ABSTRACT Aims This first‐in‐human Phase I study evaluated the safety, tolerability, pharmacokinetics (PK) and pharmacodynamics (PD) of KN069, a novel dual Glucagon‐like peptide‐1 receptor agonist (GLP‐1RA)/Glucose‐dependent insulinotropic polypeptide receptor (GIPR) antagonist in Chinese men with overweight/obesity. Materials and Methods This randomised, double‐blind trial included a single ascending dose (SAD; 12–120 mg, N = 36, 3:1 active‐to‐placebo) and a multiple ascending dose (MAD; N = 12, dose escalation 15–60 mg) phase. Safety was assessed via adverse events (AEs) and compliance. PK was analysed using a sandwich enzyme‐linked immunosorbent assay (ELISA) for Intact and Total KN069. PD included measurements of body weight, waist circumference, body mass index (BMI) and metabolic parameters. Immunogenicity was assessed by detecting anti‐drug antibodies (ADA). Results KN069 was well tolerated, with predominantly mild‐to‐moderate gastrointestinal adverse events. PK showed dose‐proportional exposure (12–90 mg) with a long half‐life for Total KN069 (899.74–1099.01 h). In the SAD part, preliminary dose‐dependent weight reductions were observed, with maximum early changes at Day 7 (90 mg: −4.71% vs. placebo: −0.41%) and sustained for up to 133 days. In the MAD part, Group B (60 mg) achieved a −2.57% mean weight reduction from baseline at Day 25, alongside a significant decrease in waist circumference ( p = 0.0446). Metabolic improvements included lower fasting glucose, triglycerides, uric acid and elevated insulin/C‐peptide. Conclusions KN069 exhibits favourable safety, long‐acting PK and preliminary dose‐dependent weight reduction alongside expected pharmacologic metabolic effects, supporting further clinical development. ClinicalTrials.gov Identifier: NCT06547775.

Hypertension remains a serious public health issue worldwide because of its strong association with cardiovascular disease and premature mortality. Although body mass index (BMI) and waist circumference (WC) are commonly used indicators of obesity, they do not always capture differences in fat distribution. This study examined whether Body Roundness Index (BRI), a newer measure of body shape, is associated with hypertension among U.S. adults. The analysis used data from the 2017–2020 National Health and Nutrition Examination Survey (NHANES). After excluding participants with incomplete demographic, anthropometric, or blood pressure data, 5,729 individuals were included. Multivariable logistic regression was applied to evaluate the association between BRI and hypertension while controlling for demographic, socioeconomic, and lifestyle characteristics. The results showed that individuals in the highest BRI quartile were more likely to have hypertension than those in the lowest quartile, even after adjustment for potential confounders. Overall, the findings indicate that BRI may be a useful marker for identifying hypertension risk in population-based research.

Background: Breakfast timing has been increasingly recognized as an important factor influencing metabolic health and body weight regulation. However, delayed breakfast consumption and irregular lifestyle behaviors are common among working adults and may contribute to obesity and metabolic disturbances. Aim of the study: This study aimed to assess breakfast consumption patterns and lifestyle behaviors among female employees and to evaluate the effect of early breakfast consumption on anthropometric measurements and lipid profile among overweight and obese participants. Methods: A two-phase study was conducted among female employees. Phase I was a cross-sectional study including 100 female participants to assess sociodemographic characteristics, anthropometric measurements, breakfast habits, and lifestyle behaviors. Phase II was a one-month intervention involving 30 overweight and obese participants from the initial sample who approved to participate in the study. Participants received an educational session promoting early breakfast consumption before 8:00 a.m. Anthropometric measurements and lipid profile parameters (total cholesterol, HDL-cholesterol, and triglycerides) were assessed before and after the intervention. Results: Phase I revealed a high prevalence of overweight and obesity, accompanied by delayed breakfast timing and unfavorable lifestyle behaviors, including physical inactivity, short sleep duration, and irregular meal patterns. Despite most participants reported regular breakfast consumption, the average breakfast time was markedly delayed. Following the intervention, significant reductions were observed in body weight, body mass index, waist circumference and hip circumference (p < 0.01). Total cholesterol and triglyceride levels also decreased significantly (p < 0.001), while HDL-cholesterol remained unchanged. Conclusion: Early breakfast consumption may represent a simple and practical nutritional strategy for improving body composition and lipid profile among overweight and obese women. Incorporating meal timing education into workplace health programs may contribute to obesity prevention and metabolic health improvement.

ABSTRACT Background: Metabolic syndrome (MetS) is a collection of physiological, biochemical, clinical, and metabolic factors associated with a fivefold to sixfold increased risk of cardiovascular diseases and type 2 diabetes mellitus. This study aimed to assess the extent, patterns, and predictors of MetS among urban adults. Materials and Methods: This community-based cross-sectional study was conducted among 536 adults of Prayagraj district, Uttar Pradesh, India, using a multistage sampling technique. A predesigned, pre-tested proforma was used to obtain sociodemographic information. Blood samples were collected aseptically for estimation of serum high-density lipoprotein-cholesterol, triglycerides, and fasting blood glucose. Blood pressure was measured using an electronic blood pressure monitor, and central obesity was assessed by measuring waist circumference using a non-stretchable tape. The National Cholesterol Education Program Adult Treatment Panel III criteria were used to diagnose MetS. Data Analysis: Data were analyzed using the Statistical Package for the Social Sciences. Statistical significance was assessed using the chi-square test. Logistic regression was used for inferential purposes. Results: The prevalence of MetS was 41.8% and increased with age ( P < 0.05). Among affected subjects, 60%–66% had hypertension, high fasting glucose or dyslipidemia, and 73.3% had central obesity. MetS showed significant associations with marital status, caste, and occupation ( P < 0.05). Higher body fat, visceral fat, and waist-to-hip ratio were significantly linked to MetS. Subjects aged 40–59 years and those who reported perceived comorbidities had higher adjusted odds of MetS. Conclusion: Thirteen out of 31 urban subjects had MetS. These findings advocate for the integration of lifestyle modification, targeted intervention for high-risk groups, and health sector reforms in terms of ensuring diagnostic facilities and linking mitigation efforts of MetS with ongoing national noncommunicable diseases programs.

Objectives . To compare the body composition of sarcopenic and non-sarcopenic femals. To evaluate the role of bioimpedance analysis and phase angle determination in the diagnosis of sarcopenia. Material and methods . The cross-sectional study included 90 women aged 61 to 83 years. Group 1 enrolled 56 women without sarcopenia, group 2 consisted of 34 sarcopenic persons. Standardized collection of complaints and medical history, anthropometry, dynamometry, Short Physical Performance Battery, including the Chair rising test, tandem, Timed-Up-and-Go, walking speed, bioimpedance analysis of body composition. Results. Waist circumference, the age is higher in group 2, p < 0.050. Body composition did not differ in both groups, p > 0.050. A potentially high level of physical performance was detected only in group 1, a low level of nutritional status only in group 2, p = 0.011, χ 2 = 10.965. A moderate feedback relationship was found between fat mass, fat-free, active cell masses, total and extracellular fluid, and basal metabolism with the relative strength of both hands. There is little correlation between the results of other functional tests and body composition. Conclusions. Age is an unmodified factor in the development of sarcopenia. The presence of abdominal type of obesity increases the risk of sarcopenia. The determination of muscle strength by the dynamometry method has a pronounced diagnostic significance. Determination of the phase angle using bioimpedance analysis plays an important role in confirming the diagnosis of sarcopenia. A low level of phase angle is an important marker and predictor of the severity of sarcopenia.

The mediating role of biological age acceleration in the associations between lifestyle factors and incident digestive system cancers: A Prospective Cohort Study from the UK Biobank.

The triglyceride-glucose frailty index (TyGFI), integrating metabolic dysfunction and physiological decline, has been proposed as a novel marker of cardiometabolic risk. Its association with metabolic dysfunction-associated steatotic liver disease (MASLD), however, remains unclear. Our objective was to assess TyGFI's predictive value for MASLD risk and to explore the mediating role of obesity indices in the relationships among insulin resistance, aging, and MASLD. Using data from 988 participants aged ≥45 years in the 2017-2018 National Health and Nutrition Examination Survey, we examined the relationship between TyGFI and MASLD. MASLD was defined by controlled attenuation parameter (CAP ≥ 278 dB/m) and at least 1 cardiometabolic risk factor. Multivariable logistic regression, restricted cubic spline, subgroup, and receiver operating characteristic analyses were conducted. Mediation analysis was applied to assess the role of obesity indicators, including body mass index, waist circumference, lipid accumulation product, and visceral adiposity index. Elevated TyGFI was independently associated with an increased prevalence of MASLD, with a significant dose-response relationship across quartiles. The association was consistent across demographic and clinical subgroups. receiver operating characteristic analysis indicated modest predictive ability (AUC = 0.62). All 4 obesity indicators correlated strongly with TyGFI and MASLD; mediation analyses showed that body mass index, waist circumference, and lipid accumulation product significantly mediated the TyGFI-MASLD association, whereas visceral adiposity index did not. TyGFI is independently associated with MASLD risk in middle-aged and older adults, with much of its effect mediated through obesity-related pathways. These findings highlight the potential of TyGFI as a risk assessment tool and underscore the importance of obesity control in MASLD prevention.

Body size is an established risk factor for several cancers, but associations with bladder cancer risk remain unclear. We pooled data from 2,533,008 participants in 30 international cohort studies to assess associations of BMI, waist circumference, and height with bladder cancer risk. Multivariable Cox regression models, including smoking status, duration, and other confounders, were run separately by cohort and sex, and results combined by random-effects meta-analysis. Incident first primary bladder cancer was diagnosed in 15,259 males and 5,188 females. For males, overweight (BMI, 25.0-29.9 kg/m2) and obesity (BMI, ≥30 kg/m2) were associated with increased risk of bladder cancer, with hazard ratios (HRs) of 1.08 (95% CI, 1.04 to 1.12) and 1.16 (95% CI, 1.10 to 1.22), respectively, when compared with normal weight (BMI, 18.5-24.9 kg/m2). The corresponding HR for females were 1.02 (95% CI, 0.95 to 1.09) and 1.04 (95% CI, 0.95 to 1.14), respectively. The HR per 5 kg/m2 increment was 1.07 (95% CI, 1.05 to 1.09) for males and 1.00 (95% CI, 0.97 to 1.04) for females. Higher waist circumference was also associated with increased risk of bladder cancer for males (HR per 10 cm increase 1.06 [95% CI, 1.03 to 1.08]) but not females (HR, 1.01 [95% CI, 0.97 to 1.04]). Results for height were largely consistent with those for BMI and waist circumference, with strong and consistent evidence for males, but not females. Larger body size is associated with increased risk of bladder cancer for males, but not females. Public health interventions to prevent overweight and obesity, along with smoking cessation and reduced occupational exposure to bladder carcinogens, are likely to reduce bladder cancer incidence worldwide.

Ultrasound often misses mild steatosis and does not assess fibrosis, a key mortality predictor in nonalcoholic fatty liver disease (NAFLD). Due to the invasiveness of biopsy and the impracticality of magnetic resonance imaging, we evaluated transient elastography (TE) and noninvasive tests (NITs) in type 1 Diabetes Mellitus. In 85 patients, NAFLD was defined using a controlled attenuation parameter (CAP) ≥248 dB/m or a Liver Stiffness Measurement (LSM) ≥7 kPa with TE, which served as the reference standard. Serum chemerin, aspartate aminotransferase/Alanine aminotransferase (AST/ALT) ratio, AST to Platelet Ratio Index, FIB-4 score, and the Enhanced Liver Fibrosis (ELF) score were assessed. The prevalence of NAFLD by TE was 23 (27%), with steatosis in 18 (21.4%) and fibrosis in 8 (9.5%). Lean NAFLD was seen in 11 patients, with 6 having fibrosis. Patients with NAFLD had higher weight, waist circumference (WC), waist-hip ratio, and systolic blood pressure. The AST/ALT ratio and chemerin performed poorly for steatosis detection, and ELF scores were not significantly elevated in fibrosis. FIB-4 was significantly higher in fibrosis (0.69) than in those without (0.40). A cut-off of 0.5 predicted fibrosis, with 75% sensitivity, 31.6% specificity and a negative predictive value of 86.2% for excluding fibrosis. LSM correlated with the FIB-4 score. Insulin resistance measured by estimated glucose disposal rate showed no difference across body mass index categories or in Metabolic Syndrome. NAFLD was observed in nearly one-quarter of individuals. NITs, including a lower FIB-4 score, could aid in screening this high-risk population.

Effects of one avocado a day for six months on cognitive performance in overweight adults: A randomized controlled trial.

Vitamin D plays an important role in glucose metabolism, lipid regulation, and inflammatory processes, and has been implicated in cardiometabolic health. However, its associations with specific metabolic biomarkers remain inconsistent, particularly in older adults. This study aimed to examine whether vitamin D deficiency is differentially associated with multiple metabolic biomarkers in a nationally representative sample of older adults. This cross-sectional study used data from the 2024 Korea National Health and Nutrition Examination Survey, including 1806 adults aged ≥65 years. Vitamin D deficiency was defined as serum 25-hydroxyvitamin D levels < 20 ng/mL. Metabolic biomarkers included fasting glucose, glycated hemoglobin (HbA1c), triglycerides, C-reactive protein (CRP), high-density lipoprotein cholesterol (HDL-C), waist circumference, and body mass index (BMI). Complex sample linear regression analyses were performed with sequential adjustment for sociodemographic factors, health behaviors, and comorbidities. In unadjusted analyses, vitamin D deficiency was associated with adverse metabolic profiles, including higher fasting glucose, HbA1c, triglycerides, waist circumference, and CRP levels, and lower HDL-C levels. After adjustment for sociodemographic factors, health behaviors, and comorbidities, significant associations remained for HbA1c (β = 0.10, p = 0.034), triglycerides (β = 0.10, p = 0.003), and waist circumference (β = 1.21, p = 0.040). No significant associations were observed for fasting glucose, HDL-C, CRP, or BMI. Vitamin D deficiency was independently associated with poorer long-term glycemic status, hypertriglyceridemia, and central adiposity in older adults, but not with other metabolic markers after adjustment. These findings suggest that the metabolic correlates of vitamin D deficiency may be domain-specific rather than generalized. Longitudinal and interventional studies are needed to clarify causality and underlying mechanisms.

Evaluation of oxidant stress markers in a population involved in a health care program.

Physical and Lifestyle Predictors of Vascular Health in Premenopausal East Asian Women: The Women's Vascular Health Project.

Effects of pistachio consumption on cognition, cardiometabolic risk factors, and life satisfaction in older adults: Protocol for a remote-based randomized controlled trial.

Despite limitations in using BMI to assess obesity, little is known about central obesity's role in pregnancy and postpartum cardiometabolic conditions. We investigated associations of central obesity with perinatal cardiometabolic conditions, independently and jointly with BMI. We examined associations of early pregnancy central obesity measures (waist circumference, waist-to-hip ratio, waist-to-height ratio, and body roundness index) with gestational diabetes mellitus, hypertensive disorders of pregnancy, postpartum prediabetes/diabetes, and postpartum chronic hypertension using modified Poisson (prenatal outcomes) and Cox (postpartum outcomes) regression. Among the 3,055 individuals in the study, there was a dose-response relationship between increasing central obesity and all outcomes, even after adjusting for BMI. Among individuals with healthy prepregnancy BMI, central obesity was associated with a higher risk of gestational diabetes mellitus (relative risks 1.92-2.42), postpartum prediabetes/diabetes (hazard ratios [HRs] 1.50-2.16), and postpartum chronic hypertension (HRs 2.04-3.63). Early pregnancy central obesity measures may enhance perinatal cardiometabolic risk assessment, helping identify at-risk individuals who could be missed using BMI alone.

Postmenopausal women with obesity often show blood abnormalities and low plasma volume, which reduce aerobic capacity and raise health risks. The purpose is to compare the effects of high-intensity (HIIWT) versus moderate-intensity interval walking training (MIIWT) on body composition, plasma volume variations (PVV), hematological parameters, muscle damage, and aerobic capacity in postmenopausal women with overweight/obesity. Thirty-two postmenopausal women with overweight/obesity were randomly assigned to HIIWT (n = 11), MIIWT (n = 11), or control (CON, n = 10) groups. The HIIWT and MIIWT groups performed intermittent walking at 90-110% and 60-80% of their 6-min-walk-test (6MWT) distance, respectively, four times per week for 10 weeks. Body composition, hematological and muscle damage markers, and 6MWT performance were assessed pre- and post-intervention. After ten weeks, PVV was calculated in all three groups. A significant group × time interaction was observed for body composition, erythrocytes, hemoglobin levels, hematocrit, creatine kinase (CK), lactate dehydrogenase (LDH), and 6MWT performance (p < 0.05). Both the HIIWT and MIIWT groups showed significant reductions in body mass, body fat, waist circumference (p < 0.05), and erythrocyte count (p = 0.010 and 0.028, respectively). Only the HIIWT group showed significant reductions in hemoglobin (p < 0.001), hematocrit (p = 0.005), CK (p = 0.002), and LDH (p = 0.009), along with a significant increase in 6MWT-performance (p = 0.002). The HIIWT group demonstrated a significantly greater increase in PVV compared to both MIIWT (p = 0.018) and CON (p < 0.001) groups. HIIWT induced superior improvements in body composition, aerobic capacity, plasma volume, and hematological and muscle-damage markers compared to MIIWT. HIIWT represents a practical strategy for improving health outcomes in postmenopausal women with overweight/obesity.

The Obesity prevalence in Tamil Nadu has escalated to 41.3% in males and 27.3% in females, representing a significant public health burden. Centralised data mining approaches for obesity prediction encounter substantial obstacles, including regulatory constraints, institutional data silos, and patient privacy concerns. This investigation presents a privacypreserving federated deep learning framework for stratifying obesity risk across distributed hospital networks in Tamil Nadu using electronic health records, advancing data-driven public health surveillance while maintaining data sovereignty. A federated learning architecture was implemented across simulated multi- institutional healthcare networks representing diverse urban, semi-urban, and rural settings. The framework integrated data from 724,115 individuals derived from national health surveys, combined with synthesised clinical features. The architecture employed deep neural networks augmented with differential privacy mechanisms and cryptographically secure aggregation protocols. Obesity risk was stratified into four categories through pattern recognition and predictive modelling approaches inherent to advanced data mining. The federated model demonstrated superior performance metrics with 94.2% accuracy and AUC-ROC of 0.963, substantially exceeding centralised approaches (92.3%, AUC 0.948) and conventional federated averaging (91.8%, AUC 0.941). Model convergence occurred within 32 communication rounds compared to 45 rounds required for standard federated averaging. Feature importance analysis identified body mass index (18.5%), waist circumference (17.2%), and chronological age (14.8%) as principal predictive factors. Population-level risk stratification revealed 14.5% classified as very high risk and 24.7% as high risk, facilitating targeted clinical intervention strategies. Federated deep learning architecture successfully enables population-level obesity risk prediction while simultaneously preserving patient privacy and maintaining institutional data autonomy. This approach demonstrates considerable scalability potential for distributed health surveillance and evidence-based intervention programming across Tamil Nadu's decentralised healthcare infrastructure

Objectives This study aimed to develop and evaluate machine learning (ML) models for predicting non-alcoholic fatty liver disease (NAFLD) in patients with type 2 diabetes mellitus (T2DM) using readily accessible clinical and biochemical indicators. Methods A total of 2,459 patients with T2DM were enrolled in this cross-sectional study. Eight ML algorithms, logistic regression (LG), k-nearest neighbors (k-NN), support vector machine (SVM), decision tree (DT), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and naïve Bayes (NB), were developed to construct predictive models. Feature selection was performed using Boruta, recursive feature elimination, and LASSO regression. Model performance was assessed using several metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, recall, F1 score, and decision curve analysis. Results Among the study population, 1,309 individuals (53.23%) were diagnosed with NAFLD. Sixteen variables, including BMI, waist circumference, systolic blood pressure, triglycerides, HDL-C, ALT, GGT, bilirubin fractions, albumin, BUN, GFR, fasting insulin, RBC, and hemoglobin, were selected as key predictors. The SVM model demonstrated the best overall performance, achieving an AUC of 0.920, accuracy of 0.839, and specificity of 0.898 in the training set, and an AUC of 0.833 and accuracy of 0.733 in the validation set. Decision curve analysis confirmed superior clinical utility of the SVM model compared with other algorithms. Conclusions ML-based models, particularly the SVM algorithm, effectively predicted NAFLD among patients with T2DM using easily accessible clinical and biochemical indicators. These findings highlight the potential utility of ML-assisted screening tools for improving early identification and risk stratification of NAFLD in diabetic populations.

ABSTRACT Objective This study aimed to compare the metabolic and inflammatory profiles of dipper and non-dipper hypertensive patients versus healthy controls, specifically evaluating the Triglyceride-glucose (TyG) index's association with nocturnal blood pressure patterns. Methods This retrospective, cross-sectional study included 325 participants (110 normotensive controls, 106 dipper hypertensive, and 109 non-dipper hypertensive). Circadian blood pressure phenotypes were defined using 24-hour ABPM according to the 2024 ESC Hypertension Guidelines. Inflammatory indices (NLR, LMR, and HALP score) and the TyG index were calculated from fasting blood samples. Results Hypertensive groups had higher BMI and waist circumference than controls (p < 0.001). HALP, NLR, and LMR did not differ between the cohorts (p > 0.05). The TyG index showed the greatest intergroup variation and was strongly associated with the non-dipper phenotype (OR = 3.6, p = 0.004). TyG was positively correlated with nocturnal SBP/DBP and negatively correlated with nocturnal SBP decline. It showed significant diagnostic performance for hypertension and non-dipper status (AUC 0.667–0.696, p < 0.001), but limited accuracy for classifying hypertensive subgroups (AUC = 0.573, p = 0.064). Conclusion The TyG index serves as a significant independently associated with the non-dipper hypertension phenotype, reflecting predominant metabolic and cardio-renal stress rather than cellular inflammation. These findings suggest that clinical management should extend beyond blood pressure control to include early optimization of insulin resistance and atherogenic lipid imbalance. Ultimately, the TyG offers a practical and cost-effective clinical tool for multidimensional cardiometabolic and cardio-renal risk assessment.

The clinical significance of metabolic syndrome (MetS) stems from its strong association with the incidence and mortality of cardiovascular disease (CVD) and type 2 diabetes (T2D), which are the top causes of death worldwide. The triglyceride-glucose (TyG) index, a surrogate marker of insulin resistance, has been proposed as a simple diagnostic tool; however, its predictive value for MetS has not been evaluated in southeastern Mexico. This study aimed to assess the diagnostic performance of the TyG index for MetS in adults from Yucatán. A cross-sectional study was conducted on 250 adults (77 men, 173 women) attending the Regional High Specialty Hospital of the Yucatan Peninsula, IMSS-Bienestar. Anthropometric and biochemical data were collected under standardized procedures. The TyG index was calculated as ln ([fasting triglycerides × fasting glucose]/2). Logistic regression and ROC curve analyses were performed to evaluate the discriminatory capacity of the TyG index, adjusted for sex and age. Higher TyG index quartiles were associated with increased age, waist circumference, blood pressure, and prevalence of MetS (P < 0.05). The TyG index showed a positive trend with the number of MetS components (P < 0.001). The area under the curve for TyG was 0.79 (95% confidence interval: 0.73-0.85, p < 0.001), improving to 0.83 after adjustment for sex and age, with 72% sensitivity and 80% specificity. The TyG index demonstrated strong predictive and discriminative ability for MetS and, as a simple, cost-effective measure derived from routine laboratory tests, represents a practical screening tool for clinicians in southeastern Mexico.