Comorbidity clusters in patients with rheumatoid arthritis identify a patient phenotype with a favourable prognosis.

OBJECTIVES: We aimed to cluster patients with rheumatoid arthritis (RA) based on comorbidities and then examine the association between these clusters and RA disease activity and mortality. METHODS: In this population-based study, residents of an eight-county region with prevalent RA on 1 January 2015 were identified. Patients were followed for vital status until death, last contact or 31 December 2021. Diagnostic codes for 5 years before the prevalence date were used to define 55 comorbidities. Latent class analysis was used to cluster patients based on comorbidity patterns. Standardised mortality ratios were used to assess mortality. RESULTS: A total of 1643 patients with prevalent RA (72% female; 94% white; median age 64 years, median RA duration 7 years) were studied. Four clusters were identified. Cluster 1 (n=686) included patients with few comorbidities, and cluster 4 (n=134) included older patients with 10 or more comorbidities. Cluster 2 (n=200) included patients with five or more comorbidities and high prevalences of depression and obesity, while cluster 3 (n=623) included the remainder. RA disease activity and survival differed across the clusters, with cluster 1 demonstrating more remission and mortality comparable to the general population. CONCLUSIONS: More than 40% of patients with prevalent RA did not experience worse mortality than their peers without RA. The cluster with the worst prognosis (<10% of patients with prevalent RA) was older, had more comorbidities and had less disease-modifying antirheumatic drug and biological use compared with the other clusters. Comorbidity patterns may hold the key to moving beyond a one-size-fits-all perspective of RA prognosis.

Biomarkers of Cellular Senescence Predict the Onset of Mobility Disability and Are Reduced by Physical Activity in Older Adults.

Studies in mice and cross-sectional studies in humans support the premise that cellular senescence is a contributing mechanism to age-associated deficits in physical function. We tested the hypotheses that circulating proteins secreted by senescent cells are (i) associated with the incidence of major mobility disability (MMD), the development of persistent mobility disability (PMMD), and decrements in physical functioning in older adults, and (ii) influenced by physical activity (PA). Using samples and data obtained longitudinally from the Lifestyle Interventions in Elders Study clinical trial, we measured a panel of 27 proteins secreted by senescent cells. Among 1 377 women and men randomized to either a structured PA intervention or a healthy aging (HA) intervention, we observed significant associations between several senescence biomarkers, most distinctly vascular endothelial growth factor A (VEGFA), tumor necrosis factor receptor 1 (TNFR1), and matrix metallopeptidase 7 (MMP7), and the onset of both MMD and PMMD. Moreover, VEGFA, GDF15, osteopontin, and other senescence biomarkers were associated with reductions in short physical performance battery scores. The change in senescence biomarkers did not differ between PA and HA participants. In the whole cohort, higher levels of PA were associated with significantly greater reductions in 10 senescence-related proteins at 12 and/or 24 months. These data reinforce cellular senescence as a contributing mechanism of age-associated functional decline and the potential for PA to attenuate this hallmark of aging. Clinical Trials Registration Number: NCT01072500.

Calorie restriction reduces biomarkers of cellular senescence in humans.

Calorie restriction (CR) with adequate nutrient intake is a potential geroprotective intervention. To advance this concept in humans, we tested the hypothesis that moderate CR in healthy young-to-middle-aged individuals would reduce circulating biomarkers of cellular senescence, a fundamental mechanism of aging and aging-related conditions. Using plasma specimens from the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE™) phase 2 study, we found that CR significantly reduced the concentrations of several senescence biomarkers at 12 and 24 months compared to an ad libitum diet. Using machine learning, changes in biomarker concentrations emerged as important predictors of the change in HOMA-IR and insulin sensitivity index at 12 and 24 months, and the change in resting metabolic rate residual at 12 months. Finally, using adipose tissue RNA-sequencing data from a subset of participants, we observed a significant reduction in a senescence-focused gene set in response to CR at both 12 and 24 months compared to baseline. Our results advance the understanding of the effects of CR in humans and further support a link between cellular senescence and metabolic health.

Kidney function in patients with adrenal adenomas: A single-center retrospective cohort study.

OBJECTIVE: Patients with nonfunctioning adrenal adenomas (NFA) and mild autonomous cortisol secretion (MACS) demonstrate an increased risk of chronic kidney disease (CKD), however factors associated with CKD are unknown. We aimed to identify the factors associated with CKD and assess the impact of adrenalectomy on kidney function in patients with NFA or MACS. DESIGN: Single-center cohort study of patients with NFA and MACS, 1999-2020. METHODS: MACS was diagnosed based on post-dexamethasone cortisol (DST) ≥ 1.8 mcg/dL. Age, sex, dysglycemia, hypertension, therapy with statin, angiotensin converting enzyme inhibitor, or angiotensin II receptor blocker were included in the multivariable analysis. Outcomes included estimated glomerular filtration rate (eGFR) at the time of diagnosis with MACS or NFA and post-adrenalectomy delta eGFR. RESULTS: Of 972 patients, 429 (44%) had MACS and 543 (56%) had NFA. At the time of diagnosis, patients with MACS had lower eGFR (median 79.6 vs 83.8 ml/min/1.73m2, p < 0.001) than patients with NFA. In a multivariable analysis, factors associated with lower eGFR were older age, hypertension, and higher DST. In 204 patients (MACS: 155, 76% and NFA: 49, 24%) treated with adrenalectomy, post adrenalectomy eGFR improved in both groups starting at 18 months up to 3.5 years of follow up. Factors associated with increased eGFR were younger age, lower pre-adrenalectomy eGFR and longer follow-up period. CONCLUSION: DST cortisol is an independent risk factor for lower eGFR in patients with adrenal adenomas. Both patients with MACS and NFA demonstrate an increase in eGFR post-adrenalectomy, especially younger patients with lower eGFR pre-adrenalectomy.

Biomarkers of cellular senescence and risk of death in humans.

A robust and heterogenous secretory phenotype is a core feature of most senescent cells. In addition to mediators of age-related pathology, components of the senescence associated secretory phenotype (SASP) have been studied as biomarkers of senescent cell burden and, in turn, biological age. Therefore, we hypothesized that circulating concentrations of candidate senescence biomarkers, including chemokines, cytokines, matrix remodeling proteins, and growth factors, could predict mortality in older adults. We assessed associations between plasma levels of 28 SASP proteins and risk of mortality over a median follow-up of 6.3 years in 1923 patients 65 years of age or older with zero or one chronic condition at baseline. Overall, the five senescence biomarkers most strongly associated with an increased risk of death were GDF15, RAGE, VEGFA, PARC, and MMP2, after adjusting for age, sex, race, and the presence of one chronic condition. The combination of biomarkers and clinical and demographic covariates exhibited a significantly higher c-statistic for risk of death (0.79, 95% confidence interval (CI): 0.76-0.82) than the covariates alone (0.70, CI: 0.67-0.74) (p < 0.001). Collectively, these findings lend further support to biomarkers of cellular senescence as informative predictors of clinically important health outcomes in older adults, including death.

Risk of dementia and psychiatric or sleep disorders after diagnosis of adrenal adenomas: a population-based cohort study.

OBJECTIVE: Adrenal adenomas are commonly encountered in clinical practice. To date, population-based data on their impact on cognition, mental health, and sleep are lacking. We aimed to study possible associations between adrenal adenomas and dementia, psychiatric or sleep disorders. DESIGN: Population-based cohort study, Olmsted County, MN, 1995-2017. METHODS: Patients with adrenal adenoma and absent overt hormone excess were age- and sex-matched 1:1 to a referent person without adrenal adenoma. Outcomes were baseline and incident diagnoses of dementia, psychiatric or sleep disorders, assessed using ICD codes. RESULTS: Of 1004 patients with adrenal adenomas, 582 (58%) were women, and median age at diagnosis was 63 years. At baseline, and after adjusting for age, sex, education, BMI, and tobacco use, patients with adenoma had higher odds of depression (adjusted odds ratio, aOR: 1.3, 95% CI, 1.1-1.6), anxiety (aOR: 1.4, 95% CI, 1.1-1.8), and substance abuse (aOR: 2.4, 95% CI, 1.7-3.4) compared to referents. During a median follow-up of 6.8 years, and after adjusting for age, sex, socioeconomic status, BMI, tobacco, and substance abuse, patients demonstrated a higher risk of psychiatric and sleep disorders [adjusted hazard ratio (95% CI)]: depression [1.7 (1.3-2.2)], anxiety [1.4, CI (1.1-1.7)], insomnia [1.4 (1.0-1.9)], sleep-related breathing disorders [1.5 (1.1-1.9)], hypersomnias [2.1 (1.0-4.2)], parasomnias [2.1 (1.0-4.2)], and sleep-related movement disorders [1.5 (1.0-2.1)], but not dementia. CONCLUSIONS: Patients with adenomas demonstrate a higher incidence of psychiatric and sleep disorders, possibly due to the underlying subtle increase in cortisol secretion.

High prevalence of frailty in patients with adrenal adenomas and adrenocortical hormone excess: a cross-sectional multi-centre study with prospective enrolment.

OBJECTIVE: Frailty, characterized by multi-system decline, increases vulnerability to adverse health outcomes and can be measured using Frailty Index (FI). We aimed to assess the prevalence of frailty in patients with adrenal disorders (based on hormonal sub-type) and examine association between FI and performance-based measures of physical function. DESIGN: Multi-centre, cross-sectional study (March 2019-August 2022). METHODS: Adult patients with adrenal disorders (non-functioning adrenal adenomas [NFA], mild autonomous cortisol secretion [MACS], Cushing syndrome [CS], primary aldosteronism [PA]) and referent subjects without adrenal disorders completed a questionnaire encompassing 47 health variables (comorbidities, symptoms, daily living activities). FI was calculated as the average score of all variables and frailty defined as FI ≥ 0.25. Physical function was assessed with hand grip, timed up-and-go test, chair rising test, 6-minute walk test, and gait speed. RESULTS: Compared to referent subjects (n = 89), patients with adrenal disorders (n = 520) showed increased age, sex, and body mass index-adjusted prevalence of frailty (CS [odds ratio-OR 19.2, 95% confidence interval-CI 6.7-70], MACS [OR 12.5, 95% CI 4.8-42.9], PA [OR 8.4, 95% CI 2.9-30.4], NFA [OR 4.5, 95% CI 1.7-15.9]). Prevalence of frailty was similar to referent subjects when post-dexamethasone cortisol was <28 nmol/L and was higher when post-dexamethasone cortisol was 28-50 nmol/L (OR 4.6, 95% CI 1.7-16.5). FI correlated with all measures of physical function (P < .001). CONCLUSIONS: Whilst frailty prevalence was highest in patients with adrenocortical hormone excess, even patients with NFA demonstrated an increased prevalence compared to the referent population. Future longitudinal studies are needed to evaluate the impact of various management strategies on frailty.

Validating the Fracture Risk Assessment Tool Score in a US Population-Based Study of Patients With Rheumatoid Arthritis.

OBJECTIVE: The World Health Organization fracture risk assessment tool (FRAX) algorithm for risk prediction of major osteoporotic and hip fractures accounts for several risk factors, including rheumatoid arthritis (RA), since individuals with RA have an excess burden of fractures. FRAX has not been validated in population-based RA cohorts in the US. We aimed to determine the accuracy of FRAX predictions for individuals with RA in the US. METHODS: This retrospective population-based cohort study included residents of Olmsted County, Minnesota, who were followed until death, migration, or last medical record review. Each patient with RA (1987 American College of Rheumatology criteria met in 1980-2007, age 40-89 years) was matched 1:1 on age and sex to an individual without RA from the same underlying population. Ten-year predictions for major osteoporotic and hip fractures were estimated using the FRAX tool. Fractures were ascertained through follow-up, truncated at 10 years. Standardized incidence ratios (SIRs) and 95% CI were calculated to compare observed and predicted fractures. RESULTS: The study included 662 patients with RA and 658 non-RA comparators (66.8% vs 66.9% female and a mean age of 60.6 vs 60.5 years, respectively). Among patients with RA, 76 major osteoporotic fractures and 21 hip fractures were observed during follow-up (median follow-up: 9.0 years) compared to 67.0 predicted major osteoporotic fractures (SIR 1.13, 95% CI 0.91-1.42) and 23.3 predicted hip fractures (SIR 0.90, 95% CI 0.59-1.38). The observed and predicted major osteoporotic and hip fracture risks were similar for patients with RA and non-RA comparators. CONCLUSION: The FRAX tool is an accurate method for estimating major osteoporotic and hip fracture risk in patients with RA.

Glucocorticoid withdrawal syndrome following surgical remission of endogenous hypercortisolism: a longitudinal observational study.

OBJECTIVE: Glucocorticoid withdrawal syndrome (GWS) is a scarcely studied phenomenon that complicates the recovery following surgical remission of hypercortisolism. We aimed to characterize the presence and trajectory of glucocorticoid withdrawal symptoms in the postoperative period and to determine presurgical predictors of GWS severity. DESIGN: Longitudinal observational study. METHODS: Glucocorticoid withdrawal symptoms were prospectively evaluated weekly for the first 12 weeks following surgical remission of hypercortisolism. Quality of life (CushingQoL and Short-Form-36) and muscle function (hand grip strength and sit-to-stand test) were assessed at the baseline and at 12 weeks after surgery. RESULTS: Prevalent symptoms were myalgias and arthralgias (50%), fatigue (45%), weakness (34%), sleep disturbance (29%), and mood changes (19%). Most symptoms persisted, while myalgias, arthralgias, and weakness worsened during weeks 5-12 postoperatively. At 12 weeks after surgery, normative hand grip strength was weaker than at baseline (mean Z-score delta -0.37, P = .009), while normative sit-to-stand test performance improved (mean Z-score delta 0.50, P = .013). Short-Form-36 Physical Component Summary score worsened (mean delta -2.6, P = .015), but CushingQoL score improved (mean delta 7.8, P < .001) at 12 weeks compared to baseline. Cushing syndrome (CS) clinical severity was predictive of postoperative GWS symptomology. CONCLUSION: Glucocorticoid withdrawal symptoms are prevalent and persistent following surgical remission of hypercortisolism with baseline CS clinical severity predictive of postoperative GWS symptom burden. Differential changes observed in muscle function and quality of life in the early postoperative period may reflect the competing influences of GWS and recovery from hypercortisolism.

Determinants of muscle function and health-related quality of life in patients with endogenous hypercortisolism: a cross-sectional study.

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.

ST-V-Net: incorporating shape prior into convolutional neural networks for proximal femur segmentation.

We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the entire cohort and then for male and female subjects separately, 90% of the subjects were used in ten-fold stratified cross-validation for training and the rest of the subjects were used to evaluate the performance of models. In the entire cohort, the proposed model achieved a Dice similarity coefficient (DSC) of 0.9888, a sensitivity of 0.9966 and a specificity of 0.9988. Compared with V-Net, the Hausdorff distance was reduced from 9.144 to 5.917 mm, and the average surface distance was reduced from 0.012 to 0.009 mm using the proposed ST-V-Net. Quantitative evaluation demonstrated excellent performance of the proposed ST-V-Net for automatic proximal femur segmentation in QCT images. In addition, the proposed ST-V-Net sheds light on incorporating shape prior to segmentation to further improve the model performance.

Smoking, early menopause and multiple sclerosis disease course.

Smoking is associated with an increased risk of multiple sclerosis (MS), and smoking and early menopause are related to poor outcomes in MS. Smoking is also associated with early menopause. To explore this intricate relationship between smoking status, age at menopause and disease course in MS, 137 women with MS and 396 age-matched controls were included in this case-control study. Age at menopause (median 49.0 vs. 50.0 years; p = 0.79) and smoking status (40.3% vs. 47.6%; p = 0.15) were similar among MS and control women. Relapsing MS onset was earlier in ever-smoker women with early menopause compared to the rest of the women (median 30.4 vs. 37.0 years; p = 0.02) and also compared to ever-smoker women with normal age at menopause (median 30.4 vs. 41.0 years; p = 0.008) and never-smoker women with early menopause (median 30.4 vs. 41.5 years; p = 0.004). Progressive MS onset was also earlier in ever-smoker women with early menopause compared to ever-smoker women with normal age at menopause (median 41.1 vs. 49.4 years; p = 0.05) and never-smoker women with early menopause (median 41.1 vs. 50.1 years; p = 0.12). Our results suggest that smoking and menopause associate with MS disease course, including the onset of relapsing and progressive MS in women.

In vitro and in vivo effects of zoledronic acid on senescence and senescence-associated secretory phenotype markers.

In addition to reducing fracture risk, zoledronic acid has been found in some studies to decrease mortality in humans and extend lifespan and healthspan in animals. Because senescent cells accumulate with aging and contribute to multiple co-morbidities, the non-skeletal actions of zoledronic acid could be due to senolytic (killing of senescent cells) or senomorphic (inhibition of the secretion of the senescence-associated secretory phenotype [SASP]) actions. To test this, we first performed in vitro senescence assays using human lung fibroblasts and DNA repair-deficient mouse embryonic fibroblasts, which demonstrated that zoledronic acid killed senescent cells with minimal effects on non-senescent cells. Next, in aged mice treated with zoledronic acid or vehicle for 8 weeks, zoledronic acid significantly reduced circulating SASP factors, including CCL7, IL-1ß, TNFRSF1A, and TGFß1 and improved grip strength. Analysis of publicly available RNAseq data from CD115+ (CSF1R/c-fms+) pre-osteoclastic cells isolated from mice treated with zoledronic acid demonstrated a significant downregulation of senescence/SASP genes (SenMayo). To establish that these cells are potential senolytic/senomorphic targets of zoledronic acid, we used single cell proteomic analysis (cytometry by time of flight [CyTOF]) and demonstrated that zoledronic acid significantly reduced the number of pre-osteoclastic (CD115+/CD3e-/Ly6G-/CD45R-) cells and decreased protein levels of p16, p21, and SASP markers in these cells without affecting other immune cell populations. Collectively, our findings demonstrate that zoledronic acid has senolytic effects in vitro and modulates senescence/SASP biomarkers in vivo. These data point to the need for additional studies testing zoledronic acid and/or other bisphosphonate derivatives for senotherapeutic efficacy.

Predicting cholangiocarcinoma in primary sclerosing cholangitis: using artificial intelligence, clinical and laboratory data.

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 (AI) 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 AI based predictive models that performed significantly better than commonly used PSC risk scores. More predictive data modalities are needed for clinical adoption of these models.

Biomarkers of cellular senescence in idiopathic pulmonary fibrosis.

BACKGROUND: Cellular senescence is a cell fate in response to diverse forms of age-related damage and stress that has been implicated in the pathogenesis of idiopathic pulmonary fibrosis (IPF). The associations between circulating levels of candidate senescence biomarkers and disease outcomes have not been specifically studied in IPF. In this study we assessed the circulating levels of candidate senescence biomarkers in individuals affected by IPF and controls and evaluated their ability to predict disease outcomes. METHODS: We measured the plasma concentrations of 32 proteins associated with senescence in Lung Tissue Research Consortium participants and studied their relationship with the diagnosis of IPF, parameters of pulmonary and physical function, health-related quality of life, mortality, and lung tissue expression of P16, a prototypical marker of cellular senescence. A machine learning approach was used to evaluate the ability of combinatorial biomarker signatures to predict disease outcomes. RESULTS: The circulating levels of several senescence biomarkers were significantly elevated in persons affected by IPF compared to controls. A subset of biomarkers accurately classified participants as having or not having the disease and was significantly correlated with measures of pulmonary function, health-related quality of life and, to an extent, physical function. An exploratory analysis revealed senescence biomarkers were also associated with mortality in IPF participants. Finally, the plasma concentrations of several biomarkers were associated with their expression levels in lung tissue as well as the expression of P16. CONCLUSIONS: Our results suggest that circulating levels of candidate senescence biomarkers are informative of disease status, pulmonary and physical function, and health-related quality of life. Additional studies are needed to validate the combinatorial biomarkers signatures that emerged using a machine learning approach.

In vitro and in vivo effects of zoledronate on senescence and senescence-associated secretory phenotype markers.

In addition to reducing fracture risk, zoledronate has been found in some studies to decrease mortality in humans and extend lifespan and healthspan in animals. Because senescent cells accumulate with aging and contribute to multiple co-morbidities, the non-skeletal actions of zoledronate could be due to senolytic (killing of senescent cells) or senomorphic (inhibition of the secretion of the senescence-associated secretory phenotype [SASP]) actions. To test this, we first performed in vitro senescence assays using human lung fibroblasts and DNA repair-deficient mouse embryonic fibroblasts, which demonstrated that zoledronate killed senescent cells with minimal effects on non-senescent cells. Next, in aged mice treated with zoledronate or vehicle for 8 weeks, zoledronate significantly reduced circulating SASP factors, including CCL7, IL-1ß, TNFRSF1A, and TGFß1 and improved grip strength. Analysis of publicly available RNAseq data from CD115+ (CSF1R/c-fms+) pre-osteoclastic cells isolated from mice treated with zoledronate demonstrated a significant downregulation of senescence/SASP genes (SenMayo). To establish that these cells are potential senolytic/senomorphic targets of zoledronate, we used single cell proteomic analysis (cytometry by time of flight [CyTOF]) and demonstrated that zoledronate significantly reduced the number of pre-osteoclastic (CD115+/CD3e-/Ly6G-/CD45R-) cells and decreased protein levels of p16, p21, and SASP markers in these cells without affecting other immune cell populations. Collectively, our findings demonstrate that zoledronate has senolytic effects in vitro and modulates senescence/SASP biomarkers in vivo . These data point to the need for additional studies testing zoledronate and/or other bisphosphonate derivatives for senotherapeutic efficacy.

Using Unsupervised Machine Learning Methods to Cluster Comorbidities in a Population-Based Cohort of Patients With Rheumatoid Arthritis.

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.

Associations between biomarkers of cellular senescence and physical function in humans: observations from the lifestyle interventions for elders (LIFE) study.

Cellular senescence is a plausible mediator of age-associated declines in physical performance. To test this premise, we examined cross-sectional associations between circulating components of the senescence-associated secretory phenotype (SASP) and measures of physical function and muscle strength in 1377 older adults. We showed significant associations between multiple SASP proteins and the short physical performance battery (SPPB), its subcomponents (gait speed, balance, chair rise time), and 400-m walk time. Activin A, ICAM1, MMP7, VEGFA, and eotaxin showed strong associations based on gradient boost machine learning (GBM), and, when combined with other proteins, effectively identified participants at the greatest risk for mobility disability (SPPB score [Formula: see text] 7). Senescence biomarkers were also associated with lower grip strength, and GBM identified PARC, ADAMTS13, and RANTES as top candidates in females, and MMP2, SOST, and MCP1 in males. These findings highlight an association between senescence biomarkers and physical performance in older adults. ClinicalTrials.gov Identifier: NCT01072500.

Rejuvenation of the aged brain immune cell landscape in mice through p16-positive senescent cell clearance.

Cellular senescence is a plausible mediator of inflammation-related tissue dysfunction. In the aged brain, senescent cell identities and the mechanisms by which they exert adverse influence are unclear. Here we used high-dimensional molecular profiling, coupled with mechanistic experiments, to study the properties of senescent cells in the aged mouse brain. We show that senescence and inflammatory expression profiles increase with age and are brain region- and sex-specific. p16-positive myeloid cells exhibiting senescent and disease-associated activation signatures, including upregulation of chemoattractant factors, accumulate in the aged mouse brain. Senescent brain myeloid cells promote peripheral immune cell chemotaxis in vitro. Activated resident and infiltrating immune cells increase in the aged brain and are partially restored to youthful levels through p16-positive senescent cell clearance in female p16-InkAttac mice, which is associated with preservation of cognitive function. Our study reveals dynamic remodeling of the brain immune cell landscape in aging and suggests senescent cell targeting as a strategy to counter inflammatory changes and cognitive decline.