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Aims/hypothesis <p>Suboptimal sleep health is linked to higher risks for incident type 2 diabetes. We aimed to assess the clinical utility of adding self-reported sleep traits to a type 2 diabetes prediction model.</p> Methods <p>In this cohort study, we used UK Biobank data and Cox proportional hazards models to examine how self-reported sleep duration and insomnia symptoms were associated with incident type 2 diabetes risk. Harrell’s C&#xa0;statistic and net reclassification improvement (NRI) were used to assess whether sleep traits improved the incident type 2 diabetes discrimination and predictive utility achieved using QDiabetes variables, with and without including a type 2 diabetes polygenic risk score (PGS). Independent replication was explored in the Nurses’ Health Study, the Nurses’ Health Study II and the Health Professionals Follow-up Study.</p> Results <p>Extremes of sleep duration and occasional or frequent insomnia symptoms were associated with higher risks for incident type 2 diabetes. In the UK Biobank and replication cohorts, adding sleep traits to the QDiabetes risk score did not improve type 2 diabetes prediction (C&#xa0;statistic: QDiabetes alone 0.8933; QDiabetes + sleep duration 0.8939; QDiabetes + insomnia 0.8931; QDiabetes + sleep traits 0.8935). The corresponding total NRI values were: 0.08 (95% CI −0.18, 0.33), 0.04 (95% CI −0.08, 0.16) and 0.04 (95% CI −0.10, 0.18). Inclusion of PGS data marginally improved the type 2 diabetes risk prediction achieved using The QDiabetes calculator, with or without the inclusion of sleep traits in the model (QDiabetes + PGS: C&#xa0;statistic 0.8945; total NRI 0.20 [95% CI 0.12, 0.28]; QDiabetes + PGS + sleep traits: C&#xa0;statistic 0.8946; total NRI 0.18 [95% CI 0.09, 0.27]).</p> Conclusions/interpretation <p>While sleep duration and insomnia symptoms were associated with type 2 diabetes risk, they are not useful for improving type 2 diabetes prediction beyond QDiabetes model performance. Inclusion of a type 2 diabetes PGS marginally improved prediction but lacked clear clinical utility.</p> Graphical Abstract <p></p>

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Clinical utility of self-reported sleep duration and insomnia symptoms in type 2 diabetes prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月2日

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  • Alison K. Wright,
  • Tianyi Huang,
  • Matthew J. Carr,
  • Arjun D. Premdayal,
  • Sushant Saluja,
  • Hassan S. Dashti,
  • Simon G. Anderson,
  • David W. Ray,
  • Samuel E. Jones,
  • Andrew R. Wood,
  • Timothy M. Frayling,
  • Michael N. Weedon,
  • Jacqueline M. Lane,
  • Richa Saxena,
  • Junxi Liu,
  • Jack Bowden,
  • Deborah A. Lawlor,
  • Susan Redline,
  • Martin K. Rutter

摘要

Aims/hypothesis

Suboptimal sleep health is linked to higher risks for incident type 2 diabetes. We aimed to assess the clinical utility of adding self-reported sleep traits to a type 2 diabetes prediction model.

Methods

In this cohort study, we used UK Biobank data and Cox proportional hazards models to examine how self-reported sleep duration and insomnia symptoms were associated with incident type 2 diabetes risk. Harrell’s C statistic and net reclassification improvement (NRI) were used to assess whether sleep traits improved the incident type 2 diabetes discrimination and predictive utility achieved using QDiabetes variables, with and without including a type 2 diabetes polygenic risk score (PGS). Independent replication was explored in the Nurses’ Health Study, the Nurses’ Health Study II and the Health Professionals Follow-up Study.

Results

Extremes of sleep duration and occasional or frequent insomnia symptoms were associated with higher risks for incident type 2 diabetes. In the UK Biobank and replication cohorts, adding sleep traits to the QDiabetes risk score did not improve type 2 diabetes prediction (C statistic: QDiabetes alone 0.8933; QDiabetes + sleep duration 0.8939; QDiabetes + insomnia 0.8931; QDiabetes + sleep traits 0.8935). The corresponding total NRI values were: 0.08 (95% CI −0.18, 0.33), 0.04 (95% CI −0.08, 0.16) and 0.04 (95% CI −0.10, 0.18). Inclusion of PGS data marginally improved the type 2 diabetes risk prediction achieved using The QDiabetes calculator, with or without the inclusion of sleep traits in the model (QDiabetes + PGS: C statistic 0.8945; total NRI 0.20 [95% CI 0.12, 0.28]; QDiabetes + PGS + sleep traits: C statistic 0.8946; total NRI 0.18 [95% CI 0.09, 0.27]).

Conclusions/interpretation

While sleep duration and insomnia symptoms were associated with type 2 diabetes risk, they are not useful for improving type 2 diabetes prediction beyond QDiabetes model performance. Inclusion of a type 2 diabetes PGS marginally improved prediction but lacked clear clinical utility.

Graphical Abstract