GRAPHICAL ABSTRACT

Highlights

• Irregular sleep patterns increase the risk of macrovascular complications.

• Sleep regularity index shows a nonlinear association with diabetic neuropathy.

• Sleep regularity is a novel indicator for predicting diabetic complications.

INTRODUCTION

Emerging evidence indicates that unhealthy sleep behaviors, such as insufficient sleep duration and irregular sleep, have been related to an increased risk of type 2 diabetes mellitus (T2DM) [1-5]. Once established, T2DM can exacerbate vascular damage through persistent hyperglycemia, driven by metabolic dysregulation, oxidative stress, and chronic inflammation, finally inducing diabetic macrovascular complications and microvascular complications [6]. Macrovascular complications, such as coronary heart disease (CHD) and stroke, together with microvascular complications such as diabetic neuropathy, kidney disease and retinopathy, are the leading causes of death and disability in people with T2DM, posing great burdens to healthcare systems and the economy [7-9]. Therefore, identification of cost-effective sleep promotion strategies is crucial for preventing and delaying the development of macrovascular and microvascular complications among individuals with T2DM.

Sleep regularity refers to day-to-day consistency in sleep-wake patterns [10]. Individuals with irregular sleep tend to go to bed and wake up at varying times across days and may also exhibit substantial variation in daily sleep duration. Adhering to a consistent sleep schedule is often recommended as a key component of good sleep practices as important as sleep duration. Evidence from a recent cohort study of adults in the UK Biobank accelerometer sub-study aged 40 to 79 years showed that both moderate and high sleep irregularity were associated with higher risk of T2DM using sleep regularity index (SRI) [4]. Meanwhile, by measuring within-person standard deviation (SD) of 7-night accelerometer-measured sleep duration, a prior study suggests that irregular sleep duration with higher diabetes risk [5]. However, there is limited data on the association of sleep regularity with diabetic complications. Most of epidemiologic studies only focused on sleep duration and often assessed sleep duration via self-reported questionnaires [11-13]. To our knowledge, sleep regularity is also a crucial dimension of sleep health that deserves attention, and sleep regularity metrics including SRI and sleep duration SD can quantify day-to-day variability in sleep-wake patterns, which traditional sleep metrics like sleep duration do not capture. Given the limited evidence on sleep regularity, studies based on objectively collected data are needed to determine whether irregular sleep is associated with risk of diabetic complications among individuals with T2DM.

Therefore, using multiple sleep regularity metrics, the aim of this study was to investigate the association between accelerometer-measured sleep regularity and macrovascular and microvascular complications among individuals with diabetes. We hypothesized that lower sleep regularity would be associated with a higher risk of vascular complications in individuals with T2DM.

METHODS

Study design

The UK Biobank is a large population-based cohort of adults recruited across the UK between 2006 and 2010. Approximately half a million participants age 40 to 69 years were recruited from 22 centers across England, Scotland, and Wales to reflect a diverse socioeconomic and demographic mixture of urban and rural residents. More information on study methods and recruitment strategies can be found elsewhere [14]. The UK Biobank received ethical approval from the research ethics committee (reference 11/NW/0382), and all participants provided written informed consent. Between 2013 and 2015 (baseline of this study), a random selection of 236,519 participants with valid email addresses were invited to participate in a 7-day wrist-worn accelerometer study [15]; 44% (>103,000 respondents) of these participants accepted the invitation and completed the accelerometer measurement. Among 92,608 participants with good quality accelerometer data, we included participants who had a minimum of 3 valid monitoring days. Finally, we identified 3,984 participants with T2DM at baseline through a validated algorithm based on self-reported disease, medication, and a diagnosis of T2DM noted in the medical history [16]. Moreover, the glycosylated hemoglobin (HbA1c) ≥48 mmol/mol (6.5%) was also used to identify diabetes at baseline, leaving a total of 3,862 participants with T2DM. When conducting analysis for related incident disease outcomes analyses, participants with a previous diagnosis of macrovascular or microvascular complications were further excluded. A flowchart of participants included in the current study is presented in Supplementary Fig. 1. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting (Supplementary Table 1).

Assessment of sleep regularity

Sleep regularity index

Participants wore Axivity AX3 (Open Lab, Newcastle University, Newcastle upon Tyne, UK) on their dominant wrist for 7 days and accelerometers were initialized to collect data at a sampling frequency of 100 Hz and a dynamic range of ±8 g [15]. We included participants who had a minimum of 3 valid monitoring days. SRI was quantified as the probability of being in the same state (asleep or awake) at any two time points 24 hours apart for each contiguous 2-day pair using an opensource algorithm operating at the epoch level [17,18]. An SRI of 100 indicates perfectly regular sleep-wake patterns with sleeping and waking at exactly the same times each day, and 0 reflects entirely random sleep-wake patterns. Based on SRI tertiles, we categorized participants as irregular (SRI <49.8; the lowest tertile), moderately irregular (SRI between 49.8 and 61.4; the second highest tertile), and regular (SRI >61.4; the highest tertile) sleepers. In the main analysis, participants in the highest tertile were the reference group. We also evaluated SRI continuously.

Sleep duration variability

Sleep duration variability was defined as the SD of accelerometer-measured sleep duration across main sleep periods, averaged over 7-night accelerometry. Based on sleep duration SD tertiles, we categorized participants as irregular (sleep duration SD >1.68 hours; the highest tertile), moderately irregular (sleep duration SD between 1.1 and 1.68 hours; the second highest tertile), and regular (sleep duration SD <1.1 hours; the lowest tertile) sleepers, and participants in the lowest tertile were the reference group. We also evaluated sleep duration SD continuously.

Ascertainment of outcomes

The outcomes of the study were (1) diabetic macrovascular complications and its subtypes including CHD (International Classification of Diseases, 10th Revision [ICD-10]: I20, I21, I22, I23, I24, I25), and stroke (ICD-10: 160, I61, I62, I63, I64), and (2) diabetic microvascular complications and its subtypes including diabetic retinopathy (ICD-10: H360), diabetic neuropathy (ICD-10: E114, E144, G590, G629, G632, and G990), diabetic kidney disease (ICD-10: E112, E142, N180, N181, N182, N183, N184, N185, N188, and N189). In this study, hospital inpatient diagnosis data were updated through November 30, 2022. The follow-up time was calculated from the beginning date of the accelerometer completion to the earliest occurrence of diabetic complications, death or the end of followup (November 30, 2022).

Covariates

Potential covariates, selected based on their correlation with sleep regularity and diabetic complications, included the following variables collected at baseline: age, sex, ethnicity, Townsend deprivation index (TDI), education, family history of cardiovascular disease (CVD), family history of hypertension, prevalence of hypertension, physical activity, smoking status, alcohol consumption, diet, body mass index (BMI), history of shift work, season of accelerometer wear, diabetes duration, HbA1c, use of antihypertensive medication, use of lipid-lowering medication, use of aspirin, and use of diabetes medication.

Statistical analysis

Baseline characteristics of participants in the entire sample were described as means and SDs for continuous variables and numbers (percentages) for categorical variables. Cox proportional hazards regression models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Missing values in covariates with a missing rate <1% (e.g., smoking status) were completely excluded, while those with a missing rate >1% (e.g., family history of hypertension) were imputed using multiple imputations by chained equations with five imputations. Linear regression model and logistic regression model with all the covariates in the fully adjusted model were used to impute continuous variables and categorical variables, respectively. The percentage of missing values is presented in Supplementary Table 2. Two models were built. In model 1, we adjusted for age (continuous, years) and sex (male, female). In model 2, we further adjusted for TDI (continuous), ethnicity (White, others), education (college/university degree, others), family history of CVD (yes, no), family history of hypertension (yes, no), prevalence of hypertension (yes, no), physical activity (continuous, min/week), smoking status (never, former, current), alcohol consumption (not current, two or less times a week, three or more times a week), BMI (continuous), diet score (0, 1, 2, 3, 4, 5), history of shift work (yes, no), season of accelerometer wear (spring, summer, autumn, winter), diabetes duration (continuous, years), HbA1c (continuous, mmol/mol), use of antihypertensive medication (yes, no), use of lipid-lowering medication (yes, no), use of aspirin (yes, no), and use of diabetes medication (yes, no). We used Schoenfeld residuals for this purpose to assess the proportional hazards assumption. Population attributable risks (PAR) and 95% CIs were also estimated. Relative risks were obtained from the multivariate model, and we estimated the PAR of moving from the highest risk (irregular) to lowest risk (regular) categories. Further, we used Fine–Gray subdistribution hazard models to account for the competing risk of death, expressing associations as subdistribution hazard ratios (SHR). We evaluated both the linear trend by modeling sleep regularity metrics as continuous variables and potential nonlinearity using restricted cubic splines with three knots at 10th, 50th, and 90th percentiles. Statistical significance of nonlinearity was assessed by a likelihood ratio test comparing the fit of the linear model to the spline model.

Two sensitivity analyses were also conducted to assess the robustness of our results. First, Cox proportional hazards regression was performed to investigate the association between sleep regularity metrics and outcomes among diabetes patients after further excluding individuals with missing covariates. Second, sleep duration was further adjusted in the model. Additionally, stratified analyses were performed by age (<60 and ≥60 years), sex (male and female), BMI (<25 and ≥25 kg/m2), diabetes duration (<3 and ≥3 years) and use of diabetes medication (yes and no) to examine whether the associations varied by these factors. Interaction terms were tested by a Wald test. Lastly, a joint analysis was conducted for the combination of sleep regularity (regular, moderately irregular, or irregular) and sleep duration groups (meeting or not meeting recommended sleep duration) with macrovascular and microvascular complications.

A two-sided P value of <0.05 was regarded as statistically significant. All analyses were carried out using STATA version 16.0 (StataCorp., College Station, TX, USA) and R version 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria) with RMS version 6.9.0.

RESULTS

Characteristics of study participants

The final analytic sample comprised 3,862 adults with a mean age of 64.7±7.0 years on enrolment. Baseline descriptive characteristics of participants and stratified by SRI tertile are shown in Table 1. A comparison between the baseline characteristics of individuals in our primary analysis sample and those excluded because of incomplete or missing data can be found in Supplementary Table 3. Participants with higher SRI were more likely to be female, White, highly educated, and more physically active; have lower TDI, lower BMI, healthier diet, lower prevalence of hypertension, and longer mean sleep duration. They were less likely to use aspirins and medications for diabetes, dyslipidemia, and hypertension.

Association between sleep regularity and macrovascular complications

During a median follow-up of 7.2 years, 410 (13.2%) composite macrovascular events occurred, including 354 (11.4%) CHD, and 75 (2.4%) stroke. Associations between sleep regularity and macrovascular complications are shown in Table 2. For composite macrovascular complications, in model 1, compared to participants in the highest tertile of SRI, those in the lowest tertile were at higher risk of composite macrovascular complications; compared to participants with in the lowest tertile of sleep duration SD, those in the highest tertile were at higher risk of composite macrovascular complications. These associations did not change substantially in the fully adjusted model 2. For subtypes of macrovascular complications, controlling for age and sex, compared to participants in the highest tertile of SRI, those in the lowest tertile were at higher risk of CHD and stroke; compared to participants with in the lowest tertile of sleep duration SD, those in the highest tertile were at higher risk of CHD and stroke. These associations were attenuated after further adjustment for other covariates. Furthermore, each 1-hour increase in sleep duration SD was associated with a 25% to 58% higher risk of overall and subtypes of incident macrovascular complications. PARs for sleep regularity are shown in Supplementary Table 4 in multivariable model. After accounting for the competing risk of death, the risk of incident stroke (SHR, 1.25; 95% CI, 0.97 to 1.61) remained significant among participants in the lowest tertile of SRI with those in the highest tertile of SRI (Supplementary Table 5). For sleep duration SD, those associations were similar to the main analysis (composite macrovascular complications [SHR, 1.44; 95% CI, 1.13 to 1.85], CHD [SHR, 1.40; 95% CI, 1.07 to 1.83], stroke [SHR, 2.10; 95% CI, 1.10 to 3.99]) (Supplementary Table 5). To further explore the nonlinear associations of sleep regularity with macrovascular complications, we assessed the dose-response associations after fully adjustment by using restricted cubic splines. Linear dose-response relationships of sleep regularity with macrovascular complications were demonstrated (all P for nonlinearity >0.05) (Supplementary Figs. 2 and 3).

Association between sleep regularity and microvascular complications

During a median follow-up of 7.2 years, 615 (17.1%) composite microvascular events occurred, including 120 (3.3%) diabetic neuropathy, 372 (10.3%) diabetic kidney disease, and 242 (6.7%) diabetic retinopathy. Associations between sleep regularity and microvascular complications are shown in Table 3. For composite microvascular complications, in model 1, compared to participants in the highest tertile of SRI, those in the lowest tertile were at higher risk of composite microvascular complications; compared to participants in the lowest tertile of sleep duration SD, those in the highest tertile were at higher risk of composite microvascular complication. For subtypes of microvascular complications, in model 1, compared to participants in the highest tertile of SRI, those in the lowest tertile were at higher risk of diabetic kidney disease and diabetic retinopathy. However, the additional adjustment for other covariates in model 2 largely diminished the association of sleep regularity with the risk of microvascular complications, and all risk estimates became nonsignificant. PARs for sleep regularity are shown in Supplementary Table 6 in multivariable model. After accounting for the competing risk of death, the results were consistent with the main analyses (Supplementary Table 7). Dose-response analysis was conducted to examine the nonlinear associations between sleep regularity and microvascular complication. There was a nonlinear association between SRI and diabetic neuropathy (P<0.05), indicating a steep HR reduction with increasing SRI before reaching approximately 56 (median value) (Fig. 1). Compared to the median SRI, HR was 1.92 (95% CI, 1.28 to 2.88) for participants with SRI at the 5th percentile. However, we didn’t observe nonlinear associations between sleep duration SD and microvascular complications (Supplementary Fig. 4).

Stratified analyses and sensitivity analyses

Consistent results were observed when analyses were stratified by age, sex, BMI, diabetes duration, and use of diabetes medication (Supplementary Tables 8 and 9). The association between sleep duration SD and stroke was significant among participants BMI ≤25 kg/m², and the test for interaction was also significant. Similar significant association between sleep duration SD and diabetic retinopathy was observed among age ≤60 years, with evidence of interaction between age and sleep duration. In the sensitivity analyses, the results were generally robust when excluding individuals with missing covariates (Supplementary Tables 10 and 11) and when additionally adjusted for sleep duration (Supplementary Tables 12 and 13). Joint sleep regularity and sleep duration analyses were presented in the Supplementary Tables 14 and 15, and no significant interaction between sleep regularity and sleep duration was detected.

DISCUSSION

In this population-based prospective cohort study, we provide a comprehensive examination of sleep regularity metrics with risk of macrovascular and microvascular complications among individuals with T2DM. Our results showed that lower SRI was significantly associated with an increased risk of incident stroke and composite macrovascular complications. Furthermore, each 1-hour increase in sleep duration SD was associated with a 25% to 58% higher risk of overall and subtypes of incident macrovascular complications. Although no significant association was observed between sleep regularity metrics and diabetic microvascular complications after accounting for all socioeconomic factors, BMI, and comorbidities, there was evidence of a nonlinear association between SRI and diabetic neuropathy. Compared to the median SRI, HR was 1.92 (95% CI, 1.28 to 2.88) for participants with SRI at the 5th percentile.

Prior accelerometry-based studies specific to T2DM patients are scarce. Nevertheless, our findings are in line with previous studies in general population, which have consistently demonstrated that irregular sleep pattern was associated with poor cardiovascular health [19-22]. For instance, a prospective study from the Multi-Ethnic Study of Atherosclerosis (MESA) using 7-day SD of sleep duration, reported that individuals with the most irregular sleep duration exhibited a higher risk of CVD risk compared to those with the most regular sleep pattern [20]. Based on the same sample from UK Biobank, Chaput et al. [19] examined the association of SRI with major adverse cardiovascular events (MACE) in general population, showing an inverse association between SRI and MACE and its subtypes. The present study expanded prior analyses by incorporating both SRI and sleep duration SD, enabling a more precise characterization of sleep regularity. Importantly, our study extended the literature regarding the influence of sleep regularity among T2DM patients, and provided extensive evidence linking sleep irregularity with macrovascular complications. However, no statistically significant association between SRI and CHD among T2DM patients was observed, potentially suggesting that the mechanisms linking sleep irregularity to macrovascular outcomes are more hypertensive than atherosclerotic.

Sleep irregularity may contribute to circadian rhythm disruption, as shown in experimental studies where irregular sleep patterns led to misalignment between clock gene expression and the external light–dark cycle [22-25]. Such circadian misalignment may increase the risk of macrovascular diseases through chronic inflammation, impaired glucose metabolism, dysregulated hormone secretion, and abnormal blood pressure regulation [26-28]. In addition, irregular sleep has been linked with altered lipid metabolism, storage, and utilization, further exacerbating cardiovascular risk [29]. More studies are warranted to clarify the exact mechanisms linking irregular sleep and macrovascular complications.

Despite the potentially detrimental impact of irregular sleep pattern on T2DM, the evidence regarding microvascular complications remains inconclusive. One study didn’t find significant difference in sleep duration SD between T2DM patients with and without diabetic retinopathy [30], whereas another study observed higher sleep duration SD among T2DM patients with diabetic retinopathy [31]. Additionally, a study of 90 outpatients in diabetes clinic suggested that different diabetic complications were related to a variety of alterations in sleep-wake pattern [32]. However, the above studies were limited by cross-sectional designs, small sample sizes, and whether SRI was associated with incident microvascular complications remains unclear. Therefore, we conducted a prospective population-based cohort study to investigate the association of SRI and sleep duration SD with the risk of diabetic microvascular complications among individuals with T2DM. Notably, there was a nonlinear association between SRI and the risk of diabetic neuropathy. Although the mechanisms underpinning the connection between sleep regularity and risk of diabetic neuropathy remain inadequately understood, several potential pathways have been proposed. First, variations in sleep onset and wake time increase sympathetic nervous system activity and affect glucose homeostasis, further elevating blood glucose levels. The hyperglycemic environment triggers oxidative stress from reactive oxygen species, which can also activate downstream effectors like protein kinase C (PKC), ultimately inducing pro-inflammatory mediators and inflammatory responses, which may induce diabetic neuropathy [33,34]. More importantly, chronic hyperglycemia contributes to a decrease in neurotrophic and angiogenic factors, and increase in apoptosis of neuroglia, accelerating the development diabetic neuropathy [35,36]. Second, irregular sleep patterns also disrupt appetite regulation, such as higher intake of high-calorie foods, promoting obesity and dyslipidemia. Recent research and clinical evidence additionally suggest that obesity and dyslipidemia also promote disease pathogenesis [37,38]. Dyslipidemia induces mitochondrial membrane depolarization and reduces adenosine triphosphate production, while long-chain saturated fatty acids impair mitochondrial trafficking, both of which further exacerbate neural injury [39]. However, no statistically significant association between sleep regularity and microvascular complications among T2DM patients were observed potentially due to limited statistical power. Further research is needed to determine whether clinical management to improve sleep regularity may serve as a potential target to reduce incidence of microvascular complications among patients with T2DM.

Our findings have important clinical and public health implications. The results suggest that the risk of macrovascular complications in patients with T2DM may be reduced by improving sleep regularity. With the growing availability of actigraphy and smartphone-based technologies, objectively measured sleep regularity is a simple and useful tool that could be incorporated into clinical practice to monitor sleep quality for patients with T2DM. Furthermore, sleep regularity assessment could be of important value in clinical practice for identifying individuals at high-risk of macrovascular complications, enabling clinicians to implement targeted sleep interventions, such as structured sleep hygiene programs, in combination with established behavioral therapies.

Our study has several strengths. This study is the first to examine the longitudinal association between sleep regularity and macrovascular and microvascular complications among patients with T2DM. We utilized accelerometer-measured sleep data that were objectively collected instead of relying on self-reported questionnaire. Moreover, we selected multiple sleep regularity metrics, providing a comprehensive assessment of sleep regularity. Nonetheless, there are some potential limitations. First, the response rate in parental UK Biobank cohort was only 5.5%, and accelerometer-measured sleep regularity was only available for a subset of participants, which raised the potential of selection bias [14]. Second, due to the limitations of the database, our findings are only derived from a relatively homogeneous cohort consisting predominantly of White British people, which may limit the generalizability of our results. Multiple studies are warranted to confirm our findings and characterize the associations across more socioeconomically and ethnically diverse populations. Third, despite adjustment for known and potential confounders, unmeasured and residual confounding could not be ruled out entirely. Fourth, potential misclassification may occur due to mainly relying on ICD codes, medication data, and laboratory results for identifying outcomes and T2DM. Fifth, the statistical analyses are likely to introduce overadjustment bias, which may reduce statistical significance and bias the estimates towards the null hypothesis. Finally, sleep was only measured at baseline. Further research with a repeated measurement of sleep regularity and sleep duration may help identify the potential link between changes in sleep pattern and diabetes complications.

In conclusion, the current prospective cohort study offers novel evidence indicating an association of sleep regularity with macrovascular and microvascular complications among T2DM patients. These results underscore the significance of considering sleep regularity as a modifiable behavioral target in public health guidelines and clinical practices related to T2DM complications prevention and management. Future interventional studies are warrant to investigate whether regular sleep can yield better outcomes in relation to T2DM complications.

SUPPLEMENTARY MATERIALS

NOTES

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTIONS

Conception or design: Y.Z., M.Z., X.L., X.T.

Acquisition, analysis, or interpretation of data: Y.Z., M.Z., X.L., X.T.

Drafting the work or revising: Y.Z., H.W., J.W., H.X.W., T.A., X.T.

Final approval of the manuscript: all authors.

FUNDING

Xiao Tan is funded by Pioneer R&D Program of Zhejiang Province (2025C01119).

ACKNOWLEDGMENTS

The authors express genuine gratitude to the participants and staff of the UK Biobank for their valuable contributions.

Fig. 1.

Dose-response association between sleep regularity index (SRI) and microvascular complications and its subtypes among participants with type 2 diabetes mellitus. Model was adjusted for age, sex, age, ethnicity, Townsend deprivation index, education, family history of cardiovascular disease, family history of hypertension, prevalence of hypertension, physical activity, smoking status, alcohol consumption, diet, body mass index, history of shift work, season of accelerometer wear, diabetes duration, glycosylated hemoglobin, use of antihypertensive medication, use of lipid-lowering medication, use of aspirin, and use of diabetes medication. (A) Dose-response association of SRI with composite microvascular complications. (B) Dose-response association of SRI with diabetic neuropathy. (C) Dose-response association of SRI with diabetic kidney disease. (D) Dose-response association of SRI with diabetic retinopathy. Bold lines represent hazard ratios (HRs), while shaded areas indicate 95% confidence intervals (CIs).

REFERENCES

• 1. Li ZH, Zhang PD, Chen Q, Gao X, Chung VC, Shen D, et al. Association of sleep and circadian patterns and genetic risk with incident type 2 diabetes: a large prospective population-based cohort study. Eur J Endocrinol 2021;185:765-74.ArticlePubMed
• 2. Larcher S, Benhamou PY, Pepin JL, Borel AL. Sleep habits and diabetes. Diabetes Metab 2015;41:263-71.ArticlePubMed
• 3. Jin X, Chen Y, Feng H, Zhou M, Chan JWY, Liu Y, et al. Association of accelerometer-measured sleep duration and different intensities of physical activity with incident type 2 diabetes in a population-based cohort study. J Sport Health Sci 2024;13:222-32.ArticlePubMedPMC
• 4. Chaput JP, Biswas RK, Ahmadi M, Cistulli PA, Sabag A, St-Onge MP, et al. Sleep irregularity and the incidence of type 2 diabetes: a device-based prospective study in adults. Diabetes Care 2024;47:2139-45.ArticlePubMedPMCPDF
• 5. Kianersi S, Wang H, Sofer T, Noordam R, Phillips A, Rutter MK, et al. Association between accelerometer-measured irregular sleep duration and type 2 diabetes risk: a prospective cohort study in the UK Biobank. Diabetes Care 2024;47:1647-55.ArticlePubMedPMCPDF
• 6. Horton WB, Barrett EJ. Microvascular dysfunction in diabetes mellitus and cardiometabolic disease. Endocr Rev 2021;42:29-55.ArticlePubMedPMCPDF
• 7. Gregg EW, Sattar N, Ali MK. The changing face of diabetes complications. Lancet Diabetes Endocrinol 2016;4:537-47.ArticlePubMed
• 8. Wong ND, Sattar N. Cardiovascular risk in diabetes mellitus: epidemiology, assessment and prevention. Nat Rev Cardiol 2023;20:685-95.ArticlePubMedPDF
• 9. American Diabetes Association. 11. Microvascular complications and foot care: standards of medical care in diabetes-2021. Diabetes Care 2021;44:S151-67.ArticlePubMedPMCPDF
• 10. Fischer D, Klerman EB, Phillips AJ. Measuring sleep regularity: theoretical properties and practical usage of existing metrics. Sleep 2021;44:zsab103.ArticlePubMedPMCPDF
• 11. He P, Li H, Ye Z, Liu M, Zhou C, Wu Q, et al. Association of a healthy lifestyle, Life’s Essential 8 scores with incident macrovascular and microvascular disease among individuals with type 2 diabetes. J Am Heart Assoc 2023;12:e029441.ArticlePubMedPMC
• 12. Huang ZG, Gao JW, Zhang HF, You S, Xiong ZC, Wu YB, et al. Cardiovascular health metrics defined by Life’s Essential 8 scores and subsequent macrovascular and microvascular complications in individuals with type 2 diabetes: a prospective cohort study. Diabetes Obes Metab 2024;26:2673-83.PubMed
• 13. Niu X, Liu H, Wang Y, Lu Y, Jiao X, Ren Y, et al. Sleep duration, mediating biomarkers, and risk of microvascular complications among individuals with type 2 diabetes: a prospective cohort study. Diabetes Res Clin Pract 2025;221:112026.ArticlePubMed
• 14. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 2015;12:e1001779.ArticlePubMedPMC
• 15. Doherty A, Jackson D, Hammerla N, Plotz T, Olivier P, Granat MH, et al. Large scale population assessment of physical activity using wrist worn accelerometers: the UK Biobank study. PLoS One 2017;12:e0169649.ArticlePubMedPMC
• 16. Eastwood SV, Mathur R, Atkinson M, Brophy S, Sudlow C, Flaig R, et al. Algorithms for the capture and adjudication of prevalent and incident diabetes in UK Biobank. PLoS One 2016;11:e0162388.ArticlePubMedPMC
• 17. Phillips AJ, Clerx WM, O’Brien CS, Sano A, Barger LK, Picard RW, et al. Irregular sleep/wake patterns are associated with poorer academic performance and delayed circadian and sleep/wake timing. Sci Rep 2017;7:3216.ArticlePubMedPMCPDF
• 18. Windred DP, Jones SE, Russell A, Burns AC, Chan P, Weedon MN, et al. Objective assessment of sleep regularity in 60 000 UK Biobank participants using an open-source package. Sleep 2021;44:zsab254.ArticlePubMedPDF
• 19. Chaput JP, Biswas RK, Ahmadi M, Cistulli PA, Rajaratnam SM, Bian W, et al. Sleep regularity and major adverse cardiovascular events: a device-based prospective study in 72 269 UK adults. J Epidemiol Community Health 2025;79:257-64.ArticlePubMedPMC
• 20. Huang T, Mariani S, Redline S. Sleep irregularity and risk of cardiovascular events: the multi-ethnic study of atherosclerosis. J Am Coll Cardiol 2020;75:991-9.PubMedPMC
• 21. Lunsford-Avery JR, Engelhard MM, Navar AM, Kollins SH. Validation of the sleep regularity index in older adults and associations with cardiometabolic risk. Sci Rep 2018;8:14158.ArticlePubMedPMCPDF
• 22. Young MJ, Heanue S, Kanki M, Moneghetti KJ. Circadian disruption and its impact on the cardiovascular system. Trends Endocrinol Metab 2025;36:842-54.ArticlePubMed
• 23. Chellappa SL, Vujovic N, Williams JS, Scheer FA. Impact of circadian disruption on cardiovascular function and disease. Trends Endocrinol Metab 2019;30:767-79.ArticlePubMedPMC
• 24. Wisor JP, O’Hara BF, Terao A, Selby CP, Kilduff TS, Sancar A, et al. A role for cryptochromes in sleep regulation. BMC Neurosci 2002;3:20.ArticlePubMedPMCPDF
• 25. Wisor JP, Pasumarthi RK, Gerashchenko D, Thompson CL, Pathak S, Sancar A, et al. Sleep deprivation effects on circadian clock gene expression in the cerebral cortex parallel electroencephalographic differences among mouse strains. J Neurosci 2008;28:7193-201.ArticlePubMedPMC
• 26. Morris CJ, Purvis TE, Hu K, Scheer FA. Circadian misalignment increases cardiovascular disease risk factors in humans. Proc Natl Acad Sci U S A 2016;113:E1402-11.ArticlePubMedPMC
• 27. Xu Y, Barnes VA, Harris RA, Altvater M, Williams C, Norland K, et al. Sleep variability, sleep irregularity, and nighttime blood pressure dipping. Hypertension 2023;80:2621-6.ArticlePubMedPMC
• 28. Gumz ML, Shimbo D, Abdalla M, Balijepalli RC, Benedict C, Chen Y, et al. Toward precision medicine: circadian rhythm of blood pressure and chronotherapy for hypertension: 2021 NHLBI workshop report. Hypertension 2023;80:503-22.ArticlePubMedPMC
• 29. Hornburg D, Wu S, Moqri M, Zhou X, Contrepois K, Bararpour N, et al. Dynamic lipidome alterations associated with human health, disease and ageing. Nat Metab 2023;5:1578-94.ArticlePubMedPMCPDF
• 30. Sirisreetreerux S, Sujirakul T, Nimitphong H, Pinyopodjanard S, Saetung S, Chailurkit LO, et al. Sleep variability, 6-sulfatoxymelatonin, and diabetic retinopathy. Sleep Breath 2021;25:1069-74.ArticlePubMedPDF
• 31. Reutrakul S, Crowley SJ, Park JC, Chau FY, Priyadarshini M, Hanlon EC, et al. Relationship between intrinsically photosensitive ganglion cell function and circadian regulation in diabetic retinopathy. Sci Rep 2020;10:1560.ArticlePubMedPMCPDF
• 32. Kadono M, Nakanishi N, Yamazaki M, Hasegawa G, Nakamura N, Fukui M. Various patterns of disrupted daily rest-activity rhythmicity associated with diabetes. J Sleep Res 2016;25:426-37.ArticlePubMedPDF
• 33. Giacco F, Brownlee M. Oxidative stress and diabetic complications. Circ Res 2010;107:1058-70.ArticlePubMedPMC
• 34. Xu J, Wu S, Wang J, Wang J, Yan Y, Zhu M, et al. Oxidative stress induced by NOX2 contributes to neuropathic pain via plasma membrane translocation of PKCε in rat dorsal root ganglion neurons. J Neuroinflammation 2021;18:106.ArticlePubMedPMCPDF
• 35. Feldman EL, Nave KA, Jensen TS, Bennett DL. New horizons in diabetic neuropathy: mechanisms, bioenergetics, and pain. Neuron 2017;93:1296-313.ArticlePubMedPMC
• 36. Goncalves NP, Vaegter CB, Andersen H, Ostergaard L, Calcutt NA, Jensen TS. Schwann cell interactions with axons and microvessels in diabetic neuropathy. Nat Rev Neurol 2017;13:135-47.ArticlePubMedPMCPDF
• 37. Andersen ST, Witte DR, Dalsgaard EM, Andersen H, Nawroth P, Fleming T, et al. Risk factors for incident diabetic polyneuropathy in a cohort with screen-detected type 2 diabetes followed for 13 years: ADDITION-Denmark. Diabetes Care 2018;41:1068-75.ArticlePubMedPDF
• 38. Christensen DH, Knudsen ST, Gylfadottir SS, Christensen LB, Nielsen JS, Beck-Nielsen H, et al. Metabolic factors, lifestyle habits, and possible polyneuropathy in early type 2 diabetes: a nationwide study of 5,249 patients in the Danish centre for strategic research in type 2 diabetes (DD2) cohort. Diabetes Care 2020;43:1266-75.ArticlePubMedPDF
• 39. Eid SA, Rumora AE, Beirowski B, Bennett DL, Hur J, Savelieff MG, et al. New perspectives in diabetic neuropathy. Neuron 2023;111:2623-41.ArticlePubMedPMC

Figure & Data

References

Citations

Citations to this article as recorded by  

About this article

Zheng Y, Zhang M, Wu H, Wei J, Wang HX, Åkerstedt T, Li X, Tan X. Objectively-Defined Sleep Regularity Is Associated with Macrovascular and Microvascular Complications among Individuals with Type 2 Diabetes Mellitus: A Cohort Study. Diabetes Metab J. 2026 Apr 17. doi: 10.4093/dmj.2025.0530. Epub ahead of print.

Received: Jun 17, 2025; Accepted: Dec 01, 2025

DOI: https://doi.org/10.4093/dmj.2025.0530.

Download citation ↓