Lipid distributions in the Global Diagnostics Network across five continents
Bibliographic record
Abstract
AIMS: Lipids are central in the development of cardiovascular disease, and the present study aimed to characterize variation in lipid profiles across different countries to improve understanding of cardiovascular risk and opportunities for risk-reducing interventions. METHODS AND RESULTS: This first collaborative report of the Global Diagnostics Network (GDN) evaluated lipid distributions from nine laboratory organizations providing clinical laboratory testing in 17 countries on five continents. This cross-sectional study assessed aggregated lipid results from patients aged 20-89 years, tested at GDN laboratories, from 2018 through 2020. In addition to mean levels, the World Health Organization total cholesterol risk target (<5.00 mmol/L, <193 mg/dL) and proportions in guideline-based low-density lipoprotein cholesterol (LDL-C) categories were assessed. This study of 461 888 753 lipid results found wide variation by country/region, sex, and age. In most countries, total cholesterol and LDL-C peaked at 50-59 years in females and 40-49 years in males. Sex- and age-group adjusted mean total cholesterol levels ranged from 4.58 mmol/L (177.1 mg/dL) in the Republic of Korea to 5.40 mmol/L (208.8 mg/dL) in Austria. Mean total cholesterol levels exceeded the World Health Organization target in Japan, Australia, North Macedonia, Switzerland, Germany, Slovakia, and Austria. Considering LDL-C categories, North Macedonia had the highest proportions of LDL-C results >4.91 mmol/L (>190 mg/dL) for both females (9.9%) and males (8.7%). LDL-C levels <1.55 mmol/L (<60 mg/dL) were most common among females in Canada (10.7%) and males in the UK (17.3%). CONCLUSION: With nearly a half billion lipid results, this study sheds light on the worldwide variability in lipid levels, which may reflect inter-country differences in genetics, lipid testing, lifestyle habits, and pharmacologic treatment. Despite variability, elevated atherogenic lipid levels are a common global problem, and these results can help inform national policies and health system approaches to mitigate lipid-mediated risk of cardiovascular disease.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".