Effect of low-carbohydrate diets on cardiometabolic risk, insulin resistance, and metabolic syndrome
Bibliographic record
Abstract
PURPOSE OF REVIEW: An obesity epidemic has resulted in increasing prevalence of insulin resistance, hyperinsulinemia, metabolic syndrome (MetS), and cardiovascular disease (CVD). The Diet-Heart Hypothesis posited that dietary fat is the culprit. Yet dietary fat reduction has contributed to the problem, not resolved it. The role of hyperinsulinemia, the genesis of its atherogenic dyslipidemia and systemic inflammation in CVD and its reversal is reviewed. RECENT FINDINGS: Overnutrition leads to weight gain and carbohydrate intolerance creating a vicious cycle of insulin resistance/hyperinsulinemia inhibiting fat utilization and encouraging fat storage leading to an atherogenic dyslipidemia characterized by hypertriglyceridemia, low HDL, and small dense LDL. The carbohydrate-insulin model better accounts for the pathogenesis of obesity, MetS, and ultimately type 2 diabetes (T2DM) and CVD. Ketogenic Diets reduce visceral obesity, increase insulin sensitivity, reverse the atherogenic dyslipidemia and the inflammatory biomarkers of overnutrition. Recent trials show very high adherence to ketogenic diet for up to 2 years in individuals with T2DM, reversing their metabolic, inflammatory and dysglycemic biomarkers as well as the 10-year estimated atherosclerotic risk. Diabetes reversal occurred in over 50% and complete remission in nearly 8%. SUMMARY: Therapeutic carbohydrate-restricted can prevent or reverse the components of MetS and T2DM.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".