Diabetes, Obesity and Metabolism

Background: GLP-1 receptor agonists (GLP1-RAs) are an established treatment for type 2 diabetes mellitus (T2DM) and obesity. Their widespread use is set to increase through both indication expansion and patent expiry. As well as efficacy, it is crucial to understand the safety of this drug class to enable optimal use. Here we demonstrate how a genetic approach can augment signal-detection and post-market authorization surveillance. Methods: We used single nucleotide polymorphisms (SNPs) in GLP1R to recapitulate the effect of agonism with GLP1RAs on circulating glucose, glycated hemoglobin (HbA1c), body mass index (BMI) and risk of type 2 diabetes (T2DM) using Mendelian randomisation. We then tested if the adverse effect highlighted by medicines regulators of pancreatitis and the emerging effect of sarcopenia were causally related to GLP1R agonism, using this approach. Analyses were conducted in UK biobank and replicated in FinnGen and All of Us, results being combined using meta-analysis. Analyses were further stratified by a priori risk factors of age and alcohol consumption. Results: Genetically proxied GLP-1R agonism was associated with a reduction in glucose (exp({beta}) = 0.95 95% CI [0.94, 0.97]), HbA1c (exp({beta}) = 0.94 95% CI [0.92, 0.95]), and BMI (exp({beta})=0.98 95% CI [0.97, 0.99]); and a reduced risk of T2DM (OR = 0.82 95% CI [0.79 to 0.86]). Risk of acute and chronic pancreatitis was however increased (OR = 1.10 95% CI [1.01 to 1.20] and OR = 1.05 95% CI [0.95, 1.17], respectively), which varied as a function of age with risk most pronounced in those aged 50-59 years-old (OR = 1.79 95% CI [1.43, 2.24], OR = 1.57 95% CI [1.16, 2.12]) and in drinkers (OR = 1.32 95% CI [1.12, 1.54], OR = 1.36 95% CI [1.12, 1.65]). Risk of sarcopenia also increased (OR 1.34; 95% CI 1.05,1,71). Conclusions: Genetically proxied agonism with GLP-1RAs recapitulated the pharmacological effects of GLP1-1RAs on glycaemic traits, BMI and T2DM risk. This approach supports a causal effect of GLP-1RAs on the well reported adverse effects of pancreatitis and further indicates age and alcohol consumption as risk modifying effects. The less well reported but emerging effect of sarcopenia appears to also be casually related to agonism at GLP-1R. These analyses suggest a genetic approach could be used as an adjunct to signal detection studies to enhance safety regulation as well as personalisation of the use of these drugs.

Aims/Hypothesis: Behavioral and phenotypic characteristics do not fully explain variability in African Americans with youth-onset type 2 diabetes (Y-T2D) treated with metformin with or without liraglutide. We hypothesized that biological heterogeneity, including genetic variation in the metformin transporter OCT1, influences metformin pharmacokinetics and hepatic glucose flux. Therefore, we sought to characterize metformin pharmacokinetics in Y-T2D and evaluate genetic variants known to modulate metformin efficacy in adults to determine the mechanisms underlying variation in treatment response. Methods: We evaluated genetic variants related to metformin transport and mechanisms of action in 30 Y-T2D using a candidate-gene approach to evaluate the association of pharmacogenetic variants with fasting glucose and gluconeogenesis. In a subset of Y-T2D randomized to 3 months of metformin (n=11) or metformin and liraglutide (n=8), we constructed a metformin population pharmacokinetic model and evaluated gene variant associations. Results: A one-compartment first-order absorption and elimination pharmacokinetic model provided the optimal fit. Metformin pharmacokinetic parameters were similar by group and not related to glycemia. The rs628031_OCT1 A allele was associated with greater metformin clearance. The rs622342_OCT1 C allele was associated with lower post-treatment fractional gluconeogenesis ({beta} [95% CI] = -8.8 [-14.13, -3.47] %, Adjusted R2 = 0.56, P = 0.003). The rs7903146_TCF7L2 T allele was associated with greater reductions in fasting glucose among those treated with metformin + liraglutide ({beta} = -1.32 [-2.42, -0.22] mmol/L, Adjusted R2 = 0.8, P<0.002), but baseline glucose and gluconeogenesis (P<0.0001) were the strongest predictors of post-treatment glycemia. Conclusion/interpretation: In Y-T2D, OCT1 gene variants rs628031 and rs622342 were associated with metformin clearance and gluconeogenesis, respectively. TCF7L2 variant rs7903146 may contribute to differences in glycemic response in youth treated with metformin and liraglutide. These findings suggest genetic variants may be important for understanding variable metformin response in Y-T2D.

Objectives: Diabetes affects over 500 million people globally and glycemia is inadequately managed. Metformin is the most frequently prescribed initial treatment for type 2 diabetes globally, yet glycemic response trajectories to metformin in routine real-world care and predictors of treatment response have not been well described. We aimed to identify glycemic response trajectories in adults prescribed metformin monotherapy as initial type 2 diabetes treatment and predictors of poor glycemic response to metformin. Design: Observational cohort study using latent class mixed models to identify hemoglobin A1c (HbA1c) trajectory classes, followed by random forests machine learning to predict trajectory class membership. Setting: US Veterans Affairs Healthcare System Participants: Adults treated with metformin alone for >30 days after diabetes diagnosis with a minimum of two HbA1c measurements from 90 days prior to two years after the first metformin prescription (N=140,413). Exposures: Demographic, laboratory, vital sign, and comorbidity data were included as predictors of metformin response trajectory Main Outcomes and Measures: We included all HbA1c measurements (487,604 total) for two years after metformin initiation to define metformin glycemic response trajectories. Results: We identified three HbA1c trajectories: stably low (89.7% of sample, mean HbA1c decrease from 7.2% to 6.6%), brisk response (7.1% of sample, mean HbA1c decrease from 11.4% to 7.0%), and non-response (3.1% of sample, mean HbA1c increase from 8.9% to 10.8%). Of those in the stably low and brisk response classes at 2 years, 91% maintained HbA1c at approximately 7% on metformin alone for 5 years after drug initiation. Prediction models could accurately predict brisk response (91% accuracy) but not metformin non-response (59% accuracy). Conclusions: Most individuals treated initially with metformin monotherapy have a beneficial and durable glycemic response. Predicting individuals who will not respond to metformin may be challenging but is evident within six months with recommended glycemic surveillance. The findings support current guidelines for HbA1c surveillance when initiating diabetes treatment.

Background Chronic gastritis and duodenitis (CGD) are highly prevalent among patients with type 2 diabetes (T2D). However, the prognostic impact of their comorbidity and the potential role of MRI-derived phenotype-tailored dietary strategies remain unclear. Methods This prospective cohort study included 453,768 UK Biobank participants. Primary endpoints were myocardial infarction, stroke, end-stage renal disease (ESRD), dementia, Parkinson's disease, and all-cause mortality. Time-dependent multivariable Cox regression assessed outcome associations, while additive interaction analyses evaluated synergistic effects between T2D and CGD. Eight healthy dietary pattern scores were analyzed. Latent profile analysis classified MRI-derived body composition phenotypes based on fat distribution and organ volume. Results T2D and CGD were positively associated, and their comorbidity increased risks of cardiovascular events, ESRD, dementia, and all-cause mortality. Additive interaction analyses demonstrated synergistic effects on myocardial infarction and all-cause mortality. The comorbidity was further associated with aggravated lipid metabolic abnormalities and multiorgan atrophy. Higher adherence to the Healthful Plant-Based Diet Index (HPDI) and Dietary Approaches to Stop Hypertension (DASH) diets attenuated the excess mortality risk related to this synergy. Dietary associations varied across T2D, CGD, and comorbid populations, while MRI-based latent profiles modified diet-outcome relationships. A nomogram integrating demographic, dietary, and body composition data demonstrated reliable long-term predictive performance for myocardial infarction, stroke, and all-cause mortality. Conclusions Comorbid T2D and CGD substantially increase adverse clinical risks and exhibit synergistic effects on myocardial infarction and all-cause mortality. These findings support routine CGD screening in T2D care and provide population-based evidence for MRI-derived phenotype-tailored dietary strategies.

Importance: Despite the benefits of statin therapy in individuals with diabetes, fewer than 70% of adults with diabetes meet contemporary guidelines for statin therapy and reducing low-density lipoprotein cholesterol (LDL) to <100 mg/dL. Evidence describing delays in statin initiation after diabetes diagnosis and associated clinical outcomes may motivate process of care interventions to improve guideline recommended care in individuals newly diagnosed with type 2 diabetes mellitus (T2D). Objective: To examine the timing of statin initiation and achievement of LDL <100 mg/dL after diabetes diagnosis, and to determine the association of early LDL reduction among statin initiators with incident atherosclerotic cardiovascular disease (ASCVD). Design: Retrospective observational cohort study using data from 2005-2021 Setting: Veterans Affairs Health Care System (VA) Participants: Individuals with newly diagnosed T2D Exposure: Primary exposure was ASCVD risk based on ACC/AHA Pooled Cohort Equations; secondary exposure was LDL <100 mg/dL in the first year after T2D diagnosis among statin initiators Main Outcomes and Measures: Co-primary outcomes were initiation of statin therapy and achievement of LDL <100 mg/dL within 5 years of diabetes diagnosis; incident 5-year ASCVD was a secondary outcome. Results: Among 100,406 individuals with newly diagnosed T2D, 59,615 were prescribed statin therapy within five years (59.4%), and 44,783 (57.5%) of those with LDL above goal achieved LDL <100 mg/dL within 5 years. Relative to those at low (<7.5%) 10-year ASCVD risk, individuals at intermediate (7.5-20%) and high (>20%) risk were more likely to be initiated on a statin (intermediate: Hazard Ratio [HR] 1.14 [95% CI 1.11, 1.17]; high: HR 1.16 [95% CI 1.13, 1.19]) and to achieve LDL <100 mg/dL (intermediate: HR 1.23 [95% CI 1.19, 1.26]; high: HR 1.34 [95% CI 1.30, 1.38]). Among those prescribed a statin within one year of diabetes diagnosis, achieving LDL <100 mg/dL in the first year after diabetes diagnosis was associated with lower risk of 5-year incident ASCVD (HR 0.84 [95% CI 0.77, 0.92]). Conclusions and Relevance: Gaps in guideline-directed primary prevention of ASCVD arise early following initial diabetes diagnosis. Guideline recommended early LDL lowering among statin initiators was associated with improved clinical outcomes.

Objective: Despite the complex and non-linear progression of diabetes, its shared pathways with atherosclerotic cardiovascular disease (ASCVD) are conventionally described using models based on single time points. We identified longitudinal diabetes clusters before diagnosis using deep learning and studied their association with ASCVD events and mortality. Methods: We analyzed 157,670 visits from 15,871 adults (25-65 years) without diabetes from four pooled U.S. cohorts (median follow-up: 22 years [IQR: 9-30]). A gated recurrent unit model with decay (GRU-D) was used to predict 1-year risk of diabetes or censoring within 10 years, by learning longitudinal embeddings across 25 clinical characteristics and biomarkers. Parallel Factor Analysis-2 (PARAFAC-2) and Gaussian mixture models (GMM) were used to group longitudinal participant representations as clusters. Landmark time Cox proportional hazards regressions, relative to last observation in the training window, were used to study covariate-adjusted associations of clusters with ASCVD and mortality. Prognostic utility of clusters beyond the PREVENT risk score was assessed using Harrell's C-index. Findings were replicated in a fifth cohort. Results: The analytic sample was aged 49 years [SD: 11], 58% female, and 68% white; 1,202 (8%) developed diabetes within the first 10 years. We identified five clusters (Cluster A to E) that differed in their clinical characteristics over time. Cluster E (46%) had the highest cumulative incidence of diabetes in the study period, followed by Cluster C (40%) and Cluster A (38%). Cluster C, which was defined by older age, high blood pressure, and suboptimal renal function at the first visit, had higher rates of ASCVD (HR: 1.09, 95%CI: 0.98-1.21) and mortality (HR: 1.08, 95%CI: 1.00-1.16), relative to Cluster A despite being similar in age and BMI at the first visit. Relative to Cluster A, all other clusters had similar or lower rates of ASCVD and mortality. We observed substantial cluster effects for three clusters (Clusters C to E), which were based on only two cohorts. The two clusters (Clusters A and B) that included participants from all four cohorts were reproduced in the fifth cohort and showed similar rates of outcomes. Clusters did not improve ASCVD prognosis, relative to a model that included only the PREVENT risk score. Conclusions: Longitudinal clusters reveal substantial heterogeneity in the period before diabetes diagnosis, and their risk for ASCVD and mortality. However, clusters discovered may, in part, be explained by cohort effects from variations in recruitment and visit patterns after recruitment.

Background Lifestyle interventions can increase the probability of remission of prediabetes to normal glucose tolerance, but their economic value remains unclear. We assessed the within-trial and lifetime-horizon modeled cost-effectiveness of intensive and conventional lifestyle interventions in risk-stratified participants with prediabetes. Methods A health economic evaluation was conducted alongside the 12-month multicenter PLIS trial (n=1,105). High-risk participants were randomized to intensive (HR-INT) or conventional (HR-CONV); low-risk participants to conventional lifestyle intervention (LR-CONV) or control (only short single consultation; LR-CTRL) with risk stratification based on insulin secretion, insulin sensitivity, and liver fat content. Within-trial analyses estimated incremental costs per additional remission to normoglycemia and per quality-adjusted life year (QALY). Lifetime cost-effectiveness was modelled using a four-state Markov Model. Findings At 12 months, HR-INT and LR-CONV increased remission compared with their respective comparators. The incremental cost per additional remission was {euro}7,081 (95% CI: dominated-47,277) for HR-INT and {euro}4,278 (1,312-11,793) for LR-CONV from a health insurance perspective. A willingness-to-pay of {euro}22,000 (HR-INT) and {euro}7,500 (LR-CONV) per additional remission corresponded to 90% probability of cost-effectiveness. Neither intervention was cost-effective in terms of QALYs gained within the 12-months period. Lifetime modelling suggested that both HR-INT and LR-CONV are not only cost-effective, but also cost-saving, relative to HR-CONV and LR-CTRL, respectively. Also in the probabilistic sensitivity analysis, most simulations indicated dominance (71.7% for HR and 88% for LR). Interpretation Based on short-term economic evaluation, the interventions assessed were cost-effective regarding additional participants with remission, not for incremental QALYs gained. Lifetime modelling suggests cost savings for both risk groups. Targeting populations with lifestyle interventions to achieve prediabetes remission seems to generate good value for money in the long term.

Objective: Patients and physicians frequently focus on HbA1c and weight alone. We hypothesized that individuals with similar HbA1c and BMI may present markedly distinct metabolic backgrounds. We investigated whether the adipo-B index- composite of adipose insulin resistance (adipo-IR) and beta-cell function (HOMA-B)-can uncover hidden heterogeneity in this clinically homogeneous population. Methods: A total of 399 newly diagnosed, drug-naive Japanese subjects with T2DM were analyzed. Histograms of HbA1c and BMI demonstrated peak distributions within HbA1c 8-10% and BMI 24-26. Based on these distributions, a clinically homogeneous subgroup was defined to minimize confounding by glycemic severity and adiposity. Metabolic parameters including FBG, insulin, FFA, HOMA-R, HOMA-B, adipo-IR, adipo-B, T-C, TG, HDL-C and non-HDL-C were analyzed. Simple regression, multivariable linear regression, and subgroup stratification analyses were performed. Results: Despite comparable HbA1c and BMI by design, adipo-B stratification revealed significant differences in HOMA-B, FFA, non-HDL-C, and TG, whereas HOMA-R stratification identified only higher insulin and adipo-IR without differences in lipids or HOMA-B. Thus, adipo-B-but not HOMA-R-identified a lipotoxic, beta-cell-stressed phenotype invisible to conventional markers. Simple regression showed significant positive correlations between adipo-B and HbA1c, FBG, FFA, T-C, TG, and non-HDL-C, and negative correlations with insulin and HOMA-B. Multivariable linear regression confirmed that adipo-B was independently associated with non-HDL cholesterol, TG, and FFA after adjustment for HbA1c and BMI. Conclusion: Even among patients with identical HbA1c and BMI, the adipo-B index uncovers clinically relevant metabolic heterogeneity, supporting its role as a functional marker of the adipose-pancreas axis and a potential tool for precision phenotyping in early T2DM.

Background: Consensus continuous glucose monitoring (CGM) metrics, including time in range (TIR), time above range (TAR), time below range (TBR), mean glucose, glucose management indicator, and glycemic variability, are essential for modern glucose assessment. However, these whole-day summaries do not explicitly partition nocturnal basal from daytime ambulatory glycemic burden. Objective: To develop and evaluate a complementary domain-based CGM framework that quantifies basal and daytime ambulatory glycemic exposure across oral glucose tolerance test (OGTT)-derived dysglycemia phenotypes. Methods: In this observational, clinic-based study, 253 individuals underwent OGTT with insulin measurement and CGM. Participants were classified using a prespecified OGTT-derived phenotyping algorithm, implemented through a deterministic rules-based web calculator, and collapsed into five groups: NoDM, Increased insulin resistance, Midzone Glycemia, Prediabetes, and Diabetes. CGM files were uniformly reprocessed by selecting the latest contiguous episode and retaining the most recent 15 calendar days with data. The 24-hour profile was partitioned into nocturnal basal (00:00 to <06:00) and daytime ambulatory (06:00 to <24:00) domains. Derived indices included Area of Basal Glycemia (ABG), Area of Prandial/Daytime Ambulatory Glycemia (APG), incremental ABG (iABG), incremental APG (iAPG), and exploratory deficit indices dABG and dAPG. Results: The final dataset contributed 3,647 analyzable CGM days. APG remained higher than ABG across all groups. Mean ABG/APG increased from 80.45/86.38 mg/dL in NoDM to 111.96/124.70 mg/dL in Diabetes. Mean iABG/iAPG increased from 5.65/6.60 to 34.12/38.91 mg/dL, whereas dABG/dAPG declined as dysglycemia worsened. Conclusions: The ABG/APG framework provides interpretable, domain-resolved CGM burden metrics that separate basal from daytime ambulatory exposure and distinguish total burden from above-threshold excess. These indices are proposed as adjunctive metrics to support dysglycemia phenotyping, early risk recognition, and treatment monitoring, but are not intended to replace established consensus CGM metrics or diagnostic criteria. External, prospective validation is required.

Aims CGM devices report glucose only within fixed limits (typically 40-400 mg/dL; 2.2-22.2 mmol/L), truncating extreme values to a boundary ("capping"). We characterised prevalence, duration, and consequences of capping in type 1 diabetes trial data. Materials and Methods We analysed 46,990,617 CGM readings from 948 participants across four publicly available clinical trial datasets (Dexcom G4 Platinum or G6 sensors). Capping prevalence, run duration, and associations with age, HbA1c and sex were characterised across all datasets. In the 77 participants of the Replace-BG trial CGM-plus-blood glucose monitor (BGM) arm, CGM-derived metrics were compared with contemporaneous BGM measurements across 1,162 non-overlapping 14-day windows. Results Between 93.5% and 100% of participants had at least one capped reading, and capped values comprised 0.47-0.98% of all readings. In the three datasets for which duration could be calculated, over 70% of upper-cap runs exceeded 15 minutes and over one third exceeded 60 minutes. Upper-limit capping was inversely associated with age (Spearman {rho} -0.20 to -0.47, p[&le;]0.002) in three of the datasets, and positively associated with baseline HbA1c ({rho} 0.39-0.62, p<0.001) in all four datasets. A within-participant analysis showed that capping burden did not predict CGM-BGM divergence in any summary metric (all p>0.2), and a systematic CGM-BGM offset in mean glucose and time in range (TIR) reflected the physiological lag between blood and interstitial fluid rather than capping artefact. Conclusions Sensor limit capping is near-universal in type 1 diabetes, produces sustained periods of right-censored glucose data disproportionately affecting younger patients, and does not substantially distort standard summary metrics at the population level. Clinicians and trialists should be aware that CGM data can confirm extreme glucose events but cannot quantify their severity.

Background: Acute pancreatitis (AP) is an established risk factor for diabetes, with approximately 20% of children developing either prediabetes or diabetes within one year of their first episode. Little is known about the diabetes pathophysiology or which individuals are at highest risk. We aimed to evaluate whether genetic risk scores (GRS) for type 1 (T1D) and polygenic risk scores (PRS) type 2 diabetes (T2D) are associated with progression to dysglycemia following AP. Methods: Clinical data were available for 123 children (mean age (IQR), 12 (8-15) years; mean body mass index (BMI), 21.8) with AP who were followed for >1 year. Array genotyping coupled with imputation using the TOPMed reference panel was performed. Genetic ancestry was predicted using a random forest classifier. GRS for T1D and T2D were calculated using either an ancestry-appropriate (T1D-GRS) or a multi-ancestry (T2D-PRS) weighted framework. To evaluate risk compared to the population we used predefined GRS thresholds from UK Biobank. Results: Among the 123 subjects, 24 developed dysglycemia (5 with diabetes and 19 with prediabetes). The majority (75.6%, n=93) of children were of European ancestry. Comparison of the T1D-GRS burden with the UK BioBank showed numerically higher proportions for any given threshold. At the top 5% threshold, 9.7% of our cohort were classified as high-risk compared to 5% in UK Biobank (p<0.05). The elevated T1D-GRS could be primarily attributed to non-HLA variants and was more enriched in those testing positive for [&ge;]1 islet-autoantibody. The T2D-PRS was also elevated in the dysglycemic group but only reached statistical significance in those who were obese. Conclusion: These findings highlight the potential role of both T1D-GRS and T2D-PRS in investigating diabetes susceptibility following AP.

Aims: Sarcopenia and sarcopenic obesity are associated with increased risks of cardiovascular (CV) disease and mortality. This study examined the associations of body composition and daily physical activity with mortality, CV events and cancer in patients with diabetes. Methods: This prospective cohort study included patients with diabetes treated at a specialised clinic in Japan between January 2018 and March 2023. Body composition, including visceral adipose tissue (VAT), was assessed by bioelectrical impedance analysis. Daily physical activity was evaluated using the non-exercise activity thermogenesis (NEAT) questionnaire, and handgrip strength (HGS) was measured by dynamometry. Cox proportional hazards models were used to assess associations with mortality, CV events, and cancer. Results: Among 2,024 patients (mean age 63.0 years, BMI 24.6 kg/m^2, HbA1c 7.8%), NEAT, HGS, and VAT were not independently associated with all-cause mortality. Higher VAT was associated with increased cancer risk (HR 1.485; 95% CI 1.101-2.003; p = 0.009). Higher HGS was inversely associated with CV event risk (HR 0.951; 95% CI 0.919-0.984; p = 0.004). NEAT was not associated with any outcome. Conclusions: Higher VAT was associated with increased cancer risk, whereas higher HGS was protective against CV events. Incorporating body composition and HGS assessments into clinical practice may improve risk stratification and management in patients with diabetes.

Introduction Stroke is a leading cause of disability and death in adults with type 2 diabetes (T2D). We evaluated the comparative stroke risk in Veterans with T2D initiated on either of two glucose-lowering medications: GLP-1 receptor agonists (GLP-1RA) or SGLT-2 inhibitors (SGLT2i). Patients and Methods We conducted a retrospective cohort study on diabetic Veterans aged 40 and older with no prior history of stroke or transient ischemic attack, who started on a GLP-1RA or SGLT2i between 2014 and 2021. Patients with contraindications or prior exposure to medication were excluded. Using national Veteran health data, we identified 195,072 [SS1.1]eligible individuals and followed them from treatment initiation until stroke, death, loss to follow up, or end of follow up, whichever came first. Primary outcome was incident stroke, and secondary outcomes included ischemic and hemorrhagic stroke. We applied Kaplan-Meier methods and Cox proportional hazards models. Adjusted associations were estimated using inverse probability weighting. Results Both unadjusted and adjusted analyses suggest GLP-1RA users have reduced stroke incidence compared SGLT-2i users[HS2.1] (HR = 0.[HS3.1]67, 95% CI 0.64-0.69; HR = 0.72, 95% CI 0.69-0.75). Similar results were found in secondary outcome and stratified analyses, with GLP-1RA users having reduced stroke risk compared to SGLT2i users for all age groups, chronic kidney disease stages, and hemoglobin A1c levels. Discussion and Conclusion GLP-1RA treatment was associated with a lower risk of stroke compared with SGLT2i treatment in Veterans with T2D. These findings were consistent for ischemic and hemorrhagic strokes, suggesting potential differences in stroke risk between the treatments.

Objective To develop clinically operational, population-representative risk-score models for detecting metabolic syndrome (MetS) in U.S. adults by incorporating the NHANES survey design. Research Design and Methods We analyzed 36,812 U.S. adults from NHANES 1988--2018. Seven models of increasing clinical complexity were trained and evaluated, ranging from basic demographics to full biochemical panels. We used a new deep-learning methodology for survey data with a predictive uncertainty quantification model. Results A model combining anthropometrics, vital signs and a basic lipid panel achieved an AUC of 0.923 at an estimated cost of 0.40 eur per individual. Adding diabetes-specific biomarkers, including fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c), yielded only marginal improvements. Conclusions This low-cost population-representative screening tool for MetS may help identify at-risk individuals and support data-driven public health interventions.

Background: Black adults bear a disproportionate burden of cardiometabolic dysfunction, yet most dietary trial evidence comes from predominantly White cohorts. Objective: To evaluate whether a personalized whole-food dietary intervention improves cardiometabolic outcomes more in Black than White young adults with overweight or obesity. Methods: In this 8-week randomized, controlled trial (ClinicalTrials.gov: NCT04635917), 112 Black and White adults (18-35 years; BMI 25-45 kg/m2) were block-randomized by race to a personalized dietary intervention providing whole foods (PD, n=57) or conventional dietary counseling at baseline (BL) using MyPlate guidelines (CD, n=55). Primary outcomes were Matsuda Index and fasting and OGTT-derived glucose, insulin, and non-esterified fatty acids. Other glucoregulatory, cardiovascular, anthropometric, appetite, and cognitive outcomes were also assessed. Outcomes were analyzed using baseline-adjusted linear models with sensitivity analyses adjusting for baseline BMI and food security score. Results: Compliance with study food consumption was 85-91%. Diet quality was higher in PD than CD (P < 0.05), with larger gains in vegetable-related outcomes among Black participants (group x race, P < 0.05). HOMA-{beta} was lower in PD than CD overall (P < 0.05). In sensitivity analyses, Black PD participants had greater fasting insulin reductions than White, especially in the latter half of intervention (week x group x race, P < 0.05), with a similar tendency for HOMA-IR. Glucose AUC 0-30 min was higher in White than Black PD participants (group x race, P < 0.05). Concentration performance was higher in PD than CD overall (P < 0.05), with larger gains in processing speed and accuracy among Black than White participants (group x race, P < 0.05). No effects were observed for cardiovascular or appetite outcomes. Conclusions: The personalized whole-food intervention produced differential effects in fasting insulin and early-phase glucose handling, and greater benefits in attention, in Black compared with White young adults with overweight or obesity during weight maintenance.

BackgroundThe discontinuation of Fasiglifam (TAK-875), a GPR40/FFAR1 full agonist, during Phase 3 clinical trials due to hepatotoxicity led to widespread abandonment of GPR40 as a viable therapeutic target for type 2 diabetes mellitus (T2DM). However, mechanistic evidence suggests that Fasiglifams hepatotoxicity arises from mitochondrial liability driven by high lipophilicity (aLogP = 5.31), rather than from on-target GPR40 signaling. We hypothesized that target-level failure was incorrectly inferred from compound-level safety concerns, and that superior candidates exist within publicly available databases. MethodsWe queried ChEMBL Release 36 (28 GB SQLite, 74 tables) for all compounds with documented GPR40/FFAR1 activity (UniProt: O14842). Compounds were filtered by EC50 [&le;] 10 nM in nM units with standard relation "=". Drug-likeness was assessed using Lipinskis Rule of Five (Ro5), aLogP, molecular weight (MW), hydrogen bond donors/acceptors (HBD/HBA), and polar surface area (PSA). A parallel analysis of Therapeutic Target Database (TTD v10.1.01, 4,298 targets) provided clinical context. A real-world evidence (RWE) patient stratification framework was constructed using EMR data from tens of millions of patients with >10 years of longitudinal follow-up. ResultsOf 2,637 GPR40-active compounds in ChEMBL 36, 526 (19.9%) demonstrated EC50 < 100 nM and 102 (3.9%) demonstrated EC50 < 10 nM. Eight compounds met stringent drug-likeness criteria (Ro5 violations = 0, aLogP < 5.0, EC50 [&le;] 1 nM). The lead compound (CHEMBL4859651) exhibited EC50 = 0.04 nM (8.75-fold more potent than Fasiglifam), MW = 297 Da (43% lower), and aLogP = 4.30 (19% lower), with zero Ro5 violations. Mean MW of the eight candidates was 317 {+/-} 28 Da versus 524 Da for Fasiglifam. A parallel GCK analysis identified a protein-protein interaction target (CHEMBL3885579, GCK-GKRP interface) harboring 40 exclusive compounds as an orthogonal strategy for partial GCK activation. ConclusionsSystematic cheminformatic analysis reveals that compounds with substantially superior activity and drug-likeness profiles relative to Fasiglifam exist within ChEMBL 36. Fasiglifams hepatotoxicity is attributable to compound-specific physicochemical properties, not GPR40-mediated toxicity. RWE patient stratification may further mitigate hepatotoxicity risk for next-generation GPR40 agonists. These findings argue for systematic reappraisal of GPR40 as a viable therapeutic target for T2DM.

Background: Kenya replaced the National Health Insurance Fund with the Social Health Authority in October 2024, making the 2022 Kenya Demographic and Health Survey the last nationally representative pre-transition baseline. Evidence on insurance coverage and treatment gaps among adults already diagnosed with hypertension or diabetes remains limited, including how these patterns differ by sex. We aimed to estimate the level, distribution, and correlates of insurance coverage and treatment gaps among diagnosed adults at the close of the NHIF era. Methods: We conducted a cross-sectional secondary analysis of the 2022 Kenya Demographic and Health Survey. Insurance status, prior diagnosis, and current medication use were reported by respondents. Analyses were sex-stratified and survey-weighted, with adjusted prevalence ratios estimated to assess associations between insurance coverage and treatment gaps. Wealth-related inequality was examined using concentration indices. Results: The analytic sample included 1,932 diagnosed adults (1,384 women and 548 men). Any insurance coverage was 47.7%, largely driven by National Health Insurance Fund enrolment (43.4%). Overall, 63.8% of diagnosed adults reported not receiving treatment, including 67.1% of women and 59.8% of men, with treatment gaps exceeding 60% across all wealth quintiles. Insurance coverage was strongly pro-rich, whereas treatment gaps were distributed across the wealth gradient. After adjustment, insurance was not strongly associated with lower treatment-gap prevalence among women or men, with no evidence of effect modification by sex. Among women, lack of money for treatment was reported as a major barrier far more frequently among the uninsured than the insured. Conclusions: Before the Social Health Authority transition, Kenya faced both incomplete insurance coverage and substantial treatment gaps among adults diagnosed with hypertension or diabetes. These findings provide a critical pre-transition benchmark and suggest that expanding insurance enrolment alone may be insufficient to close treatment gaps without improvements in benefit depth, medicine availability, and frontline readiness for chronic care delivery.

Background Cardiometabolic-based chronic disease (CMBCD) at an individual level results from complex interactions among a multi-tiered network of sociodemographic, behavioral, and metabolic factors. Though a consensus set of risk factors drives CMBCD, population context influences risk factor effects and interactions. To better understand this phenomenon, we investigated the multi-tiered networking of cardiometabolic variables across diverse populations using a comparative modelling approach. Methods and Findings Utilizing nationally representative cross-sectional data from 48 countries participating in the World Health Organization "STEPwise approach to noncommunicable disease risk factor surveillance" survey, we learned country-specific Bayesian networks including sociodemographic, behavioral, and cardiometabolic variables (adiposity, diabetes, hypertension, hyperlipidemia, and cardiovascular disease). By computing the structural Hamming distance between pairs of networks, we compared differences in network structures across regions and country income levels. We then used the learned networks to assess individual risk factor influences and interactions on cardiometabolic outcomes. Country-specific Bayesian networks varied in terms of the risk factors directly and indirectly associated with the cardiometabolic outcomes. Network structures differed significantly across regions (p = 0.023) but not across income levels (p = 0.91). These results were robust to an alternative learning algorithm, network comparison metric, and data imputation approach. Older age (60+ vs. 30-44 years old) was associated with a greater increase in probability of obesity in Europe and Central Asia (+80%) compared to other regions. Higher education was associated with increased probability of obesity (+53%), diabetes (+18%), and hypertension (+2%) in South Asia but decreased probability of obesity (-10%), diabetes (-32%), hypertension (-16%), and hyperlipidemia (-25%) in Middle East and North Africa. The interaction between age and sex in predicting obesity was significant in the highest proportion of countries in Europe and Central Asia compared to other regions. While this dataset provided standardized data across multiple countries to define cardiometabolic risk factors and drivers, there was limited data on certain health outcomes and uneven availability of data across regions. Conclusions These results revealed specific regional patterns of multi-tiered cardiometabolic risk structures, emphasizing the need for regionally tailored public health strategies rather than applying generalized consensus evidence-based models. Future research should explore the structural drivers of regional differences in inter-relationships of cardiometabolic risk factors, drivers, and disease.

Type 2 Diabetes (T2D) is a growing public health burden in West Africa, yet the effectiveness of lifestyle interventions for glycemic control in this region remains unclear. This systematic review and meta-analysis evaluated the impact of lifestyle interventions on Fasting Blood Glucose (FBG) and Glycated Hemoglobin (HbA1c) levels among adults with T2D in West Africa. A systematic search of PubMed, Scopus, Africa Journals Online, and Cairn.info was conducted following PRISMA guidelines. Randomized controlled trials (RCTs) and quasi-experimental studies evaluating lifestyle interventions (physical activity, dietary modification, and combined/educational interventions) for glycemic control in adults with T2D in West Africa were included. Meta-analysis was performed via a random-effects model with restricted maximum likelihood (REML) estimation, using mean differences (MD) as the effect measure for both FBG and HbA1c outcomes. Heterogeneity was assessed via I2 statistics, and sensitivity, subgroup, and meta-regression analyses were conducted to examine potential moderators of the observed heterogeneity. Ten studies comprising 645 participants were included. Six studies reported FBG outcomes; however, two were excluded from the FBG meta-analysis due to missing control group post-test values and absence of a control group respectively, leaving four studies for pooling. A separate set of four studies contributed to the HbA1c meta-analysis. For FBG, lifestyle interventions were associated with reduction in FBG levels (pooled MD = -1.81 mmol/L, 95% CI: -2.33 to -1.30, p < 0.001), with moderate heterogeneity (I2 = 50.76%). The certainty of evidence assessed using the GRADE approach was rated as low for FBG outcomes and very low for HbA1c outcomes, reflecting concerns about imprecision and inconsistency across studies. Leave-one-out sensitivity analysis confirmed robustness of this finding, with estimates ranging from -1.707 to -2.087 mmol/L. Neither intervention duration nor sample size significantly moderated FBG effect sizes, with the model explaining approximately 15.7% of observed heterogeneity. For HbA1c, lifestyle interventions were also associated with reduction in HbA1c levels (pooled MD = -1.044%, 95% CI: -1.594 to -0.495, p = 0.0002), though heterogeneity was exceptionally high (I2 = 98.08%), limiting interpretability of the pooled estimate. Exploratory meta-regression identified intervention duration and sample size as statistically associated with HbA1c effect size, though the model was saturated given the small number of studies and findings should not be interpreted as confirmatory evidence of moderation. Conclusion: Lifestyle interventions, including supervised physical activity, dietary modification, and community-based diabetes education, were generally associated with improvements in glycemic control among adults with type 2 diabetes in West Africa. Evidence was more consistent for fasting blood glucose, while findings for HbA1c were highly heterogeneous and should be interpreted with caution. These results suggest potential benefit, but variability across studies highlights the need for more standardized and rigorously designed trials in the region.

Background Early onset obesity in children, almost always accompanied by significant health complications, may be driven by rare genetic variants that influence appetite, metabolism, and nutrient absorption. Traditional treatment approaches are usually insufficient for those with monogenic obesity of this type. Glucagon-like peptide-1 (GLP-1) receptor agonists, such as semaglutide, and related drugs such as melanocortin 4 receptor agonists, have emerged as promising first-line treatments for severe obesity. There is no established protocol or pathway in England for identifying children with monogenic obesity who could benefit from these and similar treatments Methods We undertook early economic modelling to examine the cost-effectiveness, from a health service perspective, of implementing a new pharmacotherapeutic care pathway for the identification and treatment of monogenic obesity in children. We modelled a hypothetical population of children with hyperphagia and body mass index (BMI) three standard deviations above mean values for age and sex. We evaluated the clinical decision to initiate the pathway using a decision tree model with patient quality-adjusted life years (QALYs) and NHS healthcare costs 12 months from an initial clinic visit as outcomes, and calculated incremental cost effectiveness ratios and a cost-effectiveness acceptability curve. Results Both costs and QALYs were higher under further investigation (GBP3,247 and 0.47 QALYs) compared to no further investigation (GBP1,589 and 0.24 QALYs). The incremental cost-effectiveness ratio in the base case was GBP7,133 per QALY. Further examination of these children was therefore likely to be cost effective in this model. Conclusion A decision-tree model suggested that further investigation of severely obese children potentially eligible for treatment with semaglutide is likely to be cost-effective for the NHS. However, this result is associated with uncertainty arising from a lack of evidence for many key model parameters.