Sleep Quality and Outcomes in Preclinical Heart Failure: A Prospective UK Biobank Cohort Study.

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Published in final edited form as: Am J Med. 2025 Jun 2;138(12):1652–1661. doi: 10.1016/j.amjmed.2025.05.037 Sleep quality and outcomes in preclinical heart failure: a prospective UK Biobank cohort study Fang Zhu a , Joseph J Shearer a , Jungnam Joo b , Rui Miao b , Jason YY Wong a , Gabriel Goodney a , Jungeun Lim a , Maryam Hashemian a , Gretell Henríquez-Santos a , Ji-Eun Kim a , Sadiya S Khan c, d , Philip Greenland d , Véronique L Roger a Author information Article notes Copyright and License information a Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA b Office of Biostatistics Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA c Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois d Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois Author Role Statement: All authors had access to the data and a role in writing the manuscript. ✉ Corresponding author: Véronique L Roger, MD, MPH, Senior Investigator and Branch Chief, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA, Mailing Address: 10 Center Dr Rm 8N228A; Bethesda, MD 20892, veronique.roger@nih.gov Issue date 2025 Dec. PMC Copyright notice PMCID: PMC12772642  NIHMSID: NIHMS2127253  PMID: 40466883 The publisher's version of this article is available at Am J Med Abstract Background: Prevention is crucial in reducing heart failure (HF) morbidity and mortality. The American Heart Association guideline emphasizes the progression from preclinical to clinical HF, yet the association between sleep and outcomes in preclinical HF remains unexplored. Objectives: To examine sleep quality in a large cohort of preclinical HF, and assess its association with clinical HF and mortality risk. Methods: Using UK Biobank data from 2006–2010, we identified individuals with preclinical HF (Stage A/B). We examined self-reported sleep characteristics (duration, chronotype, insomnia, snoring, daytime sleepiness) individually and combined into a sleep quality score. Participants were followed until 2021 for clinical HF (Stage C/D) or death. Cox proportional hazard models assessed the relationship between sleep quality and clinical HF or mortality, accounting for competing risks. Interaction effects between sleep, sex, and deprivation were explored. Results: Among 311,446 preclinical HF participants (mean age 57.5 years, 49% men), sleep quality was categorized as healthy (35%), intermediate (60%), and poor (5%). Over a median 12-year follow-up, 10,780 clinical HF events and 24,996 deaths occurred. Poor sleep quality was associated with a higher risk of clinical HF (HR: 2.03; 95% CI: 1.79–2.31 in women, HR: 1.55; 95% CI: 1.41–1.71 in men). Deprivation was associated with higher HF risk across all sleep categories (P<0.01). Conclusions: Low-quality sleep was prevalent and associated with a higher risk of clinical HF and death, especially in women. Deprivation was linked to worse outcomes in both sexes. These findings highlight an opportunity to improve preclinical HF outcomes by addressing sleep quality. Keywords: sleep, heart failure, mortality, risk factors INTRODUCTION Heart failure (HF) is a growing global health concern, driven in part, by aging populations and increasing burden of risk factors ( 1 ). HF-related mortality increased in the US over the past decade ( 2 ) despite initiatives to reduce its burden ( 3 ). Hence, primary prevention is essential to reduce HF morbidity and mortality. The stage-based classification system by the American College of Cardiology/American Heart Association (ACC/AHA) ( 4 , 5 ) provides a framework to help prevent the progression from preclinical to clinical HF. The preclinical phase of HF, which identifies an enriched subset on the trajectory to clinical HF, is the optimal time for prevention through optimization of cardiovascular health ( 6 , 7 ). In 2022, the AHA incorporated sleep as a key component of cardiovascular health ( 8 ) operationalized by Life’s Essential 8. While the construct emphasizes sleep duration, other characteristics, such as sleep-disordered breathing, sleep quality, insomnia, and daytime sleepiness are increasingly recognized ( 9 – 12 ). Previous studies examined the association between sleep and clinical HF in the general population ( 10 , 13 ) and in patients with diabetes ( 14 ), and the association between sleep and mortality among those with cardiometabolic multimorbidity ( 15 ). Individual sleep variables such as chronotype or self-reported snoring have shown inconsistent associations with HF risk ( 10 , 13 ). It remains unclear if sleep quality is associated with clinical HF or mortality in individuals with preclinical HF and whether association differ by sex or social determinants of health ( 13 , 16 , 17 ). To address gaps in knowledge we aimed first to assemble a cohort of individuals with preclinical HF to characterize their sleep quality; second , to evaluate the clinical correlates of low-quality sleep; third , to examine the associations between low quality sleep and adverse outcomes, including occurrence of clinical HF and mortality, while exploring how sex and social deprivation may modify these associations. METHODS Source population and preclinical HF cohort The UK Biobank is a prospective cohort study of over 500,000 participants aged 37–73 across the UK from 2006 to 2010 ( 18 ). The UK Biobank includes sociodemographic, physical, and clinical information collected through various approaches including questionnaires, interviews, physical measures, and linkage to health records. We applied the AHA criteria ( 19 , 20 ) to assemble a cohort of preclinical HF ( Figure 1 ), which includes individuals classified as Stage A (HF risk factors) or Stage B (asymptomatic cardiac structural or functional abnormalities). Participants were classified as preclinical HF if they had any of the following clinical risk factors: prevalent atherosclerotic cardiovascular disease (ASCVD; defined as coronary artery disease, myocardial infarction, stroke, or peripheral artery disease), hypertension (blood pressure ≥130/80 mm Hg, or self-reported taking blood pressure-lowering medications), obesity (body mass index ≥30 kg/m 2 ), diabetes (self-reported physician diagnosis, self-reported use of diabetes medications, a non-fasting blood glucose level ≥200 mg/dL, or hemoglobin A1c ≥6.5%). Participants missing sleep data or having a censoring date that preceded baseline were excluded. Figure 1. Open in a new tab Flow chart of the study population. HF = heart failure, ASCVD = atherosclerotic cardiovascular disease. Exposure Assessment Sleep as the main exposure Five self-reported sleep characteristics were collected, including sleep duration, chronotype, insomnia, snoring, and daytime sleepiness ( 21 ). Sleep quality score was calculated as previously reported ( 21 ), where participants scored 1 point for each low-risk sleep factor and 0 for each high-risk factor. Low-risk sleep factors included an early chronotype (a “morning” person or more a “morning” than “evening” person), 7 to 8 hours of sleep per day, never or rarely having insomnia, no snoring, and no frequent daytime sleepiness. Scores were summed across the five components with higher scores indicating higher quality sleep. Sleep was categorized as healthy (reference group, score 4–5), intermediate (score 2–3), and poor (score 0–1) ( 21 ). Other clinical characteristics Demographic, lifestyle, and clinical variables were collected by touchscreen questionnaires and interviews. Physical activity was categorized as low [<600 metabolic equivalent (MET)-min/week], moderate (600 to 3000 MET-min/week), and high (≥ 3000 MET-min/week) ( 22 ). The Townsend deprivation index is derived from national census data by combining four area-level measures of neighborhood disadvantage, including non-home ownership, non-car ownership, unemployment, and overcrowding, and, with higher scores indicating greater deprivation. In addition, body mass index, blood pressure, glucose, hemoglobin A1c, triglycerides, total cholesterol, low-density and high-density lipoprotein cholesterol were measured. Dyslipidemia was defined as having at least one of the following based on the Adult Treatment Panel III classification ( 23 ): total cholesterol ≥200 mg/dL, triglycerides ≥150 mg/dL, low-density lipoprotein cholesterol ≥130 mg/dL, high-density lipoprotein cholesterol ≤40 mg/dL for men and ≤50 mg/dL for women, or taking lipid-lowering medication. Participants with incomplete lipid data were classified as non-dyslipidemia if three out of the four lipid measurements were within the normal range and they were not taking lipid medication. QRISK3 ( 24 ) scores were also calculated as an indicator of comorbidity for model adjustment. Assessment of outcomes Outcomes included incident clinical HF, all-cause mortality, and cardiovascular disease (CVD) mortality, classified using ICD-9 and ICD-10 (International Classification of Diseases, 9 th and 10 th Revisions, respectively) codes. HF was defined with the following codes: ICD-9: 428.0 and 428.1; ICD-10: I50.0, I50.1, I50.9, I11.0, I13.0, and I13.2. CVD mortality was defined as the underlying cause of death with ICD-10 codes I00 to I78. Follow-up started at the date of baseline visit and ended at the date of the first incident HF diagnosis (for HF analyses), death (for mortality analyses), or end of follow-up (including loss to follow-up and administrative censoring; 20 September 2021 for England and Wales, and 31 October 2021 for Scotland). Statistical analysis Baseline characteristics were summarized using means with standard deviation (SD) or counts with percentage. Crude incidence rate of clinical HF during follow-up and the 95% confidence intervals (CIs) were calculated using the Clopper–Pearson exact binomial method. All-cause mortality was calculated using the Kaplan–Meier estimator, while cardiovascular mortality was derived by cumulative incidence, treating non-cardiovascular mortality as a competing event. Cox proportional hazard models were used to examine the association between sleep quality and clinical HF or death, accounting for competing risks, using complete follow-up time as the underlying timescale. The proportional hazard assumptions were verified. Since significant interaction effects were observed between sleep and sex, we examined associations within each sex group. To adjust for the effects of potential confounders, two models were considered. Model 1 included age (continuous; 5 natural spline knots were set due to non-linear association) and race/ethnicity (White/Non-White). Model 2 further included dyslipidemia (yes/no), assessment center (21 categories), Townsend Deprivation Index (continuous), physical activity (low/medium/high/missing), smoking (current/past/never), and alcohol use (current/past/never). To examine how social deprivation modified the associations, we further stratified the analyses by five increasing social deprivation groups defined using quintiles of the Townsend deprivation index. We calculated hazard ratios (HRs) across deprivation status and sleep categories, setting the least deprivation as the reference group. We analyzed the linear trend effects of deprivation on outcomes for each sleep category by treating deprivation level as a continuous variable. Most covariates had minimal missingness (<1.4%), except for physical activity, which had a 17.8% missing rate; so a ‘missing’ category was created for this variable. Two-sided P values were considered significant at P <0.05. Statistical analyses were performed using R version 4.4.0. RESULTS The preclinical HF cohort The cohort included 311,446 individuals with preclinical HF (mean age 57.5 ± 7.8 years; 49.0% men; 95.0% White). At baseline, 95.9% had hypertension, 31.6% were obese, 7.5% had diabetes, and 5.2% had a history of ASCVD ( Table 1 ). Men had a higher prevalence of diabetes, ASCVD, smoking and alcohol use, while women had a higher prevalence of obesity. Table 1. Population characteristics of participants with preclinical heart failure in UK Biobank Overall (N=311,446) Women (N=158,851) Men (N=152,595) Sociodemographic Age, mean(SD), y 57.5 (7.8) 57.6 (7.7) 57.3 (8.0) Townsend deprivation index, mean(SD) −1.4 (3.0) −1.4 (3.0) −1.4 (3.1) Clinical conditions Obesity, No.(%) 98,087 (31.6) 51,804 (32.7) 46,283 (30.5) Hypertension, No.(%) 294,708 (95.9) 148,536 (94.9) 146,172 (97.0) Diabetes, No.(%) 23,389 (7.5) 9,346 (5.9) 14,043 (9.2) Dyslipidemia, No.(%) 269,043 (86.4) 136,924 (86.2) 132,119 (86.6) Atherosclerotic cardiovascular disease, No.(%) 16,058 (5.2) 4,695 (3.0) 11,363 (7.4) Lifestyle behaviors Physical activity, No.(%) Low 49,096 (19.2) 24,200 (19.5) 24,896 (18.9) Moderate 127,867 (50.0) 63,676 (51.2) 64,191 (48.8) High 79,000 (30.9) 36,500 (29.3) 42,500 (32.3) Smoking, No.(%) Current 31,242 (10.1) 13,007 (8.2) 18,235 (12.0) Former 113,513 (36.6) 51,562 (32.6) 61,951 (40.7) Never 165,717 (53.4) 93,784 (59.2) 71,933 (47.3) Alcohol drinking, No.(%) Current 287,178 (92.3) 143,474 (90.4) 143,704 (94.2) Former 10,722 (3.4) 5,736 (3.6) 4,986 (3.3) Never 13,317 (4.3) 9,524 (6.0) 3,793 (2.5) Individual sleep characteristics , No.(%) Sleep >8 or <7 hours/day 102,638 (33.0) 52,875 (33.3) 49,763 (32.6) Late chronotype 114,826 (36.9) 57,607 (36.3) 57,219 (37.5) Frequent insomnia 237,329 (76.2) 130,977 (82.5) 106,352 (69.7) Self-reported snoring 125,583 (40.3) 50,051 (31.5) 75,532 (49.5) Frequent daytime sleepiness 8,837 (2.8) 4,112 (2.6) 4,725 (3.1) Sleep quality score , No.(%) Healthy 110,444 (35.5) 58,182 (36.6) 52,262 (34.2) Intermediate 185,612 (59.6) 93,614 (58.9) 91,998 (60.3) Poor 15,390 (4.9) 7,055 (4.4) 8,335 (5.5) Open in a new tab Percentages exceed 100% due to rounding. Frequent insomnia was more common in women (82.5% vs 69.7%). Snoring was more prevalent in men (49.5% vs 31.5%). Approximately, 35% of the participants were categorized as having a healthy sleep score, while the prevalence of intermediate and poor sleep was 59% and 5%, respectively. Sleep quality was similar by sex, but differed by obesity, diabetes, or current smoking ( Table 2 ). Table 2. Population characteristics by sleep score Healthy score (N=110,444) Intermediate score (N=185,612) Poor score (N=15,390) Sociodemographic Age, mean(SD), y 57.4 ± 8.0 57.6 ± 7.8 56.7 ± 7.8 Men, No.(%) 52,262 (47.3) 91,998 (49.6) 8,335 (54.2) Townsend deprivation index, mean (SD) −1.60 (2.92) −1.34 (3.07) −0.76 (3.31) Clinical conditions Obesity, No.(%) 27,703 (25.2) 63,012 (34.1) 7,372 (48.2) Hypertension, No.(%) 105,244 (96.4) 175,192 (95.8) 14,273 (94.6) Diabetes, No.(%) 6,646 (6.0) 14,784 (8.0) 1,959 (12.8) Dyslipidemia, No.(%) 93,874 (85.0) 161,494 (87.0) 13,675 (88.9) Atherosclerotic cardiovascular disease, No.(%) 4,751 (4.3) 10,150 (5.5) 1,157 (7.5) Lifestyle behaviors Physical activity, No.(%) Low 14,474 (15.6) 31,072 (20.6) 3,550 (29.0) Moderate 47,142 (50.9) 75,116 (49.7) 5,609 (45.9) High 30,978 (33.5) 44,950 (29.7) 3,072 (25.1) Smoking, No.(%) Current 8,311 (7.5) 20,408 (11.0) 2,523 (16.4) Former 37,368 (33.9) 69,977 (37.8) 6,168 (40.2) Never 64,440 (58.5) 94,621 (51.1) 6,656 (43.4) Alcohol drinking, No.(%) Current 101,563 (92.0) 171,609 (92.5) 14,006 (91.1) Former 3,604 (3.3) 6,402 (3.5) 716 (4.7) Never 5,203 (4.7) 7,461 (4.0) 653 (4.2) Open in a new tab Percentages exceed 100% due to rounding. Outcomes The median follow-up was 12.5 years (IQR: 11.7–13.2). A total of 10,780 clinical HF events occurred, equating to an incidence rate of 2.9 (95% CI: 2.8 to 2.9) per 1000 person-years. A total of 24,996 deaths, among which 5,023 (20.1%) were CVD-related, occurred. This equated at 10 years to an all-cause mortality rate of 5.2% (95% CI: 5.1 to 5.3%) and a CVD mortality rate of 1.0% (95% CI: 1.0 to 1.1%). Event rates were higher in men than in women ( Table 3 ). Table 3. Association between sleep characteristics and outcomes among women and men with preclinical heart failure in UK Biobank Clinical heart failure ** All-cause mortality Cardiovascular disease mortality ** Women Event/N 3,985/158,851 9,766/158,851 1,483/158,851 Event rate (95% CI) * 2.1 (2.0, 2.1) 3.9 (3.8, 4.0) 0.6 (0.5, 0.6) Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Individual sleep characteristics Sleep >8 or <7 hours/day 1.45 (1.36, 1.55) 1.33 (1.25, 1.42) 1.26 (1.21, 1.31) 1.17 (1.12, 1.22) 1.41 (1.27, 1.57) 1.27 (1.14, 1.41) Late chronotype 1.23 (1.15, 1.31) 1.15 (1.08, 1.23) 1.19 (1.15, 1.24) 1.11 (1.06, 1.15) 1.29 (1.16, 1.43) 1.17 (1.06, 1.31) Frequent insomnia 1.28 (1.16, 1.40) 1.22 (1.11, 1.35) 0.97 (0.92, 1.02) 0.94 (0.89, 0.99) 0.93 (0.81, 1.07) 0.89 (0.77, 1.02) Self-reported snoring 1.12 (1.05, 1.20) 1.07 (1.00, 1.14) 1.04 (1.00, 1.09) 0.99 (0.95, 1.04) 1.15 (1.03, 1.28) 1.08 (0.97, 1.21) Frequent daytime sleepiness 2.27 (1.99, 2.60) 1.98 (1.73, 2.26) 1.62 (1.46, 1.78) 1.45 (1.32, 1.61) 1.99 (1.58, 2.50) 1.71 (1.35, 2.16) Sleep quality score Healthy Reference Reference Reference Reference Reference Reference Intermediate 1.36 (1.27, 1.46) 1.26 (1.17, 1.35) 1.16 (1.11, 1.21) 1.07 (1.02, 1.11) 1.18 (1.05, 1.32) 1.06 (0.95, 1.19) Poor 2.51 (2.21, 2.84) 2.03 (1.79, 2.31) 1.64 (1.50, 1.79) 1.33 (1.21, 1.45) 2.37 (1.94, 2.89) 1.79 (1.46, 2.20) Men Event/N 6,795/152,595 15,230/152,595 3,540/152,595 Event rate (95% CI) * 3.7 (3.6, 3.8) 6.5 (6.4, 6.6) 1.5 (1.5, 1.6) Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Individual sleep characteristics Sleep >8 or <7 hours/day 1.39 (1.32, 1.46) 1.29 (1.23, 1.36) 1.34 (1.30, 1.39) 1.23 (1.19, 1.27) 1.42 (1.32, 1.51) 1.28 (1.20, 1.37) Late chronotype 1.08 (1.02, 1.13) 1.01 (0.96, 1.06) 1.13 (1.10, 1.17) 1.05 (1.02, 1.09) 1.12 (1.04, 1.20) 1.03 (0.96, 1.10) Frequent insomnia 1.33 (1.26, 1.41) 1.27 (1.20, 1.35) 1.16 (1.12, 1.20) 1.11 (1.07, 1.15) 1.15 (1.06, 1.24) 1.09 (1.01, 1.17) Self-reported snoring 0.95 (0.91, 1.00) 0.98 (0.93, 1.02) 0.85 (0.83, 0.88) 0.87 (0.84, 0.90) 0.85 (0.79, 0.91) 0.87 (0.81, 0.93) Frequent daytime sleepiness 1.72 (1.55, 1.91) 1.57 (1.42, 1.74) 1.32 (1.22, 1.43) 1.19 (1.10, 1.28) 1.43 (1.23, 1.67) 1.28 (1.10, 1.49) Sleep quality score Healthy Reference Reference Reference Reference Reference Reference Intermediate 1.23 (1.16, 1.29) 1.17 (1.11, 1.24) 1.16 (1.12, 1.20) 1.09 (1.05, 1.13) 1.23 (1.14, 1.32) 1.14 (1.06, 1.23) Poor 1.77 (1.61, 1.95) 1.55 (1.41, 1.71) 1.39 (1.29, 1.49) 1.18 (1.10, 1.27) 1.43 (1.23, 1.65) 1.20 (1.03, 1.39) Open in a new tab For the individual sleep characteristics, the reference group was their low-risk level. * Crude incidence of clinical heart failure during follow-up, unit: per 1000 person-years. Ten-year all-cause and cardiovascular mortality rates were presented. ** Cause-specific hazard regression models were used, with non-heart failure death as a competing event for heart outcome and non-cardiovascular death for cardiovascular disease mortality. Model 1: adjusted for age and race. Model 2: Model 1 + assessment center, Townsend deprivation index, dyslipidemia, physical activity, smoking, and alcohol status. Sleep quality and outcomes Long or short sleep duration and frequent daytime sleepiness were associated with higher risk of adverse outcomes in both sexes ( Table 3 ). Late chronotype was associated with higher risk of all three adverse outcomes in women, but only with all-cause mortality in men. Frequent insomnia was significantly associated with all adverse outcomes in men, but only with risk of HF in women. We observed a relatively small, but statistically significant, protective association between frequent insomnia and all-cause mortality (HR: 0.94 95% CI: 0.89 to 0.99) in women. Self-reported snoring was not a risk factor in women, but was associated with lower risk of mortality in men. Intermediate and poor sleep quality were associated with all adverse outcomes. All associations were only slightly attenuated after adjusting for covariates and were stronger in women compared to men (P-interaction <0.01) ( Table 3 ). For clinical HF, intermediate sleep quality was associated with a higher risk of 26% in women and 17% in men while poor sleep was associated with a two-fold higher risk in women and 55% higher risk in men. For all-cause mortality, intermediate sleep quality was associated with a 7% higher risk in women and 9% in men. Poor sleep was associated with a 33% higher risk of all-cause mortality in women and 18% in men. For CVD mortality, intermediate sleep quality was associated with a 14% higher risk of CVD mortality in men, and poor sleep was associated with a 79% higher risk in women and 20% in men. Further adjustment for preclinical HF components or QRISK3 score did not substantially alter the associations between sleep and outcomes. Associations with social deprivation For clinical HF, worse deprivation was associated with greater risk regardless of sleep quality or sex ( Figure 2 ). For all-cause and CVD mortality, worse deprivation was associated with greater risk regardless of sleep categories for men but were limited to women with healthy and intermediate sleep categories. Figure 2. Open in a new tab Association between sleep quality pattern and outcomes among women and men stratified by socioeconomic deprivation quintile. Most healthy sleep, least deprived group is the reference group. Error bars indicate 95% CIs. HR=hazard ratio. DISCUSSION In a cohort of over 300,000 individuals with preclinical HF, two-thirds had poor sleep, especially those with obesity or diabetes. Clinical HF and mortality rates were higher in men. Poor sleep was associated with an excess risk of clinical HF and death, particularly in women, and greater deprivation was associated with increased risk of adverse outcomes across most sleep categories in both sexes. Preclinical HF Preclinical HF affects approximately 50% of adults ( 1 , 25 ), but remains poorly characterized. In the UK Biobank, 76% of the participants had preclinical HF, with a high prevalence of hypertension and dyslipidemia. Compared with US community cohorts ( 19 ), our cohort had more hypertension (96% vs 83%), less diabetes (7% vs 17%), and a similar obesity rates (over 30%). A report from the Atherosclerosis Risk in Communities Study highlighted that the prevalence of hypertension was nearly 75% and that 30% of participants with preclinical HF had diabetes ( 20 ). Differences may be related to the definition of preclinical HF, study design, such as recruitment periods, age distributions, and country of origin. However, the findings show that preclinical HF is common, with hypertension and obesity commonly observed. Preclinical HF population is likely heterogeneous, with differences in the prevalence of obesity or structural heart disease ( 4 ), which likely reflects differences in ascertainment methods. Sleep quality and risk of heart failure and mortality Sex differences in selected sleep characteristics have been reported ( 16 , 26 ), with women experiencing more insomnia and men more snoring ( 16 , 27 ). We observed similar patterns in our preclinical HF population. One study of young adults reported a higher prevalence of poor sleep among women beyond psycho-social disparities ( 28 ). The mechanisms underlying differences in sleep quality among women and men are likely multifactorial, involving biological and social factors ( 16 , 26 , 27 , 29 ). The prevalence of low-quality sleep did not differ by sex but its association on outcomes differed, with women being more vulnerable to the adverse effects of low-quality sleep. Notably, the lack of a uniform definition of sleep quality ( 30 ) poses a challenge to directly comparing our findings with others. Further, no published research has yet examined sex differences in the association between composite sleep quality and HF incidence or mortality in individuals with preclinical HF. When considering sleep characteristics individually, we found a U-shaped association with sleep duration and risk of HF and mortality, consistent with previous research ( 8 , 9 ), but without sex differences in these associations. Chen et.al ( 31 ) recently reported no sex differences in the relation between sleep duration with diabetes and CVD. However, one review indicated that women may be more susceptible to CVD and mortality of insufficient sleep (<7 hours/night) than men ( 29 ), highlighting the need for more research. Sex-specific associations between the other four sleep characteristics and HF are not well known. Herein, late chronotype was a risk factor for HF and mortality only in women, and insomnia was a risk factor for mortality only in men. Both insomnia and evening chronotype have been associated with poor cardiometabolic health and increased CVD risk ( 32 – 34 ), with individuals more likely to have an unhealthy lifestyle including poor diet, lack of physical activity, and smoking ( 35 ). In a meta-analysis, women insomniacs had a higher risk of myocardial infarction compared to men ( 36 ), while the association between insomnia and increased all-cause mortality was stronger in men ( 37 ). Sex differences in insomnia may be explained by factors such as women experiencing insomnia earlier in life, potentially coping better with sleep disturbances, and being particularly vulnerable to insomnia during hormonal changes, such as the menstrual cycle ( 37 ), but the ‘mixed’ directions of adverse outcomes suggest that the underlying mechanisms are complex and not fully understood. Daytime sleepiness and snoring are symptoms of sleep-disordered breathing, which is frequent in clinical HF ( 9 ). This may be the case also in preclinical HF. In our study, daytime sleepiness was a risk factor for adverse outcomes, particularly in women. A previous UK Biobank report did not find an overall association between snoring and HF ( 10 ) or CVD ( 21 ) in the general population.. Our results suggest that these exposures might operate differently among persons with preclinical HF. It is worth noting that women tend to report snoring less often than men ( 38 ) such that self-reported snoring data may need to be interpreted considering this observation. Social deprivation and sleep The Townsend deprivation index correlates with individual-level socioeconomic status ( 39 ). Residents living in poorer neighborhoods often face exposures that contribute to sleep deficiency ( 17 ), as well as limited social support and healthcare access and unhealthy lifestyle factors ( 40 ). Our data suggest that deprivation may increase the vulnerability to poor sleep. Even with similar sleep quality, higher deprivation was linked to a greater risk of adverse outcomes. However, the mortality risk for women with poor sleep quality was not significantly affected by social deprivation. Clinical implications Our findings indicate that individuals in the early stages of HF, before the onset of symptoms, may benefit from sleep intervention to slow the transition to symptomatic clinical HF. However, sleep was not included in the 2022 AHA/ACC/HFSA guidelines for HF management regarding lifestyle recommendations for patients across Stage A to Stage D ( 5 ). Further, recent European Society of Cardiology guidelines focus on individuals with clinical HF, with less emphasis on those at-risk ( 41 ). Strengths and limitations Strengths include the availability of rich clinical data and social deprivation indicators, allowing for thorough confounding assessment and long-term follow-up. With over 311,000 participants, offers valuable insights into sex-specific patterns and the link between sleep quality and outcomes. Additionally, our analysis of social deprivation and sleep highlights potential targets for interventions. Limitations include predominantly White participants, self-reported sleep data collected only at baseline, and the possibility of unmeasured confounding. Finally, the UK Biobank’s health volunteer bias ( 42 ), may lead to an underestimation of disease prevalence and incidence rates. Conclusion Low quality sleep was present in two-thirds of participants with preclinical HF. Poor sleep was significantly associated with higher risk of clinical HF and death, independent of other risk factors, particularly among women. Worse deprivation was associated with excess risk of adverse outcomes across most sleep categories in both women and men. These data indicate that individuals in the early stages of HF, before the onset of symptoms, may benefit from sleep intervention to slow the transition to symptomatic clinical HF. Clinical Significance. Low quality sleep was present in two-thirds of individuals with preclinical heart failure Low-quality sleep was associated with higher risk of clinical heart failure and mortality, particularly in women Individuals residing in more deprived areas were at higher risk of adverse outcomes across most sleep categories Acknowledgments: The authors thank the UK Biobank study participants and staff as well as Bill Wheeler at Information Management Services, Inc. This secondary analysis of de-identified data was exempt from IRB approval (NIH, 17-NCI-00302). Funding: The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by intramural funding from the National Cancer Institute (NCI), Division of Cancer Epidemiology and Genetics (DCEG) and the National Heart, Lung, and Blood Institute (NHLBI), Division of Intramural Research (DIR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Abbreviations and acronyms HF heart failure ASCVD atherosclerotic cardiovascular disease ACC/AHA American College of Cardiology/American Heart Association Footnotes Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Declaration of Interest Statement: None Conflict of Interest Disclosures: Fang Zhu, PhD, MPH Joseph J. Shearer, PhD, MPH Jungnam Joo, PhD Rui Miao, PhD Jason Y.Y. 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