Int J Med Sci 2018; 15(4):323-338. doi:10.7150/ijms.22747
Bone Turnover Status: Classification Model and Clinical Implications
1. Department of Geriatric Medicine, The Canberra Hospital, Canberra, ACT Health, Canberra, Australia;
2. Department of Orthopaedic Surgery, The Canberra Hospital, Canberra, ACT Health, Canberra, Australia;
3. Frankston Hospital, Peninsula Health, Melbourne, Australia
4. Australian National University Medical School, Canberra, ACT, Australia
Fisher A, Fisher L, Srikusalanukul W, Smith PN. Bone Turnover Status: Classification Model and Clinical Implications. Int J Med Sci 2018; 15(4):323-338. doi:10.7150/ijms.22747. Available from http://www.medsci.org/v15p0323.htm
Aim: To develop a practical model for classification bone turnover status and evaluate its clinical usefulness.
Methods: Our classification of bone turnover status is based on internationally recommended biomarkers of both bone formation (N-terminal propeptide of type1 procollagen, P1NP) and bone resorption (beta C-terminal cross-linked telopeptide of type I collagen, bCTX), using the cutoffs proposed as therapeutic targets. The relationships between turnover subtypes and clinical characteristic were assessed in1223 hospitalised orthogeriatric patients (846 women, 377 men; mean age 78.1±9.50 years): 451(36.9%) subjects with hip fracture (HF), 396(32.4%) with other non-vertebral (non-HF) fractures (HF) and 376 (30.7%) patients without fractures.
Resalts: Six subtypes of bone turnover status were identified: 1 - normal turnover (P1NP>32 μg/L, bCTX≤0.250 μg/L and P1NP/bCTX>100.0[(median value]); 2- low bone formation (P1NP ≤32 μg/L), normal bone resorption (bCTX≤0.250 μg/L) and P1NP/bCTX>100.0 (subtype2A) or P1NP/bCTX<100.0 (subtype 2B); 3- low bone formation, high bone resorption (bCTX>0.250 μg/L) and P1NP/bCTX<100.0; 4- high bone turnover (both markers elevated ) and P1NP/bCTX>100.0 (subtype 4A) or P1NP/bCTX<100.0 (subtype 4B). Compared to subtypes 1 and 2A, subtype 2B was strongly associated with nonvertebral fractures (odds ratio [OR] 2.0), especially HF (OR 3.2), age>75 years and hyperparathyroidism. Hypoalbuminaemia and not using osteoporotic therapy were two independent indicators common for subtypes 3, 4A and 4B; these three subtypes were associated with in-hospital mortality. Subtype 3 was associated with fractures (OR 1.7, for HF OR 2.4), age>75 years, chronic heart failure (CHF), anaemia, and history of malignancy, and predicted post-operative myocardial injury, high inflammatory response and length of hospital stay (LOS) above10 days. Subtype 4A was associated with chronic kidney disease (CKD), anaemia, history of malignancy and walking aids use and predicted LOS>20 days, but was not discriminative for fractures. Subtype 4B was associated with fractures (OR 2.1, for HF OR 2.5), age>75 years, CKD and indicated risks of myocardial injury, high inflammatory response and LOS>10 days.
Conclusions: We proposed a classification model of bone turnover status and demonstrated that in orthogeriatric patients altered subtypes are closely related to presence of nonvertebral fractures, comorbidities and poorer in-hospital outcomes. However, further research is needed to establish optimal cut points of various biomarkers and improve the classification model.
Keywords: bone turnover markers, classification, nonvertebral fracture, prediction