Sleep, Genetics, and Human Health

C H A P T E R

6 Sleep, Genetics, and Human Health Mengyu Fan1, Lu Qi2,3 1

Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; 2Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States; 3Department of Nutrition, Harvard School of Public Health, Boston, MA, United States

Abbreviations

the genome-wide association studies (GWASs) and geneeenvironment interaction studies. Because of the complexity of sleep behaviors, current epidemiological studies focus on the following major dimensions of sleep:

AHI ApneaeHypopnea Index BMI Body Mass Index CHD Coronary heart disease CVD Cardiovascular disease GRS Genetic risk score GWAS Genome-wide association study ISGEC International Sleep Genetic Epidemiology Consortium OSA Obstructive sleep apnea PDSA Physician-diagnosed sleep apnea RLS Restless legs syndrome SCN Suprachiasmatic nucleus SNP Single nucleotide polymorphism

• Sleep duration: the total amount of sleep obtained per day • Chronotype: the time of sleep • Insomnia: a sleep disorder that is characterized by difficulty falling and/or staying asleep • Snoring: the sound you make when your breathing is blocked while you are asleep • Daytime sleepiness: the ability to maintain attentive wakefulness during wake hours

INTRODUCTION SLEEP AND HUMAN DISEASES

Humans spend almost one-third of their lives on sleeping. Similar to other basic biological necessities, sleep plays a critical role in affecting human health. Sleep deficiency and untreated sleep disorders have been associated with physical and mental disturbances, injuries, loss of productivity, and increased risk of various diseases and premature death. Although environmental factors can impact sleep behavior, recent efforts have also identified genetic variations that are associated with various sleep behaviors and disorders. Emerging data also suggest potential interactions between the genetic variations and environmental factors in affecting human health. These data may improve our understanding of the etiology of sleep-related adverse health consequences and development of new preventive strategies and treatments. In this chapter, we briefly summarize the latest epidemiological findings on the relationships between the sleep behaviors and human diseases and then review the current knowledge of the genetics of sleep, with a focus on Neurological Modulation of Sleep https://doi.org/10.1016/B978-0-12-816658-1.00006-5

In recent years, growing evidence has related various sleep behaviors and disorders with human health. Increasing attention has been paid on investigations of the associations of the major sleep behaviors and disorders with chronic diseases, including type 2 diabetes, CVD, obesity, cognitive function, depression, and etc.

Sleep Duration The relations between sleep duration and chronic diseases, especially cardiovascular disease (CVD), diabetes, and obesity, have been extensively investigated, with consistent findings accrued from prospective studies in diverse populations.1,2 Meta-analysis of prospective studies found that the association between sleep duration and chronic diseases followed a “U-shaped” mannerdboth short and long sleep duration were significantly associated with an increased risk of

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© 2020 Elsevier Inc. All rights reserved.

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6. SLEEP, GENETICS, AND HUMAN HEALTH

mortality,3,4 CVD,3,5 coronary heart disease,5e7 stroke,5,8e10 type 2 diabetes mellitus,11 obesity,12,13 or depression.14 For cognitive function, meta-analysis among older adults also showed a U-shaped association between sleep duration and low cognitive function.15 However, similar studies in middle-aged populations are scarce. Limited results from prospective studies suggested that both short and long sleep duration were associated with a lower cognitive function in middleage adults.16,17

Chronotype The preference to time of sleep is governed by two clocks, the internal body clock controlling the circadian rhythms and the external social clock controlling the daily social routines. These clocks vary across individuals, thus contributing to different chronotypes between individuals. Based on these individual differences, humans can be classified as “morning types (Mtypes)” with the preference for morning activities and early bedtimes, or “evening types (E-types)” with the preference for evening activities and late bedtimes, or the intermediate types (I-types). Chronotype may therefore have a difference in their association with physical and mental health outcomes. There is emerging research showing that chronotype is related to individual’s psychological health. Specifically, the E-type has been associated with depression, anxiety, and other mental illness.18e21 In addition to mental health outcomes, chronotype is also related to other health outcomes. Several studies have reported that the E-type was associated with increased levels of cardiometabolic risk factors, including blood pressure, heart rate, and inflammatory markers that predispose to diabetes and CVD,22e26 even after adjusting for other sleep characteristics, such as sleep duration. Although these data suggest that evening chronotype might be related to metabolic abnormalities and obesity, future prospective studies are needed to examine the associations of chronotype with development of CVD and diabetes.

Insomnia Insomnia, the most prevalent sleep disorder, is characterized by subjective reports of having difficulties in falling asleep, staying asleep, or waking up too early, leading to distress or daytime impairments. Insomnia has high comorbidity with other chronic medical conditions or psychiatric disorders, particularly depression. Previous studies reported that insomnia disorder more frequently co-occurred with depression, and both insomnia and depression significantly predicted the onset of the other disorders in longitudinal studies,

suggesting a bidirectional relationship between insomnia and depression.27 Meta-analysis showed that individuals with insomnia were two times more likely to develop major depression than individuals without insomnia.28,29 Thus, early treatment programs for insomnia might reduce the risk for developing depression in the general population, and be considered a general preventive strategy in mental health care. There is also evidence suggesting an association between insomnia and hypertension,30,31 diabetes,32e34 CHD,35e38 heart failure,39 subclinical incident CVD,40,41 or CVD mortality,42 particularly when coupled with short sleep duration. Of note, all these studies used subjective means (questionnaire) to determine symptoms of insomnia, there are wide variations in how insomnia is defined and measured, and caution must be exercised when comparing studies and interpreting results. Nonetheless, these prospective data support insomnia as an important risk factor for diabetes and CVD. However, there is also a need for randomized controlled trials to further establish the causality between insomnia and CVD, and whether treatment of insomnia may improve outcomes.

Obstructive Sleep Apnea Obstructive sleep apnea (OSA) is a common, serious, and potentially life-threatening sleep disorder. OSA is characterized by frequent episodes of partial or complete collapse of upper airways during sleep, resulting in recurrent episodes of intermittent hypoxemia and arousal from sleep. OSA can be reliably diagnosed by using overnight polysomnography studies to assess the number of apneasehypopneas per hour of sleep: the ApneaeHypopnea Index (AHI). In epidemiological studies, OSA has been associated with a wide range of health-related outcomes and physiological processes. In particular, the potential influence of OSA on cardiovascular health continues to receive widespread attention. Several meta-analyses of cohort studies found individuals with moderateesevere OSA had an almost 2.5-fold higher risk of developing cardiovascular events and had a 2.0-fold higher risk of developing stroke, though the association between OSA and CHD was borderline significant and somewhat weaker.43e45 The effect sizes were similar with, or even greater than traditional risk factors. The complex mechanisms underlying the increase of consequent CVD in OSA patients are not well understood. Dysfunctions in endothelial pathways, including intermittent hypoxia, intrathoracic pressure swings, and recurrent arousals, may lead to the development of atherosclerosis, and eventually cardiovascular events.46 Evidence from population studies also shows that OSA may increase the risk of hypertension47,48 and type 2 diabetes,49,50 both of which are established risk

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

SLEEP AND HUMAN DISEASES

factors of CVD. Despite accumulative evidence supporting a positive relationship, a causal association between OSA and CVD has, however, yet to be established. Further large prospective cohort studies among the general populations and randomized controlled trials examining the relation between OSA and CVD are still required for better understanding the causality and developing effective preventive strategies. Obesity and OSA frequently coexist. The relationship between OSA and obesity remains complex and multifactorial. Many studies have reported dose-response associations between the prevalence of OSA and increased BMI. Prospective studies found that weight gain predicted the development of moderate-to-severe OSA, while weight loss was associated with a reduced OSA severity and likelihood of developing OSA.51,52 On the other hand, OSA itself may also contribute to obesity. Patients with newly diagnosed OSA had a significantly greater weight gain compared with matched subjects without sleep apnea.53 This association is supported by other studies showing that OSA leads to increases in appetite hormones and a preference for calorie-dense foods,54,55 as well as a change in the gut microbiome pattern that contributes to weight gain.56 Cumulative evidence indicates that the association between obesity and OSA seems to be bidirectional, which can lead to a cycle of worsening symptoms of both conditions. Obesity itself increases the risk for OSA, while OSA may also predispose the individual to weight gain. The coexistence of obesity and OSA may have far more serious impact on the cardiovascular and metabolic outcomes than either of these conditions. The relations between OSA and psychiatric disabilities, especially depression, have also been extensively studied. Similar to insomnia, the actual nature of OSAe depression relationship remains undiscovered. More current studies reported that depression was highly prevalent in patients with OSA, and several studies reported that individuals diagnosed with OSA were at a higher risk of depression. OSA may cause depression via sleep loss, sleep interruption, and cognitive alterations induced by recurrent hypoxemia, while weight gain and sleep disruption due to depression could worsen OSA. Furthermore, the symptoms of OSA may induce the symptoms of depression, including overall low energy, sleep disruption, reduced will and decision capacity, cognitive deficiency, and a lower quality of life. Specific attention should be paid on the patients with OSA or depressive disorder.

Snoring Snoring is not only a “common nuisance of sleep,” but one of the most important and cardinal manifestations of OSA. Many individuals who snore may not

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have significant apnea and hypopnea events. Indeed, snoring may be identified as an early marker of OSA or may also represent a separate clinical entity. Previous epidemiological studies have reported association between snoring and diabetes mellitus,57,58 incident hypertension,59 incident coronary heart disease, and stroke.60e64 These associations were independent of age, smoking, BMI, and other cardiovascular risk factors. However, other studies showed inconsistent results.65 A prospective study found that physiciandiagnosed sleep apnea (PDSA) but not habitual snoring was associated with high incident CVD events and all-cause mortality in a multiethnic adult population free of clinical CVD disease at baseline.66 A possible explanation is that PDSA and self-reported snoring each provide different information on OSA severity. Whether the risk is attributable to OSA or snoring alone remains controversial. Nevertheless, obtaining a precise diagnosis of OSA is challenging, and not suitable for large cohorts of the general population. Self-reported snoring is easily assessed compared to diagnosis, which is now widely used as a surrogate of the diagnosis of OSA in large populationebased studies and may also help clinicians identify individuals at higher risk for chronic diseases. Future studies are required to verify whether there is a causal association between snoring, OSA, and chronic diseases.

Daytime Sleepiness Daytime sleepiness, defined as the inability to stay awake and alert during the major waking periods of the day, results in unintended lapses into drowsiness or sleep. Excessive daytime sleepiness is not a disorder in itself, but a serious symptom with a variety of potential causes. The most common cause of excessive daytime sleepiness in a clinical setting is OSA. Excessive daytime sleepiness is a significant public health concern since it is associated with cognitive impairment, vehicle accidents, occupational injuries, and loss of productivity. Several studies have also found that excessive daytime sleepiness might lead to important adverse health outcomes. Longitudinal studies have shown that excessive daytime sleepiness is associated with the incidence of stroke and CHD.67e70 However, most of the previous studies did not fully control for other important sleep characteristics including sleep duration, insomnia, or snoring. A study based on the Nurses’ Health Study II found the associations between daytime sleepiness and CVD were eliminated after controlling for sleep duration, snoring, shift work, and sleep adequacy.71 The results from this cohort of women suggest the daytime sleepiness was not an independent risk factor for CVD, and the association is primarily explained by other sleep

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6. SLEEP, GENETICS, AND HUMAN HEALTH

characteristics that are established risk factors for CVD. However, since daytime sleepiness is a noticeable symptom, its presence may indicate increased cardiovascular risk due to these underlying conditions. The presence of daytime sleepiness in patients may therefore cue clinicians to assess for sleep disorders that often go undiagnosed and untreated. When looking at the association between excessive daytime sleepiness and BMI, there was also a clear increase in the risk of excessive daytime sleepiness with higher BMI classifications. Further, weight gain was a strong predictor of the incidence and persistence of excessive daytime sleepiness even in nonobese individuals.72

THE GENETICS OF SLEEP While many psychosocial and environmental correlates of sleep behavior have been identified, current understanding of the genetic architecture contributing to sleep characteristics is very limited, especially when compared to other phenotypes such as obesity and diabetes. Significant heritability of sleep characteristics has been reported from both twin and family studies. Further candidate geneebased studies have inconsistently associated several genes with the circadian clock. More recent genome-wide association studies (GWASs) have been the most important contributor to identification of genetic determinants of human sleep behaviors. Notably, earlier studies did not find or had just found limited genome-wide significant loci, partly due to the small sample size. Owing to the increasing availability of population biobanks, such as the UK Biobank, sample sizes have grown rapidly since 2015, resulting in a tremendous increase of new discoveries over the past 3 years. The important findings from GWASs of sleep characteristics, including sleep duration, chronotype, insomnia, and excessive daytime sleepiness, are summarized in this part.

GWAS of Sleep Duration In 2007, the first GWAS on sleep found suggestive associations of sleep duration with variants at the MYRIP and PROK2 loci.73 Since then, more than 70 genetic loci have been identified by a range of GWASs74e81 (Table 6.1). The vast majority of the included studies only examined self-reported sleep duration, while only the latest study analyzed accelerometer-derived measures.81 Furthermore, sleep duration was analyzed as a quantitative trait in minutes or hours per day in most studies. In the most recent GWAS among UK Biobank participants of European ancestry (N ¼ 446,118),81 a total of 78

loci for self-reported sleep duration, 27 loci for short sleep, and 8 loci for long sleep relative to normal sleep duration were identified, respectively. Only the PAX8 signal was shared across all three traits, consistently indicating associations between the minor allele and longer sleep duration, with the effect size of 2.44 min per allele. However, numerous gaps in knowledge still remain. First of all, further replication is important to validate the findings. The genetic information gathered to date is dominated by pediatric onset in European-ancestry populations. Whole-genome analysis in non-European ancestry groups will likely identify novel genes and/ or novel variants in known genes. In addition, only a tiny fraction of the variance of sleep duration is explained yet: the 78 known loci could only explain 0.69% of the variance in sleep duration.81 There is a long way to go to fully understand the involved mechanisms of the identified genes, which is of utmost importance to inform development of new therapeutic strategies.

GWAS of Chronotype Circadian rhythms are fundamental cyclical processes and are driven by an inherited central clock residing in the suprachiasmatic nucleus (SCN) of the hypothalamus. There has long been an interest in the genetic basis for circadian rhythmicity. Several clock genes associated with circadian rhythms, particular PER and CRY are identified through animal models, linkage studies, and candidate geneebased studies82,83; but these findings are not robust due to small sizes of the discovery studies. Chronotype is a physical and behavioral manifestation of circadian rhythms. Age and occupation, as well as environmental factors explain a large proportion of variance in chronotype, but genetic variation is also an important contributor. In recent years, three large-scale GWASs from European population were published. In the first GWAS, Hu and colleagues84 analyzed genetic associations of self-reported chronotype using the 23andMe cohort (n ¼ 89,283) and identified a total of 15 genome-wide significant loci. Lane et al.85 and Jones et al.79 conducted genome-wide association analyses for chronotype on data from British, caucasian subjects from the UK Biobank, a prospective study of >500,000 people in the United Kingdom. Lane and colleagues identified 12 significant loci and one suggestive locus among 100,420 subjects; while Jones and colleagues identified 16 loci associated with chronotype among 128,266 subjects in the UK Biobank. Overall, nine genes were independently identified based on nearby significant SNPs in multiple studies (four found in all three studies and

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

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THE GENETICS OF SLEEP

TABLE 6.1 Summary of Current Genome-Wide Association Study on Sleep Duration. Year

Study or Ethnic Group

N

Phenotypes

Main Finding

2007

Framingham Offspring Study

738

Continuous

71.3 Mb on chromosome 3

2013

Seven European populations

4,251

Continuous

An intronic variant (rs11046205) in the ABCC9 gene

2013

Australian Twin Registry

2,323

Continuous

5 most significant genes: RSPRY1, NIP30, CPNE2, ARL2BP, GPR68

2014

Finnish population

1,941

Continuous

3 suggestive loci

2015

Coriell Personalized Medicine Collaborative (CPMC), United States

4,401

Ordered category

2 novel candidate genes: SORCS1 and ELOVL2

2015

Cohorts for heart and aging research in genomic Epidemiology (CHARGE) consortium, European ancestry

47,180

Continuous

2 genome-wide significant loci

2016

Seven candidate gene association Resource (CARe) cohorts including African, Asian, European and Hispanic American ancestry

>25,000

Continuous

1 genome-wide significant loci

2016

British individuals from the UK Biobank study

128,266

Continuous Dichotomous: Oversleeper (9 h/day); Undersleeper (6 h/day)

1 known loci and 2 novel loci

2017

British individuals from the UK Biobank study

111,975

Continuous

1 genome-wide significant loci and 4 suggestive loci

2018

European descent from the UK Biobank

446,118

Continuous Short sleep (<7 h) Long sleep (9 h)

78 loci 27 loci 8 loci

five found in two studies). Of these identified genes, PER2 and RGS16 are both known for their roles in circadian regulation. An additional 22 SNPs were genomewide significant in just one of the three studies. In 2018, Jones and colleagues86 performed a GWAS of chronotype in 697,828 individuals from 23andMe and the UK Biobank study and increased the number of associated loci to 351. For the first time, these loci were validated in subgroup populations with activity monitorederived measures of estimates of sleep timing. Given the lack of replication across studies, these associations, particularly for those unreplicated genes with no known circadian roles, should be interpreted with great caution (Table 6.2).

GWAS of Insomnia Twin studies and family based heritability estimates suggest a heritable basis to insomnia (heritability: 22%e59% in adults).87 Specific genetic risk variants for insomnia are only recently identified at genome-wide

significance. Brief descriptions of insomnia GWAS are provided in Table 6.3. Given the heterogeneity in insomnia as a disorder, to accurately specify the phenotypes becomes particularly important in genetic studies. To date, all the studies examining potential genes involved in insomnia used self-reported measures. However, the definition of insomnia varied in different studies. In the UK Biobank study, subjects were asked, “Do you have trouble falling asleep at night or do you wake up in the middle of the night?” with responses “never/rarely,” “sometimes,” “usually,” and “prefer not to answer.” In other studies, insomnia was assessed according to a set of questions. Because the prevalence of insomnia symptoms varies by sex, several sex-stratified GWASs were performed and sex-specific loci were found. Furthermore, there is evidence of sex interactions, suggesting the genetic architecture for insomnia differed by sex. For example, stronger effects were found in women at KRT8P18, NT5C2, NMT1, CCDC148, C11ORF49, while stronger effects were found in men at CADM1 and SLC8A3.88

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

60 TABLE 6.2

6. SLEEP, GENETICS, AND HUMAN HEALTH

Summary of Recent Large Genome-Wide Association Studies on Chronotype.

Author

Year

Study

N

Phenotypes

Main Findings

Hu

2014

23andMe

89,283

Binary: Early versus late chronotype

15 loci

Lane

2015

UK Biobank study

100,420

Continuous Binary

9 loci 6 loci

Jones

2016

UK Biobank study

128,266

Either a continuous or binary

16 loci

Jones

2018

23andMe and UK Biobank

697,828

Either a continuous or binary

351 loci

Based on the existing evidence, the gene most strongly associated with insomnia complaints was MEIS1. MEIS1 encodes a homeobox protein that acts as a transcriptional regulator and activator and is thought to be important for normal development.89 Notably, Winkelmann and colleagues90 previously reported an association of multiple SNPs in MEIS1 with restless legs syndrome (RLS), and further studies suggest that MEIS1 is likely to have pleiotropic effects on both RLS and insomnia.91 In addition to MEIS1, the VRK2 locus was also shown to be associated with a greater risk of schizophrenia.92 It is clear there has been progress in our understanding of the genetics of insomnia. Novel genes are identified through GWAS, and some genes appear to be associated with other psychiatric disorders, shedding light on several potential molecular mechanisms for the development of the insomnia. For future studies, the most important goal on genetic underpinnings of insomnia is to gain insight into the etiology of the disorder to inform development of interventions for both prevention and treatment of insomnia.

GWAS of Snoring, Excessive Daytime Sleepiness, and Obstructive Sleep Apnoea OSA is a common chronic disease and is associated with high social and economic costs. Snoring and excessive daytime sleepiness are the main symptoms of OSA syndrome. However, genetic research in snoring, excessive daytime sleepiness, and OSA has being an underexplored area. For snoring, only one study has been reported to date. Lane and colleagues93 performed a GWAS of selfreported snoring in >100,000 subjects of European ancestry in the UK Biobank. In this study, three genome-wide suggestive loci were identified associated with snoring (near FBXL4, GALNT12/COL15A1, and NPLOC4) among the total population. The study also found a sex-specific locus conferring an increased risk of snoring in females only (rs138233508; p for interaction ¼ 1.35  104). Interestingly, these

suggestive associations with an increased risk of snoring seemed to be modified by obesity status. The same study group published a GWAS of excessive daytime sleepiness in 2017. Again, based on the European ancestry subjects from UK Biobank (n ¼ 111,648), Lane et al.80 identified a signal near the androgen receptor gene AR (rs73536079), with no sex-specific effects. Secondary analyses after additional adjustment for depression or BMI identified a signal near ROBO1, which encodes a neuronal axon guidance receptor previously implicated in dyslexia, and a signal near another member of the TMEM132 family, TMEM132B (rs142261172). Sensitivity analyses adjusting for factors influencing sleep traits, including self-reported sleep apnea, depression, psychiatric medication use, smoking, socioeconomic status, employment status, marital status and snoring, generated similar signals. As for OSA phenotypes, the International Sleep Genetic Epidemiology Consortium (ISGEC) has completed a series of planned GWAS Meta analyses from nine independent European ancestry cohorts (8336 cases and 76,663 controls), but the results have yet to be published. So far, only one GWAS has been published, which was conducted in 12,558 Hispanic/Latino Americans from three cohorts.94 In this study, three OSA-associated phenotypic traits were investigated, including AHI, average oxygen saturation during sleep, and average respiratory event duration. Two novel loci were identified at genome-level significance (rs11691765, GPR83 for the API; and rs35424364, C6ORF183/CCDC162P for respiratory event duration) and seven additional loci were identified with suggestive significance. Secondary sex-stratified analyses also identified one significant and several suggestive associations. Several of the identified loci overlapped genes with biologic plausibility.

GENE-SLEEP INTERACTION ON HUMAN DISEASES It has been increasingly accepted that the etiology of most common diseases involves not only discrete

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

TABLE 6.3

Summary of Genome-Wide Association Studies on Insomnia. Year

Study

Population

N

Gender

Phenotypes

Main Findings

Hammerschlag

2016

UK Biobank

European descent

113,006

Total

Never/rarely/sometimes versus usually

2 loci

53,639

Male

1 loci

59,367

Female

1 loci

Lane

Stein

2016

2017

UK Biobank

STARRS

European descent

112,586

Never/rarely versus usually Male

1 loci

Female

4 loci

European American

11,473

Total

African American

2,679

Total

Latino American

3,499

Total

At least 1 month of insomnia and reporting of at least “some of the time” on one or more of the five symptom items versus others

Chr7 (q11.22) Chr12 in NTF3 Chr9 intronic region of DEC1

Jansen

2018

UK Biobank and 23andMe

European descent

1,331,010

Total

Never/rarely/sometimes versus usually a set of questions

202 loci

Lane

2018

UK Biobank

European descent

453,379

Total

Never/rarely versus usually

48 loci

Total

Never/rarely versus sometimes/ usually

29 loci

GENE-SLEEP INTERACTION ON HUMAN DISEASES

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

Author

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6. SLEEP, GENETICS, AND HUMAN HEALTH

genetic and environmental causes but also interactions between the two. The remaining unexplained heritability may be accounted for in part by such unappreciated geneeenvironment interactions. Recent evidence from epidemiological studies supports the existence of genee environment interactions for chronic diseases, including cardiovascular and metabolic disease,95e97 infectious disease,98 trauma and injury,99 and mental health disorders.100 However, despite growing evidence regarding the traditional lifestyle and genetic background, the possible interaction between sleep characteristics and gene susceptibility is only just beginning to emerge. For the first time, a twin study suggested potential genetic interaction with sleep duration on depressive symptoms in 2012.101 Watson and colleagues found that genetic influences on depressive symptoms were moderated by habitual sleep duration. Both short (<7 h/night) and long (9 h/night) sleep were associated with the highest heritability of depressive symptoms. This work suggests that environmentally mediated treatments for depression may have the greatest opportunity for success when administered in a patient sleeping normal amounts of time. Future research should consider the effects of habitual sleep duration on treatment success. Since then, only two cross-sectional studies have been conducted for geneesleep interaction in the UK Biobank cohort study.102,103 In 2017, Celis-Morales and colleagues102 investigated whether the associations between a comprehensive and validated genetic risk scores (GRS) for obesity and adiposity outcomes were modified by sleep-related characteristics. This study provides novel evidence that the effects of the genetic predisposition to obesity appear to be augmented by sleeping behaviors, including short and long duration, evening chronotype, day napping, shift work, and night shift work. In those with a high GRS obesity, being a short sleeper was associated with a 0.6 higher BMI, and being a long sleeper was associated with a 1.1 higher BMI compared with those with similar genetic risk but normal sleep duration. In contrast, short or long sleep duration was associated with only about 0.2 higher BMI in those in the lowest quartile for GRS obesity. These findings suggest that the adverse associations of short or long sleep durations are more pronounced in those who have increased genetic predisposition to obesity and conversely less pronounced in those with lower genetic predisposition. Although the causality of this association cannot be ascertained from this study, the findings make a case for intervention studies to determine the effects of adopting healthier sleep behaviors, particularly in individuals genetically susceptible to obesity. In 2018, Vetter et al.103 conducted another study of 270,000 men and women in the UK Biobank

investigating the interaction between shift work and a cumulative GRS for type 2 diabetes (6770 prevalent cases). The study showed that current and past night shift work did not interact with genetic type 2 diabetes predisposition on the disease risk. This could be related, at least in part, to epistatic interaction among the genetic variants or because participants in the UK Biobank might be more prone to transition to other less strenuous work schedules over time (also referred to as the healthy worker effect). Taken together, limited studies, mostly among participants of European ancestry, have assessed the genee sleep interactions. It is also noteworthy that the crosssectional nature of exist studies represent a major weakness. Further prospective work will be necessary to examine the interaction of genetic risk with sleep behavior on obesity, risk of type 2 diabetes and CVDs.

FUTURE DIRECTION AND CONCLUSION REMARKS Sleep is increasingly recognized as an important lifestyle contributor to both somatic and mental health in humans. Epidemiological evidence continues to support the conclusion that sleep problems have an important and substantial negative effect on human health. However, sleep characteristics are typically correlated with each other, but most previous studies examined the effect of sleep characteristics separately, and less attention has been paid to the combined effect of multiple sleep characteristics. More importantly, previous research in sleep medicine tended to follow the pattern focusing on sleep disorders and diseases. From a public health perspective, it is important to address the concept of sleep health, which provides a positive frame rather than a negative light: as something to be sought and promoted, rather than something to be avoided and replaced. By emphasizing a positive direction of sleep in overall health may help with education and health promotion initiatives, as well as offer the field of sleep medicine new research and clinical opportunities. Results to date also showed that numerous genetic polymorphisms underlie interindividual differences in sleep behaviors. These results provide initial biological insights into the genetics of sleep behaviors and reveal shared underlying biology with health and diseases. Despite these findings, it is worthwhile to note that the genetic determinants of sleep and associated factors remain largely unknown. First of all, the current study population was mainly restricted to adults of European ancestry, and other ethnic/racial groups are underrepresented in almost all genetic studies. Discovery and replication studies in different ethnic and racial groups are needed to detect additional genetic variations and

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

REFERENCES

improve the generalizability of the findings. GWAS using objective sleep measures as outcomes, rather than self-reported measures, are also critical. Moreover, in spite of those identified specific locus, the following key questions about sleep genetics are of particular interest: to what extent sleep behaviors are affected by the genetic factors, and exactly, what genetic factors and biological pathways are involved in determining sleep behaviors. Finally, few studies have accounted for potential interaction between sleep behaviors and genetic risk. To further understand the complete etiology of a disease inclusive of multiple discrete and interacting pathways, and to determine the public health impact of sleep behaviors within a specific population so that interventions can be designed to maximize health and minimize disease, future large prospective studies in this area are needed.

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