Multiple biomarkers and their relative contributions to identifying metabolic syndrome

Lee, Jeong Gyu; Lee, Sang Yeoup; Kim, Young Joo; Jin, Hong Ki; Cho, Byung Mann; Kim, Yun Jin; Jeong, Dong Wook; Park, Hyun Ji; Kim, Ji Eun

doi:10.1016/j.cca.2009.07.006

Cited by 28 publications

(15 citation statements)

References 30 publications

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“…In some studies, serum NT-proBNP and Hcy levels have been significantly related to IR, MetS, and diabetes risk 9–12,21. However, there has been no relationship of serum NT-proBNP and Hcy levels with IR, MetS, and diabetes risk in other studies 13,14,22. The current study showed that despite the identification of one or more of IR, MetS, and diabetes risk by serum NT-proBNP and Hcy levels, there was no independent association of serum NT-proBNP and Hcy levels with IR, MetS, and diabetes risk after full adjustment covering all components of MetS and CDRS.…”

Section: Discussionmentioning

confidence: 99%

Deep analyses of the associations of a series of biomarkers with insulin resistance, metabolic syndrome, and diabetes risk in nondiabetic middle-aged and elderly individuals: results from a Chinese community-based study

Fu¹,

Ping

Luo³

2016

CIA

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ObjectiveThe current study was designed to perform deep analyses of the associations of biomarkers, including high-sensitivity C-reactive protein (hs-CRP), N-terminal prohormone of brain natriuretic peptide (NT-proBNP), and homocysteine (Hcy), with insulin resistance (IR), metabolic syndrome (MetS), and diabetes risk and evaluate the abilities of biomarkers to identify IR, MetS, and diabetes risk in Chinese community-dwelling middle-aged and elderly residents.Participants and methodsA total of 396 participants older than 45 years underwent physical examinations and laboratory analyses following standardized protocol.ResultsSerum hs-CRP concentrations were able to identify MetS, Chinese diabetes risk score (CDRS) ≥4, high-density lipoprotein-cholesterol (HDL-c) <0.9/1.0 mmol/L, and HDL-c <1.0/1.3 mmol/L (P<0.05 for all). Serum NT-proBNP concentrations were able to identify homeostasis model assessment of IR >1.5, CDRS ≥4, overweight, and blood pressure (BP) ≥140/90 mmHg (P<0.05 for all). Serum Hcy concentrations were able to identify CDRS ≥4, general obesity, overweight, and BP ≥140/90 mmHg (P<0.05 for all). Serum hs-CRP concentrations were independently associated with MetS as well as HDL-c <1.0/1.3 mmol/L and HDL-c <0.9/1.0 mmol/L (P<0.05 for all). Serum NT-proBNP concentrations were independently associated with BP ≥140/90 mmHg (P<0.05). Serum Hcy concentrations were independently associated with CDRS ≥4 (P<0.05).ConclusionSerum HDL-c levels were the major determinant of the associations between serum hs-CRP levels and MetS and the key link between inflammation and MetS. There was no other association of these biomarkers with IR, MetS, and diabetes risk after full adjustment.

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Section: Discussionmentioning

confidence: 99%

Deep analyses of the associations of a series of biomarkers with insulin resistance, metabolic syndrome, and diabetes risk in nondiabetic middle-aged and elderly individuals: results from a Chinese community-based study

Fu¹,

Ping

Luo³

2016

CIA

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“…Receiver‐operating characteristic curve analysis, a univariate analysis, is often used, 2 although multivariate analysis should also be used to evaluate the relative contribution of each liver enzyme to MetS. Xia et al.…”

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confidence: 99%

Multiple factors are required for the prediction of metabolic syndrome

Kawada

2011

Clinical and Experimental Pharmacology and Physiology

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We read with great interest the paper by Xia et al., 1 who reported an association between the metabolic syndrome (MetS) and liver enzymes by the following two methods. After setting optimal cut-off values for liver enzymes in MetS by receiver-operating characteristic (ROC) curve analysis, logistic regression analysis was conducted to predict MetS by each liver enzyme. They concluded that MetS was significantly associated with increased serum levels of alanine aminotransferase (ALT) and c-glutamyl transpeptidase (GGT), even after adjustment for age, body mass index (BMI), and lifestyle factors, such as smoking and alcohol consumption.Receiver-operating characteristic curve analysis, a univariate analysis, is often used, 2 although multivariate analysis should also be used to evaluate the relative contribution of each liver enzyme to MetS. Xia et al. 1 did not use all the three liver enzymes simultaneously as independent variables in their analysis illustrated in table 3.I recently addressed this issue using data not described in recent studies. 3,4 In 2008, a total of 2537 male workers aged 34-63 years participated in this study. Patients with fasting plasma glucose levels of under 140 mg ⁄ dL were selected, because the validity of the homeostasis model assessment for insulin resistance (HOMA-IR) decreases at plasma glucose levels exceeding 140 mg ⁄ dL or higher. Patients who showed positive hepatitis type B antigen and ⁄ or positive hepatitis type C antibody were also excluded. Patients with diabetes, hypertension, dyslipidaemia, hyperuricaemia, coronary, and ⁄ or cerebrovascular diseases were excluded. Finally, 2178 patients, who also attended a health examination in 2010, were included in the analysis. Similar to the statistical method selected by Xia et al., I also conducted a logistic regression analysis to assess the predictive ability of several factors simultaneously for MetS. I used BMI, age, serum aspartate aminotransferase (AST), serum ALT, serum GGT, the HOMA-IR, and uric acid as independent variables.A logistic regression analysis (Wald method) revealed the odds ratios (95% confidence intervals) of age, BMI, uric acid, Log-transformed value of the HOMA-IR, Log-transformed GGT, and no habitual drinking in 2008 for the presence of metabolic syndrome. 0.70 (0.49-0.99), respectively.Only serum GGT was selected among the liver enzymes as a significant predictor of MetS in my study. As significant correlations among the three liver enzymes exist, especially between ALT and AST (r = 0.757, P < 0.001), attention must also be paid to the multicollinearity of independent variables. 5 Although the prediction of MetS should be conducted using several related factors simultaneously as independent variables to determine relative significances, I understand that the strategy used by Xia et al. (table 3) was not a wrong strategy to determine the contribution of each liver enzyme to the prediction of MetS. REFERENCES 1. Xia MF, Yan HM, Lin HD et al. Elevation of liver enzymes within the normal limits and metabolic sy...

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“…To the best of our knowledge, this is the first study to determine a cut‐off point for HOMA‐IR to discriminate MetS in a Japanese population. Several reports have determined HOMA‐IR cut‐off points for the diagnosis of MetS as 1.22 15 , 1.7 16 , 2.3 17 and 2.34 18 by ROC analysis in other ethnic populations (Table 2). The possible reasons for the inconsistency might be the differences in ethnicity and clinical backgrounds, including body mass index.…”

Section: Discussionmentioning

confidence: 99%

Optimal cut‐off point for homeostasis model assessment of insulin resistance to discriminate metabolic syndrome in non‐diabetic Japanese subjects

Yamada

Moriyama

Takahashi³

2012

J of Diabetes Invest

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We have recently established a ‘health‐associated’ reference interval of homeostasis model assessment of insulin resistance (HOMA‐IR) between 0.4 and 2.4. In the present study, the aim was to establish a ‘decision‐based’ limit of HOMA‐IR for the discrimination of metabolic syndrome (MetS) in non‐diabetic Japanese subjects. The receiver–operating characteristic curve of HOMA‐IR for detecting MetS was developed using data from 6868 non‐diabetic subjects (3727 men, 3141 women). The optimal cut‐off point was determined based on the point that yielded the minimum value of the square root of [(1 – sensitivity)2 + (1 – specificity)2]. HOMA‐IR = 1.7 was determined as the optimal cut‐off value, with a sensitivity and specificity of 73.4% and 70.5% for men, and 81.5% and 77.0% for women, respectively. In conclusion, the optimal cut‐off value for HOMA‐IR to discriminate MetS in non‐diabetic Japanese subjects appears to be 1.7. (J Diabetes Invest, doi: 10.1111/j.2040‐1124.2012.00194.x, 2012)

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Multiple biomarkers and their relative contributions to identifying metabolic syndrome

Cited by 28 publications

References 30 publications

Deep analyses of the associations of a series of biomarkers with insulin resistance, metabolic syndrome, and diabetes risk in nondiabetic middle-aged and elderly individuals: results from a Chinese community-based study

Deep analyses of the associations of a series of biomarkers with insulin resistance, metabolic syndrome, and diabetes risk in nondiabetic middle-aged and elderly individuals: results from a Chinese community-based study

Multiple factors are required for the prediction of metabolic syndrome

Optimal cut‐off point for homeostasis model assessment of insulin resistance to discriminate metabolic syndrome in non‐diabetic Japanese subjects

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