Diagnosis Of Cardiovascular Diseases Research Articles

ASSESSMENT OF NT-PROBNP LEVELS IN PATIENTS WITH CARDIAC ARRHYTHMIAS

Background: NT-proBNP is a valuable biomarker for the diagnosis and treatment of cardiovascular diseases, including arrhythmias. Objective: To evaluate NT-proBNP levels in patients with arrhythmias and analyze the correlation between NT-proBNP levels and several variables. Subjects and Methods: A cross-sectional descriptive study was conducted on 158 patients with arrhythmias from December 2023 to March 2024 at the Arrhythmias Treatment Department of Cho Ray Hospital. Clinical and paraclinical characteristics were analyzed; NT-proBNP levels were compared between patients with arrhythmias with and without heart failure (HF); and the correlation between NT-proBNP levels and several factors was analyzed using R software version 4.3.1. Statistical significance was considered at P < 0.05. Results: The NT-proBNP level (Median [IQR]) in patients with arrhythmias (n = 158) was 367 (61.9-1297) pg/ml. NT-proBNP levels were higher in patients with arrhythmias with HF (n = 41) than in those without HF (n = 117), at 1273 (498-4173) vs. 210 (51-875) pg/ml (P < 0.001). Univariate analysis showed that age, weight, HGB, EF, creatinine, and eGFR were statistically significantly correlated with NT-proBNP. Conclusions: The NT-proBNP level in patients with arrhythmias was 367.3 (61.9-1297) pg/ml; it was higher in patients with arrhythmias with HF than in those without HF. NT-proBNP level was statistically significantly correlated with age, weight, HGB, EF, creatinine, and eGFR.

Ca2+-Dependent Cl− Channels in Vascular Tone Regulation during Aging

Identifying alterations caused by aging could be an important tool for improving the diagnosis of cardiovascular diseases. Changes in vascular tone regulation involve various mechanisms, like NO synthase activity, activity of the sympathetic nervous system, production of prostaglandin, endothelium-dependent relaxing, and contracting factors, etc. Surprisingly, Ca2+-dependent Cl− channels (CaCCs) are involved in all alterations of the vascular tone regulation mentioned above. Furthermore, we discuss these mechanisms in the context of ontogenetic development and aging. The molecular and electrophysiological mechanisms of CaCCs activation on the cell membrane of the vascular smooth muscle cells (VSMC) and endothelium are explained, as well as the age-dependent changes that imply the activation or inhibition of CaCCs. In conclusion, due to the diverse intracellular concentration of chloride in VSMC and endothelial cells, the activation of CaCCs depends, in part, on intracellular Ca2+ concentration, and, in part, on voltage, leading to fine adjustments of vascular tone. The activation of CaCCs declines during ontogenetic development and aging. This decline in the activation of CaCCs involves a decrease in protein level, the impairment of Ca2+ influx, and probably other alterations in vascular tone regulation.

Research on heart and lung sound separation method based on DAE–NMF–VMD

Auscultation is the most effective method for diagnosing cardiovascular and respiratory diseases. However, stethoscopes typically capture mixed signals of heart and lung sounds, which can affect the auscultation effect of doctors. Therefore, the efficient separation of mixed heart and lung sound signals plays a crucial role in improving the diagnosis of cardiovascular and respiratory diseases. In this paper, we propose a blind source separation method for heart and lung sounds based on deep autoencoder (DAE), nonnegative matrix factorization (NMF) and variational mode decomposition (VMD). Firstly, DAE is employed to extract highly informative features from the heart and lung sound signals. Subsequently, NMF clustering is applied to group the heart and lung sounds based on their distinct periodicities, achieving the separation of the mixed heart and lung sounds. Finally, variational mode decomposition is used for denoising the separated signals. Experimental results demonstrate that the proposed method effectively separates heart and lung sound signals and exhibits significant advantages in terms of standardized evaluation metrics when compared to contrast methods.

The impact of lifestyle, measured with the HLPCQ questionnaire on the prevalence of metabolic syndrome in Poland: a multicenter study

Metabolic syndrome is one of the most common health problems for people around the world. The aim of our study was to assess the prevalence of metabolic syndrome among adults without prior diagnosis of cardiovascular disease, diabetes, and chronic kidney disease. We also plan to assess the influence of certain lifestyle components on prevalence of metabolic syndrome. The study involved cardiovascularly healthy patients undergoing lab tests, measurements, and the HLPCQ questionnaire (The Healthy Lifestyle and Personal Control Questionnaire). The data were used to diagnose metabolic syndrome. Out of 1044 patients from 10 primary care facilities, 23.3% met the metabolic syndrome criteria, showing a strong link with increased blood pressure, cholesterol, and fasting glucose. Lower scores in the Organized physical exercise subscale of the HLPCQ questionnaire were noted in those with metabolic syndrome. Comparing the subscale of HLPCQ questionnaire, the lower results in Organized physical exercise subscale were found among the participants with metabolic syndrome, both male and females. Metabolic syndrome, a significant risk factor for cardiovascular disease, should be screened for actively, even in apparently healthy populations. Results obtained in our study from analysis of HLPCQ show that screening for metabolic syndrome should be preceded by prevention based on regular physical activity and proper eating habits.

Bio-inspired flexible optical micro/nano fibers sensor with nanohemisphere array microstructure for ultra-sensitive detection of skin signals

We propose the utilization of a bio-inspired nanohemispherical array of microstructure flexible optical micro/nanofibers sensor (NHSAMNFS) designed for highly sensitive detection of epidermal signals and cardiovascular health assessment. NHSAMNFS builds a flexible wearable self-mixing optical sensor by assembling MNF in a polydimethylsiloxane (PDMS) film with a surface modified nanohemispheres (400 nm diameter) array, where the micro/nanofiber (MNF) sensor is used to sense physiological signals. PDMS with a surface modified nano-hemispherical array is used to eliminate misalignment and increase the sensitivity of sensing pressure. Due to the high flexibility of PDMS and its surface modified nano-hemispherical array microstructure, the sensing element can reliably transmit pressure stimuli into interpretable optical signals with high fidelity, allowing instantaneous pressure perception. The sensor, measuring 20×20mm in size, boasts a sensitivity of 0.102mV/kPa, with a rise time of 3.7 ms and a fall time of 6.26 ms. By tracking fluctuations in the reflected light power of MNF within PDMS, real-time monitoring of the fingertip pulse wave is achieved. Extracting cardiovascular vital signs from this pulse signal enables the evaluation of cardiovascular health status, yielding results consistent with human physiological characteristics. This non-invasive, continuous, and precise cardiovascular health monitoring, facilitated by wearable devices, holds significant promise for applications in human health monitoring, as well as in the prevention and diagnosis of cardiovascular diseases.

Circulating interleukin 17A and other inflammatory proteins may predict cardiovascular disease in early rheumatoid arthritis.

The objective of this study was to investigate the impact of 92 inflammatory proteins on the risk of cardiovascular disease (CVD) in patients with early RA. This study included consecutive patients with early RA recruited 1995-2002. Stored plasma samples were analyzed for 92 inflammatory proteins. CVD diagnoses were retrieved from national in-patient and cause-of-death registries. Statistical analyses were pre-designated as hypothesis-driven or exploratory. For the latter, proteins were selected based on principal component analysis (i.e., factor loading >0.5 within main components). Potential predictors of CVD and coronary artery disease (CAD) were assessed using Cox regression. Data on baseline levels of proteins and CVD were available for 163 patients. As hypothesized, levels of IL-17A were associated with CVD [hazard ratio 1.35; 95% confidence interval 1.02-1.78, adjusted for age, sex, hypertension, diabetes, smoking and ESR], while not significantly with CAD. Osteoprotegerin (OPG) levels were significantly associated with both outcomes but only in crude models. No associations were observed for IL-6, TNF-α, monocyte chemotactic protein (MCP)-1 or IL-8. In the exploratory analyses, particularly MCP-3 had significant associations with both outcomes in crude models. Circulating IL-17A at RA diagnosis predicted future CVD, although we cannot exclude that this finding is due to multiple testing. The association was independent of traditional CVD risk factors, and of ESR at the time of diagnosis. Furthermore, OPG may be a predictor of CVD. We also identified some novel potential biomarkers for CVD in RA.

Camera-Based Seismocardiogram for Heart Rate Variability Monitoring.

Heart rate variability (HRV) is a crucial metric that quantifies the variation between consecutive heartbeats, serving as a significant indicator of autonomic nervous system (ANS) activity. It has found widespread applications in clinical diagnosis, treatment, and prevention of cardiovascular diseases. In this study, we proposed an optical model for defocused speckle imaging, to simultaneously incorporate out-of-plane translation and rotation-induced motion for highly-sensitive non-contact seismocardiogram (SCG) measurement. Using electrocardiogram (ECG) signals as the gold standard, we evaluated the performance of photoplethysmogram (PPG) signals and speckle-based SCG signals in assessing HRV. The results indicated that the HRV parameters measured from SCG signals extracted from laser speckle videos showed higher consistency with the results obtained from the ECG signals compared to PPG signals. Additionally, we confirmed that even when clothing obstructed the measurement site, the efficacy of SCG signals extracted from the motion of laser speckle patterns persisted in assessing the HRV levels. This demonstrates the robustness of camera-based non-contact SCG in monitoring HRV, highlighting its potential as a reliable, non-contact alternative to traditional contact-PPG sensors.

Levels of Small Extracellular Vesicles Containing hERG-1 and Hsp47 as Potential Biomarkers for Cardiovascular Diseases.

The diagnosis of cardiovascular disease (CVD) is still limited. Therefore, this study demonstrates the presence of human ether-a-go-go-related gene 1 (hERG1) and heat shock protein 47 (Hsp47) on the surface of small extracellular vesicles (sEVs) in human peripheral blood and their association with CVD. In this research, 20 individuals with heart failure and 26 participants subjected to cardiac stress tests were enrolled. The associations between hERG1 and/or Hsp47 in sEVs and CVD were established using Western blot, flow cytometry, electron microscopy, ELISA, and nanoparticle tracking analysis. The results show that hERG1 and Hsp47 were present in sEV membranes, extravesicularly exposing the sequences 430AFLLKETEEGPPATE445 for hERG1 and 169ALQSINEWAAQTT- DGKLPEVTKDVERTD196 for Hsp47. In addition, upon exposure to hypoxia, rat primary cardiomyocytes released sEVs into the media, and human cardiomyocytes in culture also released sEVs containing hERG1 (EV-hERG1) and/or Hsp47 (EV-Hsp47). Moreover, the levels of sEVs increased in the blood when cardiac ischemia was induced during the stress test, as well as the concentrations of EV-hERG1 and EV-Hsp47. Additionally, the plasma levels of EV-hERG1 and EV-Hsp47 decreased in patients with decompensated heart failure (DHF). Our data provide the first evidence that hERG1 and Hsp47 are present in the membranes of sEVs derived from the human cardiomyocyte cell line, and also in those isolated from human peripheral blood. Total sEVs, EV-hERG1, and EV-Hsp47 may be explored as biomarkers for heart diseases such as heart failure and cardiac ischemia.

International Migration and Cardiovascular Health: Unraveling the Disease Burden Among Migrants to North America and Europe.

Europe and North America are the 2largest recipients of international migrants from low-resource regions in the world. Here, large differences in cardiovascular disease (CVD) morbidity and death exist between migrants and the host populations. This review discusses the CVD burden and its most important contributors among the largest migrant groups in Europe and North America as well as the consequences of migration to high-income countries on CVD diagnosis and therapy. The available evidence indicates that migrants in Europe and North America generally have a higher CVD risk compared with the host populations. Cardiometabolic, behavioral, and psychosocial factors are important contributors to their increased CVD risk. However, despite these common denominators, there are important ethnic differences in the propensity to develop CVD that relate to pre- and postmigration factors, such as socioeconomic status, cultural factors, lifestyle, psychosocial stress, access to health care and health care usage. Some of these pre- and postmigration environmental factors may interact with genetic (epigenetics) and microbial factors, which further influence their CVD risk. The limited number of prospective cohorts and clinical trials in migrant populations remains an important culprit for better understanding pathophysiological mechanism driving health differences and for developing ethnic-specific CVD risk prediction and care. Only by improved understanding of the complex interaction among human biology, migration-related factors, and sociocultural determinants of health influencing CVD risk will we be able to mitigate these differences and truly make inclusive personalized treatment possible.

Hybrid U-Net and Swin-transformer network for limited-angle cardiac computed tomography

Objective. Cardiac computed tomography (CT) is widely used for diagnosis of cardiovascular disease, the leading cause of morbidity and mortality in the world. Diagnostic performance depends strongly on the temporal resolution of the CT images. To image the beating heart, one can reduce the scanning time by acquiring limited-angle projections. However, this leads to increased image noise and limited-angle-related artifacts. The goal of this paper is to reconstruct high quality cardiac CT images from limited-angle projections. Approach. The ability to reconstruct high quality images from limited-angle projections is highly desirable and remains a major challenge. With the development of deep learning networks, such as U-Net and transformer networks, progresses have been reached on image reconstruction and processing. Here we propose a hybrid model based on the U-Net and Swin-transformer (U-Swin) networks. The U-Net has the potential to restore structural information due to missing projection data and related artifacts, then the Swin-transformer can gather a detailed global feature distribution. Main results. Using synthetic XCAT and clinical cardiac COCA datasets, we demonstrate that our proposed method outperforms the state-of-the-art deep learning-based methods. Significance. It has a great potential to freeze the beating heart with a higher temporal resolution.

Recent developments in the diagnosis, treatment, and management of cardiovascular diseases through artificial intelligence and other innovative approaches

Recent developments in the diagnosis, treatment, and management of cardiovascular diseases through artificial intelligence and other innovative approaches

Examination of Cardiac Activity with ECG Monitoring Using Heart Rate Variability Methods.

The paper presents a system for analyzing cardiac activity with the possibility of continuous and remote monitoring. The created sensor mobile device monitors heart activity by means of the convenient and imperceptible registration of cardiac signals. At the same time, the behavior of the human body is also monitored through the accelerometer and gyroscope built into the device, thanks to which it is possible to signal in the event of loss of consciousness or fall (in patients with syncope). Conducting real-time cardio monitoring and the analysis of recordings using various mathematical methods (linear, non-linear, and graphical) enables the research, accurate diagnosis, timely assistance, and correct treatment of cardiovascular diseases. The paper examines the recordings of patients diagnosed with arrhythmia and syncope recorded by electrocardiography (ECG) sensors in real conditions. The obtained results are subjected to statistical analysis to determine the accuracy and significance of the obtained results. The studies show significant deviations in the patients with arrhythmia and syncope regarding the obtained values of the studied parameters of heart rate variability (HRV) from the accepted normal values (for example, the root mean square of successive differences between normal heartbeats (RMSSD) in healthy individuals is 24.02 ms, while, in patients with arrhythmia (6.09 ms) and syncope (5.21 ms), it is much lower). The obtained quantitative and graphic results identify some possible abnormalities and demonstrate disorders regarding the activity of the autonomic nervous system, which is directly related to the work of the heart.

DFA-Net: Dual multi-scale feature aggregation network for vessel segmentation in X-ray digital subtraction angiography

Even though deep learning is fascinated in fields of coronary vessel segmentation in X-ray angiography and achieves prominent progresses, most of those models probably bring high false and missed detections due to indistinct contrast between coronary vessels and background, especially for tiny sub-branches. Image improvement technique is able to better such contrast, while boosting extraneous information, e.g., other tissues with similar intensities and noise. If incorporating features derived from original and enhanced images, the segmentation performance is improved because those images comprise complementary information from different contrasts. Accordingly, inspired from advantages of contrast improvement and encoding-decoding architecture, a dual multi-scale feature aggregation network (named DFA-Net) is introduced for coronary vessel segmentation in digital subtraction angiography (DSA). DFA-Net integrates the contrast improvement using exponent transformation into a semantic segmentation network that individually accepts original and enhanced images as inputs. Through parameter sharing, multi-scale complementary features are aggregated from different contrasts, which strengthens leaning capabilities of networks, and thus achieves an efficient segmentation. Meanwhile, a risk cross-entropy loss is enforced on the segmentation, for availably decreasing false negatives, which is incorporated with Dice loss for joint optimization of the proposed strategy during training. Experimental results demonstrate that DFA-Net can not only work more robustly and effectively for DSA images under diverse conditions, but also achieve better performance, in comparison with state-of-the-art methods. Consequently, DFA-Net has high fidelity and structure similarity to the reference, providing a way for early diagnosis of cardiovascular diseases.

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review.

Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed.

Cardiovascular screening outcomes in the Dutch survivorship care program for Hodgkin lymphoma survivors.

Hodgkin lymphoma (HL) survivors are at increased risk of cardiovascular disease (CVD) due to former lymphoma treatment. In 2013, cardiovascular screening for 5-year HL survivors according to national guidelines was implemented in Dutch survivorship clinics. We aim to assess the following: (1) adherence to screening guidelines and (2) the yield of (risk factors for) CVD in the screening program. The study population consisted of 5-year HL survivors who received survivorship care at three University Medical Centers from 2013 to 2016 through 2021. Patient characteristics, cardiovascular screening procedures, and outcomes were collected from the medical records. In 186 survivors eligible for cardiovascular screening (mean age 47.8years, 60.8% female), the following diagnostics were performed: complete blood tests (81.0%, median frequency: yearly instead of advised 5-yearly evaluation), electrocardiogram (93.0%), echocardiography (94.6%). Fifty-five percent of survivors had at least one modifiable cardiovascular risk factor (i.e., current smoking, overweight, new/insufficiently controlled hypertension, dyslipidemia, or diabetes). Screening detected ≥ 1 CVD in 31.1% of survivors. Among survivors with available echocardiography report (n = 106), screening detected new aortic and/or mitral valve dysfunction(s) in 51.0% (with grades 3-4 in 4.9%) and impaired left ventricular ejection fraction in 10.3%. Adherence to the screening guidelines in the Dutch HL survivorship care program was reasonable to good and a substantial number of actionable (risk factors for) CVD were diagnosed. Our findings inform HL survivors at high risk of late cardiotoxicity about cardiovascular screening findings and demonstrate appropriate therapeutic actions after diagnosis of (risk factors for) CVD.

Social media for cardiac imagers: a review.

Cardiac imaging plays a pivotal role in the diagnosis and management of cardiovascular diseases. In the burgeoning landscape of digital technology and social media platforms, it becomes essential for cardiac imagers to know how to effectively increase the visibility and the impact of their activity. With the availability of social sites like X (formerly Twitter), Instagram and Facebook, cardiac imagers can now reach a wider audience and engage with peers, sharing their findings, insights, and discussions. The integration of persistent identifiers, such as Digital Object Identifiers (DOIs), facilitates traceability and citation of cardiac imaging publications across various digital platforms, further enhancing their discoverability. To maximize visibility, practical advice is provided, including the use of visually engaging infographics and videos, as well as the strategic implementation of relevant hashtags and keywords. These techniques can significantly improve the discoverability of cardiac imaging research through search engine optimization and social media algorithms. Tracking impact and engagement is crucial in the digital age, and this review discusses various metrics and tools to gauge the reach and influence of cardiac imaging publications. This includes traditional citation-based metrics and altmetrics. Moreover, this review underscores the importance of creating and updating professional profiles on social platforms and participating in relevant scientific communities online. The adoption of digital technology, social platforms, and a strategic approach to publication sharing can empower cardiac imaging professionals to enhance the visibility and impact of their research, ultimately advancing the field and improving patient care.

Reconstruction of arterial blood pressure waveforms based on squeeze-and-excitation network models using electrocardiography and photoplethysmography signals

Arterial blood pressure (ABP) waveforms indicate the efficiency with which a patient’s blood responds to changes in the arterial flow. ABP waveforms can be used to analyze the cardiovascular health of patients, including heart rate analysis, stress analysis, and cardiovascular disease diagnosis. However, obtaining the ABP waveform is more difficult than recording the electrocardiography (ECG) and photoplethysmography (PPG) signals. Several studies have focused on the reconstruction of the ABP waveform by recombining bio-signals. Reconstructing ABP waveforms is considered a major challenge because of signals being distorted by waveform destruction, owing to the normalization and standardization of the input ABP waveform. This paper presents a model that can simultaneously output blood pressure estimation and ABP waveforms using the raw ABP signal as the input. Herein, the squeeze-and-excitation network (SE-Net) is coupled with the multi-task learning architecture. Three signals, namely, the ECG, PPG, and ECG–PPG, were trained as feature vectors using different variants of the U-Net model, and the results are compared. Additionally, we introduce a novel method for incorporating white Gaussian noise at different signal-to-noise ratios (SNRs) to augment the training data. The objective of the proposed approach is to evaluate the ABP signal reconstruction capability of the SE-Net at various SNR levels and analyze the overall performance and robustness of the model. In the case of systolic blood pressure, the root mean square error (RMSE) of the PPG-based values is 2.58 mmHg, and the Pearson correlation coefficient is r = 0.92 (p≤ 0.001). In the case of diastolic blood pressure(DBP), the RMSE was 3.35 mmHg, and the Pearson correlation coefficient was r = 0.89 (p ≤ 0.001). The results indicate the potential for replacing ECG signals with PPG signals to overcome the constraints of optimizing the non-invasive reconstruction of ABP waveforms using SE-Net. Furthermore, we demonstrate that the SE-Net-combined multi-task learning architecture model can simultaneously perform blood pressure estimation and output ABP waveforms without damaging the raw signal.

Sequence based model using deep neural network and hybrid features for identification of 5-hydroxymethylcytosine modification

RNA modifications are pivotal in the development of newly synthesized structures, showcasing a vast array of alterations across various RNA classes. Among these, 5-hydroxymethylcytosine (5HMC) stands out, playing a crucial role in gene regulation and epigenetic changes, yet its detection through conventional methods proves cumbersome and costly. To address this, we propose Deep5HMC, a robust learning model leveraging machine learning algorithms and discriminative feature extraction techniques for accurate 5HMC sample identification. Our approach integrates seven feature extraction methods and various machine learning algorithms, including Random Forest, Naive Bayes, Decision Tree, and Support Vector Machine. Through K-fold cross-validation, our model achieved a notable 84.07% accuracy rate, surpassing previous models by 7.59%, signifying its potential in early cancer and cardiovascular disease diagnosis. This study underscores the promise of Deep5HMC in offering insights for improved medical assessment and treatment protocols, marking a significant advancement in RNA modification analysis.

Identification of diagnostic biomarkers used in the diagnosis of cardiovascular diseases and diabetes mellitus: A systematic review of quantitative studies.

To perform a systematic review of studies that sought to identify diagnostic biomarkers for the diagnosis of cardiovascular diseases (CVDs) and diabetes mellitus (DM), which could be used in low- and middle-income countries (LMICs) where there is a lack of diagnostic equipment, treatments and training. Papers were sourced from six databases: the British Nursing Index, Google Scholar, PubMed, Sage, Science Direct and Scopus. Articles published between January 2002 and January 2023 were systematically reviewed by three reviewers and appropriate search terms and inclusion/exclusion criteria were applied. A total of 18 studies were yielded, as well as 234 diagnostic biomarkers (74 for CVD and 160 for DM). Primary biomarkers for the diagnosis of CVDs included growth differentiation factor 15 and neurogenic locus notch homologue protein 1 (Notch1). For the diagnosis of DM, alpha-2-macroglobulin, C-peptides, isoleucine, glucose, tyrosine, linoleic acid and valine were frequently reported across the included studies. Advanced analytical techniques, such as liquid chromatography mass spectrometry, enzyme-linked immunosorbent assays and vibrational spectroscopy, were also repeatedly reported in the included studies and were utilized in combination with traditional and alternative matrices such as fingernails, hair and saliva. While advanced analytical techniques are expensive, laboratories in LMICs should carry out a cost-benefit analysis of their use. Alternatively, laboratories may want to explore emerging techniques such as infrared, Fourier transform-infrared and near-infrared spectroscopy, which allow sensitive noninvasive analysis.

Genetics in the diagnosis and treatment of cardiovascular diseases

Cardiovascular diseases (CVDs) remain one of the leading causes of morbidity and mortality worldwide, with genetics being a major risk factor. Genetic cardiovascular disease can occur either because of single variant (Mendelian) or polygenic influences and has been linked to inherited cardiovascular conditions (ICC) such as arrhythmias, cardiomyopathies, dyslipidemias, and aortopathies which are significant factors leading to sudden cardiac death in young adults. Timely screening, diagnosis, and management of ICC can not only provide life-saving treatment to a patient, but also identify at-risk family members. The field of pharmacogenomics (PGx) helped to understand the variable action of medications such as clopidogrel, aspirin, warfarin, and statin according to genotype. Newer technologies such as multi-omics can combine data from multiple sources such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiome. These advancements can contribute to the development of polygenic prediction scores and precision medicine tailored to individual genotypes. Substantial strides have been made in genetic-based therapeutics, gene editing technologies, and drug delivery systems, which have significantly expanded treatment options for patients with acquired or inherited CVDs. Although variable, the country- and society-specific guidelines on genetic testing for ICC and PGx and treatment are being continuously updated to keep up with ongoing research in the field. Along with appropriate knowledge, other factors including cost and availability of genetic testing play a vital role in the usage by both physicians and patients. With the advent of newer genetic testing for CVDs, a key factor is the availability of genetic counselors (GCs) who are specifically trained in cardiovascular genomics. The current review provides a concise summary of the major influences of genetics in the diagnosis and treatment of CVDs.