PKD1-Associated Arachnoid Cysts in Autosomal Dominant Polycystic Kidney Disease

Variant Spectrum of Renal Ciliopathies in Turkish Cohort and Genotype-Phenotype Association Specifically in Autosomal Dominant Polycystic Kidney Disease

Renal ciliopathies are a genetically and phenotypically heterogeneous group of diseases characterized by cystic and dysplastic kidneys. The aim of this study was to investigate the correlation between genetic changes that cause renal ciliopathies and phenotypic outcomes. The study group consisted of 137 patients diagnosed with renal ciliopathy disease. One hundred nineteen patients had ADPKD phenotype, 7 patients had ARPKD phenotype, 4 patients had nephronophthisis, 1 patient had Senior-Loken syndrome, 4 patients had Bardet-Biedl syndrome, 1 patient had Joubert syndrome and 1 patient had Meckel Gruber syndrome phenotype. Among patients with autosomal dominant polycystic kidney disease, patients with the PKD1 gene mutation had higher creatinine levels (p value: 0.020) and no arachnoid cysts were revealed in the PKD2 group (p value: 0.014). When the domains were compared, the finding of arachnoid cyst in patients with mutations in the transmembrane domain was statistically significant (p value: 0.021). Homozygous likely pathogenic variant in the TCTN1 gene was reported in a fetus who had findings of Meckel-Gruber syndrome; microphthalmia and cardiac hypoplasia were reported as novel findings. As a conclusion, we identified variant spectrum of renal ciliopathies in Turkish cohort and revealed the association between the transmembrane domain and arachnoid cyst.

Clinical Approach to Genetic Cerebral Arteriopathy in the Adult Patient With Ischemic Stroke

Genetic arteriopathies leading to stroke in adults constitute a diverse group of cerebrovascular disorders with distinct etiologies, pathophysiologic mechanisms, and clinical presentations. As imaging modalities better delineate subtle changes in cerebral vasculature and access to genetic testing increases, the detection rate for these conditions is expected to rise, particularly among young adults with idiopathic cerebral arteriopathy and stroke. Adults with stroke in the setting of a genetic cerebral arteriopathy often present with few traditional stroke risk factors and, in certain cases, have characteristic clinical features, cerebrovascular imaging findings, and often concurrent systemic vasculopathy, such as aortopathy, which are important to recognize. Given that there are over 50 recognized genetic cerebral arteriopathies that can cause ischemic and hemorrhagic stroke in young adults, it can be a significant diagnostic challenge for the practicing neurologist when faced with a genetic cerebral arteriopathy, because clinical algorithms for a systematic approach to genetic cerebral arteriopathies are lacking. In this review, we present a phenotype-driven, clinically oriented algorithm to guide the diagnostic workup when suspecting a genetic cerebral arteriopathy in an adult patient while highlighting the genetic basis of each disease, molecular mechanisms, clinical manifestations, diagnostic approaches, and emerging therapeutic strategies. Moreover, given the lack of widely available gene panels for diagnostic germline testing for genetic cerebral arteriopathies, we propose key genes to be tested and focused on in each clinical scenario, to better decipher the underlying diagnosis in these rare conditions.

Prenatal dispositions and genetic analysis of monozygotic female twins with suprasellar cysts and hydrocephalus: A case report

INTRODUCTION

We present a unique case of monozygotic female twins with virtually identical clinical and radiological presentations of supratentorial hydrocephalus and cystic formations from the suprasellar cistern.

DISCUSSION

Evaluating genetic predispositions and prenatal exposures is crucial for hydrocephalus in twins. Familial cases imply a genetic contribution to the development of these anomalies, including chromosomal abnormalities and specific variants linked to arachnoid cyst formation in various syndromes. Extensive genetic analyses found no pathogenic variants in the twins. Prenatal exposure to anti-epileptic medication was known during pregnancy and may be associated with fetal abnormalities, but not central nervous system (CNS) malformations, and was therefore not considered the cause of the condition in the twins. The twins presenting simultaneously with hydrocephalus caused by suprasellar cysts (SAC) underwent a two-step surgical management: initial ventriculoperitoneal shunt (VPS) placement followed by fenestration. Postoperative imaging showed cyst reduction, but a secondary VPS was necessary in both cases.

CONCLUSION

Genetic analysis is less likely to identify a monogenic etiology in non-syndromic cases of SACs, which are assumed to be multifactorial. There is no established evidence linking a teratogenic effect of anti-epileptic drugs to CNS malformations. Moreover, the surgical treatment of this complex condition constitutes a point of discussion.

Autosomal Dominant Polycystic Kidney Disease: Extrarenal Involvement

Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disorder, but kidneys are not the only organs involved in this systemic disorder. Individuals with the condition may display additional manifestations beyond the renal system, involving the liver, pancreas, and brain in the context of cystic manifestations, while involving the vascular system, gastrointestinal tract, bones, and cardiac valves in the context of non-cystic manifestations. Despite kidney involvement remaining the main feature of the disease, thanks to longer survival, early diagnosis, and better management of kidney-related problems, a new wave of complications must be faced by clinicians who treated patients with ADPKD. Involvement of the liver represents the most prevalent extrarenal manifestation and has growing importance in the symptom burden and quality of life. Vascular abnormalities are a key factor for patients' life expectancy and there is still debate whether to screen or not to screen all patients. Arterial hypertension is often the earliest onset symptom among ADPKD patients, leading to frequent cardiovascular complications. Although cardiac valvular abnormalities are a frequent complication, they rarely lead to relevant problems in the clinical history of polycystic patients. One of the newest relevant aspects concerns bone disorders that can exert a considerable influence on the clinical course of these patients. This review aims to provide the "state of the art" among the extrarenal manifestation of ADPKD.

Clinical findings, underlying pathogenetic processes and treatment of vascular dysfunction in autosomal dominant polycystic kidney disease

Autosomal dominant polycystic kidney disease (ADPKD) is an inherited disorder characterized by the development of fluid-filled cysts in the kidneys. The primary cause of ADPKD is mutations in the PKD1 (polycystic kidney disease 1) or PKD2 (polycystic kidney disease 2) gene. Patients with ADPKD often develop a variety of vascular abnormalities, which have a major impact on the structure and function of the blood vessels and can lead to complications such as hypertension, intracranial aneurysm (ICAN), and atherosclerosis. The progression of ADPKD involves intricate molecular and cellular processes that lead to the development of these vascular abnormalities. Our understanding of these processes remains incomplete, and available treatment options are limited. The aim of this review is to delve into the underlying mechanisms of these vascular abnormalities and to explore potential interventions.

Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease

Organ volume measurements are a key metric for managing ADPKD (the most common inherited renal disease). However, measuring organ volumes is tedious and involves manually contouring organ outlines on multiple cross-sectional MRI or CT images. The automation of kidney contouring using deep learning has been proposed, as it has small errors compared to manual contouring. Here, a deployed open-source deep learning ADPKD kidney segmentation pipeline is extended to also measure liver and spleen volumes, which are also important. This 2D U-net deep learning approach was developed with radiologist labeled T2-weighted images from 215 ADPKD subjects (70% training = 151, 30% validation = 64). Additional ADPKD subjects were utilized for prospective (n = 30) and external (n = 30) validations for a total of 275 subjects. Image cropping previously optimized for kidneys was included in training but removed for the validation and inference to accommodate the liver which is closer to the image border. An effective algorithm was developed to adjudicate overlap voxels that are labeled as more than one organ. Left kidney, right kidney, liver and spleen labels had average errors of 3%, 7%, 3%, and 1%, respectively, on external validation and 5%, 6%, 5%, and 1% on prospective validation. Dice scores also showed that the deep learning model was close to the radiologist contouring, measuring 0.98, 0.96, 0.97 and 0.96 on external validation and 0.96, 0.96, 0.96 and 0.95 on prospective validation for left kidney, right kidney, liver and spleen, respectively. The time required for manual correction of deep learning segmentation errors was only 19:17 min compared to 33:04 min for manual segmentations, a 42% time saving (p = 0.004). Standard deviation of model assisted segmentations was reduced to 7, 5, 11, 5 mL for right kidney, left kidney, liver and spleen respectively from 14, 10, 55 and 14 mL for manual segmentations. Thus, deep learning reduces the radiologist time required to perform multiorgan segmentations in ADPKD and reduces measurement variability.