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Review Article
The contribution of dietary total antioxidant capacity to type 2 diabetes risk and levels of glycemic biomarkers: a systematic review
Sorayya Kheirouri1orcid, Hamed Alizadeh2orcid

DOI: https://doi.org/10.24171/j.phrp.2024.0337
Published online: March 27, 2025

1Department of Nutrition, Faculty of Nutrition and Food Sciences, Tabriz University of Medical Sciences, Tabriz, Iran

2Patient Safety Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia, Iran

Corresponding author: Sorayya Kheirouri Department of Nutrition, Faculty of Nutrition and Food Sciences, Tabriz University of Medical Sciences, Attar Nishabouri St., Tabriz, I. R. Postal code: 5166614711, POBOX: 14711, Iran E-mail: Kheirouris@tbzmed.ac.ir
• Received: December 10, 2024   • Revised: February 16, 2025   • Accepted: February 24, 2025

© 2025 Korea Disease Control and Prevention Agency.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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  • Objectives
    This study systematically reviewed and analyzed epidemiological evidence regarding the association between dietary total antioxidant capacity (DTAC) and both the risk of developing diabetes and glycemic biomarker levels.
  • Methods
    We searched the PubMed, Scopus, ScienceDirect, and Google Scholar databases through July 2024 without imposing any date restrictions. Original studies that examined the relationship between DTAC and either the risk of developing diabetes or glycemic biomarker levels—specifically fasting blood glucose (FBG), hemoglobin A1C (HbA1C), insulin, and the homeostatic model assessment for insulin resistance (HOMA-IR)—were eligible for inclusion. After eliminating duplicates and irrelevant records, relevant studies were selected, and data were extracted through rigorous critical analysis.
  • Results
    A total of 32 articles were included in the review. Of the 19 studies that evaluated diabetes risk, 15 reported a lower risk among subjects with higher DTAC values. All 4 studies examining prediabetes risk found lower risk in participants with high DTAC scores. Additionally, significant inverse relationships were observed between DTAC values and FBG (9/15 studies), HbA1C (1/6 studies), insulin (5/6 studies), and HOMA-IR (8/9 studies).
  • Conclusion
    The majority of evidence indicates that high adherence to an antioxidant-rich diet may reduce diabetes risk and improve glycemic biomarkers, including FBG, insulin, and HOMA-IR.
Type 2 diabetes (T2D) is a chronic condition characterized by insulin resistance and elevated blood glucose levels. As of 2021, approximately 537 million adults worldwide have diabetes, with more than 90% of these cases being T2D—a figure projected to rise to 643 million by 2030 [1]. In 2021, T2D accounted for an age-standardized mortality rate of 18.5 per 100,000 individuals [2] and, in 2019, a disability-adjusted life years rate of 801.5 per 100,000 individuals [2]. T2D leads to severe complications, including cardiovascular diseases, kidney damage, and neuropathy [3]. Major contributing factors include obesity, physical inactivity, and unhealthy dietary habits [4].
There is growing interest in the role of oxidative stress and inflammation in chronic conditions such as atherosclerosis, obesity, and T2D [5]. An imbalance between prooxidants and antioxidants, resulting in oxidative stress, is a key pathogenic mechanism that increases diabetes risk [6]. Hyperglycemia promotes glucose autoxidation, non-enzymatic glycation, and impairs monocyte function, all of which lead to increased free radical production [7]. In addition, reduced levels of antioxidants exacerbate oxidative stress [8], resulting in lipid and DNA damage as observed in patients with diabetes [9].
A healthy diet rich in antioxidants and anti-inflammatory compounds may reduce the risk of chronic diseases, including cardiovascular disease, diabetes, and certain cancers [10]. Dietary intake has been linked to modulation of oxidative stress [11,12], and energy restriction has been suggested to lower levels of oxidative stress intermediaries [13]. Consumption of antioxidants is proposed as a protective strategy to mitigate oxidative damage [14], and evidence indicates that dietary antioxidants reduce T2D risk by inhibiting peroxidation chain reactions [15]. Moreover, consuming fruits and vegetables is associated with reduced incidence and mortality from various chronic diseases [16,17]. One hypothesis suggests that the collective impact of antioxidants—such as vitamins C and E, carotenoids, flavonoids, and proanthocyanidins—protects cells against free radical-induced oxidative damage [18].
Recent research indicates that higher total plasma antioxidant capacity is correlated with the intake of antioxidant-rich fruits and vegetables [19,20]. Because individual antioxidant concentrations do not fully capture the overall antioxidant potential of whole foods, the concept of dietary total antioxidant capacity (DTAC) was introduced [21]. DTAC functions as a valuable indicator of the overall antioxidant status of a diet [22,23].
Over recent decades, researchers have developed various methods to assess the total antioxidant capacity (TAC) of complex materials like foods. These assays include ferric reducing-antioxidant power (FRAP), which measures the reduction of Fe³⁺ to Fe²⁺; total radical-trapping antioxidant parameter (TRAP), which monitors protection during a controlled peroxidation reaction; Trolox equivalent antioxidant capacity (TEAC), which compares the ability of antioxidants to quench the ABTS¹⁺ radical with that of Trolox; and oxygen radical absorbance capacity (ORAC), which evaluates the complete reaction of antioxidants with biologically relevant free radicals [24,25].
In recent years, substantial research has focused on the relationship between DTAC and T2D risk [26,27]. Although a recent systematic review examined the relationship between DTAC and cardiometabolic risk factors in diverse populations, no review has comprehensively summarized the evidence linking DTAC to T2D risk and related biomarkers such as fasting blood glucose (FBG), hemoglobin A1C (HbA1C), insulin levels, and homeostatic model assessment for insulin resistance (HOMA-IR).
We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to ensure a transparent and systematic report [28].
Search Strategy
We conducted a systematic literature search in PubMed, Scopus, ScienceDirect, and Google Scholar up to July 2024 without imposing any date restrictions. Our search strategy used specific keywords relevant to the research question: “Dietary antioxidant capacity OR Dietary antioxidant index OR Dietary antioxidant intake OR Dietary total antioxidant” in title, abstract, and keywords, combined with “Diabetes OR Glycemic indices OR Metabolic indices OR insulin resistance OR insulin OR HOMA-IR OR glucose OR sugar” in title, abstract, and keywords. Only English-language original articles were included. Details of the search strategy are provided in Table S1. Reference lists of the included studies were also reviewed to identify additional relevant articles.
The research question was: “Is there an association between DTAC and T2D risk and levels of glycemic biomarkers?” Table 1 outlines the population, exposure, comparator, outcome (PECO) approach for this review, with individuals with diabetes or at risk defined as the population, low DTAC as the exposure, high DTAC as the comparator, and diabetes risk and glycemic biomarkers (FBG, HbA1C, insulin, and HOMA-IR) as the outcomes.
Eligibility Criteria
We included original articles published in English that evaluated the association between DTAC and either the risk of developing diabetes or glycemic biomarkers (including FBG, HbA1C, insulin, and HOMA-IR). Studies were excluded if they focused on a single dietary antioxidant (e.g., vitamin E or carotenoids) in relation to T2D, the effects of antioxidant supplementation on diabetes biomarkers, the impact of specific active constituents with antioxidant activity from special foods on diabetes management, interactions between DTAC and specific genotypes (e.g., the caveolin-1 gene variant, rs 3807992) on diabetes biomarkers, or the combined effect of DTAC with other factors on diabetes or glycemic biomarkers. Animal studies, theses, dissertations, conference abstracts, editorials, reviews, and posters were also excluded.
Selection of Studies
Extracted studies were transferred to an EndNote file, and duplicate articles were removed. Two independent researchers screened the remaining articles based on their titles and abstracts to identify potentially eligible studies. Full texts of the selected articles were then reviewed independently to assess eligibility and extract relevant data. Any disagreements regarding study eligibility were resolved through discussion until consensus was reached.
Data Extraction
The following information was collected: first author and year of publication, study country and design, participant demographics (including age and sex), physical condition, follow-up duration, dietary assessment method, DTAC evaluation method, covariates, and outcome measures reported as correlation indicators (odds/hazard ratios, confidence intervals, p-values, if available). Only results from fully adjusted models were considered.
In studies that employed multiple statistical approaches to assess the association between DTAC and T2D risk, priority was given to findings from regression analyses. When analyses were conducted separately by sex and an association was observed in only 1 sex, only the relevant sex-specific findings were documented.
Ethics Approval
The ethics committee of Tabriz University of Medical Sciences, Tabriz, Iran, registered and approved the protocol of this study (IR.TBZMED.REC.1401.824).
Selection of Studies
Initially, 314 studies were retrieved (Figure 1). After removing duplicates, 143 studies remained. A review of titles and abstracts identified 43 publications relevant to the study’s scope. Following critical appraisal, 11 studies were excluded because 1 was irrelevant, 1 was unavailable, 1 was a duplicate of an already included study, 1 was a letter, 5 investigated the interaction of DTAC with other factors regarding T2D, and 2 focused on single dietary antioxidants. Ultimately, 32 studies were included in the review (comprising 8 cohort studies, 20 cross-sectional studies, and 4 case-control studies) (Figure 1).
Characteristics of the Included Studies
As shown in Table 2, the included investigations [26,27,2958] were conducted across diverse global regions. The studies were published between 2010 and 2024, with the majority (n=26) published after 2015. In the cohort studies, follow-up durations ranged from 3 to 37 years, although 4 studies had excessively long follow-up periods. Most studies (23 of 32) used food frequency questionnaires (FFQs) to assess dietary intake. DTAC was measured using various methods, including FRAP (21 studies), ORAC (9 studies), TRAP (5 studies), TEAC (5 studies), a composite dietary antioxidant index (3 studies), and other methods (2 studies). Different cut-off points were employed to categorize DTAC values. Twenty-one studies focused on subjects with diabetes or prediabetes, and most studies (n=23) adjusted for potential covariates in their analyses.
Relationship between DTAC and Risk of Developing Prediabetes and Diabetes
Among the 19 studies assessing the association between DTAC and diabetes risk [27,2946], 15 reported a lower risk of diabetes in individuals with higher DTAC values [30,31,3335,3746]. In contrast, 4 studies found no significant association [27,29,32,36]. All 4 studies that evaluated the link between DTAC and prediabetes risk reported lower risk in individuals with higher DTAC values [27,40,47,48].
Relationship between DTAC and Glycemic Biomarkers
As shown in Table 2, out of 15 studies that evaluated the association between DTAC and FBG [26,27,29,31,43,48-57], 9 reported lower FBG levels in individuals with higher DTAC values [27,29,43,48,5154,57]. Six studies investigated the association between DTAC and HbA1C [27,31,49,50,55,56], with only 1 study reporting lower HbA1C levels in those with higher DTAC values [27]. Furthermore, 6 studies assessed the relationship between DTAC and insulin levels [5055], and 5 of these found that higher DTAC values were associated with lower insulin levels [5053,55]. Among the 9 studies evaluating DTAC and HOMA-IR [32,40,5055,58], 8 reported lower HOMA-IR values in subjects with higher DTAC values [32,40,5053,55,58].
A healthy and diverse diet can enhance overall health outcomes and well-being [59,60]. Antioxidants play a critical role in maintaining health by neutralizing free radicals—highly reactive molecules that can damage cells and genetic material. These free radicals arise from metabolism, exercise, and environmental exposures such as air pollution and sunlight [61]. Evidence indicates that a diet rich in antioxidants, particularly from fruits, vegetables, and legumes, reduces the risk of chronic diseases related to oxidative stress. Epidemiological studies and meta-analyses of prospective observational studies have consistently linked higher intakes of antioxidant-rich foods with reduced risks of cardiovascular diseases, cancer, and all-cause mortality [62,63]. Thus, incorporating antioxidant-rich foods into one’s diet may improve overall health and well-being [64,65].
In the present study, we reviewed both interventional and observational investigations to provide an overview of the relationship between an antioxidant-rich diet and the risk of developing diabetes or altered glycemic biomarkers. Our systematic review provides robust evidence that high DTAC is associated with decreased diabetes risk as well as improved levels of FBG, insulin, and HOMA-IR.
Oxidative stress is central to the pathophysiology of diabetes [66]. Excess production of reactive free radicals, often triggered by hyperglycemia, leads to oxidative stress, which further exacerbates diabetes and its complications [66,67]. In diabetes, oxidative stress interacts with cellular biomolecules—including proteins and lipids—resulting in harmful effects such as lipid peroxidation, which compromises cellular structure and function [68]. Genetic studies related to oxidative stress have revealed potential causal links with diabetes and microvascular complications [69]. Elevated lipid peroxidation coupled with reduced antioxidant activity has been observed in patients with diabetes [70]. Although classic antioxidants like vitamin E have not demonstrated clinical benefits in large-scale trials, a more comprehensive strategy that both prevents reactive species generation and scavenges free radicals appears promising [67]. Moreover, synergistic low-dose antioxidant blends have shown potential in reducing lipid peroxidation and restoring redox homeostasis [71]. Understanding the interplay among dietary antioxidants, lipid peroxidation, and hyperglycemia is essential for managing diabetes and its associated complications.
Oxidative stress also contributes significantly to the development of insulin resistance. Research has shown that systemic oxidative stress is correlated with insulin resistance. For instance, data from the Framingham Offspring Study revealed that individuals with higher levels of oxidative stress (measured by urinary 8-epi-prostaglandin F2α) had an increased prevalence of insulin resistance, even after adjusting for body mass index [72]. A similar positive association between oxidative stress markers and HOMA-IR has been reported in young adults [73]. Oxidative stress may impair insulin signaling by damaging cellular components such as proteins, lipids, and DNA, leading to reduced glucose uptake [74]. Additionally, the activation of inflammatory pathways further exacerbates insulin resistance [75]. Antioxidants help counteract this process by reducing inflammation; for example, Luu et al. [76] reported an inverse association between dietary antioxidant intake and inflammatory biomarkers in a study of 3,853 women, while Beharka et al. [77] demonstrated that prolonged dietary antioxidant supplementation reduced the production of specific inflammatory mediators in mouse macrophages.
Elevated oxidative stress can also impair insulin secretion. In both type 1 and type 2 diabetes, chronic hyperglycemia contributes to complications in target organs. High glucose levels trigger the production of reactive oxygen (ROS) and reactive nitrogen species, which damage DNA, proteins, and lipids, and activate stress-sensitive pathways such as nuclear factor kappa B (NF-κB) and p38 mitogen-activated protein kinase [78]. In T2D, elevated glucose and free fatty acid levels further activate these pathways, contributing to both insulin resistance and impaired insulin secretion [78]. Studies have reported increased oxidative and endoplasmic reticulum stress in pancreatic β-cells under hyperglycemic conditions, adversely affecting insulin production [79]. Furthermore, oxidative stress compromises insulin sensitivity and contributes to insulin resistance by impairing glucose uptake into cells [66]. Accumulating evidence indicates that oxidative stress plays a central role in β-cell dysfunction and insulin secretory failure in T2D [80,81].
Dietary bioactive compounds, including antioxidants, play a crucial role in protecting pancreatic β-cells from oxidative stress, which is a key factor in the development and progression of diabetes [82,83]. Dietary antioxidants may protect β-cells by inhibiting NF-κB activity [84] and by activating nuclear factor erythroid-derived factor 2-related factor 2 (Nrf2), a transcription factor that upregulates antioxidant and cytoprotective genes [85]. NF-κB is a transcription factor that regulates the expression of genes involved in inflammation and immune responses. The activation of NF-κB increases the production of pro-inflammatory cytokines, which can damage β-cells [86]. Meanwhile, Nrf2 is a transcription factor that regulates the expression of antioxidant and cytoprotective genes. Nrf2 activation upregulates antioxidant enzymes and proteins, which help to neutralize ROS and reduce oxidative stress [87]. Studies have shown that polyphenols and other dietary antioxidants can inhibit NF-κB activation and reduce oxidative stress [88] and activate the Nrf2 pathway [89], thereby protecting β-cells from oxidative damage and improving their function.
Dietary antioxidants also help maintain serum antioxidant levels and mitigate oxidative stress, which is essential for overall health [90]. Vahid et al. [91] reported that higher dietary antioxidant intake was associated with increased serum TAC. Similarly, Jewell et al. [92] found that an antioxidant blend—including vitamin E, vitamin C, and β-carotene—increased cellular protection and improved antioxidant status in dogs and cats. Avila-Escalante et al. [11] demonstrated that a high-antioxidant diet enhances plasma antioxidant capacity and reduces oxidative stress markers in individuals with various chronic diseases. Therefore, incorporating antioxidant-rich foods into the diet is crucial for maintaining overall health and preventing chronic diseases.
In summary, as illustrated in Figure 2, consumption of antioxidant-rich foods enhances circulating antioxidant levels and mitigates oxidative stress. This effect contributes to reduced insulin resistance, improved insulin secretion and sensitivity, and ultimately lower blood glucose levels, thereby decreasing the risk of T2D and its associated complications.
Strengths of the Study
The included investigations were conducted in diverse regions worldwide, enhancing the generalizability of the findings. The recent publication dates of most studies indicate that the topic is current. The presence of several prospective cohort studies with substantial sample sizes strengthens the statistical power of the results. Additionally, most studies accounted for potential confounders in their analyses of the DTAC–diabetes risk relationship, underscoring the independent role of DTAC.
Limitations of the Study
Several studies did not adequately consider covariates in their statistical analyses, and diverse statistical methods were employed across investigations. Furthermore, different techniques were used to calculate DTAC, and varying cut-off points were applied when categorizing DTAC values, which may have influenced the findings. The heterogeneity among the included studies—in terms of study populations, measured outcomes, and methodologies (including statistical analyses and DTAC evaluation methods)—precluded the performance of a meta-analysis and limited the ability to draw definitive conclusions.
The majority of existing evidence indicates that high adherence to an antioxidant-rich diet may reduce diabetes risk and improve glycemic biomarkers, including FBG, insulin, and HOMA-IR.
Implications of the Findings
The findings of this study have significant public health implications, particularly in managing T2D. They provide valuable insights for developing dietary recommendations aimed at preventing chronic diseases. Improved blood glucose parameters can lead to better glycemic control, reducing the risk of diabetes-related complications such as cardiovascular disease, neuropathy, and nephropathy. Enhanced glycemic control further contributes to overall metabolic health and quality of life for individuals with or at risk for T2D.
• Oxidative stress contributes to the pathophysiology of diabetes.
• An antioxidant-rich diet intake may reduce diabetes risk.
• An antioxidant-rich diet intake may improve glycemic biomarkers, including fasting blood glucose, insulin, and homeostatic model assessment for insulin resistance.
Supplementary data are available at https://doi.org/10.24171/j.phrp.2024.0337.
Table S1.
Association between the dietary total antioxidant capacity and risk of developing diabetes or glycemic biomarkers: the database search strategy using PubMed, Scopus, Google Scholar, and ScienceDirect.
j-phrp-2024-0337-Supplementary-Table-1.pdf

Ethics Approval

The ethics committee of Tabriz University of Medical Sciences, Tabriz, Iran, registered and approved the protocol of this study (IR.TBZMED.REC.1401.824).

Conflicts of Interest

The authors have no conflicts of interest to declare.

Funding

This study was financially supported by Tabriz University of Medical Sciences (Project No: 70938).

Availability of Data

All study-related data are included in the publication or provided as supplementary information.

Figure 1.
Flow diagram of the study. DTAC, dietary total antioxidant capacity.
j-phrp-2024-0337f1.jpg
Figure 2.
Potential mechanistic pathways for the association between dietary total antioxidant capacity and the diabetes risk and its related glycemic biomarkers.
NrF2, nuclear factor erythroid-derived factor 2-related factor 2; NF-κB, nuclear factor kappa B; IRSP, insulin receptor signaling pathway.
j-phrp-2024-0337f2.jpg
Table 1.
Description of the PECO strategy
Element Description
Population People with diabetes or at risk of diabetes
Exposure Low dietary total antioxidant capacity
Comparator People with high dietary total antioxidant capacity
Outcome Diabetes risk and diabetes-related glycemic biomarkers including FBG, HbA1C, insulin, and HOMA-IR

FBG, fasting blood glucose; HbA1c, hemoglobin A1C; HOMA-IR, homeostatic model of insulin resistance.

Table 2.
Summary and characteristics of the 28 selected studies assessing the relationship of DTAC with diabetes risk and its related glycemic biomarkers
Study Year Country/study design Target population Age (y) Method of dietary intake assessment Method of DTAC evaluation Follow-up (y) Findings
FBS HbA1C HOMA-IR Insulin Risk of diabetes Statistical analysis method/considered confounders
Bahadoran et al. [29] 2012 Iran/cohort 1,983 Adults (47% men) 40.4±13.0 168 Food-items semiquantitative FFQ ORAC: Q1, <842; Q2, 842-958; Q3, 959–1,080; Q4, >1,080 3 At baseline: FBG did not significantly differ across DTAC categories. - - - Hyperglycemia (FPG ≥100 mg/dL) did not differ across the DTAC quartiles and during 3-year follow-up, hyperglycemia did not significantly correlate with DTAC value. Age, sex, BMI, physical activity, smoking status, energy and macronutrient intakes, dietary potassium intake
DTAC was negatively associated with FBG (p-trend <0.01). After a 3 years follow-up, DTAC was not associated with FBG.
Baharirad et al. [26] 2022 Iran/cross-sectional 189 T2D patients 35–65 FFQ ORAC (tertile) - The mean values of FBS did not show statistically significant difference among the DTAC tertiles. There was no significant relationship between DTAC and FBS (tertile 3 vs. tertile 1: β, –0.90; 95% CI, –1.23 to 1.05; p=0.87; adjusted model). - - - - Logistic regression/age, sex, BMI, physical activity, and total caloric intake
Capas et al. [27] 2018 Turkey/descriptive Adults with T2D; people with diabetes, n=29 (n=20 female); healthy subjects, n=15 (n=10 female) 40–70 3 Days of 24-hour food record Modified version of the FRAP - In people with diabetes, a negative correlation was found between DTAC and FBG (r=–0.406, p=0.036). In people with diabetes, a negative correlation was found between DTAC and HbA1C (r=–0.531, p=0.004). - - DTAC did not differ between the 2 groups. Group comparison and Spearman correlation/not stated
Cetiner et al. [30] 2021 Turkey/cross-sectional People with diabetes, n=60 (n=33 female); healthy subjects, n=25 (n=14 female) Newly diagnosed T2DM, 50.8±6.8; formerly diagnosed T2DM, 51.8±7.4; controls, 45.6±8.6 3 Days of 24-hour recall FRAP, TEAC, TRAP, ORAC - - - - - DTAC was lower in diabetes than in controls (p<0.001). Groups comparison/confounders have not been considered.
Chen et al. [31] 2023 China/cross-sectional 11,956 Participants Mean>42 24-Hour dietary recall CDAI (6 dietary antioxidants) FBS did not significantly differ among the CDAI quartiles. In full adjusted model, CDAI did not independently associate with fasting glucose. Percentage of HbA1C was lower in the highest quartile of CDAI (p=0.03). In full adjusted model, CDAI was not independently associated with HbA1C. - - Percentage of people with diabetes were lower in the highest quartile of CDAI (p<0.001) Multivariable logistic regressions/age, sex, race, education, physical activity, smoking status, hypertension, and coronary heart diseases
Compared with the lowest quartile of CDAI, the highest quartile was related to reduced risk of diabetes (OR, 0.84; 95% CI, 0.71–0.99; p=0.035).
Cyunczyk et al. [32] 2022 Poland/cross-sectional 413 Adults (40% male); normoglycemia, 171 (41.40%); prediabetes, 202 (48.91%); T2D, 40 (9.69%) 49.84±9.47 3 Days of 24-hour dietary recall FRAP (Q1, ≤8.37; Q2, 8.38–11.27; Q3, 11.2–14.50; Q4, ≥14.51) - - - DTAC was inversely associated with HOMA-IR (β=–0.39, p=0.02). - The higher quartile of DTAC was significantly associated with a reduced odds ratio for the prevalence of prediabetes (Q3 vs. Q1; OR, 0.583; 95% CI, 0.309–0.945), but not with the risk of diabetes. Linear regression/age, sex, family history of diabetes, education level, physical activity, dyslipidemia, hypertension, BMI, waist circumference, smoking status, daily alcohol intake and daily energy intake
Daneshzad et al. [33] 2020 Iran/case-control Pregnant women with GDM; GDM, n=200; healthy, n=263 28.33±6.23 3 Days of a 24-hour dietary record FRAP, TRAP, TEAC; tertile values were not stated. - - - - - FRAP was significantly lower in pregnant women with GDM than in controls. The risk of GDM was 85% lower among those in the highest tertile of FRAP (OR, 0.15; 95% CI, 0.08–0.29; p-trend <0.0001). There was no significant association between the risk of GDM and TRAP as well as TEAC. Group comparison and binary logistic regression/age, BMI, energy intake, physical activity, number of offspring, carbohydrate, fat, and protein intake, and supplementation
Daneshzad et al. [49] 2020 Iran/cross-sectional 265 T2D women 59.66±8.94 168-Item semiquantitative FRAP (T1, <3.68; T2, 3.68–5.18; T3, >5.18); ORAC - FBS levels did not differ significantly across the tertiles of DTAC. HbA1C levels did not differ significantly across the tertiles of DTAC. - - - Analysis of variance/age, BMI, energy intake, physical activity, blood pressure, medication, supplement consumption
Fagherazzi et al. [34] 2018 France/cohort 402 Women at very high risk of T2DM; women with T2DM, 117 (29%); women free of T2DM, 285 (71%) 55.7±6.65 Semiquantitative FFQ (57 predefined food groups) FRAP 19 - - - - FRAP scores did not differ between 2 groups. A high DTAC was associated with less developing T2DM, in women with a moderate or high Western dietary pattern score. Group comparison and regression/not stated
El Frakchi et al. [35] 2024 Morocco/cross-sectional 254 T2D outpatients 54.52±7.21 255 Food-item FFQ FRAP; high, >10.6 mmol; low, <10.6 mmol - - - - - Percentage of people with diabesity was lower among those with higher DTAC (p=0.02). Student t-test/-
Galarregui et al. [50] 2018 Spain/cross-sectional 112 Overweight or obese adults 50.8±9 137-Item semiquantitative FFQ FRAP (T1, <8.6; T, 8.6–11.36; T3, >11.36) - Glucose level did not change significantly across the tertiles of DTAC. HgA1C level did not change significantly across the tertiles of DTAC. Subjects with higher values of TAC had significantly lower HOMA-IR (p=0.03). Subjects with higher values of TAC had significantly lower insulin concentration (p=0.01). - -
Hermsdorff et al. [51] 2011 Spain/cross-sectional 266 Healthy subjects (105 men/ 161 women) 22±3 Brazilian sample (n=123), 3 day-record; Spanish sample (n=143), 136 food-items semiquantitative FFQ TEAC - DTAC value was inversely associated with glucose (p<0.05). - DTAC values were inversely associated with HOMA-IR (p<0.05). DTAC values were inversely associated with insulin levels (p<0.05). - Multiple linear regression/age, sex, waist circumference, energy intake, smoking habit, physical activity, and vitamin supplement use
Heshmati et al. [44] 2024 Iran/prospective cohort 1,856 pregnant women; GDM, 369 18–45 168 Food-items FFQ FRAP Between February 1, 2020 and August 31, 2021 - - - - The adjusted risk of GDM decreased by 34% (95% CI, 10%–52%; p=0.023) for each DTAC score increase. Women in the highest quartile of DTAC had a lower risk of developing GDM compared to those in the lowest quartile (adjusted RR, 0.29; 95% CI, 0.12–0.68; p=0.005). Age, BMI, occupation, hypertension, diabetes, education, and working rotating shift
Jimenez-Ortega et al. [58] 2024 México/cross-sectional 830 Children and adolescents 7–18 116 Food-items FFQ Based on the intake of 6 vitamins and minerals (vitamins A, C, E, selenium, magnesium, and zinc) - - In total participants, people in the highest DAI category had low insulin resistance (OR, 0.49; 95% CI, 0.30–0.80). Female participants in the highest DAI category had significantly lower odds of developing insulin resistance than those in the lowest DAI category (OR, 0.54; 95% CI, 0.29–0.98). - - Multiple logistic regression/age, sex, BMI, smoking status, vitamin D intake, polyunsaturated intake, alcohol intake, family history of diabetes, Tanner stages, protein intake and total fat intake
Kashino et al. [36] 2019 Japan/prospective cohort 64,660 Adults (27,809 men and 36,851 women). During the 5-y period, 1,191 participants (692 men and 499 women) were newly diagnosed with T2D. 44–76 147 Food-items FFQ FRAP ORAC, TRAP (quartiles) 5 - - - - DTAC was not associated with the risk of T2D in multivariate-adjusted models. Similar associations were found in men and women. Linear and logistic regression/age, sex, study area, smoking habits, physical activity, BMI, history of hypertension, family history of diabetes mellitus, use of supplements, coffee consumption, and energy intake
Li et al. [37] 2024 China/cross-sectional 12,467 Participants (female, 65.4%); people with T2D, 1,238 (9.9%) 57.04±10.13 FFQ FRAP - - - - - Higher DATC was associated with a lower T2DM risk (OR, 0.96; 95% CI, 0.80–1.17; p-trend=0.024). Logistic regression/age, sex, smoking status, alcohol consumption, physical activity, BMI, WC, TG, HDL-C, hypertension, and health supplement intake
Liu et al. [38] 2024 China/cross-sectional 2,158 Participants (male, 52.12%) 58.87±0.41 24 Hour dietary recall CDAI - - - - - A negative correlation between CDAI and diabetic retinopathy (OR, 0.94; 95% CI, 0.90–0.98; p=0.007). Multivariate logistic regression/age, sex, race, drinking, smoking, body mass index (BMI), hypertension, etc.
Mancini et al. [39] 2018 Germany/prospective cohort 64,223 Women. During 15 years of follow-up, 1,751 women had validated T2Ds. 52±7 208 Food-items dietary questionnaire FRAP (Q1, ≤8.72; Q2, 8.73–11.29; Q3,11.30–13.93; Q4,13.94–17.55; Q5, >17.55) 15 - - - - In multivariable models, higher levels of DTAC were associated with a lower risk of T2D (OR, 0.73; 95% CI, 0.60–0.89; p<0.0001). Spline regression/smoking status, physical activity, education level, hypertension, hypercholesterolaemia, family history of diabetes, energy intake, alcohol intake, BMI
Okubo et al. [52] 2014 United Kingdom/cohort Men (1,441), women (1,253) 59–73 129 Food-item FFQ ORAC, TRAP, FRAP, TEAC 37 By all 4 assays: In women, but not in men, DTAC was inversely associated with fasting glucose (p<0.05). - By all 4 assays: In women and men, DTAC was inversely associated with HOMA-IR (p<0.05). By all 4 assays: In women and men, DTAC was inversely associated with insulin levels (p<0.05). - Multiple linear regression/age, sex, BMI, smoking status and physical activity level, dietary supplement use, and energy intake
Psaltopoulou et al. [53] 2011 Greece/epidemiological study 551 Men and 467 women; normal, n=771; IFG, n=203; T2D, n=44 Normal, 38±11; IFG, 43±10; diabetic, 52±8 Semiquantitative FFQ TRAP, FRAP, TEAC Using all 3 assays, an inverse association was found between DTAC and serum log-glucose (p=0.001). - By all 3 assays, an inverse association was found between DTAC and serum log- HOMA (p≤0.001). By all 3 assays, an inverse association was found between DTAC and serum log- insulin (p≤0.002). - Multiple regression analyses/age, sex, BMI, physical activity status, smoking habits, and energy intake
Puchau et al. [54] 2010 Spain/cross-sectional 153 Healthy young adults (101 women and 52 men) 20.8±2.7 136 Food-item FFQ and 3-day food record FRAP (low, <6.9 mmol; high, >6.9 mmol) - Serum glucose significantly differed between subjects with high and low DTAC values (p=0.006). Serum glucose was negatively associated with DTAC (p=0.03). - HOMA-IR did not significantly differ between subjects with high and low DTAC values. Insulin levels did not significantly differ between subjects with high and low DTAC values. - Groups comparison and multiple linear regression/sex and daily energy intake
Rahmani et al. [47] 2021 Iran/case-control Prediabetes, n=49; healthy control, n=98 47.42±15.98 80 Food-item FFQ FRAP (T1, ≤11.90; T2, 11.90–21.24; T3, >21.24) - - - - - Patients with prediabetes had lower DTAC scores as compared with controls. High DTAC was associated with a significantly reduced likelihood of having prediabetes (OR, 0.09; 95% CI, 0.02–0.53; p-trend=0.01). Age, sex, BMI, marital status, income, occupation, education, physical activity, dietary supplementation, family history of diabetes, and total calorie intake
Roumi et al. [45] 2024 Iran/cross-sectional 4,241 Participants; patients with T2D, n=589; individuals without T2D, n=3,611 35–70 FFQ Wright’s method - There was no significant correlation between FBG and dietary total antioxidant index. - - - Negative associations was found between T2D with total score of dietary antioxidant index (OR, 0.67; 95% CI, 0.55–0.81; p=0.001). Logistic and linear regression/age, sex, BMI, education level, marital status, occupation, physical activity, and calorie intake
Salavatizadeh et al. [55] 2022 Iran/cross-sectional 200 People with T2D 18–70 147 Food-item FFQ FRAP FBS was not different across DTAC tertiles (p=0.44). HbA1C did not differ across DTAC tertiles (p=0.67). People in the third tertile of DTAC had lower HOMA-IR level (p=0.05). People in the third tertile of DTAC had lower insulin level (p=0.01). - Kruskal-Wallis test and analysis of covariance/age, sex, diabetes duration, smoking status, physical activity, BMI, waist circumference, and energy
van der Schaft et al. [40] 2019 Netherland/cohort 5,796 Men (n=2,266) and women (n=3,530); normoglycaemia, n=4,957; prediabetes, n=839 Men, 63.4; women, 64.6 170 Items semiquantitative FFQ FRAP 15 - - Dietary FRAP was inversely associated with HOMA-IR (p<0.001). - Higher FRAP score was associated with a lower risk of T2D among the total population (HR, 0.84; 95% CI, 0.75–0.95; p=0.01) and among participants with prediabetes (HR, 0.85; 95% CI, 0.73–0.99; p=0.03), but not with risk of prediabetes. Cox proportional hazards regression/age, sex, BMI, hypertension, dyslipidemia, highest attained level of education, physical activity, smoking status, energy intake, daily alcohol intake and degree of adherence to guidelines for a healthy diet
Sezavar et al. [56] 2021 Iran/cross-sectional 170 Adults with morbid obesity 37.4±10.17 147 Food-item FFQ ORAC, FRAP (T1, <5.36; T2, 16.41–24.01; T3, >145.17) - FBS did not significantly differ across tertiles of FRAP (p=0.21) and ORAC (p=0.86). HbA1C did not significantly differ across tertiles of FRAP (p=0.22) and ORAC (p=0.99). - - - One-way ANOVA/not stated
Sohouli et al. [57] 2020 Iran/case-control NAFLD, n=158; healthy individuals, n=357 43.9±5.9 168 Food-item FFQ ORAC (tertiles) - Across increasing DTAC tertiles, the FBG level reduced (p=0.001). - - - - One-factor ANCOVA test/not stated
Sotoudeh et al. [48] 2018 Iran/case-control study 300 Individuals with and without prediabetes (n=150/group) Control, 47.7±7.2; prediabetic, 47.4±7.5 168 Food-items semiquantitative FFQ ORAC (Q1, <11,878.5; Q2, 11,878.5–16,322.1; Q3, 16,322.1–24,548.8; Q4, >24,548.8 mmol TE/100 g) - Across increasing DTAC quartiles, the participants had lower FBG (p-trend<0.001). - - - The mean DTAC was lower in individuals with prediabetes than in the control group (p<0.001). Participants in the fourth quartile of DTAC were less likely to experience prediabetes compared with those in the first quartile (OR, 0.18; 95% CI, 0.07–0.49; p<0.001). Logistic regression/BMI, physical activity, education, dietary intake of fiber, fat, energy, and coffee
Tan et al. [41] 2022 South Korean/cohort 20,594 Participants, 332 men and 360 women with T2D 40–79 106 Food-item FFQ Self-reported dietary data linked to the TAC database 5 - - - - DTAC was inversely associated with the development of T2D in women (HR, 0.58; 95% CI, 0.40–0.83; p-trend=0.0004). Among men, an approximately 15% reduced risk of developing T2D was observed for an SD increment in TAC (HR, 0.85; 95% CI, 0.75–0.96). A multivariable Cox proportional hazards regression/age, BMI, education, smoking, alcohol intake, physical activity
Zhou et al. [46] 2024 China/cross-sectional 7,982 Subjects; 48.50% male and 51.50% female; diabetic, 1,607; non-diabetic, 6,375 47.32±16.77 Two 24-hour dietary recalls CDAI - - - - - High CDAI was associated with reduced risk of diabetes mellitus in the female population (p=0.046). Multifactorial logistic regression models/age, sex, race, and education level
Zujko et al. [42] 2014 Poland/cross-sectional 80 Patients with and without T2D and 37 controls 40–65 24-Hour food recall and dietary database FRAP - - - - - DTAC was significantly higher in control than in patients with longstanding diabetes and those with newly diagnosed diabetes (p=0.01). Groups comparison/not stated
Zujko et al. [43] 2018 Poland/cross-sectional 5,690 Adults (2,554 men and 3,136 women) 50.08±16.44 A single 24-hour dietary recall FRAP (tertiles) - In women, higher DTAC was significantly associated with reduced odds of elevated blood glucose. - - - In women, higher DTAC was associated with 27.9% lower odds of diabetes (OR, 0.721; 95% CI, 0.522–0.997). Logistic regression/age, BMI, educational level, leisure time, physical activity, smoking, and alcohol intake

DTAC, dietary total antioxidant capacity; FBS, fasting blood sugar; HbA1c, hemoglobin A1C; HOMA-IR, homeostatic model assessment for insulin resistance; FFQ, food frequency questionnaire; ORAC, oxygen radical absorption capacity; FBG, fasting blood glucose; FPG, fasting plasma glucose; BMI, body mass index; T2D, type 2 diabetes; CI, confidence interval; T2DM, type 2 diabetes mellitus; FRAP, ferric reducing-antioxidant power; TEAC, Trolox equivalent antioxidant capacity; TRAP, total radical-trapping antioxidant potential; CDAI, composite dietary antioxidant index; OR, odds ratio; GDM, gestational diabetes mellitus; TAC, total antioxidant capacity; RR, risk ratio; DAI, dietary antioxidant index; WC, waist circumference; TG, triglycerides; HDL-C, high-density lipoprotein-cholesterol; IFG, impaired fasting glucose; HR, hazard ratio; ANOVA, analysis of variance; ANCOVA, analysis of covariance; SD, standard deviation.

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      The contribution of dietary total antioxidant capacity to type 2 diabetes risk and levels of glycemic biomarkers: a systematic review
      Image Image
      Figure 1. Flow diagram of the study. DTAC, dietary total antioxidant capacity.
      Figure 2. Potential mechanistic pathways for the association between dietary total antioxidant capacity and the diabetes risk and its related glycemic biomarkers.NrF2, nuclear factor erythroid-derived factor 2-related factor 2; NF-κB, nuclear factor kappa B; IRSP, insulin receptor signaling pathway.
      The contribution of dietary total antioxidant capacity to type 2 diabetes risk and levels of glycemic biomarkers: a systematic review
      Element Description
      Population People with diabetes or at risk of diabetes
      Exposure Low dietary total antioxidant capacity
      Comparator People with high dietary total antioxidant capacity
      Outcome Diabetes risk and diabetes-related glycemic biomarkers including FBG, HbA1C, insulin, and HOMA-IR
      Study Year Country/study design Target population Age (y) Method of dietary intake assessment Method of DTAC evaluation Follow-up (y) Findings
      FBS HbA1C HOMA-IR Insulin Risk of diabetes Statistical analysis method/considered confounders
      Bahadoran et al. [29] 2012 Iran/cohort 1,983 Adults (47% men) 40.4±13.0 168 Food-items semiquantitative FFQ ORAC: Q1, <842; Q2, 842-958; Q3, 959–1,080; Q4, >1,080 3 At baseline: FBG did not significantly differ across DTAC categories. - - - Hyperglycemia (FPG ≥100 mg/dL) did not differ across the DTAC quartiles and during 3-year follow-up, hyperglycemia did not significantly correlate with DTAC value. Age, sex, BMI, physical activity, smoking status, energy and macronutrient intakes, dietary potassium intake
      DTAC was negatively associated with FBG (p-trend <0.01). After a 3 years follow-up, DTAC was not associated with FBG.
      Baharirad et al. [26] 2022 Iran/cross-sectional 189 T2D patients 35–65 FFQ ORAC (tertile) - The mean values of FBS did not show statistically significant difference among the DTAC tertiles. There was no significant relationship between DTAC and FBS (tertile 3 vs. tertile 1: β, –0.90; 95% CI, –1.23 to 1.05; p=0.87; adjusted model). - - - - Logistic regression/age, sex, BMI, physical activity, and total caloric intake
      Capas et al. [27] 2018 Turkey/descriptive Adults with T2D; people with diabetes, n=29 (n=20 female); healthy subjects, n=15 (n=10 female) 40–70 3 Days of 24-hour food record Modified version of the FRAP - In people with diabetes, a negative correlation was found between DTAC and FBG (r=–0.406, p=0.036). In people with diabetes, a negative correlation was found between DTAC and HbA1C (r=–0.531, p=0.004). - - DTAC did not differ between the 2 groups. Group comparison and Spearman correlation/not stated
      Cetiner et al. [30] 2021 Turkey/cross-sectional People with diabetes, n=60 (n=33 female); healthy subjects, n=25 (n=14 female) Newly diagnosed T2DM, 50.8±6.8; formerly diagnosed T2DM, 51.8±7.4; controls, 45.6±8.6 3 Days of 24-hour recall FRAP, TEAC, TRAP, ORAC - - - - - DTAC was lower in diabetes than in controls (p<0.001). Groups comparison/confounders have not been considered.
      Chen et al. [31] 2023 China/cross-sectional 11,956 Participants Mean>42 24-Hour dietary recall CDAI (6 dietary antioxidants) FBS did not significantly differ among the CDAI quartiles. In full adjusted model, CDAI did not independently associate with fasting glucose. Percentage of HbA1C was lower in the highest quartile of CDAI (p=0.03). In full adjusted model, CDAI was not independently associated with HbA1C. - - Percentage of people with diabetes were lower in the highest quartile of CDAI (p<0.001) Multivariable logistic regressions/age, sex, race, education, physical activity, smoking status, hypertension, and coronary heart diseases
      Compared with the lowest quartile of CDAI, the highest quartile was related to reduced risk of diabetes (OR, 0.84; 95% CI, 0.71–0.99; p=0.035).
      Cyunczyk et al. [32] 2022 Poland/cross-sectional 413 Adults (40% male); normoglycemia, 171 (41.40%); prediabetes, 202 (48.91%); T2D, 40 (9.69%) 49.84±9.47 3 Days of 24-hour dietary recall FRAP (Q1, ≤8.37; Q2, 8.38–11.27; Q3, 11.2–14.50; Q4, ≥14.51) - - - DTAC was inversely associated with HOMA-IR (β=–0.39, p=0.02). - The higher quartile of DTAC was significantly associated with a reduced odds ratio for the prevalence of prediabetes (Q3 vs. Q1; OR, 0.583; 95% CI, 0.309–0.945), but not with the risk of diabetes. Linear regression/age, sex, family history of diabetes, education level, physical activity, dyslipidemia, hypertension, BMI, waist circumference, smoking status, daily alcohol intake and daily energy intake
      Daneshzad et al. [33] 2020 Iran/case-control Pregnant women with GDM; GDM, n=200; healthy, n=263 28.33±6.23 3 Days of a 24-hour dietary record FRAP, TRAP, TEAC; tertile values were not stated. - - - - - FRAP was significantly lower in pregnant women with GDM than in controls. The risk of GDM was 85% lower among those in the highest tertile of FRAP (OR, 0.15; 95% CI, 0.08–0.29; p-trend <0.0001). There was no significant association between the risk of GDM and TRAP as well as TEAC. Group comparison and binary logistic regression/age, BMI, energy intake, physical activity, number of offspring, carbohydrate, fat, and protein intake, and supplementation
      Daneshzad et al. [49] 2020 Iran/cross-sectional 265 T2D women 59.66±8.94 168-Item semiquantitative FRAP (T1, <3.68; T2, 3.68–5.18; T3, >5.18); ORAC - FBS levels did not differ significantly across the tertiles of DTAC. HbA1C levels did not differ significantly across the tertiles of DTAC. - - - Analysis of variance/age, BMI, energy intake, physical activity, blood pressure, medication, supplement consumption
      Fagherazzi et al. [34] 2018 France/cohort 402 Women at very high risk of T2DM; women with T2DM, 117 (29%); women free of T2DM, 285 (71%) 55.7±6.65 Semiquantitative FFQ (57 predefined food groups) FRAP 19 - - - - FRAP scores did not differ between 2 groups. A high DTAC was associated with less developing T2DM, in women with a moderate or high Western dietary pattern score. Group comparison and regression/not stated
      El Frakchi et al. [35] 2024 Morocco/cross-sectional 254 T2D outpatients 54.52±7.21 255 Food-item FFQ FRAP; high, >10.6 mmol; low, <10.6 mmol - - - - - Percentage of people with diabesity was lower among those with higher DTAC (p=0.02). Student t-test/-
      Galarregui et al. [50] 2018 Spain/cross-sectional 112 Overweight or obese adults 50.8±9 137-Item semiquantitative FFQ FRAP (T1, <8.6; T, 8.6–11.36; T3, >11.36) - Glucose level did not change significantly across the tertiles of DTAC. HgA1C level did not change significantly across the tertiles of DTAC. Subjects with higher values of TAC had significantly lower HOMA-IR (p=0.03). Subjects with higher values of TAC had significantly lower insulin concentration (p=0.01). - -
      Hermsdorff et al. [51] 2011 Spain/cross-sectional 266 Healthy subjects (105 men/ 161 women) 22±3 Brazilian sample (n=123), 3 day-record; Spanish sample (n=143), 136 food-items semiquantitative FFQ TEAC - DTAC value was inversely associated with glucose (p<0.05). - DTAC values were inversely associated with HOMA-IR (p<0.05). DTAC values were inversely associated with insulin levels (p<0.05). - Multiple linear regression/age, sex, waist circumference, energy intake, smoking habit, physical activity, and vitamin supplement use
      Heshmati et al. [44] 2024 Iran/prospective cohort 1,856 pregnant women; GDM, 369 18–45 168 Food-items FFQ FRAP Between February 1, 2020 and August 31, 2021 - - - - The adjusted risk of GDM decreased by 34% (95% CI, 10%–52%; p=0.023) for each DTAC score increase. Women in the highest quartile of DTAC had a lower risk of developing GDM compared to those in the lowest quartile (adjusted RR, 0.29; 95% CI, 0.12–0.68; p=0.005). Age, BMI, occupation, hypertension, diabetes, education, and working rotating shift
      Jimenez-Ortega et al. [58] 2024 México/cross-sectional 830 Children and adolescents 7–18 116 Food-items FFQ Based on the intake of 6 vitamins and minerals (vitamins A, C, E, selenium, magnesium, and zinc) - - In total participants, people in the highest DAI category had low insulin resistance (OR, 0.49; 95% CI, 0.30–0.80). Female participants in the highest DAI category had significantly lower odds of developing insulin resistance than those in the lowest DAI category (OR, 0.54; 95% CI, 0.29–0.98). - - Multiple logistic regression/age, sex, BMI, smoking status, vitamin D intake, polyunsaturated intake, alcohol intake, family history of diabetes, Tanner stages, protein intake and total fat intake
      Kashino et al. [36] 2019 Japan/prospective cohort 64,660 Adults (27,809 men and 36,851 women). During the 5-y period, 1,191 participants (692 men and 499 women) were newly diagnosed with T2D. 44–76 147 Food-items FFQ FRAP ORAC, TRAP (quartiles) 5 - - - - DTAC was not associated with the risk of T2D in multivariate-adjusted models. Similar associations were found in men and women. Linear and logistic regression/age, sex, study area, smoking habits, physical activity, BMI, history of hypertension, family history of diabetes mellitus, use of supplements, coffee consumption, and energy intake
      Li et al. [37] 2024 China/cross-sectional 12,467 Participants (female, 65.4%); people with T2D, 1,238 (9.9%) 57.04±10.13 FFQ FRAP - - - - - Higher DATC was associated with a lower T2DM risk (OR, 0.96; 95% CI, 0.80–1.17; p-trend=0.024). Logistic regression/age, sex, smoking status, alcohol consumption, physical activity, BMI, WC, TG, HDL-C, hypertension, and health supplement intake
      Liu et al. [38] 2024 China/cross-sectional 2,158 Participants (male, 52.12%) 58.87±0.41 24 Hour dietary recall CDAI - - - - - A negative correlation between CDAI and diabetic retinopathy (OR, 0.94; 95% CI, 0.90–0.98; p=0.007). Multivariate logistic regression/age, sex, race, drinking, smoking, body mass index (BMI), hypertension, etc.
      Mancini et al. [39] 2018 Germany/prospective cohort 64,223 Women. During 15 years of follow-up, 1,751 women had validated T2Ds. 52±7 208 Food-items dietary questionnaire FRAP (Q1, ≤8.72; Q2, 8.73–11.29; Q3,11.30–13.93; Q4,13.94–17.55; Q5, >17.55) 15 - - - - In multivariable models, higher levels of DTAC were associated with a lower risk of T2D (OR, 0.73; 95% CI, 0.60–0.89; p<0.0001). Spline regression/smoking status, physical activity, education level, hypertension, hypercholesterolaemia, family history of diabetes, energy intake, alcohol intake, BMI
      Okubo et al. [52] 2014 United Kingdom/cohort Men (1,441), women (1,253) 59–73 129 Food-item FFQ ORAC, TRAP, FRAP, TEAC 37 By all 4 assays: In women, but not in men, DTAC was inversely associated with fasting glucose (p<0.05). - By all 4 assays: In women and men, DTAC was inversely associated with HOMA-IR (p<0.05). By all 4 assays: In women and men, DTAC was inversely associated with insulin levels (p<0.05). - Multiple linear regression/age, sex, BMI, smoking status and physical activity level, dietary supplement use, and energy intake
      Psaltopoulou et al. [53] 2011 Greece/epidemiological study 551 Men and 467 women; normal, n=771; IFG, n=203; T2D, n=44 Normal, 38±11; IFG, 43±10; diabetic, 52±8 Semiquantitative FFQ TRAP, FRAP, TEAC Using all 3 assays, an inverse association was found between DTAC and serum log-glucose (p=0.001). - By all 3 assays, an inverse association was found between DTAC and serum log- HOMA (p≤0.001). By all 3 assays, an inverse association was found between DTAC and serum log- insulin (p≤0.002). - Multiple regression analyses/age, sex, BMI, physical activity status, smoking habits, and energy intake
      Puchau et al. [54] 2010 Spain/cross-sectional 153 Healthy young adults (101 women and 52 men) 20.8±2.7 136 Food-item FFQ and 3-day food record FRAP (low, <6.9 mmol; high, >6.9 mmol) - Serum glucose significantly differed between subjects with high and low DTAC values (p=0.006). Serum glucose was negatively associated with DTAC (p=0.03). - HOMA-IR did not significantly differ between subjects with high and low DTAC values. Insulin levels did not significantly differ between subjects with high and low DTAC values. - Groups comparison and multiple linear regression/sex and daily energy intake
      Rahmani et al. [47] 2021 Iran/case-control Prediabetes, n=49; healthy control, n=98 47.42±15.98 80 Food-item FFQ FRAP (T1, ≤11.90; T2, 11.90–21.24; T3, >21.24) - - - - - Patients with prediabetes had lower DTAC scores as compared with controls. High DTAC was associated with a significantly reduced likelihood of having prediabetes (OR, 0.09; 95% CI, 0.02–0.53; p-trend=0.01). Age, sex, BMI, marital status, income, occupation, education, physical activity, dietary supplementation, family history of diabetes, and total calorie intake
      Roumi et al. [45] 2024 Iran/cross-sectional 4,241 Participants; patients with T2D, n=589; individuals without T2D, n=3,611 35–70 FFQ Wright’s method - There was no significant correlation between FBG and dietary total antioxidant index. - - - Negative associations was found between T2D with total score of dietary antioxidant index (OR, 0.67; 95% CI, 0.55–0.81; p=0.001). Logistic and linear regression/age, sex, BMI, education level, marital status, occupation, physical activity, and calorie intake
      Salavatizadeh et al. [55] 2022 Iran/cross-sectional 200 People with T2D 18–70 147 Food-item FFQ FRAP FBS was not different across DTAC tertiles (p=0.44). HbA1C did not differ across DTAC tertiles (p=0.67). People in the third tertile of DTAC had lower HOMA-IR level (p=0.05). People in the third tertile of DTAC had lower insulin level (p=0.01). - Kruskal-Wallis test and analysis of covariance/age, sex, diabetes duration, smoking status, physical activity, BMI, waist circumference, and energy
      van der Schaft et al. [40] 2019 Netherland/cohort 5,796 Men (n=2,266) and women (n=3,530); normoglycaemia, n=4,957; prediabetes, n=839 Men, 63.4; women, 64.6 170 Items semiquantitative FFQ FRAP 15 - - Dietary FRAP was inversely associated with HOMA-IR (p<0.001). - Higher FRAP score was associated with a lower risk of T2D among the total population (HR, 0.84; 95% CI, 0.75–0.95; p=0.01) and among participants with prediabetes (HR, 0.85; 95% CI, 0.73–0.99; p=0.03), but not with risk of prediabetes. Cox proportional hazards regression/age, sex, BMI, hypertension, dyslipidemia, highest attained level of education, physical activity, smoking status, energy intake, daily alcohol intake and degree of adherence to guidelines for a healthy diet
      Sezavar et al. [56] 2021 Iran/cross-sectional 170 Adults with morbid obesity 37.4±10.17 147 Food-item FFQ ORAC, FRAP (T1, <5.36; T2, 16.41–24.01; T3, >145.17) - FBS did not significantly differ across tertiles of FRAP (p=0.21) and ORAC (p=0.86). HbA1C did not significantly differ across tertiles of FRAP (p=0.22) and ORAC (p=0.99). - - - One-way ANOVA/not stated
      Sohouli et al. [57] 2020 Iran/case-control NAFLD, n=158; healthy individuals, n=357 43.9±5.9 168 Food-item FFQ ORAC (tertiles) - Across increasing DTAC tertiles, the FBG level reduced (p=0.001). - - - - One-factor ANCOVA test/not stated
      Sotoudeh et al. [48] 2018 Iran/case-control study 300 Individuals with and without prediabetes (n=150/group) Control, 47.7±7.2; prediabetic, 47.4±7.5 168 Food-items semiquantitative FFQ ORAC (Q1, <11,878.5; Q2, 11,878.5–16,322.1; Q3, 16,322.1–24,548.8; Q4, >24,548.8 mmol TE/100 g) - Across increasing DTAC quartiles, the participants had lower FBG (p-trend<0.001). - - - The mean DTAC was lower in individuals with prediabetes than in the control group (p<0.001). Participants in the fourth quartile of DTAC were less likely to experience prediabetes compared with those in the first quartile (OR, 0.18; 95% CI, 0.07–0.49; p<0.001). Logistic regression/BMI, physical activity, education, dietary intake of fiber, fat, energy, and coffee
      Tan et al. [41] 2022 South Korean/cohort 20,594 Participants, 332 men and 360 women with T2D 40–79 106 Food-item FFQ Self-reported dietary data linked to the TAC database 5 - - - - DTAC was inversely associated with the development of T2D in women (HR, 0.58; 95% CI, 0.40–0.83; p-trend=0.0004). Among men, an approximately 15% reduced risk of developing T2D was observed for an SD increment in TAC (HR, 0.85; 95% CI, 0.75–0.96). A multivariable Cox proportional hazards regression/age, BMI, education, smoking, alcohol intake, physical activity
      Zhou et al. [46] 2024 China/cross-sectional 7,982 Subjects; 48.50% male and 51.50% female; diabetic, 1,607; non-diabetic, 6,375 47.32±16.77 Two 24-hour dietary recalls CDAI - - - - - High CDAI was associated with reduced risk of diabetes mellitus in the female population (p=0.046). Multifactorial logistic regression models/age, sex, race, and education level
      Zujko et al. [42] 2014 Poland/cross-sectional 80 Patients with and without T2D and 37 controls 40–65 24-Hour food recall and dietary database FRAP - - - - - DTAC was significantly higher in control than in patients with longstanding diabetes and those with newly diagnosed diabetes (p=0.01). Groups comparison/not stated
      Zujko et al. [43] 2018 Poland/cross-sectional 5,690 Adults (2,554 men and 3,136 women) 50.08±16.44 A single 24-hour dietary recall FRAP (tertiles) - In women, higher DTAC was significantly associated with reduced odds of elevated blood glucose. - - - In women, higher DTAC was associated with 27.9% lower odds of diabetes (OR, 0.721; 95% CI, 0.522–0.997). Logistic regression/age, BMI, educational level, leisure time, physical activity, smoking, and alcohol intake
      Table 1. Description of the PECO strategy

      FBG, fasting blood glucose; HbA1c, hemoglobin A1C; HOMA-IR, homeostatic model of insulin resistance.

      Table 2. Summary and characteristics of the 28 selected studies assessing the relationship of DTAC with diabetes risk and its related glycemic biomarkers

      DTAC, dietary total antioxidant capacity; FBS, fasting blood sugar; HbA1c, hemoglobin A1C; HOMA-IR, homeostatic model assessment for insulin resistance; FFQ, food frequency questionnaire; ORAC, oxygen radical absorption capacity; FBG, fasting blood glucose; FPG, fasting plasma glucose; BMI, body mass index; T2D, type 2 diabetes; CI, confidence interval; T2DM, type 2 diabetes mellitus; FRAP, ferric reducing-antioxidant power; TEAC, Trolox equivalent antioxidant capacity; TRAP, total radical-trapping antioxidant potential; CDAI, composite dietary antioxidant index; OR, odds ratio; GDM, gestational diabetes mellitus; TAC, total antioxidant capacity; RR, risk ratio; DAI, dietary antioxidant index; WC, waist circumference; TG, triglycerides; HDL-C, high-density lipoprotein-cholesterol; IFG, impaired fasting glucose; HR, hazard ratio; ANOVA, analysis of variance; ANCOVA, analysis of covariance; SD, standard deviation.


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