Physical activity reduces cancer mortality but not cardiovascular or all-cause mortality in obstructive sleep apnea: a National Health and Nutrition Examination Survey (NHANES)-based analysis

Introduction

Obstructive sleep apnea (OSA), a chronic disorder featuring repeated episodes of upper airway obstruction while sleeping, is strongly linked to cardiovascular disease, metabolic syndrome, and neurocognitive dysfunction (1). There is a growing global occurrence of OSA, which affects an estimated 9% of adult females and 24% of adult males (2). The prevalence continues to increase with advancing age and the growing obesity epidemic. Notably, more than 936 million adults aged 30–69 years have mild to severe OSA, with an apnea-hypopnea index (AHI) of 5 or more events per hour. Among them, 425 million meet the criteria for moderate-to-severe OSA (AHI ≥15) (3). OSA is linked to a significantly elevated mortality risk, particularly in those suffering from untreated severe disease (AHI ≥30), whose all-cause mortality (ACM) rate is 3.8 times higher than that of individuals without sleep-disordered breathing (4). Moreover, OSA constitutes a substantial global health burden, with far-reaching economic costs, affecting productivity and public safety at both individual and societal levels (5).

In recent years, physical activity interventions have garnered significant attention as a potential non-pharmacological therapy for OSA. The underlying mechanisms are thought to involve multiple synergistic pathways, including the reduction of cervical and visceral fat accumulation, thereby decreasing the risk of upper airway collapse; enhancement of upper airway dilator muscle tone, including the genioglossus muscle; improvement in sleep architecture with reduced arousal frequency; and modulation of systemic inflammatory responses (6). A randomized controlled trial (RCT) demonstrated that in sedentary overweight or obese adults, engaging in 150 minutes of moderate-intensity aerobic exercise per week exerted a moderate therapeutic effect in reducing the AHI (7). Similarly, a 12-week combined aerobic-resistance training intervention reduced the AHI by 23% and improved the oxygen desaturation index in OSA patients, with no significant body weight alterations (8). Therefore, physical activity is a prospective effective adjunct in OSA management.

The neutrophil-percentage-to-albumin ratio (NPAR) and neutrophil-to-high-density lipoprotein cholesterol ratio (NHR) are identified as integrated inflammatory-metabolic biomarkers with independent prognostic value for mortality risk in cardiovascular diseases, metabolic syndrome, and chronic illnesses (9-13). Studies indicate that exercise interventions can reduce neutrophil activity (14-16) while increasing high-density lipoprotein cholesterol (HDL-C) levels (17), thereby ameliorating systemic inflammation and lipid metabolism, potentially leading to decreased NPAR and NHR levels. OSA is a chronic inflammatory disorder with multiple systemic inflammatory markers implicated in its pathophysiology. Its severity is positively correlated with inflammatory marker levels (18). NPAR and NHR possibly mediate how physical activity influences the probability of death in the OSA population. However, relevant studies remain scarce. Therefore, our study sought to unravel the possible mediating roles of the foregoing biomarkers in the relation of physical activity to mortality risk in OSA patients.

A prior study has predominantly examined how physical activity influences OSA severity (19), while its long-term prognostic implications remain unclear. In particular, whether physical activity can reduce OSA-related ACM remains uncertain due to the lack of high-quality evidence from large-scale clinical studies. Given the known benefits of physical activity through multiple physiological pathways, it is hypothesized that appropriate physical activity may reduce mortality risk in OSA patients via multisystem mechanisms, with protective effects independent of conventional therapeutic interventions. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-825/rc).

Methods

Data source and study population

Data from the National Health and Nutrition Examination Survey (NHANES) (https://wwwn.cdc.gov/nchs/nhanes/) between 2015 and 2018 were studied to elucidate the effects of physical activity on ACM among OSA sufferers. NHANES has an intricate, stratified, multistage probability sampling design, presenting a thorough and representative evaluation of Americans. It offers extensive data on health, nutritional levels, and demographic characteristics, offering high scientific value and representativeness (20). The original cohort comprised 19,225 participants. After applying predefined eligibility criteria, 4,353 eligible adult participants with OSA were ultimately included in the analysis. Specifically, 13,517 individuals were excluded due to missing OSA diagnosis data or absence of OSA, 59 individuals due to missing physical activity data, and 1,296 individuals due to missing data on body mass index (BMI), alcohol consumption, income, or education. Our study selection process is detailed in Figure 1. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Figure 1 Flow chart of patient screening. BMI, body mass index; NHANES, National Health and Nutrition Examination Survey; OSA, obstructive sleep apnea.

Definition of OSA

Following previous research (21), participants were classified as having OSA if they responded “yes” to at least one of the following three NHANES survey questions: (I) experiencing excessive daytime sleepiness despite obtaining at least seven hours of sleep per night, reported 16–30 times; (II) experiencing gasping, snorting, or cessation of breathing at least three times per week; (III) snoring at least three times per week.

Assessment of physical activity

Detailed physical activity was assessed through the NHANES Physical Activity Questionnaire administered by trained interviewers (22). Using the Ainsworth Compendium of Physical Activities, different physical activities were assigned metabolic equivalent (MET) values to quantify energy expenditure objectively for every activity category (23). The physical activity index was calculated as Weekly Activity Frequency × Activity Duration per Session × MET. The MET values were assigned according to the suggested MET scores in the NHANES database. Participants were then categorized into two categories: sufficient activity (≥600 MET-min/week) and inadequate physical activity (<600 MET-min/week) (24).

Mediating variables

NPAR and NHR were derived from NHANES laboratory data, which were obtained through official complete blood count (CBC) measurements via the Beckman Coulter DxH 800 analyzer (Beckman Coulter, Inc., Brea, CA, USA) (24).

NHR was derived from neutrophil count (NC) (10³ cells/µL)/HDL-C (mmol/L). NHR was computed as: NC (10³ cells/µL) / HDL-C (mmol/L) (25).

The calculation method for NPAR is: neutrophil percentage (as a proportion of total white blood cell count) (%) × 100 / albumin (g/dL) (26).

Covariates

Potential confounding factors were included as covariates and encompassed age, sex, race/ethnicity (non-Hispanic White, non-Hispanic Black, Mexican American, other), educational attainment (bachelor’s degree or higher, partial college education or associate degree, high school diploma or equivalent, less than high school education), income level (≥$20,000, <$20,000), alcohol consumption (never: fewer than 12 alcoholic drinks in a lifetime and in the previous year; former: at least 12 alcoholic drinks in a lifetime but none in the previous year; current: at least 12 alcoholic drinks in the previous year), smoking (never: fewer than 100 cigarettes in a lifetime; former: at least 100 cigarettes in a lifetime but currently none; current: at least 100 cigarettes in a lifetime and currently smoking either daily or occasionally), and BMI.

Statistical analysis

Categorical variables were shown in N (%) and analyzed by the chi-squared test, whereas continuous ones were displayed in mean ± standard error (SE) and compared via the Z-test. To investigate how physical activity impacts mortality among OSA individuals, logistic regression models were employed. Three models were constructed: Model 1 was unadjusted; Model 2 involved adjustments of age, sex, and race; Model 3 was further adjusted for education, income, BMI, smoking, and alcohol consumption. Moreover, mediation analysis was performed to examine whether NPAR and NHR mediated the effect of physical activity on OSA-related mortality. Furthermore, subgroup analyses were performed to explore potential variations in the association based on covariates. A two-tailed test was applied, with statistical significance set at P<0.05. All statistical analyses were enabled by R 4.4.1, with logistic regression performed using the “survey” package and mediation analysis using the “mediation” package.

Results

Baseline characteristics of participants

The baseline characteristics vary significantly between the ACM cohort (n=137) and the non-mortality group (n=4,216) (Table 1). Baseline characteristics of the study population without excluding OSA participants are presented in Table S1. Regarding demographic factors, the mean age was notably higher in the mortality cohort. BMI levels, however, were lower in the mortality group. Race distribution varied significantly, with more non-Hispanic Whites in the mortality cohort, while other racial groups were less than in the non-mortality group. Regarding socioeconomic factors, education level and income displayed significant gradient differences. The proportion of college graduates was lower in the mortality cohort. More individuals with an annual income below $20,000 were observed in the mortality group. As for lifestyle factors, the proportion of insufficient physical activity was 62.0% in the mortality cohort. The mortality group exhibited a markedly higher prevalence of smoking, particularly with regard to former smokers. Conversely, this group showed a substantially lower percentage of current alcohol consumers. No evident sex differences were observed across groups (P=0.14). The mortality group had a higher proportion of males.

Impact of physical activity on mortality in OSA patients

To further examine how physical activity impacts death among OSA sufferers, a multivariable logistic regression model was applied (Table 2). In terms of cancer-related mortality, compared to the insufficient physical activity group, the sufficient physical activity group had an odds ratio (OR) of 0.28 [95% confidence interval (CI): 0.11–0.73; P=0.01]. Regarding cardiovascular death, the sufficient physical activity cohort had an OR of 2.09 (95% CI: 0.78–5.64, P=0.13), displaying no statistically significant relation. For ACM, the sufficient physical activity group had an OR of 0.83 (95% CI: 0.50–1.37, P=0.44).

Mediation analysis

To explore whether NHR and NPAR mediated how physical activity influences death, a causal mediation analysis was performed. For cancer-related mortality, the average causal mediation effect (ACME) for NPAR was −1.32×10⁻¹⁰ (P=0.99). The ACME for NHR was −4.11×10⁻⁹ (P=0.37). Regarding cardiovascular mortality, the ACME for NPAR was −3.06×10⁻¹⁰ (P=0.94). The ACME for NHR was 1.05×10⁻⁹ (P=0.90). In terms of ACM, the ACME for NPAR was −9.67×10⁻¹⁰ (P=0.89). The ACME for NHR was −1.75×10⁻⁸ (P=0.11). These results are detailed in Figure 2.

Figure 2 The mediating effect of NPAR and NHR in the relationship between physical activity and OSA mortality. (A) The mediating effect of NPAR and NHR in the relationship between physical activity and all-cause death from OSA. (B) The mediating effect of NPAR and NHR in the relationship between physical activity and cardiovascular death from OSA. (C) The mediating effect of NPAR and NHR in the relationship between physical activity and cancer death from OSA. *, P<0.05; **, P<0.01; ***, P<0.001. NHR, neutrophil-to-high-density lipoprotein cholesterol ratio; NPAR, neutrophil-percentage-to-albumin ratio; OSA, obstructive sleep apnea; PAI, physical activity index.

Subgroup analysis

We performed a subgroup analysis by covariates to examine potential population differences in the association between physical activity and mortality in OSA patients (Table 3). Regarding cancer-related mortality, the OR was 0.14 (95% CI: 0.05–0.40) for males and 0.76 (95% CI: 0.15–3.99) for females. Among racial groups, the OR was 0.24 (95% CI: 0.08–0.73) for non-Hispanic Whites and 0.71 (95% CI: 0.06–8.40) for other races. In terms of BMI, individuals with BMI ≥30 kg/m² had an OR of 0.24 (95% CI: 0.06–0.92), while those with BMI <30 kg/m² had an OR of 0.34 (95% CI: 0.12–0.96). Regarding income levels, the OR was 0.27 (95% CI: 0.07–0.99) for individuals with an income ≥$20,000 and 0.28 (95% CI: 0.03–2.24) for those with an income <$20,000. For cardiovascular mortality, the OR was 3.94 (95% CI: 1.09–14.23) for men and 0.66 (95% CI: 0.07–6.35) for women.

Discussion

This study is the first to elucidate the impact of physical activity on varied mortality outcomes in OSA patients. Adequate physical activity significantly lowers cancer-related mortality but does not confer protection against cardiovascular mortality, and its protective effect on ACM disappears after adjusting for confounders. These findings provide important evidence for individualized physical activity interventions in OSA patients while also highlighting the complexity of health management in this population.

This study systematically evaluated the effects of physical activity on both cause-specific and ACM among OSA patients, revealing the intricate regulation of mortality risk within this group. Notably, our study is the first to confirm the independent protective function of physical activity against cancer-linked death among OSA patients. This protective effect was particularly pronounced in males, obese individuals, and smokers, suggesting that the unique pathophysiological mechanisms of OSA may modulate the benefits of physical activity through multiple pathways. Intermittent hypoxia (IH), a hallmark of OSA, activates the HIF-1α axis, which may serve as a key biological bridge linking OSA to cancer progression. IH has been shown to promote tumor growth by inducing DNA damage and creating an immunosuppressive microenvironment, while also driving the Warburg effect, which accelerates tumor metabolic reprogramming (27,28). Regular physical activity may counteract this malignant cycle through dual mechanisms: physiological level: improved respiratory muscle function reduces nocturnal hypoxic episodes, thereby attenuating HIF-1α-driven angiogenesis (28); immunological level: activation of the β-adrenergic signaling pathway enhances CD8⁺ T cell tumor infiltration and natural killer (NK) cell cytotoxicity (29,30). Interestingly, this enhanced immune surveillance effect appears to be more pronounced in individuals with higher baseline inflammation levels, such as smokers and those with obesity. This aligns with recent epigenetic evidence suggesting that physical activity may mitigate tumor hypoxia, restore TET enzyme activity, and reverse DNA hypermethylation (31), thereby maintaining tumor suppressor gene function.

In contrast to its benefits for cancer-related mortality, physical activity did not confer a protective effect against cardiovascular mortality, a finding that challenges the conventional understanding of the universal cardiovascular benefits of exercise (32). Notably, the male subgroup even exhibited increased cardiovascular mortality risk. Several pathophysiological interactions unique to OSA patients may explain this. First, OSA is characterized by persistent sympathetic activation and oxidative stress (33), which may counteract the cardiovascular benefits of exercise (34). In males, testosterone-mediated sympathetic hyperreactivity may further exacerbate this effect. Second, the unique “nocturnal oxygen desaturation phenomenon” may lead to exacerbated myocardial reperfusion injury in response to exercise (35). Excessive free fatty acid (FFA) release triggered by exercise-induced lipolysis (36) may promote insulin resistance and lipotoxicity, offsetting metabolic benefits (37). These findings suggest that cardiovascular risk management in OSA patients requires a tailored exercise prescription strategy, distinct from that of the general population. Particular caution is needed when prescribing high-intensity exercise for male OSA patients, as it may carry unforeseen risks.

The analysis of ACM revealed a more complex sociomedical landscape. While preliminary analyses suggested a protective role of physical activity, this link became insignificant following adjustments for socioeconomic factors. This phenomenon may reflect the multidimensional interplay between health behaviors and social structures. According to the biopsychosocial model, individuals with low income and education levels face both direct limitations on exercise participation (38-40) and indirect barriers due to reduced access to healthcare resources, creating a vicious cycle of poverty, sedentary behavior, and comorbidity. Recent research on health inequalities suggests that (39,40), integrating social support policies (e.g., subsidizing exercise prescriptions) may enhance adherence to healthy behaviors by reducing economic barriers. However, the effectiveness of such interventions in the OSA population warrants further investigation. These findings highlight the need for a shift beyond traditional biomedical models to develop multidimensional intervention strategies incorporating social policy support.

Our study found that NHR and NPAR did not mediate the relation of physical activity to mortality risk in the OSA population, which contrasts sharply with findings in the general population (41,42). Previous studies on NPAR/NHR and OSA were limited. Several potential explanations exist. First, the persistent low-grade inflammatory state in OSA patients may dampen the metabolic-inflammatory homeostasis response to short-term lifestyle interventions, thereby masking single-pathway mediation effects. Second, OSA-linked IH possibly alters inflammatory signaling pathways via epigenetic modifications, reducing the predictive power of inflammatory markers. Third, composite inflammatory indices, such as NHR and NPAR, may dilute the specific effects of individual inflammatory mediators. In contrast, classic inflammatory markers such as interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and high-sensitivity C-reactive protein (hs-CRP) have been well-documented in previous studies to be closely associated with metabolic disorders, cardiovascular risks, and mortality in OSA patients (43-45). For instance, TNF-α and IL-6 can directly promote vascular endothelial injury (46), while hs-CRP serves as a powerful predictor of cardiovascular events (47). Given that physical activity and sleep mutually influence each other through multiple complex interactions in both physiological and psychological processes (48), we recommend that future studies incorporate multidimensional biomarkers to more comprehensively elucidate the underlying mediating mechanisms.

There are limitations in this study. First, our data were extracted from the NHANES database, which includes OSA data only from 2015–2016 and 2017–2018, resulting in a relatively small sample size. Second, our study design cannot fully exclude residual confounders, such as OSA severity (e.g., AHI index) and physical activity intensity or type. Subgroup analyses were also limited by sample size. Third, continuous positive airway pressure (CPAP) adherence, a potential effect modifier, was not accounted for in this study. Fourth, this study relies on questionnaire-based criteria to identify OSA rather than objective polysomnography (PSG). This approach introduces potential misclassification bias, including over-diagnosis (false positives) and under-diagnosis (false negatives), both of which may influence mortality estimates. Fifth, our physical activity assessment relied on self-reported METs without granular data on exercise types (e.g., aerobic vs. resistance training) or intensity levels. This limitation precludes analysis of whether specific physical activity modalities differentially influence mortality in OSA subgroups. Future research should leverage larger datasets to comprehensively evaluate the influence of physical activity on the likelihood of mortality in the OSA population.

Conclusions

This study identifies physical activity as an independent protective factor against cancer-related mortality in OSA patients while also revealing its potential adverse effects on cardiovascular risk and the complex sociological mediation effects. These findings challenge one-size-fits-all exercise recommendations and highlight the need for an OSA-specific risk assessment-monitoring-intervention framework.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-825/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-825/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-825/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Finamore P, Scarlata S, Cardaci V, et al. Exhaled Breath Analysis in Obstructive Sleep Apnea Syndrome: A Review of the Literature. Medicina (Kaunas) 2019;55:538. [Crossref] [PubMed]
2. Chang AC, Fox TP, Wang S, et al. Relationship between obstructive sleep apnea and the presence and severity of diabetic retinopathy. Retina 2018;38:2197-206. [Crossref] [PubMed]
3. Tiron R, Lyon G, Kilroy H, et al. Screening for obstructive sleep apnea with novel hybrid acoustic smartphone app technology. J Thorac Dis 2020;12:4476-95. [Crossref] [PubMed]
4. Fang X, Wang L, Xu C, et al. The association of lymphocyte with hypothyroidism in obstructive sleep apnea. BMC Pulm Med 2024;24:60. [Crossref] [PubMed]
5. Lyons MM, Bhatt NY, Pack AI, et al. Global burden of sleep-disordered breathing and its implications. Respirology 2020;25:690-702. [Crossref] [PubMed]
6. Andrade FM, Pedrosa RP. The role of physical exercise in obstructive sleep apnea. J Bras Pneumol 2016;42:457-64. [Crossref] [PubMed]
7. Li Y, Miao Y, Tan J, et al. Association of modifiable risk factors with obstructive sleep apnea: a Mendelian randomization study. Aging (Albany NY) 2023;15:14039-65. [Crossref] [PubMed]
8. Giannaki CD, Sakkas GK, Hadjigeorgiou GM, et al. Unfolding the role of exercise in the management of sleep disorders. Eur J Appl Physiol 2024;124:2547-60. [Crossref] [PubMed]
9. Wang R, Tao W, Chen H, et al. Investigating nonlinear associations between neutrophil percentage to albumin ratio and cardiovascular disease: a nationally representative cross-sectional study. Sci Rep 2024;14:23632. [Crossref] [PubMed]
10. Chuang SM, Liu SC, Chien MN, et al. Neutrophil-to-High-Density Lipoprotein Ratio (NHR) and Neutrophil-to-Lymphocyte Ratio (NLR) as prognostic biomarkers for incident cardiovascular disease and all-cause mortality: A comparison study. Am J Prev Cardiol 2024;20:100869. [Crossref] [PubMed]
11. Li X, Wu M, Chen M, et al. The Association Between Neutrophil-Percentage-to-Albumin Ratio (NPAR) and Mortality Among Individuals With Cancer: Insights From National Health and Nutrition Examination Survey. Cancer Med 2025;14:e70527. [Crossref] [PubMed]
12. Guo J, Chen M, Hong Y, et al. Comparison of the Predicting Value of Neutrophil to high-Density Lipoprotein Cholesterol Ratio and Monocyte to high-Density Lipoprotein Cholesterol Ratio for in-Hospital Prognosis and Severe Coronary Artery Stenosis in Patients with ST-Segment Elevation Acute Myocardial Infarction Following Percutaneous Coronary Intervention: A Retrospective Study. J Inflamm Res 2023;16:4541-57. [Crossref] [PubMed]
13. Hashemi SM, Kheirandish M, Rafati S, et al. The association between neutrophil and lymphocyte to high-density lipoprotein cholesterol ratio and metabolic syndrome among Iranian population, finding from Bandare Kong cohort study. Lipids Health Dis 2024;23:393. [Crossref] [PubMed]
14. Bartlett DB, Fox O, McNulty CL, et al. Habitual physical activity is associated with the maintenance of neutrophil migratory dynamics in healthy older adults. Brain Behav Immun 2016;56:12-20. [Crossref] [PubMed]
15. Wang R, Chen PJ, Chen WH. Diet and exercise improve neutrophil to lymphocyte ratio in overweight adolescents. Int J Sports Med 2011;32:982-6. [Crossref] [PubMed]
16. Osváth P, Szűcs M, Börzsei D, et al. Andrological Aspects of Exercise: Moderate Swimming Protects against Isoproterenol Induced Testis and Semen Abnormalities in Rats. Antioxidants (Basel) 2022;11:436. [Crossref] [PubMed]
17. Cho KH, Nam HS, Kang DJ, et al. Enhancement of High-Density Lipoprotein (HDL) Quantity and Quality by Regular and Habitual Exercise in Middle-Aged Women with Improvements in Lipid and Apolipoprotein Profiles: Larger Particle Size and Higher Antioxidant Ability of HDL. Int J Mol Sci 2023;24:1151. [Crossref] [PubMed]
18. Attia MM, Qasim MA, Alhamwi HS, et al. Using the Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio as Prognostic Markers for Obstructive Sleep Apnea: A Systematic Review and Meta-Analysis of Observational Studies. Cureus 2024;16:e72539. [Crossref] [PubMed]
19. Liu Y, Yang L, Stampfer MJ, et al. Physical activity, sedentary behaviour and incidence of obstructive sleep apnoea in three prospective US cohorts. Eur Respir J 2022;59:2100606. [Crossref] [PubMed]
20. Pan X, Zhang X, Wu X, et al. Association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and obstructive sleep apnea: a cross-sectional study from NHANES. Lipids Health Dis 2024;23:209. [Crossref] [PubMed]
21. Zhou T, Chen S, Mao J, et al. Association between obstructive sleep apnea and visceral adiposity index and lipid accumulation product: NHANES 2015-2018. Lipids Health Dis 2024;23:100. [Crossref] [PubMed]
22. Tsai YC, Chen HM, Hsiao SM, et al. Association of physical activity with cardiovascular and renal outcomes and quality of life in chronic kidney disease. PLoS One 2017;12:e0183642. [Crossref] [PubMed]
23. Ainsworth BE, Haskell WL, Whitt MC, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 2000;32:S498-504. [Crossref] [PubMed]
24. Wei X, Min Y, Xiang Z, et al. Joint association of physical activity and dietary quality with survival among US cancer survivors: a population-based cohort study. Int J Surg 2024;110:5585-94. [Crossref] [PubMed]
25. Wang Y, Shang X, Zhang Y, et al. The association between neutrophil to high-density lipoprotein cholesterol ratio and gallstones: a cross-sectional study. BMC Public Health 2025;25:157. [Crossref] [PubMed]
26. Liu CF, Chien LW. Predictive Role of Neutrophil-Percentage-to-Albumin Ratio (NPAR) in Nonalcoholic Fatty Liver Disease and Advanced Liver Fibrosis in Nondiabetic US Adults: Evidence from NHANES 2017-2018. Nutrients 2023;15:1892. [Crossref] [PubMed]
27. Shoucair S, Pu N, Habib JR, et al. Obstructive sleep apnea predicts pathologic response to neoadjuvant therapy in resected pancreatic ductal adenocarcinoma. MedComm (2020) 2022;3:e184.
28. Vargova D, Kolková Z, Dargaj J, et al. Analysis of HIF-1α expression and genetic polymorphisms in human clear cell renal cell carcinoma. Pathol Oncol Res 2023;29:1611444. [Crossref] [PubMed]
29. Baker FL, Smith KA, Mylabathula PL, et al. Exercise-induced β2-adrenergic Receptor Activation Enhances the Antileukemic Activity of Expanded γδ T-Cells via DNAM-1 Upregulation and PVR/Nectin-2 Recognition. Cancer Res Commun 2024;4:1253-67. [Crossref] [PubMed]
30. Schauer T, Djurhuus SS, Simonsen C, et al. The effects of acute exercise and inflammation on immune function in early-stage prostate cancer. Brain Behav Immun Health 2022;25:100508. [Crossref] [PubMed]
31. Zhao S, Allis CD, Wang GG. The language of chromatin modification in human cancers. Nat Rev Cancer 2021;21:413-30. [Crossref] [PubMed]
32. Fiuza-Luces C, Santos-Lozano A, Joyner M, et al. Exercise benefits in cardiovascular disease: beyond attenuation of traditional risk factors. Nat Rev Cardiol 2018;15:731-43. [Crossref] [PubMed]
33. Dobrosielski DA, Papandreou C, Patil SP, et al. Diet and exercise in the management of obstructive sleep apnoea and cardiovascular disease risk. Eur Respir Rev 2017;26:160110. [Crossref] [PubMed]
34. Ooishi Y. Correlation Between Resting Testosterone/Cortisol Ratio and Sound-Induced Vasoconstriction at Fingertip in Men. Front Physiol 2018;9:164. [Crossref] [PubMed]
35. Tsigkou V, Oikonomou E, Anastasiou A, et al. Molecular Mechanisms and Therapeutic Implications of Endothelial Dysfunction in Patients with Heart Failure. Int J Mol Sci 2023;24:4321. [Crossref] [PubMed]
36. Wang Y, Weng S, Xia C, et al. Effect of physical exercise on metabolism in patients with atrial fibrillation. Front Cardiovasc Med 2024;11:1502620. [Crossref] [PubMed]
37. Shabbir S, Khurram E, Moorthi VS, et al. The interplay between androgens and the immune response in polycystic ovary syndrome. J Transl Med 2023;21:259. [Crossref] [PubMed]
38. Ra JS. Factors associated with the combination of general and abdominal obesity in middle-aged and older Korean women: a cross-sectional study. Osong Public Health Res Perspect 2023;14:379-87. [Crossref] [PubMed]
39. Saat NZM, Hanawi SA, Farah NMF, et al. Relationship between physical activity and musculoskeletal disorders among low income housewives in Kuala Lumpur: A cross sectional study. PLoS One 2022;17:e0274305. [Crossref] [PubMed]
40. Echeverría SE, Pentakota SR, Abraído-Lanza AF, et al. Clashing paradigms: an empirical examination of cultural proxies and socioeconomic condition shaping Latino health. Ann Epidemiol 2013;23:608-13. [Crossref] [PubMed]
41. Jiang M, Sun J, Zou H, et al. Prognostic Role of Neutrophil to High-Density Lipoprotein Cholesterol Ratio for All-Cause and Cardiovascular Mortality in the General Population. Front Cardiovasc Med 2022;9:807339. [Crossref] [PubMed]
42. Liu Y, Qiu Z, Shen G, et al. Associations between neutrophil-percentage-to-albumin ratio level and all-cause mortality and cardiovascular disease-cause mortality in general population: evidence from NHANES 1999-2010. Front Cardiovasc Med 2024;11:1393513. [Crossref] [PubMed]
43. Fei Q, Tan Y, Yi M, et al. Associations between cardiometabolic phenotypes and levels of TNF-α, CRP, and interleukins in obstructive sleep apnea. Sleep Breath 2023;27:1033-42. [Crossref] [PubMed]
44. Khosla AA, Nasir K, Saxena A, et al. Association between high risk of obstructive sleep apnea and inflammatory markers in a population sample of young and middle-aged adults in the Miami Heart Study. J Clin Sleep Med 2024;20:1895-903. [Crossref] [PubMed]
45. Li C, Shi S. Gut microbiota and metabolic profiles in chronic intermittent hypoxia-induced rats: disease-associated dysbiosis and metabolic disturbances. Front Endocrinol (Lausanne) 2023;14:1224396. [Crossref] [PubMed]
46. Wei A, Ma H, Zhang L, et al. Clinical analysis of chronic active EBV infection with coronary artery dilatation and a matched case-control study. Orphanet J Rare Dis 2021;16:50. [Crossref] [PubMed]
47. Zhou XD, Chen QF, Targher G, et al. High-Sensitivity C-Reactive Protein Is Associated With Heart Failure Hospitalization in Patients With Metabolic Dysfunction-Associated Fatty Liver Disease and Normal Left Ventricular Ejection Fraction Undergoing Coronary Angiography. J Am Heart Assoc 2024;13:e032997. [Crossref] [PubMed]
48. Nguyen TD, Guinot M, Bricout VA. Effect of Daily Physical Activity on Sleep Characteristics in Children with Autism Spectrum Disorder. Sports (Basel) 2021;9:91. [Crossref] [PubMed]