Evidence shows that genetic polymorphisms in perilipin 1 gene (PLIN1) are associated with excessive accumulation of body fat and disturbances in cardiometabolic markers. Therefore, the aim of this study was to verify whether the SNP PLIN1 11482 G>A (rs894160) interacts with nutrient intake, anthropometric, body composition and cardiometabolic markers in adults with normal-weight obesity (NWO) syndrome. A cross-sectional study was carried out with 116 individuals aged 20–59 years, with normal BMI and high percentage of body fat. Anthropometric and body composition measures, glycaemic control and serum lipid markers, SNP PLIN1 11482 G>A and nutrient intake were evaluated. Interactions between nutrient intake and the SNP were determined by regression models and adjusted for potential confounders. The SNP frequency was 56·0 % GG, 38·8 % GA and 5·2 % AA. Anthropometric measures and biochemical markers were not different according to genotype, except for total cholesterol (TC), LDL-cholesterol and non-HDL-cholesterol concentrations. However, important interactions between the SNP and dietary intake were observed. Carbohydrate intake interacted with the SNP PLIN1 11482 G>A to modulate waist circumference (WC) and the homeostatic model assessment of insulin resistance index. Interaction of lipid intake and the SNP modulated TC and LDL-cholesterol concentrations, and the interaction between protein intake and the SNP tended to modulate weight, WC and BMI. The SNP PLIN1 11482 G>A seems to modulate responses in anthropometric and lipid profile biomarkers of subjects with NWO depending on the dietary macronutrient composition, which may have long-term impact on cardiometabolic markers.

Normal-weight obesity (NWO) syndrome is associated with metabolic diseases. The present study aimed to investigate the effects of 12 weeks of a high-protein (HP) v. a standard protein (SP) diet on appetite, anthropometry and body composition in NWO women. In this clinical trial, fifty NWO women were randomly allocated to HP (n 25) or SP (n 25) diet groups. Women in the HP and SP groups consumed 25 and 15 % of their total energy intake from protein for 12 weeks. Weight, fat mass (FM), lean body mass (LBM), waist circumference (WC) and appetite were evaluated at baseline and following their 3-month intervention. After 12 weeks, the LBM was higher in HP compared with no significant changes in the SP group (mean between-group difference = 1·5 kg; 95 % CI 3·1, 0·01; effect size (d) = 0·4). Furthermore, the HP group had lower FM (mean between-group difference –1·1 kg; 95 % CI 1, –3·3; d = –0·2), body fat percentage (BFP) (mean between-group difference –2 %; 95 % CI 0·7, –5·2; d = –0·3) and WC (mean between-group difference –1·4 cm; 95 % CI 0·6, –3·6; d = –0·2) at the end of the study in comparison with the SP group. In both groups, weight and appetite were unchanged over time without significant differences between groups. Twelve weeks of euenergetic diets with different dietary protein contents resulted in no significant weight loss in women with NWO. However, an HP diet significantly improved body composition (LBM, FM, BFP and WC) in this population.

Obesity indicators are known to predict the presence of type 2 diabetes mellitus (T2DM); however, evidence for which indicator best identifies undiagnosed T2DM in the Indian population is still very limited. In the present study we examined the utility of different obesity indicators to identify the presence of undiagnosed T2DM and determined their appropriate cut point for each obesity measure. Individuals were recruited from the large-scale population-based Kerala Diabetes Prevention Program. Oral glucose tolerance tests was performed to diagnose T2DM. Receiver operating characteristic (ROC) curve analyses were used to compare the association of different obesity indicators with T2DM and to determine the optimal cut points for identifying T2DM. A total of 357 new cases of T2DM and 1352 individuals without diabetes were identified. The mean age of the study participants was 46⋅4 (sd 7⋅4) years and 62 % were men. Waist circumference (WC), waist:hip ratio (WHR), waist:height ratio (WHtR), BMI, body fat percentage and fat per square of height were found to be significantly higher (P < 0⋅001) among those with diabetes compared with individuals without diabetes. In addition, ROC for WHR (0⋅67; 95 % 0⋅59, 0⋅75), WHtR (0⋅66; 95 % 0⋅57, 0⋅75) and WC (0⋅64; 95 % 0⋅55, 0⋅73) were shown to better identify patients with T2DM. The proposed cut points with an optimal sensitivity and specificity for WHR, WHtR and WC were 0⋅96, 0⋅56 and 86 cm for men and 0⋅88, 0⋅54 and 83 cm for women, respectively. The present study has shown that WHR, WHtR and WC are better than other anthropometric measures for detecting T2DM in the Indian population. Their utility in clinical practice may better stratify at-risk patients in this population than BMI, which is widely used at present.

High body fat in apparently lean individuals is a commonly described phenotype in individuals of Asian descent, but very limited consolidated scientific literature is available on this topic. This phenotype is known as ‘normal-weight obesity’ and may explain the large disparity between the prevalence of obesity (as measured by BMI) and diabetes that occurs in these individuals. Routine use of obesity indicators that best predict body fat content would help to identify these individuals in clinical practice. In this debate, we would like to highlight that even though fat and BMI have a good correlation, as suggested by Kryst et al. (2019), clinicians, public health researchers and policymakers should consider the use of these indicators in conjunction with each other rather than individually. Future research is needed on pathogenic mechanisms, diagnostic modalities and therapeutic options in these individuals which will help to further characterize and manage these patients appropriately.

Obesity indicators are useful clinical tools in the measurement of obesity, but it is important for clinicians to appropriately interpret their values in individuals with different ethnicities. Future research is needed to identify optimal cut-offs that can predict the occurrence of cardio-metabolic comorbidities in individuals of different ethnic descent. Assessment of more recently developed indicators like the Edmonton Obesity Staging System and visceral adipose tissue are able to appropriately identify metabolically at-risk individuals.