Biological Age as a Predictor of Arterial Stiffness in Young and Early Midlife Black and White Women

Abstract

BACKGROUND

Chronological age (CA) is independently associated with arterial stiffness, but it is not a sufficient measure of aging or the disparities related to disease risk. While biological age (BA) is considered a more accurate indicator of disease risk, the relationships among BA, CA, and arterial stiffness remain inconclusive.

METHODS

In 222 women (n = 143 White, n = 79 Black) enrolled in the Predictive Health Institute cohort (age range 25–49), arterial stiffness was assessed using carotid-femoral pulse wave velocity (cfPWV), measured by applanation tonometry (SphygmoCor). BA was estimated using the Klemera-Doubal method from 11 different clinical biomarkers and CA. Accelerated age (AccA) was calculated as the difference between BA and CA. Overall and race-specific associations between BA and arterial stiffness adjusting for sociodemographics, health behaviors, and clinical factors were estimated using multiple linear regression.

RESULTS

Mean (SD) CA was 41.4 (6.3) years in Black women and 39.8 (6.0) years in White women, respectively. Mean (SD) BA, AccA, and cfPWV were 43 (13.1) and 1.6 (12.9) years, and 7.3 (1.1) m/s in Black women and 36 (10.8) and −3.2 (10.6) years, and 6.3 (0.8) m/s in White women (Black–White difference in BA: 6.4 years, P = <0.001). Higher BA was associated with a 0.015 m/s per year (95% CI: 0.002, 0.029, P = 0.028) higher cfPWV after adjustment for demographic and CVD risk factors, without evidence of an interaction by race (P = 0.65).

CONCLUSIONS

BA was associated with arterial stiffness in the fully adjusted model. Targeting modifiable risk factors may promote healthy vascular aging and reduce subclinical CVD progression.

Graphical Abstract

Graphical Abstract.

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Arterial stiffness, a predictive marker of future cardiovascular events, all-cause, and cardiovascular disease (CVD) mortality,¹ is an important precursor of hypertension and hallmark of vascular aging.^2,3 Vascular aging promotes remodeling of the small arteries that is associated with increased resistance, blood pressure, and ultimately central artery stiffness.³ Arterial stiffness tends to increase with chronological age (CA)^2,4; however, emerging evidence indicates that relying solely on CA may not be optimal for assessing vascular disease risk or preventing CVD.⁵ CA represents the linear passage of time and is recognized as an insufficient indicator of aging or the disparities associated with disease risk.⁶

Biological age (BA), defined as a quantity expressing the true global state of an aging organism, has been used to quantify aging-related deficits in system integrity and estimate an individual’s level of damage accrual.^7,8 Phenotypic measures such as clinical composite biomarkers are regarded as equally or even more predictive of disease, disability, and mortality than molecular measures like DNA methylation, epigenetic clocks, and telomeres.⁹ Clinical composite biomarkers used to estimate BA are considered more effective at capturing systemic physiological dysregulation, as they account for a variety of factors such as endogenous and exogenous stressors, age-related molecular changes, morbidity, mortality, and overall health span.^9,10

Racial disparities have also been found for both arterial stiffness and BA. Prior research has shown that arterial stiffness is generally higher in Black than White women.^11,12 Emerging evidence indicates that Black populations also tend to have higher BAs and age at a faster rate relative to their CA, while the opposite trend has been observed in White populations.^13–15 These consequential findings may also coincide with vascular changes at the subclinical level, which has important clinical implications for both progression to clinical CVD risk and for its prevention.¹⁶ Thus, we sought to determine whether BA is associated with arterial stiffness in a cohort of young and early midlife adult Black and White women free of CVD. We also explored whether this association varied by race. We hypothesized that BA would be positively associated with arterial stiffness.

METHODS

Data source and study design

The datasets generated during and/or analyzed during the current study is available upon submission of an online application to access de-identified data from the senior author. The Center for Health Discovery and Well Being (CHDWB) study, conducted between March 2005 to October 2009, comprised of 462 female participants aged 18 to 77 years residing in a large southeastern metropolitan area. A detailed description regarding study methods and recruitment is provided elsewhere.¹⁷ Briefly, the CHDWB cohort was established as an initiative aimed towards integrating predictive health principles using a combination of established and cutting-edge tools to identify and measure risks and deviations in healthy employees of two large university systems who were employed for at least two years and covered by the university-sponsored health insurance plans. Participants with an acute illness, hospitalization within the past year, pregnant women, and individuals with poorly controlled medical conditions were excluded.¹⁸ Participants enrolled in CHDWB studies signed an informed consent that was approved by the Institutional review boards of both Emory University and Georgia Institute of Technology. All aspects of the study were approved by the Institutional Review Boards of both universities. The sample for the current cross-sectional study consisted of 230 women aged 25–49 years at baseline. Of the 230 participants, eight women were excluded due to missing data on carotid-femoral pulse wave velocity (cfPWV) (n = 5) and biomarker assays (n = 3) leaving a final analytic sample of 222 participants.

Arterial stiffness

Arterial stiffness was the outcome variable for this study and was measured as cfPWV, which is recognized as the gold standard method for the assessment of aortic stiffness.¹⁹ CfPWV was measured noninvasively after an overnight fast using applanation tonometry (Sphygmocor device, Atcor Medical, Sydney, Australia) using pressure waveforms at the carotid and femoral arterial sites and calculated using standardized methods.²⁰ CfPWV (meters/second) was calculated by measuring the time interval between the R-waves at each site divided by the distance. A higher PWV indicates greater arterial stiffness. Reproducibility studies in our laboratory on consecutive days demonstrated a coefficient of variation of 3.8% for cfPWV.

Biological age

BA, the primary independent variable, was calculated using the Klemera-Doubal method (KDM), defined as the age at which their biological profile would be considered normal within the reference population.^7,8 KDM BA that is greater than CA, reflects an advanced state of biological aging, which is associated with an increased risk for disease, disability, and mortality. Conversely, a KDM BA that is less than CA is associated with a decreased risk and slower pace of aging.⁸ We calculated KDM BA based on the reformulated KDM version 2,⁸ using 11 multisystem biomarkers that were available in our current dataset, including blood urea nitrogen, creatinine, total cholesterol, alkaline phosphatase, mean corpuscular volume, red blood cell distribution width, albumin, lymphocyte %, white blood cell count, C-reactive protein (CRP), systolic blood pressure, and CA. These biomarkers are correlated with aging and reflect the system integrity of various body systems including cardiovascular, immune, renal, hepatic, and metabolic function.⁹ We also calculated accelerated age (AccA), defined as the difference between BA and CA for descriptive purposes. AccA reflects the pace of aging for an individual or group such that a positive value indicates that a person is biologically older than their CA and a negative value indicates a person is biologically younger than their CA for descriptive purposes.^13,21

The KDM BA algorithm is derived from a series of regressions of individual biomarkers on CA in a reference population.^7,8 The BA estimates are based upon minimizing the distance between m regression lines and m biomarker points within an m-dimensional space of all biomarkers.²² BA is computed using the KDM equation,^7,8 which takes information from n number of individual regression lines of CA regressed onto n biomarkers. Following previous work,^8,14,21 we formed our reference population from nonpregnant Black and White females aged 25–49 (n = 410 Black, n = 436 White) from National Health and Nutrition Examination Survey III for which data collection ran between 1988 to 1994 using the 11 biomarkers listed above.

Covariates

Covariates included sociodemographic, health behaviors, and clinical factors. Age, marital status (married/divorced/separated/never married/unmarried couple), self-reported race (Black/African American or White), education (≤high school, some college, or college, and above), and household income (<$25K, $25K-<$50K, $50K-<$75K, & ≥$75K), were self-reported. Physical activity (none, 150 or 75 min/week of moderate-or vigorous-intensity physical activity or an equivalent combination of the two), alcohol consumption in the past 30 days (never, 1–2 times/week, 3–4 times/week, 5–6 times/week, daily, once/month, 2–3 times/month), smoking status (current/quit ≤12 months, never or quit >12 months), history of diabetes or taking medication, and hypertension or taking medication were self-reported. Anthropometric parameters of height and weight, body mass index (BMI) kg/m²²³ calculated using the CDC guidelines, blood pressure, and heart rate were collected by clinical staff. Psychological well-being, defined as depressive symptoms, was assessed using the Beck Depression Inventory Index-II (BDI-II).²⁴

Data analysis

Baseline characteristics were described using mean (SD) for continuous variables and frequency (proportion) for categorical variables. Pearson’s correlation coefficients for CA, BA, and cfPWV were calculated to determine their associations. Differences in sociodemographic, clinical, and health behavior characteristics by race were evaluated using Student’s t-test for normally distributed continuous variables, Mann–Whitney U test for nonnormal distributed continuous variables, and chi-square test for categorical variables. To determine whether BA is associated with arterial stiffness, we tested the association between BA and cfPWV unadjusted and adjusted for race, age, sociodemographics, health behaviors, and clinical factors. We obtained the unadjusted estimates for the association of BA with cfPWV (model 1) and used multivariable linear regression models²⁵ controlling for (model 2): race, (model 3): model 2 + CA, (model 4): model 3 + other sociodemographic variables (educational attainment, income, and marital status); (model 5): model 4 + health behaviors (alcohol consumption, smoking status, physical activity); and (model 6): model 5 + clinical factors (BMI, heart rate, a comorbid variable was created and coded as 0 [no history] or 1 [history of diabetes and hypertension], and depressive symptoms). Variance inflation factors (VIFs) were conducted to check for collinearity among variables, and the VIFs for all models were less than 5 indicating no presence of collinearity. Due to the inclusion of systolic blood pressure as part of the BA measure, we did not include it as a control variable for model adjustment. To explore whether race modified the associations between BA and cfPWV, we tested interactions between race and BA, following the same sequential adjustment as done for the primary analyses. Statistical significance was set to p < 0.05. Calculation of BA was performed using the BioAge R package,⁸ which allows users to parametrize measurement algorithms using custom sets of biomarkers, compare results of aging measurements to published versions of the KDM method, and to score the measurements in new datasets. All analyses were performed using STATA version 18 and R version 4.3.3.²⁶

RESULTS

Sample characteristics

The mean (SD) CA was similar for both groups of women (Table 1). Black women were biologically older (43.0 years), exhibited an accelerated pace of aging (1.6 years vs. −3.2 years), and had higher (stiffer) mean cfPWV measures (7.3 m/s vs. 6.3 m/s) than White women, which were biologically younger (36.6 years) and exhibited a delayed pace of aging. Biomarkers used to estimate BA were within normal range for both groups, but Black women had higher levels of inflammation (CRP 0.26 vs. 0.11 mg/dl) and lymphocyte counts (83.7% vs. 70.5%) than White women. White women were more educated, had higher income, and were more likely to be married. Black women, compared to white women, consumed less alcohol, had higher proportion of smoking, were less physically active, had higher BMI, HR, SBP, and were more likely to have been diagnosed with either diabetes or hypertension.

Table 1.

Descriptive statistics of participant characteristics stratified by race, (n = 222)

	Black Women (n = 79)	White Women (n = 143)	P-value*
Chronological Age, y, mean (SD)	41.4 (6.3)	39.8 (6.0)	0.057
KDM Biological Age, y, mean (SD)	43.0 (13.1)	36.6 (10.8)	<0.001
Accelerated age, y, mean (SD)	1.6 (12.9)	−3.2 (10.6)	0.003
Arterial Stiffness
cfPWV, m/s	7.3 (1.1)	6.3 (0.8)	<0.001
BA Biomarkers
BUN, mg/dl, mean (SD)	11.1 (2.8)	12.1 (3.0)	0.020
Creatinine, mg/dl, mean (SD)	0.78 (0.11)	0.75 (0.11)	0.089
Total cholesterol, mg/dl	192 (38.0)	188 (29.3)	0.326
ALP, units/l, median (IQR)	66 [56, 79.5]	59.5 [47.5, 70]	0.002
MCV, fl, mean (SD)	86.6 (7.0)	91.4 (5.0)	<0.001
RDW, %fl, median (IQR)	14.5 [13.8, 15.6]	13.4 [13, 13.9]	<0.001
Albumin, g/dl, mean (SD)	4.4 (0.3)	4.5 (0.3)	<0.001
Lymphocyte, %, mean (SD)	83.7 (44.0)	70.5 (37.2)	0.019
WBC, 103/ml, mean (SD)	5.8 (1.9)	6.1 (1.6)	0.345
CRP, mg/dl, median (IQR)	0.26 [0.12, 0.74]	0.11 [0.1, 0.34]	<0.001
Demographics
Education, n [%]  HS or Less  Some College  College  Graduate  Postgraduate	5 [6.3] 27 [34] 23 [29] 20 [25] 5 [6.3]	0 13 [9] 42 [28] 65 [43] 30 [20]	<0.001
Income, n [%]  <$50K  $50K–<$75K  $75K–<$100K  $100–200K  ≥$200K	26 [34] 20 [26] 14 [18] 14 [18] 2 [3]	10 [7] 20 [14] 30 [21] 57 [39] 28 [19]	<0.001
Marital Status, n [%]  Married  Divorced  Single	33 [41] 20 [25] 27 [34]	94 [63] 13 [9] 43 [29]	0.001
Health Behaviors			<0.001
Alcohol use, n [%]
None	21 [30]	7 [5]
1–2/week  3–4/week  5 or more/week	31 [44] 12 [17] 6 [9]	69 [50] 31 [22] 31 [22]
Smoking, n [%]			0.003
Yes	9 [11]	3 [2]
Physical activity, n [%]
Moderate	18 [24]	30 [21]	0.251
Vigorous	2 [3]	12 [8]
None	56 [74]	103 [71]
Clinical Variables
BMI, mean (SD)	33.2 (9.0)	25.5 (5.3)	<0.001
Heartrate, bpm, mean (SD)	74 (8.6)	69 (9.7)	0.001
SBP, mean (SD)	123 (15.1)	112 (13.1)	<0.001
History of Diabetes, n [%]	14 [18]	7 [5]	0.001
History of HTN, n [%]	34 [43]	34 [23]	0.002
BDI, mean (SD)	6.1 (6.2)	6.8 (6.3)	0.431

Abbreviations: ALP, alkaline phosphatase; BMI, body mass index; BUN, blood urea nitrogen; CfPWV, carotid-femoral pulse wave velocity; CRP, C-reactive protein; HS, high school; HTN, hypertension; Income, (K denotes thousand US dollars); IQR, interquartile range; KDM, Klemera–Doubal; MCV, mean corpuscular volume; RDW, red cell distribution width; SBP, systolic blood pressure; WBC, white blood cell count.

^*Statistical tests: Categorical variables: chi-square; continuous variables: Student’s t-test or Wilcoxon–Mann–Whitney test when appropriate.

Relationship between cfPWV, and BA, and CA.

CfPWV was more strongly correlated with BA (r = 0.36, P = <0.001) than with CA (r = 0.25, P = <0.001), (Figures 1 and 2). In an unadjusted model, BA was associated with a b = 0.031 m/s per year (95% CI: 0.021, 0.042, P = <0.001) higher cfPWV (Table 2). Upon adjusting for race (model 2), the association between BA and cfPWV was attenuated, b = 0.023 m/s per year (95% CI: 0.013, 0.033, P = <0.001). The association between BA and cfPWV remained unchanged, b = 0.020 m/s per year (95% CI: 0.009, 0.030, P = <0.001) after adjusting for CA, model 3, and after further adjustment for sociodemographics, health behaviors, clinical, and mental health factors [model 6: b = 0.015 m/s per year (95% CI: 0.002, 0.029, P = 0.03)] (Table 2), Models 3–6. While race was independently associated with cfPWV, no significant interaction was observed between racial groups in the relationship between cfPWV and BA (P = 0.65).

Figure 1.

Correlations between carotid-femoral pulse wave velocity and chronological age (n = 222). Note: **P < 0.001. Abbreviations: cfPWV: carotid-femoral pulse wave velocity.

Figure 2.

Correlations between carotid-femoral pulse wave velocity and biological age (n = 222).

Table 2.

Association of BA, race, and CA with pulse wave velocity overall

	Model 1		Model 2		Model 3		Model 4		Model 5		Model 6
	β	95% CI	β	95% CI	β	95% CI	β	95% CI	β	95% CI	β	95% CI
Klemera–Doubal Biological Age	0.031	0.021, 0.042**	0.023	0.013, 0.033**	0.020	0.009, 0.030**	0.021	0.010, 0.032**	0.018	0.006, 0.030**	0.015	0.002, 0.029*
Black vs. White Race			0.797	0.541, 1.054**	0.785	0.530, 1.039**	0.632	0.320, 0.944**	0.62	0.284, 0.965**	0.518	0.142, 0.894*
Chronological Age					0.022	0.002, 0.043*	0.030	0.007, 0.053*	0.031	0.007, 0.058*	0.036	0.011, 0.061*

^* P < 0.05,

^** P < 0.0001. Model 1: unadjusted; Model 2: Klemera-Doubal Biological Age + race; Model 3: model 2 + CA; Model 4: model 3 + sociodemographics (education-reference ≤high school; income-reference ≤$25,000, marital status-reference married); Model 5: model 4 + health behaviors (alcohol consumption, smoking status, physical activity); Model 6: model 5 + clinical and mental health factors (BMI, heart rate, hypertension & diabetes history, and depressive symptoms).

DISCUSSION

In this pooled cohort of 222 Black and White women from young adulthood through middle age, we calculated BA to determine its association with arterial stiffness, measured as cfPWV and investigated whether race modified these relationships. Our findings show that higher BA was associated with greater arterial stiffness among the overall cohort, even after unadjustment for race, CA, health behaviors, clinical and mental health factors. BA was also higher in Black than White women. Despite Black women having higher levels of arterial stiffness than White women, we did not detect any racial differences in the association between BA and arterial stiffness. Our novel findings that higher arterial stiffness in Black women is due at least partly to their higher BA fills a research gap related to the synergistic relationship between BA and arterial stiffness, two diverse indicators of aging and markers of cardiovascular events in young adults and CVD mortality.^27,28

Our findings that BA was associated with arterial stiffness after accounting for CA, both in the unadjusted and adjusted models for the overall cohort aligns with previous investigations that have identified associations between BA, psychosocial factors, and all-cause and CVD mortality.^13,15 Despite the relatively small increase in BA and arterial stiffness, and after adjusting for other key confounders of race, age, health behaviors, and CVD risk factors, this association persisted. Prior evidence linking advancing CA to aortic stiffness progression is well documented,²⁹ however, we cannot conclude that higher BA directly contributes to progressive aortic stiffness and highlights the need to have longitudinal assessments. The aging process is complex and estimation methods utilizing a composite of clinical biomarkers such as the KDM can provide a broader overview of the aging process that goes beyond a single body system.⁵ BA is considered to reflect the true age of the body’s systems beyond the shared characteristics of CA within a given population as seen in our cohort. The combined analysis of vascular functional biomarkers, such as pulse wave velocity and BA highlights the interconnectedness of the aging process and suggests that if one system is affected, others are likely impacted as well. Moreover, the data is especially useful for the assessment and targeting for CVD prevention efforts as well as identification of critical upstream factors that may influence these disparate aging patterns, thereby expediting the onset of hypertension and CVD events.

Our hypothesis that race would modify the association between BA and cfPWV was not supported despite evidence showing that mean cfPWV measures were higher among Black women compared to White women. Based on a priori power calculation of 14 total model parameters and 80% power, the study was sufficiently powered to detect two main effects and one interaction effect at a medium effect size for Cohen’s F of 0.15 if it were present. Although BA and race were both independently associated with cfPWV, it is plausible that we were unable to detect an interaction due to the true effect size was substantially smaller than what we were powered to detect. It is also plausible there was not a true interaction between race and BA suggesting there may be other clinically relevant risk factors that influence cfPWV. In this regard,larger studies with diverse populations are needed to fully investigate these relationships.³⁰ Previous investigations have found racial differences in BA such that Black populations were biologically older compared to White populations,¹³ while other studies have found social hallmarks of aging,³¹ including depressive symptoms and lower social class, as determinants of accelerated aging in Black populations.¹⁴ Likewise, studies have also consistently shown that Black women have significantly stiffer arteries than White women even after accounting for CA and risk factors.^11,12,32 Nonetheless, our findings are concerning, particularly for Black women, since these women were free of CVD at enrollment, suggesting these women have begun exhibiting evidence of vascular aging despite their younger CA.

These findings may partially underlie why Black women have some of the highest CVD-associated risk factors, poor CVD outcomes, and shorter life expectancy compared to White women.³³ According to the Weathering Hypothesis, the stress of living in a racially unjust society is a significant contributor of the physiological wear and tear of body systems, and premature morbidity and mortality that is experienced by racial ethnic populations.³⁴ The older BA observed among Black women in the current study may be evidence of this weathering phenomenon; however, we were unable to test this hypothesis due to the lack of variables associated with social hallmarks of aging in the current dataset. Greater emphasis directed at providers focused on earlier CVD risk, identification, and detection efforts may collectively mitigate the incidence of premature CVD morbidity and CVD events,³⁵ thus promoting healthier aging while reducing the economic burden associated with the management of CVD and other comorbid diseases.

Clinical implications

The consequences of accelerated aging are postulated to result in the earlier onset of chronic disease and mortality that is commonly associated with older age.³⁶ Accelerated aging, reflected in advanced BA and vascular dysfunction, maybe a significant contributor to the growing burden of hypertension among Black women, which has now surpassed that of Black men (58.4% vs. 57.5%).³⁷ Importantly, arterial stiffness has been linked to the development of left ventricular diastolic dysfunction and impaired ventricular-arterial coupling in women, but not in men, and is thought to contribute to the progression of heart failure.³⁸ Implementing effective interventions to improve cardiovascular health, along with developing sustainable behavioral and lifestyle strategies, may play a crucial role in reducing arterial stiffness, CVD progression and slowing the pace of aging.^5,29

Strengths

The current study has several strengths. We were able to investigate the relationship between BA utilizing clinical biomarkers to quantify its influence on arterial stiffness in a cohort of women under 50 years of age and free of CVD. Clinical markers used to estimate BA as applied in the current study, are considered more precise than epigenetic measures as they more effectively capture physiological dysregulation caused by exogenous and endogenous stress factors, age-related molecular alterations, morbidity and mortality risk, and overall health span.^8,9 Clinical biomarkers are affordable and suitable for detecting signs of risk decades before disease onset as well as evaluating the efficacy of interventions.⁹ This is important given that Black women have remarkably higher rates and are more likely to acquire hypertension at an earlier age than aged-matched White women.³⁹ Our cohort included Black women, as prior studies that have focused on aging and health have been limited by a lack of racial/ethnic diversity,⁴⁰ thus leaving a significant gap in our knowledge regarding its influence on racial health disparities.

Limitations

Our investigation was cross-sectional and cannot infer causality or identify patterns of aging over time. Assessment of BA and with health outcomes yields more benefit when BA is assessed at baseline and AccA longitudinally to identify variations in aging and health patterns with respect to health outcomes.²¹ Although our study was powered to detect race differences, our sample size of Black women was small and therefore larger and diverse sample sizes are needed to begin disentangling these differences. Unlike cfPWV, there is no gold standard method for estimating BA and therefore identifying the best method for risk prediction may not be suitable for use across all populations. Finally, our single-center findings may not be representative of the general population.

In this cohort of early and middle-aged women, BA was associated with arterial stiffness after accounting for clinical CVD risk factors. The clinical utility for the measurement of BA rather than CA provides a better index of vascular health in younger women. While both measures reflect the cumulative impact of life exposures on the body,^3,13 our findings emphasize the importance of targeting and addressing modifiable clinical risk factors that are often shaped by challenging social and contextual factors that may contribute to and accelerate premature aging, particularly among Black women.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by funding in whole or in part by the National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), under Contract nos. [1 U01 HL079156-01 (Quyyumi)] and [1 U01 HL79214-01 (Gibbons)]; NIH, National Center for Research Resources (NCRR) [Grant M01-RR00039] for the Emory Clinical Interaction Unit (ACTSI); and NIH/NCRR [5P20RR11104] for the Morehouse CRC; NIH [K24HL077506-06 (Vaccarino)]; NIH/NCRR [5U54RR022814 (Din)]; and the Woodruff Fund (Emory Predictive Health Initiative). Research reported in this publication was also supported by the National Institute of Nursing Research, NHLBI, and National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award Numbers [K23NR020631 (Spikes)]; [K24HL148521]; [T32HL130025]; and NIMHD [U54MD000214 (Thorpe)]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. A. A.Q. has been supported by NIH grants P01HL154996-01A1, R33HL138657-05, 5T32 HL130025, P30DK111024-07S2, R01HL166004-01, 5R01HL158141-043, 3R01HL157311-03S1, 1R01HL166004-01.

Conflict of Interest

The authors declared no conflict of interest.