Kathleen F. Kerr

Companion dogs are a valuable geroscience model because they share human environments, develop similar age-related diseases, and have compressed lifespans. The Dog Aging Project (DAP) is a large, prospective cohort designed to investigate intrinsic and extrinsic factors associated with canine aging and longevity. We analyzed 41,047 dogs from the 2024 DAP Curated Data Release with baseline Health and Life Experience Survey (HLES) data and mortality follow-up through 2024. All-cause mortality was assessed using owner-reported death dates. We estimated survival by size class, breed background (American Kennel Club-recognized vs. non-AKC/mixed-breed), and sex using Kaplan–Meier methods and Cox models. We further evaluated geographic variation and conducted a survey-wide association study (SWAS) of 804 HLES variables using Cox models stratified by demographics with false discovery rate control. Median lifespan ranged from 14 years in small dogs to less than 10 years in giant AKC-recognized breed dogs. Within size-sex strata, AKC-recognized breed dogs lived modestly shorter lives than non-AKC/mixed-breed dogs, and females had slightly longer lifespans than males. After demographic adjustment, rural-suburban-urban differences were no longer evident, but there was significant regional and state-level variation, which correlated positively with human mortality patterns ( r = 0.43, ρ = 0.63, p = 0.0017). The SWAS identified 72 baseline variables associated with mortality, primarily from Health Status, Physical Activity, Behavior, Environment, and Diet domains, with many showing clear exposure–response relationships. This initial DAP survival analysis confirms strong demographic effects on canine longevity, reveals shared geographic mortality patterns between dogs and humans, and highlights survey-based health, behavioral, and environmental factors warranting targeted causal investigation.

Background: Kidney transplant allocation currently relies on the Kidney Donor Profile Index (KDPI), a measure which has limitations. This study evaluated the potential of using KDPI and deceased donor urinary biomarkers to improve the assessment of organ quality and the prediction of allograft outcomes compared with KDPI alone. Methods: Pairs of kidney recipients from the same donor transplanted between 2010 and 2013 were followed for 3 years post-transplant from kidneys procured at five OPOs. By 3 years post transplant, concordant good outcomes were defined as both recipients having an estimated glomerular filtration rate (eGFR) ≥45 mL/min/1.73m ² , while concordant poor outcomes were defined as both recipients experiencing graft failure (return to dialysis or waitlisted) or having an eGFR <20 mL/min/1.73m. Urinary biomarkers (uromodulin (UMOD), osteopontin (OPN), and chitinase-3-like protein 1 (YKL-40)) were measured by immunoassay using a MesoScale Device (MSD) in all donors, and by lateral flow devices in a subset. Discriminatory ability was quantified with the area under the receiver operator characteristics curve (AUC). Results: We identified 474 donors with both kidneys transplanted and either concordant good outcomes (438; 92%) or concordant poor outcomes (36, 8%). For distinguishing concordant good from concordant poor outcomes, the AUC for KDPI alone was 0.80 (SE 0.04); the AUC for KDPI combined with UMOD, OPN, and YKL-40 measured by immunoassay was 0.86 (SE 0.03). In a subset of 105 donors with lateral flow device measurements, the AUC for KDPI plus three biomarkers was 0.85 (SE 0.04), compared with 0.81 (SE 0.05) for KDPI alone. Conclusions: Combining donor urinary biomarkers with KDPI improves prediction of concordant allograft outcomes, regardless of assay platform. Lateral flow device measurements can be obtained at the point-of-care and may provide additional insight into expected deceased donor allograft function, potentially aiding the kidney transplant allocation process.

Background In dementia research, affective neuropsychiatric symptoms (NPS) – depression, anxiety, and apathy – remain understudied. Improving strategies to accurately identify clinically relevant NPS is essential for more robust research. Objectives We sought to determine how often objective metrics and clinical gestalt metrics agree on NPS presence or absence. We further sought to determine optimal cut-offs for affective NPS presence/absence using the Neuropsychiatric Inventory Questionnaire (NPI-Q) severity ratings. Methods We assessed agreement for NPS presence/absence among 5 different depression metrics, 4 anxiety metrics, and 2 apathy metrics via Jaccard indices using the National Alzheimer’s Coordinating Centers (NACC) dataset. Analysis included exploring different NPIQ severity rating thresholds of >0, >1, >2, and 0 and >1. Results NPIQ cut-off >1 for presence and =0 for absence of an NPS led to the best agreement with other metrics. However, there was poor agreement for NPS presence across depression metrics (6%) and across anxiety metrics (7%). Choice of metric could greatly skew the frequency of an NPS being present. All 3 affective NPS were more common in Lewy Body Disorder compared to Alzheimer’s Disease or Vascular Cognitive Impairment, regardless of metric. Conclusions Though NPIQ severity rating cut-off choice should depend on study design, using a severity score of >1 for presence and =0 for absence may best fit clinical gestalt for affective NPS. Lewy Body Disorders present with more affective NPS than other common dementia etiologies. Future consensus on criteria for depression and anxiety syndromes in dementia may improve their identification.

Key Points: This risk prediction model can enhance post-transplant care by identifying high-risk recipients for personalized interventions. The predicted risk of 3-year graft failure is dynamically updated with each follow-up eGFR measurement. Background: The identification of kidney transplant recipients at high risk of graft failure enables timely interventions to improve outcomes. We developed and validated a risk prediction model that uses changes in eGFR to dynamically predict 3-year graft failure. Methods: The risk prediction model was developed in a prospective multicenter cohort of deceased donor kidney transplant recipients (2010–2013) and validated in three independent cohorts (a registry cohort and two electronic medical record cohorts). The two-stage approach first estimated eGFR trends using a linear mixed-effects model and then used these trends in a logistic regression model to predict 3-year graft failure. eGFR was calculated using race-free equation, and graft failure was defined as the return to dialysis or retransplantation, censored for death. The model can be used at any time within the first 3 years after transplant, and the predicted risk is dynamically updated with each additional eGFR measurement. Results: In the development cohort ( N =1114), 94 (8%) experienced graft failure within 3 years of transplant. The model's predictive accuracy improved over time with the increase in available eGFR measurements. The optimism-corrected area under the ROC curve (AUC) was 0.70 (95% confidence interval, 0.63 to 0.77) at 3 months post-transplant and reached an AUC of 0.90 (95% confidence interval, 0.85 to 0.95) at 30 months post-transplant. Performance was attenuated in the validation cohorts (AUC range 0.60–0.64 at 3 months to 0.72–0.78 at 30 months), likely due to differences in data collection approaches in ascertaining eGFR. Conclusions: This risk prediction model has the potential to enhance post-transplant care by identifying high-risk recipients who may benefit from closer monitoring and personalized interventions. Podcast: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2026_02_18_CJASNFeb.21.2.21820.mp3

Objective: To study the effects of exposure to a prior diagnosis (PD) on second opinions in breast pathology. Materials and Methods: Pathologists interpreted digital breast biopsy cases in 2 phases separated by a washout. Phase 2 interpretations were randomly assigned to PD or no PD. When presented, PD was always more or less severe than a participant’s phase 1 diagnosis. Viewing behaviors, including zoom level, were recorded during all interpretations. Twenty pathologists yielded 556 interpretations of 32 different cases. Results: Pathologists were 71% more likely to give a less severe diagnosis when exposed to a less severe PD than with no PD (RR 1.71, 95% CI 1.33–2.20, P < 0.001). In comparison, when exposed to a more severe PD than with no PD, pathologists were 27% more likely to give a more severe diagnosis, but the effect was not significant (RR 1.27, 95% CI 0.87–1.86, P = 0.223). Compared with no PD, viewing behavior shifted toward more focus on critical image regions with exposure to a less severe PD and toward higher zoom levels with exposure to a more severe PD. Discussion: Results indicate anchoring and confirmation biases from PD exposure, such that second opinions after PD exposure are not independent assessments. Viewing behaviors illustrated how PD alters the interpretive process, including increased zooming when exposed to a more severe PD. Results have implications for best practices for computer-aided diagnosis tools. Implications: When giving a second opinion, exposure to a PD can sway diagnostic classifications and alter interpretive behavior, highlighting a need for protocols that encourage independent assessments. Highlights: In pathology diagnosis, second opinions are systematically influenced by prior diagnostic information. Less severe prior diagnoses shift pathologists’ visual attention toward clinically critical regions of a pathology image, whereas more severe prior diagnoses tend to elicit increased magnification during case interpretation. Specific viewing behaviors partially mediate the effect of prior diagnoses on second opinion diagnoses. When prior diagnoses are disclosed to pathologists, anchoring and confirmation biases undermine the independence of second opinion decisions.

Key Points: Clinical characteristics and biomarkers after hospital discharge can predict major adverse kidney events among AKI survivors. Clinical impact plots based on parsimonious prediction models illustrate the potential to optimize post-AKI care by identifying high-risk patients. Background: AKI increases the risk of CKD. We aimed to identify combinations of clinical variables and biomarkers that predict long-term kidney disease risk after AKI. Methods: We analyzed data from a prospective cohort of 723 hospitalized patients with AKI in the Assessment, Serial Evaluation, and Subsequent Sequelae of AKI study. Using machine learning, we investigated 75 candidate predictors including biomarkers measured at 3-month postdischarge follow-up to predict major adverse kidney events (MAKEs) within 3 years, defined as a decline in eGFR ≥40%, development of ESKD, or death. Results: The mean age of study participants was 64±13 years, 68% were male, and 79% were of White race. Two hundred four patients (28%) developed MAKEs over 3 years of follow-up. Random forest and least absolute shrinkage and selection operator penalized regression models using all 75 predictors yielded area under the receiver-operating characteristic curve (AUC) values of 0.80 (95% confidence interval [CI], 0.69 to 0.91) and 0.79 (95% CI, 0.68 to 0.90), respectively. The most consistently selected predictors were albuminuria, soluble TNF receptor-1, and diuretic use. A parsimonious model using the top eight predictor variables showed similarly strong discrimination for MAKEs (AUC, 0.78; 95% CI, 0.66 to 0.90). Clinical impact utility analyses demonstrated that the eight-predictor model would have 55% higher efficiency of post-AKI care (number needed to screen/follow-up for a MAKE decreased from 3.55 to 1.97). For a kidney-specific outcome of eGFR decline or ESKD, a four-predictor model showed strong discrimination (AUC, 0.82; 95% CI, 0.68 to 0.96). Conclusions: Combining clinical data and biomarkers can accurately identify patients with high-risk AKI, enabling personalized post-AKI care and improved outcomes.

Four years ago, Clinical Chemistry published a special issue on molecular diagnostics. The current special issue focuses on cancer and the translation of biological discoveries into clinically useful diagnostics and therapeutic aids. There is a wide world of relevant technologies with potential applications to cancer covered in this special edition. We highlight some exciting developments that will likely impact clinical practice. Genomic alterations play an important role in the development and progression of hematological malignancies and solid tumors. They consist of both tumor entity-specific and common genomic aberrations in oncogenes (e.g., KRAS, BRAF) or tumor suppressor genes (e.g., TP53). While progression of hematological malignancies is more driven by genomic changes ( 1), metastases of solid tumors (e.g., breast cancer) are in many instances the result of adaptive transcriptional changes leading to increased plasticity and selection of tumor cells with stemness properties ( 2). Tumor cells that metastasize can undergo an epithelial-to-mesenchymal transition that allows them to become mobile and pass through barriers such as the basement membrane or the endothelial vessel wall. Besides the release of circulating tumor cells (CTCs), tumors at the primary site also release extracellular vesicles (EVs) that contain cellular cargo such as DNA, RNAs, and proteins that may be taken up by recipient cells locally or at distant sites after their journey through the blood stream ( 3, 4). The uptake of EVs can change the biology of the recipient cell and contribute to metastatic progression (e.g., support metastatic niche formation). Both CTCs and EVs are therefore biomarkers with functional properties ( 5).