Poor multi-dimensional sleep health, either estimated by sleep health score or characterized by sleep clusters, was associated with poor prognosis in the CKD population, with severe insomnia and disturbed sleep patterns associated with 19-54% higher risks of anemia, CVD, and hyperparathyroidism.
Key Findings
Results
Higher sleep health scores were significantly associated with decreased risks of anemia, CVD, and hyperparathyroidism in CKD patients.
The cohort included 15,809 participants with laboratory-observed kidney dysfunction or a CKD diagnosis during 2006-2010 from the UK Biobank.
Follow-up extended through 2022 using ICD-10 coded outcomes.
Associations were statistically significant (all p < 0.05) after adjusting for potential confounders.
Multi-dimensional sleep health was evaluated through an a priori sleep health score (SHS) and sleep health clusters categorized by latent class analysis.
Higher SHS was not reported as significantly associated with decreased mortality or cognitive impairment in the abstract.
Results
Severe insomnia with short or long sleep duration was associated with significantly higher risks of adverse outcomes in CKD patients.
Severe insomnia combined with short or long sleep duration was associated with 19-54% higher risks of anemia, CVD, and hyperparathyroidism.
These associations were identified through Cox proportional hazards models.
During follow-up, 2,416 deaths, 2,343 anemia cases, 4,087 CVD cases, 940 cognitive impairment cases, and 314 hyperparathyroidism cases occurred.
Sleep clusters were derived using latent class analysis to characterize distinct multi-dimensional sleep profiles.
Results
Severe disturbed sleep with multiple dysfunctions was associated with 19-54% higher risks of anemia, CVD, and hyperparathyroidism in CKD patients.
This sleep cluster represented a pattern characterized by multiple concurrent sleep dysfunctions beyond insomnia and sleep duration alone.
Risk elevations ranged from 19% to 54% depending on the specific outcome examined.
Associations were estimated through Cox proportional hazards models adjusted for potential confounders.
This cluster was identified alongside the severe insomnia with short or long sleep duration cluster as a high-risk sleep profile.
Results
Individual sleep factors showed great heterogeneity in their associations with adverse outcomes in the CKD population.
Each sleep factor examined individually indicated substantial variation in its relationship with different adverse outcomes.
Gender was identified as a moderating variable that interacts with some sleep exposures in relation to outcomes.
Kidney function level also interacted with some sleep exposures in determining outcome risk.
The heterogeneity of individual sleep factor associations underscored the value of multi-dimensional sleep assessment over single-factor approaches.
Methods
Latent class analysis identified distinct sleep health clusters in the CKD population that corresponded to different risk profiles for complications and mortality.
Sleep health clusters were derived using latent class analysis as a data-driven, a posteriori approach to classifying multi-dimensional sleep health.
Clusters were compared against the a priori sleep health score (SHS) as complementary analytic strategies.
Specific sleep profiles identified through clustering were proposed to help pinpoint high-risk subgroups.
The cluster approach enabled characterization of co-occurring sleep dysfunctions that individual factor analyses would miss.
Methods
The prospective cohort study included 15,809 CKD patients from the UK Biobank followed from 2006-2010 enrollment through 2022.
Participants were included based on laboratory-observed kidney dysfunction or a CKD diagnosis recorded during 2006-2010.
Incident adverse outcomes were identified via ICD-10 coding through 2022.
Outcomes assessed included all-cause mortality, anemia, cardiovascular disease (CVD), cognitive impairment, and hyperparathyroidism.
Cox proportional hazards models were used to estimate associations between sleep health measures and each outcome.
Analyses were adjusted for potential confounders.
What This Means
This research suggests that the way people with chronic kidney disease (CKD) sleep — across multiple dimensions including insomnia severity, sleep duration, and other sleep disturbances — is meaningfully linked to their risk of developing serious complications and dying. Using data from nearly 16,000 UK Biobank participants with CKD followed for over a decade, researchers found that patients with poorer overall sleep health were more likely to develop anemia, cardiovascular disease, and a parathyroid hormone disorder. People whose sleep was characterized by severe insomnia combined with either very short or very long sleep, or by multiple simultaneous sleep problems, faced 19 to 54 percent higher risks of these complications compared to those with healthier sleep patterns.
The study also found that looking at individual sleep problems in isolation tells an incomplete story — different sleep factors were associated with different outcomes, and a person's sex and kidney function level further modified these relationships. This suggests that a comprehensive, multi-dimensional view of sleep health provides more useful information than focusing on any single sleep problem, such as insomnia alone. The researchers used two complementary methods — a pre-defined sleep health score and a statistical technique called latent class analysis that groups people by their overall sleep profiles — and both approaches pointed to similar conclusions about poor sleep predicting worse outcomes.
This research suggests that identifying specific sleep problem patterns in CKD patients could help clinicians recognize who is at highest risk for complications and target sleep-related interventions more precisely. Because CKD patients already face a high burden of health complications, addressing sleep health as part of their care may offer an avenue to improve outcomes — though the observational nature of the study means causality cannot be confirmed from this data alone.
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Lei K, Li J, Xu L, Teng Y, Lu C, Qiao C, et al.. (2026). Multi-dimensional sleep patterns predict complications and mortality in chronic kidney disease patients: a prospective study from the UK Biobank.. Frontiers in public health. https://doi.org/10.3389/fpubh.2026.1889689