Tobacco use remains the single most preventable cause of cardiovascular morbidity and mortality globally, responsible for more than 1.9 million cardiovascular disease (CVD) deaths annually according to the World Health Organization.¹ In India, the Global Adult Tobacco Survey (GATS-2, 2016–17) reported that 28.6% of adults aged 15 years and above use tobacco in some form, with cigarette and bidi smoking constituting the predominant modes of tobacco consumption, particularly among middle-aged urban males.² The state of Madhya Pradesh, where the present study was conducted, registers among the highest smoking prevalence rates in India, making it a particularly important setting for investigating tobacco-related metabolic and cardiovascular risk. The cardiovascular toxicity of tobacco smoking is mediated through multiple interrelated pathways. Nicotine and tobacco combustion products activate the sympathoadrenal axis, driving catecholamine-mediated lipolysis and augmenting hepatic synthesis of very low-density lipoprotein (VLDL) particles. Reactive oxygen species (ROS) generated in cigarette smoke directly oxidise low-density lipoprotein (LDL) particles, producing oxidised LDL (ox-LDL)—a potent initiator of macrophage foam cell formation and atherogenesis. Simultaneously, ROS suppress paraoxonase-1 (PON-1) activity on high-density lipoprotein (HDL), impairing reverse cholesterol transport and accelerating HDL catabolism via upregulation of hepatic lipase.³ Through NF-κB-mediated transcription in alveolar macrophages and vascular endothelial cells, tobacco smoke also triggers the release of pro-inflammatory cytokines including interleukin-6 (IL-6) and tumour necrosis factor-alpha (TNF-α), driving the hepatic acute-phase response and elevating C-reactive protein (CRP).⁴ Chronic nicotine exposure additionally induces insulin resistance through counter-regulatory hormone release and impairs pancreatic beta-cell function, contributing to dysglycaemia.⁵ Despite a rich global literature, hospital-based case-control studies from central India that simultaneously assess the full metabolic signature of smoking—encompassing lipid fractions, glycaemic status, and the inflammatory triad of hsCRP, IL-6, and ferritin—with rigorous exclusion of confounders remain limited. The present study was therefore designed to comprehensively evaluate and compare these biochemical parameters between smokers and non-smokers attending a tertiary care centre in Indore, Madhya Pradesh, and to establish dose-response relationships between cumulative tobacco exposure (pack-year history) and the severity of metabolic derangement.

2.1 Study Design and Setting This was a hospital-based case-control study conducted in the Department of Physiology, Index Medical College, Hospital and Research Centre, Indore, Madhya Pradesh, India. The study was approved by the Institutional Ethics Committee (Human) and was conducted in accordance with the Declaration of Helsinki (2013). Written informed consent was obtained from all participants. 2.2 Participants A total of 350 adult subjects aged ≥18 years were enrolled: 175 smokers (Group I, cases) and 175 non-smokers (Group II, controls). A smoker was defined as any individual currently smoking one or more cigarettes or bidis per day for a minimum of one year. Non-smokers were those who had never smoked or had abstained for ≥5 years with no passive smoke exposure. Participants were excluded if they had known diabetes mellitus, hypertension (BP ≥140/90 mmHg), chronic liver or kidney disease, thyroid dysfunction, regular alcohol consumption, or were taking lipid-modifying medications (statins, fibrates, niacin, beta-blockers, or thiazide diuretics). Participants with serum triglycerides ≥4.5 mmol/L were also excluded. The minimum required sample size was calculated as 161 per group using the Charan and Biswas (2013) formula for case-control studies (p₁ = 11.8%, p₂ = 3.5%, 95% CI, 80% power); 175 per group were enrolled to allow for attrition. 2.3 Data Collection A structured proforma captured demographic information, anthropometric measurements (height, weight, BMI, waist circumference, waist-to-hip ratio), blood pressure, and detailed smoking history including type of tobacco product (cigarette, bidi, or both), duration, daily quantity, and pack-year history (calculated as: cigarettes per day / 20 × years of smoking). Smokers were classified as mild (1–10 cigarettes/day or 1–15 bidis/day), moderate (11–20/16–30), or heavy (≥21/≥31). 2.4 Biochemical Analysis All participants maintained a strict 12-hour overnight fast prior to blood collection. Approximately 6 mL of venous blood was collected from the antecubital vein: 4 mL into a plain vacutainer for serum biochemistry, and 2 mL into an EDTA vial for HbA1c. Serum was separated by centrifugation at 3000 rpm for 10 minutes. Total cholesterol (TC) and triglycerides (TGL) were estimated by the CHOD-PAP and GPO-PAP enzymatic colorimetric methods respectively; HDL-C was measured by the direct homogeneous method; and LDL-C was calculated using the Friedewald equation [LDL-C = TC – HDL-C – TGL/2.2 (mmol/L)]. Non-HDL-C was derived as TC minus HDL-C. All lipid assays were performed on the Beckman Coulter AU-480 automated analyser. HbA1c was estimated by the ARCHITECT Chemiluminescence Immunoassay (CLIA) platform (Abbott Diagnostics). hsCRP was measured by latex turbidimetry on the AU-480 analyser and confirmed on the Vitros Chemistry 4600 system. Serum ferritin was quantified by CLIA (ARCHITECT platform). IL-6 was estimated by sandwich ELISA using a validated kit. Lipid parameters were classified and cardiovascular risk was stratified according to the NCEP ATP III criteria. 2.5 Statistical Analysis Data were analysed using SPSS version 26.0. Continuous variables are presented as mean ± SD (normally distributed) or median with IQR (non-normal). Comparisons between groups were made using the independent Student's t-test or Mann-Whitney U test. Categorical comparisons used the chi-square test. Pearson's or Spearman's correlation was used for dose-response analyses. Binary logistic regression estimated adjusted odds ratios (OR) with 95% CI for key clinical endpoints. A p-value < 0.05 (two-tailed) was considered statistically significant.

3.1 Demographic and Anthropometric Characteristics The two groups were well matched for age (38.4 ± 9.2 vs 36.9 ± 8.7 years; p = 0.124) and gender distribution (p = 0.284). Smokers had significantly higher BMI (24.8 ± 3.1 vs 23.2 ± 2.9 kg/m²; p = 0.031), waist circumference (88.6 vs 82.4 cm; p < 0.001), and waist-to-hip ratio (0.92 vs 0.86; p < 0.001). The mean smoking duration was 14.6 ± 7.8 years with a mean pack-year history of 18.4 ± 10.2. Cigarette-only smokers constituted 56.0%, bidi-only smokers 27.4%, and dual users 16.6%. Smoking intensity was mild in 35.4%, moderate in 42.3%, and heavy in 22.3%. 3.2 Lipid Profile All lipid parameters differed significantly between groups (Table 1). Smokers had markedly higher TC, TGL, LDL-C, non-HDL-C, TC/HDL-C ratio (5.62 ± 0.84 vs 3.94 ± 0.62), and LDL-C/HDL-C ratio (3.62 ± 0.72 vs 2.36 ± 0.52), while HDL-C was significantly lower (38.6 ± 6.8 vs 48.2 ± 7.2 mg/dL; p < 0.001 for all comparisons). Table 1. Comparison of Lipid Profile Parameters between Smokers and Non-Smokers Parameter Smokers (n=175) Mean±SD Non-Smokers (n=175) Mean±SD t-value p-value Total Cholesterol (mg/dL) 214.6 ± 28.4 186.2 ± 24.8 9.18 <0.001** Triglycerides (mg/dL) 168.4 ± 38.2 128.8 ± 32.6 9.64 <0.001** LDL-Cholesterol (mg/dL) 138.2 ± 24.6 112.4 ± 20.8 9.82 <0.001** HDL-Cholesterol (mg/dL) 38.6 ± 6.8 48.2 ± 7.2 11.62 <0.001** Non-HDL Cholesterol (mg/dL) 176.0 ± 27.2 138.0 ± 24.0 12.46 <0.001** TC / HDL-C Ratio 5.62 ± 0.84 3.94 ± 0.62 19.34 <0.001** LDL-C / HDL-C Ratio 3.62 ± 0.72 2.36 ± 0.52 17.08 <0.001** ** p<0.001; Student's independent t-test. SD = standard deviation. 3.3 Glycaemic Status Mean HbA1c was significantly higher in smokers (5.82 ± 0.48 vs 5.34 ± 0.38%; t = 9.42; p = 0.002). Pre-diabetic HbA1c (5.7–6.4%) was found in 36.6% of smokers versus 18.3% of non-smokers (p < 0.001). Undetected dysglycaemia (HbA1c ≥ 6.5%) was present in 9.7% of smokers versus 4.0% of non-smokers (p = 0.038). 3.4 Inflammatory Biomarkers Smokers demonstrated a markedly elevated inflammatory profile (Table 2). Mean hsCRP was more than fourfold higher in smokers (4.82 ± 1.28 vs 1.14 ± 0.42 mg/L; p < 0.001). According to AHA/CDC risk stratification, 54.3% of smokers were in the high cardiovascular risk category (hsCRP > 3 mg/L) versus only 5.1% of non-smokers (adjusted OR = 10.86; 95% CI: 6.28–18.78; p < 0.001). IL-6 was more than threefold elevated in smokers (9.64 ± 2.46 vs 2.88 ± 0.96 pg/mL; p < 0.001). Serum ferritin was also significantly higher (142.6 ± 38.2 vs 84.4 ± 22.8 ng/mL; p < 0.001). Table 2. Comparison of Inflammatory Biomarkers between Groups Biomarker Smokers Mean±SD Non-Smokers Mean±SD t / U p-value hsCRP (mg/L) 4.82 ± 1.28 1.14 ± 0.42 U=3246 <0.001** IL-6 (pg/mL) 9.64 ± 2.46 2.88 ± 0.96 U=1842 <0.001** Serum Ferritin (ng/mL) 142.6 ± 38.2 84.4 ± 22.8 t=15.46 <0.001** HbA1c (%) 5.82 ± 0.48 5.34 ± 0.38 t=9.42 0.002** ** p<0.001 or p<0.01. Mann-Whitney U for non-normally distributed variables; t-test otherwise. 3.5 Dose-Response Correlations Within the smoker group, significant Pearson's and Spearman's correlations were found between pack-year history and all biochemical parameters: LDL-C (r = +0.624), hsCRP (ρ = +0.572), IL-6 (ρ = +0.614), ferritin (r = +0.468), HbA1c (r = +0.382), and HDL-C (r = −0.586), all p < 0.001. The prevalence of clinically significant dyslipidaemia (high LDL-C ≥ 130 mg/dL) was 62.3% in smokers versus 28.6% in non-smokers (adjusted OR = 4.12; 95% CI: 2.58–6.59; p < 0.001).

The findings of this study demonstrate a comprehensive pro-atherogenic and pro-inflammatory phenotype in chronic smokers, consistent with and extending prior published literature. The significantly higher TC, TGL, and LDL-C alongside reduced HDL-C observed in our smoker cohort align closely with Craig et al.'s meta-analysis of 54,000 participants, which reported a 9% increase in TC, 12% increase in LDL-C, and 6% reduction in HDL-C in smokers.⁶ Mehta et al. (2019) from North India similarly found mean LDL-C of 136.2 mg/dL and HDL-C of 38.5 mg/dL in middle-aged smokers,⁷ remarkably concordant with our values of 138.2 and 38.6 mg/dL respectively, validating consistency across Indian populations. The more than fourfold elevation of hsCRP in our smoker group (4.82 vs 1.14 mg/L) is consistent with findings from the NHANES III dataset, where current smokers had 1.5–2 times higher CRP than non-smokers,⁸ and with Yoshida et al. (2010), who reported mean hsCRP of 3.6 mg/L in Japanese middle-aged smokers.⁹ The adjusted OR of 10.86 for high-risk hsCRP in smokers in our study represents one of the strongest smoking–inflammation associations reported in the Indian literature. The simultaneous elevation of IL-6 (>threefold) confirms activation of the upstream cytokine cascade driving CRP production, consistent with Bermudez et al.'s (2002) Health ABC Study findings.¹⁰ The significantly higher HbA1c and elevated proportion of pre-diabetic smokers (36.6%) corroborates Willi et al.'s landmark meta-analysis demonstrating a 44% increased risk of type 2 diabetes in active smokers, mediated through nicotine-induced insulin resistance and pancreatic beta-cell toxicity.¹¹ The elevated serum ferritin—both an acute-phase reactant and a pro-oxidant through the Fenton reaction—has been linked to subclinical atherosclerosis in smokers in the Mainous et al. study and the German National Health Survey.¹² The strong dose-response correlations between pack-year history and all adverse biochemical parameters (r/ρ = 0.38–0.62) provide compelling evidence for a causal, cumulative relationship between tobacco burden and metabolic deterioration. A notable strength of this study is its simultaneous assessment of the full metabolic signature—lipid, glycaemic, and three inflammatory markers—with rigorous exclusion of confounders and inclusion of both cigarette and bidi smokers, the latter being particularly relevant to the Indian epidemiological context. The close concordance of our findings with published values from India (Kapoor and Bhimarao, 2000; Gupta et al., 2007; Yadav and Bansal, 2018) and internationally (ARIC Study; Pankow et al., 2004) strengthens the external validity of the results.

Chronic cigarette and bidi smoking is independently and strongly associated with a pro-atherogenic lipid profile, impaired glycaemic homeostasis, and a robust systemic inflammatory state characterised by markedly elevated hsCRP, IL-6, and serum ferritin in middle-aged Indian adults. All adverse parameters scale in a dose-dependent manner with cumulative tobacco exposure. These findings support routine comprehensive metabolic screening—including fasting lipid profile, HbA1c, hsCRP, and IL-6—in all adult smokers presenting to outpatient services. Smoking cessation remains the most powerful cardiovascular intervention, and these objective biomarker findings should be communicated to patients as motivational evidence of tobacco-related metabolic organ damage.

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