Prevalence of Genetically Associated Dilated Cardiomyopathy: A Systematic Literature Review and Meta-Analysis
Bibliographic record
Abstract
Background: Dilated cardiomyopathy (DCM) is a leading cause of heart failure and cardiac transplantation globally. Disease-associated genetic variants play a significant role in the development of DCM. Accurately determining the prevalence of genetically associated DCM (genetic DCM) is important for developing targeted prevention strategies. This review synthesized published literature on the global prevalence of genetic DCM across various populations, focusing on two of the most common variants: titin ( TTN ) and myosin heavy chain 7 ( MYH7 ). Methods: MEDLINE ® and Embase were searched from database inception to September 19, 2022 for English-language studies reporting the prevalence of genetic DCM within any population. Studies using family history as a proxy for genetic DCM were excluded. Results: Of 2,736 abstracts, 57 studies were included. Among the global adult or mixed (mostly adults with few pediatric patients) DCM population, median prevalence was 20.2% (interquartile range (IQR): 16.3-36.0%) for overall genetic DCM, 11.4% (IQR: 8.2-17.8%) for TTN -associated DCM, and 3.2% (IQR: 1.8-5.2%) for MYH7 -associated DCM. Global prevalence of overall pediatric genetic DCM within the DCM population was similar (weighted mean: 21.3%). Few studies reported data on the prevalence of genetic DCM within the general population. Conclusions: Our study identified variable prevalence estimates of genetic DCM across different populations and geographic locations. The current evidence may underestimate the genetic contributions due to limited screening and detection of potential DCM patients. Epidemiological studies using long-read whole genome sequencing to identify structural variants or non-coding variants are needed, as well as large cohort datasets with genotype-phenotype correlation analyses. Cardiol Res. 2024;15(4):233-245 doi: https://doi.org/10.14740/cr1680
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".