Diverging Global Burden, Risk Factors, and Temporal Trends of Early-Onset and Late-Onset Dementia: A Comprehensive Analysis of the Global Burden of Disease Study 2021
Bibliographic record
Abstract
INTRODUCTION: Early-onset dementia (EOD) and late-onset dementia (LOD) differ in etiology, clinical presentation, and societal impact. Characterizing their global burden and associated risk factors is essential for guiding targeted interventions and resource allocation. METHODS: We analyzed data from the Global Burden of Disease (GBD) 2021 study (1990-2021) to estimate incidence, mortality, and disability-adjusted life years (DALYs) for EOD (ages 40-64) and LOD (≥65 years) across 204 countries and territories. Age-standardized rates (ASRs) and estimated annual percentage changes (EAPCs) were calculated. Risk factors examined included smoking, high fasting plasma glucose (FPG), and high body mass index. Associations with the sociodemographic index (SDI) were assessed, and Bayesian age-period-cohort model was used to project trends to 2050. RESULTS: In 2021, global age-standardized incidence rates (ASIRs) were 63.58 per 100,000 for EOD and 1,173.19 per 100,000 for LOD. Corresponding age-standardized mortality rates (ASMRs) and age-standardized DALY (AS-DALY) rates were substantially higher for LOD. Point estimates for ASRs were consistently higher for females than for males across all metrics. From 1990 to 2021, the burden of EOD increased (ASIR EAPC = 0.13; ASMR EAPC = 0.08; AS-DALY EAPC = 0.08), while LOD trends remained largely stable. Regional variation was observed, with EOD burden highest in China, Canada, Brazil, and the USA and LOD in China, Germany, Italy, and Lebanon. Metabolic risks, particularly high FPG, were major contributors to LOD. SDI was positively correlated with LOD burden (r = 0.176; p < 0.001), indicating a weak but statistically significant association. Projections indicate that EOD cases will increase by 160% and LOD cases by 479% by 2050. CONCLUSIONS: The global dementia burden is rising, with distinct epidemiological patterns for EOD and LOD. Age- and region-specific risk factor profiles highlight the need for tailored prevention strategies, informed healthcare planning, and differentiated clinical management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".