A novel non-invasive method for predicting bone mineral density and fracture risk using demographic and anthropometric measures

Justin Aflatooni,Steven Martin,Adib Edilbi,Pranav Gadangi,William Singer,Robert Loving,Shreya Domakonda,Nandini Solanki,Patrick C Mcculloch,Bradley Lambert

doi:10.1016/j.smhs.2023.09.003

Abstract

Fractures are costly to treat and can significantly increase morbidity. Although dual-energy x-ray absorptiometry (DEXA) is used to screen at risk people with low bone mineral density (BMD), not all areas have access to one. We sought to create a readily accessible, inexpensive, high-throughput prediction tool for BMD that may identify people at risk of fracture for further evaluation. Anthropometric and demographic data were collected from 492 volunteers (♂275, ♀217; [44 ± 20] years; Body Mass Index (BMI) = [27.6 ± 6.0] kg/m2) in addition to total body bone mineral content (BMC, kg) and BMD measurements of the spine, pelvis, arms, legs and total body. Multiple-linear-regression with step-wise removal was used to develop a two-step prediction model for BMC followed by BMC. Model selection was determined by the highest adjusted R2, lowest error of estimate, and lowest level of variance inflation (α = 0.05). Height (HTcm), age (years), sexm=1, f=0, %body fat (%fat), fat free mass (FFMkg), fat mass (FMkg), leg length (LLcm), shoulder width (SHWDTHcm), trunk length (TRNKLcm), and pelvis width (PWDTHcm) were observed to be significant predictors in the following two-step model (p < 0.05). Step1: BMC (kg) = (0.006 3 × HT) + (−0.002 4 × AGE) + (0.171 2 × SEXm=1, f=0) + (0.031 4 × FFM) + (0.001 × FM) + (0.008 9 × SHWDTH) + (−0.014 5 × TRNKL) + (−0.027 8 × PWDTH) - 0.507 3; R2 = 0.819, SE ± 0.301. Step2: Total body BMD (g/cm2) = (−0.002 8 × HT) + (−0.043 7 × SEXm=1, f=0) + (0.000 8 × %FAT) + (0.297 0 × BMC) + (−0.002 3 × LL) + (0.002 3 × SHWDTH) + (−0.002 5 × TRNKL) + (−0.011 3 × PWDTH) + 1.379; R2 = 0.89, SE ± 0.054. Similar models were also developed to predict leg, arm, spine, and pelvis BMD (R2 = 0.796–0.864, p < 0.05). The equations developed here represent promising tools for identifying individuals with low BMD at risk of fracture who would benefit from further evaluation, especially in the resource or time restricted setting.

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