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1.  Effects of Common Genetic Variants Associated With Type 2 Diabetes and Glycemic Traits on α- and β-Cell Function and Insulin Action in Humans 
Diabetes  2013;62(8):2978-2983.
Although meta-analyses of genome-wide association studies have identified >60 single nucleotide polymorphisms (SNPs) associated with type 2 diabetes and/or glycemic traits, there is little information on whether these variants also affect α-cell function. The aim of the current study was to evaluate the effects of glycemia-associated genetic loci on islet function in vivo and in vitro. We studied 43 SNPs in 4,654 normoglycemic participants from the Finnish population-based Prevalence, Prediction, and Prevention of Diabetes-Botnia (PPP-Botnia) Study. Islet function was assessed, in vivo, by measuring insulin and glucagon concentrations during oral glucose tolerance test, and, in vitro, by measuring glucose-stimulated insulin and glucagon secretion from human pancreatic islets. Carriers of risk variants in BCL11A, HHEX, ZBED3, HNF1A, IGF1, and NOTCH2 showed elevated whereas those in CRY2, IGF2BP2, TSPAN8, and KCNJ11 showed decreased fasting and/or 2-h glucagon concentrations in vivo. Variants in BCL11A, TSPAN8, and NOTCH2 affected glucagon secretion both in vivo and in vitro. The MTNR1B variant was a clear outlier in the relationship analysis between insulin secretion and action, as well as between insulin, glucose, and glucagon. Many of the genetic variants shown to be associated with type 2 diabetes or glycemic traits also exert pleiotropic in vivo and in vitro effects on islet function.
doi:10.2337/db12-1627
PMCID: PMC3717852  PMID: 23557703
2.  Genetic Screening for the Risk of Type 2 Diabetes 
Diabetes Care  2013;36(Suppl 2):S120-S126.
doi:10.2337/dcS13-2009
PMCID: PMC3920800  PMID: 23882036
3.  Assessing the phenotypic effects in the general population of rare variants in genes for a dominant mendelian form of diabetes 
Nature genetics  2013;45(11):1380-1385.
Genome sequencing can identify individuals in the general population who harbor rare coding variants in genes for Mendelian disorders1–7 – and who consequently may have increased disease risk. However, previous studies of rare variants in phenotypically extreme individuals have ascertainment bias and may demonstrate inflated effect size estimates8–12. We sequenced seven genes for maturity-onset diabetes of the young (MODY)13 in well-phenotyped population samples14,15 (n=4,003). Rare variants were filtered according to prediction criteria used to identify disease-causing mutations: i) previously-reported in MODY, and ii) stringent de novo thresholds satisfied (rare, conserved, protein damaging). Approximately 1.5% and 0.5% of randomly selected Framingham and Jackson Heart Study individuals carried variants from these two classes, respectively. However, the vast majority of carriers remained euglycemic through middle age. Accurate estimates of variant effect sizes from population-based sequencing are needed to avoid falsely predicting a significant fraction of individuals as at risk for MODY or other Mendelian diseases.
doi:10.1038/ng.2794
PMCID: PMC4051627  PMID: 24097065
4.  Link Between GIP and Osteopontin in Adipose Tissue and Insulin Resistance 
Diabetes  2013;62(6):2088-2094.
Low-grade inflammation in obesity is associated with accumulation of the macrophage-derived cytokine osteopontin (OPN) in adipose tissue and induction of local as well as systemic insulin resistance. Since glucose-dependent insulinotropic polypeptide (GIP) is a strong stimulator of adipogenesis and may play a role in the development of obesity, we explored whether GIP directly would stimulate OPN expression in adipose tissue and thereby induce insulin resistance. GIP stimulated OPN protein expression in a dose-dependent fashion in rat primary adipocytes. The level of OPN mRNA was higher in adipose tissue of obese individuals (0.13 ± 0.04 vs. 0.04 ± 0.01, P < 0.05) and correlated inversely with measures of insulin sensitivity (r = −0.24, P = 0.001). A common variant of the GIP receptor (GIPR) (rs10423928) gene was associated with a lower amount of the exon 9–containing isoform required for transmembrane activity. Carriers of the A allele with a reduced receptor function showed lower adipose tissue OPN mRNA levels and better insulin sensitivity. Together, these data suggest a role for GIP not only as an incretin hormone but also as a trigger of inflammation and insulin resistance in adipose tissue. Carriers of the GIPR rs10423928 A allele showed protective properties via reduced GIP effects. Identification of this unprecedented link between GIP and OPN in adipose tissue might open new avenues for therapeutic interventions.
doi:10.2337/db12-0976
PMCID: PMC3661641  PMID: 23349498
5.  Genetic Variants Associated With Glycine Metabolism and Their Role in Insulin Sensitivity and Type 2 Diabetes 
Diabetes  2013;62(6):2141-2150.
Circulating metabolites associated with insulin sensitivity may represent useful biomarkers, but their causal role in insulin sensitivity and diabetes is less certain. We previously identified novel metabolites correlated with insulin sensitivity measured by the hyperinsulinemic-euglycemic clamp. The top-ranking metabolites were in the glutathione and glycine biosynthesis pathways. We aimed to identify common genetic variants associated with metabolites in these pathways and test their role in insulin sensitivity and type 2 diabetes. With 1,004 nondiabetic individuals from the RISC study, we performed a genome-wide association study (GWAS) of 14 insulin sensitivity–related metabolites and one metabolite ratio. We replicated our results in the Botnia study (n = 342). We assessed the association of these variants with diabetes-related traits in GWAS meta-analyses (GENESIS [including RISC, EUGENE2, and Stanford], MAGIC, and DIAGRAM). We identified four associations with three metabolites—glycine (rs715 at CPS1), serine (rs478093 at PHGDH), and betaine (rs499368 at SLC6A12; rs17823642 at BHMT)—and one association signal with glycine-to-serine ratio (rs1107366 at ALDH1L1). There was no robust evidence for association between these variants and insulin resistance or diabetes. Genetic variants associated with genes in the glycine biosynthesis pathways do not provide consistent evidence for a role of glycine in diabetes-related traits.
doi:10.2337/db12-0876
PMCID: PMC3661655  PMID: 23378610
6.  Early Metabolic Markers of the Development of Dysglycemia and Type 2 Diabetes and Their Physiological Significance 
Diabetes  2013;62(5):1730-1737.
Metabolomic screening of fasting plasma from nondiabetic subjects identified α-hydroxybutyrate (α-HB) and linoleoyl-glycerophosphocholine (L-GPC) as joint markers of insulin resistance (IR) and glucose intolerance. To test the predictivity of α-HB and L-GPC for incident dysglycemia, α-HB and L-GPC measurements were obtained in two observational cohorts, comprising 1,261 nondiabetic participants from the Relationship between Insulin Sensitivity and Cardiovascular Disease (RISC) study and 2,580 from the Botnia Prospective Study, with 3-year and 9.5-year follow-up data, respectively. In both cohorts, α-HB was a positive correlate and L-GPC a negative correlate of insulin sensitivity, with α-HB reciprocally related to indices of β-cell function derived from the oral glucose tolerance test (OGTT). In follow-up, α-HB was a positive predictor (adjusted odds ratios 1.25 [95% CI 1.00–1.60] and 1.26 [1.07–1.48], respectively, for each standard deviation of predictor), and L-GPC was a negative predictor (0.64 [0.48–0.85] and 0.67 [0.54–0.84]) of dysglycemia (RISC) or type 2 diabetes (Botnia), independent of familial diabetes, sex, age, BMI, and fasting glucose. Corresponding areas under the receiver operating characteristic curve were 0.791 (RISC) and 0.783 (Botnia), similar in accuracy when substituting α-HB and L-GPC with 2-h OGTT glucose concentrations. When their activity was examined, α-HB inhibited and L-GPC stimulated glucose-induced insulin release in INS-1e cells. α-HB and L-GPC are independent predictors of worsening glucose tolerance, physiologically consistent with a joint signature of IR and β-cell dysfunction.
doi:10.2337/db12-0707
PMCID: PMC3636608  PMID: 23160532
7.  A Central Role for GRB10 in Regulation of Islet Function in Man 
PLoS Genetics  2014;10(4):e1004235.
Variants in the growth factor receptor-bound protein 10 (GRB10) gene were in a GWAS meta-analysis associated with reduced glucose-stimulated insulin secretion and increased risk of type 2 diabetes (T2D) if inherited from the father, but inexplicably reduced fasting glucose when inherited from the mother. GRB10 is a negative regulator of insulin signaling and imprinted in a parent-of-origin fashion in different tissues. GRB10 knock-down in human pancreatic islets showed reduced insulin and glucagon secretion, which together with changes in insulin sensitivity may explain the paradoxical reduction of glucose despite a decrease in insulin secretion. Together, these findings suggest that tissue-specific methylation and possibly imprinting of GRB10 can influence glucose metabolism and contribute to T2D pathogenesis. The data also emphasize the need in genetic studies to consider whether risk alleles are inherited from the mother or the father.
Author Summary
In this paper, we report the first large genome-wide association study in man for glucose-stimulated insulin secretion (GSIS) indices during an oral glucose tolerance test. We identify seven genetic loci and provide effects on GSIS for all previously reported glycemic traits and obesity genetic loci in a large-scale sample. We observe paradoxical effects of genetic variants in the growth factor receptor-bound protein 10 (GRB10) gene yielding both reduced GSIS and reduced fasting plasma glucose concentrations, specifically showing a parent-of-origin effect of GRB10 on lower fasting plasma glucose and enhanced insulin sensitivity for maternal and elevated glucose and decreased insulin sensitivity for paternal transmissions of the risk allele. We also observe tissue-specific differences in DNA methylation and allelic imbalance in expression of GRB10 in human pancreatic islets. We further disrupt GRB10 by shRNA in human islets, showing reduction of both insulin and glucagon expression and secretion. In conclusion, we provide evidence for complex regulation of GRB10 in human islets. Our data suggest that tissue-specific methylation and imprinting of GRB10 can influence glucose metabolism and contribute to T2D pathogenesis. The data also emphasize the need in genetic studies to consider whether risk alleles are inherited from the mother or the father.
doi:10.1371/journal.pgen.1004235
PMCID: PMC3974640  PMID: 24699409
8.  Joint Analysis of Individual Participants’ Data from 17 Studies on the Association of the IL6 Variant -174G>C with Circulating Glucose Levels, Interleukin-6 Levels, and Body-Mass Index 
Annals of medicine  2009;41(2):128-138.
Background
Several studies have investigated associations between the -174G>C polymorphism (rs1800795) of the IL6-gene, but presented inconsistent results.
Aims
This joint analysis aimed to clarify whether IL6 -174G>C was associated with type 2 diabetes mellitus (T2DM) related quantitative phenotypes.
Methods
Individual-level data from all studies of the IL6-T2DM consortium on Caucasian subjects with available BMI were collected. As study-specific estimates did not show heterogeneity (P>0.1), they were combined by using the inverse-variance fixed-effect model.
Results
The main analysis included 9440, 7398, 24,117, or 5659 nondiabetic and manifest T2DM subjects for fasting glucose, 2-hour glucose, BMI or circulating interleukin-6 levels, respectively. IL6 -174 C-allele carriers had significantly lower fasting glucose (−0.091mmol/L, P=0.014). There was no evidence for association between IL6 -174G>C and BMI or interleukin-6. In an additional analysis of 641 subjects known to develop T2DM later on, the IL6 -174 CC-genotype was associated with higher baseline interleukin-6 (+0.75pg/mL, P=0.004), which was consistent with higher interleukin-6 in the 966 manifest T2DM subjects (+0.50pg/mL, P=0.044).
Conclusions
Our data suggest association between IL6 -174G>C and quantitative glucose, and exploratory analysis indicated modulated interleukin-6 levels in pre-diabetic subjects, being in-line with this SNP’s previously reported T2DM association and a role of circulating interleukin-6 as intermediate phenotype.
doi:10.1080/07853890802337037
PMCID: PMC3801210  PMID: 18752089
blood glucose; body mass index; diabetes mellitus; type 2; epidemiology; molecular; genes; inflammation mediators; interleukin-6; intermediate phenotype; meta-analysis; polymorphism; single nucleotide
9.  Regulation of the Pro-inflammatory Cytokine Osteopontin by GIP in Adipocytes - A role for the Transcription Factor NFAT and Phosphodiesterase 3B 
The incretin - glucose-dependent insulinotropic polypeptide (GIP) - and the pro-inflammatory cytokine osteopontin are known to have important roles in the regulation of adipose tissue functions. In this work we show that GIP stimulates lipogenesis and osteopontin expression in primary adipocytes. The GIP-induced increase in osteopontin expression was inhibited by the NFAT (the transcription factor nuclear factor of activated T-cells) inhibitor A285222. Also, the NFAT kinase glycogen synthase kinase (GSK) 3 was upregulated by GIP. To test whether cAMP might be involved in GIP mediated effects on osteopontin a number of strategies were used. Thus, the β3-adrenergic receptor agonist CL316,243 stimulated osteopontin expression, an effects which was mimicked by OPC3911, a specific inhibitor of phosphodiesterase 3. Furthermore, treatment of phosphodiesterase 3B knock-out mice with CL316,243 resulted in a dramatic upregulation of osteopontin in adipose tissue which was not the case in wild-type mice. In summary, we delineate mechanisms by which GIP stimulate osteopontin in adipocytes. Given the established link between osteopontin and insulin resistance, our data suggest that GIP by stimulating osteopontin expression, also could promote insulin resistance in adipocytes.
doi:10.1016/j.bbrc.2012.07.157
PMCID: PMC3759516  PMID: 22892131
Osteopontin; GIP; adipocytes; NFAT; phosphodiesterase3B
10.  A common variant in the melatonin receptor gene (MTNR1B) is associated with increased risk of future type 2 diabetes and impaired early insulin secretion 
Nature genetics  2008;41(1):82-88.
Genome wide association studies revealed that variation in the Melatonin Receptor 1B gene (MTNR1B) is associated with insulin and glucose concentrations. Here we show that the risk genotype of this SNP predicts future type 2 diabetes (T2D) in two large prospective studies. Specifically, the risk genotype was associated with impairment of early insulin response to both oral and intravenous glucose and with faster deterioration of insulin secretion over time. We also show that the Melatonin Receptor 1B mRNA is expressed in human islets, and immunocytochemistry confirms that it is primarily localized in β-cells in islets. Non-diabetic individuals carrying the risk allele and patients with T2D showed increased expression of the receptor in islets. Insulin release from clonal β-cells in response to glucose was inhibited in the presence of melatonin. These data suggest that the circulating hormone melatonin, which is predominantly released from the pineal gland in the brain, is involved in the pathogenesis of T2D. Given the increased expression of Melatonin Receptor 1B in individuals at risk of T2D, the pathogenic effects are likely exerted via a direct inhibitory effect on β-cells. In view of these results, blocking the melatonin ligand-receptor system could be a therapeutic avenue in T2D.
doi:10.1038/ng.288
PMCID: PMC3725650  PMID: 19060908
11.  Reduced Insulin Exocytosis in Human Pancreatic β-Cells With Gene Variants Linked to Type 2 Diabetes 
Diabetes  2012;61(7):1726-1733.
The majority of genetic risk variants for type 2 diabetes (T2D) affect insulin secretion, but the mechanisms through which they influence pancreatic islet function remain largely unknown. We functionally characterized human islets to determine secretory, biophysical, and ultrastructural features in relation to genetic risk profiles in diabetic and nondiabetic donors. Islets from donors with T2D exhibited impaired insulin secretion, which was more pronounced in lean than obese diabetic donors. We assessed the impact of 14 disease susceptibility variants on measures of glucose sensing, exocytosis, and structure. Variants near TCF7L2 and ADRA2A were associated with reduced glucose-induced insulin secretion, whereas susceptibility variants near ADRA2A, KCNJ11, KCNQ1, and TCF7L2 were associated with reduced depolarization-evoked insulin exocytosis. KCNQ1, ADRA2A, KCNJ11, HHEX/IDE, and SLC2A2 variants affected granule docking. We combined our results to create a novel genetic risk score for β-cell dysfunction that includes aberrant granule docking, decreased Ca2+ sensitivity of exocytosis, and reduced insulin release. Individuals with a high risk score displayed an impaired response to intravenous glucose and deteriorating insulin secretion over time. Our results underscore the importance of defects in β-cell exocytosis in T2D and demonstrate the potential of cellular phenotypic characterization in the elucidation of complex genetic disorders.
doi:10.2337/db11-1516
PMCID: PMC3379663  PMID: 22492527
12.  The Role of Adiposity in Cardiometabolic Traits: A Mendelian Randomization Analysis 
Fall, Tove | Hägg, Sara | Mägi, Reedik | Ploner, Alexander | Fischer, Krista | Horikoshi, Momoko | Sarin, Antti-Pekka | Thorleifsson, Gudmar | Ladenvall, Claes | Kals, Mart | Kuningas, Maris | Draisma, Harmen H. M. | Ried, Janina S. | van Zuydam, Natalie R. | Huikari, Ville | Mangino, Massimo | Sonestedt, Emily | Benyamin, Beben | Nelson, Christopher P. | Rivera, Natalia V. | Kristiansson, Kati | Shen, Huei-yi | Havulinna, Aki S. | Dehghan, Abbas | Donnelly, Louise A. | Kaakinen, Marika | Nuotio, Marja-Liisa | Robertson, Neil | de Bruijn, Renée F. A. G. | Ikram, M. Arfan | Amin, Najaf | Balmforth, Anthony J. | Braund, Peter S. | Doney, Alexander S. F. | Döring, Angela | Elliott, Paul | Esko, Tõnu | Franco, Oscar H. | Gretarsdottir, Solveig | Hartikainen, Anna-Liisa | Heikkilä, Kauko | Herzig, Karl-Heinz | Holm, Hilma | Hottenga, Jouke Jan | Hyppönen, Elina | Illig, Thomas | Isaacs, Aaron | Isomaa, Bo | Karssen, Lennart C. | Kettunen, Johannes | Koenig, Wolfgang | Kuulasmaa, Kari | Laatikainen, Tiina | Laitinen, Jaana | Lindgren, Cecilia | Lyssenko, Valeriya | Läärä, Esa | Rayner, Nigel W. | Männistö, Satu | Pouta, Anneli | Rathmann, Wolfgang | Rivadeneira, Fernando | Ruokonen, Aimo | Savolainen, Markku J. | Sijbrands, Eric J. G. | Small, Kerrin S. | Smit, Jan H. | Steinthorsdottir, Valgerdur | Syvänen, Ann-Christine | Taanila, Anja | Tobin, Martin D. | Uitterlinden, Andre G. | Willems, Sara M. | Willemsen, Gonneke | Witteman, Jacqueline | Perola, Markus | Evans, Alun | Ferrières, Jean | Virtamo, Jarmo | Kee, Frank | Tregouet, David-Alexandre | Arveiler, Dominique | Amouyel, Philippe | Ferrario, Marco M. | Brambilla, Paolo | Hall, Alistair S. | Heath, Andrew C. | Madden, Pamela A. F. | Martin, Nicholas G. | Montgomery, Grant W. | Whitfield, John B. | Jula, Antti | Knekt, Paul | Oostra, Ben | van Duijn, Cornelia M. | Penninx, Brenda W. J. H. | Davey Smith, George | Kaprio, Jaakko | Samani, Nilesh J. | Gieger, Christian | Peters, Annette | Wichmann, H.-Erich | Boomsma, Dorret I. | de Geus, Eco J. C. | Tuomi, TiinaMaija | Power, Chris | Hammond, Christopher J. | Spector, Tim D. | Lind, Lars | Orho-Melander, Marju | Palmer, Colin Neil Alexander | Morris, Andrew D. | Groop, Leif | Järvelin, Marjo-Riitta | Salomaa, Veikko | Vartiainen, Erkki | Hofman, Albert | Ripatti, Samuli | Metspalu, Andres | Thorsteinsdottir, Unnur | Stefansson, Kari | Pedersen, Nancy L. | McCarthy, Mark I. | Ingelsson, Erik | Prokopenko, Inga
PLoS Medicine  2013;10(6):e1001474.
In this study, Prokopenko and colleagues provide novel evidence for causal relationship between adiposity and heart failure and increased liver enzymes using a Mendelian randomization study design.
Please see later in the article for the Editors' Summary
Background
The association between adiposity and cardiometabolic traits is well known from epidemiological studies. Whilst the causal relationship is clear for some of these traits, for others it is not. We aimed to determine whether adiposity is causally related to various cardiometabolic traits using the Mendelian randomization approach.
Methods and Findings
We used the adiposity-associated variant rs9939609 at the FTO locus as an instrumental variable (IV) for body mass index (BMI) in a Mendelian randomization design. Thirty-six population-based studies of individuals of European descent contributed to the analyses.
Age- and sex-adjusted regression models were fitted to test for association between (i) rs9939609 and BMI (n = 198,502), (ii) rs9939609 and 24 traits, and (iii) BMI and 24 traits. The causal effect of BMI on the outcome measures was quantified by IV estimators. The estimators were compared to the BMI–trait associations derived from the same individuals. In the IV analysis, we demonstrated novel evidence for a causal relationship between adiposity and incident heart failure (hazard ratio, 1.19 per BMI-unit increase; 95% CI, 1.03–1.39) and replicated earlier reports of a causal association with type 2 diabetes, metabolic syndrome, dyslipidemia, and hypertension (odds ratio for IV estimator, 1.1–1.4; all p<0.05). For quantitative traits, our results provide novel evidence for a causal effect of adiposity on the liver enzymes alanine aminotransferase and gamma-glutamyl transferase and confirm previous reports of a causal effect of adiposity on systolic and diastolic blood pressure, fasting insulin, 2-h post-load glucose from the oral glucose tolerance test, C-reactive protein, triglycerides, and high-density lipoprotein cholesterol levels (all p<0.05). The estimated causal effects were in agreement with traditional observational measures in all instances except for type 2 diabetes, where the causal estimate was larger than the observational estimate (p = 0.001).
Conclusions
We provide novel evidence for a causal relationship between adiposity and heart failure as well as between adiposity and increased liver enzymes.
Please see later in the article for the Editors' Summary
Editors' Summary
Cardiovascular disease (CVD)—disease that affects the heart and/or the blood vessels—is a major cause of illness and death worldwide. In the US, for example, coronary heart disease—a CVD in which narrowing of the heart's blood vessels by fatty deposits slows the blood supply to the heart and may eventually cause a heart attack—is the leading cause of death, and stroke—a CVD in which the brain's blood supply is interrupted—is the fourth leading cause of death. Globally, both the incidence of CVD (the number of new cases in a population every year) and its prevalence (the proportion of the population with CVD) are increasing, particularly in low- and middle-income countries. This increasing burden of CVD is occurring in parallel with a global increase in the incidence and prevalence of obesity—having an unhealthy amount of body fat (adiposity)—and of metabolic diseases—conditions such as diabetes in which metabolism (the processes that the body uses to make energy from food) is disrupted, with resulting high blood sugar and damage to the blood vessels.
Why Was This Study Done?
Epidemiological studies—investigations that record the patterns and causes of disease in populations—have reported an association between adiposity (indicated by an increased body mass index [BMI], which is calculated by dividing body weight in kilograms by height in meters squared) and cardiometabolic traits such as coronary heart disease, stroke, heart failure (a condition in which the heart is incapable of pumping sufficient amounts of blood around the body), diabetes, high blood pressure (hypertension), and high blood cholesterol (dyslipidemia). However, observational studies cannot prove that adiposity causes any particular cardiometabolic trait because overweight individuals may share other characteristics (confounding factors) that are the real causes of both obesity and the cardiometabolic disease. Moreover, it is possible that having CVD or a metabolic disease causes obesity (reverse causation). For example, individuals with heart failure cannot do much exercise, so heart failure may cause obesity rather than vice versa. Here, the researchers use “Mendelian randomization” to examine whether adiposity is causally related to various cardiometabolic traits. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. It is known that a genetic variant (rs9939609) within the genome region that encodes the fat-mass- and obesity-associated gene (FTO) is associated with increased BMI. Thus, an investigation of the associations between rs9939609 and cardiometabolic traits can indicate whether obesity is causally related to these traits.
What Did the Researchers Do and Find?
The researchers analyzed the association between rs9939609 (the “instrumental variable,” or IV) and BMI, between rs9939609 and 24 cardiometabolic traits, and between BMI and the same traits using genetic and health data collected in 36 population-based studies of nearly 200,000 individuals of European descent. They then quantified the strength of the causal association between BMI and the cardiometabolic traits by calculating “IV estimators.” Higher BMI showed a causal relationship with heart failure, metabolic syndrome (a combination of medical disorders that increases the risk of developing CVD), type 2 diabetes, dyslipidemia, hypertension, increased blood levels of liver enzymes (an indicator of liver damage; some metabolic disorders involve liver damage), and several other cardiometabolic traits. All the IV estimators were similar to the BMI–cardiovascular trait associations (observational estimates) derived from the same individuals, with the exception of diabetes, where the causal estimate was higher than the observational estimate, probably because the observational estimate is based on a single BMI measurement, whereas the causal estimate considers lifetime changes in BMI.
What Do These Findings Mean?
Like all Mendelian randomization studies, the reliability of the causal associations reported here depends on several assumptions made by the researchers. Nevertheless, these findings provide support for many previously suspected and biologically plausible causal relationships, such as that between adiposity and hypertension. They also provide new insights into the causal effect of obesity on liver enzyme levels and on heart failure. In the latter case, these findings suggest that a one-unit increase in BMI might increase the incidence of heart failure by 17%. In the US, this corresponds to 113,000 additional cases of heart failure for every unit increase in BMI at the population level. Although additional studies are needed to confirm and extend these findings, these results suggest that global efforts to reduce the burden of obesity will likely also reduce the occurrence of CVD and metabolic disorders.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001474.
The American Heart Association provides information on all aspects of cardiovascular disease and tips on keeping the heart healthy, including weight management (in several languages); its website includes personal stories about stroke and heart attacks
The US Centers for Disease Control and Prevention has information on heart disease, stroke, and all aspects of overweight and obesity (in English and Spanish)
The UK National Health Service Choices website provides information about cardiovascular disease and obesity, including a personal story about losing weight
The World Health Organization provides information on obesity (in several languages)
The International Obesity Taskforce provides information about the global obesity epidemic
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
MedlinePlus provides links to other sources of information on heart disease, on vascular disease, on obesity, and on metabolic disorders (in English and Spanish)
The International Association for the Study of Obesity provides maps and information about obesity worldwide
The International Diabetes Federation has a web page that describes types, complications, and risk factors of diabetes
doi:10.1371/journal.pmed.1001474
PMCID: PMC3692470  PMID: 23824655
13.  Codon 72 polymorphism (rs1042522) of TP53 is associated with changes in diastolic blood pressure over time 
p53 is involved in stress response, metabolism and cardiovascular functioning. The C-allele of rs1042522 in the gene encoding for p53 is associated with longevity and cancer. In this study, we aimed to investigate the association of rs1042522 with changes in blood pressure, BMI and waist circumference using a longitudinal approach. Rs1042522 was analyzed in two longitudinal studies; the Doetinchem Cohort Study (DCS) and the Botnia Prospective Study (BPS). Changes in quantitative traits over time were investigated according to rs1042522 genotypes. An association between rs1042522 and changes in diastolic blood pressure (DBP) in the DCS over time was observed (P=0.004). Furthermore, a borderline significant association was detected with changes in waist circumference over time (P=0.03). These findings were also observed in the BPS (P=0.02 and P=0.05). The C/C-genotype (Pro/Pro) showed the most moderate time-related increase for the studied endpoints. Furthermore, data from the BPS suggested that the C/C-genotype protects against increases in glucose levels over time at 30 and 60 min during oral glucose tolerance test (P=0.01 and P=0.02). In conclusion, we found an association between the C/C-genotype of rs1042522 and changes in DBP and waist circumference over time. This might contribute to the longevity phenotype observed for the same genotype by others.
doi:10.1038/ejhg.2011.240
PMCID: PMC3355249  PMID: 22189267
p53; blood pressure; metabolism; longitudinal study
14.  A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance 
Manning, Alisa K. | Hivert, Marie-France | Scott, Robert A. | Grimsby, Jonna L. | Bouatia-Naji, Nabila | Chen, Han | Rybin, Denis | Liu, Ching-Ti | Bielak, Lawrence F. | Prokopenko, Inga | Amin, Najaf | Barnes, Daniel | Cadby, Gemma | Hottenga, Jouke-Jan | Ingelsson, Erik | Jackson, Anne U. | Johnson, Toby | Kanoni, Stavroula | Ladenvall, Claes | Lagou, Vasiliki | Lahti, Jari | Lecoeur, Cecile | Liu, Yongmei | Martinez-Larrad, Maria Teresa | Montasser, May E. | Navarro, Pau | Perry, John R. B. | Rasmussen-Torvik, Laura J. | Salo, Perttu | Sattar, Naveed | Shungin, Dmitry | Strawbridge, Rona J. | Tanaka, Toshiko | van Duijn, Cornelia M. | An, Ping | de Andrade, Mariza | Andrews, Jeanette S. | Aspelund, Thor | Atalay, Mustafa | Aulchenko, Yurii | Balkau, Beverley | Bandinelli, Stefania | Beckmann, Jacques S. | Beilby, John P. | Bellis, Claire | Bergman, Richard N. | Blangero, John | Boban, Mladen | Boehnke, Michael | Boerwinkle, Eric | Bonnycastle, Lori L. | Boomsma, Dorret I. | Borecki, Ingrid B. | Böttcher, Yvonne | Bouchard, Claude | Brunner, Eric | Budimir, Danijela | Campbell, Harry | Carlson, Olga | Chines, Peter S. | Clarke, Robert | Collins, Francis S. | Corbatón-Anchuelo, Arturo | Couper, David | de Faire, Ulf | Dedoussis, George V | Deloukas, Panos | Dimitriou, Maria | Egan, Josephine M | Eiriksdottir, Gudny | Erdos, Michael R. | Eriksson, Johan G. | Eury, Elodie | Ferrucci, Luigi | Ford, Ian | Forouhi, Nita G. | Fox, Caroline S | Franzosi, Maria Grazia | Franks, Paul W | Frayling, Timothy M | Froguel, Philippe | Galan, Pilar | de Geus, Eco | Gigante, Bruna | Glazer, Nicole L. | Goel, Anuj | Groop, Leif | Gudnason, Vilmundur | Hallmans, Göran | Hamsten, Anders | Hansson, Ola | Harris, Tamara B. | Hayward, Caroline | Heath, Simon | Hercberg, Serge | Hicks, Andrew A. | Hingorani, Aroon | Hofman, Albert | Hui, Jennie | Hung, Joseph | Jarvelin, Marjo Riitta | Jhun, Min A. | Johnson, Paul C.D. | Jukema, J Wouter | Jula, Antti | Kao, W.H. | Kaprio, Jaakko | Kardia, Sharon L. R. | Keinanen-Kiukaanniemi, Sirkka | Kivimaki, Mika | Kolcic, Ivana | Kovacs, Peter | Kumari, Meena | Kuusisto, Johanna | Kyvik, Kirsten Ohm | Laakso, Markku | Lakka, Timo | Lannfelt, Lars | Lathrop, G Mark | Launer, Lenore J. | Leander, Karin | Li, Guo | Lind, Lars | Lindstrom, Jaana | Lobbens, Stéphane | Loos, Ruth J. F. | Luan, Jian’an | Lyssenko, Valeriya | Mägi, Reedik | Magnusson, Patrik K. E. | Marmot, Michael | Meneton, Pierre | Mohlke, Karen L. | Mooser, Vincent | Morken, Mario A. | Miljkovic, Iva | Narisu, Narisu | O’Connell, Jeff | Ong, Ken K. | Oostra, Ben A. | Palmer, Lyle J. | Palotie, Aarno | Pankow, James S. | Peden, John F. | Pedersen, Nancy L. | Pehlic, Marina | Peltonen, Leena | Penninx, Brenda | Pericic, Marijana | Perola, Markus | Perusse, Louis | Peyser, Patricia A | Polasek, Ozren | Pramstaller, Peter P. | Province, Michael A. | Räikkönen, Katri | Rauramaa, Rainer | Rehnberg, Emil | Rice, Ken | Rotter, Jerome I. | Rudan, Igor | Ruokonen, Aimo | Saaristo, Timo | Sabater-Lleal, Maria | Salomaa, Veikko | Savage, David B. | Saxena, Richa | Schwarz, Peter | Seedorf, Udo | Sennblad, Bengt | Serrano-Rios, Manuel | Shuldiner, Alan R. | Sijbrands, Eric J.G. | Siscovick, David S. | Smit, Johannes H. | Small, Kerrin S. | Smith, Nicholas L. | Smith, Albert Vernon | Stančáková, Alena | Stirrups, Kathleen | Stumvoll, Michael | Sun, Yan V. | Swift, Amy J. | Tönjes, Anke | Tuomilehto, Jaakko | Trompet, Stella | Uitterlinden, Andre G. | Uusitupa, Matti | Vikström, Max | Vitart, Veronique | Vohl, Marie-Claude | Voight, Benjamin F. | Vollenweider, Peter | Waeber, Gerard | Waterworth, Dawn M | Watkins, Hugh | Wheeler, Eleanor | Widen, Elisabeth | Wild, Sarah H. | Willems, Sara M. | Willemsen, Gonneke | Wilson, James F. | Witteman, Jacqueline C.M. | Wright, Alan F. | Yaghootkar, Hanieh | Zelenika, Diana | Zemunik, Tatijana | Zgaga, Lina | Wareham, Nicholas J. | McCarthy, Mark I. | Barroso, Ines | Watanabe, Richard M. | Florez, Jose C. | Dupuis, Josée | Meigs, James B. | Langenberg, Claudia
Nature genetics  2012;44(6):659-669.
Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and beta-cell dysfunction, but contributed little to our understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways may be uncovered by accounting for differences in body mass index (BMI) and potential interaction between BMI and genetic variants. We applied a novel joint meta-analytical approach to test associations with fasting insulin (FI) and glucose (FG) on a genome-wide scale. We present six previously unknown FI loci at P<5×10−8 in combined discovery and follow-up analyses of 52 studies comprising up to 96,496non-diabetic individuals. Risk variants were associated with higher triglyceride and lower HDL cholesterol levels, suggestive of a role for these FI loci in insulin resistance pathways. The localization of these additional loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.
doi:10.1038/ng.2274
PMCID: PMC3613127  PMID: 22581228
15.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segrè, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian'an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John RB | Platou, Carl GP | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden89-, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922
16.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segré, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian’an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John R B | Platou, Carl G P | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922
17.  Variants in MTNR1B influence fasting glucose levels 
Prokopenko, Inga | Langenberg, Claudia | Florez, Jose C | Saxena, Richa | Soranzo, Nicole | Thorleifsson, Gudmar | Loos, Ruth J F | Manning, Alisa K | Jackson, Anne U | Aulchenko, Yurii | Potter, Simon C | Erdos, Michael R | Sanna, Serena | Hottenga, Jouke-Jan | Wheeler, Eleanor | Kaakinen, Marika | Lyssenko, Valeriya | Chen, Wei-Min | Ahmadi, Kourosh | Beckmann, Jacques S | Bergman, Richard N | Bochud, Murielle | Bonnycastle, Lori L | Buchanan, Thomas A | Cao, Antonio | Cervino, Alessandra | Coin, Lachlan | Collins, Francis S | Crisponi, Laura | de Geus, Eco J C | Dehghan, Abbas | Deloukas, Panos | Doney, Alex S F | Elliott, Paul | Freimer, Nelson | Gateva, Vesela | Herder, Christian | Hofman, Albert | Hughes, Thomas E | Hunt, Sarah | Illig, Thomas | Inouye, Michael | Isomaa, Bo | Johnson, Toby | Kong, Augustine | Krestyaninova, Maria | Kuusisto, Johanna | Laakso, Markku | Lim, Noha | Lindblad, Ulf | Lindgren, Cecilia M | McCann, Owen T | Mohlke, Karen L | Morris, Andrew D | Naitza, Silvia | Orrù, Marco | Palmer, Colin N A | Pouta, Anneli | Randall, Joshua | Rathmann, Wolfgang | Saramies, Jouko | Scheet, Paul | Scott, Laura J | Scuteri, Angelo | Sharp, Stephen | Sijbrands, Eric | Smit, Jan H | Song, Kijoung | Steinthorsdottir, Valgerdur | Stringham, Heather M | Tuomi, Tiinamaija | Tuomilehto, Jaakko | Uitterlinden, André G | Voight, Benjamin F | Waterworth, Dawn | Wichmann, H-Erich | Willemsen, Gonneke | Witteman, Jacqueline C M | Yuan, Xin | Zhao, Jing Hua | Zeggini, Eleftheria | Schlessinger, David | Sandhu, Manjinder | Boomsma, Dorret I | Uda, Manuela | Spector, Tim D | Penninx, Brenda WJH | Altshuler, David | Vollenweider, Peter | Jarvelin, Marjo Riitta | Lakatta, Edward | Waeber, Gerard | Fox, Caroline S | Peltonen, Leena | Groop, Leif C | Mooser, Vincent | Cupples, L Adrienne | Thorsteinsdottir, Unnur | Boehnke, Michael | Barroso, Inês | Van Duijn, Cornelia | Dupuis, Josée | Watanabe, Richard M | Stefansson, Kari | McCarthy, Mark I | Wareham, Nicholas J | Meigs, James B | Abecasis, Gonçalo R
Nature genetics  2008;41(1):77-81.
To identify previously unknown genetic loci associated with fasting glucose concentrations, we examined the leading association signals in ten genome-wide association scans involving a total of 36,610 individuals of European descent. Variants in the gene encoding melatonin receptor 1B (MTNR1B) were consistently associated with fasting glucose across all ten studies. The strongest signal was observed at rs10830963, where each G allele (frequency 0.30 in HapMap CEU) was associated with an increase of 0.07 (95% CI = 0.06-0.08) mmol/l in fasting glucose levels (P = 3.2 = × 10−50) and reduced beta-cell function as measured by homeostasis model assessment (HOMA-B, P = 1.1 × 10−15). The same allele was associated with an increased risk of type 2 diabetes (odds ratio = 1.09 (1.05-1.12), per G allele P = 3.3 × 10−7) in a meta-analysis of 13 case-control studies totaling 18,236 cases and 64,453 controls. Our analyses also confirm previous associations of fasting glucose with variants at the G6PC2 (rs560887, P = 1.1 × 10−57) and GCK (rs4607517, P = 1.0 × 10−25) loci.
doi:10.1038/ng.290
PMCID: PMC2682768  PMID: 19060907
18.  Genome-Wide Association Identifies Nine Common Variants Associated With Fasting Proinsulin Levels and Provides New Insights Into the Pathophysiology of Type 2 Diabetes 
Strawbridge, Rona J. | Dupuis, Josée | Prokopenko, Inga | Barker, Adam | Ahlqvist, Emma | Rybin, Denis | Petrie, John R. | Travers, Mary E. | Bouatia-Naji, Nabila | Dimas, Antigone S. | Nica, Alexandra | Wheeler, Eleanor | Chen, Han | Voight, Benjamin F. | Taneera, Jalal | Kanoni, Stavroula | Peden, John F. | Turrini, Fabiola | Gustafsson, Stefan | Zabena, Carina | Almgren, Peter | Barker, David J.P. | Barnes, Daniel | Dennison, Elaine M. | Eriksson, Johan G. | Eriksson, Per | Eury, Elodie | Folkersen, Lasse | Fox, Caroline S. | Frayling, Timothy M. | Goel, Anuj | Gu, Harvest F. | Horikoshi, Momoko | Isomaa, Bo | Jackson, Anne U. | Jameson, Karen A. | Kajantie, Eero | Kerr-Conte, Julie | Kuulasmaa, Teemu | Kuusisto, Johanna | Loos, Ruth J.F. | Luan, Jian'an | Makrilakis, Konstantinos | Manning, Alisa K. | Martínez-Larrad, María Teresa | Narisu, Narisu | Nastase Mannila, Maria | Öhrvik, John | Osmond, Clive | Pascoe, Laura | Payne, Felicity | Sayer, Avan A. | Sennblad, Bengt | Silveira, Angela | Stančáková, Alena | Stirrups, Kathy | Swift, Amy J. | Syvänen, Ann-Christine | Tuomi, Tiinamaija | van 't Hooft, Ferdinand M. | Walker, Mark | Weedon, Michael N. | Xie, Weijia | Zethelius, Björn | Ongen, Halit | Mälarstig, Anders | Hopewell, Jemma C. | Saleheen, Danish | Chambers, John | Parish, Sarah | Danesh, John | Kooner, Jaspal | Östenson, Claes-Göran | Lind, Lars | Cooper, Cyrus C. | Serrano-Ríos, Manuel | Ferrannini, Ele | Forsen, Tom J. | Clarke, Robert | Franzosi, Maria Grazia | Seedorf, Udo | Watkins, Hugh | Froguel, Philippe | Johnson, Paul | Deloukas, Panos | Collins, Francis S. | Laakso, Markku | Dermitzakis, Emmanouil T. | Boehnke, Michael | McCarthy, Mark I. | Wareham, Nicholas J. | Groop, Leif | Pattou, François | Gloyn, Anna L. | Dedoussis, George V. | Lyssenko, Valeriya | Meigs, James B. | Barroso, Inês | Watanabe, Richard M. | Ingelsson, Erik | Langenberg, Claudia | Hamsten, Anders | Florez, Jose C.
Diabetes  2011;60(10):2624-2634.
OBJECTIVE
Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology.
RESEARCH DESIGN AND METHODS
We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates.
RESULTS
Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10−8). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10−4), improved β-cell function (P = 1.1 × 10−5), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10−6). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets.
CONCLUSIONS
We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis.
doi:10.2337/db11-0415
PMCID: PMC3178302  PMID: 21873549
19.  Pleiotropic Effects of GIP on Islet Function Involve Osteopontin 
Diabetes  2011;60(9):2424-2433.
OBJECTIVE
The incretin hormone GIP (glucose-dependent insulinotropic polypeptide) promotes pancreatic β-cell function by potentiating insulin secretion and β-cell proliferation. Recently, a combined analysis of several genome-wide association studies (Meta-analysis of Glucose and Insulin-Related Traits Consortium [MAGIC]) showed association to postprandial insulin at the GIP receptor (GIPR) locus. Here we explored mechanisms that could explain the protective effects of GIP on islet function.
RESEARCH DESIGN AND METHODS
Associations of GIPR rs10423928 with metabolic and anthropometric phenotypes in both nondiabetic (N = 53,730) and type 2 diabetic individuals (N = 2,731) were explored by combining data from 11 studies. Insulin secretion was measured both in vivo in nondiabetic subjects and in vitro in islets from cadaver donors. Insulin secretion was also measured in response to exogenous GIP. The in vitro measurements included protein and gene expression as well as measurements of β-cell viability and proliferation.
RESULTS
The A allele of GIPR rs10423928 was associated with impaired glucose- and GIP-stimulated insulin secretion and a decrease in BMI, lean body mass, and waist circumference. The decrease in BMI almost completely neutralized the effect of impaired insulin secretion on risk of type 2 diabetes. Expression of GIPR mRNA was decreased in human islets from carriers of the A allele or patients with type 2 diabetes. GIP stimulated osteopontin (OPN) mRNA and protein expression. OPN expression was lower in carriers of the A allele. Both GIP and OPN prevented cytokine-induced reduction in cell viability (apoptosis). In addition, OPN stimulated cell proliferation in insulin-secreting cells.
CONCLUSIONS
These findings support β-cell proliferative and antiapoptotic roles for GIP in addition to its action as an incretin hormone. Identification of a link between GIP and OPN may shed new light on the role of GIP in preservation of functional β-cell mass in humans.
doi:10.2337/db10-1532
PMCID: PMC3161325  PMID: 21810601
20.  Association between parental history of diabetes and type 2 diabetes genetic risk scores in the PPP-Botnia and Framingham Offspring Studies 
Objective
Parental history of diabetes and specific gene variants are risk factors for type 2 diabetes, but the extent to which these factors are associated is unknown.
Methods
We examined the association between parental history of diabetes and a type 2 diabetes genetic risk score (GRS) in two cohort studies from Finland (population-based PPP-Botnia Study) and the US (family-based Framingham Offspring Study).
Results
Mean (95% CI) GRS increased from 16.8 (16.8–16.9) to 16.9 (16.8–17.1) to 17.1 (16.8–17.4) among PPP-Botnia participants with 0, 1, and 2 parents with diabetes, respectively (ptrend=0.03). The trend was similar among Framingham Offspring but was not statistically significant (p=0.07). The meta-analyzed p value for trend from the two studies was 0.005.
Conclusions
The very modest associations reported above suggest that the increased risk of diabetes in offspring of parents with diabetes is largely the result of shared environmental/lifestyle factors and/or hitherto unknown genetic factors.
doi:10.1016/j.diabres.2011.04.013
PMCID: PMC3156338  PMID: 21570145
Type 2 diabetes mellitus; genetic risk score; family history
21.  FTO, Type 2 Diabetes, and Weight Gain Throughout Adult Life 
Diabetes  2011;60(5):1637-1644.
OBJECTIVE
FTO is the most important polygene identified for obesity. We aimed to investigate whether a variant in FTO affects type 2 diabetes risk entirely through its effect on BMI and how FTO influences BMI across adult life span.
RESEARCH DESIGN AND METHODS
Through regression models, we assessed the relationship between the FTO single nucleotide polymorphisms rs9939609, type 2 diabetes, and BMI across life span in subjects from the Norwegian population-based HUNT study using cross-sectional and longitudinal perspectives. For replication and meta-analysis, we used data from the Malmö Diet and Cancer (MDC) and Malmö Preventive Project (MPP) cohorts, comprising a total sample of 41,504 Scandinavians.
RESULTS
The meta-analysis revealed a highly significant association for rs9939609 with both type 2 diabetes (OR 1.13; P = 4.5 × 10−8) and the risk to develop incident type 2 diabetes (OR 1.16; P = 3.2 × 10−8). The associations remained also after correction for BMI and other anthropometric measures. Furthermore, we confirmed the strong effect on BMI (0.28 kg/m2 per risk allele; P = 2.0 × 10−26), with no heterogeneity between different age-groups. We found no differences in change of BMI over time according to rs9939609 risk alleles, neither overall (∆BMI = 0.0 [−0.05, 0.05]) nor in any individual age stratum, indicating no further weight gain attributable to FTO genotype in adults.
CONCLUSIONS
We have identified that a variant in FTO alters type 2 diabetes risk partly independent of its observed effect on BMI. The additional weight gain as a result of the FTO risk variant seems to occur before adulthood, and the BMI difference remains stable thereafter.
doi:10.2337/db10-1340
PMCID: PMC3292341  PMID: 21398525
22.  A common variant of HMGA2 is associated with adult and childhood height in the general population 
Nature genetics  2007;39(10):1245-1250.
Human height is a classic, highly heritable quantitative trait. To begin to identify genetic variants influencing height, we examined genome-wide association data from 4,921 individuals. Common variants in the HMGA2 oncogene, exemplified by rs1042725, were associated with height (P = 4 × 10−8). HMGA2 is also a strong biological candidate for height, as rare, severe mutations in this gene alter body size in mice and humans, so we tested rs1042725 in additional samples. We confirmed the association in 19,064 adults from four further studies (P = 3 × 10−11, overall P = 4 × 10−16, including the genome-wide association data). We also observed the association in children (P = 1 × 10−6, N = 6,827) and a tall/short case-control study (P = 4 × 10−6, N = 3,207). We estimate that rs1042725 explains ~0.3% of population variation in height (~0.4 cm increased adult height per C allele). There are few examples of common genetic variants reproducibly associated with human quantitative traits; these results represent, to our knowledge, the first consistently replicated association with adult and childhood height.
doi:10.1038/ng2121
PMCID: PMC3086278  PMID: 17767157
23.  Detailed Physiologic Characterization Reveals Diverse Mechanisms for Novel Genetic Loci Regulating Glucose and Insulin Metabolism in Humans 
Diabetes  2010;59(5):1266-1275.
OBJECTIVE
Recent genome-wide association studies have revealed loci associated with glucose and insulin-related traits. We aimed to characterize 19 such loci using detailed measures of insulin processing, secretion, and sensitivity to help elucidate their role in regulation of glucose control, insulin secretion and/or action.
RESEARCH DESIGN AND METHODS
We investigated associations of loci identified by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) with circulating proinsulin, measures of insulin secretion and sensitivity from oral glucose tolerance tests (OGTTs), euglycemic clamps, insulin suppression tests, or frequently sampled intravenous glucose tolerance tests in nondiabetic humans (n = 29,084).
RESULTS
The glucose-raising allele in MADD was associated with abnormal insulin processing (a dramatic effect on higher proinsulin levels, but no association with insulinogenic index) at extremely persuasive levels of statistical significance (P = 2.1 × 10−71). Defects in insulin processing and insulin secretion were seen in glucose-raising allele carriers at TCF7L2, SCL30A8, GIPR, and C2CD4B. Abnormalities in early insulin secretion were suggested in glucose-raising allele carriers at MTNR1B, GCK, FADS1, DGKB, and PROX1 (lower insulinogenic index; no association with proinsulin or insulin sensitivity). Two loci previously associated with fasting insulin (GCKR and IGF1) were associated with OGTT-derived insulin sensitivity indices in a consistent direction.
CONCLUSIONS
Genetic loci identified through their effect on hyperglycemia and/or hyperinsulinemia demonstrate considerable heterogeneity in associations with measures of insulin processing, secretion, and sensitivity. Our findings emphasize the importance of detailed physiological characterization of such loci for improved understanding of pathways associated with alterations in glucose homeostasis and eventually type 2 diabetes.
doi:10.2337/db09-1568
PMCID: PMC2857908  PMID: 20185807
24.  Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis 
Voight, Benjamin F | Scott, Laura J | Steinthorsdottir, Valgerdur | Morris, Andrew P | Dina, Christian | Welch, Ryan P | Zeggini, Eleftheria | Huth, Cornelia | Aulchenko, Yurii S | Thorleifsson, Gudmar | McCulloch, Laura J | Ferreira, Teresa | Grallert, Harald | Amin, Najaf | Wu, Guanming | Willer, Cristen J | Raychaudhuri, Soumya | McCarroll, Steve A | Langenberg, Claudia | Hofmann, Oliver M | Dupuis, Josée | Qi, Lu | Segrè, Ayellet V | van Hoek, Mandy | Navarro, Pau | Ardlie, Kristin | Balkau, Beverley | Benediktsson, Rafn | Bennett, Amanda J | Blagieva, Roza | Boerwinkle, Eric | Bonnycastle, Lori L | Boström, Kristina Bengtsson | Bravenboer, Bert | Bumpstead, Suzannah | Burtt, Noisël P | Charpentier, Guillaume | Chines, Peter S | Cornelis, Marilyn | Couper, David J | Crawford, Gabe | Doney, Alex S F | Elliott, Katherine S | Elliott, Amanda L | Erdos, Michael R | Fox, Caroline S | Franklin, Christopher S | Ganser, Martha | Gieger, Christian | Grarup, Niels | Green, Todd | Griffin, Simon | Groves, Christopher J | Guiducci, Candace | Hadjadj, Samy | Hassanali, Neelam | Herder, Christian | Isomaa, Bo | Jackson, Anne U | Johnson, Paul R V | Jørgensen, Torben | Kao, Wen H L | Klopp, Norman | Kong, Augustine | Kraft, Peter | Kuusisto, Johanna | Lauritzen, Torsten | Li, Man | Lieverse, Aloysius | Lindgren, Cecilia M | Lyssenko, Valeriya | Marre, Michel | Meitinger, Thomas | Midthjell, Kristian | Morken, Mario A | Narisu, Narisu | Nilsson, Peter | Owen, Katharine R | Payne, Felicity | Perry, John R B | Petersen, Ann-Kristin | Platou, Carl | Proença, Christine | Prokopenko, Inga | Rathmann, Wolfgang | Rayner, N William | Robertson, Neil R | Rocheleau, Ghislain | Roden, Michael | Sampson, Michael J | Saxena, Richa | Shields, Beverley M | Shrader, Peter | Sigurdsson, Gunnar | Sparsø, Thomas | Strassburger, Klaus | Stringham, Heather M | Sun, Qi | Swift, Amy J | Thorand, Barbara | Tichet, Jean | Tuomi, Tiinamaija | van Dam, Rob M | van Haeften, Timon W | van Herpt, Thijs | van Vliet-Ostaptchouk, Jana V | Walters, G Bragi | Weedon, Michael N | Wijmenga, Cisca | Witteman, Jacqueline | Bergman, Richard N | Cauchi, Stephane | Collins, Francis S | Gloyn, Anna L | Gyllensten, Ulf | Hansen, Torben | Hide, Winston A | Hitman, Graham A | Hofman, Albert | Hunter, David J | Hveem, Kristian | Laakso, Markku | Mohlke, Karen L | Morris, Andrew D | Palmer, Colin N A | Pramstaller, Peter P | Rudan, Igor | Sijbrands, Eric | Stein, Lincoln D | Tuomilehto, Jaakko | Uitterlinden, Andre | Walker, Mark | Wareham, Nicholas J | Watanabe, Richard M | Abecasis, Gonçalo R | Boehm, Bernhard O | Campbell, Harry | Daly, Mark J | Hattersley, Andrew T | Hu, Frank B | Meigs, James B | Pankow, James S | Pedersen, Oluf | Wichmann, H-Erich | Barroso, Inês | Florez, Jose C | Frayling, Timothy M | Groop, Leif | Sladek, Rob | Thorsteinsdottir, Unnur | Wilson, James F | Illig, Thomas | Froguel, Philippe | van Duijn, Cornelia M | Stefansson, Kari | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2010;42(7):579-589.
By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combinedP < 5 × 10−8. These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.
doi:10.1038/ng.609
PMCID: PMC3080658  PMID: 20581827
25.  Minimal Contribution of Fasting Hyperglycemia to the Incidence of Type 2 Diabetes in Subjects With Normal 2-h Plasma Glucose 
Diabetes Care  2009;33(3):557-561.
OBJECTIVE
To assess the relative contribution of increased fasting and postload plasma glucose concentrations to the incidence of type 2 diabetes in subjects with a normal 2-h plasma glucose concentration.
RESEARCH DESIGN AND METHODS
A total of 3,450 subjects with 2-h plasma glucose concentration <140 mg/dl at baseline were followed up in the San Antonio Heart Study (SAHS) and the Botnia Study for 7–8 years. The incidence of type 2 diabetes at follow-up was related to the fasting, 1-h, and 2-h plasma glucose concentrations.
RESULTS
In subjects with 2-h plasma glucose <140 mg/dl, the incidence of type 2 diabetes increased with increasing fasting plasma glucose (FPG) and 1-h and 2-h plasma glucose concentrations. In a multivariate logistic analysis, after adjustment for all diabetes risk factors, the FPG concentration was a strong predictor of type 2 diabetes in both the SAHS and the Botnia Study (P < 0.0001). However, when the 1-h plasma glucose, but not 2-h plasma glucose, concentration was added to the model, FPG concentration was no longer a significant predictor of type 2 diabetes in both studies (NS). When subjects were matched for the level of 1-h plasma glucose concentration, the incidence of type 2 diabetes markedly increased with the increase in 1-h plasma glucose, but the increase in FPG was not associated with a significant increase in the incidence of type 2 diabetes.
CONCLUSIONS
An increase in postload glycemia in the normal range is associated with an increase in the incidence of type 2 diabetes. After controlling for 1-h plasma glucose concentration, the increase in FPG concentration is not associated with an increase in the incidence of type 2 diabetes.
doi:10.2337/dc09-1145
PMCID: PMC2827507  PMID: 20007945

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