Evidence map›Paper›PMID 42769512›Full record

ArticleAmerican journal of translational research2026

A nomogram for predicting insulin resistance in polycystic ovary syndrome based on serum amino acid profiles and anti-Müllerian hormone.

Yan Zhao, Haiyang Wang, Cuina Zhang, Hongmei He

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Article in American journal of translational research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Yan ZhaoDepartment of Laboratory, The Fourth Hospital of Shijiazhuang Shijiazhuang 050031, Hebei, China.
Haiyang WangOperating Room, Hebei Province Second Rongjun Youfu Hospital Shijiazhuang 050051, Hebei, China.
Cuina ZhangDepartment of Nephrology with Integrated Chinese and Western Medicine, Shijiazhuang Hospital of Traditional Chinese Medicine Shijiazhuang 050051, Hebei, China.
Hongmei HeDepartment of Laboratory, The Fourth Hospital of Shijiazhuang Shijiazhuang 050031, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesInsulin resistance (IR) worsens metabolic and reproductive outcomes in polycystic ovary syndrome (PCOS). This study aimed to develop and validate a nomogram incorporating serum amino acid profiles and anti-Müllerian hormone (AMH) for predicting IR in PCOS.

methodsA retrospective observational analysis was performed on 434 PCOS patients treated at The Fourth Hospital of Shijiazhuang between January 2022 and January 2026. Patients were classified as IR+ (n=184) or IR- (n=250) using a homeostasis model assessment of insulin resistance (HOMA-IR) cutoff of 2.69 and were chronologically assigned to training (n=259) and validation (n=175) cohorts. Anthropometric parameters, hormone levels and serum amino acid concentrations were measured. Multivariable logistic regression identified independent predictors for nomogram development. Receiver operating characteristic (ROC) analysis, calibration plot, bootstrap resampling and decision curve analysis (DCA) were applied to evaluate the model's performance.

resultsMultivariable analysis identified body mass index (odds ratio [OR]=1.357, 95% confidence interval [CI]: 1.180-1.561), waist circumference (OR=1.096, 95% CI: 1.053-1.141), AMH (OR=0.931, 95% CI: 0.879-0.986), leucine (OR=1.028, 95% CI: 1.012-1.044), isoleucine (OR=1.075, 95% CI: 1.045-1.107), valine (OR=1.015, 95% CI: 1.009-1.022) and tyrosine (OR=1.046, 95% CI: 1.004-1.089) as independent predictors. The nomogram demonstrated excellent discrimination in training (area under the curve [AUC]=0.945) and validation (AUC=0.917) cohorts, with good calibration (Hosmer-Lemeshow P=0.554) and clinical utility on DCA.

conclusionThis nomogram, integrating anthropometric measures, serum AMH and four amino acids, provides an accurate tool for the early identification of IR in PCOS.

Indexed as

amino acidsanti-Mullerian hormoneinsulin resistancenomogramsPolycystic ovary syndrometargeted amino acid profiling

Identifiers

PMID42769512
PMCPMC13590987

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