Validation of the ORIGIN Cardiovascular Biomarker Panel and the Value of Adding Troponin I in Dysglycemic People

Evaluating the association between genetically proxied ACE inhibition and dementias

INTRODUCTION

Angiotensin-converting enzyme (ACE) has been implicated in the metabolism of amyloid beta; however, the causal effect of ACE inhibition on risk of Alzheimer's disease (AD) dementia and other common dementias is largely unknown.

METHODS

We examined the causal association of genetically proxied ACE inhibition with four types of dementias using a two-sample Mendelian randomization (MR) approach.

RESULTS

Genetically proxied ACE inhibition was associated with increased risk of AD dementia (odds ratio per one standard deviation reduction in serum ACE [95% confidence interval]; 1.07 [1.04-1.10], P = 5 × 10-07 ) and frontotemporal dementia (1.16 [1.04-1.29], P = 0.01) but not with Lewy body dementia or vascular dementia (P > 0.05). These findings were independently replicated and remained consistent in sensitivity analyses.

DISCUSSION

This comprehensive MR study provided genetic evidence for an association between ACE inhibition and the risk for AD and frontotemporal dementias. These results should encourage further studies of the neurocognitive effects of ACE inhibition.

HIGHLIGHTS

This study evaluated genetically proxied angiotensin-converting enzyme (ACE) inhibition association with dementias. The results suggest an association between ACE inhibition and Alzheimer's disease. The results suggest an association between ACE inhibition and frontotemporal dementia. Those associations can be interpreted as potentially causal.

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.

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.

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.

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, HbA_1c, 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 HbA_1c ≥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.

Changes in the prognostic values of modern cardiovascular biomarkers in relation to duration of diabetes mellitus

BACKGROUND

Based on the complex pathophysiology of type 2 diabetes and atherosclerosis we hypothesized a dynamic change in prognostic value of cardiovascular biomarkers over time.

METHODS

In this prospective study 746 patients with type 2 diabetes mellitus, being followed up for 60 months were analysed. The primary endpoint was defined as unplanned hospitalization for cardiovascular disease or death. Beside others, especially the prognostic performance of the biomarkers of interest (GDF-15, NT-proBNP, hs-TnT) was evaluated in relation to quartiles of diabetes duration.

RESULTS

In patients having a diabetes duration below 7 years lnGDF-15 (HR 2.84; p < 0.01) and lnhs-TnT (HR 2.96; p < 0.01) were significant predictors of the primary endpoint. LnAge (HR 40.01; p < 0.01) and lnNT-proBNP (HR 1.56; p = 0.03) were significant predictors in patients with a diabetes duration between 7 and 12 years. In the third quartile (diabetes duration 12-22 years) lnurinary albumin to creatinine ratio (HR 1.25; p = 0.005) and lnNT-proBNP (HR 2.13, p < 0.001) predicted the endpoint. In patients with a diabetes duration above 22 years, lnAge (HR 75.35; p = 0.001) and lnNT-proBNP (HR 2.0; p < 0.01) were the only significant predictors of the endpoint.

CONCLUSION

Prognostic power of cardiovascular biomarkers changes dynamically in relation to duration of type 2 diabetes mellitus. In patients with shorter duration of the disease markers of subclinical cardiovascular dysfunction and inflammation perform better than markers of systemic advanced organ dysfunction and cardiovascular disease.

Serum tenascin-C is independently associated with increased major adverse cardiovascular events and death in individuals with type 2 diabetes: a French prospective cohort

AIMS/HYPOTHESIS

Tenascin-C (TN-C) is an extracellular matrix glycoprotein highly expressed in inflammatory and cardiovascular (CV) diseases. Serum TN-C has not yet been specifically studied in individuals with type 2 diabetes, a condition associated with chronic low-grade inflammation and increased CV disease risk. In this study, we hypothesised that elevated serum TN-C at enrolment in participants with type 2 diabetes would be associated with increased risk of death and major adverse CV events (MACE) during follow-up.

METHODS

We used a prospective, monocentric cohort of consecutive type 2 diabetes participants (the SURDIAGENE [SUivi Rénal, DIAbète de type 2 et GENEtique] cohort) with all-cause death as a primary endpoint and MACE (CV death, non-fatal myocardial infarction or stroke) as a secondary endpoint. We used a proportional hazard model after adjustment for traditional risk factors and the relative integrated discrimination improvement (rIDI) to assess the incremental predictive value of TN-C for these risk factors.

RESULTS

We monitored 1321 individuals (58% men, mean age 64 ± 11 years) for a median of 89 months. During follow-up, 442 individuals died and 497 had MACE. Multivariate Cox analysis showed that serum TN-C concentrations were associated with an increased risk of death (HR per 1 SD: 1.27 [95% CI 1.17, 1.38]; p < 0.0001) and MACE (HR per 1 SD: 1.23 [95% CI 1.13, 1.34]; p < 0.0001). Using TN-C concentrations on top of traditional risk factors, prediction of the risk of all-cause death (rIDI: 8.2%; p = 0.0006) and MACE (rIDI: 6.7%; p = 0.0014) improved significantly, but modestly.

CONCLUSIONS/INTERPRETATION

In individuals with type 2 diabetes, increased serum TN-C concentrations were independently associated with death and MACE. Therefore, including TN-C as a prognostic biomarker could improve risk stratification in these individuals.

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.

Novel Biomarkers for Change in Renal Function in People With Dysglycemia

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 HbA_1c, 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.

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 (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

Novel Biomarkers for Predicting Cardiovascular Disease in Patients With Diabetes

It is generally acknowledged that patients with diabetes comprise a high-risk population for the development of cardiovascular disease. However, it is perhaps less well recognized that there actually exists considerable heterogeneity in vascular risk within this patient population, with a sizable subset of individuals seemingly at low risk for major cardiovascular events despite the presence of diabetes. Because traditional clinical risk calculators have shown wide variability in their performance in the setting of diabetes, there exists a need for additional risk predictors in this patient population. In this context, there has been considerable interest in the potential utility of circulating biomarkers as clinical tools that might facilitate risk stratification and thereby guide therapeutic and preventative decision-making. Coupled with the current era of dedicated cardiovascular outcome trials in type 2 diabetes, this interest has spawned a growing literature of recent studies that evaluated potential biomarkers. To date, these studies have identified N-terminal pro-B-type natriuretic peptide, high-sensitivity cardiac troponins, and growth differentiation factor-15 as cardiovascular biomarkers of particular potential in patients with diabetes. Furthermore, recognizing the potential benefit of collective consideration of different biomarkers reflecting distinct pathophysiologic processes that might contribute to the development of cardiovascular disease, there is emerging emphasis on the evaluation of combinations of biomarkers for optimal risk prediction. Although not currently ready for clinical practice, this rapidly-growing topic of biomarker research might ultimately facilitate the goal of individualized risk stratification and thereby enable truly personalized management of diabetes.