Static Preload Inhibits Loading‐Induced Bone Formation

Srinivasan, Sundar; Balsiger, D; Huber, P.; Ausk, Brandon J.; Bain, Steven D.; Gardiner, Edith M.; Gross, Ted S.

doi:10.1002/jbm4.10087

Cited by 7 publications

(4 citation statements)

References 66 publications

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“…The effect of altering this initial (or resting) load level itself, or in combination with other loading parameters has not been systematically examined in the axial tibial loading model. A recent study using a novel off‐axis compression tibial loading model in 4‐month‐old female BALB/c mice showed that even when keeping the change from pre‐load to peak load similar (−3.8 N), increasing the static pre‐load from −0.03 to −1.5 N had detrimental effects on bone formation, even though the greatest pre‐load (−1.5 N) also corresponded to the greatest applied peak load (−5.3 N) . These results may depend upon the genetic strain, age of mouse, and loading protocol employed, but suggest that choice of pre‐load magnitude can have important effects upon the load‐induced anabolic response of both cortical and cancellous bone tissue.…”

Section: Experimental Parameters To Consider For In Vivo Loading Expementioning

confidence: 99%

Murine Axial Compression Tibial Loading Model to Study Bone Mechanobiology: Implementing the Model and Reporting Results

Main

Shefelbine

Meakin

et al. 2019

Journal Orthopaedic Research

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In vivo, tibial loading in mice is increasingly used to study bone adaptation and mechanotransduction. To achieve standardized and defined experimental conditions, loading parameters and animal-related factors must be considered when performing in vivo loading studies. In this review, we discuss these loading and animal-related experimental conditions, present methods to assess bone adaptation, and suggest reporting guidelines. This review originated from presentations by each of the authors at the workshop "Developing Best Practices for Mouse Models of In Vivo Loading" during the Preclinical Models Section at the Orthopaedic Research Society Annual Meeting, San Diego, CA, March 2017. Following the meeting, the authors engaged in detailed discussions with consideration of relevant literature. The guidelines and recommendations in this review are provided to help researchers perform in vivo loading experiments in mice, and thus further our knowledge of bone adaptation and the mechanisms involved in mechanotransduction.

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Section: Experimental Parameters To Consider For In Vivo Loading Expementioning

confidence: 99%

Murine Axial Compression Tibial Loading Model to Study Bone Mechanobiology: Implementing the Model and Reporting Results

Main

Shefelbine

Meakin

et al. 2019

Journal Orthopaedic Research

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“…z = 50 % cross sections of the mouse tibia, two commonly investigated sections within the limb (Srinivasan et al. 2019 ; DeLong et al. 2020 ; Rooney et al.…”

Section: Methodsmentioning

confidence: 99%

Cortical bone adaptation response is region specific, but not peak load dependent: insights from $$\mu$$CT image analysis and mechanostat simulations of the mouse tibia loading model

Miller,

Pickering,

Martelli

et al. 2023

Biomech Model Mechanobiol

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The two major aims of the present study were: (i) quantify localised cortical bone adaptation at the surface level using contralateral endpoint imaging data and image analysis techniques, and (ii) investigate whether cortical bone adaptation responses are universal or region specific and dependent on the respective peak load. For this purpose, we re-analyse previously published $$\mu$$ μ CT data of the mouse tibia loading model that investigated bone adaptation in response to sciatic neurectomy and various peak load magnitudes (F = 0, 2, 4, 6, 8, 10, 12 N). A beam theory-based approach was developed to simulate cortical bone adaptation in different sections of the tibia, using longitudinal strains as the adaptive stimuli. We developed four mechanostat models: universal, surface-based, strain directional-based, and combined surface and strain direction-based. Rates of bone adaptation in these mechanostat models were computed using an optimisation procedure (131,606 total simulations), performed on a single load case (F = 10 N). Subsequently, the models were validated against the remaining six peak loads. Our findings indicate that local bone adaptation responses are quasi-linear and bone region specific. The mechanostat model which accounted for differences in endosteal and periosteal regions and strain directions (i.e. tensile versus compressive) produced the lowest root mean squared error between simulated and experimental data for all loads, with a combined prediction accuracy of 76.6, 55.0 and 80.7% for periosteal, endosteal, and cortical thickness measurements (in the midshaft of the tibia). The largest root mean squared errors were observed in the transitional loads, i.e. F = 2 to 6 N, where inter-animal variability was highest. Finally, while endpoint imaging studies provide great insights into organ level bone adaptation responses, the between animal and loaded versus control limb variability make simulations of local surface-based adaptation responses challenging.

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“…This strain threshold was, however, found to vary with loading conditions, locations, loading waveform, frequency of loading etc. [4]–[10]. For example, the strain thresholds reported for the cantilever loading of mouse tibia, with trapezoidal loading waveform are about 650 με [4] and 850 με [5], with and without rest-inserted waveform, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…For axial loading of tibia, the strain threshold was found to be approximately 1100 με [9]. Bone formation rate (BFR) was further found to depend on axial preloads, with higher BFR for lower axial preload, possibly due to preload-dependent strain threshold [10].…”

Section: Introductionmentioning

confidence: 99%

Relating strain amplitude, strain threshold and bone formation rate to exogenous forcing frequency

Prasad,

Aruva,

Shekhar

et al. 2023

Preprint

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The literature supports the existence of a strain threshold, above which cortical bone adapts to exogenous mechanical loading by forming new bone. This strain threshold, however, varies with loading conditions, locations, waveforms, frequency etc. and there is a need to mathematically express the strain threshold in terms of these parameters. There have been several parametric, mathematical or numerical models in the literature for the cortical bone’s adaptation to mechanical loading, which may be already fitting some of the experimental data; however, they may not be easily and confidently derived from the first principles. To fill the gap, this work has attempted to derive the corresponding bone formation rate (BFR) rather from the first principles, namely using the energy principles. The derived model has been compared to the existing parametric models and validated with respect to the diverse experimental data available in the literature. The developed model is able to not only predict the BFR, but also helps to understand the nature and possible mathematical form of the strain threshold for cortical bone’s adaptation to mechanical loading.

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Static Preload Inhibits Loading‐Induced Bone Formation

Cited by 7 publications

References 66 publications

Murine Axial Compression Tibial Loading Model to Study Bone Mechanobiology: Implementing the Model and Reporting Results

Murine Axial Compression Tibial Loading Model to Study Bone Mechanobiology: Implementing the Model and Reporting Results

Cortical bone adaptation response is region specific, but not peak load dependent: insights from $$\mu$$CT image analysis and mechanostat simulations of the mouse tibia loading model

Relating strain amplitude, strain threshold and bone formation rate to exogenous forcing frequency

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