Artificial Intelligence Models Reveal Sex-Specific Gene Expression in Aortic Valve Calcification.
Ontology highlight
ABSTRACT:
SUBMITTER: Sarajlic P
PROVIDER: S-EPMC8165113 | biostudies-literature | 2021 May
REPOSITORIES: biostudies-literature
Publications
Artificial Intelligence Models Reveal Sex-Specific Gene Expression in Aortic Valve Calcification.
Sarajlic Philip P Plunde Oscar O Franco-Cereceda Anders A Bäck Magnus M
JACC. Basic to translational science 20210414 5
Male and female aortic stenosis patients have distinct valvular phenotypes, increasing the complexities in the evaluation of valvular pathophysiology. In this study, we present cutting-edge artificial intelligence analyses of transcriptome-wide array data from stenotic aortic valves to highlight differences in gene expression patterns between the sexes, using both sex-differentiated transcripts and unbiased gene selections. This approach enabled the development of efficient models with high pred ...[more]
Similar Datasets
Project description:ObjectiveCirculating biomarkers are useful in detection and monitoring of cardiovascular diseases. However, their role in aortic valve disease is unclear. Mechanisms are rapidly elucidated and sex differences are suggested to be involved. Therefore, we sought to identify biomarkers involved in aortic valve calcification (AVC) stratified by sex.MethodsBlood samples of 34 patients with AVC (without further overt cardiovascular disease, including absence of hemodynamic consequences of valvular calcification) were compared with 136 patients without AVC. AVC was determined using computed tomography calcium scoring. Circulating biomarkers were quantified using a novel antibody-based method (Olink Proseek Multiplex Cardiovascular Panel I) and 92 biomarkers were compared between patients with and without AVC.ResultsIn the overall population, Interleukin-1 Receptor Antagonist and pappalysin-1 were associated with increased and decreased odds of having AVC. These differences were driven by the male population [IL1RA: OR 2.79 (1.16-6.70), p = 0.022; PAPPA: OR 0.30 (0.11-0.84), p = 0.021]. Furthermore, TNF-related activation-induced cytokine (TRANCE) and fibroblast growth factor-23 were associated decreased odds of having AVC, and monocyte chemotactic protein-1 was associated with increased odds of having AVC [TRANCE: OR 0.32 (0.12-0.80), p = 0.015; FGF23: OR 0.41 (0.170-0.991), p = 0.048; MCP1: OR 2.64 (1.02-6.81), p = 0.045]. In contrast, galanin peptides and ST2 were associated with increased odds of having AVC in females [GAL: OR 12.38 (1.31-116.7), p = 0.028; ST2: OR13.64 (1.21-153.33), p = 0.034].ConclusionIn this exploratory study, we identified biomarkers involved in inflammation, fibrosis and calcification which may be associated with having AVC. Biomarkers involved in fibrosis may show higher expression in females, whilst biomarkers involved in inflammation and calcification could associate with AVC in males.
| S-EPMC7366171 | biostudies-literature
Project description:AimsTo study sex-specific differences in the amount and distribution of aortic valve calcification (AVC) and to correlate the AVC load with paravalvular leakage (PVL) post-transcatheter aortic valve intervention (TAVI).Methods and resultsThis registry included 1801 patients undergoing TAVI with a Sapien3 or Evolut valve in two tertiary care institutions. Exclusion criteria encompassed prior aortic valve replacement, suboptimal multidetector computed tomography (MDCT) quality, and suboptimal transthoracic echocardiography images. Calcium content and distribution were derived from MDCT. In this study, the median age was 81.7 (25th-75th percentile 77.5-85.3) and 54% male. Men, compared to women, were significantly younger [81.2 (25th-75th percentile 76.5-84.5) vs. 82.4 (78.2-85.9), P ≤ 0.01] and had a larger annulus area [512 mm2 (25th-75th percentile 463-570) vs. 405 mm2 (365-454), P < 0.01] and higher Agatston score [2567 (25th-75th percentile 1657-3913) vs. 1615 (25th-75th percentile 905-2484), P < 0.01]. In total, 1104 patients (61%) had none-trace PVL, 648 (36%) mild PVL, and 49 (3%) moderate PVL post-TAVI. There was no difference in the occurrence of moderate PVL between men and women (3% vs. 3%, P = 0.63). Cut-off values for the Agatston score as predictor for moderate PVL based on the receiver-operating characteristic curve were 4070 (sensitivity 0.73, specificity 0.79) for men and 2341 (sensitivity 0.74, specificity 0.73) for women.ConclusionAVC is a strong predictor for moderate PVL post-TAVI. Although the AVC load in men is higher compared to women, there is no difference in the incidence of moderate PVL. Sex-specific Agatston score cut-offs to predict moderate PVL were almost double as high in men vs. women.
| S-EPMC10229261 | biostudies-literature
Project description:AimsAortic stenosis (AS) is a common and progressive disease, which, if left untreated, results in increased morbidity and mortality. Monitoring and follow-up care can be challenging due to significant variability in disease progression. This study aimed to develop machine learning models to predict the risks of disease progression and mortality in patients with mild AS.Methods and resultsA comprehensive database including 9611 patients with serial transthoracic echocardiograms was collected from a single institution across three clinical sites. The data set included parameters from echocardiograms, electrocardiograms, laboratory values, and diagnosis codes. Data from a single clinical site were preserved as an independent test group. Machine learning models were trained to identify progression to severe stenosis and all-cause mortality and tested in their performance for endpoints at 2 and 5 years. In the independent test group, the AS progression model differentiated those with progression to severe AS within 2 and 5 years with an area under the curve (AUC) of 0.86 for both. The feature of greatest importance was aortic valve mean gradient, followed by other valve haemodynamic measurements including valve area and dimensionless index. The mortality model identified those with mortality within 2 and 5 years with an AUC of 0.84 and 0.87, respectively. Smaller reduced-input validation models had similarly robust findings.ConclusionMachine learning models can be used in patients with mild AS to identify those at high risk of disease progression and mortality. Implementation of such models may facilitate real-time, patient-specific follow-up recommendations.
| S-EPMC11750192 | biostudies-literature
Project description:BackgroundTransthoracic echocardiography (TTE) is the primary modality for diagnosing aortic stenosis (AS), yet it requires skilled operators and can be resource-intensive. We developed and validated an artificial intelligence (AI)-based system for evaluating AS that is effective in both resource-limited and advanced settings.MethodsWe created a dual-pathway AI system for AS evaluation using a nationwide echocardiographic dataset (developmental dataset, n = 8427): 1) a deep learning (DL)-based AS continuum assessment algorithm using limited 2D TTE videos, and 2) automating conventional AS evaluation. We performed internal (internal test dataset [ITDS], n = 841) and external validation (distinct hospital dataset [DHDS], n = 1696; temporally distinct dataset [TDDS], n = 772) for diagnostic value across various stages of AS and prognostic value for composite endpoints (cardiovascular death, heart failure, and aortic valve replacement).FindingsThe DL index for the AS continuum (DLi-ASc, range 0-100) increased with worsening AS severity and demonstrated excellent discrimination for any AS (AUC 0.91-0.99), significant AS (0.95-0.98), and severe AS (0.97-0.99). DLi-ASc was independent predictor for composite endpoint (adjusted hazard ratios 2.19, 1.64, and 1.61 per 10-point increase in ITDS, DHDS, and TDDS, respectively). Automatic measurement of conventional AS parameters demonstrated excellent correlation with manual measurement, resulting in high accuracy for AS staging (98.2% for ITDS, 82.1% for DHDS, and 96.8% for TDDS) and comparable prognostic value to manually-derived parameters.InterpretationThe AI-based system provides accurate and prognostically valuable AS assessment, suitable for various clinical settings. Further validation studies are planned to confirm its effectiveness across diverse environments.FundingThis work was supported by a grant from the Institute of Information & Communications Technology Planning & Evaluation (IITP) funded by the Korea government (Ministry of Science and ICT; MSIT, Republic of Korea) (No. 2022000972, Development of a Flexible Mobile Healthcare Software Platform Using 5G MEC); and the Medical AI Clinic Program through the National IT Industry Promotion Agency (NIPA) funded by the MSIT, Republic of Korea (Grant No.: H0904-24-1002).
| S-EPMC11794175 | biostudies-literature
Project description:Patients with aortic valve stenosis (AVS) have sexually dimorphic phenotypes in their valve tissue, where male valvular tissue adopts a calcified phenotype and female tissue becomes more fibrotic. The molecular mechanisms that regulate sex-specific calcification in valvular tissue remain poorly understood. Here, we explored the role of osteopontin (OPN), a pro-fibrotic but anti-calcific bone sialoprotein, in regulating the calcification of female aortic valve tissue. Recognizing that OPN mediates calcification processes, we hypothesized that aortic valvular interstitial cells (VICs) in female tissue have reduced expression of osteogenic markers in the presence of elevated OPN relative to male VICs. Human female valve leaflets displayed reduced and smaller microcalcifications, but increased OPN expression relative to male leaflets. To understand how OPN expression contributes to observed sex dimorphisms in valve tissue, we employed enzymatically degradable hydrogels as a 3D cell culture platform to recapitulate male or female VIC interactions with the extracellular matrix. Using this system, we recapitulated sex differences observed in human tissue, specifically demonstrating that female VICs exposed to calcifying medium have smaller mineral deposits within the hydrogel relative to male VICs. We identified a change in OPN dynamics in female VICs in the presence of calcification stimuli, where OPN deposition localized from the extracellular matrix to perinuclear regions. Additionally, exogenously delivered endothelin-1 to encapsulated VICs increased OPN gene expression in male cells, which resulted in reduced calcification. Collectively, our results suggest that increased OPN in female valve tissue may play a sex-specific role in mitigating mineralization during AVS progression.
| S-EPMC9842038 | biostudies-literature
Project description:Valvular heart disease (VHD) is a chronic progressive condition with an increasing prevalence in the Western world due to aging populations. VHD is often diagnosed at a late stage when patients are symptomatic and the outcomes of therapy, including valve replacement, may be sub-optimal due the development of secondary complications, including left ventricular (LV) dysfunction. The clinical application of artificial intelligence (AI), including machine learning (ML), has promise in supporting not only early and more timely diagnosis, but also hastening patient referral and ensuring optimal treatment of VHD. As physician auscultation lacks accuracy in diagnosis of significant VHD, computer-aided auscultation (CAA) with the help of a commercially available digital stethoscopes improves the detection and classification of heart murmurs. Although used little in current clinical practice, CAA can screen large populations at low cost with high accuracy for VHD and faciliate appropriate patient referral. Echocardiography remains the next step in assessment and planning management and AI is delivering major changes in speeding training, improving image quality by pattern recognition and image sorting, as well as automated measurement of multiple variables, thereby improving accuracy. Furthermore, AI then has the potential to hasten patient disposal, by automated alerts for red-flag findings, as well as decision support in dealing with results. In management, there is great potential in ML-enabled tools to support comprehensive disease monitoring and individualized treatment decisions. Using data from multiple sources, including demographic and clinical risk data to image variables and electronic reports from electronic medical records, specific patient phenotypes may be identified that are associated with greater risk or modeled to the estimate trajectory of VHD progression. Finally, AI algorithms are of proven value in planning intervention, facilitating transcatheter valve replacement by automated measurements of anatomical dimensions derived from imaging data to improve valve selection, valve size and method of delivery.
| S-EPMC7867819 | biostudies-literature
Project description:AimsThere is evidence to suggest that the subtype of aortic stenosis (AS), the degree of myocardial fibrosis (MF), and level of aortic valve calcification (AVC) are associated with adverse cardiac outcome in AS. Because little is known about their respective contribution, we sought to investigate their relative importance and interplay as well as their association with adverse cardiac events following transcatheter aortic valve replacement (TAVR).Methods and resultsOne hundred consecutive patients with severe AS and indication for TAVR were prospectively enrolled between January 2017 and October 2018. Patients underwent transthoracic echocardiography, multidetector computed tomography, and left ventricular endomyocardial biopsies at the time of TAVR. The final study cohort consisted of 92 patients with a completed study protocol, 39 (42.4%) of whom showed a normal ejection fraction (EF) high-gradient (NEFHG) AS, 13 (14.1%) a low EF high-gradient (LEFHG) AS, 25 (27.2%) a low EF low-gradient (LEFLG) AS, and 15 (16.3%) a paradoxical low-flow, low-gradient (PLFLG) AS. The high-gradient phenotypes (NEFHG and LEFHG) showed the largest amount of AVC (807 ± 421 and 813 ± 281 mm3 , respectively) as compared with the low-gradient phenotypes (LEFLG and PLFLG; 503 ± 326 and 555 ± 594 mm3 , respectively, P < 0.05). Conversely, MF was most prevalent in low-output phenotypes (LEFLG > LEFHG > PLFLG > NEFHG, P < 0.05). This was paralleled by a greater cardiovascular (CV) mortality within 600 days after TAVR (LEFLG 28% > PLFLG 26.7% > LEFHG 15.4% > NEFHG 2.5%; P = 0.023). In patients with a high MF burden, a higher AVC was associated with a lower mortality following TAVR (P = 0.045, hazard ratio 0.261, 95% confidence interval 0.07-0.97).ConclusionsMF is associated with adverse CV outcome following TAVR, which is most prevalent in low EF situations. In the presence of large MF burden, patients with large AVC have better outcome following TAVR. Conversely, worse outcome in large MF and relatively little AVC may be explained by a relative prominence of an underlying cardiomyopathy. The better survival rates in large AVC patients following TAVR indicate TAVR induced relief of severe AS-associated pressure overload with subsequently improved outcome.
| S-EPMC10375183 | biostudies-literature
Project description:miR-214 has been recently found to be significantly downregulated in calcified human aortic valves (AVs). ER stress, especially the ATF4-mediated pathway, has also been shown to be significantly upregulated in calcific AV disease. Since elevated cyclic stretch is one of the major mechanical stimuli for AV calcification and ATF4 is a validated target of miR-214, we investigated the effect of cyclic stretch on miR-214 expression as well as those of ATF4 and two downstream genes (CHOP and BCL2L1). Porcine aortic valve (PAV) leaflets were cyclically stretched at 15% for 48 h in regular medium and for 1 week in osteogenic medium to simulate the early remodeling and late calcification stages of stretch-induced AV disease, respectively. For both stages, 10% cyclic stretch served as the physiological counterpart. RT-qPCR revealed that miR-214 expression was significantly downregulated during the late calcification stage, whereas the mRNA expression of ATF4 and BCL2L1 was upregulated and downregulated, respectively, during both early remodeling and late calcification stages. When PAV leaflets were statically transfected with miR-214 mimic in osteogenic medium for 2 weeks, calcification was significantly reduced compared to the control mimic case. This implies that miR-214 may have a protective role in stretch-induced calcific AV disease.
| S-EPMC6416061 | biostudies-literature
Project description:BackgroundCellular communication network factor 3 (CCN3) has been implicated in the regulation of osteoblast differentiation. However, it is not known if CCN3 can regulate valvular calcification. While macrophages have been shown to regulate valvular calcification, the molecular and cellular mechanisms of this process remain poorly understood. In the present study, we investigated the role of macrophage-derived CCN3 in the progression of calcific aortic valve disease.MethodsMyeloid-specific knockout of CCN3 (Mye-CCN3-KO) and control mice were subjected to a single tail intravenous injection of AAV encoding mutant mPCSK9 (rAAV8/D377Y-mPCSK9) to induce hyperlipidemia. AAV-injected mice were then fed a high fat diet for 40 weeks. At the conclusion of high fat diet feeding, tissues were harvested and subjected to histologic and pathologic analyses. In vitro, bone marrow-derived macrophages (BMDM) were obtained from Mye-CCN3-KO and control mice and the expression of bone morphogenic protein signaling related gene were verified via quantitative real-time PCR and Western blotting. The BMDM conditioned medium was cocultured with human valvular intersititial cells which was artificially induced calcification to test the effect of the conditioned medium via Western blotting and Alizarin red staining.ResultsEchocardiography revealed that both male and female Mye-CCN3-KO mice displayed compromised aortic valvular function accompanied by exacerbated valve thickness and cardiac dysfunction. Histologically, Alizarin-Red staining revealed a marked increase in aortic valve calcification in Mye-CCN3-KO mice when compared to the controls. In vitro, CCN3 deficiency augmented BMP2 production and secretion from bone marrow-derived macrophages. In addition, human valvular interstitial cells cultured with conditioned media from CCN3-deficient BMDMs resulted in exaggerated pro-calcifying gene expression and the consequent calcification.ConclusionOur data uncovered a novel role of myeloid CCN3 in the regulation of aortic valve calcification. Modulation of BMP2 production and secretion in macrophages might serve as a key mechanism for macrophage-derived CCN3's anti-calcification function in the development of CAVD. Video Abstract.
| S-EPMC9854076 | biostudies-literature
Project description:BackgroundAtrioventricular block requiring permanent pacemaker (PPM) implantation is a common complication of transcatheter aortic valve replacement (TAVR). The mechanism of atrioventricular (AV) block during TAVR is not fully understood, but it may be due to the mechanical stress of TAVR deployment, resulting in possible injury to the nearby compact AV node. Aortic valve calcification (AVC) may worsen this condition and has been associated with an increased risk for post-TAVR PPM implantation. We performed a retrospective analysis to determine if AVC is predictive for long-term right ventricular (RV) pacing in post-TAVR pacemaker patients at 30 days.MethodsA total of 262 consecutive patients who underwent TAVR with a balloon-expandable valve were analyzed. AVC data were derived from contrast-enhanced computed tomography and characterized by leaflet sector and region.ResultsA total of 25 patients (11.1%) required post-TAVR PPM implantation. Seventeen patients did not require RV pacing at 30 days. Nine of these 17 patients had no RV pacing requirement within 10 days. The presence of intra-procedural heart block (P = 0.004) was the only significant difference between patients who did not require PPM and those who required PPM but they were not RV pacing-dependent at 30 days. Non-coronary cusp (NCC) calcium volume was significantly higher in patients who were pacemaker-dependent at 30 days (P = 0.01) and a calcium volume of > 239.2 mm3 in the NCC was strongly predictive of pacemaker dependence at 30 days (area under the curve (AUC) = 0.813). Pre-existing right bundle branch block (RBBB) (odds ratio (OR) 105.4, P = 0.004), bifascicular block (OR 12.5, P = 0.02), QRS duration (OR 70.43, P = 0.007) and intra-procedural complete heart block (OR 12.83, P = 0.03) were also predictive of pacemaker dependence at 30 days.ConclusionsIn patients who required PPM after TAVR, quantification of AVC by non-coronary leaflet calcium volume was found to be a novel predictor for RV pacing dependence at 30 days. The association of NCC calcification and PPM dependence may be related to the proximity of the conduction bundle to the non-coronary leaflet. Further studies are necessary to improve risk prediction for long-term RV pacing requirements following TAVR.
| S-EPMC7239596 | biostudies-literature