A machine learning based study to assess bone health in a diabetic cohort. (2022)

• Record Type: Journal Article
• Authors: Musleh, Saleh; Nazeemudeen, Anjanarani; Islam, Mohammad Tariqul; El Hajj, Nady; Alam, Tanvir
• Is Part Of: Informatics in medicine unlocked. Volume 33(2022)
• Publication Date: 2022
• Subjects: Diabetes -- Osteoporosis -- Osteopenia -- Bone mineral density -- Dual energy X-ray absorptiometry -- DXA -- Machine learning -- Qatar Biobank (QBB); BMC Bone Mineral Content -- BMD Bone Mineral Density -- BMI Body Mass Index -- DM Diabetes Mellitus -- DXA Dual Energy X-ray Absorptiometry -- MENA Middle East and North Africa -- ML Machine Learning -- OP Osteoporosis -- OPN Osteopenia -- QBB Qatar Biobank -- WHO World Health Organization; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/j.imu.2022.101079
• Languages: English
• ISSNs: 2352-9148

Abstract

Abstract: Diabetes mellitus (DM) and osteoporosis/osteopenia affect millions of people globally and are major health conditions in several countries including Qatar. Bone mineral density (BMD) is a widely accepted indicator for diagnosing osteoporosis (OP) and osteopenia (OPN). The best method for determining bone mineral density and OP/OPN risk is via dual energy X-ray absorptiometry (DXA) technology. The risk of osteoporosis-related fracture may increase for people with diabetes. Therefore, it is necessary to develop a system that can support the early detection of OP/OPN in diabetic patients. In this study, we analyzed Qatar diabetic cohorts including 500 subjects, among which 68 were OP/OPN (target) subjects and 432 were without osteoporosis/osteopenia (control) subjects. The objective of this study is to develop an ML model to distinguish diabetic OP/OPN patients from diabetic non-OP/non-OPN subjects based on their bone health indicators from full body DXA scan measurements. Based on our experiments, AdaBoost model performed the best for classifying the target group from the control group. 10-fold cross validation-based results indicate that the proposed ML model was able to distinguish the target group from the control group at 80% sensitivity, 96% specificity. To the best of our knowledge, our study is the first ML-based approach to detect the early onset of OP/OPN in diabetic cohort from Qatar. Our analyses revealed the higher level of lean mass, fat mass and bone mass for the control group compared to the target group. Higher levels of BMC, BMD from different body parts in the control group compared to the osteoporosis/osteopenia group indicate the protective effects of obesity on bone health in the Qatari diabetic cohort. Moreover, higher value of anthropometric measurements in troch, lumbar spine (L1, L2, L3, L4), pelvis and other body parts in the control group indicates that the WHO guideline can be applied to the Qatari diabetic cohort for the early detection of OP/OPN based on the proposed ML model. Further research on OP/OPN in diabetic patients is warranted in future to confirm the role of DM on bone health.