Left ventricular diastolic function in relation to the urinary proteome: a proof-of-concept study in a general population

Background: In previous studies, we identified two urinary proteomic classifiers, termed HF1 and HF2, which discriminated subclinical diastolic left ventricular (LV) dysfunction from normal. HF1 and HF2 combine information from 85 and 671 urinary peptides, mainly up- or down-regulated collagen fragments. We sought to validate these classifiers in a population study. Methods: In 745 people randomly recruited from a Flemish population (49.8 years; 51.3% women), we measured early and late diastolic peak velocities of mitral inflow (E and A) and mitral annular velocities (e' and a') by conventional and tissue Doppler echocardiography, and the urinary proteome by capillary electrophoresis coupled with mass spectrometry. Results: In the analyses adjusted for sex, age, body mass index, blood pressure, heart rate, LV mass index and intake of medications, we expressed effect sizes per 1-SD increment in the classifiers. HF1 was associated with 0.204 cm/s lower e' peak velocity (95% confidence interval, 0.057–0.351; p = 0.007) and 0.145 higher E/e' ratio (0.023–0.268; p = 0.020), while HF2 was associated with a 0.174 higher E/e' ratio (0.046–0.302; p = 0.008). According to published definitions, 67 (9.0%) participants had impaired LV relaxation and 96 (12.9%) had elevated LV filling pressure. The odds of impaired relaxation associated with HF1 was 1.38 (1.01–1.88; p = 0.043) and that of increased LV filling pressure associated with HF2 was 1.38 (1.00–1.90; p = 0.052). Conclusions: In a general population, the urinary proteome correlated with diastolic LV dysfunction, proving its utility for early diagnosis of this condition

Improving Maternal and Fetal Cardiac Monitoring Using Artificial Intelligence

Early diagnosis of possible risks in the physiological status of fetus and mother during pregnancy and delivery is critical and can reduce mortality and morbidity. For example, early detection of life-threatening congenital heart disease may increase survival rate and reduce morbidity while allowing parents to make informed decisions. To study cardiac function, a variety of signals are required to be collected. In practice, several heart monitoring methods, such as electrocardiogram (ECG) and photoplethysmography (PPG), are commonly performed. Although there are several methods for monitoring fetal and maternal health, research is currently underway to enhance the mobility, accuracy, automation, and noise resistance of these methods to be used extensively, even at home. Artificial Intelligence (AI) can help to design a precise and convenient monitoring system. To achieve the goals, the following objectives are defined in this research: The first step for a signal acquisition system is to obtain high-quality signals. As the first objective, a signal processing scheme is explored to improve the signal-to-noise ratio (SNR) of signals and extract the desired signal from a noisy one with negative SNR (i.e., power of noise is greater than signal). It is worth mentioning that ECG and PPG signals are sensitive to noise from a variety of sources, increasing the risk of misunderstanding and interfering with the diagnostic process. The noises typically arise from power line interference, white noise, electrode contact noise, muscle contraction, baseline wandering, instrument noise, motion artifacts, electrosurgical noise. Even a slight variation in the obtained ECG waveform can impair the understanding of the patient's heart condition and affect the treatment procedure. Recent solutions, such as adaptive and blind source separation (BSS) algorithms, still have drawbacks, such as the need for noise or desired signal model, tuning and calibration, and inefficiency when dealing with excessively noisy signals. Therefore, the final goal of this step is to develop a robust algorithm that can estimate noise, even when SNR is negative, using the BSS method and remove it based on an adaptive filter. The second objective is defined for monitoring maternal and fetal ECG. Previous methods that were non-invasive used maternal abdominal ECG (MECG) for extracting fetal ECG (FECG). These methods need to be calibrated to generalize well. In other words, for each new subject, a calibration with a trustable device is required, which makes it difficult and time-consuming. The calibration is also susceptible to errors. We explore deep learning (DL) models for domain mapping, such as Cycle-Consistent Adversarial Networks, to map MECG to fetal ECG (FECG) and vice versa. The advantages of the proposed DL method over state-of-the-art approaches, such as adaptive filters or blind source separation, are that the proposed method is generalized well on unseen subjects. Moreover, it does not need calibration and is not sensitive to the heart rate variability of mother and fetal; it can also handle low signal-to-noise ratio (SNR) conditions. Thirdly, AI-based system that can measure continuous systolic blood pressure (SBP) and diastolic blood pressure (DBP) with minimum electrode requirements is explored. The most common method of measuring blood pressure is using cuff-based equipment, which cannot monitor blood pressure continuously, requires calibration, and is difficult to use. Other solutions use a synchronized ECG and PPG combination, which is still inconvenient and challenging to synchronize. The proposed method overcomes those issues and only uses PPG signal, comparing to other solutions. Using only PPG for blood pressure is more convenient since it is only one electrode on the finger where its acquisition is more resilient against error due to movement. The fourth objective is to detect anomalies on FECG data. The requirement of thousands of manually annotated samples is a concern for state-of-the-art detection systems, especially for fetal ECG (FECG), where there are few publicly available FECG datasets annotated for each FECG beat. Therefore, we will utilize active learning and transfer-learning concept to train a FECG anomaly detection system with the least training samples and high accuracy. In this part, a model is trained for detecting ECG anomalies in adults. Later this model is trained to detect anomalies on FECG. We only select more influential samples from the training set for training, which leads to training with the least effort. Because of physician shortages and rural geography, pregnant women's ability to get prenatal care might be improved through remote monitoring, especially when access to prenatal care is limited. Increased compliance with prenatal treatment and linked care amongst various providers are two possible benefits of remote monitoring. If recorded signals are transmitted correctly, maternal and fetal remote monitoring can be effective. Therefore, the last objective is to design a compression algorithm that can compress signals (like ECG) with a higher ratio than state-of-the-art and perform decompression fast without distortion. The proposed compression is fast thanks to the time domain B-Spline approach, and compressed data can be used for visualization and monitoring without decompression owing to the B-spline properties. Moreover, the stochastic optimization is designed to retain the signal quality and does not distort signal for diagnosis purposes while having a high compression ratio. In summary, components for creating an end-to-end system for day-to-day maternal and fetal cardiac monitoring can be envisioned as a mix of all tasks listed above. PPG and ECG recorded from the mother can be denoised using deconvolution strategy. Then, compression can be employed for transmitting signal. The trained CycleGAN model can be used for extracting FECG from MECG. Then, trained model using active transfer learning can detect anomaly on both MECG and FECG. Simultaneously, maternal BP is retrieved from the PPG signal. This information can be used for monitoring the cardiac status of mother and fetus, and also can be used for filling reports such as partogram

Impact of a 6-wk olive oil supplementation in healthy adults on urinary proteomic biomarkers of coronary artery disease, chronic kidney disease, and diabetes (types 1 and 2): a randomized, parallel, controlled, double-blind study

Background: Olive oil (OO) consumption is associated with cardiovascular disease prevention because of both its oleic acid and phenolic contents. The capacity of OO phenolics to protect against low-density lipoprotein (LDL) oxidation is the basis for a health claim by the European Food Safety Authority. Proteomic biomarkers enable an early, presymptomatic diagnosis of disease, which makes them important and effective, but understudied, tools for primary prevention. Objective: We evaluated the impact of supplementation with OO, either low or high in phenolics, on urinary proteomic biomarkers of coronary artery disease (CAD), chronic kidney disease (CKD), and diabetes. Design: Self-reported healthy participants (n = 69) were randomly allocated (stratified block random assignment) according to age and body mass index to supplementation with a daily 20-mL dose of OO either low or high in phenolics (18 compared with 286 mg caffeic acid equivalents per kg, respectively) for 6 wk. Urinary proteomic biomarkers were measured at baseline and 3 and 6 wk alongside blood lipids, the antioxidant capacity, and glycation markers. Results: The consumption of both OOs improved the proteomic CAD score at endpoint compared with baseline (mean improvement: –0.3 for low-phenolic OO and −0.2 for high-phenolic OO; P < 0.01) but not CKD or diabetes proteomic biomarkers. However, there was no difference between groups for changes in proteomic biomarkers or any secondary outcomes including plasma triacylglycerols, oxidized LDL, and LDL cholesterol. Conclusion: In comparison with low-phenolic OO, supplementation for 6 wk with high-phenolic OO does not lead to an improvement in cardiovascular health markers in a healthy cohort. This trial was registered at www.controlled-trials.com as ISRCTN93136746

MRI of mouse heart failure

Heart failure (HF) is the inability of the heart to pump blood at a rate that satisfies the peripheral needs and is a final consequence of many pathologies. Left ventricular (LV) pressure overload and myocardial infarction are amongst the most important causes of HF. Common and important hallmarks of HF are myocardial hypertrophy, fibrosis, vascular adaptation and metabolic remodeling. The role of cardiac magnetic resonance (CMR) as a diagnostic tool for HF is rapidly increasing. The prognostic value of important measures of LV function such as ejection fraction, however, is limited. To improve diagnostic relevance and risk stratification additional MR imaging and spectroscopy techniques are therefore highly desired. For that, preclinical research in mouse models plays an important role. This goal of this thesis was to apply multiple, novel MR imaging methods and phosphorous 31PMR spectroscopy for the evaluation of mouse HF, with a focus on myocardial hypertrophy, fibrosis, perfusion and LV energy status. These techniques are part of an ever extending toolbox for mouse CMR that allows the researcher to perform a multi-parametric assessment of myocardial tissue status. Preferably, a time-efficient protocol is constructed from all these tools, which is tailored for a particular HF phenotype and yields the relevant, decisive features of the stage of development towards HF. After successful proof-of-concept studies in mice, these techniques could be translated for clinical use. Ultimately, these techniques might then contribute to improved diagnostic accuracy and a better characterization of the tissue status, new (surrogate) end-points to evaluate the success of therapies and interventions, and perhaps may even provide better prognostic markers for the disease course. The transverse aortic constriction (TAC) mouse model was used throughout this thesis as it is an important model of pressure overload induced hypertrophy and HF. Since the TAC model was first described, it has been extensively used to study various facets of pressure overload induced LV adaptation. In Chapter 2 we characterized cardiac function and morphology in a mild and severe TAC model. Mice underwent repeated measurements to evaluate the progression of cardiac parameters over time. The mild TAC mice developed a stage of compensated LV hypertrophy and mildly impaired LV function. No progressive deterioration of myocardial function was observed over time and LV maladaptation did not result in pulmonary remodeling and RV failure. LV function and morphology in severe TAC mice, on the other hand, progressively deteriorated over time resulting in overt decompensated hypertrophy, which was also indicated by profound pulmonary remodeling and impaired RV function. A repeatable method for quantitative, first-pass perfusion MRI of the mouse heart based on a dual-bolus approach was described in Chapter3. A non-saturated arterial input function was acquired from a low-dose containing Gd(DTPA)2- prebolus. The myocardial tissue response was measured from a separate high-dose full-bolus infusion. Perfusion (ml min-1 g-1) was quantified using a Fermi constrained deconvolution of the myocardial tissue response with the arterial input function. This calculation critically depends on linearity of the measured MR signal intensity with Gd(DTPA)2- concentration in the LV lumen during the prebolus and in the myocardial wall during the full-bolus. In separate experiments these assumptions were proven to be valid for our experimental conditions. Interestingly, this assumption was to the best of our knowledge never demonstrated in vivo, although Weber et al. confirmed the appropriateness of this assumption for quantitative first-pass perfusion measurements in the human heart using phantom experiments. The first-pass perfusion method was used in Chapter 4 to study myocardial perfusion in TAC mice, which was considerably decreased as compared to perfusion in control mice. Importantly, the relationship between perfusion and LV morphology and function was studied. Clear correlations were obtained between a decreased perfusion in TAC mice and the indices of LV function and morphology, e.g., LV ejection fraction, volumes as well as LV mass. Although group-averaged perfusion values in TAC mice did not change between measurements in the longitudinal study, these results revealed that with an ensuing hypertrophic growth and concomitantly declining LV function (Chapter 2) perfusion gradually diminishes. Current MRI techniques for the quantification of diffuse myocardial fibrosis suffer from severe limitations. In Chapter 5 ultra short echo time (UTE) MRI was used to study replacement and diffuse fibrosis in the ex vivo and in vivo mouse heart. Here, the MI mouse model was also used as it results in the formation of a spatially confined, collagenous scar providing an ideal model for proof-of-principle purposes. Subtraction of short- and long-TE images resulted in images highlighting tissue with short T2*, such as collagen. Indeed, a good correlation was obtained between the relative infarct volume as determined from histology and ex vivo UTE MRI. UTE MRI also resulted in signal differences between control and TAC hearts, which were related to the amount of collagen present in the hearts. Cardiovascular UTE MRI may thus provide a means for the assessment of diffuse fibrosis based on endogenous tissue contrast. Impaired myocardial energetics are thought to play an important role in HF. Chapter 6 describes 3D Image Selected In vivo Spectroscopy (ISIS) for single-voxel localized 31P-MRS of the in vivo mouse heart. From the resulting spectra the phosphocreatine-to-ATP (PCr/¿-ATP) ratio was quantified as a measure for myocardial energy status. When mice showed a markedly impaired LV systolic function and myocardial hypertrophy 7 weeks after TAC, PCr/ATP was approximately 25% lower 7 weeks as compared to control mice. Multiple studies have pointed to the possible predictive value of PCr/ATP, an important measure for myocardial energy status, for subsequent maladaptive ventricular remodeling. It is unclear though if PCr/ATP measured during the first stage of the remodeling process also predicts consecutive maladaptation. In Chapter 7 the hypothesis was therefore tested that PCr/ATP measured at the day of TAC or four days thereafter predicts subsequent remodeling. Such a relation could, however, not be established. Clear relations were obtained, on the other hand, between LV function and morphology four days after TAC and seven weeks, pointing to the importance of the severity of the initial pressure overload for maladaptive cardiac remodeling. In addition, these experiments showed an apparent decrease of PCr/ATP already at the day of TAC, whereas PCr/ATP four days after TAC was significantly decreased, pointing to the first signs of an impaired myocardial energy status

The Association of Systemic Microvascular Changes with Lung Function and Lung Density: A Cross-Sectional Study

10.1371/journal.pone.0050224PLoS ONE712

Noninvasive cardiac output and central systolic pressure from cuff-pressure and pulse wave velocity

Goal: We introduce a novel approach to estimate cardiac output (CO) and central systolic blood pressure (cSBP) from noninvasive measurements of peripheral cuff-pressure and carotid-to-femoral pulse wave velocity (cf-PWV). Methods: The adjustment of a previously validated one-dimensional arterial tree model is achieved via an optimization process. In the optimization loop, compliance and resistance of the generic arterial tree model as well as aortic flow are adjusted so that simulated brachial systolic and diastolic pressures and cf-PWV converge towards the measured brachial systolic and diastolic pressures and cf-PWV. The process is repeated until full convergence in terms of both brachial pressures and cf-PWV is reached. To assess the accuracy of the proposed framework, we implemented the algorithm on in vivo anonymized data from 20 subjects and compared the method-derived estimates of CO and cSBP to patient-specific measurements obtained with Mobil-O-Graph apparatus (central pressure) and two-dimensional transthoracic echocardiography (aortic blood flow). Results: Both CO and cSBP estimates were found to be in good agreement with the reference values achieving an RMSE of 0.36 L/min and 2.46 mmHg, respectively. Low biases were reported, namely -0.04 +/- 0.36 L/min for CO predictions and -0.27 +/- 2.51 mmHg for cSBP predictions. Significance: Our one-dimensional model can be successfully "tuned" to partially patient-specific standards by using noninvasive, easily obtained peripheral measurement data. The in vivo evaluation demonstrated that this method can potentially be used to obtain central aortic hemodynamic parameters in a noninvasive and accurate way

Impact of acute exercise intensity on pulsatile growth hormone release in men

To investigate the effects of exercise intensity on growth hormone (GH) release, 10 male subjects were tested on 6 randomly ordered occasions [1 control condition (C), 5 exercise conditions (Ex)]. Serum GH concentrations were measured in samples obtained at 10-min intervals between 0700 and 0900 (baseline) and 0900 and 1300 (exercise+ recovery). Integrated GH concentrations (IGHC) were calculated by trapezoidal reconstruction. During Ex subjects exercised for 30 min (0900–0930) at one of the following intensities [normalized to the lactate threshold (LT)]: 25 and 75% of the difference between LT and rest (0.25LT and 0.75LT, respectively), at LT, and at 25 and 75% of the difference between LT and peak (1.25LT and 1.75LT, respectively). No differences were observed among conditions for baseline IGHC. Exercise+recovery IGHC (mean ± SE: C = 250 ± 60; 0.25LT = 203 ± 69; 0.75LT = 448 ± 125; LT = 452 ± 119; 1.25LT = 512 ± 121; 1.75LT = 713 ± 115 µg · l-1 · min-1) increased linearly with increasing exercise intensity (P < 0.05). Deconvolution analysis revealed that increasing exercise intensity resulted in a linear increase in the mass of GH secreted per pulse and GH production rate [production rate increased from 16.5 ± 4.5 (C) to 32.1 ± 5.2 µg · distribution volume-1 · min-1(1.75LT), P < 0.05], with no changes in GH pulse frequency or half-life of elimination. We conclude that the GH secretory response to exercise is related to exercise intensity in a linear dose-response pattern in young men

Increased glutathionylated hemoglobin (HbSSG) in type 2 diabetes subjects with microangiopathy

Objective: Protein glutathionylation is considered an important post-translational modification in the pathogenesis of complex diseases. The aim of this study was to examine whether hemoglobin (Hb) is modified by reduced glutathione (GSH) via oxidation of the thiol groups present in diabetes and its associated microangiopathy and to determine whether oxidative imbalance has any correlation with glutathionylated Hb (HbSSG) levels. Methods: The study group consisted of a total of 130 subjects which included non-diabetic healthy control subjects (n = 30) and type 2 diabetic patients with (n = 53) and without (n = 47) microangiopathy. All subjects were assessed for glycemic and lipidemic status, while diabetic subjects were also assessed for the diagnosis of retinopathy and nephropathy. RBC lysates from all the subjects were analyzed by liquid chromatography/electrospray ionization-mass spectrometry (LC/ESI-MS) for HbSSG β-globin chains. Levels of GSH and thiobarbituric acid substances (TBARS) levels were measured by spectrophotometric and fluorimetric methods, respectively. Results: The positivity for HbSSG in diabetic subjects with microangiopathy was significantly higher (69%) compared to diabetics without microangiopathy (22%) and control subjects (14%). In univariate regression analysis, HbSSG levels were significantly associated with the duration of diabetes, HbA1c, and TBARS levels. GSH levels were negatively correlated (r = -0.57, P < 0.001) with HbSSG in diabetic subjects. A significant inverse correlation (r = -0.42, P < 0.001) between the GSH levels and HbA1c levels was also seen in diabetic subjects. Conclusions: This is perhaps the largest LC-MS-based study to demonstrate that HbSSG levels are markedly increased in diabetic subjects with microangiopathy. Since diabetic subjects also exhibited increased lipid peroxidation and decreased GSH levels, it appears that enhanced oxidative stress may account for the increased HbSSG concentrations and altered reduction-oxidation (redox) signaling