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An early, reversible cholesterolgenic etiology of diet-induced insulin resistance
(Elsevier, 2023) Covert, Jacob D.; Grice, Brian A.; Thornburg, Matthew G.; Kaur, Manpreet; Ryan, Andrew P.; Tackett, Lixuan; Bhamidipati, Theja; Stull, Natalie D.; Kim, Teayoun; Habegger, Kirk M.; McClain, Donald A.; Brozinick, Joseph T.; Elmendorf, Jeffrey S.; Anatomy, Cell Biology and Physiology, School of Medicine
Objective: A buildup of skeletal muscle plasma membrane (PM) cholesterol content in mice occurs within 1 week of a Western-style high-fat diet and causes insulin resistance. The mechanism driving this cholesterol accumulation and insulin resistance is not known. Promising cell data implicate that the hexosamine biosynthesis pathway (HBP) triggers a cholesterolgenic response via increasing the transcriptional activity of Sp1. In this study we aimed to determine whether increased HBP/Sp1 activity represented a preventable cause of insulin resistance. Methods: C57BL/6NJ mice were fed either a low-fat (LF, 10% kcal) or high-fat (HF, 45% kcal) diet for 1 week. During this 1-week diet the mice were treated daily with either saline or mithramycin-A (MTM), a specific Sp1/DNA-binding inhibitor. A series of metabolic and tissue analyses were then performed on these mice, as well as on mice with targeted skeletal muscle overexpression of the rate-limiting HBP enzyme glutamine-fructose-6-phosphate-amidotransferase (GFAT) that were maintained on a regular chow diet. Results: Saline-treated mice fed this HF diet for 1 week did not have an increase in adiposity, lean mass, or body mass while displaying early insulin resistance. Consistent with an HBP/Sp1 cholesterolgenic response, Sp1 displayed increased O-GlcNAcylation and binding to the HMGCR promoter that increased HMGCR expression in skeletal muscle from saline-treated HF-fed mice. Skeletal muscle from these saline-treated HF-fed mice also showed a resultant elevation of PM cholesterol with an accompanying loss of cortical filamentous actin (F-actin) that is essential for insulin-stimulated glucose transport. Treating these mice daily with MTM during the 1-week HF diet fully prevented the diet-induced Sp1 cholesterolgenic response, loss of cortical F-actin, and development of insulin resistance. Similarly, increases in HMGCR expression and cholesterol were measured in muscle from GFAT transgenic mice compared to age- and weight-match wildtype littermate control mice. In the GFAT Tg mice we found that these increases were alleviated by MTM. Conclusions: These data identify increased HBP/Sp1 activity as an early mechanism of diet-induced insulin resistance. Therapies targeting this mechanism may decelerate T2D development.
Effectiveness of the VA-Geriatric Resources for Assessment and Care of Elders (VA-GRACE) program: An observational cohort study
(Wiley, 2022) Schubert, Cathy C.; Perkins, Anthony J.; Myers, Laura J.; Damush, Teresa M.; Penney, Lauren S.; Zhang, Ying; Schwartzkopf, Ashley L.; Preddie, Alaina K.; Riley, Sam; Menen, Tetla; Bravata, Dawn M.; Medicine, School of Medicine
Background: As the Department of Veterans Affairs (VA) healthcare system seeks to expand access to comprehensive geriatric assessments, evidence-based models of care are needed to support community-dwelling older persons. We evaluated the VA Geriatric Resources for Assessment and Care of Elders (VA-GRACE) program's effect on mortality and readmissions, as well as patient, caregiver, and staff satisfaction. Methods: This retrospective cohort included patients admitted to the Richard L. Roudebush VA hospital (2010-2019) who received VA-GRACE services post-discharge and usual care controls who were potentially eligible for VA-GRACE but did not receive services. The VA-GRACE program provided home-based comprehensive, multi-disciplinary geriatrics assessment, and ongoing care. Primary outcomes included 90-day and 1-year all-cause readmissions and mortality, and patient, caregiver, and staff satisfaction. We used propensity score modeling with overlapping weighting to adjust for differences in characteristics between groups. Results: VA-GRACE patients (N = 683) were older than controls (N = 4313) (mean age 78.3 ± 8.2 standard deviation vs. 72.2 ± 6.9 years; p < 0.001) and had greater comorbidity (median Charlson Comorbidity Index 3 vs. 0; p < 0.001). VA-GRACE patients had higher 90-day readmissions (adjusted odds ratio [aOR] 1.55 [95%CI 1.01-2.38]) and higher 1-year readmissions (aOR 1.74 [95%CI 1.22-2.48]). However, VA-GRACE patients had lower 90-day mortality (aOR 0.31 [95%CI 0.11-0.92]), but no statistically significant difference in 1-year mortality was observed (aOR 0.88 [95%CI 0.55-1.41]). Patients and caregivers reported that VA-GRACE home visits reduced travel burden and the program linked Veterans and caregivers to needed resources. Primary care providers reported that the VA-GRACE team helped to reduce their workload, improved medication management for their patients, and provided a view into patients' daily living situation. Conclusions: The VA-GRACE program provides comprehensive geriatric assessments and care to high-risk, community-dwelling older persons with high rates of satisfaction from patients, caregivers, and providers. Widespread deployment of programs like VA-GRACE will be required to support Veterans aging in place.
Natural language processing-driven state machines to extract social factors from unstructured clinical documentation
(Oxford University Press, 2023-04-18) Allen, Katie S.; Hood, Dan R.; Cummins, Jonathan; Kasturi, Suranga; Mendonca, Eneida A.; Vest, Joshua R.; Health Policy and Management, School of Public Health
Objective: This study sought to create natural language processing algorithms to extract the presence of social factors from clinical text in 3 areas: (1) housing, (2) financial, and (3) unemployment. For generalizability, finalized models were validated on data from a separate health system for generalizability. Materials and methods: Notes from 2 healthcare systems, representing a variety of note types, were utilized. To train models, the study utilized n-grams to identify keywords and implemented natural language processing (NLP) state machines across all note types. Manual review was conducted to determine performance. Sampling was based on a set percentage of notes, based on the prevalence of social need. Models were optimized over multiple training and evaluation cycles. Performance metrics were calculated using positive predictive value (PPV), negative predictive value, sensitivity, and specificity. Results: PPV for housing rose from 0.71 to 0.95 over 3 training runs. PPV for financial rose from 0.83 to 0.89 over 2 training iterations, while PPV for unemployment rose from 0.78 to 0.88 over 3 iterations. The test data resulted in PPVs of 0.94, 0.97, and 0.95 for housing, financial, and unemployment, respectively. Final specificity scores were 0.95, 0.97, and 0.95 for housing, financial, and unemployment, respectively. Discussion: We developed 3 rule-based NLP algorithms, trained across health systems. While this is a less sophisticated approach, the algorithms demonstrated a high degree of generalizability, maintaining >0.85 across all predictive performance metrics. Conclusion: The rule-based NLP algorithms demonstrated consistent performance in identifying 3 social factors within clinical text. These methods may be a part of a strategy to measure social factors within an institution.
Feature engineering from medical notes: A case study of dementia detection
(Elsevier, 2023-03-18) Ben Miled, Zina; Dexter, Paul R.; Grout, Randall W.; Boustani, Malaz; Electrical and Computer Engineering, School of Engineering and Technology
Background and objectives: Medical notes are narratives that describe the health of the patient in free text format. These notes can be more informative than structured data such as the history of medications or disease conditions. They are routinely collected and can be used to evaluate the patient's risk for developing chronic diseases such as dementia. This study investigates different methodologies for transforming routine care notes into dementia risk classifiers and evaluates the generalizability of these classifiers to new patients and new health care institutions. Methods: The notes collected over the relevant history of the patient are lengthy. In this study, TF-ICF is used to select keywords with the highest discriminative ability between at risk dementia patients and healthy controls. The medical notes are then summarized in the form of occurrences of the selected keywords. Two different encodings of the summary are compared. The first encoding consists of the average of the vector embedding of each keyword occurrence as produced by the BERT or Clinical BERT pre-trained language models. The second encoding aggregates the keywords according to UMLS concepts and uses each concept as an exposure variable. For both encodings, misspellings of the selected keywords are also considered in an effort to improve the predictive performance of the classifiers. A neural network is developed over the first encoding and a gradient boosted trees model is applied to the second encoding. Patients from a single health care institution are used to develop all the classifiers which are then evaluated on held-out patients from the same health care institution as well as test patients from two other health care institutions. Results: The results indicate that it is possible to identify patients at risk for dementia one year ahead of the onset of the disease using medical notes with an AUC of 75% when a gradient boosted trees model is used in conjunction with exposure variables derived from UMLS concepts. However, this performance is not maintained with an embedded feature space and when the classifier is applied to patients from other health care institutions. Moreover, an analysis of the top predictors of the gradient boosted trees model indicates that different features inform the classification depending on whether or not spelling variants of the keywords are included. Conclusion: The present study demonstrates that medical notes can enable risk prediction models for complex chronic diseases such as dementia. However, additional research efforts are needed to improve the generalizability of these models. These efforts should take into consideration the length and localization of the medical notes; the availability of sufficient training data for each disease condition; and the variabilities resulting from different feature engineering techniques.
Molecular Signatures of Diabetic Kidney Disease Hiding in a Patient with Hypertension-Related Kidney Disease: A Clinical Pathologic Molecular Correlation
(Wolters Kluwer, 2022) Patel, Jiten; Torrealba, Jose R.; Poggio, Emilio D.; Bebiak, Jack; Alpers, Charles E.; Grewenow, Stephanie M.; Toto, Robert D.; Eadon, Michael T.; Kidney Precision Medicine Project; Medicine, School of Medicine
The Kidney Precision Medicine Project (KPMP) seeks to establish a molecular atlas of the kidney in health and disease and improve our understanding of the molecular drivers of CKD and AKI. Herein, we describe the case of a 66-year-old woman with CKD who underwent a protocol KPMP kidney biopsy. Her clinical history included well-controlled diabetes mellitus, hypertension, and proteinuria. The patient’s histopathology was consistent with modest hypertension-related kidney injury, without overt diabetic kidney disease. Transcriptomic signatures of the glomerulus, interstitium, and tubular subsegments were obtained from laser microdissected tissue. The molecular signatures that were uncovered revealed evidence of early diabetic kidney disease adaptation and ongoing active tubular injury with enriched pathways related to mesangial cell hypertrophy, glycosaminoglycan biosynthesis, and apoptosis. Molecular evidence of diabetic kidney disease was found across the nephron. Novel molecular assays can supplement and enrich the histopathologic diagnosis obtained from a kidney biopsy.