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Introduction

Chronic obstructive pulmonary disease (COPD) is a group of progressive respiratory disorders caused by chronic airway inflammation, and it is characterized by airway obstruction or persistent airflow limitation.¹ Major symptoms of this disease include chronic cough, sputum production, and dyspnea. Clinically, COPD often exhibits recurrent exacerbations and is difficult to cure, and the common diseases of COPD include asthma, emphysema, and chronic bronchitis.² Progressive decline in lung function leads to reduced physical activity (PA) in individuals with COPD and multiple complications, including congestive heart failure (CHF) and diabetes, as well as psychological issues (such as severe anxiety and depression). These factors severely impair patients’ quality of life and can be life-threatening. The World Health Organization reports that nearly 299 million individuals worldwide suffer from COPD. It stands as the fourth major cause of death worldwide,³ with incidence rates rising every year. For example, in 2017, the prevalence of COPD in the United States was 15.2% among current cigarette smokers, 7.6% among former smokers, and 2.8% among adults who had never smoked.⁴ COPD typically develops insidiously and is often overlooked during the early stages. Patients usually receive medical attention only after symptoms become pronounced, thereby resulting in a poor prognosis.^5,6 COPD also requires substantial healthcare resources, imposing a considerable burden on global medical and public health systems. Therefore, identifying factors influencing COPD and developing effective prevention strategies to reduce its incidence is of great importance in alleviating this global health and socioeconomic burden.

Obesity, a prevalent metabolic disorder, is linked to a range of systemic complications. It can cause functional impairments in organs and tissues, ultimately resulting in organic lesions. The relationship between obesity and COPD has received growing attention.⁷ In particular, the accumulation of visceral fat (VF) in individuals with obesity may elevate the risk of COPD.⁸ Obesity may trigger pulmonary disease by inducing oxidative stress (OS) and systemic inflammation.^7,9 Thus, a reliable indicator for assessing VF is essential for effective chronic disease control and enhancing quality of life.

The Body Roundness Index (BRI), a novel anthropometric metric based on waist circumference (WC) and height, can comprehensively reflect visceral adiposity and body fat percentage.¹⁰ Increasing evidence demonstrates that BRI holds great potential for disease risk stratification. Elevated BRI levels are associated with many chronic diseases, such as chronic kidney disease (CKD), diabetes, and cardiovascular diseases (CVD), including atherosclerosis and hypertension (HP).^11,12 Nonetheless, the relationship between BRI and COPD remains unclear. Using data from the National Health and Nutrition Examination Survey (NHANES) 2013–2018, this study aims to examine the association between BRI and COPD.

Materials and Methods

Study Design and Population

Conducted by the National Center for Health Statistics (NCHS), the NHANES employs a stratified, multistage sampling design and various data collection methods to analyze the nutritional and health status of American children and adults. All NHANES protocols were approved by the Ethics Review Board of NCHS. Written informed consent was provided by all enrolled individuals. To ensure participant confidentiality, all data were de-identified. According to the Ethical Review Methods for Life Science and Medical Research Involving Human Participants, Article 32 exempts certain research from requiring ethical approval under specific conditions. Research utilizing legally obtained public data or data derived from non-intrusive observation of public behavior does not require ethical approval.

Data from three consecutive NHANES cycles (2013–2018) were collected. A total of 29,400 individuals were initially included. Individuals were excluded if they were under 20 years of age (n = 12,343), had incomplete BRI data (n = 1766), or lacked information on key covariates, including smoking status (n = 9), alcohol consumption (n = 1011), educational background (n = 9), marital status (n = 3), and PA (n = 5). Ultimately, 14,254 individuals were enrolled (Figure 1).

Figure 1 Flowchart of participant selection.

COPD Outcomes

COPD was defined according to affirmative responses to any of the following self-reported questions: “Have you been diagnosed with emphysema?”, “Have you been diagnosed with chronic bronchitis?”, or “Has a doctor ever informed you that you have COPD?” Individuals who answered “no” to all three questions were considered non-COPD.

Calculation of Anthropometric Indices

BRI was treated as the independent variable. According to the BRI calculation formula proposed by Tomas et al,¹⁰ data on WC and height were extracted from the anthropometric measurements in NHANES for the calculation.

Assessment of Covariates

A wide range of lifestyle, demographic, and health-related variables were collected: alcohol consumption, poverty income ratio (PIR), sex, total cholesterol, educational background, total energy intake (TEI), race/ethnicity, high-density lipoprotein cholesterol (HDL), protein, total sugar, age, HP, marital status, height, carbohydrate, dietary fiber (DF), CVD, total fat, smoking status, vigorous and moderate recreational activities, weight, eosinophil count, diabetes, white blood cell count, monocyte count, WC, and BMI.

Smoking status was categorized as never smoked, current smoker, or former smoker based on whether the individuals had smoked more than 100 cigarettes over a lifetime and their duration since quitting. Individuals were defined as having diabetes according to the following criteria: self-reported physician diagnosis of diabetes, fasting blood glucose >126 mg/dL, administration of glucose-lowering medication or insulin, or glycated hemoglobin (HbA1c) ≥6.5%.¹³ HP was defined according to the following criteria: self-reported HP, current use of antihypertensive medication, average systolic blood pressure ≥130 mmHg or average diastolic blood pressure ≥80 mmHg across three measurements.¹⁴ CVD was defined if individuals reported the following physician diagnoses: coronary heart disease, CHF, heart attack, or stroke.¹³

Statistical Analysis

Following NHANES methodological guidelines, this study applied the recommended multistage probability sampling weights to account for the complex survey design during statistical analyses. BRI was divided into four quartiles based on its distribution: Q1 (1.167–3.965), Q2 (3.965–5.282), Q3 (5.282–6.940), and Q4 (6.940–23.482). The Kolmogorov–Smirnov test was used to assess the normality of continuous variables. Given that these continuous variables were non-normally distributed, they were expressed as medians and interquartile ranges, and between-group comparisons were conducted using the Kruskal–Wallis test. Categorical variables were expressed as frequencies and percentages, and between-group comparisons were made using weighted Chi-square tests. Multicollinearity among covariates was examined, and variables with a variance inflation factor (VIF) ≥ 5 were excluded. Weighted logistic regression (WLR) models were employed to investigate the association between BRI and COPD prevalence. Three models were constructed: Model 1 was unadjusted; Model 2 was adjusted for race, sex, and age; Model 3 was further adjusted for educational background, marital status, PIR, TEI, DF, smoking status, alcohol consumption, moderate PA, diabetes, HP, CVD,¹⁵ direct HDL cholesterol, and eosinophil percentage. Restricted cubic spline (RCS) analysis with five knots was employed to investigate the dose-response relationship between BRI and COPD prevalence. Subgroup analyses stratified by sex, smoking status (never, current, former), alcohol consumption, diabetes, HP, and CVD were conducted to test the robustness of the findings. Statistical significance was defined as two-sided p-values less than 0.05. R version 4.4.2 was used to conduct statistical analysis.

Result

Baseline Characteristics of Study Population

Table 1 illustrates the basic characteristics of the included 14,254 individuals (stratified by BRI quartiles). The overall prevalence of COPD was 8.3%, corresponding to a weighted estimate of 210,015,248 individuals. The median age of individuals was 48 years, with 49% men and 51% women. BRI was divided into four quartiles: Q1 (1.167–3.965), Q2 (3.965–5.282), Q3 (5.282–6.940), and Q4 (6.940–23.482).

Table 1 Baseline Characteristics of Participants

The highest BRI quartile had a greater proportion of women and Mexican Americans in comparison to the lowest BRI quartile. Moreover, higher BRI levels were associated with lower engagement in vigorous PA, older age, and being widowed. Moreover, the highest BRI group exhibited a greater prevalence of CVD and diabetes.

Associations Between BRI and the Likelihood of COPD

The association between BRI and COPD prevalence was examined using WLR models (Table 2). For continuous BRI, the results indicated that BRI was positively associated with COPD prevalence. In Model 1 (unadjusted), each unit increase in BRI was associated with a 16.8% increase in the likelihood of COPD (P < 0.001). In model 2 (adjusted for race/ethnicity, sex, and age), this association remained stable (OR = 1.141, 95% CI: 1.106–1.176, P < 0.001). In model 3 (further adjusted for comorbidities, dietary intake, and laboratory indicators), the association persisted, although slightly attenuated (OR = 1.085; 95% CI: 1.036–1.136; P = 0.002).

Table 2 WLR Analysis of BRI and COPD

For categorical BRI, Model 1 demonstrated that individuals in the highest BRI quartile had a 2.972-fold higher likelihood of COPD in comparison to those in the lowest quartile (P < 0.001). This association remained significant in Model 2 (OR = 2.162; 95% CI: 1.728–2.706; P < 0.001) and Model 3 (OR = 1.466; 95% CI: 1.091–1.969; P = 0.015).

RCS Analysis

RCS analysis examined the dose-response relationship between BRI and the likelihood of developing COPD. The results revealed an approximately linear positive association between BRI and the likelihood of developing COPD (P for nonlinearity = 0.155) (Figure 2).

Figure 2 The association between BRI and the likelihood of developing COPD through RCS analysis. The RCS curve was adjusted for race/ethnicity, sex, PIR, educational background, marital status, age, TEI, DF, smoking status, alcohol consumption, diabetes, HP, moderate PA, direct HDL cholesterol, and eosinophil percentage.

Subgroup Analysis

The robustness of the association between BRI and COPD prevalence was assessed by subgroup analyses based on sex, smoking status, alcohol consumption, age, diabetes, HP, and CVD. The results demonstrated a significant association between BRI and the likelihood of developing COPD in all subgroups except for former smokers and individuals with CVD. In addition, interaction tests revealed substantial differences in COPD prevalence across subgroups stratified by sex, HP, and CVD (Figure 3).

Figure 3 The association between BRI and COPD prevalence through subgroup analyses.

Discussion

This cross-sectional study, which involved 14,254 individuals, suggested that higher BRI levels were positively associated with COPD prevalence in the US adult population. This association was confirmed to be linear based on RCS analysis. Although no threshold effect was detected in the RCS analysis, it was observed that when BRI exceeded 5.332, COPD prevalence increased significantly with rising BRI. Subgroup analyses further supported the robustness of these findings. Interaction tests indicated that sex, HP, and CVD significantly modified this association. These results suggest that maintaining an appropriate BRI level is particularly important for COPD prevention. Moreover, BRI, due to its cost-effectiveness and efficiency, may hold substantial value in large-scale COPD screening and risk stratification.

Obesity, as a major contributor to the risk of COPD, has garnered increasing research interest. Prior studies mainly employed BMI to quantify obesity, but BMI cannot differentiate between fat mass (FM) and fat-free mass (FFM). Since FFM and FM exert distinct effects on pulmonary physiology, Zhang et al highlighted the necessity of investigating the association between fat distribution and COPD risk.¹⁶ Although WC reflects abdominal fat accumulation (AFA), it does not distinguish between subcutaneous and visceral fat. The A Body Shape Index may outperform BMI and WC in predicting ACM, but is less reliable for predicting chronic diseases.^17–19 In contrast, BRI, which integrates WC and height into a cylindrical model, more accurately estimates abdominal obesity.^20–22 Current studies have demonstrated relationships between BRI and obstructive respiratory diseases. BRI is a superior predictor of obstructive sleep apnea.²³ Xu et al demonstrated that both weight-adjusted waist index (WWI) and BRI are independent factors influencing asthma risk, with BRI exhibiting better predictive performance than WWI.²⁴ These conclusions support the utility of BRI in early COPD detection and risk stratification. Therefore, timely identification and intervention in individuals with elevated BRI may help slow disease progression and improve their quality of life.

Several mechanisms may underlie the association between BRI and COPD. Individuals with high BRI tend to have significant AFA, which increases the COPD risk. This finding has been supported by previous research.^25,26 Lam et al reported that central obesity is associated with both obstructive and restrictive ventilatory impairments in COPD.²⁶ This may be attributed to obesity-induced systemic inflammation, which can impair the normal structure and function of lung tissue.²⁷ Abnormal accumulation of visceral adipose tissue (VAT) can cause adipocyte hypoxia by impairing ventilation and reducing tissue oxygenation,^7,15 ultimately triggering inflammatory responses. The activation of immune cells (such as neutrophils, macrophages, and eosinophils) contributes to pulmonary inflammation.^28,29 Furthermore, VAT is recognized as an active endocrine organ.²⁸ In individuals with obesity, VAT homeostasis is disrupted, thereby leading to abnormal secretion of adipokines. This includes elevated levels of pro-inflammatory mediators such as leptin, IL-6, and TNF-α, and decreased levels of anti-inflammatory factors such as adiponectin. These imbalances can further stimulate the NOD-like receptor family pyrin domain-containing 3 inflammasome and IL-1β signaling,^28,30,31 potentially accelerating airway remodeling, impairing lung function, and ultimately contributing to COPD progression.

Furthermore, elevated levels of chronic low-grade inflammatory mediators related to obesity, such as TNF-α, leptin, and IL-6 secreted by adipose tissue, are associated with neutrophil-mediated OS responses.⁹ Neutrophil activation contributes to the depletion of systemic antioxidants by releasing reactive oxygen species (ROS),³² thereby reducing pulmonary defense capacity. Moreover, it can exacerbate damage to the alveolar wall by releasing proteases. In addition, ROS can activate inflammation-related signaling pathways,^32,33 alter the extracellular matrix, and stimulate goblet cell activation, thereby resulting in mucus hypersecretion and subsequent airway obstruction. Current evidence demonstrates that neutrophil elastases damage elastic fibers and mucociliary structures in the lungs, ultimately impairing mucus clearance and promoting goblet cell metaplasia and mucin production.⁹ These effects further reduce pulmonary compliance and increase airway resistance, thereby elevating COPD risk.

Moreover, inadequate PA also contributes to COPD progression among individuals with obesity. In individuals with COPD, obesity may further impair ventilation through such mechanical constraints as limited diaphragmatic movement and increased chest wall resistance.^7,34,35 Additionally, these constraints can elevate the risk of comorbid conditions, including metabolic syndrome and CVD, thus indirectly accelerating disease deterioration.^15,30

According to subgroup analyses, no significant association between BRI and COPD was observed in participants with CVD or in former smokers, suggesting that the generalizability of BRI may vary across populations. The lack of a significant association in the CVD subgroup may be attributable to residual confounding from unmeasured factors, such as medication interventions or complications, or to potential reverse causality.³⁶ After smoking cessation, systemic inflammation persisted in patients with COPD, whereas it declined in healthy former smokers.³⁷ This difference may affect the BRI–COPD relationship through pathways involving fat and lung function. Existing research reveals that although smoking cessation is frequently accompanied by weight gain, it does not lead to an elevated risk of COPD.³⁸ These findings warrant further investigation in prospective cohort studies. In addition, sex, HP, and CVD significantly modified the association between BRI and the odds of having COPD. The association between obesity and CVD may be mediated by shared intermediate metabolic risk factors, including impaired glucose tolerance, insulin resistance, HP, and hypertriglyceridemia, all of which contribute to adverse cardiovascular events.^39,40 In the CVD subgroup, several plasma markers are linked to obesity.⁴¹ For example, abnormally low natriuretic peptide levels are associated with an increase in total white adipose tissue mass.⁴² These factors may partly disrupt the BRI–COPD relationship, distinguishing this subgroup from individuals without CVD. A U.S.-based study suggests that men are more prone to exhibit AFA.⁴³ Abdominal fat, particularly VAT, increases COPD risk through metabolic and inflammatory mechanisms.⁴⁴

This study has several strengths. First, the generalizability of our results was enhanced by incorporating a nationally representative sample and applying a complex multistage probability sampling design. Second, this study systematically analyzed the association between BRI and COPD and adjusted for multiple covariates to minimize confounding. Moreover, regression and subgroup analyses further reinforced the reliability of the observed associations.

Nevertheless, some limitations should be acknowledged. First, a causal relationship between BRI and COPD cannot be established because of the cross-sectional design. Second, since the data were derived from a US population, the generalizability of the findings is uncertain and warrants further validation. Third, compared with standardized diagnoses based on lung function tests, self-reported diagnoses may substantially underestimate the true prevalence of COPD, resulting in underdiagnosis. Consequently, self-report diagnosis is an imprecise tool for identifying COPD patients,⁴⁵ potentially affecting the interpretation of the findings. Hence, these findings should be confirmed by prospective cohort studies.

Conclusion

This study observed a positive linear association between BRI and the likelihood of developing COPD. This result suggests that BRI could serve as a potential marker for assessing the likelihood of COPD and may help reduce the cost of early COPD screening. In addition, BRI has been shown to be more accurate and sensitive than BMI and WC. Therefore, these findings hold significant implications for clinical practice and epidemiological research. Future studies should further investigate whether BRI-based interventions can improve clinical outcomes in individuals with COPD, as well as explore the potential threshold value of BRI.

Data Sharing Statement

The datasets analysed during the current study are available in the National Center for Health Statistics (NCHS), https://www.cdc.gov/nchs/nhanes/about/index.html.

Ethics Approval and Informed Consent

All NHANES protocols obtained approval from the Ethics Review Board of NCHS. Written informed consent was offered by enrolled individuals.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work..

Funding

The authors declare that they did not receive any funding from any source.

Disclosure

The authors declare that they have no competing interests.

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Introduction

Glial fibrillary acidic protein (GFAP) is an intermediate filament protein in astrocytes and an important component of the astrocyte cytoskeleton, which plays an important role in astrocyte regeneration, synaptic plasticity, and reactive gliosis.^1,2 Abnormal regulation and expression of GFAP is related to various neurological disorders, including infectious diseases of the central nervous system (CNS), neurodegenerative diseases, cerebral edema, traumatic brain injury, and psychiatric disorders.³ In 2016, the United States’ Mayo Clinic Lennon team identified anti-GFAP antibodies in the cerebrospinal fluid and serum of patients with meningoencephalomyelitis and named it autoimmune GFAP astrocytopathy (GFAP-A).⁴ GFAP-A affects the brain, meninges, spinal cord, and/or optic nerve. Its clinical manifestations are complex and varied, including fever, headache, ataxia, seizures, mental and behavioral abnormalities, movement disorders, tremors, and autonomic and cognitive dysfunction.^5,6 In addition, due to the heterogeneity of the disease, GFAP-A may become life-threatening within weeks or even days in some patients due to central hypoventilation or severe autonomic dysfunction.^6–9 Currently, there are no unified standards or guidelines for the treatment of GFAP-A. Most patients with GFAP-A respond well to steroid therapy and achieve complete or partial remission upon discharge, and a favorable prognosis. However, some patients are prone to recurrence, leaving varying degrees of functional disability and even death.^5,6,8,10 Therefore, it is vital to establish biomarkers to assess appropriate steroid therapy and clinical treatment decisions for GFAP-A.

Monitoring disease progression and detecting potentially severe disease at an early stage are crucial for managing individual patients with GFAP-A, as this affects clinical treatment decisions. Studies focused on biomarkers related to disease severity and prognosis of GFAP-A are in the beginning stages. Segal et al¹¹ found that higher neurofilament light chain (NfL) concentrations were associated with magnetic resonance imaging abnormalities and a poor prognosis in patients with GFAP-A, suggesting that NfL may be a biomarker of disease severity and prognosis in GFAP-A. Fu et al¹² found that MIP-3α was positively correlated with disease severity by detecting the expression levels of 200 serum cytokines in patients with GFAP-A. Kimura et al¹³ observed elevated levels of TNF-α, IL-27, IL-6, CCL20, GFAP, S100 calcium-binding protein B, and NfL in GFAP-A cerebrospinal fluid samples, suggesting that these may be potential biomarkers of GFAP-A. However, these potential biomarkers of GFAP-A have some limitations in clinical practice. First, due to economic considerations, not all patients will undergo testing for the above biological indicators. Second, due to the requirements of the detection methods, not all hospitals are capable of conducting the detection of the above biological indicators. Finally, cytokines and chemokines in cerebrospinal fluid may be degraded during storage, resulting in inaccurate test results. Therefore, there is an urgent need for objective, inexpensive, and clinically accessible biomarkers to guide the clinical management of patients with GFAP-A.

Pathological studies have revealed the extensive infiltration of lymphocytes, monocytes, and neutrophils around intracranial blood vessels in patients with GFAP-A, which is consistent with the typical imaging feature of GFAP-A with perivascular linear radiolucent enhancement perpendicular to the ventricle of the brain.^6,14–16 These findings suggest that neuroinflammation caused by the interaction of lymphocytes, monocytes, neutrophils, microglia, and antibodies secreted by plasma cells is a possible pathogenesis of GFAP-A. Neutrophils, monocytes, and lymphocytes are inflammatory markers measured using routine blood examinations that are often used to reflect the inflammatory status in patients. The neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR) are more representative of the inflammatory state of the body than neutrophil, monocyte, or lymphocyte counts alone. Studies have observed that the NLR and MLR are associated with the severity, activity, and prognosis of some immune diseases of the CNS, including multiple sclerosis (MS), autoimmune encephalitis (AE), neuromyelitis optica spectrum disorder (NMOSD), and myelin-oligodendrocyte glycoprotein antibody-associated disease (MOGAD).^17–19 Similar to the NLR and MLR, the platelet-to-lymphocyte ratio (PLR) and systemic immune-inflammatory index (SII) are associated with the severity, activity, and prognosis of several immune diseases, including NMOSD, MOGAD, and AE.^19–21 However, the associations of the NLR, MLR, PLR, and SII with the disease severity and prognosis of patients with GFAP-A remain unclear. This study analyzed the associations between these inflammatory indices and the disease severity and prognosis of patients with GFAP-A.

Materials and Methods

Study Patients

Sixty-two patients with GFAP-A who were treated at the First Affiliated Hospital of Zhengzhou University between January 2020 and August 2024 were included in this study (Figure 1). The age range of these patients was 3 to 68 years old. This study followed the principles of the Declaration of Helsinki and was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (No. 2022-KY-0053). All patients included in this study were newly diagnosed with GFAP-A. Currently, there is no unified standard for the diagnosis of GFAP-A. Some patients are often misdiagnosed as viral encephalitis, tuberculous meningitis, or acute disseminated encephalomyelitis, leading to delayed and incorrect treatment.^22,23 We have summarized the following diagnostic points based on relevant literature: (1) acute or subacute onset; (2) clinical manifestations of meningeal, cerebral, spinal cord, or optic nerve involvement or a combination of symptoms; (3) paraventricular linear radial enhancement and/or spinal cord long-segment involvement on MRI; (4) serum and/or cerebrospinal fluid anti-GFAP antibody positivity; (5) responsive to steroid hormone therapy; (6) the exclusion of other diagnoses.^4–6,14 In addition, all blood samples used in this study were obtained prior to the administration of steroid hormones or immunosuppressants, and all relevant clinical data were available for all patients in this study. Patients were excluded from this study if it was not their first GFAP-A attack or they had comorbidities of a tumor, rheumatism, infectious diseases, hematological diseases, other autoimmune diseases, or psychiatric disorders. Patients with severe liver, kidney, or other organic diseases, or other autoimmune antibodies, such as anti-AQP4 antibodies or anti-MOG antibodies, were also excluded from this study. Pregnant patients, those who had been administered steroid hormones or immunosuppressants prior to blood collection, and those with antiplatelet drug therapy use (including aspirin and/or clopidogrel) within 4 months of the study were excluded. Finally, patients with incomplete clinical information were also excluded from this study.

Figure 1 Flowchart of included and excluded patients.

Data Collection

Basic clinical data, laboratory findings, and imaging data were collected from all of the patients. Basic clinical data included sex, age, prodromal events, time from first symptom to consultation, time from first symptom to diagnosis, need for mechanical ventilation, need for intensive care unit (ICU) treatment for at least 48h, clinical manifestations, immunotherapy, and length of hospitalization. Laboratory findings included routine blood examination results, liver function, kidney function, rheumatological and immune indices, tumor markers, cerebrospinal fluid results, and serum and/or cerebrospinal fluid antibody test results. Routine blood examinations were conducted within 24 hours of admission and before immunotherapy in all patients during acute attacks (episodes of neurological deterioration lasting more than 24 hours). Routine blood examination results included white blood cell, platelet, neutrophil, monocyte, lymphocyte, eosinophil, basophil counts. The NLR, MLR, PLR, and SII were calculated based on the routine blood examination results (NLR=neutrophils/lymphocytes; MLR=monocytes/lymphocytes; PLR=platelets/lymphocytes; SII=neutrophils*platelets/lymphocytes). Long-term follow-up was conducted after immunotherapy.

Assessment Scale

The primary endpoints of this study were disease severity at admission and prognosis. The Clinical Assessment Scale for Autoimmune Encephalitis (CASE) consists of nine items, including seizure, memory dysfunction, psychiatric symptoms, consciousness, language problem, dyskinesia/dystonia, gait instability and ataxia, brainstem dysfunction, and weakness, which compensates for the shortcomings of the modified Rankin Scale (mRS) in assessing non-motor symptoms of autoimmune encephalitis.²⁴ GFAP-A mainly manifests as a combination of the above symptoms; therefore, CASE was used to assess the disease severity of GFAP-A at admission, and patients were categorized based on the CASE score into mild (CASE ≤4) or severe (CASE ≥5) groups. The mRS was used to assess the patients’ conditions at discharge and follow-up, and patients were categorized based on the mRS score at 1-year follow-up (one year after discharge) into the good (mRS ≤2) or poor (mRS ≥3) prognosis groups.

Statistical Analysis

All statistical analyses were conducted using SPSS statistical software (version 25.0; IBM, Chicago, IL, USA). Continuous data with normal distribution were presented as mean ± standard deviation, and an independent samples t-test was used to compare the continuous data. Continuous data that did not conform to a normal distribution were presented as median and interquartile range, and the Mann–Whitney U-test was used to compare the continuous data. Categorical data were compared using the chi-square test or Fisher’s exact probability method. Spearman correlation analysis was used to test the correlations between the NLR, MLR, PLR, and SII and the severity of GFAP-A. Receiver operating characteristic (ROC) curves were used to evaluate the ability of the NLR, MLR, PLR, and SII to predict the severity of GFAP-A, and the area under the curve (AUC) was calculated. Binary logistic regression was used to analyze the risk factors affecting the disease severity and prognosis of patients with GFAP-A. This study only included patients with GFAP-A and did not compare the GFAP-A groups with healthy controls or other patient groups. All statistical analyses were two-sided, and the level of statistical significance was set at P < 0.05.

Results

Patient Characteristics

Sixty-two patients with GFAP-A were included in this study. The clinical features and laboratory findings are shown in Table 1. The clinical manifestations of the patients in this study were diverse and included fever (n=43, 69.35%), headache (n=32, 53.33%), ataxia (n=22, 35.48%), movement disorders (n=15, 24.19%), cognitive dysfunction (n=11, 17.74%), mental and behavioral abnormalities (n=6, 9.68%), seizures (n=5, 8.06%), consciousness disorders (n=17, 27.42%), autonomic dysfunction (n=34, 54.84%), tremors (n=9, 14.52%), and visual abnormalities (n=10, 16.13%). All patients were administered first-line immunotherapy, and no patient received second-line immunotherapy. Fifty-five patients were administered oral steroid hormones and/or mycophenolate mofetil after discharge.

Table 1 Clinical Features and Laboratory Findings of Patients with GFAP-A

The NLR Was Associated with the Severity of GFAP-A

Clinical Data of Patients in the Mild and Severe Groups

The mild group included 42 patients (67.74%), and the severe group included 20 patients (32.26%). The neutrophil count, NLR, MLR, PLR, and SII were significantly higher in patients in the severe group than in those in the mild group (P <0.05). The basophil count was significantly lower in patients in the severe group than in those in the mild group (P <0.05). No significant differences were observed between the two groups in terms of sex, age, prodromal events, time from first symptom to consultation, and white blood cell, platelet, monocyte, lymphocyte, or eosinophil counts (Table 2).

Table 2 Clinical Data of Patients in the Mild and Severe Groups

Correlations of the NLR, MLR, PLR, and SII with the Severity of GFAP-A

As shown in Figure 2, the NLR, MLR, and SII were positively correlated with the CASE score of patients with GFAP-A (r=0.365, P=0.003; r=0.283, P=0.026; r=0.313, P=0.013), while no significant correlation was observed between the PLR and the CASE score (r=0.236, P=0.064).

Figure 2 Correlations of the NLR, MLR, PLR, and SII with the CASE score (a–d). Abbreviations: NLR, neutrophil-to-lymphocyte ratio; MLR, monocyte-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immune-inflammatory index; CASE, the Clinical Assessment Scale for Autoimmune Encephalitis.

The Predictive Value of the NLR, MLR, PLR, and SII for the Severity of GFAP-A

Figure 3 showed ROC curves of the NLR, MLR, PLR, and SII to predict the severity of GFAP-A. The optimal cut-off value of the NLR for predicting the severity of GFAP-A was 3.05, with a sensitivity of 80%, specificity of 57.1%, and AUC of 0.729 (95% Confidence Interval/CI: 0.600–0.858, P=0.004). The optimal cut-off value of the MLR for predicting the severity of GFAP-A was 0.35, with a sensitivity of 65%, specificity of 66.7%, and AUC of 0.66 (95% CI: 0.512–0.807, P=0.044). The optimal cut-off value of the PLR for predicting the severity of GFAP-A was 176.34, with a sensitivity of 65%, specificity of 73.8%, and AUC of 0.656 (95% CI: 0.504–0.808, P=0.049). The optimal cut-off value of the SII for predicting the severity of GFAP-A was 1055.43, with a sensitivity of 60%, specificity of 76.2%, and AUC of 0.702 (95% CI: 0.563–0.841, P=0.01). Details of the AUC, optimal cut-off, sensitivity, and specificity were shown in Table 3.

Table 3 The Predictive Value of the NLR, MLR, PLR, and SII for the Severity of GFAP-A

Figure 3 ROC curves of the NLR, MLR, PLR, and SII for predicting the severity of GFAP-A. Abbreviations: ROC, receiver operating characteristic; GFAP-A, autoimmune glial fibrillary acidic protein astrocytopathy; NLR, neutrophil-to-lymphocyte ratio; MLR, monocyte-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immune-inflammatory index.

The NLR as an Independent Risk Factor for the Severity of GFAP-A

The neutrophil, lymphocyte, and basophil counts, and the NLR, MLR, and SII were correlated with the GFAP-A severity (P <0.05) (Table 4). The NLR (Odds Ratio/OR=1.238, 95% CI: 1.003–1.473, P=0.01) was identified as an independent risk factor for the severity of GFAP-A (Table 4).

Table 4 Binary Logistic Regression Analysis of Factors Related to the Severity of GFAP-A

The mRS Score at Discharge as an Independent Risk Factor for the Poor Prognosis One year After Discharge of GFAP-A

Among the 62 patients included in this study, 12 were followed up for less than 1 year, and two were lost to follow-up; therefore, these 14 patients were excluded from the follow-up analysis. The remaining 48 patients were grouped according to the mRS score at 1-year follow-up (one year after discharge). Forty-one patients were included in the good prognosis group (85.42%) and seven patients were included in the poor prognosis group (14.58%). The need for mechanical ventilation, ICU treatment, length of hospitalization, CASE score at admission and at the worst condition, and mRS score at discharge were associated with a poor prognosis one year after discharge (P <0.05) (Table 5). The mRS score at discharge (OR=7.966, 95% CI: 1.120–56.658; P=0.038) was identified as an independent risk factor for the poor prognosis one year after discharge of patients with GFAP-A (Table 6).

Table 5 Univariate Logistic Regression Analysis of Factors Associated with a Poor Prognosis in GFAP-A

Table 6 Multivariate Logistic Regression Analysis of Factors Associated with a Poor Prognosis in GFAP-A

Changes in the NLR, MLR, PLR, and SII After Administration of Immunotherapy

Routine blood examination results during follow-up were available for 32 patients, including 27 with a good prognosis and five with a poor prognosis. The NLR, MLR, and SII were not significantly different before and after immunotherapy (P >0.05); however, the PLR was significantly reduced after immunotherapy (153.01 vs 121.16, P=0.039) (Table 7). Twenty-four patients had a reduced PLR after immunotherapy. However, a decrease in the PLR after immunotherapy was not correlated with the prognosis of patients with GFAP-A (P=0.779).

Table 7 The NLR, MLR, PLR, and SII Before and After Immunotherapy in GFAP-A

Discussion

GFAP-A is a novel autoimmune inflammatory disease of the CNS associated with antibodies to GFAP. Meningitis, encephalitis, myelitis, optic neuritis, or a combination of these serve as the main clinical manifestation, and the typical imaging feature is perivascular linear radiolucent enhancement perpendicular to the ventricle of the brain.^5,6 Most patients with GFAP-A respond well to treatment such as steroid hormones, intravenous immunoglobulin, plasma exchange, immunosuppressants, and monoclonal antibodies and have a good prognosis; however, some patients relapse, and the disease may progress to residual dysfunction or death.^6,25 In the current study, the majority of patients with GFAP-A were middle-aged, and the number of male patients was higher than the number of female patients. These findings differ from those of previous studies, in which no difference in sex was reported among patients with GFAP-A.^4,6,22 These differing results may be related to racial and economic differences or a result of the insufficient sample size in the current study. Similar to previous studies, patients with GFAP-A presented with fever, headache, ataxia, movement disorders, and autonomic dysfunction in this study.^4,6 Despite the gradual increase in the number of studies regarding GFAP-A, few studies regarding biological indicators related to the disease severity and patient prognosis have been reported. Objective and inexpensive biological indicators are urgently needed to guide clinical practice.

Neuroinflammation caused by interactions between lymphocytes, monocytes, neutrophils, microglia, and antibodies secreted by plasma cells may be a pathogenetic mechanism of GFAP-A.^6,14–16 Long et al¹⁴ retrospectively analyzed 19 patients with GFAP-A, including four who underwent brain tissue biopsy. The neuropathological results suggested that the inflammatory lesions were mainly infiltrated by T lymphocytes, B lymphocytes, plasma cells, scattered neutrophils, and eosinophils with obvious activation of microglia around the cerebral blood vessels.¹⁴ Yamakawa et al²⁶ reviewed the neuropathological results at autopsy in two patients with GFAP-A, and reported findings consistent with those reported by Long et al. Iorio et al¹⁵ observed inflammatory changes in the local meningeal tissue infiltrated by macrophages and CD8⁺ T cells in a meningeal biopsy specimen of a patient with GFAP-A. Neutrophils and monocytes play important roles in the innate immune system. Neutrophils can damage the blood-brain barrier and increase its permeability by releasing a variety of proinflammatory factors, thus promoting the development of autoimmune diseases of the CNS.^27–29 Moreover, neutrophils can induce monocytes to accumulate at the site of inflammation by releasing large amounts of cytokines. Subsequently, monocytes differentiate into macrophages, which play an important role in the formation of antigen-presenting cells and activate lymphocytes to initiate acquired immunity.^28,30,31 T and B lymphocytes are vital members of acquired immunity, and cytotoxic CD8⁺ T cell-mediated immune responses play an important role in the pathogenesis of GFAP-A.^4,32 In addition, it has been reported that platelets can transmit signals for leukocyte differentiation, migration, and infiltration via the secretion of cytokines, chemokines, and their receptors, thus playing an important role in inflammatory diseases of the CNS, such as MS and MOGAD.^33–36 Platelets can increase the permeability of the blood-brain barrier by promoting neutrophil migration, thereby promoting the progression of neuroinflammation.^37,38 Neutrophils, monocytes, lymphocytes, and platelets are biological indicators derived from routine blood examinations that reflect the inflammatory state of the body. Routine blood examinations are easily performed, clinically accessible, and inexpensive, and can provide a detailed snapshot of the peripheral immune cells. Currently, a single leukocyte subtype count is commonly used for the clinical assessment of the degree of inflammation in the body, which is susceptible to physiological and/or pathological factors. In contrast, the NLR, MLR, PLR, and SII have higher stability and can more accurately assess the inflammatory degree in the body and reflect disease severity.³⁹

Monitoring disease progression and the early detection of potentially severe disease are critical for the management of individual patients with GFAP-A, as it can help determine the optimal treatment, such as the need for combination first-line immunotherapy, early use of monoclonal antibodies, or addition of immunosuppressants. However, there is currently no ideal biomarker for predicting the severity and progression of GFAP-A. The NLR, MLR, PLR, and SII are related to the severity, activity, and prognosis of several autoimmune diseases of the CNS. A study of 199 patients with AE reported that the NLR and MLR were significantly increased in patients with severe AE, and a high NLR and MLR were independent risk factors for patients with severe AE, though they were not associated with patient prognosis.¹⁷ In contrast to the conclusions of Liu et al, Qiu et al⁴⁰ found that a high NLR on admission was correlated with a poor prognosis in patients with AE, which may be due to differences in research methods, sample size, and follow-up times between the studies. Hemond et al⁴¹ found that the NLR and MLR were closely associated with the severity of neurological dysfunction and whole-brain atrophy in patients with MS, and that patients with a high NLR were more likely to relapse.⁴² Lin et al¹⁹ reported that a high PLR level was positively correlated with MOGAD activity and relapse and that the PLR can be used to differentiate between MS and NMOSD.⁴³ Moreover, Mao et al²¹ reported that the SII was related to the disease severity and prognosis of patients with AE. No studies regarding the associations of the NLR, MLR, PLR, and SII with GFAP-A have been reported. Therefore, this is the first study to investigate the associations between these inflammatory indices and the severity and prognosis of GFAP-A. In the current study, the NLR (OR=1.238, 95% CI: 1.003–1.473, P=0.01) was identified as an independent risk factor for the severity of GFAP-A and was positively correlated with the CASE score (r=0.365, P=0.003). The optimal cut-off value of the NLR was 3.05, with a sensitivity of 80%, specificity of 57.1%, and AUC of 0.729 (95% CI: 0.600–0.858, P=0.004), which supported the use of the NLR as a predictor of GFAP-A severity. In addition, the NLR, MLR, PLR, and SII measured at admission were not related to the prognosis of GFAP-A (P>0.05). The PLR significantly decreased after immunotherapy (P=0.039), though the reduction in the PLR after immunotherapy was not correlated with the prognosis of GFAP-A (P>0.05). Last, the mRS score at discharge (OR=7.966, 95% CI: 1.120–56.658; P=0.038) was identified as an independent risk factor for the poor prognosis one year after discharge of patients with GFAP-A. These findings are still in the preliminary stages and require external validation in the future. Our data suggest that an NLR of 3.05 or higher may indicate a severe condition of GFAP-A. For these patients, physicians should optimize clinical treatment decisions as early as possible, such as extending the duration of steroid therapy, combining first-line immunotherapy, or starting second-line immunotherapy early, which may reduce the likelihood of recurrence or residual neurological dysfunction in patients. However, we have not evaluated the correlation between NLR levels and the specific methods and duration of immunotherapy of the patients, and we will further investigate this correlation in the future. Taken together, these results indicate that the NLR is a potential biomarker to predict the severity of GFAP-A and monitor disease progression.

This study is not without limitations. First, it was a small-sample and single-center study, rendering it susceptible to selection bias. Second, this was a retrospective study. The presented data were retrospectively obtained from an electronic medical record system, and the follow-up of most patients was conducted retrospectively, which may lead to information and confounding biases. Therefore, a prospective, multicenter study with a larger sample size is needed to verify the conclusions of the current study. Finally, since tumors, infectious diseases, hematological disorders, and other autoimmune diseases can affect NLR, MLR, PLR, and SII, we excluded patients with these comorbidities. Therefore, the results of this study may not be extrapolated to all patients with GFAP-A.

Conclusion

This is the first study to explore the associations between the NLR and GFAP-A severity. The NLR is a clinically inexpensive, accessible, and widely-available inflammatory marker that is positively associated with the severity of GFAP-A. Therefore, this biomarker can help physicians monitor disease progression and identify patients with severe disease at an early stage, thus allowing for optimal treatment decisions.

Data Sharing Statement

The data that support the results of this study are available from the corresponding author on reasonable request.

Ethics Approval and Informed Consent

This study was performed in line with the principles of the Declaration of Helsinki and approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (No. 2022-KY-0053). Informed consent was obtained from all individual participants included in the study.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was supported by Henan Province Science and Technology Tackle Project (grant number: 252102310280), the Outstanding Young Talent Cultivation Project of Henan Science and Technology Innovation Talents (grant number: YXKC2022037), and Young and Middle-aged Academic Leaders of Henan Provincial Health Commission.

Disclosure

All authors declare no conflicts of interest in this work.

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What is a balanced diet for the heart?

What you put on your plate matters more than you think. A diet loaded with saturated fats, processed foods, and excess sugar can dramatically elevate your cholesterol levels, trigger weight gain, and skyrocket your blood pressure, all key culprits contributing to heart disease, as per the Centers for Disease Control and Prevention.

Instead of falling into these dietary traps, focus on embracing a heart-friendly diet:

1. Fruits and vegetables: “Bursting with antioxidants, fibre, and important vitamins, these powerhouses fortify your heart”, Dr Gautam Rege, Interventional Cardiologist, Jupiter Hospitals, tells Health Shots.
2. Whole grains, such as oats, brown rice, and whole wheat bread, can effectively lower cholesterol levels according to The Nutrition Source.
3. Lean proteins: Opt for fish, beans, lentils, and skinless poultry to boost your protein intake without the unhealthy fats, according to the UK’s National Health Institute.
4. Good fats: Incorporate heart-protective unsaturated fats from sources like olive oil, nuts, seeds, and avocados into your meals as per Harvard Health.
5. Pro Tip: Follow the “80-20 rule”, aim for 80% of your diet to be made up of nutrient-dense whole foods while allowing for 20% to enjoy occasional indulgences.

How to stay physically active?

Your heart is a muscle, and like any muscle, it thrives on regular exercise, as per the National Heart, Lung, and Blood Institute. A sedentary lifestyle invites a host of cardiovascular issues. In contrast, physical activity strengthens your heart, enhances blood circulation, reduces cholesterol, and maintains a healthy weight, all of which are critical factors in lowering heart attack risks.

1. Aim for 30 minutes daily: Aim for at least 30 minutes of moderate activity, such as brisk walking, cycling, or swimming, five times a week.
2. Include strength training: “Engage in strength training exercises two to three times per week to build lean muscle mass, which aids in regulating blood sugar and metabolism”, shares the cardiologist.
3. Small changes count: Even simple adjustments, like opting for stairs over elevators or taking a walking break after meals, can accumulate significant health benefits.
4. Consistency is key: Focus on enjoyable activities that encourage a daily habit of movement as per the British Heart Foundation.

How to manage stress to reduce heart attack risk?

Chronic stress is a silent killer, unleashing harmful hormones like cortisol and adrenaline that can elevate blood pressure and overstrain your heart. Moreover, stress often drives unhealthy coping mechanisms such as overeating, smoking, or increased alcohol consumption, as per Harvard Health.

To protect your heart against the attack of stress, consider these methods:

1. Mindful breathing or meditation: Dedicate 10–15 minutes daily to these practices to reduce stress levels, as per the American Heart Association.
2. Engage in relaxing hobbies: “Immerse yourself in enjoyable activities like gardening, painting, or reading that can divert your mind from stressors”, explains Dr Rege.
3. Prioritise sleep: Aim for quality rest, ideally 7–8 hours per night, as poor sleep can escalate stress and adversely affect your heart health, as per the American Heart Association.
4. Stay socially connected: Engaging with family or friends can significantly lighten emotional burdens, as per Innovation in Aging.

How to detox your body from smoking and alcohol?

Smoking isn’t just bad; it’s downright lethal for your heart. It wreaks havoc on blood vessels, diminishes oxygen levels in the blood, and accelerates plaque buildup in arteries, significantly elevating heart attack risk as per the National Heart, Lung, and Blood Institute.

• Kick the habit: Quitting smoking can drastically reduce your heart attack risk within months. If you find it challenging, professional help, support groups, or nicotine replacement therapies can ease the process, as per the American Cancer Society.
• Limit alcohol consumption: Excessive alcohol intake raises blood pressure and can trigger heart rhythm problems. If you choose to drink, moderation is essential, with no more than one drink per day for women and two for men, as recommended by the Centers for Disease Control and Prevention.

Dr Gautam Rege says, “the safest option for your heart is to avoid alcohol entirely”

How to monitor your health daily?

Prevention paired with awareness is key. Many heart risk factors, including high blood pressure, high cholesterol, and diabetes, often whisper silently until it’s too late, as per the Centers for Disease Control and Prevention. Regular health check-ups can help you catch problems early on.

1. Annual check-ups: Schedule to have your blood pressure, cholesterol, and blood glucose checked at least once a year as per the American Heart Association.
2. Track your weight and waistline: “Abdominal fat is closely correlated with increased heart disease risk; keeping an eye on these metrics can provide valuable insights”, explains the cardiologist.
3. Stay alert for symptoms: Consult a doctor immediately if you experience warning signs like chest pain, shortness of breath, or abnormal fatigue.