Associations of BMI and its fat-free and fat components with blood lipids in children: Project HeartBeat!

Fat-to-muscle ratio as a predictor for dyslipidaemia in transitional-age youth

Background

Although dyslipidaemia may have a crucial impact on cardiovascular health in adults, there is a lack of specific data in transitional-age youth. Therefore, this study attempted to evaluate the association of dyslipidaemia with fat-to-muscle ratio (FMR), and establish FMR thresholds for diagnosing dyslipidaemia in transitional-age youth.

Methods

One thousand six hundred sixty individuals aged 16 to 24 years from the baseline of a subcohort in the Northwest China Natural Population Cohort: Ningxia Project were analysed. Anthropometric characteristics were gauged by a bioelectrical impedance analyser, and dyslipidaemia components were measured using a Beckman AU480 chemistry analyser. Additionally, this study used logistic regression to estimate the risk of dyslipidaemia based on FMR quintiles, and calculate the gender-specific ideal cut-off values of dyslipidaemia and its components by the receiver operating characteristic (ROC) curve.

Results

Of the 1660 participants, aged 19.06 ± 1.14 years, 558 males and 1102 females. The prevalence of dyslipidaemia was 13.4% and was significantly associated with FMR quintiles among all participants (P < 0.05). The ideal values of FMR in diagnosing dyslipidaemia were 0.2224 for males and 0.4809 for females, while males had a higher AUC than females (0.7118 vs. 0.6656). Meanwhile, high FMR values were significantly associated with adverse outcomes of dyslipidaemia, hypercholesterolemia and hypertriglyceridaemia (P < 0.05).

Conclusions

The FMR was positively correlated with the prevalence of dyslipidaemia. The FMR can be used as an effective body composition index for diagnosing dyslipidaemia, especially in males, and preventive strategies should be initiated in transitional-age youth to decrease obesity-related dyslipidaemia.

Cardiometabolic Profile of Different Body Composition Phenotypes in Children

CONTEXT

Cardiometabolic profiles of different body composition phenotypes are poorly characterized in young children, where it is well established that high adiposity is unfavorable, but the role of lean mass is unclear.

OBJECTIVE

We hypothesized that higher lean mass attenuates cardiometabolic risk in children with high fat mass.

METHODS

In 6-year-old children (n = 377) from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) prospective birth cohort, whole-body composition was measured by quantitative magnetic resonance, a novel validated technology. Based on fat mass index (FMI) and lean mass index (LMI), 4 body composition phenotypes were derived: low FMI-low LMI (LF-LL), low FMI-high LMI (LF-HL), high FMI-low LMI (HF-LL), high FMI-high LMI (HF-HL).

MAIN OUTCOME MEASURES

Body mass index (BMI) z-score, fasting plasma glucose, insulin resistance, metabolic syndrome risk score, fatty liver index, and blood pressure.

RESULTS

Compared with the LF-HL group, children in both high FMI groups had increased BMI z-score (HF-HL: 1.43 units 95% CI [1.11,1.76]; HF-LL: 0.61 units [0.25,0.96]) and metabolic syndrome risk score (HF-HL: 1.64 [0.77,2.50]; HF-LL: 1.28 [0.34,2.21]). The HF-HL group also had increased fatty liver index (1.15 [0.54,1.77]). Girls in HF-HL group had lower fasting plasma glucose (-0.29 mmol/L [-0.55,-0.04]) and diastolic blood pressure (-3.22 mmHg [-6.03,-0.41]) than girls in the HF-LL group. No similar associations were observed in boys.

CONCLUSION

In a multi-ethnic Asian cohort, lean mass seemed to protect against some cardiometabolic risk markers linked with adiposity, but only in girls. The FMI seemed more important than lean mass index in relation to cardiometabolic profiles of young children.

Non-HDL Cholesterol Levels in Childhood and Carotid Intima-Media Thickness in Adulthood

BACKGROUND

Elevated non-high-density lipoprotein cholesterol (HDL-C) levels are used to identify children at increased cardiovascular risk, but the use of non-HDL-C in childhood to predict atherosclerosis is unclear. We examined whether the National Heart, Lung, and Blood Institute classification of youth non-HDL-C status predicts high common carotid artery intima-media thickness in adulthood.

METHODS

We analyzed data from 4 prospective cohorts among 4582 children aged 3 to 19 years who were remeasured as adults (mean follow-up of 26 years). Non-HDL-C status in youth and adulthood was classified according to cut points of the National Heart, Lung, and Blood Institute and the National Cholesterol Education Program Adult Treatment Panel III. High carotid intima-media thickness (cIMT) in adulthood was defined as at or above the study visit-, age-, sex-, race-, and cohort-specific 90th percentile of intima-media thickness.

RESULTS

In a log-binomial regression analysis adjusted with age at baseline, sex, cohort, length of follow-up, baseline BMI, and systolic blood pressure, children with dyslipidemic non-HDL-C were at increased risk of high cIMT in adulthood (relative risk [RR], 1.29; 95% confidence interval [CI], 1.07-1.55). Compared with the persistent normal group, the persistent dyslipidemia group (RR, 1.80; 95% CI, 1.37-2.37) and incident dyslipidemia (normal to dyslipidemia) groups (RR, 1.45; 95% CI, 1.07-1.96) had increased risk of high cIMT in adulthood, but the risk was attenuated for the resolution (dyslipidemia to normal) group (RR, 1.17; 95% CI, 0.97-1.41).

CONCLUSIONS

Dyslipidemic non-HDL-C levels predict youth at risk for developing high cIMT in adulthood. Those who resolve their non-HDL-C dyslipidemia by adulthood have normalized risk of developing high cIMT in adulthood.

Comparison of waist to height ratio and body indices for prediction of metabolic disturbances in the Korean population: the Korean National Health and Nutrition Examination Survey 2008-2011

BACKGROUND

The aim of the present study of the general population was to identify the best predictor of metabolic risk among the body index variables evaluated with dual-energy X-ray absorptiometry (DEXA) or anthropometric indices including the waist to height ratio (WHtR).

PATIENTS AND METHODS

Data from the Korean National Health and Nutrition Examination Survey 2008-2011 were used for the analyses. As a result, 15,965 participants were included in this study. The body mass (BM) index was calculated as the body weight divided by the height squared. The WHtR was calculated as the waist circumference divided by height. Body composition indices such as lean mass (LM), fat mass (FM), trunk fat mass (TFM), and bone mineral content (BMC) were determined by using DEXA. Skeletal muscle mass (SM) was defined as the sum of the lean soft masses of both extremities. The LM, FM, BMC, TFM, and SM indices were calculated by dividing the total LM, total FM, total BMC, TFM, or SM by the height squared.

RESULTS

The WHtR had the highest area under the curve (AUC) and was the best predictor of metabolic syndrome for both sexes. In addition, the WHtR had the highest AUCs for components of metabolic syndrome (male: AUC 0.823, 95 % confidence interval [CI] 0.814-0.832; female: AUC 0.870, 95 % CI 0.863-0.877). There was a small statistically significant difference in AUC between WHtR and the other indices. Multivariate logistic regression showed that male participants in the second, third, and fourth quartiles had a 4.0 (95 % CI, 3.1-5.2), 9.6 (95 % CI, 7.5-12.3), and 36.1 (95 % CI, 28.0-46.4) times increased risk of metabolic syndrome compared with patients in the first quartile and female participants in the second, third, and fourth quartiles had a 4.3 (95 % CI, 3.1-6.0), 18.0 (95 % CI, 13.3-24.5), and 58.5 (95 % CI, 42.9-79.9) times increased risk of metabolic syndrome compared with patients in the first quartile.

CONCLUSION

Among the BM, FM, LM, SM, TFM, and WHtR indices, WHtR is most useful to predict the presence of metabolic syndrome and insulin resistance in the Korean population.

Temporal divergence of percent body fat and body mass index in pre-teenage children: the LOOK longitudinal study

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

The index of body mass related to stature, (body mass index, BMI, kgm(-2) ), is widely used as a proxy for percent body fat (%BF) in cross-sectional and longitudinal investigations. BMI does not distinguish between lean and fat mass and in children, the cross-sectional relationship between %BF and BMI changes with age and sex.

WHAT THIS STUDY ADDS

While BMI increases linearly with age from age 8 to 12 years in both boys and girls, %BF plateaus off between 10 and 12 years. Repeated measures in children show a systematic decrease in %BF for any given BMI from age 8 to 10 to 12 years. Because changes in BMI misrepresent changes in %BF, its use as a proxy of %BF should be avoided in longitudinal studies in this age group.

BACKGROUND

Body mass index (BMI, kgm(-2) ) is commonly used as an indicator of pediatric adiposity, but with its inability to distinguish changes in lean and fat mass, its use in longitudinal studies of children requires careful consideration.

OBJECTIVE

To investigate the suitability of BMI as a surrogate of percent body fat (%BF) in pediatric longitudinal investigations.

METHODS

In this longitudinal study, healthy Australian children (256 girls and 278 boys) were measured at ages 8.0 (standard deviation 0.3), 10.0 and 12.0 years for height, weight and percent body fat (%BF) by dual-energy X-ray absorptiometry.

RESULTS

The patterns of change in the means of %BF and BMI were different (P < 0.001). While mean BMI increased linearly from 8 to 12 years of age, %BF did not change between 10 and 12 years. Relationships between %BF and BMI in boys and girls were curvilinear and varied with age (P < 0.001) and gender (P < 0.001); any given BMI corresponding with a lower %BF as a child became older.

CONCLUSION

Considering the divergence of temporal patterns of %BF and BMI between 10 and 12 years of age, employment of BMI as a proxy for %BF in absolute or age and sex standardized forms in pediatric longitudinal investigations is problematical.

A comparison of fat and lean body mass index to BMI for the identification of metabolic syndrome in children and adolescents

CONTEXT

The use of body mass index (BMI) to assess risk for cardiometabolic disease in the pediatric population may be limited by a failure to differentiate between fat and lean body mass.

OBJECTIVES

The objectives of the study were to identify biologically based criteria for the definition of obesity using fat (FMI) and lean body mass index (LBMI) and to compare the ability of FMI and LBMI to BMI to identify the presence of metabolic syndrome (MetSyn).

DESIGN

This was a cross-sectional study using National Health and Nutrition Examination Survey 1999-2006 data.

PARTICIPANTS

A total of 3004 participants aged 12-20 years with dual-energy X-ray absorptiometry body composition and fasting laboratory data participated in the study.

MAIN OUTCOME MEASURES

Adjusted odds ratios for MetSyn according to FMI and LBMI status and area under the curve for the identification of MetSyn were measured.

RESULTS

Receiver-operating characteristic curve analyses identified the 80th percentile for FMI and the 74th percentile for LBMI as the optimal cut points for the identification of MetSyn. There was no difference in the area under the curve for FMI [0.867; 95% confidence interval (CI) 0.838-0.891] vs BMI (0.868; 95% CI 0.837-0.894) Z-scores for MetSyn discrimination. Separate multivariate regression models identified odds ratios for the identification of MetSyn of 6.2 (95% CI 3.3-11.5) for BMI-Z, 6.4 (95% CI 3.7-11.1) for FMI-Z, and 4.6 (95% CI 3.0-7.1) for LBMI-Z. Models containing both FMI-Z and LBMI-Z revealed that greater LBMI-Z was associated with greater odds of low high-density lipoprotein (1.5; 95% CI 1.2-1.9), high blood pressure (1.8; 95% CI 1.1-2.9), and insulin resistance (1.8; 95% CI 1.4-2.5), independent of FMI-Z.

CONCLUSIONS

The use of FMI and LBMI does not improve upon BMI for the identification of MetSyn in the pediatric population. Unexpectedly, higher LBMI was associated with greater odds of multiple cardiometabolic risk factors independent of FMI. The use of FMI and LBMI allow for the independent evaluation of relationships between body compartments and disease and warrants future research.

Metabolic risk factors in U.S. youth with low relative muscle mass

AIMS

To examine the association between relative muscle mass (RMM) and nine risk factors for cardiovascular disease and diabetes (CVD/DM) in U.S. youth.

METHODS

We used a sample representative of the U.S. population of youth, aged 8-20 years (NHANES 1999-2004). We compared the prevalence of adverse levels of nine CVD/DM risk factors between youths in the lowest quartile of RMM and their peers in the remaining quartiles, controlling for age, sex, and race/ethnicity. We also examined variations in the adjusted prevalence of these risk factors along the entire range of RMM.

RESULTS

The adjusted prevalence of adverse levels of risk factors among youths in the lowest quartile of RMM was significantly higher for seven of the nine risk factors examined compared with their peers in the other quartiles. Over the entire range of RMM, the adjusted prevalence of adverse levels of each of these seven risk factors decreased gradually with increasing RMM values (all p for trend <0.001).

CONCLUSIONS

RMM and prevalence of adverse risk factors for CVD/DM are highly and inversely associated in U.S. youth. Among youth with low RMM, the risk of these chronic diseases could be significantly high later in life.