Атлас о факторах риска для множественного склероза: Менделианское исследование случайности.

Introduction

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system and a leading non-traumatic cause of disability among young adults of northern European ancestry [ 1 ]. Even though epidemiological studies have uncovered several modifiable risk factors for MS, such as serum vitamin D levels [ 2 ] and body mass index [ 3 ], the overall etiological basis of MS is poorly understood [ 4 ]. A recent umbrella review found consistent evidence supporting the associations of Epstein-Barr virus infection and smoking with MS risk [ 1 ]. However, the role of other environmental factors and internal conditions for MS risk have been scarcely investigated. In addition, it is unclear whether the associations reported by traditional observational studies are causal due to potential confounding, reverse causality and misclassification of such studies.

Mendelian randomization (MR) is an analytical approach that utilizes genetic variants, generally single nucleotide polymorphisms (SNPs), as instrumental variables for an exposure to diminish confounding and reserve causality, thereby strengthening the causal inference of an exposure-outcome association [ 5 ]. The rationale of minimizing confounding in MR studies is that genetic variants are randomly allocated at meiosis, and therefore, one trait is generally unrelated to other traits. Reverse causality can be avoided since genetic variants are fixed and, therefore, cannot be modified by disease onset and progression [ 5 ]. There are three key assumptions for MR analysis [ 5 ]. First, the genetic variants proposed as instrumental variables should be associated with the risk factor of interest. Second, the used genetic variants should not be associated with potential confounders. Third, the selected genetic variants should affect the risk of the outcome (e.g. MS) merely through the risk factor. Exploiting different summary genetic sources for an exposure and outcome, the two-sample MR approach infer the exposure-outcome causality with improve statistical power and less confounding bias [ 6 ].

The aim of the present study was to systematically appraise the evidence of causal associations between possible risk factors and MS using the two-sample MR design. For exposures that cannot be instrumented by genetic variants, we additionally obtained data from the latest meta-analyses through a systematic review of the literature.

Methods

Study design overview and potential risk factor identification

The overview of the study design is displayed in Fig.  1 . First, we conducted a systematic review in the PubMed database to identify possible risk factors for MS. In total, 1863 studies published in recent 5 years were screened, and 87 general risk factors were pinpointed (Supplementary Table 1). After excluding traits without suitable genetic instruments or limited genetic instruments (SNPs < 3), a total of 65 possible risk factors were included in the MR analyses. In addition, we included 18 risk factors in the systematic review.

Overview of study design

Mendelian randomization study

Instrumental variable selection

Instrumental variables for the 65 exposures were identified from genome-wide association studies (GWASs). SNPs at the genome-wide significance threshold ( p  < 5 × 10 –8 ) were proposed as instrumental variables. To mitigate against co-linearity between included SNPs, we excluded SNPs in linkage disequilibrium ( R 2  ≥ 0.01) and retained SNPs with the strongest effect on the associated trait. SNPs in the MHC gene region, which is strongly associated with MS, were removed from analyses to exclude possible pleiotropy. The variance explained by used SNPs for individual risk factor was either extracted from the original GWAS or estimated based on minor allele frequency, beta coefficient for minor allele and standard deviation (SD) for the risk factor. Information of the data sources as well as the number of SNPs used, and the variance explained by the SNPs is presented in Table 1 . Other information, such as instrumental variable selection and unit for each trait, is available in Supplementary Table 2.

Table 1 Data sources and instrumental variables used for exposures included in the MR analyses Exposure Cases or sample size Controls Population SNPs Used SNPs c Variance (%) d F statistic PubMed ID Psychiatric factor  Lifetime anxiety disorder 25 453 58 113 European 5 5 0.5 e 84 BioRxiv  Major depressive disorder 414 055 892 299 European 95 91 1.8 d 252 30718901  Sleep duration 446 118 NA European 76 3 0.7 e 41 30846698  Short sleep (< 7 h) 106 192 305 742 European 26 1 1.2 d 192 30846698  Long sleep (> 9 h) 34 184 305 742 European 6 6 0.7 d 399 30846698  Insomnia 397 972 933 038 European 238 206 2.6 e 149 30804565  Morningness 372 765 278 530 European 329 313 11.9 d 267 30696823  Restless leg syndrome 15 126 95 725 European 20 20 11.7 e 734 29029846 Autoimmune disorder  Type 1 diabetes 9934 16 956 European 18 17 6.7 e 107 21980299  Latent autoimmune diabetes in adults 2634 5947 European 3 3 0.7 d 20 30254083  Allergic rhinitis 59 762 152 358 European 33 32 4.7 d 317 30013184  Asthma 10 549 47 146 European 18 16 13.7 d 509 30552067  Eczema (atopic dermatitis) 18 900 84 166 European 20 19 7.2 d 400 26482879  Rheumatoid arthritis 18 136 49 724 European 27 24 8.1 d 221 24390342 Cardiometabolic factor  Type 2 diabetes 74 124 824 006 European 179 160 1.0 d 51 30297969  Fasting glucose a 133 010 NA European 35 35 4.8 d 192 22885924  Fasting insulin a 133 010 NA European 18 18 1.0 d 75 22885924  Hemoglobin A1c a up to 159 940 NA Mixed 48 45 4.7 e 164 28898252 Hemoglobin a 135 367 NA Mixed 25 17 NA NA 23222517 Diastolic blood pressure  > 1 million NA Mixed 271 262 5.3 e 21 30224653 Systolic blood pressure  > 1 million NA Mixed 229 222 5.7 e 26 30224653 Coronary artery disease 60 801 123 504 Mixed 45 43 1.7 e 71 26343387 Peripheral artery disease 36 424 601 044 European 18 17 3.5 d 1284 31285632 Obesity-related factor  Birth weight up to 500 000 NA European 92 92 2.5 d 139 30305743  Childhood body mass index 35 668 NA Mixed 15 15 2.2 d 53 26604143  Adulthood body mass index 250 000 NA European 963 963 7.8 d 22 30124842  Waist circumference 500 000 NA European 314 314 4.6 d 77 30305743  Lean body mass 47 227 NA European 7 7 4.6 d 325 30721968  Body fat percentage up to 500 000 NA European 364 364 5.3 d 77 30305743  Circulating adiponectin a 45 891 NA Mixed 10 10 NA NA 22479202 Hormone-related factor  Age of puberty up to 370 000 NA Mixed 339 315 7.4 e 87 28436984  Age of natural menopause up to 70 000 NA European 42 42 NA NA 26414677 Other factors  Migraine 59 674 316 078 European 33 33 4.4 d 524 27322543  Migraine without aura 2326 4580 European 6 7.8 d 97 22683712  Estimated bone mineral density 426 824 NA European 993 786 20.7 e 112 30598549  Uric acid a 288 649 NA European 123 94 5.3 e 131 31578528 Amino acid  Carnitine a 7824 NA European 18 18 13.8 d 69 24816252  Homocysteine a 44 147 NA European 16 13 3.3 d 94 23824729  Isoleucine a 16 596 NA European 4 4 1.1 d 46 27898682 Plasma fatty acid  Docosapentaenoic acid 8866 NA European 3 3 3.2 d 98 21829377  Linoleic acid 8631 NA European 3 3 2.1 d 62 24823311  Palmitoleic acid 8961 NA European 5 5 3.5 d 65 23362303  Stearic acid 8964 NA European 3 3 2.1 d 64 23362303 Mineral  Calcium a 39 400 NA European 7 7 0.9 d 51 24068962 Magnesium a 15 366 NA European 6 6 1.6 d 42 20700443 Sodium b 446 237 NA European 47 45 NA NA 31409800 Potassium b 446 238 NA European 12 12 NA NA 31409800 Iron a 48 972 NA European 5 5 3.4 d 345 25352340 Vitamin  Folate (vitamin B9) a 37 341 NA Mixed 3 3 0.8 d 100 23754956  Vitamin B12 a 45 576 NA Mixed 10 9 4.5 d 215 23754956  Vitamin D a 121 640 NA European 7 7 5.3 d 972 29343764  Vitamin E a 5006 NA European 3 3 1.7 e 29 21729881 Lifestyle factor  Alcohol drinking 941 280 NA European 87 81 2.5 e 277 30643251  Coffee consumption 375 833 NA European 15 12 0.5 e 126 31046077  Smoking initiation 1 232 091 NA European 349 307 1.0 e 36 30643251  Physical activity 377 234 NA European 6 6 0.8 d 507 29899525 Serum lipids  High-density lipoprotein cholesterol a 188 577 NA Mixed 68 68 1.6 d 45 24097068  Low-density lipoprotein cholesterol a 188 577 NA Mixed 58 58 2.4 d 80 24097068  Total cholesterol a 188 577 NA Mixed 74 74 2.6 d 68 24097068 Triglycerides a 188 577 NA Mixed 37 37 2.1 d 109 24097068 Inflammatory biomarker  Tumor necrosis factor a 30 912 NA European 3 3 0.6 e 62 –  C-reactive protein a 204 402 NA European 55 55 7.0 e 280 30388399  Immunoglobulin E a 6819 NA European 3 3 1.6 e 37 22075330 Socioeconomic status  Educational level 1 131 881 NA European 1197 789 12.0 d 129 30038396  Intelligence 269 867 NA European 230 201 5.2 d 64 29942086 Variables without controls information are continuous variables NA not available; SNP single nucleotide polymorphism a Measurement of these indicators was based on serum levels b Measurement of these indicators was based on urinary levels c Numbers of SNPs used in the present Mendelian randomization analyses d Variance estimation was based on the formula R 2  = 2 × MAF × (1 − MAF) × (beta/SD) 2  (MAF indicates minor allele frequency; beta estimation was based on MAF; and SD was one for continuous traits with SD unit or binary traits without variance information in original genome-wide association studies e For continuous traits that were not scaled into SD unit, variance explained was extracted from the original genome-wide association studies. For binary traits with variance information in original genome-wide association studies, variance was extracted from the paper directly

Data sources and instrumental variables used for exposures included in the MR analyses

Variables without controls information are continuous variables

NA not available; SNP single nucleotide polymorphism

a Measurement of these indicators was based on serum levels

b Measurement of these indicators was based on urinary levels

c Numbers of SNPs used in the present Mendelian randomization analyses

d Variance estimation was based on the formula R 2  = 2 × MAF × (1 − MAF) × (beta/SD) 2  (MAF indicates minor allele frequency; beta estimation was based on MAF; and SD was one for continuous traits with SD unit or binary traits without variance information in original genome-wide association studies

e For continuous traits that were not scaled into SD unit, variance explained was extracted from the original genome-wide association studies. For binary traits with variance information in original genome-wide association studies, variance was extracted from the paper directly

Multiple sclerosis genotyping data

Summary-level statistics for the associations of 65 risk factor-associated SNPs with MS were extracted from the discovery stage of a GWAS with 14 498 MS cases and 24 091 controls of European ancestry from 11 countries [ 7 ]. Beta and standard error for identified SNPs had been obtained by logistic regression analysis with adjustment for five population principal components. MS cases were diagnosis by Neurologists familiar with multiple sclerosis in accordance with recognised diagnostic criteria that employ a combination of clinical and laboratory-based para-clinical information. Detailed cases ascertainment criteria in every included area were specified in the published GWAS of MS [ 7 ]. Overall and country-specific disease-related features, such as sex ratio, onset age, age at examination and disease severity, is displayed in Supplementary Table 3.

Statistical analysis

The association between individual risk factor and MS risk attributable to each SNP was estimated with the Wald method. The ratio estimates for every used SNPs for one trait were combined by using the multiplicative random-effects inverse-variance weighted (IVW) meta-analysis method [ 8 ]. We used the weighted median approach as sensitivity analysis, which can provide a consistent estimate with the prerequisite that more than 50% of the weight in the analysis comes from valid instrumental variables [ 8 ]. The MR-PRESSO approach was used to correct for possible pleiotropic effects. The MR-PRESSO test detects possible outliers and provides estimates after removal of outliers, thereby correcting for horizontal pleiotropy [ 9 ]. Heterogeneity across used SNPs for a trait was measured by the Cochranes’s Q statistic and possible pleiotropy was detected by MR-Egger regression model with p for intercept ≤ 0.05 [ 8 ]. To assess the strength of the instrumental variables, F-statistics was estimated based on sample size, numbers of SNPs used, and variance explained by included SNPs [ 10 ]. Power estimation was based on a web-tool [ 11 ] and is shown in Supplementary Table 2. Odds ratios (ORs) and 95% confidence intervals (CIs) of MS were scaled to one-unit increase in corresponding units for different traits. All statistical analyses were two-sided and performed using the mrrobust package in Stata/SE 15.0 and TwoSampleMR in R 3.6.0 software. Associations with p value < 0.05 in both IVW-random effects and MR-PRESSO models were deemed as robust associations and associations with p  < 0.05 in either IVW-random effects or MR-PRESSO model and in the same direction across all analyses were regarded as suggestive associations.

Systematic review

With regard to risk factors not suitable for MR analysis, we conducted systematic reviews to obtain the latest meta-analysis including the most studies. Systematic reviews were carried out on 18 risk factors. We extracted published information, number of included studies, sample size and risk estimates. Detailed information and search strategies are documented in Supplementary Table 4.

Results

Mendelian randomization

Among 65 possible risk factors, four traits, including childhood and adulthood body mass index, serum 25-hydroxyvitamin D and physical activity, were robustly associated with risk of MS. There were suggestive associations with 5 risk factors, including type 2 diabetes, waist circumference, body fat percentage, age of puberty and high-density lipoprotein cholesterol.

Health status

Six out of 36 health status-related risk factors were associated with MS (Table 2 ). Specifically, liability to type 2 diabetes, childhood and adulthood body mass index, waist circumference and body fat percentage were positively associated with MS risk, whereas age of puberty was inversely associated with risk. Even though there was heterogeneity in the above analyses, no indication of pleiotropy was revealed in MR-Egger regression analysis (all p  > 0.05). The other 30 factors showed limited evidence for an association with MS risk.

Table 2 Associations between exposures and multiple sclerosis in Mendelian randomization analyses IVW-random effects method Weighted median method MR-PRESSO Cochrane’s Q P het P intercept Exposure OR 95% CI p OR 95% CI p OR 95% CI p Psychiatric factor  Lifetime anxiety disorder 0.95 0.76–1.18 0.617 1.03 0.86–1.25 0.720 1.02 0.85–1.22 0.840 13.0 0.011 0.673  Major depressive disorder 1.03 0.79–1.35 0.812 0.99 0.75–1.31 0.948 1.04 0.83–1.29 0.753 220.2  < 0.001 0.723  Sleep duration in hour 1.00 0.99–1.01 0.970 1.00 0.99–1.01 0.523 1.00 0.99–1.01 0.846 138.4  < 0.001 0.378  Short sleep (< 7 h) 1.31 0.99–1.74 0.055 1.06 0.76–1.48 0.741 1.31 0.99–1.74 0.068 39.7 0.023 0.621  Long sleep (> 9 h) 1.09 0.72–1.65 0.677 1.15 0.81–1.63 0.429 1.03 0.78–1.36 0.848 12.7 0.026 0.237  Insomnia 1.00 0.88–1.13 0.950 1.06 0.97–1.15 0.235 1.07 1.00–1.15 0.050 1014.8  < 0.001 0.809  Morningness 0.98 0.85–1.13 0.780 0.92 0.82–1.02 0.111 0.95 0.87–1.03 0.189 1282.4  < 0.001 0.134  Restless leg syndrome 0.98 0.93–1.04 0.535 1.00 0.92–1.08 0.985 0.98 0.93–1.04 0.542 20.2 0.384 0.561 Autoimmune disorder  Type 1 diabetes 1.09 0.93–1.29 0.288 1.00 0.91–1.09 0.963 1.05 0.99–1.13 0.162 192.2  < 0.001 0.318  Latent autoimmune diabetes in adults 0.99 0.59–1.64 0.962 1.07 0.97–1.17 0.173 – – – 89.5  < 0.001 0.268  Allergic rhinitis 0.85 0.59–1.22 0.365 1.00 0.82–1.21 0.972 0.88 0.77–1.02 0.100 324.5  < 0.001 0.361  Asthma 0.96 0.82–1.12 0.598 0.97 0.87–1.08 0.554 0.94 0.86–1.03 0.190 88.8  < 0.001 0.089  Eczema (atopic dermatitis) 1.33 0.81–2.18 0.256 1.06 0.88–1.28 0.532 1.31 1.09–1.57 0.018 489.4  < 0.001 0.497  Rheumatoid arthritis 1.05 0.87–1.27 0.598 0.95 0.82–1.09 0.437 1.02 0.90–1.15 0.801 147.2  < 0.001 0.050 Cardiometabolic factor  Type 2 diabetes 1.08 0.99–1.19 0.089 1.11 1.01–1.21 0.029 1.10 1.04–1.16 5.7 × 10 –4 737.9  < 0.001 0.978  Fasting glucose 1.14 0.90–1.46 0.282 1.01 0.80–1.27 0.956 1.14 0.85–1.53 0.405 93.0  < 0.001 0.72  Fasting insulin 1.08 0.72–1.61 0.721 1.12 0.72–1.72 0.620 0.90 0.26–3.09 0.743 31.4 0.018 0.268  Hemoglobin A1c 0.86 0.51–1.47 0.590 0.92 0.56–1.53 0.761 1.15 0.76–1.74 0.518 109.7  < 0.001 0.973  Hemoglobin 1.24 0.83–1.84 0.294 1.41 1.02–1.94 0.038 1.41 1.02–1.93 0.061 75.3  < 0.001 0.749  Diastolic blood pressure 1.00 0.98–1.02 0.978 1.00 0.97–1.02 0.906 1.00 0.98–1.02 0.698 476.6  < 0.001 0.519  Systolic blood pressure 1.00 0.98–1.01 0.681 0.99 0.98–1.01 0.386 1.00 0.98–1.01 0.445 340.1  < 0.001 0.475  Coronary artery disease 0.99 0.90–1.09 0.917 1.00 0.92–1.09 0.976 0.97 0.90–1.04 0.358 140.1  < 0.001 0.822  Peripheral artery disease 1.17 0.92–1.49 0.214 1.16 0.93–1.43 0.183 1.08 0.91–1.29 0.387 51.1  < 0.001 0.745 Obesity-related factor  Birthweight 0.94 0.68–1.30 0.700 0.97 0.76–1.23 0.787 0.97 0.79–1.20 0.773 433.0  < 0.001 0.718  Childhood body mass index 1.23 1.05–1.43 0.010 1.20 0.97–1.48 0.089 1.22 1.07–1.40 0.011 9.4 0.743 0.862  Adulthood body mass index 1.27 1.1.5–1.41 2 × 10 –6 1.25 1.08–1.44 0.003 1.28 1.16–1.40 2.9 × 10 –7 1500.9  < 0.001 0.452  Waist circumference 1.20 0.97–1.48 0.096 1.27 1.06–1.53 0.010 1.30 1.14–1.47 7.0 × 10 –5 1239.9  < 0.001 0.753  Lean body mass 1.06 0.95–1.18 0.311 1.02 0.90–1.15 0.756 1.05 0.94–1.17 0.464 14.7 0.023 0.843  Body fat percentage 1.18 0.97–1.43 0.089 1.27 1.08–1.50 0.004 1.26 1.12–1.43 2.1 × 10 –4 1345.1  < 0.001 0.909  Circulating adiponectin 1.00 0.83–1.20 0.982 1.03 0.81–1.31 0.807 1.00 0.83–1.20 0.982 9.8 0.369 0.970 Hormone-related factor  Age of puberty 0.90 0.80–1.01 0.073 0.97 0.88–1.08 0.613 0.90 0.85–0.97 0.004 1224.3  < 0.001 0.128  Age of natural menopause 0.98 0.93–1.03 0.395 1.00 0.96–1.05 0.943 0.99 0.96–1.03 0.771 126.7  < 0.001 0.464 Other factors  Migraine 1.09 0.93–1.28 0.267 1.10 0.92–1.31 0.283 1.08 0.93–1.24 0.317 64.2  < 0.001 0.838  Migraine without aura 1.03 0.93–1.14 0.574 1.02 0.91–1.14 0.789 0.180  Estimated bone mineral density 1.02 0.95–1.10 0.569 1.11 0.98–1.25 0.089 1.05 0.98–1.12 0.164 1340.7  < 0.001 0.633  Uric acid 1.03 0.92–1.14 0.633 1.00 0.92–1.09 0.952 1.01 0.93–1.10 0.743 265.7  < 0.001 0.621 Amino acid  Carnitine 0.99 0.90–1.09 0.898 1.07 0.98–1.16 0.120 1.03 0.98–1.08 0.313 42.2  < 0.001 0.206  Homocysteine 0.87 0.68–1.12 0.289 0.87 0.69–1.09 0.227 0.93 0.80–1.08 0.374 24.7 0.010 0.614  Isoleucine 0.98 0.69–1.38 0.892 1.03 0.76–1.39 0.844 0.98 0.69–1.38 0.892 7.8 0.050 0.579 Plasma fatty acid  Docosapentaenoic acid 1.03 0.71–1.50 0.867 1.05 0.72–1.53 0.805 – – – 1.2 0.544 0.476  Linoleic acid 0.99 0.98–1.02 0.762 0.99 0.97–1.02 0.552 – – – 2.9 0.235 0.527  Palmitoleic acid 0.96 0.82–1.11 0.571 0.92 0.77–1.10 0.352 0.96 0.82–1.11 0.571 4.9 0.296 0.472  Stearic acid 1.09 0.96–1.21 0.194 1.06 0.93–1.20 0.418 – – – 1.3 0.522 0.584 Mineral  Calcium 0.78 0.33–1.83 0.564 0.88 0.50–1.54 0.644 0.98 0.54–1.77 0.940 18.9 0.004 0.741  Magnesium 1.16 0.94–1.44 0.172 1.19 0.91–1.54 0.199 1.16 0.94–1.44 0.172 1.7 0.789 0.412  Sodium 2.16 1.00–4.68 0.050 1.41 0.56–3.55 0.461 2.16 1.00–4.68 0.050 78.9  < 0.001 0.642  Potassium 0.74 0.13–4.23 0.736 1.39 0.24–8.04 0.713 1.16 0.30–4.50 0.836 22.5 0.021 0.113  Iron 1.10 0.81–1.50 0.158 0.92 0.77–1.11 0.395 1.04 0.73–1.48 0.846 31.8  < 0.001 0.949 Vitamin  Folate (vitamin B9) 1.48 0.86–2.54 0.153 1.47 1.01–2.16 0.047 – – – 4.4 0.037 0.576  Vitamin B12 0.91 0.72–1.16 0.446 0.90 0.73–1.11 0.337 0.94 0.72–1.23 0.668 28.4  < 0.001 0.183  Vitamin D 0.77 0.65–0.93 0.005 0.83 0.72–0.95 0.006 0.85 0.78–0.94 0.029 17.3 0.004 0.981  Vitamin E 0.75 0.34–1.66 0.481 0.89 0.33–2.38 0.814 – – – 2.4 0.293 0.988 Lifestyle factor  Alcohol drinking 0.73 0.47–1.12 0.149 1.13 0.64–1.99 0.675 0.78 0.53–1.16 0.224 140.0  < 0.001 0.407  Coffee intake 1.00 0.99–1.01 0.969 1.01 0.99–1.02 0.099 1.00 0.99–1.01 0.538 20.0 0.045 0.597  Smoking initiation 1.04 0.91–1.19 0.550 1.12 0.96–1.32 0.146 1.05 0.93–1.19 0.420 469.2  < 0.001 0.774  Physical activity 0.12 0.05–0.32 2 × 10 –5 0.24 0.07–0.90 0.039 0.15 0.06–0.40 0.012 12.0 0.062 0.066 Serum lipids  High-density lipoprotein cholesterol 1.14 1.00–1.31 0.057 1.07 0.93–1.22 0.345 1.17 1.06–1.29 0.004 172.0  < 0.001 0.707  Low-density lipoprotein cholesterol 0.95 0.75–1.19 0.651 1.12 0.96–1.31 0.155 0.96 0.86–1.07 0.499 411.6  < 00.01 0.222  Total cholesterol 0.98 0.79–1.23 0.887 1.16 0.99–1.35 0.068 1.00 0.88–1.13 0.951 501.2  < 0.001 0.858  Triglycerides 0.90 0.79–1.02 0.098 0.94 0.81–1.09 0.385 0.88 0.78–1.00 0.052 60.0 0.007 0.808 Inflammatory biomarker  Tumor necrosis factor 8.83 0.33–237 0.194 5.80 2.06–16.3 0.001 – – – 60.7  < 0.001 0.082  C-reactive protein 1.08 0.85–1.38 0.538 0.99 0.84–1.17 0.940 1.08 0.94–1.24 0.262 386.4  < 0.001 0.681  Immunoglobulin E 0.89 0.76–1.05 0.162 0.93 0.78–1.10 0.389 – – – 1.6 0.443 0.825 Socioeconomic status  Education level 0.91 0.78–1.06 0.236 0.93 0.77–1.12 0.430 0.90 0.79–1.02 0.108 1367.8  < 0.001 0.284  Intelligence 1.08 0.91–1.29 0.369 1.04 0.85–1.28 0.707 1.08 0.93–1.25 0.294 365.8  < 0.001 0.392 CI indicates confidence interval; IVW, inverse weighted median; OR, odd ratio. P for pleiotropy is the p value for the intercept of MR-Egger analysis ( p  < 0.05 indicates the possible pleiotropy)

Associations between exposures and multiple sclerosis in Mendelian randomization analyses

CI indicates confidence interval; IVW, inverse weighted median; OR, odd ratio. P for pleiotropy is the p value for the intercept of MR-Egger analysis ( p  < 0.05 indicates the possible pleiotropy)

Nutrition and lifestyle

Genetically higher serum 25-hydroxyvitamin D levels and physical activity (moderate to vigorous level) were associated with a decreased MS risk in all models and no pleiotropy was detected (Table 2 ). There was a borderline association between urinary sodium levels and MS in both IVW-random effects and MR-PRESSO models (Table 2 ). There was no evidence of causal associations of circulating levels of amino acids, fatty acids, or other minerals and vitamins, alcohol drinking, coffee consumption, or smoking with MS risk (Table 2 ).

Internal biomarker

Genetic predisposition to higher levels of high-density lipoprotein cholesterol was suggestively associated with a lower risk of MS (Table 2 ). There was limited evidence supporting causal associations of other serum lipids, tumor necrosis factor, C-reactive protein and immunoglobulin E with MS.

Systematic review

We obtained 9 meta-analyses on 18 individual risk factors by a systematic search in PubMed. There were limited data from meta-analysis of sun exposure, pesticide-related products exposure, air pollution, exposure to farm animals and pets and antibiotic use in relation to MS. Exposure to organic solvents and Epstein Barr virus infection were positively, whereas cytomegalovirus infection, diphtheria vaccination and tetanus vaccination were inversely associated with MS risk (Supplementary Table 4).

Discussion

Using MR analysis, we found that 4 out of 65 risk factors were robustly associated with MS risk, including childhood and adulthood body mass index, serum 25-hydroxyvitamin D and physical activity. There was evidence of suggestive associations of type 2 diabetes, waist circumference, body fat percentage, age of puberty and high-density lipoprotein cholesterol with risk of MS. Evidence of latest meta-analyses showed that exposure to organic solvents, Epstein Barr virus and cytomegalovirus virus infection, and diphtheria and tetanus vaccination were associated with MS risk.

Adulthood obesity has been identified as a risk factor for MS in previous studies [ 3 , 12 ]. The present study confirmed the causal association between high body mass index and an elevated risk of MS using more than ten-fold more SNPs for adulthood body mass index compared with the previous MR study [ 3 ]. We additionally assessed the influence of birth weight, childhood body mass index, waist circumstance, body fat percentage, lean body mass, basal metabolic rate, and circulating adiponectin levels on MS. Consistent with observational findings [ 13 ], our study observed a causal positive association between childhood obesity and MS risk. Waist circumstance and body fat percentage but not lean body mass showed evidence of possible associations with MS risk, which might shed light on the possible varying effects of obesity phenotypes on MS risk and mechanisms.

Low serum 25-hydroxyvitamin D levels exert detrimental effects on MS development, which has been found in previous studies [ 2 , 14 ] and verified in the present study. Maternal and neonatal 25-hydroxyvitamin D status has also been found to be associated with MS risk in offspring or later on [ 15 , 16 ]. We observed a consistent protective effect of moderate to vigorous physical activity on MS risk, which supports observational findings [ 17 ]. In addition, increased physical activity level can act as a beneficial rehabilitation strategy for MS patients to manage symptoms, restore function, improve quality of life, and promote wellness [ 18 ]. Therefore, from the preventive and therapeutic perspectives, exercise should be promoted among individuals at high risk of MS as well as for MS patients.

Effects of nutritional factors, except vitamin D, on the risk of MS are seldom discussed. Recent prospective cohort studies did not find any associations of potassium, magnesium, calcium and iron with MS risk [ 19 , 20 ], which is overall consistent with our study. Observational evidence stated a protective effect of omega-3 polyunsaturated fatty acids [ 21 ] and a detrimental effect of total polyunsaturated fatty acids [ 22 ] on MS risk. Nonetheless, our study examined several individual plasma fatty acids levels and found null associations of these fatty acids with MS. We did not find any causal roles of amino acid and other vitamins in the onset of MS, which are scarcely explored in observational studies.

Observational data showed that the prevalence of both type 1 and type 2 diabetes was higher among MS patients compared with non-MS individuals [ 23 , 24 ]. The present study revealed a possible association between type 2 diabetes and MS. We found limited evidence supporting a causal effect of type 1 diabetes on MS risk. The reason behind a concurrence between type 1 diabetes and MS in observational studies might be shared genes contributing to susceptibility to both diseases (e.g. CLEC16A and CLECL1 ) [ 25 ], instead of a causal relationship.

Most studies have detected a decreased MS risk among individuals with postponed puberty age [ 26 , 27 ], which is consistent with our results. Several population and animal studies have indicated that puberty might influence MS risk or relapse per se or via body mass index and other pathways [ 28 , 29 ]. Conflicting findings of observational studies have revealed possible roles of cigarette smoking, alcohol drinking, and coffee consumption in the development of MS [ 12 , 30 – 32 ]. The present MR study did not confirm a causal influence of those lifestyle factors on MS risk, but we cannot exclude that we may have overlooked weak associations. The causal role of those lifestyle factors on MS risk merit further study if more SNPs are identified for those factors and in studies based on larger number of MS cases and controls.

Among internal biomarkers, previous studies found that serum lipid levels were not associated with MS risk [ 33 ]. However, high-density lipoprotein cholesterol was found to play a role in MS fatigue [ 34 ]. The present study observed a suggestive positive association between high-density lipoprotein cholesterol and risk of MS. Given inconsistent information on this association, whether high-density lipoprotein cholesterol play a casual role in the development of MS needs more study.

This is the first study to comprehensively investigate the potential risk factors for MS using MR analysis. In addition, for exposures not feasible for MR analysis, a systematic review of the literature was conducted to provide contemporary evidence of risk factors for MS. Evidence from meta-analyses of observational studies can be challenged by potential methodological limitations embedded in such studies. Thus, the findings from meta-analyses need more study. Population bias was largely reduced by using genetic data mainly from individuals with European ancestry. However, findings based on certain analyses using genetic data from multi-ancestries need to be cautiously interpreted and verified. The F-statistic for traits indicated that our results were unlikely biased by weak instruments (F-statistic > 10) [ 10 ]. However, the statistical power for some analyses was modest, suggesting that it is likely that some of the null results might suffer from “false negative” findings. Given that MR analysis reflects a lifetime exposure, the obtained effect sizes in the present study might be exaggerated and are not directly comparable with estimates derived from traditional observational studies. All MR analyses assumed linear relationships between the risk factors and MS and no interaction (e.g., the interaction between smoking and human leukocyte antigen genes [ 35 ]) or modification effects. We could not assess reverse causality through bidirectional MR analysis because suitable summary-level data were not available for most exposures. Thus, whether there are bidirectional associations between certain exposures and MS needs to be revealed in future study.

Conclusions

This MR study provides evidence of causal associations of a childhood and adulthood body mass index, serum 25-hydroxyvitamin D and physical activity with MS risk. Our complementary systematic review additionally showed that exposure to organic solvents, Epstein Barr virus and cytomegalovirus virus infection, and diphtheria and tetanus vaccination were associated with MS risk. Taken together, this study suggests that lowering obesity and Epstein Barr virus infection and increasing physical activity and serum vitamin D levels can reduce the risk of MS.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 91 kb)

Acknowledgements

Open access funding provided by Uppsala University. Summary-level data for MS were obtained from The International Multiple Sclerosis Consortium. The authors thank all investigators for sharing these data.

Author contributions

SY and SCL designed the study and formulated the conceptions. SY drafted the manuscript and conducted the statistical analyses. SY and YX conducted the systematic review. All authors contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final version of the manuscript.

Funding

Funding for this study came from the Swedish Research Council (Vetenskapsrådet; Grant Number 2019-00977) and the Swedish Research Council for Health, Working Life and Welfare (Forte; Grant Number 2018-00123). The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

Compliance with ethical standards

Conflicts of interest

We declare no competing interests.

Ethical approval

All data used in the present study are publicly available in the original GWAS studies, which obtained appropriate patient consent and ethical approval. Detailed information of used instrumental variables will be assessable upon a reasonable request to corresponding author. Ethical approval for the present MR study was not considered because these de-identified data came from summary statistics and no individual-level data were used. Summary-level genetic data for MS at the discovery stage can be downloaded at the website: https://imsgc.net/ .

Contributor Information

Shuai Yuan, Email: shuai.yuan@ki.se.

Ying Xiong, Email: ying.xiong@stud.ki.se.

Susanna C. Larsson, Email: susanna.larsson@surgsci.uu.se

References

Associated Data

Supplementary Materials

Supplementary file1 (DOCX 91 kb)