| 1. |
- Adamson, Carly, et al.
(author)
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Dapagliflozin for Heart Failure According to Body Mass Index : The DELIVER Trial.
- 2022
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In: European heart journal. - : Oxford University Press (OUP). - 0195-668X .- 1522-9645. ; 43:41, s. 4406-4417
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Journal article (peer-reviewed)abstract
- AIMS: Obesity is common and associated with unique phenotypic features in heart failure with preserved ejection fraction (HFpEF). Therefore, understanding the efficacy and safety of new therapies in HFpEF patients with obesity is important. The effects of dapagliflozin were examined according to body mass index (BMI) among patients in the Dapagliflozin Evaluation to Improve the LIVEs of Patients With PReserved Ejection Fraction Heart Failure trial. METHODS AND RESULTS: Body mass index was analysed by World Health Organization (WHO) categories and as a continuous variable using restricted cubic splines. Body mass index ranged from 15.2 to 50 kg/m2 with a mean value of 29.8 (standard deviation +/- 6.1) kg/m2. The proportions, by WHO category, were: normal weight 1343 (21.5%); overweight 2073 (33.1%); Class I obesity 1574 (25.2%); Class II obesity 798 (12.8%); and Class III obesity 415 (6.6%). Compared with placebo, dapagliflozin reduced the risk of the primary outcome to a similar extent across these categories: hazard ratio (95% confidence interval): 0.89 (0.69-1.15), 0.87 (0.70-1.08), 0.74 (0.58-0.93), 0.78 (0.57-1.08), and 0.72 (0.47-1.08), respectively (P-interaction = 0.82). The placebo-corrected change in Kansas City Cardiomyopathy Questionnaire total symptom score with dapagliflozin at 8 months was: 0.9 (-1.1, 2.8), 2.5 (0.8, 4.1), 1.9 (-0.1, 3.8), 2.7 (-0.5, 5.8), and 8.6 (4.0, 13.2) points, respectively (P-interaction = 0.03). The placebo-corrected change in weight at 12 months was: -0.88 (-1.28, -0.47), -0.65 (-1.04, -0.26), -1.42 (-1.89, -0.94), -1.17 (-1.94, -0.40), and -2.50 (-4.4, -0.64) kg (P-interaction = 0.002). CONCLUSIONS: Obesity is common in patients with HFpEF and is associated with higher rates of heart failure hospitalization and worse health status. Treatment with dapagliflozin improves cardiovascular outcomes across the spectrum of BMI, leads to greater symptom improvement in patients with obesity, compared with those without, and has the additional benefit of causing modest weight loss.
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| 2. |
- Baumann, Stefan, et al.
(author)
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Gender differences in the diagnostic performance of machine learning coronary CT angiography-derived fractional flow reserve -results from the MACHINE registry
- 2019
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In: European Journal of Radiology. - : ELSEVIER IRELAND LTD. - 0720-048X .- 1872-7727. ; 119
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Journal article (peer-reviewed)abstract
- Purpose: This study investigated the impact of gender differences on the diagnostic performance of machine-learning based coronary CT angiography (cCTA)-derived fractional flow reserve (CT-FFR mL ) for the detection of lesion-specific ischemia. Method: Five centers enrolled 351 patients (73.5% male) with 525 vessels in the MACHINE (Machine leArning Based CT angiograpHy derIved FFR: a Multi-ceNtEr) registry. CT-FFRML and invasive FFR amp;lt;= 0.80 were considered hemodynamically significant, whereas cCTA luminal stenosis amp;gt;= 50% was considered obstructive. The diagnostic performance to assess lesion-specific ischemia in both men and women was assessed on a per-vessel basis. Results: In total, 398 vessels in men and 127 vessels in women were included. Compared to invasive FFR, CT-FFRML reached a sensitivity, specificity, positive predictive value, and negative predictive value of 78% (95%CI 72-84), 79% (95%CI 73-84), 75% (95%CI 69-79), and 82% (95%CI: 76-86) in men vs. 75% (95%CI 58-88), 81 (95%CI 72-89), 61% (95%CI 50-72) and 89% (95%CI 82-94) in women, respectively. CT-FFRML showed no statistically significant difference in the area under the receiver-operating characteristic curve (AUC) in men vs. women (AUC: 0.83 [95%CI 0.79-0.87] vs. 0.83 [95%CI 0.75-0.89], p = 0.89). CT-FFRML was not superior to cCTA alone [AUC: 0.83 (95%CI: 0.75-0.89) vs. 0.74 (95%CI: 0.65-0.81), p = 0.12] in women, but showed a statistically significant improvement in men [0.83 (95%CI: 0.79-0.87) vs. 0.76 (95%CI: 0.71-0.80), p = 0.007]. Conclusions: Machine-learning based CT-FFR performs equally in men and women with superior diagnostic performance over cCTA alone for the detection of lesion-specific ischemia.
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| 3. |
- Lelliott, Christopher J., et al.
(author)
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Ablation of PGC-1beta results in defective mitochondrial activity, thermogenesis, hepatic function, and cardiac performance
- 2006
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In: PLoS biology. - : Public Library of Science (PLoS). - 1544-9173 .- 1545-7885. ; 4:11
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Journal article (peer-reviewed)abstract
- The transcriptional coactivator peroxisome proliferator-activated receptor-gamma coactivator-1beta (PGC-1beta) has been implicated in important metabolic processes. A mouse lacking PGC-1beta (PGC1betaKO) was generated and phenotyped using physiological, molecular, and bioinformatic approaches. PGC1betaKO mice are generally viable and metabolically healthy. Using systems biology, we identified a general defect in the expression of genes involved in mitochondrial function and, specifically, the electron transport chain. This defect correlated with reduced mitochondrial volume fraction in soleus muscle and heart, but not brown adipose tissue (BAT). Under ambient temperature conditions, PGC-1beta ablation was partially compensated by up-regulation of PGC-1alpha in BAT and white adipose tissue (WAT) that lead to increased thermogenesis, reduced body weight, and reduced fat mass. Despite their decreased fat mass, PGC1betaKO mice had hypertrophic adipocytes in WAT. The thermogenic role of PGC-1beta was identified in thermoneutral and cold-adapted conditions by inadequate responses to norepinephrine injection. Furthermore, PGC1betaKO hearts showed a blunted chronotropic response to dobutamine stimulation, and isolated soleus muscle fibres from PGC1betaKO mice have impaired mitochondrial function. Lack of PGC-1beta also impaired hepatic lipid metabolism in response to acute high fat dietary loads, resulting in hepatic steatosis and reduced lipoprotein-associated triglyceride and cholesterol content. Altogether, our data suggest that PGC-1beta plays a general role in controlling basal mitochondrial function and also participates in tissue-specific adaptive responses during metabolic stress.
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| 4. |
- Tesche, Christian, et al.
(author)
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Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR Results From MACHINE Registry
- 2020
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In: JACC Cardiovascular Imaging. - : ELSEVIER SCIENCE INC. - 1936-878X .- 1876-7591. ; 13:3, s. 760-770
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Journal article (peer-reviewed)abstract
- OBJECTIVESThis study was conducted to investigate the influence of coronary artery calcium (CAC) score on the diagnostic performance of machine-learning-based coronary computed tomography (CT) angiography (cCTA)-derived fractional flow reserve (CT-FFR).BACKGROUNDCT-FFR is used reliably to detect lesion-specific ischemia. Novel CT-FFR algorithms using machine-learning artificial intelligence techniques perform fast and require less complex computational fluid dynamics. Yet, influence of CAC score on diagnostic performance of the machine-learning approach has not been investigated.METHODSA total of 482 vessels from 314 patients (age 62.3 +/- 9.3 years, 77% male) who underwent cCTA followed by invasive FFR were investigated from the MACHINE (Machine Learning based CT Angiography derived FFR: a Multi-center Registry) registry data. CAC scores were quantified using the Agatston convention. The diagnostic performance of CT-FFR to detect lesion-specific ischemia was assessed across all Agatston score categories (CAC 0, >0 to <100, 100 to <400, and >=$400) on a per-vessel level with invasive FFR as the reference standard.RESULTSThe diagnostic accuracy of CT-FFR versus invasive FFR was superior to cCTA alone on a per-vessel level (78% vs. 60%) and per patient level (83% vs. 73%) across all Agatston score categories. No statistically significant differences in the diagnostic accuracy, sensitivity, or specificity of CT-FFR were observed across the categories. CT-FFR showed good discriminatory power in vessels with high Agatston scores (CAC >= 400) and high performance in low-to-intermediate Agatston scores (CAC >0 to <400) with a statistically significant difference in the area under the receiver-operating characteristic curve (AUC) (AUC: 0.71 [95% confidence interval (CI): 0.57 to 0.85] vs. 0.85 [95% CI: 0.82 to 0.89], p = 0.04). CT-FFR showed superior diagnostic value over cCTA in vessels with high Agatston scores (CAC >= 400: AUC 0.71 vs. 0.55, p = 0.04) and low-to-intermediate Agatston scores (CAC >0 to <400: AUC 0.86 vs. 0.63, p < 0.001).CONCLUSIONSMachine-learning-based CT-FFR showed superior diagnostic performance over cCTA alone in CAC with a significant difference in the performance of CT-FFR as calcium burden/Agatston calcium score increased. (Machine Learning Based CT Angiography Derived FFR: a Multicenter, Registry [MACHINE] NCT02805621). (C) 2020 by the American College of Cardiology Foundation.
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