Background: We (and others) have previously identified five clinically distinct diabetes subtypes. Currently, few models to identify diabetes subtypes are readily accessible. Further, while COVID-19 has been associated with increased risk of new-onset diabetes, it remains unknown whether the pandemic is also associated with changes in diabetes subtype distribution. Methods: We used the electronic health records of patients diagnosed with diabetes from 2010 to 2019 at the Kirklin Clinic of the University of Alabama at Birmingham (UAB) to train models to assign diabetes subtypes previously identified by hierarchical clustering. We then applied the trained model to conduct a retrospective cluster analysis of electronic health records of patients diagnosed with diabetes from 2020 to 2024 at UAB. We further validated our findings using data from the 2015–2023 National Health and Nutrition Examination Surveys (NHANES). Results: The trained classification model had an average specificity of 98% and an average sensitivity of 93%. Using the model, we identified a significant difference in the distribution of type 2 diabetes subtypes in patients at UAB and in participants in NHANES. In particular, the proportion of patients with severe insulin-dependent diabetes or severe insulin-resistant diabetes subtypes increased from 42% to 61% and 31% to 40% at the UAB and in NHANES, respectively. Conclusions: The model presented here can facilitate the identification of diabetes subtypes. The proportions of patients with severe subtypes of diabetes have seemed to increase in the more recent years following the pandemic. Further studies are required to determine the potential causes of this phenomenon.

This report explores a therapeutic hypothesis for addressing the potential effects of a loss-of-function variant in the ZC4H2 gene. We describe a pediatric female (born 2020) with developmental delay associated with an early truncation variant in ZC4H2. This genetic alteration is suspected to reduce levels of the functional ZC4H2 protein (encoded by the ZC4H2 gene). Variants in ZC4H2 are associated with Wieacker–Wolff and Miles–Carpenter syndrome, which are characterized by intellectual disability, developmental delay, congenital contractures, and seizures. The patient presents with multiple developmental manifestations, including developmental delay, intellectual disability, joint contractures and dislocations, coronal craniosynostosis, truncal hypotonia, laryngomalacia, rocker bottom feet, and intermittent esotropia. We hypothesize that targeting the downstream effects of ZC4H2 haploinsufficiency, such as synaptic dysfunction and impaired dendritic spine stability, may help mitigate the patient’s symptoms by restoring synaptic homeostasis. This report outlines the rationale for this therapeutic hypothesis, which is informed by genetic findings, prior literature, and recent mechanistic insights. Further studies are needed to validate underlying disease mechanisms and to evaluate the candidate therapy for safety and efficacy.

MAPK8IP3- related neurodevelopmental disorders are a spectrum of rare conditions caused by de novo mutations in the MAPK8IP3 gene that encodes the JIP3 protein. These disorders are associated with a spectrum of neurodevelopmental symptoms that manifest in children and cause brain abnormalities, profound intellectual disabilities, movement disorders, and developmental delays. JIP3 is required for axonal transport of proteins and organelles between the soma and the synaptic terminal of neurons, a process critical for normal brain development and function. Homozygous loss-of-function mutations in JIP3 lead to impaired axonal transport and aggregation of cargo, which result in axonal swelling and stunted elongation. Despite these severe outcomes, disease mechanisms are poorly understood, and no current treatments are available. Here we conduct thorough morphological, behavioral, and motility phenotyping in the JIP3 knockout zebrafish and identify locomotor deficits and morphological abnormalities. To identify treatment options, we used insights from expert clinicians and the artificial intelligence tool, mediKanren, to identify drug candidates hypothesized to improve patient symptoms or compensate for the loss of JIP3 at the molecular level. We then prioritized drugs that are FDA-approved, safe for children, and readily available. These collective efforts identified amantadine and levodopa as candidate therapies and rescued motor phenotypes associated with JIP3 loss-of-function in zebrafish.

This year marks the 63rd anniversary of the International Society of Nephrology, which signaled nephrology’s emergence as a modern medical discipline. In this article, we briefly trace the course of nephrology’s history to show a clear arc in its evolution—of increasing resolution in nephrological data—an arc that is converging with computational capabilities to enable precision nephrology. In general, precision medicine refers to tailoring treatment to the individual characteristics of patients. For an operational definition, this tailoring takes the form of an optimization, in which treatments are selected to maximize a patient’s expected health with respect to all available data. Because modern health data are large and high resolution, this optimization process requires computational intervention, and it must be tuned to the contours of specific medical disciplines. An advantage of this operational definition for precision medicine is that it allows us to better understand what precision medicine means in the context of a specific medical discipline. The goal of this article was to demonstrate how to instantiate this definition of precision medicine for the field of nephrology. Correspondingly, the goal of precision nephrology was to answer two related questions: ( 1 ) How do we optimize kidney health with respect to all available data? and ( 2 ) How do we optimize general health with respect to kidney data?

There are over 6,000 different rare diseases estimated to impact 300 million people worldwide. As genetic testing becomes more common practice in the clinical setting, the number of rare disease diagnoses will continue to increase, resulting in the need for novel treatment options. Identifying treatments for these disorders is challenging due to a limited understanding of disease mechanisms, small cohort sizes, interindividual symptom variability, and little commercial incentive to develop new treatments. A promising avenue for treatment is drug repurposing, where FDA-approved drugs are repositioned as novel treatments. However, linking disease mechanisms to drug action can be extraordinarily difficult and requires a depth of knowledge across multiple fields, which is complicated by the rapid pace of biomedical knowledge discovery. To address these challenges, The Hugh Kaul Precision Medicine Institute developed an artificial intelligence tool, mediKanren, that leverages the mechanistic insight of genetic disorders to identify therapeutic options. Using knowledge graphs, mediKanren enables an efficient way to link all relevant literature and databases. This tool has allowed for a scalable process that has been used to help over 500 rare disease families. Here, we provide a description of our process, the advantages of mediKanren, and its impact on rare disease patients.