An NHANES Analysis of 2005 - 2006 Data Examining the Relationship Between Diabetes Mellitus and Vitamin C Ingestion
Bibliographic record
Abstract
Background: Diabetes mellitus (DM) is one of the most common diseases afflicting the United States (U.S.) population. Vitamin C (Ascorbic acid) is considered to be one of the most potent anti-oxidants present in the body. Thus, we present the results of our epidemiologic analysis of whether Vitamin C ingestion is related to the development of diabetes mellitus. Methods: For the purpose of our study, we examined National Health and Nutrition Examination Survey (NHANES) data collected between 2005 and 2006. Of 10,34 8 participants who had data collected during this two-year period, 4979 did not meet any exclusion criteria, and were includ ed in our data analysis. We performed a multivariate logistic regression to find out whether plasma levels of vitamin C were correlated with the development of diabetes. Results: The unadjusted odds ratios of having diabetes in the four quartiles of Vitamin C starting from lowest to highest were 1, 0.87 (95% CI of 0.67 - 1.13), 0.62 (95% CI of 0.50 - 0.78) and 0.45 (95% CI of 0.359 - 0.557), respectively. When the analysis was adjusted for risk factors the odds ratios still showed a dose-response relation with odds ratios of 1, 0.79 (95% CI of 0.6-1.04), 0.58 (95% CI of 0.45-0.76) and 0.53 (95% CI of 0.43-0.63), respectively. Conclusion: Our study supports the hypothesis that higher plasma levels of Vitamin C levels are protective against the development of Diabetes Mellitus. Given the limitations of our study, a prospective, randomized stud y looking at Vitamin C ingestion to reach predefined serum levels is warranted to further investigate the necessary logistics of Vitamin C use in the prevention of chronic diseases such as diabetes. doi:10.4021/jem88e
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".