Chronic Obstructive Pulmonary Disease (COPD) is a progressive inflammatory lung disorder characterised by persistent respiratory symptoms and airflow limitation. It is a major global health concern and currently ranks as the third leading cause of death worldwide [1]. COPD is associated not only with pulmonary impairment but also with a wide range of systemic manifestations and comorbidities that significantly impact morbidity, hospitalisations, and overall quality of life [2].

One of the most important systemic comorbidities increasingly recognised in COPD patients is Metabolic Syndrome (MetS), a cluster of metabolic abnormalities including abdominal obesity, dyslipidemia, hypertension, and impaired glucose regulation [3]. MetS has been shown to accelerate atherosclerosis, heighten cardiovascular risk, and worsen clinical outcomes among affected individuals [4]. The chronic systemic inflammation present in COPD may contribute to the development of MetS, while the metabolic abnormalities themselves may worsen respiratory symptoms and lung function decline, suggesting a bidirectional relationship [5].

Studies have reported that the prevalence of MetS in COPD patients varies widely, ranging from 21% to 53%, depending on diagnostic criteria, study population, and severity of COPD [6,7]. Evidence suggests that MetS may be more common in patients with milder COPD and may decline as the disease becomes more severe, partly due to weight loss and muscle wasting in advanced stages [8]. Conversely, certain metabolic components—particularly abdominal obesity and dysglycemia—have been associated with increased exacerbation frequency and worsening airflow obstruction [9].

Given the increasing burden of COPD in India, where underdiagnosis and heterogeneous phenotypes remain challenges [10], understanding the prevalence and clinical impact of MetS is essential. The coexistence of COPD and MetS may worsen prognosis through increased systemic inflammation, endothelial dysfunction, and altered respiratory mechanics. Identifying MetS early in COPD patients may help tailor interventions aimed at reducing exacerbations, improving lung function, and preventing cardiovascular complications.

Therefore, this study was designed to estimate the prevalence of metabolic syndrome among COPD patients and to evaluate its correlation with disease severity based on GOLD 2025 criteria. Understanding these associations may provide insights into comprehensive COPD management strategies aimed at improving both pulmonary and metabolic health outcomes.

MATERIALS AND METHODS

Study Design and Setting

This cross-sectional study was conducted in the Department of General Medicine at Government Villupuram Medical College and Hospital (GVMCH), Tamil Nadu. The study was carried out over 1 year. A total of 150 patients diagnosed with Chronic Obstructive Pulmonary Disease (COPD) were included after fulfilling the eligibility criteria.

Sample Size Estimation

The sample size was calculated based on an assumed prevalence (p) of metabolic syndrome among COPD patients of 10%, obtained from previously published data. The standard formula for estimating sample size for proportions in cross-sectional studies was used:

To compensate for potential non-response or incomplete data, a 10% contingency was added. The final sample size was therefore set at 150 patients.

Inclusion Criteria

Patients were eligible for inclusion if they:

• Were diagnosed with COPD according to GOLD guidelines based on clinical history, physical examination, chest radiography, and spirometry.

Exclusion Criteria

Patients were excluded if they had any of the following:

• Bronchial asthma or other chronic respiratory diseases
• Active pulmonary tuberculosis
• Malignancy or severe comorbid illness interfering with study completion
• Use of systemic corticosteroids within the previous 3 months

Data Collection Procedure

Data were collected using a pretested, structured proforma. The following information was obtained:

1. Demographic and Clinical Characteristics

• Name, age, sex
• Presenting complaints and duration of symptoms
• Modified Medical Research Council (mMRC) Dyspnea Grade
• History of exacerbations and hospital admissions within the past year

2. Medical and Personal History

• Past medical history: diabetes mellitus, hypertension, coronary artery disease (CAD)
• Comorbid conditions: cerebrovascular accident (CVA), chronic kidney disease (CKD), bronchial asthma
• Family history of COPD or other respiratory disorders
• Personal history: height, weight, waist circumference, smoking status, alcohol intake, tobacco use
• Dietary habits

3. Physical Examination

• Vital signs: pulse rate, blood pressure, oxygen saturation
• Systemic examination with emphasis on cardiovascular and respiratory systems

4. Laboratory and Radiological Investigations

• Chest X-ray PA view
• Pulmonary function test (spirometry)
• Complete blood count
• Liver and renal function tests
• Electrocardiography (ECG)
• 2D echocardiography: ejection fraction (EF), left ventricular function, valvular assessment, interatrial and interventricular septum (IAS/IVS), presence of pulmonary hypertension
• Biochemical parameters: fasting blood sugar (FBS), postprandial blood sugar (PPBS), serum total cholesterol, triglycerides, and HbA1c

Assessment of Metabolic Syndrome

Metabolic syndrome was diagnosed using standard criteria based on the following parameters:

• Blood pressure
• Fasting plasma glucose
• Waist circumference
• Serum triglycerides
• HDL cholesterol

Outcome Measures

The primary and secondary outcomes assessed included:

• Classification of COPD severity according to GOLD 2025 criteria
• Number of exacerbations in the previous year
• Duration of hospital stay
• Presence or absence of metabolic syndrome

Statistical Analysis

Data were entered into Microsoft Excel and analysed using SPSS version 27 and Epi Info. A combination of descriptive and inferential statistical methods was applied.

Descriptive Statistics

• Continuous variables (age, BMI, FBS, lipid profile, spirometry values) were expressed as mean ± standard deviation or median with interquartile range (IQR), depending on distribution.
• Categorical variables (sex, metabolic syndrome, GOLD stage, comorbidities) were expressed as frequencies and percentages.

Inferential Statistics

• Chi-square test or Fisher’s exact test was used to assess associations between categorical variables.
• Student’s t-test or Mann–Whitney U test was applied for comparing continuous variables between two groups (with vs. without metabolic syndrome).
• ANOVA or Kruskal–Wallis test was used for comparisons involving more than two groups.
• Pearson’s or Spearman’s correlation coefficient was used to evaluate correlations between metabolic parameters and spirometric indices.

A p-value <0.05 was considered statistically significant.

RESULTS AND OBSERVATIONS;

Baseline Characteristics of the Study Population

A total of 152 patients diagnosed with Chronic Obstructive Pulmonary Disease (COPD) were enrolled in the study. The mean age of the study population was 65.2 ± 8.1 years. The majority of the patients were male (68.4%), and 31.6% were female. The mean Body Mass Index (BMI) was 25.6 ± 3.5 kg/m², and the mean waist circumference was 98.1 ± 6.2 cm (Table 1).

Table 1: Baseline Characteristics of Study Participants (n = 152)

Graph 1: Donut chart showing prevalence of Metabolic syndrome in COPD patients

Table 2: GOLD Group Distribution in Metabolic Syndrome vs Non-Metabolic Syndrome Patients

Table 3: Comparison of Key Variables Between Metabolic Syndrome and Non-Metabolic Syndrome Groups

Graph 2: Horizontal Bar Chart Showing Comparison of Clinical and Biochemical Parameters Between COPD Patients With and Without Metabolic Syndrome

Table 4: Correlation Between FEV1 and Metabolic Indicators

Graph 3: The bar plot displays the Pearson correlation coefficients (r) between FEV1 % predicted and selected metabolic parameters.

DISCUSSION

In this cross-sectional study, the prevalence of Metabolic Syndrome (MetS) among patients with Chronic Obstructive Pulmonary Disease (COPD) was found to be substantial and clinically significant. The presence of MetS was associated with higher BMI, increased waist circumference, dyslipidemia, hyperglycemia, a higher number of exacerbations, longer hospital stay, and reduced lung function. These findings highlight the strong interrelationship between COPD and metabolic dysfunction.

The observed prevalence of MetS in our study population aligns with earlier research from Western and Asian populations, where prevalence has ranged from 21% to over 50% depending on diagnostic criteria and severity of COPD [11–13]. This variability may be attributed to differences in lifestyle, ethnicity, obesity prevalence, diagnostic definitions, and disease stage. Our findings reinforce the notion that metabolic abnormalities are common and often underrecognized in COPD patients.

The higher BMI and waist circumference in the MetS group observed in our study are consistent with studies by Marquis et al. and Watz et al., who demonstrated that abdominal obesity contributes to systemic inflammation and reduced lung mechanics in COPD [14,15]. Increased visceral fat is known to promote pro-inflammatory cytokines, which may worsen airway inflammation and contribute to airflow limitation [16]. The negative correlation between FEV1 and metabolic parameters such as fasting glucose, triglycerides, and waist circumference further supports the role of metabolic dysregulation in worsening respiratory function.

Several studies have reported a paradoxical relationship between body composition and COPD outcomes. While obesity may be associated with increased metabolic burden, underweight individuals in severe COPD often have poorer outcomes due to muscle wasting and cachexia [17]. Our findings that patients with MetS tend to fall in the higher-BMI category, predominantly belonging to GOLD group B and E, support the concept that MetS is more prevalent in earlier or moderate stages of COPD, a pattern described in previous literature [18].

The current study demonstrated significantly higher exacerbation frequency and prolonged hospital stay in the MetS group. Similar observations were made by Park et al. and Lam et al., who noted that metabolic abnormalities, particularly hyperglycemia and dyslipidemia, are associated with increased risk of exacerbations and inflammation-driven airway instability [19,20]. These outcomes may be partly explained by insulin resistance and endothelial dysfunction, which impair gas exchange and increase susceptibility to infections [21].

Reduced HDL cholesterol observed in the MetS group has also been implicated in the progression of COPD. HDL possesses anti-inflammatory and antioxidant properties, and its deficiency may lead to increased oxidative stress, exacerbating airway inflammation [22]. The significant positive correlation between HDL and FEV1 in our study highlights its potential protective role in lung function.

The association between MetS and pulmonary hypertension identified on echocardiography is noteworthy. Previous studies suggest that metabolic abnormalities contribute to endothelial dysfunction and increased vascular remodelling, thereby elevating pulmonary artery pressures [23]. This emphasises the importance of comprehensive cardiovascular assessment in COPD patients with MetS.

The strengths of this study include a well-defined study population, comprehensive metabolic and respiratory assessments, and correlation analyses that highlight clinically meaningful associations. However, certain limitations must be acknowledged. The cross-sectional design precludes causal inference. Additionally, the study was conducted at a single tertiary care centre, limiting generalizability to other populations. Longitudinal studies are required to evaluate the long-term impact of MetS on COPD progression, exacerbation rates, and mortality.

Overall, the findings of this study underscore the importance of early identification and management of MetS in COPD patients. Integrating metabolic screening into COPD management protocols could potentially improve clinical outcomes, reduce hospitalization, and slow disease progression. Lifestyle interventions targeting obesity, diet, and physical activity—combined with pharmacological management—may provide a multidimensional approach for improving both metabolic and respiratory health.

CONCLUSION

This study demonstrates that Metabolic Syndrome is highly prevalent among patients with Chronic Obstructive Pulmonary Disease, and its presence is significantly associated with worse clinical outcomes. COPD patients with MetS exhibited higher BMI, larger waist circumference, impaired glucose and lipid profiles, increased frequency of exacerbations, longer hospital stays, and reduced lung function. The negative correlation between key metabolic parameters and FEV1 highlights the substantial influence of metabolic abnormalities on respiratory impairment.

These findings emphasise the importance of routine metabolic screening in all COPD patients, irrespective of disease severity. Early identification and management of metabolic risk factors may help reduce exacerbations, improve lung function, and lower hospitalisation burden. A combined approach that integrates pulmonary rehabilitation with targeted lifestyle modification and metabolic control may offer the most significant benefits. Future longitudinal studies are needed to evaluate the long-term impact of MetS on COPD progression, morbidity, and survival.

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