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The effect of different dietary restriction on weight management and metabolic parameters in people with type 2 diabetes mellitus: a network meta-analysis of randomized controlled trials

Diabetology & Metabolic Syndrome volume 16, Article number: 254 (2024) Cite this article

Abstract

Background

Type 2 diabetes mellitus (T2DM) is a globally prevalent chronic condition. Individuals with T2DM are at increased risk of developing complications associated with both macrovascular and microvascular pathologies. These comorbidities reduce patient quality of life and increase mortality. Dietary restriction is a principal therapeutic approach for managing T2DM. This study assessed the effects of various dietary regimens on body weight and metabolic profiles in T2DM patients, aiming to determine the most beneficial interventions for enhancing clinical outcomes and overall well-being.

Methods

We conducted a literature search in PubMed, Embase, and Web of Science from 2003 to April 15, 2024. The risk of bias was assessed via the Revised Cochrane risk-of-bias tool for randomized trials (RoB2). The certainty of the evidence was appraised via the confidence in network meta-analysis (CINeMA) framework. Intermittent fasting (IF) was directly compared with continuous energy restriction (CER) via Review Manager 5.4. Network meta-analysis was statistically assessed via R Studio 4.3.3 and STATA 14.0.

Results

Eighteen studies involving 1,658 participants were included. The network meta-analysis indicated that intermittent energy restriction, the twice-per-week fasting, time-restricted eating, fasting-mimicking diets (FMD), and CER interventions were more effective than conventional diets. Direct comparisons revealed that IF was as effective as CER for reducing glycated haemoglobin A1c, body weight, and body mass index. The results of the cumulative ranking analysis demonstrated that FMD had the greatest combined intervention effect, followed by TRE in terms of overall effectiveness.

Conclusions

Both IF and CER exert positive influences on weight control and metabolic profile enhancement in individuals with T2DM, with FMD as part of IF demonstrating the greatest impact. To substantiate these findings, more rigorous randomized controlled trials that directly compare the effects of the different IF regimens with one another and with the CER regimen are needed.

Introduction

Type 2 diabetes mellitus (T2DM) is a chronic noncommunicable disease that is prevalent worldwide. The International Diabetes Federation estimates that in 2021, the global population with diabetes was 537 million. This figure is projected to escalate, reaching an anticipated 783 million individuals by 2045 [1]. T2DM is characterized by chronic hyperglycaemia and a prolonged disease course, which increases the risk of both macrovascular and microvascular complications. These complications can manifest as various cardiovascular and cerebrovascular diseases, including diabetic nephropathy, diabetic cardiomyopathy, and diabetic retinopathy. These comorbidities significantly diminish the quality of life of patients with T2DM and contribute to a higher mortality rate [2,3,4]. Annually, diabetes and its complications are responsible for approximately 6.7 million deaths worldwide [1]. Consequently, effective management of T2DM is of paramount importance. The current standard treatment approaches for T2DM include dietary modifications, regular physical activity, and pharmacological interventions [5].

In previous studies, certain foods or dietary supplements—such as vitamin D probiotics and cinnamon—have improved glycosylated haemoglobin A1c (HbA1c), fasting blood glucose (FBG), and insulin levels in individuals with T2DM, suggesting their potential as clinical adjunct treatments in T2DM management [6,7,8,9,10,11]. These findings underscore the importance of dietary factors in managing diabetes. Accordingly, an expanding body of research has shown that dietary restriction (DR), which is another popular approach for managing food intake, is a potent and dependable nonpharmacological strategy for combating various metabolic disorders, including diabetes [12,13,14,15]. The predominant DR protocols include continuous energy restriction (CER) and intermittent fasting (IF). CER typically involves a consistent reduction in total daily caloric intake, with subjects consuming approximately 800–1500 kcal per day, contingent upon the specific study design. IF modalities are characterized by cycling between periods of eating and fasting and include time-restricted eating/feeding (TRE/TRF), intermittent energy restriction (IER), alternate day fasting (ADF), the twice-per-week fasting (TWF), and fasting-mimicking diets (FMD) [16, 17]. Their characteristics can be seen in Fig. 1. TRE imposes no restrictions on daily caloric intake but confines the eating window to 6–10 h per day. Within this designated time, participants are permitted to consume food ad libitum. Outside of these hours, caloric intake is strictly limited to water or zero-calorie beverages [18, 19]. Intermittent energy restriction (IER) typically involves limiting caloric intake over nonconsecutive periods. For example, in a study by Yang et al., IER was defined as the restriction of energy intake to 500–600 kcal on five days following ten days of the usual diet. This pattern is maintained every new week throughout the duration of the intervention period [20]. ADF alternates between feeding days, when caloric intake is unrestricted, and fasting days, when participants either abstain from caloric intake entirely or consume only 20–30% of their typical daily intake [21, 22]. TWF involves a two-day fast, either consecutive or nonconsecutive, within a weekly cycle. During the fast, participants consume no calories, but on the remaining five days, they can eat without restrictions [23]. FMD is a variant of periodic fasting (PF), which is typically implemented once every two weeks or months for 4–7 days in humans and 2–5 days in mice. The specifics of the FMD can be tailored to the research objectives [24]. For example, in a study by Li et al., the FMD protocol involved a two-day lead-in phase with a low-energy diet of approximately 1200 kcal/day, followed by a one-week period of very low-energy intake of approximately 300 kcal/day; the protocol concluded with a two-day transition back to a regular diet [25]. IF has been proposed as an alternative to CER because of its flexibility, ease of adherence, and comparable efficacy in weight loss and metabolic improvement [26]. Clinical trials have demonstrated that dietary interventions, such as CER and IF, effectively reduce blood pressure, lipid levels, body weight, insulin levels, and blood glucose levels and increase insulin sensitivity [27, 28], thereby mitigating T2DM. Although previous meta-analyses have evaluated the impact of various IF regimens on glycaemic control and BMI in diabetic populations [29], no study has yet systematically and comprehensively compared the effects of diverse DR approaches within T2DM cohorts.

In this study, we conducted a network meta-analysis and employed direct and indirect comparison techniques to evaluate the impact of the five most common distinct DR regimens, namely, CER, TRE, TWF, FMD and IER, on patients with T2DM. The objective of this study was to identify the DR regimen that has the most pronounced benefits in terms of weight reduction and metabolic parameter improvement, such as blood glucose levels, lipid profiles and blood pressure, for individuals with T2DM. This information may assist health care providers in selecting the most appropriate dietary regimen for people with T2DM, thereby assisting in the management and delaying the progression of diabetes.

Fig. 1
figure 1

Various forms of IF. The green sections represent eating times and the white sections represent fasting or calorie restriction periods

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Materials and methods

This systematic review and meta-analysis protocol is registered with the International Prospective Register of Systematic Reviews (PROSPERO) (Registration No. CRD42023472385). We performed this systematic review and meta-analysis according to the A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2) [30] and reported it according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 statement (Additional file 1) [31].

Search strategy

We conducted a comprehensive literature search across the PubMed, Embase, and Web of Science databases via a predefined set of keywords: ‘Fasting’, ‘Alternate Day Fasting’, ‘Time-Restricted Eating’, ‘Twice-Per-Week Fasting’, ‘Fasting Mimicking Diet’, ‘Diabetes Mellitus, Type 2’, and ‘Randomized Controlled Trial’. Furthermore, we manually searched the reference lists from the identified studies and consulted with domain experts to ensure the inclusion of all potentially relevant research (Additional file 2).

Inclusion and exclusion criteria

We included studies that met the following criteria: (1) Population: individuals with clinically diagnosed T2DM. (2) Intervention: The studies involved either CER or IF, with IF including IER, TWF, TRE, TRF, or FMD protocols. (3) Control: a control group following a normal diet (ND). (4) Outcome: Studies reported data on at least one of the following parameters: body weight, body mass index (BMI), HbA1c, FBG, total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and blood pressure. (5) Study design: Randomized controlled trials (RCTs) in a clinical setting.

We excluded studies based on the following criteria: (1) Animal research, conference abstracts, and systematic reviews. (2) Studies incorporating other types of intermittent fasting not specified in our inclusion criteria. (3) Studies that did not report original data. (4) Studies involving nonadult populations or those with acute and chronic conditions, such as gastrointestinal diseases, malignant tumours, and prediabetes, or participants taking medications that could influence the study outcomes. (5) Studies involving pregnant or nursing women.

Study selection and data collection

All the retrieved studies were imported into Endnote 20. After removing duplicate studies, two investigators independently evaluated the articles included in the review, applying the Cochrane system evaluation method. We utilized a weighted Cohen’s kappa (κ) coefficient to quantify the level of concordance between our assessments [32, 33]. Discrepancies were resolved via discussion with a third investigator.

We extracted the following variables independently according to the same criteria: first author, publication year, sample size, details of the intervention group and comparator group, duration, and outcomes. Missing information was supplemented by contacting the authors via email.

Outcome measures

The primary outcome measures employed in this study were HbA1c, body weight, and BMI. The secondary outcomes included FBG, lipid profiles, blood pressure, and insulin levels. The statistical effect sizes for all outcomes are reported as the mean differences (MDs) with 95% confidence intervals (CIs).

Bias risk assessment and evidence quality grading

Two reviewers independently examined the methodological quality of the included studies. The Revised Cochrane Risk of Bias Tool for Randomized Trials (RoB2) was employed to evaluate potential biases [34]. This tool includes five key areas: (1) bias risk stemming from the randomization procedure; (2) bias risk attributable to variances from the planned interventions (impact of intervention assignment); (3) incomplete outcome data; (4) bias risk in outcome measurement; and (5) bias risk in the reporting of selected results. Each area is evaluated via a set of specific questions designed to gauge the quality of the research. The bias risk is categorized as either “low risk of bias,” “some concerns,” or “high risk of bias” for each respective domain. The online quality assessment tool CINeMA was used to grade the quality of evidence. This software evaluates the quality of network meta-analyses by comprehensively considering six domains: risk of bias within studies, risk of bias across studies, indirectness, imprecision, inconsistency, and heterogeneity. The evidence quality was then graded accordingly. The severity of issues within each domain was categorized as nonserious, serious, or very serious. Consequently, the overall evidence quality grade for network meta-analyses was classified into four levels: high, medium, low, and very low. Following the completion of these evaluations, two researchers independently cross-verified the assessments. In cases of discrepancies, a third researcher was consulted to reach a consensus.

Statistical analysis

For the statistical analysis, R Studio version 4.3.3 and STATA version 14.0 were used. The outcome measure in our analysis is a continuous variable. To visualize the network of relationships among the variables, we utilized the “network” package in STATA. We employed an inconsistency model to evaluate the overall consistency of the data. A nonsignificant P value (P > 0.05) suggests a lack of substantial overall inconsistency within the dataset. Furthermore, the node splitting method was implemented to detect any local inconsistencies. A P value above 0.05 indicates that the direct and indirect comparisons are consistent, validating the application of the consistency model for our analysis. When the network diagram contained a closed loop, we calculated the loop-specific inconsistency factor to identify any inconsistency. The evidence from direct and indirect comparisons was considered consistent if the lower bound of the 95% CI for the inconsistency factor is zero or close to it. To quantify the ranking of the treatments, we used the surface under the cumulative ranking (SUCRA) curve, which provides a score ranging from 0 to 1, where a higher score indicates a more favourable ranking. Finally, to assess the potential for publication bias, we employed a comparison-corrected funnel plot, which is a standard method for identifying asymmetry in the published literature that may indicate bias.

Results

Research identification and selection

During the initial database search and subsequent literature citation tracking, a total of 8,109 articles were identified, from which 1,635 duplicates were removed. Following a meticulous multistep screening process, we refined our selection by focusing on pertinent study types, scrutinizing titles and abstracts systematically, and conducting a detailed review of the full texts. This rigorous approach culminated in the identification of 18 eligible studies for inclusion in our network meta-analysis (Fig. 2). We utilized a weighted Cohen’s kappa (κ) coefficient to quantify the level of concordance between our assessments.

Fig. 2
figure 2

PRISM flowchart. The flowchart of the literature search for the effects of diet restriction on weight management and metabolic parameters

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Characteristics of the included studies

The eligible studies included in this analysis were published between 2013 and 2024. The sample sizes of the included studies varied from 12 to 210 participants, resulting in a cumulative sample size of 1,658. The interventions assessed in the studies included CER, IER, TWF, TRE, and FMD for the intervention groups. Participants in the control groups primarily adhered to a normal diet. The outcome measures assessed included body weight, HbA1c, FBG, BMI, lipid profiles, insulin levels, and blood pressure. Table 1 provides an overview of the baseline characteristics of the included studies.

Table 1 The characteristics of the included studies
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Assessment of bias risk in included studies and grading of evidence quality

In this study, the RCTs included were assessed for risk of bias via ROB2, and the kappa coefficients for the five aspects included in ROB2 were calculated to be 0.64, 0.723, 0.824, 1, and 0.824. This finding indicates a good level of agreement between the two researchers in assessing the risk of bias. Summarizing the bias risk, 66.7% of the studies exhibited a “low risk bias” classification, equating to 12 out of the 18 studies reviewed. Additionally, 27.8% of the studies, or 5 out of 18, were found to have “some concerns” regarding bias. Only 5.6%, specifically 1 out of 18 studies, were identified as having a “high risk of bias.” Supplementary details pertaining to the bias risk evaluation and an overarching summary of the bias risks are presented in Fig. 3. The certainty of the evidence for the primary outcome, assessed via the CINeMA tool, varied from high to very low. Full details regarding the risk of bias and evidence certainty can be found in Additional file 3.

Fig. 3
figure 3

The result of risk of bias assessment

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Evidence network

The network diagrams illustrating primary outcomes, which included HbA1c, weight, and BMI, as well as secondary outcomes, such as FBG, lipid profiles, blood pressure, and insulin level, are presented in Figs. 4 and 5, respectively. These figures clearly visualize the direct and indirect comparisons between the five distinct types of diabetic retinopathy (DR) and ND.

Fig. 4
figure 4

Illustrations of the primary outcomes from the network meta-analysis. The network geometry for eligible comparisons is depicted for a) HbA1c, b) body weight, and c) BMI

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Fig. 5
figure 5

Outcomes of the secondary measures from the network meta-analysis are presented. The network geometry for eligible comparisons is illustrated for a) FBG, b) insulin levels, c) lipid profiles, and d) blood pressure

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Results of the network meta-analysis

Primary outcomes

IF versus CER

In a comprehensive analysis, six studies documented variations in HbA1c levels following IF protocols [35,36,37,38,39,40], specifically IER, TWF, and TRE, compared to CER interventions alone. Additionally, six studies examined weight changes [35,36,37,38,39,40], whereas five studies assessed alterations in BMI [36,37,38, 40]. Despite these observations, statistical analysis revealed no significant differences between the IF and CER groups concerning the reductions in HbA1c, weight, or BMI (Fig. 6).

Fig. 6
figure 6

Forest plots depicting the comparative effects of IF and CER on primary outcome indicators. The plots compare the impacts on a) HbA1c, b) body weight, and c) BMI

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Meta-analysis of HbAc1

Fifteen RCTs reported alterations in HbA1c following a DR intervention [25, 35,36,37,38,39,40,41,42,43,44,45,46,47,48], with the network diagram depicted in Fig. 4a. A consistency test was conducted on the compiled data, and the results indicated no overall inconsistency (P = 0.12). The node splitting method yielded P values greater than 0.05 for all nodes, indicating the absence of local inconsistency. The loop inconsistency test revealed inconsistency factor values ranging from 0.004 to 1.76, indicating no significant inconsistency within the closed loops. Compared with ND, FMD significantly decreased HbA1c levels (MD, -0.78; 95% CI, -1.31 to -0.25). No statistically significant differences were observed in any other comparisons (Additional file 4). Based on the SUCRA results presented in Fig. 7a, FMD presented the highest optimal probability, with a ranked probability of 77.8%. This figure was followed by TRE at 60.6%, CER at 59.9%, IER at 50.9%, TWF at 46.7%, and ND at 4.2%.

Fig. 7
figure 7

Cumulative rankings of primary outcomes by dietary intervention. The rankings are presented for a) HbA1c, b) body weight, and c) BMI

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Meta-analysis of weight

Fourteen studies documented weight changes (Fig. 4b) [20, 25, 35,36,37,38,39,40,41, 43,44,45,46,47]. A consistency test was conducted on the pooled data, revealing a P value of 0.85, which suggests no overall inconsistency. The results of the node splitting method indicated the absence of local inconsistency. However, the inconsistency factor values from the loop inconsistency test ranged from 0.17 to 5.49, with the lower bounds of the 95% CIs ranging from 0 to 2.21, which suggests potential inconsistency in the CER-IER-TWF closed loop. Notably, compared with ND, FMD effectively reduced weight (MD, -5.75; 95% CI, -10.60 to -0.90). No other comparisons revealed statistically significant differences (Additional file 4). The SUCRA analysis indicated that FMD had the highest probability of being optimal, with a ranked probability of 93.9% (Fig. 7b). This figure was followed by TRE at 60.6%, IER at 50.9%, TWF at 43.4%, CER at 28.8%, and ND at 22.3%.

Meta-analysis of BMI

A total of fourteen studies reported changes in BMI following DR intervention, with the network diagram presented in Fig. 4c [20, 25, 36,37,38, 40, 42,43,44,45,46, 48, 49]. The consistency of the included data was evaluated with a consistency test, which yielded a P value of 0.99, indicating no overall inconsistency. The node splitting method further confirmed this, with P values exceeding 0.05 for all nodes, signifying the absence of local inconsistency. However, the inconsistency factor values from the loop inconsistency test ranged from 0.019 to 1.409, with the lower limit of the 95% CI ranging from 0 to 0.42, suggesting potential inconsistency in the IER-TWF-ND closed loop. Importantly, the FMD reduced BMI compared with the ND (MD, -2.36; 95% CI, -3.40 to -1.33) and significantly reduced BMI compared with the IER (MD, -1.39; 95% CI, -2.46 to -0.32). However, no other comparisons identified statistically significant differences (Additional file 4). The SUCRA values revealed that FMD had the highest probability of being optimal, with a ranked probability of 95.8% (Fig. 7c). This figure was followed by IER at 70.2%, TRE at 65.3%, CER at 28.3%, TWF at 26.3%, and ND at 14.0%.

Second outcomes

Glycaemic control

A total of thirteen RCTs reported changes in FBG (Fig. 5a) [20, 25, 37, 38, 41,42,43,44,45,46, 48,49,50]. A consistency test was conducted on the included data, indicating no overall inconsistency (P = 0.94). The node splitting method also demonstrated no local inconsistency among all nodes. The inconsistency factor values for the loop inconsistency test ranged from 0.13 to 1.37, with the lower limit of the 95% CI at 0, suggesting no significant inconsistency between closed loops. Compared with ND, IER significantly reduced FBG levels (MD, -1.45; 95% CI, -2.36 to -0.53). No other comparisons yielded statistically significant differences (Additional file 4). According to the SUCRA results, the highest probability of being optimal was for IER, with a ranked probability of 93.3% (Fig. 8a).

Three studies reported changes in insulin levels (Fig. 5b) [25, 43, 50]. Given the absence of loop closures between intervention modalities, no inconsistency tests were conducted. Compared with ND, TRE could reduce insulin levels (MD, -0.42; 95% CI, -0.47 to -0.37). No other comparisons yielded statistically significant differences (Additional file 4). Based on the SUCRA analysis, FMD demonstrated the highest probability of being optimal, with a ranked probability of 87.1% (Fig. 8b).

Fig. 8
figure 8

Cumulative rankings of glycaemic control parameters by dietary intervention. The cumulative rankings are detailed for a) FBG and b) insulin levels, each categorized according to the dietary intervention applied

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Lipid profiles

Twelve studies reported the effects of DR interventions on lipid profiles (Fig. 5c) [25, 37,38,39,40, 42,43,44,45,46, 48, 49]. The studies included comparisons between CER and ND, IER and ND, TWF and ND, and TRE and ND. Consistency tests were conducted for TC, TG, LDL, and HDL, yielding P values of 0.99, 0.57, 0.06, and 0.0507, respectively. These results suggest no overall inconsistency in the outcomes for TC, TG, LDL and HDL. The node splitting method revealed no local inconsistency. The inconsistency factor values for the loop inconsistency test indicated potential inconsistencies in specific loops: for TC, the IF values ranged from 2.48 to 10.66 with a 95% CI from 0 to 8.67, suggesting inconsistency in the CER-IER-ND loop; for TG, the IF values ranged from 1.37 to 28.81 with a 95% CI from 0 to 17.00, indicating possible inconsistency in the CER-TWF-ND and CER-IER-ND loops; for LDL, the IF values ranged from 0.32 to 12.72 with a 95% CI from 0 to 5.03, suggesting possible inconsistency in the CER-TWF-ND and CER-IER-ND loops; and for HDL, the IF values ranged from 0.016 to 3.32 with a 95% CI from 0 to 1.75, indicating potential inconsistency in the CER-IER-ND and CER-IER-TWF loops. Compared with ND, FMD significantly reduced TG levels (MD, -0.96; 95% CI, -1.69 to -0.22). No other comparisons yielded statistically significant differences (Additional file 4). The SUCRA analysis indicated that the FMD had the highest probability of being optimal for TC and TG (Fig. 9a and b). For LDL, CER had the highest probability of being optimal (Fig. 9c). For HDL, FMD also had the highest probability of being optimal (Fig. 9d).

Fig. 9
figure 9

Cumulative rankings of lipid profile outcomes by dietary intervention. The rankings are organized for a) TC, b) TG, c) LDL, and d) HDL, each according to the specific diet intervention applied

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Blood pressure

Eight RCTs reported changes in blood pressure, including systolic blood pressure (SBP) and diastolic blood pressure (DBP) (Fig. 5d) [20, 39, 40, 42, 44,45,46, 48]. Consistency tests were conducted separately for SBP and DBP. The results indicated no overall inconsistency. The node splitting method further confirmed the absence of local inconsistency. Compared with ND, FMD significantly reduced both SBP (MD, -17.27; 95% CI, -30.24 to -4.30) and DBP (MD, -9.22; 95% CI, -13.54 to -4.89). However, no other differences reached statistical significance (Additional file 4). The SUCRA results indicated that FMD had the highest probability of being optimal for SBP and DBP (Fig. 10a and b).

Fig. 10
figure 10

Cumulative rankings of blood pressure outcomes by dietary intervention. The rankings are delineated for a) SBP and b) DBP, each stratified by the type of diet intervention

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Publication bias

Publication bias was assessed via an adjusted funnel plot (Additional file 5). Most of the data points were symmetrically distributed around the central axis and clustered in the central area, indicating a low level of bias across the studies. This finding might be attributed to the moderate sample sizes of most studies, which typically result in lower susceptibility to bias. However, the presence of some points extending beyond the dashed lines suggested the existence of heterogeneity among the studies.

Discussion

This network meta-analysis, for the first time synthesizes the impact of DR—which includes both IF and CER—on body weight and metabolic parameters in T2DM patients. The IF protocols examined included IER, TWF, TRE, and FMD. These findings indicate that most of the intervention regimens, both IF and CER, outperformed a ND in terms of weight management and metabolic parameter optimization. Notably, the FMD demonstrated the most pronounced improvements in body weight, blood glucose levels, and blood lipid profiles, suggesting that it may be the optimal dietary intervention strategy for enhancing the clinical outcomes of patients with T2DM.

The inconsistency test for the results revealed that, the outcome measures of weight and body mass index (BMI) may include inconsistencies between the CER-IER-TWF and IER-TWF-ND closed loops. Intermittent energy restriction (IER) typically involves limiting caloric intake during nonconsecutive periods, which is a crucial node in this loop and plays a significant role in weight reduction. In the studies included in our review, McDiarmid et al. added a Mediterranean diet to the IER regimen, and previous research has indicated that the Mediterranean diet contributes to weight loss [51], which may account for the inconsistencies observed in the loop. Regarding the secondary outcome measure of lipid profiles, inconsistencies may exist between the CER-IER-ND and CER-TWF-ND closed loops. Caloric energy restriction (CER) generally refers to calorie restriction over continuous periods, which is a key node in this loop and is important for improving lipid profiles. Compared with CER implemented in other studies included in our review, Zhang et al. implemented CER accompanied by changes in the macronutrient composition of meals, with a reduction in fat intake and an increase in fibre intake, which may have influenced the outcomes [52]. In the study by Pavlou et al., CER involved a daily energy restriction of 25% of the usual intake, equating to approximately 1800 kcal per day, which is significantly greater than the daily energy restriction observed in CER across other studies included in our review. These factors may be responsible for the inconsistencies observed in the loops. Previous research has indicated that IF may be more effective than CER in managing body weight, fat mass, and waist circumference [53]. However, other studies have reported comparable effects of IF and CER on weight loss and metabolic disorder improvement, suggesting no clear superiority of IF [54, 55]. CER improves metabolic outcomes, such as weight reduction and insulin resistance improvement, mainly by reducing daily caloric intake. The “calories in calories out” (CICO) principle underscores the pivotal role of caloric consumption adjustments in the success of dietary interventions for managing body weight and mitigating metabolic risks. The CICO (Calories In, Calories Out) theory is a straightforward concept of energy balance, positing that weight gain, loss, or maintenance is primarily determined by the relationship between the calories consumed and those expended, irrespective of the type or quality of macronutrients. If the calories consumed exceed the body’s expenditure, weight gain occurs; if they are lower, weight loss results; and when they are equal, weight remains stable [56, 57]. The CER approach, which restricts calorie intake almost daily, aims to create an energy deficit to reduce body weight, thereby improving metabolism. IF includes various regimens, each with distinct characteristics regarding caloric intake. TRE typically limits the eating window without imposing specific restrictions on daily caloric intake. However, due to the shortened eating window, calorie intake tends to decrease accordingly. Studies have indicated that, compared with standard diets, TRE can reduce energy intake by approximately 20% [58]. However, even when the calories consumed within the TRE eating window is the same as that consumed normally, TRE can still exert biological effects and improve human metabolism. TWF involves either continuous or intermittent calorie restriction on two days per week, with no restrictions on the remaining five days. Research has shown that although participants may compensate by consuming more calories on nonfasting days, the average daily caloric intake over the week is reduced [59]. During FMD, caloric intake is reduced, and after the FMD period, participants may temporarily increase their caloric intake. However, compared with that of the standard diet group, the total caloric intake of the restricted diet group remains similar [60]. Overall, in the IF dietary pattern, calorie intake during nonfasting periods may increase accordingly but may not fully compensate for the caloric deficit during fasting periods, which could be one reason for weight loss and improved metabolism. However, even when caloric intake is not affected, IF can still play a role in improving human metabolism [61]. The benefits of IF are largely attributed to metabolic shifts within the body. In the early stages of fasting, increases in hepatic glycogenolysis, white adipose tissue catabolism, fatty acid oxidation, and ketone body levels are observed in mice. These ketone bodies serve as energy sources for tissue cells. As the duration of fasting increases, ketone body levels continue to rise, becoming the primary metabolic energy source. This increase leads to metabolic shifts and a coordinated cellular stress response, promoting the clearance of damaged organelles, transient inhibition of protein synthesis, and preservation of mitochondrial function, which may contribute to the deceleration of ageing processes [28, 62,63,64]. Our study demonstrated that T2DM patients who underwent IF achieved weight loss and blood glucose level reductions comparable to those on CER. This finding indicates that IF can be a viable alternative to CER for T2DM patients, which is similar to the findings of a previous study [54, 55]. The benefits of IF extend beyond weight loss because it aligns well with the body’s biological rhythms and can be implemented in various ways. Therefore, our findings suggest that IF may serve as an effective dietary intervention for T2DM patients. However, the diversity of IF protocols in the studies included in this network meta-analysis resulted in considerable heterogeneity, potentially affecting the reliability of the findings. Future research should incorporate additional well-designed trials to enable more robust comparisons and to ascertain the optimal dietary restriction regimen for the T2DM population.

In this study, we conducted a comparative analysis of five prevalent DR regimens—TWF, TRE, IER, FMD, and CER—to evaluate their efficacy in improving body weight and metabolic parameters among patients with T2DM. Our findings indicate that the FMD is the most effective in reducing body weight, BMI, and HbA1c, and it also improves blood lipid profiles and blood pressure. TRE is the next most effective regimen. FMD are an advanced form of periodic fasting designed to mitigate adverse effects, such as hypoglycaemia and hypotension. Traditionally, the FMD diet is high in fat and low in protein and carbohydrates [64]. However, many experimental and clinical studies have implemented FMD without deliberately altering dietary composition, focusing instead on restricting food intake during fasting periods to achieve a significant reduction in caloric intake [25, 42, 45]. In this network meta-analysis, the FMD protocols predominantly consisted of a low-energy or very low-energy diet once monthly for five days over a period of three to six months. Compared with other forms of intermittent fasting, FMD necessitate minimal dietary intervention, making them more accessible and easier for participants to adhere to. Studies utilizing mouse models of both type 1 and type 2 diabetes have demonstrated that a four-day FMD can stimulate islet β-cell regeneration through the ngn3 pathway, increase insulin secretion, and maintain blood glucose homeostasis, thereby alleviating diabetes [65]. Post-FMD intervention, an approximately 10% reduction in the dosage of hypoglycaemic medications, such as metformin, was observed among T2DM patients [66]. Research has reported completion rates for FMD above 80%, both in the short term (3 months) and long term (12 months) [60, 66]. Most adverse reactions experienced by subjects undergoing FMD intervention, including dizziness, nausea, and weakness, were mild and generally tolerable and did not impede the continuation of the intervention [67, 68]. During the fasting phase of the FMD, closely monitoring the use of hypoglycaemic drugs or insulin is crucial to minimize the risk of hypoglycaemia. Evidence suggests that ceasing the intake of oral hypoglycaemic agents and short-acting insulin and adjusting the dosage of long-acting insulin during the FMD fasting period can effectively mitigate the risk of hypoglycaemia or hyperglycaemia [45]. Thus, under medical supervision, FMD can be considered a safe DR approach for T2DM patients.

TRE is distinct from other DR protocols in that it focuses on restricting the eating window rather than emphasizing caloric intake reduction. Current clinical studies of TRE typically involve a daily feeding window of 4 to 10 h, predominantly scheduled during daytime hours when humans are active. Outside of this feeding window, participants are in a fasting state, consuming only calorie-free fluids. This feeding‒fasting cycle aligns with the circadian rhythms of the body, which can lead to various health benefits, including fat reduction, decreased inflammation, increased mitochondrial volume, and enhanced cellular repair [27, 69]. Even when caloric intake is equivalent to that of the control groups, TRE has been shown to reduce body weight, improve metabolic parameters, and slow the progression of obesity and T2DM in murine models [70]. However, adherence to TRE can be challenging. In a study by Antoni et al., approximately half of the participants experienced difficulty maintaining TRE over the long term, often due to social events that temporarily disrupted their eating schedules [71]. Despite these interruptions, the metabolic benefits of TRE persist even with brief disruptions to the time-restricted feeding regimen [70]. The adverse effects associated with TRE are infrequent and generally mild and include symptoms such as dizziness and headache [72,73,74]. Overall, TRE has demonstrated acceptability and feasibility as a form of DR. Our findings also indicate that TRE positively impacts weight loss and improves blood glucose levels and other metabolic parameters in patients with T2DM.

This study also has several limitations. First, due to the limited data on ADF, which is a common form of IF, in the literature, the results are not yet comprehensive because few RCTs have focused on this topic. Second, in the absence of direct comparisons between DR regimens, such as FMD, and other dietary restrictions, indirect comparisons may not be as accurate as direct comparisons because they rely on multiple assumptions, including the transitivity of intervention effects. Furthermore, the number of studies reviewed in this meta-analysis was relatively small, which precluded subgroup analyses. Additionally, each comparison of different forms of DR was based on a limited number of randomized controlled trials, leading to insufficient sample sizes that may affect the accuracy of the results. Therefore, well-designed, multicentre, large-sample RCTs are needed to validate our findings.

In summary, this network meta-analysis is the first systematic comparison of the effects of different forms of IF and CER on body weight and metabolic parameters in individuals with T2DM. From a clinical perspective, our findings suggest that the FMD may be the primary DR option for reducing body weight and blood glucose and improving lipid profiles and blood pressure in T2DM patients. Furthermore, we determined the therapeutic effects of various DR regimens on T2DM patients, as well as their efficacy rankings. Therefore, our study has clear clinical significance and provides robust evidence to support clinical decision-making. These findings may be applicable to the development of the latest clinical practice guidelines, offering valuable decision-making references for clinicians.

Conclusions

The current evidence supports the conclusion that both IF and CER have beneficial effects on weight management and metabolic parameter improvement in patients with type 2 diabetes mellitus (T2DM), with FMD within IF showing the most significant effects. Nonetheless, further robust RCTs that directly compare the effects of various IF regimens with each other and with CER regimens are warranted to substantiate these findings.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ADF:

Alternate day fasting

BMI:

Body mass index

CER:

Continuous energy restriction

CI:

Confidence intervals

DBP:

Diastolic blood pressure

DR:

Dietary restriction

FBG:

Fasting blood glucose

FMD:

Fasting-mimicking diet

HbA1c:

Glycated haemoglobin A1c

HDL:

High-density lipoprotein

IER:

Intermittent energy restriction

IF:

Intermittent fastin

LDL:

Low-density lipoprotein

MD:

Mean differences

ND:

Normal diet

RCT:

Randomized controlled trials

SBP:

Systolic blood pressure

SUCRA:

The surface under the cumulative ranking

TC:

Total cholesterol

TG:

Triglycerides

TRE:

Time-restricted eating

TRF:

Time-restricted feeding

TWF:

Twice-per-week fasting

T2DM:

Type 2 diabetes mellitus

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Acknowledgements

We would like to thank Springer Nature Author Services for English language editing.

Funding

The work was supported by the Natural Science Foundation of China (NO.82270358; NO.U22A20286); Sichuan Science and Technology Program (NO.2022YFS0617, NO.2023YFS0417, NO.2023ZYD0095) and Sichuan Province cadre health research project (NO. ZH 2022 − 1501).

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Authors and Affiliations

  1. Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China

    Xin Zeng, Zong-zhe Jiang & Yong Xu

  2. School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China

    Qi-pei Ji

  3. Metabolic Vascular Disease Key Laboratory of Sichuan Province, Sichuan, China

    Zong-zhe Jiang & Yong Xu

  4. Sichuan Clinical Research Center for Nephropathy, Luzhou, Sichuan, China

    Zong-zhe Jiang & Yong Xu

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  1. Xin Zeng
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XZ, Z-z J and YX conceived the study and provided methodological support. XZ and Q-p J designed the search strategy, screened the studies, extracted the data, assessed the risk of bias and wrote the manuscript. All authors have read and approved the final manuscript.

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Zeng, X., Ji, Qp., Jiang, Zz. et al. The effect of different dietary restriction on weight management and metabolic parameters in people with type 2 diabetes mellitus: a network meta-analysis of randomized controlled trials. Diabetol Metab Syndr 16, 254 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-024-01492-9

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-024-01492-9

Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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