Faculty Bibliography

RATIONALE & OBJECTIVE/OBJECTIVE:Exposure to extreme heat events has been linked to increased morbidity and mortality in the general population. Patients receiving maintenance dialysis may be vulnerable to greater risks from these events, but this is not well understood. We sought to characterize the association of extreme heat events and the risk of death among patients receiving dialysis in the United States. STUDY DESIGN/METHODS:Retrospective cohort study. SETTING & PARTICIPANTS/METHODS:Data from the United States Renal Data System were used to identify adults living in US urban settlements prone to extreme heat who initiated maintenance dialysis between 1997 and 2016. EXPOSURE/METHODS:An extreme heat event was defined as a time-updated heat index (a humid-heat metric) exceeding 40.6°C for ≥2 days or 46.1°C for ≥1 day. OUTCOME/RESULTS:Death. ANALYTICAL APPROACH/METHODS:Cox proportional hazards regression to estimate the elevation in risk of death during a humid-heat event adjusted for age, sex, year of dialysis initiation, dialysis modality, poverty level, and climate region. Interactions between humid-heat and these same factors were explored. RESULTS:Among 945,251 adults in 245 urban settlements, the mean age was 63 years and 44% were female. During a median follow-up of 3.6 years, 498,049 adults were exposed to at least one of 7,154 extreme humid-heat events, and 500,025 deaths occurred. In adjusted models, there was an increased risk of death (hazard ratio 1.18; 95% confidence interval 1.15-1.20) during extreme humid-heat exposure. Relative mortality risk was higher among patients living in the Southeast (P<0.001) compared with the Southwest. LIMITATIONS/CONCLUSIONS:Possibility of exposure misclassification, did not account for land use and air pollution co-exposures. CONCLUSIONS:This study suggests that patients receiving dialysis face an increased risk of death during extreme humid-heat exposure.

Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles. Tiles are grouped into morphologically similar clusters which constitute an atlas of histomorphological phenotypes (HP-Atlas), revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. These properties are maintained in a multi-cancer study.

Physical frailty is a syndrome that typically manifests in later life, although the pathogenic process causing physical frailty likely begins decades earlier. To date, few studies have examined the biological signatures in mid-life associated with physical frailty later in life. Among 4,189 middle-aged participants (57.8 ± 5.0 years, 55.8% women) from the Atherosclerosis Risk in Community (ARIC) study, we evaluated the associations of 4,955 plasma proteins (log 2-transformed and standardized) measured using the SomaScan platform with their frailty status approximately 20 years later. Using multinomial logistic regression models adjusting for demographics, health behaviors, kidney function, total cholesterol, and comorbidities, 12 and 221 proteins were associated with prefrailty and frailty in later life, respectively (FDR p < 0.05). Top frailty-associated proteins included neurocan core protein (NCAN, OR = 0.66), fatty acid-binding protein heart (FABP3, OR = 1.62) and adipocyte (FABP4, OR = 1.65), as well proteins involved in the contactin-1 (CNTN1), toll-like receptor 5 (TLR5), and neurogenic locus notch homolog protein 1 (NOTCH1) signaling pathway relevant to skeletal muscle regeneration, myelination, and inflammation. Pathway analyses suggest midlife dysregulation of inflammation, metabolism, extracellular matrix, angiogenesis, and lysosomal autophagy among those at risk for late-life frailty. After further adjusting for midlife body mass index (BMI) - an established frailty risk factor - only CNTN1 (OR = 0.75) remained significantly associated with frailty. Post-hoc analyses demonstrated that the top 41 midlife frailty-associated proteins mediate 32% of the association between mid-life BMI and late-life frailty. Our findings provide new insights into frailty etiology earlier in the life course, enhancing the potential for prevention.

BACKGROUND:Metabolites represent a read-out of cellular processes underlying states of health and disease. METHODS:We evaluated cross-sectional and longitudinal associations between 1255 serum and 1398 urine known and unknown (denoted with "X" in name) metabolites (Metabolon HD4, 721 detected in both biofluids) and kidney function in 1612 participants of the Atherosclerosis Risk in Communities (ARIC) Study. All analyses were adjusted for clinical and demographic covariates, including for baseline eGFR and UACR in longitudinal analyses. RESULTS:At visit 5 of the ARIC study, the mean age of participants was 76 years (SD 6), 56% were women, mean eGFR was 62 ml/min/1.73m2 (SD 20), and median urine albumin-to-creatinine level (UACR) was 13 mg/g (IQR 25). In cross-sectional analysis, 675 serum and 542 urine metabolites were associated with eGFR (Bonferroni-corrected p < 4.0E-5 for serum analyses and p < 3.6E-5 for urine analyses), including 248 metabolites shared across biofluids. Fewer metabolites (75 serum and 91 urine metabolites, including 7 shared across biofluids) were cross-sectionally associated with albuminuria. Guanidinosuccinate, N2,N2-dimethylguanosine, hydroxy-N6,N6,N6-trimethyllysine, X-13844, and X-25422 were significantly associated with both eGFR and albuminuria. Over a mean follow-up of 6.6 years, serum mannose (HR 2.3 [1.6,3.2], p = 2.7E-5) and urine X-12117 (HR 1.7 [1.3,2.2], p = 1.9E-5) were risk factors for UACR doubling, whereas urine sebacate (HR 0.86 [0.80,0.92], p = 1.9E-5) was inversely associated. Compared to clinical characteristics alone, including the top 5 endogenous metabolites in serum and urine associated with longitudinal outcomes improved the outcome prediction (AUCs for eGFR decline: clinical model = 0.79, clinical + metabolites model = 0.87, p = 8.1E-6; for UACR doubling: clinical model = 0.66, clinical + metabolites model = 0.73, p = 2.9E-5). CONCLUSIONS:Metabolomic profiling in different biofluids provided distinct and potentially complementary insights into the biology and prognosis of kidney diseases.

Cardiometabolic comorbidities, diabetes (DM), hypertension (HTN), and obesity, contribute to cardiovascular disease (CVD). Circulating biomarkers facilitate prognostication for patients with CVD. We explored the relationship between cardiometabolic comorbidity burden in patients with chronic coronary disease (CCD) and biomarkers of myocardial stretch, injury, inflammation, and platelet activity. We analyzed participants from the ISCHEMIA Trials biorepository with plasma biomarkers (NT-proBNP, hs-cTnT, hs-CRP, IL-6, sCD40L, and GDF-15) and clinical risk factors [hemoglobin A1c (HbA1c), systolic blood pressure (SBP), and body mass index (BMI)] at baseline. We defined cardiometabolic comorbidities as DM, HTN, and obesity at baseline. Comorbidity burden characterized by number and severity of comorbidities. Controlled comorbidities were defined as HbA1c <7% for those with DM, SBP <130 mmHg for those with HTN and BMI <30 kg/m². Severely uncontrolled was defined as HbA1c ≥8%, SBP ≥160 mmHg, and BMI ≥35 kg/m². We performed linear regression analyses to examine the association between comorbidity burden and log-transformed biomarker levels adjusting for age, sex, eGFR controlled for hemodialysis, and left ventricular ejection fraction. A total of 752 individuals (mean age 66, 19% female, 84% white) were included in this analysis. Self-reported Black race, current smokers, history of MI and HF had greater cardiometabolic comorbidity burden. The presence of ≥ 1 severely uncontrolled comorbidity was associated with significantly higher baseline levels of hs-cTnT, hs-CRP, IL-6, and GDF-15 compared to participants with no comorbidities. In conclusion, increasing cardiometabolic comorbidity burden in patients with CCD is associated with higher levels of circulating biomarkers of myocardial injury and inflammation.

Bone regeneration remains a significant clinical challenge, often necessitating surgical approaches when healing bone defects and fracture nonunions. Within this context, the modulation of adenosine signaling pathways has emerged as a promising therapeutic option, encouraging osteoblast activation and tempering osteoclast differentiation. A literature review of the PubMed database with relevant keywords was conducted. The search criteria involved in vitro or in vivo models, with clear methodological descriptions. Only studies that included the use of indirect adenosine agonists, looking at the effects of bone regeneration, were considered relevant according to the eligibility criteria. A total of 29 articles were identified which met the inclusion and exclusion criteria, and they were reviewed to highlight the preclinical translation of adenosine agonists. While preclinical studies demonstrate the therapeutic potential of adenosine signaling in bone regeneration, its clinical application remains unrealized, underscoring the need for further clinical trials. To date, only large, preclinical animal models using indirect adenosine agonists have been successful in stimulating bone regeneration. The adenosine receptors (A₁, A_2A, A_2B, and A₃) stimulate various pathways, inducing different cellular responses. Specifically, indirect adenosine agonists act to increase the extracellular concentration of adenosine, subsequently agonizing the respective adenosine receptors. The agonism of each receptor is dependent on its expression on the cell surface, the extracellular concentration of adenosine, and its affinity for adenosine. This comprehensive review analyzed the multitude of indirect agonists currently being studied preclinically for bone regeneration, discussing the mechanisms of each agonist, their cellular responses in vitro, and their effects on bone formation in vivo.

BACKGROUND/UNASSIGNED:There is interest in identifying novel filtration markers that lead to more accurate GFR estimates than current markers (creatinine and cystatin C) and are more consistent across demographic groups. We hypothesize that large-scale metabolomics can identify serum metabolites that are strongly influenced by glomerular filtration rate (GFR) and are more consistent across demographic variables than creatinine, which would be promising filtration markers for future investigation. METHODS/UNASSIGNED:< -0.5), we assessed additional variation by age (height in children), sex, race and body mass index (BMI). RESULTS/UNASSIGNED:< -0.5), 27 were consistently not associated with age (height in children), sex or race. CONCLUSIONS/UNASSIGNED:The majority of metabolite-mGFR correlations were negative and consistent across sex, race, BMI and study. Metabolites with consistent strong negative correlations with mGFR and non-association with demographic variables may represent candidate markers to improve estimation of GFR.

INTRODUCTION/UNASSIGNED:Hyperoxaluria is a risk factor for kidney stone formation and chronic kidney disease progression. The microbiome is an important protective factor against oxalate accumulation through the activity of its oxalate-degrading enzymes (ODEs). In this cross-sectional study, we leverage multiomics to characterize the microbial community of participants with primary and enteric hyperoxaluria, as well as idiopathic calcium oxalate kidney stone (CKS) formers, focusing on the relationship between oxalate degrading functions of the microbiome. METHODS/UNASSIGNED:Patients diagnosed with type 1 primary hyperoxaluria (PH), enteric hyperoxaluria (EH), and CKS were screened for inclusion in the study. Participants completed a food frequency questionnaire recording their dietary oxalate content while fecal oxalate levels were ascertained. DNA and RNA were extracted from stool samples and sequenced. Metagenomic (MTG) and metatranscriptomic (MTT) data were processed through our bioinformatics pipelines, and microbiome diversity, differential abundance, and networks were subject to statistical analysis in relationship with oxalate levels. RESULTS/UNASSIGNED:A total of 38 subjects were recruited, including 13 healthy participants, 12 patients with recurrent CKS, 8 with PH, and 5 with EH. Urinary and fecal oxalate were significantly higher in the PH and the EH population compared to healthy controls. At the community level, alpha-diversity and beta-diversity indices were similar across all populations. The respective contributions of single bacterial species to the total oxalate degradative potential were similar in healthy and PH subjects. MTT-based network analysis identified the most interactive bacterial network in patients with PH. Patients with EH had a decreased abundance of multiple major oxalate degraders. CONCLUSION/UNASSIGNED:The composition and inferred activity of oxalate-degrading microbiota were differentially associated with host clinical conditions. Identifying these changes improves our understanding of the relationships between dietary constituents, microbiota, and oxalate homeostasis, and suggests new therapeutic approaches protecting against hyperoxaluria.

RATIONALE & OBJECTIVE/OBJECTIVE:Biomarkers that enable better identification of persons with chronic kidney disease (CKD) who are at higher risk for disease progression and adverse events are needed. This study sought to identify urine and plasma metabolites associated with progression of kidney disease. STUDY DESIGN/METHODS:Prospective metabolome-wide association study. SETTING & PARTICIPANTS/METHODS:Persons with CKD enrolled in the German CKD Study (GCKD) with metabolite measurements; with external validation within the Atherosclerosis Risk in Communities Study. EXPOSURES/METHODS:1,513 urine and 1,416 plasma metabolites (Metabolon, Inc.) measured at study entry using untargeted mass spectrometry. OUTCOMES/RESULTS:, or 40% decline in eGFR (CKE). Death from any cause was a secondary endpoint. After a median of 6.5 years follow-up, 500 persons experienced KF, 1,083 experienced CKE and 680 died. ANALYTICAL APPROACH/METHODS:Time-to-event analyses using multivariable proportional hazard regression models in a discovery-replication design, with external validation. RESULTS:5,088 GCKD participants were included in analyses of urine metabolites and 5,144 in analyses of plasma metabolites. Among 182 unique metabolites, 30 were significantly associated with KF, 49 with CKE, and 163 with death. The strongest association with KF was observed for plasma hydroxyasparagine (hazard ratio: 1.95, 95% confidence interval: 1.68-2.25). An unnamed metabolite measured in plasma and urine was significantly associated with KF, CKE, and death. External validation of the identified associations of metabolites with KF or CKE revealed direction-consistency for 88% of observed associations. Selected associations of 18 metabolites with study outcomes have not been previously reported. LIMITATIONS/CONCLUSIONS:Use of observational data and semi-quantitative metabolite measurements at a single time point. CONCLUSIONS:The observed associations between metabolites and KF, CKE or death in persons with CKD confirmed previously reported findings and also revealed several associations not previously described. These findings warrant confirmatory research in other study cohorts.