Osteopenia in a Mouse Model of Spinal Cord Injury: Effects of Age, Sex and Motor Function.
Michelle A HookAlyssa FalckRavali DundumullaMabel TerminelRachel CunninghamArthur SefianiKayla CallawayDana GaddyCédric G GeoffroyPublished in: Biology (2022)
After spinal cord injury (SCI), 80% of individuals are diagnosed with osteopenia or osteoporosis. The dramatic loss of bone after SCI increases the potential for fractures 100-fold, with post-fracture complications occurring in 54% of cases. With the age of new SCI injuries increasing, we hypothesized that a SCI-induced reduction in weight bearing could further exacerbate age-induced bone loss. To test this, young (2-3 months) and old (20-30 months) male and female mice were given a moderate spinal contusion injury (T9-T10), and recovery was assessed for 28 days (BMS, rearing counts, distance traveled). Tibial trabecular bone volume was measured after 28 days with ex vivo microCT. While BMS scores did not differ across groups, older subjects travelled less in the open field and there was a decrease in rearing with age and SCI. As expected, aging decreased trabecular bone volume and cortical thickness in both old male and female mice. SCI alone also reduced trabecular bone volume in young mice, but did not have an additional effect beyond the age-dependent decrease in trabecular and cortical bone volume seen in both sexes. Interestingly, both rearing and total activity correlated with decreased bone volume. These data underscore the importance of load and use on bone mass. While partial weight-bearing does not stabilize/reverse bone loss in humans, our data suggest that therapies that simulate complete loading may be effective after SCI.
Keyphrases
- bone loss
- bone mineral density
- spinal cord injury
- postmenopausal women
- spinal cord
- body composition
- soft tissue
- mouse model
- neuropathic pain
- type diabetes
- physical activity
- total knee arthroplasty
- body mass index
- high glucose
- high fat diet induced
- weight loss
- electronic health record
- oxidative stress
- metabolic syndrome
- big data
- skeletal muscle
- risk assessment
- diabetic rats
- artificial intelligence
- deep learning
- hip fracture