Serum CA19-9 as a predictor of incident metabolic syndrome in obese middle-aged and older men: A 9-year cohort study

DOI: https://doi.org/10.21203/rs.3.rs-2257258/v1

Abstract

The status of carbohydrate antigen 19 − 9 (CA19-9) in metabolic syndrome (MetS) is unknown. From 2007 to 2015, 1,750 participants were retrospectively reviewed; health checkup data were obtained. Participants were divided into three groups based on CA19-9 levels. Body mass index (BMI), waist circumference (WC), and blood pressure were determined. Blood samples were collected after fasting for > 8 hours, to measure biochemical parameters and tumor markers. Cox regression analysis showed that, after adjusting for covariates, the highest CA19-9 tertile was associated with an increased risk of incident MetS (P = 0.002), high systolic blood pressure (≥ 130 mmHg; P < 0.001), high WC (≥ 90 cm; P < 0.001), and high fasting plasma glucose (≥ 100 mg/dL; P = 0.001), low high-density lipoprotein (≤ 50 mg/dL; P = 0.001), and high triglyceride (≥ 150 mg/dL; P = 0.001) levels. Subgroup analysis showed that individuals in the highest CA19-9 tertile who were obese (BMI ≥ 24 kg/m2; P = 0.002), male (P = 0.001), and ≥ 50 years of age (P = 0.002) were at increased risk of incident MetS. Our results revealed a positive correlation between CA19-9 levels and MetS in obese middle-aged and older men.

Introduction

Tumor markers, such as carbohydrate antigen 19 − 9 (CA19-9) and carcinoembryonic antigen, are usually measured during health checkups in Taiwan. A small number of individuals have elevated tumor markers in the absence of a tumor. We investigated the reasons for elevated CA19-9 levels using data from the Health Management Center database.

CA19-9 was first described in 1979[1] as a monoclonal antibody synthesized by the exocrine pancreas and biliary duct cells[2]. CA19-9 is also synthesized by other organs, such as the salivary glands, endometrium, and colon[2]. CA19-9 is a well-known and valuable tumor marker for pancreatic cancer. It is also associated with malignancies of the upper gastrointestinal tract, colorectal cancer, hepatocellular cancer, and ovarian cancer[3]. Moreover, CA19-9 is elevated in benign inflammatory conditions, such as cholangitis[4], pancreatitis[5], and bronchiolitis[6].

Du et al. reported that higher CA19-9 levels were associated with a higher risk of incident diabetes mellitus (DM) and metabolic syndrome (MetS) in middle-aged and elderly Chinese individuals[7, 8]. CA19-9 is positively correlated with glycemic control in DM[9–12], as well as microvascular complications[11, 13]. CA19-9 is also associated with insulin resistance in prediabetic individuals[14]. MetS is a complex disease characterized by hypertension, central obesity, glucose intolerance, and dyslipidemia, and is associated with an increased risk of DM and cardiovascular disease[15]. The glucose metabolism and lipid profiles of individuals with MetS are similar to those of patients with DM, and both diseases often coexist[16]. Herein, we examine the association between CA19-9 and incident MetS in healthy individuals.

Results

Clinical characteristics of participants according to CA19-9 tertiles

The clinical characteristics of the participants are shown in Table 1. The mean age was 46.6 years; 86.7% were men. Participants in the highest CA19-9 tertile tended to be women (P < 0.001) and exhibited a lower body mass index (BMI) (P = 0.004) and lower serum creatinine levels (P = 0.001). Participants in the highest CA19-9 tertile had an increased risk of MetS (P = 0.04), high systolic blood pressure (SBP; ≥130 mmHg) (P = 0.015), high fasting plasma glucose levels (≥ 100 mg/dL) (P = 0.041) and high triglyceride (TG) levels (≥ 150 mg/dL) (P = 0.049).

Table 1

Baseline characteristics according to CA19-9 tertiles

Characteristic

CA19-9 tertile (U/mL)

Total

(n = 1,750)

P trend

T1 (≤ 6.58)

T2 (6.59–12.99)

T3 (13–143)

(n = 583)

(n = 582)

(n = 585)

Male sex, n (%)

545 (93.5)

503 (86.4)

469 (80.2)

1,517 (86.7)

< 0.001

Age (years), mean (SD)

47.46 (12.46)

45.99 (12.98)

46.22 (13.22)

46.56 (12.90)

0.114

BMI (kg/m2), mean (SD)

24.83 (3.20)

24.80 (3.44)

24.23 (3.47)

24.62 (3.38)

0.004

WC (cm), mean (SD)

85.25 (8.47)

84.37 (10.18)

83.56 (10.08)

84.35 (9.68)

0.116

SBP (mmHg), mean (SD)

123.18 (16.43)

123.26 (17.40)

124.34 (18.84)

123.60 (17.59)

0.471

DBP (mmHg), mean (SD)

79.61 (11.03)

79.52 (11.41)

79.78 (13.05)

79.64 (11.86)

0.933

TC (mg/dL), mean (SD)

189.49 (36.21)

191.27 (34.88)

194.36 (35.35)

191.75 (35.52)

0.121

TG (mg/dL), mean (SD)

125.87 (70.46)

135.98 (148.83)

133.56 (121.89)

131.79 (118.01)

0.428

BUN (mg/dL), mean (SD)

13.71 (3.03)

13.59 (3.56)

13.80 (4.78)

13.70 (3.86)

0.671

UA (mg/dL), mean (SD)

6.24 (1.30)

6.19 (1.45)

6.09 (1.54)

6.17 (1.44)

0.176

Creatinine (mg/dL), mean (SD)

0.93 (0.15)

0.91 (0.19)

0.89 (0.20)

0.91 (0.18)

0.001

HDL cholesterol (mg/dL), mean (SD)

52.26 (13.57)

51.14 (12.91)

52.47 (15.06)

51.96 (13.89)

0.219

LDL cholesterol (mg/dL), mean (SD)

124.41 (31.77)

123.09 (32.15)

126.15 (34.38)

124.53 (32.82)

0.462

CRP (mg/dL), mean (SD)

0.248 (0.81)

0.219 (0.338)

0.261 (0.489)

0.243 (0.599)

0.806

Albumin (mg/dL), mean (SD)

4.69 (0.25)

4.69 (0.26)

4.68 (0.29)

4.69 (0.27)

0.628

MetS, n (%)

105 (18.0)

107 (18.4)

116 (19.8)

328 (18.7)

0.040

SBP ≥ 130 mmHg, n (%)

109 (18.7)

104 (17.9)

118 (20.2)

331 (18.9)

0.015

WC ≥ 90 cm, n (%)

84 (14.4)

81 (14.0)

79 (13.5)

244 (13.9)

0.069

Fasting plasma glucose ≥ 100 mg/dL, n (%)

82 (14.0)

81 (13.9)

84 (14.4)

247 (14.1)

0.041

HDL cholesterol ≤ 50 mg/dL, n (%)

92 (15.7)

88 (15.2)

93 (15.9)

273 (15.6)

0.319

Total TG ≥ 150 mg/dL, n (%)

90 (15.4)

91 (15.6)

92 (15.7)

273 (15.6)

0.049
BMI, body mass index; BUN, blood urea nitrogen; CA19-9, carbohydrate antigen 19 − 9; CRP, C-reactive protein; DBP, diastolic blood pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MetS, metabolic syndrome; n, number; SBP, systolic blood pressure; SD, standard deviation; TC, total cholesterol; TG, triglyceride; UA, uric acid; WC, waist circumference.

 

Correlation of CA19-9 tertiles with incident MetS and its components

Cox proportional hazards models, after adjusting for covariates, were used to assess the relationship between CA19-9 and incident MetS, type 2 DM (T2DM), hypertension, and dyslipidemia. After adjusting for covariates, participants in the highest CA19-9 tertile were more likely to develop MetS (hazard ratio [HR], 2.44 [95% confidence interval (CI): 1.39–4.27]; P = 0.002), T2DM (HR, 4.27 [95% CI: 2.07–8.12]; P < 0.001), hypertension (HR, 2.61 [95% CI: 1.56–4.34]; P < 0.001), and dyslipidemia (HR, 2.24 [95% CI: 1.39–3.60]; P = 0.001) compared with those in the lowest tertile (Table 2).

Table 2

Cox proportional HRs of incident MetS, T2DM, hypertension, and dyslipidemia according to CA19-9 tertiles

Model

CA19-9 tertile

HR (95% CI)

P

MetS

1

T2 vs. T1

T3 vs. T1

1.72 (0.96–3.09)

1.98 (1.16–3.38)

0.067

0.013

2

T2 vs. T1

T3 vs. T1

1.98 (1.09–3.61)

2.46 (1.42–4.26)

0.026

0.001

3

T2 vs. T1

T3 vs. T1

2.02 (1.10–3.70)

2.44 (1.39–4.27)

0.023

0.002

T2DM

1

T2 vs. T1

T3 vs. T1

0.61 (0.17–2.17)

3.81 (1.86–7.79)

0.443

< 0.001

2

T2 vs. T1

T3 vs. T1

0.62 (0.17–2.24)

4.06 (1.98–8.32)

0.469

< 0.001

3

T2 vs. T1

T3 vs. T1

0.69 (0.19–2.49)

4.27 (2.07–8.12)

0.567

< 0.001

Hypertension

1

T2 vs. T1

T3 vs. T1

2.44 (1.47–4.04)

2.51 (1.54–4.08)

0.001

< 0.001

2

T2 vs. T1

T3 vs. T1

2.64 (1.57–4.41)

2.75 (1.68–4.51)

< 0.001

< 0.001

3

T2 vs. T1

T3 vs. T1

2.74 (1.62–4.63)

2.61 (1.56–4.34)

< 0.001

< 0.001

Dyslipidemia

1

T2 vs. T1

T3 vs. T1

2.13 (1.32–3.42)

2.19 (1.39–3.45)

0.002

0.001

2

T2 vs. T1

T3 vs. T1

1.99 (1.23–3.23)

2.27 (1.44–3.59)

0.005

< 0.001

3

T2 vs. T1

T3 vs. T1

2.14 (1.31–3.49)

2.24 (1.39–3.60)

0.002

0.001
Model 1 = unadjusted
Model 2 = adjusted for sex, age, and BMI
Model 3 = Model 2 + adjusted for TC, BUN, creatinine, UA, albumin, and CRP
BMI, body mass index; BUN, blood urea nitrogen; CA19-9, carbohydrate antigen 19 − 9; CI, confidence interval; CRP, C-reactive protein; HR, hazard ratio; MetS, metabolic syndrome; TC, total cholesterol; T2DM, type 2 diabetes mellitus; UA, uric acid.

 

To further investigate the relationship between CA19-9 and MetS components, we used Cox regression models, with adjusted covariates for each MetS component as follows: high SBP (≥ 130 mmHg), high waist circumference (WC; ≥ 90 cm), high fasting plasma glucose levels (≥ 100 mg/dL), low high-density lipoprotein (HDL) levels (≤ 50 mg/dL), and high TG levels (≥ 150 mg/dL). As shown in Table 3, after adjusting for covariates, participants in the highest CA19-9 tertile were significantly associated with high SBP (HR, 2.45 [95% CI: 1.50–4.00]; P < 0.001), high WC (HR, 2.29 [95% CI: 1.46–3.60]; P < 0.001), high fasting plasma glucose levels (HR, 2.05 [95% CI: 1.33–3.18]; P = 0.001), low HDL levels (HR, 2.24 [95% CI: 1.39–3.60]; P = 0.001), and high TG levels (HR, 2.20 [95% CI: 1.40–3.48]; P = 0.001).

Table 3

Cox proportional HRs of incident MetS components according to CA19-9 tertiles

Model

CA19-9 tertile

HR (95% CI)

P

WC ≥ 90 cm

1

T2 vs. T1

T3 vs. T1

2.13 (1.38–3.29)

2.06 (1.35–3.14)

0.001

0.001

2

T2 vs. T1

T3 vs. T1

2.60 (1.65–4.08)

2.55 (1.64–3.95)

< 0.001

< 0.001

3

T2 vs. T1

T3 vs. T1

2.57 (1.63–4.05)

2.29 (1.46–3.60)

< 0.001

< 0.001

SBP ≥ 130 mmHg

1

T2 vs. T1

T3 vs. T1

1.98 (1.20–3.29)

2.39 (1.50–3.81)

0.008

< 0.001

2

T2 vs. T1

T3 vs. T1

2.15 (1.29–3.56)

2.62 (1.63–4.21)

0.003

< 0.001

3

T2 vs. T1

T3 vs. T1

2.21 (1.31–3.72)

2.45 (1.50–4.00)

0.003

< 0.001

Fasting plasma glucose ≥ 100 mg/dL

1

T2 vs. T1

T3 vs. T1

1.57 (0.98–2.51)

1.84 (1.20–2.84)

0.061

0.005

2

T2 vs. T1

T3 vs. T1

1.66 (1.03–2.66)

1.93 (1.25–2.96)

0.036

0.003

3

T2 vs. T1

T3 vs. T1

1.78 (1.10–2.87)

2.05 (1.33–3.18)

0.020

0.001

Total TG ≥ 150 mg/dL

1

T2 vs. T1

T3 vs. T1

1.40 (0.83–2.34)

2.10 (1.35–3.26)

0.206

0.001

2

T2 vs. T1

T3 vs. T1

1.34 (0.80–2.24)

2.28 (1.47–3.56)

0.271

< 0.001

3

T2 vs. T1

T3 vs. T1

1.38 (0.82–2.32)

2.20 (1.40–3.48)

0.230

0.001

HDL cholesterol ≤ 50 mg/dL

1

T2 vs. T1

T3 vs. T1

2.13 (1.32–3.42)

2.19 (1.39–3.45)

0.002

0.001

2

T2 vs. T1

T3 vs. T1

1.99 (1.23–3.23)

2.72 (1.44–3.59)

0.005

< 0.001

3

T2 vs. T1

T3 vs. T1

2.14 (1.31–3.49)

2.24 (1.39–3.60)

0.002

0.001
Model 1 = unadjusted
Model 2 = adjusted for sex, age, and BMI
Model 3 = Model 2 + adjusted for TC, BUN, creatinine, UA, albumin, and CRP
BMI, body mass index; BUN, blood urea nitrogen; CA19-9, carbohydrate antigen 19 − 9; CI, confidence interval; CRP, C-reactive protein; HDL, high-density lipoprotein; HR, hazard ratio; MetS, metabolic syndrome; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; UA, uric acid; WC, waist circumference.

 

Subgroup analysis of the correlation between CA19-9 and incident MetS

We performed subgroup analysis of the correlation between CA19-9 and incident MetS using age-, sex-, and BMI-specific groups. Participants in the highest CA19-9 tertile who were obese (BMI ≥ 24 kg/m2) (HR, 2.56 [95% CI: 1.39–4.69]; P = 0.002), male (HR, 1.88 [95% CI: 1.28–2.76]; P = 0.001), and ≥ 50 years of age (HR, 2.75 [95% CI: 1.44–5.25]; P = 0.002) were at increased risk of incident MetS (Table 4).

Table 4

Cox proportional HRs of incident MetS according to CA19-9 tertiles as per subgroup analysis based on age, sex, and BMI

Model

CA19-9 tertile

HR (95% CI)

P

Model

CA19-9 tertile

HR (95% CI)

P

BMI < 24 kg/m2

BMI ≥ 24 kg/m2

1

T2 vs. T1

T3 vs. T1

0.51 (0.06–4.47)

1.16 (0.28–4.93)

0.541

0.836

1

T2 vs. T1

T3 vs. T1

2.13 (1.13–4.00)

2.37 (1.31–4.31)

0.019

0.005

2

T2 vs. T1

T3 vs. T1

0.47 (0.05–4.32)

1.30 (0.30–5.74)

0.500

0.726

2

T2 vs. T1

T3 vs. T1

2.19 (1.16–4.13)

2.48 (1.36–4.54)

0.016

0.003

3

T2 vs. T1

T3 vs. T1

0.41 (0.04–3.85)

1.20 (0.25–5.74)

0.438

0.819

3

T2 vs. T1

T3 vs. T1

2.09 (1.09–4.00)

2.56 (1.39–4.69)

0.026

0.002

Male sex

Female sex

1

T2 vs. T1

T3 vs. T1

1.58 (1.08–2.32)

1.79 (1.23–2.60)

0.020

0.002

1

T2 vs. T1

T3 vs. T1

0.18 (0.04–0.82)

0.40 (0.12–1.39)

0.027

0.151

2

T2 vs. T1

T3 vs. T1

1.67 (1.13–2.46)

1.93 (1.32–2.82)

0.010

0.001

2

T2 vs. T1

T3 vs. T1

0.32 (0.06–1.84)

0.65 (0.16–2.66)

0.208

0.553

3

T2 vs. T1

T3 vs. T1

1.65 (1.11–2.43)

1.88 (1.28–2.76)

0.012

0.001

3

T2 vs. T1

T3 vs. T1

0.33 (0.05–2.00)

0.50 (0.11–2.24)

0.226

0.362

Age < 50 years

Age ≥ 50 years

1

T2 vs. T1

T3 vs. T1

1.05 (0.36–3.05)

1.05 (0.34–3.24)

0.925

0.929

1

T2 vs. T1

T3 vs. T1

1.93 (0.96–3.85)

2.31 (1.25–4.26)

0.064

0.007

2

T2 vs. T1

T3 vs. T1

1.14 (0.39–3.33)

1.24 (0.40–3.86)

0.808

0.708

2

T2 vs. T1

T3 vs. T1

2.57 (1.25–5.29)

2.85 (1.50–5.40)

0.010

0.001

3

T2 vs. T1

T3 vs. T1

1.13 (0.38–3.42)

0.84 (0.25–2.90)

0.825

0.786

3

T2 vs. T1

T3 vs. T1

2.73 (1.31–5.72)

2.75 (1.44–5.25)

0.008

0.002
Model 1 = unadjusted
Model 2 = adjusted for sex, age, and BMI
Model 3 = Model 2 + adjusted for TC, BUN, creatinine, UA, albumin, and CRP
BMI, body mass index; BUN, blood urea nitrogen; CA19-9, carbohydrate antigen 19 − 9; CI, confidence interval; CRP, C-reactive protein; HR, hazard ratio; MetS, metabolic syndrome; TC, total cholesterol; UA, uric acid.

Discussion

This study demonstrates that high CA19-9 levels may be associated with incident MetS and its components, after adjusting for covariates. Furthermore, subgroup analysis showed that high serum CA19-9 levels may predict incident MetS in healthy middle-aged and older men. A small number of individuals have elevated tumor markers in the absence of a tumor. Our findings can be applied to these patients. Their increased cancer index may be related to MetS. Our results warrant close follow-up of metabolic conditions in high-risk individuals with high CA19-9 levels.

CA19-9 is secreted by duct cells of the exocrine pancreas and is elevated in conditions involving pancreatic tissue damage and specific malignancies[3]. In addition to its role as a tumor marker, CA19-9 has been proven to be associated with hemoglobin A1c, fasting plasma glucose levels, early-phase insulin secretion, and insulin resistance in prediabetic individuals[14]. Patients with DM and high CA19-9 levels were associated with poor disease control and β-cell function[13, 17, 18]. Moreover, high CA19-9 levels have been shown to predict the risk of DM and MetS in middle-aged and older Chinese individuals[7, 8]. Taken together, CA19-9 not only plays a major role in monitoring DM, but is also a predictor of incident DM and MetS.

MetS is a well-established risk factor for DM and cardiovascular disease[19] and increases the risk of all-cause and cardiovascular mortality in elderly populations[20]. MetS components, such as hypertension, central obesity, insulin resistance, and dyslipidemia, are risk factors for T2DM and cardiovascular disease[21]. The prevalence of MetS is high and increasing worldwide[22], probably due to obesity and the popularity of western diets[23, 24]. Decreased physical activity and diets high in fat and carbohydrates play a major role in central obesity and insulin resistance, which are the major pathophysiological characteristics of MetS[25]. Insulin resistance and diminished β-cell function occur before the development of MetS[26]. Furthermore, β-cell function has been shown to be negatively correlated with the severity of MetS[27]. Additionally, it has been shown that altered β-cell function may affect the exocrine activity of the pancreas, leading to increased serum CA19-9 levels[28]. It has also been proposed that CA19-9 biosynthesis is affected by insulin via regulation of intestinal galactosyltransferase activity[14, 29].

MetS components differ between men and women due to differences in hormones, lean body mass, and body fat; men have higher lean body mass, less body fat, and lower insulin sensitivity than women[30]. Insulin resistance is more prominent in men than in women[31, 32]. Our study revealed high serum CA19-9 levels to be a predictor of incident MetS in men. This may be attributed to the correlation of CA19-9 and MetS with β-cell function and the effects of sex on MetS components.

Given the adverse outcomes of MetS and the large population at risk, researchers need to focus on disease prevention and management of risk factors. Obesity, physical inactivity, smoking, and an atherogenic diet constitute acquired risk factors for MetS. Clinical management should focus on reducing these underlying risk factors according to an individual’s risk status[15]. Our findings are consistent with those of Du et al.[7, 8], who reported a positive association between CA19-9 and incident DM and MetS. Serum CA19-9 levels may provide physicians with an indication of the presence of a specific metabolic condition and influence approaches towards disease intervention. Therefore, CA19-9 levels should be determined during health checkups. High CA19-9 levels not only indicate the possibility of cancer but also warrant close follow-up of metabolic conditions.

Our study has several limitations. First, although adjustments for potential confounders were made, we could not exclude the possibility of unmeasured confounders. Nevertheless, as the participants in our study were relatively healthy, the influence of unknown confounders should be minimal. Second, this was a retrospective observational study, wherein serum CA19-9 levels were analyzed only once during the health checkup program. Participants were not subjected to surveillance and long-term follow-up. Thus, prospective cohort studies are warranted to validate our results. Third, a few participants were excluded due to inadequate information or no follow-up data; therefore, selection bias may have occurred. Finally, information regarding family history, eating habits, physical activity, smoking status, and drinking status were not collected in this study. Unadjusted data may represent a limitation of our study.

In conclusion, high CA19-9 levels may be associated with incident MetS in middle-aged and older obese men. Additionally, high CA19-9 levels correlated with MetS components. CA19-9 may be a useful marker for predicting MetS and can be routinely monitored during health checkups. Further prospective studies with larger sample sizes are warranted to confirm our findings.

Methods

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The study design was approved by the Institutional Review Board of Tri-Service General Hospital, Taipei, Taiwan. All participants signed an informed consent form. The information obtained was used for research purposes only. Data were analyzed anonymously.

Study population

From 2007 to 2015, 44,563 participants, who were healthy according to a survey conducted by the Health Management Center of Tri-Service General Hospital (a medical center in Taipei, Taiwan), were included in our study. To reduce confounding effects, we excluded individuals (n = 32,370) with a history of dyslipidemia, coronary artery disease, hypertension, DM, hepatobiliary disease, pancreatitis, or malignancy (including pancreatic cancer, ovarian cancer, and other malignancies), and those on any medication. Subjects (n = 10,443) lacking data on MetS components, lipid profiles, blood biochemistry tests, tumor marker tests, medical history, and physical examinations were also excluded.

This study involved 1,750 participants with no known medical history or chronic illness (Fig. 1). Participants were divided into three groups based on their CA19-9 levels. The CA19-9 tertiles were as follows: T1 (≤ 6.58 U/mL); T2 (6.59–12.99 U/mL); and T3 (13–143 U/mL).

General characteristics and laboratory measurements

Information regarding the participants’ medical history was obtained by questionnaire. Physical examinations were performed by experienced physicians. BMI was estimated using equation (1):


WC was measured to the nearest centimeter using a constant tension tape at the level of the umbilicus while the participants were in the standing position. Blood pressure was measured using an automatic electronic sphygmomanometer while the participants were in the sitting position, after resting for 5 minutes.

Blood samples were collected after fasting for > 8 hours via vacuum blood collection tubes containing EDTA. Serum biochemical parameters, such as TG, total cholesterol (TC), blood urine nitrogen (BUN), uric acid (UA), creatinine, C-reactive protein (CRP), HDL, low-density lipoprotein (LDL), albumin, and fasting plasma glucose, were measured using an automatic analyzer. The tumor marker, CA19-9, was measured using RIA.

Definition of MetS

According to the revised National Cholesterol Education Program’s Adult Treatment Panel III[33], MetS was defined by the presence of at least three of the following parameters: WC ≥ 90 cm in men or ≥ 80 cm in women; serum TG ≥ 150 mg/dL (1.7 mmol/L); HDL ≤ 40 mg/dL (1.03 mmol/L) in men or ≤ 50 mg/dL (1.29 mmol/L) in women; SBP ≥ 130 mmHg or diastolic blood pressure (DBP) ≥ 85 mmHg; and fasting plasma glucose ≥ 100 mg/dL (5.6 mmol/L).

Statistical analyses

Statistical analyses were performed using SPSS Statistics (released 2009) (PASW Statistics for Windows, version 18.0; SPSS Inc., Chicago, IL, USA). Continuous variables are expressed as mean ± SD, whereas categorical variables are expressed as numbers and percentages. The association between CA19-9 and incident MetS was evaluated using Cox regression models. An extended model approach was used to adjust for covariates. Model 1 was not adjusted for any covariates. Model 2 was adjusted for sex, age, and BMI. Model 3 was adjusted for the covariates in Model 2 and TC, BUN, creatinine, UA, albumin, and CRP.

Declarations

Acknowledgments

We thank the participants at the Health Management Center of Tri-Service General Hospital, Taipei, Taiwan. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author contributions

PKC and LWW did the conceptualization, validation, writing, reviewing, and editing. PKC did the data curation and original draft preparation. JMH did the formal analysis, project administration, software, supervision, and visualization. LWW did the funding acquisition, investigation, methodology, and resources. All authors read and approved the final manuscript.

Competing interests

The authors declare no competing interests.

Data availability

The datasets generated during and/or analyzed during the current study are not publicly available due to our original data is not shared for everyone to use but are available from the corresponding author on reasonable request.

References

  1. Koprowski, H. et al. Colorectal carcinoma antigens detected by hybridoma antibodies. Somatic Cell Genet. 5, 957–971 (1979).
  2. Kim, B. J. et al. How do we interpret an elevated carbohydrate antigen 19 – 9 level in asymptomatic subjects? Dig. Liver Dis. 41, 364–369 (2009).
  3. Goonetilleke, K. S. & Siriwardena, A. K. Systematic review of carbohydrate antigen (CA 19 – 9) as a biochemical marker in the diagnosis of pancreatic cancer. Eur. J. Surg. Oncol. 33, 266–270 (2007).
  4. Murray, M. D., Burton, F. R. & Di Bisceglie, A. M. Markedly elevated serum CA 19 – 9 levels in association With a benign biliary stricture due to primary sclerosing cholangitis. J. Clin. Gastroenterol. 41, 115–117 (2007).
  5. Pines, E. et al. [Unusually high level of CA 19 – 9 in chronic pancreatitis]. Gastroenterol. Clin. Biol. 19, 641–642 (1995).
  6. Mukae, H. et al. Elevation of tumor-associated carbohydrate antigens in patients with diffuse panbronchiolitis. Am. Rev. Respir. Dis. 148, 744–751 (1993).
  7. Du, R. et al. Serum CA 19 – 9 and risk of incident diabetes in middle-aged and elderly Chinese: A prospective cohort study. Acta Diabetol. 54, 201–208 (2017).
  8. Du, R. et al. Association between serum CA 19 – 9 and metabolic syndrome: A cross-sectional study. J. Diabetes 9, 1040–1047 (2017).
  9. Benhamou, P. Y., Vuillez, J. P., Halimi, S., Meffre, G. & Bachelot, I. Influence of metabolic disturbances of diabetes mellitus on serum CA 19 – 9 tumor marker. Diabete Metab. 17, 39–43 (1991).
  10. Uygur-Bayramiçli, O. et al. Type 2 Dıabetes Mellıtus and CA 19 – 9 levels. World J. Gastroenterol. 13, 5357–5359 (2007).
  11. Gul, K. et al. CA 19 – 9 level in patients with type 2 diabetes mellitus and its relation to the metabolic control and microvascular complications. Am. J. Med. Sci. 341, 28–32 (2011).
  12. Huang, Y. et al. Relationship between CA 19 – 9 levels and glucose regulation in a middle-aged and elderly Chinese population. J. Diabetes 4, 147–152 (2012).
  13. Kim, S. H. et al. Clinical implication of elevated CA 19 – 9 level and the relationship with glucose control state in patients with Type 2 diabetes. Endocrine 46, 249–255 (2014).
  14. Shimodaira, M. et al. The relation between CA 19 – 9 level and early-phase insulin secretion in normoglycemic and prediabetic subjects. Int. J. Biol. Markers 30, e169–e173 (2015).
  15. Eckel, R. H., Grundy, S. M. & Zimmet, P. Z. The metabolic syndrome. Lancet 365, 1415–1428 (2005).
  16. Halcox, J. & Misra, A. Type 2 diabetes mellitus, metabolic syndrome, and mixed dyslipidemia: How similar, how different, and how to treat? Metab. Syndr. Relat. Disord. 13, 1–21 (2015).
  17. Esteghamati, A. et al. CA 19 – 9 is associated with poor glycemic control in diabetic patients: Role of insulin resistance Clin. Lab. 60, 441–447 (2014)
  18. Yu, H. et al. Serum CA19-9 level associated with metabolic control and pancreatic beta cell function in diabetic patients. Exp. Diabetes Res. 2012, 745189 (2012).
  19. Kanbak, G., Akalin, A., Dokumacioglu, A., Ozcelik, E. & Bal, C. Cardiovascular risk assessment in patients with type 2 diabetes mellitus and metabolic syndrome: Role of biomarkers. Diabetes Metab. Syndr. 5, 7–11 (2011).
  20. Ju, S. Y., Lee, J. Y. & Kim, D. H. Association of metabolic syndrome and its components with all-cause and cardiovascular mortality in the elderly: A meta-analysis of prospective cohort studies. Med. (Baltim.) 96, e8491 (2017).
  21. Sookoian, S. & Pirola, C. J. Metabolic syndrome: From the genetics to the pathophysiology. Curr. Hypertens. Rep. 13, 149–157 (2011).
  22. Engin, A. The definition and prevalence of obesity and metabolic syndrome. Adv. Exp. Med. Biol. 960, 1–17 (2017).
  23. Syauqy, A., Hsu, C. Y., Rau, H. H. & Chao, J. C.-J. Association of dietary patterns, anthropometric measurements, and metabolic parameters with C-reactive protein and neutrophil-to-lymphocyte ratio in middle-aged and older adults with metabolic syndrome in Taiwan: A cross-sectional study. Nutr. J. 17, 106 (2018).
  24. Ranasinghe, P., Mathangasinghe, Y., Jayawardena, R., Hills, A. P. & Misra, A. Prevalence and trends of metabolic syndrome among adults in the Asia-Pacific region: A systematic review. BMC Public Health 17, 101 (2017).
  25. O’Neill, S. & O’Driscoll, L. Metabolic syndrome: A closer look at the growing epidemic and its associated pathologies. Obes. Rev. 16, 1–12 (2015).
  26. Cubeddu, L. X. & Hoffmann, I. S. Impact of traits of metabolic syndrome on β-cell function and insulin resistance in normal fasting, normal glucose tolerant subjects. Metab. Syndr. Relat. Disord. 10, 344–350 (2012).
  27. Malin, S. K. et al. β-cell Dysfunction Is Associated with Metabolic Syndrome Severity in Adults. Metab. Syndr. Relat. Disord. 12, 79–85 (2014).
  28. Williams, J. A. & Goldfine, I. D. The insulin-pancreatic acinar axis. Diabetes 34, 980–986 (1985).
  29. Lenoir, D., Gréco, S., Louisot, P. & Biol, M. C. Implication of insulin and nutritional factors in the regulation of intestinal galactosyltransferase activity during postnatal development. Metabolism 49, 526–531 (2000).
  30. Rochlani, Y., Pothineni, N. V. & Mehta, J. L. Metabolic syndrome: Does it differ Between women and men? Cardiovasc. Drugs Ther. 29, 329–338 (2015).
  31. Unwin, N., Shaw, J., Zimmet, P. & Alberti, K. G. M. M. Impaired glucose tolerance and impaired fasting glycaemia: The current status on definition and intervention. Diabet. Med. 19, 708–723 (2002).
  32. Aldhoon-Hainerová, I. et al. Glucose homeostasis and insulin resistance: Prevalence, gender differences and predictors in adolescents. Diabetol. Metab. Syndr. 6, 100 (2014).
  33. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. 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). JAMA 285, 2486–2497 (2001).