BACKGROUND: Guidelines recommend nursing home (NH) residents with cognitive impairment receive less intensive glycemic treatment and less frequent fingerstick monitoring. Our objective was to determine whether current practice aligns with guideline recommendations by examining fingerstick frequency in Veterans Affairs (VA) NH residents with diabetes across cognitive impairment levels. METHODS: We identified VA NH residents with diabetes aged ≥65 residing in VA NHs for >30 days between 2016 and 2019. Residents were grouped by cognitive impairment status based on the Cognitive Function Scale: cognitively intact, mild impairment, moderate impairment, and severe impairment. We also categorized residents into mutually exclusive glucose-lowering medication (GLM) categories: (1) no GLMs, (2) metformin only, (3) sulfonylureas/other GLMs (+/- metformin but no insulin), (4) long-acting insulin (+/- oral/other GLMs but no short-acting insulin), and (5) any short-acting insulin. Our outcome was mean daily fingersticks on day 31 of NH admission. RESULTS: Among 13,637 NH residents, mean age was 75 years and mean hemoglobin A1c was 7.0%. The percentage of NH residents on short-acting insulin varied by cognitive status from 22.7% in residents with severe cognitive impairment to 33.9% in residents who were cognitively intact. Mean daily fingersticks overall on day 31 was 1.50 (standard deviation = 1.73). There was a greater range in mean fingersticks across GLM categories compared to cognitive status. Fingersticks ranged widely across GLM categories from 0.39 per day (no GLMs) to 3.08 (short-acting insulin), while fingersticks ranged slightly across levels of cognitive impairment from 1.11 (severe cognitive impairment) to 1.59 (cognitively intact). CONCLUSION: NH residents receive frequent fingersticks regardless of level of cognitive impairment, suggesting that cognitive status is a minor consideration in monitoring decisions. Future studies should determine whether decreasing fingersticks in NH residents with moderate/severe cognitive impairment can reduce burdens without compromising safety.

Background

Electronic health record (EHR) prediction models may be easier to use in busy clinical settings since EHR data can be auto-populated into models. This study assessed whether adding functional status and/or Medicare claims data (which are often not available in EHRs) improves the accuracy of a previously developed Veterans Affairs (VA) EHR-based mortality index.

Methods

This was a retrospective cohort study of veterans aged 75 years and older enrolled in VA primary care clinics followed from January 2014 to April 2020 (n = 62,014). We randomly split participants into development (n = 49,612) and validation (n = 12,402) cohorts. The primary outcome was all-cause mortality. We performed logistic regression with backward stepwise selection to develop a 100-predictor base model using 854 EHR candidate variables, including demographics, laboratory values, medications, healthcare utilization, diagnosis codes, and vitals. We incorporated functional measures in a base + function model by adding activities of daily living (range 0-5) and instrumental activities of daily living (range 0-7) scores. Medicare data, including healthcare utilization (e.g., emergency department visits, hospitalizations) and diagnosis codes, were incorporated in a base + Medicare model. A base + function + Medicare model included all data elements. We assessed model performance with the c-statistic, reclassification metrics, fraction of new information provided, and calibration plots.

Results

In the overall cohort, mean age was 82.6 years and 98.6% were male. At the end of follow-up, 30,263 participants (48.8%) had died. The base model c-statistic was 0.809 (95% CI 0.805-0.812) in the development cohort and 0.804 (95% CI 0.796-0.812) in the validation cohort. Validation cohort c-statistics for the base + function, base + Medicare, and base + function + Medicare models were 0.809 (95% CI 0.801-0.816), 0.811 (95% CI 0.803-0.818), and 0.814 (95% CI 0.807-0.822), respectively. Adding functional status and Medicare data resulted in similarly small improvements among other model performance measures. All models showed excellent calibration.

Conclusions

Incorporation of functional status and Medicare data into a VA EHR-based mortality index led to small but likely clinically insignificant improvements in model performance.

Short-rib polydactyly syndromes (SRPS) arise from mutations in genes involved in retrograde intraflagellar transport (IFT) and basal body homeostasis, which are critical for cilia assembly and function. Recently, mutations in WDR34 or WDR60 (candidate dynein intermediate chains) were identified in SRPS. We have identified and characterized Tctex1d2, which associates with Wdr34, Wdr60 and other dynein complex 1 and 2 subunits. Tctex1d2 and Wdr60 localize to the base of the cilium and their depletion causes defects in ciliogenesis. We propose that Tctex1d2 is a novel dynein light chain important for trafficking to the cilium and potentially retrograde IFT and is a new molecular link to understanding SRPS pathology.

Somatic mutations that perturb Parkin ubiquitin ligase activity and the misregulation of iron homeostasis have both been linked to Parkinson's disease. Lactotransferrin (LTF) is a member of the family of transferrin iron binding proteins that regulate iron homeostasis, and increased levels of LTF and its receptor have been observed in neurodegenerative disorders like Parkinson's disease. Here, we report that Parkin binds to LTF and ubiquitylates LTF to influence iron homeostasis. Parkin-dependent ubiquitylation of LTF occurred most often on lysines (K) 182 and 649. Substitution of K182 or K649 with alanine (K182A or K649A, respectively) led to a decrease in the level of LTF ubiquitylation, and substitution at both sites led to a major decrease in the level of LTF ubiquitylation. Importantly, Parkin-mediated ubiquitylation of LTF was critical for regulating intracellular iron levels as overexpression of LTF ubiquitylation site point mutants (K649A or K182A/K649A) led to an increase in intracellular iron levels measured by ICP-MS/MS. Consistently, RNAi-mediated depletion of Parkin led to an increase in intracellular iron levels in contrast to overexpression of Parkin that led to a decrease in intracellular iron levels. Together, these results indicate that Parkin binds to and ubiquitylates LTF to regulate intracellular iron levels. These results expand our understanding of the cellular processes that are perturbed when Parkin activity is disrupted and more broadly the mechanisms that contribute to Parkinson's disease.