Prevalence of Iron Deficiency Among Senegalese Adolescents Aged 10–19 Years: A National Representative Study
Bibliographic record
Abstract
Objectives: To determine the associations of eating window with all-cause, cardiovascular, and cancer mortality among US adults.Methods: Cohort analysis of 33,052 participants aged >19 from NHANES (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) with available mortality data from NCHS.Dietary data were collected via 24-hour food recalls, which enabled us to determine eating window as the time elapsed between the first and last consumption of any food/ beverage containing more than 0 calories.We employed surveyweighted Cox regression with Restricted Cubic Spline (RCS) to model the relationship between eating window and mortality.Our models were adjusted for various demographic, lifestyle, and health factors.Results: Over 8 years, all-cause, cardiovascular, and cancer deaths occurred in 4,158 (8.9%), 1,277 (2.6%), and 989 (2.2%) participants, respectively.Our fully adjusted RCS model showed a non-linear U-shaped relationship between eating window and mortality, with moderate eating windows (~11-12 hours) linked to the lowest risks (p¼0.004).In contrast, shorter eating windows of 8 hours/day were associated with a significantly higher risk of all-cause mortality, with hazard ratios (HR) of 1.3 or higher in the fully adjusted RCS model, especially evident in older adults.Shorter eating windows were associated with over a 50% higher cardiovascular mortality among older adults, men, and White individuals.Extended eating windows (15 hours/ day) were associated with greater risk of all-cause mortality across the study population (HR: 1.25; 95% CI:1.01-1.55).This risk was particularly greater among Whites and was primarily driven by higher cardiovascular mortality.Conclusions: Our study highlights the complexity of dietary timing and its association with mortality, with a moderate eating window of ~11-12 hours/day being associated with the lowest mortality risks, suggesting that deviations from this range may have potential health implications.The observed variations in associations between eating windows and mortality across different population subgroups suggest the need for more personalized dietary recommendations.Further long-term evidence from intervention studies in large, diverse populations is essential to establish causal relationships and elucidate underlying mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".