The association between dietary sugar intake and neuromyelitis optica spectrum disorder: A case–control study

Multiple Sclerosis and Related Disorders 31 (2019) 112–117

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The association between dietary sugar intake and neuromyelitis optica spectrum disorder: A case–control study

T

Nasim Rezaeimanesha,b,1, Soodeh Razeghi Jahromia,b,1, Zeinab Ghorbania,c, Nahid Beladi Moghadamd, Azita Hekmatdoostb, Abdorreza Naser Moghadasia, Amir Reza Azimia, ⁎ Mohammad Ali Sahraiana, a

Multiple Sclerosis Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran Department of Clinical Nutrition and Dietetics, Faculty of Nutrition and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran c School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), Tehran, Iran d Department of Neurology, Shahid Beheshti University of Medical Sciences, Tehran, Iran b

A R T I C LE I N FO

A B S T R A C T

Keywords: Sugar Diet Neuromyelitis optica spectrum disorder Case–control

Background: Neuromyelitis optica spectrum disorder (NMOSD) is an uncommon autoimmune disease of the central nerves system (CNS) by inflammatory nature. The effects of high dietary sugar intake on inflammation and dysbiosis have been received more attention in recent years. The aim of the present study was to investigate the association between various types of dietary sugar intake and NMOSD odds and clinical features. Method: The current case–control study was conducted among 70 patients with definite NMOSD diagnosis based on 2015 international consensus criteria and 164 hospital-based controls. Demographic and anthropometric information in all participants and disease characteristics just in case group were obtained. Dietary data during the past year of study attendance was collected by a validated 168-item food frequency questionnaire. Participants were stratified into 3 tertiles according to each type of sugar intake and the third tertile considered as reference in multivariate regression models. The correlation between dietary sugar and disease features were analyzed using Pearson correlation test. Results: The mean ± SD of total sugar intake increased from 80.73 ± 17.71 to 208.71 ± 57.93 g/day across tertiles of total sugar intake. In fully adjusted model, lower intake of sugar was associates with decreased odds of NMOSD in the first tertile vs third tertile by ORs of: 0.02(CI:0.00–0.08; p-for-trend:0.00), 0.02(CI:0.00–0.10; pfor-trend:0.00), 0.23(CI:0.08–0.61; p-for-trend:0.00), 0.19(CI:0.06–0.58; p-for-trend:0.00) and 0.16(CI:0.05–0.51; p-for-trend:0.00) for glucose, fructose, galactose, lactose and sucrose, respectively. The odds of NMOSD had a 1.72-fold (CI: 1.43–2.03; p-for-trend:0.00) significant raise per every 10 g increase for total sugar intake. There was no significant correlation between various types of dietary sugar intakes and relapse rate or patients’ disability. Conclusion: The present study proposes a possible direct association between high intake of various sugar types and odds of suffering from NMOSD. More investigations are needed to prove this results.

1. Introduction Neuromyelitis optica spectrum disorder (NMOSD) is a chronic, immune mediated, demyelinating syndrome (Eskandarieh et al., 2017a; Gong et al., 2018). This recurrent inflammatory disorder of central nervous system (CNS), mainly result in vasculitic necrotising lesions in white matter and Subsequently optic neuritis and transverse myelitis as clinical symptoms (Badihian et al., 2018; Bergamaschi, 2007). NMOSD

usually occurs in young adult with more prevalence in women (Eskandarieh et al., 2017a; Badihian et al., 2018). The prevalence of NMOSD has been reported 0.86 per 100,000 subjects in Tehran, the capital city of Iran in 2016 (Eskandarieh et al., 2017b). Interaction between environmental and genetic risk factors have been suggested for etiology of NMOSD (Eskandarieh et al., 2017a). Until now, the risk factors of NMOSD have not fully been investigated but Currently the only known NMOSD environmental risk factors are:

⁎

Corresponding author. E-mail address: [email protected] (M.A. Sahraian). 1 Nasim Rezaeimanesh and Soodeh Razeghi Jahromi have equally taken part. https://doi.org/10.1016/j.msard.2019.03.028 Received 5 December 2018; Received in revised form 4 February 2019; Accepted 31 March 2019 2211-0348/ © 2019 Elsevier B.V. All rights reserved.

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Hospital-based controls were eligible for inclusion in the study if they aged more than 18 years old, did not have any past history of trauma, other neurological diseases or medical history of chronic diseases including diseases previously mentioned for case group. Were not pregnant or lactating. Also included subjects should not have followed any special diet like weight loss regimens, any kind of vegetarian diets, pregnancy or lactation regimens, etc. for a year before the study.

the past history of trauma, low level of physical activity and dietary risk factors in both sexes, the past history of abortion and low body mass index (BMI) in females (Baek et al., 2018; Eskandarieh et al., 2018). Nutritional intake is believed to be important modifiable risk factor (Baek et al., 2018; Eskandarieh et al., 2018). Low consumption of dairy, sea foods, egg, red meat, chicken, fats, fruits and vegetables at adolescence age (13–19 years old), is outlined as nutritional risk factors (Eskandarieh et al., 2018). Various studies established the notable role of inflammation in NMOSD etiology (Wang et al., 2011). The role of proinflammatory cytokines such as interleukin (IL)−2, IL-6, IL-17, T helper (Th)−17 and Th2 related cytokines, interferon-gamma (INF- γ) and Tumor necrosis factor α (TNFα) in NMOSD etiology has been suggested in different studies (Uzawa et al., 2014; Wang et al., 2013). The association between gut microbiota dysbiosis and NMOSD susceptibility is shown in recent investigations and overabundance of Clostridium perfringens leading to proinflammatory aquaporin-4 (AQP4)-specific T-cell and Bcell responses and increased level of Th17-polarizing cytokines like IL6, are proposed for explanation of this relation (Cree et al., 2016; Zamvil et al., 2017). Thus any factors that can augment inflammation may make the individual more prone to NMOSD. Dysbiosis of gut microbiota may lead to systematic inflammation which could result in neuro-inflammation by increasing in TNF-α, IL-1, and IL-6 levels (Esposito et al., 2018; Lombardi et al., 2018). Dietary intake is the main determining factors in gastrointestinal microbiome balance. Dysregulation and decrease in diversity of microbiota due to high dietary sugar intake, is reported in some studies (Beilharz et al., 2016; Lambertz et al., 2017). In addition, it is proved that dietary sugar provoke the inflammatory processes in the human body through increasing free fatty acids synthesis in the liver and also elevating the level of proinflammatory mediators such as C-reactive protein (CRP) (Beilharz et al., 2016; Lambertz et al., 2017; Della Corte et al., 2018). Up to our knowledge, there is not any study on the association between high dietary sugar intake and NMOSD risk or progression, However; lower intake of added sugar as a part of high quality diet seems to decrease disability and depression in Multiple sclerosis (MS) patients (Fitzgerald et al., 2018). High intake of sugar-sweetened drinks also has been noted to play a role in upregulation of proinflammatory pathways and dysbiosis in gut microbia which might exacerbate MS symptoms (Riccio and Rossano, 2015). The present study examined various types of dietary sugar intake as a modifiable factor for NMOSD odds, relapse rate and disability.

2.2. Protocol approval and patient consents The project protocol has been confirmed with IR.TUMS.VCR.REC.1396.4270 ethic code, in ethical committee of Tehran University of medical sciences. In addition, informed consents were obtained from all participants and the main aims of study were explained for them at the beginning of the interview. 2.3. Demographic, clinical data and anthropometric measurements All data was collected by the same person to reduce the subjective error to the minimum. A similar questionnaire including age (full years of birthday), gender (female or male), and cigarette smoking (whether the person is a current smoker or not) were filled in both group of participants during in-person interviews. Then the height and weight of study participant were taken using a tape meters without shoes by 0.5 cm accuracy (in m) and a Seca digital scale (Seca, Hamburg, Germany) without shoes and minimally clothes by 100 g accuracy (in Kg), respectively. Data on disease characteristics of NMOSD patients including relapse rate (in last year), disability level (based on Expanded Disability Status Scale (EDSS) determined by neurologist), treatment (current treatment) and disease duration (number of months passed from definite NMOSD diagnosis) were obtained from their clinical records. 2.4. Dietary assessments In order to evaluate dietary intakes, a 168-item semi-quantitive food frequency questionnaire (FFQ), validated in Iran (Esfahani et al., 2010; Mirmiran et al., 2010), was completed. Data on the average frequency and amount of consumption for each FFQ nutrition item per day, week, month or year according to special portion sizes which is mentioned in FFQ was collected from participants during the past year of study entry. The USDA Food Composition Databases were used to access the amount of various sugar types per 1 g of each FFQ item (ndb.nal.usda.gov/ndb). In order to computing the average amount of various sugar intake per day, the cumulative amount of sugars contents of all 168 FFQ food items, which were consumed per day was estimated.

2. Method 2.1. Study population

2.5. Statistical methods We designed a hospital-based case–control study among NMOSD patients with definite diagnosis. Case group participants were recruited from Sina hospital, a tertiary care referral center with the only NMOSD specialist clinic in Tehran, Iran. The NMOSD definite diagnosis in this clinic is based on the 2015 international consensus criteria for NMOSD diagnosis and neurologist's confirmation (Wingerchuk et al., 2015). Due to the uncommon nature of NMOSD, prevalence NMOSD cases were enrolled in this study, who referred to NMOSD clinic during October 2017 to July 2018. Totally 137 NMOSD patients were registered in Sina hospital and had clinical records. From 110 available and willing patients for cooperation, 70 persons were eligible for study enrollment because of having inclusion criteria as follows: aged more than 18 years old, did not change their routine diet after NMOSD diagnosis and did not suffering from chronic disease such as cardiovascular disease or hyperlipidemia, diabetes, chronic kidney or liver disease, gastrointestinal disorders and hormonal dysfunction and did not being in pregnancy or lactation situation.

Data analysis was carried out by Statistical Package for the Social Sciences (SPSS software), version 21 (Chicago: SPSS Inc. IBM Corp.). Each type of sugar was categorized into 3 tertiles of intake in gram per day. Baseline characteristics of all participants were reported as mean ± standard deviation (SD) for continuous variables and as proportion (%) for categorical variables according to total sugar tertiles. Disease features of NMOSD group were presented as mean ± SD across every type of dietary sugar. To determine P for trend in categorical values chi-square was applied and for continuous variables, the median values of each tertile of total sugar intake were considered as a continuous variable and linear regression was used. In order to compute adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the association between various type of sugars intake and the risk of NMOSD, two multivariate regression models were performed. The third tertile of each type of sugar intake was considered as reference and ORs were reported by comparing the risk of reduction in dietary sugars intake of participants in the second and first tertiles of 113

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their intake with the third tertile. In the case of total sugar, the ORs of NMOSD for each 10-gram of this variable were reported. To examine linear trend across the tertiles of each type of sugar, the median amounts in each tertile of that item was used as a continuous variable. First regression models were adjusted for age (year, continuous), gender (male, female, categorical) and energy intake (Kcal/day, continuous). The second models were additionally adjusted for BMI (kg/ m2, continuous), cigarette smoking (yes, no, categorical), carbohydrate (g/day, continuous), protein (g/day, continuous) and fat (g/day, continuous) intake. To assess the correlation between daily total sugar consumption and relapse rate or disability of NMOSD patients Pearson correlation test was applied. Significance levels were set at P-value ≤0.05.

Table 2 Disease characteristics of case group. Variables Treatment

Rituximab Azathioprine Prednisolone Cellcept Missed data

Disability level (EDSS) Disease duration (months) Relapse rate (during last year)

64.5% 29% 1.6% 3.2% 1.6% 2.45 ± 1.92 37.56 ± 20.74 0.2 ± 0.51

Treatment is reported as proportion and other data are reported as Mean ± standard deviation (SD).

Further adjustment for BMI, cigarette smoking, carbohydrate, protein and fat intake, in the second regression model demonstrated that the ORs in the second and first tertiles of each sugar types vs third tertile, respectively, were: 0.18(95%CI: 0.06–0.49) and 0.02(95%CI: 0.00–0.08) for glucose, 0.13(95%CI: 0.05–0.35) and 0.02(95%CI: 0.00–0.10) for fructose, 0.53(95%CI: 0.21–1.31) and 0.23(95%CI: 0.08–0.61) for galactose, 0.49(95%CI: 0.19–1.25) and 0.19(95%CI: 0.06–0.58) for lactose, 0.53(95%CI: 0.21–1.35) and 0.16(95%CI: 0.05–0.51) for sucrose intake, by p = 0.00 for trend in all mentioned sugar types (Table 4). Moreover, when analyzing the association between each 10 g increment in total sugar intake and the NMOSD, a 1.4-fold elevation in the disorder risk was shown in model 1 (OR: 1.40, CI: 1.25–1.58; P for trend =0.00). Additional adjustment for the mentioned variables in the second regression models led to increase in OR of NMOSD risk, across each 10-gram increase in total sugar intake (OR: 1.72, CI: 1.43–2.03; P = 0.00 for trend) (Table 5). There was no significant correlation between each type of dietary sugar and patients’ disability level or relapse rate (Table 6).

3. Result Considering inclusion and exclusion criteria, finally seventy NMOSD patients in case group and 164 healthy individuals in control group were enrolled in the present study. The mean age of subjects was 35.34 ± 9.87 and 42.94 ± 15.31 years old by 85% and 62.2% female percentage in case and control group, respectively. Baseline characteristics of study participants according to tertiles of total sugar consumption is presented in Table 1. as shown in this table, total energy, carbohydrate, protein, fat, glucose, fructose, galactose, lactose and sucrose intake had significant increasing trend across the total sugar intake tertiles (P for trend≤0.05). As its shown in Table 2, most NMOSD patients are received Rituximab (64.5%), followed by Azathioprine (29%) as treatment. The mean EDSS of NMOSD patients was 2.45 by the mean relapse rate of 0.2 during prior year of study participation. On average 37.56 months were passed from NMOSD definite diagnosis. Clinical characteristics of NMOSD patients are reported in Table 3. In the first regression model after adjusting for age, gender and total energy intake, a statistically significant decreased risk for NMOSD was found by lower intake of different types of sugars. In this context, when comparing the second and first tertile with the highest tertile of intakes the risk appeared to decreased as follows: 77% (OR:0.23, 95% CI:0.10–0.51) in the second and 94% (OR: 0.06, CI: 0.02–0.17) in the first tertile of glucose intake (P = 0.00 for trend); 84% (OR:0.16, 95% CI:0.07–0.38) in the second and 94% (OR:0.06, CI:0.02–0.17) in the first tertile of fructose intake (P for trend =0.00); 60% (OR:0.40, CI:0.18–0.89) in the first tertile of galactose intake (P for trend =0.02); 80% (OR:0.20, CI:0.07–0.54) in the first tertile of sucrose intake (P for trend =0.00); However, there were no significant odds for NMOSD prevalence in second tertile of galactose, first and second tertile of lactose and second tertile of sucrose vs third tertile in the model 1.

4. Discussion The results of the present research showed that limiting the intake of different types of dietary sugar could result in reducing the odds of NMOSD even after controlling for potential confounders and other dietary factors including age, gender, BMI, cigarette smoking, in addition to daily intake of energy, carbohydrate, protein and fat. In addition, it was highlighted that each 10 g per day increment in the intake of total sugar was accompanied by 72% increase in the odds of NMOSD. To the best of our knowledge, to date, only one study assessed the environmental risk factors of NMOSD including nutritional factors and reported that during their adolescence, these patients had less intake of

Table 1 Baseline characteristics of participants according to tertiles of total sugar intake. Tertiles of total sugar intake (g)

Female (%) Cigarette smoker (%) Age (year) BMI (kg/m2) Energy (Kcal/day) Total Carbohydrate (g/day) Total protein (g/day) Total fat (g/day) Total sugar (g/day) Total fructose (g/day) Total galactose (g/day) Total glucose (g/day) Total lactose (g/day) Total sucrose (g/day)

1st tertile

2nd tertile

3rd tertile

P for trend

78.2% 23.1% 39.64 ± 15.68 26.01 ± 4.76 1898.48 ± 507.61 232.17 ± 60.50 69.84 ± 21.96 82.33 ± 30.67 80.73 ± 17.71 16.10 ± 5.39 1.13 ± 0.67 13.74 ± 4.62 8.83 ± 5.13 27.86 ± 12.41

64.9% 9.1% 41.29 ± 11.68 27.32 ± 4.53 2298.66 ± 614.04 289.19 ± 74.83 92.06 ± 28.08 94.20 ± 29.89 126.84 ± 13.08 27.82 ± 9.29 1.62 ± 0.86 23.91 ± 8.41 15.55 ± 7.84 39.14 ± 13.19

64.6% 11.4% 40.88 ± 15.30 27.65 ± 11.37 3046.50 ± 751.92 387.82 ± 97.60 112.01 ± 29.29 127.13 ± 41.97 208.71 ± 57.93 48.86 ± 19.50 2.62 ± 1.76 44.22 ± 19.72 21.93 ± 11.73 67.25 ± 28.78

0.10 0.02 0.63 0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Plus-minus values are presented as mean ± standard deviation (SD). 114

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Table 3 Clinical features of NMOSD patients. Variable Glucose

Treatment

Fructose

Disease duration$ (month) EDSS$ Relapse rate$ (number in last year) Treatment

Galactose

Disease duration$ (month) EDSS$ Relapse rate$ (number in last year) Treatment

Lactose

Disease duration$ (month) EDSS$ Relapse rate$ (number in last year) Treatment

Sucrose

Disease duration$ (month) EDSS$ Relapse rate$ (number in last year) Treatment

Total sugar

Disease duration$ (month) EDSS$ Relapse rate$ (number in last year) Treatment

¥

Rituximab Azathioprine¥ Prednisolone¥ Cellcept¥

Rituximab¥ Azathioprine¥ Prednisolone¥ Cellcept¥

Rituximab¥ Azathioprine¥ Prednisolone¥ Cellcept¥

Rituximab¥ Azathioprine¥ Prednisolone¥ Cellcept¥

Rituximab¥ Azathioprine¥ Prednisolone¥ Cellcept¥

Rituximab¥ Azathioprine¥ Prednisolone¥ Cellcept¥

Disease duration$ (month) EDSS$ Relapse rate$ (number in last year)

1st tertile

2nd tertile

3rd tertile

72.7% 27.3% 0.0% 0.0% 40.67 ± 32.14 1.95 ± 1.34 0.09 ± 0.30 75.0% 25.0% 0.0% 0.0% 41.40 ± 30.39 1.86 ± 1.31 0.08 ± 0.29 43.8% 43.8% 6.3% 0.0% 34.44 ± 17.58 2.63 ± 1.83 0.33 ± 0.62 60.0% 26.7% 6.7% 0.0% 33.26 ± 19.96 2.96 ± 1.62 0.31 ± 0.63 69.2% 30.8% 0.0% 0.0% 47.14 ± 27.71 2.35 ± 1.48 0.23 ± 0.60 83.3% 16.7% 0.0% 0.0% 39.45 ± 29.05 2.17 ± 1.42 0.25 ± 0.62

66.7% 27.8% 0.0% 5.6% 36.13 ± 19.44 3.82 ± 2.38 0.41 ± 0.71 60.0% 33.3% 0.0% 6.7% 40.77 ± 21.27 4.07 ± 2.26 0.5 ± 0.76 77.8% 22.2% 0.0% 0.0% 38.12 ± 22.39 2.41 ± 2.28 0.06 ± 0.24 65.2% 30.4% 0.0% 4.3% 40.20 ± 22.66 2.21 ± 2.20 0.04 ± 0.21 62.5% 29.2% 4.2% 4.2% 36.00 ± 17.30 2.74 ± 2.31 0.18 ± 0.39 50.0% 38.9% 5.6% 5.6% 43.13 ± 23.87 3.63 ± 2.48 0.19 ± 0.40

60.6% 30.3% 3.0% 3.0% 37.38 ± 18.49 1.80 ± 1.30 0.13 ± 0.42 62.9% 28.6% 2.9% 2.9% 35.55 ± 17.89 1.92 ± 1.51 0.12 ± 0.41 67.9% 25.0% 7.1% 0.0% 39.21 ± 22.06 2.38 ± 1.79 0.21 ± 0.57 66.7% 29.2% 0.0% 4.2% 38.75 ± 20.02 2.35 ± 1.82 0.29 ± 0.62 64.0% 28.0% 4.0% 0.0% 34.00 ± 18.22 2.24 ± 1.78 0.20 ± 0.58 65.6% 28.1% 3.1% 0.0% 34.50 ± 15.87 1.95 ± 1.51 0.19 ± 0.54

$

These data are presented as Mean ± standard deviation (SD). These data are presented as percentage of NMOSD patients in each tertile of dietary sugar intake (remained percents are related to missed data). EDSS: Expanded Disability Status Scale.

¥

liver production of free fatty acids (FFA) that is known as lipotoxicity theory, which finally result in causing proinflammatory responses and release of reactive oxygen species (ROS). Furthermore, it has been revealed that high sugar consumption for a period of one week could reduce the diversity in microbiome composition of the gut (Beilharz et al., 2016; Lambertz et al., 2017; Della Corte et al., 2018). In general, it has been widely known that consuming a diet with high amounts of energy, sugars and fats (especially animal fats), in addition to fried and processed foods can lead to disturbances in neurological functions such as learning and memory. These disturbances may be mediated by a variety of mechanisms including impairing mitochondrial functions, inducing oxidative stress, provoking pro-inflammatory responses, dysbiosis in gut microflora, and impairing immunological functions of intestine which ultimately could result in increasing the risk for chronic inflammatory disorders (Francis et al., 2013; Pistell et al., 2010; Riccio and Rossano, 2015). Moreover, systemic inflammation, which could lead to CNS inflammation, may also occur subsequent to microflora dysbiosis and increased intestinal permeability that might be induced by dietary sugar or fats (Esposito et al., 2018). In this regard, the association between the gut and the brain which is also known as “gut-brain axis indicates that gut microflora dysbiosis result in augmented neuro-inflammation by stimulating proinflmmatory cytokines including TNF-α, IL-1, and IL-6

animal protein as well as vegetable in comparison with the control subjects (Eskandarieh et al., 2018). Moreover, there have been two studies investigating the relationship between nutritional data and EDSS in addition to fatigue levels in NMOSD suffers. Both of these reports demonstrated significant inverse associations between intake of whole grain, fish, and EDSS scores in addition to negative relationships between fruits and vegetables consumption and fatigue scores (Maljaei et al., 2018b,a). There is extensive evidence to support effects of Rituximab as a treatment for NMOSD patients for reducing patients’ disability and relapse rate. In 2011, Bedi et.al reported a significant decreasement in NMOSD patients relapse rate from 1.8 to 0.0 and improvement or stabilizing in EDSS (Bedi et al., 2011). Approximately 65% of our NMOSD patients had been treated with Rituximab and non-significant correlation between EDSS or relapse rate and sugar intake in our result may be referred to beneficial effects of rituximab for controlling disease. A possible explanation for the positive association between increased dietary sugar consumption and augmented odds of this autoimmune inflammatory disorder might be related to the pro-inflammatory effects of different types of sugar in addition to their influences on gut microflora dysbiosis. The effects of sugar consumption on inducing inflammation seems to be mediated through increasing the levels of proinflmmatory factors such as CRP and also stimulating the 115

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Table 4 Odds ratios (ORs) and 95% confidence intervals (95% CI) for NMOSD prevalence through tertiles of various types of sugar. Variable ¥

Glucose No. of cases/ Modela Modelb Fructose ¥ No. of cases/ Modela Modelb Galactose ¥ No. of cases/ Modela Modelb Lactose ¥ No. of cases/ Modela Modelb Sucrose ¥ No. of cases/ Modela Modelb

Glucose(g/day)

Fructose(g/day)

Galactose(g/day)

Lactose(g/day)

Sucrose(g/day)

controls

controls

controls

controls

controls

1st tertile

2nd tertile

3rd tertile

13.29 11/66 0.06(0.02–0.17) 0.02(0.00–0.08) 15.52 12/66 0.06(0.02–0.17) 0.02(0.00–0.10) 0.84 19/59 0.40(0.18–0.89) 0.23(0.08–0.61) 6.46 21/57 0.76(0.33–1.72) 0.19(0.06–0.58) 23.76 15/63 0.20(0.07–0.54) 0.16(0.05–0.51)

23.17 19/60 0.23(0.10–0.51) 0.18(0.06–0.49) 27.56 16/62 0.16(0.07–0.38) 0.13(0.05–0.35) 1.56 20/57 0.55(0.26–1.18) 0.53(0.21–1.31) 13.22 24/54 0.90(0.42–1.93) 0.49(0.19–1.25) 39.34 25/53 0.49(0.22–1.09) 0.53(0.21–1.35)

39.89 40/38 reference reference 45.26 42/36 reference reference 2.53 31/48 reference reference 24.95 25/53 reference reference 65.09 30/48 reference reference

P for trend

0.00 0.00

0.00 0.00

0.02 0.00

0.53 0.00

0.00 0.00

¥ These values are presented as median. a Regression model adjusted for age, gender and total energy intake. b Regression model adjusted for age, body mass index, gender, cigarette smoking, carbohydrate, protein, fat and energy intake.

hs-CRP as a consequence of stimulating the gene expression of NF-kB and AP-1 (Esposito et al., 2018). Furthermore, it has been suggested that in addition to inflammation, gut dysbiosis may also play a role in NMOSD pathogenesis (Zamvil et al., 2017). The present study also revealed that intake of about 15.5 g fructose per day may be accompanied by 98% reduced risk of NMOSD compared to the subjects had a median intake of about 45 g fructose per day. Among different types of dietary sugars, fructose, whether in the composition of sucrose or corn syrup, seems to have a major contribution in the proinflammatory effects of total dietary sugar. TNFα, and IL-6 are among the well-known proinflammatory cytokines which are reported to be increased by high fructose consumption. In addition, intake of this sugar component might result in gut microbiota dysbiosis, in addition to impairment in intestinal mucosal barrier and increasing its permeability (Gong et al., 2018; Lambertz et al., 2017; Della Corte et al., 2018; Stanhope, 2016). We also found that consuming about 13 g/d of glucose could attenuate the risk of NMOSD by 98% when comparing with those who consume about 40 g/d of this type of sugar agent. Glucose is one of the other well studied components of dietary sugars that has been widely associated with higher level of oxidative markers and proinflammatory factors as well as vascular damage that seems to occur as a consequence of increased FFA production (Della Corte et al., 2018).

Table 5 Odds ratios (ORs) and 95% confidence intervals (95% CI) for NMOSD prevalence per each 10-gram increment in total sugar intake. Variable

per 10-gram increase in total sugar intake

P for trend

Total sugar intake

Modela Modelb

0.00 0.00

1.40(1.25–1.58) 1.72(1.43–2.03)

a

Regression model adjusted for age, gender and total energy intake. Regression model adjusted for age, body mass index, gender, cigarette smoking, carbohydrate, protein, fat and energy intake. b

Table 6 Correlation between different types of dietary sugar intake and patients. Variable

Glucose Fructose Galactose Lactose Sucrose Total sugar ¥

EDSS r

P-value¥

Relapse rate r

P-value¥

−0.23 −0.19 0.07 0.00 −0.08 −0.17

0.08 0.14 0.60 0.96 0.55 0.21

0.12 0.11 0.11 0.22 0.00 0.11

0.36 0.36 0.39 0.08 0.99 0.36

Analyzing the correlation using Pearson correlation test.

(Lombardi et al., 2018). In accordance with NMOSD, several studies have emphasized that dysbiosis in gut microflora could disturb immune system in animal models of MS. For example, it has been suggested that probiotics and high vegetable consumption might improve the microflora profile and could alleviate disease severity in these models (Esposito et al., 2018; Saresella et al., 2017). Therefore, according to “gut-brain axis” hypothesis, it can be assumed that modifying dietary intakes in favor of less fat and sugar consumption can also lead to improving microflora profile and suppressing inflammatory process and thus reducing the susceptibility for NMOSD (Lombardi et al., 2018). Moreover, previous researches showed up-regulation of the two transcription factors including NF-κB and Activator protein 1 (AP-1) with proinflammatory effects in MS and NMOSD. On the other hand, as mentioned, it has been found that a diet with high energy, high fat, and high carbohydrate content could exert proinflammatory effects by means of ROS formation, and elevating TNF-α, IFN-γ, IL-1β, IL-2, 6, 18,

4.1. Strengths and weaknesses Our study is one of the limited number of studies which investigate NMOSD dietary risk factors. According to NMOSD rarity we enrolled an appropriate sample size to this study. One of this study limitation is recall bias that is an inherent aspect of case–control studies. 5. Conclusion Collectively, the results of the present research showed that limiting the intake of different types of dietary sugar could result in reducing the likelihood of being assigned to NMOSD even after controlling for confounding variables. In addition, it was highlighted that each 10 g per day increment in the intake of total sugar was accompanied by 72% increase in the odds of NMOSD but there was no significant correlation between sugar intake and disease characteristics including relapse rate and disability level. More studies including well-designed cohort 116

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studies and randomized controlled-trials are needed to explore the effects of sugar consumption on NMO onset or progression.

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Funding This study was funded by Multiple Sclerosis Research Center, Tehran University of Medical Sciences (grant number: 96-02-18835793). Conflict of interest The authors declare that there is no conflict of interest. References Badihian, S., Manouchehri, N., Mirmosayyeb, O., Ashtari, F., Shaygannejad, V., 2018. Neuromyelitis optica spectrum disorder and menstruation. Rev. Neurol. (Paris) 174 (10), 716–721. Baek, S.-H., Kim, J.-S., Jang, M.-j., Kim, Y.H., Kwon, O., Oh, J.-H., et al., 2018. Low body mass index can be associated with the risk and poor outcomes of neuromyelitis optica with aquaporin-4 immunoglobulin G in women. J. Neurol. Neurosurg. Psychiatry 89 (11), 1228–1230 jnnp-2017-317202. Bedi, G.S., Brown, A.D., Delgado, S.R., Usmani, N., Lam, B.L., Sheremata, W.A., 2011. Impact of rituximab on relapse rate and disability in neuromyelitis optica. Mult. Scler. 17 (10), 1225–1230. Beilharz, J.E., Kaakoush, N.O., Maniam, J., Morris, M.J., 2016. The effect of short-term exposure to energy-matched diets enriched in fat or sugar on memory, gut microbiota and markers of brain inflammation and plasticity. Brain Behav. Immun. 57, 304–313. Bergamaschi, R., 2007. Immune agents for the treatment of Devic's neuromyelitis optica. Neurol. Sci. 28 (5), 238–240. Cree, B.A., Spencer, C.M., Varrin-Doyer, M., Baranzini, S.E., Zamvil, S.S., 2016. Gut microbiome analysis in neuromyelitis optica reveals overabundance of Clostridium perfringens. Ann. Neurol. 80 (3), 443–447. Della Corte, K.W., Perrar, I., Penczynski, K.J., Schwingshackl, L., Herder, C., Buyken, A.E., 2018. Effect of dietary sugar intake on biomarkers of subclinical inflammation: a systematic review and meta-analysis of intervention studies. Nutrients 10 (5), 606. Esfahani, F.H., Asghari, G., Mirmiran, P., Azizi, F., 2010. Reproducibility and relative validity of food group intake in a food frequency questionnaire developed for the Tehran lipid and glucose study. J. Epidemiol. 20 (2), 150–158. Eskandarieh, S., Nedjat, S., Abdollahpour, I., Azimi, A.R., Moghadasi, A.N., Asgari, N., et al., 2018. Environmental risk factors in neuromyelitis optica spectrum disorder: a case–control study. Acta Neurol. Belg. 118 (2), 277–287. Eskandarieh, S., Nedjat, S., Abdollahpour, I., Moghadasi, A.N., Azimi, A.R., Sahraian, M.A., 2017a. Comparing epidemiology and baseline characteristic of multiple sclerosis and neuromyelitis optica: a case-control study. Mult. Scler. Relat. Disord. 12, 39–43. Eskandarieh, S., Nedjat, S., Azimi, A.R., Moghadasi, A.N., Sahraian, M.A., 2017b. Neuromyelitis optica spectrum disorders in Iran. Mult. Scler. Relat. Disord. 18, 209–212. Esposito, S., Bonavita, S., Sparaco, M., Gallo, A., Tedeschi, G., 2018. The role of diet in

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