Determinants of serious health outcome-free status in middle-aged and older people with dysglycaemia: Exploratory analysis of the ORIGIN trial.

AIM: To assess clinical and biochemical measurements that can identify people with dysglycaemia (i.e. diabetes or pre-diabetes) who remain free of serious outcomes during follow-up. MATERIALS AND METHODS: We conducted exploratory analyses using data from the Outcomes Reduction with an Initial Glargine Intervention (ORIGIN) study to identify independent determinants of outcome-free status in 12 537 middle-aged and older adults with prediabetes and early type 2 diabetes from 40 countries. Serious outcome-free status was defined as the absence of major cardiovascular outcomes, kidney or retinal outcomes, peripheral artery disease, dementia, cancer, any hospitalization, or death during follow-up. RESULTS: In total, 3328 (26.6%) participants remained free of serious outcomes during a median follow-up of 6.2 years (IQR 5.8, 6.7). Independent clinical determinants of outcome-free status included younger age, female sex, non-White ethnicity, shorter diabetes duration, absence of previous cardiovascular disease, current or former smokers, higher grip strength, Mini-Mental State Examination score, and ankle-brachial index, lower body mass index and kidney disease index, and non-use of renin-angiotensin system drugs and beta-blockers. In a subset of 8401 people with baseline measurements of 238 biomarkers, growth differentiation factor 15, kidney injury molecule-1, N-terminal pro-brain natriuretic peptide, uromodulin, C-reactive protein, factor VII and ferritin were independent determinants. The combination of clinical determinants and biomarkers best identified participants who remained outcome-free (C-statistics 0.71, 95% confidence interval 0.70-0.73; net reclassification improvement 0.55, 95% confidence interval 0.48-0.58). CONCLUSIONS: A set of routinely measured clinical characteristics and seven protein biomarkers identify middle-aged and older people with prediabetes or early type 2 diabetes as least likely to experience serious outcomes during follow-up.

Identifying blood biomarkers for type 2 diabetes subtyping: a report from the ORIGIN trial.

AIMS/HYPOTHESIS: Individuals with diabetes can be clustered into five subtypes using up to six routinely measured clinical variables. We hypothesised that circulating protein levels might be used to distinguish between these subtypes. We recently used five of these six variables to categorise 7017 participants from the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial into these subtypes: severe autoimmune diabetes (SAID, n=241), severe insulin-deficient diabetes (SIDD, n=1594), severe insulin-resistant diabetes (SIRD, n=914), mild obesity-related diabetes (MOD, n=1595) and mild age-related diabetes (MARD, n=2673). METHODS: Forward-selection logistic regression models were used to identify a subset of 233 cardiometabolic protein biomarkers that were independent determinants of one subtype vs the others. We then assessed the performance of adding identified biomarkers (one after one, from the most discriminant to the least) to predict each subtype vs the others using area under the receiver operating characteristic curve (AUC ROC). Models were adjusted for age, sex, ethnicity, C-peptide level, diabetes duration and glucose-lowering medication usage at blood collection. RESULTS: A total of 25 biomarkers were independent determinants of subtypes, including 13 for SIDD, 2 for SIRD, 7 for MOD and 11 for MARD (all p<4.3 × 10-5). The performance of the biomarker sets (comprising 1 to 25 biomarkers), assessed through the AUC ROC, ranged from 0.611 to 0.734, 0.723 to 0.861, 0.672 to 0.742, and 0.651 to 0.751, for SIDD, SIRD, MOD and MARD, respectively. No biomarkers other than GAD antibodies were determinants of SAID. CONCLUSIONS/INTERPRETATION: We identified 25 serum biomarkers, as independent determinants of type 2 diabetes subtypes, that could be combined into a diagnostic test for subtyping. TRIAL REGISTRATION: ORIGIN trial, ClinicalTrials.gov NCT00069784.

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.

Validation of the classification for type 2 diabetes into five subgroups: a report from the ORIGIN trial.

AIMS/HYPOTHESIS: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS/INTERPRETATION: 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. TRIAL REGISTRATION: ORIGIN trial, ClinicalTrials.gov NCT00069784.

Patients With High Genome-Wide Polygenic Risk Scores for Coronary Artery Disease May Receive Greater Clinical Benefit From Alirocumab Treatment in the ODYSSEY OUTCOMES Trial.

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<0.001); this PRS relationship was not explained by baseline LDL-C or other established risk factors. Both the absolute and relative reduction of MACE by alirocumab compared with placebo was greater in high versus low PRS patients. There was an absolute reduction by alirocumab in high versus low PRS groups of 6.0% and 1.5%, respectively, and a relative risk reduction by alirocumab of 37% in the high PRS group (hazard ratio, 0.63 [95% CI, 0.46-0.86]; 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.

Obesity and weight loss are inversely related to mortality and cardiovascular outcome in prediabetes and type 2 diabetes: data from the ORIGIN trial.

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) < 22 kg/m2] normal (22-24.9), overweight (25-29.9), obesity Grades 1-3 (30-34.9, 35-39.9, ≥40 kg/m2, respectively). Outcome variables included total and CV mortality and composite outcomes of CV death, non-fatal stroke, or myocardial infarction plus revascularization or heart failure hospitalization. Follow-up was 6.2 years (interquartile range 5.8-6.7 years). After multivariable adjustment, lowest risks were seen in patients with overweight and mild obesity for total mortality [overweight: hazard ratio (HR) 0.80 (95% confidence interval (CI) 0.69-0.91); obesity Grade 1: HR 0.82 (0.71-0.95), both P < 0.01)] and CV mortality [overweight: HR 0.79 (0.66-0.94); obesity Grade 1: 0.79 (0.65-0.95), all compared to patients with normal BMI, P < 0.05]. Obesity of any severity was not associated with higher mortality. Low body weight was related to higher mortality [HR 1.28 (1.02-1.61); CV mortality: HR 1.34 (1.01-1.79), P < 0.05]. A continued 2-year weight loss was associated with higher risk of mortality [HR 1.32 (1.18-1.46), P < 0.0001] and CV mortality [HR 1.18 (1.02-1.35), compared to patients without weight loss, P < 0.05]. In turn, weight gain was not related to any adverse outcome. 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.

Serum kidney injury molecule 1 and ß2-microglobulin perform as well as larger biomarker panels for prediction of rapid decline in renal function in type 2 diabetes.

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 ß2-microglobulin (B2M) showed the most consistent effects, with standardised odds ratios for progression of at least 1.4 (p < 0.0003) in all cohorts. A combination of B2M and KIM-1 added to clinical covariates, including baseline eGFR and albuminuria, modestly improved prediction, increasing the area under the curve in the SDR, Go-DARTS and CARDS by 0.079, 0.073 and 0.239, respectively. Neither the inclusion of additional Luminex biomarkers on top of B2M and KIM-1 nor a sparse mass spectrometry panel, nor the larger multiplatform panels previously identified, consistently improved prediction further across all validation sets. CONCLUSIONS/INTERPRETATION: Serum KIM-1 and B2M independently improve prediction of renal decline from an eGFR of 30-75 ml min-1 [1.73 m]-2 in type 2 diabetes beyond clinical factors and prior eGFR and are robust to varying sample storage conditions. Larger panels of biomarkers did not improve prediction beyond these two biomarkers.

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.

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.

Identifying Novel Biomarkers for Cardiovascular Events or Death in People With Dysglycemia.

BACKGROUND: Serum biomarkers may identify people at risk for cardiovascular (CV) outcomes. Biobanked serum samples from 8494 participants with dysglycemia in the completed Outcome Reduction With Initial Glargine Intervention trial were assayed for 284 biomarkers to identify those that could identify people at risk for a CV outcome or death when added to clinical measurements. METHODS AND RESULTS: A multiplex analysis measured a panel of cardiometabolic biomarkers in 1 mL of stored frozen serum from every participant who provided biobanked blood. After eliminating undetectable or unanalyzable biomarkers, 8401 participants who each had a set of 237 biomarkers were analyzed. Forward-selection Cox regression models were used to identify biomarkers that were each independent determinants of 3 different incident outcomes: (1) the composite of myocardial infarction, stroke, or CV death; (2) these plus heart failure hospitalization or revascularization; and (3) all-cause death. When added to clinical variables, 10 biomarkers were independent determinants of the 1405 CV composite outcomes observed during follow-up; 9 biomarkers (including 8 of these 10) were independent determinants of the 2435 expanded composite outcomes; and 15 (including the 10 CV composite biomarkers) were independent determinants of the 1340 deaths. Adjusted C statistics increased from 0.64 for the clinical variables to 0.71 and 0.68 for the 2 CV composite outcomes, respectively, with the greatest increase to 0.75 for death (P<0.001 for the change). CONCLUSIONS: A systematic hypothesis-free approach identified combinations of up to 15 cardiometabolic biomarkers as independent determinants of CV outcomes or death in people with dysglycemia. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00069784.

Biomarkers of rapid chronic kidney disease progression in type 2 diabetes.

Here we evaluated the performance of a large set of serum biomarkers for the prediction of rapid progression of chronic kidney disease (CKD) in patients with type 2 diabetes. We used a case-control design nested within a prospective cohort of patients with baseline eGFR 30-60 ml/min per 1.73 m(2). Within a 3.5-year period of Go-DARTS study patients, 154 had over a 40% eGFR decline and 153 controls maintained over 95% of baseline eGFR. A total of 207 serum biomarkers were measured and logistic regression was used with forward selection to choose a subset that were maximized on top of clinical variables including age, gender, hemoglobin A1c, eGFR, and albuminuria. Nested cross-validation determined the best number of biomarkers to retain and evaluate for predictive performance. Ultimately, 30 biomarkers showed significant associations with rapid progression and adjusted for clinical characteristics. A panel of 14 biomarkers increased the area under the ROC curve from 0.706 (clinical data alone) to 0.868. Biomarkers selected included fibroblast growth factor-21, the symmetric to asymmetric dimethylarginine ratio, ß2-microglobulin, C16-acylcarnitine, and kidney injury molecule-1. Use of more extensive clinical data including prebaseline eGFR slope improved prediction but to a lesser extent than biomarkers (area under the ROC curve of 0.793). Thus we identified several novel associations of biomarkers with CKD progression and the utility of a small panel of biomarkers to improve prediction.

Vaspin: A Novel Biomarker Linking Gluteofemoral Body Fat and Type 2 Diabetes Risk.

OBJECTIVE: To determine whether adiposity depots modulate vaspin levels and whether vaspin predicts type 2 diabetes (T2D) risk, through epidemiological and genetic analyses. RESEARCH DESIGN AND METHODS: We assessed the relationship of plasma vaspin concentration with incident and prevalent T2D and adiposity-related variables in 1) the Prospective Urban and Rural Epidemiology (PURE) biomarker substudy (N = 10,052) and 2) the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial (N = 7,840), using regression models. We then assessed whether vaspin is causally associated with T2D and whether genetic variants associated with MRI-measured adiposity depots modulate vaspin levels, using two-sample Mendelian randomization (MR). RESULTS: A 1-SD increase in circulating vaspin levels was associated with a 16% increase in incident T2D in the PURE cohort (hazard ratio 1.16; 95% CI 1.09-1.23; P = 4.26 × 10-7) and prevalent T2D in the ORIGIN cohort (odds ratio [OR] 1.16; 95% CI 1.07-1.25; P = 2.17 × 10-4). A 1-unit increase in BMI and triglyceride levels was associated with a 0.08-SD (95% CI 0.06-0.10; P = 2.04 × 10-15) and 0.06-SD (95% CI 0.04-0.08; P = 4.08 × 10-13) increase, respectively, in vaspin in the PURE group. Consistent associations were observed in the ORIGIN cohort. MR results reinforced the association between vaspin and BMI-adjusted T2D risk (OR 1.01 per 1-SD increase in vaspin level; 95% CI 1.00-1.02; P = 2.86 × 10-2) and showed that vaspin was increased by 0.10 SD per 1-SD decrease in genetically determined gluteofemoral adiposity (95% CI 0.02-0.18; P = 2.01 × 10-2). No relationships were found between subcutaneous or visceral adiposity and vaspin. CONCLUSIONS: These findings support that higher vaspin levels are related to increased T2D risk and reduced gluteofemoral adiposity, positioning vaspin as a promising clinical predictor for T2D.

Cardioprotective effects of lixisenatide in rat myocardial ischemia-reperfusion injury studies.

BACKGROUND: Lixisenatide is a glucagon-like peptide-1 analog which stimulates insulin secretion and inhibits glucagon secretion and gastric emptying. We investigated cardioprotective effects of lixisenatide in rodent models reflecting the clinical situation. METHODS: The acute cardiac effects of lixisenatide were investigated in isolated rat hearts subjected to brief ischemia and reperfusion. Effects of chronic treatment with lixisenatide on cardiac function were assessed in a modified rat heart failure model after only transient coronary occlusion followed by long-term reperfusion. Freshly isolated cardiomyocytes were used to investigate cell-type specific mechanisms of lixisenatide action. RESULTS: In the acute setting of ischemia-reperfusion, lixisenatide reduced the infarct-size/area at risk by 36% ratio without changes on coronary flow, left-ventricular pressure and heart rate. Treatment with lixisenatide for 10 weeks, starting after cardiac ischemia and reperfusion, improved left ventricular end-diastolic pressure and relaxation time and prevented lung congestion in comparison to placebo. No anti-fibrotic effect was observed. Gene expression analysis revealed a change in remodeling genes comparable to the ACE inhibitor ramipril. In isolated cardiomyocytes lixisenatide reduced apoptosis and increased fractional shortening. Glucagon-like peptide-1 receptor (GLP1R) mRNA expression could not be detected in rat heart samples or isolated cardiomyocytes. Surprisingly, cardiomyocytes isolated from GLP-1 receptor knockout mice still responded to lixisenatide. CONCLUSIONS: In rodent models, lixisenatide reduced in an acute setting infarct-size and improved cardiac function when administered long-term after ischemia-reperfusion injury. GLP-1 receptor independent mechanisms contribute to the described cardioprotective effect of lixisenatide. Based in part on these preclinical findings patients with cardiac dysfunction are currently being recruited for a randomized, double-blind, placebo-controlled, multicenter study with lixisenatide. TRIAL REGISTRATION: (ELIXA, ClinicalTrials.gov Identifier: NCT01147250).

Sex hormone-binding globulin and heart failure hospitalizations in patients with dysglycemia: Experiences from the outcome reduction with an Initial Glargine Intervention trial.

INTRODUCTION: Sex hormone-binding globulin (SHBG), which binds most of circulating testosterone in blood, has been linked to dysglycemia and cardiovascular disease but the relationship with heart failure remains unclear. AIM: To study the relation between SHBG and heart failure hospitalizations. METHODS: SHBG levels were analysed in dysglycemic participants at high cardiovascular risk (n = 8401) followed for a median of 6.2 years in the Outcome Reduction with an Initial Glargine Intervention trial. Cox regression was used to estimate hazard ratios (HRs) per one standard deviation increase for heart failure hospitalizations adjusted for age, comorbidities, biochemical data (including testosterone) and pharmacological treatment. RESULTS: 5553 men and 2848 women were included. Heart failure hospitalizations occurred in 349 (6.3 %) men and 123 (4.3 %) women. One standard deviation increase in SHBG was independently associated with an increased risk of heart failure hospitalizations in men (HR 1.15, 95 % CI 1.03-1.28; p = 0.011) but not in women (HR 1.15; 95 % CI 0.96-1.39; p = 0.14). CONCLUSIONS: In patients with dysglycemia and high cardiovascular risk, increasing SHBG was associated with greater risk of HF hospitalizations independent of testosterone concentrations in men but not in women, suggesting the effects could be mediated through androgen-independent pathways.

Biomarkers Associated With Severe COVID-19 Among Populations With High Cardiometabolic Risk: A 2-Sample Mendelian Randomization Study.

Importance: Cardiometabolic parameters are established risk factors for COVID-19 severity. The identification of causal or protective biomarkers for COVID-19 severity may facilitate the development of novel therapies. Objective: To identify protein biomarkers that promote or reduce COVID-19 severity and that mediate the association of cardiometabolic risk factors with COVID-19 severity. Design, Setting, and Participants: This genetic association study using 2-sample mendelian randomization (MR) was conducted in 2022 to investigate associations among cardiometabolic risk factors, circulating biomarkers, and COVID-19 hospitalization. Inputs for MR included genetic and proteomic data from 4147 participants with dysglycemia and cardiovascular risk factors collected through the Outcome Reduction With Initial Glargine Intervention (ORIGIN) trial. Genome-wide association study summary statistics were obtained from (1) 3 additional independent plasma proteome studies, (2) genetic consortia for selected cardiometabolic risk factors (including body mass index [BMI], type 2 diabetes, type 1 diabetes, and systolic blood pressure; all n >10 000), and (3) the COVID-19 Host Genetics Initiative (n = 5773 hospitalized and 15 497 nonhospitalized case participants with COVID-19). Data analysis was performed in July 2022. Exposures: Genetically determined concentrations of 235 circulating proteins assayed with a multiplex biomarker panel from the ORIGIN trial for the initial analysis. Main Outcomes and Measures: Hospitalization status of individuals from the COVID-19 Host Genetics Initiative with a positive COVID-19 test result. Results: Among 235 biomarkers tested in samples totaling 22 101 individuals, MR analysis showed that higher kidney injury molecule-1 (KIM-1) levels reduced the likelihood of COVID-19 hospitalization (odds ratio [OR] per SD increase in KIM-1 levels, 0.86 [95% CI, 0.79-0.93]). A meta-analysis validated the protective association with no observed directional pleiotropy (OR per SD increase in KIM-1 levels, 0.91 [95% CI, 0.88-0.95]). Of the cardiometabolic risk factors studied, only BMI was associated with KIM-1 levels (0.17 SD increase in biomarker level per 1 kg/m2 [95% CI, 0.08-0.26]) and COVID-19 hospitalization (OR per 1-SD biomarker level, 1.33 [95% CI, 1.18-1.50]). Multivariable MR analysis also revealed that KIM-1 partially mitigated the association of BMI with COVID-19 hospitalization, reducing it by 10 percentage points (OR adjusted for KIM-1 level per 1 kg/m2, 1.23 [95% CI, 1.06-1.43]). Conclusions and Relevance: In this genetic association study, KIM-1 was identified as a potential mitigator of COVID-19 severity, possibly attenuating the increased risk of COVID-19 hospitalization among individuals with high BMI. Further studies are required to better understand the underlying biological mechanisms.

Biomarkers of Prevalent and Incident Cognitive Dysfunction in People with Dysglycemia: Data from the ORIGIN Trial.

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 < 24 or dementia) using forward selection with an inclusion p-value < 0.0002 to account for multiplicity. RESULTS: During a median follow-up period of 6.2 years, 939 individuals developed cognitive dysfunction. After accounting for 17 clinical risk factors, glargine allocation, and the baseline MMSE, three biomarkers (α-2 Macroglobulin, HR 1.19; 95% CI 1.12, 1.27; Macrophage Inflammatory Protein 1α, HR 1.11; 95% CI 1.06, 1.16; and Growth Hormone, HR 0.91; 95% CI 0.87, 0.96) independently predicted incident cognitive dysfunction (p < 0.0002). Addition of these biomarkers to a model that included clinical risk factors, however, did not improve the ability to predict cognitive dysfunction. CONCLUSION: Addition of independent biomarkers to clinical risk factors for cognitive dysfunction in people with dysglycemia did not predict incident cognitive dysfunction better than clinical risk factors alone.

Distribution and characteristics of newly-defined subgroups of type 2 diabetes in randomised clinical trials: Post hoc cluster assignment analysis of over 12,000 study participants.

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.

Protein Biomarkers and Cardiovascular Outcomes in People With Type 2 Diabetes and Acute Coronary Syndrome: The ELIXA Biomarker Study.

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.