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Comparative efficacy of various mind-body exercise types on cardiometabolic health in patients with type 2 diabetes: a network meta-analysis of randomized controlled trials

BMC Cardiovascular Disorders volume 25, Article number: 291 (2025) Cite this article

Abstract

Objective

This study aims to compare the efficacy of different mind-body exercises (MBEs) on cardiometabolic risk factors in patients with type 2 diabetes mellitus (T2DM) using a network meta-analysis of randomized controlled trials (RCTs).

Methods

This study followed PRISMA guidelines and was registered in PROSPERO (CRD42025630741). A systematic search of PubMed, Cochrane Library, Web of Science, and Embase was conducted up to December 15, 2024, using MeSH terms related to mind-body therapies and cardiometabolic risk in type 2 diabetes. Randomized controlled trials (RCTs) evaluating mind-body exercises (MBEs) on glucose metabolism, body composition, cardiovascular physiology, and lipid metabolism were included. Data extraction and risk of bias assessment (RoB 2 tool) were performed independently by two reviewers. Network meta-analysis was conducted using R (gemtc package) and Stata 17.0, with effect sizes reported as mean difference (MD) or standardized mean difference (SMD). Evidence quality was assessed using CINeMA.

Results

This network meta-analysis compared the effects of various mind-body exercise interventions on ten cardiometabolic risk factors. Meditative Exercise (ME) was most effective in reducing fasting plasma glucose (SUCRA = 97.9%, SMD = -7.23, 95% CI: -8.27 to -6.20), while Mindfulness Intervention Training (MIT) showed the greatest benefit for glycated hemoglobin (SUCRA = 92.2%, MD = -0.78, 95% CI: -1.12 to -0.44) and blood pressure reduction (SBP: SUCRA = 86.1%, MD = -13.00, 95% CI: -17.22 to -8.78; DBP: SUCRA = 99.8%, MD = -6.00, 95% CI: -7.64 to -4.36), significantly outperforming conventional exercise. Yoga with Meditation (YWM) was most effective in lowering body mass index (SUCRA = 99.4%, MD = -2.90, 95% CI: -4.05 to -1.75). CINeMA assessments rated most comparisons as very low certainty due to within-study bias and between-study heterogeneity. Nevertheless, consistency was supported by node-splitting analysis, and no significant publication bias was detected, indicating robust and reliable findings.

Conclusion

Compared with conventional exercise intervention, MBE exerts unique and superior effects on various cardiometabolic risk factors in T2DM, underscoring their potential as effective and integrative interventions for personalized diabetes management. Clinicians should consider incorporating MBEs, such as MIT, ME, and YWM, into treatment plans based on individual patient needs, particularly for glycemic control, weight management, and cardiovascular health. Further research is warranted to explore the long-term benefits and optimal implementation strategies, especially given the heterogeneity in intervention protocols and the relatively short duration of the included trials.

Peer Review reports

Introduction

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder resulting from insufficient insulin secretion or insulin resistance, characterized by impaired glucose homeostasis [1]. Globally, the prevalence of diabetes continues to rise, with an estimated 415 million people currently affected—around 193 million of whom have yet to be diagnosed. T2DM accounts for over 90% of all cases, and its complications include both microvascular and macrovascular conditions such as retinopathy, nephropathy, and cardiovascular disease (CVD). These complications not only pose a severe threat to patients’ quality of life but also place a significant economic burden on healthcare system [2]. T2DM is closely associated with various cardiometabolic risk factors, such as elevated fasting plasma glucose (FPG), reduced levels of high-density lipoprotein cholesterol (HDL-C), and increased levels of low-density lipoprotein cholesterol (LDL-C) [3, 4]. These risk factors significantly elevate the likelihood of cardiovascular disease (CVD) in T2DM patients [5]. If inadequately managed, T2DM can also lead to chronic complications [6], including nephropathy, retinopathy [7], and other conditions that severely impact patients’ quality of life.

Currently, the primary approaches to managing T2DM include pharmacotherapy, healthy dietary practices, and physical activity [8, 9]. However, these strategies may have limitations in reducing cardiovascular risk. Moreover, T2DM patients often face psychological burdens such as stress, anxiety, and depression [10, 11], which further complicate disease management. These psychological burdens further compound the complexity of disease management. As a result, researchers have increasingly recognized that neither physical activity alone nor medication alone can fully address the multifaceted challenges of type 2 diabetes. Consequently, comprehensive strategies—particularly those combining physical activity with psychological regulation—have become a key focus in diabetes management research [12].

Mind-body exercises (MBEs) represent an integrative intervention approach combining physical activity with psychological regulation [13]. Research suggests that mind-body exercises offer significant benefits for overall health and cardiometabolic indicators [14]. For instance, yoga—a widely practiced form of meditative exercise—can substantially improve anthropometric measurements, cardiovascular risk factors, physical function, and quality of life [15]. Pilates training has been shown to effectively enhance anthropometric measures, body composition, glucose and lipid metabolism, and blood pressure in overweight or obese women [16]. Moreover, mindfulness training, as an effective method to alleviate chronic stress, can regulate both behavioral and neurobiological mechanisms related to stress response [17]. Although conventional exercise also yields notable improvements in glucose metabolism and cardiometabolic health, mind-body exercises have emerged as an important complementary intervention in type 2 diabetes management, owing to their unique psychological benefits [18]. Nevertheless, although numerous studies have investigated the effects of individual mind-body exercises or different exercise modes cardiometabolic risk factors among patients with type 2 diabetes the relative efficacy of different types of mind-body exercises has yet to be systematically compared [19,20,21].

In this context, the present study employs a network meta-analysis approach to systematically integrate findings from existing randomized controlled trials (RCTs). The study aims to compare the effectiveness of various MBEs and their combined interventions on cardiometabolic risk factors in patients with T2DM. physical activity, home-based stretching exercises, and standard care are included as control groups. Outcome measures focus on four key aspects of cardiometabolic health: glucose metabolism, body composition, cardiovascular physiology, and lipid metabolism. This study aims to evaluate and compare the effectiveness of various MBEs in addressing cardiometabolic risk factors among patients with T2DM through a network meta-analysis of RCTs.

Materials and methods

Study design and protocol registration

The literature synthesis protocol for this study has been registered with the International Prospective Register of Systematic Reviews (PROSPERO, registration number: CRD42025630741). The study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [22]. This research adheres fully to all recommended reporting items listed in the PRISMA-P checklist. The PRISMA checklist is provided in Appendix 1, Tables S1.5.

Literature search

We conducted a systematic search of the PubMed, Cochrane Library, Web of Science, and Embase databases from their inception to December 15, 2024. The search strategy utilized the following MeSH terms: “Mind-Body Therapies,” “Cardiometabolic Risk,” “Diabetes Mellitus,” and “Randomized Controlled Trial.” Only peer-reviewed articles published in English were included. The detailed search strategies are presented in Appendix 1, Tables S1.1-S1.4.

Eligibility criteria

Studies were included based on the PICOS framework to ensure methodological rigor and relevance. The population consisted of individuals aged 18 years or older who were clinically diagnosed with type 2 diabetes mellitus (T2DM). The interventions examined various types of mind-body exercises (MBEs), with comparisons focusing on their differential effects. Outcomes were categorized into four key cardiometabolic risk factors: (a) glucose metabolism, measured by fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c); (b) body composition, assessed through waist circumference (WC) and body mass index (BMI); (c) cardiovascular physiology, including systolic blood pressure (SBP) and diastolic blood pressure (DBP); and (d) lipid metabolism, encompassing triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and total cholesterol (TC). Only randomized controlled trials (RCTs) were included to maintain the highest standard of evidence quality.

Studies were excluded if they involved non-T2DM participants or interventions where the experimental and control groups received the same type of MBE. Reviews, case reports, non-randomized trials, research protocols, and conference abstracts were also excluded. Additionally, duplicate publications, studies without accessible full texts, or those with incomplete data were not considered.

Data extraction

Two independent reviewers performed literature screening and data extraction, followed by cross-verification. Disagreements were resolved through discussion. Screening was conducted in two stages: initially reviewing titles and abstracts to exclude irrelevant studies, and subsequently assessing full texts to determine eligibility. Data extracted included: first author, country and year of the study, participant characteristics, sample size of the intervention and control groups, intervention frequency and duration, intervention methods, outcomes, and result data.

Literature management was conducted using EndNote X9. Full-text articles of eligible studies were retrieved, and necessary data were extracted and organized to ensure accuracy and completeness.

Risk of bias assessment

The risk of bias was evaluated using the Cochrane Collaboration’s RoB 2 tool. The assessment covered the following domains: randomization process, deviations from intended interventions, missing outcome data, measurement of outcomes, and selection of reported results. The overall risk of bias across these domains was deemed low, though some concerns were noted [23].

Data analysis

In this study, we used the gemtc package in R (version 4.3.3) to construct a network relationship diagram, visually illustrating the direct and indirect comparisons among different interventions [24]. Subsequent analyses were conducted using Stata 17.0 to ensure the robustness of the analytical models [25]. All study data were continuous variables. For variables with consistent units, the mean difference (MD) was used as the effect size metric, whereas for variables with inconsistent units, the standardized mean difference (SMD) was applied. A global inconsistency test was first conducted, and if the P-value was greater than 0.05, the consistency model was employed; otherwise, the inconsistency model was used for further analysis. Heterogeneity within the network was assessed using the node-splitting method.The relative ranking of interventions was estimated using the surface under the cumulative ranking curve (SUCRA), where a higher SUCRA value indicated a better ranking of the intervention. The study results were presented using a multidimensional approach, including a ranking table to display the relative rankings of interventions, a forest plot for pairwise comparisons, and a league table for intuitive visualization.For effect size evaluation, a 95% confidence interval (CI) containing zero indicated no statistically significant difference, while a CI with both limits above or below zero suggested a significant positive or negative effect, respectively [26]. Additionally, publication bias was assessed using funnel plots and Egger’s test to enhance the reliability and interpretability of the findings.

Assessment using cinema

This study systematically evaluated the quality of evidence for network estimates using the Confidence in Network Meta-Analysis (CINeMA) framework, which has been applied in previous studies as well [27]. CINeMA follows a standardized assessment process and evaluates evidence quality across six key domains: within-study bias, reporting bias, indirectness, imprecision, heterogeneity, and incoherence.The quality of evidence was classified into four levels: high, moderate, low, and very low. Notably, all included RCTs were initially assumed to provide high-quality evidence. During the assessment, if any of the six domains were found to be deficient, the corresponding evidence level was downgraded accordingly. The extent of downgrading varied depending on the severity of the issue, with a possible reduction by one or two levels [28].

Results

Literature search process and results

Through the literature search, a total of 1,302 studies were identified. After excluding duplicates, registrations, reviews, study protocols, letters, books, correspondence, and conference abstracts, 435 studies remained. Screening the titles and abstracts further reduced the number to 308. Following a detailed full-text review, 17 studies were ultimately included [29,30,31,32,33,34,35,36,37,38,39,40,41,42,43]. Figure 1 presents the flowchart illustrating the literature screening process.

Fig. 1
figure 1

Literature screening flow chart

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Study characteristics

Table 1 provides a detailed summary of the baseline characteristics of the included studies. A total of 1,447 patients were analyzed across the 17 included studies, all of whom were diagnosed with type 2 diabetes. The studies were conducted between 2008 and 2023 and originated from eight different countries. Among them, two were three-arm trials, while the remaining 15 were two-arm studies. The sample sizes ranged from 20 to 300 participants, with a mean age of 63.8 years. The intervention frequency varied from once to eight times per week, with each session lasting between 30 min and 4 h, and the intervention duration ranging from 6 weeks to 6 months.Gender distribution was not specified in two studies, while three studies included only female participants. A total of 11 different intervention types were analyzed. The outcome measures were classified into four categories to comprehensively assess metabolic health and cardiovascular risk: Glucose metabolism indicators (FPG, HbA1c).Body composition and anthropometric measures (BMI, WC).Cardiovascular physiological indicators (SBP, DBP).Lipid profile (HDL-C, LDL-C, TG, TC). Figure 2 presents a summary of the risk of bias assessment. Overall, the primary sources of bias stemmed from the lack of clear blinding procedures for participants and assessors, which raised concerns about the reliability of outcome data. Appendix 4, Figure S1 provides a detailed quality assessment of each included study. As a result, a total of 17 studies were ultimately analyzed.

Table 1 Basic information included in the study
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Fig. 2
figure 2

Cochrane risk of bias percentile chart

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Network meta-analysis results

The network meta-analysis results presented in Table 2 indicate significant differences in the effectiveness of various interventions in improving multiple health indicators.For FPG, ME had the highest SUCRA value (97.9%), indicating the most substantial improvement, while the CG had the lowest (5.9%). Compared to CG, ME significantly reduced FPG (SMD: -7.23, 95% CI: -8.27, -6.20).Regarding glycated HbA1c, MIT demonstrated the best effect (SUCRA: 92.2%), whereas LY showed the poorest performance (SUCRA: 0.0%).Compared to CG, MIT significantly reduced HbA1c (MD: -0.78, 95% CI: -1.12, -0.44).For BMI, the combination of YWM was the most effective intervention (SUCRA: 99.4%), while MIT had the lowest effectiveness (SUCRA: 15.9%). Compared to CG, YWM significantly reduced BMI (MD: -2.90, 95% CI: -4.05, -1.75).In terms of blood pressure, MIT ranked highest for both SBP and DBP (SUCRA: 86.1% and 99.8%, respectively), whereas conventional exercise (CE) had the least effect (SUCRA: 6.1% and 5.1%). Compared to CE, MIT significantly reduced SBP (MD: -13.00, 95% CI: -17.22, -8.78) and DBP (MD: -6.00, 95% CI: -7.64, -4.36).A detailed network evidence diagram (Appendix 5, Figure S2.1-S2.10) visually represents the direct and indirect comparisons between interventions, providing structural insights into the network meta-analysis. Node-splitting analysis results (Appendix 2, Table S2.1-S2.3) were used to assess inconsistencies within the network model. The findings indicated that most comparisons were consistent, further supporting the robustness of this study’s conclusions.Comprehensive SUCRA ranking results are presented in Appendix 7, Figure S4.1-S4.10, quantifying the relative effectiveness of each intervention for specific outcome measures. Detailed pairwise comparison results are available in league tables (Appendix 3, Table S3.1-S3.10) and forest plots (Appendix 6, Figure S3.1-S3.10), which provide direct comparisons of all interventions, including MD and their 95% CI, offering comprehensive data support.Additionally, publication bias was assessed using funnel plots and Egger’s test (Appendix 8, Table S4; Figure S5.1-S5.10). The results indicated no significant publication bias, further strengthening the reliability of the study findings.

Table 2 Basic information included in the study
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CINeMA evidence assessment

We utilized the CINeMA tool to evaluate the quality of evidence for each outcome in the network meta-analysis. The results indicated that two comparisons had low-quality evidence, while most comparisons were rated as very low quality. The quality of evidence for these comparisons was affected by within-study bias and between-study heterogeneity.The detailed results of the CINeMA assessment are presented in Appendix 9, Figure S6.1-S6.10 and Table S5.1-S5.10.

Discussion

The results of the current network meta-analysis demonstrate that different MBE interventions exhibit distinct advantages in improving glucose metabolism, body composition, cardiovascular physiology, and lipid profiles. ME and MIT were particularly effective in glucose metabolism, with ME achieving the greatest reduction in FPG, and MIT showing a slight advantage in reducing HbA1c. For body composition, YWM demonstrated the most pronounced improvements in BMI and WC. In contrast, MIT had the weakest effect on BMI management, and CE performed least effectively for WC reduction. Regarding cardiovascular physiology, MIT showed significant improvements in SBP and DBP, outperforming Pilates and Taiji, whereas CE exhibited the least favorable outcomes. For lipid profile improvement, low-intensity integrative mind-body interventions (e.g., MIT, Hatha Yoga, Pilates, YWM) showed significant benefits compared to control group, which were relatively weaker.

The findings of this study align well with previous research, reinforcing the positive effects of exercise on cardiometabolic health in individuals with type 2 diabetes and overweight/obesity. Batrakoulis found that combined training is the most effective exercise type for improving outcomes such as blood pressure, blood glucose, lipid profile, and physical fitness [44]. Similarly, Al-Mhanna showed that combined aerobic and resistance training improves BMI, glycemic control, inflammation, blood pressure, cardiorespiratory fitness, and quality of life [45]. Recent evidence also supports resistance training alone as beneficial for this population, with improvements in body composition, lipid levels, glucose metabolism, and insulin sensitivity [46]. This suggests resistance training can be a valuable option, especially for those who cannot perform high-intensity or combined workouts.In terms of lipid metabolism, narrative reviews indicate that moderate-to-high intensity, long-duration exercise is especially effective in increasing HDL-C and lowering triglycerides, LDL-C, and total cholesterol in people with T2DM and obesity [47, 48]. High-intensity interval training (HIIT) also shows promise, though its long-term adherence may be a concern [49]. Overall, current evidence supports using personalized, structured, and multicomponent exercise programs as a key non-pharmacological strategy for managing T2DM and obesity. Both combined and resistance training have clear benefits. Future studies should explore how different exercise doses affect health outcomes to improve exercise recommendations.

Given these established benefits of MBEs, it is important to compare their underlying physiological and psychological mechanisms with those of exercise to better understand their distinct yet complementary roles in T2DM management.While MBEs and exercise both offer benefits for T2DM management, they operate through distinct yet complementary physiological and psychological mechanisms. From a physiological perspective, TE (e.g., aerobic and resistance training) primarily improves glycemic control by enhancing insulin sensitivity, increasing skeletal muscle glucose uptake, and promoting mitochondrial function [50,51,52]. In contrast, MBEs, characterized by slow, controlled movements and breath regulation, modulate glucose metabolism through autonomic nervous system regulation, reducing cortisol levels and improving vagal tone, which may contribute to better glycemic control [53, 54]. From a psychological perspective, MBEs offer greater stress reduction and emotional regulation compared to TE, largely due to their emphasis on mindfulness, deep breathing, and relaxation techniques [55]. Chronic stress and psychological distress are known to exacerbate insulin resistance and metabolic dysregulation, and MBEs may counteract these effects by activating the hypothalamic-pituitary-adrenal axis and downregulating sympathetic nervous system activity [56].

Glucose metabolism

ME combines meditation with low-intensity physical activities, such as yoga or Taiji, effectively alleviating psychological stress while directly enhancing insulin sensitivity and glucose uptake through aerobic activity [57, 58]. Specifically, ME activates the parasympathetic nervous system, promoting metabolic balance [59], while low-intensity exercises further increase glucose utilization by skeletal muscles [60]. Additionally, ME may reduce chronic inflammatory markers, such as C-reactive protein and tumor necrosis factor-α [61], improve pancreatic function, and inhibit hepatic glucose production, contributing to significant reductions in FPG [62].

MIT, on the other hand, significantly reduces HbA1c levels through multiple mechanisms, including regulation of the hypothalamic-pituitary-adrenal axis [57, 63]. By alleviating psychological stress and reducing cortisol levels [64], MIT enhances insulin sensitivity [65, 66]. Moreover, MIT fosters dietary mindfulness, reduces binge eating behaviors, and enhances sleep quality, collectively contributing to improved glycemic control [67, 68]. It also bolsters self-management abilities and psychological resilience, aiding in the stabilization of blood glucose levels and delaying glycation processes [69].

Body composition

The significant effectiveness of YWM can be attributed to the multidimensional mechanisms of yoga, which functions as a low-to-moderate intensity and integrative form of exercise. Yoga postures and movements actively recruit major muscle groups and enhance basal metabolic rate, thereby promoting increased fat oxidation and energy expenditure [70, 71]. Moreover, the integration of yoga and meditation has been shown to effectively reduce cortisol levels [72, 73]. Elevated cortisol is closely associated with fat storage, particularly in the accumulation of abdominal fat [74]. By regulating cortisol levels, YWM contributes to reductions in BMI and WC, highlighting its potential for improving body composition and metabolic health. This combination of physiological and endocrine mechanisms underpins the superior outcomes of YWM in body composition management.Yoga postures and breathing exercises, such as abdominal breathing and twisting poses, stimulate the digestive system, improve gastrointestinal function, and promote waste elimination [75]. These play a vital role in reducing bloating, decreasing fat accumulation, and optimizing metabolic balance.

In contrast, MIT primarily targets psychological regulation and has limited direct effects on energy expenditure, resulting in weaker BMI management outcomes. CE, while increasing physical activity levels, lacks the holistic regulation of core metabolic and psychological functions, leading to suboptimal performance in WC management.

Cardiovascular physiology

MIT reduces psychological stress levels through mindfulness meditation, decreasing excessive sympathetic nervous system activity while enhancing parasympathetic nervous system regulation [76, 77]. These neuroregulatory mechanisms directly impact vascular tone, reducing peripheral vascular resistance and effectively improving SBP and DBP. Additionally, MIT modulates systemic inflammation and oxidative stress, enhancing endothelial function by promoting nitric oxide release and slowing the progression of atherosclerosis, thereby contributing to blood pressure control [78, 79]. MIT alleviation of anxiety and emotional fluctuations also significantly reduces the adverse effects of long-term psychological stress on blood pressure [80], which is particularly important for managing blood pressure variability in patients.

Pilates improves SBP by strengthening core muscle groups through low-intensity full-body exercises, improving posture and respiratory efficiency, and optimizing blood circulation and cardiac pumping function [81, 82]. The breathing techniques emphasized in Pilates, which resemble abdominal breathing, promote slow and deep breathing [83]. This activates the vagus nerve, significantly enhancing parasympathetic nervous activity [84], thereby reducing heart rate and regulating blood pressure [85]. These synergistic mechanisms make Pilates an effective low-intensity intervention for cardiovascular health.

In contrast, while high-intensity physical exercise may offer limited short-term benefits for blood pressure regulation [86], it lacks a comprehensive approach to managing psychological stress. Consequently, its performance in improving SBP and DBP is significantly inferior to that of MIT and other low-intensity, integrative exercise interventions.

Lipid profile

Low-intensity, integrative interventions (e.g., MIT, HY, Pilates, and YWM) improve lipid metabolism through multiple mechanisms. MIT and HY reduces psychological stress, decreases sympathetic nervous activity, and enhances parasympathetic function, effectively raising HDL-C levels and reducing TG and LDL-C production [76, 77]. Additionally, MIT alleviates systemic inflammation and oxidative stress [78], improving endothelial function and lowering the risk of atherosclerosis, thereby further optimizing lipid metabolism [79]. HY also activates core muscle groups and deep tissues [87], boosting basal metabolic rate and reducing visceral fat, effectively lowering LDL-C and TG levels [88, 89]. Pilates strengthens core muscles and adjusts posture, promoting blood circulation and lipid metabolism, reducing TG and LDL-C levels while improving overall metabolic health [90]. YWM, combining the dual benefits of yoga and meditation, creates a synergistic effect through physical movement and psychological regulation, significantly reducing systemic inflammation while improving HDL-C function and lowering LDL-C and TG levels [91, 92].

In comparison, mind-body exercises such as yoga and tai chi, while effective in enhancing flexibility and providing specific benefits for mind-body regulation [65, 93, 94], demonstrate limited effects on basal metabolic rate and lipid metabolism [95]. This limitation may be attributed to their insufficient intensity to adequately stimulate whole-body metabolic activity.

Clinical significance and practical implications

This meta-analysis offers valuable clinical insights into the management of patients with type 2 diabetes. First, clinicians and exercise professionals can personalize mind-body exercise regimens according to specific patient profiles, including primary risk factors (e.g., blood glucose, blood pressure, lipid abnormalities), age, physical mobility, psychological stress levels, and personal preferences. For example, older adults or individuals with limited mobility may particularly benefit from gentle, low-impact interventions such as YWM or Pilates, given their emphasis on flexibility, balance, and mindfulness. Conversely, patients experiencing significant psychological stress or requiring improved mental health support may derive substantial benefits from ME or MIT.

For patients specifically needing tight glycemic control, higher efficacy interventions such as MIT or ME might be prioritized, whereas those targeting reductions in body weight or waist circumference could benefit most from consistent practice of YWM or Pilates. Additionally, integrating aerobic and resistance training alongside MBE significantly enhances glycemic management, reduces inflammation, improves blood pressure, and enhances cardiorespiratory fitness. Exercise professionals are thus encouraged to consider combined exercise modalities in their prescriptions.

Second, adherence represents a critical barrier in T2DM exercise interventions, particularly among patients resistant to high-intensity or complex exercise routines. MBEs generally feature low-to-moderate intensity, gentle movements, and psychological integration, making them more accessible and sustainable. Consequently, these practices are feasible for diverse settings, including community centers, home-based environments, and hospital rehabilitation programs.

Long-term implications also warrant consideration. While the analyzed studies primarily involved short-term interventions, sustained engagement in MBE could potentially offer enduring benefits, such as prolonged glycemic control, continuous psychological improvement, and sustained reductions in cardiometabolic risks. However, maintaining adherence over the long-term poses ongoing challenges that require supportive structures, regular follow-ups, and possibly hybrid models blending supervised and independent practice.

Finally, clinical decision-making must balance exercise benefits with patient safety and feasibility. MBEs are generally safe but may require careful adaptation or contraindication awareness for specific conditions, such as severe joint limitations, recent surgeries, or advanced neuropathy. Clinicians and exercise professionals should evaluate individual patient risks, adjust exercise intensity appropriately, and provide clear instructions to maximize benefits while minimizing potential harms.

In summary, personalized, patient-centered exercise prescriptions aligned with specific health profiles, functional capabilities, and psychological needs are essential to improve adherence, optimize clinical outcomes, and ensure safe, sustainable management of T2DM.

Conclusions and future directions

Overall, this study further corroborates the positive impact of mind-body exercises on cardiometabolic health indicators in T2DM patients. In clinical practice, the integrative nature of MBEs should be leveraged to simultaneously alleviate the burden of glycemic and weight management while enhancing overall mental well-being. Looking ahead, larger, multi-center randomized controlled trials with extended follow-up periods are needed to investigate the optimal implementation strategies for various MBEs and to address issues of adherence. Such research will provide more robust evidence to support comprehensive T2DM management.

Strengths and limitations

This study provides a comprehensive evaluation of MBEs through a robust network meta-analysis, enabling a nuanced comparison of their relative efficacy across a broad spectrum of cardiometabolic risk factors in T2DM. The exclusive inclusion of RCTs, adherence to PRISMA guidelines, and the assessment of diverse outcome measures (e.g., glucose metabolism, body composition, cardiovascular physiology, and lipid profiles) collectively enhance both the methodological rigor and clinical applicability of our findings.

However, certain limitations warrant consideration. First, heterogeneity in intervention protocols—such as varied session duration and frequency—may have influenced the pooled effect sizes. Second, relatively short trial durations (6 weeks to 6 months) limit insights into the long-term sustainability and real-world adherence of these interventions. Third, incomplete reporting of participant characteristics (e.g., sex distribution, diabetes severity) could mask subgroup-specific responses or limit the generalizability of our conclusions. Despite these constraints, the present analysis offers valuable comparative evidence supporting MBEs as potentially effective adjunctive interventions in the comprehensive management of T2DM.

Conclusion

This study provides robust evidence that MBEs offer distinct and superior benefits in managing cardiometabolic risk factors in patients with T2DM. ME and MIT showed significant improvements in glucose metabolism, with ME being the most effective for reducing FPG and MIT for HbA1c. YWM demonstrated the greatest efficacy in improving body composition, including BMI and WC. For cardiovascular health, MIT, Pilates, and Taiji were the most effective in lowering SBP and DBP. In lipid metabolism, low-intensity integrative interventions, such as YWM, HY, and Pilates, optimized HDL-C, TG, and LDL-C.

Exercise interventions, while beneficial in some aspects, were generally less effective in managing these risk factors. The findings underscore the potential of MBEs as integrative and personalized approaches to improve metabolic health, cardiovascular function, and overall well-being in T2DM patients. Future research should focus on the long-term effects of these interventions and explore strategies for their integration into routine diabetes care.

Data availability

The raw data supporting the conclusions of this article will be made available by the corresponding author, without undue reservation.

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  1. Physical Education Teaching Department, Wuxi Taihu University, Binhu District, Wuxi City, 214000, Jiangsu Province, China

    Xi Li

  2. School of Physical Education, Daqing Normal University, Daqing City, Heilongjiang Province, China

    Menglong Gao

  3. Yangming Central Primary School, Wuxi City, Jiangsu Province, China

    Jiao Hua

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LX: Conceptualization, Methodology, Investigation, Writing—original draft preparation. GML: Conceptualization, Writing—review and editing, Supervision, Project administration. HJ: Methodology, Formal analysis, Data curation. All authors have read and agreed to the published version of the manuscript.

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Appendices: Table S1.1: Search Strategies-PubMed. Table S1.2: Search Strategies-Web of Science. Table S1.3: Search Strategies-Embase. Table S1.4: Search Strategies- Cochrane Library. Table S1.5: Prisma checklist. Table S2.1: FPG Node splitting method. Table S2.2: HDL-C Node splitting method. Table S2.3: TC Node splitting method. Table S3.1: FPG league table. Table S3.2: HbA1c league table. Table S3.3: BMI league table. Table S3.4: WC league table. Table S3.5: SBP league table. Table S3.6: DBP league table. Table S3.7: HDL-C league table. Table S3.8: LDL-C league table. Table S3.9: TG league table. Table S3.10: TC league table. Table S4: Egger test. Table S5.1: FPG Reasons for downgrading. Table S5.2: HbA1c Reasons for downgrading. Table S5.3: BMI Reasons for downgrading. Table S5.4: WC Reasons for downgrading. Table S5.5: SBP Reasons for downgrading. Table S5.6: DBP Reasons for downgrading. Table S5.7: HDL-C Reasons for downgrading. Table S5.8: LDL-C Reasons for downgrading. Table S5.9: TG Reasons for downgrading. Table S5.10: TC Reasons for downgrading. Figure S1: Risk of bias summary. Figure S2.1: FPG network map. Figure S2.2: HbA1c network map. Figure S2.3: BMI network map. Figure S2.4: WC network map. Figure S2.5: SBP network map. Figure S2.6: DBP network map. Figure S2.7: HDL-C network map. Figure S2.8: LDL-C network map. Figure S2.9: TG network map. Figure S2.10: TC network map. Figure S3.1: FPG forest plot for pairwise comparison. Figure S3.2: HbA1c forest plot for pairwise comparison. Figure S3.3: BMI forest plot for pairwise comparison. Figure S3.4: WC forest plot for pairwise comparison. Figure S3.5: SBP forest plot for pairwise comparison. Figure S3.6: DBP forest plot for pairwise comparison. Figure S3.7: HDL-C forest plot for pairwise comparison. Figure S3.8: LDL-C forest plot for pairwise comparison. Figure S3.9: TG forest plot for pairwise comparison. Figure S3.10: TC forest plot for pairwise comparison. Figure S4.1: FPG SUCRA ordination diagram. Figure S4.2: HbA1c SUCRA ordination diagram. Figure S4.3: BMI SUCRA ordination diagram. Figure S4.4: WC SUCRA ordination diagram. Figure S4.5: SBP SUCRA ordination diagramFigure S4.6: DBP SUCRA ordination diagram. Figure S4.7: HDL-C SUCRA ordination diagram. Figure S4.8: LDL-C SUCRA ordination diagram. Figure S4.9: TG SUCRA ordination diagram. Figure S4.10: TC SUCRA ordination diagram. Figure S5.1: FPG funnel plot of publication bias. Figure S5.2: HbA1c funnel plot of publication bias. Figure S5.3: BMI funnel plot of publication bias. Figure S5.4: WC funnel plot of publication bias. Figure S5.5: SBP funnel plot of publication bias. Figure S5.6: DBP funnel plot of publication bias. Figure S5.7: HDL-C funnel plot of publication bias. Figure S5.8: LDL-C funnel plot of publication bias. Figure S5.9:TG funnel plot of publication bias. Figure S5.10: TC funnel plot of publication bias. Figure S6.1: FPG Network Evidence Plot with Risk of Bias. Figure S6.2: HbA1c Network Evidence Plot with Risk of Bias. Figure S6.3: BMI Network Evidence Plot with Risk of Bias. Figure S6.4: WC Network Evidence Plot with Risk of Bias. Figure S6.5: SBP Network Evidence Plot with Risk of Bias. Figure S6.6: DBP Network Evidence Plot with Risk of Bias. Figure S6.7: HDL-C Network Evidence Plot with Risk of Bias. Figure S6.8: LDL-C Network Evidence Plot with Risk of Bias. Figure S6.9: TG Network Evidence Plot with Risk of Bias. Figure S6.10: TC Network Evidence Plot with Risk of Bias

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Li, X., Gao, M. & Hua, J. Comparative efficacy of various mind-body exercise types on cardiometabolic health in patients with type 2 diabetes: a network meta-analysis of randomized controlled trials. BMC Cardiovasc Disord 25, 291 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12872-025-04745-1

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BMC Cardiovascular Disorders

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