Prevalence and Treatment of Familial Hypercholesterolemia and Severe Hypercholesterolemia in Older Adults in Ontario, Canada
Bibliographic record
Abstract
Background: A simplified Canadian definition was recently developed to enable identification of individuals with familial hypercholesterolemia (FH) and severe hypercholesterolemia in the general population. Our objective was to use a modified version of this new definition to assess contemporary disease prevalence, treatment patterns, and low-density lipoprotein cholesterol (LDL-C) control in Ontario, Canada. Methods: eam (CANHEART) database, which was created by linking 19 population-based health databases in Ontario. Hypercholesterolemia was identified using LDL-C values. Cholesterol reduction and lipid-lowering treatment were assessed at time of diagnosis and after at least 2 and 5 years' follow-up. Results: Among 922,464 individuals, 2440 (0.26%) met criteria for definite or probable FH, and 72,893 (7.90%) for severe hypercholesterolemia. At diagnosis, mean LDL-C concentration was 9.52 mmol/L for those with definite FH, 5.83 mmol/L for those with probable FH, 5.73 mmol/L for those with severe hypercholesterolemia, and 3.33 mmol/L for all other individuals. After > 5 years, LDL-C concentration remained elevated at 3.58 mmol/L for those with definite FH, 2.72 mmol/L for those with probable FH, and 2.93 mmol/L for those with severe hypercholesteremia. Use of statin therapy was initially high (83% of those with definite FH, 78% of those with probable FH, 62% of those with severe hypercholesterolemia); however, fewer patients remained on statins at follow-up at > 5 years (62% of those with definite FH, 67% of those with probable FH, 58% of those with severe hypercholesterolemia). Conclusions: Among older Ontarians, we estimated that 1 in 378 individuals had FH, and 1 in 13 had severe hypercholesterolemia. Despite being at substantially increased cardiovascular risk, these patients acheived suboptimal LDL-C level control and fewer were on medical therapy at follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".