Association of a Low-Carbohydrate High-Fat Diet With Plasma Lipid Levels and Cardiovascular Risk
Bibliographic record
Abstract
Low-carbohydrate high-fat (LCHF) diets have attracted interest for a variety of conditions. In some individuals, these diets trigger hypercholesterolemia. There are limited data on their effects on cardiovascular disease risk. To investigate the association between LCHF dietary patterns, lipid levels and incident major adverse cardiovascular events (MACE). In a cohort from the UK Biobank, participants with ≥ one 24-hour dietary questionnaire were identified. A LCHF diet was defined as <100g and/or <25% total daily energy (TDE) from carbohydrates/day and >45% TDE fat, with participants on a standard diet (SD) not meeting these criteria. Each LCHF case was age- and sex-matched 1:4 to SD individuals. From 2034 LCHF and 8136 SD identified participants, 305 LCHF and 1220 SD individuals completed an enrollment assessment concurrently with lipids collection. In this cohort, low-density lipoprotein-cholesterol (LDL-C) and apolipoprotein B (apoB) levels were significantly increased in the LCHF vs SD group (p<0.001). 11.1% LCHF and 6.2% SD individuals demonstrated severe hypercholesterolemia (LDL-C >5 mmol/L, p<0.001). After 11.8 years, 9.8% LCHF vs 4.3% participants experienced a MACE (p<0.001). This difference remained significant after adjustment for cardiovascular risk factors (HR 2.18, 95% CI 1.39-3.43, p<0.001). Individuals with an elevated LDL-C polygenic risk score had the highest concentrations of LDL-C on a LCHF diet. Similar significant changes in lipid levels and MACE associations were confirmed in the entire cohort and in ≥2 dietary surveys. Consumption of a LCHF diet was associated with increased LDL-C and apoB, and an increased risk of incident MACE.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".