C.S. Yajnik

Body fat measurement in indian men : comparison of three methods based on a two-compartment model

Obesity is a major risk factor for diabetes and related disorders. The current classification of obesity is based on body mass index (BMI, kg/m2), which is a surrogate for the total body fat. Since the relationship between BMI and body fat varies in different populations, an independent validation of the BMI–body fat relationship in the population of interest is desirable.OBJECTIVES:(1) To study the validity of field methods of measuring body fat (multiple skinfolds and bioimpedance) against a criterion method (deuterium dilution) and (2) To compare the prevalence of obesity (WHO 2000 criteria for BMI) with adiposity (body fat >25%) in middle-aged Indian men in rural and urban Pune.DESIGN:Community-based multistage stratified random sampling of middle-aged men from rural and urban Pune for study of body composition and cardiovascular risk. A third of these men, selected to represent wide BMI distribution, were studied for body fat measurements by specific methods.SUBJECTS:A total of 141 healthy men, approximately similar number from rural, urban slums and middle class from Pune. They were 39.3 (±6.2) y old and had a BMI of 21.9 (±3.7) kg/m2.MEASUREMENTS:Anthropometry (height, weight and multiple skinfold thicknesses) by trained observers using standardised technique to calculate body fat by Durnin and Womersleys equation. Total body water and body fat by bioelectrical impedance analysis (BIA) and deuterium oxide dilution (D2O).RESULTS:Mean total body fat was 14.3 kg (23.0%) by anthropometry, 16.5 kg (26.0%) by BIA and 15.3 kg (24.6%) by D2O method. Although there was a good correlation between fat estimation by three methods (r=∼0.9, P<0.001 all), compared to D2O method anthropometry underestimated body fat by 1.0 kg and BIA overestimated fat by 1.2 kg (P<0.001 both). Using the standard cut-point of 25% body fat for ‘adiposity’ 29.5% rural, 46.0% slum and 75.0% middle class men were adipose. These proportions were considerably higher than the number of men who were ‘preobese’ (BMI≥25–29.9 kg/m2, 9.0% rural, 22.0% urban slums and 27.0% urban middle class) and ‘obese’ (BMI >30 kg/m2, 4.0% urban slums, none in rural and urban middle class).CONCLUSION:We recommend that future studies assessing risk for chronic diseases in Indians should measure adiposity by anthropometry (multiple skinfolds) or BIA (calibrated for Indians) rather than relying only on BMI cut-points.

The ketosis-resistance in fibro-calculous-pancreatic-diabetes. 1. Clinical observations and endocrine-metabolic measurements during oral glucose tolerance test.

We measured circulating levels of C-peptide, pancreatic glucagon, cortisol, growth hormone and metabolites (glucose, non-esterified fatty acids, glycerol and 3-hydroxybutyrate) in fibro-calculous-pancreatic diabetic (FCPD, n = 28), insulin-dependent diabetic (IDDM, n = 28) and non-diabetic control (n = 27) subjects during an oral glucose tolerance test. There was no difference in the two diabetic groups in age (FCPD 24 +/- 2, IDDM 21 +/- 2 years, mean +/- SEM), BMI (FCPD 16.0 +/- 0.6, IDDM 15.7 +/- 0.4 kg/m2), triceps skinfold thickness (FCPD 8 +/- 1, IDDM 7 +/- 1 mm), glycaemic status (fasting plasma glucose, FCPD 12.5 +/- 1.5, IDDM 14.5 +/- 1.2 mmol/l), fasting plasma C-peptide (FCPD 0.13 +/- 0.03, IDDM 0.08 +/- 0.01 nmol/l), peak plasma C-peptide during OGTT (FCPD 0.36 +/- 0.10, IDDM 0.08 +/- 0.03 nmol/l) and fasting plasma glucagon (FCPD 35 +/- 4, IDDM 37 +/- 4 ng/l). FCPD patients, however, showed lower circulating concentrations of non-esterified fatty acids (0.73 +/- 0.11 mmol/l), glycerol (0.11 +/- 0.02 mmol/l) and 3-hydroxybutyrate (0.15 +/- 0.03 mmol/l) compared to IDDM patients (1.13 +/- 0.14, 0.25 +/- 0.05 and 0.29 +/- 0.08 mmol/l, respectively). This could be due to enhanced sensitivity of adipose tissue lipolysis to the suppressive action of circulating insulin and possibly also to insensitivity of hepatic ketogenesis to glucagon. Our results also demonstrate preservation of alpha-cell function in FCPD patients when beta-cell function is severely diminished, suggesting a more selective beta-cell dysfunction or destruction than hitherto believed.

The Relationship Between Obesity, Plasma Immunoreactive Insulin Concentration and Blood Pressure in Newly Diagnosed Indian Type 2 Diabetic Patients

The association of blood pressure with clinical and biochemical measures was studied in 185 newly diagnosed Type 2 diabetic patients, 74 impaired‐glucose‐tolerant (IGT) and 128 non‐diabetic control subjects. Hyperglycaemic subjects were older than control subjects (controls 40 (24–59) years, IGT 48 (29–64) years, diabetic 43 (29–60) years, median (5th‐95th centile) both p < 0.05). They were also more obese (body mass index (BMI) controls 23.5 kg m−2 (17.2–29.9), IGT 26.0 kg m−2 (19.8–33.9), diabetic 24.2 kg m−2 (19.3–32.2)) and with a greater waist‐hip ratio (controls 0.83 (0.70–0.98), IGT 0.88 (0.75–0.98), diabetic 0.89 (0.75–1.00)). Blood pressure was significantly higher in both IGT (systolic 127mmHg (108–162), diastolic 80 mmHg (66–99)) and diabetic patients (systolic 130 mmHg (104–160), diastolic 84 mmHg (66–102)) compared to non‐diabetic controls (systolic 120 mmHg (100–151), diastolic 80 mmHg (60–94)). Univariate analysis showed that in diabetic patients systolic blood pressure was related to age (r = 0.17, p < 0.05), BMI (r= 0.23, p < 0.01) and plasma immunoreactive insulin (fasting and post glucose, r= ˜ 0.25, p<0.01) but not to C‐peptide concentrations; diastolic blood pressure to BMI (r= 0.35, p < 0.001), waist‐hip ratio (r = 0.23, p < 0.01) and plasma immunoreactive insulin (fasting r= 0.30, p < 0.001, post glucose r = ˜ 0.20, p < 0.05) but not to C‐peptide concentrations. Multivariate analysis revealed that systolic blood pressure in diabetic patients was related to BMI (p < 0.01) and fasting immunoreactive insulin (p < 0.05) while diastolic blood pressure was related to BMI (p < 0.001) and waist‐hip ratio (p < 0.01). Thus, blood pressure is associated with obesity even in our relatively non‐obese population and it is also associated with plasma immunoreactive insulin concentrations. The mechanism of these associations remains to be established.

Circulating lipids and cardiovascular risk in newly diagnosed non‐insulin‐dependent diabetic subjects in India

Circulating concentrations of total cholesterol, triglycerides, non‐esterified fatty acids (NEFA), glycerol, and 3‐hydroxybutyrate (3‐HB) were measured in 133 subjects with normal glucose tolerance (NGT), 78 with impaired‐glucose‐tolerance (IGT) and 189 non‐insulin dependent (Type 2) diabetic (NIDDM) patients. Plasma cholesterol concentration was similar in the three groups; NGT (4.2 (2.3–7.5) mmol l−1 , median (range)), IGT (4.7 (2.7–6.3)) and NIDDM (4.3 (2.3–6.9)). Plasma triglycerides (NGT 0.88 (0.37–2.80), IGT 1.26 (0.43–3.82) and NIDDM 1.38 (0.62–3.91) mmol l−1 ) and NEFA (NGT 0.81 (0.29–1.58), IGT 1.02 (0.33–1.87) and NIDDM 1.02 (0.48–2.77) mmol l−1 ) were higher in the two hyperglycaemic groups, but blood 3‐HB concentration was similar in the three groups. Plasma cholesterol concentration in these subjects is lower than that reported in white Caucasians in the UK and USA and migrant Indian NIDDM patients in the UK. In NIDDM patients plasma cholesterol concentration was related to age, body mass index (BMI), and plasma glucose concentration while plasma triglyceride concentration was related to plasma NEFA and insulin (IRI) concentration. Evidence of ischaemia on electrocardiography in patients with diabetes was associated with higher age, blood pressure, plasma triglyceride, glucose, and IRI concentrations. © 1997 by John Wiley & Sons, Ltd.

Lack of replication of association of THSD7A with obesity.

With interest we read the recently published article by Nizamuddin et al. that reported a novel locus THSD7A to be associated with body mass index (BMI) in the Indian population. The discovery is important for two reasons; one because obesity as an intermediate trait is a shared risk factor for many disease conditions including type 2 diabetes and cardiovascular disorders, and second, THSD7A is proposed to be a population-specific and male-specific locus, although the latter is not highlighted in the observations. Several obesity-associated loci, such as FTO, MC4R, TMEM18, and so on have been identified in European populations, and we and others have reported similar association for them in the Indian population. However, Nizamuddin et al. failed to replicate them in their study samples and hence, the results need to be interpreted with caution. We performed replication analysis using normal adults from two homogeneous populations. Parents of children in the Pune Maternal Nutrition Study (PMNS; Indo-Europeans from Western India; n=1761(829 males/932 females)) and the Parthenon Study ((PS; Dravidians from South India; n=830 (400 males/430 females)) were genotyped for rs1526538 in THSD7A by Sanger-sequencing using primers described earlier (Supplementary Table 1). Frequency of the risk allele ‘T’ at rs1526538 was 0.49 in both the cohorts and similar to the reported study and in South Asian ancestry populations in 1000 G data. Association analysis using linear regression additive model, adjusted for age and gender failed to detect significant or suggestive association of rs1526538 with BMI in PMNS (β=− 0.0007, P=0.996) or PS (β=− 0.300, P=0.133) or on meta-analysis (β=− 0.091; P=0.407) or any other anthropometric parameters (Table 1). Further analysis comparing underweight, normal, overweight and obese groups using standard WHO criteria did not replicate the association of THSD7A with obesity (β=− 0.042, P=0.136). Gender-specific analysis also showed similar results. It is intriguing that we could not replicate THSD7A as an Indian population-specific obesity locus, despite having a sample size that was four times larger and 499% powered to detect the association with BMI at same effect size as the original study (β=−1.0104 for P=10). We can only speculate the possible reasons which may be related to the choice of samples and population stratification leading to probable spurious association. The premise of the reported study was to identify novel BMI-associated loci in South Indians as they are not related to any group outside Indian subcontinent. However, the discovery set comprises small number of individuals from different geographical regions, which surprisingly did not show population stratification on principal component analysis (PCA), although the original study demonstrated several clusters. This could be attributed to failure to include the known and established samples identified in their earlier study as positive control for PCA. The same could be the reason for their failure to replicate association of two strongest obesity-associated loci, FTO and MC4R. In conclusion, our findings underline the importance of choosing appropriate samples for association analysis. They also imply that the status of THSD7A needs to be validated in another population, avoiding any population stratification in a country with huge genetic diversity.

Candidate genes linking maternal nutrient exposure to offspring health via DNA methylation: a review of existing evidence in humans with specific focus on one-carbon metabolism.

Abstract Background Mounting evidence suggests that nutritional exposures during pregnancy influence the fetal epigenome, and that these epigenetic changes can persist postnatally, with implications for disease risk across the life course. Methods We review human intergenerational studies using a three-part search strategy. Search 1 investigates associations between preconceptional or pregnancy nutritional exposures, focusing on one-carbon metabolism, and offspring DNA methylation. Search 2 considers associations between offspring DNA methylation at genes found in the first search and growth-related, cardiometabolic and cognitive outcomes. Search 3 isolates those studies explicitly linking maternal nutritional exposure to offspring phenotype via DNA methylation. Finally, we compile all candidate genes and regions of interest identified in the searches and describe their genomic locations, annotations and coverage on the Illumina Infinium Methylation beadchip arrays. Results We summarize findings from the 34 studies found in the first search, the 31 studies found in the second search and the eight studies found in the third search. We provide details of all regions of interest within 45 genes captured by this review. Conclusions Many studies have investigated imprinted genes as priority loci, but with the adoption of microarray-based platforms other candidate genes and gene classes are now emerging. Despite a wealth of information, the current literature is characterized by heterogeneous exposures and outcomes, and mostly comprise observational associations that are frequently underpowered. The synthesis of current knowledge provided by this review identifies research needs on the pathway to developing possible early life interventions to optimize lifelong health.