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. 2021 Jul 9;13(7):2355. doi: 10.3390/nu13072355

Folic Acid Supplementation Improves Glycemic Control for Diabetes Prevention and Management: A Systematic Review and Dose-Response Meta-Analysis of Randomized Controlled Trials

Omid Asbaghi 1, Damoon Ashtary-Larky 2, Reza Bagheri 3, Seyedeh Parisa Moosavian 4, Hadi Pourmirzaei Olyaei 5, Behzad Nazarian 6, Mahnaz Rezaei Kelishadi 7, Alexei Wong 8, Darren G Candow 9, Frédéric Dutheil 10, Katsuhiko Suzuki 11,*, Amirmansour Alavi Naeini 7,*
Editors: Teresa Partearroyo, Gregorio Varela-Moreiras
PMCID: PMC8308657  PMID: 34371867

Abstract

Background: There is a growing interest in the considerable benefits of dietary supplementations, such as folic acid, on the glycemic profile. We aimed to investigate the effects of folic acid supplementation on glycemic control markers in adults. Methods: Randomized controlled trials examining the effects of folic acid supplementation on glycemic control markers published up to March 2021 were detected by searching online databases, including Scopus, PubMed, Embase, and ISI web of science, using a combination of related keywords. Mean change and standard deviation (SD) of the outcome measures were used to estimate the mean difference between the intervention and control groups at follow-up. Meta-regression and non-linear dose-response analysis were conducted to evaluate the association between pooled effect size and folic acid dosage (mg/day) and duration of the intervention (week). From 1814 detected studies, twenty-four studies reported fasting blood glucose (FBG), fasting insulin, hemoglobin A1C (HbA1C), and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) as an outcome measure. Results: Results revealed significant reductions in FBG (weighted mean difference (WMD): −2.17 mg/dL, 95% CI: −3.69, −0.65, p = 0.005), fasting insulin (WMD: −1.63 pmol/L, 95% CI: −2.53, −0.73, p < 0.001), and HOMA-IR (WMD: −0.40, 95% CI: −0.70, −0.09, p = 0.011) following folic acid supplementation. No significant effect was detected for HbA1C (WMD: −0.27%, 95% CI: −0.73, 0.18, p = 0.246). The dose-response analysis showed that folic acid supplementation significantly changed HOMA-IR (r = −1.30, p-nonlinearity = 0.045) in non-linear fashion. However, meta-regression analysis did not indicate a linear relationship between dose, duration, and absolute changes in FBG, HOMA-IR, and fasting insulin concentrations. Conclusions: Folic acid supplementation significantly reduces some markers of glycemic control in adults. These reductions were small, which may limit clinical applications for adults with type II diabetes. Further research is necessary to confirm our findings.

Keywords: folic acid, glycemic control, diabetes, meta-analysis

1. Introduction

Glycemic management among individuals suffering from chronic metabolic disease seems indispensable in preventing further complications [1]. The global incidence of diabetes mellitus, one of the primary endocrine disorders associated with poor glycemic control, has progressively climbed to 415 million adults; and this number will reach 649 million by 2040 [2]. Type 2 diabetes mellitus (T2DM) is the predominant type of metabolic disease, accounting for 90–95% of cases, which are characterized by high blood glucose, insulin resistance (Homeostatic Model Assessment for Insulin Resistance [HOMA-IR]), and relative lack of insulin [3,4]. To combat T2DM complications, several efficient approaches have been recommended, such as lifestyle modifications, pharmacological treatment, exercise interventions, changes in either dietary intakes, the inclusion of food supplements, or in combination [5,6].

It has been assumed that dietary supplements have led to promising outcomes for T2DM treatment [7,8,9,10]. Namely, folic acid is known as one of the members of the vitamin B complex, which is crucial for red blood cell (RBC) increment and other body cells as an essential vitamin. Folic acid is also recognized as folic acid, folate, vitamin B9, and vitamin B11 [11]. One of the forms of folic acid naturally in food products is “folate”, a water-soluble vitamin [12]. Evidence indicates that folic acid supplementation could positively affect inflammatory and oxidative stress markers [13,14]. However, the reported effects of folic acid on glycemic control have been inconclusive. For instance, folic acid supplementation reduced plasma concentrations of homocysteine and improves glycemic control, insulin resistance, and vitamin B12 in those with T2DM who consume high doses of metformin [15]. In addition, in patients with metabolic syndrome, folate supplementation decreases insulin resistance and improves endothelial dysfunction [16]. According to a few small randomized controlled trials (RCTs), folic acid supplementation also has possible advantages for decreasing insulin resistance [17,18]. On the other hand, some previous studies failed to show any benefit in patients with T2DM. For instance, a study has shown an increase in glucose concentrations caused by a short-term, low-dose folic acid supplementation [19]. Moreover, Mangoni et al. reported that although folic acid supplementation can significantly decrease serum concentrations of homocysteine, there were no significant changes in lipid and glycemic profile following folic acid supplementation in patients with T2DM [20]. Similar outcomes were observed in other studies [21,22].

Some meta-analytic studies have evaluated the efficacy of folic acid supplementation on various glycemic control markers [23,24,25]. However, these meta-analyses either failed to include some relevant studies, did not perform subgroup or dose-response analysis, meta-regression, or had low overall certainty of evidence across the studies included. Collectively, findings on the effects of folic acid supplementation on glycemic control remain unclear, which demonstrates a need for an updated comprehensive systematic review and meta-analysis of RCTs on this topic. Consequently, we performed this systematic review and meta-analysis of published RCTs to summarize available findings on the influence of folic acid supplementation on glycemic control in adults.

2. Materials and Methods

This study was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guides [26].

2.1. Search Strategy

A systematic search was carried out on electronic databases, including Scopus, PubMed, Embase, and ISI web of science up to March 2021. Consequently, all the studies, which met the inclusion criteria, were added into Endnote software for screening. In our search strategy, we used MeSH terms, keywords, and abstracts, as well as ((“folate”[Title/Abstract] OR “folic acid”[Title/Abstract] OR “Vitamin M”[Title/Abstract] OR “Vitamin B9”[Title/Abstract] OR “Folacin”[Title/Abstract] OR “Folvite”[Title/Abstract] OR “Pteroylglutamic Acid”[Title/Abstract] OR “folates”[Title/Abstract] OR “tetrahydrofolates”[Title/Abstract] OR “Formyltetrahydrofolates”[Title/Abstract]) OR “methylTHF” [Title/Abstract] OR “THF” [Title/Abstract] AND (“glucose tolerance”[Title/Abstract] OR “insulin resistance”[Title/Abstract] OR FBG[Title/Abstract] OR “fasting blood glucose”[Title/Abstract] OR HbA1c[Title/Abstract] OR “hemoglobin A1c”[Title/Abstract] OR HOMA-IR[Title/Abstract] OR “homeostatic model assessment”[Title/Abstract] OR Insulin[Title/Abstract] OR “fasting blood sugar”[Title/Abstract] OR FBS[Title/Abstract])) AND (Intervention[Title/Abstract] OR “Intervention Study”[Title/Abstract] OR “Intervention Studies”[Title/Abstract] OR “controlled trial”[Title/Abstract] OR randomized[Title/Abstract] OR randomized[Title/Abstract] OR random[Title/Abstract] OR randomly[Title/Abstract] OR placebo[Title/Abstract] OR “clinical trial”[Title/Abstract] OR Trial[Title/Abstract] OR “randomized controlled trial”[Title/Abstract] OR “randomized clinical trial”[Title/Abstract] OR RCT[Title/Abstract] OR blinded[Title/Abstract] OR “double blind”[Title/Abstract] OR “double blinded”[Title/Abstract] OR trial[Title/Abstract] OR “clinical trial”[Title/Abstract] OR trials[Title/Abstract] OR “Pragmatic Clinical Trial”[Title/Abstract] OR “Cross-Over Studies”[Title/Abstract] OR “Cross-Over”[Title/Abstract] OR “Cross-Over Study”[Title/Abstract] OR parallel[Title/Abstract] OR “parallel study”[Title/Abstract] OR “parallel trial”[Title/Abstract]). We did not use limitations on time of publication and language. Additionally, unpublished studies and duplicate citations were removed. Two authors (BN and MRK) completed the research process individually and in duplicate. Meeting and consultations with another researcher (OA) solved any disagreements in these regards.

2.2. Inclusion Criteria

Two investigators selected qualified studies that conform to the following criteria: studies that evaluated the effect of folic acid supplementation on glycemic control in adult populations (aged > 18 years old), those that reported glycemic control values (fasting blood glucose (FBG), fasting insulin, hemoglobin A1C (HbA1C), homeostatic model assessment for insulin resistance (HOMA-IR)) at baseline and the end of the intervention, and parallel or crossover studies with a control group.

2.3. Exclusion Criteria

In vitro studies, animal experiments, case reports, trials without a control group, observational studies, studies using folic acid supplementation in combination with other components, and those investigations that did not report inclusion criteria were excluded from the analysis.

2.4. Data Extraction

The following data were extracted from qualified studies by two independent investigators (BN and MRK) using a predetermined abstraction form: (I) first author’s name; (II) publication year; (III) study design and blinding; (IV) duration of the study; (V) characteristics of participants (gender, age, and diseases) in each group; (VI) location of the study; (VII) sample size in control and intervention groups; (VIII) type and dose of placebo and folic acid; and (IX) mean and standard deviation (SD) of outcomes of the glycemic control markers at baseline and the end of the study. When data were published in two different studies, we included the latest one. Moreover, if data for glycemic control markers were reported in various units, we converted to the most commonly used. In cases of the absence of relevant data, we communicated to corresponding authors through e-mail to receive the required information. If there was any inconsistency, that was settled by consensus.

2.5. Quality Assessment

To measure the risk of bias for each included study, we used Cochrane collaboration’s tool [27], which contained seven domains: allocation concealment, performance bias, random sequence generation, reporting bias, and detection, attrition bias, and other sources of bias. The quality assessment procedure was described in our earlier studies [8,28]. Moreover, two independent investigators (OA and DAL) performed the risk of bias assessment.

2.6. Statistical Analysis

The mean differences and SD for the intervention and control groups were extracted to launch an investigation into the effect size for glycemic control markers. Additionally, weighted mean differences (WMDs) with 95% confidence intervals (CIs) were calculated by a random-effects model. Cochran’s Q test was used to evaluate between-study heterogeneity and I2 measurement was used for quantification. I2 greater than 40% or p < 0.05 was deemed as high between-study heterogeneity [29]. To distinguish probable sources of heterogeneity, we performed a subgroup analysis in conformity with the baseline serum fasting blood glucose concentrations, study duration (<12 and ≥12 weeks), intervention dosage (<5 and ≥5 mg/d), diabetes status (non-T2DM and T2DM), and sex (both sexes, male and female). To conduct sensitivity analysis, we removed each study one after another and recalculated the pooled assessments. To detect potential publication bias, funnel plots and Egger’s regression test were performed. Meta-regression and non-linear dose-response analysis was conducted to evaluate the association between pooled effect size and folic acid dosage (mg/day) and duration of the intervention (week) [30]. Statistical analysis was conducted using STATA, version 11.2 (Stata Corp, College Station, TX, USA). In all analyses, the statistically substantial value was considered as p < 0.05.

2.7. Certainty Assessment

The overall certainty of evidence across the studies was graded according to the GRADE guidelines (Grading of Recommendations Assessment, Development, and Evaluation) working group. According to the corresponding evaluation criteria, the quality of evidence was classified into four categories: high, moderate, low, and very low [31].

3. Results

3.1. Study Selection

The initial search yielded 1814 studies; however, 419 of those were removed due to duplication. Another 1395 studies were excluded due to unrelated titles and abstracts (n = 978), animal studies (n = 210), and review studies (n = 167). Consequently, 40 relevant studies remained for full-text review. Among these, 16 studies were excluded because of a lack of reporting of glycemic control markers. Finally, 24 studies [15,17,20,21,22,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50] were included in the present meta-analysis (Figure 1).

Figure 1.

Figure 1

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Flowchart of study selection for inclusion studies.

3.2. Characteristics of the Included Studies

The characteristics of the included studies are shown in Table 1. In total, 34,646 volunteers were included (case = 17,396; control = 17,249), and the dates of publications were between 1998 and 2018. Twenty-one studies were designed as parallel studies and three as a crossover. The study duration ranged between 3 and 234 weeks, and the sample size ranged from 20 to 20,030 participants. Participants’ ages ranged from 24 to 65 years, while baseline body mass index (BMI) varied between 23.9 and 32.3 kg/m−2. Two studies enrolled only males, while nine studies enrolled females. The remained studies enrolled both females and males. Results from quality assessments are indicated in Table 2.

Table 1.

Characteristic of included studies.

Studies Country Study Design Participant Sample Size and Sex Sample Size Trial Duration
(Week)		 Means Age Means BMI Intervention
IG CG IG CG IG CG Folic Acid Dose (mg/d) Control Group
Aarsand et al., 1998 Norway RA/DB/PC
(parallel)		 type 2 diabetes 28: 21M, 7F 14 14 12 56.7 ± 10.47 61.6 ± 9.35 29.2 ± 5.23 28.3 ± 4.11 0.25 PC
Doshi et al., 2001 United Kingdom RA/PC
(parallel)		 Coronary artery disease 50: 44M, 6F 50 50 6 57 ± 8 57 ± 8 28.5 ± 4.4 28.5 ± 4.4 5 PC
Doshi et al., 2002 United Kingdom RA/PC
(crossover)		 Coronary artery disease 33: 30M 3F 16 17 6 55 ± 7 56 ± 7 NR NR 5 PC
Kilicdag et al., 2005 Turkey RA
(parallel)		 Polycystic ovarian syndrome patients 31: 31F 17 14 12 24.94 ± 6.67 24.14 ± 6.92 28.58 ± 5.43 26.02 ± 5.98 0.348 No intervention
Mangoni et al., 2005 Australia RA/DB/PC
(parallel)		 Type 2 diabetes 26: 14M,12F 13 13 4 55.3 ± 4.32 57.6 ± 4.68 30.5 ± 3.96 32.3 ± 4.68 5 PC
Sheu et al., 2005 Taiwan RA/DB/PC
(parallel)		 Obese women 74: 74F 36 38 12 43 ± 12 40 ± 12.32 29.6 ± 3.6 29.3 ± 4.93 5 PC
Villa et al., 2005 Italy RA/PC
(parallel)		 Postmenopausal 20: 20F 10 10 8 55.4 ± 6.95 53.1 ± 7.27 29.7 ± 4.74 26.91 ± 5.88 7.5 PC
Moat et al., 2006 (A) USA RA/DB/PC
(parallel)		 Coronary artery disease 59: 52M, 7F 30 15 6 61 ± 7 61 ± 7 28.5 ± 4.4 29.6 ± 4.1 0.4 PC
Moat et al., 2006 (B) USA RA/DB/PC
(parallel)		 Coronary artery disease 54: 46M, 8F 25 14 6 60 ± 7 61 ± 7 29.9 ± 4.4 29.6 ± 4.1 5 PC
Solini et al., 2006 Italy RA/PC
(parallel)		 Overweight subjects 60: 19M, 41F 30 30 12 50 ± 7 49 ± 8 27.5 ± 0.6 27.4 ± 0.6 2.5 PC
Title et al., 2006 Canada RA/DB/PC
(crossover)		 Type 2 diabetes 19: 9M,10F 19 19 2 54.5 ± 5.9 54.5 ± 5.9 NR NR 10 PC
Moens et al., 2007 Belgium RA/DB/PC
(crossover)		 Acute myocardial infarction 40: 35M, 5F 20 20 6 57 ± 11 56 ± 14 NR NR 10 PC
Mao et al., 2008 (A) China RA/DB
(parallel)		 Mild to moderate primary hypertension 295: 120M, 175F 146 75 8 57.4 ± 10 57.3 ± 10 25.5 ± 3.3 25.7 ± 3.2 0.4 No intervention
Mao et al., 2008 (B) China RA/DB
(parallel)		 Mild to moderate primary hypertension 297: 126M, 171F 148 74 8 56.6 ± 9.6 57.3 ± 10 25.8 ± 3.6 25.7 ± 3.2 0.8 No intervention
Palomba et al., 2010 Italy DB/PC
(parallel)		 Polycystic ovary syndrome 47: 47F 23 24 25 26.9 ± 3.1 26.4 ± 2.8 27.9 ± 2.6 28.1 ± 3.1 0.4 PC
Aghamohammadi khiavi et al., 2011 Iran RA/DB/PC
(parallel)		 Type 2 diabetes mellitus 68: 68M 34 34 8 58.7 ± 7.2 55.6 ± 9.3 27.4 ± 3.2 27.8 ± 4 5 PC
Gargari et al., 2011 Iran RA/DB/PC
(parallel)		 Overweight and obese men with type 2 diabetes 48: 48M 24 24 8 59.4 ± 7.6 57 ± 10.1 28.8 ± 2.7 28.5 ± 3.3 5 PC
Grigoletti et al., 2013 Brazil RA/DB/PC
(parallel)		 HIV-infected individuals 30: 14M, 16F 15 15 4 45 ± 7.74 45 ± 7.74 23.9 ± 4.96 23.9 ± 3.11 5 PC
Asemi et al., 2014 (A) Iran RA/DB/PC
(parallel)		 Overweight women with polycystic ovary syndrome 81: 81F 27 14 8 24.3 ± 5.0 24.7 ± 5.0 27.2 ± 5.0 27.9 ± 4.7 1 PC
Asemi et al., 2014 (B) Iran RA/DB/PC
(parallel)		 Overweight women with polycystic ovary syndrome 81: 81F 27 13 8 25.1 ± 4.5 24.7 ± 5.0 29.3 ± 4.6 27.9 ± 4.7 5 PC
Cagnacci et al., 2009 Italy RA/DB/PC
(parallel)		 Postmenopausal 30: 30F 15 15 3 55.8 ± 4.26 54.5 ± 4.64 26.3 ± 5.03 27.5 ± 5.03 15 PC
Asemi et al., 2016 Iran RA/DB/PC
(parallel)		 Cervical intraepithelial neoplasia grade 1 58: 58F 29 29 25 36.8 ± 8.8 39.1 ± 9.1 28.2 ± 3.5 29.8 ± 6.4 5 PC
Hashemi et al., 2016 Iran RA/TB/PC
(parallel)		 Pre-eclamptic patients 85: 85F 43 42 8 30.82 ± 4.08 31.2 ± 4.3 25.19 ± 2.53 24.63 ± 2.64 5 PC
Qin et al., 2016 China RA/DB
(parallel)		 Hypertension 20,030: 8295M, 11,735F 10,014 10,016 234 59.9 ± 7.6 60 ± 7.5 24.9 ± 3.7 24.9 ± 3.7 0.8 No intervention
Talari et al., 2016 Iran RA/DB/PC
(parallel)		 Metabolic syndrome 60: 26M, 34F 30 30 12 62.1 ± 9.6 65.4 ± 11.5 29.8 ± 3.8 29.8 ± 4.4 5 PC
Li et al., 2017 (A) China RA/DB
(parallel)		 Diabetics 1636: 585M, 1051F 800 836 229 60.1 ± 7.2 59.9 ± 7.3 26.3 ± 3.7 26.4 ± 3.5 0.8 No intervention
Li et al., 2017 (B) China RA/DB
(parallel)		 Nondiabetics 11,435: 4444M, 6991F 5711 5724 229 59.4 ± 7.5 59.4 ± 7.6 25.5 ± 3.5 25.5 ± 3.5 0.8 No intervention
Bahmani et al., 2018 Iran RA/DB/PC
(parallel)		 Endometrial hyperplasia 60: 60F 30 30 12 44.4 ± 6.5 44.7 ± 3.1 30.7 ± 4.6 30.5 ± 3.8 5 PC
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Abbreviations: CG, control group; DB, double-blinded; F, female; IG, intervention group; M, male; NR, not reported; PC, placebo-controlled; RA, randomized; TB, triple-blinded; the references Moat et al., 2006, Mao et al., 2008, and Asemi et al., 2014 have 2 separate arms due to differences in population or folic acid supplementation dose (A and B).

Table 2.

Quality assessment.

Studies Random Sequence Generation Allocation Concealment Selective Reporting Other Sources of Bias Blinding (Participants and Personnel) Blinding (Outcome Assessment) Incomplete Outcome Data Overall
Aarsand et al., 1998 L H H H L H H Fair
Doshi et al., 2001 L H H H H H L Fair
Doshi et al., 2002 L H H H H H L Fair
Kilicdag Doshi et al., 2005 L H H H H H L Fair
Mangoni et al., 2005 L H H H L H H Fair
Sheu et al., 2005 L H H H L H L Good
Villa et al., 2005 L H H H H H L Fair
Moat et al., 2006 L H H H L H L Good
Solini et al., 2006 L H H H H H L Fair
Title et al., 2006 L H H H L H L Good
Moens et al., 2007 L H H H L H L Good
Mao et al., 2008 L H H H L H L Good
Cagnacci et al., 2009 L H H H L H L Good
Palomba et al., 2010 L H H H L H L Good
Gargari et al., 2011 L H L H L H L Good
khiavi et al., 2011 L H L H L H L Good
Grigoletti et al., 2013 L H H H L H L Good
Asemi et al., 2014 L H H H L H L Good
Asemi et al., 2016 L H H H L H L Good
Hashemi et al., 2016 L L H H L L L Good
Qin et al., 2016 L H H H L H L Good
Talari et al., 2016 L H H H L H L Good
Bahmani et al., 2018 L H H H L H L Good
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Abbreviations: L, low; H, high.

3.3. The Effect of Folic Acid Supplementation on FBG

The results of our analysis demonstrated that folic acid supplementation significantly reduced FBG concentrations ((WMD = −2.17 mg/dL; 95% CI: −3.69, −0.65, p = 0.005), (Figure 2A)). However, there was a significant between-study heterogeneity [(I2 = 81.5%, p < 0.001)]. Therefore, we conducted a subgroup analysis to detect the source of heterogeneity. We found that intervention dose, trial duration, and participant’s sex were the sources of heterogeneity. In addition, subgroup analysis demonstrated that folic acid supplementation significantly reduced FBG concentrations when the study duration was <12 weeks (p = 0.006) in participants with higher baseline FBG concentrations (FBG ≥ 100 mg/dL, p = 0.043). Moreover, when the dosage was ≥5 (mg/d), a significant decrease in FBG concentrations was detected (p = 0.021). These decreases were significant in non-diabetic participants (p = 0.030). Lastly, in those studies that enrolled both males (p < 0.001) and females (p = 0.001) with elevated FBG, a significant reduction in FBG concentrations was found ((p = 0.043); (Table 3)).

Figure 2.

Figure 2

Figure 2

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Forest plot detailing weighted mean difference and 95% confidence intervals (CIs) for the effect of acid folic supplementation on; (A) FBG; (B) HbA1c; (C) fasting insulin; (D) HOMA-IR.

Table 3.

Subgroup analyses of folic acid supplementation on glycemic control in adults.

NO Sample Size (Intervention/Control) WMD (95%CI) p-Value Heterogeneity
P Heterogeneity I2 P between Sub-Groups Tau-Squared
Subgroup analyses of folic acid supplementation on FBG
Overall effect 27 17,379/17,235 −2.17 (−3.69, −0.65) 0.005 <0.001 81.5% 7.4032
Baseline FBG (mg/dL)
<100 16 6365/6182 −2.14 (−4.36, −0.06) 0.057 <0.001 85.7% 0.824 13.20
≥100 11 11,014/11,053 −4.06 (−7.83, −0.29) 0.043 <0.001 71.9% 17.26
Trial duration (week)
<12 17 662/464 −5.32 (−9.11, −1.53) 0.006 <0.001 86.5% 0.026 41.15
≥12 10 16,717/16,771 −0.79 (−1.81, 0.22) 0.126 0.041 48.6% 0.74
Intervention dose (mg/d)
<5 10 16,943/16,822 −1.40 (−3.23, 0.43) 0.135 <0.001 84.9% 0.006 4.53
≥5 17 436/413 −3.58 (−6.62, −0.54) 0.021 <0.001 78.3% 21.89
Diabetes status
non-T2DM 20 10,764/10,571 −2.34 (−4.46, −0.22) 0.030 <0.001 83.7% 0.243 14.16
T2DM 7 6615/6664 −4.87 (−10.15, 0.39) 0.070 0.001 73.6% 24.12
Sex
Both sexes 16 17,081/16,962 0.11 (−0.55, 0.77) 0.905 0.653 0.0% <0.001 0.00
Female 9 240/215 −9.53 (−14.71, −4.35) 0.001 <0.001 90.8% 39.33
Male 2 58/58 −18.81 (−26.87, −10.74) <0.001 0.729 0.0% 0.00
Overall analyses of folic acid supplementation on HbA1c
Overall effect 4 85/85 −0.27 (−0.73, 0.18) 0.246 0.007 74.9% 0.16
Subgroup analyses of folic acid supplementation on fasting insulin
Overall effect 12 315/291 −1.63 (−2.53, −0.73) <0.001 0.001 65.8% 1.3281
Trial duration (week)
<12 6 137/110 −1.28 (−2.73, 0.16) 0.082 0.001 76.0% 0.939 2.08
≥12 6 178/181 −2.03 (−3.31, −0.75) 0.002 0.045 55.8% 1.27
Intervention dose (mg/d)
<5 3 80/68 −0.99 (−1.94, −0.04) 0.040 0.365 0.9% 0.082 0.00
≥5 9 235/223 −1.86 (−3.00, −0.71) 0.001 0.001 70.5% 1.76
Diabetes status
non-T2DM 10 257/233 −1.96 (−2.92, −1.00) <0.001 0.002 65.3% 0.015 1.22
T2DM 2 58/58 0.02 (−1.45, 1.51) 0.972 0.604 0.0% 0.00
Sex
Both sexes 2 60/60 −2.37 (−5.89, 1.13) 0.185 0.119 58.8% 0.032 4.36
Female 8 197/173 −2.01 (−3.144, −0.88) <0.001 0.002 69.0% 1.50
Male 2 58/58 0.02 (−1.45, 1.51) 0.972 0.604 0.0% 0.00
Subgroup analyses of folic acid supplementation on HOMA-IR
Overall effect 12 322/295 −0.40 (−0.70, −0.09) 0.011 <0.001 80.9% 0.17
Trial duration (week)
<12 5 127/100 −0.62 (−0.64, −0.59) <0.001 0.654 0.0% <0.001 0.00
≥12 7 195/195 −0.31 (−0.83, 0.19) 0.224 <0.001 83.7% 0.35
Intervention dose (mg/d)
<5 4 97/82 0.02 (−0.68, 0.73) 0.949 <0.001 84.0% <0.001 0.38
≥5 8 225/213 −0.62 (−0.64, −0.60) <0.001 0.615 0.0% 0.00
Diabetes status
non-T2DM 10 264/237 −0.43 (−0.77, −0.08) 0.016 <0.001 83.9% 0.192 0.19
T2DM 2 58/58 −0.26 (−0.79, 0.27) 0.339 0.857 0.0% 0.00
Sex
Both sexes 2 58/58 −0.75 (−1.91, 0.39) 0.198 0.049 74.3% 0.103 0.54
Female 8 204/177 −0.38 (−0.82, 0.06) 0.092 <0.001 85.8% 0.28
Male 2 60/60 −0.26 (−0.79, 0.27) 0.339 0.857 0.0% 0.00
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Abbreviations: CI, confidence interval; WMD, weighted mean differences; FBG, fasting blood glucose; HbA1c, hemoglobin A1c; HOMA-IR (homeostatic model assessment for insulin resistance); p-Value in bold: significant difference.

3.4. The Effect of Folic Acid Supplementation on HbA1c

Overall analysis of the data from four effect sizes demonstrated that folic acid supplementation did not significantly affect HbA1c ((WMD = −0.27%; 95% CI: −0.73, 0.18, p = 0.246), (Figure 2B)). However, significant heterogeneity existed (I2 = 74.9%, p = 0.007). In addition, due to the low number of effect sizes, we could not conduct subgroup analysis (Table 3).

3.5. The Effect of Folic Acid Supplementation on Fasting Insulin

There was a significant reduction in fasting insulin concentrations following folic acid supplementation ((WMD = −1.63 uU/mL; 95% CI: −2.53, −0.73, p < 0.001), (Figure 2C)) with significant between-study heterogeneity ((I2 = 65.8%, p = 0.001)). The reduction was significant when study duration was ≥12 weeks (p = 0.002). In addition, the effects of folic acid supplementation at dosages <5 (p = 0.040) and ≥5 ((mg/d); p = 0.001)) efficaciously reduced fasting insulin concentrations that were statistically significant in non-T2M patients (p < 0.001). Astonishingly, folic acid supplementation favorably attenuated fasting insulin concentrations in females (p < 0.001) but not in men ((p = 0.972); (Table 3)).

3.6. The Effect of Folic Acid Supplementation on HOMA-IR

A significant reduction for HOMA-IR ((WMD = −0.40; 95% CI: −0.70, −0.09, p = 0.011), (Figure 2D)) was found following folic acid supplementation. In addition, when supplementation duration was <12 weeks, a significant decrease in HOMA-IR was found (p < 0.001). Moreover, doses ≥5 mg/d significantly reduced HOMA-IR (p < 0.001). These decrements were significant in non-T2DM patients (p = 0.016); (Table 3)).

3.7. Publication Bias

Based on Egger’s regression test, there was no evidence of publication bias for studies examining the effects of folic acid supplementation on HbA1c (p = 0.485), fasting insulin concentrations (p = 0.964), and HOMA-IR (p = 0.240). However, based on the Egger regression test, there was a significant publication bias for FBG (p = 0.028). Additionally, Begg’s test revealed that there is no publication bias for FBG (p = 0.588), HbA1c (p = 1.000), fasting insulin (p = 0.537), and HOMA-IR (p = 0.732). The visually inspected funnel plot test also confirmed this point (Figure 3A–D).

Figure 3.

Figure 3

Figure 3

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Funnel plot for the effect of folic acid supplementation on; (A) FBG; (B) HbA1c; (C) fasting insulin; (D) HOMA-IR.

3.8. Grade Assessment

Grade assessment demonstrated very low quality for FBG concentrations due to serious limitations regarding indirectness, publication bias, and very serious limitations about inconsistency. HbA1c very low because of serious limitation for inconsistency, indirectness and imprecision. Fasting insulin and HOMA-IR had low quality of evidence due to inconsistency, and indirectness (Table 4).

Table 4.

GRADE profile of folic acid supplementation for FBG, HbA1c, fasting insulin and HOMA-IR scores in adults.

Quality Assessment Summary of Findings Quality of Evidence
Outcomes Risk of Bias Inconsistency Indirectness Imprecision Publication Bias Number of Intervention/Control WMD (95%CI)
FBG No serious limitations Very serious limitations a Serious limitations e No serious limitations Serious limitations g 17,379/17,235 −2.17 (−3.69, −0.65) ⊕◯◯◯
Very low
HbA1c No serious limitations Serious limitations b Serious limitations e Serious limitations f No serious limitations 85/85 −0.27 (−0.73, 0.18) ⊕◯◯◯
Very low
Fasting insulin No serious limitations Serious limitations c Serious limitations e No serious limitations No serious limitations 315/291 −1.63 (−2.53, −0.73) ⊕⊕◯◯
Low
HOMA-IR No serious limitations Very serious limitations d Serious limitations e No serious limitations No serious limitations 322/295 −0.40 (−0.70, −0.09) ⊕⊕◯◯
Low
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a The test for heterogeneity is significant, and the I2 is moderate, 83.2%. b The test for heterogeneity is significant, and the I2 is moderate, 74.9% c The test for heterogeneity is significant, and the I2 is moderate, 65.8%, d The test for heterogeneity is significant, and the I2 is moderate, 80.9%, e studies conducted subject to various conditions, f values are distributed within opposite direction across studies. g The Egger’s test for publication bias is significant (p = 0.039). Based on the number of limitations, the quality of outcomes is divided into four categories: high (⊕⊕⊕⊕), moderate (⊕⊕⊕◯), low (⊕⊕◯◯) and very low (⊕◯◯◯) quality.

3.9. Sensitivity Analysis

Sensitivity analysis for FBG, HbA1c, and fasting insulin concentrations did not show evidence of sensitivity; however, sensitivity analysis for HOMA-IR demonstrated that the overall effect was influenced by the elimination of a study conducted by Aghamohammadi khiavi et al. [42] (WMD = −0.15; 95% CI: −0.71, 0.39).

3.10. Non-Linear Dose-Response between Dose and Duration of Folic Acid Supplementation and Glycemic Control

We conducted a non-linear dose-response analysis between the dose and duration of folic acid supplementation for FBG, HOMA-IR, and fasting insulin concentrations. Dose-response analysis showed that folic acid supplementation significantly changed HOMA-IR (r = −1.30, p-nonlinearity = 0.045) in a non-linear fashion (Figure 4A–C). However, folic acid supplementation did not affect FBG, HOMA-IR, and fasting insulin concentrations based on the duration of the intervention (Figure 5A–C).

Figure 4.

Figure 4

Figure 4

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Non-linear dose-response relations between dose of folic acid supplementation (mg/day) and absolute mean differences in (A) FBG; (B) fasting insulin; (C) HOMA-IR.

Figure 5.

Figure 5

Figure 5

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Non-linear dose-response relations between duration of intervention (weeks) and absolute mean differences in (A) FBG; (B) fasting insulin; (C) HOMA-IR.

3.11. Meta-Regression Analysis

Meta-regression was performed to investigate the potential association between a decrease in FBG, HOMA-IR, fasting insulin, dosage (mg/d), and duration (weeks) of folic acid supplementation. Meta-regression analysis did not indicate a linear relationship between dose (Figure 6A–C), duration (Figure 7A–C), and absolute changes in FBG, HOMA-IR, and fasting insulin concentrations.

Figure 6.

Figure 6

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Random-effects meta-regression plots of the association between dose of folic acid (mg/day) and weighted mean difference of (A) FBG; (B) fasting insulin; (C) HOMA-IR.

Figure 7.

Figure 7

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Random-effects meta-regression plots of the association between duration of intervention (weeks) and weighted mean difference of (A) FBG; (B) fasting insulin; (C) HOMA-IR.

4. Discussion

In this dose-response meta-analysis, we evaluated the effects of folic acid supplementation on glycemic control markers in adults. Results showed that folic acid supplementation reduced FBG, fasting insulin, and HOMA-IR, without any significant alterations in HbA1c when compared to a control group. Meanwhile, subgroup analyses on the dosage of folic acid supplementation (≥5 mg/d vs. <5 mg/d) showed that glycemic-improvement was more evident following higher doses. However, it should be noted that the improvements in measures of glycemic control following folic acid supplementation were relatively small and may not reach clinical importance.

Previous meta-analyses have reported inconsistent results regarding the efficacy of folic acid supplementation on various glycemic control markers, most notably FBG [23]. Akbari et al. examined the effects of folic acid supplementation in patients with metabolic diseases and found that folic acid supplementation resulted in significant decreases in insulin concentrations and HOMA-IR but did not affect FBG and HbA1c [23]. In another meta-analysis by Lind et al., folic acid supplementation lowered fasting insulin concentrations and HOMA-IR, with no effects observed for FBG or HbA1c [24]. Our results show improvements in glycemic markers of FBG, fasting insulin, and HOMA-IR, which is in agreement with the meta-analysis performed by Zhao et al., who found beneficial effects of folic acid supplementation on insulin resistance, FBG, and insulin [25]. However, we expand on the results of Zhao et al. because we performed sub-analyses involving a dose-response. According to our dose-response analysis, folic acid supplementation significantly improves insulin resistance in a non-linear fashion. Based on this finding, increasing the dose of folic acid supplementation to 5 mg/day can significantly improve insulin resistance. These findings are in line with previous observational studies, which reported that higher folate status could be associated with insulin resistance in people with obesity [51] and non-diabetic cohorts [52]. It seems that these beneficial effects of folic acid supplementation might be mediated by a decline in homocysteine concentrations [53].

Our results also demonstrate that folic acid supplementation resulted in a greater decline in fasting insulin concentrations in women compared with men. The reason for this phenomenon is not clear; however, some evidence suggests that folate concentrations and dietary folate intakes are significantly lower in females than males, which is reported in observational studies [15,54]. Moreover, it seems that men require more folic acid intake than women to achieve the same blood folate concentrations, mainly because men have a larger lean mass [55]. In addition, men had a much higher prevalence of cigarette smoking and drinking than women, and generally, smokers had lower plasma and red blood cell folate concentrations than nonsmokers [56,57,58]. On the other hand, some previous studies reported the positive relationship between serum folate and estrogen concentrations and the potential effects of folic acid supplementation on increasing estrogen concentrations among women [59,60]. Estrogen might decrease insulin concentrations by some possible mechanisms, including its anti-inflammatory and antioxidant properties such as maintaining glucose homeostasis and decreasing body fat [61]. Further studies are needed to evaluate the gender-specific effects of folic acid supplementation on glycemic markers.

Even though our subgroup analysis showed that individuals with higher FBS (>100 mg/dL) had greater improvements in this glycemic marker compared to those with lower FBS (<100 mg/dL), it should be noted that the effects of folic acid supplementation in our study were mostly observed in non-diabetic individuals. One possible explanation for the more relevant outcomes in non-diabetics might be the low number of studies on cohorts with diabetes (only 6 of the 24 included studies). Moreover, these studies were mostly short-term (less than 2 months) and consequently the complete benefit of folic acid supplementation may not have been observed during this period.

Our results failed to show significant decreases in HbA1c following folic acid supplementation, despite improvements in FBG, fasting insulin, and HOMA-IR. RBC life span is a known determinant of HbA1C [62], and it has been reported to average 80–115 days [63,64]. The four included studies, which evaluated HbA1C changes following folic acid supplementation had durations ranging from 4 to 12 weeks. Consequently, a plausible reason for the insignificant changes in HbA1C is the short period of interventions. Long-term studies evaluating HbA1C levels after folic acid supplementation are warranted.

The positive effects of folic acid supplementation on glycemic control markers may be related to habitual folic acid intake. Global folate status statistics indicate that folate deficiency varies from 5% to 20% in different countries [65]. In addition, 11 surveys reported the prevalence of folate insufficiency, which was >40% in most countries [66]. These data suggest the hypothesis that replenishment of folic acid concentrations in those who suffer from folate deficiency and insufficiency may have more favorable effects for improving glycemic control markers compared to individuals with normal folate concentrations. Based on World Health Organization (WHO) guidelines, concentrations of 13.5–45.3 nmol/L (6–20 ng/mL) is considered a normal range and a serum folate concentrations of <13.4 nmol/L (or <5.9 ng/mL) is interpreted as a possible deficiency [67]. From all 23 included studies, 17 studies reported baseline folate concentrations [15,20,21,22,32,33,34,35,37,38,39,40,41,42,43,47] and the baseline concentrations of folate in 9 studies are considered as possibly deficient [15,20,21,22,32,38,40,42,43]. Moreover, the recommended dietary allowance (RDA) for folic acid is 400 μg/day for both adult men and women [68]. From six studies, which did not reported baseline folate concentrations, four studies reported dietary intake of folic acid [44,45,48,50], and in all four of these studies, the average dietary folate intake was less than 280 μg/day. Therefore, since the participants of most included studies had lower folate concentrations than normal or low dietary intakes, folic acid supplementation showed significant improvement in the glycemic profile. From a mechanistic perspective, oxidative stress also leads to the production of reactive oxygen species (ROS) [69]. It has been shown that oxidative stress has been linked to the development of insulin resistance, β-cell dysfunction, impaired glucose tolerance, and mitochondrial dysfunction [70]. Alternatively, it has been mentioned that folic acid supplementation has antioxidant properties [71]. ROS-induced insulin resistance is mediated by some mechanism, including c-Jun N-terminal protein kinase (JNK) activation, tumor necrosis factor-alpha (TNF-α) increment decrease glucose uptake. In vitro and in vivo studies showed that ascorbic acid supplementation could decrease both JNK activation [72,73] and TNF-α gene expression [74]. Thirdly, inflammation plays an important role in the development of insulin resistance [75,76]. Previous investigations have revealed that proinflammatory cytokines such as TNF-α and IL-6 could impair insulin signaling, which may cause insulin resistance [75,76]. Moreover, both in vitro and in vivo studies have shown that folic acid supplementation may inhibit the proinflammatory process by inhibiting NF-kB activation [77,78]. Finally, folate-deficiency-induced homocysteinemia may have resulted in glycemic disorders [79,80,81]. Even earlier stages of folate deficiency are expressed by rising homocysteine concentrations, which appear at higher folic acid intakes [65]. Further mechanistic studies are needed to evaluate the possible mechanisms of the effects of folic acid supplementation on glycemic control.

This meta-analysis contains some strengths and limitations. The main strength of this study is the relatively acceptable number of studies and high sample size. Another advantage is the lack of publication bias in the analysis. Furthermore, we performed a dose-response analysis to evaluate the association between pooled effect size, dosage (mg/day), and duration of folic acid supplementation. Regarding limitations, it should be mentioned that all but three trials lasted less than three months; therefore, our analysis is unable to show the long-term effects of folic acid supplementation on glycemic profile. Moreover, there was a notable heterogeneity in the results of the investigations included.

In conclusion, folic acid supplementation may improve glycemic profile by decreasing FBG, fasting insulin, and HOMA-IR without altering HbA1c. It should be noted that the glycemic control properties of folic acid supplementation were small and may not reach clinical importance.

Author Contributions

O.A., B.N., D.A.-L. and S.P.M. designed and conceived the study, searched databases, screened studies, and extracted data; O.A. performed the statistical analyses; R.B., M.R.K., F.D., K.S., A.W. and D.G.C. interpreted the results and drafted the manuscript with contributions from H.P.O. and A.A.N. All authors reviewed and commented on subsequent drafts of the manuscript. O.A. and D.A.-L. have the primary responsibility for the final content. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data sharing is applicable with email to corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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