Body composition trajectories into adolescence according to age at pubertal growth spurt

Relationship between sexual maturation and anthropometric and blood pressure indicators in teenagers

This study aimed to investigate the relationship between sexual maturation and anthropometric and blood pressure indicators in teenagers. This was a population-based cross-sectional study, conducted with 345 teenagers, aged 10 to 19 years, between 2018 and 2020. In this study, data referent to sociodemographic and anthropometric variables, blood pressure, and sexual maturation were collected. The data analysis was performed by applying the Principle Component Analysis (PCA), which generated three components and then tested the correlation between sexual maturation and the generated components. Most of the teenagers were female (53%), normotensive (66.1%), and with a normal weight (73%). A positive correlation was found between breast development and component 1 and component 2, as well as a negative correlation between the breasts and component 3. In the boys, the development of genitals and pubic hair was positively correlated with component 2 and inversely correlated with component 3. It could therefore be concluded that there is a relationship between sexual maturation and the anthropometric and blood pressure indicators, which proved to be representative variables for cardiovascular risk in teenagers, even if not in their entirety.

Relationship between sexual maturation and anthropometric and blood pressure indicators in teenagers

Abstract This study aimed to investigate the relationship between sexual maturation and anthropometric and blood pressure indicators in teenagers. This was a population-based cross-sectional study, conducted with 345 teenagers, aged 10 to 19 years, between 2018 and 2020. In this study, data referent to sociodemographic and anthropometric variables, blood pressure, and sexual maturation were collected. The data analysis was performed by applying the Principle Component Analysis (PCA), which generated three components and then tested the correlation between sexual maturation and the generated components. Most of the teenagers were female (53%), normotensive (66.1%), and with a normal weight (73%). A positive correlation was found between breast development and component 1 and component 2, as well as a negative correlation between the breasts and component 3. In the boys, the development of genitals and pubic hair was positively correlated with component 2 and inversely correlated with component 3. It could therefore be concluded that there is a relationship between sexual maturation and the anthropometric and blood pressure indicators, which proved to be representative variables for cardiovascular risk in teenagers, even if not in their entirety.

Body height, weight, and Body Mass Index - magnitude and pace of secular changes in children and adolescents from Kraków (Poland) between 1983 and 2020

OBJECTIVE

The aim of this study was to analyze the occurrence and pace of secular trends regarding body height, weight, and Body Mass Index (BMI) among children and adolescents from Kraków (Poland) examined in 1983, 2000, 2010, and 2020.

METHODS

The study group consisted of 17 407 individuals (8650 girls and 8757 boys) aged 3-18 included in four cross-sectional studies conducted in 1983, 2000, 2010, and 2020.Analyzed anthropometric characteristics included body height (measured according to Martin's technique, with an anthropometer) and body weight. BMI was calculated as follows: body weight (kg)/body height² (m).

RESULTS

Results of the current study suggest that there was a cessation of previously observed secular increase of the body height in the examined population in the recent decade. Additionally, secular changes regarding body height, body mass, and BMI occurred noticeably slower in the recent decade compared to the previous years.

CONCLUSIONS

In conclusion, the rapid increase of body height in the years 1983-2000 and later secular increase of the body mass and BMI in 2000-2010 were probably associated mainly with significant socio-economic progress of the country. On the other hand, deceleration of those trends in 2010-2020, especially in regards to body height, may be associated with reaching the maximum genetically attainable body height in the examined population.

What Is the Impact of Energy Expenditure on Energy Intake?

Coupling energy intake (EI) to increases in energy expenditure (EE) may be adaptively, compensatorily, or maladaptively leading to weight gain. This narrative review examines if functioning of the homeostatic responses depends on the type of physiological perturbations in EE (e.g., due to exercise, sleep, temperature, or growth), or if it is influenced by protein intake, or the extent, duration, timing, and frequency of EE. As different measures to increase EE could convey discrepant neuronal or humoral signals that help to control food intake, the coupling of EI to EE could be tight or loose, which implies that some ways to increase EE may have advantages for body weight regulation. Exercise, physical activity, heat exposure, and a high protein intake favor weight loss, whereas an increase in EE due to cold exposure or sleep loss likely contributes to an overcompensation of EI, especially in vulnerable thrifty phenotypes, as well as under obesogenic environmental conditions, such as energy dense high fat—high carbohydrate diets. Irrespective of the type of EE, transient elevations in the metabolic rate seem to be general risk factors for weight gain, because a subsequent decrease in energy requirement is not compensated by an adequate adaptation of appetite and EI.

Impact of Energy Turnover on the Regulation of Energy and Macronutrient Balance

Energy turnover, defined as the average daily total metabolic rate, can be normalized for basal metabolic rate in order to compare physical activity level between individuals, whereas normalization of energy turnover for energy intake (energy flux) allows investigation of its impact on regulation of energy partitioning independent of energy balance. Appetite sensations better correspond to energy requirements at a high compared with a low energy turnover. Adaptation of energy intake to habitual energy turnover may, however, contribute to the risk of weight gain associated with accelerated growth, pregnancy, detraining in athletes, or after weight loss in people with obesity. The dose-response relationship between energy turnover and energy intake as well as the metabolic effects of energy turnover varies with the habitual level of physical activity and the etiology of energy turnover (e.g., cold-induced thermogenesis, growth, or lactation; aerobic vs. anaerobic exercise). Whether a high energy turnover due to physical activity or exercise may compensate for adverse effects of overfeeding or an unhealthy diet needs to be further investigated using the concept of energy flux. In summary, the beneficial effects of a high energy turnover on regulation of energy and macronutrient balance facilitate the prevention and treatment of obesity and associated metabolic risk.

Diagnosis of obesity based on body composition-associated health risks-Time for a change in paradigm

Traditional diagnosis and understanding of the pathophysiology of obesity are based on excessive fat storage due to a chronically positive energy balance characterized by body mass index (BMI). Quantitative and qualitative analysis of lean and adipose tissue compartments by body composition analysis reveals that characterization of obesity as "overfat" does not facilitate a comprehensive understanding of obesity-associated health risk. Instead of being related to fat mass, body composition characteristics underlying BMI-associated prognosis may depend (i) on accelerated growth by a gain in lean mass or fat-free mass (FFM) in children with early BMI rebound or adolescents with early puberty; (ii) on a low muscle mass in aging, associated chronic disease, or severe illness; and (iii) on impaired adipose tissue expandability with respect to cardiometabolic risk. It is therefore time to call the adipocentric paradigm of obesity into question and to avoid the use of BMI and body fat percentage. By contrast, obesity should be seen in face of a limited FFM/muscle mass together with a limited capacity of fat storage.

[Body phenotypes in adolescence: is it necessary to adjust body mass index according to sexual maturation stages?]

This study aimed to analyze the effect of sexual maturation on body phenotypes, based on multivariate analysis of adolescents' nutritional status. This was a cross-sectional study with a sample of 833 adolescent schoolchildren 10 to 15 years of age, selected by complex sampling in Piracicaba, São Paulo State, Brazil. Body phenotypes were defined by principal components analysis (PCA), based on age, anthropometric data (body mass, height, skinfolds, and waist circumference), body composition (phase angle measured by bioelectrical impedance), and biochemical parameters (triglycerides, glucose, total cholesterol/LDL ratio, hemoglobin). Body phenotypes were classified as: P1adiposity characterized as positive association with skinfolds, body mass, and waist circumference; P2growth characterized as positive association between height and age; P3biochemical characterized as positive association with total cholesterol/LDL ratio, triglycerides, and glucose; and P4metabolic characterized as positive association with phase angle and hemoglobin and negative association with glucose. The phenotypes were later considered outcomes in the mixed effects analysis, incorporating sex at the first level and sexual maturation, age, sex, and socioeconomic score at the second level. This analysis allowed measuring the effect of sexual maturation on body phenotypes. Our results indicate that sexual maturation only a displayed relevant explanation of body phenotype that represents linear growth (height and age). Thus, it is not necessary to adjust anthropometric measures by sexual maturation.

Lean mass reference curves in adolescents using dual-energy x-ray absorptiometry (DXA)

The body composition phenotype of low lean mass (LM) has been associated with metabolic disorders and impaired physical functioning in the pediatric population. Abnormalities in body composition may be identified using reference curves; however, no reference data on LM is available for Brazilian adolescents. The purpose of this study was to present reference data, including percentile curves, of whole body LM, lean mass index (LMI), appendicular lean mass (ALM), and fat mass for Southern Brazilian adolescents. This was a cross-sectional study of adolescents aged 12–17 years from a southern region in Brazil, who had body composition assessed using dual energy x-ray absorptiometry (DXA). Percentile values and reference curves employing the Lambda, Mu and Sigma method (LMS) were computed for LM, LMI (lean mass/height²), ALM and fat mass. Data on 541 adolescents (68.6% boys) was included. Sex differences in growth trajectories were observed for absolute and adjusted LM, with boys presenting greater LM quantity with advancing ages than girls (66.9% and 17.4% difference between the ages of 12 and 17 for boys and girls, respectively). The values corresponding to the lowest percentile (3^rd) of LMI ranged between 10.63 to 13.93 kg/m² in boys and 11.13 to 12.03 kg/m² among girls aged 12–17 years. This study established the first LM, LMI, and ALM reference curves in Southern Brazilian adolescents, which can potentially be used in association with functional measures to identify LM abnormalities during growth.

Dietary Patterns in Primary School are of Prospective Relevance for the Development of Body Composition in Two German Pediatric Populations

This study performed comparative analyses in two pediatric cohorts to identify dietary patterns during primary school years and examined their relevance to body composition development. Nutritional and anthropometric data at the beginning of primary school and two or four years later were available from 298 and 372 participants of IDEFICS-Germany (Identification and prevention of Dietary-induced and lifestyle-induced health Effects In Children and infants Study) and the KOPS (Kiel Obesity Prevention Study) cohort, respectively. Principal component analyses (PCA) and reduced rank regression (RRR) were used to identify dietary patterns at baseline and patterns of change in food group intake during primary school years. RRR extracted patterns explaining variations in changes in body mass index (BMI), fat mass index (FMI), and waist-to-height-ratio (WtHR). Associations between pattern adherence and excess gain in BMI, FMI, or WtHR (>75th percentile) during primary school years were examined using logistic regression. Among PCA patterns, only a change towards a more Mediterranean food choice during primary school years were associated with a favorable body composition development in IDEFICS-Germany (p < 0.05). In KOPS, RRR patterns characterized by a frequent consumption of fast foods or starchy carbohydrate foods were consistently associated with an excess gain in BMI and WtHR (all p < 0.005). In IDEFICS-Germany, excess gain in BMI, FMI, and WtHR were predicted by a frequent consumption of nuts, meat, and pizza at baseline and a decrease in the consumption frequency of protein sources and snack carbohydrates during primary school years (all p < 0.01). The study confirms an adverse impact of fast food consumption on body composition during primary school years. Combinations of protein and carbohydrate sources deserve further investigation.

The gonadal function in obese adolescents: review

This review deals with the relationship between obesity in male adolescents and gonadal function. The article is structured in two main paragraphs; the first one is about population studies that have assessed puberty timing and its mode of onset in relation with body weight to evaluate if and how the latter can influence the gonadal function in this phase of life. These studies analyze issues such as increased BMI and early onset of male puberty, gender differences, secular trend toward early onset of puberty in males, effects of a different body composition on male puberty and consequences of a different stage of childhood obesity on the onset of male puberty. The second paragraph examines the possible mechanisms through which, obesity may alter the timing of puberty in young males, including the role of SHBG, leptin, insulin resistance, ghrelin, GH-IGF-1 axis, AR polymorphisms, primary testicular dysfunction, retinol binding protein 4 (RBP-4) and liver function abnormalities. However, despite the numerous studies in the literature, the etiology of gonadal disfunction in obese adolescents on puberty remains uncertain.

Nutrition and pubertal development

Nutrition is one of the most important factors affecting pubertal development. Puberty entails a progressive nonlinear process starting from prepubescent to full sexual maturity through the interaction and cooperation of biological, physical, and psychological changes. Consuming an adequate and balanced healthy diet during all phases of growth (infancy, childhood and puberty) appears necessary both for proper growth and normal pubertal development. Girls begin puberty at an earlier age compared to past decades. Excessive eating of many processed, high-fat foods, may be the cause of this phenomenon. Overweight or obese children are more likely to enter puberty early. Some evidence suggests that obesity can accelerate the onset of puberty in girls and may delay the onset of puberty in boys. Moreover, the progression of puberty is affected by nutrition. On the other hand, puberty triggers a growth spurt, which increases nutritional needs including macro and micronutrients. Increased caloric, protein, iron, calcium, zinc and folate needs have to be provided during this critical period of rapid growth. Severe primary or secondary malnutrition also can delay the onset and progression of puberty. The higher incidence of anorexia nervosa and bulimia in adolescents imposes a nutritional risk on pubertal development. Moreover, many environmental endocrine disruptors (EDs) have been identified that can significantly impair the normal course of puberty. This mini-review sums up some important findings in this important complex that link nutrition and pubertal development.

The physiology and timing of male puberty

PURPOSE OF REVIEW

To describe available markers of male puberty, discuss associations between adiposity and pubertal timing and to review recent evidence of a possible secular trend in male pubertal timing.

RECENT FINDINGS

An expert panel reviewing existing American pubertal data from boys in 2005 could not confirm a secular trend in male pubertal timing. National Health and Nutrition Examination Survey III findings have been confirmed by the National Institute of Child Health and Human Development study reporting a mean age of 10.4 years for Caucasian boys entering Tanner stage G2. Furthermore, the Copenhagen Puberty Study reported a 3 months decline in pubertal onset during a 15-year period (from 11.92 years in 1991 to 11.66 years in 2008).A negative association between obesity and early puberty was found in the National Institute of Child Health and Human Development study, in contrast to the positive association found in a Danish study. Other studies have not been able to document an association between prepubertal BMI and age at pubertal onset.

SUMMARY

Evaluation of Tanner stage and especially assessment of testicular volume should both be used in epidemiological studies. We speculate that the association between fat mass and pubertal timing may be nonlinear and recent studies may indicate a small decline in age at pubertal onset in boys.

Childhood body mass index trajectories: modeling, characterizing, pairwise correlations and socio-demographic predictors of trajectory characteristics

BACKGROUND

Modeling childhood body mass index (BMI) trajectories, versus estimating change in BMI between specific ages, may improve prediction of later body-size-related outcomes. Prior studies of BMI trajectories are limited by restricted age periods and insufficient use of trajectory information.

METHODS

Among 3,289 children seen at 81,550 pediatric well-child visits from infancy to 18 years between 1980 and 2008, we fit individual BMI trajectories using mixed effect models with fractional polynomial functions. From each child's fitted trajectory, we estimated age and BMI at infancy peak and adiposity rebound, and velocity and area under curve between 1 week, infancy peak, adiposity rebound, and 18 years.

RESULTS

Among boys, mean (SD) ages at infancy BMI peak and adiposity rebound were 7.2 (0.9) and 49.2 (11.9) months, respectively. Among girls, mean (SD) ages at infancy BMI peak and adiposity rebound were 7.4 (1.1) and 46.8 (11.0) months, respectively. Ages at infancy peak and adiposity rebound were weakly inversely correlated (r = -0.09). BMI at infancy peak and adiposity rebound were positively correlated (r = 0.76). Blacks had earlier adiposity rebound and greater velocity from adiposity rebound to 18 years of age than whites. Higher birth weight z-score predicted earlier adiposity rebound and higher BMI at infancy peak and adiposity rebound. BMI trajectories did not differ by birth year or type of health insurance, after adjusting for other socio-demographics and birth weight z-score.

CONCLUSIONS

Childhood BMI trajectory characteristics are informative in describing childhood body mass changes and can be estimated conveniently. Future research should evaluate associations of these novel BMI trajectory characteristics with adult outcomes.

Gender differences in relationship between fat-free mass index and fat mass index among Korean children using body composition chart

PURPOSE

This study aimed to investigate gender differences in the relationship between fat-free mass index (FFMI) and fat mass index (FMI) by applying body composition chart on Korean elementary students.

MATERIALS AND METHODS

Data from 965 healthy Korean children of 8 to 12 years of age (501 boys, 464 girls) were obtained. FFMI and FMI were plotted on the body composition chart, and the differences in the relationships between FFMI and FMI were separately evaluated by gender or grade.

RESULTS

Weight was heavier and BMI was higher in 3rd and 4th grade boys compare to girls. The value of FFM was higher in boys, but FM was not different. In subgroup analysis by grade, significant gender by FFMI interaction (p=0.015) was found, indicating that the slope of the lines for FMI vs. FFMI was different between boys and girls (Figure was not shown). In subgroup analysis by gender, grade by FFMI interaction was significant in boys, indicating that FMI vs. FFMI relationship differed according to grade only in boys.

CONCLUSION

Boys are leaner than girls, despite having similar BMI. Gender difference in the direction of the change of the FFMI and FMI relationship is evident in children.

Prepubertal glucocorticoid status and pubertal timing

CONTEXT

Whether prepubertal glucocorticoid status impacts on the timing of puberty is not clear.

OBJECTIVE

The objective of the study was to examine the relationship between prepubertal glucocorticoid status and early or late pubertal markers, independent of adrenarchal and nutritional status.

DESIGN AND PARTICIPANTS

Prospective cohort study of healthy Caucasian children (n = 111, 56 boys) who provided both 24-h urine samples and weighed dietary records 1 and 2 yr before the start of pubertal growth spurt [age at take-off (ATO)].

MEASUREMENTS

Major urinary glucocorticoid and androgen metabolites determined by gas chromatography-mass spectrometry analysis were summed to assess daily overall cortisol (ΣC21) and adrenal androgen secretion; urinary free cortisol and cortisone measured by RIA were summed (UFF+UFE) as an indicator of potentially bioactive free glucocorticoids.

MAIN OUTCOMES

The main outcomes included ATO, age at peak height velocity, age at menarche/voice break, ages at Tanner stage 2 for breast (girls) and genital (boys) development, and pubic hair.

RESULTS

In girls ΣC21, but not UFF+UFE, was associated with pubertal markers after adjusting for overall adrenal androgen, urinary nitrogen, and body fat. Girls with higher ΣC21 (fourth quartile) reached ATO 0.7 yr (P = 0.01) and menarche 0.9 yr later (P = 0.006) than girls with lower ΣC21 (first quartile). The ΣC21 tended to be also positively associated with age at Tanner stage 2 for breast (P = 0.1), Tanner stage 2 for pubic hair (P = 0.1), and age at peak height velocity (P = 0.06). In boys, neither the ΣC21 nor UFF+UFE was related to pubertal timing.

CONCLUSION

An individually higher prepubertal glucocorticoid secretion level, even in physiological range, appears to delay early and late pubertal timing of healthy girls, particularly their onset of pubertal growth spurt and menarche.

Association of dietary energy density in childhood with age and body fatness at the onset of the pubertal growth spurt

The aim of the present study was to examine the association of pre-pubertal dietary energy density (ED) with both age and body fatness at the start of the pubertal growth spurt (age at take-off, ATO). Analyses included 219 DOrtmund Nutritional and Anthropometric Longitudinally Designed Study participants with sufficient height measurements to estimate ATO who provided 3 d weighed dietary records at baseline, i.e. 2 and 3 years before ATO (mean age 6·9 (sd 1·2) years). Mean energy intakes and amounts of foods/drinks consumed at baseline were derived from the records. ED (kJ/g) was calculated based on (1) all foods and drinks (ED_all), (2) foods and energy-containing drinks (ED_energy), (3) foods and milk as a drink, but no other beverages (ED_milk) and (4) foods only, solid or liquid (ED_food). Using multiple regression analyses, the association between the ED variables and ATO was investigated. Furthermore, Z-scores of BMI and fat mass index (FMI) at ATO were considered as outcomes to reflect body fatness at puberty onset. The results showed that ED at baseline was not associated with ATO, regardless of the ED method used. For example, mean ATO in the lowest v. highest tertile of ED_food was 9·3 (95 % CI 9·0, 9·5) v. 9·4 (95 % CI 9·1, 9·7) years, Ptrend = 0·8 (adjusted for sex, maternal age, birth weight, dietary protein, dietary fibre, baseline BMI Z-score). Similarly, ED was not independently associated with BMI or FMI Z-score at ATO (Ptrend = 0·3–0·9). In conclusion, dietary ED in childhood did not influence timing or body fatness at ATO in this cohort of healthy, free-living children.