The Associations between Blood and Urinary Concentrations of Metal Metabolites, Obesity, Hypertension, Type 2 Diabetes, and Dyslipidemia among US Adults: NHANES 1999–2016
Bibliographic record
Abstract
Background. Heavy metals are well known to be associated with cancer outcomes, but its association with obesity and cardiometabolic risk outcomes requires further study. Methods. Adult data from the National Health and Examination Survey (NHANES Continuous 1999–2016, n = 12,636 to 32,012) with data for blood or urinary metals concentrations and body mass index were used. The study aim was twofold: (1) to determine the association between heavy metals and obesity and (2) to examine the influence of heavy metals on the relationship between obesity and hypertension, type 2 diabetes, and dyslipidemia. Logistic regression was used to examine the main effects and interaction effects of metals and obesity for the odds of prevalent hypertension, type 2 diabetes, and dyslipidemia. Models were adjusted for age, gender, ethnicity, smoking status, physical active status, and poverty-income ratio, with additional adjustment for creatinine in models with the urinary measures of heavy metals. High-low concentration categories were defined by grouping metal quintiles with the most similar associations with obesity. Results. Blood lead had a negative linear association with obesity (odds ratio (OR) = 0.42, 95% confidence interval (CI) = 0.37–0.47). In those with obesity, high blood lead was associated with lower risk of prevalent dyslipidemia, while no association was found in those without obesity. The study observed a curvilinear relationship between urinary antimony and obesity with the moderate group having the highest odds of obesity (OR = 1.36, 1.16–1.59). However, the relationship between urinary antimony and prevalent hypertension and dyslipidemia risk was linear, positive, and independent of obesity. While not associated with prevalent obesity risk, high urinary uranium was associated with 30% ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>P</a:mi> <a:mo>=</a:mo> <a:mn>0.01</a:mn> </a:math> ) higher odds for prevalent type 2 diabetes. Conclusions. The impact of environmental factors on obesity and health may be complex, and this study reinforces the heterogeneous relationship between various metals, obesity, and obesity-related metabolic diseases even at levels observed in the general population.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".