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Context: Sulfate is critical in the biotransformation of multiple compounds via sulfation. These compounds include neurotransmitters, proteoglycans, xenobiotics, and hormones such as dehydroepiandrosterone (DHEA). Sulfation reactions are thought to be rate-limited by endogenous sulfate concentrations. The gene, SLC13A1, encodes the sodium-sulfate cotransporter NaS1, responsible for sulfate (re)absorption in the intestines and kidneys. We previously reported two rare, non-linked, nonsense variants in SLC13A1 (R12X and W48X) associated with hyposulfatemia (P = 9 × 10−20). Objective: To examine the effect of serum sulfate concentration and sulfate-lowering genotype on DHEA homeostasis. Design: Retrospective cohort study. Setting: Academic research. Patients: Participants of the Amish Pharmacogenomics of Anti-Platelet Intervention (PAPI) Study and the Amish Hereditary and Phenotype Intervention (HAPI) Study. Main outcome measures: DHEA, DHEA-S, and DHEA-S/DHEA ratio. Results: Increased serum sulfate was associated with decreased DHEA-S (P = 0.03) and DHEA-S/DHEA ratio (P = 0.06) in males but not females. Female SLC13A1 nonsense variant carriers, who had lower serum sulfate (P = 9 × 10−13), exhibited 14% lower DHEA levels (P = 0.01) and 7% higher DHEA-S/DHEA ratios compared to female non-carriers (P = 0.002). Consistent with this finding, female SLC13A1 nonsense variant carriers also had lower total testosterone levels compared to non-carrier females (P = 0.03). Conclusions: Our results demonstrate an inverse relationship between serum sulfate, and DHEA-S and DHEA-S/DHEA ratio in men, while also suggesting that the sulfate-lowering variants, SLC13A1 R12X and W48X, decrease DHEA and testosterone levels, and increase DHEA-S/DHEA ratio in women. While paradoxical, these results illustrate the complexity of the mechanisms involved in DHEA homeostasis and warrant additional studies to better understand sulfate's role in hormone physiology.

White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery.In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94–0.95; p