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Mice deficient in the lipid sensor GPR120 develop obesity, glucose intolerance and fatty liver when fed a high-fat diet, and a loss-of-function variant in the GPR120 gene strongly contributes to increased obesity in human. A signal for obesity The G-protein-coupled receptor GPR120 is a receptor for free fatty acids, and is involved in homeostasis mechanisms such as fat-cell generation and the regulation of appetite. Here it is shown that without GPR120, mice on a high-fat diet develop obesity, glucose intolerance and fatty liver. In humans, GPR120 expression in adipose tissue is shown to be significantly elevated in obesity. The authors also identify a mutation that inhibits GPR120 signalling activity and is associated with an increased risk for obesity in Europeans. Free fatty acids provide an important energy source as nutrients, and act as signalling molecules in various cellular processes 1 , 2 , 3 , 4 . Several G-protein-coupled receptors have been identified as free-fatty-acid receptors important in physiology as well as in several diseases 3 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 . GPR120 (also known as O3FAR1) functions as a receptor for unsaturated long-chain free fatty acids and has a critical role in various physiological homeostasis mechanisms such as adipogenesis, regulation of appetite and food preference 5 , 6 , 14 , 15 , 16 . Here we show that GPR120-deficient mice fed a high-fat diet develop obesity, glucose intolerance and fatty liver with decreased adipocyte differentiation and lipogenesis and enhanced hepatic lipogenesis. Insulin resistance in such mice is associated with reduced insulin signalling and enhanced inflammation in adipose tissue. In human, we show that GPR120 expression in adipose tissue is significantly higher in obese individuals than in lean controls. GPR120 exon sequencing in obese subjects reveals a deleterious non-synonymous mutation (p.R270H) that inhibits GPR120 signalling activity. Furthermore, the p.R270H variant increases the risk of obesity in European populations. Overall, this study demonstrates that the lipid sensor GPR120 has a key role in sensing dietary fat and, therefore, in the control of energy balance in both humans and rodents.

Increased adiposity is linked with higher risk for cardiometabolic diseases. We aimed to determine to what extent elevated body mass index (BMI) within the normal weight range has causal effects on the detailed systemic metabolite profile in early adulthood. We used Mendelian randomization to estimate causal effects of BMI on 82 metabolic measures in 12,664 adolescents and young adults from four population-based cohorts in Finland (mean age 26 y, range 16-39 y; 51% women; mean ± standard deviation BMI 24 ± 4 kg/m(2)). Circulating metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics and biochemical assays. In cross-sectional analyses, elevated BMI was adversely associated with cardiometabolic risk markers throughout the systemic metabolite profile, including lipoprotein subclasses, fatty acid composition, amino acids, inflammatory markers, and various hormones (p<0.0005 for 68 measures). Metabolite associations with BMI were generally stronger for men than for women (median 136%, interquartile range 125%-183%). A gene score for predisposition to elevated BMI, composed of 32 established genetic correlates, was used as the instrument to assess causality. Causal effects of elevated BMI closely matched observational estimates (correspondence 87% ± 3%; R(2)= 0.89), suggesting causative influences of adiposity on the levels of numerous metabolites (p<0.0005 for 24 measures), including lipoprotein lipid subclasses and particle size, branched-chain and aromatic amino acids, and inflammation-related glycoprotein acetyls. Causal analyses of certain metabolites and potential sex differences warrant stronger statistical power. Metabolite changes associated with change in BMI during 6 y of follow-up were examined for 1,488 individuals. Change in BMI was accompanied by widespread metabolite changes, which had an association pattern similar to that of the cross-sectional observations, yet with greater metabolic effects (correspondence 160% ± 2%; R(2) = 0.92). Mendelian randomization indicates causal adverse effects of increased adiposity with multiple cardiometabolic risk markers across the metabolite profile in adolescents and young adults within the non-obese weight range. Consistent with the causal influences of adiposity, weight changes were paralleled by extensive metabolic changes, suggesting a broadly modifiable systemic metabolite profile in early adulthood. Please see later in the article for the Editors' Summary.

To assess the frequency and perinatal outcomes of gestational diabetes mellitus (GDM) defined by the criteria according to the International Association of Diabetes in Pregnancy Study Group (IADPSG) and the National Institute for Health and Care Excellence (NICE) diagnostic criteria for GDM. A retrospective cohort study. Six secondary and tertiary delivery hospitals in Finland in 2009. Pregnant women (N = 4,033) and their offspring. We used data on comprehensive screening of pregnant women with a 2-h 75-g oral glucose tolerance test (OGTT), performed between gestational weeks 24 and 40. OGTT glucose concentrations were used to identify women who fulfilled IADPSG and NICE criteria. While cut-offs according to Finnish national criteria partly overlapped with both criteria, a subgroup of IADPSG- or NICE-positive GDM women remained undiagnosed by Finnish criteria and hence non-treated. They were analysed as subgroups and compared to controls who were negative with all cut-offs. GDM prevalence, birth weight SD score (BWSDS), large for gestational age (LGA) and caesarean section (CS) rates. Among the 4,033 women screened for GDM, 1,249 (31.0%) and 529 (13.1%) had GDM according to the IADPSG and NICE criteria, respectively. The LGA rate was similar in both groups. Regardless of the diagnostic criteria, women with GDM had a higher risk of induced delivery and CSs than controls. In IADPSG-positive non-treated women, offspring's BWSDS and CS rate were higher than in controls. GDM prevalence was 2.4-fold higher according to the IADPSG compared with the NICE criteria but the LGA rate did not differ. BWSDS and CS rate were increased already with mild untreated hyperglycaemia.

Background Gestational diabetes mellitus (GDM) is a common pregnancy-related disorder and a well-known risk factor for adverse pregnancy outcomes. There are conflicting findings on the association of GDM with the risk of congenital anomalies (CAs) in offspring. In this study, we aimed to determine study whether maternal GDM is associated with an increased risk of major CAs in offspring. Methods This Finnish Gestational Diabetes (FinnGeDi) register-based study included 6,597 women with singleton pregnancies and a diagnosis of GDM and 51,981 singleton controls with no diabetes identified from the Finnish Medical Birth Register (MBR) in 2009. Data from MBR were combined in this study with the Register of Congenital Malformations, which includes the data of CAs. We used logistic regression to calculate odds ratios (OR) for CAs, together with their 95% confidence intervals (CIs), adjusting for maternal age, parity, pre-pregnancy body mass index (BMI), and maternal smoking status. Results The risk of major CAs was higher in the GDM-exposed ( n  = 336, 5.09%) than in the non-exposed group ( n  = 2,255, 4.33%) (OR: 1.18, 95% CI: 1.05–1.33, p  = 0.005). The adjusted OR (aOR) was 1.14 (95% CI: 1.00-1.30, p  = 0.047). There was a higher overall prevalence of CAs, particularly chromosomal abnormalities (0.52% vs. 0.21%), in the GDM-exposed group (OR: 2.49, 95% Cl: 1.69–3.66, p  < 0.001). The aOR was 1.93 (95% Cl: 1.25–2.99, p  = 0.003). Conclusions Offspring exposed to GDM have a higher prevalence of major CAs. Of note, risk factors other than GDM, such as older maternal age and a higher pre-pregnancy BMI, diminished the between group differences in the prevalence of major CAs. Nevertheless, our findings suggest that offspring exposed to maternal GDM are more likely to be diagnosed with a chromosomal abnormality, independent of maternal age, parity, pre-pregnancy BMI, and smoking.

Nelson Freimer and colleagues report the first genome-wide association study of a longitudinal birth cohort (the Northern Finland Birth Cohort 1966). The results include new associations for nine quantitative metabolic traits. Genome-wide association studies (GWAS) of longitudinal birth cohorts enable joint investigation of environmental and genetic influences on complex traits. We report GWAS results for nine quantitative metabolic traits (triglycerides, high-density lipoprotein, low-density lipoprotein, glucose, insulin, C-reactive protein, body mass index, and systolic and diastolic blood pressure) in the Northern Finland Birth Cohort 1966 (NFBC1966), drawn from the most genetically isolated Finnish regions. We replicate most previously reported associations for these traits and identify nine new associations, several of which highlight genes with metabolic functions: high-density lipoprotein with NR1H3 ( LXRA ), low-density lipoprotein with AR and FADS1 - FADS2 , glucose with MTNR1B , and insulin with PANK1 . Two of these new associations emerged after adjustment of results for body mass index. Gene–environment interaction analyses suggested additional associations, which will require validation in larger samples. The currently identified loci, together with quantified environmental exposures, explain little of the trait variation in NFBC1966. The association observed between low-density lipoprotein and an infrequent variant in AR suggests the potential of such a cohort for identifying associations with both common, low-impact and rarer, high-impact quantitative trait loci.

Pre-eclampsia is a common and complex pregnancy disorder that often involves impaired placental development. In order to identify altered gene expression in pre-eclamptic placenta, we sequenced placental transcriptomes of nine pre-eclamptic and nine healthy pregnant women in pools of three. The differential gene expression was tested both by including all the pools in the analysis and by excluding some of the pools based on phenotypic characteristics. From these analyses, we identified altogether 53 differently expressed genes, a subset of which was validated by qPCR in 20 cases and 19 controls. Furthermore, we conducted pathway and functional analyses which revealed disturbed vascular function and immunological balance in pre-eclamptic placenta. Some of the genes identified in our study have been reported by numerous microarray studies ( BHLHE40 , FSTL3 , HK2 , HTRA4 , LEP , PVRL4 , SASH1 , SIGLEC6 ), but many have been implicated in only few studies or have not previously been linked to pre-eclampsia ( ARMS2 , BTNL9, CCSAP , DIO2 , FER1L4 , HPSE , LOC100129345 , LYN , MYO7B , NCMAP , NDRG1 , NRIP1, PLIN2 , SBSPON, SERPINB9, SH3BP5 , TET3 , TPBG , ZNF175 ). Several of the molecules produced by these genes may have a role in the pathogenesis of pre-eclampsia and some could qualify as biomarkers for prediction or detection of this pregnancy complication.

OBJECTIVE: Early pubertal onset in females is associated with increased risk for adult obesity and cardiovascular disease, but whether this relationship is independent of preceding childhood growth events is unclear. Furthermore, the association between male puberty and adult disease remains unknown. To clarify the link between puberty and adult health, we evaluated the relationship between pubertal timing and risk factors for type 2 diabetes and cardiovascular disease in both males and females from a large, prospective, and randomly ascertained birth cohort from Northern Finland. RESEARCH DESIGN AND METHODS: Pubertal timing was estimated based on pubertal height growth in 5,058 subjects (2,417 males and 2,641 females), and the relationship between puberty and body weight, glucose and lipid homeostasis, and blood pressure at age 31 years was evaluated with linear regression modeling. RESULTS: Earlier pubertal timing associated with higher adult BMI, fasting insulin, diastolic blood pressure, and decreased HDL cholesterol in both sexes (P < 0.002) and with higher total serum cholesterol, LDL cholesterol, and triglycerides in males. The association with BMI and diastolic blood pressure remained statistically significant in both sexes, as did the association with insulin levels and HDL cholesterol concentrations in males after adjusting for covariates reflecting both fetal and childhood growth including childhood BMI. CONCLUSIONS: We demonstrate independent association between earlier pubertal timing and adult metabolic syndrome-related derangements both in males and females. The connection emphasizes that the mechanisms advancing puberty may also contribute to adult metabolic disorders.

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. 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. 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<0·001). 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<0·001). The three equations for childhood obesity were converted into simple Excel risk calculators for potential clinical use. 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.

Common Variation in the FTO Gene Alters Diabetes-Related Metabolic Traits to the Extent Expected Given Its Effect on BMI Rachel M. Freathy 1 , Nicholas J. Timpson 2 3 , Debbie A. Lawlor 3 4 , Anneli Pouta 5 , Yoav Ben-Shlomo 4 , Aimo Ruokonen 5 , Shah Ebrahim 6 , Beverley Shields 1 , Eleftheria Zeggini 2 , Michael N. Weedon 1 , Cecilia M. Lindgren 2 7 , Hana Lango 1 , David Melzer 1 , Luigi Ferrucci 8 , Giuseppe Paolisso 9 , Matthew J. Neville 7 , Fredrik Karpe 7 , Colin N.A. Palmer 10 , Andrew D. Morris 10 , Paul Elliott 11 , Marjo-Riitta Jarvelin 5 11 , George Davey Smith 3 4 , Mark I. McCarthy 2 7 , Andrew T. Hattersley 1 and Timothy M. Frayling 1 1 Peninsula Medical School, Exeter, U.K 2 Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K 3 MRC Centre for Causal Analyses in Translational Epidemiology, Bristol University, Bristol, U.K 4 Department of Social Medicine, Bristol University, Bristol, U.K 5 National Public Health Institute and University of Oulu, Oulu, Finland 6 London School of Hygiene and Tropical Medicine, London, U.K 7 Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, U.K 8 National Institute on Aging, National Institutes of Health, Bethesda, Maryland 9 II University of Naples, Naples, Italy 10 Ninewells Hospital and Medical School, University of Dundee, Nethergate, Dundee, Scotland, U.K 11 Department of Epidemiology and Public Health, Imperial College, London, U.K Corresponding author: Prof. Timothy M. Frayling, Institute of Biomedical and Clinical Science, Peninsula Medical School, Magdalen Rd., Exeter, EX1 2LU, U.K. E-mail: tim.frayling{at}pms.ac.uk Abstract OBJECTIVE— Common variation in the FTO gene is associated with BMI and type 2 diabetes. Increased BMI is associated with diabetes risk factors, including raised insulin, glucose, and triglycerides. We aimed to test whether FTO genotype is associated with variation in these metabolic traits. RESEARCH DESIGN AND METHODS— We tested the association between FTO genotype and 10 metabolic traits using data from 17,037 white European individuals. We compared the observed effect of FTO genotype on each trait to that expected given the FTO -BMI and BMI-trait associations. RESULTS— Each copy of the FTO rs9939609 A allele was associated with higher fasting insulin (0.039 SD [95% CI 0.013–0.064]; P = 0.003), glucose (0.024 [0.001–0.048]; P = 0.044), and triglycerides (0.028 [0.003–0.052]; P = 0.025) and lower HDL cholesterol (0.032 [0.008–0.057]; P = 0.009). There was no evidence of these associations when adjusting for BMI. Associations with fasting alanine aminotransferase, γ-glutamyl-transferase, LDL cholesterol, A1C, and systolic and diastolic blood pressure were in the expected direction but did not reach P < 0.05. For all metabolic traits, effect sizes were consistent with those expected for the per allele change in BMI. FTO genotype was associated with a higher odds of metabolic syndrome (odds ratio 1.17 [95% CI 1.10–1.25]; P = 3 × 10 −6 ). CONCLUSIONS— FTO genotype is associated with metabolic traits to an extent entirely consistent with its effect on BMI. Sample sizes of >12,000 individuals were needed to detect associations at P < 0.05. Our findings highlight the importance of using appropriately powered studies to assess the effects of a known diabetes or obesity variant on secondary traits correlated with these conditions. ALT, alanine aminotransferase BWHHS, British Women's Heart and Health Study EFSOCH, Exeter Family Study of Childhood Health GGT, γ-glutamyl-transferase NCEP, National Cholesterol Education Program NFBC1966, Northern Finland Birth Cohort of 1966 NIA, National Institute on Aging SNP, single nucleotide polymorphism UKT2D GCC, U.K. Type 2 Diabetes Genetics Consortium Collection Footnotes Published ahead of print at http://diabetes.diabetesjournals.org on 17 March 2008. DOI: 10.2337/db07-1466. Additional information for this article can be found in an online appendix at http://dx.doi.org/10.2337/db07-1466 . R.M.F. and N.J.T. contributed equally to this work. M.-R.J., G.D.S., M.I.M., A.T.H., and T.M.F. contributed equally to this work. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Accepted January 27, 2008. Received October 13, 2007. DIABETES

Introduction Maternal metabolism changes substantially during pregnancy. However, few studies have used metabolomics technologies to characterize changes across gestation. Objectives and methods We applied liquid chromatography–mass spectrometry (LC–MS) based non-targeted metabolomics to determine whether the metabolic profile of serum differs throughout the pregnancy between pre-eclamptic and healthy women in the FINNPEC (Finnish Genetics of Preeclampsia Consortium) Study. Serum samples were available from early and late pregnancy. Results Progression of pregnancy had large-scale effects to the serum metabolite profile. Altogether 50 identified metabolites increased and 49 metabolites decreased when samples of early pregnancy were compared to samples of late pregnancy. The metabolic signatures of pregnancy were largely shared in pre-eclamptic and healthy women, only urea, monoacylglyceride 18:1 and glycerophosphocholine were identified to be increased in the pre-eclamptic women when compared to healthy controls. Conclusions Our study highlights the need of large-scale longitudinal metabolomic studies in non-complicated pregnancies before more detailed understanding of metabolism in adverse outcomes could be provided. Our findings are one of the first steps for a broader metabolic understanding of the physiological changes caused by pregnancy per se.