The relationship between nutritional status and thyroid function among adults in the USA: NHANES 2007–2012
Bibliographic record
Abstract
Controlled Nutritional Status (CONUT) scores have been developed as quantitative tools that can be employed to gauge the nutritional status of individual patients. However, there has been very little research investigating the association between these CONUT scores and the function of the thyroid. As such, the present study was designed to address this research gap through the evaluation of a representative cohort of American adults. National Health and Nutrition Examination Survey (NHANES) data were herein used to separate subjects into those with normal nutritional status (CONUT score: 0–1) from those who were malnourished (CONUT scores > 1). Associations between these CONUT scores and the function of the thyroid were investigated through linear regression modeling, employing weighted analytical strategies and subgroup analyses. Overall, 8082 individuals from the NHANES 2007–2012 cohort were enrolled in this analysis. The weighted mean CONUT score for these individuals was 0.72 (0.02), with 6661 participants (weighted proportion: 83.12%) falling within the normal nutritional status group and 1421 participants (weighted proportion: 16.88%) within the malnourished group. In adjusted analyses, subjects who were malnourished were found to present with an increase in FT4 levels ( β = 0.033; p < 0.001 together with reduced TT3 levels ( β = −3.526; p = 0.01). The present data offer evidence in support of higher CONUT scores, which correspond to malnutrition, being related to increases in FT4 levels together with reductions in TT3 levels. More studies will be crucial to further probe the mechanistic drivers of these results.
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 | 0.002 |
| 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".