Is the association of continuous metabolic syndrome risk score with body mass index independent of physical activity? The CASPIAN-III study

Heshmat, Ramin; Shafiee, Gita; Kelishadi, Roya; Babaki, Amir Eslami Shahr; Motlagh, Mohammad Esmaeil; Arefirad, Tahereh; Ardalan, Gelayol; Ataie‐Jafari, Asal; Asayesh, Hamid; Mohammadi, Rasool; Qorbani, Mostafa

doi:10.4162/nrp.2015.9.4.404

Cited by 8 publications

(4 citation statements)

References 43 publications

(48 reference statements)

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“…This cluster of risk factors is a better predictor of cardiovascular health than is any of the components individually [ 11 , 12 ]. Since the prevalence of MetS is lower in children and adolescents than in adults, many epidemiologic studies have used a continuous value of the MetS risk score (cMetS) instead of a binary distinction between MetS and potential risk factors [ 13 , 14 , 15 ]. The cMetS is more sensitive and less error-prone than a dichotomous approach [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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Associations between Sugar Intake from Different Food Sources and Adiposity or Cardio-Metabolic Risk in Childhood and Adolescence: The Korean Child–Adolescent Cohort Study

et al. 2015

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The increasing prevalence of childhood obesity is a serious public health problem associated with co-morbidities in adulthood, as well as childhood. This study was conducted to identify associations between total sugar intake and sugar intake from different foods (fruit, milk, and sugar-sweetened beverages (SSBs)), and adiposity and continuous metabolic syndrome scores (cMetS) among Korean children and adolescents using cohort data. The study subjects were children (n = 770) who participated in the 4th year (2008) of the Korean Child–Adolescent Cohort Study (KoCAS). Dietary intake data were collected via three-day 24-h food records, and sugar intake was calculated for the total sugar content of foods using our database compiled from various sources. Anthropometric measurements, assessments of body composition, and blood sample analysis were performed at baseline and at follow-up four years later. The cMetS was calculated based on waist circumference, triglycerides, high-density lipoprotein cholesterol, glucose, and mean arterial blood pressure. According to multiple linear regression analysis, there were no significant associations between total sugar intake and adiposity and cMetS. However, higher intake of fruit sugar at baseline was significantly associated with lower body mass index (BMI) z-scores and body fat percentages at baseline (β = −0.10, p = 0.02 and β = −0.78, p < 0.01, respectively). At follow-up, sugar intake from fruit at baseline was still negatively associated with the above outcomes, but only the relationship with BMI z-scores retained statistical significance (β = −0.08, p < 0.05). There was a significant positive relationship between consumption of sugar from SSBs and cMetS at baseline (β = 0.04, p = 0.02), but that relationship was not observed at follow-up (p = 0.83). Differences in consumption sugars from fruit and SSBs might play an important role in the risk of adiposity and metabolic disease in children and adolescents. Our results suggest that strategies for reducing sugar intake need to target particular food groups. Consequently, this information could be of value to obesity- and metabolic disease-prevention strategies.

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Section: Introductionmentioning

confidence: 99%

“…The cMetS is more sensitive and less error-prone than a dichotomous approach [ 16 ]. Cardio-metabolic risk is a progressive function of other risk factors such as MetS components [ 15 ], and so this study used a continuous assessment to detect changes in cardio-metabolic risk.…”

Section: Introductionmentioning

confidence: 99%

Associations between Sugar Intake from Different Food Sources and Adiposity or Cardio-Metabolic Risk in Childhood and Adolescence: The Korean Child–Adolescent Cohort Study

et al. 2015

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“…To the best of our knowledge, this is the largest study showing that unfavorable body composition parameters (obesity, visceral adiposity, and sarcopenia) additively increase the risk for MS. Previous studies have shown that visceral adiposity [ 7 – 11 ], sarcopenia [ 30 – 32 ], and obesity [ 20 – 23 ] were associated with an increased risk for MS. However, most of these studies analyzed the association of only a single body composition parameter with MS; studies analyzing the association between multiple body composition parameters and MS have been rare [ 43 , 45 ].…”

Section: Discussionmentioning

confidence: 99%

Association of obesity, visceral adiposity, and sarcopenia with an increased risk of metabolic syndrome: A retrospective study

et al. 2021

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Aims Metabolic syndrome (MS) is a global health problem associated with an increased risk of diabetes mellitus (DM), cardiovascular disease (CVD), and cancer. Body composition parameters, including obesity, visceral adiposity, and sarcopenia contribute to the development of MS and CVD. Previous studies have investigated the association of individual body composition parameters with MS. Studies analyzing the association between multiple body composition parameters and MS have been rare. We aimed to investigate the association between MS and multiple body composition parameters, including obesity, visceral adiposity, and sarcopenia. Methods A total of 13,620 subjects who underwent voluntary routine checkups at the Health Care Center of our institution between October 2014 and December 2019 were enrolled. Only data from the first examination of subjects who underwent repeated checkups were included. Clinical and laboratory data were collected. Skeletal muscle mass and visceral fat area (VFA) were measured using bioelectrical impedance analysis. Appendicular skeletal muscle mass (ASM) was divided by body weight (in kg) and expressed as a percentage (calculated as, ASM% = ASM × 100/Weight). Data were compared between the groups based on obesity, VFA, and ASM%. Logistic regression analysis was performed to determine the risk of MS in each group. Results Body mass index and VFA were significantly higher in subjects with MS than in those without MS. ASM% was significantly lower in subjects with MS than in those without MS. Subjects with obesity, visceral adiposity, or sarcopenia had a higher prevalence of MS than those without. As the number of metabolic components increased from 0 to 5, we identified a decreasing trend of ASM% and an increasing trend of VFA and BMI (P for trend < 0.001 for all). In the paired analyses, all the three body composition parameters showed additive effects in predicting MS. In the logistic regression analysis, the three parameters were associated with an increased risk of MS after adjustment for age, sex, hypertension, DM, dyslipidemia, smoking, alcohol intake, and C-reactive protein. Conclusions Obesity, visceral adiposity, and sarcopenia showed additive effects on MS prediction. Subjects with obesity, visceral adiposity, or sarcopenia were significantly associated with the increased risk of MS after adjustment for multiple confounders. Increasing skeletal muscle and reducing visceral fat may be strategies for the prevention or treatment of MS.

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“…The z-score was the earliest statistical technique used to generate a cMetS score by Batey et al [34] in 1997. The cMetS is computed using the z-score by taking the difference between the sample mean of each attribute and each sample value in the population, and dividing the result by the standard deviation of each attribute in the population [35], [36], [38], [43], [45]. The PCA operates by decreasing the dimensionality of the attributes of a dataset in order to make data more interpretable while preserving the relevant information [47].…”

Section: A Statistical Techniquesmentioning

confidence: 99%

Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP

et al. 2019

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Metabolic Syndrome (MetS) constitutes of metabolic abnormalities that lead to noncommunicable diseases, such as type II diabetes, cardiovascular diseases, and cancer. Early and accurate diagnosis of this abnormality is required to prevent its further progression to these diseases. This paper aims to diagnose the risk of MetS using a new non-clinical approach called ''genetically optimized Bayesian adaptive resonance theory mapping'' (GOBAM). We evolve the Bayesian adaptive resonance theory mapping (BAM) by using genetic algorithm to optimize the parameters of BAM and its training input sequence. We use the GOBAM algorithm to classify individuals as either being at risk of MetS or not at risk of MetS with a related posterior probability, which ranges between 0 and 1. A data set of 11 237 Malaysians from the CLUSTer study stratified by age and gender into four subcategories was used to evaluate the proposed GOBAM algorithm. The comparative evaluation of our results suggested that the GOBAM performs significantly better than other classical adaptive resonance theory mapping models on the area under the receiver operating characteristic curves (AUC) and others criteria. Our algorithm gives an AUC of 86.42 %, 87.04 %, 91.08 %, and 89.24 % for the young female, middle aged female, young male, and middle-aged male subcategories, respectively. The proposed model can be used to support medical practitioners in accurate and early diagnosis of MetS. INDEX TERMS Metabolic syndrome, adaptive resonance theory, Bayesian ARTMAP, genetic algorithm.

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Is the association of continuous metabolic syndrome risk score with body mass index independent of physical activity? The CASPIAN-III study

Cited by 8 publications

References 43 publications

Associations between Sugar Intake from Different Food Sources and Adiposity or Cardio-Metabolic Risk in Childhood and Adolescence: The Korean Child–Adolescent Cohort Study

Associations between Sugar Intake from Different Food Sources and Adiposity or Cardio-Metabolic Risk in Childhood and Adolescence: The Korean Child–Adolescent Cohort Study

Association of obesity, visceral adiposity, and sarcopenia with an increased risk of metabolic syndrome: A retrospective study

Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP

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