ArticleJournal of human reproductive sciences
Utility of Visceral Adiposity Index and Lipid Accumulation Products to Define Metabolically-Unhealthy Polycystic Ovary Syndrome in Asian Indian Women - A Cross Sectional Study.
Article in Journal of human reproductive sciences. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
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Who cites it
4 citing papers in PubMed, 2 syntheses or guidelines pooled it, 7 citations in OpenAlex.
- The accuracy of lipid accumulation product to predict metabolic syndrome in PCOS: a meta-analysis and comparative analysis with other indicators.Frontiers in endocrinology · 2026Pooled it
- Lipid Accumulation Product Index as a Marker of Metabolic Syndrome in Women With Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis.Endocrinology, diabetes & metabolism · 2025Pooled it
- Visceral Adiposity Index (VAI) Levels and Metabolic Risk Across Phenotypes of Polycystic Ovary Syndrome (PCOS).Medicina (Kaunas, Lithuania) · 2025Article
- Visceral Adiposity Index in Relation to Rotterdam Phenotypes of Polycystic Ovary Syndrome.Biomedicines · 2025Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Polycystic ovary syndrome (PCOS) women are at risk of developing diabetes, cardiovascular disease and metabolic syndrome (MetS) due to insulin resistance (IR) and hyperandrogenism (HA). Both visceral adiposity index (VAI) and lipid accumulation product (LAP) are simple outpatient department-based metric tools that have been introduced to screen PCOS women who are metabolically unhealthy and are at risk of development of MetS. Aims: The aim of the study was to evaluate VAI and LAP in women with PCOS and to correlate them with metabolic and endocrine markers. The study also assessed these parameters amongst different PCOS phenotypes and determined their usefulness to define metabolically healthy PCOS (MH-PCOS) and metabolically unhealthy PCOS (MU-PCOS). Settings and Design: The design of the study was a cross-sectional study. Materials and Methods: Two hundred PCOS women were included in the study, and all the clinical, anthropometric, hormonal, biochemical and metabolic markers were assessed. The cohort was divided into MH-PCOS and MU-PCOS by the modified National Cholesterol Education Programme criteria. VAI and LAP were calculated and correlated with clinical, endocrine and metabolic parameters. Statistical Analysis Used: Univariate and multivariate logistic regression analysis was used to study the independent role of VAI and LAP to predict MetS. Adjusted and unadjusted odds ratios were calculated. Receiver-operating characteristic (ROC) analysis was done to define cut-offs in Asian Indian women. Results: VAI and LAP had good ability to correctly discriminate MU-PCOS from MH-PCOS (area under the curve [AUC] [95% confidence interval (CI)]: 0.89 [0.82-0.95]) and (AUC [95% CI [0.81-0.92] =0.86) using ROC, respectively. The sensitivity of VAI and LAP corresponding to the optimal cut-off of ≥2.76 and ≥48.06 (Youden) was 84.09% and 79.55%, respectively. Similarly, the specificity of VAI and LAP was 85.26% and 79.49%, respectively. VAI has a positive predictive value of 61.7% (95% CI [23.7%-40.3%]) and a negative predictive value of 95% (95% CI [88%-99.1%]). LAP has a positive predictive value of 53% (95% CI [40.3%-65.4%]) and a negative predictive value of 93.3% (95% CI [87.6%-96.9%]). PCOS women having VAI ≥ 2.76 had 19.3 times ([95% CI: 6.50-57.70]) more chance of developing MetS. PCOS women having LAP (≥48.06) have 3.7 times ([95% CI: 1.35-10.60]) more odds. There was no difference between ROC curves of VAI and LAP ( Conclusion: VAI cut-off ≥ 2.76 and LAP with a cut-off of ≥ 48.06 may be used as markers for predicting MetS amongst PCOS women.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.