Front. Neurol. Frontiers in Neurology Front. Neurol. 1664-2295 Frontiers Media S.A. 10.3389/fneur.2022.765321 Neurology Original Research Sleep, Pain, and Neurodegeneration: A Mendelian Randomization Study Grover Sandeep Sharma Manu * International Age-related Macular Degeneration Genomics Consortium (IAMDGC) Centre for Genetic Epidemiology, Institute for Clinical Epidemiology and Applied Biometry, University of Tübingen, Tübingen, Germany

Edited by: Rosanna Tortelli, University College London, United Kingdom

Reviewed by: Xinghao Yu, Soochow University, China; Yiqiang Zhan, Helmholtz Association of German Research Centers (HZ), Germany

 *Correspondence: Manu Sharma manu.sharma@uni-tuebingen.de

This article was submitted to Dementia and Neurodegenerative Diseases, a section of the journal Frontiers in Neurology

 02 05 2022 2022 13 765321 27 08 2021 14 03 2022 Copyright © 2022 Grover, Sharma, and International Age-related Macular Degeneration Genomics Consortium (IAMDGC). 2022 Grover, Sharma, and International Age-related Macular Degeneration Genomics Consortium (IAMDGC)

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Our aim was to determine whether the genetic liability to sleep and pain-related traits have a causal effect on risk of neurodegeneration in individuals of predominantly European ancestry. We selected five neurodegenerative disorders, namely, age-related macular degeneration (AMD), Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), and Parkinson's disease (PD). Sleep duration (SD), short sleep (SS), long sleep (LS), chronotype (CHR), morning person (MP), insomnia (INS), and multisite chronic pain (MCP) were considered as exposures. We conducted Mendelian randomization (MR) using an inverse-variance weighted (IVW) method to compute causal effect estimates using latest available GWAS data sets. The MP phenotype was observed as the strongest risk factor for genetic liability to AMD (ORIVW = 1.192; 95% CI 1.078, 1.318, P = 0.0007). We observed suggestive evidence of risky effects of CHR on AMD (P = 0.0034), SS on AD (P = 0.0044), and INS on ALS (P = 0.0123). However, we failed to observe any role of pain. The results were robust on sensitivity analyses. Our study highlighted the role of MP as a risk factor for AMD.

 Mendelian randomization causal inference neurodegenerative disorders sleep pain chronotype EU Joint Programme – Neurodegenerative Disease Research10.13039/100013278 Michael J. Fox Foundation for Parkinson's Research10.13039/100000864 Deutsche Forschungsgemeinschaft10.13039/501100001659 Introduction

Patients with neurodegenerative diseases (NDDs) often experience disruptions in circadian rhythmic activities (1, 2). Many patients with NDD and circadian disruptions also complain of painful symptoms of variable origins and intensities (3). Both sleep and pain could often be treated and, thereby, can help maintain a stable quality of life in the absence of any disease-modifying treatment for NDDs (4). A greater understanding of the etiological relationship between sleep, pain, and neurodegeneration could, thereby, enable better management of NDDs.

It is well-recognized that circadian dysfunction in old age is due to degeneration of the suprachiasmatic nucleus (SCN) in the anterior hypothalamus, directly connected to the light-sensing retina (5). Different NDDs further exhibit marked heterogeneity in manifestation of circadian disruptions, which could be attributed to loss of different neuronal subpopulations in the SCN. Clinically, patients with Alzheimer's disease (AD) often show sleep-wake rhythm disorder, and patients with PD show a reduction in the amplitude of the circadian rhythm (6, 7). A limited number of longitudinal studies have demonstrated the potential influence of circadian disruptions on predisposition to AD, PD, and related markers of neurodegeneration (8, 9).

Similar to the involvement of specific brain regions directly influencing circadian rhythms, several brain regions, also referred to as pain matrix, have been shown to be activated during pain perception (10, 11). The pain matrix comprising the primary (S1) and secondary (S2) somatosensory cortices, insula, anterior cingulate cortex (ACC), amygdala, prefrontal cortex (PFC), and thalamus, further shows differential activation during acute and chronic pain (12). Aging is specifically known to increase the likelihood of chronic pain and may amplify the neurodegeneration process (13, 14).

To date, the sparse number of large longitudinal studies and clinical trials has limited our progress in understanding the relationship between sleep, pain, and onset or progression of neurodegeneration, necessitating the need for searching alternative approaches for judging the causality. A two-sample Mendelian randomization (MR) is one such approach that employs instruments or proxy markers of risk factor in one population to judge causality of the risk factor with an outcome in an independent population (15–17).

So far, limited studies have employed a genetic instrument-based approach to judge the etiological relationship between sleep, pain, and NDDs. A recent MR study showed an absence of the role of genetic liability to sleep duration (SD) in influencing predisposition to AD (18). On the contrary, another report showed an association of genetic liability with sleep efficiency with AD (19). A couple of studies showed increased risk of ALS due to daytime sleepiness (19, 20). Considering the highly varied role of various behavioral biomarkers of circadian rhythm on neurodegeneration and potential overlapping etiology of sleep and pain, we adopted a highly comprehensive approach by exploiting the availability of genetic instruments for various markers of circadian rhythm, namely, SD (21), short sleep (SS) (21), long sleep (LS) (21), chronotype (CHR) (22), morning person (MP) (22), insomnia (INS) (23), and multisite chronic pain (MCP) (24), and NDDs, namely, AD (25), AMD (26, 27), ALS (28), MS (29), and PD (30, 31) to dissect the bi-directional relationship between sleep, pain, and neurodegeneration using two-sample MR approach.

 Methods Identification and Correlation Among Data Sets

We employed a two-sample MR study design using summary estimates to examine the lifelong effect of sleep and pain-related traits on genetic liability to neurodegeneration in European populations. We used latest available discovery cohorts of meta-analyses of GWAS data sets in the literature. We identified single nucleotide polymorphisms (SNPs) that influence circadian rhythm-related traits, including SD (21), SS (21), LS (21), CHR (22), MP (22), INS (23), and MCP (24) (Table 1). We adopted a P cutoff of 5 × 10−8 to select the genetic instruments. Concerning the outcome data sets, we used the discovery cohort of a recent meta-analysis of GWAS on AD (25), AMD (26), ALS (28), MS (29), and PD (30). Before judging the causal role of sleep and pain in predisposition to PD, we checked for any potential correlation between different sleep and pain-related traits and different NDDs. We specifically employed a cross-trait LD score regression (LDSC) method to evaluate the genome-wide correlation between traits (https://github.com/bulik/ldsc) (32).

Details of discovery GWAS datasets and prioritized instruments used for direct and reverse causal analysis in the present study.

S.No. Phenotype References Maximum sample size P Number of analyzed SNPs Number of significant SNPs Number of significant SNPs (post-clumping) (R2 < 0.001) Average F-statistics Median (range) R2 (%)
Sleep-related traits
1 Sleep duration (SD) (21) 446,118 5 × 10−8 14,661,601 7,926 74 34.7 (29.6-220.9) 0.731%
2 Short sleep (SS) (21) 106,192 cases/305,742 controls 5 × 10−8 14,661,601 859 26 34.1 (29.9-77.0) 0.045%
3 Long sleep (LS) (21) 34,184 cases/305,742 controls 5 × 10−8 14,661,601 3,901 9 32.4 (29.9-53.0) 0.006%
4 Chronotype (CHR) (22) 449,734 5 × 10−8 11,977,111 15,152 156 39.4 (28.2-209.4) 2.683%
5 Morning person (MP) (22) 252,287 cases/150,908 controls 5 × 10−8 11,977,111 1,0949 127 37.9 (29.0-168.5) 5.748%
6 Insomnia (INS) (23) 109,389 cases/277,144 controls 5 × 10−8 10,862,567 463 13 34.4 (30.4-94.7) 0.712%
Pain-related trait
1 Mulisite chronic pain (MCP) (24) 387,649 5 × 10−8 9,926,106 1,746 41 34.1 (30.0-54.6) 0.341%
Disease trait
Data sets used for main analysis
1 Alzheimer's disease (AD) (25) 16,144 cases/17,832 controls 5 × 10−8 12,023,830 7,218 42 47.5 (29.2-382.5) NA
2 Age-related macular degeneration (AMD) (26) 71,880 cases/383,378 controls 5 × 10−8 3,367,299 2,357 27 42.2 (30.2-422.5) NA
3 Amyotrophic lateral sclerosis (ALS) (28) 12,577 cases/23,475 controls 5 × 10−8 8,709,452 125 4 37.2 (32.2-80.1) NA
4 Multiple Sclerosis (MS) (29) 47,351 cases/68,284 controls 5 × 10−8 8,593,650 26,403 74 41.9 (29.8-561.9) NA
5 Parkinson's disease (PD) (30) 33,674 cases,449,056 controls 5 × 10−8 17,513,773 3,465 23 43.6 (30.0-181.5) NA
Data sets used for sensitivity analysis
1 Alzheimer's disease (AD) (without UKB) (27) 17,008 cases/37,154 controls 5 × 10−8 7,055,881 1,090 18 37.9 (29.7-82.4) NA
2 Parkinson's disease (PD) (without UKB) (31) 9,581 cases/33,245 controls 5 × 10−8 8,543,957 3,209 9 49.8 (33.1-175.7) NA

Direct analysis was done using PD as an outcome and reverse was done using sleep and pain-related traits as outcomes.

Since the study analyzed secondary data (publicly available data) that contained information at the population-level (summary-level data), informed consent and ethical approval were waived off for this study.

 Causal Effect Estimation

The prioritized SNP IDs and positions were synchronized with the NCBI GRCh37 assembly. We further checked for the validity of MR assumptions by excluding SNPs with F-statistics <10 and loci known to be directly involved in neurodegeneration based on existing evidence from previously published literature.

As the selected genetic instruments could be correlated, we performed clumping of significantly associated SNPs on each GWAS data set with the clump_data function of the TwoSampleMR package (version 0.4.25) in R (version 3.6.1). We employed a clumping window of 10,000 kb and linkage disequilibrium (LD; i.e., r2) cutoff of 0.001, and used the European population in the 1,000 Genome Phase 3v5 data set to identify the leading SNPs.

The leading SNPs were further checked for availability in the respective outcome data sets. When possible, if a specific SNP was not available, a proxy SNP (r2 > 0.8) was used. We further computed the pooled variance (R2) for the respective risk factor using effect estimates (βx) and effect allele frequencies (EAFs) of individual genetic instruments, i.e., R2 = 2*βx2*EAF*(1–EAF). Detectable risky and protective effect estimates at 80% power were computed for each NDD as an outcome at various pooled variances explained by the genetic instruments (ranging from 0.25 to 7.5%) using the Mendelian Randomisation Power Calculator (http://cnsgenomics.com/shiny/mRnd). To compute the effect estimates at specific variance for a given outcome, we employed a sample size of each outcome data set, the proportion of patients in the same data set, and a threshold P of 1.42 × 10−3.

We used the inverse variance-weighted (IVW) effect method as the primary method to compute the causal effect estimates, as used previously (17). We computed the causal estimates as odds ratio (OR) per unit of standard deviation (SD) for continuous traits and ORs for the outcome per unit log-odds of categorical traits. We employed a conservative Bonferroni correction of the significance level to account for 35 independent tests, including forward and reverse MR (threshold P = 1.42 × 10−3, i.e., 0.05/35). Heterogeneity was judged using the Cochran's Q-statistic and I2 for the IVW method along with Rucker's Q-statistic, and the intercept deviation test for the MR-Egger's method (17). All the scripts used for the primary MR analysis have been provided as part of the a R-based mrpipeline package (https://github.com/CGEatTuebingen/mrpipeline). We used a previously published data set to replicate the findings before employing the package for to this study (17). The mrpipeline package is currently under the developmental phase, with a plan to integrate external databases, including GWAS and tissue expression repositories in the future. We also performed an Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) global test to evaluate horizontal pleiotropy (33). Lastly, we performed an MR Steiger test of directionality to validate the assumption that a given exposure causes an outcome using the TwoSampleMR package (version 0.4.25) in R (version 3.6.1).

 Sensitivity Analysis

Several approaches were employed to rule out the influence of potential pleiotropic variants on the overall results. We used multiple modern MR methods, including the MR-Egger, weighted median (WME), and weighted mode (MBE) methods, to check the reliability of the estimates, as used in previous studies (16, 17). Since most of the recent meta-analyses of GWAS compute effect estimates by pooling UK Biobank (UKB) data sets with previously available data sets, and the existence of any overlapping samples in exposure and outcome datasets could bias the effect estimates toward the confounded observational estimates, we also used the NDD datasets without UKB samples, when required (27, 31). We conducted MR in the reverse direction to check and confirm the directionality of the observed associations.

We further employed a leave-one-out and leave-one-group-out cross-validation approach to rule out the influence of outlier variants known to be associated with confounders of the relationship between the respective exposure and outcome data sets. We specifically employed the Phenoscanner database (http://phenoscanner.medschl.cam.ac.uk) to identify genetic variants associated with potential confounders. However, in the absence of knowledge of potential confounders, we adopted a more conservative approach, and all genetic loci known to be associated with non-sleep-related traits were assumed to be pleiotropic loci. We identified such loci by searching for all genetic variants in high LD with genetic instruments prioritized for this study using r2 > 0.9 for previously reported associations in European populations. We used visual approaches, including scatter plots and funnel plots, to identify outlier variants.

We also performed a sensitivity analysis by adjusting for potential confounders using a multivariable MR method. As and when appropriate, we adjusted for quantity of sleep, sleep preference for a given time of day, and pain, the phenotypes of interest investigated in this study. As multiple, highly correlated, and overlapping traits representing both quantity of sleep (LS, SS, INS, and SD) and sleep preference (CHR, MP) were available, we performed a variable selection procedure to select the optimal variable that represented each category. Such an approach prevented us from conducting an overadjustment and avoided loss of power inherent with multiple variable regression methods. We selected SD to adjust for the quantity of sleep, as a continuous variable is more informative than a binary trait. Similarly, we selected CHR as a variable of choice representing sleep preference. Specifically, the genetic associations of instruments with respective NDDs were regressed on the genetic associations with all the risk factors (SD, sleep pattern, and pain) in a single regression model using IVW method. Genetic instruments entered into the multivariable regression model were allowed to be associated with any of the risk factor under consideration.

We further evaluated the potential biological influence of different brain regions on their respective contribution to the causal effect estimate by analyzing gene expression data for available genetic variants from the Genotype-Tissue Expression Project (https://www.gtexportal.org).

 Results Identification and Correlation Among Data Sets

Details of discovery GWAS data sets used for the causal analysis in this study are shown in Table 1. The minimum number of individuals available for a specific NDD ranged from 12,557 ALS cases to 71,880 AD cases, which are broadly in consensus with their respective prevalence.

The pairwise genetic correlation analysis of complete GWAS data sets failed to show correlation of any of the NDDs with sleep or pain-related traits (Supplementary Table 1). Expectedly, a highly significant correlation was observed among the traits representing SD (SS, LS, SD, and INS) and among those representing sleep pattern (CHR and MP). Notably, MCP was strongly correlated with all the markers of SD (rg ranging from 0.28 for LS to 0.59 for INS), suggesting a need for conducting a multivariable analysis adjusting for MCP when judging the independent association of sleep markers with NDDs or vice versa.

 Causal Effect Estimation

The genetic instruments were identified that influence sleep and pain-related traits through latest publicly available meta-analysis of GWAS summary datasets (Table 1). Overall, we identified 771 genetic instruments to check the bidirectional causality between sleep, pain, and neurodegeneration, with F-statistic for individual SNPs ranging from 28.2 to 422.5. The detectable effect estimates for different NDDs as outcomes at 80% power and a type-1 error rate of 1.42 × 10−3 are further shown in Supplementary Table 2.

The data used for computation of causal effect estimates are provided in Supplementary Table 3. The causal effect estimates using various MR approaches and heterogeneity analysis measures used to judge the robustness of the estimates are provided in Table 2 for the direct causal estimates for NDDs as outcomes. We observed a highly significant causal effect of MP on genetic liability to AMD (ORIVW = 1.192; 95% CI 1.078, 1.318, P = 0.0007). Heterogeneity check confirmed the reliability of the observed association with absence of any heterogeneity in the distribution of effect estimates of individual genetic variants (I2 = 0.0%, Cochran's Q-test P = 0.9288, Rucker's Q-test P = 0.9414, MR-PRESSO global test P = 0.8420). The distribution of individual SNP-level effect estimates and the effect estimates computed with different MR methods for the effect of MP on AMD is further shown as scatter and funnel plots in Figure 1. We observed a similar directionality of causal effect estimates using the WME method (ORWME = 1.126; 95% CI = 1.044, 1.214). We also observed a similar trend using a highly correlated but continuous trait, CHR on AMD (ORIVW = 1.269; 95% CI 1.083, 1.486, P = 0.0034). The directionality of findings was further confirmed by a significantly higher variance explained by genetic instruments for MP and CHR than that explained by the respective genetic instruments for AMD (PSteiger = 2.1 × 10−98 and PSteiger = 1.65 × 10−24). In contrast, we did not observe any direct role of pain on predisposition to AMD.

Causal effect estimates using different Mendelian randomization (MR) methods and heterogeneity analysis of causal effect estimates for neurodegeneratice disorders (NDDs) using various sleep and pain-related traits as exposures.

Trait Mendelian randomization (MR) methodology Number of SNPs Direct causal effect estimates Tests of heterogeneity
OR 95% CI P
Alzheimer's disease (AD)
Sleep duration (SD) Inverse variance weighted method (IVW) 71 0.992 0.956-1.029 0.6567 MR-Egger intercept (P) 0.2022
MR-Egger method 0.909 0.791-1.045 0.1783 I2 (IVW) 0.0%
Weighted median method (WME) 0.998 0.971-1.026 0.9436 Cochran's Q-test (IVW) (P) 0.5815
Weighted mode method (NOME 1.026 0.934-1.127 0.5951 Rucker's Q-test (P) 0.6021
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9763
MR-PRESSO global test (P) 0.4270
Short sleep (SS) Inverse variance weighted method (IVW) 26 1.256 1.081-1.459 0.0044 MR-Egger intercept (P) 0.7405
MR-Egger method 1.121 0.547-2.299 0.7457 I2 (IVW) 0.0%
Weighted median method (WME) 1.219 1.103-1.347 0.0586 Cochran's Q-test (IVW) (P) 0.5847
Weighted mode method (NOME 1.362 0.952-1.949 0.1032 Rucker's Q-test (P) 0.5279
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9994
MR-PRESSO global test (P) 0.4520
Long sleep (LS) Inverse variance weighted method (IVW) 6 0.877 0.527-1.460 0.5381 MR-Egger intercept (P) 0.4714
MR-Egger method 1.443 0.231-9.010 0.6076 I2 (IVW) 0.0%
Weighted median method (WME) 0.979 0.763-1.255 0.9341 Cochran's Q-test (IVW) (P) 0.4411
Weighted mode method (NOME 1.082 0.525-2.232 0.8385 Rucker's Q-test (P) 0.3854
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.8662
MR-PRESSO global test (P) 0.4640
Chronotype (CHR) Inverse variance weighted method (IVW) 153 0.995 0.973-1.018 0.6729 MR-Egger intercept (P) 0.0941
MR-Egger method 0.937 0.871-1.009 0.0850 I2 (IVW) 28.6%
Weighted median method (WME) 0.995 0.980-1.009 0.7090 Cochran's Q-test (IVW) (P) 0.0008
Weighted mode method (NOME 1.025 0.934-1.125 0.6074 Rucker's Q-test (P) 0.0013
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9810
MR-PRESSO global test (P) <0.001
Morning person (MP) Inverse variance weighted method (IVW) 123 1.001 0.986-1.017 0.8441 MR-Egger intercept (P) 0.0364
MR-Egger method 0.953 0.909-1.001 0.0533 I2 (IVW) 23.5%
Weighted median method (WME) 1.004 0.994-1.014 0.7228 Cochran's Q-test (IVW) (P) 0.0127
Weighted mode method (NOME 1.022 0.962-1.086 0.4776 Rucker's Q-test (P) 0.0234
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9643
MR-PRESSO global test (P) 0.0030
Insomnia (INS) Inverse variance weighted method (IVW) 13 0.981 0.939-1.024 0.3448 MR-Egger intercept (P) 0.8399
MR-Egger method 0.968 0.836-1.120 0.6342 I2 (IVW) 8.4%
Weighted median method (WME) 0.977 0.953-1.001 0.3529 Cochran's Q-test (IVW) (P) 0.3621
Weighted mode method (NOME 0.983 0.914-1.059 0.6655 Rucker's Q-test (P) 0.2882
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9987
MR-PRESSO global test (P) 0.2800
Multisite chronic pain (MCP) Inverse variance weighted method (IVW) 32 1.373 0.884-2.133 0.1523 MR-Egger intercept (P) 0.0029
MR-Egger method 25.956 3.919-171.909 0.0014 I2 (IVW) 13.1%
Weighted median method (WME) 1.143 0.866-1.509 0.6338 Cochran's Q-test (IVW) (P) 0.2575
Weighted mode method (NOME 1.026 0.305-3.457 0.9667 Rucker's Q-test (P) 0.6690
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.7320
MR-PRESSO global test (P) 0.1730
Age-related macular degeneration (AMD)
Sleep duration (SD) Inverse variance weighted method (IVW) 69 1.242 0.925-1.667 0.1475 MR-Egger intercept (P) 0.0252
MR-Egger method 0.397 0.141-1.117 0.0792 I2 (IVW) 0.0%
Weighted median method (WME) 1.165 0.935-1.451 0.4888 Cochran's Q-test (IVW) (P) 0.5105
Weighted mode method (NOME 1.198 0.546-2.629 0.6545 Rucker's Q-test (P) 0.6381
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9302
MR-PRESSO global test (P) 0.2830
Short sleep (SS) Inverse variance weighted method (IVW) 25 0.520 0.144-1.881 0.3041 MR-Egger intercept (P) 0.8015
MR-Egger method 0.249 0.001-113.336 0.6431 I2 (IVW) 10.0%
Weighted median method (WME) 0.723 0.320-1.631 0.6936 Cochran's Q-test (IVW) (P) 0.3198
Weighted mode method (NOME 0.967 0.050-18.823 0.9826 Rucker's Q-test (P) 0.2735
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9970
MR-PRESSO global test (P) 0.2210
Long sleep (LS) Inverse variance weighted method (IVW) 6 1.355 0.004-491.772 0.8997 MR-Egger intercept (P) 0.7103
MR-Egger method 41.952 NA 0.6982 I2 (IVW) 44.8%
Weighted median method (WME) 2.690 0.321-22.563 0.6612 Cochran's Q-test (IVW) (P) 0.1066
Weighted mode method (NOME 3.168 0.005-1904.134 0.7383 Rucker's Q-test (P) 0.0654
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9745
MR-PRESSO global test (P) 0.0730
Chronotype (CHR) Inverse variance weighted method (IVW) 150 1.269 1.083-1.486 0.0034 MR-Egger intercept (P) 0.5248
MR-Egger method 1.086 0.653-1.805 0.7503 I2 (IVW) 1.9%
Weighted median method (WME) 1.171 1.048-1.308 0.1556 Cochran's Q-test (IVW) (P) 0.4204
Weighted mode method (NOME 0.954 0.533-1.707 0.8736 Rucker's Q-test (P) 0.4104
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9963
MR-PRESSO global test (P) 0.0920
Morning person (MP) Inverse variance weighted method (IVW) 121 1.192 1.078-1.318 0.0007 MR-Egger intercept (P) 0.1273
MR-Egger method 0.941 0.682-1.297 0.7075 I2 (IVW) 0.0%
Weighted median method (WME) 1.126 1.044-1.214 0.1197 Cochran's Q-test (IVW) (P) 0.9288
Weighted mode method (NOME 1.008 0.682-1.491 0.9662 Rucker's Q-test (P) 0.9414
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9771
MR-PRESSO global test (P) 0.8420
Insomnia (INS) Inverse variance weighted method (IVW) 13 1.135 0.826-1.560 0.4017 MR-Egger intercept (P) 0.2253
MR-Egger method 2.158 0.686-6.793 0.1678 I2 (IVW) 0.0%
Weighted median method (WME) 1.113 0.927-1.336 0.5694 Cochran's Q-test (IVW) (P) 0.8587
Weighted mode method (NOME 1.120 0.622-2.016 0.7119 Rucker's Q-test (P) 0.9109
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.7715
MR-PRESSO global test (P) 0.8530
Multisite chronic pain (MCP) Inverse variance weighted method (IVW) 31 1.014 0.580-1.774 0.9597 MR-Egger intercept (P) 0.1034
MR-Egger method 0.120 0.009-1.702 0.1129 I2 (IVW) 3.6%
Weighted median method (WME) 1.279 0.897-1.825 0.4931 Cochran's Q-test (IVW) (P) 0.4092
Weighted mode method (NOME 1.250 0.303-5.158 0.7594 Rucker's Q-test (P) 0.5018
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9093
MR-PRESSO global test (P) 0.2860
Amyotrophic lateral sclerosis (ALS)
Sleep duration (SD) Inverse variance weighted method (IVW) 71 1.003 0.743-1.355 0.9844 MR-Egger intercept (P) 0.2855
MR-Egger method 0.569 0.191-1.696 0.3069 I2 (IVW) 0.0%
Weighted median method (WME) 1.011 0.797-1.283 0.9621 Cochran's Q-test (IVW) (P) 0.5246
Weighted mode method (NOME 0.988 0.410-2.384 0.9795 Rucker's Q-test (P) 0.5307
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9829
MR-PRESSO global test (P) 0.3920
Short sleep (SS) Inverse variance weighted method (IVW) 26 0.839 0.231-3.052 0.7818 MR-Egger intercept (P) 0.7964
MR-Egger method 1.837 0.003-1038.801 0.8447 I2 (IVW) 4.7%
Weighted median method (WME) 0.693 0.296-1.624 0.6705 Cochran's Q-test (IVW) (P) 0.3947
Weighted mode method (NOME 0.587 0.022-15.815 0.7541 Rucker's Q-test (P) 0.3451
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9969
MR-PRESSO global test (P) 0.3180
Long sleep (LS) Inverse variance weighted method (IVW) 6 0.746 0.003-218.829 0.8994 MR-Egger intercept (P) 0.8766
MR-Egger method 0.223 NA 0.8550 I2 (IVW) 40.2%
Weighted median method (WME) 0.505 0.060-4.219 0.7606 Cochrane Q-test (IVW) (P) 0.1375
Weighted mode method (NOME 0.350 0.001-242.154 0.7659 Rucker's Q-test (P) 0.0814
assumptions) (MBE) Rucker's test statistic/ Cochrane Q-statistic 0.9919
MR-PRESSO global test (P) 0.1370
Chronotype (CHR) Inverse variance weighted method (IVW) 153 0.914 0.781-1.070 0.2605 MR-Egger intercept (P) 0.8658
MR-Egger method 0.876 0.524-1.467 0.6134 I2 (IVW) 0.0%
Weighted median method (WME) 0.976 0.868-1.097 0.8343 Cochran's Q-test (IVW) (P) 0.5552
Weighted mode method (NOME 1.112 0.642-1.924 0.7058 Rucker's Q-test (P) 0.5325
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 1.0000
MR-PRESSO global test (P) 0.2740
Morning person (MP) Inverse variance weighted method (IVW) 122 0.934 0.841-1.037 0.2007 MR-Egger intercept (P) 0.9094
MR-Egger method 0.952 0.674-1.344 0.7779 I2 (IVW) 0.0%
Weighted median method (WME) 0.944 0.873-1.020 0.4607 Cochran's Q-test (IVW) (P) 0.8461
Weighted mode method (NOME 1.012 0.711-1.439 0.9480 Rucker's Q-test (P) 0.8302
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9998
MR-PRESSO global test (P) 0.7370
Insomnia (INS) Inverse variance weighted method (IVW) 13 1.551 1.121-2.145 0.0123 MR-Egger intercept (P) 0.4410
MR-Egger method 1.100 0.404-2.993 0.8383 I2 (IVW) 0.0%
Weighted median method (WME) 1.480 1.203-1.821 0.0828 Cochran's Q-test (IVW) (P) 0.5894
Weighted mode method (NOME 1.386 0.762-2.522 0.3063 Rucker's Q-test (P) 0.5559
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9432
MR-PRESSO global test (P) 0.5290
Multisite chronic pain (MCP) Inverse variance weighted method (IVW) 35 1.472 0.902-2.401 0.1176 MR-Egger intercept (P) 0.3001
MR-Egger method 0.412 0.034-5.066 0.4772 I2 (IVW) 16.8%
Weighted median method (WME) 1.456 1.085-1.954 0.2097 Cochran's Q-test (IVW) (P) 0.1938
Weighted mode method (NOME 1.586 0.484-5.195 0.4512 Rucker's Q-test (P) 0.1943
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9726
MR-PRESSO global test (P) 0.0740
Multiple sclerosis (MS)
Sleep duration (SD) Inverse variance weighted method (IVW) 70 1.002 0.732-1.371 0.9909 MR-Egger intercept (P) 0.2162
MR-Egger method 2.014 0.632-6.423 0.2323 I2 (IVW) 9.3%
Weighted median method (WME) 1.133 0.911-1.408 0.5684 Cochran's Q-test (IVW) (P) 0.2622
Weighted mode method (NOME assumptions) (MBE) 1.168 0.581-2.346 0.6641 Rucker's Q-test (P) 0.2822
Rucker's Q-test statistic/Cochran's Q-test statistic 0.9763
MR-PRESSO global test (P) 0.0810
Short sleep (SS) Inverse variance weighted method (IVW) 26 4.780 0.939-24.326 0.0588 MR-Egger intercept (P) 0.8463
MR-Egger method 10.264 NA 0.5641 I2 (IVW) 42.7%
Weighted median method (WME) 1.740 0.732-4.137 0.5284 Cochran's Q-test (IVW) (P) 0.0120
Weighted mode method (NOME 0.724 0.046-11.380 0.8199 Rucker's Q-test (P) 0.0083
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 1.0017
MR-PRESSO global test (P) 0.0010
Long sleep (LS) Inverse variance weighted method (IVW) 5 0.296 0.001-90.815 0.5866 MR-Egger intercept (P) 0.1757
MR-Egger method NA NA 0.2011 I2 (IVW) 28.5%
Weighted median method (WME) 4.452 0.479-41.384 0.5397 Cochran's Q-test (IVW) (P) 0.2318
Weighted mode method (NOME 5.847 0.016-2101.701 0.5880 Rucker's Q-test (P) 0.4244
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.4997
MR-PRESSO global test (P) 0.2190
Chronotype (CHR) Inverse variance weighted method (IVW) 154 1.022 0.715-1.461 0.9041 MR-Egger intercept (P) 0.2977
MR-Egger method 0.553 0.164-1.863 0.3370 I2 (IVW) 30.1%
Weighted median method (WME) 0.940 0.836-1.055 0.5928 Cochran's Q-test (IVW) (P) 0.0004
Weighted mode method (NOME assumptions) (MBE) 0.853 0.467-1.558 0.6058 Rucker's Q-test (P) 0.0001
assumptions) (MBE) Rucker's test statistic/Cochrane Q-statistic 1.0236
MR-PRESSO global test (P) <0.001
Morning person (MP) Inverse variance weighted method (IVW) 124 0.963 0.856-1.084 0.5337 MR-Egger intercept (P) 0.4641
MR-Egger method 0.833 0.554-1.254 0.3788 I2 (IVW) 22.3%
Weighted median method (WME) 0.964 0.894-1.039 0.6234 Cochran's Q-test (IVW) (P) 0.0176
Weighted mode method (NOME 0.931 0.634-1.367 0.7172 Rucker's Q-test (P) 0.0167
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9954
MR-PRESSO global test (P) 0.0010
Insomnia (INS) Inverse variance weighted method (IVW) 13 0.936 0.648-1.352 0.7029 MR-Egger intercept (P) 0.5462
MR-Egger method 0.658 0.179-2.422 0.4944 I2 (IVW) 19.9%
Weighted median method (WME) 0.850 0.696-1.038 0.4327 Cochran's Q-test (IVW) (P) 0.2429
Weighted mode method (NOME 0.859 0.495-1.489 0.5974 Rucker's Q-test (P) 0.2091
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9650
MR-PRESSO global test (P) 0.1920
Multisite chronic pain (MCP) Inverse variance weighted method (IVW) 34 1.444 0.861-2.422 0.1577 MR-Egger intercept (P) 0.9268
MR-Egger method 1.635 0.101-26.412 0.7212 I2 (IVW) 28.7%
Weighted median method (WME) 1.197 0.890-1.609 0.5483 Cochran's Q-test (IVW) (P) 0.0619
Weighted mode method (NOME 1.237 0.417-3.668 0.7038 Rucker's Q-test (P) 0.0483
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 1.0010
MR-PRESSO global test (P) 0.0220
Parkinson's disease (PD)
Sleep duration (SD) Inverse variance weighted method (IVW) 70 0.934 0.649-1.343 0.7085 MR-Egger intercept (P) 0.3304
MR-Egger method 0.475 0.115-1.970 0.3003 I2 (IVW) 6.3%
Weighted median method (WME) 0.805 0.626-1.034 0.3889 Cochran's Q-test (IVW) (P) 0.3284
Weighted mode method (NOME 0.652 0.244-1.743 0.3968 Rucker's Q-test (P) 0.3251
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9874
MR-PRESSO global test (P) 0.1550
Short sleep (SS) Inverse variance weighted method (IVW) 26 3.485 0.810-14.993 0.0903 MR-Egger intercept (P) 0.8351
MR-Egger method 1.742 0.002-1841.723 0.8708 I2 (IVW) 0.0%
Weighted median method (WME) 2.734 1.025-7.290 0.3149 Cochran's Q-test (IVW) (P) 0.4655
Weighted mode method (NOME 3.892 0.113-133.950 0.4587 Rucker's Q-test (P) 0.4079
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 1.0006
MR-PRESSO global test (P) 0.2820
Long sleep (LS) Inverse variance weighted method (IVW) 6 0.506 0.002-121.424 0.7627 MR-Egger intercept (P) 0.1399
MR-Egger method 0.000 0.000-158.569 0.1383 I2 (IVW) 17.0%
Weighted median method (WME) 0.075 0.006-0.938 0.3522 Cochran's Q-test (IVW) (P) 0.3036
Weighted mode method (NOME 0.019 0.000-37.008 0.3521 Rucker's Q-test (P) 0.5936
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.4629
MR-PRESSO global test (P) 0.2590
Chronotype (CHR) Inverse variance weighted method (IVW) 155 0.921 0.753-1.125 0.4158 MR-Egger intercept (P) 0.5143
MR-Egger method 1.116 0.603-2.065 0.7250 I2 (IVW) 20.9%
Weighted median method (WME) 0.875 0.763-1.003 0.3280 Cochran's Q-test (IVW) (P) 0.0149
Weighted mode method (NOME 0.805 0.496-1.308 0.3823 Rucker's Q-test (P) 0.0141
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9966
MR-PRESSO global test (P) <0.001
Morning person (MP) Inverse variance weighted method (IVW) 125 1.026 0.898-1.173 0.7011 MR-Egger intercept (P) 0.8819
MR-Egger method 0.996 0.658-1.509 0.9863 I2 (IVW) 15.2%
Weighted median method (WME) 0.938 0.858-1.024 0.4666 Cochran's Q-test (IVW) (P) 0.0847
Weighted mode method (NOME 0.903 0.650-1.254 0.5446 Rucker's Q-test (P) 0.0754
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 1.0001
MR-PRESSO global test (P) 0.0080
Insomnia (INS) Inverse variance weighted method (IVW) 13 1.100 0.692-1.747 0.6609 MR-Egger intercept (P) 0.4819
MR-Egger method 0.628 0.108-3.657 0.5726 I2 (IVW) 34.1%
Weighted median method (WME) 0.891 0.694-1.146 0.6549 Cochran's Q-test (IVW) (P) 0.1093
Weighted mode method (NOME 0.654 0.278-1.539 0.3500 Rucker's Q-test (P) 0.0953
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9578
MR-PRESSO global test (P) 0.1730
Multisite chronic pain (MCP) Inverse variance weighted method (IVW) 34 0.696 0.435-1.113 0.1259 MR-Egger intercept (P) 0.2551
MR-Egger method 0.200 0.021-1.876 0.1531 I2 (IVW) 0.0%
Weighted median method (WME) 0.728 0.532-0.995 0.3184 Cochran's Q-test (IVW) (P) 0.6387
Weighted mode method (NOME 0.545 0.163-1.8118 0.3304 Rucker's Q-test (P) 0.6541
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9573
MR-PRESSO global test (P) 0.5230

Graphical representation of causal association analysis and assessment of pleiotropy. (A) Scatterplot showing causal effect estimates computed using various MR methods for the association of morning person (MP) as exposure with age-related macular degeneration (AMD) as outcome. (B) Funnel plot showing the extent of heterogeneity among the individual Wald ratio estimates for morning person (MP) as exposure with age-related macular degeneration (AMD) as outcome. IVW, inverse variance-weighted method; WME, weighted median method; MBE, weighted mode method (NOME assumptions).

We further observed a suggestive risky causal effect of SS on genetic liability to AD (ORIVW = 1.256; 95% CI 1.081, 1.459, P = 0.0044). Heterogeneity check further confirmed the reliability of the observed association with absence of any heterogeneity in the distribution of effect estimates of individual genetic variants (I2 = 0%, Cochrane P =0.5847, Rucker's Q-test P = 0.5279, MR-PRESSO global test P = 0.4270). A similar directionality in the causal effect estimates was also observed using the WME method (OR = 1.121; 95% CI 1.103, 1.347). However, we did not observe any role of pain in predisposition to AD.

We also observed a suggestive risky causal effect of INS on genetic liability to ALS (ORIVW = 1.551; 95% CI 1.121, 2.145, P = 0.0123). On the other hand, we failed to observe any role of pain in predisposition to ALS.

We did not observe any direct role of sleep and pain-related traits in predisposition to MS. Similarly, our MR analysis failed to detect a role of the sleep and pain-related traits in predisposition to PD.

 Sensitivity Analysis

Concerning direct MR, the association of SS with AD was lost after the exclusion of overlapping UKB samples (data not shown). In the reverse MR, PD showed suggestion of a strong protective effect against CHR and MP after the exclusion of overlapping UKB samples (data not shown). Reverse causal estimates for various sleep and pain-related traits using various NDDs as exposure are shown in Table 3. Our reverse casual check confirmed the directionality of the observed associations of MP and CHR with AMD, as we failed to observe any effect of AMD on MP and CHR. Our reverse causal check also confirmed the role of SS in predisposition to AD, as we failed to observe the causal effect of AD on SS. Interestingly, all the sleep-related traits except for SS were observed to be influenced by genetic predisposition to AD when employing non-IVW methods for judging causal effects of sleep-related traits on AD. Lastly, our reverse casual check confirmed the role of INS in predisposition to ALS. On the contrary, our findings suggested a causal role of genetic predisposition to ALS in LS with a consistent significant risk effect using the IVW, WME, and MBE methods.

Causal effect estimates using different Mendelian randomization methods and heterogeneity analysis of causal effect estimates for various sleep and pain-related traits using Neurodegenerative disorders (NDDs) asexposures.

Trait MR methodology Number of SNPs Reverse causal effect estimates Tests of heterogeneity
β or OR 95% CI P
Alzheimer's disease (AD)
Sleep duration (SD) Inverse variance weighted method (IVW) 26 −0.0370 −0.0879-0.0140 0.1482 MR-Egger intercept (P) 0.0857
MR-Egger method −0.1046 −0.1977-−0.0114 0.0293 I2 (IVW) 0.0%
Weighted median method (WME) −0.0583 −0.0924-−0.0242 0.0996 Cochran's Q-test (IVW) (P) 0.6942
Weighted mode method (NOME −0.0854 −0.1644-−0.0064 0.0443 Rucker's Q-test (P) 0.8057
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.8557
MR-PRESSO global test (P) 0.5860
Short sleep (SS) Inverse variance weighted method (IVW) 26 1.004 0.983-1.026 0.6774 MR-Egger intercept (P) 0.0745
MR-Egger method 1.035 0.995-1.076 0.0862 I2 (IVW) 0.0%
Weighted median method (WME) 1.023 1.008-1.038 0.1287 Cochran's Q-test (IVW) (P) 0.9298
Weighted mode method (NOME 1.026 0.993-1.060 0.1416 Rucker's Q-test (P) 0.9787
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.7827
MR-PRESSO global test (P) 0.9120
Long sleep (LS) Inverse variance weighted method (IVW) 26 0.984 0.968-1.000 0.0479 MR-Egger intercept (P) 0.9637
MR-Egger method 0.984 0.955-1.014 0.2819 I2 (IVW) 0.0%
Weighted median method (WME) 0.986 0.975-0.998 0.2373 Cochran's Q-test (IVW) (P) 0.7454
Weighted mode method (NOME 0.973 0.944-1.002 0.0841 Rucker's Q-test (P) 0.6947
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 1.0005
MR-PRESSO global test (P) 0.6330
Chronotype (CHR) Inverse variance weighted method (IVW) 26 1.033 0.950-1.123 0.4365 MR-Egger intercept (P) 0.3102
MR-Egger method 1.101 0.945-1.284 0.2055 I2 (IVW) 42.8%
Weighted median method (WME) 1.123 1.075-1.173 0.0141 Cochran's Q-test (IVW) (P) 0.0118
Weighted mode method (NOME 1.118 1.016-1.230 0.0306 Rucker's Q-test (P) 0.0158
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9434
MR-PRESSO global test (P) 0.0030
Morning person (MP) Inverse variance weighted method (IVW) 26 1.055 0.922-1.205 0.4212 MR-Egger intercept (P) 0.5355
MR-Egger method 1.123 0.877-1.438 0.3423 I2 (IVW) 0.4%
Weighted median method (WME) 1.169 1.088-1.256 0.0388 Cochrane Q-test (IVW) (P) 0.0324
Weighted mode method (NOME 1.177 1.003-1.381 0.0570 Rucker's Q-test (P) 0.0300
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.976
MR-PRESSO global test (P) 0.0110
Insomnia (INS) Inverse variance weighted method (IVW) 26 0.916 0.799-1.051 0.2011 MR-Egger intercept (P) 0.0599
MR-Egger method 0.757 0.596-0.960 0.0239 I2 (IVW) 23.7%
Weighted median method (WME) 0.871 0.799-0.948 0.1171 Cochran's Q-test (IVW) (P) 0.1372
Weighted mode method (NOME 0.851 0.714-1.017 0.0888 Rucker's Q-test (P) 0.2441
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.8665
MR-PRESSO global test (P) 0.0920
Multisite chronic pain (MCP) Inverse variance weightedmethod (IVW) 26 −0.0371 −0.1073-0.0329 0.3181 MR-Egger intercept (P) 0.3786
MR-Egger method −0.0839 −0.2125-0.0447 0.1908 I2 (IVW) 33.2%
Weighted median method (WME) −0.0294 −0.0688-0.0100 0.4629 Cochran's Q-test (IVW) (P) 0.0527
Weighted mode method (NOME −0.0837 −0.1884-0.0209 0.1293 Rucker's Q-test (P) 0.0575
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9566
MR-PRESSO global test (P) 0.0140
Amyotrophic lateral sclerosis (ALS)
Sleep duration (SD) Inverse variance weighted method (IVW) 4 0.0249 −0.0054-0.0554 0.0797 MR-Egger intercept (P) 0.8095
MR-Egger method 0.0816 −0.0963-0.1327 0.5654 I2 (IVW) 0.0%
Weighted median method (WME) 0.0250 0.0138-0.03611 0.1099 Cochran's Q-test (IVW) (P) 0.9179
Weighted mode method (NOME 0.0255 −0.0010-0.0521 0.1565 Rucker's Q-test (P) 0.803
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.8697
MR-PRESSO global test (P) 0.9350
Short sleep (SS) Inverse variance weighted method (IVW) 4 0.9980 0.985-1.011 0.6380 MR-Egger intercept (P) 0.9973
MR-Egger method 0.9980 0.951-1.048 0.8711 I2 (IVW) 0.0%
Weighted median method (WME) 0.9990 0.995-1.004 0.9524 Cochran's Q-test (IVW) (P) 0.7351
Weighted mode method (NOME 1.0070 0.990-1.012 0.9091 Rucker's Q-test (P) 0.5285
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 1.002
MR-PRESSO global test (P) 0.6800
Long sleep (LS) Inverse variance weighted method (IVW) 4 1.0125 1.002-1.023 0.0316 MR-Egger intercept (P) 0.8888
MR-Egger method 1.0109 0.964-1.059 0.4300 I2 (IVW) 4.0%
Weighted median method (WME) 1.0134 1.009-1.017 0.0409 Cochran's Q-test (IVW) (P) 0.373
Weighted mode method (NOME 1.0139 1.005-1.022 0.0530 Rucker's Q-test (P) 0.2079
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 1.0058
MR-PRESSO global test (P) 0.4410
Chronotype (CHR) Inverse variance weighted method (IVW) 4 1.0263 0.990-1.064 0.1068 MR-Egger intercept (P) 0.5384
MR-Egger method 1.0488 0.915-1.202 0.2709 I2 (IVW) 0.0%
Weighted median method (WME) 1.0294 1.016-1.043 0.1207 Cochran's Q-test (IVW) (P) 0.7821
Weighted mode method (NOME 1.0328 1.001-1.065 0.1339 Rucker's Q-test (P) 0.7597
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.5093
MR-PRESSO global test (P) 0.7830
Morning person (MP) Inverse variance weighted method (IVW) 4 1.0383 0.977-1.103 0.1433 MR-Egger intercept (P) 0.5715
MR-Egger method 1.0733 0.854-1.348 0.3137 I2 (IVW) 0.0%
Weighted median method (WME) 1.0483 1.024-1.073 0.1327 Cochran's Q-test (IVW) (P) 0.6704
Weighted mode method (NOME 1.0536 1.001-1.109 0.1397 Rucker's Q-test (P) 0.5750
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.7132
MR-PRESSO global test (P) 0.6050
Insomnia (INS) Inverse variance weighted method (IVW) 4 1.0148 0.947-1.087 0.5445 MR-Egger intercept (P) 0.9715
MR-Egger method 1.0125 0.782-1.311 0.8555 I2 (IVW) 0.0%
Weighted median method (WME) 1.0245 0.999-1.051 0.4104 Cochran's Q-test (IVW) (P) 0.6546
Weighted mode method (NOME 1.0326 0.974-1.095 0.3607 Rucker's Q-test (P) 0.4449
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9989
MR-PRESSO global test (P) 0.6330
Multisite chronic pain (MCP) Inverse variance weightedmethod (IVW) 4 0.0045 −0.0280-0.0372 0.6848 MR-Egger intercept (P) 0.5017
MR-Egger method −0.0169 −0.13931-0.1054 0.6112 I2 (IVW) 0.0%
Weighted median method (WME) 0.0050 −0.0069-0.0169 0.7024 Cochran's Q-test (IVW) (P) 0.5233
Weighted mode method (NOME −0.0055 −0.0326-0.0216 0.7177 Rucker's Q-test (P) 0.4402
Rucker's Q-test statistic/Cochran's Q-test statistic 0.7313
MR-PRESSO global test (P) 0.4220
Age related macular degeneration (AMD)
Sleep duration (SD) Inverse variance weighted method (IVW) 38 −0.0005 −0.0077-0.0067 0.8752 MR-Egger intercept (P) 0.6714
MR-Egger method −0.0026 −0.0151-0.0099 0.6746 I2 (IVW) 47.3%
Weighted median method (WME) 0.0014 −0.0022-0.0051 0.7060 Cochran's Q-test (IVW) (P) 0.0008
Weighted mode method (NOME 0.0018 −0.0067-0.0103 0.6725 Rucker's Q-test (P) 0.0006
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9966
MR-PRESSO global test (P) <0.001
Short sleep (SS) Inverse variance weighted method (IVW) 38 0.999 0.997-1.001 0.2404 MR-Egger intercept (P) 0.7791
MR-Egger method 0.999 0.995-1.003 0.6506 I2 (IVW) 6.5%
Weighted median method (WME) 0.999 0.997-1.000 0.3791 Cochran's Q-test (IVW) (P) 0.3558
Weighted mode method (NOME 0.997 0.993-1.001 0.1737 Rucker's Q-test (P) 0.3151
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.999
MR-PRESSO global test (P) 0.2540
Long sleep (LS) Inverse variance weighted method (IVW) 38 0.999 0.996-1.001 0.3476 MR-Egger intercept (P) 0.4268
MR-Egger method 0.997 0.993-1.002 0.2361 I2 (IVW) 55.4%
Weighted median method (WME) 1.000 0.999-1.001 0.9789 Cochran's Q-test (IVW) (P) <0.0001
Weighted mode method (NOME 1.001 0.997-1.004 0.6871 Rucker's Q-test (P) <0.0001
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9767
MR-PRESSO global test (P) <0.001
Chronotype (CHR) Inverse variance weighted method (IVW) 38 1.005 0.994-1.015 0.3798 MR-Egger intercept (P) 0.6299
MR-Egger method 1.001 0.983-1.019 0.9068 I2 (IVW) 63.8%
Weighted median method (WME) 1.004 0.999-1.009 0.4304 Cochran's Q-test (IVW) (P) <0.0001
Weighted mode method (NOME 1.000 0.986-1.014 0.9980 Rucker's Q-test (P) <0.0001
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 1.003
MR-PRESSO global test (P) <0.001
Morning person (MP) Inverse variance weighted method (IVW) 38 1.007 0.991-1.025 0.3592 MR-Egger intercept (P) 0.5705
MR-Egger method 1.001 0.973-1.030 0.9438 I2 (IVW) 58.3%
Weighted median method (WME) 1.007 0.999-1.016 0.4027 Cochran's Q-test (IVW) (P) <0.0001
Weighted mode method (NOME 1.005 0.983-1.028 0.6701 Rucker's Q-test (P) <0.0001
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 1.0019
MR-PRESSO global test (P) <0.001
Insomnia (INS) Inverse variance weighted method (IVW) 37 0.994 0.981-1.008 0.3903 MR-Egger intercept (P) 0.4319
MR-Egger method 0.987 0.965-1.010 0.2580 I2 (IVW) 27.6%
Weighted median method (WME) 1.000 0.991-1.009 0.9879 Rucker's Q-test (P) 0.0656
Weighted mode method (NOME 1.005 0.980-1.032 0.6813 Rucker's Q-test (P) 0.0614
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9833
MR-PRESSO global test (P) 0.0280
Multisite chronic pain (MCP) Inverse variance weighted method (IVW) 42 −0.0028 −0.0089-0.0033 0.3574 MR-Egger intercept (P) 0.0548
MR-Egger method −0.0112 −0.0216-−0.0008 0.0358 I2 (IVW) 30.8%
Weighted median method (WME) −0.0034 −0.0075-0.0008 0.4127 Cochran's Q-test (IVW) (P) 0.0321
Weighted mode method (NOME −0.0021 −0.0119-0.0077 0.6812 Rucker's Q-test (P) 0.0686
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.911
MR-PRESSO global test (P) 0.0110
Multiple sclerosis (MS)
Sleep duration (SD) Inverse variance weighted method (IVW) 70 0.0032 −0.0024-0.0088 0.2586 MR-Egger intercept (P) 0.6248
MR-Egger method 0.0015 −0.0076-0.0105 0.7477 I2 (IVW) 53.2%
Weighted median method (WME) 0.0044 0.0012-0.0076 0.1841 Cochran's Q-test (IVW) (P) <0.0001
Weighted mode method (NOME 0.0038 −0.0021-0.0097 0.2164 Rucker's Q-test (P) <0.0001
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9996
MR-PRESSO global test (P) NA
Short sleep (SS) Inverse variance weighted method (IVW) 70 1.000 0.998-1.002 0.9521 MR-Egger intercept (P) 0.7509
MR-Egger method 1.000 0.997-1.004 0.8329 I2 (IVW) 49.7%
Weighted median method (WME) 0.999 0.997-1.000 0.4187 Cochran's Q-test (IVW) (P) <0.0001
Weighted mode method (NOME 0.999 0.997-1.002 0.6820 Rucker's Q-test (P) <0.0001
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9997
MR-PRESSO global test (P) NA
Long sleep (LS) Inverse variance weighted method (IVW) 70 1.002 1.001-1.003 0.0040 MR-Egger intercept (P) 0.6275
MR-Egger method 1.001 1.000-1.003 0.1459 I2 (IVW) 0.8%
Weighted median method (WME) 1.002 1.001-1.003 0.1231 Cochran's Q-test (IVW) (P) 0.4591
Weighted mode method (NOME 1.002 1.000-1.003 0.0726 Rucker's Q-test (P) 0.4326
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9969
MR-PRESSO global test (P) NA
Chronotype (CHR) Inverse variance weighted method (IVW) 70 1.003 0.996-1.009 0.3943 MR-Egger intercept (P) 0.2149
MR-Egger method 0.998 0.987-1.008 0.6608 I2 (IVW) 52.3%
Weighted median method (WME) 1.000 0.997-1.004 0.9476 Cochran's Q-test (IVW) (P) <0.0001
Weighted mode method (NOME 1.002 0.996-1.008 0.5352 Rucker's Q-test (P) <0.0001
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9815
MR-PRESSO global test (P) NA
Morning person (MP) Inverse variance weighted method (IVW) 70 1.004 0.993-1.014 0.4939 MR-Egger intercept (P) 0.1581
MR-Egger method 0.994 0.978-1.011 0.4975 I2 (IVW) 49.4%
Weighted median method (WME) 0.998 0.992-1.004 0.7365 Cochran's Q-test (IVW) (P) <0.0001
Weighted mode method (NOME 1.002 0.992-1.013 0.6579 Rucker's Q-test (P) <0.0001
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.973
MR-PRESSO global test (P) NA
Insomnia (INS) Inverse variance weighted method (IVW) 67 1.000 0.991-1.011 0.8216 MR-Egger intercept (P) 0.5171
MR-Egger method 1.005 0.989-1.021 0.5208 I2 (IVW) 27.6%
Weighted median method (WME) 0.999 0.991-1.007 0.8708 Cochran's Q-test (IVW) (P) 0.0217
Weighted mode method (NOME 1.003 0.990-1.015 0.6794 Rucker's Q-test (P) 0.0190
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9956
MR-PRESSO global test (P) NA
Multisite chronic pain (MCP) Inverse variance weightedmethod (IVW) 70 −0.0008 −0.0060-0.0043 0.6868 MR-Egger intercept (P) 0.7972
MR-Egger method −0.0017 −0.0100-0.0067 0.6902 I2 (IVW) 39.0%
Weighted median method (WME) −0.0032 −0.0066-0.0001 0.3276 Cochran's Q-test (IVW) (P) 0.0006
Weighted mode method (NOME −0.0043 −0.0100-0.0013 0.1428 Rucker's Q-test (P) 0.0005
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9992
MR-PRESSO global test (P) NA
Parkinson's disease (PD)
Sleep duration (SD) Inverse variance weighted method (IVW) 23 0.0098 −0.0048-0.0245 0.1798 MR-Egger intercept (P) 0.93
MR-Egger method 0.0113 −0.0266-0.0492 0.5417 I2 (IVW) 68.2%
Weighted median method (WME) 0.0061 −0.0001-0.0125 0.3089 Cochran's Q-test (IVW) (P) <0.0001
Weighted mode method (NOME −0.0001 −0.0193-0.0190 0.9877 Rucker's Q-test (P) <0.0001
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 1.0032
MR-PRESSO global test (P) <0.001
Short sleep (SS) Inverse variance weighted method (IVW) 23 0.999 0.995-1.002 0.3680 MR-Egger intercept (P) 0.8565
MR-Egger method 0.998 0.988-1.007 0.6047 I2 (IVW) 19.0%
Weighted median method (WME) 0.999 0.997-1.001 0.6722 Cochran's Q-test (IVW) (P) 0.2058
Weighted mode method (NOME 1.002 0.993-1.010 0.6937 Rucker's Q-test (P) 0.1678
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9982
MR-PRESSO global test (P) 0.1170
Long sleep (LS) Inverse variance weighted method (IVW) 23 1.002 0.998-1.007 0.2488 MR-Egger intercept (P) 0.7717
MR-Egger method 1.004 0.993-1.016 0.4337 Cochran's Q-test (IVW) (P) 61.8%
Weighted median method (WME) 0.999 0.997-1.001 0.4874 Rucker's Q-test (P) <0.0001
Weighted mode method (NOME 0.998 0.994-1.002 0.3944 Rucker's Q-test (P) <0.0001
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 1.0081
MR-PRESSO global test (P) <0.001
Chronotype (CHR) Inverse variance weighted method (IVW) 23 0.992 0.978-1.007 0.3039 MR-Egger intercept (P) 0.4215
MR-Egger method 0.979 0.943-1.017 0.2560 I2 (IVW) 60.6%
Weighted median method (WME) 1.002 0.994-1.009 0.8195 Cochran's Q-test (IVW) (P) 0.0001
Weighted mode method (NOME 1.013 0.956-1.072 0.6693 Rucker's Q-test (P) 0.0001
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9688
MR-PRESSO global test (P) <0.001
Morning person (MP) Inverse variance weighted method (IVW) 23 0.991 0.967-1.015 0.4437 MR-Egger intercept (P) 0.5548
MR-Egger method 0.974 0.915-1.038 0.4014 I2 (IVW) 60.0%
Weighted median method (WME) 0.998 0.985-1.011 0.8945 Cochran's Q-test (IVW) (P) 0.0001
Weighted mode method (NOME 0.943 0.875-1.016 0.1385 Rucker's Q-test (P) 0.0001
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9819
MR-PRESSO global test (P) <0.001
Insomnia (INS) Inverse variance weighted method (IVW) 23 1.002 0.980-1.024 0.8525 MR-Egger intercept (P) 0.8117
MR-Egger method 0.996 0.942-1.053 0.8829 I2 (IVW) 34.8%
Weighted median method (WME) 0.991 0.979-1.004 0.5141 Cochran's Q-test (IVW) (P) 0.0524
Weighted mode method (NOME 0.967 0.931-1.004 0.0898 Rucker's Q-test (P) 0.0398
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9967
MR-PRESSO global test (P) 0.0240
Multisite chronic pain (MCP) Inverse variance weightedmethod (IVW) 23 −0.0054 −0.0170-0.0062 0.3590 MR-Egger intercept (P) 0.3476
MR-Egger method −0.0178 −0.0472-0.0115 0.2202 I2 (IVW) 48.3%
Weighted median method (WME) −0.0093 −0.0155-−0.0032 0.1373 Cochran's Q-test (IVW) (P) 0.0054
Weighted mode method (NOME −0.0105 −0.0274-0.0062 0.2277 Rucker's Q-test (P) 0.0061
assumptions) (MBE) Rucker's Q-test statistic/Cochran's Q-test statistic 0.9565
MR-PRESSO global test (P) 0.001

We failed to observe the predominant influence of any of the single variants on causal the effect estimates of MP with AMD, as shown in Supplementary Table 4. Similarly, the observed associations of CHR with AMD, SS with AD, and INS with ALS were retained (Supplementary Table 5). Among SNPs used for causal effect estimation of MP and CHR with AMD, 46 and 51 were identified as potential pleitropic variants for respective estimations (Supplementary Table 6). However, exclusion of these SNPs did not influence the observed casual association of MP and CHR with AMD (OR = 1.202, 95% CI 1.055, 1.370; OR = 1.262, 95% CI 1.049, 1.520). On the contrary, associations of SS with AD and INS with ALS were lost, which could be attributed to the presence of a high proportion of pleiotropic SNPs in the genetic instruments for SS and INS.

The sensitivity analysis using the multivariable MR approach also yielded similar results with the retention of the association of MP and CHR with AMD (OR = 1.184, 95% CI 1.083, 1.284; OR = 1.162, 95% CI 1.060, 1.263) (Supplementary Table 7).

Concerning the influence of specific brain regions, we specifically identified a high proportion of SNPs influencing brain expression in the cerebellum and basal ganglia region (Table 4). However, exclusion of these SNPs did not affect the overall causal association of CHR and MP with AMD. Similarly, we failed to observe the effect of any of the other brain regions on the observed associations. We also failed to observe any influence of brain region-specific expression on other observed associations (data not shown).

Sensitivity analysis of causal effect estimates of sleep-related traits on neurodegeneration by exploring potential influence of specific brain region using variants involved in regional expression.

Causal effect estimates of MP with AMD Causal effect estimates of CHR with AMD
Brain region Number of SNPs involved in expression Number of SNPs remaining IVW OR 95% CI P Number of SNPs involved in expression Number of SNPs IVW OR 95% CI P
Amygdala 5 116 1.184 1.069-1.312 0.0014 6 144 1.245 1.061-1.462 0.0077
Anterior cingulate cortex (BA24) 8 113 1.188 1.070-1.318 0.0014 12 138 1.269 1.088-1.479 0.0027
Brain—caudate (basal ganglia) 14 107 1.180 1.061-1.313 0.0027 19 131 1.262 1.075-1.482 0.0049
Brain—Cerebellar Hemisphere 13 108 1.185 1.066-1.317 0.0019 17 133 1.285 1.098-1.504 0.0020
Brain—cerebellum 16 105 1.186 1.065-1.320 0.0021 21 129 1.271 1.079-1.497 0.0044
Brain—cortex 13 108 1.175 1.058-1.306 0.0030 17 133 1.252 1.071-1.462 0.0050
Brain—cerebellar hemisphere 13 108 1.185 1.066-1.317 0.0019 17 133 1.285 1.098-1.504 0.0020
Brain—frontal cortex (BA9) 14 107 1.176 1.057-1.307 0.0031 15 135 1.264 1.083-1.476 0.0033
Brain—hippocampus 7 114 1.197 1.080-1.328 0.0008 11 139 1.296 1.111-1.511 0.0011
Brain—hypothalamus brain 0 121 1.192 1.078-1.318 0.0007 0 150 1.269 1.083-1.486 0.0034
Brain—nucleus accumbens (basal ganglia) 12 109 1.189 1.070-1.320 0.0015 17 133 1.268 1.082-1.486 0.0037
Brain—putamen (basal ganglia) 8 113 1.195 1.078-1.326 0.0009 12 138 1.266 1.086-1.475 0.0028
Brain—spinal cord (cervical c-1) 5 116 1.198 1.081-1.326 0.0007 9 141 1.295 1.113-1.508 0.0010
Brain—substantia nigra 2 119 1.200 1.084-1.328 0.0005 5 145 1.301 1.121-1.511 0.0007
Discussion

The use of GWAS data in MR-based approaches has opened up opportunities to assess and define clinically relevant signatures for a diverse spectrum of diseases. Our study supports the role of a person's underlying circadian rhythm in genetic predisposition to neurodegeneration. We found an association of genetically predicted MP trait with AMD. The correlated trait CHR also had a suggestive risk association with AMD. We also found suggestive evidence for a possible association of genetically predicted SS with AD, and INS with ALS. Surprisingly, however, our study found no evidence to support the association between pain and NDDs.

To date, evidence from observational studies has shown a remarkable heterogeneity in the association of different circadian traits with various NDDs. A recent study investigating the incidence of AMD in 108,225 participants observed that patients with INS were 33% more likely to have subsequent AMD (HR 1.33; 95% CI 1.18, 1.48) (34). Previously, an observational study on 57 patients with neovascular AMD and 108 controls found a significantly increased risk of neovascular AMD in patients sleeping <6 h compared to those sleeping 7-8 h (OR 3.29; 95% CI 1.32, 8.27) (35). Another study failed to detect an association with LS in 316 patients with neovascular AMD compared to 500 patients without AMD (36). However, the study did find an association of LS with geographic atrophy, an advanced form of AMD, in 61 individuals (presence of a discrete area of atrophy with a diameter of ≥175 μm). A recent observational study further reported that individuals who take an afternoon nap are 60% less likely to be diagnosed with late AMD (56 with late AMD vs. 1,204 without AMD) (37). As darkness is known to stimulate the secretion of melatonin from the pineal gland, our findings are in agreement with previous studies showing that increased melatonin synthesis could play a protective role in the pathophysiology of AMD (38). However, a recent randomized controlled trial (RCT) failed to show any beneficial effect of low-level night-time light therapy on the progression of AMD (39).

In contrast to previously reported findings from epidemiological studies, we failed to observe any association of INS, SS, and LS with AMD using the genetic data in this study. However, we observed that MP is more likely to be predisposed to AMD (OR 1.19; 95% CI 1.08, 1.32). Our study suggests that more prolonged exposure to daylight in such individuals could increase the risk for AMD. Our findings are in contrast to a recent meta-analysis of observational studies demonstrating the absence of an association between sunlight exposure and AMD (OR 1.12; 95% CI 0.76, 1.67) (40). One of the possible reasons for this discrepancy could be that only one of the 14 studies included in the meta-analysis was a cohort study. The only included cohort study was a 10-year follow-up study, which demonstrated that participants exposed to summer sun for more than 5 h a day were more likely to show increased retinal pigment (RR 2.99; 95% CI 1.18, 7.6) and develop early age-related maculopathy (RR 2.2; 95% CI 1.02, 4.73) in comparison to those exposed for <2 h per day (41). It has also been suggested that excessive light exposure may induce phototoxic damage to the retinal pigmental epithelium and possibly contribute to the gradual worsening of vision in AMD (42–44).

Compared to the impact of circadian rhythms on other NDDs, the role of sleep-related traits has been well-investigated in AD but with mixed findings. Previous studies have predominantly focused on sleep-wake rhythmicity, showing higher incidence of sleep fragmentations and lower amplitude of circadian rhythmicity in patients with moderate or severe AD (1). Concerning SD, both LS and SS have been previously shown to be linked with the risk of dementia (8, 45, 46). A 17-year longitudinal study investigating sleep characteristics in 11, 247 old-aged Swedish individuals (> 65 years at baseline) observed an association of short (≤ 6 h) and extended (> 9 h) time in bed with a higher incidence of dementia (HR 1.4, 95% CI 1.06, 1.85; HR 1.11, 95% CI 1, 1.24) (8). Our results are in agreement with a previously published study (8). Indeed, we observed a strong causal role of SS in predisposition to AD (OR 1.26; 95% CI 1.08, 1.46). However, our results need to be treated with caution, as the association was lost after excluding the overlapping UKB samples from the AD data set, as demonstrated previously (18). It is also possible that the association was lost because of decrease in sample size, necessitating replication with larger AD data sets in the future.

Sleep disturbances are also frequently observed in patients with ALS. Our MR analysis also suggested a possible causal role of INS in ALS (OR 1.55; 95% CI 1.12, 2.14). A previous observational study has demonstrated decreased sleep efficiency and fragmented sleep architecture in 59 patients with ALS (47). Another study reported the presence of sleep disturbances in more than 2/3 of 40 patients with ALS. The study further reported a diagnosis of INS in 65% of the patients (48). These results are in agreement with a previous study reporting a significantly higher prevalence of INS in 90 patients with motor neuron disease compared to 96 healthy controls (48.9 vs. 31.3%, p = 0.014) (49). In summary, reports of sleep disturbance among patients with ALS in small sample-sized observational studies and the suggestive causal role of INS in ALS in this study necessitate a need for conducting large-scale epidemiological studies.

Despite the consistent findings of excessive daytime sleepiness or altered sleep timing in patients with PD, our MR findings demonstrate the absence of any causal role of sleep-related traits in predisposition to PD (1). One possible explanation could be that dopaminergic treatment might have influenced the sleeping behavior of patients with PD, as excessive daytime sleepiness is known to be one of the common side effects of dopaminergic treatment. In such a scenario, causal analysis using biological markers of circadian rhythms such as core body temperature, cortisol, and melatonin rhythms, might potentially shed light on the true relationship between sleep-related traits and PD.

We also failed to observe any causal association of sleep-related traits with MS, although sleep disturbance is a common symptom of MS (1). It is suggested that the sleep disorders observed in patients with MS could be a secondary cause of fatigue, a symptom that affects 9 of 10 patients with MS (50).

Among all NDDs, high prevalence of pain has been observed in patients with AD and PD (1). Assessment of pain in such patients of is often challenging because of associated cognitive and motor impairments (51). Nevertheless, the use of genetic instruments of pain on a general population shows that MCP does not play any causal role in AD and PD. A recent cross-sectional study investigating pain in 100 patients with PD patients showed that pain is more prevalent in patients with advanced-stage PD than patients with early-stage PD suggesting pain to be a consequence of the disease rather than a cause (52). Moreover, pain is a broad concept, and inconsistencies in the measurement of number of available pain behavior rating scales often limit their application in clinical settings.

Our study has several strengths and limitations. We adopted a highly comprehensive approach involving the exploration of several sleep-related traits and pain with commonly prevalent NDD. We further employed multiple MR methods and heterogeneity and sensitivity analysis approaches to confirm the reliability of the observed associations. Concerning limitations, previous observational studies have shown that the impact of sleep and pain-related traits may be dependent on the stage of neurodegeneration or severity of an NDD (2). However, we could not conduct such a stratified analysis because of the non-availability of an individual-level data set for respective NDD. Furthermore, pain is a highly complex trait, and the lack of genetic instruments specific for neuropathic and nociceptive pain may undermine the findings of this study. The possibility of nociceptive pain confounding the causal relationship between neuropathic pain and neurodegeneration cannot be ruled out. One critical assumption for MR is that the effect of a genetic instrument for the main exposure on disease outcome is mediated by its influence on the intermediate trait. As genetic variants associated with sleep (duration or pattern) are highly correlated with pain and other sleep-related traits (duration or pattern), we addressed the potential pleiotropic effect by conducting a multivariable analysis. Our findings of causal association between sleep pattern (CHR or MP) and AMD remained robust after adjusting for the potential pleiotropic effect of SD and pain. However, despite adopting a multivariable MR approach, the possibility of residual confounding due to our inability to simultaneously adjust for all the highly correlated SD-related traits (SD or LS or SS) cannot be ruled out.

Using genetic data, we provide strong evidence that being an MP is a causal risk factor for genetic liability to AMD. There is a necessity for conducting large-scale epidemiological cohort studies to confirm our findings. Additional research is also required to understand the biological pathways underlying these associations, including causal analysis with biochemical makers of sleep and correlated traits associated with sleep.

 Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

 Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants' legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.

 Author Contributions

SG designed and conceptualized the study, conducted data extraction, analyzed the data, drafted the manuscript, and revised the final draft. MS supervised the overall study and revised the final draft. Both authors contributed to the article and approved the submitted version.

 Funding

This study was, in part, supported by the EU Joint Programme-Neurodegenerative Diseases Research (JPND) project under the aegis of JPND (www.jpnd.eu) through Germany, BMBF, funding code 01ED1406. MS was further funded by the Michael J. Fox Foundation, USA Genetic Diversity in PD Program: GAP-India Grant ID: 17473 and supported by grants from the German Research Council (DFG/SH 599/6-1 to MS), and MSA Coalition.

 Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

We acknowledge the study participants and the investigators from the International Genomics of Alzheimer's Patients (IGAP), International Parkinson's Disease Genomics Consortium (IPDGC), project MinE, International Multiple Sclerosis Genetics Consortium (IMSGC), International Sleep Genetic Epidemiology Consortium (ISGEC), UK Biobank, and UK Biobank sleep and chronotype research group for sharing the summary statistics in their GWAS data sets. We also acknowledge UK biobank resources under application number 65949.

 Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fneur.2022.765321/full#supplementary-material

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