Publications

BACKGROUND: Computed tomography (CT) captures the quantity, density, and distribution of subcutaneous and visceral (SAT and VAT) adipose tissue compartments. These metrics may change with age and sex.

OBJECTIVE: The study aims to provide age-, sex-, and vertebral level-specific reference values for SAT on chest CT and for SAT and VAT on abdomen CT.

MATERIALS AND METHODS: This secondary analysis of an observational study describes SAT and VAT measurements in participants of the Framingham Heart Study without known cancer diagnosis who underwent at least 1 of 2 CT examinations between 2002 and 2011. We used a previously validated machine learning-assisted pipeline and rigorous quality assurance to segment SAT at the fifth, eighth, and tenth thoracic vertebra (T5, T8, T10) and SAT and VAT at the third lumbar vertebra (L3). For each metric, we measured cross-sectional area (cm2) and mean attenuation (Hounsfield units [HU]) and calculated index (area/height2) (cm2/m2) and gauge (attenuation × index) (HU × cm2/m2). We summarized body composition metrics by age and sex and modeled sex-, age-, and vertebral level-specific reference curves.

RESULTS: We included 14,898 single-level measurements from up to 4 vertebral levels of 3797 scans of 3730 Framingham Heart Study participants (1889 [51%] male with a mean [standard deviation] age of 55.6 ± 10.6 years; range, 38-81 years). The mean VAT index increased with age from 65 (cm2/m2) in males and 29 (cm2/m2) in females in the <45-year-old age group to 99 (cm2/m2) in males and 60 (cm2/m2) in females in >75-year-old age group. The increase of SAT with age was less pronounced, resulting in the VAT/SAT ratio increasing with age. A free R package and online interactive visual web interface allow access to reference values.

CONCLUSIONS: This study establishes age-, sex-, and vertebral level-specific reference values for CT-assessed SAT at vertebral levels T5, T8, T10, and L3 and VAT at vertebral level L3.

Osteoporosis and cardiovascular disease frequently occur together in older adults; however, a causal relationship between these 2 common conditions has not been established. By the time clinical cardiovascular disease develops, it is often too late to test whether vascular dysfunction developed before or after the onset of osteoporosis. Therefore, we assessed the association of vascular function, measured by tonometry and brachial hemodynamic testing, with bone density, microarchitecture, and strength, measured by HR-pQCT, in 1391 individuals in the Framingham Heart Study. We hypothesized that decreased vascular function (pulse wave velocity, primary pressure wave, brachial pulse pressure, baseline flow amplitude, and brachial flow velocity) contributes to deficits in bone density, microarchitecture and strength, particularly in cortical bone, which is less protected from excessive blood flow pulsatility than the trabecular compartment. We found that individuals with increased carotid-femoral pulse wave velocity had lower cortical volumetric bone mineral density (tibia: -0.21 [-0.26, -0.15] standardized beta [95% CI], radius: -0.20 [-0.26, -0.15]), lower cortical thickness (tibia: -0.09 [-0.15, -0.04], radius: -0.07 [-0.12, -0.01]) and increased cortical porosity (tibia: 0.20 [0.15, 0.25], radius: 0.21 [0.15, 0.27]). However, these associations did not persist after adjustment for age, sex, height, and weight. These results suggest that vascular dysfunction with aging may not be an etiologic mechanism that contributes to the co-occurrence of osteoporosis and cardiovascular disease in older adults. Further study employing longitudinal measures of HR-pQCT parameters is needed to fully elucidate the link between vascular function and bone health.

The purpose of this study was to develop a machine learning model to reconstruct time series kinematic and kinetic profiles of the ankle and knee joint across six different tasks using an ankle-mounted IMU. Four male collegiate basketball players performed repeated tasks, including walking, jogging, running, sidestep cutting, max-height jumping, and stop-jumping, resulting in a total of 102 movements. Ankle and knee flexion-extension angles and moments were estimated using motion capture and inverse dynamics and considered 'actual data' for the purpose of model fitting. Synchronous acceleration and angular velocity data were collected from right ankle-mounted IMUs. A time-series feature extraction model was used to determine a set of features used as input to a random forest regression model to predict the ankle and knee kinematics and kinetics. Five-fold cross-validation was performed to verify the model accuracy, and statistical parametric mapping was used to determine the difference between the predicted and experimental time series. The random forest regression model predicted the time-series profiles of the ankle and knee flexion-extension angles and moments with high accuracy (Kinematics: R2 ranged from 0.782 to 0.962, RMSE ranged from 2.19° to 11.58°; Kinetics: R2 ranged from 0.711 to 0.966, RMSE ranged from 0.10 Nm/kg to 0.41 Nm/kg). There were differences between predicted and actual time series for the knee flexion-extension moment during stop-jumping and walking. An appropriately trained feature-based regression model can predict time series knee and ankle joint angles and moments across a wide range of tasks using a single ankle-mounted IMU.

There is a common belief that antiosteoporosis medications are less effective in older adults. This study used data from randomized controlled trials (RCTs) to determine whether the anti-fracture efficacy of treatments and their effects on BMD differ in people ≥70 compared to those <70 yr. We used individual patient data from 23 RCTs of osteoporosis medications collected as part of the FNIH-ASBMR SABRE project. We assessed the following fractures: radiographic vertebral, non-vertebral, hip, all clinical, and all fractures. We used Cox proportional hazard regression to estimate treatment effect for clinical fracture outcomes, logistic regression for the radiographic vertebral fracture outcome, and linear regression to estimate treatment effect on 24-mo change in hip and spine BMD in each age subgroup. The analysis included 123 164 (99% female) participants; 43% being ≥70 yr. Treatment with anti-osteoporosis drugs significantly and similarly reduced fractures in both subgroups (eg, odds ratio [OR] = 0.47 and 0.51 for vertebral fractures in those below and above 70 yr, interaction P = .19; hazard ratio [HR] for all fractures: 0.72 vs 0.70, interaction P = .20). Results were similar when limited to bisphosphonate trials with the exception of hip fracture risk reduction which was somewhat greater in those <70 (HR = 0.44) vs ≥70 (HR = 0.79) yr (interaction P = .02). Allocation to anti-osteoporotic drugs resulted in significantly greater increases in hip and spine BMD at 24 mo in those ≥70 compared to those <70 yr. In summary, anti-osteoporotic medications similarly reduced the risk of fractures regardless of age, and the few small differences in fracture risk reduction by age were of uncertain clinical significance.

Diabetes, a disease marked by consistent high blood glucose levels, is associated with various complications such as neuropathy, nephropathy, retinopathy, and cardiovascular disease. Notably, skeletal fragility has emerged as a significant complication in both type 1 (T1D) and type 2 (T2D) diabetic patients. This review examines noninvasive imaging studies that evaluate skeletal outcomes in adults with T1D and T2D, emphasizing distinct skeletal phenotypes linked with each condition and pinpointing gaps in understanding bone health in diabetes. Although traditional DXA-BMD does not fully capture the increased fracture risk in diabetes, recent techniques such as quantitative computed tomography, peripheral quantitative computed tomography, high-resolution quantitative computed tomography, and MRI provide insights into 3D bone density, microstructure, and strength. Notably, existing studies present heterogeneous results possibly due to variations in design, outcome measures, and potential misclassification between T1D and T2D. Thus, the true nature of diabetic skeletal fragility is yet to be fully understood. As T1D and T2D are diverse conditions with heterogeneous subtypes, future research should delve deeper into skeletal fragility by diabetic phenotypes and focus on longitudinal studies in larger, diverse cohorts to elucidate the complex influence of T1D and T2D on bone health and fracture outcomes.

BACKGROUND: Bone stress injury (BSI) is a common overuse injury in active women. BSIs can be classified as high-risk (pelvis, sacrum, and femoral neck) or low-risk (tibia, fibula, and metatarsals). Risk factors for BSI include low energy availability, menstrual dysfunction, and poor bone health. Higher vertical load rates during running have been observed in women with a history of BSI.

PURPOSE/HYPOTHESIS: The purpose of this study was to characterize factors associated with BSI in a population of premenopausal women, comparing those with a history of high-risk or low-risk BSI with those with no history of BSI. It was hypothesized that women with a history of high-risk BSI would be more likely to exhibit lower bone mineral density (BMD) and related factors and less favorable bone microarchitecture compared with women with a history of low-risk BSI. In contrast, women with a history of low-risk BSI would have higher load rates.

STUDY DESIGN: Cross-sectional study; Level of evidence, 3.

METHODS: Enrolled were 15 women with a history of high-risk BSI, 15 with a history of low-risk BSI, and 15 with no history of BSI. BMD for the whole body, hip, and spine was standardized using z scores on dual-energy x-ray absorptiometry. High-resolution peripheral quantitative computed tomography was used to quantify bone microarchitecture at the radius and distal tibia. Participants completed surveys characterizing factors that influence bone health-including sleep, menstrual history, and eating behaviors-utilizing the Eating Disorder Examination Questionnaire (EDE-Q). Each participant completed a biomechanical assessment using an instrumented treadmill to measure load rates before and after a run to exertion.

RESULTS: Women with a history of high-risk BSI had lower spine z scores than those with low-risk BSI (-1.04 ± 0.76 vs -0.01 ± 1.15; P < .05). Women with a history of high-risk BSI, compared with low-risk BSI and no BSI, had the highest EDE-Q subscores for Shape Concern (1.46 ± 1.28 vs 0.76 ± 0.78 and 0.43 ± 0.43) and Eating Concern (0.55 ± 0.75 vs 0.16 ± 0.38 and 0.11 ± 0.21), as well as the greatest difference between minimum and maximum weight at current height (11.3 ± 5.4 vs 7.7 ± 2.9 and 7.6 ± 3.3 kg) (P < .05 for all). Women with a history of high-risk BSI were more likely than those with no history of BSI to sleep <7 hours on average per night during the week (80% vs 33.3%; P < .05). The mean and instantaneous vertical load rates were not different between groups.

CONCLUSION: Women with a history of high-risk BSI were more likely to exhibit risk factors for poor bone health, including lower BMD, while load rates did not distinguish women with a history of BSI.

Several new peripheral dual-energy X-ray absorptiometry (DXA) devices designed for assessment of bone and body composition in rodents have been developed. We compared the performance (accuracy and precision) of two of these devices, the InAlyzer and the iNSiGHT, to those of an established device, the PIXImus. We measured total body bone mineral content (BMC), bone mineral density (BMD), and body composition (lean and fat mass) on the three DXA devices in 18 male C57Bl/6 J mice (6 each of ages 8, 14, and 24 weeks, weighing 22 to 33 g). DXA body composition measures were compared to whole-body nuclear magnetic resonance (NMR) outcomes. BMC of the femur was also compared to ex vivo micro-computed tomography (microCT). Total body BMD from the InAlyzer and iNSiGHT devices was strongly correlated to that from PIXImus (R2 = 0.83 and 0.82, respectively), but was  25 % higher than PIXImus. Total body BMC measures by InAlyzer were strongly associated with those from PIXImus (R2 = 0.86), whereas those from iNSiGHT were only weakly correlated (R2 = 0.29). Femur BMC from InAlyzer was strongly correlated with microCT outcomes, whereas iNSiGHT was only weakly correlated. InAlyzer and iNSiGHT fat mass measures were very strongly correlated with PIXImus and NMR outcomes (R2 = 0.91 to 0.97), with slightly weaker associations for lean mass (R2 = 0.81 to 0.76). Short-term precision of InAlyzer and iNSiGHT measurements were excellent, and akin to those from the PIXImus for both body composition and bone measures, ranging between 0.39 and 3.2 %. With faster scan times, closed X-ray source and excellent precision, the new devices are both satisfactory replacements for the now discontinued PIXImus system. However, given the accuracy of the bone and body composition measures, the InAlyzer may be preferable for studies where musculoskeletal changes are the main interest.

BACKGROUND: Loss of muscle mass is a known feature of sarcopenia and predicts poor clinical outcomes. Although muscle metrics can be derived from routine computed tomography (CT) images, sex-specific reference values at multiple vertebral levels over a wide age range are lacking.

OBJECTIVE: The aim of this study was to provide reference values for skeletal muscle mass and attenuation on thoracic and abdominal CT scans in the community-based Framingham Heart Study cohort to aid in the identification of sarcopenia.

MATERIALS AND METHODS: This secondary analysis of a prospective trial describes muscle metrics by age and sex for participants from the Framingham Heart Study without prior history of cancer who underwent at least 1 CT scan between 2002 and 2011. Using 2 previously validated machine learning algorithms followed by human quality assurance, skeletal muscle was analyzed on a single axial CT image per level at the 5th, 8th, 10th thoracic, and 3rd lumbar vertebral body (T5, T8, T10, L3). Cross-sectional muscle area (cm 2 ), mean skeletal muscle radioattenuation (SMRA, in Hounsfield units), skeletal muscle index (SMI, in cm 2 /m 2 ), and skeletal muscle gauge (SMRA·SMI) were calculated. Measurements were summarized by age group (<45, 45-54, 55-64, 65-74, ≥75 years), sex, and vertebral level. Models enabling the calculation of age-, sex-, and vertebral-level-specific reference values were created and embedded into an open access online Web application.

RESULTS: The cohort consisted of 3804 participants (1917 [50.4%] males; mean age, 55.6 ± 11.8 years; range, 33-92 years) and 7162 CT scans. Muscle metrics qualitatively decreased with increasing age and female sex.

CONCLUSIONS: This study established age- and sex-specific reference values for CT-based muscle metrics at thoracic and lumbar vertebral levels. These values may be used in future research investigating the role of muscle mass and attenuation in health and disease, and to identify sarcopenia.

BACKGROUND: Bone stress injuries (BSIs) are common in female runners, and recurrent BSI rates are high. Previous work suggests an association between higher impact loading during running and tibial BSI. However, it is unknown whether impact loading and fatigue-related loading changes discriminate women with a history of multiple BSIs. This study compared impact variables at the beginning of a treadmill run to exertion and the changes in those variables with exertion among female runners with no history of BSI as well as among those with a history of single or multiple BSIs.

METHODS: We enrolled 45 female runners (aged 18-40 years) for this cross-sectional study: having no history of diagnosed lower extremity BSI (N-BSI, n = 14); a history of 1 lower extremity BSI (1-BSI, n = 16); and diagnosed by imaging, or a history of multiple (≥3) lower extremity BSIs (M-BSI, n = 15). Participants completed a 5-km race speed run on an instrumented treadmill while wearing an Inertial Measurement Unit. The vertical average loading rate (VALR), vertical instantaneous loading rate (VILR), vertical stiffness during impact via instrumented treadmill, and tibial shock determined as the peak positive tibial acceleration via Inertial Measurement Unit were measured at the beginning and the end of the run.

RESULTS: There were no differences between groups in VALR, VILR, vertical stiffness, or tibial shock in a fresh or exerted condition. However, compared to N-BSI, women with M-BSI had greater increase with exertion in VALR (-1.8% vs. 6.1%, p = 0.01) and VILR (1.5% vs. 4.8%, p = 0.03). Similarly, compared to N-BSI, vertical stiffness increased more with exertion among women with M-BSI (-0.9% vs. 7.3%, p = 0.006) and 1-BSI (-0.9% vs. 1.8%, p = 0.05). Finally, compared to N-BSI, the increase in tibial shock from fresh to exerted condition was greater among women with M-BSI (0.9% vs. 5.5%, p = 0.03) and 1-BSI (0.9% vs. 11.2%, p = 0.02).

CONCLUSION: Women with 1-BSI or M-BSIs experience greater exertion-related increases in impact loading than women with N-BSI. These observations imply that exertion-related changes in gait biomechanics may contribute to risk of BSI.