Childhood exposure to phthalates, a class of chemicals with known reproductive and developmental effects, has been hypothesized to increase the risk of obesity, but this association is not well ...understood in preschool children. We examined the association between urinary concentrations of phthalate metabolites and concurrently measured body mass index (BMI) and skinfolds among children between the ages of two and five years.
We collected anthropometric measures and biomonitoring data on approximately 200 children enrolled in the Maternal-Infant Research on Environmental Chemicals Child Development Plus study. We measured 22 phthalate metabolites in children's urine and used the 19 metabolites detected in at least 40% of samples. Our primary outcome was BMI z-scores calculated using the World Health Organization growth standards. Skinfold z-scores were secondary outcomes. We used multivariable linear regression to evaluate the association between tertiles of phthalate concentrations and each anthropometric measure. We also used weighted quantile sum regression to identify priority exposures of concern.
Our analytic sample included 189 singleton-born children with complete anthropometric data. Children with concentrations of the parent compound di-n-butyl phthalate (∑DnBP) in the third tertile had 0.475 (95% CI: 0.068, 0.883) higher BMI z-scores than those in the lower tertile. ∑DnBP was identified as a priority exposure in the weighted quantile sum regression BMI model.
In this population of Canadian preschool aged children, we identified DnBP as a potential chemical of concern in regard to childhood obesity. Future research with serial phthalate measurements and anthropometric measurements in young children will help confirm these findings.
Body composition in infancy plays a central role in the programming of metabolic diseases. Fat mass (FM) is determined by personal and environmental factors. Anthropometric measurements allow for ...estimations of FM in many age groups; however, correlations of these measurements with FM in early stages of life are scarcely reported. The aim of this study was to evaluate anthropometric and clinical correlates of FM in healthy term infants at 6 months of age.
Healthy term newborns (n = 102) from a prospective cohort. Weight, length, skinfolds (biceps, triceps, subscapular and the sum -SFS-) and waist circumference (WC) were measured at 6 months. Body mass index (BMI) and WC/length ratio were computed. Type of feeding during the first 6 months of age was recorded. Air displacement plethysmography was used to asses FM (percentage -%-) and FM index (FMI) was calculated. Correlations and general linear models were performed to evaluate associations.
Significant correlations were observed between all anthropometric measurements and FM (% and index)(p < 0.001). Exclusive/predominant breastfed infants had higher FM and anthropometric measurements at 6 months. Models that showed the strongest associations with FM (% and index) were SFS + WC + sex + type of feeding.
Anthropometry showed good correlations with FM at 6 months of age. Skinfolds sum and waist circumference were the strongest anthropometric variables associated to FM. Exclusive/predominant breastfeeding was strongly associated with FM.
Bauer, P, Majisik, A, Mitter, B, Csapo, R, Tschan, H, Hume, P, Martínez-Rodríguez, A, and Makivic, B. Body composition of competitive bodybuilders: a systematic review of published data and ...recommendations for future work. J Strength Cond Res 37(3): 726-732, 2023-The purpose of this review was to systematically summarize studies measuring the body composition of competitive bodybuilding athletes to provide recommended values for preparation and during competition. The protocol was preregistered with PROSPERO (CRD42020197921) and followed the guidelines of the Preferred Reported Items for Systematic Reviews and Meta-Analysis. A search of 5 electronic databases (PubMed, Web of Science, SportDiscus, CINAHL, and Scopus) was conducted to retrieve all relevant publications from January 1, 2000, up to June 13, 2021. Of 16 studies meeting the inclusion criteria, 6 presented longitudinal data on competition preparation and were discussed in detail. In the general preparation phase, body fat levels of bodybuilding athletes ranged between 15.3 and 25.2% (female) and from 9.6 to 16.3% (male). Close to competition, however, body fat levels were substantially lower, ranging from 8.1 to 18.3% for female and 5.8-10.7% for male athletes. All studies comparing relative body fat values at various time points during competition preparation found significant reductions between 30 and 60% in relative body fat, whereas lean mass was mostly maintained. Findings from the studies included in this review suggest that most bodybuilding competitors keep resistance training volume high while increasing aerobic training volume when preparing for competition. Findings on energy intake and macronutrient distribution were unclear and should be addressed in future studies. Further research, especially on contest preparation, is warranted and should include more details about training programs, nutritional strategies, psychosocial situation, anabolic androgen steroid, and supplement use as well as measurement protocols and preparation.
Height is required for the assessment of growth and nutritional status, as well as for predictions and standardization of physiological parameters. To determine whether arm span, mid-upper arm and ...waist circumferences and sum of four skinfolds can be used to predict height, the relationships between these anthropometric variables were assessed among Ellisras rural children aged 8-18 years.
The following parameters were measured according to the International Society for the Advancement of Kinathropometry: height, arm span, mid-upper arm circumference, waist circumference and four skinfolds (suprailiac, subscapular, triceps and biceps). Associations between the variables were assessed using Pearson correlation coefficients and linear regression models.
Ellisras Longitudinal Study (ELS), Limpopo Province, South Africa.
Boys (n 911) and girls (n 858) aged 8-18 years.
Mean height was higher than arm span, with differences ranging from 4 cm to 11·5 cm between boys and girls. The correlation between height and arm span was high (ranging from 0·74 to 0·91) with P<0·001. The correlation between height and mid-upper arm circumference, waist circumference and sum of four skinfolds was low (ranging from 0·15 to 0·47) with P<0·00 among girls in the 15-18 years age group.
Arm span was found to be a good predictor of height. The sum of four skinfolds was significantly associated with height in the older age groups for girls, while waist circumference showed a negative significant association in the same groups.
To assess whether adiposity measures differed according to joint categories of sleep duration and sleep variability in a sample of Mexican adolescents.
A sample of 528 Mexico City adolescents aged ...9-17 years wore wrist actigraphs for 6-7 days. Average sleep duration was categorized as age-specific sufficient or insufficient. Sleep variability, the standard deviation of sleep duration, was split at the median into stable versus variable. Adiposity measures—body mass index (BMI)-for-age Z score (BMIz), triceps skinfolds, waist circumference, and percent body fat—were collected by trained assistants. We regressed adiposity measures on combined sleep duration and variability categories. Log binomial models were used to estimate prevalence ratios and 95% CI for obesity (>2 BMIz) by joint categories of sleep duration and variability, adjusting for sex, age, and maternal education.
Approximately 40% of the adolescents had insufficient sleep and 13% were obese. Relative to sufficient-stable sleepers, adolescents with insufficient-stable sleep had higher adiposity across all 4 measures (eg, adjusted difference in BMIz was 0.68; 95% CI, 0.35-1.00) and higher obesity prevalence (prevalence ratio, 2.54; 95% CI, 1.36-4.75). Insufficient-variable sleepers had slightly higher BMIz than sufficient-stable sleepers (adjusted difference, 0.30; 95% CI, 0.00-0.59).
Adolescents with consistently insufficient sleep could be at greater risk for obesity. The finding that insufficient-variable sleepers had only slightly higher adiposity suggests that opportunities for “catch-up” sleep may be protective.
Bongiovanni, T, Lacome, M, Rodriguez, C, and Tinsley, GM. Tracking body composition over a competitive season in elite soccer players using laboratory- and field-based assessment methods. J Strength ...Cond Res 38(3): e104-e115, 2024-The purpose of this study was to describe body composition changes in professional soccer players over the course of a competitive playing season and compare the ability of different assessment methods to detect changes. Twenty-one elite male soccer players (age: 23.7 ± 4.8 years; height: 185.0 ± 5.2 cm; body mass: 80.7 ± 5.5 kg; body fat: 12.8 ± 2.2%) playing for an Italian national second league (Serie B) championship team were assessed at 4 time points throughout a competitive season: T0 (mid-October), T1 (mid-December), T2 (mid-February), and T3 (end of April). Dual-energy x-ray absorptiometry (DXA), skinfolds (SKF), and bioelectrical impedance analysis were performed at each time point, and multiple SKF-based equations were applied. A modified 4-compartment (4C) model was also produced. Data were analyzed using repeated measures analysis of variance, relevant post hoc tests, and Pearson's correlations. Dual-energy x-ray absorptiometry, 4C, and the SKF-based equations of Reilly and Civar detected differences in fat-free mass (FFM) between time points, with the most differences observed for DXA. Fat-free mass increased from T0 values to a peak at T2, followed by a decrease by T3, although FFM values remained higher than T0. Fat-free mass gain was primarily driven by increases in the lower limbs. Fat-free mass changes between all methods were significantly correlated, with correlation coefficients of 0.70-0.97. No significant differences between time points were observed for absolute fat mass or body fat percentage, although significant correlations between several methods for change values were observed. Select laboratory and field methods can detect changes in FFM over the course of a season in elite, professional soccer athletes, with a more limited ability to detect changes in adiposity-related variables. For SKF in this population, the equation of Reilly is recommended.
Bioelectrical impedance analysis (BIA) and anthropometry are considered alternatives to well-established reference techniques for assessing body composition. In team sports, the percentage of fat ...mass (FM%) is one of the most informative parameters, and a wide range of predictive equations allow for its estimation through both BIA and anthropometry. Although it is not clear which of these two techniques is more accurate for estimating FM%, the choice of the predictive equation could be a determining factor. The present study aimed to examine the validity of BIA and anthropometry in estimating FM% with different predictive equations, using dual X-ray absorptiometry (DXA) as a reference, in a group of futsal players. A total of 67 high-level male futsal players (age 23.7 ± 5.4 years) underwent BIA, anthropometric measurements, and DXA scanning. Four generalized, four athletic, and two sport-specific predictive equations were used for estimating FM% from raw bioelectric and anthropometric parameters. DXA-derived FM% was used as a reference. BIA-based generalized equations overestimated FM% (ranging from 1.13 to 2.69%, p < 0.05), whereas anthropometry-based generalized equations underestimated FM% in the futsal players (ranging from −1.72 to −2.04%, p < 0.05). Compared to DXA, no mean bias (p > 0.05) was observed using the athletic and sport-specific equations. Sport-specific equations allowed for more accurate and precise FM% estimations than did athletic predictive equations, with no trend (ranging from r = −0.217 to 0.235, p > 0.05). Regardless of the instrument, the choice of the equation determines the validity in FM% prediction. In conclusion, BIA and anthropometry can be used interchangeably, allowing for valid FM% estimations, provided that athletic and sport-specific equations are applied.
Preseason training optimises adaptations in the physical qualities required in rugby union athletes. Sleep can be compromised during periods of intensified training. Therefore, we investigated the ...relationship between sleep quantity and changes in physical performance over a preseason phase in professional rugby union athletes.
Twenty-nine professional rugby union athletes (Mean ± SD, age: 23 ± 3 years) had their sleep duration monitored for 3 weeks using wrist actigraphy. Strength and speed were assessed at baseline and at week 3. Aerobic capacity and body composition were assessed at baseline, at week 3 and at week 5. Participants were stratified into 2 groups for analysis: <7 h 30 min sleep per night (LOW,
= 15) and >7 h 30 min sleep per night (HIGH,
= 14).
A significant group x time interaction was determined for aerobic capacity (
= 0.02,
= 1.25) at week 3 and for skinfolds at week 3 (
< 0.01,
= 0.58) and at week 5 (
= 0.02,
= 0.92), in favour of the HIGH sleep group. No differences were evident between groups for strength or speed measures (
≥ 0.05).
This study highlights that longer sleep duration during the preseason may assist in enhancing physical qualities including aerobic capacity and body composition in elite rugby union athletes.
Acute coronavirus disease 2019 infection has been shown to negatively affect body composition among adult and malnourished or obesity children. Our aim is to longitudinally evaluate body composition ...in children affected by the Multisystem Inflammatory Syndrome (MIS-C).
In this cohort study, we recruited 40 patients affected by MIS-C, aged 2-18 years old, who were admitted in our clinic between December 2020 and February 2021. Physical examination for each participant included weight, height, body mass index (BMI) z score, circumferences, and skinfolds assessment. The same measurements were repeated during outpatient follow-up at 10 (T2), 30 (T3), 90 (T4), and 180 (T5) days after hospital discharge. Fat mass and fat free mass were calculated according to skinfolds predictive equations for children and adolescents. A control group was randomly selected among patients attending a pediatric nutritional outpatient clinic.
BMI z score significantly decrease between preadmission and hospital discharge. Similarly, arm circumference z score, arm muscular area z score, and arm fat area z score significantly decreased, during hospital stay. Fat mass index (FMI) significantly increased over time, peaking at T3. Fat free mass index decreased during hospitalization.
To the best of our knowledge, this is the first study to assess body composition in a numerically large pediatric MIS-C population from acute infection to 6 months after triggering event. FMI and anthropometric parameters linked to fat deposits were significantly higher 6 months after acute event. Thus, limiting physical activity and having sedentary lifestyle may lead to an accumulation of adipose tissue even in healthy children who experienced MIS-C and long hospitalization.
Prediction equations are commonly used to estimate body fat from anthropometric measurements, but are population specific. We aimed to establish and validate a body composition prediction formula for ...Asian newborns, and compared the performance of this formula with that of a published equation.
Among 262 neonates (174 from day 0, 88 from days 1-3 post delivery) from a prospective cohort study, body composition was measured using air-displacement plethysmography (PEA POD), with standard anthropometric measurements, including triceps and subscapular skinfolds. Using fat mass measurement by PEA POD as a reference, stepwise linear regression was utilized to develop a prediction equation in a randomly selected subgroup of 62 infants measured on days 1-3, which was then validated in another subgroup of 200 infants measured on days 0-3.
Regression analyses revealed subscapular skinfolds, weight, gender and gestational age were significant predictors of neonatal fat mass, explaining 81.1% of the variance, but not triceps skinfold or ethnicity. By Bland-Altman analyses, our prediction equation revealed a non-significant bias with limits of agreement (LOA) similar to those of a published equation for infants measured on days 1-3 (95% LOA: (-0.25, 0.26) kg vs (-0.23, 0.21) kg) and on day 0 (95% LOA: (-0.19, 0.17) kg vs (-0.17, 0.18) kg). The published equation, however, exhibited a systematic bias in our sample.
Our equation requires only one skinfold site measurement, which can significantly reduce time and effort. It does not require the input of ethnicity and, thus, aid its application to other Asian neonatal populations.
PDF
