Obesity contributes to increased morbidity and mortality, yet its metabolic heterogeneity remains underexplored in sub-Saharan Africa. This study examined associations between body composition, insulin sensitivity, inflammation, and the metabolically healthy profile (MHP), and assessed cardiovascular risk among Cameroonian adults with and without obesity. In this cross-sectional study, 490 participants from the West region of Cameroon underwent anthropometric and metabolic assessments. Ten-year cardiovascular risk was estimated using the Framingham risk score. Logistic regression identified predictors of MHP and determinants of elevated cardiovascular risk. The prevalence of MHP was 59.6% among individuals with normal weight, 11.5% among those with overweight, and 38.2% among those with obesity. Participants with overweight, obesity, android obesity or insulin resistance were 92%, 59%, 58%, and 46% less likely to have a MHP than participants in the control groups, respectively. Inflammation defined solely by CRP-hs was not predictive. Compared with metabolically unhealthy counterparts, metabolically healthy obese (OR = 6.27; 95% CI: 3.15-12.48) and overweight (OR = 2.50; 95% CI: 0.72-8.65) individuals had substantially lower predicted 10-year cardiovascular risk. Metabolic phenotyping reveals that not all Cameroonians with overweight and obesity present the same cardiovascular risk. Incorporating fat distribution and insulin sensitivity into clinical evaluation could improve early identification of high-risk individuals and guide tailored prevention strategies.

The distribution of adipose tissue is a key determinant of obesity-related disease risk. When the capacity of subcutaneous adipose tissue to safely store energy is exceeded, lipid overflow into visceral depots occurs, increasing free fatty acid flux, promoting insulin resistance, and triggering chronic low-grade inflammation. More than 80 years ago, the physician Jean Vague recognized the association between android (central) obesity and cardiometabolic disease; subsequent advances from imaging technologies have expanded this framework to include ectopic fat accumulation within non-adipose organs. Ectopic fat deposition in the liver, pancreas, heart, kidneys, skeletal muscle, and upper airway contributes to organ-specific dysfunction through mechanisms including lipotoxicity, adipokine dysregulation, oxidative stress, and local inflammatory signaling. This review summarizes the evidence linking visceral and ectopic fat to metabolic dysfunction-associated steatotic liver disease and steatohepatitis, type 2 diabetes, cardiovascular and renal disease, sarcopenic obesity, and obstructive sleep apnea. Importantly, visceral and ectopic fat depots are modifiable. Lifestyle-induced energy deficit, incretin-based pharmacotherapy, and metabolic bariatric surgery reduce visceral and ectopic fat and improve glycemic control, liver fat content, cardiovascular risk factors, physical function, and sleep-disordered breathing. Incorporating ectopic fat characterization into clinical practice may enhance risk stratification and support more personalized management of obesity phenotypes.

Whether modulation of adipose tissue distribution alone can improve insulin sensitivity independently of weight loss remains unclear. In this randomized, double-blind, placebo-controlled 90-day trial, we investigated whether adults with obesity could achieve metabolic improvement through modulation of adipose tissue distribution independent of weight loss. Fifty-six participants received oral placebo or Pep19 (5 or 10 mg daily). Regional fat distribution and metabolic parameters were assessed longitudinally. Pep19 at 10 mg reduced central adiposity, as demonstrated by a reduced android-to-gynoid fat ratio and decreased abdominal skinfold thickness, without affecting body weight, caloric intake, or physical activity. Glycated hemoglobin (HbA1c) and HOMA-IR were significantly reduced, despite unchanged fasting glucose, indicating improved insulin sensitivity. These findings suggest that Pep19 promotes favorable changes in adipose tissue distribution accompanied by improvements in insulin sensitivity independent of significant weight loss, supporting further evaluation in larger and longer-term clinical studies.

Background/Objectives: Body mass index (BMI) is the most widely used measure for obesity classification, yet it does not directly measure body fat and may substantially underestimate obesity prevalence. Simultaneous evaluation of the diagnostic accuracy of bioelectrical impedance analysis (BIA), BMI, and waist-to-hip ratio (WHR) against dual-energy X-ray absorptiometry (DEXA) for obesity classification is lacking in Saudi Arabia. We aimed to quantify the diagnostic performance of these three surrogate measures against DEXA in Saudi adults. Methods: We conducted a cross-sectional diagnostic accuracy study reported according to the STARD guideline at Prince Sultan Military Medical City, Riyadh, Saudi Arabia. We enrolled a convenience sample of 399 adults aged 18-65 years recruited from primary health care and outpatient clinic waiting areas. Each participant underwent InBody 770 BIA, anthropometric measurements (BMI, WHR), and DEXA (Lunar iDXA, GE) on the same day. We evaluated BIA body fat percentage against DEXA body fat percentage, BMI ≥ 30 kg/m2 against DEXA body fat percentage and fat mass index (FMI)-defined obesity, and WHR against the DEXA android-to-gynoid ratio. We calculated sensitivity, specificity, positive and negative predictive values, likelihood ratios, and area under the receiver operating characteristic curve (AUC), overall and stratified by sex. Results: DEXA classified 86.0% of participants as having obesity, compared with 62.9% by BIA and 24.8% by BMI; against DEXA body fat percentage, BMI ≥ 30 kg/m2 had a sensitivity of 28.9% and specificity of 100%. When FMI replaced body fat percentage as the reference standard, BMI sensitivity increased to 68.5% (specificity 97.3%, AUC 0.95). BIA achieved a sensitivity of 72.3%, specificity of 94.4%, and AUC of 0.86. WHR had the lowest sensitivity for central obesity (54.0%, specificity 94.8%, AUC 0.80). BIA showed the strongest correlation with DEXA body fat percentage (Spearman ρ = 0.82), followed by BMI (ρ = 0.60). Conclusions: BMI alone missed two-thirds of participants with DEXA-defined obesity in this Saudi adult cohort. Although BIA was more sensitive, it still missed 27.7% of individuals with DEXA-defined obesity. These findings support integrating body composition assessment into clinical practice and suggest that national obesity prevalence estimates based on BMI alone substantially underestimate the true burden.

Background and Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent chronic liver disease and frequently improves after bariatric surgery. Conventional postoperative follow-up is mainly based on weight-loss outcomes, such as Body Mass Index (BMI), excess weight loss (EWL), total weight loss (TWL), and routine biochemical parameters, which may not fully characterize hepatic fat response. Ultrasound-derived fat fraction (UDFF) provides a quantitative non-invasive assessment of hepatic steatosis, while DXA-derived visceral adipose tissue (VAT) reflects central adiposity and metabolic risk. This study aimed to evaluate whether combined UDFF and DXA-VAT assessment provides complementary information to conventional bariatric follow-up parameters after bariatric surgery. Methods: We conducted a prospective longitudinal cohort study on 41 patients with severe obesity who underwent bariatric surgery between July 2025 and May 2026. Anthropometric, body composition, metabolic, hepatic, and biochemical parameters were assessed at baseline (T0) and 6 months postoperatively (T1). UDFF, utilizing a DAX ultrasound transducer, was used for hepatic steatosis assessment, while DXA was performed for VAT, android fat mass, total body fat percentage, and lean mass quantification. Correlation analyses, multivariable linear regression, and an exploratory clinical discordance analysis were performed. Results: At 6 months after surgery, patients showed significant improvements in body weight, visceral adiposity, insulin resistance, biochemical parameters, and hepatic steatosis. VAT reduction showed the strongest association with UDFF reduction, whereas conventional weight-loss outcomes, anthropometric, and metabolic parameters did not fully identify patients with residual steatosis. These findings suggest that hepatic fat improvement after bariatric surgery is more closely related to changes in visceral adiposity than to weight loss alone. Conclusions: Combined UDFF and DXA-derived VAT assessment may provide a practical dual-compartment imaging framework for postoperative MASLD monitoring. This approach captures both hepatic fat response and visceral adiposity remodeling, offering information that complements conventional bariatric follow-up based on weight-loss outcomes.