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Indian journal of endocrinology and metabolism

January 1, 2026 · via Europe PMC

Evaluation of Serum Irisin Levels in Individuals with Gender Incongruence and Its Metabolic Correlations.

Kamath PU, Golamari UR, Nair AS, Kalra P.

research-articleOpen access

AI summary — for doctors, generated from the abstract only

Serum irisin correlated with some metabolic parameters in transgender individuals but had limited value for predicting metabolic syndrome.

What was studied

A cross-sectional study of 123 age- and BMI-matched transgender and cisgender participants measuring serum irisin and metabolic indices and assessing prediction of metabolic syndrome using affirmed and birth-assigned gender criteria.

Key findings

  • In transgender women, irisin inversely correlated with diastolic blood pressure (r = -0.307, P = 0.036)
  • In transgender men, irisin inversely correlated with HDL (r = -0.339) and prolactin (r = -0.357)
  • Irisin had limited predictive value for metabolic syndrome; specific statistic not fully captured in the abstract
  • Triglyceride/HDL and triglyceride-glucose indices offered better prediction

Clinical relevance

Suggests irisin is not a useful metabolic syndrome predictor in this population and that affirmed-gender criteria may aid risk assessment; the small sample limits conclusions.

Who should care

Endocrinology

Limitations

Small cross-sectional study; AUC values are truncated in the abstract.

Irisin is a weak predictor of metabolic syndrome in transgender individuals.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.

Abstract

Introduction

Irisin, a myokine implicated in energy homeostasis, has been proposed as a biomarker for metabolic dysfunction. This study aimed to assess serum irisin levels and their associations with metabolic parameters in gender-incongruent individuals compared to healthy cisgender controls and to assess the predictive value of irisin and related metabolic indices for metabolic syndrome (MetS) using both affirmed and birth-assigned gender criteria.

Methods

A cross-sectional "observational, non-randomised" study was conducted from June 2024 to February 2025 among 123 participants (transgender women, transgender men, cisgender women and cisgender men), matched for age and body mass index. Individuals with confounding metabolic conditions were excluded. Serum irisin was measured using a sandwich enzyme-linked immunosorbent assay, and metabolic parameters were evaluated. Statistical analyses assessed the diagnostic utility of irisin and various metabolic indices for MetS.

Results

In transgender women, serum irisin showed inverse correlation with diastolic blood pressure (r = -0.307, P = 0.036). In transgender men, it was inversely correlated with high-density lipoprotein (HDL) cholesterol (r = -0.339, P = 0.046) and prolactin (r = -0.357, P = 0.035). Receiver operating characteristic analysis revealed a limited predictive value of irisin for MetS (area under the curve (AUC) <0.5). The triglyceride (Ty)/HDL index performed better in transgender women (AUC = 0.748) and the triglyceride-glucose index (Ty/G index) in transgender men (AUC = 0.67).

Conclusion

Serum irisin is associated with select metabolic parameters but has limited utility in predicting MetS in gender incongruent individuals. Affirmed gender-based assessment enhances risk stratification, with Ty/HDL and Ty/G indices offering better predictive value in transgender populations.

DOI

10.4103/ijem.ijem_631_25

PMID

41918608

PMCID

PMC13035306

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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