Platelet parameters in Chinese older adults with metabolic syndrome

in Endocrine Connections

Correspondence should be addressed to X Chen or C-W Pan: cx1708@126.com or pcwonly@gmail.com

*(X-J Yang and L-Y Zhang contributed equally to this work)

Purpose:

We aimed to examine the associations of platelet parameters with the presence of metabolic syndrome in community-dwelling older Chinese adults.

Methods:

Study sample was from the Weitang Geriatric Diseases Study, which included 4338 individuals aged 60 years or above. The mean age of the participants was 68 years. Metabolic syndrome was defined based on the Adult Treatment Panel III criteria. Platelet parameters were assessed using an automated hematology analyzer. Multiple logistic regression models were fitted to examine relationships between the platelet parameters and the presence of metabolic syndrome after adjusting for potential confounders.

Results:

The adjusted odds ratio (95% CI) of metabolic syndrome for the highest quartile of platelet parameters (platelet count, mean platelet volume, plateletcrit, platelet distribution width, platelet larger cell ratio) when compared to the lowest quartile were 1.32 (1.06, 1.64), 1.00 (0.81, 1.24), 1.37 (1.10, 1.71), 1.45 (1.14, 1.83), 1.11 (0.89, 1.39), respectively. Hypertension and diabetes modified the relationship between platelet distribution width and metabolic syndrome with the associations being significant in hypertensive and non-diabetic groups. The levels of platelet distribution width increased with the risk of metabolic syndrome in men but not in women.

Conclusion:

The levels of platelet count, plateletcrit and platelet distribution width increased in older adults with metabolic syndrome, suggesting that these parameters may be useful biomarkers for further risk appraisal of metabolic syndrome in aged population.

Abstract

Purpose:

We aimed to examine the associations of platelet parameters with the presence of metabolic syndrome in community-dwelling older Chinese adults.

Methods:

Study sample was from the Weitang Geriatric Diseases Study, which included 4338 individuals aged 60 years or above. The mean age of the participants was 68 years. Metabolic syndrome was defined based on the Adult Treatment Panel III criteria. Platelet parameters were assessed using an automated hematology analyzer. Multiple logistic regression models were fitted to examine relationships between the platelet parameters and the presence of metabolic syndrome after adjusting for potential confounders.

Results:

The adjusted odds ratio (95% CI) of metabolic syndrome for the highest quartile of platelet parameters (platelet count, mean platelet volume, plateletcrit, platelet distribution width, platelet larger cell ratio) when compared to the lowest quartile were 1.32 (1.06, 1.64), 1.00 (0.81, 1.24), 1.37 (1.10, 1.71), 1.45 (1.14, 1.83), 1.11 (0.89, 1.39), respectively. Hypertension and diabetes modified the relationship between platelet distribution width and metabolic syndrome with the associations being significant in hypertensive and non-diabetic groups. The levels of platelet distribution width increased with the risk of metabolic syndrome in men but not in women.

Conclusion:

The levels of platelet count, plateletcrit and platelet distribution width increased in older adults with metabolic syndrome, suggesting that these parameters may be useful biomarkers for further risk appraisal of metabolic syndrome in aged population.

Introduction

Metabolic syndrome (MetS) affects about one-fourth of the global adult population. The prevalence of MetS has been increasing rapidly in the mainland of China over the past few decades (1), reaching 33.9% (31.0% in men and 36.8% in women) in 2010 (2). MetS refers to a cluster of metabolic risk factors that promote atherosclerotic cardiovascular disease and type 2 diabetes mellitus (3). Patients are not diagnosed with MetS until they manifest multiple symptoms related to obesity, dyslipidemia, hyperglycemia, and/or hypertension (3). However, any combinations of these conditions take heavy tolls on an individual’s long-term health. Therefore, strategies are needed for prevention and early detection of this condition.

Platelet parameters may be promising biomarkers for predicting early signs of metabolic abnormalities. The pathophysiology of MetS includes inflammation, insulin resistance, oxidative stress and endothelial dysfunction and triggers platelet hyperactivity (4). Common platelet parameters that reflect platelet reactivity include platelet count (PC), platelet distribution width (PDW), mean platelet volume (MPV), plateletcrit (PCT) and platelet larger cell ratio (P-LCR). Among them, MPV was relatively more widely investigated in epidemiological studies and was reported to be positively associated with cardiovascular events and type 2 diabetes mellitus, but its associations with MetS was inconsistent in meta-analyses (5, 6). Other platelet parameters are also commonly available in clinical routines and there was limited evidence supporting their roles in developing MetS. Future investigations, especially on aged population, examining the associations between a broad spectrum of platelet parameters and MetS would contribute to identifying reliable blood biomarkers for screening and early detection of MetS.

Based on a large sample of community-dwelling Chinese adults aged 60 years or older, the present study aimed to examine the association of five major platelet parameters (PC, PDW, MPV, PCT, and P-LCR) with MetS. The findings might have crucial implications in developing a cheap and easy-to-operate way for MetS screening in the communities.

Methods

Study population

The Weitang Geriatric Diseases Study is a community-based study conducted in Weitang town located in Suzhou of China from August 2014 to January 2015. The detailed study protocol and some other findings have been reported in previous reports (7, 8, 9, 10, 11). In brief, invitation letters were sent to 6030 residents aged 60 years or older in the Weitang town based on official records. Older adults were excluded if he or she had migrated from the residing address, lived in the town less than 6 months, or had died. As a result, 5613 adults were considered ‘eligible’ to participate. Among them, 4611 attended the clinical examination and 4579 had complete data including blood samples. In addition, 241 participants with a medical history of diseases which could potentially influence the distributions of platelet parameters (e.g. liver, blood, inflammation, tumor) were excluded from the current analyses. Ultimately, 4338 healthy participants were included.

The Weitang Geriatrics Study complied with the Helsinki Declaration and was approved by the Institutional Review Board of the Soochow University. All participants involved in this study have signed informed consent.

Assessment and definition of metabolic syndrome components

Anthropometric measurements are performed by trained medical staff following standardized procedures. Body height in centimeters and weight in kilograms were measured without shoes using a wall-mounted measuring stadiometer and a digital scale. BMI was calculated using weight in kilograms divided by height in meters squared (kg/m2). Blood pressure (BP) was measured at least three times with a minimum 5-min interval by well-trained nurses using a digital automatic blood pressure monitor. Measurements were taken under the following conditions: participants had been seated for at least 5 min after questionnaire completion, refrained from smoking and at least 30 min after blood sampling. The average of the last two readings was used in the analysis. Venous blood samples were taken after fasting for 12 h. Blood high-density lipoprotein cholesterol (HDL-C), triglycerides, and glucose levels were measured using a chemistry analyzer (Roche cobas c 501).

According to the modified Adult Treatment Panel III (ATP III) guidelines (12), MetS was diagnosed when a participant had three or more of the following conditions: (1) BMI ≥25 kg/m2; (2) systolic blood pressure (SBP) ≥130 mmHg or diastolic blood pressure (DBP) ≥85 mmHg or history of antihypertensive medication; (3) blood triglycerides ≥150 mg/dL (1.7 mmol/L); (4) HDL-C of lower than 40 mg/dL (men) and 50 mg/dL (women); and (5) fasting plasma glucose ≥7.0 mmol/L or have been diagnosed with diabetes mellitus (13).

Assessment of platelet parameters

Five types of platelet parameters were measured in this study including platelet count (PC), mean platelet volume (MPV), platelet distribution width (PDW), plateletcrit (PCT) and large platelet ratio (P-LCR) by professional physicians in a contracted clinical laboratory. We collected the blood samples in ethylenediamine tetra acetic acid (EDTA) tube and processed them within 4 h. The results of measurement were recorded based on public unit of measurement. For platelet parameters, normal reference ranges were as follows: PC, 150–450 × 109/L; MPV, 6.7–9.6 fL; PCT, 0.15–0.31% for women and 0.14–0.28 % for men (14); PDW, 15.0–17.6% (15) and P-LCR, 21.3–51.2% for men and 21.1–51.4% for women (16).

Assessment of covariates

Socio-demographic data, medical history, smoking status, alcohol consumption and tea consumption were obtained through a standardized questionnaire carried out by trained interviewers. Education attainment from low to high is divided into five levels: no formal education, primary school, middle school, high school and university or above. Smoking status was classified into ‘never’, ‘current’ and ‘former smoker’, while alcohol and tea consumption was divided into ‘drinkers’ and ‘non-drinkers’.

Statistical analysis

All statistical analyses were performed using commercial software IBM SPSS version 25.0 (Spss Inc). Descriptive analyses included mean and s.d. for continuous variables and frequency for categorical and ordinal variables. The differences in platelet parameters between older adults with and without MetS were compared using the Student’s t-test. Participants were divided into four quartiles by the level of platelet parameters. Multiple logistic regression models were fitted to examine relationships between the quartiles of platelet parameters and the presence of MetS after adjusting for sex, age, smoking status and alcohol drinking status as covariates. Adjusted odds ratios (ORs) and corresponding 95% CIs were used to assess the strength of the platelet-MetS relationship. We also assessed the association of MetS components with platelet parameters in multivariable models.

Since hypertension and diabetes mellitus are the major causes of MetS, we further conducted a stratified analysis of the population with these two conditions. The relationships between platelet parameters and MetS and its components in different disease status were analyzed. We also investigated the potential interaction between gender and platelet parameters, and the results of the gender stratification analysis are shown in a forest plot. In all analyses, two-sided P values <0.05 were considered as the level of statistical significance.

Results

In this analysis, 820 out of 4338 (18.9%) individuals were found to be affected by MetS based on the ATP III criteria. Platelet parameters of the study population by metabolic syndrome status are displayed in Table 1. Subjects with MetS had significantly higher levels of PC (167 ± 48.5), PDW (16.1 ± 1.06) and PCT (0.16 ± 0.04) compared to those without (PC: 161 ± 50.3, PDW: 16.0 ± 1.14, PCT: 0.16 ± 0.04, P < 0.05 for all). However, levels of MPV and P-LCR were not different between the two groups. When stratified by gender, higher levels of PDW was observed (P < 0.01) in men with MetS. In women, levels of PC and PCT were significantly higher in subjects with MetS compared to those without.

Table 1

Platelet parameters of the study population by metabolic syndrome status.

Platelet parametersnMetabolic syndromeP value
AbsentPresent
All personsn = 4338n = 3518n = 820
 Platelet count, mean (s.d.), 109/L162 (50.1)161 (50.3)167 (48.5)<0.01
 Mean platelet volume, mean (s.d.), fL9.84 (1.29)9.84 (1.29)9.84 (1.30)0.94
 Platelet crit, mean (s.d.), %0.16 (0.04)0.16 (0.04)0.16 (0.04)<0.01
 Platelet distribution width, mean (s.d.), %16.1 (1.13)16.0 (1.14)16.1 (1.06)<0.05
 Large platelet ratio, mean (s.d.), %35.7 (10.7)35.6 (10.6)36.0 (10.7)0.35
Menn = 2117n = 1832n = 285
 Platelet count, mean (s.d.), 109/L159 (50.2)159 (50.6)160 (47.5)0.77
 Mean platelet volume, mean (s.d.), fL9.78 (1.29)9.78 (1.29)9.79 (1.32)0.83
 Platelet crit, mean (s.d.), %0.15 (0.04)0.15 (0.04)0.15 (0.04)0.61
 Platelet distribution width, mean (s.d.), %16.1 (1.17)16.0 (1.20)16.3 (0.94)<0.01
 Large platelet ratio, mean (s.d.), %35.0 (10.5)34.9 (10.5)35.7 (10.8)0.21
Womenn = 2221n = 1686n = 535
 Platelet count, mean (s.d.), 109/L166 (49.6)165 (49.9)171 (48.6)<0.05
 Mean platelet volume, mean (s.d.), fL9.91 (1.29)9.92 (1.29)9.87 (1.30)0.40
 Platelet crit, mean (s.d.), %0.16 (0.04)0.16 (0.04)0.16 (0.04)<0.01
 Platelet distribution width, mean (s.d.), %16.0 (1.09)16.0 (1.08)16.1 (1.12)0.41
 Large platelet ratio, mean (s.d.), %36.4 (10.8)36.4 (10.8)36.2 (10.7)0.62

Table 2 shows the associations between platelet parameters and MetS after adjusting for potential confounders. We found that participants with MetS tended to have higher levels of PDW, PC and PCT. For example, the OR of MetS for the highest vs lowest quartile of PDW, PC and PCT were 1.45 (95% CI:1.14–1.83), 1.32 (95% CI:1.06–1.64) and 1.37 (95% CI:1.10–1.71) in multiple-adjusted models, respectively (all P < 0.05). No significant associations were observed between levels of MPV/P-LCR and the presence of MetS.

Table 2

Relationships of platelet parameters to the prevalence of metabolic syndrome.

Unadjusted modelMultiple adjusted modela
OR (95% CI)P valueOR (95% CI)P value
Platelet count, 109/L1.002 (1.001, 1.004)<0.011.001 (1.000, 1.003)0.05
 Q1 (<129)ReferenceReference
 Q2 (130–160)1.21 (0.97, 1.51)0.091.22 (0.97, 1.52)0.09
 Q3 (161–191)1.12 (0.90, 1.40)0.331.05 (0.84, 1.32)0.66
 Q4 (>192)1.45 (1.17, 1.80)<0.011.32 (1.06, 1.64)<0.05
Mean platelet volume, fL1.00 (0.94, 1.06)0.940.99 (0.93, 1.05)0.61
 Q1 (<8.9)ReferenceReference
 Q2 (9.0–9.7)0.98 (0.79, 1.21)0.840.97 (0.78, 1.20)0.76
 Q3 (9.8–10.6)0.91 (0.73, 1.13)0.400.87 (0.70, 1.09)0.22
 Q4 (>10.7)1.04 (0.84, 1.29)0.721.00 (0.81, 1.24)0.99
Platelet crit, %1.38 (1.15, 1.66)<0.011.24 (1.03, 1.49)<0.05
 Q1 (<0.130)ReferenceReference
 Q2 (0.131–0.154)1.19 (0.95, 1.49)0.121.14 (0.91, 1.43)0.26
 Q3 (0.155–0.180)1.26 (1.01, 1.57)<0.051.15 (0.92, 1.44)0.22
 Q4 (>0.181)1.58 (1.28, 1.97)<0.011.37 (1.10, 1.71)<0.01
Platelet distribution width, %1.09 (1.02, 1.17)<0.051.11 (1.03, 1.19)<0.01
 Q1 (<15.8)ReferenceReference
 Q2 (15.9–16.1)1.31 (1.05, 1.64)<0.051.32 (1.06, 1.66)<0.05
 Q3 (16.2–16.5)1.59 (1.28, 1.96)<0.011.65 (1.33, 2.05)<0.01
 Q4 (>16.5)1.36 (1.08, 1.72)<0.011.45 (1.14, 1.83)<0.01
Platelet larger cell ratio, %1.003 (0.996, 1.011)0.351.00 (0.99, 1.01)0.70
 Q1 (<27.9)ReferenceReference
 Q2 (28.0–34.7)1.08 (0.87, 1.34)0.511.07 (0.86, 1.34)0.54
 Q3 (34.8–42.0)1.16 (0.94, 1.44)0.171.12 (0.90, 1.40)0.30
 Q4 (>42.0)1.17 (0.94, 1.45)0.161.11 (0.89, 1.39)0.34

aAdjusted for gender, age, smoking status (never, current and former smoker), and alcohol drinking status (drinkers and non-drinkers).

We also assessed the association of MetS components with platelet parameters (Table 3). Results indicated that the increased PC and PCT level was significantly correlated with three MetS factors (P < 0.05 for all) and that the level of PDW was correlated with the high blood triglycerides (OR:1.13, 95% CI: (1.05, 1.21)).

Table 3

Odds ratios (ORs) and 95% CIs for the association between platelet parameters and each criterion of metabolic syndrome.

Overweight/obesityElevated BPElevated TGReduced HDL-CElevated FPG
ORa (95% CI)ORa (95% CI)ORa (95% CI)ORa (95% CI)ORa (95% CI)
Platelet count, 109/L1.001 (1.000, 1.003)1.004 (1.002, 1.005)1.002 (1.001, 1.004)1.002 (1.000, 1.003)0.999 (0.997, 1.001)
P = 0.13P < 0.01P < 0.01P < 0.05P = 0.52
Mean platelet volume, fL1.01 (0.96, 1.08)1.00 (0.94, 1.07)0.97 (0.92, 1.03)0.92 (0.87, 0.98)1.04 (0.96, 1.12)
P = 0.66P = 0.96P = 0.36P < 0.01P = 0.32
Platelet crit, %1.25 (1.03, 1.50)1.75 (1.39, 2.20)1.32 (1.11, 1.58)1.12 (0.94, 1.34)0.84 (0.61, 1.16)
P < 0.05P < 0.01P < 0.01P = 0.22P = 0.28
Platelet distribution width, %1.03 (0.96, 1.10)1.01 (0.94, 1.09)1.13 (1.05, 1.21)1.05 (0.98, 1.12)1.05 (0.95, 1.15)
P = 0.48P = 0.77P < 0.01P = 0.20P = 0.34
Platelet larger cell ratio, %1.004 (0.996, 1.011)1.00 (0.99, 1.01)1.00 (0.99, 1.01)0.992 (0.985, 0.999)1.01 (1.00, 1.02)
P = 0.32P = 0.96P = 0.36P < 0.05P = 0.12

aAdjusted for gender, age, smoking status (never, current and former smoker), and alcohol drinking status (drinkers and non-drinkers).

BP, blood pressure; FPG, fasting plasma glucose; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides.

We further examined the relationship between platelet parameters and MetS in subgroup analyses stratified by the presence of diabetes or hypertension and gender (Tables 4 and 5). We found that the presence of hypertension or diabetes modified the relationship between PDW and MetS. Among individuals with hypertension, levels of PDW showed a positive association with MetS (P < 0.01), while this associations were not observed in non-hypertensive ones. In the stratified analysis of diabetes, however, this relationship was only significant in men without diabetes. Meanwhile, the relationships between PC/PCT and MetS were always significant in diabetic participants. In addition, significant interaction effect of gender on the relationship between levels of PDW and MetS were observed (P value for interaction <0.01). In subsequent analyses stratified by gender (Fig. 1), the levels of PDW increased with the risk of MetS in men but not in women.

Figure 1
Figure 1

Subgroup analyses of the association between platelet parameters and MetS. Interactions between platelet parameters and sex on the MetS were tested by the likelihood ratio test with adjustment for the same variables as multiple model in Table 2 in addition to sex. Odds ratio and 95% CIs were showed by forest plot.

Citation: Endocrine Connections 9, 7; 10.1530/EC-20-0209

Table 4

Associations of platelet parameters with metabolic syndrome by diabetes.

OR (95% CI)P ValueOR (95% CI)P ValueOR (95% CI)P ValueOR (95% CI)P ValueOR (95% CI)P Value
PC, 109/LMPV, fLPCT, %PDW, %P-LCR, %
Diabetes
 All persons1.009 (1.003, 1.014)<0.05All persons0.87 (0.72, 1.06)0.16All persons1.84 (1.04, 3.25)<0.05All persons0.96 (0.79, 1.06)0.68All persons0.98 (0.96, 1.00)0.99
 Q1 (≤121)ReferenceQ1 (≤8.90)ReferenceQ1 (≤0.13)ReferenceQ1 (≤15.9)ReferenceQ1 (≤28.3)Reference
 Q2 (121–157)1.32 (0.68, 2.57)0.41Q2 (8.91–9.80)1.12 (0.57, 2.19)0.74Q2 (0.14–0.15)0.86 (0.44, 1.68)0.66Q2 (16.0–16.2)0.93 (0.48, 1.81)0.84Q2 (28.4–35.6)1.64 (0.83, 3.26)0.16
 Q3 (158–192)1.65 (0.84, 3.23)0.15Q3 (9.81–10.70)0.48 (0.24, 0.95)<0.05Q3 (0.16–0.18)1.40 (0.71, 2.76)0.34Q3 (16.3–16.5)0.66 (0.34, 1.27)0.22Q3 (35.7–43.0)0.91 (0.46, 1.79)0.78
 Q4 (>192)1.91 (0.96, 3.82)0.07Q4 (>10.70)1.05 (0.53, 2.08)0.89Q4 (>0.18)1.45 (0.72, 2.91)0.30Q4 (>16.5)0.94 (0.48, 1.85)0.85Q4 (>43.0)0.77 (0.39, 1.50)0.44
 Men1.004 (0.997, 1.011)0.28Men1.02 (0.78, 1.34)0.23Men1.92 (0.81, 4.57)0.14Men1.17 (0.89, 1.56)0.26Men1.01 (0.98, 1.04)0.61
 Women1.008 (0.999, 1.012)0.06Women0.74 (0.56, 0.97)<0.05Women1.71 (0.79, 3.69)0.18Women0.71 (0.47, 1.08)0.11Women0.96 (0.92, 0.99)<0.05
Non-diabetes
 All persons1.001 (1.000, 1.003)0.16All persons0.99 (0.93, 1.06)0.83All persons1.20 (0.97, 1.48)0.90All persons1.15 (1.05, 1.25)<0.05All persons1.00 (1.00, 1.01)0.44
 Q1 (≤129)ReferenceQ1 (≤8.90)ReferenceQ1 (≤0.13)ReferenceQ1 (≤15.8)ReferenceQ1 (≤27.8)Reference
 Q2 (130–160)1.32 (1.03, 1.69)<0.05Q2 (8.91–9.70)0.96 (0.76, 1.22)0.76Q2 (0.14–0.15)1.23 (0.96, 1.60)0.11Q2 (15.9–16.1)1.51 (1.17, 1.94)<0.01Q2 (27.9–34.5)1.10 (0.86, 1.41)0.46
 Q3 (161–191)1.09 (0.84, 1.40)0.52Q3 (9.71–10.6)0.92 (0.72, 1.18)0.52Q3 (0.16–0.18)1.26 (0.98, 1.62)0.07Q3 (16.2–16.5)1.88 (1.47, 2.40)<0.01Q3 (34.6–41.9)1.13 (0.88, 1.44)0.33
 Q4 (>191)1.36 (1.06, 1.74)<0.05Q4 (>10.6)0.98 (0.77, 1.25)0.87Q4 (>0.18)1.38 (1.08, 1.78)<0.05Q4 (>16.5)1.58 (1.21, 2.06)<0.01Q4 (>41.9)1.13 (0.88, 1.44)0.33
 Men0.999 (0.996, 1.002)0.56Men1.00 (0.90, 1.12)0.98Men0.86 (0.59, 1.26)0.43Men1.25 (1.09, 1.45)<0.01Men1.01 (0.99, 1.02)0.30
 Women1.002 (1.000, 1.004)<0.05Women0.99 (0.91, 1.08)0.81Women1.42 (1.09, 1.84)<0.01Women1.09 (0.98, 1.21)0.12Women1.00 (0.99, 1.01)0.81

Adjusted for gender, age, smoking status (never, current and former smoker), and alcohol drinking status (drinkers and non-drinkers).

Table 5

Associations of platelet parameters with metabolic syndrome by hypertension.

OR (95% CI)P ValueOR (95% CI)P ValueOR (95% CI)P ValueOR (95% CI)P ValueOR (95% CI)P Value
PC, 109/LMPV, fLPCT, %PDW, %P-LCR, %
Hypertension
 All persons1.001 (0.998, 1.002)0.71All persons0.97 (0.90, 1.04)0.37All persons1.04 (0.82, 1.30)0.77All persons1.12 (1.02, 1.22)<0.05All persons1.00 (0.99, 1.01)0.81
 Q1 (≤131)ReferenceQ1 (≤8.90)ReferenceQ1 (≤0.13)ReferenceQ1 (≤15.9)ReferenceQ1 (≤28.1)Reference
 Q2 (132–163)1.19 (0.91, 1.57)0.21Q2 (8.91–9.70) 0.86 (0.65, 1.13)0.27Q2 (0.14–0.16)1.17 (0.89, 1.54)0.27Q2 (16.0–16.1)1.54 (1.16, 2.04)<0.01Q2 (28.2–35.2)1.08 (0.82, 1.42)0.58
 Q3 (164–195)1.07 (0.81, 1.40)0.65Q3 (9.71–10.70) 0.812 (0.62, 1.06)0.12Q3 (0.17–0.18)1.04 (0.79, 1.38)0.78Q3 (16.2–16.5)1.91 (1.45, 2.52)<0.01Q3 (35.3–42.4)1.04 (0.79, 1.37)0.77
 Q4 (>195)1.13 (0.86, 1.49)0.39Q4 (>10.70)0.90 (0.69, 1.18)0.45Q4 (>0.18)1.10 (0.84, 1.45)0.48Q4 (>16.5)1.52 (1.13, 2.04)<0.01Q4 (>42.4)1.06 (0.81, 1.40)0.67
 Men0.999 (0.996, 1.002)0.44Men1.03 (0.92, 1.06)0.57Men0.89 (0.60, 1.32)0.56Men1.22 (1.05, 1.41)<0.01Men1.01 (1.00, 1.03)0.13
 Women1.001 (0.999, 1.004)0.25Women0.93 (0.84, 1.02)0.11Women1.15 (0.86, 1.35)0.36Women1.05 (0.93, 1.17)0.43Women1.00 (0.98, 1.01)0.36
Non-hypertension
 All persons1.002 (0.999, 1.004)0.27All persons0.99 (0.89, 1.04)0.86All persons1.23 (0.86, 1.76)0.25All persons1.06 (0.93, 1.21)0.38All persons1.00 (0.99, 1.01)0.91
 Q1 (≤126)ReferenceQ1 (≤8.90)ReferenceQ1 (≤0.13)ReferenceQ1 (≤15.8)ReferenceQ1 (≤27.6)Reference
 Q2 (127–156)1.14 (0.77, 1.70)0.51Q2 (8.91–9.60)1.37 (0.94, 2.01)0.11Q2 (0.14–0.15)0.87 (0.58, 1.31)0.51Q2 (15.9–16.1)0.98 (0.66, 1.47)0.93Q2 (27.7–34.0)1.15 (0.77, 1.70)0.49
 Q3 (157–187)1.03 (0.70, 1.54)0.87Q3 (9.61–10.50)0.95 (0.64, 1.43)0.82Q3 (0.16–0.18)0.94 (0.63, 1.40)0.75Q3 (16.2–16.5)1.35 (0.93, 1.96)0.11Q3 (34.1–41.6)1.05 (0.71, 1.56)0.81
 Q4 (>187)1.25 (0.85, 1.84)0.26Q4 (>10.50)1.14 (0.77, 1.68)0.52Q4 (>0.18)1.21 (0.83, 1.77)0.33Q4 (>16.5)1.29 (0.86, 1.94)0.23Q4 (>41.6)1.19 (0.81, 1.76)0.38
 Men0.999 (0.994, 1.001)0.74Men0.94 (0.77, 1.14)0.50Men0.77 (0.40, 1.49)0.44Men1.20 (0.94, 1.53)0.15Men1.00 (0.98, 1.02)0.91
 Women1.003 (0.999, 1.006)0.14Women1.02 (0.89, 1.06)0.78Women1.50 (0.98, 2.31)0.06Women1.00 (0.85, 1.17)0.99Women1.00 (0.98, 1.02)0.75

Adjusted for gender, age, smoking status (never, current and former smoker), and alcohol drinking status (drinkers and non-drinkers).

Table 6 shows that high BP and high triglycerides were accompanied by increased levels of PC and PCT in non-diabetes participants. Meanwhile, as shown in Table 7, PC and PCT levels were associated with high triglycerides in patients with hypertension.

Table 6

Associations of platelet parameters with components of metabolic syndrome by diabetes.

Elevated BPElevated BMIElevated TGReduced HDL-C
OR (95% CI)P valueOR (95% CI)P valueOR (95% CI)P valueOR (95% CI)P value
Diabetes
 PC, 109/L1.00 (0.99, 1.01)0.561.00 (1.00, 1.01)0.451.01 (1.00, 1.01)<0.051.00 (1.00, 1.01)0.74
 MPV, fL1.07 (0.74, 1.54)0.731.00 (0.82, 1.22)0.990.84 (0.69, 1.03)0.100.93 (0.76, 1.14)0.49
 PCT, %1.97 (0.65, 6.00)0.231.36 (0.76, 2.43)0.311.63 (0.93, 2.87)0.091.04 (0.59, 1.83)0.90
 PDW, %1.27 (0.67, 1.97)0.081.19 (0.66, 2.16)0.561.09 (0.88, 1.35)0.550.90 (0.73, 1.11)0.32
 P-LCR, %1.01 (0.97, 1.06)0.581.00 (0.89, 1.03)0.880.98 (0.96, 1.01)0.190.99 (0.96, 1.01)0.29
Non-diabetes
 PC, 109/L1.004 (1.002, 1.006)<0.051.000 (0.999, 1.003)0.231.002 (1.000, 1.003)<0.051.002 (1.000, 1.003)<0.05
 MPV, fL1.00 (0.93, 1.07)0.941.02 (0.96, 1.08)0.590.99 (0.93, 1.05)0.610.92 (0.86, 0.98)<0.01
 PCT, %1.77 (1.40, 2.24)<0.051.22 (1.00, 1.49)0.051.29 (1.07, 1.55)<0.011.13 (0.93, 1.21)0.21
 PDW, %1.00 (0.93, 1.08)0.921.02 (0.95, 1.10)0.611.13 (1.05, 1.22)<0.011.07 (0.99, 1.15)0.09
 P-LCR, %1.00 (1.00, 1.01)0.821.00 (1.00, 1.01)0.291.01 (1.00, 1.01)0.190.99 (0.99, 1.10)<0.05

Adjusted for gender, age, smoking status (never, current and former smoker), and alcohol drinking status (drinkers and non-drinkers).

BP blood pressure, TG triglycerides, HDL-C high-density lipoprotein cholesterol.

Table 7

Associations of platelet parameters with components of metabolic syndrome by hypertension.

Elevated FPGElevated BMIElevated TGReduced HDL-C
OR (95% CI)P valueOR (95% CI)P valueOR (95% CI)P valueOR (95% CI)P value
Hypertension
 PC, 109/L0.998 (0.996, 1.001)0.211.000 (0.998, 1.002)0.951.002 (1.000, 1.004)<0.051.002 (1.000, 1.004)<0.05
 MPV, fL1.03 (0.94, 1.12)0.601.03 (0.95, 1.11)0.470.93 (0.87, 1.01)0.070.90 (0.83, 0.97)<0.01
 PCT, %1.14 (0.10, 2.73)0.200.96 (0.18, 21.01)0.581.25 (1.01, 1.56)<0.051.11 (0.89, 1.40)0.35
 PDW, %1.06 (0.95, 1.19)0.271.03 (0.94, 1.12)0.551.09 (1.00, 1.19)0.051.03 (0.94, 1.12)0.55
 P-LCR, %1.01 (1.00, 1.02)0.701.01 (1.00, 1.02)0.201.00 (0.99, 1.01)0.080.99 (0.98, 1.00)<0.05
Non-hypertension
 PC, 109/L0.999 (0.995, 1.003)0.701.002 (0.998, 1.004)0.181.000 (0.998, 1.003)0.541.001 (0.998, 1.003)0.66
 MPV, fL1.04 (0.90, 1.27)0.600.97 (0.88, 1.08)0.611.02 (0.93, 1.13)0.631.95 (0.87, 1.04)0.25
 PCT, %0.96 (0.58, 1.60)0.871.28 (0.92, 1.78)0.151.16 (0.85, 1.59)0.350.96 (0.71, 1.30)0.79
 PDW, %0.98 (0.83, 1.15)0.801.00 (0.89, 1.12)0.991.19 (1.05, 1.36)<0.011.07 (0.96, 1.19)0.25
 P-LCR, %1.01 (0.99, 1.03)0.441.00 (0.99, 1.01)0.961.01 (1.00, 1.02)0.141.00 (0.98, 1.01)0.37

Adjusted for gender, age, smoking status (never, current and former smoker), and alcohol drinking status (drinkers and non-drinkers).

FPG, fasting plasma glucose; HDL-C, high-density lipoprotein cholesterol; TG, triglycerides.

Discussion

In the present study, we found that some platelet parameters such as PC, PCT and PDW were significantly associated with MetS in a large sample of community-dwelling older adults. In general, older individuals with MetS tended to have higher PC, PCT and PDW levels compared with those without. Moreover, gender and the presence of hypertension and diabetes modified the association between PDW and MetS. These findings added new knowledge on role of platelets in the pathogenesis of MetS and may help to develop early diagnostic strategies for MetS-related complications.

To the best of our knowledge, this is the first study assessing the relationship of platelet parameters with MetS in older adults. PDW represents the morphologic changes and the activity of platelets (17). Therefore, elevated PDW indicates the presence of larger, younger and more active platelets in peripheral blood (6, 18, 19). The biological mechanisms underlying observed associations between MetS and PDW are not fully elucidated, but some possibilities have been suggested. Insulin resistance plays an important role in the development of MetS and type 2 diabetes mellitus. Vascular dysfunction caused by insulin resistance may lead to a decrease in prostacyclin and nitric oxide production by the vascular endothelium, resulting in promoting increased platelet activity (20). Furthermore, a plethora of studies have indicated that platelet activation is associated with the development of atherosclerosis and thrombosis, which is a result of MetS components such as hypertension, obesity, and inflammation. People with atherosclerosis and thrombosis exhibit greater platelet activation, which promotes increased peripheral platelet consumption, resulting in platelets produced by the bone marrow being larger and more active.

We also found that levels of PC/PCT were higher in older adults with MetS than those without. In accordance with results of Table 3, both PC and PCT were associated with three components of MetS. First, it is suggested that metabolic disorders are often accompanied by low-grade inflammation (21), characterized by increased secretion of a variety of adipokines and cytokines such as adiponectin, interleukin 6 (IL-6), and tumor necrosis factor-α (TNF-α), leading to changes in levels of PC (22). Meanwhile, the value of PCT is derived from PC and MPV with increased PC levels being associated with higher PCT levels. Secondly, physical activity showed a significant and inverse dose-response relationship with levels of PC (23). PC can also be influenced by nutritional status, which is closely related to metabolic function. Martin et al. (24) demonstrated that the animals on the high-cholesterol diet had significantly higher levels of PC than the control group, which indicates that a high-cholesterol diet produces might change platelet production. The development of MetS is highly dependent of physical activity, nutrition, lifestyle, and family genetics and these risk factors for MetS may, at least partially, explain the relationship between PC/PCT and MetS. This also explains the increased levels of PC and PCT in people who have both high BP and high triglycerides. Our study suggested that the presence of hypertension modified the association between increased PDW levels in people who had both hypertension and MetS. High blood pressure might cause endothelial dysfunction and vascular damage, which promotes platelet activation and adhesion (25). According to previous studies, PDW is a more specific marker of platelet activation than MPV (26). Furthermore, hypertension is a major risk factor for cardiovascular disease (CVD) (27) and PDW associated with or predicting cardiovascular disorders (28).

Our study showed that the association of PC/PCT with MetS was gender specific and was significant in women rather than men. This finding was consistent with some previous studies (29, 30). We provided some explanations for the gender difference in the PC/PCT-MetS associations. First, women tend to have higher body fat rates than men with similar BMI. Pro-inflammatory cytokines are released by perivascular white adipose tissue, which may increase the numbers of platelets and lead to chronic low-grade inflammation (31). Second, there are differences in lifestyles between men and women. Chinese men are significantly more likely to smoke and drink alcohol than women. Cigarette smoking has been shown to increase platelet count and platelet aggregation (32, 33), while alcohol consumption can lead to abnormalities in platelet function (34, 35). Third, higher testosterone levels might be associated with a reduced risk of MetS in men and an elevated risk in women (36). Thus, one may hypothesize that gender-specific sex hormones may explain some of this phenomenon. Our study had a relatively large sample size so that the study power was sufficient to detect the association between platelet parameters and MetS as well as some possible interaction effects. However, some limitations exist in the current study. First, the causal relationship could not be identified because of the nature of the cross-sectional design. It is also likely that MetS may result in a higher level of platelet parameters. In addition, although some confounding factors have been adjusted, other potential confounding effects (such as long-term medication) may still exist and may distort the findings.

In conclusion, our study provided preliminary evidence on the association between platelet parameters including PC, PCT and PDW and MetS in older Chinese adults. Higher PC/PCT levels tended to increase the risk of MetS in women and increased PDW levels in hypertensive patients was associated with MetS. Longitudinal cohort studies are warranted to confirm the findings and evaluate the power and ability of platelet parameters in predicting MetS and its associated complications.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

This study was supported by the Science and Technology Bureau of Xiangcheng District in Suzhou, China, under grant no. XJ201706 and the Health Commission of Suzhou under grant no. GSWS2019090.

Ethical approval

This study was conducted complying with the tenets of the Helsinki Declaration and with the approval of the Institutional Review Board of Soochow University.

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    Subgroup analyses of the association between platelet parameters and MetS. Interactions between platelet parameters and sex on the MetS were tested by the likelihood ratio test with adjustment for the same variables as multiple model in Table 2 in addition to sex. Odds ratio and 95% CIs were showed by forest plot.

  • 1

    GuDReynoldsKWuXChenJDuanXReynoldsRFWheltonPKHeJ. Prevalence of the metabolic syndrome and overweight among adults in China. Lancet 2005 365 13981405. (https://doi.org/10.1016/S0140-6736(05)66375-1)

    • Search Google Scholar
    • Export Citation
  • 2

    LuJWangLLiMXuYJiangYWangWLiJMiSZhangMLiY Metabolic syndrome among adults in China: the 2010 China noncommunicable disease surveillance. Journal of Clinical Endocrinology and Metabolism 2017 102 507515. (https://doi.org/10.1210/jc.2016-2477)

    • Search Google Scholar
    • Export Citation
  • 3

    AlbertiKGZimmetPShawJ & IDF Epidemiology Task Force Consensus Group. The metabolic syndrome – a new worldwide definition. Lancet 2005 366 10591062. (https://doi.org/10.1016/S0140-6736(05)67402-8)

    • Search Google Scholar
    • Export Citation
  • 4

    SantilliFVazzanaNLianiRGuagnanoMTDaviG. Platelet activation in obesity and metabolic syndrome. Obesity Reviews 2012 13 2742. (https://doi.org/10.1111/j.1467-789X.2011.00930.x)

    • Search Google Scholar
    • Export Citation
  • 5

    ChuSGBeckerRCBergerPBBhattDLEikelboomJWKonkleBMohlerERReillyMPBergerJS. Mean platelet volume as a predictor of cardiovascular risk: a systematic review and meta-analysis. Journal of Thrombosis and Haemostasis 2010 8 148156. (https://doi.org/10.1111/j.1538-7836.2009.03584.x)

    • Search Google Scholar
    • Export Citation
  • 6

    ZaccardiFRoccaBPitoccoDTaneseLRizziAGhirlandaG. Platelet mean volume, distribution width, and count in type 2 diabetes, impaired fasting glucose, and metabolic syndrome: a meta-analysis. Diabetes/Metabolism Research and Reviews 2015 31 402410. (https://doi.org/10.1002/dmrr.2625)

    • Search Google Scholar
    • Export Citation
  • 7

    YangXJTianSMaQHSunHPXuYPanCW. Leukocyte-related parameters in older adults with metabolic syndrome. Endocrine 2020 68 312319. (https://doi.org/10.1007/s12020-020-02243-2)

    • Search Google Scholar
    • Export Citation
  • 8

    QianYXLiuJHMaQHSunHPXuYPanCW. Associations of sleep durations and sleep-related parameters with metabolic syndrome among older Chinese adults. Endocrine 2019 66 240248. (https://doi.org/10.1007/s12020-019-02064-y)

    • Search Google Scholar
    • Export Citation
  • 9

    LiuJHMaQHSunHPXuYPanCW. Depressive symptom as a mediator of the influence of self-reported sleep quality on falls: a mediation analysis. Aging and Mental Health 2020 16. (https://doi.org/10.1080/13607863.2020.1711860)

    • Search Google Scholar
    • Export Citation
  • 10

    LiuJHQianYXMaQHSunHPXuYPanCW. Depressive symptoms and metabolic syndrome components among older Chinese adults. Diabetology and Metabolic Syndrome 2020 12 18. (https://doi.org/10.1186/s13098-020-00526-2)

    • Search Google Scholar
    • Export Citation
  • 11

    LiuJHZhangYJMaQHSunHPXuYPanCW. Elevated blood neutrophil to lymphocyte ratio in older adults with cognitive impairment. Archives of Gerontology and Geriatrics 2020 88 104041. (https://doi.org/10.1016/j.archger.2020.104041)

    • Search Google Scholar
    • Export Citation
  • 12

    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 2001 285 24862497. (https://doi.org/10.1001/jama.285.19.2486)

    • Search Google Scholar
    • Export Citation
  • 13

    SabanayagamCWangJJMitchellPTanAGTaiESAungTSawSMWongTY. Metabolic syndrome components and age-related cataract: the Singapore Malay eye study. Investigative Ophthalmology and Visual Science 2011 52 23972404. (https://doi.org/10.1167/iovs.10-6373)

    • Search Google Scholar
    • Export Citation
  • 14

    KimMJParkPWSeoYHKimKHSeoJYJeongJHParkMJJungJWAhnJY. Reference intervals for platelet parameters in Korean adults using Advia 2120. Annals of Laboratory Medicine 2013 33 364366. (https://doi.org/10.3343/alm.2013.33.5.364)

    • Search Google Scholar
    • Export Citation
  • 15

    HoffmannJJvan den BroekNMCurversJ. Reference intervals of reticulated platelets and other platelet parameters and their associations. Archives of Pathology and Laboratory Medicine 2013 137 16351640. (https://doi.org/10.5858/arpa.2012-0624-OA)

    • Search Google Scholar
    • Export Citation
  • 16

    ZhangGMXiaYJZhangGMZhuBLHuLYMaXBZhouYT. Laboratory reference intervals of complete blood count for apparently healthy elderly people in Shuyang, China. Clinical Laboratory 2014 60 20812087. (https://doi.org/10.7754/clin.lab.2014.140320)

    • Search Google Scholar
    • Export Citation
  • 17

    KaratekeAKurtRKBalogluA. Relation of platelet distribution width (PDW) and platelet crit (PCT) to preeclampsia. Ginekologia Polska 2015 86 372375. (https://doi.org/10.17772/gp/2425)

    • Search Google Scholar
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  • 18

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