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

Authors

Abrar Ahmad, Institutionen för Kliniska Vetenskaper, Malmö
Lee Ling Lim, Universiti Malaya
Mario Luca Morieri, Azienda Ospedale Università Padova
Claudia Ha ting Tam, Chinese University of Hong Kong
Feifei Cheng, The Second Affiliated Hospital of Chongqing Medical University
Tinashe Chikowore, University of the Witwatersrand Faculty of Health Sciences
Monika Dudenhöffer-Pfeifer, Institutionen för Kliniska Vetenskaper, Malmö
Hugo Fitipaldi, Institutionen för Kliniska Vetenskaper, Malmö
Chuiguo Huang, Chinese University of Hong Kong
Sarah Kanbour, Aman Hospital
Sudipa Sarkar, Johns Hopkins University School of Medicine
Robert Wilhelm Koivula, University of Oxford Medical Sciences Division
Ayesha A. Motala, The Nelson R. Mandela Medical School
Sok Cin Tye, Universitair Medisch Centrum Groningen
Gechang Yu, Chinese University of Hong Kong
Yingchai Zhang, Chinese University of Hong Kong
Michele Provenzano, IRCCS Azienda Ospedaliero-Universitaria di Bologna
Diana Sherifali, McMaster University
Russell J. de Souza, McMaster University, Faculty of Health Sciences
Russell J. de Souza, McMaster University, Faculty of Health Sciences
Deirdre Kay Tobias, Harvard T.H. Chan School of Public Health
Paul W. Franks, Institutionen för Kliniska Vetenskaper, Malmö
Stephen S. Rich, University of Virginia School of Medicine
Robert Wagner, Deutsches Diabetes-Zentrum
Tina Vilsbøll, Steno Diabetes Center Copenhagen
Kimberly K. Vesco, Kaiser Permanente Center for Health Research
Miriam S. Udler, Massachusetts General Hospital
Tiinamaija Tuomi, Helsinki University Hospital
Arianne Sweeting, Faculty of Medicine and Health
Emily K. Sims, Indiana University School of Medicine
Jennifer L. Sherr, Yale School of Medicine
Robert K. Semple, Edinburgh Medical School
Rebecca M. Reynolds, Edinburgh Medical School
Maria J. Redondo, Baylor College of Medicine

Document Type

Article

Publication Date

12-1-2024

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.

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