Association between Dietary Inflammatory Index Scores and Diabetes Sensorimotor Polyneuropathy in Patients with Type 2 Diabetes Mellitus: A Case-Control Study
Bibliographic record
Abstract
Background. Diabetes sensorimotor polyneuropathy (DSPN) is a common complication of diabetes. Diet has been previously related to DSPN. However, no studies have investigated the relationship between the inflammatory potential of the whole diet and DSPN. So, we aimed to examine the association between dietary inflammatory index (DII) and DSPN in Iranian adults. Methods. A total of 185 subjects with DSPN and 185 sex- and age-matched controls were selected in this case-control study. A 168-item validated food frequency questionnaire (FFQ) was used to assay dietary intakes. DII was calculated based on the developed formula. The Toronto clinical neuropathy score was applied to define DSPN. Binary logistic regression was used to estimate the odds ratios (ORs) and 95% confidence intervals (95% CIs) of DII in relation to DSPN. Results. Mean values of age and BMI for all the participants were reported as 50.79 and 28.60, respectively. Also, the median (IQR) of DII for all the participants was estimated as −0.091 (−0.93, 1.07). Our findings suggest that participants in the highest quartile of the DII had higher odds of DSPN (OR = 1.76; 95% CI: 1.03, 3.36) ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>p</a:mi> </a:math> -trend = 0.01) compared to subjects in the lowest quartile of DII scores after adjustment for age and sex. Additionally, a strong association was observed after adjusting for energy intake, physical activity, education, smoking status, economic status, marital status, job, BMI, and WC in model 2 (OR = 2.23, 95% CI = 1.13–4.39) ( <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>p</c:mi> </c:math> -trend = 0.0048). Conclusion. Higher DII score was associated with an increased risk of DSPN. Therefore, it is possible that a diet rich in anti-inflammatory nutrients and foods could improve and prevent DSPN.
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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) | 0.001 | 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".