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Serum alkaline phosphatase level is positively associated with metabolic syndrome: A nationwide population-based study
Journal Pre-proofs Serum alkaline phosphatase level is positively associated with metabolic syndrome: A nationwide population-based study Ji-Hye Kim, Hye Sun Lee, Hye-Min Park, Yong-Jae Lee PII: DOI: Reference:
S0009-8981(19)32079-0 https://doi.org/10.1016/j.cca.2019.10.015 CCA 15876
To appear in:
Clinica Chimica Acta
Received Date: Revised Date: Accepted Date:
1 April 2019 12 October 2019 16 October 2019
Please cite this article as: J-H. Kim, H. Sun Lee, H-M. Park, Y-J. Lee, Serum alkaline phosphatase level is positively associated with metabolic syndrome: A nationwide population-based study, Clinica Chimica Acta (2019), doi: https://doi.org/10.1016/j.cca.2019.10.015
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Serum alkaline phosphatase level is positively associated with metabolic syndrome: A nationwide population-based study
Ji-Hye Kim, MDa,b, Hye Sun Lee, PhDc, Hye-Min Park, MDb,d and Yong-Jae Lee, MD, MPH, PhDd* aDepartment
of Health Promotion, Yonsei University Health System, Severance Check-up,
Seoul, Korea bDepartment
of Medicine, Yonsei University Graduate School, Seoul, Korea
cBiostatistics
Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea
dDepartment
of Family Medicine, Yonsei University College of Medicine, Seoul, Korea
Corresponding author: Yong-Jae Lee MD, MPH, PhD. Department of Family Medicine Yonsei University College of Medicine Gangnam Severance Hospital, 211 Eonju-ro, Gangnam-gu, Seoul, Republic of Korea, 06273. Tel: +82 2 2019 2630 Fax: +82 3462 8209 Cell: +82 10 7292 9169, E-mail: [email protected]
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Abstract Background: Serum alkaline phosphatase (ALP), a useful marker of hepatobiliary or bone disorder, has recently emerged as a biomarker of chronic low-grade inflammation and cardiometabolic disease. This study aimed to examine the association of serum ALP level with metabolic syndrome (MetS) in apparently healthy adults. Methods: A cross-sectional study was conducted to examine the relationship between serum ALP level and MetS in 7,101 men and 8,873 women aged 19 to 75 years using data from the 2008 to 2011 Korean National Health and Nutrition Examination Survey. The odds ratios (ORs) and 95% confidence intervals (CIs) for MetS were calculated using multiple logistic regression analyses across serum ALP quartiles (Q1: ≤190 U/L; Q2: 191–224 U/L; Q3: 225– 263 U/L; and Q4: ≥264 U/L for men and Q1: ≤163 U/L; Q2: 164–201 U/L; Q3: 202–251 U/L; and Q4: ≥252 U/L for women). Results: The mean values of most cardiometabolic variables, HOMA-IR, and leukocyte count gradually increased with serum ALP quartile. The prevalence of MetS significantly increased in accordance with serum ALP quartile. In comparison with those of individuals in the lowest quartile, the OR (95% CI) for MetS in the highest quartile was 1.32 (1.05-1.64) in men and 1.99 (1.42-3.81) in women after adjusting for age, cigarette smoking, alcohol intake, regular exercise, household income, education level, occupation, AST, ALT, and GGT levels. Conclusion: Serum ALP level was positively and independently associated with MetS in men and women.
Keywords: alkaline phosphatase; metabolic syndrome; insulin resistance; inflammation.
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1. Introduction Metabolic syndrome (MetS) is characterized by a cluster of several cardiometabolic risk factors including abdominal obesity, elevated blood pressure, glucose intolerance, and atherogenic dyslipidemia [1]. The prevalence of MetS in adults has increased globally in recent decades, and this upward trend is becoming a significant threat to public health with increased incidence risk of cardiovascular disease and type 2 diabetes [2,3]. Although the pathophysiology of MetS remains a major challenge, accumulating evidence has shown that insulin resistance and subclinical low-grade inflammation play a key role in the development of MetS [4,5]. Alkaline phosphatase (ALP) is a ubiquitous metalloenzyme that catalyzes the hydrolysis of monophosphate esters in an alkaline pH environment [6-9]. ALP is present in serum and the external cell surface of most cells; is particularly rich in the liver, bone, and placenta; and plays an integral role in metabolism within the hepatobiliary and skeletal systems [10,11]. In this regard, serum ALP has long been considered a useful marker of hepatobiliary or bone disorders [12,13]. However, emerging evidence also shows that serum ALP activity is modestly increased in cardiometabolic diseases such as hypertension, type 2 diabetes, dyslipidemia, and peripheral arterial disease [14,15], which are increasingly being seen as inflammatory disorders. In light of these novel findings, we hypothesized that there would be a positive association between serum ALP activity and MetS; however, limited data exist linking serum ALP activity with MetS, with associated inconsistent and conflicting results [16-18].
2. Materials and Methods 2.1. Survey overview and study population
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This cross-sectional study used data obtained from the 2008 to 2011 Korean National Health and Nutrition Examination Survey (KNHANES IV and V), which was conducted by the Korea Centers for Disease Control and Prevention. The KNHANES is a nationwide, representative, population-based survey performed to evaluate the health and nutritional status of Koreans. The survey consists of a health interview survey, a nutrition survey, and a health examination survey. Sampling units demonstrated a stratified, multistage, probabilitysampling design that was based on sex, age, and geographical area using household registries. We extracted 25,935 participants (11,538 men and 14,397 women) whose ALP data were available from the 2008 to 2011 KNHANES dataset. We excluded participants with the following conditions: children and adolescents aged ≤ 18 y; pregnant females; individuals with a osteoporosis and a history of hip, wrist, or vertebral fracture; individuals with a history of hepatitis B infection, hepatitis C infection, thyroid disease, chronic kidney disease, coronary artery disease, cerebrovascular disease and/or cancer; individuals with aspartate aminotransferase (AST) ≥80 U/l, alanine aminotransferase (ALT) ≥80 U/l, or γglutamyltransferase (GGT) ≥80 U/l; those who had not fasted for 12 hours prior to blood sampling, and those whose MetS components and/or insulin data were missing. After these exclusions, a total of 16,748 individuals (7,101 men and 8,873 women) aged 19 to 75 y were included in our final analysis. The KNHANES received ethical approval from the Institutional Review Board (IRB) of the Korea Centers for Disease Control and Prevention and written consent was obtained from all of the participants. In addition, the study was conducted in accordance with the ethical principles of the Declaration of Helsinki.
2.2. Data collection Trained medical staff obtained anthropometric measurements following a standardized procedure. Height was measured to the nearest 0.1 cm with a measuring rod
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attached to a balanced beam scale (Seca 225; Seca) using a Frankfurt horizontal plane, with subjects standing as straight as possible and inhaling deeply. Body weight was measured using a digital electronic scale with the subjects wearing light indoor clothing without shoes after adjusting the scale to zero prior to measurement (GL-6000-20; G-tech). Waist circumference was measured by a trained technician to the nearest 0.1 cm in a horizontal plane at a level midway between the lower rib margin and the iliac crest following normal expiration. Self-reported cigarette smoking, alcohol consumption, and physical activity status were determined from the questionnaires. Each participant was categorized as a nonsmoker, ex-smoker, or current smoker with respect to smoking status. Participants were also asked about their frequency of alcohol intake and leisure-time physical activity on a weekly basis. Alcohol intake was divided into none, ≤ once per week, and ≥ twice per week. The short form of International Physical Activity Questionnaire (IPAQ) was adopted to determine the frequency of physical activity. Additionally, all subjects were instructed to record their daily engagement in mild, moderate, or vigorous intensity of activity during the previous week. Then, we estimated the quantity of physical activity (MET-h/wk) on the basis of physical activity frequency and intensity [19]. Regular exercise was divided into three groups according to the frequency of moderate-intensity exercise, as follows: none, once per week, and ≥ twice per week. Educational level was classified as either middle school or below, high school, and college or above. Household income was classified into four quartiles from lowest to highest. Occupation was classified into three categories: unemployed (i.e., retired individuals, students, and housewives); blue-collar workers (e.g., agriculture, forestry, fishery workers, craft and related trade workers, plant and machine operators and assemblers, and elementary occupations); and white-collar workers (e.g., managers, professionals, technicians, clerks, and service/sales workers) according to the Korean Standard Classification of Occupations. Systolic blood pressure (SBP) and diastolic blood pressure
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(DBP) were assessed three times in the right upper arm using a standard mercury sphygmomanometer (Baumanometer; Baum), and the mean of the second and third blood pressure readings was used for analysis. Blood samples were obtained from the antecubital vein after each participant had fasted overnight for a minimum of 12 hours. Fasting plasma glucose (FPG), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C) aspartate aminotransferase (AST), alanine aminotransferase (ALT), γ-glutamyltransferase (GGT), and ALP activities were measured using the Hitachi 7600-110 automated chemistry analyzer (Hitachi Co.). The intra-assay and inter-assay CVs for ALP were 3.2% and 3.3%, respectively. Fasting serum insulin was assessed using a 1470 WIZARD gamma-counter (PerkinElmer). The Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) value was calculated using the following formula: fasting plasma glucose (mg/dl) × fasting insulin (µIU/ml)/405. Leucocyte counts were determined by an automated blood cell counter (XE2100D; Sysmex, Kobe, Japan).
2.3. Definition of MetS MetS was defined as the presence of at least three of the following criteria according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) [1,20] : abdominal obesity according to the Asian-Pacific criteria (waist circumference ≥ 90 cm in men and ≥ 80 cm in women) [21]; high BP (SBP ≥ 130 mmHg or DBP ≥ 85 mmHg); high FPG (≥ 100 mg/dl); high TG (≥ 150 mg/dl; and low HDL-C (< 40 mg/dl in men or < 50 mg/dl in women). Individuals who reported taking anti-hypertensive medication or anti-diabetes medications were considered to have elevated blood pressure or elevated fasting glucose.
2.4. Statistical analysis
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Participants were categorized into serum ALP quartiles as follows: Q1: ≤190 U/l; Q2: 191–224 U/l; Q3: 225–263 U/l; and Q4: ≥264 U/l for men and Q1: ≤163 U/l; Q2: 164– 201 U/l; Q3: 202–251 U/l; and Q4: ≥252 U/l for women). The results were expressed as mean and standard error (SE) or percentage (SE) for quantitative variables. A weighted oneway analysis of variance test or 2 test was used to compare the means of different variables between four categories in the field. To assess the relationships with insulin resistance and MetS according to serum ALP quartile, the odds ratios (ORs) and 95% confidence intervals (CIs) for MetS were calculated using multiple logistic regression analyses across serum ALP quartiles. Moreover, factor analysis using principal component analysis varimax rotation was performed. The data were analyzed using SAS software, version 9.4 (SAS institute Inc.) and SPSS Statistics (ver. 25, IBM Corp.). Two-sided P-values < 0.05 were considered statistically significant.
3. Results Table 1 shows the demographic and biochemical characteristics of the participants in relation to serum ALP quartile. In the fourth quartile of serum ALP, the mean values of age, waist circumference, BP, FPG, TG, and AST, ALT, and GGT levels were highest, whereas HDL-C level was lowest for both men and women. In addition, the proportion of household income in the highest quartile and the education level of college or above was lowest in the highest quartile, whereas blue collar workers were most prevalent in the fourth quartile of serum ALP for both men and women. Table 2 shows the proportion of metabolic syndrome and its components by serum ALP quartile. For men, the proportion of MetS and high BP, high TG, low HDL-cholesterol significantly increased in accordance with serum ALP quartile. For women, the proportion of MetS and all components of MetS with increasing ALP quartile.
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Fig. 1 and 1 illustrate the mean HOMA-IR values and the leukocyte counts, respectively, which were gradually increased according to serum ALP quartile (all P values < 0.001). Table 3 shows the results of multiple logistic regression analysis to assess the odds for predicting MetS in terms of serum ALP quartile. Compared with the lowest quartile group, the OR (95% CI) for MetS of the highest quartile was 2.06 (1.23–3.45) after adjusting for age, cigarette smoking, alcohol intake, regular exercise, household income, education level, and occupation. These associations remained valid after additional adjustment for inflammatory markers such as leukocyte count and hepatic enzymes such as AST, ALT, and GGT. In comparison with the lowest quartile, the OR (95% CI) for MetS in the highest quartile was 1.32 (1.05-1.64) in men and 1.99 (1.42-3.81) in women after adjusting for age, cigarette smoking, alcohol intake, regular exercise, household income, education level, occupation, AST, ALT, and GGT levels. In subgroup analysis of each component of MetS, serum ALP quartiles were significantly associated with high TG and low HDL-cholesterol for men and obesity, high BP, high FPG, and high TG for women. Factor analysis using principal component analysis varimax rotation was performed. The number of factors among five MetS components was determined Eigen value >1 and both men and women have two factors. For men, factor 1 is combination of TG and HDL-C, and factor 2 is a combination of waist, BP, and FPG. For women, factor1 is a combination of waist, BP, and FPG, and factor 2 is a combination of TG and HDL-C (Table 4). Each factor formula of component score coefficient×standardized value for the MetS components were as follows: The formula of factor 1 was 0.230xwaist-0.176xBP-0.022xFPG+0.507xTG+0.643x HDL-C and factor 2 was 0.289xwaist+0.614xBP+0.528xFPG+0.051xTG-0.229xHDL-C in men. The formula of factor 1 was is 0.362xwaist+0.541xBP+0.511xFPG+0.049xTG0.246xHDL-C and factor 2 was 0.145xwaist-0.167xBP-0.112xFPG+0.487xTG+0.727xHDL-
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C in women. Table 5 shows the relationship between factor score and serum ALP quartiles. Each factor was found to have a significant correlation with serum ALP quartiles.
4. Discussion In this nationally representative cross-sectional study, we found that serum ALP activity was positively and independently associated with MetS in men and women after adjusting for potential confounding variables. These positive associations remained after additionally adjusting for hepatic enzymes, suggesting that higher levels of ALP are associated with a higher prevalence of MetS, irrespective of liver dysfunction. Previous researchers have reported associations between serum ALP activity and MetS, with inconsistent and conflicting results. In a cross-sectional community-based survey of the association between osteocalcin and MetS in Korean men and 567 postmenopausal women, the relationship of ALP activity and MetS was found to be not statistically significant when adjusted for age, body mass index, and osteocalcin [16]. On the contrary, several observational studies have suggested that serum ALP activity may be higher in individuals with MetS versus in those without MetS. Data from the Insulin Resistance Atherosclerosis Study, a multicenter observational epidemiological study in the United Satates, which included a follow-up average of 5.2 y, showed a postive association of serum ALP activity with MetS among 633 participants [22]. Compared to the lowest ALP quartile, the OR (95% CI) for MetS in the highest ALP quartile was 2.28 (1.4–4.20) after adjusting for age, sex, ethnicity, clinics, and alcohol consumption. Kim et al. reported similar results in a prospective study with a 4-y follow-up period among 14,224 apparently healthy middle-aged Korean employees [23]. However, sex difference was not fully considered in the previous studies, since there is a significant sex difference in serum ALP distribution. Our study
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confirmed the positive association of serum ALP activity with MetS through sex-specific multiple logistic regression analyses. Some mechanisms could explain the significant relationship between serum ALP activity and MetS. Although the pathophysiology of MetS is not fully understood, insulin resistance and subclinical low-grade inflammation play a key role in the development of MetS. Thus, insulin resistance could be an underlying mechanism linking serum ALP activity and MetS. Serum ALP is known to be associated with insulin resistance, especially hepatic steatosis [24], which is seen as a hepatic manifestation of MetS [25,26]. Actually, in the present study, insulin resistance, as measured by HOMA-IR, gradually increased in accordance with serum ALP quartiles. Subclinical inflammation could also explain the positive associations between them. MetS is associated with chronic inflammatory responses following the stages of abnormal cytokine production, increased acute phase reactants, and activation of inflammatory signaling pathways [27,28]. Recent studies have shown that elevated C-reactive protein (CRP) level is strongly associated with MetS and its components. Moreover, serum ALP activity was positively associated with CRP in a previous study [29]. In the present study, leukocyte count, another nonspecific marker of systemic inflammation, also gradually increased according to serum ALP quartiles. Another noteworthy finding in our study was that the ORs for MetS in accordance with ALP quartiles were higher in women than in men. Moreover, among MetS components, serum ALP activities were significantly associated with high triglyceride and low HDL-cholesterol for men, whereas serum ALP activities were significantly associated with abdominal obesity, high blood pressure, high plasma glucose, and high triglyceride for women. Although the reason for the discrepancies between men and women in the relationship of serum ALP activities with MetS and components is unclear, the sex differences the distribution of total body adipose tissue and sex hormones could explained the sex-specific relationships between them [30,31]. Women
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might have a greater amount of total body adipose tissue compared with men at the same BMI [32], which could be the major source of circulating free fatty acids and proinflammatory cytokines with inducing insulin resistance and an atherogenic lipid profiles [30,33]. Importantly, though, our study had several limitations. First, it is a retrospective observational analysis of an existing database that limits causal inferences. Additional research is needed to elucidate longitudinal associations. Second, the present investigation is a representative study of a single ethnic group; results from other populations are required to more clearly understand the complex mechanisms of MetS. Third, since this study used secondary data from the KNHANES, we did not take into consideration the amount of alcohol consumed. We used frequency of alcohol intake instead of the amount of alcohol consumed as a confounding variable in multiple logistic regression analysis models. Lastly, we could not determine whether hepatic ALP or bone ALP was associated with MetS because we did not measure ALP isoenzymes. Cheung et al. reported the relationships between serum bone-specific ALP activities and MetS components among 3773 participants of the 19992004 NHANES in the United States [34]. To minimize this limitation, we performed analysis adjusting for the hepatobiliary markers such as AST, ALT, and GGT as confounding factors. Despite these potential limitations, the KNHANES offered a large sample population that is nationally representative, with appropriate sampling design and quality control. Furthermore, our analysis considers many potential covariates including socioeconomic status that can confound the observed associations. In conclusion, serum ALP activity was positively and independently associated with MetS, suggesting that serum ALP activity may be a useful additional measure in assessment of MetS.
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Acknowledgements We thank all those who conducted the 2008 to 2011 KNHANES, as well as the participants in the survey.
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Figure legends Fig. 1. Mean values of HOMA-IR according to serum ALP quartile in men and women Fig. 2. Mean leukocyte count according to serum ALP quartile in men and women
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19 Table1 Demographic and clinical characteristics of study population by alkaline phosphatase quartiles in men and women Men (U/l) Women (U/l) Unweighted N Age (y) Waist circumference (cm) Body mass index (kg/m2) Systolic BP (mmHg) Diastolic BP (mmHg) Fasting plasma glucose (mg/dl) Triglyceride (mg/dl) HDL-cholesterol (mg/dl) AST (U/l) ALT (U/l) GGT (U/l) Current smoker (%) Regular drinker (%)a Regular exercise (%)b Household income (%) Quartile 1 (lowest) Quartile 2 Quartile 3 Quartile 4 (highest) Education level (%) ≤ Middle school High school ≥ College Occupation (%) Unemployed White-collar workers Blue-collar workers
Q1 (≤190)
Q2 (191-224)
Q3 (225-263)
Q4 (≥264)
1809 42.1 (0.4) 83.4 (0.3) 23.9 (0.1) 119.9 (0.4) 79.8 (0.3) 95.8 (0.4) 140.3 (3.3) 48.4 (0.3) 22.1 (0.2) 23.0 (0.4) 39.3 (1.5) 44.9 (1.4) 45.2 (1.5) 36.8 (1.4)
1784 42.2 (0.4) 84.0 (0.3) 24.1 (0.1) 120.5 (0.4) 80.5 (0.3) 97.1 (0.6) 154.6 (4.1) 46.3 (0.3) 23.1 (0.3) 25.6 (0.5) 43.9 (1.8) 45.9 (1.4) 39.4 (1.3) 34.7 (1.3)
1751 42.7 (0.4) 84.1 (0.3) 24.0 (0.1) 121.3 (0.4) 80.7 (0.3) 97.4 (0.6) 162.8 (3.8) 45.7 (0.3) 24.0 (0.3) 26.4 (0.5) 45.9 (1.7) 48.1 (1.4) 36.8 (1.4) 35.1 (1.3)
1757 45.2 (0.4) 84.3 (0.3) 24.0 (0.1) 123.0 (0.5) 81.2 (0.3) 101.9 (0.9) 171.5 (4.5) 45.4 (0.3) 25.6 (0.3) 28.5 (0.5) 58.9 (2.6) 51.6 (1.5) 37.0 (1.4) 36.2 (1.4)
10.5 (0.8) 25.1 (1.4) 29.7 (1.4) 34.7 (1.5)
10.1 (0.8) 25.2 (1.2) 33.4 (1.4) 31.3 (1.3)
14.2 (0.9) 26.5 (1.4) 30.8 (1.3) 28.5 (1.4)
15.9 (1.0) 26.8 (1.3) 30.7 (1.3) 36.2 (1.4)
P-value <0.001 0.012 NS <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.027 <0.001 NS <0.001
Q1 (≤163)
Q2 (164-201)
Q3 (202-251)
Q4 (≥252)
2225 37.9 (0.2) 73.7 (0.2) 22.1 (0.1) 108.7 (0.4) 71.5 (0.3) 90.0 (0.3) 85.9 (1.2) 53.6 (0.3) 17.3 (0.1) 13.6 (0.2) 16.1 (0.3) 8.0 (0.7) 14.0 (0.9) 31.0 (1.1)
2266 40.3 (0.3) 76.1 (0.2) 22.8 (0.1) 112.1 (0.4) 73.1 (0.3) 91.5 (0.3) 102.0 (1.6) 52.9 (0.3) 18.3 (0.1) 15.1 (0.2) 18.3 (0.4) 7.9 (0.7) 13.1 (0.9) 28.1 (1.1)
2184 45.7 (0.4) 78.7 (0.3) 23.6 (0.1) 116.8 (0.5) 75.0 (0.3) 94.3 (0.3) 113.7 (1.9) 51.6 (0.3) 19.9 (0.2) 17.8 (0.3) 21.7 (0.6) 6.2 (0.7) 10.2 (0.8) 30.9 (1.2)
2198 54.5 (0.4) 81.5 (0.2) 24.2 (0.1) 123.4 (0.5) 76.9 (0.3) 101.0 (0.7) 132.0 (2.3) 49.3 (0.3) 22.2 (0.2) 20.1 (0.3) 25.8 (0.7) 6.2 (0.6) 8.4 (0.9) 28.7 (1.3)
9.9 (0.8) 25.5 (1.2) 32.3 (1.3) 32.3 (1.3)
14.4 (0.9) 25.3 (1.2) 29.4 (1.2) 30.9 (1.4)
17.8 (1.0) 26.7 (1.1) 28.8 (1.2) 26.7 (1.2)
25.3 (1.1) 26.2 (1.2) 26.0 (1.2) 22.5 (1.1)
<0.001 17.3 (1.1) 41.6 (1.5) 42.1 (1.4)
19.0 (1.0) 44.0 (1.4) 37.0 (1.4)
20.1 (1.1) 42.0 (1.5) 37.9 (1.5)
27.5 (1.3) 42.8 (1.5) 29.7 (1.3)
19.3 (1.1) 50.2 (1.4) 30.5 (1.5)
16.8 (1.0) 49.1 (1.5) 34.1 (1.4)
20.5 (1.2) 42.2 (1.5) 37.3 (1.5)
25.0 (1.2) 34.8 (1.3) 40.2 (1.5)
P value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.010 <0.001 <0.001 <0.001
<0.001 14.0 (0.9) 43.6 (1.3) 42.4 (1.3)
22.3 (1.0) 44.6 (1.3) 33.1 (1.2)
36.1 (1.3) 38.4 (1.3) 25.5 (1.2)
58.4 (1.3) 26.5 (1.2) 15.1 (0.9)
39.3 (1.3) 48.2 (1.2) 12.5 (0.9)
47.0 (1.2) 39.5 (1.3) 13.5 (0.8)
49.0 (1.3) 32.7 (1.2) 18.3 (1.0)
54.9 (1.4) 21.2 (1.1) 23.9 (1.4)
<0.001
<0.001
20 Abbreviations: BP, blood pressure; HDL, high density lipoprotein. AST, aspartate aminotransferase; ALT, alanine aminotransferase; GGT, γ-glutamyltransferase; Data are expressed as the mean (SE) or percentage (SE). P values were calculated using weighted ANOVA-test or weighted chi-square test. aAlcohol intake ≥ twice/week. cModerate intensity physical exercise ≥ twice/week. Table2 Proportion of metabolic syndrome and its components by alkaline phosphatase quartiles in men and women Men Women Q1
Q2
Q3
Q4
Metabolic syndrome 22.6 (1.0) 24.7 (1.2) 26.9 (1.2) 30.9 (1.3) Abdominal obesity 23.4 (1.2) 25.2 (1.2) 25.0 (1.3) 26.2 (1.3) High blood pressure 37.0 (1.3) 38.6 (1.4) 40.0 (1.4) 43.0 (1.4) High plasma glucose 27.3 (1.2) 28.0 (1.2) 26.5 (1.2) 31.7 (1.3) High triglyceride 31.4 (1.3) 36.1 (1.3) 38.5 (1.4) 41.5 (1.5) Low HDL-cholesterol 23.1 (1.0) 29.9 (1.3) 33.6 (1.3) 35.0 (1.4) Data are expressed as percentage (SE). P values were calculated using weighted 2 test.
P-value
Q1
Q2
Q3
Q4
P value
<0.001 NS 0.022 0.016 <0.001 <0.001
8.9 (0.6) 21.6 (1.0) 11.2 (0.8) 10.4 (0.7) 8.8 (0.7) 41.7 (1.2)
17.6 (0.7) 31.0 (1.2) 17.4 (0.9) 14.5 (0.9) 16.1 (0.8) 42.3 (1.3)
24.7 (0.8) 43.2 (1.4) 26.7 (1.1) 20.9 (1.0) 19.8 (1.0) 47.2 (1.8)
39.1 (1.1) 55.1 (1.4) 38.8 (1.2) 31.5 (1.1) 31.7 (1.2) 57.0 (1.3)
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Table 3. Odds ratios and 95% confidence intervals for metabolic syndrome according to serum alkaline phosphatase quartiles in men and women Men Women Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Model 1
1.00 (reference)
1.13 (0.94-1.35)
1.25 (1.05-1.47)
1.40 (1.17-1.67)
1.00 (reference)
1.69 (1.36-2.10)
2.07 (1.69-2.54)
2.91 (2.37-3.57)
Model 2
1.00 (reference)
1.07 (0.87-1.33)
1.30 (1.05-1.60)
1.48 (1.19-1.83)
1.00 (reference)
1.41 (1.09-1.82)
1.41 (1.09-1.81)
1.99 (1.53-2.59)
Model 3
1.00 (reference)
1.03 (0.84-1.27)
1.21 (0.98-1.49)
1.32 (1.05-1.64)
1.00 (reference)
1.46 (1.10-2.19)
1.47 (1.04-2.07)
1.99 (1.42-3.81)
Met-S
21 Abdominal obesity Model 1
1.00 (reference)
1.10 (0.92-1.32)
1.08 (0.89-1.30)
1.09 (0.91-1.32)
1.00 (reference)
1.47 (1.24-1.75)
2.02 (1.71-2.39)
2.31 (1.92-1.77)
Model 2
1.00 (reference)
1.00 (0.77-1.31)
1.01 (0.76-1.34)
1.09 (0.81-1.46)
1.00 (reference)
1.45 (1.21-1.75-
2.00 (1.66-2.41)
2.17 (1.74-2.70)
Model 3
1.00 (reference)
0.98 (0.74-1.28)
0.97 (0.73-1.29)
1.02 (0.75-1.37)
1.00 (reference)
1.49 (1.17-1.89)
1.72 (1.34-2.20)
1.80 (1.35-2.40)
Model 1
1.00 (reference)
1.07 (0.91-1.26)
1.07 (0.90-1.27)
1.17 (0.98-1.39)
1.00 (reference)
1.35 (1.32-1.65)
1.65 (1.32-2.04)
1.71 (1.39-2.10)
Model 2
1.00 (reference)
1.08 (0.91-1.28)
1.10 (0.91-1.32)
1.25 (1.03-1.50)
1.00 (reference)
1.23 (0.97-1.56)
1.48 (1.16-1.90)
1.48 (1.15-1.90)
Model 3
1.00 (reference)
1.05 (0.8-1.25)
1.05 (0.86-1.26)
1.16 (0.95-1.40)
1.00 (reference)
1.35 (0.99-1.84)
1.45 (1.12-2.16)
1.49 (1.06-2.08)
Model 1
1.00 (reference)
1.03 (0.86-1.23)
0.92 (0.77-1.09)
1.06 (0.88-1.28)
1.00 (reference)
1.25 (1.01-1.81)
1.47 (1.20-1.81)
1.74 (1.41-2.14)
Model 2
1.00 (reference)
1.06 (0.87-1.28)
1.00 (0.83-1.20)
1.18 (0.96-1.44)
1.00 (reference)
1.28 (1.01-1.61)
1.58 (1.25-2.00)
1.84 (1.45-2.34)
Model 3
1.00 (reference)
1.00 (0.82-1.21)
0.90 (0.74-1.10)
1.01 (0.82-1.24)
1.00 (reference)
1.27 (1.00-1.88)
1.38 (1.92-1.78)
1.46 (1.05-2.02)
Model 1
1.00 (reference)
1.24 (1.05-1.45)
1.36 (1.15-1.60)
1.48 (1.25-1.75)
1.00 (reference)
1.80 (1.44-2.26)
1.87 (1.48-2.37)
2.65 (2.11-3.32)
Model 2
1.00 (reference)
1.28 (1.08-1.52)
1.49 (1.24-1.78)
1.65 (1.34-1.99)
1.00 (reference)
1.69 (1.17-2.18)
1.55 (1.17-2.04)
2.19 (1.68-2.87)
Model 3
1.00 (reference)
1.21 (1.02-1.44)
1.40 (1.11-1.61)
1.43 (1.15-1.70)
1.00 (reference)
1.68 (1.36-2.60)
1.88 (1.34-2.64)
2.28 (1.60-3.24)
Model 1
1.00 (reference)
1.43 (1.20-1.68)
1.68 (1.42-1.99)
1.74 (1.47-2.04)
1.00 (reference)
0.97 (0.83-1.13)
1.04 (0.90-1.21)
1.28 (1.09-1.49)
Model 2
1.00 (reference)
1.32 (1.10-1.58)
1.59 (1.31-1.93)
1.61 (1.35-1.93)
1.00 (reference)
0.88 (0.74-1.03)
0.83 (0.72-0.98)
0.94 (0.79-1.12)
High blood pressure
High plasma glucose
High triglyceride
Low HDL-cholesterol
Model 3 1.00 (reference) 1.30 (1.08-1.56) 1.56 (1.29-1.91) 1.57 (1.31-1.89) 1.00 (reference) 0.88 (0.70-1.10) 0.88 (0.71-1.09) 1.01 (0.80-1.27) Model 1: adjusted for age; Model 2 adjusted for age, smoking status, alcohol intake, regular exercise, household income, education level, and occupation; Model 3 adjusted for age, smoking status, alcohol intake, regular exercise, household income, education level, occupation, AST, ALT, and GGT activities.
22 Table 4. Factor loading of the components of metabolic syndrome Men Kaiser-Meyer-Olkin measure 0.633 Batlett’s p-value <0.001 Total loading 54.472 (factor 1: 27.945, factor 2:26.527) Factor loading Factor 1 Factor 2 Abdominal obesity 0.411 0.454 Blood pressure -0.058 0.760 Fasting plasma glucose 0.131 0.694 Triglyceride concentration 0.724 0.223 HDL cholesterol 0.827 -0.106
Women 0.698 <0.001 56.654 (factor 1: 27.750, factor 2: 26.903) Factor loading Factor 1 Factor 2 0.604 0.358 0.730 0.019 0.709 0.078 0.292 0.678 -0.039 0.867
Table 5. Relationship between factor score and serum ALP quartiles Overall P-value Q1
Q2
Q3
Q4
Post-hoc P-value Q1 vs Q2 Q1 vs Q3
Q1 vs Q4 Q2 vs Q3 Q2 vs Q4 Q3 vs Q4 Men Factor 1 -0.121±0.955 0.001±0.996 0.041±1.015 0.076±1.022 <0.001 <0.001 <0.001 <0.001 0.235 0.025 0.295 Factor 2 -0.020±0.995 -0.027±0.984 -0.045±0.989 0.091±1.026 <0.001 0.825 0.450 0.001 0.590 <0.001 <0.001 Women Factor 1 -0.393±0.773 -0.169±0.905 0.109±1.030 0.439±1.061 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 Factor 2 -0.183±0.883 -0.085±0.982 0.018±1.007 0.244±1.066 <0.001 0.001 <0.001 <0.001 <0.001 <0.001 <0.001 The formula of factor 1 was 0.230xwaist-0.176xBP-0.022xFPG+0.507xTG+0.643x HDL-C and factor 2 was 0.289xwaist+0.614xBP+0.528xFPG+0.051xTG-0.229xHDL-C in men. The formula of factor 1 was is 0.362xwaist+0.541xBP+0.511xFPG+0.049xTG-0.246xHDL-C and factor 2 was 0.145xwaist-0.167xBP-0.112xFPG+0.487xTG+0.727xHDL-C in women.
23
24 ●
Serum alkaline phosphatase (ALP), a useful marker of hepatobiliary or bone disorder, has recently
emerged as a biomarker of chronic low-grade inflammation and cardiometabolic disease. ●
This study aimed to examine the association of serum ALP level with metabolic syndrome (MetS) in
apparently healthy adults. ●
Serum ALP level was positively and independently associated with MetS, suggesting that serum ALP
level may be a useful additional measure in assessment of MetS.
S0009-8981(19)32079-0 https://doi.org/10.1016/j.cca.2019.10.015 CCA 15876
To appear in:
Clinica Chimica Acta
Received Date: Revised Date: Accepted Date:
1 April 2019 12 October 2019 16 October 2019
Please cite this article as: J-H. Kim, H. Sun Lee, H-M. Park, Y-J. Lee, Serum alkaline phosphatase level is positively associated with metabolic syndrome: A nationwide population-based study, Clinica Chimica Acta (2019), doi: https://doi.org/10.1016/j.cca.2019.10.015
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1
Serum alkaline phosphatase level is positively associated with metabolic syndrome: A nationwide population-based study
Ji-Hye Kim, MDa,b, Hye Sun Lee, PhDc, Hye-Min Park, MDb,d and Yong-Jae Lee, MD, MPH, PhDd* aDepartment
of Health Promotion, Yonsei University Health System, Severance Check-up,
Seoul, Korea bDepartment
of Medicine, Yonsei University Graduate School, Seoul, Korea
cBiostatistics
Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea
dDepartment
of Family Medicine, Yonsei University College of Medicine, Seoul, Korea
Corresponding author: Yong-Jae Lee MD, MPH, PhD. Department of Family Medicine Yonsei University College of Medicine Gangnam Severance Hospital, 211 Eonju-ro, Gangnam-gu, Seoul, Republic of Korea, 06273. Tel: +82 2 2019 2630 Fax: +82 3462 8209 Cell: +82 10 7292 9169, E-mail: [email protected]
2
Abstract Background: Serum alkaline phosphatase (ALP), a useful marker of hepatobiliary or bone disorder, has recently emerged as a biomarker of chronic low-grade inflammation and cardiometabolic disease. This study aimed to examine the association of serum ALP level with metabolic syndrome (MetS) in apparently healthy adults. Methods: A cross-sectional study was conducted to examine the relationship between serum ALP level and MetS in 7,101 men and 8,873 women aged 19 to 75 years using data from the 2008 to 2011 Korean National Health and Nutrition Examination Survey. The odds ratios (ORs) and 95% confidence intervals (CIs) for MetS were calculated using multiple logistic regression analyses across serum ALP quartiles (Q1: ≤190 U/L; Q2: 191–224 U/L; Q3: 225– 263 U/L; and Q4: ≥264 U/L for men and Q1: ≤163 U/L; Q2: 164–201 U/L; Q3: 202–251 U/L; and Q4: ≥252 U/L for women). Results: The mean values of most cardiometabolic variables, HOMA-IR, and leukocyte count gradually increased with serum ALP quartile. The prevalence of MetS significantly increased in accordance with serum ALP quartile. In comparison with those of individuals in the lowest quartile, the OR (95% CI) for MetS in the highest quartile was 1.32 (1.05-1.64) in men and 1.99 (1.42-3.81) in women after adjusting for age, cigarette smoking, alcohol intake, regular exercise, household income, education level, occupation, AST, ALT, and GGT levels. Conclusion: Serum ALP level was positively and independently associated with MetS in men and women.
Keywords: alkaline phosphatase; metabolic syndrome; insulin resistance; inflammation.
3
1. Introduction Metabolic syndrome (MetS) is characterized by a cluster of several cardiometabolic risk factors including abdominal obesity, elevated blood pressure, glucose intolerance, and atherogenic dyslipidemia [1]. The prevalence of MetS in adults has increased globally in recent decades, and this upward trend is becoming a significant threat to public health with increased incidence risk of cardiovascular disease and type 2 diabetes [2,3]. Although the pathophysiology of MetS remains a major challenge, accumulating evidence has shown that insulin resistance and subclinical low-grade inflammation play a key role in the development of MetS [4,5]. Alkaline phosphatase (ALP) is a ubiquitous metalloenzyme that catalyzes the hydrolysis of monophosphate esters in an alkaline pH environment [6-9]. ALP is present in serum and the external cell surface of most cells; is particularly rich in the liver, bone, and placenta; and plays an integral role in metabolism within the hepatobiliary and skeletal systems [10,11]. In this regard, serum ALP has long been considered a useful marker of hepatobiliary or bone disorders [12,13]. However, emerging evidence also shows that serum ALP activity is modestly increased in cardiometabolic diseases such as hypertension, type 2 diabetes, dyslipidemia, and peripheral arterial disease [14,15], which are increasingly being seen as inflammatory disorders. In light of these novel findings, we hypothesized that there would be a positive association between serum ALP activity and MetS; however, limited data exist linking serum ALP activity with MetS, with associated inconsistent and conflicting results [16-18].
2. Materials and Methods 2.1. Survey overview and study population
4
This cross-sectional study used data obtained from the 2008 to 2011 Korean National Health and Nutrition Examination Survey (KNHANES IV and V), which was conducted by the Korea Centers for Disease Control and Prevention. The KNHANES is a nationwide, representative, population-based survey performed to evaluate the health and nutritional status of Koreans. The survey consists of a health interview survey, a nutrition survey, and a health examination survey. Sampling units demonstrated a stratified, multistage, probabilitysampling design that was based on sex, age, and geographical area using household registries. We extracted 25,935 participants (11,538 men and 14,397 women) whose ALP data were available from the 2008 to 2011 KNHANES dataset. We excluded participants with the following conditions: children and adolescents aged ≤ 18 y; pregnant females; individuals with a osteoporosis and a history of hip, wrist, or vertebral fracture; individuals with a history of hepatitis B infection, hepatitis C infection, thyroid disease, chronic kidney disease, coronary artery disease, cerebrovascular disease and/or cancer; individuals with aspartate aminotransferase (AST) ≥80 U/l, alanine aminotransferase (ALT) ≥80 U/l, or γglutamyltransferase (GGT) ≥80 U/l; those who had not fasted for 12 hours prior to blood sampling, and those whose MetS components and/or insulin data were missing. After these exclusions, a total of 16,748 individuals (7,101 men and 8,873 women) aged 19 to 75 y were included in our final analysis. The KNHANES received ethical approval from the Institutional Review Board (IRB) of the Korea Centers for Disease Control and Prevention and written consent was obtained from all of the participants. In addition, the study was conducted in accordance with the ethical principles of the Declaration of Helsinki.
2.2. Data collection Trained medical staff obtained anthropometric measurements following a standardized procedure. Height was measured to the nearest 0.1 cm with a measuring rod
5
attached to a balanced beam scale (Seca 225; Seca) using a Frankfurt horizontal plane, with subjects standing as straight as possible and inhaling deeply. Body weight was measured using a digital electronic scale with the subjects wearing light indoor clothing without shoes after adjusting the scale to zero prior to measurement (GL-6000-20; G-tech). Waist circumference was measured by a trained technician to the nearest 0.1 cm in a horizontal plane at a level midway between the lower rib margin and the iliac crest following normal expiration. Self-reported cigarette smoking, alcohol consumption, and physical activity status were determined from the questionnaires. Each participant was categorized as a nonsmoker, ex-smoker, or current smoker with respect to smoking status. Participants were also asked about their frequency of alcohol intake and leisure-time physical activity on a weekly basis. Alcohol intake was divided into none, ≤ once per week, and ≥ twice per week. The short form of International Physical Activity Questionnaire (IPAQ) was adopted to determine the frequency of physical activity. Additionally, all subjects were instructed to record their daily engagement in mild, moderate, or vigorous intensity of activity during the previous week. Then, we estimated the quantity of physical activity (MET-h/wk) on the basis of physical activity frequency and intensity [19]. Regular exercise was divided into three groups according to the frequency of moderate-intensity exercise, as follows: none, once per week, and ≥ twice per week. Educational level was classified as either middle school or below, high school, and college or above. Household income was classified into four quartiles from lowest to highest. Occupation was classified into three categories: unemployed (i.e., retired individuals, students, and housewives); blue-collar workers (e.g., agriculture, forestry, fishery workers, craft and related trade workers, plant and machine operators and assemblers, and elementary occupations); and white-collar workers (e.g., managers, professionals, technicians, clerks, and service/sales workers) according to the Korean Standard Classification of Occupations. Systolic blood pressure (SBP) and diastolic blood pressure
6
(DBP) were assessed three times in the right upper arm using a standard mercury sphygmomanometer (Baumanometer; Baum), and the mean of the second and third blood pressure readings was used for analysis. Blood samples were obtained from the antecubital vein after each participant had fasted overnight for a minimum of 12 hours. Fasting plasma glucose (FPG), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C) aspartate aminotransferase (AST), alanine aminotransferase (ALT), γ-glutamyltransferase (GGT), and ALP activities were measured using the Hitachi 7600-110 automated chemistry analyzer (Hitachi Co.). The intra-assay and inter-assay CVs for ALP were 3.2% and 3.3%, respectively. Fasting serum insulin was assessed using a 1470 WIZARD gamma-counter (PerkinElmer). The Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) value was calculated using the following formula: fasting plasma glucose (mg/dl) × fasting insulin (µIU/ml)/405. Leucocyte counts were determined by an automated blood cell counter (XE2100D; Sysmex, Kobe, Japan).
2.3. Definition of MetS MetS was defined as the presence of at least three of the following criteria according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) [1,20] : abdominal obesity according to the Asian-Pacific criteria (waist circumference ≥ 90 cm in men and ≥ 80 cm in women) [21]; high BP (SBP ≥ 130 mmHg or DBP ≥ 85 mmHg); high FPG (≥ 100 mg/dl); high TG (≥ 150 mg/dl; and low HDL-C (< 40 mg/dl in men or < 50 mg/dl in women). Individuals who reported taking anti-hypertensive medication or anti-diabetes medications were considered to have elevated blood pressure or elevated fasting glucose.
2.4. Statistical analysis
7
Participants were categorized into serum ALP quartiles as follows: Q1: ≤190 U/l; Q2: 191–224 U/l; Q3: 225–263 U/l; and Q4: ≥264 U/l for men and Q1: ≤163 U/l; Q2: 164– 201 U/l; Q3: 202–251 U/l; and Q4: ≥252 U/l for women). The results were expressed as mean and standard error (SE) or percentage (SE) for quantitative variables. A weighted oneway analysis of variance test or 2 test was used to compare the means of different variables between four categories in the field. To assess the relationships with insulin resistance and MetS according to serum ALP quartile, the odds ratios (ORs) and 95% confidence intervals (CIs) for MetS were calculated using multiple logistic regression analyses across serum ALP quartiles. Moreover, factor analysis using principal component analysis varimax rotation was performed. The data were analyzed using SAS software, version 9.4 (SAS institute Inc.) and SPSS Statistics (ver. 25, IBM Corp.). Two-sided P-values < 0.05 were considered statistically significant.
3. Results Table 1 shows the demographic and biochemical characteristics of the participants in relation to serum ALP quartile. In the fourth quartile of serum ALP, the mean values of age, waist circumference, BP, FPG, TG, and AST, ALT, and GGT levels were highest, whereas HDL-C level was lowest for both men and women. In addition, the proportion of household income in the highest quartile and the education level of college or above was lowest in the highest quartile, whereas blue collar workers were most prevalent in the fourth quartile of serum ALP for both men and women. Table 2 shows the proportion of metabolic syndrome and its components by serum ALP quartile. For men, the proportion of MetS and high BP, high TG, low HDL-cholesterol significantly increased in accordance with serum ALP quartile. For women, the proportion of MetS and all components of MetS with increasing ALP quartile.
8
Fig. 1 and 1 illustrate the mean HOMA-IR values and the leukocyte counts, respectively, which were gradually increased according to serum ALP quartile (all P values < 0.001). Table 3 shows the results of multiple logistic regression analysis to assess the odds for predicting MetS in terms of serum ALP quartile. Compared with the lowest quartile group, the OR (95% CI) for MetS of the highest quartile was 2.06 (1.23–3.45) after adjusting for age, cigarette smoking, alcohol intake, regular exercise, household income, education level, and occupation. These associations remained valid after additional adjustment for inflammatory markers such as leukocyte count and hepatic enzymes such as AST, ALT, and GGT. In comparison with the lowest quartile, the OR (95% CI) for MetS in the highest quartile was 1.32 (1.05-1.64) in men and 1.99 (1.42-3.81) in women after adjusting for age, cigarette smoking, alcohol intake, regular exercise, household income, education level, occupation, AST, ALT, and GGT levels. In subgroup analysis of each component of MetS, serum ALP quartiles were significantly associated with high TG and low HDL-cholesterol for men and obesity, high BP, high FPG, and high TG for women. Factor analysis using principal component analysis varimax rotation was performed. The number of factors among five MetS components was determined Eigen value >1 and both men and women have two factors. For men, factor 1 is combination of TG and HDL-C, and factor 2 is a combination of waist, BP, and FPG. For women, factor1 is a combination of waist, BP, and FPG, and factor 2 is a combination of TG and HDL-C (Table 4). Each factor formula of component score coefficient×standardized value for the MetS components were as follows: The formula of factor 1 was 0.230xwaist-0.176xBP-0.022xFPG+0.507xTG+0.643x HDL-C and factor 2 was 0.289xwaist+0.614xBP+0.528xFPG+0.051xTG-0.229xHDL-C in men. The formula of factor 1 was is 0.362xwaist+0.541xBP+0.511xFPG+0.049xTG0.246xHDL-C and factor 2 was 0.145xwaist-0.167xBP-0.112xFPG+0.487xTG+0.727xHDL-
9
C in women. Table 5 shows the relationship between factor score and serum ALP quartiles. Each factor was found to have a significant correlation with serum ALP quartiles.
4. Discussion In this nationally representative cross-sectional study, we found that serum ALP activity was positively and independently associated with MetS in men and women after adjusting for potential confounding variables. These positive associations remained after additionally adjusting for hepatic enzymes, suggesting that higher levels of ALP are associated with a higher prevalence of MetS, irrespective of liver dysfunction. Previous researchers have reported associations between serum ALP activity and MetS, with inconsistent and conflicting results. In a cross-sectional community-based survey of the association between osteocalcin and MetS in Korean men and 567 postmenopausal women, the relationship of ALP activity and MetS was found to be not statistically significant when adjusted for age, body mass index, and osteocalcin [16]. On the contrary, several observational studies have suggested that serum ALP activity may be higher in individuals with MetS versus in those without MetS. Data from the Insulin Resistance Atherosclerosis Study, a multicenter observational epidemiological study in the United Satates, which included a follow-up average of 5.2 y, showed a postive association of serum ALP activity with MetS among 633 participants [22]. Compared to the lowest ALP quartile, the OR (95% CI) for MetS in the highest ALP quartile was 2.28 (1.4–4.20) after adjusting for age, sex, ethnicity, clinics, and alcohol consumption. Kim et al. reported similar results in a prospective study with a 4-y follow-up period among 14,224 apparently healthy middle-aged Korean employees [23]. However, sex difference was not fully considered in the previous studies, since there is a significant sex difference in serum ALP distribution. Our study
10
confirmed the positive association of serum ALP activity with MetS through sex-specific multiple logistic regression analyses. Some mechanisms could explain the significant relationship between serum ALP activity and MetS. Although the pathophysiology of MetS is not fully understood, insulin resistance and subclinical low-grade inflammation play a key role in the development of MetS. Thus, insulin resistance could be an underlying mechanism linking serum ALP activity and MetS. Serum ALP is known to be associated with insulin resistance, especially hepatic steatosis [24], which is seen as a hepatic manifestation of MetS [25,26]. Actually, in the present study, insulin resistance, as measured by HOMA-IR, gradually increased in accordance with serum ALP quartiles. Subclinical inflammation could also explain the positive associations between them. MetS is associated with chronic inflammatory responses following the stages of abnormal cytokine production, increased acute phase reactants, and activation of inflammatory signaling pathways [27,28]. Recent studies have shown that elevated C-reactive protein (CRP) level is strongly associated with MetS and its components. Moreover, serum ALP activity was positively associated with CRP in a previous study [29]. In the present study, leukocyte count, another nonspecific marker of systemic inflammation, also gradually increased according to serum ALP quartiles. Another noteworthy finding in our study was that the ORs for MetS in accordance with ALP quartiles were higher in women than in men. Moreover, among MetS components, serum ALP activities were significantly associated with high triglyceride and low HDL-cholesterol for men, whereas serum ALP activities were significantly associated with abdominal obesity, high blood pressure, high plasma glucose, and high triglyceride for women. Although the reason for the discrepancies between men and women in the relationship of serum ALP activities with MetS and components is unclear, the sex differences the distribution of total body adipose tissue and sex hormones could explained the sex-specific relationships between them [30,31]. Women
11
might have a greater amount of total body adipose tissue compared with men at the same BMI [32], which could be the major source of circulating free fatty acids and proinflammatory cytokines with inducing insulin resistance and an atherogenic lipid profiles [30,33]. Importantly, though, our study had several limitations. First, it is a retrospective observational analysis of an existing database that limits causal inferences. Additional research is needed to elucidate longitudinal associations. Second, the present investigation is a representative study of a single ethnic group; results from other populations are required to more clearly understand the complex mechanisms of MetS. Third, since this study used secondary data from the KNHANES, we did not take into consideration the amount of alcohol consumed. We used frequency of alcohol intake instead of the amount of alcohol consumed as a confounding variable in multiple logistic regression analysis models. Lastly, we could not determine whether hepatic ALP or bone ALP was associated with MetS because we did not measure ALP isoenzymes. Cheung et al. reported the relationships between serum bone-specific ALP activities and MetS components among 3773 participants of the 19992004 NHANES in the United States [34]. To minimize this limitation, we performed analysis adjusting for the hepatobiliary markers such as AST, ALT, and GGT as confounding factors. Despite these potential limitations, the KNHANES offered a large sample population that is nationally representative, with appropriate sampling design and quality control. Furthermore, our analysis considers many potential covariates including socioeconomic status that can confound the observed associations. In conclusion, serum ALP activity was positively and independently associated with MetS, suggesting that serum ALP activity may be a useful additional measure in assessment of MetS.
12
Acknowledgements We thank all those who conducted the 2008 to 2011 KNHANES, as well as the participants in the survey.
13
Figure legends Fig. 1. Mean values of HOMA-IR according to serum ALP quartile in men and women Fig. 2. Mean leukocyte count according to serum ALP quartile in men and women
14
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19 Table1 Demographic and clinical characteristics of study population by alkaline phosphatase quartiles in men and women Men (U/l) Women (U/l) Unweighted N Age (y) Waist circumference (cm) Body mass index (kg/m2) Systolic BP (mmHg) Diastolic BP (mmHg) Fasting plasma glucose (mg/dl) Triglyceride (mg/dl) HDL-cholesterol (mg/dl) AST (U/l) ALT (U/l) GGT (U/l) Current smoker (%) Regular drinker (%)a Regular exercise (%)b Household income (%) Quartile 1 (lowest) Quartile 2 Quartile 3 Quartile 4 (highest) Education level (%) ≤ Middle school High school ≥ College Occupation (%) Unemployed White-collar workers Blue-collar workers
Q1 (≤190)
Q2 (191-224)
Q3 (225-263)
Q4 (≥264)
1809 42.1 (0.4) 83.4 (0.3) 23.9 (0.1) 119.9 (0.4) 79.8 (0.3) 95.8 (0.4) 140.3 (3.3) 48.4 (0.3) 22.1 (0.2) 23.0 (0.4) 39.3 (1.5) 44.9 (1.4) 45.2 (1.5) 36.8 (1.4)
1784 42.2 (0.4) 84.0 (0.3) 24.1 (0.1) 120.5 (0.4) 80.5 (0.3) 97.1 (0.6) 154.6 (4.1) 46.3 (0.3) 23.1 (0.3) 25.6 (0.5) 43.9 (1.8) 45.9 (1.4) 39.4 (1.3) 34.7 (1.3)
1751 42.7 (0.4) 84.1 (0.3) 24.0 (0.1) 121.3 (0.4) 80.7 (0.3) 97.4 (0.6) 162.8 (3.8) 45.7 (0.3) 24.0 (0.3) 26.4 (0.5) 45.9 (1.7) 48.1 (1.4) 36.8 (1.4) 35.1 (1.3)
1757 45.2 (0.4) 84.3 (0.3) 24.0 (0.1) 123.0 (0.5) 81.2 (0.3) 101.9 (0.9) 171.5 (4.5) 45.4 (0.3) 25.6 (0.3) 28.5 (0.5) 58.9 (2.6) 51.6 (1.5) 37.0 (1.4) 36.2 (1.4)
10.5 (0.8) 25.1 (1.4) 29.7 (1.4) 34.7 (1.5)
10.1 (0.8) 25.2 (1.2) 33.4 (1.4) 31.3 (1.3)
14.2 (0.9) 26.5 (1.4) 30.8 (1.3) 28.5 (1.4)
15.9 (1.0) 26.8 (1.3) 30.7 (1.3) 36.2 (1.4)
P-value <0.001 0.012 NS <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.027 <0.001 NS <0.001
Q1 (≤163)
Q2 (164-201)
Q3 (202-251)
Q4 (≥252)
2225 37.9 (0.2) 73.7 (0.2) 22.1 (0.1) 108.7 (0.4) 71.5 (0.3) 90.0 (0.3) 85.9 (1.2) 53.6 (0.3) 17.3 (0.1) 13.6 (0.2) 16.1 (0.3) 8.0 (0.7) 14.0 (0.9) 31.0 (1.1)
2266 40.3 (0.3) 76.1 (0.2) 22.8 (0.1) 112.1 (0.4) 73.1 (0.3) 91.5 (0.3) 102.0 (1.6) 52.9 (0.3) 18.3 (0.1) 15.1 (0.2) 18.3 (0.4) 7.9 (0.7) 13.1 (0.9) 28.1 (1.1)
2184 45.7 (0.4) 78.7 (0.3) 23.6 (0.1) 116.8 (0.5) 75.0 (0.3) 94.3 (0.3) 113.7 (1.9) 51.6 (0.3) 19.9 (0.2) 17.8 (0.3) 21.7 (0.6) 6.2 (0.7) 10.2 (0.8) 30.9 (1.2)
2198 54.5 (0.4) 81.5 (0.2) 24.2 (0.1) 123.4 (0.5) 76.9 (0.3) 101.0 (0.7) 132.0 (2.3) 49.3 (0.3) 22.2 (0.2) 20.1 (0.3) 25.8 (0.7) 6.2 (0.6) 8.4 (0.9) 28.7 (1.3)
9.9 (0.8) 25.5 (1.2) 32.3 (1.3) 32.3 (1.3)
14.4 (0.9) 25.3 (1.2) 29.4 (1.2) 30.9 (1.4)
17.8 (1.0) 26.7 (1.1) 28.8 (1.2) 26.7 (1.2)
25.3 (1.1) 26.2 (1.2) 26.0 (1.2) 22.5 (1.1)
<0.001 17.3 (1.1) 41.6 (1.5) 42.1 (1.4)
19.0 (1.0) 44.0 (1.4) 37.0 (1.4)
20.1 (1.1) 42.0 (1.5) 37.9 (1.5)
27.5 (1.3) 42.8 (1.5) 29.7 (1.3)
19.3 (1.1) 50.2 (1.4) 30.5 (1.5)
16.8 (1.0) 49.1 (1.5) 34.1 (1.4)
20.5 (1.2) 42.2 (1.5) 37.3 (1.5)
25.0 (1.2) 34.8 (1.3) 40.2 (1.5)
P value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.010 <0.001 <0.001 <0.001
<0.001 14.0 (0.9) 43.6 (1.3) 42.4 (1.3)
22.3 (1.0) 44.6 (1.3) 33.1 (1.2)
36.1 (1.3) 38.4 (1.3) 25.5 (1.2)
58.4 (1.3) 26.5 (1.2) 15.1 (0.9)
39.3 (1.3) 48.2 (1.2) 12.5 (0.9)
47.0 (1.2) 39.5 (1.3) 13.5 (0.8)
49.0 (1.3) 32.7 (1.2) 18.3 (1.0)
54.9 (1.4) 21.2 (1.1) 23.9 (1.4)
<0.001
<0.001
20 Abbreviations: BP, blood pressure; HDL, high density lipoprotein. AST, aspartate aminotransferase; ALT, alanine aminotransferase; GGT, γ-glutamyltransferase; Data are expressed as the mean (SE) or percentage (SE). P values were calculated using weighted ANOVA-test or weighted chi-square test. aAlcohol intake ≥ twice/week. cModerate intensity physical exercise ≥ twice/week. Table2 Proportion of metabolic syndrome and its components by alkaline phosphatase quartiles in men and women Men Women Q1
Q2
Q3
Q4
Metabolic syndrome 22.6 (1.0) 24.7 (1.2) 26.9 (1.2) 30.9 (1.3) Abdominal obesity 23.4 (1.2) 25.2 (1.2) 25.0 (1.3) 26.2 (1.3) High blood pressure 37.0 (1.3) 38.6 (1.4) 40.0 (1.4) 43.0 (1.4) High plasma glucose 27.3 (1.2) 28.0 (1.2) 26.5 (1.2) 31.7 (1.3) High triglyceride 31.4 (1.3) 36.1 (1.3) 38.5 (1.4) 41.5 (1.5) Low HDL-cholesterol 23.1 (1.0) 29.9 (1.3) 33.6 (1.3) 35.0 (1.4) Data are expressed as percentage (SE). P values were calculated using weighted 2 test.
P-value
Q1
Q2
Q3
Q4
P value
<0.001 NS 0.022 0.016 <0.001 <0.001
8.9 (0.6) 21.6 (1.0) 11.2 (0.8) 10.4 (0.7) 8.8 (0.7) 41.7 (1.2)
17.6 (0.7) 31.0 (1.2) 17.4 (0.9) 14.5 (0.9) 16.1 (0.8) 42.3 (1.3)
24.7 (0.8) 43.2 (1.4) 26.7 (1.1) 20.9 (1.0) 19.8 (1.0) 47.2 (1.8)
39.1 (1.1) 55.1 (1.4) 38.8 (1.2) 31.5 (1.1) 31.7 (1.2) 57.0 (1.3)
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Table 3. Odds ratios and 95% confidence intervals for metabolic syndrome according to serum alkaline phosphatase quartiles in men and women Men Women Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Model 1
1.00 (reference)
1.13 (0.94-1.35)
1.25 (1.05-1.47)
1.40 (1.17-1.67)
1.00 (reference)
1.69 (1.36-2.10)
2.07 (1.69-2.54)
2.91 (2.37-3.57)
Model 2
1.00 (reference)
1.07 (0.87-1.33)
1.30 (1.05-1.60)
1.48 (1.19-1.83)
1.00 (reference)
1.41 (1.09-1.82)
1.41 (1.09-1.81)
1.99 (1.53-2.59)
Model 3
1.00 (reference)
1.03 (0.84-1.27)
1.21 (0.98-1.49)
1.32 (1.05-1.64)
1.00 (reference)
1.46 (1.10-2.19)
1.47 (1.04-2.07)
1.99 (1.42-3.81)
Met-S
21 Abdominal obesity Model 1
1.00 (reference)
1.10 (0.92-1.32)
1.08 (0.89-1.30)
1.09 (0.91-1.32)
1.00 (reference)
1.47 (1.24-1.75)
2.02 (1.71-2.39)
2.31 (1.92-1.77)
Model 2
1.00 (reference)
1.00 (0.77-1.31)
1.01 (0.76-1.34)
1.09 (0.81-1.46)
1.00 (reference)
1.45 (1.21-1.75-
2.00 (1.66-2.41)
2.17 (1.74-2.70)
Model 3
1.00 (reference)
0.98 (0.74-1.28)
0.97 (0.73-1.29)
1.02 (0.75-1.37)
1.00 (reference)
1.49 (1.17-1.89)
1.72 (1.34-2.20)
1.80 (1.35-2.40)
Model 1
1.00 (reference)
1.07 (0.91-1.26)
1.07 (0.90-1.27)
1.17 (0.98-1.39)
1.00 (reference)
1.35 (1.32-1.65)
1.65 (1.32-2.04)
1.71 (1.39-2.10)
Model 2
1.00 (reference)
1.08 (0.91-1.28)
1.10 (0.91-1.32)
1.25 (1.03-1.50)
1.00 (reference)
1.23 (0.97-1.56)
1.48 (1.16-1.90)
1.48 (1.15-1.90)
Model 3
1.00 (reference)
1.05 (0.8-1.25)
1.05 (0.86-1.26)
1.16 (0.95-1.40)
1.00 (reference)
1.35 (0.99-1.84)
1.45 (1.12-2.16)
1.49 (1.06-2.08)
Model 1
1.00 (reference)
1.03 (0.86-1.23)
0.92 (0.77-1.09)
1.06 (0.88-1.28)
1.00 (reference)
1.25 (1.01-1.81)
1.47 (1.20-1.81)
1.74 (1.41-2.14)
Model 2
1.00 (reference)
1.06 (0.87-1.28)
1.00 (0.83-1.20)
1.18 (0.96-1.44)
1.00 (reference)
1.28 (1.01-1.61)
1.58 (1.25-2.00)
1.84 (1.45-2.34)
Model 3
1.00 (reference)
1.00 (0.82-1.21)
0.90 (0.74-1.10)
1.01 (0.82-1.24)
1.00 (reference)
1.27 (1.00-1.88)
1.38 (1.92-1.78)
1.46 (1.05-2.02)
Model 1
1.00 (reference)
1.24 (1.05-1.45)
1.36 (1.15-1.60)
1.48 (1.25-1.75)
1.00 (reference)
1.80 (1.44-2.26)
1.87 (1.48-2.37)
2.65 (2.11-3.32)
Model 2
1.00 (reference)
1.28 (1.08-1.52)
1.49 (1.24-1.78)
1.65 (1.34-1.99)
1.00 (reference)
1.69 (1.17-2.18)
1.55 (1.17-2.04)
2.19 (1.68-2.87)
Model 3
1.00 (reference)
1.21 (1.02-1.44)
1.40 (1.11-1.61)
1.43 (1.15-1.70)
1.00 (reference)
1.68 (1.36-2.60)
1.88 (1.34-2.64)
2.28 (1.60-3.24)
Model 1
1.00 (reference)
1.43 (1.20-1.68)
1.68 (1.42-1.99)
1.74 (1.47-2.04)
1.00 (reference)
0.97 (0.83-1.13)
1.04 (0.90-1.21)
1.28 (1.09-1.49)
Model 2
1.00 (reference)
1.32 (1.10-1.58)
1.59 (1.31-1.93)
1.61 (1.35-1.93)
1.00 (reference)
0.88 (0.74-1.03)
0.83 (0.72-0.98)
0.94 (0.79-1.12)
High blood pressure
High plasma glucose
High triglyceride
Low HDL-cholesterol
Model 3 1.00 (reference) 1.30 (1.08-1.56) 1.56 (1.29-1.91) 1.57 (1.31-1.89) 1.00 (reference) 0.88 (0.70-1.10) 0.88 (0.71-1.09) 1.01 (0.80-1.27) Model 1: adjusted for age; Model 2 adjusted for age, smoking status, alcohol intake, regular exercise, household income, education level, and occupation; Model 3 adjusted for age, smoking status, alcohol intake, regular exercise, household income, education level, occupation, AST, ALT, and GGT activities.
22 Table 4. Factor loading of the components of metabolic syndrome Men Kaiser-Meyer-Olkin measure 0.633 Batlett’s p-value <0.001 Total loading 54.472 (factor 1: 27.945, factor 2:26.527) Factor loading Factor 1 Factor 2 Abdominal obesity 0.411 0.454 Blood pressure -0.058 0.760 Fasting plasma glucose 0.131 0.694 Triglyceride concentration 0.724 0.223 HDL cholesterol 0.827 -0.106
Women 0.698 <0.001 56.654 (factor 1: 27.750, factor 2: 26.903) Factor loading Factor 1 Factor 2 0.604 0.358 0.730 0.019 0.709 0.078 0.292 0.678 -0.039 0.867
Table 5. Relationship between factor score and serum ALP quartiles Overall P-value Q1
Q2
Q3
Q4
Post-hoc P-value Q1 vs Q2 Q1 vs Q3
Q1 vs Q4 Q2 vs Q3 Q2 vs Q4 Q3 vs Q4 Men Factor 1 -0.121±0.955 0.001±0.996 0.041±1.015 0.076±1.022 <0.001 <0.001 <0.001 <0.001 0.235 0.025 0.295 Factor 2 -0.020±0.995 -0.027±0.984 -0.045±0.989 0.091±1.026 <0.001 0.825 0.450 0.001 0.590 <0.001 <0.001 Women Factor 1 -0.393±0.773 -0.169±0.905 0.109±1.030 0.439±1.061 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 Factor 2 -0.183±0.883 -0.085±0.982 0.018±1.007 0.244±1.066 <0.001 0.001 <0.001 <0.001 <0.001 <0.001 <0.001 The formula of factor 1 was 0.230xwaist-0.176xBP-0.022xFPG+0.507xTG+0.643x HDL-C and factor 2 was 0.289xwaist+0.614xBP+0.528xFPG+0.051xTG-0.229xHDL-C in men. The formula of factor 1 was is 0.362xwaist+0.541xBP+0.511xFPG+0.049xTG-0.246xHDL-C and factor 2 was 0.145xwaist-0.167xBP-0.112xFPG+0.487xTG+0.727xHDL-C in women.
23
24 ●
Serum alkaline phosphatase (ALP), a useful marker of hepatobiliary or bone disorder, has recently
emerged as a biomarker of chronic low-grade inflammation and cardiometabolic disease. ●
This study aimed to examine the association of serum ALP level with metabolic syndrome (MetS) in
apparently healthy adults. ●
Serum ALP level was positively and independently associated with MetS, suggesting that serum ALP
level may be a useful additional measure in assessment of MetS.