PKD1 mutant clones within cirrhotic livers inhibit steatohepatitis without promoting cancer.

Somatic mutations in non-malignant tissues are selected for because they confer increased clonal fitness. However, it is uncertain whether these clones can benefit organ health. Here, ultra-deep targeted sequencing of 150 liver samples from 30 chronic liver disease patients revealed recurrent somatic mutations. PKD1 mutations were observed in 30% of patients, whereas they were only detected in 1.3% of hepatocellular carcinomas (HCCs). To interrogate tumor suppressor functionality, we perturbed PKD1 in two HCC cell lines and six in vivo models, in some cases showing that PKD1 loss protected against HCC, but in most cases showing no impact. However, Pkd1 haploinsufficiency accelerated regeneration after partial hepatectomy. We tested Pkd1 in fatty liver disease, showing that Pkd1 loss was protective against steatosis and glucose intolerance. Mechanistically, Pkd1 loss selectively increased mTOR signaling without SREBP-1c activation. In summary, PKD1 mutations exert adaptive functionality on the organ level without increasing transformation risk.

Magnesium Disorders: Core Curriculum 2024.

Magnesium is ubiquitous in nature. It sits at the origin of the food chain, occupying the center of chlorophyl in plants. In humans, magnesium is critical to diverse molecular and catalytic processes, including energy transfer and maintenance of the genome. Despite its abundance, hypomagnesemia is common and often goes undiagnosed. This is in spite of epidemiologic data linking low magnesium with chronic diseases including diabetes mellitus. Clinically significant hypermagnesemia is encountered less frequently, but the presentation may be dramatic. Advances in molecular biology and the elucidation of the genetic causes of magnesium disorders have enhanced our understanding of their pathophysiology. Treatment approaches are also changing. The repurposing of newer medications, such as sodium/glucose cotransporter 2 inhibitors, offers new therapeutic options. In this review we integrate knowledge in this rapidly evolving field to provide clinicians and trainees with a resource for approaching common clinical scenarios involving magnesium disorders.

Claudin-2 protects against colitis-associated cancer by promoting colitis-associated mucosal healing.

Patients with inflammatory bowel disease (IBD) are susceptible to colitis-associated cancer (CAC). Chronic inflammation promotes the risk for CAC. In contrast, mucosal healing predicts improved prognosis in IBD and reduced risk of CAC. However, the molecular integration among colitis, mucosal healing, and CAC remains poorly understood. Claudin-2 (CLDN2) expression is upregulated in IBD; however, its role in CAC is not known. The current study was undertaken to examine the role for CLDN2 in CAC. The AOM/DSS-induced CAC model was used with WT and CLDN2-modified mice. High-throughput expression analyses, murine models of colitis/recovery, chronic colitis, ex vivo crypt culture, and pharmacological manipulations were employed in order to increase our mechanistic understanding. The Cldn2KO mice showed significant inhibition of CAC despite severe colitis compared with WT littermates. Cldn2 loss also resulted in impaired recovery from colitis and increased injury when mice were subjected to intestinal injury by other methods. Mechanistic studies demonstrated a possibly novel role of CLDN2 in promotion of mucosal healing downstream of EGFR signaling and by regulation of Survivin expression. An upregulated CLDN2 expression protected from CAC and associated positively with crypt regeneration and Survivin expression in patients with IBD. We demonstrate a potentially novel role of CLDN2 in promotion of mucosal healing in patients with IBD and thus regulation of vulnerability to colitis severity and CAC, which can be exploited for improved clinical management.

The role of claudin-2 in kidney function and dysfunction.

Claudin-2 is a tight junction protein expressed in leaky epithelia where it forms paracellular pores permeable to cations and water. The paracellular pore formed by claudin-2 is important in energy-efficient cation and water transport in the proximal tubules of the kidneys. Mounting evidence now suggests that claudin-2 may modulate cellular processes often altered in disease, including cellular proliferation. Also, dysregulation of claudin-2 expression has been linked to various diseases, including kidney stone disease and renal cell carcinoma. However, the mechanisms linking altered claudin-2 expression and function to disease are poorly understood and require further investigation. The aim of this review is to discuss the current understanding of the role of claudin-2 in kidney function and dysfunction. We provide a general overview of the claudins and their organization in the tight junction, the expression, and function of claudin-2 in the kidney, and the evolving evidence for its role in kidney disease.

Metabolomic profiling to identify early urinary biomarkers and metabolic pathway alterations in autosomal dominant polycystic kidney disease.

Autosomal dominant polycystic kidney disease (ADPKD) is characterized by the formation of numerous fluid-filled cysts that lead to progressive loss of functional nephrons. Currently, there is an unmet need for diagnostic and prognostic indicators of early stages of the disease. Metabolites were extracted from the urine of patients with early-stage ADPKD (n = 48 study participants) and age- and sex-matched normal controls (n = 47) and analyzed by liquid chromatography-mass spectrometry. Orthogonal partial least squares-discriminant analysis was used to generate a global metabolomic profile of early ADPKD for the identification of metabolic pathway alterations and discriminatory metabolites as candidates of diagnostic and prognostic biomarkers. The global metabolomic profile exhibited alterations in steroid hormone biosynthesis and metabolism, fatty acid metabolism, pyruvate metabolism, amino acid metabolism, and the urea cycle. A panel of 46 metabolite features was identified as candidate diagnostic biomarkers. Notable putative identities of candidate diagnostic biomarkers for early detection include creatinine, cAMP, deoxycytidine monophosphate, various androgens (testosterone; 5-α-androstane-3,17,dione; trans-dehydroandrosterone), betaine aldehyde, phosphoric acid, choline, 18-hydroxycorticosterone, and cortisol. Metabolic pathways associated with variable rates of disease progression included steroid hormone biosynthesis and metabolism, vitamin D3 metabolism, fatty acid metabolism, the pentose phosphate pathway, tricarboxylic acid cycle, amino acid metabolism, sialic acid metabolism, and chondroitin sulfate and heparin sulfate degradation. A panel of 41 metabolite features was identified as candidate prognostic biomarkers. Notable putative identities of candidate prognostic biomarkers include ethanolamine, C20:4 anandamide phosphate, progesterone, various androgens (5-α-dihydrotestosterone, androsterone, etiocholanolone, and epiandrosterone), betaine aldehyde, inflammatory lipids (eicosapentaenoic acid, linoleic acid, and stearolic acid), and choline. Our exploratory data support metabolic reprogramming in early ADPKD and demonstrate the ability of liquid chromatography-mass spectrometry-based global metabolomic profiling to detect metabolic pathway alterations as new therapeutic targets and biomarkers for early diagnosis and tracking disease progression of ADPKD.NEW & NOTEWORTHY To our knowledge, this study is the first to generate urinary global metabolomic profiles from individuals with early-stage ADPKD with preserved renal function for biomarker discovery. The exploratory dataset reveals metabolic pathway alterations that may be responsible for early cystogenesis and rapid disease progression and may be potential therapeutic targets and pathway sources for candidate biomarkers. From these results, we generated a panel of candidate diagnostic and prognostic biomarkers of early-stage ADPKD for future validation.

Biomarkers in Polycystic Kidney Disease: Are We There?

This article describes the use of prognostic, predictive, and response biomarkers that have been developed for autosomal dominant polycystic kidney disease and their use in clinical care or drug development. We focus on biochemical markers that can be assayed in patients' blood and urine and their association with the outcome of decreased glomerular filtration rate. There have been several studies on prognostic biomarkers. The most promising ones have been markers of tubular injury, inflammation, metabolism, or the vasopressin-urinary concentration axis. So far, none have been shown to be superior to kidney volume-based biomarkers. Several biomarkers are additive to kidney volume and genotype in prognostic models, but there have been few direct comparisons between the biochemical markers to identify the best ones. Moreover, there is a lack of uniformity in the statistical tools used to assess and compare biomarkers. There have been few reports of predictive and response biomarkers, and none are suitable surrogate endpoints. The U.S. Food and Drug Administration's Biomarker Qualification Program provides a regulatory pathway to approve biomarkers for use across multiple drug-development programs.

Utility of new image-derived biomarkers for autosomal dominant polycystic kidney disease prognosis using automated instance cyst segmentation.

New image-derived biomarkers for patients affected by autosomal dominant polycystic kidney disease are needed to improve current clinical management. The measurement of total kidney volume (TKV) provides critical information for clinicians to drive care decisions. However, patients with similar TKV may present with very different phenotypes, often requiring subjective decisions based on other factors (e.g., appearance of healthy kidney parenchyma, a few cysts contributing significantly to overall TKV, etc.). In this study, we describe a new technique to individually segment cysts and quantify biometric parameters including cyst volume, cyst number, parenchyma volume, and cyst parenchyma surface area. Using data from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study the utility of these new parameters was explored, both quantitatively as well as visually. Total cyst number and cyst parenchyma surface area showed superior prediction of the slope of estimated glomerular filtration rate decline, kidney failure and chronic kidney disease stages 3A, 3B, and 4, compared to TKV. In addition, presentations such as a few large cysts contributing significantly to overall kidney volume were shown to be much better stratified in terms of outcome predictions. Thus, these new image biomarkers, which can be obtained automatically, will have great utility in future studies and clinical care for patients affected by autosomal dominant polycystic kidney disease.

The lonidamine derivative H2-gamendazole reduces cyst formation in polycystic kidney disease.

Autosomal dominant polycystic kidney disease (ADPKD) is a debilitating renal neoplastic disorder with limited treatment options. It is characterized by the formation of large fluid-filled cysts that develop from kidney tubules through abnormal cell proliferation and cyst-filling fluid secretion driven by cAMP-dependent Cl- secretion. We tested the effectiveness of the indazole carboxylic acid H2-gamendazole (H2-GMZ), a derivative of lonidamine, to inhibit these processes using in vitro and in vivo models of ADPKD. H2-GMZ was effective in rapidly blocking forskolin-induced, Cl--mediated short-circuit currents in human ADPKD cells, and it significantly inhibited both cAMP- and epidermal growth factor-induced proliferation of ADPKD cells. Western blot analysis of H2-GMZ-treated ADPKD cells showed decreased phosphorylated ERK and decreased hyperphosphorylated retinoblastoma levels. H2-GMZ treatment also decreased ErbB2, Akt, and cyclin-dependent kinase 4, consistent with inhibition of heat shock protein 90, and it decreased levels of the cystic fibrosis transmembrane conductance regulator Cl- channel protein. H2-GMZ-treated ADPKD cultures contained a higher proportion of smaller cells with fewer and smaller lamellipodia and decreased cytoplasmic actin staining, and they were unable to accomplish wound closure even at low H2-GMZ concentrations, consistent with an alteration in the actin cytoskeleton and decreased cell motility. Experiments using mouse metanephric organ cultures showed that H2-GMZ inhibited cAMP-stimulated cyst growth and enlargement. In vivo, H2-GMZ was effective in slowing postnatal cyst formation and kidney enlargement in the Pkd1flox/flox: Pkhd1-Cre mouse model. Thus, H2-GMZ treatment decreases Cl- secretion, cell proliferation, cell motility, and cyst growth. These properties, along with its reported low toxicity, suggest that H2-GMZ might be an attractive candidate for treatment of ADPKD.NEW & NOTEWORTHY Autosomal dominant polycystic kidney disease (ADPKD) is a renal neoplastic disorder characterized by the formation of large fluid-filled cysts that develop from kidney tubules through abnormal cell proliferation and cyst-filling fluid secretion driven by cAMP-dependent Cl- secretion. This study shows that the lonidamine derivative H2-GMZ inhibits Cl- secretion, cell proliferation, and cyst growth, suggesting that it might have therapeutic value for the treatment of ADPKD.

Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records.

Importance: Acute kidney injury (AKI) is a heterogeneous syndrome prevalent among hospitalized patients. Personalized risk estimation and risk factor identification may allow effective intervention and improved outcomes. Objective: To develop and validate personalized AKI risk estimation models using electronic health records (EHRs), examine whether personalized models were beneficial in comparison with global and subgroup models, and assess the heterogeneity of risk factors and their outcomes in different subpopulations. Design, Setting, and Participants: This diagnostic study analyzed EHR data from 1 tertiary care hospital and used machine learning and logistic regression to develop and validate global, subgroup, and personalized risk estimation models. Transfer learning was implemented to enhance the personalized model. Predictor outcomes across subpopulations were analyzed, and metaregression was used to explore predictor interactions. Adults who were hospitalized for 2 or more days from November 1, 2007, to December 31, 2016, were included in the analysis. Patients with moderate or severe kidney dysfunction at admission were excluded. Data were analyzed between August 28, 2019, and May 8, 2022. Exposures: Clinical and laboratory variables in the EHR. Main Outcomes and Measures: The main outcome was AKI of any severity, and AKI was defined using the Kidney Disease: Improving Global Outcomes serum creatinine criteria. Performance of the models was measured with area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve, and calibration. Results: The study cohort comprised 76 957 inpatient encounters. Patients had a mean (SD) age of 55.5 (17.4) years and included 42 159 men (54.8%). The personalized model with transfer learning outperformed the global model for AKI estimation in terms of AUROC among general inpatients (0.78 [95% CI, 0.77-0.79] vs 0.76 [95% CI, 0.75-0.76]; P < .001) and across the high-risk subgroups (0.79 [95% CI, 0.78-0.80] vs 0.75 [95% CI, 0.74-0.77]; P < .001) and low-risk subgroups (0.74 [95% CI, 0.73-0.75] vs 0.71 [95% CI, 0.70-0.72]; P < .001). The AUROC improvement reached 0.13 for the high-risk subgroups, such as those undergoing liver transplant and cardiac surgery. Moreover, the personalized model with transfer learning performed better than or comparably with the best published models in well-studied AKI subgroups. Predictor outcomes varied significantly between patients, and interaction analysis uncovered modifiers of the predictor outcomes. Conclusions and Relevance: Results of this study demonstrated that a personalized modeling with transfer learning is an improved AKI risk estimation approach that can be used across diverse patient subgroups. Risk factor heterogeneity and interactions at the individual level highlighted the need for agile, personalized care.

Contribution of phosphate and FGF23 to CKD progression.

PURPOSE OF REVIEW: Progressive forms of chronic kidney disease (CKD) exhibit kidney inflammation and fibrosis that drive continued nephron loss; however, factors responsible for the development of these common pathologic features remain poorly defined. Recent investigations suggest pathways involved in maintaining urinary phosphate excretion in CKD may be contributing to kidney function decline. This review provides an update on recent evidence linking altered phosphate homeostasis to CKD progression. RECENT FINDINGS: High dietary phosphate intake and increased serum concentrations of fibroblast growth factor 23 (FGF23) both increase urinary phosphate excretion and are associated with increased risk of kidney function decline. Recent investigations have discovered high concentrations of tubular phosphate promote phosphate-based nanocrystal formation that drives tubular injury, cyst formation, and fibrosis. SUMMARY: Studies presented in this review highlight important scientific discoveries that have molded our current understanding of the contribution of altered phosphate homeostasis to CKD progression. The collective observations from these investigations implicate phosphaturia, and the resulting formation of phosphate-based crystals in tubular fluid, as unique risk factors for kidney function decline. Developing a better understanding of the relationship between tubular phosphate handling and kidney pathology could result in innovative strategies for improving kidney outcomes in patients with CKD.

Volume Progression and Imaging Classification of Polycystic Liver in Early Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND AND OBJECTIVES: The progression of polycystic liver disease is not well understood. The purpose of the study is to evaluate the associations of polycystic liver progression with other disease progression variables and classify liver progression on the basis of patient's age, height-adjusted liver cystic volume, and height-adjusted liver volume. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Prospective longitudinal magnetic resonance images from 670 patients with early autosomal dominant polycystic kidney disease for up to 14 years of follow-up were evaluated to measure height-adjusted liver cystic volume and height-adjusted liver volume. Among them, 245 patients with liver cyst volume >50 ml at baseline were included in the longitudinal analysis. Linear mixed models on log-transformed height-adjusted liver cystic volume and height-adjusted liver volume were fitted to approximate mean annual rate of change for each outcome. The association of sex, body mass index, genotype, baseline height-adjusted total kidney volume, and Mayo imaging class was assessed. We calculated height-adjusted liver cystic volume ranges for each specific age and divided them into five classes on the basis of annual percentage increase in height-adjusted liver cystic volume. RESULTS: The mean annual growth rate of height-adjusted liver cystic volume was 12% (95% confidence interval, 11.1% to 13.1%; P<0.001), whereas that for height-adjusted liver volume was 2% (95% confidence interval, 1.9% to 2.6%; P<0.001). Women had higher baseline height-adjusted liver cystic volume than men, but men had higher height-adjusted liver cystic volume growth rate than women by 2% (95% confidence interval, 0.4% to 4.5%; P=0.02). Whereas the height-adjusted liver cystic volume growth rate decreased in women after menopause, no decrease was observed in men at any age. Body mass index, genotype, and baseline height-adjusted total kidney volume were not associated with the growth rate of height-adjusted liver cystic volume or height-adjusted liver volume. According to the height-adjusted liver cystic volume growth rate, patients were classified into five classes (number of women, men in each class): A (24, six); B (44, 13); C (43, 48); D (28, 17); and E (13, nine). CONCLUSIONS: Compared with height-adjusted liver volume, the use of height-adjusted liver cystic volume showed greater separations in volumetric progression of polycystic liver disease. Similar to the Mayo imaging classification for the kidney, the progression of polycystic liver disease may be categorized on the basis of patient's age and height-adjusted liver cystic volume.

Claudin-2 promotes colorectal cancer liver metastasis and is a biomarker of the replacement type growth pattern.

Claudin-2 promotes breast cancer liver metastasis by enabling seeding and early cancer cell survival. We now demonstrate that Claudin-2 is functionally required for colorectal cancer liver metastasis and that Claudin-2 expression in primary colorectal cancers is associated with poor overall and liver metastasis-free survival. We have examined the role of Claudin-2, and other claudin family members, as potential prognostic biomarkers of the desmoplastic and replacement histopathological growth pattern associated with colorectal cancer liver metastases. Immunohistochemical analysis revealed higher Claudin-2 levels in replacement type metastases when compared to those with desmoplastic features. In contrast, Claudin-8 was highly expressed in desmoplastic colorectal cancer liver metastases. Similar observations were made following immunohistochemical staining of patient-derived xenografts (PDXs) that we have established, which faithfully retain the histopathology of desmoplastic or replacement type colorectal cancer liver metastases. We provide evidence that Claudin-2 status in patient-derived extracellular vesicles may serve as a relevant prognostic biomarker to predict whether colorectal cancer patients have developed replacement type liver metastases. Such a biomarker will be a valuable tool in designing optimal treatment strategies to better manage patients with colorectal cancer liver metastases.

Prognostic Value of Fibroblast Growth Factor 23 in Autosomal Dominant Polycystic Kidney Disease.

INTRODUCTION: Autosomal dominant polycystic kidney disease (ADPKD) is characterized by progressive cyst growth and a loss of functioning renal mass, but a decline in glomerular filtration rate (GFR) and onset of end-stage renal disease (ESRD) occur late in the disease course. There is therefore a great need for early prognostic biomarkers in this disorder. METHODS: We measured baseline serum fibroblast growth factor 23 (FGF23) levels in 192 patients with ADPKD from the Consortium for Radiologic Imaging Studies of PKD (CRISP) cohort that were followed for a median of 13 years and tested the association between FGF23 levels and change over time in height-adjusted total kidney volume (htTKV), GFR, and time to the composite endpoints of ESRD, death, and doubling of serum creatinine. RESULTS: Patients in the highest quartile for baseline FGF23 level had a higher rate of increase in htTKV (0.95% per year, P = 0.0016), and faster rate of decline in GFR (difference of -1.03 ml/min/1.73 m2 per year, P = 0.005) compared with the lowest quartile, after adjusting for other covariates, including htTKV and genotype. The highest quartile of FGF23 was also associated with a substantial increase in risk for the composite endpoint of ESRD, death, or doubling of serum creatinine (hazard ratio [HR] of 2.45 in the fully adjusted model, P = 0.03). CONCLUSION: FGF23 is a prognostic biomarker for disease progression and clinically important outcomes in ADPKD, and has additive value to established imaging and genetic biomarkers.

A Computable Phenotype for Autosomal Dominant Polycystic Kidney Disease.

Background: A computable phenotype is an algorithm used to identify a group of patients within an electronic medical record system. Developing a computable phenotype that can accurately identify patients with autosomal dominant polycystic kidney disease (ADPKD) will assist researchers in defining patients eligible to participate in clinical trials and other studies. Our objective was to assess the accuracy of a computable phenotype using International Classification of Diseases 9th and 10th revision (ICD-9/10) codes to identify patients with ADPKD. Methods: We reviewed four random samples of approximately 250 patients on the basis of ICD-9/10 codes from the EHR from the Kansas University Medical Center database: patients followed in nephrology clinics who had ICD-9/10 codes for ADPKD (Neph+), patients seen in nephrology clinics without ICD codes for ADPKD (Neph-), patients who were not followed in nephrology clinics with ICD codes for ADPKD (No Neph+), and patients not seen in nephrology clinics without ICD codes for ADPKD (No Neph-). We reviewed the charts and determined ADPKD status on the basis of internationally accepted diagnostic criteria for ADPKD. Results: The computable phenotype to identify patients with ADPKD who attended nephrology clinics has a sensitivity of 99% (95% confidence interval [95% CI], 96.4 to 99.7) and a specificity of 84% (95% CI, 79.5 to 88.1). For those who did not attend nephrology clinics, the sensitivity was 97% (95% CI, 93.3 to 99.0), and a specificity was 82% (95% CI, 77.4 to 86.1). Conclusion: A computable phenotype using the ICD-9/10 codes can correctly identify most patients with ADPKD, and can be utilized by researchers to screen health care records for cohorts of patients with ADPKD with acceptable accuracy.

Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction.

Artificial intelligence (AI) has demonstrated promise in predicting acute kidney injury (AKI), however, clinical adoption of these models requires interpretability and transportability. Non-interoperable data across hospitals is a major barrier to model transportability. Here, we leverage the US PCORnet platform to develop an AKI prediction model and assess its transportability across six independent health systems. Our work demonstrates that cross-site performance deterioration is likely and reveals heterogeneity of risk factors across populations to be the cause. Therefore, no matter how accurate an AI model is trained at the source hospital, whether it can be adopted at target hospitals is an unanswered question. To fill the research gap, we derive a method to predict the transportability of AI models which can accelerate the adaptation process of external AI models in hospitals.

Biological Efficacy and Safety of Niacinamide in Patients With ADPKD.

INTRODUCTION: Autosomal dominant polycystic kidney disease (ADPKD) is characterized by progressive cyst enlargement, leading to kidney failure. Sirtuin-1 is upregulated in ADPKD and accelerates disease progression by deacetylating p53. Niacinamide is a dietary supplement that inhibits sirtuins at high doses. METHODS: We conducted an open-label, single-arm intervention trial (study 1, N = 10), and a randomized, double blinded, placebo-controlled trial (study 2, N = 36) to assess the biological activity and safety of niacinamide. Patients with ADPKD were given 30 mg/kg oral niacinamide or placebo, for 12 months. The primary endpoint was the ratio of acetylated p53 to total p53 protein in peripheral blood mononuclear cells (PBMCs). RESULTS: There was no sustained effect of niacinamide on acetylated/total p53 in either study and no difference between placebo and niacinamide arms. There was no difference in the change in height-adjusted total kidney volume over 12 months between niacinamide and placebo. Niacinamide was generally well tolerated. The most common adverse effects were nausea, diarrhea, gastroesophageal reflux, headache, and acneiform rash but there was no difference in their incidence between niacinamide and placebo. CONCLUSIONS: In conclusion, niacinamide is safe and well-tolerated in patients with ADPKD. However, we were unable to detect a sustained inhibition of sirtuin activity over 12 months of treatment, and there was no signal to suggest a beneficial effect on any efficacy measure.

Changing relative risk of clinical factors for hospital-acquired acute kidney injury across age groups: a retrospective cohort study.

BACKGROUND: Likelihood of developing acute kidney injury (AKI) increases with age. We aimed to explore whether the predictability of AKI varies between age groups and assess the volatility of risk factors using electronic medical records (EMR). METHODS: We constructed a retrospective cohort of adult patients from all inpatient units of a tertiary care academic hospital and stratified it into four age groups: 18-35, 36-55, 56-65, and > 65. Potential risk factors collected from EMR for the study cohort included demographics, vital signs, medications, laboratory values, past medical diagnoses, and admission diagnoses. AKI was defined based on the Kidney Disease Improving Global Outcomes (KDIGO) serum creatinine criteria. We analyzed relative importance of the risk factors in predicting AKI using Gradient Boosting Machine algorithm and explored the predictability of AKI across age groups using multiple machine learning models. RESULTS: In our cohort, older patients showed a significantly higher incidence of AKI than younger adults: 18-35 (7.29%), 36-55 (8.82%), 56-65 (10.53%), and > 65 (10.55%) (p < 0.001). However, the predictability of AKI decreased with age, where the best cross-validated area under the receiver operating characteristic curve (AUROC) achieved for age groups 18-35, 36-55, 56-65, and > 65 were 0.784 (95% CI, 0.769-0.800), 0.766 (95% CI, 0.754-0.777), 0.754 (95% CI, 0.741-0.768), and 0.725 (95% CI, 0.709-0.737), respectively. We also observed that the relative risk of AKI predictors fluctuated between age groups. CONCLUSIONS: As complexity of the cases increases with age, it is more difficult to quantify AKI risk for older adults in inpatient population.

Expanded Imaging Classification of Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND: The Mayo Clinic imaging classification of autosomal dominant polycystic kidney disease (ADPKD) uses height-adjusted total kidney volume (htTKV) and age to identify patients at highest risk for disease progression. However, this classification applies only to patients with typical diffuse cystic disease (class 1). Because htTKV poorly predicts eGFR decline for the 5%-10% of patients with atypical morphology (class 2), imaging-based risk modeling remains unresolved. METHODS: Of 558 adults with ADPKD in the HALT-A study, we identified 25 patients of class 2A with prominent exophytic cysts (class 2Ae) and 43 patients of class 1 with prominent exophytic cysts; we recalculated their htTKVs to exclude exophytic cysts. Using original and recalculated htTKVs in association with imaging classification in logistic and mixed linear models, we compared predictions for developing CKD stage 3 and for eGFR trajectory. RESULTS: Using recalculated htTKVs increased specificity for developing CKD stage 3 in all participants from 82.6% to 84.2% after adjustment for baseline age, eGFR, BMI, sex, and race. The predicted proportion of class 2Ae patients developing CKD stage 3 using a cutoff of 0.5 for predicting case status was better calibrated to the observed value of 13.0% with recalculated htTKVs (45.5%) versus original htTKVs (63.6%). Using recalculated htTKVs reduced the mean paired difference between predicted and observed eGFR from 17.6 (using original htTKVs) to 4.0 ml/min per 1.73 m2 for class 2Ae, and from -1.7 (using original htTKVs) to 0.1 ml/min per 1.73 m2 for class 1. CONCLUSIONS: Use of a recalculated htTKV measure that excludes prominent exophytic cysts facilitates inclusion of class 2 patients and reclassification of class 1 patients in the Mayo classification model.