Early-Onset Hypertension and Sex-Specific Residual Risk for Cardiovascular Disease in Type 2 Diabetes.

OBJECTIVE: To investigate whether the sex disparities in type 2 diabetes-associated cardiovascular disease (CVD) risks may be related to early-onset hypertension that could benefit from intensive blood pressure (BP) control. RESEARCH DESIGN AND METHODS: We analyzed intensive versus standard BP control in relation to incident CVD events in women and men with type 2 diabetes, based on their age of hypertension diagnosis. RESULTS: Among 3,792 adults with type 2 diabetes (49% women), multivariable-adjusted CVD risk was increased per decade earlier age at hypertension diagnosis (hazard ratio 1.11 [1.03-1.21], P = 0.006). Excess risk associated with early-diagnosed hypertension was attenuated in the presence of intensive versus standard antihypertensive therapy in women (P = 0.036) but not men (P = 0.76). CONCLUSIONS: Women with type 2 diabetes and early-onset hypertension may represent a higher-risk subpopulation that not only contributes to the female excess in diabetes-related CVD risk but may benefit from intensive BP control.

Paradigm shift in biomarker translation: a pipeline to generate clinical grade biomarker candidates from DIA-MS discovery.

Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package. This innovative tool uses the discovery cohort analysis to select precursors, peptides, and proteins that adhere to established targeted assay criteria. TEAQ was applied to Data-Independent Acquisition MS data from plasma samples acquired on an Orbitrap™ Astral™ MS. Identified precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation from 11-point loading curves under three throughputs, to develop a resource for clinical-grade targeted assays. From a clinical cohort of individuals with inflammatory bowel disease (n=492), TEAQ successfully identified 1116 signature peptides for 327 quantifiable proteins from 1180 identified proteins. Embedding stringent selection criteria adaptable to targeted assay development into the analysis of discovery data will streamline the transition to validation and clinical studies.

Deep learning evaluation of echocardiograms to identify occult atrial fibrillation.

Atrial fibrillation (AF) often escapes detection, given its frequent paroxysmal and asymptomatic presentation. Deep learning of transthoracic echocardiograms (TTEs), which have structural information, could help identify occult AF. We created a two-stage deep learning algorithm using a video-based convolutional neural network model that (1) distinguished whether TTEs were in sinus rhythm or AF and then (2) predicted which of the TTEs in sinus rhythm were in patients who had experienced AF within 90 days. Our model, trained on 111,319 TTE videos, distinguished TTEs in AF from those in sinus rhythm with high accuracy in a held-out test cohort (AUC 0.96 (0.95-0.96), AUPRC 0.91 (0.90-0.92)). Among TTEs in sinus rhythm, the model predicted the presence of concurrent paroxysmal AF (AUC 0.74 (0.71-0.77), AUPRC 0.19 (0.16-0.23)). Model discrimination remained similar in an external cohort of 10,203 TTEs (AUC of 0.69 (0.67-0.70), AUPRC 0.34 (0.31-0.36)). Performance held across patients who were women (AUC 0.76 (0.72-0.81)), older than 65 years (0.73 (0.69-0.76)), or had a CHA2DS2VASc ≥2 (0.73 (0.79-0.77)). The model performed better than using clinical risk factors (AUC 0.64 (0.62-0.67)), TTE measurements (0.64 (0.62-0.67)), left atrial size (0.63 (0.62-0.64)), or CHA2DS2VASc (0.61 (0.60-0.62)). An ensemble model in a cohort subset combining the TTE model with an electrocardiogram (ECGs) deep learning model performed better than using the ECG model alone (AUC 0.81 vs. 0.79, p = 0.01). Deep learning using TTEs can predict patients with active or occult AF and could be used for opportunistic AF screening that could lead to earlier treatment.

Deep Learning-Derived Myocardial Strain.

BACKGROUND: Echocardiographic strain measurements require extensive operator experience and have significant intervendor variability. Creating an automated, open-source, vendor-agnostic method to retrospectively measure global longitudinal strain (GLS) from standard echocardiography B-mode images would greatly improve post hoc research applications and may streamline patient analyses. OBJECTIVES: This study was seeking to develop an automated deep learning strain (DLS) analysis pipeline and validate its performance across multiple applications and populations. METHODS: Interobserver/-vendor variation of traditional GLS, and simulated effects of variation in contour on speckle-tracking measurements were assessed. The DLS pipeline was designed to take semantic segmentation results from EchoNet-Dynamic and derive longitudinal strain by calculating change in the length of the left ventricular endocardial contour. DLS was evaluated for agreement with GLS on a large external dataset and applied across a range of conditions that result in cardiac hypertrophy. RESULTS: In patients scanned by 2 sonographers using 2 vendors, GLS had an intraclass correlation of 0.29 (95% CI: -0.01 to 0.53, P = 0.03) between vendor measurements and 0.63 (95% CI: 0.48-0.74, P < 0.001) between sonographers. With minor changes in initial input contour, step-wise pixel shifts resulted in a mean absolute error of 3.48% and proportional strain difference of 13.52% by a 6-pixel shift. In external validation, DLS maintained moderate agreement with 2-dimensional GLS (intraclass correlation coefficient [ICC]: 0.56, P = 0.002) with a bias of -3.31% (limits of agreement: -11.65% to 5.02%). The DLS method showed differences (P < 0.0001) between populations with cardiac hypertrophy and had moderate agreement in a patient population of advanced cardiac amyloidosis: ICC was 0.64 (95% CI: 0.53-0.72), P < 0.001, with a bias of 0.57%, limits of agreement of -4.87% to 6.01% vs 2-dimensional GLS. CONCLUSIONS: The open-source DLS provides lower variation than human measurements and similar quantitative results. The method is rapid, consistent, vendor-agnostic, publicly released, and applicable across a wide range of imaging qualities.

Genetic regulation and targeted reversal of lysosomal dysfunction and inflammatory sterol metabolism in pulmonary arterial hypertension.

Vascular inflammation critically regulates endothelial cell (EC) pathophenotypes, particularly in pulmonary arterial hypertension (PAH). Dysregulation of lysosomal activity and cholesterol metabolism have known inflammatory roles in disease, but their relevance to PAH is unclear. In human pulmonary arterial ECs and in PAH, we found that inflammatory cytokine induction of the nuclear receptor coactivator 7 (NCOA7) both preserved lysosomal acidification and served as a homeostatic brake to constrain EC immunoactivation. Conversely, NCOA7 deficiency promoted lysosomal dysfunction and proinflammatory oxysterol/bile acid generation that, in turn, contributed to EC pathophenotypes. In vivo, mice deficient for Ncoa7 or exposed to the inflammatory bile acid 7α-hydroxy-3-oxo-4-cholestenoic acid (7HOCA) displayed worsened PAH. Emphasizing this mechanism in human PAH, an unbiased, metabolome-wide association study (N=2,756) identified a plasma signature of the same NCOA7-dependent oxysterols/bile acids associated with PAH mortality (P<1.1x10-6). Supporting a genetic predisposition to NCOA7 deficiency, in genome-edited, stem cell-derived ECs, the common variant intronic SNP rs11154337 in NCOA7 regulated NCOA7 expression, lysosomal activity, oxysterol/bile acid production, and EC immunoactivation. Correspondingly, SNP rs11154337 was associated with PAH severity via six-minute walk distance and mortality in discovery (N=93, P=0.0250; HR=0.44, 95% CI [0.21-0.90]) and validation (N=630, P=2x10-4; HR=0.49, 95% CI [0.34-0.71]) cohorts. Finally, utilizing computational modeling of small molecule binding to NCOA7, we predicted and synthesized a novel activator of NCOA7 that prevented EC immunoactivation and reversed indices of rodent PAH. In summary, we have established a genetic and metabolic paradigm and a novel therapeutic agent that links lysosomal biology as well as oxysterol and bile acid processes to EC inflammation and PAH pathobiology. This paradigm carries broad implications for diagnostic and therapeutic development in PAH and in other conditions dependent upon acquired and innate immune regulation of vascular disease.

Pulmonary primary oxysterol and bile acid synthesis as a predictor of outcomes in pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) is a rare and fatal vascular disease with heterogeneous clinical manifestations. To date, molecular determinants underlying the development of PAH and related outcomes remain poorly understood. Herein, we identify pulmonary primary oxysterol and bile acid synthesis (PPOBAS) as a previously unrecognized pathway central to PAH pathophysiology. Mass spectrometry analysis of 2,756 individuals across five independent studies revealed 51 distinct circulating metabolites that predicted PAH-related mortality and were enriched within the PPOBAS pathway. Across independent single-center PAH studies, PPOBAS pathway metabolites were also associated with multiple cardiopulmonary measures of PAH-specific pathophysiology. Furthermore, PPOBAS metabolites were found to be increased in human and rodent PAH lung tissue and specifically produced by pulmonary endothelial cells, consistent with pulmonary origin. Finally, a poly-metabolite risk score comprising 13 PPOBAS molecules was found to not only predict PAH-related mortality but also outperform current clinical risk scores. This work identifies PPOBAS as specifically altered within PAH and establishes needed prognostic biomarkers for guiding therapy in PAH.

Sex Differences in Association of Physical Activity With All-Cause and Cardiovascular Mortality.

BACKGROUND: Although physical activity is widely recommended for reducing cardiovascular and all-cause mortality risks, female individuals consistently lag behind male individuals in exercise engagement. OBJECTIVES: The goal of this study was to evaluate whether physical activity derived health benefits may differ by sex. METHODS: In a prospective study of 412,413 U.S. adults (55% female, age 44 ± 17 years) who provided survey data on leisure-time physical activity, we examined sex-specific multivariable-adjusted associations of physical activity measures (frequency, duration, intensity, type) with all-cause and cardiovascular mortality from 1997 through 2019. RESULTS: During 4,911,178 person-years of follow-up, there were 39,935 all-cause deaths including 11,670 cardiovascular deaths. Regular leisure-time physical activity compared with inactivity was associated with 24% (HR: 0.76; 95% CI: 0.73-0.80) and 15% (HR: 0.85; 95% CI: 0.82-0.89) lower risk of all-cause mortality in women and men, respectively (Wald F = 12.0, sex interaction P < 0.001). Men reached their maximal survival benefit of HR 0.81 from 300 min/wk of moderate-to-vigorous physical activity, whereas women achieved similar benefit at 140 min/wk and then continued to reach a maximum survival benefit of HR 0.76 also at ∼300 min/wk. Sex-specific findings were similar for cardiovascular death (Wald F = 20.1, sex interaction P < 0.001) and consistent across all measures of aerobic activity as well as muscle strengthening activity (Wald F = 6.7, sex interaction P = 0.009). CONCLUSIONS: Women compared with men derived greater gains in all-cause and cardiovascular mortality risk reduction from equivalent doses of leisure-time physical activity. These findings could enhance efforts to close the "gender gap" by motivating especially women to engage in any regular leisure-time physical activity.

Machine Learning and Bias in Medical Imaging: Opportunities and Challenges.

Bias in health care has been well documented and results in disparate and worsened outcomes for at-risk groups. Medical imaging plays a critical role in facilitating patient diagnoses but involves multiple sources of bias including factors related to access to imaging modalities, acquisition of images, and assessment (ie, interpretation) of imaging data. Machine learning (ML) applied to diagnostic imaging has demonstrated the potential to improve the quality of imaging-based diagnosis and the precision of measuring imaging-based traits. Algorithms can leverage subtle information not visible to the human eye to detect underdiagnosed conditions or derive new disease phenotypes by linking imaging features with clinical outcomes, all while mitigating cognitive bias in interpretation. Importantly, however, the application of ML to diagnostic imaging has the potential to either reduce or propagate bias. Understanding the potential gain as well as the potential risks requires an understanding of how and what ML models learn. Common risks of propagating bias can arise from unbalanced training, suboptimal architecture design or selection, and uneven application of models. Notwithstanding these risks, ML may yet be applied to improve gain from imaging across all 3A's (access, acquisition, and assessment) for all patients. In this review, we present a framework for understanding the balance of opportunities and challenges for minimizing bias in medical imaging, how ML may improve current approaches to imaging, and what specific design considerations should be made as part of efforts to maximize the quality of health care for all.

Blood pressure variability supersedes heart rate variability as a real-world measure of dementia risk.

Blood pressure variability (BPV) and heart rate variability (HRV) have been associated with Alzheimer's Disease and Related Dementias (ADRD) in rigorously controlled studies. However, the extent to which BPV and HRV may offer predictive information in real-world, routine clinical care is unclear. In a retrospective cohort study of 48,204 adults (age 54.9 ± 17.5 years, 60% female) receiving continuous care at a single center, we derived BPV and HRV from routinely collected clinical data. We use multivariable Cox models to evaluate the association of BPV and HRV, separately and in combination, with incident ADRD. Over a median 3 [2.4, 3.0] years, there were 443 cases of new-onset ADRD. We found that clinically derived measures of BPV, but not HRV, were consistently associated with incident ADRD. In combined analyses, only patients in both the highest quartile of BPV and lowest quartile of HRV had increased ADRD risk (HR 2.34, 95% CI 1.44-3.81). These results indicate that clinically derived BPV, rather than HRV, offers a consistent and readily available metric for ADRD risk assessment in a real-world patient care setting. Thus, implementation of BPV as a widely accessible tool could allow clinical providers to efficiently identify patients most likely to benefit from comprehensive ADRD screening.

Racial and Ethnic Disparities in Co-Occurrence of Nocturnal Hypertension and Nocturnal Blood Pressure Decreases.

This cross-sectional study assesses racial and ethnic disparities in co-occurrence of nocturnal hypertension and blunted nocturnal decreases in blood pressure.

How Should We Approach Faculty Who Create Hostile Learning Environments for Underrepresented Students and Trainees?

Faculty members who demonstrate resistance to or lack of skill in addressing negative bias in practice and learning environments can erode safety, especially among underrepresented students. This commentary on a case suggests how educators and leaders should respond to problematic behaviors of unwilling or unskilled faculty, prevent mistreatment of students and colleagues, and facilitate continuous faculty development. This commentary also considers strategies for motivating equity and building health care cultures of accountability.

Postural orthostatic tachycardia syndrome after COVID-19 vaccination.

BACKGROUND: There is an association between coronavirus disease 2019 (COVID-19) mRNA vaccination and the incidence or exacerbation of postural orthostatic tachycardia syndrome (POTS). OBJECTIVE: The purpose of this study was to characterize patients reporting new or exacerbated POTS after receiving the mRNA COVID-19 vaccine. METHODS: We prospectively collected data from sequential patients in a POTS clinic between July 2021 and June 2022 reporting new or exacerbated POTS symptoms after COVID-19 vaccination. Heart rate variability (HRV) and skin sympathetic nerve activity (SKNA) were compared against those of 24 healthy controls. RESULTS: Ten patients (6 women and 4 men; age 41.5 ± 7.9 years) met inclusion criteria. Four patients had standing norepinephrine levels > 600 pg/mL. All patients had conditions that could raise POTS risk, including previous COVID-19 infection (N = 4), hypermobile Ehlers-Danlos syndrome (N = 6), mast cell activation syndrome (N = 6), and autoimmune (N = 7), cardiac (N = 7), neurological (N = 6), or gastrointestinal conditions (N = 4). HRV analysis indicated a lower ambulatory root mean square of successive differences (46.19 ±24 ms; P = .042) vs control (72.49 ± 40.8 ms). SKNA showed a reduced mean amplitude (0.97 ± 0.052 µV; P = .011) vs control (1.2 ± 0.31 µV) and burst amplitude (1.67 ± 0.16 µV; P = .018) vs control (4. 3 ± 4.3 µV). After 417.2 ± 131.4 days of follow-up, all patients reported improvement with the usual POTS care, although 2 with COVID-19 reinfection and 1 with small fiber neuropathy did have relapses of POTS symptoms. CONCLUSION: All patients with postvaccination POTS had pre-existing conditions. There was no evidence of myocardial injuries or echocardiographic abnormalities. The decreased HRV suggests a sympathetic dominant state. Although all patients improved with guideline-directed care, there is a risk of relapse.

Assessing the post hoc effectiveness of tixagevimab-cilgavimab for prevention of SARS-CoV-2 infections in solid organ transplant recipients.

BACKGROUND: Tixagevimab-cilgavimab (Tix-Cil) was authorized for prophylaxis against COVID-19 in immunocompromised patients from December 2021 through January 2023. Real-world effectiveness for solid organ transplant (SOT) recipients has been unclear. METHODS: We enrolled 911 SOT recipients into a longitudinal COVID-19 serology study, of whom 381 (42%) received ≥1 dose of Tix-Cil. We collected and analyzed data on incident SARS-CoV-2 infections and antibody kinetics for all patients from January 2022 to March 2023, including periods dominated by Omicron BA and BQ subvariants. RESULTS: Over 253 ± 131 days of follow-up, there were 324 new-onset SARS-CoV-2 infections: 117 (31%) in Tix-Cil treated and 207 (39%) in Tix-Cil untreated patients (p = .012). In analyses adjusting for demographic, clinical, and COVID-19 exposure factors, any Tix-Cil treatment was associated with lower infection risk (OR 0.52, 95% CI 0.27-0.96, p = .039) throughout the surveillance period including when more resistant BQ.1 and BQ.1.1 subvariants had emerged (12/1/2022 onwards). Among treated patients, receiving a Tix-Cil dose was associated with substantial and sustained increase in anti-spike IgG antibody and angiotensin-converting enzyme 2 binding inhibition levels (Abbott Architect assay) that together also demonstrated association with lower infection risk (p = .042). During the full surveillance period, the frequency of infections requiring hospitalization was low overall (N = 26, 2.9% of the total cohort) and not significantly different between Tix-Cil recipients (N = 12, 3.2% of treated patients) and non-Tix-Cil recipients (N = 14, 2.6% of untreated patients) with unadjusted p = .31 for between-group difference. CONCLUSION: In a large cohort of SOT recipients, we found that Tix-Cil reduced infection risk even amidst emergent Omicron subvariants. Additionally, the extent of measurable humoral response to Tix-Cil may indicate relative effectiveness. Pre-exposure monoclonal antibody therapy may represent a strategy that will continue to offer clinical benefit for immunocompromised persons who are known to derive limited protection from vaccinations.

Cardiac Abnormalities in Hispanic/Latina Women With Prior De Novo Hypertensive Disorders of Pregnancy.

BACKGROUND: Hypertensive disorders of pregnancy (HDP) are associated with long-term maternal risks for cardiovascular disease for reasons that remain incompletely understood. METHODS: The HCHS/SOL (Hispanic Community Health Study/Study of Latinos), a multi-center community-based cohort of Hispanic/Latino adults recruited 2008 to 2011, was used to evaluate the associations of history of de novo HDP (gestational hypertension, preeclampsia, eclampsia) with echocardiographic measures of cardiac structure and function in Hispanic/Latina women with ≥1 prior pregnancy and the proportion of association mediated by current hypertension (>140/90 mm Hg or antihypertensive therapy). RESULTS.: The study cohort included 5168 Hispanic/Latina women with an average age (SD) of 58.7 (9.7) years at time of echocardiogram. Prior de novo HDP was reported by 724 (14%) of the women studied and was associated with lower left ventricle (LV) ejection fraction -0.66 (95% confidence interval [CI], -1.21 to -0.11), higher LV relative wall thickness 0.09 (95% CI, 0-0.18), and 1.39 (95% CI, 1.02-1.89) higher risk of abnormal LV geometry after adjusting for blood pressure and other confounders. The proportion of the association mediated by current hypertension between HDP and LV ejection fraction was 0.09 (95% CI, 0.03-0.45), LV relative wall thickness was 0.28 (95% CI, 0.16-0.51), abnormal LV geometry was 0.14 (95% CI, 0.12-0.48), concentric left ventricular hypertrophy was 0.31 (95% CI, 0.19-0.86), and abnormal LV diastolic dysfunction was 0.58 (95% CI, 0.26-0.79). CONCLUSIONS.: In a large cohort of Hispanic/Latina women those with history of de novo HDP had detectable and measurable subclinical alterations in cardiac structure and both systolic and diastolic dysfunction that were only partially mediated by current hypertension.