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Obesity and Metabolic Syndrome
Comparison of Competitive Models of Metabolic Syndrome Using Structural Equation Modeling: A Confirmatory Factor Analysis
Karimollah Hajian-Tilaki
Diabetes Metab J. 2018;42(5):433-441.   Published online October 22, 2018
DOI: https://doi.org/10.4093/dmj.2018.0010
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  • 41 Download
  • 4 Web of Science
  • 4 Crossref
AbstractAbstract PDFPubReader   
Background

The purpose of this study was to apply the structural equation modeling (SEM) to compare the fitness of different competing models (one, two, and three factors) of the metabolic syndrome (MetS) in Iranian adult population.

Methods

Data are given on the cardiometabolic risk factors of 841 individuals with nondiabetic adults from a cross-sectional population-based study of glucose, lipids, and MetS in the north of Iran. The three conceptual hypothesized models (single factor, two correlated factors, and three correlated latent factors) were evaluated by using confirmatory factor analysis with the SEM approach. The summary statistics of correlation coefficients and the model summary fitting indexes were calculated.

Results

The findings show that a single-factor model and a two-correlated factor model had a poorer summary fitting index compared with a three-correlated factor model. All fitting criteria met the conceptual hypothesized three-correlated factor model for both sexes. However, the correlation structure between the three underlying constructs designating the MetS was higher in women than in men.

Conclusion

These results indicate the plausibility of the pathophysiology and etiology of MetS being multifactorial, rather than a single factor, in a nondiabetic Iranian adult population.

Citations

Citations to this article as recorded by  
  • Structural Equation Modelling for Predicting the Relative Contribution of Each Component in the Metabolic Syndrome Status Change
    José E. Teixeira, José A. Bragada, João P. Bragada, Joana P. Coelho, Isabel G. Pinto, Luís P. Reis, Paula O. Fernandes, Jorge E. Morais, Pedro M. Magalhães
    International Journal of Environmental Research and Public Health.2022; 19(6): 3384.     CrossRef
  • New risk score model for identifying individuals at risk for diabetes in southwest China
    Liying Li, Ziqiong Wang, Muxin Zhang, Haiyan Ruan, Linxia Zhou, Xin Wei, Ye Zhu, Jiafu Wei, Sen He
    Preventive Medicine Reports.2021; 24: 101618.     CrossRef
  • Definition and early diagnosis of metabolic syndrome in children
    Gunter Matthias Christian Flemming, Sarah Bussler, Antje Körner, Wieland Kiess
    Journal of Pediatric Endocrinology and Metabolism.2020; 33(7): 821.     CrossRef
  • Calcium-Sensing Receptor in Adipose Tissue: Possible Association with Obesity-Related Elevated Autophagy
    Pamela Mattar, Sofía Sanhueza, Gabriela Yuri, Lautaro Briones, Claudio Perez-Leighton, Assaf Rudich, Sergio Lavandero, Mariana Cifuentes
    International Journal of Molecular Sciences.2020; 21(20): 7617.     CrossRef
Factor Structure of Indices of the Second Derivative of the Finger Photoplethysmogram with Metabolic Components and Other Cardiovascular Risk Indicators
Tomoyuki Kawada, Toshiaki Otsuka
Diabetes Metab J. 2013;37(1):40-45.   Published online February 15, 2013
DOI: https://doi.org/10.4093/dmj.2013.37.1.40
  • 3,416 View
  • 29 Download
  • 8 Crossref
AbstractAbstract PDFPubReader   
Background

The second derivative of the finger photoplethysmogram (SDPTG) is an indicator of arterial stiffness. The present study was conducted to clarify the factor structure of indices of the SDPTG in combination with components of the metabolic syndrome (MetS), to elucidate the significance of the SDPTG among various cardiovascular risk factors.

Methods

The SDPTG was determined in the second forefinger of the left hand in 1,055 male workers (mean age, 44.2±6.4 years). Among 4 waves of SDPTG components, the ratios of the height of the "a" wave to that of the "b" and "d" waves were expressed as b/a and d/a, and used as SDPTG indices for the analysis.

Results

Principal axis factoring analysis was conducted using age, SDPTG indices, components of MetS, and the serum levels of C-reactive protein (CRP) and uric acid. Three factors were extracted, and the SDPTG indices were categorized in combination with age as the third factor. Metabolic components and the SDPTG indices were independently categorized. These three factors explained 44.4% of the total variation. Multiple logistic regression analysis revealed age, d/a, serum uric acid, serum CRP, and regular exercise as independent determinants of the risk of MetS. The odds ratios (95% confidence intervals) were 1.08 (1.04 to 1.11), 0.10 (0.01 to 0.73), 1.24 (1.06 to 1.44), 3.59 (2.37 to 5.42), and 0.48 (0.28 to 0.82), respectively.

Conclusion

The SDPTG indices were categorized in combination with age, and they differed in characteristics from components of MetS or inflammatory markers. In addition, this cross-sectional study also revealed decrease of the d/a as a risk factor for the development of MetS.

Citations

Citations to this article as recorded by  
  • Presence of hypertension might pose a potential pitfall in detection of diabetes mellitus non-invasively using the second derivative of photoplethysmography
    Ahmet Taş, Yaren Alan, Ilke Kara, Abdullah Savas, Muhammed Ikbal Bayhan, Diren Ekici, Zeynep Atay, Fatih Sezer, Cagla Kitapli, Sabahattin Umman, Murat Sezer
    Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization.2024;[Epub]     CrossRef
  • Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review
    Malak Abdullah Almarshad, Md Saiful Islam, Saad Al-Ahmadi, Ahmed S. BaHammam
    Healthcare.2022; 10(3): 547.     CrossRef
  • Can biomarkers be used to improve diagnosis and prediction of metabolic syndrome in childhood cancer survivors? A systematic review
    Vincent G. Pluimakers, Selveta S. van Santen, Marta Fiocco, Marie‐Christine E. Bakker, Aart J. van der Lelij, Marry M. van den Heuvel‐Eibrink, Sebastian J. C. M. M. Neggers
    Obesity Reviews.2021;[Epub]     CrossRef
  • Age-Related Changes in the Characteristics of the Elderly Females Using the Signal Features of an Earlobe Photoplethysmogram
    Jeong-Woo Seo, Jungmi Choi, Kunho Lee, Jaeuk U. Kim
    Sensors.2021; 21(23): 7782.     CrossRef
  • Screening Test on Metabolic Syndrome Using Electro Interstitial Scan Instrument


    Phawit Norchai, Thipaporn Tharavanij, Picha Suwannahitatorn, Thammanard Charernboon
    Medical Devices: Evidence and Research.2020; Volume 13: 237.     CrossRef
  • Evaluation of Cardiorespiratory Function During Cardiopulmonary Exercise Testing in Untreated Hypertensive and Healthy Subjects
    Yahui Zhang, Zhihao Jiang, Lin Qi, Lisheng Xu, Xingguo Sun, Xinmei Chu, Yanling Liu, Tianjing Zhang, Stephen E. Greenwald
    Frontiers in Physiology.2018;[Epub]     CrossRef
  • Second derivative of the finger photoplethysmogram and cardiovascular mortality in middle-aged and elderly Japanese women
    Noriko Inoue, Hideshi Kawakami, Hideya Yamamoto, Chikako Ito, Saeko Fujiwara, Hideo Sasaki, Yasuki Kihara
    Hypertension Research.2017; 40(2): 207.     CrossRef
  • Photoplethysmogram second derivative review: Analysis and applications
    K Qawqzeh Yousef, Uldis Rubins, Alharbi Mafawez
    Scientific Research and Essays.2015; 10(21): 633.     CrossRef
Clustering Characteristics of Risk Variables of Metabolic Syndrome in Korean Rural Populations.
Yong Moon Park, Hyuk Sang Kwon, Sun Young Lim, Jin Hee Lee, Sung Rae Kim, Kun Ho Yoon, Bong Yun Cha, Ho Young Son, Yong Gyu Park, Dong Suk Kim, Kwang ho Meng, Won Chul Lee
Korean Diabetes J. 2006;30(3):177-189.   Published online May 1, 2006
DOI: https://doi.org/10.4093/jkda.2006.30.3.177
  • 2,321 View
  • 17 Download
  • 4 Crossref
AbstractAbstract PDF
BACKGROUND
The risks of both type 2 diabetes mellitus and cardiovascular disease are mainly associated with the metabolic syndrome which is characterized by clustering of metabolic risk factors, including abdominal obesity, glucose intolerance, hypertension, and dyslipidemia. This study aimed to examine the relations among metabolic risk variables and the underlying structure of the metabolic syndrome that unites related components. METHODS: Subjects were selected by stratified random cluster sampling among persons aged over 40 years from a rural area. Waist circumference, BMI, fasting glucose, fasting insulin, triglycerides, HDL cholesterol, systolic blood pressure, and diastolic blood pressure were used as risk variables of metabolic syndrome. Factor analysis, a multivariate correlation statistical technique, was performed on a dataset from nondiabetic 3,443 men and women without history of coronary heart disease. RESULTS: Exploratory factor analysis identified three factors in both gender (obesity, hypertension, and dyslipidemia-insulin resistance in men; obesity-insulin resistance, hypertension, and dyslipidemia in women). Fasting insulin was a common contributor to the structure of metabolic syndrome in male subjects, smokers and alcohol drinking group. Confirmatory factor analysis based on the results of exploratory factor analysis revealed that metabolic syndrome was represented primarily by obesity factor in men, obesity-insulin resistance factor in women, and that dyslipidemia factor was highly correlated with obesity factor in men, with insulin resistance factor in women. CONCLUSION: Underlying structure of metabolic syndrome was different between men and women, and obesity might be a primary target for prevention of both type 2 diabetes mellitus and cardiovascular disease in Korea.

Citations

Citations to this article as recorded by  
  • Disjoint factor analysis with cross-loadings
    Maurizio Vichi
    Advances in Data Analysis and Classification.2017; 11(3): 563.     CrossRef
  • Factors associated with control of blood pressure among elderly people diagnosed with hypertension in a rural area of South Korea: The Chungju Metabolic Disease Cohort Study (CMC study)
    Hong-Seok Lee, Yong-Moon Park, Hyuk-Sang Kwon, Jin Hee Lee, Kun-Ho Yoon, Ho Young Son, Dong Suk Kim, Hyeon Woo Yim, Won-Chul Lee
    Blood Pressure.2010; 19(1): 31.     CrossRef
  • Optimal Waist Circumference Cutoff Value Reflecting Insulin Resistance as a Diagnostic Criterion of Metabolic Syndrome in a Nondiabetic Korean Population Aged 40 Years and Over: The Chungju Metabolic Disease Cohort (CMC) Study
    Yong-Moon Park, Hyuk-Sang Kwon, Sun Young Lim, Jin-Hee Lee, Kun-Ho Yoon, Ho-Young Son, Hyeon Woo Yim, Won-Chul Lee
    Yonsei Medical Journal.2010; 51(4): 511.     CrossRef
  • Prevalence, Awareness, Treatment, and Control of Hypertension Among People Over 40 Years Old in a Rural Area of South Korea: The Chungju Metabolic Disease Cohort (CMC) Study
    Hong-Seok Lee, Yong-Moon Park, Hyuk-Sang Kwon, Jin-Hee Lee, Young Joon Park, Sun Young Lim, Seung-Hwan Lee, Kun-Ho Yoon, Ho-Young Son, Dong Suk Kim, Hyeon Woo Yim, Won-Chul Lee
    Clinical and Experimental Hypertension.2010; 32(3): 166.     CrossRef
Association between Hyperleptinemia and Metabolic Syndrome in an Urban Korean Community.
Jee Young Oh, Young Sun Hong, Yeon Ah Sung
Korean Diabetes J. 2003;27(4):313-322.   Published online August 1, 2003
  • 1,104 View
  • 18 Download
AbstractAbstract PDF
BACKGROUND
To determine whether hyperleptinemia is a principal component of metabolic syndrome in a Korean population using factor analysis. METHODS: Metabolic syndrome was defined by the NCEP-ATP III guideline. An oral glucose tolerance test was performed, and plasma samples for leptin and lipid profiles were collected from 199 men and 426 women who had no history of diabetes, hypertension, dyslipidemia, or of taking lipid-lowering, antihypertensive, or antihyperglycemic medications. RESULTS: Leptin level was correlated with overall and central obesity, blood pressure, and glucose or insulin levels in men and women aged 30 to 83. Before and after adjustment for BMI, leptin level was significantly and positively correlated, in women only, with insulin and with insulin resistance, as assessed by a homeostasis model assessment (HOMA) (Ps<0.0001). Factor analysis identified the following four factors from among the metabolic syndrome variables; an obesity/hyperinsulinemia factor, a glucose intolerance factor, a hypertension factor, and a dyslipidemia factor in men. Leptin was clustered as an obesity/ hyperinsulinemia and a dyslipidemia factor in men. In women, four different groups were found: an obesity/hypertension factor, a glucose intolerance factor, an obesity/dyslipidemia factor, and an obesity/hyperinsulinemia factor. Leptin was clustered as an obesity/hyperinsulinemia factor in women. CONCLUSION: Our research suggests that leptin level is associated with metabolic syndrome in relation to obesity and hyperinsulinemia. Moreover, obesity, as opposed to hyperinsulinemia, is related to hypertension or dyslipidemia in women only, and this gender differences may reflect different roles of central adiposity on metabolic abnormalities.
Clustering of Risk Variables in Insulin Resistance Syndrome in Jungup District, Korea.
Sang Wook Kim, Myung Hoe Huh, Young Il Kim, Jin Yub Kim, Eun Sook Kim, Moo Song Lee, Joong Yeol Park, Sung Kwan Hong, Ki Up Lee
Korean Diabetes J. 1999;23(6):843-856.   Published online January 1, 2001
  • 1,105 View
  • 16 Download
AbstractAbstract PDF
BACKGROUND
Insulin resistance syndrome (IRS), a clustering of hypertension, impaired glucose tolerance, low HDL cholesterol and high triglyceride, is prevalent in Korea. We studied the correlational structure of IRS using factor analysis to evaluate whether a single process underlies in the clustering of these risk factors. METHODS: Factor analysis was performed using data from 1,018 non-diabetic subjects (388 men and 630 women) who participated in the Jungup epidemiological study. RESULTS: Factor analysis reduced 9 correlated risk factors to 4 independent factors, each reflecting a different aspect of IRS: hypertension factor (increased systolic and diastolic blood pressure), glucose intolerance factor (increased fasting and postload glucose), obesity factor (increased body mass index, waist circumference, and increased insulin), and dyslipidemia factor (increased trigly- cerides and decreased HDL cholesterol). Increased insulin was also loaded into dyslipidemia factor in men and glucose intolerance factor in women. These factors explained about 70% of the total variance in the data. Three factors such as the glucose intolerance factor, the dyslipidemia factor and the obesity factor, were linked through mutual association with hyperinsulinemia, while hypertension factor was not associated with hyperin- sulinemia. Age-adjusted mean BP by BMI tertile and fasting insulin level tertile for men and women increased progressively with increase in BMI in men and women. There was no significant elevation of mean BP according to increase in fasting insulin level. In contrast to premenopausal women in whom hyperinsulinemia show mutual association with the glucose intolerance factor, the dyslipidemia factor, and the obesity factor, hyperinsulinemia was only loaded into obesity factor in postmenopausal women. CONCLUSION: These results suggested that more than one process underlies the clustering of IRS. In sulin resistance alone did not seem to be the single underlying mechanism of IRS. Especially, hypertension was not correlated with hyperin- sulinemia.

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