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Association between erythrocyte parameters and metabolic syndrome in urban Han Chinese: a longitudinal cohort study

  • Shuo Wu1,
  • Haiyan Lin1,
  • Chengqi Zhang2,
  • Qian Zhang1,
  • Dongzhi Zhang3,
  • Yongyuan Zhang1,
  • Wenjia Meng1,
  • Zhenxin Zhu1,
  • Fang Tang2,
  • Fuzhong Xue1 and
  • Yanxun Liu1Email author
Contributed equally
BMC Public Health201313:989

https://doi.org/10.1186/1471-2458-13-989

Received: 23 April 2013

Accepted: 8 October 2013

Published: 21 October 2013

Abstract

Background

Although various cross-sectional studies have shown that erythrocyte parameters, including red blood cell (RBC), hemoglobin (Hb) and hematocrit (HCT), were linked with metabolic syndrome (MetS), few longitudinal studies have been used to confirm their relationship. The study, therefore, constructed a large-scale longitudinal cohort in urban Chinese population to highlight and confirm the association between erythrocyte parameters and MetS/its components.

Methods

A longitudinal cohort with 6,453 participants was established based on the routine health check-up systems to follow up MetS, and Generalized Estimating Equation (GEE) model was used to detect the association between erythrocyte parameters and MetS/its components (obesity, hyperglycemia, dyslipidemia, and hypertension).

Results

287 MetS occurred over the four-year follow-up, leading to a total incidence density of 14.19 per 1,000 person-years (287/20218 person-years). Both RBC and Hb were strongly associated with MetS (RR/95% CI, P value; 3.016/1.525-5.967, 0.002 for RBC; 3.008/1.481-6.109, 0.002 for Hb), with their dose–response trends detected. All three erythrocyte parameters (RBC, Hb and HCT) were found to be associated with obesity, hypertension and dyslipidemia with similar dose–response trends respectively, while only Hb showed a significant association with hyperglycemia.

Conclusions

Elevated erythrocyte parameters were confirmed to be associated with MetS/its components in urban Chinese population, suggesting that erythrocyte parameters might be served as a potential predictor for risk of MetS.

Keywords

Metabolic syndrome (MetS)Erythrocyte parametersLongitudinal cohort studyGeneralized estimated equation (GEE)

Background

The metabolic syndrome (MetS) is characterized by obesity, hyperglycemia, dyslipidemia, hypertension and insulin resistance (IR) [1, 2]. Various cross-sectional studies have demonstrated that erythrocyte parameters, including red blood cell (RBC), hemoglobin (Hb) and hematocrit (HCT), were associated with MetS [28]. These cross-sectional studies showed that elevated RBC was associated with MetS in Taiwan [2], Israel [3], Korea [4], Japan [5, 6], Hb in Thailand [7] and Japan [6], HCT in Thailand [7] and Japan [6, 8]. These positive associations were further detected in an Ethiopian cohort [9]. Furthermore, in a Japanese cohort [10], HCT was reported to be positively associated with insulin resistance, which is the basic pathogenesis for MetS. As most current results were reported from cross-sectional studies, and few from cohort studies, further longitudinal cohort studies are required to confirm the assumption in different populations.

The study, we established a longitudinal cohort with 6,453 participants based on the routine health check-up systems in urban Chinese population to follow up MetS, and each individual in this cohort was undergone at least three repeated health checks in the five years (January 2005 to January 2010). Furthermore, Generalized Estimating Equation (GEE) model, which could handle the repeat measurement data with high autocorrelation in the framework of logistic regression model [1114], was used to detect the association between erythrocyte parameters and MetS/its components (obesity, hyperglycemia, dyslipidemia, and hypertension).

Methods

Study population

A large scale longitudinal cohort was set up in 2005 on middle-to-upper class urban Han Chinese who attended routine health check-up at the Centers for Health Management of Shandong Provincial Hospital and Shandong Provincial Qianfoshan Hospital. Four groups of participants without cerebral infarction, cardiovascular disease, coronary artery bypass surgery, MetS and its single component in their first check-up at the year of 2005, 2006, 2007 and 2008 were included in the baseline of our longitudinal cohort study respectively. Figure 1 showed the total of 6453 participants having at least three repeated health check-up within five years (January 2005 to January 2010), and the samples of repeated surveys each year.
Figure 1

The samples of repeated surveys at each year.

All individuals in the longitudinal cohort underwent a general health questionnaire, anthropometric measurements, and laboratory tests. The general health questionnaire covered the current status of smoking, alcohol intake, diet, sleeping quality and physical activity. Anthropometric measurements involved height, weight, and blood pressure. Both height and weight were measured with light clothing without shoes. Body mass index (BMI) was calculated as weight/height2 (kg/m2) as an evidence of obesity. Blood pressure was measured using Omron HEM-907 by the cuff-oscillometric method on the right arm in sitting position after a 5-min rest, and the mean systolic and diastolic blood pressure values of two measurements were recorded respectively. While the participant was fasting, a venous blood sample was taken for laboratory test. Laboratory tests included RBC, Hb, HCT, white blood count (WBC), platelet distribution width (PDW), mean platelet volume (MPV), thrombocytocrit (PCT), glucose, total cholesterol (CHOL), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TG), gamma-glutamyl transpeptidase (GGT), serum albumin (ALB), serum globulins (GLO), blood urea nitrogen (BUN), and serum creatinine (SCr), etc. This study was approved by the Ethics Committee of School of Public Health, Shandong University, and all participants were given informed written consent.

Definition of the metabolic syndrome

Considering the target population was Chinese with their specific physiological characteristics, Diabetes Branch of the Chinese Medical Association (CDS) [15] was used as the MetS diagnostic criteria, which is very popular for the Chinese population in clinical practice. MetS was defined as presence of three or more of the following four medical conditions: 1) overweight or obesity, i.e. BMI ≥25.0 Kg/m2; 2) hypertension, i.e. systolic blood pressure (SBC) ≥140 mmHg, or diastolic blood pressure (DBP) ≥90 mmHg, or previously diagnosed; 3) dyslipidemia, i.e., fasting TG ≥1.7 mmol/L, or fasting HDL <0.9 mmol/L; 4) hyperglycemia, i.e. fasting blood-glucose (FPG) ≥6.1 mmol/L, or 2 h Post-meal Glucose (PG) ≥7.8 mmol/L, or previously diagnosed.

Statistical analysis

To account for missing values, multiple imputation was performed. Since imputation method was depended on the patterns of the missing data and the types of the imputed variables, without loss of generality, the Markov chain Monte Carlo (MCMC) method was chosen according to MI Procedure of SAS 9.1.3 [16]. Most variables had less than 2% missing observations before imputation except diet, drinking, smoking, quality of sleep and physical activity having less than 10% missing values. The original continuous erythrocyte parameters were categorized into 4 levels (Q1-Q4) using the 3 quartiles of P25, P50 and P75 as critical values, with ≤ P25 for Q1, >P25 and ≤ P50 for Q2, >P50 and ≤ P75 for Q3, and > P75 for Q4 respectively. Summary statistics were used to illustrate the distribution characteristics for variables of interest at each repeated surveys, and student's t test for continuous variables and chi-square test for categorical variables were used to detect the statistical significances compared with the first survey (baseline). As GEE model could handle the repeat measurement data with high autocorrelation in the framework of logistic regression model [1114], it was used to detect the association between erythrocyte parameters and MetS/its components. Simple GEE model was firstly used to select variables associated with MetS/its components, then variables which were significant at the level of 0.05 in the simple GEE analysis entered the multiple GEE model to adjust the potential confounding. The 'Logit' link function was chosen in GEE analysis, with significance level 0.05. All the statistical analyses were performed on SAS 9.1.3.

Results

Table 1 summarized the characteristics of erythrocyte parameters levels and other potential confounding factors of the participants at each repeated survey, which showed that most factors were generally higher than that in the first survey (baseline). A total of 294 cases of MetS occurred over the four-year follow-up, leading to a total incidence density of 14.19 per 1,000 person-years (287/20218 person-years). During the follow up, 3 participants were diagnosed as cerebral infarction (person-years), 141 participants were diagnosed as cardiovascular disease (person-years) and no-one underwent coronary artery bypass surgery (see Additional file 1: Table S20).
Table 1

Distribution of erythrocyte parameters and other potential confounding factors

Variables

The 1st survey (N = 6453)

The 2nd survey (N = 5300)

The 3rd survey (N = 5732)

The 4th surveys (N = 4542)

The 5th survey (N = 3346)

age

38.563 ± 11.444

39.798 ± 11.572

40.619 ± 11.406*

42.413 ± 11.495*

43.317 ± 11.352*

sex

     

male

2688

2196

2415

1861

1351

female

3765

3104

3317

2681

1995

RBC

4.769 ± 0.456

4.705 ± 0.454*

4.698 ± 0.454*

4.678 ± 0.463*

4.7 ± 0.432*

Hb

142.454 ± 14.885

142.845 ± 15.27

141.73 ± 15.169*

142.569 ± 15.381

142.115 ± 16.465

HCT

42.888 ± 3.905

42.408 ± 3.951*

42.015 ± 3.889*

42.191 ± 3.964*

41.846 ± 3.869*

GGT

18.004 ± 16.393

20.016 ± 20.032*

19.383 ± 16.419*

21.08 ± 20.193*

21.336 ± 20.323*

ALB

46.575 ± 2.427

45.789 ± 2.784*

45.362 ± 2.742*

45.128 ± 2.69*

44.991 ± 2.436*

GLO

27.016 ± 3.838

27.133 ± 3.973

28.253 ± 3.972*

29.128 ± 4.046*

30.314 ± 3.826*

BUN

4.746 ± 1.197

4.698 ± 1.175*

4.664 ± 1.154*

4.772 ± 1.204

4.812 ± 1.145*

S-Cr

76.943 ± 13.736

77.897 ± 13.842*

77.502 ± 14.072*

78.587 ± 14.694*

77.012 ± 12.743

WBC

6.167 ± 1.466

6.069 ± 1.483*

6.054 ± 1.479*

6.142 ± 1.501

6.182 ± 1.489

PDW

12.346 ± 1.712

12.323 ± 1.722

12.226 ± 1.671*

12.164 ± 1.669*

12.088 ± 1.63*

MPV

10.454 ± 0.811

10.448 ± 0.946

10.42 ± 0.803*

10.398 ± 0.802*

10.428 ± 0.796

PCT

0.247 ± 0.089

0.257 ± 0.320*

0.254 ± 0.266*

0.249 ± 0.055

0.245 ± 0.054

Diet

     

Vegetarian

3454

2194*

2098*

1906*

1214*

normal

1863

792

750

550

463

meat-based

1118

2283

2866

2064

1653

sea food

18

31

18

22

16

Drinking

     

no

3698

2959

3333

2731*

1977

yes

2755

2341

2399

1811

1369

Smoking

     

no

5274

4323

4678

3726

2718

yes

1179

977

1054

816

628

Sleep

     

≥fair

6270

5173

5557

4373*

3250

<fair

183

127

175

169

96

Exercise

     

never/seldom

4694

3902

4267*

3469*

2477

often

1759

1398

1465

1073

869

*P < 0.05 compared with the first survey (baseline);

The abbreviations of the variables: RBC = red blood cell; Hb = Hemoglobin; HCT = Hematocrit; GGT = gamma-glutamyl transpeptidase; ALB = serum albumin; GLO = serum globulins; BUN = blood urea nitrogen; S-Cr = serum creatinine; WBC = white blood cell; PDW = Platelet distribution width; MPV = mean platele volume; PCT = Thrombocytocrit; Diet: 0: Vegetarian, 1: normal, 2: meat-based 3: sea food (the major kinds of food used to have); Drinking: 0: never, 1: seldom, 2: often, wine, 3: often beer, 4: often, Chinese spirits, 5: often, mixed all kinds; Smoking: 0: never, 1: seldom, 2: quit, 3: 1-4/d, 4: 5 -15/d, 5: >15/d; Quality of sleep: 0: excellent, 1: well, 2: fair 3: poor, 4: very poor (evaluated by themselves); Physical activity 0: never, 1: seldom (1–2 times a week), 2: often or everyday (more than 3 times a week).

Table 2 showed the selected variables associated with MetS at α = 0.05 level. It indicated that each of the 3 erythrocyte parameters (RBC, Hb, and HTC) with 9 potential confounding factors, including gender, age, GGT, GLO, BUN, WBC, diet, drinking and smoking might be linked with MetS. Also, each of the 3 erythrocyte parameters might be linked with the four components of MetS with their specific potential confounding factors respectively (see Additional file 2: Table S1, Additional file 3: Table S2, Additional file 4: Table S3 and Additional file 5: Table S4 for details).
Table 2

The association analyses result from simple GEE model (MetS as dependent variable)

Quartiles

Estimate

ERR

Z

P > |Z|

RR

Lower 95% confidence limits

Upper 95% confidence limits

red blood cell

       

Q4

1.284

0.244

5.271

<0.001

3.612

2.241

5.824

Q3

0.602

0.261

2.308

0.021

1.825

1.095

3.042

Q2

0.050

0.292

0.170

0.865

1.051

0.593

1.864

Q1

ref

ref

ref

ref

ref

ref

ref

hemoglobin

       

Q4

1.271

0.209

6.078

<0.001

3.564

2.366

5.370

Q3

0.492

0.232

2.118

0.034

1.635

1.037

2.577

Q2

0.251

0.240

1.045

0.296

1.285

0.803

2.056

Q1

ref

ref

ref

ref

ref

ref

ref

hematocrit

       

Q4

1.005

0.210

4.776

<0.001

2.732

1.809

4.127

Q3

0.406

0.234

1.736

0.083

1.500

0.949

2.371

Q2

0.123

0.249

0.496

0.620

1.131

0.694

1.844

Q1

ref

ref

ref

ref

ref

ref

ref

gender

−0.946

0.157

−6.034

<0.001

0.388

0.286

0.528

age

0.410

0.039

10.596

<0.001

1.507

1.397

1.625

GGT

0.012

0.002

7.235

<0.001

1.012

1.009

1.016

ALB

−0.050

0.025

−1.981

0.048

0.951

0.906

0.999

GLO

0.072

0.015

4.863

<0.001

1.074

1.044

1.106

BUN

0.177

0.049

3.599

<0.001

1.193

1.084

1.314

S-Cr

0.010

0.006

1.668

0.095

1.010

0.998

1.022

WBC

0.284

0.031

9.061

<0.001

1.328

1.249

1.412

PDW

0.005

0.042

0.119

0.905

1.005

0.926

1.091

MPV

−0.093

0.094

−0.983

0.326

0.912

0.758

1.096

PCT

−0.588

1.229

−0.478

0.632

0.555

0.050

6.176

Diet

0.223

0.073

3.049

0.002

1.250

1.083

1.442

Drinking

0.173

0.046

3.796

0.001

1.189

1.087

1.300

Smoking

0.143

0.044

3.246

0.001

1.153

1.058

1.257

Sleep

0.109

0.076

1.432

0.152

1.115

0.961

1.294

Exercise

−0.013

0.159

−0.080

0.937

0.987

0.723

1.348

Table 3 illustrated the summarized results of the association analyses between erythrocyte parameters and MetS/its components after adjusting potential confounding factors by multiple GEE model. (Confounding variables were not shown, detailed information seeing Additional file 6: Table S5, Additional file 7: Tables S6, Additional file 8: Table S7, Additional file 9: Table S8, Additional file 10: Table S9, Additional file 11: Table S10, Additional file 12: Table S11, Additional file 13: Table S12, Additional file 14: Table S13, Additional file 15: Table S14, Additional file 16: Table S15, Additional file 17: Table S16, Additional file 18: Table S17, Additional file 19: Table S18 and Additional file 20: Table S19.). It revealed that the top quartiles (Q4) of RBC and Hb were strongly associated with MetS (RR/95%CI, P value; 3.016/1.525-5.967, 0.002 for RBC; 3.008/1.481-6.109, 0.002 for Hb) using Q1 as reference level. Although no significant for Q2 and Q3, trends of increased of RR were observed from Q2 to Q4, indicating that there were dose–response trends between the 2 erythrocyte parameters and MetS. In addition, all three erythrocyte parameters (RBC, Hb and HCT) were found to be associated with obesity, hypertension and dyslipidemia with similar dose–response trends respectively, while only Hb showed a significant association with hyperglycemia.
Table 3

The summary results of the association analyses between erythrocyte parameters and MetS/its components after adjusting potential factors by multiple GEE model

Parameters

MetS

Obesity

Hyperglycemia

Hypertension

Dyslipdemia

  

Estimate

P > |Z|

RR

Estimate

P > |Z|

RR

Estimate

P > |Z|

RR

Estimate

P > |Z|

RR

Estimate

P > |Z|

RR

Red blood cell

Q4

1.104

0.002

3.016

0.474

<0.001

1.606

0.215

0.202

1.239

0.484

0.001

1.622

0.431

<0.001

1.539

Q3

0.523

0.081

1.688

0.364

0.001

1.439

0.075

0.606

1.078

0.271

0.042

1.312

0.251

0.002

1.285

Q2

−0.039

0.895

0.961

0.006

0.955

1.006

0.068

0.605

1.071

0.113

0.389

1.120

0.123

0.103

1.131

Q1

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

Hemolglobin

Q4

1.101

0.002

3.008

0.693

<0.001

2.000

0.654

0.001

1.923

0.749

<0.001

2.114

0.536

<0.001

1.709

Q3

0.454

0.144

1.575

0.447

<0.001

1.564

0.519

0.001

1.680

0.390

0.006

1.477

0.254

0.003

1.289

Q2

0.340

0.162

1.405

0.387

<0.001

1.473

0.307

0.025

1.359

0.273

0.035

1.314

0.082

0.251

1.086

Q1

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

Hemocrite

Q4

0.583

0.079

1.792

0.362

0.009

1.436

0.028

0.874

1.029

0.368

0.021

1.445

0.257

0.009

1.293

Q3

0.214

0.479

1.238

0.297

0.013

1.346

0.151

0.320

1.163

0.324

0.018

1.383

0.195

0.018

1.215

Q2

0.034

0.892

1.035

0.220

0.028

1.246

0.014

0.915

1.014

0.149

0.250

1.161

0.003

0.969

1.003

 

Q1

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

ref

Discussions

This study mainly attempted to confirm the association between erythrocyte parameters and MetS/its components using the longitudinal cohort. Although the longitudinal cohort study was based on routine health check-up in urban Han Chinese population from middle to upper socioeconomic strata, the positive associations between erythrocyte parameters and MetS/its components were observed, which were also detected in other two cohort study [9].

RBC had been reported to be associated with MetS in various populations by cross-sectional studies [26] and in Ethiopia by a cohort studies [9]. At present study, we not only confirmed that RBC was associated with MetS and its 3 single components (dyslipidemia, obesity and hypertension), but also observed the dose–response trends (seeing Table 3 or Additional file 7: Table S6, Additional file 8: Tables S7 and Additional file 9: Table S8). These results highlighted the positive association between RBC and MetS. In pathogenesis, this linkage might be explained by the insulin resistance(IR) mechanisms in the development of MetS, because insulin and insulin growth factors I and II supporting erythropoiesis in both vitro [1721] and vivo [2224] had been detected in laboratory studies.

As another important erythrocyte parameter, Hb also had been reported to be associated with MetS in Thailand [7] and Japan [6] by Cross-sectional studies, as well as in Ethiopia [9] by a cohort study. This positive association was also detected in our longitudinal cohort study with a potential dose–response trend between them (seeing Table 3 or Additional file 11: Table S10, Additional file 12: Table S11, Additional file 13: Table S12, Additional file 14: Table S13 and Additional file 15: Table S14). The possible mechanism might be supported by the following pathogenesis. Hb is a well recognized carrier and buffer of nitric oxide (NO), and various compounds of Hb with NO can affect Hb-oxygen affinity of the whole blood [25]. Disturbed NO synthesis may exert an adverse effect on endothelial dysfunction through the L-arginine-NO pathway [26]. Furthermore, endothelial dysfunction was reported to be associated with MetS [27, 28]. All these evidences expect the association between Hb and MetS/its components in population level.

Elevated HCT could increase blood viscosity and peripheral resistance to blood flow, and further contribute to IR [2931]. Therefore, the association between HCT and MetS/its components should be observed in population level. In this paper, HCT associating with obesity, hypertension and dyslipdemia were all detected in urban Han Chinese population (seeing Table 3 or Additional file 17: Table S16, Additional file 18: Table S17, Additional file 19: Table S18), while no statistical significant association between HCT and MetS/ hyperglycemia was found (seeing Table 3, Additional file 16: Table S15 or Additional file 20: Table S19). Similar results were also observed in Ethiopia [9] and Japan [9] by cohort studies, as well as in Thailand [7] and Japan [9, 11] by Cross-sectional studies.

Several limitations of this study must be considered. a) Selection bias might exist due to the samples just from routine health check-up population for middle-to-upper class urban Han Chinese. b) Owing to the absence of waist circumference measurement, the diagnostic criteria of MetS was just based on China Diabetes Federation, rather than international standard criteria. c) The medication history and menstrual history of participants might be significant confounding factors, but they were absent in our database. d) Hematological parameter categories were based on a single assessment of blood, which may cause a misclassification bias. It is, therefore, desired to conduct a perfect longitudinal cohort study in general population for further highlighting the association between erythrocyte parameters and MetS.

Conclusion

In conclusion, elevated erythrocyte parameters were confirmed to be associated with MetS/its components in urban Chinese population, suggesting that erythrocyte parameters might be a potential predictor for risk of MetS.

Notes

Declarations

Funding

This work was supported by grants from the National Nature Science Fund (No. 81273177) of China. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Authors’ Affiliations

(1)
Department of Epidemiology and Biostatistics, School of Public Health, Shandong University
(2)
Health Management Center, Shandong Provincial QianFoShan Hospital
(3)
Center for Health Management, Provincial Hospital affiliated to Shandong University

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