Association Between Platelet-to-Lymphocyte Ratio and Insulin Resistance in Cancer Survivors: A Cross-Sectional Study Based on the NHANES 2005 - 2018
Notice bibliographique
Résumé
Background: The incidence of insulin resistance (IR) among cancer survivors is significantly higher than that in the general population. Current diagnostic methods for IR are complex, often requiring fasting blood samples, specialized laboratory tests, and sometimes invasive procedures, which limit their routine clinical application. This study investigated the association between platelet-to-lymphocyte ratio (PLR), a readily available marker from routine blood tests, and IR in cancer survivors, aiming to find a more straightforward predictor of IR that could potentially simplify screening and monitoring processes in this high-risk population. Methods: This cross-sectional study analyzed data from 1,418 cancer survivors from the NHANES 2005-2018 database. IR was assessed by three indicators: homeostasis model assessment of insulin resistance (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), and the triglyceride-glucose (TyG) index. Multivariable logistic regression models were used to examine the relationship between PLR quartiles and IR, with restricted cubic spline (RCS) analyses to evaluate non-linear relationships. Covariates included demographic (age, gender, race/ethnicity), socioeconomic (marital status, education, family poverty income ratio), lifestyle (smoking status, alcohol consumption, sleep duration, physical activity) and health status variables (body mass index (BMI), history of cardiovascular disease, hypertension status). Subgroup analyses were conducted to identify sensitive populations. Results: The 1,418 cancer survivors (weighted population: 20,233,847; median age 69.0 years; 58.4% female) with low HOMA-IR (< 2.5), high QUICKI (≥ 0.33) and low TyG index (< 4.68) demonstrated more favorable metabolic profiles, including lower PLR values, better socioeconomic status, better lifestyle, and lower chronic disease burden. Compared to the lowest PLR quartile (Q1), individuals in the highest quartile (Q4) showed significantly increased IR risk when assessed by QUICKI (hazard ratio (HR) = 1.61, 95% confidence interval (CI): 1.02 - 2.53, P = 0.040) and the TyG index (HR = 1.56, 95% CI: 1.02 - 2.45, P = 0.035). Each increment in PLR quartile was associated with a 19% (QUICKI) and 15% (TyG) increased risk of IR. Receiver operating characteristic (ROC) analysis results indicated that PLR cutoff values in the range of 124 - 137 can serve as screening thresholds for IR in cancer survivors. In the subgroup analysis, the PLR and IR showed no significant interaction across all examined subgroup characteristics in both the HOMA-IR and QUICKI models (P > 0.05). However, in the TyG index model, individuals in the Q4 compared to the Q1 demonstrated greater susceptibility to IR in those with physical activity < 600 metabolic equivalent of task (MET)-mins/week (odds ratio (OR) = 2.28, 95% CI: 1.12 - 4.61); and those with a history of hypertension (OR = 1.95, 95% CI: 1.05 - 3.63), with P values for interaction of 0.007 and 0.036, respectively. Conclusions: Higher PLR levels indicated a significantly increased risk of IR in cancer survivors, especially when assessed by QUICKI and the TyG index. This relationship showed no significant interaction between subgroups in the HOMA-IR and QUICKI models, but in the TyG index model, and the association was more pronounced among individuals with low physical activity and those with a history of hypertension. As a simple, cost-effective biomarker derived from routine blood tests, PLR offers potential clinical value for the assessment of IR risk in cancer survivors, especially in high-risk subgroups.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».