Develop and Evaluate a New and Effective Approach for Predicting Dyslipidemia in Steel Workers

Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years

BACKGROUND

Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology.

RESULTS

We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology.

CONCLUSIONS

The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

Probiotics combined with atorvastatin administration in the treatment of hyperlipidemia: A randomized, double-blind, placebo-controlled clinical trial

BACKGROUND

Hyperlipidemia is a common feature of chronic diseases. The aim of this work was designed to assess the role of probiotics (Lactobacillus casei Zhang, Bifidobactetium animalis subsp. lactis V9, and Lactobacillus plantarum P-8) in the treatment of hyperlipidemia.

METHODS

Thirty three patients with hyperlipidemia were randomly divided into a probiotic group (n = 18) and a control group (n = 15). The probiotic group was administered probiotics (2 g once daily) and atorvastatin 20 mg (once daily), and the control group was administered a placebo (2 g once daily) and atorvastatin 20 mg (once daily). Serum and fecal samples were gathered for subsequent analyses.

RESULTS

Time had a significant effect on the total cholesterol (TC), triglycerides (TG), and low-density lipoprotein-cholesterol (LDL-C) levels in the probiotic and control groups (P < .05). The gut microbial abundance in the probiotic group was markedly higher than that in the control group following 3-month probiotic treatment (P < .05). At the phylum level, probiotics exerted no notable effects on the relative abundance of Firmicutes, Bacteroidetes, and Actinobacteria but elevated that of Tenericutes and reduced Proteobacteria. At the genus level, probiotics increased the relative abundance of Bifidobacterium, Lactobacillus, and Akkermansia, and decreased that of Escherichia, Eggerthella, and Sutterella relative to the control group in months 1, 2, and 3 (P < .05).

CONCLUSIONS

Probiotics optimize the gut microbiota structure and decrease the amount of harmful bacteria in patients with hyperlipidemia. Probiotics can influence the composition of gut microorganisms and increase their diversity and abundance in vivo. It is recommended to use probiotics combined with atorvastatin to treat patients with hyperlipidemia.

Prediction of hypercholesterolemia using machine learning techniques

Purpose

Hypercholesterolemia is a major risk factor for a wide range of cardiovascular diseases. Developing countries are more susceptible to hypercholesterolemia and its complications due to the increasing prevalence and the lack of adequate resources for conducting screening and/or prevention programs. Using machine learning techniques to identify factors contributing to hypercholesterolemia and developing predictive models can help early detection of hypercholesterolemia, especially in developing countries.

Methods

Data from the nationwide 2016 STEPs study in Iran were used to identify socioeconomic, lifestyle, and metabolic risk factors associated with hypercholesterolemia. Furthermore, the predictive power of the identified risk factors was assessed using five commonly used machine learning algorithms (random forest; gradient boosting; support vector machine; logistic regression; artificial neural network) and 10-fold cross validation in terms of specificity, sensitivity, and the area under the receiver operating characteristic curve.

Results

A total of 14,667 individuals were included in this study, of those 12.8% (n = 1879) had (undiagnosed) hypercholesterolemia. Based on multivariate logistic regression analysis the five most important risk factors for hypercholesterolemia were: older age (for the elderly group: OR = 2.243; for the middle-aged group: OR = 1.869), obesity-related factors including high BMI status (morbidly obese: OR = 1.884; obese: OR = 1.499; overweight: OR = 1.426) and AO (OR = 1.339), raised BP (hypertension: OR = 1.729; prehypertension: OR = 1.577), consuming fish once or twice per week (OR = 1.261), and having risky diet (OR = 1.163). Furthermore, all the five hypercholesterolemia prediction models achieved AUC around 0.62, and models based on random forest (AUC = 0.6282; specificity = 65.14%; sensitivity = 60.51%) and gradient boosting (AUC = 0.6263; specificity = 64.11%; sensitivity = 61.15%) had the optimal performance.

Conclusion

The study shows that socioeconomic inequalities, unhealthy lifestyle, and metabolic syndrome (including obesity and hypertension) are significant predictors of hypercholesterolemia. Therefore controlling these factors is necessary to reduce the burden of hypercholesterolemia. Furthermore, machine learning algorithms such as random forest and gradient boosting can be employed for hypercholesterolemia screening and its timely diagnosis. Applying deep learning algorithms as well as techniques for handling the class overlap problem seems necessary to improve the performance of the models.

Artificial intelligence in cardiovascular prevention: new ways will open new doors

Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.

The relationships of shift work, hair cortisol concentration and dyslipidaemia: a cohort study in China

BACKGROUND

Currently, cardiovascular disease is the leading cause of death, and dyslipidaemia is an independent and modifiable major risk factor. Previous studies on shift work with dyslipidaemia and hair cortisol concentration (HCC) have yielded conflicting results. The aim of this study was to clarify the association between shift work, dyslipidaemia, and HCC. We further explored the mediating effect of HCC.

METHODS

In this cohort study, baseline data were collected from participants in May 2013. The cohort included 2170 participants- 1348 shift workers and 822 non-shift workers- who were followed up for 6 years with four questionnaire surveys from July 2014, October 2015, and May to December 2019. Hair samples were collected from 340 participants during the baseline period for HCC testing with an automated radioimmunoassay. Dyslipidaemia was defined using the National Cholesterol Education Program Adult Treatment Panel III diagnostic criteria.

RESULTS

Shift workers had a higher risk of dyslipidaemia than workers on the fixed day shift (two-shift RR = 1.408, 95% CI: 1.102-1.798; three-shift RR = 1.478, 95% CI: 1.134-1.926; four-shift RR = 1.589, 95% CI: 1.253-2.015). Additionally, shift workers had higher HCC levels than fixed day shift workers, with geometric mean concentration (GMC) ± geometric standard difference (GSD) = 2.625 ± 2.012 ng/g, two-shift GMC ± GSD = 3.487 ± 1.930 ng/g, three-shift GMC ± GSD = 2.994 ± 1.813 ng/g, and four-shift GMC ± GSD = 3.143 ± 1.720 ng/g. High HCC was associated with a high incidence of dyslipidaemia. After controlling for confounding factors, this study showed that HCC played a role in mediating dyslipidaemia in shift workers and accounted for 16.24% of the effect.

CONCLUSIONS

Shift work was linked to increased risk of dyslipidaemia compared with fixed day shift work. Higher HCC was associated with a higher prevalence of dyslipidaemia. HCC had a significant mediating effect on dyslipidaemia in shift workers.

A Predictive Model for Abnormal Bone Density in Male Underground Coal Mine Workers

The dark and humid environment of underground coal mines had a detrimental effect on workers’ skeletal health. Optimal risk prediction models can protect the skeletal health of coal miners by identifying those at risk of abnormal bone density as early as possible. A total of 3695 male underground workers who attended occupational health physical examination in a coal mine in Hebei, China, from July to August 2018 were included in this study. The predictor variables were identified through single-factor analysis and literature review. Three prediction models, Logistic Regression, CNN and XG Boost, were developed to evaluate the prediction performance. The training set results showed that the sensitivity of Logistic Regression, XG Boost and CNN models was 74.687, 82.058, 70.620, the specificity was 80.986, 89.448, 91.866, the F1 scores was 0.618, 0.919, 0.740, the Brier scores was 0.153, 0.040, 0.156, and the Calibration-in-the-large was 0.104, 0.020, 0.076, respectively, XG Boost outperformed the other two models. Similar results were obtained for the test set and validation set. A two-by-two comparison of the area under the ROC curve (AUC) of the three models showed that the XG Boost model had the best prediction performance. The XG Boost model had a high application value and outperformed the CNN and Logistic regression models in prediction.

The diverse life-course cohort (DLCC): protocol of a large-scale prospective study in China

The Diverse Life-Course Cohort (DLCC) is a large-scale prospective study including around 130,000 participants in mainland China. The primary aims of DLCC include contributing to knowledge on noncommunicable chronic disease determinants, particularly cardiometabolic diseases, and exploring the long-term effect of ambient air pollutants or other environmental risk factors on health among all-age populations. The cohort consists of several sub-populations that cover the whole life-course and diverse resources: from premarital to adolescents, adults from workplace and communities ranged from 18 to 93 years old. Baseline assessment (2017–2021) included face-to-face standardized questionnaire interview and measurements to assess social and biological factors of health. Blood samples were collected from each participant (except for children younger than 6) to establish the biobank. DLCC consists of two visits. Visit 1 was conducted from 2017, and 114850 individuals from one of the world-class urban agglomerations: Beijing, Tianjin, and Hebei area were recruited. By the end of 2021, at least one follow-up was carried out, with an overall follow-up rate of 92.33%. In 2021, we initiated Visit 2, newly recruited 9,866 adults from Guangdong province (South China) and Hebei province (Central China), with research focuses on the comparations on ambient pollution hazards and other unique dietary or environmental risks for health. The baseline survey of Visit 2 was finished in July 2021. DLCC is still ongoing with a long-term follow-up design, and not limited by the current funding period. With reliable data and the well-established biobank which consists of over 120,000 individuals’ blood samples, DLCC will provide invaluable resources for scientific research.