Skeletal muscle-focused guideline development: hierarchical model incorporating muscle form, function, and clinical outcomes

Accurate prediction of three-dimensional humanoid avatars for anthropometric modeling

OBJECTIVE

To evaluate the hypothesis that anthropometric dimensions derived from a person's manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient body composition analysis and metabolic disease risk stratification in clinical settings.

METHODS

Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations. A new prospective sample of 84 adults had ground-truth measurements of 6 body circumferences, 7 volumes, and 7 surface areas with a 20-camera 3D reference scanner. 3D humanoid avatars were generated on these participants with manifold regression including age, weight, height, DXA %fat, and BIA impedances as potential predictor variables. Ground-truth and predicted avatar anthropometric dimensions were quantified with the same software.

RESULTS

Following exploratory studies, one manifold prediction model was moved forward for presentation that included age, weight, height, and %fat as covariates. Predicted and ground-truth avatars had similar visual appearances; correlations between predicted and ground-truth anthropometric estimates were all high (R2s, 0.75-0.99; all p < 0.001) with non-significant mean differences except for arm circumferences (%Δ ~ 5%; p < 0.05). Concordance correlation coefficients ranged from 0.80-0.99 and small but significant bias (p < 0.05-0.01) was present with Bland-Altman plots in 13 of 20 total anthropometric measurements. The mean waist to hip circumference ratio predicted by manifold regression was non-significantly different from ground-truth scanner measurements.

CONCLUSIONS

3D avatars predicted from demographic, physical, and other accessible characteristics can produce body representations with accurate anthropometric dimensions without a 3D scanner. Combining manifold regression algorithms into established body composition methods such as DXA, BIA, and other accessible methods provides new research and clinical opportunities.

Serum creatinine and serum cystatin C as an index of muscle mass in adults

PURPOSE OF REVIEW

Serum creatinine reflects both muscle mass and kidney function. Serum cystatin C has recently been recommended as an additional marker for estimating kidney function, and use of both markers together may provide an index of muscle mass. This review aims to describe the biological basis for and recent research examining the relationship of these markers to muscle mass in a range of adult populations and settings.

RECENT FINDINGS

This review identified 67 studies, 50 of which had direct measures of muscle mass, and almost all found relationships between serum creatinine and cystatin C and muscle mass and related outcomes. Most studies have been performed in older adults, but similar associations were found in general populations as well as in subgroups with cancer, chronic kidney disease (CKD), and other morbid conditions. Creatinine to cystatin C ratio was the measure examined the most often, but other measures showed similar associations across studies.

SUMMARY

Measures of serum creatinine and cystatin C together can be an index of muscle mass. They are simple and reliable measures that can be used in clinical practice and research. Further study is needed to determine actionable threshold values for each measure and clinical utility of testing and intervention.

Accurate Prediction of Three-Dimensional Humanoid Avatars for Anthropometric Modeling

Objective

To evaluate the hypothesis that anthropometric dimensions derived from a person's manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient metabolic disease risk stratification in clinical settings.

Methods

Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations. A new prospective sample of 84 adults had ground-truth measurements of 6 body circumferences, 7 volumes, and 7 surface areas with a 20-camera 3D reference scanner. 3D humanoid avatars were generated on these participants with manifold regression including age, weight, height, DXA %fat, and BIA impedances as potential predictor variables. Ground-truth and predicted avatar anthropometric dimensions were quantified with the same software.

Results

Following exploratory studies, one manifold prediction model was moved forward for presentation that included age, weight, height, and %fat as covariates. Predicted and ground-truth avatars had similar visual appearances; correlations between predicted and ground-truth anthropometric estimates were all high (R2s, 0.75-0.99; all p < 0.001) with non-significant mean differences except for arm circumferences (%D ~ 5%; p < 0.05). Concordance correlation coefficients ranged from 0.80-0.99 and small but significant bias (p < 0.05 - 0.01) was present with Bland-Altman plots in 13 of 20 total anthropometric measurements. The mean waist to hip circumference ratio predicted by manifold regression was non-significantly different from ground-truth scanner measurements.

Conclusions

3D avatars predicted from demographic, physical, and other accessible characteristics can produce body representations with accurate anthropometric dimensions without a 3D scanner. Combining manifold regression algorithms into established body composition methods such as DXA, BIA, and other accessible methods provides new research and clinical opportunities.

Muscle parameters in fragility fracture risk prediction in older adults: A scoping review

Half of osteoporotic fractures occur in patients with normal/osteopenic bone density or at intermediate or low estimated risk. Muscle measures have been shown to contribute to fracture risk independently of bone mineral density. The objectives were to review the measurements of muscle health (muscle mass/quantity/quality, strength and function) and their association with incident fragility fractures and to summarize their use in clinical practice. This scoping review follows the PRISMA-ScR guidelines for reporting. Our search strategy covered the three overreaching concepts of 'fragility fractures', 'muscle health assessment' and 'risk'. We retrieved 14 745 references from Medline Ovid SP, EMBASE, Web of Science Core Collection and Google Scholar. We included original and prospective studies on community-dwelling adults aged over 50 years that analysed an association between at least one muscle parameter and incident fragility fractures. We systematically extracted 17 items from each study, including methodology, general characteristics and results. Data were summarized in tables and graphically presented in adjusted forest plots. Sixty-seven articles fulfilled the inclusion criteria. In total, we studied 60 muscle parameters or indexes and 322 fracture risk ratios over 2.8 million person-years (MPY). The median (interquartile range) sample size was 1642 (921-5756), age 69.2 (63.5-73.6) years, follow-up 10.0 (4.4-12.0) years and number of incident fragility fractures 166 (88-277). A lower muscle mass was positively/not/negatively associated with incident fragility fracture in 28 (2.0), 64 (2.5) and 10 (0.2 MPY) analyses. A lower muscle strength was positively/not/negatively associated with fractures in 53 (1.3), 57 (1.7 MPY) and 0 analyses. A lower muscle function was positively/not/negatively associated in 63 (1.9), 45 (1.0 MPY) and 0 analyses. An in-depth analysis shows how each single muscle parameter was associated with each fragility fractures subtype. This review summarizes markers of muscle health and their association with fragility fractures. Measures of muscle strength and function appeared to perform better for fracture risk prediction. Of these, hand grip strength and gait speed are likely to be the most practical measures for inclusion in clinical practice, as in the evaluation of sarcopenia or in further fracture risk assessment scores. Measures of muscle mass did not appear to predict fragility fractures and might benefit from further research, on D3-creatine dilution test, lean mass indexes and artificial intelligence methods.