The genetic structure of a tribal population, the Yanomama indians. VII. Anthropometric differences among Yanomama villages

Anthropometric data on 12 variables in 19 villages of the Yanomama Indians demonstrate significant heterogeneity in physique among villages of this tribe. Mahalanobis' distances (D²) calculated from the data lead to the tentative conclusion of a general correspondence between anthropometric and geographic distances separating villages. The mean stature of the Yanomama is smaller than that of most other South American tribes which have been measured, and the Yanomama are genetically distinct from the other small Indians as shown by genetic distances based on allele frequencies for a variety of genetic markers. Since some subjects were measured more than once by the same and by different observers, it was possible to calculate approximate estimates of variance within and between observers. Univariate analysis indicates that face height and nose height are especially susceptible to systematic differences in technique between observers. The variances obtained in this field study compare favorably with those of some classical laboratory studies described in the literature. It was found that measurement error nevertheless probably makes a substantial contribution to anthropometric distance between villages. The median error variance as a fraction of that of Herskovits ('30) is 0.62 for the seven measurements in common with this study. The median value of the error variance for the 12 variables in this study is between 16% and 17% of the total variance.     

Anthropometry as a predictor of bench press performance done at different loads

The purpose of our study was to examine the ability of anthropometric variables (body mass, total arm length, biacromial width) to predict bench press performance at both maximal and submaximal loads. Our methods required 36 men to visit our laboratory and submit to anthropometric measurements, followed by lifting as much weight as possible in good form one time (1 repetition maximum, 1RM) in the exercise. They made 3 more visits in which they performed 4 sets of bench presses to volitional failure at 1 of 3 (40, 55, or 75% 1RM) submaximal loads. An accelerometer (Myotest Inc., Royal Oak MI) measured peak force, velocity, and power after each submaximal load set. With stepwise multivariate regression, our 3 anthropometric variables attempted to explain significant amounts of variance for 13 bench press performance indices. For criterion measures that reached significance, separate Pearson product moment correlation coefficients further assessed if the strength of association each anthropometric variable had with the criterion was also significant. Our analyses showed that anthropometry explained significant amounts (p < 0.05) of variance for 8 criterion measures. It was concluded that body mass had strong univariate correlations with 1RM and force-related measures, total arm length was moderately associated with 1RM and criterion variables at the lightest load, whereas biacromial width had an inverse relationship with the peak number of repetitions performed per set at the 2 lighter loads. Practical applications suggest results may help coaches and practitioners identify anthropometric features that may best predict various measures of bench press prowess in athletes.     

Leptin,Insulin, and Obesity‐related Phenotypes: Genetic Influences on Levels and Longitudinal Changes

This study estimated the genetic and environmental determinants of plasma leptin and insulin levels and of obesity‐related phenotypes. Included in this analysis were family members from 80 families living in kibbutz settlements, who participated in two examinations 8–10 years apart. We estimated that polygenes explained 30–50% of the adjusted leptin and insulin levels and 30–70% of the anthropometric phenotypes. This study demonstrated a significant genetic influence on longitudinal changes in leptin and BMI (h² = 0.45) and small‐to‐moderate heritability estimates for changes in insulin and other obesity‐related phenotypes. In bivariate genetic analyses, we observed positive genetic correlations between leptin and anthropometric phenotypes, suggesting that shared effects of the same sets of loci account for 20–30% of the additive genetic variance in these pairs of variables. Shared genetic factors also account for 20–25% of the additive genetic variance in insulin—anthropometric pairs of variables.     

Genome-wide linkage analysis of systolic blood pressure: a comparison of two approaches to phenotype definition

Problem 1 of the Genetic Analysis Workshop 13(GAW13) contains longitudinal data of cardiovascular measurements from 330 pedigrees. The longitudinal data complicates the phenotype definition because multiple measurements are taken on each individual. To address this complication, we propose an approach that uses generalized estimating equations to obtain residuals for each time point for each person. The mean residual is then taken as the new phenotype with which to use in a variance components linkage analysis. We compare our phenotype definition approach to an approach that first reduces the multiple measurements to a single measurement and then models these summary statistics as regression terms in a variance components analysis. For each approach, multipoint linkage analysis was performed using the residuals and the SOLAR computer program. Our results show little difference between the methods based on the LOD scores.     

A penalized spline approach to functional mixed effects model analysis

In this article, we propose penalized spline (P-spline)-based methods for functional mixed effects models with varying coefficients. We decompose longitudinal outcomes as a sum of several terms: a population mean function, covariates with time-varying coefficients, functional subject-specific random effects, and residual measurement error processes. Using P-splines, we propose nonparametric estimation of the population mean function, varying coefficient, random subject-specific curves, and the associated covariance function that represents between-subject variation and the variance function of the residual measurement errors which represents within-subject variation. Proposed methods offer flexible estimation of both the population- and subject-level curves. In addition, decomposing variability of the outcomes as a between- and within-subject source is useful in identifying the dominant variance component therefore optimally model a covariance function. We use a likelihood-based method to select multiple smoothing parameters. Furthermore, we study the asymptotics of the baseline P-spline estimator with longitudinal data. We conduct simulation studies to investigate performance of the proposed methods. The benefit of the between- and within-subject covariance decomposition is illustrated through an analysis of Berkeley growth data, where we identified clearly distinct patterns of the between- and within-subject covariance functions of children's heights. We also apply the proposed methods to estimate the effect of antihypertensive treatment from the Framingham Heart Study data.     

Relationships among body composition measures in community-dwelling older women

Objective: To examine whether simple anthropometric measures provide a good estimate of total and visceral fat in 146 community‐dwelling, older white women (mean age, 74.0 ± 4.1 years). Research Methods and Procedures: Total body fat and visceral fat were measured using electron beam computed tomography (EBT). Anthropometric parameters (height, weight, BMI, sagittal diameter, and waist circumference) were measured using standard techniques. Total percentage body fat was assessed using DXA. Spearman correlations were used to examine the association between the measures. Linear regression, controlling for age, was used to examine the associations between the anthropometric parameters and total and visceral body fat measured by EBT. Results: Correlations among body composition measures ranged from ρ = 0.46 to 0.93 (p < 0.0001). EBT total fat was strongly correlated with both DXA estimates of total percentage fat (ρ = 0.86) and BMI (ρ = 0.89). Separate linear regression models indicated that BMI, waist circumference, sagittal diameter, and DXA total percentage fat were each independently related to EBT total fat. BMI had the strongest linear relationship, explaining 80% of the model variance (p < 0.0001). Linear regression indicated that BMI, waist circumference, and sagittal diameter were each independently related to EBT visceral fat, with BMI and sagittal diameter explaining ～53% of the model variance (p < 0.0001). Discussion: The use of simple anthropometric measures such as BMI, sagittal diameter, and waist circumference may be an appropriate alternative for more expensive techniques when assessing total fat but should be used with caution when estimating visceral body fat.     

Structured latent growth curves for twin data.

We describe methods to fit structured latent growth curves to data from MZ and DZ twins. The well-known Gompertz, logistic and exponential curves may be written as a function of three components - asymptote, initial value, and rate of change. These components are allowed to vary and covary within individuals in a structured latent growth model. Such models are highly economical, requiring a small number of parameters to describe covariation across many occasions of measurement. We extend these methods to analyse longitudinal data from MZ and DZ twins and focus on the estimation of genetic and environmental variation and covariation in each of the asymptote, initial and rate of growth factors. For illustration, the models are fitted to longitudinal Bayley Infant Mental Development Scale data published by McArdle (1986). In these data, all three components of growth appear strongly familial with the majority of variance associated with the shared environment; differences between the models were not great. Occasion-specific residual factors not associated with the curve components account for approximately 40% of variance of which a significant proportion is additive genetic. Though the growth curve model fit less well than some others, they make restrictive, falsifiable predictions about the mean, variance and twin covariance of other (not yet measured) occasions of measurement.     

Daytime sleepiness in the obese: not as simple as obstructive sleep apnea

Objective: Excessive daytime sleepiness is a common symptom in obese patients, but what drives this condition is unclear. The objective was to look for clinical, anthropometric, biochemical, and polysomnographic predictors of excessive daytime sleepiness as measured by the Epworth Sleepiness Scale (ESS) in obese patients. Research Methods and Procedures: The ESS questionnaire was completed by 1055 consecutive patients presenting for obesity surgery. Those at high risk for obstructive sleep apnea (n = 331) had diagnostic overnight polysomnography preoperatively. All patients had preoperative clinical, hematologic, and biochemical measurements and completed multiple questionnaires. Results: There was no significant relationship between ESS score and any measure of diagnostic polysomnography factors, including total apnea hypopnea index. Subtle increases in ESS scores were reported in men, older patients, and those with type 2 diabetes. However, general demographic, anthropometric, and biochemical measures of the metabolic syndrome explained only 3% of ESS score variance, and inflammatory markers of C‐reactive protein and total white cell count were not predictive. Poor Short Form‐36 energy scores (b = ?0.18, p < 0.001) and high Beck Depression Inventory scores were predictive of higher ESS scores (b = 0.15, p < 0.001) and, along with increasing age and male gender, explained 10% of variance. Symptoms related to disturbed nocturnal sleep explained 30% of variance. Conclusion: In severely obese subjects, increased daytime sleepiness does not seem to be driven by obstructive sleep apnea, the degree of obesity, or anthropometric, metabolic, or inflammatory markers of the metabolic syndrome. It is, however, associated with poor energy, symptoms of depression, and symptoms of nocturnal sleep disturbance.     

Interobserver errors in anthropometry

To present basic information on the interobserver precision and accuracy of 32 selected anthropometric measurement items, six observers measured each of 37 subjects once in two days. The data were analyzed by using ANOVA, and mean absolute bias, standard deviation of bias, and mean absolute bias in standard deviation unit were used as measures of bias. By comparing the results of the two days, the effects of the practice on measurement errors were also investigated. Variance was overestimated by more than 10% in five measurements. Interobserver error variance and random error variance were highly correlated with each other. Measures of the bias were significantly correlated with interobserver and especially with random error variances. The interobserver errors were drastically reduced on the second day in the measurement items in which the causes of the interobserver errors could be specified. It was speculated that even when the definitions of the landmarks and measurement items were clear, the ambiguity in the practical procedures in locating landmarks, applying instruments, and so on, permitted each observer to develop his or her own measurement technique, and it in turn caused interobserver errors. To minimize interobserver and random errors, the standardization of measurement technique should be extended to the details of the practical procedures.     

Population structure and quantitative characters

A migration matrix model is used to investigate the behavior of neutral polygenic characters in subdivided populations. It is shown that gametic disequilibrium has a large effect on the variance among groups but none at all on its expectation. The variance of among-group variance is substantial and does not depend on the number of loci contributing to variance in the character. It is just as large for polygenic characters as for single loci with the same additive variance. This implies that one polygenic character contains exactly as much information about population relationships as one single-locus marker. The theory is compared with observed differentiation of dermatoglyphic and anthropometric characters among Bougainville islanders.     

A univariate and multivariate examination of measurement error in anthropometry

Replicate anthropometric measurements on 20 male and 22 female Eskimos were examined using analysis of variance, product-moment correlation coefficients, and canonical variates with Mahalanobis' D² distances. Analysis of variance indicated that 12 of the 16 variables could be measured comparably by two investigators. Those variables with readily defined endpoints yielded the highest correlations between the results of two anthropometrists. The multivariate analysis demonstrated a high level of discrimination between two sets of data taken on the same group of subjects. This suggests that population comparisons using data from two or more investigators could be significantly affected by measurement error.     

Anthropometry as a predictor of vertical jump heights derived from an instrumented platform

The current study purpose examined the vertical height-anthropometry relationship with jump data obtained from an instrumented platform. Our methods required college-aged (n = 177) subjects to make 3 visits to our laboratory to measure the following anthropometric variables: height, body mass, upper arm length (UAL), lower arm length, upper leg length, and lower leg length. Per jump, maximum height was measured in 3 ways: from the subjects' takeoff, hang times, and as they landed on the platform. Standard multivariate regression assessed how well anthropometry predicted the criterion variance per gender (men, women, pooled) and jump height method (takeoff, hang time, landing) combination. Z-scores indicated that small amounts of the total data were outliers. The results showed that the majority of outliers were from jump heights calculated as women landed on the platform. With the genders pooled, anthropometry predicted a significant (p < 0.05) amount of variance from jump heights calculated from both takeoff and hang time. The anthropometry-vertical jump relationship was not significant from heights calculated as subjects landed on the platform, likely due to the female outliers. Yet anthropometric data of men did predict a significant amount of variance from heights calculated when they landed on the platform; univariate correlations of men's data revealed that UAL was the best predictor. It was concluded that the large sample of men's data led to greater data heterogeneity and a higher univariate correlation. Because of our sample size and data heterogeneity, practical applications suggest that coaches may find our results best predict performance for a variety of college-aged athletes and vertical jump enthusiasts.     

Sedentism, seasonality, and economic status: A multivariate analysis of maternal dietary and health statuses between pastoral and agricultural Ariaal and Rendille communities in northern Kenya

This paper examines longitudinal data to assess the effects of the recent transition from pastoralism to sedentary agriculture for Ariaal and Rendille mothers in northern Kenya. Dietary, morbidity, and anthropometric data resulting from bimonthly repeated surveys of the pastoral community of Lewogoso and the sedentary agricultural community of Songa, covering the period from September 1994-July 1995, were used to test two hypotheses: 1) that sedentism is associated with changes in diet, seasonality, morbidity, and socioeconomic differentiation, and 2) that these changes affect maternal body composition. The first hypothesis is partly supported, with starch replacing milk in the sedentary diet. Repeated-measures multivariate analysis of variance revealed no seasonal effects for diet in the agricultural sample, while starch consumption was significantly affected by seasonality for the pastoral sample. The agricultural community also featured economic dietary disparities favoring wealthier mothers, while no such effect was found in the pastoral community. However, no seasonal or economic effects were found for morbidity patterns in either sample. The second hypothesis is also partially supported, as a generalized estimating equations analysis revealed differences in the way diet and economic strata influence maternal anthropometric values. Economic status was significantly associated with maternal arm-fat area in Lewogoso. Milk was a significant factor associated with maternal weight and arm-muscle area in Songa. These findings are discussed in light of development policy implications.     

Prediction of body composition in habitually active middle-aged men.

In 45 physically active men (ages 35-67 yr) who underwent hydrostatic weighing to determine body composition, multiple regression equations were developed for the prediction of body density (D), lean body weight (LBW), fat body weight (FBW), and % fat using selected anthropometric measurements. The prediction accuracy for these parameters using several previously generated anthropometric regression equations was also determined. With equations developed from the present data a substantially higher correlation was obtained between measured and predicted LBW (r = 0.95) than between measured and predicted D (r = 0.85), FBW (r = 0.88), or % fat (r = 0.84). When previously developed equations were applied to the present sample, correlations between measured and predicted values were considerably lower (4-42%) than in the original studies; this reduction was least in the case of LBW. Analysis of previous data indicated that in selected populations total body weight can account for a relatively large fraction of the variance in LBW. LBW may be estimated quite accurately (r greater than or equal to 0.90) in physically active men with one of several regression equations which include total body weight as an independent variable.     

The influence of anthropometric characteristics to the handgrip and pinch strength in 6-10-year old children

The aim of the study was to investigate the relationships between handgrip and pinch strength values with basic body (body height, body mass, BMI) and with specific hand anthropometric parameters (fingers spans, lengths and perimeters) in prepubertal children. Body height, body mass and five fingers spans, lengths and perimeters were measured in 461 6-10 year old Estonian children according to Visnapuu & Jürim?e (2007). BMI was calculated (kg/m2). The maximal handgrip strength of the right and left hand was measured with hand dynamometer. The right and left key and tip pinch were measured with a pinch gauge. Body height, as a rule, in combination with BMI, was the strongest predictor of handgrip strength, especially in older children (about 40-60 % of the total variance, R2 x 100). From the hand anthropometry, the most important span parameter was FS2 (see explanations in the methods), which explained about 10-50% (R2 x 100) of the total variance. In older groups, the FS1 and FS3 were added to the models. From the length parameters, the most important was IFL, which in younger groups together with MFL explained 10-30% of the total variance, and in older groups the addition of RFL increased the influence to 45% (R2 x 100). From the perimeters, the most important one was P2 which explained the variability of the handgrip strength in younger groups by 15-30% (R2 x 100) and in older groups together with P3 and P4 even 30-40%. In children, the basic anthropometric parameters (body height and BMI) contribute more to the prediction of handgrip strength than the specific anthropometric parameters. With increasing age the contribution of basic and specific hand anthropometry increases and the relation between anthropometry and handgrip strength is stronger in boys compared with girls. The relation of basic and hand anthropometry to the tip and key pinch strength is relatively low.     

Optimal allocation of replicates for measurement evaluation studies

Optimal experimental design is important for the efficient use of modern highthroughput technologies such as microarrays and proteomics. Multiple factors including the reliability of measurement system, which itself must be estimated from prior experimental work, could influence design decisions. In this study, we describe how the optimal number of replicate measures （technical replicates） for each biological sample （biological replicate） can be determined. Different allocations of biological and technical replicates were evaluated by minimizing the variance of the ratio of technical variance （measurement error） to the total variance （sum of sampling error and measurement error）. We demonstrate that if the number of biological replicates and the number of technical replicates per biological sample are variable, while the total number of available measures is fixed, then the optimal allocation of replicates for measurement evaluation experiments requires two technical replicates for each biological replicate. Therefore, it is recommended to use two technical replicates for each biological replicate if the goal is to evaluate the reproducibility of measurements.     

Influence of anthropometric characteristics on changes in maximal exercise ventilation and breathing pattern during growth in boys.

The aim of this study was to investigate the effect of growth on ventilation and breathing pattern during maximal exercise oxygen consumption (VO2max) and their relationships with anthropometric characteristics. Seventy six untrained schoolboys, aged 10.5-15.5 years, participated in this study. Anthropometric measurements made included body mass, height, armspan, lean body mass, and body surface area. During an incremental exercise test, maximal ventilation (VEmax), tidal volume (VTmax), breathing frequency (fmax), inspiratory and expiratory times (tImax and tEmax), total duration of respiratory cycle (tTOTmax), mean inspiratory flow (VT/tImax), and inspiration fraction (tI/tTOTmax) were measured at VO2max. A power function was calculated between anthropometric characteristics and ventilatory variables to determine the allometric constants. The results showed firstly, that VEmax, VTmax, tImax, tEmax, tTOTmax, and VT/tImax increased with age and anthropometric characteristics (P less than 0.001), fmax decreased (P less than 0.001), and tI/tTOTmax remained constant during growth; secondly that lean body mass explained the greatest percentage of variance of VEmax (62.1%), VTmax (76.8%), and VT/tImax (70.6%), while anthropometric characteristics explained a slight percentage of variance of fmax and timing; and thirdly that VEmax, VTmax, and VT/tImax normalized by lean body mass did not change significantly with age. We concluded that at VO2max there were marked changes in ventilation and breathing pattern with growth. The changes in VEmax, VTmax, and VT/tImax were strongly related to the changes in lean body mass.     

Semiparametric variance components models for genetic studies with longitudinal phenotypes

In a family-based genetic study such as the Framingham Heart Study (FHS), longitudinal trait measurements are recorded on subjects collected from families. Observations on subjects from the same family are correlated due to shared genetic composition or environmental factors such as diet. The data have a 3-level structure with measurements nested in subjects and subjects nested in families. We propose a semiparametric variance components model to describe phenotype observed at a time point as the sum of a nonparametric population mean function, a nonparametric random quantitative trait locus (QTL) effect, a shared environmental effect, a residual random polygenic effect and measurement error. One feature of the model is that we do not assume a parametric functional form of the age-dependent QTL effect, and we use penalized spline-based method to fit the model. We obtain nonparametric estimation of the QTL heritability defined as the ratio of the QTL variance to the total phenotypic variance. We use simulation studies to investigate performance of the proposed methods and apply these methods to the FHS systolic blood pressure data to estimate age-specific QTL effect at 62cM on chromosome 17.     

A simulation-based marginal method for longitudinal data with dropout and mismeasured covariates

Longitudinal data often contain missing observations and error-prone covariates. Extensive attention has been directed to analysis methods to adjust for the bias induced by missing observations. There is relatively little work on investigating the effects of covariate measurement error on estimation of the response parameters, especially on simultaneously accounting for the biases induced by both missing values and mismeasured covariates. It is not clear what the impact of ignoring measurement error is when analyzing longitudinal data with both missing observations and error-prone covariates. In this article, we study the effects of covariate measurement error on estimation of the response parameters for longitudinal studies. We develop an inference method that adjusts for the biases induced by measurement error as well as by missingness. The proposed method does not require the full specification of the distribution of the response vector but only requires modeling its mean and variance structures. Furthermore, the proposed method employs the so-called functional modeling strategy to handle the covariate process, with the distribution of covariates left unspecified. These features, plus the simplicity of implementation, make the proposed method very attractive. In this paper, we establish the asymptotic properties for the resulting estimators. With the proposed method, we conduct sensitivity analyses on a cohort data set arising from the Framingham Heart Study. Simulation studies are carried out to evaluate the impact of ignoring covariate measurement error and to assess the performance of the proposed method.     

Prediction of Visceral Adipose Tissue from Simple Anthropometric Measurements in Youths with Obesity

OWENS, SCOTT, MARK LITAKER, JERRY ALLISON, SHARON RIGGS, MICHAEL FERGUSON, AND BERNARD GUTIN. Prediction of visceral adipose tissue from simple anthropometric measurements in youths with obesity. Obes Res. 1999;7:16–22. Objective : Although visceral adipose tissue (VAT) is the component of body composition most highly associated with cardiovascular risk factors, its measurement requires expensive procedures, such as magnetic resonance imaging. This study examined the ability of simple demographic and anthropometric measurements to predict magnetic resonance imaging-derived VAT in 76 apparently healthy, black and white youths with obesity who were 7 years to 16 years of age. Research Methods and Procedures : Stepwise multiple linear regression was used to develop a prediction equation for VAT based on 13 simple anthropometric variables (height, weight, body mass index, triceps skinfold, calf skinfold, sagittal diameter, waist circumference, hip circumference, thigh circumference, waist/hip ratio, waist/thigh ratio, sagittal diameter/thigh ratio, and percent body fat from the sum of calf and triceps skinfolds) and three demographic variables (age, gender and ethnicity). Results : The stepwise multiple regression procedure yielded a final model that included two anthropometric variables (sagittal diameter and waist/hip ratio) and one demographic variable (ethnicity). The prediction equation was: VAT = ?124.06+16.67 (ethnicity)+4.15 (sagittal diameter)-+17.89 (waist/hip ratio), where ethnicity was coded as 0 = black and 1 = white. The model explained 63% of the variance in VAT and was associated with a measurement error of 23.9%. Discussion : Although the model seems to lack sufficient explanatory power for routine use in clinical settings with individual patients, it may have some utility in epidemiological studies given its relatively small (<25%) standard error of estimate.