0760 Sleep Health Profiles Across Six Harmonized Cohorts and Their Association with Future Depressive Symptoms
Bibliographic record
Abstract
Abstract Introduction Depression is a leading cause of disability in older adults, yet it remains underdiagnosed and undertreated. Establishing common multivariable sleep health profiles may eventually help to identify at-risk older adults and match them with appropriate treatments. However, profiles identified across cohorts often differ because of inconsistent measures and methods, hampering progress towards large-scale initiatives. We aimed to overcome this challenge using harmonized data across six cohorts. Methods We harmonized five self-report sleep health indicators across six epidemiologic cohorts of adults aged >60 from the United States and Netherlands (N=613 - 3,123). We performed latent class analysis in each cohort. Generalizability and comparability of findings were assessed using several indices, including cluster stability and novel distance metrics. Generalized linear mixed-effects modeling was used to relate sleep health profiles to the risk of increased depressive symptoms over time in each cohort. Results Two sleep health profiles were common across all cohorts: ‘Good Sleep’ (GS; average sleep duration, high quality and efficiency) and ‘Poor Sleep’ (PS; short sleep duration, low quality and efficiency, high daytime sleepiness). Three cohorts indicated an ‘Inefficient Sleep’ profile (IS) and three cohorts indicated a ‘Long Sleep’ profile (LS). PS had high and sustained depressive symptoms over 3-15 years of follow-up, especially relative to GS and IS (Risk Ratios [RRs]=1.47-3.44 for PS vs.GS; 1.75-2.32 for PS vs. IS; and 1.10-1.15 for PS vs. LS). Conclusion GS and PS profiles were generalizable across cohorts. Older adults with the PS profile had heightened depressive symptoms over multiple years of follow-up, especially relative to GS and IS profiles. Next steps involve quantifying the impact of screening using sleep health profiles and gathering evidence that interventions targeting sleep health profiles reduce onset or severity of depression. Support (if any) Wallace (RF1AG056331); Rush Alzheimer’s Disease Center (R01AG017917, R01AG022018); Osteoporotic Fractures in Men Study (U01AG027810, U01AG042124, U01AG042139, U01AG042140, U01AG042143, U01AG042145, U01AG042168, U01AR066160, UL1TR000128, R01HL071194, R01HL070848, R01HL070847, R01HL070842, R01HL070841, R01HL070837, R01HL070838, R01HL070839); Study of Osteoporotic Fractures (R01AG005407, R01AR35582, R01AR35583, R01AR35584, R01AG005394, R01AG027574, R01AG027576, and R01AG026720); Rotterdam Study (Erasmus Medical Center and Erasmus University, Netherlands Organization for the Health Research and Development, Ministries of Education and Health, European Commission, Rotterdam Municipality).
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".