GWAS of random glucose in 476,326 individuals provide insights into diabetes pathophysiology, complications and treatment stratification

Conventional measurements of fasting and postprandial blood glucose levels investigated in genome-wide association studies (GWAS) cannot capture the effects of DNA variability on ‘around the clock’ glucoregulatory processes. Here we show that GWAS meta-analysis of glucose measurements under nonstandardized conditions (random glucose (RG)) in 476,326 individuals of diverse ancestries and without diabetes enables locus discovery and innovative pathophysiological observations. We discovered 120 RG loci represented by 150 distinct signals, including 13 with sex-dimorphic effects, two cross-ancestry and seven rare frequency signals. Of these, 44 loci are new for glycemic traits. Regulatory, glycosylation and metagenomic annotations highlight ileum and colon tissues, indicating an underappreciated role of the gastrointestinal tract in controlling blood glucose. Functional follow-up and molecular dynamics simulations of lower frequency coding variants in glucagon-like peptide-1 receptor (GLP1R), a type 2 diabetes treatment target, reveal that optimal selection of GLP-1R agonist therapy will benefit from tailored genetic stratification. We also provide evidence from Mendelian randomization that lung function is modulated by blood glucose and that pulmonary dysfunction is a diabetes complication. Our investigation yields new insights into the biology of glucose regulation, diabetes complications and pathways for treatment stratification.</p

Stability of the associations between early life risk indicators and adolescent overweight over the evolving obesity epidemic.

BACKGROUND: Pre- and perinatal factors and preschool body size may help identify children developing overweight, but these factors might have changed during the development of the obesity epidemic. OBJECTIVE: We aimed to assess the associations between early life risk indicators and overweight at the age of 9 and 15 years at different stages of the obesity epidemic. METHODS: We used two population-based Northern Finland Birth Cohorts including 4111 children born in 1966 (NFBC1966) and 5414 children born in 1985-1986 (NFBC1986). In both cohorts, we used the same a priori defined prenatal factors, maternal body mass index (BMI), birth weight, infant weight (age 5 months and 1 year), and preschool BMI (age 2-5 years). We used internal references in early childhood to define percentiles of body size (90) and generalized linear models to study the association with overweight, according to the International Obesity Taskforce (IOTF) definitions, at the ages of 9 and 15 years. RESULTS: The prevalence of overweight at the age of 15 was 9% for children born in 1966 and 16% for children born in 1986. However, medians of infant weight and preschool BMI changed little between the cohorts, and we found similar associations between maternal BMI, infant weight, preschool BMI, and later overweight in the two cohorts. At 5 years, children above the 90th percentile had approximately a 12 times higher risk of being overweight at the age of 15 years compared to children below the 50th percentile in both cohorts. CONCLUSIONS: The associations between early body size and adolescent overweight showed remarkable stability, despite the increase in prevalence of overweight over the 20 years between the cohorts. Using consequently defined internal percentiles may be a valuable tool in clinical practice

Preschool weight and body mass index in relation to central obesity and metabolic syndrome in adulthood.

BACKGROUND: If preschool measures of body size routinely collected at preventive health examinations are associated with adult central obesity and metabolic syndrome, a focused use of these data for the identification of high risk children is possible. The aim of this study was to test the associations between preschool weight and body mass index (BMI) and adult BMI, central obesity and metabolic alterations. METHODS: The Northern Finland Birth Cohort 1966 (NFBC1966) (N = 4111) is a population-based cohort. Preschool weight (age 5 months and 1 year) and BMI (age 2-5 years) were studied in relation to metabolic syndrome as well as BMI, waist circumference, lipoproteins, blood pressure, and fasting glucose at the age of 31 years. Linear regression models and generalized linear regression models with log link were used. RESULTS: Throughout preschool ages, weight and BMI were significantly linearly associated with adult BMI and waist circumference. Preschool BMI was inversely associated with high-density lipoprotein levels from the age of 3 years. Compared with children in the lower half of the BMI range, the group of children with the 5% highest BMI at the age of 5 years had a relative risk of adult obesity of 6.2(95% CI:4.2-9.3), of adult central obesity of 2.4(95% CI:2.0-2.9), and of early onset adult metabolic syndrome of 2.5(95% CI:1.7-3.8). CONCLUSIONS: High preschool BMI is consistently associated with adult obesity, central obesity and early onset metabolic syndrome. Routinely collected measures of body size in preschool ages can help to identify children in need of focused prevention due to their increased risk of adverse metabolic alterations in adulthood

SCOPA and META-SCOPA: software for the analysis and aggregation of genome-wide association studies of multiple correlated phenotypes

Abstract Background: Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits

Bifidobacterium Is Enriched in Gut Microbiome of Kashmiri Women with Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) is a very common endocrine condition in women in India. Gut microbiome alterations were shown to be involved in PCOS, yet it is remarkably understudied in Indian women who have a higher incidence of PCOS as compared to other ethnic populations. During the regional PCOS screening program among young women, we recruited 19 drug naive women with PCOS and 20 control women at the Sher-i-Kashmir Institute of Medical Sciences, Kashmir, North India. We profiled the gut microbiome in faecal samples by 16S rRNA sequencing and included 40/58 operational taxonomic units (OTUs) detected in at least 1/3 of the subjects with relative abundance (RA) ≥ 0.1%. We compared the RAs at a family/genus level in PCOS/non-PCOS groups and their correlation with 33 metabolic and hormonal factors, and corrected for multiple testing, while taking the variation in day of menstrual cycle at sample collection, age and BMI into account. Five genera were significantly enriched in PCOS cases: Sarcina, Megasphaera, and previously reported for PCOS Bifidobacterium, Collinsella and Paraprevotella confirmed by different statistical models. At the family level, the relative abundance of Bifidobacteriaceae was enriched, whereas Peptococcaceae was decreased among cases. We observed increased relative abundance of Collinsella and Paraprevotella with higher fasting blood glucose levels, and Paraprevotella and Alkalibacterium with larger hip, waist circumference, weight, and Peptococcaceae with lower prolactin levels. We also detected a novel association between Eubacterium and follicle-stimulating hormone levels and between Bifidobacterium and alkaline phosphatase, independently of the BMI of the participants. Our report supports that there is a relationship between gut microbiome composition and PCOS with links to specific reproductive health metabolic and hormonal predictors in Indian women

Estimation of newborn risk for child or adolescent obesity:lessons from longitudinal birth cohorts

OBJECTIVES: Prevention of obesity should start as early as possible after birth. We aimed to build clinically useful equations estimating the risk of later obesity in newborns, as a first step towards focused early prevention against the global obesity epidemic. METHODS: We analyzed the lifetime Northern Finland Birth Cohort 1986 (NFBC1986) (N = 4,032) to draw predictive equations for childhood and adolescent obesity from traditional risk factors (parental BMI, birth weight, maternal gestational weight gain, behaviour and social indicators), and a genetic score built from 39 BMI/obesity-associated polymorphisms. We performed validation analyses in a retrospective cohort of 1,503 Italian children and in a prospective cohort of 1,032 U.S. children. RESULTS: In the NFBC1986, the cumulative accuracy of traditional risk factors predicting childhood obesity, adolescent obesity, and childhood obesity persistent into adolescence was good: AUROC = 0·78[0·74-0.82], 0·75[0·71-0·79] and 0·85[0·80-0·90] respectively (all p\u3c0·001). Adding the genetic score produced discrimination improvements ≤1%. The NFBC1986 equation for childhood obesity remained acceptably accurate when applied to the Italian and the U.S. cohort (AUROC = 0·70[0·63-0·77] and 0·73[0·67-0·80] respectively) and the two additional equations for childhood obesity newly drawn from the Italian and the U.S. datasets showed good accuracy in respective cohorts (AUROC = 0·74[0·69-0·79] and 0·79[0·73-0·84]) (all p\u3c0·001). The three equations for childhood obesity were converted into simple Excel risk calculators for potential clinical use. CONCLUSION: This study provides the first example of handy tools for predicting childhood obesity in newborns by means of easily recorded information, while it shows that currently known genetic variants have very little usefulness for such prediction

Life-Course Analysis of a Fat Mass and Obesity-Associated (FTO) Gene Variant and Body Mass Index in the Northern Finland Birth Cohort 1966 Using Structural Equation Modeling

The association between variation in the fat mass and obesity-associated (FTO) gene and adulthood body mass index (BMI; weight (kg)/height (m)2) is well-replicated. More thorough analyses utilizing phenotypic data over the life course may deepen our understanding of the development of BMI and thus help in the prevention of obesity. The authors used a structural equation modeling approach to explore the network of variables associated with BMI from the prenatal period to age 31 years (1965–1997) in 4,435 subjects from the Northern Finland Birth Cohort 1966. The use of structural equation modeling permitted the easy inclusion of variables with missing values in the analyses without separate imputation steps, as well as differentiation between direct and indirect effects. There was an association between the FTO single nucleotide polymorphism rs9939609 and BMI at age 31 years that persisted after controlling for several relevant factors during the life course. The total effect of the FTO variant on adult BMI was mostly composed of the direct effect, but a notable part was also arising indirectly via its effects on earlier BMI development. In addition to well-established genetic determinants, many life-course factors such as physical activity, in spite of not showing mediation or interaction, had a strong independent effect on BMI

Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression

Motivation: A typical genome-wide association study searches for associations between single nucleotide polymorphisms (SNPs) and a univariate phenotype. However, there is a growing interest to investigate associations between genomics data and multivariate phenotypes, for example, in gene expression or metabolomics studies. A common approach is to perform a univariate test between each genotype–phenotype pair, and then to apply a stringent significance cutoff to account for the large number of tests performed. However, this approach has limited ability to uncover dependencies involving multiple variables. Another trend in the current genetics is the investigation of the impact of rare variants on the phenotype, where the standard methods often fail owing to lack of power when the minor allele is present in only a limited number of individuals. Results: We propose a new statistical approach based on Bayesian reduced rank regression to assess the impact of multiple SNPs on a high-dimensional phenotype. Because of the method’s ability to combine information over multiple SNPs and phenotypes, it is particularly suitable for detecting associations involving rare variants. We demonstrate the potential of our method and compare it with alternatives using the Northern Finland Birth Cohort with 4702 individuals, for whom genome-wide SNP data along with lipoprotein profiles comprising 74 traits are available. We discovered two genes (XRCC4 and MTHFD2L) without previously reported associations, which replicated in a combined analysis of two additional cohorts: 2390 individuals from the Cardiovascular Risk in Young Finns study and 3659 individuals from the FINRISK study. Availability and implementation: R-code freely available for download at http://users.ics.aalto.fi/pemartti/gene_metabolome/. Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Genetic Variant in HK1 Is Associated With a Proanemic State and A1C but Not Other Glycemic Control–Related Traits

OBJECTIVE A1C is widely considered the gold standard for monitoring effective blood glucose levels. Recently, a genome-wide association study reported an association between A1C and rs7072268 within HK1 (encoding hexokinase 1), which catalyzes the first step of glycolysis. HK1 deficiency in erythrocytes (red blood cells [RBCs]) causes severe nonspherocytic hemolytic anemia in both humans and mice. RESEARCH DESIGN AND METHODS The contribution of rs7072268 to A1C and the RBC-related traits was assessed in 6,953 nondiabetic European participants. We additionally analyzed the association with hematologic traits in 5,229 nondiabetic European individuals (in whom A1C was not measured) and 1,924 diabetic patients. Glucose control–related markers other than A1C were analyzed in 18,694 nondiabetic European individuals. A type 2 diabetes case-control study included 7,447 French diabetic patients. RESULTS Our study confirms a strong association between the rs7072268–T allele and increased A1C (β = 0.029%; P = 2.22 × 10−7). Surprisingly, despite adequate study power, rs7072268 showed no association with any other markers of glucose control (fasting- and 2-h post-OGTT–related parameters, n = 18,694). In contrast, rs7072268–T allele decreases hemoglobin levels (n = 13,416; β = −0.054 g/dl; P = 3.74 × 10−6) and hematocrit (n = 11,492; β = −0.13%; P = 2.26 × 10−4), suggesting a proanemic effect. The T allele also increases risk for anemia (836 cases; odds ratio 1.13; P = 0.018). CONCLUSIONS HK1 variation, although strongly associated with A1C, does not seem to be involved in blood glucose control. Since HK1 rs7072268 is associated with reduced hemoglobin levels and favors anemia, we propose that HK1 may influence A1C levels through its anemic effect or its effect on glucose metabolism in RBCs. These findings may have implications for type 2 diabetes diagnosis and clinical management because anemia is a frequent complication of the diabetes state