Precision Prediction for CVD Risk in T2D

Launching a groundbreaking systematic review, our international team of experts embarked on a mission to redefine cardiovascular risk prediction in type 2 diabetes, exploring new biomarkers and genetic markers beyond traditional factors.
Like

Share this post

Choose a social network to share with, or copy the URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

Genesis of our Systematic Review

In 2020, as part of an international consortium – the ADA/EASD Precision Medicine Diabetes – we convened 23 experts from 11 countries across four continents with a crucial objective: to enhance cardiovascular risk prediction in type 2 diabetes patients. Recognizing the substantial impact of cardiovascular disease on morbidity and mortality in this population, our team embarked on a mission to identify potential biomarkers, genetic markers, and risk scores that could provide more precise predictions.

Objective

Our ambitious goal was to systematically review the medical literature to identify promising markers that could augment cardiovascular risk prediction in type 2 diabetes, beyond traditional factors like hypertension, smoking, and dyslipidemia. Through collaborative weekly meetings across various time zones, a diverse group of experts developed a rigorous study protocol. We embarked on the arduous task of sifting through nearly 9,400 scientific studies that met our initial criteria.

What we Did

Following extensive screening, we narrowed our focus to 416 relevant studies, meticulously extracting data on 321 biomarkers, 48 genetic markers, and 47 risk scores. We aimed to understand how these factors could improve predictive utility beyond established traditional markers. This involved capturing metrics such as the Net Reclassification Index (NRI) and the Integrated Discrimination Index (IDI), and assessing the improvement in the c-statistic. Our stepwise evaluation process was designed to categorize the predictive utility of each biomarker, ensuring a comprehensive assessment in clinical contexts.

Key Findings

  • Our analysis revealed that only 13 biomarkers significantly improved prediction accuracy. Notably, NT-proBNP emerged as a powerful predictor, with its utility potentially extending beyond heart failure to various cardiovascular outcomes.
  • Other biomarkers demonstrated varying levels of utility, with some like coronary computed tomography angiography and single-photon emission computed tomography (SPECT) showing moderate predictive capabilities despite limited evidence.
  • Among the 79 genetic biomarkers identified, a fraction showed a positive association with cardiovascular outcomes, with the isoform e-4 in APOE and the Genetic Risk Score for Coronary Heart Disease (GRS-CHD) demonstrating moderate predictive ability.
  • Risk scores are increasingly being integrated into clinical practice. Our study evaluated the discriminative ability of 27 risk scores across 47 studies, finding that their effectiveness varied, particularly when applied to populations different from the initial development cohort. This emphasizes the need for caution and the importance of considering population-specific factors in health assessments.

Take-Aways and Future Directions

  • This study represents one of the most extensive and detailed summaries of prognostic factors for cardiovascular disease in type 2 diabetes. It highlights several new findings and underscores significant knowledge gaps.
  • The standout biomarker, NT-proBNP, warrants prospective testing to evaluate its potential in altering clinical practices for better prediction of cardiovascular risk in type 2 diabetes patients.
  • Our findings also emphasize the need for more rigorous studies to robustly test these markers and convincingly demonstrate their added predictive value.

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Follow the Topic

Diabetes Complications
Life Sciences > Health Sciences > Clinical Medicine > Diseases > Diabetes > Diabetes Complications
Biomarkers
Life Sciences > Health Sciences > Clinical Medicine > Diagnosis > Biomarkers
Genetic Markers
Life Sciences > Biological Sciences > Genetics and Genomics > Evolutionary Genetics > Genetic Markers
Cardiovascular Diseases
Life Sciences > Health Sciences > Clinical Medicine > Diseases > Cardiovascular Diseases

Related Collections

With collections, you can get published faster and increase your visibility.

Biology of rare genetic disorders

This cross-journal Collection between Nature Communications, Communications Biology, npj Genomic Medicine and Scientific Reports brings together research articles that provide new insights into the biology of rare genetic disorders, also known as Mendelian or monogenic disorders.

Publishing Model: Open Access

Deadline: Jan 31, 2025

Carbon dioxide removal, capture and storage

In this cross-journal Collection, we bring together studies that address novel and existing carbon dioxide removal and carbon capture and storage methods and their potential for up-scaling, including critical questions of timing, location, and cost. We also welcome articles on methodologies that measure and verify the climate and environmental impact and explore public perceptions.

Publishing Model: Open Access

Deadline: Mar 22, 2025