Prevalence, incidence, and definition of severe hypertriglyceridemia: A comprehensive review and weighted summary
Bibliographic record
Abstract
BACKGROUND: Guidelines vary in defining severe hyperglyceridemia (sHTG), affecting estimates of disease burden. This review examined sHTG definitions in clinical guidelines and prevalence and incidence in the general adult population across countries. METHODS: Embase and MEDLINE® were searched on 14 March, 2025 to identify guidelines on sHTG definitions and studies on prevalence/incidence. Prevalence estimates were stratified by triglyceride (TG) threshold to define sHTG type, with studies having substantial bias or methodological issues excluded from analysis. RESULTS: The most commonly used threshold for defining sHTG among 18 identified guidelines was TG > 500 mg/dL. Overall pooled prevalence estimates for unspecified/mixed (primary or secondary/acquired) sHTG defined as TG > 500 mg/dL, TG > 886 mg/dL, and TG > 1000 mg/dL were 1:88 (1.14%), 1:526 (0.19%), and 1:556 (0.18%), respectively. Population-based studies on primary sHTG were limited, with the United States (US) reporting prevalence of 1:125 (0.80%) and Spain 1:667 (0.15%) for sHTG defined as TG > 500 mg/dL. Incidence data were also scarce; for unspecified/mixed sHTG, Canada reported cumulative incidence of 1:400 adults for TG 886 to 1771 mg/dL and 1:2500 for TG > 1771 mg/dL, while Denmark reported incidence of 39 per 100,000 person-years for TG > 886 mg/dL. A US study reported 24 per 100,000 person-years for primary sHTG with TG > 500 mg/dL. CONCLUSION: This review highlights variability in sHTG definitions and prevalence/incidence across regions, with the US and China showing higher prevalence of sHTG than Europe. Standardization of nomenclature, definitions, and TG thresholds is necessary to improve comparability. Longitudinal studies should be conducted to identify patients with persistently elevated TGs to obtain more accurate estimates of sHTG incidence.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.023 | 0.026 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".