Relationship between body mass index and percentage of body fat, estimated by bio-electrical impedance among adult females in a rural community of North India: A cross-sectional study

Age-appropriate BMI cut-offs for malnutrition among older adults in India

With the increasing prevalence of obesity in India, body mass index (BMI) has garnered importance as a disease predictor. The current World Health Organization (WHO) body mass index (BMI) cut-offs may not accurately portray these health risks in older adults aged 60 years and above. This study aims to define age-appropriate cut-offs for older adults (60-74 years and 75 years and above) and compare the performance of these cut-offs with the WHO BMI cut-offs using cardio-metabolic conditions as outcomes. Using baseline data from the Longitudinal Ageing Study in India (LASI), classification and regression tree (CART) cross-sectional analysis was conducted to obtain age-appropriate BMI cut-offs based on cardio-metabolic conditions as outcomes. Logistic regression models were estimated to compare the association of the two sets of cut-offs with cardio-metabolic outcomes. The area under the receiver operating characteristic curve (AUC), sensitivity and specificity were estimated. Agreement with waist circumference, an alternate measure of adiposity, was conducted. For older adults aged 60-74 years and 75 years and above, the cut-off for underweight reduced from < 18.5 to < 17.4 and < 13.3 respectively. The thresholds for overweight and obese increased for older adults aged 60-74 years old from >  = 25 to > 28.8 and >  = 30 to > 33.7 respectively. For older adults aged 75 years and above, the thresholds decreased for both categories. The largest improvement in AUC was observed in older adults aged 75 years and above. The newly derived cut-offs also demonstrated higher sensitivity and specificity among all age-sex stratifications. There is a need to adopt greater rigidity in defining overweight/obesity among older adults aged 75 years and above, as opposed to older adults aged 60-74 years old among whom the thresholds need to be less conservative. Further stratification in the low risk category could also improve BMI classification among older adults. These age-specific thresholds may act as improved alternatives of the current WHO BMI thresholds and improve classification among older adults in India.

Associations of body adiposity index, body mass index, waist circumference, and percentage of body fat in young female Emirati adults

Body Adiposity Index (BAI), which relies on an individual's hip circumference and height, was proposed as an alternative anthropometric measurement to Body Mass Index (BMI). Although this measure has been validated across different populations, its accuracy in predicting percent body fat (%BF) in the United Arab Emirates has not yet been assessed. The objective of this study was to examine the association between BAI, BMI, Waist Circumference (WC), and %BF in young female Emirati adults and determine the relative accuracy of BAI when predicting %BF. A retrospective cross-sectional study was conducted among 95 Emirati women between the ages of 17 and 27. The %BF was measured using the dual-energy X-ray absorption (DXA) scanner. Anthropometric measurements were collected, and BMI and BAI were calculated. BMI and %BF (r = 0.823, p <0.001) showed a greater association than that between BAI and %BF (r = 0.702, p <0.001). A linear regression analysis revealed that BMI was the single best predictor of %BF in the sample (r2 = 0.678, p<0.001). The variation around the regression line for BAI comparisons with %BF (standard error of estimate = 4.879) was greater than BMI comparisons (standard error of estimate = 3.889). BAI was found to significantly underestimate %BF at higher adiposity levels (mean difference = 8.7%). The ROC curve analysis demonstrated that BMI had a higher discriminatory capacity (AUC = 0.891) over WC and BAI. The results demonstrated that BMI was a better predictor of %BF in the sample than BAI and WC. Thus, BMI may be more useful in assessing adiposity in young female Emirati adults than BAI. However, the potential of BAI as an alternative measure of adiposity should continue to be examined.

Palm oil as part of a high-fat diet: advances and challenges, or possible risks of pathology?

Nutritional status disorders have the most significant impact on the development of cardiovascular and oncologic diseases; therefore, the interest in the study of palm oil as among the leading components of nutrition has been increasing. The data examined in this review were sourced from the Scopus, SCIE (Web of Science), PubMed and PubMed Central, MEDLINE, CAPlus/SciFinder, and Embase databases; experts in the field; bibliographies; and abstracts from review analyses from the past 15 years. This review summarizes recent research data focusing on the quantitative and qualitative composition of nutrition of modern humans; concepts of the relationship between high-fat diets and disorders of insulin functioning and transport and metabolism of fatty acids; analyses of data regarding the palmitic acid (16:0) to oleic acid (18:1) ratio; and the effect of diet based on palm oil consumption on cardiovascular risk factors and lipid and lipoprotein levels. Several studies suggest a potential vector contributing to the transmission of maternal, high-fat-diet-induced, addictive-like behaviors and obesogenic phenotypes across generations. The relationship between cholesterol accumulation in lysosomes that may lead to lysosome dysfunction and inhibition of the autophagy process is analyzed, as is the progression of inflammatory diseases, atherosclerosis, nonalcoholic liver inflammation, and obesity with associated complications. Data are discussed from analyses of differences between rodent models and human population studies in the investigated different effects of palm oil consumption as a high-fat diet component. A conclusion is reached that the results cannot be generalized in human population studies because no similar effects were observed. Although there are numerous published reports, more studies are necessary to elucidate the complex regulatory mechanisms in digestive and nutrition processes, because there are great differences in lipoprotein profiles between rodents and humans, which makes it difficult to reproduce the pathology of many diseases caused by different types of the high-fat diet.

Relationship Between Body Mass Index and Body Fat Percentage in a Group of Indian Participants: A Cross-Sectional Study From a Tertiary Care Hospital

Background Body mass index (BMI) is an important indicator of overweight and obesity. Unlike BMI, body fat percentage (BF%) can be utilized to estimate body composition regardless of weight and height. The association between BMI and BF%, as well as the impact of age and gender, may help estimate the prevalence of obesity more clearly. This study aimed to assess the relationship between BMI and BF%, examine the effect of age and gender on this relationship, and establish the linearity/curvilinearity of this relationship. Methodology The body composition analysis of 1,150 participants in various institutional events (institution foundation day) during 2019 and 2023 was performed using the Accuniq bio-electrical impedance analyzer (BIA) (Accuniq, Netherlands). The participants included undergraduate, postgraduate medical, and PhD students, as well as employees of All India Institute of Medical Sciences, New Delhi. Age groups were categorized as under 17 years, young adults (18-25 years), adults (26-44 years), middle-aged adults (45-59 years), and older adults (≥60 years). Pearson's correlation coefficient (r) was used for analyzing the relationship between BMI and BF%. To assess the effect of age and gender on this relationship, multiple regression analysis was applied, and polynomial regression was applied to test its linearity. The data were analyzed using SPSS version 25 (IBM Corp., Armonk, NY, USA). Results Males made up a larger proportion of the participants (56.3%; 647). The mean age of the participants was 36.5 ± 13.6 years. The mean BMI and BF% were 24.7 ± 4.0 kg/m2 and 29.1 ± 8.7%, respectively. A significant and moderate positive correlation (r = 0.630, p < 0.01) was observed between BMI and BF%. The mean ± SD of BMI and BF% had a directly proportional relationship with age. Among both genders, females showed a greater correlation (r = 0.852). Both age and gender had a significant effect on this relationship, with gender impacting more than age (β = 0.488, p < 0.000). The curvilinear nature of the relationship between BMI and BF% was demonstrated with the female model showing a more precise fit (R2 = 0.72, standard error of the estimate = 3.3%). Conclusions The relationship between BMI and BF% was significant and positive in this group of Indians. This relationship was significantly impacted by age and gender and was curvilinear in nature. Females had a higher association than males between BMI and BF%. The study suggests that BMI, BF%, and the effects of age and gender should be taken into consideration when predicting obesity.

Different correlation of body mass index with body fatness and obesity-related biomarker according to age, sex and race-ethnicity

The relationship between body mass index (BMI) and body fatness could differ according to age, sex, and race-ethnicity. We aimed to evaluate in which contexts BMI could be a good measure for body fatness compared to dual-energy X-ray absorptiometry (DXA) derived measures. The study population included 18,061 participants (9141 men and 8920 women) aged 18 and older who tested DXA from the National Health and Nutrition Examination Survey (NHANES) database from 1999 to 2006, and 8107 men and 10,754 women with DXA data from Korea NHANES from 2008 to 2011 to represent the Asian population. We calculated Pearson correlation coefficients between BMI and DXA derived fat mass index (FMI) and percentage body fat (PBF) depending on age, sex, and race-ethnicity. The correlation between BMI, FMI and PBF and obesity-related biomarkers was also estimated among the subgroup with both DXA and information on each biomarker. BMI was strongly correlated with FMI (r = 0.944 in men and 0.976 in women), PBF (r = 0.735 in men and 0.799 in women), and truncal fat mass (r = 0.914 in men and 0.941 in women) with correlations stronger in women than in men except for with waist-height ratio (r = 0.921 in men and 0.911 in women). The correlation between BMI and DXA derived adiposity weakened with age in both sexes. BMI was less correlated with FMI (r = 0.840 in men and 0.912 in women), PBF (r = 0.645 in men and 0.681 in women), and truncal fat mass (r = 0.836 in men and 0.884 in women) in Korean compared to other race-ethnicities. Among obesity-related biomarkers, insulin was the most strongly correlated to body adiposity indices in both sexes and strength of these correlations generally decreased with age. BMI predicted obesity-related biomarkers as well as FMI and truncal fat mass and superior to PBF. BMI could be a good measure for body fatness, particularly among young age groups, women, the US population, but less so in Korean populations. The lower correlation between BMI and body fatness in older compared to younger age groups could be related to increasing PBF and decreasing lean body mass.

Indian Phenotype Characteristics Among Patients with Type 2 Diabetes Mellitus: Insights from a Non-interventional Nationwide Registry in India

Background: Indian patients with type 2 diabetes mellitus (T2D) constitute one-sixth of affected adults globally. Here, we evaluate the association of body mass index (BMI) with body fat percentage (BF%) and glycated haemoglobin (HbA1c) levels among patients with T2D in India. Method: This was a cross-sectional Indian registry study across 845 geographically diverse zones between December 2017 and August 2019. Results: Of 37,927 patients, 55.6% were men, with a mean ± standard deviation age of 54.2 ± 11.5 years and HbA1c of 8.3 ± 1.71%. Mean ± standard deviation BMI and BF% were 27.0 ± 4.6 kg/m2 and 32.0 ± 8.0%, respectively. Overall, 15.4% of patients were overweight, and 25.0% were obese. Despite fewer males (20.7%) having BMI-based obesity than females (31.2%), around three-quarters of both sexes had BF%-defined obesity (males 77.2%; females 71.2%). One-third of males (34.6%) and 41.9% of females had BF%-defined obesity despite normal BMI. The association was substantiated by a moderately significant correlation (r=0.51) between BMI and BF% in the overall population (p<0.0001). Conclusion: This pan-India registry presents a real-world reflection of the Asian Indian phenotype: high BF% despite lower BMI in people with T2D. This highlights the importance of primordial and primary prevention, and may guide decisions on the choice of agents for glycaemic control, with a preference for drugs that promote weight loss or are weight neutral.