Abigail Brooks is a strategic content lead overseeing 

She joined MJH Life Sciences in August 2023 shortly after graduating from Monmouth University where she earned her BA in Communication with a concentration in Public Relations/Journalism and later an MA in Interactive Digital Media. She enjoys traveling, running, and reading books. She can be reached at abrooks@mjhlifesciences.com.

Credit: International Society of Nephrology

A post hoc analysis of the phase 3 STEP UP trial found that semaglutide 7.2 mg and 2.4 mg were each associated with significant increases in estimated glomerular filtration rate measured by the creatinine-cystatin C equation (eGFRcre-cys) compared with placebo at 72 weeks in adults with 

The findings, which were presented at at the American Diabetes Association (ADA) Scientific Sessions 2026 in New Orleans, Louisiana, by Peter Rossing, MD, PhD, showed both semaglutide doses also produced significantly greater reductions in high-sensitivity C-reactive protein (hsCRP) versus placebo, suggesting a measurable reduction in systemic inflammation alongside the observed 

The analysis adds to a growing body of evidence examining the renal and inflammatory effects of GLP-1 receptor agonist therapy in populations beyond T2D, with methodological attention paid to a key limitation of standard eGFR measurement in the setting of substantial weight loss.

Why eGFRcre-cys Matters in Weight Loss Trials

Standard creatinine-based eGFR (eGFRcre) is subject to confounding by reductions in muscle mass that accompany significant weight loss, a recognized limitation when assessing kidney function changes in obesity trials. Investigators used both the CKD-EPI 2021 creatinine equation and the combined creatinine-cystatin C equation to characterize eGFR changes, with the latter considered less susceptible to weight loss-related muscle mass reduction.

At week 72, eGFRcre ratios to baseline were similar across all three groups, with few participants shifting eGFR category, a pattern the investigators attributed in part to the confounding effect of lean mass loss on creatinine-based estimates. In contrast, eGFRcre-cys increased significantly with both semaglutide 7.2 mg and 2.4 mg versus placebo (estimated treatment difference [ETD] at week 72, 3.31 mL/min/1.73 m2; 95% CI, 1.63–5.00; 

<.001 and 3.21 mL/min/1.73 m2; 95% CI, 1.08–5.33; 

= .001, respectively), and more participants in both active treatment groups shifted to the eGFRcre-cys ≥90 mL/min/1.73 m2 category by week 72.

STEP UP Trial Design and Kidney Outcome Findings

The STEP UP trial randomly assigned adults with obesity but without diabetes to once-weekly subcutaneous semaglutide 7.2 mg (n = 1005), semaglutide 2.4 mg (n = 201), or placebo (n = 201). The present post hoc analysis assessed changes in eGFRcre, eGFRcre-cys, eGFR category shifts, and hsCRP from baseline to week 72 using observed values from the on-treatment period.

At baseline, approximately two-thirds of participants across all groups had normal kidney function, defined as eGFRcre ≥90 mL/min/1.73 m2 (67.3%, 70.6%, and 69.7% in the semaglutide 7.2 mg, 2.4 mg, and placebo groups, respectively). Baseline mean eGFRcre values were 99.0 mL/min/1.73 m2, 100 mL/min/1.73 m2, and 99.2 mL/min/1.73 m2 across the 3 groups.

Regarding hsCRP, both semaglutide groups demonstrated significantly greater median reductions from baseline versus placebo at week 72 (estimated treatment ratio [ETR], 0.42; 95% CI, 0.36–0.50; 

<.001 for 7.2 mg vs placebo and 0.47; 95% CI, 0.38–0.58; 

<.001 for 2.4 mg vs placebo), reflecting reduced systemic inflammation. The difference between the 2 semaglutide doses for hsCRP reduction was not statistically significant (ETR, 0.90; 95% CI, 0.77–1.05; 

Semaglutide Mechanism and Renal Relevance in Obesity

Semaglutide is a GLP-1 receptor agonist approved at 2.4 mg weekly (Wegovy, Novo Nordisk) for chronic weight management in adults with obesity or overweight with ≥ 1 weight-related comorbidity, and at lower doses (Ozempic) for glycemic management and cardiovascular risk reduction in T2D.

Most recently, the US Food and Drug Administration approved higher dose semaglutide injection 7.2 mg to be used along with a reduced calorie diet and increased physical activity to help adults with obesity lose weight and keep it off, provided they have tolerated the 2.4 mg dosage for ≥ 4 weeks and additional weight reduction is clinically indicated. The approval was based on the results from the STEP UP trial program.

GLP-1 receptors are expressed in the kidney, and preclinical and clinical data have suggested potential renoprotective effects of GLP-1 receptor agonism independent of glucose lowering, including reductions in intraglomerular pressure and inflammation. In populations with obesity but without T2D, the renal implications of sustained GLP-1 receptor agonist therapy have been less extensively characterized, making data from STEP UP of potential interest for understanding the drug class's broader organ-level effects.

Rossing P, Jacobsen CSF, HjelmesÆth J, et al. High-Dose Semaglutide Is Associated with Preservation of Kidney Function in People Living with Obesity without Diabetes. Presented at American Diabetes Association (ADA) Scientific Sessions 2026 in New Orleans, Louisiana, June 5-8, 2026.

Górriz JL, Soler MJ, Navarro-González JF, et al. GLP-1 receptor agonists and diabetic kidney disease: a call of attention to nephrologists. 

Brooks A. FDA Approves Higher Dose Semaglutide (Wegovy HD) Injection 7.2 mg for Obesity. 

. March 19, 2026. Accessed June 5, 2026. 

https://www.hcplive.com/view/fda-approves-higher-dose-semaglutide-wegovy-hd-injection-7-2-mg-for-obesity

Articles

A post hoc analysis of STEP UP in adults with obesity without diabetes showed semaglutide 7.2 mg and 2.4 mg improved kidney function when assessed with the combined creatinine–cystatin C eGFR equation at 72 weeks versus placebo. Creatinine-only eGFR appeared relatively unchanged, consistent with confounding from weight loss–related lean mass reductions. Both doses also significantly lowered hsCRP, indicating reduced systemic inflammation. Findings support potential renoprotective and anti-inflammatory effects of GLP-1 receptor agonism beyond glycemic control.

A post-hoc analysis of the STEP UP trial found that semaglutide 7.2 mg and 2.4 mg were linked to eGFR increases in adults with obesity who did not have diabetes. 


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High-Dose Semaglutide Preserves Kidney Function, Reduces Inflammation in Obesity Without Diabetes

New data from the phase 3 STEP UP T2D trial suggest that a higher subcutaneous dose of semaglutide 7.2 mg was associated with numerically greater proportions of patients with 

 reaching proposed BMI and waist-to-height ratio (WHtR) treatment targets, as well as low-risk thresholds for key cardiometabolic markers, compared with the 2.4 mg dose or placebo at 72 weeks.

The findings were presented by Carel Le Roux, MBChB, MSC, PhD, as a late-breaking poster at the American Diabetes Association (ADA) Scientific Sessions 2026 in New Orleans, Louisiana.

STEP UP T2D Trial Design and Study Population

STEP UP T2D was a phase 3, randomized, placebo-controlled trial evaluating once-weekly subcutaneous semaglutide 7.2 mg and semaglutide 2.4 mg dose in adults with obesity and T2D. The analysis presented at ADA included 512 participants, including 307 receiving semaglutide 7.2 mg, 103 receiving semaglutide 2.4 mg, and 102 receiving placebo.

The mean T2D duration at baseline was 8.4 years (SD, 6.4), and mean HbA1c was 8.1% (SD, 0.9), reflecting a population with established, moderately uncontrolled diabetes. No participants met the BMI or WHtR treatment targets at baseline, which were defined as a BMI < 27 kg/m2 and a WHtR < 0.53, respectively — thresholds that have been proposed as clinically meaningful goals for obesity management in the context of metabolic risk reduction.

Cardiometabolic outcomes assessed at week 72 included normoglycemia, HDL-cholesterol, blood pressure, and triglycerides, each evaluated against established low-risk reference thresholds.

At week 72, numerically greater proportions of participants in the semaglutide 7.2 mg group achieved the BMI target of < 27 kg/m2, the WHtR target of < 0.53, or both, compared with those receiving semaglutide 2.4 mg or placebo. Of note, the same directional pattern was observed across the cardiometabolic low-risk thresholds, with the 7.2 mg cohort showing numerically higher rates of reaching target ranges for normoglycemia, HDL-cholesterol, blood pressure, and triglycerides.

Findings are consistent with the direction seen in the parent STEP UP trial, which evaluated semaglutide 7.2 mg in participants with obesity but without T2D. However, the investigators noted that overall proportions achieving the anthropometric and cardiometabolic targets were lower in the T2D population than in STEP UP, reflecting the additional metabolic burden and treatment complexity associated with T2D.

Clinical Context: Obesity and T2D as Compounding CVD Risk Factors

Adults with both obesity and T2D represent a population at substantially elevated CVD risk, and achieving clinically meaningful reductions in BMI and central adiposity, reflected by WHtR, has emerged as a treatment goal beyond glycemic control alone. The WHtR threshold of 0.53 and BMI target < 27 kg/m2 have been proposed as more sensitive indicators of cardiometabolic risk than traditional weight-based metrics, particularly in populations with metabolic comorbidities.

Semaglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, is approved at 2.4 mg weekly (Wegovy, Novo Nordisk) for chronic weight management in adults with obesity or overweight with weight-related comorbidities, and at 1 mg and 2 mg weekly (Ozempic) for glycemic control in T2D and cardiovascular risk reduction.

On March 19, 2026, the US Food and Drug Administration approved higher dose semaglutide injection 7.2 mg to be used along with a reduced calorie diet and increased physical activity to help adults with obesity lose weight and keep it off, provided they have tolerated the 2.4 mg dosage for ≥ 4 weeks and additional weight reduction is clinically indicated. The approval was based on the results from the STEP UP trial program.

The 7.2 mg dose evaluated in the present research represents an investigational escalation intended to assess whether a higher dose can extend the magnitude of weight and metabolic benefit. The data presented at ADA suggest a dose-dependent response pattern, though the absolute gap in target achievement compared with the non-T2D population underscores the challenge of normalizing metabolic parameters in the setting of T2D.

Limitations and Unmet Need in the T2D Population

The investigators acknowledged that despite the dose-dependent numerical advantage observed with semaglutide 7.2 mg, overall BMI and WHtR target achievement in STEP UP T2D remained lower than that seen in STEP UP, which enrolled participants without diabetes. This finding highlights the continued unmet need in a population in which metabolic disease burden, longer T2D duration, and existing CVD risk complicate weight and cardiometabolic normalization.

Le Roux C, Das S, Harder-Lauridsen NM, et al. STEP UP T2D: Participants Achieving BMI, WHtR, and Cardiometabolic Treatment Targets. Presented at American Diabetes Association (ADA) Scientific Sessions 2026 in New Orleans, Louisiana, June 5-8, 2026.

Ashwell M, Gibson S. Waist-to-height ratio as an indicator of 'early health risk': simpler and more predictive than using a 'matrix' based on BMI and waist circumference. 

Phase 3 STEP UP T2D randomized 512 adults with obesity and established T2D (mean HbA1c 8.1%, duration 8.4 years) to once-weekly semaglutide 7.2 mg, 2.4 mg, or placebo for 72 weeks. No participants met proposed anthropometric targets at baseline (BMI <27 kg/m²; WHtR <0.53). At week 72, semaglutide 7.2 mg yielded numerically higher attainment of BMI/WHtR targets and low-risk thresholds for glycemia, HDL-C, blood pressure, and triglycerides versus 2.4 mg or placebo, though responses lagged non‑T2D cohorts. The 7.2 mg dose was recently FDA-approved for obesity after 2.4 mg tolerance.

STEP UP T2D data suggest semaglutide 7.2 mg's superiority to the 2.4 mg dose and placebo for proposed BMI and waist-to-height ratio treatment targets.

Type 2 Diabetes

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Semaglutide 7.2 mg Shows Greater BMI, Cardiometabolic Gains in T2D

A novel electronic health record (EHR)-based machine learning model accurately identified adults at high risk of developing 

 up to a decade in advance, according to data presented at the 

2026 Scientific Sessions of the American Diabetes Association (ADA)

According to the US Centers for Disease Control and Prevention, more than 40 million people in the US have diabetes, and about 90% to 95% of them have type 2 diabetes. People with overweight, who are physically active less than 3 times a week, are 45 years of age or older, and have prediabetes are at an increased risk of developing type 2 diabetes.

EHR-based machine learning model predicted type 2 diabetes risk at 1, 3, and 10 years in 3,365,464 adults at Kaiser Permanente Northern California

Training AUC 0.886 (95% CI, 0.883–0.888); validation AUC 0.883 (95% CI, 0.88–0.886)

At a high-risk threshold of >1.2%, sensitivity was 74% and specificity was 82% over up to 10 years of follow-up

Authors plan prospective clinical testing to evaluate impact on prevention program enrollment and diabetes incidence

Current diabetes prevention programs cannot serve the more than 60% of US adults carrying type 2 diabetes risk factors. The gradual, often asymptomatic progression of the disease limits the ability of health systems to prioritize prevention resources toward the patients most likely to benefit. As such, the present model represents a potential step toward more precise, scalable risk stratification using data already available in the clinical record.

"These findings represent a potential advancement over existing approaches for identifying individuals at risk of developing type 2 diabetes by enabling earlier, more precise detection and supporting a more targeted, proactive approach to prevention," Luis Rodriguez, PhD, MPH, RD, lead author of the study, said in a statement.¹ "Our model has the potential to create an opportunity for clinicians and health systems to focus prevention efforts on the high-risk individuals often missed by traditional screening who have the most to gain from prevention and treatment."

The retrospective cohort study analyzed 3,365,464 adults aged 18 to 70 receiving care at Kaiser Permanente Northern California between 2012 and 2024. Median patient age was 39 years, and 55% were female.

Researchers applied a hazard-based super learning approach combining multiple survival-analysis models to generate individualized risk estimates at 1, 3, and 10 years. Input variables included routine clinical and demographic data collected at medical visits including age, weight, blood glucose levels, medical history, and medications, as well as publicly available social determinants data including access to healthy food and walkable areas.

During a median follow-up of 5.4 years, the observed type 2 diabetes incidence was 10.7 per 1,000 person-years.

The training model achieved an area under the curve (AUC) of 0.886 (95% CI, 0.883 to 0.888), with the validation model scoring 0.883 (95% CI, 0.88 to 0.886). 1-year calibration was near-ideal, with a mean predicted risk of 1.03% against an observed rate of 1.01%.

At the threshold defining high risk, set at > 1.2% risk, the model demonstrated a sensitivity of 74% and a specificity of 82% over up to 10 years of follow-up.

These performance characteristics suggest the model can meaningfully discriminate between high- and lower-risk individuals using data clinicians already collect, without requiring additional testing or referrals. The combination of routinely captured EHR variables with socioeconomic context data allows risk estimation to account for structural factors influencing diabetes development beyond traditional clinical metrics.

Investigators described plans to test the model prospectively in a clinical setting to assess whether its deployment increases engagement in type 2 diabetes prevention programs and reduces incidence.

American Diabetes Association. Machine learning model accurately predicts long-term risk of type 2 diabetes. Published June 5, 2026. Accessed June 5, 2026. 

Rodriguez LA. Machine-learning modeling for T2DM prediction in over 3 million adults [abstract 2321-P]. Presented at the American Diabetes Association (ADA) 2026 Scientific Sessions in New Orleans, LA, June 5-8, 2026.

US Centers for Disease Control and Prevention. Type 2 Diabetes. May 15, 2024. Accessed June 5, 2026. 

https://www.cdc.gov/diabetes/about/about-type-2-diabetes.html

A hazard-based super learning model leveraging routinely captured EHR data plus social determinants reliably predicted type 2 diabetes risk up to 10 years before onset in a large integrated health system. In 3.37 million adults aged 18–70, discrimination was strong (AUC ~0.88) with near-ideal 1-year calibration. Using a >1.2% high-risk threshold, sensitivity reached 74% and specificity 82% over long-term follow-up. The approach aims to enable scalable, earlier risk stratification to better target prevention resources and engagement.

An EHR-based machine learning model predicted 10-year type 2 diabetes risk with near-ideal calibration in over 3 million adult patients.

Machine Learning Model Predicts Type 2 Diabetes Risk Up To 10 Years Out

Tim Smith joined the MJH Life Sciences team in 2022 and is currently an associate editor for HCPLive. He is also the executive producer of two podcasts: Skin of Color Savvy and The Medical Sisterhood. Previously, he was the producer of the Lungcast podcast. He graduated from UC Berkeley and worked in multimedia journalism as a staff writer prior to joining MJH. You can contact him at tsmith@mjhlifesciences.com.

New data suggest a well-performing foundation artificial intelligence (AI) model, despite surpassing those with under 3 years of 

 experience, will still not outperform physicians in dermatology with more than 10 years of experience.

A modern AI foundation model attained higher diagnostic accuracy than doctors with fewer than 3 years of dermatology experience (72.2% vs 68.2%; 

Physicians with more than 10 years of experience showed the highest overall level of diagnostic accuracy (74.2%), outperforming all of the AI models assessed.

The results highlight the potential of AI as a diagnostic support tool while underscoring its current limitations versus expert clinicians in real-world skin lesion diagnoses.

Despite the strong performance of AI applications in skin cancer detection research, these new data suggest routine clinical practice may still require experienced dermatologists, particularly when assessing the wide array of lesions in real-world scenarios.

 The findings were authored by investigators such as Julien Anriot, MD, of Claude Bernard University Lyon in France.

Anriot and coauthors’ multi-institutional diagnostic analysis involved a comparison between the performance of several AI-based diagnostic systems with that of physicians who varied in their levels of dermatologic experience with skin lesions.

Their analysis was created to evaluated whether AI models could accurately classify such lesions in a specific dataset intended to reflect everyday clinical settings, including uncommon and diagnostically challenging lesions.

1117 dermatologic cases were included by Anriot et al and their data gathered in the timeframe between March 2023 - August 2025. The investigators’ dataset included both clinical and dermoscopic images along with metadata linked with these images. The team looked at 3 AI systems, including a first-generation convolutional neural network (CNN) as well as 2 newer foundation models, PanDerm unimodal and PanDerm multimodal.

These models’ performance was compared with that of 652 physician readers, all of whom provided a total of 1092 testing iterations. Those assessed represented a plethora of levels of diagnostic experiences, from less than 1 year of dermatology experience to more than 10 years in practice. 33 years of age was the median participant age (interquartile range [IQR], 29-37 years). Additionally, Anriot and colleagues noted 85.7% were reported as female.

The main endpoint of their study was multi-class diagnostic accuracy for classification of patients’ skin lesions. The investigators’ secondary endpoints included specificity, sensitivity, and balanced accuracy for distinguishing benign lesions from malignant lesions.

How Does AI Perform Against Clinicians in Skin Lesion Diagnosis?

Overall, the investigative team concluded all physician readers outperformed the CNN model which was older. Human readers were found to have a mean (SD) diagnostic accuracy level of 65.9% (10.5%) as opposed to 56.7% (3.9%) for the CNN. This was noted by the team to be a statistically significant difference (

Among the AI systems assessed by Anriot and coauthors, the PanDerm unimodal foundation model showed the greatest level of performance. The model achieved a mean (SD) accuracy of 72.2% (3.5%), described as significantly exceeding the performance of physicians with under 3 years of dermatology experience, who attained a mean (SD) accuracy of 68.2% (7.6%). Anriot et al noted the 4.0-percentage-point difference had statistical significance (95% CI, 3.2-4.9 percentage points; 

Despite this conclusion, the investigators still noted the highest overall diagnostic accuracy among the most experienced physicians with around 10 years of experience. Those who had more than 10 years of experience attained a mean (SD) multi-class diagnostic accuracy of 74.2% (5.7%), outperforming all evaluated AI models on the study's primary endpoint. By comparison, rates of lesion accuracy were 72.2% (3.5%) for the PanDerm unimodal model, 66.3% (3.8%) for the PanDerm multimodal model, and 56.7% (3.9%) for the CNN.

What Do These Data Suggest Regarding AI versus Physician Performance?

Anriot and colleagues’ data found the newer foundation-model AI systems may be capable of matching the diagnostic performance of clinicians with 3 - 10 years of experience. The AI models’ performance surpassing those with less than 3 years of experience.

At the same time, these data were noted to underscore the continued value of expert clinical judgment. They highlighted a lack of AI models in this analysis with performance exceeding that of dermatologists with more than a decade of experience. The team concluded while modern AI models show promise as support tools in the dermatology space, notable gaps do persist between algorithmic performance and expert-level clinical evaluations.

“Future practice should integrate human-AI collaboration, with AI supporting less experienced clinicians and providing expert triage assistance and help to minimize fatigue-related diagnostic errors,” Anriot and colleauges wrote.

Anriot J, Yan S, Coste C, et al. Limits of Artificial Intelligence Models for Skin Cancer Diagnosis in Realistic Settings. JAMA Dermatol. Published online June 03, 2026. 

Smith T. Where Teledermatology is Today: Expanding Access and Navigating Limits. HCPLive. March 13, 2026. Accessed June 5, 2026. 

https://www.hcplive.com/view/ai-in-dermatology-discussing-downsides-to-the-adoption-of-artificial-intelligence

A multi-institutional diagnostic analysis compared three AI systems (a legacy CNN and two PanDerm foundation models) with 652 physicians across 1,117 real-world dermatology cases containing clinical/dermoscopic images and metadata, including uncommon, challenging lesions. Physicians outperformed the CNN, while PanDerm unimodal achieved the best AI accuracy and exceeded readers with <3 years’ experience. However, dermatologists with >10 years’ experience achieved the highest multi-class accuracy, surpassing all tested AI models. Results support AI as an adjunct for triage and decision support.

New findings point to remaining gaps in AI diagnostic success for skin lesions compared with experienced clinicians in dermatology.

Dermatology

The HCPLive dermatology page is a resource for medical news and expert insights on skin disease. This page features expert-led coverage, articles, videos and research on the therapies and development of treatments for dermatologic conditions, and more.

Experienced Physicians Outperform AI Model in Skin Lesion Diagnoses

Alexis Marcotte-Chénard, PhD, is a professor at the University of Quebec in Outaouais.

Brief, Regular Movement Breaks Can Improve Cardiovascular Risk in T2D

 who are largely sedentary can improve cardiovascular risk factors through brief movement breaks, or “exercise snacks,” according to a recent study.

American Diabetes Association (ADA) Scientific Sessions 2026

 in New Orleans, Louisiana, by Alexis Marcotte-Chénard, PhD, a professor at the University of Quebec in Outaouais, this study reflects a much more accessible method of improving the ABCs of diabetes – hemoglobin A1c, blood pressure, and LDL cholesterol.

“If you tell a patient that they should do 150 minutes a week of moderate to vigorous physical activity, and they’re not active, then it seems like a lot,” Marcotte-Chénard told 

 in an exclusive interview. “But if you tell them to do 4 or 5 minutes a day, spread throughout the day, while you’re doing different activities, then many people would find this more accessible than doing just a long bout of physical activity.”

Marcotte-Chénard and colleagues conducted the present study to examine the impact of a 12-week movement break intervention on the ABC target and its collective components. A total of 69 adult patients with T2D, of whom 46 were female, were randomly assigned to movement breaks performed at either a high intensity (n = 31) or low intensity (n = 38).

The team instructed patients to complete 4 1-minute movement breaks per day for ≥5 days per week, with a total goal of ≥20 movement breaks per week. Patients were assigned the intervention via a smartphone application, which provided customizable prompts and demonstration videos for each movement break. Marcotte-Chénard and colleagues measured ABC target components before and after the 12-week intervention.

Ultimately, the proportion of patients achieving their targets increased from 72% to 79% (

 = 0.02) for A1c and from 54% to 61% for blood pressure (

 = .04). However, the proportion of patients achieving their LDL-C targets was unchanged (49 to 53%; 

 = .37; all main effects of time). Additionally, the proportion of patients achieving the triple ABC target was unchanged from baseline to post-intervention (21 to 27%; 

When treated as continuous variables, Marcotte-Chénard and colleagues found a main effect of time for systolic (-5.4 [-8.3 to -2.5] mmHg, 

 <.001) and diastolic (-3.5 [-5.2 to -1.8] mmHg, 

 <.001) blood pressure, with no difference between groups (

 ≥.015). No main effects of time or between-group differences were found for A1c or LDL-C (all 

Ultimately, the team concluded that these “exercise snacks” have the capacity to improve cardiovascular risk markers when performed regularly, regardless of intensity. Marcotte-Chénard and colleagues do, however, note the need for further research into the broader applicability of these findings, as well as how they could be implemented in a clinical setting.

“I think it all depends on the patient’s preference and their physical capacity,” Marcotte-Chénard said. “A typical movement break should be something that a patient could perform well in their environment. For intensity, I would say moderate to vigorous intensity. And for frequency, I would say that the best way for people living with type 2 diabetes would be after a meal.”

Editors’ Note: Marcotte-Chénard reports no relevant disclosures.

Marcotte-Chénard M, Sandilands R, Babir F, et al. Movement Breaks Improve Cardiovascular Risk Factors in Adults Living With Type 2 Diabetes: A Randomized Trial. Abstract presented at the American Diabetes Association (ADA) Scientific Sessions 2026, New Orleans, LA. June 5-8, 2026.

Zhu Y, Lin L, Zhang J, et al. Age-stratified associations of glycemia, blood pressure, and cholesterol with mortality in diabetes: A prospective cohort study. BMC Med. Published online May 30, 2026. 

Videos

Alexis Marcotte-Chénard, PhD, discusses his presentation on “exercise snacks” and how they can lower blood pressure and improve HbA1c in sedentary patients.

From hepatitis D's first FDA-approved therapy to advances in hepatitis B and PBC, May delivered major developments in hepatology.

Hepatology

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Hepatitis C page, resources on the topics of medical news and expert insight into HCV can be found. Content includes articles, interviews, videos, podcasts, and breaking news on hepatitis C virus research, treatment, and drug development.

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5 Hepatology Headlines You Missed in May 2026

Chelsie Derman is an associate editor for 

who covers allergy and  psychiatry. She joined MJH Life Sciences in September 2023 after graduating from The College of New Jersey with a bachelor’s degree in journalism and professional writing. In her free time, she enjoys creative writing and reading.

From FDA-approved at-home TMS and accelerated protocols to conversational AI matching group therapy outcomes, here are 5 psychiatry updates from May every clinician should know.

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5 Psychiatry Headlines You Missed in May 2026

ACCESS phase 2b data show aleniglipron 120 mg produced 11.3% placebo-adjusted weight loss at 36 weeks, with continued reductions in the OLE.

ACCESS: Aleniglipron Achieves Up To 11.3% Weight Loss in Phase 2b Obesity Trial

Vleugels discusses Part B findings on investigational IL-13 inhibitor zumilokibart (APG777) in adult patients with moderate-to-severe atopic dermatitis.

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Atopic Dermatitis condition center page, audiences can find a variety of coverage mediums for medical news and updates, including articles, video interviews, podcasts, and breaking news on dermatitis research, treatment, and drug development.

Q&A: Zumilokibart Effective for Atopic Dermatitis, With Ruth Ann Vleugels, MD, MPH, MBA

Rachael Sood, NP, is a diabetes nurse practitioner and Owner and CEO of The Diabetes Collective.

Sood discusses emerging diabetes technologies, from automated insulin delivery to dual glucose-ketone monitoring, at ADA 2026.

Type 1 Diabetes

Diabetes Technology at a Turning Point: What’s New and What’s Next, With Rachael Sood, NP

Ryan Livingston joined the MJH Life Sciences team in March of 2025, shortly after graduating from TCNJ with a bachelor’s degree in English. He enjoys creative writing, reading, and scuba diving. He can be reached at rlivingston@mjhlifesciences.com.

Teplizumab, which has recently seen a substantial label expansion into pediatric patients, is matching its safety and efficacy data in the clinic.

TEPLI-REAL: First Real-World Study Confirming Teplizumab Treatment Patterns in T1D

From a one-time CRISPR therapy cutting HAE attacks by 87% to CSU biomarkers and emerging ILIT research, here are the 5 allergy stories worth catching up on from May 2026.

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5 Allergy Headlines You Missed in May 2026

Learn how clinicians reach ultra-low LDL targets in high-risk patients using ezetimibe, PCSK9s, and smarter insurance documentation.

Navigating Access, Shared Decision-Making, and Long-Term Adherence in ASCVD

Why patients quit cholesterol therapy—and how LDL checkups, reminders, and longer-acting injectables improve lifelong medication adherence.

Personalizing Therapy Across High-Risk Conditions and Comorbidities in ASCVD

Pediatric Dermatologist at Children’s Hospital Colorado

In the final segment, the clinicians evaluate the guideline's nuanced positioning of phototherapy relative to newer systemic agents and highlight a gap.

Latest News

High-Dose Semaglutide Preserves Kidney Function, Reduces Inflammation in Obesity Without Diabetes

Semaglutide 7.2 mg Shows Greater BMI, Cardiometabolic Gains in T2D

Machine Learning Model Predicts Type 2 Diabetes Risk Up To 10 Years Out

Experienced Physicians Outperform AI Model in Skin Lesion Diagnoses

Brief, Regular Movement Breaks Can Improve Cardiovascular Risk in T2D