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Confirming a Biological Pathway in the Metabolic Syndrome—Insight from the NHANES 1999–2002
Author(s) -
Lin LianYu,
Kuo HsuKo,
Li HungYuan,
Hwang JueyJen,
Lin JouWei
Publication year - 2008
Publication title -
obesity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.438
H-Index - 199
eISSN - 1930-739X
pISSN - 1930-7381
DOI - 10.1038/oby.2008.429
Subject(s) - national health and nutrition examination survey , dyslipidemia , insulin resistance , metabolic syndrome , medicine , obesity , anthropometry , endocrinology , inflammation , mediation , systemic inflammation , diabetes mellitus , physiology , environmental health , population , political science , law
The objective of this study was to examine the role of obesity in the development of the metabolic syndrome (MS). A total of 3,596 whites aged 19 years and above, who participated in the National Health and Nutrition Examination Survey (NHANES) 1999–2002, were included for analysis. Anthropometric measurements, biochemical profiles, and high‐sensitivity C‐reactive protein (CRP) were measured. A structural equation model (SEM) was constructed to elucidate a pathway in which obesity initiated the cascade leading to full MS. The results of SEM demonstrated that obesity was positively associated with elevated CRP level ( B = 0.05, P < 0.001). This higher inflammatory state directed to insulin resistance ( B = 0.32, P < 0.001), which in turn was positively associated with dyslipidemia ( B = 0.06, P < 0.001). Obesity could also directly and positively affect blood pressure ( B = 0.51, P < 0.001), without the mediation of insulin resistance and/or inflammation. The results of the cross‐sectional analysis in the white subjects have shown that obesity has a strong influence on hypertension that obtains little additional influence from inflammation or insulin resistance. The metabolic profile in the NHANES group has been confirmatory with the statement that there is a sequential effect from obesity to inflammation, insulin resistance, and dyslipidemia. This approach has allowed to inferring important biological insights about the nature of the relationships among the components of MS.