This prospective cohort study evaluated whether baseline sleep quality predicts subsequent glycaemic outcomes in adults with type 2 diabetes mellitus (T2DM). The analysis used repeated fasting blood glucose measurements and longitudinal pattern analyses to relate baseline sleep measures to later glycaemic status.
Participants were drawn from the Taizhou Diabetes Family-based Cohort in Zhejiang Province, China. The analytic sample comprised 1,421 adults with T2DM who had a baseline sleep quality assessment and at least one follow-up fasting blood glucose measurement.
Sleep quality at baseline was measured using the Pittsburgh Sleep Quality Index (PSQI). The PSQI score was used both as a continuous variable and categorically to define poor sleep quality. The source reports PSQI as the exposure of interest without providing additional subgroup cutpoints beyond the categorical designation of poor sleep quality.
Follow-up fasting blood glucose (FBG) measurements were obtained through 30 June 2025. Primary outcomes were: (1) continuous FBG levels during follow-up and (2) a binary FBG status defined as FBG ≥7.0 mmol/L at follow-up. Secondary outcomes included measures of glycaemic variability and membership in latent glycaemic trajectory classes identified over the follow-up period.
In models that adjusted for covariates (fully adjusted models as reported in the source), poorer baseline sleep quality was associated with less favourable glycaemic outcomes:
Each 1-point increase in PSQI score was associated with a 0.096 mmol/L higher fasting blood glucose level (95% CI 0.081 to 0.111).
When comparing categories, poor sleep quality was associated with a 0.669 mmol/L higher fasting blood glucose level (95% CI 0.547 to 0.791).
For the binary outcome (FBG ≥7.0 mmol/L):
Each 1-point increase in PSQI score was associated with higher odds of FBG ≥7.0 mmol/L (odds ratio [OR] 1.10, 95% CI 1.08 to 1.12).
Poor sleep quality was associated with 1.61-fold higher odds of FBG ≥7.0 mmol/L (95% CI 1.37 to 1.91).
These reported associations indicate a graded relationship between worse sleep quality and higher fasting glucose and higher likelihood of exceeding the glycaemic threshold used in this study.
Higher PSQI scores were associated with greater glycaemic variability during follow-up. In addition, poorer baseline sleep quality increased the odds of membership in less favourable longitudinal glycaemic trajectory groups. The source reports these associations qualitatively and provides the statistical direction and significance but does not list detailed trajectory class descriptions or numeric class-specific estimates in the provided summary.
Among adults with T2DM in this family-based Chinese cohort, poorer baseline sleep quality was consistently associated with higher fasting blood glucose, worse glycaemic control as defined by FBG ≥7.0 mmol/L, greater glycaemic variability, and less favourable long-term glycaemic trajectories.
These findings suggest that simple assessment of sleep quality using the PSQI may help identify patients at higher risk of suboptimal long-term glycaemic management. The study implies that sleep quality is a potentially relevant and easily assessable clinical marker, though the source does not report intervention data or whether improving sleep quality would change these outcomes.
Limitations and missing details from the source
The source summary does not provide the full list of covariates included in the fully adjusted models, the exact PSQI cutoff used to define poor sleep quality, the number or timing of FBG measurements per participant, or class-specific trajectory parameters. Those details were not reported in the provided excerpt and would require consulting the full article for comprehensive appraisal.
Overall, the reported results support a link between baseline sleep disturbances and adverse glycaemic measures in adults with T2DM in this cohort, underscoring the potential value of sleep assessment in routine diabetes care.