Ischemic stroke in young patients with obesity: the role of chronic non-specific inflammation

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Introduction

The incidence of ischemic stroke (IS) in young adults under 45–50 years of age is increasing worldwide. Currently, the prevalence of IS among cerebrovascular disorders reaches 20% [1, 2]. In most studies, “young stroke” is defined as the first acute cerebrovascular event in adults aged 18–50 years. The pathogenesis of this condition involves the interplay of multiple modifiable and genetic risk factors that interact in a complex manner [3].

IS of undetermined etiology, or cryptogenic stroke, is common in young patients, and its proportion is higher in this group compared to older age groups [4, 5]. This fact limits the choice of secondary prevention strategies and emphasizes the need to search for additional IS causes.

Recent studies associate the increasing incidence of IS in young adults with the rising prevalence of vascular risk factors, including arterial hypertension, type 2 diabetes, and metabolic syndrome (MS), with particular emphasis on dyslipidemia and obesity [2, 6].

In this context, MS is considered a factor triggering premature cellular damage, based on abnormal processes such as oxidative stress and cytokine imbalance [7, 8]. The concept of cerebro-metabolic health integrates the understanding of the close relationship between cerebrovascular disorders and metabolic disturbances [9]. This becomes particularly relevant given the growing number of individuals with unfavorable lifestyle characteristics, which necessitates a deep understanding of the disease mechanisms and the identification of opportunities for its prevention and treatment at various stages of progression [10].

Obesity contributes to stroke through multiple direct and indirect pathophysiological mechanisms, including metabolic, endocrine, structural, humoral, hemodynamic, functional, and immunological alterations [11]. The close relationship between inflammation and atherosclerosis has a long history, with key milestones shaping modern understanding. Rudolf Virchow first proposed that atherosclerosis involves an inflammation, though this theory long remained undeveloped [12]. The connection between metabolic disorders, alongside inflammation and obesity, and atherosclerosis has been established [13]. Inflammation mediated by innate and adaptive immune responses is recognized as a central factor in atherogenesis, from endothelial dysfunction to plaque formation, progression, and rupture. Metabolic inflammation orchestrates complex interactions between lipids, immune cells, and the vascular wall, providing a mechanistic link between obesity and atherosclerosis: adipokines (e.g., leptin, adiponectin) secreted by adipose tissue induce insulin resistance, hypercoagulability, lipid accumulation, and smooth muscle cell proliferation [14, 15].

There is accumulating evidence that immune function and metabolism are not independent processes but interconnected through complex relationships. Currently, the hyperproduction of pro-inflammatory cytokines by visceral adipose tissue is considered an important linking pathogenetic mechanism in the cardiac, vascular, and endocrine comorbidity [16]. The activity of immune cells is tightly regulated by metabolic pathways, which can modulate immune responses by influencing inflammation, vascular integrity, and thrombotic potential [17].

However, it is known that brain cells and endothelial cells can synthesize inflammatory mediators (tumor necrosis factor-α [TNF-α], interleukins [IL]-6, -8, and -10) in response to acute cerebral ischemia-mediated damage after stroke, thereby exacerbating neuronal injury [18]. Cytokines (IL-6, IL-8, TNF-α) capable of stimulating pro-inflammatory processes increase acute-phase protein levels, and their indicators correlate with the size of the lesion. Other counter-regulatory cytokines (IL-10) suppress the activity of pro-inflammatory ILs, and their lower concentrations are associated with worse stroke outcomes [19–21]. Thus, studying the cytokine profile reflecting chronic nonspecific inflammation and determining its role in the ischemic stroke in young adults remain unresolved and important research tasks.

Our previous research demonstrated that obesity is the most prevalent among the most frequently analyzed traditional risk factors for IS of unknown etiology. A correlation was established between severe early outcomes and elevated body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR); the role of systemic inflammation combined with endothelial dysfunction and platelet hyperaggregation in increasing prothrombogenic potential was revealed [22]. The current study stage was based on confirming the hypothesis that chronic nonspecific inflammation initiated by adipose tissue may play a significant role in the IS of unknown etiology in young individuals.

Study aim: to assess the role of inflammatory mediators, biochemical and hemostasis statuses in young patients with ischemic stroke of unknown etiology and obesity.

Materials and Methods

In of a prospective study, complaints, medical history, clinical status, and laboratory parameters were analyzed in patients aged 18–50 years with primary IS verified by instrumental methods, hospitalized at Regional Vascular Center No. 2 of the City Clinical Hospital of Novosibirsk Region between 2022 and 2025.

Patients were divided into groups according to current obesity criteria: Group 1 (main) — 34 patients with obesity (BMI ≥ 30.0 kg/m²), mean age 40.8 ± 7.6 years; Group 2 (comparison) — 32 patients with normal BMI values (18.5–24.9 kg/m²), mean age 41.0 ± 7.8 years.

In each group, the following were determined:

• anthropometric parameters (height, weight, BMI, mean systolic and diastolic blood pressure); WC, hip circumference (HC), WHR;
• blood chemistry parameters: fasting blood glucose, C-reactive protein (CRP), uric acid, total cholesterol (TC), low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol, triglycerides, homocysteine;
• hemostasis parameters: factor VIII, factor IX, von Willebrand factor (vWF), antithrombin III activity, protein C activity;
• inflammatory activity markers — leukocyte and neutrophil counts;
• levels of TNF-α, IL-6, IL-8, and IL-10.

Interleukin profile assessment was performed during the early recovery period (32 ± 2 days) after IS in both patient groups to exclude erroneous results due to the immunological response to IS.

Statistical analysis was conducted using Prism software version 10. All parameters were tested for normality of distribution using the Shapiro-Wilk test. Data are presented as mean (M) and standard deviation (SD) for normally distributed variables. For non-normally distributed data, median (Me) and interquartile range ([Q₁; Q₃]) were used. Comparison of two groups for normally distributed quantitative variables was performed using Welch’s t-test. Comparison of two groups for non-normally distributed variables was performed using the Mann–Whitney U test. The chi-square test was applied for comparing qualitative variables. Statistical analysis included two-way ANOVA to assess the effects of sex, obesity, and their interaction on the study parameters. The Šidák correction was used to adjust p-values for pairwise comparisons (p_adj), with statistical significance set at p < 0.05.

Results

The anthropometric characteristics of the 66 examined patients are presented in Table 1. Comparative analysis revealed no intergroup differences in age, sex, or presence of risk factors such as smoking and arterial hypertension. However, the groups differed significantly in weight, BMI, and anthropometric parameters — WC and WHR.

 

Table 1. Anthropometric characteristics of examined young patients with IS

Parameter		Group 1 (n = 34)	Group 2 (n = 32)	p
Mean age, years	Мe [Q1; Q3]	42.0 [36.00; 42.25]	43.5 [36.0; 48.0]	0.8706
Sex:
male	n (%)	16 (47.06)	22 (68.75)	0.0871
female	n (%)	18 (52.94)	10 (31.25)
Body weight, kg	М ± SD (min–max)	96.53 ± 11.02 (80–130)	67.44 ± 8.14 (53–85)	< 0.0001
BMI, kg/m2;	Мe [Q1; Q3]	34.6 [31.2; 37.2]	23.9 [21.0; 24.2]	< 0.0001
WC, cm
male	Мe [Q1; Q3]	110.0 [101.0; 123.3]	88.0 [75.5; 99.5]	0.0001
female	Мe [Q1; Q3]	106.5 [99.75; 125.50]	73.0 [69.5; 86.0]	0.0001
HC, cm
male	Мe [Q1; Q3]	107.5 [99.25; 116.50]	98.50 [89.75; 102.00]	0.002
female	Мe [Q1; Q3]	102.0 [98.75; 120.30]	110.5 [97.0; 116.5]	0.8596
WHR
male	Мe [Q1; Q3]	1.025 [0.93; 1.13]	0.905 [0.83; 1.02]	0.0028
female	Мe [Q1; Q3]	1.055 [0.93; 1.13]	0.61 [0.65; 0.89]	0.0001
Smoking	n (%)	14 (41.2)	17 (53.2)	0.0885
Systolic blood pressure, mm Hg	М ± SD	135.90 ± 20.08	129.60 ± 16.90	0.1414
Diastolic blood pressure, mm Hg	М ± SD	87.20 ± 14.02	84.31 ± 11.90	0.331

 

When assessing these parameters, a sex-specific specificity analysis was performed. Statistically significant differences were found among male groups for all parameters: WC (p = 0.0001), HC (p = 0.002), and WHR (p = 0.0028). In the analysis of female groups, significant differences were observed for WC and WHR (p = 0.0001), while no significant differences were found for HC.

Due to identified differences in sex distribution between groups (47% males in Group 1 vs. 69% in Group 2; p = 0.0871), an additional analysis was conducted to exclude potential sex-related confounding effects.

Comparative analysis of laboratory parameters between groups of patients with IS of unknown etiology depending on obesity status revealed significant differences in several key parameters: LDL-C, HDL-C, uric acid, homocysteine levels, and values of blood coagulation factors VIII, IX, von Willebrand factor (vWF), and antithrombin III (Table 2).

 

Table 2. Laboratory parameters in young patients with IS of unknown etiology stratified by sex

Parameter		Group 1 (n = 34)	Group 2 (n = 32)	p1	p2	p3	p4	padj
Total cholesterol, mmol/L
male	М ± SD	5.15 ± 1.32	4.70 ± 1.23	0.8145	0.0437	0.2836	0.2925	0.5204
female	М ± SD	5.77 ± 1.30	5.48 ± 1.60				0.6359	0.8268
Triglycerides, mmol/L
male	Мe [Q1; Q3]	1.80 [1.30; 2.83]	0.88 [0.65; 1.88]	0.2943	0.5861	0.1225	0.0210	0.0873
female	Мe [Q1; Q3]	1.51 [0.97; 2.23]	1.21 [1.03; 1.47]				0.5316	0.9334
LDL-C, mmol/L
male	Мe [Q1; Q3]	3.50 [2.76; 4.37]	2.35 [2.07; 3.45]	0.6689	0.4209	0.0382	0.0177	0.0994
female	Мe [Q1; Q3]	3.75 [3.09; 4.03]	3.15 [1.80; 4.40]				0.4561	0.4761
HDL-C, mmol/L
male	М ± SD	1.05 ± 0.37	1.37 ± 0.53	0.7686	0.0084	0.0112	0.0348	0.0521
female	М ± SD	1.38 ± 0.19	1.67 ± 0.56				0.1916	0.2537
Glucose, mmol/L
male	Мe [Q1; Q3]	6.40 [5.70; 7.77]	6.12 [4.60; 7.07]	0.6011	0.3673	0.6559	0.3753	0.9977
female	Мe [Q1; Q3]	5.65 [4.87; 7.00]	6.30 [4.90; 7.17]				0.3873	0.7780
Uric acid, mmol/L
male	М ± SD	368.9 ± 93.9	304.9 ± 75.9	0.7057	0.0426	0.0011	0.0329	0.0416
female	М ± SD	333.2 ± 78.3	253.3 ± 83.2				0.0229	0.0326
Protein C activity, %
male	Мe [Q1; Q3]	107.0 [78.7; 123.0]	105.0 [95.2; 115.5]	0.3458	0.4147	0.2233	0.6557	0.9702
female	Мe [Q1; Q3]	123.8 [97.0; 141.2]	88.3 [75.7; 140.1]				0.4424	0.2960
Homocysteine, mmol/L
male	Мe [Q1; Q3]	12.7 [10.9; 28.7]	11.7 [9.3; 14.7]	0.1688	0.4287	0.0001	0.0997	0.0838
female	Мe [Q1; Q3]	15.9 [12.3; 42.1]	9.8 [7.5; 13.1]				0.0013	0.0016
Coagulation factor VIII, %
male	Мe [Q1; Q3]	144.5 [131.8; 157.3]	119.1 [88.2; 139.5]	0.3498	0.7233	0.0241	0.0091	0.0268
female	Мe [Q1; Q3]	139.0 [135.7; 147.0]	135.5 [83.3; 160.3]				0.4006	0.6104
Coagulation factor IX, %
male	М ± SD	134.4 ± 27.7	113.4 ± 39.1	0.5611	0.6684	0.0030	0.0612	0.1076
female	М ± SD	135.6 ± 27.9	104.9 ± 31.9				0.0209	0.0396
vWF, %
male	М ± SD	227.9 ± 29.5	122.2 ± 25.5	0.0181	0.0413	< 0.0001	< 0.0001	< 0.0001
female	М ± SD	194.4 ± 35.9	124.7 ± 19.3				< 0.0001	< 0.0001
Antithrombin III activity, %
male	М ± SD	98.7 ± 9.2	108.6 ± 11.1	0.8243	0.8841	0.0041	0.0048	0.0296
female	М ± SD	99.9 ± 12.8	108.4 ± 16.1				0.1704	0.1487
WBC ×109/L
male	Мe [Q1; Q3]	10.3 [8.7; 11.6]	8.1 [6.9; 9.5]	0.0451	0.0661	0.9935	0.0076	0.2197
female	Мe [Q1; Q3]	9.7 [7.1; 12.3]	8.9 [7.9; 13.4]				0.8322	0.3381
Monocytes ×109/L
male	Мe [Q1; Q3]	0.55 [0.40; 0.75]	0.57 [0.35; 0.63]	0.1907	0.6392	0.3622	0.5054	0.9389
female	Мe [Q1; Q3]	0.45 [0.35; 0.70]	0.47 [0.31; 0.94]				0.8598	0.2772

Note. Here and in Table 3: p₁ — statistical significance of the interaction between sex and obesity factors (calculated using two-way ANOVA); p₂ — statistical significance of the sex factor effect (calculated using two-way ANOVA); p₃ — statistical significance of the obesity factor effect (calculated using two-way ANOVA); p₄ — statistical significance of differences between groups within each sex subgroup (men and women separately), calculated using pairwise comparisons (Student’s t-test for normally distributed data or Mann–Whitney U test for non-normally distributed data).

 

When examining sex effects, statistically significant influences were found for total cholesterol (p = 0.0437), HDL-C (p = 0.0112), uric acid (p = 0.0326), and vWF (p < 0.0001).

Interaction between obesity and sex factors was detected for vWF (p = 0.0181) and WBC count (p = 0.0451).

Sex stratification revealed significant obesity effects on key markers in both men and women: triglyceride levels, LDL-C, HDL-C, uric acid, homocysteine, blood coagulation factors VIII and IX, vWF, WBC, and antithrombin III activity. However, after Šidák correction, significant sex differences persisted for HDL-C (p_adj = 0.0112), uric acid (p_adj = 0.0326), vWF (p_adj < 0.0001), homocysteine (p_adj = 0.0016), blood coagulation factor IX (p_adj = 0.0396), and antithrombin III (p_adj = 0.0296).

Comparative analysis of cytokine status assessment also showed statistically significant differences between the two patient groups (Table 3). When examining parameters reflecting nonspecific inflammation, significant differences were found between obese and non-obese groups for most inflammation markers: TNF-α (р < 0.0001), IL-8 (р = 0.0162), IL-10 (р = 0.0045), CRP (р < 0.0001), and neutrophils (р = 0.0199) (Table 3).

 

Table 3. Characteristics of the immune status in young patients with IS of unknown etiology by sex

Parameter		Group 1 (n = 34)	Group 2 (n = 32)	p1	p2	p3	p4	padj
TNF-α, pg/mL
male	М ± SD	36.31 ± 8.57	23.53 ± 6.68	0.2432	0.2080	< 0.0001	< 0.0001	< 0.0001
female	М ± SD	31.49 ± 8.13	23.34 ± 7.23				0.0126	0.0178
IL-6, pg/mL
male	Мe [Q1; Q3]	3.51 [2.01; 5.71]	3.24 [2.01; 5.25]	0.9636	0.3797	0.4532	0.7423	0.7828
female	Мe [Q1; Q3]	3.15 [2.03; 4.52]	3.07 [1.67; 4.72]				0.7683	0.8748
IL-8, pg/mL
male	М ± SD	22.64 ± 8.36	14.86 ± 6.57	0.1064	0.9982	0.0162	0.0046	0.0042
female	М ± SD	19.54 ± 7.00	17.97 ± 7.98				0.6083	0.8315
IL-10, pg/mL
male	Мe [Q1; Q3]	12.70 [11.08; 17.43]	9.50 [7.15; 13.02]	0.6800	0.2490	0.0045	0.0069	0.0635
female	Мe [Q1; Q3]	10.56 [9.10; 14.75]	9.75 [8.70; 10.88]				0.1749	0.4838
CRP, mg/L
male	Мe [Q1; Q3]	27.30 [11.55; 48.27]	1.31 [0.80; 6.06]	0.8055	0.4652	< 0.0001	< 0.0001	0.0005
female	Мe [Q1; Q3]	31.40 [8.52; 54.90]	3.20 [1.28; 11.80]				0.0030	0.0018
Neutrophils, ×109/L
male	М ± SD	7.53 ± 2.38	5.24 ± 2.73	0.4312	0.3507	0.0199	0.0178	0.0310
female	М ± SD	7.63 ± 3.55	6.48 ± 1.80				0.5876	0.5131

 

Discussion

The modern concept of cerebrometabolic health is crucial for understanding various pathophysiological mechanisms of cerebrovascular disorders. This study may additionally support the contribution of dysmetabolic changes to the transformation and rejuvenation of stroke.

Epidemiological studies demonstrate an increasing incidence of IS among young individuals, associated with rising prevalence of traditional risk factors typical for elderly populations: hypertension, dyslipidemia, type 2 diabetes, and obesity [23]. A particularly pronounced trend is the increase in obesity among young IS patients, with cryptogenic stroke cases showing significantly higher obesity rates compared to sex- and age-matched control groups [24, 25].

Metabolic disturbances (insulin resistance and dyslipidemia) enhance prothrombogenic potential by promoting endothelial dysfunction and activating coagulation/deactivating anticoagulation systems.

MS represents a cluster of metabolic abnormalities that significantly elevate stroke risk. Inflammation, a fundamental physiological response aimed at maintaining homeostasis, plays a central role in MS. This persistent inflammatory state contributes to MS pathogenesis by inducing insulin resistance, endothelial dysfunction, and adipose tissue remodeling. Diagnostic criteria for MS, including central obesity, dyslipidemia, hyperglycemia, and hypertension, are linked to inflammation mediated by activation of both innate and adaptive immune systems. Among these mechanisms, chronic low-grade inflammation stands out as a critically important factor [26]. The relationship between inflammation and metabolic dysfunction is often described by the term “immunometabolism” [27]. Impaired immune response and its failure to resolve, as observed in metabolic disorders, should not be overlooked in clinical patient management due to its association with population mortality [28].

Obesity is characterized by disruption of innate and acquired immunity, as well as central and peripheral meta-inflammation [29].

Chronic unresolved systemic inflammation and adipose tissue inflammation trigger cardiometabolic diseases associated with obesity [30]. The concept of “metabolic inflammatory syndrome” is proposed by some researchers as a new integrative medicine concept [31].

Analysis of metabolic predictors for IS course in young adults demonstrated the undeniable mutually potentiating contribution of disorders of carbohydrate and lipid metabolism, prothrombotic blood state, and obesity status [22]. The identified differences in main fat distribution parameters in men, along with differences in WC and WHR despite the absence of statistically significant differences in HC between female IS patient groups, emphasize the contribution of abdominal fat distribution to the realization of increased cerebrovascular event risk.

Metabolic inflammation in obesity is closely associated with elevated levels of circulating proinflammatory cytokines. The first evidence of obesity-related inflammation was mentioned in a study reporting increased TNF-α in patients with high BMI [32]. Obesity-mediated changes, primarily meta-inflammation, impair vascular endothelial function, triggering prothrombotic and proatherogenic states that lead to platelet activation, leukocyte adhesion, vasoconstriction, pro-oxidation, mitogenesis, coagulation disorders, atherosclerosis, and thrombosis with subsequent cardiovascular complications [33]. Neutrophils are known to promote monocyte infiltration into the intima by releasing reactive oxygen species and proinflammatory cytokines such as TNF-α, IL-6, and IL-8 [14].

Our results indicate that patients with obesity show significantly altered endothelial dysfunction markers (vWF) compared to the IS group with normal BMI, thereby creating a favorable background for vascular wall remodeling and lipid/foam cell accumulation, which in turn leads to arterial obstruction.

The potential causal role of specific cytokines in stroke pathogenesis remains incompletely studied and controversial. TNF-α is considered a primary pro-inflammatory cytokine. In the brain, microglial cells initiate local TNF-α production immediately after ischemia. Both neuroprotective effects and adverse effects of TNF-α have been reported [34].

Our study in young stroke patients stratified by obesity revealed significant differences in their immune status. Young obese patients demonstrated significantly elevated levels of pro-inflammatory cytokines and inflammatory markers compared to stroke patients with normal body weight. Elevated TNF-α levels confirm its active involvement in obesity-related inflammation and potentiation of endothelial dysfunction, which may aggravate cerebral ischemia progression.

IL-6, primarily produced by monocyte-derived macrophages, lymphoid cells, T-cells, B-cells, granulocytes, mast cells, and endothelial cells, is a multifunctional cytokine, though its role in ischemic stroke pathogenesis remains debatable. Circulating IL-6 increases after ischemic stroke, particularly in patients with infarcts >3 cm, and may serve as a prognostic marker according to some data [34]. Elevated IL-6 levels induced by ischemia, hypoxia, oxidative stress, vascular occlusion, and inflammation partly trigger acute-phase protein production in the liver, thereby stimulating leukocytosis and thrombosis, ultimately causing cardio-cerebrovascular diseases including ischemic stroke [20].

Regarding IL-6, our results revealed no statistically significant differences between the studied patient groups. This may indicate that during the acute phase of IS, IL-6 levels are determined more by the fact of ischemia itself and the subsequent inflammatory response than by obesity. In this patient cohort, obesity does not enhance IL-6 production in response to ischemia, or its role in stroke pathogenesis in obese patients is compensated by other mechanisms, including the influence of the temporal factor.

Elevated IL-8 levels after IS are associated with neutrophil mobilization, increased activity, and migration to the ischemic area, exacerbating local inflammation, expanding ischemic lesions, and worsening severe disease course and disability [35]. Our data demonstrated a significant increase in IL-8 levels in obese patients.

IL-10 is an anti-inflammatory cytokine primarily produced by monocytes. In the brain, this molecule promotes the survival of both neuronal and glial cells by antagonizing pro-apoptotic cytokine activity [34]. The increase in IL-10 levels likely aims to limit the damaging effects of pro-inflammatory cytokines such as TNF-α and IL-8 and protect neuronal and glial cells from apoptosis. However, despite IL-10 anti-inflammatory effect, its elevation failed to fully neutralize inflammation, as evidenced by high CRP and other pro-inflammatory marker levels in this group.

In cardiovascular diseases, adipose tissue dysfunction is a critical factor for increased thrombosis risk, and specifically, visceral obesity affects IS risk through immune system imbalance and metabolic disturbances, promoting a prothrombotic state [36]. Visceral adipose tissue sustains low-grade inflammation, oxidative stress, and endothelial dysfunction, leading to a prothrombotic state and playing a key role in the development of cardiovascular events [24]. Circulating cytokines in obese patients are suggested to stimulate coagulation and inhibit fibrinolytic activity, with abdominal obesity being most significant for thrombotic complications.

Given the multifactorial nature of mechanisms governing hemostasis and blood rheology in obesity, and the lack of consensus on the interplay between obesity, thrombosis risk, and metainflammation severity, more detailed study of this problem appears necessary.

While strengths of our study include results confirming our hypothesis that adipose tissue-initiated chronic nonspecific inflammation plays a key role in IS in young obese patients, possible limitations exist related to the small sample size.

Conclusion

Cryptogenic IS in young individuals with obesity is accompanied by increased prothrombogenic activity of the blood, systemic inflammatory changes, and hyperproduction of cytokines. The mutually potentiating processes of metabolic inflammation and endothelial dysfunction may serve as key factors in the initiation and development of hemostasis disorders, which play a leading role in the pathogenesis of acute cerebrovascular events.

Impaired cerebrometabolic health due to obesity, systemic inflammation, and endothelial dysfunction constitutes a key link in the pathogenesis of IS in young patients. This necessitates a revision of traditional preventive approaches with emphasis on early detection of metabolic and inflammatory changes and their personalized management.

About the authors

Maria S. Ponomareva

Novosibirsk State Medical University; State Novosibirsk Regional Clinical Hospital

Author for correspondence.
Email: annaly-nevrologii@neurology.ru
ORCID iD: 0000-0001-5141-3292

neurologist, Neurology and Neurosurgery Center, laboratory assistant, Neurology department

Russian Federation, Novosibirsk; Novosibirsk

Larisa A. Shchepankevich

Novosibirsk State Medical University; State Novosibirsk Regional Clinical Hospital

Email: annaly-nevrologii@neurology.ru
ORCID iD: 0000-0001-6951-2205

Dr. Sci. (Med.), Head, Neurology department, Chief, Neurology and Neurosurgery Center, curator, Neurology department

Russian Federation, Novosibirsk; Novosibirsk

Boris B. Pinkhasov

Novosibirsk State Medical University

Email: annaly-nevrologii@neurology.ru
ORCID iD: 0000-0002-4579-425X

Dr. Sci. (Med.), Head, Pathophysiology department

Russian Federation, Novosibirsk

Мarine M. Tanashyan

Russian Center of Neurology and Neurosciences

Email: annaly-nevrologii@neurology.ru
ORCID iD: 0000-0002-5883-8119

Dr. Sci. (Med.), Professor, Full Member of RAS, Deputy Director for research, Head, 1^st Neurological department, Institute of Clinical and Preventive Neurology

Russian Federation, Moscow

Ksenia V. Antonova

Russian Center of Neurology and Neurosciences

Email: annaly-nevrologii@neurology.ru
ORCID iD: 0000-0003-2373-2231

Dr. Sci. (Med.), leading researcher, 1^st Neurological department, Institute of Clinical and Preventive Neurology

Russian Federation, Moscow

Elena V. Taneeva

State Novosibirsk Regional Clinical Hospital

Email: annaly-nevrologii@neurology.ru
ORCID iD: 0000-0002-6538-6069

Head, Stroke unit No. 2

Russian Federation, Novosibirsk

References

Supplementary files