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. 2018 Aug 16;13(8):e0201585.
doi: 10.1371/journal.pone.0201585. eCollection 2018.

Adipokines demonstrate the interacting influence of central obesity with other cardiometabolic risk factors of metabolic syndrome in Hong Kong Chinese adults

Rashmi Supriya  1 , Bjorn T Tam  1 , Angus P Yu  2 , Paul H Lee  3 , Christopher W Lai  1 , Kenneth K Cheng  1 , Sonata Y Yau  4 , Lawrence W Chan  1 , Benjamin Y Yung  1 , Sinead Sheridan  2 , Parco M Siu  2
Affiliations

Adipokines demonstrate the interacting influence of central obesity with other cardiometabolic risk factors of metabolic syndrome in Hong Kong Chinese adults

Rashmi Supriya et al. PLoS One. .

Abstract

Objective: Metabolic syndrome (MetS) or prediabetes is a complex disorder that is defined by a clustering of cardiometabolic risk factors, including obesity, hypertriglyceridemia, reduced high-density lipoprotein (HDL) cholesterol, hypertension, and insulin resistance. Among cardiometabolic risk factors, central obesity plays a key role in the development of MetS through alterations in the secretion of adipokines and interacts with other MetS risk factors to unfavorably influence overall cardiometabolic risk. Obesity has grasped epidemic proportions in Asia, which has the highest number of people with diabetes in the world. But, the importance of central obesity in the clustering of all four MetS risk factors or vice versa in predicting severity of MetS has not yet been investigated in Asian population. Therefore, the present study examined the influence of central obesity on circulating levels of adipokines through its interaction with the clustering of cardiometabolic risk factors of MetS including hyperglycemia, hypertriglyceridemia, dyslipidemia and hypertension in Hong Kong Chinese adults.

Subjects: Blood samples from 83 Hong Kong Chinese adults, who were previously screened for MetS according to the guideline of the United States National Cholesterol Education Program Expert Panel Adult Treatment Panel III criteria were selected. Insulin and adipokines, including visfatin, chemerin, plasminogen activator inhibitor-1 (PAI-1), resistin, C-C motif chemokine ligand 2 (CCL-2), interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-10 (IL-10), tumour necrosis factor-α (TNF-α), leptin and adiponectin were assessed.

Results: The interacting effect of central obesity with all of the other four MetS risk factors increased the proinflammatory status of adipokines (TNF-α, leptin) and decreased the anti-inflammatory status of adipokine (adiponectin).

Conclusion: Our results indicate that the inflammatory status of MetS may be more severe in the presence of central obesity. Adipokines, as biomarkers for pathophysiological changes, may help to improve early patient identification and to predict MetS-associated morbidity and mortality.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flowchart of the study design.
Fig 2
Fig 2. The interaction of central obesity with the clustering of the other 4 MetS risk factors on adipokines.
The line graphs represent the direction and slope of interaction effect of central obesity and the clustering of the other 4 MetS risk factors (high fasting blood glucose, high triglycerides, low HDL and high systolic and diastolic BP) on adipokines including TNF-α (A), leptin (B) and adiponectin (C) in Hong Kong Chinese women categorized into four groups: 1) subjects with none of the cardiometabolic risk factors (N4RF_NO; n = 20), 2) subjects with only central obesity without the other 4 MetS cardiometabolic risk factors (N4RF_O; n = 35), 3) subjects without central obesity but with the other 4 MetS cardiometabolic risk factors (4RF_NO; n = 9), and 4) subjects with all five MetS cardiometabolic risk factors (4RF_O; n = 19). Data are expressed in estimated marginal means. Statistical significance was accepted at P < 0.05.
Fig 3
Fig 3. Main effect of obesity on adipokines.
The line graphs (A-D) represent the means of insulin, chemerin, IL-6, and PAI-1 of subjects without central obesity (n = 29) versus subjects with central obesity (n = 54) irrespective of the presence of the clustering of 4 MetS risk factors (high fasting blood glucose, high triglycerides, low HDL and high systolic and diastolic BP). The data are expressed as the mean ± 1 standard deviation. Statistical significance was accepted at P < 0.05.
Fig 4
Fig 4. Main effect of the clustering of 4 MetS risk factors on adipokines.
The line graphs (A-D) represent the means of insulin, chemerin, IL-8, and visfatin of subjects without the clustering of 4 MetS risk factors (high fasting blood glucose, high triglycerides, low HDL and high systolic and diastolic BP) (n = 46) versus subjects with the clustering of 4 MetS risk factors (n = 37) irrespective of the presence or absence of central obesity. The data are expressed as the mean ± 1 standard deviation. Statistical significance was accepted at P < 0.05.
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Grants and funding

We declare all the funding or sources of support received during this specific study as the following, and all the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This study was supported by the Hong Kong Research Grants Council Hong Kong Ph.D. Fellowship Scheme (RTVX PF13-11753), the Hong Kong Polytechnic University Research Fund (RTAS and 1-ZE17), The University of Hong Kong Seed Fund for Basic Research, and the Hong Kong Research Grants Council General Research Fund (PolyU 5632/10M).