Relationship between medical history and multiple sclerosis: A-case-control study

Abstract

This project sought to explore the potential association between medical history and the development of multiple sclerosis (MS) by conducting a retrospective study. This population-based case-control study included 200 MS cases and 2 control groups of 200 patients and healthy individuals each. Data was collected through face-to-face interviews, medical file reviews, and an electronic checklist. Multivariable analysis was used to calculate odds ratios and 95% confidence intervals to estimate the risk of each medical history on MS occurrences. Of 600 participants, 381 (63.5%) individuals were female. The mean age of the participants was 36.5 ± 11.9 years. The adjusted risks of MS were 4.40; 95% CI: 1.73 to 11.1 for measles and 4.75; 95% CI: 2.05 to 11 for amoxicillin consumption. The adjusted MS odds for autoimmune disease including 4.63; 95% CI: 0.35 to 60.6 for psoriasis and 7.15; 95% CI: 1.87 to 27.2 for myasthenia gravis. On the other hand, the calculated adjusted odds of MS occurrence were 0.14; 95% CI: 0.03 to 0.69 for seizure and 0.17; 95% CI: 0.02 to 1.49 for epilepsy. This study suggested that individuals with autoimmune diseases should be monitored more closely, as they may be at an increased risk of developing other autoimmune conditions, particularly MS.

1. Introduction

Multiple sclerosis (MS) is a debilitating disorder that is characterized by inflammation and the degenerative process of demyelination, leading to progressive axonal and gliosis damage.^[1] The exact cause of MS is still unknown, but it is believed to be the result of a combination of genetic, environmental, and immunological factors.^[2] Recent research has revealed that the prevalence of MS in Iranian provinces is on the rise, with Sistan-Baluchistan and Khuzestan having the lowest incidence and Tehran having the highest. This research indicates that the overall prevalence of MS in Iran is high, with a rate of 100 per 100,000.^[3] This is evidenced by the recent study that revealed a dramatic rise in the prevalence of MS in Tehran, Iran, from 51.9 per 100,000 in 2010 to 151.3 per 100,000 in 2020 – a 3-fold increase.^[4]

As suggested by health hypotheses, surge in autoimmune diseases is thought to be linked to a decrease in exposure to antigens. The immune system’s inflammatory process and subsequent cytokine release have been linked to an increased incidence of autoimmune diseases such as inflammatory bowel disease, myasthenia gravis (MG), type 1 diabetes mellitus (T1DM), and psoriasis.^[5] Epidemiological studies have highlighted the impact of environmental factors on the development of MS in genetically predisposed individuals. Vitamin D deficiency, family history of MS, certain viruses, particularly Epstein-Barr virus, and geographical location have all been identified as risk factors for MS.^[6]

Recent research has revealed that the prevalence of MS in Mazandaran province has been on the rise in recent years, affecting both genders and those with a family history of MS.^[7–9] A variety of factors have been identified as contributing to the increased risk of MS, including geographical area, demographic findings, genetic susceptibility, family history of MS, underlying diseases such as chronic or viral illnesses, and exposure to toxins.^[10,11] The exact relationship between MS and the mentioned factors has yet to be determined, and there are many unanswered questions. Further research is needed to determine whether these factors act as protective agents or risk factors for MS. This study aimed to evaluate the MS risk of these factors in different geographic regions, in order to modify the effect size of each factor on MS occurrence in different populations. By doing so, we designed a case-control study to indicate a better understanding of the role of these indicators and their effect size on MS occurrence.

2. Methods

2.1. Study design and population

This case-control study enrolled 600 participants. The aim of the study was to explore the association between MS and related factors, rather than to prove the association between MS and a specific factor. The study period spanned from August 1, 2020 to January 30, 2021, with the enrollment and data collection process taking 6 months. The participants were divided into 3 groups: case group (MS patients) and 2 control groups including patient control, and population-based control (or healthy control).

2.2. Case group

This group included patients with confirmed MS diagnosis based on the McDonald Criteria 2017^[12] and diagnostic tests performed by neurologists referred to the MS clinic of Bu-Ali Sina Hospital for follow-up. In order to be eligible for this study, participants also had to be aged 18 or over and living in Mazandaran province. Those who were unwilling to take part or had incomplete medical records were excluded. Our clinic serves more than 1000 MS patients from various cities in Mazandaran province and its surrounding areas. Two hundred participants were randomly chosen (n = 200) without any restrictions on time or day. Two physicians conducted a face-to-face interview with MS patients using a pre-prepared checklist and reviewed their medical files in order to gather data for this study.

2.3. Control groups

Considering that the MS patients were representative of the MS cases of Mazandaran province, so the population-based control was designed along with the patient control group to accurately assess the association between MS risk and potential factors.

1.2.3. Population-based control.

In order to be included in the study, participants had to be aged 18 or over, living in Mazandaran province, and were not MS patients (n = 200). Those who declined to participate or failed to complete the checklist were excluded. Data collection was conducted using an electronic checklist. To create an electronic checklist, one of the authors first created an account in Google using email address. Then, the author uploaded the checklist (in Word format) to Google Form. An electronic checklist could be accessed via the following link: https://forms.gle/KWh7heebmUFA5aXt5. The received link from Google attached to the explanations regarding the conditions of participation and the objectives of the study. Then, a list of all cities of Mazandaran province was prepared, and 10 cities were randomly selected such as Sari, Qaem shahr, Babol, Amol, Noor, Chalus, Shahsavar (Tonekabon), Ramsar, Behshahr, and Galogah. We employed a weighted sampling approach to distribute the checklist among the cities. We utilized exclusive telegram channels from randomly chosen cities. We searched for the links of the selected cities’ telegram channels on Google. We discussed the study objectives and how to complete the checklist with the channel admins. Most of the admins shared the checklist in their channels without any compensation; however, 2 cities’ admins received a fee for disseminating the checklist to their members. The channels members were presented with the checklist link, along with details about the objectives of the study, the voluntary nature of participation in the research project, the procedure for completing it, the confidentiality of the responders’ information, and the significance of providing accurate answers to each part of the checklist. It is worth noting that the explanations accompanying the checklist for each city state that if you are a resident of the specified city, you are eligible to take part in the study and complete the checklist. The completed checklist was automatically sent to the author’s email address.

2.2.3. Patient control.

This group included patients (n = 200) referred to the neurology clinic of Bu-Ali Sina Hospital. In order to be included in this group, patients had to be aged 18 or above, who had underlying neurological disease, but were not MS patients. Data was collected through face-to-face interviews and medical file reviews by 2 physicians. Patients who were unwilling to participate or had incomplete medical files were excluded from the study.

2.4. Data collection

A pre-prepared checklist was completed for each participant by conducting an interview, reviewing their medical records, and utilizing an electronic checklist. The checklist content encompassed the participants’ demographic characteristics (age, gender, marital status, number of children, and level of education), psychosocial conditions (death of first-degree relatives, family breakdown, duration of homelessness, history of imprisonment, severe illness of first-degree family members, injury and illness, suicide of first-degree family members, divorce, death of spouse, indebtedness, dismissal, unemployment, and being a burden on family), immigration to another city or country, retirement, taking an entrance exam (any period during the last 6 months). Moreover, the participants’ medical history of depression, measles, rubella, mumps, chicken pox, hepatitis B, influenza, systemic lupus erythematous, rheumatoid arthritis, hypothyroidism and hyperthyroidism, Crohn’s disease, ulcerative colitis, inflammatory bowel disease (IBS), leukemia, T1DM, celiac disease, psoriasis, MG, Hodgkin’s and non-Hodgkin’s lymphoma, melanoma, skin cancer (non-melanoma), migraine, seizure, epilepsy, vertigo, headache, Amnesia, Parkinson, hypertension, type 2 diabetes mellitus (T2DM), and kidney diseases was also recorded. Also, the duration of the disease was recorded. Moreover, the family history of T1DM, MS, and leukemia, head trauma and its frequency, antibiotic consumption from 13 to 19 years old, antibiotic prescription for 2 weeks or more during the last 3 years, and the duration of consumption were also recorded.

2.5. Ethics

The participants were thoroughly informed about their role in the project and the objectives of the study. In addition, informed written consent was obtained from the case group and the patient control group by the physicians during the interview. For the normal population group, it was mentioned that completing the checklist indicated their consent to take part in the study. The study proposal was approved by the ethics committee of Mazandaran University of Medical Sciences, ensuring that the study adhered to the ethical standards outlined in the 1964 Declaration of Helsinki and its subsequent amendments or similar ethical standards (approval ID: IR. MAZUMS.REC.1400.8335).

2.6. Statistical analysis

The collected data were analyzed with STATA software version 13 (StataCorp, College Station, TX). Data were displayed as frequencies (percent) or mean (standard deviation). Chi-square or Fisher’s exact test was used to compare the 3 groups’ variables, including qualitative variables, and the analysis of variance^[13] or Kruskal–Wallis H test was also used for the quantitative variables. To study the relationship between MS and medical records, an MS risk assessment was performed by calculating the odds ratios (ORs, with a 95% confidence interval [CI]) using a logistic regression model. Multiple potential confounders entered the final model to estimate an adjusted OR for each medical history, encompassing age, gender, level of education, death of relatives, family breakdown, duration of homelessness, severe illness of family members, injury and illness, indebtedness, separation from spouse, and entrance exam. The model’s goodness of fit was assessed using the Hosmer and Lemeshow test. Multicollinearity between the independent variables was ultimately checked in the final model using the LMCOL command and calculating the VIF index (variance inflation factor). In this study, P < .05 was set as the significance level.

3. Result

Of 600 participants, 63.5 % (n = 381) of them were female. The mean age was 36.5 ± 11.9 years. The variables including age, female sex, married, having children, illiterate, elementary literacy, middle literacy, diploma literacy, postgraduate literacy, bachelor of literacy, relatives death, family breakdown, homelessness periods, severe illness of 1st degree family members, injury and illness, spouse separation, being in debt’s money, entrance exam were significant between the 3 groups (P < .05). A summary of the basic and sociodemographic characteristics have been presented in Table 1.

Table 1.

Basic and sociodemographic findings of the participants.

Demographic findings	Groups			P value
	MS	Patients control	Normal population
	(N = 200)	(N = 200)	(N = 200)
Age, yr	33.8 ± 9.10	40.2 ± 13.8	35.3 ± 11.2	<.001
Sex, female	147 (73.5%)	133 (66.5%)	101 (50.5%)	<.001
Married	147 (73.5%)	157 (78.5%)	129 (64.5%)	<.001
Having children	123 (61.5%)	152 (76%)	99 (49.5%)	<.001
Illiterate	2 (1%)	13 (6.50%)	0 (0%)	<.001
Elementary literacy	10 (5%)	18 (9%)	4 (2%)	<.001
Middle literacy	22 (11%)	22 (11%)	9 (4.5%)	.03
High school	9 (4.5%)	12 (6%)	9 (4.5%)	.72
Diploma literacy	56 (28%)	55 (27.5%)	33 (16.5%)	.01
Postgraduate literacy	15 (7.5%)	19 (9.5%)	38 (19%)	<.001
Bachelor of literacy	67 (33.5%)	44 (22%)	78 (39%)	<.001
Relatives death	51 (25.5%)	47 (23.5%)	25 (12.5%)	.01
Mother	21 (10.5%)	23 (11.5%)	10 (5%)
Father	24 (12%)	15 (7.5%)	10 (5%)
Sister	2 (1%)	4 (2%)	5 (2.5%)
Brother	2 (1%)	4 (2%)	0 (0%)
Child	2 (1%)	1 (0.5%)	0 (0%)
Family breakdown	10 (5%)	17 (8.5%)	3 (1.5%)	.01
Homelessness periods	3 (1.5%)	0 (0%)	0 (0%)	.04
imprisonment history	1 (0.5%)	0 (0%)	0 (0%)	.36
Severe illness of 1st degree family members	21 (10.5%)	38 (19%)	17 (8.5%)	<.001
Injury and illness	20 (10%)	40 (20%)	25 (12.5%)	.01
Suicide of first-degree family members	2 (1%)	3 (1.5%)	0 (0%)	.24
Spouse separation	17 (8.5%)	4 (2%)	3 (1.5%)	<.001
Spouse death	3 (1.5%)	1 (0.5%)	1 (0.5%)	.44
Being in debt’s money	30 (15%)	38 (19%)	20 (10%)	.03
Dismissal from employment	3 (1.5%)	0 (0%)	2 (1%)	.24
Unemployment and being a burden on the family	11 (5.5%)	4 (2%)	6 (3%)	.14
immigration to another city or country	9 (4.5%)	8 (4%)	4 (2%)	.35
Retirement	4 (2%)	2 (1%)	0 (0%)	.13
Entrance exam	21 (10.5%)	8 (4%)	26 (13%)	<.001
Psychosocial conditions during the last 6 mo				.13
Marriage	1 (0.5%)	1 (0.5%)	7 (3.5%)
Relatives death	8 (4%)	7 (3.5%)	3 (1.5%)
Family breakdown	1 (0.5%)	1 (0.5%)	0 (0%)
Homelessness periods	0 (0%)	0 (0%)	0 (0%)
Imprisonment history	0 (0%)	0 (0%)	0 (0%)
Severe illness of 1st degree family members	1 (0.5%)	6 (3%)	5 (2.5%)
Injury and illness	1 (0.5%)	2 (1%)	3 (1.5%)
Suicide of first-degree family members	0 (0%)	0 (0%)	0 (0%)
Spouse separation	1 (0.5%)	0 (0%)	0 (0%)
Spouse death	1 (0.5%)	0 (0%)	0 (0%)
Being in debt’s money	8 (4%)	6 (3%)	2 (1%)
Dismissal from employment	2 (1%)	0 (0%)	0 (0%)
Unemployment and being a burden on the family	2 (1%)	1 (0.5%)	2 (1%)
immigration to another city or country	1 (0.5%)	0 (0%)	2 (1%)
Retirement	0 (0%)	0 (0%)	0 (0%)
Entrance exam	0 (0%)	1 (0.5%)	0 (0%)

Data are shown as standard deviation ± mean or (percentage) number. Bold values indicate significant changes between the groups.

P value was obtained by Fisher’s exact test and Student t test.

Variables such as lifetime history of depression, measles, rubella, mumps, chickenpox, migraine, headache, epilepsy, amnesia, hypertension, T2DM, head trauma, frequency of head trauma, amoxicillin consumption and the duration of these variables (week) were significant between the 3 groups (P < .05). Also, penicillin consumption (using ≥ 2 weeks during the last 3 years), lifetime history of seizure, hyperthyroidism, and MG, family history of T1DM, family history of MS, duration of Parkinson, and duration of hypothyroidism were significant between 3 groups (P < .05).

Among the medical records, depression and migraine were the most common, reported in 106 and 103 of the participants, respectively. The duration of depression was significantly more in the MS group (P < .001). History of chickenpox, mumps, and measles were more prevalent than other viral diseases which chickenpox and measles duration were more in the MS group significantly (P < .001 and P < .001, respectively). Analyzing antibiotic profile showed Amoxicillin usage and its duration were significantly more in the MS group (P < .001 and P = .02, respectively). Among the autoimmune disease, MG was significant more prevalent in the MS group (P < .001). Systemic lupus erythematous, Crohn’s disease, ulcerative colitis, celiac, leukemia, Hodgkin’s and non-Hodgkin’s lymphoma, melanoma, and IBS were reported in none of the participants. The findings concerning the participants’ medical history and the duration of the aforementioned disease have been presented in Table 2.

Table 2.

The distribution and duration of the medical history of the 3 groups.

Variables/medical history	Groups{number (percentage), Mean. ± St.Dev}			P value
	MS	Patients control	Normal population
	(N = 200)	(N = 200)	(N = 200)
Depression	41 (20.5%)	50 (25%)	15 (7.5%)	<.001 *
Duration of depression (wk)	75.4 ± 260.2	56 ± 153.3	12.8 ± 59.9	<.001 †
Measles	29 (14.5%)	25 (12.5%)	7 (3.5%)	<.001 *
Duration of measles (wk)	208.9 ± 543.2	190.7 ± 569.5	62.8 ± 336.3	<.001 †
Rubella	12 (6%)	11 (5.5%)	0 (0%)	<.001 *
Duration of Rubella (wk)	78.2 ± 330.9	102.3 ± 462	0	<.001 †
Mumps	24 (12%)	33 (16.5%)	5 (2.5%)	<.001 *
Duration of Mumps (wk)	156 ± 442.5	215.9 ± 541.3	27.3 ± 178.5	<.001 †
Hepatitis-B	0 (0%)	0 (0%)	1 (0.5%)	.40*
Duration of Hepatitis-B (wk)	0	0	4.32 ± 61.04	.36†
Chickenpox	83 (41.5%)	76 (38%)	28 (14%)	<.001 *
Duration of Chickenpox (wk)	452.4 ± 690.7	394.8 ± 586.6	145.3 ± 436.5	<.001 †
Influenza	4 (2%)	3 (1.5%)	3 (1.5%)	.90*
Migraine	35 (17.5%)	65 (32.5%)	3 (1.5%)	<.001 *
Duration of Migraine (wk)	106.4 ± 222.9	80.2 ± 248.9	4.32 ± 38.8	<.001 †
Seizure	2 (1%)	21 (10.5%)	0 (0%)	<.001 *
Duration of seizure (wk)	8.68 ± 88.5	13 ± 80.1	0	.15†
Parkinson	6 (3%)	8 (4%)	1 (0.5%)	.06*
Duration of Parkinson (wk)	25.9 ± 159.6	6.72 ± 43.5	0.02 ± 0.35	.01 †
Headache	9 (4.5%)	36 (18%)	1 (0.5%)	<.001 *
Duration of Headache (wk)	0.03 ± 0.17	14.7 ± 84.9	0	<.001 †
Epilepsy	1 (0.5%)	9 (4.5%)	0 (0%)	<.001
Duration of Epilepsy (wk)	3.6 ± 50.9	29.4 ± 173.5	0	<.001
Amnesia	7 (3.5%)	10 (5%)	0 (0%)	<.001
Duration of Amnesia (wk)	0.15 ± 0.73	9.3 ± 50.5	0	<.001 †
Rheumatoid arthritis	0 (0%)	4 (2%)	2 (1%)	.13*
Duration of Rheumatoid arthritis (wk)	0	13.2 ± 140.3	8.16 ± 102.6	.41†
Hypothyroidism	11 (5.5%)	21 (10.5%)	10 (5%)	.05*
Duration of Hypothyroidism (wk)	25.2 ± 133.1	54 ± 237	14.4 ± 72.6	.04 †
Hyperthyroidism	1 (0.5%)	6 (3%)	0 (0%)	.02 *
Duration of Hyperthyroidism (wk)	3.36 ± 47.5	6.98 ± 59.9	0	.28†
Type 1 diabetes mellitus	1 (0.5%)	1 (0.5%)	2 (1%)	.77*
Duration of Type 1 diabetes mellitus (wk)	2.4 ± 33.9	5.76 ± 81.4	6.24 ± 75.8	.82†
Psoriasis	2 (1%)	0 (0%)	1 (0.5%)	.36*
Duration of Psoriasis (wk)	3.6 ± 37.8	0	1.4 ± 33.9	.45†
Myasthenia gravis	11 (5.5%)	3 (1.5%)	0	<.001 *
Duration of Myasthenia gravis (wk)	0	3.6 ± 31.8	0	.07†
Kidney disease	5 (2.5%)	6 (3%)	6 (3%)	.94
Duration of Kidney disease (wk)	5.06 ± 41	4.09 ± 32.9	8.22 ± 56.9	.62†
Hypertension	2 (1%)	11 (5.5%)	1 (0.5%)	<.001
Duration of Hypertension (wk)	9.12 ± 100.7	32.4 ± 164.3	0.48 ± 6.78	.01 †
Type 2 diabetes mellitus	3 (1.5%)	14 (7%)	3 (1.5%)	<.001 *
Duration of type 2 diabetes mellitus (wk)	9.6 ± 92.1	45.06 ± 224.2	2.88 ± 27.4	<.001 †
Skin cancer, non-melanoma	1 (0.5%)	0)0%)	0 (0%)	.36
Family history of type 1 diabetes mellitus	3 (1.5%)	0	11 (5.5%)	<.001 *
Family history of Leukemia	1 (0.5%)	5 (2.5%)	5 (2.5%)	.22*
Family history of MS	18 (9%)	13 (6.5%)	2 (1%)	<.001 *
Head trauma	38 (19%)	21 (10.5%)	11 (5.5%)	<.001 *
Frequency of Head trauma	0.22 ± 0.55	0.11 ± 0.34	0.06 ± 0.30	<.001 †
Antibiotic consumption for 2 wk or more during the last 3 years	37 (18.5%)	29 (14.5%)	28 (14%)	.39*
Antibiotic consumption between 13–19 years old	12 (6%)	5 (2.5%)	4 (3.5%)	.06*
Duration of Antibiotic consumption between 13–19 years old (week)	0.08 ± 0.44	0.10 ± 0.61	0.13 ± 0.56	.65†
Co-Amoxiclav (using ≥ 2 wk during the last 3 years)	8 (4%)	5 (2.5%)	9 (4.5%)	.54*
Duration of Co-Amoxiclav (week)	0.07 ± 0.35	0.07 ± 0.45	0.11 ± 0.50	.58†
Gentamycin (using ≥ 2 wk during the last 3 years)	2 (1%)	1 (0.5%)	0	.40*
Duration of Gentamycin (wk)	0.03 ± 0.35	0.005 ± 0.07	0	.19†
Cefexim (using ≥ 2 wk during the last 3 yr)	7 (3.5%)	6 (3%)	2 (1%)	.23*
Duration of Cefexim (wk)	0.11 ± 0.71	0.06 ± 0.40	0.01 ± 0.15	.11†
Cefalexin (using ≥ 2 wk during the last 3 yr)	7 (3.5%)	3 (1.5%)	1 (0.5%)	.07*
Duration of Cefalexin (wk)	0.08 ± 0.44	0.03 ± 0.29	0.01 ± 0.14	.08†
Penicillin (using ≥ 2 wk during the last 3 yr)	4 (2%)	0	0	.01 *
Duration of Penicillin (wk)	0.05 ± 0.57	0	0	.16†
Azithromycin (using ≥ 2 wk during the last 3 yr)	7 (3.5%)	4 (2%)	4 (2%)	.54*
Duration of Azithromycin (wk)	0.08 ± 0.44	0.04 ± 0.32	0.03 ± 0.25	.39†
Amoxicillin (using ≥ 2 wk during the last 3 yr)	18 (9%)	7 (3.5%)	4 (2%)	<.001 *
Duration of Amoxicillin (wk)	0.18 ± 0.60	0.10 ± 0.65	0.03 ± 0.25	.02 †
Metronidazole (using ≥ 2 wk during the last 3 yr)	5 (2.5%)	4 (2%)	0	.09*
Duration of Metronidazole (wk)	0.10 ± 0.71	0.07 ± 0.63	0	.16†

Data are shown as standard deviation ± mean or (percentage) number. Bold values indicate significant changes between the groups.

MS = multiple sclerosis.

^*P value was obtained by Fisher’s exact test.

^†P value was calculated by Mann–Whitney U test.

The risk assessment of the medical histories indicated a crude odds of MS risk calculated for MG was the largest (OR 7.70, 95% CI: 2.12–27.9), followed by psoriasis (OR 4.03, 95% CI: 0.36–44.7). On the other hand, a crude odds obtained for seizure was the lowest (OR 0.18, 95% CI: 0.04–0.78). Among the viral diseases, a crude odds of MS risk was obtained the highest for Rubella (OR 2.25, 95% CI: 0.97–5.21), followed by measles (OR 1.95, 95% CI: 1.14–3.32). Among the antibiotic profile, a crude odds of MS risk calculated for amoxicillin and cefalexin were the largest (OR 3.49, 95% CI: 1.61–7.55, and OR 3.5, 95% CI: 1.03–12.4, respectively). A crude odds of MS risk calculated for amnesia was the largest among the neurological diseases (OR 1.41, 95% CI: 0.53–3.77). The estimated ORs of the medical histories associated with the MS risk are listed in Table 3.

Table 3.

MS risk assessment (Crude OR) of medical histories

Variables/medical history	Reference group (Crude OR (95%CI))
	Patient control	Normal population	All controls
Depression	(0.77, 0.48–1.23)	(3.1, 1.69–5.96)	(1.32, 0.86–2.05)
Measles	(1.18, 0.66–2.10)	(4.67, 1.99–10.94)	(1.95, 1.14–3.32)
Rubella	(1.09, 0.47–2.54)	–	(2.25, 0.97–5.21)
Mumps	(1, 0.98–1.02)	(5.34, 1.99–14.3)	(1.30, 0.75–2.24)
Chickenpox	(0.98, 0.92–1.04)	(4.35, 2.67–7.10)	(0.99, 0.96–1.01)
Influenza	(1.34, 0.29–6.06)	(1.34, 0.29–6.06)	(1.34, 0.37–4.80)
Migraine	(0.44, 0.27–0.70)	(13.9, 4.20–46.1)	(1.03, 0.66–1.62)
Seizure	(0.08, 0.01–0.37)	–	(0.18, 0.04–0.78)
Parkinson	(0.74, 0.25–2.17)	(6.15, 0.73–51.5)	(1.3, 0.47–3.82)
Headache	(0.21, 0.10–0.45)	(9.37, 1.17–74.7)	(0.46, 0.21–0.97)
Epilepsy	(0.10, 0.01–0.84)	–	(0.21, 0.02–1.7)
Amnesia	(0.68, 0.25–1.84)	–	(1.41, 0.53–3.77)
Hypothyroidism	(0.49, 0.23–1.05)	(1.1, 0.45–2.66)	(0.69, 0.34–1.40)
Hyperthyroidism	(1.06, 0.87–1.30)	–	(1.1, 0.85–1.42)
Type 1 diabetes mellitus	(1, 0.06–16)	(0.49, 0.04–5.53)	(0.66, 0.06–6.43)
Psoriasis	–	(2.01, 0.18–22.3)	(4.03, 0.36–44.7)
Myasthenia gravis	(3.82, 1.04–13.9)	–	(7.70, 2.12–27.9)
Hypertension	(0.17, 0.03–0.79)	(2.01, 0.18–22.3)	(0.32, 0.07–1.47)
Type 2 diabetes mellitus	(0.20, 0.05–0.71)	(1, 0.19–5.01)	(0.34, 0.09–1.18)
Kidney disease	(0.82, 0.24–2.76)	(0.82, 0.24–2.76)	(0.82, 0.28–2.38)
Family history of type 1 diabetes mellitus	–	(0.26, 0.07–0.95)	(0.53, 0.14–1.95)
Family history of Leukemia	(0.19, 0.02–1.69)	(0.19, 0.02–1.69)	(0.19, 0.02–1.54)
Family history of MS	(1.42, 0.67–2.98)	(9.79, 2.24–42.7)	(2.53, 1.25–5.15)
Head trauma	(1.99, 1.12–3.54)	(4.03, 1.99–8.14)	(2.69, 1.62–4.47)
Antibiotic consumption for 2 wk or more during the last 3 yr	(1.33, 0.78–2.27)	(1.39, 0.81–2.38)	(1.36, 0.86–2.15)
Antibiotic consumption between 13–19 years old	(2.48, 0.86–7.20)	(3.12, 0.99–9.86)	(2.77, 1.14–6.69)
Co-Amoxiclav (using ≥ 2 wk during the last 3 yr)	(1.62, 0.52–5.05)	(0.88, 0.33–2.34)	(1.14, 0.47–2.78)
Cefixime (using ≥ 2 weeks during the last 3 yr)	(1.17, 0.38–3.55)	(3.5, 0.73–17.5)	(1.77, 0.63–4.97)
Cefalexin (using ≥ 2 weeks during the last 3 years)	(2.38, 0.60–9.34)	(7.21, 0.87–59.2)	(3.5, 1.03–12.4)
Azithromycin (using ≥ 2 wk during the last 3 yr)	(1.77, 0.51–6.16)	(1.77, 0.51–6.16)	(1.77, 0.63–4.97)
Amoxicillin (using ≥ 2 wk during the last 3 yr)	(2.72, 1.11–6.68)	(4.84, 1.60–14.5)	(3.49, 1.61–7.55)
Metronidazole (using ≥ 2 wk during the last 3 yr)	(1.25, 0.33–4.74)	–	(2.5, 0.67–9.55)

CI = confidence interval, MS = multiple sclerosis, OR = odds ratio.

After adjusting the ORs for medical histories, the calculated odds of MS risk was still largest for MG (OR 7.15, 95% CI: 1.87–27.2) and smallest for seizure (OR 0.14, 95% CI: 0.03–0.69), while the effect of potential confounders was controlled. Among the viral disease, measles had the highest adjusted MS risk (OR 4.40, 95% CI: 1.73–11.1), followed by Rubella (OR 0.99, 95% CI 0.96–1.02). The adjusted MS risk for amoxicillin was the largest among antibiotic profile (OR 4.75, 95% CI: 2.05–11), followed by cefalexin (OR 3.32, 95% CI: 0.85–12.9). The adjusted MS risk calculated for psoriasis was OR 4.63, 95% CI: 0.35 to 60.6. The adjusted MS risk was obtained OR 3.14, 95% CI: 1.77 to 5.58 for head trauma. Among the neurological diseases, the adjusted MS risk estimated for Amnesia was still the highest (OR 2.82, 95% CI: 0.94–8.43). The calculated adjusted ORs of medical histories have been presented in Table 4.

Table 4.

MS risk assessment (Adjusted OR) of medical histories.

Variables/medical history	Reference group (Adjusted OR* (95%CI))
	Patient control	Normal population	All controls
Depression	(0.88, 0.52–1.50)	(2.86, 1.41–5.78)	(2.85, 1.41–5.76)
Measles	(1.38, 0.73–2.60)	(4.33, 1.70–11)	(4.40, 1.73–11.1)
Rubella	(1.51, 0.60–3.77)	–	(1.31, 0.81–2.11)
Mumps	(1, 0.98–1.01)	(5.84, 2.01–16.9)	(1.25, 0.70–2.22)
Chickenpox	(0.87, 0.54–1.39)	(4.51, 2.61–7.81)	(0.99, 0.96–1.02)
Influenza	(1, 0.20–4.95)	(0.69, 0.13–3.61)	(1.20, 0.31–4.65)
Migraine	(0.41, 0.24–0.70)	(12.42, 3.57–43.2)	(0.91, 0.56–1.49)
Seizure	(0.05, 0.01–0.27)	–	(0.14, 0.03–0.69)
Parkinson	(1.43, 0.40–5.05)	(10.3, 1.04–101.8)	(2.54, 0.78–8.27)
Headache	(0.16, 0.07–0.39)	(13.6, 1.53–122.3)	(0.42, 0.19–0.94)
Epilepsy	(0.09, 0.01–0.81)	–	(0.17, 0.02–1.49)
Amnesia	(1.42, 0.45–4.49)	–	(2.82, 0.94–8.43)
Hypothyroidism	(0.54, 0.23–1.28)	(0.68, 0.25–1.80)	(0.56, 0.26–1.21)
Hyperthyroidism	(1.08, 0.85–1.36)	–	(1.12, 0.82–1.53)
Type 1 diabetes mellitus	(1.10, 0.06–20.1)	(0.29, 0.01–4.63)	(0.62, 0.05–6.71)
Psoriasis	–	(2.12, 0.14–30.2)	(4.63, 0.35–60.6)
Myasthenia gravis	(3.22, 0.82–12.5)	–	(7.15, 1.87–27.2)
Hypertension	(0.83, 0.14–4.71)	(2.37, 0.09–60.1)	(0.87, 0.16–4.73)
Type 2 diabetes mellitus	(0.59, 0.14–2.52)	(1.7, 0.28–10.7)	(0.72, 0.18–2.87)
Kidney disease	(1.12, 0.28–4.41)	(0.45, 0.11–1.83)	(0.93, 0.30–2.91)
Family history of type 1 diabetes mellitus	–	(0.05, 0.01–0.26)	(0.27, 0.06–1.09)
Family history of Leukemia	(0.22, 0.02–2.05)	(0.22, 0.02–2.11)	(0.21, 0.02–1.76)
Family history of MS	(1.43, 0.63–3.24)	(9.99, 2.14–46.4)	(2.62, 1.23–5.57)
Head trauma	(1.98, 1.02–3.82)	(4.85, 2.13–11.06)	(3.14, 1.77–5.58)
Antibiotic consumption for 2 wk or more during the last 3 yr	(1.57, 0.84–2.91)	(1.06, 0.57–1.98)	(1.31, 0.79–2.19)
Antibiotic consumption between 13–19 yr old	(2.13, 0.67–6.72)	(2.61, 0.71–9.53)	(2.52, 0.95–6.65)
Co-Amoxiclav (using ≥ 2 wk during the last 3 yr)	(1.94, 0.56–6.66)	(0.61, 0.18–2.03)	(1.12, 0.43–2.91)
Cefixime (using ≥ 2 wk during the last 3 yr)	(0.54, 0.14–2.09)	(3.82, 0.65–22.2)	(1.43, 0.44–4.61)
Cefalexin (using ≥ 2 wk during the last 3 yr)	(1.60, 0.34–7.42)	(11.6, 1.16–115.8)	(3.32, 0.85–12.9)
Azithromycin (using ≥ 2 wk during the last 3 yr)	(2.13, 0.54–8.40)	(1.35, 0.30–6.14)	(2.14, 0.66–6.86)
Amoxicillin (using ≥ 2 wk during the last 3 yr)	(3.90, 1.43–10.6)	(6.91, 2.07–23)	(4.75, 2.05–11)
Metronidazole (using ≥ 2 wk during the last 3 yr)	(1.70, 0.36–7.94)	–	(2.40, 0.54–10.6)

CI = confidence interval, MS = multiple sclerosis, OR = odds ratio.

^*Odds ratio were adjusted according to age, sex, education, death of relatives, family breakdown, period of homelessness, severe illness of family members, injury and illness, being in debt’s money, separation from spouse, and entrance exam.

4. Discussion

This population-based case-control study revealed that MG, psoriasis, measles, head trauma, and amoxicillin consumption were significantly associated with an increased risk of MS, with a greater impact than other medical histories. Conversely, a history of seizure and epilepsy had the weakest association with MS risk.

Our research uncovered that individuals with a past of head trauma had a MS rate more than 2-fold that of the control groups. Furthermore, it was demonstrated that a lifetime history of head trauma was associated with an increased risk of MS of 1.6 to 2 times. However, a systematic review and meta-analysis conducted by Warren et al (which included 13 case-controls and 3 cohorts) did not support the association between traumatic injury and MS risk, which was contrary to our results.^[14] Abdollahpour et al^[15] study did not reveal a significant association between the frequency of head injury and the onset of MS which this finding was contrary to our own. Neuroinflammation, which is associated with head trauma, can lead to the development of primary and tertiary lesions. Neuronal inflammation is a complex process that involves a variety of factors, including tissue damage, blood-brain barrier permeability, production of reactive oxygen species, and the release of cytokines.^[16] Glial cells, such as oligodendrocytes, astrocytes, and microglia, are also involved in this process, which can manifest in both local and systemic forms. Local glial activation can lead to a systemic immune response.^[17]

The severity of a traumatic injury, the age at which it occurred, and the immune response to it all have a major impact on the risk of developing MS.^[18] A study conducted in Sweden by Montgomery et al^[19] found that having one or more concussions during adolescence was associated with an increased risk of MS, while no such relationship was observed for the childhood period. Lunny et al^[20] meta-analysis included 4 cohort studies and 36 case-control studies, which revealed a slight positive association between the risk of developing MS and a history of childhood head trauma among the higher quality case-control studies. The extent of neuronal damage caused by traumatic brain injury (TBI) is directly proportional to the severity of the injury. The long-term consequences of TBI can be far-reaching, with chronic neuroinflammation being one of the most significant. While the initial impact of a TBI may not be immediately apparent, the inflammation that follows can lead to tertiary lesions and neurodegeneration. It is also important to note that TBI is more likely to occur in male gender and certain age groups, such as young adults, adolescents, and children aged 0 to 4 years. These individuals are also more likely to experience more severe TBI.^[17]

The MS group had a significantly higher prevalence of a history of measles than the control group, with the highest risk of MS associated with measles, followed by rubella and mumps. However, Abdollahpour et al^[21] found that a history of measles and rubella was associated with a decreased risk of MS, which was contrary to our findings. Our findings were in agreement with Abbasi et al,^22] who found that a history of mumps, measles, and rubella was associated with an increased risk of MS. Only a history of measles in Maroufi et al^[23] study was consistent with our finding and was linked to a higher probability of MS. However, the results of the study by Ahlgren et al^[24] did not corroborate our findings and they did not observe a significant association between the history of rubella, measles and mumps with MS risk. Their findings further supports the notion that vaccination against viral diseases such as measles, mumps, and rubella does not play a role in MS progression. In 2017, a systematic review was conducted to assess the impact of vaccination on the risk of developing MS. The results of the review showed that there was no meaningful association between vaccination against viral diseases such as measles, mumps, and rubella and MS risk.^[25] Reports suggest that the central nervous system is being targeted by the invasion of wild strains of rubella, measles, and mumps.^[26] Studies have been conducted to examine the association between measles antibodies in serum and cerebral spinal fluid in MS patients, and it is believed that the high pathogenicity of measles strains in humans may be responsible for the increased prevalence of MS.^[22,27] It has been suggested that molecular antigenic similarity between pathogens and host antigens, as well as the destruction of host central nervous system tissue by antibodies produced by stimulated B lymphocytes and the damaging of myelin sheaths by cytokines produced by stimulated T lymphocytes (innocent bystander), may be mechanisms that contribute to the relationship between viral diseases and the risk of developing MS.^[28]

The prevalence of MG and psoriasis was significantly higher in the MS group than in the control group, with MG increasing the risk of MS by 6-fold and psoriasis by 3-fold. This suggests that among autoimmune diseases, MG and psoriasis are the most likely to be associated with MS. Langer-Gould et al^[29] study findings were in agreement with our results, which showed that MG and psoriasis were associated with an increased risk of MS. Siddiqui et al^[30] and Abdollahpour et al^[15] also argued that psoriasis was linked to a heightened probability of MS, which was consistent with our study.

It has been observed that the strongest association exists between neuromyelitis optica spectrum disorder (NMOSD) and MS.^[31,32] Both NMOSD and MG are immune-mediated diseases with similar underlying mechanisms. MS is more closely related to NMOSD than MG, as it is not caused by specific antibodies,^[33] although antinuclear antibodies have been found in 40% of MG patients and 30% of MS patients.^[34]

It is widely believed that MG and MS occur more frequently than previously thought. Both diseases are believed to be caused by T and B lymphocytes, respectively, but evidence suggests that both humoral and cellular immunity play a role in their pathogenesis. Furthermore, it has been suggested that MG and MS share a similar immunogenetic basis, and that the various clinical phenotypes may be attributed to different factors and triggers.^[35,36] It has been proposed that the loss of self-tolerance is the root cause of multiple autoimmune diseases, which is attributed to the deregulation of regulatory T and the lack of effector T repression.^[37]

The importance of the MS prodrome phase in understanding the progression of MS has been studied. Although the mechanisms and duration of the prodrome are not clear, they highlighted the signs and symptoms that may appear before the onset of MS.^[38] Patients with underlying autoimmune conditions are at an increased risk of developing other autoimmune diseases, particularly MS.^[39] To help reduce this risk, physicians should pay close attention to psychosocial issues, medical history, family history of disease, and environmental factors during routine visits with these patients. Doing so can play a major role in preventing the onset of additional autoimmune diseases.

Our study indicated that the use of amoxicillin 4 times more prevalent among the MS group than the control group. Our study also found that the use of antibiotics for 2 weeks or more during the last 3 years may be associated with an increased risk of MS progression, which is consistent with the results of Baldin et al^[40] study. According to our findings, the use of amoxicillin and cefalexin (both ≥ 2 weeks during the last 3 years) was more associated to MS risk than other antibiotics. TERNÁK et al investigated the association between annual antibiotic use and the prevalence of MS in 30 European countries from 1997 to 2018. It was suggested that countries with higher use of tetracycline and narrow spectrum penicillin had a higher prevalence of MS.^[41] In contrast to our findings, Abdollahpour et al found that antibiotic administration for more than 14 days in the last 3 years was associated with a decreased risk of MS. Additionally, they reported that penicillin and cephalosporin consumption was linked to a reduced progression of MS.^[21] Alonso et al^[42] findings were consistent with our study, which revealed that both short-term (less than 1 week) and long-term (less than 2 weeks) antibiotic usage within the last 3 years was associated with an increased risk of MS. Furthermore, using cephalosporin for either a short or long period of time within the last 3 years was also linked to an increased risk of MS.

Antibiotics have been found to have a regulatory effect on gut flora. Therefore, their misuse can lead to microbiome-related diseases such as MS in humans. Studies have demonstrated that alterations in gut microbiota composition can cause changes in the inflammatory cytokines release and T regulatory function.^[41,43] Additionally, a mouse model study has revealed that antibiotic administration can be used to prevent experimental autoimmune encephalomyelitis. The findings suggest that the alteration of gut microbiota has a strong association with the development of autoimmune diseases.^[44] Different antibiotics have been found to have varying impacts on the microbiota population, which can either stimulate or protect against inflammatory cytokines and immune mechanisms.^[41] This could explain why some individuals are more prone to autoimmune diseases than others.

It is evident that the efficacy of antibiotics in various contexts and among different demographics must be more thoroughly assessed. The pattern of antibiotic prescription in patients should be reevaluated, particularly in Mediterranean countries,^[45] where the primary focus should be on the indiscriminate use of antibiotics by individuals and the prescribing of antibiotics without proper indications by physicians. This issue is further highlighted when the age range of those prescribed antibiotics and the functioning of their immune systems are taken into account. By exploring the relationship between antibiotic use and MS risk, we can gain a better understanding of the role of antibiotics in autoimmune diseases, and thus, reduce the conflicting results that are often reported.

The clinical importance of this study is due to the fact that there is still not enough information about co-morbidities and their role in the MS onset and prevalence. From an epidemiological and therapeutic standpoint, the presence of risk factors and co-morbidities is of utmost importance. For instance, the presence of autoimmune disease can alter the type of drug treatment for MS, leaving the physicians with limited options for drug selection. Reports suggest that interferon (IFNb) or disease-modifying drugs (DMDs) used to treat MS can lead to either a flare-up or improvement of psoriasis.^[46,47] Also, psoriasis induced by rituximab has been observed in a MS patient.^[48] Additionally, it is essential to be aware that the common diseases and risk factors associated with MS can vary depending on the geographical region and community. Identifying demographic, epidemiological and medical history in each area is a key to preventing the onset of autoimmune diseases, particularly MS, and is of great importance in managing these patients.

5. Study limitations

Our findings may be subject to selection bias due to the fact that the control group (normal population) was sourced from Telegram channels. Therefore, readers should bear this in mind when interpreting the results. Additionally, the retrospective study design may have caused recall bias. To better demonstrate the association between the factor and the outcome, a cohort study would be more suitable. Another limitation, the protopathic estimation of the antibiotic calculated OR was associated with the initial symptoms onset and the time of MS diagnosis. However, we were unable to take into account major variables such as vitamin D deficiency and particular HLA due to the high cost and lack of access to a normal population group.^[49,50] Our study was not tailored to any particular factor or medical history, but rather focused on exploring the factors most associated with MS development, which could lead to an overestimation of our results. Therefore, it is recommended that future studies hone in on the factors with the strongest association to MS occurrence in order to modify its progression.

6. Conclusion

This study sought to assess the impact of various medical histories on the risk of developing MS. Results showed that MG and psoriasis, as well as a history of amoxicillin consumption (≥2 weeks during the last 3 years), had the strongest association with MS. Consequently, medical professionals should be especially vigilant when treating individuals with existing autoimmune conditions. Due to their increased susceptibility to other autoimmune disorders, particularly MS, it is essential to assess their predisposing factors, such as underlying disease, family history, and environmental factors, during their follow-up care.

Author contributions

Conceptualization: Fatemeh esfandiari, Mobin Ghazaiean, Seyed Mohammad Baghbanian.

Data curation: Fatemeh esfandiari, Mobin Ghazaiean, Hadi Darvishi-Khezri, Seyed Mohammad Baghbanian.

Formal analysis: Hadi Darvishi-Khezri.

Investigation: Fatemeh esfandiari, Mobin Ghazaiean, Hadi Darvishi-Khezri, Seyed Mohammad Baghbanian.

Methodology: Fatemeh esfandiari, Mobin Ghazaiean, Hadi Darvishi-Khezri, Seyed Mohammad Baghbanian.

Project administration: Fatemeh esfandiari, Mobin Ghazaiean, Hadi Darvishi-Khezri, Seyed Mohammad Baghbanian.

Software: Hadi Darvishi-Khezri.

Supervision: Seyed Mohammad Baghbanian.

Validation: Fatemeh esfandiari, Mobin Ghazaiean, Hadi Darvishi-Khezri, Seyed Mohammad Baghbanian.

Visualization: Fatemeh esfandiari, Mobin Ghazaiean, Hadi Darvishi-Khezri, Seyed Mohammad Baghbanian.

Writing – original draft: Fatemeh esfandiari, Mobin Ghazaiean, Hadi Darvishi-Khezri, Seyed Mohammad Baghbanian.

Writing – review & editing: Fatemeh esfandiari, Mobin Ghazaiean, Hadi Darvishi-Khezri, Seyed Mohammad Baghbanian.