Impact of adolescent obesity on middle-age health of women given data MAR

We analyze adolescent BMI and middle-age systolic blood pressure (SBP) repeatedly measured on women enrolled in the Fels Longitudinal Study (FLS) between 1929 and 2010 to address three questions: Do adolescent-specific growth rates in body mass index (BMI) and menarche affect middle-age SBP? Do they moderate the aging effect on middle-age SBP? Have the effects changed over historical time? To address the questions, we propose analyzing a growth curve model (GCM) that controls for age, birth-year cohort, and historical time. However, several complications in the data make the GCM analysis nonstandard. First, the person-specific adolescent BMI and middle-age SBP trajectories are unobservable. Second, missing data are substantial on BMI, SBP, and menarche. Finally, modeling the latent trajectories for BMI and SBP, repeatedly measured on two distinct sets of unbalanced time points, are computationally intensive. We adopt a bivariate GCM for BMI and SBP with correlated random coefficients. To efficiently handle missing values of BMI, SBP, and menarche assumed missing at random, we estimate their joint distribution by maximum likelihood via the EM algorithm where the correlated random coefficients and menarche are multivariate normal. The estimated distribution will be transformed to the desired GCM for SBP that includes the random coefficients of BMI and menarche as covariates. We demonstrate unbiased estimation by simulation. We find that adolescent growth rates in BMI and menarche are positively associated with, and moderate, the aging effect on SBP in middle age, controlling for age, cohort, and historical time, but the effect sizes are at most modest. The aging effect is significant on SBP, controlling for cohort and historical time, but not vice versa.     

Estimation of the mixing proportion in a mixture of two normal distributions from simple, rapid measurements

A mixture of two or more normal distributions often provides an adequate model for the distribution of a population consisting of varying proportions of component subpopulations. We consider here the problem of estimating the mixing proportion in a mixture of two normal distributions, the parameters of which can be assumed known. Very large samples may be needed if reasonably precise estimates are to be obtained, thus bringing into consideration the cost or time involved in obtaining large numbers of exact measurements and computing the estimates from them. Simple estimators based on simple, rapidly obtained measurements may then be attractive alternatives provided efficiency losses are not too great. Three such estimators studied here are based on (a) the number of observations less than a fixed point r, (b) the nembers less than s and greater than t, and (c) the sample mean. Optimal choices of the points r, s and t are considered, and the efficiencies of the estimators relative to maximum likelihood estimators (MLE) using the full data are obtained. The simple estimators often perform sufficiently well to make the collection of full data not worthwhile in practice.     

A Semiparametric Joint Model for Longitudinal and Survival Data with Application to Hemodialysis Study

Summary .  In many longitudinal clinical studies, the level and progression rate of repeatedly measured biomarkers on each subject quantify the severity of the disease and that subject's susceptibility to progression of the disease. It is of scientific and clinical interest to relate such quantities to a later time-to-event clinical endpoint such as patient survival. This is usually done with a shared parameter model. In such models, the longitudinal biomarker data and the survival outcome of each subject are assumed to be conditionally independent given subject-level severity or susceptibility (also called frailty in statistical terms). In this article, we study the case where the conditional distribution of longitudinal data is modeled by a linear mixed-effect model, and the conditional distribution of the survival data is given by a Cox proportional hazard model. We allow unknown regression coefficients and time-dependent covariates in both models. The proposed estimators are maximizers of an exact correction to the joint log likelihood with the frailties eliminated as nuisance parameters, an idea that originated from correction of covariate measurement error in measurement error models. The corrected joint log likelihood is shown to be asymptotically concave and leads to consistent and asymptotically normal estimators. Unlike most published methods for joint modeling, the proposed estimation procedure does not rely on distributional assumptions of the frailties. The proposed method was studied in simulations and applied to a data set from the Hemodialysis Study.     

Two Requirements for Obtaining Valid Common Patterns under Different Assumptions in Vicariance Biogeography

In vicariance biogeography, widespread or sympatric taxa can be dealt with under assumptions 0, 1, and 2. Data from cladogenetic relationships among taxa of a monophyletic group and their distribution over areas are assumed, in the order 0 → 1 → 2, to represent decreasing information about vicariance events. A less strict assumption carries a larger solution set, i.e., the number of possible area cladograms increases with the decrease in strictness of the assumption applied. We formulate two requirements for obtaining valid general area cladograms from data of several monophyletic groups of taxa. First, the assumptions, and with them the sets of area cladograms derived under these assumptions, should be inclusive. Second, sets of single group area cladograms should be compared for different monophyletic groups under a single assumption. When these two requirements are met, area cladograms become consistent with respect to the processes (vicariance, extinction, and dispersal) that are a priori assumed. The explanatory power increases for any particular monophyletic group of taxa when the set of valid general area cladograms contains a subset of area cladograms derived under a more strict assumption. We discuss examples from literature of how violation of these two requirements affects the results.     

Analyses of gene frequencies of mates

A genic analysis of variance of data on mate pairs for a codominant gene is developed. This analysis provides estimators of the correlation, F, of genes within individuals, of the correlation, Θ, of genes between mates, and of various variances—all relative to the correlation or variation among genes of nonmates. The data are manipulated into marginal distributions to produce another method of obtaining the same estimators. Several examples are given of how assumptions about the model and parameters modify the estimators and which were utilized in constructing χ² tests of hypotheses concerning F and Θ.—A recessive gene is also considered. Only the frequency of recessive genotypes and the correlation of recessive mates are estimable in this case unless one makes very demanding assumptions about the model.—Numerical examples of the analysis of variance and estimators are given for both a codominant and recessive gene.     

Milk intake and total dairy consumption: associations with early menarche in NHANES 1999-2004

Background

Several components of dairy products have been linked to earlier menarche.

Methods/Findings

This study assessed whether positive associations exist between childhood milk consumption and age at menarche or the likelihood of early menarche (<12 yrs) in a U.S sample. Data derive from the National Health and Nutrition Examination Survey (NHANES) 1999–2004. Two samples were utilized: 2657 women age 20–49 yrs and 1008 girls age 9–12 yrs. In regression analysis, a weak negative relationship was found between frequency of milk consumption at 5–12 yrs and age at menarche (daily milk intake β = −0.32, P<0.10; “sometimes/variable milk intake” β = −0.38, P<0.06, each compared to intake rarely/never). Cox regression yielded no greater risk of early menarche among those who drank milk “sometimes/varied” or daily vs. never/rarely (HR: 1.20, P<0.42, HR: 1.25, P<0.23, respectively). Among the 9–12 yr olds, Cox regression indicated that neither total dairy kcal, calcium and protein, nor daily milk intake in the past 30 days contributed to early menarche. Girls in the middle tertile of milk intake had a marginally lower risk of early menarche than those in the highest tertile (HR: 0.6, P<0.06). Those in the lowest tertiles of dairy fat intake had a greater risk of early menarche than those in the highest (HR: 1.5, P<0.05, HR: 1.6, P<0.07, lowest and middle tertile, respectively), while those with the lowest calcium intake had a lower risk of early menarche (HR: 0.6, P<0.05) than those in the highest tertile. These relationships remained after adjusting for overweight or overweight and height percentile; both increased the risk of earlier menarche. Blacks were more likely than Whites to reach menarche early (HR: 1.7, P<0.03), but not after controlling for overweight.

Conclusions

There is some evidence that greater milk intake is associated with an increased risk of early menarche, or a lower age at menarche.     

Heritability of age at menarche in girls from the Fels Longitudinal Study

Menarche is the hallmark maturational event of female childhood. Many studies indicated a significant genetic contribution to the timing of the onset of menstruation, but most of these studies were limited by the use of retrospective data and by the use of data from only certain types of relatives (i.e., mothers and daughters, sisters, or twin sisters). The primary goal of this study was to use a modern maximum likelihood quantitative genetic method to estimate the heritability (h(2)) of age at menarche, using familial data collected over the course of the 74-year-old Fels Longitudinal Study. The secondary goal was to review earlier studies of the heritability of age at menarche. The study of the heritability of age at menarche presented here is unique for two reasons. First, because of the Fels Longitudinal Study's serial design, age-at-menarche data were collected prospectively from most participants. Second, because the Fels Longitudinal Study is a family study that has been conducted for decades, age-at-menarche data are available from many types of female relatives spanning multiple households and generations. The best-fitting and most parsimonious quantitative genetic model included provision for a secular decrease in age at menarche, and estimated the h(2) of age at menarche to be 0.49+/- 0.13 (95% confidence interval of h(2),=0.24-0.73). The results of this study are in general agreement with the findings of most previous studies of genetic influences on age at menarche, and suggest that it is reasonable to consider it well-established that approximately half the phenotypic variation among girls from developed nations in the timing of menarche is due to genetic factors.     

The Cannabinoid Receptor 2 Q63R Variant Modulates the Relationship between Childhood Obesity and Age at Menarche

Background

The ovary is an important site where gene variants modulate pubertal timing. The cannabinoid receptor 2 (CB2) is expressed in the ovary, plays a role in folliculogenesis and ovulation, and can be modulated by estrogens. Obesity is strictly associated with early menarche and is characterized by sex hormone and endocannabinoid derangement.

Aim

In this study, we investigated the role of the CB2 receptor in determining the age at menarche in obese girls.

Methods

We studied a cohort of 240 obese girls (age 11.9±3 years; BMI z-score 2.8±0.8). The age at menarche (if it had already occurred) was recorded at the time of the visit or via phonecall. The CNR2 rs35761398 polymorphism, which leads to the CB2 Q63R variant, was detected by the TaqMan assay.

Results

In total, 105 patients were homozygous for the R63-coding allele (RR), 113 were QR and 22 were QQ. Variance analysis revealed a significantly earlier age of menarche in subjects carrying the Q63 allele, which was also found after adjusting for BMI z-score (11±1.2 vs. 11.6±1.2 years, p = 0.0003). Logistic regression analysis demonstrated that patients homozygous for the Q allele had a 2.2-fold higher risk (odds ratio = 2.2; CI1.1–3.4; p = 0.02) of presenting with an early menarche (age at menarche <12 years).

Conclusion

We demonstrated for the first time the association between the CB2 Q63R functional variant and the age at menarche in a cohort of Italian obese girls.     

The influence of age at menarche on the fertility of Chinese women

This paper examines the effect of age at menarche on children ever born (CEB). We use data from the 1997 Sample Survey of Population and Reproductive Health conducted by the China Population Information and Research Center and the State Family Planning Commission. Poisson regression models are estimated for 10,919 ever married Chinese Han women. The influence of a woman's age at menarche on her CEB is examined while controlling for the social effects of rural/urban residency, education, her number of fecund years, whether her first birth occurred before or after the initiation of China's one child policy, and her age at first marriage. The results support our hypothesized positive association between age at menarche and CEB. That is, the later a woman's age at menarche, the greater her number of children ever born. Holding the other five independent variables constant, we show that for every additional month in age at menarche, a Chinese Han woman's mean number of children ever born increases by 0.5 percent. Some of the implications of these results are explored.     

Single versus serial assessment of skeletal age: Either,both or neither?

Assessments of skeletal age are a valuable adjunct to the clinical evaluation of physical maturity but are more meaningful when considered in relation to chronological age, especially over time, than as separate entities. Data on 51 girls from the Child Research Council study series gave a correlation coefficient of 0.51 between skeletal age (SA) at menarche and chronological age (CA) at menarche — a value in close agreement with data reported from other studies. With a range in SA of 11.58 to 14.89 years, these data were examined further for changes in SA related to timing of adolescence. SA was greater than CA in each of the nine girls whose menarche occurred between 10.5 and 12 years of age. SA was equal to CA in one girl, greater than CA in eight girls and less than CA in 11 girls with menarche between 12.15 and 13.4 years. Of the 22 girls with menarche after 13.5 years, one had SA = CA at 14.89 years and the other 21 all had SA less than CA. An r of 0.84 was calculated between the values of CA minus SA at menarche and CA at menarche. Similar relationships were found between SA and CA at age of maximum increment in growth in height for these girls and for 53 boys in the study series. Longitudinal data for height, weight and SA for four boys and five girls demonstrate the problems of prediction of the timing of adolescence and of adult size from skeletal ages in the childhood years.     

High variation in developmental instability under non-normal developmental error: a Bayesian perspective

The developmental mechanisms behind developmental instability (DI) are only poorly understood. Nevertheless, fluctuating asymmetry (FA) is often used a surrogate for DI. Based on statistical arguments it is often assumed that individual levels of FA are only weakly associated with the underlying DI. Patterns in FA therefore need to be interpreted with caution, and should ideally be transformed into patterns in DI. In order to be able to achieve that, assumptions about the distribution of developmental errors must be made. Current models assume that errors during development are additive and independent such that they yield a normal distribution. The observation that the distribution of FA is often leptokurtic has been interpreted as evidence for between-individual variation in DI. This approach has led to unrealistically high estimates of between-individual variation in DI, and potentially incorrect interpretations of patterns in FA, especially at the individual level. Recently, it has been suggested that the high estimates of variation in DI may be biased upward because either developmental errors are log-normal or gamma distributed and/or low measurement resolution of FA. A proper estimation of the amount (and shape) of heterogeneity in DI is crucial for the interpretation of patterns in FA and their transformation into patterns in DI. Yet, incorrect model assumptions may render misleading inferences. We therefore develop a statistical model to evaluate the sensitivity of results under the normal error model against the two alternative distributions as well as to investigate the importance of low measurement resolution. An analysis of simulated and empirical data sets indicated that bias due to misspecification of the developmental error distribution can be substantial, yet, did not appear to reduce estimates of variation in DI in empirical data sets to a large extent. Effects of low measurement resolution were neglectable. The importance of these results are discussed in the context of the interpretation of patterns in FA.     

Selection of likelihood parameters for complex models determines the effectiveness of Bayesian calibration

Assessing the parameter uncertainty of complex ecosystem models is a key challenge for improving our understanding of real world abstractions, such as those for explaining carbon and nitrogen cycle at ecosystem scale and associated biosphere-atmosphere-hydrosphere exchange processes. The lack of data about the variance of measurements forces scientists to revisit assumptions used in estimating the parameter distribution of complex ecosystem models.An increasingly used tool for assessing parameter uncertainty of complex ecosystem models is Bayesian calibration. In this paper, we generate two data sets which may represent a seasonal temperature curve or the seasonality of soil carbon dioxide flux and a single high peak put on a low background signal as is e.g. typical for soil nitrous oxide emission. Based on these examples we illustrate that commonly used assumptions for measurement uncertainty can lead to a sampling of wrong areas in the parameter space, incorrect parameter dependencies, and an underestimation of parameter uncertainties. This step needs particular attention by modelers as these issues lead to erroneous model simulations a) in present and future domains, b) misinterpretations of process feedback and functioning of the model, and c) to an underestimation of model uncertainty (e.g. for soil greenhouse gas fluxes).We also test the extension of the Bayesian framework with a model error term to compensate the effects caused by the false assumption of a perfect model and show that this approach can alleviate the observed problems in estimating the model parameter distribution.     

Genetic and environmental effects on age at menarche,and its relationship with reproductive health in twins

INTRODUCTION:

Menarche or first menstrual period is a landmark in reproductive life span and it is the most prominent change of puberty. The timing of menarche can be under the influence of genes as well as individual environmental factors interacting with genetic factors.

OBJECTIVE:

Our study objectives were (a) to investigate the heritability of age of menarche in twins, (b) to obtain the association between age of menarche and childhood factors, and reproductive events/behavior, (c) to examine whether or not having a male co-twin affects early/late menarche.

METHODOLOGY:

A group of female-female identical (n = 108, 54 pairs), non-identical twins (n = 68, 34 pairs) and 17 females from opposite-sex twin sets were identified from twin registries of Malaysia and Iran. Genetic analysis was performed via two methods of Falconers’ formula and maximum likelihood.

RESULTS:

Heritability was found to be 66% using Falconers’ formula and 15% using univariate twin analysis. Model analysis revealed that shared environmental factors have a major contribution in determining the age of menarche (82%) followed by non-shared environment (18%).

DISCUSSION:

Result of this study is consistent with that of the literature. Timing of menarche could be under the influence of shared and non-shared environmental effects. Hirsutism was found to have a higher frequency among subjects with late menarche. There was no significant difference in age of menarche between females of opposite-sex twins and females of same-sex twins.

CONCLUSION:

It is concluded that twin models provide a powerful means of examining the total genetic contribution to age of menarche. Longitudinal studies of twins may clarify the type of environmental effects that determine the age of menarche.     

The genetic divergence of three populations

This paper considers the effect of the genetic divergence on the genetic composition of three samples drawn from three populations at some time after the populations had split. It generalizes the two-sample case studied earlier by Watterson (1985a). Under the assumptions that (i) mating is at random, (ii) the genes at a locus can be any of infinitely many alleles and all mutants are assumed to be new alleles, and (iii) no selective differences exist, we find the probability distribution of the sample gene configurations. From this distribution the single-sample allelic distribution after one-step and two-step bottlenecks and the allelic distribution in the two-sample case can be obtained as marginal distributions. Some numerical results on the number of alleles in common in the three samples are compared with those obtained by Watterson's simulation method; the agreement is excellent. Also, the probability that the three samples are monomorphic for the same allele is found, and numerical examples are given.     

Semiparametric frailty models for zero‐inflated event count data in the presence of informative dropout

Recurrent events data are commonly encountered in medical studies. In many applications, only the number of events during the follow‐up period rather than the recurrent event times is available. Two important challenges arise in such studies: (a) a substantial portion of subjects may not experience the event, and (b) we may not observe the event count for the entire study period due to informative dropout. To address the first challenge, we assume that underlying population consists of two subpopulations: a subpopulation nonsusceptible to the event of interest and a subpopulation susceptible to the event of interest. In the susceptible subpopulation, the event count is assumed to follow a Poisson distribution given the follow‐up time and the subject‐specific characteristics. We then introduce a frailty to account for informative dropout. The proposed semiparametric frailty models consist of three submodels: (a) a logistic regression model for the probability such that a subject belongs to the nonsusceptible subpopulation; (b) a nonhomogeneous Poisson process model with an unspecified baseline rate function; and (c) a Cox model for the informative dropout time. We develop likelihood‐based estimation and inference procedures. The maximum likelihood estimators are shown to be consistent. Additionally, the proposed estimators of the finite‐dimensional parameters are asymptotically normal and the covariance matrix attains the semiparametric efficiency bound. Simulation studies demonstrate that the proposed methodologies perform well in practical situations. We apply the proposed methods to a clinical trial on patients with myelodysplastic syndromes.     

Correlates and predictors of the age of menarche

In a cross-sectional study of 452 girls between 10 and 16 years of age 36 indices of physical and 50 of mental development were tested for their correlation with age at menarche and chronological age, as well as for their predictive power for estimating menarche by multiple regression analysis. Indices of physical maturity and body weight when adjusted for chronological age showed the highest partial correlation coefficients with age at menarche. Among mental characters which show lower intercorrelations with menarche occurred the highest correlation coefficients for a handmotor factor "Spurennachzeichnen" and a factor "Gruppenabh?ngigkeit" (which indicates a type of social motivation). In general physical and mental factors correlate higher with chronological age than with age at menarche. By multiple regression analysis we determined 14 physical and 25 mental predictors explaining 21% and 17% respectively of the variance of age at menarche. The error of the estimate predicting menarche on body weight without knowledge of onset of menarche is +/- 1 year. Using chronological age in a sample of girls before menarche the error of the estimate only is +/- 6 months. To compare the predictive power of chronological age combined with body weight or with skeletal age the time interval is calculated within which 95% of girls attain menarche. The range of prediction extends from 4.3 to 1 year on chronological age (11-16 years); using mean body weight it can be improved by 1.8 to 6 months, while using mean skeletal age an improvement of 0.2 to 3.9 months is possible compared with body weight. The correlations between age at menarche and physical and mental variables are attributed to a common hormonal influence on rate of development.     

Menopause in Blackfeet women--a life span perspective

A lifespan perspective, combining quantitative and qualitative approaches, is used to examine factors related to the timing of menopause in Blackfeet women of northern Montana (USA). Cross-sectional survey data demonstrate a median age at menopause using a status quo method of 51.6 years, and a mean age of 47.0 +/- 5.0 years among those women who had already experienced menopause. Age at menopause is inversely associated with age at menarche and having been breastfed, and positively associated with use of contraceptives, household income, and current or recent employment. Household income and age at menarche influence menopause age jointly in multivariate models. These and other patterns are examined in the lives of two women with very divergent ages at menopause. Although these data support an effect of early life influences on shaping reproductive trajectories that culminate in menopause, environmental factors and human agency during adult life may play a modifying role.     

Age at menarche, schooling, and sexual debut in northern Malawi

Background

Age at sexual debut is a key behavioural indicator used in HIV behavioural surveillance. Early age at menarche may precipitate early sex through perceived readiness for sex, or through school drop-out, but this is rarely studied. We investigated trends and circumstances of sexual debut in relation to schooling and age at menarche.

Methods and Findings

A cross-sectional sexual behaviour survey was conducted on all individuals age 15–59 within a demographic surveillance site in Karonga District, Malawi. Time trends were assessed using birth cohorts. Survival analysis was used to estimate the median age at menarche, sexual debut and first marriage. The 25^th centile was used to define “early” sex, and analyses of risk factors for early sex were restricted to those who had reached that age, and were done using logistic regression. Of the 8232 women and 7338 men resident in the area, 88% and 78%, respectively, were seen, and, 94% and 92% of these were interviewed. The median reported age at first sex was 17.5 for women and 18.8 for men. For women, ages at menarche, sexual debut and first marriage did not differ by birth cohort. For men, age at sexual debut and first marriage decreased slightly in later birth cohorts. For both men and women increased schooling was associated with later sexual debut and a longer delay between sexual debut and first marriage, but the associations were stronger for women. Earlier age at menarche was strongly associated with earlier sexual debut and marriage and lower schooling levels. In women early sexual debut (<16 years) was less likely in those with menarche at age 14–15 (odds ratio (OR) 0.31, 95%CI 0.26–0.36), and ≥16 (OR 0.04, 95%CI 0.02–0.05) compared to those with menarche at <14. The proportion of women who completed primary school was 46% in those with menarche at <14, 60% in those with menarche at 14–15 and 70% in those with menarche at ≥16. The association between age at menarche and schooling was partly explained by age at sexual debut. The association between age at menarche and early sex was not altered by adjusting for schooling.

Conclusions

Women with early menarche start sex and marry early, leading to school drop-out. It is important to find ways to support those who reach menarche early to access the same opportunities as other young women.     

Statistical Efficiency in Distance Sampling

Distance sampling is a technique for estimating the abundance of animals or other objects in a region, allowing for imperfect detection. This paper evaluates the statistical efficiency of the method when its assumptions are met, both theoretically and by simulation. The theoretical component of the paper is a derivation of the asymptotic variance penalty for the distance sampling estimator arising from uncertainty about the unknown detection parameters. This asymptotic penalty factor is tabulated for several detection functions. It is typically at least 2 but can be much higher, particularly for steeply declining detection rates. The asymptotic result relies on a model which makes the strong assumption that objects are uniformly distributed across the region. The simulation study relaxes this assumption by incorporating over-dispersion when generating object locations. Distance sampling and strip transect estimators are calculated for simulated data, for a variety of overdispersion factors, detection functions, sample sizes and strip widths. The simulation results confirm the theoretical asymptotic penalty in the non-overdispersed case. For a more realistic overdispersion factor of 2, distance sampling estimation outperforms strip transect estimation when a half-normal distance function is correctly assumed, confirming previous literature. When the hazard rate model is correctly assumed, strip transect estimators have lower mean squared error than the usual distance sampling estimator when the strip width is close enough to its optimal value (± 75% when there are 100 detections; ± 50% when there are 200 detections). Whether the ecologist can set the strip width sufficiently accurately will depend on the circumstances of each particular study.     

Brief communication: menarche is related to fat distribution

The energy demands of pregnancy and lactation together with the accumulation of stored fat in human females during development suggest that a critical level of fat may be required for menarche; but multivariate analyses have supported the alternative view that skeletal growth is the main factor. However, significant differences between upper- and lower-body (gluteofemoral) fat suggest that fat distribution may be more relevant than total fat. Using cross-sectional data from the third National Health and Nutrition Examination Survey (NHANES III) for females aged 10-14, we show that menarche is more closely related to fat distribution than to skeletal maturity. Unit increases in hip circumference are associated with 24% higher odds of menarche while increases in waist circumference and triceps skinfold lower the odds by 7 and 9%, respectively. Those with menarche despite low levels of total body fat have relatively more fat stored in gluteofemoral depots than those without menarche or those with menarche and greater total amounts of fat. In young women with completed growth, age at menarche is negatively related to hip and thigh circumference and positively related to waist circumference, stature, and biiliac breadth; and blood leptin levels are much more strongly related to gluteofemoral than upper-body fat, suggesting that leptin may convey information about fat distribution to the hypothalamus during puberty. Fat distribution may be relevant because gluteofemoral fat may provide neurodevelopmentally important fatty acid reserves.