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. 2025 Aug 29;104(35):e43997. doi: 10.1097/MD.0000000000043997

Lipidome profiles and sleep disorders: A Mendelian randomization analysis of insomnia, sleep terrors, sleep apnea, and circadian rhythm disturbances

Jiawei Li a,*, Jiaqi Shi b, Yan Chen c, Ying Guo d
PMCID: PMC12401233  PMID: 40898522

Abstract

Sleep disorders, such as insomnia, sleep terrors, sleep apnea, and sleep-wake schedule disorders, pose a significant public health challenge worldwide, yet their underlying pathophysiological mechanisms are not fully understood. Lipids, beyond being structural membrane components, actively regulate neuroinflammation, circadian rhythms, and neuronal signaling, all implicated in sleep disorder pathophysiology. This study employed two-sample Mendelian randomization (TSMR) to explore the causal relationships between the lipidome and these sleep disorders, analyzing a comprehensive GWAS dataset with 179 lipid species. Heterogeneity and pleiotropy were assessed using Cochran Q test, MR-Egger intercept test, and MR-PRESSO global test, and sensitivity analyses were done to check the influence of individual single nucleotide polymorphisms. The analysis revealed significant causal associations between specific lipid species and sleep disorders. For insomnia, several lipid species, including sterol ester (27:1/20:3), ceramides (d40:1, d42:1, d42:2), phosphatidylcholine (15:0_18:2), and sphingomyelin (d40:1), demonstrated potential protective effects (OR < 1). In contrast, for sleep terrors, phosphatidylcholines (16:0_22:4, O–16:0_16:1, O–16:0_18:2) and sphingomyelin (d34:0) were associated with increased risk (OR > 1), while triacylglycerol (46:2) showed a protective effect. For sleep apnea, cholesterol levels exhibited a protective effect (OR = 0.96), whereas specific phosphatidylcholines (16:1_18:0) and triacylglycerols (52:2, 52:3, 58:8) were associated with increased risk. Circadian rhythm disturbances were influenced by various lipid species, with diacylglycerol (18:1_18:3) and phosphatidylcholine (16:1_18:0) posing risk-increasing effects, while phosphatidylethanolamines (O–16:1_20:4, O–18:1_20:4) demonstrated protective roles. This study elucidates the complex interplay between lipid metabolism and sleep regulation, identifying specific lipid species that may serve as potential biomarkers or therapeutic targets for sleep disorders.

Keywords: insomnia, lipidome, Mendelian randomization, sleep terrors, sleep-wake schedule disorders, sleep apnea, sleep disorders

1. Introduction

Sleep disorders represent a significant public health concern, affecting a substantial portion of the population worldwide.[1,2] These disorders encompass a range of conditions, including insomnia, sleep terrors, sleep apnea, and disorders of the sleep-wake schedule, which collectively impact not only the quality of life but also contribute to various comorbidities such as cardiovascular diseases, metabolic disorders, and mental health issues.[3–5] Despite extensive research into the epidemiology and clinical manifestations of these sleep disorders, the underlying pathophysiological mechanisms remain incompletely understood.[6] This gap in knowledge underscores the need for novel approaches to elucidate the factors contributing to their development and progression.

Lipids are essential biomolecules that play critical roles in various physiological processes, including cell signaling, membrane integrity, energy storage, and metabolism.[7] The lipidome, which refers to the comprehensive profile of lipids in biological systems, has emerged as a promising area of research for understanding the molecular underpinnings of various diseases.[8,9] Lipidomic studies have revealed that subtle differences in lipid species can profoundly influence their biological functions, highlighting the potential for lipids to serve as biomarkers or therapeutic targets.[8,10] Recent advances in lipidomics have shown that lipid profiles can be modulated by genetic factors, lifestyle, and environmental influences, making them potential contributors to the pathogenesis of sleep disorders.[11,12] For instance, alterations in lipid metabolism have been implicated in conditions such as obstructive sleep apnea, where dyslipidaemia is often observed.[13,14] Additionally, lipids are involved in the regulation of circadian rhythms and neuroinflammation, both of which are critical factors in sleep regulation and sleep disorders.[15–17]

Mendelian randomization (MR) is a powerful genetic epidemiological method that leverages genetic variants as IVs to investigate causal relationships between exposures and outcomes.[18] This approach has been widely used to explore the causal effects of various exposures on disease outcomes, providing robust evidence that is less susceptible to confounding compared to traditional observational studies.[18] To date, the causal impact of the lipidome on sleep disorders remains largely unexplored. The objective of this study is to employ two-sample Mendelian randomization (TSMR) to examine the potential causal associations between plasma lipidome and several sleep disorders, including insomnia, sleep terrors, sleep apnea, and disorders of the sleep-wake schedule. By elucidating these relationships, we aim to provide novel insights into the pathophysiology of sleep disorders and identify potential therapeutic targets for their management.

2. Materials and methods

2.1. Study design

The study design is schematically represented in Figure 1. The causal associations between 179 plasma lipid species and 5 sleep disorders were investigated using TSMR analysis. This method employs genetic variants as IVs to infer causal relationships between exposures and outcomes, with the robustness of the findings verified through sensitivity analyses. The validity of the MR results relies on 3 fundamental assumptions: the genetic variants are strongly associated with the lipid species; these variants are not associated with known confounders; and the genetic variants influence sleep disorders exclusively through the lipid species, without direct effects on the sleep disorders themselves.[19]

Figure 1.

Figure 1.

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Schematic representation of the study design for investigating the causal associations between plasma lipidome and sleep disorders using TSMR analysis. TSMR = two-sample Mendelian randomization.

2.2. Data sources

For exposure variables, aggregated statistics were obtained from genome-wide association studies (GWAS) of lipids conducted in 7174 unrelated Finnish individuals within the GeneRISK cohort. These studies examined single nucleotide polymorphisms (SNPs) associated with 13 lipid categories and 179 lipid subclasses (Table S1, Supplemental Digital Content, https://links.lww.com/MD/P809). The data were sourced from the GWAS repository (https://www.ebi.ac.uk/gwas/, GCST90277238 to GCST90277416).[20]

Outcome data for insomnia, sleep terrors, sleep apnea, and disorders of the sleep-wake schedule were retrieved from the Finnish database (https://www.finngen.fi/). Specifically, the insomnia study (ID: finngen_R12_F5_INSOMNIA) included 6776 cases and 490,763 controls from the European population. The sleep terrors study (ID: finngen_R12_F5_SLEEPTERRORS) encompassed 76 cases and 490,763 controls. The sleep apnea study (ID: finngen_R12_G6_SLEEPAPNO) involved 56,885 cases and 441,137 controls. Lastly, the disorder of the sleep-wake schedule study (ID: finngen_R12_F5_SLEEPWAKE) included 633 cases and 490,763 controls.

2.3. Instrumental variable extraction

In MR analysis, the selection of instrumental variables (IVs) is crucial to ensure the reliability of the study outcomes.[21] Given the limited number of IVs meeting the stringent threshold (P < 5 × 10−8), the threshold was adjusted to P < 1 × 10−6 to include a larger set of IVs, thereby enhancing the robustness of the results.[22] Additionally, SNPs were pruned using an r2 threshold of < 0.001 within a 10,000 KB window to minimize linkage disequilibrium (LD) and ensure the independence of each IV. The f-statistic of IVs was calculated to exclude weak IVs, with an f-statistic > 10 indicating no bias due to weak IVs.[23]

2.4. Statistical analysis

All statistical analyses were conducted using R version 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria). The primary method used to estimate the causal effects between plasma lipid species and sleep disorders was the inverse variance weighted (IVW) method, implemented using the “TwoSampleMR” R package (version 0.5.6) available on the MR-Base platform (https://mrcieu.github.io/TwoSampleMR/). The IVW method provides a consistent estimate of causal effects under the assumption that all genetic variants are valid IVs (i.e., no horizontal pleiotropy). It combines the Wald ratio estimates for each SNP by weighting them according to the inverse of their variance, thereby giving more influence to SNPs with greater precision. To assess the robustness of the IVW estimates, we conducted several sensitivity analyses. First, we used alternative MR methods, including MR-Egger regression, weighted median, simple mode, and weighted mode, to evaluate consistency across different modeling assumptions. Second, we performed a leave-one-out analysis, in which each SNP was sequentially excluded to determine whether the overall estimate was driven by any single variant. This approach helps to identify potentially influential SNPs and assess the stability of the causal estimates. Heterogeneity among the IVs was assessed using Cochran Q test,[24,25] and directional pleiotropy was evaluated using the MR-Egger intercept test and the MR-PRESSO global test.[26–28] A P-value > 0.05 in these tests was considered indicative of no significant pleiotropy or heterogeneity. All reported effect estimates are presented as odds ratios (OR) with 95% confidence intervals (CI), and statistical significance was defined as P < .05.

3. Results

3.1. Causal effects of lipidome and insomnia

The study evaluated the causal effect of various lipidome on insomnia using TSMR methodology. For sterol esters, sterol ester (27:1/20:3) levels showed a potential protective effect against insomnia, with an OR of 0.911 (95% CI: 0.852–0.972, P = .005). Among ceramides, ceramide (d40:1) had an OR of 0.931 (95% CI: 0.869–0.997, P = .042), ceramide (d42:1) had an OR of 0.924 (95% CI: 0.859–0.994, P = .034), and ceramide (d42:2) had an OR of 0.930 (95% CI: 0.872–0.992, P = .027), all suggesting possible protective roles. Phosphatidylcholine (15:0_18:2) also exhibited a potential protective effect, with an OR of 0.940 (95% CI: 0.884–0.999, P = .048). Sphingomyelin (d40:1) had an OR of 0.920 (95% CI: 0.871–0.972, P = .003), indicating it might also play a protective role in reducing the risk of insomnia (Fig. 2). Figure S1 (Supplemental Digital Content, https://links.lww.com/MD/P808) presents the outcomes derived from various analytical methods, with the IVW method serving as the primary indicator. The alignment of results across the other 4 methods, coupled with a significance threshold of P < .05, bolsters the confidence in establishing causality. Evaluations using Cochran Q test, MR-Egger intercept test, and MR-PRESSO global test did not reveal any significant concerns. Additionally, sensitivity analyses were conducted using the leave-one-out method to evaluate the influence of individual SNPs on the robustness of the MR estimates (Fig. S2, Supplemental Digital Content, https://links.lww.com/MD/P808).

Figure 2.

Figure 2.

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Forest plot depicting the causal effects of various lipid species on the risk of insomnia. The OR and 95% CI are shown for sterol esters, ceramides, phosphatidylcholines, and sphingomyelins. A value of OR < 1 indicates a potential protective effect, while OR > 1 suggests an increased risk. 95% CI = 95% confidence interval, OR = odds ratio.

3.2. Causal effects of lipidome and sleep terrors

TSMR analysis elucidated significant associations between several lipids and sleep terrors. Among the phosphatidylcholines, phosphatidylcholine (16:0_22:4), phosphatidylcholine (O–16:0_16:1), and phosphatidylcholine (O–16:0_18:2) exhibited notable links. Phosphatidylcholine (16:0_22:4) had an OR of 2.324 (95% CI: 1.269–4.255), phosphatidylcholine (O–16:0_16:1) had an OR of 3.275 (95% CI: 1.449–7.404), and phosphatidylcholine (O–16:0_18:2) had an OR of 2.473 (95% CI: 1.142–5.356). These values suggest that these phosphatidylcholines might increase the risk of sleep terrors. Sphingomyelin (d34:0) also showed a significant association with sleep terrors, with an OR of 1.996 (95% CI: 1.104–3.608), indicating that it could be a potential risk factor. Regarding triacylglycerols, triacylglycerol (46:2) and triacylglycerol (49:2) presented different outcomes. Triacylglycerol (46:2) had an OR of 0.423 (95% CI: 0.205–0.875), suggesting a possible protective effect against sleep terrors. In contrast, triacylglycerol (49:2) had an OR of 2.411 (95% CI: 1.010–5.756), implying that it might enhance the risk of sleep terrors (Fig. 3). Figure S3 (Supplemental Digital Content, https://links.lww.com/MD/P808) presents the outcomes derived from various analytical methods, with the IVW method serving as the primary indicator. The alignment of results across the other 4 methods, coupled with a significance threshold of P < .05, bolsters the confidence in establishing causality. Evaluations using Cochran Q test, MR-Egger intercept test, and MR-PRESSO global test did not reveal any significant concerns. Additionally, sensitivity analyses were conducted using the leave-one-out method to evaluate the influence of individual SNPs on the robustness of the MR estimates (Fig. S4, Supplemental Digital Content, https://links.lww.com/MD/P808).

Figure 3.

Figure 3.

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Forest plot showing the causal associations between lipid species and sleep terrors. The OR and 95% CI are presented for phosphatidylcholines, sphingomyelins, and triacylglycerols. Lipids with OR > 1 are associated with increased risk, whereas those with OR < 1 indicate potential protective effects. 95% CI = 95% confidence interval, OR = odds ratio.

3.3. Causal effects of lipidome and sleep apnea

TSMR analysis revealed significant causal associations between specific lipidome and sleep apnea. Cholesterol levels demonstrated a protective effect against sleep apnea (OR = 0.96, 95% CI: 0.931–0.997, P = .031). Among glycerophospholipids, phosphatidylcholine (16:0_16:0) (OR = 0.95, 95% CI: 0.92–0.97, P < .001), phosphatidylcholine (O–16:0_22:5) (OR = 0.96, 95% CI: 0.93–0.99, P = .016), and phosphatidylcholine (O–18:2_20:4) (OR = 0.97, 95% CI: 0.94–1.00, P = .034) were inversely associated with sleep apnea risk. Conversely, phosphatidylcholine (16:1_18:0) exhibited a risk-increasing effect (OR = 1.05, 95% CI: 1.01–1.08, P = .013). Within the sphingomyelin category, sphingomyelin (d36:2) (OR = 0.95, 95% CI: 0.91–0.99, P = .011) and sphingomyelin (d40:2) (OR = 0.97, 95% CI: 0.94–0.99, P = .007) demonstrated protective effects. Notably, triacylglycerols, including triacylglycerol (52:2) (OR = 1.03, 95% CI: 1.00–1.07, P = .024), triacylglycerol (52:3) (OR = 1.03, 95% CI: 1.01–1.06, P = .020), and triacylglycerol (58:8) (OR = 1.04, 95% CI: 1.01–1.07, P = .005), were consistently associated with elevated risk of sleep apnea. Phosphatidylinositol (18:0_18:1) (OR = 1.03, 95% CI: 1.00–1.06, P = .037) and phosphatidylinositol (18:0_18:2) (OR = 1.03, 95% CI: 1.01–1.04, P = .008) also emerged as risk factors (Fig. 4). Figure S5 (Supplemental Digital Content, https://links.lww.com/MD/P808) presents the outcomes derived from various analytical methods, with the IVW method serving as the primary indicator. The alignment of results across the other 4 methods, coupled with a significance threshold of P < .05, bolsters the confidence in establishing causality. Evaluations using Cochran Q test, MR-Egger intercept test, and MR-PRESSO global test did not reveal any significant concerns. Additionally, sensitivity analyses were conducted using the leave-one-out method to evaluate the influence of individual SNPs on the robustness of the MR estimates (Fig. S6, Supplemental Digital Content, https://links.lww.com/MD/P808).

Figure 4.

Figure 4.

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Forest plot illustrating the causal effects of multiple lipid species on sleep apnea. The OR and 95% CI are displayed for sterol esters, ceramides, phosphatidylcholines, phosphatidylinositols, sphingomyelins, and triacylglycerols. Lipids with OR < 1 suggest protective effects, while OR > 1 indicates increased risk. 95% CI = 95% confidence interval, OR = odds ratio.

3.4. Causal effects of lipidome and disorder of the sleep-wake schedule

Multiple lipid species were found to have significant associations with sleep-wake schedule disorders. Diacylglycerol (18:1_18:3) and Phosphatidylcholine (16:1_18:0) have OR of 1.326 (95% CI: 1.001–1.757) and 1.395 (95% CI: 1.036–1.877) respectively, indicating a potential risk – increasing effect on the disorder. In contrast, Phosphatidylethanolamine (O–16:1_20:4) and Phosphatidylethanolamine (O–18:1_20:4) have ORs of 0.772 (95% CI: 0.596–0.999) and 0.743 (95% CI: 0.590–0.935), suggesting a possible protective effect. Other lipids like Phosphatidylinositol (18:1_18:1), Sphingomyelin (d38:2), Triacylglycerol (50:4), and Triacylglycerol (56:7) also show significant associations with varying ORs, collectively indicating that different lipid species may have diverse impacts on the sleep-wake schedule disorder (Fig. 5). Figure S7 (Supplemental Digital Content, https://links.lww.com/MD/P808) presents the outcomes derived from various analytical methods, with the IVW method serving as the primary indicator. The alignment of results across the other 4 methods, coupled with a significance threshold of P < .05, bolsters the confidence in establishing causality. Evaluations using Cochran Q test, MR-Egger intercept test, and MR-PRESSO global test did not reveal any significant concerns. Additionally, sensitivity analyses were conducted using the leave-one-out method to evaluate the influence of individual SNPs on the robustness of the MR estimates (Fig. S8, Supplemental Digital Content, https://links.lww.com/MD/P808).

Figure 5.

Figure 5.

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Forest plot depicting the causal associations between lipid species and disorders of the sleep-wake schedule. The OR and 95% CI are shown for sterol esters, ceramides, diacylglycerols, phosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, sphingomyelins, and triacylglycerols. Lipids with OR < 1 indicate potential protective effects, whereas OR > 1 suggests increased risk. 95% CI = 95% confidence interval, OR = odds ratio.

4. Discussion

This study represents the first comprehensive investigation into the causal associations between the lipidome and various sleep disorders, including insomnia, sleep terrors, sleep apnea, and disorders of the sleep-wake schedule. By employing a TSMR approach, we aimed to elucidate the potential causal links between 179 plasma lipid species and these sleep disorders, providing novel insights into their pathophysiology and potential therapeutic targets. Our findings revealed significant associations between several lipid species and sleep disorders. For instance, sterol esters, ceramides, and certain phosphatidylcholines were identified as potential protective factors against insomnia and sleep apnea. Conversely, specific phosphatidylcholines and sphingomyelins were associated with increased risks of sleep terrors and sleep apnea. Notably, triacylglycerols exhibited mixed effects, with some species showing protective roles while others increased the risk of sleep disorders. These results also highlight the complex interplay between lipid metabolism and sleep regulation.[15,29] Lipids play crucial roles in various physiological processes, including cell signaling, membrane integrity, and energy metabolism.[30–32] Recent advances in lipidomics have shown that lipid profiles can be modulated by genetic factors, lifestyle, and environmental influences, making them potential contributors to the pathogenesis of sleep disorders.[14] For example, alterations in lipid metabolism have been implicated in obstructive sleep apnea, where dyslipidaemia is often observed.[14] Additionally, lipids are involved in the regulation of circadian rhythms and neuroinflammation, both of which are critical factors in sleep regulation and sleep disorders.[29,33]

In insomnia, our study identified certain sterol esters, ceramides, phosphatidylcholines, and sphingomyelins demonstrated protective effects against insomnia. For instance, sterol ester (27:1/20:3), ceramide (d40:1), ceramide (d42:1), ceramide (d42:2), phosphatidylcholine (15:0_18:2), and sphingomyelin (d40:1) were associated with a reduced risk of insomnia. This finding aligns with previous research suggesting that lipid metabolism may influence sleep quality through neuroinflammatory pathways.[34] Insomnia is often associated with increased oxidative stress and inflammation, and certain lipids may modulate these processes to affect sleep regulation.[34] For instance, ceramides have been shown to influence mitochondrial function and cellular stress responses, which could potentially impact sleep quality.[35,36]

In the context of sleep terrors, our analysis revealed significant associations with several phosphatidylcholines, sphingomyelins, and triacylglycerols. Specifically, phosphatidylcholine (16:0_22:4), phosphatidylcholine (O–16:0_16:1), phosphatidylcholine (O–16:0_18:2), and sphingomyelin (d34:0) were identified as potential risk factors for sleep terrors. In contrast, triacylglycerol (46:2) exhibited a protective effect. Phosphatidylcholines are essential components of cell membranes and play roles in cell signaling and inflammation.[37,38] The identified phosphatidylcholines (e.g., 16:0_22:4, O–16:0_16:1) showed increased risk associations with sleep terrors, suggesting that alterations in membrane fluidity or signaling pathways involving these lipids may contribute to the pathogenesis of this disorder. Similarly, sphingomyelin (d34:0) was identified as a potential risk factor, highlighting the importance of sphingolipid metabolism in sleep regulation.

In sleep apnea, multiple lipid species exhibited significant associations.[14,39,40] Cholesterol showed a protective effect. Among glycerophospholipids, certain phosphatidylcholines (e.g., 16:0_16:0, O–16:0_22:5, O–18:2_20:4) were inversely associated with sleep apnea risk, while phosphatidylcholine (16:1_18:0) increased risk. In sphingomyelins, sphingomyelin (d36:2) and (d40:2) had protective effects. Conversely, triacylglycerols (e.g., 52:2, 52:3, 58:8) were linked to higher sleep apnea risk. Phosphatidylinositols (18:0_18:1 and 18:0_18:2) also emerged as risk factors. These findings highlight the complex interplay between lipid profiles and sleep apnea risk. These findings underscore the complex role of lipid metabolism in sleep apnea. Dyslipidaemia is a common comorbidity in sleep apnea patients,[14,41,42] and our results suggest that specific lipid species may influence the development or progression of this disorder. For example, ceramides have been implicated in the regulation of insulin sensitivity and inflammation, both of which are associated with sleep apnea.[39,41] Additionally, the identified phosphatidylcholines and phosphatidylinositols may influence cellular signaling pathways that contribute to the pathophysiology of sleep apnea.[41,43]

Disorders of the sleep-wake schedule were also associated with multiple lipid species.[44–46] Diacylglycerol (18:1_18:3) and phosphatidylcholine (16:1_18:0) exhibited potential risk-increasing effects, while phosphatidylethanolamine (O–16:1_20:4) and phosphatidylethanolamine (O–18:1_20:4) demonstrated protective effects. Other lipids, such as phosphatidylinositol (18:1_18:1), sphingomyelin (d38:2), triacylglycerol (50:4), and triacylglycerol (56:7), also showed significant associations with varying OR. Circadian regulation of lipid metabolism has been previously reported, with certain lipids showing diurnal variations.[15,47,48] Our results suggest that disruptions in lipid metabolism may contribute to the development of sleep-wake schedule disorders through alterations in circadian signaling pathways.

Despite these novel findings, our study has several limitations. Firstly, the study cohort predominantly comprised individuals of European ancestry, restricting the generalizability of our results to other ethnic groups. Secondly, the use of a screening threshold of P < 1 × 10−6 for lipid-associated SNPs may have introduced some bias, although this approach was necessary to include a sufficient number of IVs. Thirdly, the lack of individual patient data in the GWAS dataset limited our ability to conduct further stratification analyses within the sleep disorder cohorts. Lastly, while our study establishes potential causal associations, it does not elucidate the underlying mechanisms by which lipids influence sleep disorders. Further research is needed to explore these mechanisms and validate our findings in diverse populations.

5. Conclusions

In conclusion, this study offers valuable insights into the causal associations between specific lipid species and sleep disorders. The identified lipid species may serve as potential biomarkers or therapeutic targets for the prevention and management of sleep disorders. Further research is warranted to elucidate the underlying mechanisms by which these lipid species influence sleep disorders, paving the way for the development of more effective treatments and improved patient outcomes.

Author contributions

Conceptualization: Jiawei Li, Jiaqi Shi, Yan Chen.

Data curation: Jiaqi Shi.

Formal analysis: Ying Guo.

Investigation: Jiawei Li.

Methodology: Jiaqi Shi.

Project administration: Jiawei Li.

Supervision: Yan Chen.

Validation: Yan Chen.

Writing – original draft: Ying Guo.

Writing – review & editing: Jiawei Li, Ying Guo.

Supplementary Material

medi-104-e43997-s001.xlsx (14.8KB, xlsx)

Abbreviations:

95% CI
95% confidence interval
GWAS
genome-wide association studies
IVs
instrumental variables
IVW
inverse variance weighted
LD
linkage disequilibrium
MR
Mendelian randomization
OR
odds ratio
SNPs
single nucleotide polymorphisms
TSMR
two-sample Mendelian randomization

This study did not involve human subjects, human tissue, or animal subjects, and thus, no ethical approval was required. The study protocol adhered to the guidelines established by the journal.

The authors have no funding and conflicts of interest to declare.

The datasets generated during and/or analyzed during the current study are publicly available.

Supplemental Digital Content is available for this article.

How to cite this article: Li J, Shi J, Chen Y, Guo Y. Lipidome profiles and sleep disorders: A Mendelian randomization analysis of insomnia, sleep terrors, sleep apnea, and circadian rhythm disturbances. Medicine 2025;104:35(e43997).

JL and JS contributed equally to this work.

All authors have read and agreed to the published version of the manuscript.

Contributor Information

Jiaqi Shi, Email: sjiaqi0607@163.com.

Yan Chen, Email: epi_sleep@163.com.

Ying Guo, Email: hlj_zyy_gy@163.com.

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