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Sex differences in deterioration of sleep properties associated with aging: a 12-year longitudinal cohort study

2021; American Academy of Sleep Medicine; Volume: 17; Issue: 5 Linguagem: Inglês

10.5664/jcsm.9072

1550-9397

Hyeon Jin Kim, Regina E. Y. Kim, Soriul Kim, Sol Ah Kim, Song E. Kim, Seung Ku Lee, Hyang Woon Lee, Chol Shin,

Circadian rhythm and melatonin

Free AccessScientific InvestigationsSex differences in deterioration of sleep properties associated with aging: a 12-year longitudinal cohort study Hyeon Jin Kim, MD, Regina E.Y. Kim, PhD, Soriul Kim, PhD, Sol Ah Kim, BS, Song E. Kim, PhD, Seung Ku Lee, PhD, Hyang Woon Lee, MD, PhD, Chol Shin, MD, PhD Hyeon Jin Kim, MD Departments of Neurology and Medical Science, College of Medicine, Ewha Womans University and Ewha Medical Research Institute, Seoul, Republic of Korea *Contributed equally. , Regina E.Y. Kim, PhD Institute of Human Genomic Study, College of Medicine, Korea University, Ansan, Republic of Korea Department of Psychiatry, University of Iowa, Iowa City, Iowa *Contributed equally. , Soriul Kim, PhD Institute of Human Genomic Study, College of Medicine, Korea University, Ansan, Republic of Korea , Sol Ah Kim, BS Departments of Neurology and Medical Science, College of Medicine, Ewha Womans University and Ewha Medical Research Institute, Seoul, Republic of Korea Graduate Program in System Health Science and Engineering, and , Song E. Kim, PhD Departments of Neurology and Medical Science, College of Medicine, Ewha Womans University and Ewha Medical Research Institute, Seoul, Republic of Korea , Seung Ku Lee, PhD Institute of Human Genomic Study, College of Medicine, Korea University, Ansan, Republic of Korea , Hyang Woon Lee, MD, PhD Departments of Neurology and Medical Science, College of Medicine, Ewha Womans University and Ewha Medical Research Institute, Seoul, Republic of Korea Graduate Program in System Health Science and Engineering, and Computational Medicine, Ewha Womans University, Seoul, Republic of Korea , Chol Shin, MD, PhD Address correspondence to: Hyang Woon Lee, MD, PhD, Departments of Neurology, Medical Science, Computational Medicine, Ewha Womans University School of Medicine and Ewha Medical Research Institute, 1071, Anyangcheon-ro, Yangcheon-gu, Seoul 07985, Republic of Korea; Tel: +82-2-2650-2673; Fax: +82-2-2650-5958; Email: E-mail Address: [email protected]; and Chol Shin, MD, PhD, FCCP, Division of Pulmonary, Sleep, and Critical Care Medicine, Department of Internal Medicine, Korea University Ansan Hospital and Institute of Human Genomics Study, Korea University, 516, Gojan-1-dong, Danwon-gu, Ansan City, Gyeonggi-do 15355, Republic of Korea; Tel: +82-31-412-5541; Fax: +82-31-412-5604; Email: E-mail Address: [email protected] Institute of Human Genomic Study, College of Medicine, Korea University, Ansan, Republic of Korea Division of Pulmonary, Sleep, and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Korea University Ansan Hospital, Ansan, Republic of Korea Published Online:May 1, 2021https://doi.org/10.5664/jcsm.9072Cited by:10SectionsAbstractEpubPDFSupplemental Material ShareShare onFacebookTwitterLinkedInRedditEmail ToolsAdd to favoritesDownload CitationsTrack Citations AboutABSTRACTStudy Objectives:The sleep patterns of humans are greatly influenced by age and sex and have various effects on overall health as they change continuously during the lifespan. We investigated age-dependent changes in sleep properties and their relation to sex in middle-aged individuals.Methods:We analyzed data from 2,640 participants (mean age of 49.8 ± 6.8 years at baseline, 50.6% women) in the Korean Genome and Epidemiology Study, which assessed sleep habits using the Pittsburgh Sleep Quality Index and other clinical characteristics. We analyzed the sleep habit changes that occurred between baseline and a follow-up point (mean interval: 12.00 ± 0.16 years). Associations of age and sex with 9 sleep characteristics were evaluated.Results:Age was associated with most of the sleep characteristics cross-sectionally and longitudinally (P < .05), except for the time in bed at baseline (P = .455) and change in sleep duration (P = .561). Compared with men, women had higher Pittsburgh Sleep Quality Index scores, shorter time in bed, shorter sleep duration, and longer latency at baseline (P ≤ .001). Longitudinal deterioration in Pittsburgh Sleep Quality Index score, habitual sleep efficiency, duration, and latency was more prominent in women (P < .001). The sex differences in these longitudinal sleep changes were mainly noticeable before age 60 years (P < .05). Worsening of Pittsburgh Sleep Quality Index scores, habitual sleep efficiency, and latency was most evident in perimenopausal women. Men presented with greater advancement of chronotype (P = .006), with the peak sex-related difference occurring when they were in their late 40s (P = .048).Conclusions:Aging is associated with substantial deterioration in sleep quantity and quality as well as chronotype advancement, with the degree and timing of these changes differing by sex.Citation:Kim HJ, Kim REY, Kim S, et al. Sex differences in deterioration of sleep properties associated with aging: a 12-year longitudinal cohort study. J Clin Sleep Med. 2021;17(5):964–972.BRIEF SUMMARYCurrent Knowledge/Study Rationale: To date, few studies have presented longitudinal follow-up data of individual sleep measures, and even fewer have measured phase advance in relation to biological sex during the aging process. The identification of sleep profile changes in the pre-aging population is fundamental for understanding the role of sleep in healthy aging.Study Impact: Our study presents the interactive effects of age and sex on sleep behavior changes stratified by age, which could be used to guide future research on the extent to which sleep behavior changes should be addressed in men and women in late adulthood. Studies on the association between aging and sleep properties should further consider the multifactorial effects of sex and hormonal status.INTRODUCTIONAge-related changes in sleep profiles usually start in middle age around the 50s or even earlier, accompanied by a gradual increase in sleep complaints.1 In addition to the aging process, medical and psychiatric comorbidities in middle-aged adults, as well as primary sleep disorders, contribute to further deterioration in sleep properties.2,3 Consequently, it is quite challenging to distinguish general aging-associated sleep changes from pathological sleep conditions in this age group. Since evidence for the effect of sleep on the overall physical and mental health of the adults aged 40s and older population has increased in recent years, establishing a norm for how sleep changes with age will provide a valuable foundation for clinicians and researchers.The circadian system is a core determinant of sleep timing and structure, interacting with the homeostatic sleep-wake cycle to achieve consolidated sleep.4 The endogenous oscillation of biological timing in humans is synchronized to the 24-hour cycle by external inputs through entrainment. Age-related degeneration in any of the coordination systems involved in generating the circadian rhythms may, therefore, contribute to altered sleep-wake propensity.5,6 Age-related neural dysfunctions leading to decreased homeostatic sleep pressure buildup during wakefulness or decreased sleep need in itself are other suggested main factors leading to changes in sleep behavior.7,8Chronological regression of sleep patterns after middle age differs between individuals and, therefore, interindividual sleep profile variability is much more significant in adults aged 40s and older compared with the younger population.9 In addition to aging, sex is well known to influence intrinsic sleep structure.10 There is substantial evidence that women generally have more sleep-related self-reported complaints than men, but these are not always consistent with objective measures.11 Investigations on sex-related differences in sleep at different levels, from genetic regulation to regulation by the hypothalamic-pituitary-gonadal axis and reproductive hormones, have mostly been confined to the laboratory setting.12,13 Therefore, the interplay between sex-associated and aging effects on sleep patterns in real life remains unclear. Moreover, most of the research so far has focused on the amount of sleep without considering sleep timing preference (the chronotype) and has not included many middle-aged people,14 many of whom are also undergoing radical sex hormone changes.A thorough investigation of sleep behavior evolution in a middle-aged population stratified by age group in each sex is needed to provide a coherent interpretation of the research data on the consequences of inadequate sleep. Considering the substantial interindividual variability, the effects of the aging process on sleep physiology in individuals should be compared with the norm in their age group to assess the degree of pathological deviation.15,16 For this, reference data on sleep profiles derived from a representative cohort are crucial for selecting a target group that needs to be closely monitored. To date, however, there have been few large-scale longitudinal studies investigating a comprehensive panel of sleep measures including chronotype and their influencing factors, and even fewer in the Asian population.17–20 Our study aimed to fill this knowledge gap by delineating the sex-related differences in age-related effects on longitudinal sleep behavior changes in middle to late adulthood in a Korean population.METHODSStudy design and participantsWe studied a subset of participants in the Korean Genome and Epidemiology (KoGES) Ansan Study.21 This prospective study recruited randomly selected community-dwelling individuals aged 40–69 years in 2001 to investigate the genetic and environmental etiology of common complex diseases in Koreans and causes of death with long-term follow-up. A physical/medical examination and a questionnaire-based interview were administered every 2 years to compile longitudinal data. Each participant provided an informed-consent form, and the Institutional Review Board of the Korea University Ansan Hospital approved the study procedure.Due to multiple revisions in the sleep behavior survey format during the KoGES study, we only compared data from the second and eighth examinations, which we used as the baseline and follow-up time points, respectively, for our study. Among 4,023 participants for whom baseline sleep behavior data were available (collected during the second KoGES examination, 2003–2004), 3,083 participants completed the follow-up examination (eighth KoGES examination, 2015–2016), with a mean interval of 12.00 ± 0.16 years. The main reasons for attrition were contact loss (n = 666), refusal (n = 153), moving away (n = 59), and hospitalization (n = 56). We observed no distinct differences in the baseline characteristics between responders and nonresponders. We excluded participants who reported psychiatric illness (n = 3) or shift-working history (n = 206) as well as those taking any hypnotic medication (n = 69). After further elimination of those who reported extremes of any sleep behavior (n = 24; those who reported a midsleep time between 9 am and 6 pm on workdays), our final cohort had a total of 2,640 participants.Sleep questionnairesWe investigated sleep behavior profiles on workdays and free days separately. The weekly average was then calculated as follows:[(VariableWorkdays× 5)+(VariableFree days× 2)]/7The Pittsburgh Sleep Quality Index (PSQI) components related to sleep quantity include sleep latency and self-aware actual sleep duration in addition to bedtime and wake time during the past month.22 Time in bed (TIB) was calculated as the time difference between bedtime and wake time and then used to calculate habitual sleep efficiency (HSE; = % sleep duration/TIB). Social jetlag was defined as the discrepancy between the midsleep time on free days vs workdays, which represents the discrepancy between social and biological time.23 Mid-sleep time on free days corrected for sleep debt on weekdays (MSFsc) was also calculated as the midpoint between bedtime and wake time to estimate each individual's chronotype, as follows:24MSFsc=Midsleep timeFree days – 0.5×(TIBFree days – TIBAverage).Statistical analysisStatistical analysis was performed using the statistical software R (R version 3.6.1, R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org/). In the demographic characteristics analysis, age at baseline (second KoGES examination) was used and sex differences were evaluated using the t test or chi-square tests. In the cross-sectional sleep profiles analysis, the effects of age and sex were reported from 1 linear regression model after adjusting for body mass index (kg/m2), smoking intensity (pack-years), alcohol consumption (g/d), hypertension, diabetes mellitus, and occupation.21,25SleepBaseline=βAge×Age+βFemale×Female+βCovariate×CovariateIn the longitudinal change analysis, values at baseline were subtracted from the corresponding values at the follow-up point and adjustments were made for each individual's baseline measurements and precise follow-up interval.ΔSleep=βAge×Age+βFemale×Female+βCovariate×Covariate+βSleep(T1)×Sleep (T1)+βinterval×Interval,whereΔSleep=Sleep (T2)-Sleep (T1);T1=baseline;T2=follow-upCorrelation matrices for the paired association between sleep properties were computed and, after adjusting for the covariates mentioned above, corresponding P values for each longitudinal change in sleep behavior were reported.To investigate associations between sleep behaviors and menstruation status, we categorized the female study participants into 3 groups based on their menopausal status during our study period26: (1) the premenopause group with persistent menstruation up to the end of the data-collection period, (2) the perimenopause group of women who had menstruation at baseline but reported menopause at the final visit, and (3) the postmenopause group with menopause reported at baseline. A linear regression model was used to analyze changes in sleep properties across menopausal groups after adjusting for the covariates mentioned above. Strata of menopausal status were used to show patterns of association, and tests for trend (P for trend) were conducted based on a general linear regression model with status strata ordered from pre- to peri- to postmenopausal.27ΔSleep=βtrend×[Menopause status]+βCovariate×Covariate+βinterval×Interval+βAge×AgeRESULTSDemographic characteristicsWe analyzed data from 2,640 individuals (50.6% female) whose average age was 49.8 years at baseline. Detailed sex-segregated demographic characteristics at baseline are presented in Table 1. The mean follow-up interval was slightly longer in women (P = .040). Most of the men were economically active (88.8%) at baseline. In contrast, a majority of female participants reported being homemakers (63.6%). Hypertension (22.9% vs 32.3%; P < .001) and diabetes (13.2% vs 18.1%; P < .001) were reported more frequently in men than in women. Body mass index was not different between the sexes (P = .181). Men reported considerably more unhealthy lifestyles, including smoking experience and alcohol consumption (P < .001, respectively).Table 1 Demographic characteristics at baseline.Women (n = 1,335) (50.6%)Men (n = 1,305) (49.4%)P*Age, y49.82 ± 6.9349.71 ± 6.70.685Follow-up duration, y12.00 ± 0.1511.99 ± 0.17.040Occupation,† n (%)<.001 Intellectual185 (13.9)595 (45.6) Mixed232 (17.4)523 (40.1) Elementary52 (3.9)40 (3.1) Homemaker849 (63.6)1 (0.1)Hypertension, n (%)305 (22.9)421 (32.3)<.001 Systolic blood pressure, mm Hg109.64 ± 14.68115.38 ± 14.03 Diastolic blood pressure, mm Hg72.63 ± 10.2278.57 ± 10.43Diabetes, n (%)176 (13.2)236 (18.1)<.001Body mass index, kg/m224.46 ± 2.9224.61 ± 2.70.181Smoking status, n (%)<.001 Never1,289 (96.6)294 (22.5) Ex-smoker15 (1.1)545 (41.8) Current31 (2.3)466 (35.7)Alcohol, g/d1.89 ± 6.1919.87 ± 30.82<.001 Heavy (≥15 g/d), n (%)38 (2.9)527 (40.4)<.001Data are means ± SDs or the number of observations, n (%). n = 2,640. *P = overall group differences between the sexes by t test or chi-square test. †Occupation was investigated and classified into 4 categories: intellectual (including senior officials, managers, and professionals), mixed (including armed forces, technicians and associate professionals, service workers and shop and market sales workers, skilled agricultural and fishery workers, craft and related trades workers, plant and machine operators and assemblers), elementary (elementary occupations consist of simple and routine tasks which mainly require the use of hand-held tools and often some physical effort), and homemakers. Students, retirees, and unemployed people were classified as economically inactive. SD = standard deviation.Sleep properties with agingThe mean values of 9 sleep properties and their associations with age at baseline are summarized in the upper left portion of Table 2. Cross-sectionally, older age was substantially associated with deterioration in all sleep characteristics except for TIB (P = .455). Older age was associated with higher PSQI score (P < .001); longer sleep latency (P = .001); lower HSE (P < .001), which was mostly driven by shorter sleep duration (P < .001); smaller social jetlag (P < 0.001); and earlier chronotype (MSFsc, P < .001).Table 2 Sleep properties at baseline and their change at follow-up.TotalβAge†P†WomenMenβW/M‡P‡Baseline measure PSQI, score4.2 (2.3)0.03<.0014.4 (2.4)4.0 (2.2)0.67<.001 Sleep latency, min16.0 (16.9)0.16.00117.0 (18.1)15.0 (15.6)4.22<.001 HSE, %94.2 (10.6)−0.24<.00194.3 (11.2)94.1 (10.0)−0.34.508 Time in bed, h6.7 (1.2)0.00.4556.6 (1.3)6.9 (1.1)−0.18.001 Sleep duration, h6.3 (1.2)−0.01<.0016.2 (1.2)6.4 (1.2)−0.21<.001 MSFsc, 24-h3:05 (1:11)−0:03<.0012:59 (1:07)3:12 (1:14)−0:03.307 Midsleep time (F), 24-h3:15 (1:15)−0:04<.0013:11 (1:12)3:18 (1:18)0:03.289 Midsleep time (W), 24-h2:54 (1:05)−0.02<.0012:48 (0:59)2:59 (1:10)−0:02.346 Social jetlag, 24-h0:24 (0:40)−0:01<.0010:26 (0:37)0:23 (0:42)0:05.004Longitudinal change ΔPSQI, score0.7 (3.0)0.02.0110.9 (3.1)0.5 (2.8)0.80<.001 ΔSleep latency, min1.2 (22.7)0.22<.0013.5 (26.7)−1.1 (17.6)6.86<.001 ΔHSE, %−4.0 (14.4)−0.26<.001−5.6 (14.9)−2.3 (13.7)−3.71<.001 ΔTime in bed, h0.3 (1.4)0.02<.0010.4 (1.4)0.2 (1.4)0.02.769 ΔSleep duration, h0.0 (1.4)0.00.561−0.1 (1.4)0.0 (1.3)−0.23<.001 ΔMSFsc, 24-h−0:29 (1:22)−0:01<.001−0:19 (1:16)−0:38 (1:26)0:09.006 ΔMidsleep time (F), 24-h−0:35 (1:19)−0:01<.001−0:29 (1:12)−0:42 (1:25)0:08.013 ΔMidsleep time (W), 24-h−0:23 (1:12)−0:01<.001−0:16 (1:07)−0:31 (1:16)0:07.023 ΔSocial jetlag, 24-h−0:11 (0:54)−0:01<.001−0:11 (0:55)−0:11 (0:53)0:03.126The adjusted mean (SD) values at baseline (upper portion) and change at follow-up (lower portion) are shown. βAge† and βW/M‡ were computed in the same model. βAge† (presented with its corresponding P†) is the estimated association of age to the corresponding sleep characteristic after adjusting for sex, body mass index (kg/m2), smoking intensity (pack-years), alcohol consumption (g/d), hypertension, diabetes mellitus, and occupation. For longitudinal comparisons, additional adjustments for the variable at baseline and the visit interval (years) were made. βW/M‡ (presented with its corresponding P‡) reflects the association of the measures in women relative to men. F = free days, HSE = habitual sleep efficiency, MSFsc = mid-sleep time on free days corrected for sleep debt on weekdays, PSQI = Pittsburgh Sleep Quality Index, SD = standard deviation, W = workdays, Δ = change.The longitudinal observations were consistent with our baseline findings in that advancing age was associated with accelerated deterioration in sleep properties (Table 2; lower left, βAge†). We observed accelerated changes in PSQI scores (+0.02/year, P = .011), sleep latency (+0.22 minutes/year, P < .001), and HSE (−0.26%/year, P < .001) driven by an accelerated increase in TIB (+0.02 hours/year, P < .001). Also, the 1-year increase in age was associated with accelerated advance in chronotype (MSFsc, −1 minute/year, P < .001) and decrease in social jetlag (−1 minute/year, P < .001).Sleep properties by sexThe right side of Table 2 summarizes the sleep characteristics at baseline and after 12 years for each sex. At baseline, women reported higher PSQI scores (P < .001), longer sleep latency (P < .001), shorter TIB (P = .001), shorter sleep duration (P < .001), and larger social jetlag (P = .004) than men. There were no observed sex-associated differences in HSE, MSFsc, or midsleep time (either on free days or workdays) at baseline.The longitudinal change in many sleep properties also differed between the sexes. Women reported a greater reduction in sleep quality (represented by a larger increase in PSQI, 0.9 vs 0.5; P < .001). Sleep latency increased in women but decreased slightly in men (+3.5 vs −1.1 minutes, P < .001). Women exhibited a greater decrease in HSE (−5.6% vs −2.3%, P < .001) resulting from a larger decrease in sleep duration (−0.1 vs 0.0 hours, P < .001) in addition to increased TIB (P = .769). No definite sex-related difference was observed in longitudinal social jetlag change (P = .126). The overall degree of chronotype advancement at the 12-year follow-up point was more remarkable in men than in women as revealed by changes in MSFsc (−38 vs −19 minutes, P = .006) and midsleep time (P = .013 for free days and P = .023 for workdays).Longitudinal sleep deteriorations across age groups in each sexFurther investigation by age and sex is summarized in Figure 1 and Table S1 in the supplemental material. The results suggest that sex differences in longitudinal sleep changes could differ by age subgroup. The degree of longitudinal PSQI increase was generally higher for women in most age groups, showing a tendency for accelerated deterioration after the mid-50s. The increment of sleep latency was more remarkable for women than for men in all age groups, and the sex-related difference in this variable was mainly observed before age 60 years. The degree of HSE decrease was generally greater for women participants in most age groups, and the sex differences in HSE change were observed prominently in participants in their 40s and late 50s.Figure 1: Longitudinal changes in sleep properties between the sexes across age groups.Dotted lines represent the mean change in sleep properties over the follow-up period in men (blue triangles) and women (red squares) participants by age group. Significant differences between the sexes after adjusting for all covariates are marked by asterisks: *P < .05, **P < .005, and ***P < .001. The black dotted horizontal lines (zero on the y axis) indicate the level that would represent no change. HSE = habitual sleep efficiency, MSFsc = mid-sleep time on free days corrected for sleep debt on weekdays, PSQI = Pittsburgh Sleep Quality Index.Download FigureThe most substantial chronotype advance was observed in men in their 40s and women in their 50s. The degree of chronotype advancement was generally greater in men than in women in all age groups, and a sex-related difference was mainly noticeable in participants in their 40s (45–49 years old for change [Δ] in MSFsc, −43 minutes in men and −18 minutes in women; P < 0.05). In participants older than their mid-50s, the degree of chronotype advancement tended to be gradually attenuated in both sexes.Pairwise correlation of longitudinal sleep change propertiesWe next investigated paired correlations between longitudinal sleep change properties in each sex (Table 3). In both sexes, sleep quality deterioration reflected in increased PSQI during the follow-up period was associated with the following measures (in order of strongest to weakest correlation): decreased sleep duration (r = −.60 in women, r = −.61 in men; both P < .001), decreased HSE (r = −.57 in women, r = −.56 in men; both P < .001), increased sleep latency (r = .45 in women, r = .44 in men; both P < .001), and decreased TIB (r = −.15 in women, r = −.20 in men; both P < .001).Table 3 Adjusted correlation matrices of longitudinal changes in sleep properties in each sex.ΔPSQIΔLatencyΔHSEΔTIBΔDurationΔMSFscΔMST (F)ΔMST (W)Women ΔLatency0.45***1.00 ΔHSE−0.57***−0.32***1.00 ΔTIB−0.15***0.13***−0.31***1.00 ΔDuration−0.60***−0.15***0.52***0.62***1.00 ΔMSFsc0.010.030.03−0.020.031.00 ΔMST (F)0.020.020.05−0.030.030.70***1.00 ΔMST (W)0.020.040.02−0.07−0.010.76***0.66***1.00 ΔSJL0.04−0.030.010.020.020.33***0.26***−0.03Men ΔLatency0.44***1.00 ΔHSE−0.56***−0.33***1.00 ΔTIB−0.20***0.07*−0.27***1.00 ΔDuration−0.61***−0.16***0.51***0.66***1.00 ΔMSFsc0.00−0.010.020.040.07*1.00 ΔMST (F)−0.01−0.010.07**0.000.09*0.82***1.00 ΔMST (W)−0.02−0.020.06*−0.030.040.77***0.76***1.00 ΔSJL0.050.01−0.010.09**0.07*0.26***0.20***0.06***Correlation significances are noted after adjusting for body mass index (kg/m2), smoking intensity (pack-years), alcohol consumption (g/d), hypertension, diabetes mellitus, occupation, and visit interval (years). *P < .05, **P < .005, ***P < .001. F = free days, HSE = habitual sleep efficiency, MSFsc = mid-sleep time on free days corrected for sleep debt on weekdays, MST = midsleep time, PSQI = Pittsburgh Sleep Quality Index, SJL = social jetlag, TIB = time in bed, W = workdays.Longitudinal changes in chronotype measures (ΔMSFsc, Δmidsleep time) were highly correlated with each other in both sexes (r > .6, P < .001) and also correlated with decreased social jetlag (for ΔMSFsc: r = .33 in women, r = .26 in men; both P < .001). However, we did not find any significant linear association of longitudinal chronotype advance and sleep quality/quantity deteriorations. Only in men did advanced chronotype measured by midsleep time (free days and workdays) exhibit a slight tendency to correspond with decreased HSE and duration, but this finding was marginal (r = .07 and r = .06, respectively; both P < .05).Effects of menstruation status on sleep characteristicsBaseline characteristics of the women across menopausal statuses are shown in Table S2. At baseline, similar numbers of participants were in the premenopause and postmenopause groups (n = 728 [54.5%] vs n = 607 [45.5%]). The postmenopause group's average age was higher, as expected (55.07 ± 6.62 years vs 45.44 ± 3.03 years, respectively; P < .001). Sleep profiles differed widely between the pre- and postmenopause groups cross-sectionally at baseline. However, after adjusting for general health, lifestyle, and age characteristics, only sleep latency (15.2 vs 19.1 minutes, P = .018) and social jetlag (35 vs 15 minutes, P = .005) remained significantly different.The effects of menstruation status transition in women on the longitudinal sleep profile changes are shown in Table 4. At follow-up, only 29 women remained in the premenopause group. When comparing the 3 groups classified by menopausal status, longitudinal PSQI scores, sleep latency, and HSE seemed to deteriorate the most in the group of women who underwent menopause during the course of the study (perimenopause), but there was no significant association after covariate adjustment. Although chronotype advancement was most prominent in the group of postmenopausal women from the baseline, workday midsleep time deterioration was affected significantly by menopausal status transition even after covariate adjustment (P = .011).Table 4 Adjusted mean (95% confidence interval) changes in the sleep characteristics stratified by menopausal status in women.Menopausal StatusP for TrendPre (n = 29)Peri (n = 697)Post (n = 606)ΔPSQI, score0.7 (−0.5 to 1.8)1.1 (0.8 to 1.4)0.7 (0.4 to 1.0).225ΔSleep latency, min4.7 (−5.2 to 14.5)5.2 (2.9 to 7.5)1.5 (−1.1 to 4.1).098ΔHSE, %−4.1 (−9.7 to 1.5)−6.0 (−7.3 to −4.7)−5.2 (−6.7 to −3.7).697ΔTime in bed, min9.9 (−21.2 to 41.0)19.6 (12.3 to 26.9)25.3 (17.0 to 33.5).283ΔSleep duration, min−7.6 (−38.5 to 23.2)−6.9 (−14.1 to 0.4)1.4 (−6.8 to 9.5).220ΔMSFsc, 24-h−0:05 (−0:34 to 0:22)−0:15 (−0:22 to −0:08)−0:25 (−0:33 to −0:18).061ΔMidsleep time (F), 24-h−0:21 (−0:48 to 0:05)−0:27 (−0:33 to −0:20)−0:32 (−0:39 to −0:24).322ΔMidsleep time (W), 24-h−0:06 (−0:31 to 0:18)−0:10 (−0:16 to −0:04)−0:24 (−0:30 to −0:17).011ΔSocial jetlag, 24-h−0:18 (−0:39 to 0:02)−0:15 (−0:19 to −0:10)−0:06 (−0:12 to −0:01).059P for trend is reported for menopausal status after adjusting for body mass index (kg/m2), smoking intensity (pack-years), alcohol consumption (g/d), hypertension, diabetes mellitus, occupation, and visit interval (years) in addition to baseline age. F = free days, HSE = habitual sleep efficiency, MSFsc = mid-sleep time on free days corrected for sleep debt on weekdays, n = number of observations, PSQI = Pittsburgh Sleep Quality Index, W = workdays.DISCUSSIONThis study described the effects of sex and age on sleep patterns cross-sectionally and longitudinally in a community-dwelling Korean cohort. We further investigated the degree and timing of sleep changes in each sex on age-stratified groups. To our knowledge, this is the first study to investigate the long-term association between sex and sleep profile changes in the normal aging process. Although many studies have demonstrated differences in sleep behavior across the lifespan cross-sectionally, not many have investigated longitudinal follow-up data to consider the independent and interactive effects of sex in a late-adulthood population experiencing significant hormonal changes in addition to aging.9,14,28 The findings of this study can be summarized as follows:Sleep profiles undergo substantial deterioration from late adulthood onward.The degree of deterioration in the quality and quantity of sleep tends to be more accelerated with aging.Sleep profiles and their aging-related deterioration vary with sex, with differences mainly noticeable before age 60.Chronotype advance occurs mainly before age 50 with more pronounced changes in men.Radical changes in hormonal status at the beginning of the aging process are also important factors that must be considered in studying the sleep profiles after the 40s.Our results are well