INTRODUCTION
Obstructive sleep apnea (OSA) is a highly prevalent sleep-disordered breathing condition characterized by recurrent episodes of partial or complete upper airway obstruction during sleep, leading to intermittent hypoxia (IH), sleep fragmentation, and daytime dysfunction [1]. Affecting hundreds of millions globally, OSA is a major modifiable risk factor for cardiometabolic diseases, including hypertension, insulin resistance, type 2 diabetes, and cardiovascular events, and is largely mediated by chronic low-grade systemic inflammation [2,3].
Previous meta-analyses have confirmed that OSA independently elevates pro-inflammatory cytokines, such as interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and C-reactive protein (CRP), with pooled estimates showing significantly higher levels in OSA patients than in controls [4,5]. These elevations persist despite adjustments for obesity and contribute to endothelial dysfunction, oxidative stress, and metabolic dysregulation. Continuous positive airway pressure (CPAP) therapy, the mainstay treatment, reduces these markers in randomized trials, supporting a causal link [6].
A pivotal mechanism involves IH, the hallmark of OSA, which stabilizes hypoxia-inducible factor-1α (HIF-1α), a master regulator of cellular response to low oxygen. HIF-1α upregulates genes that promote inflammation, angiogenesis, and metabolic adaptation, amplifying cytokine production (such as IL-6 and TNF-α) in both hypoxic tissues and adipose depots in obese individuals [7,8]. Clinical studies have demonstrated chronic HIF-1α upregulation in OSA serum with correlations to severity metrics such as the apnea-hypopnea index (AHI) and especially the oxygen desaturation index (ODI), highlighting IH severity as a key driver of inflammation over AHI alone in some contexts [9,10].
Obesity is a major bidirectional risk factor for OSA, with fat deposition in the upper airway contributing to collapse, and IH exacerbating adipose tissue dysfunction [11]. However, substantial evidence indicates that OSA and its inflammatory consequences occur independently of obesity [12]. Some studies have shown elevated systemic inflammation (such as higher CRP, IL-6, and TNF-α levels) in obese OSA patients, while non-obese OSA patients exhibit similar metabolic and inflammatory derangements, including insulin resistance and lipid abnormalities, although often less severe than their obese counterparts [13,14]. Non-obese OSA may involve craniofacial factors, visceral fat redistribution, or heightened IH sensitivity, underscoring OSA’s role of OSA beyond that of adiposity alone. In India, the prevalence of overweight and obesity among adults has risen substantially, reaching approximately 23%–24% in recent national surveys. OSA affects an estimated 11% of adults (higher in men), which amplifies the cardiometabolic burden in both obese and non-obese populations [15,16].
Despite substantial global evidence, key gaps remain in the literature. Most studies originate from Western cohorts, with limited data from Asian populations, where genetic, dietary, and anthropometric factors may influence OSA-inflammation interactions. Adult-specific research in India is scarce, particularly regarding hypoxia mediators such as HIF-1α. Although circulating HIF-1α has been studied in general OSA populations, demonstrating chronic upregulation linked to IH and desaturation events, evidence specifically in obese adults with OSA is limited, indirect, and mostly absent [7–10].
This observational case-control study bridges these gaps in a rigorously matched cohort of Indian adults with obesity. By quantifying IL-6, TNF-α, CRP, and HIF-1α levels while prioritizing ODI as the primary hypoxia metric in predictive models, this study elucidates the independent association of IH with systemic inflammation beyond obesity alone. These elements provide mechanistic depth, clinical translatability, and region-specific insights, advancing our understanding of an underrepresented population.
The primary objective was to compare the serum concentrations of IL-6, TNF-α, CRP, and HIF-1α between obese adults with OSA and body mass index (BMI)-matched controls without OSA. The secondary objectives were to examine the associations of these biomarkers with OSA severity indicators (AHI, ODI, minimum oxygen saturation, and Epworth Sleepiness Scale [ESS] score), anthropometric and metabolic parameters (BMI, WHR, and Homeostatic Model Assessment for Insulin Resistance as HOMA-IR), and to identify independent predictors of inflammation through multivariable regression.
METHODS
Study Design and Ethical Approval
This hospital-based observational case-control study was conducted at the Sleep Medicine and Obesity Clinic of a tertiary care teaching hospital between May 2024 and December 2025. The study protocol was approved by the Institutional Ethics Committee of Institute of Medical Sciences and SUM Hospital (IEC/IMS.SH/SOA/2024/045 dated 27.04.24) and was conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants prior to enrolment. This study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology guidelines for case-control studies [17].
Sample Size Estimation
The sample size was calculated using the G*Power software (version 3.1.9.7, Heinrich Heine University) to detect clinically meaningful differences in serum IL-6 levels between obese adults with and without OSA. Based on an effect size of 0.70, two-tailed α=0.05, and 95% power, a minimum of 45 participants per group was required [18]. The final analyzed sample comprised 45 participants per group after the exclusion of incomplete data or other protocol criteria.
Study Population and Recruitment
Consecutive adult patients who were referred for evaluation of suspected sleep-disordered breathing were screened. The participants were assigned to two groups: obese adults with newly diagnosed OSA (OSA group, n=45) and BMI-matched obese adults without OSA (control group, n=45). Frequency matching was applied for age (±5 years) and BMI (±3 kg/m2). Controls were recruited from individuals referred for non-OSA complaints (e.g., insomnia) or routine health assessments.
Inclusion and Exclusion
The participants were adults aged 18–65 years with a BMI ≥30 kg/m2 who provided informed consent [19]. OSA was diagnosed using attended in-laboratory polysomnography with an AHI ≥5 events/h according to the American Academy of Sleep Medicine (AASM) criteria. The controls had an AHI <5 events/h [20]. Exclusion criteria included acute or chronic inflammatory/infectious conditions, autoimmune diseases, malignancy, chronic kidney or liver disease, pregnancy, prior OSA diagnosis or treatment, use of corticosteroids/immunosuppressants/anti-inflammatory drugs within the previous four weeks, recent cardiovascular events (<6 months), or any condition likely to confound inflammatory biomarker levels. Smoking status, alcohol consumption, and hypertension were recorded but did not constitute the exclusion criteria.
Clinical and Anthropometric Assessment
Height, weight, BMI, waist and hip circumferences, and WHR were measured using standardized protocols. Daytime sleepiness was assessed with the ESS [21]. Blood pressure was recorded as the average of two seated readings using a calibrated automated sphygmomanometer.
Polysomnography
Overnight attended in-laboratory polysomnography was performed using a multichannel digital system. Sleep stages and respiratory events were manually scored according to the AASM criteria, specifically the rules for scoring respiratory events, as detailed in the 2012 update of the AASM Manual [22]. Key parameters recorded included the AHI, ODI events with ≥3% desaturation per hour, mean and minimum nocturnal oxygen saturation (SpO2), total sleep time, and T90 (percentage of total sleep time with SpO2 <90%). ODI was prespecified a priori as the primary metric of IH for all correlation, regression, and mediation analyses because the study focused on the repetitive desaturation-reoxygenation cycles that mechanistically stabilize HIF-1α and are linked to inflammatory pathways. Although T90 was recorded as part of standard oximetry reporting, it was not included in the primary predictive models.
Blood Sample Collection
Fasting venous blood samples were collected between 07:00 and 09:00 hours in the morning, immediately after polysomnography. Although HIF-1α protein can undergo rapid degradation upon reoxygenation in acute settings, a previous study in OSA patients has demonstrated chronically elevated circulating HIF-1α levels that remain stable between evening (pre-polysomnography) and morning (post-polysomnography) measurements [7]. This indicates that repeated nightly cycles of IH lead to sustained upregulation of circulating HIF-1α rather than transient fluctuations, supporting the reliability of morning sampling to reflect the cumulative nocturnal hypoxic burden in OSA patients. Samples were centrifuged at 3,000 rpm for 10 minutes, and serum was aliquoted and stored at −80°C until batch analysis.
Measurement of Biomarkers
Serum concentrations of IL-6, CRP, TNF-α, and HIF-1α were quantified using commercially available high-sensitivity ELISA kits (Elabscience), according to the manufacturer’s instructions. All samples were analyzed in duplicate in a single batch by personnel blinded to the group allocation. The intra- and inter-assay coefficients of variation were maintained below 10% and 12%, respectively. All biomarker concentrations were expressed in pg/mL.
Metabolic Assessment
The fasting plasma glucose and insulin levels were measured using standard automated laboratory methods. Insulin resistance was calculated using the HOMA-IR formula: HOMA-IR=(fasting insulin [μU/mL]×fasting glucose [mmol/L])/22.5 [23].
Statistical Analysis
Statistical analyses were performed using IBM SPSS Statistics version 26.0 (IBM Corp.). Normality of continuous variables was assessed using the Shapiro–Wilk test and visual inspection of histograms. Normally distributed data are expressed as mean± standard deviation with 95% confidence intervals (CIs), whereas non-normally distributed variables are presented as medians (range). Between-group comparisons were performed using the independent samples t-test or Mann–Whitney U test, as appropriate. Multiple comparisons were adjusted using Bonferroni correction (significance threshold set at p<0.003). Bonferroni correction was applied for 16 comparisons.
Associations between hypoxia indices and inflammatory biomarkers (or HIF-1α) were evaluated using multiple linear regression analysis, adjusted for age, BMI, and sex. The predictor variables included SpO2, creatinine, cholesterol, AHI, ODI, ESS score, and HOMA-IR. Collinearity among the predictor variables was assessed using variance inflation factors (VIF). The VIF values ranged from 1.8 to 3.6, indicating acceptable levels of multicollinearity and allowing all variables to be retained in the regression models. Although AHI and ODI are moderately to highly correlated (as both reflect OSA severity), they were both included because they capture complementary aspects of the disorder. AHI quantifies respiratory event frequency, while ODI specifically measures the frequency of desaturation events (IH). This approach allowed direct comparison of their independent associations with inflammatory biomarkers and HIF-1α. The VIF values remained well below the conventional threshold of 5 (or 10), confirming that multicollinearity did not unduly influence the models.
The mediation effects of HIF-1α on the relationship between IH (ODI) and inflammatory biomarkers (IL-6, CRP, and TNF-α) were assessed using bootstrapped indirect effect analysis with 95% CIs (5,000 resamples). Statistical significance was defined as a two-tailed p-value of <0.05, unless otherwise adjusted. Smoking status, alcohol consumption, and hypertension were recorded for all participants but were not included as covariates in the primary regression models due to the modest sample size and to avoid overfitting. Major potential confounders (such as acute/chronic inflammatory conditions, corticosteroid or anti-inflammatory drug use, recent cardiovascular events, and other comorbidities) were excluded by study design.
RESULTS
Ninety obese participants (BMI ≥30 kg/m2) were enrolled: 45 with newly diagnosed OSA group and 45 BMI- and age-matched obese controls without OSA. All participants underwent in-laboratory polysomnography, with no losses to analysis and negligible missing data (<1% for primary variables).
Participant Characteristics
The demographic and anthropometric variables were well balanced between the groups, confirming effective matching (Table 1). Age, BMI, and WHR showed no significant differences after the Bonferroni correction (all p>0.003). In contrast, OSA participants demonstrated profound nocturnal hypoxia and sleep disruption, with significantly lower median SpO2, higher AHI, ODI, and ESS scores (all p<0.001, effect sizes r≈0.82–0.87) (Table 1). Metabolic parameters also differed markedly, including higher creatinine, total cholesterol, and HOMA-IR in the OSA group (all p<0.001, large effect sizes) (Table 1), whereas fasting glucose levels were comparable (p=0.180). Smoking status, alcohol consumption, and hypertension did not differ significantly between the OSA and control groups (all p>0.003 after Bonferroni correction).
Inflammatory Biomarkers and HIF-1α
Systemic inflammation was substantially greater in participants with OSA, with significantly higher serum levels of IL-6, CRP, TNF-α, and HIF-1α (all p<0.001 after Bonferroni correction, effect sizes r≈0.83–0.86) (Table 1).
Independent Predictors of Inflammation and HIF-1α
Multiple linear regression models (adjusted for age, BMI, and sex) identified the ODI as the dominant predictor of all inflammatory biomarkers and HIF-1α (Table 2). Standardized coefficients for the ODI were consistently large and highly significant (β=0.40–0.67, all p<0.001). HOMA-IR emerged as an additional strong independent predictor across the outcomes (β=0.29–0.50, all p<0.001). Mean SpO2 was inversely associated with HIF-1α (β=−0.32, p<0.001), and cholesterol was positively associated with CRP, TNF-α, and HIF-1α (all p≤0.007). AHI significantly contributed only to CRP and HIF-1α levels (p≤0.013). No collinearity issues were observed (all VIF <5). Notably, ODI remained the strongest independent predictor even after simultaneous adjustment for AHI, supporting its incremental value beyond overall event frequency in this obese cohort.
Mediation by HIF-1α
Bootstrapped mediation analysis (5,000 resamples) confirmed HIF-1α as a significant partial mediator of the association between IH and inflammation (Table 3 and Fig. 1). The indirect effects were substantial and significant for IL-6 (0.25; 95% CI, 0.15–0.35), CRP (0.20; 95% CI, 0.10–0.30), and TNF-α (0.35; 95% CI, 0.22–0.48), with robust total and direct effects in all pathways. These results demonstrate that IH, predominantly quantified by ODI, is independently associated with systemic inflammation in obese OSA via HIF-1α stabilization, with insulin resistance contributing additively.
DISCUSSION
The present findings highlight a strong association of IH with systemic inflammation in obese individuals with OSA, with the ODI serving as the predominant predictor of elevated inflammatory biomarkers and HIF-1α levels. This observation aligns with emerging evidence that ODI, reflecting the frequency and depth of desaturation, better captures the hypoxic inflammatory burden than AHI alone. Recent proteomic analyses in male OSA cohorts have similarly shown that ODI, but not AHI or REM-specific AHI, is associated with multiple inflammatory and cardiovascular proteins, suggesting that desaturation events are linked to redox-sensitive pathways leading to inflammation [24].
HIF-1α has emerged as a significant partial mediator linking IH to increased IL-6, CRP, and TNF-α levels [24]. This mediation supports the well-described mechanism by which recurrent hypoxic cycles stabilize HIF-1α, leading to transcriptional activation of pro-inflammatory genes in immune cells, endothelial tissue, and adipose depots [8,25,26]. In vitro and animal models of IH consistently demonstrate that HIF-1α upregulation is associated with NF-κB activation, oxidative stress, and cytokine release, including IL-6 and TNF-α [27,28]. Human studies further corroborate chronic HIF-1α elevation in OSA serum, often correlating with desaturation severity rather than event frequency, and persisting despite adjustments for obesity [9,29].
In the present study, both ODI and HOMA-IR emerged as strong independent predictors of inflammatory biomarkers and HIF-1α. This suggests that IH and insulin resistance contribute additively to systemic inflammation in obese adults with OSA. Although a formal statistical interaction analysis was not performed due to sample size limitations, experimental evidence indicates that IH can promote adipose tissue inflammation and impair insulin signaling [30,31]. These findings raise the possibility of synergistic effects, whereby the inflammatory impact of IH may be amplified in the presence of higher insulin resistance. Whether IH acts as the predominant driver or primarily in the context of underlying metabolic dysfunction due to obesity warrants further investigation in larger cohorts with explicit interaction testing.
These results extend predominantly Western data to an Indian obese cohort. South Asian populations, including Asian Indians, are known to exhibit a distinct “thin-fat” phenotype characterized by higher visceral and abdominal adiposity at lower BMI levels compared with Western populations [32]. This ethnic predisposition is often accompanied by greater insulin resistance and inflammatory responses even at comparable BMI values. In the present study, we used WHR as a validated surrogate marker of central/visceral adiposity, and groups were rigorously matched on both BMI and WHR. While direct imaging-based measures of visceral fat (such as CT or MRI) were not available, the matched design and inclusion of WHR help minimize confounding by overall and regional adiposity. Prior studies in Asian Indians with OSA have similarly noted elevated inflammatory markers, although often without mechanistic dissection via HIF-1α [33,34]. The independent association of IH with inflammation beyond obesity in our matched cohort reinforces that OSA confers additional inflammatory risk, consistent with observations in diverse ethnic groups where OSA severity correlates with systemic inflammation even after BMI adjustment [14,35].
While the present findings emphasize the ODI as a strong predictor of systemic inflammation and HIF-1α levels, we acknowledge that other metrics of nocturnal hypoxemia, such as T90 or CT90, are widely used to quantify the overall hypoxic burden [29,36]. Unlike ODI, which primarily reflects the frequency of desaturation events, T90 captures the duration and cumulative exposure to hypoxemia and has been associated with cardiovascular risk, myocardial ischemia, and some cardiometabolic outcomes in OSA cohorts [36]. Some studies suggest that T90 or more advanced hypoxic burden indices may outperform ODI or AHI in predicting sustained cardiovascular events or mortality, whereas ODI may better represent the cyclical, intermittent nature of hypoxia that drives rapid HIF-1α stabilization and acute redox-sensitive inflammatory responses [29,36].
In the current analysis, we prespecified ODI as the primary hypoxia metric because our mechanistic focus was on IH as the trigger for HIF-1α-mediated inflammation in obese Indian adults with OSA. We recorded T90 as part of standard polysomnography reporting but did not include it in the primary predictive models. Future studies integrating both event-based (ODI) and duration-based (T90 or area-based hypoxic burden) metrics could offer a more comprehensive assessment of nocturnal hypoxic load and its differential contributions to inflammatory versus long-term cardiometabolic pathways.
Clinical Implications
Clinical evaluation of obese OSA patients should prioritize hypoxic burden metrics, such as ODI, for improved risk stratification, as these may better predict inflammatory and cardiometabolic sequelae than AHI alone. Interventions targeting desaturation events, such as optimized CPAP or adjunctive therapies modulating HIF-1α pathways, hold promise for reducing associated inflammation. In high-prevalence regions such as India, incorporating simple ODI-based screening with routine metabolic and inflammatory profiling could facilitate early intervention and reduce the cardiometabolic burden in resource-constrained settings.
Strengths
Strengths include rigorous age- and BMI-matched controls to isolate OSA effects, gold-standard attended polysomnography with standardized AASM scoring, comprehensive multivariable adjustment for confounders, and application of bootstrapped mediation analysis to provide mechanistic insight into HIF-1α’s role. This represents one of the first evaluations of HIF-1α-mediated pathways in an Indian obese OSA population, addressing a notable gap in the region-specific data.
Limitations
The cross-sectional design precludes establishing causality; longitudinal studies are needed to determine whether reducing IH reverses HIF-1α upregulation and inflammation. Although adequately powered for primary comparisons, the sample size may have limited the detection of subtle interactions or subgroup effects. Smoking status, alcohol consumption, medication use (such as statins), and specific comorbidities were recorded but not forced into the primary regression models; residual confounding from these factors therefore cannot be entirely ruled out. Some control participants were referred for insomnia or other non-OSA complaints. Although all controls had AHI <5 events/h on polysomnography and the groups were rigorously matched for age and BMI, residual confounding from underlying sleep complaints in the control group cannot be completely excluded due to the observational design of the study. Recruitment from a single tertiary center introduces potential selection bias, restricting generalizability to community or non-obese populations. Future studies should incorporate multi-center cohorts, objective measures of adipose hypoxia, larger sample sizes to allow fuller adjustment for lifestyle and medication-related confounders, and interventional designs to validate these pathways and therapeutic responses.
Conclusions
In obese Indian adults with OSA, IH quantified by ODI was strongly associated with increased levels of systemic inflammatory biomarkers, including IL-6, CRP, and TNF-α. HIF-1α partially mediates these associations, supporting a potential mechanistic link between nocturnal hypoxic burden and inflammatory activation. Compared with AHI, ODI appeared to better reflect inflammatory risk in this population. These findings highlight the importance of hypoxic burden metrics in the cardiometabolic assessment of OSA, and suggest that pathways involving HIF-1α may represent potential targets for future therapeutic investigations.
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