Prediction of future visceral adiposity and application to cancer research: The Multiethnic Cohort Study.

BACKGROUND: We previously developed a prediction score for MRI-quantified abdominal visceral adipose tissue (VAT) based on concurrent measurements of height, body mass index (BMI), and nine blood biomarkers, for optimal performance in five racial/ethnic groups. Here we evaluated the VAT score for prediction of future VAT and examined if enhancement with additional biomarkers, lifestyle behavior information, and medical history improves the prediction. METHODS: We examined 500 participants from the Multiethnic Cohort (MEC) with detailed data (age 50-66) collected 10 years prior to their MRI assessment of VAT. We generated three forecasted VAT prediction models: first by applying the original VAT equation to the past data on the predictors ("original"), second by refitting the past data on anthropometry and biomarkers ("refit"), and third by building a new prediction model based on the past data enhanced with lifestyle and medical history ("enhanced"). We compared the forecasted prediction scores to future VAT using the coefficient of determination (R2). In independent nested case-control data in MEC, we applied the concurrent and forecasted VAT models to assess association of the scores with subsequent incident breast cancer (950 pairs) and colorectal cancer (831 pairs). RESULTS: Compared to the VAT prediction by the concurrent VAT score (R2 = 0.70 in men, 0.68 in women), the forecasted original VAT score (R2 = 0.54, 0.48) performed better than past anthropometry alone (R2 = 0.47, 0.40) or two published scores (VAI, METS-VF). The forecasted refit (R2 = 0.61, 0.51) and enhanced (R2 = 0.62, 0.55) VAT scores each showed slight improvements. Similar to the concurrent VAT score, the forecasted VAT scores were associated with breast cancer, but not colorectal cancer. Both the refit score (adjusted OR for tertile 3 vs. 1 = 1.27; 95% CI: 1.00-1.62) and enhanced score (1.27; 0.99-1.62) were associated with breast cancer independently of BMI. CONCLUSIONS: Predicted VAT from midlife data can be used as a surrogate to assess the effect of VAT on incident diseases associated with obesity, as illustrated for postmenopausal breast cancer.

Variations in bioelectrical impedance devices impact raw measures comparisons and subsequent prediction of body composition using recommended estimation equations.

BACKGROUND & AIMS: Bioelectrical impedance analysis (BIA) for body composition estimation is increasingly used in clinical and field settings to guide nutrition and training programs. Due to variations among BIA devices and the proprietary prediction equations used, studies have recommended the use of raw measures of resistance (R) and reactance (Xc) within population-specific equations to predict body composition. OBJECTIVE: We compared raw measures from three BIA devices to assess inter-device variation and the impact of differences on body composition estimations. METHODS: Raw R, Xc, impedance (Z) parameters were measured on a calibrated phantom and athletes using tetrapolar supine (BIASUP4), octapolar supine (BIASUP8), and octapolar standing (BIASTA8) devices. Measures of R and Xc were compared across devices and graphed using BIA vector analysis (BIVA) and raw parameters were entered into recommended athlete-specific equations for predicting fat-free mass (FFM) and appendicular lean soft tissue (ALST). Whole-body FFM and regional ALST were compared across devices and to a criterion five-compartment (5C) model and dual energy X-ray absorptiometry for ALST. RESULTS: Data from 73 (23.2 ± 4.8 y) athletes were included in the analyses. Technical differences were observed between Z (range 12.2-50.1Ω) measures on the calibrated phantom. Differences in whole-body impedance were apparent due to posture (technological) and electrode placement (biological) factors. This resulted in raw measures for all three devices showing greater dehydration on BIVA compared to published norms for athletes using a separate BIA device. Compared to the 5C FFM, significant differences (p < 0.05) were observed on all three equations for BIASUP8 and BIASTA8, with constant error (CE) from -2.7 to -4.6 kg; no difference was observed for BIASUP4 or when device-specific algorithms were used. Published equations resulted in differences as large as 8.8 kg FFM among BIA devices. For ALST, even after a correction in the error of the published empirical equation, all three devices showed significant (p < 0.01) CE from -1.6 to -2.9 kg. CONCLUSIONS: Raw bioimpedance measurements differ among devices due to technical, technological, and biological factors, limiting interchangeability of data across BIA systems. Professionals should be aware of these factors when purchasing systems, comparing data to published reference ranges, or when applying published empirical body composition prediction equations.

The Prospective Association between Early Life Growth and Breast Density in Young Adult Women.

Breast density is a strong intermediate endpoint to investigate the association between early-life exposures and breast cancer risk. This study investigates the association between early-life growth and breast density in young adult women measured using Optical Breast Spectroscopy (OBS) and Dual X-ray Absorptiometry (DXA). OBS measurements were obtained for 536 female Raine Cohort Study participants at ages 27-28, with 268 completing DXA measurements. Participants with three or more height and weight measurements from ages 8 to 22 were used to generate linear growth curves for height, weight and body mass index (BMI) using SITAR modelling. Three growth parameters (size, velocity and timing) were examined for association with breast density measures, adjusting for potential confounders. Women who reached their peak height rapidly (velocity) and later in adolescence (timing) had lower OBS-breast density. Overall, women who were taller (size) had higher OBS-breast density. For weight, women who grew quickly (velocity) and later in adolescence (timing) had higher absolute DXA-breast density. Overall, weight (size) was also inversely associated with absolute DXA-breast density, as was BMI. These findings provide new evidence that adolescent growth is associated with breast density measures in young adult women, suggesting potential mediation pathways for breast cancer risk in later life.

Accurate Prediction of Three-Dimensional Humanoid Avatars for Anthropometric Modeling.

Objective: To evaluate the hypothesis that anthropometric dimensions derived from a person's manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient metabolic disease risk stratification in clinical settings. Methods: Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations. A new prospective sample of 84 adults had ground-truth measurements of 6 body circumferences, 7 volumes, and 7 surface areas with a 20-camera 3D reference scanner. 3D humanoid avatars were generated on these participants with manifold regression including age, weight, height, DXA %fat, and BIA impedances as potential predictor variables. Ground-truth and predicted avatar anthropometric dimensions were quantified with the same software. Results: Following exploratory studies, one manifold prediction model was moved forward for presentation that included age, weight, height, and %fat as covariates. Predicted and ground-truth avatars had similar visual appearances; correlations between predicted and ground-truth anthropometric estimates were all high (R2s, 0.75-0.99; all p < 0.001) with non-significant mean differences except for arm circumferences (%D ~ 5%; p < 0.05). Concordance correlation coefficients ranged from 0.80-0.99 and small but significant bias (p < 0.05 - 0.01) was present with Bland-Altman plots in 13 of 20 total anthropometric measurements. The mean waist to hip circumference ratio predicted by manifold regression was non-significantly different from ground-truth scanner measurements. Conclusions: 3D avatars predicted from demographic, physical, and other accessible characteristics can produce body representations with accurate anthropometric dimensions without a 3D scanner. Combining manifold regression algorithms into established body composition methods such as DXA, BIA, and other accessible methods provides new research and clinical opportunities.

Faecal Immunochemical Test (FIT) Sensitivity; A Five Year Audit.

Introduction: Colorectal cancer has a high prevalence and mortality rate in the United Kingdom. Cancerous colorectal lesions often bleed into the gastrointestinal lumen. The faecal immunochemical test (FIT) detects haemoglobin (Hb) in the faeces of patients and is used as a first line test in the diagnosis of colorectal cancer. Materials and Methods: A retrospective audit of all FIT performed and all colorectal cancers diagnosed in the Hull and East Riding of Yorkshire counties of the United Kingdom (population approximately 609,300) between 2018 and 2022 was conducted. FIT were performed using a HM-JACKarc analyser from Kyowa medical. The predominant symptom suggestive of colorectal cancer which prompted the FIT was recorded. Colorectal cancer was diagnosed using the gold standard of histological biopsy following colonoscopy. Results: Between 2018 and 2022, 56,202 FIT were performed on symptomatic patients. Follow on testing identified 1,511 with colorectal cancer. Of these people, only 450 people with a confirmed colorectal cancer had a FIT within the 12 months preceding their diagnosis. Of these 450 FIT results, 36 had a concentration of <10 µg/g and may be considered to be a false negative. The sensitivity of FIT in the patients identified was 92.00%. The most common reason stated by the clinician for a FIT being performed in patients with colorectal cancer was a change in bowel habits, followed by iron deficient anaemia. The number of patients diagnosed with colorectal cancer decreased in 2020, but increased significantly in 2021. Discussion: This study shows that 8.00% of people diagnosed with colorectal cancer in the Hull and East Riding of Yorkshire regions had a negative FIT. This study also shows that the SARS-CoV-2 pandemic affected the number of people diagnosed with colorectal cancer, and therefore skews the prevalence and pre-test probability of a positive test. There are many reasons why a FIT could produce a false negative result, the most likely being biological factors affecting the stability of haemoglobin within the gastrointestinal tract, or pre-analytical factors influencing faecal sampling preventing the detection of haemoglobin. Some colorectal lesions do not protrude into the gastrointestinal lumen and are less likely to bleed. Conclusion: This is the first study showing data from outside of a structured clinical trial and provides the largest study to date showing the sensitivity of FIT in a routine clinical setting. This study also provides evidence for the impact COVID-19 had on the rate of colorectal cancer diagnosis.

Advancing body composition assessment in patients with cancer: First comparisons of traditional versus multicompartment models.

BACKGROUND AND AIMS: Measurement of body composition using computed tomography (CT) scans may be a viable clinical tool for low muscle mass assessment in oncology. However, longitudinal assessments are often infeasible with CT. Clinically accessible body composition technologies can be used to track changes in fat-free mass (FFM) or muscle, though their accuracy may be impacted by cancer-related physiological changes. The purpose of this study was to examine the agreement among accessible body composition method with criterion methods for measures of whole-body FFM measurements and, when possible, muscle mass for the classification of low muscle in patients with cancer. METHODS: Patients with colorectal cancer were recruited to complete measures of whole-body DXA, air displacement plethysmography (ADP), and bioelectrical impedance analysis (BIA). These measures were used alone, or in combination to construct the criterion multicompartment (4C) mode for estimating FFM. Patients also underwent abdominal CT scans as part of routine clinical assessment. Agreement of each method with 4C model was analyzed using mean constant error (CE = criterion - alternative), linear regression including root mean square error (RMSE), Bland-Altman limits of agreement (LoA) and mean percentage difference (MPD). Additionally, appendicular lean soft tissue index (ALSTI) measured by DXA and predicted by CT were compared for the absolute agreement, while the ALSTI values and skeletal muscle index by CT were assessed for agreement on the classification of low muscle mass. RESULTS: Forty-five patients received all measures for the 4C model and 25 had measures within proximity of clinical CT measures. Compared to 4C, DXA outperformed ADP and BIA by showing the strongest overall agreement (CE = 1.96 kg, RMSE = 2.45 kg, MPD = 98.15 ± 2.38%), supporting its use for body composition assessment in patients with cancer. However, CT cutoffs for skeletal muscle index or CT-estimated ALSTI were lower than DXA ALSTI (average 1.0 ± 1.2 kg/m2) with 24.0% to 32.0% of patients having a different low muscle classification by CT when compared to DXA. CONCLUSIONS: Despite discrepancies between clinical body composition assessment and the criterion multicompartment model, DXA demonstrates the strongest agreement with 4C. Disagreement between DXA and CT for low muscle mass classification prompts further evaluation of the measures and cutoffs used with each technique. Multicompartment models may enhance our understanding of body composition variations at the individual patient level and improve the applicability of clinically accessible technologies for classification and monitoring change over time.

Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging.

The body size and composition assessment is commonly included in the routine management of healthy athletes as well as of different types of patients to personalize the training or rehabilitation strategy. The digital anthropometric analyses described in the following protocol can be performed with recently introduced systems. These new tools and approaches have the potential to be widely used in clinical settings because they are very simple to operate and enable the rapid collection of accurate and reproducible data. One system consists of a rotating platform with a weight measurement plate, three infrared cameras, and a tablet built into a tower, while the other system consists of a tablet mounted on a holder. After image capture, the software of both systems generates a de-identified three-dimensional humanoid avatar with associated anthropometric and body composition variables. The measurement procedures are simple: a subject can be tested in a few minutes and a comprehensive report (including the three-dimensional scan and body size, shape, and composition measurements) is automatically generated.

Evaluation of body shape as a human body composition assessment in isolated conditions and remote environments.

Individuals in isolated and extreme environments can experience debilitating side-effects including significant decreases in fat-free mass (FFM) from disuse and inadequate nutrition. The objective of this study was to determine the strengths and weaknesses of three-dimensional optical (3DO) imaging for monitoring body composition in either simulated or actual remote environments. Thirty healthy adults (ASTRO, male = 15) and twenty-two Antarctic Expeditioners (ABCS, male = 18) were assessed for body composition. ASTRO participants completed duplicate 3DO scans while standing and inverted by gravity boots plus a single dual-energy X-ray absorptiometry (DXA) scan. The inverted scans were an analog for fluid redistribution from gravity changes. An existing body composition model was used to estimate fat mass (FM) and FFM from 3DO meshes. 3DO body composition estimates were compared to DXA with linear regression and reported with the coefficient of determination (R2) and root mean square error (RMSE). ABCS participants received only duplicate 3DO scans on a monthly basis. Standing ASTRO meshes achieved an R2 of 0.76 and 0.97 with an RMSE of 2.62 and 2.04 kg for FM and FFM, while inverted meshes achieved an R2 of 0.52 and 0.93 with an RMSE of 2.84 and 3.23 kg for FM and FFM, respectively, compared to DXA. For the ABCS arm, mean weight, FM, and FFM changes were -0.47, 0.06, and -0.54 kg, respectively. Simulated fluid redistribution decreased the accuracy of estimated body composition values from 3DO scans. However, FFM stayed robust. 3DO imaging showed good absolute accuracy for body composition assessment in isolated and remote environments.

Evaluation of visceral adipose tissue thresholds for elevated metabolic syndrome risk across diverse populations: A systematic review.

Beyond obesity, excess levels of visceral adipose tissue (VAT) significantly contribute to the risk of developing metabolic syndrome (MetS), although thresholds for increased risk vary based on population, regions of interest, and units of measure employed. We sought to determine whether a common threshold exists that is indicative of heightened MetS risk across all populations, accounting for sex, age, BMI, and race/ethnicity. A systematic literature review was conducted in September 2023, presenting threshold values for elevated MetS risk. Standardization equations harmonized the results from DXA, CT, and MRI systems to facilitate a comparison of threshold variations across studies. A total of 52 papers were identified. No single threshold could accurately indicate elevated risk for both males and females across varying BMI, race/ethnicity, and age groups. Thresholds fluctuated from 70 to 165.9 cm2, with reported values consistently lower in females. Generally, premenopausal females and younger adults manifested elevated risks at lower VAT compared to their older counterparts. Notably, Asian populations exhibited elevated risks at lower VAT areas (70-136 cm2) compared to Caucasian populations (85.6-165.9 cm2). All considered studies reported associations of VAT without accommodating covariates. No single VAT area threshold for elevated MetS risk was discernible post-harmonization by technology, units of measure, and region of interest. This review summarizes available evidence for MetS risk assessment in clinical practice. Further exploration of demographic-specific interactions between VAT area and other risk factors is imperative to comprehensively delineate overarching MetS risk.

Examining the impact of the COVID-19 pandemic on homecare services among individuals with traumatic and non-traumatic spinal cord injuries.

STUDY DESIGN: Descriptive repeated-cross sectional retrospective longitudinal cohort study. OBJECTIVE: To investigate the impact of the COVID-19 pandemic on homecare services in individuals with traumatic or non-traumatic Spinal Cord Injury (SCI). SETTING: Health administrative database in Ontario, Canada. METHODS: A repeated cross-sectional study using linked health administrative databases from March 2015 to June 2022. Monthly homecare utilization was assessed in 3381 adults with SCI using Autoregressive Integrated Moving Average (ARIMA) models. RESULTS: Compared to pre-pandemic levels, between March 2020 to June 2022, the traumatic group experienced a decrease in personal and/or homemaking services, as well as an increase in nursing visits from April 2020-March 2022 and June 2022. Case management increased at various times for the traumatic group, however therapies decreased in May 2020 only. The non-traumatic group experienced a decrease in personal and/or homemaking services in July 2020, as well as an increase in nursing visits from March 2020 to February 2021 and sporadically throughout 2020. Case management also increased at certain points for the non-traumatic group, but therapies decreased in April 2020, July 2020, and September 2021. CONCLUSION: The traumatic group had decreases in personal and/or homemaking services. Both groups had increases in nursing services, increases in case management, and minimal decreases in therapies at varying times during the pandemic. Investigation is warranted to understand the root cause of these changes, and if they resulted in adverse outcomes.

Value contribution of blood-based neurofilament light chain as a biomarker in multiple sclerosis using multi-criteria decision analysis.

Introduction: Multiple sclerosis (MS) is a chronic autoimmune demyelinating disease that represents a leading cause of non-traumatic disability among young and middle-aged adults. MS is characterized by neurodegeneration caused by axonal injury. Current clinical and radiological markers often lack the sensitivity and specificity required to detect inflammatory activity and neurodegeneration, highlighting the need for better approaches. After neuronal injury, neurofilament light chains (NfL) are released into the cerebrospinal fluid, and eventually into blood. Thus, blood-based NfL could be used as a potential biomarker for inflammatory activity, neurodegeneration, and treatment response in MS. The objective of this study was to determine the value contribution of blood-based NfL as a biomarker in MS in Spain using the Multi-Criteria Decision Analysis (MCDA) methodology. Materials and methods: A literature review was performed, and the results were synthesized in the evidence matrix following the criteria included in the MCDA framework. The study was conducted by a multidisciplinary group of six experts. Participants were trained in MCDA and scored the evidence matrix. Results were analyzed and discussed in a group meeting through reflective MCDA discussion methodology. Results: MS was considered a severe condition as it is associated with significant disability. There are unmet needs in MS as a disease, but also in terms of biomarkers since no blood biomarker is available in clinical practice to determine disease activity, prognostic assessment, and response to treatment. The results of the present study suggest that quantification of blood-based NfL may represent a safe option to determine inflammation, neurodegeneration, and response to treatments in clinical practice, as well as to complement data to improve the sensitivity of the diagnosis. Participants considered that blood-based NfL could result in a lower use of expensive tests such as magnetic resonance imaging scans and could provide cost-savings by avoiding ineffective treatments. Lower indirect costs could also be expected due to a lower impact of disability consequences. Overall, blood-based NfL measurement is supported by high-quality evidence. Conclusion: Based on MCDA methodology and the experience of a multidisciplinary group of six stakeholders, blood-based NfL measurement might represent a high-value-option for the management of MS in Spain.

Effect of Intermittent vs. Continuous Energy Restriction on Visceral Fat: Protocol for The Healthy Diet and Lifestyle Study 2 (HDLS2).

Obesity in the United States and Western countries represents a major health challenge associated with an increased risk of metabolic diseases, including cardiovascular disease, hypertension, diabetes, and certain cancers. Our past work revealed a more pronounced obesity-cancer link in certain ethnic groups, motivating us to develop a tailored dietary intervention called the Healthy Diet and Lifestyle 2 (HDLS2). The study protocol is described herein for this randomized six-month trial examining the effects of intermittent energy restriction (5:2 Diet) plus the Mediterranean dietary pattern (IER + MED) on visceral adipose tissue (VAT), liver fat, and metabolic biomarkers, compared to a standard MED with daily energy restriction (DER + MED), in a diverse participant group. Using MRI and DXA scans for body composition analysis, as well as metabolic profiling, this research aims to contribute to nutritional guidelines and strategies for visceral obesity reduction. The potential benefits of IER + MED, particularly regarding VAT reduction and metabolic health improvement, could be pivotal in mitigating the obesity epidemic and its metabolic sequelae. The ongoing study will provide essential insights into the efficacy of these energy restriction approaches across varied racial/ethnic backgrounds, addressing an urgent need in nutrition and metabolic health research. Registered Trial, National Institutes of Health, ClinicalTrials.gov (NCT05132686).

Time-specific impact of trace metals on breast density of adolescent girls in Santiago, Chile.

Whether trace metals modify breast density, the strongest predictor for breast cancer, during critical developmental stages such as puberty remains understudied. Our study prospectively evaluated the association between trace metals at Tanner breast stage B1 (n = 291) and at stages both B1 and B4 (n = 253) and breast density at 2 years post-menarche among Chilean girls from the Growth and Obesity Cohort Study. Dual-energy x-ray absorptiometry assessed the volume of dense breast tissue (absolute fibroglandular volume [FGV]) and percent breast density (%FGV). Urine trace metals included arsenic, barium, cadmium, cobalt, cesium, copper, magnesium, manganese, molybdenum, nickel, lead, antimony, selenium, tin, thallium, vanadium, and zinc. At B1, a doubling of thallium concentration resulted in 13.69 cm3 increase in absolute FGV (ß: 13.69, 95% confidence interval [CI]: 2.81, 24.52), while a doubling of lead concentration resulted in a 7.76 cm3 decrease in absolute FGV (ß: -7.76, 95%CI: -14.71, -0.73). At B4, a doubling of barium concentration was associated with a 10.06 cm3 increase (ß: 10.06, 95% CI: 1.44, 18.60), copper concentration with a 12.29 cm3 increase (ß: 12.29, 95% CI: 2.78, 21.56), lead concentration with a 9.86 cm3 increase (ß: 9.86, 95% CI: 0.73, 18.98), antimony concentration with a 12.97 cm3 increase (ß: 12.97, 95% CI: 1.98, 23.79) and vanadium concentration with a 13.14 cm3 increase in absolute FGV (ß: 13.14, 95% CI: 2.73, 23.58). Trace metals may affect pubertal breast density at varying developmental stages with implications for increased susceptibility for breast cancer.

Non-invasive biomarkers for detecting progression toward hypovolemic cardiovascular instability in a lower body negative pressure model.

Occult hemorrhages after trauma can be present insidiously, and if not detected early enough can result in patient death. This study evaluated a hemorrhage model on 18 human subjects, comparing the performance of traditional vital signs to multiple off-the-shelf non-invasive biomarkers. A validated lower body negative pressure (LBNP) model was used to induce progression towards hypovolemic cardiovascular instability. Traditional vital signs included mean arterial pressure (MAP), electrocardiography (ECG), plethysmography (Pleth), and the test systems utilized electrical impedance via commercial electrical impedance tomography (EIT) and multifrequency electrical impedance spectroscopy (EIS) devices. Absolute and relative metrics were used to evaluate the performance in addition to machine learning-based modeling. Relative EIT-based metrics measured on the thorax outperformed vital sign metrics (MAP, ECG, and Pleth) achieving an area-under-the-curve (AUC) of 0.99 (CI 0.95-1.00, 100% sensitivity, 87.5% specificity) at the smallest LBNP change (0-15 mmHg). The best vital sign metric (MAP) at this LBNP change yielded an AUC of 0.6 (CI 0.38-0.79, 100% sensitivity, 25% specificity). Out-of-sample predictive performance from machine learning models were strong, especially when combining signals from multiple technologies simultaneously. EIT, alone or in machine learning-based combination, appears promising as a technology for early detection of progression toward hemodynamic instability.

The distribution of breast density in women aged 18 years and older.

PURPOSE: Age and body mass index (BMI) are critical considerations when assessing individual breast cancer risk, particularly for women with dense breasts. However, age- and BMI-standardized estimates of breast density are not available for screen-aged women, and little is known about the distribution of breast density in women aged < 40. This cross-sectional study uses three different modalities: optical breast spectroscopy (OBS), dual-energy X-ray absorptiometry (DXA), and mammography, to describe the distributions of breast density across categories of age and BMI. METHODS: Breast density measures were estimated for 1,961 Australian women aged 18-97 years using OBS (%water and %water + %collagen). Of these, 935 women had DXA measures (percent and absolute fibroglandular dense volume, %FGV and FGV, respectively) and 354 had conventional mammographic measures (percent and absolute dense area). The distributions for each breast density measure were described across categories of age and BMI. RESULTS: The mean age was 38 years (standard deviation = 15). Median breast density measures decreased with age and BMI for all three modalities, except for DXA-FGV, which increased with BMI and decreased after age 30. The variation in breast density measures was largest for younger women and decreased with increasing age and BMI. CONCLUSION: This unique study describes the distribution of breast density measures for women aged 18-97 using alternative and conventional modalities of measurement. While this study is the largest of its kind, larger sample sizes are needed to provide clinically useful age-standardized measures to identify women with high breast density for their age or BMI.

The DIRAC framework: Geometric structure underlies roles of diversity and accuracy in combining classifiers.

Combining classification systems potentially improves predictive accuracy, but outcomes have proven impossible to predict. Similar to improving binary classification with fusion, fusing ranking systems most commonly increases Pearson or Spearman correlations with a target when the input classifiers are "sufficiently good" (generalized as "accuracy") and "sufficiently different" (generalized as "diversity"), but the individual and joint quantitative influence of these factors on the final outcome remains unknown. We resolve these issues. Building on our previous empirical work establishing the DIRAC (DIversity of Ranks and ACcuracy) framework, which accurately predicts the outcome of fusing binary classifiers, we demonstrate that the DIRAC framework similarly explains the outcome of fusing ranking systems. Specifically, precise geometric representation of diversity and accuracy as angle-based distances within rank-based combinatorial structures (permutahedra) fully captures their synergistic roles in rank approximation, uncouples them from the specific metrics of a given problem, and represents them as generally as possible.

Breast composition during and after puberty: the Chilean Growth and Obesity Cohort Study.

BACKGROUND: Breast density (BD) is a strong risk factor for breast cancer. Little is known about how BD develops during puberty. Understanding BD trajectories during puberty and its determinants could be crucial for promoting preventive actions against breast cancer (BC) at early ages. The objective of this research is to characterize % fibroglandular volume (%FGV), absolute fibroglandular volume (AFGV), and breast volume (BV) at different breast Tanner stages until 4-year post menarche in a Latino cohort and to assess determinants of high %FGV and AFGV during puberty and in a fully mature breast. METHODS: This is a longitudinal follow-up of 509 girls from low-middle socioeconomic status of the Southeast area of Santiago, recruited at a mean age of 3.5 years. The inclusion criteria were singleton birth born, birthweight between 2500 and 4500 g with no medical or mental disorder. A trained dietitian measured weight and height since 3.5 years old and sexual maturation from 8 years old (breast Tanner stages and age at menarche onset). Using standardized methods, BD was measured using dual-energy X-ray absorptiometry (DXA) in various developmental periods (breast Tanner stage B1 until 4 years after menarche onset). RESULTS: In the 509 girls, we collected 1,442 breast DXA scans; the mean age at Tanner B4 was 11.3 years. %FGV increased across breast Tanner stages and peaked 250 days after menarche. AFGV and BV peaked 2 years after menarche onset. Girls in the highest quartiles of %FGV, AFGV, and BV at Tanner B4 and B5 before menarche onset had the highest values thereafter until 4 years after menarche onset. The most important determinants of %FGV and AFGV variability were BMI z-score (R2 = 44%) and time since menarche (R2 = 42%), respectively. CONCLUSION: We characterize the breast development during puberty, a critical window of susceptibility. Although the onset of menarche is a key milestone for breast development, we observed that girls in the highest quartiles of %FGV and AFGV tracked in that group afterwards. Following these participants in adulthood would be of interest to understand the changes in breast composition during this period and its potential link with BC risk.

3D Convolutional Deep Learning for Nonlinear Estimation of Body Composition from Whole-Body Morphology.

Total and regional body composition are strongly correlated with metabolic syndrome and have been estimated non-invasively from 3D optical scans using linear parameterizations of body shape and linear regression models. Prior works produced accurate and precise predictions on many, but not all, body composition targets relative to the reference dual X-Ray absorptiometry (DXA) measurement. Here, we report the effects of replacing linear models with nonlinear parameterization and regression models on the precision and accuracy of body composition estimation in a novel application of deep 3D convolutional graph networks to human body composition modeling. We assembled an ensemble dataset of 4286 topologically standardized 3D optical scans from four different human body shape databases, DFAUST, CAESAR, Shape Up! Adults, and Shape Up! Kids and trained a parameterized shape model using a graph convolutional 3D autoencoder (3DAE) in lieu of linear PCA. We trained a nonlinear Gaussian process regression (GPR) on the 3DAE parameter space to predict body composition via correlations to paired DXA reference measurements from the Shape Up! scan subset. We tested our model on a set of 424 randomly withheld test meshes and compared the effects of nonlinear computation against prior linear models. Nonlinear GPR produced up to 20% reduction in prediction error and up to 30% increase in precision over linear regression for both sexes in 10 tested body composition variables. Deep shape features produced 6-8% reduction in prediction error over linear PCA features for males only and a 4-14% reduction in precision error for both sexes. Our best performing nonlinear model predicting body composition from deep features outperformed prior work using linear methods on all tested body composition prediction metrics in both precision and accuracy. All coefficients of determination (R2) for all predicted variables were above 0.86. We show that GPR is a more precise and accurate method for modeling body composition mappings from body shape features than linear regression. Deep 3D features learned by a graph convolutional autoencoder only improved male body composition accuracy but improved precision in both sexes. Our work achieved lower estimation RMSEs than all previous work on 10 metrics of body composition.