Comparative efficacy of exercise modes on inflammatory adipocytokines in patients with breast cancer: a systematic review with pairwise and network meta-analyses 

Comparative efficacy of exercise modes on inflammatory adipocytokines in patients with breast cancer: a systematic review with pairwise and network meta-analyses | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Comparative efficacy of exercise modes on inflammatory adipocytokines in patients with breast cancer: a systematic review with pairwise and network meta-analyses Mousa Khalafi, Mohammad Hossein Sakhaei, Faeghe Ghasemi, Saeid Fatolahi, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8968752/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Introduction and aim. Adipocytokines play a key role in the pathogenesis and progression of breast cancer and are increasingly recognized as potential therapeutic targets. The anti-inflammatory effects of regular exercise training are well-established, but its effects on inflammatory adipocytokines in individuals with breast cancer have not been elucidated. This systematic review and pairwise network meta-analyses aimed to (1) investigate the overall effects of exercise training on pro- and anti-inflammatory markers, and (2) compare the efficacy of different exercise modes using pairwise and network meta-analytic approaches. Methods . PubMed, Web of Science, and Scopus were systematically searched from inception to January 2025 using four groups of keywords, including “exercise”, “cytokine”, “breast cancer” and “randomized”. Randomized controlled trials investigating the effects of exercise training compared with a control (CON) or other modes of exercise on circulating IL-6, TNF-α, IL-10, CRP, leptin, and adiponectin in patients with breast cancer were included. Standardized mean differences (SMD) with 95% confidence intervals (CIs) were calculated using random-effects models for both pairwise and network meta-analyses. Results . Twenty-five studies involving 1,219 patients with breast cancer were. Compared with CON, exercise training led to significantly larger reductions in IL-6 [SMD: -0.39, p=0.02] and leptin [SMD: -0.77, p=0.01], and non-significant trends toward reductions in TNF-α [SMD: -0.30, p=0.05] and CRP [SMD: -0.47, p=0.07], but had no effect on IL-10 or adiponectin. In addition, high-intensity interval training (HIIT) reduced IL-6 [SMD: -1.08, p=0.01], with non-significant trends toward reduced TNF-α [SMD: -1.40, p=0.09] and raised IL-10 [SMD: 0.34, p=0.09]. Combined training (CT) decreased TNF-α [SMD: -0.65, p=0.006], and showed a trend towards lower leptin [SMD: -0.62, p=0.06]. No individual exercise mode impacted on in CRP or adiponectin compared with CON. Subgroup analyses suggested that intervention duration and patients’ clinical status influenced the magnitude of these effects. Conclusion . Exercise training appears to be effective in attenuating some markers of chronic low-grade inflammation by modulating key adipose-derived pro- and anti-inflammatory cytokines in patients with breast cancer, with HIIT and CT may confer superior benefits. Health sciences/Biomarkers Health sciences/Diseases Biological sciences/Immunology Health sciences/Medical research Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction Breast cancer is a major global health challenge, being the most commonly diagnosed cancer and the fifth leading cause of cancer-related mortality worldwide [1]. Breast cancer development and progression are influenced not only by tumor-intrinsic factors but also by systemic host factors such as chronic low-grade inflammation and dysregulated adipocytokines [2]. Obesity and physical inactivity are associated with an inflammatory milieu that can promote breast cancer pathogenesis and worsen survival [3-5]. In overweight or obese subjects, adipose tissue releases pro-inflammatory cytokines (e.g., interleukin-6 [IL-6], tumor necrosis factor-alpha [TNF-α]), and adipokines (e.g., leptin), contributing to a chronic inflammatory environment that promotes tumor progression by enhancing angiogenesis and impairing anti-tumor immune responses [3, 6, 7]. Raised IL-6, TNF-α, and C-reactive protein (CRP) are linked to poorer prognosis in breast cancer, correlating with higher rates of recurrence, metastasis, and mortality [3, 8-10]. In contrast, anti-inflammatory factors like interleukin-10 (IL-10) and adiponectin may exert protective effects. Adiponectin, an adipocyte-derived hormone, promotes an anti-inflammatory, insulin-sensitizing state and can directly inhibit breast cancer cell growth via activation of AMP-activated protein kinase (AMPK) and downregulation of proliferative pathway PI3K/Akt [11]. Higher adiponectin is associated with lower breast cancer risk, whereas leptin, a pro-inflammatory adipokine upregulated in obesity, enhances breast cancer cell proliferation and survival, angiogenesis, and invasion through estrogen-dependent and -independent mechanisms [11]. A leptin/adiponectin imbalance observed in obesity (characterized by hyperleptinemia and hypoadiponectinemia) is a major factor linking excess adiposity to breast cancer progression [11, 12]. Thus, targeting chronic inflammation and adipocytokine dysregulation is increasingly recognized as an important strategy in breast cancer management. Exercise training is emerging as a potent lifestyle intervention to attenuate chronic inflammation and favorably modulate adipocytokines in both healthy and clinical populations, with regular exercise exerting anti-inflammatory effects [13]. Exercise reduces visceral and total body fat mass, thereby decreasing the source of pro-inflammatory adipokines including IL-6, TNF-α, and leptin, often with increases in adiponectin [13, 14]. Whilst, each acute bout of exercise provokes a transient release of muscle-derived cytokines (myokines), notably IL-6, which paradoxically mediates anti-inflammatory effects by stimulating IL-10 production and inhibiting TNF-α and IL-1β signaling through AMPK activation [14, 15]. Exercise also shifts immune cell profiles toward a less inflammatory state—such as increasing anti-inflammatory M2 macrophages over pro-inflammatory M1 in adipose tissue—and improves metabolic factors like insulin resistance that are closely linked to inflammation [16, 17]. These multifactorial actions help explain why long-term (chronic) exercise is associated with reduced systemic inflammation and improved outcomes in cancer survivors [3, 13]. In breast cancer patients and survivors, numerous randomized controlled trials (RCTs) have examined the effects of exercise on inflammatory markers. Many reports exercise reduces circulating IL-6 and CRP, and in some cases, downregulates TNF-α and leptin while upregulating adiponectin, although such findings have not been uniform [18-20]. The heterogeneity in inflammatory outcomes may relate to differences in exercise modes, intensities, and durations, as well as patient factors. Notably, prior meta-analyses have provided evidence that overall, exercise has beneficial effects on inflammatory biomarkers in breast cancer [18]. Similarly, structured exercise can induce small-to-moderate improvements in pro-inflammatory cytokines in this population [14, 21]. However, these reviews have typically evaluated exercise in general and have not directly compared different exercise modes (e.g., aerobic vs. resistance vs. combined training). It remains unclear whether certain modes of exercise confer superior anti-inflammatory effects in breast cancer patients, which represents a critical knowledge gap for exercise oncology. We present a systematic review along with the first network meta-analysis comparing the efficacy of different exercise modalities on key pro- and anti-inflammatory cytokines and adipokines (IL-6, TNF-α, IL-10, CRP, leptin, and adiponectin) in patients with breast cancer. By integrating direct and indirect evidence, this approach enables the ranking of exercise interventions based on their anti-inflammatory effects. Our objectives are to quantify the overall effects of exercise training and identify which modes offer the greatest benefits. We have also examined the potential effect modifiers, such as the patients’ clinical status (during vs. post-treatment) and intervention durations. This work addresses a key evidence gap and aims to refine exercise prescriptions by identifying optimal exercise modes to reduce chronic inflammation and support long-term outcomes in breast cancer care. Methods This systematic review and meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [22] and the Cochrane Handbook for Systematic Reviews of Interventions [23], and was prospectively registered on the International Prospective Register of Systematic Reviews (PROSPERO) with ID: CRD420251231590. Search Strategy. A systematic search was conducted across multiple databases, including PubMed, Web of Science, and Scopus, from inception to January 2025. The search incorporated four groups of keywords related to “exercise”, “cytokine”, “breast cancer” and “randomized trials”. When available on the databases, filters were applied to restrict results to human studies, English language publications, and articles. The detailed search strategy for each database is provided in Supplementary Table 1. To minimize the risk of missing relevant studies, the reference lists of included studies and previously published meta-analyses [18, 24] were manually screened. Additional searches were conducted using Google Scholar, and through checking reference lists of eligible studies (the snowballing method) to identify eligible studies. The searches were conducted by one author (M Kh). Eligibility Criteria. Peer-reviewed and English language studies were deemed eligible if they met following criteria defined according to the PICOS framework: Population: participants were adults ≥ 18 years of age, with a confirmed breast cancer diagnosis, regardless of disease stage or clinical status; Intervention: studies that investigated any mode of exercise training, including aerobic, resistance, combined (aerobic and resistance), or high-intensity interval training (HIIT), with an intervention duration ≥ 2 weeks, regardless of frequency, intensity and total training volume; Comparator: studies that included a non-exercise control group (CON) or another exercise intervention arm differing from the primary arm; Outcomes: studies that assessed and reported inflammatory adipocytokines including IL-6, TNF-α, IL-10, CRP, leptin, or adiponectin; and Study design: randomized controlled or clinical trials with parallel group designs. Exclusion criteria comprised studies that were non-original, non-English language, non-peer-reviewed, and non-randomized. Studies investigating exercise training in combination with co-interventions, such as dietary interventions, were also excluded. Study Selection. Three authors (FG, MM, AZ) independently screened the studies, and any disagreements were resolved through discussion with other authors (MKh and SF). All retrieved studies were imported into EndNote (v21) to remove duplicate records and facilitate the screening process. Screening was conducted based on the study titles and abstracts, followed by the full-text assessment of potentially eligible studies according to the a priori inclusion and exclusion criteria. Data Extraction and Synthesis. Data from included studies were independently extracted by two authors (FG, MHS), and any disagreements were resolved through discussion with additional authors (MKh and ShM). Extracted data and information included study details, including first author and years of publication; participants' information, including study sample size, age, biological sex, health status; intervention and comparator characteristics, including CON type, exercise mode, intensity, time, duration, and frequency; and data for the specified outcome of interest. For statistical analyses, mean changes and standard deviations (SDs) along with sample sizes for each group were extracted. When required, these values were calculated from pre- and post-intervention data. In addition, if studies reported outcomes as medians and interquartile ranges (IQRs), standard errors, and confidence intervals (CIs), these were converted to means and SDs using established statistical methods [23, 25, 26]. When numerical data were available only in graphical form, values were extracted using GetData Graph Digitizer software. Risk of Bias Assessment. Two authors (MHS and HR) independently assessed the risk of bias using the Cochrane Risk of Bias Tool (ROB) [27], and discrepancies were resolved through discussion or with a third author (MKh). Statistical Analyses. Pairwise meta-analysis was conducted using the comprehensive meta-analysis version 3 software (CMA3). Effect sizes were calculated as standardized mean difference (SMD) with 95% confidence intervals (CIs). The SMD was selected to account for variations in measurement methods across studies. A random-effects model was applied to pool data, based on the assumption of underlying heterogeneity among the included randomized trials. To assess heterogeneity amongst included studies, I 2 statistics and Q values were calculated. I 2 values was interpreted as low (0−25%), moderate (26−50%), substantial (51−75%), and considerable (> 75%) heterogeneity; and for the Q statistic, p-values < 0.05 were considered indicative of statistically significant heterogeneity. Publication bias was evaluated through visual inspection of funnel plots and Egger’s regression tests, with p-values < 0.05 indicating potential bias. Sensitivity analyses were performed by sequentially removing individual studies to determine the influence of each study on the overall pooled estimates. In addition, subgroup analysis was performed based on patients’ clinical status (with cancer and cancer survivors) and intervention durations (medium-term: < 16 weeks and longer-term: ≥ 16 weeks). For the network meta-analyses, the Netmeta package in R software (version 4.4.1) was used within a frequentist framework. Both direct and indirect evidence were synthesized using random effects models to calculate SMDs with corresponding 95% CIs, applying the same rationale used in the pairwise meta-analyses to account for anticipated heterogeneity across studies. Network geometry plots were generated to illustrate the relationships among the intervention arms. Forest plots and league tables were produced to present the standardized mean differences (SMDs) and their 95% confidence intervals (CIs) for all pairwise comparisons between interventions. To rank the interventions, P-scores were calculated, with higher P-scores indicating higher likelihood of better effectiveness relative to the other exercise modes. To assess heterogeneity among the included studies, I², tau (τ), tau-squared (τ²), and Cochran’s Q statistics were calculated. To evaluate the consistency assumption within the network, both global and local approaches were applied. Global consistency was assessed using the Q statistic under a full design-by-treatment interaction random-effects model, whereas local consistency was examined using node-splitting models. In addition, publication bias was assessed using Egger’s tests, with p-values < 0.05 indicating potential bias. Results Study characteristics. A total of 2,511 records were identified through database searches, of which 1,866 records were duplicates and therefore excluded, and 1,866 articles were entered into the screening process. A total of 1,866 articles were screened based on the titles and abstracts, of which 1,779 articles were excluded, and 87 articles entered the full-text screening. Subsequently, 63 articles were excluded for the reasons detailed in Figure 1. Finally, 25 articles met all inclusion criteria and were included in the meta-analyses [28-52]. All included studies were randomized trials with parallel designs. A total of 1,219 participants were included, with mean ages ranging from 31 to 70 years old and mean BMIs ranging from 26 to 40 kg.m 2 . All participants were overweight or obese with or without comorbidities such as metabolic syndrome or polycystic ovary syndrome (PCOS). Comprehensive details of participants’ characteristics, intervention protocols, and outcome measures are presented in Table 1. In addition, the methodological quality of the included studies is summarized in the Supplementary Figure 13-18 with having low risk, some concerns, or high risk of bias. Most studies are expected to fall within the “some concerns” or “high risk” categories. Pairwise meta-analysis IL-6. Exercise training reduced IL-6 significantly more than CON [SMD: -0.39 (95% CI: -0.74 to -0.05), p=0.02; 22 trials] (Figure 2), with substantial and statistically significant heterogeneity amongst studies (I 2 =84.27, p=0.001). Visual interpretation of the funnel plots suggested publication bias, although not confirmed by the Egger’s test (p=0.44). Sensitivity analysis, conducted by removing individual studies, resulted in a reduced effect size and a non-statistically significant result. After accounting for the missing studies with the trim and fill method, the overall effect size increased [SMD: -0.72; 95% CI: -1.05, -0.40]. Subgroup analyses showed that although not statistically significant, when compared with CON, exercise training tended to lead to larger reductions in IL-6 in both patients with cancer [SMD: -0.48 (95% CI: -1.01 to 0.03), p=0.06] and in cancer survivors [SMD: -0.38 (95% CI: -0.89 to 0.11), p=0.13]. In addition, exercise training tended to reduce IL-6 more than CON in both medium [SMD: -0.29 (95% CI: -0.62 to 0.03), p=0.08] and longer-term [SMD: -0.59 (95% CI: -1.40 to 0.20), p=0.14] interventions. TNF-α. Exercise training trended toward a reduction in TNF-α compared with CON, not reaching statistical significance [SMD: -0.30 (95% CI: -0.61 to 0.00), p=0.05; 16 trials] (Figure 3), with substantial and statistically significant heterogeneity amongst included studies (I 2 =71.29, p=0.001). Visual interpretation of the funnel plots suggested publication bias, not confirmed by the Egger’s test (p=0.68). Sensitivity analysis, conducted by removing individual studies, resulted in a reduced effect size and a non-significant result. After accounting for the missing studies with the trim and fill method, the overall effect size increased [SMD: -0.51; 95% CI: -0.81, -0.21]. Subgroup analyses showed that although not statistically significant, as compared to CON, exercise training led to larger reductions in TNF-α in both patients with cancer [SMD: -0.25 (95% CI: -0.54 to 0.04), p=0.09] and in cancer survivors [SMD: -0.35 (95% CI: -0.84 to 0.13), p=0.15]. Subgroup analysis based on intervention duration indicated that neither medium-term or longer-term interventions were significant. IL-10. Exercise training did not lead to significantly larger changes in IL-10 compared with CON [SMD: 0.03 (95% CI: -0.21 to 0.27), p=0.80; 9 trials] (Figure 4), with no heterogeneity amongst studies (I 2 =0.00, p=0.53). Both the visual interpretation of the funnel plots and Egger’s test results did not show publication bias (p=0.86). Subgroup analyses based on intervention durations and patients’ clinical status indicated that neither medium-term or longer-term interventions and patients with cancer and cancer survivors were significantly different from CON for IL-10. CRP. Exercise training trended toward a larger reduction in CRP compared with CON [SMD: -0.47 (95% CI: -1.00 to 0.05), p=0.07; 13 trials] (Figure 5), with substantial heterogeneity amongst included studies (I 2 =89.83, p=0.001). Visual interpretation of the funnel plots suggested publication bias that was not confirmed by the Egger’s test (p=0.55). Sensitivity analysis, conducted by removing individual studies sequentially, resulted in a reduced effect size and a non-statistically significance result. After accounting for the missing studies with the trim and fill method, the overall effect size increased [SMD: -0.82; 95% CI: -1.33, -0.31]. Subgroup analyses showed that exercise training reduced CRP significantly more than CON in both patients with cancer and in cancer survivors [SMD: -0.63 (95% CI: -1.22 to 0.04), p=0.03], and trended toward a larger reduction in CRP in medium-term interventions [SMD: -0.98 (95% CI: -2.24 to 0.26), p=0.12]. Leptin. Exercise training reduced leptin significantly more than CON [SMD: -0.77 (95% CI: -1.41 to -0.14), p=0.01; 12 trials] (Figure 6), with substantial heterogeneity amongst included studies (I 2 =92.06, p=0.001). Visual interpretation of the funnel plots suggested publication bias that was not confirmed by the Egger’s test (p=0.12). Sensitivity analysis, conducted by removing individual studies sequentially, resulted in a reduced effect size and a non-statistically significant result. After accounting for the missing studies with the trim and fill method, the overall effect size increased [SMD: -1.22; 95% CI: -1.90, -0.53]. Subgroup analyses showed that exercise training reduced leptin significantly more than CON in cancer survivors [SMD: -1.06 (95% CI: -1.93 to -0.19), p=0.01], with medium-term interventions [SMD: -0.53 (95% CI: -1.00 to -0.06), p=0.02] and trended toward a larger reduction with longer-term interventions [SMD: -1.03 (95% CI: -2.39 to 0.31), p=0.13]. Adiponectin. Exercise training did not increase adiponectin significantly more than CON [SMD: 0.48 (95% CI: -0.17 to 1.13), p=0.15; 11 trials] (Figure 7), with substantial heterogeneity amongst included studies (I 2 =91.65, p=0.001). Visual interpretation of the funnel plots suggested publication bias that was not confirmed by the Egger’s test (p=0.34). Sensitivity analysis, conducted by removing individual studies sequentially, resulted in a reduced effect size and a non-statistically significant result. After accounting for the missing studies with the trim and fill method, the overall effect size increased [SMD: 0.89; 95% CI: 0.27, 1.51]. Subgroup analyses based on intervention durations and patients’ clinical status indicated that neither medium-term or longer-term interventions and patients with cancer and cancer survivors were significantly different from CON for adiponectin. Network meta-analysis IL-6. Twenty studies involving 26 pairwise comparisons, with 6 intervention arms and 7 study designs were included in the network meta-analysis (Supplementary Figure 1). Compared with CON, only HIIT led to significantly larger reductions in IL-6 [SMD: -1.08 (95% CI: -1.98 to -0.18), p=0.01], and AT, RT, CT, and Yoga/Tai chi did not lead to significantly different effects (Figure 8) with no significant differences between exercise modes (Supplementary Table 3). According to the P-score rankings, the highest ranking, indicating the likelihood of being most effective among the compared exercise modes, was observed for HIIT (0.92), followed by CT (0.62), AT (0.53), RT (0.50), and Yoga/Tai Chi (0.21) (Figure 8). There was high heterogeneity amongst included studies (tau^2 = 0.5625; tau = 0.7500; I^2 = 84.9% [77.5%; 89.9%]), with no global inconsistency (Q= 4.83, df=4, p=0.30). Furthermore, the results of the node splitting analysis based on comparison of direct and indirect results showed no significant inconsistency between results (Supplementary Table 4). No evidence of publication bias was detected based on the Egger’s test (p=0.46) (Supplementary Figure 2). TNF-α. Thirteen studies involving 19 pairwise comparisons, with 5 intervention arms and 5 study designs were included in the network meta-analysis (Supplementary Figure 3). Compared with CON, CT led to significantly larger reductions in TNF-α [SMD: -0.65 (95% CI: -1.11 to -0.18), p=0.006], while HIIT trended toward a larger reduction, but did not reach statistical significance [SMD: -1.40 (95% CI: -1.40 to -0.10), p=0.09]. When compared with CON, neither AT nor RT demonstrated a significantly larger effect on TNF-α (Figure 9). In addition, CT, led to significantly larger reductions in TNF-α as compared with RT [SMD: -0.97 (95% CI: -1.80 to -0.14), p=0.02], and HIIT trended toward a larger reduction in TNF-α as compared with RT [SMD: -0.97 (95% CI: -1.99 to 0.04), p=0.05] (Supplementary Table 5). The highest P-score ranking was observed for CT (0.85), followed by HIIT (0.82), AT (0.50), and RT (0.08) (Figure 8). There was moderate heterogeneity amongst included studies (tau 2 = 0.1813; tau = 0.4257; I 2 = 62.4% [31.4%; 79.4%]), with no global inconsistency (Q= 0.15, df=3, p=0.98). Furthermore, the results of the node splitting analysis based on comparison of direct and indirect results showed no significant inconsistency between results (Supplementary Table 6). No evidence of publication bias was detected based on the Egger’s test (p=0.09) (Supplementary Figure 4). IL-10. Seven studies involving 11 pairwise comparisons, with 5 intervention arms and 4 study designs, were included in the network meta-analysis (Supplementary Figure 5). Compared with CON, HIIT trended toward a larger increase in IL-10, although the difference did not reach statistical significance [SMD: 0.34 (95% CI: -0.05 to 0.75), p=0.09], with neither AT, CT, and RT having an effect (Figure 10), or differences between exercise modes (Supplementary Table 7). The highest P-score ranking was observed for HIIT (0.88), followed by AT (0.69), RT (0.28), and CT (0.19) (Figure 8). There was no to moderate heterogeneity amongst included studies (tau 2 = 0; tau = 0; I 2 = 0% [0.0%; 74.6%]), with no global inconsistency (Q= 0.06, df=1, p=0.80). Furthermore, the results of the node splitting analysis based on comparison of direct and indirect results, showed no significant inconsistency between results (Supplementary Table 8). No evidence of publication bias was detected based on the Egger’s test (p=0.09) (Supplementary Figure 6). CRP. Thirteenstudies involving 13 pairwise comparisons, with 5 intervention arms and 4 study designs were included in the network meta-analysis (Supplementary Figure 7). Compared with CON, none of the exercise modes produced significantly larger reductions in CRP (Figure 11), with no differences between any exercise (Supplementary Table 9). The highest P-score ranking was observed for CT (0.74), followed by RT (0.53), Yoga-Tai Chi (0.50), and AT (0.44) (Figure 11). There was high heterogeneity amongst included studies (tau 2 = 1.2589; tau = 1.1220; I 2 = 92.2% [87.8%; 95.0%]). The global inconsistency test could not be calculated (Q=0.0, df=0), indicating no variability in study designs within the network. No evidence of publication bias was detected based on the Egger’s test (p=0.56) (Supplementary Figure 8). Leptin. Ten studiesinvolving 14 pairwise comparisons, with 5 intervention arms and 4 study designs, were included in the network meta-analysis (Supplementary Figure 9). Compared with CON, CT trended toward a larger reduction in leptin [SMD: -0.62 (95% CI: -0.88 to 0.06), p=0.06], with no effect of AT, RT, and HIIT (Figure 12), or differences between exercise modes (Supplementary Table 10). The highest P-score ranking was observed for CT (0.77), followed by HIIT (0.62), AT (0.48), and RT (0.42) (Figure 12). There was high heterogeneity amongst studies (tau 2 = 1.6646; tau = 1.2902; I 2 = 94% [90.6%; 96.1%]) (Supplementary Table 4), and no global inconsistency (Q= 0.19, df=1, p=0.66). Furthermore, the results of the node splitting analysis based on comparison of direct and indirect results, showed no significant inconsistency between results (Supplementary Table 11). No evidence of publication bias was detected based on the Egger’s test (p=0.23) (Supplementary Figure 10). Adiponectin. Nine studies involving 13 pairwise comparisons with 5 intervention arms and 4 study designs were included in the network meta-analysis (Supplementary Figure 11). Compared with CON, none of exercise modes produced a significantly larger increase in adiponectin (Figure 13), with no differences between modes (Supplementary Table 12). The highest P-score ranking was observed for CT (0.78), followed by HIIT (0.67), AT (0.47), and RT (0.26) (Figure 13). There was high heterogeneity amongst included studies (tau 2 = 1.4951; tau = 1.2227; I 2 = 92.9% [88.4%; 95.7%]) (Supplementary Table 4), with no global inconsistency (Q= 0.09, df=1, p=0.75). Furthermore, the results of the node splitting analysis based on comparison of direct and indirect results, showed no significant inconsistency between results (Supplementary Table 13). No evidence of publication bias was detected based on the Egger’s test (p=0.93) (Supplementary Figure 12). Discussion Summary of Main Findings Our systematic review with pairwise and network meta-analyses indicates that exercise training confers significant anti-inflammatory benefits for breast cancer patients and survivors with small to moderate effects. Regular exercise leads to reductions in circulating concentrations of select inflammatory cytokines and adipokines, notably IL-6 and leptin. Furthermore, network meta-analysis indicated HIIT was the most effective in lowering IL-6, and CT showed the largest benefit for TNF-α. Overall, our findings reinforce the role of exercise as a complementary therapy to help improve inflammatory profiles in breast cancer and suggest that HIIT and CT are most effective [53, 54]. Comparison with Existing Evidence Our results extend prior research on the effects of exercise training on markers of inflammation in populations with breast cancer that have consistently reported that exercise reduced circulating CRP and other inflammatory markers in breast cancer survivors [24, 53]. For example, Abbasi et al. found significant decreases in CRP with exercise, although changes in TNF-α, IL-6, IL-8, IL-10, IL-1β, and INF-ɣ were not significantly different compared with control in adult women with breast cancer (18 studies combining 11 effects sizes) [24]. Whilst more recent larger studies demonstrated favorable reductions in IL-6 and TNF-α [21]. We confirm that exercise reduces select pro-inflammatory cytokine and adipokine such as IL-6 and leptin, with more consistent effects observed with CT and HIIT. Consequently, a growing body of evidence supports positive biological effects on inflammatory pathways in breast cancer patients. Notably, our network approach allowed direct comparison of exercise modalities, yielding novel insights. Prior meta-analyses have typically categorized interventions as aerobic training (AT), resistance training (RT), or combined exercise without differentiating between the magnitude of intensity [21]. We expand on these analyses by comparing the efficacy of HIIT to other exercise modes, and indicates that vigorous-intensity exercise may offer greater anti-inflammatory benefits compared with moderate-intensity [14, 38]. Furthermore, combined aerobic and resistance training is a more effective approach for improving outcomes in breast cancer survivors [21, 53], as found after 16 weeks of supervised CT in overweight or obese participants [55]. Conversely, earlier single-mode exercise trials have frequently shown no changes in adiponectin or leptin among breast cancer survivors, possibly due to suboptimal intensities or insufficient intervention durations [56]. Our analysis helps reconcile these discrepancies by confirming that any regular exercise training is beneficial, with the magnitude of inflammatory improvement greater with more intensive and multifaceted training. Thus, our findings are in broad agreement with existing literature while providing a nuanced hierarchy of exercise modalities consistent with both recent meta-analytic evidence and high-quality clinical trials [21, 55]. Subgroup Analyses and Effect Modifiers Where data allowed, we explored whether participant or intervention characteristics modified the effects of exercise training. Although our primary network analysis did not show a statistically significant difference in outcomes between shorter and longer programs, the direction of effects and previous evidence suggests that exercise duration and dose are important considerations [14]. Prolonged training most likely allows sustained weight loss and metabolic adaptation, thereby amplifying anti-inflammatory effects. Exercise intensity therefore has the potential to compensate for shorter program durations in targeting specific inflammatory markers [21]. Baseline patient characteristics also appear to affect responsiveness to exercise, with subgroup trends from our meta-analysis, indicating that elevated baseline adiposity and inflammation was associated with larger improvements in inflammatory markers [53]. By contrast, in mixed-weight populations, IL-6 reductions are sometimes attenuated [24]. Excess adipose tissue is a key source of pro-inflammatory signaling, and higher adiposity may lead to more pronounced anti-inflammatory benefits from exercise training, mediated by reduced leptin [53]. Another factor is the phase of breast cancer treatment although data are limited, exercising during active treatment (such as chemo-therapy) may be feasible and blunt therapy-related inflammatory surges [57]. Moreover, patients who stayed physically active during chemotherapy were more likely to have no detectable cancer cells at the end of treatment [57]. These observations suggest that exercise in overweight or obese patients under therapy, or in remission exhibit inflammatory improvements. Biological Mechanisms for Anti-inflammatory Effects Exercise exerts multi-faceted biological effects likely to improve inflammatory markers and adipokines, with skeletal muscle having a major role as it releases several “myokines” during and after exercise. During exercise, pro-inflammatory IL-6 rises rapidly, largely due to immune cells that infiltrate and respond to muscle activity and remodeling [58], which then timulates the release of anti-inflammatory cytokines such as IL-10 to suppress TNF-α synthesis, thereby shifting the cytokine milieu towards anti-inflammation [59]. Regular exercise and associated changes in myokines modulate immune function by reducing circulating pro-inflammatory monocytes and macrophages, while enhancing the activity of cytotoxic T cells and natural killer cells that help target and eliminate tumor cells [58, 60-62]. Overall, each bout of exercise provokes a transient inflammatory-like response that ultimately leads to longer-term anti-inflammatory adaptations [63]. In addition to its direct effects on circulating cytokines, exercise also favorably influences adipose-tissue related biomarkers, and is particularly relevant for breast cancer patients, many of whom are overweight or obese. In our analyses, exercise, especially HIIT and CT, was associated with meaningful improvements in adipokines such as leptin, indicating a beneficial shift in adipose tissue biology, thereby reducing inflammation and tumor growth [2, 54]. Regular exercise combats adipose tissue by reducing fat mass (and even “remodeling” fat) to lower the production of pro-inflammatory adipocytokines [64] [65, 66]. This shift is critical because leptin not only fuels inflammation by activating macrophages and T-cells, but also directly stimulates breast cancer cell proliferation via the JAK2/STAT3 signaling pathway [54, 67]. High leptin in the tumor microenvironment can increase angiogenesis and contribute to poorer prognosis [54]. Additionally, regular exercise can promote a healthier adipose tissue phenotype through the browning of white fat, a process that may enhance energy expenditure, reduce pro-inflammatory adipokines (e.g., IL-6, TNF-α, leptin), and limit M1 pro-inflammatory macrophage infiltration, thereby attenuating tumor-promoting inflammation [68, 69]. Collectively, these adaptations mean that patients who exercise regularly typically exhibit lower basal levels of IL-6 and TNF-α and a more favorable adipokine profile than their sedentary peers [21, 53]. Furthermore, improved insulin sensitivity and reduced insulin/IGF-1 signaling with exercise provides further anti-inflammatory and anti-cancer benefits [70-73]. In summary, regular exercise creates an environment less conducive to cancer progression by suppressing inflammatory through myokine-driven immune modulation and transforming adipose tissue into a more metabolically healthy and anti-inflammatory state. Strengths and Limitations Strengths: To our knowledge, this is the systematic review with both pairwise and network meta-analyses comparing different exercise modes on inflammatory adipocytokines in breast cancer patients. By integrating data from multiple RCTs and applying such an approach, we could compare and rank the relative efficacy of each exercise modalities thereby extending previous meta-analyses that evaluated exercise as a single, uniform intervention. This increased our power to discern differences between exercise modes, and facilitates tailored exercise prescriptions, that was complemented by a rigorous methodology (preregistered protocol, extensive database search, and risk-of-bias assessment). Our analysis was comprehensive, including breast cancer patients during treatment and post-treatment (survivors), thereby enhancing generalizability across the spectrum of breast cancer care. The Limitations: There was considerable heterogeneity among the included trials, with exercise interventions varying in dose, length, frequency, supervision, and adherence. Sources of heterogeneity (e.g. duration, intensity) were examined through subgroup and meta-regression analyses, but residual confounding may remain. Additionally, some exercise modes (particularly HIIT) had a relatively small number of trials, so although we incorporated indirect evidence to enhance comparisons, rankings for less-studied exercise modalities should be interpreted with caution until supported by additional high-quality RCTs. The inflammatory markers examined were often secondary outcomes in the original trials, leading to issues such as incomplete reporting and potential publication bias (studies with non-significant cytokine findings are less likely to publish detailed results). Funnel plot inspection did not reveal substantial publication bias, however, this possibility cannot be ruled out due to the relatively small number of studies available for each outcome. Patient populations also differed between studies, some trials focused on survivors with obesity, who were on long-term endocrine therapy, while others included patients in active chemotherapy or radiotherapy. This clinical diversity, while enhancing generalizability, might limit the precision of our estimates for any one subgroup. So although, changes in IL-6 or leptin are biologically relevant, they represent surrogate endpoints, and it remains uncertain whether these translate into lower cancer recurrence or improved survival outcomes. Although cross-sectional data show a strong association between lower inflammation and improved outcomes, whether reducing inflammation through exercise will improve patient prognosis is not established. Finally, standardization of cytokine assays was lacking across studies, variations in assay sensitivity and timing of blood sampling (e.g., acute post-exercise vs. resting levels) could influence results. Considering these limitations, our findings should be interpreted as hypothesis-generating. Future Research Directions This meta-analysis underscores several areas for future investigation, for which there is a need for large, high-quality RCTs directly comparing exercise modalities with inflammatory markers and clinical outcomes as endpoints. It would be especially impactful to integrate biomarker endpoints into ongoing exercise-oncology trials or survivorship programs, thereby linking mechanistic changes to patient outcomes. Mechanistic studies are also warranted to establish how exercise exerts its anti-inflammatory and anti-cancer effects in this population. A combination of lifestyle interventions should be explored, as diet-induced weight loss itself can reduce inflammation, so combining exercise with nutritional strategies might have additive or synergistic effects. Finally, implementation research is crucial, so although exercise is a safe and effective adjunct therapy (with numerous ancillary benefits like improved fitness, quality of life, and fatigue reduction), real-world uptake remains suboptimal. Conclusion This systematic review with pairwise and network meta-analyses provides evidence that exercise, particularly CT and HIIT, may be effective for reducing chronic inflammation in patients with breast cancer. Clinicians should therefore consider prescribing tailored exercise programs as part of breast cancer management, not only to improve fitness and quality of life, but to harness the effects of exercise training on reducing the tumor-promoting inflammatory milieu. It reinforces the growing recognition of exercise as a key component of breast cancer care and the need for continued research to refine exercise prescriptions across cancer populations. Declarations Author Contributions M.Kh., M.H.S., M.E.S., S.K.R., and H.R. conceptualized and designed the protocol.M.Kh., M.H.S., F.G., H.R., Sh.M., A.S.Z., M.M., S.F. carried out the screenings and reviews, and the analysis of the articles. M.Kh., M.H.S., K.O., and H.R. drafted the manuscript, and K.S., M.E.S. and S.K.R. revised the manuscript. All authors read and approved the final manuscript. Funding . Non. Institutional review board statement Not applicable. Consent to Participate Not applicable. Clinical trial number Not applicable. Conflict of Interest The authors declare no conflicts of interest. Data Availability All data generated or analyzed for this study are contained within this published article. Acknowledgments The authors have nothing to report. References Ferlay, J., et al., Global cancer observatory: cancer today. Lyon: International agency for research on cancer, 2020. 20182020 . 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Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table13.docx SupplementaryFile.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviews received at journal 08 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers invited by journal 27 Mar, 2026 Editor assigned by journal 20 Mar, 2026 Editor invited by journal 20 Mar, 2026 Submission checks completed at journal 14 Mar, 2026 First submitted to journal 13 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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10:30:27","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":54910,"visible":true,"origin":"","legend":"","description":"","filename":"Table13.docx","url":"https://assets-eu.researchsquare.com/files/rs-8968752/v1/156722b9c0f444197610ce0e.docx"},{"id":105908341,"identity":"5f2a8a26-6f5d-42a7-bf5b-00e51bd28639","added_by":"auto","created_at":"2026-04-01 10:36:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1533418,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-8968752/v1/d4e95c232b5bfdbe0d3f7f54.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative efficacy of exercise modes on inflammatory adipocytokines in patients with breast cancer: a systematic review with pairwise and network meta-analyses ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is a major global health challenge, being the most commonly diagnosed cancer and the fifth leading cause of cancer-related mortality worldwide [1]. Breast cancer development and progression are influenced not only by tumor-intrinsic factors but also by systemic host factors such as chronic low-grade inflammation and dysregulated adipocytokines [2]. Obesity and physical inactivity are associated with an inflammatory milieu that can promote breast cancer pathogenesis and worsen survival [3-5]. In overweight or obese subjects, adipose tissue releases pro-inflammatory cytokines (e.g., interleukin-6 [IL-6], tumor necrosis factor-alpha [TNF-α]), and adipokines (e.g., leptin), contributing to a chronic inflammatory environment that promotes tumor progression by enhancing angiogenesis and impairing anti-tumor immune responses [3, 6, 7]. Raised IL-6, TNF-α, and C-reactive protein (CRP) are linked to poorer prognosis in breast cancer, correlating with higher rates of recurrence, metastasis, and mortality [3, 8-10]. In contrast, anti-inflammatory factors like interleukin-10 (IL-10) and adiponectin may exert protective effects. Adiponectin, an adipocyte-derived hormone, promotes an anti-inflammatory, insulin-sensitizing state and can directly inhibit breast cancer cell growth via activation of AMP-activated protein kinase (AMPK) and downregulation of proliferative pathway PI3K/Akt [11]. Higher adiponectin is associated with lower breast cancer risk, whereas leptin, a pro-inflammatory adipokine upregulated in obesity, enhances breast cancer cell proliferation and survival, angiogenesis, and invasion through estrogen-dependent and -independent mechanisms [11]. A leptin/adiponectin imbalance observed in obesity (characterized by hyperleptinemia and hypoadiponectinemia) is a major factor linking excess adiposity to breast cancer progression [11, 12]. Thus, targeting chronic inflammation and adipocytokine dysregulation is increasingly recognized as an important strategy in breast cancer management.\u003c/p\u003e\n\u003cp\u003eExercise training is emerging as a potent lifestyle intervention to attenuate chronic inflammation and favorably modulate adipocytokines in both healthy and clinical populations, with regular exercise exerting anti-inflammatory effects [13]. Exercise reduces visceral and total body fat mass, thereby decreasing the source of pro-inflammatory adipokines including IL-6, TNF-α, and leptin, often with increases in adiponectin [13, 14]. Whilst, each acute bout of exercise provokes a transient release of muscle-derived cytokines (myokines), notably IL-6, which paradoxically mediates anti-inflammatory effects by stimulating IL-10 production and inhibiting TNF-α and IL-1β signaling through AMPK activation [14, 15]. Exercise also shifts immune cell profiles toward a less inflammatory state—such as increasing anti-inflammatory M2 macrophages over pro-inflammatory M1 in adipose tissue—and improves metabolic factors like insulin resistance that are closely linked to inflammation [16, 17]. These multifactorial actions help explain why long-term (chronic) exercise is associated with reduced systemic inflammation and improved outcomes in cancer survivors [3, 13]. In breast cancer patients and survivors, numerous randomized controlled trials (RCTs) have examined the effects of exercise on inflammatory markers. Many reports exercise reduces circulating IL-6 and CRP, and in some cases, downregulates TNF-α and leptin while upregulating adiponectin, although such findings have not been uniform [18-20]. The heterogeneity in inflammatory outcomes may relate to differences in exercise modes, intensities, and durations, as well as patient factors. Notably, prior meta-analyses have provided evidence that overall, exercise has beneficial effects on inflammatory biomarkers in breast cancer [18]. Similarly, structured exercise can induce small-to-moderate improvements in pro-inflammatory cytokines in this population [14, 21]. However, these reviews have typically evaluated exercise in general and have not directly compared different exercise modes (e.g., aerobic vs. resistance vs. combined training). It remains unclear whether certain modes of exercise confer superior anti-inflammatory effects in breast cancer patients, which represents a critical knowledge gap for exercise oncology.\u003c/p\u003e\n\u003cp\u003eWe present a systematic review along with the first network meta-analysis comparing the efficacy of different exercise modalities on key pro- and anti-inflammatory cytokines and adipokines (IL-6, TNF-α, IL-10, CRP, leptin, and adiponectin) in patients with breast cancer. By integrating direct and indirect evidence, this approach enables the ranking of exercise interventions based on their anti-inflammatory effects. Our objectives are to quantify the overall effects of exercise training and identify which modes offer the greatest benefits. We have also examined the potential effect modifiers, such as the patients’ clinical status (during vs. post-treatment) and intervention durations. This work addresses a key evidence gap and aims to refine exercise prescriptions by identifying optimal exercise modes to reduce chronic inflammation and support long-term outcomes in breast cancer care.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis systematic review and meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [22] and the Cochrane Handbook for Systematic Reviews of Interventions [23], and was prospectively registered on the International Prospective Register of Systematic Reviews (PROSPERO) with ID: CRD420251231590.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSearch Strategy.\u0026nbsp;\u003c/strong\u003eA systematic search was conducted across multiple databases, including PubMed, Web of Science, and Scopus, from inception to January 2025. The search incorporated four groups of keywords related to “exercise”, “cytokine”, “breast cancer” and “randomized trials”. When available on the databases, filters were applied to restrict results to human studies, English language publications, and articles. The detailed search strategy for each database is provided in Supplementary Table 1. To minimize the risk of missing relevant studies, the reference lists of included studies and previously published meta-analyses [18, 24] were manually screened. Additional searches were conducted using Google Scholar, and through checking reference lists of eligible studies (the snowballing method) to identify eligible studies. The searches were conducted by one author (M Kh).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEligibility Criteria.\u0026nbsp;\u003c/strong\u003ePeer-reviewed and English language studies were deemed eligible if they met following criteria defined according to the PICOS framework: Population: participants were adults ≥ 18 years of age, with a confirmed breast cancer diagnosis, regardless of disease stage or clinical status; Intervention: studies that investigated any mode of exercise training, including aerobic, resistance, combined (aerobic and resistance), or high-intensity interval training (HIIT), with an intervention duration ≥ 2 weeks, regardless of frequency, intensity and total training volume; Comparator: studies that included a non-exercise control group (CON) or another exercise intervention arm differing from the primary arm; Outcomes: studies that assessed and reported inflammatory adipocytokines including IL-6, TNF-α, IL-10, CRP, leptin, or adiponectin; and Study design: randomized controlled or clinical trials with parallel group designs. Exclusion criteria comprised studies that were non-original, non-English language, non-peer-reviewed, and non-randomized. Studies investigating exercise training in combination with co-interventions, such as dietary interventions, were also excluded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Selection.\u0026nbsp;\u003c/strong\u003eThree authors (FG, MM, AZ) independently screened the studies, and any disagreements were resolved through discussion with other authors (MKh and SF). All retrieved studies were imported into EndNote (v21) to remove duplicate records and facilitate the screening process. Screening was conducted based on the study titles and abstracts, followed by the full-text assessment of potentially eligible studies according to the a priori inclusion and exclusion criteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Extraction and Synthesis.\u0026nbsp;\u003c/strong\u003eData from included studies were independently extracted by two authors (FG, MHS), and any disagreements were resolved through discussion with additional authors (MKh and ShM). Extracted data and information included study details, including first author and years of publication; participants' information, including study sample size, age, biological sex, health status; intervention and comparator characteristics, including CON type, exercise mode, intensity, time, duration, and frequency; and data for the specified outcome of interest. For statistical analyses, mean changes and standard deviations (SDs) along with sample sizes for each group were extracted. When required, these values were calculated from pre- and post-intervention data. In addition, if studies reported outcomes as medians and interquartile ranges (IQRs), standard errors, and confidence intervals (CIs), these were converted to means and SDs using established statistical methods [23, 25, 26]. When numerical data were available only in graphical form, values were extracted using GetData Graph Digitizer software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk of Bias Assessment.\u0026nbsp;\u003c/strong\u003eTwo authors (MHS and HR) independently assessed the risk of bias using the Cochrane Risk of Bias Tool (ROB) [27], and discrepancies were resolved through discussion or with a third author (MKh).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analyses.\u0026nbsp;\u003c/strong\u003ePairwise meta-analysis was conducted using the comprehensive meta-analysis version 3 software (CMA3). Effect sizes were calculated as standardized mean difference (SMD) with 95% confidence intervals (CIs). The SMD was selected to account for variations in measurement methods across studies. A random-effects model was applied to pool data, based on the assumption of underlying heterogeneity among the included randomized trials. To assess heterogeneity amongst included studies, I\u003csup\u003e2\u0026nbsp;\u003c/sup\u003estatistics and Q values were calculated. I\u003csup\u003e2\u003c/sup\u003e values was interpreted as low (0−25%), moderate (26−50%), substantial (51−75%), and considerable (\u0026gt; 75%) heterogeneity; and for the Q statistic, p-values \u0026lt; 0.05 were considered indicative of statistically significant heterogeneity.\u0026nbsp;Publication bias was evaluated through visual inspection of funnel plots and Egger’s regression tests, with p-values \u0026lt; 0.05 indicating potential bias. Sensitivity analyses were performed by sequentially removing individual studies to determine the influence of each study on the overall pooled estimates.\u0026nbsp;In addition, subgroup analysis was performed based on\u0026nbsp;patients’ clinical status (with cancer and cancer survivors) and intervention durations (medium-term: \u0026lt; 16 weeks and longer-term: ≥ 16 weeks).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the network meta-analyses, the Netmeta package in R software (version 4.4.1) was used within a frequentist framework. Both direct and indirect evidence were synthesized using random effects models to calculate SMDs with corresponding 95% CIs, applying the same rationale used in the pairwise meta-analyses to account for anticipated heterogeneity across studies. Network geometry plots were generated to illustrate the relationships among the intervention arms. Forest plots and league tables were produced to present the standardized mean differences (SMDs) and their 95% confidence intervals (CIs) for all pairwise comparisons between interventions. To rank the interventions, P-scores were calculated, with higher P-scores indicating higher likelihood of better effectiveness relative to the other exercise modes. To assess heterogeneity among the included studies, I², tau (τ), tau-squared (τ²), and Cochran’s Q statistics were calculated. To evaluate the consistency assumption within the network, both global and local approaches were applied. Global consistency was assessed using the Q statistic under a full design-by-treatment interaction random-effects model, whereas local consistency was examined using node-splitting models. In addition, publication bias was assessed using Egger’s tests, with p-values \u0026lt; 0.05 indicating potential bias.\u003c/p\u003e"},{"header":"Results ","content":"\u003cp\u003e\u003cstrong\u003eStudy characteristics.\u003c/strong\u003e A total of 2,511 records were identified\u0026nbsp;through database searches, of which 1,866 records were duplicates and therefore excluded, and 1,866 articles were entered into the screening process. A total of 1,866 articles were screened based on the titles and abstracts, of which 1,779 articles were excluded, and 87 articles entered the full-text screening. Subsequently, 63 articles were excluded for the reasons detailed in Figure 1. Finally, 25 articles met all inclusion criteria and were included in the meta-analyses [28-52]. All included studies were randomized trials with parallel designs. A total of 1,219 participants were included, with mean ages ranging from 31 to 70 years old and mean BMIs ranging from 26 to 40 kg.m\u003csup\u003e2\u003c/sup\u003e. All participants were overweight or obese with or without comorbidities such as metabolic syndrome or polycystic ovary syndrome (PCOS). Comprehensive details of participants’ characteristics, intervention protocols, and outcome measures are presented in Table 1. In addition, the methodological quality of the included studies is summarized in the Supplementary\u0026nbsp;Figure 13-18\u0026nbsp;with having low risk, some concerns, or high risk of bias. Most studies are expected to fall within the “some concerns” or “high risk” categories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePairwise meta-analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIL-6.\u003c/strong\u003e Exercise training reduced IL-6 significantly more than CON [SMD: -0.39 (95% CI: -0.74 to -0.05), p=0.02; 22 trials] (Figure 2), with substantial and statistically significant heterogeneity amongst studies (I\u003csup\u003e2\u003c/sup\u003e=84.27, p=0.001). Visual interpretation of the funnel plots suggested publication bias, although not confirmed by the Egger’s test (p=0.44). Sensitivity analysis, conducted by removing individual studies, resulted in a reduced effect size and a non-statistically significant result. After accounting for the missing studies with the trim and fill method, the overall effect size increased\u0026nbsp;[SMD: -0.72; 95% CI: -1.05, -0.40]. Subgroup analyses showed that although not statistically significant, when compared with CON, exercise training tended to lead to larger reductions in IL-6 in both patients with cancer [SMD: -0.48 (95% CI: -1.01 to 0.03), p=0.06] and in cancer survivors [SMD: -0.38 (95% CI: -0.89 to 0.11), p=0.13]. In addition, exercise training tended to reduce IL-6 more than CON in both medium [SMD: -0.29 (95% CI: -0.62 to 0.03), p=0.08] and longer-term [SMD: -0.59 (95% CI: -1.40 to 0.20), p=0.14] interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTNF-α.\u0026nbsp;\u003c/strong\u003eExercise training trended toward a reduction in TNF-α compared with CON, not reaching statistical significance [SMD: -0.30 (95% CI: -0.61 to 0.00), p=0.05; 16 trials] (Figure 3), with substantial and statistically significant heterogeneity amongst included studies (I\u003csup\u003e2\u003c/sup\u003e=71.29, p=0.001). Visual interpretation of the funnel plots suggested publication bias, not confirmed by the Egger’s test (p=0.68). Sensitivity analysis, conducted by removing individual studies, resulted in a reduced effect size and a non-significant result. After accounting for the missing studies with the trim and fill method, the overall effect size increased\u0026nbsp;[SMD: -0.51; 95% CI: -0.81, -0.21]. Subgroup analyses showed that although not statistically significant, as compared to CON, exercise training led to larger reductions in TNF-α in both patients with cancer [SMD: -0.25 (95% CI: -0.54 to 0.04), p=0.09] and in cancer survivors [SMD: -0.35 (95% CI: -0.84 to 0.13), p=0.15]. Subgroup analysis based on intervention duration indicated that neither medium-term or longer-term interventions were significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIL-10.\u0026nbsp;\u003c/strong\u003eExercise training did not lead to significantly larger changes in IL-10 compared with CON [SMD: 0.03 (95% CI: -0.21 to 0.27), p=0.80; 9 trials] (Figure 4), with no heterogeneity amongst studies (I\u003csup\u003e2\u003c/sup\u003e=0.00, p=0.53). Both the visual interpretation of the funnel plots and Egger’s test results did not show publication bias (p=0.86). Subgroup analyses based on intervention durations and patients’ clinical status indicated that neither medium-term or longer-term interventions and patients with cancer and cancer survivors were significantly different from CON for IL-10.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRP.\u0026nbsp;\u003c/strong\u003eExercise training trended toward a larger reduction in CRP compared with CON [SMD: -0.47 (95% CI: -1.00 to 0.05), p=0.07; 13 trials] (Figure 5), with substantial heterogeneity amongst included studies (I\u003csup\u003e2\u003c/sup\u003e=89.83, p=0.001). Visual interpretation of the funnel plots suggested publication bias that was not confirmed by the Egger’s test (p=0.55). Sensitivity analysis, conducted by removing individual studies sequentially, resulted in a reduced effect size and a non-statistically significance result. After accounting for the missing studies with the trim and fill method, the overall effect size increased\u0026nbsp;[SMD: -0.82; 95% CI: -1.33, -0.31]. Subgroup analyses showed that exercise training reduced CRP significantly more than CON in both patients with cancer and in cancer survivors [SMD: -0.63 (95% CI: -1.22 to 0.04), p=0.03], and trended toward a larger reduction in CRP in medium-term interventions [SMD: -0.98 (95% CI: -2.24 to 0.26), p=0.12].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLeptin.\u0026nbsp;\u003c/strong\u003eExercise training reduced leptin significantly more than CON [SMD: -0.77 (95% CI: -1.41 to -0.14), p=0.01; 12 trials] (Figure 6), with substantial heterogeneity amongst included studies (I\u003csup\u003e2\u003c/sup\u003e=92.06, p=0.001). Visual interpretation of the funnel plots suggested publication bias that was not confirmed by the Egger’s test (p=0.12). Sensitivity analysis, conducted by removing individual studies sequentially, resulted in a reduced effect size and a non-statistically significant result. After accounting for the missing studies with the trim and fill method, the overall effect size increased\u0026nbsp;[SMD: -1.22; 95% CI: -1.90, -0.53]. Subgroup analyses showed that exercise training reduced leptin significantly more than CON in cancer survivors [SMD: -1.06 (95% CI: -1.93 to -0.19), p=0.01], with medium-term interventions [SMD: -0.53 (95% CI: -1.00 to -0.06), p=0.02] and trended toward a larger reduction with longer-term interventions [SMD: -1.03 (95% CI: -2.39 to 0.31), p=0.13].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdiponectin.\u0026nbsp;\u003c/strong\u003eExercise training did not increase adiponectin significantly more than CON [SMD: 0.48 (95% CI: -0.17 to 1.13), p=0.15; 11 trials] (Figure 7), with substantial heterogeneity amongst included studies (I\u003csup\u003e2\u003c/sup\u003e=91.65, p=0.001). Visual interpretation of the funnel plots suggested publication bias that was not confirmed by the Egger’s test (p=0.34). Sensitivity analysis, conducted by removing individual studies sequentially, resulted in a reduced effect size and a non-statistically significant result. After accounting for the missing studies with the trim and fill method, the overall effect size increased\u0026nbsp;[SMD: 0.89; 95% CI: 0.27, 1.51]. Subgroup analyses based on intervention durations and patients’ clinical status indicated that neither medium-term or longer-term interventions and patients with cancer and cancer survivors were significantly different from CON for adiponectin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork meta-analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIL-6.\u003c/strong\u003e Twenty\u0026nbsp;studies involving 26 pairwise comparisons, with 6 intervention arms and 7 study designs were included in the network meta-analysis (Supplementary\u0026nbsp;Figure 1). Compared with CON, only HIIT led to significantly larger reductions in IL-6 [SMD: -1.08 (95% CI: -1.98 to -0.18), p=0.01], and AT, RT, CT, and Yoga/Tai chi did not lead to significantly different effects (Figure 8) with no significant differences between exercise modes (Supplementary Table 3). According to the P-score rankings, the highest ranking, indicating the likelihood of being most effective among the compared exercise modes, was observed for HIIT (0.92), followed by CT (0.62), AT (0.53), RT (0.50), and Yoga/Tai Chi (0.21) (Figure 8). There was high heterogeneity amongst included studies (tau^2 = 0.5625; tau = 0.7500; I^2 = 84.9% [77.5%; 89.9%]), with no global inconsistency (Q= 4.83, df=4, p=0.30). Furthermore, the results of the node splitting analysis based on comparison of direct and indirect results showed no significant inconsistency between results (Supplementary Table 4). No evidence of publication bias was detected based on the Egger’s test (p=0.46) (Supplementary\u0026nbsp;Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTNF-α.\u0026nbsp;\u003c/strong\u003eThirteen studies involving 19 pairwise comparisons, with 5 intervention arms and 5 study designs were included in the network meta-analysis (Supplementary\u0026nbsp;Figure 3). Compared with CON, CT led to significantly larger reductions in TNF-α [SMD: -0.65 (95% CI: -1.11 to -0.18), p=0.006], while HIIT trended toward a larger reduction, but did not reach statistical significance [SMD: -1.40 (95% CI: -1.40 to -0.10), p=0.09]. When compared with CON, neither AT nor RT demonstrated a significantly larger effect on TNF-α (Figure 9). In addition, CT, led to significantly larger reductions in TNF-α as compared with RT [SMD: -0.97 (95% CI: -1.80 to -0.14), p=0.02], and HIIT trended toward a larger reduction in TNF-α as compared with RT [SMD: -0.97 (95% CI: -1.99 to 0.04), p=0.05] (Supplementary Table 5). The highest P-score ranking was observed for CT (0.85), followed by HIIT (0.82), AT (0.50), and RT (0.08) (Figure 8). There was moderate heterogeneity amongst included studies (tau\u003csup\u003e2\u003c/sup\u003e = 0.1813; tau = 0.4257; I\u003csup\u003e2\u003c/sup\u003e = 62.4% [31.4%; 79.4%]), with no global inconsistency (Q= 0.15, df=3, p=0.98). Furthermore, the results of the node splitting analysis based on comparison of direct and indirect results showed no significant inconsistency between results (Supplementary Table 6). No evidence of publication bias was detected based on the Egger’s test (p=0.09) (Supplementary\u0026nbsp;Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIL-10.\u0026nbsp;\u003c/strong\u003eSeven studies involving 11 pairwise comparisons, with 5 intervention arms and 4 study designs, were included in the network meta-analysis (Supplementary\u0026nbsp;Figure 5). Compared with CON, HIIT trended toward a larger increase in IL-10, although the difference did not reach statistical significance [SMD: 0.34 (95% CI: -0.05 to 0.75), p=0.09], with neither AT, CT, and RT having an effect (Figure 10), or differences between exercise modes (Supplementary Table 7). The highest P-score ranking was observed for HIIT (0.88), followed by AT (0.69), RT (0.28), and CT (0.19) (Figure 8). There was no to moderate heterogeneity amongst included studies (tau\u003csup\u003e2\u003c/sup\u003e = 0; tau = 0; I\u003csup\u003e2\u003c/sup\u003e = 0% [0.0%; 74.6%]), with no global inconsistency (Q= 0.06, df=1, p=0.80). Furthermore, the results of the node splitting analysis based on comparison of direct and indirect results, showed no significant inconsistency between results (Supplementary Table 8). No evidence of publication bias was detected based on the Egger’s test (p=0.09) (Supplementary\u0026nbsp;Figure 6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRP.\u0026nbsp;\u003c/strong\u003eThirteenstudies involving 13 pairwise comparisons, with 5 intervention arms and 4 study designs were included in the network meta-analysis (Supplementary\u0026nbsp;Figure 7). Compared with CON, none of the exercise modes produced significantly larger reductions in CRP (Figure 11), with no differences between any exercise (Supplementary Table 9). The highest P-score ranking was observed for CT (0.74), followed by RT (0.53), Yoga-Tai Chi (0.50), and AT (0.44) (Figure 11). There was high heterogeneity amongst included studies (tau\u003csup\u003e2\u003c/sup\u003e = 1.2589; tau = 1.1220; I\u003csup\u003e2\u003c/sup\u003e = 92.2% [87.8%; 95.0%]). The global inconsistency test could not be calculated (Q=0.0, df=0), indicating no variability in study designs within the network. No evidence of publication bias was detected based on the Egger’s test (p=0.56) (Supplementary\u0026nbsp;Figure 8).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLeptin.\u0026nbsp;\u003c/strong\u003eTen studiesinvolving 14 pairwise comparisons, with 5 intervention arms and 4 study designs, were included in the network meta-analysis (Supplementary\u0026nbsp;Figure 9). Compared with CON, CT trended toward a larger reduction in leptin [SMD: -0.62 (95% CI: -0.88 to 0.06), p=0.06], with no effect of AT, RT, and HIIT (Figure 12), or differences between exercise modes (Supplementary Table 10). The highest P-score ranking was observed for CT (0.77), followed by HIIT (0.62), AT (0.48), and RT (0.42) (Figure 12). There was high heterogeneity amongst studies (tau\u003csup\u003e2\u003c/sup\u003e = 1.6646; tau = 1.2902; I\u003csup\u003e2\u003c/sup\u003e = 94% [90.6%; 96.1%]) (Supplementary\u0026nbsp;Table 4), and no global inconsistency (Q= 0.19, df=1, p=0.66). Furthermore, the results of the node splitting analysis based on comparison of direct and indirect results, showed no significant inconsistency between results (Supplementary\u0026nbsp;Table 11). No evidence of publication bias was detected based on the Egger’s test (p=0.23) (Supplementary\u0026nbsp;Figure 10).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdiponectin.\u0026nbsp;\u003c/strong\u003eNine studies involving 13 pairwise comparisons with 5 intervention arms and 4 study designs were included in the network meta-analysis (Supplementary\u0026nbsp;Figure 11). Compared with CON, none of exercise modes produced a significantly larger increase in adiponectin (Figure 13), with no differences between modes (Supplementary Table 12). The highest P-score ranking was observed for CT (0.78), followed by HIIT (0.67), AT (0.47), and RT (0.26) (Figure 13). There was high heterogeneity amongst included studies (tau\u003csup\u003e2\u003c/sup\u003e = 1.4951; tau = 1.2227; I\u003csup\u003e2\u003c/sup\u003e = 92.9% [88.4%; 95.7%]) (Supplementary Table 4), with no global inconsistency (Q= 0.09, df=1, p=0.75). Furthermore, the results of the node splitting analysis based on comparison of direct and indirect results, showed no significant inconsistency between results (Supplementary Table 13). No evidence of publication bias was detected based on the Egger’s test (p=0.93) (Supplementary Figure 12).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eSummary of Main Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur systematic review with pairwise and network meta-analyses indicates that exercise training confers significant anti-inflammatory benefits for breast cancer patients and survivors with small to moderate effects. Regular exercise leads to reductions in circulating concentrations of select inflammatory cytokines and adipokines, notably IL-6 and leptin. Furthermore, network meta-analysis indicated HIIT was the most effective in lowering IL-6, and CT showed the largest benefit for TNF-α. Overall, our findings reinforce the role of exercise as a complementary therapy to help improve inflammatory profiles in breast cancer and suggest that HIIT and CT are most effective [53, 54].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison with Existing Evidence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur results extend prior research on the effects of exercise training on markers of inflammation in populations with breast cancer that have consistently reported that exercise reduced circulating CRP and other inflammatory markers in breast cancer survivors [24, 53]. For example, Abbasi et al. found significant decreases in CRP with exercise, although changes in TNF-α, IL-6, IL-8, IL-10, IL-1β, and INF-ɣ\u0026nbsp;were not significantly different compared with control in adult women with breast cancer (18 studies combining 11 effects sizes)\u0026nbsp;[24]. Whilst more recent larger studies demonstrated favorable reductions in IL-6 and TNF-α\u0026nbsp;[21]. We confirm that exercise reduces select pro-inflammatory cytokine and adipokine such as IL-6 and leptin, with more consistent effects observed with CT and HIIT. Consequently, a growing body of evidence supports positive biological effects on inflammatory pathways in breast cancer patients.\u003c/p\u003e\n\u003cp\u003eNotably, our network approach allowed direct comparison of exercise modalities, yielding novel insights. Prior meta-analyses have typically categorized interventions as aerobic training (AT), resistance training (RT), or combined exercise without differentiating between the magnitude of intensity [21]. We expand on these analyses by comparing the efficacy of HIIT to other exercise modes, and indicates that vigorous-intensity exercise may offer greater anti-inflammatory benefits compared with moderate-intensity [14, 38]. Furthermore, combined aerobic and resistance training is a more effective approach for improving outcomes in breast cancer survivors [21, 53], as found after 16 weeks of supervised CT in overweight or obese participants [55]. Conversely, earlier single-mode exercise trials have frequently shown no changes in adiponectin or leptin among breast cancer survivors, possibly due to suboptimal intensities or insufficient intervention durations [56]. Our analysis helps reconcile these discrepancies by confirming that any regular exercise training is beneficial, with the magnitude of inflammatory improvement greater with more intensive and multifaceted training. Thus, our findings are in broad agreement with existing literature while providing a nuanced hierarchy of exercise modalities consistent with both recent meta-analytic evidence and high-quality clinical trials [21, 55].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup Analyses and Effect Modifiers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhere data allowed, we explored whether participant or intervention characteristics modified the effects of exercise training. Although our primary network analysis did not show a statistically significant difference in outcomes between shorter and longer programs, the direction of effects and previous evidence suggests that exercise duration and dose are important considerations [14]. Prolonged training most likely allows sustained weight loss and metabolic adaptation, thereby amplifying anti-inflammatory effects. Exercise intensity therefore has the potential to compensate for shorter program durations in targeting specific inflammatory markers [21].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBaseline patient characteristics also appear to affect responsiveness to exercise, with subgroup trends from our meta-analysis, indicating that elevated baseline adiposity and inflammation was associated with larger improvements in inflammatory markers [53]. By contrast, in mixed-weight populations, IL-6 reductions are sometimes attenuated [24]. Excess adipose tissue is a key source of pro-inflammatory signaling, and higher adiposity may lead to more pronounced anti-inflammatory benefits from exercise training, mediated by reduced leptin [53]. Another factor is the phase of breast cancer treatment although data are limited, exercising during active treatment (such as chemo-therapy) may be feasible and blunt therapy-related inflammatory surges [57]. Moreover, patients who stayed physically active during chemotherapy were more likely to have no detectable cancer cells at the end of treatment [57]. These observations suggest that exercise in overweight or obese patients under therapy, or in remission exhibit inflammatory improvements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiological Mechanisms for Anti-inflammatory Effects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExercise exerts multi-faceted biological effects likely to improve inflammatory markers and adipokines, with skeletal muscle having a major role as it releases several “myokines” during and after exercise. During exercise, pro-inflammatory IL-6 rises rapidly, largely due to immune cells that infiltrate and respond to muscle activity and remodeling [58], which then timulates the release of anti-inflammatory cytokines such as IL-10 to suppress TNF-α synthesis, thereby shifting the cytokine milieu towards anti-inflammation [59]. Regular exercise and associated changes in myokines modulate immune function by reducing circulating pro-inflammatory monocytes and macrophages, while enhancing the activity of cytotoxic T cells and natural killer cells that help target and eliminate tumor cells [58, 60-62]. Overall, each bout of exercise provokes a transient inflammatory-like response that ultimately leads to longer-term anti-inflammatory adaptations [63].\u003c/p\u003e\n\u003cp\u003eIn addition to its direct effects on circulating cytokines, exercise also favorably influences adipose-tissue related biomarkers, and is particularly relevant for breast cancer patients, many of whom are overweight or obese. In our analyses, exercise, especially HIIT and CT, was associated with meaningful improvements in adipokines such as leptin, indicating a beneficial shift in adipose tissue biology, thereby reducing inflammation and tumor growth [2, 54]. Regular exercise combats adipose tissue by reducing fat mass (and even “remodeling” fat) to lower the production of pro-inflammatory adipocytokines [64] [65, 66]. This shift is critical because leptin not only fuels inflammation by activating macrophages and T-cells, but also directly stimulates breast cancer cell proliferation via the JAK2/STAT3 signaling pathway [54, 67]. High leptin in the tumor microenvironment can increase angiogenesis and contribute to poorer prognosis [54]. Additionally, regular exercise can promote a healthier adipose tissue phenotype through the browning of white fat, a process that may enhance energy expenditure, reduce pro-inflammatory adipokines (e.g., IL-6, TNF-α, leptin), and limit M1 pro-inflammatory macrophage infiltration, thereby attenuating tumor-promoting inflammation [68, 69]. Collectively, these adaptations mean that patients who exercise regularly typically exhibit lower basal levels of IL-6 and TNF-α and a more favorable adipokine profile than their sedentary peers [21, 53]. Furthermore, improved insulin sensitivity and reduced insulin/IGF-1 signaling with exercise provides further anti-inflammatory and anti-cancer benefits [70-73]. In summary, regular exercise creates an environment less conducive to cancer progression by suppressing inflammatory through myokine-driven immune modulation and transforming adipose tissue into a more metabolically healthy and anti-inflammatory state.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths:\u003c/strong\u003e To our knowledge, this is the systematic review with both pairwise and network meta-analyses comparing different exercise modes on inflammatory adipocytokines in breast cancer patients. By integrating data from multiple RCTs and applying such an approach, we could compare and rank the relative efficacy of each exercise modalities thereby extending previous meta-analyses that evaluated exercise as a single, uniform intervention. This increased our power to discern differences between exercise modes, and facilitates tailored exercise prescriptions, that was complemented by a rigorous methodology (preregistered protocol, extensive database search, and risk-of-bias assessment). Our analysis was comprehensive, including breast cancer patients during treatment and post-treatment (survivors), thereby enhancing generalizability across the spectrum of breast cancer care. The\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations:\u003c/strong\u003e There was considerable heterogeneity among the included trials, with exercise interventions varying in dose, length, frequency, supervision, and adherence. Sources of heterogeneity (e.g. duration, intensity) were examined through subgroup and meta-regression analyses, but residual confounding may remain. Additionally, some exercise modes (particularly HIIT) had a relatively small number of trials, so although we incorporated indirect evidence to enhance comparisons, rankings for less-studied exercise modalities should be interpreted with caution until supported by additional high-quality RCTs. The inflammatory markers examined were often secondary outcomes in the original trials, leading to issues such as incomplete reporting and potential publication bias (studies with non-significant cytokine findings are less likely to publish detailed results). Funnel plot inspection did not reveal substantial publication bias, however, this possibility cannot be ruled out due to the relatively small number of studies available for each outcome. Patient populations also differed between studies, some trials focused on survivors with obesity, who were on long-term endocrine therapy, while others included patients in active chemotherapy or radiotherapy. This clinical diversity, while enhancing generalizability, might limit the precision of our estimates for any one subgroup. So although, changes in IL-6 or leptin are biologically relevant, they represent surrogate endpoints, and it remains uncertain whether these translate into lower cancer recurrence or improved survival outcomes. Although cross-sectional data show a strong association between lower inflammation and improved outcomes, whether reducing inflammation through exercise will improve patient prognosis is not established. Finally, standardization of cytokine assays was lacking across studies, variations in assay sensitivity and timing of blood sampling (e.g.,\u0026nbsp;acute post-exercise vs. resting levels) could influence results. Considering these limitations, our findings should be interpreted as hypothesis-generating.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Research Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis meta-analysis underscores several areas for future investigation, for which there is a need for large, high-quality RCTs directly comparing exercise modalities with inflammatory markers and clinical outcomes as endpoints. It would be especially impactful to integrate biomarker endpoints into ongoing exercise-oncology trials or survivorship programs, thereby linking mechanistic changes to patient outcomes. Mechanistic studies are also warranted to establish how exercise exerts its anti-inflammatory and anti-cancer effects in this population. A combination of lifestyle interventions should be explored, as diet-induced weight loss itself can reduce inflammation, so combining exercise with nutritional strategies might have additive or synergistic effects. Finally, implementation research is crucial, so although exercise is a safe and effective adjunct therapy (with numerous ancillary benefits like improved fitness, quality of life, and fatigue reduction), real-world uptake remains suboptimal.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis systematic review with pairwise and network meta-analyses provides evidence that exercise, particularly CT and HIIT, may be effective for reducing chronic inflammation in patients with breast cancer. Clinicians should therefore consider prescribing tailored exercise programs as part of breast cancer management, not only to improve fitness and quality of life, but to harness the effects of exercise training on reducing the tumor-promoting inflammatory milieu. It reinforces the growing recognition of exercise as a key component of breast cancer care and the need for continued research to refine exercise prescriptions across cancer populations.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.Kh., M.H.S., M.E.S., S.K.R., and H.R. conceptualized and designed the protocol.M.Kh., M.H.S., F.G., H.R., Sh.M., A.S.Z., M.M., S.F.\u0026nbsp;carried out the screenings and reviews, and the analysis of the articles. M.Kh.,\u0026nbsp;M.H.S., K.O., and H.R. drafted the manuscript, and K.S., M.E.S. and S.K.R. revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eNon.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional review board statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed for this study are contained within this published article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have nothing to report.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFerlay, J., et al., \u003cem\u003eGlobal cancer observatory: cancer today.\u003c/em\u003e Lyon: International agency for research on cancer, 2020. \u003cstrong\u003e20182020\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eIyengar, N.M., et al., \u003cem\u003eObesity and cancer mechanisms: tumor microenvironment and inflammation.\u003c/em\u003e Journal of clinical oncology, 2016. \u003cstrong\u003e34\u003c/strong\u003e(35): p. 4270-4276.\u003c/li\u003e\n\u003cli\u003eCastro-Espin, C., et al., \u003cem\u003ePrognostic role of pre-diagnostic circulating inflammatory biomarkers in breast cancer survival: evidence from the EPIC cohort study.\u003c/em\u003e British journal of cancer, 2024. \u003cstrong\u003e131\u003c/strong\u003e(9): p. 1496-1505.\u003c/li\u003e\n\u003cli\u003eLahart, I.M., et al., \u003cem\u003ePhysical activity, risk of death and recurrence in breast cancer survivors: a systematic review and meta-analysis of epidemiological studies.\u003c/em\u003e Acta oncologica, 2015. \u003cstrong\u003e54\u003c/strong\u003e(5): p. 635-654.\u003c/li\u003e\n\u003cli\u003eIyengar, N.M., C.A. 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R225-R241.\u003c/li\u003e\n\u003cli\u003eSimone, V., et al., \u003cem\u003eObesity and breast cancer: molecular interconnections and potential clinical applications.\u003c/em\u003e The oncologist, 2016. \u003cstrong\u003e21\u003c/strong\u003e(4): p. 404-417.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8968752/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8968752/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction and aim. \u003c/strong\u003eAdipocytokines play a key role in the pathogenesis and progression of breast cancer and are increasingly recognized as potential therapeutic targets. The anti-inflammatory effects of regular exercise training are well-established, but its effects on inflammatory adipocytokines in individuals with breast cancer have not been elucidated. This systematic review and pairwise network meta-analyses aimed to (1) investigate the overall effects of exercise training on pro- and anti-inflammatory markers, and (2) compare the efficacy of different exercise modes using pairwise and network meta-analytic approaches.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e. PubMed, Web of Science, and Scopus were systematically searched from inception to January 2025 using four groups of keywords, including “exercise”, “cytokine”, “breast cancer” and “randomized”. Randomized controlled trials investigating the effects of exercise training compared with a control (CON) or other modes of exercise on circulating IL-6, TNF-α, IL-10, CRP, leptin, and adiponectin in patients with breast cancer were included. Standardized mean differences (SMD) with 95% confidence intervals (CIs) were calculated using random-effects models for both pairwise and network meta-analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e. Twenty-five studies involving 1,219 patients with breast cancer were. Compared with CON, exercise training led to significantly larger reductions in IL-6 [SMD: -0.39, p=0.02] and leptin [SMD: -0.77, p=0.01], and non-significant trends toward reductions in TNF-α [SMD: -0.30, p=0.05] and CRP [SMD: -0.47, p=0.07], but had no effect on IL-10 or adiponectin. In addition, high-intensity interval training (HIIT) reduced IL-6 [SMD: -1.08, p=0.01], with non-significant trends toward reduced TNF-α [SMD: -1.40, p=0.09] and raised IL-10 [SMD: 0.34, p=0.09]. Combined training (CT) decreased TNF-α [SMD: -0.65, p=0.006], and showed a trend towards lower leptin [SMD: -0.62, p=0.06]. No individual exercise mode impacted on in CRP or adiponectin compared with CON. Subgroup analyses suggested that intervention duration and patients’ clinical status influenced the magnitude of these effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e. Exercise training appears to be effective in attenuating some markers of chronic low-grade inflammation by modulating key adipose-derived pro- and anti-inflammatory cytokines in patients with breast cancer, with HIIT and CT may confer superior benefits.\u003c/p\u003e","manuscriptTitle":"Comparative efficacy of exercise modes on inflammatory adipocytokines in patients with breast cancer: a systematic review with pairwise and network meta-analyses ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 10:11:21","doi":"10.21203/rs.3.rs-8968752/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-13T01:58:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T12:50:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333580554545079943442079966510796584364","date":"2026-04-11T09:59:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250373833482914721724359123391177845217","date":"2026-04-09T06:37:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-08T17:16:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139680979765992963531293596879017990849","date":"2026-04-08T17:10:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202849141990689879263855570762721021049","date":"2026-04-08T16:19:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26436762153203385224107428141711605543","date":"2026-04-08T15:31:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-27T07:51:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-20T17:06:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-20T11:22:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-14T06:10:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-13T15:52:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d4b1497-d763-4af0-bf60-a993eaa2ee76","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65340975,"name":"Health sciences/Biomarkers"},{"id":65340976,"name":"Health sciences/Diseases"},{"id":65340977,"name":"Biological sciences/Immunology"},{"id":65340978,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-05-12T07:24:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 10:11:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8968752","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8968752","identity":"rs-8968752","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}