Cardiovascular Risk Prediction in Type 2 Diabetes

Type 2 diabetes markedly elevates the risk of atherosclerotic and heart-failure events, yet risk is heterogeneous across individuals. Traditional prediction relies on demographic variables, glycaemic control, blood pressure and lipid profiles, but these models often underperform when applied to diverse populations or fail to capture subclinical disease. Recent advances seek to refine stratification by incorporating novel biomarkers, genetic scores, advanced imaging and machine-learning algorithms. The aim is to move from one-size-fits-all to precision risk assessment, identifying patients at highest risk who may benefit from targeted therapies such as SGLT-2 inhibitors or PCSK9 inhibitors, while avoiding overtreatment of lower-risk individuals. Global studies underscore the need for recalibration of existing tools to account for regional differences in baseline risk, and for rigorous evaluation of incremental predictive value beyond established factors. In parallel, regulatory and clinical practice guidelines increasingly call for transparent reporting of model performance, including discrimination, calibration and net reclassification, to ensure robust translation into routine care.

Research from Nature Portfolio

A comprehensive systematic review and meta-analysis of longitudinal studies has synthesised evidence on prognostic factors in people with type 2 diabetes. Out of hundreds of candidate markers, only a small subset has demonstrated clear incremental predictive utility beyond conventional risk factors. N-terminal pro B-type natriuretic peptide (NT-proBNP) emerged with the strongest evidence for forecasting cardiovascular outcomes, followed by troponin-T, the triglyceride-glucose (TyG) index and a coronary heart disease Genetic Risk Score. Imaging measures such as pulse-wave velocity and coronary computed tomography angiography showed moderate benefit. The study highlights that many proposed markers lack rigorous validation and underscores the importance of standardised reporting and external validation to confirm clinical utility.

Cardiovascular Risk Prediction in Type 2 Diabetes publication trend

The graph below shows the total number of articles in cardiovascular risk prediction in type 2 diabetes across all publications each year (not limited to Nature Index journals).

Technical terms

NT-proBNP: Cardiac neurohormone released in response to ventricular stretch, indicative of heart-failure risk.

Troponin-T: Myocardial protein marker of cardiomyocyte injury, used to detect subclinical cardiac damage.

TyG index: Logarithmic product of fasting triglycerides and glucose, reflecting insulin resistance.

Concordance index (C-index): Measure of a model’s ability to rank individuals by risk, analogous to the area under the ROC curve.

Calibration: Agreement between predicted and observed event rates across risk strata.

References

1. Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis. Communications Medicine (2024).
2. Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review. Cardiovascular Diabetology (2023).
3. NT-proBNP improves prediction of cardiorenal complications in type 2 diabetes: the Hong Kong Diabetes Biobank. Diabetologia (2024).
4. Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings. Diabetologia (2022).