n-terminal pro-b-type natriuretic peptide (nt-probnp) in type 2 diabetes mellitus

Conditions: Type2diabetes; Heart Failure; Cardiovascular Complication

Summary: more than 400 million people have type 2 diabetes (t2d) globally, and the burden of diabetes-related cardiovascular complications is increasing. cardiovascular disease (cvd) affects approximately one-third of all individuals with t2d and accounts for half of all deaths in this population despite major advances in the treatment of the disease. among the different types of cvd, heart failure (hf) is frequently the first cvd manifestation in individuals with t2d. although the link between t2d and cvd is widely recognised, the absolute risk of cardiovascular events varies among individuals with t2d. as such, effective risk-stratification tool that accurately identify t2d patients at the highest risk of developing incident or recurrent cardiovascular (cv) events is needed. b-type natriuretic peptide (bnp) and its inactive n-terminal precursor nt-probnp are biomarkers of myocardial stress. they been shown to incrementally improve predictive discrimination of death and cv events in high-risk individuals with t2d. an nt-probnp-based cvd/hf risk stratification strategy has not been prospectively tested in the multi-ethnic t2d population in singapore. in this study, we aim to: 1. evaluate the predictive value of nt-probnp for death and cv events compared to traditional risk markers \[e.g. hba1c, albuminuria, high sensitivity c-reactive protein (hscrp), high sensitivity troponin-t (hstnt)\] in a cohort of t2d patients with or without established cvd (defined as ischaemic heart disease, myocardial infarct, unstable angina, prior coronary artery revascularisation, stroke, transient ischaemic attack or pad) attending a tertiary diabetes care centre. (patients with history of hf will be excluded.) 2. compare the performance of nt-probnp as a single biomarker for cv risk prediction to risk scoring algorithms in t2d patients.

this is a prospective single-site, observational study to evaluate the predictive value of nt-probnp for death and cv events compared to traditional cv risk markers (e.g., hba1c, albuminuria, hscrp and hstnt) in t2d patients with or without established cvd (defined as ischaemic heart disease, myocardial infarct, unstable angina, prior coronary artery revascularisation, stroke, transient ischaemic attack or pad). patients with history of hf will be excluded. the study will also compare the performance of nt-probnp as a single biomarker for cv risk prediction to other risk scoring algorithms such as uk prospective diabetes study (ukpds) risk engine, risk equations for complications of type 2 diabetes (recode) and university of hong kong-singapore (hku-sg) risk scores. the study will prospectively recruit 1200 adults (aged 40 and above) with t2d from singapore general hospital (sgh) over a period of 18 months. informed consent will be obtained from patients before the start of any procedures. there will be 1 baseline visit to collect demographic information, and clinical and laboratory data. baseline point-of-care echocardiography will also be performed for all patients. all patients will subsequently be observed over 5 years for the occurrence of death or cv events through monitoring of electronic health records (ehr) and telephone contacts.

Eligibility criteria:
inclusion criteria: 1. type 2 diabetes (t2d) of at least 6 months' duration 2. age 40 and older exclusion criteria: 1. known history of heart failure (self-reported and medical records review) 2. estimated glomerular filtration rate (egfr) \<15 ml/min/1.73m2 \[using the ckd-epidemiology collaboration (ckd-epi) 2021 equation for glomerular filtration rate\] based on the last known egfr within 12 months of study recruitment) 3. renal replacement therapy 4. systemic treatment with corticosteroids or immunosuppressants 5. pregnant or nursing women 6. active cancer disease 7. serious disease with life expectancy \<1 year as judged by the doctor 8. any condition that, in the investigator's opinion, would interfere with a subject's ability to comply with study protocol or procedures

Keywords: diabetes risk prediction