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Identification of BMI-related high-risk feature combinations for diabetes among young adults with normal baseline fasting plasma glucose using interpretable machine learning: a health check-up cohort study.

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Title: Identification of BMI-related high-risk feature combinations for diabetes among young adults with normal baseline fasting plasma glucose using interpretable machine learning: a health check-up cohort study.
Authors: Xu Z; Nursing Department, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China., Zhang Y; Nursing Department, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China., Zhang H; Nursing Department, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Source: Frontiers in endocrinology [Front Endocrinol (Lausanne)] 2026 May 15; Vol. 17, pp. 1850071. Date of Electronic Publication: 2026 May 15 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Research Foundation] Country of Publication: Switzerland NLM ID: 101555782 Publication Model: eCollection Cited Medium: Print ISSN: 1664-2392 (Print) Linking ISSN: 16642392 NLM ISO Abbreviation: Front Endocrinol (Lausanne) Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22Xu+Z%22">Xu Z</searchLink>; Nursing Department, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.<br /><searchLink fieldCode="AU" term="%22Zhang+Y%22">Zhang Y</searchLink>; Nursing Department, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.<br /><searchLink fieldCode="AU" term="%22Zhang+H%22">Zhang H</searchLink>; Nursing Department, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
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  Data: <searchLink fieldCode="JN" term="%22101555782%22">Frontiers in endocrinology</searchLink> [Front Endocrinol (Lausanne)] 2026 May 15; Vol. 17, pp. 1850071. <i>Date of Electronic Publication: </i>2026 May 15 (<i>Print Publication: </i>2026).
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Frontiers+Research+Foundation]%22">Frontiers Research Foundation] </searchLink><i>Country of Publication: </i>Switzerland <i>NLM ID: </i>101555782 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Print <i>ISSN: </i>1664-2392 (Print) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2216642392%22">16642392 </searchLink><i>NLM ISO Abbreviation: </i>Front Endocrinol (Lausanne) <i>Subsets: </i>MEDLINE
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        Value: 10.3389/fendo.2026.1850071
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        Text: English
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        StartPage: 1850071
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      – TitleFull: Identification of BMI-related high-risk feature combinations for diabetes among young adults with normal baseline fasting plasma glucose using interpretable machine learning: a health check-up cohort study.
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            NameFull: Xu Z
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            NameFull: Zhang Y
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            – D: 15
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              Text: 2026 May 15
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              Y: 2026
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