Prevalence and associated comorbidities of restless legs syndrome (RLS): Data from a large population-based door-to-door survey on 19176 adults in Tehran, Iran
Bibliographic record
Abstract
BACKGROUND: Discrepancies have been reported in the prevalence rate of restless legs syndrome (RLS) among different ethnic groups and geographic populations. Furthermore, there are disagreements on determinant factors and associated comorbidities of RLS. We aimed to estimate prevalence of RLS and investigate its associated comorbid conditions and risk factors in a large population-based door-to-door survey. METHODS: Following a multistage random sampling from the households lived in 22 urban districts of Tehran, Iran, 19176 participants with ≥30 years of age were recruited. Trained surveyors filled study checklist consisting of baseline characteristics, risk factors and comorbidity profile and the International RLS Study Group (IRLSSG) diagnostic criteria through face-to-face interviews. RESULTS: In total, 1580 individuals were positively screened for RLS resulting in a standardized prevalence rate of 60.0/1000. There was a gradual increase in RLS prevalence by advancing age, however, sex difference disappeared after adjustment. Parkinsonism [adjusted odds' ratio (adj-OR) = 7.4 (95% CI: 5.3-10.4)], peripheral neuropathy [adj-OR = 3.7 (95% CI: 3.3-4.1)], subjective cognitive impairment (SCI) [adj-OR = 3.1 (95% CI: 2.7-3.4)], acting out dreams [adj-OR = 2.8 (95% CI: 2.5-3.2)], hyposmia [adj-OR = 2.5 (95% CI: 2.2-2.9)], active smoking [adj-OR = 1.5 (95% CI: 1.3-1.9)] and additional number of cardiometabolic diseases associated with higher risk of RLS [adj-OR = 1.6 (95% CI: 1.2-2.3)]. CONCLUSION: Our findings showed that neuro-cognitive co-morbidities such as parkinsonism, peripheral neuropathy, SCI, acting out dreams and hyposmia as well as cardio-metabolic risk factors and diseases were independent determinants of RLS. It is recommended to screen individuals with either these comorbid conditions for RLS or the ones with RLS for the accompanying diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".