Association between biological aging and the risk of stroke: Results from the National Health and Nutrition Examination Survey and Mendelian randomization analysis

doi:10.21203/rs.3.rs-4994277/v1

Download PDF

Article

Association between biological aging and the risk of stroke: Results from the National Health and Nutrition Examination Survey and Mendelian randomization analysis

https://doi.org/10.21203/rs.3.rs-4994277/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Introduction:

Stroke is an acute cerebrovascular disease that seriously affects lifespan, and aging is generally considered an important risk factor for stroke. This study aimed to explore the association between biological aging and stroke risk using well-validated measures of aging.

Methods

We employed the methods of Klemera-Doubal (KDMAge) and phenotypic age (PhenoAge) as alternative measures of biological aging in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. In order to analyze the data, we employed logistic regression, trend p-value, restricted cubic spline (RCS) analysis, and subgroup analysis. Furthermore, a bidirectional two-sample Mendelian randomization (MR) analysis was conducted to assess the potential causal impact of biological aging (including intrinsic age acceleration, telomere length, facial aging, and frailty index) on stroke and stroke subtypes.

Results

A cross-sectional analysis of 34,856 participants revealed that higher biological age or biological age acceleration was associated with an increased risk of stroke. Furthermore, multivariate logistic regression analysis performed in 5 models revealed a statistically significant association between biological age or biological age acceleration and stroke risk. RCS analysis showed that there is a nonlinear relationship between KDMAge and KDMAge acceleration and stroke. Subgroup analysis revealed a moderating effect of alcohol consumption on the association between KDMAge acceleration and stroke risk. Finally, MR analysis revealed that the frailty index was associated with an increased risk of stroke (OR: 1.57, 95% CI: 1.36–1.83, p < 0.001). In contrast, reverse MR analysis revealed that stroke was associated with PhenoAge accelerated (OR: 1.54, 95% CI:1.12–2.12,p = 0.008), frailty index (OR:1.11,95% CI:1.05–1.17, p < 0.001) and facial aging (OR:1.02, 95% CI:1.01–1.03, p = 0.001). These findings provide evidence for a potential causal relationship between biological aging and stroke risk.

Conclusion

There is a significant association between biological aging and stroke. It is possible that biological aging may be a risk factor for stroke, and that stroke may further accelerate the process of biological aging. Consequently, it is of paramount importance to identify the acceleration of biological aging through biological aging measures with the objective of reducing the risk of stroke and the occurrence of adverse events.

Health sciences/Diseases/Neurological disorders/Stroke

Health sciences/Pathogenesis/Clinical genetics

Health sciences/Risk factors

Health sciences/Biomarkers/Predictive markers

biological aging

stroke

epidemiology

Mendelian randomization

genetics

Stroke is a serious acute cerebrovascular disease that is characterized by brain tissue damage caused by cerebral blood circulation disorder. This disorder is one of the main causes of long-term disability and death ¹. Stroke is the fifth leading cause of death in the United States, with approximately 795,000 people suffering a new or recurrent stroke each year ². The prevalence of stroke is projected to increase by 3.4 million people between 2012 and 2030 as the population ages and post-stroke mortality decreases ³. Therefore, it is of great importance to identify risk factors for stroke in order to reduce the burden of the disease.

The effects of biological aging on stroke are complex and involve multiple levels of decline, including the decline of immune system function, the decline of cell function, and the shortening of telomere length (TL) ^4–6. Biological aging measure can reflect the degree of aging of an individual at a physiological level, thus helping to predict potential disease risk, such as cardiovascular disease and neurodegenerative diseases ^7–9. However, studies on the association between biological aging and stroke risk are still relatively scarce, and the results are still inconsistent. Therefore, elucidate the mechanism of biological aging in the pathogenesis of stroke is of great significance for understanding the pathophysiological process of stroke and developing a new prevention and treatment model.

The objective of this study was to examine the relationship between biological aging and stroke prevalence using data from the National Health and Nutrition Examination Survey (NHANES). Given the limitations of observational studies in elucidating causal relationships, we employed data from a large-scale genome-wide association study (GWAS) of biological aging measures and stroke to perform a bidirectional two-sample Mendelian randomization (MR) analysis to ascertain the potential causal effects of biological aging on stroke risk.

2.1 Observational study design

The NHANES provides a representative sample of the United States population. The NHANES involves an interview conducted at the participant's home, a clinical and laboratory examination at a mobile examination center (MEC), and information on demographics, health status, and body composition. A total of 101,316 subjects were screened from the NHANES 1999–2018. Individuals lacking information on stroke, age calculated by the Klemera-Doubal method (KDMAge), and phenotypic age (PhenoAge) indicators were excluded from the study (n = 65,410). Furthermore, individuals outside the age range of 20 to 80 years were excluded (n = 1,050). The final sample size included 34,856 participants.

2.2 Study variables and criteria

Based on our previous research, KDMAge and PhenoAge were used to assess biological aging¹⁰. PhenoAge was determined using nine aging-related variables, including albumin level, creatinine level, blood glucose level, C-reactive protein (CRP) level, lymphocyte percentage, mean cell volume, erythrocyte distribution width, alkaline phosphatase level, and white blood cell count ¹¹. PhenoAge calculation formula is as follows:

$$\:\text{P}\text{h}\text{e}\text{n}\text{o}\text{t}\text{y}\text{p}\text{i}\text{c}\:\text{a}\text{g}\text{e}=141.50+\frac{Ln\left[-0.00553\times\:Ln\left(\text{e}\text{x}\text{p}\left(\frac{-1.51714\times\:\text{e}xp\left(xb\right)}{0.0076927}\right)\right)\right]}{0.09165}$$

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.

KDMAge was determined based on eight aging-related variables, including ln-CRP, blood creatinine, glycosylated hemoglobin, serum albumin, serum total cholesterol, serum urea nitrogen, serum alkaline phosphatase, and systolic blood pressure ¹². KDMAge calculation formula is as follows:

$$\:B{A}_{E}=\frac{{\varSigma\:}_{j=1}^{m}\left({x}_{j}-{q}_{j}\right)\left(\frac{{k}_{j}}{{s}_{j}^{2}}\right)}{{\varSigma\:}_{j=1}^{m}{\left(\frac{{k}_{j}}{{s}_{j}}\right)}^{2}}$$

$$\:{r}_{char}=\frac{{\varSigma\:}_{j=1}^{m}\frac{{r}_{j}^{2}}{\sqrt{1-{r}_{j}^{2}}}}{{\varSigma\:}_{j=1}^{m}\frac{{r}_{j}}{\sqrt{1-{r}_{j}^{2}}}}$$

$$\:{s}_{BA}^{2}=\frac{{\sum\:}_{j=1}^{n}{\left(\left(B{A}_{Ei}-C{A}_{i}\right)-\frac{{\sum\:}_{i=1}^{n}\left(B{A}_{Ei}-C{A}_{i}\right)}{n}\right)}^{2}}{n}\:-\left(\frac{1-{r}_{c}^{2}}{{r}_{c}^{2}}\right)\times\:\left(\frac{{\left(C{A}_{max}-C{A}_{min}\right)}^{2}}{12m}\right)$$

$$\:KDMAge=\frac{{\sum\:}_{j=1}^{m}\left({x}_{j}-{q}_{j}\right)\left(\frac{{k}_{j}}{{s}_{j}^{2}}\right)+\frac{CA}{{s}_{BA}^{2}}}{{\sum\:}_{j=1}^{m}{\left(\frac{{k}_{j}}{{s}_{j}}\right)}^{2}+\frac{1}{{s}_{BA}^{2}}}$$

The values j and i represent the number of biomarkers and samples respectively. The values k, q, and s are the regression slope, intercept, and the root means squared error of a biomarker regressed on chronological age, respectively. The value r _j ² represents the variance explained by regression of chronological age on biomarkers.

The definition of stroke was derived from self-reported personal interview data regarding the question, "Has a doctor or other health professional ever informed you that you had a stroke?" The sociodemographic characteristics of the participants were defined as follows: age (continuous), sex (male or female), race (non-Hispanic white, non-Hispanic black, or other), educational level (less than high school, high school or equivalent, or college and above), poverty-income ratio (PIR) (< 1.0, 1.0-2.9, or ≥ 3.0), marital status (married, never married, or other), smoking (never smoker, former smoker, or current smoker), drinking (never drinker, occasional drinker, or frequent drinker), body mass index (BMI) (< 25.0, 25.0-29.9, or ≥ 30.0), diabetes (yes or no), hypertension (yes or no), triglycerides, and total cholesterol.

2.3 Statistical analysis

The complex sampling design and sampling weight of NHANES were fully considered in the analysis. Continuous variables were represented by weighted median (interquartile range, IQR) or weighted mean ± standard deviation (SD). Categorical variables are presented as numbers and weighted percentages. The R package "BioAge" was employed to calculate KDMAge and PhenoAge. To assess the biological aging status of individuals, KDMAge acceleration or PhenoAge acceleration were defined as the residual values computed by the regression of KDMAge or PhenoAge relative to chronological age. Subsequently, KDMAge acceleration or PhenoAge acceleration was classified into different quartiles. Accelerated aging was defined as a KDMAge acceleration or PhenoAge acceleration greater than 0, whereas non-accelerated aging was defined as a KDMAge acceleration or PhenoAge acceleration less than or equal to 0 ¹³. To illustrate the incremental effect of each 10-year increase, we divide the value of KDMAge, PhenoAge, KDMAge acceleration, and PhenoAge acceleration by 10. This provides a more intuitive explanation. This provides a more intuitive explanation. We employ multiple imputation methods to address the issue of missing data in covariates ¹⁴.

Logistic regression was used to quantify the association between biological aging and stroke by calculating the odds ratio (OR) and its 95% confidence interval (CI). A series of stepwise adjusted models were constructed. The first was a crude model with unadjusted covariates. Subsequently, Model 1 was adjusted for age, sex, and race. Model 2 included additional variables, including educational level, PIR, marital status, smoking, drinking, and BMI. Model 3 adjusted for diabetes and hypertension based on Model 2. Model 4 included triglycerides and total cholesterol based on Model 3. The trend p-value was calculated using integer values (1, 2, 3, and 4). The dose-response relationship between biological aging and stroke risk was assessed using restricted cubic splines (RCS). Four values were located at the 5th, 35th, 65th, and 95th percentiles, respectively. In addition, we performed interaction and subgroup analyses to examine potential discrepancies between biological aging and stroke.

2.4 Selection of genetic instruments for biological aging and stroke

We used the GWAS database of accelerated aging phenotype, TL, facial aging (FA), and frailty index (FI) to evaluate biological aging. Accelerated aging phenotypes was derived from a meta statistical data of 34,710 individuals collected from 28 cohorts, including GrimAge acceleration, Hannum age acceleration, Intrinsic epigenetic age acceleration, and PhenoAge acceleration ¹⁵. TL was derived from a GWAS meta-analysis of 472,174 individuals of European ancestry ¹⁶. FA were obtained from the United Kingdom Biobank (UKB) ¹⁷, which included 423,999 participants. FI was derived from a meta-analysis datasets ¹⁸, including 164,610 UKB participants and 10,616 TwinGene participants. We obtained summary statistical data on the genetic association with stroke and stroke subtypes from the GIGASTROKE ¹⁹, which consisted of 73 652 stroke patients and 1 234 808 controls, including 62 100 cases of ischemic stroke (IS), 10 804 cases of cardioembolic stroke (CES), 6399 cases of large artery stroke (LAS) and 6811 cases of small vessel stroke (SVS). In addition, we obtained similar data from the MEGASTROKE ²⁰, which consisted of 40,585 stroke patients and 406,111 controls, including 34,217 cases of IS, 7,193 cases of CES, 4,373 cases of LAS, and 5,386 cases of SVS. The detailed descriptive data of GWAS are shown in Supplementary Table 1. This MR study was conducted in accordance with the STROBE-MR guidelines ²¹.

2.5 Selection of genetic instruments

Single nucleotide polymorphisms (SNPs) were utilized as instrumental variables (IVs) and must satisfy the following assumptions: (1) Genetic variation was significantly associated with exposure; (2) They are independent of confounding factors; (3) They can only affect the outcome through exposure. Therefore, we selected IVs according to the following criteria: (1) SNPs associated with exposure were considered eligible at a genome-wide significance threshold of p < 5 × 10^− 8; (2) SNPs in linkage disequilibrium were eliminated by PLINK, and independent SNPs were retained (r² < 0.001, clumping window = 10,000 kB). (3) F-statistic was performed on each SNP to determine the strength of the association. SNP with an F-statistic greater than 10 were retained to reduce the influence of weak instrumental variables. (4) The ambiguous palindrome SNP is removed (harmonize data action-3). (5) Outlier SNPs are identified using MR-PRESSO and Radial MR.

2.6 Mendelian randomization analysis

We used inverse variance weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode methods to analyze the causal relationship between biological aging and stroke. The main results are evaluated using the IVW method because it has higher estimation accuracy and test capability ²². The random effects model was used to meta merge the MR Results from different GWAS sources. If the result of the IVW method is significant (p < 0.05), even if the result of the other method is not significant and no pleiotropy and heterogeneity are found, it can be considered a positive result as long as the beta values of the other method are in the same direction ²³. In addition, we used the reverse MR method to study the causal relationship between stroke and biological aging. MR-PRESSO and Radial MR were used to detect and exclude abnormal values ²⁴. Scatter plots and funnel plots were used to evaluate the causal effect and stability of the results. MR-Egger intercept was used to test the pleiotropy of IVs in the GWAS dataset. The heterogeneity was assessed by Cochran's Q. In addition, a leave-one-out analysis was performed to determine whether the MR estimate was influenced or biased by a single SNP.

All statistical analyses were performed with R, version 4.3.3. All tests were two-tailed, and the significance level was set at p < 0.05. Figure 1 summarizes the population selection and research process used in this study.

3.1 Baseline characteristics

A total of 34,856 NHANES participants were included in this study, which is sufficient to represent 150,333,463 U.S. citizens. Participants were 46.33 ± 16.34 years of age, including 16,891 males and 17,965 female. Table 1 categorizes participants into non-stroke (n = 33,687) and stroke groups (n = 1,169) based on stroke status. In addition to KDMAge acceleration (p = 0.055), all factors including age, sex, race, education level, PIR, marital status, smoking status, alcohol consumption, BMI, diabetes, hypertension, triglycerides, total cholesterol, KDMAge, PhenoAge, PhenoAge acceleration, KDMAge accelerated aging, and PhenoAge accelerated aging were significantly different between the two groups (p ≤ 0.001).

Table 1

Weighted comparison in basic characteristics.
Characteristic	Total (n = 34,856)	Non-Stroke (n = 33,687)	Stroke (n = 1,169)	P
Age, years	46.33 ± 16.34	45.92 ± 16.18	62.79 ± 14.17	< 0.001
Sex, n (%)				< 0.001
Male	16,891 (48.6)	16,316 (48.8)	575 (41.6)
Female	17,965 (51.4)	17,371 (51.2)	594 (58.4)
Race, n (%)				< 0.001
Non-Hispanic white	15,822 (69.6)	15,236 (69.5)	586 (71.1)
Non-Hispanic black	6,741 (10.3)	6,435 (10.1)	306 (14.7)
Others	12,293 (20.2)	12,016 (20.3)	277 (14.3)
Educational level, n (%)				< 0.001
Less than high school	9,505 (16.9)	9,071 (16.6)	434 (27.2)
High school or equivalent	8,206 (24.8)	7,896 (24.6)	310 (31.3)
College and above	17,145 (58.3)	16,720 (58.7)	425 (41.1)
PIR, n (%)				< 0.001
<1.0	6,797 (13.2)	6,504 (13.0)	293 (19.7)
1-2.9	14,863 (36.1)	14,268 (35.8)	595 (47.7)
≥3.0	13,196 (50.8)	12,915 (51.2)	281 (32.5)
Marital status, n (%)				< 0.001
Married	18,799 (56.8)	18,219 (56.9)	580 (52.1)
Never married	8,507 (24.6)	8,355 (24.8)	152 (14.3)
Others	7,550 (18.6)	7,113 (18.3)	437 (33.6)
Smoking, n (%)				< 0.001
Never smoker	18,699 (53.0)	18,225 (53.3)	474 (41.7)
Ever smoker	8,682 (25.0)	8,270 (24.8)	412 (31.7)
Current smoker	7,475 (22.0)	7,192 (21.9)	283 (26.6)
Drinking, n (%)				< 0.001
Never drinker	5,006 (11.3)	4,774 (11.1)	232 (20.0)
Occasional drinker	5,224 (13.4)	5,013 (13.2)	211 (18.3)
Frequent drinker	24,626 (75.3)	23,900 (75.7)	726 (61.6)
BMI, n (%)				< 0.001
<25.0	10,061 (31.3)	9811 (31.5)	250 (21.7)
25-29.9	11,983 (33.5)	11588 (33.5)	395 (33.2)
≥30.0	12,812 (35.2)	12288 (35.0)	524 (45.1)
Diabetes, n (%)	5,334 (11.2)	4,896 (10.6)	438 (33.7)	< 0.001
Hypertension, n (%)	13,126 (33.0)	12,216 (31.9)	910 (74.6)	< 0.001
Triglycerides, mmol/L	1.67 ± 1.28	1.66 ± 1.28	1.93 ± 1.25	< 0.001
Total cholesterol, mmol/L	5.10 ± 1.05	5.10 ± 1.05	4.97 ± 1.21	0.001
KDMAge, years	39.55 (28.37, 52.90)	39.13 (28.17, 52.23)	58.62 (45.54, 74.92)	< 0.001
PhenoAge, years	42.26 (29.79, 55.67)	41.82 (29.49, 54.96)	64.94 (51.96, 75.23)	< 0.001
KDMAge acceleration, years	-5.00 (-13.62, 3.95)	-5.02 (-13.59, 3.87)	-3.42 (-15.53, 9.18)	0.055
PhenoAge acceleration, years	-2.98 (-5.76, -0.04)	-3.02 (-5.80, -0.10)	-0.72 (-4.23, 3.00)	< 0.001
KDMAge accelerated aging, n (%)	12,597 (35.3)	12,092 (35.1)	505 (42.1)	0.001
PhenoAge accelerated aging, n (%)	9,609 (24.8)	9,039 (24.3)	570 (44.3)	< 0.001
Continuous variables were presented as mean ± SD or median (IQR). Categorical variables were presented as n (%). BMI, body mass index; PIR, poverty income ratio; PhenoAge, Phenotypic age; KDMAge, biological age as calculated by the Klemera-Doubal method; SD, standard deviation; IQR, interquartile range; n, numbers of participants; %, weighted percentage; P, weighted P.

3.2 Multivariable regression analysis

Table 2 provides a summary of the potential effects of biological aging on the incidence of stroke. The data were divided into quartiles based on the level of KDMAge acceleration and PhenoAge acceleration (quartiles 1–4). In the crude model, we found that each 10-year increase in KDMAge significantly increased the risk of stroke by 1.57 times (95%CI: 1.44–1.51, p < 0.001). Similarly, the risk of stroke also increased significantly with each 10-year increase in PhenoAge (OR: 1.92, 95%CI: 1.85-2.00, p < 0.001). Further analysis revealed a significant positive association between the fourth quartile of KDMAge acceleration and stroke (p for trend = 0.007) and a positive association between all quartiles of PhenoAge acceleration and stroke (p for trend < 0.001). The incidence of stroke in the KDMAge accelerated aging group was significantly higher than that in the non-KDMAge accelerated aging group (OR: 1.36, 95%CI: 1.21–1.53, p < 0.001). Similarly, the incidence of stroke was significantly higher in the PhenoAge accelerated aging group than in the non-PhenoAge accelerated aging group (OR: 2.59, 95%CI: 2.31–2.92, p < 0.001). These associations remained significant after controlling for multiple potential confounders. For example, the potential impact of PhenoAge on stroke incidence every 10-year remains significantly positive after adjusting for sex, age, and race/ethnicity (OR: 2.53, 95%CI: 2.26–2.83, p < 0.001). After further adjustment for education level, PIR, marital status, smoking status, alcohol consumption and BMI, the risk of stroke increased to 2.16 times (95%CI: 1.92–2.43, p < 0.001). After further adjustment for diabetes mellitus and hypertension, the risk of stroke remained significantly increased (OR: 1.72, 95%CI: 1.52–1.95, p < 0.001). In model 4, which further adjusted for triglycerides and total cholesterol, the risk of stroke remained significantly increased with each 10-year increase in PhenoAge (OR: 1.66, 95%CI: 1.46–1.88, p < 0.001).

Table 2

Weighted logistic regressions of associations between biological aging and stroke.
	Crude model		Model 1		Model 2		Model 3		Model 4
	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P
KDMAge, 10 years	1.47 (1.44–1.51)	< 0.001	1.22 (1.18–1.26)	< 0.001	1.19 (1.15–1.22)	< 0.001	1.11 (1.07–1.14)	< 0.001	1.13 (1.10–1.17)	< 0.001
KDMAge acceleration, 10 years
Continuous	1.15 (1.10–1.19)	< 0.001	1.22 (1.18–1.26)	< 0.001	1.19 (1.15–1.22)	< 0.001	1.11 (1.07–1.14)	< 0.001	1.13 (1.10–1.17)	< 0.001
Quartile 1	Reference		Reference		Reference		Reference		Reference
Quartile 2	0.71 (0.60–0.85)		1.16 (0.98–1.38)		1.12 (0.95–1.33)		1.01 (0.85–1.20)		1.05 (0.89–1.25)
Quartile 3	0.60 (0.50–0.71)		1.16 (0.97–1.39)		1.10 (0.92–1.31)		0.92 (0.77–1.10)		0.99 (0.83–1.18)
Quartile 4	1.22 (1.05–1.42)		2.20 (1.89–2.57)		1.94 (1.66–2.26)		1.42 (1.21–1.66)		1.58 (1.35–1.86)
		0.007^a		< 0.001^a		< 0.001^a		< 0.001^a		< 0.001^a
Non-KDMAge accelerated aging	Reference		Reference		Reference		Reference		Reference
KDMAge accelerated aging	1.36 (1.21–1.53)	< 0.001	1.82 (1.62–2.05)	< 0.001	1.65 (1.47–1.86)	< 0.001	1.33 (1.18–1.51)	< 0.001	1.42 (1.26–1.61)	< 0.001
PhenoAge, 10 years	1.92 (1.85-2.00)	< 0.001	2.53 (2.26–2.83)	< 0.001	2.16 (1.92–2.43)	< 0.001	1.72 (1.52–1.95)	< 0.001	1.66 (1.46–1.88)	< 0.001
PhenoAge acceleration, 10 years
Continuous	3.02 (2.70–3.39)	< 0.001	2.53 (2.26–2.83)	< 0.001	2.16 (1.92–2.43)	< 0.001	1.72 (1.52–1.95)	< 0.001	1.66 (1.46–1.88)	< 0.001
Quartile 1	Reference		Reference		Reference		Reference		Reference
Quartile 2	1.25 (1.02–1.54)		1.26 (1.03–1.54)		1.17 (0.95–1.42)		1.07 (0.88–1.31)		1.05 (0.86–1.28)
Quartile 3	1.62 (1.34–1.98)		1.59 (1.31–1.94)		1.36 (1.12–1.65)		1.15 (0.94–1.39)		1.11 (0.91–1.35)
Quartile 4	3.40 (2.85–4.05)		2.95 (2.47–3.52)		2.32 (1.93–2.79)		1.72 (1.42–2.07)		1.62 (1.34–1.96)
		< 0.001^a		< 0.001^a		< 0.001^a		< 0.001^a		< 0.001^a
Non-PhenoAge accelerated aging	Reference		Reference		Reference		Reference		Reference
PhenoAge accelerated aging	2.59 (2.31–2.92)	< 0.001	2.26 (2.01–2.54)	< 0.001	1.93 (1.71–2.17)	< 0.001	1.58 (1.39–1.79)	< 0.001	1.52 (1.34–1.72)	< 0.001
Model 1: adjusted for sex, age, and race. Model 2: Model 1 + adjusted for education level, poverty–income ratio, marital status, smoking, drinking, and body mass index. Model 3: Model 2 + adjusted for diabetes and hypertension. Model 4: Model 3 + adjusted for triglycerides and total cholesterol. PhenoAge, Phenotypic age; KDMAge, biological age as calculated by the Klemera-Doubal method; OR, odds ratio; 95% CI, 95% confidence interval. ^a P for trend.

3.3 Dose–response relationship between biological aging and stroke

According to the RCS analysis, there was a nonlinear relationship between KDMAge and Phenoage and the prevalence of stroke after adjusting for age, sex, race/ethnicity, education level, PIR, marital status, smoking status, alcohol consumption, BMI, diabetes, and hypertension. Figure 2 shows that there was a significant “S-shaped” relationship between KDMAge and the prevalence of stroke (p for nonlinear = 0.001). In addition, we also found a “J-type” relationship between PhenoAge and the prevalence of stroke (p for nonlinear < 0.001). It is noteworthy that although the straight line showed an upward trend with increasing PhenoAge or PhenoAge acceleration, no significant nonlinear relationship was found with the prevalence of stroke.

3.4 Subgroup analysis

Subgroup analyses further examined the association between biological aging and stroke risk in various subgroups defined by age, sex, race, education level, PIR, marital status, smoking, drinking, BMI, hypertension, and diabetes. The results showed that the incidence of stroke was positively associated with KDMAge acceleration among participants age ≥ 60 years, women, non-Hispanic black, less than high school, other marital status, smoking, diabetes, and hypertension (p < 0.05). In contrast, the incidence of stroke was negatively associated with KDMAge acceleration in the group of PIR ≥ 3.0 and frequent drinker (p < 0.05). It is noteworthy that the stratified analysis of drinking showed an interaction (p for interaction = 0.002). In addition, age ≥ 60 years, sex, non-Hispanic white, non-Hispanic black, less than high school, high school or equivalent, married, other marital status, smoking, 30 > BMI ≥ 25, BMI ≥ 30, hypertensive, non-hypertensive, diabetes, and non- diabetes was positively associated with PhenoAge acceleration (p < 0.05). In contrast, the incidence of stroke in the PIR ≥ 3.0 group was negatively associated with PhenoAge acceleration (p = 0.048). We did not find a statistically significant interaction in our stratified analysis of the association between PhenoAge acceleration and stroke. The results of subgroup analysis are shown in Figure.3.

3.5 Causal effects of biological aging on stroke

A variety of MR methods were employed to assess the impact of genetically predicted biological aging (including four accelerated aging phenotypes, TL, FI, and FA) on stroke and stroke subtypes (including IS, CES, LAS, and SVS). The results of the IVW method are presented in Fig. 4. The analysis of stroke GWAS data from the GIGASTROKE consortium revealed a positive association between genetically predicted FI and stroke (OR: 1.61, 95% CI: 1.35–1.92, p < 0.001). Further research revealed that FI was associated with IS (OR: 1.58, 95% CI: 1.29–1.93, p < 0.001) and LAS (OR: 2.69, 95% CI: 1.45–4.99, p = 0.002). The analysis of stroke GWAS data from the MEGASTROKE consortium revealed a positive association between genetically predicted FI and stroke (OR: 1.49, 95% CI: 1.12–1.98, p < 0.001), while TL was negatively associated with stroke (OR: 0.91, 95% CI: 0.84–0.99, p = 0.033). After meta-merging the IVW results of stroke GWAS data from the two consortiums, the positive correlation between FI and stroke was still observed (OR: 1.57, 95% CI: 1.36–1.83, p < 0.001). Further analysis of stroke subtypes revealed that GrimAge acceleration was negatively associated with LAS (OR: 0.86, 95% CI: 0.76–0.96, p = 0.010), while intrinsic epigenetic age acceleration was positively associated with CES (OR: 1.04, 95% CI: 1.01–1.07, p = 0.004). FI is positively correlated with IS (OR: 1.52, 95% CI: 1.29–1.79, p < 0.001), LAS (OR: 2.78, 95% CI: 1.74–4.42, p < 0.001), and SVS (OR: 1.79, 95% CI: 1.15–2.79, p = 0.010). A negative correlation was observed between FA and SVS (OR: 0.50, 95% CI: 0.30–0.83, p = 0.007).

3.6 Causal effects of stroke on biological aging

A reverse MR analysis was conducted to investigate the relationship between biological aging and stroke. The results of the IVW method are presented in Fig. 5. The analysis of stroke GWAS data from the GIGASTROKE consortium revealed that stroke was associated with PhenoAge acceleration (OR: 1.64, 95% CI: 1.14–2.36, p = 0.008), FI (OR: 1.08, 95% CI: 1.04–1.12, p < 0.001), and FA (OR: 1.02, 95% CI: 1.00-1.03, p = 0.010). In the MEGASTROKE consortium, stroke was positively associated with FI (OR: 1.14, 95% CI: 1.08–1.21, p < 0.001) and FA (OR: 1.02, 95% CI: 1.00-1.04, p = 0.020). The results of meta-analysis showed that stroke was associated with PhenoAge acceleration (OR: 1.54, 95% CI: 1.12–2.12, p = 0.008), FI (OR: 1.11, 95% CI: 1.05–1.17, p < 0.001), and FA (OR: 1.02, 95% CI: 1.01–1.03, p = 0.001). Further analysis revealed a positive correlation between IS and FI (OR: 1.09, 95% CI: 1.04–1.13, p < 0.001), as well as a positive correlation between LAS and FI (OR: 1.04, 95% CI: 1.00-1.08, p = 0.041).

3.7 Sensitivity analysis

Following the removal of samples with missing covariates from the NHANES data, the same analysis was performed, resulting in the confirmation of the robustness of the analysis. Furthermore, the other four MR methods highlight the overall robustness of the MR results (Supplementary Table 2–3). Our MR analysis demonstrated no heterogeneity or pleiotropy. The MR-Egger regression method was employed to assess the potential for horizontal pleiotropy between the SNP and the results. This analysis did not identify any evidence of such pleiotropy (Supplement Table 4). As illustrated in Supplementary Table 5, according to Cochran's Q tests, the results of the sensitivity analysis did not indicate any evidence of heterogeneity. Furthermore, the leave-one-out sensitivity analysis demonstrated that no single SNP could influence the causal relationship, thereby reinforcing the robustness of our conclusions.

The objective of this study was to examine the relationship between biological aging and stroke. Findings from the NHANES indicate that biological aging is significantly positively associated with an increased risk of stroke. Specifically, there was a non-linear relationship between KDMAge and KDMAge acceleration and stroke risk, whereas there was a linear relationship between PhenoAge and PhenoAge acceleration and stroke risk. Subgroup analyses demonstrated the robustness of our conclusions and further revealed the moderating effect of alcohol consumption on the association between KDMAge acceleration and stroke risk. Furthermore, we conducted a two-sample MR analysis using multiple GWAS datasets and meta-analyzed the results to assess the causal relationship between biological aging and stroke and its subtypes. The MR results indicate the potential for a bidirectional causal relationship between biological aging and stroke. For instance, FI may act as a bidirectional causal factor in the occurrence of stroke and its subtypes. This finding lends support to the potential role of genetically predicted biological aging in stroke risk. In conclusion, our findings indicate that biological aging increases the risk of stroke, and that stroke may accelerate the process of biological aging to a certain extent.

Aging is an unavoidable risk factor for stroke and the onset of various cardiovascular diseases, including stroke, is closely related to biological aging. One study indicates that individuals with accelerated biological aging are at an increased risk of stroke and cardiovascular disease, as well as a corresponding increase in mortality ²⁵. Another study demonstrated a significant correlation between cardiovascular health and accelerated epigenetic age ²⁶. Biological aging involves multiple dimensions such as cellular senescence, epigenetic changes, inflammation, immune system decline, changes in metabolism and organ homeostasis ²⁷. KDMAge and PhenoAge fully consider these factors to effectively identify people whose biological age is older than their actual age ^28,29. Therefore, KDMAge and PhenoAge have the potential to be used to identify individuals with accelerated biological aging to help prevent the occurrence of stroke.

MR employs SNPs related to exposure factors as IVs, effectively eliminating systematic bias, thus successfully avoiding confounding factors in population studies. This approach provides effects similar to randomized controlled trials and can help us understand the causal relationship between exposure factors and outcomes ³⁰. Despite the failure of our study to identify a potential causal role of biological age acceleration in stroke development, further analysis suggests that GrimAge acceleration may be a contributing factor in the development of CES. FI provides a comprehensive assessment of individual vulnerability and health status by considering multiple health deficits, which are associated with stroke and adverse stroke outcomes ^31,32. The results of our study indicate that FI may be a causal factor in the occurrence of stroke and its subtypes. This evidence indicates that biological aging plays an important role in the development of stroke and that the risk of stroke can be effectively predicted by assessing specific markers of biological aging.

In a reverse MR analysis, we identified a potential positive causal relationship between genetically predicted stroke and PhenoAge acceleration, FI, and GrimAge acceleration. This association was found to be more significant in certain stroke subtypes. This indicates that stroke may contribute to the process of biological aging to a certain extent. Aging impairs cerebral blood vessel and white matter repair and regeneration during stroke recovery. This is evidenced by the observation that older rats exhibit lower blood vessel density in the brain 14 days post-stroke compared with young rats and exhibit angiogenesis at the bulk transcriptome level. The expression of related molecules is weakened ³³. Another study demonstrated that angiogenesis and oligodendrogenesis exhibit differential patterns across the lifespan ³⁴. Moreover, a considerable number of studies have demonstrated the correlation between biological aging and the recovery from stroke, as well as the unfavorable prognosis associated with stroke ^35–37. In conclusion, our findings underscore the necessity of considering biological aging in stroke patients during their recovery. They also provide valuable insights into the association between biological aging and stroke. Moreover, they suggest that additional interventions may be necessary to slow biological aging in these patients. This, in turn, should prevent the adverse consequences that would otherwise result.

Indeed, the decline in physical function, cognitive function, memory, and reaction ability that is associated with stroke can be considered a manifestation of the aging process ²⁷. However, the underlying mechanisms explaining the relationship between biological aging and stroke remain incompletely understood. One potential explanation is that biological aging may create a pro-inflammatory environment that triggers an inflammatory response by affecting the immune system, thereby increasing the risk of stroke ³⁸. This inflammatory milieu may exacerbate secondary brain injury by exacerbating blood-brain barrier damage, microvascular failure, brain edema, oxidative stress, and directly inducing neuronal cell death ³⁹. In addition, biological aging may also increase the risk of stroke by affecting vascular function and blood rheology ⁴⁰. As individuals age, there is a concomitant decrease in the elasticity of blood vessels, which increases the risk of arteriosclerosis. These factors may result in inadequate blood flow and thrombosis, which can ultimately lead to a stroke. Future research should continue to explore these mechanisms in order to develop effective interventions to prevent stroke and mitigate post-stroke impairment.

In addition, our research yielded some unexpected findings. We observed an inverse association between GrimAge acceleration and LAS, as well as between FA and SVS. These findings were not in accordance with our initial expectations. It is postulated that this discrepancy may be attributed to the fact that intrinsic epigenetic age is generally considered a relatively stable indicator. However, in practical applications, the profound impact of environmental and lifestyle factors on human populations may be overlooked. These factors may influence the expression of biological aging markers, thereby contributing to the observed associations ⁴¹. This result indicates that the influence of the environment and lifestyle must be considered when selecting biological aging indicators. These external factors may significantly influence an individual's biological aging process and, as a consequence, the risk of related diseases.

This study possesses several notable strengths that render it a significant contribution to the existing body of knowledge regarding the relationship between biological aging and stroke. Firstly, this is the largest population-based study of its kind to date. Second, we investigate potential nonlinear relationships between biological aging and stroke, offering new insights into the nature of this association. Third, we conducted a further investigation of the interaction of covariates between biological aging and stroke through subgroup analysis. This can assist in providing a more comprehensive comprehension of the manner in which disparate elements operate collectively to influence the risk of stroke. Fourth, this is the inaugural MR study to investigate the causal relationship between genetically determined biological aging and stroke. Finally, two sets of GWAS databases were integrated for analysis, and meta-analysis was employed to supplement and verify the association between biological aging and stroke. These measures effectively reduce the impact of population heterogeneity on the research conclusions. Concurrently, a comprehensive sensitivity analysis was conducted on the MR results to reinforce the reliability of the research conclusions.

It should be noted that the findings of this study are not without limitations. Firstly, the study sample was predominantly comprised of individuals of American and European ancestry, which somewhat constrains the applicability of our findings to other racial or ethnic groups. Secondly, our study is dependent on data from the NHANES, and it must be acknowledged that any representative sample may be limited in its ability to adequately capture the unique characteristics of all subpopulations. This is particularly evident in the case of language barriers, health conditions, socioeconomic status, or other factors that may result in certain subgroups being underrepresented or even excluded from inclusion in studies. Third, our study involved some self-reported data, which may be affected by recall bias or reporting bias, thus causing a certain degree of interference with the accuracy of the study results. Fourth, although two-sample MR analysis was employed to assess potential causal relationships, this approach may not be able to fully determine causal relationships due to the influence of various potential confounders. Fifth, it is acknowledged that a multitude of environmental, lifestyle, and psychological factors are established risk factors for biological aging and stroke. Consequently, future research should further investigate the role of environmental and lifestyle factors in the relationship between biological aging and disease risk.

Our study used NHANES1999-2018 data for a cross-sectional study and a two-sample MR method to examine the relationship between biological aging and stroke. The research results show that there is a positive correlation between biological aging and stroke, and MR analysis shows that biological aging may have a bidirectional causal effect on stroke risk. Sensitivity analysis demonstrates the robustness of our results. In summary, our study contributes to a better understanding of the interaction mechanisms between biological aging and stroke. The utilization of alternative methodologies to discern accelerated biological aging may prove to be a promising avenue for the prevention of both primary and secondary strokes. Moreover, the identification and intervention in the aging process have the potential to significantly impact the reduction of stroke morbidity and mortality.

Conflict of interest

The authors declare no conflicts of interest concerning this study.

Funding

This study was supported by the Taishan Scholar Youth Expert Plan in Shandong Province.

Author Contribution

Each author has made substantial contributions to the manuscript. Zhaoqi Zhang and Xingru Zhao drafted this article and contributed to the editing and revision. Kai Yang downloaded the data set and performed bioinformatics analysis. Xin Li edited the manuscript substantially. Wei Liu provided statistical methods for correction and analysis. All authors had read and agreed to the final version of the manuscript.

Acknowledgement

The authors thank the staff and participants of NHANES, UK Biobank, GIGASTROKE, and MEGASTROKE for making the data publicly available.

Data Availability

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

Hankey, G. J. & Stroke Lancet (London England) 389, 641–654 (2017).
Tsao, C. W. et al. Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association. Circulation. 147, e93–e621 (2023).
Boehme, A. K., Esenwa, C. & Elkind, M. S. V. Stroke Risk Factors, Genetics, and Prevention. Circ. Res. 120, 472–495 (2017).
Ding, H. et al. Telomere length and risk of stroke in Chinese. Stroke. 43, 658–663 (2012).
Ritzel, R. M. et al. Aging alters the immunological response to ischemic stroke. Acta Neuropathol. (berl). 136, 89–110 (2018).
Yousufuddin, M. & Young, N. Aging and ischemic stroke. Aging (milano). 11, 2542–2544 (2019).
Li, X. et al. Accelerated aging mediates the associations of unhealthy lifestyles with cardiovascular disease, cancer, and mortality. J. Am. Geriatr. Soc. 72, 181–193 (2024).
Cai, W. et al. Dysfunction of the neurovascular unit in ischemic stroke and neurodegenerative diseases: An aging effect. Ageing Res. Rev. 34, 77–87 (2017).
Klopack, E. T., Crimmins, E. M., Cole, S. W., Seeman, T. E. & Carroll, J. E. Accelerated epigenetic aging mediates link between adverse childhood experiences and depressive symptoms in older adults: Results from the Health and Retirement Study. SSM - Popul. health. 17, 101071 (2022).
Zhang, Z. et al. Biological aging mediates the association between periodontitis and cardiovascular disease: results from a national population study and Mendelian randomization analysis. Clin. Epigenetics. 16, 116 (2024).
Levine, M. E. et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (milano). 10, 573–591 (2018).
Klemera, P. & Doubal, S. A new approach to the concept and computation of biological age. Mech. Ageing Dev. 127, 240–248 (2006).
Chen, L. et al. Associations between biological ageing and the risk of, genetic susceptibility to, and life expectancy associated with rheumatoid arthritis: a secondary analysis of two observational studies. Lancet Healthy Longev. 5, e45–e55 (2024).
van Buuren, S. & Groothuis-Oudshoorn, K. mice: Multivariate Imputation by Chained Equations in R. J. Stat. Softw. 45, 1–67 (2011).
McCartney, D. L. et al. Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging. Genome Biol. 22, 194 (2021).
Codd, V. et al. Polygenic basis and biomedical consequences of telomere length variation. Nat. Genet. 53, 1425–1433 (2021).
Jiang, L. et al. A resource-efficient tool for mixed model association analysis of large-scale data. Nat. Genet. 51, 1749–1755 (2019).
Atkins, J. L. et al. A genome-wide association study of the frailty index highlights brain pathways in ageing. Aging Cell. 20, e13459 (2021).
Mishra, A. et al. Stroke genetics informs drug discovery and risk prediction across ancestries. Nature. 611, 115–123 (2022).
Malik, R. et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat. Genet. 50, 524–537 (2018).
Burgess, S. et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open. Res. 4, 186 (2019).
Verbanck, M., Chen, C. Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).
Wang, S. et al. Systemic inflammatory regulators and risk of acute-on-chronic liver failure: A bidirectional mendelian-randomization study. Front. Cell. Dev. Biol. 11, 1125233 (2023).
Bowden, J. et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int. J. Epidemiol. 47, 1264–1278 (2018).
Forrester, S. N. et al. Racial differences in the association of accelerated aging with future cardiovascular events and all-cause mortality: the coronary artery risk development in young adults study, 2007–2018. Ethn. Health. 27, 997–1009 (2022).
Pottinger, T. D. et al. Association of cardiovascular health and epigenetic age acceleration. Clin. Epigenetics. 13, 42 (2021).
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. Hallmarks of aging: An expanding universe. Cell. 186, 243–278 (2023).
Paneni, F., Diaz Cañestro, C., Libby, P., Lüscher, T. F. & Camici, G. G. The Aging Cardiovascular System: Understanding It at the Cellular and Clinical Levels. J. Am. Coll. Cardiol. 69, 1952–1967 (2017).
Liberale, L. et al. Inflammation, Aging, and Cardiovascular Disease: JACC Review Topic of the Week. J. Am. Coll. Cardiol. 79, 837–847 (2022).
Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23, R89–98 (2014).
Burton, J. K. et al. Prevalence and implications of frailty in acute stroke: systematic review & meta-analysis. Age Ageing. 51, afac064 (2022).
Liu, W. et al. Genetically predicted frailty index and risk of stroke and Alzheimer’s disease. Eur. J. Neurol. 29, 1913–1921 (2022).
Jin, C. et al. Leveraging single-cell RNA sequencing to unravel the impact of aging on stroke recovery mechanisms in mice. Proc. Natl. Acad. Sci. 120, e2300012120 (2023).
Baltan, S., Shi, Y., Keep, R. F. & Chen, J. The effect of aging on brain injury and recovery after stroke. Neurobiol. Dis. 126, 1–2 (2019).
Buga, A. M. et al. Transcriptomics of Post-Stroke Angiogenesis in the Aged Brain. Front. Aging Neurosci. 6, (2014).
Engler-Chiurazzi, E. B., Monaghan, K. L., Wan, E. C. K. & Ren, X. Role of B cells and the aging brain in stroke recovery and treatment. GeroScience. 42, 1199–1216 (2020).
Petcu, E. B., Smith, R. A., Miroiu, R. I. & Opris, M. M. Angiogenesis in old-aged subjects after ischemic stroke: a cautionary note for investigators. J. Angiogenesis Res. 2, 26 (2010).
Anrather, J. & Iadecola, C. Inflammation and Stroke: An Overview. Neurotherapeutics: J. Am. Soc. Experimental Neurother. 13, 661–670 (2016).
Shi, K. et al. Global brain inflammation in stroke. Lancet Neurol. 18, 1058–1066 (2019).
Ungvari, Z., Kaley, G., de Cabo, R., Sonntag, W. E. & Csiszar, A. Mechanisms of vascular aging: new perspectives. J. Gerontol. A. 65, 1028–1041 (2010).
Lu, A. T. et al. GWAS of epigenetic aging rates in blood reveals a critical role for TERT. Nat. Commun. 9, 387 (2018).

No competing interests reported.

SupplementaryMaterial.docx

Download PDF

Reviewers agreed at journal
20 Nov, 2024
Reviewers agreed at journal
17 Nov, 2024
Reviewers invited by journal
18 Oct, 2024
Editor assigned by journal
04 Oct, 2024
Editor invited by journal
26 Sep, 2024
Submission checks completed at journal
06 Sep, 2024
First submitted to journal
28 Aug, 2024

You are reading this latest preprint version

Association between biological aging and the risk of stroke: Results from the National Health and Nutrition Examination Survey and Mendelian randomization analysis

Status:

Version 1

Abstract

Introduction:

Methods

Results

Conclusion

Figures

1 Introduction

2 Methods

3 Results

4 Discussion

5 Conclusions

Declarations

Conflict of interest

Funding

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1