Abstract
Objective:
To determine the association between measures of overall diet quality (dietary indices/pattems) and risk of MS.
Methods:
Over 185,000 women in the Nurses’ Health Study (NHS) and Nurses’ Health Study II (NHSII) completed semi-quantitative food frequency questionnaires every 4 years. There were 480 incident MS cases. Diet quality was assessed using the Alternative Healthy Eating Index-2010 (AHEI-2010), Alternate Mediterranean Diet (aMED) index, and Dietary Approaches to Stop Hypertension (DASH) index. Principal components analysis was used to determine major dietary patterns. We calculated the hazard (HR) of MS with Cox multivariate models adjusted for age, latitude of residence at age 15, body mass index at age 18, supplemental vitamin D intake, and cigarette smoking.
Results:
None of the dietary indices, AHEI-2010, aMED, or DASH, at baseline was statistically significantly related to the risk of MS. The principle components analysis identified “Western” and “prudent” dietary patterns, neither of which was associated with MS risk (HR, top vs. bottom quintile: Western, 0.81 (p=0.31) and prudent, 0.96 (p=0.94)). When the analysis was repeated using cumulative average dietary pattern scores, the results were unchanged.
Conclusion:
There was no evidence of an association between overall diet quality and risk of developing MS among women.
Keywords: multiple sclerosis, diet, incidence, epidemiology
Introduction
The contribution of diet to disease risk and course is of great interest to many with chronic diseases, including multiple sclerosis (MS). In recent years, distinct patterns in the gut microbiota have been described in individuals with MS, and diet is known to affect gut microbiota composition.1 Previous studies investigating diet and MS risk have yielded null or inconsistent results,2 and have concentrated on individual nutrients or foods. 3,4 In prospective studies, no association has been found between intake of carotenoids, vitamin C, and vitamin E, saturated fat4, caffeine and alcohol5, or sodium intake6 and MS risk. However, higher vitamin A serum levels7 and greater intake of vitamin D8 and polyunsaturated fatty acids have been associated with a decreased MS risk.9 Describing diet quality is a more global approach to nutritional assessment and a high quality diet emphasizes an overall pattern of intake that is generally low in saturated fat, processed meats and refined sugars, and high in vegetables, fruit, nuts, and fish.
Diet quality has been identified as an important etiologic factor for neurologic diseases, including cerebrovascular disease10, 11and cognitive impairment12–14 and other chronic systemic disorders, including diabetes15, cardiac disease16, 17, and certain types of cancer.18 The objective of this study was to explore the association between diet quality and the risk of MS using two large, prospective cohorts of women.
Methods
Study population
The Nurses’ Health Study (NHS) enrolled 121,700 female registered nurses aged 30 to 55 living in the United States in1976 and the Nurses’ Health Study II (NHS II) enrolled 116,671 female nurses aged 25 to 42 in 1989. Women are asked every 2 years to complete mailed questionnaires concerning their medical histories and health-related behaviors. In 1984 in the NHS and 1991 in NHSII, women completed a semiquantitative food frequency questionnaire (FF Q) to report their typical diet, and then every 4 years thereafter. Women were excluded from this analysis if their FFQ responses resulted in an implausible estimate of total energy intake (i.e. <600 kcal/day or >3,500 kcal/day), if they omitted more than 70 food items, or if they left two or more sections of the FFQ blank.
Standard Protocol Approvals, Registrations, and Patient Consents
Ethics approval was obtained through the Brigham and Women’s Hospital and the Office of Human Research Administration at the Harvard T.H. Chan School of Public Health.
Ascertainment of MS Cases
Incident cases of MS were identified by nurses’ self-report. Women were then asked for permission to contact their neurologists for confirmation of the diagnosis and a copy of their relevant medical records. Their treating physicians were asked whether the diagnosis of MS was definite, probable, possible, or incompatible with the clinical picture. Beginning in 2003, medical records were reviewed by the study neurologist (TC). We included both definite and probable cases in this analysis, an approach previously validated, for a total of 480 cases.19 The diagnosis was supported by MRI evidence of demyelinating lesions in 76% of cases for the NHS and 89% for the NHS II.
Dietary exposures
Using a validated FFQ20,21, participants report what their average consumption was of approximately 130 food items over the past year, with 9 possible options ranging from “never” to “6 or more times per day.” Portion sizes for each food are provided as appropriate (e.g. one slice of bread). Nutrient values for each food were determined from the Harvard University Food Composition Database using US Department of Agriculture data and manufacturer information22, and individual intake of a specific nutrient is calculated by multiplying the amount of the nutrient in a food item by the frequency of consumption of the food item (frequency of “1 per day” weighted as 1), summed across all foods consumed containing the specific nutrient.
Several dietary indices for healthy eating have been created and widely used in studies of chronic disease in the NHS cohorts.18,23–26, For each FFQ year, the Alternative Healthy Eating Index-2010 (AHEI-2010),23 the Alternate Mediterranean Diet (aMED) index,25 and the Dietary Approaches to Stop Hypertension (DASH) index,24, 26 were calculated (Table 1).
Table 1:
Dietary indices by food and nutrient components
| Food Group | AHEI-2010 | aMED | DASH |
|---|---|---|---|
| Vegetables (servings/d) | ↑ | ↑ | ↑ |
| Fruit (servings/d) | ↑ | ↑ | ↑ |
| Nuts and legumes (servings/d) | ↑ | ↑ | ↑ |
| Whole grains (servings/d) | ↑ | ↑ | N/A |
| Omega-3 PUFAs | ↑ | N/A | N/A |
| Moderate alcohol (g/d) | ↑ | ↑ | N/A |
| Sweetened beverages (servings/d) | ↓ | N/A | ↓ |
| Red and processed meats (servings/d) | ↓ | ↓ | ↓ |
| Sodium (mg/d) | ↓ | N/A | ↓ |
| Fish and seafood (servings/d) | N/A | ↑ | N/A |
| Monounsaturated: saturated fat | N/A | ↑ | N/A |
| trans fat | ↓ | N/A | N/A |
| Low-fat dairy products (servings/d) | N/A | N/A | ↑ |
↑ Consumption increases index score; ↓ Consumption decreases index score; N/A does not contribute to index score
The AHEI-2010 index was developed previously based on foods and nutrients predictive of chronic disease risk and is comprised of 11 components with higher intakes of vegetables, fruit, nuts/legumes, whole grains, and omega-3 polyunsaturated fatty acids, moderate alcohol, and lower intakes of sugar-sweetened beverages/juice, red/processed meats, trans fat, and sodium all contributing to higher index scores, indicating a higher quality diet. Each component is rated on a scale of 0–10, with a total index score that ranges from 0–110.23 The aMED index assesses adherence to the Mediterranean style diet- -a point is awarded for greater than the cohort median intake of the following: vegetables (excluding potatoes), legumes, fruit, nuts, whole grains, fish, and the ratio of monounsaturated to saturated fats.25 In addition, 1 point is awarded for alcohol intake of 5–15 g daily, and another point is awarded for red and processed meat consumption below the median intake. Total scores range from 0 to 9, with higher scores representing better adherence to the Mediterranean diet. The DASH index assesses adherence to the Dietary Approaches to Stop Hypertension (DASH) diet and is created by allocating points based on the quintile of intake derived from the cohorts.24, 26 For favorable components, fruits, vegetables, nut and legumes, and low-fat dairy products, 1 point is granted per increasing quintile of intake (i.e. 1 point for first quintile, 2 points for second quintile, etc.), and for unfavorable components, sodium intake, sweetened beverages, and red and processed meats, a point is subtracted per increasing quintile of intake. The DASH index ranges from 8–40 with higher scores indicating a better adherence to the DASH style diet.
Principal component analysis was utilized to derive predominant dietary patterns in the two cohorts. First, food items were collapsed into 38 groups based on similar nutrient compositions or culinary use. Principal component analysis with orthogonal rotation was then applied and dietary patterns were derived utilizes the correlations between food groups. Factor scores were calculated by the sum of an individual’s reported intake for each food group weighted by factor loadings, with higher scores representing better adherence to the given pattem. Two major dietary pattem factors were derived: the “prudent” and “Western” patterns.26 The “prudent” pattern was high in vegetables, fruit, legumes, fish, poultry, and whole grains, while the “Western” pattern was characterized by a higher consumption of red and processed meats, refined grains, and sweets, as previously described.18, 26
Co-variates
Every 2 years women update their smoking status and current smokers report the number of cigarettes per day they use. Pack-years of smoking is derived from this information and was included in the model as a time-varying covariate. In 1980 in NHS and 1989 in NHSII women reported their weight at age 18. Using adult height reported on the baseline questionnaires, we calculated BMI at age 18 in kg/m2 (<23, 23–24.9, 25–26.9, 27–29.9, or ≥30). In 1992 in NHS and 1993 in NHSII, women reported their state of residence at age 15 and tier of latitude (north, middle, south) was assigned as previously described.19 Women reported their ancestry choosing as many of the following categories as applied: Southern European/Mediterranean, Scandinavian, other Caucasian, African American, Hispanic, Asian, Native American, and/or other. Ancestry categories for analysis were created as previously described.19 Supplemental vitamin D intake, (0, <400, >=400 IU/day) was derived from the FFQs and was included in the model as a timevarying covariate.
Statistical Analysis
Person-time was computed from the return of the baseline questionnaires until the date of MS diagnosis, death from any cause, or conclusion of follow-up (June 2004 for the NHS and June 2009 for the NHS II). Baseline and mean cumulative dietary pattern scores were categorized for each index/pattern by quintiles that were created based on the distribution of the index/pattern in the cohort. Tests for linear trend across the quintiles were conducted by modeling the median value of each quintile as a continuous variable. We conducted separate analyses for the NHS and NHS II, and a pooled analysis over both cohorts using the inverse of the variance as the weight and the Q statistic to assess heterogeneity (fixed effects models).27 We used Cox proportional hazards models to estimate hazard ratios (HR) and 95% confidence intervals (CI). All statistical analyses were conducted with SAS v.9.
In a sensitivity analysis, we examined dietary patterns as determined by FFQ scores from the date immediately preceding the MS diagnosis as the primary exposure. We also conducted sensitivity analyses with modification of co-variates: 1) omitting BMI at age 18; 2) adding a measure of socio-economic status (SES), as determined by marital status and spouse’s highest level of educational achievement.
Results
Nurses with healthier diets were slightly older, more likely to reside in the northern tier during their teenage years, and had slightly greater vitamin D supplemental intake (Table 2). Scandinavian ancestry and BMI at age 18 were relatively consistent across the dietary index quintiles.
Table 2:
Age-standardized characteristics of NHS and NHSII by baseline diet quality scores
| AHEI-2010 | aMED | DASH | Prudent Pattern | Western Pattern | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Q1 | Q3 | Q5 | Q1 | Q3 | Q5 | Q1 | Q3 | Q5 | Q1 | Q3 | Q5 | Q1 | Q3 | Q5 | |
| NHS | |||||||||||||||
| n | 16343 | 16345 | 16343 | 19458 | 15859 | 17026 | 16481 | 18589 | 14527 | 16343 | 16344 | 16345 | 16346 | 16340 | 16345 |
| Age, yrs, mean (sd) | 49.4 (7.2) | 51 (7.1) | 52.8 (6.8) | 49.8 (7.2) | 51.1 (7.1) | 52.4 (7.0) | 48.7 (6.9) | 51.2 (7.1) | 53.4 (6.8) | 49.3 (7.1) | 51.1 (7.1) | 52.5 (7) | 53 (6.8) | 50.8 (7.1) | 49.3 (7.2) |
| Smoking, pkyr mean (sd) | 13.9 (19.2) | 12.4 (17.6) | 11.8 (16.6) | 14.5 (19.2) | 12.4 (17.5) | 10.4 (15.7) | 15.7 (19.3) | 12.2 (17.6) | 9.9 (15.4) | 15.0 (19.2) | 12.4 (17.6) | 10.8 (16.2) | 12.5 (17.4) | 12.4 (17.7) | 12.5 (18.0) |
| Scandinavian ancestry, % | 4 | 4 | 4 | 3 | 4 | 4 | 3 | 4 | 5 | 3 | 4 | 5 | 4 | 4 | 4 |
| Residence in northern tier age 15, % | 34 | 36 | 36 | 33 | 36 | 38 | 33 | 37 | 37 | 33 | 36 | 37 | 35 | 36 | 35 |
| Supplemental vitamin D intake IU/d, mean (sd) | 95 (181) | 120 (206) | 172 (258) | 99 (189) | 123 (210) | 163 (246) | 79 (171) | 123 (206) | 184 (260) | 96 (189) | 122 (206) | 163 (250) | 161 (247) | 121 (205) | 100 (189) |
| BMI age 18, kg/m2, mean (sd) | 21.2 (3.0) | 21.4 (3.0) | 21.6 (3.0) | 21.3 (3.1) | 21.4 (3.0) | 21.5 (2.9) | 21.1 (3.1) | 21.5 (3) | 21.6 (3) | 21.2 (3) | 21.4 (2.9) | 21.7 (3.1) | 21.6 (3) | 21.3 (2.9) | 21.3 (3) |
| NHSII | |||||||||||||||
| n | 19090 | 19090 | 19090 | 19316 | 18289 | 24131 | 21492 | 14015 | 21608 | 19090 | 19090 | 19090 | 19090 | 19090 | 19090 |
| Age, yrs, mean (sd) | 35.4 (4.8) | 36.1 (4.6) | 36.9 (4.5) | 35.7 (4.8) | 36.1 (4.7) | 36.6 (4.5) | 35.9 (4.8) | 36.1 (4.7) | 36.4 (4.6) | 35.5 (4.8) | 36.2 (4.6) | 36.8 (4.5) | 36.6 (4.7) | 36.1 (4.6) | 35.7 (4.7) |
| Smoking, pkyr mean (sd) | 4.1 (8.1) | 4.0 (7.6) | 4.3 (7.4) | 4.6 (8.4) | 4.1 (7.8) | 3.8 (7.0) | 5.2 (8.9) | 3.9 (7.3) | 3.4 6.6) | 4.6 (8.4) | 3.9 (7.5) | 4.1 (7.3) | 4.2 (7.6) | 4.0 (7.4) | 4.4 (8.2) |
| Scandinavian ancestry, % | 4 | 4 | 5 | 4 | 5 | 5 | 4 | 4 | 5 | 4 | 5 | 5 | 4 | 4 | 5 |
| Residence in northern tier age 15, % | 26 | 31 | 32 | 27 | 30 | 32 | 25 | 30 | 33 | 24 | 31 | 33 | 31 | 30 | 27 |
| Supplemental vitamin D intake IU/d, mean (sd) | 112 (175) | 129 (190) | 152 (218) | 104 (174) | 126 (191) | 152 (208) | 91 (167) | 128 (194) | 170 (215) | 96 (170) | 129 (192) | 160 (215) | 142 (213) | 128 (189) | 119 (184) |
| BMI age 18, kg/m2, mean (sd) | 21.1 (3.5) | 21.2 (3.3) | 21.5 (3.4) | 21.3 (3.4) | 21.3 (3.5) | 21.2 (3.3) | 21.2 (3.5) | 21.3 (3.2) | 21.3 (3.3) | 21.1 (3.4) | 21.2 (3.2) | 21.6 (3.6) | 21.4 (3.3) | 21.2 (3.3) | 21.3 (3.6) |
There were 130 incident cases of MS identified in the NHS and 350 cases in the NHS II. As-expected, the baseline dietary indices were correlated. The Spearman correlation coefficients were AHEI-2010 and aMED 0.56 (NHS), 0.52 (NHSII); AHEI-2010 and DASH 0.66 (NHS), 0.55 (NHSII); aMED and DASH 0.68 (NHS); 0.71 (NHSII) (all p <0.0001). Using the baseline AHEI-2010, aMED, and DASH scores, none of the indices was significantly associated with the risk of developing MS. (Table 3). Similarly, adherence to a Western or prudent dietary pattern was not associated with the risk of MS and no clear trend was observed across the quintiles.
Table 3:
Multivariable* hazard ratios of MS by quintile of baseline dietary index or pattern score in the NHS (1984–2004) and NHS II (1991–2009)
| Dietary Index or Pattern | Q1 | Q2 | Q3 | Q4 | Q5 | p for trend |
|---|---|---|---|---|---|---|
| AHEI-2010 | ||||||
| n/person-years | 90/616,671 | 99/619,426 | 90/619,093 | 117/619,637 | 84/621,081 | |
| HR (95% CI) | 1.0 (ref) | 1.07 (0.81–1.43) | 0.97 (0.72–1.31) | 1.25 (0.94–1.66) | 0.89 (0.65–1.21) | 0.77 |
| aMED | ||||||
| n/person-years | 104/676,836 | 77/559,474 | 88/596,788 | 95/542,796 | 116/720,014 | |
| HR (95% CI) | 1.0 (ref) | 0.90 (0.67–1.22) | 0.96 (0.72–1.28) | 1.13 (0.85–1.51) | 1.03 (0.78–1.37) | 0.43 |
| DASH | ||||||
| n/person-years | 105/657,840 | 97/620,570 | 89/576,246 | 75/611,193 | 114/630,058 | |
| HR (95% CI) | 1.0 (ref) | 1.03 (0.78–1.36) | 1.12 (0.84–1.50) | 0.81 (0.60–1.10) | 1.18 (0.89–1.57) | 0.47 |
| Western Pattern | ||||||
| n/person-years | 92/615,043 | 107/619,057 | 98/620,009 | 105/621,558 | 78/620,240 | |
| HR (95% CI) | 1.0 (ref) | 1.15 (0.86–1.53) | 1.03 (0.76–1.41) | 1.11 (0.80–1.55) | 0.81 (0.53–1.23) | 0.31 |
| Prudent Pattern | ||||||
| n/person-years | 99/614,498 | 90/619,857 | 102/620,958 | 95/621,857 | 94/618,738 | |
| HR (95% CI) | 1.0 (ref) | 0.90 (0.68–1.20) | 1.04 (0.78–1.38) | 0.97 (0.72–1.30) | 0.96 (0.70–1.32) | 0.93 |
adjusted for age, energy intake, smoking, BMI at age 18, vitamin D intake, latitude of residence at age 15, ethnicity.
There was no association between the cumulative dietary scores and risk of MS. (Table 4). No trend was observed across the quintiles for the AHEI-2010, aMED, and DASH scores or prudent or Western dietary patterns. Similarly, the results remained the same when we calculated dietary scores from the date immediately preceding the MS diagnosis as the primary exposure. There was no meaningful change in the results with omission of BMI at age 18 as a co-variate, or addition of SES as a co-variate.
Table 4:
Multivariable* hazard ratios of MS by quintile of mean cumulative dietary index or pattern score in the NHS (1984–2004) and NHS II (1991–2009)
| Dietary Index or Pattern | Q1 | Q2 | Q3 | Q4 | Q5 | p for trend |
|---|---|---|---|---|---|---|
| AHEI-2010 | ||||||
| n/person-years | 95/640,581 | 95/640,408 | 98/626,653 | 108/609,704 | 84/578,562 | |
| HR (95% CI) | 1.0 (ref) | 0.97 (0.73–1.29) | 0.99 (0.74–1.32) | 1.11 (0.84–1.47) | 0.89 (0.65–1.20) | 0.73 |
| aMED | ||||||
| n/person-years | 113/670,689 | 104/681,500 | 99/632,155 | 87/589,497 | 77/522,067 | |
| HR (95% CI) | 1.0 (ref) | 0.88 (0.67–1.15) | 0.98 (0.74–1.30) | 0.97 (0.72–1.30) | 0.96 (0.70–1.31) | 0.93 |
| DASH | ||||||
| n/person-years | 111/641,088 | 90/619,517 | 103/648,815 | 82/601,051 | 94/585,437 | |
| HR (95% CI) | 1.0 (ref) | 0.90 (0.68–1.19) | 0.93 (0.71–1.24)§ | 0.88 (0.66–1.19) | 1.03 (0.77–1.39) | 0.85 |
| Western Pattern | ||||||
| n/person-years | 97/594,907 | 98/624,221 | 115/628,206 | 97/632,470 | 73/616,101 | |
| HR (95% CI) | 1.0 (ref) | 0.96 (0.72–1.29) | 1.09 (0.81–1.47) | 0.92 (0.66–1.27) | 0.67 (0.44–1.01) | 0.07 |
| Prudent Pattern | ||||||
| n/person-years | 96/587,222 | 91/621,317 | 103/631,396 | 96/635,517 | 94/620,457 | |
| HR (95% CI) | 1.0 (ref) | 0.92 (0.69–1.23) | 1.05 (0.79–1.40) | 0.98 (0.73–1.32) | 0.99 (0.72–1.37) | 0.88 |
adjusted for age, energy intake, smoking, BMI at age 18, vitamin D intake, latitude of residence at age 15, ethnicity. §Test for heterogeneity: p=0.014
Discussion
We did not find evidence for an association between overall diet quality and the risk of MS in this study of two large prospective cohorts of women. This result was consistent across the indices in both individual cohorts and in the pooled analysis, and for both baseline and mean cumulative dietary patterns.
The diet quality indices and diet patterns have been used in multiple investigations with associations observed with a host of other chronic diseases,10, 11, 13, 15, 17, 18, 23, 24, 26 including another neurologic disorder, Parkinson’s disease (PD).26 In a pooled analysis of the NHS and the Health Professionals Follow-up Study, the prudent diet pattern and the AHEI were associated with a significantly lower risk of PD.26 The lack of association between higher diet quality and MS risk using any of the indices studied suggests that variations in dietary quality within the range commonly observed in the U. S. population are not a major determinant of MS risk.
Strengths of our study included the availability of dietary data at multiple time points and detailed information on established confounders of MS risk. Further, assessment of overall diet quality provides a more global approach to nutritional intake. This approach helps to account for people’s tendency to eat certain foods in tandem, and thus may allow for greater power to detect dietary effects. One consideration, however, is that the dietary indices are derived using food consumption data and in some cases relative to the whole cohort (e.g. the aMED score is based on what an individual’s intake of a certain food group/nutrient is relative to the median of the whole cohort). Thus, we cannot rule out the possibility that an inverse association between better diet quality and MS risk exists in populations with diets even healthier than the NHS cohorts used here.
Another consideration is that while the AHEI-2010, DASH, and aMED have been employed to study the effect of dietary practices in a variety of diseases, they were originally conceived based upon known nutritional influences on cardiometabolic health.17, 23–26 Other diet patterns may be more relevant to the risk of inflammatory processes24, 28, as one recent study derived a dietary pattern associated with the risk of depression using inflammatory biomarkers, including C-reactive protein and TNFα,29, 30 although a previous study in these cohorts did find higher scores for the AHEI and aMED were associated with a less inflammatory profile.25 Other studies have explored the effects of plant-based diets on chronic disease,31–33 and these deserve further study with respect to MS risk.
Principal component analysis is a commonly used method for deriving dietary patterns that involves certain assumptions such as the nature and number of food groups, and the number of factors to retain. With smaller sample sizes there is a danger of accepting factors which are unstable or not fully relevant, but here we had >185,000 respondents. There can be loss of information concerning particular nutrients or smaller combinations of foods, a limitation inherent to the use of dietary patterns in general.
FFQs are a well-established method for assessing nutritional intake but have some limitations as they can be subject to recall bias. However, multiple validation studies over serveral decades20,21 have demonstrated that FFQs correlate reasonably well with other methods of dietary assessments such as repeated 7-day dietary records and 24-hour recalls.
This work does not address whether there may be a critical window during childhood, adolescence, and/or early adulthood when dietary practices could affect the risk of MS, as all participants in this study were at least 25 years of age at assessment of their diet. Several studies have found an association between BMI in adolescence/young adulthood and the risk of developing MS, particularly among women. Only female nurses were enrolled in the cohorts thus these results do not provide any information about the relationship between diet quality and MS risk in men.
Importantly, our results provide no information on whether there is an association between diet quality and disease course in those who already have MS. A recent crosssectional study suggested that a healthy diet is associated with a lesser symptom burden in those with existing MS, although the direction of the causal relationship remains uncertain.39
In conclusion, in this large, prospective study, we found no evidence for an association between diet quality and MS risk. This does not preclude an effect of specific nutrients on MS risk, or the effects of diet quality on the wellness or progression of individuals with MS.
Acknowledgements
We thank Dr. Stephanie Chiuve for her thoughtful comments on an earlier draft of this manuscript.
Study Funding: This work was supported by the US National Institutes of Health (grants UM1 CA186107, UM1 CA176726).
Footnotes
Disclosures:
Dr. Dalia Rotstein has received research support from the Consortium of Multiple Sclerosis Centers (CMSC), Multiple Sclerosis Society of Canada, and Biogen Idec. She has served as a speaker or consultant for Sanofi Aventis, EMD Serono, Novartis, Biogen, and Roche.
Marianna Cortese has nothing to declare.
Teresa T. Fung has nothing to declare.
Tanuja Chitnis has served on the advisory boards for clinical trials for Novartis and Sanofi-Genzyme. She has received compensation for advisory/consulting for Biogen, Novartis and Sanofi-Genzyme. She has received financial support for research activities from Merck-Serono and Verily.
Alberto Ascherio serves on a scientific advisory board for the Michael J. Fox Foundation; received speaker honoraria from Merck Serono; and receives research support from the US Department of Defense(Army) [W81XWH-05–1-0117 (PI)], the NIH [R01 NS045893 (PI), R01 NS047467 (PI), R01 NS48517 (PI), NINDS R01 NS042194 (PI), and R01 NS046635 (PI)], and the Michael J. Fox Foundation (Co-I).
Kassandra Munger has nothing to declare.
Contributor Information
Dalia L. Rotstein, Department of Medicine (Neurology), University of Toronto, Toronto, Ontario, Canada.
Marianna Cortese, Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, Department of Clinical Medicine, University of Bergen, Bergen, Norway, Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway..
Teresa T. Fung, Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, Department of Nutrition Simmons College, Boston, MA.
Tanuja Chitnis, Partners Multiple Sclerosis Center, Brigham and Women’s Hospital, Boston, MA.
Ascherio, Dept of Nutrition, Harvard T.H. Chan School of Public Health, Boston MA, Dept of Epidemiology, Harvard T.H. Chan School of Public Health, Boston MA, Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA..
Kassandra L. Munger, Dept of Nutrition, Harvard T.H. Chan School of Public Health, Boston MA.
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