Gastroenterology

Among hospitalized patients with cirrhosis, a machine learning (ML) model enhanced mortality prediction compared with traditional methods and was consistent across country income levels in a large global study.

“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.

Among hospitalized patients with cirrhosis, a machine learning (ML) model enhanced mortality prediction compared with traditional methods and was consistent across country income levels in a large global study.

“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.

Among hospitalized patients with cirrhosis, a machine learning (ML) model enhanced mortality prediction compared with traditional methods and was consistent across country income levels in a large global study.

“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.

Private equity (PE) acquisition of gastroenterology (GI) practices led to higher colonoscopy prices, utilization, and spending with no commensurate effect on quality, an economic analysis found. Price increases ranged from about 5% to about 7%.

In view of the growing trend to such acquisitions, policy makers should monitor the impact of PE investment in medical practices, according to researchers led by health economist Daniel R. Arnold, PhD, of the Department of Health Services, Policy & Practice in the School of Public Health at Brown University in Providence, Rhode Island. “In a previous study of ours, gastroenterology stood out as a particularly attractive specialty to private equity,” Arnold told GI & Hepatology News.

Published in JAMA Health Forum, the economic evaluation of more than 1.1 million patients and 1.3 million colonoscopies concluded that PE acquisitions of GI sites are difficult to justify.

 

The Study

This difference-in-differences event study and economic evaluation analyzed data from US GI practices acquired by PE firms from 2015 to 2021. Commercial insurance claims covering more than 50 million enrollees were used to calculate price, spending, utilization, and quality measures from 2012 to 2021, with all data analyzed from April to September 2024.

The main outcomes were price, spending per physician, number of colonoscopies per physician, number of unique patients per physician, and quality, as defined by polyp detection, incomplete colonoscopies, and four adverse event measures: cardiovascular, serious and nonserious GI events, and any other adverse events.

The mean age of patients was 47.1 years, and 47.8% were men. The sample included 718, 851 colonoscopies conducted by 1494 physicians in 590, 900 patients across 1240 PE-acquired practice sites and 637, 990 control colonoscopies conducted by 2550 physicians in 527,380 patients across 2657 independent practice sites.

Among the findings:

• Colonoscopy prices at PE-acquired sites increased by 4.5% (95% CI, 2.5-6.6; P < .001) vs independent practices. That increase was much lower than that reported by Singh and colleagues for  .
• The estimated price increase was 6.7% (95% CI, 4.2-9.3; P < .001) when only colonoscopies at PE practices with market shares above the 75th percentile (24.4%) in 2021 were considered. Both increases were in line with other research, Arnold said.
• Colonoscopy spending per physician increased by 16.0% (95% CI, 8.4%-24.0%; P < .001), while the number of colonoscopies and the number of unique patients per physician increased by 12.1% (95% CI, 5.3-19.4; P < .001) and 11.3% (95% CI, 4.4%-18.5%; P < .001), respectively. These measures, however, were already increasing before PE acquisition.
• No statistically significant associations were detected for the six quality measures analyzed.

Could such cost-raising acquisitions potentially be blocked by concerned regulators? 

“No. Generally the purchases are at prices below what would require notification to federal authorities,” Arnold said. “The Department of Justice/Federal Trade Commission hinted at being willing to look at serial acquisitions in their 2023 Merger Guidelines, but until that happens, these will likely continue to fly under the radar.”

Still, as evidence of PE-associated poorer quality outcomes as well as clinician burnout continues to emerge, Arnold added, “I would advise physicians who get buyout offers from PE to educate themselves on what could happen to patients and staff if they choose to sell.”

Offering an outsider’s perspective on the study, health economist Atul Gupta, PhD, an assistant professor of healthcare management in the Wharton School at the University of Pennsylvania in Philadelphia, called it an “excellent addition to the developing literature examining the effects of private equity ownership of healthcare providers.” Very few studies have examined the effects on prices and quality for the same set of deals and providers. “This is important because we want to be able to do an apples-to-apples comparison of the effects on both outcomes before judging PE ownership,” he told GI & Hepatology News.

In an accompanying editorial , primary care physician Jane M. Zhu, MD, an associate professor of medicine at Oregon Health & Science University in Portland, Oregon, and not involved in the commercial-insurance-based study, said one interpretation of the findings may be that PE acquisition focuses on reducing inefficiencies, improving access by expanding practice capacity, and increasing throughput. “Another interpretation may be that PE acquisition is focused on the strategic exploitation of market and pricing power. The latter may have less of an impact on clinical measures like quality of care, but potentially, both strategies could be at play.” 

Since this analysis focused on the commercial population, understanding how patient demographics may change after PE acquisition is a future avenue for exploration. “For instance, a potential explanation for both the price and utilization shifts might be if payer mix shifted toward more commercially insured patients at the expense of Medicaid or Medicare patients,” she wrote.

Zhu added that the impact of PE on prices and spending, by now replicated across different settings and specialties, is far clearer than the effect of PE on access and quality. “The analysis by Arnold et al is a welcome addition to the literature, generating important questions for future study and transparent monitoring as investor-owners become increasingly influential in healthcare.”

Going forward, said Gupta, an open question is whether the harmful effects of PE ownership of practices are differentially worse than those of other corporate entities such as insurers and hospital systems.

“There are reasons to believe that PE could be worse in theory. For example, their short-term investment horizon may force them to take measures that others will not as well as avoid investing into capital improvements that have a long-run payoff,” he said. “Their uniquely high dependence on debt and unbundling of real estate can severely hurt financial solvency of providers.” But high-quality evidence is lacking to compare the effects from these two distinct forms of corporatization.

The trend away from individual private practice is a reality, Arnold said. “The administrative burden on solo docs is becoming too much and physicians just seem to want to treat patients and not deal with it. So the options at this point really become selling to a hospital system or private equity.”

This study was funded by a grant from the philanthropic foundation Arnold Ventures (no family relation to Daniel Arnold). 

Arnold reported receiving grants from Arnold Ventures during the conduct of the study. Gupta had no competing interests to declare. Zhu reported receiving grants from the Agency for Healthcare Research and Quality during the submitted work and from the National Institutes of Health, National Institute for Health Care Management Foundation, and American Psychological Association, as well as personal fees from Cambia outside the submitted work.

A version of this article appeared on Medscape.com.

Private equity (PE) acquisition of gastroenterology (GI) practices led to higher colonoscopy prices, utilization, and spending with no commensurate effect on quality, an economic analysis found. Price increases ranged from about 5% to about 7%.

In view of the growing trend to such acquisitions, policy makers should monitor the impact of PE investment in medical practices, according to researchers led by health economist Daniel R. Arnold, PhD, of the Department of Health Services, Policy & Practice in the School of Public Health at Brown University in Providence, Rhode Island. “In a previous study of ours, gastroenterology stood out as a particularly attractive specialty to private equity,” Arnold told GI & Hepatology News.

Published in JAMA Health Forum, the economic evaluation of more than 1.1 million patients and 1.3 million colonoscopies concluded that PE acquisitions of GI sites are difficult to justify.

 

The Study

This difference-in-differences event study and economic evaluation analyzed data from US GI practices acquired by PE firms from 2015 to 2021. Commercial insurance claims covering more than 50 million enrollees were used to calculate price, spending, utilization, and quality measures from 2012 to 2021, with all data analyzed from April to September 2024.

The main outcomes were price, spending per physician, number of colonoscopies per physician, number of unique patients per physician, and quality, as defined by polyp detection, incomplete colonoscopies, and four adverse event measures: cardiovascular, serious and nonserious GI events, and any other adverse events.

The mean age of patients was 47.1 years, and 47.8% were men. The sample included 718, 851 colonoscopies conducted by 1494 physicians in 590, 900 patients across 1240 PE-acquired practice sites and 637, 990 control colonoscopies conducted by 2550 physicians in 527,380 patients across 2657 independent practice sites.

Among the findings:

• Colonoscopy prices at PE-acquired sites increased by 4.5% (95% CI, 2.5-6.6; P < .001) vs independent practices. That increase was much lower than that reported by Singh and colleagues for  .
• The estimated price increase was 6.7% (95% CI, 4.2-9.3; P < .001) when only colonoscopies at PE practices with market shares above the 75th percentile (24.4%) in 2021 were considered. Both increases were in line with other research, Arnold said.
• Colonoscopy spending per physician increased by 16.0% (95% CI, 8.4%-24.0%; P < .001), while the number of colonoscopies and the number of unique patients per physician increased by 12.1% (95% CI, 5.3-19.4; P < .001) and 11.3% (95% CI, 4.4%-18.5%; P < .001), respectively. These measures, however, were already increasing before PE acquisition.
• No statistically significant associations were detected for the six quality measures analyzed.

Could such cost-raising acquisitions potentially be blocked by concerned regulators? 

“No. Generally the purchases are at prices below what would require notification to federal authorities,” Arnold said. “The Department of Justice/Federal Trade Commission hinted at being willing to look at serial acquisitions in their 2023 Merger Guidelines, but until that happens, these will likely continue to fly under the radar.”

Still, as evidence of PE-associated poorer quality outcomes as well as clinician burnout continues to emerge, Arnold added, “I would advise physicians who get buyout offers from PE to educate themselves on what could happen to patients and staff if they choose to sell.”

Offering an outsider’s perspective on the study, health economist Atul Gupta, PhD, an assistant professor of healthcare management in the Wharton School at the University of Pennsylvania in Philadelphia, called it an “excellent addition to the developing literature examining the effects of private equity ownership of healthcare providers.” Very few studies have examined the effects on prices and quality for the same set of deals and providers. “This is important because we want to be able to do an apples-to-apples comparison of the effects on both outcomes before judging PE ownership,” he told GI & Hepatology News.

In an accompanying editorial , primary care physician Jane M. Zhu, MD, an associate professor of medicine at Oregon Health & Science University in Portland, Oregon, and not involved in the commercial-insurance-based study, said one interpretation of the findings may be that PE acquisition focuses on reducing inefficiencies, improving access by expanding practice capacity, and increasing throughput. “Another interpretation may be that PE acquisition is focused on the strategic exploitation of market and pricing power. The latter may have less of an impact on clinical measures like quality of care, but potentially, both strategies could be at play.” 

Since this analysis focused on the commercial population, understanding how patient demographics may change after PE acquisition is a future avenue for exploration. “For instance, a potential explanation for both the price and utilization shifts might be if payer mix shifted toward more commercially insured patients at the expense of Medicaid or Medicare patients,” she wrote.

Zhu added that the impact of PE on prices and spending, by now replicated across different settings and specialties, is far clearer than the effect of PE on access and quality. “The analysis by Arnold et al is a welcome addition to the literature, generating important questions for future study and transparent monitoring as investor-owners become increasingly influential in healthcare.”

Going forward, said Gupta, an open question is whether the harmful effects of PE ownership of practices are differentially worse than those of other corporate entities such as insurers and hospital systems.

“There are reasons to believe that PE could be worse in theory. For example, their short-term investment horizon may force them to take measures that others will not as well as avoid investing into capital improvements that have a long-run payoff,” he said. “Their uniquely high dependence on debt and unbundling of real estate can severely hurt financial solvency of providers.” But high-quality evidence is lacking to compare the effects from these two distinct forms of corporatization.

The trend away from individual private practice is a reality, Arnold said. “The administrative burden on solo docs is becoming too much and physicians just seem to want to treat patients and not deal with it. So the options at this point really become selling to a hospital system or private equity.”

This study was funded by a grant from the philanthropic foundation Arnold Ventures (no family relation to Daniel Arnold). 

Arnold reported receiving grants from Arnold Ventures during the conduct of the study. Gupta had no competing interests to declare. Zhu reported receiving grants from the Agency for Healthcare Research and Quality during the submitted work and from the National Institutes of Health, National Institute for Health Care Management Foundation, and American Psychological Association, as well as personal fees from Cambia outside the submitted work.

A version of this article appeared on Medscape.com.

Private equity (PE) acquisition of gastroenterology (GI) practices led to higher colonoscopy prices, utilization, and spending with no commensurate effect on quality, an economic analysis found. Price increases ranged from about 5% to about 7%.

In view of the growing trend to such acquisitions, policy makers should monitor the impact of PE investment in medical practices, according to researchers led by health economist Daniel R. Arnold, PhD, of the Department of Health Services, Policy & Practice in the School of Public Health at Brown University in Providence, Rhode Island. “In a previous study of ours, gastroenterology stood out as a particularly attractive specialty to private equity,” Arnold told GI & Hepatology News.

Published in JAMA Health Forum, the economic evaluation of more than 1.1 million patients and 1.3 million colonoscopies concluded that PE acquisitions of GI sites are difficult to justify.

 

The Study

This difference-in-differences event study and economic evaluation analyzed data from US GI practices acquired by PE firms from 2015 to 2021. Commercial insurance claims covering more than 50 million enrollees were used to calculate price, spending, utilization, and quality measures from 2012 to 2021, with all data analyzed from April to September 2024.

The main outcomes were price, spending per physician, number of colonoscopies per physician, number of unique patients per physician, and quality, as defined by polyp detection, incomplete colonoscopies, and four adverse event measures: cardiovascular, serious and nonserious GI events, and any other adverse events.

The mean age of patients was 47.1 years, and 47.8% were men. The sample included 718, 851 colonoscopies conducted by 1494 physicians in 590, 900 patients across 1240 PE-acquired practice sites and 637, 990 control colonoscopies conducted by 2550 physicians in 527,380 patients across 2657 independent practice sites.

Among the findings:

• Colonoscopy prices at PE-acquired sites increased by 4.5% (95% CI, 2.5-6.6; P < .001) vs independent practices. That increase was much lower than that reported by Singh and colleagues for  .
• The estimated price increase was 6.7% (95% CI, 4.2-9.3; P < .001) when only colonoscopies at PE practices with market shares above the 75th percentile (24.4%) in 2021 were considered. Both increases were in line with other research, Arnold said.
• Colonoscopy spending per physician increased by 16.0% (95% CI, 8.4%-24.0%; P < .001), while the number of colonoscopies and the number of unique patients per physician increased by 12.1% (95% CI, 5.3-19.4; P < .001) and 11.3% (95% CI, 4.4%-18.5%; P < .001), respectively. These measures, however, were already increasing before PE acquisition.
• No statistically significant associations were detected for the six quality measures analyzed.

Could such cost-raising acquisitions potentially be blocked by concerned regulators? 

“No. Generally the purchases are at prices below what would require notification to federal authorities,” Arnold said. “The Department of Justice/Federal Trade Commission hinted at being willing to look at serial acquisitions in their 2023 Merger Guidelines, but until that happens, these will likely continue to fly under the radar.”

Still, as evidence of PE-associated poorer quality outcomes as well as clinician burnout continues to emerge, Arnold added, “I would advise physicians who get buyout offers from PE to educate themselves on what could happen to patients and staff if they choose to sell.”

Offering an outsider’s perspective on the study, health economist Atul Gupta, PhD, an assistant professor of healthcare management in the Wharton School at the University of Pennsylvania in Philadelphia, called it an “excellent addition to the developing literature examining the effects of private equity ownership of healthcare providers.” Very few studies have examined the effects on prices and quality for the same set of deals and providers. “This is important because we want to be able to do an apples-to-apples comparison of the effects on both outcomes before judging PE ownership,” he told GI & Hepatology News.

In an accompanying editorial , primary care physician Jane M. Zhu, MD, an associate professor of medicine at Oregon Health & Science University in Portland, Oregon, and not involved in the commercial-insurance-based study, said one interpretation of the findings may be that PE acquisition focuses on reducing inefficiencies, improving access by expanding practice capacity, and increasing throughput. “Another interpretation may be that PE acquisition is focused on the strategic exploitation of market and pricing power. The latter may have less of an impact on clinical measures like quality of care, but potentially, both strategies could be at play.” 

Since this analysis focused on the commercial population, understanding how patient demographics may change after PE acquisition is a future avenue for exploration. “For instance, a potential explanation for both the price and utilization shifts might be if payer mix shifted toward more commercially insured patients at the expense of Medicaid or Medicare patients,” she wrote.

Zhu added that the impact of PE on prices and spending, by now replicated across different settings and specialties, is far clearer than the effect of PE on access and quality. “The analysis by Arnold et al is a welcome addition to the literature, generating important questions for future study and transparent monitoring as investor-owners become increasingly influential in healthcare.”

Going forward, said Gupta, an open question is whether the harmful effects of PE ownership of practices are differentially worse than those of other corporate entities such as insurers and hospital systems.

“There are reasons to believe that PE could be worse in theory. For example, their short-term investment horizon may force them to take measures that others will not as well as avoid investing into capital improvements that have a long-run payoff,” he said. “Their uniquely high dependence on debt and unbundling of real estate can severely hurt financial solvency of providers.” But high-quality evidence is lacking to compare the effects from these two distinct forms of corporatization.

The trend away from individual private practice is a reality, Arnold said. “The administrative burden on solo docs is becoming too much and physicians just seem to want to treat patients and not deal with it. So the options at this point really become selling to a hospital system or private equity.”

This study was funded by a grant from the philanthropic foundation Arnold Ventures (no family relation to Daniel Arnold). 

Arnold reported receiving grants from Arnold Ventures during the conduct of the study. Gupta had no competing interests to declare. Zhu reported receiving grants from the Agency for Healthcare Research and Quality during the submitted work and from the National Institutes of Health, National Institute for Health Care Management Foundation, and American Psychological Association, as well as personal fees from Cambia outside the submitted work.

A version of this article appeared on Medscape.com.

Changes in sleep metrics detected with wearable technology could serve as an inflammation marker and potentially predict inflammatory bowel disease (IBD) flareups, regardless of whether a patient has symptoms, 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.

Changes in sleep metrics detected with wearable technology could serve as an inflammation marker and potentially predict inflammatory bowel disease (IBD) flareups, regardless of whether a patient has symptoms, 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.

Changes in sleep metrics detected with wearable technology could serve as an inflammation marker and potentially predict inflammatory bowel disease (IBD) flareups, regardless of whether a patient has symptoms, 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.

Emerging research indicates that phenotype-based testing may help identify which biologic process is driving an individual’s obesity, 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.

Emerging research indicates that phenotype-based testing may help identify which biologic process is driving an individual’s obesity, 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.

Emerging research indicates that phenotype-based testing may help identify which biologic process is driving an individual’s obesity, 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.

Low rates of retained gastric contents were seen in endoscopy patients on GLP-1 receptor agonists ( RAs), a retrospective multicenter cross-sectional analysis reported in The American Journal of Gastroenterology. Moreover, most instances occurred in patients using the drugs for type 2 diabetes (T2D) rather than for weight loss alone.

The findings suggest adopting an individualized approach rather than universal preoperative withholding of GLP-1 RAs before endoscopy, concluded Jennifer Phan, MD, medical director of the Hoag Advanced Endoscopy Center in Newport Beach, California, and colleagues. These agents are associated with slowed gastric emptying, possibly raising the risk for pulmonary aspiration. The study identified comorbid uncontrolled T2D as a risk factor for retained gastric contents.

Recommendations from gastroenterological societies and the American Society of Anesthesiologists (ASA) differ regarding pre-endoscopic holding of these ubiquitous agents used for obesity and T2D. “Many patients undergo routine endoscopic procedures, and there was concern from the anesthesia safety perspective for retained gastric contents,” Phan told GI & Hepatology News. “At first these events were seen in a handful of cases; however, out of precaution this resulted in a statement from the ASA recommending that patients hold their GLP-1 medications for at least 1 week prior to a routine endoscopic procedure.”

That guidance resulted in protocol changes within endoscopy units, cancelled procedures, and potential delays in patient care. “We wanted to study whether this concern was clinically valid and to help identify which subgroup of patients are at highest risk in order to best inform anesthesia and endoscopy practices,” Phan added.

The ASA updated its guidance in 2023.

The current study aligns with other research showing that rates of clinically relevant retained gastric contents are < 10%, Phan said. For instance, the American Gastroenterological Association (AGA) published a rapid clinical practice update in November 2023 that found insufficient evidence to support patients stopping the medications before endoscopic procedures. AGA guidance suggests an individual approach for each patient on a GLP-1 RA rather than a blanket statement on how to manage all patients taking the medications.

“Our initial hypothesis was that the rates of clinically relevant retained gastric contents in patients on GLP-1 RA medications would be low,” Phan noted. “This was born out of anecdotal experience of the limited number of aborted procedures we experienced before the ASA statement.” 

Her group also hypothesized that the indication for which the GLP-1 RA was prescribed would be important, with patients taking GLP-1 RA medications for diabetes potentially having a higher likelihood of retained contents given the concomitant propensity for delayed gastric motility related to uncontrolled hyperglycemia.

 

The Study

The investigators identified 815 patients on confirmed GLP-1 RA medications of various types receiving endoscopy from 2021 to 2023 at four centers. Demographics, prescribing practices, and procedure outcomes were captured. GLP-1 RA management of preoperative holding was retroactively classified per ASA guidance.

Of the 815 patients (mean age, 67.7 years; 57.7% women; 53.9% White individuals), 70 (8.7%) exhibited retained gastric contents on endoscopy. Of these 65 (93%) had T2D with a median A1c of 6.5%. Among those with retained contents, most had a minimal (10, 14.3%) or moderate (31, 44.3%) amount of food retained, although 29 (41.4%) had a large quantity. Only one patient required unplanned intubation because of a large quantity of residual content, and none had aspiration events.

In multivariate analysis, the odds ratio of retention in those with diabetes was 4.1. “Given the predominance of diabetes in those with retained gastric contents, we highlight the potential to risk-stratify patients who require further preprocedural consideration,” the authors wrote.

Those with GLP-1 RA held per ASA guidance (406, 49.8%) were less likely to have retained contents (4.4% vs 12.7%; P < .001), but no significant differences for intubation (0% vs 2%; P = .53) or aborting procedure rates (28% vs 18%; P = .40) due to gastric retention were observed.

On multivariable analysis, the likelihood of food retention increased by 36% (95% CI, 1.15-1.60) for every 1% increase in glycosylated hemoglobin after adjusting for GLP-1 RA type and preoperative medication hold.

“Our study can help to differentiate which patients can be at largest risk for retained gastric contents,” Phan said, noting the impact of increasing percentages of A1C. “There’s a 36% increased likelihood of food retention in patients on GLP-1 medications, so a blanket policy to hold GLP-1s in patients who are nondiabetic and taking the medication for obesity may not be the best approach. But if patients have uncontrolled hyperglycemia, then an approach of caution is clinically valid.” In that context, holding the GLP-1 RA injection or lengthening the preoperative clear-liquid diet policy should be considered.

She noted that the study results are generalizable because the study was conducted across multiple types of hospital systems, both university and county, and included all types of GLP-1 RA.

Offering an anesthesiologist’s perspective on the study, Paul Potnuru, MD, an assistant professor in the Department of Anesthesiology, Critical Care, and Pain Medicine at UTHealth Houston and not involved in the study, called the findings “somewhat reassuring” but said the risk for aspiration was still a consideration.

A recent review, however, reported that the risk for GLP-1 RA-associated pulmonary aspiration was low.

Potnuru acknowledged that the original ASA guidance on preoperative GLP-1 RA cessation led to some confusion. “There were not a lot of data on the issue, but some studies found that even with stopping GLP-1s 2 weeks preoperatively some patients still retained gastric content,” he told GI & Hepatology News.

A study at his center recently reported that 56% of GLP-1 RA users had increased pre-anesthesia residual gastric content compared with 19% of nonusers.

From the anesthesiologist’s clinical vantage point, the margin of safety is an issue even if aspiration risk is low. “If there’s a 1 in 1000 chance or even a 1 in 3000 chance, that can be considered too high,” Potnuru said.

He further noted that the current study included only 815 patients, not nearly enough for definitive data. In addition, a retrospective study based on medical records can’t really capture all the real-world procedural changes made in the operating room. “It’s common for anesthesiologists not to document all cases of intubation, for example,” he said.

While the ideal is a completely empty stomach, he agreed that a practical alternative to stopping GLP-1 RA therapy, especially that prescribed for diabetes, would be a 24-hour liquid diet, which would clear the stomach quickly. “If you stop these drugs in patients taking them for diabetes, you get a worsening of their glycemic control,” he said.

He noted that patients have different risk tolerances, with some willing to go ahead even if ultrasound shows gastric retention, while some opt to cancel.

Prospective studies are needed, Potnuru added, “because you find more if you know what you’re looking for.” His center is starting a clinical trial in 150 patients to assess the impact of a 24-hour, liquids-only diet on gastric retention.

According to Phan, other research is following GLP-1 RA users undergoing colonoscopy. “Future studies can look at the added value of point-of-care abdominal ultrasound to see if it increases precision preoperative management in these patients on GLP-1 medications.”

Other groups are examining the safety of these agents in the general context of sedation. “It’s worth noting that the studies are being done on currently available medications and may not apply to future medications such as triple agonists or anti-amylins that may come on the market in the near future,” Phan said.

This study received no financial support. Neither the study authors nor Potnuru had any conflicts of interest.

A version of this article appeared on Medscape.com.

Low rates of retained gastric contents were seen in endoscopy patients on GLP-1 receptor agonists ( RAs), a retrospective multicenter cross-sectional analysis reported in The American Journal of Gastroenterology. Moreover, most instances occurred in patients using the drugs for type 2 diabetes (T2D) rather than for weight loss alone.

The findings suggest adopting an individualized approach rather than universal preoperative withholding of GLP-1 RAs before endoscopy, concluded Jennifer Phan, MD, medical director of the Hoag Advanced Endoscopy Center in Newport Beach, California, and colleagues. These agents are associated with slowed gastric emptying, possibly raising the risk for pulmonary aspiration. The study identified comorbid uncontrolled T2D as a risk factor for retained gastric contents.

Recommendations from gastroenterological societies and the American Society of Anesthesiologists (ASA) differ regarding pre-endoscopic holding of these ubiquitous agents used for obesity and T2D. “Many patients undergo routine endoscopic procedures, and there was concern from the anesthesia safety perspective for retained gastric contents,” Phan told GI & Hepatology News. “At first these events were seen in a handful of cases; however, out of precaution this resulted in a statement from the ASA recommending that patients hold their GLP-1 medications for at least 1 week prior to a routine endoscopic procedure.”

That guidance resulted in protocol changes within endoscopy units, cancelled procedures, and potential delays in patient care. “We wanted to study whether this concern was clinically valid and to help identify which subgroup of patients are at highest risk in order to best inform anesthesia and endoscopy practices,” Phan added.

The ASA updated its guidance in 2023.

The current study aligns with other research showing that rates of clinically relevant retained gastric contents are < 10%, Phan said. For instance, the American Gastroenterological Association (AGA) published a rapid clinical practice update in November 2023 that found insufficient evidence to support patients stopping the medications before endoscopic procedures. AGA guidance suggests an individual approach for each patient on a GLP-1 RA rather than a blanket statement on how to manage all patients taking the medications.

“Our initial hypothesis was that the rates of clinically relevant retained gastric contents in patients on GLP-1 RA medications would be low,” Phan noted. “This was born out of anecdotal experience of the limited number of aborted procedures we experienced before the ASA statement.” 

Her group also hypothesized that the indication for which the GLP-1 RA was prescribed would be important, with patients taking GLP-1 RA medications for diabetes potentially having a higher likelihood of retained contents given the concomitant propensity for delayed gastric motility related to uncontrolled hyperglycemia.

 

The Study

The investigators identified 815 patients on confirmed GLP-1 RA medications of various types receiving endoscopy from 2021 to 2023 at four centers. Demographics, prescribing practices, and procedure outcomes were captured. GLP-1 RA management of preoperative holding was retroactively classified per ASA guidance.

Of the 815 patients (mean age, 67.7 years; 57.7% women; 53.9% White individuals), 70 (8.7%) exhibited retained gastric contents on endoscopy. Of these 65 (93%) had T2D with a median A1c of 6.5%. Among those with retained contents, most had a minimal (10, 14.3%) or moderate (31, 44.3%) amount of food retained, although 29 (41.4%) had a large quantity. Only one patient required unplanned intubation because of a large quantity of residual content, and none had aspiration events.

In multivariate analysis, the odds ratio of retention in those with diabetes was 4.1. “Given the predominance of diabetes in those with retained gastric contents, we highlight the potential to risk-stratify patients who require further preprocedural consideration,” the authors wrote.

Those with GLP-1 RA held per ASA guidance (406, 49.8%) were less likely to have retained contents (4.4% vs 12.7%; P < .001), but no significant differences for intubation (0% vs 2%; P = .53) or aborting procedure rates (28% vs 18%; P = .40) due to gastric retention were observed.

On multivariable analysis, the likelihood of food retention increased by 36% (95% CI, 1.15-1.60) for every 1% increase in glycosylated hemoglobin after adjusting for GLP-1 RA type and preoperative medication hold.

“Our study can help to differentiate which patients can be at largest risk for retained gastric contents,” Phan said, noting the impact of increasing percentages of A1C. “There’s a 36% increased likelihood of food retention in patients on GLP-1 medications, so a blanket policy to hold GLP-1s in patients who are nondiabetic and taking the medication for obesity may not be the best approach. But if patients have uncontrolled hyperglycemia, then an approach of caution is clinically valid.” In that context, holding the GLP-1 RA injection or lengthening the preoperative clear-liquid diet policy should be considered.

She noted that the study results are generalizable because the study was conducted across multiple types of hospital systems, both university and county, and included all types of GLP-1 RA.

Offering an anesthesiologist’s perspective on the study, Paul Potnuru, MD, an assistant professor in the Department of Anesthesiology, Critical Care, and Pain Medicine at UTHealth Houston and not involved in the study, called the findings “somewhat reassuring” but said the risk for aspiration was still a consideration.

A recent review, however, reported that the risk for GLP-1 RA-associated pulmonary aspiration was low.

Potnuru acknowledged that the original ASA guidance on preoperative GLP-1 RA cessation led to some confusion. “There were not a lot of data on the issue, but some studies found that even with stopping GLP-1s 2 weeks preoperatively some patients still retained gastric content,” he told GI & Hepatology News.

A study at his center recently reported that 56% of GLP-1 RA users had increased pre-anesthesia residual gastric content compared with 19% of nonusers.

From the anesthesiologist’s clinical vantage point, the margin of safety is an issue even if aspiration risk is low. “If there’s a 1 in 1000 chance or even a 1 in 3000 chance, that can be considered too high,” Potnuru said.

He further noted that the current study included only 815 patients, not nearly enough for definitive data. In addition, a retrospective study based on medical records can’t really capture all the real-world procedural changes made in the operating room. “It’s common for anesthesiologists not to document all cases of intubation, for example,” he said.

While the ideal is a completely empty stomach, he agreed that a practical alternative to stopping GLP-1 RA therapy, especially that prescribed for diabetes, would be a 24-hour liquid diet, which would clear the stomach quickly. “If you stop these drugs in patients taking them for diabetes, you get a worsening of their glycemic control,” he said.

He noted that patients have different risk tolerances, with some willing to go ahead even if ultrasound shows gastric retention, while some opt to cancel.

Prospective studies are needed, Potnuru added, “because you find more if you know what you’re looking for.” His center is starting a clinical trial in 150 patients to assess the impact of a 24-hour, liquids-only diet on gastric retention.

According to Phan, other research is following GLP-1 RA users undergoing colonoscopy. “Future studies can look at the added value of point-of-care abdominal ultrasound to see if it increases precision preoperative management in these patients on GLP-1 medications.”

Other groups are examining the safety of these agents in the general context of sedation. “It’s worth noting that the studies are being done on currently available medications and may not apply to future medications such as triple agonists or anti-amylins that may come on the market in the near future,” Phan said.

This study received no financial support. Neither the study authors nor Potnuru had any conflicts of interest.

A version of this article appeared on Medscape.com.

Low rates of retained gastric contents were seen in endoscopy patients on GLP-1 receptor agonists ( RAs), a retrospective multicenter cross-sectional analysis reported in The American Journal of Gastroenterology. Moreover, most instances occurred in patients using the drugs for type 2 diabetes (T2D) rather than for weight loss alone.

The findings suggest adopting an individualized approach rather than universal preoperative withholding of GLP-1 RAs before endoscopy, concluded Jennifer Phan, MD, medical director of the Hoag Advanced Endoscopy Center in Newport Beach, California, and colleagues. These agents are associated with slowed gastric emptying, possibly raising the risk for pulmonary aspiration. The study identified comorbid uncontrolled T2D as a risk factor for retained gastric contents.

Recommendations from gastroenterological societies and the American Society of Anesthesiologists (ASA) differ regarding pre-endoscopic holding of these ubiquitous agents used for obesity and T2D. “Many patients undergo routine endoscopic procedures, and there was concern from the anesthesia safety perspective for retained gastric contents,” Phan told GI & Hepatology News. “At first these events were seen in a handful of cases; however, out of precaution this resulted in a statement from the ASA recommending that patients hold their GLP-1 medications for at least 1 week prior to a routine endoscopic procedure.”

That guidance resulted in protocol changes within endoscopy units, cancelled procedures, and potential delays in patient care. “We wanted to study whether this concern was clinically valid and to help identify which subgroup of patients are at highest risk in order to best inform anesthesia and endoscopy practices,” Phan added.

The ASA updated its guidance in 2023.

The current study aligns with other research showing that rates of clinically relevant retained gastric contents are < 10%, Phan said. For instance, the American Gastroenterological Association (AGA) published a rapid clinical practice update in November 2023 that found insufficient evidence to support patients stopping the medications before endoscopic procedures. AGA guidance suggests an individual approach for each patient on a GLP-1 RA rather than a blanket statement on how to manage all patients taking the medications.

“Our initial hypothesis was that the rates of clinically relevant retained gastric contents in patients on GLP-1 RA medications would be low,” Phan noted. “This was born out of anecdotal experience of the limited number of aborted procedures we experienced before the ASA statement.” 

Her group also hypothesized that the indication for which the GLP-1 RA was prescribed would be important, with patients taking GLP-1 RA medications for diabetes potentially having a higher likelihood of retained contents given the concomitant propensity for delayed gastric motility related to uncontrolled hyperglycemia.

 

The Study

The investigators identified 815 patients on confirmed GLP-1 RA medications of various types receiving endoscopy from 2021 to 2023 at four centers. Demographics, prescribing practices, and procedure outcomes were captured. GLP-1 RA management of preoperative holding was retroactively classified per ASA guidance.

Of the 815 patients (mean age, 67.7 years; 57.7% women; 53.9% White individuals), 70 (8.7%) exhibited retained gastric contents on endoscopy. Of these 65 (93%) had T2D with a median A1c of 6.5%. Among those with retained contents, most had a minimal (10, 14.3%) or moderate (31, 44.3%) amount of food retained, although 29 (41.4%) had a large quantity. Only one patient required unplanned intubation because of a large quantity of residual content, and none had aspiration events.

In multivariate analysis, the odds ratio of retention in those with diabetes was 4.1. “Given the predominance of diabetes in those with retained gastric contents, we highlight the potential to risk-stratify patients who require further preprocedural consideration,” the authors wrote.

Those with GLP-1 RA held per ASA guidance (406, 49.8%) were less likely to have retained contents (4.4% vs 12.7%; P < .001), but no significant differences for intubation (0% vs 2%; P = .53) or aborting procedure rates (28% vs 18%; P = .40) due to gastric retention were observed.

On multivariable analysis, the likelihood of food retention increased by 36% (95% CI, 1.15-1.60) for every 1% increase in glycosylated hemoglobin after adjusting for GLP-1 RA type and preoperative medication hold.

“Our study can help to differentiate which patients can be at largest risk for retained gastric contents,” Phan said, noting the impact of increasing percentages of A1C. “There’s a 36% increased likelihood of food retention in patients on GLP-1 medications, so a blanket policy to hold GLP-1s in patients who are nondiabetic and taking the medication for obesity may not be the best approach. But if patients have uncontrolled hyperglycemia, then an approach of caution is clinically valid.” In that context, holding the GLP-1 RA injection or lengthening the preoperative clear-liquid diet policy should be considered.

She noted that the study results are generalizable because the study was conducted across multiple types of hospital systems, both university and county, and included all types of GLP-1 RA.

Offering an anesthesiologist’s perspective on the study, Paul Potnuru, MD, an assistant professor in the Department of Anesthesiology, Critical Care, and Pain Medicine at UTHealth Houston and not involved in the study, called the findings “somewhat reassuring” but said the risk for aspiration was still a consideration.

A recent review, however, reported that the risk for GLP-1 RA-associated pulmonary aspiration was low.

Potnuru acknowledged that the original ASA guidance on preoperative GLP-1 RA cessation led to some confusion. “There were not a lot of data on the issue, but some studies found that even with stopping GLP-1s 2 weeks preoperatively some patients still retained gastric content,” he told GI & Hepatology News.

A study at his center recently reported that 56% of GLP-1 RA users had increased pre-anesthesia residual gastric content compared with 19% of nonusers.

From the anesthesiologist’s clinical vantage point, the margin of safety is an issue even if aspiration risk is low. “If there’s a 1 in 1000 chance or even a 1 in 3000 chance, that can be considered too high,” Potnuru said.

He further noted that the current study included only 815 patients, not nearly enough for definitive data. In addition, a retrospective study based on medical records can’t really capture all the real-world procedural changes made in the operating room. “It’s common for anesthesiologists not to document all cases of intubation, for example,” he said.

While the ideal is a completely empty stomach, he agreed that a practical alternative to stopping GLP-1 RA therapy, especially that prescribed for diabetes, would be a 24-hour liquid diet, which would clear the stomach quickly. “If you stop these drugs in patients taking them for diabetes, you get a worsening of their glycemic control,” he said.

He noted that patients have different risk tolerances, with some willing to go ahead even if ultrasound shows gastric retention, while some opt to cancel.

Prospective studies are needed, Potnuru added, “because you find more if you know what you’re looking for.” His center is starting a clinical trial in 150 patients to assess the impact of a 24-hour, liquids-only diet on gastric retention.

According to Phan, other research is following GLP-1 RA users undergoing colonoscopy. “Future studies can look at the added value of point-of-care abdominal ultrasound to see if it increases precision preoperative management in these patients on GLP-1 medications.”

Other groups are examining the safety of these agents in the general context of sedation. “It’s worth noting that the studies are being done on currently available medications and may not apply to future medications such as triple agonists or anti-amylins that may come on the market in the near future,” Phan said.

This study received no financial support. Neither the study authors nor Potnuru had any conflicts of interest.

A version of this article appeared on Medscape.com.

WASHINGTON, DC — A low-emulsifier-containing diet led to a threefold increased likelihood of improvement in symptoms of Crohn’s disease compared with an emulsifier-containing diet in a randomized double-blind dietary trial involving 154 patients with mildly active disease living across the United Kingdom.

The findings were reported at Gut Microbiota for Health (GMFH) World Summit 2025 by Benoit Chassaing, PhD, of the Institut Pasteur, Paris, France, whose research leading up to the trial has demonstrated that food additive emulsifiers —ubiquitous in processed foods — alter microbiota composition and lead to microbiota encroachment into the mucus layer of the gut and subsequent chronic gut inflammation.

Patients in the ADDapt trial, which was also reported in an abstract earlier this year at the European Crohn’s and Colitis Organization (ECCO) 2025 Congress, had a Crohn’s disease activity index (CDAI) of 150-250 and evidence of inflammation (faecal calprotectin (FCP) ≥ 150 µg/g or endoscopy/radiology). All “had been exposed in their regular diets to emulsifiers,” said Chassaing, a co-investigator, during a GMFH session on “Dietary Drivers of Health and Disease.”

They were randomized to either a low-emulsifier diet or to a low-emulsifier diet followed by emulsifier “resupplementation” — a design meant to “account for the very strong placebo effect that is always observed with dietary studies,” he said.

All patients received dietary counseling, a smart phone app and barcode scan to support shopping, and weekly support. They also received supermarket foods for 25% of their needs that were either free of emulsifiers or contained emulsifiers, and they were provided three snacks per day that were emulsifier-free or contained carrageenan, carboxymethycellulse (CMC), and polysorbate-80 (P80) — dietary emulsifiers that are commonly added to processed foods to enhance texture and extend shelf-life.

In the intention-to-treat (ITT) analysis, 49% of patients in the intervention group reached the primary endpoint of a 70-point reduction or more in CDAI response after 8 weeks compared with 31% of those in the control group (P = .019), with an adjusted relative risk of response of 3.1 (P = .003), Chassaing shared at the GMFH meeting, convened by the American Gastroenterological Association and the European Society of Neurogastroenterology and Motility.

In the per-protocol analysis (n = 119), 61% and 47% of patients in the intervention and control groups, respectively, reached the primary outcome of CDAI response, with an adjusted relative risk of response of 3.0 (P = .018), he said.

Secondary endpoints included CDAI remission at 24 weeks, and according to the abstract for the ECCO Congress, in the ITT analysis, patients in the intervention group were more than twice as likely to experience remission.

Chassaing noted at the GMFH meeting that as part of the study, he and coinvestigators have been investigating the participants’ gut microbiota with metagenomic analyses. The study was led by Kevin Whelan, PhD, head of the Department of Nutritional Sciences at King’s College London, London, England.

 

Can Emulsifier-Sensitive Individuals Be Identified?

In murine model research 10 years ago, Chassaing showed that the administration of CMC and P80 results in microbiota encroachment into the mucus layer of the gut, alterations in microbiota composition — including an increase in bacteria that produce pro-inflammatory flagellin — and development of chronic inflammation.

Wild-type mice treated with these compounds developed metabolic disease, and mice that were modified to be predisposed to colitis had a higher incidence of robust colitis. Moreover, fecal transplantation from emulsifier-treated mice to germ-free mice reproduced these changes, “clearly suggesting that the microbiome itself is sufficient to drive chronic inflammation,” he said.

In recent years, in humans, analyses from the large French NutriNet-Sante prospective cohort study have shown associations between exposure to food additive emulsifiers and the risk for cardiovascular disease, the risk for cancer (overall, breast, and prostate), and the risk for type 2 diabetes.

But to explore causality and better understand the mechanisms of emulsifier-driven changes on the microbiota, Chassaing and his colleagues also launched the FRESH study (Functional Research on Emulsifier in Humans), a double-blind randomized controlled-feeding study of the emulsifier CMC. For 11 days, nine healthy patients consumed an emulsifier-free diet and 11 consumed an identical diet enriched with 15 g/d of CMC.

Patients on the CMC-containing diet had reduced microbiota diversity and depletions of an array of microbiota-related metabolites, but only a small subset had profound alterations in microbiota composition and increased microbiota encroachment into the mucus layer. “Some seemed to be resistant to CMC-induced microbiota encroachment, while some were highly susceptible,” Chassaing said.

The pilot study raised the question, he said, of whether there is an “infectivity component” — some kind of “sensitive” gut microbiota composition — that may be associated with dietary emulsifier-driven inflammation and disease.

In other murine research, Chassaing and his team found that germ-free mice colonized with Crohn’s disease-associated adherent-invasive E coli (AIEC) and subsequently given CMC or P80 developed chronic inflammation and metabolic dysregulation, “clearly demonstrating that you can convert resistant mice to sensitive mice just by adding one bacteria to the ecosystem,” he said. “The presence of AIEC alone was sufficient to drive the detrimental effects of dietary emulsifiers.”

(In vitro research with transcriptomic analysis then showed that the emulsifiers directly elicit AIEC virulence gene expression, Chassaing and his coauthors wrote in their 2020 paper, facilitating AIEC’s “penetration of the mucus layer and adherence to epithelial cells and resulting in activation of host pro-inflammatory signaling.”)

“We don’t think it’s solely the AIEC bacteria that will drive emulsifier sensitivity, though…we think it’s more complex,” Chassaing said at the meeting. Overall, the findings raise the question of whether emulsifier-sensitive individuals can be identified.

This, he said, is one of his most recent research questions. His lab has led the development of an in vitro microbiota model built to predict an individual’s sensitivity to emulsifiers. In a study published in April, the model recapitulated the differential CMC sensitivity observed in the earlier FRESH study, suggesting that an individual’s sensitivity to emulsifiers can indeed be predicted by examining their baseline microbiota.

 

Interpreting the Epidemiology

Chassaing’s research arch illustrates the synergy between epidemiological research, basic/translational research, and clinical interventional research that’s needed to understand the diet-microbiome intersection in inflammatory bowel disease, said Ashwin Ananthakrishnan, MBBS, MPH, AGAF, associate professor of medicine at Massachusetts General Hospital, Boston, in an interview at the meeting.

“It’s a good example of how to really span the spectrum, starting from the big picture and going deeper to understand mechanisms, and starting from mechanisms and expanding it out,” Ananthakrishnan said.

In his own talk about research on IBD, Ananthakrishnan said that epidemiological data have shown over the past 10-15 years that total dietary fiber is inversely associated with the risk for Crohn’s disease (with the strongest associations with fiber from fruits and vegetables). Studies have also shown that a higher intake of polyunsaturated fatty acids is associated with a lower risk for ulcerative colitis, whereas “an n-6-fatty acid-rich diet is associated with a higher risk of ulcerative colitis,” he said.

Dietary cohort studies, meanwhile, have shed light on the influence of dietary patterns — such as the Mediterranean diet and diets with high inflammatory potential—on IBD. A diet rich in ultra-processed foods has also been shown in a prospective cohort study to be associated with a higher risk for Crohn’s disease, with certain categories of ultra-processed foods (eg, breads and breakfast foods) having the strongest associations.

Such studies are limited in part, however, by inadequate assessment of potentially relevant variables such as emulsifiers, preservatives, and how the food is processed, he said.

And in interpreting the epidemiological research on fiber and IBD, for instance, one must appreciate that “there are a number of mechanisms by which fiber is impactful…there’s a big picture to look at,” Ananthakrishnan said. Fiber “can affect the microbiome, clearly, it can affect the gut barrier, and it can affect bile acids, and there are detailed translational studies in support of each of these.”

But there are other constituents of fruits and vegetables “that could potentially influence disease risk, such as AhR ligands and polyphenols,” he said. “And importantly, people not eating a lot of fiber may be eating a lot of ultra-processed foods.”

Most interventional studies of fiber have not shown a benefit of a high-fiber diet, Ananthakrishnan said, but there are multiple possible reasons and factors at play, including potential population differences (eg, in inflammatory status or baseline microbiota), shortcomings of the interventions, and potentially inaccurate outcomes.

Abigail Johnson, PhD, RDN, associate director of the Nutrition Coordinating Center, University of Minnesota Twin Cities, which supports dietary analysis, said during the session that the focus of dietary research is “moving toward understanding overall dietary patterns” as opposed to focusing more narrowly on vitamins, minerals, and macronutrients such as proteins, fats, and carbohydrates.

This is an improvement, though “we still don’t have good approaches for understanding [the contributions of] things like additives and emulsifiers, food preparation and cooking, and food processing,” said Johnson, assistant professor in the Division of Epidemiology and Community Health at University of Minnesota Twin Cities. “Perhaps by looking at things at the food level we can overcome some of these limitations.”

Ananthakrishnan reported being a consultant for Geneoscopy and receiving a research grant from Takeda. Chassaing did not report any financial disclosures. Johnson reported that she had no financial disclosures.

A version of this article appeared on Medscape.com.

WASHINGTON, DC — A low-emulsifier-containing diet led to a threefold increased likelihood of improvement in symptoms of Crohn’s disease compared with an emulsifier-containing diet in a randomized double-blind dietary trial involving 154 patients with mildly active disease living across the United Kingdom.

The findings were reported at Gut Microbiota for Health (GMFH) World Summit 2025 by Benoit Chassaing, PhD, of the Institut Pasteur, Paris, France, whose research leading up to the trial has demonstrated that food additive emulsifiers —ubiquitous in processed foods — alter microbiota composition and lead to microbiota encroachment into the mucus layer of the gut and subsequent chronic gut inflammation.

Patients in the ADDapt trial, which was also reported in an abstract earlier this year at the European Crohn’s and Colitis Organization (ECCO) 2025 Congress, had a Crohn’s disease activity index (CDAI) of 150-250 and evidence of inflammation (faecal calprotectin (FCP) ≥ 150 µg/g or endoscopy/radiology). All “had been exposed in their regular diets to emulsifiers,” said Chassaing, a co-investigator, during a GMFH session on “Dietary Drivers of Health and Disease.”

They were randomized to either a low-emulsifier diet or to a low-emulsifier diet followed by emulsifier “resupplementation” — a design meant to “account for the very strong placebo effect that is always observed with dietary studies,” he said.

All patients received dietary counseling, a smart phone app and barcode scan to support shopping, and weekly support. They also received supermarket foods for 25% of their needs that were either free of emulsifiers or contained emulsifiers, and they were provided three snacks per day that were emulsifier-free or contained carrageenan, carboxymethycellulse (CMC), and polysorbate-80 (P80) — dietary emulsifiers that are commonly added to processed foods to enhance texture and extend shelf-life.

In the intention-to-treat (ITT) analysis, 49% of patients in the intervention group reached the primary endpoint of a 70-point reduction or more in CDAI response after 8 weeks compared with 31% of those in the control group (P = .019), with an adjusted relative risk of response of 3.1 (P = .003), Chassaing shared at the GMFH meeting, convened by the American Gastroenterological Association and the European Society of Neurogastroenterology and Motility.

In the per-protocol analysis (n = 119), 61% and 47% of patients in the intervention and control groups, respectively, reached the primary outcome of CDAI response, with an adjusted relative risk of response of 3.0 (P = .018), he said.

Secondary endpoints included CDAI remission at 24 weeks, and according to the abstract for the ECCO Congress, in the ITT analysis, patients in the intervention group were more than twice as likely to experience remission.

Chassaing noted at the GMFH meeting that as part of the study, he and coinvestigators have been investigating the participants’ gut microbiota with metagenomic analyses. The study was led by Kevin Whelan, PhD, head of the Department of Nutritional Sciences at King’s College London, London, England.

 

Can Emulsifier-Sensitive Individuals Be Identified?

In murine model research 10 years ago, Chassaing showed that the administration of CMC and P80 results in microbiota encroachment into the mucus layer of the gut, alterations in microbiota composition — including an increase in bacteria that produce pro-inflammatory flagellin — and development of chronic inflammation.

Wild-type mice treated with these compounds developed metabolic disease, and mice that were modified to be predisposed to colitis had a higher incidence of robust colitis. Moreover, fecal transplantation from emulsifier-treated mice to germ-free mice reproduced these changes, “clearly suggesting that the microbiome itself is sufficient to drive chronic inflammation,” he said.

In recent years, in humans, analyses from the large French NutriNet-Sante prospective cohort study have shown associations between exposure to food additive emulsifiers and the risk for cardiovascular disease, the risk for cancer (overall, breast, and prostate), and the risk for type 2 diabetes.

But to explore causality and better understand the mechanisms of emulsifier-driven changes on the microbiota, Chassaing and his colleagues also launched the FRESH study (Functional Research on Emulsifier in Humans), a double-blind randomized controlled-feeding study of the emulsifier CMC. For 11 days, nine healthy patients consumed an emulsifier-free diet and 11 consumed an identical diet enriched with 15 g/d of CMC.

Patients on the CMC-containing diet had reduced microbiota diversity and depletions of an array of microbiota-related metabolites, but only a small subset had profound alterations in microbiota composition and increased microbiota encroachment into the mucus layer. “Some seemed to be resistant to CMC-induced microbiota encroachment, while some were highly susceptible,” Chassaing said.

The pilot study raised the question, he said, of whether there is an “infectivity component” — some kind of “sensitive” gut microbiota composition — that may be associated with dietary emulsifier-driven inflammation and disease.

In other murine research, Chassaing and his team found that germ-free mice colonized with Crohn’s disease-associated adherent-invasive E coli (AIEC) and subsequently given CMC or P80 developed chronic inflammation and metabolic dysregulation, “clearly demonstrating that you can convert resistant mice to sensitive mice just by adding one bacteria to the ecosystem,” he said. “The presence of AIEC alone was sufficient to drive the detrimental effects of dietary emulsifiers.”

(In vitro research with transcriptomic analysis then showed that the emulsifiers directly elicit AIEC virulence gene expression, Chassaing and his coauthors wrote in their 2020 paper, facilitating AIEC’s “penetration of the mucus layer and adherence to epithelial cells and resulting in activation of host pro-inflammatory signaling.”)

“We don’t think it’s solely the AIEC bacteria that will drive emulsifier sensitivity, though…we think it’s more complex,” Chassaing said at the meeting. Overall, the findings raise the question of whether emulsifier-sensitive individuals can be identified.

This, he said, is one of his most recent research questions. His lab has led the development of an in vitro microbiota model built to predict an individual’s sensitivity to emulsifiers. In a study published in April, the model recapitulated the differential CMC sensitivity observed in the earlier FRESH study, suggesting that an individual’s sensitivity to emulsifiers can indeed be predicted by examining their baseline microbiota.

 

Interpreting the Epidemiology

Chassaing’s research arch illustrates the synergy between epidemiological research, basic/translational research, and clinical interventional research that’s needed to understand the diet-microbiome intersection in inflammatory bowel disease, said Ashwin Ananthakrishnan, MBBS, MPH, AGAF, associate professor of medicine at Massachusetts General Hospital, Boston, in an interview at the meeting.

“It’s a good example of how to really span the spectrum, starting from the big picture and going deeper to understand mechanisms, and starting from mechanisms and expanding it out,” Ananthakrishnan said.

In his own talk about research on IBD, Ananthakrishnan said that epidemiological data have shown over the past 10-15 years that total dietary fiber is inversely associated with the risk for Crohn’s disease (with the strongest associations with fiber from fruits and vegetables). Studies have also shown that a higher intake of polyunsaturated fatty acids is associated with a lower risk for ulcerative colitis, whereas “an n-6-fatty acid-rich diet is associated with a higher risk of ulcerative colitis,” he said.

Dietary cohort studies, meanwhile, have shed light on the influence of dietary patterns — such as the Mediterranean diet and diets with high inflammatory potential—on IBD. A diet rich in ultra-processed foods has also been shown in a prospective cohort study to be associated with a higher risk for Crohn’s disease, with certain categories of ultra-processed foods (eg, breads and breakfast foods) having the strongest associations.

Such studies are limited in part, however, by inadequate assessment of potentially relevant variables such as emulsifiers, preservatives, and how the food is processed, he said.

And in interpreting the epidemiological research on fiber and IBD, for instance, one must appreciate that “there are a number of mechanisms by which fiber is impactful…there’s a big picture to look at,” Ananthakrishnan said. Fiber “can affect the microbiome, clearly, it can affect the gut barrier, and it can affect bile acids, and there are detailed translational studies in support of each of these.”

But there are other constituents of fruits and vegetables “that could potentially influence disease risk, such as AhR ligands and polyphenols,” he said. “And importantly, people not eating a lot of fiber may be eating a lot of ultra-processed foods.”

Most interventional studies of fiber have not shown a benefit of a high-fiber diet, Ananthakrishnan said, but there are multiple possible reasons and factors at play, including potential population differences (eg, in inflammatory status or baseline microbiota), shortcomings of the interventions, and potentially inaccurate outcomes.

Abigail Johnson, PhD, RDN, associate director of the Nutrition Coordinating Center, University of Minnesota Twin Cities, which supports dietary analysis, said during the session that the focus of dietary research is “moving toward understanding overall dietary patterns” as opposed to focusing more narrowly on vitamins, minerals, and macronutrients such as proteins, fats, and carbohydrates.

This is an improvement, though “we still don’t have good approaches for understanding [the contributions of] things like additives and emulsifiers, food preparation and cooking, and food processing,” said Johnson, assistant professor in the Division of Epidemiology and Community Health at University of Minnesota Twin Cities. “Perhaps by looking at things at the food level we can overcome some of these limitations.”

Ananthakrishnan reported being a consultant for Geneoscopy and receiving a research grant from Takeda. Chassaing did not report any financial disclosures. Johnson reported that she had no financial disclosures.

A version of this article appeared on Medscape.com.

WASHINGTON, DC — A low-emulsifier-containing diet led to a threefold increased likelihood of improvement in symptoms of Crohn’s disease compared with an emulsifier-containing diet in a randomized double-blind dietary trial involving 154 patients with mildly active disease living across the United Kingdom.

The findings were reported at Gut Microbiota for Health (GMFH) World Summit 2025 by Benoit Chassaing, PhD, of the Institut Pasteur, Paris, France, whose research leading up to the trial has demonstrated that food additive emulsifiers —ubiquitous in processed foods — alter microbiota composition and lead to microbiota encroachment into the mucus layer of the gut and subsequent chronic gut inflammation.

Patients in the ADDapt trial, which was also reported in an abstract earlier this year at the European Crohn’s and Colitis Organization (ECCO) 2025 Congress, had a Crohn’s disease activity index (CDAI) of 150-250 and evidence of inflammation (faecal calprotectin (FCP) ≥ 150 µg/g or endoscopy/radiology). All “had been exposed in their regular diets to emulsifiers,” said Chassaing, a co-investigator, during a GMFH session on “Dietary Drivers of Health and Disease.”

They were randomized to either a low-emulsifier diet or to a low-emulsifier diet followed by emulsifier “resupplementation” — a design meant to “account for the very strong placebo effect that is always observed with dietary studies,” he said.

All patients received dietary counseling, a smart phone app and barcode scan to support shopping, and weekly support. They also received supermarket foods for 25% of their needs that were either free of emulsifiers or contained emulsifiers, and they were provided three snacks per day that were emulsifier-free or contained carrageenan, carboxymethycellulse (CMC), and polysorbate-80 (P80) — dietary emulsifiers that are commonly added to processed foods to enhance texture and extend shelf-life.

In the intention-to-treat (ITT) analysis, 49% of patients in the intervention group reached the primary endpoint of a 70-point reduction or more in CDAI response after 8 weeks compared with 31% of those in the control group (P = .019), with an adjusted relative risk of response of 3.1 (P = .003), Chassaing shared at the GMFH meeting, convened by the American Gastroenterological Association and the European Society of Neurogastroenterology and Motility.

In the per-protocol analysis (n = 119), 61% and 47% of patients in the intervention and control groups, respectively, reached the primary outcome of CDAI response, with an adjusted relative risk of response of 3.0 (P = .018), he said.

Secondary endpoints included CDAI remission at 24 weeks, and according to the abstract for the ECCO Congress, in the ITT analysis, patients in the intervention group were more than twice as likely to experience remission.

Chassaing noted at the GMFH meeting that as part of the study, he and coinvestigators have been investigating the participants’ gut microbiota with metagenomic analyses. The study was led by Kevin Whelan, PhD, head of the Department of Nutritional Sciences at King’s College London, London, England.

 

Can Emulsifier-Sensitive Individuals Be Identified?

In murine model research 10 years ago, Chassaing showed that the administration of CMC and P80 results in microbiota encroachment into the mucus layer of the gut, alterations in microbiota composition — including an increase in bacteria that produce pro-inflammatory flagellin — and development of chronic inflammation.

Wild-type mice treated with these compounds developed metabolic disease, and mice that were modified to be predisposed to colitis had a higher incidence of robust colitis. Moreover, fecal transplantation from emulsifier-treated mice to germ-free mice reproduced these changes, “clearly suggesting that the microbiome itself is sufficient to drive chronic inflammation,” he said.

In recent years, in humans, analyses from the large French NutriNet-Sante prospective cohort study have shown associations between exposure to food additive emulsifiers and the risk for cardiovascular disease, the risk for cancer (overall, breast, and prostate), and the risk for type 2 diabetes.

But to explore causality and better understand the mechanisms of emulsifier-driven changes on the microbiota, Chassaing and his colleagues also launched the FRESH study (Functional Research on Emulsifier in Humans), a double-blind randomized controlled-feeding study of the emulsifier CMC. For 11 days, nine healthy patients consumed an emulsifier-free diet and 11 consumed an identical diet enriched with 15 g/d of CMC.

Patients on the CMC-containing diet had reduced microbiota diversity and depletions of an array of microbiota-related metabolites, but only a small subset had profound alterations in microbiota composition and increased microbiota encroachment into the mucus layer. “Some seemed to be resistant to CMC-induced microbiota encroachment, while some were highly susceptible,” Chassaing said.

The pilot study raised the question, he said, of whether there is an “infectivity component” — some kind of “sensitive” gut microbiota composition — that may be associated with dietary emulsifier-driven inflammation and disease.

In other murine research, Chassaing and his team found that germ-free mice colonized with Crohn’s disease-associated adherent-invasive E coli (AIEC) and subsequently given CMC or P80 developed chronic inflammation and metabolic dysregulation, “clearly demonstrating that you can convert resistant mice to sensitive mice just by adding one bacteria to the ecosystem,” he said. “The presence of AIEC alone was sufficient to drive the detrimental effects of dietary emulsifiers.”

(In vitro research with transcriptomic analysis then showed that the emulsifiers directly elicit AIEC virulence gene expression, Chassaing and his coauthors wrote in their 2020 paper, facilitating AIEC’s “penetration of the mucus layer and adherence to epithelial cells and resulting in activation of host pro-inflammatory signaling.”)

“We don’t think it’s solely the AIEC bacteria that will drive emulsifier sensitivity, though…we think it’s more complex,” Chassaing said at the meeting. Overall, the findings raise the question of whether emulsifier-sensitive individuals can be identified.

This, he said, is one of his most recent research questions. His lab has led the development of an in vitro microbiota model built to predict an individual’s sensitivity to emulsifiers. In a study published in April, the model recapitulated the differential CMC sensitivity observed in the earlier FRESH study, suggesting that an individual’s sensitivity to emulsifiers can indeed be predicted by examining their baseline microbiota.

 

Interpreting the Epidemiology

Chassaing’s research arch illustrates the synergy between epidemiological research, basic/translational research, and clinical interventional research that’s needed to understand the diet-microbiome intersection in inflammatory bowel disease, said Ashwin Ananthakrishnan, MBBS, MPH, AGAF, associate professor of medicine at Massachusetts General Hospital, Boston, in an interview at the meeting.

“It’s a good example of how to really span the spectrum, starting from the big picture and going deeper to understand mechanisms, and starting from mechanisms and expanding it out,” Ananthakrishnan said.

In his own talk about research on IBD, Ananthakrishnan said that epidemiological data have shown over the past 10-15 years that total dietary fiber is inversely associated with the risk for Crohn’s disease (with the strongest associations with fiber from fruits and vegetables). Studies have also shown that a higher intake of polyunsaturated fatty acids is associated with a lower risk for ulcerative colitis, whereas “an n-6-fatty acid-rich diet is associated with a higher risk of ulcerative colitis,” he said.

Dietary cohort studies, meanwhile, have shed light on the influence of dietary patterns — such as the Mediterranean diet and diets with high inflammatory potential—on IBD. A diet rich in ultra-processed foods has also been shown in a prospective cohort study to be associated with a higher risk for Crohn’s disease, with certain categories of ultra-processed foods (eg, breads and breakfast foods) having the strongest associations.

Such studies are limited in part, however, by inadequate assessment of potentially relevant variables such as emulsifiers, preservatives, and how the food is processed, he said.

And in interpreting the epidemiological research on fiber and IBD, for instance, one must appreciate that “there are a number of mechanisms by which fiber is impactful…there’s a big picture to look at,” Ananthakrishnan said. Fiber “can affect the microbiome, clearly, it can affect the gut barrier, and it can affect bile acids, and there are detailed translational studies in support of each of these.”

But there are other constituents of fruits and vegetables “that could potentially influence disease risk, such as AhR ligands and polyphenols,” he said. “And importantly, people not eating a lot of fiber may be eating a lot of ultra-processed foods.”

Most interventional studies of fiber have not shown a benefit of a high-fiber diet, Ananthakrishnan said, but there are multiple possible reasons and factors at play, including potential population differences (eg, in inflammatory status or baseline microbiota), shortcomings of the interventions, and potentially inaccurate outcomes.

Abigail Johnson, PhD, RDN, associate director of the Nutrition Coordinating Center, University of Minnesota Twin Cities, which supports dietary analysis, said during the session that the focus of dietary research is “moving toward understanding overall dietary patterns” as opposed to focusing more narrowly on vitamins, minerals, and macronutrients such as proteins, fats, and carbohydrates.

This is an improvement, though “we still don’t have good approaches for understanding [the contributions of] things like additives and emulsifiers, food preparation and cooking, and food processing,” said Johnson, assistant professor in the Division of Epidemiology and Community Health at University of Minnesota Twin Cities. “Perhaps by looking at things at the food level we can overcome some of these limitations.”

Ananthakrishnan reported being a consultant for Geneoscopy and receiving a research grant from Takeda. Chassaing did not report any financial disclosures. Johnson reported that she had no financial disclosures.

A version of this article appeared on Medscape.com.

Neighborhood-level social determinants of health (SDOH) are associated with the burden, comorbidities, and mortality of metabolic dysfunction-associated steatotic disease (MASLD). These health mediators should be considered along with individual SDOH in clinical care and healthcare quality and equity improvement, a large retrospective study of adults with MASLD at a multi-state healthcare institution concluded.

Across quartiles, patients in the most disadvantaged neighborhoods (according to home addresses) vs the least disadvantaged had worse outcomes and were also disproportionately Hispanic, Black, and Native American/Alaska Native, more often Spanish-speaking in primary language, and more often uninsured or on Medicaid, according to Karn Wijarnpreecha, MD, MPH, of the Division of Gastroenterology and Hepatology at University of Arizona College of Medicine–Phoenix, and colleagues writing in Clinical Gastroenterology and Hepatology.

Even after adjustment for measures in the Social Deprivation Index (SDI), the incidence of death, cirrhosis, diabetes mellitus (DM), and major adverse cardiovascular events (MACE) was higher in Native American/Alaska Native patients compared with their non-Hispanic White counterparts. The SDI is a composite measure of seven demographic characteristics from the American Community Survey, with scores ranging from 1 to 100 and weighted based on characteristics from national percentile rankings.

Aligning with the growing prevalence of obesity and DM, MASLD has increased substantially over the past three decades, and is now the leading cause of chronic liver disease in this country and the world. 

This rise in prevalence has underscored health disparities in MASLD and prompted research into linkd between liver disease and SDOH, defined as the conditions under which people are born, grow, live, work, and age. These are fundamental drivers of health disparities, including those in MASLD.
 

Study Details

Primary outcomes were MASLD burden, mortality, and comorbidities by neighborhood SDOH, assessed using the SDI in cross-sectional and longitudinal analyses.

A total of 69,191 adult patients (more than 50% female) diagnosed with MASLD were included, 45,003 of whom had at least 365 days of follow-up. They were treated from July 2012 to June 2023 in Banner Health Systems, a network that includes primary-, secondary-, and tertiary-care centers in Arizona, Colorado, Wyoming, Nevada, Nebraska, and California.

The median follow-up time was 48 months. Among patients across SDI quartiles (age range 49 to 62 years), 1390 patients (3.1%) died, 902 (2.0%) developed cirrhosis, 1087 (2.4%) developed LRE, 6537 (14.5%) developed DM, 2057 (4.6%) developed cancer, and 5409 (12.0%) developed MACE.

Those living in the most disadvantaged quartile of neighborhoods compared with the least had the following higher odds:

• cirrhosis, adjusted odds ratio [aOR], 1.42 (P < .001)
• any cardiovascular (CVD) disease, aOR, 1.20 (P < .001),
• coronary artery disease, aOR, 1.17 (P < .001)
• congestive heart failure, aOR, 1.43 (P < .001)
• cerebrovascular accident, aOR, 1.19 (P = .001)
• DM, aOR, 1.57 (P < .001)
• hypertension, aOR, 1.38 (P < .001).

They also had increased incidence of death (adjusted hazard ratio [aHR], 1.47; P < .001), LRE (aHR, 1.31; P = .012), DM (aHR, 1.47; P < .001), and MACE (aHR, 1.24; P < .001). 

The study expands upon previous SDOH-related research in liver disease and is the largest analysis of neighborhood-level SDOH in patients with MASLD to date. “Our findings are consistent with a recent study by Chen et al of over 15,900 patients with MASLD in Michigan that found neighborhood-level social disadvantage was associated with increased mortality and incident LREs and CVD,” Wijarnpreecha and colleagues wrote. 

“Beyond screening patients for individual-level SDOH, neighborhood-level determinants of health should also be considered, as they are important mediators between the environment and the individual,” they added, calling for studies to better understand the specific neighborhood SDOH that drive the disparate outcomes. In practice, integration of these measures into medical records might inform clinicians which patients would benefit from social services or help guide quality improvement projects and community partnerships.

Wijarnpreecha had no conflicts of interest to disclose. Several coauthors reported research support, consulting/advisory work, or stock ownership for various private-sector companies.
 

Neighborhood-level social determinants of health (SDOH) are associated with the burden, comorbidities, and mortality of metabolic dysfunction-associated steatotic disease (MASLD). These health mediators should be considered along with individual SDOH in clinical care and healthcare quality and equity improvement, a large retrospective study of adults with MASLD at a multi-state healthcare institution concluded.

Across quartiles, patients in the most disadvantaged neighborhoods (according to home addresses) vs the least disadvantaged had worse outcomes and were also disproportionately Hispanic, Black, and Native American/Alaska Native, more often Spanish-speaking in primary language, and more often uninsured or on Medicaid, according to Karn Wijarnpreecha, MD, MPH, of the Division of Gastroenterology and Hepatology at University of Arizona College of Medicine–Phoenix, and colleagues writing in Clinical Gastroenterology and Hepatology.

Even after adjustment for measures in the Social Deprivation Index (SDI), the incidence of death, cirrhosis, diabetes mellitus (DM), and major adverse cardiovascular events (MACE) was higher in Native American/Alaska Native patients compared with their non-Hispanic White counterparts. The SDI is a composite measure of seven demographic characteristics from the American Community Survey, with scores ranging from 1 to 100 and weighted based on characteristics from national percentile rankings.

Aligning with the growing prevalence of obesity and DM, MASLD has increased substantially over the past three decades, and is now the leading cause of chronic liver disease in this country and the world. 

This rise in prevalence has underscored health disparities in MASLD and prompted research into linkd between liver disease and SDOH, defined as the conditions under which people are born, grow, live, work, and age. These are fundamental drivers of health disparities, including those in MASLD.
 

Study Details

Primary outcomes were MASLD burden, mortality, and comorbidities by neighborhood SDOH, assessed using the SDI in cross-sectional and longitudinal analyses.

A total of 69,191 adult patients (more than 50% female) diagnosed with MASLD were included, 45,003 of whom had at least 365 days of follow-up. They were treated from July 2012 to June 2023 in Banner Health Systems, a network that includes primary-, secondary-, and tertiary-care centers in Arizona, Colorado, Wyoming, Nevada, Nebraska, and California.

The median follow-up time was 48 months. Among patients across SDI quartiles (age range 49 to 62 years), 1390 patients (3.1%) died, 902 (2.0%) developed cirrhosis, 1087 (2.4%) developed LRE, 6537 (14.5%) developed DM, 2057 (4.6%) developed cancer, and 5409 (12.0%) developed MACE.

Those living in the most disadvantaged quartile of neighborhoods compared with the least had the following higher odds:

• cirrhosis, adjusted odds ratio [aOR], 1.42 (P < .001)
• any cardiovascular (CVD) disease, aOR, 1.20 (P < .001),
• coronary artery disease, aOR, 1.17 (P < .001)
• congestive heart failure, aOR, 1.43 (P < .001)
• cerebrovascular accident, aOR, 1.19 (P = .001)
• DM, aOR, 1.57 (P < .001)
• hypertension, aOR, 1.38 (P < .001).

They also had increased incidence of death (adjusted hazard ratio [aHR], 1.47; P < .001), LRE (aHR, 1.31; P = .012), DM (aHR, 1.47; P < .001), and MACE (aHR, 1.24; P < .001). 

The study expands upon previous SDOH-related research in liver disease and is the largest analysis of neighborhood-level SDOH in patients with MASLD to date. “Our findings are consistent with a recent study by Chen et al of over 15,900 patients with MASLD in Michigan that found neighborhood-level social disadvantage was associated with increased mortality and incident LREs and CVD,” Wijarnpreecha and colleagues wrote. 

“Beyond screening patients for individual-level SDOH, neighborhood-level determinants of health should also be considered, as they are important mediators between the environment and the individual,” they added, calling for studies to better understand the specific neighborhood SDOH that drive the disparate outcomes. In practice, integration of these measures into medical records might inform clinicians which patients would benefit from social services or help guide quality improvement projects and community partnerships.

Wijarnpreecha had no conflicts of interest to disclose. Several coauthors reported research support, consulting/advisory work, or stock ownership for various private-sector companies.
 

Neighborhood-level social determinants of health (SDOH) are associated with the burden, comorbidities, and mortality of metabolic dysfunction-associated steatotic disease (MASLD). These health mediators should be considered along with individual SDOH in clinical care and healthcare quality and equity improvement, a large retrospective study of adults with MASLD at a multi-state healthcare institution concluded.

Across quartiles, patients in the most disadvantaged neighborhoods (according to home addresses) vs the least disadvantaged had worse outcomes and were also disproportionately Hispanic, Black, and Native American/Alaska Native, more often Spanish-speaking in primary language, and more often uninsured or on Medicaid, according to Karn Wijarnpreecha, MD, MPH, of the Division of Gastroenterology and Hepatology at University of Arizona College of Medicine–Phoenix, and colleagues writing in Clinical Gastroenterology and Hepatology.

Even after adjustment for measures in the Social Deprivation Index (SDI), the incidence of death, cirrhosis, diabetes mellitus (DM), and major adverse cardiovascular events (MACE) was higher in Native American/Alaska Native patients compared with their non-Hispanic White counterparts. The SDI is a composite measure of seven demographic characteristics from the American Community Survey, with scores ranging from 1 to 100 and weighted based on characteristics from national percentile rankings.

Aligning with the growing prevalence of obesity and DM, MASLD has increased substantially over the past three decades, and is now the leading cause of chronic liver disease in this country and the world. 

This rise in prevalence has underscored health disparities in MASLD and prompted research into linkd between liver disease and SDOH, defined as the conditions under which people are born, grow, live, work, and age. These are fundamental drivers of health disparities, including those in MASLD.
 

Study Details

Primary outcomes were MASLD burden, mortality, and comorbidities by neighborhood SDOH, assessed using the SDI in cross-sectional and longitudinal analyses.

A total of 69,191 adult patients (more than 50% female) diagnosed with MASLD were included, 45,003 of whom had at least 365 days of follow-up. They were treated from July 2012 to June 2023 in Banner Health Systems, a network that includes primary-, secondary-, and tertiary-care centers in Arizona, Colorado, Wyoming, Nevada, Nebraska, and California.

The median follow-up time was 48 months. Among patients across SDI quartiles (age range 49 to 62 years), 1390 patients (3.1%) died, 902 (2.0%) developed cirrhosis, 1087 (2.4%) developed LRE, 6537 (14.5%) developed DM, 2057 (4.6%) developed cancer, and 5409 (12.0%) developed MACE.

Those living in the most disadvantaged quartile of neighborhoods compared with the least had the following higher odds:

• cirrhosis, adjusted odds ratio [aOR], 1.42 (P < .001)
• any cardiovascular (CVD) disease, aOR, 1.20 (P < .001),
• coronary artery disease, aOR, 1.17 (P < .001)
• congestive heart failure, aOR, 1.43 (P < .001)
• cerebrovascular accident, aOR, 1.19 (P = .001)
• DM, aOR, 1.57 (P < .001)
• hypertension, aOR, 1.38 (P < .001).

They also had increased incidence of death (adjusted hazard ratio [aHR], 1.47; P < .001), LRE (aHR, 1.31; P = .012), DM (aHR, 1.47; P < .001), and MACE (aHR, 1.24; P < .001). 

The study expands upon previous SDOH-related research in liver disease and is the largest analysis of neighborhood-level SDOH in patients with MASLD to date. “Our findings are consistent with a recent study by Chen et al of over 15,900 patients with MASLD in Michigan that found neighborhood-level social disadvantage was associated with increased mortality and incident LREs and CVD,” Wijarnpreecha and colleagues wrote. 

“Beyond screening patients for individual-level SDOH, neighborhood-level determinants of health should also be considered, as they are important mediators between the environment and the individual,” they added, calling for studies to better understand the specific neighborhood SDOH that drive the disparate outcomes. In practice, integration of these measures into medical records might inform clinicians which patients would benefit from social services or help guide quality improvement projects and community partnerships.

Wijarnpreecha had no conflicts of interest to disclose. Several coauthors reported research support, consulting/advisory work, or stock ownership for various private-sector companies.