Estimating the Prevalence of Hypercholesterolemia in Indigenous Populations
Bibliographic record
Abstract
Hypercholesterolemia is a common condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C) and increased risk of atherosclerotic cardiovascular disease (ASCVD). Indigenous populations experience disproportionate rates of ASCVD, however, the extent to which hypercholesterolemia contributes to this burden is unknown. This study aimed to estimate the prevalence of hypercholesterolemia, severe hypercholesterolemia, and familial hypercholesterolemia (FH) in Indigenous populations in Canada, the United States, Australia, and New Zealand. We searched MEDLINE, EMBASE, Web of Science, Native Health Database, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews for peer-reviewed studies reporting on hypercholesterolemia and elevated LDL-C in Indigenous populations. All diagnostic criteria used to classify hypercholesterolemia were included. Pooled prevalence and 95% CIs were calculated using a random-effects model. There were no studies reporting the prevalence of FH and one study reporting the prevalence of severe hypercholesterolemia in Indigenous populations. The pooled prevalence of hypercholesterolemia was 28.9% or ∼1 in 3 to 1 in 4 individuals (95% CI: 22.4%-36.4%) and 12.6% (95% CI: 7.7%-19.9%) using an LDL-C cutoff of ≥3.5 mmol/L (135 mg/dL). The pooled prevalence in Indigenous populations in North America was 24.3% (95% CI: 17.1%-33.3%) compared with 40.0% (95% CI: 31.3%-49.3%) in Australia. Meta-regression showed diabetes had a significant effect on prevalence (P = 0.022). Hypercholesterolemia is prevalent in Indigenous communities and may contribute to the high burden of ASCVD these populations face. There is insufficient research on FH and severe hypercholesterolemia in Indigenous populations worldwide.
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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 | 0.022 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".