Genome-wide association studies and polygenic risk score phenome-wide association studies across complex phenotypes in the human phenotype project.

BACKGROUND: Genome-wide association studies (GWASs) associate phenotypes and genetic variants across a study cohort. GWASs require large-scale cohorts with both phenotype and genetic sequencing data, limiting studied phenotypes. The Human Phenotype Project is a longitudinal study that has measured a wide range of clinical and biomolecular features from a self-assignment cohort over 5 years. The phenotypes collected are quantitative traits, providing higher-resolution insights into the genetics of complex phenotypes. METHODS: We present the results of GWASs and polygenic risk score phenome-wide association studies with 729 clinical phenotypes and 4,043 molecular features from the Human Phenotype Project. This includes clinical traits that have not been previously associated with genetics, including measures from continuous sleep monitoring, continuous glucose monitoring, liver ultrasound, hormonal status, and fundus imaging. FINDINGS: In GWAS of 8,706 individuals, we found significant associations between 169 clinical traits and 1,184 single-nucleotide polymorphisms. We found genes associated with both glycemic control and mental disorders, and we quantify the strength of genetic signals in serum metabolites. In polygenic risk score phenome-wide association studies for clinical traits, we found 16,047 significant associations. CONCLUSIONS: The entire set of findings, which we disseminate publicly, provides newfound resolution into the genetic architecture of complex human phenotypes. FUNDING: E.S. is supported by the Minerva foundation with funding from the Federal German Ministry for Education and Research and by the European Research Council and the Israel Science Foundation.

Mexican Biobank advances population and medical genomics of diverse ancestries.

Latin America continues to be severely underrepresented in genomics research, and fine-scale genetic histories and complex trait architectures remain hidden owing to insufficient data1. To fill this gap, the Mexican Biobank project genotyped 6,057 individuals from 898 rural and urban localities across all 32 states in Mexico at a resolution of 1.8 million genome-wide markers with linked complex trait and disease information creating a valuable nationwide genotype-phenotype database. Here, using ancestry deconvolution and inference of identity-by-descent segments, we inferred ancestral population sizes across Mesoamerican regions over time, unravelling Indigenous, colonial and postcolonial demographic dynamics2-6. We observed variation in runs of homozygosity among genomic regions with different ancestries reflecting distinct demographic histories and, in turn, different distributions of rare deleterious variants. We conducted genome-wide association studies (GWAS) for 22 complex traits and found that several traits are better predicted using the Mexican Biobank GWAS compared to the UK Biobank GWAS7,8. We identified genetic and environmental factors associating with trait variation, such as the length of the genome in runs of homozygosity as a predictor for body mass index, triglycerides, glucose and height. This study provides insights into the genetic histories of individuals in Mexico and dissects their complex trait architectures, both crucial for making precision and preventive medicine initiatives accessible worldwide.

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

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

BMI change during childhood, DNA methylation change at TXNIP, and glucose change during midlife.

OBJECTIVE: This study investigated whether changes in DNA methylation (DNAm) at TXNIP are associated with glycemic changes and whether such an association differs with early-life adiposity changes. METHODS: A total of 594 Bogalusa Heart Study participants who had blood DNAm measurements at two time points in midlife were included. Of them, 353 participants had at least four BMI measurements during childhood and adolescence. The incremental area under the curve was calculated as a measure of long-term trends of BMI during childhood and adolescence. RESULTS: Increase in DNAm at TXNIP was significantly associated with decrease in fasting plasma glucose (FPG) independent of covariates (p < 0.001). The study found that the strength of this relationship was significantly modified by a trend of increasing BMI during childhood and adolescence (p-interaction = 0.003). Each 1% increase in DNAm at TXNIP was associated with a 2.90- (0.77) mg/dL decrease in FPG among participants with the highest tertile of BMI incremental area under the curve and a 0.96- (0.38) mg/dL decrease among those with the middle tertile, whereas no association was observed among participants with the lowest tertile. CONCLUSIONS: These results indicate that changes in blood DNAm at TXNIP are significantly associated with changes in FPG in midlife, and this association was modified by BMI trends during childhood and adolescence.

Polymorphisms of mtDNA in the D-loop region moderate the associations of BMI with HOMA-IR and HOMA-ß among women with polycystic ovary syndrome: a cross-sectional study.

PURPOSE: Polycystic ovary syndrome (PCOS) is one of the leading causes of infertility in women of childbearing age, and many patients with PCOS have obesity and insulin resistance (IR). Although obesity is related to an increased risk of IR, in clinical practice, PCOS patients exhibit different effects on improving insulin sensitivity after weight loss. Therefore, in the present study, we aimed to examine the moderating effect of polymorphisms of mtDNA in the D-loop region on the associations of body mass index (BMI) with the homeostasis model assessment of insulin resistance index (HOMA-IR) and pancreatic ß cell function index (HOMA-ß) among women with PCOS. METHODS: Based on a cross-sectional study, women with PCOS were recruited from the Reproductive Center of the First Affiliated Hospital of Anhui Medical University from 2015 to 2018. A total of 520 women who were diagnosed with PCOS based on the revised 2003 Rotterdam criteria were included in the study. Peripheral blood was collected from these patients, followed by DNA extraction, PCR amplification, and sequencing at baseline. HOMA-IR and HOMA-ß were calculated according to blood glucose-related indices. Moderating effect models were performed with BMI as an independent variable, polymorphisms of mtDNA in the D-loop region as moderators, and ln (HOMA-IR) and ln (HOMA-ß) as dependent variables. To verify the stability of moderating effect, sensitivity analysis was performed with the quantitative insulin sensitivity check index (QUICKI), fasting plasma glucose/fasting insulin (G/I), and fasting insulin as dependent variables. RESULTS: BMI was positively associated with ln (HOMA-IR) and ln (HOMA-ß) (ß = 0.090, p < 0.001; ß = 0.059, p < 0.001, respectively), and the relationship between BMI and ln (HOMA-IR) or ln (HOMA-ß) was moderated by the polymorphisms of mtDNA in the D-loop region. Compared with the respective wild-type, the variant -type of m.16217 T > C enhanced the association between BMI and HOMA-IR, while the variant-type of m.16316 A > G weakened the association. On the other hand, the variant-type of m.16316 A > G and m.16203 A > G weakened the association between BMI and HOMA-ß, respectively. The results of QUICKI and fasting insulin as dependent variables were generally consistent with HOMA-IR, and the results of G/I as dependent variables were generally consistent with HOMA-ß. CONCLUSION: Polymorphisms of mtDNA in the D-loop region moderate the associations of BMI with HOMA-IR and HOMA-ß among women with PCOS.

Genome-wide association study and functional characterization identifies candidate genes for insulin-stimulated glucose uptake.

Distinct tissue-specific mechanisms mediate insulin action in fasting and postprandial states. Previous genetic studies have largely focused on insulin resistance in the fasting state, where hepatic insulin action dominates. Here we studied genetic variants influencing insulin levels measured 2 h after a glucose challenge in >55,000 participants from three ancestry groups. We identified ten new loci (P < 5 × 10-8) not previously associated with postchallenge insulin resistance, eight of which were shown to share their genetic architecture with type 2 diabetes in colocalization analyses. We investigated candidate genes at a subset of associated loci in cultured cells and identified nine candidate genes newly implicated in the expression or trafficking of GLUT4, the key glucose transporter in postprandial glucose uptake in muscle and fat. By focusing on postprandial insulin resistance, we highlighted the mechanisms of action at type 2 diabetes loci that are not adequately captured by studies of fasting glycemic traits.

KCNQ1 rs2237892 polymorphism modify the association between short-term ambient particulate matter exposure and fasting blood glucose: A family-based study.

BACKGROUND: The association between particulate matter and fasting blood glucose (FBG) has shown conflicting results. Genome-wide association studies have shown that KCNQ1 rs2237892 polymorphism is associated with the risk of diabetes. Whether KCNQ1 rs2237892 polymorphism might modify the association between particulate matter and FBG is still uncertain. METHODS: Data collected from a family-based cohort study in Northern China, were used to perform the analysis. A generalized additive Gaussian model was used to examine the short-term effects of air pollutants on FBG. We further conducted interaction analyses by including a cross-product term of air pollutants by rs2237892 within KCNQ1 gene. RESULTS: A total of 4418 participants were included in the study. In the single pollutant model, the FBG level increased 0.0031 mmol/L with per 10 µg/m3 elevation in fine particular matter (PM2.5) for lag 0 day. After additional adjustments for nitrogen dioxide (NO2) and sulfur dioxide (SO2), similar results were observed for lag 0-2 days. As for particulate matter with particle size below 10 µm (PM10), the significant association between the daily average concentration of the pollutant and FBG level was observed for lag 0-3 days. Additionally, rs2237892 in KCNQ1 gene modified the association between PM and FBG level. The higher risk of FBG levels associated with elevations in PM10 and PM2.5 were more evident as the number of risk allele C increased. Individuals with a CC genotype had the highest risk of elevation in FBG levels. CONCLUSION: Short-term exposures to PM2.5 and PM10 were associated with higher FBG levels. Additionally, rs2237892 in KCNQ1 gene might modify the association between the air pollutants and FBG levels.

The melatonin receptor 1B gene links circadian rhythms and type 2 diabetes mellitus: an evolutionary story.

Disturbed circadian rhythms have been a risk factor for type 2 diabetes mellitus (T2DM). Melatonin is the major chronobiotic hormone regulating both circadian rhythm and glucose homeostasis. The rs10830963 (G allele) of the melatonin receptor 1B (MTNR1B) gene has the strongest genetic associations with T2DM according to several genome-wide association studies. The MTNR1B rs10830963 G allele is also associated with disturbed circadian phenotypes and altered melatonin secretion, both factors that can elevate the risk of diabetes. Furthermore, evolutionary studies implied the presence of selection pressure and ethnic diversity in MTNR1B, which was consistent with the "thrifty gene" hypothesis in T2DM. The rs10830963 G risk allele is associated with delayed melatonin secretion onset in dim-light and prolonged duration of peak melatonin. This delayed melatonin secretion may help human ancestors adapt to famine or food shortages during long nights and early mornings and avoid nocturnal hypoglycemia but confers susceptibility to T2DM due to adequate energy intake in modern society. We provide new insight into the role of MTNR1B variants in T2DM via disturbed circadian rhythms from the perspective of the "thrifty gene" hypothesis; these data indicate a novel target for the prevention and treatment of susceptible populations with the thrifty genotype.

Gastrointestinal Consequences of Type 2 Diabetes Mellitus and Impaired Glycemic Homeostasis: A Mendelian Randomization Study.

OBJECTIVE: We conducted a Mendelian randomization (MR) study to examine the associations of type 2 diabetes and glycemic traits with gastrointestinal diseases (GDs). RESEARCH DESIGN AND METHODS: Uncorrelated genetic variants associated with type 2 diabetes (n = 231), fasting insulin (n = 38), fasting glucose (n = 71), and hemoglobin A1c (n = 75) at the genome-wide significance were selected as instrument variables. Genetic associations with 23 common GDs were obtained from the FinnGen and UK Biobank studies and other large consortia. RESULTS: Genetic liability to type 2 diabetes was associated with the risk of 12 GDs. Per 1-unit increase in the log-transformed odds ratio (OR) of type 2 diabetes, the OR was 1.06 (95% CI, 1.03-1.09) for gastroesophageal reflux disease, 1.12 (95% CI, 1.07-1.17) for gastric ulcer, 1.11 (95% CI, 1.03-1.20) for acute gastritis, 1.07 (95% CI, 1.01-1.13) for chronic gastritis, 1.08 (95% CI, 1.03-1.12) for irritable bowel syndrome, 1.04 (95% CI, 1.01-1.07) for diverticular disease, 1.08 (95% CI, 1.02-1.14) for acute pancreatitis, 1.09 (95% CI, 1.05-1.12) for cholelithiasis, 1.09 (95% CI, 1.05-1.13) for cholelithiasis with cholecystitis, 1.29 (95% CI, 1.17-1.43) for nonalcoholic fatty liver disease, 1.12 (95% CI, 1.03-1.21) for liver cirrhosis, and 0.93 (95% CI, 0.89-0.97) for ulcerative colitis. Genetically predicted higher levels of fasting insulin and glucose were associated with six and one GDs, respectively. CONCLUSIONS: Associations were found between genetic liability to type 2 diabetes and an increased risk of a broad range of GDs, highlighting the importance of GD prevention in patients with type 2 diabetes.

The contribution of common and rare genetic variants to variation in metabolic traits in 288,137 East Asians.

Metabolic traits are heritable phenotypes widely-used in assessing the risk of various diseases. We conduct a genome-wide association analysis (GWAS) of nine metabolic traits (including glycemic, lipid, liver enzyme levels) in 125,872 Korean subjects genotyped with the Korea Biobank Array. Following meta-analysis with GWAS from Biobank Japan identify 144 novel signals (MAF ≥ 1%), of which 57.0% are replicated in UK Biobank. Additionally, we discover 66 rare (MAF < 1%) variants, 94.4% of them co-incident to common loci, adding to allelic series. Although rare variants have limited contribution to overall trait variance, these lead, in carriers, substantial loss of predictive accuracy from polygenic predictions of disease risk from common variant alone. We capture groups with up to 16-fold variation in type 2 diabetes (T2D) prevalence by integration of genetic risk scores of fasting plasma glucose and T2D and the I349F rare protective variant. This study highlights the need to consider the joint contribution of both common and rare variants on inherited risk of metabolic traits and related diseases.

Phenome-wide analysis of Taiwan Biobank reveals novel glycemia-related loci and genetic risks for diabetes.

To explore the complex genetic architecture of common diseases and traits, we conducted comprehensive PheWAS of ten diseases and 34 quantitative traits in the community-based Taiwan Biobank (TWB). We identified 995 significantly associated loci with 135 novel loci specific to Taiwanese population. Further analyses highlighted the genetic pleiotropy of loci related to complex disease and associated quantitative traits. Extensive analysis on glycaemic phenotypes (T2D, fasting glucose and HbA1c) was performed and identified 115 significant loci with four novel genetic variants (HACL1, RAD21, ASH1L and GAK). Transcriptomics data also strengthen the relevancy of the findings to metabolic disorders, thus contributing to better understanding of pathogenesis. In addition, genetic risk scores are constructed and validated for absolute risks prediction of T2D in Taiwanese population. In conclusion, our data-driven approach without a priori hypothesis is useful for novel gene discovery and validation on top of disease risk prediction for unique non-European population.

A haplotype in the dipeptidyl peptidase 4 gene impacts glycemic-related traits of Brazilian older adults.

Dipeptidyl peptidase 4 (DPP4) regulates various physiological pathways and has a pivotal role in glucose homeostasis. The objective of this study was to verify the association of a haplotype constituted by two single nucleotide polymorphisms (rs2268894 and rs6741949) in the DPP4 gene with type 2 diabetes mellitus (T2DM) and fasting glycemia-related variables in a sample of Brazilian older adults, taking serum levels and enzymatic activity of DPP4 into account. Clinical, biochemical, and anthropometric characteristics as well as DPP4 serum levels and enzymatic activity were determined in 800 elderly (≥60 years old) individuals. Assessment of polymorphic sites was performed by real-time PCR whereas haplotypes were inferred from genotypic frequencies. Statistical analyses compared measures and proportions according to T2DM diagnosis and DPP4 haplotypic groups. The most common haplotype consisted of the T-rs2268894/G-rs6741949 string, which was 20% more frequent among non-diabetics. Considering non-diabetic patients alone, carriers of the T/G haplotype had significantly lower levels of blood glucose, insulin, HOMA-IR index, and DPP4 activity. Among diabetic patients, the T/G haplotype was associated with lower DPP4 levels whereas glycemic scores were not affected by allelic variants. Our results suggested that the genetic architecture of DPP4 affects the glycemic profile and DPP4 serum levels and activity among elderly individuals according to the presence or absence of T2DM, with a possible implication of the T/G haplotype to the risk of T2DM onset.

Genetic and Phenotypic Factors Affecting Glycemic Response to Metformin Therapy in Patients with Type 2 Diabetes Mellitus.

Metformin is an oral hypoglycemic agent widely used in clinical practice for treatment of patients with type 2 diabetes mellitus (T2DM). The wide interindividual variability of response to metformin therapy was shown, and recently the impact of several genetic variants was reported. To assess the independent and combined effect of the genetic polymorphism on glycemic response to metformin, we performed an association analysis of the variants in ATM, SLC22A1, SLC47A1, and SLC2A2 genes with metformin response in 299 patients with T2DM. Likewise, the distribution of allele and genotype frequencies of the studied gene variants was analyzed in an extended group of patients with T2DM (n = 464) and a population group (n = 129). According to our results, one variant, rs12208357 in the SLC22A1 gene, had a significant impact on response to metformin in T2DM patients. Carriers of TT genotype and T allele had a lower response to metformin compared to carriers of CC/CT genotypes and C allele (p-value = 0.0246, p-value = 0.0059, respectively). To identify the parameters that had the greatest importance for the prediction of the therapy response to metformin, we next built a set of machine learning models, based on the various combinations of genetic and phenotypic characteristics. The model based on a set of four parameters, including gender, rs12208357 genotype, familial T2DM background, and waist-hip ratio (WHR) showed the highest prediction accuracy for the response to metformin therapy in patients with T2DM (AUC = 0.62 in cross-validation). Further pharmacogenetic studies may aid in the discovery of the fundamental mechanisms of type 2 diabetes, the identification of new drug targets, and finally, it could advance the development of personalized treatment.

Gene-environment interaction in type 2 diabetes in Korean cohorts: Interaction of a type 2 diabetes polygenic risk score with triglyceride and cholesterol on fasting glucose levels.

Type 2 diabetes (T2D) is caused by genetic and environmental factors as well as gene-environment interactions. However, these interactions have not been systematically investigated. We analyzed these interactions for T2D and fasting glucose levels in three Korean cohorts, HEXA, KARE, and CAVAS, using the baseline data with a multiple regression model. Two polygenic risk scores for T2D (PRST2D ) and fasting glucose (PRSFG ) were calculated using 488 and 82 single nucleotide polymorphisms (SNP) for T2D and fasting glucose, respectively, which were extracted from large-scaled genome-wide association studies with multiethnic data. Both lifestyle risk factors and T2D-related biochemical measurements were assessed. The effect of interactions between PRST2D -triglyceride (TG) and PRST2D -total cholesterol (TC) on fasting glucose levels was observed as follows: ß ± SE = 0.0005 ± 0.0001, p = 1.06 × 10-19 in HEXA, ß ± SE = 0.0008 ± 0.0001, p = 2.08 × 10-8 in KARE for TG; ß ± SE = 0.0006 ± 0.0001, p = 2.00 × 10-6 in HEXA, ß ± SE = 0.0020 ± 0.0004, p = 2.11 × 10-6 in KARE, ß ± SE = 0.0007 ± 0.0004, p = 0.045 in CAVAS for TC. PRST2D -based classification of the participants into four groups showed that the fasting glucose levels in groups with higher PRST2D were more adversely affected by both the TG and TC. In conclusion, blood TG and TC levels may affect the fasting glucose level through interaction with T2D genetic factors, suggesting the importance of consideration of gene-environment interaction in the preventive medicine of T2D.

Quantitative trait loci, G×E and G×G for glycemic traits: response to metformin and placebo in the Diabetes Prevention Program (DPP).

The complex genetic architecture of type-2-diabetes (T2D) includes gene-by-environment (G×E) and gene-by-gene (G×G) interactions. To identify G×E and G×G, we screened markers for patterns indicative of interactions (relationship loci [rQTL] and variance heterogeneity loci [vQTL]). rQTL exist when the correlation between multiple traits varies by genotype and vQTL occur when the variance of a trait differs by genotype (potentially flagging G×G and G×E). In the metformin and placebo arms of the DPP (n = 1762) we screened 280,965 exomic and intergenic SNPs, for rQTL and vQTL patterns in association with year one changes from baseline in glycemia and related traits (insulinogenic index [IGI], insulin sensitivity index [ISI], fasting glucose and fasting insulin). Significant (p < 1.8 × 10-7) rQTL and vQTL generated a priori hypotheses of individual G×E tests for a SNP × metformin treatment interaction and secondarily for G×G screens. Several rQTL and vQTL identified led to 6 nominally significant (p < 0.05) metformin treatment × SNP interactions (4 for IGI, one insulin, and one glucose) and 12G×G interactions (all IGI) that exceeded experiment-wide significance (p < 4.1 × 10-9). Some loci are directly associated with incident diabetes, and others are rQTL and modify a trait's relationship with diabetes (2 diabetes/glucose, 2 diabetes/insulin, 1 diabetes/IGI). rs3197999, an ISI/insulin rQTL, is a possible gene damaging missense mutation in MST1, is associated with ulcerative colitis, sclerosing cholangitis, Crohn's disease, BMI and coronary artery disease. This study demonstrates evidence for context-dependent effects (G×G & G×E) and the complexity of these T2D-related traits.

KCNQ1 variant rs163184 is a potential biomarker of glycemic response to exenatide.

Aim: To examine the association between variant rs163184 in the type 2 diabetes mellitus (T2DM) susceptibility gene KCNQ1 and exenatide glycemic response in the Chinese population. Patients & methods: We included 100 T2DM patients from the CONFIDENCE study and investigated the association between rs163184 and glycemic response to exenatide, by using a multivariate linear model with adjustment for baseline glucose status and other covariates. Results: The G allele of rs163184 was associated with a 0.34% (p = 0.016) lower glycosylated hemoglobin reduction after 48 weeks of exenatide treatment. Similar significant associations were observed when glycemic response to exenatide was evaluated with fasting blood glucose or postprandial blood glucose reduction. Conclusion: We found that rs163184 in the gene KCNQ1 was associated with reduced glycemic response to exenatide in T2DM patients. The effect size observed in this study was large enough to be considered clinically relevant in stratified medicine.

Metabolites and Genes behind Cardiac Metabolic Remodeling in Mice with Type 1 Diabetes Mellitus.

Metabolic remodeling is at the heart of diabetic cardiomyopathy. High glycemic fluctuations increase metabolic stress in the type 1 diabetes mellitus (T1DM) heart. There is a lack of understanding on how metabolites and genes affect metabolic remodeling in the T1DM heart. We hypothesize that differential expression of metabolic genes and metabolites synergistically influence metabolic remodeling preceding T1DM cardiomyopathy. To test our hypothesis, we conducted high throughput analysis of hearts from adult male hyperglycemic Ins2+/- (Akita) and littermate normoglycemic Ins2+/+ (WT) mice. The Akita mouse is a spontaneous, genetic model of T1DM that develops increased levels of consistent glycemic variability without the off-target cardiotoxic effects present in chemically- induced models of T1DM. After validating the presence of a T1DM phenotype, we conducted metabolomics via LC-MS analysis and genomics via next-generation sequencing in left ventricle tissue from the Akita heart. Ingenuity Pathway Analyses revealed that 108 and 30 metabolic pathways were disrupted within the metabolomics and genomics datasets, respectively. Notably, a comparison between the two analyses showed 15 commonly disrupted pathways, including ketogenesis, ketolysis, cholesterol biosynthesis, acetyl CoA hydrolysis, and fatty acid biosynthesis and beta-oxidation. These identified metabolic pathways predicted by the differential expression of metabolites and genes provide the foundation for understanding metabolic remodeling in the T1DM heart. By limited experiment, we revealed a predicted disruption in the metabolites and genes behind T1DM cardiac metabolic derangement. Future studies targeting these genes and metabolites will unravel novel therapies to prevent/improve metabolic remodeling in the T1DM heart.

Novel diabetes gene discovery through comprehensive characterization and integrative analysis of longitudinal gene expression changes.

Type 2 diabetes is a complex, systemic disease affected by both genetic and environmental factors. Previous research has identified genetic variants associated with type 2 diabetes risk; however, gene regulatory changes underlying progression to metabolic dysfunction are still largely unknown. We investigated RNA expression changes that occur during diabetes progression using a two-stage approach. In our discovery stage, we compared changes in gene expression using two longitudinally collected blood samples from subjects whose fasting blood glucose transitioned to a level consistent with type 2 diabetes diagnosis between the time points against those who did not with a novel analytical network approach. Our network methodology identified 17 networks, one of which was significantly associated with transition status. This 822-gene network harbors many genes novel to the type 2 diabetes literature but is also significantly enriched for genes previously associated with type 2 diabetes. In the validation stage, we queried associations of genetically determined expression with diabetes-related traits in a large biobank with linked electronic health records. We observed a significant enrichment of genes in our identified network whose genetically determined expression is associated with type 2 diabetes and other metabolic traits and validated 31 genes that are not near previously reported type 2 diabetes loci. Finally, we provide additional functional support, which suggests that the genes in this network are regulated by enhancers that operate in human pancreatic islet cells. We present an innovative and systematic approach that identified and validated key gene expression changes associated with type 2 diabetes transition status and demonstrated their translational relevance in a large clinical resource.

Reducing sarcolipin expression improves muscle metabolism in mdx mice.

Duchenne muscular dystrophy (DMD) is an inherited muscle wasting disease. Metabolic impairments and oxidative stress are major secondary mechanisms that severely worsen muscle function in DMD. Here, we sought to determine whether germline reduction or ablation of sarcolipin (SLN), an inhibitor of sarco/endoplasmic reticulum (SR) Ca2+ ATPase (SERCA), improves muscle metabolism and ameliorates muscle pathology in the mdx mouse model of DMD. Glucose and insulin tolerance tests show that glucose clearance rate and insulin sensitivity were improved in the SLN haploinsufficient mdx (mdx:sln+/-) and SLN-deficient mdx (mdx:sln-/-) mice. The histopathological analysis shows that fibrosis and necrosis were significantly reduced in muscles of mdx:sln+/- and mdx:sln-/- mice. SR Ca2+ uptake, mitochondrial complex protein levels, complex activities, mitochondrial Ca2+ uptake and release, and mitochondrial metabolism were significantly improved, and lipid peroxidation and protein carbonylation were reduced in the muscles of mdx:sln+/- and mdx:sln-/- mice. These data demonstrate that reduction or ablation of SLN expression can improve muscle metabolism, reduce oxidative stress, decrease muscle pathology, and protects the mdx mice from glucose intolerance.

Associations Between Glycemic Traits and Colorectal Cancer: A Mendelian Randomization Analysis.

BACKGROUND: Glycemic traits-such as hyperinsulinemia, hyperglycemia, and type 2 diabetes-have been associated with higher colorectal cancer risk in observational studies; however, causality of these associations is uncertain. We used Mendelian randomization (MR) to estimate the causal effects of fasting insulin, 2-hour glucose, fasting glucose, glycated hemoglobin (HbA1c), and type 2 diabetes with colorectal cancer. METHODS: Genome-wide association study summary data were used to identify genetic variants associated with circulating levels of fasting insulin (n = 34), 2-hour glucose (n = 13), fasting glucose (n = 70), HbA1c (n = 221), and type 2 diabetes (n = 268). Using 2-sample MR, we examined these variants in relation to colorectal cancer risk (48 214 case patient and 64 159 control patients). RESULTS: In inverse-variance models, higher fasting insulin levels increased colorectal cancer risk (odds ratio [OR] per 1-SD = 1.65, 95% confidence interval [CI] = 1.15 to 2.36). We found no evidence of any effect of 2-hour glucose (OR per 1-SD = 1.02, 95% CI = 0.86 to 1.21) or fasting glucose (OR per 1-SD = 1.04, 95% CI = 0.88 to 1.23) concentrations on colorectal cancer risk. Genetic liability to type 2 diabetes (OR per 1-unit increase in log odds = 1.04, 95% CI = 1.01 to 1.07) and higher HbA1c levels (OR per 1-SD = 1.09, 95% CI = 1.00 to 1.19) increased colorectal cancer risk, although these findings may have been biased by pleiotropy. Higher HbA1c concentrations increased rectal cancer risk in men (OR per 1-SD = 1.21, 95% CI = 1.05 to 1.40), but not in women. CONCLUSIONS: Our results support a causal effect of higher fasting insulin, but not glucose traits or type 2 diabetes, on increased colorectal cancer risk. This suggests that pharmacological or lifestyle interventions that lower circulating insulin levels may be beneficial in preventing colorectal tumorigenesis.