Evidence map›Paper›PMID 41446289›Full record

ArticleFrontiers in psychiatry2025

Quantitative computed tomography for assessing body composition in schizophrenia: a potential indicator of insulin resistance.

Xinping Kuai, Fangsong Zhang, Mengyang Han, Jianye Zhang, Chanjun Ding, Lili Yuan, Qicheng Yuan, Lei Chen, Ziyue Xu, Xuexue Wang and 1 more

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Xinping KuaiDepartment of Radiology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Fangsong ZhangDepartment of Radiology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Mengyang HanDepartment of Radiology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jianye ZhangDepartment of Radiology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Chanjun DingDepartment of Radiology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lili YuanDepartment of Radiology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Qicheng YuanDepartment of Radiology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lei ChenDepartment of Radiology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ziyue XuDepartment of Radiology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xuexue WangDepartment of Radiology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jinhong WangDepartment of Radiology, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To investigate the relationship between the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) index and organ body composition, and to evaluate the potential of quantitative computed tomography (QCT) parameters as biomarkers for indicating insulin resistance (IR) in schizophrenia. Materials and methods: A total of 924 patients with schizophrenia were enrolled and categorized into non-IR (HOMA-IR ≤ 2.5) and IR (HOMA-IR > 2.5) groups. Quantitative computed tomography (QCT) systematically measured parameters such as bone mineral density (BMD), hepatic fat fraction, and areas of subcutaneous adipose tissues (SAT) and visceral adipose tissues (VAT). Univariate and multivariate logistic regression analyses were performed to identify independent predictors of IR. The indicative performance of individual parameters and combined models-including a QCT parameters-only model and a model integrating QCT parameters with clinical covariates (gender, age, BMI)-was evaluated using receiver operating characteristic (ROC) curve analysis. Results: Patients in the IR group (n=332) demonstrated significantly elevated levels of all QCT parameters compared to the non-IR group (n=592): BMD (125.27 vs. 114.01 mg/cm³), liver fat (10.14% vs. 6.61%), SAT (138.55 vs. 82.20 cm²), and VAT (222.83 vs. 131.92 cm²) (all Conclusion: Visceral adipose tissue and liver fat content, as measured by QCT, are robust and independent indicator of insulin resistance in patients with schizophrenia. A model combining QCT parameters with basic clinical covariates offers the best predictive performance for identifying IR risk in this population, highlighting the critical role of ectopic fat deposition in metabolic dysregulation.

Indexed as

insulin resistancequantitative computed tomographyschizophreniasubcutaneous adipose tissuevisceral adipose tissue

Identifiers

PMID41446289
PMCPMC12722831

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.