Stability of the factorial structure of metabolic syndrome from childhood to adolescence: a 6-year follow-up study

Martínez‐Vizcaíno, Vicente; Ortega, Francisco B.; Solera-Martínez, Montserrat; Ruiz, Jonatan R.; Labayen, Idoia; Eensoo, Diva; Harro, Jaanus; Loit, Helle‐Mai; Veidebaum, Toomas; Sjöstróm, Michael

doi:10.1186/1475-2840-10-81

Cited by 24 publications

(26 citation statements)

References 31 publications

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“…Taken together, these results suggest that CVD risk factor levels are fairly stable from early childhood to adulthood. We did not find any sex differences in the tracking of clustered CVD risk (data not shown), which is similar to the results from some (11,12), but not all, studies (18). In the current study, all of the single risk factors included in the summed z-score had a positive tracking coefficient ( Table 2), which means that they all contributed to the tracking coefficient for the sum of z-score.…”

Section: Articlessupporting

confidence: 76%

“…We found moderate to high levels of tracking between all the measured time points, and the risk of having a high sum of z-score at a second time point was between 2.3 and 30.8 times higher for children having a high sum of z-score at the first time point. Similarly, the only other study examining the stability of clustered CVD risk factors from childhood (age: 9 y) to adolescence (age: 15 y) in a sample of Swedish and Estonian children found moderate overall tracking coefficients for boys and girls, respectively (11). Moreover, studies looking at clustered CVD risk factor tracking from childhood or adolescence to adulthood displayed similar results (12)(13)(14)(15).…”

Section: Discussionsupporting

confidence: 55%

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Tracking of clustered cardiovascular disease risk factors from childhood to adolescence

et al. 2012

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Background: clustering of cardiovascular disease (cVD) risk factors has been found in children as young as 9 y of age. however, the stability of this clustering over the course of childhood has yet to be determined. The purpose of this study was to determine the tracking of clustered cVD risk from young school age through adolescence and to examine differences in tracking between levels of overweight/obesity and cardiorespiratory fitness (VO 2peak ). Methods: Beginning at 6 y, children (n = 434) were measured three times in 7 y. anthropometrics, blood pressure, and VO 2peak were measured. Fasting blood samples were analyzed for cVD risk factors. a clustered risk score (z-score) was constructed by adding sex-specific z-scores for blood pressure, homeostatic model assessment (hOMa-IR), triglyceride (TG), skinfolds, and negative values of high-density lipoprotein cholesterol (hDLc) and VO 2peak . results: significant tracking coefficients were found between clustered z-score at all time intervals (r = 0.514, 0.559, and 0.381 between ages 6-9, 9-13, and 6-13 y, respectively, all P < 0.0001). Tracking was higher for low-fit children, whereas no clear pattern was found for different levels of body fat. conclusion: We found that clustered z-score is a fairly stable characteristic through childhood. Implementation of preventive strategies could therefore start at early school age.

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Section: Articlessupporting

confidence: 76%

Section: Discussionsupporting

confidence: 55%

Tracking of clustered cardiovascular disease risk factors from childhood to adolescence

et al. 2012

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“…Correlation coefficients in- fatness determined by waist circumference and aerobic fitness were higher than other variables, similar to previous studies 23,36) . Also, moderate tracking of TG and blood pressure was consistent with previous studies 23,24,26,36) . Although it is difficult to explain these phenomena in detail, we suggest that single risk factors in the short-term were moderate to high stable from childhood to adolescence in Japanese children.…”

Section: Discussionsupporting

confidence: 77%

Tracking of clustered metabolic syndrome risk factor in Japanese children: 3-year follow-up study

Sasayama

Adachi

2017

JPFSM

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The purpose of this study was to investigate the tracking of clustered metabolic syndrome (MetS) risk factor taking into account fatness and aerobic fitness from childhood to adolescence in Japanese children. This cohort study included 113 participants (47 boys and 66 girls) who were measured for MetS risk factors at 10 years and 13 years of age. Clustered MetS risk factors (MetS risk score) were calculated from the total gender-specific values (zscores) of the following five parameters: waist to height ratio (W/H), predictedtriglycerides, high density lipoprotein cholesterol, and mean arterial pressure. The tracking coefficient of MetS risk score from childhood to adolescence was 0.647 (p < 0.001). Correlation coefficients of MetS risk scores (without W/H) between 10 and 13 years of age in the high W/H group (r = 0.713, p < 0.001) were higher than those in the low W/H group (r = 0.402, p < 0.01) In addition, correlation coefficients of MetS risk scores (without pV ・ O2peak) between 10 and 13 years of age in the low pV ・ O2peak (r = 0.630, p < 0.001) were higher than those in the high pV ・ O2peak (r = 0.452, p < 0.01). In conclusion, we found that MetS risk was stable from childhood to adolescence in Japanese children. Furthermore, our results show that both fatness and fitness are crucial for tracking MetS risk.

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“…The cardiometabolic risk score 1 is an average value computed from the gender-and age-specific z scores of the sum of 4 skinfolds, homoeostasis model assessment index, systolic blood pressure, triglyceride, total cholesterol/HDL ratio, and cardiorespiratory fitness (VO 2max ; this score was inverted by multiplying by -1) 31 ; the score 2 is an average value computed from the gender-and age-specific z scores of waist circumference, fasting insulin, triglyceride/HDL cholesterol ratio, and mean arterial pressure. 32,33 any marked extent for any of the confounders considered.…”

Section: Figurementioning

confidence: 99%

“…Mean arterial pressure was calculated. 30 We computed 2 well-established cardiometabolic risk scores (gender-and age-specific z scores) to be used in the main analyses, as proposed by Andersen et al 31 and Martínez-Vizcaino et al 32,33 See the biomarkers included in each score in the legend to Table 2. For sensitivity analyses, we selected these 2 risk scores, which largely differ from each other in the risk factors included (eg, markers of total versus abdominal fatness, homeostasis model assessment index versus fasting insulin, etc, for the Andersen's versus Martinez-Vizcaino scores, respectively).…”

Section: Physical Fitnessmentioning

confidence: 99%

Health Inequalities in Urban Adolescents: Role of Physical Activity, Diet, and Genetics

et al. 2014

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WHAT'S KNOWN ON THIS SUBJECT: Individuals living in Mediterranean countries have historically had a lower risk of cardiovascular disease. Important changes in diet and lifestyle have taken place in these countries in recent years, and it is unknown how these changes might influence current cardiovascular health. WHAT THIS STUDY ADDS:Fitness and fatness levels indicate that urban adolescents from southern Europe are less healthy than those from central northern Europe. The extent to which these differences might be explained by physical activity, diet, and genetics is analyzed and discussed in this article. abstract OBJECTIVE: Coordinated European projects relying on standardized methods are needed to identify health inequalities across Europe. This study aimed to compare fitness, fatness, and cardiometabolic risk between urban adolescents from the south and center-north of Europe and to explore whether physical activity (PA) and other factors might explain these differences. METHODS:The Healthy Lifestyle in Europe by Nutrition in Adolescence cross-sectional project comprised 3528 adolescents from the south (4 cities) and central-north (6 cities) of Europe, 1089 of whom provided blood samples for analysis. Fitness (strength, speed-agility, and cardiorespiratory fitness), total and abdominal fatness (anthropometry and bioelectrical impedance), and cardiometabolic risk (z scores including fitness, fatness, blood lipids, insulin resistance, and blood pressure) were assessed. The analyses were adjusted for socioeconomic factors, objectively measured PA (accelerometry), total energy intake and diet quality, and genetic variants of the FTO rs9939609 polymorphism. RESULTS:Adolescents from southern Europe were less fit and fatter according to all markers (P , .001). Differences in cardiometabolic risk scores were not consistent. Adolescents from the south were less active and this would largely explain the differences observed in speedagility and cardiorespiratory fitness. Differences in total and abdominal fatness could not be explained by PA, energy intake, diet quality, or FTO rs9939609 polymorphism.CONCLUSIONS: Fitness and fatness levels indicate that urban adolescents from the south are less healthy than those from centralnorthern Europe. Our data suggest that differences in PA might explain differences in important health-related fitness components, yet factors explaining the differences in fatness encountered remain unknown. Pediatrics 2014;133:e884-e895

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Stability of the factorial structure of metabolic syndrome from childhood to adolescence: a 6-year follow-up study

Cited by 24 publications

References 31 publications

Tracking of clustered cardiovascular disease risk factors from childhood to adolescence

Tracking of clustered cardiovascular disease risk factors from childhood to adolescence

Tracking of clustered metabolic syndrome risk factor in Japanese children: 3-year follow-up study

Health Inequalities in Urban Adolescents: Role of Physical Activity, Diet, and Genetics

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