Huntington Medical Research Institutes

ABSTRACTBackgroundGlucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) have gained rapid popularity for both medical and consumer‐directed weight loss. This growth has been accompanied by increased public discussion regarding facial aesthetic changes, commonly referred to as “Ozempic face,” characterized by volume depletion and cutaneous laxity.ObjectiveTo quantify temporal patterns of public search interest in a widely known GLP‐1 RA and evaluate corresponding awareness of its cosmetic facial sequelae using infodemiologic methods.MethodsGoogle Trends data for “Ozempic” and related frequently co‐searched queries were analyzed from November 2021 to December 2024. Trends in relative search volume (RSV) were examined, with particular focus on terms associated with facial aesthetics, including “Ozempic face” and “plastic surgeons Ozempic face.”ResultsRSV for “Ozempic” showed a steady upward trajectory over the study period. Queries related to facial aesthetic consequences exhibited substantial proportional increases. Notably, “Ozempic face” demonstrated a 4600% rise in RSV, and searches for “plastic surgeons Ozempic face” similarly grew markedly.ConclusionsPublic interest in GLP‐1 RAs is strongly associated with rising awareness and concern about their facial aesthetic effects. These trends suggest that aesthetic practitioners should expect more patient inquiries regarding GLP‐1–related facial changes and should proactively integrate counseling and corrective treatment options into clinical practice.

Background/Objectives: Alzheimer’s disease (AD) is the leading cause of dementia, which is an inevitable consequence of aging. Early detection of AD, or detection during the pre-AD stage, is beneficial, as it enables timely intervention to reduce modifiable risk factors, which may help prevent or delay the progression to dementia. On the one hand, plasma biomarkers have demonstrated great promise in predicting cognitive decline. On the other hand, in recent years, ocular imaging features, particularly the thickness of retinal layers measured by spectral-domain optical coherence tomography (SD-OCT), are emerging as possible non-invasive, non-contact surrogate markers for early detection and monitoring of neurodegeneration. This pilot study aims to identify retinal layer thickness changes across the entire retina linked to plasma AD biomarkers in cognitively healthy (CH) elderly individuals at risk for AD. Methods: Eleven CH individuals (20 eyes total) were classified in the pre-AD stage by plasma β-amyloid (Aβ)42/40 ratio < 0.10 and underwent SD-OCT. A deep-learning-derived automated algorithm was used to segment retinal layers on OCT (with manual correction when needed). Multiple layer thicknesses throughout the entire retina (including the inner retina, the outer retina, and the choroid) were measured in the inner ring (1–3 mm) and outer ring (3–6 mm) of the Early Treatment Diabetic Retinopathy Study (ETDRS). Relationships between retinal layers and plasma biomarkers were analyzed by ridge regression/bootstrapping. Results: Results showed that photoreceptor inner segment (PR-IS) thinning had the largest size effect with neurofilament light chain. Additional findings revealed thinning or thickening of the other retinal layers in association with increasing levels of glial fibrillary acidic protein and phosphorylated tau at threonine 181 and 217 (p-tau181 and p-tau217). Conclusions: This pilot study suggests that retinal layer-specific signatures exist, with PR-IS thinning as the largest effect, indicating neurodegeneration in pre-AD. Further research is needed to confirm the findings of this pilot study using larger longitudinal pre-AD cohorts and comparative analyses with healthy aging adults.

In recent years, pleckstrin homology domain leucine-rich repeat protein phosphatase-1 (PHLPP1) and 2 (PHLPP2) have emerged as key players in regulating various survival signaling pathways, including Akt, and contribute towards cardiovascular disease development. This review highlights the diverse mechanisms regulating PHLPP1/2 at transcriptional, translational, and post-translational levels and discusses their role in cardiovascular function and disease. We further explore the therapeutic potential of targeting PHLPP1/2 using small molecule inhibitors, peptide inhibitors, microRNAs, long noncoding RNAs, and natural compounds. While the divergent roles of PHLPP1 and PHLPP2 in maintaining cellular homeostasis and their dysregulation in cancer and other diseases are well documented, their regulation and downstream targets in the heart under normal and pathological states remain unclear. Future studies are warranted to discover the regulatory mechanisms of PHLPP1/2, identify novel cardiac stress-regulated substrate kinases and binding partners, and develop therapies that target these phosphatases independently for clinical translation.

Introduction: New innovations in AI allow opportunistic detection of non-coronary features on coronary artery calcium (CAC) scans, enabling screening for a range of conditions, and improved cardiovascular disease (CVD) prediction. Myosteatosis, excessive fat infiltration into skeletal muscle, is increasingly recognized as a marker of systemic metabolic dysfunction and can be quantified in CT using the mean attenuation of skeletal muscle. We evaluated AI-measured myosteatosis in thoracic skeletal muscle for predicting future atrial fibrillation (AF), heart failure (HF), and total CVD. Methods: We used baseline CAC scans and 15-year follow-up data from 5,489 asymptomatic participants (47.8% male) in the Multi-Ethnic Study of Atherosclerosis (MESA). Myosteatosis was operationally defined as the lowest quartile of thoracic skeletal muscle mean attenuation (males&lt;33 Hounsfield Units (HU) and females&lt;27 HU). Hazard ratios [HR] for bottom vs top quartile of mean muscle CT density were evaluated using proportional hazards regression models adjusted for CVD risk factors, inflammatory markers, and social determinants of health. Results: Myosteatosis was associated with worse outcomes in both sexes: HRs in males were 4.59 (95% CI, 3.52–5.99) for AF, 8.46 (4.61–15.52) for HF, and 3.56 (2.89–4.37) for total CVD, with corresponding HRs in females of 4.68 (3.48–6.29), 8.01 (3.62–17.72), and 4.37 (3.42–5.57), respectively. After full adjustment, associations remained significant for HF (1.93 [1.31–2.82]), AF (1.78 [1.26–2.50]), and total CVD (1.44 [1.09–1.91]) in males, and for AF (1.69 [1.17–2.45]) and total CVD (1.75 [1.29–2.39]) in females. Individuals in the top quartile of CAC (&gt;89.5 HU) who also had myosteatosis had greater 15-year incidence of AF (45.4%) and HF (21.8%) than those in either group alone (CAC, AF: 29%, HF: 9.5%; myosteatosis, AF: 20.9%, HF: 5.3%). Conclusion: Thoracic skeletal myosteatosis in CAC scans is an independent predictor of AF, HF, and total CVD over 15 years. Improving clinical outcomes through the detection of myosteatosis, and other opportunistic findings in CAC scans as part of the AI-CVD initiative, merits further investigation.

Introduction: Myocardial infarct size (IS) is the most robust endpoint for evaluating cardioprotective strategies in preclinical ischemia/reperfusion studies. The gold standard for IS quantification in preclinical studies (triphenyl tetrazolium chloride (TTC) staining) is traditionally performed manually and is prone to inter-operator variability. Here, we propose a deep learning segmentation pipeline to automate IS quantification in TTC-stained rat heart sections. Methods: We used n=165 Sprague-Dawley rats (150–300 g, 1–2 months, 69% female). Myocardial infarction (MI) was induced using a standard occlusion/reperfusion model by occluding the proximal left coronary artery for 30 minutes, followed by 3 hours of reperfusion. After euthanasia, the left ventricle (LV) was excised, transversely sliced, and incubated in 1% TTC at 37 °C for 15 minutes to distinguish necrotic myocardium (pale white) from viable tissues (brick red, Fig. 1). Manual IS was quantified by contouring infarcted and total LV areas in each slice using ImageJ (NIH, USA). To automate the IS measurement from TTC-stained heart slices, we implemented a deep learning segmentation pipeline based on the mask region-based convolutional neural network (Mask R-CNN) architecture. Ground truth masks for infarcted regions and LV area were created using VGG Image Annotator. Images from n=140 rats were used for training, as well as an additional 1,400 images generated by data augmentation. All training and preprocessing pipelines were conducted in Python. Dice similarity coefficient (Dice score) was used to evaluate the model performance. The best-performing Mask R-CNN model was blindly tested on 25 additional MI rats. Results: Infarct sizes calculated from Mask R-CNN-generated segmentations showed strong agreement with the ones from expert-annotated manual segmentations from TTC-stained LV slices (R = 0.97, p &lt; 0.0001) when tested on heart slices from 25 additional MI rats, supporting the model’s accuracy and validity. Conclusions: Our results demonstrate that deep learning segmentation accurately and automatically quantifies infarct size from TTC-stained images without operator input. This automated approach is rapid, reproducible, and unbiased, significantly reducing inter-operator variability and manual workload in preclinical studies. By streamlining infarct size assessment in preclinical cardio-protection studies, it has the potential to improve consistency and translational value in cardiac research.

Introduction: Immune checkpoint inhibitors (ICIs) have radically altered cancer therapy. Understanding adverse cardiovascular events (ACEs) associated with ICIs and identifying patients at risk remains crucial. Methods: Data from a single center retrospective cohort was evaluated. The primary outcome was ACE, a composite of myocardial infarction (MI), coronary artery disease (CAD), stroke (CVA), peripheral vascular disease (PVD), myocarditis, heart failure, valvular disease, pericardial disease and arrhythmias. Secondary outcomes were individual components of ACE and all-cause mortality. Pre- and post-ICI imaging parameters from echocardiography (echo) and cardiac magnetic resonance (CMR) studies were evaluated. Cox regression analysis for ACE and all-cause mortality were also conducted on propensity-score matched populations. Results: 5,145 patients with cancer treated with ICIs, between 2013 and 2024, were included. 41% (n=2,109) of patients experienced ACE in median follow up time of 1.00 year (IQR 0.57-2.95 years), with MI/CAD/CVA/PVD (CVD) being the most common (n=1,194, 23.2%) ( Figure 1 ). In multivariate analysis on propensity-score matched cohort (by age, gender, type of malignancy and pre-ICI ACE), dual ICI therapy (hazard ratio [HR] 1.28, confidence interval [CI] 1.12–1.46), age (HR 1.01, CI 1.00–1.02), male sex (HR 1.19, CI 1.01–1.42), smoking (HR 1.23, CI 1.02-1.47), hypertension (HR 1.22, CI 1.01-1.47), and central nervous system malignancy (HR 1.42, CI 1.08–1.87), were associated with ACE ( Figure 2 ). Pre-ICI CVD was associated with lower risk of post-ICI ACE (HR 0.67, CI 0.55-0.81). CVD (HR 1.22, CI 1.02-1.46) and heart failure (HR 1.32, CI 1.08-1.61) were associated with increased risk of death ( Figure 3 ). For patients with echo completed pre-ICI, 24.0% (n=338/1,405) had abnormal left ventricular ejection fraction (LVEF) and 35.7% (n=187/524) had abnormal global longitudinal strain (GLS), while post ICI 28.5% (n=464/1,626) had abnormal LVEF and 41.8% (n=272/652) abnormal GLS. Of patients with CMR pre-ICI, 32.9% (n=26/79) had left ventricular late gadolinium enhancement (LVLGE) and 8.9% (n=5/56) had abnormal T2 imaging; post ICI, 49.5% (n=110/222) had LVLGE and 23.5% (n=39/166) had abnormal T2 imaging. Conclusion: ACE are common in patients receiving ICI; and pre-ICI CVD was associated increased risk of death. Further work, possibly guided by multimodality cardiac imaging, could identify patients at higher risk and guide preventative care.

Introduction/Background: Myosteatosis, defined as pathological fat infiltration into skeletal muscle, is an emerging marker of metabolic dysfunction and cardiovascular risk, particularly when measured in abdominal CT. However, its association with lung health and risk of chronic obstructive pulmonary disease (COPD) is not well established. The AI-CVD initiative aims to extract all useful opportunistic screening information from coronary artery calcium scans and combines them with traditional risk factors to create a stronger predictor of cardiovascular diseases. We hypothesized that myosteatosis measured from cardiac CT scans using AI-CVD is associated with increased risk of incident COPD in a population free of clinical cardiovascular disease at baseline. Methods/Approach: A retrospective cohort analysis was conducted using baseline data from Exam 1 of the Multi-Ethnic Study of Atherosclerosis including men and women aged 45 to 84 free of cardiovascular disease at baseline. Myosteatosis was quantified using AI-CVD to segment muscle and compute thoracic skeletal muscle density as a proxy for fat infiltration. Chronic obstructive pulmonary disease (COPD) was defined using clinical diagnosis with ICD codes. Proportional hazards models were used to assess the association between myosteatosis and incident COPD disease over 15 years. Models were adjusted for confounders including age, sex, pack years of smoking, emphysema, body mass index, inflammation, diabetes, and socioeconomic status. Results/Data: A total of 283 cases of incident COPD were identified. Individuals in the lowest quartile of muscle attenuation had significantly higher cumulative incidence compared to other quartiles. In minimally adjusted models, the hazard ratio comparing the lowest to highest quartile was 1.87. In fully adjusted models, the association remained significant with a hazard ratio of 1.32. Conclusion(s): AI-based quantification of myosteatosis on routine cardiac CT scans independently predicts future risk of COPD. Adverse muscle composition in the pectoralis, intercostal, and paraspinal muscles may precede lung function decline. Opportunistic assessment of myosteatosis could enable early identification of individuals at elevated risk and support preventive interventions at elevated risk for COPD and guide preventive strategies before clinical disease onset.

Introduction: Septic shock is a common and often lethal disease. Myocardial dysfunction is a frequent complication in patients with septic shock. Common methods of assessing cardiac function, including left ventricular ejection fraction, are typically dependent on loading conditions, which may complicate interpretation. Left ventricular stroke work index (LVSWI), which incorporates LV preload and afterload, has been associated with adverse outcomes in several populations of chronic heart disease and in the cardiac intensive care unit (ICU). Given the known challenges with imaging critically ill patients, speckle tracking analysis may allow for calculation of stroke volume in patients where imaging may be limited. Research Question: Is LVSWI from speckle tracking associated with mortality in sepsis and septic shock? Methods: This is a secondary analysis of a prospectively studied cohort of patients admitted to one of two intensive care units with sepsis or septic shock. We performed transthoracic echocardiography on these patients within the first 24 hours of ICU admission. We used TomTec Image Arena to calculate stroke volume from an apical 4 chamber view. We calculated LVSWI as 0.0136 * (Stroke Volume / body surface area)*(mean arterial pressure – (1.9 + 1.24*E/e’)). We performed logistic regression for 28-day mortality, adjusting for Acute Physiology and Chronic Health Evaluation (APACHE II) score and receipt of vasopressors and mechanical ventilation. Results: We studied 398 patients, 35% of whom received vasopressors, and 26% of whom were mechanically ventilated. Average APACHE II score was 26 ± 10 points, with a 23% mortality. Stroke volume by speckle tracking was measured in 343 patients (86.2%) compared to 230 patients with conventional stroke volume by Doppler. LVSWI averaged 3.0 ± 1.7 Dg min/m 2 . Survivors had higher LVSWI than nonsurvivors (3.1 vs 2.5 Dg min/m 2 , p = 0.01). On univariable analysis, LVSWI was associated with 28-day mortality (OR 0.79, 95% CI 0.65-0.96, p 0.02). This association persisted after adjusting for APACHE II and receipt of vasopressor and mechanical ventilation (OR 0.74, 95%CI 0.59-0.93, p &lt; 0.01) Conclusion: LVSWI from speckle tracking is associated with mortality in patients with sepsis and septic shock. LVSWI may be a useful clinical measurement in critically ill septic patients