Human body mass estimation: a comparison of "morphometric" and "mechanical" methods

In the past, body mass was reconstructed from hominin skeletal remains using both "mechanical" methods which rely on the support of body mass by weight-bearing skeletal elements, and "morphometric" methods which reconstruct body mass through direct assessment of body size and shape. A previous comparison of two such techniques, using femoral head breadth (mechanical) and stature and bi-iliac breadth (morphometric), indicated a good general correspondence between them (Ruff et al. [1997] Nature 387:173-176). However, the two techniques were never systematically compared across a large group of modern humans of diverse body form. This study incorporates skeletal measures taken from 1,173 Holocene adult individuals, representing diverse geographic origins, body sizes, and body shapes. Femoral head breadth, bi-iliac breadth (after pelvic rearticulation), and long bone lengths were measured on each individual. Statures were estimated from long bone lengths using appropriate reference samples. Body masses were calculated using three available femoral head breadth (FH) formulae and the stature/bi-iliac breadth (STBIB) formula, and compared. All methods yielded similar results. Correlations between FH estimates and STBIB estimates are 0.74-0.81. Slight differences in results between the three FH estimates can be attributed to sampling differences in the original reference samples, and in particular, the body-size ranges included in those samples. There is no evidence for systematic differences in results due to differences in body proportions. Since the STBIB method was validated on other samples, and the FH methods produced similar estimates, this argues that either may be applied to skeletal remains with some confidence.     

Predicting body mass of bonobos (Pan paniscus) with human-based morphometric equations

A primate's body mass covaries with numerous ecological, physiological, and behavioral characteristics. This versatility and potential to provide insight into an animal's life has made body mass prediction a frequent and important objective in paleoanthropology. In hominin paleontology, the most commonly employed body mass prediction equations (BMPEs) are “mechanical” and “morphometric”: uni- or multivariate linear regressions incorporating dimensions of load-bearing skeletal elements and stature and living bi-iliac breadth as predictor variables, respectively. The precision and accuracy of BMPEs are contingent on multiple factors, however, one of the most notable and pervasive potential sources of error is extrapolation beyond the limits of the reference sample. In this study, we use a test sample requiring extrapolation—56 bonobos (Pan paniscus) from the Lola ya Bonobo sanctuary in Kinshasa, Democratic Republic of the Congo—to evaluate the predictive accuracy of human-based morphometric BMPEs. We first assess systemic differences in stature and bi-iliac breadth between humans and bonobos. Due to significant differences in the scaling relationships of body mass and stature between bonobos and humans, we use panel regression to generate a novel BMPE based on living bi-iliac breadth. We then compare the predictive accuracy of two previously published morphometric equations with the novel equation and find that the novel equation predicts bonobo body mass most accurately overall (41 of 56 bonobos predicted within 20% of their observed body mass). The novel BMPE is particularly accurate between 25 and 45 kg. Given differences in limb proportions, pelvic morphology, and body tissue composition between the human reference and bonobo test samples, we find these results promising and evaluate the novel BMPE's potential application to fossil hominins.     

The influence of body proportions on femoral and tibial midshaft shape in hunter‐gatherers

Variation in femoral and tibial diaphyseal shape is used as an indicator of adaptation to patterns of terrestrial mobility. Recent experimentation has implied that lower limb diaphyseal shape may be primarily influenced by lower limb length, and less so by mobility patterns. If valid, this would, at most, render previous interpretations of mobility patterns based on analyses of diaphyseal shape questionable, and, at least, require additional standardization that considers the influence of limb length. Although the consequences could be profound, this implication has yet to be directly tested. Additionally, the influence of body breadth on tibial shape (and to a lesser extent femoral shape) remains uncertain. Tibial and femoral cross‐sectional midshaft shape measurements, taken from nine Pleistocene and Holocene skeletal populations, were compared against lower limb length, limb segment length, and bi‐iliac breadth. Generally, limb length and limb segment length do not significantly influence femoral or tibial midshaft shape. After controlling for body mass greater bi‐iliac breadth is associated with a relative mediolateral strengthening of the femoral midshaft, while the influence of a wider body shape (BIB/length) is associated with a relative M‐L strengthening of the tibia and femur of males, and the tibia of females. We conclude that; (1) mechanical interpretations of lower limb diaphyseal shape are most parsimonious due to the lack of evidence for a consistent relationship between segment length and shape; however, (2) further work is required to investigate the influence of bi‐iliac breadth on both femoral and tibial midshaft shape. Am J Phys Anthropol, 2010. © 2010 Wiley‐Liss, Inc.     

Allometric equations for predicting body mass of dinosaurs

We use data from the literature to compare two statistical procedures for estimating mass (or size) of quadrupedal dinosaurs and other extraordinarily large animals in extinct lineages. Both methods entail extrapolation from allometric equations fitted to data for a reference group of contemporary animals having a body form similar to that of the dinosaurs. The first method is the familiar one of fitting a straight line to logarithmic transformations, followed by back-transformation of the resulting equation to a two-parameter power function in the arithmetic scale. The second procedure entails fitting a two-parameter power function directly to arithmetic data for the extant forms by nonlinear regression. In the example presented here, the summed circumferences for humerus plus femur for 33 species of quadrupedal mammals was the predictor variable in the reference sample and body mass was the response variable. The allometric equation obtained by back-transformation from logarithms was not a good fit to the largest species in the reference sample and presumably led to grossly inaccurate estimates for body mass of several large dinosaurs. In contrast, the allometric equation obtained by nonlinear regression described data in the reference sample quite well, and it presumably resulted in better estimates for body mass of the dinosaurs. The problem with the traditional analysis can be traced to change in the relationship between predictor and response variables attending transformation, thereby causing measurements for large animals not to be weighted appropriately in fitting models by least squares regression. Extrapolations from statistical models obtained by back-transformation from lines fitted to logarithms are unlikely to yield reliable predictions for body size in extinct animals. Numerous reports on the biology of dinosaurs, including recent studies of growth, may need to be reconsidered in light of our findings.     

New regression equations for predicting peak expiratory flow in adults.

An earlier study of peak expiratory flow (PEF) in normal adults contained too few men aged over 55 and women aged over 65 for the regression equations to be used for prediction in older people. A subsequent study was therefore carried out on an additional 23 men and 29 women aged 55 or over who were lifelong non-smokers and satisfied the same strict criteria of normality that had been used in the original study. The data from both studies were combined and a new model used to calculate equations for the regression of PEF on age and height in the two sexes. With this model predicted values could be derived for men and women aged between 15 and 85. These new equations gave predicted values in men and women aged less than 55 and 65, respectively, which were almost identical with those reported previously. The new regression equations for PEF enable values to be predicted for people aged 15-85 and so enhance the accuracy of testing in the elderly.     

Body mass and stature estimation based on the first metatarsal in humans

Archaeological assemblages often lack the complete long bones needed to estimate stature and body mass. The most accurate estimates of body mass and stature are produced using femoral head diameter and femur length. Foot bones including the first metatarsal preserve relatively well in a range of archaeological contexts. In this article we present regression equations using the first metatarsal to estimate femoral head diameter, femoral length, and body mass in a diverse human sample. The skeletal sample comprised 87 individuals (Andamanese, Australasians, Africans, Native Americans, and British). Results show that all first metatarsal measurements correlate moderately to highly (r = 0.62-0.91) with femoral head diameter and length. The proximal articular dorsoplantar diameter is the best single measurement to predict both femoral dimensions. Percent standard errors of the estimate are below 5%. Equations using two metatarsal measurements show a small increase in accuracy. Direct estimations of body mass (calculated from measured femoral head diameter using previously published equations) have an error of just over 7%. No direct stature estimation equations were derived due to the varied linear body proportions represented in the sample. The equations were tested on a sample of 35 individuals from Christ Church Spitalfields. Percentage differences in estimated and measured femoral head diameter and length were less than 1%. This study demonstrates that it is feasible to use the first metatarsal in the estimation of body mass and stature. The equations presented here are particularly useful for assemblages where the long bones are either missing or fragmented, and enable estimation of these fundamental population parameters in poorly preserved assemblages.     

Stature and body mass estimation from skeletal remains in the European Holocene

Techniques that are currently available for estimating stature and body mass from European skeletal remains are all subject to various limitations. Here, we develop new prediction equations based on large skeletal samples representing much of the continent and temporal periods ranging from the Mesolithic to the 20th century. Anatomical reconstruction of stature is carried out for 501 individuals, and body mass is calculated from estimated stature and biiliac breadth in 1,145 individuals. These data are used to derive stature estimation formulae based on long bone lengths and body mass estimation formulae based on femoral head breadth. Prediction accuracy is superior to that of previously available methods. No systematic geographic or temporal variation in prediction errors is apparent, except in tibial estimation of stature, where northern and southern European formulae are necessary because of the presence of relatively longer tibiae in southern samples. Thus, these equations should bebroadly applicable to European Holocene skeletal samples.     

Associations between height,body mass,and frequency of decayed,extracted, and filled deciduous teeth among two cohorts of Taiwanese first graders

In this study, heights, weights, and numbers of decayed, extracted, and filled (DEF) deciduous teeth of 300 first‐graders from a less affluent area of Taipei were compared with those of 277 first‐graders from a more affluent one. Parents of all children self‐identified as having ancestors from Fujian. This study tested the hypothesis that synergisms between under‐nutrition and disease form part of a causal pathway contributing to the risk of deciduous caries. Within the less affluent community significant inverse associations between height and body mass index, as proxies for nutritional status, and the frequency of DEF deciduous teeth were anticipated. These associations were not expected in the more affluent community where nutritional status was adequate. An alternative hypothesis, that parental behavior potentially correlated with parental education, occupational backgrounds, housing, or family size contributed independently to offspring nutritional status and caries risk, was evaluated with available data. Consistent with the primary hypothesis, regression analyses revealed significant negative slopes of height (P = 0.002) and log BMI (P = 0.036) on total DEF deciduous teeth in the less affluent group, but not in the more affluent one. Direct tests of slope coefficients in the two groups indicate a significant difference for height (P = 0.041) but not log BMI (P = 0.29). Inclusion of parental education, occupational categories, housing, and numbers of siblings in the regression model provided no support to the alternative hypothesis. Results suggest that improving nutritional status significantly lowers caries risk, though most variation is probably attributable to other factors. Am J Phys Anthropol, 2009. © 2009 Wiley‐Liss, Inc.     

Preliminary evidence for an association between LRP-1 genotype and body mass index in humans

Background/Aims

The LDL receptor-related protein-1 gene (LRP-1) has been associated with obesity in animal models, but no such association has yet been reported in humans. As data suggest this increase in fat mass may be mediated through a mechanism involving the clearance of plasma triglyceride-rich lipoproteins (TGRL), where the LRP interacts with apolipoprotein E (ApoE) on chylomicron remnants, we aimed to examine (1) whether there was an association between 3 single nucleotide polymorphisms (SNPs) on LRP-1 with body mass index (BMI) and (2) whether any association between LRP-1 SNPs and BMI could be modified by polymorphisms on the ApoE gene when comparing the wild type ε3/ε3 genotype against mutant ApoE allele (ε2/ε4) carriers.

Methods/Results

We used data from 1,036 men and women (mean age±SD = 49±16 y) participating in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) Study. Mixed linear models, which controlled for age, sex, alcohol intake and smoking, as well as family pedigree and center of data collection were calculated. Models that used LRP-1 genotype as a predictor of BMI revealed that individuals who were homozygous for the minor allele at the LRP-1 {"type":"entrez-protein","attrs":{"text":"I10701","term_id":"592781","term_text":"I10701"}}I10701 locus had BMIs, on average, 1.03 kg/m² higher than major allele carriers (P = 0.03). In subsequent mixed linear models that included main effects of LRP-1 {"type":"entrez-protein","attrs":{"text":"I10701","term_id":"592781","term_text":"I10701"}}I10701 SNP and ApoE alleles, and an interaction term the two genotypes, there was no interaction detected between the LRP-1 {"type":"entrez-protein","attrs":{"text":"I70701","term_id":"3006836","term_text":"gb|I70701.1|I70701"}}I70701 genotype with either the ApoE ε2 or ε4 allele carriers (P>0.05).

Conclusions

This has implications for starting to understand pathways from genotype to human BMI, which may operate through TGRL uptake at the LRP-1 receptor. This may pave the way for future research into individualized dietary interventions.     

Two-stage support vector regression approach for predicting accessible surface areas of amino acids

We address the problem of predicting solvent accessible surface area (ASA) of amino acid residues in protein sequences, without classifying them into buried and exposed types. A two-stage support vector regression (SVR) approach is proposed to predict real values of ASA from the position-specific scoring matrices generated from PSI-BLAST profiles. By adding SVR as the second stage to capture the influences on the ASA value of a residue by those of its neighbors, the two-stage SVR approach achieves improvements of mean absolute errors up to 3.3%, and correlation coefficients of 0.66, 0.68, and 0.67 on the Manesh dataset of 215 proteins, the Barton dataset of 502 nonhomologous proteins, and the Carugo dataset of 338 proteins, respectively, which are better than the scores published earlier on these datasets. A Web server for protein ASA prediction by using a two-stage SVR method has been developed and is available (http://birc.ntu.edu.sg/~ pas0186457/asa.html).     

New equations for estimating body cell mass from bioimpedance parallel models in healthy older Germans

The objectives of this study were to assess for elderly Germans the validity of existing equations for predicting body cell mass (BCM) and to develop from single- and multifrequency bioimpedance (SFBIA, MFBIA) models new prediction equations. In a data-splitting approach, validation and cross-validation were performed in 160 healthy elderly (60- to 90-yr) subjects. BCM was determined using a tetrapolar bioimpedance analyzer (800 microA; 4 fixed frequencies: 1, 5, 50, and 100 kHz; electrodes placed to hand, wrist, ankle, and foot) and whole body (40)K counting as a reference method. New prediction equations were derived by multiple stepwise regression analysis. The Bland-Altman procedure was used for methods comparison. Relative to whole body counting, the manufacturer's equation overestimated BCM by 9% in men (P < 0.0001, paired t-test) and 4% in women (P = 0.002). Compared with the manufacturer's equation, the newly derived equations (r = 0.92, RMSE = 6-9%) improved accuracy (pure error = 13 vs. 7-8%) and reduced bias and limits of agreement. SFBIA and MFBIA equations did not differ in precision or accuracy. We conclude that the newly derived equations improved BCM estimates in the elderly compared with existing equations. There was no advantage of MFBIA over SFBIA equations.     

Relationships between bone mass and micro-architecture at the mandible and iliac bone in edentulous subjects: a dual X-ray absorptiometry, computerised tomography and microcomputed tomography study

doi: 10.1111/j.1741‐2358.2011.00527.x Relationships between bone mass and micro‐architecture at the mandible and iliac bone in edentulous subjects: a dual X‐ray absorptiometry, computerised tomography and microcomputed tomography study Objectives: To compare bone volume, bone mineral density, cortical thickness and bone micro‐architecture in a series of paired mandibular and iliac bone samples analysed by various imagery techniques to see whether relationships exist between the various techniques and between mandibular and iliac bone. Materials and methods: Bone samples from the mandible and ilium were harvested in 20 cadavers and analysed by dual energy X‐ray absorptiometry (DXA), computerised tomography (CT) on a conventional hospital machine and microCT. Results: Significant correlations were found between Hounsfield density obtained by CT, and bone mass determined by microCT but not with DXA values. Cortical thickness measurements were well correlated between CT and microCT. No relationships were found between mandibular and iliac bone, when considering mineral density, cortical thickness, bone volume or micro‐architecture. Conclusion: In clinical practice, CT remains the most appropriate routine means for bone qualitative and quantitative evaluation at the mandible. In this ex vivo study, these results confirm that mandibular bone status does not reflect the axial skeletal one and assist in the placement of implants with dental prostheses in old or osteoporotic patients.     

Geographical patterns of body mass distribution are robust even when inserting uncertainty in average estimates of species body mass

Pardiñas, Valenzuela, and Salazar‐Bravo (2017) are concerned that eventual differences in species mean body masses and in the phylogenetic hypothesis used in Maestri et al. (2016)—compared with those available on other potential sources—could affect the results of our original article. Here, we used a new phylogenetic hypothesis to conduct the same analyses of the original article, and we randomly sampled 1000 values of body mass within approximately 35% upper and lower intervals around the mean body mass for each species included in our database. We show that our previous results and conclusions are robust and valid, and they persist despite uncertainty in mean body mass estimation. We argue that sampling variation and uncertainty in both species mean body mass estimation and phylogenetic hypothesis are to be expected and should not always be confused with inaccuracies.     

Disentangling basal and accumulated body mass for cross‐population comparisons

Measures of human body mass confound 1) well‐established population differences in body form and 2) exposure to obesogenic environments, posing challenges for using body mass index (BMI) in cross‐population studies of body form, energy reserves, and obesity‐linked disease risk. We propose a method for decomposing population BMI by estimating basal BMI (bBMI) among young adults living in extremely poor, rural households where excess body mass accumulation is uncommon. We test this method with nationally representative, cross‐sectional Demographic and Health Surveys (DHS) collected from 69,916 rural women (20–24 years) in 47 low‐income countries. Predicting BMI by household wealth, we estimate country‐level bBMI as the average BMI of young women (20–24 years) living in rural households with total assets <400 USD per capita. Above 400 USD per capita, BMI increases with both wealth and age. Below this point, BMI hits a baseline floor showing little effect of either age or wealth. Between‐country variation in bBMI (range of 4.3 kg m^?2) is reliable across decades and age groups (R² = 0.83–0.88). Country‐level estimates of bBMI show no relation to diabetes prevalence or country‐level GDP (R² < 0.05), supporting its independence from excess body mass. Residual BMI (average BMI minus bBMI) shows better fit with both country‐level GDP (R² = 0.55 vs. 0.40) and diabetes prevalence (R² = 0.23 vs. 0.17) than does conventional BMI. This method produces reliable estimates of bBMI across a wide range of nationally representative samples, providing a new approach to investigating population variation in body mass. Am J Phys Anthropol 153:542–550, 2014. © 2013 Wiley Periodicals, Inc.     

Intrapopulation variation in stature and body proportions: social status and sex differences in an Italian medieval population (Trino Vercellese, VC)

The phenotypic expression of adult body size and shape results from synergistic interactions between hereditary factors and environmental conditions experienced during growth. Variation in body size and shape occurs even in genetically relatively homogeneous groups, due to different occurrence, duration, and timing of growth insults. Understanding the causes and patterns of intrapopulation variation can foster meaningful information on early life conditions in living and past populations. This study assesses the pattern of biological variation in body size and shape attributable to sex and social status in a medieval Italian population. The sample includes 52 (20 female, 32 male) adult individuals from the medieval population of Trino Vercellese, Italy. Differences in element size and overall body size (skeletal height and body mass) were assessed through Monte Carlo methods, while univariate non-parametric tests and Principal Component Analysis (PCA) were employed to examine segmental and overall body proportions. Discriminant Analysis was employed to determine the predictive value of individual skeletal elements for social status in the population. Our results highlight a distinct pattern in body size and shape variation in relation to status and sex. Male subsamples exhibit significant postcranial variation in body size, while female subsamples express smaller, nonsignificant differences. The analysis of segmental proportions highlighted differences in trunk/lower limb proportions between different status samples, and PCA indicated that in terms of purely morphological variation high status males were distinct from all other groups. The pattern observed likely resulted from a combination of biological factors and cultural practices.     

IntApop: A web service for predicting apoptotic protein interactions in humans

Apoptosis, a type of cell death, is necessary for maintaining tissue homeostasis and removing malignant cells. Interrupted apoptosis process contributes to carcinogenesis, developmental defects, autoimmune diseases and neurological disorders. Due to the complexity of the process, the molecular dynamics and relative interactions of individual proteins responsible for the activation or inhibition of apoptosis should be researched systematically. In this study, we integrate known protein interactions from databases DIP, IntAct, MINT, HPRD and BioGRID by Naïve Bayes classifier. The receiver operation characteristic (ROC) curve with the area under the ROC curve (AUC) of 0.797 indicates it has a good performance in prediction. Then, we predict the global human apoptotic protein interactions network. Within it, we not only identify the already known interactions of caspases (caspase-8/-10, caspase-9, caspase-3/-6/-7) and Bcl-2 family, but also reveal that Bid can interact with casein kinases (CSK21/22/2B, KC1A, KC1E); both of B2LA1 and B2CL2 can interact with Bid, Bax and Bak; caspase-8 interacts with autophagic proteins (MLP3B, MLP3A and LRRk2). Consequently, we make an initial step to develop the web service IntApop that provides an appropriate platform for apoptosis researchers, systems biologists and translational clinician scientists to predict apoptotic protein interactions in human. In addition, the interaction network can be visualized online, making it a widely applicable systems biology tool for apoptosis and cancer researchers.     

Comparing performances of logistic regression and neural networks for predicting melatonin excretion patterns in the rat exposed to ELF magnetic fields

Various studies have been reported on the bioeffects of magnetic field exposure; however, no consensus or guideline is available for experimental designs relating to exposure conditions as yet. In this study, logistic regression (LR) and artificial neural networks (ANNs) were used in order to analyze and predict the melatonin excretion patterns in the rat exposed to extremely low frequency magnetic fields (ELF‐MF). Subsequently, on a database containing 33 experiments, performances of LR and ANNs were compared through resubstitution and jackknife tests. Predictor variables were more effective parameters and included frequency, polarization, exposure duration, and strength of magnetic fields. Also, five performance measures including accuracy, sensitivity, specificity, Matthew's Correlation Coefficient (MCC) and normalized percentage, better than random (S) were used to evaluate the performance of models. The LR as a conventional model obtained poor prediction performance. Nonetheless, LR distinguished the duration of magnetic fields as a statistically significant parameter. Also, horizontal polarization of magnetic fields with the highest logit coefficient (or parameter estimate) with negative sign was found to be the strongest indicator for experimental designs relating to exposure conditions. This means that each experiment with horizontal polarization of magnetic fields has a higher probability to result in “not changed melatonin level” pattern. On the other hand, ANNs, a more powerful model which has not been introduced in predicting melatonin excretion patterns in the rat exposed to ELF‐MF, showed high performance measure values and higher reliability, especially obtaining 0.55 value of MCC through jackknife tests. Obtained results showed that such predictor models are promising and may play a useful role in defining guidelines for experimental designs relating to exposure conditions. In conclusion, analysis of the bioelectromagnetic data could result in finding a relationship between electromagnetic fields and different biological processes. Bioelectromagnetics 31:164–171, 2010. © 2009 Wiley‐Liss, Inc.     

Development of equations for predicting metabolisable energy concentrations in compound feeds for pigs

In 2006 in Germany new recommendations for the supply of energy and nutrients to pigs were published, including a modification of the energy evaluation on the basis of metabolisable energy for pigs (ME_S). It was the objective of this meta-analysis to calculate equations that can be used for predicting ME_S in compound feeds for pigs on the basis of their nutrient concentrations. Data from digestibility studies with a total of 290 compound feeds were provided by different research institutions. Feeds for both piglets and growing-finishing pigs were contained in the data set. The ME_S concentration ranged from 10.4–16.6 MJ/kg DM, with the majority of data ranging between 15 and 16 MJ/kg DM. The data were processed with a cross validation method and a multiple regression approach by using ME_S as the independent variable. A corrected Akaike-Information-Criterion (AIC_ cor) was used for model evaluation. Various models were developed and validated independently. Calculations were made both for piglet and growing-finishing pig feeds, separately and together, for all feeds. It was shown that the ME_S concentration in compound feeds can be predicted with good accuracy if the concentrations of crude protein, crude fat, crude fibre, and starch are known. Prediction equations were presented. The lack of data for highly fibrous feeds as well as the analysis of different fibre fractions was identified as a challenge for further improvement of the prediction equations.