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Background: Diabetes and cardiovascular disease increase the risk of incident cognitive dysfunction. Identification of novel biochemical markers for cognitive dysfunction may identify people at the highest risk while yielding insights regarding the pathophysiology of cognitive dysfunction. Objective: To identify cardiovascular biomarkers in serum that are independent predictors of cognitive dysfunction in individuals with dysglycemia. Methods: This analysis was conducted in 8,365 participants in the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial whose stored serum was analyzed for 238 cardio-metabolic biomarkers and completed a baseline Mini-Mental State Examination (MMSE). Fine and Gray sub distribution hazard models accounting for the competing risk of death accounting for clinical risk factors and the baseline MMSE were used to identify biomarkers that predicted incident cognitive dysfunction (MMSE

Data analyses from Swedish individuals with newly diagnosed diabetes have suggested that diabetes could be classified into five subtypes that differ with respect to the progression of dysglycaemia and the incidence of diabetes consequences. We assessed this classification in a multiethnic cohort of participants with established and newly diagnosed diabetes, randomly allocated to insulin glargine vs standard care. In total, 7017 participants from the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial were assigned to the five predefined diabetes subtypes (namely, severe auto-immune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, mild age-related diabetes) based on the age at diabetes diagnosis, BMI, HbA1c, fasting C-peptide levels and the presence of glutamate decarboxylase antibodies at baseline. Differences between diabetes subtypes in cardiovascular and renal outcomes were investigated using Cox regression models for a median follow-up of 6.2 years. We also compared the effect of glargine vs standard care on hyperglycaemia, defined by having a mean post-randomisation HbA1c ≥6.5%, between subtypes. The five diabetes subtypes were replicated in the ORIGIN trial and exhibited similar baseline characteristics in Europeans and Latin Americans, compared with the initially described clusters in the Swedish cohort. We confirmed differences in renal outcomes, with a higher incidence of events in the severe insulin-resistant diabetes subtype compared with the mild age-related diabetes subtype (i.e., chronic kidney disease stage 3A: HR 1.49 [95% CI 1.31, 1.71]; stage 3B: HR 2.25 [1.82, 2.78]; macroalbuminuria: HR 1.56 [1.22, 1.99]). No differences were observed in the incidence of retinopathy and cardiovascular diseases after adjusting for multiple hypothesis testing. Diabetes subtypes also differed in glycaemic response to glargine, with a particular benefit of receiving glargine (vs standard care) in the severe insulin-deficient diabetes subtype compared with the mild age-related diabetes subtype, with a decreased occurrence of hyperglycaemia by 13% (OR 1.36 [1.30, 1.41] on glargine; OR 1.49 [1.43, 1.57] on standard care; p for interaction subtype × intervention = 0.001). Cluster analysis enabled the characterisation of five subtypes of diabetes in a multiethnic cohort. Both the incidence of renal outcomes and the response to insulin varied between diabetes subtypes. These findings reinforce the clinical utility of applying precision medicine to predict comorbidities and treatment responses in individuals with diabetes. ORIGIN trial, ClinicalTrials.gov NCT00069784.

Vaspin (visceral adipose tissue-derived serpinA12) is an adipokine suspected to be involved in type 2 diabetes (T2D) pathogenesis, linking obesity and T2D. We sought to assess epidemiological and genetic associations of vaspin with T2D and related variables. We assessed the relationship of body-mass index (BMI) and waist-hip ratio (WHR) and plasma vaspin concentration, along with the relationship between plasma vaspin and adiposity-related variables and T2D in (i) a case-cohort within the Prospective Urban and Rural Epidemiology (PURE) prospective multi-country study (N=10,052) , and (ii) the Outcome Reduction with Initial Glargine Intervention (ORIGIN) Trial (N=7,840) of participants with dysglycemia. We next investigated the predictive value of vaspin for T2D risk. All models adjusted for age, sex, and ethnicity. Using two-sample Mendelian randomization (MR) , we analyzed if genetically higher vaspin is causal for T2D. The MR analysis used 34 independent genetic variants (linkage disequilibrium r2 In PURE, a 1 unit increase in BMI and WHR was associated with a 0.01 SD (95% CI 0.01 - 0.02; P = 2.04x10−15) and 0.45 SD (95% CI, 0.21 - 0.70; P = 2.87 × 10−4) increase in plasma vaspin respectively. A 1 SD increase in plasma vaspin was associated with higher triglycerides (0.09 mmol/L; 95% CI, 0.07 - 0.12, P = 4.08x10−13) . Directionally-consistent associations were observed in ORIGIN. A 1 SD increase in plasma vaspin was associated with a 19% increase in incident T2D risk in PURE (HR, 1.19; 95% CI, 1.12 - 1.26; P = 6.36x10−8) and a 16% increase in prevalent T2D in ORIGIN (OR, 1.16; 95% CI, 1.07 - 1.25; P = 2.17x10−4) . Sensitivity analyses showed vaspin's specificity for T2D. Genetically-higher vaspin was associated with increased BMI-adjusted T2D risk (OR, 1.02; 95% CI, 1.00 - 1.03; P = 5.46x10−3) . Our data suggests that vaspin is a predictor for T2D risk, a modest causal factor for T2D development, and might represent a potential T2D therapeutic target. Disclosure H. Wang: None. M. Chong: None. N. Perrot: None. J. Feiner: None. S. Hess: Employee; Sanofi. S. Yusuf: None. H. C. Gerstein: Advisory Panel; Abbott, Eli Lilly and Company, Hanmi Pharm. Co., Ltd., Novo Nordisk, Pfizer Inc., Sanofi, Viatris Inc., Consultant; Kowa Company, Ltd., Other Relationship; DKSH, Eli Lilly and Company, Sanofi, Zuellig Pharma Holdings Pte. Ltd., Research Support; AstraZeneca, Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk, Sanofi. G. Pare: Advisory Panel; Amgen Inc., Bayer AG, Sanofi, Research Support; Bayer AG. M. Pigeyre: n/a. Funding PURE biomarker study was sponsored by Bayer and CIHR. Origin (NCT00069784) and the biomarker-substudy in Origin were sponsored by Sanofi and CIHR.

Diabetes (DM) can be classified into 5 subtypes characterized by distinct progression in dysglycaemia and complications. Using 5 clinical variables, we categorized 7017 participants from the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial into 1/auto-immune DM (n=241) , 2/insulin-deficient DM (n=1594) , 3/insulin-resistant DM (n=914) , 4/obesity-related DM (n=1595) , 5/age-related DM (n=2673) .Yet, whether blood biomarkers are associated with these subtypes is unknown. Forward-selection logistic regression models were used to identify biomarkers that were each independent determinant of one cluster versus the others, among 233 selected cardiometabolic proteins measured at baseline. Models were adjusted for age, sex, ethnicity, C-peptide level, diabetes duration. A total of 13, 2, 7 and biomarkers were independent determinants of DM subtypes 2 to 5 respectively (all P Disclosure M.Pigeyre: n/a. H.C.Gerstein: Advisory Panel; Abbott, Eli Lilly and Company, Hanmi Pharm. Co., Ltd., Novo Nordisk, Pfizer Inc., Sanofi, Viatris Inc., Consultant; Kowa Company, Ltd., Other Relationship; DKSH, Eli Lilly and Company, Sanofi, Zuellig Pharma Holdings Pte. Ltd., Research Support; AstraZeneca, Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk, Sanofi. L.Groop: None. S.Hess: Employee; Sanofi. G.Pare: Advisory Panel; Amgen Inc., Bayer AG, Sanofi, Research Support; Bayer AG. Funding NCT00069784 Canadian Institute of Health Research Sanofi

Adding novel protein biomarkers to routine clinical risk factors may identify people with type 2 diabetes and acute coronary syndrome who are at highest risk for cardiovascular (CV) outcomes and death. Methods: Bio-banked baseline serum from 5128 of 6069 ELIXA (Evaluating Lixisenatide in Acute Coronary Syndrome) trial (NCT 01147250) participants was analyzed to identify independent risk factors for incident major adverse CV events (MACE, defined as a nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death) , and death. A multiplex analysis of 1.8 ml of serum measured the concentration of 49 proteins. Forward-selection Cox models that identified proteins that independently predicted these outcomes were compared to previously validated biomarkers identified in the Outcomes Reduction with an Initial Glargine Intervention (ORIGIN) trial data (NCT00069784) . Results: Forty-two proteins were analyzed in 4957 participants who had 630 (12.7%) MACE outcomes and 349 (7.0%) deaths during a median follow-up period of 2.1 years. When added to clinical risk factors, the independent hazard ratios (HR; 95% confidence intervals) of MACE per standard deviation (SD) were NT-proBNP (1.54; 1,41, 1.68) , osteoprotegerin (1.18; 1.08, 1.30) and trefoil factor 3 (1.18, 1.08, 1.29) . HRs per SD for death were NT-proBNP (2.01; 1.78, 2.29) , osteoprotegerin (1.34; 1.18, 2.52) and angiopoietin-2 (1.28; 1.15, 1.44) . C statistics for MACE and death were 0.70 (0,68, 0.72) and 0.79 (0.76, 0.81) respectively compared to 0.63 (0.61, 0.65) and 0.66 (0.63, 0.69) for clinical variables alone. These proteins had all been previously identified and validated in ORIGIN. Notably, NT-proBNP alone plus clinical risk factors yielded C statistics of 0.69 (0.67, 0.71) and 0.78 (0.75, 0.80) for MACE and death respectively. Conclusion: NT-proBNP and other proteins independently predict CV outcomes in people with type 2 diabetes following acute coronary syndrome. Adding other biomarkers only marginally increased NT-proBNP's prognostic value. Disclosure H. C. Gerstein: Advisory Panel; Abbott, Eli Lilly and Company, Hanmi Pharm. Co., Ltd., Novo Nordisk, Pfizer Inc., Sanofi, Viatris Inc., Consultant; Kowa Company, Ltd., Other Relationship; DKSH, Eli Lilly and Company, Sanofi, Zuellig Pharma Holdings Pte. Ltd., Research Support; AstraZeneca, Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk, Sanofi. S. Hess: Employee; Sanofi. B. Claggett: Consultant; Amgen Inc., Biogen, Cardurion, Corvia, MyoKardia, Novartis AG. J. Tardif: Consultant; AstraZeneca, DalCor Pharmaceuticals, HLS Therapeutics Inc., Pendopharm, Other Relationship; DalCor Pharmaceuticals, Research Support; Amarin Corporation, AstraZeneca, Ceapro Inc., DalCor Pharmaceuticals, ESPERION Therapeutics, Inc., Ionis Pharmaceuticals, Novartis Pharmaceuticals Corporation, Pfizer Inc., REGENXBIO Inc., Sanofi. M. A. Pfeffer: Consultant; AstraZeneca, Boehringer Ingelheim and Eli Lilly Alliance, Corvidia Therapeutics, GlaxoSmithKline plc., Lexicon Pharmaceuticals, Inc., Novartis Pharmaceuticals Corporation, Novo Nordisk, Peerbridge, Sanofi, Other Relationship; DalCor Pharmaceuticals, National Heart, Lung, and Blood Institute, Research Support; Novartis Pharmaceuticals Corporation. Funding Sanofi

OBJECTIVE To use protein biomarkers to identify people with type 2 diabetes at high risk of cardiovascular outcomes and death. RESEARCH DESIGN AND METHODS Biobanked serum from 4,957 ELIXA (Evaluation of Lixisenatide in Acute Coronary Syndrome) trial participants was analyzed. Forward-selection Cox models identified independent protein risk factors for major adverse cardiovascular events (MACE) and death that were compared with a previously validated biomarker panel. RESULTS NT-proBNP and osteoprotegerin predicted both outcomes. In addition, trefoil factor 3 predicted MACE, and angiopoietin-2 predicted death (C = 0.70 and 0.79, respectively, compared with 0.63 and 0.66 for clinical variables alone). These proteins had all previously been identified and validated. Notably, C statistics for just NT-proBNP plus clinical risk factors were 0.69 and 0.78 for MACE and death, respectively. CONCLUSIONS NT-proBNP and other proteins independently predict cardiovascular outcomes in people with type 2 diabetes following acute coronary syndrome. Adding other biomarkers only marginally increased NT-proBNP’s prognostic value.

Background: Alirocumab, an antibody that blocks PCSK9 (proprotein convertase subtilisin/kexin type 9), was associated with reduced major adverse cardiovascular events (MACE) and death in the ODYSSEY OUTCOMES trial (Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab). In this study, higher baseline levels of low-density lipoprotein cholesterol (LDL-C) predicted greater benefit from alirocumab treatment. Recent studies indicate high polygenic risk scores (PRS) for coronary artery disease (CAD) identify individuals at higher risk who derive increased benefit from statins. We performed post hoc analyses to determine whether high PRS for CAD identifies higher-risk individuals, independent of baseline LDL-C and other known risk factors, who might derive greater benefit from alirocumab treatment. Methods: ODYSSEY OUTCOMES was a randomized, double-blind, placebo-controlled trial comparing alirocumab or placebo in 18 924 patients with acute coronary syndrome and elevated atherogenic lipoproteins despite optimized statin treatment. The primary endpoint (MACE) comprised death of CAD, nonfatal myocardial infarction, ischemic stroke, or unstable angina requiring hospitalization. A genome-wide PRS for CAD comprising 6 579 025 genetic variants was evaluated in 11 953 patients with available DNA samples. Analysis of MACE risk was performed in placebo-treated patients, whereas treatment benefit analysis was performed in all patients. Results: The incidence of MACE in the placebo group was related to PRS for CAD: 17.0% for high PRS patients (>90th percentile) and 11.4% for lower PRS patients (≤90th percentile; P P =0.004) versus a 13% reduction in the low PRS group (hazard ratio, 0.87 [95% CI, 0.78–0.98]; P =0.022; interaction P =0.04). Conclusions: A high PRS for CAD is associated with elevated risk for recurrent MACE after acute coronary syndrome and a larger absolute and relative risk reduction with alirocumab treatment, providing an independent tool for risk stratification and precision medicine.

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.

Metformin is the most commonly prescribed medication for type 2 diabetes, owing to its glucose-lowering effects, which are mediated through the suppression of hepatic glucose production (reviewed in refs. 1-3). However, in addition to its effects on the liver, metformin reduces appetite and in preclinical models exerts beneficial effects on ageing and a number of diverse diseases (for example, cognitive disorders, cancer, cardiovascular disease) through mechanisms that are not fully understood1-3. Given the high concentration of metformin in the liver and its many beneficial effects beyond glycemic control, we reasoned that metformin may increase the secretion of a hepatocyte-derived endocrine factor that communicates with the central nervous system4. Here we show, using unbiased transcriptomics of mouse hepatocytes and analysis of proteins in human serum, that metformin induces expression and secretion of growth differentiating factor 15 (GDF15). In primary mouse hepatocytes, metformin stimulates the secretion of GDF15 by increasing the expression of activating transcription factor 4 (ATF4) and C/EBP homologous protein (CHOP; also known as DDIT3). In wild-type mice fed a high-fat diet, oral administration of metformin increases serum GDF15 and reduces food intake, body mass, fasting insulin and glucose intolerance; these effects are eliminated in GDF15 null mice. An increase in serum GDF15 is also associated with weight loss in patients with type 2 diabetes who take metformin. Although further studies will be required to determine the tissue source(s) of GDF15 produced in response to metformin in vivo, our data indicate that the therapeutic benefits of metformin on appetite, body mass and serum insulin depend on GDF15.

OBJECTIVE Diabetes is a major risk factor for renal function decline and failure. The availability of multiplex panels of biochemical markers provides the opportunity to identify novel biomarkers that can better predict changes in renal function than routinely available clinical markers. RESEARCH DESIGN AND METHODS The concentration of 239 biochemical markers was measured in stored serum from participants in the biomarker substudy of Outcome Reduction With Initial Glargine Intervention (ORIGIN) trial. Repeated-measures mixed-effects models were used to compute the annual change in eGFR (measured as mL/min/1.73 m2/year) for the 7,482 participants with a recorded baseline and follow-up eGFR. Linear regression models using forward selection were used to identify the independent biomarker determinants of the annual change in eGFR after accounting for baseline HbA1c, baseline eGFR, and routinely measured clinical risk factors. The incidence of the composite renal outcome (i.e., renal replacement therapy, renal death, renal failure, albuminuria progression, doubling of serum creatinine) and death within each fourth of change in eGFR predicted from these models was also estimated. RESULTS During 6.2 years of median follow-up, the median annual change in eGFR was −0.18 mL/min/1.73 m2/year. Fifteen biomarkers independently predicted eGFR decline after accounting for cardiovascular risk factors, as did 12 of these plus 1 additional biomarker after accounting for renal risk factors. Every 0.1 mL/min/1.73 m2 predicted annual fall in eGFR predicted a 13% (95% CI 12, 14%) higher mortality. CONCLUSIONS Adding up to 16 biomarkers to routinely measured clinical risk factors improves the prediction of annual change in eGFR in people with dysglycemia.

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.

Publisher Copyright: © 2022 Aims: Newly-defined subgroups of type 2 diabetes mellitus (T2DM) have been reported from real-world cohorts but not in detail from randomised clinical trials (RCTs). Methods: T2DM participants, uncontrolled on different pre-study therapies (n = 12.738; 82 % Caucasian; 44 % with diabetes duration > 10 years) from 14 RCTs, were assigned to new subgroups according to age at onset of diabetes, HbA1c, BMI, and fasting C-peptide using the nearest centroid approach. Subgroup distribution, characteristics and influencing factors were analysed. Results: In both, pooled and single RCTs, “mild-obesity related diabetes” predominated (45 %) with mean BMI of 35 kg/m2. “Severe insulin-resistant diabetes” was found least often (4.6 %) and prevalence of “mild age-related diabetes” (23.9 %) was mainly influenced by age at onset of diabetes and age cut-offs. Subgroup characteristics were widely comparable to those from real-world cohorts, but all subgroups showed higher frequencies of diabetes-related complications which were associated with longer diabetes duration. A high proportion of “severe insulin-deficient diabetes” (25.4 %) was identified with poor pre-study glycaemic control. Conclusions: Classification of RCT participants into newly-defined diabetes subgroups revealed the existence of a heterogeneous population of T2DM. For future RCTs, subgroup-based randomisation of T2DM will better define the target population and relevance of the outcomes by avoiding clinical heterogeneity.

Metformin is the most commonly prescribed medication for type 2 diabetes, owing to its glucose-lowering effects, which are mediated through the suppression of hepatic glucose production (reviewed in refs. 1-3). However, in addition to its effects on the liver, metformin reduces appetite and in preclinical models exerts beneficial effects on ageing and a number of diverse diseases (for example, cognitive disorders, cancer, cardiovascular disease) through mechanisms that are not fully understood1-3. Given the high concentration of metformin in the liver and its many beneficial effects beyond glycemic control, we reasoned that metformin may increase the secretion of a hepatocyte-derived endocrine factor that communicates with the central nervous system4. Here we show, using unbiased transcriptomics of mouse hepatocytes and analysis of proteins in human serum, that metformin induces expression and secretion of growth differentiating factor 15 (GDF15). In primary mouse hepatocytes, metformin stimulates the secretion of GDF15 by increasing the expression of activating transcription factor 4 (ATF4) and C/EBP homologous protein (CHOP; also known as DDIT3). In wild-type mice fed a high-fat diet, oral administration of metformin increases serum GDF15 and reduces food intake, body mass, fasting insulin and glucose intolerance; these effects are eliminated in GDF15 null mice. An increase in serum GDF15 is also associated with weight loss in patients with type 2 diabetes who take metformin. Although further studies will be required to determine the tissue source(s) of GDF15 produced in response to metformin in vivo, our data indicate that the therapeutic benefits of metformin on appetite, body mass and serum insulin depend on GDF15.

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 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)

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

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.

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 Conclusions: We identified novel biomarkers associated with blood pressure using MR. These markers could prove useful for risk assessment and as potential therapeutic targets.

Metformin is the most commonly prescribed medication for type 2 diabetes, owing to its glucose-lowering effects, which are mediated through the suppression of hepatic glucose production (reviewed in refs.

The causal relationship between angiotensin-converting enzyme (ACE) inhibition and protection from type 2 diabetes (T2D) remains uncertain. While three large randomized control trials (RCTs) suggested a protective effect of ACE inhibitors compared to placebo, three other RCTs failed to observe an effect, including the only trial dedicated to this question. We hypothesized that ACE concentration-lowering genetic variants could be used to infer the pharmacological effect of ACE inhibitors on T2D risk using a Mendelian Randomization (MR) approach. We first assessed the association between T2D prevalence and ACE serum level in the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial (N=8,197). We next investigated whether genetically lowered ACE level was causally linked to the risk of T2D using (i) a two-sample MR analysis applied to the DIAbetes Genetics Replication And Meta-analysis consortium (n=26,676 T2D cases; 132,532 controls), and (ii) an ACE concentration-lowering genetic risk score in the UK Biobank cohort (N=341,872). We then compared the genetically determined effect of lower ACE level on T2D risk to the pharmacological inhibition of ACE vs. placebo, which was evaluated through a meta-analysis of six RCTs (N=31,200). Lower ACE serum level was associated with reduced T2D prevalence (OR, 0.89; 95% CI, 0.82-0.96; P=3.50x10-3) in the ORIGIN trial. Through MR analyses, a 1 SD lower genetically determined ACE level predicted a lower risk of T2D (OR, 0.92; 95% CI, 0.89-0.95; P=1.79x10-7). This result was replicated in the UK Biobank (OR, 0.97; 95% CI, 0.96-0.99, P=8.73x10-4). RCT meta-analysis showed a reduction in T2D incidence on ACE inhibitors vs. placebo (OR, 0.76; 95% CI, 0.60-0.97; P=2.59x10-2), which was consistent with the findings from MR analyses (P for comparison=0.95). Our results support a protective effect of long-term ACE inhibition on T2D risk and suggest to consider patient's risk for T2D when prescribing blood-pressure lowering drugs. Disclosure M. Pigeyre: None. J. Sjaarda: None. M. Chong: None. S. Hess: None. J. Bosch: Advisory Panel; Self; Bayer AG. S. Yusuf: None. H. Gerstein: Advisory Panel; Self; Abbott, AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk A/S, Sanofi. Research Support; Self; AstraZeneca, Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk A/S, Sanofi. Other Relationship; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Sanofi. G. Pare: Consultant; Self; Amgen Inc., Bristol-Myers Squibb Company, Lexicomp, Sanofi. Research Support; Self; Canada Research Chair in Genetic and Molecular Epidemiology, CISCO Professorship in Integrated Health Biosystems, Sanofi. Funding Sanofi; Canadian Institutes of Health Research

Diabetes is the most common cause of renal failure accounting for more than 50% of cases. Whereas HbA1c, age and albuminuria are independent risk factors for renal decline, adding novel serum biomarkers to routine clinical risk factors may help identify patients at high risk for renal decline. One ml of stored serum from 7,482 ORIGIN participants with a recorded eGFR at baseline and either the 2 year and/or final visit was assayed for 237 biomarkers using Myriad RBM’s customized human discovery multi-analyte profile 250+ panel, and hs troponin and anti-GAD biomarkers were measured locally. During a median follow-up of 6.2 years, 1,646 (22%) developed the renal outcome comprising renal failure, albuminuria regression or doubling of some creatinine. A linear mixed model using a forward selection approach was used to identify the subset of biomarkers that independently predicted a decline in eGFR after accounting for 9 clinical risk factors for renal decline including age, sex, smoking, body mass index, mean arterial pressure, log (albumin:creatinine), cholesterol, baseline eGFR and HbA1c. A Bonferroni-corrected P value for inclusion in the model of less than 0.00021 (i.e., 0.05/239) identified 13 significant and independent biomarkers that included: alpha 1 microglobulin (beta = -0.157); NT-pro BNP (beta = -0.081); IGF binding protein 4 (beta = -0.110); growth differentiation factor 15 (beta = -0.091); RAGE (beta = -0.052); myoglobin (beta = -0.073); growth regulated alpha protein (beta = 0.058); fibulin-1C (beta = -0.048); apolipoprotein A-4 (beta = -0.057); apolipoprotein A-2 (beta = 0.065); fas ligand (beta = -0.099); eotaxin E (beta = -0.052); and beta amyloid 1-40 (beta = -0.057). The addition of these selected biomarkers to the clinical variables improved the variance of the eGFR from 0.053 to 0.155 (difference in log likelihood = 849, P < 0.001). These novel biomarkers are potential therapeutic targets. They can also help identify dysglycemic people at risk for renal decline. (NCT00069784) Disclosure H. Gerstein: Advisory Panel; Self; Abbott, AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk A/S, Sanofi. Research Support; Self; AstraZeneca, Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk A/S, Sanofi. Other Relationship; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Sanofi. G. Pare: Consultant; Self; Amgen Inc., Bristol-Myers Squibb Company, Lexicomp, Sanofi. Research Support; Self; Canada Research Chair in Genetic and Molecular Epidemiology, CISCO Professorship in Integrated Health Biosystems, Sanofi. M.J. McQueen: None. S. Lee: None. A. Kannt: Employee; Self; Sanofi. S. Hess: None. Funding Sanofi; Innovative Medicines Initiative (115974); European Union; European Federation of Pharmaceutical Industries; JDRF

Aims The association of body weight and weight change with mortality and cardiovascular (CV) outcome in patients with diabetes mellitus (DM) is not clearly established. We assessed the relationship between weight, weight change, and outcomes in patients with established CV risk factors and type 2 DM or pre-diabetes. Methods and results A total of 12 521 participants from the ORIGIN trial were grouped in BMI categories of low body weight [body mass index (BMI) Conclusion Obesity in patients with DM or pre-diabetes and CV risk profile was not associated with higher mortality or adverse CV outcome. The lowest mortality risk was seen in patients with overweight and moderate obesity (BMI 25–35 kg/m2). Weight loss was an independent risk factor for higher mortality compared to no weight loss.

Aims: Total and free testosterone and sex hormone-binding globulin may affect cardiovascular prognosis in women. The objective was to study the association between sex hormones and prognosis in women with dysglycemia and high cardiovascular risk. Methods: This epidemiological report included dysglycemic women from the Outcome Reduction with an Initial Glargine Intervention trial ( n = 2848) with baseline total testosterone and sex hormone-binding globulin. Free testosterone was calculated with the Vermeulen formula. Cox regression analyses adjusted for variables including age, previous diseases and pharmacological treatments were used to estimate the association between these levels and the composite cardiovascular outcome (death from cardiovascular causes, nonfatal myocardial infarction or nonfatal stroke) and all-cause mortality per one standard deviation. Results: Patients (73% post-menopausal) were followed for a median of 6.1 years during which 377 cardiovascular events and 389 deaths occurred. In Cox analyses, total and free testosterone were not associated with any outcomes, but sex hormone-binding globulin was related to all-cause mortality in age adjusted (HR 1.15; 95% CI 1.06–1.24; p Conclusions: Increasing levels of baseline sex hormone-binding globulin were associated with an increased risk of all-cause mortality in dysglycemic women at high cardiovascular risk. Trial registration ClinicalTrials.gov no. NCT00069784.

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.

AIMS/HYPOTHESIS: As part of the Surrogate Markers for Micro- and Macrovascular Hard Endpoints for Innovative Diabetes Tools (SUMMIT) programme we previously reported that large panels of biomarkers derived from three analytical platforms maximised prediction of progression of renal decline in type 2 diabetes. Here, we hypothesised that smaller (n ≤ 5), platform-specific combinations of biomarkers selected from these larger panels might achieve similar prediction performance when tested in three additional type 2 diabetes cohorts. METHODS: We used 657 serum samples, held under differing storage conditions, from the Scania Diabetes Registry (SDR) and Genetics of Diabetes Audit and Research Tayside (GoDARTS), and a further 183 nested case-control sample set from the Collaborative Atorvastatin in Diabetes Study (CARDS). We analysed 42 biomarkers measured on the SDR and GoDARTS samples by a variety of methods including standard ELISA, multiplexed ELISA (Luminex) and mass spectrometry. The subset of 21 Luminex biomarkers was also measured on the CARDS samples. We used the event definition of loss of >20% of baseline eGFR during follow-up from a baseline eGFR of 30-75 ml min-1 [1.73 m]-2. A total of 403 individuals experienced an event during a median follow-up of 7 years. We used discrete-time logistic regression models with tenfold cross-validation to assess association of biomarker panels with loss of kidney function. RESULTS: Twelve biomarkers showed significant association with eGFR decline adjusted for covariates in one or more of the sample sets when evaluated singly. Kidney injury molecule 1 (KIM-1) and β2-microglobulin (B2M) showed the most consistent effects, with standardised odds ratios for progression of at least 1.4 (p

Aims: Testosterone and its binding protein sex hormone-binding globulin have been associated with cardiovascular disease and dysglycaemia. However, information on the prognostic implication in patients at high cardiovascular risk with dysglycaemia is inconsistent. The study objective was to determine whether testosterone and/or sex hormone-binding globulin predict cardiovascular events or death in dysglycaemic patients. Methods: Dysglycaemic males at high cardiovascular risk ( n = 5553) who participated in the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial and provided baseline blood samples were studied. Testosterone and sex hormone-binding globulin were measured at baseline and used to estimate free testosterone. Low levels of total and free testosterone were defined as ≤300 ng/dl and ≤7 ng/dl, respectively. Patients were followed for six years for cardiovascular events (defined as the composite of cardiovascular death, non-fatal myocardial infarction or stroke) and all-cause mortality. Results: The mean total and free testosterone levels were 416.6 ng/dl and 8.4 ng/dl, and low levels were present in 13% and 37% of the patients. The median sex hormone-binding globulin level was 35 nmol/l. In Cox regression models adjusted for age, previous diseases and pharmacological treatment, neither total nor free testosterone predicted cardiovascular events. However, a one-standard-deviation increase in sex hormone-binding globulin predicted both cardiovascular events (hazard ratio 1.07; 95% confidence interval 1.00–1.14; p = 0.03) and all-cause mortality (hazard ratio 1.13; 95% confidence interval 1.06–1.21; p Conclusion: Sex hormone-binding globulin, but not total testosterone, predicts cardiovascular disease and all-cause mortality in dysglycaemic males at high cardiovascular risk.

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 (β = 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. ClinicalTrials.gov Identifier NCT00069784