Summary Height is a highly heritable, classic polygenic trait with ∼700 common associated variants identified so far through genome-wide association studies. Here, we report 83 height-associated coding variants with lower minor allele frequencies (range of 0.1-4.8%) and effects of up to 2 cm/allele (e.g. in IHH, STC2, AR and CRISPLD2), >10 times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (+1-2 cm/allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes mutated in monogenic growth disorders and highlight new biological candidates (e.g. ADAMTS3, IL11RA, NOX4) and pathways (e.g. proteoglycan/glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate to large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways.

Mutations in mitochondrial DNA (mtDNA) associate with various disease states. A few mtDNA mutations strongly associate with diabetes, with the most common mutation being the A3243G mutation in the mitochondrial DNA-encoded tRNA(Leu,UUR) gene. This article describes clinical characteristics of mitochondrial diabetes and its molecular diagnosis. Furthermore, it outlines recent developments in the pathophysiological and molecular mechanisms leading to a diabetic state. A gradual development of pancreatic ␤-cell dysfunction upon aging, rather than insulin resistance, is the main mechanism in developing glucose intolerance. Carriers of the A3243G mutation show during a hyperglycemic clamp at 10 mmol/l glucose a marked reduction in firstand second-phase insulin secretion compared with noncarriers. The molecular mechanism by which the A3243G mutation affects insulin secretion may involve an attenuation of cytosolic ADP/ATP levels leading to a resetting of the glucose sensor in the pancreatic ␤-cell, such as in maturity-onset diabetes of the young (MODY)-2 patients with mutations in glucokinase. Unlike in MODY2, which is a nonprogressive form of diabetes, mitochondrial diabetes does show a pronounced age-dependent deterioration of pancreatic function indicating involvement of additional processes. Furthermore, one would expect that all mtDNA mutations that affect ATP synthesis lead to diabetes. This is in contrast to clinical observations. The origin of the age-dependent deterioration of pancreatic function in carriers of the A3243G mutation and the contribution of ATP and other mitochondrion-derived factors such as reactive oxygen species to the development of diabetes is discussed. Diabetes 53 (Suppl. 1):S103-S109, 2004 D iabetes is a collection of diseases characterized by the presence of chronic hyperglycemia. Maintenance of normal glucose homeostasis involves the action of a glucose sensor in the pancreatic ␤-cell that detects an increase in blood glucose concentration and converts that into increased secretion of insulin. Increased circulating insulin concentrations suppress hepatic glucose output and stimulate glucose uptake by muscle and adipose tissue.Pathophysiological mechanisms leading to diabetes can involve an inappropriate secretion of insulin, insulin resistance of the liver, muscle and fat, or combined defects. The risk of an individual to develop diabetes involves a complex interaction between genetic and environmental factors. Gene variants that have been identified to contribute to the major forms of diabetes, such as autoimmune type 1 diabetes and metabolic syndrome-associated type 2 diabetes, are "low penetrance" variants that modulate the susceptibility of an individual to develop diabetes or that protect against the disease (1-3).A number of gene mutants have been identified in the past decade that represent high penetrance risk genes for diabetes. Carriers of these gene mutants have a nearly 100% chance to develop diabetes during their life span. These so-called monogenetic forms of diabetes com...

High serum levels of total and LDL cholesterol are important risk factors in the development of atherosclerotic coronary artery disease. Cholesterol metabolism is affected by nutritional, environmental and genetic factors. Neuropeptide Y (NPY), which is widely expressed in both the central and peripheral nervous systems, has an important role in the hypothalamic regulation of energy balance by stimulating food intake and favoring energy storage through increased lipoprotein lipase activity in white adipose tissue. As a part of ongoing study of the genetic basis of obesity, we screened the NPY gene for sequence variants. We report here the identification of a common Leu(7)-to-Pro(7) polymorphism in the signal peptide of NPY. Presence of this Pro(7) in NPY was associated with higher serum levels of total and LDL cholesterol in obese subjects participating in two independent Finnish and Dutch studies. Furthermore, normal-weight Finns with Pro(7) also had higher serum levels of total and LDL cholesterol than did subjects with Leu(7)/Leu(7), as analyzed in three subsequent determinations at 5-year intervals during a 10-year follow-up period. The NPY polymorphism was not associated with higher cholesterol levels in normal-weight Dutch. Our study provides evidence that NPY is linked to cholesterol metabolism and that the polymorphism producing Pro(7) in NPY is one of the strongest genetic factors identified thus far affecting serum cholesterol, particularly in obese subjects.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Aims/hypothesis Genome-wide association studies have recently identified novel type 2 diabetes susceptibility gene regions. We assessed the effects of six of these regions on insulin secretion as determined by a hyperglycaemic clamp. Methods Variants of the HHEX/IDE, CDKAL1, SLC30A8, IGF2BP2 and CDKN2A/CDKN2B genes were genotyped in a cohort of 146 participants with NGT and 126 with IGT from the Netherlands and Germany, who all underwent a hyperglycaemic clamp at 10 mmol/l glucose. Results Variants of CDKAL1 and IGF2BP2 were associated with reductions in first-phase insulin secretion (34% and 28%, respectively). The disposition index was also significantly reduced. For gene regions near HHEX/IDE, SLC30A8 and CDKN2A/CDKN2B we did not find significant associations with first-phase insulin secretion (7-18% difference between genotypes; all p>0.3). None of the variants showed a significant effect on second-phase insulin secretion in our cohorts (2-8% difference between genotypes, all p>0.3). Furthermore, the gene variants were not associated with the insulin sensitivity index. Conclusions Variants of CDKAL1 and IGF2BP2 attenuate the first phase of glucose-stimulated insulin secretion but show no effect on the second phase of insulin secretion. Our results, based on hyperglycaemic clamps, provide further insight into the pathogenic mechanism behind the association of these gene variants with type 2 diabetes.

The incretin hormone glucagon-like peptide 1 (GLP-1) promotes glucose homeostasis and enhances β-cell function. GLP-1 receptor agonists (GLP-1 RAs) and dipeptidyl peptidase-4 (DPP-4) inhibitors, which inhibit the physiological inactivation of endogenous GLP-1, are used for the treatment of type 2 diabetes. Using the Metabochip, we identified three novel genetic loci with large effects (30–40%) on GLP-1–stimulated insulin secretion during hyperglycemic clamps in nondiabetic Caucasian individuals (TMEM114; CHST3 and CTRB1/2; n = 232; all P ≤ 8.8 × 10−7). rs7202877 near CTRB1/2, a known diabetes risk locus, also associated with an absolute 0.51 ± 0.16% (5.6 ± 1.7 mmol/mol) lower A1C response to DPP-4 inhibitor treatment in G-allele carriers, but there was no effect on GLP-1 RA treatment in type 2 diabetic patients (n = 527). Furthermore, in pancreatic tissue, we show that rs7202877 acts as expression quantitative trait locus for CTRB1 and CTRB2, encoding chymotrypsinogen, and increases fecal chymotrypsin activity in healthy carriers. Chymotrypsin is one of the most abundant digestive enzymes in the gut where it cleaves food proteins into smaller peptide fragments. Our data identify chymotrypsin in the regulation of the incretin pathway, development of diabetes, and response to DPP-4 inhibitor treatment.

Background - The blood metabolome incorporates cues from the environment as well as the host's genetic background, potentially offering a holistic view of an individual's health status. Methods - We have compiled a vast resource of 1 H-NMR metabolomics and phenotypic data encompassing over 25,000 samples derived from 26 community and hospital-based cohorts. Results - Using this resource, we constructed a metabolomics-based age predictor (metaboAge) to calculate an individual's biological age. Exploration in independent cohorts demonstrates that being judged older by one's metabolome, as compared to one's chronological age, confers an increased risk on future cardiovascular disease, mortality and functionality in older individuals. A web-based tool for calculating metaboAge (metaboage.researchlumc.nl) allows easy incorporation in other epidemiological studies. Access to data can be requested at bbmri.nl/samples-images-data. Conclusions - In summary, we present a vast resource of metabolomics data and illustrate its merit by constructing a metabolomics-based score for biological age that captures aspects of current and future cardio-metabolic health.

Aims/hypothesis Variation in fasting plasma glucose (FPG) within the normal range is a known risk factor for the development of type 2 diabetes. Several reports have shown that genetic variation in the genes for glucokinase (GCK), glucokinase regulatory protein (GCKR), islet-specific glucose 6 phosphatase catalytic subunit-related protein (G6PC2) and melatonin receptor type 1B (MTNR1B) is associated with FPG. In this study we examined whether these loci also contribute to type 2 diabetes susceptibility. Methods A random selection from the Dutch New Hoorn Study was used for replication of the association with FGP (2,361 non-diabetic participants). For the genetic association study we extended the study sample with 2,628 participants with type 2 diabetes. Risk allele counting was used to calculate a four-gene risk allele score for each individual. Results Variants of the GCK, G6PC2 and MTNR1B genes but not GCKR were associated with FPG (all, p≤0.001; GCKR, p=0.23). Combining these four genes in a risk allele score resulted in an increase of 0.05 mmol/l (0.04-0.07) per additional risk allele (p=2×10 −13). Furthermore, participants with less than three or more than five risk alleles showed significantly different type 2 diabetes susceptibility compared with the most common group with four risk alleles respectively). The age at diagnosis was also significantly associated with the number of risk alleles (p=0.009). Conclusions A combined risk allele score for singlenucleotide polymorphisms in four known FPG loci is significantly associated with FPG and HbA 1c in a Dutch population-based sample of non-diabetic participants. Carriers of low or high numbers of risk alleles show significantly different risks for type 2 diabetes compared with the reference group.