Citations

Abstract Insulin resistance (IR) is a modifiable risk factor for dementia, yet its effects on brain metabolism and function remain unclear. In older adults, greater IR was associated with reduced cerebral glucose uptake (indicating impaired mitochondrial metabolism), atrophy, and weakened connectivity between brain regions critical for cognition. In neuron-specific insulin receptor knockout mice, brain IR produced deficits in hippocampal- and prefrontal-dependent tasks accompanied by reduced brain mitochondrial ATP and elevated reactive oxygen species. To evaluate reversibility of IR-induced brain deficits, forty older adults with IR were randomized to 40-weeks of metformin or placebo. Metformin improved insulin sensitivity, increased brain glucose uptake, strengthened cognitive network connectivity, and preserved whole-brain and regional volumes implicated in decision-making and learning. Metformin also improved processing speed and working memory. Collectively, these findings highlight IR as a driver of brain metabolism and support the concept that insulin sensitization can prevent neurobiological deficits in older people with IR.

Abstract Objectives Determine differences in quantitative stenosis severity measurements for ultra-high-resolution (UHR) photon-counting detector (PCD) coronary CT angiography (cCTA) relative to energy-integrating detector (EID) cCTA in a large patient cohort. Methods Adult participants seen between November 2022 and March 2023 underwent a clinical dual-source EID-CT cCTA and a research dual-source PCD-CT cCTA on the same day. Percent diameter stenosis (PDS) was measured, and stenosis severity was assigned based on the PDS of the most severe lesion per patient to determine a coronary artery disease reporting and data system (CAD-RADS) score. Agreement between EID-CT and PCD-CT for PDS and CAD-RADS was determined. Results A total of 112 participants were enrolled, yielding 82 subjects with at least 1 stenosis at PCD-CT (mean age, 68.0 ± 10.8 years; 69.5% [57/82] males). A total of 177 paired stenoses were quantified. The percent decrease in mean PDS from PCD-CT (24.3%) to EID-CT (29.4%) was 17.3% (median difference in PDS: −5.0%, 95% CI: −5.7% to −4.0%). The assigned CAD-RADS score changed in 31/82 subjects. In 11/31 subjects, the most severe stenosis was missed at EID-CT due to partial volume averaging of a small calcification, yet PCD clearly identified stenosis at those locations. In 20/31 subjects, the CAD-RADS score decreased at PCD-CT due to the decreased calcium blooming resulting from the improved spatial resolution. Conclusions UHR PCD cCTA decreases quantitative measures of stenosis severity when a stenosis is identified at EID, leading to decreases in CAD-RADS assignments. Advances in knowledge With its improved spatial resolution, PCD-CT identifies stenoses missed on EID-CT.

Abstract Neurofibrillary tangles are dynamic neuropathologic hallmarks of Alzheimer’s disease with a hypothesized lifespan morphologically-defined by three maturity levels: pretangles, mature tangles, and ghost tangles. To better understand the progression of tangle pathophysiology, we characterized tangle maturity level predilection of 15 tau antibodies recognizing a broad range of linear, phosphorylation, conformational, and truncation epitopes in the hippocampus of 24 postmortem brains. We developed the tangle maturity scoring system to semi-quantitatively evaluate each tangle maturity level. Based on proportions of tangle maturity levels, we classified antibodies as “early” (mostly pretangles and mature tangles), “middling” (mature tangles with pretangles and ghost tangles), and “advanced” (mostly ghost tangles and mature tangles) tangle maturity markers. To summarize tangle maturity predilection, we developed the tangle maturity scale to integrate individual tangle maturity scores. Correlations showed stronger relationships between tangle maturity scale and subsector thickness for more advanced tangle maturity markers in CA1 and subiculum, whereas Braak tangle stage remained consistently correlated throughout markers of the tangle lifespan. To aid in scoring hippocampi, we used machine learning to recognize tangle maturity levels, which performed comparably to a domain expert and showed similar relationships by Spearman correlation. Pattern recognition software was used to assess tangle and neuritic tau burden separately, which generally correlated with Braak stage and neuronal counts. However, tangle-derived tau burden more consistently correlated with hippocampal subsector thickness. In conclusion, we developed manual and automated scoring systems to evaluate tangle maturity levels, demonstrating early 4R, phosphorylated, and oligomeric tau accumulation preceding more advanced 3R and truncated tau. Our study provides supportive evidence of disease-relevant ordering of tau posttranslational modifications in the brain, which may have implications for theragnostic development. These findings underscore the promise of computerized quantitative analyses (i.e., pathomics) for high-throughput feature extraction from whole-slide images to enhance our understanding of microscopically observed morphologic changes.

INTRODUCTION: The SPEC-AI Nigeria trial (NCT05438576) was designed to use artificial intelligence (AI) to screen for pregnancy-related cardiomyopathy in the peripartum period. Using data from this study, we evaluated the utility of AI-predicted delta age (adjusted AI-predicted age − chronological age) as a surrogate for biological age. METHODS: We included 1,187 pregnant and postpartum women enrolled between August 2022 and September 2023 with follow-up through May 2024. Standard 12-lead electrocardiograms (ECGs) were recorded at study entry to generate AI age predictions. Artificial intelligence-predicted age was adjusted using estimated reference ranges obtained from a community-dwelling cohort of 25,144 individuals with AI-ECG age estimated and documented chronological age. Logistic and Cox-proportional hazards regression were used to examine associations with comorbid cardiovascular conditions and maternal mortality, respectively. RESULTS: Adjusted AI-predicted age was significantly higher among women with any cardiovascular condition, peripartum cardiomyopathy, or who died within 18 months (7, 14, and 23 years older, respectively) compared to those without these conditions who had values similar to the normal reference ranges (adjusted AI-predicted age difference less than 1 year). Artificial intelligence-predicted delta age greater than the 75th percentile was associated with an odds ratio (OR) of 2.06 for any cardiovascular condition, OR of 4.98 for left ventricular systolic dysfunction, and a hazard ratio of 32.81 for all-cause mortality; all values of P<.001. CONCLUSIONS/IMPLICATIONS: Artificial intelligence-ECG-derived biological age appears to be a potentially useful measure of cardiovascular health status and risk among pregnant and postpartum women. However, its role in monitoring maternal health requires further exploration.

Abstract Context Higher gluconeogenesis (GNG) contributes to higher nocturnal endogenous glucose production (EGP) in type 2 diabetes (T2D). Studies using 13C magnetic resonance spectroscopy (MRS) have confirmed lower hepatic glycogen content in subjects with T2D than in subjects with no diabetes (ND). Objective We determined the role of glycogen loading (GL) vs nonglycogen loading (NGL) on the contribution of GNG to nocturnal EGP in T2D. Methods In total, 14 subjects with T2D and 15 matched subjects with ND were studied on 2 occasions, with GL (60% carbohydrate) vs NGL (40% carbohydrate) isocaloric meals for 3 days, in random order in the overnight state. [6,6-2H2] glucose was infused to measure EGP, deuterium labelled water was used to measure GNG, and 13C MRS scans were performed in fed and fasted states to measure hepatic glycogen content. Results Hepatic glycogen content and nocturnal EGP were higher (P &lt; .05) in GL vs NGL in both cohorts. The % GNG to EGP averaged ∼50% in subjects with ND throughout the night after both meals. In contrast, % GNG to nocturnal EGP in T2D was lower with GL vs NGL and matched the pattern observed in subjects with ND with GL lowering overnight rates of GNG in subjects with T2D. Conclusion Selective targeting of GNG at night with appropriate medications could reduce nocturnal and early morning fasting hyperglycemia and hepatic insulin resistance in people with T2D.

BACKGROUND: Plasma NT-proBNP (N-terminal pro-B-type natriuretic peptide) is commonly used to diagnose heart failure with preserved ejection fraction (HFpEF), but its diagnostic performance in the ambulatory/outpatient setting is unknown because previous studies lacked objective reference standards. METHODS: Among patients with chronic dyspnea, diagnosis of HFpEF or noncardiac dyspnea was determined conclusively by exercise catheterization in a derivation cohort (n=414), multicenter validation cohort 1 (n=560), validation cohort 2 (n=207), and a nonobese Japanese validation cohort 3 (n=77). Optimal NT-proBNP cut points for HFpEF rule out (optimizing sensitivity) and rule in (optimizing specificity) were derived and tested, stratified by obesity and atrial fibrillation. Derived cut points were tested in 3 additional validation cohorts (cohorts 4–6) in whom HFpEF was diagnosed by resting catheterization only (n=260), previous hospitalization for heart failure (n=447), or exercise echocardiography (n=517), respectively. RESULTS: Current recommended rule-out NT-proBNP threshold <125 pg/mL had 82% sensitivity (95% CI, 77%–88%) with a body mass index (BMI) <35 kg/m 2 , decreasing to 67% (95% CI, 58%–77%) with a BMI ≥35 kg/m 2 . A lower rule-out NT-proBNP threshold <50 pg/mL displayed good sensitivity with a BMI <35 kg/m 2 (97% [95% CI, 95%–99%]), with a modest decline in sensitivity with a BMI ≥35 kg/m 2 (86% [95% CI, 79%–93%]); diagnostic thresholds were confirmed in validation cohorts 1 and 2 (91% [95% CI, 88%–95%] and 86% [95% CI, 80%–93%] with a BMI <35 kg/m 2 ; 80% [95% CI, 74%–87%] and 84% [95% CI, 74%–93%] with a BMI ≥35 kg/m 2 ). Current consensus age- and BMI-stratified rule-in thresholds demonstrated only 65% specificity (95% CI, 57%–72%). Rule-in NT-proBNP threshold ≥500 pg/mL had 85% specificity (95% CI, 78%–91%) with a BMI <35 kg/m 2 (87% [95% CI, 80%–94%] and 90% [95% CI, 81%–99%] in validation cohorts), with 100% specificity at a BMI ≥35 kg/m 2 (93% [95% CI, 81%–100%] and 100% in validation cohorts). With a BMI ≥35 kg/m 2 , lower rule-in thresholds (≥220 pg/mL) provided good specificity (88% [95% CI, 73%–100%]; 93% [95% CI, 81%–100%] and 100% in validation cohorts). Findings were consistent in validation cohorts 3 through 6 (sensitivity of <50 pg/mL, 93%–98%; specificity of ≥500 pg/mL, 82%–89%). NT-proBNP provided no incremental discrimination among patients with history of AF; ≥98% of patients with AF and dyspnea were found to have HFpEF in our cohorts. CONCLUSIONS: In patients with chronic unexplained dyspnea, current rule-in and rule-out NT-proBNP diagnostic thresholds lead to unacceptably high error rates, with important interactions by obesity and AF status. In our study, NT-proBNP provided little value in those with AF and dyspnea because the presence of AF is by itself a robust biomarker of HFpEF. Use of separate rule-in and rule-out diagnostic thresholds stratified by BMI reduces miscategorization and can guide more appropriate use of exercise testing for possible HFpEF.

Background: Effective pain management is crucial for both comfort and outcomes, yet predicting and managing this pain is difficult. This study aimed to analyze postoperative pain in patients undergoing hand surgery at the Mayo Clinic Florida, examining how patient characteristics and anxiety affect pain outcomes. Methods: We conducted a single-arm clinical trial at Mayo Clinic Florida, recruiting patients undergoing hand surgery. Preoperative pain and anxiety were assessed using the Pain Catastrophizing Scale (PCS) and State-Trait Anxiety Inventory (STAI). Postoperatively, patients used an iPhone app to record pain levels and medication use every four hours. Devices were collected three days after surgery. We analyzed the relationship between demographics, PCS, STAI scores, and pain levels using linear and logistic regression models. All statistical tests were two-sided with significance set at p < 0.05, analyzed with R4.2.2. Results: Data were collected from 62 patients (62.9% women, 37.1% men) undergoing hand surgery. Participants were mainly White (90.3%), with 50% being in the middle-aged adult group. Most had low anxiety levels (80.6% STAI-S, 82.3% STAI-T) and low catastrophizing (61.3% PCS). Postoperative pain was low, with median scores between 1.0 and 2.0 over three days. Demographics, anxiety, and catastrophizing were not significant predictors of pain levels. Logistic regression showed time as a significant factor, with pain levels peaking on Day 3. Conclusions: Postoperative pain after hand surgery was generally low, with time being a significant predictor of increased pain. Demographic factors, anxiety, and catastrophizing did not significantly affect pain levels. Pain management should emphasize time-sensitive interventions and ongoing monitoring.

Abstract Background We sought to evaluate key performance indicators related to an internally developed and deployed artificial intelligence (AI)-augmented kidney stone composition test system for potential improvements in test quality, efficiency, cost-effectiveness, and staff satisfaction. Methods We compared quality, efficiency, staff satisfaction, and financial data from the 6 months after the AI-augmented laboratory test system was deployed (test period) with data from the same 6-month period in the previous year (control period) to determine if AI-augmentation improved key performance indicators of this laboratory test. Results In the 6 months following the deployment (test period) of the AI-augmented kidney stone composition test system, 44 830 kidney stones were analyzed. Of these, 92% of kidney stones were eligible for AI-assisted interpretation. Out of these AI-eligible stones, 45% were able to be auto-released by the AI-augmented test system without human secondary review. Furthermore, the new AI-augmented kidney stone test system resulted in an apparent 40% reduction in incorrect laboratory results. Additionally, the new AI-augmented test system improved laboratory efficiency by 20%, improved staff satisfaction, and reduced the average analysis cost per kidney stone by $0.23. Conclusions The AI-augmented test system improved test quality, efficiency, cost-effectiveness and staff satisfaction related to this kidney stone composition test.