Introduction: Obesity is a heterogeneous metabolic disease in which individuals with similar body weight may exhibit markedly different physiological risk profiles. Conventional monitoring using weight alone may fail to capture underlying metabolic and cellular changes during lifestyle intervention. Integrating body composition and biochemical markers may improve risk stratification in clinical practice. Methodology: A prospective observational analysis was conducted among participants enrolled in a structured lifestyle programme. Baseline and follow-up assessments included biochemical markers (fasting glucose, renal function, lipid profile, and liver enzymes) alongside body composition parameters derived from bioelectrical impedance analysis, including percent body fat, visceral fat area, skeletal muscle index (SMI), and phase angle (PhA). Changes over time and associations between metabolic and body composition variables were analyzed. Results: Metabolic responses varied substantially despite similar anthropometric profiles. Renal function improved significantly, with increased estimated glomerular filtration rate (eGFR) observed over time (p = 0.01), and was inversely associated with fasting glucose (r = −0.41, p <0.01). Lipid abnormalities persisted, although reductions in lowdensity lipoprotein cholesterol were noted (p = 0.04). Total cholesterol remained elevated and correlated positively with fasting glucose (r = 0.36, p = 0.02). Improvements in aspartate aminotransferase were observed (p = 0.03), while other liver markers remained stable. Notably, body composition parameters, including PhA and SMI, demonstrated variability independent of weight change, reflecting heterogeneous physiological adaptation. Conclusion Metabolic and physiological responses to lifestyle intervention are heterogeneous and not fully captured by changes in body weight alone. The integration of bioimpedance-derived parameters with biochemical markers provides a more comprehensive approach to metabolic risk stratification and may support more personalized clinical management in obesity care.
Biomarkers ; Risk Assessment ; Body Composition

Introduction: Cafeteria environments within universities have a significant influence over sustainable and healthy dietary behaviours. Hence, this study aimed to understand the nutritional landscape of food options in university cafeterias and to identify the factors contributing to unhealthy food options from the perspective of food service vendors. Methods: Using a mixed-methods approach, 122 food options from all 12 residential campus cafeterias in Universiti Malaya (UM) were collected between October and December 2024. All food options were grouped into (i) bread, (ii) meat dishes, (iii) drinks, (iv) Western dishes, and (v) rice- or noodle-based fried food group; they were further analysed for calories, fat, saturated fat, dietary fibre, and salt using the Malaysian Food Composition Database (MyFCD) and the Singapore Food Composition Database (SFCD). Nutrient classification (high vs. low) was based on the criteria outlined in the Labelling Guidelines 2023. Additionally, food service vendors were interviewed to explore operational challenges, with data collection ceasing upon reaching thematic saturation. The interviews were then transcribed and analysed using thematic analysis. Results: Quantitative findings showed that there were 36 bread-based meals, 16 meat-based dishes, 18 beverages, 30 Westernstyle meals, and 22 rice- or noodle-based fried foods. The study also revealed an over-reliance on high-calorie, high-fat, and low-fibre foods, with limited availability of sustainable and nutritious options. Qualitative findings highlighted the influence of affordability, vendor practices, and student preferences on campus food choices. Conclusion: The study underscored the need for internal policy intervention, improved vendor education and sustainability practices, and increased accessibility to healthier meals on campus.

INTRODUCTIONWeight-and-height-based anthropometric indices have long been used for obesity screening among adolescents.However, the ability of their age-and-sex-specific reference values in classifying adolescent as "obese" in different populations was not fully established. Our study aimed to validate the existing international (BMI-for-age charts from WHO, CDC, IOTF) and local cut-offs [percent weight for height (PWH)] for obesity against body fat percentage, as assessed by 4 skinfolds measurement.

MATERIALS AND METHODSA cross-sectional sample of 6991 adolescents aged 12 to 18 years was measured. All anthropometric measurements were compliant with the internationally accepted protocol. Obesity was defined as percentage body fat greater than or equal to 95 percentile, specific to age and sex. The validity of the existing classification criteria in detecting obesity was evaluated by comparing their respective diagnostic accuracy.

RESULTSBoth prevalence of obesity and diagnostic accuracy indices varied by the classification criteria. While all criteria generated very high specificity rates with the lowest being 95%, their sensitivity rates were low ranging from 43% to 71%. Youden's index suggested that CDC and WHO criteria had optimal sensitivity and specificity. ROC analysis showed that overall performance could be improved by refining the existing cut-offs.

CONCLUSIONSClinical validity of weight-and-height-based classification systems for obesity screening in Asian adolescents is poorer than expected, and this could be improved by refining the existing cut-offs.

Adolescent ; Anthropometry ; Asian Continental Ancestry Group ; Body Height ; Body Mass Index ; Body Weight ; Child ; Cross-Sectional Studies ; Female ; Humans ; Male ; Mass Screening ; Obesity ; diagnosis