Association of oxidative balance score with biological aging in US adults: a quantile regression analysis

doi:10.21203/rs.3.rs-5240208/v1

Background

The oxidative stress theory of aging is one of the prominent theories in the field of aging research. The Oxidative Balance Score (OBS) serves as a comprehensive tool for evaluating the effects of different diets and lifestyles on the oxidative/antioxidant system, however, its correlation with biological aging remains ambiguous. We thus conducted this study to explore the relationship between OBS and biological aging in American adults using quantitative measures.

Methods

We conducted a cross-sectional study using the NHANES 1999–2018 database. We examined several biological aging indicators, including biological age, phenotypic age, telomere length, and serum klotho levels. A weighted multiple linear regression model and smoothed fitted curves were employed to analyze the linear and nonlinear relationship between OBS and biological aging markers. Additionally, quantile regression was utilized to further explore their associations.

Results

A significant negative correlation was found between OBS and both biological and phenotypic ages, whereas a significant positive correlation was observed between telomere length and serum klotho levels. Upon comparing the highest tertile of OBS with the lowest tertile, the fully adjusted β values for OBS in the highest tertile were − 0.663 (-0.867, -0.458), -1.792 (-2.190, -1.393), and 32.332 (8.193, 56.471) for biological age, phenotypic age, and serum klotho, respectively. Notably, the positive correlation between telomere length [0.031 (0.007, 0.056)] and OBS was significant only in the partially adjusted model. The negative correlation between OBS and biological aging was consistent in individuals aged over 60 years. In quantile regression, the negative correlation between OBS, DOBS, and LOBS was most significant at the 0.93 percentile of biological age and 0.95 percentile of phenotypic age. Moreover, the serum klotho concentration exhibited a positive correlation with LOBS across all quantiles, with the strongest correlation observed at the 0.95 percentile.

Conclusions

Our study suggests a dose-response relationship between OBS and biological aging, indicating adopting an antioxidant-rich diet and lifestyle may yield beneficial effects on aging. These findings offer theoretical insights into strategies for aging prevention.

OBS

biological aging

dietary

lifestyle

NHANES

quantile regression

Population ageing is a global phenomenon. Around one fifth of the total global burden of disease is due to illness in people aged 60 and over.¹ Aging is characterized by low low-grade inflammation (inflammaging), while long-term inflammation is a factor driving the development and progression of many chronic noncommunicable diseases, including respiratory diseases, cardiovascular and metabolic diseases, and neurodegenerative diseases.^2–6 There are two prominent perspectives on the aging of organisms: one that views it as a disease state, and another that perceives it as a natural physiological adaptation.^7,8 Regardless of which perspective is taken, both agree that aging is a significant risk factor for most common age-related diseases.⁹ Therefore, the issue of aging is particularly important. If we put it in the context of a disease, it suggests that there are opportunities to intervene or modify the attributes of aging, or even reverse age-related diseases.¹⁰Alternatively, if aging is considered as an inevitable natural process, efforts can be made to slow down its progression and thereby extend one's healthspan and longevity.⁹

There are differences in aging rates between people due to differences in mortality and disease vulnerability.¹¹Furthermore, studies have shown that there are differences in the effects of aging in individual organs and systems.¹²Therefore, we have reasons to question whether the state of health and aging in living organisms can be fully explained only by chronological age. It is possible to determine the degree of biological or physiological aging of a person by comparing the physiological function and state of the reference population. Several molecular areas of aging measures that have been proposed so far include: DNA methylation age, leukocyte telomere length and serum klotho concentration.^13–15 However, their poor ability to predict beyond their chronological age, and their unaffordability makes them inferior to biological and phenotypic age in clinical-based performance.^11,16 In simple terms, biological age and phenotypic age correspond to the average biological status and the level of mortality risk associated with the age at a given time in the reference population.

Of all the theories of aging, one of the most popular is the perspective of aging driven by oxidative stress (OS).¹⁷ The theory clarifies that the accumulation of oxidative damage within the body over time is the main cause of aging and various age-related diseases. Oxidative stress is caused by a disruption of the balance between pro-oxidants and antioxidants, which triggers changes in the structure and oxidation state of key biological molecules.¹⁸ Adjustable elements such as diet, obesity, or medications have the potential to have an impact on one's organism's oxidative stress system.¹⁹

Oxidative Balance Score (OBS) is a global balance tool for assessing an individual's redox status, which captures the effects of various diets and lifestyles on the entire oxidative/antioxidant system.²⁰ In general, higher OBS indicates stronger exposure to antioxidants than pro-oxidants. Several studies have demonstrated a negative association between OBS and age-related diseases, such as osteoarthritis, cardiovascular disease, and cancer.^21–23 However, there is limited research focusing on the association between OBS and biological aging. Existing studies on biological aging predominantly emphasize median values and lack differentiation among individuals at various stages of the aging process. Hence, this study aims to evaluate the correlation between OBS and biological aging in U.S. adults and to investigate potential variations in its effects across different aging populations, utilizing data from the National Health and Nutrition Examination Survey (NHANES).

2.1 Study design and participants

This cross-sectional study was conducted using the NHANES database from 2005 to 2018, encompassing subjects aged 20 to 79. Individuals aged 80 years and older were categorized as ≥ 80 in the database. Following the exclusion of participants who were pregnant, had missing oxidative balance score components, or had a daily energy intake outside the normal range (males: 500–8000 kcal; females: 500–5000 kcal), a total of 27,935 subjects were included in this study for follow-up analysis, as depicted in Fig. 1.

2.2 Oxidative balance score

Data on the total daily intake of nutrients for the study subjects were available in the NHANES diet section, and dietary intakes for 16 nutrients, including dietary fiber, carotene, riboflavin, niacin, vitamin B6, total folate, vitamin B12, vitamin C, vitamin E, calcium, magnesium, zinc, copper, selenium, total fat, and iron, were obtained from the first dietary review interview. Data were obtained from physical examination and questionnaire data for 4 lifestyle factors, including BMI, physical activity, alcohol consumption, and smoking. Total fat, iron, BMI, alcohol consumption, and smoking are considered to pro-oxidants, while the rest are considered antioxidants. There were three groups of alcohol consumption, heavy drinkers (women: ≥15 g/day, men: ≥30 g/day), non-heavy drinkers (women: 0–15 g/day, men: 0–30 g/day), and non-drinkers, who were assigned a score of 0, 1, and 2, respectively. Afterwards, the other components were grouped by sex and then into three groups according to their triems, with antioxidants assigned a score of 0–2 in groups 1–3 and pro-oxidants assigned a score of 2 − 0 in groups 1-3.²⁴ The higher the OBS score, the more significant the antioxidant exposure.

2.3 Biological aging measurement

Biological age was calculated using the modified Klemera-Dubar method (KDM).¹⁶ We used the following 14 biomarkers to calculate biological age and phenotypic age: albumin, alkaline phosphatase, total cholesterol, creatinine, glycosylated hemoglobin, systolic blood pressure, urea nitrogen, uric acid, lymphocyte percentage, mean cell volume and white blood cell count, glucose, C-reactive protein, erythrocyte distribution width.^25,26 Measurements of serum klotho and telomere length are described in detail in NHANES.

2.4 Covariate assessment

In this study, sociodemographic characteristics were adjusted, including age, gender, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other), marital status (never married/married/living with a partner, widow/divorced/separated), educational level (less than high school, high school, some college or above), and household poverty index ratio (PIR, the ratio of household income to poverty line, < 1.3, 1.3–3.5, > 3.5). In addition, self-reported chronic conditions (including diabetes, hypertension, dyslipidemia, stroke, coronary heart disease, angina pectoris, heart attack, congestive heart failure, emphysema, bronchitis, arthritis, cancer/malignancy, thyroid condition, liver condition, and chronic kidney disease) were included as covariates.²⁷

2.5 Statistical analysis

All analyses were performed under complex sampling. Statistical descriptions of continuous variables were expressed as mean ± standard deviation (\(\:\overline{X}\pm\:SD\)) or median and interquartile range M (P25, P75), and differences between groups were compared using a t-test or grouped rank-sum test. Categorical variables were described using rates or composition ratios, and chi-square tests were used to compare differences between groups. Multivariable linear regression models were used to study the linear association between OBS and biological aging, and different models were used to adjust for covariates, including the non-adjusted model (model I), sociodemographic variables adjusting for model II (only age, sex, and race were adjusted), model III (model II plus marital status, educational level and PIR were adjusted) and fully adjusted model IV (model III plus self-reported chronic conditions were adjusted). OBS is considered both a continuous and a categorical variable (divided into tertiles) and the first tertile is used as the reference value. β is used to represent the correlation between OBS and biological aging. We further applied a smooth curve fitting and piecewise regression model to examine the dose-response relationship of OBS on biological aging. And to explore whether this association changed by confounders, we performed interaction analyses and stratified analyses.

In addition, quantile regression was used to evaluate the difference in the effect of OBS, both dietary and lifestyles, on different quantiles of biological aging. IBM SPSS Statistics 24.0 and R software were used for statistical analysis, and a two-sided P-value of < 0.05 was considered statistically significant.

3.1 Basic characteristics of participants

Table 1 shows the characteristics of the 27,042 participants. The mean age of the participants was 45.60±0.23 years, with 12,917(48.79%) males and 14,125(51.21%) females. In comparison to the bottom OBS group, participants with top OBS group are more likely to be female, older, non-Hispanic white and never married/married/living with a partner; more likely with no or only one chronic disease; more likely to have higher educational level.

3.2 Association between OBS and biological aging

Table 2 presents the association between OBS and biological aging. Participants with a higher OBS were associated with a lower biological age and phenotypic age. After key demographic variables and all potential confounding variables adjustment, each increase in OBS was associated with decrease in both biological age [model 2: -0.057(-0.068,-0.046); model 4: -0.043(-0.054,-0.031)] and phenotypic age [model 2: -0.144(-0.162,-0.125); model 4: -0.125(-0.148,-0.103)]. Furthermore, the significant association persisted when classifying OBS into tertiles. Compared with those in the lowest tertile of OBS, participants were 0.663 and 1.792-year younger than those in the highest tertile. We found an opposite association between OBS with both telomere length and serum klotho concentration. After adjusted for key demographic variables and all potential confounding variables, each increase in OBS was associated with an increase in telomere length [model 2: 0.002(0.000, 0.004); model 4: 0.002(-0.001, 0.005)] and an increase in serum klotho concentration [model 2: 2.800(1.527, 4.074); model 4: 2.283(0.823, 3.742)]. Additionally, in fully adjusted model, the β values for serum Klotho concentration among participants in the highest OBS tertile were 32.332 pg/ml, compared with those in the lowest tertile. However, this negative correlation was not maintained with telomere length after adjusting for all covariates (Fig.2).

3.3 Association between dietary OBS/lifestyle OBS and biological aging

We divided OBS into dietary OBS and lifestyle OBS. Table 3 and Table 4 independently show the association between them with biological aging. Their correlation with biological aging was consistent with overall OBS, but lifestyle OBS is independent of telomere length. Intriguingly, lifestyle OBS has a greater impact on biological aging than dietary OBS. Biological age and phenotypic age were negatively associated with both dietary and lifestyle OBS and was statistically significant after adjusting for all variables [biological age and DOBS: -0.393(-0.595, -0.192), biological age and LOBS: -1.453(-1.673, -1.233); phenotypic age and DOBS: -1.396(-1.761, -1.031), phenotypic age and LOBS: -3.755(-4.330, -3.180)]. In addition, dietary OBS was positively correlated with telomere length and serum klotho concentration only in model 1 and model 2[telomere length in model 2: 0.030(0.003, 0.056), serum klotho concentration in model 2: 26.525(3.660, 49.391)]. Lifestyle OBS has nearly twice as much impact on Klotho as overall OBS [klotho and LOBS in model 4: 62.497(43.947, 81.048)] (Fig.3).

3.4 Subgroup analysis of the association between oxidative balance score with biological aging

Table 5 shows the results of the subgroup analyses of OBS and biological aging after stratification by covariates such as sex and age. The relationship between OBS and different biological aging measures consistently maintained the same trend across all subgroups, except in the subgroup of people younger than 60 years showed a positive association between OBS with biological age [0.048(0.011, 0.085)]. We found that the association between OBS and biological age was significantly stronger when stratified by certain variables, such as the association between OBS and biological age was -0.096(-0.139, -0.053) in the subgroup of people aged 60 years and older of age (P value for interaction <0.001).

3.5 The relationship between OBS and different quantiles of biological aging

Table 6-8 shows the association between OBS and dietary/lifestyle OBS with biological aging in different quantiles of the biological aging distribution. From the figure 3, we can found that the negative regression coefficient β between both biological age and phenotypic age remaining flat in the middle quantiles have a sharp increasing trend in the high quartiles with OBS and dietary/lifestyle OBS. The negative correlation with OBS, DOBS and LOBS was the highest at the 0.93 quantile of biological age, which β-coefficient were -0.079, -0.061 and -0.508, respectively. For phenotypic age is at the 0.95 quantile and the β-coefficient were -0.203, -0.149 and -1.398, respectively.

The correlation between OBS with telomere length was similar to that with dietary OBS. The negative correlation with OBS was the highest at the 0.84 quantile which β-coefficient was 0.003, and DOBS was at the 0.87 which β-coefficient was 0.003. However, lifestyle OBS had no significance for telomere length at any quantile. Meanwhile, the relationship between OBS with serum klotho concentration was significant at the 0.08–0.77 quantiles and the β-coefficient was more than 2.000 at the 0.17 and 0.71 quantile. There was also a significance for DOBS at the 0.12-0.55 quantiles and 0.68-0.73 quantiles except 0.29 quantile. However, lifestyle OBS was positively correlated with klotho concentration at each quantile and was strongest at the high end of the quantile which β-coefficient was 26.559 at the 0.95 quantile (Fig.4).

In this study, we investigated the correlation between OBS and biological aging using a cohort of 27,935 individuals from the NHANES database. Our findings revealed a negative association between total OBS, dietary OBS, and lifestyle OBS with both biological age and phenotypic age. Furthermore, we observed a positive correlation between OBS and telomere length, as well as serum klotho levels, with the exception of lifestyle OBS and Klotho concentration. These findings emphasize the significance of adopting an antioxidant-rich diet and lifestyle. Furthermore, our study offers potential theoretical insights for the prevention of aging.

This study is the first to examine OBS using a composite bio aging marker predictor. Currently, there is limited direct evidence regarding the association between OBS and biological aging. A cross-sectional study involving 3220 participants found a positive correlation between OBS and telomere length in women.²⁴ Additionally, a negative correlation was observed between dietary OBS and both biological age and phenotypic age.²⁸ The relationship between OBS and aging is highly consistent. OBS contains various ingredients, many of which have demonstrated anti-aging effects. For instance, lycopene, a carotenoid, has been shown to reduce HbA1c and FPG levels in patients with T2DM.²⁹ Moreover, consuming lycopene has been found to decrease the risk of cardiovascular disease by 17%.³⁰ Another study involving a large cohort found that higher dietary fiber intake is associated with a lower risk of cardiovascular and coronary heart disease.³¹ Intake of multivitamins has also been linked to a reduced risk of cardiovascular and coronary heart disease morbidity and mortality.^32,33 Furthermore, research suggests that longer telomere length is associated with higher dietary intakes of vitamins C, E, and A, as well as β-carotene and folic acid.³⁴ In men, an increase in serum copper/zinc ratios and copper concentrations, which are biomarkers associated with aging, has been found to be linearly associated with a higher risk of chronic obstructive pulmonary disease. Muscle loss and obesity may also increase the risk of metabolic disorders and mortality. Smoking is a well-established risk factor for aging, supported by strong evidence. Even in old age, smoking remains an independent risk factor for cardiovascular events and mortality, increasing cardiovascular mortality by more than five years. However, quitting smoking in these age groups can still be beneficial in reducing the risk of morbidity. Additionally, smoking has been associated with poorer cognitive performance, and long-term smoking may accelerate brain aging.

Most studies on the OBS have traditionally separated it into diet and lifestyle components,^35–38 with only a limited number of studies examining OBS holistically.³⁹ Our research indicates that lifestyle OBS shows a stronger correlation with aging compared to dietary OBS, a finding that is consistent with other studies in the field. For instance, a cross-sectional study on OBS and sleep quality revealed that lifestyle-related OBS had a more significant impact on sleep quality than diet-related OBS.³⁶ Additionally, a study involving 6341 participants demonstrated that lifestyle-related OBS was more effective in reducing the risk of non-alcoholic fatty liver disease (NAFLD) than diet-related OBS.³⁷ Furthermore, a prospective study conducted in Iowa found that lifestyle-related OBS, rather than diet-related OBS, was linked to higher mortality rates in older women.³⁸ Importantly, our results show that lifestyle OBS has more than double the effect on serum Klotho levels compared to dietary OBS. Similarly, a study in Spain involving middle-aged sedentary adults identified lean mass index (LMI) as the primary determinant of Klotho levels, although dietary factors may also play a role.⁴⁰ Improving lifestyle may have a significant impact on slowing the aging process. However, it is important to note that aging is a multifaceted process that cannot be solely attributed to diet and lifestyle choices. Therefore, further high-quality prospective studies are necessary to validate our findings.

Our research findings indicate gender disparities in the impact of oxidative stress on aging. We hypothesize that certain factors may have influenced this phenomenon. For instance, male hormones are believed to trigger oxidative stress, and a study revealed that men, particularly those with higher body mass index and older age, were more vulnerable to oxidative stress compared to women.^41,42 Surprisingly, our study reveals a contrasting pattern in the correlation between oxidative stress and telomere length. Our results demonstrate a significant association between oxidative stress and telomere length in women rather than men, a conclusion consistent with a cross-sectional study involving 3220 participants.24 Furthermore, healthy lifestyle choices, including antioxidant-rich behaviors, might benefit elderly individuals.^43,44 The prevalence of antioxidant-rich diet and lifestyle choices could potentially slow down the aging process in individuals over 60 years of age. However, a positive correlation was observed between OBS scores and biological age in individuals over 60 years old. Due to the cross-sectional nature of the study, establishing a causal relationship or determining the chronological order of exposure and outcome is not feasible. The wide age range within the group under 60 years old suggests the need for further stratification in future studies. The intricate nature of the OBS, encompassing both pro-oxidants and antioxidants, alongside the complexity of aging, suggests that a comprehensive understanding of aging cannot solely rely on dietary and lifestyle OBS. Further high-quality prospective studies are essential to validate these findings.

In quantile regression, it has been observed that there is an increase in the negative association between OBS and high quantiles of biological age and phenotypic age. This could be attributed to the gradual accumulation of excess reactive oxygen species (ROS) in the body as aging, leading to an imbalance in antioxidant defenses, an acute rise in oxidative stress, and ultimately an inflammatory state. Consequently, individuals who are aging more significantly may be more susceptible to changes in oxidative/antioxidant levels compared to those with milder senescence.⁴⁵ The correlation between OBS and telomere length and serum klotho level remains relatively stable at meaningful quantiles, except for a positive correlation between lifestyle OBS and serum klotho concentration quantile. This finding suggests that lifestyle factors such as smoking, which induce the release of pro-inflammatory cytokines and promote inflammation, may lead to an increase in serum klotho levels. It is important to note that serum klotho levels not only serve as an anti-aging indicator but also as an anti-inflammatory molecule. A study conducted in Japan discovered significantly higher serum klotho levels in smokers compared to never-smokers.⁴⁶ Additionally, subjects experiencing inflammation-related stress, including sleep deprivation and high psychological stress, also exhibited elevated serum klotho levels. Lack of sleep and psychological stress have been associated with increased inflammatory markers.^47,48 These findings support the hypothesis that the rise in klotho is a compensatory response to stress and that higher klotho levels can partly be explained by elevated inflammatory levels and, consequently, heightened sensitivity to LOBS.

This study is pioneering in utilizing a composite biomarker predictor to investigate the correlation between OBS and aging. The key strength lies in its dual perspective on OBS analysis and its exploration of aging correlations from various angles. By incorporating multiple bioindicators, this research offers a more comprehensive understanding of the link between OBS and aging, offering empirical evidence for aging mechanisms and anti-aging strategies. It is important to acknowledge that the cross-sectional study design used in this research can only establish correlations, not causal relationships. Therefore, future studies should incorporate longitudinal data tracking and further analysis to validate the stability and reliability of the observed associations.

In summary, this study highlights a dose-response relationship between OBS and biological aging, suggesting that an antioxidant-rich diet and lifestyle may provide beneficial effects on aging. These findings contribute theoretical insights into strategies for aging prevention.

OBS	Oxidative balance score
DOBS	Dietary oxidative balance score
LOBS	Lifestyle oxidative balance score

Acknowledgements

We thank the NHANES staff for their excellent work on study design and data collection.

Author contributions

JYJ, LSY and YY designed research; JYJ and FS conducted research and analyzed data; JYJ and HLY wrote the paper; and QSF, CXW, WRY, LSW and ZAM critically reviewed the paper. All authors read and approved the final manuscript.

Funding

This work was supported by Department of Science and Technology of Jilin Province [grant number: 20240601018RC].

Data availability

Data described in the manuscript are publicly and freely available without restriction at https://www.cdc.gov/nchs/nhanes/index.htm.

Ethics approval and consent to participate

NHANES is conducted by the Centers for Disease Control and Preven- tion. (CDC) and the National Center for Health Statistics (NCHS). The NCHS Research. Ethics Review Committee reviewed and approved the NHANES study protocol. All participants signed written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Table 1 Basic characteristics of participants in the 1999–2018 NHANES
Characteristic	Total	Oxidative balance score			P value
Characteristic	Total	Tertile 1	Tertile 2	Tertile 3	P value
Age (years)	45.60±0.23	45.31±0.29	45.61±0.28	45.85±0.32	0.337
Gender, (%)					0.005
Male	12917(48.79)	5102(53.26)	5028(49.77)	3995(51.05)
Female	14125(51.21)	4256(46.74)	4805(50.23)	3856(48.95)
Race/ethnicity, (%)					<0.001
Non-Hispanic White	12836(71.55)	3996(66.54)	4725(71.46)	4115(75.92)
Non-Hispanic Black	5282(9.64)	2502(15.08)	1770(8.98)	1010(5.75)
Mexican American	4440(7.42)	1544(7.43)	1638(7.65)	1258(7.17)
Other Race	4484(11.38)	1316(10.95)	1700(11.91)	1468(11.17)
Education level, (%)					<0.001
Less than high school	6181(22.74)	2488(28.81)	2281(23.65)	1412(16.60)
High school	5573(12.32)	2516(17.42)	1932(12.01)	1125(8.34)
Some college or above	15274(64.90)	4347(53.77)	5615(64.34)	5312(75.06)
Marital status, (%)					<0.001
Never married/Married/Living with a partner	21921(84.28)	7299(81.71)	8019(84.39)	6603(86.91)
Divorced/Separated/Widowed	4845(15.50)	1928(18.29)	1734(15.61)	1183(13.09)
Family PIR, (%)					<0.001
<1.3	6706(16.88)	2859(24.89)	2315(17.10)	1532(12.87)
1.3–3.5	9114(44.94)	2429(37.49)	3405(47.63)	3280(56.23)
>3.5	9240(32.51)	3334(37.62)	3391(35.28)	2515(30.90)
Self-reported chronic conditions, (%)					<0.001
0	6202(29.14)	1765(29.57)	2239(31.94)	2198(36.43)
1	6507(25.13)	2239(28.57)	2326(27.63)	1942(28.81)
≥2	9670(34.51)	3583(41.86)	3565(40.43)	2522(34.76)
Biological age (years)	45.28±0.22	45.56±0.30	45.34±0.28	44.98±0.31	0.244
Phenotypic age (years)	39.95±0.28	40.91±0.35	40.04±0.36	39.07±0.37	<0.001
Telomere length	1.07±0.01	1.06±0.02	1.07±0.02	1.09±0.02	0.041
Klotho (pg/ml)	844.61± 5.61	828.32± 7.46	836.93± 8.39	864.55± 8.90	0.006
Mean ± SD for continuous variables: the P value for Mann–Whitney U test; (%) for categorical variables: the P value for chi-square test; Oxidative balance score tertile ranges: Tertile 1 = [3,17); Tertile 2 = [17,25); Tertile 3 = [25,37]; PIR, Ratio of family income to poverty.

Table 2 Associations between oxidative balance score with biological aging
Biological aging	Model 1 [β (95% CI)]	Model 2 [β (95% CI)]	Model 3 [β (95% CI)]	Model 4 [β (95% CI)]
Biological age (years)
OBS	-0.039(-0.080,0.002)	-0.057(-0.068, -0.046)**	-0.047(-0.058, -0.036)**	-0.043(-0.054, -0.031)**
T1	Ref	Ref	Ref	Ref
T2	-0.215(-0.945,0.514)	-0.315(-0.519, -0.112)*	-0.185(-0.385, 0.015)	-0.189(-0.410, 0.033)
T3	-0.573(-1.287,0.142)	-0.902(-1.096, -0.709)**	-0.716(-0.902, -0.530)**	-0.663(-0.867, -0.458)**
P for trend	0.110	<0.001	<0.001	<0.001
Phenotypic age (years)
OBS	-0.112(-0.165, -0.058)**	-0.144(-0.162, -0.125)**	-0.123(-0.140, -0.105)**	-0.125(-0.148, -0.103)**
T1	Ref	Ref	Ref	Ref
T2	-0.875(-1.709, -0.040)**	-0.959(-1.265, -0.652)**	-0.762(-1.054, -0.471)**	-0.855(-1.234, -0.476)**
T3	-1.835(-2.726, -0.944)**	-2.082(-2.403, -1.761)**	-1.746(-2.049, -1.444)**	-1.792(-2.190, -1.393)**
P for trend	<0.001	<0.001	<0.001	<0.001
Telomere length
OBS	0.002(0.000, 0.003)*	0.002(0.000, 0.004)*	0.002(0.000, 0.003)*	0.002(-0.001, 0.005)
T1	Ref	Ref	Ref	Ref
T2	0.010(-0.019,0.039)	0.017(-0.011, 0.045)	0.018(-0.010, 0.045)	0.022(-0.007, 0.051)
T3	0.030(0.006, 0.055)*	0.037(0.011, 0.062)*	0.031(0.007, 0.056)*	0.037(-0.004, 0.079)
P for trend	0.015	0.007	0.015	0.069
Klotho (pg/mL)
OBS	2.545(1.262, 3.828)**	2.800(1.527, 4.074)**	2.500(1.142, 3.858)**	2.283(0.823, 3.742)**
T1	Ref	Ref	Ref	Ref
T2	8.605(-9.401,26.610)	11.282(-6.198, 28.762)	8.564(-10.002, 27.131)	6.886(-12.739, 26.512)
T3	36.226(14.465, 57.986)**	39.982(18.541, 61.422)**	36.086(13.811, 58.361)*	32.332(8.193, 56.471)*
P for trend	<0.001	<0.001	0.002	0.009
Model 1: no covariates were adjusted; Model 2: age, gender, and race were adjusted; Model 3: age, gender, race, education level, marital status, PIR were adjusted; Model 4: age, gender, race, education level, marital status, PIR and self-reported chronic conditions were adjusted; OBS Oxidative Balance Score, PIR Ratio of family income to poverty, T tertile; P<0.05 *P<0.001

Table 3 Associations between dietary oxidative balance score with biological aging
Biological aging	Model 1 [β (95% CI)]	Model 2 [β (95% CI)]		Model 3 [β (95% CI)]	Model 4 [β (95% CI)]
Biological age (years)
DOBS	-0.030(-0.073,0.013)	-0.036(-0.048, -0.025)**		-0.025(-0.037, -0.013)**	-0.026(-0.038, -0.013)**
T1	Ref	Ref		Ref	Ref
T2	-0.049(-0.809,0.712)	-0.254(-0.446, -0.062)*		-0.112(-0.307, 0.083)	-0.129(-0.348, 0.089)
T3	-0.579(-1.259,0.100)	-0.570(-0.760, -0.380)**		-0.384(-0.571, -0.197)**	-0.393(-0.595, -0.192)**
P for trend	0.080	<0.001		<0.001	<0.001
Phenotypic age (years)
DOBS	0.101(-0.155, -0.047)**	-0.109(-0.127, -0.090)**		-0.086(-0.104, -0.068)**	-0.093(-0.117, -0.069)**
T1	Ref	Ref		Ref	Ref
T2	-0.681(-1.614, 0.252)	-0.852(-1.194, -0.510)**		-0.650(-0.989, -0.310)**	-0.772(-1.180, -0.363)**
T3	-1.752(-2.598, -0.906)**	-1.624(-1.918, -1.330)**		-1.286(-1.563, -1.008)**	-1.396(-1.761, -1.031)**
P for trend	<0.001	<0.001		<0.001	<0.001
Telomere length
DOBS	0.002(0.000,0.004)*	0.002(0.000, 0.004)*		0.002(0.000, 0.003)	0.003(0.000, 0.005)
T1	Ref	Ref		Ref	Ref
T2	0.018(-0.008,0.044)	0.024(-0.003, 0.051)		0.024(-0.002, 0.050)	0.023(-0.013, 0.060)
T3	0.028(0.002, 0.054)*	0.030(0.003, 0.056)*		0.024(-0.002, 0.051)	0.034(-0.011, 0.080)
P for trend	0.035		0.031	0.071	0.129
Klotho (pg/mL)
DOBS	1.896(0.481, 3.311)*	2.047(0.646, 3.447)*		1.680(0.196, 3.163)*	1.568(-0.020, 3.156)
T1	Ref	Ref		Ref	Ref
T2	1.050(-19.686,21.787)	3.154(-17.190, 23.498)		-0.728(-21.994, 20.537)	-0.172(-22.439, 22.095)
T3	25.842(2.799, 48.885)*	26.525(3.660, 49.391)*		22.149(-1.999, 46.297)	20.431(-5.263, 46.124)
P for trend	0.027	0.023		0.068	0.113
Model 1: no covariates were adjusted; Model 2: age, gender, and race were adjusted; Model 3: age, gender, race, education level, marital status, PIR were adjusted; Model 4: age, gender, race, education level, marital status, PIR and self-reported chronic conditions were adjusted; DOBS Dietary Oxidative Balance Score, PIR Ratio of family income to poverty, T tertile; P<0.05 *P<0.001

Table 4 Associations between lifestyle oxidative balance score with biological aging
Biological aging	Model 1 [β (95% CI)]	Model 2 [β (95% CI)]	Model 3 [β (95% CI)]	Model 4 [β (95% CI)]
Biological age (years)
LOBS	-0.261(-0.459, -0.062)*	-0.528(-0.580, -0.476)**	-0.510(-0.562, -0.459)**	-0.421(-0.479, -0.363)**
T1	Ref	Ref	Ref	Ref
T2	0.298(-0.342, 0.939)	-0.766(-0.945, -0.588)**	-0.742(-0.920, -0.563)**	-0.525(-0.718, -0.332)**
T3	-1.427(-2.234, -0.620)**	-1.799(-2.001, -1.597)**	-1.740(-1.936, -1.545)**	-1.453(-1.673, -1.233)**
P for trend	0.003	<0.001	<0.001	<0.001
Phenotypic age (years)
LOBS	-0.461(-0.730, -0.191)**	-0.977(-1.067, -0.886)**	-0.919(-1.015, -0.823)**	-0.869(-0.991, -0.747)**
T1	Ref	Ref	Ref	Ref
T2	-0.305(-1.064, 0.454)	-1.892(-2.144, -1.641)**	-1.745(-2.003, -1.486)**	-1.569(-1.874, -1.263)**
T3	-2.389(-3.597, -1.180)**	-4.158(-4.588, -3.729)**	-3.951(-4.416, -3.485)**	-3.755(-4.330, -3.180)**
P for trend	<0.001	<0.001	<0.001	<0.001
Telomere length
LOBS	0.000(-0.010,0.009)	0.006(-0.003, 0.015)	0.005(-0.005, 0.014)	-0.005(-0.014, 0.004)
T1	Ref	Ref	Ref	Ref
T2	0.003(-0.021, 0.026)	0.013(-0.010, 0.036)	0.011(-0.013, 0.036)	0.005(-0.024, 0.035)
T3	-0.009(-0.046, 0.028)	0.012(-0.022, 0.046)	0.003(-0.033, 0.039)	-0.018(-0.057, 0.021)
P for trend	0.64	0.436	0.787	0.394
Klotho (pg/mL)
LOBS	20.216(15.105, 25.328)**	22.935(17.676, 28.195)**	22.605(16.731, 28.480)**	20.491(14.625, 26.358)**
T1	Ref	Ref	Ref	Ref
T2	23.155(4.568, 41.741)*	29.375(10.148, 48.602)*	31.620(12.560, 50.679)*	25.941(6.316, 45.566)*
T3	62.693(44.499, 80.887)**	70.859(53.005, 88.713)**	70.822(51.761, 89.884)**	62.497(43.947, 81.048)**
P for trend	<0.001	<0.001	<0.001	<0.001
Model 1: no covariates were adjusted; Model 2: age, gender, and race were adjusted; Model 3: age, gender, race, education level, marital status, PIR were adjusted; Model 4: age, gender, race, education level, marital status, PIR and self-reported chronic conditions were adjusted; LOBS Lifestyle Oxidative Balance Score, PIR Ratio of family income to poverty, T tertile; P<0.05 *P<0.001

Table 5 Subgroup analysis of the association between oxidative balance score with biological aging
Subgroup	Biological age β (95% CI)	Phenotypic age β (95% CI)	Telomere length β (95% CI)	Klotho β (95%CI)
Sex
Male	-0.015(-0.056, 0.026)	-0.140(-0.206, -0.075)**	0.000(-0.003, 0.004)	2.575(0.601, 4.549)*
Female	0.040(-0.002, 0.082)	0.015(-0.043, 0.073)	0.004(0.001, 0.007)*	1.690( -0.190, 3.570)
P for interaction	0.096	<0.001	0.053	0.813
Age
<60	0.048(0.011, 0.085)*	-0.032(-0.085, 0.021)	0.001(-0.001,0.004)	1.604(-0.095, 3.303)
≥ 60	-0.096(-0.139, -0.053)**	-0.156(-0.219, -0.094)**	0.004(0.001, 0.008)*	3.574(1.420, 5.728)**
P for interaction	<0.001	<0.001	0.022	0.133
Race/ethnicity
Non-Hispanic White	0.023(-0.017, 0.062)	-0.044(-0.100, 0.013)	0.003(0.000, 0.006)*	2.901(1.046, 4.756)*
Non-Hispanic Black	-0.059(-0.116, -0.002)*	-0.148(-0.248, -0.048)*	0.002(-0.003, 0.006)	0.722(-3.064, 4.507)
Mexican American	-0.025(-0.097, 0.047)	-0.118(-0.215, -0.021)*	-0.001(-0.004, 0.002)	0.943(-3.228, 5.114)
Other race/multiracial	0.007(-0.059, 0.074)	-0.192(-0.315, -0.069)*	-0.005(-0.013, 0.003)	-0.692(-3.319, 1.935)
P for interaction	0.011	0.008	0.015	0.220
Education level
Less than high school	-0.033(-0.102, 0.035)	-0.142(-0.245, -0.039)*	-0.001(-0.005, 0.004)	1.737(-0.736, 4.210)
High school grad/GED	0.015(-0.059, 0.090)	-0.020(-0.115, 0.075)	0.005(0.000, 0.009)*	1.442(-1.580, 4.464)
Some college or above	0.019(-0.016, 0.054)	-0.068(-0.122, -0.014)*	0.002(-0.001, 0.005)	2.576(0.892, 4.260)*
P for interaction	0.469	0.105	0.192	0.741
Marital status
Never married/Married/Living with a partner	0.018(-0.015, 0.050)	-0.052(-0.101, -0.004)*	0.002(0.000, 0.005)	2.735(1.234, 4.236)**
Divorced/Separated/Widowed	-0.019(-0.086, 0.047)	-0.131(-0.229, -0.034)*	0.003(-0.002, 0.008)	0.573(-2.001, 3.146)
P for interaction	0.221	0.174	0.918	0.144
Family PIR
<1.3	0.021(-0.031, 0.073)	-0.103(-0.195, -0.010)*	-0.003(-0.009, 0.003)	2.419(-0.705, 5.542)
1.3-3.49	-0.002(-0.055, 0.051)	-0.080(-0.153, -0.007)*	0.004(0.001, 0.008)*	1.739(-0.266, 3.745)
≥3.5	0.015(-0.034, 0.063)	-0.049(-0.113, 0.015)	0.002(-0.001, 0.005)	2.554(0.493, 4.615)*
P for interaction	0.677	0.363	0.016	0.824
Self-reported chronic conditions
0	0.029(-0.022, 0.079)	-0.063(-0.143, 0.017)	-	2.670(-0.655, 5.995)
1	0.044(-0.006, 0.094)	-0.019(-0.084, 0.047)	0.001(-0.003, 0.004)	0.547(-2.175, 3.269)
≥2	-0.018(-0.062, 0.026)	-0.099(-0.167, -0.031)*	0.003(0.001, 0.006)*	2.839(1.059, 4.619)*
P for interaction	0.018	0.101	0.157	0.638
Age, gender, race, education level, marital status, PIR and self-reported chronic conditions were adjusted; OBS Oxidative Balance Score, PIR the ratio of income to poverty; P<0.05 *P<0.001

Table 6 Quantile regression analysis of association between oxidative balance score and biological aging
Quantile	Biological age	Phenotypic age	Telomere length	Klotho
0.1	-0.030(-0.043, -0.017)**	-0.086(-0.106, -0.065)**	0.002(0.000, 0.003)*	1.015(0.048, 1.982)*
0.2	-0.037(-0.049, -0.026)**	-0.095(-0.113, -0.076)**	0.001(0.000, 0.003)*	1.949(0.937, 2.960)**
0.3	-0.039(-0.050, -0.028)**	-0.108(-0.124, -0.092)**	0.002(0.000, 0.003)*	1.337(0.399, 2.276)*
0.4	-0.038(-0.050, -0.027)**	-0.111(-0.127, -0.095)**	0.002(0.001, 0.003)*	1.491(0.517, 2.465)*
0.5	-0.039(-0.050, -0.027)**	-0.112(-0.129, -0.094)**	0.001(0.000, 0.003)	1.557(0.498, 2.616)*
0.6	-0.039(-0.050, -0.027)**	-0.118(-0.137, -0.098)**	0.001(-0.001, 0.003)	1.378(0.203, 2.553)*
0.7	-0.044(-0.057, -0.031)**	-0.120(-0.143, -0.097)**	0.002(0.000, 0.004)*	1.925(0.606, 3.244)*
0.8	-0.059(-0.074, -0.045)**	-0.134(-0.161, -0.107)**	0.002(0.000, 0.004)*	1.596(-0.028, 3.222)
0.9	-0.066(-0.087, -0.046)**	-0.154(-0.205, -0.103)**	0.002(-0.001, 0.004)	0.437(-2.092, 2.967)
Age, gender, race, education level, marital status, PIR and self-reported chronic conditions were adjusted; PIR the ratio of income to poverty; P<0.05 *P<0.001

Table 7 Quantile regression analysis of association between dietary oxidative balance score and biological aging
Quantile	Biological age	Phenotypic age	Telomere length	Klotho
0.1	-0.016(-0.030, -0.002)*	-0.058(-0.079, -0.037)**	0.002(0.000, 0.003)*	0.679(-0.324, 1.681)
0.2	-0.023(-0.035, -0.011)**	-0.068(-0.087, -0.049)**	0.001(0.000, 0.003)*	1.298(0.242, 2.355)*
0.3	-0.023(-0.034, -0.011)**	-0.080(-0.096, -0.063)**	0.002(0.000, 0.003)*	1.027(0.052, 2.002)*
0.4	-0.022(-0.033, -0.010)**	-0.078(-0.096, -0.060)**	0.002(0.001, 0.004)*	1.091(0.083, 2.099)*
0.5	-0.021(-0.033, -0.010)**	-0.082(-0.100, -0.063)**	0.002(0.000, 0.003)	1.197(0.089, 2.304)*
0.6	-0.024(-0.036, -0.013)**	-0.086(-0.107, -0.065)**	0.001(0.000, 0.003)	0.896(-0.314, 2.104)
0.7	-0.028(-0.041, -0.014)**	-0.089(-0.113, -0.065)**	0.003(0.000, 0.004)*	1.435(0.066, 2.804)*
0.8	-0.042(-0.057, -0.026)**	-0.101(-0.129, -0.072)**	0.003(0.000, 0.005)*	1.017(-0.676, 2.710)
0.9	-0.049(-0.071, -0.027)**	-0.120(-0.171, -0.070)**	0.002(-0.001, 0.005)	-0.170(-2.797, 2.457)
Age, gender, race, education level, marital status, PIR and self-reported chronic conditions were adjusted; PIR the ratio of income to poverty; P<0.05 *P<0.001

Table 8 Quantile regression analysis of association between lifestyle oxidative balance score and biological aging
Quantile	Biological age	Phenotypic age	Telomere length	Klotho
0.1	-0.405(-0.463, -0.346)**	-0.745(-0.839, -0.651)**	-0.001(-0.008, 0.005)	11.008(5.868, 16.148)**
0.2	-0.415(-0.467, -0.363)**	-0.704(-0.782, -0.626)**	-0.001(-0.008, 0.006)	12.762(7.479, 18.045)**
0.3	-0.450(-0.459, -0.361)**	-0.757(-0.833, -0.682)**	-0.003(-0.011, 0.005)	12.146(7.614, 16.678)**
0.4	-0.439(-0.490, -0.388)**	-0.806(-0.888, -0.724)**	-0.001(-0.008, 0.006)	13.108(8.110, 18.105)**
0.5	-0.424(-0.476, -0.372)**	-0.844(-0.924, -0.764)**	-0.004(-0.011, 0.004)	13.936(8.832, 19.039)**
0.6	-0.423(-0.475, -0.371)**	-0.892(-0.981, -0.802)**	-0.002(-0.010, 0.006)	14.310(8.582, 20.037)**
0.7	-0.455(-0.515, -0.394)**	-0.888(-0.979, -0.796)**	-0.002(-0.012, 0.007)	16.113(9.522, 22.703)**
0.8	-0.493(-0.560, -0.426)**	-0.951(-1.077, -0.824)**	-0.003(-0.013, 0.007)	13.239(5.189, 21.288)*
0.9	-0.405(-0.463, -0.346)**	-0.745(-0.839, -0.651)**	-0.001(-0.008, 0.005)	11.008(5.868, 16.148)**
Age, gender, race, education level, marital status, PIR and self-reported chronic conditions were adjusted; PIR the ratio of income to poverty; P<0.05 *P<0.001

No competing interests reported.

Association of oxidative balance score with biological aging in US adults: a quantile regression analysis

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

1 Introduction

2 Methods

2.1 Study design and participants

2.2 Oxidative balance score

2.3 Biological aging measurement

2.4 Covariate assessment

2.5 Statistical analysis

3 Results

4 Discussion

5 Conclusions

Abbreviations

Declarations

References

Tables

Additional Declarations

Status:

Version 1