ABSTRACT
Background
To investigate the association of serum alkaline phosphatase (ALP) with incident metabolic syndrome (MetS) and its components, as well as the influence of sex on this relationship among Iranian adults.
Methods
The multivariable Cox proportional hazards regression models were applied to assess the associations between ALP both as continuous and categorical variables with incident MetS and its components.
Results
Among 831 subjects (467 women) with a mean age of 44.51 years, during a median follow‐up of 15.6 years, 597 MetS cases (336 women) occurred. Interaction was found between ALP quartiles and sex (p‐value = 0.006). Among women, increasing levels of ALP across the second to fourth quartiles were associated with hazard ratios (HRs) of 1.269, 1.491, and 2.092 for MetS, respectively (p for trend < 0.001). Among men, no association was found between ALP and incident MetS. Among women, the second and fourth quartiles of ALP were associated with incident high triglycerides (TG), with HRs of 1.793 and 1.815, respectively. Moreover, a 1‐SD increase in ALP conferred a 17.9% higher risk of low high‐density lipoprotein cholesterol (HDL‐C). Among men, a 1‐SD increase in ALP was associated with an HR of 1.222 for incident high waist circumference (WC) (All p‐values < 0.05).
Conclusion
Sex significantly influenced the impact of serum ALP on the incidence of MetS and its components. In women, ALP was a strong harbinger for incident MetS and its dyslipidemia components. However, among men, the increasing value of ALP was associated with incident central obesity but not MetS.
Keywords: alkaline phosphatase, components, metabolic syndrome, sex differences
In this prospective study among Iranian adults over a 15‐year follow‐up, we found that serum alkaline phosphatase (ALP) levels were significantly associated with the risk of developing metabolic syndrome (MetS) and its components in a sex‐specific manner. Higher ALP was associated with a heightened risk of MetS and dyslipidemia in women, whereas in men, it was associated mainly with increased central obesity but not MetS. These findings highlight ALP as a simple biomarker with different predictive values across sexes for cardiometabolic risk.
1. Introduction
Metabolic syndrome (MetS) is a complex of impaired metabolic factors, including central obesity, elevated fasting plasma glucose (FPG), high blood pressure (BP), increased serum level of triglycerides (TG), and reduced high‐density lipoprotein cholesterol (HDL‐C) as first described by Reaven [1]. MetS is increasingly becoming a global public health problem that increases the risk of non‐communicable diseases (NCDs) such as cardiovascular diseases (CVD), type 2 diabetes mellitus (T2DM), stroke, and cardiovascular mortality [2, 3]. Comparable to other countries in the Middle East and North Africa (MENA) region, Iran has a high prevalence of MetS [4, 5, 6, 7, 8]. In 2016, a nationwide study in Iran showed that the prevalence of MetS was 38.3% among adults above 18 years, with a higher prevalence in urban areas [9]. Additionally, every year, in the adult population over 20 years of age in Iran, about 5% of people are diagnosed with MetS, an issue that is majorly related to progressive diet westernization and low physical activity [10].
The associations of liver function disorders manifested by elevated liver enzymes, i.e., alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma‐glutamyltransferase (GGT) with MetS have been evaluated in a few prospective studies [11, 12, 13].
Prior studies have suggested that ALP, which is generally known as a clinical biomarker of bone and hepatobiliary disorders, is associated with NCDs, including insulin resistance (IR) [14], T2DM [15], high BP [16], and MetS [11], with inconsistent findings. To our knowledge, the associations between serum ALP level and incident MetS and its components have been examined only in very few studies conducted among the American and Korean populations [11, 13].
The purpose of the current study was to examine the association of ALP, an available simple biomarker, with incident MetS and its components within a population‐based cohort of Iranian adults in a region with a high burden of cardiometabolic risk factors [17].
2. Methods
2.1. Study Population
The current prospective study is conducted within the Tehran Lipid and Glucose Study (TLGS) framework, a longitudinal population‐based study consisting of a representative sample of the Tehranian urban population aged ≥ 3 years and aimed to assess the prevalence and incidence of NCDs, inquiring about their risk factors, and implementing a healthy lifestyle. As per the TLGS design, the participants' enrollment was performed using the multistage cluster random sampling method and carried out in two phases: the first phase [1999–2001, n = 15,005], the second phase [2001–2005, n = 3550], and triennial follow‐ups were performed thereafter [(2005–2008), (2008–2011), (2011–2015), and (2015–2018)]. The details of the TLGS design and methodology have been reported elsewhere [18, 19].
Participants of the first phase of the TLGS aged ≥ 21 years with available ALP data were enrolled in the current study (n = 3619). Those with a history of cancer (n = 21) and missing data of MetS components (n = 140) or confounders (n = 24) were excluded, leaving 3434 eligible participants. Additional exclusions were those with prevalent outcomes at the baseline and subjects without any follow‐up (Figure 1).
FIGURE 1.
Flowchart of the study population. TLGS, Tehran Lipid and Glucose Study; ALP, alkaline phosphatase; n, number; MetS, metabolic syndrome; WC, waist circumference; BP, blood pressure; FPG, fasting plasma glucose; TG, triglycerides; HDL‐C, high density lipoprotein cholesterol.
2.2. Patient and Public Involvement
Participants were not involved in setting of the research agenda.
2.3. Data Collection (Clinical, Anthropometric and Laboratory Measurements)
To gather information on demographics, educational level, smoking status, medications, history of CVD, and family history of type 2 diabetes mellitus (FH‐DM), a pre‐tested questionnaire was completed by a trained interviewer. Also, the level of physical activity was evaluated using the Lipid Research Clinic (LRC) questionnaire [20]. Details of anthropometric measurements and blood pressure assessments were reported elsewhere [18, 21]. Waist circumference (WC) was measured at the level of the umbilicus by a tape meter. Body mass index (BMI) was calculated as weight in kg divided by the square of height in meters.
The participants had a 12–14 h overnight fasting before collection of a venous blood sample between 7 and 9 AM. For determination of FPG and TG concentration, enzymatic colorimetric methods were used. HDL‐C was measured using the same method after precipitation of the apolipoprotein‐B‐containing lipoproteins. The serum creatinine level was assayed by the Jaffe kinetic calorimetric method. Levels of serum ALP were measured using the DGKC (Deutsche Gesells chaftfür Klinische Chemie) method. All biochemical analyses were performed in the TLGS research laboratory using commercial kits (Pars Azmoon Inc., Iran) and a Selectra 2 auto‐analyzer (Vital Scientific, The Netherlands). Lyophilized serum controls in two different concentrations (TruLab N and TruLab P; Pars Azmoun Company) were used to monitor the performance of assays. All intra‐ and inter‐assay coefficients of variations (CVs) were less than 3.0%.
2.4. Definition of Terms
To define MetS and its components as the main outcomes, the criteria of the Joint Interim Statement of the International Diabetes Federation Task Force on Epidemiology and Prevention were used [22]. Subjects who had at least three of the following criteria were diagnosed as cases of MetS: (a) elevated WC (≥ 90 cm for both genders, the defined cut‐off to identify the Iranian population at risk of CVD risk factors that require lifestyle change) [23], (b) elevated BP (≥ 130/85 mmHg or using anti‐hypertensive medication), (c) elevated FPG (≥ 5.6 mmol/L or use of glucose‐lowering drugs), (d) elevated TG (≥ 1.7 mmol/L or use of lipid‐lowering drugs), and (e) low HDL‐C (< 1.03 mmol/L and < 1.29 mmol/L for men and women, respectively).
The estimated glomerular filtration rate (eGFR), expressed as mL/min/1.73 m2, was calculated using the abbreviated prediction equation provided by the Chronic Kidney Disease Epidemiology Collaboration (CKD‐EPI) study [24].
A positive FH‐DM was defined as the presence of T2DM in at least one parent or sibling of the participants. Subjects who had previous ischemic heart disease and/or cerebrovascular events were considered to have a positive history of CVD.
The smoking status was categorized into two groups: current smoker (daily or occasional use of cigarettes or other smoking implements) and never/past smoker (never smoker or quitting smoking for at least 1 year before study entry). Regarding physical activity, participants who had vigorous physical activity at least three days per week were considered physically active [20]. Self‐reported educational status was classified into three categories: less than 6 years (reference group), 6–12 years, and more than 12 years of schooling. For marital status, three groups were considered as single (reference group), married, and widowed/divorced.
2.5. Statistical Analysis
Baseline characteristics of the study participants were reported as mean [standard deviation (SD)] for continuous variables and frequency (percentage) for categorical variables. Baseline characteristics according to quartiles of serum ALP concentrations were compared using one‐way analysis of variance (ANOVA) and Chi‐squared tests as appropriate.
Cox proportional hazards regression models were applied to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between serum ALP and incident MetS and each of its components. Censoring was defined as leaving the residence area, death, loss to follow‐up, or end of follow‐up. The event date for the incident cases of MetS and its components was defined as mid‐time between the date of follow‐up visit at which the outcomes were diagnosed for the first time and the most recent follow‐up visit prior to the diagnosis; hence, the follow‐up time was defined as the difference between the calculated mid‐time date and the date at which the subjects entered the study. For those without outcome (censored subjects), the survival time was the interval between the first and the last observation dates.
Considering MetS as the main outcome, two models were run: model 1, adjusted for age and sex (in the whole population), and model 2, further adjusted for BMI, eGFR, smoking status, physical activity level, education, marital status, history of CVD, and FH‐DM. Considering each MetS component as the outcome, a third model was also run that further adjusted for other MetS components. All analyses were run in two manners, across serum ALP quartiles and per 1‐SD increase in serum ALP. Since we found a significant interaction between ALP quartiles and sex for incident MetS (p‐value = 0.006), our analyses were performed in a sex‐stratified manner; moreover, to compare our findings with other studies in this field, we also performed our data analyses in the whole population as well. The proportional hazards assumptions in the Cox models were assessed both graphically and using the Schoenfeld residual test. Generally, all proportionality assumptions were appropriate. We set the statistical significance level at a two‐tailed type I error of < 0.05 and used Stata version 17 (StataCorp LLC, College Station, TX, USA) and SPSS version 20.0 (SPSS Inc., Chicago, IL, USA) for all statistical analyses.
2.6. Ethics Statement
The Institutional Review Board of the Research Institute for Endocrine Sciences (RIES), Shahid Beheshti University of Medical Sciences, Tehran, Iran, approved the current study (approval ID: IR.SBMU.ENDOCRINE.REC.1402.061, Date: 5 November 2023). All participants read and signed written informed consent.
3. Results
The study population consisted of 831 subjects (467 women) with a mean age of 44.51 (SD: 12.98) years. The baseline demographic, anthropometric, and biochemical characteristics of the study participants by serum ALP quartiles in each sex are presented in Tables 1 and 2 for women and men, respectively. Generally, compared to the lowest quartiles of serum ALP, women in the fourth quartile were older and had higher values of WC, SBP, DBP, and lower values of eGFR. In addition, the proportion of those with a history of CVD and subjects with less than 6 years of education was higher in the fourth quartile. In men, those in the fourth quartile had higher values of FPG and a higher proportion of current smokers compared to the first quartile.
TABLE 1.
Baseline characteristics of women by serum alkaline phosphatase quartiles: Tehran Lipid and Glucose Study.
| Variables | Serum alkaline phosphatase quartiles (U/L) | p | |||
|---|---|---|---|---|---|
| Q1 (< 157) | Q2 (157–189) | Q3 (189–234) | Q4 (≥ 234) | ||
| n = 116 | n = 116 | n = 116 | n = 119 | ||
| Age (year) | 38.35 ± 9.11 | 42.33 ± 11.73 | 48.68 ± 13.80 | 50.02 ± 12.26 | < 0.001 |
| WC (cm) | 83.12 ± 9.65 | 85.23 ± 8.23 | 87.42 ± 10.00 | 88.0168 ± 9.58 | < 0.001 |
| BMI (kg/m2) | 26.69 ± 3.95 | 26.91 ± 3.59 | 27.29 ± 4.06 | 27.30 ± 3.89 | 0.555 |
| SBP (mmHg) | 110.96 ± 12.16 | 114.41 ± 13.46 | 115.66 ± 14.49 | 121.17 ± 19.87 | < 0.001 |
| DBP (mmHg) | 73.84 ± 7.98 | 75.34 ± 7.62 | 75.78 ± 8.25 | 78.21 ± 11.147 | 0.002 |
| FPG (mmol/L) | 4.87 ± 0.66 | 4.96 ± 1.46 | 5.16 ± 1.33 | 5.19 ± 1.67 | 0.194 |
| TG (mmol/L) | 2.08 ± 1.14 | 2.00 ± 0.92 | 1.97 ± 1.11 | 1.93 ± 1.04 | 0.739 |
| HDL‐C (mmol/L) | 1.23 ± 0.30 | 1.23 ± 0.32 | 1.31 ± 0.33 | 1.30 ± 0.33 | 0.104 |
| ALP (U/L) | 134.88 ± 15.69 | 171.51 ± 9.34 | 208.86 ± 12.63 | 291.19 ± 73.96 | < 0.001 |
| eGFR (mL/min/1.73 m2) | 77.38 ± 11.73 | 74.43 ± 13.45 | 72.55 ± 14.01 | 72.43 ± 13.83 | 0.015 |
| Smoking Status | 0.244 | ||||
| Never/Past | 113 (97.4) | 113 (97.4) | 109 (94.0) | 117 (98.3) | |
| Current | 3 (2.6) | 3 (2.6) | 7 (6.0) | 2 (1.7) | |
| Education | < 0.001 | ||||
| < 6 years | 24 (20.7) | 43 (37.1) | 63 (54.3) | 69 (58.0) | |
| 6–12 years | 74 (63.8) | 63 (54.3) | 44 (37.9) | 44 (37.0) | |
| > 12 years | 18 (15.5) | 10 (8.6) | 9 (7.8) | 6 (5.0) | |
| Marital status | 0.005 | ||||
| Single | 12 (10.3) | 5 (4.3) | 11 (9.5) | 4 (3.4) | |
| Married | 97 (83.6) | 107 (92.2) | 89 (76.7) | 99 (83.2) | |
| Divorced/Widowed | 7 (6.0) | 4 (3.4) | 16 (13.8) | 16 (13.4) | |
| Low physical activity (Yes) | 82 (70.7) | 85 (73.3) | 79 (68.1) | 90 (75.6) | 0.606 |
| History of CVD (Yes) | 0 (0.0) | 1 (0.9) | 7 (6.0) | 5 (4.2) | 0.016 |
| FH‐DM (Yes) | 32 (27.6) | 37 (31.9) | 32 (27.6) | 34 (28.6) | 0.871 |
Note: Data are shown as mean ± SD or number (percent) for categorical variables.
Abbreviations: ALP, Alkaline phosphatase; BMI, body mass index; CVD, cardiovascular disease; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FH‐DM, family history of type 2 diabetes mellitus; FPG, fasting plasma glucose; HDL‐C, high density lipoprotein cholesterol; SBP, systolic blood pressure; SD, standard deviation; TG, triglycerides; WC, waist circumference.
TABLE 2.
Baseline characteristics of men by serum alkaline phosphatase quartiles: Tehran Lipid and Glucose Study.
| Variables | Serum alkaline phosphatase quartiles (U/L) | p | |||
|---|---|---|---|---|---|
| Q1 (< 167) | Q2 (167–195) | Q3 (195–230) | Q4 (≥ 230) | ||
| n = 90 | n = 87 | n = 95 | n = 92 | ||
| Age (year) | 44.54 ± 13.16 | 44.77 ± 13.69 | 43.15 ± 12.94 | 43.80 ± 13.61 | 0.839 |
| WC (cm) | 85.76 ± 8.65 | 86.51 ± 7.95 | 86.73 ± 8.84 | 86.00 ± 7.65 | 0.850 |
| BMI (kg/m2) | 24.80 ± 2.98 | 25.25 ± 3.09 | 25.33 ± 3.53 | 24.59 ± 3.19 | 0.341 |
| SBP (mmHg) | 116.10 ± 14.18 | 115.67 ± 15.21 | 114.83 ± 13.68 | 114.70 ± 9.79 | 0.874 |
| DBP (mmHg) | 75.01 ± 8.32 | 74.47 ± 7.94 | 75.97 ± 9.82 | 76.50 ± 6.82 | 0.353 |
| FPG (mmol/L) | 4.92 ± 0.50 | 5.40 ± 1.96 | 5.02 ± 0.57 | 5.08 ± 0.93 | 0.036 |
| TG (mmol/L) | 2.45 ± 1.26 | 2.45 ± 1.19 | 2.82 ± 1.83 | 2.89 ± 2.10 | 0.142 |
| HDL‐C (mmol/L) | 1.11 ± 0.26 | 1.11 ± 0.28 | 1.06 ± 0.26 | 1.03 ± 0.24 | 0.058 |
| ALP (U/L) | 142.87 ± 22.53 | 179.02 ± 8.95 | 209.66 ± 10.01 | 269.07 ± 36.85 | < 0.001 |
| eGFR (mL/min/1.73 m2) | 77.94 ± 14.63 | 80.52 ± 12.85 | 80.76 ± 14.42 | 82.73 ± 14.01 | 0.148 |
| Smoking Status | < 0.001 | ||||
| Never/Past | 74 (82.2) | 57 (65.5) | 63 (66.3) | 45 (48.9) | |
| Current | 16 (17.8) | 30 (34.5) | 32 (33.7) | 47 (51.1) | |
| Education | 0.162 | ||||
| < 6 years | 14 (15.6) | 24 (27.6) | 21 (22.1) | 20 (21.7) | |
| 6–12 years | 53 (58.9) | 48 (55.2) | 62 (65.3) | 59 (64.1) | |
| > 12 years | 23 (25.6) | 15 (17.2) | 12 (12.6) | 13 (14.1) | |
| Marital status | 0.763 | ||||
| Single | 11 (12.2) | 10 (11.5) | 12 (12.6) | 9 (9.8) | |
| Married | 79 (87.8) | 77 (88.5) | 83 (87.4) | 82 (89.1) | |
| Divorced/Widowed | 0 (0.0) | 0 (0.0) | 0 (0.0) | 1 (1.1) | |
| Low physical activity (Yes) | 70 (77.8) | 64 (73.6) | 80 (84.2) | 77 (83.7) | 0.225 |
| History of CVD (Yes) | 6 (6.7) | 6 (6.9) | 4 (4.2) | 5 (5.4) | 0.854 |
| FH‐DM (Yes) | 33 (36.7) | 24 (27.6) | 20 (21.1) | 20 (21.7) | 0.062 |
Note: Data are shown as mean ± SD or number (percent) for categorical variables.
Abbreviations: ALP, Alkaline phosphatase; BMI, body mass index; CVD, cardiovascular disease; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FH‐DM, family history of type 2 diabetes mellitus; FPG, fasting plasma glucose; HDL‐C, high density lipoprotein cholesterol; SBP, systolic blood pressure; SD, standard deviation; TG, triglycerides; WC, waist circumference.
The median follow‐up time was 15.6 years [interquartile range (IQR): 12.9–16.7]. Moreover, the median time to incident MetS [n = 597 (467 women)] was 6.93 years (IQR: 8.21–914). Figure 2 illustrates the cumulative incidence of MetS stratified by baseline ALP quartiles among women, men, and the whole population. Accordingly, the increasing value of ALP level was accompanied by a higher incidence of MetS in the whole population as well as women (p for log‐rank test < 0.0001) but not men.
FIGURE 2.
The cumulative incidence of metabolic syndrome (MetS) according to the baseline serum alkaline phosphatase (ALP) quartiles among women, men, and the whole population.
Figure 3 and Table S1 show the HRs for incident MetS by quartiles and per 1‐SD increase in serum ALP levels among the whole population, women, and men. Investigating the associations between serum ALP quartiles and incident MetS in the multivariate model showed that among women, the increment in the serum ALP levels across the quartiles was associated with incident MetS; the second, third, and fourth quartiles of ALP were associated with HRs of 1.269, 1.491, and 2.092 for incident MetS (p for trend < 0.001) (Model 2). Moreover, a 1‐SD increase in serum ALP was associated with a 19.5% higher risk of incident MetS (HR: 1.195, 95% CI: 1.094–1.305). Among men, no significant association was found between increasing values of serum ALP and incident MetS, even in model 1. The result of the association between ALP levels and MetS incidence among the whole population was in line with the findings among women.
FIGURE 3.
Risk of incident metabolic syndrome according to serum alkaline phosphatase. (A) as categorical variable (first quartile as reference), (B) as continuous variable (per 1 SD increase); Hazard ratios are adjusted for age, sex (for whole population), body mass index, estimated glomerular filtration rate, smoking, physical activity, education, marital status, history of cardiovascular diseases, and family history of diabetes mellitus.
The associations between serum ALP levels, whether as a categorical or continuous variable, and the incidence of each of the MetS components among women, men, and the whole population are shown in Tables 3, 4, 5, respectively. Among women (Table 3), we found the second and fourth quartiles of ALP were significantly associated with incident high TG component; the corresponding HRs were 1.793 and 1.815, respectively (p‐value = 0.010). Moreover, the third and fourth quartiles of ALP were also associated with incident low HDL‐C component; the corresponding HRs were 1.839 and 1.529, respectively (p‐value = 0.003). Moreover, a 1‐SD increase in ALP was associated with a 17.9% higher risk of low HDL‐C (1.179, 95% CI: 1.040–1.336). Among men (Table 4), increasing values of serum ALP levels were associated with high WC (p for trends 0.008). The fourth quartile of serum ALP showed a 73.0% (HR 1.730, 1.226–2.442) higher risk of incident high WC compared to the first quartile. Moreover, a 1‐SD increase in serum ALP among men resulted in a 22.2% and a 13.0% higher risk of high WC and high FPG, respectively. Although for the latter, we did not find a significant trend of increased risk across quartiles.
TABLE 3.
HRs (95% CIs) for incident MetS components by quartiles of ALP in women: Tehran Lipid and Glucose Study.
| Quartiles of alkaline phosphatase (U/L) | p for trend | Per 1 SD increase in ALP | ||||
|---|---|---|---|---|---|---|
| High WC | Q1 (< 157), n = 131 | Q2 (157–192), n = 133 | Q3 (192–236), n = 132 | Q4 (≥ 236), n = 133 | ||
| Model 1 | Reference | 1.119 (0.842–1.486) | 0.799 (0.589–1.084) | 1.017 (0.751–1.376) | 0.135 | 0.943 (0.846–1.052) |
| Model 2 | Reference | 1.005 (0.754–1.340) | 0.733 (0.540–0.994) | 0.982 (0.721–1.339) | 0.100 | 0.990 (0.880–1.114) |
| Model 3 | Reference | 1.009 (0.757–1.346) | 0.739 (0.545–1.004) | 0.986 (0.722–1.346) | 0.116 | 0.991 (0.880–1.115) |
| High BP | Q1 (< 158), n = 179 | Q2 (158–191), n = 187 | Q3 (191–232), n = 193 | Q4 (≥ 232), n = 190 | ||
| Model 1 | Reference | 1.287 (0.966–1.715) | 1.213 (0.907–1.621) | 1.231 (0.919–1.648) | 0.359 | 1.016 (0.928–1.112) |
| Model 2 | Reference | 1.235 (0.924–1.652) | 1.151 (0.857–1.545) | 1.207 (0.901–1.617) | 0.509 | 1.020 (0.929–1.119) |
| Model 3 | Reference | 1.243 (0.929–1.664) | 1.139 (0.848–1.531) | 1.203 (0.896–1.614) | 0.492 | 1.025 (0.934–1.126) |
| High FPG | Q1 (< 160), n = 237 | Q2 (160–197), n = 254 | Q3 (197–239), n = 248 | Q4 (≥ 239), n = 247 | ||
| Model 1 | Reference | 0.783 (0.610–1.005) | 0.907 (0.706–1.166) | 0.948 (0.735–1.222) | 0.245 | 1.010 (0.927–1.101) |
| Model 2 | Reference | 0.749 (0.583–0.961) | 0.873 (0.678–1.123) | 0.926 (0.719–1.192) | 0.131 | 1.014 (0.930–1.106) |
| Model 3 | Reference | 0.710 (0.551–0.914) | 0.849 (0.659–1.093) | 0.880 (0.682–1.136) | 0.063 | 1.002 (0.919–1.093) |
| High TG | Q1 (< 160), n = 78 | Q2 (160–197), n = 75 | Q3 (197–240), n = 83 | Q4 (≥ 240), n = 78 | ||
| Model 1 | Reference | 1.656 (1.121–2.447) | 1.318 (0.882–1.968) | 1.654 (1.109–2.467) | 0.039 | 1.076 (0.961–1.203) |
| Model 2 | Reference | 1.821 (1.225–2.708) | 1.296 (0.857–1.961) | 1.862 (1.223–2.835) | 0.007 | 1.103 (0.980–1.241) |
| Model 3 | Reference | 1.793 (1.202–2.675) | 1.255 (0.826–1.909) | 1.815 (1.182–2.785) | 0.010 | 1.062 (0.944–1.196) |
| Low HDL‐C | Q1 (< 170), n = 86 | Q2 (170–205), n = 88 | Q3 (205–247), n = 86 | Q4 (≥ 247), n = 88 | ||
| Model 1 | Reference | 1.207 (0.856–1.702) | 2.010 (1.421–2.843) | 1.648 (1.166–2.330) | < 0.001 | 1.197 (1.064–1.348) |
| Model 2 | Reference | 1.209 (0.850–1.720) | 2.028 (1.426–2.882) | 1.599 (1.125–2.272) | < 0.001 | 1.193 (1.058–1.346) |
| Model 3 | Reference | 1.135 (0.794–1.623) | 1.839 (1.284–2.635) | 1.529 (1.068–2.190) | 0.003 | 1.179 (1.040–1.336) |
Note: Model 1: adjusted for age. Model 2: model 1 + adjusted for BMI, eGFR, smoking, physical activity, education, marital status, history of CVD, and FH‐DM. Model 3: model 2 + adjusted for other MetS components.
Abbreviations: ALP, alkaline phosphatase; BMI, body mass index; BP, blood pressure; CI, confidence interval; CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate; FH‐DM, family history of type 2 diabetes mellitus; FPG, fasting plasma glucose; HDL‐C, high density lipoprotein cholesterol; HR, hazard ratio; MetS, metabolic syndrome; n, number; Q, quartile; SD, standard deviation; TG, triglycerides; U/L, unit per liter; WC, waist circumference.
TABLE 4.
HRs (95% CIs) for incident MetS components by quartiles of ALP in men: Tehran Lipid and Glucose Study.
| Quartiles of alkaline phosphatase (U/L) | p for trend | Per 1 SD increase in ALP | ||||
|---|---|---|---|---|---|---|
| High WC | Q1 (< 169), n = 96 | Q2 (169–198), n = 102 | Q3 (198–234), n = 99 | Q4 (≥ 234), n = 100 | ||
| Model 1 | Reference | 1.151 (0.826–1.604) | 1.318 (0.950–1.829) | 1.550 (1.122–2.141) | 0.049 | 1.163 (1.041–1.299) |
| Model 2 | Reference | 1.091 (0.780–1.528) | 1.423 (1.013–1.999) | 1.697 (1.212–2.376) | 0.007 | 1.210 (1.072–1.364) |
| Model 3 | Reference | 1.085 (0.773–1.524) | 1.366 (0.959–1.948) | 1.730 (1.226–2.442) | 0.008 | 1.222 (1.080–1.383) |
| High BP | Q1 (< 167), n = 168 | Q2 (167–198), n = 169 | Q3 (198–231), n = 175 | Q4 (≥ 231), n = 172 | ||
| Model 1 | Reference | 1.003 (0.761–1.321) | 0.882 (0.667–1.166) | 0.881 (0.666–1.166) | 0.657 | 1.032 (0.860–1.054) |
| Model 2 | Reference | 1.043 (0.787–1.380) | 0.906 (0.684–1.201) | 0.949 (0.710–1.268) | 0.779 | 0.870 (0.875–1.075) |
| Model 3 | Reference | 1.042 (0.786–1.382) | 0.895 (0.674–1.188) | 0.941 (0.703–1.259) | 0.732 | 0.966 (0.870–1.072) |
| High FPG | Q1 (< 167), n = 201 | Q2 (167–197), n = 201 | Q3 (197–230), n = 204 | Q4 (≥ 230), n = 202 | ||
| Model 1 | Reference | 1.013 (0.772–1.330) | 1.372 (1.059–1.777) | 1.161 (0.892–1.510) | 0.056 | 1.137 (1.038–1.246) |
| Model 2 | Reference | 0.980 (0.745–1.290) | 1.286 (0.990–1.671) | 1.152 (0.880–1.508) | 0.140 | 1.140 (1.037–1.253) |
| Model 3 | Reference | 0.966 (0.734–1.273) | 1.300 (1.001–1.690) | 1.122 (0.857–1.469) | 0.109 | 1.130 (1.028–1.244) |
| High TG | Q1 (< 164), n = 33 | Q2 (164–185), n = 35 | Q3 (185–218), n = 34 | Q4 (≥ 218), n = 35 | ||
| Model 1 | Reference | 0.701 (0.384–1.280) | 0.848 (0.460–1.560) | 0.583 (0.311–1.092) | 0.360 | 0.827 (0.667–1.025) |
| Model 2 | Reference | 0.587 (0.311– 1.108) | 0.858 (0.446–1.652) | 0.459 (0.226–0.930) | 0.114 | 0.796 (0.626–1.012) |
| Model 3 | Reference | 0.622 (0.322–1.201) | 0.887 (0.460–1.709) | 0.458 (0.226–0.930) | 0.123 | 0.772 (0.604–0.987) |
| Low HDL‐C | Q1 (< 164), n = 79 | Q2 (164–192), n = 82 | Q3 (192–227), n = 83 | Q4 (≥ 227), n = 83 | ||
| Model 1 | Reference | 1.034 (0.712–1.500) | 1.034 (0.714–1.498) | 1.334 (0.929–1.917) | 0.353 | 1.115 (0.982–1.266) |
| Model 2 | Reference | 1.031 (0.697–1.523) | 1.021 (0.690–1.511) | 1.290 (0.880–1.891) | 0.485 | 1.105 (0.967–1.263) |
| Model 3 | Reference | 1.079 (0.729–1.596) | 1.005 (0.679–1.486) | 1.203 (0.822–1.761) | 0.739 | 1.070 (0.934–1.225) |
Note: Model 1: adjusted for age. Model 2: model 1 + adjusted for BMI, eGFR, smoking, physical activity, education, marital status, history of CVD, and FH‐DM. Model 3: model 2 + adjusted for other MetS components.
Abbreviations: ALP, alkaline phosphatase; BMI, body mass index; BP, blood pressure; CI, confidence interval; CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate; FH‐DM, family history of type 2 diabetes mellitus; FPG, fasting plasma glucose; HDL‐C, high density lipoprotein cholesterol; HR, hazard ratio; MetS, metabolic syndrome; n, number; Q, quartile; SD, standard deviation; TG, triglycerides; U/L, unit per liter; WC, waist circumference.
TABLE 5.
HRs (95% CIs) for incident MetS components by quartiles of ALP in the whole population.
| Quartiles of alkaline phosphatase (U/L) | p for trend | Per 1 SD increase in ALP | ||||
|---|---|---|---|---|---|---|
| High WC | Q1 (< 161), n = 224 | Q2 (161–195), n = 233 | Q3 (195–234), n = 232 | Q4 (≥ 234), n = 237 | ||
| Model 1 | Reference | 1.090 (0.877–1.353) | 1.051 (0.844–1.309) | 1.296 (1.045–1.606) | 0.088 | 1.057 (0.985–1.136) |
| Model 2 | Reference | 0.991 (0.797–1.233) | 0.953 (0.763–1.191) | 1.283 (1.030–1.598) | 0.025 | 1.093 (1.012–1.181) |
| Model 3 | Reference | 1.001 (0.804–1.245) | 0.961 (0.767–1.203) | 1.276 (1.022–1.593) | 0.037 | 1.089 (1.008–1.177) |
| High BP | Q1 (< 163) n = 357 | Q2 (163–195) n = 350 | Q3 (195–232) n = 367 | Q4 (≥ 232) n = 359 | ||
| Model 1 | Reference | 1.191 (0.978–1.450) | 1.064 (0.873–1.297) | 1.082 (0.887–1.320) | 0.368 | 1.000 (0.936–1.070) |
| Model 2 | Reference | 1.157 (0.948–1.412) | 1.031 (0.845–1.259) | 1.088 (0.889–1.331) | 0.480 | 1.005 (0.939–1.076) |
| Model 3 | Reference | 1.157 (0.948–1.413) | 1.020 (0.835–1.246) | 1.071 (0.875–1.311) | 0.459 | 1.002 (0.936–1.073) |
| High FPG | Q1 (< 163), n = 436 | Q2 (163–197), n = 457 | Q3 (197–234), n = 445 | Q4 (≥ 234), n = 456 | ||
| Model 1 | Reference | 0.918 (0.764–1.102) | 1.155 (0.966–1.382) | 1.047 (0.874–1.254) | 0.082 | 1.052 (0.992–1.016) |
| Model 2 | Reference | 0.881 (0.732–1.059) | 1.110 (0.927–1.330) | 1.025 (0.854–1.230) | 0.086 | 1.059 (0.997–1.126) |
| Model 3 | Reference | 0.858 (0.713–1.033) | 1.101 (0.919–1.319) | 0.989 (0.823–1.188) | 0.058 | 1.047 (0.985–1.113) |
| High TG | Q1 (< 163), n = 112 | Q2 (163–194), n = 109 | Q3 (194–234), n = 115 | Q4 (≥ 234), n = 115 | ||
| Model 1 | Reference | 1.294 (0.934–1.794) | 1.053 (0.750–1.480) | 1.398 (1.011–1.934) | 0.119 | 1.037 (0.936–1.148) |
| Model 2 | Reference | 1.294 (0.930–1.803) | 1.033 (0.732–1.458) | 1.426 (1.017–2.001) | 0.097 | 1.043 (0.937–1.161) |
| Model 3 | Reference | 1.288 (0.924–1.795) | 1.001 (0.708–1.415) | 1.372 (0.973–1.933) | 0.127 | 1.013 (0.909–1.129) |
| Low HDL‐C | Q1 (< 165), n = 165 | Q2 (165–199), n = 168 | Q3 (199–237), n = 172 | Q4 (≥ 237), n = 170 | ||
| Model 1 | Reference | 1.009 (0.783–1.300) | 1.388 (1.084–1.778) | 1.437 (1.123–1.839) | 0.002 | 1.159 (1.064–1.263) |
| Model 2 | Reference | 0.983 (0.761–1.271) | 1.369 (1.064–1.763) | 1.387 (1.079–1.783) | 0.004 | 1.147 (1.051–1.251) |
| Model 3 | Reference | 0.977 (0.755–1.263) | 1.304 (1.010–1.683) | 1.337 (1.037–1.723) | 0.019 | 1.129 (1.032–1.236) |
Note: Model 1: adjusted for age and sex. Model 2: model 1 + adjusted for BMI, eGFR, smoking, physical activity, education, marital status, history of CVD, and FH‐DM. Model 3: model 2 + adjusted for other MetS components.
Abbreviations: ALP, alkaline phosphatase; BMI, body mass index; BP, blood pressure; CI, confidence interval; CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate; FH‐DM, family history of type 2 diabetes mellitus; FPG, fasting plasma glucose; HDL‐C, high density lipoprotein cholesterol; HR, hazard ratio; MetS, metabolic syndrome; n, number; Q, quartile; SD, standard deviation; TG, triglycerides; U/L, unit per liter; WC, waist circumference.
Interestingly, in contrast to women, among men (p for interaction < 0.05), increasing values of ALP were associated with a lower risk of incident high TG component that did not reach a significant level. Moreover, the fourth quartile of ALP was associated with a 55% lower risk of incident high TG component. In the whole population (Table 5), increasing values of serum ALP levels were associated with high WC and low HDL‐C (p for trends 0.037 and 0.019, respectively). The highest quartile of serum ALP (ALP > 232 U/L) was associated with HRs of 1.276 and 1.337 for incident high WC and low HDL‐C, respectively. Moreover, a 1‐SD increase in ALP levels was associated with higher risks of high WC [HR: 1.089 (1.008–1.177)] and low HDL‐C [HR: 1.129 (1.032–1.236)].
As a sensitivity analysis, we repeated the analyses with WC cut‐off values provided by the National Cholesterol Education Program Adult Treatment Panel III (NCEP‐ATP‐III) (in women: ≥ 88 cm, in men: ≥ 102 cm) [25]. The results remained essentially unchanged. Accordingly, a 1‐SD increase in serum ALP was significantly associated with a higher risk of MetS among women (HR: 1.21, 95% CI: 1.10–1.34) and the whole population (HR: 1.15, 1.07–1.23) but not for men (HR: 1.05, 0.95–1.17). Moreover, among the whole population as well as men, a 1‐SD increase in serum ALP was associated with abdominal obesity applying NCEP‐ATP‐III cut‐off values with HRs of 1.09 (1.02–1.17), and 1.15 (1.04–1.28), respectively. The relationship between serum ALP and other MetS components was not affected using alternative WC cut‐off points (data not shown).
4. Discussion
In this study conducted within the framework of TLGS, we assessed the association of the increase in serum levels of ALP with the incidence of MetS and its components, independent of a large set of covariates, including age, general adiposity, eGFR, smoking, physical activity, educational level, marital status, history of CVD, and FH‐DM. Accordingly, we found that the relationship between serum ALP and our outcomes was significantly different between men and women. Among women, there was a dose–response ALP‐MetS relationship. Moreover, higher ALP levels were also associated with dyslipidemic components of MetS (high TG and low HDL‐C). In men, increases in serum ALP were only associated with central obesity but not incident MetS. Also, when studying the population as a whole, increasing values of serum ALP were associated with incident MetS as well as high WC and low HDL‐C components.
Only a few prospective studies assessed the ALP‐MetS relationship. In the Insulin Resistance Atherosclerosis Study (IRAS), the highest ALP quartile compared to the lowest was associated with an almost 2‐fold increase in MetS risk; this association significantly attenuated after further adjustment for WC and impaired glucose tolerance status [11]. Similarly, in our data analysis, among the whole population, the highest quartile of ALP was associated with more than 60% incident MetS compared to the first quartile. However, in the Mexico City Diabetes Study [12], only raised GGT but not ALP levels were an independent predictor of MetS features, including glucose intolerance, diabetes, and dyslipidemia. Also, in a study by Kim et al. [13], among a Korean population during 4 years of follow‐up, an increase in ALP was associated with incident MetS, and those in the highest quintile had a 50% higher risk for MetS compared to those in the lowest quintile.
Despite the notable disparity in the serum ALP distribution between men and women, the majority of earlier research did not thoroughly account for the sex differences. In a cross‐sectional study conducted in Thailand, it was shown that increasing levels of serum ALP were associated with prevalent MetS among men but not women [26]. In a population‐based, case–control study, it was shown that a 5‐unit increase in serum ALP was associated with higher odds of prevalent MetS among women than men (1.16 vs. 1.07, respectively) [27]. Recently, in a Korean National Health and Nutrition Examination Survey (KNHANES), the fourth quartile of ALP (> 264 U/L for men and > 252 U/L for women) was associated with 30 and 100% higher odds ratios for prevalent MetS in men and women, respectively [28]. We extend previous studies by showing a linear MetS–ALP association in our study population as a whole. Additionally, using appropriate statistical analysis, we found that this association was mainly related to the women population.
Regarding MetS components, the researchers of IRAS also demonstrated that the increasing value of ALP (as shown by SD of log‐ALP) was significantly associated with hyperglycemia, high TG, and low HDL‐C; however, no associations were found for abdominal obesity or high BP [11]. Similarly, in our data analysis among the whole population, we found a 1‐SD increase in ALP was associated with about 9% and 13% higher risks for incident abdominal obesity and low HDL‐C, respectively. Importantly, we also found that among women, there was a significant association between ALP and incident high TG; i.e., both the second and fourth quartiles of ALP were associated with about an 80% higher risk of incident high TG. In the Korean cohort [13], researchers also found that higher ALP values were significantly associated with the dyslipidemia components of MetS, as well as abdominal obesity (that was marginally significant, p for trend: 0.08). The KNHANES study showed that dyslipidemia components for men, and all of the MetS components for women excluding low HDL‐C, were significantly influenced by the increase in ALP levels [28]. Liu et al., in a meta‐analysis of prospective studies conducted among the American population from two cohorts, demonstrated that a 1‐SD increment in log‐ALT was associated with an 11% higher risk of abdominal obesity [29]. Here, we extended the previous findings by showing the association of serum ALP as another liver function test with incident abdominal obesity, particularly among Iranian men. In our sex‐stratified analysis, we found that increasing ALP levels were associated with the atherogenic dyslipidemic profile of high TG and low HDL‐C in women and central obesity in men. Although the explanation for the differences in the impact of serum ALP activity on MetS components between men and women is unknown, sex differences in total body adipose tissue distribution and sex hormones could be explanatory [30, 31]. Women may have more total body adipose tissue than men at the same BMI [32], which could be a key source of circulating free fatty acids and pro‐inflammatory cytokines, promoting IR and atherogenic lipid profiles [33, 34].
The mechanisms linking serum ALP, MetS, and its components are not fully explored, but few potential links have been suggested. The exact pathophysiology of MetS remains incompletely understood, with IR [35] and subclinical low‐grade inflammation [36] recognized as significant contributors. IR may connect serum ALP activity and MetS, as serum ALP is linked to IR, particularly in cases of hepatic steatosis and non‐alcoholic fatty liver disease (NAFLD) [37, 38]. The notion of considering NAFLD, a recognized risk enhancer for MetS components, as the hepatic manifestation in the cluster of MetS has increasingly gained support from the body of evidence [39, 40]. This marker, among other liver markers, is known to be associated with hepatic fat content and deposition [41], which has central etiologic roles in the development of MetS [42]. Moreover, subclinical inflammation may, to some extent, account for the observed associations between these factors. MetS is linked to chronic inflammation, which occurs as a result of aberrant cytokine production, a rise in acute phase reactants, and the activation of inflammatory signaling pathways [43, 44]. Recent studies have demonstrated significant associations between elevated levels of C reactive protein (CRP), MetS, and its components [45, 46]. Additionally, previous studies found an association between serum ALP activity and markers of inflammation, such as CRP and leukocyte count [28, 47]. However, the KNHANES study [28] found a significant ALP‐MetS association in the presence of CRP. Additionally, in the United States National Health and Nutrition Examination Survey (NHANES) study, researchers found plasma ALP levels significantly correlated with plasma CRP concentrations [48]. Endothelial dysfunction [49] and vascular calcification [50] are also among other risk factors that are closely correlated with ALP activity.
Certain limitations should be acknowledged; first, data on other confounding factors, including CRP, vitamin D, and other liver enzymes, including ALT and AST, were unavailable. However, it is of note that the prevalence of hepatitis B and C among the Iranian population had decreasing trends in recent years, reaching 1.09% and 0.3%, respectively [51, 52]. Second, we did not have information about nutritional intake at the recruitment time, so we could not consider this important confounder in our data analysis. Third, although it was previously shown that a longitudinal increase of liver enzymes (i.e., GGT) even within the normal range was strongly associated with MetS [53, 54], we did not have data on serum ALP after the first measurement. Despite this, using only a single measurement of ALP, we found a strong and robust association with MetS among the whole population and women. Finally, according to the study area, this study cannot be generalized to other rural zones of the country. As strengths, to our best, this is the first study to examine the association between serum ALP levels and incident MetS and its components in a prospective manner, particularly exploring the sex differences as well.
5. Conclusions
In conclusion, during more than 15 years of follow‐up among the Iranian population, we found that sex was a significant modifier in the impact of ALP levels on the development of MetS and its components. Hence, ALP levels were a strong harbinger for the incidence of MetS and dyslipidemia components among women. However, among men, the increasing values of ALP were associated with the development of central adiposity but not MetS or other components.
Author Contributions
Conception and design: Maryam Tohidi, Farzad Hadaegh, and Fereidoun Azizi. Acquisition of data, or interpretation of data: Parto Hadaegh, Mitra Hasheminia, Amir Abdi, Maryam Tohidi, Farzad Hadaegh. Drafting the work or revising: Parto Hadaegh, Amir Abdi, Maryam Tohidi, Farzad Hadaegh, Mitra Hasheminia, Fereidoun Azizi. Final approval of the manuscript: Maryam Tohidi, Farzad Hadaegh, Parto Hadaegh, Amir Abdi, Mitra Hasheminia, Fereidoun Azizi.
Ethics Statement
The Institutional Review Board of the Research Institute for Endocrine Sciences (RIES), Shahid Beheshti University of Medical Sciences, Tehran, Iran, approved the current study (approval ID: IR.SBMU.ENDOCRINE.REC.1402.061, Date: 5 November 2023). All participants read and signed written informed consent.
Consent
Consent obtained directly from patients and all participants read and signed written informed consent.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Table S1. HRs (95% CIs) for incident MetS by quartiles of ALP: Tehran lipid and glucose study.
Acknowledgments
We express our gratitude to the TLGS participants and executive team for their enthusiastic cooperation.
Funding: The authors received no specific funding for this work.
ICMJE Declaration: Authors disclose no relationships/activities/interests related to the content of this manuscript.
Data Availability Statement
Data are available from the corresponding author on reasonable request.
References
- 1. Reaven G. M. and Chen Y. D., “Role of Insulin in Regulation of Lipoprotein Metabolism in Diabetes,” Diabetes/Metabolism Reviews 4, no. 7 (1988): 639–652. [DOI] [PubMed] [Google Scholar]
- 2. Cameron A. J., Shaw J. E., and Zimmet P. Z., “The Metabolic Syndrome: Prevalence in Worldwide Populations,” Endocrinology and Metabolism Clinics 33, no. 2 (2004): 351–375. [DOI] [PubMed] [Google Scholar]
- 3. Belete R., Ataro Z., Abdu A., and Sheleme M., “Global Prevalence of Metabolic Syndrome Among Patients With Type I Diabetes Mellitus: A Systematic Review and Meta‐Analysis,” Diabetology & Metabolic Syndrome 13, no. 1 (2021): 1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Ansarimoghaddam A., Adineh H. A., Zareban I., Iranpour S., HosseinZadeh A., and Kh F., “Prevalence of Metabolic Syndrome in Middle‐East Countries: Meta‐Analysis of Cross‐Sectional Studies,” Diabetes & Metabolic Syndrome 12, no. 2 (2018): 195–201. [DOI] [PubMed] [Google Scholar]
- 5. Al‐Rubeaan K., Bawazeer N., Al Farsi Y., et al., “Prevalence of Metabolic Syndrome in Saudi Arabia ‐ a Cross Sectional Study,” BMC Endocrine Disorders 18, no. 1 (2018): 16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Shin S. and Jee H., “Prevalence of Metabolic Syndrome in the Gulf Cooperation Council Countries: Meta‐Analysis of Cross‐Sectional Studies,” Journal of Exercise Rehabilitation 16, no. 1 (2020): 27–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Alzeidan R. A., Rabiee F., Mandil A. A., Hersi A. S., and Ullah A. A., “Changes in Dietary Habits, Physical Activity and Status of Metabolic Syndrome Among Expatriates in Saudi Arabia,” Eastern Mediterranean Health Journal = La Revue De Sante De La Mediterranee Orientale = Al‐Majallah Al‐Sihhiyah Li‐Sharq Al‐Mutawassit 23, no. 12 (2018): 836–844. [DOI] [PubMed] [Google Scholar]
- 8. Atasi M. and Kammar‐García A., “Dietary Patterns and Their Association With Metabolic Syndrome and Their Components in Middle‐Class Adults From Damascus, Syria: A Cross‐Sectional Study,” Journal of Nutrition and Metabolism 2022 (2022): 5621701. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Tabatabaei‐Malazy O., Saeedi Moghaddam S., Rezaei N., et al., “A Nationwide Study of Metabolic Syndrome Prevalence in Iran; a Comparative Analysis of Six Definitions,” PLoS One 16, no. 3 (2021): e0241926. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Hadaegh F., Hasheminia M., Lotfaliany M., Mohebi R., Azizi F., and Tohidi M., “Incidence of Metabolic Syndrome Over 9 Years Follow‐Up; the Importance of Sex Differences in the Role of Insulin Resistance and Other Risk Factors,” PLoS One 8, no. 9 (2013): e76304. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Hanley A. J., Williams K., Festa A., Wagenknecht L. E., R. B. D'Agostino, Jr. , and Haffner S. M., “Liver Markers and Development of the Metabolic Syndrome: The Insulin Resistance Atherosclerosis Study,” Diabetes 54, no. 11 (2005): 3140–3147. [DOI] [PubMed] [Google Scholar]
- 12. Nannipieri M., Gonzales C., Baldi S., et al., “Liver Enzymes, the Metabolic Syndrome, and Incident Diabetes: The Mexico City Diabetes Study,” Diabetes Care 28, no. 7 (2005): 1757–1762. [DOI] [PubMed] [Google Scholar]
- 13. Kim M. K., Baek K. H., Kang M. I., et al., “Serum Alkaline Phosphatase, Body Composition, and Risk of Metabolic Syndrome in Middle‐Aged Korean,” Endocrine Journal 60, no. 3 (2013): 321–328. [DOI] [PubMed] [Google Scholar]
- 14. Son D. H., Ha H. S., and Lee Y. J., “Association of Serum Alkaline Phosphatase With the TG/HDL Ratio and TyG Index in Korean Adults,” Biomolecules 11, no. 6 (2021): 882. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Zhang Y., Zhou C., Li J., et al., “Serum Alkaline Phosphatase Levels and the Risk of New‐Onset Diabetes in Hypertensive Adults,” Cardiovascular Diabetology 19, no. 1 (2020): 186. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Shimizu Y., Nakazato M., Sekita T., et al., “Association Between Alkaline Phosphatase and Hypertension in a Rural Japanese Population: The Nagasaki Islands Study,” Journal of Physiological Anthropology 32, no. 1 (2013): 10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Azizi F., Hadaegh F., Hosseinpanah F., et al., “Metabolic Health in the Middle East and North Africa,” Lancet Diabetes and Endocrinology 7, no. 11 (2019): 866–879. [DOI] [PubMed] [Google Scholar]
- 18. Azizi F., Ghanbarian A., Momenan A. A., et al., “Prevention of Non‐Communicable Disease in a Population in Nutrition Transition: Tehran Lipid and Glucose Study Phase II,” Trials 10 (2009): 5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Azizi F., Takyar M., and Zadeh‐Vakili A., “Contributions and Implications of the Tehran Lipid and Glucose Study,” International Journal of Endocrinology and Metabolism 16, no. 4 (2018): e84792. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Ainsworth B. E., D. R. Jacobs, Jr. , and Leon A. S., “Validity and Reliability of Self‐Reported Physical Activity Status: The Lipid Research Clinics Questionnaire,” Medicine and Science in Sports and Exercise 25, no. 1 (1993): 92–98. [DOI] [PubMed] [Google Scholar]
- 21. Azizi F., Zadeh‐Vakili A., and Takyar M., “Review of Rationale, Design, and Initial Findings: Tehran Lipid and Glucose Study,” International Journal of Endocrinology and Metabolism 16, no. 4 (2018): e84777. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Alberti K. G., Eckel R. H., Grundy S. M., et al., “Harmonizing the Metabolic Syndrome: A Joint Interim Statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity,” Circulation 120, no. 16 (2009): 1640–1645. [DOI] [PubMed] [Google Scholar]
- 23. Azizi F., Khalili D., Aghajani H., et al., “Appropriate Waist Circumference Cut‐Off Points Among Iranian Adults: The First Report of the Iranian National Committee of Obesity,” Archives of Iranian Medicine 13, no. 3 (2010): 243–244. [PubMed] [Google Scholar]
- 24. Levey A. S., Stevens L. A., Schmid C. H., et al., “A New Equation to Estimate Glomerular Filtration Rate,” Annals of Internal Medicine 150, no. 9 (2009): 604–612. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Expert Panel on Detection E and Treatment of High Blood Cholesterol in A , “Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III),” Journal of the American Medical Association 285, no. 19 (2001): 2486–2497. [DOI] [PubMed] [Google Scholar]
- 26. Perera S., Lohsoonthorn V., Jiamjarasrangsi W., Lertmaharit S., and Williams M. A., “Association Between Elevated Liver Enzymes and Metabolic Syndrome Among Thai Adults,” Diabetes & Metabolic Syndrome 2, no. 3 (2008): 171–178. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Zhang L., Ma X., Jiang Z., et al., “Liver Enzymes and Metabolic Syndrome: A Large‐Scale Case‐Control Study,” Oncotarget 6, no. 29 (2015): 26782–26788. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Kim J. H., Lee H. S., Park H. M., and Lee Y. J., “Serum Alkaline Phosphatase Level Is Positively Associated With Metabolic Syndrome: A Nationwide Population‐Based Study,” Clinica Chimica Acta 500 (2020): 189–194. [DOI] [PubMed] [Google Scholar]
- 29. Liu Z., Que S., Ning H., Wang L., and Peng T., “Elevated Alanine Aminotransferase Is Strongly Associated With Incident Metabolic Syndrome: A Meta‐Analysis of Prospective Studies,” PLoS One 8, no. 12 (2013): e80596. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Power M. L. and Schulkin J., “Sex Differences in Fat Storage, Fat Metabolism, and the Health Risks From Obesity: Possible Evolutionary Origins,” British Journal of Nutrition 99, no. 5 (2008): 931–940. [DOI] [PubMed] [Google Scholar]
- 31. Tramunt B., Smati S., Grandgeorge N., et al., “Sex Differences in Metabolic Regulation and Diabetes Susceptibility,” Diabetologia 63, no. 3 (2020): 453–461. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Blaak E., “Gender Differences in Fat Metabolism,” Current Opinion in Clinical Nutrition and Metabolic Care 4, no. 6 (2001): 499–502. [DOI] [PubMed] [Google Scholar]
- 33. Alexopoulos N., Katritsis D., and Raggi P., “Visceral Adipose Tissue as a Source of Inflammation and Promoter of Atherosclerosis,” Atherosclerosis 233, no. 1 (2014): 104–112. [DOI] [PubMed] [Google Scholar]
- 34. Geer E. B. and Shen W., “Gender Differences in Insulin Resistance, Body Composition, and Energy Balance,” Gender Medicine 6, no. Suppl 1 (2009): 60–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Zhao X., An X., Yang C., Sun W., Ji H., and Lian F., “The Crucial Role and Mechanism of Insulin Resistance in Metabolic Disease,” Frontiers in Endocrinology 14 (2023): 1149239. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Esser N., Legrand‐Poels S., Piette J., Scheen A. J., and Paquot N., “Inflammation as a Link Between Obesity, Metabolic Syndrome and Type 2 Diabetes,” Diabetes Research and Clinical Practice 105, no. 2 (2014): 141–150. [DOI] [PubMed] [Google Scholar]
- 37. Sesti G., Fiorentino T. V., Hribal M. L., Sciacqua A., and Perticone F., “Association of Hepatic Insulin Resistance Indexes to Nonalcoholic Fatty Liver Disease and Related Biomarkers,” Nutrition, Metabolism, and Cardiovascular Diseases 23, no. 12 (2013): 1182–1187. [DOI] [PubMed] [Google Scholar]
- 38. Pantsari M. W. and Harrison S. A., “Nonalcoholic Fatty Liver Disease Presenting With an Isolated Elevated Alkaline Phosphatase,” Journal of Clinical Gastroenterology 40, no. 7 (2006): 633–635. [DOI] [PubMed] [Google Scholar]
- 39. Buzzetti E., Pinzani M., and Tsochatzis E. A., “The Multiple‐Hit Pathogenesis of Non‐Alcoholic Fatty Liver Disease (NAFLD),” Metabolism 65, no. 8 (2016): 1038–1048. [DOI] [PubMed] [Google Scholar]
- 40. Godoy‐Matos A. F., Silva Júnior W. S., and Valerio C. M., “NAFLD as a Continuum: From Obesity to Metabolic Syndrome and Diabetes,” Diabetology & Metabolic Syndrome 12, no. 1 (2020): 60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Chen S. C., Tsai S. P., Jhao J. Y., Jiang W. K., Tsao C. K., and Chang L. Y., “Liver Fat, Hepatic Enzymes, Alkaline Phosphatase and the Risk of Incident Type 2 Diabetes: A Prospective Study of 132,377 Adults,” Scientific Reports 7, no. 1 (2017): 4649. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Yki‐Järvinen H., “Non‐Alcoholic Fatty Liver Disease as a Cause and a Consequence of Metabolic Syndrome,” Lancet Diabetes and Endocrinology 2, no. 11 (2014): 901–910. [DOI] [PubMed] [Google Scholar]
- 43. Kerner A., Avizohar O., Sella R., et al., “Association Between Elevated Liver Enzymes and C‐Reactive Protein: Possible Hepatic Contribution to Systemic Inflammation in the Metabolic Syndrome,” Arteriosclerosis, Thrombosis, and Vascular Biology 25, no. 1 (2005): 193–197. [DOI] [PubMed] [Google Scholar]
- 44. Hotamisligil G. S., Arner P., Caro J. F., Atkinson R. L., and Spiegelman B. M., “Increased Adipose Tissue Expression of Tumor Necrosis Factor‐Alpha in Human Obesity and Insulin Resistance,” Journal of Clinical Investigation 95, no. 5 (1995): 2409–2415. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Koziarska‐Rościszewska M., Gluba‐Brzózka A., Franczyk B., and Rysz J., “High‐Sensitivity C‐Reactive Protein Relationship With Metabolic Disorders and Cardiovascular Diseases Risk Factors,” Life 11, no. 8 (2021): 742. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Mirhafez S. R., Ebrahimi M., Saberi Karimian M., et al., “Serum High‐Sensitivity C‐Reactive Protein as a Biomarker in Patients With Metabolic Syndrome: Evidence‐Based Study With 7284 Subjects,” European Journal of Clinical Nutrition 70, no. 11 (2016): 1298–1304. [DOI] [PubMed] [Google Scholar]
- 47. Cheung B. M., Ong K. L., Cheung R. V., et al., “Association Between Plasma Alkaline Phosphatase and C‐Reactive Protein in Hong Kong Chinese,” Clinical Chemistry and Laboratory Medicine 46, no. 4 (2008): 523–527. [DOI] [PubMed] [Google Scholar]
- 48. Webber M., Krishnan A., Thomas N. G., and Cheung B. M., “Association Between Serum Alkaline Phosphatase and C‐Reactive Protein in the United States National Health and Nutrition Examination Survey 2005–2006,” Clinical Chemistry and Laboratory Medicine 48, no. 2 (2010): 167–173. [DOI] [PubMed] [Google Scholar]
- 49. Perticone F., Perticone M., Maio R., et al., “Serum Alkaline Phosphatase Negatively Affects Endothelium‐Dependent Vasodilation in Naive Hypertensive Patients,” Hypertension 66, no. 4 (2015): 874–880. [DOI] [PubMed] [Google Scholar]
- 50. Azpiazu D., Gonzalo S., and Villa‐Bellosta R., “Tissue Non‐Specific Alkaline Phosphatase and Vascular Calcification: A Potential Therapeutic Target,” Current Cardiology Reviews 15, no. 2 (2019): 91–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51. Mahmud S., Akbarzadeh V., and Abu‐Raddad L. J., “The Epidemiology of Hepatitis C Virus in Iran: Systematic Review and Meta‐Analyses,” Scientific Reports 8, no. 1 (2018): 150. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52. Rezaei N., Asadi‐Lari M., Sheidaei A., et al., “Epidemiology of Hepatitis B in Iran From 2000 to 2016: A Systematic Review and Meta‐Regression Analysis,” Archives of Iranian Medicine 23, no. 3 (2020): 189–196. [PubMed] [Google Scholar]
- 53. Ryu S., Chang Y., Woo H. Y., et al., “Longitudinal Increase in Gamma‐Glutamyltransferase Within the Reference Interval Predicts Metabolic Syndrome in Middle‐Aged Korean Men,” Metabolism 59, no. 5 (2010): 683–689. [DOI] [PubMed] [Google Scholar]
- 54. Ali N., Samadder M., Mahmud F., and Islam F., “Association Between Liver Enzymes and Metabolic Syndrome: A Study in Bangladeshi Adults,” Expert Review of Endocrinology and Metabolism 18, no. 6 (2023): 541–547. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1. HRs (95% CIs) for incident MetS by quartiles of ALP: Tehran lipid and glucose study.
Data Availability Statement
Data are available from the corresponding author on reasonable request.