Guillain-Barré Syndrome (GBS) is an acute, immune-mediated polyradiculoneuropathy that presents with rapidly progressive symmetrical weakness, hyporeflexia or areflexia, and varying degrees of sensory, autonomic, and cranial nerve involvement. It remains the most common cause of acute flaccid paralysis worldwide since the near-eradication of poliomyelitis [1]. The pathogenesis of GBS is largely attributed to aberrant immune responses following antecedent infections, such as Campylobacter jejuni, cytomegalovirus, Epstein-Barr virus, and Zika virus, which induce molecular mimicry and subsequent peripheral nerve damage [2,3].

Epidemiologically, GBS has an annual incidence ranging between 0.8 and 2.4 per 100,000 persons, with a slight male predominance and a higher occurrence among older adults [4]. However, considerable variability in incidence has been observed across different geographic regions, populations, and time periods. Among these, seasonal variation has been frequently reported, suggesting a link between climatic conditions, infection rates, and GBS onset [5]. Studies from various parts of the world, including India, have identified peaks in GBS cases during monsoon and post-monsoon seasons, coinciding with increased prevalence of gastrointestinal and respiratory infections [6,7].

Moreover, the electrophysiological variants of GBS, including Acute Inflammatory Demyelinating Polyradiculoneuropathy (AIDP), Acute Motor Axonal Neuropathy (AMAN), Acute Motor and Sensory Axonal Neuropathy (AMSAN), and Miller Fisher Syndrome (MFS), exhibit diverse regional patterns. In Western countries, AIDP predominates, whereas AMAN is more prevalent in parts of Asia and Latin America [8]. The subtype distribution may also be influenced by seasonal and environmental factors, further emphasizing the need to study local epidemiological trends.

Despite advances in diagnosis and management, GBS continues to pose significant challenges in resource-limited settings due to delayed diagnosis, lack of immunotherapy access, and underreporting. Understanding local epidemiological patterns and temporal distribution can aid in the early recognition of disease surges, efficient allocation of healthcare resources, and improved patient outcomes.

Study Design

This study was designed as a retrospective observational study.

Study Setting

The research was conducted at the Department of Neurology, Indira Gandhi Institute of Medical Sciences (IGIMS), Bailey Road, Patna, which is a tertiary care center serving a wide population of Bihar and neighboring regions. The study utilized data obtained from the hospital’s neurology ward, outpatient department (OPD), and medical records section.

Study Duration

The study covered a period from June 2023 to April 2025, allowing for the observation of seasonal fluctuations across two consecutive years and ensuring a comprehensive data set for analysis.

Participants

A total of 79 patients diagnosed with Guillain-Barré Syndrome (GBS) were included in the study. These participants were identified based on their clinical and electrophysiological diagnoses documented in hospital records during the study period.

Inclusion Criteria

• Patients of all age groups and genders diagnosed with Guillain-Barré Syndrome as per Brighton Criteria.
• Patients admitted or treated at IGIMS, Patna, between June 2023 and April 2025.
• Availability of complete medical records, including seasonal and demographic data.

Exclusion Criteria

• Patients with incomplete or missing clinical or diagnostic data.
• Patients with recurrent episodes or previously diagnosed chronic inflammatory demyelinating polyneuropathy (CIDP).
• Cases suspected to be secondary to trauma, metabolic, or toxic causes.

Bias

To minimize selection bias, all consecutive cases meeting the inclusion criteria within the defined study period were enrolled. Information bias was addressed by cross-verifying data from multiple hospital sources (OPD registers, inpatient files, and diagnostic reports). However, as a retrospective study, there remained potential limitations related to data completeness and documentation accuracy.

Data Collection

Relevant clinical, demographic, and temporal data were extracted from medical records, case files, admission registers, and laboratory reports. Data included patient age, sex, date of onset, seasonal/month of onset, GBS variant, and neurophysiological findings. All data were recorded in a structured pro forma designed for the study.

Procedure

The diagnosis of GBS in each case was confirmed based on clinical criteria and supported by nerve conduction studies (NCS). Cases were categorized into electrophysiological subtypes such as AIDP, AMAN, AMSAN, and others. Seasonal variation was analyzed by grouping cases into four seasonal categories: pre-monsoon (March–May), monsoon (June–September), post-monsoon (October–November), and winter (December–February).

Statistical Analysis

Data were analyzed using SPSS version 23.0 (IBM Corp., Armonk, NY, USA). Categorical variables were summarized as frequencies and percentages, while continuous variables were presented as mean ± standard deviation (SD). The Chi-square test was used to assess associations between GBS occurrence and seasonal patterns. A p-value <0.05 was considered statistically significant.

A total of 79 patients diagnosed with Guillain-Barré Syndrome were included in the study. The mean age of the patients was 42.7 ± 16.3 years, ranging from 12 to 78 years. Male predominance was observed, with 52 males (65.8%) and 27 females (34.2%), resulting in a male-to-female ratio of 1.9:1.

Table 1: Demographic Profile of Participants (n = 79)

The majority of patients belonged to the 20–60 years age group, highlighting the common occurrence of GBS among economically productive age groups.

Seasonal Distribution of GBS Cases

A clear seasonal trend was observed. The monsoon season (June–September) accounted for the highest number of cases (32; 40.5%), followed by the post-monsoon season (October–November) with 19 cases (24.1%).

Table 2: Seasonal Distribution of GBS Cases (n = 79)

The peak incidence during the monsoon season may correlate with increased gastrointestinal and respiratory infections, known GBS triggers.

Electrophysiological Variants of GBS

The most common subtype identified was Acute Inflammatory Demyelinating Polyradiculoneuropathy (AIDP), seen in 43 patients (54.4%). AMAN (Acute Motor Axonal Neuropathy) accounted for 20.3%, while AMSAN (Acute Motor and Sensory Axonal Neuropathy) was seen in 17.7%. Other atypical variants were observed in a few cases.

Table 3: Electrophysiological Variants (n = 79)

AIDP remains the predominant variant in the Indian population, consistent with global data. However, a significant proportion of axonal variants (AMAN and AMSAN) were also observed.

Seasonal Distribution of Electrophysiological Subtypes

Among the 32 cases reported during the monsoon season, AIDP was the most common (n = 21), followed by AMAN (n = 6). The axonal variants were more commonly seen during monsoon and post-monsoon periods.

Table 4: Seasonal Distribution of GBS Subtypes (n = 79)

Axonal variants (AMAN, AMSAN) showed a slight rise in incidence during monsoon and winter, suggesting a possible seasonal influence in their pathogenesis.

Statistical Analysis

A statistically significant association was found between season of onset and number of GBS cases (p = 0.02; Chi-square test). However, no significant difference was noted in the distribution of GBS subtypes across age groups (p > 0.05). There was also no statistically significant sex predilection among the electrophysiological subtypes (p = 0.36).

The study included 79 patients diagnosed with (GBS) over a period of nearly two years. The demographic analysis revealed a male predominance, with males comprising 65.8% of the study population. The mean age of the patients was 42.7 years, and the majority (70.9%) belonged to the 20–60 years’ age group, suggesting that GBS most commonly affects adults in their productive age, consistent with previous literature.

A notable finding was the seasonal variation in the incidence of GBS. The monsoon season (June–September) witnessed the highest number of cases (40.5%), followed by the post-monsoon period (24.1%). This pattern aligns with the hypothesis that infections, especially gastrointestinal and respiratory illnesses which are more common during the rainy season, may act as potential triggers for GBS. The seasonal difference was found to be statistically significant (p = 0.02), indicating a true seasonal association rather than a chance occurrence.

In terms of electrophysiological classification, AIDP (Acute Inflammatory Demyelinating Polyradiculoneuropathy) was the most prevalent subtype, observed in 54.4% of cases, followed by AMAN (20.3%) and AMSAN (17.7%). This trend is consistent with global and Indian data, where AIDP is typically the dominant form. However, a relatively high frequency of axonal variants (AMAN and AMSAN combined: 38%) was also recorded, especially during the monsoon and winter seasons, possibly indicating environmental or infectious influences on pathogenesis.

Further analysis showed that AIDP was the most common subtype across all seasons, but axonal variants were more prevalent during monsoon and winter, which could suggest a variation in antecedent infections or regional environmental factors influencing nerve involvement patterns. The distribution of GBS subtypes across different age groups and between sexes was not statistically significant, implying no preferential age or sex association with specific electrophysiological patterns.

In conclusion, the study highlights a clear seasonal trend and subtype distribution in GBS incidence at a tertiary care center in Bihar. These findings reinforce the need for heightened clinical suspicion and preparedness during peak seasons, particularly the monsoon, when the disease burden appears to rise.

Recent studies have reaffirmed the seasonal and geographical variability in Guillain-Barré Syndrome (GBS) incidence across multiple regions and populations. A study in Iran reported the highest GBS incidence during winter, with Acute Inflammatory Demyelinating Polyneuropathy (AIDP) being the most frequent subtype across all seasons [9]. Similarly, a nationwide retrospective cohort in Thailand observed a significantly higher incidence during the rainy and winter seasons compared to summer, and also noted an increasing trend in the use of IVIg therapy over the 13-year period [10]. In China, regional differences were evident; southern and southwestern regions reported peaks in winter and spring, while northern areas exhibited summer and autumn peaks, suggesting climatic influence on disease patterns [11]. A Spanish nationwide study during the COVID-19 pandemic showed a winter peak in GBS cases and a marked decrease in incidence during 2020, likely linked to lockdown-induced reductions in infectious triggers [12]. Using Google Trends data, researchers in the United States found GBS search peaks in October, aligning with colder months and correlating with sharp temperature changes—possibly mirroring trends seen in traditional epidemiological data [13].

Pediatric-focused studies also show seasonal variation. In Iranian children, GBS incidence was higher in summer and autumn, with respiratory infections frequently reported as preceding events [14]. A broader meta-analysis covering China and international data found that enteric infection rates were positively associated with GBS incidence, and global variation was influenced by both climate and infectious disease exposure [15]. Additionally, a study from eastern Turkey revealed the highest incidence in spring, with AMAN and AMSAN subtypes linked to prior gastroenteritis. Severity at admission was found to be a strong predictor of poor outcomes [16].

This study demonstrates a significant seasonal variation in Guillain-Barré Syndrome incidence, with a peak during the monsoon season, likely linked to seasonal infections. AIDP was the most common electrophysiological subtype, followed by axonal variants. These findings highlight the importance of early recognition and preparedness during high-incidence periods to ensure timely diagnosis and management.

1. Willison HJ, Jacobs BC, van Doorn PA. Guillain-Barré syndrome. Lancet. 2016;388(10045):717-27.
2. Zhang G, Li Q, Liu Y, Li R, Yu Y, Xu Y, et al. Guillain-Barré syndrome associated with COVID-19: a systematic review. Eur J Neurol. 2022;29(7):1910-22.
3. Koga M, Yuki N. Campylobacter jejuni-associated Guillain-Barré syndrome. Curr Opin Infect Dis. 2021;34(5):396-403.
4. McGrogan A, Madle GC, Seaman HE, de Vries CS. The epidemiology of Guillain-Barré syndrome worldwide. Neuroepidemiology. 2009;32(2):150-63.
5. Ropper AH, Samuels MA, Klein JP. Adams and Victor’s Principles of Neurology. 11th ed. New York: McGraw-Hill Education; 2019. p.1312-23.
6. Patil H, Kulkarni G, Kamble N. Seasonal trends and clinical profile of Guillain-Barré syndrome in South India. J Neurosci Rural Pract. 2020;11(4):583-7.
7. Yadav S, Goyal P, Agrawal D, Srivastava A, Saxena R. Seasonal variation and electrophysiological patterns in Guillain-Barré syndrome: A prospective observational study from Northern India. J Clin Neurosci. 2021;92:141-5.
8. Aladawi M, Sheikh M, Elshamam MN, Zaidan M, Basha M, Juma A, et al. Guillain-Barré syndrome: A systematic review of subtype incidence and geographical distribution. J Neurol Sci. 2021;429:119917.
9. Hatami H, Fatehi F, Heshmat R, Dirandeh E. Seasonal variation of Guillain-Barré syndrome in Iranian patients: a retrospective study. SDH. 2021;7:1-7.
10. Kasemsap N, Vorasoot N, Kongbunkiat K, Tiamkao S, Chotmongkol V, Sawanyawisuth K, et al. The epidemiology of Guillain‐Barré syndrome in Thailand over 13 years (2005‐2017): A nationwide population-based retrospective cohort study. J Peripher Nerv Syst. 2021;26:202-208.
11. Yao J, Liu Y, Liu S, Lu Z. Regional Differences of Guillain-Barré Syndrome in China: From South to North. Front Aging Neurosci. 2022;14:1-8.
12. Blanco-Ruiz M, Martín-Aguilar L, Caballero-Ávila M, Lleixà C, Pascual-Goñi E, Collet-Vidiella R, et al. A nationwide Guillain–Barré syndrome epidemiological study in Spain during the COVID‐19 years. Eur J Neurol. 2024;e16439.
13. Giordano A, Vabanesi M, Dalla Costa G, Cerri F, Comi G, Martinelli V, Fazio R. Assessing seasonal dynamics of Guillain-Barré syndrome with search engine query data. Neurol Sci. 2019;40:1015-1018.
14. Ashrafi M, Mohammadalipoor A, Naeini AR, Amanat M, Heidari M, Badv RS, et al. Clinical characteristics and electrodiagnostic features of Guillain-Barré syndrome among the pediatric population. J Child Neurol. 2020;35:448-455.
15. Xu L, Zhao C, Bao Y, Liu Y, Liang Y, Wei J, et al. Variation in worldwide incidence of Guillain-Barré syndrome: a population-based study in urban China and existing global evidence. Front Immunol. 2024;15:1-15.
16. Kiraz M, Yılgör A, Milanlıoğlu A, Çilingir V, Çağaç A, Özkan S. Clinical subtypes, seasonality, and short-term prognosis of Guillain-Barré syndrome in an Eastern city of Turkey. Neurol Asia. 2022;27:1-6.