Impact of Circulating Triglycerides Concentration on Atherosclerotic Disease Status in Middle-Aged Saudi Arabian Dwellers

Identification of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis

Background

Atherosclerosis is a significant contributor to cardiovascular disease, and conventional diagnostic methods frequently fall short in the timely and accurate detection of early-stage atherosclerosis. Abnormal lipid metabolism plays a critical role in the development of atherosclerosis. Consequently, the identification of new diagnostic markers is essential for the precise diagnosis of this condition.

Method

The datasets related to atherosclerosis utilized in this research were obtained from the GEO database (GSE2470, GSE24495, GSE100927 and GSE43292). The ssGSEA technique was first utilized to assess lipid metabolism scores in samples affected by atherosclerosis, thereby aiding in the discovery of important regulatory genes linked to lipid metabolism via WGCNA. Following this, differential expression analysis and functional evaluations were carried out, after which various machine learning approaches were employed to determine significant diagnostic genes for atherosclerosis. A diagnostic model was then developed and validated through several machine learning algorithms. Furthermore, molecular docking studies were conducted to analyze the binding affinity of these key markers with therapeutic agents for atherosclerosis. The ssGSEA technique was also used to measure immune cell scores in atherosclerotic samples, aiding the exploration of the connection between key diagnostic markers and immune cells. Finally, the expression variations of the identified pivotal genes were confirmed through experimental validation.

Result

WGCNA identified 302 lipid metabolism-related genes in atherosclerotic samples, and functional analysis revealed that these genes are associated with multiple immune pathways. Through further differential analysis and screening using machine learning algorithms, APLNR, PCDH12, PODXL, SLC40A1, TM4SF18, and TNFRSF25 were identified as key diagnostic genes for atherosclerosis. The diagnostic model we constructed was confirmed to predict the occurrence of atherosclerosis with high accuracy, and molecular docking studies indicated that these six key diagnostic genes have potential as drug targets. Additionally, the ssGSEA algorithm further validated the association of these diagnostic genes with various immune cells. Finally, the expression levels of these six genes were experimentally confirmed.

Conclusion

Our study introduces novel lipid metabolism-related diagnostic markers for atherosclerosis and emphasizes their potential as immune-related drug targets. This research provides a valuable approach for the predictive diagnosis and targeted therapy of atherosclerosis.

A Cross-Sectional Study of the Current Management of Hypertriglyceridemia

Objectives To estimate the prevalence of hypertriglyceridemia (HTG), determine the association between HTG and the risk of ischemic heart disease and major adverse cardiovascular events. Lastly, to assess the management outcomes of HTG in terms of the different drugs in the treatment plan.  Methods A retrospective, longitudinal study at a tertiary hospital was conducted. All who came in were screened. Patients with HTG (TAG [triacylglyceride] 2.3 mmol/L) in the last five years were included in the study. The data included the demographic variables, potential causes, and the methods of management. All data were recorded in a standard data collection form and analyzed by an appropriate statistical tool, using the John Macintosh Project (JMP) software version 15 (Cary, NC: SAS Institute Inc.). Results Of 300 patients included, 174 (58.0%) were male, with a mean age of 57.8±13.4 years. Pre-treatment, the mean triglycerides (TG) was 3.2±2.3 mmol/L, low-density lipoprotein (LDL) 2.7±1.3 mmol/L, high-density lipoprotein (HDL) 0.93±0.30 mmol/L, and the total cholesterol (TC)was 5.2±1.3 mmol/L. All the patients have prescribed a statin, 144 (48.0%) received aspirin, six (2.0%) fenofibrate, and three (1.0%) gemfibrozil. At the follow-up, the level of the TG was 2.6±1.3 mmol/L (P=0.001), LDL 2.5±1.2 mmol/L (P=0.006) and total cholesterol (TC) 4.7±1.5 mmol/L (P=0.001). Almost a third (28.2%) developed cardiac complications, five (1.6%) presented with unstable angina, six (2.0%) as non-ST segment elevation myocardial infarction (NSTEMI), three (1.0%) had ST segment elevation myocardial infarction (STEMI), and 19 (6.3%) had heart failure. A small proportion (17.3%) had a percutaneous coronary intervention, 27 (9.0%) had single-vessel disease, 12 (4.0%) two-vessel disease, and 13 (4.3%) three-vessel disease. Conclusions Many physicians do not pay attention to HTG in everyday practice, although HTG contributes significantly to the occurrence of coronary heart disease. In our study, the majority had mixed hyperlipidemia. One-third of patients with high triglycerides developed ischemic heart disease. The use of fenofibrate and gemfibrozil was not high. A low occurrence of pancreatitis was noted in our series.

Determinants of Brachial-Ankle Pulse Wave Velocity and Vascular Aging in Healthy Older Subjects

BACKGROUND Vascular aging is characterized by increasing arterial stiffness as measured by pulse wave velocity. The present study evaluated the factors influencing vascular aging in Chinese healthy older subjects. MATERIAL AND METHODS Disease- and treatment-free aged (≥60 years) participants were recruited from 2014 to 2019. Cardiometabolic risk factors and brachial-ankle pulse wave velocity (baPWV) were assessed. We defined healthy vascular aging (HVA) as the lowest 10% and early vascular aging (EVA) as the highest 10% of the baPWV distribution, after adjustment for age and blood pressure (BP). We fitted linear and logistic regression models to assess the determinants. RESULTS In all, 794 subjects (mean age 66.5±6.8 years, 71.0% male) were recruited; the 10th and 90th percentiles of baPWV were 1278 cm/s and 1955 cm/s, respectively. Age, BP, heart rate, and triglycerides were all positively associated with baPWV, whereas male subjects and body mass index (BMI) were negatively associated with baPWV. The number of participants diagnosed with either HVA or EVA was 80. Logistic regression models showed that sex, BMI, heart rate, and triglycerides were associated with HVA and EVA after adjustment for age, BP, and other confounding factors. CONCLUSIONS Male, high BMI, low heart rate, and low triglycerides are protective factors for vascular aging in the healthy aged population. Management of BMI, heart rate, triglycerides in a reasonable range may help to alleviate the vascular aging process.