This study is the first meta-analysis detailing the effects of CT in patients with type 2 diabetes and analyzing the effects of CT on multiple aspects of glycemic control, body composition, blood pressure, and VO2max. Long-term CT significantly reduced HbA1c and FBG levels compared with the control group, and the optimal intervention effect was achieved at a dose of 1030 METs-min/week; in addition, CT significantly improved BMI, BFP, blood pressure, and VO2max levels in patients with type 2 diabetes. In addition, the intervention effect of CT may be moderated by baseline HbA1c levels, with a low BMI yielding better results than a high BMI.

The results of this study show consistency with recent related studies4–7 on several key points. In particular, our findings support the notion that CT has a positive impact on patients with type 2 diabetes, such as glycemic control, blood pressure, and BMI, which is similar to the findings of the study by Al-Mhanna et al,5 which reported similar improvements in the obese type 2 diabetes population. In addition, in contrast to these previous studies, our results further emphasize the potential benefits of different CT doses in the comprehensive management of diabetes and provide an optimal CT dose for clinical practice as a preliminary reference.

HbA1c, a key indicator of glycemic control, is widely used in clinical practice for screening, diagnosis, glycemic control, and efficacy confirmation in patients with diabetes.2 Our study found that CT significantly reduced HbA1c and FBG levels. Several previous studies4–7 have confirmed the benefits of CT in improving HbA1c levels, potentially due to the upregulation of glucose transporter type 4 expression at the cellular level.24 By increasing the cellular uptake of glucose, CT effectively increases the overall metabolic efficiency of glucose in the body,25 ultimately leading to lower blood glucose levels. Therefore, by lowering the average blood glucose concentration, CT indirectly lowered HbA1c levels. Benham et al26 found a dose-response relationship between exercise frequency and HbA1c levels. Gallardo-Gómez et al11 observed a similar relationship between total physical activity and HbA1c levels, identifying an optimal dose of 1100 METs-min/week. The present study confirmed a dose-response relationship between CT intensity and HbA1c levels, identifying an optimal dose of 1030 MET-min/week, which is equivalent to 270 min of moderate-intensity CT or 150 min of high-intensity CT per week. Notably, exercise at only 370 MET-min/week significantly lowered HbA1c levels, well below the minimum weekly exercise dose (600 MET-min) recommended by the WHO.27 Therefore, CT is an important intervention in managing type 2 diabetes and effective for achieving the HbA1c target (6.5%) set by the International Expert Committee on Diabetes.28 Furthermore, Bassi et al24 showed that HbA1c improved significantly in a shorter intervention cycle (12 weeks), consistent with our finding that CT showed stable effects across the intervention durations (12/24/36/48 weeks) (figure 6).

The meta-analysis results showed that CT effectively reduced BMI levels in patients with type 2 diabetes, which is consistent with the findings from two previous RCTs.4 5 In addition, we found that CT had a greater benefit for BFP. CT combines the advantages of aerobic and strength training, effectively improving body fat status by increasing insulin sensitivity and decreasing leptin secretion and fat accumulation in adipose tissue.29 Strength training helps maintain and increase lean body mass, enhancing upper and lower extremity strength and improving the resting metabolic rate.30 Therefore, CT can overcome the limitations of aerobic or strength training alone and improve body composition more comprehensively in patients with type 2 diabetes. However, Dobrosielski et al31 noted that although CT reduced the percentages of total body fat, abdominal fat, and subcutaneous fat, it had no significant effect on visceral fat reduction. Therefore, future studies are required to explore the specific effects of CT on different areas of body fat.

In patients without type 2 diabetes, both aerobic and strength training are effective treatment strategies for lowering blood pressure.26 However, the evidence is less consistent in patients with type 2 diabetes. According to a joint statement by the American College of Sports Medicine and the American Diabetes Association,32 exercise may slightly reduce systolic blood pressure (SBP) in patients with type 2 diabetes, but the reduction in diastolic blood pressure (DBP) is not significant. Previous studies have suggested that diabetes-related metabolic abnormalities may impair vascular function by limiting vasodilatory capacity and exacerbating vasoconstrictor responses, which may lead to the remodeling and hardening of arterial structures, subsequently increasing SBP.33 Nonetheless, the results of the present study confirmed that CT significantly reduced SBP and DBP in patients with type 2 diabetes, possibly because CT intervention increased vascular elasticity and improved metabolic profiles, which may be effective in reversing these unfavorable vascular changes. These findings emphasize the potential value of CT in managing type 2 diabetes, particularly for improving blood pressure control.

Both epidemiological and clinical studies have shown that patients with long-term type 2 diabetes tend to experience a decline in cardiorespiratory fitness, which is negatively correlated with mortality in patients with diabetes.34 In addition, Bassi et al24 confirmed that CT significantly increases VO2max, which is closely associated with a decrease in HbA1c, consistent with our findings. This process reduces the affinity of hemoglobin for oxygen, thereby making oxygen more readily available to tissues during exercise, significantly enhancing the VO2max of patients.35

The present study identified several strengths through meta-analysis. First, the use of advanced Bayesian meta-analysis offers flexibility in dealing with complex data structures, accurately modeling multi-arm studies, crossover designs, and nested data in a hierarchy. Additionally, it allows the synthesis of information from different sources, including prior knowledge and experimental data, to enhance analysis accuracy and robustness. This approach also outperforms traditional methods in managing and exploring data heterogeneity, estimating its magnitude and exploring potential sources, such as differences in study design and sample characteristics. These properties make multilevel Bayesian meta-analysis a powerful tool for dealing with complex and variable data.37 Second, by combining pairwise meta-analysis and dose-response analysis based on natural spline modeling, it is possible to gain a more comprehensive understanding of how the study results are obtained at different dose levels rather than just at the level of the combined effect. This approach enhances the credibility of the evidence by providing a contemporaneous training prescription, which can serve as a guideline for both clinical practitioners and people with diabetes.

This study had some limitations. First, the literature’s quality was low because of the limitations of blinded assessment in the included RCTs, potentially affecting the overall quality of the study. Second, the limited number of included studies prevented the analysis of key metrics such as insulin levels and insulin resistance indices, thereby limiting our ability to fully assess CT’s impact on glycemic control in patients with type 2 diabetes. Finally, owing to the limitations of the included literature, we did not categorize for different ages, genders, and levels of obesity when presenting exercise dosage recommendations, potentially reducing the generalizability and replicability of the study results.