基于孟德尔随机化的饮食因素与失眠症状因果关系研究

doi:10.3969/j.issn.1674-8115.2026.06.010

摘要/Abstract

摘要：

目的·通过两样本孟德尔随机化（Mendelian randomization，MR）方法探讨多种饮食因素与失眠症状之间的因果关系。方法·从公开的全基因组关联研究（Genome-Wide Association Studies，GWAS）数据库中提取与多种饮食因素以及失眠症状相关的单核苷酸多态性（single nucleotide polymorphism，SNP）数据作为工具变量。以逆方差加权法作为主要分析方法，同时采用加权中位数法、MR-Egger回归、加权众数法及简单众数法为辅助方法。异质性使用Cochran's Q检验判断，多效性使用MR-Egger截距检验评估，同时使用留一法（leave-one-out）分析单个SNP对结果的影响程度。结果·6种饮食因素与失眠症状存在潜在因果关系，其中酒精（OR=1.07，95%CI 1.01~1.12，P=0.018）和家禽（OR=1.12，95%CI 1.00~1.24，P=0.041）摄入与失眠症状呈正向关联；油性鱼肉（OR=0.91，95%CI 0.84~1.00，P=0.047）、谷物（OR=0.92，95%CI 0.87~0.99，P=0.017）、新鲜水果（OR=0.84，95%CI 0.72~0.98，P=0.022）和水果干（OR=0.77，95%CI 0.69~0.85，P=5.256×10^-7）摄入与失眠症状呈负向关联。经过Bonferroni校正后，水果干摄入与失眠症状之间的因果关系仍具有统计学意义。结论·不同饮食因素与失眠症状之间可能存在因果关联。研究结果为进一步探讨饮食因素在睡眠健康中的作用机制及潜在干预方向提供了遗传学证据。

关键词: 失眠, 饮食因素, 孟德尔随机化研究

Abstract:

Objective ·To investigate the causal relationships between multiple dietary factors and insomnia symptoms by using a two-sample Mendelian randomization (MR) approach. Methods ·Single nucleotide polymorphisms (SNPs) associated with various dietary factors and insomnia symptoms were extracted from publicly available Genome-Wide Association Studies (GWAS) databases and used as instrumental variables. The inverse variance weighted (IVW) method was applied as the primary analysis, complemented by the weighted median, MR-Egger regression, weighted mode, and simple mode methods. Heterogeneity was assessed by using Cochran's Q test, while horizontal pleiotropy was evaluated by using the MR-Egger intercept test. Leave-one-out analyses were conducted to examine the influence of individual SNPs on the overall results. Results ·Six dietary factors were identified as having potential causal associations with insomnia symptoms. Alcohol intake (OR=1.07, 95%CI 1.01‒1.12, P=0.018) and poultry intake (OR=1.12, 95%CI 1.00‒1.24, P=0.041) were positively associated with insomnia symptoms.. In contrast, oily fish (OR=0.91, 95%CI 0.84‒1.00, P=0.047), cereals (OR=0.92, 95%CI 0.87‒0.99, P=0.017), fresh fruit (OR=0.84, 95%CI 0.72‒0.98, P=0.022), and dried fruit (OR=0.77, 95%CI 0.69‒0.85, P=5.256×10^-7) intake were negatively associated with insomnia symptoms. After Bonferroni correction, the causal relationship between dried fruit intake and insomnia symptoms remained statistically significant. Conclusion ·The findings support potential causal associations between different dietary factors and insomnia symptoms, providing genetic evidence for further investigation into the role of dietary factors in sleep health and potential intervention strategies.

Key words: insomnia, dietary factor, Mendelian randomization study

中图分类号:

R749

崔郭宇, 王秋云, 谈皓舟, 杨谦梓, 罗艳. 基于孟德尔随机化的饮食因素与失眠症状因果关系研究[J]. 上海交通大学学报（医学版）, 2026, 46(6): 778-785.

Cui Guoyu, Wang Qiuyun, Tan Haozhou, Yang Qianzi, Luo Yan. Mendelian randomization study on the causal relationship between dietary factors and insomnia symptoms[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2026, 46(6): 778-785.

图/表 4

参考文献 30

[1]	Perlis M L, Posner D, Riemann D, et al. Insomnia[J]. Lancet, 2022, 400(10357): 1047-1060.
[2]	Morin C M, Jarrin D C. Epidemiology of insomnia: prevalence, course, risk factors, and public health burden[J]. Sleep Med Clin, 2022, 17(2): 173-191.
[3]	Wang S C, Zheng X T, Huang J X, et al. Sleep characteristics and risk of Alzheimer's disease: a systematic review and meta-analysis of longitudinal studies[J]. J Neurol, 2024, 271(7): 3782-3793.
[4]	Li L Q, Gan Y, Zhou X G, et al. Insomnia and the risk of hypertension: a meta-analysis of prospective cohort studies[J]. Sleep Med Rev, 2021, 56: 101403.
[5]	Hertenstein E, Feige B, Gmeiner T, et al. Insomnia as a predictor of mental disorders: a systematic review and meta-analysis[J]. Sleep Med Rev, 2019, 43: 96-105.
[6]	Madari S, Golebiowski R, Mansukhani M P, et al. Pharmacological management of insomnia[J]. Neurotherapeutics, 2021, 18(1): 44-52.
[7]	Morin C M, Buysse D J. Management of insomnia[J]. N Engl J Med, 2024, 391(3): 247-258.
[8]	Şahin-Bodur G, Kemaneci S, Tunçer E, et al. Evaluation of the relationship between the Mediterranean diet adherence and sleep quality in adults[J]. Sleep Breath, 2024, 28(1): 511-518.
[9]	Vlahoyiannis A, Giannaki C D, Sakkas G K, et al. A systematic review, meta-analysis and meta-regression on the effects of carbohydrates on sleep[J]. Nutrients, 2021, 13(4): 1283.
[10]	Gangwisch J E, Hale L, St-Onge M P, et al. High glycemic index and glycemic load diets as risk factors for insomnia: analyses from the Women's Health Initiative[J]. Am J Clin Nutr, 2020, 111(2): 429-439.
[11]	Larsson S C, Butterworth A S, Burgess S. Mendelian randomization for cardiovascular diseases: principles and applications[J]. Eur Heart J, 2023, 44(47): 4913-4924.
[12]	Richmond R C, Davey Smith G. Mendelian randomization: concepts and scope[J]. Cold Spring Harb Perspect Med, 2022, 12(1): a040501.
[13]	Elsworth B, Lyon M, Alexander T, et al. The MRC IEU OpenGWAS data infrastructure[EB/OL]. [2025-08-10]. https://www.biorxiv.org/content/10.1101/2020.08.10.244293v1.
[14]	Lyon M S, Andrews S J, Elsworth B, et al. The variant call format provides efficient and robust storage of GWAS summary statistics[J]. Genome Biol, 2021, 22(1): 32.
[15]	Liu M Z, Jiang Y, Wedow R, et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use[J]. Nat Genet, 2019, 51(2): 237-244.
[16]	Pirastu N, McDonnell C, Grzeszkowiak E J, et al. Using genetic variation to disentangle the complex relationship between food intake and health outcomes[J]. PLoS Genet, 2022, 18(6): e1010162.
[17]	Bowden J, Spiller W, Del Greco M F, et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression[J]. Int J Epidemiol, 2018, 47(6): 2100.
[18]	Verbanck M, Chen C Y, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases[J]. Nat Genet, 2018, 50(5): 693-698.
[19]	Davies N M, Holmes M V, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians[J]. BMJ, 2018, 362: k601.
[20]	Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression[J]. Int J Epidemiol, 2015, 44(2): 512-525.
[21]	Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome[J]. eLife, 2018, 7: e34408.
[22]	Tan X, Alén M, Wang K, et al. Effect of six-month diet intervention on sleep among overweight and obese men with chronic insomnia symptoms: a randomized controlled trial[J]. Nutrients, 2016, 8(11): 751.
[23]	Golmohammadi M, Attari V E, Salimi Y, et al. The effect of MIND diet on sleep status, mental health, and serum level of BDNF in overweight/obese diabetic women with insomnia: a randomized controlled trial[J]. Sci Rep, 2025, 15(1): 8237.
[24]	Zhang C, Du X X. Application of Mendelian randomization to explore the genetic association between drinking habits of different beverages and sleep disorder[J]. Arch Med Sci, 2025, 21(4): 1310-1322.
[25]	Sekula P, Del Greco M F, Pattaro C, et al. Mendelian randomization as an approach to assess causality using observational data[J]. J Am Soc Nephrol, 2016, 27(11): 3253-3265.
[26]	Li S Y, Ma M W, Hu L M, et al. Association between fish consumption and sleep disorders among Chinese adults: a cross-sectional study[J]. Ann Med, 2025, 57(1): 2491663.
[27]	Meng X, Li Y, Li S, et al. Dietary sources and bioactivities of melatonin[J]. Nutrients, 2017, 9(4): 367.
[28]	Maruani J, Reynaud E, Chambe J, et al. Efficacy of melatonin and ramelteon for the acute and long-term management of insomnia disorder in adults: a systematic review and meta-analysis[J]. J Sleep Res, 2023, 32(6): e13939.
[29]	Jansen E C, She R C, Rukstalis M, et al. Changes in fruit and vegetable consumption in relation to changes in sleep characteristics over a 3-month period among young adults[J]. Sleep Health, 2021, 7(3): 345-352.
[30]	Reed Z E, Wootton R E, Khouja J N, et al. Exploring pleiotropy in Mendelian randomisation analyses: what are genetic variants associated with 'cigarette smoking initiation' really capturing?[J]. Genet Epidemiol, 2025, 49(1): e22583.

Category	Trait	Population	Sample size/n	SNP number/n	Open GWAS ID
Liquids	Alcoholic drinks per week	European	335 394	11 887 865	ieu-b-73
	Water consumption	European	445 799	5 733 790	ebi-a-GCST90096929
	Tea consumption	European	434 171	5 733 790	ebi-a-GCST90096926
	Milk intake	European	64 943	9 851 867	ukb-b-2966
	Yogurt intake	European	64 949	9 851 867	ukb-b-7753
	Coffee intake	European	428 860	9 851 867	ukb-b-5237
Meat	Pork consumption	European	187 202	5 733 790	ebi-a-GCST90096919
	Beef consumption	European	241 092	5 733 790	ebi-a-GCST90096901
	Lamb consumption	European	189 984	5 733 790	ebi-a-GCST90096915
	Poultry intake	European	461 900	9 851 867	ukb-b-8006
	Oily fish consumption	European	297 881	5 733 790	ebi-a-GCST90096918
	Non-oily fish consumption	European	318 136	5 733 790	ebi-a-GCST90096917
Staple foods	White rice intake	European	64 949	9 851 867	ukb-b-14690
Grains	Cereal intake	European	441 640	9 851 867	ukb-b-15926
Fruit	Fresh fruit consumption	European	433 186	5 733 790	ebi-a-GCST90096912
Fruit	Dried fruit consumption	European	409 125	5 733 790	ebi-a-GCST90096909
Vegetable	Cooked vegetable intake	European	448 651	9 851 867	ukb-b-8089
Vegetable	Salad/raw vegetable intake	European	435 435	9 851 867	ukb-b-1996
Nuts	Salted nuts intake	European	64 949	9 851 867	ukb-b-15960
Nuts	Unsalted nuts intake	European	64 949	9 851 867	ukb-b-12217
Outcome	Insomnia	European	462 341	9 851 867	ukb-b-3957

Category	Trait	Population	Sample size/n	SNP number/n	Open GWAS ID
Liquids	Alcoholic drinks per week	European	335 394	11 887 865	ieu-b-73
	Water consumption	European	445 799	5 733 790	ebi-a-GCST90096929
	Tea consumption	European	434 171	5 733 790	ebi-a-GCST90096926
	Milk intake	European	64 943	9 851 867	ukb-b-2966
	Yogurt intake	European	64 949	9 851 867	ukb-b-7753
	Coffee intake	European	428 860	9 851 867	ukb-b-5237
Meat	Pork consumption	European	187 202	5 733 790	ebi-a-GCST90096919
	Beef consumption	European	241 092	5 733 790	ebi-a-GCST90096901
	Lamb consumption	European	189 984	5 733 790	ebi-a-GCST90096915
	Poultry intake	European	461 900	9 851 867	ukb-b-8006
	Oily fish consumption	European	297 881	5 733 790	ebi-a-GCST90096918
	Non-oily fish consumption	European	318 136	5 733 790	ebi-a-GCST90096917
Staple foods	White rice intake	European	64 949	9 851 867	ukb-b-14690
Grains	Cereal intake	European	441 640	9 851 867	ukb-b-15926
Fruit	Fresh fruit consumption	European	433 186	5 733 790	ebi-a-GCST90096912
Fruit	Dried fruit consumption	European	409 125	5 733 790	ebi-a-GCST90096909
Vegetable	Cooked vegetable intake	European	448 651	9 851 867	ukb-b-8089
Vegetable	Salad/raw vegetable intake	European	435 435	9 851 867	ukb-b-1996
Nuts	Salted nuts intake	European	64 949	9 851 867	ukb-b-15960
Nuts	Unsalted nuts intake	European	64 949	9 851 867	ukb-b-12217
Outcome	Insomnia	European	462 341	9 851 867	ukb-b-3957

Category	Exposure	IV number/n	β	SE	P value	OR (95%CI)
Liquids	Alcoholic drinks per week	34	0.064	0.027	0.018	1.07 (1.01‒1.12)
	Water consumption	56	-0.004	0.015	0.770	1.00 (0.97‒1.02)
	Tea consumption	26	-0.042	0.066	0.520	0.96 (0.84‒1.09)
	Milk intake	6	-0.019	0.049	0.690	0.98 (0.89‒1.08)
	Yogurt intake	3	0.019	0.048	0.699	1.02 (0.93‒1.12)
	Coffee intake	39	0.004	0.029	0.905	1.00 (0.95‒1.06)
Meat	Pork consumption	5	0.348	0.418	0.405	1.42 (0.62‒3.21)
	Beef consumption	6	-0.214	0.409	0.601	0.81 (0.36‒1.80)
	Lamb consumption	20	0.155	0.229	0.501	1.17 (0.74‒1.83)
	Poultry intake	7	0.112	0.055	0.041	1.12 (1.00‒1.24)
	Oily fish consumption	42	-0.090	0.045	0.047	0.91 (0.84‒1.00)
	Non-oily fish consumption	5	-0.239	0.186	0.198	0.79 (0.55‒1.13)
Staple foods	White rice intake	3	0.005	0.137	0.972	1.00 (0.77‒1.32)
Grains	Cereal intake	39	-0.078	0.033	0.017	0.92 (0.87‒0.99)
Fruits	Fresh fruit consumption	47	-0.178	0.078	0.022	0.84 (0.72‒0.98)
Fruits	Dried fruit consumption	41	-0.267	0.053	5.256×10^-7	0.77 (0.69‒0.85)
Vegetables	Cooked vegetable intake	17	-0.052	0.089	0.559	0.95 (0.80‒1.13)
Vegetables	Salad/raw vegetable intake	18	0.064	0.066	0.332	1.07 (0.94‒1.21)
Nuts	Salted nuts intake	7	-0.012	0.067	0.855	0.99 (0.87‒1.13)

Category	Exposure	IV number/n	β	SE	P value	OR (95%CI)
Liquids	Alcoholic drinks per week	34	0.064	0.027	0.018	1.07 (1.01‒1.12)
	Water consumption	56	-0.004	0.015	0.770	1.00 (0.97‒1.02)
	Tea consumption	26	-0.042	0.066	0.520	0.96 (0.84‒1.09)
	Milk intake	6	-0.019	0.049	0.690	0.98 (0.89‒1.08)
	Yogurt intake	3	0.019	0.048	0.699	1.02 (0.93‒1.12)
	Coffee intake	39	0.004	0.029	0.905	1.00 (0.95‒1.06)
Meat	Pork consumption	5	0.348	0.418	0.405	1.42 (0.62‒3.21)
	Beef consumption	6	-0.214	0.409	0.601	0.81 (0.36‒1.80)
	Lamb consumption	20	0.155	0.229	0.501	1.17 (0.74‒1.83)
	Poultry intake	7	0.112	0.055	0.041	1.12 (1.00‒1.24)
	Oily fish consumption	42	-0.090	0.045	0.047	0.91 (0.84‒1.00)
	Non-oily fish consumption	5	-0.239	0.186	0.198	0.79 (0.55‒1.13)
Staple foods	White rice intake	3	0.005	0.137	0.972	1.00 (0.77‒1.32)
Grains	Cereal intake	39	-0.078	0.033	0.017	0.92 (0.87‒0.99)
Fruits	Fresh fruit consumption	47	-0.178	0.078	0.022	0.84 (0.72‒0.98)
Fruits	Dried fruit consumption	41	-0.267	0.053	5.256×10^-7	0.77 (0.69‒0.85)
Vegetables	Cooked vegetable intake	17	-0.052	0.089	0.559	0.95 (0.80‒1.13)
Vegetables	Salad/raw vegetable intake	18	0.064	0.066	0.332	1.07 (0.94‒1.21)
Nuts	Salted nuts intake	7	-0.012	0.067	0.855	0.99 (0.87‒1.13)