The global burden of disease attributable to high fasting plasma glucose in 204 countries and territories, 1990–2019: An updated analysis for the Global Burden of Disease Study 2019
Bibliographic record
Abstract
AIMS: High fasting plasma glucose (HFPG) is an independent risk factor for several adverse health outcomes and has become a serious public health problem. We aimed to evaluate the spatial pattern and temporal trend of disease burden attributed to HFPG from 1990 to 2019 using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. MATERIALS AND METHODS: Using data from GBD 2019, we estimated the numbers and age-standardized rates of deaths and disability-adjusted life years (DALYs) attributed to HFPG by calendar year, age, gender, country, region, Socio-demographic Index (SDI), and specific causes. The joinpoint regression analysis was used to assess the temporal trends of deaths and DALYs from 1990 to 2019. RESULTS: In 2019, globally, the numbers of deaths and DALYs attributable to HFPG were approximately 6.50 million and 172.07 million, respectively, with age-standardized rates of 83.00 per 100,000 people and 2104.26 per 100,000 people, respectively. From 1990 to 2019, the global numbers of deaths and DALYs attributed to HFPG have over doubled. The age-standardized rate of DALYs showed an increasing trend, particularly in males and in regions with middle SDI or below. The leading causes of the global disease burden attributable to HFPG in 2019 were diabetes mellitus, ischaemic heart disease, stroke, and chronic kidney disease. CONCLUSIONS: HFPG is an important contributor to increasing the global and regional disease burden. Necessary measures should be taken to curb the growing burden attributed to HFPG, particularly in males and in regions with middle SDI or below.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".