Polymorphic variants and risk of diabetic peripheral neuropathy in patients with type 2 diabetes mellitus: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Neuropathy is a frequent complication of diabetes mellitus, a disease that is growing exponentially worldwide. Genetic research has emerged as an important tool for better understanding its predisposition, although a systematic synthesis of existing evidence is needed to better comprehend its association. The objective of this review was to determine the association between polymorphic variants identified through massive genomic testing and the risk of peripheral diabetic neuropathy in patients with type 2 diabetes mellitus. METHODS: Inclusion criteria were case-control, cohort, and cross-sectional studies examining polymorphic variants and diabetic neuropathy (DNP) risk in type 2 diabetes, studies using GWAS, EWAS, or microarray for identifying genetic polymorphisms, studies involving adults, and articles in English or Spanish. Exclusion criteria included case reports, case series, ecological studies, editor letters, reviews, or secondary studies and conference abstracts. Exhaustive search in PubMed, Scopus, and Web of Science databases, using keywords. Risk of bias was determined through Newcastle-Ottawa scale. A qualitative synthesis of the results was performed (frequency), including meta-analysis where applicable (forest plot and funnel plot). RESULTS: The searching strategy identified 370 studies, from which 7 were chosen for the systematic review, included 9478 participants. The quality of the studies was mostly good, but a significant heterogeneity in methods was found. We identify a significant association between peripheral neuropathy and plenty of single nucleotide variants (SNVs). Just the SNV rs10555080 in the gene THEG5 showed a higher likelihood of neuropathy (OR:1,34; IC 95%: 1,19 - 1,49). DISCUSSION: This study faced limitations due to heterogeneity in DNP definitions, genotyping methods, and a focus on white and Arab populations, limiting generalization. Only English and Spanish articles were included, potentially excluding relevant research in other languages. Multiple SNVs were identified through genomic testing that were associated with peripheral diabetic neuropathy in patients with type 2 diabetes mellitus; however, the SNVs were not similar between studies. TRIAL REGISTRATION: This research received no funding and was registered in PROSPERO (ID CRD42024505256).
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".