Identification of four novel loci associated with psychotropic drug-induced weight gain in a Swiss psychiatric longitudinal study: A GWAS analysis.

Patients suffering from mental disorders are at high risk of developing cardiovascular diseases, leading to a reduction in life expectancy. Genetic variants can display greater influence on cardiometabolic features in psychiatric cohorts compared to the general population. The difference is possibly due to an intricate interaction between the mental disorder or the medications used to treat it and metabolic regulations. Previous genome wide association studies (GWAS) on antipsychotic-induced weight gain included a low number of participants and/or were restricted to patients taking one specific antipsychotic. We conducted a GWAS of the evolution of body mass index (BMI) during early (i.e., ≤ 6) months of treatment with psychotropic medications inducing metabolic disturbances (i.e., antipsychotics, mood stabilizers and some antidepressants) in 1135 patients from the PsyMetab cohort. Six highly correlated BMI phenotypes (i.e., BMI change and BMI slope after distinct durations of psychotropic treatment) were considered in the analyses. Our results showed that four novel loci were associated with altered BMI upon treatment at genome-wide significance (p < 5 × 10-8): rs7736552 (near MAN2A1), rs11074029 (in SLCO3A1), rs117496040 (near DEFB1) and rs7647863 (in IQSEC1). Associations between the four loci and alternative BMI-change phenotypes showed consistent effects. Replication analyses in 1622 UK Biobank participants under psychotropic treatment showed a consistent association between rs7736552 and BMI slope (p = 0.017). These findings provide new insights into metabolic side effects induced by psychotropic drugs and underline the need for future studies to replicate these associations in larger cohorts.

Partner choice, confounding and trait convergence all contribute to phenotypic partner similarity.

Partners are often similar in terms of their physical and behavioural traits, such as their education, political affiliation and height. However, it is currently unclear what exactly causes this similarity-partner choice, partner influence increasing similarity over time or confounding factors such as shared environment or indirect assortment. Here, we applied Mendelian randomization to the data of 51,664 couples in the UK Biobank and investigated partner similarity in 118 traits. We found evidence of partner choice for 64 traits, 40 of which had larger phenotypic correlation than causal effect. This suggests that confounders contribute to trait similarity, among which household income, overall health rating and education accounted for 29.8, 14.1 and 11.6% of correlations between partners, respectively. Finally, mediation analysis revealed that most causal associations between different traits in the two partners are indirect. In summary, our results show the mechanisms through which indirect assortment increases the observed partner similarity.

Polynomial Mendelian randomization reveals non-linear causal effects for obesity-related traits.

Causal inference is a critical step in improving our understanding of biological processes, and Mendelian randomization (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the three core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear. Here, we propose PolyMR, an MR-based method that provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than existing methods. We applied this method to data from the UK Biobank, testing for effects between anthropometric traits and continuous health-related phenotypes, and found most of these (84%) to have causal effects that deviate significantly from linear. These deviations ranged from slight attenuation at the extremes of the exposure distribution, to large changes in the magnitude of the effect across the range of the exposure (e.g., a 1 kg/m2 change in BMI having stronger effects on glucose levels if the initial BMI was higher), to non-monotonic causal relationships (e.g., the effects of BMI on cholesterol forming an inverted U shape). Finally, we show that the linearity assumption of the causal effect may lead to the misinterpretation of health risks at the individual level or heterogeneous effect estimates when using cohorts with differing average exposure levels.

Genetically proxied therapeutic inhibition of antihypertensive drug targets and risk of common cancers: A mendelian randomization analysis.

BACKGROUND: Epidemiological studies have reported conflicting findings on the potential adverse effects of long-term antihypertensive medication use on cancer risk. Naturally occurring variation in genes encoding antihypertensive drug targets can be used as proxies for these targets to examine the effect of their long-term therapeutic inhibition on disease outcomes. METHODS AND FINDINGS: We performed a mendelian randomization analysis to examine the association between genetically proxied inhibition of 3 antihypertensive drug targets and risk of 4 common cancers (breast, colorectal, lung, and prostate). Single-nucleotide polymorphisms (SNPs) in ACE, ADRB1, and SLC12A3 associated (P < 5.0 × 10-8) with systolic blood pressure (SBP) in genome-wide association studies (GWAS) were used to proxy inhibition of angiotensin-converting enzyme (ACE), ß-1 adrenergic receptor (ADRB1), and sodium-chloride symporter (NCC), respectively. Summary genetic association estimates for these SNPs were obtained from GWAS consortia for the following cancers: breast (122,977 cases, 105,974 controls), colorectal (58,221 cases, 67,694 controls), lung (29,266 cases, 56,450 controls), and prostate (79,148 cases, 61,106 controls). Replication analyses were performed in the FinnGen consortium (1,573 colorectal cancer cases, 120,006 controls). Cancer GWAS and FinnGen consortia data were restricted to individuals of European ancestry. Inverse-variance weighted random-effects models were used to examine associations between genetically proxied inhibition of these drug targets and risk of cancer. Multivariable mendelian randomization and colocalization analyses were employed to examine robustness of findings to violations of mendelian randomization assumptions. Genetically proxied ACE inhibition equivalent to a 1-mm Hg reduction in SBP was associated with increased odds of colorectal cancer (odds ratio (OR) 1.13, 95% CI 1.06 to 1.22; P = 3.6 × 10-4). This finding was replicated in the FinnGen consortium (OR 1.40, 95% CI 1.02 to 1.92; P = 0.035). There was little evidence of association of genetically proxied ACE inhibition with risk of breast cancer (OR 0.98, 95% CI 0.94 to 1.02, P = 0.35), lung cancer (OR 1.01, 95% CI 0.92 to 1.10; P = 0.93), or prostate cancer (OR 1.06, 95% CI 0.99 to 1.13; P = 0.08). Genetically proxied inhibition of ADRB1 and NCC were not associated with risk of these cancers. The primary limitations of this analysis include the modest statistical power for analyses of drug targets in relation to some less common histological subtypes of cancers examined and the restriction of the majority of analyses to participants of European ancestry. CONCLUSIONS: In this study, we observed that genetically proxied long-term ACE inhibition was associated with an increased risk of colorectal cancer, warranting comprehensive evaluation of the safety profiles of ACE inhibitors in clinical trials with adequate follow-up. There was little evidence to support associations across other drug target-cancer risk analyses, consistent with findings from short-term randomized controlled trials for these medications.

Socio-economic position as a moderator of cardiometabolic outcomes in patients receiving psychotropic treatment associated with weight gain: results from a prospective 12-month inception cohort study and a large population-based cohort.

Weight gain and metabolic complications are major adverse effects of many psychotropic drugs. We aimed to understand how socio-economic status (SES), defined as the Swiss socio-economic position (SSEP), is associated with cardiometabolic parameters after initiation of psychotropic medications known to induce weight gain. Cardiometabolic parameters were collected in two Swiss cohorts following the prescription of psychotropic medications. The SSEP integrated neighborhood-based income, education, occupation, and housing condition. The results were then validated in an independent replication sample (UKBiobank), using educational attainment (EA) as a proxy for SES. Adult patients with a low SSEP had a higher risk of developing metabolic syndrome over one year versus patients with a high SSEP (Hazard ratio (95% CI) = 3.1 (1.5-6.5), n = 366). During the first 6 months of follow-up, a significant negative association between SSEP and body mass index (BMI), weight change, and waist circumference change was observed (25 ≤ age < 65, n = 526), which was particularly important in adults receiving medications with the highest risk of weight gain, with a BMI difference of 0.86 kg/m2 between patients with low versus high SSEP (95% CI: 0.03-1.70, n = 99). Eventually, a causal effect of EA on BMI was revealed using Mendelian randomization in the UKBiobank, which was notably strong in high-risk medication users (beta: -0.47 SD EA per 1 SD BMI; 95% CI: -0.46 to -0.27, n = 11,314). An additional aspect of personalized medicine was highlighted, suggesting the patients' SES represents a significant risk factor. Particular attention should be paid to patients with low SES when initiating high cardiometabolic risk psychotropic medications.

Associations Between High Plasma Methylxanthine Levels, Sleep Disorders and Polygenic Risk Scores of Caffeine Consumption or Sleep Duration in a Swiss Psychiatric Cohort.

Objective: We first sought to examine the relationship between plasma levels of methylxanthines (caffeine and its metabolites) and sleep disorders, and secondarily between polygenic risk scores (PRS) of caffeine consumption or sleep duration with methylxanthine plasma levels and/or sleep disorders in a psychiatric cohort. Methods: Plasma levels of methylxanthines were quantified by ultra-high performance liquid chromatography/tandem mass spectrometry. In inpatients, sleep disorder diagnosis was defined using ICD-10 "F51.0," sedative drug intake before bedtime, or hospital discharge letters, while a subgroup of sedative drugs was used for outpatients. The PRS of coffee consumption and sleep duration were constructed using publicly available GWAS results from the UKBiobank. Results: 1,747 observations (1,060 patients) were included (50.3% of observations with sleep disorders). Multivariate analyses adjusted for age, sex, body mass index, setting of care and psychiatric diagnoses showed that patients in the highest decile of plasma levels of methylxanthines had more than double the risk for sleep disorders compared to the lowest decile (OR = 2.13, p = 0.004). PRS of caffeine consumption was associated with plasma levels of caffeine, paraxanthine, theophylline and with their sum (ß = 0.1; 0.11; 0.09; and 0.1, pcorrected = 0.01; 0.02; 0.02; and 0.01, respectively) but not with sleep disorders. A trend was found between the PRS of sleep duration and paraxanthine levels (ß = 0.13, pcorrected = 0.09). Discussion: Very high caffeine consumption is associated with sleep disorders in psychiatric in- and outpatients. Future prospective studies should aim to determine the benefit of reducing caffeine consumption in high caffeine-consuming patients suffering from sleep disorders.

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.

ACE and Type 2 Diabetes Risk: A Mendelian Randomization Study.

OBJECTIVE: To determine whether ACE inhibitors reduce the risk of type 2 diabetes using a Mendelian randomization (MR) approach. RESEARCH DESIGN AND METHODS: A two-sample MR analysis included 17 independent genetic variants associated with ACE serum concentration in 4,147 participants from the Outcome Reduction with Initial Glargine INtervention (ORIGIN) (clinical trial reg. no. NCT00069784) trial, and their effects on type 2 diabetes risk were estimated from 18 studies of the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium. A genetic risk score (GRS) underpinning lower ACE concentration was then tested for association with type 2 diabetes prevalence in 341,872 participants, including 16,320 with type 2 diabetes, from the UK Biobank. MR estimates were compared after standardization for blood pressure change, with the estimate obtained from a randomized controlled trial (RCT) meta-analysis of ACE inhibitors versus placebo (n = 31,200). RESULTS: Genetically lower ACE concentrations were associated with a lower risk of type 2 diabetes (odds ratio [OR] per SD 0.92 [95% CI 0.89-0.95]; P = 1.79 × 10-7). This result was replicated in the UK Biobank (OR per SD 0.97 [0.96-0.99]; P = 8.73 × 10-4). After standardization, the ACE GRS was associated with a larger decrease in type 2 diabetes risk per 2.4-mmHg lower mean arterial pressure (MAP) compared with that obtained from an RCT meta-analysis (OR per 2.4-mmHg lower MAP 0.19 [0.07-0.51] vs. 0.76 [0.60-0.97], respectively; P = 0.007 for difference). CONCLUSIONS: These results support the causal protective effect of ACE inhibitors on type 2 diabetes risk and may guide therapeutic decision making in clinical practice.

Influence of Genetic Ancestry on Human Serum Proteome.

Disease risk varies significantly between ethnic groups, however, the clinical significance and implications of these observations are poorly understood. Investigating ethnic differences within the human proteome may shed light on the impact of ancestry on disease risk. We used admixture mapping to explore the impact of genetic ancestry on 237 cardiometabolic biomarkers in 2,216 Latin Americans within the Outcomes Reduction with an Initial Glargine Intervention (ORIGIN) study. We developed a variance component model in order to determine the proportion of variance explained by inter-ancestry differences, and we applied it to the biomarker panel. Multivariable linear regression was used to identify and localize genetic loci affecting biomarker variability between ethnicities. Variance component analysis revealed that 5% of biomarkers were significantly impacted by genetic admixture (p < 0.05/237), including C-peptide, apolipoprotein-E, and intercellular adhesion molecule 1. We also identified 46 regional associations across 40 different biomarkers (p < 1.13 × 10-6). An independent analysis revealed that 34 of these 46 regions were associated at genome-wide significance (p < 5 × 10-8) with their respective biomarker in either Europeans or Latin populations. Additional analyses revealed that an admixture mapping signal associated with increased C-peptide levels was also associated with an increase in diabetes risk (odds ratio [OR] = 6.07 per SD, 95% confidence interval [CI] 1.44 to 25.56, p = 0.01) and surrogate measures of insulin resistance. Our results demonstrate the impact of ancestry on biomarker levels, suggesting that some of the observed differences in disease prevalence have a biological basis, and that reference intervals for those biomarkers should be tailored to ancestry. Specifically, our results point to a strong role of ancestry in insulin resistance and diabetes risk.

PPARA Polymorphism Influences the Cardiovascular Benefit of Fenofibrate in Type 2 Diabetes: Findings From ACCORD-Lipid.

The cardiovascular benefits of fibrates have been shown to be heterogeneous and to depend on the presence of atherogenic dyslipidemia. We investigated whether genetic variability in the PPARA gene, coding for the pharmacological target of fibrates (PPAR-α), could be used to improve the selection of patients with type 2 diabetes who may derive cardiovascular benefit from addition of this treatment to statins. We identified a common variant at the PPARA locus (rs6008845, C/T) displaying a study-wide significant influence on the effect of fenofibrate on major cardiovascular events (MACE) among 3,065 self-reported white subjects treated with simvastatin and randomized to fenofibrate or placebo in the ACCORD-Lipid trial. T/T homozygotes (36% of participants) experienced a 51% MACE reduction in response to fenofibrate (hazard ratio 0.49; 95% CI 0.34-0.72), whereas no benefit was observed for other genotypes (P interaction = 3.7 × 10-4). The rs6008845-by-fenofibrate interaction on MACE was replicated in African Americans from ACCORD (N = 585, P = 0.02) and in external cohorts (ACCORD-BP, ORIGIN, and TRIUMPH, total N = 3059, P = 0.005). Remarkably, rs6008845 T/T homozygotes experienced a cardiovascular benefit from fibrate even in the absence of atherogenic dyslipidemia. Among these individuals, but not among carriers of other genotypes, fenofibrate treatment was associated with lower circulating levels of CCL11-a proinflammatory and atherogenic chemokine also known as eotaxin (P for rs6008845-by-fenofibrate interaction = 0.003). The GTEx data set revealed regulatory functions of rs6008845 on PPARA expression in many tissues. In summary, we have found a common PPARA regulatory variant that influences the cardiovascular effects of fenofibrate and that could be used to identify patients with type 2 diabetes who would derive benefit from fenofibrate treatment, in addition to those with atherogenic dyslipidemia.

Identification of Circulating Proteins Associated With Blood Pressure Using Mendelian Randomization.

BACKGROUND: Hypertension is a common modifiable risk factor for cardiovascular disease and mortality. Pathophysiological mechanisms leading to hypertension remain incompletely understood. Mendelian randomization (MR) allows the evaluation of the causal role of markers by minimizing the risk of biases such as reverse causation and confounding. We aimed to identify novel circulating proteins associated with blood pressure through a comprehensive screen of 227 blood biomarkers using MR. METHODS: Genetic determinants of 227 biomarkers were identified in ORIGIN (Outcome Reduction With Initial Glargine Intervention; URL: http://www.clinicaltrials.gov. Unique identifier: NCT00069784) participants (N=4147) and combined with genetic effects on systolic blood pressure, diastolic blood pressure, mean arterial pressure, and pulse pressure from the International Consortium for Blood Pressure (74 064 individuals) using MR. Results were replicated in the UK Biobank (up to 319 103 individuals) and using another biomarker dataset (N=3301). MR analyses with cardiovascular risk factors and outcomes as well as other biomarkers were performed to further evaluate the mechanisms involved. RESULTS: Six biomarkers were associated with blood pressure using MR after adjustment for multiple hypothesis testing. Relationships between NT-proBNP (N-terminal Pro-B-type natriuretic peptide), systolic blood pressure, and diastolic blood pressure confirmed previous reports. Novel circulating proteins associated with blood pressure were also identified. uPA (urokinase-type plasminogen activator) was related to systolic blood pressure; ADM (adrenomedullin) was related to systolic blood pressure and pulse pressure; IL (interleukin) 16 was related to diastolic blood pressure; cFn (cellular fibronectin) and IGFBP3 (insulin-like growth factor-binding protein 3) were related to pulse pressure. With the exception of IL16 and diastolic blood pressure (P=0.58), these relationships were validated in the UK Biobank (P<0.0001). Further MR analyses with cardiovascular risk factors and outcomes showed relationships between NT-proBNP and large-artery atherosclerotic stroke, IGFBP3 and diabetes mellitus as well as cFn and body mass index. CONCLUSIONS: We identified novel biomarkers associated with blood pressure using MR. These markers could prove useful for risk assessment and as potential therapeutic targets.

Novel Drug Targets for Ischemic Stroke Identified Through Mendelian Randomization Analysis of the Blood Proteome.

BACKGROUND: Novel, effective, and safe drugs are warranted for treatment of ischemic stroke. Circulating protein biomarkers with causal genetic evidence represent promising drug targets, but no systematic screen of the proteome has been performed. METHODS: First, using Mendelian randomization (MR) analyses, we assessed 653 circulating proteins as possible causal mediators for 3 different subtypes of ischemic stroke: large artery atherosclerosis, cardioembolic stroke, and small artery occlusion. Second, we used MR to assess whether identified biomarkers also affect risk for intracranial bleeding, specifically intracerebral and subarachnoid hemorrhages. Third, we expanded this analysis to 679 diseases to test a broad spectrum of side effects associated with hypothetical therapeutic agents for ischemic stroke that target the identified biomarkers. For all MR analyses, summary-level data from genome-wide association studies (GWAS) were used to ascertain genetic effects on circulating biomarker levels versus disease risk. Biomarker effects were derived by meta-analysis of 5 GWAS (N≤20 509). Disease effects were derived from large GWAS analyses, including MEGASTROKE (N≤322 150) and UK Biobank (N≤408 961) studies. RESULTS: Several biomarkers emerged as causal mediators for ischemic stroke. Causal mediators for cardioembolic stroke included histo-blood group ABO system transferase, coagulation factor XI, scavenger receptor class A5 (SCARA5), and tumor necrosis factor-like weak inducer of apoptosis (TNFSF12). Causal mediators for large artery atherosclerosis included ABO, cluster of differentiation 40, apolipoprotein(a), and matrix metalloproteinase-12. SCARA5 (odds ratio [OR]=0.78; 95% CI, 0.70-0.88; P=1.46×10-5) and TNFSF12 (OR=0.86; 95% CI, 0.81-0.91; P=7.69×10-7) represent novel protective mediators of cardioembolic stroke. TNFSF12 also increased the risk of subarachnoid (OR=1.53; 95% CI, 1.31-1.78; P=3.32×10-8) and intracerebral (OR=1.34; 95% CI, 1.14-1.58; P=4.05×10-4) hemorrhages, whereas SCARA5 decreased the risk of subarachnoid hemorrhage (OR=0.61; 95% CI, 0.47-0.81; P=5.20×10-4). Multiple side effects beyond stroke were identified for 6 of 7 biomarkers, most (75%) of which were beneficial. No adverse side effects were found for coagulation factor XI, apolipoprotein(a), and SCARA5. CONCLUSIONS: Through a systematic MR screen of the circulating proteome, causal roles for 5 established and 2 novel biomarkers for ischemic stroke were identified. Side-effect profiles were characterized to help inform drug target prioritization. In particular, SCARA5 represents a promising target for treatment of cardioembolic stroke, with no predicted adverse side effects.

Identification of Novel Causal Blood Biomarkers Linking Metabolically Favorable Adiposity With Type 2 Diabetes Risk.

OBJECTIVE: Observations of a metabolically unhealthy normal weight phenotype suggest that a lack of favorable adiposity contributes to an increased risk of type 2 diabetes. We aimed to identify causal blood biomarkers linking favorable adiposity with type 2 diabetes risk for use in cardiometabolic risk assessments. RESEARCH DESIGN AND METHODS: A weighted polygenic risk score (PRS) underpinning metabolically favorable adiposity was validated in the UK Biobank (n = 341,872) and the Outcome Reduction With Initial Glargine Intervention (ORIGIN Trial) (n = 8,197) and tested for association with 238 blood biomarkers. Associated biomarkers were investigated for causation with type 2 diabetes risk using Mendelian randomization and for its performance in predictive models for incident major adverse cardiovascular events (MACE). RESULTS: Of the 238 biomarkers tested, only insulin-like growth factor-binding protein (IGFBP)-3 concentration was associated with the PRS, where a 1 unit increase in PRS predicted a 0.28-SD decrease in IGFBP-3 blood levels (P < 0.05/238). Higher IGFBP-3 levels causally increased type 2 diabetes risk (odds ratio 1.26 per 1 SD genetically determined IGFBP-3 level [95% CI 1.11-1.43]) and predicted a higher incidence of MACE (hazard ratio 1.13 per 1 SD IGFBP-3 concentration [95% CI 1.07-1.20]). Adding IGFBP-3 concentrations to the standard clinical assessment of metabolic health enhanced the prediction of incident MACE, with a net reclassification improvement of 11.5% in normal weight individuals (P = 0.004). CONCLUSIONS: We identified IGFBP-3 as a novel biomarker linking a lack of favorable adiposity with type 2 diabetes risk and a predictive marker for incident cardiovascular events. Using IGFBP-3 blood concentrations may improve the risk assessment of cardiometabolic diseases.

A Mendelian Randomization-Based Approach to Identify Early and Sensitive Diagnostic Biomarkers of Disease.

BACKGROUND: Identifying markers of chronic kidney disease (CKD) that occur early in the disease process and are specific to loss of kidney function rather than other underlying causes of disease may allow earlier, more accurate identification of patients who will develop CKD. We therefore sought to identify diagnostic blood markers of early CKD that are caused by loss of kidney function by using an innovative "reverse Mendelian randomization" (MR) approach. METHODS: We applied this technique to genetic and biomarker data from 4147 participants in the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial, all with known type 2 diabetes, impaired fasting glucose, or impaired glucose tolerance. Two-sample MR was conducted using variants associated with creatinine-based eGFR (eGFRcrea) from the CKDGen Consortium (n = 133814) to estimate the effect of genetically decreased eGFRcrea on 238 serum biomarkers. RESULTS: With reverse MR, trefoil factor 3 (TFF3) was identified as a protein that is increased owing to decreased eGFRcrea (ß = 1.86 SD per SD decrease eGFRcrea; 95% CI, 0.95-2.76; P = 8.0 × 10-5). Reverse MR findings were consistent with epidemiological associations for incident CKD in ORIGIN (OR = 1.28 per SD increase in TFF3; 95% CI, 1.18-1.38; P = 4.58 × 10-10). Addition of TFF3 significantly improved discrimination for incident CKD relative to eGFRcrea alone (net reclassification improvement = 0.211; P = 9.56 × 10-12) and in models including additional risk factors. CONCLUSIONS: Our results suggest TFF3 is a valuable diagnostic marker for early CKD in dysglycemic populations and acts as a proof of concept for the application of this novel MR technique to identify diagnostic biomarkers for other chronic diseases. CLINICALTRIALSGOV IDENTIFIER: NCT00069784.

Genetic Tools for Coronary Risk Assessment in Type 2 Diabetes: A Cohort Study From the ACCORD Clinical Trial.

OBJECTIVE: We evaluated whether the increasing number of genetic loci for coronary artery disease (CAD) identified in the general population could be used to predict the risk of major CAD events (MCE) among participants with type 2 diabetes at high cardiovascular risk. RESEARCH DESIGN AND METHODS: A weighted genetic risk score (GRS) derived from 204 variants representative of all the 160 CAD loci identified in the general population as of December 2017 was calculated in 5,360 and 1,931 white participants in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) and Outcome Reduction With Initial Glargine Intervention (ORIGIN) studies, respectively. The association between GRS and MCE (combining fatal CAD events, nonfatal myocardial infarction, and unstable angina) was assessed by Cox proportional hazards regression. RESULTS: The GRS was associated with MCE risk in both ACCORD and ORIGIN (hazard ratio [HR] per SD 1.27, 95% CI 1.18-1.37, P = 4 × 10-10, and HR per SD 1.35, 95% CI 1.16-1.58, P = 2 × 10-4, respectively). This association was independent from interventions tested in the trials and persisted, though attenuated, after adjustment for classic cardiovascular risk predictors. Adding the GRS to clinical predictors improved incident MCE risk classification (relative integrated discrimination improvement +8%, P = 7 × 10-4). The performance of this GRS was superior to that of GRS based on the smaller number of CAD loci available in previous years. CONCLUSIONS: When combined into a GRS, CAD loci identified in the general population are associated with CAD also in type 2 diabetes. This GRS provides a significant improvement in the ability to correctly predict future MCE, which may increase further with the discovery of new CAD loci.

Blood CSF1 and CXCL12 as Causal Mediators of Coronary Artery Disease.

BACKGROUND: Identification of biomarkers that cause coronary artery disease (CAD) has led to important advances in prevention and treatment. Epidemiological analyses have identified many biomarker-CAD relationships; however, these associations may arise from reverse causation and/or confounding and therefore may not represent true causal associations. Mendelian randomization (MR) analyses overcome these limitations. OBJECTIVES: This study sought to identify causal mediators of CAD through a comprehensive screen of 237 biomarkers using MR. METHODS: MR was performed by identifying genetic determinants of 227 biomarkers in ORIGIN (Outcome Reduction With Initial Glargine Intervention) trial participants (N = 4,147) and combining these with genetic effects on CAD from the CARDIoGRAM consortium (60,801 cases and 123,504 controls). Blood concentrations of novel biomarkers identified by MR were then tested for association with incident major adverse cardiovascular events in ORIGIN. RESULTS: Six biomarkers were found to be causally linked to CAD after adjustment for multiple hypothesis testing. The causal role of 4 of these is well documented, whereas macrophage colony-stimulating factor 1 (CSF1) and stromal cell-derived factor 1 (CXCL12) have not previously been reported, to the best of our knowledge. MR analysis predicted an 18% higher risk of CAD per SD increase in CSF1 (odds ratio: 1.18; 95% confidence interval: 1.08 to 1.30; p = 2.1 × 10-4) and epidemiological analysis identified a 16% higher risk of major adverse cardiovascular events per SD (hazard ratio: 1.16; 95% confidence interval: 1.09 to 1.23; p < 0.001). Elevated CXCL12 levels were also identified as a causal risk factor for CAD with consistent epidemiological results. Furthermore, genetically predicted CSF1 and CXCL12 levels were associated with CAD in the UK Biobank (n = 343,735). CONCLUSIONS: The study identified CSF1 and CXCL12 as causal mediators of CAD in humans. Understanding the mechanism by which these markers mediate CAD will provide novel insights into CAD and could lead to new approaches to prevention. These results support targeting inflammatory processes and macrophages, in particular, to prevent CAD, consistent with the recent CANTOS (Canakinumab Antiinflammatory Thrombosis Outcome Study). (Outcome Reduction With Initial Glargine Intervention [ORIGIN]; NCT00069784).

Blood HER2 and Uromodulin as Causal Mediators of CKD.

Many biomarkers have been epidemiologically linked with CKD; however, the possibility that such associations are due to reverse causation or confounding limits the utility of these biomarkers. To overcome this limitation, we used a Mendelian randomization (MR) approach to identify causal mediators of CKD. We performed MR by first identifying genetic determinants of 227 serum protein biomarkers assayed in 4147 participants of the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial who had early or prediabetes, and assessing the effects of these biomarkers on CKD in the CKD genetics consortium (n=117,165; 12,385 cases) using the inverse-variance weighted (fixed-effects) method. We then estimated the relationship between the serum concentration of each biomarker identified and incident CKD in ORIGIN participants. MR identified uromodulin (UMOD) and human EGF receptor 2 (HER2) as novel, causal mediators of CKD (UMOD: odds ratio [OR], 1.30 per SD; 95% confidence interval [95% CI], 1.25 to 1.35; P<5×10-20; HER2: OR, 1.30 per SD; 95% CI, 1.14 to 1.48; P=8.0×10-5). Consistent with these findings, blood HER2 concentration associated with CKD events in ORIGIN participants (OR, 1.07 per SD; 95% CI, 1.01 to 1.13; P=0.01). Additional exploratory MR analyses identified angiotensin-converting enzyme (ACE) as a regulator of HER2 levels (ß=0.13 per SD; 95% CI, 0.08 to 0.16; P=2.5×10-7). This finding was corroborated by an inverse relationship between ACE inhibitor use and HER2 levels. Thus, UMOD and HER2 are independent causal mediators of CKD in humans, and serum HER2 levels are regulated in part by ACE. These biomarkers are potential therapeutic targets for CKD prevention.

Growth Differentiation Factor 15 as a Novel Biomarker for Metformin.

OBJECTIVE: Metformin is a commonly used glucose-lowering drug. However, apart from glycemic measures, no biomarker for its presence or dose has been identified. RESEARCH DESIGN AND METHODS: A total of 237 biomarkers were assayed in baseline serum from 8,401 participants (2,317 receiving metformin) in the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial. Regression models were used to identify biomarkers for metformin use. RESULTS: Growth differentiation factor 15 (GDF15) was strongly linked to metformin, such that the odds of metformin use per SD increase in level varied from 3.73 (95% CI 3.40, 4.09) to 3.94 (95% CI 3.59, 4.33) depending on the other included variables. For the remaining 25 linked biomarkers, the odds ranged from 0.71 to 1.24. A 1.64 ng/mL higher GDF15 level predicted a 188-mg higher metformin dose (P < 0.0001). CONCLUSIONS: GDF15 levels are a biomarker for the use of metformin in people with dysglycemia, and its concentration reflects the dose of metformin.