Literature DB >> 30650103

Non-Apnea Sleep Disorder associates with increased risk of incident heart failure-A nationwide population-based cohort study.

I-Duo Wang1, Wu-Chien Chien2,3, Chi-Hsiang Chung3, Pei-Yi Tsai4, Shan-Yueh Chang1, Fan-Chun Meng1, Chung-Kan Peng1,5.   

Abstract

PURPOSE: Non-apnea sleep disorder (NASD) increases the risk of cardiovascular events, such as hypertension and ischemic heart disease (IHD). Patients with heart failure (HF) are at higher risk for sleep disorder; however, there is no documentation on NASD's association with HF to date. Therefore, our study aimed to determine whether NASD increases the risk of incident HF.
MATERIALS AND METHODS: Using the outpatient and inpatient data from Taiwan's Longitudinal Health Insurance Database, we conducted a nationwide cohort study of patients with a first-time diagnosis of NASD in the year 2000 and followed up the risk of incident heart failure until December 31, 2013. We calculated risks and incidence ratios of HF for patients with NASD compared with the general population. The cumulative incidence of NASD and the subsequent risk of HF are assessed by the Kaplan-Meier method and Cox regression using a matched comparison cohort of HF patients without NASD.
RESULTS: The NASD cohort had an adjusted hazard ratio (HR) of incident HF 19.7% higher than that of the cohort without NASD (95% CI = 1.130-1.270; p<0.001). In the NASD population, the mean interval to HF in males and females were 5.00±3.69 years and 5.00±3.66 years, respectively. The Kaplan-Meier analysis indicated that after the seventh year, the incidence of HF was higher in the NASD cohort than in the control cohort till the end of the follow up.
CONCLUSIONS: Our study demonstrates that NASD patients are associated with a higher risk of incident HF.

Entities:  

Mesh:

Year:  2019        PMID: 30650103      PMCID: PMC6334965          DOI: 10.1371/journal.pone.0209673

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


1. Introduction

Sleep disorders (SDs) affect the quality of life of the general population in modern society [1]. Sleep disorders are a group of diseases characterized by the presence of disturbances in the amount, quality, or timing of sleep, or in the disturbance of physiological conditions associated with sleep [2]. According to the International Classification of Sleep Disorders (ICSD-3; American Academy of Sleep Medicine [AASM], 2014), SD has seven categories: insomnia, sleep-related breathing disorders, central disorders of hypersomnolence, circadian rhythm sleep disorders, parasomnias, sleep-related movement disorders, and other sleep disorders [2]. In the previous literature, obstructive sleep apnea (OSA) increases the risk of cardiovascular and cerebrovascular events [3, 4]. However, recent studies have shown that non-apnea sleep disorder (NASD) is also associated with an increased risk of hypertension, coronary artery disease (CAD), and ischemic stroke [5-7]. Heart failure (HF) is defined as a condition in which the heart loses the ability to pump enough blood to the body's tissues. Patients with HF are at a higher risk for SD, with an incidence rate between 23% and 73% [8, 9]. On the other hand, the correlation between SD and subsequent HF has rarely been documented. Laugsand et al. demonstrated an association between insomnia and the risk of incident heart failure in the Nord-Trøndelag Health study (HUNT) [10], but currently there is no documentation of the association between NASD and HF. In the present study, we conducted the largest and the longest follow up period longitudinal nationwide population-based cohort study to date discussing the association between NASD and HF. We used Taiwan National Health Insurance Research Database (NHIRD) to investigate whether NASD increases the subsequent risk of HF while taking several cardiovascular risk factors, comorbidities, and demographic features into consideration.

2. Methods

2.1. Data source

Our results were obtained from the Taiwan National Health Insurance Research Database (NHIRD). Taiwan started the National Health Insurance (NHI) program in 1995. It covers approximately 99% of the 23.74 million individuals in the general population and has contracts with 97% of the hospitals and clinics in Taiwan [11]. The NHIRD includes information on medical facility registries, medication prescriptions, and outpatient, inpatient, and emergency visiting data for Taiwan’s general public. It offers representative data for medical and health-related research fields due to its high accuracy and validity [12, 13]. The NHIRD encrypts the identification numbers of all NHIRD records to ensure the privacy of all persons registered in the program. Additionally, our study protocol was also approved by the Institutional Review Board of the Tri-Service General Hospital (TSGHIRB No.2-105-05–082).

2.2. Participants

This cohort study was based on the cumulative outpatients and inpatients population of 1,685,500 from Taiwan’s Longitudinal Health Insurance Database in the year 2000 (LHID; 2000) following from 2000 to 2013. The data were extracted according to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). Patients newly diagnosed with NASD under sedative usage who visited hospitals as outpatients more than 3 times, or who were hospitalized were included, for a total of 20,985 individuals. The date of diagnosis of NASD was used as the index date, and patients were followed until the appearance of heart failure (ICD-9-CM: 428). We defined the use of sedative (benzodiazepines and Z-drugs) by following the Anatomic Therapeutic Chemical classification system codes: N05BA and N05CD for benzodiazepines and N05CF for Z-drugs. The benzodiazepines enrolled in this study were diazepam, nitrazepam, chlordiazepoxide, clobazam, oxazepam, alprazolam, lorazepam and temazepam, whereas then Z-drugs included zopiclone and zolpidem. We excluded patients with sleep apnea, NASD diagnosis prior to the year 2000, diagnosis of heart failure prior to the index date, age less than 20 years old, patients who have received radiation therapy (ICD-9-CM V58.0) or chemotherapy (ICD-9-CM V58.1), and patients with unknown gender (Fig 1). NASD included sleep disturbances, unspecified (ICD-9-CM code: 780.50); other insomnia (ICD-9-CM code: 780.52); other hypersomnia (ICD-9-CM code: 780.54); disruptions of the 24-hour sleep-wake cycle (ICD-9-CM code: 780.55); dysfunctions associated with sleep stages or arousal from sleep (ICD-9-CM code: 780.56); unspecified sleep-related movement disorder (ICD-9-CM code 780.58); other sleep disturbances (ICD-9-CM code: 780.59), specific disorders of sleep of nonorganic origin (ICD-9-CM code: 307.4) and parasomnia (ICD-9-CM code: 327.44). Participants who matched our criteria were assigned to the study group. For each NASD patient, 1 comparison patient that was matched by gender, age, index date, and comorbidities was added to the control cohort. Common comorbidities, such as hypertension (ICD-9-CM code: 401–405), type 2 diabetes mellitus (DM; ICD-9-CM code: 250), hyperlipidemia (ICD-9-CM code: 272), chronic kidney disease (CKD; ICD-9-CM code: 585), ischemic heart disease (IHD; ICD-9-CM code: 410–414), stroke (ICD-9-CM code: 430–438), obesity (ICD-9-CM code: 278), anxiety (ICD-9-CM code: 300.00), and depression (ICD-9-CM code: 296.2–296.3, 300.4), were included to compare the baseline characteristics between the two groups.
Fig 1

Flowchart of study sample selection from National Health Insurance Research Database in Taiwan.

2.3. Statistical analysis

The demographic features and common comorbidities between the study and comparison cohorts were compared using chi-squared tests. The mean ages of the cohorts were compared using a Student's t-test. We assessed the incidence (per 105 person-years) of age, sex, and common comorbidities of HF for both the NASD and the control cohorts using Cox proportional hazard regression analysis to estimate the hazard ratios (HRs) with 95% confidence intervals (CIs). Each variant was adjusted for demographic characteristics and common comorbidities. The Kaplan-Meier method was used to evaluate the cumulative incidence of NASD and subsequent HF. All analyses were conducted using SPSS 22.0 software, with p< 0.05 considered statistically significant for the two-tailed tests.

3. Results

The study included 20,000 patients with NASD diagnosed in the year 2000 and 20,000 patients in the control cohort. The study was conducted between 2000 and 2013. The mean follow-up time was 10.36 ± 4.06 and 10.52 ± 3.78 years for the NASD and comparison cohorts, respectively (S1 Table). At the end of follow-up, 3,498 participants (17.49%) in the NASD cohort developed HF, compared to 2,687 participants in the control cohort (13.46%; p <0.001; Fig 1). Females were dominant in the study population (59.88%) and 41.66% of patients were younger than 50 years of age. The distribution of age and sex were similar between the study and control cohorts. The baseline data are listed in the Table 1 while the follow-up results of the demographic characteristics and common comorbidities are shown in Table 2.
Table 1

Characteristics of study in the baseline.

With NASD (n = 20,000)Without NASD (n = 20,000)P
Characteristicsn%n%
Gender0.999
    Male8,02540.138,02540.13
    Female11,97559.8811,97559.88
Age (yrs)53.27 ± 14.2953.09 ± 16.080.237
Age group0.999
    20–49 yrs8,33241.668,33241.66
    50–64 yrs5,99930.005,99930.00
    ≧65 yrs5,66928.355,66928.35
Comorbidities
    Hypertension3,58817.943,20016.00<0.001***
    DM1,1745.877163.58<0.001***
    Hyperlipidemia4002.003081.54<0.001***
    CKD460.23410.210.668
    IHD8084.043721.86<0.001***
    Stroke5662.832811.41<0.001***
    Obesity70.0440.020.549
    Anxiety1,5227.835552.81<0.001***
    Depression1,5988.226113.10<0.001***

P: Chi-square / Fisher exact test for the category variable and t-test for the continue variable.

*P < 0.05

**P < 0.01

***P < 0.001

Chronic kidney disease (CKD) is a condition characterized by a gradual loss of kidney function over time. Ischemic heart disease (IHD) refers to insufficient blood supply to the coronary arteries.

Table 2

Characteristics of study in the endpoint.

With NASD (n = 20,000)Without NASD (n = 20,000)P
Characteristicsn%n%
HF3,49817.492,68713.46<0.001***
Gender0.999
    Male8,02540.138,02540.13
    Female11,97559.8811,97559.88
Age (yrs)61.88 ± 17.1462.27 ± 16.330.020*
Age group<0.001***
    20–49 yrs4,62423.125,41127.06
    50–64 yrs7,02735.144,86224.31
    ≧65 yrs8,34941.759,72748.64
Comorbidities
    Hypertension3,83219.163,20016.16<0.001***
    DM1,899.471,5877.94<0.001***
    Hyperlipidemia9844.929304.650.214
    CKD5432.724022.01<0.001***
    IHD1,3616.811,0825.41<0.001***
    Stroke9404.707413.71<0.001***
    Obesity100.0590.050.819
    Anxiety1,6778.396173.09<0.001***
    Depression1,7918.967903.95<0.001***

P: Chi-square / Fisher exact test for the category variable and t-test for the continue variable.

*P < 0.05

**P < 0.01

***P < 0.001

P: Chi-square / Fisher exact test for the category variable and t-test for the continue variable. *P < 0.05 **P < 0.01 ***P < 0.001 Chronic kidney disease (CKD) is a condition characterized by a gradual loss of kidney function over time. Ischemic heart disease (IHD) refers to insufficient blood supply to the coronary arteries. P: Chi-square / Fisher exact test for the category variable and t-test for the continue variable. *P < 0.05 **P < 0.01 ***P < 0.001 During the follow-up period, women had a higher percentage of HF in both cohorts. The average age was 61.88 ± 17.14 and 62.27 ± 16.33 years for the study and the comparison cohorts, respectively (p < 0.001; Table 2). The incidence of HF increased with age in both cohorts, with 41.75% of the patients aged 65 years had HF (p = 0.02; Table 2). Compared with the control cohort, the NASD cohort had a higher risk of HF and a higher prevalence of hypertension, DM, CKD, IHD, stroke, anxiety, and depression (p<0.001; Table 2). Kaplan-Meier analysis indicated that in the seventh year after diagnosis, the NASD cohort had a higher incidence of HF than the general population cohort (p = 0.036) and this difference persisted until the end of follow-up (log-rank p<0.001; Fig 2). The average time interval to HF was 5.00±3.62 and 5.62±3.68 years in the NASD population and the population without NASD, respectively. In the NASD population, the average time duration to HF was 5.00±3.69 years in males and 5.00±3.66 years in females (S2 Table).
Fig 2

The Kaplan-Meier analysis for cumulative incidence of HF among patients aged over 20 years with and without NASD.

Patients with NASD had a higher risk of HF compared with the control cohort (crude HR = 1.289 [95% CI, 1.226–1.356]; p<0.001). After adjusting for cardiovascular and other concomitant comorbidities, the HR of developing HF for patients with NASD decreased to 1.197 (95% CI = 1.130–1.270; p<0.001; Table 3). The influence of age on HF was significant, with an adjusted HR for HF of 3.639 (95% CI = 2.894–3.978; p<0.001) in the ≧65-year age group and an adjusted HR for HF of 2.466 (95% CI = 2.050–2.898; p<0.001) in the 50-to 64-year age group, compared to the 20-to 49-year age group. Additionally, the multivariable Cox regression model also showed that comorbidities, including hypertension, DM, IHD, anxiety, and depression are associated with an increased risk of HF (all p<0.05; Table 3). IHD had the highest HR for HF, with an adjusted HR = 3.601 (95% CI = 2.917–4.001; p<0.001), followed by hypertension (adjusted HR = 1.600 [95% CI, 1.301–1.874]; p<0.001). Psychological disorders such as anxiety and depression were associated with increased risk for HF, with adjusted HRs of 1.218 (95% CI = 1.016–2.514; p = 0.031) and 1.385 (95% CI = 1.107–1.662; p<0.001), respectively.
Table 3

Cox regression analysis for the risk of incident HF with interaction of comorbidities.

CharacteristicsCrude HR95% CIPAdjusted HR95% CIP
NASD (reference: without)1.2891.226–1.356<0.001***1.1971.130–1.270<0.001***
Male (reference: female)1.0040.954–1.0560.1890.9640.852–1.0320.068
Age group (reference: 20–49 yrs)
    50–64 yrs2.9042.572–3.280<0.001***2.4662.050–2.898<0.001***
    ≧65 yrs5.4814.901–6.129<0.001***3.6392.894–3.978<0.001***
Comorbidities (reference: without)
    Hypertension2.0651.951–2.858<0.001***1.6001.301–1.874<0.001***
    DM1.2141.111–1.312<0.001***1.0191.001–1.5210.049*
    Hyperlipidemia0.4780.365–0.592<0.001***0.3830.201–0.655<0.001***
    CKD1.4011.183–1.609<0.001***1.1510.972–1.4360.074
    IHD5.2814.175–6.778<0.001***3.6012.917–4.001<0.001***
    Stroke0.7020.600–0.8450.001**0.5980.312–0.772<0.001***
    Obesity1.1230.356–2.9870.6431.2440.527–3.4110.726
    Anxiety1.3231.025–2.6330.027*1.2181.016–2.5140.031*
    Depression1.4291.125–1.774<0.001***1.3851.107–1.662<0.001***

HR = hazard ratio, CI = confidence interval, Adjusted HR: Adjusted variables listed in the table

*P < 0.05

**P < 0.01

***P < 0.001

HR = hazard ratio, CI = confidence interval, Adjusted HR: Adjusted variables listed in the table *P < 0.05 **P < 0.01 ***P < 0.001 The overall incidence rate of HF in NASD was 1,404.45 per 105 person-years (Table 4). Men and women were both affected by NASD with the incidence rate of 1,441.92 in males and 1,380.18 in females per 105 person-years. When adjusted for other covariates, females had a higher HR (adjusted HR = 1.198 [95% CI, 1.134–1.286]; p<0.001) for HF compared to males (adjusted HR = 1.195 [95% CI, 1.128–1.268]; p = 0.001; Table 4). The incidence of HF increased with age in both cohorts. Also, NASD had a higher impact of HF in the age group ≧65 years with the adjusted HR 1.269 (95% CI = 1.140–1.345; p<0.001) compared to patients without NASD (Table 4). The risk of HF in NASD patients increased with concomitant comorbidities. Hypertension, DM, hyperlipidemia, CKD, IHD, stroke and obesity were associated with higher risk of HF in NASD patients compared to the patients without NASD (all p<0.05; Table 4).
Table 4

Incidence and HRs of incident HF for NASD cohort compared with non-NASD cohort by demographic characteristics and comorbidities.

With NASD (n = 20,000)Without NASD (n = 20.000)With vs. Without NASD (reference)
StratifiedHF eventsPYsRateHF eventsPYsRateAdjusted HR95% CIP
Overall3,498249,065.121,404.452,687254,381.961,056.291.1971.130–1.270<0.001***
GenderMale1,41297,925.131,441.921,093100,731.241,085.071.1951.128–1.2680.001**
Female2,086151,139.991,380.181,594153,650.721,037.421.1981.134–1.286<0.001***
Age group20–49 yrs14560,869.76238.2114156,035.70251.631.0651.005–1.1650.045*
50–64 yrs47269,242.99681.6649284,890.32579.571.1840.894–1.2080.126
≧65 yrs2,881118,952.372,421.982,054113,455.931,810.391.2691.140–1.345<0.001***
HypertensionWith2,626214,830.011,222.362,032219,930.98923.931.1861.099–1.2540.003**
Without87234,235.112,547.0965534,450.981,901.251.2451.137–1.801<0.001***
DMWith3,187228,913.261,392.232,465233,404.541,056.111.1221.007–1.2650.040*
Without31120,151.861,543.2822220,977.421,058.281.3450.865–2.9780.266
HyperlipidemiaWith3,428234,467.141,462.042,622237,402.761,104.451.1941.098–1.8010.003*
Without7014,597.98479.526516,979.20382.821.2200.826–1.9750.595
CKDWith3,397243,293.531,396.262,618249,068.991,051.111.1921.101–1.3650.001**
Without1015,771.591,749.95695,312.971,298.711.2730.744–2.0140.348
IHDWith2,518231,601.001,087.212,256244,286.74923.501.0601.025–1.2450.027*
Without98017,464.125,611.5143110,095.224,269.351.1831.000–1.3360.050*
StrokeWith3,408239,400.891,423.552,632246,299.711,068.621.1991.101–1.2570.007*
Without909,664.23931.27558,082.25680.501.2340.567–2.4380.486
ObesityWith3,498248,890.831,405.442,687254,281.411,056.701.1971.130–1.270<0.001***
Without0174.2900100.550---
AnxietyWith3,298239,089.501,379.402,589245,638.451,053.991.1780.672–1.5400.412
Without2009,975.622,004.89988,743.511,120.831.6451.428–2.001<0.001***
DepressionWith3,243239,066.401,356.532,580245,136.561,052.471.1600.611–1.5130.387
Without2559,998.722,550.331079,245.401,157.331.9781.702–2.256<0.001***

PYs = person-years, Rate = incidence (per 105 PYs), Adjusted HR = adjusted hazard ratio: adjusted variables listed in Table 3, CI = confidence interval

*P < 0.05

**P < 0.01

***P < 0.001

PYs = person-years, Rate = incidence (per 105 PYs), Adjusted HR = adjusted hazard ratio: adjusted variables listed in Table 3, CI = confidence interval *P < 0.05 **P < 0.01 ***P < 0.001 The risk of different types of NASD on incident HF is presented in Table 5. Both the insomnia and the non-insomnia groups were associated with a risk of heart failure. Among patients with NASD, insomnia patients accounted for an adjusted HR with a 36.5% increased risk of heart failure (95% CI = 1.185–1.784; p<0.001) whereas in non-insomnia patients, the adjusted risk of heart failure was 1.199 (95% CI = 1.100–1.420; p = 0.001). In the non-insomnia group, patients with unspecified sleep-related movement disorder and other sleep disturbances were associated with adjusted risks of 2.764 (95% CI = 1.682–3.895; p<0.001) and 2.053 (95% CI = 1.298–3.112; p<0.001), respectively.
Table 5

Incidence rates and HRs of incident HF in patients with different types of NASDs.

Types of NASDsHF eventsPYsRateAdjusted HR95% CIP
Without NASD (n = 20,000)2,687254,381.961,056.29Reference
With NASD (n = 20,000)3,498249,065.121,404.451.1971.130–1.270<0.001***
    Insomnia (n = 13,851)2,211140,170.651,577.361.3651.185–1.784<0.001***
    Non-insomnia (n = 6,149)1,287108,894.471,181.881.1991.100–1.4200.001**
    Sleep disturbances (n = 2,875)41640,977.231,015.200.9880.477–1.9450.489
    Hypersomnia (n = 81)19,195.1210.880.0080.001–18.8420.845
    Disruptions of 24-hour sleep-wake cycle (n = 6)031.2700-0.901
    Dysfunctions associated with sleep stages or arousal from sleep (n = 51)35,394.1055.620.0410.005–11.7750.810
    Unspecified sleep-related movement disorder (n = 33)2257,258.933,099.632.7641.682–3.895<0.001***
    Other sleep disturbances (n = 675)30811,256.742,736.142.0531.298–3.112<0.001***
    Specific non-organic sleep disorder (n = 2,100)31331,656.11988.750.9020.415–1.9010.342
    Parasomnia (n = 31)01.8700-0.945
    Sleep-related movement disorders (n = 297)213,123.10672.410.6450.347–1.8640.475

PYs = person-years, Rate = incidence (per 105 PYs), Adjusted HR = adjusted hazard ratio: adjusted variables listed in Table 3, CI = confidence interval.

*P < 0.05

**P < 0.01

***P < 0.001

PYs = person-years, Rate = incidence (per 105 PYs), Adjusted HR = adjusted hazard ratio: adjusted variables listed in Table 3, CI = confidence interval. *P < 0.05 **P < 0.01 ***P < 0.001

4. Discussion

Our study is the largest nationwide cohort study to date discussing the association between NASD and incident heart failure. The mean follow-up time in the NASD cohort was 10.36 ± 4.06 years. Previous articles in the literature have only focused on the correlation between insomnia and the risk of heart failure. Laugsand et al., who had a mean follow-up of 11.3 years, showed that the number of insomnia symptoms cumulatively increased the risk of incident HF [10]. Two other small studies also investigated the association of insomnia and incident heart failure. Ingelsson et al. [14] studied 2,314 middle-aged men prospectively over 30 years of follow-up. Newman [15] discovered that daytime sleepiness predicted incident HF and cardiovascular mortality in a 5-year follow-up. However, none of them described an association between NASD and incident HF. In our study, the overall incidence ratio of incident HF was 33% higher in the NASD cohort than in the control cohort. The crude HR in the study cohort was 1.289. After adjusting for cardiovascular and other concomitant comorbidities, the HR decreased to 1.197, indicating that gender, age, and other covariates had an interaction with NASD, leading to an increased risk of HF. Women were predominant in our NASD study, making up 59.88% of the population. This has also been shown in other studies, suggesting that females are more likely to have NASD than males [16, 17]. After adjusting for age and other comorbidities, women with NASD had a higher risk of developing HF than men (adjusted HR of 1.198 in women and 1.195 in men). Our result was similar to the findings of Laugsand LE et al in the Nord-Trøndelag Health study (HUNT) [10]. However, they did not calculate the time duration between NASD diagnosis and the development of HF. In this study, the average time interval to HF was 5.00 ± 3.62 years in the NASD population, which was similar to the finding from the Kaplan-Meier analysis showing that after seven years, NASD patients had a higher risk of developing HF than the control cohort. There was no significant difference in the average time duration to HF in males and females (5.00 ± 3.69 and 5.00 ± 3.66 years, respectively). Age is another important risk factor for HF. Patients aged ≧65 years carried 5.481 times the risk for HF, while participants in the 50- to 64-year age group had a 2.904-fold risk, compared to the 20- to 49-year-olds. However, when adjusting for gender and other concomitant comorbidities, the HR decreased to 3.639 and 2.466 times for the ≧65-year age group and the 50- to 64-year age group, respectively. This finding can be explained by the presence of more comorbidities in older adults that may interact with NASD, leading to the development of HF. The pathophysiology of NASD and its association with HF has not yet been fully understood. Insomnia and other NASDs are disorders of hyperarousal that are accompanied by activation of stress hormones and increased activity of the hypothalamic-pituitary-adrenal (HPA) axis and sympathetic nervous system [18]. The activated stress response increases heart rate, blood pressure, and the secretion of pro-inflammatory cytokines and catecholamines, but it decreases heart rate variability [19]. Patients with NASD are associated with an unhealthy lifestyle and concomitant comorbidities, including hypertension, type 2 diabetes, CAD, CKD, and ischemic stroke [5–7, 20, 21]. This may lead to endothelial dysfunction, atherosclerosis, and progressive left ventricular remodeling [10], leading to subsequent HF. In our study, we found that both the insomnia and non-insomnia groups were associated with a risk of heart failure. The insomnia patients had a 36.5% increased risk of heart failure, compared to a 19.9% increased risk in the non-insomnia group patients. Psychological distress, such as anxiety and depression, is an important factor for HF that may have confounding interactions with NASD, thereby increasing the risk of HF. However, in previous studies, the association between insomnia and cardiovascular disease was not adjusted for these psychological factors [22]. In our study, we have adjusted for many covariates, including gender, age, and cardiovascular risk factors, and studied the impact of anxiety and depression on HF. Anxiety alone increased the risk of HF by 21.8%, while depression was associated with a 38.5% increase. Our study have several limitations. First, we could not obtain detailed patient information such as smoking habits, alcohol consumption, physical activity, socioeconomic status, or family history from the NHIRD. Second, there were selection bias while using the NHIRD; thus resulting in the low numbers of some diagnosis in the NASD categories. Third, there were no objective measurements of NASD in this study, nor did we use polysomnography. Despite the limitations, this study provides a nationwide population-based cohort on the association between NASDs and the risk of incident heart failure. In conclusion, we have identified that NASD increases the risk of incident heart failure. An increasing number of people suffer from NASD in modern society; therefore, we believe that our study can act as a wake-up call for clinical practitioners as well as for the general public in the prevention of cardiovascular events.

Years of follow-up.

(DOC) Click here for additional data file.

Years to HF.

(DOC) Click here for additional data file.
  20 in total

1.  Characteristics of sleep in patients with stable heart failure versus a comparison group.

Authors:  Nancy S Redeker; Sherry Stein
Journal:  Heart Lung       Date:  2006 Jul-Aug       Impact factor: 2.210

2.  Daytime sleepiness predicts mortality and cardiovascular disease in older adults. The Cardiovascular Health Study Research Group.

Authors:  A B Newman; C F Spiekerman; P Enright; D Lefkowitz; T Manolio; C F Reynolds; J Robbins
Journal:  J Am Geriatr Soc       Date:  2000-02       Impact factor: 5.562

3.  Sleep disturbances independently predict heart failure in overweight middle-aged men.

Authors:  Erik Ingelsson; Lars Lind; Johan Arnlöv; Johan Sundström
Journal:  Eur J Heart Fail       Date:  2006-08-01       Impact factor: 15.534

Review 4.  Classification of sleep disorders.

Authors:  Michael J Thorpy
Journal:  Neurotherapeutics       Date:  2012-10       Impact factor: 7.620

Review 5.  Are sleep and depression independent or overlapping risk factors for cardiometabolic disease?

Authors:  Elizabeth J Mezick; Martica Hall; Karen A Matthews
Journal:  Sleep Med Rev       Date:  2010-05-23       Impact factor: 11.609

6.  Nonapnea sleep disorders are associated with subsequent ischemic stroke risk: a nationwide, population-based, retrospective cohort study.

Authors:  Wei-Shih Huang; Chon-Haw Tsai; Cheng-Li Lin; Fung-Chang Sung; Yen-Jung Chang; Chia-Hung Kao
Journal:  Sleep Med       Date:  2013-10-16       Impact factor: 3.492

7.  Sleep disorders and increased risk of subsequent acute coronary syndrome in individuals without sleep apnea: a nationwide population-based cohort study.

Authors:  Wei-Sheng Chung; Cheng-Li Lin; Yung-Fu Chen; John Y Chiang; Fung-Chang Sung; Yen-Jung Chang; Chia-Hung Kao
Journal:  Sleep       Date:  2013-12-01       Impact factor: 5.849

8.  Self-reported obstructive sleep apnea, simple snoring, and various markers of sleep-disordered breathing as predictors of cardiovascular risk.

Authors:  Teemu J Niiranen; Erkki Kronholm; Harri Rissanen; Markku Partinen; Antti M Jula
Journal:  Sleep Breath       Date:  2015-09-12       Impact factor: 2.816

9.  Nonapnea sleep disorders and incident chronic kidney disease: a population-based retrospective cohort study.

Authors:  Shih-Ting Huang; Cheng-Li Lin; Tung-Min Yu; Te-Cheng Yang; Chia-Hung Kao
Journal:  Medicine (Baltimore)       Date:  2015-01       Impact factor: 1.889

10.  The association between insomnia and cardiovascular diseases.

Authors:  Kai Spiegelhalder; Cathy Scholtes; Dieter Riemann
Journal:  Nat Sci Sleep       Date:  2010-05-04
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