The efficacy of low-carbohydrate diets on glycemic control in type 2 diabetes: a comprehensive overview of meta-analyses of controlled clinical trials

Abstract

Objective

This umbrella meta-analysis comprehensively evaluates the effectiveness of low-carbohydrate diets (LCDs) in managing glycemic control among individuals with type 2 diabetes mellitus (T2DM).

Methods

Following PRISMA guidelines and registered in the PROSPERO database for transparency, a thorough literature search was conducted across key databases, including EMBASE, Scopus, and PubMed. The review focuses on randomized controlled trials (RCTs) targeting adults with T2DM, with methodological quality assessed using the AMSTAR2 tool.

Results

The findings reveal that LCDs resulted in significant reductions in hemoglobin A1c (HbA1c) levels in 16 out of 21 studies, indicating substantial short-term improvements in glycemic control. However, four studies reported no significant changes, highlighting individual variability in responses. While some studies showed reductions in fasting blood glucose, results were inconclusive overall. Insulin sensitivity improved during the short term, but these benefits were not sustained over the long term.

Conclusion

Overall, the analysis suggests that LCDs provide considerable short-term glycemic control benefits for T2DM patients, alongside potential long-term advantages. Further research is necessary to develop personalized dietary strategies that address the diverse needs of different populations, ultimately enhancing the effectiveness of diabetes management.

Introduction

Diabetes mellitus, especially type 2 diabetes (T2DM), has emerged as a significant global health challenge, impacting millions of people worldwide [1]. According to the International Diabetes Federation, approximately 463 million adults were living with diabetes in 2019, and this number is projected to rise to 700 million by 2045 [2]. This condition is primarily characterized by insulin resistance and disturbances in glucose metabolism, which can lead to a range of serious complications [3]. Among these complications are cardiovascular disease, neuropathy, and nephropathy, all of which significantly contribute to morbidity and mortality rates associated with diabetes [4]. The management of T2DM primarily emphasizes weight loss, as excess body weight is a major factor in insulin resistance and inadequate blood sugar control [5]. While pharmacological therapy (e.g., metformin, SGLT2 inhibitors) remains essential, these interventions often present limitations including side effects (gastrointestinal distress in 20–30% of patients) and high costs [6, 7]. Similarly, while herbal remedies show promise in recent studies, challenges remain regarding standardization and long-term safety [8, 9]. Effective weight management is critical for improving glycemic control and minimizing the risk of complications associated with diabetes [10].

Various strategies, including dietary modifications and increased physical activity, are employed to facilitate weight loss [11]. Systematic reviews and meta-analyses indicate that diets characterized by a low glycemic index, high fiber content, and Mediterranean principles can enhance glucose metabolism [12]. Among these approaches, low-carbohydrate diets (LCD) have garnered significant attention for their potential effectiveness in managing T2DM [13]. The LCD is defined as a program in which carbohydrate intake is limited to less than 130 g per day, accounting for approximately 26% of total daily energy intake from carbohydrates [14]. LCD emphasizes the reduction of carbohydrate intake, which has been shown to improve glycemic control and facilitate weight loss [15]. Studies have demonstrated that such diets can significantly decrease fasting blood glucose levels and lower hemoglobin A1c (HbA1c), positioning them as effective strategies for managing T2DM [16]. By promoting more stable blood sugar levels and aiding in weight management, LCDs are instrumental in minimizing the risk of diabetes-related complications.

Several meta-analyses have highlighted the effectiveness of LCDs in improving glycemic control [15, 17–19]. However, some studies do not support the long-term use of LCDs for maintaining glycemic control in individuals [16, 20]. This umbrella study aims to synthesize existing evidence regarding the effectiveness of LCDs in managing glycemic control in T2DM.

Methods

The umbrella review was conducted following the principles outlined in the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines [21]. The protocol for the umbrella study has been registered in the PROSPERO database (CRD42024620490).

Search strategy and study selection

The scientific databases EMBASE, Scopus, Web of Science, Cochrane Central Library, and PubMed were searched from their inception through 24-07-2025 with all searches completed on this date to ensure consistency and comprehensiveness.The search strategy was formulated using the following MeSH terms and keywords: (((“Diet, Carbohydrate-Restricted“[Mesh] OR “Diet, High-Protein Low-Carbohydrate“[Mesh]) OR (((“carbohydrate-restricted diet“[Title/Abstract]) OR (“carbohydrate restriction“[Title/Abstract])) OR (“low carbohydrate diet“[Title/Abstract]))) AND (((meta-analysis[Title/Abstract]) OR (meta[Title/Abstract])) OR (Meta-analysis[Publication Type]))) AND ((“Diabetes Mellitus, Type 2“[Mesh]) OR (((“type 2 diabetes mellitus“[Title/Abstract]) OR (“Diabetes“[Title/Abstract])) OR (T2DM[Title/Abstract]))).

Inclusion and exclusion criteria

This umbrella meta-analysis synthesized data from randomized controlled trials (RCTs) investigating the effects of LCDs and related dietary interventions on glycemic control in individuals with confirmed T2DM. The primary outcomes assessed included changes in glycated hemoglobin (HbA1c), fasting plasma glucose (FPG), and related metabolic markers, comparing LCDs to control or alternative diets.

The inclusion criteria focused on individuals aged 18 years or older, diagnosed with T2DM, and having a body mass index (BMI) in the normal, overweight, or obese range. Eligible participants had a history of treatment with specific dietary regimens or glucose-lowering medications. They demonstrated a willingness to adhere to dietary interventions and participate in long-term follow-up assessments.

Exclusion criteria comprised individuals with type 1 diabetes, gestational diabetes, or other forms of secondary diabetes. Participants with severe comorbidities, including advanced renal failure, liver disease, or cardiovascular conditions, were excluded. Additionally, pregnant or breastfeeding women, those with a history of eating disorders, and individuals unwilling to comply with dietary or follow-up requirements were not included. Studies of low methodological quality, such as in vitro, in vivo, or ex vivo research, case reports, quasi-experimental studies, and controlled clinical trials categorized as low quality, were also excluded.

Methodological quality assessment

Two independent reviewers (SA and YY) utilized the Assessing the Methodological Quality of Systematic Reviews 2 (AMSTAR2) questionnaire to evaluate the methodological quality of the included articles. Any discrepancies in their assessments were resolved through discussion with the senior author CY. The AMSTAR2 questionnaire consists of 16 items, each rated as ‘Yes,’ ‘Partial Yes,’ ‘No,’ or ‘Not a Meta-analysis.’ The results from the AMSTAR2 checklist were categorized into four quality levels: “Critically low quality,” “low quality,” “moderate quality,” and “high quality.”

Study selection and data extraction

Data from eligible studies were extracted by two independent reviewers (SA and YY). Initially, the titles and abstracts of the articles were screened. Articles were then evaluated for full-text availability and excluded if they did not pertain to our research question. Subsequently, relevant data were collected and organized in a standardized Excel spreadsheet, which included the first author’s name, year of publication, sample size, participants’ age and gender, country of study, journal of publication, and health condition. Any discrepancies in the data extraction process were resolved through discussions with a third reviewer.

Results

Study selection

All the studies included in the umbrella meta-analysis utilized randomized controlled trial (RCT) designs, ensuring methodological rigor. Out of 76 articles identified, 10 were excluded due to duplication. Of the remaining studies, 46 focused on diabetes and underwent detailed evaluation. After applying the inclusion criteria, 21 studies were deemed eligible which are summarized in Fig. 1.

Fig. 1.

PRISMA Flowchart of the study selection process

Results of individual studies

Participant sample sizes: distribution across large-scale, moderate, and small-scale studies

The number of participants in the included studies varied significantly, reflecting the diversity in study scale and design. Large-scale trials, with more than 1000 participants, were conducted in 10 studies, such as Silverii et al. (2020) [22] with 3301 participants, with 2412 participants, Huntriss et al. (2017) [23] with 2204 participants, Korsmo-Haugen et al. (2019) [24] with 2178 participants, Apekey et al. (2022) [25] with 1391 participants, Snorgaard et al. (2017) [19] with 1376 participants, Goldenberg et al. (2020) [26] with 1357 participants Zaki et al. (2022) [27] with 1287 participants, Fan et al. (2016) [17] with 1080 participants, Luo et al. (2022) [28] with 1074 participants and McArdle et al. (2019) [29] with 2132 participants (Table 11). These studies provided robust statistical power and offered generalizable results.

Table 1.

Study characteristics investigating the impact of LCD on glycemic control in type 2 diabetes

Author ID	Study Design	Male/Female	Age (Years)	Study Number	Participants (N)	Duration of intervention (Months)	Interventional Diet	Comparison Diet	The mean duration of diabetes	Main Outcomes	Medication	Effect Size (CI)
Kirk et al. (2008)	RCT	NR	58	13	T2DM (263)	3.33 months	LCD	Control diet	NR	↓ HbA1c ↓ Glucose	Doses of insulin or oral hypoglycemic agents	[MD: -2.295% CI: (-2.98, -1.42)] [MD: -48.0 mg/dL 95% CI: (-70.34, -25.66)
Snorgaard et al. (2016)	RCT	M	58.5	10	T2DM (1376)	11.5 months	LCD (energy percentage below 45%)	HCD	NR	↓ HbA1c at 3 and 6 months No significant effect on HbA1c at 1 year	Glucose-lowering medication	[MD :0.34%, 95% CI: (0.06-0.63)] -
Fan et al.(2016)	RCT	F/M	>18	10	T2DM (1080)	14.2 months	LCD (intake of 130 g of carbohydrates per day with any other type of diet)	Caloric restriction	NR	↓ HbA1c	NR	[WMD: -0.33%; 95% CI: ( -0.51%, -0.151%)]
Meng et al.(2017)	RCT	NR	NR	9	T2DM (734)	11.25 months	LCD (<130 g carbohydrate/day or 26% of daily energy from carbohydrates)	Normal or high carbohydrate diet	NR	No significant effect on FPG concentration ↓ HbA1c	NR	- [WMD: 0.44; 95% CI: (0.61-0.26 )]
Huntriss et al. (2017)	RCT	NR	>18	18	T2DM (2204)	14.5 months	LCD	Control diet		↓ HbA1c	Diabetes medications	[MD: -0.28%, 95% CI: ( -0.53, -0.02)]
Korsmo-Haugen et al. (2019)	RCT	NR	NR	23	Normal, overweight and obese T2DM (2178)	13.5 months	LCD-group (very low-carbohydrate diets, VLCD: 21–70 g carbohydrates and moderate LCD: 30-40 E% carbohydrates)	HCD	1-17 years	↓ HbA1c	Insulin therapy, anti-hypertensive drugs, lipid lowering medications, oral hypoglycemic agents such as metformine, sulfonylurea, thiazolidinedione	Total: (MD : -0.09%, 95% CI -0.17, -0.01)
McArdle et al. (2019)	RCT	NR	NR	25	T2DM (2132)	1.08 months	Restricted the proportion or quantity of dietary carbohydrate	An active control diet that does not include carbohydrate restriction	NR	No effects on HbA1C Restriction of carbohydrate to 50–130 g per day has beneficial effects on HbA1c in trials up to 6 months	Diabetes medication	- [WMD -0.49% (-0.75, -0.23)]
Goldenberg et al. (2020)	RCT	F	57	23	T2DM (1357)	17.14 months	LCD (<10% daily calories from carbohydrates or <50 g/d	Any active controls including competing dietary programs higher in carbohydrates (≥26%)	NR	↓ HbA1c at 6 months ↓ HbA1c at 12 months ↓ FPG at 6 months no difference in FPG at 12 months	Diabetes medication	[MD:–0.47%, 95% CI: (–0.60, –0.34)] [MD:–0.23%, 95% CI: (–0.46% - 0.00%)] [MD:–0.73, 95% CI: (–1.19,–0.27)] -
Silverii et al. (2020)	RCT	NR	58.25	37	T2DM (3301)	17.88 months	LCD ( 26–45% of total calories from carbohydrates))/VLCD(< 26% of total calories from carbohydrates and/or less than 130 g of carbohydrates daily)	Balanced Carbohydrate diets( 5–60% of total calories from carbohydrates)	NR	↓ HbA1c at 3 months No difference at 6 and 12 months ↑ HbA1c at 24 months ↓ HbA1c at 3 months ↓ HbA1c at 6 months No difference at 12 and 24 months	NR	LCD: [MD - 0.17%, 95% CI: ( - 0.27, - 0.07)] - LCD: [MD 0.23%, 95% CI: (0.02, 0.44)] VLCD: [MD: - 0.43% 95% CI: ( - 0.60, - 0.26%) VLCD: [MD: - 0.40% 95% CI: ( - 0.59, - 0.22)] -
Choi et al. (2020)	RCT	F	44	14	T2DM (734)	1.41 months	Very low-calorie-ketogenic diet, low carbohydrate, ketogenic diet, low-carbohydrate ketogenic diet, very low-carbohydrate ketogenic diet	comparator diets	NR	↓ FPG ↓HbA1c	NR	[KD: SMD, -0.25; 95% CI: ( -0.50, -0.00)] [KD : SMD, -0.48; 95% CI : ( -0.69 ,-0.27)]
Yuan et al. (2020)	RCT	NR	41	13	T2DM (567)	6.46 months	KD	Control diet	7-10 year	↓ FBG ↓HbA1c	Glucose-lowering medications	[MD:1.29 mmol/L 95% CI: (-1.78 to -0.79)] [MD:1.07 95% CI: (-1.37 to -0.78)]
Li et al. (2021)	RCT	F	59	12	T2DM (761)	9.43 months	(<26% of daily calories from carbohydrates or <130 g/day	LFD	8.9-14.13 years	↓ HbA1c Benefcial effect of LCDs on HbA1c started from short terms 3–4 months ; and 6–8 months Persists at 1.5 years disappeared at 2 years	NR	Total: [WMD : 0.35% 95% CI: ( -0.45, -0.24)] [WMD = -0.41%; 95% CI: (-0.57, -0.25)] [WMD = -0.34%; 95% CI: ( -0.52, -0.15)], [WMD = -0.4%; 95% CI: (-0.63, -0.16)] a [WMD = -0.01%; 95% CI: ( -0.34, -0.32)]
Rafiullah et al.(2021)	RCT	F/M	57.5	8	T2DM (648)	11.25 months	VLCK( carbohydrate intake < 50 g/d or < 10% of total energy)	Control diet( Carbohydrate content: 37% to 60% of the TEI)	NR	↓ HbA1c at 3 months ↓ HbA1c at 6 months	Antidiabetic medications	[WMD:- 0.61%; 95%CI: ( -0.82 ,- 0.40)] [WMD: -0.58%; 95%CI: ( -0.85 - 0.32)]
Luo et al. (2022)	RCT	F/M	34.25	21	Obese/Overweight patients with T2DM (1074)	14.5 months	LCKD/VLCKD	Non-ketogenic diet	NR	↓ HbA1c ↓ FPG	NR	[SMD, -0.53; 95% CI: ( -0.78 , -0.29)] [SMD, -0.75; 95% CI: (-1.15 to -0.35)]
Parry-Strong et al. (2022)	RCT	M	58.3	8	T2DM (606)	11.25 months	VLC/KDs (≤50 g carbohydrate or ≤10% total energy from carbohydrate per day)	Diet containing a carbohydrate content above this threshold	NR	No significant effect on HbA1c ↓HbA1cat 12 months	Oral hypoglycemic agents and insulin	- [MD:-0.65% 95% CI: ( -0.99 to -0.31)]
Zhou et al. (2022)	RCT	NR	43	8	Overweight T2DM (611)	7.5 month	KD	Other types of diets		No significant differences on fasting glucose, fasting insulin, HOMA-IR ↓HbA1c	antidiabetic treatment)	- [SMD: -0.38; 95% CI: ( -0.61 to -0.16)]
Zaki et al. (2022)	RCT	M/F	55	16	Normal weight and obese T2DM (1287)	11.5 months	LCD (14-20% total energy from carbohydrates), KD(daily intake; 30–50 g of carbohydrate, 60 g protein, 130 g fat, and 1500±50 kcal of total calories), VLCD(14% total energy from carbohydrates, 28% protein, and 58% total fat (35%,LCKD (< 20 g daily intake of dietary carbohydrate), VLCK (a very low-calorie diet consisting of 600- 800 kcal/day, low carbohydrate of <50 g/day from vegetables and lipids (10 g from olive oil daily)	Other diets	NR	↓ HbA1c	NR	LCD: (SMD : -0.27%; 95% CI; -0.60%, 0.07% KD : (SMD: -1.45%; 95% CI; -2.73%, -0.17%)
Apekey et al. (2022)	RCT	F/M	51.9	22	Overweight or obese with T2DM (1391)	11.5 months	LCD ( (<26% of total energy or <130 g of carbohydrate a day) VLCD: Reduce carb intake to between 20–50 g of nonfiber carbohydrates a day KD: carbohydrate 30–50 g, protein 60 g, fat 130 g, and total calories (1500 ± 50) Kcal LCKD: Reduce carbohydrate intake over 7–10 days to between 20–50 g of carbohydrates a day with the goal of achieving nutritional ketosis	LFD	0.3–13.5 years	No significant differences in fasting glucose levels at 3, 6, 12, and 24 months ↓ HbA1c at 3 months No differences in HbA1C at 6, 12, and 24 months ↓ Fasting insulin at 3 months No significant differences in insulin at 6, 12, and 24 months ↓ HOMA-IR at 3 months No significant difference in HOMA2-%B at all time points	Oral glucose-lowering medication	- [MD: -0.41% 95% CI: (-0.62, -0.20)] - [MD: -2.83 µIU/mL 95% CI( -4.73, -0.93)] - [MD: -0.71 95% CI(-1.05, -0.37)] -
Choy et al. (2023)	RCT	NR	51	11	T2DM (541)	12.25 months	KD	Isocaloric high CHO diet	NR	↓HbA1cat 3–6 months No statistically significant differences in HbA1c at other time points	NR	[SMD: 0.59; 95%CI: ( 1.18 to - 0.01)]
Ichikawa et al. (2024)	RCT	NR	NR	6	Overweight and obese T2DM (524)	18 months	LCD	Low-fat diets, a standard recommended American Diabetes Association calorie-restricted diet, a traditional diabetic diet, high-carbohydrate and protein-restricted diet	NR	No significant differences in changes in HbA1c	NR	-
Hironaka. et al. (2024)	RCT	NR	56.75	6	Overweight T2DM (400)	9 months	LCD (<50–130 g/day)	Low fat diet, Traditional diabetic diet, IBW(kg) 9 25 kcal/day, IBW(kg) 9 28 kcal/day	NR	↓ HbA1c	NR	[SMD: -0.55%, 95% CI: (-0.77 ,-0.33)]

Moreover, moderate-sized trials studies comprised 9 studies such as Li et al. (2021) [30] with 761 participants, Meng et al. (2017) [18] with 734 participants, Choi et al. (2020) [31] with 734 participants, Rafiullah et al. (2021) [32] with 648 participants, Zhou et al. (2022) [33] with 611 participants, Parry-Strong et al. (2022) [34] with 606 participants, Yuan et al. (2020) [35] with 567 participants, Choy et al. (2023) [36] with 541 participants and Ichikawa et al. (2024) [37] with 524 participants (Table 1).

Smaller-scale trials (< 500 participants) were conducted in 2 studies, including Hironaka. et al. (2024) [38] with 400 participants, and Kirk et al. (2008) [39] with 263 participants (Table 1). These smaller studies allowed for more detailed and focused investigations, albeit with less statistical power than larger trials.

Age distribution of participants

The age distribution of participants in the included studies primarily focused on middle-aged adults, with limited inclusion of younger and older populations. The majority of studies, a total of 15, reported mean participant ages ranging from 35 to 65 years. For instance, studies such as Kirk et al. (2008) [39] (mean age 58 years), Snorgaard et al. (2016) [19] (58.5 years), Goldenberg et al. (2020) [26] (57 years), Li et al. (2021) [30] (59 years), Rafiullah et al. (2021) [32] (57.5 years), Hironaka et al. (2024) [38] (56.75 years), and Silverii et al. (2020) [22] (58.25 years) exemplify this focus. Additionally, Parry-Strong et al. (2022) [34] (58.3 years), Choy et al. (2023) [36] (51 years), Apekey et al. (2022) [25] (51.9 years), and Zaki et al. (2022) [27] (55 years) similarly targeted this demographic (Table 1). These studies primarily involved individuals at higher risk for T2D and metabolic complications, emphasizing the relevance of dietary interventions in this age group.

In contrast, younger participants with a mean age below 35 years were included in only one study, Luo et al. (2022) [28], which reported a mean age of 34.25 years. This limited representation reflects a gap in addressing early-onset diabetes or pre-diabetic conditions in younger populations. Furthermore, no studies exclusively focused on older adults with a mean age exceeding 65 years, although some included older participants as part of their broader age range. Several studies, including Meng et al. (2017) [18], Huntriss et al. (2017) [23], Korsmo-Haugen et al. (2019) [24], McArdle et al. (2019) [29], and Ichikawa et al. (2024) [37], did not provide specific age data, which limits the generalizability of their findings (Table 1). Overall, the predominant focus on middle-aged adults highlights the demographic most affected by T2D and underscores the importance of tailored dietary interventions for this population.

Evaluating short-term and long-term effects of LCDs

The studies included in this review evaluated the short-term and long-term effects of LCDs on individuals with T2DM. Short-term interventions, defined as those lasting six months or less, were conducted in three studies, with intervention durations ranging from 1.08 months in McArdle et al. (2019) [29] to 3.33 months in Kirk et al. (2008) [39] and Choi et al. (2020) [31] to 1.41 months (Table 1). These studies primarily focused on examining the immediate effects of LCD on metabolic markers, particularly HbA1c and glucose levels.

Long-term interventions, defined as those lasting more than six months were conducted in four studies, including Yuan et al. (2020) [35] with a duration of 6.46 months, Li et al. (2021) [30] with 9.43 months, Zhou et al. (2022) [33] with 7.5 months, and Hironaka et al. (2024) [38] with 9 months (Table 1). These studies aimed to evaluate the prolonged effects of LCD on metabolic outcomes such as HbA1c and fasting glucose. The extended duration of these interventions allowed for the assessment of the sustainability of LCDs and their long-term impact on diabetes management.

The following studies evaluated the long-term effects ofLCDs for durations longer than 10 months: Snorgaard et al. (2016) [19] with 11.5 months, Fan et al. (2016) [17] with 14.2 months, Meng et al. (2017) [18] with 11.25 months, Huntriss et al. (2017) [23] with 14.5 months, Korsmo-Haugen et al. (2019) [24] with 13.5 months, Goldenberg et al. (2020) [26] with 17.14 months, Silverii et al. (2020) [22] with 17.88 months, Rafiullah et al. (2021) [32] with 11.25 months, Luo et al. (2022) [28] with 14.5 months, Parry-Strong et al. (2022) [34] with 11.25 months, Zaki et al. (2022) [27] with 11.5 months, Apekey et al. (2022) [25] with 11.5 months, Choy et al. (2023) [36] with 12.25 months, and Ichikawa et al. (2024) [37] with 18 months (Table 1). These studies were instrumental in examining the sustained impact of LCD on metabolic outcomes, particularly focusing on the long-term management of T2DM, highlighting their effectiveness and sustainability over extended intervention periods.

Overview of low-carbohydrate dietary interventions: lcds, vlcds, and KDs in reviewed studies

The studies reviewed in this analysis primarily focused on various low-carbohydrate dietary interventions, including LCDs, VLCDs, and Ketogenic Diets (KDs), to assess their effects on individuals with T2DM.

Among the studies, Kirk et al. (2008) [39], Snorgaard et al. (2016) [19], Fan et al. (2016) [17], Meng et al. (2017) [18], Huntriss et al. (2017) [23], Goldenberg et al. (2020) [26], Silverii et al. (2020) [22], Zaki et al. (2022) [27], Apekey et al. (2022) [25], Ichikawa et al. (2024) [37], and Hironaka et al. (2024) [38], McArdle et al. (2019) [29] administered LCDs as the primary intervention. Notably, Korsmo-Haugen et al. (2019) [24] and Silverii et al. (2020) [22] not only included LCDs but also implemented VLCDs as part of their interventions (Table 1).

Further, Li et al. (2021) [30] exclusively used VLCDs, while Zaki et al. (2022) [27] utilized a combination of LCD, VLCD, LCKD, and VLCKD. Additionally, Apekey et al. (2022) [25] incorporated LCD, VLCD, KD, and LCKD into their dietary protocols. Choi et al. (2020) [31] focused specifically on KDs, while Yuan et al. (2020) [35] used a KD as the intervention. Rafiullah et al. (2021) [32] and Luo et al. (2022) [28] implemented VLCK and LCK D/VLCKD interventions, respectively. Parry-Strong et al. (2022) [34] used VLC/KDs (≤ 50 g carbohydrates or ≤ 10% of total energy from carbohydrates per day), and Zhou et al. (2022) [33] only used KDs (Table 1).

This wide variety of dietary approaches, including LCD, VLCD, and KD, highlights the flexibility and adaptability of low-carbohydrate interventions in the management of T2DM, with different studies combining one or more of these dietary strategies to assess their effects on metabolic outcomes such as HbA1c, fasting glucose, and insulin sensitivity. The diversity of interventions across the studies underscores the need for further research to determine the most effective and sustainable low-carbohydrate diet strategy for diabetes management.

Comparison groups: assessing low-fat, high-carbohydrate, and calorie-restricted diets in LCD studies

Control diets in the reviewed studies included a range of dietary approaches to compare with low-carbohydrate interventions. Examples include general or high-carbohydrate diets (HCDs), as seen in Kirk et al. (2008) [39] and Snorgaard et al. (2016) [19], where carbohydrate intake exceeded the low-carbohydrate threshold. Caloric restriction without specific carbohydrate limitations was applied by Fan et al. (2016) [17], while Meng et al. (2017) [18] implemented normal or high-carbohydrate diets reflecting typical dietary patterns (Table 1).

Low-fat diets (LFDs) served as controls in Apekey et al. (2022) [25] and Ichikawa et al. (2024) [37], with carbohydrate intake higher than those in the intervention groups. Non-ketogenic diets were utilized by Luo et al. (2022) [28], while calorie-restricted diets aligned with the American Diabetes Association (ADA) guidelines were followed by Ichikawa et al. (2024) [37]. Isocaloric high-carbohydrate diets were employed by Choy et al. (2023) [36] to maintain energy balance. Some studies, including Goldenberg et al. (2020) [26], used dietary programs where carbohydrate intake exceeded 26% of total daily energy (Table 1).

These control strategies facilitated meaningful comparisons with LCD and underscored the relative metabolic advantages of the latter.

Medication use in LCDs

Various studies have explored the use of different diabetes medications, including insulin therapy, oral hypoglycemic agents, and glucose-lowering treatments. Kirk et al. (2008) [39] and Parry-Strong et al. (2022) [34] focused on insulin and oral hypoglycemic agents, while Snorgaard et al. (2016) [19] and Yuan et al. (2020) [35] examined glucose-lowering medications. Huntriss et al. (2017) [23], McArdle et al. (2019) [29], and Goldenberg et al. (2020) [26] broadly discussed diabetes medications and their role in blood glucose control. Korsmo-Haugen et al. (2019) [24] provided a more comprehensive approach, including insulin therapy, antihypertensive drugs, lipid-lowering agents, and specific oral hypoglycemic medications such as metformin, sulfonylureas, and thiazolidinediones. Additionally, Rafiullah et al. (2021) [32] and Zhou et al. (2022) [33] emphasized the importance of antidiabetic treatments in managing diabetes and its related complications. Finally, Apekey et al. (2022) [25] concentrated on oral glucose-lowering medications as a primary intervention (Table 1). Together, these studies underline the variety of pharmacological strategies employed in diabetes management.

While these studies did not provide information regarding the use of medications, they included Hironaka et al. (2024) [38], Ichikawa et al. (2024) [37], Choy et al. (2023) [36], Zaki et al. (2022) [27], Luo et al. (2022) [28], Li et al. (2021) [30], Choi et al. (2020) [31], Silverii et al. (2020) [22], Meng et al. (2017) [18], and Fan et al. (2016) [17] (Table 1).

Impact of LCDs on glycemic control

Results on HbA1c levels from low-carbohydrate diets

The impact of LCDs on glycemic control has been assessed in 21 studies, with significant reductions in HbA1c reported in 12. Specifically, Kirk et al. (2008) [39], Fan et al. (2016) [17], Huntriss et al. (2017) [23], Meng et al. (2017) [18], Korsmo-Haugen et al. (2019) [24], Choi et al. (2020) [31], Yuan et al. (2020) [35], Li et al. (2021) [30], Zhou et al. (2022) [33], Luo et al. (2022) [28], Zaki et al. (2022) [27], and Hironaka et al. (2024) [38] all demonstrated notable decreases in HbA1c levels (Table 1). These findings suggest that LCDs can effectively improve glycemic control, particularly in the short term.

In contrast to the studies reporting significant reductions in HbA1c, 5 studies found no significant effects on HbA1c levels. Snorgaard et al. (2016) [19] reported no significant effect after one year of intervention, while McArdle et al. (2019) [29] found no effects on HbA1c at all. Similarly, Parry-Strong et al. (2022) [34] noted no significant impact on HbA1c. Lastly, Ichikawa et al. (2024) [37] reported no significant differences in changes in HbA1c (Table 1). These findings highlight the variability in outcomes associated with low-carbohydrate diets, suggesting that while LCDs may benefit some individuals, they do not yield consistent results across all studies.

Mixed results on HbA1c levels from LCDs over time

Many studies have investigated the impact of LCDs on HbA1c levels across different periods. Several studies have examined the effects of LCDs on HbA1c levels over varying timeframes. Snorgaard et al. (2016) [19] reported a reduction in HbA1c at 3 and 6 months, but no significant effect was observed at 1 year. McArdle et al. (2019) [29] found that while there were no overall effects on HbA1c, restricting carbohydrates to 50–130 g per day could have beneficial effects, particularly within trials lasting up to 6 months. Goldenberg et al. (2020) [26] demonstrated reductions in HbA1c at both 6 and 12 months.

In the short term, Silverii et al. (2020) [22] noted a decrease in HbA1c at 3 months, but found no difference at 6 and 12 months, with an increase at 24 months. Li et al. (2021) [30] indicated that the beneficial effects of LCDs on HbA1c began within the first 3–4 months and persisted for up to 1.5 years, but disappeared by 2 years. Rafiullah et al. (2021) [32] reported reductions in HbA1c at both 3 and 6 months.

However, some studies showed no significant effects on HbA1c at extended time points. Parry-Strong et al. (2022) [34] noted no significant impact on HbA1c, although a decrease was observed at 12 months. Apekey et al. (2022) [25] reported a decrease in HbA1c at 3 months but found no differences at 6, 12, and 24 months, while Choy et al. (2023) [36] similarly found a decrease at 3–6 months with no statistically significant differences at other time points. Collectively, these findings highlight the mixed and time-dependent effects of LCD on HbA1c levels (Table 11).

Impact of LCDs on fasting glucose levels

Several studies assessed the effects of LCDs on fasting glucose levels. Kirk et al. (2008) [39] reported a decrease in glucose levels, while Choi et al. (2020) [31] and Yuan et al. (2020) [35] also observed reductions in fasting plasma glucose (FPG), respectively. Luo et al. (2022) [28] similarly found a decrease in FBG. However, Meng et al. (2017) [18] reported no significant effect on FPG concentration, indicating variability in responses. Additionally, Apekey et al. (2022) [25] found no significant differences in fasting glucose levels at 3, 6, 12, and 24 months. Goldenberg et al. (2020) [26] noted a decrease in FPG at 6 months, further emphasizing the mixed outcomes associated with LCDs on glucose levels. Additionally, Zhou et al. (2022) [33] reported no significant differences in fasting glucose levels (Table 1).

Impact of LCDs on insulin and related metabolic markers

Zhou et al. (2022) [33] reported no significant differences in fasting insulin and fasting glucose levels. Apekey et al. (2022) [25] also noted a decrease in fasting insulin at 3 months but found no significant differences at 6, 12, and 24 months, as well as a reduction in HOMA-IR at 3 months with no significant difference in HOMA2B-% at any time point. Collectively, these findings highlight the mixed and time-dependent effects of LCDs on HbA1c and related metabolic markers (Table 1).

Results of quality assessment of articles using the AMSTAR checklist

In this study, the quality of 21 articles was assessed using the AMSTAR checklist. The findings revealed that Rafiullah et al. (2021) [32]achieved the highest quality score of 16, followed closely by Li et al. (2021) [30] and Hironaka et al. (2024) [15], both scoring 15. Other notable articles, such as Parry-Strong et al. (2022) [34], Zhou et al. (2022) [33], and Apekey et al. (2022) [25] also garnered scores of 15, indicating robust methodologies and compelling results. In the medium-quality category, Korsmo-Haugen et al. (2019) [24] and McArdle et al. (2019) [29] scored 13, while Choy et al. (2023) [36] and Ichikawa et al. (2024) [16] also received scores of 13. Conversely, Kirk et al. (2008) [39] and Yuan et al. (2020) [35] received the lowest scores of 4, highlighting significant weaknesses in their methodologies. Other articles, including Snorgaard et al. (2016) [19] (score of 12), Fan et al. (2016) [17] (score of 11), Meng et al. (2017) [18] (score of 12), Huntriss et al. (2017) [23] (score of 12), Luo et al. (2022) [28] (score of 13), Goldenberg et al. (2020) [26] (score of 11), Silverii et al. (2020) [20] (score of 14), Choi et al. (2020) [31] (score of 14), Zaki et al. (2022) [27] (score of 13), contributed to a diverse range of scores, showcasing variability in quality across the 21 studies (Table 2). Overall, the results indicate that most articles display good quality, especially those published in 2021 and beyond, with high-scoring articles typically employing comprehensive research methods and presenting convincing findings. In contrast, lower-scoring articles may benefit from reevaluating their methodologies and analyses. These findings can assist researchers and authors in identifying strengths and weaknesses in their work, fostering improvements in future research endeavors.

Table 2.

Results of the assessment of the methodological quality of the meta-analyses

Study	Q11	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	Q12	Q13	Q14	Q15	Q16	Quality score
Kirk et al. (2008)	+	-	+	-	-	-	-	+	-	-	+	-	-	-	-	-	4
Snorgaard et al. (2016)	+	-	+	-	+	+	+	+	+	+	+	+	-	+	-	+	12
Fan et al.(2016)	+	-	+	+	+	+	+	+	-	-	+	-	-	+	+	+	11
Meng et al.(2017)	+	-	+	+	+	+	+	+	-	+	+	-	-	+	+	+	12
Huntriss et al. (2017)	+	-	+	+	+	-	+	+	+	+	+	+	+	-	-	+	12
Korsmo-Haugen et al. (2019)	+	+	+	+	+	+	-	+	+	+	+	+	+	-	+	-	13
McArdle et al. (2019)	+	+	+	+	+	+	-	+	+	+	+	+	+	-	-	+	14
Goldenberg et al. (2020)	+	+	+	-	+	-	+	+	+	+	+	+	+	+	+	+	11
Silverii et al. (2020)	+	+	+	-	+	+	+	+	+	+	+	+	-	+	+	+	14
Choi et al. (2020)	+	+	+	+	+	+	+	+	+	+	+	+	-	-	+	+	14
Yuan et al. (2020)	+	+	-	-	-	-	-	+	-	-	+	-	-	-	-	-	4
Li et al. (2021)	+	+	+	+	+	+	+	+	+	+	+	+	+	-	+	+	15
Rafiullah et al.(2021)	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	+	16
Luo et al. (2022)	+	-	+	+	+	+	+	+	+	-	+	+	-	+	+	+	13
Parry-Strong et al. (2022)	+	+	+	+	+	+	+	+	+	+	+	+	-	+	+	+	15
Zhou et al. (2022)	+	+	+	+	+	+	+	+	+	+	+	+	+	+	-	+	15
Zaki et al. (2022)	+	+	+	+	+	+	+	+	+	-	+	+	+	-	-	+	13
Apekey et al. (2022)	+	+	+	+	+	+	+	+	+	+	+	+	+	-	+	+	15
Choy et al. (2023)	+	+	+	+	-	-	+	+	+	+	+	+	-	+	+	+	13
Ichikawa et al. (2024)	+	+	+	+	-	+	+	+	+	-	+	+	-	+	+	-	12
Hironaka. et al. (2024)	+	+	+	+	+	+	+	+	+	+	+	+	-	+	+	+	15

¹Q1: Was an ‚a priori‘ design provided?, Q2: Was there duplicate study selection and data extraction?, Q3: Was a comprehensive literature search (at least two databases) performed?, Q4: Was the status of publication (i.e. grey literature) used as an inclusion criterion?, Q5: Was a list of studies (included and excluded) provided?, Q6: Were the characteristics of the included studies provided?, Q7: Was the scientific quality of the included studies assessed and documented?, Q8: Was the scientific quality of the included studies used appropriately in formulation conclusions?, Q9: Were the methods used to combine the findings of studies appropriate?, Q10: Was the likelihood of publication bias assessed?, Q11: Was the conflict of interest included?

The result of assess the methodological quality using AMSTAR: each item for included studies (? : can’t answer; - : means no; + : means yes)

Discussion

The findings of this umbrella meta-analysis offer valuable insights into the role of LCDs, VLCDs, and KD diets with a focus on both short-term and long-term effects in managing T2DM. By focusing exclusively on randomized controlled trials (RCTs), the included studies uphold a high standard of methodological rigor, enhancing the reliability of the results. The variation in participant numbers across large, moderate, and small-scale studies demonstrates the diversity in research approaches to dietary interventions for T2DM. Large-scale trials, involving over 1,000 participants, provide robust evidence with broad applicability, while smaller trials enable a more focused examination of specific dietary impacts. Nevertheless, the limited number of smaller studies highlights the need for further research addressing unique or underrepresented subgroups of the diabetic population. The predominance of middle-aged adults (35–65 years) among study participants reflects the demographic most frequently affected by T2DM. This age group is particularly significant, as it often experiences the greatest burden of diabetes-related metabolic complications.

Short-term interventions, lasting six months or less, primarily demonstrated the immediate benefits of LCDs in improving key metabolic markers such as HbA1c and glucose levels. Studies like McArdle et al. (2019) [29], Kirk et al. (2008) [39], and Choi et al. (2020) [31] emphasized the rapid improvements achievable within a short period, particularly in glycemic control. These findings support the role of LCDs as effective initial interventions for managing blood sugar levels in T2DM patients. However, the limited duration of these studies raises questions about the sustainability of such benefits without continued dietary adherence.

The review also evaluated the prolonged impact of LCDs, with intervention durations exceeding six months. Studies such as Yuan et al. (2020) [35], Li et al. (2021) [30], and Hironaka et al. (2024) [38] demonstrated sustained improvements in HbA1c and fasting glucose levels, suggesting that long-term adherence to LCDs could provide lasting metabolic benefits. Importantly, interventions extending beyond 10 months, reinforced the potential of LCDs to manage T2DM effectively over extended periods [17, 18, 22–28, 32, 34, 36, 37, 40]. These findings highlight the importance of dietary sustainability in achieving and maintaining glycemic control.

A variety of low-carbohydrate dietary interventions, including standard LCDs, VLCDs, and KDs, were implemented across the reviewed studies. This diversity reflects the adaptability of low-carbohydrate strategies to different patient needs and preferences. For instance, studies like Kirk et al. (2008) [39], Huntriss et al. (2017) [23], Ichikawa et al. (2024) [16], and Hironaka. et al. (2024) [15] focused solely on LCDs, while others, such as Li et al. (2021) [30] and Parry-Strong et al. (2022) [34], utilized stricter VLCDs or KDs with carbohydrate restrictions as low as 50 g per day or 10% of total energy intake. The inclusion of mixed interventions in studies like Zaki et al. (2022) [27] further underscores the potential for tailored approaches to optimize outcomes in diverse populations.

The role of medications in conjunction with LCDs is a key aspect of diabetes management. Several studies have explored how various diabetes treatments, including insulin therapy, oral hypoglycemic agents, and other glucose-lowering medications, interact with dietary interventions. For example, Kirk et al. (2008) [39] and Parry-Strong et al. (2022) [34] investigated the effects of insulin and oral medications in combination with low-carbohydrate diets. Their findings suggest that dietary changes could enhance the effectiveness of these medications, improving overall blood glucose control and potentially reducing the reliance on insulin.

Other studies, such as those by Snorgaard et al. (2016) [19] and Yuan et al. (2020) [35], specifically looked at how glucose-lowering medications interacted with carbohydrate restriction. These studies support the idea that a low-carbohydrate diet could work synergistically with medications to achieve better glycemic control, making it a valuable option for people with T2D. Furthermore, studies that took a more comprehensive approach, such as Korsmo-Haugen et al. (2019) [24], included not only diabetes medications but also antihypertensive and lipid-lowering treatments. This broader focus highlights the potential benefits of LCDs in addressing multiple metabolic issues common in diabetes, such as high blood pressure and dyslipidemia.

The studies by Rafiullah et al. (2021) [32] and Zhou et al. (2022) [33] further emphasized the importance of medication in the context of diabetes and its complications, underlining the potential for LCDs to complement pharmacological treatment. In contrast, some studies, like Apekey et al. (2022) [25], focused more on oral glucose-lowering medications without detailing the broader pharmacological context, which could limit understanding of how LCDs fit into the larger picture of diabetes care.

Another issue noted in the literature is the lack of detailed reporting on the use of medications in some studies [17, 18, 22, 27, 28, 30, 31, 36–38]. This lack of consistency makes it difficult to fully understand the relationship between diet and medication.

The impact of LCDs on glycemic control and metabolic outcomes has been a subject of considerable interest, with a range of results observed across studies. Several studies, including those by Kirk et al. (2008) [39], Fan et al. (2016) [17], Huntriss et al. (2017) [23], and others, have reported significant reductions in HbA1c levels, suggesting that LCDs can effectively improve glycemic control. These findings align with the growing body of evidence supporting the potential of low-carbohydrate dietary interventions to manage T2D and improve long-term metabolic health. The reductions in HbA1c observed in these studies may reflect the direct impact of carbohydrate restriction on glucose metabolism, as LCDs are known to reduce insulin resistance and promote better glucose utilization.

However, the variability in results, reporting no significant changes in HbA1c levels suggests that the effectiveness of LCDs on glycemic control is not consistent across all populations [29, 34, 37]. This variability may be influenced by factors such as baseline metabolic status, the degree of carbohydrate restriction, adherence to the diet, and the duration of the intervention. These mixed results point to the need for further research to identify which subgroups may benefit most from LCDs and to explore the optimal duration and carbohydrate intake levels for achieving sustained glycemic improvements.

In terms of the time-dependent effects of low-carbohydrate diets, several studies highlighted the transient nature of the benefits. For example, while reductions in HbA1c were noted in the short term (3–6 months) in studies by Silverii et al. (2020) [20] and Li et al. (2021) [30], these improvements were often not sustained beyond 1 to 2 years. This pattern suggests that while LCDs may offer short-term benefits for glycemic control, their long-term effectiveness may be limited unless sustained dietary adherence or additional interventions are introduced. The decrease in HbA1c levels observed in the first 3–6 months may be due to the initial weight loss and improved insulin sensitivity that commonly accompany low-carbohydrate interventions.

The mixed effects of LCDs on fasting glucose levels further reinforce the need for personalized approaches to dietary interventions. While several studies, such as those by Kirk et al. (2008) [39], Choi et al. (2020) [31], and Yuan et al. (2020) [35], reported decreases in fasting glucose, other studies found no significant effects. This variability suggests that LCDs may not always lead to significant changes in fasting glucose levels, which could be attributed to individual variations in metabolic response or the type of LCD implemented. Additionally, the lack of consistent findings in studies by Goldenberg et al. (2020) [26] and Zhou et al. (2022) [33] underscores the complexity of fasting glucose regulation and the potential influence of other factors such as medication use, physical activity, and overall calorie intake.

The effects of LCDs on insulin levels and related metabolic markers, such as HOMA-IR, also exhibited mixed results. While some studies observed reductions in fasting insulin levels and improvements in insulin sensitivity, others did not find significant changes. These discrepancies may reflect differences in the methodological design of the studies, such as variations in the duration of the interventions, the degree of carbohydrate restriction, and the metabolic characteristics of the participants. It is important to note that while short-term reductions in fasting insulin and HOMA-IR suggest improved insulin sensitivity, the longer-term impact on these markers remains unclear and warrants further investigation.

The overall quality of the studies assessed using the AMSTAR checklist varied significantly, with most studies demonstrating moderate to high quality. A substantial proportion of articles, particularly those published in recent years, exhibited strong methodological rigor and comprehensive reporting, reflecting advancements in research standards over time. However, a few studies were identified with notable methodological weaknesses, which may limit the reliability of their findings. This variation underscores the need for consistent application of high-quality research practices to ensure the robustness and reproducibility of results.

This study has several limitations. First, the absence of detailed demographic data, such as socioeconomic status or physical activity levels, limits the generalizability of the findings and their applicability to diverse populations. Second, the heterogeneity in dietary protocols and intervention durations complicates direct comparisons across studies. Third, the overview design—while valuable for synthesizing meta-analyses—precluded sensitivity analyses to assess how low-quality studies (identified via AMSTAR-2) might influence pooled results; this underscores the need for cautious interpretation of conclusions. Fourth the long-term sustainability and adherence to strict diets like VLCDs and KDs remain unclear, especially regarding their feasibility in everyday life. Finally, the inconsistent reporting of dietary interventions and medication regimens reduces the clarity on how these factors interact to improve outcomes.

Conclusion

This umbrella meta-analysis highlights the potential of low-carbohydrate diets, including LCDs, VLCDs, and KDs, in improving glycemic control and metabolic outcomes in T2DM. Short-term benefits, such as reductions in HbA1c and fasting glucose, support their role in initial management, while long-term studies suggest sustained benefits with adherence. However, variability in outcomes underscores the need for personalized approaches. Future research should focus on diverse populations, consistent reporting of interventions, and the long-term feasibility of these diets to optimize their application in diabetes care.

Author contributions

Y.Y., S.A., P.J., C.Y: Drafting manuscript, Data curation, Study design, Data analysis, Final approval.

Funding

None.

Data availability

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

Declarations

Competing interests

The authors declare no competing interests.

Human ethics and consent to participate

None.