Edited by: Gaetano Santulli, Albert Einstein College of Medicine, United States
Reviewed by: Aysegul Atmaca, Ondokuz Mayıs University, Türkiye; Juan Velasco, Yale University, United States
*Correspondence: Cuiling Wu,
†These authors have contributed equally to this work
This article was submitted to Clinical Diabetes, a section of the journal Frontiers in Endocrinology
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The relationship between oxidative balance score (OBS) and diabetes remains poorly understood and may be gender-specific. We conducted a cross-sectional study to investigate the complex association between OBS and diabetes among US adults.
Overall, 5,233 participants were included in this cross-sectional study. The exposure variable was OBS, composed of scores for 20 dietary and lifestyle factors. Multivariable logistic regression, subgroup analysis, and restricted cubic spline (RCS) regression were applied to examine the relationship between OBS and diabetes.
Compared to the lowest OBS quartile group (Q1), the multivariable-adjusted odds ratio (OR) (95% confidence interval (CI) for the highest OBS quartile group (Q4) was 0.602 (0.372–0.974) (
In summary, high OBS was negatively associated with diabetes risk in a gender-dependent manner.
The spread of Western lifestyles has gradually increased the use of high-calorie and high-fat diets, sedentary lifestyles, and the number of adults with diabetes (
A large body of evidence demonstrated that oxidative stress plays a crucial role in the development and progression of diabetes (
Herein, we investigated the relationship between OBS and the prevalence of diabetes. We examined the possible effects of OBS on diabetes using data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to March 2020.
The NHANES was a national cross-sectional study assessing the health and nutrition status of adults and children in the US population. The study used a “stratified multistage probability sampling,” in which the information was collected from relevant interviews, examinations, dietary questionnaires, and laboratory measurements. In total, 5,233 participants were chosen from 2007 to March 2020. Exclusion criteria were as follows: age of participants was <20 or ≥80 years, participants without dietary or lifestyle data, participants without known diabetes status, and variables with missing values (
Flow diagram.
The development and calculation of the OBS have been reported previously (
The diagnostic criteria for diabetes were as follows: previous diagnosis of diabetes by a physician, glycated hemoglobin (HbA1c) >6.5%, fasting glucose ≥7.0 mmol/L, random blood glucose ≥11.1 mmol/L, 2-h oral glucose tolerance test (OGTT) ≥11.1 mmol/L, and use of diabetes medication or insulin.
Based on the existing literature and clinical consideration, we selected covariates that could play roles as potential confounders in the associations between OBS and diabetes. The standardized household interviews were used to obtain the demographic characteristics, including age, gender, race, educational level, and poverty income ratio (PIR). Age was divided into three groups (20–39, 40–59, and 60–79 years), with 20–39 years as the reference. Race was divided into non-Hispanic white, non-Hispanic black, Mexican American, and others, with non-Hispanic black as the reference. Education level was graded into primary school or less, middle and high school, and college or higher, with college or higher as the reference. Poverty was defined as PIR ≤ 1.0 and divided into two categories of PIR (≤1.0 and >1.0), with PIR ≤ 1.0 as the reference. White blood cell (WBC) count, platelet (Plt) count, neutrophil (Neu) count, lymphocyte (Lym) count, and hemoglobin (Hb) level were obtained from the laboratory data. Chronic kidney disease (CKD), cardiovascular disease (CVD), hypertension, dyslipidemia, and smoking are important risk factors for diabetes. Therefore, these diseases were included in the analysis. According to the KDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases, albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR) were used to define CKD. ACR ≥ 30 mg/g (3 mg/mmol) and eGFR < 60 ml/min/1.73 m2 were defined as diagnostic criteria of CKD. CVD was defined as congestive heart failure, coronary heart disease, heart attack, angina, and stroke. The diagnostic criteria for hypertension were as follows: average systolic blood pressure ≥140 mmHg or average diastolic blood pressure ≥90 mmHg after at least three times of measurement, use of anti-hypertensive drugs, and subject- or physician-reported diagnosis of hypertension. The diagnostic criteria for dyslipidemia were as follows: total cholesterol level ≥5.18 mmol/L, triglyceride level ≥150 mg/dl, high-density lipoprotein-cholesterol <1.04 mmol/L in men and <1.30 mmol/L in women, low-density lipoprotein-cholesterol ≥3.37 mmol/L, or the use of cholesterol-lowering drugs. Smoking status was defined into three categories: never (smoked less than 100 cigarettes in life), former (smoked more than 100 cigarettes in life and does not smoke now), and smoked more than 100 cigarettes in life and smokes some days or every day. No or never was taken as the reference for all of the above conditions.
Considering the complexity of the sampling method, the study subject was a weighted statistical analysis. Continuous variables are presented as mean (standard deviation (SD)), and categorical variables are summarized as frequency (percentage). For baseline characteristics, categorical variables were compared using the chi-square test, and continuous variables were compared using the t-test or one-way analysis of variance. Multivariable logistic regression models (crude models to model 3) were used to investigate the relationship between OBS and diabetes after adjusting for different potential confounders. The crude model was not adjusted for any covariates. Model 1 was adjusted for age, gender, race, and education. Model 2 was further adjusted for WBC count, Neu count, Hb count, and Plt count. Model 3 was adjusted for the variables in model 2 and additional confounders, including CKD, CVD, smoking status, hypertension, and dyslipidemia. We further assessed the heterogeneity between OBS and diabetes through subgroup analysis for the following variables: age groups, gender, race, education, CKD, CVD, hypertension, dyslipidemia, and smoking status. We applied restricted cubic spline (RCS) analysis with four knots to evaluate the non-linear associations between OBS and diabetes risk. R statistical software (version 4.2.2) was applied for all statistical analyses and mapping. Alpha was set at <0.05 for statistical significance, and all analyses were two-sided. A two-sided
In total, 5,233 participants from NHANES (2007 to March 2020) were enrolled in the present study, of whom 622 (11.89%) had diabetes. Among all participants with diabetes, men (461, 11.64%) showed a higher prevalence than women (161, 5.61%). Participants with diabetes were older and had lower education levels, but PIR did not differ between the two groups (
Baseline characteristics of all participants by diabetes.
Variables | Overall |
Non-DM |
DM |
|
---|---|---|---|---|
Age (years) | 45.23 (0.41) | 44.23 (0.42) | 55.11 (0.78) | <0.0001 |
Gender, n (%) | <0.0001 | |||
Female | 2,062 (39.4) | 1,901 (94.39) | 161 (5.61) | |
Male | 3,171 (60.6) | 2,710 (88.36) | 461 (11.64) | |
Age group, n (%) | <0.0001 | |||
20–39 | 2,103 (40.19) | 2,023 (97.12) | 80 (2.88) | |
40–59 | 1,951 (37.28) | 1,675 (89.39) | 276 (10.61) | |
60–79 | 1,179 (22.53) | 913 (81.29) | 266 (18.71) | |
Education, n (%) | <0.001 | |||
College and higher | 3,495 (66.79) | 3,136 (91.79) | 359 (8.21) | |
Middle and high school | 1,535 (29.33) | 1,314 (88.92) | 221 (11.08) | |
Primary school and less | 203 (3.88) | 161 (79.08) | 42 (20.92) | |
Race, n (%) | 0.01 | |||
Black | 1,029 (19.66) | 868 (87.89) | 161 (12.11) | |
Mexican | 623 (11.91) | 520 (86.97) | 103 (13.03) | |
Other | 989 (18.9) | 873 (90.43) | 116 (9.57) | |
White | 2,592 (49.53) | 2,350 (91.57) | 242 (8.43) | |
PIR | 0.65 | |||
≤1 | 812 (15.52) | 719 (90.23) | 93 (9.77) | |
>1 | 4,421 (84.48) | 3,892 (90.94) | 529 (9.06) | |
WBC (×109/L) | 7.01 (0.05) | 6.98 (0.05) | 7.36 (0.14) | 0.01 |
Neu (×109/L) | 4.12 (0.04) | 4.08 (0.04) | 4.45 (0.10) | <0.001 |
Lym (×109/L) | 2.10 (0.01) | 2.11 (0.01) | 2.08 (0.07) | 0.7 |
Hb (g/L) | 14.52 (0.03) | 14.50 (0.03) | 14.69 (0.08) | 0.03 |
Plt (×106/L) | 237.68 (1.57) | 238.57 (1.61) | 228.78 (4.45) | 0.03 |
CKD, n (%) | <0.0001 | |||
No | 4,693 (89.68) | 4,246 (92.36) | 447 (7.64) | |
Yes | 540 (10.32) | 365 (75.37) | 175 (24.63) | |
CVD, n (%) | <0.0001 | |||
No | 4,910 (93.83) | 4,390 (91.86) | 520 (8.14) | |
Yes | 323 (6.17) | 221 (73.09) | 102 (26.91) | |
Hypertension, n (%) | <0.0001 | |||
No | 3,485 (66.6) | 3,273 (95.35) | 212 (4.65) | |
Yes | 1,748 (33.4) | 1,338 (80.82) | 410 (19.18) | |
Dyslipidemia, n (%) | <0.0001 | |||
No | 1841,(35.18) | 1,747 (96.01) | 94 (3.99) | |
Yes | 3,392 (64.82) | 2,864 (88.28) | 528 (11.72) | |
Smoking status, n (%) | <0.001 | |||
Never | 2,570 (49.11) | 2,327 (92.49) | 243 (7.51) | |
Former | 1,364 (26.07) | 1,139 (87.38) | 225 (12.62) | |
Now | 1,299 (24.82) | 1,145 (91.65) | 154 (8.35) | |
OBS | 29.69 (0.08) | 29.76 (0.08) | 28.99 (0.20) | <0.001 |
Dietary OBS | 25.55 (0.06) | 25.58 (0.07) | 25.28 (0.16) | 0.06 |
Lifestyle OBS | 4.13 (0.04) | 4.17 (0.04) | 3.71 (0.09) | <0.0001 |
All values represented are weighted means (standard deviation) or counts (weighted percentage).
SD, standard deviation; PIR, poverty income ratio; WBC, white blood cells; Neu, neutrophil; Lym, lymphocyte; Hb, hemoglobin; Plt, platelet; CKD, chronic kidney disease; CVD, cardiovascular disease; OBS, oxidative balance score; DM, diabetes.
All 5,233 individuals were categorized into four groups according to OBS quartiles: Q1 (OBS, 5 to 28; median, 26), Q2 (OBS, 29 to 30; median, 30), Q3 (OBS, 31 to 32; median, 31), and Q4 (OBS, 32 to 36; median, 33). As the reference group, participants in the first quartile group (Q1) with lower OBS were more likely to be white and have higher educational levels and PIR. In addition, participants in the Q1 had a lower incidence of CKD, diabetes, hypertension, dyslipidemia, and smoking. Gender was not significantly different between OBS quartiles, suggesting an evenly balanced distribution of men and women in all quartiles (
Baseline characteristics of all participants by the OBS quartile.
Variables | Overall |
Q1 |
Q2 |
Q3 |
Q4 |
|
---|---|---|---|---|---|---|
Age (years) | 45.23 (0.41) | 43.32 (0.60) | 44.62 (0.64) | 46.13 (0.60) | 47.31 (0.78) | <0.001 |
Gender, n (%) | 0.1 | |||||
Female | 2,062 (39.4) | 604 (25.23) | 494 (23.55) | 577 (30.41) | 387 (20.81) | |
Male | 3,171 (60.6) | 1,004 (27.72) | 816 (26.18) | 845 (28.22) | 506 (17.88) | |
Age group, n (%) | <0.001 | |||||
20–39 | 2,103 (40.19) | 681 (29.60) | 537 (25.45) | 545 (27.61) | 340 (17.34) | |
40–59 | 1,951 (37.28) | 597 (26.93) | 515 (25.96) | 538 (29.31) | 301 (17.80) | |
60–79 | 1,179 (22.53) | 330 (20.14) | 258 (22.41) | 339 (31.87) | 252 (25.57) | |
Education, n (%) | <0.0001 | |||||
College and higher | 3,495 (66.79) | 854 (21.20) | 827 (23.76) | 1,054 (31.39) | 760 (23.64) | |
Middle and high school | 1,535 (29.33) | 677 (42.07) | 425 (28.62) | 319 (22.96) | 114 (6.35) | |
Primary school and less | 203 (3.88) | 77 (40.38) | 58 (31.00) | 49 (20.89) | 19 (7.73) | |
Race, n (%) | <0.0001 | |||||
Black | 1,029 (19.66) | 499 (49.20) | 269 (26.55) | 192 (17.63) | 69 (6.61) | |
Mexican | 623 (11.91) | 177 (28.60) | 189 (29.51) | 182 (30.09) | 75 (11.81) | |
Other | 989 (18.9) | 247 (25.12) | 214 (23.42) | 285 (28.86) | 243 (22.60) | |
White | 2,592 (49.53) | 685 (24.32) | 638 (24.81) | 763 (30.34) | 506 (20.53) | |
PIR | <0.0001 | |||||
≤1 | 812 (15.52) | 377 (44.18) | 197 (23.27) | 167 (21.67) | 71 (10.89) | |
>1 | 4,421 (84.48) | 1,231 (24.87) | 1,113 (25.27) | 1,255 (29.90) | 822 (19.95) | |
WBC (×109/L) | 7.01 (0.05) | 7.51 (0.10) | 7.21 (0.07) | 6.81 (0.07) | 6.37 (0.09) | <0.0001 |
Neu (×109/L) | 4.12 (0.04) | 4.46 (0.07) | 4.24 (0.05) | 3.97 (0.05) | 3.71 (0.07) | <0.0001 |
Lym (×109/L) | 2.10 (0.01) | 2.21 (0.03) | 2.16 (0.03) | 2.07 (0.02) | 1.93 (0.03) | <0.0001 |
Hb (g/L) | 14.52 (0.03) | 14.63 (0.06) | 14.58 (0.06) | 14.47 (0.05) | 14.35 (0.06) | 0.002 |
Plt (×106/L) | 237.68 (1.57) | 243.98 (2.24) | 237.34 (2.58) | 237.81 (2.56) | 229.12 (3.06) | 0.002 |
DM, n (%) | 0.004 | |||||
No | 4,611 (88.11) | 1,360 (25.79) | 1,156 (24.98) | 1,269 (29.61) | 826 (19.62) | |
Yes | 622 (11.89) | 248 (35.61) | 154 (26.12) | 153 (24.37) | 67 (13.89) | |
CKD, n (%) | 0.003 | |||||
No | 4,693 (89.68) | 1,399 (25.90) | 1,190 (24.99) | 1,297 (29.85) | 807 (19.26) | |
Yes | 540 (10.32) | 209 (34.87) | 120 (26.07) | 125 (21.63) | 86 (17.43) | |
CVD, n (%) | 0.65 | |||||
No | 4,910 (93.83) | 1,485 (26.66) | 1,227 (24.94) | 1,348 (29.08) | 850 (19.32) | |
Yes | 323 (6.17) | 123 (27.07) | 83 (27.62) | 74 (30.06) | 43 (15.25) | |
Hypertension, n (%) | <0.0001 | |||||
No | 3,485 (66.6) | 960 (24.48) | 861 (24.30) | 962 (29.26) | 702 (21.95) | |
Yes | 1,748 (33.4) | 648 (31.62) | 449 (26.84) | 460 (28.85) | 191 (12.70) | |
Dyslipidemia, n (%) | <0.001 | |||||
No | 1,841 (35.18) | 527 (23.98) | 440 (23.82) | 499 (28.70) | 375 (23.50) | |
Yes | 3,392 (64.82) | 1,081 (28.05) | 870 (25.72) | 923 (29.35) | 518 (16.88) | |
Smoking status, n (%) | <0.0001 | |||||
Never | 2,570 (49.11) | 573 (18.40) | 590 (21.97) | 802 (34.28) | 605 (25.35) | |
Former | 1,364 (26.07) | 345 (22.86) | 345 (26.36) | 407 (29.35) | 267 (21.43) | |
Now | 1,299 (24.82) | 690 (50.65) | 375 (30.62) | 213 (17.01) | 21 (1.72) | |
OBS | 29.69 (0.08) | 25.10 (0.13) | 29.55 (0.02) | 31.50 (0.02) | 33.50 (0.03) | <0.0001 |
Dietary OBS | 25.55 (0.06) | 22.13 (0.15) | 25.89 (0.05) | 26.97 (0.04) | 27.73 (0.03) | <0.0001 |
Lifestyle OBS | 4.13 (0.04) | 2.96 (0.05) | 3.66 (0.05) | 4.53 (0.04) | 5.77 (0.03) | <0.0001 |
All values represented are weighted means (standard deviation) or counts (weighted percentage). The OBS was divided into four levels by quartile (5 < Q1 ≤ 28, 29 < Q2 ≤ 30, 31 < Q3 ≤ 32, and 32 < Q4 ≤36).
WBC, white blood cells; Neu, neutrophil; Lym, lymphocyte; Hb, hemoglobin; Plt, platelet; CKD, chronic kidney disease; CVD, cardiovascular disease; OBS, oxidative balance score; DM, diabetes.
The results of the multivariable logistic regressions showed that OBS was significantly associated with diabetes (
Association of the OBS with diabetes, NHANES 2007–March 2020.
Diabetes | OR (95% CI); |
|||||||
---|---|---|---|---|---|---|---|---|
Crude model | Model 1 | Model 2 | Model 3 | |||||
Continuous | 0.95 (0.93, 0.97) | <0.0001 | 0.94 (0.92, 0.97) | <0.0001 | 0.95 (0.92, 0.98) | <0.001 | 0.96 (0.94, 0.99) | <0.009 |
Q1 | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | 1.00 (ref) | ||||
Q2 | 0.76 (0.56, 1.02) | 0.07 | 0.71 (0.52, 0.98) | 0.04 | 0.73 (0.53, 1.00) | 0.05 | 0.76 (0.55, 1.06) | 0.10 |
Q3 | 0.60 (0.42, 0.85) | 0.005 | 0.55 (0.38, 0.78) | 0.001 | 0.58 (0.40, 0.83) | 0.003 | 0.62 (0.43, 0.90) | 0.01 |
Q4 | 0.51 (0.33, 0.79) | 0.003 | 0.44 (0.28, 0.69) | <0.001 | 0.48 (0.30, 0.76) | 0.002 | 0.60 (0.37, 0.97) | 0.04 |
<0.001 | <0.0001 | <0.001 | 0.007 |
The OBS was converted from a continuous variable to a categorical variable (quartiles). Data are presented as OR (95% CI). Crude model was adjusted with no covariates.
OBS, oxidative balance score; NHANES, National Health and Nutrition Examination Survey.
Subgroup analysis was performed based on gender, age group, race, education, CVD, CKD, dyslipidemia, hypertension, and smoking status (
Subgroup analyses of the association between OBS and diabetes, NHANES 2007–March 2020.
Variables | Q1 | Q2 | Q3 | Q4 | ||
---|---|---|---|---|---|---|
Age group | 0.211 | |||||
60–79 | Ref | 1.063 (0.637, 1.776) | 1.031 (0.579, 1.833) | 0.821 (0.480, 1.403) | 0.456 | |
20–39 | Ref | 0.574 (0.290, 1.138) | 0.334 (0.153, 0.726) | 0.212 (0.067, 0.672) | 0.001 | |
40–59 | Ref | 0.639 (0.416, 0.982) | 0.423 (0.261, 0.684) | 0.345 (0.142, 0.838) | 0.001 | |
Gender | 0.044 | |||||
Male | Ref | 0.838 (0.570, 1.232) | 0.592 (0.410, 0.855) | 0.614 (0.363, 1.041) | 0.016 | |
Female | Ref | 0.456 (0.214, 0.970) | 0.468 (0.246, 0.891) | 0.120 (0.036, 0.401) | <0.0001 | |
Race | 0.832 | |||||
White | Ref | 0.723 (0.460, 1.136) | 0.552 (0.326, 0.933) | 0.520 (0.290, 0.933) | 0.009 | |
Mexican | Ref | 1.008 (0.478, 2.123) | 0.665 (0.298, 1.485) | 0.617 (0.152, 2.499) | 0.26 | |
Black | Ref | 0.835 (0.483, 1.446) | 0.719 (0.381, 1.357) | 0.186 (0.063, 0.550) | 0.017 | |
Other | Ref | 0.545 (0.236, 1.256) | 0.603 (0.242, 1.502) | 0.305 (0.129, 0.721) | 0.029 | |
Education | 0.187 | |||||
College and higher | Ref | 0.835 (0.551, 1.264) | 0.696 (0.432, 1.118) | 0.519 (0.304, 0.887) | 0.009 | |
Middle and high school | Ref | 0.667 (0.387, 1.149) | 0.419 (0.207, 0.848) | 0.626 (0.220, 1.782) | 0.05 | |
Primary school and less | Ref | 0.192 (0.044, 0.841) | 0.091 (0.024, 0.345) | 0.074 (0.011, 0.501) | <0.001 | |
CKD | 0.069 | |||||
No | Ref | 0.598 (0.420, 0.852) | 0.516 (0.344, 0.774) | 0.470 (0.289, 0.764) | <0.001 | |
Yes | Ref | 1.309 (0.599, 2.859) | 0.776 (0.371, 1.624) | 0.312 (0.121, 0.804) | 0.011 | |
CVD | 0.268 | |||||
No | Ref | 0.727 (0.526, 1.005) | 0.508 (0.339, 0.760) | 0.393 (0.242, 0.638) | <0.0001 | |
Yes | Ref | 0.543 (0.201, 1.462) | 0.693 (0.320, 1.500) | 0.958 (0.253, 3.627) | 0.872 | |
Smoking status | 0.472 | |||||
Never | Ref | 0.534 (0.283, 1.007) | 0.415 (0.236, 0.729) | 0.353 (0.182, 0.685) | 0.001 | |
Former | Ref | 0.947 (0.532, 1.686) | 0.736 (0.439, 1.236) | 0.493 (0.254, 0.955) | 0.015 | |
Now | Ref | 0.621 (0.334, 1.154) | 0.285 (0.147, 0.551) | 0.203 (0.070, 0.590) | <0.001 | |
Hypertension | 0.121 | |||||
No | Ref | 0.436 (0.256, 0.744) | 0.538 (0.298, 0.971) | 0.470 (0.229, 0.962) | 0.038 | |
Yes | Ref | 1.022 (0.694, 1.506) | 0.611 (0.417, 0.894) | 0.601 (0.334, 1.082) | 0.01 | |
Dyslipidemia | 0.239 | |||||
Yes | Ref | 0.675 (0.480, 0.949) | 0.572 (0.382, 0.858) | 0.531 (0.328, 0.860) | 0.002 | |
No | Ref | 0.944 (0.412, 2.163) | 0.427 (0.160, 1.136) | 0.188 (0.073, 0.486) | <0.001 |
Data are presented as OR (95% CI). Adjusted for age, gender, race, education, WBC, Neu, Hb, Plt, CKD, CVD, smoking status, hypertension, and dyslipidemia.
WBC, white blood cells; Neu, neutrophil; Hb, hemoglobin; Plt, platelet; CKD, chronic kidney disease; CVD, cardiovascular disease; OBS, oxidative balance score.
The associations between OBS and diabetes in men and women were further evaluated using the RCS curves and the multivariable logistic regression (model 3). First, we found an inverted-U relationship (
RCS analysis of the association between OBS and diabetes. The association was adjusted for age, gender, race, education, WBC count, Neu count, Hb level, Plt count, CKD, CVD, smoking status, hypertension, and dyslipidemia. The median OBS was chosen as the reference.
For the first time, our large-scale cross-sectional study evaluated the association of OBS with diabetes based on NHANES (from 2007 to March 2020). In this study, we confirmed that the OBS and lifestyle OBS in participants without diabetes were significantly higher than those in participants with diabetes. Higher OBS and lifestyle OBS were associated with decreased risk of diabetes. After confounding factors were adjusted, it was shown that the effect of OBS on diabetes significantly relied on gender. In women and all participants, the association between OBS and diabetes showed an inverted-U relationship. In men, there was a linear relationship between OBS and the risk of diabetes.
Numerous clinical and animal studies linked oxidative stress to diabetes incidence and progression. Oxidative stress occurs when the amount of reactive oxygen species (ROS) exceeds the neutralizing capacity of antioxidants (
Similar to previous studies, subgroup analysis and RCS analysis revealed that gender significantly affected the correlations between OBS and diabetes. Studies have shown that the serum glucose level and oxidative stress of female diabetic rats were lower than those of male diabetic rats. At the same time, female diabetic rats had lower hydrogen peroxide levels and xanthine oxidase activity (
The study has several advantages. First, our study found the association between OBS and diabetes for the first time and uncovered the gender-specific effects of OBS on the prevalence of diabetes. Second, the NHANES used a stratified, multistage sampling method, which increases the generalizability of our findings to non-institutionalized populations. Third, this study adjusted the results for several confounders. In addition, there are several limitations to this study. Even though we controlled for potential confounders, the role of unknown or unmeasured confounders cannot be ruled out. However, the cross-sectional nature of our study makes it difficult to infer causality. To increase the utility of our findings, the predictive value of OBS in diabetes needs to be further verified through prospective studies. Finally, dietary OBS was not significantly different between diabetic and non-diabetic groups in our study; therefore, the effects of dietary OBS in predicting diabetes risk remain unclear.
In conclusion, this cross-sectional study indicated that OBS, especially lifestyle OBS, was negatively associated with the prevalence of diabetes. OBS had an inverted-U relationship with the prevalence of diabetes in nationally representative adults of the USA. In addition, we found that the negative correlation between OBS and diabetes was clearer among female participants than in male participants.
Publicly available datasets were analyzed in this study. This data can be found here:
The studies involving human participants were reviewed and approved by National Center for Health Statistics Ethics Review Board Approval. The patients/participants provided their written informed consent to participate in this study.
CW and CR conceptualized this study; performed the literature search, study design, data curation, data analysis, and data interpretation; and drafted the original manuscript. YS participated in the study design and critically revised the manuscript. YS, HG, XP and LZ conceived the study and participated in study design, coordination, data collection, and analysis. All authors contributed to the article and approved the submitted version.
This work was supported by the academic leader of Changzhi Medical College (No. XS202002), a grant from the Health Research Project of Shanxi Province (NO. 2020132).
The authors declare that this study was conducted in the absence of any commercial or financial relationships that could serve as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
The Supplementary Material for this article can be found online at: