ReviewHerz2025
Big data and cardiovascular risk-insights into obesity, diabetes, and coronary heart disease.
Review in Herz, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Combined B-vitamin supplementation on homocysteine and vascular outcomes in coronary heart disease: a meta-analysis.Annals of medicine · 2026Pooled it
- Folic Acid Therapy is Associated with a Lower Risk of Incident Diabetes and Better Glycemic Status in Patients with Coronary Heart Disease: Findings from a Multicenter Propensity Score-Matched Analysis.Drug design, development and therapy · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiovascular diseases (CVD) remain a major global health burden. Obesity and type 2 diabetes mellitus (T2DM) are key modifiable risk factors for coronary heart disease (CHD). The emergence of big data has revolutionized cardiovascular research by enabling deeper risk stratification and detection of complex interactions among clinical, lifestyle, and molecular variables.
objectiveThis article reviews how big data has advanced our understanding of the links between obesity, T2DM, and CHD. It highlights key findings from large cohort studies and international consortia as well as methodological innovations transforming cardiovascular epidemiology.
resultsThe data reveal that obesity and diabetes show significant regional differences in prevalence and incidence and are associated with other risk factor such as hypertension. Large-scale cohorts and consortia have confirmed that diabetes substantially increases CVD and mortality risk two- to fourfold and is linked to an up to 75% higher mortality rate, with earlier onset and poor glycemic control worsening outcomes. Novel approaches, including polygenic risk scores, machine learning, and real-world data integration, have improved prediction and causal inference. The interplay between obesity and diabetes is a major driver of CHD burden.
conclusionBig data has enhanced our understanding of cardiovascular risks associated with obesity and diabetes, improved risk prediction models, and provided a foundation for precision prevention strategies. Continued investment in large cohorts, data harmonization, and digital health tools is essential in order to translate these insights into effective public health strategies and reduce the global CVD burden.
Indexed as
Identifiers
40553126What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.