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The Chinese University of Hong Kong (CUHK)’s Faculty of Medicine has confirmed that N-terminal pro B-type natriuretic peptide (NT-proBNP) — a hormonal molecule produced and secreted from ventricular cells — can be used to independently predict the risk of developing cardiovascular disease and renal disease in patients with type 2 diabetes. Researchers believe NT-proBNP can serve as a clinical biomarker for predicting and stratifying the risk of cardiovascular and kidney complications in diabetic patients, thereby enabling more intensive risk factor management for patients in need. The findings have been published in Diabetologia, the official journal of the European Association for the Study of Diabetes (EASD).

NT-proBNP is an established biomarker for heart failure

NT-proBNP is released into the blood when the heart ventricles are stressed. It is normal to have some NT-proBNP in the bloodstream, but higher-than-normal levels compared to people of the same age and sex may be a sign of heart failure. The peptide is an established biomarker for heart failure and other cardiovascular complications in people with or without diabetes. Recent data suggests it may also be a useful biomarker for renal dysfunction.

Professor Ronald Ma Ching-wan, S.H. Ho Professor of Diabetes and Head (Academic Affairs) in the Division of Endocrinology and Diabetes at CU Medicine, said: “Although people with diabetes are at higher risk of developing cardiovascular and kidney diseases than those without, it remains a challenge for clinicians to predict who among this population is most at risk. NT-proBNP is a reliable biomarker for heart failure, but data for other complications was limited, especially for Asian populations. We therefore initiated a study to evaluate the clinical utility of NT-proBNP for predicting cardiorenal complications in Chinese individuals with type 2 diabetes, and to compare its performance in precision prognostics with established clinical risk factors.”  

NT-proBNP has good predictive ability for different cardiorenal endpoints in diabetes

A CU Medicine research team analysed data from 1,993 individuals with type 2 diabetes, with NT-proBNP measured from the Hong Kong Diabetes Biobank, a multicentre prospective diabetes cohort and biobank coordinated by CUHK, to investigate the associations between elevated NT-proBNP and the incidence of cardiovascular and renal complications.

Key findings are as follow:

• Baseline findings showed 1 in 4 patients (24.7%) with diabetes had elevated NT-proBNP (defined as ≥ 125pg/ml).
• The frequency of prevalent cardiorenal complications at baseline was 2-4 times higher in patients with elevated NT-proBNP compared with those with a NT-proBNP level within the normal range.
• Elevated NT-proBNP was associated with incident cardiovascular complications, including atrial fibrillation, cardiovascular diseases and congestive heart failure, after adjustment for covariates.
• Elevated NT-proBNP was associated with incident chronic kidney disease, kidney failure and a 40% decrease in estimated glomerular filtration rate (eGFR), after adjustment for covariates.

The team further examined the utility of NT-proBNP for predicting different cardiorenal endpoints in diabetes and proved the biomarker has good predictive ability, with a C index[1] over 0.8, for a variety of cardiorenal complications (please refer to Table 1 for more detailed data).

“Our study proved the biomarker NT-proBNP has utility in identifying those at increased risk of cardiovascular and renal complications. We found that incorporating NT-proBNP into established risk prediction equations can enhance the ability to predict each cardiorenal complication, beyond using clinical risk factors alone. We now have effective medications that can help to reduce cardiorenal complications in people with diabetes. Early identification of patients who are at higher risk of future cardiorenal complications may facilitate early intervention and treatment,” Professor Ma added.  

[1] C-index means the concordance index. It evaluates the performance of a predictive algorithm trained to recognise the presence of a disease. The value ranges between 0.5 and 1: a value of 0.5 indicates that the model has no ability to discriminate between low- and high-risk subjects, whereas a value of 1 indicates that the model can perfectly discriminate between these two groups.