Association between Life’s Essential 8 and abdominal aortic calcification: findings from a national study

doi:10.21203/rs.3.rs-4987264/v1

Background and aims

Data are lacking regarding Life’s Essential 8 (LE8), a cardiovascular health (CVH) scoring system recently updated by the American Heart Association, and abdominal aortic calcification (AAC). We aimed to detect the associations of total and individual CVH metrics based on LE8 with AAC-score and AAC prevalence.

Methods

We conducted a cross-sectional study from the National Health and Nutrition Examination Survey (NHANES) 2013–2014. Individuals with a score exceeding 0 or 6 were considered as having AAC and severe AAC, respectively. Total and individual CVH metric scores were stratified into low level (0–49), intermediate level (50–74), and high level (75–100). Multivariable linear or logistic regression models were performed to calculate β or odds ratios (ORs) and corresponding 95% confidence interval (CI) for the associations of total and individual CVH metrics with AAC. For further exploration, restricted cubic spline curves and subgroup analyses were performed.

Results

2,403 eligible participants were finally included. Compared to those with a low total CVH score, adults with a high total CVH score were associated with lower AAC-score [β: -0.50, (-0.95, -0.06), P = 0.0277] and reduced prevalence of AAC (OR: 0.57, (0.41, 0.80), P = 0.0013] and severe AAC [OR: 0.52, (0.30, 0.90), P = 0.0197]. There was a nonlinear and linear dose–response inverse associations of total CVH score with AAC and severe AAC, respectively. For the eight individual CVH metrics, well management of nicotine exposure, blood glucose and blood pressure was the major individual contributor to prevent AAC. Equally, enough engagement of physical activity could partly reduce AAC risk.

Conclusions

The ideal CVH assessed by Life’s Essential 8 metrics was significantly associated with reduced AAC risk. We emphasize that earlier efforts on promoting CVH metrics should be highlighted to reduce AAC prevalence and subsequent CVD in later life.

Cardiovascular health

Life’s Essential 8

Abdominal aortic calcification

NHANES

Cardiovascular disease (CVD), a prominent contributor to mortality and disability worldwide,¹ has caused a heavy economic burden on the medical system, with an average annual cost of nearly $400 billion from 2016 to 2017 in United States (US).² As universally known, vascular atherosclerosis has been identified as a major etiological factor underlying CVD and therefore, early taking measures to maintain vascular health is priceless and crucial. Notably, vascular calcification is a potential risk factor for the progression and rupture of atherosclerotic plaques with close relationship to CVD events.³ Additionally, given that abdominal aortic calcification (AAC) occurs earlier than coronary artery calcification, AAC has emerged as a superior indicator to predict subclinical CVD and related mortality.⁴ The marking system of Kauppila is currently the most commonly used measuring method of AAC, and it was scanned through lateral lumber X-ray to evaluate the severity of AAC according to AAC-24 score.⁵ Increasing evidence from previous studies did support that elevated AAC was associated with CVD events, including aneurysm rupture, myocardial infarction, stroke and even death, etc.^6–8 Thus, studies should be imperatively conducted to control AAC as an early preventive measure against CVD.

Last decades, epidemiological studies have confirmed that poor diet, physical inactivity, smoking, insufficient sleep duration, diabetes mellitus, hypertension, and dyslipidemia among adults are the main risk factors for CVD occurrence and progress.⁹ In 2010, the American Heart Association (AHA) established the “Life’s Simple 7 (LS7)” to promote ideal cardiovascular health (CVH).¹⁰ It included ideal goals for three behavioral and four health factors of high dietary quality, enough physical activity engagement, no cigarette exposure, and normal control of body mass index (BMI), total cholesterol, blood glucose and blood pressure. However, there were some limitations of LS7 such as a crude additive scoring and less sensitivity to interindividual, intraindividual or intrapopulation variation.¹¹ To address these limitations, the AHA in 2022 recommended the "Life’s Essential 8 (LE8)" to maintain optimal CVH, which introduced a new metric of sleep health as compared to LS7, and updated and refined the other seven metrics of CVH.¹¹ Although several mentioned above risk factors, including smoking, diabetes mellitus and hypertension, etc., have been well established for relationships to the incidence and severity of AAC,¹² there is no research comparing these individual metrics together. Moreover, the emerging practical LE8 seems to be superior and particularly, the association between CVH assessed by LS7 or even LE8 and AAC has not been investigated until now. In order to better prevent CVD in the early stage, we conducted a large-scale epidemiological analysis to better understand whether ideal CVH evaluated by LE8 is linked to lower AAC scores and reduced risk of AAC.

Study population

The National Health and Nutrition Examination Survey (NHANES) is a research program that assesses the health and nutritional status of US civilian population. The protocol was approved by the National Center for Health Statistics Research Ethics Review Board, and all participants provided written informed consent. As information of AAC was only available in NHANES 2013–2014, data from this period were used for present study. After excluding participants without completed and valid AAC scan (n = 7,035) or CVH metrics (n = 737), a total of 2,403 adults aged 40–80 years were included in this study (Fig. 1).

Measurements of cardiovascular metrics in updated Life’s Essential 8

According to the latest AHA recommendation, CVH was estimated by 8 updated cardiovascular metrics of LE8.¹¹ Details of LE8 score measurements and calculation were previously described.^11,13 Briefly, information and each score of diet, physical activity, nicotine exposure and sleep health were measured and calculated through self-reported questionnaires in present study. Blood lipid, blood glucose, blood pressure and BMI were objectively measured using standardized methods. Each component within LE8 was scored from 0 to 100 (the best CVH) and subsequently, LE8 was calculated as the unweighted mean of scores of all components. To note that, participants with completed data of the eight components were eligible to final analyses.

Abdominal aortic calcification assessment

As reported by the protocol in NHANES, AAC was assessed by dual-energy X-ray absorptiometry of vertebrae L1–L4 with the Kauppila scoring system. Participants who were aged below 40 years old, pregnancy, weighted over 450 ponds were not enrolled to AAC scan. Each eligible subject could obtain a potential score range of 0 to 24 for the total score. Individuals with a score of 0 were categorized as AAC-free, while those with exceeding 0 were considered as having AAC. Particularly, an AAC score above 6 was widely recognized as a threshold for severe AAC.^5,14

Covariate information

The covariates such as age (40–54, 55–64, and 65–80 years), gender (male and female), ethnicity (Mexican American, non-Hispanic White, non-Hispanic Black, and others), marital status (married, divorced/separated/widowed, and unmarried/cohabitation), education level (high school, lower or more than high school), family income to poverty ratio (< 1.0, 1.0 to 3.0, and > 3.0), family history diseases (heart disease and diabetes mellitus) and statin therapy were obtained from standardized questionnaires in the household interview. Family income to poverty ratio < 1.00 indicated household income below the poverty threshold, and otherwise > 3.00 implied that household income was more than triple the poverty threshold. Estimated glomerular filtration rate (eGFR) was calculated to evaluate kidney function of each participant following the Chronic Kidney Disease Epidemiology Collaboration equation, and we accordingly defined chronic kidney disease (CKD) by eGFR < 60 ml/min/1.73 m2.¹⁵ Each covariable detailed acquisition process and measuring methods are available at www.cdc.gov/nchs/nhanes.

Statistical analysis

Due to the complex sampling design of NHANES, sample weights, clustering, and stratification were incorporated in all analyses in present study. Baseline characteristics of all participants were stratified by total CVH scores of 0–49 (low), 50–74 (intermediate), and 75–100 points (high), with the continuous and categorical variables reported as the mean (standard error) and numbers with percentages, respectively. To detect the differences across groups, χ2 tests (categorical variables), one-way ANOVA tests (normal distribution), or Kruskal-Wallis tests (skewed distribution) were performed, in which bivariate associations between AAC-score, total CVH scores and baseline continuous variables were examined using the Spearman correlation analysis.

Multivariate regression analyses with two models were adopted to evaluate the independent associations of combined and single CVH metric scores (low level as the reference) with AAC-score as well as risks of AAC and severe AAC. In model I, we adjusted for age, gender and ethnicity; Model II was fully adjusted for age, gender, ethnicity, marital status, education level, family income to poverty ratio, statin therapy, CKD, family history of heart disease and diabetes mellitus, diet, nicotine exposure, physical activity, sleep health, BMI, blood glucose, blood lipids, and blood pressure. When combined or single CVH metrics were evaluated, corresponding metrics were excluded from the adjustment in Model II.

Additionally, we generated restricted cubic spline curves with three knots at the 10th, 50th and 90th percentiles of total CVH scores to explore dose–response effects of total CVH scores as a continuous variable. Finally, we conducted several stratified analyses with fully adjustments by age (40–54, 55–64, and 65–80 years), gender (male and female), ethnicity (Mexican American, non-Hispanic White, non-Hispanic Black, and others), marital status (married, divorced/separated/widowed, and unmarried/cohabitation), education level (high school, lower or more than high school), family income to poverty ratio (< 1.0, 1.0 to 3.0, and > 3.0), statin therapy (yes or no), CKD (yes or no), and family history of heart disease and diabetes mellitus (yes or no). To test for statistical significance of interactions, interaction terms between total CVH scores and different stratified factors were generated and examined. If necessary, the possible interactions between all adjusted factors were also tested. All analyses were performed using EmpowerStats software with R (version 3.4.3), and a 2-sided P-value of < 0.05 was set as the threshold for statistical significance.

The weighted demographic features of the 2,403 subjects in this study across the three categories of total CVH scores (low, intermediate, high) are shown in Table 1. Among all the subjects, 46.93% were male, 73.30% were non-Hispanic white and 44.91% of those aged from 40 to 54 years. Only 27.59% of adults engaged a high total CVH score, whereas 13.44% had a low score. Overall, there were significant difference in baseline characteristics between low and high CVH score groups, in which those with higher CVH scores were more likely to be male, younger, non-Hispanic White, married, well-educated and high family income (all P < 0.05). Besides, participants with a history of diabetes mellitus and heart disease were less likely to have high CVH scores (all P < 0.001). Similarly, there were less participants with CKD and statin therapy in high CVH score group (all P < 0.001).

The associations of total and individual CVH scores with AAC are shown in Table 2. In the initial analyses after adjusting for age, gender and ethnicity, the increase in per-10 total CVH score was significantly associated with reduced AAC-score and AAC prevalence [β: -0.25, 95% CI: (-0.34, -0.15), P < 0.0001; OR: 0.82 (0.77, 0.88), P < 0.0001; respectively]. As compared to those with low total CVH scores, participants with high score level were highly correlated with lower AAC-score [β: -0.91(-1.34, -0.47), P < 0.0001] and decreased odds of AAC [OR: 0.46 (0.34, 0.63), P < 0.0001]. In further analyses after adjusting for all potential confounders, the increase in per-10 total CVH score and participants with high CVH score level were still positive in lower AAC-score and ACC prevalence [β: -0.14 (-0.23, -0.04), P = 0.0064; OR: 0.87 (0.81, 0.94), P = 0.0002; β: -0.50(-0.95, -0.06), P = 0.0277; OR: 0.57 (0.41, 0.80), P = 0.0013; respectively]. Similar trends towards reduced AAC-score and risks of AAC were generally observed for higher individual CVH scores of physical activity, nicotine, blood glucose and blood pressure, which was not case for diet and sleep health. On the contrary, elevated score of BMI was significantly correlated to increased AAC-score and risks of AAC, indicating that there was inversed association between BMI and AAC. Furthermore, as shown in Table 3, fully adjusted ORs for severe AAC in per-10 total CVH score increasement and participants with high CVH score level were 0.84 (0.74, 0.95) and 0.52 (0.30, 0.90), respectively. It is intriguing that higher individual CVH scores of nicotine, blood glucose and blood pressure remained significant association with lower risks of severe AAC, whereas there was no correlation between physical activity and severe AAC.

Moreover, to assess the associations between AAC-score, total CVH scores and baseline continuous variables, we performed the Spearman correlation analyses. As shown in Fig. 2 and Table S1, AAC-score was mostly positively associated with systolic blood pressure, blood glucose, HbA1c and sleep duration, whereas inversed correlations were found between AAC-score and total CVH scores, diastolic blood pressure, non-high-density lipoprotein cholesterol and physical activity time. In Fig. 3, we generated restricted cubic spline curves to detect the dose–response effects of total CVH scores with AAC and severe AAC, which indicated that total CVH scores were correlated with AAC in a nonlinear association and severe AAC in a linear association. These associations were consistent with the categorical results in Tables 2&3.

Furthermore, the results of several subgroups analyses indicated that the patterns of higher total CVH scores in relation to reduced AAC-score and lower risks of AAC and severe AAC remained generally consistent across subgroup patients who were female, aged from 40 to 65 years, non-Hispanic White, high family income, no statin therapy, CKD-free, with a family history of diabetes mellitus but not heart disease (Tables S2-S4). Notably, no statistical interaction was observed in each subgroup.

The major findings in the present cross-sectional study were that adults with a high level of total or individual CVH metrics in LE8 mostly yielded a reduced risks of AAC-score and AAC prevalence. Although they were not approximately consistent on the dose-response effects, there were always similar associations of increased total CVH score with reduced risks of AAC and severe AAC. Our individual CVH metrics estimates indicated that enough physical activity engagement and better control of nicotine exposure, blood glucose and blood pressure levels might largely prevent AAC prevalent. Overall, our findings did support the new CVH metrics named LE8 as a part of predicting the future AAC.

Our results using LE8 scoring system were consistent with the findings from previous studies that have shown a protective effect of ideal CVH in LS7 on the development of coronary artery calcification and subclinical atherosclerosis.^16,17 To our knowledge, this is the first study with a large sample size to investigate the association between CVH in LE8 and AAC risk. Additionally, we simultaneously analysed the correlation of individual LE8 metric with the AAC-score and AAC prevalence in the same population with consistent adjustments, probably avoiding unstable results due to heterogeneity between different studies. Recently, one large population-based study demonstrated that LE8 yielded a strong, graded and inverse association with coronary artery stenosis and coronary artery calcification.¹⁸ Also, it was proved to have slightly higher capacity to predict the presence of any coronary artery stenosis than LS7.¹⁸ Likewise, the dose-response finding of our study suggested nearly any improvements of total CVH score in LE8 could be beneficial for preventing AAC, particularly among those who might maintain LE8 score of over 60. That is the supportive approach of “some improvements are better than none, more is slightly better” and a critical finding of present study. Of note, we did not compare and validate the superior predictive capacity LE8 than LS7 in AAC in this study. Previously, monitoring LE8 scores as like as LS7 scores has been identified to detect participants with considerably higher coronary atherosclerosis burden before cardiovascular events occur.^19,20 As mentioned above that, on one hand, CVH constructed by LE8 metrics could avoid the interindividual, intraindividual or intrapopulation differences, indicating a high-quality and reliable generality.¹¹ On the other hand, AAC might arise an opportunity to early recognize atherosclerosis than coronary artery.⁴ Collectively, our results might also corroborate that the LE8 score was useful to monitor CVH and prevent cardiovascular disease in spite of ease calculation of LS7.

For the individual CVH metrics, we found that well management of nicotine exposure, blood glucose and blood pressure was the major individual contributor to prevent AAC. Equally, enough engagement of physical activity could partly reduce AAC risk. Till now, substantially evidence has revealed positive associations of those four factors with reducing AAC risk and subsequent CVD.^9,21,22 However, there were negative impacts of diet, sleep health and blood lipids, as well as an inverse correlation of BMI on reducing AAC. These results were similar to previous study, in which individual CVH metrics such as diet and total cholesterol were not correlated to the prevalence of coronary artery calcification in adults with and without type 1 diabetes.²³ Interestingly, increasing number of ideal CVH metrics were all significantly associated with lower odds of prevalence and progression of coronary artery calcification.²³ Probably, some interactions among those behavioral and health factors existed and undoubtedly, they were influenced by each other as following as the Spearman correlation analysis. It meant that individual metrics not directly associated with AAC might improve AAC through others that were directly related to AAC. For example, blood glucose and systolic blood pressure are positively interacted with sleep duration and BMI. In addition, BMI as an established paradoxical paradigm is not new, and an inverse correlation between BMI and coronary artery calcification has been identified in a previous large sample research with percutaneous coronary intervention patients.²⁴ Clearly, the interaction between those individual CVH metrics and the detailed mechanism underlying the anti-vascular calcification benefits of them merit further investigation. Besides, subgroup analysis indicated that patients who were female, aged from 40 to 65 years, non-Hispanic White, high family income, no statin therapy, CKD-free, with a family history of diabetes mellitus but not heart disease might have more benefit from ideal CVH based on the new definition of LE8. In fact, AAC is a long-term, systematic result of multiple factors, such as genetic susceptibility, lifestyle, medications, endocrine metabolism abnormalities and external environmental interference, etc. In sight of the multifactorial nature of pathogenesis of AAC, to detect these specific populations who could be beneficial from ideal CVH against AAC in earlier stage might be inspiring and meaningful. In a word, our data validate the updated CVH scoring system in LE8 and underscore its key role in reducing AAC.

There were some inevitable limitations in present study. First, as a cross-sectional observational study, causality could not be determined between the CVH evaluated by LE8 and AAC. Secondly, since all participants are US residents, extending the results to other populations with different demographic characteristics might be limited. Third, each metric in LE8 was only available at baseline, which might influence the quality of CVH information and constrict us to assess detailed impact of fluctuations of CVH on the prevalence of AAC. Finally, some subgroups with a small number of events might lead to the non-statistically significant associations of several individual CVH metrics with AAC and potentially exhibit a statistical bias. Thus, the results should be interpreted with caution in clinical practise.

We found that a high total CVH score in LE8 was inversely associated with AAC-score and AAC prevalence. No nicotine exposure, normal control of blood glucose and blood pressure as well as enough physical activity engagement were the leading urgent individual contributors to reduce AAC risk. Our findings emphasize that earlier efforts on promoting CVH metrics should be highlighted to reduce AAC prevalence and subsequent CVD in later life.

CRediT authorship contribution statement

JP, XQ, RX and MZ conceived and designed the study. JP, JL, XJ and XY collected and analysed the data. JP drafted the paper and RX and MZ revised the manuscript. All authors have reviewed the final manuscript.

Ethics approval

This study was approved by the ethics review board of the National Center for Health Statistics.

Acknowledgements

This research was conducted with open access to NHANES data, and we are grateful to each staff and participant in NHANES.

Consent to participate

The written informed consents were obtained from each participant.

Consent for publication

Not applicable.

Funding Statement

Not applicable.

Declaration of competing interest

All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Data availability statement

The detailed datasets about the surveys are available at www.cdc.gov/nchs/nhanes.

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Table 1. Characteristics of US adults by three categories of total cardiovascular health (CVH) score, 2013-2014^a
	Participants, No. (%)
	Total CVH scores
Characteristics	All participants	0-49	50-74	75-100	P-value
Participants, No.	2403	323	1417	663
Total CVH score
Mean score (SE)	67.67 (0.57)	42.04 (0.31)	63.82 (0.20)	83.92 (0.27)	<0.001
Age group					0.011
40–54 years	993 (44.91)	109 (38.72)	548 (42.56)	336 (51.36)
55–64 years	623 (27.03)	91 (28.38)	387 (28.08)	145 (24.66)
65–80 years	787 (28.06)	123 (32.90)	482 (29.36)	182 (23.98)
Male	1131 (46.93)	129 (41.57)	708 (49.24)	294 (44.64)	0.014
Marital status					<0.001
Married	1470 (67.00)	173 (59.95)	833 (63.43)	464 (75.93)
Divorced/separated/widowed	655 (23.50)	114 (29.21)	408 (27.00)	133 (15.17)
Unmarried/cohabitation	278 (9.50)	36 (10.84)	176 (9.58)	66 8.89)
Family income, income/poverty ratio					<0.001
< 1.0	381 (9.75)	87 (18.11)	226 (10.33)	68 (5.77)
1.0 to < 3.0	1006 (35.22)	153 (43.62)	637 (39.86)	216 (23.89)
> 3.0	1016 (55.03)	83 (38.27)	554 (49.81)	379 (70.34)
Race/ethnicity					<0.001
Mexican American	308 (6.42)	39 (6.38)	202 (7.47)	67 (4.56)
Non-Hispanic White	1128 (73.30)	149 (69.15)	658 (72.41)	321 (76.36)
Non-Hispanic Black	461 (9.50)	87 (15.07)	292 (10.63)	82 (5.51)
Other	506 (10.78)	48 (9.40)	265 (9.49)	193 (13.57)
Education					<0.001
Lower than high school	491 (13.88)	109 (26.67)	294 (14.26)	88 (8.69)
High school	540 (21.27)	95 (26.37)	357 (26.55)	88 (9.93)
More than high school	1372 (64.85)	119 (46.96)	766 (59.18)	487 (81.38)
Family history of diabetes mellitus	1001 (38.69)	179 (56.65)	611 (40.13)	211 (29.58)	<0.001
Family history of heart disease	291 (13.76)	55 (20.49)	185 (15.57)	51 (8.05)	<0.001
Chronic kidney disease	270 (9.90)	58 (17.60)	155 (9.35)	57 (8.08)	<0.001
Statin therapy	692 (29.03)	119 (34.23)	444 (32.76)	129 (20.45)	<0.001
Diet, mean score (SE)
Mean score (SE)	44.42 (1.43)	22.68 (1.65)	38.83 (1.39)	62.41 (1.58)	<0.001
Physical activity
Mean (SE), min/week	897.32 (39.42)	1016.80 (208.57)	966.49 (69.42)	788.37 (42.36)	0.006
Mean score (SE)	69.41 (1.09)	24.86 (2.38)	65.82 (1.57)	92.11 (0.99)	<0.001
Nicotine exposure
Mean score (SE)	73.76 (1.53)	43.21 (3.10)	70.74 (2.06)	90.31 (1.24)	<0.001
Sleep health
Mean (SE), h/day	6.94 (0.03)	6.47 (0.17)	6.93 (0.03)	7.13 (0.04)	0.002
Mean score (SE)	83.08 (0.74)	64.79 (2.10)	82.02 (1.04)	91.65 (0.84)	<0.001
Body mass index
Mean (SE), kg/m²	28.34 (0.17)	33.01 (0.33)	29.27 (0.26)	24.96 (0.13)	<0.001
Mean score (SE)	62.52 (0.94)	33.96 (1.71)	56.97 (1.42)	82.90 (0.93)	<0.001
Blood lipids (non-HDL cholesterol)
Mean (SE), mg/dl	3.64 (0.02)	4.32 (0.10)	3.73 (0.04)	3.24 (0.04)	<0.001
Mean score (SE)	61.79 (0.63)	42.52 (2.26)	57.63 (0.84)	76.29 (1.33)	<0.001
Blood glucose
Fasting glucose, mean (SE), mg/dl	5.88 (0.04)	6.88 (0.17)	5.93 (0.03)	5.44 (0.03)	<0.001
HbA1c, mean (SE), %	5.71 (0.03)	6.46 (0.11)	5.73 (0.02)	5.38 (0.02)	<0.001
Mean score (SE)	82.80 (0.75)	59.80 (2.29)	80.72 (0.71)	94.90 (0.87)	<0.001
Blood pressure
Systolic, mean (SE), mmHg	124.81 (0.62)	133.12 (1.23)	127.45 (0.98)	117.05 (0.76)	<0.001
Diastolic, mean (SE), mmHg	71.23 (0.31)	72.53 (0.72)	72.10 (0.42)	69.22 (0.40)	<0.001
Mean score (SE)	63.54 (1.06)	44.47 (2.63)	57.81 (1.55)	80.77 (1.29	<0.001
SE, standard error. ^aAll estimates accounted for complex survey designs, and all percentages were weighted.

Table 2. Associations between Life’s Essential 8 cardiovascular health (CVH) score and abdominal aortic calcification (AAC), NHANES 2013–2014
	AAC scores, β (95% CI), P value		AAC, OR (95% CI), P value
CVH components	Model 1^a	Model 2^b	Model 1^a	Model 2^b
Total CVH
Total CVH score (10 increase)	-0.25 (-0.34, -0.15) <0.0001	-0.14 (-0.23, -0.04) 0.0064	0.82 (0.77, 0.88) <0.0001	0.87 (0.81, 0.94) 0.0002
Low level (0-49)	Reference	Reference	Reference	Reference
Intermediate level (50-74)	-0.37 (-0.76, 0.02) 0.0663	-0.16 (-0.54, 0.23) 0.4280	0.83 (0.64, 1.09) 0.1829	0.93 (0.70, 1.22) 0.5896
High level (75-100)	-0.91 (-1.34, -0.47) <0.0001	-0.50 (-0.95, -0.06) 0.0277	0.46 (0.34, 0.63) <0.0001	0.57 (0.41, 0.80) 0.0013
P for trend	<0.001	0.015	<0.001	<0.001
Diet
Diet score (10 increase)	-0.02 (-0.06, 0.02) 0.2824	0.00 (-0.04, 0.04) 0.8957	0.96 (0.94, 0.99) 0.0197	0.98 (0.95, 1.01) 0.2663
Low level (0-49)	Reference	Reference	Reference	Reference
Intermediate level (50-74)	0.03 (-0.29, 0.35) 0.8530	0.17 (-0.14, 0.48) 0.2736	0.96 (0.76, 1.21) 0.7415	1.06 (0.84, 1.35) 0.6207
High level (75-100)	-0.16 (-0.47, 0.15) 0.3154	0.03 (-0.28, 0.34) 0.8378	0.80 (0.64, 1.00) 0.0510	0.91 (0.71, 1.15) 0.4250
P for trend	0.345	0.973	0.057	0.310
Physical activity
Physical activity score (10 increase)	-0.05 (-0.08, -0.02) 0.0007	-0.04 (-0.07, -0.01) 0.0134	0.97 (0.95, 0.99) 0.0023	0.97 (0.95, 1.00) 0.0225
Low level (0-49)	Reference	Reference	Reference	Reference
Intermediate level (50-74)	-0.25 (-0.90, 0.41) 0.4630	-0.03 (-0.67, 0.60) 0.9168	1.09 (0.69, 1.73) 0.7006	1.19 (0.74, 1.92) 0.4735
High level (75-100)	-0.48 (-0.76, -0.19) 0.0011	-0.33 (-0.60, -0.05) 0.0219	0.75 (0.62, 0.92) 0.0057	0.81 (0.66, 1.00) 0.0533
P for trend	0.001	0.035	0.005	0.054
Nicotine exposure
Nicotine exposure score (10 increase)	-0.08 (-0.12, -0.05) <0.0001	-0.06 (-0.10, -0.02) 0.0009	0.93 (0.91, 0.95) <0.0001	0.94 (0.91, 0.97) <0.0001
Low level (0-49)	Reference	Reference	Reference	Reference
Intermediate level (50-74)	-0.76 (-1.50, -0.02) 0.0447	-0.56 (-1.28, 0.16) 0.1278	0.63 (0.37, 1.08) 0.0906	0.72 (0.41, 1.25) 0.2449
High level (75-100)	-0.66 (-0.98, -0.33) <0.0001	-0.46 (-0.79, -0.13) 0.0068	0.53 (0.42, 0.67) <0.0001	0.60 (0.47, 0.77) <0.0001
P for trend	<0.001	0.007	<0.001	<0.001
Sleep health
Sleep health score (10 increase)	-0.00 (-0.06, 0.05) 0.8757	0.02 (-0.03, 0.07) 0.3653	0.98 (0.95, 1.02) 0.3662	1.00 (0.96, 1.04) 0.8985
Low level (0-49)	Reference	Reference	Reference	Reference
Intermediate level (50-74)	-0.20 (-0.61, 0.21) 0.3324	-0.08 (-0.47, 0.32) 0.7018	0.80 (0.59, 1.08) 0.1420	0.89 (0.65, 1.22) 0.4566
High level (75-100)	-0.03 (-0.39, 0.33) 0.8860	0.14 (-0.21, 0.49) 0.4267	0.88 (0.68, 1.14) 0.3166	0.99 (0.76, 1.31) 0.9635
P for trend	0.828	0.397	0.533	0.910
Body mass index
Body mass index score (10 increase)	0.06 (0.02, 0.10) 0.0037	0.12 (0.07, 0.16) <0.0001	1.05 (1.02, 1.09) 0.0011	1.10 (1.06, 1.13) <0.0001
Low level (0-49)	Reference	Reference	Reference	Reference
Intermediate level (50-74)	0.56 (0.25, 0.87) 0.0004	0.79 (0.49, 1.09) <0.0001	1.43 (1.14, 1.79) 0.0019	1.69 (1.33, 2.14) <0.0001
High level (75-100)	0.34 (0.00, 0.67) 0.0474	0.72 (0.38, 1.06) <0.0001	1.36 (1.06, 1.73) 0.0141	1.80 (1.38, 2.36) <0.0001
P for trend	0.028	<0.001	0.010	<0.001
Blood lipids (non-HDL cholesterol)
Blood lipids score (10 increase)	-0.02 (-0.06, 0.03) 0.4151	-0.01 (-0.05, 0.03) 0.6082	0.97 (0.94, 1.00) 0.0482	0.97 (0.94, 1.00) 0.0645
Low level (0-49)	Reference	Reference	Reference	Reference
Intermediate level (50-74)	-0.43 (-0.77, -0.08) 0.0164	0.02 (-0.33, 0.36) 0.9285	0.65 (0.50, 0.85) 0.0015	0.79 (0.60, 1.05) 0.1070
High level (75-100)	0.14 (-0.16, 0.44) 0.3543	0.01 (-0.28, 0.30) 0.9590	0.90 (0.73, 1.11) 0.3059	0.83 (0.67, 1.04) 0.1064
P for trend	0.309	0.909	0.356	0.115
Blood glucose
Blood glucose score (10 increase)	-0.14 (-0.19, -0.09) <0.0001	-0.09 (-0.14, -0.04) 0.0005	0.94 (0.91, 0.97) 0.0005	0.97 (0.93, 1.01) 0.0978
Low level (0-49)	Reference	Reference	Reference	Reference
Intermediate level (50-74)	-1.00 (-1.40, -0.59) <0.0001	-0.67 (-1.09, -0.26) 0.0015	0.74 (0.56, 0.98) 0.0378	0.86 (0.63, 1.16) 0.3171
High level (75-100)	-1.25 (-1.62, -0.88) <0.0001	-0.85 (-1.24, -0.45) <0.0001	0.62 (0.48, 0.80) 0.0003	0.76 (0.57, 1.02) 0.0722
P for trend	<0.001	<0.001	<0.001	0.171
Blood pressure
Blood pressure score (10 increase)	-0.11 (-0.15, -0.07) <0.0001	-0.08 (-0.13, -0.04) 0.0001	0.92 (0.89, 0.95) <0.0001	0.93 (0.90, 0.97) <0.0001
Low level (0-49)	Reference	Reference	Reference	Reference
Intermediate level (50-74)	-0.51 (-0.88, -0.15) 0.0061	-0.37 (-0.73, -0.01) 0.0430	0.71 (0.55, 0.91) 0.0079	0.77 (0.59, 1.00) 0.0509
High level (75-100)	-0.67 (-0.99, -0.34) <0.0001	-0.51 (-0.83, -0.19) 0.0018	0.63 (0.50, 0.78) <0.0001	0.68 (0.54, 0.86) 0.0015
P for trend	<0.001	<0.001	<0.001	<0.001
OR, odds ratio; CI, confidence interval; NHANES, National Health and Nutrition Examination Survey. ^aAdjusted for age, sex, race/ethnicity. ^bMultivariable-adjusted models were adjusted for age, sex, race/ethnicity, education level, marital status, ratio of family income poverty, statin therapy, family history of heart disease and diabetes mellitus, chronic kidney disease, diet, nicotine exposure, physical activity, sleep health, body mass index, blood glucose, blood lipids, and blood pressure; When total or each CVH metrics were evaluated, corresponding metrics were excluded from the adjustment.

Table 3. Associations between Life’s Essential 8 cardiovascular health (CVH) score and severe abdominal aortic calcification (AAC), NHANES 2013–2014
	Severe AAC, OR (95% CI), P value
CVH components	Model 1^a	Model 2^b
Total CVH
Total CVH score (10 increase)	0.77 (0.69, 0.86) <0.0001	0.84 (0.74, 0.95) 0.0056
Low level (0-49)	Reference	Reference
Intermediate level (50-74)	0.69 (0.46, 1.02) 0.0652	0.77 (0.50, 1.18) 0.2247
High level (75-100)	0.39 (0.24, 0.65) 0.0003	0.52 (0.30, 0.90) 0.0197
P for trend	<0.001	0.019
Diet
Diet score (10 increase)	0.98 (0.93, 1.03) 0.3649	0.99 (0.94, 1.05) 0.8322
Low level (0-49)	Reference	Reference
Intermediate level (50-74)	1.04 (0.72, 1.50) 0.8460	1.15 (0.77, 1.71) 0.4911
High level (75-100)	0.85 (0.59, 1.22) 0.3789	0.96 (0.64, 1.43) 0.8454
P for trend	0.396	0.907
Physical activity
Physical activity score (10 increase)	0.96 (0.93, 1.00) 0.0297	0.98 (0.94, 1.01) 0.1898
Low level (0-49)	Reference	Reference
Intermediate level (50-74)	0.81 (0.38, 1.73) 0.5849	0.83 (0.37, 1.87) 0.6483
High level (75-100)	0.72 (0.53, 0.98) 0.0386	0.82 (0.58, 1.15) 0.2508
P for trend	0.039	0.319
Nicotine exposure
Nicotine exposure score (10 increase)	0.90 (0.87, 0.94) <0.0001	0.91 (0.87, 0.95) <0.0001
Low level (0-49)	Reference	Reference
Intermediate level (50-74)	0.31 (0.10, 0.91) 0.0333	0.31 (0.10, 1.01) 0.0512
High level (75-100)	0.45 (0.31, 0.66) <0.0001	0.50 (0.33, 0.74) 0.0007
P for trend	<0.001	<0.001
Sleep health
Sleep health score (10 increase)	1.00 (0.94, 1.06) 0.9476	1.02 (0.95, 1.09) 0.5836
Low level (0-49)	Reference	Reference
Intermediate level (50-74)	1.03 (0.61, 1.76) 0.9012	1.20 (0.68, 2.12) 0.5254
High level (75-100)	1.06 (0.67, 1.68) 0.7955	1.22 (0.74, 2.00) 0.4280
P for trend	0.786	0.681
Body mass index
Body mass index score (10 increase)	1.06 (1.01, 1.12) 0.0197	1.13 (1.06, 1.19) <0.0001
Low level (0-49)	Reference	Reference
Intermediate level (50-74)	1.67 (1.15, 2.41) 0.0065	2.27 (1.52, 3.40) <0.0001
High level (75-100)	1.46 (0.98, 2.19) 0.0635	2.19 (1.39, 3.48) 0.0008
P for trend	0.059	<0.001
Blood lipids (non-HDL cholesterol)
Blood lipids score (10 increase)	0.99 (0.94, 1.04) 0.6539	1.00 (0.94, 1.06) 0.8803
Low level (0-49)	Reference	Reference
Intermediate level (50-74)	0.59 (0.36, 0.95) 0.0312	1.03 (0.60, 1.79) 0.9137
High level (75-100)	1.13 (0.81, 1.58) 0.4658	0.99 (0.69, 1.43) 0.9715
P for trend	0.353	0.594
Blood glucose
Blood glucose score (10 increase)	0.87 (0.83, 0.92) <0.0001	0.89 (0.83, 0.94) 0.0001
Low level (0-49)	Reference	Reference
Intermediate level (50-74)	0.42 (0.28, 0.62) <0.0001	0.42 (0.26, 0.66) 0.0002
High level (75-100)	0.34 (0.24, 0.50) <0.0001	0.38 (0.24, 0.59) <0.0001
P for trend	<0.001	<0.001
Blood pressure
Blood pressure score (10 increase)	0.90 (0.86, 0.95) <0.0001	0.92 (0.87, 0.97) 0.0026
Low level (0-49)	Reference	Reference
Intermediate level (50-74)	0.76 (0.52, 1.13) 0.1750	0.87 (0.57, 1.33) 0.5226
High level (75-100)	0.54 (0.38, 0.78) 0.0009	0.61 (0.42, 0.90) 0.0135
P for trend	<0.001	0.006
OR, odds ratio; CI, confidence interval; NHANES, National Health and Nutrition Examination Survey. ^aAdjusted for age, sex, race/ethnicity. ^bMultivariable-adjusted models were adjusted for age, sex, race/ethnicity, education level, marital status, ratio of family income poverty, statin therapy, family history of heart disease and diabetes mellitus, chronic kidney disease, diet, nicotine exposure, physical activity, sleep health, body mass index, blood glucose, blood lipids, and blood pressure; When total or each CVH metrics were evaluated, corresponding metrics were excluded from the adjustment.

No competing interests reported.

Association between Life’s Essential 8 and abdominal aortic calcification: findings from a national study

Status:

Version 1

Abstract

Figures

INTRODUCTION

MATERIALS AND METHODS

Study population

Abdominal aortic calcification assessment

Covariate information

Statistical analysis

Results

DISCUSSION

CONCLUSION

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1