How shape-based anthropometry can complement traditional anthropometric techniques: a cross-sectional study

Patient perceptions of three-dimensional (3D) surface imaging technology and traditional methods used to assess anthropometry

Background

Obesity and overweight are commonplace, yet attrition rates in weight management clinics are high. Traditional methods of body measurement may be a deterrent due to invasive and time-consuming measurements and negative experiences of how data are presented back to individuals. Emerging new technologies, such as three-dimensional (3D) surface imaging technology, might provide a suitable alternative. This study aimed to understand acceptability of traditional and 3D surface imaging-based body measures, and whether perceptions differ between population groups.

Methods

This study used a questionnaire to explore body image, body measurement and shape, followed by a qualitative semi-structured interview and first-hand experience of traditional and 3D surface imaging-based body measures.

Results

49 participants responded to the questionnaire and 26 participants attended for the body measurements and interview over a 2-month period. There were 3 main themes from the qualitative data 1) Use of technology, 2) Participant experience, expectations and perceptions and 3) Perceived benefits and uses.

Conclusion

From this study, 3D-surface imaging appeared to be acceptable to patients as a method for anthropometric measurements, which may reduce anxiety and improve attrition rates in some populations. Further work is required to understand the scalability, and the role and implications of these technologies in weight management practice. (University Research Ethics Committee reference number ER41719941).

Comparative efficacy of anthropometric indices in predicting 10-year ASCVD risk: insights from NHANES data

Background

Cardiovascular diseases remain a leading cause of morbidity and mortality worldwide. Accurately predicting the 10-year risk of Atherosclerotic Cardiovascular Disease (ASCVD) is crucial for timely intervention and management. This study aimed to evaluate the predictive performance of six anthropometric indices in assessing the 10-year ASCVD risk.

Methods

Utilizing data from the National Health and Nutrition Examination Survey (NHANES) database (1999-2018), the study involved 11,863 participants after applying exclusion criteria. Six anthropometric indices-waist circumference (WC), body mass index (BMI), waist-to-height ratio (WHtR), a body shape index (ABSI), body roundness index (BRI), and waist-to-height0.5 ratio (WHT.5R)-were calculated. The 10-year ASCVD risk was assessed using the 2013 ACC/AHA guidelines & pooled cohort equations model. Participants were divided into two groups based on an ASCVD risk threshold of 7.5%. Statistical analysis included chi-square tests, odds ratios, and receiver operating characteristic (ROC) curves.

Results

The study found significant differences in baseline characteristics between participants with ASCVD risk less than 7.5% and those with a risk greater than or equal to 7.5%, stratified by gender. In both male and female groups, individuals with higher ASCVD risk exhibited higher age, waist circumference, BMI, and a higher prevalence of health-compromising behaviors. ABSI emerged as the most accurate predictor of ASCVD risk, with the highest area under the curve (AUC) values in both genders. The optimal cut-off values for ABSI was established for effective risk stratification (cut-off value = 0.08).

Conclusion

The study underscores the importance of anthropometric indices, particularly ABSI, in predicting the 10-year risk of ASCVD. These findings suggest that ABSI, along with other indices, can be instrumental in identifying individuals at higher risk for ASCVD, thereby aiding in early intervention and prevention strategies.

Torso Shape Improves the Prediction of Body Fat Magnitude and Distribution

Background: As obesity increases throughout the developed world, concern for the health of the population rises. Obesity increases the risk of metabolic syndrome, a cluster of conditions associated with type-2 diabetes. Correctly identifying individuals at risk from metabolic syndrome is vital to ensure interventions and treatments can be prescribed as soon as possible. Traditional anthropometrics have some success in this, particularly waist circumference. However, body size is limited when trying to account for a diverse range of ages, body types and ethnicities. We have assessed whether measures of torso shape (from 3D body scans) can improve the performance of models predicting the magnitude and distribution of body fat. Methods: From 93 male participants (age 43.1 ± 7.4) we captured anthropometrics and torso shape using a 3D scanner, body fat volume using an air displacement plethysmography device (BODPOD^®) and body fat distribution using bioelectric impedance analysis. Results: Predictive models containing torso shape had an increased adjusted R² and lower mean square error when predicting body fat magnitude and distribution. Conclusions: Torso shape improves the performance of anthropometric predictive models, an important component of identifying metabolic syndrome risk. Future work must focus on fast, low-cost methods of capturing the shape of the body.

Identification of the Best Anthropometric Index for Predicting the 10-Year Cardiovascular Disease in Southwest China: A Large Single-Center, Cross-Sectional Study

INTRODUCTION

This population-based cross-sectional study aimed to identify the best predictor of the 10-year cardiovascular (CV) high risk among old and new anthropometric indices.

METHODS

We investigated 76,915 adults older than 18 years of age living in southwest China. Ten obesity indices were calculated. The 10-year cardiovascular disease (CVD) risk was estimated using the Framingham risk score. Receiver operating characteristic curve analysis was performed to assess the ability of the anthropometric index to predict the 10-year high risk of CVD events.

RESULTS

The waist-to-hip ratio (WHR) had the highest area under the curve (AUC) value (0.711; sensitivity: 62.22%, specificity: 42.73%) in men, while the body fat index (BAI) had the lowest AUC value (0.624, sensitivity: 49.07%, specificity: 54.84%). The waist-to-height ratio (WHtR) and the body roundness index (BRI) showed the highest AUC value (0.751, sensitivity: 39.24%, 39.83%, specificity: 75.68%, 68.59%) in women, while the BAI showed the lowest AUC value (0.671, sensitivity: 53.15%, specificity: 57.14%).

CONCLUSIONS

The WHR was the best anthropometric measure for assessing the 10-year high risk of CVD in men, while the WHtR and BRI were the best measures for women. In men, the WHR should be < 0.88, and in women, the WHtR should be < 0.502 or the BRI should be < 3.41.

Modification and refinement of three‐dimensional reconstruction to estimate body volume from a simulated single‐camera image

Objective

Body volumes (BV) are used for calculating body composition to perform obesity assessments. Conventional BV estimation techniques, such as underwater weighing, can be difficult to apply. Advanced machine learning techniques enable multiple obesity-related body measurements to be obtained using a single-camera image; however, the accuracy of BV calculated using these techniques is unknown. This study aims to adapt and evaluate a machine learning technique, synthetic training for real accurate pose and shape (STRAPS), to estimate BV.

Methods

The machine learning technique, STRAPS, was applied to generate three-dimensional (3D) models from simulated two-dimensional (2D) images; these 3D models were then scaled with body stature and BV were estimated using regression models corrected for body mass. A commercial 3D scan dataset with a wide range of participants (n = 4318) was used to compare reference and estimated BV data.

Results

The developed methods estimated BV with small relative standard errors of estimation (<7%) although performance varied when applied to different groups. The BV estimated for people with body mass index (BMI) < 30 kg/m² (1.9% for males and 1.8% for females) were more accurate than for people with BMI ≥ 30 kg/m² (6.9% for males and 2.4% for females).

Conclusions

The developed method can be used for females and males with BMI < 30 kg/m² in BV estimation and could be used for obesity assessments at home or clinic settings.

Modelling of human torso shape variation inferred by geometric morphometrics

Traditional body measurement techniques are commonly used to assess physical health; however, these approaches do not fully represent the complex shape of the human body. Three-dimensional (3D) imaging systems capture rich point cloud data that provides a representation of the surface of 3D objects and have been shown to be a potential anthropometric tool for use within health applications. Previous studies utilising 3D imaging have only assessed body shape based on combinations and relative proportions of traditional body measures, such as lengths, widths and girths. Geometric morphometrics (GM) is an established framework used for the statistical analysis of biological shape variation. These methods quantify biological shape variation after the effects of non-shape variation-location, rotation and scale-have been mathematically held constant, otherwise known as the Procrustes paradigm. The aim of this study was to determine whether shape measures, identified using geometric morphometrics, can provide additional information about the complexity of human morphology and underlying mass distribution compared to traditional body measures. Scale-invariant features of torso shape were extracted from 3D imaging data of 9,209 participants form the LIFE-Adult study. Partial least squares regression (PLSR) models were created to determine the extent to which variations in human torso shape are explained by existing techniques. The results of this investigation suggest that linear combinations of body measures can explain 49.92% and 47.46% of the total variation in male and female body shape features, respectively. However, there are also significant amounts of variation in human morphology which cannot be identified by current methods. These results indicate that Geometric morphometric methods can identify measures of human body shape which provide complementary information about the human body. The aim of future studies will be to investigate the utility of these measures in clinical epidemiology and the assessment of health risk.