Association of muscle strength with abdominal aortic calcification

Abstract

Background

Abdominal aortic calcification, a subclinical marker of atherosclerosis, shares pathophysiological pathways with musculoskeletal decline. Although grip strength is an established predictor of cardiovascular outcomes, its association with abdominal aortic calcification in sex-specific analysis remains underexplored.

Methods

This cross-sectional study analyzed data from 1683 adults in National Health and Nutrition Examination Survey 2013–2014. Abdominal aortic calcification was quantified via dual-energy X-ray absorptiometry using Kauppila scores (L1–L4), with abdominal aortic calcification defined as a score >0. Muscle strength was evaluated using a standardized grip strength measurement (Takei dynamometer). Multivariable logistic regression, adjusted for demographic, metabolic, and lifestyle factors, were used to examine sex-specific associations. Restricted cubic splines were applied to evaluate dose–response relationships.

Results

Among 903 men (mean age, 58.3 ± 12.0 years) and 780 women (mean age, 57.2 ± 11.9 years), each 1-kg increase in grip strength was associated with a 1.8% reduction in abdominal aortic calcification risk (odds ratio, 0.982; 95% confidence interval: 0.971–0.993) in men and a 2.6% reduction (odds ratio, 0.974; 95% confidence interval: 0.955–0.993) in women. Dose–response curves demonstrated linear inverse relationships (p < 0.05). Subgroup analyses confirmed consistency of this association across age, body mass index, hypertension, and diabetes status (all p for interaction > 0.05).

Conclusion

Muscle strength exhibits an independent, dose-dependent inverse association with the prevalence of abdominal aortic calcification, which persists across demographic subgroups. These findings support the use of grip strength as a practical biomarker for abdominal aortic calcification risk stratification and highlight musculoskeletal health as a potential target for interventions to prevent vascular calcification.

Keywords

Muscle strength abdominal aortic calcification cardiovascular disease risk factors

Introduction

Cardiovascular disease (CVD) is a major public health concern and the leading cause of mortality worldwide.¹ Abdominal aortic calcification (AAC) is a common form of vascular calcification characterized by disrupted metabolism of calcium and phosphorus as well as abnormal deposition of mineralized plaques within the arterial wall. Its incidence increases progressively with age.^2,3 Numerous studies have demonstrated that AAC is an independent risk factor for cardiovascular events and a reliable biomarker of atherosclerotic CVD.^4–6 A retrospective study has demonstrated that computed tomography (CT)–based AAC is a robust predictor of CVD.⁷ Although traditional risk factors such as hypertension, diabetes, and hyperlipidemia contribute to vascular calcification, emerging evidence suggests that musculoskeletal health plays a protective role.^8–10

Muscle strength is a key indicator of musculoskeletal health.¹¹ Although grip strength is a simple and objective measure of muscle strength and an established marker of cardiovascular health, evidence regarding its association with AAC remains limited and inconclusive.^12,13 The National Health and Nutrition Examination Survey (NHANES) provides a robust, nationally representative dataset to investigate this relationship. Using data from NHANES 2013–2014, this study examined the association between muscle strength, assessed via grip strength, and the prevalence of AAC, with adjustment for key demographic, metabolic, and lifestyle confounders.¹³ Given established sex-based differences in muscle mass and cardiovascular risk, we conducted stratified analyses to evaluate potential variations in this association between men and women.^14,15

Given the shared pathophysiological pathways underlying diminished muscle strength and vascular calcification, integrating grip strength into risk prediction models for AAC holds significant clinical promise. Key drivers, including chronic inflammation (e.g. elevated αtumor necrosis factor-alpha (TNF-α) and Interleukin-1 beta (IL-1β)), oxidative stress, and endocrine dysregulation, synergistically promote myofiber loss and vascular medial calcification.^16–19 Therefore, grip strength may serve not only as an indicator of overall muscle health but also as an integrative marker of subclinical risk. Elucidating the independent association between grip strength and AAC will provide direct evidence for developing early screening tools based on grip strength measurement and will advance the establishment of personalized resistance exercise regimens as effective nonpharmacological strategies to mitigate the progression of arterial calcification. We hypothesized that higher muscle strength is inversely associated with the prevalence of AAC and that this relationship persists after adjusting for confounding factors. Additionally, we examined whether this association varies across subgroups defined by age, body mass index (BMI), hypertension, and diabetes status. Our findings may provide insights into the potential role of muscle strength in mitigating vascular calcification and inform future interventions targeting musculoskeletal health to reduce cardiovascular risk.

Methods

Study design and population

This study used data from NHANES 2013–2014. NHANES is a continuous, cross-sectional survey designed to assess data on health, nutrition, and demographics of the noninstitutionalized population in the United States through a multistage probability sampling method. Data were obtained from publicly available files and the NHANES study protocol was approved by the National Center for Health Statistics Ethics Review Board, with all participants providing written informed consent. All participant information was deidentified. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.

For the present analysis, all participants from NHANES 2013–2014 were screened for inclusion. As shown in Figure 1, participants were excluded if any of the following data were unavailable: AAC data (n = 7035), muscle strength data (n = 282), or other covariates of interest (n = 370). Furthermore, to minimize potential confounding in grip strength assessment, participants with a history of hand or wrist surgery (n = 141) or those reporting recent hand pain, aching, or stiffness (n = 664) were excluded. After these exclusions, the final analytical cohort comprised 1683 participants.

Figure 1.

Flowchart showing the selection of NHANES 2013–2014 participants for analysis. AAC: abdominal aorta calcification; NHANES: National Health and Nutrition Examination Survey.

Assessment of muscle strength

Grip strength was measured using a Takei digital grip strength dynamometer (model T.K.K. 5401) as an objective assessment of muscle strength. A trained examiner provided standardized instructions and demonstrated the testing protocol to each participant. The grip span of the dynamometer was individually adjusted according to hand size, followed by a practice trial to ensure proper understanding of the procedure and optimal grip adjustment.

Participants were instructed to exert maximal force on the dynamometer with one hand while exhaling to prevent intrathoracic pressure buildup. The test was then repeated for the contralateral hand following the same protocol. Three consecutive measurements were obtained for each hand, with trials alternating between hands and a 60-s rest interval between measurements on the same hand to minimize fatigue. For analysis, combined grip strength was calculated as the sum of the highest recorded values from both hands and expressed in kilograms.²⁰

AAC measurements

The severity of AAC was quantified using AAC score, which was determined based on the Kauppila semi-quantitative scoring system.²¹ The AAC scores and relevant data were obtained from the publicly available NHANES 2013–2014 database. In the original survey, lateral dual-energy X-ray absorptiometry (DXA) scans of the thoracolumbar spine were acquired at mobile examination centers using a Hologic Discovery A densitometer (Hologic, Marlborough; MA, USA). AAC was assessed at vertebral levels L1–L4 via the instant vertebral assessment protocol, with each segment scored from 0 to 3. The total AAC score was calculated as the sum of scores across these four vertebrae, resulting in a possible range of 0 to 24, with higher scores indicating more severe calcification. According to NHANES documentation, all scans were initially assessed by trained radiologists. A rigorous quality control protocol was implemented, including a second review by an expert radiologist for a randomly selected subset of scans to ensure consistency. Detailed quantification protocols, including Kauppila scoring quality assurance procedures, are available at https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/DXXAAG_H.htm. For the present analysis, AAC was defined as a total score >0, with a score >6 indicating severe AAC.

Measurements of other covariates

Based on previous studies and clinical judgment, multiple potential confounding variables were considered, including age, sex, race, BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), smoking status, drinking status, hypertension, diabetes, hyperlipidemia, education level, poverty–income ratio (PIR), fasting plasma glucose (FPG), total cholesterol (TC), triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), serum creatinine, alkaline phosphatase, serum calcium, and serum phosphate.

Participants’ demographic and anthropometric characteristics; lifestyle; and cardiovascular risk factors, including age, sex, race, smoking status, drinking status, education level, and PIR were collected using standardized questionnaires by trained interviewers. Smoking status was classified as current (smoked more than 100 cigarettes and currently smoke occasionally or daily), former (smoked more than 100 cigarettes but currently are nonsmoker), or never (smoked less than 100 cigarettes in their lifetime).²² Participants were categorized by alcohol intake as current (consumed ≥12 drinks in the past year), former (did not drink in the past year but had consumed ≥12 drinks in their lifetime), or never (consumed <12 drinks in their lifetime).²³ Education level was classified as less than high school, high school or equivalent, and above high school. Hypertension was defined as an average SBP ≥140 mmHg and/or DBP ≥90 mmHg, a self-reported diagnosis, or use of antihypertensive medications.²² Diabetes was defined as FPG ≥7.0 mmol/L, a self-reported diagnosis, or use of diabetes medication or insulin.²⁴ Hyperlipidemia was defined by any of the following criteria: (a) use of lipid-lowering medications; (b) hypertriglyceridemia (triglycerides ≥150 mg/dL); (c) hypercholesterolemia (TC ≥200 mg/dL, LDL-C ≥130 mg/dL, or HDL-C <40 mg/dL).²⁴ Detailed descriptions of BMI, blood pressure, FPG, TC, triglycerides, HDL-C, LDL-C, serum creatinine, alkaline phosphatase, serum calcium, and serum phosphate measurements are available at www.cdc.gov/Nchs/Data/nahnes.

Study definitions and endpoints

This analysis included participants aged ≥40 years. The following methods and definitions were applied: 1. AAC was quantified using the Kauppila score (range, 0–24) derived from DXA scans, with AAC defined as a score >0. 2. Muscle strength was measured as the sum of maximum bilateral grip strength and categorized into sex-specific tertiles. 3 The primary outcome was the prevalence of AAC (score >0), whereas secondary analyses examined severe AAC (score >6). Muscle strength tertiles served as the primary exposure.

Statistical analysis

Normally distributed continuous data were expressed as mean ± SD, whereas non-normally distributed continuous data were expressed as median (interquartile range). Categorical data were expressed as numbers (percentages). Given the significant differences in muscle strength between sexes, we analyzed muscle strength using sex-specific tertiles. Differences among groups were evaluated using analysis of variance (ANOVA) or the Kruskal-Wallis H test, as appropriate, for continuous variables and χ² test for categorical variables. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between muscle strength and AAC. Univariate and multivariate models were performed, adjusting for age, alkaline phosphatase, BMI, diabetes, drinking status, education level, FPG, hyperlipidemia, hypertension, LDL-C, PIR, race, SBP, serum calcium, serum creatinine, serum phosphate, smoking status, and triglycerides. Additionally, we used a restricted cubic spline regression model with three knots to assess the dose–response association between muscle strength and AAC. We further performed subgroup analysis stratifying participants according to age, BMI, hypertension, and diabetes.

All analyses were conducted using Statistical Package for Social Sciences (SPSS) version 26 (Inc.; Chicago, IL) and R version 4.3.1. A two-sided p value <0.05 was considered statistically significant.

Results

Characteristics according to muscle strength levels

The demographic and clinical characteristics of male and female participants are summarized in Tables 1 and 2, respectively. Among male participants (n = 903), the mean age was 58.3 ± 12.0 years, 409 (45.3%) were non-Hispanic White, 173 (19.2%) were current smokers, 754 (83.5%) were current drinkers, 380 (42.1%) had hypertension, 148 (16.4%) had diabetes, and 450 (49.8%) had hyperlipidemia. Additionally, 268 (29.7%) participants exhibited AAC, and the mean muscle strength was 84.9 ± 17.2 kg. Comparative analysis revealed that participants with greater muscle strength were significantly younger and more likely to be non-Hispanic White. They also had higher education levels and were more likely to be never or current smokers along with a higher prevalence of hypertension. Notably, the highest muscle strength group exhibited higher mean values for BMI, DBP, PIR, TC levels, and LDL-C levels, whereas demonstrated lower levels of FPG, HDL-C, and alkaline phosphatase. In contrast, no statistically significant differences were observed across muscle strength tertiles for SBP; drinking status; diabetes prevalence; hyperlipidemia prevalence; and triglycerides, serum creatinine, or serum phosphate levels.

Table 1.

Characteristics of male participants in NHANES 2013–2014 according to muscle strength tertiles.

	Total(n = 903)	Tertile 1(n = 301)	Tertile 2 (n = 299)	Tertile 3 (n = 303)	p value
Age, years	58.3 ± 12.0	66.0 ± 11.7	57.0 ± 10.8	52.0 ± 9.0	<0.001
Race, n (%)					0.001
Mexican American	107 (11.8%)	44 (14.6%)	35 (11.7%)	28 (9.2%)
Other Hispanic	74 (8.2%)	30 (10.0%)	26 (8.7%)	18 (5.9%)
Non-Hispanic White	409 (45.3%)	127 (42.2%)	134 (44.8%)	148 (48.8%)
Non-Hispanic Black	186 (20.6%)	44 (14.6%)	63 (21.1%)	79 (26.1%)
Other Race	127 (14.1%)	56 (18.6%)	41 (13.7%)	30 (9.9%)
BMI, kg/m²	28.1 ± 4.8	27.0 ± 4.3	27.9 ± 4.7	29.5 ± 5.0	<0.001
SBP, mmHg	126.9 ± 16.5	129.0 ± 18.5	125.9 ± 15.6	125.8 ± 15.0	0.052
DBP, mmHg	71.9 ± 13.4	68.7 ± 14.8	71.1 ± 13.6	75.7 ± 10.7	<0.001
Smoking status, n (%)					0.002
Never	422 (46.7%)	138 (45.8%)	128 (42.8%)	156 (51.5%)
Former	308 (34.1%)	121 (40.2%)	106 (35.5%)	81 (26.7%)
Current	173 (19.2%)	42 (14.0%)	65 (21.7%)	66 (21.8%)
Drinking status, n (%)					0.215
Never	73 (8.1%)	31 (10.3%)	21 (7.0%)	21 (6.9%)
Former	76 (8.4%)	31 (10.3%)	24 (8.0%)	21 (6.9%)
Current	754 (83.5%)	239 (79.4%)	254 (84.9%)	261 (86.1%)
Hypertension, n (%)	380 (42.1%)	147 (48.8%)	115 (38.5%)	118 (38.9%)	0.014
Diabetes, n (%)	148 (16.4%)	59 (19.6%)	46 (15.4%)	43 (14.2%)	0.169
Hyperlipidemia, n (%)	450 (49.8%)	150 (49.8%)	150 (50.2%)	150 (49.5%)	0.987
Education level, n (%)					0.021
Less than high school	183 (20.3%)	76 (25.2%)	61 (20.4%)	46 (15.2%)
High school or equivalent	205 (22.7%)	70 (23.3%)	60 (20.1%)	75 (24.8%)
Above high school	515 (57.0%)	155 (51.5%)	178 (59.5%)	182 (60.1%)
Poverty–income ratio	3.0 (1.3, 5.0)	2.2 (1.2, 4.4)	2.6 (1.3, 5.0)	3.5 (1.6, 5.0)	0.002
FPG, mmol/L	6.2 ± 2.6	6.5 ± 2.8	6.1 ± 2.4	6.0 ± 2.5	0.003
TC, mg/dL	188.8 ± 43.8	183.5 ± 44.7	187.9 ± 44.0	195.0 ± 41.9	0.004
Triglyceride, mg/dL	132.0 (88.0, 212.0)	125.0 (80.5, 197.0)	140.0 (87.0, 217.0)	134.0 (90.0, 221.0)	0.083
HDL-C, mg/dL	48.5 ± 14.7	50.2 ± 15.9	47.7 ± 14.8	47.6 ± 13.2	0.032
LDL-C, mg/dL	112.4 ± 38.0	106.9 ± 39.7	111.6 ± 37.8	118.6 ± 35.5	<0.001
Serum creatinine, mg/dL	88.4 (78.7, 102.5)	89.3 (76.0, 106.5)	87.5 (78.7, 99.9)	88.4 (80.4, 102.5)	0.479
Alkaline phosphatase, U/L	66.8 ± 23.1	69.0 ± 23.3	67.2 ± 25.0	64.2 ± 20.8	0.022
Serum calcium, mmol/L	2.4 ± 0.1	2.4 ± 0.1	2.4 ± 0.1	2.4 ± 0.1	0.021
Serum phosphate, mmol/L	1.2 ± 0.2	1.2 ± 0.2	1.2 ± 0.2	1.2 ± 0.2	0.752
Muscle strength, kg	84.9 ± 17.2	66.1 ± 9.7	85.4 ± 3.9	103.2 ± 9.2	<0.001
AAC, n (%)	268 (29.7%)	132 (43.9%)	82 (27.4%)	54 (17.8%)	<0.001

Male grip strength tertiles were defined as follows: tertile 1 < 78.5 kg, tertile 2 = 78.5–92.1 kg, and tertile 3 > 92.1 kg.

AAC: abdominal aortic calcification; BMI: body mass index; DBP: diastolic blood pressure; FPG: fasting plasma glucose; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; SBP: systolic blood pressure; TC: total cholesterol.

Table 2.

Characteristics of female participants in NHANES 2013–2014 according to muscle strength tertiles.

	Total(n = 780)	Tertile 1(n = 259)	Tertile 2(n = 261)	Tertile 3(n = 260)	p value
Age, years	57.2 ± 11.9	64.6 ± 11.9	56.3 ± 10.8	50.6 ± 8.3	<0.001
Race, n (%)					<0.001
Mexican American	83 (10.6%)	34 (13.1%)	26 (10.0%)	23 (8.8%)
Other Hispanic	77 (9.9%)	30 (11.6%)	30 (11.5%)	17 (6.5%)
Non-Hispanic White	377 (48.3%)	136 (52.5%)	131 (50.2%)	110 (42.3%)
Non-Hispanic Black	145 (18.6%)	19 (7.3%)	43 (16.5%)	83 (31.9%)
Other Race	98 (12.6%)	40 (15.4%)	31 (11.9%)	27 (10.4%)
BMI, kg/m²	28.6 ± 6.2	27.5 ± 5.9	28.2 ± 6.4	30.1 ± 6.2	<0.001
SBP, mmHg	125.6 ± 19.3	131.3 ± 21.7	124.2 ± 18.8	121.3 ± 15.6	<0.001
DBP, mmHg	69.8 ± 12.8	67.6 ± 15.2	71.0 ± 11.5	70.9 ± 10.9	0.005
Smoking status, n (%)					0.767
Never	491 (62.9%)	168 (64.9%)	156 (59.8%)	167 (64.2%)
Former	161 (20.6%)	52 (20.1%)	58 (22.2%)	51 (19.6%)
Current	128 (16.4%)	39 (15.1%)	47 (18.0%)	42 (16.2%)
Drinking status, n (%)					<0.001
Never	148 (19.0%)	60 (23.2%)	36 (13.8%)	52 (20.0%)
Former	135 (17.3%)	59 (22.8%)	44 (16.9%)	32 (12.3%)
Current	497 (63.7%)	140 (54.1%)	181 (69.3%)	176 (67.7%)
Hypertension, n (%)	348 (44.6%)	148 (57.1%)	102 (39.1%)	98 (37.7%)	<0.001
Diabetes, n (%)	104 (13.3%)	49 (18.9%)	34 (13.0%)	21 (8.1%)	0.001
Hyperlipidemia, n (%)	332 (42.6%)	132 (51.0%)	105 (40.2%)	95 (36.5%)	0.003
Education level, n (%)					0.005
Less than high school	149 (19.1%)	67 (25.9%)	47 (18.0%)	35 (13.5%)
High school or equivalent	157 (20.1%)	53 (20.5%)	47 (18.0%)	57 (21.9%)
Above high school	474 (60.8%)	139 (53.7%)	167 (64.0%)	168 (64.6%)
Poverty–income ratio	2.5 (1.2, 4.6)	2.2 (1.1, 3.8)	3.0 (1.2, 5.0)	2.8 (1.2, 4.7)	0.021
FPG, mmol/L	5.9 ± 2.5	6.3 ± 3.1	5.9 ± 2.7	5.5 ± 1.4	0.001
TC, mg/dL	203.4 ± 44.2	202.1 ± 40.9	204.5 ± 39.8	203.7 ± 51.1	0.723
Triglyceride, mg/dL	120.5 (81.3, 185.8)	131.0 (88.0, 203.0)	126.0 (87.0, 185.5)	108.5 (71.0, 173.8)	0.001
HDL-C, mg/dL	59.1 ± 17.0	59.3 ± 17.5	59.7 ± 16.9	58.3 ± 16.6	0.457
LDL-C, mg/dL	117.3 ± 39.3	114.8 ± 35.2	119.0 ± 34.9	117.9 ± 46.7	0.397
Serum creatinine, mg/dL	71.1 ± 25.7	72.9 ± 33.6	68.7 ± 17.8	71.7 ± 23.0	0.178
Alkaline phosphatase, U/L	67.8 ± 23.9	73.0 ± 30.3	66.9 ± 19.3	63.4 ± 19.5	<0.001
Serum calcium, mmol/L	2.4 ± 0.1	2.4 ± 0.1	2.4 ± 0.1	2.4 ± 0.1	0.164
Serum phosphate, mmol/L	1.3 ± 0.2	1.3 ± 0.2	1.3 ± 0.2	1.2 ± 0.2	0.001
Muscle strength, kg	55.0 ± 11.1	42.8 ± 6.1	55.1 ± 2.8	66.9 ± 6.2	<0.001
AAC, n (%)	214 (27.4%)	99 (38.2%)	72 (27.6%)	43 (16.5%)	<0.001

Female grip strength tertiles were defined as follows: tertile 1 < 50.1 kg, tertile 2 = 50.1–59.7 kg, and tertile 3 > 59.7 kg.

Among female participants (n = 780), the mean age was 57.2 ± 11.9 years, 377 (48.3%) were non-Hispanic White, 128 (16.4%) were current smokers, 497 (63.7%) were current drinkers, 348 (44.6%) had hypertension, 104 (13.3%) had diabetes, and 332 (42.6%) had hyperlipidemia. Additionally, 214 (27.4%) participants exhibited AAC, and the mean muscle strength was 55.0 ± 11.1 kg. Higher muscle strength tertiles were inversely associated with age and positively associated with education level. These participants included a lower proportion of non-Hispanic White and former drinkers and exhibited lower rates of hypertension, diabetes, and hyperlipidemia. The strongest tertile demonstrated elevated biometric parameters, including BMI, DBP, PIR, and serum phosphate levels along with decreased SBP and FPG and triglyceride levels. No statistically significant intertertile differences were observed for smoking status and levels of TC, HDL-C, LDL-C, serum creatinine, or serum calcium. Visualization of AAC score distributions via histograms revealed comparable patterns between sexes, with no statistically significant differences in shape or severity (p > 0.05, Supplementary Figure 1).

Association between muscle strength and AAC

Table 3 presents the association between muscle strength and AAC in multivariable-adjusted analyses. When analyzed as a continuous variable, each 1-kg increase in muscle strength was inversely associated with AAC prevalence, corresponding to a 1.8% risk reduction in males (OR, 0.982; 95% CI: 0.971–0.993) and a 2.6% reduction in females (OR, 0.974; 95% CI: 0.955–0.993). This dose–response relationship remained significant when muscle strength was analyzed in tertiles. Compared with the lowest tertile (reference), males in the highest muscle strength tertile demonstrated markedly lower AAC risk (OR, 0.440; 95% CI: 0.283–0.683), whereas females showed a similar but nonsignificant trend (OR, 0.615; 95% CI: 0.368–1.027). To address potential limitations from smaller sample sizes in tertile groups, additional analyses were performed using dichotomized muscle strength classifications (Supplementary Table 1). Unadjusted and adjusted models consistently showed a significantly lower risk of AAC risk in the high muscle strength group compared with the low-strength group across both sexes. Additionally, a supplementary analysis using a clinically relevant AAC threshold (>6) yielded consistent results, with higher muscle strength showing a significant inverse association with severe AAC in males and a nonsignificant trend in females (Supplementary Table 2). Multivariable-adjusted restricted cubic spline regression models revealed significant linear associations between muscle strength and AAC prevalence in male and female cohorts (Figures 2 and 3).

Table 3.

Association between muscle strength and the risk of AAC stratified by sex.

	No. events/total	Model 1		Model 2		Model 3
	No. events/total	OR (95% CI)	p value	OR (95% CI)	p value	OR (95% CI)	p value
Male
Tertile 1	132/301	Reference	–	Reference	–	Reference	–
Tertile 2	82/299	0.484 (0.344–0.680)	<0.001	0.666 (0.461–0.962)	0.030	0.676 (0.461–0.993)	0.046
Tertile 3	54/303	0.278 (0.191–0.403)	<0.001	0.447 (0.295–0.679)	<0.001	0.440 (0.283–0.683)	<0.001
Muscle strength, kg	268/903	0.968 (0.959–0.977)	<0.001	0.981 (0.971–0.991)	<0.001	0.982 (0.971–0.993)	0.001
Female
Tertile 1	99/259	Reference	–	Reference	–	Reference	–
Tertile 2	72/261	0.616 (0.426–0.891)	0.010	0.823 (0.550–1.231)	0.342	0.901 (0.590–1.378)	0.631
Tertile 3	43/260	0.320 (0.212–0.484)	<0.001	0.553 (0.341–0.897)	0.016	0.615 (0.368–1.027)	0.063
Muscle strength, kg	214/780	0.952 (0.938–0.967)	<0.001	0.971 (0.953–0.988)	0.001	0.974 (0.955–0.993)	0.007

Model 1: Unadjusted.

Model 2: Adjusted for age and race.

Model 3: Adjusted for Model 2 covariates along with alkaline phosphatase, body mass index, drinking status, education level, fasting plasma glucose, history of diabetes, history of hyperlipidemia, history of hypertension, low-density lipoprotein cholesterol, poverty–income ratio, serum calcium, serum creatinine, serum phosphate, smoking status, systolic blood pressure, and triglyceride.

AAC: abdominal aorta calcification; CI: confidence interval; OR: odds ratio.

Figure 2.

Adjusted odds ratios for AAC prevalence by muscle strength in male participants. Each odds ratio was calculated using a muscle strength of 85.5 kg as the reference. The odds ratio was adjusted for age, alkaline phosphatase, body mass index, diabetes, drinking status, education level, fasting plasma glucose, hyperlipidemia, hypertension, low-density lipoprotein cholesterol, poverty–income ratio, race, serum calcium, serum creatinine, serum phosphate, smoking status, systolic blood pressure, and triglyceride. The red solid line represents the odds ratio across the whole range of muscle strength. The red dotted line represents the 95% CI. The black dotted line indicates the reference line (HR = 1). Histograms show the frequency distribution of muscle strength. AAC: abdominal aorta calcification; CI: confidence interval; HR: hazard ratio.

Figure 3.

Adjusted odds ratios of AAC prevalence by muscle strength in female participants. Each odds ratio was calculated using a muscle strength of 55.5 kg as the reference. The odds ratio was adjusted for age, alkaline phosphatase, body mass index, diabetes, drinking status, education level, fasting plasma glucose, hyperlipidemia, hypertension, low-density lipoprotein cholesterol, poverty–income ratio, race, serum calcium, serum creatinine, serum phosphate, smoking status, systolic blood pressure, and triglyceride. The red solid line represents the odds ratio across the full range of muscle strength. The red dotted line represents the 95% CI. The black dotted line indicates the reference line (HR = 1). Histograms show the frequency distribution of muscle strength. AAC: abdominal aorta calcification; CI: confidence interval; HR: hazard ratio.

Subgroup analysis

When participants were stratified by key demographic and clinical variables, including age (<60 or ≥60 years), BMI (<28 or ≥28 kg/m²), hypertension status (yes or no), and diabetes status (yes or no), the inverse association between muscle strength and AAC prevalence remained statistically significant across all subgroups. This consistent relationship was observed in male and female cohorts, as shown in Figures 4 and 5. Further stratified analyses confirmed that the magnitude of this association did not vary substantially between subgroups, reinforcing the generalizability of our findings.

Figure 4.

Subgroup analysis of the association between muscle strength and AAC prevalence in male participants. Logistic regression was performed after adjustment for age, alkaline phosphatase, body mass index, diabetes, drinking status, education level, fasting plasma glucose, hyperlipidemia, hypertension, low-density lipoprotein cholesterol, poverty–income ratio, race, serum calcium, serum creatinine, serum phosphate, smoking status, systolic blood pressure, and triglyceride. AAC: abdominal aorta calcification.

Figure 5.

Subgroup analysis of the association between muscle strength and AAC prevalence in female participants. Logistic regression was performed after adjustment for age, alkaline phosphatase, body mass index, diabetes, drinking status, education level, fasting plasma glucose, hyperlipidemia, hypertension, low-density lipoprotein cholesterol, poverty–income ratio, race, serum calcium, serum creatinine, serum phosphate, smoking status, systolic blood pressure, and triglyceride. AAC: abdominal aorta calcification.

Discussion

Our study demonstrated a significant inverse association between muscle strength, assessed via grip strength, and AAC prevalence in a nationally representative adult population. Notably, this relationship persisted in sex-specific analyses and remained robust after adjustment for key demographic, metabolic, and lifestyle confounders. These findings suggest that muscle strength may serve as an independent marker of vascular health, with potential implications for CVD risk stratification and prevention strategies.

The observed inverse association between muscle strength and AAC is consistent with emerging evidence linking musculoskeletal health to cardiovascular outcomes. Previous studies have shown that sarcopenia and low muscle strength are associated with increased arterial stiffness, endothelial dysfunction, and higher CVD mortality.^25–27 Our study extends this literature by specifically examining AAC, a well-validated biomarker of subclinical atherosclerosis, and demonstrating that the association between muscle strength and AAC remains significant even after accounting for traditional CVD risk factors.

Conversely, the causal pathway may operate in the reverse direction. Advanced vascular calcification may contribute to reduced muscle strength through several mechanisms. Recent studies suggest that severe AAC can impair blood flow to skeletal muscles, leading to tissue ischemia and dysfunction.²⁸ Furthermore, the systemic inflammatory milieu associated with progressive atherosclerosis may directly promote muscle wasting and weakness.²⁹ This bidirectional relationship underscores the complex interplay between musculoskeletal and vascular health.

The sex-specific differences in our findings warrant further discussion. Although males and females exhibited a dose-dependent reduction in AAC risk with increasing muscle strength, the effect size was greater in males (OR, 0.440 for the highest vs. lowest tertile) than in females (OR, 0.615; nonsignificant trend). This discrepancy may reflect biological differences in muscle composition, hormonal influences, or variations in vascular calcification pathophysiology between sexes.^14,15 Nevertheless, the consistent inverse trend in both groups supports the notion that muscle strength is a correlate of vascular health irrespective of sex.

Several biologically plausible mechanisms may explain the observed inverse association between muscle strength and AAC. However, these mechanisms remain speculative, and it is possible that both parameters share common upstream risk factors, such as sedentary lifestyle or chronic inflammation. Within this conceptual framework, skeletal muscle functions as an endocrine organ by secreting myokines (e.g. irisin and interleukin-6), which can exert anti-inflammatory effects, potentially counteracting the chronic low-grade inflammation implicated in vascular calcification pathogenesis.^30,31 Furthermore, greater muscle mass is associated with improved metabolic homeostasis, thereby attenuating insulin resistance and dyslipidemia, which are established contributors to atherosclerotic calcification.^32,33 Finally, enhanced muscle strength is associated with improved endothelial function and arterial compliance, which may reduce hemodynamic stress and subsequent vascular stiffening.^34,35 Collectively, these pathways suggest potential biological links and also acknowledge that the association may reflect shared risk factors.

The clinical implications of these findings are threefold. First, grip strength measurement, a simple and cost-effective tool, can be implemented as an adjunct risk stratification tool to identify individuals at a higher risk of subclinical atherosclerosis. Second, our results support the hypothesis that resistance training and structured physical activity programs, particularly those targeting muscle strength preservation, should be incorporated into existing CVD prevention guidelines, with particular relevance for aging populations. Third, the observed sex-specific variation in the association between muscle strength and vascular calcification underscores the need for further mechanistic investigations. Such studies may facilitate the development of sex-tailored preventive interventions aimed to optimize cardiovascular risk reduction.

Several methodological limitations of our study warrant consideration. First, the cross-sectional design inherently precludes causal inference, and the observed association may be partly explained by shared risk factors (e.g. inflammation and sedentary behavior) rather than a direct protective effect. Second, although we implemented comprehensive multivariable adjustments for available confounders, including history of hypertension, diabetes, and dyslipidemia, these adjustments were partial. Consequently, residual confounding from unmeasured or imperfectly measured variables remains possible. Critically, data on chronic kidney disease, a major determinant of vascular calcification and muscle weakness, were unavailable, representing a significant limitation. Additionally, information on medication use (e.g. statins and antihypertensives), vitamin D status, and osteoporosis, which are key factors in the muscle–bone–vascular axis, was unavailable and constitutes important unmeasured confounding. Third, although our DXA-based AAC assessment represents a validated approach, its relatively lower sensitivity compared with CT may result in systematic underestimation of calcification burden, potentially attenuating effect estimates. These limitations highlight important directions for future research but do not invalidate our core findings regarding the muscle strength–AAC association.

In conclusion, our study provides novel evidence of an independent association between higher muscle strength and lower AAC prevalence in both sexes, with consistent effects across key demographic and clinical subgroups. These findings highlight the potential of musculoskeletal health as a marker of vascular health. Future research should employ longitudinal designs to investigate whether muscle-strengthening interventions can attenuate AAC progression and improve cardiovascular outcomes.

Supplemental Material

sj-pdf-1-imr-10.1177_03000605261421738 - Supplemental material for Association of muscle strength with abdominal aortic calcification

Supplemental material, sj-pdf-1-imr-10.1177_03000605261421738 for Association of muscle strength with abdominal aortic calcification by Xiao-Xue Li, Feng-Shun Wang, Hao Wang, Li-Jun Gao and Kai Liu in Journal of International Medical Research

Footnotes

Acknowledgments

We extend our gratitude to the participants, staff, and investigators of the NHANES study.

Authors’ contributions

XXL and FSW had full access to the data and performed the analyses; HW assisted in data interpretation; LJG helped in the data methods and presentation; XXL and KL critically revised the manuscript and supervised the study analyses. All authors read and approved the final manuscript.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Consent for publication

Not applicable.

Declaration of conflicting interests

The authors declare that they have no competing interests.

Ethics approval and consent to participate

The study was approved by the institutional review boards of all NHANES field centers, and informed consent was obtained from all participants.

Funding

None.

Supplemental material

Supplemental material for this article is available online.

ORCID iD

Kai Liu

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