Body composition by computed tomography vs. dual energy x-ray absorptiometry: Long-term prediction of all-cause mortality in the Health ABC cohort

Deep learning-quantified body composition from positron emission tomography/computed tomography and cardiovascular outcomes: a multicentre study

BACKGROUND AND AIMS

Positron emission tomography (PET)/computed tomography (CT) myocardial perfusion imaging (MPI) is a vital diagnostic tool, especially in patients with cardiometabolic syndrome. Low-dose CT scans are routinely performed with PET for attenuation correction and potentially contain valuable data about body tissue composition. Deep learning and image processing were combined to automatically quantify skeletal muscle (SM), bone and adipose tissue from these scans and then evaluate their associations with death or myocardial infarction (MI).

METHODS

In PET MPI from three sites, deep learning quantified SM, bone, epicardial adipose tissue (EAT), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intermuscular adipose tissue (IMAT). Sex-specific thresholds for abnormal values were established. Associations with death or MI were evaluated using unadjusted and multivariable models adjusted for clinical and imaging factors.

RESULTS

This study included 10 085 patients, with median age 68 (interquartile range 59-76) and 5767 (57%) male. Body tissue segmentations were completed in 102 ± 4 s. Higher VAT density was associated with an increased risk of death or MI in both unadjusted [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.37-1.43] and adjusted (HR 1.24, 95% CI 1.19-1.28) analyses, with similar findings for IMAT, SAT, and EAT. Patients with elevated VAT density and reduced myocardial flow reserve had a significantly increased risk of death or MI (adjusted HR 2.49, 95% CI 2.23-2.77).

CONCLUSIONS

Volumetric body tissue composition can be obtained rapidly and automatically from standard cardiac PET/CT. This new information provides a detailed, quantitative assessment of sarcopenia and cardiometabolic health for physicians.

Fat-Free Mass: Friend or Foe to Metabolic Health?

BACKGROUND

Fat mass (FM) and fat-free mass (FFM) are body composition estimates commonly reported in research studies and clinical settings. Recently, fat-free mass indexed to height (fat-free mass index; FFMI) has been shown to be positively associated with impaired insulin sensitivity or insulin resistance. Consequently, hypertrophic resistance training which can increase FFM was also questioned. This paper sets out to evaluate these propositions.

METHODS

In this narrative review, we discuss possible reasons that link FFMI to adverse metabolic health outcomes including the limitations of the body composition model that utilizes FFM. The safety of resistance training is also briefly discussed.

RESULTS

Approximately 50% of FFM is comprised of skeletal muscle (SM), with the other 50% being viscera, skin, and bone; FFM and SM cannot be conflated. FFM and fat mass (FM) can both rise with increasing body weight and adiposity, indicating a positive correlation between the two compartments. Risk assessment models not adequately adjusting for this correlation may cause erroneous conclusions, however which way FM and FFM are indexed. Adipose tissue accumulation with weight gain, measured by dual-energy X-ray absorptiometry or bioelectrical impedance, can inflate FFM estimates owing to increased connective tissue. Increased adiposity can also result in fat deposition within skeletal muscle disrupting metabolic health. Importantly, non-skeletal muscle components of the FFM, i.e., the liver and pancreas, both critical in metabolic health, can also be negatively affected by the same lifestyle factors that impact SM. The most frequently used body composition techniques used to estimate FM and FFM cannot detect muscle, liver or pancreas fat infiltration. Prospective evidence demonstrates that resistance training is a safe and effective exercise modality across all ages, especially in older adults experiencing age- or disease-related declines in muscle health.

CONCLUSIONS

The association between FFM and insulin resistance is largely an artefact driven by inadequate assessment of skeletal muscle. If FM and FFM are used, at the minimum, they need to be evaluated in context with one another. Body composition methods, such as magnetic resonance imaging, which measures skeletal muscle rather than fat-free mass, and adipose tissue as well as muscle ectopic fat, are preferred methods. Resistance training is important in achieving and maintaining good health across the lifespan. While strength and power are critical components of resistance training, the reduction of skeletal mass through ageing or disease may require hypertrophic training to mitigate and slow down the progression of this often-inevitable process.

Thigh muscle by CT images as a predictor of mortality in patients with newly diagnosed colorectal cancer

This study aimed to evaluate the prognostic value of thigh muscle assessed by CT images to predict overall mortality in patients with colorectal cancer (CRC). This was a multicenter cohort study including adults (≥ 18 years old) newly diagnosed with CRC, who performed a diagnostic computed tomography (CT) exam including thigh regions. CT images were analyzed to evaluate skeletal muscle (SM in cm2), skeletal muscle index (SMI in cm2/m2), and skeletal muscle density (SMD in HU). Muscle abnormalities (low SM, SMI, and SMD) were defined as the values below the median by sex. Kaplan-Meyer curves and hazard ratios (HRs) for low SM, SMI and SMD were evaluated for overall mortality, stratified by sex. A total of 257 patients were included in the final analysis. Patients' mean age was 62.6 ± 12.1 years, and 50.2% (n = 129) were females. In males, low thigh SMI was associated with shorter survival (log-rank P = .02). Furthermore, this low thigh SMI (cm2/m2) was independently associated with higher mortality rates (HR adjusted 2.08, 95% CI 1.03-4.18). Our additional findings demonstrated that low SMD was independently associated with overall mortality among early-stage patients (I-III) (HR adjusted 2.78, 95% CI 1.26-6.15).

Predictive value of sarcopenia components for all-cause mortality: findings from population-based cohorts

BACKGROUND

Low grip strength and gait speed are associated with mortality. However, investigation of the additional mortality risk explained by these measures, over and above other factors, is limited.

AIM

We examined whether grip strength and gait speed improve discriminative capacity for mortality over and above more readily obtainable clinical risk factors.

METHODS

Participants from the Health, Aging and Body Composition Study, Osteoporotic Fractures in Men Study, and the Hertfordshire Cohort Study were analysed. Appendicular lean mass (ALM) was ascertained using DXA; muscle strength by grip dynamometry; and usual gait speed over 2.4-6 m. Verified deaths were recorded. Associations between sarcopenia components and mortality were examined using Cox regression with cohort as a random effect; discriminative capacity was assessed using Harrell's Concordance Index (C-index).

RESULTS

Mean (SD) age of participants (n = 8362) was 73.8(5.1) years; 5231(62.6%) died during a median follow-up time of 13.3 years. Grip strength (hazard ratio (95% CI) per SD decrease: 1.14 (1.10,1.19)) and gait speed (1.21 (1.17,1.26)), but not ALM index (1.01 (0.95,1.06)), were associated with mortality in mutually-adjusted models after accounting for age, sex, BMI, smoking status, alcohol consumption, physical activity, ethnicity, education, history of fractures and falls, femoral neck bone mineral density (BMD), self-rated health, cognitive function and number of comorbidities. However, a model containing only age and sex as exposures gave a C-index (95% CI) of 0.65(0.64,0.66), which only increased to 0.67(0.67,0.68) after inclusion of grip strength and gait speed.

CONCLUSIONS

Grip strength and gait speed may generate only modest adjunctive risk information for mortality compared with other more readily obtainable risk factors.

The Health, Aging, and Body Composition (Health ABC) Study-Ground-Breaking Science for 25 Years and Counting

BACKGROUND

The Health, Aging, and Body Composition Study is a longitudinal cohort study that started just over 25 years ago. This ground-breaking study tested specific hypotheses about the importance of weight, body composition, and weight-related health conditions for incident functional limitation in older adults.

METHODS

Narrative review with analysis of ancillary studies, career awards, publications, and citations.

RESULTS

Key findings of the study demonstrated the importance of body composition as a whole, both fat and lean mass, in the disablement pathway. The quality of the muscle in terms of its strength and its composition was found to be a critical feature in defining sarcopenia. Dietary patterns and especially protein intake, social factors, and cognition were found to be critical elements for functional limitation and disability. The study is highly cited and its assessments have been widely adopted in both observational studies and clinical trials. Its impact continues as a platform for collaboration and career development.

CONCLUSIONS

The Health ABC provides a knowledge base for the prevention of disability and promotion of mobility in older adults.

The Epidemiology and Societal Impact of Aging-Related Functional Limitations: A Looming Public Health Crisis

Functional impairment and disability become increasingly common with aging. As more people are reaching old age, the number of people needing care will rise, creating a crisis of need for care. Population studies and clinical trials have demonstrated the importance of the detection of early loss of strength and walking speed in predicting disability and in designing interventions to prevent functional decline. There is a large societal burden linked to age-related disorders. Physical activity is to date the only intervention that has prevented disability in a long-term clinical trial, but is difficult to sustain. Novel interventions are needed to maintain function in late life.

Association of muscle wasting with mortality risk among adults: A systematic review and meta-analysis of prospective studies

The relationship between muscle wasting and mortality risk in the general population remains unclear. Our study was conducted to examine and quantify the associations between muscle wasting and all-cause and cause-specific mortality risks. PubMed, Web of Science and Cochrane Library were searched until 22 March 2023 for main data sources and references of retrieved relevant articles. Prospective studies investigating the associations of muscle wasting with risks of all-cause and cause-specific mortality in the general population were eligible. A random-effect model was used to calculate the pooled relative risk (RR) and 95% confidence intervals (CIs) for the lowest versus normal categories of muscle mass. Subgroup analyses and meta-regression were performed to investigate the potential sources of heterogeneities among studies. Dose-response analyses were conducted to evaluate the relationship between muscle mass and mortality risk. Forty-nine prospective studies were included in the meta-analysis. A total of 61 055 deaths were ascertained among 878 349 participants during the 2.5- to 32-year follow-up. Muscle wasting was associated with higher mortality risks of all causes (RR = 1.36, 95% CI, 1.28 to 1.44, I²  = 94.9%, 49 studies), cardiovascular disease (CVD) (RR = 1.29, 95% CI, 1.05 to 1.58, I²  = 88.1%, 8 studies), cancer (RR = 1.14, 95% CI, 1.02 to 1.27, I²  = 38.7%, 3 studies) and respiratory disease (RR = 1.36, 95% CI, 1.11 to 1.67, I²  = 62.8%, 3 studies). Subgroup analyses revealed that muscle wasting, regardless of muscle strength, was significantly associated with a higher all-cause mortality risk. Meta-regression showed that risks of muscle wasting-related all-cause mortality (P = 0.06) and CVD mortality (P = 0.09) were lower in studies with longer follow-ups. An approximately inverse linear dose-response relationship was observed between mid-arm muscle circumference and all-cause mortality risk (P < 0.01 for non-linearity). Muscle wasting was associated with higher mortality risks of all causes, CVD, cancer and respiratory disease in the general population. Early detection and treatment for muscle wasting might be crucial for reducing mortality risk and promoting healthy longevity.

Deep learning predicts all-cause mortality from longitudinal total-body DXA imaging

Background

Mortality research has identified biomarkers predictive of all-cause mortality risk. Most of these markers, such as body mass index, are predictive cross-sectionally, while for others the longitudinal change has been shown to be predictive, for instance greater-than-average muscle and weight loss in older adults. And while sometimes markers are derived from imaging modalities such as DXA, full scans are rarely used. This study builds on that knowledge and tests two hypotheses to improve all-cause mortality prediction. The first hypothesis is that features derived from raw total-body DXA imaging using deep learning are predictive of all-cause mortality with and without clinical risk factors, meanwhile, the second hypothesis states that sequential total-body DXA scans and recurrent neural network models outperform comparable models using only one observation with and without clinical risk factors.

Methods

Multiple deep neural network architectures were designed to test theses hypotheses. The models were trained and evaluated on data from the 16-year-long Health, Aging, and Body Composition Study including over 15,000 scans from over 3000 older, multi-race male and female adults. This study further used explainable AI techniques to interpret the predictions and evaluate the contribution of different inputs.

Results

The results demonstrate that longitudinal total-body DXA scans are predictive of all-cause mortality and improve performance of traditional mortality prediction models. On a held-out test set, the strongest model achieves an area under the receiver operator characteristic curve of 0.79.

Conclusion

This study demonstrates the efficacy of deep learning for the analysis of DXA medical imaging in a cross-sectional and longitudinal setting. By analyzing the trained deep learning models, this work also sheds light on what constitutes healthy aging in a diverse cohort.

Intermuscular adipose tissue in Type 2 diabetes mellitus: Non-invasive quantitative imaging and clinical implications

Intermuscular adipose tissue (IMAT) is an ectopic fat depot found beneath the fascia and within the muscles. IMAT modulates muscle insulin sensitivity and triggers local and systemic chronic low-grade inflammation by producing cytokines and chemokines, which underlie the pathogenesis of Type 2 diabetes mellitus (T2DM). Imaging techniques have been increasingly used to non-invasively quantify IMAT in patients with diabetes in research and healthcare settings. In this study, we systematically reviewed the cell of origin and definition of IMAT, and the use of quantitative and functional imaging technology pertinent to the etiology, risk factors, lifestyle modification, and therapeutic treatment of diabetes. The purpose of this article is to provide important insight into the current understanding of IMAT and future prospects of targeting IMAT for T2DM control.