Diabetes

DENVER — Unlike traditional antidiabetic therapies, which have never been associated with a significant reduction in stroke in a major trial, some of the newer drugs are showing that benefit, but the protection is not linked to tighter glycemic control.

In patients with type 2 diabetes mellitus (T2DM), the evidence is strong that “they are not working through glycemic control per se,” according to Larry B. Goldstein, MD, chair of neurology, University of Kentucky School of Medicine, Louisville. “But it is not yet clear what the mechanism of benefit is.”

Ted Bosworth/MDedge News

Stroke Prevention With Antidiabetic Drugs

“What has changed is that we have new ways of glycemic control, and some of these do show protection against stroke,” Dr. Goldstein said. Yet, the newer drugs do not do a better job at sustained reductions of HbA1c or other measures of reaching lower blood glucose reductions when adherence is similar.

“The level of glucose control with the newer agents is really about the same,” Dr. Goldstein said at the annual meeting of the American Academy of Neurology, where he led a symposium called Controversies in Stroke Treatment and Prevention.

The newer agents, such as sodium glucose co-transport-2 inhibitors (SGLT-2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA), have been associated with significant and clinically meaningful reductions in cardiovascular events. However, it is not clear that even these two medications perform similarly for stroke prevention specifically.

Of these two drug classes, Dr. Goldstein said the evidence most strongly supports GLP-1 receptor agonists. He cited one meta-analysis of eight randomized studies that calculated a risk reduction of about 15% whether calculated for fatal or nonfatal strokes. For each the protection was highly statistically significant (P = .0002 and P < .001, respectively).

In contrast, the effect of SGLT-2 inhibitors is weaker. In a study that distilled data from large cardiovascular trials with GLP-1RA, SGLT2i, dipeptidyl peptidase-4 inhibitors (DPP4i), and pioglitazone, a thiazolidinedione, only GLP-1RA drugs were associated with a highly significant (P < .001) reduction in risk of stroke. The risk reduction for pioglitazone reached significance (P = .025), but there was no signal of risk reduction for SGLT2i (P = .88) or for DPP4i (P = .5).
 

Weight Loss Is Potential Mechanism

Looking to explain the protection from stroke associated with some of the newer antidiabetic therapies, Gordon Kelley, MD, who leads the stroke program for AdventHealth Medical Group, Shawnee Mission, Kansas, suggested that weight loss is probably important.

“In our group, we work as a team to manage stroke risk in patients with diabetes, so I am not much involved in the choice of antidiabetic therapies, but it does seem that SGLT2 inhibitors and the GLP-1 receptor agonists share weight loss as an effect beyond glucose control,” he said.

Dr. Goldstein agreed that weight loss is a potential contributor to the cardiovascular benefits of GLP-1RA and SGLT2i, but he indicated that it might not help explain the reduction in stroke, an effect demonstrated repeatedly with GLP-1RA but inconsistently with SGLT2i.

The argument against weight loss as the critical mechanism of stroke prevention from newer antidiabetic drugs is strengthened by studies that suggest weight loss with SGLT2i appears to be even better than on GLP-1RA. In a study published in a pharmacy journal, weight loss was about twice as great among T2DM patients after 6 months of treatment managed with SGLT2i relative to those on a GLP-1RA (-2.8 vs 1.15 kg; P = .014).
 

Newer Antidiabetic Agents Guideline Recommended

In the 2019 American College of Cardiology/American Heart Association guidelines on the Primary Prevention of Cardiovascular Disease, stroke reduction is not discussed as an isolated risk, but these guidelines do recommend GLP-1RA or SGLT2i after metformin for glycemic control in T2DM patients with atherosclerotic cardiovascular disease (ASCVD) risk factors. This is based on evidence that drugs of both classes reduce risk for ASCVD events. The risk reduction has been particularly strong for heart failure.

For the risk of stroke specifically in patients with T2DM, Dr. Goldstein recommended calculating the ASCVD risk with the simple but well validated ACC risk calculator that is available online and is quickly completed when values for patient risk factors are readily available. For those with greater than 10% risk of an event in the next 10 years, he thinks GLP-1RA are a reasonable choice for prevention of stroke and other ASCVD events.

“GLP-1RA is mentioned in the guidelines, so this is supported,” said Dr. Goldstein, although adding that his choice of this class over SGLT2i is a personal if informed recommendation. He believes that the data favor GLP-1RA even if the exact mechanism of this protection is yet to be identified.

Dr. Goldstein and Dr. Kelley report no potential conflicts of interest.

DENVER — Unlike traditional antidiabetic therapies, which have never been associated with a significant reduction in stroke in a major trial, some of the newer drugs are showing that benefit, but the protection is not linked to tighter glycemic control.

In patients with type 2 diabetes mellitus (T2DM), the evidence is strong that “they are not working through glycemic control per se,” according to Larry B. Goldstein, MD, chair of neurology, University of Kentucky School of Medicine, Louisville. “But it is not yet clear what the mechanism of benefit is.”

Ted Bosworth/MDedge News

Stroke Prevention With Antidiabetic Drugs

“What has changed is that we have new ways of glycemic control, and some of these do show protection against stroke,” Dr. Goldstein said. Yet, the newer drugs do not do a better job at sustained reductions of HbA1c or other measures of reaching lower blood glucose reductions when adherence is similar.

“The level of glucose control with the newer agents is really about the same,” Dr. Goldstein said at the annual meeting of the American Academy of Neurology, where he led a symposium called Controversies in Stroke Treatment and Prevention.

The newer agents, such as sodium glucose co-transport-2 inhibitors (SGLT-2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA), have been associated with significant and clinically meaningful reductions in cardiovascular events. However, it is not clear that even these two medications perform similarly for stroke prevention specifically.

Of these two drug classes, Dr. Goldstein said the evidence most strongly supports GLP-1 receptor agonists. He cited one meta-analysis of eight randomized studies that calculated a risk reduction of about 15% whether calculated for fatal or nonfatal strokes. For each the protection was highly statistically significant (P = .0002 and P < .001, respectively).

In contrast, the effect of SGLT-2 inhibitors is weaker. In a study that distilled data from large cardiovascular trials with GLP-1RA, SGLT2i, dipeptidyl peptidase-4 inhibitors (DPP4i), and pioglitazone, a thiazolidinedione, only GLP-1RA drugs were associated with a highly significant (P < .001) reduction in risk of stroke. The risk reduction for pioglitazone reached significance (P = .025), but there was no signal of risk reduction for SGLT2i (P = .88) or for DPP4i (P = .5).
 

Weight Loss Is Potential Mechanism

Looking to explain the protection from stroke associated with some of the newer antidiabetic therapies, Gordon Kelley, MD, who leads the stroke program for AdventHealth Medical Group, Shawnee Mission, Kansas, suggested that weight loss is probably important.

“In our group, we work as a team to manage stroke risk in patients with diabetes, so I am not much involved in the choice of antidiabetic therapies, but it does seem that SGLT2 inhibitors and the GLP-1 receptor agonists share weight loss as an effect beyond glucose control,” he said.

Dr. Goldstein agreed that weight loss is a potential contributor to the cardiovascular benefits of GLP-1RA and SGLT2i, but he indicated that it might not help explain the reduction in stroke, an effect demonstrated repeatedly with GLP-1RA but inconsistently with SGLT2i.

The argument against weight loss as the critical mechanism of stroke prevention from newer antidiabetic drugs is strengthened by studies that suggest weight loss with SGLT2i appears to be even better than on GLP-1RA. In a study published in a pharmacy journal, weight loss was about twice as great among T2DM patients after 6 months of treatment managed with SGLT2i relative to those on a GLP-1RA (-2.8 vs 1.15 kg; P = .014).
 

Newer Antidiabetic Agents Guideline Recommended

In the 2019 American College of Cardiology/American Heart Association guidelines on the Primary Prevention of Cardiovascular Disease, stroke reduction is not discussed as an isolated risk, but these guidelines do recommend GLP-1RA or SGLT2i after metformin for glycemic control in T2DM patients with atherosclerotic cardiovascular disease (ASCVD) risk factors. This is based on evidence that drugs of both classes reduce risk for ASCVD events. The risk reduction has been particularly strong for heart failure.

For the risk of stroke specifically in patients with T2DM, Dr. Goldstein recommended calculating the ASCVD risk with the simple but well validated ACC risk calculator that is available online and is quickly completed when values for patient risk factors are readily available. For those with greater than 10% risk of an event in the next 10 years, he thinks GLP-1RA are a reasonable choice for prevention of stroke and other ASCVD events.

“GLP-1RA is mentioned in the guidelines, so this is supported,” said Dr. Goldstein, although adding that his choice of this class over SGLT2i is a personal if informed recommendation. He believes that the data favor GLP-1RA even if the exact mechanism of this protection is yet to be identified.

Dr. Goldstein and Dr. Kelley report no potential conflicts of interest.

DENVER — Unlike traditional antidiabetic therapies, which have never been associated with a significant reduction in stroke in a major trial, some of the newer drugs are showing that benefit, but the protection is not linked to tighter glycemic control.

In patients with type 2 diabetes mellitus (T2DM), the evidence is strong that “they are not working through glycemic control per se,” according to Larry B. Goldstein, MD, chair of neurology, University of Kentucky School of Medicine, Louisville. “But it is not yet clear what the mechanism of benefit is.”

Ted Bosworth/MDedge News

Stroke Prevention With Antidiabetic Drugs

“What has changed is that we have new ways of glycemic control, and some of these do show protection against stroke,” Dr. Goldstein said. Yet, the newer drugs do not do a better job at sustained reductions of HbA1c or other measures of reaching lower blood glucose reductions when adherence is similar.

“The level of glucose control with the newer agents is really about the same,” Dr. Goldstein said at the annual meeting of the American Academy of Neurology, where he led a symposium called Controversies in Stroke Treatment and Prevention.

The newer agents, such as sodium glucose co-transport-2 inhibitors (SGLT-2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA), have been associated with significant and clinically meaningful reductions in cardiovascular events. However, it is not clear that even these two medications perform similarly for stroke prevention specifically.

Of these two drug classes, Dr. Goldstein said the evidence most strongly supports GLP-1 receptor agonists. He cited one meta-analysis of eight randomized studies that calculated a risk reduction of about 15% whether calculated for fatal or nonfatal strokes. For each the protection was highly statistically significant (P = .0002 and P < .001, respectively).

In contrast, the effect of SGLT-2 inhibitors is weaker. In a study that distilled data from large cardiovascular trials with GLP-1RA, SGLT2i, dipeptidyl peptidase-4 inhibitors (DPP4i), and pioglitazone, a thiazolidinedione, only GLP-1RA drugs were associated with a highly significant (P < .001) reduction in risk of stroke. The risk reduction for pioglitazone reached significance (P = .025), but there was no signal of risk reduction for SGLT2i (P = .88) or for DPP4i (P = .5).
 

Weight Loss Is Potential Mechanism

Looking to explain the protection from stroke associated with some of the newer antidiabetic therapies, Gordon Kelley, MD, who leads the stroke program for AdventHealth Medical Group, Shawnee Mission, Kansas, suggested that weight loss is probably important.

“In our group, we work as a team to manage stroke risk in patients with diabetes, so I am not much involved in the choice of antidiabetic therapies, but it does seem that SGLT2 inhibitors and the GLP-1 receptor agonists share weight loss as an effect beyond glucose control,” he said.

Dr. Goldstein agreed that weight loss is a potential contributor to the cardiovascular benefits of GLP-1RA and SGLT2i, but he indicated that it might not help explain the reduction in stroke, an effect demonstrated repeatedly with GLP-1RA but inconsistently with SGLT2i.

The argument against weight loss as the critical mechanism of stroke prevention from newer antidiabetic drugs is strengthened by studies that suggest weight loss with SGLT2i appears to be even better than on GLP-1RA. In a study published in a pharmacy journal, weight loss was about twice as great among T2DM patients after 6 months of treatment managed with SGLT2i relative to those on a GLP-1RA (-2.8 vs 1.15 kg; P = .014).
 

Newer Antidiabetic Agents Guideline Recommended

In the 2019 American College of Cardiology/American Heart Association guidelines on the Primary Prevention of Cardiovascular Disease, stroke reduction is not discussed as an isolated risk, but these guidelines do recommend GLP-1RA or SGLT2i after metformin for glycemic control in T2DM patients with atherosclerotic cardiovascular disease (ASCVD) risk factors. This is based on evidence that drugs of both classes reduce risk for ASCVD events. The risk reduction has been particularly strong for heart failure.

For the risk of stroke specifically in patients with T2DM, Dr. Goldstein recommended calculating the ASCVD risk with the simple but well validated ACC risk calculator that is available online and is quickly completed when values for patient risk factors are readily available. For those with greater than 10% risk of an event in the next 10 years, he thinks GLP-1RA are a reasonable choice for prevention of stroke and other ASCVD events.

“GLP-1RA is mentioned in the guidelines, so this is supported,” said Dr. Goldstein, although adding that his choice of this class over SGLT2i is a personal if informed recommendation. He believes that the data favor GLP-1RA even if the exact mechanism of this protection is yet to be identified.

Dr. Goldstein and Dr. Kelley report no potential conflicts of interest.

TOPLINE:

No significant association was found between the use of glucagon-like peptide 1 receptor agonists (GLP-1 RAs) and thyroid cancer over nearly 4 years.

METHODOLOGY:

• A cohort study using data from nationwide registers in Denmark, Norway, and Sweden between 2007 and 2021 included 145,410 patients who initiated GLP-1 RAs and 291,667 propensity score-matched patients initiating dipeptidyl peptidase 4 (DPP4) inhibitors as active comparators.
• Additional analysis included 111,744 who initiated GLP-1 RAs and 148,179 patients initiating sodium-glucose cotransporter 2 (SGLT2) inhibitors.
• Overall, mean follow-up time was 3.9 years, with 25% followed for more than 6 years.

TAKEAWAY:

• The most common individual GLP-1 RAs were liraglutide (57.3%) and semaglutide (32.9%).
• During follow-up, there were 76 incident thyroid cancer cases among GLP-1 RA users and 184 cases in DPP4 inhibitor users, giving incidence rates per 10,000 of 1.33 and 1.46, respectively, a nonsignificant difference (hazard ratio [HR], 0.93; 95% CI, 0.66-1.31).
• Papillary thyroid cancer was the most common thyroid cancer subtype, followed by follicular and medullary, with no significant increases in risk with GLP-1 RAs by cancer type, although the numbers were small.
• In the SGLT2 inhibitor comparison, there was also no significantly increased thyroid cancer risk for GLP-1 RAs (HR, 1.16; 95% CI, 0.65-2.05).

IN PRACTICE:

“Given the upper limit of the confidence interval, the findings are incompatible with more than a 31% increased relative risk of thyroid cancer. In absolute terms, this translates to no more than 0.36 excess cases per 10 000 person-years, a figure that should be interpreted against the background incidence of 1.46 per 10,000 person-years among the comparator group in the study populations.”

SOURCE:

This study was conducted by Björn Pasternak, MD, PhD, of the Karolinska Institutet, Stockholm, and colleagues. It was published online on April 10, 2024, in The BMJ.

LIMITATIONS:

Relatively short follow-up for cancer risk. Risk by individual GLP-1 RA not analyzed. Small event numbers. Observational, with potential for residual confounding and time-release bias.

DISCLOSURES:

The study was supported by grants from the Swedish Cancer Society and the Swedish Research Council. Dr. Pasternak was supported by a consolidator investigator grant from Karolinska Institutet. Some of the coauthors had industry disclosures.

A version of this article appeared on Medscape.com.

TOPLINE:

No significant association was found between the use of glucagon-like peptide 1 receptor agonists (GLP-1 RAs) and thyroid cancer over nearly 4 years.

METHODOLOGY:

• A cohort study using data from nationwide registers in Denmark, Norway, and Sweden between 2007 and 2021 included 145,410 patients who initiated GLP-1 RAs and 291,667 propensity score-matched patients initiating dipeptidyl peptidase 4 (DPP4) inhibitors as active comparators.
• Additional analysis included 111,744 who initiated GLP-1 RAs and 148,179 patients initiating sodium-glucose cotransporter 2 (SGLT2) inhibitors.
• Overall, mean follow-up time was 3.9 years, with 25% followed for more than 6 years.

TAKEAWAY:

• The most common individual GLP-1 RAs were liraglutide (57.3%) and semaglutide (32.9%).
• During follow-up, there were 76 incident thyroid cancer cases among GLP-1 RA users and 184 cases in DPP4 inhibitor users, giving incidence rates per 10,000 of 1.33 and 1.46, respectively, a nonsignificant difference (hazard ratio [HR], 0.93; 95% CI, 0.66-1.31).
• Papillary thyroid cancer was the most common thyroid cancer subtype, followed by follicular and medullary, with no significant increases in risk with GLP-1 RAs by cancer type, although the numbers were small.
• In the SGLT2 inhibitor comparison, there was also no significantly increased thyroid cancer risk for GLP-1 RAs (HR, 1.16; 95% CI, 0.65-2.05).

IN PRACTICE:

“Given the upper limit of the confidence interval, the findings are incompatible with more than a 31% increased relative risk of thyroid cancer. In absolute terms, this translates to no more than 0.36 excess cases per 10 000 person-years, a figure that should be interpreted against the background incidence of 1.46 per 10,000 person-years among the comparator group in the study populations.”

SOURCE:

This study was conducted by Björn Pasternak, MD, PhD, of the Karolinska Institutet, Stockholm, and colleagues. It was published online on April 10, 2024, in The BMJ.

LIMITATIONS:

Relatively short follow-up for cancer risk. Risk by individual GLP-1 RA not analyzed. Small event numbers. Observational, with potential for residual confounding and time-release bias.

DISCLOSURES:

The study was supported by grants from the Swedish Cancer Society and the Swedish Research Council. Dr. Pasternak was supported by a consolidator investigator grant from Karolinska Institutet. Some of the coauthors had industry disclosures.

A version of this article appeared on Medscape.com.

TOPLINE:

No significant association was found between the use of glucagon-like peptide 1 receptor agonists (GLP-1 RAs) and thyroid cancer over nearly 4 years.

METHODOLOGY:

• A cohort study using data from nationwide registers in Denmark, Norway, and Sweden between 2007 and 2021 included 145,410 patients who initiated GLP-1 RAs and 291,667 propensity score-matched patients initiating dipeptidyl peptidase 4 (DPP4) inhibitors as active comparators.
• Additional analysis included 111,744 who initiated GLP-1 RAs and 148,179 patients initiating sodium-glucose cotransporter 2 (SGLT2) inhibitors.
• Overall, mean follow-up time was 3.9 years, with 25% followed for more than 6 years.

TAKEAWAY:

• The most common individual GLP-1 RAs were liraglutide (57.3%) and semaglutide (32.9%).
• During follow-up, there were 76 incident thyroid cancer cases among GLP-1 RA users and 184 cases in DPP4 inhibitor users, giving incidence rates per 10,000 of 1.33 and 1.46, respectively, a nonsignificant difference (hazard ratio [HR], 0.93; 95% CI, 0.66-1.31).
• Papillary thyroid cancer was the most common thyroid cancer subtype, followed by follicular and medullary, with no significant increases in risk with GLP-1 RAs by cancer type, although the numbers were small.
• In the SGLT2 inhibitor comparison, there was also no significantly increased thyroid cancer risk for GLP-1 RAs (HR, 1.16; 95% CI, 0.65-2.05).

IN PRACTICE:

“Given the upper limit of the confidence interval, the findings are incompatible with more than a 31% increased relative risk of thyroid cancer. In absolute terms, this translates to no more than 0.36 excess cases per 10 000 person-years, a figure that should be interpreted against the background incidence of 1.46 per 10,000 person-years among the comparator group in the study populations.”

SOURCE:

This study was conducted by Björn Pasternak, MD, PhD, of the Karolinska Institutet, Stockholm, and colleagues. It was published online on April 10, 2024, in The BMJ.

LIMITATIONS:

Relatively short follow-up for cancer risk. Risk by individual GLP-1 RA not analyzed. Small event numbers. Observational, with potential for residual confounding and time-release bias.

DISCLOSURES:

The study was supported by grants from the Swedish Cancer Society and the Swedish Research Council. Dr. Pasternak was supported by a consolidator investigator grant from Karolinska Institutet. Some of the coauthors had industry disclosures.

A version of this article appeared on Medscape.com.

You are what you eat, as the adage goes. But a growing body of evidence indicates that it’s not just what and how much you eat that influence your health. How fast and when you eat also play a role.

Research now indicates that these two factors may affect the risk for gastrointestinal problems, obesity, and type 2 diabetes (T2D). Because meal timing and speed of consumption are modifiable, they present new opportunities to change patient behavior to help prevent and perhaps address these conditions.

Not So Fast

Most people are well acquainted with the short-term gastrointestinal effects of eating too quickly, which include indigestion, gas, bloating, and nausea. But regularly eating too fast can cause long-term consequences.

Obtaining a sense of fullness is key to staving off overeating and excess caloric intake. However, it takes approximately 20 minutes for the stomach to alert the brain to feelings of fullness. Eat too quickly and the fullness signaling might not set in until you’ve consumed more calories than intended. Research links this habit to excess body weight.

The practice also can lead to gastrointestinal diseases over the long term because overeating causes food to remain in the stomach longer, thus prolonging the time that the gastric mucosa is exposed to gastric acids.

A study of 10,893 adults in Korea reported that those with the fastest eating speed (< 5 min/meal) had a 1.7 times greater likelihood of endoscopic erosive gastritis than those with the slowest times (≥ 15 min/meal). Faster eating also was linked to increased risk for functional dyspepsia in a study involving 89 young-adult female military cadets in Korea with relatively controlled eating patterns.

On the extreme end of the spectrum, researchers who performed an assessment of a competitive speed eater speculated that the observed physiological accommodation required for the role (expanding the stomach to form a large flaccid sac) makes speed eaters vulnerable to morbid obesity, gastroparesis, intractable nausea and vomiting, and the need for gastrectomy.

The risk for metabolic changes and eventual development of T2D also appear to be linked to how quickly food is consumed.

Two clinical studies conducted in Japan — a cohort study of 2050 male factory workers and a nationwide study with 197,825 participants — identified a significant association between faster eating and T2D and insulin resistance. A case-control study involving 234 patients with new onset T2D and 468 controls from Lithuania linked faster eating to a greater than twofold risk for T2D. And a Chinese cross-sectional study of 7972 adults indicated that faster eating significantly increased the risk for metabolic syndrome, elevated blood pressure, and central obesity in adults.

Various hypotheses have been proposed to explain why fast eating may upset metabolic processes, including a delayed sense of fullness contributing to spiking postprandial glucose levels, lack of time for mastication causing higher glucose concentrations, and the triggering of specific cytokines (eg, interleukin-1 beta and interleukin-6) that lead to insulin resistance. It is also possible that the association is the result of people who eat quickly having relatively higher body weights, which translates to a higher risk for T2D.

However, there’s an opportunity in the association of rapid meal consumption with gastrointestinal and metabolic diseases, as people can slow the speed at which they eat so they feel full before they overeat.

A 2019 study in which 21 participants were instructed to eat a 600-kcal meal at a “normal” or “slow” pace (6 minutes or 24 minutes) found that the latter group reported feeling fuller while consuming fewer calories.

This approach may not work for all patients, however. There’s evidence to suggest that tactics to slow down eating may not limit the energy intake of those who are already overweight or obese.

Patients with obesity may physiologically differ in their processing of food, according to Michael Camilleri, MD, consultant in the Division of Gastroenterology and Hepatology at Mayo Clinic in Rochester, Minnesota.

“We have demonstrated that about 20%-25% of people with obesity actually have rapid gastric emptying,” he told this news organization. “As a result, they don’t feel full after they eat a meal and that might impact the total volume of food that they eat before they really feel full.”

The Ideal Time to Eat

It’s not only the speed at which individuals eat that may influence outcomes but when they take their meals. Research indicates that eating earlier in the day to align meals with the body’s circadian rhythms in metabolism offers health benefits.

“The focus would be to eat a meal that syncs during those daytime hours,” Collin Popp, PhD, MS, RD, a research scientist at the NYU Grossman School of Medicine in New York, told this news organization. “I typically suggest patients have their largest meal in the morning, whether that’s a large or medium-sized breakfast, or a big lunch.”

A recent cross-sectional study of 2050 participants found that having the largest meal at lunch protected against obesity (odds ratio [OR], 0.71), whereas having it at dinner increased the risk for obesity (OR, 1.67) and led to higher body mass index.

Consuming the majority of calories in meals earlier in the day may have metabolic health benefits, as well.

A 2015 randomized controlled trial involving 18 adults with obesity and T2D found that eating a high-energy breakfast and a low-energy dinner leads to reduced hyperglycemia throughout the day compared with eating a low-energy breakfast and a high-energy dinner.

Time-restricted eating (TRE), a form of intermittent fasting, also can improve metabolic health depending on the time of day.

A 2023 meta-analysis found that TRE was more effective at reducing fasting glucose levels in participants who were overweight and obese if done earlier rather than later in the day. Similarly, a 2022 study involving 82 healthy patients without diabetes or obesity found that early TRE was more effective than mid-day TRE at improving insulin sensitivity and that it improved fasting glucose and reduced total body mass and adiposity, while mid-day TRE did not.

A study that analyzed the effects of TRE in eight adult men with overweight and prediabetes found “better insulin resistance when the window of food consumption was earlier in the day,» noted endocrinologist Beverly Tchang, MD, an assistant professor of clinical medicine at Weill Cornell Medicine with a focus on obesity medication.

Patients May Benefit From Behavioral Interventions

Patients potentially negatively affected by eating too quickly or at late hours may benefit from adopting behavioral interventions to address these tendencies. To determine if a patient is a candidate for such interventions, Dr. Popp recommends starting with a simple conversation.

“When I first meet patients, I always ask them to describe to me a typical day for how they eat — when they’re eating, what they’re eating, the food quality, who are they with — to see if there’s social aspects to it. Then try and make the recommendations based on that,” said Dr. Popp, whose work focuses on biobehavioral interventions for the treatment and prevention of obesity, T2D, and other cardiometabolic outcomes.

Dr. Tchang said she encourages her patients to be mindful of hunger and fullness cues.

“Eat if you’re hungry; don’t force yourself to eat if you’re not hungry,” she said. “If you’re not sure whether you’re hungry or not, speak to a doctor because this points to an abnormality in your appetite-regulation system, which can be helped with GLP-1 [glucagon-like peptide 1] receptor agonists.”

Adjusting what patients eat can help them improve their meal timing.

“For example, we know that a high-fiber diet or a diet that has a large amount of fat in it tends to empty from the stomach slower,” Dr. Camilleri said. “That might give a sensation of fullness that lasts longer and that might prevent, for instance, the ingestion of the next meal.”

Those trying to eat more slowly are advised to seek out foods that are hard in texture and minimally processed.

A study involving 50 patients with healthy weights found that hard foods are consumed more slowly than soft foods and that energy intake is lowest with hard, minimally processed foods. Combining hard-textured foods with explicit instructions to reduce eating speed has also been shown to be an effective strategy. For those inclined to seek out technology-based solution, evidence suggests that a self-monitoring wearable device can slow the eating rate.

Although the evidence is mounting that the timing and duration of meals have an impact on certain chronic diseases, clinicians should remember that these two factors are far from the most important contributors, Dr. Popp said.

“We also have to consider total caloric intake, food quality, sleep, alcohol use, smoking, and physical activity,” he said. “Meal timing should be considered as under the umbrella of health that is important for a lot of folks.”

A version of this article appeared on Medscape.com.

You are what you eat, as the adage goes. But a growing body of evidence indicates that it’s not just what and how much you eat that influence your health. How fast and when you eat also play a role.

Research now indicates that these two factors may affect the risk for gastrointestinal problems, obesity, and type 2 diabetes (T2D). Because meal timing and speed of consumption are modifiable, they present new opportunities to change patient behavior to help prevent and perhaps address these conditions.

Not So Fast

Most people are well acquainted with the short-term gastrointestinal effects of eating too quickly, which include indigestion, gas, bloating, and nausea. But regularly eating too fast can cause long-term consequences.

Obtaining a sense of fullness is key to staving off overeating and excess caloric intake. However, it takes approximately 20 minutes for the stomach to alert the brain to feelings of fullness. Eat too quickly and the fullness signaling might not set in until you’ve consumed more calories than intended. Research links this habit to excess body weight.

The practice also can lead to gastrointestinal diseases over the long term because overeating causes food to remain in the stomach longer, thus prolonging the time that the gastric mucosa is exposed to gastric acids.

A study of 10,893 adults in Korea reported that those with the fastest eating speed (< 5 min/meal) had a 1.7 times greater likelihood of endoscopic erosive gastritis than those with the slowest times (≥ 15 min/meal). Faster eating also was linked to increased risk for functional dyspepsia in a study involving 89 young-adult female military cadets in Korea with relatively controlled eating patterns.

On the extreme end of the spectrum, researchers who performed an assessment of a competitive speed eater speculated that the observed physiological accommodation required for the role (expanding the stomach to form a large flaccid sac) makes speed eaters vulnerable to morbid obesity, gastroparesis, intractable nausea and vomiting, and the need for gastrectomy.

The risk for metabolic changes and eventual development of T2D also appear to be linked to how quickly food is consumed.

Two clinical studies conducted in Japan — a cohort study of 2050 male factory workers and a nationwide study with 197,825 participants — identified a significant association between faster eating and T2D and insulin resistance. A case-control study involving 234 patients with new onset T2D and 468 controls from Lithuania linked faster eating to a greater than twofold risk for T2D. And a Chinese cross-sectional study of 7972 adults indicated that faster eating significantly increased the risk for metabolic syndrome, elevated blood pressure, and central obesity in adults.

Various hypotheses have been proposed to explain why fast eating may upset metabolic processes, including a delayed sense of fullness contributing to spiking postprandial glucose levels, lack of time for mastication causing higher glucose concentrations, and the triggering of specific cytokines (eg, interleukin-1 beta and interleukin-6) that lead to insulin resistance. It is also possible that the association is the result of people who eat quickly having relatively higher body weights, which translates to a higher risk for T2D.

However, there’s an opportunity in the association of rapid meal consumption with gastrointestinal and metabolic diseases, as people can slow the speed at which they eat so they feel full before they overeat.

A 2019 study in which 21 participants were instructed to eat a 600-kcal meal at a “normal” or “slow” pace (6 minutes or 24 minutes) found that the latter group reported feeling fuller while consuming fewer calories.

This approach may not work for all patients, however. There’s evidence to suggest that tactics to slow down eating may not limit the energy intake of those who are already overweight or obese.

Patients with obesity may physiologically differ in their processing of food, according to Michael Camilleri, MD, consultant in the Division of Gastroenterology and Hepatology at Mayo Clinic in Rochester, Minnesota.

“We have demonstrated that about 20%-25% of people with obesity actually have rapid gastric emptying,” he told this news organization. “As a result, they don’t feel full after they eat a meal and that might impact the total volume of food that they eat before they really feel full.”

The Ideal Time to Eat

It’s not only the speed at which individuals eat that may influence outcomes but when they take their meals. Research indicates that eating earlier in the day to align meals with the body’s circadian rhythms in metabolism offers health benefits.

“The focus would be to eat a meal that syncs during those daytime hours,” Collin Popp, PhD, MS, RD, a research scientist at the NYU Grossman School of Medicine in New York, told this news organization. “I typically suggest patients have their largest meal in the morning, whether that’s a large or medium-sized breakfast, or a big lunch.”

A recent cross-sectional study of 2050 participants found that having the largest meal at lunch protected against obesity (odds ratio [OR], 0.71), whereas having it at dinner increased the risk for obesity (OR, 1.67) and led to higher body mass index.

Consuming the majority of calories in meals earlier in the day may have metabolic health benefits, as well.

A 2015 randomized controlled trial involving 18 adults with obesity and T2D found that eating a high-energy breakfast and a low-energy dinner leads to reduced hyperglycemia throughout the day compared with eating a low-energy breakfast and a high-energy dinner.

Time-restricted eating (TRE), a form of intermittent fasting, also can improve metabolic health depending on the time of day.

A 2023 meta-analysis found that TRE was more effective at reducing fasting glucose levels in participants who were overweight and obese if done earlier rather than later in the day. Similarly, a 2022 study involving 82 healthy patients without diabetes or obesity found that early TRE was more effective than mid-day TRE at improving insulin sensitivity and that it improved fasting glucose and reduced total body mass and adiposity, while mid-day TRE did not.

A study that analyzed the effects of TRE in eight adult men with overweight and prediabetes found “better insulin resistance when the window of food consumption was earlier in the day,» noted endocrinologist Beverly Tchang, MD, an assistant professor of clinical medicine at Weill Cornell Medicine with a focus on obesity medication.

Patients May Benefit From Behavioral Interventions

Patients potentially negatively affected by eating too quickly or at late hours may benefit from adopting behavioral interventions to address these tendencies. To determine if a patient is a candidate for such interventions, Dr. Popp recommends starting with a simple conversation.

“When I first meet patients, I always ask them to describe to me a typical day for how they eat — when they’re eating, what they’re eating, the food quality, who are they with — to see if there’s social aspects to it. Then try and make the recommendations based on that,” said Dr. Popp, whose work focuses on biobehavioral interventions for the treatment and prevention of obesity, T2D, and other cardiometabolic outcomes.

Dr. Tchang said she encourages her patients to be mindful of hunger and fullness cues.

“Eat if you’re hungry; don’t force yourself to eat if you’re not hungry,” she said. “If you’re not sure whether you’re hungry or not, speak to a doctor because this points to an abnormality in your appetite-regulation system, which can be helped with GLP-1 [glucagon-like peptide 1] receptor agonists.”

Adjusting what patients eat can help them improve their meal timing.

“For example, we know that a high-fiber diet or a diet that has a large amount of fat in it tends to empty from the stomach slower,” Dr. Camilleri said. “That might give a sensation of fullness that lasts longer and that might prevent, for instance, the ingestion of the next meal.”

Those trying to eat more slowly are advised to seek out foods that are hard in texture and minimally processed.

A study involving 50 patients with healthy weights found that hard foods are consumed more slowly than soft foods and that energy intake is lowest with hard, minimally processed foods. Combining hard-textured foods with explicit instructions to reduce eating speed has also been shown to be an effective strategy. For those inclined to seek out technology-based solution, evidence suggests that a self-monitoring wearable device can slow the eating rate.

Although the evidence is mounting that the timing and duration of meals have an impact on certain chronic diseases, clinicians should remember that these two factors are far from the most important contributors, Dr. Popp said.

“We also have to consider total caloric intake, food quality, sleep, alcohol use, smoking, and physical activity,” he said. “Meal timing should be considered as under the umbrella of health that is important for a lot of folks.”

A version of this article appeared on Medscape.com.

You are what you eat, as the adage goes. But a growing body of evidence indicates that it’s not just what and how much you eat that influence your health. How fast and when you eat also play a role.

Research now indicates that these two factors may affect the risk for gastrointestinal problems, obesity, and type 2 diabetes (T2D). Because meal timing and speed of consumption are modifiable, they present new opportunities to change patient behavior to help prevent and perhaps address these conditions.

Not So Fast

Most people are well acquainted with the short-term gastrointestinal effects of eating too quickly, which include indigestion, gas, bloating, and nausea. But regularly eating too fast can cause long-term consequences.

Obtaining a sense of fullness is key to staving off overeating and excess caloric intake. However, it takes approximately 20 minutes for the stomach to alert the brain to feelings of fullness. Eat too quickly and the fullness signaling might not set in until you’ve consumed more calories than intended. Research links this habit to excess body weight.

The practice also can lead to gastrointestinal diseases over the long term because overeating causes food to remain in the stomach longer, thus prolonging the time that the gastric mucosa is exposed to gastric acids.

A study of 10,893 adults in Korea reported that those with the fastest eating speed (< 5 min/meal) had a 1.7 times greater likelihood of endoscopic erosive gastritis than those with the slowest times (≥ 15 min/meal). Faster eating also was linked to increased risk for functional dyspepsia in a study involving 89 young-adult female military cadets in Korea with relatively controlled eating patterns.

On the extreme end of the spectrum, researchers who performed an assessment of a competitive speed eater speculated that the observed physiological accommodation required for the role (expanding the stomach to form a large flaccid sac) makes speed eaters vulnerable to morbid obesity, gastroparesis, intractable nausea and vomiting, and the need for gastrectomy.

The risk for metabolic changes and eventual development of T2D also appear to be linked to how quickly food is consumed.

Two clinical studies conducted in Japan — a cohort study of 2050 male factory workers and a nationwide study with 197,825 participants — identified a significant association between faster eating and T2D and insulin resistance. A case-control study involving 234 patients with new onset T2D and 468 controls from Lithuania linked faster eating to a greater than twofold risk for T2D. And a Chinese cross-sectional study of 7972 adults indicated that faster eating significantly increased the risk for metabolic syndrome, elevated blood pressure, and central obesity in adults.

Various hypotheses have been proposed to explain why fast eating may upset metabolic processes, including a delayed sense of fullness contributing to spiking postprandial glucose levels, lack of time for mastication causing higher glucose concentrations, and the triggering of specific cytokines (eg, interleukin-1 beta and interleukin-6) that lead to insulin resistance. It is also possible that the association is the result of people who eat quickly having relatively higher body weights, which translates to a higher risk for T2D.

However, there’s an opportunity in the association of rapid meal consumption with gastrointestinal and metabolic diseases, as people can slow the speed at which they eat so they feel full before they overeat.

A 2019 study in which 21 participants were instructed to eat a 600-kcal meal at a “normal” or “slow” pace (6 minutes or 24 minutes) found that the latter group reported feeling fuller while consuming fewer calories.

This approach may not work for all patients, however. There’s evidence to suggest that tactics to slow down eating may not limit the energy intake of those who are already overweight or obese.

Patients with obesity may physiologically differ in their processing of food, according to Michael Camilleri, MD, consultant in the Division of Gastroenterology and Hepatology at Mayo Clinic in Rochester, Minnesota.

“We have demonstrated that about 20%-25% of people with obesity actually have rapid gastric emptying,” he told this news organization. “As a result, they don’t feel full after they eat a meal and that might impact the total volume of food that they eat before they really feel full.”

The Ideal Time to Eat

It’s not only the speed at which individuals eat that may influence outcomes but when they take their meals. Research indicates that eating earlier in the day to align meals with the body’s circadian rhythms in metabolism offers health benefits.

“The focus would be to eat a meal that syncs during those daytime hours,” Collin Popp, PhD, MS, RD, a research scientist at the NYU Grossman School of Medicine in New York, told this news organization. “I typically suggest patients have their largest meal in the morning, whether that’s a large or medium-sized breakfast, or a big lunch.”

A recent cross-sectional study of 2050 participants found that having the largest meal at lunch protected against obesity (odds ratio [OR], 0.71), whereas having it at dinner increased the risk for obesity (OR, 1.67) and led to higher body mass index.

Consuming the majority of calories in meals earlier in the day may have metabolic health benefits, as well.

A 2015 randomized controlled trial involving 18 adults with obesity and T2D found that eating a high-energy breakfast and a low-energy dinner leads to reduced hyperglycemia throughout the day compared with eating a low-energy breakfast and a high-energy dinner.

Time-restricted eating (TRE), a form of intermittent fasting, also can improve metabolic health depending on the time of day.

A 2023 meta-analysis found that TRE was more effective at reducing fasting glucose levels in participants who were overweight and obese if done earlier rather than later in the day. Similarly, a 2022 study involving 82 healthy patients without diabetes or obesity found that early TRE was more effective than mid-day TRE at improving insulin sensitivity and that it improved fasting glucose and reduced total body mass and adiposity, while mid-day TRE did not.

A study that analyzed the effects of TRE in eight adult men with overweight and prediabetes found “better insulin resistance when the window of food consumption was earlier in the day,» noted endocrinologist Beverly Tchang, MD, an assistant professor of clinical medicine at Weill Cornell Medicine with a focus on obesity medication.

Patients May Benefit From Behavioral Interventions

Patients potentially negatively affected by eating too quickly or at late hours may benefit from adopting behavioral interventions to address these tendencies. To determine if a patient is a candidate for such interventions, Dr. Popp recommends starting with a simple conversation.

“When I first meet patients, I always ask them to describe to me a typical day for how they eat — when they’re eating, what they’re eating, the food quality, who are they with — to see if there’s social aspects to it. Then try and make the recommendations based on that,” said Dr. Popp, whose work focuses on biobehavioral interventions for the treatment and prevention of obesity, T2D, and other cardiometabolic outcomes.

Dr. Tchang said she encourages her patients to be mindful of hunger and fullness cues.

“Eat if you’re hungry; don’t force yourself to eat if you’re not hungry,” she said. “If you’re not sure whether you’re hungry or not, speak to a doctor because this points to an abnormality in your appetite-regulation system, which can be helped with GLP-1 [glucagon-like peptide 1] receptor agonists.”

Adjusting what patients eat can help them improve their meal timing.

“For example, we know that a high-fiber diet or a diet that has a large amount of fat in it tends to empty from the stomach slower,” Dr. Camilleri said. “That might give a sensation of fullness that lasts longer and that might prevent, for instance, the ingestion of the next meal.”

Those trying to eat more slowly are advised to seek out foods that are hard in texture and minimally processed.

A study involving 50 patients with healthy weights found that hard foods are consumed more slowly than soft foods and that energy intake is lowest with hard, minimally processed foods. Combining hard-textured foods with explicit instructions to reduce eating speed has also been shown to be an effective strategy. For those inclined to seek out technology-based solution, evidence suggests that a self-monitoring wearable device can slow the eating rate.

Although the evidence is mounting that the timing and duration of meals have an impact on certain chronic diseases, clinicians should remember that these two factors are far from the most important contributors, Dr. Popp said.

“We also have to consider total caloric intake, food quality, sleep, alcohol use, smoking, and physical activity,” he said. “Meal timing should be considered as under the umbrella of health that is important for a lot of folks.”

A version of this article appeared on Medscape.com.

SAN DIEGO — A liquid biopsy assay that combines a microRNA signature and a well-known biomarker for pancreatic cancer has demonstrated an accuracy of 97% for detecting stage I/II pancreatic ductal adenocarcinoma, the most common type of pancreatic cancer.

It is quite encouraging to know we have a blood test that could potentially find this disease early, said Ajay Goel, PhD, a molecular diagnostics specialist at City of Hope in Duarte, California, who presented the findings at the annual meeting of the American Association for Cancer Research (AACR).

Dr. Goel and colleagues developed a signature for pancreatic cancer based on microRNAs identified in the exomes shed from pancreatic cancers and cell-free DNA markers found in the blood of patients with the disease.

Their initial assay tested blood samples for this signature in a training cohort of 252 people in Japan, approximately 60% of whom had pancreatic cancer. The rest were healthy controls. The assay was then tested in validation cohorts of 400 subjects, half with pancreatic cancer and half controls, in China and South Korea.

In both the initial and validation tests, the microRNA assay had an accuracy of about 90% for stage I/II pancreatic cancer, already far better than commercially available assays.

In an additional validation cohort in the United States with 139 patients with pancreatic cancer and 193 controls at six centers across the country, the researchers found that adding carbohydrate antigen 19-9 — a well-known marker of pancreatic cancer — to the assay boosted the test’s accuracy to 97%.

The test performed the same whether the tumor was in the head or tail of the pancreas.

“We are very excited about this data,” said Dr. Goel.

The technology was recently licensed to Pharus Diagnostics for commercial development, which will likely include a prospective screening trial, he told this news organization.

Because pancreatic cancer is fairly uncommon, Dr. Goel did not anticipate the test being used for general screening but rather for screening high-risk patients such as those with newly diagnosed type 2 diabetes, a family history of pancreatic cancer, or predisposing genetic mutations.

“It should be a very inexpensive test; it doesn’t cost us much to do in the lab,” he added.

Study moderator Ryan Corcoran, MD, PhD, a gastrointestinal (GI) oncologist at Massachusetts General Hospital, Boston, saw the potential.

“As a GI oncologist, I know how lethal and hard to treat pancreatic cancer is,” he said. A test that could reliably detect pancreatic cancer early, with an acceptable false-positive rate, would be extremely useful.

“The cure rate is many, many times higher,” if we detect it before it has a chance to spread, he explained.

In the meantime, Dr. Goel said there’s more work to be done.

Almost 4,000 subjects have been enrolled in ongoing validation efforts, and efforts are underway to use the test to screen thousands of banked blood samples from the PLCO, a prospective cancer screening trial in healthy subjects.

The researchers also want to see if the test can distinguish benign pancreatic cysts from ones that turn cancerous.

The idea is to find the earliest possible signs of this disease to see if we can find it not “at the moment of clinical diagnosis, but possibly 6 months, 1 year, 2 years earlier” than with radiologic imaging, Dr. Goel said.

The work was funded by the National Cancer Institute and others. Dr. Goel is a consultant for Pharus Diagnostics and Cellomics. Dr. Corcoran is a consultant for, has grants from, and/or holds stock in numerous companies, including Pfizer, Novartis, Eli Lilly, and Revolution Medicines.

A version of this article appeared on Medscape.com.

SAN DIEGO — A liquid biopsy assay that combines a microRNA signature and a well-known biomarker for pancreatic cancer has demonstrated an accuracy of 97% for detecting stage I/II pancreatic ductal adenocarcinoma, the most common type of pancreatic cancer.

It is quite encouraging to know we have a blood test that could potentially find this disease early, said Ajay Goel, PhD, a molecular diagnostics specialist at City of Hope in Duarte, California, who presented the findings at the annual meeting of the American Association for Cancer Research (AACR).

Dr. Goel and colleagues developed a signature for pancreatic cancer based on microRNAs identified in the exomes shed from pancreatic cancers and cell-free DNA markers found in the blood of patients with the disease.

Their initial assay tested blood samples for this signature in a training cohort of 252 people in Japan, approximately 60% of whom had pancreatic cancer. The rest were healthy controls. The assay was then tested in validation cohorts of 400 subjects, half with pancreatic cancer and half controls, in China and South Korea.

In both the initial and validation tests, the microRNA assay had an accuracy of about 90% for stage I/II pancreatic cancer, already far better than commercially available assays.

In an additional validation cohort in the United States with 139 patients with pancreatic cancer and 193 controls at six centers across the country, the researchers found that adding carbohydrate antigen 19-9 — a well-known marker of pancreatic cancer — to the assay boosted the test’s accuracy to 97%.

The test performed the same whether the tumor was in the head or tail of the pancreas.

“We are very excited about this data,” said Dr. Goel.

The technology was recently licensed to Pharus Diagnostics for commercial development, which will likely include a prospective screening trial, he told this news organization.

Because pancreatic cancer is fairly uncommon, Dr. Goel did not anticipate the test being used for general screening but rather for screening high-risk patients such as those with newly diagnosed type 2 diabetes, a family history of pancreatic cancer, or predisposing genetic mutations.

“It should be a very inexpensive test; it doesn’t cost us much to do in the lab,” he added.

Study moderator Ryan Corcoran, MD, PhD, a gastrointestinal (GI) oncologist at Massachusetts General Hospital, Boston, saw the potential.

“As a GI oncologist, I know how lethal and hard to treat pancreatic cancer is,” he said. A test that could reliably detect pancreatic cancer early, with an acceptable false-positive rate, would be extremely useful.

“The cure rate is many, many times higher,” if we detect it before it has a chance to spread, he explained.

In the meantime, Dr. Goel said there’s more work to be done.

Almost 4,000 subjects have been enrolled in ongoing validation efforts, and efforts are underway to use the test to screen thousands of banked blood samples from the PLCO, a prospective cancer screening trial in healthy subjects.

The researchers also want to see if the test can distinguish benign pancreatic cysts from ones that turn cancerous.

The idea is to find the earliest possible signs of this disease to see if we can find it not “at the moment of clinical diagnosis, but possibly 6 months, 1 year, 2 years earlier” than with radiologic imaging, Dr. Goel said.

The work was funded by the National Cancer Institute and others. Dr. Goel is a consultant for Pharus Diagnostics and Cellomics. Dr. Corcoran is a consultant for, has grants from, and/or holds stock in numerous companies, including Pfizer, Novartis, Eli Lilly, and Revolution Medicines.

A version of this article appeared on Medscape.com.

SAN DIEGO — A liquid biopsy assay that combines a microRNA signature and a well-known biomarker for pancreatic cancer has demonstrated an accuracy of 97% for detecting stage I/II pancreatic ductal adenocarcinoma, the most common type of pancreatic cancer.

It is quite encouraging to know we have a blood test that could potentially find this disease early, said Ajay Goel, PhD, a molecular diagnostics specialist at City of Hope in Duarte, California, who presented the findings at the annual meeting of the American Association for Cancer Research (AACR).

Dr. Goel and colleagues developed a signature for pancreatic cancer based on microRNAs identified in the exomes shed from pancreatic cancers and cell-free DNA markers found in the blood of patients with the disease.

Their initial assay tested blood samples for this signature in a training cohort of 252 people in Japan, approximately 60% of whom had pancreatic cancer. The rest were healthy controls. The assay was then tested in validation cohorts of 400 subjects, half with pancreatic cancer and half controls, in China and South Korea.

In both the initial and validation tests, the microRNA assay had an accuracy of about 90% for stage I/II pancreatic cancer, already far better than commercially available assays.

In an additional validation cohort in the United States with 139 patients with pancreatic cancer and 193 controls at six centers across the country, the researchers found that adding carbohydrate antigen 19-9 — a well-known marker of pancreatic cancer — to the assay boosted the test’s accuracy to 97%.

The test performed the same whether the tumor was in the head or tail of the pancreas.

“We are very excited about this data,” said Dr. Goel.

The technology was recently licensed to Pharus Diagnostics for commercial development, which will likely include a prospective screening trial, he told this news organization.

Because pancreatic cancer is fairly uncommon, Dr. Goel did not anticipate the test being used for general screening but rather for screening high-risk patients such as those with newly diagnosed type 2 diabetes, a family history of pancreatic cancer, or predisposing genetic mutations.

“It should be a very inexpensive test; it doesn’t cost us much to do in the lab,” he added.

Study moderator Ryan Corcoran, MD, PhD, a gastrointestinal (GI) oncologist at Massachusetts General Hospital, Boston, saw the potential.

“As a GI oncologist, I know how lethal and hard to treat pancreatic cancer is,” he said. A test that could reliably detect pancreatic cancer early, with an acceptable false-positive rate, would be extremely useful.

“The cure rate is many, many times higher,” if we detect it before it has a chance to spread, he explained.

In the meantime, Dr. Goel said there’s more work to be done.

Almost 4,000 subjects have been enrolled in ongoing validation efforts, and efforts are underway to use the test to screen thousands of banked blood samples from the PLCO, a prospective cancer screening trial in healthy subjects.

The researchers also want to see if the test can distinguish benign pancreatic cysts from ones that turn cancerous.

The idea is to find the earliest possible signs of this disease to see if we can find it not “at the moment of clinical diagnosis, but possibly 6 months, 1 year, 2 years earlier” than with radiologic imaging, Dr. Goel said.

The work was funded by the National Cancer Institute and others. Dr. Goel is a consultant for Pharus Diagnostics and Cellomics. Dr. Corcoran is a consultant for, has grants from, and/or holds stock in numerous companies, including Pfizer, Novartis, Eli Lilly, and Revolution Medicines.

A version of this article appeared on Medscape.com.

Statins raise the risks for increased glucose levels and the development of type 2 diabetes among people who don’t have it at baseline, but those risks are outweighed by the cardiovascular benefit, new data suggested.

The findings come from an analysis of individual participant data from a total of 23 randomized trials of statin therapy involving 154,664 individuals. In people without diabetes at baseline, statin therapy produces a dose-dependent increase in the risk for diabetes diagnosis, particularly among those whose glycemia marker levels are already at the diagnostic threshold.

Statins also tend to raise glucose levels in people who already have diabetes, but “the diabetes-related risks arising from the small changes in glycemia resulting from statin therapy are greatly outweighed by the benefits of statins on major vascular events when the direct clinical consequences of these outcomes are taken into consideration,” wrote the authors of the Cholesterol Treatment Trialists’ (CTT) Collaboration in their paper, published online in The Lancet Diabetes & Endocrinology.

Moreover, they say, “since the effect of statin therapy on measures of glycemia within an individual is small, there is likely to be little clinical benefit in measuring glucose concentrations and A1c values routinely after starting statin therapy with the aim of making comparisons to values taken before the initiation of a statin. However, people should continue to be screened for diabetes and associated risk factors and have their glycemic control monitored in accordance with current clinical guidelines.”

The CTT is co-led by Christina Reith, MBChB, PhD, and David Preiss, PhD, FRCPath, MRCP, both of the Nuffield Department of Population Health, University of Oxford, England.

In an accompanying editorial, Hertzel C. Gerstein, MD, and Marie Pigeyre, MD, PhD, both of McMaster University and Hamilton Health Sciences, Hamilton, Ontario, Canada, noted that the decreased absolute annual incidence of life-threatening cardiovascular outcomes with statins in people at high risk for type 2 diabetes “clearly exceeds the 0.1%-1.3% per year increased absolute incidence of type 2 diabetes.”

Dr. Gerstein and Dr. Pigeyre also said “these findings emphasize the importance of holistic care. As people at risk for cardiovascular outcomes are also at risk for type 2 diabetes, any prescription of a statin should be accompanied by promoting proven strategies to prevent or delay diabetes, such as modest weight reduction and increased physical activity. Finally, these findings emphasize the importance of always being alert for harmful adverse effects, even with the most beneficial and successful preventive therapies.”
 

Statins Raise Diabetes Risk, Glucose Levels Slightly

The meta-analysis of trials in the CTT Collaboration included individual participant data from 19 double-blind randomized, controlled trials with a median follow-up of 4.3 years comparing statins with placebo in a total of 123,940 participants, including 18% who had known type 2 diabetes at randomization. Also analyzed were another four double-blind trials of lower- vs higher-intensity statins involving a total of 30,724 participants followed for a median of 4.9 years, with 15% having diabetes at baseline.

In the 19 trials of low- or moderate-intensity statins vs placebo, statins resulted in a significant 10% increase in new-onset diabetes compared with placebo (rate ratio, 1.10), while high-intensity statins raised the risk by an also significant 36% (1.36). This translated to a mean absolute excess of 0.12% per year of treatment.

Compared with less intensive statin therapy, more intensive statin therapy resulted in a significant 10% proportional increase in new-onset diabetes (1.10), giving an absolute annual excess of 0.22%.

In the statin vs placebo trials, differences in A1c values from placebo were 0.06 percentage points higher for low- or moderate-intensity statins and 0.08 points greater for high-intensity statins.

Nearly two thirds (62%) of the excess cases of new-onset diabetes occurred among participants in the highest quarter of the baseline glycemia distribution for both low-intensity or moderate-intensity and high-intensity statin therapy.

And among participants who already had diabetes at baseline, there was a significant 10% relative increase in worsening glycemia (defined by adverse glycemic event, A1c increase of ≥ 0.5 percentage points, or medication escalation) with low- or moderate-intensity statins compared with placebo and a 24% relative increase in the high-intensity trials.

The Nuffield Department of Population Health has an explicit policy of not accepting any personal honoraria payments directly or indirectly from the pharmaceutical and food industries. It seeks reimbursement to the University of Oxford for the costs of travel and accommodation to participate in scientific meetings. Dr. Reith reported receiving funding to the University of Oxford from the UK National Institute for Health and Care Research Health Technology Assessment Programme and holding unpaid roles on the Clinical Data Interchange Standards Consortium as a board member and WHO as a scientific advisor. Dr. Preiss reported receiving funding to his research institution (but no personal funding) from Novartis for the ORION 4 trial of inclisiran, Novo Nordisk for the ASCEND PLUS trial of semaglutide, and Boehringer Ingelheim and Eli Lilly for the EMPA-KIDNEY trial and being a committee member for a National Institute for Health and Care Excellence guideline.

Dr. Gerstein holds the McMaster-Sanofi Population Health Institute Chair in Diabetes Research and Care. He reported research grants from Eli Lilly, AstraZeneca, Novo Nordisk, Hanmi, and Merck; continuing medical education grants to McMaster University from Eli Lilly, Abbott, Sanofi, Novo Nordisk, and Boehringer Ingelheim; honoraria for speaking from AstraZeneca, Eli Lilly, Novo Nordisk, DKSH, Zuellig Pharma, Sanofi, and Jiangsu Hanson; and consulting fees from Abbott, Eli Lilly, Novo Nordisk, Pfizer, Carbon Brand, Sanofi, Kowa, and Hanmi. Pigeyre had no disclosures.
 

A version of this article appeared on Medscape.com.

Statins raise the risks for increased glucose levels and the development of type 2 diabetes among people who don’t have it at baseline, but those risks are outweighed by the cardiovascular benefit, new data suggested.

The findings come from an analysis of individual participant data from a total of 23 randomized trials of statin therapy involving 154,664 individuals. In people without diabetes at baseline, statin therapy produces a dose-dependent increase in the risk for diabetes diagnosis, particularly among those whose glycemia marker levels are already at the diagnostic threshold.

Statins also tend to raise glucose levels in people who already have diabetes, but “the diabetes-related risks arising from the small changes in glycemia resulting from statin therapy are greatly outweighed by the benefits of statins on major vascular events when the direct clinical consequences of these outcomes are taken into consideration,” wrote the authors of the Cholesterol Treatment Trialists’ (CTT) Collaboration in their paper, published online in The Lancet Diabetes & Endocrinology.

Moreover, they say, “since the effect of statin therapy on measures of glycemia within an individual is small, there is likely to be little clinical benefit in measuring glucose concentrations and A1c values routinely after starting statin therapy with the aim of making comparisons to values taken before the initiation of a statin. However, people should continue to be screened for diabetes and associated risk factors and have their glycemic control monitored in accordance with current clinical guidelines.”

The CTT is co-led by Christina Reith, MBChB, PhD, and David Preiss, PhD, FRCPath, MRCP, both of the Nuffield Department of Population Health, University of Oxford, England.

In an accompanying editorial, Hertzel C. Gerstein, MD, and Marie Pigeyre, MD, PhD, both of McMaster University and Hamilton Health Sciences, Hamilton, Ontario, Canada, noted that the decreased absolute annual incidence of life-threatening cardiovascular outcomes with statins in people at high risk for type 2 diabetes “clearly exceeds the 0.1%-1.3% per year increased absolute incidence of type 2 diabetes.”

Dr. Gerstein and Dr. Pigeyre also said “these findings emphasize the importance of holistic care. As people at risk for cardiovascular outcomes are also at risk for type 2 diabetes, any prescription of a statin should be accompanied by promoting proven strategies to prevent or delay diabetes, such as modest weight reduction and increased physical activity. Finally, these findings emphasize the importance of always being alert for harmful adverse effects, even with the most beneficial and successful preventive therapies.”
 

Statins Raise Diabetes Risk, Glucose Levels Slightly

The meta-analysis of trials in the CTT Collaboration included individual participant data from 19 double-blind randomized, controlled trials with a median follow-up of 4.3 years comparing statins with placebo in a total of 123,940 participants, including 18% who had known type 2 diabetes at randomization. Also analyzed were another four double-blind trials of lower- vs higher-intensity statins involving a total of 30,724 participants followed for a median of 4.9 years, with 15% having diabetes at baseline.

In the 19 trials of low- or moderate-intensity statins vs placebo, statins resulted in a significant 10% increase in new-onset diabetes compared with placebo (rate ratio, 1.10), while high-intensity statins raised the risk by an also significant 36% (1.36). This translated to a mean absolute excess of 0.12% per year of treatment.

Compared with less intensive statin therapy, more intensive statin therapy resulted in a significant 10% proportional increase in new-onset diabetes (1.10), giving an absolute annual excess of 0.22%.

In the statin vs placebo trials, differences in A1c values from placebo were 0.06 percentage points higher for low- or moderate-intensity statins and 0.08 points greater for high-intensity statins.

Nearly two thirds (62%) of the excess cases of new-onset diabetes occurred among participants in the highest quarter of the baseline glycemia distribution for both low-intensity or moderate-intensity and high-intensity statin therapy.

And among participants who already had diabetes at baseline, there was a significant 10% relative increase in worsening glycemia (defined by adverse glycemic event, A1c increase of ≥ 0.5 percentage points, or medication escalation) with low- or moderate-intensity statins compared with placebo and a 24% relative increase in the high-intensity trials.

The Nuffield Department of Population Health has an explicit policy of not accepting any personal honoraria payments directly or indirectly from the pharmaceutical and food industries. It seeks reimbursement to the University of Oxford for the costs of travel and accommodation to participate in scientific meetings. Dr. Reith reported receiving funding to the University of Oxford from the UK National Institute for Health and Care Research Health Technology Assessment Programme and holding unpaid roles on the Clinical Data Interchange Standards Consortium as a board member and WHO as a scientific advisor. Dr. Preiss reported receiving funding to his research institution (but no personal funding) from Novartis for the ORION 4 trial of inclisiran, Novo Nordisk for the ASCEND PLUS trial of semaglutide, and Boehringer Ingelheim and Eli Lilly for the EMPA-KIDNEY trial and being a committee member for a National Institute for Health and Care Excellence guideline.

Dr. Gerstein holds the McMaster-Sanofi Population Health Institute Chair in Diabetes Research and Care. He reported research grants from Eli Lilly, AstraZeneca, Novo Nordisk, Hanmi, and Merck; continuing medical education grants to McMaster University from Eli Lilly, Abbott, Sanofi, Novo Nordisk, and Boehringer Ingelheim; honoraria for speaking from AstraZeneca, Eli Lilly, Novo Nordisk, DKSH, Zuellig Pharma, Sanofi, and Jiangsu Hanson; and consulting fees from Abbott, Eli Lilly, Novo Nordisk, Pfizer, Carbon Brand, Sanofi, Kowa, and Hanmi. Pigeyre had no disclosures.
 

A version of this article appeared on Medscape.com.

Statins raise the risks for increased glucose levels and the development of type 2 diabetes among people who don’t have it at baseline, but those risks are outweighed by the cardiovascular benefit, new data suggested.

The findings come from an analysis of individual participant data from a total of 23 randomized trials of statin therapy involving 154,664 individuals. In people without diabetes at baseline, statin therapy produces a dose-dependent increase in the risk for diabetes diagnosis, particularly among those whose glycemia marker levels are already at the diagnostic threshold.

Statins also tend to raise glucose levels in people who already have diabetes, but “the diabetes-related risks arising from the small changes in glycemia resulting from statin therapy are greatly outweighed by the benefits of statins on major vascular events when the direct clinical consequences of these outcomes are taken into consideration,” wrote the authors of the Cholesterol Treatment Trialists’ (CTT) Collaboration in their paper, published online in The Lancet Diabetes & Endocrinology.

Moreover, they say, “since the effect of statin therapy on measures of glycemia within an individual is small, there is likely to be little clinical benefit in measuring glucose concentrations and A1c values routinely after starting statin therapy with the aim of making comparisons to values taken before the initiation of a statin. However, people should continue to be screened for diabetes and associated risk factors and have their glycemic control monitored in accordance with current clinical guidelines.”

The CTT is co-led by Christina Reith, MBChB, PhD, and David Preiss, PhD, FRCPath, MRCP, both of the Nuffield Department of Population Health, University of Oxford, England.

In an accompanying editorial, Hertzel C. Gerstein, MD, and Marie Pigeyre, MD, PhD, both of McMaster University and Hamilton Health Sciences, Hamilton, Ontario, Canada, noted that the decreased absolute annual incidence of life-threatening cardiovascular outcomes with statins in people at high risk for type 2 diabetes “clearly exceeds the 0.1%-1.3% per year increased absolute incidence of type 2 diabetes.”

Dr. Gerstein and Dr. Pigeyre also said “these findings emphasize the importance of holistic care. As people at risk for cardiovascular outcomes are also at risk for type 2 diabetes, any prescription of a statin should be accompanied by promoting proven strategies to prevent or delay diabetes, such as modest weight reduction and increased physical activity. Finally, these findings emphasize the importance of always being alert for harmful adverse effects, even with the most beneficial and successful preventive therapies.”
 

Statins Raise Diabetes Risk, Glucose Levels Slightly

The meta-analysis of trials in the CTT Collaboration included individual participant data from 19 double-blind randomized, controlled trials with a median follow-up of 4.3 years comparing statins with placebo in a total of 123,940 participants, including 18% who had known type 2 diabetes at randomization. Also analyzed were another four double-blind trials of lower- vs higher-intensity statins involving a total of 30,724 participants followed for a median of 4.9 years, with 15% having diabetes at baseline.

In the 19 trials of low- or moderate-intensity statins vs placebo, statins resulted in a significant 10% increase in new-onset diabetes compared with placebo (rate ratio, 1.10), while high-intensity statins raised the risk by an also significant 36% (1.36). This translated to a mean absolute excess of 0.12% per year of treatment.

Compared with less intensive statin therapy, more intensive statin therapy resulted in a significant 10% proportional increase in new-onset diabetes (1.10), giving an absolute annual excess of 0.22%.

In the statin vs placebo trials, differences in A1c values from placebo were 0.06 percentage points higher for low- or moderate-intensity statins and 0.08 points greater for high-intensity statins.

Nearly two thirds (62%) of the excess cases of new-onset diabetes occurred among participants in the highest quarter of the baseline glycemia distribution for both low-intensity or moderate-intensity and high-intensity statin therapy.

And among participants who already had diabetes at baseline, there was a significant 10% relative increase in worsening glycemia (defined by adverse glycemic event, A1c increase of ≥ 0.5 percentage points, or medication escalation) with low- or moderate-intensity statins compared with placebo and a 24% relative increase in the high-intensity trials.

The Nuffield Department of Population Health has an explicit policy of not accepting any personal honoraria payments directly or indirectly from the pharmaceutical and food industries. It seeks reimbursement to the University of Oxford for the costs of travel and accommodation to participate in scientific meetings. Dr. Reith reported receiving funding to the University of Oxford from the UK National Institute for Health and Care Research Health Technology Assessment Programme and holding unpaid roles on the Clinical Data Interchange Standards Consortium as a board member and WHO as a scientific advisor. Dr. Preiss reported receiving funding to his research institution (but no personal funding) from Novartis for the ORION 4 trial of inclisiran, Novo Nordisk for the ASCEND PLUS trial of semaglutide, and Boehringer Ingelheim and Eli Lilly for the EMPA-KIDNEY trial and being a committee member for a National Institute for Health and Care Excellence guideline.

Dr. Gerstein holds the McMaster-Sanofi Population Health Institute Chair in Diabetes Research and Care. He reported research grants from Eli Lilly, AstraZeneca, Novo Nordisk, Hanmi, and Merck; continuing medical education grants to McMaster University from Eli Lilly, Abbott, Sanofi, Novo Nordisk, and Boehringer Ingelheim; honoraria for speaking from AstraZeneca, Eli Lilly, Novo Nordisk, DKSH, Zuellig Pharma, Sanofi, and Jiangsu Hanson; and consulting fees from Abbott, Eli Lilly, Novo Nordisk, Pfizer, Carbon Brand, Sanofi, Kowa, and Hanmi. Pigeyre had no disclosures.
 

A version of this article appeared on Medscape.com.

TOPLINE:

In people with type 2 diabetes (T2D), higher levels of arm and trunk fat are associated with an increased risk for cardiovascular disease (CVD) and mortality, while higher levels of leg fat are associated with a reduced risk for these conditions.

METHODOLOGY:

• People with T2D have a twofold to fourfold higher risk for CVD and mortality, and evidence shows obesity management helps delay complications and premature death, but an elevated body mass index (BMI) may be insufficient to measure obesity.
• In the “obesity paradox,” people with elevated BMI may have a lower CVD risk than people of normal weight.
• Researchers prospectively investigated how regional body fat accumulation was associated with CVD risk in 21,472 people with T2D (mean age, 58.9 years; 60.7% men; BMI about 29-33) from the UK Biobank (2006-2010), followed up for a median of 7.7 years.
• The regional body fat distribution in arms, trunk, and legs was assessed using bioelectrical impedance analysis.
• The primary outcomes were the incidence of CVD, all-cause mortality, and CVD mortality.

TAKEAWAY:

• Participants in the highest quartile of arm fat percentage (multivariate-adjusted hazard ratio [HR], 1.63; 95% CI, 1.29-2.05) and trunk fat percentage (HR, 1.27; 95% CI, 1.06-1.52) were at a higher risk for CVD than those in the lowest quartile.
• However, participants in the highest quartile of leg fat percentage had a lower risk for CVD than those in the lowest quartile (HR, 0.72; 95% CI, 0.58-0.90).
• A nonlinear relationship was observed between higher leg fat percentage and lower CVD risk and between higher trunk fat percentage and higher CVD risk, whereas a linear relationship was noted between higher arm fat percentage and higher CVD risk.
• The patterns of association were similar for both all-cause mortality and CVD mortality. Overall patterns were similar for men and women.

IN PRACTICE:

“Our findings add to the understanding of body fat distribution in patients with T2D, which highlights the importance of considering both the amount and the location of body fat when assessing CVD and mortality risk among patients with T2D,” wrote the authors.

SOURCE:

The study led by Zixin Qiu, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, was published online in The Journal of Clinical Endocrinology & Metabolism.

LIMITATIONS:

As body fat was measured only once at the beginning of the study, its changing association over time could not be assessed. Moreover, the findings were primarily based on predominantly White UK adults, potentially restricting their generalizability to other population groups. Furthermore, diabetes was diagnosed using self-reported medical history, medication, and hemoglobin A1c levels, implying that some cases may have gone undetected at baseline. 

DISCLOSURES:

This study was funded by grants from the National Natural Science Foundation of China, Hubei Province Science Fund for Distinguished Young Scholars, and Fundamental Research Funds for the Central Universities. The authors declared no conflicts of interest.

A version of this article appeared on Medscape.com.

TOPLINE:

In people with type 2 diabetes (T2D), higher levels of arm and trunk fat are associated with an increased risk for cardiovascular disease (CVD) and mortality, while higher levels of leg fat are associated with a reduced risk for these conditions.

METHODOLOGY:

• People with T2D have a twofold to fourfold higher risk for CVD and mortality, and evidence shows obesity management helps delay complications and premature death, but an elevated body mass index (BMI) may be insufficient to measure obesity.
• In the “obesity paradox,” people with elevated BMI may have a lower CVD risk than people of normal weight.
• Researchers prospectively investigated how regional body fat accumulation was associated with CVD risk in 21,472 people with T2D (mean age, 58.9 years; 60.7% men; BMI about 29-33) from the UK Biobank (2006-2010), followed up for a median of 7.7 years.
• The regional body fat distribution in arms, trunk, and legs was assessed using bioelectrical impedance analysis.
• The primary outcomes were the incidence of CVD, all-cause mortality, and CVD mortality.

TAKEAWAY:

• Participants in the highest quartile of arm fat percentage (multivariate-adjusted hazard ratio [HR], 1.63; 95% CI, 1.29-2.05) and trunk fat percentage (HR, 1.27; 95% CI, 1.06-1.52) were at a higher risk for CVD than those in the lowest quartile.
• However, participants in the highest quartile of leg fat percentage had a lower risk for CVD than those in the lowest quartile (HR, 0.72; 95% CI, 0.58-0.90).
• A nonlinear relationship was observed between higher leg fat percentage and lower CVD risk and between higher trunk fat percentage and higher CVD risk, whereas a linear relationship was noted between higher arm fat percentage and higher CVD risk.
• The patterns of association were similar for both all-cause mortality and CVD mortality. Overall patterns were similar for men and women.

IN PRACTICE:

“Our findings add to the understanding of body fat distribution in patients with T2D, which highlights the importance of considering both the amount and the location of body fat when assessing CVD and mortality risk among patients with T2D,” wrote the authors.

SOURCE:

The study led by Zixin Qiu, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, was published online in The Journal of Clinical Endocrinology & Metabolism.

LIMITATIONS:

As body fat was measured only once at the beginning of the study, its changing association over time could not be assessed. Moreover, the findings were primarily based on predominantly White UK adults, potentially restricting their generalizability to other population groups. Furthermore, diabetes was diagnosed using self-reported medical history, medication, and hemoglobin A1c levels, implying that some cases may have gone undetected at baseline. 

DISCLOSURES:

This study was funded by grants from the National Natural Science Foundation of China, Hubei Province Science Fund for Distinguished Young Scholars, and Fundamental Research Funds for the Central Universities. The authors declared no conflicts of interest.

A version of this article appeared on Medscape.com.

TOPLINE:

In people with type 2 diabetes (T2D), higher levels of arm and trunk fat are associated with an increased risk for cardiovascular disease (CVD) and mortality, while higher levels of leg fat are associated with a reduced risk for these conditions.

METHODOLOGY:

• People with T2D have a twofold to fourfold higher risk for CVD and mortality, and evidence shows obesity management helps delay complications and premature death, but an elevated body mass index (BMI) may be insufficient to measure obesity.
• In the “obesity paradox,” people with elevated BMI may have a lower CVD risk than people of normal weight.
• Researchers prospectively investigated how regional body fat accumulation was associated with CVD risk in 21,472 people with T2D (mean age, 58.9 years; 60.7% men; BMI about 29-33) from the UK Biobank (2006-2010), followed up for a median of 7.7 years.
• The regional body fat distribution in arms, trunk, and legs was assessed using bioelectrical impedance analysis.
• The primary outcomes were the incidence of CVD, all-cause mortality, and CVD mortality.

TAKEAWAY:

• Participants in the highest quartile of arm fat percentage (multivariate-adjusted hazard ratio [HR], 1.63; 95% CI, 1.29-2.05) and trunk fat percentage (HR, 1.27; 95% CI, 1.06-1.52) were at a higher risk for CVD than those in the lowest quartile.
• However, participants in the highest quartile of leg fat percentage had a lower risk for CVD than those in the lowest quartile (HR, 0.72; 95% CI, 0.58-0.90).
• A nonlinear relationship was observed between higher leg fat percentage and lower CVD risk and between higher trunk fat percentage and higher CVD risk, whereas a linear relationship was noted between higher arm fat percentage and higher CVD risk.
• The patterns of association were similar for both all-cause mortality and CVD mortality. Overall patterns were similar for men and women.

IN PRACTICE:

“Our findings add to the understanding of body fat distribution in patients with T2D, which highlights the importance of considering both the amount and the location of body fat when assessing CVD and mortality risk among patients with T2D,” wrote the authors.

SOURCE:

The study led by Zixin Qiu, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, was published online in The Journal of Clinical Endocrinology & Metabolism.

LIMITATIONS:

As body fat was measured only once at the beginning of the study, its changing association over time could not be assessed. Moreover, the findings were primarily based on predominantly White UK adults, potentially restricting their generalizability to other population groups. Furthermore, diabetes was diagnosed using self-reported medical history, medication, and hemoglobin A1c levels, implying that some cases may have gone undetected at baseline. 

DISCLOSURES:

This study was funded by grants from the National Natural Science Foundation of China, Hubei Province Science Fund for Distinguished Young Scholars, and Fundamental Research Funds for the Central Universities. The authors declared no conflicts of interest.

A version of this article appeared on Medscape.com.

TOPLINE:

Monthly cycles of a fasting-mimicking diet (FMD) may slow metabolic and immune system aging and reduce the risk for metabolic disease.

METHODOLOGY:

• In two clinical trials, monthly 5-day cycles of an FMD (a proprietary line of plant-based, low-calorie, and low-protein food products) showed lower body weight, body fat, and blood pressure at 3 months.
• Researchers assessed secondary outcomes for the impact of the diet on risk factors for metabolic syndrome and biomarkers associated with aging and age-related diseases.
• This study looked at data from nearly half of the original 184 participants (aged 18-70 years) from the two clinical trials who went through three to four monthly cycles, adhering to 5 days of an FMD in either a crossover design compared with a normal diet or an intervention group compared with people following a Mediterranean diet.
• Abdominal fat and hepatic fat were measured using an MRI in a subset of representative participants. The study also assessed metabolic blood markers and lipids and lymphoid-to-myeloid ratios (for immune aging).
• Biological age estimation was calculated from seven clinical chemistry measures, and life expectancy and mortality risk estimates and a simulation of continued FMD cycles were based on the National Health and Nutrition Examination Survey.

TAKEAWAY:

• In 15 volunteers measured by MRI, the body mass index (P = .0002), total body fat (P = .002), subcutaneous adipose tissue (P = .008), visceral adipose tissue (P = .002), and hepatic fat fraction (P = .049) reduced after the third FMD cycle, with a 50% reduction in liver fat for the five people with hepatic steatosis.
• In 11 participants with prediabetes, insulin resistance (measured by homeostatic model assessment) reduced from 1.473 to 1.209 (P = .046), while A1c levels dropped from 5.8 to 5.43 (P = .032) after the third FMD cycle.

• The lymphoid-to-myeloid ratio improved (P = .005) in all study participants receiving three FMD cycles, indicating an immune aging reversal.
• The estimated median biological age of the 86 participants who completed three FMD cycles in both trials decreased by nearly 2.5 years, independent of weight loss.

IN PRACTICE:

“Together our findings indicate that the FMD is a feasible periodic dietary intervention that reduces disease risk factors and biological age,” the authors wrote.

SOURCE:

The study, led by Sebastian Brandhorst, PhD, Leonard Davis School of Gerontology, University of Southern California (USC), Los Angeles, and Morgan E. Levine, PhD, Department of Pathology, Yale School of Medicine, New Haven, Connecticut, was published in Nature Communications.

LIMITATIONS:

The study estimated the effects of monthly FMD cycles based on results from two clinical trials and included a small subset of trial volunteers. By study measures, the cohort was healthier and biologically younger than average people of similar chronological age. Of the 86 participants, 24 who underwent FMD cycles exhibited increased biological age. The simulation did not consider compliance, dropout, mortality, or the bias that may arise owing to enthusiastic volunteers. Estimated risk reductions assume an effect of change in biological age, which hasn’t been proven. Projections from extending the effects of FMD to a lifelong intervention may require cautious interpretation.

DISCLOSURES:

The study was supported by the USC Edna Jones chair fund and funds from NIH/NIA and the Yale PEPPER Center. The experimental diet was provided by L-Nutra Inc. Some authors declared an equity interest in L-Nutra, with one author’s equity to be assigned to the nonprofit foundation Create Cures. Others disclosed no conflicts of interest.
 

A version of this article appeared on Medscape.com.

TOPLINE:

Monthly cycles of a fasting-mimicking diet (FMD) may slow metabolic and immune system aging and reduce the risk for metabolic disease.

METHODOLOGY:

• In two clinical trials, monthly 5-day cycles of an FMD (a proprietary line of plant-based, low-calorie, and low-protein food products) showed lower body weight, body fat, and blood pressure at 3 months.
• Researchers assessed secondary outcomes for the impact of the diet on risk factors for metabolic syndrome and biomarkers associated with aging and age-related diseases.
• This study looked at data from nearly half of the original 184 participants (aged 18-70 years) from the two clinical trials who went through three to four monthly cycles, adhering to 5 days of an FMD in either a crossover design compared with a normal diet or an intervention group compared with people following a Mediterranean diet.
• Abdominal fat and hepatic fat were measured using an MRI in a subset of representative participants. The study also assessed metabolic blood markers and lipids and lymphoid-to-myeloid ratios (for immune aging).
• Biological age estimation was calculated from seven clinical chemistry measures, and life expectancy and mortality risk estimates and a simulation of continued FMD cycles were based on the National Health and Nutrition Examination Survey.

TAKEAWAY:

• In 15 volunteers measured by MRI, the body mass index (P = .0002), total body fat (P = .002), subcutaneous adipose tissue (P = .008), visceral adipose tissue (P = .002), and hepatic fat fraction (P = .049) reduced after the third FMD cycle, with a 50% reduction in liver fat for the five people with hepatic steatosis.
• In 11 participants with prediabetes, insulin resistance (measured by homeostatic model assessment) reduced from 1.473 to 1.209 (P = .046), while A1c levels dropped from 5.8 to 5.43 (P = .032) after the third FMD cycle.

• The lymphoid-to-myeloid ratio improved (P = .005) in all study participants receiving three FMD cycles, indicating an immune aging reversal.
• The estimated median biological age of the 86 participants who completed three FMD cycles in both trials decreased by nearly 2.5 years, independent of weight loss.

IN PRACTICE:

“Together our findings indicate that the FMD is a feasible periodic dietary intervention that reduces disease risk factors and biological age,” the authors wrote.

SOURCE:

The study, led by Sebastian Brandhorst, PhD, Leonard Davis School of Gerontology, University of Southern California (USC), Los Angeles, and Morgan E. Levine, PhD, Department of Pathology, Yale School of Medicine, New Haven, Connecticut, was published in Nature Communications.

LIMITATIONS:

The study estimated the effects of monthly FMD cycles based on results from two clinical trials and included a small subset of trial volunteers. By study measures, the cohort was healthier and biologically younger than average people of similar chronological age. Of the 86 participants, 24 who underwent FMD cycles exhibited increased biological age. The simulation did not consider compliance, dropout, mortality, or the bias that may arise owing to enthusiastic volunteers. Estimated risk reductions assume an effect of change in biological age, which hasn’t been proven. Projections from extending the effects of FMD to a lifelong intervention may require cautious interpretation.

DISCLOSURES:

The study was supported by the USC Edna Jones chair fund and funds from NIH/NIA and the Yale PEPPER Center. The experimental diet was provided by L-Nutra Inc. Some authors declared an equity interest in L-Nutra, with one author’s equity to be assigned to the nonprofit foundation Create Cures. Others disclosed no conflicts of interest.
 

A version of this article appeared on Medscape.com.

TOPLINE:

Monthly cycles of a fasting-mimicking diet (FMD) may slow metabolic and immune system aging and reduce the risk for metabolic disease.

METHODOLOGY:

• In two clinical trials, monthly 5-day cycles of an FMD (a proprietary line of plant-based, low-calorie, and low-protein food products) showed lower body weight, body fat, and blood pressure at 3 months.
• Researchers assessed secondary outcomes for the impact of the diet on risk factors for metabolic syndrome and biomarkers associated with aging and age-related diseases.
• This study looked at data from nearly half of the original 184 participants (aged 18-70 years) from the two clinical trials who went through three to four monthly cycles, adhering to 5 days of an FMD in either a crossover design compared with a normal diet or an intervention group compared with people following a Mediterranean diet.
• Abdominal fat and hepatic fat were measured using an MRI in a subset of representative participants. The study also assessed metabolic blood markers and lipids and lymphoid-to-myeloid ratios (for immune aging).
• Biological age estimation was calculated from seven clinical chemistry measures, and life expectancy and mortality risk estimates and a simulation of continued FMD cycles were based on the National Health and Nutrition Examination Survey.

TAKEAWAY:

• In 15 volunteers measured by MRI, the body mass index (P = .0002), total body fat (P = .002), subcutaneous adipose tissue (P = .008), visceral adipose tissue (P = .002), and hepatic fat fraction (P = .049) reduced after the third FMD cycle, with a 50% reduction in liver fat for the five people with hepatic steatosis.
• In 11 participants with prediabetes, insulin resistance (measured by homeostatic model assessment) reduced from 1.473 to 1.209 (P = .046), while A1c levels dropped from 5.8 to 5.43 (P = .032) after the third FMD cycle.

• The lymphoid-to-myeloid ratio improved (P = .005) in all study participants receiving three FMD cycles, indicating an immune aging reversal.
• The estimated median biological age of the 86 participants who completed three FMD cycles in both trials decreased by nearly 2.5 years, independent of weight loss.

IN PRACTICE:

“Together our findings indicate that the FMD is a feasible periodic dietary intervention that reduces disease risk factors and biological age,” the authors wrote.

SOURCE:

The study, led by Sebastian Brandhorst, PhD, Leonard Davis School of Gerontology, University of Southern California (USC), Los Angeles, and Morgan E. Levine, PhD, Department of Pathology, Yale School of Medicine, New Haven, Connecticut, was published in Nature Communications.

LIMITATIONS:

The study estimated the effects of monthly FMD cycles based on results from two clinical trials and included a small subset of trial volunteers. By study measures, the cohort was healthier and biologically younger than average people of similar chronological age. Of the 86 participants, 24 who underwent FMD cycles exhibited increased biological age. The simulation did not consider compliance, dropout, mortality, or the bias that may arise owing to enthusiastic volunteers. Estimated risk reductions assume an effect of change in biological age, which hasn’t been proven. Projections from extending the effects of FMD to a lifelong intervention may require cautious interpretation.

DISCLOSURES:

The study was supported by the USC Edna Jones chair fund and funds from NIH/NIA and the Yale PEPPER Center. The experimental diet was provided by L-Nutra Inc. Some authors declared an equity interest in L-Nutra, with one author’s equity to be assigned to the nonprofit foundation Create Cures. Others disclosed no conflicts of interest.
 

A version of this article appeared on Medscape.com.

Diabetic foot complications represent a major global health challenge, with a high prevalence among patients with diabetes. A diabetic foot ulcer (DFU) not only affects the patient›s quality of life but also increases the risk for amputation.

Worldwide, a DFU occurs every second, and an amputation occurs every 20 seconds. The limitations of current detection and intervention methods underline the urgent need for innovative solutions.

Recent advances in artificial intelligence (AI) have paved the way for individualized risk prediction models for chronic wound management. These models use deep learning algorithms to analyze clinical data and images, providing personalized treatment plans that may improve healing outcomes and reduce the risk for amputation.

AI-powered tools can also be deployed for the diagnosis of diabetic foot complications. Using image analysis and pattern recognition, AI tools are learning to accurately detect signs of DFUs and other complications, facilitating early and effective intervention. Our group and others have been working not only on imaging devices but also on thermographic tools that — with the help of AI — can create an automated “foot selfie” to predict and prevent problems before they start.

AI’s predictive capabilities are instrumental to its clinical value. By identifying patients at high risk for DFUs, healthcare providers can implement preemptive measures, significantly reducing the likelihood of severe complications.

Although the potential benefits of AI in diabetic foot care are immense, integrating these tools into clinical practice poses challenges. These include ensuring the reliability of AI predictions, addressing data privacy concerns, and training healthcare professionals on the use of AI technologies.

As in so many other areas in our lives, AI holds the promise to revolutionize diabetic foot and limb preservation, offering hope for improved patient outcomes through early detection, precise diagnosis, and personalized care. However, realizing this potential requires ongoing research, development, and collaboration across the medical and technological fields to ensure these innovative solutions can be effectively integrated into standard care practices.

Dr. Armstrong is professor of surgery, Keck School of Medicine of University of Southern California, Los Angeles, California. He has disclosed the following relevant financial relationships: Partially supported by National Institutes of Health; National Institute of Diabetes; Digestive and Kidney Disease Award Number 1R01124789-01A1.

A version of this article first appeared on Medscape.com.

Diabetic foot complications represent a major global health challenge, with a high prevalence among patients with diabetes. A diabetic foot ulcer (DFU) not only affects the patient›s quality of life but also increases the risk for amputation.

Worldwide, a DFU occurs every second, and an amputation occurs every 20 seconds. The limitations of current detection and intervention methods underline the urgent need for innovative solutions.

Recent advances in artificial intelligence (AI) have paved the way for individualized risk prediction models for chronic wound management. These models use deep learning algorithms to analyze clinical data and images, providing personalized treatment plans that may improve healing outcomes and reduce the risk for amputation.

AI-powered tools can also be deployed for the diagnosis of diabetic foot complications. Using image analysis and pattern recognition, AI tools are learning to accurately detect signs of DFUs and other complications, facilitating early and effective intervention. Our group and others have been working not only on imaging devices but also on thermographic tools that — with the help of AI — can create an automated “foot selfie” to predict and prevent problems before they start.

AI’s predictive capabilities are instrumental to its clinical value. By identifying patients at high risk for DFUs, healthcare providers can implement preemptive measures, significantly reducing the likelihood of severe complications.

Although the potential benefits of AI in diabetic foot care are immense, integrating these tools into clinical practice poses challenges. These include ensuring the reliability of AI predictions, addressing data privacy concerns, and training healthcare professionals on the use of AI technologies.

As in so many other areas in our lives, AI holds the promise to revolutionize diabetic foot and limb preservation, offering hope for improved patient outcomes through early detection, precise diagnosis, and personalized care. However, realizing this potential requires ongoing research, development, and collaboration across the medical and technological fields to ensure these innovative solutions can be effectively integrated into standard care practices.

Dr. Armstrong is professor of surgery, Keck School of Medicine of University of Southern California, Los Angeles, California. He has disclosed the following relevant financial relationships: Partially supported by National Institutes of Health; National Institute of Diabetes; Digestive and Kidney Disease Award Number 1R01124789-01A1.

A version of this article first appeared on Medscape.com.

Diabetic foot complications represent a major global health challenge, with a high prevalence among patients with diabetes. A diabetic foot ulcer (DFU) not only affects the patient›s quality of life but also increases the risk for amputation.

Worldwide, a DFU occurs every second, and an amputation occurs every 20 seconds. The limitations of current detection and intervention methods underline the urgent need for innovative solutions.

Recent advances in artificial intelligence (AI) have paved the way for individualized risk prediction models for chronic wound management. These models use deep learning algorithms to analyze clinical data and images, providing personalized treatment plans that may improve healing outcomes and reduce the risk for amputation.

AI-powered tools can also be deployed for the diagnosis of diabetic foot complications. Using image analysis and pattern recognition, AI tools are learning to accurately detect signs of DFUs and other complications, facilitating early and effective intervention. Our group and others have been working not only on imaging devices but also on thermographic tools that — with the help of AI — can create an automated “foot selfie” to predict and prevent problems before they start.

AI’s predictive capabilities are instrumental to its clinical value. By identifying patients at high risk for DFUs, healthcare providers can implement preemptive measures, significantly reducing the likelihood of severe complications.

Although the potential benefits of AI in diabetic foot care are immense, integrating these tools into clinical practice poses challenges. These include ensuring the reliability of AI predictions, addressing data privacy concerns, and training healthcare professionals on the use of AI technologies.

As in so many other areas in our lives, AI holds the promise to revolutionize diabetic foot and limb preservation, offering hope for improved patient outcomes through early detection, precise diagnosis, and personalized care. However, realizing this potential requires ongoing research, development, and collaboration across the medical and technological fields to ensure these innovative solutions can be effectively integrated into standard care practices.

Dr. Armstrong is professor of surgery, Keck School of Medicine of University of Southern California, Los Angeles, California. He has disclosed the following relevant financial relationships: Partially supported by National Institutes of Health; National Institute of Diabetes; Digestive and Kidney Disease Award Number 1R01124789-01A1.

A version of this article first appeared on Medscape.com.

Among adults with type 2 diabetes (T2D) older than 65 years, a body mass index (BMI) in the moderate overweight category (26-28) appears to offer better protection from cardiovascular death than does a BMI in the “normal” range, new data suggested.

On the other hand, the study findings also suggest that the “normal” range of 23-25 is optimal for middle-aged adults with T2D.

The findings reflect a previously demonstrated phenomenon called the “obesity paradox,” in which older people with overweight may have better outcomes than leaner people due to factors such as bone loss, frailty, and nutritional deficits, study lead author Shaoyong Xu, of Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China, told this news organization.

“In this era of population growth and aging, the question arises as to whether obesity or overweight can be beneficial in improving survival rates for older individuals with diabetes. This topic holds significant relevance due to the potential implications it has on weight management strategies for older adults. If overweight does not pose an increased risk of cardiovascular mortality, it may suggest that older individuals are not necessarily required to strive for weight loss to achieve so-called normal values.”

Moreover, Dr. Xu added, “inappropriate weight loss and being underweight could potentially elevate the risk of cardiovascular events, myocardial infarction, cerebral infarction, and all-cause mortality.”

Thus, he said, “while there are general guidelines recommending a BMI below 25, our findings suggest that personalized BMI targets may be more beneficial, particularly for different age groups and individuals with specific health conditions.”

Asked to comment, Ian J. Neeland, MD, director of cardiovascular prevention, University Hospitals Harrington Heart & Vascular Institute, Case Western Reserve University, Cleveland, Ohio, pointed out that older people who are underweight or in lower weight categories may be more likely to smoke or have undiagnosed cancer, or that “their BMI is not so much reflective of fat mass as of low muscle mass, or sarcopenia, and that is definitely a risk factor for adverse outcomes and risks. ... And those who have slightly higher BMIs may be maintaining muscle mass, even though they’re older, and therefore they have less risk.”

However, Dr. Neeland disagreed with the authors’ conclusions regarding “optimal” BMI. “Just because you have different risk categories based on BMI doesn’t mean that’s ‘optimal’ BMI. The way I would interpret this paper is that there’s an association of mildly overweight with better outcomes in adults who are over 65 with type 2 diabetes. We need to try to understand the mechanisms underlying that observation.”

Dr. Neeland advised that for an older person with T2D who has low muscle mass and frailty, “I wouldn’t recommend necessarily targeted weight loss in that person. But I would potentially recommend weight loss in addition to resistance training, muscle building, and endurance training, and therefore reducing fat mass. The goal would be not so much weight loss but reduction of body fat and maintaining and improving muscle health.”
 

U-Shaped Relationship Found Between Age, BMI, and Cardiovascular Disease (CVD) Risk

The data come from the UK Biobank, a population-based prospective cohort study of adults in the United Kingdom. A total of 22,874 participants with baseline T2D were included in the current study. Baseline surveys were conducted between 2006 and 2010, and follow-up was a median of 12.52 years. During that time, 891 people died of CVD.

Hazard ratios were adjusted for baseline variables including age, sex, smoking history, alcohol consumption, level of physical exercise, and history of CVDs.

Compared with people with BMI a < 25 in the group who were aged 65 years or younger, those with a BMI of 25.0-29.9 had a 13% higher risk for cardiovascular death. However, among those older than 65 years, a BMI between 25.0 and 29.9 was associated with an 18% lower risk.

A U-shaped relationship was found between BMI and the risk for cardiovascular death, with an optimal BMI cutoff of 24.0 in the under-65 group and a 27.0 cutoff in the older group. Ranges of 23.0-25.0 in the under-65 group and 26.0-28 in the older group were associated with the lowest cardiovascular risk.

In contrast, there was a linear relationship between both waist circumference and waist-to-height ratio and the risk for cardiovascular death, making those more direct measures of adiposity, Dr. Xu told this news organization.

“For clinicians, our data underscores the importance of considering age when assessing BMI targets for cardiovascular health. Personalized treatment plans that account for age-specific BMI cutoffs and other risk factors may enhance patient outcomes and reduce CVD mortality,” Dr. Xu said.

However, he added, “while these findings suggest an optimal BMI range, it is crucial to acknowledge that these cutoff points may vary based on gender, race, and other factors. Our future studies will validate these findings in different populations and attempt to explain the mechanism by which the optimal nodal values exist in people with diabetes at different ages.”

Dr. Neeland cautioned, “I think more work needs to be done in terms of not just identifying the risk differences but understanding why and how to better risk stratify individuals and do personalized medicine. I think that’s important, but you have to have good data to support the strategies you’re going to use. These data are observational, and they’re a good start, but they wouldn’t directly impact practice at this point.”

The data will be presented at the European Congress on Obesity taking place May 12-15 in Venice, Italy.

The authors declared no competing interests. Study funding came from several sources, including the Young Talents Project of Hubei Provincial Health Commission, China, Hubei Provincial Natural Science Foundation of China, the Science and Technology Research Key Project of the Education Department of Hubei Province China, and the Sanuo Diabetes Charity Foundation, China, and the Xiangyang Science and Technology Plan Project, China. Dr. Neeland is a speaker and/or consultant for Boehringer Ingelheim, Novo Nordisk, Bayer, and Eli Lilly and Company.
 

A version of this article appeared on Medscape.com.

Among adults with type 2 diabetes (T2D) older than 65 years, a body mass index (BMI) in the moderate overweight category (26-28) appears to offer better protection from cardiovascular death than does a BMI in the “normal” range, new data suggested.

On the other hand, the study findings also suggest that the “normal” range of 23-25 is optimal for middle-aged adults with T2D.

The findings reflect a previously demonstrated phenomenon called the “obesity paradox,” in which older people with overweight may have better outcomes than leaner people due to factors such as bone loss, frailty, and nutritional deficits, study lead author Shaoyong Xu, of Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China, told this news organization.

“In this era of population growth and aging, the question arises as to whether obesity or overweight can be beneficial in improving survival rates for older individuals with diabetes. This topic holds significant relevance due to the potential implications it has on weight management strategies for older adults. If overweight does not pose an increased risk of cardiovascular mortality, it may suggest that older individuals are not necessarily required to strive for weight loss to achieve so-called normal values.”

Moreover, Dr. Xu added, “inappropriate weight loss and being underweight could potentially elevate the risk of cardiovascular events, myocardial infarction, cerebral infarction, and all-cause mortality.”

Thus, he said, “while there are general guidelines recommending a BMI below 25, our findings suggest that personalized BMI targets may be more beneficial, particularly for different age groups and individuals with specific health conditions.”

Asked to comment, Ian J. Neeland, MD, director of cardiovascular prevention, University Hospitals Harrington Heart & Vascular Institute, Case Western Reserve University, Cleveland, Ohio, pointed out that older people who are underweight or in lower weight categories may be more likely to smoke or have undiagnosed cancer, or that “their BMI is not so much reflective of fat mass as of low muscle mass, or sarcopenia, and that is definitely a risk factor for adverse outcomes and risks. ... And those who have slightly higher BMIs may be maintaining muscle mass, even though they’re older, and therefore they have less risk.”

However, Dr. Neeland disagreed with the authors’ conclusions regarding “optimal” BMI. “Just because you have different risk categories based on BMI doesn’t mean that’s ‘optimal’ BMI. The way I would interpret this paper is that there’s an association of mildly overweight with better outcomes in adults who are over 65 with type 2 diabetes. We need to try to understand the mechanisms underlying that observation.”

Dr. Neeland advised that for an older person with T2D who has low muscle mass and frailty, “I wouldn’t recommend necessarily targeted weight loss in that person. But I would potentially recommend weight loss in addition to resistance training, muscle building, and endurance training, and therefore reducing fat mass. The goal would be not so much weight loss but reduction of body fat and maintaining and improving muscle health.”
 

U-Shaped Relationship Found Between Age, BMI, and Cardiovascular Disease (CVD) Risk

The data come from the UK Biobank, a population-based prospective cohort study of adults in the United Kingdom. A total of 22,874 participants with baseline T2D were included in the current study. Baseline surveys were conducted between 2006 and 2010, and follow-up was a median of 12.52 years. During that time, 891 people died of CVD.

Hazard ratios were adjusted for baseline variables including age, sex, smoking history, alcohol consumption, level of physical exercise, and history of CVDs.

Compared with people with BMI a < 25 in the group who were aged 65 years or younger, those with a BMI of 25.0-29.9 had a 13% higher risk for cardiovascular death. However, among those older than 65 years, a BMI between 25.0 and 29.9 was associated with an 18% lower risk.

A U-shaped relationship was found between BMI and the risk for cardiovascular death, with an optimal BMI cutoff of 24.0 in the under-65 group and a 27.0 cutoff in the older group. Ranges of 23.0-25.0 in the under-65 group and 26.0-28 in the older group were associated with the lowest cardiovascular risk.

In contrast, there was a linear relationship between both waist circumference and waist-to-height ratio and the risk for cardiovascular death, making those more direct measures of adiposity, Dr. Xu told this news organization.

“For clinicians, our data underscores the importance of considering age when assessing BMI targets for cardiovascular health. Personalized treatment plans that account for age-specific BMI cutoffs and other risk factors may enhance patient outcomes and reduce CVD mortality,” Dr. Xu said.

However, he added, “while these findings suggest an optimal BMI range, it is crucial to acknowledge that these cutoff points may vary based on gender, race, and other factors. Our future studies will validate these findings in different populations and attempt to explain the mechanism by which the optimal nodal values exist in people with diabetes at different ages.”

Dr. Neeland cautioned, “I think more work needs to be done in terms of not just identifying the risk differences but understanding why and how to better risk stratify individuals and do personalized medicine. I think that’s important, but you have to have good data to support the strategies you’re going to use. These data are observational, and they’re a good start, but they wouldn’t directly impact practice at this point.”

The data will be presented at the European Congress on Obesity taking place May 12-15 in Venice, Italy.

The authors declared no competing interests. Study funding came from several sources, including the Young Talents Project of Hubei Provincial Health Commission, China, Hubei Provincial Natural Science Foundation of China, the Science and Technology Research Key Project of the Education Department of Hubei Province China, and the Sanuo Diabetes Charity Foundation, China, and the Xiangyang Science and Technology Plan Project, China. Dr. Neeland is a speaker and/or consultant for Boehringer Ingelheim, Novo Nordisk, Bayer, and Eli Lilly and Company.
 

A version of this article appeared on Medscape.com.

Among adults with type 2 diabetes (T2D) older than 65 years, a body mass index (BMI) in the moderate overweight category (26-28) appears to offer better protection from cardiovascular death than does a BMI in the “normal” range, new data suggested.

On the other hand, the study findings also suggest that the “normal” range of 23-25 is optimal for middle-aged adults with T2D.

The findings reflect a previously demonstrated phenomenon called the “obesity paradox,” in which older people with overweight may have better outcomes than leaner people due to factors such as bone loss, frailty, and nutritional deficits, study lead author Shaoyong Xu, of Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China, told this news organization.

“In this era of population growth and aging, the question arises as to whether obesity or overweight can be beneficial in improving survival rates for older individuals with diabetes. This topic holds significant relevance due to the potential implications it has on weight management strategies for older adults. If overweight does not pose an increased risk of cardiovascular mortality, it may suggest that older individuals are not necessarily required to strive for weight loss to achieve so-called normal values.”

Moreover, Dr. Xu added, “inappropriate weight loss and being underweight could potentially elevate the risk of cardiovascular events, myocardial infarction, cerebral infarction, and all-cause mortality.”

Thus, he said, “while there are general guidelines recommending a BMI below 25, our findings suggest that personalized BMI targets may be more beneficial, particularly for different age groups and individuals with specific health conditions.”

Asked to comment, Ian J. Neeland, MD, director of cardiovascular prevention, University Hospitals Harrington Heart & Vascular Institute, Case Western Reserve University, Cleveland, Ohio, pointed out that older people who are underweight or in lower weight categories may be more likely to smoke or have undiagnosed cancer, or that “their BMI is not so much reflective of fat mass as of low muscle mass, or sarcopenia, and that is definitely a risk factor for adverse outcomes and risks. ... And those who have slightly higher BMIs may be maintaining muscle mass, even though they’re older, and therefore they have less risk.”

However, Dr. Neeland disagreed with the authors’ conclusions regarding “optimal” BMI. “Just because you have different risk categories based on BMI doesn’t mean that’s ‘optimal’ BMI. The way I would interpret this paper is that there’s an association of mildly overweight with better outcomes in adults who are over 65 with type 2 diabetes. We need to try to understand the mechanisms underlying that observation.”

Dr. Neeland advised that for an older person with T2D who has low muscle mass and frailty, “I wouldn’t recommend necessarily targeted weight loss in that person. But I would potentially recommend weight loss in addition to resistance training, muscle building, and endurance training, and therefore reducing fat mass. The goal would be not so much weight loss but reduction of body fat and maintaining and improving muscle health.”
 

U-Shaped Relationship Found Between Age, BMI, and Cardiovascular Disease (CVD) Risk

The data come from the UK Biobank, a population-based prospective cohort study of adults in the United Kingdom. A total of 22,874 participants with baseline T2D were included in the current study. Baseline surveys were conducted between 2006 and 2010, and follow-up was a median of 12.52 years. During that time, 891 people died of CVD.

Hazard ratios were adjusted for baseline variables including age, sex, smoking history, alcohol consumption, level of physical exercise, and history of CVDs.

Compared with people with BMI a < 25 in the group who were aged 65 years or younger, those with a BMI of 25.0-29.9 had a 13% higher risk for cardiovascular death. However, among those older than 65 years, a BMI between 25.0 and 29.9 was associated with an 18% lower risk.

A U-shaped relationship was found between BMI and the risk for cardiovascular death, with an optimal BMI cutoff of 24.0 in the under-65 group and a 27.0 cutoff in the older group. Ranges of 23.0-25.0 in the under-65 group and 26.0-28 in the older group were associated with the lowest cardiovascular risk.

In contrast, there was a linear relationship between both waist circumference and waist-to-height ratio and the risk for cardiovascular death, making those more direct measures of adiposity, Dr. Xu told this news organization.

“For clinicians, our data underscores the importance of considering age when assessing BMI targets for cardiovascular health. Personalized treatment plans that account for age-specific BMI cutoffs and other risk factors may enhance patient outcomes and reduce CVD mortality,” Dr. Xu said.

However, he added, “while these findings suggest an optimal BMI range, it is crucial to acknowledge that these cutoff points may vary based on gender, race, and other factors. Our future studies will validate these findings in different populations and attempt to explain the mechanism by which the optimal nodal values exist in people with diabetes at different ages.”

Dr. Neeland cautioned, “I think more work needs to be done in terms of not just identifying the risk differences but understanding why and how to better risk stratify individuals and do personalized medicine. I think that’s important, but you have to have good data to support the strategies you’re going to use. These data are observational, and they’re a good start, but they wouldn’t directly impact practice at this point.”

The data will be presented at the European Congress on Obesity taking place May 12-15 in Venice, Italy.

The authors declared no competing interests. Study funding came from several sources, including the Young Talents Project of Hubei Provincial Health Commission, China, Hubei Provincial Natural Science Foundation of China, the Science and Technology Research Key Project of the Education Department of Hubei Province China, and the Sanuo Diabetes Charity Foundation, China, and the Xiangyang Science and Technology Plan Project, China. Dr. Neeland is a speaker and/or consultant for Boehringer Ingelheim, Novo Nordisk, Bayer, and Eli Lilly and Company.
 

A version of this article appeared on Medscape.com.

This case is a little out of the ordinary, but we would love to find out how readers would handle it.

Diana is a 51-year-old woman with a history of depression, obesity, hypertension, type 2 diabetes, and coronary artery disease. She has come in for a routine visit for her chronic illnesses. She seems very distant and has a flat affect during the initial interview. When you ask about any recent stressful events, she begins crying and explains that her daughter was just deported, leaving behind a child and boyfriend.

Their country of origin suffers from chronic instability and violence. Diana’s father was murdered there, and Diana was the victim of sexual assault. “I escaped when I was 18, and I tried to never look back. Until now.” Diana is very worried about her daughter’s return to that country. “I don’t want her to have to endure what I have endured.”

You spend some time discussing the patient’s mental health burden and identify a counselor and online resources that might help. You wonder if Diana’s adverse childhood experiences (ACEs) might have contributed to some of her physical illnesses.

ACEs and Adult Health

The effects of trauma run long and deep. ACEs have been associated with higher risks for multiple chronic conditions, even among adults aged 60 years or older. Therefore, clinicians should consider a patient’s history of ACEs as part of their evaluation of risk for chronic illness.

One of the most pronounced and straightforward links is that between ACEs and depression. In the Southern Community Cohort Study of more than 38,200 US adults, the highest odds ratio between ACEs and chronic disease was for depression. Persons who reported more than three ACEs had about a twofold increase in the risk for depression compared with persons without ACEs. There was a monotonic increase in the risk for depression and other chronic illnesses as the burden of ACEs increased.

In another study from the United Kingdom, each additional ACE was associated with a significant 11% increase in the risk for incident diabetes during adulthood. Researchers found that both depression symptoms and cardiometabolic dysfunction mediated the effects of ACEs in promoting higher rates of diabetes.

Depression and diabetes are significant risk factors for coronary artery disease, so it is not surprising that ACEs are also associated with a higher risk for coronary events. A review by Godoy and colleagues described how ACEs promote neuroendocrine, autonomic, and inflammatory dysfunction, which in turn leads to higher rates of traditional cardiovascular risk factors such as diabetes and obesity. Ultimately, the presence of four or more ACEs is associated with more than a twofold higher risk for cardiovascular disease compared with no ACEs.

Many of the pathologic processes that promote cardiovascular disease also increase the risk for dementia. Could the reach of ACEs span decades to promote a higher risk for dementia among older adults? A study by Yuan and colleagues of 7222 Chinese adults suggests that the answer is yes. This study divided the cohort into persons with a history of no ACEs, household dysfunction during childhood, or mistreatment during childhood. Child mistreatment was associated with higher rates of diabetes, depression, and cardiovascular disease, as well as an odds ratio of 1.37 (95% CI, 1.12 to 1.68) for cognitive impairment.

The magnitude of the effects ACEs can have on well-being is reinforced by epidemiologic data surrounding ACEs. According to the US Centers for Disease Control and Prevention (CDC), 64% of US adults report at least one ACE and 17% experienced at least four ACEs. Risk factors for ACEs include being female, American Indian or Alaska Native, or unemployed.

How do we reduce the impact of ACEs? Prevention is key. The CDC estimates that nearly 2 million cases of adult heart disease and more than 20 million cases of adult depression could be avoided if ACEs were eliminated.

But what is the best means to pragmatically reduce ACEs in our current practice models? How do we discover a history of ACEs in patients, and what are the best practices in managing persons with a positive history? We will cover these critical subjects in a future article, but for now, please provide your own comments and pearls regarding the prevention and management of ACEs.

Dr. Vega, health sciences clinical professor, family medicine, University of California, Irvine, disclosed ties with GlaxoSmithKline and Johnson and Johnson. Ms. Hurtado, MD candidate, University of California, Irvine School of Medicine, has disclosed no relevant financial relationships.

A version of this article appeared on Medscape.com.

This case is a little out of the ordinary, but we would love to find out how readers would handle it.

Diana is a 51-year-old woman with a history of depression, obesity, hypertension, type 2 diabetes, and coronary artery disease. She has come in for a routine visit for her chronic illnesses. She seems very distant and has a flat affect during the initial interview. When you ask about any recent stressful events, she begins crying and explains that her daughter was just deported, leaving behind a child and boyfriend.

Their country of origin suffers from chronic instability and violence. Diana’s father was murdered there, and Diana was the victim of sexual assault. “I escaped when I was 18, and I tried to never look back. Until now.” Diana is very worried about her daughter’s return to that country. “I don’t want her to have to endure what I have endured.”

You spend some time discussing the patient’s mental health burden and identify a counselor and online resources that might help. You wonder if Diana’s adverse childhood experiences (ACEs) might have contributed to some of her physical illnesses.

ACEs and Adult Health

The effects of trauma run long and deep. ACEs have been associated with higher risks for multiple chronic conditions, even among adults aged 60 years or older. Therefore, clinicians should consider a patient’s history of ACEs as part of their evaluation of risk for chronic illness.

One of the most pronounced and straightforward links is that between ACEs and depression. In the Southern Community Cohort Study of more than 38,200 US adults, the highest odds ratio between ACEs and chronic disease was for depression. Persons who reported more than three ACEs had about a twofold increase in the risk for depression compared with persons without ACEs. There was a monotonic increase in the risk for depression and other chronic illnesses as the burden of ACEs increased.

In another study from the United Kingdom, each additional ACE was associated with a significant 11% increase in the risk for incident diabetes during adulthood. Researchers found that both depression symptoms and cardiometabolic dysfunction mediated the effects of ACEs in promoting higher rates of diabetes.

Depression and diabetes are significant risk factors for coronary artery disease, so it is not surprising that ACEs are also associated with a higher risk for coronary events. A review by Godoy and colleagues described how ACEs promote neuroendocrine, autonomic, and inflammatory dysfunction, which in turn leads to higher rates of traditional cardiovascular risk factors such as diabetes and obesity. Ultimately, the presence of four or more ACEs is associated with more than a twofold higher risk for cardiovascular disease compared with no ACEs.

Many of the pathologic processes that promote cardiovascular disease also increase the risk for dementia. Could the reach of ACEs span decades to promote a higher risk for dementia among older adults? A study by Yuan and colleagues of 7222 Chinese adults suggests that the answer is yes. This study divided the cohort into persons with a history of no ACEs, household dysfunction during childhood, or mistreatment during childhood. Child mistreatment was associated with higher rates of diabetes, depression, and cardiovascular disease, as well as an odds ratio of 1.37 (95% CI, 1.12 to 1.68) for cognitive impairment.

The magnitude of the effects ACEs can have on well-being is reinforced by epidemiologic data surrounding ACEs. According to the US Centers for Disease Control and Prevention (CDC), 64% of US adults report at least one ACE and 17% experienced at least four ACEs. Risk factors for ACEs include being female, American Indian or Alaska Native, or unemployed.

How do we reduce the impact of ACEs? Prevention is key. The CDC estimates that nearly 2 million cases of adult heart disease and more than 20 million cases of adult depression could be avoided if ACEs were eliminated.

But what is the best means to pragmatically reduce ACEs in our current practice models? How do we discover a history of ACEs in patients, and what are the best practices in managing persons with a positive history? We will cover these critical subjects in a future article, but for now, please provide your own comments and pearls regarding the prevention and management of ACEs.

Dr. Vega, health sciences clinical professor, family medicine, University of California, Irvine, disclosed ties with GlaxoSmithKline and Johnson and Johnson. Ms. Hurtado, MD candidate, University of California, Irvine School of Medicine, has disclosed no relevant financial relationships.

A version of this article appeared on Medscape.com.

This case is a little out of the ordinary, but we would love to find out how readers would handle it.

Diana is a 51-year-old woman with a history of depression, obesity, hypertension, type 2 diabetes, and coronary artery disease. She has come in for a routine visit for her chronic illnesses. She seems very distant and has a flat affect during the initial interview. When you ask about any recent stressful events, she begins crying and explains that her daughter was just deported, leaving behind a child and boyfriend.

Their country of origin suffers from chronic instability and violence. Diana’s father was murdered there, and Diana was the victim of sexual assault. “I escaped when I was 18, and I tried to never look back. Until now.” Diana is very worried about her daughter’s return to that country. “I don’t want her to have to endure what I have endured.”

You spend some time discussing the patient’s mental health burden and identify a counselor and online resources that might help. You wonder if Diana’s adverse childhood experiences (ACEs) might have contributed to some of her physical illnesses.

ACEs and Adult Health

The effects of trauma run long and deep. ACEs have been associated with higher risks for multiple chronic conditions, even among adults aged 60 years or older. Therefore, clinicians should consider a patient’s history of ACEs as part of their evaluation of risk for chronic illness.

One of the most pronounced and straightforward links is that between ACEs and depression. In the Southern Community Cohort Study of more than 38,200 US adults, the highest odds ratio between ACEs and chronic disease was for depression. Persons who reported more than three ACEs had about a twofold increase in the risk for depression compared with persons without ACEs. There was a monotonic increase in the risk for depression and other chronic illnesses as the burden of ACEs increased.

In another study from the United Kingdom, each additional ACE was associated with a significant 11% increase in the risk for incident diabetes during adulthood. Researchers found that both depression symptoms and cardiometabolic dysfunction mediated the effects of ACEs in promoting higher rates of diabetes.

Depression and diabetes are significant risk factors for coronary artery disease, so it is not surprising that ACEs are also associated with a higher risk for coronary events. A review by Godoy and colleagues described how ACEs promote neuroendocrine, autonomic, and inflammatory dysfunction, which in turn leads to higher rates of traditional cardiovascular risk factors such as diabetes and obesity. Ultimately, the presence of four or more ACEs is associated with more than a twofold higher risk for cardiovascular disease compared with no ACEs.

Many of the pathologic processes that promote cardiovascular disease also increase the risk for dementia. Could the reach of ACEs span decades to promote a higher risk for dementia among older adults? A study by Yuan and colleagues of 7222 Chinese adults suggests that the answer is yes. This study divided the cohort into persons with a history of no ACEs, household dysfunction during childhood, or mistreatment during childhood. Child mistreatment was associated with higher rates of diabetes, depression, and cardiovascular disease, as well as an odds ratio of 1.37 (95% CI, 1.12 to 1.68) for cognitive impairment.

The magnitude of the effects ACEs can have on well-being is reinforced by epidemiologic data surrounding ACEs. According to the US Centers for Disease Control and Prevention (CDC), 64% of US adults report at least one ACE and 17% experienced at least four ACEs. Risk factors for ACEs include being female, American Indian or Alaska Native, or unemployed.

How do we reduce the impact of ACEs? Prevention is key. The CDC estimates that nearly 2 million cases of adult heart disease and more than 20 million cases of adult depression could be avoided if ACEs were eliminated.

But what is the best means to pragmatically reduce ACEs in our current practice models? How do we discover a history of ACEs in patients, and what are the best practices in managing persons with a positive history? We will cover these critical subjects in a future article, but for now, please provide your own comments and pearls regarding the prevention and management of ACEs.

Dr. Vega, health sciences clinical professor, family medicine, University of California, Irvine, disclosed ties with GlaxoSmithKline and Johnson and Johnson. Ms. Hurtado, MD candidate, University of California, Irvine School of Medicine, has disclosed no relevant financial relationships.

A version of this article appeared on Medscape.com.