Across much of the developing world, illness profiles are changing. Urban populations, once burdened by infections, now face a surge of lifestyle-driven disorders. Among these, metabolic syndrome (MetS) has become a silent but expanding threat. It represents not a single illness, but a network of disturbances—excess abdominal fat, abnormal lipids, rising blood pressure, and impaired glucose use—that together strain the cardiovascular system (1–3). India mirrors this global pattern. Rapid urbanization, dietary excess, and reduced physical activity have pushed the estimated national prevalence of MetS to nearly one-third of adults in major cities (4,5). For years, its consequences were viewed mainly through the lens of heart disease or diabetes. Yet accumulating evidence points elsewhere: the lungs may also be drawn into this metabolic spiral. Normal breathing relies on the flexibility of the chest wall, the recoil of lung tissue, and intact microcirculation. Each of these can be quietly altered by obesity, insulin resistance, and chronic low-grade inflammation, core elements of MetS (6,7). Adipose tissue is not inert; it acts as an endocrine organ releasing cytokines such as TNF-α and IL-6, which trigger oxidative stress and fibrosis within airway structures (12). Hyperglycemia adds another layer by stiffening vascular endothelium and impairing gas diffusion (13). International datasets reinforce this relationship. In the ARIC and NHANES III cohorts, individuals with metabolic syndrome showed measurable reductions in both FEV₁ and FVC, independent of smoking status or age (11). Asian research echoes the same trend. Korean, Japanese, and Chinese studies have linked the number of MetS components to a stepwise fall in lung capacity, most often showing a restrictive pattern (8–10). Despite these insights, Indian data remain limited, particularly from clinical settings where cardiometabolic comorbidities overlap. Recognizing respiratory impairment early in such patients could allow lifestyle correction and timely intervention before irreversible loss of function occurs. The present work, therefore, set out to evaluate lung function among adults with metabolic syndrome and to explore how individual metabolic factors influence the degree of impairment.

Study Design and Setting This was a hospital-based cross-sectional observational study carried out in the Department of General Medicine, Government Stanley Medical College and Hospital, Chennai, Tamil Nadu. The study period extended for six months, from December 2023 to August 2024. Ethical clearance was obtained from the Institutional Human Ethics Committee before commencement. Study Population Participants were adult inpatients diagnosed with metabolic syndrome as per the National Cholesterol Education Program – Adult Treatment Panel III (NCEP ATP III) criteria. The target age group was 20 to 65 years. All participants provided written informed consent before inclusion. Inclusion Criteria 1. Adults fulfilling the NCEP ATP III criteria for metabolic syndrome. 2. Known cases of type 2 diabetes mellitus or systemic hypertension under medical management. Exclusion Criteria 1. Patients with pre-existing respiratory diseases such as bronchial asthma, COPD, or pulmonary fibrosis. 2. Individuals with cardiac disorders, thyroid dysfunction, neurogenic illness, or pregnancy. 3. Those on corticosteroids, antipsychotic drugs, or with orthopedic limitations affecting spirometry. Sample Size Determination The required sample size was estimated from earlier regional data reporting approximately 53% prevalence of impaired lung function among metabolic syndrome patients. Using the standard formula n = (Z² × p × q) / d² and considering a 10% relative precision with 1.96 as the Z value, the calculated sample size was 96. To compensate for nonresponse, the final sample size was rounded to 106 participants. Sampling Method Eligible subjects were selected through convenience sampling from medical wards during the study period. Each underwent structured assessment and investigations as per the study protocol. Data Collection Procedure Demographic details, anthropometric parameters (age, sex, weight, height, waist circumference), and vital signs were recorded using a standardized proforma. Blood pressure was measured with a calibrated sphygmomanometer. Biochemical investigations included fasting blood sugar and fasting lipid profile. Waist circumference was measured midway between the lower rib and the iliac crest with the patient standing. Pulmonary Function Testing Each participant underwent spirometry using a portable computerized spirometer calibrated daily. Testing was performed in a sitting posture following standard reproducibility criteria. Parameters recorded included: ● Forced Expiratory Volume in one second (FEV1) ● Forced Vital Capacity (FVC) ● FEV1/FVC ratio ● Forced Expiratory Flow 25–75% (FEF25–75) Based on the observed values and reference equations, participants were categorized as having: ● Normal pattern ● Obstructive pattern (FEV1/FVC < 70%) Restrictive pattern (FEV1/FVC ≥ 70% with reduced FVC) ● Mixed pattern (both reduced) ● Small airway disease (reduced FEF25–75) Each test was repeated at least three times, and the best of consistent readings was taken for analysis. Statistical Analysis Data were entered and analyzed using IBM SPSS Statistics version 25. Continuous variables were expressed as mean ± standard deviation, while categorical data were presented as frequencies and percentages. Differences between lung function categories were assessed using one-way ANOVA. Associations between categorical variables were analyzed using the chi-square test. Correlations between pulmonary indices and metabolic parameters were examined using Pearson’s correlation coefficient. The level of statistical significance was fixed at p < 0.05.

A total of 106 participants diagnosed with metabolic syndrome were enrolled during the study period. The overall mean age was 50.9 ± 9.9 years, with a range from 23 to 68 years. The gender distribution showed a mild male predominance, with 59 males (55.7%) and 47 females (44.3%). Demographic and Metabolic Characteristics The descriptive statistics of the study population are summarized in Table 1. Mean fasting blood sugar was 188.3 ± 64.1 mg/dL, mean triglycerides 195.0 ± 49.2 mg/dL, and mean waist circumference 94.1 ± 9.2 cm. Around 76.4% of the participants were hypertensive. The mean HDL level was 43.3 ± 6.2 mg/dL. Table 1. Demographic and metabolic characteristics of study participants (n = 106) Parameter Mean ± SD Range Frequency (%) Age (years) 50.9 ± 9.9 23–68 – Male – – 55.7 Female – – 44.3 Fasting Blood Sugar (mg/dL) 188.3 ± 64.1 94–321 – HDL (mg/dL) 43.3 ± 6.2 29–66 – Triglycerides (mg/dL) 195.0 ± 49.2 112–328 – Waist Circumference (cm) 94.1 ± 9.2 77–124 – Hypertensive – – 76.4 Figure 1. Bar chart showing the distribution of metabolic parameters among study participants. Bar chart displaying mean values of fasting blood sugar, triglycerides, HDL, and waist circumference with distinct colors and value labels. Spirometry Findings The spirometric values are presented in Table 2. The mean FEV1 was 82.8 ± 15.2, FVC 87.1 ± 13.3, FEV1/FVC ratio 96.0 ± 17.9, and FEF25–75 99.5 ± 13.4. Based on these measurements, 58.5% of participants had normal lung function, while 41.5% demonstrated some form of impairment. Table 2. Spirometric indices among participants (n = 106) Spirometric Parameter Mean ± SD Minimum Maximum FEV1 82.8 ± 15.2 46 122 FVC 87.1 ± 13.3 54 125 FEV1/FVC (%) 96.0 ± 17.9 62 133 FEF25–75 (%) 99.5 ± 13.4 69 131 Lung function patterns were categorized as normal (58.5%), restrictive (17.9%), obstructive (10.4%), mixed (10.4%), and small airway disease (2.8%). These proportions are illustrated in Figure 2. Figure 2. Pie chart depicting the distribution of lung function patterns among participants. Pie chart representing the percentages of normal, restrictive, obstructive, mixed, and small airway disease patterns Association between Metabolic Parameters and Lung Function Patterns Comparative analysis demonstrated significant variation in fasting blood sugar, triglycerides, and waist circumference across different lung function categories. Participants with restrictive or mixed patterns exhibited higher mean fasting blood sugar and triglyceride levels. Waist circumference was highest among those with restrictive lung disease (102.9 ± 10.2 cm, p < 0.001). Hypertension was observed in 81 individuals (76.4%), but its association with lung pattern was not statistically significant. Strength of Association To examine the overall strength of association between metabolic components and lung impairment, participants were dichotomized into two groups: those with normal spirometry (n=62) and those with any form of lung disease (n=44). Chi-square analysis revealed significant associations for waist circumference, blood pressure, triglycerides, and fasting blood sugar (p < 0.05), whereas HDL cholesterol showed no significant correlation. These trends are visualized in Figure 4. Table 3. Comparison of metabolic parameters across lung function categories Parameter Normal (n=62) Obstructive (n=11) Restrictive (n=19) Mixed (n=11) Small Airway (n=3) p-value Fasting Blood Sugar (mg/dL) 176.3 ± 60.6 199.2 ± 63.4 208.2 ± 57.0 197.8 ± 86.9 233.3 ± 67.2 0.002 Triglycerides (mg/dL) 191.8 ± 51.1 207.7 ± 40.2 196.3 ± 54.8 203.7 ± 39.5 173.7 ± 46.5 0.003 HDL (mg/dL) 43.7 ± 5.6 43.6 ± 6.4 44.4 ± 7.9 39.0 ± 4.5 42.7 ± 8.1 0.187 Waist Circumference (cm) 92.3 ± 7.7 90.9 ± 8.7 102.9 ± 10.2 94.3 ± 8.2 85.7 ± 3.2 <0.001 Figure 3. Donut chart showing the correlation between waist circumference and the presence of lung impairment. Donut chart with two segments – normal vs. impaired lung function – demonstrating higher waist circumference in the impaired group. Figure 4. Stacked bar chart illustrating the strength of association between metabolic parameters and lung function impairment. Stacked bar chart comparing proportions of abnormal vs. normal lung function for each parameter Gender-Based Comparison Restrictive changes were more frequent among males (17 out of 59) than females (2 out of 47), a statistically significant difference (p = 0.008). Obstructive and mixed patterns showed near-equal gender distribution. This finding suggests possible gender-related differences in fat distribution and metabolic risk profiles influencing lung mechanics. Relationship with the Number of Metabolic Syndrome Components When stratified by the number of diagnostic criteria fulfilled, participants meeting four or five components demonstrated a higher frequency of restrictive or mixed lung disease compared with those fulfilling only three criteria. Though this trend did not reach statistical significance (p = 0.13), the directionality supports the cumulative burden effect of metabolic abnormalities on pulmonary function. Summary of Findings In summary, nearly two out of five participants with metabolic syndrome showed abnormal spirometric patterns, with restrictive defects being the most prevalent. Lung function impairment displayed significant correlations with elevated fasting blood sugar, triglyceride levels, blood pressure, and waist circumference, whereas HDL showed no such link. Male participants demonstrated a higher proportion of restrictive changes compared to females, and the likelihood of pulmonary dysfunction appeared to increase progressively with the number of metabolic syndrome components present.

This study explored how metabolic syndrome influences lung function in adults attending a tertiary hospital in South India. A little over two-fifths of participants showed some degree of spirometric impairment, most often restrictive in pattern. The finding suggests that the metabolic syndrome, though classically defined by vascular and endocrine risks, may also exert measurable effects on respiratory mechanics. The dominance of restrictive changes seen here is broadly consistent with international and Asian data. Studies from Korea, China, and Western populations have reported lower FVC and FEV₁ among individuals meeting metabolic syndrome criteria, irrespective of smoking or age (6–11). Similar to those observations, our participants with higher fasting glucose, triglycerides, and waist circumference values tended to have poorer spirometric indices. This pattern points toward a mix of physical and inflammatory influences rather than isolated mechanical restriction. The physiological explanation lies partly in central adiposity. Increased intra-abdominal pressure elevates the diaphragm and reduces chest wall compliance, while adipose tissue-derived cytokines such as interleukin-6 and tumor necrosis factor-α promote systemic inflammation and oxidative stress (12). Chronic hyperglycemia adds vascular stiffness and microangiopathy, further restricting gas exchange (13). Together, these processes can gradually reduce lung volumes even before overt respiratory disease develops. In this study, the strongest predictors of reduced lung capacity were waist circumference and fasting blood sugar. Both parameters reflect visceral fat and insulin resistance, features already known to impair endothelial function. Comparable results were reported by Leone and co-workers, who identified abdominal obesity as the best single predictor of low FVC in metabolic syndrome (7). Ford et al. later confirmed the association in a large U.S. sample, emphasizing the importance of metabolic control in maintaining pulmonary health (11). Gender-related differences were also evident. Restrictive changes appeared more frequently among males, possibly due to greater visceral fat distribution and lower HDL levels, trends well documented in South Asian populations (7). The observation that lung impairment increased with the number of metabolic components supports the concept of cumulative burden: the more severe the metabolic dysregulation, the steeper the decline in ventilatory reserve. From a clinical standpoint, these findings highlight the need to include spirometry as a supplementary assessment in metabolic clinics. Early recognition of declining lung function can strengthen lifestyle counseling and pharmacological control, especially in patients with obesity, diabetes, or hypertriglyceridemia. Even modest reductions in weight and glycemic load have been linked with measurable improvement in lung function after structured interventions (14). This study, however, was limited by its cross-sectional design and single-centre scope. Diffusion parameters such as DLCO and serum inflammatory markers were not measured, and body composition analysis was not available. Larger multicentric studies that track metabolic improvement alongside lung function changes could provide stronger causal evidence. In essence, the lungs appear to mirror the metabolic health of the individual. The restrictive patterns observed here underscore how adiposity and insulin resistance extend their influence beyond the heart and vessels, subtly reshaping respiratory physiology in Indian adults. LIMITATIONS The study was conducted at a single centre with a modest sample size, which may restrict broader generalization. Its cross-sectional design allows only association, not causality. Parameters such as diffusion capacity (DLCO), inflammatory biomarkers, and detailed body composition analysis were not included. Follow-up evaluation after metabolic optimization was also not performed. Future longitudinal, multicentric studies integrating inflammatory markers and imaging-based fat distribution analysis could further clarify the temporal link between metabolic syndrome and pulmonary dysfunction.

Metabolic syndrome is associated with significant deterioration in pulmonary function, most notably restrictive changes. The link between central obesity, raised fasting glucose, and hypertriglyceridemia with reduced spirometric indices highlights the systemic nature of this condition. Early spirometry in metabolic syndrome can aid in detecting silent respiratory compromise and guide lifestyle or therapeutic measures aimed at improving overall metabolic control.

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