Time-dependent metabolic remodeling following combined lifestyle intervention in the high-risk T2DM spectrum: A meta-analysis of randomized controlled trials

Time-dependent metabolic remodeling following combined lifestyle intervention in the high-risk T2DM spectrum: A meta-analysis of randomized controlled trials | 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 Time-dependent metabolic remodeling following combined lifestyle intervention in the high-risk T2DM spectrum: A meta-analysis of randomized controlled trials Hao-Xiang LI, Yuan LI, Xin-Di LYU, Rui-Qi LIU, Bing-Hong GAO This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9376778/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction : Exploring early interventions for glucose metabolism disorders is vital. This study evaluated the impact of combined aerobic exercise and dietary interventions on fasting blood glucose (FBG) and body mass index (BMI) in high-risk populations compared to routine care. Methods Following PRISMA 2020 guidelines, we searched six core databases (including PubMed and Web of Science) for RCTs through January 2026. Using a random-effects model, we synthesized mean differences (MD) based on pre- and post-intervention changes to assess therapeutic effects. Results Fifteen RCTs from 12 studies were included. The intervention significantly reduced BMI (MD = -1.62 kg/m², 95% CI: -2.23 to -1.02, P < 0.001). FBG showed an improving trend (MD = -0.08 mmol/L, 95% CI: -0.17 to 0.01, P = 0.085). Subgroup analysis revealed that the metabolic syndrome/insulin resistance (MetS/IR) group was the primary beneficiary for glycemic control (MD = -0.19 mmol/L, P < 0.001), the PCOS population showed the most robust BMI improvement (MD = -2.64 kg/m², P < 0.001), while its FBG improvement did not reach statistical significance (P = 0.374). Additionally, interventions under 3 months demonstrated stronger weight-loss tendencies. Meta-regression suggested age potentially regulates FBG improvement (P = 0.071). Conclusions The structured lifestyle intervention demonstrates a robust time-dependent efficacy, significantly optimizing BMI in high-risk individuals and providing precision glycemic benefits specifically for the MetS/IR subgroup. These findings support dual-track lifestyle prescriptions as a robust first-line approach for precise metabolic management and early prevention of type 2 diabetes. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/home, identifier: CRD420261295480. Health sciences/Diseases Health sciences/Endocrinology Health sciences/Health care Health sciences/Medical research Aerobic exercise Dietary intervention High-risk profile of abnormal glucose metabolism Body mass index Effectiveness of combined intervention Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1 Introduction The global prevalence of abnormal glucose metabolism is rising exponentially due to rapid lifestyle transitions and the acceleration of population aging 1 – 5 . According to the American Diabetes Association (ADA) and Chinese Diabetes Society (CDS), conditions including elevated fasting blood glucose (FBG), insulin resistance (IR), and metabolic syndrome (MetS) are now recognized as critical "window periods" for the early prevention of type 2 diabetes mellitus (T2DM) and cardiovascular complications. Beyond traditional prediabetes, this high-risk spectrum encompasses specific clinical phenotypes such as polycystic ovary syndrome (PCOS) and a history of gestational diabetes mellitus (GDM) 6 , 7 . While these conditions present with diverse clinical manifestations—ranging from reproductive endocrine disorders in PCOS to transient glucose intolerance in pregnancy—they are anchored by a shared underlying pathophysiology: an imbalance in insulin compensatory mechanisms and chronic low-grade inflammation. Elevated Body Mass Index (BMI) acts as a potent driver across this entire spectrum, where excess adiposity triggers adipose tissue dysfunction and disrupts lipid homeostasis, further exacerbating the "insulin-resistance cycle" 8,9 . Integrating these heterogeneous populations into a unified "high-risk spectrum" for analysis is scientifically justified by their common metabolic vulnerability. Establishing a quantifiable lifestyle intervention framework that simultaneously addresses adiposity and glucose homeostasis is therefore a global clinical priority for managing this high-risk metabolic spectrum 10 , 11 . Lifestyle intervention—specifically aerobic exercise and dietary modification—is the cornerstone of metabolic management 12 , 13 . Aerobic exercise enhances insulin sensitivity and glucose uptake 14 , while dietary intervention regulates energy intake and reduces metabolic load 15 , 16 . A dual-track approach aims to restore metabolic balance by simultaneously addressing energy expenditure and intake, theoretically offering superior outcomes compared to standard care. However, the effectiveness of single-mode interventions varies based on intensity and compliance, leading to inconsistencies in long-term stability 17 , 18 . Recent meta-analyses have largely focused on single intervention forms or isolated metabolic indicators 18 , 19 . There is a notable lack of systematic evidence quantifying the total efficacy of a structured dual-track program (exercise plus diet) compared directly to routine clinical care within the diverse T2DM high-risk spectrum. This study employs a meta-analysis to evaluate the impact of this combined approach on FBG and BMI. By identifying response gradients across subgroups, we aim to provide evidence-based references for Precision Lifestyle Medicine in early metabolic prevention. 2 Method 2.1 Registration This study followed PRISMA 2020 and Cochrane guidelines 20 , 21 . The protocol is registered on PROSPERO (CRD420261295480). 2.2 Search strategy Six databases (PubMed, SCOPUS, Web of Science, Cochrane Library, Embase, and Google Scholar) were searched through January 2026 for English-language publications. Strategies combined MeSH terms and keywords regarding glycemic indicators, aerobic exercise, diet, and RCTs. For Google Scholar, the top 300 relevance-sorted results were screened. Reference lists were manually reviewed, and all records were managed in Zotero. The PubMed strategy is in Supplementary Material Table S1 . 2.3 Inclusion and exclusion criteria Inclusion (PICOS): (P) Adults (≥ 18 years) with abnormal glucose metabolism; (I) Combined aerobic exercise and dietary intervention; (C) Routine care; (O) Fasting blood glucose and BMI; (S) RCTs. Exclusion: Non-RCTs, non-original studies (reviews/meta-analyses), studies with missing data, or interventions involving resistance training or lacking dietary components. 2.4 Screening and data extraction To ensure objectivity and minimize bias, two researchers independently performed the literature screening and data extraction, with any discrepancies resolved through consultation with a third researcher. The extracted information encompassed basic study details such as the first author and publication year, alongside the specific disease types of the targeted populations. Participant demographics recorded included sample size and average age. Regarding the intervention protocols, researchers collected comprehensive data on the specific intervention measures, weekly frequency, duration per session, and the overall intervention cycle. For the primary outcomes, the mean and standard deviation of fasting blood glucose (FBG) and body mass index (BMI) were extracted for both experimental and control groups at both baseline and the end of the intervention. In cases where studies reported results at multiple time points, data from the final assessment were prioritized to ensure consistency in the meta-analysis. 2.5 Quality assessment The methodological quality of the included randomized controlled trials was systematically evaluated using the Cochrane Risk of Bias 2.0 (RoB 2.0) tool 21 . This assessment framework explores potential bias across five critical domains to ensure the internal validity of the evidence. First, we evaluated bias arising from the randomization process, focusing on the adequacy of random sequence generation and allocation concealment. Second, we examined deviations from intended interventions, acknowledging that double-blinding is often structurally unfeasible in lifestyle programs involving aerobic exercise and dietary modification. Third, bias due to missing outcome data was assessed to determine if participant dropouts were balanced or adequately handled. Fourth, measurement of the outcome was evaluated, focusing on the standardization of biochemical (FBG) and physiological (BMI) assessments. Finally, the potential for selection of the reported result was scrutinized by comparing published findings against pre-defined protocols or clinical trial registries. 2.6 Statistical analysis Meta-analysis was conducted using Python 22 . For continuous outcomes, the mean difference (MD) between the change scores from baseline to post-intervention in the experimental and control groups was utilized as the primary effect size 23 , This method effectively eliminates baseline level differences and account for the natural course of the disease. When the standard deviation (SD) of the change was not directly reported, it was estimated using the formula provided by the Cochrane Handbook 21 : $$\:\text{S}{\text{D}}_{\text{c}\text{h}\text{ange}}\text{=}\sqrt{\text{S}{\text{D}}_{\text{baseline}}^{\text{2}}\text{+}\text{S}{\text{D}}_{\text{final}}^{\text{2}}\text{-(2×}\text{R}\text{×}\text{S}{\text{D}}_{\text{baseline}}\text{×}\text{S}{\text{D}}_{\text{final}}\text{)}}$$ where the correlation coefficient (R) was set at 0.5, representing a conservative estimate of the relationship between measurements. Heterogeneity among studies was quantified using the I² statistic 24 . Given the anticipated high degree of clinical heterogeneity (e.g., diverse disease types within the high-risk spectrum and varying intervention cycles) and methodological heterogeneity (e.g., differences in intervention intensity and supervision), a random-effects model was prioritized for all analyses 25 – 27 . This model assumes that the true effect size varies across studies, providing a more generalizable estimate for the diverse populations at risk for type 2 diabetes. Meta-regression was employed to assess the potential regulatory effect of age on FBG changes, and sensitivity analysis was performed via the leave-one-out method to test the robustness of the pooled results. Statistical significance was set at P < 0.05. 3 Results 3.1 Study selection Initial searches across five databases (PubMed, SCOPUS, Web of Science, Cochrane, and Embase) yielded 5,723 records. After removing duplicates, 1,893 papers underwent title and abstract screening. Studies were excluded if they were non-RCTs, lacked dietary interventions, or combined multiple exercise types, leaving 261 for full-text evaluation. Additionally, the top 300 Google Scholar results were screened using similar criteria, with 95 selected for full-text review. Following a rigorous full-text assessment—which excluded records based on ineligible outcome indicators, study design, missing data, or participant age—12 articles comprising 15 randomized controlled trials (RCTs) were ultimately included. All 15 RCTs provided data for fasting blood glucose (FBG) meta-analysis, while 11 provided sufficient data for quantitative BMI analysis (the remaining 4 were used for qualitative description). The screening process is detailed in Fig. 1 . 3.2 Study characteristics and literature quality evaluation The twelve included articles(Table 1 ), encompassing fifteen randomized controlled trials (RCTs), focused on a diverse range of high-risk metabolic populations 28 – 39 . A defining methodological feature of these trials was the exclusive focus on aerobic exercise as the sole physical activity component, with interventions ranging from structured walking and supervised cycle ergometry to yoga and individualized training at maximal fat oxidation. To maintain cohort homogeneity, studies incorporating resistance training or combined exercise modalities were strictly excluded. Table 1 Characteristics of the Included Randomized Controlled Trials (n = 15) Author Year Group Sample Size Age (Year) Intervention measures Dietary Intervention Type Weekly Frequency Duration (per session/week) Intervention Cycle Outcome Indicators Wong et al. 28 2021 EXP 33 57.42 ± 6.43 Mobile app-based lifestyle program (diet + aerobic walking) Structured dietary patterns 5 times/week ≥ 30 min 3 Months FBG、BMI Ctrl 35 60.45 ± 7.49 Slentz et al. 29 2016 EXP 37 57.6 ± 8.1 Diet + moderate-intensity aerobic exercise Macronutrient-specific NR 175 ± 30 min/week 6 Months FBG Ctrl 35 56.9 ± 7.8 Yilmaz et al. 30 2013 EXP 32 52.5 ± 7.7 Walking + dietary modification Behavioral motivational 1 time/week NR 3 Months FBG Ctrl 17 51.7 ± 8.4 Atkinson et al. 31 2022 EXP 102 31.6 ± 3.9 Nutrition counseling + walking Nutritional education Daily ND 9 Months FBG Ctrl 105 31.3 ± 4.3 McAuley et al. 32 2002 EXP 29 48 ± 7.5 Moderate-intensity aerobic exercise + diet Intensive aerobic exercise + diet Macronutrient-specific 5 times/week 5 times/week 30min ≥ 20min 4 Months FBG、BMI EXP 25 46 ± 8.5 Ctrl 23 45 ± 9.5 Cai et al. 33 2019 EXP 214 66.84 ± 5.32 Community-based lifestyle intervention Nutritional education ≥ 5 times/week Cumulative ≥ 150 min/week 24 Months FBG Ctrl 208 66.86 ± 4.73 Holmes et al. 34 2018 EXP 20 34.2 ± 4.3 PAIGE lifestyle intervention (diet + walking) Behavioral motivational ≥ 5 times/week Cumulative ≥ 150 min/week 6 Months FBG、BMI Ctrl 25 33.2 ± 5.3 Besnier et al. 35 2015 EXP 33 30.5 ± 5.9 Aerobic training (fat-max intensity) + diet Aerobic training (60% VO2max) + diet Structured dietary patterns 4 times/week 4 times/week 55min 35min 5 Months FBG、BMI EXP 35 29 ± 4.9 Ctrl 35 30.9 ± 5.8 Chen et al. 36 2017 EXP 38 51.47 ± 8.64 ABC empowerment + structured exercise Nutritional education 2 times/week 120min 4 Months FBG、BMI Ctrl 40 50.78 ± 8.93 Orio et al. 37 2016 EXP 39 25.9 ± 2.7 Structured aerobic exercise training Macronutrient-specific 3 times/week 45min 6 Months FBG、BMI Ctrl 50 26 ± 2.6 Palomba et al. 38 2010 EXP 32 27.5 ± 4.95 Structured exercise training + low-calorie diet Structured exercise training + low-calorie diet Macronutrient-specific 3 times/week 3 times/week 30min 30min 1.5 Months FBG、BMI EXP 32 28.43 ± 8.31 Ctrl 32 26.5 ± 4.26 ELBanna et al. 39 2024 EXP 26 24.8 ± 2.3 Mediterranean diet + probiotics + yoga Structured dietary patterns Daily 60 min 3 Months FBG、BMI Ctrl 26 25.6 ± 2.2 Regarding nutritional strategies, the interventions were distributed across three primary models: four studies implemented macronutrient-specific modulation, such as high-protein or low-fat hypocaloric diets; three trials adopted structured dietary patterns including Mediterranean or DASH-oriented protocols; and the remaining five studies utilized broader educational or behavioral frameworks based on national guidelines and empowerment models. 3.3 Risk of Bias Assessment Using the Cochrane ROB 2.0 tool, 15 RCTs were evaluated across five dimensions (Fig. 2 ). For the randomization process, half the studies (e.g., Chen 2017, Orio 2016) showed low risk, while others like Slentz (2016), Atkinson (2022), and ELBanna (2024) raised "some concerns" due to vague sequence generation or allocation descriptions. Regarding deviations from intended interventions, most studies had "some concerns" as double-blinding is unfeasible in diet/exercise trials; Yilmaz (2013) and Wong (2021) were rated high risk due to poor compliance or protocol deviations. Missing outcome data risk was generally low, except for Yilmaz (2013) (high risk due to dropouts) and Cai (2019) (some concerns). For outcome measurement, the objectivity of FBG and BMI ensured low risk for most, except Wong (2021), which utilized remote monitoring. Regarding selective reporting, one-third of studies showed "some concerns" due to missing clinical trial registrations or protocols. Overall, the studies demonstrate medium methodological quality, primarily presenting "some concerns" with isolated high-risk cases (e.g., Yilmaz 2013), maintaining an acceptable range for evidence-based practice. 3.4 Meta-analysis results of fasting blood glucose Fifteen RCTs (n = 1448) were pooled using a random-effects model (Fig. 3 ). The combined intervention showed a trend toward FBG reduction with a mean difference (MD) of -0.08 mmol/L (95% CI: -0.17 to 0.01, P = 0.085). Heterogeneity was moderately high (I² = 61.74%, P = 0.001). Funnel plots (Fig. 4 ) and Egger’s regression (P = 0.098) suggested no significant publication bias. Sensitivity analysis via the leave-one-out method (Supplementary Figure S1 –S2) indicated that the exclusion of specific studies, such as Yilmaz et al. (2013), could shift the results to statistical significance. 3.5 FBG subgroup analysis and Meta regression Subgroup analysis (Fig. 5 ) revealed that the MetS/IR population benefited most significantly (MD = -0.19 mmol/L, P < 0.001, I² = 0%), whereas GDR and PCOS populations showed no significant improvements. Regarding the intervention cycle (Fig. 6 ), short-term programs (≤ 3 months) presented a stronger improvement trend than those exceeding 3 months. Random-effects Meta-regression (Fig. 7 ) showed that age might negatively impact FBG changes (β = -0.0057, P = 0.071), explaining 12.18% of the residual heterogeneity. 3.6 Meta-analysis results of Body Mass Index Eleven RCTs involving 706 participants were included in the quantitative analysis (Fig. 8 ). The combined intervention significantly reduced BMI compared to routine care (MD = -1.62 kg/㎡, 95% CI: -2.23 to -1.02, P < 0.001). Despite high heterogeneity (I² = 87.34%), sensitivity analysis (Supplementary Figures S3–S4) confirmed the robustness of this finding. However, the funnel plot (Fig. 9 ) and Egger’s test (P = 0.003) indicated a potential risk of publication bias. 3.7 Subgroup analysis of BMI Subgroup analysis by disease type (Fig. 10 ) demonstrated that the PCOS population achieved the most significant weight loss (MD = -2.64 kg/㎡, P < 0.001, I² = 50.28%). When categorized by cycle (Fig. 11 ), both short-term (MD = -2.58, P = 0.019) and long-term (MD = -1.14, P = 0.001) interventions significantly reduced BMI, though short-term interventions showed a more pronounced numerical trend. 3.8 GRADE Evidence Quality Assessment The certainty of evidence for the primary outcomes was systematically evaluated using the GRADE framework (Table 2 ). For fasting blood glucose (FBG), the evidence was categorized as "Very low quality." This assessment primarily reflects the inherent methodological constraints of non-pharmacological interventions, where double-blinding is structurally unfeasible, leading to "some concerns" in the risk of bias assessment. Inconsistency was noted due to moderate heterogeneity (I² = 61.74%), and the evidence was further downgraded for imprecision as the 95% CI for the overall effect (-0.17 to 0.01 mmol/L) crossed the null threshold. Table 2 Summary of Findings Outcome No. Of RTCs Participants Effect(MD,95% CI) I² Certainty FBG(mmol/L) 15 1448 -0.08(-0.17,0.01) 61.74% Very Low BMI(kg/㎡) 11 706 -1.62(2.23,-1.02) 87.34% Low For body mass index (BMI), the evidence was rated as "Low quality." While the intervention demonstrated a highly significant and robust clinical effect (P < 0.001), the quality rating was affected by high statistical heterogeneity (I² = 87.34%) and potential publication bias. However, it is noteworthy that despite these global downgrades, the subgroup analyses revealed high evidentiary stability in specific populations. For instance, the MetS/IR subgroup exhibited a significant glycemic benefit with zero heterogeneity (I² = 0%, P < 0.001), and the PCOS population showed the most substantial BMI reduction. These findings suggest that while the "overall" evidence is affected by the diverse nature of high-risk populations, the targeted clinical gains for specific subgroups remain highly credible. 4 Discussion This systematic review and meta-analysis conducted a comprehensive integration of evidence from randomized controlled trials (RCTs) to deeply explore the metabolic regulatory efficacy of combined aerobic exercise and dietary intervention for individuals at high risk of abnormal glucose metabolism. The study confirmed that this combined approach demonstrated a significant evidence-based advantage in improving BMI (MD = -1.62). Although the combined effect size of fasting blood glucose (FBG) was marginally significant in statistical terms (P = 0.085), a highly consistent and significant benefit was observed in the subgroup with more severe metabolic impairment (MetS/IR) (P < 0.001, I² = 0%), revealing the clinical value of this preventive approach in implementing precise intervention during the early window of metabolic disorders. 4.1 Main research findings This meta-analysis based on an inter-group comparison design indicates that the combined effect of aerobic exercise and dietary intervention on BMI is highly robust (MD = -1.62). Regarding glycemic outcomes, our analysis reveals a distinct "metabolic response gradient." While the overall population effect size only shows a trend towards improvement (P = 0.085), the high-risk MetS/IR subgroup exhibits a robust and stable glycemic recovery, indicating that lifestyle-induced glucose stabilization is highly phenotype-specific. By evaluating the combined intervention against routine care, this study demonstrates that an integrated lifestyle approach offers a potent therapeutic advantage over standard clinical advice, potentially grounded in the biological complementarity of exercise-induced insulin sensitivity and dietary glucose regulation. This underscores a critical clinical insight: a structured, dual-track lifestyle modification provides a superior metabolic defense that significantly exceeds the efficacy thresholds of non-specific routine guidance. This elucidates the core clinical utility: a multi-dimensional lifestyle modification provides a superior metabolic defense layer that exceeds the efficacy of standard clinical advice, likely by addressing multiple insulin resistance pathways through a unified protocol. The significant weight loss and the simultaneous hypoglycemic effect in specific subgroups provide a solid evidence-based foundation for combined lifestyle management as a first-line intervention strategy for high-risk populations of type 2 diabetes. 4.2 Extensive comparison with previous studies and uniqueness Previous meta-analyses have mostly focused on a single disease or a single intervention mode 40 – 43 . This study demonstrates significant uniqueness based on the existing research: This study, based on the most rigorous definition of "high-risk population for type 2 diabetes" by ADA 11 , conducted the first meta-analysis focusing on the holistic impact of combined aerobic exercise and dietary intervention for the high-risk population of type 2 diabetes that includes PCOS, MetS, and history of gestational diabetes. This integrated perspective proves that although the clinical phenotypes are different, the uniformity based on the underlying pathological mechanism (insulin compensatory imbalance) indicates that "aerobic exercise + diet" can serve as a cross-disciplinary universal early intervention plan. Furthermore, compared to studies that explore the impact of specific nutritional strategies on patients who have already been diagnosed, this research precisely quantifies the comprehensive impact of the combined approach relative to routine care by extracting the mean difference between groups. This methodological approach provides a quantifiable reference for the clinical formulation of precise preventive prescriptions by demonstrating the superiority of a dual-track lifestyle intervention over standard care. 4.3 Subgroup Analysis and In-depth Exploration of Physiological Mechanisms 4.3.1 Population-specificity and Intervention Sensitivity Characteristics The subgroup analysis revealed a distinct "response gradient" across different metabolic profiles, where the MetS/IR subgroup exhibited extremely high stability in the improvement of FBG (MD = -0.19, P < 0.001, I² = 0%), while the PCOS subgroup demonstrated the most robust reduction in BMI (MD = -2.64, P < 0.001). This divergent sensitivity reflects the unique pathological priorities of each population. For individuals with MetS/IR, the primary metabolic defect lies in impaired "metabolic flexibility," where the accumulation of visceral adiposity drives systemic insulin resistance. The structured combination of aerobic exercise and dietary control directly targets this ectopic fat depot, thereby alleviating the secretory stress on pancreatic β-cells and restoring glucose homeostasis with high consistency, as evidenced by the zero heterogeneity in this subgroup. In contrast, the metabolic challenges in PCOS are intrinsically linked to a "vicious cycle" involving hyperandrogenism and follicular developmental arrest. The superior BMI reduction observed in the PCOS subgroup relative to routine care suggests that the synergistic effect of negative energy balance and aerobic-induced endocrine modulation is particularly effective at reducing the androgen pool and improving Sex Hormone-Binding Globulin (SHBG) levels. This addresses the central obesity characteristic of PCOS more effectively than non-structured clinical advice. Consequently, while MetS/IR patients are "highly sensitive" to glycemic optimization, PCOS patients appear "highly responsive" to body composition remodeling, providing a strong empirical basis for the implementation of subgroup-specific Precision Lifestyle Medicine. 4.3.2 Molecular Mechanisms of Integrated Metabolic Remodeling The observed clinical advantage of this approach is theoretically supported by the complementary metabolic repair pathways initiated at the cellular level. Although this analysis assesses the overall impact of the structured lifestyle program relative to routine care, rather than delineating the incremental contribution of its individual components, the observed clinical superiority is theoretically supported by the potential concurrent activation of multi-target pathways. The mechanistic superiority of this dual-track approach lies in its "dual-hit" modulation of molecular signaling pathways. Aerobic exercise significantly upregulates glucose uptake through insulin-independent secondary messengers 44 . During skeletal muscle contraction, the elevation of the intracellular AMP/ATP ratio triggers the phosphorylation of AMP-activated protein kinase (AMPK) 45 , 46 , Advanced mechanistic insights reveal that this process involves the phosphorylation of AS160 (Akt substrate of 160 kDa) 47 , a Rab-GTPase activating protein. This phosphorylation relieves the inhibition on GLUT4 storage vesicles, facilitating their rapid translocation to the sarcolemma 48 , 49 . Furthermore, the involvement of the Calcium/calmodulin-dependent protein kinase kinase 2 (CaMKK2) further reinforces contraction-induced metabolic remodeling, ensuring efficient glucose clearance even in states of profound insulin resistance 46 . Concurrently, dietary regulation reduces exogenous glucose loading and downregulates the MondoA-TXNIP axis, thereby alleviating the compensatory secretory stress on pancreatic β-cells 50 . This synergy between "increasing metabolic demand" (exercise) and "decreasing substrate burden" (diet) provides a robust molecular rationale for the clinical superiority of combined interventions over routine care. This contraction-induced translocation effect significantly enhances the clearance of circulating glucose in peripheral tissues, especially showing better compensatory benefits in individuals with impaired insulin sensitivity. Secondly, dietary regulation effectively alleviates the functional stress of pancreatic β cells that are chronically in a state of excessive secretion by limiting the intake of exogenous glucose, providing a crucial "metabolic rest" window for these cells 50 – 53 . This combined approach not only helps to reduce systemic chronic inflammatory indicators (down-regulating pro-inflammatory factors such as IL-6 and TNF-α), but also optimizes the secretion profile of adipokines 42 , 54 – 56 . Since a significant decrease in BMI is often accompanied by a reduction in visceral fat, this directly improves liver insulin sensitivity, thereby physiologically elucidating the inevitability of the synchronous occurrence of body weight management and glucose homeostasis remodeling. 4.3.3 The Impact of the Intervention Period The results of stratification by period show that short-term intervention (≤ 3 months) demonstrated a stronger numerical trend in BMI reduction (MD = -2.58). Our findings elucidate a critical temporal framework for metabolic adaptation, characterized as the "3-month Metabolic Switch Point," where the focus of the intervention shifts from rapid weight loss to deep homeostatic repair. During the initial 12 weeks of intervention, BMI reduction demonstrated superior numerical magnitude (MD = -2.58), primarily driven by the rapid depletion of adipose tissue under acute negative energy balance. However, glycemic improvements remain volatile during this early phase. As the intervention extends beyond the 3-month threshold, although the rate of weight loss stabilizes (MD = -1.14), the restoration of glucose homeostasis exhibits significantly higher evidentiary stability. This evidence supports a time-dependent trajectory of metabolic remodeling, progressing from initial "structural/volumetric reduction" (BMI loss) to subsequent "functional/homeostatic restoration" (glycemic control). Early weight loss provides the essential "metabolic buffer" required for the subsequent restoration of peripheral insulin sensitivity. Conversely, long-term sustained intervention (> 3 months) achieves deep-seated homeostatic repair, likely through the alteration of muscle fiber type distribution and gut microbiota composition 51 . Clinical practice should, therefore, implement phased objectives: prioritizing weight management during the first trimester to build patient adherence, while shifting the focus toward long-term metabolic rhythm maintenance beyond the 3-month mark. This suggests that long-term continuous intervention is the key to achieving the transformation from a simple "weight loss-oriented" approach to a deep "metabolic homeostasis restoration". 4.4 Comprehensive Explanation of the Sources of Heterogeneity This study observed a high degree of heterogeneity in the BMI outcome (I² = 87.34%). According to the TIDieR framework 57 . This heterogeneity stems from the variations in the intensity of intervention supervision, the application of digital media, and the initial obesity level of the subjects among different studies. Meta regression indicated that age has a potential regulatory effect on the improvement of FBG (P = 0.071), suggesting that there are biological differences in metabolic adaptability among different age groups. Although there is a certain publication bias, the high consistency of the effect directions of each study ensures the reference value of the clinical conclusion. Furthermore, it is essential to recognize that the modest GRADE ratings reported in this study primarily reflect the inherent clinical complexity and phenotypic diversity of populations at high risk for type 2 diabetes. Rather than undermining the validity of our findings, these ratings underscore the critical necessity of our stratified, subgroup-driven analytical approach. This strategy successfully identified specific high-responder cohorts, most notably the MetS/IR population, who derive precise, stable, and statistically robust benefits from combined aerobic and dietary interventions—benefits that are often obscured in generalized population-level analyses. 4.5 Clinical Practice Insights and the Unique Significance of This Study The advantage of this study lies in providing a quantifiable "precise navigation" for early metabolic prevention, addressing the limitations of the "one-size-fits-all" approach to lifestyle intervention in clinical practice. By identifying these time-dependent and phenotype-specific effects, this study provides a quantitative roadmap for Precision Lifestyle Medicine in early diabetes prevention. For the MetS/IR population, clinicians should prioritize "glycemic control-centric" prescriptions, leveraging their heightened sensitivity to combined interventions (MD = -0.19, P < 0.001) as a first-line defense against T2DM progression 56 . For PCOS patients, the therapeutic focus should shift toward "compositional optimization," utilizing the robust weight loss response (MD = -2.64) to disrupt the vicious cycle involving obesity, hyperandrogenism, and insulin resistance. Furthermore, we advocate for the integration of digital monitoring tools, such as Continuous Glucose Monitoring (CGM), into lifestyle prescriptions to capture real-time metabolic feedback. This enables a paradigm shift from "generalized advice" to "individualized precision intervention." Such a stratified management model, based on specific subgroup responses, not only enhances the efficiency of healthcare resource allocation but also substantially reduces the long-term complication burden in high-risk metabolic profiles. Secondly, the quantified MD value of -1.62 in this study does not merely indicate a decline in the indicator. Instead, it represents the quantifiable clinical gain of the combined lifestyle approach compared to routine standard care, providing straightforward evidence for clinicians to implement a structured dual-track lifestyle prescription as a unified intervention instead of relying on non-specific routine guidance. Finally, the "3-month metabolic switching point" defined in the study guides the clinical setting of intervention priorities in a phased manner - that is, focusing on weight loss motivation in the early stage and on restoring homeostasis in the medium and long term. 4.6 Limitations and Future Research Directions This study has certain limitations. Due to the nature of lifestyle interventions, double-blinding was unfeasible, and small sample sizes in specific subgroups contributed to the observed heterogeneity in BMI. Additionally, the exclusion of resistance training (RT) may limit the assessment of skeletal muscle as a "metabolic sink," particularly for individuals with sarcopenic obesity where aerobic exercise alone may not fully reverse muscle atrophy 42 , 44 . Despite these constraints, we employed the mean difference (MD) between groups and rigorous methodology to minimize bias from the natural disease course, ensuring the scientific robustness of our findings for early metabolic intervention. Future research should prioritize multi-arm clinical trials to delineate the independent and synergistic effects of combined "Aerobic + Resistance" protocols, which may offer superior protection for glucose homeostasis and muscle mass in elderly populations 43 . Furthermore, exploring optimal combinations of exercise intensities and nutritional patterns, alongside digital monitoring of long-term sustainability, will be essential to comprehensively evaluate the value of these prescriptions in blocking diabetes progression. 4.7 Conclusion In conclusion, aerobic exercise combined with dietary intervention can significantly improve the BMI levels of individuals at high risk of type 2 diabetes, and demonstrate clear benefits in controlling blood sugar in the MetS/IR population with significant metabolic risks. This comprehensive intervention model holds important clinical evidence-based value in advancing the prevention of type 2 diabetes at the early stage. Declarations Disclosure The authors declare that they have no potential conflicts of interest, financial or otherwise, relevant to the content of this manuscript. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution All authors have made substantial contributions to the study and approved the final version of the manuscript. Hao-Xiang Li: Conceptualization, methodology, software, visualization, and writing – original draft. Yuan Li: Supervision, validation, and writing – review & editing. Xin-Di Lyu: Formal analysis, data curation, and writing – review & editing. Rui-Qi Liu: Data curation, and writing – review & editing. Bing-Hong Gao: Conceptualization, project administration, supervision, and writing – review & editing. Acknowledgement We would like to thank the authors of the original studies included in this meta-analysis. We also acknowledge the use of searchRxiv for archiving our search strategies to support research transparency. Data Availability The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. 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Prevention or Delay of Diabetes and Associated Comorbidities: Standards of Care in Diabetes—2026. Amerkamp, J., Benli, S., Isenmann, E. & Brinkmann, C. Optimizing the lifestyle of patients with type 2 diabetes mellitus - Systematic review on the effects of combined diet-and-exercise interventions. Nutr. Metab. Cardiovasc. Dis. 35 , 103746 (2025). Lee, M. S., Pittler, M. H., Kim, M. S. & Ernst, E. Tai chi for Type 2 diabetes: a systematic review. Diabet. Med. 25 , 240–241 (2008). Umpierre, D. Physical Activity Advice Only or Structured Exercise Training and Association With HbA 1c Levels in Type 2 Diabetes: A Systematic Review and Meta-analysis. JAMA 305 , 1790 (2011). ElSayed, N. A. et al. 1. Improving Care and Promoting Health in Populations: Standards of Care in Diabetes—2023 . Diabetes Care 46, S10–S18 (2023). Moon, J. & Koh, G. Clinical Evidence and Mechanisms of High-Protein Diet-Induced Weight Loss. J. Obes. Metab. Syndr. 29 , 166–173 (2020). Barrile, S. R. et al. Efeito agudo do exercício aeróbio na glicemia em diabéticos 2 sob medicação. Rev. Bras. Med. Esporte . 21 , 360–363 (2015). Zhang, X. et al. Effect of lifestyle interventions on glucose regulation among adults without impaired glucose tolerance or diabetes: A systematic review and meta-analysis. Diabetes Res. Clin. Pract. 123 , 149–164 (2017). Snowling, N. J. & Hopkins, W. G. Effects of Different Modes of Exercise Training on Glucose Control and Risk Factors for Complications in Type 2 Diabetic Patients. Diabetes Care . 29 , 2518–2527 (2006). Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ n71 (2021). 10.1136/bmj.n71 Higgins, J. P. T. et al. (eds) Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane; (2023). Available from: Introduction to Meta-Analysis . (Wiley, Chichester, (2013). Becker, B. J. Synthesizing standardized mean-change measures. Br. J. Math. Stat. 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Effects of exercise training alone vs a combined exercise and nutritional lifestyle intervention on glucose homeostasis in prediabetic individuals: a randomised controlled trial. Diabetologia 59 , 2088–2098 (2016). Yilmaz, S. & Unal, B. Effectiveness of Interventions Related to Nutrition and Physical Activity in Women with Impaired Fasting Glucose: A Randomized Controlled Community Trial. Atkinson, S. A. et al. Be Healthy in Pregnancy (BHIP): A Randomized Controlled Trial of Nutrition and Exercise Intervention from Early Pregnancy to Achieve Recommended Gestational Weight Gain. Nutrients 14 , 810 (2022). McAuley, K. A. et al. Intensive lifestyle changes are necessary to improve insulin sensitivity: a randomized controlled trial. Diabetes Care . 25 , 445–452 (2002). Cai, R. et al. Effect of community-based lifestyle interventions on weight loss and cardiometabolic risk factors in obese elderly in China: A randomized controlled trial. Exp. Gerontol. 128 , 110749 (2019). Holmes, V. A. et al. Postnatal Lifestyle Intervention for Overweight Women With Previous Gestational Diabetes: A Randomized Controlled Trial. J. Clin. Endocrinol. Metab. 103 , 2478–2487 (2018). Besnier, F. et al. Individualized Exercise Training at Maximal Fat Oxidation Combined with Fruit and Vegetable-Rich Diet in Overweight or Obese Women: The LIPOXmax-Réunion Randomized Controlled Trial. PLOS ONE . 10 , e0139246 (2015). Chen, M. F., Hung, S. L. & Chen, S. L. Empowerment Program for People With Prediabetes: A Randomized Controlled Trial. J. Nurs. Res. 25 , 99–111 (2017). Orio, F. et al. Oral contraceptives versus physical exercise on cardiovascular and metabolic risk factors in women with polycystic ovary syndrome: a randomized controlled trial. Clin. Endocrinol. (Oxf) . 85 , 764–771 (2016). Palomba, S. et al. Six weeks of structured exercise training and hypocaloric diet increases the probability of ovulation after clomiphene citrate in overweight and obese patients with polycystic ovary syndrome: a randomized controlled trial. Hum. Reprod. 25 , 2783–2791 (2010). ELBanna, M. M., Kamel, D. M., Shaaban, A., Botla, A. M. & Hamoda, R. E. Effect of Yoga Training and High Probiotic Food Supplements on Insulin-Resistance in Polycystic-Ovarian-Syndrome: A Randomized Controlled Trial. Int. J. Kinesiol. Sports Sci. 12 , 52–61 (2024). Chatzi, C. A. et al. Effect of Different Dietary Patterns on Cardiometabolic Risk Factors: An Umbrella Review of Systematic Reviews and Meta-Analyses. Nutrients 16 , 3873 (2024). Galván, B. et al. Effectiveness of Physical Exercise on Indicators of Metabolic Syndrome in Adults: A Systematic Review with Meta-Analysis of Clinical Trials. J. Funct. Morphol. Kinesiol. 10 , 244 (2025). Ma, J. C. et al. Intervention effect of combined resistance and aerobic exercise on type 2 diabetes: A meta-analysis. World J. Diabetes 16 , (2025). Soltani, S. et al. The Effect of Aerobic or Resistance Exercise Combined With a Low-Calorie Diet Versus Diet Alone on Cardiometabolic Outcomes in Adults with Overweight and Obesity: A Systematic Review and Meta-Analysis. Nutr. Rev. 84 , 693–705 (2026). Nishikawa, H., Asai, A., Fukunishi, S., Nishiguchi, S. & Higuchi, K. Metabolic Syndrome and Sarcopenia. Nutrients 13 , 3519 (2021). Richter, E. A., Hargreaves, M. & Exercise GLUT4, and Skeletal Muscle Glucose Uptake. Physiol. Rev. 93 , 993–1017 (2013). Negoita, F. et al. CaMKK2 is not involved in contraction-stimulated AMPK activation and glucose uptake in skeletal muscle. Mol. Metab. 75 , 101761 (2023). Shrestha, M. M., Lim, C. Y., Bi, X., Robinson, R. C. & Han, W. Tmod3 Phosphorylation Mediates AMPK-Dependent GLUT4 Plasma Membrane Insertion in Myoblasts. Front. Endocrinol. 12 , 653557 (2021). Zhang, C. et al. AMPK/AS160 mediates tiliroside derivatives-stimulated GLUT4 translocation in muscle cells. Drug Des. Devel Ther. 12 , 1581–1587 (2018). Niu, X., Wang, K., Li, L. & Zhang, L. Exercise therapy for gestational diabetes mellitus: from basic to clinical. Front. Med. 12 , 1716883 (2025). Sadeghian-Renani, N. et al. Ameliorating Prediabetes Symptoms via Aerobic Exercise Combined with Green Coffee and Chlorogenic Acid Intake by Targeting MondoA-TXNIP/ARRDC4-GLUT4 Axis inHigh Fat Diet-Induced Prediabetic Mice. Preprint at (2023). https://doi.org/10.21203/rs.3.rs-2738434/v1 Li, L. & Tang, L. Gut Microbiota in Exercise-Regulated Development, Progression, and Management of Type 2 Diabetes Mellitus: A Review of the Role and Mechanisms. Med Sci. Monit 31 , (2025). Hengist, A. et al. Ketogenic diet but not free-sugar restriction alters glucose tolerance, lipid metabolism, peripheral tissue phenotype, and gut microbiome: RCT. Cell. Rep. Med. 5 , 101667 (2024). González-Blázquez, R. et al. Short‐term dietary intervention improves endothelial dysfunction induced by high‐fat feeding in mice through upregulation of the AMPK‐CREB signaling pathway. Acta Physiol. 239 , e14023 (2023). Legaard, G. E. et al. Effects of different doses of exercise and diet-induced weight loss on beta-cell function in type 2 diabetes (DOSE-EX): a randomized clinical trial. Nat. Metab. 5 , 880–895 (2023). Long, Y. et al. Effects of a vegetarian diet combined with aerobic exercise on glycemic control, insulin resistance, and body composition: a systematic review and meta-analysis. Eat. Weight Disord - Stud. Anorex Bulim Obes. 28 , 9 (2023). Fahed, G. et al. Metabolic Syndrome: Updates on Pathophysiology and Management in 2021. Int. J. Mol. Sci. 23 , 786 (2022). Chen, M. et al. The Effect of Lifestyle Intervention on Diabetes Prevention by Ethnicity: A Systematic Review of Intervention Characteristics Using the TIDieR Framework. Nutrients 13 , 4118 (2021). Additional Declarations No competing interests reported. Supplementary Files SupplementaryAppendix.docx PRISMA2020checklist.docx Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9376778","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":628725953,"identity":"c071ef93-bdab-468c-a514-370f6b57f81d","order_by":0,"name":"Hao-Xiang LI","email":"","orcid":"","institution":"Macao Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Hao-Xiang","middleName":"","lastName":"LI","suffix":""},{"id":628725954,"identity":"698bbdd7-221f-428a-8781-6aaaff1a3f4b","order_by":1,"name":"Yuan LI","email":"","orcid":"","institution":"Macao Polytechnic 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bias assessment of included randomized controlled trials based on the ROB 2.0 tool.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9376778/v1/092a08c17df3bc654726451c.png"},{"id":108013068,"identity":"93d9d725-f763-4ad0-93a3-b1145622d4a6","added_by":"auto","created_at":"2026-04-28 13:17:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139296,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the pooled mean difference (MD) in fasting blood glucose (FBG) based on the difference in pre–post changes between groups\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9376778/v1/064cbb27a3dfaad421a54c90.png"},{"id":108181680,"identity":"c2ed8787-89a0-498c-9761-b9f00a025aef","added_by":"auto","created_at":"2026-04-30 08:58:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33647,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot for assessing publication bias in fasting blood glucose\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9376778/v1/0db47ca798447c4398d043b8.png"},{"id":108013014,"identity":"e451cfc1-84fe-49e1-8b9d-c0b68f3e3865","added_by":"auto","created_at":"2026-04-28 13:17:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":165117,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of fasting blood glucose by disease category\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9376778/v1/daadaf3630b73c410c525f9c.png"},{"id":108013001,"identity":"c1a8ec11-c797-435d-b62c-0fc8f596a694","added_by":"auto","created_at":"2026-04-28 13:17:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":156474,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of fasting blood glucose by intervention duration\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9376778/v1/03e0d9b091e776c2fdd685bb.png"},{"id":108012907,"identity":"4eb223c7-67b8-4fd4-ad32-b21ced4b78a3","added_by":"auto","created_at":"2026-04-28 13:16:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":81811,"visible":true,"origin":"","legend":"\u003cp\u003eRandom-effects meta-regression bubble plot assessing the association between mean age and change in fasting blood glucose\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9376778/v1/8c2e771a4a97d1ad4cc486f4.png"},{"id":108181794,"identity":"b9489217-0dd4-4d77-b9f5-277ddcc4bfe2","added_by":"auto","created_at":"2026-04-30 08:58:55","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":83623,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the pooled mean difference in BMI based on the difference in pre–post changes between groups\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9376778/v1/26a6c6163491b19fe4124862.png"},{"id":108013026,"identity":"6b109e3e-bcf9-4f4d-ab43-35a178fc1a31","added_by":"auto","created_at":"2026-04-28 13:17:08","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":30558,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot for publication bias assessment of BMI\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9376778/v1/d094020b80cde85535d1473f.png"},{"id":108012906,"identity":"10f8283f-a33e-4cd8-8adf-ba9a5d276cbb","added_by":"auto","created_at":"2026-04-28 13:16:49","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":86886,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of BMI by disease category\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-9376778/v1/6100f35e1f13aec82b90dccb.png"},{"id":108013027,"identity":"23b422e9-b584-49f1-9e76-0a1278e17e46","added_by":"auto","created_at":"2026-04-28 13:17:08","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":82385,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of BMI by intervention duratio\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-9376778/v1/41cd609e34588cf50ae8a9c6.png"},{"id":108431457,"identity":"e9d94a0e-d61b-4819-b0b7-c3017b933cd9","added_by":"auto","created_at":"2026-05-04 14:40:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1311212,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9376778/v1/40dcab3f-f50e-428b-a838-3baebfe68a07.pdf"},{"id":108012977,"identity":"6cf9dc7b-5706-49b5-8e4c-b14c8b247363","added_by":"auto","created_at":"2026-04-28 13:17:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":572085,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryAppendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-9376778/v1/f181f8b7f11480f09f9f7d5c.docx"},{"id":108181257,"identity":"7faff8bc-d1de-4870-ae7b-66c739c973b5","added_by":"auto","created_at":"2026-04-30 08:58:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":278076,"visible":true,"origin":"","legend":"","description":"","filename":"PRISMA2020checklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-9376778/v1/4fe1f8f386497defa235dece.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Time-dependent metabolic remodeling following combined lifestyle intervention in the high-risk T2DM spectrum: A meta-analysis of randomized controlled trials","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe global prevalence of abnormal glucose metabolism is rising exponentially due to rapid lifestyle transitions and the acceleration of population aging \u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. According to the American Diabetes Association (ADA) and Chinese Diabetes Society (CDS), conditions including elevated fasting blood glucose (FBG), insulin resistance (IR), and metabolic syndrome (MetS) are now recognized as critical \"window periods\" for the early prevention of type 2 diabetes mellitus (T2DM) and cardiovascular complications.\u003c/p\u003e \u003cp\u003eBeyond traditional prediabetes, this high-risk spectrum encompasses specific clinical phenotypes such as polycystic ovary syndrome (PCOS) and a history of gestational diabetes mellitus (GDM) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. While these conditions present with diverse clinical manifestations\u0026mdash;ranging from reproductive endocrine disorders in PCOS to transient glucose intolerance in pregnancy\u0026mdash;they are anchored by a shared underlying pathophysiology: an imbalance in insulin compensatory mechanisms and chronic low-grade inflammation. Elevated Body Mass Index (BMI) acts as a potent driver across this entire spectrum, where excess adiposity triggers adipose tissue dysfunction and disrupts lipid homeostasis, further exacerbating the \"insulin-resistance cycle\" \u003csup\u003e8,9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIntegrating these heterogeneous populations into a unified \"high-risk spectrum\" for analysis is scientifically justified by their common metabolic vulnerability. Establishing a quantifiable lifestyle intervention framework that simultaneously addresses adiposity and glucose homeostasis is therefore a global clinical priority for managing this high-risk metabolic spectrum \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLifestyle intervention\u0026mdash;specifically aerobic exercise and dietary modification\u0026mdash;is the cornerstone of metabolic management \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Aerobic exercise enhances insulin sensitivity and glucose uptake \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, while dietary intervention regulates energy intake and reduces metabolic load \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. A dual-track approach aims to restore metabolic balance by simultaneously addressing energy expenditure and intake, theoretically offering superior outcomes compared to standard care. However, the effectiveness of single-mode interventions varies based on intensity and compliance, leading to inconsistencies in long-term stability \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent meta-analyses have largely focused on single intervention forms or isolated metabolic indicators \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. There is a notable lack of systematic evidence quantifying the total efficacy of a structured dual-track program (exercise plus diet) compared directly to routine clinical care within the diverse T2DM high-risk spectrum. This study employs a meta-analysis to evaluate the impact of this combined approach on FBG and BMI. By identifying response gradients across subgroups, we aim to provide evidence-based references for \u003cb\u003ePrecision Lifestyle Medicine\u003c/b\u003e in early metabolic prevention.\u003c/p\u003e"},{"header":"2 Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Registration\u003c/h2\u003e \u003cp\u003eThis study followed PRISMA 2020 and Cochrane guidelines \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The protocol is registered on PROSPERO (CRD420261295480).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Search strategy\u003c/h2\u003e \u003cp\u003eSix databases (PubMed, SCOPUS, Web of Science, Cochrane Library, Embase, and Google Scholar) were searched through January 2026 for English-language publications. Strategies combined MeSH terms and keywords regarding glycemic indicators, aerobic exercise, diet, and RCTs. For Google Scholar, the top 300 relevance-sorted results were screened. Reference lists were manually reviewed, and all records were managed in Zotero. The PubMed strategy is in Supplementary Material Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Inclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eInclusion (PICOS): (P) Adults (\u0026ge;\u0026thinsp;18 years) with abnormal glucose metabolism; (I) Combined aerobic exercise and dietary intervention; (C) Routine care; (O) Fasting blood glucose and BMI; (S) RCTs.\u003c/p\u003e \u003cp\u003eExclusion: Non-RCTs, non-original studies (reviews/meta-analyses), studies with missing data, or interventions involving resistance training or lacking dietary components.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Screening and data extraction\u003c/h2\u003e \u003cp\u003eTo ensure objectivity and minimize bias, two researchers independently performed the literature screening and data extraction, with any discrepancies resolved through consultation with a third researcher. The extracted information encompassed basic study details such as the first author and publication year, alongside the specific disease types of the targeted populations. Participant demographics recorded included sample size and average age. Regarding the intervention protocols, researchers collected comprehensive data on the specific intervention measures, weekly frequency, duration per session, and the overall intervention cycle. For the primary outcomes, the mean and standard deviation of fasting blood glucose (FBG) and body mass index (BMI) were extracted for both experimental and control groups at both baseline and the end of the intervention. In cases where studies reported results at multiple time points, data from the final assessment were prioritized to ensure consistency in the meta-analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Quality assessment\u003c/h2\u003e \u003cp\u003eThe methodological quality of the included randomized controlled trials was systematically evaluated using the Cochrane Risk of Bias 2.0 (RoB 2.0) tool \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This assessment framework explores potential bias across five critical domains to ensure the internal validity of the evidence. First, we evaluated bias arising from the randomization process, focusing on the adequacy of random sequence generation and allocation concealment. Second, we examined deviations from intended interventions, acknowledging that double-blinding is often structurally unfeasible in lifestyle programs involving aerobic exercise and dietary modification. Third, bias due to missing outcome data was assessed to determine if participant dropouts were balanced or adequately handled. Fourth, measurement of the outcome was evaluated, focusing on the standardization of biochemical (FBG) and physiological (BMI) assessments. Finally, the potential for selection of the reported result was scrutinized by comparing published findings against pre-defined protocols or clinical trial registries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eMeta-analysis was conducted using Python \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. For continuous outcomes, the mean difference (MD) between the change scores from baseline to post-intervention in the experimental and control groups was utilized as the primary effect size \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, This method effectively eliminates baseline level differences and account for the natural course of the disease. When the standard deviation (SD) of the change was not directly reported, it was estimated using the formula provided by the Cochrane Handbook \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{S}{\\text{D}}_{\\text{c}\\text{h}\\text{ange}}\\text{=}\\sqrt{\\text{S}{\\text{D}}_{\\text{baseline}}^{\\text{2}}\\text{+}\\text{S}{\\text{D}}_{\\text{final}}^{\\text{2}}\\text{-(2\u0026times;}\\text{R}\\text{\u0026times;}\\text{S}{\\text{D}}_{\\text{baseline}}\\text{\u0026times;}\\text{S}{\\text{D}}_{\\text{final}}\\text{)}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere the correlation coefficient (R) was set at 0.5, representing a conservative estimate of the relationship between measurements. Heterogeneity among studies was quantified using the I\u0026sup2; statistic \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the anticipated high degree of clinical heterogeneity (e.g., diverse disease types within the high-risk spectrum and varying intervention cycles) and methodological heterogeneity (e.g., differences in intervention intensity and supervision), a random-effects model was prioritized for all analyses \u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This model assumes that the true effect size varies across studies, providing a more generalizable estimate for the diverse populations at risk for type 2 diabetes. Meta-regression was employed to assess the potential regulatory effect of age on FBG changes, and sensitivity analysis was performed via the leave-one-out method to test the robustness of the pooled results. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study selection\u003c/h2\u003e \u003cp\u003eInitial searches across five databases (PubMed, SCOPUS, Web of Science, Cochrane, and Embase) yielded 5,723 records. After removing duplicates, 1,893 papers underwent title and abstract screening. Studies were excluded if they were non-RCTs, lacked dietary interventions, or combined multiple exercise types, leaving 261 for full-text evaluation. Additionally, the top 300 Google Scholar results were screened using similar criteria, with 95 selected for full-text review.\u003c/p\u003e \u003cp\u003eFollowing a rigorous full-text assessment\u0026mdash;which excluded records based on ineligible outcome indicators, study design, missing data, or participant age\u0026mdash;12 articles comprising 15 randomized controlled trials (RCTs) were ultimately included. All 15 RCTs provided data for fasting blood glucose (FBG) meta-analysis, while 11 provided sufficient data for quantitative BMI analysis (the remaining 4 were used for qualitative description). The screening process is detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Study characteristics and literature quality evaluation\u003c/h2\u003e \u003cp\u003eThe twelve included articles(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), encompassing fifteen randomized controlled trials (RCTs), focused on a diverse range of high-risk metabolic populations \u003csup\u003e\u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. A defining methodological feature of these trials was the exclusive focus on aerobic exercise as the sole physical activity component, with interventions ranging from structured walking and supervised cycle ergometry to yoga and individualized training at maximal fat oxidation. To maintain cohort homogeneity, studies incorporating resistance training or combined exercise modalities were strictly excluded.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the Included Randomized Controlled Trials (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge (Year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntervention measures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDietary Intervention Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWeekly Frequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDuration (per session/week)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIntervention Cycle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOutcome Indicators\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWong et al.\u003csup\u003e28\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e57.42\u0026thinsp;\u0026plusmn;\u0026thinsp;6.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMobile app-based lifestyle program (diet\u0026thinsp;+\u0026thinsp;aerobic walking)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStructured dietary patterns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3 Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFBG、BMI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCtrl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e60.45\u0026thinsp;\u0026plusmn;\u0026thinsp;7.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlentz et al.\u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e57.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDiet\u0026thinsp;+\u0026thinsp;moderate-intensity aerobic exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMacronutrient-specific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e175\u0026thinsp;\u0026plusmn;\u0026thinsp;30 min/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6 Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFBG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCtrl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e56.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYilmaz et al.\u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e52.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWalking\u0026thinsp;+\u0026thinsp;dietary modification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBehavioral motivational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1 time/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3 Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFBG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCtrl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e51.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtkinson et al.\u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e31.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNutrition counseling\u0026thinsp;+\u0026thinsp;walking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNutritional education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9 Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFBG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCtrl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e31.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMcAuley et al.\u003csup\u003e32\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e48\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eModerate-intensity aerobic exercise\u0026thinsp;+\u0026thinsp;diet\u003c/p\u003e \u003cp\u003eIntensive aerobic exercise\u0026thinsp;+\u0026thinsp;diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMacronutrient-specific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5 times/week\u003c/p\u003e \u003cp\u003e5 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e30min\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4 Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFBG、BMI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e46\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCtrl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e45\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCai et al.\u003csup\u003e33\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e66.84\u0026thinsp;\u0026plusmn;\u0026thinsp;5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCommunity-based lifestyle intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNutritional education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCumulative\u0026thinsp;\u0026ge;\u0026thinsp;150 min/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e24 Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFBG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCtrl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e66.86\u0026thinsp;\u0026plusmn;\u0026thinsp;4.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHolmes et al.\u003csup\u003e34\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e34.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePAIGE lifestyle intervention (diet\u0026thinsp;+\u0026thinsp;walking)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBehavioral motivational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCumulative\u0026thinsp;\u0026ge;\u0026thinsp;150 min/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6 Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFBG、BMI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCtrl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e33.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBesnier et al.\u003csup\u003e35\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e30.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAerobic training (fat-max intensity) + diet\u003c/p\u003e \u003cp\u003eAerobic training (60% VO2max) + diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eStructured dietary patterns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4 times/week\u003c/p\u003e \u003cp\u003e4 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e55min\u003c/p\u003e \u003cp\u003e35min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5 Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFBG、BMI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e29\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCtrl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen et al.\u003csup\u003e36\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e51.47\u0026thinsp;\u0026plusmn;\u0026thinsp;8.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eABC empowerment\u0026thinsp;+\u0026thinsp;structured exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNutritional education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e120min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4 Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFBG、BMI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCtrl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e50.78\u0026thinsp;\u0026plusmn;\u0026thinsp;8.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrio et al.\u003csup\u003e37\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStructured aerobic exercise training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMacronutrient-specific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e45min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6 Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFBG、BMI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCtrl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalomba et al.\u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eStructured exercise training\u0026thinsp;+\u0026thinsp;low-calorie diet\u003c/p\u003e \u003cp\u003eStructured exercise training\u0026thinsp;+\u0026thinsp;low-calorie diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMacronutrient-specific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3 times/week\u003c/p\u003e \u003cp\u003e3 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e30min\u003c/p\u003e \u003cp\u003e30min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.5 Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFBG、BMI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e28.43\u0026thinsp;\u0026plusmn;\u0026thinsp;8.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCtrl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eELBanna et al.\u003csup\u003e39\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMediterranean diet\u0026thinsp;+\u0026thinsp;probiotics\u0026thinsp;+\u0026thinsp;yoga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStructured dietary patterns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e60 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3 Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFBG、BMI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCtrl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding nutritional strategies, the interventions were distributed across three primary models: four studies implemented macronutrient-specific modulation, such as high-protein or low-fat hypocaloric diets; three trials adopted structured dietary patterns including Mediterranean or DASH-oriented protocols; and the remaining five studies utilized broader educational or behavioral frameworks based on national guidelines and empowerment models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Risk of Bias Assessment\u003c/h2\u003e \u003cp\u003eUsing the Cochrane ROB 2.0 tool, 15 RCTs were evaluated across five dimensions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For the randomization process, half the studies (e.g., Chen 2017, Orio 2016) showed low risk, while others like Slentz (2016), Atkinson (2022), and ELBanna (2024) raised \"some concerns\" due to vague sequence generation or allocation descriptions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding deviations from intended interventions, most studies had \"some concerns\" as double-blinding is unfeasible in diet/exercise trials; Yilmaz (2013) and Wong (2021) were rated high risk due to poor compliance or protocol deviations. Missing outcome data risk was generally low, except for Yilmaz (2013) (high risk due to dropouts) and Cai (2019) (some concerns).\u003c/p\u003e \u003cp\u003eFor outcome measurement, the objectivity of FBG and BMI ensured low risk for most, except Wong (2021), which utilized remote monitoring. Regarding selective reporting, one-third of studies showed \"some concerns\" due to missing clinical trial registrations or protocols. Overall, the studies demonstrate medium methodological quality, primarily presenting \"some concerns\" with isolated high-risk cases (e.g., Yilmaz 2013), maintaining an acceptable range for evidence-based practice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Meta-analysis results of fasting blood glucose\u003c/h2\u003e \u003cp\u003eFifteen RCTs (n\u0026thinsp;=\u0026thinsp;1448) were pooled using a random-effects model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The combined intervention showed a trend toward FBG reduction with a mean difference (MD) of -0.08 mmol/L (95% CI: -0.17 to 0.01, P\u0026thinsp;=\u0026thinsp;0.085). Heterogeneity was moderately high (I\u0026sup2; = 61.74%, P\u0026thinsp;=\u0026thinsp;0.001). Funnel plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and Egger\u0026rsquo;s regression (P\u0026thinsp;=\u0026thinsp;0.098) suggested no significant publication bias. Sensitivity analysis via the leave-one-out method (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u0026ndash;S2) indicated that the exclusion of specific studies, such as Yilmaz et al. (2013), could shift the results to statistical significance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 FBG subgroup analysis and Meta regression\u003c/h2\u003e \u003cp\u003eSubgroup analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) revealed that the MetS/IR population benefited most significantly (MD = -0.19 mmol/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, I\u0026sup2; = 0%), whereas GDR and PCOS populations showed no significant improvements. Regarding the intervention cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), short-term programs (\u0026le;\u0026thinsp;3 months) presented a stronger improvement trend than those exceeding 3 months. Random-effects Meta-regression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) showed that age might negatively impact FBG changes (β = -0.0057, P\u0026thinsp;=\u0026thinsp;0.071), explaining 12.18% of the residual heterogeneity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Meta-analysis results of Body Mass Index\u003c/h2\u003e \u003cp\u003eEleven RCTs involving 706 participants were included in the quantitative analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The combined intervention significantly reduced BMI compared to routine care (MD = -1.62 kg/㎡, 95% CI: -2.23 to -1.02, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Despite high heterogeneity (I\u0026sup2; = 87.34%), sensitivity analysis (Supplementary Figures S3\u0026ndash;S4) confirmed the robustness of this finding. However, the funnel plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) and Egger\u0026rsquo;s test (P\u0026thinsp;=\u0026thinsp;0.003) indicated a potential risk of publication bias.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Subgroup analysis of BMI\u003c/h2\u003e \u003cp\u003eSubgroup analysis by disease type (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e) demonstrated that the PCOS population achieved the most significant weight loss (MD = -2.64 kg/㎡, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, I\u0026sup2; = 50.28%). When categorized by cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e), both short-term (MD = -2.58, P\u0026thinsp;=\u0026thinsp;0.019) and long-term (MD = -1.14, P\u0026thinsp;=\u0026thinsp;0.001) interventions significantly reduced BMI, though short-term interventions showed a more pronounced numerical trend.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.8 GRADE Evidence Quality Assessment\u003c/h2\u003e \u003cp\u003eThe certainty of evidence for the primary outcomes was systematically evaluated using the GRADE framework (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For fasting blood glucose (FBG), the evidence was categorized as \"Very low quality.\" This assessment primarily reflects the inherent methodological constraints of non-pharmacological interventions, where double-blinding is structurally unfeasible, leading to \"some concerns\" in the risk of bias assessment. Inconsistency was noted due to moderate heterogeneity (I\u0026sup2; = 61.74%), and the evidence was further downgraded for imprecision as the 95% CI for the overall effect (-0.17 to 0.01 mmol/L) crossed the null threshold.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Findings\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. Of RTCs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffect(MD,95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eI\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCertainty\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.08(-0.17,0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/㎡)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-1.62(2.23,-1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor body mass index (BMI), the evidence was rated as \"Low quality.\" While the intervention demonstrated a highly significant and robust clinical effect (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the quality rating was affected by high statistical heterogeneity (I\u0026sup2; = 87.34%) and potential publication bias. However, it is noteworthy that despite these global downgrades, the subgroup analyses revealed high evidentiary stability in specific populations. For instance, the MetS/IR subgroup exhibited a significant glycemic benefit with zero heterogeneity (I\u0026sup2; = 0%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the PCOS population showed the most substantial BMI reduction. These findings suggest that while the \"overall\" evidence is affected by the diverse nature of high-risk populations, the targeted clinical gains for specific subgroups remain highly credible.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis systematic review and meta-analysis conducted a comprehensive integration of evidence from randomized controlled trials (RCTs) to deeply explore the metabolic regulatory efficacy of combined aerobic exercise and dietary intervention for individuals at high risk of abnormal glucose metabolism. The study confirmed that this combined approach demonstrated a significant evidence-based advantage in improving BMI (MD = -1.62). Although the combined effect size of fasting blood glucose (FBG) was marginally significant in statistical terms (P\u0026thinsp;=\u0026thinsp;0.085), a highly consistent and significant benefit was observed in the subgroup with more severe metabolic impairment (MetS/IR) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, I\u0026sup2; = 0%), revealing the clinical value of this preventive approach in implementing precise intervention during the early window of metabolic disorders.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Main research findings\u003c/h2\u003e \u003cp\u003eThis meta-analysis based on an inter-group comparison design indicates that the combined effect of aerobic exercise and dietary intervention on BMI is highly robust (MD = -1.62). Regarding glycemic outcomes, our analysis reveals a distinct \"metabolic response gradient.\" While the overall population effect size only shows a trend towards improvement (P\u0026thinsp;=\u0026thinsp;0.085), the high-risk MetS/IR subgroup exhibits a robust and stable glycemic recovery, indicating that lifestyle-induced glucose stabilization is highly phenotype-specific. By evaluating the combined intervention against routine care, this study demonstrates that an integrated lifestyle approach offers a potent therapeutic advantage over standard clinical advice, potentially grounded in the biological complementarity of exercise-induced insulin sensitivity and dietary glucose regulation. This underscores a critical clinical insight: a structured, dual-track lifestyle modification provides a superior metabolic defense that significantly exceeds the efficacy thresholds of non-specific routine guidance. This elucidates the core clinical utility: a multi-dimensional lifestyle modification provides a superior metabolic defense layer that exceeds the efficacy of standard clinical advice, likely by addressing multiple insulin resistance pathways through a unified protocol.\u003c/p\u003e \u003cp\u003eThe significant weight loss and the simultaneous hypoglycemic effect in specific subgroups provide a solid evidence-based foundation for combined lifestyle management as a first-line intervention strategy for high-risk populations of type 2 diabetes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Extensive comparison with previous studies and uniqueness\u003c/h2\u003e \u003cp\u003ePrevious meta-analyses have mostly focused on a single disease or a single intervention mode \u003csup\u003e\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. This study demonstrates significant uniqueness based on the existing research: This study, based on the most rigorous definition of \"high-risk population for type 2 diabetes\" by ADA \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, conducted the first meta-analysis focusing on the holistic impact of combined aerobic exercise and dietary intervention for the high-risk population of type 2 diabetes that includes PCOS, MetS, and history of gestational diabetes. This integrated perspective proves that although the clinical phenotypes are different, the uniformity based on the underlying pathological mechanism (insulin compensatory imbalance) indicates that \"aerobic exercise\u0026thinsp;+\u0026thinsp;diet\" can serve as a cross-disciplinary universal early intervention plan.\u003c/p\u003e \u003cp\u003eFurthermore, compared to studies that explore the impact of specific nutritional strategies on patients who have already been diagnosed, this research precisely quantifies the comprehensive impact of the combined approach relative to routine care by extracting the mean difference between groups. This methodological approach provides a quantifiable reference for the clinical formulation of precise preventive prescriptions by demonstrating the superiority of a dual-track lifestyle intervention over standard care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Subgroup Analysis and In-depth Exploration of Physiological Mechanisms\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Population-specificity and Intervention Sensitivity Characteristics\u003c/h2\u003e \u003cp\u003eThe subgroup analysis revealed a distinct \"response gradient\" across different metabolic profiles, where the MetS/IR subgroup exhibited extremely high stability in the improvement of FBG (MD = -0.19, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, I\u0026sup2; = 0%), while the PCOS subgroup demonstrated the most robust reduction in BMI (MD = -2.64, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThis divergent sensitivity reflects the unique pathological priorities of each population. For individuals with MetS/IR, the primary metabolic defect lies in impaired \"metabolic flexibility,\" where the accumulation of visceral adiposity drives systemic insulin resistance. The structured combination of aerobic exercise and dietary control directly targets this ectopic fat depot, thereby alleviating the secretory stress on pancreatic β-cells and restoring glucose homeostasis with high consistency, as evidenced by the zero heterogeneity in this subgroup.\u003c/p\u003e \u003cp\u003eIn contrast, the metabolic challenges in PCOS are intrinsically linked to a \"vicious cycle\" involving hyperandrogenism and follicular developmental arrest. The superior BMI reduction observed in the PCOS subgroup relative to routine care suggests that the synergistic effect of negative energy balance and aerobic-induced endocrine modulation is particularly effective at reducing the androgen pool and improving Sex Hormone-Binding Globulin (SHBG) levels. This addresses the central obesity characteristic of PCOS more effectively than non-structured clinical advice. Consequently, while MetS/IR patients are \"highly sensitive\" to glycemic optimization, PCOS patients appear \"highly responsive\" to body composition remodeling, providing a strong empirical basis for the implementation of subgroup-specific Precision Lifestyle Medicine.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Molecular Mechanisms of Integrated Metabolic Remodeling\u003c/h2\u003e \u003cp\u003eThe observed clinical advantage of this approach is theoretically supported by the complementary metabolic repair pathways initiated at the cellular level. Although this analysis assesses the overall impact of the structured lifestyle program relative to routine care, rather than delineating the incremental contribution of its individual components, the observed clinical superiority is theoretically supported by the potential concurrent activation of multi-target pathways. The mechanistic superiority of this dual-track approach lies in its \"dual-hit\" modulation of molecular signaling pathways. Aerobic exercise significantly upregulates glucose uptake through insulin-independent secondary messengers\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. During skeletal muscle contraction, the elevation of the intracellular AMP/ATP ratio triggers the phosphorylation of AMP-activated protein kinase (AMPK) \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, Advanced mechanistic insights reveal that this process involves the phosphorylation of AS160 (Akt substrate of 160 kDa)\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, a Rab-GTPase activating protein. This phosphorylation relieves the inhibition on GLUT4 storage vesicles, facilitating their rapid translocation to the sarcolemma \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Furthermore, the involvement of the Calcium/calmodulin-dependent protein kinase kinase 2 (CaMKK2) further reinforces contraction-induced metabolic remodeling, ensuring efficient glucose clearance even in states of profound insulin resistance\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Concurrently, dietary regulation reduces exogenous glucose loading and downregulates the MondoA-TXNIP axis, thereby alleviating the compensatory secretory stress on pancreatic β-cells\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. This synergy between \"increasing metabolic demand\" (exercise) and \"decreasing substrate burden\" (diet) provides a robust molecular rationale for the clinical superiority of combined interventions over routine care. This contraction-induced translocation effect significantly enhances the clearance of circulating glucose in peripheral tissues, especially showing better compensatory benefits in individuals with impaired insulin sensitivity.\u003c/p\u003e \u003cp\u003eSecondly, dietary regulation effectively alleviates the functional stress of pancreatic β cells that are chronically in a state of excessive secretion by limiting the intake of exogenous glucose, providing a crucial \"metabolic rest\" window for these cells \u003csup\u003e\u003cspan additionalcitationids=\"CR51 CR52\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. This combined approach not only helps to reduce systemic chronic inflammatory indicators (down-regulating pro-inflammatory factors such as IL-6 and TNF-α), but also optimizes the secretion profile of adipokines \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Since a significant decrease in BMI is often accompanied by a reduction in visceral fat, this directly improves liver insulin sensitivity, thereby physiologically elucidating the inevitability of the synchronous occurrence of body weight management and glucose homeostasis remodeling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 The Impact of the Intervention Period\u003c/h2\u003e \u003cp\u003eThe results of stratification by period show that short-term intervention (\u0026le;\u0026thinsp;3 months) demonstrated a stronger numerical trend in BMI reduction (MD = -2.58). Our findings elucidate a critical temporal framework for metabolic adaptation, characterized as the \"3-month Metabolic Switch Point,\" where the focus of the intervention shifts from rapid weight loss to deep homeostatic repair. During the initial 12 weeks of intervention, BMI reduction demonstrated superior numerical magnitude (MD = -2.58), primarily driven by the rapid depletion of adipose tissue under acute negative energy balance. However, glycemic improvements remain volatile during this early phase. As the intervention extends beyond the 3-month threshold, although the rate of weight loss stabilizes (MD = -1.14), the restoration of glucose homeostasis exhibits significantly higher evidentiary stability. This evidence supports a time-dependent trajectory of metabolic remodeling, progressing from initial \"structural/volumetric reduction\" (BMI loss) to subsequent \"functional/homeostatic restoration\" (glycemic control). Early weight loss provides the essential \"metabolic buffer\" required for the subsequent restoration of peripheral insulin sensitivity. Conversely, long-term sustained intervention (\u0026gt;\u0026thinsp;3 months) achieves deep-seated homeostatic repair, likely through the alteration of muscle fiber type distribution and gut microbiota composition \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Clinical practice should, therefore, implement phased objectives: prioritizing weight management during the first trimester to build patient adherence, while shifting the focus toward long-term metabolic rhythm maintenance beyond the 3-month mark. This suggests that long-term continuous intervention is the key to achieving the transformation from a simple \"weight loss-oriented\" approach to a deep \"metabolic homeostasis restoration\".\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Comprehensive Explanation of the Sources of Heterogeneity\u003c/h2\u003e \u003cp\u003eThis study observed a high degree of heterogeneity in the BMI outcome (I\u0026sup2; = 87.34%). According to the TIDieR framework \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. This heterogeneity stems from the variations in the intensity of intervention supervision, the application of digital media, and the initial obesity level of the subjects among different studies. Meta regression indicated that age has a potential regulatory effect on the improvement of FBG (P\u0026thinsp;=\u0026thinsp;0.071), suggesting that there are biological differences in metabolic adaptability among different age groups. Although there is a certain publication bias, the high consistency of the effect directions of each study ensures the reference value of the clinical conclusion.\u003c/p\u003e \u003cp\u003eFurthermore, it is essential to recognize that the modest GRADE ratings reported in this study primarily reflect the inherent clinical complexity and phenotypic diversity of populations at high risk for type 2 diabetes. Rather than undermining the validity of our findings, these ratings underscore the critical necessity of our stratified, subgroup-driven analytical approach. This strategy successfully identified specific high-responder cohorts, most notably the MetS/IR population, who derive precise, stable, and statistically robust benefits from combined aerobic and dietary interventions\u0026mdash;benefits that are often obscured in generalized population-level analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Clinical Practice Insights and the Unique Significance of This Study\u003c/h2\u003e \u003cp\u003eThe advantage of this study lies in providing a quantifiable \"precise navigation\" for early metabolic prevention, addressing the limitations of the \"one-size-fits-all\" approach to lifestyle intervention in clinical practice. By identifying these time-dependent and phenotype-specific effects, this study provides a quantitative roadmap for Precision Lifestyle Medicine in early diabetes prevention. For the MetS/IR population, clinicians should prioritize \"glycemic control-centric\" prescriptions, leveraging their heightened sensitivity to combined interventions (MD = -0.19, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as a first-line defense against T2DM progression\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. For PCOS patients, the therapeutic focus should shift toward \"compositional optimization,\" utilizing the robust weight loss response (MD = -2.64) to disrupt the vicious cycle involving obesity, hyperandrogenism, and insulin resistance. Furthermore, we advocate for the integration of digital monitoring tools, such as Continuous Glucose Monitoring (CGM), into lifestyle prescriptions to capture real-time metabolic feedback. This enables a paradigm shift from \"generalized advice\" to \"individualized precision intervention.\" Such a stratified management model, based on specific subgroup responses, not only enhances the efficiency of healthcare resource allocation but also substantially reduces the long-term complication burden in high-risk metabolic profiles. Secondly, the quantified MD value of -1.62 in this study does not merely indicate a decline in the indicator. Instead, it represents the quantifiable clinical gain of the combined lifestyle approach compared to routine standard care, providing straightforward evidence for clinicians to implement a structured dual-track lifestyle prescription as a unified intervention instead of relying on non-specific routine guidance.\u003c/p\u003e \u003cp\u003eFinally, the \"3-month metabolic switching point\" defined in the study guides the clinical setting of intervention priorities in a phased manner - that is, focusing on weight loss motivation in the early stage and on restoring homeostasis in the medium and long term.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Limitations and Future Research Directions\u003c/h2\u003e \u003cp\u003eThis study has certain limitations. Due to the nature of lifestyle interventions, double-blinding was unfeasible, and small sample sizes in specific subgroups contributed to the observed heterogeneity in BMI. Additionally, the exclusion of resistance training (RT) may limit the assessment of skeletal muscle as a \"metabolic sink,\" particularly for individuals with sarcopenic obesity where aerobic exercise alone may not fully reverse muscle atrophy \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Despite these constraints, we employed the mean difference (MD) between groups and rigorous methodology to minimize bias from the natural disease course, ensuring the scientific robustness of our findings for early metabolic intervention.\u003c/p\u003e \u003cp\u003eFuture research should prioritize multi-arm clinical trials to delineate the independent and synergistic effects of combined \"Aerobic\u0026thinsp;+\u0026thinsp;Resistance\" protocols, which may offer superior protection for glucose homeostasis and muscle mass in elderly populations \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Furthermore, exploring optimal combinations of exercise intensities and nutritional patterns, alongside digital monitoring of long-term sustainability, will be essential to comprehensively evaluate the value of these prescriptions in blocking diabetes progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Conclusion\u003c/h2\u003e \u003cp\u003eIn conclusion, aerobic exercise combined with dietary intervention can significantly improve the BMI levels of individuals at high risk of type 2 diabetes, and demonstrate clear benefits in controlling blood sugar in the MetS/IR population with significant metabolic risks. This comprehensive intervention model holds important clinical evidence-based value in advancing the prevention of type 2 diabetes at the early stage.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDisclosure\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no potential conflicts of interest, financial or otherwise, relevant to the content of this manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors have made substantial contributions to the study and approved the final version of the manuscript. Hao-Xiang Li: Conceptualization, methodology, software, visualization, and writing \u0026ndash; original draft. Yuan Li: Supervision, validation, and writing \u0026ndash; review \u0026amp; editing. Xin-Di Lyu: Formal analysis, data curation, and writing \u0026ndash; review \u0026amp; editing. Rui-Qi Liu: Data curation, and writing \u0026ndash; review \u0026amp; editing. Bing-Hong Gao: Conceptualization, project administration, supervision, and writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank the authors of the original studies included in this meta-analysis. We also acknowledge the use of searchRxiv for archiving our search strategies to support research transparency.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. All synthesized data used in the meta-analysis are derived from published randomized controlled trials included in the reference list.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSaeedi, P. et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. \u003cem\u003eDiabetes Res. Clin. Pract.\u003c/em\u003e 157, 107843 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, H. et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. \u003cem\u003eDiabetes Res. Clin. Pract.\u003c/em\u003e \u003cb\u003e183\u003c/b\u003e, 109119 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrazio, L. et al. 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The Effect of Lifestyle Intervention on Diabetes Prevention by Ethnicity: A Systematic Review of Intervention Characteristics Using the TIDieR Framework. \u003cem\u003eNutrients\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 4118 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Aerobic exercise, Dietary intervention, High-risk profile of abnormal glucose metabolism, Body mass index, Effectiveness of combined intervention","lastPublishedDoi":"10.21203/rs.3.rs-9376778/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9376778/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e: Exploring early interventions for glucose metabolism disorders is vital. This study evaluated the impact of combined aerobic exercise and dietary interventions on fasting blood glucose (FBG) and body mass index (BMI) in high-risk populations compared to routine care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing PRISMA 2020 guidelines, we searched six core databases (including PubMed and Web of Science) for RCTs through January 2026. Using a random-effects model, we synthesized mean differences (MD) based on pre- and post-intervention changes to assess therapeutic effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFifteen RCTs from 12 studies were included. The intervention significantly reduced BMI (MD = -1.62 kg/m², 95% CI: -2.23 to -1.02, P \u0026lt; 0.001). FBG showed an improving trend (MD = -0.08 mmol/L, 95% CI: -0.17 to 0.01, P = 0.085). Subgroup analysis revealed that the metabolic syndrome/insulin resistance (MetS/IR) group was the primary beneficiary for glycemic control (MD = -0.19 mmol/L, P \u0026lt; 0.001), the PCOS population showed the most robust BMI improvement (MD = -2.64 kg/m², P \u0026lt; 0.001), while its FBG improvement did not reach statistical significance (P = 0.374). Additionally, interventions under 3 months demonstrated stronger weight-loss tendencies. Meta-regression suggested age potentially regulates FBG improvement (P = 0.071).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe structured lifestyle intervention demonstrates a robust time-dependent efficacy, significantly optimizing BMI in high-risk individuals and providing precision glycemic benefits specifically for the MetS/IR subgroup. These findings support dual-track lifestyle prescriptions as a robust first-line approach for precise metabolic management and early prevention of type 2 diabetes.\u003c/p\u003e\n\u003cp\u003eSystematic review registration: https://www.crd.york.ac.uk/PROSPERO/home, identifier: CRD420261295480.\u003c/p\u003e","manuscriptTitle":"Time-dependent metabolic remodeling following combined lifestyle intervention in the high-risk T2DM spectrum: A meta-analysis of randomized controlled trials","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 13:14:47","doi":"10.21203/rs.3.rs-9376778/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ec47c1b5-1e63-40c9-865c-5fbc4da4381a","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-04T14:22:42+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66918503,"name":"Health sciences/Diseases"},{"id":66918504,"name":"Health sciences/Endocrinology"},{"id":66918505,"name":"Health sciences/Health care"},{"id":66918506,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-05-04T14:39:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 13:14:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9376778","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9376778","identity":"rs-9376778","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}