Abstract
Objective
This study aimed to investigate the relationship between three cardiovascular health scores—Life's Simple 7 (LS7), Life's Essential 8 (LE8), and Life's Crucial 9 (LC9)—and the prevalence of abdominal aortic calcification (AAC) and severe AAC among middle-aged and elderly individuals using data from NHANES 2013–2014.
Methods
We analyzed data from 2484 adults aged 40 years and older with dual-energy X-ray absorptiometry (DXA) scans. Logistic regression models evaluated associations between each cardiovascular health score and AAC prevalence. The discriminatory ability of each score was compared using receiver operating characteristic (ROC) analysis, with area under the curve (AUC) values, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).
Results
In fully adjusted models, low LS7 scores were significantly associated with higher odds of AAC (OR = 2.16 [1.33–3.50]) and severe AAC (OR = 3.21 [1.45–7.13]), compared to high LS7 scores. While similar associations were observed for LE8 and LC9, LS7 demonstrated the strongest ability to differentiate individuals with and without AAC, as reflected by its higher AUC values. Adding LS7 to fully adjusted models improved model discrimination for both AAC and severe AAC, with significant NRI and IDI values.
Conclusion
These findings suggest that lower cardiovascular health scores are associated with increased AAC and severe AAC prevalence, with LS7 demonstrating the highest discriminatory ability.
Keywords: LS7, LE8, LC9, AAC, NHANES
1. Introduction
Vascular calcification (VC) refers to the deposition of calcium-phosphate complexes within the blood vessels. Abdominal aortic calcification (AAC) involves the buildup of calcium-phosphate deposits within the abdominal section of the aorta [1]. AAC is a significant marker of cardiovascular disease and systemic metabolic dysfunction [2]. Its prevalence increases with age and it is strongly associated with a range of cardiovascular and metabolic risk factors [3]. Among adults aged 65–69 years, approximately 60 % show signs of AAC, and this prevalence increases dramatically with age, reaching 96 % among individuals aged 85 years and older [3]. It is often used as an indicator of generalized atherosclerosis and cardiovascular disease risk [4]. Severe AAC has been associated with adverse cardiovascular outcomes, particularly in elderly individuals or those with multiple risk factors [5]. AAC can be detected through imaging techniques such as X-ray, CT scans, or ultrasound. In clinical practice, abdominal CT scans are often used to assess the extent of AAC, particularly in patients at high risk for cardiovascular disease [6]. Developed by Kauppila and colleagues, the Kauppila AAC score is a quantitative tool derived from lateral radiographs of the lumbar spine [7,8]. These radiographs, which are widely used in clinical practice for routine diagnostics, allow for the assessment of vascular calcifications. The Kauppila AAC score is commonly employed to evaluate the extent of abdominal aortic calcification, serving as a surrogate marker for atherosclerosis and cardiovascular risk [7,8].
By focusing on seven key components: managing blood pressure, controlling cholesterol, reducing blood sugar, staying physically active, eating a healthy diet, maintaining a healthy weight, and avoiding tobacco use, the American Heart Association (AHA) established Life's Simple 7 (LS7) for cardiovascular health (CVH) [9]. In 2020, AHA introduced an updated framework and added sleep health to enhance and expand CVH assessment, Life's Essential 8 (LE8) [10]. Life's Crucial 9 (LC9) is a framework that builds on the LE8 and introduces a significant new dimension by adding psychological health as a crucial component [11]. Recent research indicated that the average LE8 score in U.S. adults was 64.7 out of 100, and less than 20 % of U.S. adults achieved an LE8 score of 80 or above [12,13]. A majority of American adults may not be meeting the ideal criteria for cardiovascular health.
The concept of CVH and its quantification through tools has been pivotal in both cardiovascular wellness and overall longevity. Studies consistently showed that higher LS7 scores were strongly associated with lower risks for cardiovascular events [14]. Several large cohort studies have demonstrated that individuals with higher LE8 scores have significantly lower risk of developing cardiovascular disease, including conditions such as coronary artery disease, heart failure, stroke, and peripheral artery disease [[15], [16], [17], [18]]. Research has shown a progressive, negative correlation between the LE8 score and all-cause mortality in multiple populations, including middle-aged and elderly adults [13]. Given the close relationship between AAC and cardiovascular disease (CVD), promoting LE8 metrics could serve as an essential management approach to reduce the risk of both AAC and severe AAC. Some researchers supported that the updated LE8 scoring system offer a more detailed and nuanced view of cardiovascular health and risk compared with LS7 metrics [19]. The roles of LS7 and LE8 in CVD prevention and management are well-established. However, research on LC9 remains relatively limited, and there is still uncertainty about whether it performs better in cardiovascular disease risk assessment and prevention.
While individual studies have assessed the predictive value of LS7, LE8, and LC9 in relation to cardiovascular outcomes, there is a lack of research directly comparing the effectiveness of these three frameworks in predicting the prevalence of AAC. The primary aim of this study was to investigate how the LS7, LE8, and LC9 scores relate to the prevalence of AAC and severe AAC in middle-aged and elderly individuals using data from the National Health and Nutrition Examination Survey (NHANES). A secondary aim was to evaluate the comparative discriminatory performance of these scores in identifying individuals at risk of AAC and severe AAC.
2. Materials and methods
2.1. Study population
This analysis is based on data from the NHANES, a cross-sectional, nationally representative survey conducted by the National Center for Health Statistics (NCHS) [20]. NHANES uses a complex, multistage probability sampling design to collect extensive health data from the non-institutionalized U.S. population, enabling generalizability across demographic and socioeconomic groups. All participants provided informed consent, and the NHANES protocol was reviewed and approved by the NCHS Research Ethics Review Board to ensure compliance with ethical standards.
In this study, participants included adults aged 40 years and older who completed dual-energy X-ray absorptiometry (DXA) scans as part of NHANES 2013–2014 (n = 3708). The following exclusion criteria were applied: 1) Participants with invalid IVA Lateral Spine scans, defined as scans where one or more vertebrae (L1–L4) could not be assessed (n = 190); 2) Participants ineligible for DXA scanning due to pregnancy (n = 3), weight exceeding 204 kg (n = 1), or other contraindications (n = 374); 3) Participants with missing data on components of the LC9 index (n = 656). The LC9 score was calculated for individuals with data available on at least 8 of the 9 components. After applying these criteria, the final analytic sample consisted of 2484 participants with complete DXA scans and sufficient data for LC9 calculation (Fig. S1).
2.2. Application of LS7, LE8, and LC9 in evaluating cardiovascular health
This study employed three CVH metrics—LS7, LE8, and LC9—to evaluate participants' CVH status, with detailed criteria in Tables S1 and S2. LS7, developed by the AHA in 2010 [9], includes four behavioral metrics (smoking status, body mass index [BMI], physical activity, and diet) and three medical indicators (blood pressure, blood glucose, and total cholesterol), each scored as 0 (poor CVH), 1 (intermediate CVH), or 2 (ideal CVH), yielding a total score between 0 and 14 points. LE8, introduced in 2020 [10], expands upon LS7 by adding sleep health and updating the scoring algorithm to enhance CVH assessment. LE8 includes four health behaviors (diet, physical activity, nicotine exposure, and sleep) and four health factors (BMI, non-high-density lipoprotein cholesterol, blood glucose, and blood pressure), each scored on a 0–100 scale, with the overall score categorized as high (80–100), moderate (50–79), or low CVH (0–49). Recognizing mental health's role in CVH [21], LC9 further incorporates a depression measure using the Patient Health Questionnaire-9 (PHQ-9), which translates PHQ-9 scores into a 0–100 scale to reflect depressive symptom levels. The LC9 score, calculated as the average of LE8 metrics and the depression score, thus provides a more comprehensive measure of CVH by integrating both physical and mental health aspects.
2.3. Assessment of AAC
In this study, AAC was assessed using lateral spine images obtained through dual-energy X-ray absorptiometry (DXA) within the NHANES 2013–2014 protocol (https://wwwn.cdc.gov/.Nchs/.Nhanes/.2013-2014/.DXXAAC_H.htm). The DXA-based method allows for precise and minimally invasive imaging of the aorta with low radiation exposure, providing a reliable measure of AAC while ensuring participant safety. Images were obtained from participants aged 40 and older at the NHANES mobile examination centers using Hologic Discovery A densitometers with Apex 3.2 software. AAC scoring was performed based on the Kauppila system, which evaluates calcification in four sections of the abdominal aorta corresponding to the lumbar vertebrae L1–L4. Each segment was visually assessed for calcification extent, with scores ranging from 0 (no calcification) to 3 (extensive calcification covering over two-thirds of the aortic wall). The cumulative AAC score ranges from 0 to 24, with higher scores indicating greater calcification severity. The presence of AAC was defined as a score greater than 0, while severe AAC was defined as scores above 6, based on established thresholds reported in prior studies [[22], [23], [24]].
2.4. Covariates
In this study, key demographic and socioeconomic covariates were included to address potential confounding factors. Age was grouped into two categories—40–59 years and 60 years and older—to represent midlife and older adulthood. Sex was classified as male or female, while race/ethnicity was categorized as non-Hispanic White, non-Hispanic Black, or other, enabling the examination of disparities across these groups. Education level was divided into below high school, high school, and above high school to capture variations in socioeconomic status and health literacy. Marital status was recorded as married/living with a partner or single/divorced/widowed, given its relevance to health behaviors and support networks. Finally, the poverty-income ratio (PIR), a measure of family income relative to the federal poverty level, was included as a socioeconomic indicator. PIR is calculated by dividing family income by the poverty threshold for a given household size and composition, as set by the U.S. Census Bureau each year. In this study, PIR was categorized into three groups: ≤1.0 (at or below the poverty level), 1.1–3.0, and >3.0, to reflect a spectrum of economic conditions [25,26].
2.5. Statistical analysis
All analyses were weighted to ensure they reflect the U.S. population. Baseline characteristics are summarized with continuous variables presented as means with standard errors (SEs) and categorical variables as frequencies with percentages. The distribution of cardiovascular health scores—LE8 and LC9—across levels of LS7 was first examined using box plots, followed by Pearson correlation analysis to assess the relationships between LS7 and both LE8 and LC9.
The prevalence of AAC and severe AAC was calculated across different cardiovascular health levels (LS7, LE8, and LC9) within the low, moderate, and high health categories, age-adjusted to the 2010 U S. Census population for standardization. Linear regression models were then used to evaluate associations between cardiovascular health scores and AAC scores, while logistic regression models estimated the odds of both AAC and severe AAC across cardiovascular health levels. Restricted cubic spline (RCS) regression analyses, with knots at the 10th, 50th, and 90th percentiles, were conducted to assess potential non-linear associations between cardiovascular health scores and AAC prevalence.
To compare the effectiveness of LS7, LE8, and LC9 in distinguishing AAC and severe AAC, receiver operating characteristic (ROC) analyses were performed, with the area under the curve (AUC) values compared across the scores using DeLong's test for statistical comparison. To assess improvements in discrimination when adding cardiovascular health scores to fully adjusted models, changes in AUC were evaluated along with the net reclassification improvement (NRI), and integrated discrimination improvement (IDI) metrics. NRI quantifies the extent to which a new model correctly reclassifies individuals into appropriate risk categories, while IDI measures the overall improvement in risk prediction by evaluating the separation between predicted probabilities for cases and non-cases.
Finally, two sensitivity analyses were conducted: one excluding participants with a baseline history of CVD and another focusing on participants with data on all nine components of LC9 (as opposed to at least eight in the primary analysis). These analyses provided a robust examination of the associations between cardiovascular health scores and AAC. Analyses were conducted using R (4.3.2) software, with statistical significance set at P < 0.05.
3. Results
3.1. Baseline characteristics of the participants
In this study of 2484 middle-aged and older adults from NHANES 2013–2014, 53 % were women (Table 1). Age distribution was balanced, with 58.2 % of participants aged 40–59 years and 41.8 % aged 60 years or older. Mean scores for cardiovascular health indicators were 8.0 (SE = 0.07) for LS7, 67.3 (SE = 0.49) for LE8, and 69.7 (SE = 0.49) for LC9. Most participants were non-Hispanic White (73.6 %), with a majority achieving educational levels above high school (64.9 %). Marital status data indicated that 69.7 % were married or cohabiting, and 58.5 % had a family PIR above 3.0. Moreover, Table S3 provides a comparison of baseline characteristics between participants with and without AAC or severe AAC.
Table 1.
Baseline characteristics of the middle-aged and older participants by sex in NHANES 2013–2014.
| Total n = 2484 | Women n = 1316 | Men n = 1168 | |
|---|---|---|---|
| Age, years | |||
| 40–59 | 1300 (58.22) | 694 (56.92) | 606 (59.69) |
| ≥60 | 1184 (41.78) | 622 (43.08) | 562 (40.31) |
| Race/ethnicity, % | |||
| Non-Hispanic White | 1173 (73.62) | 645 (73.63) | 528 (73.61) |
| Non-Hispanic Black | 485 (9.63) | 235 (9.70) | 250 (9.55) |
| Other race | 826 (16.75) | 436 (16.66) | 390 (16.84) |
| Education level, % | |||
| Below high school | 498 (13.56) | 255 (13.86) | 243 (13.22) |
| High school | 565 (21.51) | 299 (21.92) | 266 (21.05) |
| Above high school | 1421 (64.93) | 762 (64.22) | 659 (65.73) |
| Marital status, % | |||
| Married/living with partner | 1605 (69.69) | 749 (62.07) | 856 (78.34) |
| Single/divorced/widowed | 879 (30.31) | 567 (37.93) | 312 (21.66) |
| Family PIR, % | |||
| ≤1.0 | 416 (10.32) | 243 (11.68) | 173 (8.78) |
| 1.1–3.0 | 920 (31.18) | 500 (33.42) | 420 (28.62) |
| >3.0 | 1148 (58.50) | 573 (54.90) | 575 (62.60) |
| HEI-2015 score | 53.19 (0.60) | 54.01 (0.56) | 52.25 (0.75) |
| Moderate-vigorous physical activity, min/day | 681.04 (21.60) | 602.06 (25.06) | 770.63 (30.45) |
| Smoking status, % | |||
| Never smoker | 1350 (55.36) | 803 (59.55) | 547 (50.61) |
| Former smoker | 706 (28.42) | 297 (23.60) | 409 (33.89) |
| Current smoker | 427 (16.22) | 216 (16.85) | 211 (15.50) |
| Sleep times, h/day | 6.94 (0.03) | 6.96 (0.05) | 6.91 (0.04) |
| Body mass index, kg/m2 | 28.60 (0.17) | 28.60 (0.22) | 28.59 (0.19) |
| Total cholesterol, mg/dL | 195.38 (0.81) | 200.85 (0.95) | 189.16 (1.24) |
| HDL cholesterol, mg/dL | 55.02 (0.34) | 60.50 (0.42) | 48.81 (0.54) |
| HbA1c, % | 5.75 (0.02) | 5.71 (0.03) | 5.80 (0.04) |
| Systolic BP, mmHg | 124.68 (0.52) | 123.80 (0.57) | 125.66 (0.80) |
| Diastolic BP, mmHg | 70.80 (0.32) | 69.57 (0.41) | 72.21 (0.39) |
| Anti-hypertensive drugs, % | |||
| No | 1395 (59.90) | 722 (58.47) | 673 (61.54) |
| Yes | 1088 (40.09) | 593 (41.53) | 495 (38.46) |
| Diabetes mellitus, n (%) | |||
| No | 1913 (82.06) | 1041 (84.07) | 872 (79.78) |
| Yes | 571 (17.94) | 275 (15.93) | 296 (20.22) |
| PHQ‐9 score | 3.05 (0.15) | 3.70 (0.23) | 2.31 (0.16) |
| Life's Simple 7 score, % | |||
| Low (0–5) | 555 (26.51) | 312 (26.91) | 243 (26.06) |
| Moderate (6–9) | 1503 (58.93) | 762 (58.13) | 741 (59.84) |
| High (10–14) | 426 (14.56) | 242 (14.96) | 184 (14.11) |
| LS7 (0–14) score | 8.01 (0.07) | 8.01 (0.10) | 7.99 (0.09) |
| Life's Essential 8 score, % | |||
| Low (0–49) | 412 (20.63) | 245 (22.80) | 167 (18.16) |
| Moderate (50–79) | 1731 (68.07) | 864 (64.57) | 867 (72.03) |
| High (80–100) | 341 (11.31) | 207 (12.63) | 134 (9.81) |
| LE8 (0–100) score | 67.30 (0.49) | 67.47 (0.68) | 67.11 (0.51) |
| Life's Crucial 9 score, % | |||
| Low (0–49) | 230 (7.55) | 145 (8.46) | 85 (6.53) |
| Moderate (50–79) | 1754 (67.85) | 888 (65.63) | 866 (70.37) |
| High (80–100) | 500 (24.60) | 283 (25.92) | 217 (23.11) |
| LC9 (0–100) score | 69.71 (0.49) | 69.58 (0.69) | 69.86 (0.51) |
| AAC, % | |||
| No | 1746 (71.78) | 935 (72.21) | 811 (71.28) |
| Yes | 738 (28.22) | 381 (27.79) | 357 (28.72) |
| Severe AAC, % | |||
| No | 2258 (92.35) | 1194 (91.87) | 1064 (92.89) |
| Yes | 226 (7.65) | 122 (8.13) | 104 (7.11) |
| AAC (0–24) score | 1.44 (0.12) | 1.47 (0.15) | 1.40 (0.14) |
Abbreviations: AAC, abdominal aortic calcification; PIR, poverty-to-income ratio; HEI, Healthy Eating Index; HDL, high-density lipoprotein; HbA1c, glycated hemoglobin; BP, blood pressure; PHQ, Patient Health Questionnaire. Continuous variables are described as means ± SEs. Categorical variables are presented as numbers (percentages). Sampling weights were applied for calculation of demographic descriptive statistics; N reflect the study sample while percentages reflect the survey-weighted data.
Distributions of LS7, LE8, and LC9 scores across demographic groups were approximately normal, with most participants clustered in the moderate score range (Fig. S2). Both LE8 and LC9 scores showed an upward trend with increasing LS7 scores (Fig. 1), and LS7 was strongly correlated with LE8 and LC9 (correlation coefficients of 0.891 and 0.885, respectively; all P < 0.01).
Fig. 1.
Correlation of Life's simple 7 (LS7) with Life's essential 8 (LE8) and Life's crucial 9 (LC9) scores. (A) Correlation between LS7 and LE8; (B) Correlation between LS7 and LC9. Correlation coefficients were 0.891 and 0.885, respectively (all P < 0.01).
The prevalence of AAC, age-adjusted to the 2010 U S. Census, demonstrated an inverse association with cardiovascular health scores (Fig. 2). In the low cardiovascular health group, overall AAC prevalence was highest, at 30.2 % (95 % CI: 26.9–33.5 %) for LS7, 29.4 % (95 % CI: 21.6–37.2 %) for LE8, and 28.9 % (95 % CI: 21.7–36.1 %) for LC9. Prevalence decreased in the high cardiovascular health group to 19.3 % (95 % CI: 15.5–23.1 %) for LS7, 17.3 % (95 % CI: 14.2–20.4 %) for LE8, and 17.8 % (95 % CI: 14.0–21.6 %) for LC9. Severe AAC showed a similar trend: the low cardiovascular health group had the highest prevalence (LS7: 9.6 %, LE8: 10.4 %, LC9: 9.9 %), while the high health group had substantially lower rates (LS7: 4.3 %, LE8: 4.8 %, LC9: 4.9 %).
Fig. 2.
Age-adjusted prevalence of abdominal aortic calcification (AAC) and severe AAC across cardiovascular health score levels. Prevalence of AAC and severe AAC is presented for low, moderate, and high categories of cardiovascular health scores: Life's Simple 7 (LS7), Life's Essential 8 (LE8), and Life's Crucial 9 (LC9), age-adjusted to the 2010 U S. Census population.
3.2. Association between cardiovascular health scores and AAC prevalence
In the linear regression analysis (Table S4), lower cardiovascular health scores (LS7, LE8, and LC9) were consistently associated with higher AAC scores across all models. Participants in the low cardiovascular health category had the highest AAC scores compared to those in the high category. Similar trends were observed with LE8 and LC9, where moderate and low scores were also significantly associated with elevated AAC levels. Logistic regression analysis of cardiovascular health scores with AAC prevalence showed that those with moderate and low LS7, LE8, and LC9 scores had significantly higher odds of AAC prevalence than those in the high health category (Fig. 3 and Table S5). In the fully adjusted Model 2, participants with moderate LS7 scores had 2.11 times the odds (95 % CI: 1.20–3.70) of AAC compared to those in the high health category, and those in the low LS7 category had 2.16 times the odds (95 % CI: 1.33–3.50). Similar patterns were observed with LE8 and LC9; those with moderate LE8 scores had 2.28 times the odds (95 % CI: 1.37–3.80) of AAC, while moderate LC9 scores were associated with a 2.33-fold increase (95 % CI: 1.41–3.83). The logistic regression results for severe AAC revealed similar trends (Fig. 3 and Table S6). In Model 2, the moderate and low LS7 groups had 2.96 (95 % CI: 1.15–7.61) and 3.21 (95 % CI: 1.45–7.13) times the odds of severe AAC, respectively, compared to the high health group. LE8 and LC9 scores also demonstrated this association; participants with moderate LE8 scores had nearly twice the odds (OR = 1.97, 95 % CI: 1.05–3.70) of severe AAC, while low LE8 scores were associated with even greater odds (OR = 2.30, 95 % CI: 1.20–4.39). Similarly, moderate LC9 scores increased the odds of severe AAC by 2.05 times (95 % CI: 1.05–4.01). However, the association with low LC9 scores was not statistically significant for severe AAC (OR = 1.99, 95 % CI: 0.69–5.71).
Fig. 3.
Logistic regression analysis of cardiovascular health score (Life's Simple 7 [LS7], Life's Essential 8 [LE8], and Life's Crucial 9 [LC9]) with the prevalence of Abdominal Aortic Calcification (AAC) and Severe AAC. Analyses were adjusted for age (40–59, or ≥60), sex (male or female), race/ethnicity (non-Hispanic White, non-Hispanic Black or other race), education level (below high school, high school, or above high school), marital status (married/living with partner, or single/divorced/widowed), and family PIR (≤1.0, 1.1–3.0, or >3.0).
RCS regression analysis revealed a nonlinear inverse association between cardiovascular health scores (LS7, LE8, and LC9) and AAC prevalence (Fig. S3). For AAC, inflection points at LS7, LE8, and LC9 scores of 6.5, 56.8, and 60.3, respectively, marked a threshold beyond which higher scores were associated with a steep decline in AAC prevalence. A similar pattern was observed for severe AAC, with inflection points at 5.7, 56.7, and 58.0 for LS7, LE8, and LC9, respectively. Severe AAC prevalence remained stable at lower scores but declined notably once scores exceeded these points (Fig. S4). Logistic regression analyses showed inverse associations between specific components of the LS7 and LC9 scores with the prevalence of AAC and severe AAC. For LS7, smoking, blood pressure, and fasting blood glucose (FBG) were notably associated with lower AAC and severe AAC prevalence (Table S7). Similarly, within LC9, physical activity, blood pressure, and FBG/HbA1c displayed significant inverse associations with both AAC and severe AAC across adjusted models (Table S8).
3.3. Comparing the capability of cardiovascular health scores to distinguish AAC
The ability of LS7, LE8, and LC9 scores to differentiate AAC and severe AAC was evaluated using ROC analysis. For AAC (Fig. 3A), LS7 achieved the highest AUC at 0.60 (95 % CI: 0.58–0.63), which, although modest in discriminatory ability, significantly outperformed LE8 (AUC 0.58, 95 % CI: 0.56–0.61, P < 0.01) and LC9 (AUC 0.58, 95 % CI: 0.56–0.60, P < 0.01). The difference between LE8 and LC9 was not statistically significant (P = 0.34). A similar pattern was observed for severe AAC (Fig. 3B), with LS7 demonstrating an AUC of 0.64 (95 % CI: 0.61–0.68), again modest in its discrimination but significantly higher than LE8 (AUC 0.60, 95 % CI: 0.56–0.64, P < 0.01) and LC9 (AUC 0.60, 95 % CI: 0.56–0.63, P < 0.01).
Adding each cardiovascular health score (LS7, LE8, and LC9) to the fully adjusted model resulted in modest improvements in discrimination for both AAC and severe AAC (Table S9). For AAC, the inclusion of LS7 increased the AUC from 0.68 to 0.70, with significant net reclassification improvement (NRI: 0.21, 95 % CI: 0.12–0.30) and integrated discrimination improvement (IDI: 0.01, 95 % CI: 0.01–0.02). Similar modest improvements were observed with LE8 and LC9. For severe AAC, adding LS7 raised the AUC from 0.77 to 0.79, with significant NRI and IDI values, further underscoring improved discrimination. While all three cardiovascular health scores enhanced the model's discriminatory ability, LS7, as the simplest score, demonstrated comparable or slightly superior performance relative to the more complex LE8 and LC9 in distinguishing AAC and severe AAC prevalence. These findings highlight the utility of LS7 as a practical tool for cardiovascular risk stratification, despite the overall modest discriminatory capabilities indicated by the AUC values Fig. 4.
Fig. 4.
Comparing the capability of cardiovascular health score (Life's Simple 7 [LS7], Life's Essential 8 [LE8], and Life's Crucial 9 [LC9]) to distinguish Abdominal Aortic Calcification (AAC) (A) and Severe AAC (B) using receiver operating characteristic (ROC) curves.
3.4. Sensitivity analysis
To assess the robustness of the findings we conducted a series of sensitivity analyses. The sensitivity analysis further validated the primary findings across different subsets of participants. After excluding those with a history of cardiovascular disease (Table S10 and Table S11), higher cardiovascular health scores (LS7, LE8, and LC9) remained inversely associated with the prevalence of AAC and severe AAC. In the analysis restricted to participants with complete data on all nine components of the LC9 score (Table S12 and Table S13), the associations were consistent with the primary results. Higher scores in LS7, LE8, and LC9 were linked to reduced odds of both AAC and severe AAC, with notable reductions in prevalence among those with high scores compared to moderate or low scores.
4. Discussion
In this study, we estimated the relationships between three cardiovascular health scores and the prevalence of AAC and severe AAC in 2484 middle-aged and elderly individuals utilizing data from the NHANES 2013–2014 cohort. We found that lower LS7 scores and LE8 scores were associated with a higher prevalence of AAC and severe AAC. Low LC9 scores were correlated with higher odds of AAC but not severe AAC. LS7 demonstrated superior discriminatory for both AAC and severe AAC. LS7 could be considered as a practical tool for clinical risk stratification regarding cardiovascular health in middle-aged and elderly populations.
Previous studies have explored the relationships between CVH scores with the prevalence and prognosis of CVD, including AAC. The large population-based cohort study involving 65,494 low-risk adults illustrated that higher LS7 score were correlated with a lower likelihood of coronary artery calcification [27]. A U.S.-based multiethnic cohort study used LS7 metrics to assess the relationships between CVH and aortic calcification [28]. The study demonstrated that higher scores on LS7 were significantly correlated with a lower risk of various types of aortic calcification [28]. Over a 20-year follow-up, the Coronary Artery Risk Development in Young Adults study found that individuals who improved their CVH metrics during this critical period had a reduced risk of coronary arteries calcification by the time they reached middle age [29]. The study by Hasbani et al. demonstrated that higher LS7 score was consistently associated with longer life expectancy free of coronary heart disease, even among individuals with varying levels of genetic risk for CVD [14]. According to our results, participants with low LS7 scores had significantly higher odds of both AAC (OR = 2.16, 95 % CI: 1.33–3.50) and severe AAC (OR = 3.21, 95 % CI: 1.45–7.13) when compared to those with high LS7 scores.
Cai et al. recently suggested that LE8 showed a negative and nonlinear link with the risk of AAC among participants aged ≥40 years [24]. Prioritizing these areas, such as such as nicotine exposure, blood glucose levels, and blood pressure evaluations, can be crucial for effectively managing and reducing the risk of AAC [24]. Middle-aged and older individuals with AAC, particularly those with lower LE8 scores, were at substantially heightened risk for both all-cause and cardiovascular mortality over a median follow-up period of six years [17]. Liu et al. provided evidence that better LE8 cardiovascular health metrics were associated with reduced AAC risk among 2478 adults aged 40 or above, with nicotine exposure, blood glucose, and blood pressure being the strongest individual predictors among LE8 components [30]. Other studies also pointed that each 1-point increase in the LE8 score was linked to a 2 % reduction in the prevalence of severe AAC [31]. The inverse association between LE8 scores and severe AAC was especially pronounced among adults aged 60 years and older [31]. Consistent with above research, our results showed that those with low LE8 scores a 130 % higher prevalence of AAC (OR = 2.30, 95 % CI: 1.20–4.39) compared with participants with high level of LE8 scores.
LC9 adds the depression score to LE8, which might provide a slight improvement in discrimination and reclassification. Ge et al. assessed the predictive ability of LC9 on the cardiovascular and all‐cause mortality among 16, 290 adults without cardiovascular disease in a large cohort study [11]. They found LC9 score had a slightly stronger association with cardiovascular and all-cause mortality outcomes, but the improvement was minor [11]. Specifically, there was similar predictive strength between the LE8 and LC9: for cardiovascular mortality, the HR is 0.80 for LE8 and 0.77 for LC9; while for all-cause mortality, the HR is 0.82 for LE8 and 0.80 for LC9 [11]. The improvement of LC9 appeared limited and may not translate into meaningful changes in clinical practice. In our study, LC9 scores were associated with higher odds of AAC (OR = 1.99, 95 % CI: 1.02–3.90), but did not show a statistically significant association with severe AAC. And the statistical efficiency of LC9 on the prevalence of AAC was slightly lower than LS7 and LE8.
The underlying mechanisms of relationships between CVH and AAC are not yet fully understood. Research has consistently pointed to the significant roles that metabolic syndrome and lifestyle factors play in the development of AAC [23,32]. Metabolic syndrome which is characterized by conditions such as obesity, hypertension, dyslipidemia, and insulin resistance can contribute to vascular calcification processes [32]. During metabolic syndrome, the body experiences persistent inflammation, which leads to increased levels of pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α) and interleukins (such as IL-6, IL-1β) [33]. These cytokines can promote vascular inflammation, endothelial dysfunction, and smooth muscle cell proliferation, contributing to the calcification process in blood vessels [33]. The release of microparticles (MPs) from endothelial cells can carry bioactive molecules [34]. When vascular smooth muscle cells (VSMCs) phagocytose these microparticles, the bone morphogenetic protein-2 can trigger a phenotypic change in VSMCs and promote a more osteoblastic phenotype [34]. While BMP-2 is primarily known for its involvement in bone formation, it also acts as a pro-inflammatory cytokine that can stimulate endothelial activation [35]. These findings may offer potential mechanistic insights into how CVH scores may influence the development of AAC.
Notably, LS7 demonstrated the highest AUC values for distinguishing AAC. Recent research found that there was no significant difference between the LS7 and LE8 scores in predicting 10-year CVD risk among older adults [36]. While LE8 includes notably sleep health compared to LS7, these additions may not significantly enhance the model's ability to predict long-term CVD risk in an older population. The components of LS7, such as managing blood pressure and cholesterol, have direct effects on vascular health and the calcification process. In contrast, LE8 and LC9 may involve broader lifestyle and environmental factors, which might not have as direct an impact on calcification prediction. Although depression is a well-established risk factor for cardiovascular disease, the impact of it on AAC might be not notable. The LE8 scoring system's approach to sleep highlights that excessively extended sleep durations are not rated as beneficial. In fact, the role of sleep durations in the development of AAC is unclear. Some studies suggested that poor sleep quality and waking earlier than planned were associated with higher AAC, even after adjusting for additional covariates [37]. Other research showed that long sleep duration may be associated with an increased risk of AAC among male adults with chronic kidney disease [38]. Recently, depression is increasingly recognized as an important factor in cardiovascular health [39]. Depression can both result from and exacerbate traditional CVD risk factors like hypertension, obesity, diabetes, and smoking, which creates a feedback loop where each condition potentially worsens the other [[40], [41], [42], [43], [44]]. The predictive value of depression for CVD might be diminished by its correlation with traditional risk factors. However, these findings also raise the question of whether researchers should revisit and potentially refine its criteria for ideal sleep and depression in the context of AAC. While CVH scores may correlate with CVD risk, they should not be interpreted purely as risk prediction tools.
Our study demonstrates that LS7 outperforms the newer LE8 and LC9 scores in distinguishing AAC and severe AAC, likely due to its simplicity and focus on core CVH metrics. In contrast, the expanded frameworks of LE8 and LC9 include additional components, such as sleep health and mental health, which, while beneficial for comprehensive health assessment, may contribute less directly to vascular outcomes like AAC. These components are also more susceptible to subjectivity and measurement variability, which could dilute their associations with AAC. Moreover, the broader scope of LE8 and LC9 may reduce the relative weight of traditional CVH metrics that are more strongly linked to vascular calcification. This underscores the trade-offs between simplicity and comprehensiveness in CVH metrics, highlighting the need for further refinement of LE8 and LC9 to enhance their utility in predicting specific outcomes such as AAC.
There are several strengths in our study. This is the first investigation to examine the relationships between all three CVH scores and the prevalence of AAC. By analyzing these distinct scores, we provided a comprehensive assessment of how different approaches to measuring cardiovascular health relate to AAC and severe AAC prevalence. The findings suggest that LS7 may be the most practical and effective score for clinical applications targeting AAC. This superiority may be attributed to LS7's simplicity and focus on core CVH metrics, which are easier to implement and interpret in clinical and public health settings. Unlike LE8 and LC9, which incorporate additional components such as sleep health and mental health, LS7's fewer components may reduce complexity and measurement variability, leading to more robust associations. These results highlight the potential trade-offs between simplicity and comprehensiveness in CVH metrics and underscore the importance of selecting tools that balance these factors based on the clinical or research context. Additionally, our results demonstrated that higher scores in LS7, LE8, and LC9 were consistently associated with lower prevalence of AAC, even after excluding participants with a history of cardiovascular disease. These findings reinforce the importance of promoting optimal CVH, particularly among middle-aged and elderly adults, to mitigate the burden of subclinical atherosclerosis and related cardiovascular risks.
Our study has some key limitations. First, NHANES data is cross-sectional, which limits the ability to draw causal conclusions about the relationship between cardiovascular health scores and AAC. Second, several components of the cardiovascular health scores relied on self-reported data, introducing potential biases such as recall bias and inaccuracies, which could influence the observed associations with AAC. For example, self-reported sleep duration may be prone to over- or underestimation, recall bias, and subjective interpretation of sleep quality, leading to potential misclassification and impacting the reliability of the sleep health metric in the context of cardiovascular health assessment. Third, while NHANES provides a representative sample of the U.S. population, the generalizability of our findings to non-U.S. populations may be limited. Differences in risk factors, lifestyles, healthcare systems, and cardiovascular health profiles across regions could influence the applicability of these results. For instance, populations with distinct dietary patterns, physical activity levels, or prevalence of chronic conditions may exhibit different associations between cardiovascular health scores and AAC. Further studies in diverse international cohorts are essential to validate these findings and assess their broader relevance. Forth, although the study adjusted for various factors, there may still be unmeasured confounders that could influence the observed associations. Lastly, the AUC values for LS7 in distinguishing AAC and severe AAC were moderate, indicating limited discriminatory ability. While LS7 showed statistically significant associations with AAC and performed modestly better than LE8 and LC9, its AUC values were only slightly above random (0.5). This suggests that the clinical utility of LS7 for predicting AAC should be interpreted with caution, and further refinement of cardiovascular health scores is needed to enhance their discriminatory performance.
5. Conclusions
This study highlights an inverse association between cardiovascular health scores and the prevalence of AAC and severe AAC among middle-aged and elderly individuals. Participants with lower cardiovascular health scores, particularly in LS7, exhibited higher odds of AAC and severe AAC. While LS7 demonstrated modestly better discriminatory performance compared to LE8 and LC9, its clinical utility may be limited due to the modest discriminatory ability observed. These findings underscore the potential role of cardiovascular health metrics in identifying vascular calcification risk, but further refinement and validation of these tools are necessary to enhance their applicability in clinical practice.
CRediT authorship contribution statement
Qianliang Ying: Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation. Zongze Chen: Writing – review & editing, Validation, Methodology. Xiaobo Xue: Writing – review & editing, Supervision, Resources, Project administration, Methodology, Conceptualization.
Disclosure statement
The authors have no competing interests to declare.
Ethics statement
All participants provided written informed consent and study procedures were approved by the National Center for Health Statistics Research Ethics Review Board (Protocol Number: Protocol #2011–17).
Funding
None.
Acknowledgments
We appreciate the people who contributed to the NHANES data we studied.
Handling Editor: D Levy
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijcrp.2025.200389.
Appendix A. Supplementary data
The following is the supplementary data to this article:
Data availability
NHANES data described in this manuscript are available at https://wwwn.cdc.gov/nchs/nhanes/.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
NHANES data described in this manuscript are available at https://wwwn.cdc.gov/nchs/nhanes/.