Phenotypic Age Acceleration and Risk of All-Cause and Cardiovascular Mortality in US Adults With Asthma: A Retrospective Cohort Study

Abstract

Background

Phenotypic age acceleration (PhenoAgeAccel) reflects biological aging, with higher values indicating faster aging. While linked to adverse outcomes in the general population, its relation to mortality in asthma remains unclear.

Methods

We analyzed 3817 adults with physician-diagnosed asthma from NHANES (1999-2010, 2015-2018) with mortality follow-up through 2019. PhenoAgeAccel was calculated from clinical biomarkers, and associations with all-cause and cardiovascular mortality were assessed using weighted Cox models, restricted cubic splines, and subgroup/sensitivity analyses.

Results

During a median follow-up of 13.2 years, higher PhenoAgeAccel was independently associated with greater risks of all-cause mortality (HR = 1.03, 95% CI: 1.03-1.04) and cardiovascular mortality (HR = 1.02, 95% CI: 1.01-1.04). Compared with the lowest quartile, participants in the highest had a 3.42-fold higher all-cause and 2.72-fold higher cardiovascular mortality risk. Associations were nonlinear for all-cause but roughly linear for cardiovascular mortality, and consistent across subgroups and sensitivity tests.

Conclusions

Higher PhenoAgeAccel was independently associated with increased risks of all-cause and cardiovascular mortality among adults with asthma, indicating that biological aging markers may serve as useful tools for risk assessment and individualized care.

Graphical Abstract

Keywords

asthma PhenoAgeAccel biological aging all-cause mortality cardiovascular mortality

Introduction

Asthma is a prevalent chronic respiratory disorder, currently affecting nearly 300 million people worldwide and approximately 8% of adults in the United States.^1,2 Despite advances in therapeutic strategies, individuals with asthma remain at substantially elevated risk of comorbidities such as obesity, cardiovascular disease, and diabetes, which markedly increase the probability of premature mortality.^3-5 Conventional methods of risk stratification remain inadequate, emphasizing the need for novel prognostic biomarkers.

Unlike chronological age, biological aging captures the cumulative decline in physiological integrity and is increasingly recognized as a major determinant of chronic disease burden and mortality risk.⁶ Phenotypic age, derived from routine clinical biomarkers, has been validated as a reliable indicator of biological aging.⁷ The residual difference between phenotypic age and chronological age, termed phenotypic age acceleration (PhenoAgeAccel), reflects whether an individual’s biological aging rate is faster or slower than expected. Previous research has demonstrated that higher PhenoAgeAccel values are associated with increased all-cause and cause-specific mortality,^8,9 as well as cardiometabolic dysfunction and functional decline.¹⁰

Asthma exhibits several overlapping pathophysiological processes with accelerated aging, including persistent inflammation, oxidative stress, and immune dysregulation.^11-13 Moreover, evidence indicates that individuals with asthma present with shorter telomere length and accelerated epigenetic aging.^14,15 Nevertheless, the prognostic significance of PhenoAgeAccel within asthma populations has not yet been clarified.

In this study, we employed data from the nationally representative U.S. National Health and Nutrition Examination Survey (NHANES) to investigate the association between PhenoAgeAccel and the risks of all-cause and cardiovascular mortality in individuals with asthma. We hypothesized that PhenoAgeAccel would be significantly correlated with these mortality outcomes in a dose–response manner. Our findings are expected to enhance understanding of biological aging in asthma and contribute to the development of novel prognostic tools for clinical practice, ultimately improving long-term disease management.

Method

Study Population

NHANES is a continuous, nationwide survey assessing the health and nutritional status of the civilian, non-institutionalized U.S. population. It combines standardized interviews, thorough physical examinations, and laboratory testing. To ensure national representativeness, NHANES utilizes a complex, multistage, stratified probability sampling design.¹⁶ The study protocol was approved by the Research Ethics Review Board of the National Center for Health Statistics (NCHS), and all participants provided written informed consent. NHANES datasets are publicly accessible (accessed July 12, 2025; URL: https://www.cdc.gov/nchs/nhanes/). The present study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.

For this analysis, we included data from 8 survey cycles conducted between 1999-2010 and 2015-2018, focusing on adults aged 20 years or older. These cycles were selected based on the availability of biomarkers required to calculate phenotypic age. We included participants aged ≥20 years who reported a physician diagnosis of asthma. Exclusion criteria were missing parameters for phenotypic age calculation, incomplete mortality follow-up, or missing key covariates.

Assessment of PhenoAgeAccel

Phenotypic Age is derived by combining an individual’s chronological age with a panel of specific biomarkers associated with aging. These biomarkers include serum albumin, creatinine, glucose, C-reactive protein, lymphocyte percentage, mean corpuscular volume, red cell distribution width, alkaline phosphatase, and white blood cell count.^17,18 PhenoAgeAccel is defined as the residual obtained from regressing phenotypic age on chronological age, expressed in years. This residual reflects the discrepancy between biological and chronological age, with positive values indicating accelerated biological aging and negative values suggesting a slower aging process.

The formula used to calculate phenotypic age is presented as follows:

Phenotypic age = 141.50 + \frac{Ln [- 0.00553 \times Ln (\exp (\frac{- 1.51714 \times \exp (xb)}{0.0076927}))]}{0.09165}

where xb = −19.907 − 0.0336 × albumin +0.0095 × creatinine +0.1953 × glucose +0.0954 × ln CRP − 0.0120 × lymphocyte percent +0.0268 × mean cell volume +0.3306 × red cell distribution width +0.00188 × alkaline phosphatase +0.0554 × white blood cell count +0.0804 × chronological age.

Definition of Asthma

In NHANES, participants were defined as having asthma if they reported a physician-diagnosed condition and answered “yes” to the question, “Have you ever been told by a doctor that you have asthma?”.¹⁹

Assessment of Mortality

Mortality status was determined by linking NHANES records to the National Death Index (NDI) through December 31, 2019. Causes of death were coded according to the International Classification of Diseases, Tenth Revision (ICD-10). All-cause mortality encompassed deaths from any cause, whereas cardiovascular mortality was limited to deaths from heart disease (I00–I09, I11, I13, I20–I51) or cerebrovascular disease (I60–I69). Follow-up duration was measured from the baseline examination date to the date of death or the end of follow-up.

Covariates Definitions

Covariates were selected according to previous literature and clinical relevance,^19,20 including age, sex, race/ethnicity (non-Hispanic White, non-Hispanic Black, Mexican American, or other), education level (<9 years, 9-12 years, or >12 years), household income, smoking, alcohol consumption, body mass index (BMI), cardiovascular disease (CVD), hypertension, and diabetes. Income was stratified by poverty income ratio (PIR) as low (≤1.3), middle (>1.3-3.5), or high (>3.5).²¹ Smoking and drinking were categorized as never, former, or current.^22,23 BMI was calculated from standardized height and weight. CVD was defined as a history of coronary heart disease, angina, stroke, myocardial infarction, or heart failure.¹⁹ Hypertension was identified by self-report, antihypertensive medication use, or an average blood pressure ≥140/90 mmHg across 3 standardized readings.²⁴ Diabetes was determined from self-report, antidiabetic medication use, or laboratory evidence (random plasma glucose ≥11.1 mmol/L, fasting plasma glucose ≥7.0 mmol/L, or glycated hemoglobin >6.5%).²⁵

Statistical Analysis

Sampling weights provided by NCHS were applied to account for the complex multistage probability sampling design.¹⁶ For the 1999-2002 cycle, the weight was calculated as two-eighths of the 4-year Mobile Examination Center (MEC) sample weight; for the 2003-2010 and 2015-2018 cycles, it was one-eighth of the 2-year MEC weight. Baseline characteristics were summarized overall and by quartiles of PhenoAgeAccel (Q1 < −6.78; Q2: −6.78 to < −1.99; Q3: −1.99 to <4.29; Q4 ≥ 4.29). Continuous variables were expressed as weighted means with standard errors (SE), whereas categorical variables were presented as unweighted counts and weighted proportions. Differences across quartiles were evaluated using adjusted linear regression models for continuous variables and chi-square tests for categorical variables.

Associations between PhenoAgeAccel and mortality outcomes were examined using Cox proportional hazards models. The unadjusted model included no covariates. Model 1 adjusted for age, sex, race/ethnicity, education, and poverty income ratio, while Model 2 further incorporated smoking, alcohol consumption, BMI, CVD, diabetes, and hypertension. PhenoAgeAccel was analyzed both as a continuous variable (per 1-unit increase) and as categorical quartiles. Outcomes included all-cause mortality and cardiovascular mortality. Potential nonlinear associations were explored with restricted cubic spline (RCS) regression, and threshold effect analysis was performed to identify inflection points. Kaplan–Meier curves were constructed to illustrate cumulative mortality across quartiles, and differences were assessed by stratified log-rank tests. Numbers at risk were reported at 0, 40, 80, 120, 160, 200, and 240 months.

Subgroup analyses were conducted to evaluate the associations across different populations, and results were visually summarized in forest plots. Sensitivity analyses included (1) excluding participants with extreme PhenoAgeAccel values (upper and lower 1%), (2) repeating the models without applying survey weights, and (3) handling missing data using multiple imputation by chained equations (MICE). All analyses were performed using R software (version 4.2.2) and Free Statistics software (version 2.2). A two-sided P-value <0.05 was considered statistically significant.

Results

Study Population

Among 43 752 NHANES participants aged ≥20 years (1999-2010, 2015-2018), 38 147 without asthma were excluded, leaving 5605 participants. After further excluding 874 with missing phenotypic age data, 8 with incomplete mortality information, and 906 with missing covariates, the final analytic sample comprised 3817 participants (Figure 1).

Figure 1.

Flowchart of Participant Selection From the US NHANES

Baseline Characteristics

Baseline characteristics of the 3817 adults with asthma, stratified by PhenoAgeAccel quartiles, are presented in Table 1. The mean age was 45.1 years. Females comprised 2152 participants (57.6 %), and 1961 (72.5 %) were non-Hispanic White. A total of 2069 (61.2 %) had attained education beyond high school, whereas 1262 (23.9 %) reported low household income. Never-smokers accounted for 1824 participants (48.3 %), and 2707 (76.0 %) reported current alcohol use. The mean BMI was 29.8 kg/m². Cardiovascular disease, hypertension, and diabetes were reported in 590 (11.9 %), 1754 (39.5 %), and 746 (14.3 %) participants, respectively. Participants in higher PhenoAgeAccel quartiles were generally older, more frequently non-Hispanic Black, and exhibited lower socioeconomic status, higher BMI, and a greater prevalence of cardiometabolic comorbidities.

Table 1.

Baseline Characteristics of Study Individuals According to Quartiles of the PhenoAgeAccel Index. Categorical Variables Were Presented as Unweighted Frequencies With Weighted Percentages, While Continuous Variables Were Expressed as Means With Standard Errors (SE). Group Comparisons Were Performed Using Linear Regression Models for Continuous Variables and Chi-Square Tests for Categorical Variables

Characteristic	Participants
Characteristic	Overall (3817)	Q1	Q2	Q3	Q4	P value
Age, mean (SE), years	45.06 (0.34)	44.14 (0.55)	42.65 (0.58)	45.70 (0.71)	49.15 (0.82)	<0.001
Sex, n (%)						0.018
Male	1665 (42.45)	401 (37.49)	446 (45.59)	416 (44.34)	402 (43.58)
Female	2152 (57.55)	624 (62.51)	554 (54.41)	490 (55.66)	484 (56.42)
Race/ethnicity, n (%)						<0.001
Non-Hispanic White	1961 (72.48)	610 (78.25)	564 (75.74)	427 (69.76)	360 (61.95)
Non-Hispanic Black	857 (11.60)	164 (7.74)	198 (10.04)	224 (13.19)	271 (18.03)
Mexican American	408 (4.23)	125 (3.42)	100 (3.80)	93 (4.47)	90 (5.82)
Other	591 (11.70)	126 (10.59)	138 (10.43)	162 (12.59)	165 (14.20)
Education level, n (%)						<0.001
<9	318 (4.13)	78 (3.79)	66 (3.00)	70 (4.29)	104 (6.09)
9-12	1430 (34.63)	339 (30.54)	389 (36.69)	346 (33.83)	356 (39.17)
>12	2069 (61.24)	608 (65.67)	545 (60.31)	490 (61.88)	426 (54.74)
Family income, n (%)						<0.001
Low (≤1.3)	1262 (23.88)	254 (15.93)	322 (25.27)	312 (25.37)	374 (32.75)
Medium (1.3-3.5)	1376 (34.46)	349 (32.21)	347 (33.23)	351 (36.45)	329 (37.36)
High (>3.5)	1179 (41.66)	422 (51.86)	331 (41.50)	243 (38.18)	183 (29.89)
Smoking status, n (%)						<0.001
Never	1824 (48.27)	575 (55.84)	465 (46.83)	426 (46.00)	358 (41.01)
Former	1059 (27.56)	290 (27.91)	272 (25.83)	238 (27.18)	259 (29.91)
Current	934 (24.18)	160 (16.25)	263 (27.34)	242 (26.83)	269 (29.08)
Alcohol drinking status, n (%)						0.070
Never	444 (9.07)	124 (10.59)	104 (7.49)	97 (8.66)	119 (9.40)
Former	666 (14.89)	158 (13.22)	164 (14.74)	158 (14.23)	186 (18.56)
Current	2707 (76.04)	743 (76.19)	732 (77.77)	651 (77.11)	581 (72.03)
BMI, mean (SE), kg/m²	29.81 (0.18)	26.41 (0.21)	29.18 (0.28)	30.98 (0.32)	34.69 (0.42)	<0.001
CVD, n (%)	590 (11.86)	80 (6.36)	131 (9.77)	142 (13.63)	237 (21.45)	<0.001
Hypertension, n (%)	1754 (39.48)	377 (31.22)	398 (35.22)	440 (41.53)	539 (56.18)	<0.001
Diabetes, n (%)	746 (14.25)	69 (4.02)	103 (8.56)	167 (13.37)	407 (39.62)	<0.001
PhenoAgeAccel, mean (SE)	−1.79 (0.22)	−10.08 (0.09)	−4.50 (0.05)	0.85 (0.08)	12.05 (0.42)	<0.001

Association Between PhenoAgeAccel and All-Cause Mortality in Asthma

In the fully adjusted Cox regression model (Table 2), higher PhenoAgeAccel was independently associated with an increased risk of all-cause mortality (HR = 1.03; 95% CI: 1.03-1.04; P < 0.001). Compared with Q1, individuals in Q4 had a markedly elevated risk (HR = 3.42; 95% CI: 2.36-4.94), and a significant dose–response association was observed (P for trend <0.001). Threshold effect analysis (Table 3) indicated a stronger risk gradient when PhenoAgeAccel was below 10.83 (HR = 1.08; 95% CI: 1.06-1.10) than when at or above this value (HR = 1.02; 95% CI: 1.02-1.03; both P < 0.001). Restricted cubic spline analysis (Figure 2A) further confirmed a statistically significant non-linear association (P for non-linearity = 0.005; P for overall <0.001).

Table 2.

Multivariable Cox Regression Analyses of the Association Between PhenoAgeAccel and All-Cause and CVD Mortality in Patients With Asthma. Crude Model: Unadjusted Model; Model 1: Adjusted for Age, Sex, Race/Ethnicity, Education Level, and Household Income; Model 2: Adjusted for Covariates in Model 1 Plus Smoking Status, Alcohol Consumption, BMI, CVD, Diabetes, and Hypertension

	Crude model		Model 1		Model 2
	HR(95%CI)	P value	HR(95%CI)	P value	HR(95%CI)	P value
All-cause mortality
PhenoAgeAccel	1.04(1.03, 1.05)	<0.001	1.04(1.04, 1.05)	<0.001	1.03(1.03, 1.04)	<0.001
Q1	Reference		Reference		Reference
Q2	1.36(1.01, 1.83)	0.045	1.28(0.94, 1.74)	0.115	1.2(0.89, 1.62)	0.238
Q3	2.23(1.63, 3.06)	<0.001	1.87(1.41, 2.48)	<0.001	1.62(1.22, 2.15)	<0.001
Q4	6.26(4.46, 8.79)	<0.001	4.19(3.02, 5.80)	<0.001	3.42(2.36, 4.94)	<0.001
P for trend		<0.001		<0.001		<0.001
Cardiovascular mortality
PhenoAgeAccel	1.04(1.03, 1.05)	<0.001	1.04(1.03, 1.05)	<0.001	1.02(1.01, 1.04)	<0.001
Q1	Reference		Reference		Reference
Q2	1.74(0.96, 3.14)	0.067	1.59(0.85, 2.98)	0.15	1.37(0.71, 2.66)	0.35
Q3	2.62(1.35, 5.11)	0.005	1.89(0.94, 3.81)	0.076	1.24(0.56, 2.74)	0.591
Q4	8.94(4.93, 16.21)	<0.001	5.02(2.69, 9.37)	<0.001	2.72(1.34, 5.52)	0.006
P for trend		<0.001		<0.001		0.017

Abbreviations: HR, Hazard Ratio; CI, Confidence Interval; Q, Quartile.

Table 3.

Threshold Effect Analysis of the Association Between PhenoAgeAccel and the Risk of All-Cause Mortality in Patients With Asthma

PhenoAgeAccel	Adjusted model
PhenoAgeAccel	HR (95% CI)	P value
<10.83	1.08(1.06, 1.10)	<0.001
≥10.83	1.02(1.02, 1.03)	<0.001

Figure 2.

Restricted Cubic Spline Regression Analysis Depicting the Dose–Response Associations of PhenoAgeAccel With (A) All-Cause Mortality and (B) Cardiovascular Mortality Among Patients With Asthma

Association Between PhenoAgeAccel and Cardiovascular Mortality in Asthma

In the fully adjusted Cox regression model (Table 2), elevated PhenoAgeAccel was independently linked to a higher risk of cardiovascular mortality (HR = 1.02; 95% CI: 1.01-1.04; P < 0.001). Relative to Q1, individuals in Q4 exhibited more than twice the risk (HR = 2.72; 95% CI: 1.34-5.52). Although the risk estimate for Q3 was not statistically significant (HR = 1.24; 95% CI: 0.56-2.74), the overall dose–response trend across quartiles remained significant (P for trend = 0.017). Restricted cubic spline modelling (Figure 2B) indicated a significant overall association (P overall = 0.006) and no evidence of non-linearity (P for non-linearity = 0.553), supporting an approximately linear relationship between PhenoAgeAccel and cardiovascular mortality.

Survival Analysis

Kaplan–Meier analysis (Figure 3) showed progressively lower survival across increasing PhenoAgeAccel quartiles. Participants in the highest quartile (Q4) had the lowest survival for both all-cause (Figure 3A) and cardiovascular mortality (Figure 3B), with significant differences among quartiles (log-rank P < 0.001), indicating a graded increase in mortality risk with higher PhenoAgeAccel.

Figure 3.

Kaplan–Meier Survival Curves Illustrating Time-Dependent Differences in (A) All-Cause Mortality and (B) Cardiovascular Mortality Across Groups Stratified by PhenoAgeAccel

Subgroup Analyses

Subgroup analyses demonstrated that elevated PhenoAgeAccel was consistently linked to higher all-cause (Figure 4A) and cardiovascular mortality (Figure 4B) across categories of age, sex, race/ethnicity, BMI, smoking, and alcohol consumption. While modest variations in hazard ratios were observed among subgroups, the overall direction of association was uniform. Importantly, no significant effect modification was detected (all P for interaction >0.05), indicating that the relationship between PhenoAgeAccel and mortality risk was broadly stable across demographic and lifestyle characteristics.

Figure 4.

Stratified Analyses Showing the Associations of PhenoAgeAccel With (A) All-Cause Mortality Risk and (B) Cardiovascular Mortality Risk Among Patients With Asthma

Sensitivity Analysis

Sensitivity analyses supported the stability of our findings. After excluding extreme values, disregarding survey weights, and applying multiple imputation, higher PhenoAgeAccel continued to show a significant association with increased risks of all-cause (Table 4) and cardiovascular mortality (Table 5). The highest quartile exhibited the most pronounced risk compared with the reference group. These consistent trends across various analytic approaches further underscore the robustness of the primary results.

Table 4.

Sensitivity Analysis of the Association Between PhenoAgeAccel and All-Cause Mortality in Patients With Asthma. Crude Model: Unadjusted Model; Model 1: Adjusted for Age, Sex, Race/Ethnicity, Education Level, and Household Income; Model 2: Adjusted for Covariates in Model 1 Plus Smoking Status, Alcohol Consumption, BMI, CVD, Diabetes, and Hypertension

	Crude model		Model 1		Model 2
	HR (95%CI)	P value	HR (95%CI)	P value	HR (95%CI)	P value
Excluding participants within the upper and lower 1% extremes of the PhenoAgeAccel
PhenoAgeAccel	1.08(1.06, 1.09)	<0.001	1.06(1.04, 1.07)	<0.001	1.05(1.03, 1.06)	<0.001
Q1	Reference		Reference		Reference
Q2	1.33(0.98, 1.81)	0.068	1.23(0.90, 1.67)	0.193	1.16(0.86, 1.57)	0.337
Q3	2.2(1.59, 3.05)	<0.001	1.8(1.36, 2.38)	<0.001	1.56(1.17, 2.08)	0.002
Q4	5.76(4.03, 8.25)	<0.001	3.82(2.73, 5.34)	<0.001	3.09(2.13, 4.49)	<0.001
P for trend		<0.001		<0.001		<0.001
Without applying complex sampling weights
PhenoAgeAccel	1.04 (1.03∼1.04)	<0.001	1.03 (1.03∼1.04)	<0.001	1.03 (1.03∼1.04)	<0.001
Q1	Reference		Reference		Reference
Q2	1.24 (0.98∼1.58)	0.071	1.19 (0.93∼1.51)	0.159	1.14 (0.89∼1.45)	0.291
Q3	2.08 (1.64∼2.65)	<0.001	1.84 (1.44∼2.35)	<0.001	1.67 (1.29∼2.15)	<0.001
Q4	4.94 (3.95∼6.17)	<0.001	3.58 (2.83∼4.52)	<0.001	3.15 (2.42∼4.09)	<0.001
P for trend		<0.001		<0.001		<0.001
Multiple imputation for missing data
PhenoAgeAccel	1.04(1.03, 1.05)	<0.001	1.04(1.04, 1.05)	<0.001	1.03(1.03, 1.04)	<0.001
Q1	Reference		Reference		Reference
Q2	1.3(0.98, 1.72)	0.069	1.31(0.98, 1.74)	0.065	1.18(0.88, 1.56)	0.264
Q3	2.22(1.63, 3.03)	<0.001	1.95(1.48, 2.56)	<0.001	1.62(1.23, 2.13)	<0.001
Q4	5.72(4.23, 7.71)	<0.001	3.93(2.90, 5.33)	<0.001	3.1(2.22, 4.33)	<0.001
P for trend		<0.001		<0.001		<0.001

Abbreviations: HR, Hazard Ratio; CI, Confidence Interval; Q, Quartile.

Table 5.

Sensitivity Analysis of the Association Between PhenoAgeAccel and CVD Mortality in Patients With Asthma. Crude Model: Unadjusted Model; Model 1: Adjusted for Age, Sex, Race/Ethnicity, Education Level, and Household Income; Model 2: Adjusted for Covariates in Model 1 Plus Smoking Status, Alcohol Consumption, BMI, CVD, Diabetes, and Hypertension

	Crude model		Model 1		Model 2
	HR (95%CI)	P value	HR (95%CI)	P value	HR (95%CI)	P value
Excluding participants within the upper and lower 1% extremes of the PhenoAgeAccel
PhenoAgeAccel	1.09(1.07, 1.10)	<0.001	1.06(1.04, 1.08)	<0.001	1.04(1.01, 1.06)	0.003
Q1	Reference		Reference		Reference
Q2	1.73(0.95, 3.14)	0.074	1.52(0.81, 2.86)	0.193	1.31(0.68, 2.54)	0.415
Q3	2.61(1.33, 5.12)	0.005	1.81(0.89, 3.67)	0.1	1.16(0.52, 2.58)	0.713
Q4	8.88(4.84, 16.28)	<0.001	4.96(2.67, 9.23)	<0.001	2.61(1.28, 5.31)	0.008
P for trend		<0.001		<0.001		0.027
Without applying complex sampling weights
PhenoAgeAccel	1.04 (1.03∼1.05)	<0.001	1.03 (1.02∼1.04)	<0.001	1.02 (1.01∼1.03)	<0.001
Q1	Reference		Reference		Reference
Q2	1.32 (0.8∼2.17)	0.271	1.24 (0.75∼2.04)	0.402	1.15 (0.69∼1.9)	0.595
Q3	2 (1.2∼3.34)	0.007	1.61 (0.96∼2.71)	0.073	1.27 (0.74∼2.18)	0.386
Q4	5.61 (3.55∼8.86)	<0.001	3.67 (2.27∼5.93)	<0.001	2.51 (1.47∼4.28)	0.001
P for trend		<0.001		<0.001		0.001
Multiple imputation for missing data
PhenoAgeAccel	1.04(1.03, 1.05)	<0.001	1.04(1.03, 1.05)	<0.001	1.03(1.01, 1.04)	<0.001
Q1	Reference		Reference		Reference
Q2	1.62(0.93, 2.82	0.087	1.62(0.90, 2.91)	0.105	1.41(0.77, 2.59)	0.262
Q3	2.52(1.40, 4.52	0.002	1.98(1.05, 3.75)	0.035	1.33(0.66, 2.66)	0.427
Q4	7.92(4.63, 13.53	<0.001	4.69(2.65, 8.28)	<0.001	2.66(1.42, 4.98)	0.002
P for trend		<0.001		<0.001		0.009

Abbreviations: HR, Hazard Ratio; CI, Confidence Interval; Q, Quartile.

Discussion

In this nationally representative cohort of U.S. adults with asthma, elevated PhenoAgeAccel was consistently linked to higher risks of all-cause and cardiovascular mortality after full adjustment for demographic, socioeconomic, lifestyle, and clinical variables. These findings underscore the potential utility of PhenoAgeAccel as a prognostic biomarker for mortality among individuals with asthma. The threshold analysis further indicated that intervention benefits may be maximized when PhenoAgeAccel is below 10.83.

Our findings extend prior work linking biological age acceleration to adverse health outcomes. Previous studies in the general population have demonstrated that PhenoAgeAccel predicts all-cause mortality, cardiovascular disease, and multimorbidity.^7,26,27 However, evidence in asthma populations has been limited. Li et al reported that each unit increase in intrinsic epigenetic age acceleration (IEAA) was associated with a 5.1% higher risk of allergic asthma (inverse-variance weighted [IVW] odds ratio [OR] = 1.051, P = 0.025).²⁸ Similarly, Belsky et al. observed that leukocyte telomere length was significantly shorter in individuals with asthma compared with non-asthmatic controls (β = −0.31, P < 0.001).¹⁴ Both findings suggest premature aging phenotypes among asthma patients. Yet, the direct relationship between PhenoAgeAccel and mortality in asthma had not been clearly established. Our study fills this gap, demonstrating that PhenoAgeAccel provides valuable prognostic information for survival in asthma.

The biological pathways linking PhenoAgeAccel to mortality risk in asthma are likely multifactorial. Asthma is characterized by chronic systemic inflammation, which may induce oxidative stress, mitochondrial dysfunction, and cellular senescence, thereby accelerating biological aging.^29-31 Oxidative stress enhances inflammatory signaling through activation of nuclear factor erythroid 2–related factor 2 (Nrf2), nuclear factor kappa B (NF-κB), and mitogen-activated protein kinase (MAPK) pathways.³² Mitochondrial dysfunction leads to excessive reactive oxygen species (ROS) generation, aggravating local tissue damage.³³ In addition, persistent low-grade inflammation may disrupt metabolic and immune homeostasis, further promoting systemic aging.³⁴ Elevated C-reactive protein and leukocyte counts—key components of PhenoAge—reflect this inflammatory burden.³⁵ Asthma has also been linked to endothelial dysfunction, arterial stiffness, and elevated cardiovascular risk,^36,37 which may explain the strong association with cardiovascular mortality. Furthermore, long-term glucocorticoid exposure, frequently used in asthma management, may exacerbate metabolic disturbances and accelerate aging-related processes.³⁸ Together, these mechanisms provide a biologically plausible explanation for the observed associations between phenotypic aging and poor outcomes in asthma.

This study also has important strengths. First, the large sample size increased statistical power and enhanced the generalizability of our findings to a broader asthma population. Second, the use of a composite biomarker to capture biological aging allowed for a more comprehensive assessment than reliance on single markers. Third, the application of advanced statistical approaches, including spline modeling and threshold analyses, enabled detailed evaluation of dose–response patterns. Nonetheless, several limitations must be acknowledged. Asthma diagnosis was based on self-report, raising the possibility of misclassification. The absence of information on asthma severity and exacerbation history may have introduced unmeasured confounding, although subgroup and sensitivity analyses supported the robustness of our results. Additionally, PhenoAgeAccel was calculated only at baseline, limiting our ability to assess longitudinal changes. Finally, as PhenoAgeAccel is phenotype-based and does not incorporate genomic or multi-omics data, causal inferences require further validation in experimental models.

Conclusion

In summary, this study provides novel evidence that elevated PhenoAgeAccel is strongly associated with increased risks of all-cause and cardiovascular mortality in individuals with asthma. These findings suggest that PhenoAgeAccel may serve as an important independent prognostic biomarker in this population. Future research should aim to elucidate the underlying mechanisms and determine whether interventions targeting biological aging can improve survival outcomes in asthma.

Footnotes

Acknowledgement

We would like to express our sincere appreciation to the participants and staff of the National Health and Nutrition Examination Survey for their valuable contributions.

ORCID iD

Feng Lu

Ethical Considerations

The Ethics Review Board of the National Center for Health Statistics approved the human studies. The studies adhered to local legislation and institutional requirements. Participants gave written informed consent for study involvement.

Consent to Participate

All individuals provided written consent.

Author Contributions

Cuili Lin collected the relevant data; Yanyan Ye performed the statistical analysis and data visualization; Huijuan Cheng drafted the initial manuscript, and Feng Lu subsequently revised it. All authors contributed to the discussion and approved the final version of the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Publicly available datasets were analyzed in this study. These data are available at .

References

Stern

Pier

Litonjua

. Asthma epidemiology and risk factors. Semin Immunopathol. 2020;42:5-15.

Xiong

Jiang

Sun

Chen

Luo

. Association between body roundness index and weight-adjusted waist index with asthma prevalence among US adults: the NHANES cross-sectional study, 2005–2018. Sci Rep. 2025;15:9781.

Hirata

. Asthma as risk for incident cardiovascular disease and its subtypes. Hypertens Res. 2023;46:2056-2058.

Kauppi

Linna

Jantunen

, et al. Chronic comorbidities contribute to the burden and costs of persistent asthma. Mediators Inflamm. 2015;2015:819194.

Alves

de Mello

Matos

ASL

Cruz

ÁA

. Clinical features and associated factors with severe asthma in Salvador, Brazil. J Bras Pneumol. 2020;46:e20180341.

Xie

, et al. Associations of biological aging with the morbidity and all-cause mortality of patients with lung cancer. Sci Rep. 2025;15:17880.

Yang

, et al. Association between phenotypic age and mortality in patients with multivessel coronary artery disease. Dis Markers. 2022;2022:4524032.

Chen

Wang

Duan

, et al. Associations of lung function impairment and biological aging with mortality and cardiovascular disease incidence: findings from UK biobank participants. Front Public Health. 2025;13:1635195.

Zhang

Hou

Zhang

Liu

. PhenoAgeAccel is associated with all-cause and cardiovascular mortality in patients with diabetes and prediabetes: a cohort study. Diabetes Res Clin Pract. 2025;226:112375.

10.

Kwan

KJS

Xie

, et al. Evaluating the potential of phenotypic age to enhance cardiovascular risk prediction over chronological age in the UK biobank. Sci Rep. 2025;15:27858.

11.

Wan

Srikaram

Guntupalli

Chen

Gao

. Cellular senescence in asthma: from pathogenesis to therapeutic challenges. EBioMedicine. 2023;94:104717.

12.

Soma

Nagata

. Immunosenescence, inflammaging, and lung senescence in asthma in the elderly. Biomolecules. 2022;12:1456.

13.

Melinte

O-E

Stavarache

Dobrin

, et al. Oxidative stress and risk factors in adult patients with bronchial asthma: a clinical analysis of representative biomarkers. J Clin Med. 2025;14:4007.

14.

Belsky

Shalev

Sears

, et al.

Is chronic asthma associated with shorter leukocyte telomere length at midlife?

Am J Respir Crit Care Med. 2014;190:384-391.

15.

Wang

Z-N

Yang

, et al. Potential role of cellular senescence in asthma. Front Cell Dev Biol. 2020;8:59.

16.

Zipf

Chiappa

Porter

Ostchega

Lewis

Dostal

. National health and nutrition examination survey: plan and operations, 1999-2010. Vital Health Stat 1 Progr Collect. Proced. 2013;56:1-37.

17.

Chen

Zhao

Liu

, et al. Biological aging mediates the associations between urinary metals and osteoarthritis among U.S. adults. BMC Med. 2022;20:207.

18.

Levine

Quach

, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging. 2018;10:573-591.

19.

Liu

. Association of advanced lung cancer inflammation index with all-cause and cardiovascular mortality in US patients with asthma. Front Nutr. 2025;12:1525115.

20.

Wang

Zhao

. Association of neutrophil percentage-to-albumin ratio with all-cause and respiratory disease-related mortality in US adults with asthma. BMC Pulm Med. 2025;25:313.

21.

Zhu

Tao

Zhao

. Association of life’s essential 8 with all-cause mortality in asthma patients: evidence from NHANES 2005–2018. Front Nutr. 2025;12:1603875.

22.

Luo

Chen

Kong

Cao

. Relationship between advanced lung cancer inflammation index and all-cause and cause-specific mortality among chronic inflammatory airway diseases patients: a population-based study. Front Immunol. 2025;16:1585927.

23.

Wang

Zheng

Song

Sun

. Sarcopenic obesity, physical activity, and their combined impact on biological aging in US adults. BMC Public Health. 2025;25:2226.

24.

Tang

Zhang

Luo

Huang

Zhu

. Association of dietary live microbes and nondietary prebiotic/probiotic intake with cognitive function in older adults: evidence from NHANES. J Gerontol A Biol Sci Med Sci. 2024;79:glad175.

25.

Zhang

Liu

. Systemic immune-inflammation index and systemic inflammation response index levels are associated with coronary heart disease prevalence in the asthmatic population: a cross-sectional analysis of the NHANES 2011–2018. Front Public Health. 2025;13:1514016.

26.

Jia

, et al. Accelerated biological aging: unveiling the path to cardiometabolic multimorbidity, dementia, and mortality. Front Public Health. 2024;12:1423016.

27.

Jain

Binder

Chen

, et al. The association of epigenetic age acceleration and multimorbidity at age 90 in the women’s health initiative. J Gerontol A Biol Sci Med Sci. 2023;78:2274-2281.

28.

Sun

Fan

Jiang

. Epigenetic age acceleration and allergic diseases: a bidirectional two-sample Mendelian randomization study. Clin Epigenetics. 2025;17:117.

29.

Frølich

Dove

Leong-Smith

, et al. Airway extracellular copper concentrations increase with age and are associated with oxidative stress independent of disease state: a case-control study including patients with asthma and COPD. Antioxidants. 2025;14:1006.

30.

Fatima

Bhuwalka

Tahseen

Parvez

Kumar

Jahan

. Relation between systemic cell ratios and indices with oxidative stress in the severity of asthma: a south Indian study. Egypt J Bronchol. 2025;19:49.

31.

Chaiwong

Liwsrisakun

Inchai

, et al. Biomarkers of oxidative stress, systemic inflammation and thrombosis in adult asthmatic patients treated with inhaled corticosteroids during exposure to fine particulate matter. J Clin Med. 2025;14:2360.

32.

Lee

Choi

H-K

Shin

Kim

G-D

. Naturally derived bioactive compounds as regulators of oxidative stress and inflammation in asthma. Chin Med. 2025;20:94.

33.

Reddy

. Mitochondrial dysfunction and oxidative stress in asthma: implications for mitochondria-targeted antioxidant therapeutics. Pharmaceuticals. 2011;4:429-456.

34.

Zhang

Wang

Qian

Huang

. Inflammation and aging: signaling pathways and intervention therapies. Signal Transduct Target Ther. 2023;8:239.

35.

Yagi

Mizukoshi

Maruiwa

Isogai

Funao

Fujita

. Accelerated biological aging in patients with degenerative spine diseases: the impact of modifiable lifestyle factors on phenotypic age. Spine J. 2025;S1529-9430(25):181. doi:10.1016/j.spinee.2025.04.001

36.

Moore

Brotto

Fuhr

, et al. Impact of airway challenges on cardiovascular risk in asthma – a randomized controlled trial. PLoS One. 2023;18:e0288623.

37.

Ambrosino

Nolano

Candia

Grassi

Maniscalco

. Addressing oxidative stress and endothelial dysfunction in chronic respiratory diseases: the role of exercise and multidisciplinary rehabilitation. Antioxidants. 2024;13:1543.

38.

Stanley

Chapaneri

Khezrian

, et al. Predicting risk of morbidities associated with oral corticosteroid prescription for asthma. Pragmat Obs Res. 2025;16:95-109.