Atkinson, Elizabeth J.

Abstract Objective Prospective data on determinants of muscle strength impairment and quality of life in patients with various subtypes and severity of endogenous hypercortisolism are lacking. Design Single-center cross-sectional study, 2019 to 2022. Methods Patients with Cushing syndrome (CS) and mild autonomous cortisol secretion (MACS) were assessed with clinical and biochemical severity scores, muscle function (nondominant hand grip strength and sit-to-stand test), and quality of life (Short Form-36 [SF36] and CushingQoL). Referent subjects were recruited from the local population undergoing abdominal imaging for reasons other than suspected adrenal disorder. Results Of 164 patients, 81 (49%) had MACS, 14 (9%) had adrenal CS, 60 (37%) had pituitary CS, and 9 (5%) had ectopic CS. Median age was 53 years (interquartile range: 42-63 years), and 126 (77%) were women. The SF36 mental component score was similarly low in patients with MACS vs CS, but physical component score was lower in CS when compared to MACS (mean of 34.0 vs 40.5, P = .001). Compared to MACS, patients with CS had lower scores on the standardized CushingQoL (mean of 47.1 vs 34.2, P < .001). Compared to referent subjects, patients with MACS demonstrated reduced muscle strength, similar to patients with CS (mean sit to stand Z-score of −0.47 vs −0.54, P = .822). Clinical severity (r = −0.22, P = .004) but not biochemical severity was associated with sit-to-stand test performance. Conclusions Both patients with overt CS and MACS demonstrate reduced muscle strength and low quality of life. The clinical severity score utilized is associated with both physical and psychosocial components of CushingQoL and with the physical component of SF36.

Abstract New strategies to preserve cognitive function could broadly benefit the aging population. Cellular senescence is a hypothesized mediator of inflammation-related tissue dysfunction in aging. Using single-cell RNA-sequencing, we discovered an age-related brain myeloid population exhibiting overlapping senescent and disease-associated activation signatures, including upregulation of chemoattractant factors. We confirmed senescent brain myeloid cells promote peripheral immune cell chemotaxis in vitro. Through mass cytometry, we demonstrate age- and sex-dependent increases in activated resident and infiltrating brain immune cells are reduced through systemic targeting of p16-positive senescent cells, which is associated with improvements in executive function and spatial learning tasks. Our high-dimensional results reveal dynamic remodeling of the age-dependent brain immune cell landscape and implicate senescent cell targeting as a strategy to counter inflammatory changes and cognitive decline.

Abstract Background: Primary sclerosing cholangitis (PSC) patients have a risk of developing cholangiocarcinoma (CCA). Establishing predictive models for CCA in PSC is important. Methods: In a large cohort of 1,459 PSC patients seen at Mayo Clinic (1993-2020), we quantified the impact of clinical/laboratory variables on CCA development using univariate and multivariate Cox models and predicted CCA using statistical and artificial intelligence approaches. We explored plasma bile acid (BA) levels’ predictive power of CCA (subset of 300 patients, BA cohort). Results: Eight significant risk factors (false discovery rate: 20%) were identified with univariate analysis; prolonged inflammatory bowel disease (IBD) was the most important one. IBD duration, PSC duration, and total bilirubin remained significant (p<0.05) with multivariate analysis. Clinical/laboratory variables predicted CCA with cross-validated C-indexes of 0.68–0.71 at different time points of disease, significantly better compared to commonly used PSC risk scores. Lower chenodeoxycholic acid, higher conjugated fraction of lithocholic acid and hyodeoxycholic acid, and higher ratio of cholic acid to chenodeoxycholic acid were predictive of CCA. BAs predicted CCA with a cross-validated C-index of 0.66 (std: 0.11, BA cohort), similar to clinical/laboratory variables (C-index=0.64, std: 0.11, BA cohort). Combining BAs with clinical/laboratory variables leads to the best average C-index of 0.67 (std: 0.13, BA cohort). Conclusions: In a large PSC cohort, we identified clinical and laboratory risk factors for CCA development and demonstrated the first artificial intelligence based predictive models that performed significantly better than common risk scores. More predictive data modalities are needed for clinical adoption of these models.

Objective To identify clusters of comorbidities in patients with rheumatoid arthritis (RA) using 4 methods and to compare to patients without RA. Methods In this retrospective, population‐based study, residents of 8 Minnesota counties with prevalent RA as of January 1, 2015 were identified. Age‐, sex‐, and county‐matched non‐RA comparators were selected from the same underlying population. Diagnostic codes were retrieved for 5 years before January 1, 2015. Using 2 codes ≥30 days apart, 44 previously defined morbidities and 11 nonoverlapping chronic disease categories based on Clinical Classifications Software were defined. Unsupervised machine learning methods of interest included hierarchical clustering, factor analysis, K‐means clustering, and network analysis. Results Two groups of 1,643 patients with and without RA (72% female; mean age 63.1 years in both groups) were studied. Clustering of comorbidities revealed strong associations among mental/behavioral comorbidities and among cardiovascular risk factors and diseases. The clusters were associated with age and sex. Differences between the 4 clustering methods were driven by comorbidities that are rare and those that were weakly associated with other comorbidities. Common comorbidities tended to group together consistently across approaches. The instability of clusters when using different random seeds or bootstrap sampling impugns the usefulness and reliability of these methods. Clusters of common comorbidities between RA and non‐RA cohorts were similar. Conclusion Despite the higher comorbidity burden in patients with RA compared to the general population, clustering comorbidities did not identify substantial differences in comorbidity patterns between the RA and non‐RA cohorts. The instability of clustering methods suggests caution when interpreting clustering using 1 method.

Objective To comprehensively assess multimorbidity burden in patients with rheumatoid arthritis (RA) in order to unify the multimorbidity definition for RA research and clinical practice. Methods In this population-based study, residents of eight Minnesota counties with prevalent RA on 1 January 2015 were identified. Age, sex and county-matched non-RA comparators were selected from the same population. Diagnostic codes were retrieved for 5 years before 1 January 2015. Using two codes ≥30 days apart, 44 previously defined morbidities and 78 non-overlapping chronic disease categories based on Clinical Classification Software were defined. Prevalence of each morbidity in the RA versus non-RA cohorts was compared using false discovery rate to adjust for multiple comparisons. Morbidities more common in RA than non-RA and those with prevalence ≥5% were retained. Results 1643 patients with RA and 1643 non-RA subjects (72% women; mean age 63.1 years) were studied. Using the 44 morbidities, multimorbidity (defined as 2+ morbidities) was present in 1411 (86%) of RA and 1164 (71%) of non-RA subjects (p<0.001) with 5+ morbidities present in 907 (55%) of RA and 619 (38%) of non-RA (p<0.001). Patients with RA had significantly higher prevalence of 24 of the 44 morbidities compared with non-RA, especially interstitial lung disease, fibromyalgia, osteoarthritis and osteoporosis. Among the additional 78 categories, 7 were significantly higher in RA than non-RA, including organic sleep disorders, vitamin D deficiency and foot ulcers. Conclusion Patients with RA have a higher prevalence of multimorbidity compared with non-RA subjects. These results confirm the list of 44 morbidities and add several other morbidities of interest in RA.

Abstract Although cellular senescence is increasingly recognized as driving multiple age-related co-morbidities through the senescence-associated secretory phenotype (SASP), in vivo senescent cell identification, particularly in bulk or single cell RNA-sequencing (scRNA-seq) data remains challenging. Here, we generated a novel gene set (SenMayo) and first validated its enrichment in bone biopsies from two aged human cohorts. SenMayo also identified senescent cells in aged murine brain tissue, demonstrating applicability across tissues and species. For direct validation, we demonstrated significant reductions in SenMayo in bone following genetic clearance of senescent cells in mice, with similar findings in adipose tissue from humans in a pilot study of pharmacological senescent cell clearance. In direct comparisons, SenMayo outperformed all six existing senescence/SASP gene sets in identifying senescent cells across tissues and in demonstrating responses to senescent cell clearance. We next used SenMayo to identify senescent hematopoietic or mesenchymal cells at the single cell level from publicly available human and murine bone marrow/bone scRNA-seq data and identified monocytic and osteolineage cells, respectively, as showing the highest levels of senescence/SASP genes. Using pseudotime and cellular communication patterns, we found senescent hematopoietic and mesenchymal cells communicated with other cells through common pathways, including the Macrophage Migration Inhibitory Factor (MIF) pathway, which has been implicated not only in inflammation but also in immune evasion, an important property of senescent cells. Thus, SenMayo identifies senescent cells across tissues and species with high fidelity. Moreover, using this senescence panel, we were able to characterize senescent cells at the single cell level and identify key intercellular signaling pathways associated with these cells, which may be particularly useful for evolving efforts to map senescent cells (e.g., SenNet). In addition, SenMayo represents a potentially clinically applicable panel for monitoring senescent cell burden with aging and other conditions as well as in studies of senolytic drugs.

Abstract The burden of senescent cells (SnCs), which do not divide but are metabolically active and resistant to death by apoptosis, is increased in older adults and those with chronic diseases. These individuals are also at the greatest risk for morbidity and mortality from SARS‐CoV‐2 infection. SARS‐CoV‐2 complications include cytokine storm and multiorgan failure mediated by the same factors as often produced by SnCs through their senescence‐associated secretory phenotype (SASP). The SASP can be amplified by infection‐related pathogen‐associated molecular profile factors. Senolytic agents, such as Fisetin, selectively eliminate SnCs and delay, prevent, or alleviate multiple disorders in aged experimental animals and animal models of human chronic diseases, including obesity, diabetes, and respiratory diseases. Senolytics are now in clinical trials for multiple conditions linked to SnCs, including frailty; obesity/diabetes; osteoporosis; and cardiovascular, kidney, and lung diseases, which are also risk factors for SARS‐CoV‐2 morbidity and mortality. A clinical trial is underway to test if senolytics decrease SARS‐CoV‐2 progression and morbidity in hospitalized older adults. We describe here a National Institutes of Health‐funded, multicenter, placebo‐controlled clinical trial of Fisetin for older adult skilled nursing facility (SNF) residents who have been, or become, SARS‐CoV‐2 rtPCR‐positive, including the rationale for targeting fundamental aging mechanisms in such patients. We consider logistic challenges of conducting trials in long‐term care settings in the SARS‐CoV‐2 era, including restricted access, consent procedures, methods for obtaining biospecimens and clinical data, staffing, investigational product administration issues, and potential solutions for these challenges. We propose developing a national network of SNFs engaged in interventional clinical trials.