Polygenic Propensity for Longevity, APOE-ε4 Status, Dementia Diagnosis, and Risk for Cause-Specific Mortality: A Large Population-Based Longitudinal Study of Older Adults

To deepen the understanding of genetic mechanisms influencing mortality risk, we investigated the impact of genetic predisposition to longevity and APOE-ε4, on all-cause mortality and specific causes of mortality. We further investigated the mediating effects of dementia on these relationships. Using data on 7 131 adults aged ≥50 years (mean = 64.7 years, standard deviation [SD] = 9.5) from the English Longitudinal Study of Aging, genetic predisposition to longevity was calculated using the polygenic score approach (PGSlongevity). APOE-ε4 status was defined according to the absence or presence of ε4 alleles. The causes of death were ascertained from the National Health Service central register, which was classified into cardiovascular diseases, cancers, respiratory illness, and all other causes of mortality. Of the entire sample, 1 234 (17.3%) died during an average 10-year follow-up. One-SD increase in PGSlongevity was associated with a reduced risk for all-cause mortality (hazard ratio [HR] = 0.93, 95% confidence interval [CI]: 0.88-0.98, p = .010) and mortalities due to other causes (HR = 0.81, 95% CI: 0.71-0.93, p = .002) in the following 10 years. In gender-stratified analyses, APOE-ε4 status was associated with a reduced risk for all-cause mortality and mortalities related to cancers in women. Mediation analyses estimated that the percent excess risk of APOE-ε4 on other causes of mortality risk explained by the dementia diagnosis was 24%, which increased to 34% when the sample was restricted to adults who were aged ≤75 years old. To reduce the mortality rate in adults who are aged ≥50 years old, it is essential to prevent dementia onset in the general population.

Schizophrenia polygenic risk score and type 2 diabetes onset in older adults with no schizophrenia diagnosis

OBJECTIVES

An association between type 2 diabetes (T2DM) and schizophrenia has long been observed, and recent research revealed presence of shared genetic factors. However, epidemiological evidence was inconsistent, some reported insignificant contribution of genetic factors to T2DM-schizophrenia comorbidity. Prior works studied people with schizophrenia, particularly, antipsychotic-naive patients, or those during the first psychotic experience to limit schizophrenia-related environmental factors. In contrast, we controlled such factors by utilizing a general population sample of individuals undiagnosed with schizophrenia. We hypothesized that if schizophrenia genetics impact T2DM development and such impact is not fully mediated by schizophrenia-related environment, people with high polygenic schizophrenia risk would exhibit elevated T2DM incidence.

METHODS

Using a population-representative sample of adults aged ≥50 from English Longitudinal Study of Ageing ( n  = 5968, 493 T2DM cases, average follow-up 8.7 years), we investigated if schizophrenia polygenic risk score (PGS-SZ) is associated with T2DM onset. A proportional hazards model with interval censoring was adjusted for age and sex (Model 1), and age, sex, BMI, hypertension, cardiovascular diseases, exercise, smoking, depressive symptoms and T2DM polygenic risk score (Model 2). According to the power calculations, hazard rates > 1.14 per standard deviation in PGS-SZ could be detected.

RESULTS

We did not observe a significant association between PGS-SZ and T2DM incidence (hazard ratio 1.04; 95% CI 0.93-1.15; and 1.01, 95% CI 0.94-1.09).

CONCLUSION

Our results suggest low contribution of the intrinsic biological mechanisms driven by the polygenic risk of schizophrenia on future T2DM onset. Further research is needed.

Predicting type 2 diabetes prevalence for people with severe mental illness in a multi-ethnic East London population

BACKGROUND AND AIMS

Prevalence of type two diabetes mellitus (T2DM) in people with severe mental illness (SMI) is 2-3 times higher than in general population. Predictive modelling has advanced greatly in the past decade, and it is important to apply cutting-edge methods to vulnerable groups. However, few T2DM prediction models account for the presence of mental illness, and none seemed to have been developed specifically for people with SMI. Therefore, we aimed to develop and internally validate a T2DM prevalence model for people with SMI.

METHODS

We utilised a large cross-sectional sample representative of a multi-ethnic population from London (674,000 adults); 10,159 people with SMI formed our analytical sample (1,513 T2DM cases). We fitted a linear logistic regression and XGBoost as stand-alone models and as a stacked ensemble. Age, sex, body mass index, ethnicity, area-based deprivation, past hypertension, cardiovascular diseases, prescribed antipsychotics, and SMI illness were the predictors.

RESULTS

Logistic regression performed well while detecting T2DM presence for people with SMI: area under the receiver operator curve (ROC-AUC) was 0.83 (95 % CI 0.79-0.87). XGBoost and LR-XGBoost ensemble performed equally well, ROC-AUC 0.83 (95 % CI 0.79-0.87), indicating a negligible contribution of non-linear terms to predictive power. Ethnicity was the most important predictor after age. We demonstrated how the derived models can be utilised and estimated a 2.14 % (95 %CI 2.03 %-2.24 %) increase in T2DM prevalence in East London SMI population in 20 years' time, driven by the projected demographic changes.

CONCLUSIONS

Primary care data, the setting where prediction models could be most fruitfully used, provide enough information for well-performing T2DM prevalence models for people with SMI. We demonstrated how thorough internal cross-validation of an ensemble of a linear and machine-learning model can quantify the predictive value of non-linearity in the data.

B cell-dependent subtypes and treatment-based immune correlates to survival in stage 3 and 4 lung adenocarcinomas

Lung cancer is the leading cause of cancer-related deaths worldwide. Surgery and chemoradiation are the standard of care in early stages of non-small cell lung cancer (NSCLC), while immunotherapy is the standard of care in late-stage NSCLC. The immune composition of the tumor microenvironment (TME) is recognized as an indicator for responsiveness to immunotherapy, although much remains unknown about its role in responsiveness to surgery or chemoradiation. In this pilot study, we characterized the NSCLC TME using mass cytometry (CyTOF) and bulk RNA sequencing (RNA-Seq) with deconvolution of RNA-Seq being performed by Kassandra, a recently published deconvolution tool. Stratification of patients based on the intratumoral abundance of B cells identified that the B-cell rich patient group had increased expression of CXCL13 and greater abundance of PD1+ CD8 T cells. The presence of B cells and PD1+ CD8 T cells correlated positively with the presence of intratumoral tertiary lymphoid structures (TLS). We then assessed the predictive and prognostic utility of these cell types and TLS within publicly available stage 3 and 4 lung adenocarcinoma (LUAD) RNA-Seq datasets. As previously described by others, pre-treatment expression of intratumoral 12-chemokine TLS gene signature is associated with progression free survival (PFS) in patients who receive treatment with immune checkpoint inhibitors (ICI). Notably and unexpectedly pre-treatment percentages of intratumoral B cells are associated with PFS in patients who receive surgery, chemotherapy, or radiation. Further studies to confirm these findings would allow for more effective patient selection for both ICI and non-ICI treatments.

Abstract P4-09-02: A molecular classification system for basal-like breast cancer based on the tumor microenvironment is prognostic for survival

Recent advancements in molecular profiling have revealed distinct breast cancer subtypes, but many clinical NGS assays rely on gene panels, such as PAM50, limiting their clinical utility. Basal-like breast cancer (BLBC), one of the most aggressive subtypes, has highly variable molecular and clinical characteristics. Now, the tumor microenvironment (TME) is recognized as a vital participant in tumor progression and therapeutic response. The development of more refined classifications based on the TME, capable of accounting for tissue heterogeneity, may improve NGS clinical utility for BLBC. Here, we apply our transcriptomic-based approach, recently described by Bagaev et. al., to classify the BLBC TME into discrete immune portraits, to potentially improve clinical outcomes and facilitate therapeutic decisions. We collected a cohort of 1,708 BLBC samples based on the expression levels of 50 genes (PAM50) from 10 publicly available datasets, with clinical outcomes available (n = 819). Using methodology described by Bagaev et. al., 31 functional gene expression signatures (Fges) were selected, and unsupervised dense Louvain clustering was performed to identify TME subtypes. A novel RNA-seq deconvolution algorithm was used to determine the cell types within the TME. Validation of the histological features, including stroma, tumor infiltrating lymphocytes (TILs), and tertiary lymphoid structures (TLS), relative to gene expression patterns in the TME subtypes was performed by automated and manual annotation of BLBC H&E slides (n = 146) from an independent TCGA cohort. Overall survival (OS) analysis was performed using Kaplan-Meier and Cox regression methods.

We revealed 5 BLBC subtypes with distinct expression patterns: immune-enriched, non-fibrotic (IE, 19%), B-cell–enriched, TLS-like (TLS, 25.5%), granulocyte-enriched (G, 12.8%), fibrotic (F, 28%), and immune desert (D, 17.7%) (Table). IE tumors featured an active immune TME, with high immune checkpoint expression and T cell activity. The TLS subtype also had an immune-rich TME, presenting high levels of B cells, T helper cells, and TLS (p < 0.001). The TLS subtype exhibited the highest number of stromal TILs on TCGA H&E slides. The G subtype was characterized by high expression of granulocytes and granulocyte traffic molecules. In accordance with our findings, deconvolution predicted the highest percent of neutrophils in the G subtype (p < 0.001). The F subtype demonstrated the highest levels of angiogenesis, stromal Fges, and VEGFR1-3, FGFR1, and EGFR expression. By histological evaluation, 84% of F subtype samples demonstrated a medium or high level of fibrosis. The D subtype showed a high proliferation rate and low stromal and immune Fges. Indicative of proliferation rate, CCNB1 and cyclin B1 were highest in G, D, and IE subtypes. OS analysis revealed a significant association between TME subtypes (TLS = baseline; log HR G = 0.87, p < 0.05; IE = 0.39, p = 0.18; F = 0.99, p < 0.05; D = 1.21, p < 0.05), and survival outcomes. The immune-enriched subtypes, IE and TLS, demonstrated good prognosis and higher expression of immune checkpoint genes, while immune desert D and granulocyte-enriched G subtypes exhibited the worst OS.

Using our transcriptomic-based approach, BLBC was classified into 5 distinct subtypes, each with unique therapeutic vulnerabilities. Further investigation of these TME subtypes may lead to potential clinical utility as a prognostic tool to improve clinical decision making.

Table. Characteristics of Basal-like breast cancer (BLBC) tumor microenvironment (TME) subtypes. TLS - Tertiary lymphoid structures; CAF - cancer-associated fibroblasts

Citation Format: Svetlana Khorkova, Diana Shamsutdinova, Vladimir Kushnarev, Lev Popyvanov, Daniil Dymov, Anastasia Zotova, Ivan Valiev, Zoya Antysheva, Anna Love, Jessica H. Brown, Alexander Bagaev, Nikita Kotlov, Nathan Fowler. A molecular classification system for basal-like breast cancer based on the tumor microenvironment is prognostic for survival [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-09-02.

Abstract P6-04-15: AI-based prediction of tertiary lymphoid structures and lymphocyte immune infiltration in breast carcinomas

Introduction Tertiary lymphoid structures (TLSs) and tumor-infiltrating lymphocytes (TILs) in breast carcinomas are prognostic for survival and predictive of certain therapy responses. The presence of TLSs and TILs are identified by manual pathological examination; however, this method often lacks reproducibility, limiting its use in routine clinical practice. Here, we demonstrate that morphological evaluation of whole slide images (WSIs) using an artificial intelligence (AI)-based analytic workflow comprised of convolutional neural network (CNN) deep learning models that accurately and reproducibly characterizes TILs, measured as the lymphocyte immune-infiltrated area (LIIA), and TLSs in the tumor microenvironment (TME) of breast carcinomas. Methods We collected a cohort of 445 TCGA breast cancer H&E WSIs, including clinical and sequencing data, and divided this cohort into luminal invasive lobular carcinoma (ILC) (n = 192), HER2-enriched (n = 110), and basal-like (n = 143) molecular subtypes. After 55 samples were excluded due to artifacts or incomplete clinical annotation, a total of 390 samples were analyzed. A combination of CNN-based deep learning models was used to detect and classify the tumor area, TLSs present in the TME, TLS density (number of TLS per mm2 of tumor), and lymphocyte-rich regions. The LIIA was calculated as the area of the stromal and TIL components of the TME. Validation was performed by manually annotating 10 random WSIs from the dataset. Spatial model predictions of the tumor and TLSs were combined to identify TLS locations. Each model’s predictions were verified by univariate (Kaplan-Meier) and multivariate (Cox regression) survival analyses, and the log-rank test was used to calculate overall survival. Additionally, the relationship between TLSs and LIIAs with CD274 expression (PD-L1) and a high tumor mutational burden (TMB > 10) was analyzed. Statistical analyses included Spearman’s rank correlation and Mann-Whitney tests. Results TLS were detected in 53% (n = 207) of the samples, with a mean density of 26.02 TLS/mm2 (Q3 = 5.53 TLS/mm2). TLS density was higher in basal-like subtype samples compared to luminal and HER2-enriched subtypes. While LIIA and TMB-high samples exhibited a significant relationship (p = 0.00001), no significant association was found between TME and TLS quantities or density. PD-L1 gene expression exhibited weak to moderate correlations with predicted LIIA in basal-like (r = 0.38, p = 0.00001) and HER2-enriched subtypes (r = 0.38, p = 0.0001). The luminal subtype had no significant correlation between PD-L1 expression and predicted LIIA. As a result, LIIA and TLS were characterized as positive prognostic factors for the basal-like subtype. After adjusting for age, stage, and grade, the LIIA and TLS density were found to be significant independent positive prognostic overall survival factors for the basal-like subtype (LIIA HR: 0.02, p = 0.003; TLS-high group HR: 0.09, p = 0.002). For the HER2-enriched subtype, TLS density was also a significant predictor (HR: 0.05, p = 0.035), while LIIA was not a statistically significant prognostic factor (HR: 0.0002, p = 0.08). Associations were not observed between the TLSs and LIIA between the ILC subtypes and survival outcomes. The same result was observed for univariate analyses. Conclusion The developed analytic pipeline accurately identified the presence of LIIA and TLS on H&E slides, demonstrating the potential of CNN for automated characterization of the breast cancer TME. AI-based TLS and LIIA quantification can be a robust tool for pathology processes, offering additional information to help in clinical decision-making. This approach can be used to detect features of immune morphology biomarkers in other cancer types.

Citation Format: Vladimir Kushnarev, Daniil Dymov, Nadezhda Lukashevich, Lev Popyvanov, Anna Belozerova, Diana Shamsutdinova, Aida Akaeva, Yury Popov, Svetlana Khorkova, Ivan Valiev, Anastasia Zotova, Jessica H. Brown, Anna Love, Alexander Bagaev, Ekaterina Postovalova, Nathan Fowler. AI-based prediction of tertiary lymphoid structures and lymphocyte immune infiltration in breast carcinomas [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-04-15.

Multiregional single-cell proteogenomic analysis of ccRCC reveals cytokine drivers of intratumor spatial heterogeneity

Intratumor heterogeneity (ITH) represents a major challenge for anticancer therapies. An integrated, multidimensional, multiregional approach dissecting ITH of the clear cell renal cell carcinoma (ccRCC) tumor microenvironment (TME) is employed at the single-cell level with mass cytometry (CyTOF), multiplex immunofluorescence (MxIF), and single-nucleus RNA sequencing (snRNA-seq) and at the bulk level with whole-exome sequencing (WES), RNA-seq, and methylation profiling. Multiregional analyses reveal unexpected conservation of immune composition within each individual patient, with profound differences among patients, presenting patient-specific tumor immune microenvironment signatures despite underlying genetic heterogeneity from clonal evolution. Spatial proteogenomic TME analysis using MxIF identifies 14 distinct cellular neighborhoods and, conversely, demonstrated architectural heterogeneity among different tumor regions. Tumor-expressed cytokines are identified as key determinants of the TME and correlate with clinical outcome. Overall, this work signifies that spatial ITH occurs in ccRCC, which may drive clinical heterogeneity and warrants further interrogation to improve patient outcomes.

A digital imaging analysis (DIA) platform for identifying tertiary lymphoid structures (TLS) in lung adenocarcinoma (LUAD)

3142

Background: Previous studies of non-small cell lung cancer (NSCLC) have shown that TLS can be predictive of therapy response and a positive prognostic factor for survival. Currently, TLS identification is manually performed by pathologists with limited morphological criteria. Standardizing TLS detection with an automated DIA workflow could guide clinical trials in precision medicine by improving patient stratification. Here, we investigate the reproducibility and sensitivity of our DIA platform for evaluating TLS in LUAD using digital histopathology and machine learning. Methods: TLS were assessed by 3 pathologists on whole slide images (WSI) in a validation cohort of 22 LUAD samples using current TLS characterization criteria of dense lymphoid structures, the presence/absence of a germinal center, and high endothelial venules (HEVs). The intraclass correlation coefficient (ICC) was used to measure reproducibility between pathologists. The BostonGene DIA platform was used to train models for automated TLS detection. Quantitative measurements of area, lymphocyte number, and density of each TLS were obtained. A prospective cohort of 8 samples was used to compare pathologist and DIA identification of TLS. Normalized numbers of TLS in the tumor area were used for cohort stratification for overall survival (OS) analysis using the Kaplan-Meier method in an independent clinical cohort of 104 TCGA-LUAD patients. Results: A panel of 3 pathologists identified 326 unique TLS from 22 samples. Between-pathologist detection of TLS, independent of germinal center or HEV criteria, resulted in good reproducibility with an ICC of 0.77. Our DIA platform exhibited excellent reproducibility with an ICC of 0.94 when compared to validated prospective cohort annotation. In total, 155 and 189 TLS were identified by pathologists and our DIA platform, respectively. The DIA platform demonstrated a markedly improved sensitivity of 0.91 for TLS identification. Furthermore, OS analysis revealed that a TLS density greater than 0.94 TLS per mm2 of tumor assessed by DIA is a statistically significant independent biomarker of better OS in the LUAD cohort from TCGA. Conclusions: These results demonstrate the BostonGene DIA platform detects TLS in LUAD, with improved reproducibility and sensitivity over previous methods. Additionally, the DIA platform showed a TLS density greater than 0.94 TLS per mm2 of tumor is a positive prognostic marker for OS in LUAD. Standardized TLS DIA identification can be exploited in digital pathology applications for future clinical trials, informing clinicians of predictive and prognostic information during the decision-making process.

Abstract 2742: Integrated multiregional transcriptomic and multi-parameter single-cell imaging analysis of clear cell renal cell carcinoma elucidates diverse cellular communities present within the tumor microenvironment

Although genomic analyses of clear cell renal cell carcinoma (ccRCC) patients have revealed targetable pathways that have led to FDA-approved therapies, the responses to these therapies remain limited. The significant success of immune checkpoint inhibitors and anti-angiogenic agents suggest that ccRCC has a unique tumor microenvironment composition and tumor behavior that influences therapeutic response. Here, we describe an integrated proteogenomic method to study intratumoral heterogeneity (ITH), microenvironment composition, tumor spatial behavior, and cellular communities in ccRCC.

A unique AI-based segmentation platform for multiplex immunofluorescence (MxIF) was developed to analyze an entire tissue slide at single-cell resolution, including 70 regions of interest per slide, providing significant information regarding spatial architecture. Primary ccRCC tumors collected from patients were biopsied at multiple locations and subjected to MxIF (20 markers, n = 10 sites, 4 pts, ~1,000,000 cells), RNA-seq (n = 8 sites, 3 pts) and CyTOF (n = 21 sites, 6 pts), allowing integrated multi-omics analysis at the single-cell level.

Integrated analysis showed that genomic intratumor heterogeneity (ITH) was remarkably similar across all regions biopsied from the same patient, and the cellular populations present within each region of the same patient were alike. However, some cell types such as TCM CD4 T cells and Tem CD38 and Tem PD1+CD69+CD38- CD8 T cells, and CD163+PDL1+LAMP+ showed great inter-patient differences in the proportion of these cell populations. Notably, 14 CD4 T cell, 13 CD8 T cell, and 10 macrophage subpopulations were identified across the ccRCC tumors. Moreover, 14 microenvironment proximity communities based on MxIF imaging analysis of ~1 million cells were identified. The presence of B cell- and T cell-enriched communities (e.g., CD8 T cell enrichment and T-cell enrichment at the tumor border) correlated with the expression of interferon-gamma, PD1, IL-6, IL-10, PD-L1, CXCL13, and others. Macrophage-enriched communities correlated with the expression of CXCL12 and PDGFRB. Finally, tertiary lymphoid structures within the “B cell-enriched” communities correlating with the expression of CXCL13 were found in two tumors collected from one patient, with subsequent validation via H&E staining. Further, B cell repertoire (BCR) analysis of RNA-seq of the tumors from this patient showed the presence of a large B cell clone in the tumors.

In conclusion, via MxIF, 14 distinct spatial microenvironment communities with unique cytokine expression patterns were identified in ccRCC. Uncovering the spatial behavior of tumors can lead to the development of effective therapies personalized for each patient based on microenvironment composition and architecture.

Citation Format: Natalia Miheecheva, Akshaya Ramachandran, Yang Lyu, Ekaterina Postovalova, Viktor Svekolkin, Ilia Galkin, Pavel Ovcharov, Diana Shamsutdinova, Vladimir Zyrin, Alexander Bagaev, Krystle Nomie, Felix Frenkel, Ravshan Ataullakhanov, James J. Hsieh. Integrated multiregional transcriptomic and multi-parameter single-cell imaging analysis of clear cell renal cell carcinoma elucidates diverse cellular communities present within the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2742.

The impact of Dapagliflozin, and Empagliflozin on Na+/Li+ countertransport in erythrocyte membranes of healthy volunteers in acute drug tests

Background

The formation of the effect of SGLT 2 inhibitors on the cardiovascular system implies inhibition of the Na+/H+exchanger (NHE) that leads to a decrease in the concentration of Na+, which in turn contributes to a decrease in the concentration of Ca2+ in the cardiomyocyte (S. Verma, J. McMurray, 2018). However, this effect may be different against the background of exposure to empagliflozin and dapagliflozin. A study of A. Baartscheer (2017) demonstrated the reduction of [Na+], systolic and diastolic [Ca+] in cardiomyocyte is affected by Empagliflozin. N.N. Hamouda's research shows that Dapagliflozin reduce [Na+], systolic, but not diastolic [Ca2+] in cardiomyocytes. In this regard, studying the Na+/Na+ exchanger (which is the mode of the NHE operation) activity estimated by the Na+/Li+countertransport (SLC) in the erythrocyte membrane (K. Morgan, M. Canessa, 1990; M. Canessa, 1989) is of great interest.

Purpose

A study of the impact of Dapagliflozin, and Empagliflozin on SLC in erythrocyte membranes of healthy volunteers in acute drug tests.

Materials and methods

10 healthy volunteers (7 men and 3 women) were included in the study. The mean age of the study group was 24±0,6 years. The volunteers took singly Dapagliflozin, and Empagliflozin separately in one-month intervals. The activity of SLC was determined before drug consumption, as well as 2, 12, and 24 hours after consumption by the method of M.Canessa (1980). All data were tested for normality using the Kolmogorov–Smirnov test and statistically tested by paired Student t-test.

Results

Mean speeds of SLC before the drugs were administered was 289.1±33.1 mmol/L cell*h in the Dapagliflozin group and 287.8±37.3 mmol/L cell*h in the Empagliflozin group (p=0.979). Two hours after Dapagliflozin was administered, the speed of SLC increased to 76.3±17.0 mmol/L cell*h (95% CI: 37.9–114.7) and was maximum 365±41.6 mmol/L cell*h (p=0.002) for observation period. Then after 12 and 24 hours decreasing SLC activity was observed. However, the values were higher than the initial level and amounted to 335.5±39.18 mmol/L cell*h (p=0.024) after 12 hours and 331±31.8 mmol/L cell*h (p=0.001) after 24 hours. Two hours after Empagliflozin was administered, the speed of SLC not significantly changed and amounted to 287.8±37.3mmol/L cell*h (p=0.7). After 12 hours, the speed not significantly increased to 23.4±11.9 (95% CI: 4.6–51.4) mmol/L cell*h and amounted to 311.1±39.2 (p=0.08), with the subsequent decrease of speed within 24 hours to 277.6±40.5 mmol/L cell*h (p=0.5). Thus Dapagliflozin and Empagliflozin have different effects on the function of Na+/Na+ exchanger which may differently affect the changing of the [Na+] and [Ca2+] in the cardiomyocyte.

Conclusions

1. When Dapagliflozin was administered the SLC speed reached its maximum level after 2h and maintained higher than initial level after 12h and 24h.

2. When Empagliflozin was administered no significant change in SLC speed was observed.

Funding Acknowledgement

Type of funding source: None