Associations between alkaline phosphatase levels and abdominal aortic calcification via statistical logistic modeling and machine learning

Associations between alkaline phosphatase levels and abdominal aortic calcification via statistical logistic modeling and machine learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Associations between alkaline phosphatase levels and abdominal aortic calcification via statistical logistic modeling and machine learning Chunyu Yin, Long Cao, Jiao Zhao, Jianping Bai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6779641/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background: Vascular calcification is associated with serum alkaline phosphatase (ALP) levels. This study aimed to investigate the relationship between ALP and abdominal aortic calcification (AAC). Methods: Data from the 2013–2014 National Health and Nutrition Examination Survey (NHANES) were utilized for this study. Weighted multivariable regression analyses and subgroup analyses were conducted to investigate the independent relationship between ALP levels and AAC scores. Restricted cubic splines (RCS) regression was employed to explore nonlinear relationships. Additionally, Boruta, random forest, Lasso, and logistic combined with SHAP interpretable analysis were utilized for screening variables and ranking their importance. Results: A total of 1,605 middle-aged adult participants were included in the analysis. ALP was positively associated with AAC scores among middle-aged females (β=0.008, 95% CI: 0.002–0.014, P=0.011). The risk of AAC was 3.04 times greater (OR=3.045, 95% CI: 1.019--9.098, P=0.047) in middle-aged females (≥93 U/L vs. <59 U/L). A significant relationship between ALP levels and AAC was observed among middle-aged females, with one notable inflection point identified at an ALP level of 63 U/L in the RCS regression. All four machine learning algorithms interpret ALP as one of the most significant features infecting AAC among middle-aged females. Conclusions: High ALP levels were associated with elevated AAC scores and an increased risk of AAC among middle-aged females. Abdominal CT or abdominal X-ray examinations are recommended when the ALP level exceeds 63 U/L during routine biochemical assessments. alkaline phosphatase ALP abdominal aortic calcification AAC aortic calcification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Abdominal aortic calcification (AAC) is a distinct manifestation of large artery calcification characterized by the accumulation of calcium phosphate crystals within the vascular wall 1 . From an imaging perspective, the presence of AAC is typically detected by means of CT or lateral spine images from standard radiographs or dual-energy X-ray absorptiometry (DXA) machines, with calcification manifesting as either patchy or diffuse distribution 2,3 . However, these two types of calcification tend to exhibit different histological characteristics. Intimal calcification, for example, atherosclerosis, is more frequently associated with patchy distribution, whereas medial calcification, for example, aging, diabetes, chronic kidney disease and systemic inflammatory disease, is predominantly linked to diffuse distribution 4,5 . Coronary artery calcification (CAC) usually occurs in the intimal layer, whereas AAC primarily manifests within the medial layer 6 . Nevertheless, several epidemiological studies across diverse populations continue to demonstrate an association between AAC and cardiovascular events 7–9 . Furthermore, the AAC has been shown to be a more effective predictor of cardiovascular events than the Framingham risk score or CAC 10,11 . Importantly, AAC does not directly mirror atherosclerosis, thus precluding the possibility that the observed correlation can be explained solely by the degree of systemic atherosclerosis. To further explore this relationship, identifying potential factors affecting AAC is vital, as identifying these factors will contribute to the prevention and treatment of cardiovascular disease (CVD). Alkaline phosphatase (ALP) is a glycoprotein that plays a crucial role in catalyzing the hydrolysis of inorganic pyrophosphate, which is instrumental in regulating vascular calcification 12,13 . ALP has been found to be independently associated with CVD mortality 14 . Additionally, elevated serum ALP levels present an increased adjusted risk for all-cause mortality, incident stroke, CVD, and the occurrence of composite outcomes 15 . A limited number of studies have investigated the relationship between serum ALP levels and aortic calcification in patients with renal failure 16 . Nevertheless, research examining the relationship between ALP and aortic calcification within the general population is relatively rare. Therefore, the present study utilized data from the United States National Health and Nutrition Examination Survey (NHANES), which was conducted between 2013 and 2014, with the objective of investigating the association between serum ALP and AAC. Given that age can notably increase the incidence of AAC, we specifically focused our research on middle-aged individuals aged 40–59 years. Methods 1. Data source and participants The data utilized in this study were obtained from the NHANES database. The NHANSE dataset employs a sophisticated multistage, stratified, and clustered probability sampling methodology, which yields a representative cross-section of the U.S. population. The objective of the present study was to elucidate the correlation between ALP levels and the AAC score. To gain further insight into this issue, the only annual cohort with both ALP levels and AAC scores from 2013–2014 was selected for detailed analysis. In this year, a total of 10,175 individuals were enrolled. After the exclusion of participants with missing data on ALP levels (n = 3,623) and subsequently on AAC scores (n = 3,535), as well as individuals with refused information regarding other variables such as alcohol consumption (n = 7), hypertension (n = 3), diabetes (n = 2), and smoking status (n = 2), a total of 3,003 participants were included in the study. Considering the unavoidable physiological calcification of vascular structures associated with the aging process, our research further focused on the demographics of middle-aged adults, specifically individuals aged 40–59 years (n = 1,605). The participant selection flowchart is presented in Fig. 1. To guarantee ethical compliance, the NHANES study was granted approval by the NCHS Ethics Review Board. Additionally, written informed consent was obtained from all individuals who participated in the survey. No further ethical reviews are necessary for this research project. 2. Exposure and outcome definitions The DxC system employs a kinetic rate methodology utilizing 2-amino-2-methyl-1-propanol (AMP) buffer to quantify ALP activity in serum or plasma samples. The reaction in question occurs at an alkaline pH of 10.3. The system monitors the rate of change in absorbance at 410 nm over a fixed time interval. The rate of change in absorbance is directly proportional to the activity of ALP in the serum. The unit of measurement for ALP is the IU/L. In lieu of the single-year circle of 2013–2014, the serum ALP concentration was meticulously stratified using a reference dataset comprising a substantial cohort of 60,056 participants sourced from the NHANES database, spanning the period from 1999–2020. The participants were classified into four quartiles on the basis of their ALP levels: Q1 (≤ 58 U/L), Q2 (59–72 U/L), Q3 (73–92 U/L), and Q4 (≥ 93 U/L). This stratification is more logical and representative, thus facilitating comparison with the methodologies employed in other studies 17–19 . The principal outcome variables examined in the present study were the AAC score and the presence of abdominal aortic calcification. Abdominal aortic calcification can be accurately identified on lateral spine images intended for vertebral fracture assessment (VFA) obtained with dual-energy digital emission X-ray absorptiometry (DXA). DXA scans were administered to individuals aged 40 years and older who met the eligibility criteria. Pregnant females who had used radiographic contrast material (barium) in the previous seven days and those with a body weight exceeding 450 pounds were excluded from the DXA examination. All the scans in the DXXAAC_H file were viewed via Optasia SpinAnalyzer software, and both AAC-24 and AAC-8 scoring semi-quantitative techniques were used for the evaluation 20,21 . The AAC score was used to assess the severity of calcification in the abdominal aorta. An elevated AAC score is indicative of more pronounced calcification. The score was quantified via the Kauppila scoring system, which involves the analysis of lateral lumbar spine images obtained via DXA (Densitometer Discovery A, Hologic, Marlborough, MA, USA) 20 . In the scoring method for AAC-24, the anterior and posterior aortic walls are divided into four segments, which correspond to the areas in front of the lumbar vertebrae L1–L4. Within each of the eight aforementioned segments, the presence of aortic calcification was identified through visual examination. This was observed as either diffuse white stippling of the aorta, extending out to the anterior and/or posterior aortic walls, or white linear calcification of the anterior and/or posterior walls. The scores were obtained separately for the anterior and posterior aortic walls, resulting in ranges from 0–6 for each vertebral level and 0–24 for the total score. The total AAC score (variable code: DXXAAC24) was obtained directly from the NHANES dataset. In the present study, the term "AAC" was defined as a total score of > 0 on the AAC scale. 3. Covariates Given the limited research investigating the relationship between the ALP level and AAC, the selection of covariates was undertaken through a comprehensive review of literature, complemented by a systematic deductive reasoning process that illuminated their potential correlation with both the ALP level and various forms of vascular calcification. Continuous variables in our analysis included age, systolic blood pressure (SBP), diastolic blood pressure (DBP), aspartate transaminase (AST), alanine transaminase (ALT), serum creatinine (Scr), hemoglobin A1c (HbA1c), serum uric acid (UA), serum calcium, serum phosphorus, total cholesterol (TC), and total 25-hydroxyvitamin D [25(OH)D] levels. The categorical variables included sex, race, educational attainment, body mass index (BMI), hypertension status, and diabetes status. BMI was categorized into three groups: <25 kg/m², 25–29.9 kg/m², and ≥ 30 kg/m². Detailed measurement procedures for these variables can be found in publicly available documentation at https://www.cdc.gov/nchs/nhanes/. To address missing covariate data, we employed a missing interpolation method utilizing the MI program of R software to impute the absent values. 4. Statistical analysis All the statistical analyses were conducted in accordance with the guidelines established by the Centers for Disease Control and Prevention (CDC). Sampling weights were employed to derive nationally representative prevalence estimates for the non-institutionalized population of the United States. Continuous variables are presented as the means with standard errors, whereas categorical variables are shown as percentages with their standard errors. To assess differences among groups stratified by AAC level and sex among middle-aged adults, both continuous and categorical ALP levels were subjected to statistical analysis. We conducted a weighted one-way analysis of variance for normally distributed data, employed Kruskal-Wallis tests for skewed data, and utilized χ² tests for categorical variables. Univariate and multivariate linear regression, as well as multivariate logistic regression, were performed on three distinct models to further investigate the relationship between ALP and AAC. Model 1 did not incorporate any covariate adjustments. Model 2 was adjusted for factors such as age, race, and education level. Model 3 further refined the analysis by adjusting for an additional set of variables: age, race, education level, BMI, SBP, DBP, Scr, HbA1c, UA, serum calcium, serum phosphorus, TC, total 25(OH)D, and the presence of hypertension and diabetes. The efficacy of ALP in identifying AAC was evaluated through the use of receiver operating characteristic (ROC) curves and calculation of the area under the curve (AUC). Subgroup analyses and interaction tests were conducted to ascertain the consistency of the association between ALP levels and AAC across different subgroups. Additionally, weighted restricted cubic splines (RCS) regression was employed to investigate the nonlinear relationships between ALP and AAC. We further employed the Boruta algorithm, the random forest algorithm, the Lasso algorithm, and the logistic algorithm combined with SHAP interpretable analysis for the purpose of screening variables and ranking their importance. All analyses were conducted via R software (version 4.4.2), and statistical significance was determined with a two-sided P value < 0.05. Results 1. Baseline characteristics of the participants A total of 1,605 middle-aged adult participants, comprising 778 males and 827 females, were included in the analysis. The baseline characteristics of all participants categorized by sex are presented in Table 1 . Significant differences were observed among a number of factors, including AST, ALT, Scr, phosphorus, uric acid, SBP, DBP, TC, 25(OH)D, diabetes, alcohol use and smoking. Given that the subjects in the study were middle-aged, there was no statistically significant difference in age between the sexes. Notably, no significant differences were observed in the levels of ALP, calcium, or AAC. Continuous variables are presented as the means ± standard errors, whereas categorical variables are presented as percentages (standard errors). ALP, alkaline phosphatase; AST, aspartate aminotransferase; ALT, alanine aminotransferase; Scr, serum creatinine; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycohemoglobin; 25(OH)D, 25-hydroxyvitamin D; AAC, abdominal aortic calcification. Table 1 Baseline characteristics of the participants according to age and sex. Variable Overall Participants Middle-Aged-Adults (40–59 years old) Total (n = 3,003) Male (n = 1,449) Female (n = 1,554) P value Total (n = 1,605) Male (n = 778) Female (n = 827) P value Age (year) 57.41 ± 0.28 56.95 ± 0.28 57.84 ± 0.33 0.002 49.34 ± 0.20 49.55 ± 0.26 49.12 ± 0.27 0.24 ALP (U/L) 65.78 ± 0.76 64.22 ± 1.02 67.24 ± 0.89 0.02 65.30 ± 0.92 64.67 ± 1.35 65.93 ± 1.20 0.49 AST (U/L) 25.47 ± 0.52 26.56 ± 0.51 24.45 ± 0.67 0.002 25.86 ± 0.77 27.31 ± 0.72 24.39 ± 1.07 0.01 ALT (U/L) 24.79 ± 0.59 28.03 ± 0.84 21.74 ± 0.48 < 0.0001 26.51 ± 0.82 30.61 ± 1.20 22.34 ± 0.69 < 0.0001 Calcium (mmol/L) 2.36 ± 0.00 2.36 ± 0.00 2.37 ± 0.00 0.23 2.36 ± 0.00 2.36 ± 0.00 2.35 ± 0.01 0.53 Scr (umol/L) 81.86 ± 0.69 92.28 ± 1.25 72.07 ± 0.90 < 0.0001 78.97 ± 0.97 88.38 ± 1.18 69.41 ± 1.38 < 0.0001 Phosphorus (mmol/L) 1.23 ± 0.01 1.19 ± 0.01 1.26 ± 0.01 < 0.0001 1.23 ± 0.01 1.20 ± 0.01 1.25 ± 0.01 < 0.001 Uric acid (umol/L) 322.14 ± 1.78 355.44 ± 2.59 290.87 ± 2.22 < 0.0001 317.30 ± 2.37 356.40 ± 3.00 277.56 ± 2.87 < 0.0001 BMI (kg/m 2 ) 28.55 ± 0.16 28.49 ± 0.17 28.62 ± 0.23 0.62 28.72 ± 0.24 28.56 ± 0.23 28.88 ± 0.33 0.32 SBP (mmHg) 125.36 ± 0.47 126.11 ± 0.59 124.66 ± 0.51 0.03 120.60 ± 0.54 122.27 ± 0.63 118.89 ± 0.73 < 0.001 DBP (mmHg) 71.17 ± 0.32 72.56 ± 0.43 69.86 ± 0.39 < 0.001 73.34 ± 0.31 75.00 ± 0.44 71.66 ± 0.45 < 0.0001 HbA1c (%) 5.78 ± 0.03 5.82 ± 0.04 5.74 ± 0.03 0.09 5.67 ± 0.03 5.72 ± 0.06 5.62 ± 0.03 0.09 Total cholesterol (mmol/L) 5.06 ± 0.02 4.90 ± 0.03 5.21 ± 0.02 < 0.0001 5.17 ± 0.02 5.09 ± 0.05 5.24 ± 0.04 0.04 25(OH)D (nmol/L) 74.92 ± 1.31 70.27 ± 1.22 79.29 ± 1.84 < 0.001 69.79 ± 1.44 66.46 ± 1.42 73.18 ± 2.06 0.01 Hypertension (%) 0.02 0.73 No 55.89(0.04) 58.31(1.14) 53.62(1.79) 66.76(0.05) 67.22(1.45) 66.30(2.53) Yes 44.11(0.02) 41.69(1.14) 46.38(1.79) 33.24(0.02) 32.78(1.45) 33.70(2.53) Race (%) 0.53 0.56 Mexican American 6.99(0.02) 7.33(1.78) 6.67(1.59) 8.65(0.02) 8.82(2.06) 8.49(1.86) Other Hispanic 4.60(0.01) 4.34(0.86) 4.85(0.89) 5.43(0.01) 5.00(1.11) 5.86(1.04) Non-Hispanic White 71.47(0.07) 71.91(3.22) 71.05(3.23) 67.00(0.07) 67.81(3.66) 66.18(3.58) Non-Hispanic Black 9.75(0.01) 9.37(1.19) 10.10(1.50) 10.78(0.01) 10.23(1.36) 11.34(1.57) Other Race - Including Multi-Racial 7.19(0.01) 7.05(0.76) 7.32(0.89) 8.14(0.01) 8.15(0.93) 8.12(1.18) Education level (%) 0.94 0.28 Less than high school 15.14(0.02) 15.13(2.00) 15.14(1.89) 14.75(0.02) 15.22(2.22) 14.27(1.78) High school or GED 21.75(0.02) 21.96(1.62) 21.54(1.75) 21.43(0.03) 22.84(2.53) 19.99(2.18) Above high school 63.12(0.05) 62.91(2.94) 63.31(2.97) 63.82(0.06) 61.93(3.92) 65.74(3.30) Diabetes (%) 0.05 0.05 No 83.39(0.05) 81.76(1.52) 84.92(1.03) 88.56(0.06) 87.20(1.50) 89.94(1.02) Border 3.61(0.01) 4.57(0.74) 2.72(0.40) 2.67(0.00) 3.42(0.53) 1.90(0.45) Yes 12.99(0.01) 13.67(1.03) 12.36(0.96) 8.77(0.01) 9.38(1.38) 8.16(0.99) Alcohol using (%) < 0.0001 < 0.001 No 21.98(0.02) 12.70(1.54) 30.69(2.44) 18.08(0.02) 11.46(2.05) 24.81(2.54) Yes 78.02(0.05) 87.30(1.54) 69.31(2.44) 81.92(0.06) 88.54(2.05) 75.19(2.54) Smoke (%) < 0.001 0.02 No 54.20(0.04) 49.13(2.01) 58.97(1.93) 56.29(0.04) 53.56(2.14) 59.07(2.72) Yes 45.80(0.03) 50.87(2.01) 41.03(1.93) 43.71(0.03) 46.44(2.14) 40.93(2.72) AAC (%) 0.79 0.19 Normal 71.13(0.05) 70.85(1.60) 71.39(2.25) 81.40(0.05) 79.95(2.66) 82.89(2.69) AAC 28.87(0.02) 29.15(1.60) 28.61(2.25) 18.60(0.03) 20.05(2.66) 17.11(2.69) Table 2 Associations of ALP with the AAC score and AAC. Male (40–59 years) β (95% CI) Model 1 P Value Model 2 P Value Model 3 P Value AAC Scores Continues ALP Continues 0.003 (-0.003,0.008) 0.347 0.001(-0.006,0.008) 0.658 -0.002 (-0.006, 0.002) 0.331 OR (95% CI) AAC Categories ALP Continues 1.000 (0.993,1.007) 0.965 0.999 (0.990,1.007) 0.729 0.993 (0.986,1.001) 0.087 AAC Categories Model 1 P Value Model 2 P Value Model 3 P Value ALP Q1 < 59 U/L Ref Ref Ref ALP 59 ≤ Q2 < 73 U/L 1.144 (0.674,1.943) 0.589 1.176 (0.608,2.275) 0.555 1.126 (0.643,1.973) 0.658 ALP 73 ≤ Q3 < 93 U/L 1.335 (0.679,2.626) 0.370 1.257 (0.567,2.787) 0.493 1.067 (0.596,1.910) 0.817 ALP Q4 ≥ 93U/L 1.060 (0.536,2.098) 0.855 1.028 (0.411,2.571) 0.941 0.725 (0.357,1.472) 0.348 P for trend 0.461 0.597 0.789 Female (40–59 years) β (95% CI) Model 1 P Value Model 2 P Value Model 3 P Value AAC Scores Continues ALP Continues 0.009 (0.003,0.015) 0.006* 0.007 (0.002,0.013) 0.020* 0.008 (0.002,0.014) 0.011* OR (95% CI) AAC Categories ALP Continues 1.013 (1.001,1.024) 0.030* 1.011 (0.997,1.024) 0.099 1.011 (0.998,1.024) 0.090 AAC Categories Model 1 P Value Model 2 P Value Model 3 P Value ALP Q1 < 59 U/L Ref Ref Ref ALP 59 ≤ Q2 < 73 U/L 1.529 (0.708,3.303) 0.253 1.437 (0.525, 3.933) 0.397 1.373 (0.638,2.951) 0.392 ALP 73 ≤ Q3 < 93 U/L 1.982 (0.916,4.288) 0.077 1.761 (0.708, 4.380) 0.171 1.588 (0.752,3.357) 0.207 ALP Q4 ≥ 93U/L 3.368 (1.404,8.078) 0.011* 3.022 (0.873,10.459) 0.071 3.045 (1.019,9.098) 0.047* P for trend 0.008* 0.049* 0.042* 2. Association between ALP level and AAC The relationship between ALP levels and AAC scores was analyzed via weighted linear regression, whereas weighted logistic regression was employed to investigate the association between ALP (continuous or quartile) and AAC (see Table 2 ). Our analysis results revealed a significant positive correlation between the continuous variable ALP and the AAC scores across all three models in middle-aged females. In the fully adjusted Model 3, this positive association remained statistically significant (β = 0.008, 95% CI: 0.002–0.014, P = 0.011), indicating that each unit increase in the ALP level was associated with a 0.008 higher AAC score. The correlation between the ALP level and AAC was statistically significant in Model 1, indicating that a one-unit increase in the ALP level was associated with a 1.3% increase in the risk of developing AAC. However, after adjusting for other potential confounding variables, this type of relationship was attenuated and non-significant. To further elucidate the stability of the association between ALP and AAC, ALP was converted into quartiles. The prevalence of AAC also increased in quartile four (Q4) compared with quartile one (Q1) of ALP (P for trend < 0.05, in Model 2 and Model 3). Following the adjustment of all covariates, the risk of AAC was 3.04 times greater in middle-aged females with ALP levels of 93 U/L or above than in those with ALP levels below 59 U/L. Nevertheless, the results demonstrated no notable association between ALP and AAC among middle-aged males. 3. Subgroup analysis Subgroup analyses were conducted to evaluate the reliability and robustness of the association between ALP and AAC across various subgroups. The summarized findings from these subgroup analyses are presented in Fig. 2 and Suppl. Figures 1–3 . For middle-aged females, higher levels of ALP tend to be associated with increased AAC among individuals who are Non-Hispanic White, Non-Hispanic Black, have an educational level above high school, smoke, have a low BMI, and do not have hypertension or diabetes. Nevertheless, the positive correlation between ALP levels and AAC scores appears to become more stable across different racial and BMI subgroups. Notably, the P values for interactions were all statistically negative across various subgroups. Consistent with the findings obtained from previous analyses of middle-aged males, no significant relationship was observed between ALP and AAC among the subgroups. 4. Analysis of RCS regression The RCS regression was adjusted for all potential covariates. A significant relationship between the ALP level and AAC outcome was observed (P overall < 0.05) in middle-aged females, with one significant inflection point at an ALP level of 63 U/L. However, the P-nonlinear value exceeded 0.05, suggesting an unconfirmed nonlinear relationship between ALP and AAC outcome (Fig. 3 ). For middle-aged males, both the P-overall and P-nonlinear values were greater than 0.05, suggesting that there is neither a significant association nor a nonlinear relationship between ALP levels and AAC outcomes (Fig. 4 ). 5. The Importance of ALP for AAC via Machine Learning The utilization of machine learning algorithms could facilitate a more comprehensive evaluation of the significance of ALP in relation to AAC. The study subjects (middle-aged females and males) were divided into training and test sets, with the ratio of subjects in each set being 8:2. Given that the relationship between ALP and AAC was identified only in middle-aged women, the subsequent analysis focused exclusively on this demographic factor. Then, the Boruta algorithm, the random forest algorithm, the Lasso algorithm, and the logistic algorithm combined with SHAP interpretable analysis were utilized for the purpose of screening variables and ranking their importance. The Boruta algorithm indicates SBP, ALP, and BMI as the confirmed features (Fig. 5 A); the random forest algorithm indicates ALP as the third important variable (Fig. 5 B, Suppl. Figures 4–6 ); the Lasso algorithm indicates five important features (Fig. 5 C, Suppl. Figure 8A-B ), with ALP ranking fourth; and logistic regression with SHAP interpretable analysis indicates that ALP is the fourth most important feature (Fig. 5 D, Suppl. Figure 9 ). The random forest model predicted that elevated AAC in middle-aged females is associated with both (1) low BMI combined with high ALP and (2) advanced age combined with high ALP. ( Suppl. Figure 7 ). Discussion In this cross-sectional study of 1,605 middle-aged adults, elevated ALP levels demonstrated a significant independent association with both higher AAC scores and increased AAC risk, especially among middle-aged women, whereas no such association was observed in male participants. Specifically, we observed that middle-aged females with higher ALP levels tended to have higher AAC scores (0.008 units higher) and a greater risk of AAC (OR of 3.045 for the Q4 group than for the Q1 group). These findings suggest that increasing ALP may prompt the occurrence of AAC in middle-aged females. Importantly, the interaction effect between ALP and AAC was not statistically significant across various subgroups categorized by race, sex, age, body mass index, hypertension, and diabetes status, indicating the independence of ALP as a risk factor for AAC among middle-aged females. Furthermore, the odds ratio of AAC was significantly correlated with elevated ALP levels exceeding 63 U/L in middle-aged females. Finally, the importance of ALP for AAC was confirmed again among middle-aged women via four machine learning algorithms, Boruta, random forest, Lasso, and the logistic algorithm combined with SHAP interpretable analysis. In recent years, studies have acknowledged that the AAC has transitioned from being an inconsequential imaging feature, often identified serendipitously, to becoming an early indicator with predictive significance for various diseases, including stroke, aortic valve disorders, myocardial infarction, emphysema, and chronic obstructive pulmonary disease 9 , 22 . In the context of coronary diseases, AAC is significantly associated with stenosis of the precerebral arteries, myocardial infarction, ischemic heart disease, and cardiovascular mortality 7 , 11 , 23 . This association has been consistent across studies in different epidemiological populations 7 , 9 , 23 . Furthermore, compared with calcification of the coronary artery itself, AAC appears to be a superior indicator of cardiovascular events 10 . This conclusion was reinforced by the research of Wong N. D., which indicates that among individuals with AAC, approximately 40% of women and 20% of men do not exhibit CAC. Moreover, among those without CAC, nearly 60% of women and over 50% of men presented evidence of subclinical CVD elsewhere, with the majority involving AAC 24 . Consequently, it is plausible that the occurrence of AAC may precede that of CAC, thus offering potential advantages for predicting cardiovascular events. Further research demonstrated that AAC functions as a risk factor for myocardial infarction that is not contingent upon LDL, suggesting that AAC may contribute to cardiovascular disease risk in a manner comparable to hypercholesterolemia 22 . Indeed, this conclusion aligns with the histological observations - unlike coronary atherosclerosis, which is predominantly observed in the intimal layer, AAC occurs more frequently in the medial layer 22 . These findings naturally prompt researchers to explore alternative mineralization mechanisms that may influence the incidence of cardiovascular events. Importantly, fully formed bone tissue is present in atherosclerotic arteries, indicating that vascular calcification involves a complex and regulated process of biomineralization 25 . Another significant clue is that the high prevalence of arterial calcification observed in end-stage renal disease (ESRD) exceeds what can be attributed exclusively to traditional cardiovascular risk factors, including aging, diabetes, hypertension, and dyslipidemia, which highlights a substantial association between vascular calcification and abnormalities in bone and mineral metabolism 26 . The bone‒vascular axis hypothesis thus emerged, highlighting the critical influence of bone-derived cell types and endocrine/paracrine signals on vascular health 27 . ALP is classified as an orthophosphate monoester phosphohydrolase and is frequently measured in clinical practice as a biomarker for hepatic or bone-related diseases 18 . Moreover, bone-derived ALP, an ectoenzyme that plays a crucial role in the process of bone mineralization, is specifically localized to the mineralizing segments of arteries 27 . Recent population-based evidence indicates that elevated ALP levels (even within the normal range) are significantly associated with an increased risk of CVD and that higher ALP levels are a risk factor for all-cause and cardiovascular mortality in individuals with or without kidney disease 28 , 29 . ALP promotes vascular mineralization by catalyzing the hydrolysis of inorganic pyrophosphate, a key inhibitor of vascular calcification, thereby increasing its breakdown and facilitating vessel calcification 30 . Ren et al discovered that patients with elevated serum ALP levels are at increased risk of developing coronary calcification, particularly spotty calcification, as well as a minimum lumen area of less than 4.0 mm² 31 . Our team's prior research initially demonstrated a positive correlation between elevated serum ALP levels and thoracic aortic calcification 32 . Nevertheless, the relationship between AAC and the serum ALP level remains equivocal, particularly in the context of the general population, resulting in considerable confusion. Owing to the high prevalence of AAC, which ranges from 34% in individuals aged 45–54 years to 94% in those aged 75–84 years, understanding the relationship between AAC and ALP is crucial for assessing the impact of ALP on large artery calcification 24 . Hence, the current study offers a crucial contribution to the existing body of knowledge in this field, thereby providing the "missing piece of the puzzle". Nevertheless, careful judgment is crucial when evaluating the conclusions drawn from this study, as several elements warrant thorough examination. First, both the severity of calcification and the incidence of extreme cases clearly increase significantly with increasing age 22 . Consequently, the present study, which utilized data from NHANSE, focused on individuals within the 40–59 age group, categorized as middle-aged individuals. The impact of age on AAC is minimal in middle-aged individuals, thus allowing for a more accurate representation of the impact of other factors, such as ALP, on AAC. Furthermore, middle-aged individuals constitute a critical demographic for intervention strategies aimed at combating disease, underscoring the clinical importance of research pertaining to subsequent disease treatment. However, it is important to note that while a positive correlation exists between ALP and AAC, this relationship should not be readily extrapolated to broader age groups. To substantiate these conclusions, it is imperative to gather more extensive data and undertake careful consideration along with a rigorous study design. Second, the positive correlation between ALP and AAC identified in the conclusion is predominantly observed in middle-aged women, whereas this relationship is significantly reduced in middle-aged men. In our primary analysis, no substantial relationship was identified between ALP and AAC in middle-aged males. However, when the AAC score was categorized into severe calcification and non-severe calcification based on a threshold of 6 points, a significant relationship was identified. The univariate analysis indicated that ALP had an odds ratio of 1.011 (95% CI: 1.00-1.022), with a p value of 0.035. Given the limited number of middle-aged males who presented with severe AAC (n = 16), the robustness of these findings may be questioned. As a result, this component of the analysis has been excluded from the results section of the article. We propose that sex differences may be attributed to hormonal factors. Given that women aged 40–59 years are in perimenopause, they face a heightened risk of osteoporosis, which suggests an elevated potential risk for vascular calcification as well 33 . Future research should focus on examining the impact of estrogen levels on calcification processes more thoroughly. From our perspective, it would be a misjudgment to assume that there is no correlation between ALP and AAC in males. The observed discrepancies may stem from limitations related to X-ray detection resolution and the inherent subjectivity involved in measuring AAC. Additionally, ALP levels typically exhibit changes prior to observable alterations in vascular calcification levels, indicating the possibility of staggered peaks. Therefore, it will be essential for future studies to obtain more objective imaging data concerning aortic calcification to establish better correlations. Third, in the subgroup analysis, while the interaction was not statistically significant, we observed inconsistencies in the correlation between ALP and AAC across various subgroups. For example, among subgroups categorized by hypertension, diabetes, and BMI, the positive correlation between ALP and AAC was found to be more pronounced in middle-aged women who did not have the aforementioned risk factors. In contrast, in subgroups characterized by alcohol use and smoking, a significant positive correlation between ALP and AAC was observed among middle-aged women who presented these risk factors. When different ethnic subgroups were analyzed, a significant correlation was identified between ALP and AAC in non-Hispanic middle-aged women, thus highlighting an additional phenomenon that merits further consideration. These findings indicate the possibility of heterogeneity in the relationship between ALP and AAC within subgroups. However, this discrepancy has not yet reached statistical significance. The present study places greater emphasis on the primary effect of ALP, with the subgroup outcomes serving exclusively as exploratory observations. In subsequent studies, it is recommended that the sample size model be expanded, such as through the balancing of sample size differences between subgroups, with a view to rendering conclusions clearer. Fourth, when ALP levels exceeded 63 U/L, the odds ratio of AAC demonstrated an increased correlation with increasing ALP levels. These findings suggest that even when the ALP level is within the normal range, there remains an elevated risk for AAC. This effect of ALP aligns closely with the observations reported in studies investigating the relationship between ALP and coronary artery disease 13 , 29 . This finding indicates that when the ALP level exceeds 63 U/L during routine biochemical assessments, it is advisable to consider abdominal CT or abdominal X-ray examinations. These imaging modalities can provide further insights into the status of AAC and may also aid in assessing the risk of CHD. Further research is still needed to determine whether ALP is merely a marker of the aortic calcification burden or whether it has a direct impact on systemic vascular disease, including cardiovascular disease. Haarhaus et al . demonstrated that medications, such as the extra-terminal (BET) protein inhibitor apabetalone, which lowers serum ALP levels, are associated with a reduced risk of cardiovascular events 34 . However, additional research is essential to ascertain whether the reduction in ALP contributes to the improvement in CVD by mitigating vascular calcification and to explore a possible dose-response relationship between ALP and the reduction in aortic calcification. Strengths and limitations The strengths of this study include the utilization of nationally representative NHANES data, which employ standardized procedures; the application of sampling weights to increase the representativeness of the findings; and the incorporation of covariate adjustment to mitigate confounding bias. However, there are three limitations. First, the cross-sectional design restricts our ability to establish a causal relationship between ALP levels and AAC, necessitating longitudinal studies with larger sample sizes for verification. Second, we were unable to detect inflammatory markers such as high-sensitivity C-reactive protein (hs-CRP), tumor necrosis factor-alpha (TNF-ɑ), and interleukin-1 beta (IL-1β). Consequently, the potential role of ALP in AAC through inflammatory pathways could not be investigated. Finally, because the NHANES does not conduct dual-energy X-ray absorptiometry (DXA) testing on individuals under 40 years of age, AAC data for this age group are lacking, thereby limiting the comprehensiveness of our analysis regarding age associations. Conclusion High ALP levels were associated with elevated AAC scores and an increased risk of AAC among middle-aged females. Abdominal CT or abdominal X-ray examinations are recommended when the ALP level exceeds 63 U/L during routine biochemical assessments. However, further large-scale prospective studies are necessary to elucidate the precise causality of this relationship. Abbreviations ALP alkaline phosphatase AAC abdominal aortic calcification CAC coronary artery calcification CVD cardiovascular disease DXA dual-energy X-ray absorptiometry VAF vertebral fracture assessment CDC Centers for Disease Control and Prevention RCS restricted cubic spline ROC receiver operating characteristic AUC area under the curve NHANES National Health and Nutrition Examination Survey Declarations Author Contributions JB and CY contributed to the conception and design of the study, interpretation of the data, and revision of the manuscript. LC and CY wrote the manuscript and acquired the data. JZ and CY performed the statistical analysis. All the authors contributed to manuscript revision and read and approved the submitted version. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Science and Technology Project of Tianjin Municipal Health Commission (grant number TJWJ2023QN116) to L.C. and the Yanzhen Talents Scheme supported by Zhengzhou Joint Logistics Support Center of PLA to L.C. and the 983rd Hospital Science and Technology Incubation Program (983YN23F005). ORCID Jianping Bai https://orcid.org/0000-0001-9805-7133 Data availability statement The datasets analyzed in the current study are available from the corresponding author upon reasonable request. The raw data that support the findings of this study are available from the links: https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/DEMO_H.xpt https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/BIOPRO_H.xpt https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/DXXAAC_H.xpt https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/BPQ_H.xpt https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/DIQ_H.xpt https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/SMQ_H.xpt https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/ALQ_H.xpt https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/BMX_H.xpt https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/BPX_H.xpt https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/BIOPRO_H.xpt https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/TCHOL_H.xpt https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/GHB_H.xpt https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/VID_H.xpt Ethics approval and consent to participate National Center for Health Statistics (NCHS) Ethics Review Board (ERB) ensures protections of all human participants in NCHS studies and surveys. The NCHS ERB reviews and approves NHANES survey protocols. The study protocol (Protocol #2011-17, Continuation of Protocol #2011-17) was approved by the NCHS Research Ethics Review Board and all participants provided written informed consent prior to participation. (please visit: https://www.cdc.gov/nchs/nhanes/about/erb.html#print) Conflicts of interest The author(s) declare that there are no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Consent for publication Not applicable. References Azpiazu D, Gonzalo S, Villa-Bellosta R. Tissue Non-Specific Alkaline Phosphatase and Vascular Calcification: A Potential Therapeutic Target. Curr Cardiol Rev. 2019;15:91–5. Desai MY, Cremer PC, Schoenhagen P. Thoracic Aortic Calcification: Diagnostic, Prognostic, and Management Considerations. JACC Cardiovasc Imaging. 2018;11:1012–26. Leow K, et al. Prognostic Value of Abdominal Aortic Calcification: A Systematic Review and Meta-Analysis of Observational Studies. J Am Heart Assoc. 2021;10:e017205. Vos A, et al. Predominance of Nonatherosclerotic Internal Elastic Lamina Calcification in the Intracranial Internal Carotid Artery. Stroke. 2016;47:221–3. Narula N, et al. Pathology of Peripheral Artery Disease in Patients With Critical Limb Ischemia. J Am Coll Cardiol. 2018;72:2152–63. Doherty TM, et al. Molecular, endocrine, and genetic mechanisms of arterial calcification. Endocr Rev. 2004;25:629–72. Hoffmann U, et al. Cardiovascular Event Prediction and Risk Reclassification by Coronary, Aortic, and Valvular Calcification in the Framingham Heart Study. J Am Heart Assoc. 2016;5:e003144. Chen H-C, et al. Abdominal aortic calcification score can predict future coronary artery disease in hemodialysis patients: a 5-year prospective cohort study. BMC Nephrol. 2018;19:313. Bartstra JW, Mali WPTM, Spiering W. de Jong, P. A. Abdominal aortic calcification: from ancient friend to modern foe. Eur J Prev Cardiol. 2021;28:1386–91. Abdominal aortic calcium. coronary artery calcium, and cardiovascular morbidity and mortality in the Multi-Ethnic Study of Atherosclerosis - PubMed. https://pubmed.ncbi.nlm.nih.gov/24812323/ O’Connor SD, Graffy PM, Zea R, Pickhardt PJ. Does Nonenhanced CT-based Quantification of Abdominal Aortic Calcification Outperform the Framingham Risk Score in Predicting Cardiovascular Events in Asymptomatic Adults? Radiology. 2019;290:108–15. Haarhaus M, et al. Alkaline Phosphatase: An Old Friend as Treatment Target for Cardiovascular and Mineral Bone Disorders in Chronic Kidney Disease. Nutrients. 2022;14:2124. Liu K, et al. Elevated Levels of Serum Alkaline Phosphatase are Associated with Increased Risk of Cardiovascular Disease: A Prospective Cohort Study. J Atheroscler Thromb. 2023;30:795–819. Li J-W, Xu C, Fan Y, Wang Y, Xiao Y-B. Can serum levels of alkaline phosphatase and phosphate predict cardiovascular diseases and total mortality in individuals with preserved renal function? A systemic review and meta-analysis. PLoS ONE. 2014;9:e102276. Kabootari M, et al. Serum alkaline phosphatase and the risk of coronary heart disease, stroke and all-cause mortality: Tehran Lipid and Glucose Study. BMJ Open. 2018;8:e023735. Chang C-H, Liou H-H, Wu C-K. Moderate-severe aortic arch calcification and high serum alkaline phosphatase comodify the risk of cardiovascular events and mortality among chronic hemodialysis patients. Ren Fail. 2025;47:2449572. Ye Y, Zhao X, Tu C, Li Q, Zeng Y. Elevated Serum Levels of Alkaline Phosphatase and the Risk of Low Responsiveness to Clopidogrel. Int Heart J. 2020;61:1135–41. Guo W, et al. Serum alkaline phosphatase is associated with arterial stiffness and 10-year cardiovascular disease risk in a Chinese population. Eur J Clin Invest. 2021;51:e13560. Krishnamurthy VR, et al. Associations of serum alkaline phosphatase with metabolic syndrome and mortality. Am J Med. 2011;124:e5661–7. Kauppila LI, et al. New indices to classify location, severity and progression of calcific lesions in the abdominal aorta: a 25-year follow-up study. Atherosclerosis. 1997;132:245–50. Schousboe JT, Wilson KE, Hangartner TN. Detection of aortic calcification during vertebral fracture assessment (VFA) compared to digital radiography. PLoS ONE. 2007;2:e715. Sethi A, et al. Calcification of the abdominal aorta is an underappreciated cardiovascular disease risk factor in the general population. Front Cardiovasc Med. 2022;9:1003246. Chen H-C, et al. Abdominal aortic calcification score can predict future coronary artery disease in hemodialysis patients: a 5-year prospective cohort study. BMC Nephrol. 2018;19:313. Wong ND, et al. Abdominal aortic calcium and multisite atherosclerosis: the Multiethnic Study of Atherosclerosis. Atherosclerosis. 2011;214:436–41. Jayalath RW, Mangan SH, Golledge J. Aortic calcification. Eur J Vasc Endovasc Surg. 2005;30:476–88. Chen N-C, Hsu C-Y, Chen C-L. The Strategy to Prevent and Regress the Vascular Calcification in Dialysis Patients. Biomed Res Int 2017, 9035193 (2017). Thompson B, Towler DA. Arterial calcification and bone physiology: role of the bone-vascular axis. Nat Rev Endocrinol. 2012;8:529–43. Fan Y, Jin X, Jiang M, Fang N. Elevated serum alkaline phosphatase and cardiovascular or all-cause mortality risk in dialysis patients: A meta-analysis. Sci Rep. 2017;7:13224. Panh L, et al. Association between serum alkaline phosphatase and coronary artery calcification in a sample of primary cardiovascular prevention patients. Atherosclerosis. 2017;260:81–6. Schoppet M, Shanahan CM. Role for alkaline phosphatase as an inducer of vascular calcification in renal failure? Kidney Int. 2008;73:989–91. Ren Y, et al. Serum alkaline phosphatase levels are associated with coronary artery calcification patterns and plaque vulnerability. Catheter Cardiovasc Interv. 2021;97(Suppl 2):1055–62. Cao L, Zhang H, Niu Z, Ma T, Guo W. Aortic mineralization triggers the risk of acute type B aortic dissection. Atherosclerosis. 2024;395:118519. Mukaiyama K, et al. Elevation of serum alkaline phosphatase (ALP) level in postmenopausal women is caused by high bone turnover. Aging Clin Exp Res. 2015;27:413–8. Haarhaus M, et al. Apabetalone lowers serum alkaline phosphatase and improves cardiovascular risk in patients with cardiovascular disease. Atherosclerosis. 2019;290:59–65. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6779641","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":476562075,"identity":"7dcfb570-a8a0-424b-8bfe-a21988a8f750","order_by":0,"name":"Chunyu Yin","email":"","orcid":"","institution":"Chinese PLA General Hospital First Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Chunyu","middleName":"","lastName":"Yin","suffix":""},{"id":476562076,"identity":"fa0d0659-f158-41e2-81e4-562d9b8d5cf2","order_by":1,"name":"Long Cao","email":"","orcid":"","institution":"Chinese PLA 983 Hospital of Joint Logistics Support Force","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Cao","suffix":""},{"id":476562077,"identity":"b097e2ac-f68d-4299-9c8d-2b7042bd76e6","order_by":2,"name":"Jiao Zhao","email":"","orcid":"","institution":"Chinese PLA General Hospital First Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jiao","middleName":"","lastName":"Zhao","suffix":""},{"id":476562078,"identity":"1a731a07-764e-4444-8bdc-6f171a9828c5","order_by":3,"name":"Jianping Bai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYDCCAzwMDAk8EkAWYwMDQ4WNHT8z8+EHJGg5k5Ys2c6WZkBQCxwwth1m3HCeR0ECnw6+22cPfnggYyFvLt3c9vBrWxqz8WEeBgOGGptoXFokz+UlSwAdZrhzzsF2Y5lzNnxmh3kPPGA4lpbbgEOLwRkeA5AWxg03EtukJcrSmM0O8yUYMDYcxqfF+AdQiz1EC9thxs3NQEMIaDED2ZII0iL5AeR9ZgJaJM/wpVkAtSRvuHOwTRoUyBKHgYGcgMcvfGd4D9/82VNnu+F2+zPJH6Co7D98+MGHGhucWsCAsQdIAOOCGR5HCfiUg8EPiBbGHwRVjoJRMApGwUgEAKmMXi6nN9RVAAAAAElFTkSuQmCC","orcid":"","institution":"Chinese PLA 983 Hospital of Joint Logistics Support Force","correspondingAuthor":true,"prefix":"","firstName":"Jianping","middleName":"","lastName":"Bai","suffix":""}],"badges":[],"createdAt":"2025-05-29 22:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6779641/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6779641/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85744794,"identity":"ed9c142a-4b7e-4900-916e-ad34220bceb3","added_by":"auto","created_at":"2025-07-01 09:19:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108634,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of participantselection. NHANES, National Health and Nutrition Examination Survey. ALP, alkaline phosphatase. AAC, abdominal aortic calcification.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6779641/v1/3e9547fd5f1845c5ad412551.png"},{"id":85746144,"identity":"a68596b2-dc1a-4420-8c58-34e4d749d87c","added_by":"auto","created_at":"2025-07-01 09:27:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":145636,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the association between ALP and AAC scores in middle-aged females.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6779641/v1/4cf7ded16e28b0219a95abf5.jpg"},{"id":85748036,"identity":"e3da5848-a164-4301-bd01-32a3f03fd09a","added_by":"auto","created_at":"2025-07-01 09:43:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":89829,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between ALP and AAC in middle-aged females was illustrated through a fully adjusted restricted cubic spline (RCS) model. The blue bar represents the probability density. Ref representsthe reference point.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6779641/v1/2a3a4cecf502c177188043c9.jpg"},{"id":85744795,"identity":"8f8d4db5-4417-4524-8388-b6d4b510b7e8","added_by":"auto","created_at":"2025-07-01 09:19:11","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":98602,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between ALP and AAC in middle-aged males was illustrated through a fully adjusted restricted cubic spline (RCS) model. The blue bar represents the probability density.\u003cstrong\u003e \u003c/strong\u003eRef representsthe reference point.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6779641/v1/395d17d6d80f86c0dd12b6f3.jpg"},{"id":85746146,"identity":"5abc5816-b783-4d5c-bd1f-b8df33ed5a38","added_by":"auto","created_at":"2025-07-01 09:27:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":567331,"visible":true,"origin":"","legend":"\u003cp\u003eFeature selection process for AAC based on machine learning among middle-aged females. (A) The feature selection process is based on the Boruta algorithm. The horizontal axis represents the variable name, and the vertical axis represents the Z value of each variable. The green boxes and lines represent the confirmed variables, whereas the red boxes and lines represent the rejected variables in the model calculation. (B) Distribution of the minimal depth among the trees of the forest for the important variables. The mean of the distribution is indicated by a vertical black bar, accompanied by a value label. The scale of the X-axis extends from 0 to the maximum number of trees in which any variable was utilized for splitting. (C) Variable importance plots from a LASSO regression fit to middle-aged females. (D) SHAP interprets the logistic model. All the samples and features are illustrated, with each row representing a feature and the x-axis representing the SHAP value. The yellow dots represent higher feature values, whereas the purple dots represent lower feature values. All four algorithms indicate that ALP is among the top four features affecting AAC among middle-aged females.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6779641/v1/515beca783cc39ad43c318b6.png"},{"id":85748909,"identity":"c9edc035-9b4b-4e31-a390-a2005d9fe223","added_by":"auto","created_at":"2025-07-01 09:51:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2242805,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6779641/v1/31726021-aeaa-4874-b6c0-1ac133acd689.pdf"},{"id":85748037,"identity":"fe434f59-cc30-4ca1-92e3-cad9a6e76ae2","added_by":"auto","created_at":"2025-07-01 09:43:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":736778,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6779641/v1/02682eceea02315d22d0e163.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between alkaline phosphatase levels and abdominal aortic calcification via statistical logistic modeling and machine learning","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAbdominal aortic calcification (AAC) is a distinct manifestation of large artery calcification characterized by the accumulation of calcium phosphate crystals within the vascular wall\u003csup\u003e1\u003c/sup\u003e. From an imaging perspective, the presence of AAC is typically detected by means of CT or lateral spine images from standard radiographs or dual-energy X-ray absorptiometry (DXA) machines, with calcification manifesting as either patchy or diffuse distribution\u003csup\u003e2,3\u003c/sup\u003e. However, these two types of calcification tend to exhibit different histological characteristics. Intimal calcification, for example, atherosclerosis, is more frequently associated with patchy distribution, whereas medial calcification, for example, aging, diabetes, chronic kidney disease and systemic inflammatory disease, is predominantly linked to diffuse distribution\u003csup\u003e4,5\u003c/sup\u003e. Coronary artery calcification (CAC) usually occurs in the intimal layer, whereas AAC primarily manifests within the medial layer\u003csup\u003e6\u003c/sup\u003e. Nevertheless, several epidemiological studies across diverse populations continue to demonstrate an association between AAC and cardiovascular events\u003csup\u003e7\u0026ndash;9\u003c/sup\u003e. Furthermore, the AAC has been shown to be a more effective predictor of cardiovascular events than the Framingham risk score or CAC\u003csup\u003e10,11\u003c/sup\u003e. Importantly, AAC does not directly mirror atherosclerosis, thus precluding the possibility that the observed correlation can be explained solely by the degree of systemic atherosclerosis. To further explore this relationship, identifying potential factors affecting AAC is vital, as identifying these factors will contribute to the prevention and treatment of cardiovascular disease (CVD).\u003c/p\u003e\n\u003cp\u003eAlkaline phosphatase (ALP) is a glycoprotein that plays a crucial role in catalyzing the hydrolysis of inorganic pyrophosphate, which is instrumental in regulating vascular calcification\u003csup\u003e12,13\u003c/sup\u003e. ALP has been found to be independently associated with CVD mortality\u003csup\u003e14\u003c/sup\u003e. Additionally, elevated serum ALP levels present an increased adjusted risk for all-cause mortality, incident stroke, CVD, and the occurrence of composite outcomes\u003csup\u003e15\u003c/sup\u003e. A limited number of studies have investigated the relationship between serum ALP levels and aortic calcification in patients with renal failure\u003csup\u003e16\u003c/sup\u003e. Nevertheless, research examining the relationship between ALP and aortic calcification within the general population is relatively rare.\u003c/p\u003e\n\u003cp\u003eTherefore, the present study utilized data from the United States National Health and Nutrition Examination Survey (NHANES), which was conducted between 2013 and 2014, with the objective of investigating the association between serum ALP and AAC. Given that age can notably increase the incidence of AAC, we specifically focused our research on middle-aged individuals aged 40\u0026ndash;59 years.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003e1. Data source and participants\u003c/h3\u003e\n\u003cp\u003eThe data utilized in this study were obtained from the NHANES database. The NHANSE dataset employs a sophisticated multistage, stratified, and clustered probability sampling methodology, which yields a representative cross-section of the U.S. population. The objective of the present study was to elucidate the correlation between ALP levels and the AAC score. To gain further insight into this issue, the only annual cohort with both ALP levels and AAC scores from 2013\u0026ndash;2014 was selected for detailed analysis. In this year, a total of 10,175 individuals were enrolled. After the exclusion of participants with missing data on ALP levels (n\u0026thinsp;=\u0026thinsp;3,623) and subsequently on AAC scores (n\u0026thinsp;=\u0026thinsp;3,535), as well as individuals with refused information regarding other variables such as alcohol consumption (n\u0026thinsp;=\u0026thinsp;7), hypertension (n\u0026thinsp;=\u0026thinsp;3), diabetes (n\u0026thinsp;=\u0026thinsp;2), and smoking status (n\u0026thinsp;=\u0026thinsp;2), a total of 3,003 participants were included in the study. Considering the unavoidable physiological calcification of vascular structures associated with the aging process, our research further focused on the demographics of middle-aged adults, specifically individuals aged 40\u0026ndash;59 years (n\u0026thinsp;=\u0026thinsp;1,605). The participant selection flowchart is presented in Fig.\u0026nbsp;1.\u003c/p\u003e\n\u003cp\u003eTo guarantee ethical compliance, the NHANES study was granted approval by the NCHS Ethics Review Board. Additionally, written informed consent was obtained from all individuals who participated in the survey. No further ethical reviews are necessary for this research project.\u003c/p\u003e\n\u003cdiv\u003e\n\u003ch2\u003e2. Exposure and outcome definitions\u003c/h2\u003e\nThe DxC system employs a kinetic rate methodology utilizing 2-amino-2-methyl-1-propanol (AMP) buffer to quantify ALP activity in serum or plasma samples. The reaction in question occurs at an alkaline pH of 10.3. The system monitors the rate of change in absorbance at 410 nm over a fixed time interval. The rate of change in absorbance is directly proportional to the activity of ALP in the serum. The unit of measurement for ALP is the IU/L. In lieu of the single-year circle of 2013\u0026ndash;2014, the serum ALP concentration was meticulously stratified using a reference dataset comprising a substantial cohort of 60,056 participants sourced from the NHANES database, spanning the period from 1999\u0026ndash;2020. The participants were classified into four quartiles on the basis of their ALP levels: Q1 (\u0026le;\u0026thinsp;58 U/L), Q2 (59\u0026ndash;72 U/L), Q3 (73\u0026ndash;92 U/L), and Q4 (\u0026ge;\u0026thinsp;93 U/L). This stratification is more logical and representative, thus facilitating comparison with the methodologies employed in other studies\u003csup\u003e17\u0026ndash;19\u003c/sup\u003e.\u003cbr /\u003e\n\u003cp\u003eThe principal outcome variables examined in the present study were the AAC score and the presence of abdominal aortic calcification. Abdominal aortic calcification can be accurately identified on lateral spine images intended for vertebral fracture assessment (VFA) obtained with dual-energy digital emission X-ray absorptiometry (DXA). DXA scans were administered to individuals aged 40 years and older who met the eligibility criteria. Pregnant females who had used radiographic contrast material (barium) in the previous seven days and those with a body weight exceeding 450 pounds were excluded from the DXA examination. All the scans in the DXXAAC_H file were viewed via Optasia SpinAnalyzer software, and both AAC-24 and AAC-8 scoring semi-quantitative techniques were used for the evaluation\u003csup\u003e20,21\u003c/sup\u003e. The AAC score was used to assess the severity of calcification in the abdominal aorta. An elevated AAC score is indicative of more pronounced calcification. The score was quantified via the Kauppila scoring system, which involves the analysis of lateral lumbar spine images obtained via DXA (Densitometer Discovery A, Hologic, Marlborough, MA, USA)\u003csup\u003e20\u003c/sup\u003e. In the scoring method for AAC-24, the anterior and posterior aortic walls are divided into four segments, which correspond to the areas in front of the lumbar vertebrae L1\u0026ndash;L4. Within each of the eight aforementioned segments, the presence of aortic calcification was identified through visual examination. This was observed as either diffuse white stippling of the aorta, extending out to the anterior and/or posterior aortic walls, or white linear calcification of the anterior and/or posterior walls. The scores were obtained separately for the anterior and posterior aortic walls, resulting in ranges from 0\u0026ndash;6 for each vertebral level and 0\u0026ndash;24 for the total score. The total AAC score (variable code: DXXAAC24) was obtained directly from the NHANES dataset. In the present study, the term \"AAC\" was defined as a total score of \u0026gt;\u0026thinsp;0 on the AAC scale.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e3. Covariates\u003c/h3\u003e\n\u003cp\u003eGiven the limited research investigating the relationship between the ALP level and AAC, the selection of covariates was undertaken through a comprehensive review of literature, complemented by a systematic deductive reasoning process that illuminated their potential correlation with both the ALP level and various forms of vascular calcification. Continuous variables in our analysis included age, systolic blood pressure (SBP), diastolic blood pressure (DBP), aspartate transaminase (AST), alanine transaminase (ALT), serum creatinine (Scr), hemoglobin A1c (HbA1c), serum uric acid (UA), serum calcium, serum phosphorus, total cholesterol (TC), and total 25-hydroxyvitamin D [25(OH)D] levels. The categorical variables included sex, race, educational attainment, body mass index (BMI), hypertension status, and diabetes status. BMI was categorized into three groups: \u0026lt;25 kg/m\u0026sup2;, 25\u0026ndash;29.9 kg/m\u0026sup2;, and \u0026ge;\u0026thinsp;30 kg/m\u0026sup2;. Detailed measurement procedures for these variables can be found in publicly available documentation at https://www.cdc.gov/nchs/nhanes/. To address missing covariate data, we employed a missing interpolation method utilizing the MI program of R software to impute the absent values.\u003c/p\u003e\n\u003cdiv\u003e\n\u003ch2\u003e4. Statistical analysis\u003c/h2\u003e\nAll the statistical analyses were conducted in accordance with the guidelines established by the Centers for Disease Control and Prevention (CDC). Sampling weights were employed to derive nationally representative prevalence estimates for the non-institutionalized population of the United States. Continuous variables are presented as the means with standard errors, whereas categorical variables are shown as percentages with their standard errors. To assess differences among groups stratified by AAC level and sex among middle-aged adults, both continuous and categorical ALP levels were subjected to statistical analysis. We conducted a weighted one-way analysis of variance for normally distributed data, employed Kruskal-Wallis tests for skewed data, and utilized \u0026chi;\u0026sup2; tests for categorical variables. Univariate and multivariate linear regression, as well as multivariate logistic regression, were performed on three distinct models to further investigate the relationship between ALP and AAC. Model 1 did not incorporate any covariate adjustments. Model 2 was adjusted for factors such as age, race, and education level. Model 3 further refined the analysis by adjusting for an additional set of variables: age, race, education level, BMI, SBP, DBP, Scr, HbA1c, UA, serum calcium, serum phosphorus, TC, total 25(OH)D, and the presence of hypertension and diabetes. The efficacy of ALP in identifying AAC was evaluated through the use of receiver operating characteristic (ROC) curves and calculation of the area under the curve (AUC). Subgroup analyses and interaction tests were conducted to ascertain the consistency of the association between ALP levels and AAC across different subgroups. Additionally, weighted restricted cubic splines (RCS) regression was employed to investigate the nonlinear relationships between ALP and AAC. We further employed the Boruta algorithm, the random forest algorithm, the Lasso algorithm, and the logistic algorithm combined with SHAP interpretable analysis for the purpose of screening variables and ranking their importance. All analyses were conducted via R software (version 4.4.2), and statistical significance was determined with a two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e1. Baseline characteristics of the participants\u003c/h2\u003e\nA total of 1,605 middle-aged adult participants, comprising 778 males and 827 females, were included in the analysis. The baseline characteristics of all participants categorized by sex are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Significant differences were observed among a number of factors, including AST, ALT, Scr, phosphorus, uric acid, SBP, DBP, TC, 25(OH)D, diabetes, alcohol use and smoking. Given that the subjects in the study were middle-aged, there was no statistically significant difference in age between the sexes. Notably, no significant differences were observed in the levels of ALP, calcium, or AAC.\n\u003cp\u003eContinuous variables are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard errors, whereas categorical variables are presented as percentages (standard errors). ALP, alkaline phosphatase; AST, aspartate aminotransferase; ALT, alanine aminotransferase; Scr, serum creatinine; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycohemoglobin; 25(OH)D, 25-hydroxyvitamin D; AAC, abdominal aortic calcification.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBaseline characteristics of the participants according to age and sex.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eOverall Participants\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eMiddle-Aged-Adults (40\u0026ndash;59 years old)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3,003)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,449)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,554)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,605)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;778)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;827)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge (year)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALP (U/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAST (U/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALT (U/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCalcium (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eScr (umol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhosphorus (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUric acid (umol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e322.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e355.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e290.87\u0026thinsp;\u0026plusmn;\u0026thinsp;2.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e317.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e356.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e277.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSBP (mmHg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e125.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e124.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e120.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e118.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDBP (mmHg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHbA1c (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25(OH)D (nmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55.89(0.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.31(1.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.62(1.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.76(0.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.22(1.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.30(2.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44.11(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.69(1.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.38(1.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.24(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.78(1.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.70(2.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRace (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMexican American\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.99(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.33(1.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.67(1.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.65(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.82(2.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.49(1.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther Hispanic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.60(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.34(0.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.85(0.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.43(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.00(1.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.86(1.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-Hispanic White\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.47(0.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.91(3.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.05(3.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.00(0.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.81(3.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.18(3.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.75(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.37(1.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.10(1.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.78(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.23(1.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.34(1.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.19(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.05(0.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.32(0.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.14(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.15(0.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.12(1.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEducation level (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLess than high school\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.14(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.13(2.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.14(1.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.75(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.22(2.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.27(1.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh school or GED\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.75(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.96(1.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.54(1.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.43(0.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.84(2.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.99(2.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbove high school\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.12(0.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62.91(2.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.31(2.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.82(0.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.93(3.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.74(3.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.05\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.05\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83.39(0.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81.76(1.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84.92(1.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88.56(0.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.20(1.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89.94(1.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBorder\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.61(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.57(0.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.72(0.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.67(0.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.42(0.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.90(0.45)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.99(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.67(1.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.36(0.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.77(0.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.38(1.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.16(0.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlcohol using (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.98(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.70(1.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.69(2.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.08(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.46(2.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.81(2.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78.02(0.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.30(1.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.31(2.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81.92(0.06)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88.54(2.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75.19(2.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSmoke (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.20(0.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.13(2.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.97(1.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.29(0.04)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.56(2.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.07(2.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.80(0.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50.87(2.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.03(1.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.71(0.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.44(2.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40.93(2.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAAC (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.13(0.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70.85(1.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.39(2.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81.40(0.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79.95(2.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82.89(2.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAAC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.87(0.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.15(1.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.61(2.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.60(0.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.05(2.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.11(2.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations of ALP with the AAC score and AAC.\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eMale (40\u0026ndash;59 years)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAAC Scores Continues\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALP Continues\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003 (-0.003,0.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.347\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001(-0.006,0.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.658\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.002 (-0.006, 0.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.331\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eOR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAAC Categories\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALP Continues\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.000 (0.993,1.007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.965\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999 (0.990,1.007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.729\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.993 (0.986,1.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.087\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAAC Categories\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALP Q1\u0026thinsp;\u0026lt;\u0026thinsp;59 U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALP 59\u0026thinsp;\u0026le;\u0026thinsp;Q2\u0026thinsp;\u0026lt;\u0026thinsp;73 U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.144 (0.674,1.943)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.589\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.176 (0.608,2.275)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.555\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.126 (0.643,1.973)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.658\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALP 73\u0026thinsp;\u0026le;\u0026thinsp;Q3\u0026thinsp;\u0026lt;\u0026thinsp;93 U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.335 (0.679,2.626)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.370\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.257 (0.567,2.787)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.493\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.067 (0.596,1.910)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.817\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALP Q4\u0026thinsp;\u0026ge;\u0026thinsp;93U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.060 (0.536,2.098)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.855\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.028 (0.411,2.571)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.941\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.725 (0.357,1.472)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.348\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eP for trend\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.461\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.597\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.789\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFemale (40\u0026ndash;59 years)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAAC Scores Continues\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALP Continues\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009 (0.003,0.015)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.006*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007 (0.002,0.013)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.020*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008 (0.002,0.014)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.011*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eOR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAAC Categories\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALP Continues\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.013 (1.001,1.024)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.030*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.011 (0.997,1.024)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.099\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.011 (0.998,1.024)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.090\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAAC Categories\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALP Q1\u0026thinsp;\u0026lt;\u0026thinsp;59 U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALP 59\u0026thinsp;\u0026le;\u0026thinsp;Q2\u0026thinsp;\u0026lt;\u0026thinsp;73 U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.529 (0.708,3.303)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.253\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.437 (0.525, 3.933)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.397\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.373 (0.638,2.951)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.392\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALP 73\u0026thinsp;\u0026le;\u0026thinsp;Q3\u0026thinsp;\u0026lt;\u0026thinsp;93 U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.982 (0.916,4.288)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.077\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.761 (0.708, 4.380)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.171\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.588 (0.752,3.357)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.207\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALP Q4\u0026thinsp;\u0026ge;\u0026thinsp;93U/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.368 (1.404,8.078)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.011*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.022 (0.873,10.459)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.045 (1.019,9.098)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.047*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eP for trend\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.008*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.049*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.042*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e2. Association between ALP level and AAC\u003c/h2\u003e\nThe relationship between ALP levels and AAC scores was analyzed via weighted linear regression, whereas weighted logistic regression was employed to investigate the association between ALP (continuous or quartile) and AAC (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Our analysis results revealed a significant positive correlation between the continuous variable ALP and the AAC scores across all three models in middle-aged females. In the fully adjusted Model 3, this positive association remained statistically significant (\u0026beta;\u0026thinsp;=\u0026thinsp;0.008, 95% CI: 0.002\u0026ndash;0.014, P\u0026thinsp;=\u0026thinsp;0.011), indicating that each unit increase in the ALP level was associated with a 0.008 higher AAC score. The correlation between the ALP level and AAC was statistically significant in Model 1, indicating that a one-unit increase in the ALP level was associated with a 1.3% increase in the risk of developing AAC. However, after adjusting for other potential confounding variables, this type of relationship was attenuated and non-significant. To further elucidate the stability of the association between ALP and AAC, ALP was converted into quartiles. The prevalence of AAC also increased in quartile four (Q4) compared with quartile one (Q1) of ALP (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.05, in Model 2 and Model 3). Following the adjustment of all covariates, the risk of AAC was 3.04 times greater in middle-aged females with ALP levels of 93 U/L or above than in those with ALP levels below 59 U/L. Nevertheless, the results demonstrated no notable association between ALP and AAC among middle-aged males.\u003c/div\u003e\n\u003ch3\u003e3. Subgroup analysis\u003c/h3\u003e\n\u003cp\u003eSubgroup analyses were conducted to evaluate the reliability and robustness of the association between ALP and AAC across various subgroups. The summarized findings from these subgroup analyses are presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cstrong\u003eSuppl. Figures\u0026nbsp;1\u0026ndash;3\u003c/strong\u003e. For middle-aged females, higher levels of ALP tend to be associated with increased AAC among individuals who are Non-Hispanic White, Non-Hispanic Black, have an educational level above high school, smoke, have a low BMI, and do not have hypertension or diabetes. Nevertheless, the positive correlation between ALP levels and AAC scores appears to become more stable across different racial and BMI subgroups. Notably, the P values for interactions were all statistically negative across various subgroups. Consistent with the findings obtained from previous analyses of middle-aged males, no significant relationship was observed between ALP and AAC among the subgroups.\u003c/p\u003e\n\u003ch3\u003e4. Analysis of RCS regression\u003c/h3\u003e\n\u003cp\u003eThe RCS regression was adjusted for all potential covariates. A significant relationship between the ALP level and AAC outcome was observed (P overall\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in middle-aged females, with one significant inflection point at an ALP level of 63 U/L. However, the P-nonlinear value exceeded 0.05, suggesting an unconfirmed nonlinear relationship between ALP and AAC outcome (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). For middle-aged males, both the P-overall and P-nonlinear values were greater than 0.05, suggesting that there is neither a significant association nor a nonlinear relationship between ALP levels and AAC outcomes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e5. The Importance of ALP for AAC via Machine Learning\u003c/h2\u003e\nThe utilization of machine learning algorithms could facilitate a more comprehensive evaluation of the significance of ALP in relation to AAC. The study subjects (middle-aged females and males) were divided into training and test sets, with the ratio of subjects in each set being 8:2. Given that the relationship between ALP and AAC was identified only in middle-aged women, the subsequent analysis focused exclusively on this demographic factor. Then, the Boruta algorithm, the random forest algorithm, the Lasso algorithm, and the logistic algorithm combined with SHAP interpretable analysis were utilized for the purpose of screening variables and ranking their importance. The Boruta algorithm indicates SBP, ALP, and BMI as the confirmed features (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA); the random forest algorithm indicates ALP as the third important variable (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB, \u003cstrong\u003eSuppl. Figures\u0026nbsp;4\u0026ndash;6\u003c/strong\u003e); the Lasso algorithm indicates five important features (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC, \u003cstrong\u003eSuppl. Figure\u0026nbsp;8A-B\u003c/strong\u003e), with ALP ranking fourth; and logistic regression with SHAP interpretable analysis indicates that ALP is the fourth most important feature (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD, \u003cstrong\u003eSuppl. Figure\u0026nbsp;9\u003c/strong\u003e). The random forest model predicted that elevated AAC in middle-aged females is associated with both (1) low BMI combined with high ALP and (2) advanced age combined with high ALP. (\u003cstrong\u003eSuppl. Figure\u0026nbsp;7\u003c/strong\u003e).\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u0026nbsp;\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cross-sectional study of 1,605 middle-aged adults, elevated ALP levels demonstrated a significant independent association with both higher AAC scores and increased AAC risk, especially among middle-aged women, whereas no such association was observed in male participants. Specifically, we observed that middle-aged females with higher ALP levels tended to have higher AAC scores (0.008 units higher) and a greater risk of AAC (OR of 3.045 for the Q4 group than for the Q1 group). These findings suggest that increasing ALP may prompt the occurrence of AAC in middle-aged females. Importantly, the interaction effect between ALP and AAC was not statistically significant across various subgroups categorized by race, sex, age, body mass index, hypertension, and diabetes status, indicating the independence of ALP as a risk factor for AAC among middle-aged females. Furthermore, the odds ratio of AAC was significantly correlated with elevated ALP levels exceeding 63 U/L in middle-aged females. Finally, the importance of ALP for AAC was confirmed again among middle-aged women via four machine learning algorithms, Boruta, random forest, Lasso, and the logistic algorithm combined with SHAP interpretable analysis.\u003c/p\u003e\n\u003cp\u003eIn recent years, studies have acknowledged that the AAC has transitioned from being an inconsequential imaging feature, often identified serendipitously, to becoming an early indicator with predictive significance for various diseases, including stroke, aortic valve disorders, myocardial infarction, emphysema, and chronic obstructive pulmonary disease\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In the context of coronary diseases, AAC is significantly associated with stenosis of the precerebral arteries, myocardial infarction, ischemic heart disease, and cardiovascular mortality\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This association has been consistent across studies in different epidemiological populations\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Furthermore, compared with calcification of the coronary artery itself, AAC appears to be a superior indicator of cardiovascular events\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This conclusion was reinforced by the research of Wong N. D., which indicates that among individuals with AAC, approximately 40% of women and 20% of men do not exhibit CAC. Moreover, among those without CAC, nearly 60% of women and over 50% of men presented evidence of subclinical CVD elsewhere, with the majority involving AAC\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Consequently, it is plausible that the occurrence of AAC may precede that of CAC, thus offering potential advantages for predicting cardiovascular events. Further research demonstrated that AAC functions as a risk factor for myocardial infarction that is not contingent upon LDL, suggesting that AAC may contribute to cardiovascular disease risk in a manner comparable to hypercholesterolemia\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Indeed, this conclusion aligns with the histological observations - unlike coronary atherosclerosis, which is predominantly observed in the intimal layer, AAC occurs more frequently in the medial layer\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. These findings naturally prompt researchers to explore alternative mineralization mechanisms that may influence the incidence of cardiovascular events. Importantly, fully formed bone tissue is present in atherosclerotic arteries, indicating that vascular calcification involves a complex and regulated process of biomineralization\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Another significant clue is that the high prevalence of arterial calcification observed in end-stage renal disease (ESRD) exceeds what can be attributed exclusively to traditional cardiovascular risk factors, including aging, diabetes, hypertension, and dyslipidemia, which highlights a substantial association between vascular calcification and abnormalities in bone and mineral metabolism\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The bone‒vascular axis hypothesis thus emerged, highlighting the critical influence of bone-derived cell types and endocrine/paracrine signals on vascular health\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eALP is classified as an orthophosphate monoester phosphohydrolase and is frequently measured in clinical practice as a biomarker for hepatic or bone-related diseases\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Moreover, bone-derived ALP, an ectoenzyme that plays a crucial role in the process of bone mineralization, is specifically localized to the mineralizing segments of arteries\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Recent population-based evidence indicates that elevated ALP levels (even within the normal range) are significantly associated with an increased risk of CVD and that higher ALP levels are a risk factor for all-cause and cardiovascular mortality in individuals with or without kidney disease\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. ALP promotes vascular mineralization by catalyzing the hydrolysis of inorganic pyrophosphate, a key inhibitor of vascular calcification, thereby increasing its breakdown and facilitating vessel calcification\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Ren \u003cem\u003eet al\u003c/em\u003e discovered that patients with elevated serum ALP levels are at increased risk of developing coronary calcification, particularly spotty calcification, as well as a minimum lumen area of less than 4.0 mm²\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Our team's prior research initially demonstrated a positive correlation between elevated serum ALP levels and thoracic aortic calcification\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the relationship between AAC and the serum ALP level remains equivocal, particularly in the context of the general population, resulting in considerable confusion. Owing to the high prevalence of AAC, which ranges from 34% in individuals aged 45–54 years to 94% in those aged 75–84 years, understanding the relationship between AAC and ALP is crucial for assessing the impact of ALP on large artery calcification\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Hence, the current study offers a crucial contribution to the existing body of knowledge in this field, thereby providing the \"missing piece of the puzzle\". Nevertheless, careful judgment is crucial when evaluating the conclusions drawn from this study, as several elements warrant thorough examination. First, both the severity of calcification and the incidence of extreme cases clearly increase significantly with increasing age\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Consequently, the present study, which utilized data from NHANSE, focused on individuals within the 40–59 age group, categorized as middle-aged individuals. The impact of age on AAC is minimal in middle-aged individuals, thus allowing for a more accurate representation of the impact of other factors, such as ALP, on AAC. Furthermore, middle-aged individuals constitute a critical demographic for intervention strategies aimed at combating disease, underscoring the clinical importance of research pertaining to subsequent disease treatment. However, it is important to note that while a positive correlation exists between ALP and AAC, this relationship should not be readily extrapolated to broader age groups. To substantiate these conclusions, it is imperative to gather more extensive data and undertake careful consideration along with a rigorous study design. Second, the positive correlation between ALP and AAC identified in the conclusion is predominantly observed in middle-aged women, whereas this relationship is significantly reduced in middle-aged men. In our primary analysis, no substantial relationship was identified between ALP and AAC in middle-aged males. However, when the AAC score was categorized into severe calcification and non-severe calcification based on a threshold of 6 points, a significant relationship was identified. The univariate analysis indicated that ALP had an odds ratio of 1.011 (95% CI: 1.00-1.022), with a p value of 0.035. Given the limited number of middle-aged males who presented with severe AAC (n = 16), the robustness of these findings may be questioned. As a result, this component of the analysis has been excluded from the results section of the article. We propose that sex differences may be attributed to hormonal factors. Given that women aged 40–59 years are in perimenopause, they face a heightened risk of osteoporosis, which suggests an elevated potential risk for vascular calcification as well\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Future research should focus on examining the impact of estrogen levels on calcification processes more thoroughly. From our perspective, it would be a misjudgment to assume that there is no correlation between ALP and AAC in males. The observed discrepancies may stem from limitations related to X-ray detection resolution and the inherent subjectivity involved in measuring AAC. Additionally, ALP levels typically exhibit changes prior to observable alterations in vascular calcification levels, indicating the possibility of staggered peaks. Therefore, it will be essential for future studies to obtain more objective imaging data concerning aortic calcification to establish better correlations. Third, in the subgroup analysis, while the interaction was not statistically significant, we observed inconsistencies in the correlation between ALP and AAC across various subgroups. For example, among subgroups categorized by hypertension, diabetes, and BMI, the positive correlation between ALP and AAC was found to be more pronounced in middle-aged women who did not have the aforementioned risk factors. In contrast, in subgroups characterized by alcohol use and smoking, a significant positive correlation between ALP and AAC was observed among middle-aged women who presented these risk factors. When different ethnic subgroups were analyzed, a significant correlation was identified between ALP and AAC in non-Hispanic middle-aged women, thus highlighting an additional phenomenon that merits further consideration. These findings indicate the possibility of heterogeneity in the relationship between ALP and AAC within subgroups. However, this discrepancy has not yet reached statistical significance. The present study places greater emphasis on the primary effect of ALP, with the subgroup outcomes serving exclusively as exploratory observations. In subsequent studies, it is recommended that the sample size model be expanded, such as through the balancing of sample size differences between subgroups, with a view to rendering conclusions clearer. Fourth, when ALP levels exceeded 63 U/L, the odds ratio of AAC demonstrated an increased correlation with increasing ALP levels. These findings suggest that even when the ALP level is within the normal range, there remains an elevated risk for AAC. This effect of ALP aligns closely with the observations reported in studies investigating the relationship between ALP and coronary artery disease\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. This finding indicates that when the ALP level exceeds 63 U/L during routine biochemical assessments, it is advisable to consider abdominal CT or abdominal X-ray examinations. These imaging modalities can provide further insights into the status of AAC and may also aid in assessing the risk of CHD.\u003c/p\u003e\n\u003cp\u003eFurther research is still needed to determine whether ALP is merely a marker of the aortic calcification burden or whether it has a direct impact on systemic vascular disease, including cardiovascular disease. Haarhaus \u003cem\u003eet al\u003c/em\u003e. demonstrated that medications, such as the extra-terminal (BET) protein inhibitor apabetalone, which lowers serum ALP levels, are associated with a reduced risk of cardiovascular events\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. However, additional research is essential to ascertain whether the reduction in ALP contributes to the improvement in CVD by mitigating vascular calcification and to explore a possible dose-response relationship between ALP and the reduction in aortic calcification.\u003c/p\u003e\n"},{"header":"Strengths and limitations","content":"\u003cp\u003eThe strengths of this study include the utilization of nationally representative NHANES data, which employ standardized procedures; the application of sampling weights to increase the representativeness of the findings; and the incorporation of covariate adjustment to mitigate confounding bias. However, there are three limitations. First, the cross-sectional design restricts our ability to establish a causal relationship between ALP levels and AAC, necessitating longitudinal studies with larger sample sizes for verification. Second, we were unable to detect inflammatory markers such as high-sensitivity C-reactive protein (hs-CRP), tumor necrosis factor-alpha (TNF-ɑ), and interleukin-1 beta (IL-1β). Consequently, the potential role of ALP in AAC through inflammatory pathways could not be investigated. Finally, because the NHANES does not conduct dual-energy X-ray absorptiometry (DXA) testing on individuals under 40 years of age, AAC data for this age group are lacking, thereby limiting the comprehensiveness of our analysis regarding age associations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHigh ALP levels were associated with elevated AAC scores and an increased risk of AAC among middle-aged females. Abdominal CT or abdominal X-ray examinations are recommended when the ALP level exceeds 63 U/L during routine biochemical assessments. However, further large-scale prospective studies are necessary to elucidate the precise causality of this relationship.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eALP \u0026nbsp;alkaline phosphatase\u003c/p\u003e\n\u003cp\u003eAAC abdominal aortic calcification\u003c/p\u003e\n\u003cp\u003eCAC coronary artery calcification\u003c/p\u003e\n\u003cp\u003eCVD cardiovascular disease\u003c/p\u003e\n\u003cp\u003eDXA \u0026nbsp;dual-energy X-ray absorptiometry\u003c/p\u003e\n\u003cp\u003eVAF vertebral fracture assessment\u003c/p\u003e\n\u003cp\u003eCDC Centers for Disease Control and Prevention\u003c/p\u003e\n\u003cp\u003eRCS \u0026nbsp;restricted cubic spline\u003c/p\u003e\n\u003cp\u003eROC\u0026nbsp;receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp;area under the curve\u003c/p\u003e\n\u003cp\u003eNHANES National Health and Nutrition Examination Survey\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJB and CY contributed to the conception and design of the study, interpretation of the data, and revision of the manuscript. LC and CY wrote the manuscript and acquired the data. JZ and CY performed the statistical analysis. All the authors contributed to manuscript revision and read and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Science and Technology Project of Tianjin Municipal Health Commission (grant number TJWJ2023QN116) to L.C. and the Yanzhen Talents Scheme supported by Zhengzhou Joint Logistics Support Center of PLA to L.C. and the 983rd Hospital Science and Technology Incubation Program (983YN23F005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJianping Bai https://orcid.org/0000-0001-9805-7133\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eavailability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eThe raw data that support the findings of this study are available from the links: https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/DEMO_H.xpt\u003c/p\u003e\n\u003cp\u003ehttps://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/BIOPRO_H.xpt\u003c/p\u003e\n\u003cp\u003ehttps://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/DXXAAC_H.xpt\u003c/p\u003e\n\u003cp\u003ehttps://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/BPQ_H.xpt\u003c/p\u003e\n\u003cp\u003ehttps://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/DIQ_H.xpt\u003c/p\u003e\n\u003cp\u003ehttps://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/SMQ_H.xpt\u003c/p\u003e\n\u003cp\u003ehttps://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/ALQ_H.xpt\u003c/p\u003e\n\u003cp\u003ehttps://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/BMX_H.xpt\u003c/p\u003e\n\u003cp\u003ehttps://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/BPX_H.xpt\u003c/p\u003e\n\u003cp\u003ehttps://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/BIOPRO_H.xpt\u003c/p\u003e\n\u003cp\u003ehttps://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/TCHOL_H.xpt\u003c/p\u003e\n\u003cp\u003ehttps://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/GHB_H.xpt\u003c/p\u003e\n\u003cp\u003ehttps://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/VID_H.xpt\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Center for Health Statistics (NCHS) Ethics Review Board (ERB) ensures protections of all human participants in NCHS studies and surveys. The NCHS ERB reviews and approves NHANES survey protocols. The study protocol (Protocol #2011-17, Continuation of Protocol #2011-17) was approved by the NCHS Research Ethics Review Board and all participants provided written informed consent prior to participation.\u003c/p\u003e\n\u003cp\u003e(please visit: https://www.cdc.gov/nchs/nhanes/about/erb.html#print)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that there are no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAzpiazu D, Gonzalo S, Villa-Bellosta R. Tissue Non-Specific Alkaline Phosphatase and Vascular Calcification: A Potential Therapeutic Target. 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Aortic mineralization triggers the risk of acute type B aortic dissection. Atherosclerosis. 2024;395:118519.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMukaiyama K, et al. Elevation of serum alkaline phosphatase (ALP) level in postmenopausal women is caused by high bone turnover. Aging Clin Exp Res. 2015;27:413\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaarhaus M, et al. Apabetalone lowers serum alkaline phosphatase and improves cardiovascular risk in patients with cardiovascular disease. Atherosclerosis. 2019;290:59\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"alkaline phosphatase, ALP, abdominal aortic calcification, AAC, aortic calcification","lastPublishedDoi":"10.21203/rs.3.rs-6779641/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6779641/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Vascular calcification is associated with serum alkaline phosphatase (ALP) levels. This study aimed to investigate the relationship between ALP and abdominal aortic calcification (AAC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Data from the 2013–2014 National Health and Nutrition Examination Survey (NHANES) were utilized for this study. Weighted multivariable regression analyses and subgroup analyses were conducted to investigate the independent relationship between ALP levels and AAC scores. Restricted cubic splines (RCS) regression was employed to explore nonlinear relationships. Additionally, Boruta, random forest, Lasso, and logistic combined with SHAP interpretable analysis were utilized for screening variables and ranking their importance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 1,605 middle-aged adult participants were included in the analysis. ALP was positively associated with AAC scores among middle-aged females (β=0.008, 95% CI: 0.002–0.014, P=0.011). The risk of AAC was 3.04 times greater (OR=3.045, 95% CI: 1.019--9.098, P=0.047) in middle-aged females (≥93 U/L vs. \u0026lt;59 U/L). A significant relationship between ALP levels and AAC was observed among middle-aged females, with one notable inflection point identified at an ALP level of 63 U/L in the RCS regression. All four machine learning algorithms interpret ALP as one of the most significant features infecting AAC among middle-aged females.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e High ALP levels were associated with elevated AAC scores and an increased risk of AAC among middle-aged females. Abdominal CT or abdominal X-ray examinations are recommended when the ALP level exceeds 63 U/L during routine biochemical assessments.\u003c/p\u003e","manuscriptTitle":"Associations between alkaline phosphatase levels and abdominal aortic calcification via statistical logistic modeling and machine learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 09:19:06","doi":"10.21203/rs.3.rs-6779641/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-06-26T01:14:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-24T18:08:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-05T11:49:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-03T14:46:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-06-03T14:41:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6be9b2e1-53e2-4c97-a6b8-fa21e81b40c2","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-01T09:19:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-01 09:19:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6779641","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6779641","identity":"rs-6779641","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}