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Wegovy Approved for MASH With Fibrosis, No Cirrhosis
The once-weekly 2.4 mg semaglutide subcutaneous injection is given in conjunction with a reduced calorie diet and increased physical activity.
Among people living with overweight or obesity globally, 1 in 3 also have MASH.
The accelerated approval was based on part-one results from the ongoing two-part, phase-3 ESSENCE trial, in which Wegovy demonstrated a significant improvement in liver fibrosis with no worsening of steatohepatitis, as well as resolution of steatohepatitis with no worsening of liver fibrosis, compared with placebo at week 72. Those results were published online in April in The New England Journal of Medicine.
For the trial, 800 participants were randomly assigned to either Wegovy (534 participants) or placebo (266 participants) in addition to lifestyle changes. The mean age was 56 years and the mean BMI was 34. Most patients were white individuals (67.5%) and women (57.1%), and 55.9% of the patients had type 2 diabetes; 250 patients (31.3%) had stage II fibrosis and 550 (68.8%) had stage III fibrosis. Participants were on stable doses of lipid-lowering, glucose-management, and weight-loss medications.
At week 72, the first primary endpoint showed 63% of the 534 people treated with Wegovy achieved resolution of steatohepatitis and no worsening of liver fibrosis compared with 34% of 266 individuals treated with placebo — a statistically significant difference.
The second primary endpoint showed 37% of people treated with Wegovy achieved improvement in liver fibrosis and no worsening of steatohepatitis compared with 22% of those treated with placebo, also a significant difference.
A confirmatory secondary endpoint at week 72 showed 33% of patients treated with Wegovy achieved both resolution of steatohepatitis and improvement in liver fibrosis compared with 16% of those treated with placebo — a statistically significant difference in response rate of 17%.
In addition, 83.5% of the patients in the semaglutide group maintained the target dose of 2.4 mg until week 72.
Wegovy is also indicated, along with diet and physical activity, to reduce the risk for major cardiovascular events in adults with known heart disease and with either obesity or overweight. It is also indicated for adults and children aged 12 years or older with obesity, and some adults with overweight who also have weight-related medical problems, to help them lose excess body weight and keep the weight off.
What’s Next for Wegovy?
In February 2025, Novo Nordisk filed for regulatory approval in the EU, followed by regulatory submission in Japan in May 2025. Also in May, the FDA accepted a filing application for oral semaglutide 25 mg.
Furthermore, “There’s an expected readout of part 2 of ESSENCE in 2029, which aims to demonstrate treatment with Wegovy lowers the risk of liver-related clinical events, compared to placebo, in patients with MASH and F2 or F3 fibrosis at week 240,” a Novo Nordisk spokesperson told GI & Hepatology News.
Although the company has the technology to produce semaglutide as a pill or tablet, she said, “the US launch of oral semaglutide for obesity will be contingent on portfolio prioritization and manufacturing capacity.” The company has not yet submitted the 50 mg oral semaglutide to regulatory authorities.
“The oral form requires more active pharmaceutical ingredient (API),” she noted. “Given that we have a fixed amount of API, the injectable form enables us to treat more patients. We are currently expanding our oral and injectable production capacities globally with the aim of serving as many patients as possible. It requires time to build, install, validate, and ramp-up these production processes.”
A version of this article appeared on Medscape.com.
The once-weekly 2.4 mg semaglutide subcutaneous injection is given in conjunction with a reduced calorie diet and increased physical activity.
Among people living with overweight or obesity globally, 1 in 3 also have MASH.
The accelerated approval was based on part-one results from the ongoing two-part, phase-3 ESSENCE trial, in which Wegovy demonstrated a significant improvement in liver fibrosis with no worsening of steatohepatitis, as well as resolution of steatohepatitis with no worsening of liver fibrosis, compared with placebo at week 72. Those results were published online in April in The New England Journal of Medicine.
For the trial, 800 participants were randomly assigned to either Wegovy (534 participants) or placebo (266 participants) in addition to lifestyle changes. The mean age was 56 years and the mean BMI was 34. Most patients were white individuals (67.5%) and women (57.1%), and 55.9% of the patients had type 2 diabetes; 250 patients (31.3%) had stage II fibrosis and 550 (68.8%) had stage III fibrosis. Participants were on stable doses of lipid-lowering, glucose-management, and weight-loss medications.
At week 72, the first primary endpoint showed 63% of the 534 people treated with Wegovy achieved resolution of steatohepatitis and no worsening of liver fibrosis compared with 34% of 266 individuals treated with placebo — a statistically significant difference.
The second primary endpoint showed 37% of people treated with Wegovy achieved improvement in liver fibrosis and no worsening of steatohepatitis compared with 22% of those treated with placebo, also a significant difference.
A confirmatory secondary endpoint at week 72 showed 33% of patients treated with Wegovy achieved both resolution of steatohepatitis and improvement in liver fibrosis compared with 16% of those treated with placebo — a statistically significant difference in response rate of 17%.
In addition, 83.5% of the patients in the semaglutide group maintained the target dose of 2.4 mg until week 72.
Wegovy is also indicated, along with diet and physical activity, to reduce the risk for major cardiovascular events in adults with known heart disease and with either obesity or overweight. It is also indicated for adults and children aged 12 years or older with obesity, and some adults with overweight who also have weight-related medical problems, to help them lose excess body weight and keep the weight off.
What’s Next for Wegovy?
In February 2025, Novo Nordisk filed for regulatory approval in the EU, followed by regulatory submission in Japan in May 2025. Also in May, the FDA accepted a filing application for oral semaglutide 25 mg.
Furthermore, “There’s an expected readout of part 2 of ESSENCE in 2029, which aims to demonstrate treatment with Wegovy lowers the risk of liver-related clinical events, compared to placebo, in patients with MASH and F2 or F3 fibrosis at week 240,” a Novo Nordisk spokesperson told GI & Hepatology News.
Although the company has the technology to produce semaglutide as a pill or tablet, she said, “the US launch of oral semaglutide for obesity will be contingent on portfolio prioritization and manufacturing capacity.” The company has not yet submitted the 50 mg oral semaglutide to regulatory authorities.
“The oral form requires more active pharmaceutical ingredient (API),” she noted. “Given that we have a fixed amount of API, the injectable form enables us to treat more patients. We are currently expanding our oral and injectable production capacities globally with the aim of serving as many patients as possible. It requires time to build, install, validate, and ramp-up these production processes.”
A version of this article appeared on Medscape.com.
The once-weekly 2.4 mg semaglutide subcutaneous injection is given in conjunction with a reduced calorie diet and increased physical activity.
Among people living with overweight or obesity globally, 1 in 3 also have MASH.
The accelerated approval was based on part-one results from the ongoing two-part, phase-3 ESSENCE trial, in which Wegovy demonstrated a significant improvement in liver fibrosis with no worsening of steatohepatitis, as well as resolution of steatohepatitis with no worsening of liver fibrosis, compared with placebo at week 72. Those results were published online in April in The New England Journal of Medicine.
For the trial, 800 participants were randomly assigned to either Wegovy (534 participants) or placebo (266 participants) in addition to lifestyle changes. The mean age was 56 years and the mean BMI was 34. Most patients were white individuals (67.5%) and women (57.1%), and 55.9% of the patients had type 2 diabetes; 250 patients (31.3%) had stage II fibrosis and 550 (68.8%) had stage III fibrosis. Participants were on stable doses of lipid-lowering, glucose-management, and weight-loss medications.
At week 72, the first primary endpoint showed 63% of the 534 people treated with Wegovy achieved resolution of steatohepatitis and no worsening of liver fibrosis compared with 34% of 266 individuals treated with placebo — a statistically significant difference.
The second primary endpoint showed 37% of people treated with Wegovy achieved improvement in liver fibrosis and no worsening of steatohepatitis compared with 22% of those treated with placebo, also a significant difference.
A confirmatory secondary endpoint at week 72 showed 33% of patients treated with Wegovy achieved both resolution of steatohepatitis and improvement in liver fibrosis compared with 16% of those treated with placebo — a statistically significant difference in response rate of 17%.
In addition, 83.5% of the patients in the semaglutide group maintained the target dose of 2.4 mg until week 72.
Wegovy is also indicated, along with diet and physical activity, to reduce the risk for major cardiovascular events in adults with known heart disease and with either obesity or overweight. It is also indicated for adults and children aged 12 years or older with obesity, and some adults with overweight who also have weight-related medical problems, to help them lose excess body weight and keep the weight off.
What’s Next for Wegovy?
In February 2025, Novo Nordisk filed for regulatory approval in the EU, followed by regulatory submission in Japan in May 2025. Also in May, the FDA accepted a filing application for oral semaglutide 25 mg.
Furthermore, “There’s an expected readout of part 2 of ESSENCE in 2029, which aims to demonstrate treatment with Wegovy lowers the risk of liver-related clinical events, compared to placebo, in patients with MASH and F2 or F3 fibrosis at week 240,” a Novo Nordisk spokesperson told GI & Hepatology News.
Although the company has the technology to produce semaglutide as a pill or tablet, she said, “the US launch of oral semaglutide for obesity will be contingent on portfolio prioritization and manufacturing capacity.” The company has not yet submitted the 50 mg oral semaglutide to regulatory authorities.
“The oral form requires more active pharmaceutical ingredient (API),” she noted. “Given that we have a fixed amount of API, the injectable form enables us to treat more patients. We are currently expanding our oral and injectable production capacities globally with the aim of serving as many patients as possible. It requires time to build, install, validate, and ramp-up these production processes.”
A version of this article appeared on Medscape.com.
Cirrhosis Mortality Prediction Boosted by Machine Learning
“This highly inclusive, representative, and globally derived model has been externally validated,” Jasmohan Bajaj, MD, AGAF, professor of medicine at Virginia Commonwealth University in Richmond, Virginia, told GI & Hepatology News. “This gives us a crystal ball. It helps hospital teams, transplant centers, gastroenterology and intensive care unit services triage and prioritize patients more effectively.”
The study supporting the model, which Bajaj said “could be used at this stage,” was published online in Gastroenterology. The model is available for downloading at https://silveys.shinyapps.io/app_cleared/.
CLEARED Cohort Analyzed
Wide variations across the world regarding available resources, outpatient services, reasons for admission, and etiologies of cirrhosis can influence patient outcomes, according to Bajaj and colleagues. Therefore, they sought to use ML approaches to improve prognostication for all countries.
They analyzed admission-day data from the prospective Chronic Liver Disease Evolution And Registry for Events and Decompensation (CLEARED) consortium, which includes inpatients with cirrhosis enrolled from six continents. The analysis compared ML approaches with logistical regression to predict inpatient mortality.
The researchers performed internal validation (75/25 split) and subdivision using World-Bank income status: low/low-middle (L-LMIC), upper middle (UMIC), and high (HIC). They determined that the ML model with the best area-under-the-curve (AUC) would be externally validated in a US-Veteran cirrhosis inpatient population.
The CLEARED cohort included 7239 cirrhosis inpatients (mean age, 56 years; 64% men; median MELD-Na, 25) from 115 centers globally; 22.5% of centers belonged to LMICs, 41% to UMICs, and 34% to HICs.
A total of 808 patients (11.1%) died in the hospital.
Random-Forest analysis showed the best AUC (0.815) with high calibration. This was significantly better than parametric logistic regression (AUC, 0.774) and LASSO (AUC, 0.787) models.
Random-Forest also was better than logistic regression regardless of country income-level: HIC (AUC,0.806), UMIC (AUC, 0.867), and L-LMICs (AUC, 0.768).
Of the top 15 important variables selected from Random-Forest, admission for acute kidney injury, hepatic encephalopathy, high MELD-Na/white blood count, and not being in high income country were variables most predictive of mortality.
In contrast, higher albumin, hemoglobin, diuretic use on admission, viral etiology, and being in a high-income country were most protective.
The Random-Forest model was validated in 28,670 veterans (mean age, 67 years; 96% men; median MELD-Na,15), with an inpatient mortality of 4% (1158 patients).
The final Random-Forest model, using 48 of the 67 original covariates, attained a strong AUC of 0.859. A refit version using only the top 15 variables achieved a comparable AUC of 0.851.
Clinical Relevance
“Cirrhosis and resultant organ failures remain a dynamic and multidisciplinary problem,” Bajaj noted. “Machine learning techniques are one part of multi-faceted management strategy that is required in this population.”
If patients fall into the high-risk category, he said, “careful consultation with patients, families, and clinical teams is needed before providing information, including where this model was derived from. The results of these discussions could be instructive regarding decisions for transfer, more aggressive monitoring/ICU transfer, palliative care or transplant assessments.”
Meena B. Bansal, MD, system chief, Division of Liver Diseases, Mount Sinai Health System in New York City, called the tool “very promising.” However, she told GI & Hepatology News, “it was validated on a VA [Veterans Affairs] cohort, which is a bit different than the cohort of patients seen at Mount Sinai. Therefore, validation in more academic tertiary care medical centers with high volume liver transplant would be helpful.”
Furthermore, said Bansal, who was not involved in the study, “they excluded those that receiving a liver transplant, and while only a small number, this is an important limitation.”
Nevertheless, she added, “Artificial intelligence has great potential in predictive risk models and will likely be a tool that assists for risk stratification, clinical management, and hopefully improved clinical outcomes.”
This study was partly supported by a VA Merit review to Bajaj and the National Center for Advancing Translational Sciences, National Institutes of Health. No conflicts of interest were reported by any author.
A version of this article appeared on Medscape.com.
“This highly inclusive, representative, and globally derived model has been externally validated,” Jasmohan Bajaj, MD, AGAF, professor of medicine at Virginia Commonwealth University in Richmond, Virginia, told GI & Hepatology News. “This gives us a crystal ball. It helps hospital teams, transplant centers, gastroenterology and intensive care unit services triage and prioritize patients more effectively.”
The study supporting the model, which Bajaj said “could be used at this stage,” was published online in Gastroenterology. The model is available for downloading at https://silveys.shinyapps.io/app_cleared/.
CLEARED Cohort Analyzed
Wide variations across the world regarding available resources, outpatient services, reasons for admission, and etiologies of cirrhosis can influence patient outcomes, according to Bajaj and colleagues. Therefore, they sought to use ML approaches to improve prognostication for all countries.
They analyzed admission-day data from the prospective Chronic Liver Disease Evolution And Registry for Events and Decompensation (CLEARED) consortium, which includes inpatients with cirrhosis enrolled from six continents. The analysis compared ML approaches with logistical regression to predict inpatient mortality.
The researchers performed internal validation (75/25 split) and subdivision using World-Bank income status: low/low-middle (L-LMIC), upper middle (UMIC), and high (HIC). They determined that the ML model with the best area-under-the-curve (AUC) would be externally validated in a US-Veteran cirrhosis inpatient population.
The CLEARED cohort included 7239 cirrhosis inpatients (mean age, 56 years; 64% men; median MELD-Na, 25) from 115 centers globally; 22.5% of centers belonged to LMICs, 41% to UMICs, and 34% to HICs.
A total of 808 patients (11.1%) died in the hospital.
Random-Forest analysis showed the best AUC (0.815) with high calibration. This was significantly better than parametric logistic regression (AUC, 0.774) and LASSO (AUC, 0.787) models.
Random-Forest also was better than logistic regression regardless of country income-level: HIC (AUC,0.806), UMIC (AUC, 0.867), and L-LMICs (AUC, 0.768).
Of the top 15 important variables selected from Random-Forest, admission for acute kidney injury, hepatic encephalopathy, high MELD-Na/white blood count, and not being in high income country were variables most predictive of mortality.
In contrast, higher albumin, hemoglobin, diuretic use on admission, viral etiology, and being in a high-income country were most protective.
The Random-Forest model was validated in 28,670 veterans (mean age, 67 years; 96% men; median MELD-Na,15), with an inpatient mortality of 4% (1158 patients).
The final Random-Forest model, using 48 of the 67 original covariates, attained a strong AUC of 0.859. A refit version using only the top 15 variables achieved a comparable AUC of 0.851.
Clinical Relevance
“Cirrhosis and resultant organ failures remain a dynamic and multidisciplinary problem,” Bajaj noted. “Machine learning techniques are one part of multi-faceted management strategy that is required in this population.”
If patients fall into the high-risk category, he said, “careful consultation with patients, families, and clinical teams is needed before providing information, including where this model was derived from. The results of these discussions could be instructive regarding decisions for transfer, more aggressive monitoring/ICU transfer, palliative care or transplant assessments.”
Meena B. Bansal, MD, system chief, Division of Liver Diseases, Mount Sinai Health System in New York City, called the tool “very promising.” However, she told GI & Hepatology News, “it was validated on a VA [Veterans Affairs] cohort, which is a bit different than the cohort of patients seen at Mount Sinai. Therefore, validation in more academic tertiary care medical centers with high volume liver transplant would be helpful.”
Furthermore, said Bansal, who was not involved in the study, “they excluded those that receiving a liver transplant, and while only a small number, this is an important limitation.”
Nevertheless, she added, “Artificial intelligence has great potential in predictive risk models and will likely be a tool that assists for risk stratification, clinical management, and hopefully improved clinical outcomes.”
This study was partly supported by a VA Merit review to Bajaj and the National Center for Advancing Translational Sciences, National Institutes of Health. No conflicts of interest were reported by any author.
A version of this article appeared on Medscape.com.
“This highly inclusive, representative, and globally derived model has been externally validated,” Jasmohan Bajaj, MD, AGAF, professor of medicine at Virginia Commonwealth University in Richmond, Virginia, told GI & Hepatology News. “This gives us a crystal ball. It helps hospital teams, transplant centers, gastroenterology and intensive care unit services triage and prioritize patients more effectively.”
The study supporting the model, which Bajaj said “could be used at this stage,” was published online in Gastroenterology. The model is available for downloading at https://silveys.shinyapps.io/app_cleared/.
CLEARED Cohort Analyzed
Wide variations across the world regarding available resources, outpatient services, reasons for admission, and etiologies of cirrhosis can influence patient outcomes, according to Bajaj and colleagues. Therefore, they sought to use ML approaches to improve prognostication for all countries.
They analyzed admission-day data from the prospective Chronic Liver Disease Evolution And Registry for Events and Decompensation (CLEARED) consortium, which includes inpatients with cirrhosis enrolled from six continents. The analysis compared ML approaches with logistical regression to predict inpatient mortality.
The researchers performed internal validation (75/25 split) and subdivision using World-Bank income status: low/low-middle (L-LMIC), upper middle (UMIC), and high (HIC). They determined that the ML model with the best area-under-the-curve (AUC) would be externally validated in a US-Veteran cirrhosis inpatient population.
The CLEARED cohort included 7239 cirrhosis inpatients (mean age, 56 years; 64% men; median MELD-Na, 25) from 115 centers globally; 22.5% of centers belonged to LMICs, 41% to UMICs, and 34% to HICs.
A total of 808 patients (11.1%) died in the hospital.
Random-Forest analysis showed the best AUC (0.815) with high calibration. This was significantly better than parametric logistic regression (AUC, 0.774) and LASSO (AUC, 0.787) models.
Random-Forest also was better than logistic regression regardless of country income-level: HIC (AUC,0.806), UMIC (AUC, 0.867), and L-LMICs (AUC, 0.768).
Of the top 15 important variables selected from Random-Forest, admission for acute kidney injury, hepatic encephalopathy, high MELD-Na/white blood count, and not being in high income country were variables most predictive of mortality.
In contrast, higher albumin, hemoglobin, diuretic use on admission, viral etiology, and being in a high-income country were most protective.
The Random-Forest model was validated in 28,670 veterans (mean age, 67 years; 96% men; median MELD-Na,15), with an inpatient mortality of 4% (1158 patients).
The final Random-Forest model, using 48 of the 67 original covariates, attained a strong AUC of 0.859. A refit version using only the top 15 variables achieved a comparable AUC of 0.851.
Clinical Relevance
“Cirrhosis and resultant organ failures remain a dynamic and multidisciplinary problem,” Bajaj noted. “Machine learning techniques are one part of multi-faceted management strategy that is required in this population.”
If patients fall into the high-risk category, he said, “careful consultation with patients, families, and clinical teams is needed before providing information, including where this model was derived from. The results of these discussions could be instructive regarding decisions for transfer, more aggressive monitoring/ICU transfer, palliative care or transplant assessments.”
Meena B. Bansal, MD, system chief, Division of Liver Diseases, Mount Sinai Health System in New York City, called the tool “very promising.” However, she told GI & Hepatology News, “it was validated on a VA [Veterans Affairs] cohort, which is a bit different than the cohort of patients seen at Mount Sinai. Therefore, validation in more academic tertiary care medical centers with high volume liver transplant would be helpful.”
Furthermore, said Bansal, who was not involved in the study, “they excluded those that receiving a liver transplant, and while only a small number, this is an important limitation.”
Nevertheless, she added, “Artificial intelligence has great potential in predictive risk models and will likely be a tool that assists for risk stratification, clinical management, and hopefully improved clinical outcomes.”
This study was partly supported by a VA Merit review to Bajaj and the National Center for Advancing Translational Sciences, National Institutes of Health. No conflicts of interest were reported by any author.
A version of this article appeared on Medscape.com.
FROM GASTROENTEROLOGY
Sleep Changes in IBD Could Signal Inflammation, Flareups
, an observational study suggested.
Sleep data from 101 study participants over a mean duration of about 228 days revealed that altered sleep architecture was only apparent when inflammation was present — symptoms alone did not impact sleep cycles or signal inflammation.
“We thought symptoms might have an impact on sleep, but interestingly, our data showed that measurable changes like reduced rapid eye movement (REM) sleep and increased light sleep only occurred during periods of active inflammation,” Robert Hirten, MD, associate professor of Medicine (Gastroenterology), and Artificial Intelligence and Human Health, at the Icahn School of Medicine at Mount Sinai, New York City, told GI & Hepatology News.
“It was also interesting to see distinct patterns in sleep metrics begin to shift over the 45 days before a flare, suggesting the potential for sleep to serve as an early indicator of disease activity,” he added.
“Sleep is often overlooked in the management of IBD, but it may provide valuable insights into a patient’s underlying disease state,” he said. “While sleep monitoring isn’t yet a standard part of IBD care, this study highlights its potential as a noninvasive window into disease activity, and a promising area for future clinical integration.”
The study was published online in Clinical Gastroenterology and Hepatology.
Less REM Sleep, More Light Sleep
Researchers assessed the impact of inflammation and symptoms on sleep architecture in IBD by analyzing data from 101 individuals who answered daily disease activity surveys and wore a wearable device.
The mean age of participants was 41 years and 65.3% were women. Sixty-three participants (62.4%) had Crohn’s disease (CD) and 38 (37.6%) had ulcerative colitis (UC).
Almost 40 (39.6%) participants used an Apple Watch; 50 (49.5%) used a Fitbit; and 11 (10.9%) used an Oura ring. Sleep architecture, sleep efficiency, and total hours asleep were collected from the devices. Participants were encouraged to wear their devices for at least 4 days per week and 8 hours per day and were not required to wear them at night. Participants provided data by linking their devices to ehive, Mount Sinai’s custom app.
Daily clinical disease activity was assessed using the UC or CD Patient Reported Outcome-2 survey. Participants were asked to answer at least four daily surveys each week.
Associations between sleep metrics and periods of symptomatic and inflammatory flares, and combinations of symptomatic and inflammatory activity, were compared to periods of symptomatic and inflammatory remission.
Furthermore, researchers explored the rate of change in sleep metrics for 45 days before and after inflammatory and symptomatic flares.
Participants contributed a mean duration of 228.16 nights of wearable data. During active inflammation, they spent a lower percentage of sleep time in REM (20% vs 21.59%) and a greater percentage of sleep time in light sleep (62.23% vs 59.95%) than during inflammatory remission. No differences were observed in the mean percentage of time in deep sleep, sleep efficiency, or total time asleep.
During symptomatic flares, there were no differences in the percentage of sleep time in REM sleep, deep sleep, light sleep, or sleep efficiency compared with periods of inflammatory remission. However, participants slept less overall during symptomatic flares compared with during symptomatic remission.
Compared with during asymptomatic and uninflamed periods, during asymptomatic but inflamed periods, participants spent a lower percentage of time in REM sleep, and more time in light sleep; however, there were no differences in sleep efficiency or total time asleep.
Similarly, participants had more light sleep and less REM sleep during symptomatic and inflammatory flares than during asymptomatic and uninflamed periods — but there were no differences in the percentage of time spent in deep sleep, in sleep efficiency, and the total time asleep.
Symptomatic flares alone, without inflammation, did not impact sleep metrics, the researchers concluded. However, periods with active inflammation were associated with a significantly smaller percentage of sleep time in REM sleep and a greater percentage of sleep time in light sleep.
The team also performed longitudinal mapping of sleep patterns before, during, and after disease exacerbations by analyzing sleep data for 6 weeks before and 6 weeks after flare episodes.
They found that sleep disturbances significantly worsen leading up to inflammatory flares and improve afterward, suggesting that sleep changes may signal upcoming increased disease activity. Evaluating the intersection of inflammatory and symptomatic flares, altered sleep architecture was only evident when inflammation was present.
“These findings raise important questions about whether intervening on sleep can actually impact inflammation or disease trajectory in IBD,” Hirten said. “Next steps include studying whether targeted sleep interventions can improve both sleep and IBD outcomes.”
While this research is still in the early stages, he said, “it suggests that sleep may have a relationship with inflammatory activity in IBD. For patients, it reinforces the value of paying attention to sleep changes.”
The findings also show the potential of wearable devices to guide more personalized monitoring, he added. “More work is needed before sleep metrics can be used routinely in clinical decision-making.”
Validates the Use of Wearables
Commenting on the study for GI & Hepatology News, Michael Mintz, MD, a gastroenterologist at Weill Cornell Medicine and NewYork-Presbyterian in New York City, observed, “Gastrointestinal symptoms often do not correlate with objective disease activity in IBD, creating a diagnostic challenge for gastroenterologists. Burdensome, expensive, and/or invasive testing, such as colonoscopies, stool tests, or imaging, are frequently required to monitor disease activity.”
“This study is a first step in objectively monitoring inflammation in a patient-centric way that does not create undue burden to our patients,” he said. “It also provides longitudinal data that suggests changes in sleep patterns can pre-date disease flares, which ideally can lead to earlier intervention to prevent disease complications.”
Like Hirten, he noted that clinical decisions, such as changing IBD therapy, should not be based on the results of this study. “Rather this provides validation that wearable technology can provide useful objective data that correlates with disease activity.”
Furthermore, he said, it is not clear whether analyzing sleep data is a cost-effective way of monitoring IBD disease activity, or whether that data should be used alone or in combination with other objective disease markers, to influence clinical decision-making.
“This study provides proof of concept that there is a relationship between sleep characteristics and objective inflammation, but further studies are needed,” he said. “I am hopeful that this technology will give us another tool that we can use in clinical practice to monitor disease activity and improve outcomes in a way that is comfortable and convenient for our patients.”
This study was supported by a grant to Hirten from the US National Institutes of Health. Hirten reported receiving consulting fees from Bristol Meyers Squibb, AbbVie; stock options from Salvo Health; and research support from Janssen, Intralytix, EnLiSense, Crohn’s and Colitis Foundation. Mintz declared no competing interests.
A version of this article appeared on Medscape.com.
, an observational study suggested.
Sleep data from 101 study participants over a mean duration of about 228 days revealed that altered sleep architecture was only apparent when inflammation was present — symptoms alone did not impact sleep cycles or signal inflammation.
“We thought symptoms might have an impact on sleep, but interestingly, our data showed that measurable changes like reduced rapid eye movement (REM) sleep and increased light sleep only occurred during periods of active inflammation,” Robert Hirten, MD, associate professor of Medicine (Gastroenterology), and Artificial Intelligence and Human Health, at the Icahn School of Medicine at Mount Sinai, New York City, told GI & Hepatology News.
“It was also interesting to see distinct patterns in sleep metrics begin to shift over the 45 days before a flare, suggesting the potential for sleep to serve as an early indicator of disease activity,” he added.
“Sleep is often overlooked in the management of IBD, but it may provide valuable insights into a patient’s underlying disease state,” he said. “While sleep monitoring isn’t yet a standard part of IBD care, this study highlights its potential as a noninvasive window into disease activity, and a promising area for future clinical integration.”
The study was published online in Clinical Gastroenterology and Hepatology.
Less REM Sleep, More Light Sleep
Researchers assessed the impact of inflammation and symptoms on sleep architecture in IBD by analyzing data from 101 individuals who answered daily disease activity surveys and wore a wearable device.
The mean age of participants was 41 years and 65.3% were women. Sixty-three participants (62.4%) had Crohn’s disease (CD) and 38 (37.6%) had ulcerative colitis (UC).
Almost 40 (39.6%) participants used an Apple Watch; 50 (49.5%) used a Fitbit; and 11 (10.9%) used an Oura ring. Sleep architecture, sleep efficiency, and total hours asleep were collected from the devices. Participants were encouraged to wear their devices for at least 4 days per week and 8 hours per day and were not required to wear them at night. Participants provided data by linking their devices to ehive, Mount Sinai’s custom app.
Daily clinical disease activity was assessed using the UC or CD Patient Reported Outcome-2 survey. Participants were asked to answer at least four daily surveys each week.
Associations between sleep metrics and periods of symptomatic and inflammatory flares, and combinations of symptomatic and inflammatory activity, were compared to periods of symptomatic and inflammatory remission.
Furthermore, researchers explored the rate of change in sleep metrics for 45 days before and after inflammatory and symptomatic flares.
Participants contributed a mean duration of 228.16 nights of wearable data. During active inflammation, they spent a lower percentage of sleep time in REM (20% vs 21.59%) and a greater percentage of sleep time in light sleep (62.23% vs 59.95%) than during inflammatory remission. No differences were observed in the mean percentage of time in deep sleep, sleep efficiency, or total time asleep.
During symptomatic flares, there were no differences in the percentage of sleep time in REM sleep, deep sleep, light sleep, or sleep efficiency compared with periods of inflammatory remission. However, participants slept less overall during symptomatic flares compared with during symptomatic remission.
Compared with during asymptomatic and uninflamed periods, during asymptomatic but inflamed periods, participants spent a lower percentage of time in REM sleep, and more time in light sleep; however, there were no differences in sleep efficiency or total time asleep.
Similarly, participants had more light sleep and less REM sleep during symptomatic and inflammatory flares than during asymptomatic and uninflamed periods — but there were no differences in the percentage of time spent in deep sleep, in sleep efficiency, and the total time asleep.
Symptomatic flares alone, without inflammation, did not impact sleep metrics, the researchers concluded. However, periods with active inflammation were associated with a significantly smaller percentage of sleep time in REM sleep and a greater percentage of sleep time in light sleep.
The team also performed longitudinal mapping of sleep patterns before, during, and after disease exacerbations by analyzing sleep data for 6 weeks before and 6 weeks after flare episodes.
They found that sleep disturbances significantly worsen leading up to inflammatory flares and improve afterward, suggesting that sleep changes may signal upcoming increased disease activity. Evaluating the intersection of inflammatory and symptomatic flares, altered sleep architecture was only evident when inflammation was present.
“These findings raise important questions about whether intervening on sleep can actually impact inflammation or disease trajectory in IBD,” Hirten said. “Next steps include studying whether targeted sleep interventions can improve both sleep and IBD outcomes.”
While this research is still in the early stages, he said, “it suggests that sleep may have a relationship with inflammatory activity in IBD. For patients, it reinforces the value of paying attention to sleep changes.”
The findings also show the potential of wearable devices to guide more personalized monitoring, he added. “More work is needed before sleep metrics can be used routinely in clinical decision-making.”
Validates the Use of Wearables
Commenting on the study for GI & Hepatology News, Michael Mintz, MD, a gastroenterologist at Weill Cornell Medicine and NewYork-Presbyterian in New York City, observed, “Gastrointestinal symptoms often do not correlate with objective disease activity in IBD, creating a diagnostic challenge for gastroenterologists. Burdensome, expensive, and/or invasive testing, such as colonoscopies, stool tests, or imaging, are frequently required to monitor disease activity.”
“This study is a first step in objectively monitoring inflammation in a patient-centric way that does not create undue burden to our patients,” he said. “It also provides longitudinal data that suggests changes in sleep patterns can pre-date disease flares, which ideally can lead to earlier intervention to prevent disease complications.”
Like Hirten, he noted that clinical decisions, such as changing IBD therapy, should not be based on the results of this study. “Rather this provides validation that wearable technology can provide useful objective data that correlates with disease activity.”
Furthermore, he said, it is not clear whether analyzing sleep data is a cost-effective way of monitoring IBD disease activity, or whether that data should be used alone or in combination with other objective disease markers, to influence clinical decision-making.
“This study provides proof of concept that there is a relationship between sleep characteristics and objective inflammation, but further studies are needed,” he said. “I am hopeful that this technology will give us another tool that we can use in clinical practice to monitor disease activity and improve outcomes in a way that is comfortable and convenient for our patients.”
This study was supported by a grant to Hirten from the US National Institutes of Health. Hirten reported receiving consulting fees from Bristol Meyers Squibb, AbbVie; stock options from Salvo Health; and research support from Janssen, Intralytix, EnLiSense, Crohn’s and Colitis Foundation. Mintz declared no competing interests.
A version of this article appeared on Medscape.com.
, an observational study suggested.
Sleep data from 101 study participants over a mean duration of about 228 days revealed that altered sleep architecture was only apparent when inflammation was present — symptoms alone did not impact sleep cycles or signal inflammation.
“We thought symptoms might have an impact on sleep, but interestingly, our data showed that measurable changes like reduced rapid eye movement (REM) sleep and increased light sleep only occurred during periods of active inflammation,” Robert Hirten, MD, associate professor of Medicine (Gastroenterology), and Artificial Intelligence and Human Health, at the Icahn School of Medicine at Mount Sinai, New York City, told GI & Hepatology News.
“It was also interesting to see distinct patterns in sleep metrics begin to shift over the 45 days before a flare, suggesting the potential for sleep to serve as an early indicator of disease activity,” he added.
“Sleep is often overlooked in the management of IBD, but it may provide valuable insights into a patient’s underlying disease state,” he said. “While sleep monitoring isn’t yet a standard part of IBD care, this study highlights its potential as a noninvasive window into disease activity, and a promising area for future clinical integration.”
The study was published online in Clinical Gastroenterology and Hepatology.
Less REM Sleep, More Light Sleep
Researchers assessed the impact of inflammation and symptoms on sleep architecture in IBD by analyzing data from 101 individuals who answered daily disease activity surveys and wore a wearable device.
The mean age of participants was 41 years and 65.3% were women. Sixty-three participants (62.4%) had Crohn’s disease (CD) and 38 (37.6%) had ulcerative colitis (UC).
Almost 40 (39.6%) participants used an Apple Watch; 50 (49.5%) used a Fitbit; and 11 (10.9%) used an Oura ring. Sleep architecture, sleep efficiency, and total hours asleep were collected from the devices. Participants were encouraged to wear their devices for at least 4 days per week and 8 hours per day and were not required to wear them at night. Participants provided data by linking their devices to ehive, Mount Sinai’s custom app.
Daily clinical disease activity was assessed using the UC or CD Patient Reported Outcome-2 survey. Participants were asked to answer at least four daily surveys each week.
Associations between sleep metrics and periods of symptomatic and inflammatory flares, and combinations of symptomatic and inflammatory activity, were compared to periods of symptomatic and inflammatory remission.
Furthermore, researchers explored the rate of change in sleep metrics for 45 days before and after inflammatory and symptomatic flares.
Participants contributed a mean duration of 228.16 nights of wearable data. During active inflammation, they spent a lower percentage of sleep time in REM (20% vs 21.59%) and a greater percentage of sleep time in light sleep (62.23% vs 59.95%) than during inflammatory remission. No differences were observed in the mean percentage of time in deep sleep, sleep efficiency, or total time asleep.
During symptomatic flares, there were no differences in the percentage of sleep time in REM sleep, deep sleep, light sleep, or sleep efficiency compared with periods of inflammatory remission. However, participants slept less overall during symptomatic flares compared with during symptomatic remission.
Compared with during asymptomatic and uninflamed periods, during asymptomatic but inflamed periods, participants spent a lower percentage of time in REM sleep, and more time in light sleep; however, there were no differences in sleep efficiency or total time asleep.
Similarly, participants had more light sleep and less REM sleep during symptomatic and inflammatory flares than during asymptomatic and uninflamed periods — but there were no differences in the percentage of time spent in deep sleep, in sleep efficiency, and the total time asleep.
Symptomatic flares alone, without inflammation, did not impact sleep metrics, the researchers concluded. However, periods with active inflammation were associated with a significantly smaller percentage of sleep time in REM sleep and a greater percentage of sleep time in light sleep.
The team also performed longitudinal mapping of sleep patterns before, during, and after disease exacerbations by analyzing sleep data for 6 weeks before and 6 weeks after flare episodes.
They found that sleep disturbances significantly worsen leading up to inflammatory flares and improve afterward, suggesting that sleep changes may signal upcoming increased disease activity. Evaluating the intersection of inflammatory and symptomatic flares, altered sleep architecture was only evident when inflammation was present.
“These findings raise important questions about whether intervening on sleep can actually impact inflammation or disease trajectory in IBD,” Hirten said. “Next steps include studying whether targeted sleep interventions can improve both sleep and IBD outcomes.”
While this research is still in the early stages, he said, “it suggests that sleep may have a relationship with inflammatory activity in IBD. For patients, it reinforces the value of paying attention to sleep changes.”
The findings also show the potential of wearable devices to guide more personalized monitoring, he added. “More work is needed before sleep metrics can be used routinely in clinical decision-making.”
Validates the Use of Wearables
Commenting on the study for GI & Hepatology News, Michael Mintz, MD, a gastroenterologist at Weill Cornell Medicine and NewYork-Presbyterian in New York City, observed, “Gastrointestinal symptoms often do not correlate with objective disease activity in IBD, creating a diagnostic challenge for gastroenterologists. Burdensome, expensive, and/or invasive testing, such as colonoscopies, stool tests, or imaging, are frequently required to monitor disease activity.”
“This study is a first step in objectively monitoring inflammation in a patient-centric way that does not create undue burden to our patients,” he said. “It also provides longitudinal data that suggests changes in sleep patterns can pre-date disease flares, which ideally can lead to earlier intervention to prevent disease complications.”
Like Hirten, he noted that clinical decisions, such as changing IBD therapy, should not be based on the results of this study. “Rather this provides validation that wearable technology can provide useful objective data that correlates with disease activity.”
Furthermore, he said, it is not clear whether analyzing sleep data is a cost-effective way of monitoring IBD disease activity, or whether that data should be used alone or in combination with other objective disease markers, to influence clinical decision-making.
“This study provides proof of concept that there is a relationship between sleep characteristics and objective inflammation, but further studies are needed,” he said. “I am hopeful that this technology will give us another tool that we can use in clinical practice to monitor disease activity and improve outcomes in a way that is comfortable and convenient for our patients.”
This study was supported by a grant to Hirten from the US National Institutes of Health. Hirten reported receiving consulting fees from Bristol Meyers Squibb, AbbVie; stock options from Salvo Health; and research support from Janssen, Intralytix, EnLiSense, Crohn’s and Colitis Foundation. Mintz declared no competing interests.
A version of this article appeared on Medscape.com.
FROM CLINICAL GASTROENTEROLOGY AND HEPATOLOGY
Can Nonresponders to Antiobesity Medicines Be Predicted?
, enabling clinicians to better tailor antiobesity medication (AOM) to the patient.
Currently, patient response to AOMs varies widely, with some patients responding robustly to AOMs and others responding weakly or not at all.
For example, trials of the GLP-1 semaglutide found that 32%-39.6% of people are “super responders,” achieving weight loss in excess of 20%, and a subgroup of 10.2%-16.7% of individuals are nonresponders. Similar variability was found with other AOMs, including the GLP-1 liraglutide and tirzepatide, a dual GLP-1/glucose-dependent insulinotropic polypeptide receptor agonist.
Studies of semaglutide suggest that people with obesity and type 2 diabetes (T2D) lose less weight on the drug than those without T2D, and men tend to lose less weight than women.
However, little else is known about predictors of response rates for various AOMs, and medication selection is typically based on patient or physician preference, comorbidities, medication interactions, and insurance coverage.
Although definitions of a “nonresponder” vary, the Endocrine Society’s latest guideline, which many clinicians follow, states that an AOM is considered effective if patients lose more than 5% of their body weight within 3 months.
Can nonresponders and lower responders be identified and helped? Yes, but it’s complicated.
“Treating obesity effectively means recognizing that not all patients respond the same way to the same treatment, and that’s not a failure; it’s a signal,” said Andres Acosta, MD, PhD, an obesity expert at Mayo Clinic, Rochester, Minnesota, and a cofounder of Phenomix Sciences, a biotech company in Menlo Park, California.
“Obesity is not a single disease. It’s a complex, multifactorial condition driven by diverse biological pathways,” he told GI & Hepatology News. “Semaglutide and other GLP-1s primarily act by reducing appetite and slowing gastric emptying, but not all patients have obesity that is primarily driven by appetite dysregulation.”
Phenotype-Based Profiling
Figuring out what drives an individual’s obesity is where a phenotype-based profiling test could possibly help.
Acosta and colleagues previously used a variety of validated studies and questionnaires to identify four phenotypes that represent distinct biologic drivers of obesity: hungry brain (abnormal satiation), emotional hunger (hedonic eating), hungry gut (abnormal satiety), and slow burn (decreased metabolic rate). In their pragmatic clinical trial, phenotype-guided AOM selection was associated with 1.75-fold greater weight loss after 12 months than the standard approach to drug selection, with mean weight loss of 15.9% and 9%, respectively.
“If a patient’s obesity isn’t primarily rooted in the mechanisms targeted by a particular drug, their response will naturally be limited,” Acosta said. “It’s not that they’re failing the medication; the medication simply isn’t the right match for their biology.”
For their new study, published online in Cell Metabolism, Acosta and colleagues built on their previous research by analyzing the genetic and nongenetic factors that influenced calories needed to reach satiation (Calories to Satiation [CTS]) in adults with obesity. They then used machine learning techniques to develop a CTS gene risk score (CTS-GRS) that could be measured by a DNA saliva test.
The study included 717 adults with obesity (mean age, 41; 75% women) with marked variability in satiation, ranging from 140 to 2166 kcals to reach satiation.
CTS was assessed through an ad libitum meal, combined with physiological and behavioral evaluations, including calorimetry, imaging, blood sampling, and gastric emptying tests. The largest contributors to CTS variability were sex and genetic factors, while other anthropometric measurements played lesser roles.
Various analyses and assessments of participants’ CTS-GRS scores showed that individuals with a high CTS-GRS, or hungry brain phenotype, experienced significantly greater weight loss when treated with phentermine/topiramate than those with a low CTS-GRS, or hungry gut, phenotype. After 52 weeks of treatment, individuals with the hungry brain phenotype lost an average of 17.4% of their body weight compared with 11.2% in those with the hungry gut phenotype.
An analysis of a separate 16-week study showed that patients with the hungry gut phenotype responded better to the GLP-1 liraglutide, losing 6.4% total body weight, compared to 3.3% for those with the hungry brain phenotype.
Overall, the CTS-GRS test predicted drug response with up to 84% accuracy (area under the curve, 0.76 in men and 0.84 in women). The authors acknowledged that these results need to be replicated prospectively and in more diverse populations to validate the test’s predictive ability.
“This kind of phenotype-based profiling allows us to predict which patients are more likely to respond and who might need a different intervention,” Acosta said. “It’s a critical step toward eliminating trial-and-error in obesity treatment.”
The test (MyPhenome test) is used at more than 80 healthcare clinics in the United States, according to Phenomix Sciences, which manufactures it. A company spokesperson said the test does not require FDA approval because it is used to predict obesity phenotypes to help inform treatment, but not to identify specific medications or other interventions. “If it were to do the latter,” the spokesperson said, “it would be considered a ‘companion diagnostic’ and subject to the FDA clearance process.”
What to Do if an AOM Isn’t Working?
It’s one thing to predict whether an individual might do better on one drug vs another, but what should clinicians do meanwhile to optimize weight loss for their patients who may be struggling on a particular drug?
“Efforts to predict the response to GLP-1 therapy have been a hot topic,” noted Sriram Machineni, MD, associate professor at Montefiore Medical Center, Bronx, New York, and founding director of the Fleischer Institute Medical Weight Center at Montefiore Einstein. Although the current study showed that genetic testing could predict responders, like Acosta, he agreed that the results need to be replicated in a prospective manner.
“In the absence of a validated tool for predicting response to specific medications, we use a prioritization process for trialing medications,” Machineni told GI & Hepatology News. “The prioritization is based on the suitability of the side-effect profile to the specific patient, including contraindications; benefits independent of weight loss, such as cardiovascular protection for semaglutide; average efficacy; and financial accessibility for patients.”
Predicting responders isn’t straightforward, said Robert Kushner, MD, professor of medicine and medical education at the Feinberg School of Medicine at Northwestern University and medical director of the Wellness Institute at Northwestern Memorial Hospital in Chicago.
“Despite looking at baseline demographic data such as race, ethnicity, age, weight, and BMI, we are unable to predict who will lose more or less weight,” he told GI & Hepatology News. The one exception is that women generally lose more weight than men. “However, even among females, we cannot discern which females will lose more weight than other females,” he said.
If an individual is not showing sufficient weight loss on a particular medication, “we first explore potential reasons that can be addressed, such as the patient is not taking the medication or is skipping doses,” Kushner said. If need be, they discuss changing to a different drug to improve compliance. He also stresses the importance of making lifestyle changes in diet and physical activity for patients taking AOMs.
Often patients who do not lose at least 5% of their weight within 3 months are not likely to respond well to that medication even if they remain on it. “So, early response rates determine longer-term success,” Kushner said.
Acosta said that if a patient isn’t responding to one class of medication, he pivots to a treatment better aligned with their phenotype. “That could mean switching from a GLP-1 to a medication like [naltrexone/bupropion] or trying a new method altogether,” he said. “The key is that the treatment decision is rooted in the patient’s biology, not just a reaction to short-term results. We also emphasize the importance of long-term follow-up and support.”
The goal isn’t just weight loss but also improved health and quality of life, Acosta said. “Whether through medication, surgery, or behavior change, what matters most is tailoring the care plan to each individual’s unique biology and needs.”
The new study received support from the Mayo Clinic Clinical Research Trials Unit, Vivus Inc., and Phenomix Sciences. Acosta is supported by a National Institutes of Health grant.
Acosta is a co-founder and inventor of intellectual property licensed to Phenomix Sciences Inc.; has served as a consultant for Rhythm Pharmaceuticals, Gila Therapeutics, Amgen, General Mills, Boehringer Ingelheim, Currax Pharmaceuticals, Nestlé, Bausch Health, and Rare Diseases; and has received research support or had contracts with Vivus Inc., Satiogen Pharmaceuticals, Boehringer Ingelheim, and Rhythm Pharmaceuticals. Machineni has been involved in semaglutide and tirzepatide clinical trials and has been a consultant to Novo Nordisk, Eli Lilly and Company, and Rhythm Pharmaceuticals. Kushner is on the scientific advisory board for Novo Nordisk.
A version of this article appeared on Medscape.com.
, enabling clinicians to better tailor antiobesity medication (AOM) to the patient.
Currently, patient response to AOMs varies widely, with some patients responding robustly to AOMs and others responding weakly or not at all.
For example, trials of the GLP-1 semaglutide found that 32%-39.6% of people are “super responders,” achieving weight loss in excess of 20%, and a subgroup of 10.2%-16.7% of individuals are nonresponders. Similar variability was found with other AOMs, including the GLP-1 liraglutide and tirzepatide, a dual GLP-1/glucose-dependent insulinotropic polypeptide receptor agonist.
Studies of semaglutide suggest that people with obesity and type 2 diabetes (T2D) lose less weight on the drug than those without T2D, and men tend to lose less weight than women.
However, little else is known about predictors of response rates for various AOMs, and medication selection is typically based on patient or physician preference, comorbidities, medication interactions, and insurance coverage.
Although definitions of a “nonresponder” vary, the Endocrine Society’s latest guideline, which many clinicians follow, states that an AOM is considered effective if patients lose more than 5% of their body weight within 3 months.
Can nonresponders and lower responders be identified and helped? Yes, but it’s complicated.
“Treating obesity effectively means recognizing that not all patients respond the same way to the same treatment, and that’s not a failure; it’s a signal,” said Andres Acosta, MD, PhD, an obesity expert at Mayo Clinic, Rochester, Minnesota, and a cofounder of Phenomix Sciences, a biotech company in Menlo Park, California.
“Obesity is not a single disease. It’s a complex, multifactorial condition driven by diverse biological pathways,” he told GI & Hepatology News. “Semaglutide and other GLP-1s primarily act by reducing appetite and slowing gastric emptying, but not all patients have obesity that is primarily driven by appetite dysregulation.”
Phenotype-Based Profiling
Figuring out what drives an individual’s obesity is where a phenotype-based profiling test could possibly help.
Acosta and colleagues previously used a variety of validated studies and questionnaires to identify four phenotypes that represent distinct biologic drivers of obesity: hungry brain (abnormal satiation), emotional hunger (hedonic eating), hungry gut (abnormal satiety), and slow burn (decreased metabolic rate). In their pragmatic clinical trial, phenotype-guided AOM selection was associated with 1.75-fold greater weight loss after 12 months than the standard approach to drug selection, with mean weight loss of 15.9% and 9%, respectively.
“If a patient’s obesity isn’t primarily rooted in the mechanisms targeted by a particular drug, their response will naturally be limited,” Acosta said. “It’s not that they’re failing the medication; the medication simply isn’t the right match for their biology.”
For their new study, published online in Cell Metabolism, Acosta and colleagues built on their previous research by analyzing the genetic and nongenetic factors that influenced calories needed to reach satiation (Calories to Satiation [CTS]) in adults with obesity. They then used machine learning techniques to develop a CTS gene risk score (CTS-GRS) that could be measured by a DNA saliva test.
The study included 717 adults with obesity (mean age, 41; 75% women) with marked variability in satiation, ranging from 140 to 2166 kcals to reach satiation.
CTS was assessed through an ad libitum meal, combined with physiological and behavioral evaluations, including calorimetry, imaging, blood sampling, and gastric emptying tests. The largest contributors to CTS variability were sex and genetic factors, while other anthropometric measurements played lesser roles.
Various analyses and assessments of participants’ CTS-GRS scores showed that individuals with a high CTS-GRS, or hungry brain phenotype, experienced significantly greater weight loss when treated with phentermine/topiramate than those with a low CTS-GRS, or hungry gut, phenotype. After 52 weeks of treatment, individuals with the hungry brain phenotype lost an average of 17.4% of their body weight compared with 11.2% in those with the hungry gut phenotype.
An analysis of a separate 16-week study showed that patients with the hungry gut phenotype responded better to the GLP-1 liraglutide, losing 6.4% total body weight, compared to 3.3% for those with the hungry brain phenotype.
Overall, the CTS-GRS test predicted drug response with up to 84% accuracy (area under the curve, 0.76 in men and 0.84 in women). The authors acknowledged that these results need to be replicated prospectively and in more diverse populations to validate the test’s predictive ability.
“This kind of phenotype-based profiling allows us to predict which patients are more likely to respond and who might need a different intervention,” Acosta said. “It’s a critical step toward eliminating trial-and-error in obesity treatment.”
The test (MyPhenome test) is used at more than 80 healthcare clinics in the United States, according to Phenomix Sciences, which manufactures it. A company spokesperson said the test does not require FDA approval because it is used to predict obesity phenotypes to help inform treatment, but not to identify specific medications or other interventions. “If it were to do the latter,” the spokesperson said, “it would be considered a ‘companion diagnostic’ and subject to the FDA clearance process.”
What to Do if an AOM Isn’t Working?
It’s one thing to predict whether an individual might do better on one drug vs another, but what should clinicians do meanwhile to optimize weight loss for their patients who may be struggling on a particular drug?
“Efforts to predict the response to GLP-1 therapy have been a hot topic,” noted Sriram Machineni, MD, associate professor at Montefiore Medical Center, Bronx, New York, and founding director of the Fleischer Institute Medical Weight Center at Montefiore Einstein. Although the current study showed that genetic testing could predict responders, like Acosta, he agreed that the results need to be replicated in a prospective manner.
“In the absence of a validated tool for predicting response to specific medications, we use a prioritization process for trialing medications,” Machineni told GI & Hepatology News. “The prioritization is based on the suitability of the side-effect profile to the specific patient, including contraindications; benefits independent of weight loss, such as cardiovascular protection for semaglutide; average efficacy; and financial accessibility for patients.”
Predicting responders isn’t straightforward, said Robert Kushner, MD, professor of medicine and medical education at the Feinberg School of Medicine at Northwestern University and medical director of the Wellness Institute at Northwestern Memorial Hospital in Chicago.
“Despite looking at baseline demographic data such as race, ethnicity, age, weight, and BMI, we are unable to predict who will lose more or less weight,” he told GI & Hepatology News. The one exception is that women generally lose more weight than men. “However, even among females, we cannot discern which females will lose more weight than other females,” he said.
If an individual is not showing sufficient weight loss on a particular medication, “we first explore potential reasons that can be addressed, such as the patient is not taking the medication or is skipping doses,” Kushner said. If need be, they discuss changing to a different drug to improve compliance. He also stresses the importance of making lifestyle changes in diet and physical activity for patients taking AOMs.
Often patients who do not lose at least 5% of their weight within 3 months are not likely to respond well to that medication even if they remain on it. “So, early response rates determine longer-term success,” Kushner said.
Acosta said that if a patient isn’t responding to one class of medication, he pivots to a treatment better aligned with their phenotype. “That could mean switching from a GLP-1 to a medication like [naltrexone/bupropion] or trying a new method altogether,” he said. “The key is that the treatment decision is rooted in the patient’s biology, not just a reaction to short-term results. We also emphasize the importance of long-term follow-up and support.”
The goal isn’t just weight loss but also improved health and quality of life, Acosta said. “Whether through medication, surgery, or behavior change, what matters most is tailoring the care plan to each individual’s unique biology and needs.”
The new study received support from the Mayo Clinic Clinical Research Trials Unit, Vivus Inc., and Phenomix Sciences. Acosta is supported by a National Institutes of Health grant.
Acosta is a co-founder and inventor of intellectual property licensed to Phenomix Sciences Inc.; has served as a consultant for Rhythm Pharmaceuticals, Gila Therapeutics, Amgen, General Mills, Boehringer Ingelheim, Currax Pharmaceuticals, Nestlé, Bausch Health, and Rare Diseases; and has received research support or had contracts with Vivus Inc., Satiogen Pharmaceuticals, Boehringer Ingelheim, and Rhythm Pharmaceuticals. Machineni has been involved in semaglutide and tirzepatide clinical trials and has been a consultant to Novo Nordisk, Eli Lilly and Company, and Rhythm Pharmaceuticals. Kushner is on the scientific advisory board for Novo Nordisk.
A version of this article appeared on Medscape.com.
, enabling clinicians to better tailor antiobesity medication (AOM) to the patient.
Currently, patient response to AOMs varies widely, with some patients responding robustly to AOMs and others responding weakly or not at all.
For example, trials of the GLP-1 semaglutide found that 32%-39.6% of people are “super responders,” achieving weight loss in excess of 20%, and a subgroup of 10.2%-16.7% of individuals are nonresponders. Similar variability was found with other AOMs, including the GLP-1 liraglutide and tirzepatide, a dual GLP-1/glucose-dependent insulinotropic polypeptide receptor agonist.
Studies of semaglutide suggest that people with obesity and type 2 diabetes (T2D) lose less weight on the drug than those without T2D, and men tend to lose less weight than women.
However, little else is known about predictors of response rates for various AOMs, and medication selection is typically based on patient or physician preference, comorbidities, medication interactions, and insurance coverage.
Although definitions of a “nonresponder” vary, the Endocrine Society’s latest guideline, which many clinicians follow, states that an AOM is considered effective if patients lose more than 5% of their body weight within 3 months.
Can nonresponders and lower responders be identified and helped? Yes, but it’s complicated.
“Treating obesity effectively means recognizing that not all patients respond the same way to the same treatment, and that’s not a failure; it’s a signal,” said Andres Acosta, MD, PhD, an obesity expert at Mayo Clinic, Rochester, Minnesota, and a cofounder of Phenomix Sciences, a biotech company in Menlo Park, California.
“Obesity is not a single disease. It’s a complex, multifactorial condition driven by diverse biological pathways,” he told GI & Hepatology News. “Semaglutide and other GLP-1s primarily act by reducing appetite and slowing gastric emptying, but not all patients have obesity that is primarily driven by appetite dysregulation.”
Phenotype-Based Profiling
Figuring out what drives an individual’s obesity is where a phenotype-based profiling test could possibly help.
Acosta and colleagues previously used a variety of validated studies and questionnaires to identify four phenotypes that represent distinct biologic drivers of obesity: hungry brain (abnormal satiation), emotional hunger (hedonic eating), hungry gut (abnormal satiety), and slow burn (decreased metabolic rate). In their pragmatic clinical trial, phenotype-guided AOM selection was associated with 1.75-fold greater weight loss after 12 months than the standard approach to drug selection, with mean weight loss of 15.9% and 9%, respectively.
“If a patient’s obesity isn’t primarily rooted in the mechanisms targeted by a particular drug, their response will naturally be limited,” Acosta said. “It’s not that they’re failing the medication; the medication simply isn’t the right match for their biology.”
For their new study, published online in Cell Metabolism, Acosta and colleagues built on their previous research by analyzing the genetic and nongenetic factors that influenced calories needed to reach satiation (Calories to Satiation [CTS]) in adults with obesity. They then used machine learning techniques to develop a CTS gene risk score (CTS-GRS) that could be measured by a DNA saliva test.
The study included 717 adults with obesity (mean age, 41; 75% women) with marked variability in satiation, ranging from 140 to 2166 kcals to reach satiation.
CTS was assessed through an ad libitum meal, combined with physiological and behavioral evaluations, including calorimetry, imaging, blood sampling, and gastric emptying tests. The largest contributors to CTS variability were sex and genetic factors, while other anthropometric measurements played lesser roles.
Various analyses and assessments of participants’ CTS-GRS scores showed that individuals with a high CTS-GRS, or hungry brain phenotype, experienced significantly greater weight loss when treated with phentermine/topiramate than those with a low CTS-GRS, or hungry gut, phenotype. After 52 weeks of treatment, individuals with the hungry brain phenotype lost an average of 17.4% of their body weight compared with 11.2% in those with the hungry gut phenotype.
An analysis of a separate 16-week study showed that patients with the hungry gut phenotype responded better to the GLP-1 liraglutide, losing 6.4% total body weight, compared to 3.3% for those with the hungry brain phenotype.
Overall, the CTS-GRS test predicted drug response with up to 84% accuracy (area under the curve, 0.76 in men and 0.84 in women). The authors acknowledged that these results need to be replicated prospectively and in more diverse populations to validate the test’s predictive ability.
“This kind of phenotype-based profiling allows us to predict which patients are more likely to respond and who might need a different intervention,” Acosta said. “It’s a critical step toward eliminating trial-and-error in obesity treatment.”
The test (MyPhenome test) is used at more than 80 healthcare clinics in the United States, according to Phenomix Sciences, which manufactures it. A company spokesperson said the test does not require FDA approval because it is used to predict obesity phenotypes to help inform treatment, but not to identify specific medications or other interventions. “If it were to do the latter,” the spokesperson said, “it would be considered a ‘companion diagnostic’ and subject to the FDA clearance process.”
What to Do if an AOM Isn’t Working?
It’s one thing to predict whether an individual might do better on one drug vs another, but what should clinicians do meanwhile to optimize weight loss for their patients who may be struggling on a particular drug?
“Efforts to predict the response to GLP-1 therapy have been a hot topic,” noted Sriram Machineni, MD, associate professor at Montefiore Medical Center, Bronx, New York, and founding director of the Fleischer Institute Medical Weight Center at Montefiore Einstein. Although the current study showed that genetic testing could predict responders, like Acosta, he agreed that the results need to be replicated in a prospective manner.
“In the absence of a validated tool for predicting response to specific medications, we use a prioritization process for trialing medications,” Machineni told GI & Hepatology News. “The prioritization is based on the suitability of the side-effect profile to the specific patient, including contraindications; benefits independent of weight loss, such as cardiovascular protection for semaglutide; average efficacy; and financial accessibility for patients.”
Predicting responders isn’t straightforward, said Robert Kushner, MD, professor of medicine and medical education at the Feinberg School of Medicine at Northwestern University and medical director of the Wellness Institute at Northwestern Memorial Hospital in Chicago.
“Despite looking at baseline demographic data such as race, ethnicity, age, weight, and BMI, we are unable to predict who will lose more or less weight,” he told GI & Hepatology News. The one exception is that women generally lose more weight than men. “However, even among females, we cannot discern which females will lose more weight than other females,” he said.
If an individual is not showing sufficient weight loss on a particular medication, “we first explore potential reasons that can be addressed, such as the patient is not taking the medication or is skipping doses,” Kushner said. If need be, they discuss changing to a different drug to improve compliance. He also stresses the importance of making lifestyle changes in diet and physical activity for patients taking AOMs.
Often patients who do not lose at least 5% of their weight within 3 months are not likely to respond well to that medication even if they remain on it. “So, early response rates determine longer-term success,” Kushner said.
Acosta said that if a patient isn’t responding to one class of medication, he pivots to a treatment better aligned with their phenotype. “That could mean switching from a GLP-1 to a medication like [naltrexone/bupropion] or trying a new method altogether,” he said. “The key is that the treatment decision is rooted in the patient’s biology, not just a reaction to short-term results. We also emphasize the importance of long-term follow-up and support.”
The goal isn’t just weight loss but also improved health and quality of life, Acosta said. “Whether through medication, surgery, or behavior change, what matters most is tailoring the care plan to each individual’s unique biology and needs.”
The new study received support from the Mayo Clinic Clinical Research Trials Unit, Vivus Inc., and Phenomix Sciences. Acosta is supported by a National Institutes of Health grant.
Acosta is a co-founder and inventor of intellectual property licensed to Phenomix Sciences Inc.; has served as a consultant for Rhythm Pharmaceuticals, Gila Therapeutics, Amgen, General Mills, Boehringer Ingelheim, Currax Pharmaceuticals, Nestlé, Bausch Health, and Rare Diseases; and has received research support or had contracts with Vivus Inc., Satiogen Pharmaceuticals, Boehringer Ingelheim, and Rhythm Pharmaceuticals. Machineni has been involved in semaglutide and tirzepatide clinical trials and has been a consultant to Novo Nordisk, Eli Lilly and Company, and Rhythm Pharmaceuticals. Kushner is on the scientific advisory board for Novo Nordisk.
A version of this article appeared on Medscape.com.
Novel Gene Risk Score Predicts Outcomes After RYGB Surgery
SAN DIEGO –
The findings suggested that the MyPhenome test (Phenomix Sciences) can help clinicians identify the patients most likely to benefit from bariatric procedures and at a greater risk for long-term weight regain after surgery.
“Patients with both a high genetic risk score and rare mutations in the leptin-melanocortin pathway (LMP) had significantly worse outcomes, maintaining only 4.9% total body weight loss [TBWL] over 15 years compared to up to 24.8% in other genetic groups,” Phenomix Sciences Co-founder Andres Acosta, MD, PhD, told GI & Hepatology News.
The study included details on the score’s development and predictive capability. It was presented at Digestive Disease Week® (DDW) 2025
‘More Precise Bariatric Care’
The researchers recently developed a machine learning-assisted gene risk score for calories to satiation (CTSGRS), which mainly involves genes in the LMP. To assess the role of the score with or without LMP gene variants on weight loss and weight recurrence after RYGB, they identified 707 patients with a history of bariatric procedures from the Mayo Clinic Biobank. Patients with duodenal switch, revisional procedures, or who used antiobesity medications or became pregnant during follow-up were excluded.
To make predictions for 442 of the patients, the team first collected anthropometric data up to 15 years after RYGB. Then they used a two-step approach: Assessing for monogenic variants in the LMP and defining participants as carriers (LMP+) or noncarriers (LMP-). Then they defined the gene risk score (CTSGRS+ or CTSGRS-).
The result was four groups: LMP+/CTSGRS+, LMP+/CTSGRS-, LMP-/CTSGRS+, and LMP-/CTSGRS-. Multiple regression analysis was used to analyze TBWL percentage (TBWL%) between the groups at different timepoints, adjusting for baseline weight, age, and gender.
At the 10-year follow-up, the LMP+/CTSGRS+ group demonstrated a significantly higher weight recurrence (regain) of TBW% compared to the other groups.
At 15 years post-RYGB, the mean TBWL% for LMP+/CTSGRS+ was -4.9 vs -20.3 for LMP+/CTSGRS-, -18.0 for LMP-/CTSGRS+, and -24.8 for LMP-/CTSGRS-.
Further analyses showed that the LMP+/CTSGRS+ group had significantly less weight loss than LMP+/CTSGRS- and LMP-/CTSGRS- groups.
Based on the findings, the authors wrote, “Genotyping patients could improve the implementation of individualized weight-loss interventions, enhance weight-loss outcomes, and/or may explain one of the etiological factors associated with weight recurrence after RYGB.”
Acosta noted, “We’re actively expanding our research to include more diverse populations by age, sex, and race. This includes ongoing analysis to understand whether certain demographic or physiological characteristics affect how the test performs, particularly in the context of bariatric surgery.”
The team also is investigating the benefits of phenotyping for obesity comorbidities such as heart disease and diabetes, he said, and exploring whether early interventions in high-risk patients can prevent long-term weight regain and improve outcomes.
In addition, Acosta said, the team recently launched “the first prospective, placebo-controlled clinical trial using the MyPhenome test to predict response to semaglutide.” That study is based on earlier findings showing that patients identified with a Hungry Gut phenotype lost nearly twice as much weight on semaglutide compared with those who tested negative.
Overall, he concluded, “These findings open the door to more precise bariatric care. When we understand a patient’s biological drivers of obesity, we can make better decisions about the right procedure, follow-up, and long-term support. This moves us away from a one-size-fits-all model to care rooted in each patient’s unique biology.”
Potentially Paradigm-Shifting
Onur Kutlu, MD, associate professor of surgery and director of the Metabolic Surgery and Metabolic Health Program at the Miller School of Medicine, University of Miami, in Miami, Florida, commented on the study for GI & Hepatology News. “By integrating polygenic risk scores into predictive models, the authors offer an innovative method for identifying patients at elevated risk for weight regain following RYGB.”
“Their findings support the hypothesis that genetic predisposition — particularly involving energy homeostasis pathways — may underlie differential postoperative trajectories,” he said. “This approach has the potential to shift the paradigm from reactive to proactive management of weight recurrence.”
Because current options for treat weight regain are “suboptimal,” he said, “prevention becomes paramount. Preoperative identification of high-risk individuals could inform surgical decision-making, enable earlier interventions, and facilitate personalized postoperative monitoring and support.”
“If validated in larger, prospective cohorts, genetic risk stratification could enhance the precision of bariatric care and improve long-term outcomes,” he added. “Future studies should aim to validate these genetic models across diverse populations and explore how integration of behavioral, psychological, and genetic data may further refine patient selection and care pathways.”
The study was funded by Mayo Clinic and Phenomix Sciences. Gila Therapeutics and Phenomix Sciences licensed Acosta’s research technologies from the University of Florida and Mayo Clinic. Acosta declared receiving consultant fees in the past 5 years from Rhythm Pharmaceuticals, Gila Therapeutics, Amgen, General Mills, BI, Currax, Nestle, Phenomix Sciences, Bausch Health, and RareDiseases, as well as funding support from the National Institutes of Health, Vivus Pharmaceuticals, Novo Nordisk, Apollo Endosurgery, Satiogen Pharmaceuticals, Spatz Medical, and Rhythm Pharmaceuticals. Kutlu declared having no conflicts of interest.
A version of this article appeared on Medscape.com.
SAN DIEGO –
The findings suggested that the MyPhenome test (Phenomix Sciences) can help clinicians identify the patients most likely to benefit from bariatric procedures and at a greater risk for long-term weight regain after surgery.
“Patients with both a high genetic risk score and rare mutations in the leptin-melanocortin pathway (LMP) had significantly worse outcomes, maintaining only 4.9% total body weight loss [TBWL] over 15 years compared to up to 24.8% in other genetic groups,” Phenomix Sciences Co-founder Andres Acosta, MD, PhD, told GI & Hepatology News.
The study included details on the score’s development and predictive capability. It was presented at Digestive Disease Week® (DDW) 2025
‘More Precise Bariatric Care’
The researchers recently developed a machine learning-assisted gene risk score for calories to satiation (CTSGRS), which mainly involves genes in the LMP. To assess the role of the score with or without LMP gene variants on weight loss and weight recurrence after RYGB, they identified 707 patients with a history of bariatric procedures from the Mayo Clinic Biobank. Patients with duodenal switch, revisional procedures, or who used antiobesity medications or became pregnant during follow-up were excluded.
To make predictions for 442 of the patients, the team first collected anthropometric data up to 15 years after RYGB. Then they used a two-step approach: Assessing for monogenic variants in the LMP and defining participants as carriers (LMP+) or noncarriers (LMP-). Then they defined the gene risk score (CTSGRS+ or CTSGRS-).
The result was four groups: LMP+/CTSGRS+, LMP+/CTSGRS-, LMP-/CTSGRS+, and LMP-/CTSGRS-. Multiple regression analysis was used to analyze TBWL percentage (TBWL%) between the groups at different timepoints, adjusting for baseline weight, age, and gender.
At the 10-year follow-up, the LMP+/CTSGRS+ group demonstrated a significantly higher weight recurrence (regain) of TBW% compared to the other groups.
At 15 years post-RYGB, the mean TBWL% for LMP+/CTSGRS+ was -4.9 vs -20.3 for LMP+/CTSGRS-, -18.0 for LMP-/CTSGRS+, and -24.8 for LMP-/CTSGRS-.
Further analyses showed that the LMP+/CTSGRS+ group had significantly less weight loss than LMP+/CTSGRS- and LMP-/CTSGRS- groups.
Based on the findings, the authors wrote, “Genotyping patients could improve the implementation of individualized weight-loss interventions, enhance weight-loss outcomes, and/or may explain one of the etiological factors associated with weight recurrence after RYGB.”
Acosta noted, “We’re actively expanding our research to include more diverse populations by age, sex, and race. This includes ongoing analysis to understand whether certain demographic or physiological characteristics affect how the test performs, particularly in the context of bariatric surgery.”
The team also is investigating the benefits of phenotyping for obesity comorbidities such as heart disease and diabetes, he said, and exploring whether early interventions in high-risk patients can prevent long-term weight regain and improve outcomes.
In addition, Acosta said, the team recently launched “the first prospective, placebo-controlled clinical trial using the MyPhenome test to predict response to semaglutide.” That study is based on earlier findings showing that patients identified with a Hungry Gut phenotype lost nearly twice as much weight on semaglutide compared with those who tested negative.
Overall, he concluded, “These findings open the door to more precise bariatric care. When we understand a patient’s biological drivers of obesity, we can make better decisions about the right procedure, follow-up, and long-term support. This moves us away from a one-size-fits-all model to care rooted in each patient’s unique biology.”
Potentially Paradigm-Shifting
Onur Kutlu, MD, associate professor of surgery and director of the Metabolic Surgery and Metabolic Health Program at the Miller School of Medicine, University of Miami, in Miami, Florida, commented on the study for GI & Hepatology News. “By integrating polygenic risk scores into predictive models, the authors offer an innovative method for identifying patients at elevated risk for weight regain following RYGB.”
“Their findings support the hypothesis that genetic predisposition — particularly involving energy homeostasis pathways — may underlie differential postoperative trajectories,” he said. “This approach has the potential to shift the paradigm from reactive to proactive management of weight recurrence.”
Because current options for treat weight regain are “suboptimal,” he said, “prevention becomes paramount. Preoperative identification of high-risk individuals could inform surgical decision-making, enable earlier interventions, and facilitate personalized postoperative monitoring and support.”
“If validated in larger, prospective cohorts, genetic risk stratification could enhance the precision of bariatric care and improve long-term outcomes,” he added. “Future studies should aim to validate these genetic models across diverse populations and explore how integration of behavioral, psychological, and genetic data may further refine patient selection and care pathways.”
The study was funded by Mayo Clinic and Phenomix Sciences. Gila Therapeutics and Phenomix Sciences licensed Acosta’s research technologies from the University of Florida and Mayo Clinic. Acosta declared receiving consultant fees in the past 5 years from Rhythm Pharmaceuticals, Gila Therapeutics, Amgen, General Mills, BI, Currax, Nestle, Phenomix Sciences, Bausch Health, and RareDiseases, as well as funding support from the National Institutes of Health, Vivus Pharmaceuticals, Novo Nordisk, Apollo Endosurgery, Satiogen Pharmaceuticals, Spatz Medical, and Rhythm Pharmaceuticals. Kutlu declared having no conflicts of interest.
A version of this article appeared on Medscape.com.
SAN DIEGO –
The findings suggested that the MyPhenome test (Phenomix Sciences) can help clinicians identify the patients most likely to benefit from bariatric procedures and at a greater risk for long-term weight regain after surgery.
“Patients with both a high genetic risk score and rare mutations in the leptin-melanocortin pathway (LMP) had significantly worse outcomes, maintaining only 4.9% total body weight loss [TBWL] over 15 years compared to up to 24.8% in other genetic groups,” Phenomix Sciences Co-founder Andres Acosta, MD, PhD, told GI & Hepatology News.
The study included details on the score’s development and predictive capability. It was presented at Digestive Disease Week® (DDW) 2025
‘More Precise Bariatric Care’
The researchers recently developed a machine learning-assisted gene risk score for calories to satiation (CTSGRS), which mainly involves genes in the LMP. To assess the role of the score with or without LMP gene variants on weight loss and weight recurrence after RYGB, they identified 707 patients with a history of bariatric procedures from the Mayo Clinic Biobank. Patients with duodenal switch, revisional procedures, or who used antiobesity medications or became pregnant during follow-up were excluded.
To make predictions for 442 of the patients, the team first collected anthropometric data up to 15 years after RYGB. Then they used a two-step approach: Assessing for monogenic variants in the LMP and defining participants as carriers (LMP+) or noncarriers (LMP-). Then they defined the gene risk score (CTSGRS+ or CTSGRS-).
The result was four groups: LMP+/CTSGRS+, LMP+/CTSGRS-, LMP-/CTSGRS+, and LMP-/CTSGRS-. Multiple regression analysis was used to analyze TBWL percentage (TBWL%) between the groups at different timepoints, adjusting for baseline weight, age, and gender.
At the 10-year follow-up, the LMP+/CTSGRS+ group demonstrated a significantly higher weight recurrence (regain) of TBW% compared to the other groups.
At 15 years post-RYGB, the mean TBWL% for LMP+/CTSGRS+ was -4.9 vs -20.3 for LMP+/CTSGRS-, -18.0 for LMP-/CTSGRS+, and -24.8 for LMP-/CTSGRS-.
Further analyses showed that the LMP+/CTSGRS+ group had significantly less weight loss than LMP+/CTSGRS- and LMP-/CTSGRS- groups.
Based on the findings, the authors wrote, “Genotyping patients could improve the implementation of individualized weight-loss interventions, enhance weight-loss outcomes, and/or may explain one of the etiological factors associated with weight recurrence after RYGB.”
Acosta noted, “We’re actively expanding our research to include more diverse populations by age, sex, and race. This includes ongoing analysis to understand whether certain demographic or physiological characteristics affect how the test performs, particularly in the context of bariatric surgery.”
The team also is investigating the benefits of phenotyping for obesity comorbidities such as heart disease and diabetes, he said, and exploring whether early interventions in high-risk patients can prevent long-term weight regain and improve outcomes.
In addition, Acosta said, the team recently launched “the first prospective, placebo-controlled clinical trial using the MyPhenome test to predict response to semaglutide.” That study is based on earlier findings showing that patients identified with a Hungry Gut phenotype lost nearly twice as much weight on semaglutide compared with those who tested negative.
Overall, he concluded, “These findings open the door to more precise bariatric care. When we understand a patient’s biological drivers of obesity, we can make better decisions about the right procedure, follow-up, and long-term support. This moves us away from a one-size-fits-all model to care rooted in each patient’s unique biology.”
Potentially Paradigm-Shifting
Onur Kutlu, MD, associate professor of surgery and director of the Metabolic Surgery and Metabolic Health Program at the Miller School of Medicine, University of Miami, in Miami, Florida, commented on the study for GI & Hepatology News. “By integrating polygenic risk scores into predictive models, the authors offer an innovative method for identifying patients at elevated risk for weight regain following RYGB.”
“Their findings support the hypothesis that genetic predisposition — particularly involving energy homeostasis pathways — may underlie differential postoperative trajectories,” he said. “This approach has the potential to shift the paradigm from reactive to proactive management of weight recurrence.”
Because current options for treat weight regain are “suboptimal,” he said, “prevention becomes paramount. Preoperative identification of high-risk individuals could inform surgical decision-making, enable earlier interventions, and facilitate personalized postoperative monitoring and support.”
“If validated in larger, prospective cohorts, genetic risk stratification could enhance the precision of bariatric care and improve long-term outcomes,” he added. “Future studies should aim to validate these genetic models across diverse populations and explore how integration of behavioral, psychological, and genetic data may further refine patient selection and care pathways.”
The study was funded by Mayo Clinic and Phenomix Sciences. Gila Therapeutics and Phenomix Sciences licensed Acosta’s research technologies from the University of Florida and Mayo Clinic. Acosta declared receiving consultant fees in the past 5 years from Rhythm Pharmaceuticals, Gila Therapeutics, Amgen, General Mills, BI, Currax, Nestle, Phenomix Sciences, Bausch Health, and RareDiseases, as well as funding support from the National Institutes of Health, Vivus Pharmaceuticals, Novo Nordisk, Apollo Endosurgery, Satiogen Pharmaceuticals, Spatz Medical, and Rhythm Pharmaceuticals. Kutlu declared having no conflicts of interest.
A version of this article appeared on Medscape.com.
FROM DDW 2025
Walnuts Cut Gut Permeability in Obesity
, a small study showed.
“Less than 10% of adults are meeting their fiber needs each day, and walnuts are a source of dietary fiber, which helps nourish the gut microbiota,” study coauthor Hannah Holscher, PhD, RD, associate professor of nutrition at the University of Illinois at Urbana-Champaign, told GI & Hepatology News.
Holscher and her colleagues previously conducted a study on the effects of walnut consumption on the human intestinal microbiota “and found interesting results,” she said. Among 18 healthy men and women with a mean age of 53 years, “walnuts enriched intestinal microorganisms, including Roseburia that provide important gut-health promoting attributes, like short-chain fatty acid production. We also saw lower proinflammatory secondary bile acid concentrations in individuals that ate walnuts.”
The current study, presented at NUTRITION 2025 in Orlando, Florida, found similar benefits among 30 adults with obesity but without diabetes or gastrointestinal disease.
Walnut Halves, Walnut Oil, Corn Oil — Compared
The researchers aimed to determine the impact of walnut consumption on the gut microbiome, serum and fecal bile acid profiles, systemic inflammation, and oral glucose tolerance to a mixed-meal challenge.
Participants were enrolled in a randomized, controlled, crossover, complete feeding trial with three 3-week conditions, each identical except for walnut halves (WH), walnut oil (WO), or corn oil (CO) in the diet. A 3-week washout separated each condition.
“This was a fully controlled dietary feeding intervention,” Holscher said. “We provided their breakfast, lunch, snacks and dinners — all of their foods and beverages during the three dietary intervention periods that lasted for 3 weeks each. Their base diet consisted of typical American foods that you would find in a grocery store in central Illinois.”
Fecal samples were collected on days 18-20. On day 20, participants underwent a 6-hour mixed-meal tolerance test (75 g glucose + treatment) with a fasting blood draw followed by blood sampling every 30 minutes.
The fecal microbiome and microbiota were assessed using metagenomic and amplicon sequencing, respectively. Fecal microbial metabolites were quantified using gas chromatography-mass spectrometry.
Blood glucose, insulin, and inflammatory biomarkers (interleukin-6, tumor necrosis factor-alpha, C-reactive protein, and lipopolysaccharide-binding protein) were quantified. Fecal and circulating bile acids were measured via liquid chromatography tandem mass spectrometry.
Gut permeability was assessed by quantifying 24-hour urinary excretion of orally ingested sucralose and erythritol on day 21.
Linear mixed-effects models and repeated measures ANOVA were used for the statistical analysis.
The team found that Roseburia spp were greatest following WH (3.9%) vs WO (1.6) and CO (1.9); Lachnospiraceae UCG-001 and UCG-004 were also greatest with WH vs WO and CO.
WH fecal isobutyrate concentrations (5.41 µmol/g) were lower than WO (7.17 µmol/g) and CO (7.77). Similarly, fecal isovalerate concentrations were lowest with WH (7.84 µmol/g) vs WO (10.3µmol/g) and CO (11.6 µmol/g).
In contrast, indoles were highest in WH (36.8 µmol/g) vs WO (6.78 µmol/g) and CO (8.67µmol/g).
No differences in glucose concentrations were seen among groups. The 2-hour area under the curve (AUC) for insulin was lower with WH (469 µIU/mL/min) and WO (494) vs CO (604 µIU/mL/min).
The 4-hour AUC for glycolithocholic acid was lower with WH vs WO and CO. Furthermore, sucralose recovery was lowest following WH (10.5) vs WO (14.3) and CO (14.6).
“Our current efforts are focused on understanding connections between plasma bile acids and glycemic control (ie, blood glucose and insulin concentrations),” Holscher said. “We are also interested in studying individualized or personalized responses, since people had different magnitudes of responses.”
In addition, she said, “as the gut microbiome is one of the factors that can underpin the physiological response to the diet, we are interested in determining if there are microbial signatures that are predictive of glycemic control.”
Because the research is still in the early stages, at this point, Holscher simply encourages people to eat a variety of fruits, vegetables, whole grains, legumes and nuts to meet their daily fiber recommendations and support their gut microbiome.
This study was funded by a USDA NIFA grant. No competing interests were reported.
A version of this article appeared on Medscape.com .
, a small study showed.
“Less than 10% of adults are meeting their fiber needs each day, and walnuts are a source of dietary fiber, which helps nourish the gut microbiota,” study coauthor Hannah Holscher, PhD, RD, associate professor of nutrition at the University of Illinois at Urbana-Champaign, told GI & Hepatology News.
Holscher and her colleagues previously conducted a study on the effects of walnut consumption on the human intestinal microbiota “and found interesting results,” she said. Among 18 healthy men and women with a mean age of 53 years, “walnuts enriched intestinal microorganisms, including Roseburia that provide important gut-health promoting attributes, like short-chain fatty acid production. We also saw lower proinflammatory secondary bile acid concentrations in individuals that ate walnuts.”
The current study, presented at NUTRITION 2025 in Orlando, Florida, found similar benefits among 30 adults with obesity but without diabetes or gastrointestinal disease.
Walnut Halves, Walnut Oil, Corn Oil — Compared
The researchers aimed to determine the impact of walnut consumption on the gut microbiome, serum and fecal bile acid profiles, systemic inflammation, and oral glucose tolerance to a mixed-meal challenge.
Participants were enrolled in a randomized, controlled, crossover, complete feeding trial with three 3-week conditions, each identical except for walnut halves (WH), walnut oil (WO), or corn oil (CO) in the diet. A 3-week washout separated each condition.
“This was a fully controlled dietary feeding intervention,” Holscher said. “We provided their breakfast, lunch, snacks and dinners — all of their foods and beverages during the three dietary intervention periods that lasted for 3 weeks each. Their base diet consisted of typical American foods that you would find in a grocery store in central Illinois.”
Fecal samples were collected on days 18-20. On day 20, participants underwent a 6-hour mixed-meal tolerance test (75 g glucose + treatment) with a fasting blood draw followed by blood sampling every 30 minutes.
The fecal microbiome and microbiota were assessed using metagenomic and amplicon sequencing, respectively. Fecal microbial metabolites were quantified using gas chromatography-mass spectrometry.
Blood glucose, insulin, and inflammatory biomarkers (interleukin-6, tumor necrosis factor-alpha, C-reactive protein, and lipopolysaccharide-binding protein) were quantified. Fecal and circulating bile acids were measured via liquid chromatography tandem mass spectrometry.
Gut permeability was assessed by quantifying 24-hour urinary excretion of orally ingested sucralose and erythritol on day 21.
Linear mixed-effects models and repeated measures ANOVA were used for the statistical analysis.
The team found that Roseburia spp were greatest following WH (3.9%) vs WO (1.6) and CO (1.9); Lachnospiraceae UCG-001 and UCG-004 were also greatest with WH vs WO and CO.
WH fecal isobutyrate concentrations (5.41 µmol/g) were lower than WO (7.17 µmol/g) and CO (7.77). Similarly, fecal isovalerate concentrations were lowest with WH (7.84 µmol/g) vs WO (10.3µmol/g) and CO (11.6 µmol/g).
In contrast, indoles were highest in WH (36.8 µmol/g) vs WO (6.78 µmol/g) and CO (8.67µmol/g).
No differences in glucose concentrations were seen among groups. The 2-hour area under the curve (AUC) for insulin was lower with WH (469 µIU/mL/min) and WO (494) vs CO (604 µIU/mL/min).
The 4-hour AUC for glycolithocholic acid was lower with WH vs WO and CO. Furthermore, sucralose recovery was lowest following WH (10.5) vs WO (14.3) and CO (14.6).
“Our current efforts are focused on understanding connections between plasma bile acids and glycemic control (ie, blood glucose and insulin concentrations),” Holscher said. “We are also interested in studying individualized or personalized responses, since people had different magnitudes of responses.”
In addition, she said, “as the gut microbiome is one of the factors that can underpin the physiological response to the diet, we are interested in determining if there are microbial signatures that are predictive of glycemic control.”
Because the research is still in the early stages, at this point, Holscher simply encourages people to eat a variety of fruits, vegetables, whole grains, legumes and nuts to meet their daily fiber recommendations and support their gut microbiome.
This study was funded by a USDA NIFA grant. No competing interests were reported.
A version of this article appeared on Medscape.com .
, a small study showed.
“Less than 10% of adults are meeting their fiber needs each day, and walnuts are a source of dietary fiber, which helps nourish the gut microbiota,” study coauthor Hannah Holscher, PhD, RD, associate professor of nutrition at the University of Illinois at Urbana-Champaign, told GI & Hepatology News.
Holscher and her colleagues previously conducted a study on the effects of walnut consumption on the human intestinal microbiota “and found interesting results,” she said. Among 18 healthy men and women with a mean age of 53 years, “walnuts enriched intestinal microorganisms, including Roseburia that provide important gut-health promoting attributes, like short-chain fatty acid production. We also saw lower proinflammatory secondary bile acid concentrations in individuals that ate walnuts.”
The current study, presented at NUTRITION 2025 in Orlando, Florida, found similar benefits among 30 adults with obesity but without diabetes or gastrointestinal disease.
Walnut Halves, Walnut Oil, Corn Oil — Compared
The researchers aimed to determine the impact of walnut consumption on the gut microbiome, serum and fecal bile acid profiles, systemic inflammation, and oral glucose tolerance to a mixed-meal challenge.
Participants were enrolled in a randomized, controlled, crossover, complete feeding trial with three 3-week conditions, each identical except for walnut halves (WH), walnut oil (WO), or corn oil (CO) in the diet. A 3-week washout separated each condition.
“This was a fully controlled dietary feeding intervention,” Holscher said. “We provided their breakfast, lunch, snacks and dinners — all of their foods and beverages during the three dietary intervention periods that lasted for 3 weeks each. Their base diet consisted of typical American foods that you would find in a grocery store in central Illinois.”
Fecal samples were collected on days 18-20. On day 20, participants underwent a 6-hour mixed-meal tolerance test (75 g glucose + treatment) with a fasting blood draw followed by blood sampling every 30 minutes.
The fecal microbiome and microbiota were assessed using metagenomic and amplicon sequencing, respectively. Fecal microbial metabolites were quantified using gas chromatography-mass spectrometry.
Blood glucose, insulin, and inflammatory biomarkers (interleukin-6, tumor necrosis factor-alpha, C-reactive protein, and lipopolysaccharide-binding protein) were quantified. Fecal and circulating bile acids were measured via liquid chromatography tandem mass spectrometry.
Gut permeability was assessed by quantifying 24-hour urinary excretion of orally ingested sucralose and erythritol on day 21.
Linear mixed-effects models and repeated measures ANOVA were used for the statistical analysis.
The team found that Roseburia spp were greatest following WH (3.9%) vs WO (1.6) and CO (1.9); Lachnospiraceae UCG-001 and UCG-004 were also greatest with WH vs WO and CO.
WH fecal isobutyrate concentrations (5.41 µmol/g) were lower than WO (7.17 µmol/g) and CO (7.77). Similarly, fecal isovalerate concentrations were lowest with WH (7.84 µmol/g) vs WO (10.3µmol/g) and CO (11.6 µmol/g).
In contrast, indoles were highest in WH (36.8 µmol/g) vs WO (6.78 µmol/g) and CO (8.67µmol/g).
No differences in glucose concentrations were seen among groups. The 2-hour area under the curve (AUC) for insulin was lower with WH (469 µIU/mL/min) and WO (494) vs CO (604 µIU/mL/min).
The 4-hour AUC for glycolithocholic acid was lower with WH vs WO and CO. Furthermore, sucralose recovery was lowest following WH (10.5) vs WO (14.3) and CO (14.6).
“Our current efforts are focused on understanding connections between plasma bile acids and glycemic control (ie, blood glucose and insulin concentrations),” Holscher said. “We are also interested in studying individualized or personalized responses, since people had different magnitudes of responses.”
In addition, she said, “as the gut microbiome is one of the factors that can underpin the physiological response to the diet, we are interested in determining if there are microbial signatures that are predictive of glycemic control.”
Because the research is still in the early stages, at this point, Holscher simply encourages people to eat a variety of fruits, vegetables, whole grains, legumes and nuts to meet their daily fiber recommendations and support their gut microbiome.
This study was funded by a USDA NIFA grant. No competing interests were reported.
A version of this article appeared on Medscape.com .
Patient Navigation Boosts Follow-Up Colonoscopy Completion
The intervention led to a significant 13-point increase in follow-up colonoscopy completion at 1 year, compared with usual care (55.1% vs 42.1%), according the study, which was published online in Annals of Internal Medicine.
“Patients with an abnormal fecal test results have about a 1 in 20 chance of having colorectal cancer found, and many more will be found to have advanced adenomas that can be removed to prevent cancer,” Gloria Coronado, PhD, of Kaiser Permanente Center for Health Research, Portland, Oregon, and University of Arizona Cancer Center, Tucson, said in an interview.
“It is critical that these patients get a follow-up colonoscopy,” she said. “Patient navigation can accomplish this goal.”
‘Highly Effective’ Intervention
Researchers compared the effectiveness of a patient navigation program with that of usual care outreach in increasing follow-up colonoscopy completion after an abnormal stool test. They also developed a risk-prediction model that calculated a patient’s probability of obtaining a follow-up colonoscopy without navigation to determine if the addition of this intervention had a greater impact on those determined to be less likely to follow through.
The study included 967 patients from a community health center in Washington State who received an abnormal fecal test result within the prior month. The mean age of participants was 61 years, approximately 45% were women and 77% were White, and 18% preferred a Spanish-language intervention. In total, 479 patients received the intervention and 488 received usual care.
The intervention was delivered by a patient navigator who mailed introductory letters, sent text messages, and made live phone calls. In the calls, the navigators addressed the topics of barrier assessment and resolution, bowel preparation instruction and reminders, colonoscopy check-in, and understanding colonoscopy results and retesting intervals.
Patients in the usual-care group were contacted by a referral coordinator to schedule a follow-up colonoscopy appointment. If they couldn’t be reached initially, up to two follow-up attempts were made at 30 and 45 days after the referral date.
Patient navigation resulted in a significant 13% increase in follow-up, and those in this group completed a colonoscopy 27 days sooner than those in the usual care group (mean, 229 days vs 256 days).
Contrary to the authors’ expectation, the effectiveness of the intervention did not vary by patients’ predicted likelihood of obtaining a colonoscopy without navigation.
Notably, 20.3% of patients were unreachable or lost to follow-up, and 29.7% did not receive navigation. Among the 479 patients assigned to navigation, 79 (16.5%) declined participation and 56 (11.7%) were never reached.
The study was primarily conducted during the height of the COVID-19 pandemic, which created additional systemic and individual barriers to completing colonoscopies.
Nevertheless, the authors wrote, “our findings suggest that patient navigation is highly effective for patients eligible for colonoscopy.”
“Most patients who were reached were contacted with six or fewer phone attempts,” Coronado noted. “Further efforts are needed to determine how to reach and motivate patients [who did not participate] to get a follow-up colonoscopy.”
Coronado and colleagues are exploring ways to leverage artificial intelligence and virtual approaches to augment patient navigation programs — for example, by using a virtual navigator or low-cost automated tools to provide education to build patient confidence in getting a colonoscopy.
‘A Promising Tool’
“Colonoscopy completion after positive stool-based testing is critical to mitigating the impact of colon cancer,” commented Rajiv Bhuta, MD, assistant professor of clinical gastroenterology & hepatology, Lewis Katz School of Medicine, Temple University, Philadelphia, who was not involved in the study. “While prior studies assessing navigation have demonstrated improvements, none were as large enrollment-wise or as generalizable as the current study.”
That said, Bhuta said in an interview that the study could have provided more detail about coordination and communication with local gastrointestinal practices.
“Local ordering and prescribing practices vary and can significantly impact compliance rates. Were colonoscopies completed via an open access pathway or were the patients required to see a gastroenterologist first? How long was the average wait time for colonoscopy once scheduled? What were the local policies on requiring an escort after the procedure?”
He also noted that some aspects of the study — such as access to reduced-cost specialty care and free ride-share services — may limit generalizable to settings without such resources.
He added: “Although patient navigators for cancer treatment have mandated reimbursement, there is no current reimbursement for navigators for abnormal screening tests, another barrier to wide-spread implementation.”
Bhuta said that the dropout rate in the study mirrors that of his own real-world practice, which serves a high-risk, low-resource community. “I would specifically like to see research that provides behavioral insights on why patients respond positively to navigation — whether it is due to reminders, emotional support, or logistical assistance. Is it systemic barriers or patient disinterest or both that drives noncompliance?”
Despite these uncertainties and the need to refine implementation logistics, Bhuta concluded, “this strategy is a promising tool to reduce disparities and improve colorectal cancer outcomes. Clinicians should advocate for or implement structured follow-up systems, particularly in high-risk populations.”
The study was funded by the US National Cancer Institute. Coronado received a grant/contract from Guardant Health. Bhuta declared no relevant conflicts of interest.
A version of this article appeared on Medscape.com.
The intervention led to a significant 13-point increase in follow-up colonoscopy completion at 1 year, compared with usual care (55.1% vs 42.1%), according the study, which was published online in Annals of Internal Medicine.
“Patients with an abnormal fecal test results have about a 1 in 20 chance of having colorectal cancer found, and many more will be found to have advanced adenomas that can be removed to prevent cancer,” Gloria Coronado, PhD, of Kaiser Permanente Center for Health Research, Portland, Oregon, and University of Arizona Cancer Center, Tucson, said in an interview.
“It is critical that these patients get a follow-up colonoscopy,” she said. “Patient navigation can accomplish this goal.”
‘Highly Effective’ Intervention
Researchers compared the effectiveness of a patient navigation program with that of usual care outreach in increasing follow-up colonoscopy completion after an abnormal stool test. They also developed a risk-prediction model that calculated a patient’s probability of obtaining a follow-up colonoscopy without navigation to determine if the addition of this intervention had a greater impact on those determined to be less likely to follow through.
The study included 967 patients from a community health center in Washington State who received an abnormal fecal test result within the prior month. The mean age of participants was 61 years, approximately 45% were women and 77% were White, and 18% preferred a Spanish-language intervention. In total, 479 patients received the intervention and 488 received usual care.
The intervention was delivered by a patient navigator who mailed introductory letters, sent text messages, and made live phone calls. In the calls, the navigators addressed the topics of barrier assessment and resolution, bowel preparation instruction and reminders, colonoscopy check-in, and understanding colonoscopy results and retesting intervals.
Patients in the usual-care group were contacted by a referral coordinator to schedule a follow-up colonoscopy appointment. If they couldn’t be reached initially, up to two follow-up attempts were made at 30 and 45 days after the referral date.
Patient navigation resulted in a significant 13% increase in follow-up, and those in this group completed a colonoscopy 27 days sooner than those in the usual care group (mean, 229 days vs 256 days).
Contrary to the authors’ expectation, the effectiveness of the intervention did not vary by patients’ predicted likelihood of obtaining a colonoscopy without navigation.
Notably, 20.3% of patients were unreachable or lost to follow-up, and 29.7% did not receive navigation. Among the 479 patients assigned to navigation, 79 (16.5%) declined participation and 56 (11.7%) were never reached.
The study was primarily conducted during the height of the COVID-19 pandemic, which created additional systemic and individual barriers to completing colonoscopies.
Nevertheless, the authors wrote, “our findings suggest that patient navigation is highly effective for patients eligible for colonoscopy.”
“Most patients who were reached were contacted with six or fewer phone attempts,” Coronado noted. “Further efforts are needed to determine how to reach and motivate patients [who did not participate] to get a follow-up colonoscopy.”
Coronado and colleagues are exploring ways to leverage artificial intelligence and virtual approaches to augment patient navigation programs — for example, by using a virtual navigator or low-cost automated tools to provide education to build patient confidence in getting a colonoscopy.
‘A Promising Tool’
“Colonoscopy completion after positive stool-based testing is critical to mitigating the impact of colon cancer,” commented Rajiv Bhuta, MD, assistant professor of clinical gastroenterology & hepatology, Lewis Katz School of Medicine, Temple University, Philadelphia, who was not involved in the study. “While prior studies assessing navigation have demonstrated improvements, none were as large enrollment-wise or as generalizable as the current study.”
That said, Bhuta said in an interview that the study could have provided more detail about coordination and communication with local gastrointestinal practices.
“Local ordering and prescribing practices vary and can significantly impact compliance rates. Were colonoscopies completed via an open access pathway or were the patients required to see a gastroenterologist first? How long was the average wait time for colonoscopy once scheduled? What were the local policies on requiring an escort after the procedure?”
He also noted that some aspects of the study — such as access to reduced-cost specialty care and free ride-share services — may limit generalizable to settings without such resources.
He added: “Although patient navigators for cancer treatment have mandated reimbursement, there is no current reimbursement for navigators for abnormal screening tests, another barrier to wide-spread implementation.”
Bhuta said that the dropout rate in the study mirrors that of his own real-world practice, which serves a high-risk, low-resource community. “I would specifically like to see research that provides behavioral insights on why patients respond positively to navigation — whether it is due to reminders, emotional support, or logistical assistance. Is it systemic barriers or patient disinterest or both that drives noncompliance?”
Despite these uncertainties and the need to refine implementation logistics, Bhuta concluded, “this strategy is a promising tool to reduce disparities and improve colorectal cancer outcomes. Clinicians should advocate for or implement structured follow-up systems, particularly in high-risk populations.”
The study was funded by the US National Cancer Institute. Coronado received a grant/contract from Guardant Health. Bhuta declared no relevant conflicts of interest.
A version of this article appeared on Medscape.com.
The intervention led to a significant 13-point increase in follow-up colonoscopy completion at 1 year, compared with usual care (55.1% vs 42.1%), according the study, which was published online in Annals of Internal Medicine.
“Patients with an abnormal fecal test results have about a 1 in 20 chance of having colorectal cancer found, and many more will be found to have advanced adenomas that can be removed to prevent cancer,” Gloria Coronado, PhD, of Kaiser Permanente Center for Health Research, Portland, Oregon, and University of Arizona Cancer Center, Tucson, said in an interview.
“It is critical that these patients get a follow-up colonoscopy,” she said. “Patient navigation can accomplish this goal.”
‘Highly Effective’ Intervention
Researchers compared the effectiveness of a patient navigation program with that of usual care outreach in increasing follow-up colonoscopy completion after an abnormal stool test. They also developed a risk-prediction model that calculated a patient’s probability of obtaining a follow-up colonoscopy without navigation to determine if the addition of this intervention had a greater impact on those determined to be less likely to follow through.
The study included 967 patients from a community health center in Washington State who received an abnormal fecal test result within the prior month. The mean age of participants was 61 years, approximately 45% were women and 77% were White, and 18% preferred a Spanish-language intervention. In total, 479 patients received the intervention and 488 received usual care.
The intervention was delivered by a patient navigator who mailed introductory letters, sent text messages, and made live phone calls. In the calls, the navigators addressed the topics of barrier assessment and resolution, bowel preparation instruction and reminders, colonoscopy check-in, and understanding colonoscopy results and retesting intervals.
Patients in the usual-care group were contacted by a referral coordinator to schedule a follow-up colonoscopy appointment. If they couldn’t be reached initially, up to two follow-up attempts were made at 30 and 45 days after the referral date.
Patient navigation resulted in a significant 13% increase in follow-up, and those in this group completed a colonoscopy 27 days sooner than those in the usual care group (mean, 229 days vs 256 days).
Contrary to the authors’ expectation, the effectiveness of the intervention did not vary by patients’ predicted likelihood of obtaining a colonoscopy without navigation.
Notably, 20.3% of patients were unreachable or lost to follow-up, and 29.7% did not receive navigation. Among the 479 patients assigned to navigation, 79 (16.5%) declined participation and 56 (11.7%) were never reached.
The study was primarily conducted during the height of the COVID-19 pandemic, which created additional systemic and individual barriers to completing colonoscopies.
Nevertheless, the authors wrote, “our findings suggest that patient navigation is highly effective for patients eligible for colonoscopy.”
“Most patients who were reached were contacted with six or fewer phone attempts,” Coronado noted. “Further efforts are needed to determine how to reach and motivate patients [who did not participate] to get a follow-up colonoscopy.”
Coronado and colleagues are exploring ways to leverage artificial intelligence and virtual approaches to augment patient navigation programs — for example, by using a virtual navigator or low-cost automated tools to provide education to build patient confidence in getting a colonoscopy.
‘A Promising Tool’
“Colonoscopy completion after positive stool-based testing is critical to mitigating the impact of colon cancer,” commented Rajiv Bhuta, MD, assistant professor of clinical gastroenterology & hepatology, Lewis Katz School of Medicine, Temple University, Philadelphia, who was not involved in the study. “While prior studies assessing navigation have demonstrated improvements, none were as large enrollment-wise or as generalizable as the current study.”
That said, Bhuta said in an interview that the study could have provided more detail about coordination and communication with local gastrointestinal practices.
“Local ordering and prescribing practices vary and can significantly impact compliance rates. Were colonoscopies completed via an open access pathway or were the patients required to see a gastroenterologist first? How long was the average wait time for colonoscopy once scheduled? What were the local policies on requiring an escort after the procedure?”
He also noted that some aspects of the study — such as access to reduced-cost specialty care and free ride-share services — may limit generalizable to settings without such resources.
He added: “Although patient navigators for cancer treatment have mandated reimbursement, there is no current reimbursement for navigators for abnormal screening tests, another barrier to wide-spread implementation.”
Bhuta said that the dropout rate in the study mirrors that of his own real-world practice, which serves a high-risk, low-resource community. “I would specifically like to see research that provides behavioral insights on why patients respond positively to navigation — whether it is due to reminders, emotional support, or logistical assistance. Is it systemic barriers or patient disinterest or both that drives noncompliance?”
Despite these uncertainties and the need to refine implementation logistics, Bhuta concluded, “this strategy is a promising tool to reduce disparities and improve colorectal cancer outcomes. Clinicians should advocate for or implement structured follow-up systems, particularly in high-risk populations.”
The study was funded by the US National Cancer Institute. Coronado received a grant/contract from Guardant Health. Bhuta declared no relevant conflicts of interest.
A version of this article appeared on Medscape.com.
FROM ANNALS OF INTERNAL MEDICINE
Model May Predict Which UC Patients Risk Rehospitalization
a preliminary modeling study suggests.
“Absence of a gastroenterologist consultation within the year prior to admission, male sex, shorter length of hospital stay, and narcotic prescription at the time of discharge were independently associated with the risk for 90-day rehospitalization for a UC-related indication,” study author Sanjay Murthy, MD, associate professor of gastroenterology at the University of Ottawa, Ontario, Canada, and staff gastroenterologist at the Inflammatory Bowel Disease Centre at The Ottawa Hospital, said in an interview.
“While some hospital readmissions are likely unavoidable, a subset of them, particularly readmissions that occur soon after discharge, may be preventable with early and intensive postdischarge outpatient management,” he said. “Identifying those who are at high risk for early readmission is a rational first step toward applying targeted outpatient interventions that reduce this risk.”
The study was published in The Journal of the Canadian Association of Gastroenterology.
Major Predictor Variables
The researchers conducted a retrospective study in adults with UC who were admitted to The Ottawa Hospital between 2009 and 2016 for a UC flare or UC-related complication, excluding bowel cancer. Using medical records and administrative health databases, they derived and validated a multivariable logistic regression model of 90-day UC-related rehospitalization risk.
Participants’ mean age at UC diagnosis was 35.3 years and 50.4% were men. In the year before the index hospitalization, 138 (55.6%) participants had a gastroenterologist visit, whereas 41 (16.5%) were hospitalized.
During the index hospitalization, 42 (16.9%) patients were newly diagnosed with UC, and 25 (10.1%) underwent intra-abdominal surgery. At discharge, 34 (13.7%) patients were prescribed an outpatient narcotic. The mean length of hospital stay was 9.97 days. Twenty-seven individuals (10.9%) were rehospitalized within 90 days of discharge.
Out of 35 variables, the model identified the following four as significant predictors of 90-day rehospitalization: gastroenterologist consultation within the prior year (adjusted odds ratio [aOR], 0.09), male sex (aOR, 3.77), length of hospital stay (aOR, 0.93), and discharge with narcotics prescription (aOR, 5.94).
The model had 77.8% sensitivity, 80.9% specificity, 33% positive predictive value, and 96.7% negative predictive value for predicting high vs low risk for 90-day hospital readmission.
The researchers noted several study limitations. The cohort was relatively small, which limited the statistical power for model building and identifying variable associations with the outcome. In addition, the study was conducted in a single tertiary care center, which limits its generalizability. Retrospective data may have affected the accuracy of the measurements, and information on some relevant variables was not available.
Nevertheless, Murthy said, “optimally applying our prediction model at the point of hospital discharge would have classified only about a quarter of individuals in our cohort as being at high-risk for 90-day readmission and potentially needing targeted early outpatient intervention, and this would have captured close to 80% of individuals who were destined for early readmission.”
“However, our research is still preliminary and requires considerably more work to ensure that the findings are suitable for application to clinical practice,” he added. “In the meantime, practitioners may reflect on the potential importance of the major predictor variables identified in our study within their practices.”
Careful Follow-Up Key
Rajiv Bhuta, MD, assistant professor of clinical gastroenterology and hepatology at Temple University and a gastroenterologist at Temple University Hospital, both in Philadelphia, Pennsylvania, commented on the study but was not involved in it.
“The model performed fairly well (c-statistic of 0.78) using four variables: Gastroenterologist consultation within the prior year (protective), male sex (higher risk), length of stay (marginally protective), and narcotic prescription at discharge (higher risk). These are intuitive predictors that align with prior literature on UC hospitalizations,” said Bhuta.
“From a clinical perspective, this type of tool could be useful for targeting high-risk patients for early outpatient interventions (eg, close gastroenterology follow-up and pain management strategies). The negative predictive value (96.7%) suggests that it is particularly good at identifying patients at low risk for rehospitalization, which may help prioritize resource allocation more efficiently. However, practical implementation will require external validation and integration into electronic medical records to automatically flag high-risk patients at discharge.”
In addition, Bhuta noted, “the study only examines patient data through 2016. Why have the last 8 years been excluded? Given the small sample size and the sea change in available inflammatory bowel disease therapies since 2016, there could be significantly different findings with more current data.”
Furthermore, there is a lack of specific data supporting the protective effect of a gastroenterology visit in the previous year, and the readmission rate was lower than that reported by others (10% vs 20%), which, he said “may skew their findings.”
“The strong protective effect of prior gastroenterologist visits underscores the importance of specialty proactive disease management in these complex patients,” Bhuta continued. “Narcotic prescriptions at discharge may indicate inadequate disease activity control, thus making these patients important targets for close follow-up. Narcotics are generally not required once successful disease control has been achieved with steroids or biologics.
“While promising, this tool should not yet replace clinical judgment until it undergoes external validation,” he concluded. “In the meantime, clinicians should focus on structured outpatient follow-up and careful discharge planning to minimize UC-related rehospitalizations.”
This study was funded by a grant provided to Murthy by the department of medicine at the University of Ottawa. Murthy and Bhuta declared having no relevant financial relationships.
A version of this article appeared on Medscape.com .
a preliminary modeling study suggests.
“Absence of a gastroenterologist consultation within the year prior to admission, male sex, shorter length of hospital stay, and narcotic prescription at the time of discharge were independently associated with the risk for 90-day rehospitalization for a UC-related indication,” study author Sanjay Murthy, MD, associate professor of gastroenterology at the University of Ottawa, Ontario, Canada, and staff gastroenterologist at the Inflammatory Bowel Disease Centre at The Ottawa Hospital, said in an interview.
“While some hospital readmissions are likely unavoidable, a subset of them, particularly readmissions that occur soon after discharge, may be preventable with early and intensive postdischarge outpatient management,” he said. “Identifying those who are at high risk for early readmission is a rational first step toward applying targeted outpatient interventions that reduce this risk.”
The study was published in The Journal of the Canadian Association of Gastroenterology.
Major Predictor Variables
The researchers conducted a retrospective study in adults with UC who were admitted to The Ottawa Hospital between 2009 and 2016 for a UC flare or UC-related complication, excluding bowel cancer. Using medical records and administrative health databases, they derived and validated a multivariable logistic regression model of 90-day UC-related rehospitalization risk.
Participants’ mean age at UC diagnosis was 35.3 years and 50.4% were men. In the year before the index hospitalization, 138 (55.6%) participants had a gastroenterologist visit, whereas 41 (16.5%) were hospitalized.
During the index hospitalization, 42 (16.9%) patients were newly diagnosed with UC, and 25 (10.1%) underwent intra-abdominal surgery. At discharge, 34 (13.7%) patients were prescribed an outpatient narcotic. The mean length of hospital stay was 9.97 days. Twenty-seven individuals (10.9%) were rehospitalized within 90 days of discharge.
Out of 35 variables, the model identified the following four as significant predictors of 90-day rehospitalization: gastroenterologist consultation within the prior year (adjusted odds ratio [aOR], 0.09), male sex (aOR, 3.77), length of hospital stay (aOR, 0.93), and discharge with narcotics prescription (aOR, 5.94).
The model had 77.8% sensitivity, 80.9% specificity, 33% positive predictive value, and 96.7% negative predictive value for predicting high vs low risk for 90-day hospital readmission.
The researchers noted several study limitations. The cohort was relatively small, which limited the statistical power for model building and identifying variable associations with the outcome. In addition, the study was conducted in a single tertiary care center, which limits its generalizability. Retrospective data may have affected the accuracy of the measurements, and information on some relevant variables was not available.
Nevertheless, Murthy said, “optimally applying our prediction model at the point of hospital discharge would have classified only about a quarter of individuals in our cohort as being at high-risk for 90-day readmission and potentially needing targeted early outpatient intervention, and this would have captured close to 80% of individuals who were destined for early readmission.”
“However, our research is still preliminary and requires considerably more work to ensure that the findings are suitable for application to clinical practice,” he added. “In the meantime, practitioners may reflect on the potential importance of the major predictor variables identified in our study within their practices.”
Careful Follow-Up Key
Rajiv Bhuta, MD, assistant professor of clinical gastroenterology and hepatology at Temple University and a gastroenterologist at Temple University Hospital, both in Philadelphia, Pennsylvania, commented on the study but was not involved in it.
“The model performed fairly well (c-statistic of 0.78) using four variables: Gastroenterologist consultation within the prior year (protective), male sex (higher risk), length of stay (marginally protective), and narcotic prescription at discharge (higher risk). These are intuitive predictors that align with prior literature on UC hospitalizations,” said Bhuta.
“From a clinical perspective, this type of tool could be useful for targeting high-risk patients for early outpatient interventions (eg, close gastroenterology follow-up and pain management strategies). The negative predictive value (96.7%) suggests that it is particularly good at identifying patients at low risk for rehospitalization, which may help prioritize resource allocation more efficiently. However, practical implementation will require external validation and integration into electronic medical records to automatically flag high-risk patients at discharge.”
In addition, Bhuta noted, “the study only examines patient data through 2016. Why have the last 8 years been excluded? Given the small sample size and the sea change in available inflammatory bowel disease therapies since 2016, there could be significantly different findings with more current data.”
Furthermore, there is a lack of specific data supporting the protective effect of a gastroenterology visit in the previous year, and the readmission rate was lower than that reported by others (10% vs 20%), which, he said “may skew their findings.”
“The strong protective effect of prior gastroenterologist visits underscores the importance of specialty proactive disease management in these complex patients,” Bhuta continued. “Narcotic prescriptions at discharge may indicate inadequate disease activity control, thus making these patients important targets for close follow-up. Narcotics are generally not required once successful disease control has been achieved with steroids or biologics.
“While promising, this tool should not yet replace clinical judgment until it undergoes external validation,” he concluded. “In the meantime, clinicians should focus on structured outpatient follow-up and careful discharge planning to minimize UC-related rehospitalizations.”
This study was funded by a grant provided to Murthy by the department of medicine at the University of Ottawa. Murthy and Bhuta declared having no relevant financial relationships.
A version of this article appeared on Medscape.com .
a preliminary modeling study suggests.
“Absence of a gastroenterologist consultation within the year prior to admission, male sex, shorter length of hospital stay, and narcotic prescription at the time of discharge were independently associated with the risk for 90-day rehospitalization for a UC-related indication,” study author Sanjay Murthy, MD, associate professor of gastroenterology at the University of Ottawa, Ontario, Canada, and staff gastroenterologist at the Inflammatory Bowel Disease Centre at The Ottawa Hospital, said in an interview.
“While some hospital readmissions are likely unavoidable, a subset of them, particularly readmissions that occur soon after discharge, may be preventable with early and intensive postdischarge outpatient management,” he said. “Identifying those who are at high risk for early readmission is a rational first step toward applying targeted outpatient interventions that reduce this risk.”
The study was published in The Journal of the Canadian Association of Gastroenterology.
Major Predictor Variables
The researchers conducted a retrospective study in adults with UC who were admitted to The Ottawa Hospital between 2009 and 2016 for a UC flare or UC-related complication, excluding bowel cancer. Using medical records and administrative health databases, they derived and validated a multivariable logistic regression model of 90-day UC-related rehospitalization risk.
Participants’ mean age at UC diagnosis was 35.3 years and 50.4% were men. In the year before the index hospitalization, 138 (55.6%) participants had a gastroenterologist visit, whereas 41 (16.5%) were hospitalized.
During the index hospitalization, 42 (16.9%) patients were newly diagnosed with UC, and 25 (10.1%) underwent intra-abdominal surgery. At discharge, 34 (13.7%) patients were prescribed an outpatient narcotic. The mean length of hospital stay was 9.97 days. Twenty-seven individuals (10.9%) were rehospitalized within 90 days of discharge.
Out of 35 variables, the model identified the following four as significant predictors of 90-day rehospitalization: gastroenterologist consultation within the prior year (adjusted odds ratio [aOR], 0.09), male sex (aOR, 3.77), length of hospital stay (aOR, 0.93), and discharge with narcotics prescription (aOR, 5.94).
The model had 77.8% sensitivity, 80.9% specificity, 33% positive predictive value, and 96.7% negative predictive value for predicting high vs low risk for 90-day hospital readmission.
The researchers noted several study limitations. The cohort was relatively small, which limited the statistical power for model building and identifying variable associations with the outcome. In addition, the study was conducted in a single tertiary care center, which limits its generalizability. Retrospective data may have affected the accuracy of the measurements, and information on some relevant variables was not available.
Nevertheless, Murthy said, “optimally applying our prediction model at the point of hospital discharge would have classified only about a quarter of individuals in our cohort as being at high-risk for 90-day readmission and potentially needing targeted early outpatient intervention, and this would have captured close to 80% of individuals who were destined for early readmission.”
“However, our research is still preliminary and requires considerably more work to ensure that the findings are suitable for application to clinical practice,” he added. “In the meantime, practitioners may reflect on the potential importance of the major predictor variables identified in our study within their practices.”
Careful Follow-Up Key
Rajiv Bhuta, MD, assistant professor of clinical gastroenterology and hepatology at Temple University and a gastroenterologist at Temple University Hospital, both in Philadelphia, Pennsylvania, commented on the study but was not involved in it.
“The model performed fairly well (c-statistic of 0.78) using four variables: Gastroenterologist consultation within the prior year (protective), male sex (higher risk), length of stay (marginally protective), and narcotic prescription at discharge (higher risk). These are intuitive predictors that align with prior literature on UC hospitalizations,” said Bhuta.
“From a clinical perspective, this type of tool could be useful for targeting high-risk patients for early outpatient interventions (eg, close gastroenterology follow-up and pain management strategies). The negative predictive value (96.7%) suggests that it is particularly good at identifying patients at low risk for rehospitalization, which may help prioritize resource allocation more efficiently. However, practical implementation will require external validation and integration into electronic medical records to automatically flag high-risk patients at discharge.”
In addition, Bhuta noted, “the study only examines patient data through 2016. Why have the last 8 years been excluded? Given the small sample size and the sea change in available inflammatory bowel disease therapies since 2016, there could be significantly different findings with more current data.”
Furthermore, there is a lack of specific data supporting the protective effect of a gastroenterology visit in the previous year, and the readmission rate was lower than that reported by others (10% vs 20%), which, he said “may skew their findings.”
“The strong protective effect of prior gastroenterologist visits underscores the importance of specialty proactive disease management in these complex patients,” Bhuta continued. “Narcotic prescriptions at discharge may indicate inadequate disease activity control, thus making these patients important targets for close follow-up. Narcotics are generally not required once successful disease control has been achieved with steroids or biologics.
“While promising, this tool should not yet replace clinical judgment until it undergoes external validation,” he concluded. “In the meantime, clinicians should focus on structured outpatient follow-up and careful discharge planning to minimize UC-related rehospitalizations.”
This study was funded by a grant provided to Murthy by the department of medicine at the University of Ottawa. Murthy and Bhuta declared having no relevant financial relationships.
A version of this article appeared on Medscape.com .
FROM THE JOURNAL OF THE CANADIAN ASSOCIATION OF GASTROENTEROLOGY
Quality, Not Type, of Diet Linked to Microbiome Health
new research suggested.
For example, red meat was a strong driver of omnivore microbiomes, with corresponding signature microbes that are negatively correlated with host cardiometabolic health.
In contrast, the signature microbes found in vegans’ gut microbiomes were correlated with favorable cardiometabolic markers and were enriched in omnivores who ate more plant-based foods.
“From the viewpoint of the impact of diet on the gut microbiome, what seems to be more important is the diversity of healthy plant-based foods that are consumed,” principal author Nicola Segata, PhD, University of Trento in Italy, said in an interview. “Whether this comes within a vegan or an omnivore diet is less crucial, as long as there is no specific overconsumption of unhealthy food categories, such as red meat.”
Excluding broad categories of foods also can have consequences, he added. “For example, we saw that the exclusion of dairy fermented foods is associated with decreased presence of potentially probiotic microbes that are constitutive of such foods. Avoiding meat or dairy products does not necessarily have a positive effect if it does not come with a variety of quality plant-based products.”
The study was published online in Nature Microbiology.
Diet Tied to Microbial Signature
The researchers analyzed biological samples from 21,561 individuals across five multi-national cohorts to map how differences in diet patterns (omnivore, vegetarian, and vegan) are reflected in gut microbiomes.
They found that the three diet patterns are highly distinguishable by their microbial profiles and that each diet has corresponding unique signature microbes, including those tied to digestion of specific types of food and sometimes those derived from food itself.
The microbiomes of omnivores had an increased presence of bacteria associated with meat digestion, such as Alistipes putredinis, which is involved in protein fermentation. Omnivores also had more bacteria associated with both inflammatory bowel disease and increased colon cancer risk, such as Ruminococcus torques and Bilophila wadsworthia.
The microbiomes of vegans had an abundance of bacteria involved in fiber fermentation, such as several species of Bacteroides and Firmicutes phyla, which help produce short-chain fatty acids. These compounds have beneficial effects on gut health by reducing inflammation and helping to maintain a better homeostatic balance between an individual’s metabolism and immune system.
The main difference between vegetarians and vegans was the presence in vegetarians’ microbiomes of Streptococcus thermophilus, a bacterium found mainly in dairy products and used in the production of yogurt.
Dietary factors within each diet pattern, such as the amount of plant-based food, shape the microbiome more than the type of diet and are important for gut health, according to the authors. For example, by eating more plant-based foods, people with an omnivorous diet can bring the proportion of beneficial signature microbes in their microbiomes more in line with the levels in people who are vegan or vegetarian.
“Since our data showed that omnivores on average ingest significantly fewer healthy plant-based foods than vegetarians or vegans, optimizing the quality of omnivore diets by increasing dietary plant diversity could lead to better gut health,” they wrote.
The ultimate goal, Segata said, is “a precision nutrition approach that recommends foods based on the configuration of the microbiome of patients and of the aspects of the microbiome one wants to enhance. We are not there yet, but it is nonetheless important to know which foods are usually boosting which types of members of the gut microbiome.”
His team is currently analyzing changes in the gut microbiome induced by diet changes among thousands of participants in various cohorts.
“This is one of the next steps toward unraveling causality along the diet-microbiome-health axis, together with the cultivation of specific microbiome members of interest for potential prebiotic and probiotic strategies,” he said.
Conventional Dietary Advice for Now
The findings are consistent with those of previous studies, Jack Gilbert, MD, director of the Microbiome and Metagenomics Center at the University of California, San Diego, and president of Applied Microbiology International, Cambridge, England, said in an interview.
“Future research needs to focus on whether the gut microbial signature can predict those that develop cardiovascular disease in each cohort — ie, the n-of-1 studies, whereby a vegan develops cardiovascular disease, or a carnivore does not,” said Gilbert, who was not involved in the study.
With more data, he said, “we can also start examining these trends over time to understand what might be going on with these ‘oddballs.’ ”
“There is not much you can do with the ‘eat a healthy balanced diet’ routine,” he noted. “If I got a microbiome signature, I could potentially tell you what to eat to optimize your blood glucose trends and your lipid panels but not to handle long-term disease risk, yet. So sticking with the guideline-recommended dietary advice seems best, until we can provide more nuanced advice for the patient.
“Importantly, I would also like to see time-resolved data,” he added. “Signatures can fluctuate over time, even over days, and so collecting a few weeks of stool samples would help us to better align the microbiome signatures to clinical endpoints.”
Segata is a consultant to and receives options from ZOE. Gilbert is a member of the scientific advisory boards of Holobiome, BiomeSense, EcoBiomics Canadian Research Program, MASTER EU, Sun Genomics, and Oath; the editorial advisory board for The Scientist; and the external advisory board for the Binational Early Asthma & Microbiome Study. He is also an adviser for Bened Life.
A version of this article appeared on Medscape.com.
new research suggested.
For example, red meat was a strong driver of omnivore microbiomes, with corresponding signature microbes that are negatively correlated with host cardiometabolic health.
In contrast, the signature microbes found in vegans’ gut microbiomes were correlated with favorable cardiometabolic markers and were enriched in omnivores who ate more plant-based foods.
“From the viewpoint of the impact of diet on the gut microbiome, what seems to be more important is the diversity of healthy plant-based foods that are consumed,” principal author Nicola Segata, PhD, University of Trento in Italy, said in an interview. “Whether this comes within a vegan or an omnivore diet is less crucial, as long as there is no specific overconsumption of unhealthy food categories, such as red meat.”
Excluding broad categories of foods also can have consequences, he added. “For example, we saw that the exclusion of dairy fermented foods is associated with decreased presence of potentially probiotic microbes that are constitutive of such foods. Avoiding meat or dairy products does not necessarily have a positive effect if it does not come with a variety of quality plant-based products.”
The study was published online in Nature Microbiology.
Diet Tied to Microbial Signature
The researchers analyzed biological samples from 21,561 individuals across five multi-national cohorts to map how differences in diet patterns (omnivore, vegetarian, and vegan) are reflected in gut microbiomes.
They found that the three diet patterns are highly distinguishable by their microbial profiles and that each diet has corresponding unique signature microbes, including those tied to digestion of specific types of food and sometimes those derived from food itself.
The microbiomes of omnivores had an increased presence of bacteria associated with meat digestion, such as Alistipes putredinis, which is involved in protein fermentation. Omnivores also had more bacteria associated with both inflammatory bowel disease and increased colon cancer risk, such as Ruminococcus torques and Bilophila wadsworthia.
The microbiomes of vegans had an abundance of bacteria involved in fiber fermentation, such as several species of Bacteroides and Firmicutes phyla, which help produce short-chain fatty acids. These compounds have beneficial effects on gut health by reducing inflammation and helping to maintain a better homeostatic balance between an individual’s metabolism and immune system.
The main difference between vegetarians and vegans was the presence in vegetarians’ microbiomes of Streptococcus thermophilus, a bacterium found mainly in dairy products and used in the production of yogurt.
Dietary factors within each diet pattern, such as the amount of plant-based food, shape the microbiome more than the type of diet and are important for gut health, according to the authors. For example, by eating more plant-based foods, people with an omnivorous diet can bring the proportion of beneficial signature microbes in their microbiomes more in line with the levels in people who are vegan or vegetarian.
“Since our data showed that omnivores on average ingest significantly fewer healthy plant-based foods than vegetarians or vegans, optimizing the quality of omnivore diets by increasing dietary plant diversity could lead to better gut health,” they wrote.
The ultimate goal, Segata said, is “a precision nutrition approach that recommends foods based on the configuration of the microbiome of patients and of the aspects of the microbiome one wants to enhance. We are not there yet, but it is nonetheless important to know which foods are usually boosting which types of members of the gut microbiome.”
His team is currently analyzing changes in the gut microbiome induced by diet changes among thousands of participants in various cohorts.
“This is one of the next steps toward unraveling causality along the diet-microbiome-health axis, together with the cultivation of specific microbiome members of interest for potential prebiotic and probiotic strategies,” he said.
Conventional Dietary Advice for Now
The findings are consistent with those of previous studies, Jack Gilbert, MD, director of the Microbiome and Metagenomics Center at the University of California, San Diego, and president of Applied Microbiology International, Cambridge, England, said in an interview.
“Future research needs to focus on whether the gut microbial signature can predict those that develop cardiovascular disease in each cohort — ie, the n-of-1 studies, whereby a vegan develops cardiovascular disease, or a carnivore does not,” said Gilbert, who was not involved in the study.
With more data, he said, “we can also start examining these trends over time to understand what might be going on with these ‘oddballs.’ ”
“There is not much you can do with the ‘eat a healthy balanced diet’ routine,” he noted. “If I got a microbiome signature, I could potentially tell you what to eat to optimize your blood glucose trends and your lipid panels but not to handle long-term disease risk, yet. So sticking with the guideline-recommended dietary advice seems best, until we can provide more nuanced advice for the patient.
“Importantly, I would also like to see time-resolved data,” he added. “Signatures can fluctuate over time, even over days, and so collecting a few weeks of stool samples would help us to better align the microbiome signatures to clinical endpoints.”
Segata is a consultant to and receives options from ZOE. Gilbert is a member of the scientific advisory boards of Holobiome, BiomeSense, EcoBiomics Canadian Research Program, MASTER EU, Sun Genomics, and Oath; the editorial advisory board for The Scientist; and the external advisory board for the Binational Early Asthma & Microbiome Study. He is also an adviser for Bened Life.
A version of this article appeared on Medscape.com.
new research suggested.
For example, red meat was a strong driver of omnivore microbiomes, with corresponding signature microbes that are negatively correlated with host cardiometabolic health.
In contrast, the signature microbes found in vegans’ gut microbiomes were correlated with favorable cardiometabolic markers and were enriched in omnivores who ate more plant-based foods.
“From the viewpoint of the impact of diet on the gut microbiome, what seems to be more important is the diversity of healthy plant-based foods that are consumed,” principal author Nicola Segata, PhD, University of Trento in Italy, said in an interview. “Whether this comes within a vegan or an omnivore diet is less crucial, as long as there is no specific overconsumption of unhealthy food categories, such as red meat.”
Excluding broad categories of foods also can have consequences, he added. “For example, we saw that the exclusion of dairy fermented foods is associated with decreased presence of potentially probiotic microbes that are constitutive of such foods. Avoiding meat or dairy products does not necessarily have a positive effect if it does not come with a variety of quality plant-based products.”
The study was published online in Nature Microbiology.
Diet Tied to Microbial Signature
The researchers analyzed biological samples from 21,561 individuals across five multi-national cohorts to map how differences in diet patterns (omnivore, vegetarian, and vegan) are reflected in gut microbiomes.
They found that the three diet patterns are highly distinguishable by their microbial profiles and that each diet has corresponding unique signature microbes, including those tied to digestion of specific types of food and sometimes those derived from food itself.
The microbiomes of omnivores had an increased presence of bacteria associated with meat digestion, such as Alistipes putredinis, which is involved in protein fermentation. Omnivores also had more bacteria associated with both inflammatory bowel disease and increased colon cancer risk, such as Ruminococcus torques and Bilophila wadsworthia.
The microbiomes of vegans had an abundance of bacteria involved in fiber fermentation, such as several species of Bacteroides and Firmicutes phyla, which help produce short-chain fatty acids. These compounds have beneficial effects on gut health by reducing inflammation and helping to maintain a better homeostatic balance between an individual’s metabolism and immune system.
The main difference between vegetarians and vegans was the presence in vegetarians’ microbiomes of Streptococcus thermophilus, a bacterium found mainly in dairy products and used in the production of yogurt.
Dietary factors within each diet pattern, such as the amount of plant-based food, shape the microbiome more than the type of diet and are important for gut health, according to the authors. For example, by eating more plant-based foods, people with an omnivorous diet can bring the proportion of beneficial signature microbes in their microbiomes more in line with the levels in people who are vegan or vegetarian.
“Since our data showed that omnivores on average ingest significantly fewer healthy plant-based foods than vegetarians or vegans, optimizing the quality of omnivore diets by increasing dietary plant diversity could lead to better gut health,” they wrote.
The ultimate goal, Segata said, is “a precision nutrition approach that recommends foods based on the configuration of the microbiome of patients and of the aspects of the microbiome one wants to enhance. We are not there yet, but it is nonetheless important to know which foods are usually boosting which types of members of the gut microbiome.”
His team is currently analyzing changes in the gut microbiome induced by diet changes among thousands of participants in various cohorts.
“This is one of the next steps toward unraveling causality along the diet-microbiome-health axis, together with the cultivation of specific microbiome members of interest for potential prebiotic and probiotic strategies,” he said.
Conventional Dietary Advice for Now
The findings are consistent with those of previous studies, Jack Gilbert, MD, director of the Microbiome and Metagenomics Center at the University of California, San Diego, and president of Applied Microbiology International, Cambridge, England, said in an interview.
“Future research needs to focus on whether the gut microbial signature can predict those that develop cardiovascular disease in each cohort — ie, the n-of-1 studies, whereby a vegan develops cardiovascular disease, or a carnivore does not,” said Gilbert, who was not involved in the study.
With more data, he said, “we can also start examining these trends over time to understand what might be going on with these ‘oddballs.’ ”
“There is not much you can do with the ‘eat a healthy balanced diet’ routine,” he noted. “If I got a microbiome signature, I could potentially tell you what to eat to optimize your blood glucose trends and your lipid panels but not to handle long-term disease risk, yet. So sticking with the guideline-recommended dietary advice seems best, until we can provide more nuanced advice for the patient.
“Importantly, I would also like to see time-resolved data,” he added. “Signatures can fluctuate over time, even over days, and so collecting a few weeks of stool samples would help us to better align the microbiome signatures to clinical endpoints.”
Segata is a consultant to and receives options from ZOE. Gilbert is a member of the scientific advisory boards of Holobiome, BiomeSense, EcoBiomics Canadian Research Program, MASTER EU, Sun Genomics, and Oath; the editorial advisory board for The Scientist; and the external advisory board for the Binational Early Asthma & Microbiome Study. He is also an adviser for Bened Life.
A version of this article appeared on Medscape.com.
FROM NATURE MICROBIOLOGY
Leaving ED Without Being Seen Entails Increasing Risks
Higher rates of leaving the emergency department (ED) without being seen are linked to increased short-term mortality or hospitalization, according to a cohort study in Ontario, Canada.
“We found that after 2020, there was a 14% higher risk for death or hospitalization within 7 days” among patients who left without being seen (LWBS), Candace McNaughton, MD, PhD, associate professor of medicine at the University of Toronto and scientist at Sunnybrook Research Institute, both in Toronto, Ontario, Canada, told this news organization.
“When we looked at death by itself, there was a 46% higher risk after 2020,” she said. “Even 30 days after a LWBS ED visit, there was still a 5% increased risk for death/hospitalization and a 24% increased risk for death.”
The study was published in the Journal of the American College of Emergency Physicians Open.
LWBS Rates Increased
Researchers used linked administrative data to analyze temporal trends in monthly rates of ED and LWBS visits for adults in Ontario from 2014 to 2023.
They compared the composite outcome of 7-day all-cause mortality or hospitalization following an LWBS ED visit in April 2022‒March 2023 (recent period) with that following an LWBS ED visit in April 2014‒March 2020 (baseline period), after adjustment for age, sex, and Charlson Comorbidity Index (CCI).
In the two periods, patient characteristics were similar across age, sex, neighborhood-level income quartile, history of being unhoused, rurality, CCI, day, time, and mode of arrival. The median age was 40 years for the baseline period and 42 years for the recent period.
Temporal trends showed sustained increases in monthly LWBS rates after 2020, despite fewer monthly ED visits. The rate of LWBS ED visits after April 1, 2020, exceeded the baseline period’s single-month LWBS maximum of 4% in 15 of 36 months.
The rate of 7-day all-cause mortality or hospitalization was 3.4% in the recent period vs 2.9% in the baseline period (adjusted risk ratio [aRR], 1.14), despite similar rates of post-ED outpatient visits (7-day recent and baseline, 38.9% and 39.7%, respectively).
Similar trends were seen at 30 days for all-cause mortality or hospitalization (6.2% in the recent period vs 5.8% at baseline; aRR, 1.05) despite similar rates of post-ED outpatient visits (59.4% and 59.7%, respectively).
After April 1, 2020, monthly ED visits and the proportion of patients who LWBS varied widely.
The proportion of LWBS visits categorized as emergent on the Canadian Triage and Acuity Scale was higher during the recent period (12.9% vs 9.2% in the baseline period), and fewer visits were categorized as semiurgent (22.6% vs 31.9%, respectively). This finding suggested a higher acuity of illness among patients who LWBS in the recent period.
LWBS Visits ‘Not Benign’
Results of a preplanned subgroup analysis examining the risk for all-cause mortality after an LWBS visit were “particularly notable,” the authors wrote, with a 46% higher adjusted risk for death at 7 days and 24% higher adjusted risk at 30 days.
The observational study had several limitations, however. The authors could not draw conclusions regarding direct causes of the increased risk for severe short-term adverse health outcomes after an LWBS ED visit, and residual confounding is possible. Cause-of-death information was not available to generate hypotheses for future studies of potential causes. Furthermore, the findings may not be generalizable to systems without universal access to healthcare.
Nevertheless, the findings are a “concerning signal [and] should prompt interventions to address system- and population-level causes,” the authors wrote.
“Unfortunately, because of politics, since 2020, ED closures in Ontario have become more and more common and seem to be affecting more and more Ontarians,” said McNaughton. “It would be surprising if ED closure didn’t play some role in our findings.”
She added, “It is important to note that people in our study were relatively young, with a median age in their 40s; this makes our findings all the more concerning. Clinicians should be aware that LWBS ED visits are not necessarily benign, particularly when rates of LWBS ED visits are high.”
Unanswered Questions
The study raised the following questions that the authors are or will be investigating, according to McNaughton:
- Which patients are at greatest risk for bad outcomes if they leave the ED without being seen, and why?
- How much of the findings might be related to recent ED closures, longer ED wait times, or other factors? Are there geographic variations in risk?
- What can be done in the ED to prevent LWBS ED visits, and what can be changed outside the ED to prevent LWBS ED visits? For example, what can hospitals do to reduce boarding in the ED? If patients leave without being seen, should they be contacted to try to meet their health needs in other ways?
- What worked in terms of maintaining access to outpatient medical care, despite the considerable disruptions starting in 2020, and how can continued success be ensured?
To address the current situation, McNaughton said, “We need consistent, predictable, and sustained investment in our public healthcare system. We need long-term, consistent funding for primary care, ED care, as well as hospital and long-term care.”
“It takes years to recruit and train the teams of people necessary to provide the high-quality medical care that Canadians have a right to. There are no shortcuts,” she concluded.
‘Tragic Situation’
American College of Emergency Physicians (ACEP) spokesperson Jesse Pines, MD, chief of clinical innovation at US Acute Care Solutions; clinical professor of emergency medicine at George Washington University in Washington, DC; and professor of emergency medicine at Drexel University in Philadelphia, commented on the study for this news organization.
“Similar to what the authors found in their report, LWBS and other metrics — specifically boarding — have progressively increased in the United States, in particular, since the early part of 2021,” he said. “The primary factor in the US driving this, and one that ACEP is trying to address on a national scale, is the boarding of admitted patients.”
When the number of boarded patients increases, there is less space in the ED for new patients, and waits increase, Pines explained. Some patients leave without being seen, and a subset of those patients experience poor outcomes. “It’s a tragic situation that is worsening.”
“Emergency physicians like me always worry when patients leave without being seen,” he said. While some of those patients have self-limited conditions that will improve on their own, “some have critical life-threatening conditions that require care and hospitalization. The worry is that these patients experience poorer outcomes,” Pines said. “The authors showed that this is increasingly the case in Canada. The same is likely true in the US.”
The study was funded by the Canadian Institutes of Health Research. McNaughton and Pines declared no relevant financial relationships.
A version of this article first appeared on Medscape.com.
Higher rates of leaving the emergency department (ED) without being seen are linked to increased short-term mortality or hospitalization, according to a cohort study in Ontario, Canada.
“We found that after 2020, there was a 14% higher risk for death or hospitalization within 7 days” among patients who left without being seen (LWBS), Candace McNaughton, MD, PhD, associate professor of medicine at the University of Toronto and scientist at Sunnybrook Research Institute, both in Toronto, Ontario, Canada, told this news organization.
“When we looked at death by itself, there was a 46% higher risk after 2020,” she said. “Even 30 days after a LWBS ED visit, there was still a 5% increased risk for death/hospitalization and a 24% increased risk for death.”
The study was published in the Journal of the American College of Emergency Physicians Open.
LWBS Rates Increased
Researchers used linked administrative data to analyze temporal trends in monthly rates of ED and LWBS visits for adults in Ontario from 2014 to 2023.
They compared the composite outcome of 7-day all-cause mortality or hospitalization following an LWBS ED visit in April 2022‒March 2023 (recent period) with that following an LWBS ED visit in April 2014‒March 2020 (baseline period), after adjustment for age, sex, and Charlson Comorbidity Index (CCI).
In the two periods, patient characteristics were similar across age, sex, neighborhood-level income quartile, history of being unhoused, rurality, CCI, day, time, and mode of arrival. The median age was 40 years for the baseline period and 42 years for the recent period.
Temporal trends showed sustained increases in monthly LWBS rates after 2020, despite fewer monthly ED visits. The rate of LWBS ED visits after April 1, 2020, exceeded the baseline period’s single-month LWBS maximum of 4% in 15 of 36 months.
The rate of 7-day all-cause mortality or hospitalization was 3.4% in the recent period vs 2.9% in the baseline period (adjusted risk ratio [aRR], 1.14), despite similar rates of post-ED outpatient visits (7-day recent and baseline, 38.9% and 39.7%, respectively).
Similar trends were seen at 30 days for all-cause mortality or hospitalization (6.2% in the recent period vs 5.8% at baseline; aRR, 1.05) despite similar rates of post-ED outpatient visits (59.4% and 59.7%, respectively).
After April 1, 2020, monthly ED visits and the proportion of patients who LWBS varied widely.
The proportion of LWBS visits categorized as emergent on the Canadian Triage and Acuity Scale was higher during the recent period (12.9% vs 9.2% in the baseline period), and fewer visits were categorized as semiurgent (22.6% vs 31.9%, respectively). This finding suggested a higher acuity of illness among patients who LWBS in the recent period.
LWBS Visits ‘Not Benign’
Results of a preplanned subgroup analysis examining the risk for all-cause mortality after an LWBS visit were “particularly notable,” the authors wrote, with a 46% higher adjusted risk for death at 7 days and 24% higher adjusted risk at 30 days.
The observational study had several limitations, however. The authors could not draw conclusions regarding direct causes of the increased risk for severe short-term adverse health outcomes after an LWBS ED visit, and residual confounding is possible. Cause-of-death information was not available to generate hypotheses for future studies of potential causes. Furthermore, the findings may not be generalizable to systems without universal access to healthcare.
Nevertheless, the findings are a “concerning signal [and] should prompt interventions to address system- and population-level causes,” the authors wrote.
“Unfortunately, because of politics, since 2020, ED closures in Ontario have become more and more common and seem to be affecting more and more Ontarians,” said McNaughton. “It would be surprising if ED closure didn’t play some role in our findings.”
She added, “It is important to note that people in our study were relatively young, with a median age in their 40s; this makes our findings all the more concerning. Clinicians should be aware that LWBS ED visits are not necessarily benign, particularly when rates of LWBS ED visits are high.”
Unanswered Questions
The study raised the following questions that the authors are or will be investigating, according to McNaughton:
- Which patients are at greatest risk for bad outcomes if they leave the ED without being seen, and why?
- How much of the findings might be related to recent ED closures, longer ED wait times, or other factors? Are there geographic variations in risk?
- What can be done in the ED to prevent LWBS ED visits, and what can be changed outside the ED to prevent LWBS ED visits? For example, what can hospitals do to reduce boarding in the ED? If patients leave without being seen, should they be contacted to try to meet their health needs in other ways?
- What worked in terms of maintaining access to outpatient medical care, despite the considerable disruptions starting in 2020, and how can continued success be ensured?
To address the current situation, McNaughton said, “We need consistent, predictable, and sustained investment in our public healthcare system. We need long-term, consistent funding for primary care, ED care, as well as hospital and long-term care.”
“It takes years to recruit and train the teams of people necessary to provide the high-quality medical care that Canadians have a right to. There are no shortcuts,” she concluded.
‘Tragic Situation’
American College of Emergency Physicians (ACEP) spokesperson Jesse Pines, MD, chief of clinical innovation at US Acute Care Solutions; clinical professor of emergency medicine at George Washington University in Washington, DC; and professor of emergency medicine at Drexel University in Philadelphia, commented on the study for this news organization.
“Similar to what the authors found in their report, LWBS and other metrics — specifically boarding — have progressively increased in the United States, in particular, since the early part of 2021,” he said. “The primary factor in the US driving this, and one that ACEP is trying to address on a national scale, is the boarding of admitted patients.”
When the number of boarded patients increases, there is less space in the ED for new patients, and waits increase, Pines explained. Some patients leave without being seen, and a subset of those patients experience poor outcomes. “It’s a tragic situation that is worsening.”
“Emergency physicians like me always worry when patients leave without being seen,” he said. While some of those patients have self-limited conditions that will improve on their own, “some have critical life-threatening conditions that require care and hospitalization. The worry is that these patients experience poorer outcomes,” Pines said. “The authors showed that this is increasingly the case in Canada. The same is likely true in the US.”
The study was funded by the Canadian Institutes of Health Research. McNaughton and Pines declared no relevant financial relationships.
A version of this article first appeared on Medscape.com.
Higher rates of leaving the emergency department (ED) without being seen are linked to increased short-term mortality or hospitalization, according to a cohort study in Ontario, Canada.
“We found that after 2020, there was a 14% higher risk for death or hospitalization within 7 days” among patients who left without being seen (LWBS), Candace McNaughton, MD, PhD, associate professor of medicine at the University of Toronto and scientist at Sunnybrook Research Institute, both in Toronto, Ontario, Canada, told this news organization.
“When we looked at death by itself, there was a 46% higher risk after 2020,” she said. “Even 30 days after a LWBS ED visit, there was still a 5% increased risk for death/hospitalization and a 24% increased risk for death.”
The study was published in the Journal of the American College of Emergency Physicians Open.
LWBS Rates Increased
Researchers used linked administrative data to analyze temporal trends in monthly rates of ED and LWBS visits for adults in Ontario from 2014 to 2023.
They compared the composite outcome of 7-day all-cause mortality or hospitalization following an LWBS ED visit in April 2022‒March 2023 (recent period) with that following an LWBS ED visit in April 2014‒March 2020 (baseline period), after adjustment for age, sex, and Charlson Comorbidity Index (CCI).
In the two periods, patient characteristics were similar across age, sex, neighborhood-level income quartile, history of being unhoused, rurality, CCI, day, time, and mode of arrival. The median age was 40 years for the baseline period and 42 years for the recent period.
Temporal trends showed sustained increases in monthly LWBS rates after 2020, despite fewer monthly ED visits. The rate of LWBS ED visits after April 1, 2020, exceeded the baseline period’s single-month LWBS maximum of 4% in 15 of 36 months.
The rate of 7-day all-cause mortality or hospitalization was 3.4% in the recent period vs 2.9% in the baseline period (adjusted risk ratio [aRR], 1.14), despite similar rates of post-ED outpatient visits (7-day recent and baseline, 38.9% and 39.7%, respectively).
Similar trends were seen at 30 days for all-cause mortality or hospitalization (6.2% in the recent period vs 5.8% at baseline; aRR, 1.05) despite similar rates of post-ED outpatient visits (59.4% and 59.7%, respectively).
After April 1, 2020, monthly ED visits and the proportion of patients who LWBS varied widely.
The proportion of LWBS visits categorized as emergent on the Canadian Triage and Acuity Scale was higher during the recent period (12.9% vs 9.2% in the baseline period), and fewer visits were categorized as semiurgent (22.6% vs 31.9%, respectively). This finding suggested a higher acuity of illness among patients who LWBS in the recent period.
LWBS Visits ‘Not Benign’
Results of a preplanned subgroup analysis examining the risk for all-cause mortality after an LWBS visit were “particularly notable,” the authors wrote, with a 46% higher adjusted risk for death at 7 days and 24% higher adjusted risk at 30 days.
The observational study had several limitations, however. The authors could not draw conclusions regarding direct causes of the increased risk for severe short-term adverse health outcomes after an LWBS ED visit, and residual confounding is possible. Cause-of-death information was not available to generate hypotheses for future studies of potential causes. Furthermore, the findings may not be generalizable to systems without universal access to healthcare.
Nevertheless, the findings are a “concerning signal [and] should prompt interventions to address system- and population-level causes,” the authors wrote.
“Unfortunately, because of politics, since 2020, ED closures in Ontario have become more and more common and seem to be affecting more and more Ontarians,” said McNaughton. “It would be surprising if ED closure didn’t play some role in our findings.”
She added, “It is important to note that people in our study were relatively young, with a median age in their 40s; this makes our findings all the more concerning. Clinicians should be aware that LWBS ED visits are not necessarily benign, particularly when rates of LWBS ED visits are high.”
Unanswered Questions
The study raised the following questions that the authors are or will be investigating, according to McNaughton:
- Which patients are at greatest risk for bad outcomes if they leave the ED without being seen, and why?
- How much of the findings might be related to recent ED closures, longer ED wait times, or other factors? Are there geographic variations in risk?
- What can be done in the ED to prevent LWBS ED visits, and what can be changed outside the ED to prevent LWBS ED visits? For example, what can hospitals do to reduce boarding in the ED? If patients leave without being seen, should they be contacted to try to meet their health needs in other ways?
- What worked in terms of maintaining access to outpatient medical care, despite the considerable disruptions starting in 2020, and how can continued success be ensured?
To address the current situation, McNaughton said, “We need consistent, predictable, and sustained investment in our public healthcare system. We need long-term, consistent funding for primary care, ED care, as well as hospital and long-term care.”
“It takes years to recruit and train the teams of people necessary to provide the high-quality medical care that Canadians have a right to. There are no shortcuts,” she concluded.
‘Tragic Situation’
American College of Emergency Physicians (ACEP) spokesperson Jesse Pines, MD, chief of clinical innovation at US Acute Care Solutions; clinical professor of emergency medicine at George Washington University in Washington, DC; and professor of emergency medicine at Drexel University in Philadelphia, commented on the study for this news organization.
“Similar to what the authors found in their report, LWBS and other metrics — specifically boarding — have progressively increased in the United States, in particular, since the early part of 2021,” he said. “The primary factor in the US driving this, and one that ACEP is trying to address on a national scale, is the boarding of admitted patients.”
When the number of boarded patients increases, there is less space in the ED for new patients, and waits increase, Pines explained. Some patients leave without being seen, and a subset of those patients experience poor outcomes. “It’s a tragic situation that is worsening.”
“Emergency physicians like me always worry when patients leave without being seen,” he said. While some of those patients have self-limited conditions that will improve on their own, “some have critical life-threatening conditions that require care and hospitalization. The worry is that these patients experience poorer outcomes,” Pines said. “The authors showed that this is increasingly the case in Canada. The same is likely true in the US.”
The study was funded by the Canadian Institutes of Health Research. McNaughton and Pines declared no relevant financial relationships.
A version of this article first appeared on Medscape.com.