Model diagram. Individuals in the simulated population are assigned demographic characteristics based on the joint probabilities of being in each age, sex, location, and income group given population demographic estimates for India.

<p>They are then assigned a probability of having diabetes, either diagnosed or undiagnosed, and having various associated co-morbid risk factors, based on the joint probabilities of these prevalence rates and factors listed in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001827#pmed.1001827.s004" target="_blank">S2 Table</a> and illustrated in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001827#pmed.1001827.s001" target="_blank">S1 Fig</a> based on prior population estimates for India. Individuals are then subject to the screening instruments listed in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001827#pmed.1001827.t001" target="_blank">Table 1</a>, from which the model estimates positive and negative test results and subsequent diabetes complications with and without treatment.</p

Comparison of instrument performance in published sub-national populations versus synthetic national population [6–8,15].

<p>95% credible intervals are shown in parentheses. In all cases, the screening instrument is the first-stage test, and individuals testing positive are then subject to fasting blood glucose testing for diagnostic confirmation.</p><p>Comparison of instrument performance in published sub-national populations versus synthetic national population [<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001827#pmed.1001827.ref006" target="_blank">6</a>–<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001827#pmed.1001827.ref008" target="_blank">8</a>,<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001827#pmed.1001827.ref015" target="_blank">15</a>].</p

Alternative risk factors included in survey-based screening instruments proposed for detecting undiagnosed diabetes in India [6–8].

<p>The model subjects each simulated individual to each of the listed screening instruments to identify how many people would test positive or negative by each instrument.</p><p>Alternative risk factors included in survey-based screening instruments proposed for detecting undiagnosed diabetes in India [<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001827#pmed.1001827.ref006" target="_blank">6</a>–<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001827#pmed.1001827.ref008" target="_blank">8</a>].</p

Correlations (Spearman’s ρ) between abdominal fat thicknesses and independent variables in men (n = 1,371) and women (n = 1,434).

<p>Correlations (Spearman’s ρ) between abdominal fat thicknesses and independent variables in men (n = 1,371) and women (n = 1,434).</p

Comparison between the Pelotas (Brazil) birth cohort sample with information on ultrasound measurements of abdominal fat in 2012–13 and the subsample with genomic ancestry data according to sex, socioeconomic position indicators and BMI.

<p>Comparison between the Pelotas (Brazil) birth cohort sample with information on ultrasound measurements of abdominal fat in 2012–13 and the subsample with genomic ancestry data according to sex, socioeconomic position indicators and BMI.</p

Triangle plot of the 1982 Pelotas (Brazil) birth cohort’s members according to ancestry admixture proportions.

<p>Each symbol represents an individual. Each person was genotyped and ancestry-informative markers were used to provide information on African, Native American, and European ancestry.</p

Description of the study sample.

<p>Description of the study sample.</p

Standardized regression coefficients¹ for visceral and subcutaneous abdominal fat distribution according to ancestry markers and socioeconomic position indicators, adjusted for current BMI.

<p>Standardized regression coefficients<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0179085#t005fn001" target="_blank">¹</a> for visceral and subcutaneous abdominal fat distribution according to ancestry markers and socioeconomic position indicators, adjusted for current BMI.</p

Characteristics of the study sample.

<p>Characteristics of the study sample.</p

Associations of stunting at age 2 years with glycated haemglobin, total cholesterol and HDL cholesterol, and fat free mass at age 30 years by sex.

<p>Associations of stunting at age 2 years with glycated haemglobin, total cholesterol and HDL cholesterol, and fat free mass at age 30 years by sex.</p