OBJECTIVE: We determined associations between adipokines and abnormal body composition in patients with rheumatoid arthritis (RA). METHODS: Combining data from three RA cohorts, whole-body dual-energy absorptiometry measures of appendicular lean mass and fat mass indices were converted to age-, sex-, and race- and ethnicity-specific Z scores. Lean mass relative to fat mass was determined based on prior methods. Independent associations between body composition profiles and circulating levels of adiponectin, leptin, and fibroblast growth factor (FGF)-21 were assessed using linear and logistic regression models adjusting for demographic characteristics and study cohort. We also determined the improvement in the area under the curve (AUC) for prediction of low lean mass when adipokines were added to predictive models that included clinical factors such as demographic characteristics, study, and body mass index (BMI). RESULTS: Among 419 participants, older age was associated with higher levels of all adipokines, whereas higher C-reactive protein level was associated with lower adiponectin levels and higher FGF-21 levels. Greater fat mass was strongly associated with lower adiponectin levels and higher leptin and FGF-21 levels. Higher levels of adiponectin, leptin, and FGF-21 were independently associated with low lean mass. The addition of adiponectin and leptin levels to regression models improved prediction of low lean mass when combined with demographic characteristics, study, and BMI (AUC 0.75 vs. 0.66). CONCLUSION: Adipokines are associated with both excess adiposity and low lean mass in patients with RA. Improvements in the prediction of body composition abnormalities suggest that laboratory screening could help identify patients with altered body composition who may be at greater risk of adverse outcomes.

Objective

We aimed to determine if there were sex differences in lean body mass (LBM) in patients with rheumatoid arthritis (RA) when compared with sex- and race-specific National Health and Nutrition Examination Survey (NHANES) reference data, and to investigate the impact of sex differences in risk factors for LBM deficits.

Methods

Dual x-ray absorptiometry measures of whole body LBM and appendicular LBM (arms and legs, appendicular lean mass [ALM]) were obtained on a total of 190 subjects from 2 independent cohorts (141 from San Francisco [SF], 49 from Philadelphia [PA]), expressed as indices adjusted for height (LBM index and ALM index, kg/m(2) ), and converted to sex- and race-specific Z scores relative to age and based on NHANES data. Sarcopenia was defined using 4 different sex-specific definitions. Multivariable linear and logistic regression analyses adjusted for disease activity, disease duration, physical activity, anti-cyclic citrullinated peptide seropositivity, fat mass index, and glucocorticoid use.

Results

While there were significant differences between the 2 cohorts, ALM index Z scores were significantly lower in men compared to women in both (SF: -1.43 versus -0.43, P < 0.0001; PA: -0.83 versus -0.06, P = 0.03). Observed sex differences were significant after adjustment in multivariable analyses within both cohorts. Odds of sarcopenia were 3 to 8 times greater in men in the SF cohort. Men in the PA cohort also had a higher, but nonsignificant, risk of sarcopenia.

Conclusion

RA is associated with significant LBM deficits, with greater deficits observed in men. Future study may help elucidate the mechanisms driving greater deficits among men.

Objectives

To determine whether a novel measure of appendicular lean mass relative to fat mass is associated with physical functioning in RA.

Methods

In a cross-sectional design, three independent RA cohorts were retrospectively analysed. Whole-body DXA measures of appendicular lean mass index (ALMI, kg/m 2 ) and fat mass index (FMI, kg/m 2 ) were converted to age, sex and race-specific Z-scores using published National Health and Nutrition Examination Survey reference ranges. Adiposity-adjusted ALMI Z-scores (ALMI FMI ) were determined using a published method to adjust for normal associations between ALMI and FMI Z-scores. Associations between ALMI Z-scores, ALMI FMI Z-scores and physical functioning were assessed after adjusting for age, sex and study. Functional outcomes assessed included the HAQ, Valued Life Activities assessment and Short Physical Performance Battery. Low lean for age was defined as a Z-score of -1 or less.

Results

Our sample consisted of 442 patients with RA. The combined cohort had a mean ALMI Z-score of - 0.51 (1.08) and a mean ALMI FMI Z-score of - 0.58 (1.53), suggesting muscle mass deficits compared with a nationally representative sample. Greater ALMI FMI Z-scores demonstrated stronger associations with better functional outcomes compared with ALMI Z-scores. Associations were not attenuated with adjustment for systemic inflammation or pain. The FMI Z-score was independently associated with physical functioning, with a stronger association seen among patients with greater FMI Z-score. Adiposity-adjusted definitions of low lean mass more clearly identified those with functional impairment.

Conclusion

Estimates of appendicular lean mass that are adjusted for adiposity demonstrate stronger positive associations with functional outcomes compared with unadjusted estimates.

Background

To determine if adiponectin levels are associated with weight loss, low muscle mass, and physical functioning among the elderly and to determine independent associations with incident disability and death.

Methods

Included were 3,044 participants from the Health, Aging and Body Composition Study, who had whole-body dual energy absorptiometry performed to evaluate appendicular lean mass index (ALMI, kg/m2) and fat mass index (FMI, kg/m2), computed tomography measures of thigh muscle density, weight histories, estimates of physical functioning, and adiponectin levels at enrollment. Associations between adiponectin levels and body composition, weight loss, and physical functioning were assessed in multivariable linear regression models. Associations between adiponectin and incident disability and mortality were assessed in mediation analyses, adjusting for other factors.

Results

Greater adiponectin at baseline was independently associated with low FMI Z-score, lower waist circumference, low ALMI Z-score, low muscle density, a history of weight loss, and poor physical functioning (all p < .05). Greater adiponectin levels (per SD) were associated with incident disability [HR: 1.14 (1.08, 1.20), p < .001] and greater mortality [HR: 1.17 (1.10, 1.25), p < .001] in models adjusting for demographic factors, adiposity, and comorbid conditions. The association was completely attenuated and no longer significant (all p > 0.05) when adjusting for body composition, muscle density, weight loss, and physical functioning at baseline.

Conclusions

Greater serum adiponectin levels are associated with historical weight loss, low skeletal muscle mass, low muscle density, and poor physical functioning. High adiponectin is associated with a greater risk of incident disability and death, but not independently of these factors.

Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading patterns. We tested an AI-based digital twin of a pathologist, vPatho, on 2603 histological images of prostate tissue stained with hematoxylin and eosin. We analyzed various factors influencing tumor grade discordance between the vPatho system and six human pathologists. Our results demonstrated that vPatho achieved comparable performance in prostate cancer detection and tumor volume estimation, as reported in the literature. The concordance levels between vPatho and human pathologists were examined. Notably, moderate to substantial agreement was observed in identifying complementary histological features such as ductal, cribriform, nerve, blood vessel, and lymphocyte infiltration. However, concordance in tumor grading decreased when applied to prostatectomy specimens (κ = 0.44) compared to biopsy cores (κ = 0.70). Adjusting the decision threshold for the secondary Gleason pattern from 5 to 10% improved the concordance level between pathologists and vPatho for tumor grading on prostatectomy specimens (κ from 0.44 to 0.64). Potential causes of grade discordance included the vertical extent of tumors toward the prostate boundary and the proportions of slides with prostate cancer. Gleason pattern 4 was particularly associated with this population. Notably, the grade according to vPatho was not specific to any of the six pathologists involved in routine clinical grading. In conclusion, our study highlights the potential utility of AI in developing a digital twin for a pathologist. This approach can help uncover limitations in AI adoption and the practical application of the current grading system for prostate cancer pathology.

Coronary artery disease (CAD), the most common manifestation of cardiovascular disease, remains the most common cause of mortality in the United States. Risk assessment is key for primary prevention of coronary events and coronary artery calcium (CAC) scoring using computed tomography (CT) is one such non-invasive tool. Despite the proven clinical value of CAC, the current clinical practice implementation for CAC has limitations such as the lack of insurance coverage for the test, need for capital-intensive CT machines, specialized imaging protocols, and accredited 3D imaging labs for analysis (including personnel and software). Perhaps the greatest gap is the millions of patients who undergo routine chest CT exams and demonstrate coronary artery calcification, but their presence is not often reported or quantitation is not feasible. We present two deep learning models that automate CAC scoring demonstrating advantages in automated scoring for both dedicated gated coronary CT exams and routine non-gated chest CTs performed for other reasons to allow opportunistic screening. First, we trained a gated coronary CT model for CAC scoring that showed near perfect agreement (mean difference in scores = -2.86; Cohen's Kappa = 0.89, P < 0.0001) with current conventional manual scoring on a retrospective dataset of 79 patients and was found to perform the task faster (average time for automated CAC scoring using a graphics processing unit (GPU) was 3.5 ± 2.1 s vs. 261 s for manual scoring) in a prospective trial of 55 patients with little difference in scores compared to three technologists (mean difference in scores = 3.24, 5.12, and 5.48, respectively). Then using CAC scores from paired gated coronary CT as a reference standard, we trained a deep learning model on our internal data and a cohort from the Multi-Ethnic Study of Atherosclerosis (MESA) study (total training n = 341, Stanford test n = 42, MESA test n = 46) to perform CAC scoring on routine non-gated chest CT exams with validation on external datasets (total n = 303) obtained from four geographically disparate health systems. On identifying patients with any CAC (i.e., CAC ≥ 1), sensitivity and PPV was high across all datasets (ranges: 80-100% and 87-100%, respectively). For CAC ≥ 100 on routine non-gated chest CTs, which is the latest recommended threshold to initiate statin therapy, our model showed sensitivities of 71-94% and positive predictive values in the range of 88-100% across all the sites. Adoption of this model could allow more patients to be screened with CAC scoring, potentially allowing opportunistic early preventive interventions.