A Mendelian Randomization Study of Metabolite Profiles, Fasting Glucose, and Type 2 Diabetes

Mendelian randomization: causal inference leveraging genetic data

Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.

Blood lipid profile, HbA1c, fasting glucose, and diabetes: a cohort study and a two-sample Mendelian randomization analysis

PURPOSE

The prevalence of diabetes is increasing worldwide. The associations between the lipid profile and glycated hemoglobin (HbA1c), fasting glucose, and diabetes remain unclear, so we aimed to perform a cohort study and a two-sample Mendelian randomization (MR) study to investigate the causality between blood lipid profile and HbA1c, fasting glucose, and diabetes.

METHODS

A total of 25,171 participants from the Taiwan Biobank were enrolled. We applied a cohort study and an MR study to assess the association between blood lipid profile and HbA1c, fasting glucose, and diabetes. The summary statistics were obtained from the Asian Genetic Epidemiology Network (AGEN), and the estimates between the instrumental variables (IVs) and outcomes were calculated using the inverse-variance weighted (IVW) method. A series of sensitivity analyses were performed.

RESULTS

In the cohort study, high-density lipoprotein cholesterol (HDL-C) was negatively associated with HbA1c, fasting glucose, and diabetes, while the causal associations between HDL-C and HbA1c (βIVW = - 0.098, p = 0.003) and diabetes (βIVW = - 0.594, p < 0.001) were also observed. Furthermore, there was no pleiotropy effect in this study using the MR-Egger intercept test and MR-PRESSO global test.

CONCLUSIONS

Our results support the hypothesis that a genetically determined increase in HDL-C is causally related to a reduction in HbA1c and a lower risk of diabetes.

A two-sample Mendelian randomization study explores metabolic profiling of different glycemic traits

We assessed the causal relation of four glycemic traits and type 2 diabetes liability with 167 metabolites using Mendelian randomization with various sensitivity analyses and a reverse Mendelian randomization analysis. We extracted instruments for fasting glucose, 2-h glucose, fasting insulin, and glycated hemoglobin from the Meta-Analyses of Glucose and Insulin-related traits Consortium (n = 200,622), and those for type 2 diabetes liability from a meta-analysis of multiple cohorts (148,726 cases, 965,732 controls) in Europeans. Outcome data were from summary statistics of 167 metabolites from the UK Biobank (n = 115,078). Fasting glucose and 2-h glucose were not associated with any metabolite. Higher glycated hemoglobin was associated with higher free cholesterol in small low-density lipoprotein. Type 2 diabetes liability and fasting insulin were inversely associated with apolipoprotein A1, total cholines, lipoprotein subfractions in high-density-lipoprotein and intermediate-density lipoproteins, and positively associated with aromatic amino acids. These findings indicate hyperglycemia-independent patterns and highlight the role of insulin in type 2 diabetes development. Further studies should evaluate these glycemic traits in type 2 diabetes diagnosis and clinical management.

The Relationship of Circulating Choline and Choline-Related Metabolite Levels with Health Outcomes: A Scoping Review of Genome-Wide Association Studies and Mendelian Randomization Studies

Choline is essential for proper liver, muscle, brain, lipid metabolism, cellular membrane composition, and repair. Understanding genetic determinants of circulating choline metabolites can help identify new determinants of choline metabolism, requirements, and their link to disease endpoints. We conducted a scoping review to identify studies assessing the association of genetic polymorphisms on circulating choline and choline-related metabolite concentrations and subsequent associations with health outcomes. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement scoping review extension. Literature was searched to September 28, 2022, in 4 databases: Embase, MEDLINE, Web of Science, and the Biological Science Index. Studies of any duration in humans were considered. Any genome-wide association study (GWAS) investigating genetic variant associations with circulating choline and/or choline-related metabolites and any Mendelian randomization (MR) study investigating the association of genetically predicted circulating choline and/or choline-related metabolites with any health outcome were considered. Qualitative evidence is presented in summary tables. From 1248 total reviewed articles, 53 were included (GWAS = 27; MR = 26). Forty-two circulating choline-related metabolites were tested in association with genetic variants in GWAS studies, primarily trimethylamine N-oxide, betaine, sphingomyelins, lysophosphatidylcholines, and phosphatidylcholines. MR studies investigated associations between 52 total unique choline metabolites and 66 unique health outcomes. Of these, 47 significant associations were reported between 16 metabolites (primarily choline, lysophosphatidylcholines, phosphatidylcholines, betaine, and sphingomyelins) and 27 health outcomes including cancer, cardiovascular, metabolic, bone, and brain-related outcomes. Some articles reported significant associations between multiple choline types and the same health outcome. Genetically predicted circulating choline and choline-related metabolite concentrations are associated with a wide variety of health outcomes. Further research is needed to assess how genetic variability influences choline metabolism and whether individuals with lower genetically predicted circulating choline and choline-related metabolite concentrations would benefit from a dietary intervention or supplementation.

Adiposity and lipid metabolism indicators mediate the adverse effect of glucose metabolism indicators on oogenesis and embryogenesis in PCOS women undergoing IVF/ICSI cycles

BACKGROUND

Polycystic ovary syndrome (PCOS) women have high incidences of dyslipidemia, obesity, impaired glucose tolerance (IGT), diabetes, and insulin resistance (IR) and are fragile to female infertility. Obesity and dyslipidemia may be the intermediate biological mechanism for the associations between glucose metabolism dysfunction and abnormal oogenesis and embryogenesis.

METHODS

This retrospective cohort study was performed at a university-affiliated reproductive center. A total of 917 PCOS women aged between 20 and 45 undergoing their first IVF/ICSI embryo transfer cycles from January 2018 to December 2020 were involved. Associations between glucose metabolism indicators, adiposity and lipid metabolism indicators, and IVF/ICSI outcomes were explored using multivariable generalized linear models. Mediation analyses were further performed to examine the potential mediation role of adiposity and lipid metabolism indicators.

RESULTS

Significant dose-dependent relationships were found between glucose metabolism indicators and IVF/ICSI early reproductive outcomes and between glucose metabolism indicators and adiposity and lipid metabolism indicators (all P < 0.05). Also, we found significant dose-dependent relationships between adiposity and lipid metabolism indicators and IVF/ICSI early reproductive outcomes (all P < 0.05). The mediation analysis indicated that elevated FPG, 2hPG, FPI, 2hPI, HbA1c, and HOMA2-IR were significantly associated with decreased retrieved oocyte count, MII oocyte count, normally fertilized zygote count, normally cleaved embryo count, high-quality embryo count, or blastocyst formation count after controlling for adiposity and lipid metabolism indicators. Serum TG mediated 6.0-31.0% of the associations; serum TC mediated 6.1-10.8% of the associations; serum HDL-C mediated 9.4-43.6% of the associations; serum LDL-C mediated 4.2-18.2% of the associations; and BMI mediated 26.7-97.7% of the associations.

CONCLUSIONS

Adiposity and lipid metabolism indicators (i.e., serum TG, serum TC, serum HDL-C, serum LDL-C, and BMI) are significant mediators of the effect of glucose metabolism indicators on IVF/ICSI early reproductive outcomes in PCOS women, indicating the importance of preconception glucose and lipid management and the dynamic equilibrium of glucose and lipid metabolism in PCOS women.

Metabolic drivers of dysglycemia in pregnancy: ethnic-specific GWAS of 146 metabolites and 1-sample Mendelian randomization analyses in a UK multi-ethnic birth cohort

Introduction

Gestational diabetes mellitus (GDM) is the most common pregnancy complication worldwide and is associated with short- and long-term health implications for both mother and child. Prevalence of GDM varies between ethnicities, with South Asians (SAs) experiencing up to three times the risk compared to white Europeans (WEs). Recent evidence suggests that underlying metabolic difference contribute to this disparity, but an investigation of causality is required.

Methods

To address this, we paired metabolite and genomic data to evaluate the causal effect of 146 distinct metabolic characteristics on gestational dysglycemia in SAs and WEs. First, we performed 292 GWASs to identify ethnic-specific genetic variants associated with each metabolite (P ≤ 1 x 10-5) in the Born and Bradford cohort (3688 SA and 3354 WE women). Following this, a one-sample Mendelian Randomisation (MR) approach was applied for each metabolite against fasting glucose and 2-hr post glucose at 26-28 weeks gestation. Additional GWAS and MR on 22 composite measures of metabolite classes were also conducted.

Results

This study identified 15 novel genome-wide significant (GWS) SNPs associated with tyrosine in the FOXN and SLC13A2 genes and 1 novel GWS SNP (currently in no known gene) associated with acetate in SAs. Using MR approach, 14 metabolites were found to be associated with postprandial glucose in WEs, while in SAs a distinct panel of 11 metabolites were identified. Interestingly, in WEs, cholesterols were the dominant metabolite class driving with dysglycemia, while in SAs saturated fatty acids and total fatty acids were most commonly associated with dysglycemia.

Discussion

In summary, we confirm and demonstrate the presence of ethnic-specific causal relationships between metabolites and dysglycemia in mid-pregnancy in a UK population of SA and WE pregnant women. Future work will aim to investigate their biological mechanisms on dysglycemia and translating this work towards ethnically tailored GDM prevention strategies.

Nuclear magnetic resonance-determined lipoprotein profile and risk of breast cancer: a Mendelian randomization study

PURPOSE

While crudely quantified lipoproteins have been reported to affect the risk of breast cancer, the effects of subclass lipoproteins characterized by particle size, particle number, and lipidomes remain unknown.

METHODS

Utilizing nuclear magnetic resonance-based GWAS of 85 lipoprotein traits, we performed two-sample univariable Mendelian randomization (MR) to evaluate the causal relationship between each trait with breast cancer (Ncase/control = 133,384/113,789) and with its estrogen receptor (ER) subtypes. Then, we applied multivariable MR to investigate the independent effects considering both general and central obesity.

RESULTS

In univariable MR, a heterogeneous effect of subclass high-density lipoproteins (HDL) was observed, in which small HDL traits (ORs ranged from 0.89 to 0.94) were associated with a decreased risk of breast cancer while non-small HDLs traits (OR ranged from 1.04 to 1.08) were associated with an increased risk of breast cancer. Very-low-density lipoproteins (VLDL) traits and serum total triglycerides (TG) were associated with a decreased risk of breast cancer (ORs ranged from 0.88 to 0.94). Similar association patterns were found for ER + subtype. In multivariable MR, only the protective effects of small HDL, VLDL and TG on ER + subtype remained significant.

CONCLUSION

We identified a heterogeneous effect of subclass HDLs and a consistent protective effect of VLDL on breast cancer. Only the effects of small HDL and VLDL on ER + subtype remained robust after controlling for obesity. These findings provide new insight into the causal pathway underlying lipoproteins and breast cancer.

Metabolic profiling of MRI-measured liver fat in the UK Biobank

OBJECTIVE

Liver fat associates with obesity-related metabolic disturbances and may precede incident diseases. Metabolomic profiles of liver fat in the UK Biobank were investigated.

METHODS

Regression models assessed the associations between 180 metabolites and proton density liver fat fraction (PDFF) measured 5 years later through magnetic resonance imaging, as the difference (in SD units) of each log metabolite measure with 1-SD higher PDFF among those without chronic disease and not taking statins, and by diabetes and cardiovascular diseases.

RESULTS

After accounting for confounders, multiple metabolites were associated positively with liver fat (p < 0.0001 for 152 traits), particularly extremely large and very large lipoprotein particle concentrations, very low-density lipoprotein triglycerides, small high-density lipoprotein particles, glycoprotein acetyls, monounsaturated and saturated fatty acids, and amino acids. Extremely large and large high-density lipoprotein concentrations had strong inverse associations with liver fat. Associations were broadly comparable among those with versus without vascular metabolic conditions, although negative, rather than positive, associations were observed between intermediate-density and large low-density lipoprotein particles among those with BMI ≥25 kg/m² , diabetes, or cardiovascular diseases. Metabolite principal components showed a 15% significant improvement in risk prediction for PDFF relative to BMI, which was twice as great (but nonsignificant) compared with conventional high-density lipoprotein cholesterol and triglycerides.

CONCLUSIONS

Hazardous metabolomic profiles are associated with ectopic hepatic fat and are relevant to risk of vascular-metabolic disease.

Adiposity and NMR-measured lipid and metabolic biomarkers among 30,000 Mexican adults

BACKGROUND

Adiposity is a major cause of morbidity and mortality in part due to effects on blood lipids. Nuclear magnetic resonance (NMR) spectroscopy provides direct information on >130 biomarkers mostly related to blood lipid particles.

METHODS

Among 28,934 Mexican adults without chronic disease and not taking lipid-lowering therapy, we examine the cross-sectional relevance of body-mass index (BMI), waist circumference (WC), waist-hip ratio (WHR), and hip circumference (HC) to NMR-measured metabolic biomarkers. Confounder-adjusted associations between each adiposity measure and NMR biomarkers are estimated before and after mutual adjustment for other adiposity measures.

RESULTS

Markers of general (ie, BMI), abdominal (ie, WC and WHR) and gluteo-femoral (ie, HC) adiposity all display similar and strong associations across the NMR-platform of biomarkers, particularly for biomarkers that increase cardiometabolic risk. Higher adiposity associates with higher levels of Apolipoprotein-B (about 0.35, 0.30, 0.35, and 0.25 SD higher Apolipoprotein-B per 2-SD higher BMI, WHR, WC, and HC, respectively), higher levels of very low-density lipoprotein particles (and the cholesterol, triglycerides, and phospholipids within these lipoproteins), higher levels of all fatty acids (particularly mono-unsaturated fatty acids) and multiple changes in other metabolic biomarkers including higher levels of branched-chain amino acids and the inflammation biomarker glycoprotein acetyls. Associations for general and abdominal adiposity are fairly independent of each other but, given general and abdominal adiposity, higher gluteo-femoral adiposity is associated with a strongly favourable cardiometabolic lipid profile.

CONCLUSIONS

Our results provide insight to the lipidic and metabolomic signatures of different adiposity markers in a previously understudied population where adiposity is common but lipid-lowering therapy is not.

Gamma-glutamyl transferase to high-density lipoprotein cholesterol ratio: A valuable predictor of type 2 diabetes mellitus incidence

BACKGROUND

Gamma-glutamyl transferase (GGT) and high-density lipoprotein cholesterol (HDL-C) have been proven to be valuable predictors of type 2 diabetes mellitus (T2DM). The aim of this study was to investigate the association between GGT/HDL-C ratio and incident T2DM.

METHODS

The study retrospectively analyzed 15453 participants from 2004 to 2015. Cox proportional hazards regression models and Kaplan-Meier curves were used to elucidate the effect of GGT/HDL-C ratio on T2DM. Restricted cubic spline (RCS) analysis was performed to explore any non-linear correlation between GGT/HDL-C ratio and the risk of T2DM. The predictive performance of GGT, HDL-C and GGT/HDL-C ratio for T2DM was evaluated utilizing receiver-operating-characteristic (ROC) curves.

RESULTS

During a median follow-up of 5.39 years, 373 cases of incident T2DM were observed. Kaplan-Meier curves showed that the cumulative probabilities of T2DM increased in the participants with higher GGT/HDL-C ratio significantly (P < 0.001). Cox models further clarified that high GGT/HDL-C ratio was an independent risk factor for T2DM (HR = 1.01, 95% CI = 1.00-1.01, P = 0.011). Linear positive correlation between GGT/HDL-C ratio and the risk of T2DM was demonstrated through RCS analysis. In the ROC analysis, GGT/HDL-C ratio (AUC = 0.75, 95% CI = 0.73-0.77) showed competitive role in the prediction of T2DM compared with single GGT and HDL-C.

CONCLUSIONS

The GGT/HDL-C ratio could serve as a valuable predictor of T2DM, and the risk of T2DM increases in the condition of higher GGT/HDL-C ratio.

Effect of Metabolite Levels on Type 2 Diabetes Mellitus and Glycemic Traits: A Mendelian Randomization Study

OBJECTIVE

To assess the causal associations of plasma levels of metabolites with type 2 diabetes mellitus (T2DM) and glycemic traits.

METHODS

Two-sample mendelian randomization (MR) was conducted to assess the causal associations. Genetic variants strongly associated with metabolites at genome-wide significance level (P < 5 × 10-8) were selected from public genome-wide association studies, and single-nucleotide polymorphisms of outcomes were obtained from the Diabetes Genetics Replication and Meta-analysis consortium for T2DM and from the Meta-Analyses of Glucose and Insulin-related Traits Consortium for fasting glucose, insulin, and glycated hemoglobin (HbA1c). The Wald ratio and inverse-variance weighted methods were used for analyses, and MR-Egger was used for sensitivity analysis.

RESULTS

The β estimates per 1-SD increase of arachidonic acid (AA) level was 0.16 (95% CI, 0.078-0.242; P < 0.001). Genetic predisposition to higher plasma AA levels were associated with higher fasting glucose levels (β 0.10 [95% CI, 0.064-0.134], P < 0.001), higher HbA1c levels (β 0.04 [95% CI, 0.027-0.061]), and lower fasting insulin levels (β -0.025 [95% CI, -0.047 to -0.002], P = 0.033). Besides, 2-hydroxybutyric acid (2-HBA) might have a positive causal effect on glycemic traits.

CONCLUSIONS

Our findings suggest that AA and 2-HBA may have causal associations on T2DM and glycemic traits. This is beneficial for clarifying the pathogenesis of T2DM, which would be valuable for early identification and prevention for T2DM.

HDL and type 2 diabetes: the chicken or the egg?

HDL is a complex macromolecular cluster of various components, such as apolipoproteins, enzymes and lipids. Quality evidence from clinical and epidemiological studies led to the principle that HDL-cholesterol (HDL-C) levels are inversely correlated with the risk of CHD. Nevertheless, the failure of many cholesteryl ester transfer protein inhibitors to protect against CVD casts doubts on this principle and highlights the fact that HDL functionality, as dictated by its proteome and lipidome, also plays an important role in protecting against metabolic disorders. Recent data indicate that HDL-C levels and HDL particle functionality are correlated with the pathogenesis and prognosis of type 2 diabetes mellitus, a major risk factor for CVD. Hyperglycaemia leads to reduced HDL-C levels and deteriorated HDL functionality, via various alterations in HDL particles' proteome and lipidome. In turn, reduced HDL-C levels and impaired HDL functionality impact the performance of key organs related to glucose homeostasis, such as pancreas and skeletal muscles. Interestingly, different structural alterations in HDL correlate with distinct metabolic abnormalities, as indicated by recent data evaluating the role of apolipoprotein A1 and lecithin-cholesterol acyltransferase deficiency in glucose homeostasis. While it is becoming evident that not all HDL disturbances are causatively associated with the development and progression of type 2 diabetes, a bidirectional correlation between these two conditions exists, leading to a perpetual self-feeding cycle.

Stratification of Type 2 Diabetes by Age of Diagnosis in the UK Biobank Reveals Subgroup-Specific Genetic Associations and Causal Risk Profiles

The pathogenesis of type 2 diabetes (T2D) might change with increasing age. Here, we used a stratification based on age of diagnosis to gain insight into the genetics and causal risk factors of T2D across different age-groups. We performed genome-wide association studies (GWAS) on T2D and T2D subgroups based on age of diagnosis (<50, 50-60, 60-70, and >70 years) (total of 24,986 cases). As control subjects, participants were at least 70 years of age at the end of follow-up without developing T2D (N =187,130). GWAS identified 208 independent lead single nucleotide polymorphism (SNPs) mapping to 69 loci associated with T2D (P < 1.0e-8). Among others, SNPs mapped to CDKN2B-AS1 and multiple independent SNPs mapped to TCF7L2 were more strongly associated with cases diagnosed after age 70 years than with cases diagnosed before age 50 years. Based on the different case groups, we performed two-sample Mendelian randomization. Most notably, we observed that of the investigated risk factors, the association between BMI and T2D attenuated with increasing age of diagnosis. Collectively, our results indicate that stratification of T2D based on age of diag-nosis reveals subgroup-specific genetics and causal determinants, supporting the hypothesis that the pathogenesis of T2D changes with increasing age.

A Scoping Review: Metabolomics Signatures Associated with Animal and Plant Protein Intake and Their Potential Relation with Cardiometabolic Risk

The dietary shift from animal protein (AP) to plant protein (PP) sources is encouraged for both environmental and health reasons. For instance, PPs are associated with lower cardiovascular and diabetes risks compared with APs, although the underlying mechanisms mostly remain unknown. Metabolomics is a valuable tool for globally and mechanistically characterizing the impact of AP and PP intake, given its unique ability to provide integrated signatures and specific biomarkers of metabolic effects through a comprehensive snapshot of metabolic status. This scoping review is aimed at gathering and analyzing the available metabolomics data associated with PP- and AP-rich diets, and discusses the metabolic effects underlying these metabolomics signatures and their potential implication for cardiometabolic health. We selected 24 human studies comparing the urine, plasma, or serum metabolomes associated with diets with contrasted AP and PP intakes. Among the 439 metabolites reported in those studies as able to discriminate AP- and PP-rich diets, 46 were considered to provide a robust level of evidence, according to a scoring system, especially amino acids (AAs) and AA-related products. Branched-chain amino acids, aromatic amino acids (AAAs), glutamate, short-chain acylcarnitines, and trimethylamine-N-oxide, which are known to be related to an increased cardiometabolic risk, were associated with AP-rich diets, whereas glycine (rather related to a reduced risk) was associated with PP-rich diets. Tricarboxylic acid (TCA) cycle intermediates and products from gut microbiota AAA degradation were also often reported, but the direction of their associations differed across studies. Overall, AP- and PP-rich diets result in different metabolomics signatures, with several metabolites being plausible candidates to explain some of their differential associations with cardiometabolic risk. Additional studies specifically focusing on protein type, with rigorous intake control, are needed to better characterize the associated metabolic phenotypes and understand how they could mediate differential AP and PP effects on cardiometabolic risk.

Genetics of Sleep and Insights into Its Relationship with Obesity

Considerable recent advancements in elucidating the genetic architecture of sleep traits and sleep disorders may provide insight into the relationship between sleep and obesity. Despite the considerable involvement of the circadian clock in sleep and metabolism, few shared genes, including FTO, were implicated in genome-wide association studies (GWASs) of sleep and obesity. Polygenic scores composed of signals from GWASs of sleep traits show largely null associations with obesity, suggesting lead variants are unique to sleep. Modest genome-wide genetic correlations are observed between many sleep traits and obesity and are largest for snoring.Notably, U-shaped positive genetic correlations with body mass index (BMI) exist for both short and long sleep durations. Findings from Mendelian randomization suggest robust causal effects of insomnia on higher BMI and, conversely, of higher BMI on snoring and daytime sleepiness. Bidirectional effects between sleep duration and daytime napping with obesity may also exist. Limited gene-sleep interaction studies suggest that achieving favorable sleep, as part of a healthy lifestyle, may attenuate genetic predisposition to obesity, but whether these improvements produce clinically meaningful reductions in obesity risk remains unclear. Investigations of the genetic link between sleep and obesity for sleep disorders other than insomnia and in populations of non-European ancestry are currently limited. Expected final online publication date for the Annual Review of Nutrition, Volume 41 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A network analysis framework of genetic and nongenetic risks for type 2 diabetes

Both genetic and nongenetic factors have been found to be associated with type 2 diabetes, however, the correlation between them is still unclear. In the present study, we aimed to fully decipher the nongenetic and genetic factor association network for type 2 diabetes. We identified risk factors for type 2 diabetes by systematically searching for related meta-analyses and genome-wide association studies (GWAS) database. Among a total of 27,822 studies screened, 202 articles were eligible, from which 174 nongenetic factors and 210 genetic factors associated with type 2 diabetes were identified. Then, we obtained 584 associations between the nongenetic and genetic factors of type 2 diabetes, based on which a risk factor association network was conducted. The nongenetic factors could be classified into seven categories according to the Global Burden of Diseases (GBD). Of these seven categories of nongenetic factors, five were found to be correlated with genes associated with type 2 diabetes, including environmental risks, behavioral risks, metabolic risks, related disease of type 2 diabetes, and treatments. Specifically, air pollutants of environmental risks, alcohol using of behavioral risks, obesity of metabolic risks, rheumatoid arthritis of related disease risk, and simvastatin of treatment was correlated with the largest number of genes. In summary, the correlation between genetic factors and nongenetic factors identified in this study indicates that there is a common phenotype-genotype association in type 2 diabetes, with the combinations of genotypes ("genetic signature") clustering in phenotypes related to type 2 diabetes. Thus, we should take a systematic approach to explore the relationship of various factors for type 2 diabetes, as well as other noncommunicable diseases.

Causal Inference Methods to Integrate Omics and Complex Traits

Major biotechnological advances have facilitated a tremendous boost to the collection of (gen-/transcript-/prote-/methyl-/metabol-)omics data in very large sample sizes worldwide. Coordinated efforts have yielded a deluge of studies associating diseases with genetic markers (genome-wide association studies) or with molecular phenotypes. Whereas omics-disease associations have led to biologically meaningful and coherent mechanisms, the identified (non-germline) disease biomarkers may simply be correlates or consequences of the explored diseases. To move beyond this realm, Mendelian randomization provides a principled framework to integrate information on omics- and disease-associated genetic variants to pinpoint molecular traits causally driving disease development. In this review, we show the latest advances in this field, flag up key challenges for the future, and propose potential solutions.

Triangulating evidence from longitudinal and Mendelian randomization studies of metabolomic biomarkers for type 2 diabetes

The number of people affected by Type 2 diabetes mellitus (T2DM) is close to half a billion and is on a sharp rise, representing a major and growing public health burden. Given its mild initial symptoms, T2DM is often diagnosed several years after its onset, leaving half of diabetic individuals undiagnosed. While several classical clinical and genetic biomarkers have been identified, improving early diagnosis by exploring other kinds of omics data remains crucial. In this study, we have combined longitudinal data from two population-based cohorts CoLaus and DESIR (comprising in total 493 incident cases vs. 1360 controls) to identify new or confirm previously implicated metabolomic biomarkers predicting T2DM incidence more than 5 years ahead of clinical diagnosis. Our longitudinal data have shown robust evidence for valine, leucine, carnitine and glutamic acid being predictive of future conversion to T2DM. We confirmed the causality of such association for leucine by 2-sample Mendelian randomisation (MR) based on independent data. Our MR approach further identified new metabolites potentially playing a causal role on T2D, including betaine, lysine and mannose. Interestingly, for valine and leucine a strong reverse causal effect was detected, indicating that the genetic predisposition to T2DM may trigger early changes of these metabolites, which appear well-before any clinical symptoms. In addition, our study revealed a reverse causal effect of metabolites such as glutamic acid and alanine. Collectively, these findings indicate that molecular traits linked to the genetic basis of T2DM may be particularly promising early biomarkers.

Multi-omics study for interpretation of genome-wide association study

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with complex traits, including a wide variety of diseases. Despite the successful identification of associated loci, interpreting GWAS findings remains challenging and requires other biological resources. Omics, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics, are increasingly used in a broad range of research fields. Integrative analyses applying GWAS with these omics data are expected to expand our knowledge of complex traits and provide insight into the pathogenesis of complex diseases and their causative factors. Recently, associations between genetic variants and omics data have been comprehensively evaluated, providing new information on the influence of genetic variants on omics. Furthermore, recent advances in analytic methods, including single-cell technologies, have revealed previously unknown disease mechanisms. To advance our understanding of complex traits, integrative analysis using GWAS with multi-omics data is needed. In this review, I describe successful examples of integrative analyses based on omics and GWAS, discuss the limitations of current multi-omics analyses, and provide a perspective on future integrative studies.

Diet, Physical Activity and Adiposity as Determinants of Circulating Amino Acid Levels in a Multiethnic Asian Population

Profiles of circulating amino acids have been associated with cardiometabolic diseases. We investigated the associations between dietary protein intake, physical activity and adiposity and serum amino acid profiles in an Asian population. We used data from 3009 male and female participants from the Singapore Prospective Study Program cohort. Dietary and physical activity data were obtained from validated questionnaires; anthropometric measurements were collected during a health examination; and fasting concentrations of 16 amino acids were measured using targeted LC-MS. The association between lifestyle factors and amino acid levels was modeled using multiple linear regression with adjustment for other sociodemographic and lifestyle factors and correction for multiple testing. We observed significant associations between seafood intake (β-coefficient 0.132, 95% CI 0.006, 0.257 for a 100% increment), physical activity (β-coefficient −0.096, 95% CI −0.183, −0.008 in the highest versus lowest quartile) and adiposity (BMI β-coefficient 0.062, 95% CI 0.054, 0.070 per kg/m²; waist circumference β-coefficient 0.034, 95% CI 0.031, 0.037 per cm) and branched-chain amino acid levels (expressed per-SD). We also observed significant interactions with sex for the association between meat and seafood and total intakes and BCAA levels (P for interaction 0.007), which were stronger in females than in males. Our findings suggest novel associations between modifiable lifestyle factors and amino acid levels in Asian populations.

Early Metabolic Features of Genetic Liability to Type 2 Diabetes: Cohort Study With Repeated Metabolomics Across Early Life

OBJECTIVE

Type 2 diabetes develops for many years before diagnosis. We aimed to reveal early metabolic features characterizing liability to adult disease by examining genetic liability to adult type 2 diabetes in relation to metabolomic traits across early life.

RESEARCH DESIGN AND METHODS

Up to 4,761 offspring from the Avon Longitudinal Study of Parents and Children were studied. Linear models were used to examine effects of a genetic risk score (162 variants) for adult type 2 diabetes on 229 metabolomic traits (lipoprotein subclass-specific cholesterol and triglycerides, amino acids, glycoprotein acetyls, and others) measured at age 8 years, 16 years, 18 years, and 25 years. Two-sample Mendelian randomization (MR) was also conducted using genome-wide association study data on metabolomic traits in an independent sample of 24,925 adults.

RESULTS

At age 8 years, associations were most evident for type 2 diabetes liability (per SD higher) with lower lipids in HDL subtypes (e.g., -0.03 SD [95% CI -0.06, -0.003] for total lipids in very large HDL). At 16 years, associations were stronger with preglycemic traits, including citrate and with glycoprotein acetyls (0.05 SD; 95% CI 0.01, 0.08), and at 18 years, associations were stronger with branched-chain amino acids. At 25 years, associations had strengthened with VLDL lipids and remained consistent with previously altered traits, including HDL lipids. Two-sample MR estimates among adults indicated persistent patterns of effect of disease liability.

CONCLUSIONS

Our results support perturbed HDL lipid metabolism as one of the earliest features of type 2 diabetes liability, alongside higher branched-chain amino acid and inflammatory levels. Several features are apparent in childhood as early as age 8 years, decades before the clinical onset of disease.

Integrating untargeted metabolomics, genetically informed causal inference, and pathway enrichment to define the obesity metabolome

BACKGROUND

Obesity and its associated diseases are major health problems characterized by extensive metabolic disturbances. Understanding the causal connections between these phenotypes and variation in metabolite levels can uncover relevant biology and inform novel intervention strategies. Recent studies have combined metabolite profiling with genetic instrumental variable (IV) analysis (Mendelian randomization) to infer the direction of causality between metabolites and obesity, but often omitted a large portion of untargeted profiling data consisting of unknown, unidentified metabolite signals.

METHODS

We expanded upon previous research by identifying body mass index (BMI)-associated metabolites in multiple untargeted metabolomics datasets, and then performing bidirectional IV analysis to classify metabolites based on their inferred causal relationships with BMI. Meta-analysis and pathway analysis of both known and unknown metabolites across datasets were enabled by our recently developed bioinformatics suite, PAIRUP-MS.

RESULTS

We identified ten known metabolites that are more likely to be causes (e.g., alpha-hydroxybutyrate) or effects (e.g., valine) of BMI, or may have more complex bidirectional cause-effect relationships with BMI (e.g., glycine). Importantly, we also identified about five times more unknown than known metabolites in each of these three categories. Pathway analysis incorporating both known and unknown metabolites prioritized 40 enriched (p < 0.05) metabolite sets for the cause versus effect groups, providing further support that these two metabolite groups are linked to obesity via distinct biological mechanisms.

CONCLUSIONS

These findings demonstrate the potential utility of our approach to uncover causal connections with obesity from untargeted metabolomics datasets. Combining genetically informed causal inference with the ability to map unknown metabolites across datasets provides a path to jointly analyze many untargeted datasets with obesity or other phenotypes. This approach, applied to larger datasets with genotype and untargeted metabolite data, should generate sufficient power for robust discovery and replication of causal biological connections between metabolites and various human diseases.

Biomarkers for type 2 diabetes

Background

The prevalence and incidence of type 2 diabetes (T2D), representing >90% of all cases of diabetes, are increasing rapidly worldwide. Identification of individuals at high risk of developing diabetes is of great importance as early interventions might delay or even prevent full-blown disease. T2D is a complex disease caused by multiple genetic loci in interplay with lifestyle and environmental factors. Recently over 400 distinct association signals were published; these explain 18% of the risk of T2D.

Scope of review

In this review there is a major focus on risk factors and genetic and non-genetic biomarkers for the risk of T2D identified especially in large prospective population-based studies, and studies testing causality of the biomarkers for T2D in Mendelian randomization studies. Another focus is on understanding genome-phenome interplay in the classification of individuals with T2D into subgroups.

Major conclusions

Several recent large population-based studies and their meta-analyses have identified multiple potential genetic and non-genetic biomarkers for the risk of T2D. Combination of genetic variants and physiologically characterized pathways improves the classification of individuals with T2D into subgroups, and is also paving the way to a precision medicine approach, in T2D.

Metabolomics analyses in non-diabetic middle-aged individuals reveal metabolites impacting early glucose disturbances and insulin sensitivity

INTRODUCTION

Several plasma metabolites have been associated with insulin resistance and type 2 diabetes mellitus.

OBJECTIVES

We aimed to identify plasma metabolites associated with different indices of early disturbances in glucose metabolism and insulin sensitivity.

METHODS

This cross-sectional study was conducted in a subsample of the Leiden Longevity Study comprising individuals without a history of diabetes mellitus (n = 233) with a mean age of 63.3 ± 6.7 years of which 48.1% were men. We tested for associations of fasting glucose, fasting insulin, HOMA-IR, Matsuda Index, Insulinogenic Index and glycated hemoglobin with metabolites (Swedish Metabolomics Platform) using linear regression analysis adjusted for age, sex and BMI. Results were validated internally using an independent metabolomics platform (Biocrates platform) and replicated externally in the independent Netherlands Epidemiology of Obesity (NEO) study (Metabolon platform) (n = 545, mean age of 55.8 ± 6.0 years of which 48.6% were men). Moreover, in the NEO study, we replicated our analyses in individuals with diabetes mellitus (cases: n = 36; controls = 561).

RESULTS

Out of the 34 metabolites, a total of 12 plasma metabolites were associated with different indices of disturbances in glucose metabolism and insulin sensitivity in individuals without diabetes mellitus. These findings were validated using a different metabolomics platform as well as in an independent cohort of non-diabetics. Moreover, tyrosine, alanine, valine, tryptophan and alpha-ketoglutaric acid levels were higher in individuals with diabetes mellitus.

CONCLUSION

We found several plasma metabolites that are associated with early disturbances in glucose metabolism and insulin sensitivity of which five were also higher in individuals with diabetes mellitus.

Metabolomic and Lipidomic Signatures of Metabolic Syndrome and its Physiological Components in Adults: A Systematic Review

The aim of this work was to conduct a systematic review of human studies on metabolite/lipid biomarkers of metabolic syndrome (MetS) and its components, and provide recommendations for future studies. The search was performed in MEDLINE, EMBASE, EMB Review, CINHAL Complete, PubMed, and on grey literature, for population studies identifying MetS biomarkers from metabolomics/lipidomics. Extracted data included population, design, number of subjects, sex/gender, clinical characteristics and main outcome. Data were collected regarding biological samples, analytical methods, and statistics. Metabolites were compiled by biochemical families including listings of their significant modulations. Finally, results from the different studies were compared. The search yielded 31 eligible studies (2005–2019). A first category of articles identified prevalent and incident MetS biomarkers using mainly targeted metabolomics. Even though the population characteristics were quite homogeneous, results were difficult to compare in terms of modulated metabolites because of the lack of methodological standardization. A second category, focusing on MetS components, allowed comparing more than 300 metabolites, mainly associated with the glycemic component. Finally, this review included also publications studying type 2 diabetes as a whole set of metabolic risks, raising the interest of reporting metabolomics/lipidomics signatures to reflect the metabolic phenotypic spectrum in systems approaches.

Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug-metabolite atlas

Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands has constructed an atlas of drug-metabolite associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance. The atlas includes a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug-metabolite associations after evaluation of confounders including co-treatment. We show that the effect estimates of statins on metabolites from the cross-sectional study are comparable to those from intervention and genetic observational studies. Further data integration links proton pump inhibitors to circulating metabolites, liver function, hepatic steatosis and the gut microbiome. Our atlas provides a tool for targeted experimental pharmaceutical research and clinical trials to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualization of the atlas (http://bbmri.researchlumc.nl/atlas/).

Pleiotropic Effects of a KCNQ1 Variant on Lipid Profiles and Type 2 Diabetes: A Family-Based Study in China

OBJECTIVE

The genetic variant rs2237895, located in the Potassium Voltage-Gated Channel Subfamily Q Member 1 (KCNQ1) gene, has been replicated to be associated with type 2 diabetes mellitus (T2DM) susceptibility, but the relationship with lipids is conflicting. Furthermore, the common genetic predisposition to T2DM and lipids was not fully detected.

METHODS

In total, 5839 individuals (2220 were T2DM patients) across 2885 families were included. The effect of rs2237895 on T2DM and lipids was estimated using linear regression and logistic regression models after adjustment for multiple covariates. Mediation analysis was then used to test whether KCNQ1 participated in T2DM pathogenesis via lipid-mediated pathways.

RESULTS

Per allele-C of rs2237895 was associated with 17% (11-23%, P < 0.001) increased T2DM risk. Moreover, it was correlated with 5% (1-9%, P < 0.001) increased T2DM risk. Moreover, it was correlated with 5% (1-9%, P < 0.001) increased T2DM risk. Moreover, it was correlated with 5% (1-9%, P < 0.001) increased T2DM risk. Moreover, it was correlated with 5% (1-9%, P < 0.001) increased T2DM risk. Moreover, it was correlated with 5% (1-9%, P < 0.001) increased T2DM risk. Moreover, it was correlated with 5% (1-9%.

CONCLUSION

KCNQ1 had pleiotropic effects on lipids and T2DM, and the unexpected genetic effect on association of HDL-C with T2DM was observed, indicating the different pathways to lipids and T2DM. Further research studies are needed to verify potential biological mechanisms.

Lipidomic profiling identifies signatures of metabolic risk

BACKGROUND

Metabolic syndrome (MetS), the clustering of metabolic risk factors, is associated with cardiovascular disease risk. We sought to determine if dysregulation of the lipidome may contribute to metabolic risk factors.

METHODS

We measured 154 circulating lipid species in 658 participants from the Framingham Heart Study (FHS) using liquid chromatography-tandem mass spectrometry and tested for associations with obesity, dysglycemia, and dyslipidemia. Independent external validation was sought in three independent cohorts. Follow-up data from the FHS were used to test for lipid metabolites associated with longitudinal changes in metabolic risk factors.

RESULTS

Thirty-nine lipids were associated with obesity and eight with dysglycemia in the FHS. Of 32 lipids that were available for replication for obesity and six for dyslipidemia, 28 (88%) replicated for obesity and five (83%) for dysglycemia. Four lipids were associated with longitudinal changes in body mass index and four were associated with changes in fasting blood glucose in the FHS.

CONCLUSIONS

We identified and replicated several novel lipid biomarkers of key metabolic traits. The lipid moieties identified in this study are involved in biological pathways of metabolic risk and can be explored for prognostic and therapeutic utility.

How to Integrate Personalized Medicine into Prevention? Recommendations from the Personalized Prevention of Chronic Diseases (PRECeDI) Consortium

Medical practitioners are increasingly adopting a personalized medicine (PM) approach involving individually tailored patient care. The Personalized Prevention of Chronic Diseases (PRECeDI) consortium project, funded within the Marie Skłodowska Curie Action (MSCA) Research and Innovation Staff Exchange (RISE) scheme, had fostered collaboration on PM research and training with special emphasis on the prevention of chronic diseases. From 2014 to 2018, the PRECeDI consortium trained 50 staff members on personalized prevention of chronic diseases through training and research. The acquisition of skills from researchers came from dedicated secondments from academic and nonacademic institutions aimed at training on several research topics related to personalized prevention of cancer and cardiovascular and neurodegenerative diseases. In detail, 5 research domains were addressed: (1) identification and validation of biomarkers for the primary prevention of cardiovascular diseases, secondary prevention of Alzheimer disease, and tertiary prevention of head and neck cancer; (2) economic evaluation of genomic applications; (3) ethical-legal and policy issues surrounding PM; (4) sociotechnical analysis of the pros and cons of informing healthy individuals on their genome; and (5) identification of organizational models for the provision of predictive genetic testing. Based on the results of the research carried out by the PRECeDI consortium, in November 2018, a set of recommendations for policy makers, scientists, and industry has been issued, with the main goal to foster the integration of PM approaches in the field of chronic disease prevention.

ApoAI-derived peptide increases glucose tolerance and prevents formation of atherosclerosis in mice

AIMS/HYPOTHESIS

Finding new treatment alternatives for individuals with diabetes with severe insulin resistance is highly desired. To identify novel mechanisms that improve glucose uptake in skeletal muscle, independently from insulin levels and signalling, we have explored the therapeutic potential of a short peptide sequence, RG54, derived from apolipoprotein A-I (ApoA-I).

METHODS

INS-1E rat clonal beta cells, C2C12 rat muscle myotubes and J774 mouse macrophages were used to study the impact of RG54 peptide on glucose-stimulated insulin secretion, glucose uptake and cholesterol efflux, respectively. GTTs were carried out on diet-induced insulin-resistant and Lepr^db diabetic mouse models treated with RG54 peptide, and the impact of RG54 peptide on atherosclerosis was evaluated in Apoe^-/- mice. Control mice received ApoA-I protein, liraglutide or NaCl.

RESULTS

The synthetic RG54 peptide induced glucose uptake in cultured muscle myotubes by a similar amount as insulin, and also primed pancreatic beta cells for improved glucose-stimulated insulin secretion. The findings were verified in diet-induced insulin-resistant and Lepr^db diabetic mice, jointly confirming the physiological effect. The RG54 peptide also efficiently catalysed cholesterol efflux from macrophages and prevented the formation of atherosclerotic plaques in Apoe^-/- mice.

CONCLUSIONS/INTERPRETATION

The RG54 peptide exhibits good prospects for providing glucose control and reducing the risk of cardiovascular disease in individuals with severe insulin resistance.

Role of metabolic syndrome and its components as mediators of the genetic effect on type 2 diabetes: A family-based study in China

BACKGROUND

Metabolic syndrome (MetS) share a genetic basis with type 2 diabetes (T2D). However, whether MetS and its components mediate genetic susceptibility to T2D is not completely understood.

METHODS

We assessed the effects of MetS and its components on associations T2D and 18 genome-wide association studies-identified variants using a two-stage strategy based on parametric models involving 7110 Chinese participants (2436 were T2D patients) across 2885 families. Multilevel logistic regression was used to account for the intrafamilial correlation.

RESULTS

Metabolic syndrome significantly mediated the effect of a melatonin receptor 1B (MTNR1B) polymorphism on T2D risk (OR of average causal mediation effect [OR_ACME ] 1.004; 95% confidence interval [CI] 1.001-1.008; P = 0.018). In addition, low high-density lipoprotein cholesterol (HDL-C) levels mediated the genetic effects of MTNR1B (OR_ACME 1.012; 95% CI 1.007-1.015; P < 0.001), solute carrier family 30 member 8 (SLC30A8; OR_ACME 1.001; 95% CI 1.000-1.007; P < 0.040), B-cell lymphoma/leukemia 11A (BCL11A; OR_ACME 1.009; 95% CI 1.007-1.016; P < 0.001), prospero homeobox 1 (PROX1; OR_ACME 1.005; 95% CI 1.003-1.011; P < 0.001) and a disintegrin and metallopeptidase with thrombospondin type 1 motif 9 (ADAMTS9; OR_ACME 1.006; 95% CI 1.001-1.009; P = 0.022), whereas increased fasting blood glucose (FBG) significantly mediated the genetic effect of BCL11A (OR_ACME 1.017; 95% CI 1.003-1.021; P = 0.012).

CONCLUSIONS

This study provides evidence that MetS and two of its components (HDL-C, FBG) may be involved in mediating the genetic predisposition to T2D, which emphasize the importance of maintaining normal HDL-C and FBG levels.

An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis

Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D.

Unexplained reciprocal regulation of diabetes and lipoproteins

PURPOSE OF REVIEW

Type 2 diabetes is associated with a characteristic dyslipidemia that may exacerbate cardiovascular risk. The causes of, and the effects of new antihyperglycemia medications on, this dyslipidemia, are under investigation. In an unexpected reciprocal manner, lowering LDL-cholesterol with statins slightly increases the risk of diabetes. Here we review the latest findings.

RECENT FINDINGS

The inverse relationship between LDL-cholesterol and diabetes has now been confirmed by multiple lines of evidence. This includes clinical trials, genetic instruments using aggregate single nucleotide polymorphisms, as well as at least eight individual genes - HMGCR, NPC1L1, HNF4A, GCKR, APOE, PCKS9, TM6SF2, and PNPLA3 - support this inverse association. Genetic and pharmacologic evidence suggest that HDL-cholesterol may also be inversely associated with diabetes risk. Regarding the effects of diabetes on lipoproteins, new evidence suggests that insulin resistance but not diabetes per se may explain impaired secretion and clearance of VLDL-triglycerides. Weight loss, bariatric surgery, and incretin-based therapies all lower triglycerides, whereas SGLT2 inhibitors may slightly increase HDL-cholesterol and LDL-cholesterol.

SUMMARY

Diabetes and lipoproteins are highly interregulated. Further research is expected to uncover new mechanisms governing the metabolism of glucose, fat, and cholesterol. This topic has important implications for treating type 2 diabetes and cardiovascular disease.