Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis

Ahmad A 1 , Lim LL 2 , Morieri ML 3 , Tam CH 4 , Cheng F 5 , Chikowore T 6 Show all authors , Dudenhöffer-Pfeifer M 1 , Fitipaldi H 1 , Huang C 4 , Kanbour S 7 , Sarkar S 8 , Koivula RW 9 , Motala AA 10 , Tye SC 11 , Yu G 4 , Zhang Y 4 , Provenzano M 12 , Sherifali D 13 , de Souza RJ 14 , Tobias DK 15 , ADA/EASD PMDI , Gomez MF 16 , Ma RCW 17 , Mathioudakis N 18

Affiliations 

  • 1 Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden
  • 2 Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
  • 3 Metabolic Disease Unit, University Hospital of Padova, Padova, Italy
  • 4 Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
  • 5 Health Management Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China
  • 6 MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
  • 7 AMAN Hospital, Doha, Qatar
  • 8 Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
  • 9 Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
  • 10 Department of Diabetes and Endocrinology, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
  • 11 Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, Groningen, the Netherlands
  • 12 Nephrology, Dialysis and Renal Transplant Unit, IRCCS-Azienda Ospedaliero-Universitaria di Bologna, Alma Mater Studiorum University of Bologna, Bologna, Italy
  • 13 Heather M. Arthur Population Health Research Institute, McMaster University, Ontario, Canada
  • 14 Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
  • 15 Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
  • 16 Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden. [email protected]
  • 17 Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China. [email protected]
  • 18 Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. [email protected]
Commun Med (Lond), 2024 Jan 22;4(1):11.
PMID: 38253823 DOI: 10.1038/s43856-023-00429-z

Abstract

BACKGROUND: Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D).

METHODS: We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies.

RESULTS: Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort.

CONCLUSIONS: Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

Similar publications