Resistencia a la insulina y su relación en el diagnóstico de diabetes tipo 2: revisión sistemática
Bibliographic record
Abstract
Insulin resistance is a metabolic disorder characterized by a decreased response of peripheral tissues to insulin, leading to compensatory hyperinsulinemia and promoting the development of type 2 diabetes mellitus (T2DM). This study aimed to identify, analyze, and synthesize the pathophysiological mechanisms, the main diagnostic biomarkers—especially the HOMA-IR index and triglyceride and glucose derivatives—and the methodologies used for the early detection of insulin resistance and its relationship to the diagnosis of T2DM. A systematic review was conducted following the PRISMA guidelines, with searches in databases such as Scopus, PubMed, and SciELO, applying defined inclusion criteria and assessing methodological quality using validated scales (PEDro, Newcastle-Ottawa, and AMSTAR-2). Of the studies analyzed, HOMA-IR was the most frequently used biomarker, with cutoff points between 2.1 and 2.8, while the TyG and TyG-BMI indices showed good diagnostic performance in Latin American populations. The results demonstrate that insulin resistance is strongly associated with visceral obesity, chronic inflammation, and metabolic dysfunction, and that its early detection allows for timely intervention to reduce the risk of progression to type 2 diabetes. It is concluded that, although there has been significant progress in alternative biomarkers, standardization of population-specific cutoff points is still needed to strengthen the clinical utility of these tools.
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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 | 0.035 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.028 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".