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
© 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/).
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.
Study | Year | Country/study design | Target population | Age (y) | Method of dietary intake assessment | Method of DTAC evaluation | Follow-up (y) |
Findings |
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---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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.
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 |
FBG, fasting blood glucose; HbA1c, hemoglobin A1C; HOMA-IR, homeostatic model of insulin resistance.
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.