Additional file 3: Table S2. of Abdominal fat depots associated with insulin resistance and metabolic syndrome risk factors in black African young adults

Pearson’s correlation coefficients (P-values) between body fat parameters and individual metabolic traits in women. (XLS 11 kb

Additional file 1: Figure S1. of Abdominal fat depots associated with insulin resistance and metabolic syndrome risk factors in black African young adults

MRI image taken at the L4 vertebral body, showing visceral adipose tissue (VAT) in green, superficial subcutaneous adipose tissue (S-SCAT) in blue, and deep subcutaneous adipose tissue. (D_SCAT) in pink. (TIFF 429 kb

Additional file 1: of Vomiting in pregnancy is associated with a higher risk of low birth weight: a cohort study

Description of data: the data that was associated with the manuscript entitled “Vomiting in pregnancy is associated with a higher risk of low birth weight: a cohort study” by Petry et al. ( https://doi.org/10.1186/s12884-018-1786-1 ) (XLSX 149 kb

Additional file 1: Table S1. of Visceral and subcutaneous abdominal adiposity and pulmonary function in 30-year-old adults: a cross-sectional analysis nested in a birth cohort

Association between pulmonary function (z-score) and confounding variables. 1982 Pelotas Birth Cohort (n = 3438). (DOCX 58 kb

Distinct body mass index trajectories to young-adulthood obesity and their different cardiometabolic consequences

Objective: Different body mass index (BMI) trajectories that result in obesity may have diverse health consequences, yet this heterogeneity is poorly understood. We aimed to identify distinct classes of individuals who share similar BMI trajectories and examine associations with cardiometabolic health. Approach and Results: Using data on 3,549 participants in the Avon Longitudinal Study of Parents and Children (ALSPAC), a growth mixture model was developed to capture heterogeneity in BMI trajectories between 7·5 and 24·5 years. Differences between identified classes in height growth curves, body composition trajectories, early-life characteristics, and a panel of cardiometabolic health measures at 24·5 years were investigated. The best mixture model had six classes. There were two normal-weight classes: “normal-weight [non-linear]” (35% of sample) and “normal-weight [linear]” (21%). Two classes resulted in young-adulthood overweight: “normal-weight increasing to overweight” (18%) and “normal-weight or overweight” (16%). Two classes resulted in young-adulthood obesity: “normal-weight increasing to obesity” (6%) and “overweight or obesity” (4%). The “normal weight increasing to overweight” class had more unfavourable levels of trunk fat, blood pressure, insulin, high density lipoprotein cholesterol, left ventricular mass, and E/e′ ratio compared to the “always normal weight or overweight” class, despite the average BMI trajectories for both classes converging at ~26 kg/m2 at 24·5 years. Similarly, the “normal-weight increasing to obesity” class had a worse cardiometabolic profile than the “always overweight or obese” class. Conclusions: Individuals with high and stable BMI across childhood may have lower cardiometabolic disease risk than individuals who do not become overweight or obese until late adolescence

Additional file 3: of Identifying and correcting epigenetics measurements for systematic sources of variation

Figure S3. Quantile-quantile (QQ) plots for CpG site-specific analysis with respect to smoking using standard adjustment (a), residuals (b), ComBat (c) and SVA (d) correcting methods for the M values. The inflation factor λ is defined as the ratio of the median of the observed log10 transformed p values from the CpG site-specific analysis and the median of the expected log10 transformed p values. (PDF 110 kb

Additional file 2: of Identifying and correcting epigenetics measurements for systematic sources of variation

Figure S2. Quantile-quantile (QQ) plots for CpG site-specific analysis with respect to smoking using standard adjustment (a), residuals (b), ComBat (c) and SVA (d) correcting methods for the β values. The inflation factor λ is defined as the ratio of the median of the observed log10 transformed p values from the CpG site-specific analysis and the median of the expected log10 transformed p values. (PDF 110 kb

Additional file 1: of Identifying and correcting epigenetics measurements for systematic sources of variation

Figure S1. Box plots of global methylation (M values) according to laboratory factors: batch (a), chip position within batches (b), sample position within chips (c). (PDF 99 kb

Disease event definitions, incident and prevalent events by disease and medication use in the UCLEB consortium.

<p>Total incident CHD =  incident non-fatal MI or revascularization plus fatal CHD (ICD codes I20–I25, I516).</p><p>Total prevalent CHD  =  prevalent non-fatal MI or revascularization.</p><p>Total incident stroke  =  incident non-fatal stroke (ischaemic & haemorrhagic combined, but excluding TIA) plus fatal stroke (ICD codes I60×, I61×, I62, I629 I63×, I64× I65× I66×, I67, I672, I678, I679, I69×, G46×, G450, G451, G452, G453).</p><p>Total diabetes defined by a combination of self-report, medical history review, use of glucose lowering medication, or fasting glucose >7 mmol/L.</p>*<p>1958BC are currently undertaking case ascertainment.</p

Information on genotyped and imputed SNPs.

*<p>The WHII study was typed at two centres with different array readers, which were compared and shown to be concordant.</p