Causal association of lifestyle factors, clinical features in the risk of sleep disorders: Based on mendelian randomization analysis

Causal association of lifestyle factors, clinical features in the risk of sleep disorders: Based on mendelian randomization analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Causal association of lifestyle factors, clinical features in the risk of sleep disorders: Based on mendelian randomization analysis Jingyu Xu, Baojuan Wang, Wenbin Zhu, Haiqing Ao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5092259/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective : To identify potential genetic risk factors for sleep disorders (SD) and to explore the causal associations between lifestyle factors and clinical features with SD, given the limitations of traditional observational studies. Methods : Data from published genome-wide association studies (GWAS) were analyzed, encompassing 34 lifestyle factors and 17 clinical features as potential exposures influencing SD. These analyses yielded information on 51 exposures and 4 outcomes. Outcomes consisted of four categories: non-organic sleep disorders (NSD), narcolepsy, rapid eye movement (REM) sleep behavior disorders (RSBD), and obstructive sleep apnea (OSA). All genetic variables for exposure and outcome were derived from individuals of European ancestry. Two-sample MR analysis was conducted, with inverse variance weighted (IVW) as the primary method for evaluating causal effects. Weighted median estimation (WME), MR-Egger (MRE), simple mode (SM), and weighted mode (WM) were used as supplementary evaluation methods. Results : (1) The protective factors causally associated with NSD include overall physical activity time (OR: 0.35; 95%CI: 0.13-0.99; P =0.048) and fresh fruit intake (OR: 0.30; 95%CI: 0.12-0.75; P =0.010). (2) The risk factors that are causally related to narcolepsy include smoking (OR: 1.02; 95%CI: 1.01-1.03; P =0.001), alcoholic drinks (OR: 1.02; 95%CI: 1.01-1.03; P =0.013 and OR: 1.02; 95%CI: 1.01-1.03; P< 0.001), cereal intake (OR: 1.03; 95%CI: 1.00-1.07; P =0.029), salad/raw vegetable intake (OR: 1.10; 95%CI: 1.03-1.16; P= 0.002), TDI (OR: 1.05; 95%CI: 1.00-1.10; P =0.036), overall health rating (OR: 1.07; 95%CI: 1.05-1.10; P< 0.001), BMI (OR: 1.02; 95%CI: 1.02-1.03; P< 0.001), FINS (OR: 1.06; 95%CI: 1.03-1.09; P <0.001), TG (OR: 1.02; 95%CI: 1.01-1.03; P <0.001) and hypertension (OR: 1.08; 95%CI: 1.02-1.15; P =0.011). (3) The protective factors causally associated with narcolepsy include tea intake (OR: 0.95; 95%CI: 0.93-0.97; P< 0.001), non-oily fish intake (OR: 0.91; 95%CI: 0.84-0.99; P= 0.021), years of schooling (OR: 0.97; 95%CI: 0.96-0.99; P< 0.001), cognitive performance (OR: 0.98; 95%CI: 0.97-0.99; P= 0.001), average total household income before tax (OR: 0.94; 95%CI: 0.93-0.96; P< 0.001), ApoA-I (OR: 0.99; 95%CI: 0.99-1.00; P =0.036), HDL (OR: 0.99; 95%CI: 0.98-0.99; P <0.001). (4) The risk factors that are causally related to OSA include smoking (OR: 1.20; 95%CI: 1.08-1.34; P =0.001 and OR: 1.15; 95%CI: 1.06-1.25; P =0.001), alcohol intake (OR: 1.12; 95%CI: 1.01-1.24; P =0.037), coffee intake (OR: 1.25; 95%CI: 1.00-1.56; P =0.046), pork intake (OR: 2.55; 95%CI: 1.37-4.74; P= 0.003), TDI (OR: 1.56; 95%CI: 1.12-2.19; P =0.009), overall health rating (OR: 2.76; 95%CI: 2.20-3.46; P< 0.001), BMI (OR: 1.97; 95%CI: 1.85-2.11; P< 0.001), WHR (OR: 1.30; 95%CI: 1.08-1.55; P= 0.004), TG (OR: 1.06; 95%CI: 1.02-1.11; P =0.009), hypertension (OR: 2.72; 95%CI: 1.73-4.26; P <0.001) and CRP (OR: 1.06; 95%CI: 1.01-1.11; P =0.016). (5) The protective factors causally associated with OSA include bread intake (OR: 0.63; 95%CI: 0.49-0.83; P =0.001), cereal intake (OR: 0.71; 95%CI: 0.56-0.90; P =0.005), dried fruit intake (OR: 0.64; 95%CI: 0.50-0.83; P= 0.001), years of schooling (OR: 0.72; 95%CI: 0.66-0.79; P< 0.001), cognitive performance (OR: 0.79; 95%CI: 0.71-0.87; P< 0.001), average total household income before tax (OR: 0.78; 95%CI: 0.66-0.93; P =0.005), FPG (OR: 0.84; 95%CI: 0.75-0.94; P =0.003), ApoA-I (OR: 0.94; 95%CI: 0.90-0.99; P =0.017) and HDL (OR: 0.89; 95%CI: 0.85-0.93; P <0.001). (6) After multivariate analysis through adjusting BMI, the causal association between ApoA-I, TG, HDL and narcolepsy still exists. The causal association between hypertension and OSA still exists. Conclusions : The evidence from this study suggests that among 34 lifestyle factors: (1) overall physical activity time and fresh fruit intake are protective factors for NSD. (2) Smoking and alcohol drinks, cereal intake, salad/raw vegetable intake, poverty, poor overall health rating, and high BMI are risk factors for narcolepsy. Tea intake, non oily fish intake, education and cognition, and high income are protective factors for narcolepsy. (3) Smoking and alcohol intake, coffee intake, pork intake, poverty, poor overall health rating, high BMI, and high WHR are risk factors for OSA. Bread and cereal intake, dried fruit intake, education and cognition, and high income are protective factors for OSA. Among the 17 clinical features: (1) hypertension is a risk factor for NSD. (2) High FINS, high TG, hypertension, and high CRP are risk factors for narcolepsy. ApoA-I and HDL are protective factors for narcolepsy. (3) High TG, hypertension, and high CRP are risk factors for OSA. FPG, ApoA-I, and HDL are protective factors for OSA. There is not enough evidence to suggest that other relationships meet the criteria for causal association established. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Lifestyle factors Clinical features Sleep disorders Risk factors Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction As is well known, many critical life activities, including memory and emotion processing, as well as the active period of glucose and lipid metabolism in the brain, occur during sleep (Aalling et al., 2018 , Whitehurst et al., 2022 ). Sleep, a readily observable and perceived behavior in daily life, has been extensively studied and is a vital health indicator in clinical practice. Due to its widespread impact, sleep problems have become a key topic of global discussion. One significant consequence of sleep problems is their interference with the body's normal metabolic activities. An animal experiment by Wang et al. (Wang et al., 2024 ) demonstrated that insomnia can cause damage to hippocampal neurons, activate the hypothalamic-pituitary-adrenal (HPA) axis, and increase the expression of inflammatory factors, such as IL-2, IL-6, and TNF-α, in rats. Further research has revealed an association between insomnia and reduced signaling of cyclic adenosine monophosphate (cAMP). Yin C's study (Yin et al., 2024 ) reported cognitive impairment and changes in gut microbiota caused by insomnia in rats. Additionally, research suggests a link between insomnia and the activation of the TLR4/MyD88/NF-κB signaling pathway. Moreover, sleep problems can significantly affect mental health. Sleep disorders (SD) are often comorbid with mental disorders such as anxiety and depression (Gauld et al., 2021 ). Chellappa SL et al.'s study (Chellappa & Aeschbach, 2022 ) showed that insomnia affects approximately 50% of anxiety disorder patients, exacerbating their anxiety relapse. Research by Arora S (Arora et al., 2024 ) demonstrated an association between SD and decreased physical fitness (poor physical ability) and negative psychological states (depression and anxiety). SD is also a common complication in patients with depression (Solelhac et al., 2024 ). In conclusion, sleep is closely related to both physical and mental health. Sleep problems can lead to a series of negative effects, both physically and psychologically. This study focuses on four types of sleep disorders: non-organic sleep disorders (NSD), narcolepsy, rapid eye movement (REM) sleep behavior disorders (RSBD), and obstructive sleep apnea (OSA). NSD is often caused by psychological and lifestyle factors and is the most common type of SD (Edinger et al., 2021 ). NSD patients frequently exhibit chronic insomnia or disrupted sleep cycles due to disrupted circadian rhythms (San & Arranz, 2024 ), leading to psychological fragility, emotional fluctuations, memory loss, and a lack of concentration (San & Arranz, 2024 ), significantly impacting their quality of life. Narcolepsy is primarily manifested as an irresistible urge to sleep during the daytime (Konofal, 2024 ), not only affecting work and social life but also posing a safety hazard (Gupta et al., 2017 ). In addition to excessive sleepiness, patients may also experience symptoms such as muscle weakness, sleep paralysis, and hallucinations (Konofal, 2024 ), all of which hint at abnormal emotional reactions and psychological states in patients (Yu et al., 2024 ). RSBD is an SD caused by neurological disorders characterized by the loss of dreaming and muscle weakness during REM periods (Park et al., 2024 ). RSBD is often associated with Parkinson's disease, dementia, and ischemic stroke (Pace et al., 2018 , Postuma et al., 2019 ). Pesonen AK et al.’s study (Pesonen et al., 2024 ) found that REM periods primarily serve emotional processing functions. During this period, the activity of brain regions related to emotions, such as the amygdala, striatum, hippocampus, and medial prefrontal cortex, increases (Knobloch et al., 2024 , Lee et al., 2024 , Lim et al., 2024 ). These brain regions have been associated with negative emotions like anxiety and depression (Knobloch et al., 2024 , Lee et al., 2024 , Lim et al., 2024 ). Animal experiments conducted by Cai G (Cai et al., 2022 ) demonstrated that sleep interruption induces anxiety-like behaviors in mice during REM periods, while Saadati N et al.'s study (Saadati et al., 2022 ) found that it induces depression-like behaviors. Therefore, RSBD has been associated with mental disorders. Finally, OSA is characterized by the narrowing or collapse of the upper respiratory tract during sleep (Mao et al., 2024 ), leading to fragmented sleep (disrupted sleep rhythms) and intermittent hypoxia (IH), which can easily cause neuroinflammation and cognitive impairment (Ou et al., 2024 ). OSA has been associated with various mental disorders, such as anxiety, depression, and schizophrenia (Xu et al., 2023 ). Given their widespread influence and extensive study, these four types of SD were included in this study for analysis. The causes of SD are multifaceted. Among them, lifestyle factors and clinical features are two primary categories of influencing factors. Dzierzewski JM's research (Dzierzewski et al., 2021 ) demonstrated a negative correlation between sleep health and weekly fast food intake, TV/social media usage, and sedentary time. Additionally, it revealed a positive correlation with moderate to vigorous exercise time. Excessive intake of caffeine and sugar can also lead to poor sleep quality (De Pasquale et al., 2024 ). Emmy HKI's study showed that long-term use of electronic devices before bedtime can disrupt the normal sleep-wake cycle (Emmy et al., 2024 ). Therefore, this study aimed to quantitatively analyze the impact of lifestyle factors and clinical features on various types of SD as comprehensively as possible. It is important to note that while many diseases can also cause sleep problems, they were not included as influencing factors in this study, as diseases result from the combined action of multiple pathological factors. Traditional clinical observational studies frequently employ correlation coefficients and regression analysis to examine the relationship between factors and diseases. While this method can ensure authenticity in the real world, it has three limitations. First, there is a high likelihood of confounding factors beyond the investigated factors. Second, traditional statistical methods are unable to determine the direction of correlation. Third, it is challenging to accurately explain the influence of individual factors, and a multi-factor fitting model is necessary for a comprehensive assessment. Mendelian randomization (MR) analysis utilizes genetic variation (random allocation of alleles), such as single nucleotide polymorphisms (SNPs), as instrumental variables (IVs) to calculate correlation. This approach is not affected by postnatal confounding factors and provides relatively stable results. Additionally, MR clearly distinguishes between exposures and outcomes in statistical analysis, enabling the determination of the direction of causal effects. Furthermore, causal effect values can accurately represent the influence of exposures, provided the assumption of IVs is satisfied. Therefore, this study employed MR to comprehensively evaluate the effects of each independent influencing factor on the four types of SD. Methods 1 Study design This study quantitatively analyzed the influence of various factors on four types of SD through multiple two-sample MR analyses. As shown in Fig. 1 . The aim was to identify factors related to SD and provide evidence-based support. MR analysis requires the identification of exposure and outcome, followed by the calculation of causal effects using IVs representing these variables. SNPs, single base changes randomly assigned during the genetic process, are considered natural genetic variations with a frequency greater than 1% (Wang et al., 2024 ). SNPs have been associated with susceptibility to numerous diseases, including diabetes, polycystic ovary syndrome, cervical cancer, mental illness, and others (Liu et al., 2024 , Raheem et al., 2024 , Wang et al., 2024 , Wootton et al., 2024 , Xu et al., 2021 ). Therefore, in genetic association studies, SNPs are commonly used as IVs. Using IVs can effectively calculate effect values. Genetic differences eliminating the influence of various postnatal confounding factors and making the causal effect between exposure and outcome more convincing, accounting for the use of MR to analyze influencing factors in this study. During MR analysis, it is crucial to carefully select the IVs for exposure and outcome (SNP screening). These IVs should meet several criteria: (1) they should come from two independent samples (Sekula et al., 2016 ), (2) all samples should share the same genetic background (Davey & Hemani, 2014 ), and (3) the IVs should satisfy three key assumptions for causal reasoning: correlation, exclusivity, and independence (Davies et al., 2018 ). The correlation assumption states that the IV must be strongly correlated with the exposure. The exclusivity assumption stipulates that the IV only affects outcomes through the exposure and not via other pathways. The independence assumption requires that the IV be independent of confounding factors between the exposure and outcome. In this study, seven types of lifestyle factors (a total of 34 factors) and four types of clinical features (a total of 17 factors) served as exposures, as shown in Fig. 2 . While the four types of SD were the outcomes. All exposure and outcome data were sourced from publicly available Genome-wide Association Studies (GWAS), eliminating the need for ethical approval. After identifying the exposure, outcome, and IVs, statistical analysis was conducted. The study design of this MR analysis adheres to the STROBE-MR statement (Skrivankova et al., 2021 ). For further details, please refer to the supplementary materials (Table S1 ). 2 Data sources All data in this study were collected from publicly available GWAS. A total of 10 databases were involved, with 9 databases providing data for exposures and 4 databases providing data for outcomes. The source databases for exposure and outcome data are detailed in Fig. 1 . 2.1 Expose This study included lifestyle factors and clinical features as exposures. Lifestyle factors encompassed seven categories: exercise, smoking, beverages, diet, education, income, and physical condition. Clinical features included four categories: glucose metabolism, lipid metabolism, blood pressure, and inflammation. Each category contained several indicators, totaling 34 lifestyle factors and 17 clinical features. For each indicator's GWAS dataset, the author recorded its characteristics, including name, population source, sample size, number of SNPs (SNPn), GWAS number, publication year, database or consortium, and PubMed identifier (PMID). Details are recorded in Table 1. Table.1 Characteristics of the expose GWAS summary data. Expose Population Sample nSNP GWAS ID Publication Consortium PMID Mean F-statistic lifestyle factors 1. Physical activity APAM European 91,084 11,796,201 GCST006099 2018 UK biobank 29899525 33.489 Vigorous physical activity European 261,055 11,803,978 GCST006098 2018 UK biobank 29899525 39.467 Strenuous sports or other exercises European 350,492 11,807,536 GCST006100 2018 UK biobank 29899525 37.479 Moderate to vigorous physical activity levels European 377,234 11,808,007 GCST006097 2018 UK biobank 29899525 34.166 Sedentary behaviour duration European 91,105 9,926,106 GCST006913 2018 UK biobank 30531941 33.485 Walking duration European 91,105 9,926,106 GCST007110 2018 UK biobank 30531941 31.352 Moderate intensity activity duration European 91,105 9,926,106 GCST006915 2018 UK biobank 30531941 32.091 Overall physical activity time European 91,105 9,926,106 GCST006912 2018 UK biobank 30531941 35.624 2. Smoke Smoking initiation European 607,291 11,802,365 ieu-b-4877 2019 GSCAN 30643251 43.353 Smoking status (ever vs never smokers) European 518,633 19,524,200 GCST007327 2019 UK biobank 30643258 44.206 3. Drink Alcoholic drinks per week European 335,394 11,887,865 ieu-b-73 2019 GSCAN 30643251 88.897 Tea intake European 447,485 9,851,867 ukb-b-6066 2018 MRC-IEU NA 66.920 Alcohol intake frequency European 462,346 9,851,867 ukb-b-5779 2018 MRC-IEU NA 56.050 Coffee intake European 376,372 5,645,580 PMID31046077 2019 UK biobank 31046077 95.936 4. Diet Bread intake European 452,236 9,851,867 ukb-b-11348 2018 MRC-IEU NA 42.127 Dried fruit intake European 421,764 9,851,867 ukb-b-16576 2018 MRC-IEU NA 43.178 Salad / raw vegetable intake European 435,435 9,851,867 ukb-b-1996 2018 MRC-IEU NA 38.691 Beef intake European 461,053 9,851,867 ukb-b-2862 2018 MRC-IEU NA 40.133 Oily fish intake European 460,443 9,851,867 ukb-b-2209 2018 MRC-IEU NA 45.578 Lamb / mutton intake European 460,006 9,851,867 ukb-b-14179 2018 MRC-IEU NA 40.903 Cereal intake European 441,640 9,851,867 ukb-b-15926 2018 MRC-IEU NA 47.073 Cooked vegetable intake European 448,651 9,851,867 ukb-b-8089 2018 MRC-IEU NA 37.762 Fresh fruit intake European 446,462 9,851,867 ukb-b-3881 2018 MRC-IEU NA 47.166 Non-oily fish intake European 460,880 9,851,867 ukb-b-17627 2018 MRC-IEU NA 46.153 Pork intake European 460,162 9,851,867 ukb-b-5640 2018 MRC-IEU NA 37.557 Polyunsaturated fatty acids European 114,999 12,321,875 met-d-PUFA 2020 IEU OpenGWAS Project NA 144.567 Saturated fatty acids European 114,999 12,321,875 met-d-SFA 2020 IEU OpenGWAS Project NA 120.741 5. Education Years of schooling European 766,345 10,101,242 ieu-a-1239 2018 SSGAC 30038396 50.714 Cognitive performance European 257,841 10,066,414 GCST006572 2018 SSGAC 30038396 44.118 6. Income Average total household income before tax European 397,751 9,851,867 ukb-b-7408 2018 MRC-IEU NA 41.226 Townsend deprivation index at recruitment European 462,464 9,851,867 ukb-b-10011 2018 MRC-IEU NA 35.760 7. Physical condition Overall health rating European 460,844 9,851,867 ukb-b-6306 2018 MRC-IEU NA 41.349 Body mass index (BMI) European 681,275 2,336,260 ieu-b-40 2018 GIANT 30124842 87.279 Waist-to-hip ratio (WHR) European 212,244 2,560,782 ieu-a-73 2015 GIANT 25673412 47.196 Clinical features 8. Sugar metabolism Fasting plasma glucose (FPG) European 58,074 2,599,409 GCST005186 2012 NA 22581228 97.379 Fasting insulin (FINS) European 281,416 12,229,036 GCST90002238 2021 NA 34059833 52.535 9. Lipid metabolism Triglycerides (TG) European 441,016 12,321,875 ieu-b-111 2020 UK Biobank 32203549 174.015 Apolipoprotein B (ApoB) European 439,214 12,321,875 ieu-b-108 2020 UK Biobank 32203549 245.050 High density lipoprotein (HDL) European 403,943 12,321,875 ieu-b-109 2020 UK Biobank 32203549 185.936 Apolipoprotein A-I (ApoA-I) European 393,193 12,321,875 ieu-b-107 2020 UK Biobank 32203549 179.160 Low density lipoprotein (LDL) European 440,546 12,321,875 ieu-b-110 2020 UK Biobank 32203549 202.204 Total cholesterol levels (TCL) European 437,878 4,232,052 GCST90025953 2021 NA 34226706 239.215 Apolipoprotein E plasma levels (ApoE) European 400 5,188,525 GCST90085710 2022 NA 35264221 30.013 10. Blood pressure Hypertension European 463,010 9,851,867 ukb-b-12493 2018 MRC-IEU NA 50.017 11. Inflammation C-Reactive protein level (CRP) European 204,402 2,414,379 ieu-b-35 2018 NA 30388399 197.085 Tumor necrosis factor alpha levels (TNF-α) European 3,454 9,500,449 GCST004426 2016 NA 27989323 24.006 Interleukin-1-beta levels (IL-1β) European 3,309 9,983,642 GCST004448 2016 NA 27989323 16.426 Interleukin-18 levels (IL-18) European 3,636 9,785,222 GCST004441 2016 NA 27989323 70.294 Interleukin-17 levels (IL-17) European 7,760 9,786,653 GCST004442 2016 NA 27989323 38.972 Interleukin-6 levels (IL-6) European 8,189 9,790,590 GCST004446 2016 NA 27989323 25.313 Interleukin-10 levels (IL-10) European 7,681 9,793,415 GCST004444 2016 NA 27989323 169.706 NOTE : APAM: Accelerometer-based physical activity measurement (average acceleration); Hypertension: Diagnoses from ICD10-I10. 2.2 Outcome The study focused on four types of SDs: NSD, narcolepsy, RSBD, and OSA. Diagnostic criteria were established using the International Classification of Diseases-10th edition (ICD-10) (Spiegelhalder et al., 2022 ) and the International Classification of Sleep Disorders-third edition (ICSD-3) (Ruoff & Rye, 2016 ). NSD (diagnosed from ICD10: F51) is characterized by a constellation of symptoms, including insomnia, disturbances in sleep-wake rhythms, night startles, and nightmares. Narcolepsy (diagnosed from ICSD3) is further classified into type I (NT1) and type II (NT2). NT1 is defined by excessive daytime sleepiness, cataplexy (abrupt loss of muscle tone triggered by strong emotions), a sleep latency of 8 minutes or less, and the presence of two or more sleep-onset rapid eye movement periods (SOREMPs) in the Multiple Sleep Latency Test (MSLT). Additionally, NT1 is associated with low levels of hypocretin-1 (Hcrt-1) in cerebrospinal fluid (CSF), typically below 110 pg/mL or less than one-third of the average value in the normal population (Siegenthaler et al., 2023 ). NT2, on the other hand, does not exhibit cataplexy or low Hcrt-1 levels, but it may include other symptoms such as sleep paralysis, fragmented nocturnal sleep, hypnopompic-hypnogogic hallucinations, and automatic behaviors. RSBD is diagnosed based on ICSD-3, including repeated episodes of sleep-related vocalizations or complex motor behaviors during the REM sleep phase. Patients with RSBD may also exhibit REM sleep without tonia, accompanied by repeated dream-playing behaviors, which can sometimes be associated with increased tonic and/or phasic myoelectric activity. It is important to note that RSBD must not be attributable to other sleep disorders, mental disorders, or the effects of drugs or substances (Hogl & Stefani, 2017 ). OSA is diagnosed according to ICD-10 codes (ICD-10: G47.3, ICD-9: 3472A) and is determined based on a combination of subjective symptoms, clinical examination, and sleep registration. A diagnosis of OSA requires an apnea-hypopnea index (AHI) or respiratory event index (REI) of at least 5 events per hour (Gottlieb & Punjabi, 2020 , Malhotra et al., 2018 ). For each GWAS dataset associated with the outcome, the following metadata were recorded: study name, population source, sample size, number of SNPs, GWAS accession number, publication year, originating database or consortium, and PMID. These details are summarized in Table 2. Table.2 Characteristics of the outcome GWAS summary data. Outcome Population Sample nSNP GWAS ID Publication Consortium PMID Nonorganic sleep disorders (NSD) European 216,454 16,380,458 finn-b-F5_SLEEP 2021 FinnGen NA Narcolepsy European 460,913 9,851,867 ukb-b-5776 2018 MRC-IEU NA REM sleep behavior disorder (RSBD) European 9,447 2,777,418 GCST90204200 2022 NA 36470867 Obstructive sleep apnea (OSA) European 341,071 10,914,272 G6_SLEEPAPNO 2021 FinnGen NA 2.3 adjustment Body mass index (BMI) is a widely recognized metric reflecting overall physical condition. According to ICD-10, individuals with a BMI ≥ 24.0 are classified as overweight, while those with a BMI ≥ 30.0 are considered obese. Extensive research has established a strong association between BMI and the functional integrity of various bodily systems. As early as 2004, the American Heart Association acknowledged the close relationship between obesity and cardiovascular disease (Klein et al., 2004 ). Subsequent studies have consistently demonstrated a correlation between elevated BMI and an increased risk of impaired glucose metabolism, diabetes, and hyperuricemia (Aghaei et al., 2024 , Gou et al., 2024 , Liberty et al., 2024 ). Moreover, obesity is often accompanied by abnormal lipid metabolism, a significant contributor to pathological conditions such as dyslipidemia, insulin resistance, and inflammation (Albitar et al., 2024 ). Recent cross-sectional studies conducted by Gildner TE have revealed a positive association between obesity and elevated levels of the inflammatory cytokine CRP (Gildner et al., 2024 ). Sharma C's case-control study further supported this finding, demonstrating that obese patients exhibit higher levels of CRP, IL-6, TNF-α, catalase, and nitric oxide (NO), markers of oxidative stress (Sharma et al., 2024 ). Given the established causal relationship between clinical features (exposures) and sleep disorders (outcomes), BMI may serve as a common mediator in this process. Emerging research has unequivocally identified high BMI as a potent predictor of sleep disorder breathing (SDB) (Al-Naimi et al., 2024 ). Recognizing the pivotal role of BMI and its amenability to lifestyle interventions, BMI has become a cornerstone of clinical practice. Consequently, BMI was incorporated as an adjustment variable in multivariate Mendelian randomization (MVMR) analysis of this study. The data sources for BMI are detailed in Table 1. 3 Selection of IV To identify IVs that meet the stringent requirements for MR analysis, the following steps were undertaken. After establishing the GWAS datasets for the exposure and outcome, SNPs within these datasets were subjected to a meticulous screening process. Three primary criteria were employed for SNP selection: (1) a genome-wide significance threshold of P < 5×10⁻ 8 was initially applied. If no SNPs met this stringent criterion, the threshold was relaxed to P < 5×10 − 6 (Xiang et al., 2022 ); (2) SNPs exhibiting linkage disequilibrium, defined as r² 10,000 kilobases, were excluded (Machiela & Chanock, 2015 ); (3) SNPs with an F-statistic ≤ 10 were also eliminated. The F-statistic serves as a valuable tool for assessing the strength of IV bias. SNPs with an F-statistic > 10 are considered strong IVs, demonstrating a robust association with the exposure and exhibiting minimal susceptibility to bias, yielding relatively stable results (Pierce et al., 2011 ). The F-statistic is calculated using the formula F = R² / (1-R²) × (N-k-1) / k, where N represents the sample size of the GWAS study, k denotes the number of SNPs, and R² signifies the proportion of exposure variance explained by each IV (Levin et al., 2020 ). R² is calculated using the formula R² = 2 × EAF × (1 - EAF) × β², where EAF is the allele frequency and β is the allele effect size (Codd et al., 2013 ). The average F-statistic for each exposure dataset is presented in Table 1. In summary, all SNPs retained in the analysis exhibited F-values greater than 10. Subsequently, the MR Steiger filtering method was employed to remove SNPs that demonstrated a weaker correlation with the exposure compared to the outcome (Hemani et al., 2017 ). To mitigate the influence of confounding factors, SNPs related to age, gender, and primary diagnoses other than sleep disorders were retrieved and excluded from the analysis using the PhenoScanner database ( http://www.phenoscanner.medschl.cam.ac.uk ) (Kamat et al., 2019 ). Finally, the MR-PRESSO method was implemented for sensitivity analysis to identify and remove any outlier SNPs (Verbanck et al., 2018 ). This iterative process continued until no outlier SNPs remained, resulting in the final set of included IVs (SNPs). 4 Statistical analysis To systematically investigate the causal relationship between exposure and outcome, we employed several MR methods: Inverse Variance Weighted (IVW), Weighted Median Estimation (WME), MR-Egger (MRE), Simple Mode (SM), and Weighted Mode (WM). Among these, IVW serves as the primary analytical method, while the remaining four methods provide supplementary insights. In cases where IVW analysis fails to yield statistically significant results but at least three of the other four methods demonstrate statistical significance, a potential causal association is inferred (Davies et al., 2018 ). The causal effect values corresponding to each SNP were evaluated using the Wald ratio. The results were ultimately presented in the form of odds ratios (ORs) and their corresponding 95% confidence interval (95%CI). To address the issue of multiple testing, we implemented the Bonferroni correction. The significance level was adjusted to α = 0.05 / 51, “51” reflecting the total number of exposures. A P -value less than 0.00098 (0.05 / 51) was considered to indicate a strongly significant causal association, while a P-value between 0.00098 and 0.05 was deemed to represent a nominally significant causal association. Collectively, these findings provide evidence for a potential association between exposure and outcome. To assess the robustness of our findings, we conducted a comprehensive sensitivity analysis employing Cochran's Q, leave-one-out (LOO), MR-Egger intercept, and MR-PRESSO methods. Cochran's Q and LOO were utilized to detect heterogeneity among the IVs. A Q-pval greater than 0.5 indicates the absence of heterogeneity, while a Q-pval less than or equal to 0.5 suggests the presence of heterogeneity. The LOO method involves sequentially removing individual SNPs to observe if significant changes occur in the effect value and sensitivity value. To identify horizontal pleiotropy, we employed the MR-Egger intercept and MR-PRESSO methods. An MR-Egger intercept approaching zero suggests the absence of horizontal pleiotropy. Outlier SNPs detected by MR-PRESSO were removed, and the remaining SNPs were subjected to MR analysis again until no outliers were present. This iterative process ensured the robustness of our findings by mitigating the potential influence of confounding factors. Following the univariate MR analysis, multivariate MR was conducted on clinical features with significant causal associations with the outcome. In this context, IVW was the primary evaluation method, while WME, MRE, SM, and WM functioned as supplementary approaches. MVMR offers the distinct advantage of incorporating both exposure and adjustment variables into a single model for analysis, thereby enabling the assessment of exposure independence (Burgess & Thompson, 2015 ). All of the aforementioned statistical analyses were executed using the "TwoSampleMR" and "MR-PRESSO" software packages within the R software environment, version 4.1.2. Results 1 MR analysis of lifestyle factors and four types of SD Detailed F-values and additional information for each SNP can be found in the supplementary materials (Tables S2 and S3). The IVs of this MR satisfies three key assumptions. Given that the IVW method serves as the primary analytical method, the preliminary results based on IVW are presented in Fig. 3. The lifestyle factors identified as being associated with NSD include overall physical activity time (OR: 0.35; 95%CI: 0.13–0.99; P = 0.048) and fresh fruit intake (OR: 0.30; 95%CI: 0.12–0.75; P = 0.010). For narcolepsy, the associated lifestyle factors encompass smoking (OR: 1.02; 95%CI: 1.01–1.03; P = 0.001), alcoholic drinks (OR: 1.02; 95%CI: 1.01–1.03; P = 0.013 and OR: 1.02; 95%CI: 1.01–1.03; P < 0.001), tea intake (OR: 0.95; 95%CI: 0.93–0.97; P < 0.001), cereal intake (OR: 1.03; 95%CI: 1.00-1.07; P = 0.029), non-oily fish intake (OR: 0.91; 95%CI: 0.84–0.99; P = 0.021), salad/raw vegetable intake (OR: 1.10; 95%CI: 1.03–1.16; P = 0.002), years of schooling (OR: 0.97; 95%CI: 0.96–0.99; P < 0.001), cognitive performance (OR: 0.98; 95%CI: 0.97–0.99; P = 0.001), average total household income before tax (OR: 0.94; 95%CI: 0.93–0.96; P < 0.001), townsend deprivation index (TDI) (OR: 1.05; 95%CI: 1.00-1.10; P = 0.036), overall health rating (OR: 1.07; 95%CI: 1.05–1.10; P < 0.001), and BMI (OR: 1.02; 95%CI: 1.02–1.03; P < 0.001). In the case of OSA, the identified lifestyle factors include smoking (OR: 1.20; 95%CI: 1.08–1.34; P = 0.001 and OR: 1.15; 95%CI: 1.06–1.25; P = 0.001), alcohol intake (OR: 1.12; 95%CI: 1.01–1.24; P = 0.037), coffee intake (OR: 1.25; 95%CI: 1.00-1.56; P = 0.046), bread intake (OR: 0.63; 95%CI: 0.49–0.83; P = 0.001), cereal intake (OR: 0.71; 95%CI: 0.56–0.90; P = 0.005), dried fruit intake (OR: 0.64; 95%CI: 0.50–0.83; P = 0.001), pork intake (OR: 2.55; 95%CI: 1.37–4.74; P = 0.003), years of schooling (OR: 0.72; 95%CI: 0.66–0.79; P < 0.001), cognitive performance (OR: 0.79; 95%CI: 0.71–0.87; P < 0.001), average total household income before tax (OR: 0.78; 95%CI: 0.66–0.93; P = 0.005), TDI (OR: 1.56; 95%CI: 1.12–2.19; P = 0.009), overall health rating (OR: 2.76; 95%CI: 2.20–3.46; P < 0.001), BMI (OR: 1.97; 95%CI: 1.85–2.11; P < 0.001), and waist-to-hip ratio (WHR) (OR: 1.30; 95%CI: 1.08–1.55; P = 0.004). To date, no lifestyle factors have been associated with RSBD. When the IVW analysis failed to yield statistically significant results but at least three of the remaining four supplementary methods demonstrated statistical significance, a potential causal association was inferred. The results obtained from these supplementary methods are provided in the supplementary materials (Table S4). However, no significant correlations were observed in this analysis. The number and characteristics of available SNPs for each exposure are detailed in the supplementary materials (Tables S2 and S4). 2 MR results of clinical features and four types of SD The statistical results derived from the IVW method are presented in Fig. 4. Hypertension (OR: 4.68; 95%CI: 1.11–19.77; P = 0.036) was identified as the clinical feature associated with NSD. Clinical features linked to narcolepsy include fasting insulin (FINS) (OR: 1.06; 95%CI: 1.03–1.09; P < 0.001), triglycerides (TG) (OR: 1.02; 95%CI: 1.01–1.03; P < 0.001), apolipoprotein A-I (ApoA-I) (OR: 0.99; 95%CI: 0.99-1.00; P = 0.036), high-density lipoprotein (HDL) (OR: 0.99; 95%CI: 0.98–0.99; P < 0.001), and hypertension (OR: 1.08; 95%CI: 1.02–1.15; P = 0.011). For OSA, the associated clinical features encompass FPG (OR: 0.84; 95%CI: 0.75–0.94; P = 0.003), TG (OR: 1.06; 95%CI: 1.02–1.11; P = 0.009), ApoA-I (OR: 0.94; 95%CI: 0.90–0.99; P = 0.017), HDL (OR: 0.89; 95%CI: 0.85–0.93; P < 0.001), hypertension (OR: 2.72; 95%CI: 1.73–4.26; P < 0.001), and CRP (OR: 1.06; 95%CI: 1.01–1.11; P = 0.016). To date, no clinical features have been associated with RSBD. When the IVW analysis failed to yield statistically significant results but at least three of the remaining four supplementary methods demonstrated statistical significance, a potential causal association was inferred. The results obtained from these supplementary methods are provided in the supplementary materials (Table S5). Through this evaluation, a potential causal association was identified between CRP and hypersomnia among the clinical features, as illustrated in Fig. 5. The remaining unmentioned parts did not exhibit any statistically significant causal associations. The number and characteristics of available SNPs for each exposure are detailed in the supplementary materials (Tables S3 and S5). 3 MVMR results of clinical features and four types of SD Given its established association with various diseases, BMI was incorporated as an adjustment variable in the multivariate MVMR analysis among lifestyle factors. Previous studies have consistently demonstrated a significant correlation between BMI and the characteristics of numerous diseases, including inflammatory factors and blood lipid levels (Yan et al., 2024). Consequently, it is imperative to conduct multivariate analyses incorporating BMI for clinical features identified as having a significant causal relationship with sleep disorders. To account for the potential influence of individual clinical features, we conducted an adjustment analysis. As illustrated in Fig. 6, the effect of hypertension on NSD was no longer significant after adjusting for BMI, suggesting that the impact of hypertension on NSD is mediated through BMI. In contrast, Fig. 7 demonstrates a statistically significant causal correlation between ApoA-I, HDL, TG, and narcolepsy, even after adjusting for BMI. This indicates that these factors are independently associated with the risk of narcolepsy. As depicted in Fig. 8, after adjusting for BMI, all clinical features were found to be non-significantly correlated with OSA. This suggests that the impact of these clinical features on OSA is primarily mediated through BMI and that they are not independent risk factors. Secondly, a comprehensive MVMR analysis was conducted on the clinical features associated with sleep disorders (narcolepsy and OSA), focusing on those with multiple significant features. As depicted in Fig. 9, after comprehensive adjustment, only the correlation between triglycerides (TG) and narcolepsy remained statistically significant (compared to individual adjustment). This finding suggests that the impact of TG on narcolepsy is independent of all the aforementioned clinical features and BMI, establishing it as a significant independent risk factor. As illustrated in Fig. 10, after comprehensive adjustment, only the correlation between hypertension and OSA exhibited statistical significance. However, this correlation was not observed when adjusted separately. This observation indicates the presence of confounding SNPs associated with other clinical factors when adjusted separately. The significance of the correlation between hypertension and OSA becomes apparent only after removing SNPs related to these other clinical features during comprehensive adjustment. Based on this comprehensive assessment, we conclude that a potential causal relationship exists between hypertension and OSA, and this correlation is statistically significant. 4 Sensitivity analysis To assess heterogeneity in the results, Cochran's Q and LOO analyses were conducted. Cochran's Q was employed to comprehensively evaluate heterogeneity across each causal relationship. The results revealed heterogeneity between smoking, years of schooling, cognitive performance, BMI, hypertension, various lipoproteins, and narcolepsy. Additionally, heterogeneity was observed between alcohol intake, oily fish intake, years of schooling, cognitive performance, overall health rating, ApoA-I, ApoB, HDL, TCL, and OSA. No heterogeneity was detected in the remaining causal relationships. To mitigate the potential impact of heterogeneity on the MR results, a random effects model was selected for analysis. In contrast, causal correlations unaffected by heterogeneity were analyzed using a fixed effects model. The results of Cochran's Q are presented in the supplementary materials (Table S6). Furthermore, to enhance the robustness of the analysis, additional heterogeneity testing was performed on each SNP using the LOO method. The results demonstrated that eliminating any individual SNP did not significantly alter the causal effect values, suggesting that the MR analysis results are reliable. The LOO results are available in the supplementary materials (Tables S7 and S8). To assess the presence of horizontal pleiotropy in the causal relationships, the MR-Egger intercept and MR-PRESSO methods were employed. The results indicated a certain degree of pleiotropy in the causal correlations between smoking, oily fish intake, and OSA. However, no pleiotropy was observed in the remaining causal relationships. The results of the pleiotropy test are provided in the supplementary materials (Table S6). Furthermore, no outlier SNPs were identified in this MR analysis, suggesting that the results are reliable. Disscussion This study leverages the genetic information of a large-scale population to systematically analyze the modifiable factors that influence the risk of SDs in both daily life and clinical practice. In contrast to observational studies, the results obtained from gene prediction offer a more theoretical perspective, as they can effectively eliminate the confounding effects of numerous factors. This approach provides valuable insights into the upper limit of the predictive capability of these influencing factors on SDs. The exposures in this study were categorized into two broad groups: seven types of lifestyle factors and four types of clinical features. The outcomes encompassed four distinct types of SDs. Given the complexity of the factors involved, the subsequent analysis and discussion will be organized according to the respective exposure categories. 1 lifestyle factors NSD is the most prevalent type of sleep disorder (Edinger et al., 2021 ). It is also widely recognized as the SD most closely linked to unhealthy lifestyle habits and psychological factors (Remi et al., 2019 ). A large-scale cross-sectional study conducted by Zheng YB corroborates this assertion (Zheng et al., 2024 ). This study identified prolonged overall physical activity time and increased fresh fruit intake as protective factors for NSD. Moreover, sensitivity analyses demonstrated that the causal association between these factors and NSD is not influenced by heterogeneity or pleiotropy, indicating the robustness of the findings. Previous studies examining different types have also yielded consistent conclusions. Lee K's retrospective study suggests that daily regular exercise can effectively treat NSD by regulating the sleep-wake cycle (Lee et al., 2024 ). Zhu W's follow-up survey further supports this association, revealing a significant correlation between physical exercise and NSD (Zhu et al., 2024 ). Collectively, this evidence underscores the beneficial effects of exercise on sleep. Conversely, eating disorders have been established as a contributing factor to SDs arising from mental disorders (Regli et al., 2024 ). In the present study, this correlation was further confirmed by the observed positive association between fresh fruit intake and sleep. As health factors that can be readily modified through daily lifestyle interventions, the benefits of exercise and fresh fruit intake for NSD warrant emphasis in clinical health education. The global incidence rate of narcolepsy is estimated to be approximately 0.026% (Barateau et al., 2023 ). The debilitating symptoms of excessive sleepiness, sleep paralysis, and hallucinations significantly impair patients' physical, mental, and social well-being (Schiappa et al., 2018 ). Consequently, narcolepsy represents a critical category within sleep disorders. A cross-sectional study by Varallo G revealed that individuals with narcolepsy exhibit poorer coping strategies, emotional regulation abilities, and overall quality of life compared to healthy controls (Varallo et al., 2024 ). In recent years, there has been a growing emphasis on managing the mental health and quality of life of patients with narcolepsy (Barker et al., 2020 ). The latest European guidelines strongly advocate for exercise, weight control, and dietary adjustments as beneficial interventions for narcolepsy (Bassetti et al., 2021 ). In this study, smoking, alcohol consumption, cereal intake, salad/raw vegetable intake, poor overall health rating, and high BMI were identified as risk factors for narcolepsy. Conversely, tea intake, non-oily fish intake, longer education time, healthy cognitive performance, and high income emerged as protective factors. While heterogeneity influenced the results for smoking, BMI, and education level, the causal associations remained significant after employing a random effects model. For the remaining factors unaffected by heterogeneity, fixed effects models were utilized. Importantly, all significant factors identified in this study were not affected by pleiotropy, indicating the stability of their causal associations with narcolepsy. Smoking cessation, alcohol reduction, dietary adjustments, and weight loss (for obese individuals) can contribute to the health management of patients with narcolepsy. Black J's survey study demonstrated a significantly higher obesity rate among individuals with narcolepsy compared to healthy controls (Black et al., 2017 ). Furthermore, cognitive guidance and enhanced education are crucial for children with narcolepsy, facilitating their adaptation to normal school life and emotional management. Thieux M's case-control study revealed that children with narcolepsy exhibited deficits in intelligence assessment and cognitive regulation compared to healthy children (Thieux et al., 2022 ). The findings of this study also underscore the importance of education in narcolepsy, as longer school life and healthy cognitive performance were identified as protective factors. A systematic review by Bassetti CLA further supported the notion of impaired cognitive function in patients with narcolepsy, encompassing attention, executive function, and memory function (Bassetti et al., 2019 ). Currently, many clinicians and researchers acknowledge that cognitive impairment is a destructive symptom of narcolepsy (Harel et al., 2024 ). Moreover, high-income families are more likely to benefit from health management programs addressing smoking, alcohol consumption, weight management, and education (Krahn et al., 2022 ). Patients with OSA may experience excessive sleepiness, but IH during sleep is often more severe (Patel, 2019 ). OSA has been linked to various health complications, including heart disease, diabetes, stroke, and other conditions (Hidayat et al., 2024 , Kwon et al., 2024 , Tenda et al., 2024 ). These adverse outcomes are associated with oxidative stress induced by IH (Donkor et al., 2024 ). The Consensus on Diagnosis and Management of OSA published in JAMA recommends lifestyle interventions as an effective treatment for OSA (Gottlieb & Punjabi, 2020 ). Wu S et al.'s study (Wu et al., 2024 ) further demonstrated that adherence to the Life's Simple 7 metrics (LS7) is associated with a reduced risk of OSA. LS7 encompasses weight loss, smoking cessation, healthy diet, exercise, blood pressure control, cholesterol control, and blood sugar control. This study emphasizes the importance of both lifestyle factors and clinical features in OSA management. In our study, smoking, alcohol intake, coffee intake, pork intake, poor overall health rating, high BMI, and high WHR were identified as risk factors for OSA. Conversely, bread intake, cereal intake, dried fruit intake, longer education, healthy cognition, and high income were associated with a reduced risk. While alcohol consumption, education, and overall health rating exhibited some heterogeneity, the causal associations remained statistically significant after employing a random effects model. Except for smoking, all other significant factors were not affected by pleiotropy, indicating the stability of their causal associations with OSA. Zheng YB's cross-sectional study similarly found that six lifestyle factors, including healthy diet, regular physical exercise, never smoking, never drinking alcohol, short-term sedentary behavior, and normal weight, can reduce the risk of OSA (Zheng et al., 2024 ). Li Y's study corroborated the negative impact of poor overall health rating and high BMI on OSA (Li et al., 2023 ). Our study validated the stability of these findings. Additionally, the causal association between WHR and an increased risk of OSA highlights the detrimental effects of abdominal fat accumulation. Deng H's cross-sectional study also confirmed a positive correlation between WHR and OSA risk (Deng et al., 2023 ). The increasing prevalence of OSA due to rising obesity rates has been widely recognized (Lyons et al., 2020 ). Interventions targeting the aforementioned lifestyle habits may benefit OSA patients. Similarly, strengthening education and increasing income can positively impact cognitive regulation, environmental adaptation, and mental health in children and adults with OSA (Isaiah et al., 2021 , Vaienti et al., 2024 ). These findings align with the study results of Chang MS (Chang et al., 2024 ), emphasizing the importance of education and cognitive training. In summary, this analysis reveals a strong association between lifestyle factors and both narcolepsy and OSA. These findings align with the lifestyle interventions advocated in clinical guidelines. Notably, these lifestyle factors exhibit interrelationships. For instance, smoking, drinking, and diet can influence overall health rating and BMI, while income can impact diet and education. However, it is crucial to acknowledge that these relationships may be influenced by other unidentified genetic factors. None of these factors can be considered a sole determinant of SDs, and their impact on SDs can only be assessed through data-driven analysis. The occurrence and progression of SDs are the result of complex interplay among various factors. Therefore, while the identified lifestyle factors associated with SDs hold public health significance, these findings should be interpreted with caution. Nonetheless, improving these lifestyle factors can contribute to the management of SD patients. Regarding the non-statistically significant lifestyle factors, this MR analysis does not provide sufficient evidence to establish a causal association with SDs. Larger sample sizes of genetic variables and observational studies are necessary to validate these findings. 2 Clinical features Insomnia is increasingly prevalent among the general population. Surveys indicate that one-third of individuals in developed countries experience sleep challenges at least weekly, and 6–15% are diagnosed with NSD (Krystal et al., 2019 ). Globally, the incidence rate of NSD ranges from 5–20%, while up to 30–35% of adults exhibit insomnia symptoms (Nguyen et al., 2019 ). These data highlight the emergence of NSD as a significant public health concern. The causes of NSD often involve disrupted circadian rhythms and psychological factors (Riemann et al., 2022 ), making it challenging to detect significant laboratory abnormalities. This aligns with the majority of clinical observations (Ramos et al., 2023 ). In this study, no significant causal relationship was identified between glucose and lipid metabolism indicators, inflammatory indicators, and NSD. Sleep disorders frequently co-occur with mental illnesses, forming an interactive relationship (San & Arranz, 2024 ). Statistics reveal that approximately 70% of patients with mental disorders experience SDs (Hombali et al., 2019 ). NSD is primarily characterized by difficulty falling asleep, disrupted sleep, difficulty falling back asleep after waking up, and daytime dysfunction caused by early awakening (Riemann et al., 2022 ). These symptoms pose significant challenges to patients' social activities and psychological well-being. Liu RT's systematic review (Liu et al., 2020 ) identified SD as a risk factor for suicidal ideation and behavior. Additionally, Cappuccio FP's study (Cappuccio et al., 2011 ) demonstrated that SD can increase the risk of coronary heart disease, stroke, and overall cardiovascular disease. The findings of this study reveal a significant increase in NSD risk associated with hypertension. Hypertension, as the most common chronic cardiovascular disease, shares a genetic association with NSD, narcolepsy, and OSA. This represents a significant causal relationship, consistent with the well-established close association between SDs and cardiovascular disease (Hale et al., 2020 ). While our understanding of narcolepsy in terms of neural transmission and neuroimmunity has advanced considerably (Mahoney et al., 2019 ), significant gaps remain in other areas. Investigating clinical features with potential causal associations can contribute to exploring mechanisms from alternative perspectives. This study identified FINS, TG, hypertension, and CRP as risk factors for narcolepsy. Conversely, ApoA-I and HDL were found to be protective factors. Cross-sectional studies by Casale S and Ponziani V (Casale et al., 2023 , Ponziani et al., 2016 ) similarly reported a significant decrease in HDL levels and a significant increase in blood pressure in patients with narcolepsy. However, Casale S's study (Casale et al., 2023 ) did not detect any differences in FPG or FINS, possibly due to the inclusion of pediatric patients. Another study (Poli et al., 2009 ) on adult narcolepsy revealed higher levels of FINS, TCL, and TG in addition to low HDL and hypertension, aligning with the findings of our study. In this context, age may reflect the impact of the duration of abnormal metabolism on pathological progression. However, this MR analysis did not establish a causal correlation between TCL and narcolepsy. Miyagawa T's study (Miyagawa et al., 2013 ) also identified an association between high TG and narcolepsy but not high TCL, suggesting that TCL might indicate changes during disease development rather than an inducing factor. The causal associations between FINS, TG, HDL, and narcolepsy suggest that alterations in these factors may contribute to the development of narcolepsy. These findings also confirm the association between glucose and lipid metabolism and narcolepsy. Regarding inflammation, the presence of autoimmune reactions in patients with narcolepsy has been extensively studied (Bonvalet et al., 2017 ). The prevailing hypothesis posits that CD4 + T cell activation is the primary pathogenic mechanism, although specific autoantibodies have yet to be discovered (Bonvalet et al., 2017 ). Neuroimmunity, primarily mediated by cellular immunity, contributes to the loss of orexin neurons, resulting in a deficiency of the neurotransmitter orexin/Hcrt-1 (Zhan et al., 2024 ). This neurotransmitter deficiency is considered the primary cause of narcolepsy. CRP is a prominent peripheral inflammatory marker. A previous study (Aran et al., 2009 ) did not find significant differences in CRP levels between patients with narcolepsy and healthy individuals. Unfortunately, there is currently limited research on peripheral immunity and inflammatory responses in patients with narcolepsy. While this study suggests a potential causal association between CRP and narcolepsy, the primary statistical method, IVW, did not confirm this difference. Therefore, the relationship between CRP and narcolepsy warrants further investigation. MVMR analysis adjusted for BMI revealed that the causal correlations between TG, ApoA-I, HDL, and narcolepsy persisted, indicating that their associations with narcolepsy are not influenced by BMI. Other risk factors, such as FINS, hypertension, and CRP, were to some extent influenced by BMI. A comprehensive MVMR analysis further demonstrated that only TG is causally associated with narcolepsy, suggesting its independent and significant risk factor role, uninfluenced by BMI or other clinical features. During the examination of OSA, TG, hypertension, CRP were associated with an increased risk of OSA, while FPG, ApoA-I, HDL were associated with a reduced risk. Comprehensive MVMR analysis revealed a persistent causal association between hypertension and OSA, indicating its independent association with an increased risk of OSA. The lack of statistical significance in the causal association between hypertension and OSA after adjusting for BMI alone suggests that the role of hypertension in increasing the risk of OSA is independent of other clinical features but may still be influenced by BMI. Conversely, the absence of statistically significant causal associations between all significant clinical features and OSA after adjusting for BMI alone indicates that the causal associations between these features and OSA are influenced by BMI. This was further confirmed by the comprehensive MVMR analysis. After adjusting for all significant clinical features, the causal association between BMI and OSA became even more apparent, highlighting the crucial role of BMI as a predictor for OSA. The accumulation of fat in the throat, chest wall, and abdominal wall can reduce the diameter of the respiratory tract, increasing the risk of OSA (Wyszomirski et al., 2023 ). This mechanism represents an important pathophysiology of OSA. The OSA management guidelines also emphasize the importance of weight loss in OSA patients (Chang et al., 2020 ). OSA is closely linked to diabetes and metabolic syndrome (Bloomgarden, 2023 , Drager et al., 2013 ). Khalil M's systematic review revealed a prevalence of OSA as high as 60% in adult diabetes patients (Khalil et al., 2020 ). The results of this study suggest that this association is related to high BMI and high WHR, key indicators of obesity. Given anatomical factors, OSA can be readily considered an accompanying syndrome of obesity. While the association between OSA and other diseases is statistically related to obesity indicators, the characteristic pathological manifestation of OSA, intermittent hypoxia (IH), is another crucial factor in inducing complications. IH can contribute to cardiovascular disease, diabetes, and systemic inflammation (Drager et al., 2013 , Karkinski et al., 2017 , Koh et al., 2022 , Tang et al., 2024 ). Drager LF's study demonstrated that IH exacerbates FPG levels (Drager et al., 2011 ). Animal experiments conducted by Savransky V and Drager LF revealed that IH disrupts ApoA-I-controlled lipid synthesis and impairs TG clearance (Drager et al., 2012 , Savransky et al., 2008 ). Ryan DH's research similarly suggested that controlling FPG and TG is an important intervention for alleviating OSA complications (Ryan & Yockey, 2017 ). Wang J's systematic review found significantly higher serum CRP levels in OSA patients compared to healthy controls (Wang et al., 2015 ). The causal relationships between FPG, TG, CRP, and OSA identified in this study corroborate these associations. Overall, OSA is not solely caused by upper respiratory collapse. Abnormal glucose and lipid metabolism (high glucose and hyperlipidemia), hypertension, and inflammation can all contribute to the development of OSA. It is important to note that these causal associations cannot be entirely separated from the influence of high BMI. In conclusion, sleep disorders are not merely sleep problems but have a profound impact on various aspects of quality of life. They are closely linked to mental health, cardiovascular disease, diabetes, and multisystem diseases. Sleep problems should not be underestimated. Regardless of the type of SD, lifestyle interventions have been widely recognized as crucial components of effective management. The findings of this study offer detailed health management strategies encompassing multiple dimensions, including smoking and drinking habits, diet, education, cognition, income, and body fat (BMI and WHR). These results hold significant public health guidance implications. The aforementioned clinical features are readily monitored health indicators in daily life. Based on the results above, a comprehensive summary of the lifestyle factors and clinical features that exhibit a potential causal association with the four types of sleep disorders was obtained. As shown in Fig. 11 . Statistical analysis revealed that two lifestyle factors and one clinical feature were associated with NSD. Hypersomnia, on the other hand, was linked to 13 lifestyle factors and 6 clinical features. OSA was associated with 15 lifestyle factors and 6 clinical features. No factors were identified to be associated with RSBD. Monitoring these indicators can not only aid in preventing SDs but also contribute to exploring the underlying mechanisms of their occurrence. The simplicity of these factors should not be overlooked, as even seemingly straightforward factors can play a crucial role in the development and progression of SDs. limitations Based on extensive GWAS data, this study comprehensively investigated the causal relationships between lifestyle factors, clinical features, and four types of sleep disorders. The findings of this study provide valuable insights into the health management of SD patients. However, this analysis is subject to certain limitations. Firstly, no association with RSBD was identified at the genetic level among the 51 exposures examined. While these results align with previous studies (Zhang et al., 2024 ), sensitivity analysis did not reveal heterogeneity or pleiotropy in this context. This suggests the potential existence of other influencing factors that have yet to be discovered. Secondly, for some exposures, such as vigorous physical activity, moderate-intensity activity duration, walking duration, and sedentary behavior, the availability of SNPs was limited, which reduced their statistical power. Consequently, no strong correlations were observed between these factors and SDs. Further research with larger datasets is necessary to validate these findings. Finally, for the non-significant exposures, it cannot be definitively concluded that they are not associated with SDs. Additional large-scale observational studies are required to verify these results. Conclusion Among the 34 lifestyle factors examined, this study identified overall physical activity time and fresh fruit intake as protective factors for NSD. For narcolepsy, smoking, alcohol drinks, cereal intake, salad/raw vegetable intake, poverty, poor overall health rating, and high BMI were found to be risk factors, while tea intake, non-oily fish intake, education and cognition, and high income were identified as protective factors. In the case of OSA, smoking, alcohol intake, coffee intake, pork intake, poverty, poor overall health rating, high BMI, and high WHR emerged as risk factors, whereas bread and cereal intake, dried fruit intake, education and cognition, and high income were associated with reduced risk. While potential causal associations were observed between these factors and the respective sleep disorders, there is currently insufficient evidence to establish a causal relationship between other lifestyle factors and sleep disorders. Among the 17 clinical features examined, hypertension was identified as a risk factor for NSD. For narcolepsy, high FINS, high TG, hypertension, and high CRP emerged as risk factors, while ApoA-I and HDL were found to be protective factors. In the case of OSA, high TG, hypertension, and high CRP were associated with increased risk, whereas FPG, ApoA-I, and HDL were associated with reduced risk. Insufficient evidence exists to support a causal association between the remaining clinical features and sleep disorders. In summary, these findings underscore the significance of these factors in sleep disorder patients. These modifiable factors represent key targets for preventing and managing sleep disorders and hold valuable clinical guidance implications. By addressing these factors, we can potentially alleviate the health burden associated with sleep disorders. Abbreviations Initial: A-G: APAM: Accelerometer-based physical activity measurement (average acceleration); Apo: Apolipoprotein; BMI: Body Mass Index; BP: Blood pressure; cAMP: Cyclic adenosine monophosphate; CI: Confidence interval; CRP: C-reactive protein; CSF: Cerebrospinal fluid; EDS: Excessive daytime sleepiness; FINS: Fasting insulin; FPG: Fasting plasma glucose; GWAS: Genome-wide association study. Initial: H-N: Hcrt-1: Hypocretin-1; HDL: High density lipoprotein; HPA: Hypothalamic pituitary adrenal; ICD: International classification of diseases; ICSD: International classification of sleep disorders; IH: Intermittent hypoxia; IL: Interleukin; IV: Instrumental variable; IVW: Inverse variance weighted; LDL: Low density lipoprotein; LOO: Leave one out; MyD: Marrow differentiation factor; MR: Mendelian randomization; MRE: MR-Egger; MSLT: Multiple sleep latency test; MVMR: Multivariate mendelian randomization; NF-κB: Nuclear factor-κB; NO: Nitric oxide; NSD: Nonorganic sleep disorders; NTx: Narcolepsy of type x. Initial: O-T: OR: Odds ratio; OSA: Obstructive sleep apnea; PMID: PubMed unique identifier; PSG: Polysomnography; REM: Rapid eye movement; RSBD: REM sleep behavior disorder; SD: Sleep disorders; SDB: Sleep disorder breathing; SM: Simple mode; SNP: Single nucleotide polymorphism; SOREMPs: Sleep onset rapid eye movement periods; TCL: Total cholesterol levels; TDI: Townsend deprivation index; TG: Triglyceride; TLR: Toll-like receptors; TNF-α: Tumor necrosis factor-α. Initial: U-Z: WHR: Waist to hip ratio; WM: Weighted mode; WME: Weighted median estimation. Declarations Data Availability Data is provided within the manuscript or supplementary information files. Funding None. Acknowledgements The authors would like to thank freescience (www.home-for-researchers.com) for the help with the English language. Authors’ contributions Design: Haiqing Ao and Jingyu Xu; Data collection: Jingyu Xu and Baojuan Wang; Data analysis: Jingyu Xu and Wenbin Zhu; Visualization: Jingyu Xu and Wenbin Zhu; Drafting of the article: Jingyu Xu; Critical revision of the article for important intellectual content: Haiqing Ao and Jingyu Xu; Study supervision: Haiqing Ao. All the authors approved the final article. Conflicts of Interest The authors declare that they have no conflicts of interest. References Aalling, N.N., Nedergaard, M. & Dinuzzo, M. (2018) Cerebral Metabolic Changes During Sleep. CURR NEUROL NEUROSCI, 18 , 57. Aghaei, M., Joukar, F., Hasanipour, S., Ranjbar, Z.A., Naghipour, M. & Mansour-Ghanaei, F. (2024) The association between waist-to-hip ratio (WHR) with diabetes in the PERSIAN Guilan cohort study population. BMC ENDOCR DISORD, 24 , 113. Albitar, O., D'Souza, C.M. & Adeghate, E.A. (2024) Effects of Lipoproteins on Metabolic Health. NUTRIENTS, 16. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5092259","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":384923780,"identity":"ca705cc4-3711-4ca5-b05f-a89af290b353","order_by":0,"name":"Jingyu 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1","display":"","copyAsset":false,"role":"figure","size":942652,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of MR of lifestyle factors and clinical features with four types of SD\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5092259/v1/de20c4bd8b45f26193f4f007.png"},{"id":71738021,"identity":"b1a1df30-3130-4241-9dd5-bb08d2a5611f","added_by":"auto","created_at":"2024-12-18 07:45:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":627207,"visible":true,"origin":"","legend":"\u003cp\u003eCategories of lifestyle factors and clinical features (Expose)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5092259/v1/9aa6e080ef088d22ce4de5a2.png"},{"id":71734410,"identity":"8ef76d32-c573-492a-bf8d-b2f07e33123b","added_by":"auto","created_at":"2024-12-18 07:21:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":373743,"visible":true,"origin":"","legend":"\u003cp\u003eMR results of 34 lifestyle factors with 4 types of SD (IVW)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5092259/v1/84695e8ee0368d9d8145a0e5.png"},{"id":71736586,"identity":"87d03294-f6fc-4956-b2e6-1e9e1cae5bb4","added_by":"auto","created_at":"2024-12-18 07:37:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":257158,"visible":true,"origin":"","legend":"\u003cp\u003eMR results of 17 clinical features with 4 types of SD (IVW)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5092259/v1/d4d92ee4e9802662622b46aa.png"},{"id":71735650,"identity":"5162f023-c981-413f-a672-44adfcf2819a","added_by":"auto","created_at":"2024-12-18 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07:21:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":207955,"visible":true,"origin":"","legend":"\u003cp\u003eMVMR results of clinical features with narcolepsy\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5092259/v1/5d2014f9df139980d82a9e60.png"},{"id":71736588,"identity":"5e9cb1c0-c919-444d-a9ad-356008cba5ff","added_by":"auto","created_at":"2024-12-18 07:37:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":205450,"visible":true,"origin":"","legend":"\u003cp\u003eMVMR results of clinical features with OSA\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5092259/v1/919e0918e59523e996debce8.png"},{"id":71734420,"identity":"df7092ca-ba01-47aa-a4fd-3e7156edc2f8","added_by":"auto","created_at":"2024-12-18 07:21:32","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":195438,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive MVMR results of clinical features with narcolepsy\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5092259/v1/3bda9ae98900bfe609920170.png"},{"id":71735649,"identity":"ce01538d-d39d-4685-bb40-0854d461f37a","added_by":"auto","created_at":"2024-12-18 07:29:32","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":245537,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive MVMR results of clinical features with OSA\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-5092259/v1/cc02266bc7c975e0e464c38d.png"},{"id":71736589,"identity":"10171c72-0dc8-4edc-a3a5-ab6a98903ebd","added_by":"auto","created_at":"2024-12-18 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07:21:32","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":27294,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsS1STROBE.docx","url":"https://assets-eu.researchsquare.com/files/rs-5092259/v1/64ed826e7eb733cae4e91504.docx"},{"id":71734422,"identity":"f106595c-fad5-42da-b0cd-b3837edc84c0","added_by":"auto","created_at":"2024-12-18 07:21:32","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":2603832,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsS2S8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5092259/v1/0bfc60c2dbe795d8c41c2c88.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal association of lifestyle factors, clinical features in the risk of sleep disorders: Based on mendelian randomization analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs is well known, many critical life activities, including memory and emotion processing, as well as the active period of glucose and lipid metabolism in the brain, occur during sleep (Aalling et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Whitehurst et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Sleep, a readily observable and perceived behavior in daily life, has been extensively studied and is a vital health indicator in clinical practice. Due to its widespread impact, sleep problems have become a key topic of global discussion. One significant consequence of sleep problems is their interference with the body's normal metabolic activities. An animal experiment by Wang et al. (Wang et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) demonstrated that insomnia can cause damage to hippocampal neurons, activate the hypothalamic-pituitary-adrenal (HPA) axis, and increase the expression of inflammatory factors, such as IL-2, IL-6, and TNF-α, in rats. Further research has revealed an association between insomnia and reduced signaling of cyclic adenosine monophosphate (cAMP). Yin C's study (Yin et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported cognitive impairment and changes in gut microbiota caused by insomnia in rats. Additionally, research suggests a link between insomnia and the activation of the TLR4/MyD88/NF-κB signaling pathway. Moreover, sleep problems can significantly affect mental health. Sleep disorders (SD) are often comorbid with mental disorders such as anxiety and depression (Gauld et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Chellappa SL et al.'s study (Chellappa \u0026amp; Aeschbach, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) showed that insomnia affects approximately 50% of anxiety disorder patients, exacerbating their anxiety relapse. Research by Arora S (Arora et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) demonstrated an association between SD and decreased physical fitness (poor physical ability) and negative psychological states (depression and anxiety). SD is also a common complication in patients with depression (Solelhac et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In conclusion, sleep is closely related to both physical and mental health. Sleep problems can lead to a series of negative effects, both physically and psychologically.\u003c/p\u003e \u003cp\u003eThis study focuses on four types of sleep disorders: non-organic sleep disorders (NSD), narcolepsy, rapid eye movement (REM) sleep behavior disorders (RSBD), and obstructive sleep apnea (OSA). NSD is often caused by psychological and lifestyle factors and is the most common type of SD (Edinger et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). NSD patients frequently exhibit chronic insomnia or disrupted sleep cycles due to disrupted circadian rhythms (San \u0026amp; Arranz, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), leading to psychological fragility, emotional fluctuations, memory loss, and a lack of concentration (San \u0026amp; Arranz, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), significantly impacting their quality of life. Narcolepsy is primarily manifested as an irresistible urge to sleep during the daytime (Konofal, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), not only affecting work and social life but also posing a safety hazard (Gupta et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition to excessive sleepiness, patients may also experience symptoms such as muscle weakness, sleep paralysis, and hallucinations (Konofal, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), all of which hint at abnormal emotional reactions and psychological states in patients (Yu et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). RSBD is an SD caused by neurological disorders characterized by the loss of dreaming and muscle weakness during REM periods (Park et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). RSBD is often associated with Parkinson's disease, dementia, and ischemic stroke (Pace et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Postuma et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Pesonen AK et al.\u0026rsquo;s study (Pesonen et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that REM periods primarily serve emotional processing functions. During this period, the activity of brain regions related to emotions, such as the amygdala, striatum, hippocampus, and medial prefrontal cortex, increases (Knobloch et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Lee et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Lim et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These brain regions have been associated with negative emotions like anxiety and depression (Knobloch et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Lee et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Lim et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Animal experiments conducted by Cai G (Cai et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrated that sleep interruption induces anxiety-like behaviors in mice during REM periods, while Saadati N et al.'s study (Saadati et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that it induces depression-like behaviors. Therefore, RSBD has been associated with mental disorders. Finally, OSA is characterized by the narrowing or collapse of the upper respiratory tract during sleep (Mao et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), leading to fragmented sleep (disrupted sleep rhythms) and intermittent hypoxia (IH), which can easily cause neuroinflammation and cognitive impairment (Ou et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). OSA has been associated with various mental disorders, such as anxiety, depression, and schizophrenia (Xu et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Given their widespread influence and extensive study, these four types of SD were included in this study for analysis.\u003c/p\u003e \u003cp\u003eThe causes of SD are multifaceted. Among them, lifestyle factors and clinical features are two primary categories of influencing factors. Dzierzewski JM's research (Dzierzewski et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated a negative correlation between sleep health and weekly fast food intake, TV/social media usage, and sedentary time. Additionally, it revealed a positive correlation with moderate to vigorous exercise time. Excessive intake of caffeine and sugar can also lead to poor sleep quality (De Pasquale et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Emmy HKI's study showed that long-term use of electronic devices before bedtime can disrupt the normal sleep-wake cycle (Emmy et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, this study aimed to quantitatively analyze the impact of lifestyle factors and clinical features on various types of SD as comprehensively as possible. It is important to note that while many diseases can also cause sleep problems, they were not included as influencing factors in this study, as diseases result from the combined action of multiple pathological factors.\u003c/p\u003e \u003cp\u003eTraditional clinical observational studies frequently employ correlation coefficients and regression analysis to examine the relationship between factors and diseases. While this method can ensure authenticity in the real world, it has three limitations. First, there is a high likelihood of confounding factors beyond the investigated factors. Second, traditional statistical methods are unable to determine the direction of correlation. Third, it is challenging to accurately explain the influence of individual factors, and a multi-factor fitting model is necessary for a comprehensive assessment. Mendelian randomization (MR) analysis utilizes genetic variation (random allocation of alleles), such as single nucleotide polymorphisms (SNPs), as instrumental variables (IVs) to calculate correlation. This approach is not affected by postnatal confounding factors and provides relatively stable results. Additionally, MR clearly distinguishes between exposures and outcomes in statistical analysis, enabling the determination of the direction of causal effects. Furthermore, causal effect values can accurately represent the influence of exposures, provided the assumption of IVs is satisfied. Therefore, this study employed MR to comprehensively evaluate the effects of each independent influencing factor on the four types of SD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1 Study design\u003c/h2\u003e \u003cp\u003eThis study quantitatively analyzed the influence of various factors on four types of SD through multiple two-sample MR analyses. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The aim was to identify factors related to SD and provide evidence-based support. MR analysis requires the identification of exposure and outcome, followed by the calculation of causal effects using IVs representing these variables. SNPs, single base changes randomly assigned during the genetic process, are considered natural genetic variations with a frequency greater than 1% (Wang et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). SNPs have been associated with susceptibility to numerous diseases, including diabetes, polycystic ovary syndrome, cervical cancer, mental illness, and others (Liu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Raheem et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Wang et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Wootton et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Xu et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, in genetic association studies, SNPs are commonly used as IVs. Using IVs can effectively calculate effect values. Genetic differences eliminating the influence of various postnatal confounding factors and making the causal effect between exposure and outcome more convincing, accounting for the use of MR to analyze influencing factors in this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring MR analysis, it is crucial to carefully select the IVs for exposure and outcome (SNP screening). These IVs should meet several criteria: (1) they should come from two independent samples (Sekula et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), (2) all samples should share the same genetic background (Davey \u0026amp; Hemani, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and (3) the IVs should satisfy three key assumptions for causal reasoning: correlation, exclusivity, and independence (Davies et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The correlation assumption states that the IV must be strongly correlated with the exposure. The exclusivity assumption stipulates that the IV only affects outcomes through the exposure and not via other pathways. The independence assumption requires that the IV be independent of confounding factors between the exposure and outcome. In this study, seven types of lifestyle factors (a total of 34 factors) and four types of clinical features (a total of 17 factors) served as exposures, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. While the four types of SD were the outcomes. All exposure and outcome data were sourced from publicly available Genome-wide Association Studies (GWAS), eliminating the need for ethical approval. After identifying the exposure, outcome, and IVs, statistical analysis was conducted. The study design of this MR analysis adheres to the STROBE-MR statement (Skrivankova et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For further details, please refer to the supplementary materials (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2 Data sources\u003c/h3\u003e\n\u003cp\u003eAll data in this study were collected from publicly available GWAS. A total of 10 databases were involved, with 9 databases providing data for exposures and 4 databases providing data for outcomes. The source databases for exposure and outcome data are detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003e2.1 Expose\u003c/h3\u003e\n\u003cp\u003eThis study included lifestyle factors and clinical features as exposures. Lifestyle factors encompassed seven categories: exercise, smoking, beverages, diet, education, income, and physical condition. Clinical features included four categories: glucose metabolism, lipid metabolism, blood pressure, and inflammation. Each category contained several indicators, totaling 34 lifestyle factors and 17 clinical features. For each indicator's GWAS dataset, the author recorded its characteristics, including name, population source, sample size, number of SNPs (SNPn), GWAS number, publication year, database or consortium, and PubMed identifier (PMID). Details are recorded in Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eTable.1 Characteristics of the expose GWAS summary data.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enSNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGWAS ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePublication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConsortium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePMID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean F-statistic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003elifestyle factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1. Physical activity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91,084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,796,201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST006099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29899525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous physical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e261,055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,803,978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST006098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29899525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrenuous sports or other exercises\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350,492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,807,536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST006100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29899525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate to vigorous physical activity levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e377,234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,808,007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST006097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29899525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedentary behaviour duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91,105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,926,106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST006913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30531941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWalking duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91,105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,926,106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST007110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30531941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate intensity activity duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91,105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,926,106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST006915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30531941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall physical activity time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91,105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,926,106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST006912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30531941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35.624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2. Smoke\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking initiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e607,291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,802,365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eieu-b-4877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGSCAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30643251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status (ever vs never smokers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e518,633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19,524,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST007327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30643258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3. Drink\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcoholic drinks per week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335,394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,887,865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eieu-b-73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGSCAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30643251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTea intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e447,485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-6066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e66.920\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol intake frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e462,346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-5779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e56.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoffee intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e376,372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,645,580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePMID31046077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31046077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4. Diet\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBread intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e452,236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-11348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDried fruit intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e421,764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-16576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalad / raw vegetable intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e435,435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeef intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e461,053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-2862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOily fish intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e460,443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-2209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45.578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLamb / mutton intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e460,006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-14179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40.903\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCereal intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e441,640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-15926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCooked vegetable intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e448,651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-8089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFresh fruit intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e446,462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-3881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-oily fish intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e460,880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-17627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePork intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e460,162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-5640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolyunsaturated fatty acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,321,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emet-d-PUFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIEU OpenGWAS Project\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e144.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaturated fatty acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,321,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emet-d-SFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIEU OpenGWAS Project\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e120.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5. Education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e766,345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,101,242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eieu-a-1239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSSGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30038396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e50.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e257,841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,066,414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST006572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSSGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30038396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6. Income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage total household income before tax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e397,751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-7408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTownsend deprivation index at recruitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e462,464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-10011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35.760\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7. Physical condition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall health rating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e460,844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-6306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (BMI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e681,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,336,260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eieu-b-40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGIANT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30124842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e87.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist-to-hip ratio (WHR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212,244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,560,782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eieu-a-73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGIANT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25673412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical features\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8. Sugar metabolism\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting plasma glucose (FPG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58,074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,599,409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST005186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22581228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e97.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting insulin (FINS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281,416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,229,036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST90002238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34059833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e52.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9. Lipid metabolism\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (TG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e441,016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,321,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eieu-b-111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK Biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32203549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e174.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoprotein B (ApoB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e439,214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,321,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eieu-b-108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK Biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32203549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e245.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh density lipoprotein (HDL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e403,943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,321,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eieu-b-109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK Biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32203549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e185.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoprotein A-I (ApoA-I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e393,193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,321,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eieu-b-107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK Biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32203549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e179.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow density lipoprotein (LDL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e440,546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,321,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eieu-b-110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUK Biobank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32203549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e202.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol levels (TCL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e437,878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,232,052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST90025953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34226706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e239.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApolipoprotein E plasma levels (ApoE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,188,525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST90085710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35264221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10. Blood pressure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e463,010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-12493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e50.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11. Inflammation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-Reactive protein level (CRP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204,402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,414,379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eieu-b-35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30388399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e197.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor necrosis factor alpha levels (TNF-α)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,500,449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST004426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27989323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-1-beta levels (IL-1β)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,983,642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST004448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27989323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-18 levels (IL-18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,785,222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST004441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27989323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e70.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-17 levels (IL-17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,786,653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST004442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27989323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-6 levels (IL-6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,790,590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST004446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27989323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-10 levels (IL-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,793,415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST004444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27989323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e169.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNOTE\u003c/b\u003e: APAM: Accelerometer-based physical activity measurement (average acceleration); Hypertension: Diagnoses from ICD10-I10.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e2.2 Outcome\u003c/h3\u003e\n\u003cp\u003eThe study focused on four types of SDs: NSD, narcolepsy, RSBD, and OSA. Diagnostic criteria were established using the \u003cem\u003eInternational Classification of Diseases-10th edition\u003c/em\u003e (ICD-10) (Spiegelhalder et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the \u003cem\u003eInternational Classification of Sleep Disorders-third edition\u003c/em\u003e (ICSD-3) (Ruoff \u0026amp; Rye, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). NSD (diagnosed from ICD10: F51) is characterized by a constellation of symptoms, including insomnia, disturbances in sleep-wake rhythms, night startles, and nightmares. Narcolepsy (diagnosed from ICSD3) is further classified into type I (NT1) and type II (NT2). NT1 is defined by excessive daytime sleepiness, cataplexy (abrupt loss of muscle tone triggered by strong emotions), a sleep latency of 8 minutes or less, and the presence of two or more sleep-onset rapid eye movement periods (SOREMPs) in the Multiple Sleep Latency Test (MSLT). Additionally, NT1 is associated with low levels of hypocretin-1 (Hcrt-1) in cerebrospinal fluid (CSF), typically below 110 pg/mL or less than one-third of the average value in the normal population (Siegenthaler et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). NT2, on the other hand, does not exhibit cataplexy or low Hcrt-1 levels, but it may include other symptoms such as sleep paralysis, fragmented nocturnal sleep, hypnopompic-hypnogogic hallucinations, and automatic behaviors. RSBD is diagnosed based on ICSD-3, including repeated episodes of sleep-related vocalizations or complex motor behaviors during the REM sleep phase. Patients with RSBD may also exhibit REM sleep without tonia, accompanied by repeated dream-playing behaviors, which can sometimes be associated with increased tonic and/or phasic myoelectric activity. It is important to note that RSBD must not be attributable to other sleep disorders, mental disorders, or the effects of drugs or substances (Hogl \u0026amp; Stefani, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). OSA is diagnosed according to ICD-10 codes (ICD-10: G47.3, ICD-9: 3472A) and is determined based on a combination of subjective symptoms, clinical examination, and sleep registration. A diagnosis of OSA requires an apnea-hypopnea index (AHI) or respiratory event index (REI) of at least 5 events per hour (Gottlieb \u0026amp; Punjabi, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Malhotra et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For each GWAS dataset associated with the outcome, the following metadata were recorded: study name, population source, sample size, number of SNPs, GWAS accession number, publication year, originating database or consortium, and PMID. These details are summarized in Table\u0026nbsp;2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eTable.2 Characteristics of the outcome GWAS summary data.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enSNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGWAS ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePublication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConsortium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePMID\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonorganic sleep disorders (NSD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216,454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16,380,458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003efinn-b-F5_SLEEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNarcolepsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e460,913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-5776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREM sleep behavior disorder (RSBD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,777,418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCST90204200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36470867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstructive sleep apnea (OSA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e341,071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10,914,272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG6_SLEEPAPNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e2.3 adjustment\u003c/h3\u003e\n\u003cp\u003eBody mass index (BMI) is a widely recognized metric reflecting overall physical condition. According to ICD-10, individuals with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;24.0 are classified as overweight, while those with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;30.0 are considered obese. Extensive research has established a strong association between BMI and the functional integrity of various bodily systems. As early as 2004, the American Heart Association acknowledged the close relationship between obesity and cardiovascular disease (Klein et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Subsequent studies have consistently demonstrated a correlation between elevated BMI and an increased risk of impaired glucose metabolism, diabetes, and hyperuricemia (Aghaei et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Gou et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Liberty et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, obesity is often accompanied by abnormal lipid metabolism, a significant contributor to pathological conditions such as dyslipidemia, insulin resistance, and inflammation (Albitar et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recent cross-sectional studies conducted by Gildner TE have revealed a positive association between obesity and elevated levels of the inflammatory cytokine CRP (Gildner et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Sharma C's case-control study further supported this finding, demonstrating that obese patients exhibit higher levels of CRP, IL-6, TNF-α, catalase, and nitric oxide (NO), markers of oxidative stress (Sharma et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Given the established causal relationship between clinical features (exposures) and sleep disorders (outcomes), BMI may serve as a common mediator in this process. Emerging research has unequivocally identified high BMI as a potent predictor of sleep disorder breathing (SDB) (Al-Naimi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recognizing the pivotal role of BMI and its amenability to lifestyle interventions, BMI has become a cornerstone of clinical practice. Consequently, BMI was incorporated as an adjustment variable in multivariate Mendelian randomization (MVMR) analysis of this study. The data sources for BMI are detailed in Table\u0026nbsp;1.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3 Selection of IV\u003c/h2\u003e \u003cp\u003eTo identify IVs that meet the stringent requirements for MR analysis, the following steps were undertaken. After establishing the GWAS datasets for the exposure and outcome, SNPs within these datasets were subjected to a meticulous screening process. Three primary criteria were employed for SNP selection: (1) a genome-wide significance threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10⁻\u003csup\u003e8\u003c/sup\u003e was initially applied. If no SNPs met this stringent criterion, the threshold was relaxed to \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e (Xiang et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); (2) SNPs exhibiting linkage disequilibrium, defined as r\u0026sup2;\u0026lt; 0.001 and a physical distance\u0026thinsp;\u0026gt;\u0026thinsp;10,000 kilobases, were excluded (Machiela \u0026amp; Chanock, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); (3) SNPs with an F-statistic\u0026thinsp;\u0026le;\u0026thinsp;10 were also eliminated. The F-statistic serves as a valuable tool for assessing the strength of IV bias. SNPs with an F-statistic\u0026thinsp;\u0026gt;\u0026thinsp;10 are considered strong IVs, demonstrating a robust association with the exposure and exhibiting minimal susceptibility to bias, yielding relatively stable results (Pierce et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The F-statistic is calculated using the formula F\u0026thinsp;=\u0026thinsp;R\u0026sup2; / (1-R\u0026sup2;) \u0026times; (N-k-1) / k, where N represents the sample size of the GWAS study, k denotes the number of SNPs, and R\u0026sup2; signifies the proportion of exposure variance explained by each IV (Levin et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). R\u0026sup2; is calculated using the formula R\u0026sup2; = 2 \u0026times; EAF \u0026times; (1 - EAF) \u0026times; β\u0026sup2;, where EAF is the allele frequency and β is the allele effect size (Codd et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The average F-statistic for each exposure dataset is presented in Table\u0026nbsp;1. In summary, all SNPs retained in the analysis exhibited F-values greater than 10. Subsequently, the MR Steiger filtering method was employed to remove SNPs that demonstrated a weaker correlation with the exposure compared to the outcome (Hemani et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To mitigate the influence of confounding factors, SNPs related to age, gender, and primary diagnoses other than sleep disorders were retrieved and excluded from the analysis using the PhenoScanner database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phenoscanner.medschl.cam.ac.uk\u003c/span\u003e\u003cspan address=\"http://www.phenoscanner.medschl.cam.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Kamat et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Finally, the MR-PRESSO method was implemented for sensitivity analysis to identify and remove any outlier SNPs (Verbanck et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This iterative process continued until no outlier SNPs remained, resulting in the final set of included IVs (SNPs).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e4 Statistical analysis\u003c/h3\u003e\n\u003cp\u003eTo systematically investigate the causal relationship between exposure and outcome, we employed several MR methods: Inverse Variance Weighted (IVW), Weighted Median Estimation (WME), MR-Egger (MRE), Simple Mode (SM), and Weighted Mode (WM). Among these, IVW serves as the primary analytical method, while the remaining four methods provide supplementary insights. In cases where IVW analysis fails to yield statistically significant results but at least three of the other four methods demonstrate statistical significance, a potential causal association is inferred (Davies et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The causal effect values corresponding to each SNP were evaluated using the Wald ratio. The results were ultimately presented in the form of odds ratios (ORs) and their corresponding 95% confidence interval (95%CI). To address the issue of multiple testing, we implemented the Bonferroni correction. The significance level was adjusted to α\u0026thinsp;=\u0026thinsp;0.05 / 51, \u0026ldquo;51\u0026rdquo; reflecting the total number of exposures. A \u003cem\u003eP\u003c/em\u003e-value less than 0.00098 (0.05 / 51) was considered to indicate a strongly significant causal association, while a P-value between 0.00098 and 0.05 was deemed to represent a nominally significant causal association. Collectively, these findings provide evidence for a potential association between exposure and outcome.\u003c/p\u003e \u003cp\u003eTo assess the robustness of our findings, we conducted a comprehensive sensitivity analysis employing Cochran's Q, leave-one-out (LOO), MR-Egger intercept, and MR-PRESSO methods. Cochran's Q and LOO were utilized to detect heterogeneity among the IVs. A Q-pval greater than 0.5 indicates the absence of heterogeneity, while a Q-pval less than or equal to 0.5 suggests the presence of heterogeneity. The LOO method involves sequentially removing individual SNPs to observe if significant changes occur in the effect value and sensitivity value. To identify horizontal pleiotropy, we employed the MR-Egger intercept and MR-PRESSO methods. An MR-Egger intercept approaching zero suggests the absence of horizontal pleiotropy. Outlier SNPs detected by MR-PRESSO were removed, and the remaining SNPs were subjected to MR analysis again until no outliers were present. This iterative process ensured the robustness of our findings by mitigating the potential influence of confounding factors.\u003c/p\u003e \u003cp\u003eFollowing the univariate MR analysis, multivariate MR was conducted on clinical features with significant causal associations with the outcome. In this context, IVW was the primary evaluation method, while WME, MRE, SM, and WM functioned as supplementary approaches. MVMR offers the distinct advantage of incorporating both exposure and adjustment variables into a single model for analysis, thereby enabling the assessment of exposure independence (Burgess \u0026amp; Thompson, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). All of the aforementioned statistical analyses were executed using the \"TwoSampleMR\" and \"MR-PRESSO\" software packages within the R software environment, version 4.1.2.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e1 MR analysis of lifestyle factors and four types of SD\u003c/h2\u003e\n \u003cp\u003eDetailed F-values and additional information for each SNP can be found in the supplementary materials (Tables S2 and S3). The IVs of this MR satisfies three key assumptions.\u003c/p\u003e\n \u003cp\u003eGiven that the IVW method serves as the primary analytical method, the preliminary results based on IVW are presented in Fig.\u0026nbsp;3. The lifestyle factors identified as being associated with NSD include overall physical activity time (OR: 0.35; 95%CI: 0.13–0.99; \u003cem\u003eP\u003c/em\u003e = 0.048) and fresh fruit intake (OR: 0.30; 95%CI: 0.12–0.75; \u003cem\u003eP\u003c/em\u003e = 0.010).\u003c/p\u003e\n \u003cp\u003eFor narcolepsy, the associated lifestyle factors encompass smoking (OR: 1.02; 95%CI: 1.01–1.03; \u003cem\u003eP\u003c/em\u003e = 0.001), alcoholic drinks (OR: 1.02; 95%CI: 1.01–1.03; \u003cem\u003eP\u003c/em\u003e = 0.013 and OR: 1.02; 95%CI: 1.01–1.03; \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001), tea intake (OR: 0.95; 95%CI: 0.93–0.97; \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001), cereal intake (OR: 1.03; 95%CI: 1.00-1.07; \u003cem\u003eP\u003c/em\u003e = 0.029), non-oily fish intake (OR: 0.91; 95%CI: 0.84–0.99; \u003cem\u003eP =\u003c/em\u003e 0.021), salad/raw vegetable intake (OR: 1.10; 95%CI: 1.03–1.16; \u003cem\u003eP\u003c/em\u003e = 0.002), years of schooling (OR: 0.97; 95%CI: 0.96–0.99; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), cognitive performance (OR: 0.98; 95%CI: 0.97–0.99; \u003cem\u003eP\u003c/em\u003e = 0.001), average total household income before tax (OR: 0.94; 95%CI: 0.93–0.96; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), townsend deprivation index (TDI) (OR: 1.05; 95%CI: 1.00-1.10; \u003cem\u003eP\u003c/em\u003e = 0.036), overall health rating (OR: 1.07; 95%CI: 1.05–1.10; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and BMI (OR: 1.02; 95%CI: 1.02–1.03; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n \u003cp\u003eIn the case of OSA, the identified lifestyle factors include smoking (OR: 1.20; 95%CI: 1.08–1.34; \u003cem\u003eP\u003c/em\u003e = 0.001 and OR: 1.15; 95%CI: 1.06–1.25; \u003cem\u003eP\u003c/em\u003e = 0.001), alcohol intake (OR: 1.12; 95%CI: 1.01–1.24; \u003cem\u003eP\u003c/em\u003e = 0.037), coffee intake (OR: 1.25; 95%CI: 1.00-1.56; \u003cem\u003eP\u003c/em\u003e = 0.046), bread intake (OR: 0.63; 95%CI: 0.49–0.83; \u003cem\u003eP\u003c/em\u003e = 0.001), cereal intake (OR: 0.71; 95%CI: 0.56–0.90; \u003cem\u003eP\u003c/em\u003e = 0.005), dried fruit intake (OR: 0.64; 95%CI: 0.50–0.83; \u003cem\u003eP =\u003c/em\u003e 0.001), pork intake (OR: 2.55; 95%CI: 1.37–4.74; \u003cem\u003eP =\u003c/em\u003e 0.003), years of schooling (OR: 0.72; 95%CI: 0.66–0.79; \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001), cognitive performance (OR: 0.79; 95%CI: 0.71–0.87; \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001), average total household income before tax (OR: 0.78; 95%CI: 0.66–0.93; \u003cem\u003eP\u003c/em\u003e = 0.005), TDI (OR: 1.56; 95%CI: 1.12–2.19; \u003cem\u003eP\u003c/em\u003e = 0.009), overall health rating (OR: 2.76; 95%CI: 2.20–3.46; \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001), BMI (OR: 1.97; 95%CI: 1.85–2.11; \u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001), and waist-to-hip ratio (WHR) (OR: 1.30; 95%CI: 1.08–1.55; \u003cem\u003eP\u003c/em\u003e = 0.004). To date, no lifestyle factors have been associated with RSBD.\u003c/p\u003e\n \u003cp\u003eWhen the IVW analysis failed to yield statistically significant results but at least three of the remaining four supplementary methods demonstrated statistical significance, a potential causal association was inferred. The results obtained from these supplementary methods are provided in the supplementary materials (Table S4). However, no significant correlations were observed in this analysis. The number and characteristics of available SNPs for each exposure are detailed in the supplementary materials (Tables S2 and S4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e2 MR results of clinical features and four types of SD\u003c/h2\u003e\n \u003cp\u003eThe statistical results derived from the IVW method are presented in Fig.\u0026nbsp;4. Hypertension (OR: 4.68; 95%CI: 1.11–19.77; \u003cem\u003eP\u003c/em\u003e = 0.036) was identified as the clinical feature associated with NSD.\u003c/p\u003e\n \u003cp\u003eClinical features linked to narcolepsy include fasting insulin (FINS) (OR: 1.06; 95%CI: 1.03–1.09; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), triglycerides (TG) (OR: 1.02; 95%CI: 1.01–1.03; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), apolipoprotein A-I (ApoA-I) (OR: 0.99; 95%CI: 0.99-1.00; \u003cem\u003eP\u003c/em\u003e = 0.036), high-density lipoprotein (HDL) (OR: 0.99; 95%CI: 0.98–0.99; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and hypertension (OR: 1.08; 95%CI: 1.02–1.15; \u003cem\u003eP\u003c/em\u003e = 0.011).\u003c/p\u003e\n \u003cp\u003eFor OSA, the associated clinical features encompass FPG (OR: 0.84; 95%CI: 0.75–0.94; \u003cem\u003eP\u003c/em\u003e = 0.003), TG (OR: 1.06; 95%CI: 1.02–1.11; \u003cem\u003eP\u003c/em\u003e = 0.009), ApoA-I (OR: 0.94; 95%CI: 0.90–0.99; \u003cem\u003eP\u003c/em\u003e = 0.017), HDL (OR: 0.89; 95%CI: 0.85–0.93; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), hypertension (OR: 2.72; 95%CI: 1.73–4.26; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and CRP (OR: 1.06; 95%CI: 1.01–1.11; \u003cem\u003eP\u003c/em\u003e = 0.016). To date, no clinical features have been associated with RSBD.\u003c/p\u003e\n \u003cp\u003eWhen the IVW analysis failed to yield statistically significant results but at least three of the remaining four supplementary methods demonstrated statistical significance, a potential causal association was inferred. The results obtained from these supplementary methods are provided in the supplementary materials (Table S5). Through this evaluation, a potential causal association was identified between CRP and hypersomnia among the clinical features, as illustrated in Fig.\u0026nbsp;5. The remaining unmentioned parts did not exhibit any statistically significant causal associations. The number and characteristics of available SNPs for each exposure are detailed in the supplementary materials (Tables S3 and S5).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3 MVMR results of clinical features and four types of SD\u003c/h2\u003e\n \u003cp\u003eGiven its established association with various diseases, BMI was incorporated as an adjustment variable in the multivariate MVMR analysis among lifestyle factors. Previous studies have consistently demonstrated a significant correlation between BMI and the characteristics of numerous diseases, including inflammatory factors and blood lipid levels (Yan et al., 2024). Consequently, it is imperative to conduct multivariate analyses incorporating BMI for clinical features identified as having a significant causal relationship with sleep disorders.\u003c/p\u003e\n \u003cp\u003eTo account for the potential influence of individual clinical features, we conducted an adjustment analysis. As illustrated in Fig.\u0026nbsp;6, the effect of hypertension on NSD was no longer significant after adjusting for BMI, suggesting that the impact of hypertension on NSD is mediated through BMI. In contrast, Fig.\u0026nbsp;7 demonstrates a statistically significant causal correlation between ApoA-I, HDL, TG, and narcolepsy, even after adjusting for BMI. This indicates that these factors are independently associated with the risk of narcolepsy. As depicted in Fig.\u0026nbsp;8, after adjusting for BMI, all clinical features were found to be non-significantly correlated with OSA. This suggests that the impact of these clinical features on OSA is primarily mediated through BMI and that they are not independent risk factors.\u003c/p\u003e\n \u003cp\u003eSecondly, a comprehensive MVMR analysis was conducted on the clinical features associated with sleep disorders (narcolepsy and OSA), focusing on those with multiple significant features. As depicted in Fig.\u0026nbsp;9, after comprehensive adjustment, only the correlation between triglycerides (TG) and narcolepsy remained statistically significant (compared to individual adjustment). This finding suggests that the impact of TG on narcolepsy is independent of all the aforementioned clinical features and BMI, establishing it as a significant independent risk factor. As illustrated in Fig.\u0026nbsp;10, after comprehensive adjustment, only the correlation between hypertension and OSA exhibited statistical significance. However, this correlation was not observed when adjusted separately. This observation indicates the presence of confounding SNPs associated with other clinical factors when adjusted separately. The significance of the correlation between hypertension and OSA becomes apparent only after removing SNPs related to these other clinical features during comprehensive adjustment. Based on this comprehensive assessment, we conclude that a potential causal relationship exists between hypertension and OSA, and this correlation is statistically significant.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e4 Sensitivity analysis\u003c/h2\u003e\n \u003cp\u003eTo assess heterogeneity in the results, Cochran's Q and LOO analyses were conducted. Cochran's Q was employed to comprehensively evaluate heterogeneity across each causal relationship. The results revealed heterogeneity between smoking, years of schooling, cognitive performance, BMI, hypertension, various lipoproteins, and narcolepsy. Additionally, heterogeneity was observed between alcohol intake, oily fish intake, years of schooling, cognitive performance, overall health rating, ApoA-I, ApoB, HDL, TCL, and OSA. No heterogeneity was detected in the remaining causal relationships. To mitigate the potential impact of heterogeneity on the MR results, a random effects model was selected for analysis. In contrast, causal correlations unaffected by heterogeneity were analyzed using a fixed effects model. The results of Cochran's Q are presented in the supplementary materials (Table S6). Furthermore, to enhance the robustness of the analysis, additional heterogeneity testing was performed on each SNP using the LOO method. The results demonstrated that eliminating any individual SNP did not significantly alter the causal effect values, suggesting that the MR analysis results are reliable. The LOO results are available in the supplementary materials (Tables S7 and S8).\u003c/p\u003e\n \u003cp\u003eTo assess the presence of horizontal pleiotropy in the causal relationships, the MR-Egger intercept and MR-PRESSO methods were employed. The results indicated a certain degree of pleiotropy in the causal correlations between smoking, oily fish intake, and OSA. However, no pleiotropy was observed in the remaining causal relationships. The results of the pleiotropy test are provided in the supplementary materials (Table S6). Furthermore, no outlier SNPs were identified in this MR analysis, suggesting that the results are reliable.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Disscussion","content":"\u003cp\u003eThis study leverages the genetic information of a large-scale population to systematically analyze the modifiable factors that influence the risk of SDs in both daily life and clinical practice. In contrast to observational studies, the results obtained from gene prediction offer a more theoretical perspective, as they can effectively eliminate the confounding effects of numerous factors. This approach provides valuable insights into the upper limit of the predictive capability of these influencing factors on SDs. The exposures in this study were categorized into two broad groups: seven types of lifestyle factors and four types of clinical features. The outcomes encompassed four distinct types of SDs. Given the complexity of the factors involved, the subsequent analysis and discussion will be organized according to the respective exposure categories.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e1 lifestyle factors\u003c/h2\u003e \u003cp\u003eNSD is the most prevalent type of sleep disorder (Edinger et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is also widely recognized as the SD most closely linked to unhealthy lifestyle habits and psychological factors (Remi et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A large-scale cross-sectional study conducted by Zheng YB corroborates this assertion (Zheng et al., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study identified prolonged overall physical activity time and increased fresh fruit intake as protective factors for NSD. Moreover, sensitivity analyses demonstrated that the causal association between these factors and NSD is not influenced by heterogeneity or pleiotropy, indicating the robustness of the findings. Previous studies examining different types have also yielded consistent conclusions. Lee K's retrospective study suggests that daily regular exercise can effectively treat NSD by regulating the sleep-wake cycle (Lee et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Zhu W's follow-up survey further supports this association, revealing a significant correlation between physical exercise and NSD (Zhu et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Collectively, this evidence underscores the beneficial effects of exercise on sleep. Conversely, eating disorders have been established as a contributing factor to SDs arising from mental disorders (Regli et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the present study, this correlation was further confirmed by the observed positive association between fresh fruit intake and sleep. As health factors that can be readily modified through daily lifestyle interventions, the benefits of exercise and fresh fruit intake for NSD warrant emphasis in clinical health education.\u003c/p\u003e \u003cp\u003eThe global incidence rate of narcolepsy is estimated to be approximately 0.026% (Barateau et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The debilitating symptoms of excessive sleepiness, sleep paralysis, and hallucinations significantly impair patients' physical, mental, and social well-being (Schiappa et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Consequently, narcolepsy represents a critical category within sleep disorders. A cross-sectional study by Varallo G revealed that individuals with narcolepsy exhibit poorer coping strategies, emotional regulation abilities, and overall quality of life compared to healthy controls (Varallo et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In recent years, there has been a growing emphasis on managing the mental health and quality of life of patients with narcolepsy (Barker et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The latest European guidelines strongly advocate for exercise, weight control, and dietary adjustments as beneficial interventions for narcolepsy (Bassetti et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, smoking, alcohol consumption, cereal intake, salad/raw vegetable intake, poor overall health rating, and high BMI were identified as risk factors for narcolepsy. Conversely, tea intake, non-oily fish intake, longer education time, healthy cognitive performance, and high income emerged as protective factors. While heterogeneity influenced the results for smoking, BMI, and education level, the causal associations remained significant after employing a random effects model. For the remaining factors unaffected by heterogeneity, fixed effects models were utilized. Importantly, all significant factors identified in this study were not affected by pleiotropy, indicating the stability of their causal associations with narcolepsy. Smoking cessation, alcohol reduction, dietary adjustments, and weight loss (for obese individuals) can contribute to the health management of patients with narcolepsy. Black J's survey study demonstrated a significantly higher obesity rate among individuals with narcolepsy compared to healthy controls (Black et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, cognitive guidance and enhanced education are crucial for children with narcolepsy, facilitating their adaptation to normal school life and emotional management. Thieux M's case-control study revealed that children with narcolepsy exhibited deficits in intelligence assessment and cognitive regulation compared to healthy children (Thieux et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The findings of this study also underscore the importance of education in narcolepsy, as longer school life and healthy cognitive performance were identified as protective factors. A systematic review by Bassetti CLA further supported the notion of impaired cognitive function in patients with narcolepsy, encompassing attention, executive function, and memory function (Bassetti et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Currently, many clinicians and researchers acknowledge that cognitive impairment is a destructive symptom of narcolepsy (Harel et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, high-income families are more likely to benefit from health management programs addressing smoking, alcohol consumption, weight management, and education (Krahn et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePatients with OSA may experience excessive sleepiness, but IH during sleep is often more severe (Patel, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). OSA has been linked to various health complications, including heart disease, diabetes, stroke, and other conditions (Hidayat et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Kwon et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Tenda et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These adverse outcomes are associated with oxidative stress induced by IH (Donkor et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The \u003cem\u003eConsensus on Diagnosis and Management of OSA\u003c/em\u003e published in JAMA recommends lifestyle interventions as an effective treatment for OSA (Gottlieb \u0026amp; Punjabi, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Wu S et al.'s study (Wu et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) further demonstrated that adherence to the Life's Simple 7 metrics (LS7) is associated with a reduced risk of OSA. LS7 encompasses weight loss, smoking cessation, healthy diet, exercise, blood pressure control, cholesterol control, and blood sugar control. This study emphasizes the importance of both lifestyle factors and clinical features in OSA management. In our study, smoking, alcohol intake, coffee intake, pork intake, poor overall health rating, high BMI, and high WHR were identified as risk factors for OSA. Conversely, bread intake, cereal intake, dried fruit intake, longer education, healthy cognition, and high income were associated with a reduced risk. While alcohol consumption, education, and overall health rating exhibited some heterogeneity, the causal associations remained statistically significant after employing a random effects model. Except for smoking, all other significant factors were not affected by pleiotropy, indicating the stability of their causal associations with OSA. Zheng YB's cross-sectional study similarly found that six lifestyle factors, including healthy diet, regular physical exercise, never smoking, never drinking alcohol, short-term sedentary behavior, and normal weight, can reduce the risk of OSA (Zheng et al., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Li Y's study corroborated the negative impact of poor overall health rating and high BMI on OSA (Li et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our study validated the stability of these findings. Additionally, the causal association between WHR and an increased risk of OSA highlights the detrimental effects of abdominal fat accumulation. Deng H's cross-sectional study also confirmed a positive correlation between WHR and OSA risk (Deng et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The increasing prevalence of OSA due to rising obesity rates has been widely recognized (Lyons et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Interventions targeting the aforementioned lifestyle habits may benefit OSA patients. Similarly, strengthening education and increasing income can positively impact cognitive regulation, environmental adaptation, and mental health in children and adults with OSA (Isaiah et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Vaienti et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings align with the study results of Chang MS (Chang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), emphasizing the importance of education and cognitive training.\u003c/p\u003e \u003cp\u003eIn summary, this analysis reveals a strong association between lifestyle factors and both narcolepsy and OSA. These findings align with the lifestyle interventions advocated in clinical guidelines. Notably, these lifestyle factors exhibit interrelationships. For instance, smoking, drinking, and diet can influence overall health rating and BMI, while income can impact diet and education. However, it is crucial to acknowledge that these relationships may be influenced by other unidentified genetic factors. None of these factors can be considered a sole determinant of SDs, and their impact on SDs can only be assessed through data-driven analysis. The occurrence and progression of SDs are the result of complex interplay among various factors. Therefore, while the identified lifestyle factors associated with SDs hold public health significance, these findings should be interpreted with caution. Nonetheless, improving these lifestyle factors can contribute to the management of SD patients. Regarding the non-statistically significant lifestyle factors, this MR analysis does not provide sufficient evidence to establish a causal association with SDs. Larger sample sizes of genetic variables and observational studies are necessary to validate these findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2 Clinical features\u003c/h2\u003e \u003cp\u003eInsomnia is increasingly prevalent among the general population. Surveys indicate that one-third of individuals in developed countries experience sleep challenges at least weekly, and 6\u0026ndash;15% are diagnosed with NSD (Krystal et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Globally, the incidence rate of NSD ranges from 5\u0026ndash;20%, while up to 30\u0026ndash;35% of adults exhibit insomnia symptoms (Nguyen et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These data highlight the emergence of NSD as a significant public health concern. The causes of NSD often involve disrupted circadian rhythms and psychological factors (Riemann et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), making it challenging to detect significant laboratory abnormalities. This aligns with the majority of clinical observations (Ramos et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, no significant causal relationship was identified between glucose and lipid metabolism indicators, inflammatory indicators, and NSD. Sleep disorders frequently co-occur with mental illnesses, forming an interactive relationship (San \u0026amp; Arranz, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Statistics reveal that approximately 70% of patients with mental disorders experience SDs (Hombali et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). NSD is primarily characterized by difficulty falling asleep, disrupted sleep, difficulty falling back asleep after waking up, and daytime dysfunction caused by early awakening (Riemann et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These symptoms pose significant challenges to patients' social activities and psychological well-being. Liu RT's systematic review (Liu et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) identified SD as a risk factor for suicidal ideation and behavior. Additionally, Cappuccio FP's study (Cappuccio et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) demonstrated that SD can increase the risk of coronary heart disease, stroke, and overall cardiovascular disease. The findings of this study reveal a significant increase in NSD risk associated with hypertension. Hypertension, as the most common chronic cardiovascular disease, shares a genetic association with NSD, narcolepsy, and OSA. This represents a significant causal relationship, consistent with the well-established close association between SDs and cardiovascular disease (Hale et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile our understanding of narcolepsy in terms of neural transmission and neuroimmunity has advanced considerably (Mahoney et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), significant gaps remain in other areas. Investigating clinical features with potential causal associations can contribute to exploring mechanisms from alternative perspectives. This study identified FINS, TG, hypertension, and CRP as risk factors for narcolepsy. Conversely, ApoA-I and HDL were found to be protective factors. Cross-sectional studies by Casale S and Ponziani V (Casale et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Ponziani et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) similarly reported a significant decrease in HDL levels and a significant increase in blood pressure in patients with narcolepsy. However, Casale S's study (Casale et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) did not detect any differences in FPG or FINS, possibly due to the inclusion of pediatric patients. Another study (Poli et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) on adult narcolepsy revealed higher levels of FINS, TCL, and TG in addition to low HDL and hypertension, aligning with the findings of our study. In this context, age may reflect the impact of the duration of abnormal metabolism on pathological progression. However, this MR analysis did not establish a causal correlation between TCL and narcolepsy. Miyagawa T's study (Miyagawa et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) also identified an association between high TG and narcolepsy but not high TCL, suggesting that TCL might indicate changes during disease development rather than an inducing factor. The causal associations between FINS, TG, HDL, and narcolepsy suggest that alterations in these factors may contribute to the development of narcolepsy. These findings also confirm the association between glucose and lipid metabolism and narcolepsy. Regarding inflammation, the presence of autoimmune reactions in patients with narcolepsy has been extensively studied (Bonvalet et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The prevailing hypothesis posits that CD4\u003csup\u003e+\u003c/sup\u003e T cell activation is the primary pathogenic mechanism, although specific autoantibodies have yet to be discovered (Bonvalet et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Neuroimmunity, primarily mediated by cellular immunity, contributes to the loss of orexin neurons, resulting in a deficiency of the neurotransmitter orexin/Hcrt-1 (Zhan et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This neurotransmitter deficiency is considered the primary cause of narcolepsy. CRP is a prominent peripheral inflammatory marker. A previous study (Aran et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) did not find significant differences in CRP levels between patients with narcolepsy and healthy individuals. Unfortunately, there is currently limited research on peripheral immunity and inflammatory responses in patients with narcolepsy. While this study suggests a potential causal association between CRP and narcolepsy, the primary statistical method, IVW, did not confirm this difference. Therefore, the relationship between CRP and narcolepsy warrants further investigation. MVMR analysis adjusted for BMI revealed that the causal correlations between TG, ApoA-I, HDL, and narcolepsy persisted, indicating that their associations with narcolepsy are not influenced by BMI. Other risk factors, such as FINS, hypertension, and CRP, were to some extent influenced by BMI. A comprehensive MVMR analysis further demonstrated that only TG is causally associated with narcolepsy, suggesting its independent and significant risk factor role, uninfluenced by BMI or other clinical features.\u003c/p\u003e \u003cp\u003eDuring the examination of OSA, TG, hypertension, CRP were associated with an increased risk of OSA, while FPG, ApoA-I, HDL were associated with a reduced risk. Comprehensive MVMR analysis revealed a persistent causal association between hypertension and OSA, indicating its independent association with an increased risk of OSA. The lack of statistical significance in the causal association between hypertension and OSA after adjusting for BMI alone suggests that the role of hypertension in increasing the risk of OSA is independent of other clinical features but may still be influenced by BMI. Conversely, the absence of statistically significant causal associations between all significant clinical features and OSA after adjusting for BMI alone indicates that the causal associations between these features and OSA are influenced by BMI. This was further confirmed by the comprehensive MVMR analysis. After adjusting for all significant clinical features, the causal association between BMI and OSA became even more apparent, highlighting the crucial role of BMI as a predictor for OSA. The accumulation of fat in the throat, chest wall, and abdominal wall can reduce the diameter of the respiratory tract, increasing the risk of OSA (Wyszomirski et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This mechanism represents an important pathophysiology of OSA. The OSA management guidelines also emphasize the importance of weight loss in OSA patients (Chang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). OSA is closely linked to diabetes and metabolic syndrome (Bloomgarden, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Drager et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Khalil M's systematic review revealed a prevalence of OSA as high as 60% in adult diabetes patients (Khalil et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The results of this study suggest that this association is related to high BMI and high WHR, key indicators of obesity. Given anatomical factors, OSA can be readily considered an accompanying syndrome of obesity. While the association between OSA and other diseases is statistically related to obesity indicators, the characteristic pathological manifestation of OSA, intermittent hypoxia (IH), is another crucial factor in inducing complications. IH can contribute to cardiovascular disease, diabetes, and systemic inflammation (Drager et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Karkinski et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Koh et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Tang et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Drager LF's study demonstrated that IH exacerbates FPG levels (Drager et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Animal experiments conducted by Savransky V and Drager LF revealed that IH disrupts ApoA-I-controlled lipid synthesis and impairs TG clearance (Drager et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Savransky et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Ryan DH's research similarly suggested that controlling FPG and TG is an important intervention for alleviating OSA complications (Ryan \u0026amp; Yockey, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Wang J's systematic review found significantly higher serum CRP levels in OSA patients compared to healthy controls (Wang et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The causal relationships between FPG, TG, CRP, and OSA identified in this study corroborate these associations. Overall, OSA is not solely caused by upper respiratory collapse. Abnormal glucose and lipid metabolism (high glucose and hyperlipidemia), hypertension, and inflammation can all contribute to the development of OSA. It is important to note that these causal associations cannot be entirely separated from the influence of high BMI.\u003c/p\u003e \u003cp\u003eIn conclusion, sleep disorders are not merely sleep problems but have a profound impact on various aspects of quality of life. They are closely linked to mental health, cardiovascular disease, diabetes, and multisystem diseases. Sleep problems should not be underestimated. Regardless of the type of SD, lifestyle interventions have been widely recognized as crucial components of effective management. The findings of this study offer detailed health management strategies encompassing multiple dimensions, including smoking and drinking habits, diet, education, cognition, income, and body fat (BMI and WHR). These results hold significant public health guidance implications. The aforementioned clinical features are readily monitored health indicators in daily life. Based on the results above, a comprehensive summary of the lifestyle factors and clinical features that exhibit a potential causal association with the four types of sleep disorders was obtained. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. Statistical analysis revealed that two lifestyle factors and one clinical feature were associated with NSD. Hypersomnia, on the other hand, was linked to 13 lifestyle factors and 6 clinical features. OSA was associated with 15 lifestyle factors and 6 clinical features. No factors were identified to be associated with RSBD. Monitoring these indicators can not only aid in preventing SDs but also contribute to exploring the underlying mechanisms of their occurrence. The simplicity of these factors should not be overlooked, as even seemingly straightforward factors can play a crucial role in the development and progression of SDs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003elimitations\u003c/h2\u003e \u003cp\u003eBased on extensive GWAS data, this study comprehensively investigated the causal relationships between lifestyle factors, clinical features, and four types of sleep disorders. The findings of this study provide valuable insights into the health management of SD patients. However, this analysis is subject to certain limitations. Firstly, no association with RSBD was identified at the genetic level among the 51 exposures examined. While these results align with previous studies (Zhang et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), sensitivity analysis did not reveal heterogeneity or pleiotropy in this context. This suggests the potential existence of other influencing factors that have yet to be discovered. Secondly, for some exposures, such as vigorous physical activity, moderate-intensity activity duration, walking duration, and sedentary behavior, the availability of SNPs was limited, which reduced their statistical power. Consequently, no strong correlations were observed between these factors and SDs. Further research with larger datasets is necessary to validate these findings. Finally, for the non-significant exposures, it cannot be definitively concluded that they are not associated with SDs. Additional large-scale observational studies are required to verify these results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAmong the 34 lifestyle factors examined, this study identified overall physical activity time and fresh fruit intake as protective factors for NSD. For narcolepsy, smoking, alcohol drinks, cereal intake, salad/raw vegetable intake, poverty, poor overall health rating, and high BMI were found to be risk factors, while tea intake, non-oily fish intake, education and cognition, and high income were identified as protective factors. In the case of OSA, smoking, alcohol intake, coffee intake, pork intake, poverty, poor overall health rating, high BMI, and high WHR emerged as risk factors, whereas bread and cereal intake, dried fruit intake, education and cognition, and high income were associated with reduced risk. While potential causal associations were observed between these factors and the respective sleep disorders, there is currently insufficient evidence to establish a causal relationship between other lifestyle factors and sleep disorders.\u003c/p\u003e \u003cp\u003eAmong the 17 clinical features examined, hypertension was identified as a risk factor for NSD. For narcolepsy, high FINS, high TG, hypertension, and high CRP emerged as risk factors, while ApoA-I and HDL were found to be protective factors. In the case of OSA, high TG, hypertension, and high CRP were associated with increased risk, whereas FPG, ApoA-I, and HDL were associated with reduced risk. Insufficient evidence exists to support a causal association between the remaining clinical features and sleep disorders.\u003c/p\u003e \u003cp\u003eIn summary, these findings underscore the significance of these factors in sleep disorder patients. These modifiable factors represent key targets for preventing and managing sleep disorders and hold valuable clinical guidance implications. By addressing these factors, we can potentially alleviate the health burden associated with sleep disorders.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eInitial: A-G:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAPAM:\u0026nbsp;Accelerometer-based physical activity measurement (average acceleration); Apo: Apolipoprotein; BMI: Body Mass Index; BP: Blood pressure; cAMP: Cyclic adenosine monophosphate; CI: Confidence interval; CRP: C-reactive protein; CSF: Cerebrospinal fluid; EDS: Excessive daytime sleepiness; FINS: Fasting insulin; FPG: Fasting plasma glucose; GWAS: Genome-wide association study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInitial: H-N:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHcrt-1: Hypocretin-1; HDL: High density lipoprotein; HPA: Hypothalamic pituitary adrenal; ICD: International classification of diseases; ICSD: International classification of sleep disorders; IH: Intermittent hypoxia; IL: Interleukin; IV: Instrumental variable; IVW: Inverse variance weighted; LDL: Low density lipoprotein; LOO: Leave one out; MyD: Marrow differentiation factor; MR: Mendelian randomization; MRE: MR-Egger; MSLT: Multiple sleep latency test; MVMR: Multivariate mendelian randomization; NF-κB: Nuclear factor-κB; NO: Nitric oxide; NSD: Nonorganic sleep disorders; NTx: Narcolepsy of type x.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInitial: O-T:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOR: Odds ratio; OSA: Obstructive sleep apnea; PMID: PubMed unique identifier; PSG: Polysomnography; REM: Rapid eye movement; RSBD: REM sleep behavior disorder; SD: Sleep disorders; SDB: Sleep disorder breathing; SM: Simple mode; SNP: Single nucleotide polymorphism; SOREMPs: Sleep onset rapid eye movement periods; TCL:\u0026nbsp;Total cholesterol levels; TDI: Townsend deprivation index; TG: Triglyceride; TLR: Toll-like receptors; TNF-α: Tumor necrosis factor-α.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInitial: U-Z:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWHR: Waist to hip ratio; WM: Weighted mode; WME: Weighted median estimation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank freescience (www.home-for-researchers.com) for the help with the English language.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDesign: Haiqing Ao and Jingyu Xu; Data collection: Jingyu Xu and Baojuan Wang; Data analysis: Jingyu Xu and Wenbin Zhu; Visualization: Jingyu Xu and Wenbin Zhu; Drafting of the article: Jingyu Xu; Critical revision of the article for important intellectual content: Haiqing Ao and Jingyu Xu; Study supervision: Haiqing Ao. All the authors approved the final article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAalling, N.N., Nedergaard, M. \u0026amp; Dinuzzo, M. (2018) Cerebral Metabolic Changes During Sleep. \u003cem\u003eCURR NEUROL NEUROSCI,\u003c/em\u003e 18\u003cstrong\u003e,\u003c/strong\u003e 57.\u003c/li\u003e\n\u003cli\u003eAghaei, M., Joukar, F., Hasanipour, S., Ranjbar, Z.A., Naghipour, M. \u0026amp; Mansour-Ghanaei, F. (2024) The association between waist-to-hip ratio (WHR) with diabetes in the PERSIAN Guilan cohort study population. \u003cem\u003eBMC ENDOCR DISORD,\u003c/em\u003e 24\u003cstrong\u003e,\u003c/strong\u003e 113.\u003c/li\u003e\n\u003cli\u003eAlbitar, O., D\u0026apos;Souza, C.M. \u0026amp; Adeghate, E.A. 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(2024) Influence of smartphone addiction on sleep quality of college students: The regulatory effect of physical exercise behavior. \u003cem\u003ePLOS ONE,\u003c/em\u003e 19\u003cstrong\u003e,\u003c/strong\u003e e0307162.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lifestyle factors, Clinical features, Sleep disorders, Risk factors, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-5092259/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5092259/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/em\u003e: To identify potential genetic risk factors for sleep disorders (SD) and to explore the causal associations between lifestyle factors and clinical features with SD, given the limitations of traditional observational studies.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e: Data from published genome-wide association studies (GWAS) were analyzed, encompassing 34 lifestyle factors and 17 clinical features as potential exposures influencing SD. These analyses yielded information on 51 exposures and 4 outcomes. Outcomes consisted of four categories: non-organic sleep disorders (NSD), narcolepsy, rapid eye movement (REM) sleep behavior disorders (RSBD), and obstructive sleep apnea (OSA). All genetic variables for exposure and outcome were derived from individuals of European ancestry. Two-sample MR analysis was conducted, with inverse variance weighted (IVW) as the primary method for evaluating causal effects. Weighted median estimation (WME), MR-Egger (MRE), simple mode (SM), and weighted mode (WM) were used as supplementary evaluation methods.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e:\u003c/p\u003e\n\u003cp\u003e(1) The protective factors causally associated with NSD include overall physical activity time (OR: 0.35; 95%CI: 0.13-0.99; \u003cem\u003eP\u003c/em\u003e=0.048) and fresh fruit intake (OR: 0.30; 95%CI: 0.12-0.75; \u003cem\u003eP\u003c/em\u003e=0.010).\u003c/p\u003e\n\u003cp\u003e(2) The risk factors that are causally related to narcolepsy include smoking (OR: 1.02; 95%CI: 1.01-1.03; \u003cem\u003eP\u003c/em\u003e=0.001), alcoholic drinks (OR: 1.02; 95%CI: 1.01-1.03; \u003cem\u003eP\u003c/em\u003e=0.013 and OR: 1.02; 95%CI: 1.01-1.03; \u003cem\u003eP\u0026lt;\u003c/em\u003e0.001), cereal intake (OR: 1.03; 95%CI: 1.00-1.07; \u003cem\u003eP\u003c/em\u003e=0.029), salad/raw vegetable intake (OR: 1.10; 95%CI: 1.03-1.16; \u003cem\u003eP=\u003c/em\u003e0.002), TDI (OR: 1.05; 95%CI: 1.00-1.10; \u003cem\u003eP\u003c/em\u003e=0.036), overall health rating (OR: 1.07; 95%CI: 1.05-1.10; \u003cem\u003eP\u0026lt;\u003c/em\u003e0.001), BMI (OR: 1.02; 95%CI: 1.02-1.03; \u003cem\u003eP\u0026lt;\u003c/em\u003e0.001), FINS (OR: 1.06; 95%CI: 1.03-1.09; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), TG (OR: 1.02; 95%CI: 1.01-1.03; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and hypertension (OR: 1.08; 95%CI: 1.02-1.15; \u003cem\u003eP\u003c/em\u003e=0.011).\u003c/p\u003e\n\u003cp\u003e(3) The protective factors causally associated with narcolepsy include tea intake (OR: 0.95; 95%CI: 0.93-0.97; \u003cem\u003eP\u0026lt;\u003c/em\u003e0.001), non-oily fish intake (OR: 0.91; 95%CI: 0.84-0.99; \u003cem\u003eP=\u003c/em\u003e0.021), years of schooling (OR: 0.97; 95%CI: 0.96-0.99; \u003cem\u003eP\u0026lt;\u003c/em\u003e0.001), cognitive performance (OR: 0.98; 95%CI: 0.97-0.99; \u003cem\u003eP=\u003c/em\u003e0.001), average total household income before tax (OR: 0.94; 95%CI: 0.93-0.96; \u003cem\u003eP\u0026lt;\u003c/em\u003e0.001), ApoA-I (OR: 0.99; 95%CI: 0.99-1.00; \u003cem\u003eP\u003c/em\u003e=0.036), HDL (OR: 0.99; 95%CI: 0.98-0.99; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e(4) The risk factors that are causally related to OSA include smoking (OR: 1.20; 95%CI: 1.08-1.34; \u003cem\u003eP\u003c/em\u003e=0.001 and OR: 1.15; 95%CI: 1.06-1.25; \u003cem\u003eP\u003c/em\u003e=0.001), alcohol intake (OR: 1.12; 95%CI: 1.01-1.24; \u003cem\u003eP\u003c/em\u003e=0.037), coffee intake (OR: 1.25; 95%CI: 1.00-1.56; \u003cem\u003eP\u003c/em\u003e=0.046), pork intake (OR: 2.55; 95%CI: 1.37-4.74; \u003cem\u003eP=\u003c/em\u003e0.003), TDI (OR: 1.56; 95%CI: 1.12-2.19; \u003cem\u003eP\u003c/em\u003e=0.009), overall health rating (OR: 2.76; 95%CI: 2.20-3.46; \u003cem\u003eP\u0026lt;\u003c/em\u003e0.001), BMI (OR: 1.97; 95%CI: 1.85-2.11; \u003cem\u003eP\u0026lt;\u003c/em\u003e0.001), WHR (OR: 1.30; 95%CI: 1.08-1.55; \u003cem\u003eP=\u003c/em\u003e0.004), TG (OR: 1.06; 95%CI: 1.02-1.11; \u003cem\u003eP\u003c/em\u003e=0.009), hypertension (OR: 2.72; 95%CI: 1.73-4.26; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and CRP (OR: 1.06; 95%CI: 1.01-1.11; \u003cem\u003eP\u003c/em\u003e=0.016).\u003c/p\u003e\n\u003cp\u003e(5) The protective factors causally associated with OSA include bread intake (OR: 0.63; 95%CI: 0.49-0.83; \u003cem\u003eP\u003c/em\u003e=0.001), cereal intake (OR: 0.71; 95%CI: 0.56-0.90; \u003cem\u003eP\u003c/em\u003e=0.005), dried fruit intake (OR: 0.64; 95%CI: 0.50-0.83; \u003cem\u003eP=\u003c/em\u003e0.001), years of schooling (OR: 0.72; 95%CI: 0.66-0.79; \u003cem\u003eP\u0026lt;\u003c/em\u003e0.001), cognitive performance (OR: 0.79; 95%CI: 0.71-0.87; \u003cem\u003eP\u0026lt;\u003c/em\u003e0.001), average total household income before tax (OR: 0.78; 95%CI: 0.66-0.93; \u003cem\u003eP\u003c/em\u003e=0.005), FPG (OR: 0.84; 95%CI: 0.75-0.94; \u003cem\u003eP\u003c/em\u003e=0.003), ApoA-I (OR: 0.94; 95%CI: 0.90-0.99; \u003cem\u003eP\u003c/em\u003e=0.017) and HDL (OR: 0.89; 95%CI: 0.85-0.93; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e(6) After multivariate analysis through adjusting BMI, the causal association between ApoA-I, TG, HDL and narcolepsy still exists. The causal association between hypertension and OSA still exists.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/em\u003e: The evidence from this study suggests that among 34 lifestyle factors: (1) overall physical activity time and fresh fruit intake are protective factors for NSD. (2) Smoking and alcohol drinks, cereal intake, salad/raw vegetable intake, poverty, poor overall health rating, and high BMI are risk factors for narcolepsy. Tea intake, non oily fish intake, education and cognition, and high income are protective factors for narcolepsy. (3) Smoking and alcohol intake, coffee intake, pork intake, poverty, poor overall health rating, high BMI, and high WHR are risk factors for OSA. Bread and cereal intake, dried fruit intake, education and cognition, and high income are protective factors for OSA. Among the 17 clinical features: (1) hypertension is a risk factor for NSD. (2) High FINS, high TG, hypertension, and high CRP are risk factors for narcolepsy. ApoA-I and HDL are protective factors for narcolepsy. (3) High TG, hypertension, and high CRP are risk factors for OSA. FPG, ApoA-I, and HDL are protective factors for OSA. There is not enough evidence to suggest that other relationships meet the criteria for causal association established.\u003c/p\u003e","manuscriptTitle":"Causal association of lifestyle factors, clinical features in the risk of sleep disorders: Based on mendelian randomization analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 07:21:27","doi":"10.21203/rs.3.rs-5092259/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"76ddf1e2-9715-402b-9aa3-0f1e684e80dc","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41001567,"name":"Health sciences/Diseases"},{"id":41001568,"name":"Health sciences/Health care"},{"id":41001569,"name":"Health sciences/Medical research"},{"id":41001570,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-04-16T13:25:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-18 07:21:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5092259","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5092259","identity":"rs-5092259","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}