Social determinants of health and biological age among diverse U.S. Adults, NHANES 2011–2018

Abstract

Background

Social determinants of health are fundamental drivers of health inequities. Education and income are inversely associated with accelerated aging, but less is known about differences in their association with aging by race/ethnicity. We examined the sex-specific association between each social determinant of health with biological age and by race/ethnicity.

Methods

Data were sourced from four two-year National Health and Nutrition Examination Survey cycles (2011–2018). Education and household income were self-reported. The Klemera-Doubal Method, an algorithm using biomarkers from different organ systems, was used to calculate biological age among 6,213 females and 5,938 males aged 30–75 years who were Mexican, other Hispanic, non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, or other/multi-racial. We used multivariable linear regression models to determine the association between education and income and biological age.

Results

Compared with a college education, less than a high school education was associated with higher biological age by 3.17 years (95% CI: 1.69, 4.65) among females only; associations were strongest among non-Hispanic Black, other Hispanic, and non-Hispanic Asian female adults. Compared with an annual income of ≥$75,000, an income <$25,000 was associated with higher biological age by 4.94 years (95% CI: 3.40, 6.47) among males and 2.74 years among females (95% CI: 1.48, 4.00); associations were strongest among non-Hispanic White and non-Hispanic Asian adults, and Mexican and other Hispanic males.

Conclusions

These findings demonstrate that adverse levels of education and income are associated with greater biological age among females and males, and racial/ethnic minority adults in particular. Targeting upstream sources of social disadvantage among racial/ethnic minority groups, in conjunction with improvements to income and education, may promote healthy aging in these populations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-025-23803-z.

Background

Social determinants of health are fundamental drivers of health inequities [1]. People of low socio-economic status are burdened by premature morbidity and mortality [2, 3] and have an accelerated decline in physical, physiological, and cognitive health [4]. For example, lower education and income have been shown to be particularly influential and are strongly associated with many adverse age-related health outcomes such as disability, cognitive deficiency, and multi-morbidity [5] and inversely associated with accelerated aging [6, 7]. Socio-economic disparities are further compounded by nativity status and length of time spent in the U.S., which reflects the social challenges experienced by immigrants [8]. This is particularly salient for racial and ethnic minority populations who, owing to social disadvantage [8], disproportionately are of lower socio-economic status [9], and exposed to psychosocial stressors such as stigma and discrimination [10]. Persistent stress from life-course social disadvantage has been posited to result in earlier aging [11] and exacerbate health disparities among racial and ethnic minority populations [12]. In fact, aging speed differs by race/ethnicity, with non-Hispanic Black adults aging at a faster pace compared with non-Hispanic White adults [13] and Hispanic adults [14].

The impact of socio-economic status on health and aging is not homogenous across race/ethnic groups. For example, the “diminishing returns hypothesis” posits that as socio-economic status levels increase, non-Hispanic Black adults do not have an equivalent improvement in health compared to non-Hispanic White adults [15]. Less is known about the association between social determinants of health and aging speed among Hispanic and non-Hispanic Asian adults, groups that represent fast-growing segments of the U.S. population [16, 17]. By 2060, ethnic and racial minorities will represent the majority of the U.S. population [16, 17]. As the U.S. population becomes increasingly diverse, understanding how education, income, and nativity/length of time spent in the U.S. drive disparities in biological aging in traditionally understudied and underserved groups is critical for achieving health equity.

Chronological age refers to the amount of time passed since birth, while biological age refers to the phenotypic changes associated with the gradual aging process across the lifespan [18]. Contemporary research in aging has emphasized the utilization of biological age instead of chronological age as a marker of the body’s degradation and breakdown [19]. Among the several algorithms used in the calculation of biological age, the Klemera-Doubal Method is regarded as the most valid [20]. The overarching goals of this study were to describe differences in aging speed by social determinants of health and race/ethnicity, determine whether social determinants of health are associated with biological age, and examine whether this association is modified by race/ethnicity.

Methods

Study population

The National Health and Nutrition Examination Survey (NHANES) is an ongoing, cross-sectional survey, that is representative of the non-institutionalized U.S. civilian population. The NHANES has been conducted in two-year cycles continuously since 1999. Asian Americans were oversampled beginning with the 2011–2012 data cycle. Data for this study were sourced from four NHANES cycles (2011–2018). Trained interviewers collected demographic, socio-economic, dietary, and health-related information in the participant’s home. Participants attended a mobile examination center where physical examinations and laboratory testing were conducted under standardized protocols. NHANES protocols were approved by the Ethics Review Board of the National Center for Health Statistics and all methods were performed in accordance with the relevant guidelines and regulations. Written informed consent was obtained prior to data collection.

Measures

Social determinants of health

Education, household income, and nativity/years residing in the U.S. were obtained from self-reported questionnaires. Education: was measured from participants’ highest educational level attained at the time of interview (categorized as: less than high school, high school/GED equivalent, some college or an Associate degree, or a college graduate and above). Household income: was measured from participants' total annual household income (categorized as: <$ 25 K, $25K-<$ 55 K, $55K-<$ 75 K, or $75K+). Nativity/years residing in the U.S.: was measured from participants' country of birth in combination with reported length of time spent in the U.S. Participants were categorized as U.S. born, foreign born and residing in the U.S. for ≥ 10 years, or foreign born and residing in the U.S. < 10 years.

Biological age

Biological age was calculated using the Klemera-Doubal Method [21]. The Klemera-Doubal Method is a multi-step process that involves: (1) the identification of biomarkers to be used in the Klemera-Doubal algorithm, (2) the utilization of selected biomarkers in a reference population, and (3) the application of the reference training parameters in the analytic dataset. Biological age is then computed using an algorithm that utilizes the parameter values derived from the reference population and biomarker values from the analytic sample [22]. Each step is outlined in further detail below.

Identification of biomarkers

We first identified biomarkers that are associated with aging [23] and were consistently collected across all NHANES data cycles. The following 15 biomarkers were considered: blood urea nitrogen, serum creatinine, albumin, alanine aminotransferase, white blood cell count, total cholesterol, high-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol (total cholesterol minus high-density lipoprotein cholesterol), glycosylated hemoglobin, waist circumference, body mass index, systolic blood pressure, pulse pressure (the difference between systolic and diastolic blood pressure), alkaline phosphatase, and albumin-to-creatinine ratio. The biomarkers identified account for the aging process in different organ systems [23].

Selection of biomarkers

To remain independent of biological age estimation in the analytic dataset, we derived Klemera-Doubal-biological age algorithm parameters using NHANES data cycles 2007–2008 and 2009–2010 as the reference population [24]. We derived models separately for males and females, as biomarker distributions differ by sex [25]. We used correlation analyses to obtain sex-specific Pearson correlation coefficients between each biomarker with chronological age. We selected significantly correlated biomarkers with chronological age at r > 0.10 [22] and used a threshold of r > 0.7 to determine multi-collinearity between the biomarkers. From the initial list of 15 biomarkers, nine were selected in males and females. Among males, the following were retained: blood urea nitrogen, serum creatinine, albumin, alanine aminotransferase, non-high-density lipoprotein cholesterol, glycosylated hemoglobin, waist circumference, pulse pressure, and albumin-to-creatinine ratio. Among females, the following were retained: blood urea nitrogen, serum creatinine, white blood cell count, non-high-density lipoprotein cholesterol, glycosylated hemoglobin, waist circumference, pulse pressure, alkaline phosphatase, and albumin-to-creatinine ratio. We re-ran the correlation analysis after removing missing data from the nine biomarkers, and excluded biomarker values that were more than five standard deviations away from the sex-specific mean [24]. Each significantly correlated biomarker was then individually regressed on chronological age to obtain the intercept, slope, and root mean squared error [21]. A final set of parameters contained the intercept, slope, root mean squared error, and correlation coefficients.

Application

Described in detail elsewhere [22, 24], in Step 1, we calculated an initial estimate of biological age using an equation containing biomarker values from the analytic sample, and the intercept, slope, and root mean squared error values from the reference dataset. In Step 2, we calculated an overall correlation coefficient using the significant correlation coefficients obtained in the reference dataset. In Step 3, we calculated the variance in biological age using the initial biological age estimate from Step 1, the overall correlation coefficient from Step 2, and each individual chronological age. In Step 4, we obtained a robust estimate of biological age using a modified Step 1 equation that included the scaling variance factor derived in Step 3. Biological age was calculated for all males and females, separately. As a secondary outcome, to characterize aging speed, we calculated aging difference, defined as the difference between biological age and chronological age. A positive aging difference was indicative of accelerated aging, while a negative aging difference was indicative of decelerated aging.

As an alternative measure, we also estimated sex-independent biological age in the whole sample (i.e., in males and females as one group). Eight biomarkers were retained after the correlation analyses for this secondary measure: blood urea nitrogen, serum creatinine, albumin, glycosylated hemoglobin, waist circumference, systolic blood pressure, alkaline phosphatase, and albumin-to-creatinine ratio. Subsequent steps were followed in the same manner.

Other variables of interest

Participants self-reported demographic and health behaviors via standardized questionnaires. Participants reported race and Hispanic origin and identified as non-Hispanic White, non-Hispanic Black, Mexican, other Hispanic, non-Hispanic Asian, or another race (which includes multi-racial). Participants also disclosed marital status (married or living with a partner, or never married/widowed/divorced/separated), health insurance status (yes/no), alcohol use (yes/no on consumption of at least 12 alcoholic beverages in the past year), smoking status (current smoker, former smoker, or never smoker), and history of cardiovascular disease (self-reported history of congestive heart failure, coronary heart disease, angina/angina pectoris, a heart attack, or a stroke). Physical activity was measured from the physical activity questionnaire (based off the Global Physical Activity Questionnaire). Participants self-reported whether they engaged in vigorous intensity (e.g., running or playing basketball) or moderate intensity (e.g., brisk walking, swimming, or golf) recreational activities for at least 10 min. continuously in a typical week, or no recreational activity.

Analytic sample

Of the 39,156 adult and child participants from NHANES 2011–2018 cycles, we excluded n = 15,331 children (< 18 years of age) and n = 7,178 adults outside of the inclusionary age range (30–75 year olds were included to ensure biomarkers reflected age-related variation and to minimize survival bias by not including people with better than average longevity). We also excluded participants missing information on all three social determinants of health (education, income, or nativity/years residing in the U.S.) (n = 1,916). Given that laboratory values vary during pregnancy, we excluded pregnant females [11] (n = 94) and participants without complete information on all biomarkers utilized in the Klemera-Doubal calculation (n = 2,486). The final analytic sample included 12,151 adults (5,938 males and 6,213 females). A study flow chart is shown in Fig. 1.

Fig. 1.

Cohort inclusion and exclusion flow chart, NHANES 2011–2018

Statistical analysis

We described population characteristics (age group, nativity/years residing in the U.S., education, income, health insurance, marital status, alcohol use, smoking history, physical activity level, and history of cardiovascular disease) by sex and stratified by race/ethnicity. We determined whether characteristics differed by race/ethnicity using an analysis of variance for continuous variables or chi-square tests for proportions. For males and females, we estimated mean aging difference by race/ethnicity and social determinants of health. Next, we used multivariable linear regression models to determine the association between education, income, and nativity/years residing in the U.S. with biological age. Model 1 was adjusted for race/ethnicity. Model 2 included Model 1 covariates in addition to education, income, health insurance, marital status, and nativity/years residing in the U.S. Model 3 included Model 2 covariates in addition to alcohol use, smoking history, physical activity level, and history of cardiovascular disease. We considered Model 3 to be our fully adjusted model in education and income analyses. We also included an additional Model 4 which included Model 3 covariates and chronological age. We considered Model 4 to be our fully adjusted model in nativity analyses. To determine whether race/ethnicity modified the association between social determinants of health and biological age, we tested the multiplicative interaction between each social determinant of health and race/ethnicity. Finally, to facilitate sex-based comparisons of our results, in a sensitivity analysis, we repeated our multivariable linear regression models using our alternative (sex-independent) biological age variable as an outcome. We did so, as statistically comparing results across males and females was not possible with sex-specific calculation of biological age (our main biological age measure). Data management was conducted using SAS 15.1 and all analyses were conducted in SUDAAN V11.0.4 and accounted for the complex survey design of NHANES, including 8-year mobile examination center weights.

Results

Among males, mean chronological age was 50.4 years, 38.8% were non-Hispanic White, 21.4% non-Hispanic Black, 13.0% Mexican, 10.0% other Hispanic, and 12.9% non-Hispanic Asian adults (Table 1). More than a third were college graduates (34.5%), 47.4% had an annual household income ≥ $75,000, and 82.6% were U.S. born. Mexican, other Hispanic and non-Hispanic Black males were more likely to be younger and of lower SES (P < 0.05). Among females, mean chronological age was 51.0 years, 36.9% were non-Hispanic White, 22.0% non-Hispanic Black, 13.3% Mexican, 11.1% other Hispanic, and 13.5% non-Hispanic Asian adults (Table 2). More than a third were college graduates (34.6%), 41.8% had an annual household income ≥ $75,000, and 82.6% were U.S. born. Mexican, other Hispanic, non-Hispanic Black and non-Hispanic Asian females tended to be younger (30–44 years) and have less than a high school (HS) education, while non-Hispanic Black, Mexican, and other Hispanic females had a lower household income (P < 0.05).

Table 1.

Male baseline demographics, and stratified by race/ethnicity, NHANES 2011–2018

								Race/Ethnicity
							Overall (N = 5,938)	Non-Hispanic Whites (n = 2,307)	Non-Hispanic Blacks (n = 1,269)		Mexicans (n = 768)	Other Hispanics (n = 596)	Non-Hispanic Asians (n = 763)	Other (n = 235)
Characteristic							% (SE)	% (SE)	% (SE)		% (SE)	% (SE)	% (SE)	% (SE)
Age* (years)							50.4 (0.3)	51.5 (0.3)	49.1 (0.4)		46.0 (0.5)	47.4 (0.6)	47.9 (0.5)	49.0 (1.1)
Age categories*, %
30–44							36.1 (1.0)	32.2 (1.3)	38.7 (1.7)		51.5 (2.0)	47.4 (2.6)	44.0 (2.0)	43.1 (4.7)
45–54							24.1 (0.8)	23.9 (1.1)	26.5 (1.5)		24.4 (1.5)	22.9 (2.2)	25.5 (1.7)	21.2 (4.7)
55–64							24.1 (0.9)	25.9 (1.2)	23.0 (1.2)		16.7 (1.6)	19.1 (1.4)	19.8 (1.6)	20.7 (4.4)
65–75							15.7 (0.8)	18.0 (1.1)	11.8 (0.9)		7.4 (0.8)	10.6 (1.1)	10.7 (1.1)	15.0 (3.2)
Education*, %
< HS education							13.7 (0.9)	8.6 (1.0)	18.5 (1.5)		44.9 (2.4)	26.6 (2.2)	13.3 (1.4)	12.8 (2.5)
HS graduate							22.7 (1.1)	22.3 (1.4)	29.2 (1.4)		24.3 (2.1)	24.6 (2.2)	12.0 (1.4)	20.7 (3.5)
Some college							29.1 (0.9)	30.5 (1.2)	31.6 (1.3)		19.2 (2.1)	27.6 (2.1)	15.1 (1.6)	38.5 (4.5)
College grad							34.5 (1.6)	38.6 (2.0)	20.7 (1.6)		11.5 (1.3)	21.1 (2.3)	59.6 (2.9)	28.0 (5.7)
Household Income*, %
< $25K							15.3 (0.9)	11.8 (1.1)	27.2 (1.9)		24.8 (2.2)	27.3 (2.3)	10.4 (1.3)	19.1 (2.8)
$25K-<$55K							23.9 (1.0)	21.1 (1.2)	30.3 (1.6)		36.4 (2.3)	30.0 (2.5)	20.5 (1.8)	29.3 (4.2)
$55K-<$75K							13.5 (0.7)	13.4 (0.8)	12.5 (0.9)		15.6 (1.7)	12.6 (1.7)	12.1 (1.5)	15.4 (3.1)
≥ $75K							47.4 (1.7)	53.7 (2.0)	30.0 (2.3)		23.2 (2.1)	30.1 (2.6)	57.1 (2.9)	36.2 (5.6)
Nativity/years residing in the U.S.*, %
U.S. Born							82.6 (1.0)	96.0 (0.5)	87.6 (1.4)		39.2 (3.0)	30.3 (3.1)	10.5 (1.4)	85.8 (3.8)
≥ 10 years in U.S.							14.1 (0.8)	3.1 (0.5)	9.5 (1.3)		53.4 (2.6)	55.8 (2.8)	68.1 (2.1)	11.9 (3.7)
< 10 years in U.S.							3.4 (0.4)	0.9 (0.3)	2.9 (0.5)		7.4 (1.4)	13.9 (2.0)	21.4 (2.1)	2.3 (1.2)
Has health insurance*, %							83.8 (0.9)	88.0 (1.1)	77.1 (1.6)		63.3 (2.4)	71.9 (2.1)	87.1 (1.4)	77.8 (3.2)
Married*, %							67.4 (1.2)	69.1 (1.5)	49.2 (1.7)		69.3 (2.0)	64.6 (1.9)	83.8 (1.3)	58.9 (5.0)
≥ 12 alcoholic drinks in past year*, %							77.9 (0.9)	81.0 (1.1)	71.0 (1.4)		78.6 (1.9)	75.4 (2.7)	57.1 (2.4)	66.9 (4.9)
Current smoker*, %							20.8 (0.7)	19.3 (0.9)	31.3 (1.4)		18.5 (1.8)	20.8 (2.1)	15.0 (1.5)	35.3 (4.5)
Physical activity level*, %
None							44.3 (1.2)	42.6 (1.5)	47.7 (1.8)		53.5 (2.0)	48.0 (3.0)	40.4 (2.1)	47.6 (4.2)
Moderate							27.7 (1.2)	29.9 (1.6)	21.3 (1.1)		19.6 (1.9)	21.8 (1.9)	26.7 (2.0)	27.8 (4.1)
Vigorous							28.1 (1.3)	27.5 (1.7)	31.0 (1.6)		26.9 (2.2)	30.2 (3.1)	32.9 (2.0)	24.6 (4.2)
History of CVD*, %							9.4 (0.5)	10.0 (0.7)	10.0 (0.8)		5.0 (0.8)	6.9 (1.1)	5.3 (0.8)	16.0 (4.4)

Missing Data: Alcohol 4.7%, History of CVD 0.2%, Insurance 0.1%

Abbreviations: NHANES National Health and Nutrition Examination Survey, CVD Cardiovascular Disease, HS High school

*Indicates significant differences by race/ethnicity (P <0.05)

Table 2.

Female baseline demographics, and stratified by race/ethnicity, NHANES 2011–2018

		Race/Ethnicity
	Overall (N = 6,213)	Non-Hispanic Whites (n = 2,290)		Non-Hispanic Blacks (n = 1,367)	Mexican (n = 825)	Other Hispanic (n = 691)	Non-Hispanic Asians (n = 841)	Other (n = 199)
Characteristic	% (SE)	% (SE)		% (SE)	% (SE)	% (SE)	% (SE)	% (SE)
Age* (years)	51.0 (0.2)	52.3 (0.3)		49.0 (0.4)	46.4 (0.5)	48.6 (0.5)	49.2 (0.6)	48.3 (1.2)
Age categories*, %
30–44	33.9 (0.9)	29.2 (1.2)		40.9 (1.7)	51.5 (2.3)	42.1 (2.1)	41.0 (2.2)	45.4 (5.5)
45–54	24.7 (0.9)	25.1 (1.2)		24.7 (1.2)	23.7 (1.7)	25.1 (1.6)	24.4 (1.8)	19.5 (3.9)
55–64	24.6 (0.7)	26.4 (1.0)		23.1 (1.5)	16.0 (1.3)	20.1 (1.6)	20.1 (1.3)	24.2 (3.9)
65–75	16.8 (0.6)	19.3 (0.8)		11.4 (0.7)	8.8 (1.0)	12.7 (1.0)	14.5 (1.7)	10.9 (2.6)
Education*, %
< HS education	11.7 (0.8)	7.0 (0.9)		13.4 (1.0)	41.9 (2.4)	25.9 (2.5)	15.2 (1.8)	7.9 (2.7)
HS graduate	19.9 (0.7)	19.3 (1.0)		23.5 (1.2)	22.2 (1.6)	20.3 (1.9)	13.5 (1.4)	24.4 (5.3)
Some college	33.9 (1.0)	35.4 (1.4)		37.8 (1.4)	23.9 (1.8)	30.9 (2.5)	19.0 (1.5)	42.6 (5.0)
College grad	34.6 (1.6)	38.3 (2.0)		25.3 (1.5)	12.1 (1.4)	22.9 (2.2)	52.3 (2.2)	25.0 (4.7)
Household Income*, %
< $25K	18.3 (0.9)	14.1 (1.2)		31.4 (1.6)	30.5 (2.5)	32.1 (2.0)	13.3 (1.7)	19.9 (3.0)
$25K-<$55K	26.6 (0.9)	24.5 (1.3)		33.9 (1.5)	32.7 (1.7)	29.2 (2.2)	22.8 (1.6)	36.1 (4.6)
$55K-<$75K	13.3 (0.7)	13.5 (0.8)		11.5 (1.1)	14.5 (1.3)	11.9 (1.7)	11.7 (1.6)	16.5 (4.2)
≥ $75K	41.8 (1.5)	47.8 (1.9)		23.2 (1.7)	22.4 (2.3)	26.8 (2.6)	52.2 (3.0)	27.6 (4.9)
Nativity/years residing in the U.S.*, %
U.S. Born	82.6 (1.0)	95.9 (0.6)		88.9 (1.6)	43.8 (2.5)	30.7 (2.8)	7.7 (1.1)	83.8 (4.4)
≥ 10 years in U.S.	14.2 (0.8)	3.5 (0.5)		9.4 (1.5)	48.4 (2.3)	55.0 (2.6)	70.8 (1.7)	11.4 (3.7)
< 10 years in U.S.	3.3 (0.3)	0.6 (0.2)		1.6 (0.4)	7.8 (1.2)	14.3 (1.8)	21.5 (1.7)	4.8 (3.0)
Has health insurance*, %	87.5 (0.6)	91.2 (0.8)		82.9 (1.2)	63.7 (1.8)	77.7 (2.2)	88.9 (1.0)	90.3 (1.9)
Married*, %	59.8 (0.9)	63.4 (1.0)		33.9 (1.2)	59.1 (2.1)	53.2 (2.4)	77.4 (1.7)	51.1 (5.4)
≥ 12 alcoholic drinks in past year*, %	61.2 (1.3)	68.3 (1.6)		50.2 (1.7)	47.7 (2.3)	48.4 (2.9)	26.0 (1.5)	60.6 (4.9)
Current smoker*, %	16.9 (0.8)	18.1 (1.1)		19.4 (1.3)	11.4 (1.6)	9.4 (1.4)	2.3 (0.5)	34.4 (4.9)
Physical activity level*, %
None	45.5 (1.2)	41.8 (1.5)		52.4 (2.0)	57.9 (2.0)	54.1 (2.4)	47.5 (2.0)	53.3 (4.9)
Moderate	32.4 (1.0)	34.5 (1.4)		28.4 (1.5)	24.2 (1.6)	26.7 (1.5)	34.5 (1.9)	27.2 (4.7)
Vigorous	22.1 (1.1)	23.7 (1.4)		19.2 (1.4)	17.8 (1.7)	19.3 (2.0)	18.0 (1.4)	19.5 (3.1)
History of CVD*, %	6.8 (0.4)	6.8 (0.6)		9.2 (0.9)	4.2 (0.8)	5.8 (1.0)	3.3 (0.7)	12.5 (3.2)

Missing Data: Alcohol 7.8%, History of CVD 0.2%, Smoker 0.1%

Abbreviations: NHANES National Health and Nutrition Examination Survey, CVD Cardiovascular Disease, HS High school

*Indicates significant differences by race/ethnicity (P <0.05)

Biological age and aging difference by sex, race/ethnicity, and social determinants of health

Among males: mean biological age was 49.1 or 1.3 years (95% CI: −1.7, −0.8) lower than chronological age (Fig. 2). By race/ethnicity, aging difference was highest in non-Hispanic Black (3.8 years, 95% CI: 3.1, 4.5) and lowest in non-Hispanic White (−2.1 years, 95% CI: −2.6, −1.6) and non-Hispanic Asian males (−3.4 years, 95% CI: −4.0, −2.8). Among females: mean biological age was 50.4 or 0.6 years (95% CI: −0.8, −0.4) lower than chronological age. By race/ethnicity, aging difference was highest in non-Hispanic Black (1.9 years, 95% CI: 1.5, 2.2) and lowest in non-Hispanic White (−1.1 years, 95% CI: −1.4, −0.8) and non-Hispanic Asian females (−2.2 years, 95% CI: −2.6, −1.7). Figure 3 shows non-Hispanic Black males and females had accelerated aging, while non-Hispanic White and non-Hispanic Asian males and females had decelerated aging across all levels of education and income. Figure 4 shows that among immigrants, Mexican males and non-Hispanic Asian males and females had decelerated aging, while among those U.S. born, non-Hispanic Black and Mexican males and females had accelerated aging.

Fig. 2.

Aging difference among males and females and stratified by race/ethnicity, NHANES 2011–2018

Fig. 3.

Aging difference by sex, by education and income, and stratified by race/ethnicity, NHANES 2011–2018

Fig. 4.

Aging difference by sex, by nativity/years residing in the U.S., and stratified by race/ethnicity, NHANES 2011–2018

Associations between social determinants of health and biological age

Among males, from fully adjusted models (Table 3, model 3), an income <$ 25 K compared to ≥$ 75 K was associated with greater biological age by 4.94 years (95% CI: 3.40, 6.47). Compared to U.S. born males, biological age was 2.46 years lower (95% CI: −3.55, −1.38) among males who had lived ≥ 10 years in the U.S., and 2.79 years lower (95% CI: −4.15, −1.43) among males who had lived < 10 years in the U.S. (model 4). Associations between educational attainment and biological age were not significant (P > 0.05) among males. Among females, compared to college graduates, biological age was greater by 3.14 years among females who were high school graduates (95% CI: 2.03, 4.26) and greater by 3.17 years (95% CI: 1.69, 4.65) among females with less than a high school education. Compared to females with an income ≥$ 75 K, biological age was greater by 2.74 years among females with an income <$ 25 K (95% CI: 1.48, 4.00). Compared to U.S. born females, biological age was 1.28 years lower (95% CI: −1.85, −0.71) among females who had lived ≥ 10 years in the U.S., and 1.50 years lower (95% CI: −2.53, −0.46) among females who had lived < 10 years in the U.S.

Table 3.

Multivariable adjusted associations between social determinants of health and biological age among males and females

	Males (n = 5,938)				Females (n = 6,213)
	Model 1	Model 2	Model 3	Model 4	Model 1	Model 2	Model 3	Model 4
	ß (95% CI)	ß (95% CI)	ß (95% CI)	ß (95% CI)	ß (95% CI)	ß (95% CI)	ß (95% CI)	ß (95% CI)
Education
College graduate	-	-	-	-	-	-	-	-
Some college	1.34* (0.07, 2.60)	0.61 (−0.71, 1.93)	−0.74 (−2.02, 0.54)	0.77 (−0.09, 1.62)	2.62* (1.26, 3.98)	1.89* (0.57, 3.21)	0.90 (−0.30, 2.10)	0.52* (0.08, 0.95)
HS graduate	1.37 (−0.15, 2.89)	0.62 (−0.87, 2.12)	−1.22 (−2.80, 0.35)	0.44 (−0.80, 1.68)	5.45* (4.29, 6.62)	4.60* (3.43, 5.77)	3.14* (2.03, 4.26)	0.79* (0.24, 1.35)
Less than HS	2.39* (0.81, 3.97)	1.61 (−0.20, 3.41)	−0.80 (−2.52, 0.91)	0.18 (−1.04, 1.39)	5.27* (3.79, 6.75)	4.62* (3.18, 6.06)	3.17* (1.69, 4.65)	0.80* (0.09, 1.52)
Income
≥ $75K	-	-	-	-	-	-	-	-
$55K-<$75K	2.51* (0.81, 4.22)	2.91* (1.13, 4.69)	1.76* (0.07, 3.45)	1.03 (−0.19, 2.26)	2.70* (1.21, 4.18)	2.20* (0.66, 3.74)	1.61* (0.15, 3.06)	0.43 (−0.21, 1.07)
$25K-<$55K	2.72* (1.40, 4.04)	4.65* (3.13, 6.16)	2.72* (1.22, 4.22)	0.80 (−0.03, 1.63)	3.60* (2.34, 4.86)	3.08* (1.78, 4.37)	2.05* (0.96, 3.14)	0.46 (−0.06, 0.98)
< $25K	4.13* (2.74, 5.51)	7.19* (5.56, 8.82)	4.94* (3.40, 6.47)	1.09 (−0.12, 2.30)	4.58* (3.17, 6.00)	3.99* (2.65, 5.33)	2.74* (1.48, 4.00)	0.67* (0.06, 1.27)
Nativity/years residing in the U.S.
U.S. Born	-	-	-	-	-	-	-	-
≥ 10 years in the U.S.	−2.90* (−4.48, −1.33)	−2.75* (−4.14, −1.37)	−2.31* (−3.60, −1.03)	−2.46* (−3.55, −1.38)	−0.15 (−1.61, 1.31)	−0.08 (−1.52, 1.36)	−0.63 (−1.97, 0.72)	−1.28* (−1.85, −0.71)
< 10 years in the U.S.	−9.68* (−11.45, −7.92)	−9.59* (−11.39, −7.80)	−8.10* (−9.73, −6.48)	−2.79* (−4.15, −1.43)	−6.58* (−8.99, −4.16)	−5.88* (−8.20, −3.55)	−6.31* (−8.38, −4.24)	−1.50* (−2.53, −0.46)

Model 1: adjusted for race/ethnicity

Model 2: adjusted for Model 1 variables and health insurance, marital status, education (in income and nativity models), income (in education and nativity models), and nativity (in education and income models)

Model 3: adjusted for Model 1 + Model 2 variables, and alcohol use, smoking history, physical activity, and history of CVD

Model 4: adjusted for Model 1 + Model 2 + Model 3 variables, and chronological age

*Significant at P < 0.05

Associations between social determinants of health and biological age according to race/ethnicity

Associations between all social determinants of health (education, income, nativity/years residing in the U.S.) and biological age significantly differed by race/ethnicity (P for interaction < 0.10) (Fig. 5). Among males, compared to college graduates, less than a high school education was associated with greater biological age by 6.42 years among non-Hispanic Asian males only (95% CI: 3.25, 9.25). Compared to an income ≥$ 75 K, an income <$ 25 K was associated with greater biological age by 5.58 years among non-Hispanic White (95% CI: 3.33, 7.83), 6.43 years among Mexican (95% CI: 3.46, 9.40), 3.68 years among other Hispanic (95% CI: 0.34, 7.03), and 5.40 years (95% CI: 0.99, 9.81) among non-Hispanic Asian males. Compared to being U.S. born, living in the U.S. <10 years was associated with lower biological age by 6.61 years among non-Hispanic Black (95% CI: −10.01, −3.17), by 3.87 years among Mexican (95% CI: −6.37, −1.37) and by 2.63 years (95% CI: −5.23, −0.03) among other Hispanic males. Among females, compared to college graduates, less than a high school education was associated with greater biological age by 4.65 years among non-Hispanic Black (95% CI: 2.01, 7.29), 5.78 years among other Hispanic (95% CI: 1.34, 6.61), and 7.92 years among non-Hispanic Asian females (95% CI: 3.87, 11.97). Compared to an income ≥$ 75 K, an income <$ 25 K was associated with greater biological age by 2.91 years among non-Hispanic White females (95% CI: 1.24, 4.59) and 4.96 years among non-Hispanic Asian females (95% CI: 0.59, 9.33). Compared to being U.S. born, living in the U.S. <10 years was associated with lower biological age by 2.48 years (95% CI: −4.82, −0.14) among non-Hispanic Black females.

Fig. 5.

Sex-specific Forest plots showing multivariable adjusted associations between social determinants of health and biological age, stratified by race/ethnicity, NHANES 2011-2018 (Fully adjusted models)

Sensitivity analyses

Results of our sensitivity analysis using sex-independent biological age were consistent with our main results showing that social determinants of health were associated with biological age among males and females and that race/ethnicity modifies the association (results not shown).

Discussion

In a nationally representative study of U.S. adults, we found stark differences in biological age and aging difference by social determinants of health and race/ethnicity. Aging difference was lowest (most favorable) among non-Hispanic Asian adults and highest (least favorable) among non-Hispanic Black adults. Likewise, aging difference was higher among non-Hispanic Black adults compared to all other race/ethnic groups, across all levels of education and income (Fig. 3). In multivariable models, lower income, less educational attainment and more time spent in the U.S. were associated with greater biological age, with differences by race/ethnicity. Common measures of social determinants of health (education, income, and nativity/years residing in the U.S.), as well with the intersection of race/ethnic background, play an important role in aging.

Black-White differences in biological aging have been demonstrated by many prior studies [11, 13, 26, 27]. Results from the Coronary Artery Risk Development in Young Adults study, showed that by age 45, Black adults were on average 10 years older than their chronological age, while White adults were roughly 1.5 years younger [26]. Likewise, findings from the Health and Retirement Study showed that non-Hispanic Black males were about 2 years and non-Hispanic Black females were 1 year older than their chronological age, while non-Hispanic White males and females were roughly 1 year younger than their chronological age [27]. Using the Klemera-Doubal method, we found that on average, non-Hispanic Black males and females were 3.8 and 1.9 years older, respectively, while non-Hispanic White males and females were 2.1 years and 1.1 years younger, respectively, than their chronological age (Fig. 2). Among Hispanic adults, research on differences between biological age and chronological age have been mixed. Though, some studies point to accelerated aging among Hispanic adults [14, 27], we found no significant difference between biological age and chronological age among other Hispanic persons and faster aging among only Mexican females. The “Hispanic Paradox” posits that despite an unfavorable socio-economic and biological risk profile, Hispanic adults have equivalent or better health outcomes and a longer life expectancy compared to non-Hispanic White adults [28]. Our results are consistent with this phenomenon, as we did not find any indication of accelerated aging among Mexican males and other Hispanic people, despite relative socio-economic disadvantage in this population. Less is known about biological age among Asian American populations. To date, most research on aging in Asian people has been conducted in mainland China. Research in Chinese adults suggests slower biological aging [29], with one study showing no difference between biological age and chronological age [30]. In our study, one of the few to include Asian Americans, we found non-Hispanic Asian males were 3.4 years younger and non-Hispanic Asian females were 2.2 years younger than their chronological age. However, we emphasize that these results should be interpreted with caution. Asian Americans are a heterogeneous population. At the national level, aggregated Asian Americans have favorable socio-economic profiles which may explain our findings [31]. This is in contrast to regional or localized data from smaller studies of Asian sub-groups showing adverse socio-economic conditions and health disparities [32].

Socio-economic disadvantage has been linked to accelerated aging [6, 7, 27]. Findings from the Healthy Aging in Neighborhoods of Diversity Across the Life Span study found that income below the poverty level was associated with accelerated aging among Black and White adults [33]. Similarly, we found the lowest compared to the highest income level was associated with a greater biological age by roughly six years in non-Hispanic White and Mexican males, four years among other Hispanic males, and five years among non-Hispanic Asian males, and by three and five years among non-Hispanic White and non-Hispanic Asian females, respectively (Fig. 5). Education, an important social determinant of health, has also been implicated in the aging process. Among participants of the Multi-Ethnic Study of Atherosclerosis, lower education was associated with accelerated aging [7]. Interestingly, in our study, lower educational attainment was associated with greater biological age among females only (Table 3). Associations between social determinants of health and health outcomes have previously been described among females in the literature [34]. A potential explanation for different associations by sex may be attributed to differences in reactivity to socio-economic stressors. For example, in the Midlife in the United States study, lower socio-economic status was associated with a larger increase in negative emotions among females but not males, and this mediated the association between socio-economic status and physical health [35]. In fact, research from the Health and Retirement study has shown that education is a more potent predictor of behavior changes in females [36]. Therefore, compared to males, females with lower levels of education may be more vulnerable to unhealthier lifestyle choices [37] and prone to stress and depression, which negatively impact their health [35] and can contribute to advanced biological aging.

The ‘Weathering Hypothesis’ attributes the earlier manifestation of disease experienced by ethnic minority populations, and in particular non-Hispanic Black adults, to the cumulative effect of a lifetime of exposure to social or economic adversity [11]. Persistent coping from stressors related to stigmatization and discrimination leads to unhealthy behaviors [38] and exacerbates physiological deterioration [11]. Consequently, despite socio-economic status improvement, non-Hispanic Black adults may not experience a net benefit in health. This “diminishing returns hypothesis” is evident in our study, as we found accelerated aging among non-Hispanic Black adults across all levels of education and income (Fig. 3). In contrast, all other race/ethnic groups showed signs of less aging with greater education and income attained. Despite the protective effect of high socio-economic status on healthy aging [39], our results are consistent with weathering among non-Hispanic Black males and females. A possible explanation for this association is that non-Hispanic Black adults of higher socio-economic status experience greater discrimination (which is not reflected through socio-economic status metrics), whether racial, gender, or lifetime, compared to other groups, including even those of lower socio-economic status [40].

Immigrant health advantages have long been reported in the U.S., with foreign-born adults on average with a life expectancy 3.4 years longer compared with the native-born population [41]. Consistent with prior literature [27], in our study, we found lower biological age among adults who were foreign-born compared to their U.S. born counterparts (Table 3). Among immigrants, we found a shorter duration of U.S. residence was associated with slower aging, especially among racial/ethnic minority populations (Table 3; Fig. 2). Acculturation, or the process by which one adopts the cultural patterns of the host country [42], has been linked with unhealthier lifestyle choices [43] and dysregulation of biological stress markers, a precursor to adverse health outcomes [44], which may explain our findings and others. For example, the attenuation of immigrant health advantages with longer duration of time spent in the U.S. has been documented [45]. In a study of diverse older adults, foreign-born Hispanics exhibited slower aging compared to U.S. born Hispanics [27]. Among non-Hispanic Asian adults, longer duration of U.S. residence is associated with greater consumption of ultra-processed food, a risk factor for adverse cardiometabolic health [46]. Our results have population-wide implications. As the Hispanic population has shifted from predominantly an immigrant makeup to a majority U.S. born [17] and the non-Hispanic Asian population acculturates [47], effort is needed to help these groups retain the immigrant health advantage.

The current study is not without limitations. First, the cross-sectional design of this study limits our ability to assess life-course improvements in socio-economic status. Further, NHANES aggregates racial/ethnic populations, thereby masking sub-group differences. For example, previous research among Hispanic [48] and non-Hispanic Asian [49] adults have found differential associations in health outcomes by background group. As previous research in aging in Hispanics has been in mainly Mexican populations, future research should assess the role of social determinants of health with biological age among disaggregated diverse adults, particularly of Hispanic or Asian descent. To prevent misinterpretation of the Hispanic results, we analyzed Mexican and other Hispanic adults independently. Additionally, an increasing concern with national surveys has been the declining response rate in contemporary survey cycles. Likewise, recent immigrants, who may be healthier compared to those U.S. born, may decline to participate because of language barriers [50]. Finally, not all potential biomarkers were available across all NHANES data cycles. It is possible that including biomarkers not readily available, such as C-reactive protein and forced expiratory volume, would have improved biological age estimation given the relationship between inflammation and lung function with aging [19, 23]. We also acknowledge that slightly more than 15% of participants had missing biomarker information. We compared characteristics from those included in the study with those participants excluded from the study due to missing biomarker information (Supplemental Table 1). Participants excluded from the study were more likely non-Hispanic Black, had less than a high school education, had less than a $25,000 income, and were smokers. Excluded participants were more likely to be socio-economically disadvantaged and have a higher prevalence of cardiovascular disease. Given the healthier sample in our study, our results are likely to be an attenuation of the effect of adverse levels of social determinants of health on biological age. As biological age indices were derived in a sex-specific manner and different biomarkers were retained among males and females, we were unable to test for statistical interactions by sex. However, in our sensitivity analyses, which utilized a sex-independent biological age, we did find statistically significant interactions by sex, suggesting that our observed differences by sex persist despite our use of sex-specific biological age algorithims. This study also has notable strengths. To the best of our knowledge, this study is the first to characterize biological age among a representative and contemporary sample of U.S. Mexican, Other Hispanic and non-Hispanic Asian adults. In addition, we examine the role of several social determinants of health (education, income, and nativity/years residing in the U.S.) in relation to biological age among racial/ethnic minority populations. Further, we leveraged a novel approach in biological age estimation and utilized the Klemera-Doubal Method in all our analyses.

Conclusions

In summary, in a representative sample of diverse U.S. adults we found evidence of accelerated aging among non-Hispanic Black adults compared to all other race/ethnic groups across all levels of education and income. We also found significant associations between social determinants of health (education, income, and nativity/years in the U.S.) with biological age. We found evidence of a protective effect of foreign-born nativity on biological age among females and males, with an attenuation of the health advantage with a longer duration of residency in the U.S. These findings demonstrate that adverse levels of social determinants of health are associated with premature biological aging and may have long-term implications for health outcomes. Further, given the accelerated aging among non-Hispanic Black adults even at higher levels of income or education, targeting upstream sources of social disadvantage among racial/ethnic minority groups, in conjunction with improvements to income and education, are likely required to help achieve health equity in aging.

Abbreviations

NHANES

National Health and Nutrition Examination Survey

CVD

Cardiovascular Disease

HS

High school

Authors’ contributions

R.A.M. was involved in conceptualization, data curation, formal analysis, methodology, and manuscript writing. T.E. was involved in conceptualization (supporting), methodology (supporting), supervision (lead), and manuscript writing (supporting). M.M.L. was involved in methodology (supporting) and supervision (supporting). D.J.L. and T.R. were involved in supervision (supporting). T.E., M.M.L., D.J.L., T.R., K.L.K., and A.Z.A.H reviewed and edited the manuscript. R.A.M. is the guarantor of this work, and, as such, had full access to the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Funding

This work was supported by the National Institutes of Health/National Heart, Lung, and Blood Institute (5T32HL007426-45 to R.A.M.).

This work was supported by the National Institutes of Aging (R01AG089174 to A.Z.A.H. and K99AG084769 to K.L.K).

Data availability

NHANES data are de-identified and publicly available through the National Center for Health Statistics and can be accessed via: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.

Declarations

Ethics approval and consent to participate

“Not applicable”.

Consent for publication

“Not applicable”.

Competing interests

The authors declare no competing interests.