Standardised questionnaires will be used to collect demographic, symptom and medical history data. Sociodemographic information includes age, gender, body mass index(BMI), hours of sleep, marital status, literacy, nature of work and type of occupation. Clinical data includes past and present illnesses (acute and chronic), substance use and family history. Coronary heart disease-related information includes duration of CHD, number of diseased vessels and degree of stenosis, whether PCI was performed, and medication. Past medical history, smoking history, and alcohol consumption history will be also recorded. The vital sign data, such as temperature, respiration, pulse, and blood pressure, will be also included.

At baseline, all participants will undergo a cognitive assessment. Overall cognitive function will be assessed using the MoCA scale. Neuropsychological scale screening tests are of significant value in identifying cognitive impairment. Currently, the Brief Mental State Examination (MMSE) and the MoCA are the most commonly used cognitive assessment tools. Of the two, the MoCA is more comprehensive and sensitive than the MMSE, covering seven dimensions, including attention and concentration, executive function, memory, language, visual-spatial ability, orientation, and abstraction, and is suitable for use in a wide range of clinical practices. In a study of cognitive function assessment in middle-aged and elderly patients (11), the evaluation results of the MoCA scale are more accurate than those of the MMSE, providing more objective results for the assessment of cognitive dysfunction, which is worth promoting and applying in clinical practice. Moreover, MoCA is advantageous in the diagnosis of MCI in the elderly (12, 13). Therefore, the group chose MoCA as a screening tool for determining the presence of MCI.

Test results within 7 days before and after screening are acceptable. The laboratory test contents are shown in Table 2.

Multi-omics techniques will be applied in this study to my high-throughput data for differential markers at the micro-level of MCI in older adults with CHD. Early in the morning of the second day, the enrolled patients will have 2 mL of peripheral venous blood drawn by the nurse in the blood collection room in an EDTA anticoagulation tube. The upper layer of plasma will be retained after centrifugation (3,000 rpm, 4 °C for 15 min), and then frozen in a −80 °C refrigerator for non-target metabolomic and proteomic assays. Collect 3–5 g of fresh faecal samples, place them in sterile collection tubes and store them in a −80 °C refrigerator within 30 min for macrogenomics testing. A cranial nuclear magnetic examination will be performed to collect information on neuroimaging techniques. For an overview of all research and design objectives of the Multi-omics, see Table 3.

Research has shown (14) that cognitive function declines significantly faster in the years following the diagnosis of CHD than before the onset of the disease. Clinical symptoms are often early warning signs of subsequent disease progression. In elderly patients with CHD, some of the clinical symptoms manifested may have a significant impact on whether cognitive impairment occurs. Systematically collecting and analysing patients’ symptom information not only helps to identify potential risk factors at an early stage but also allows for timely treatment adjustments, thus effectively preventing cognitive impairment from occurring. This will not only improve patients’ quality of life but may also delay or even avoid cognitive decline. The symptom information will collect from patients mainly includes: the degree and frequency of chest pain and chest tightness episodes, Canadian Cardiovascular Society (CCS) grading assessment, long-term physical discomfort symptoms, diet, sleep, exercise, and physical appearance and morphology.

All researchers completed Good Clinical Practice (GCP) training and received instruction on the study procedures prior to study initiation, including administration of the MoCA scale, and standardized collection of blood and faecal samples, to ensure rigor and consistency in data and sample acquisition. All investigators demonstrated a thorough understanding of the clinical study protocol, were proficient in all study procedures, and were authorized by the Principal Investigator to conduct the study.

SPSS 25.0 will be used to analyse the data, and the descriptive statistics will be presented as mean ± standard deviation (x ± s) or as a percentage for each group. Based on the normality of the distribution of continuous variables, either the t-test or the Mann–Whitney U-test will be used to detect differences in baseline characteristics. The unordered categorical variables of baseline data between the groups will be tested by the Chi-square test or Fisher's exact test. To identify the influencing factors of MCI combined with CHD in the elderly, clinical data (e.g., past medical history, family history, laboratory tests and clinical symptoms) will be analysed using the aforementioned univariate analysis method. Variables with a P-value < 0.1 will be selected and incorporated into subsequent multivariate logistic regression models to control for confounding biases. The predictive value of the relevant indices for the outcome will be assessed using the receiver operating characteristic (ROC) curves.

We will integrate multi-omics features with clinical phenotypic data to construct a multi-omics mechanism network diagram corresponding to the clinical phenotype of MCI combined with CHD in the elderly. We will establish the correlation between micro-phenotypic features (multi-omics data and laboratory tests) and macro-phenotypic clinical manifestations (physiological and pathological characteristics). Before multi-omics integration, feature selection and dimensionality reduction analysis will be performed at each omics level. Principal Component Analysis (PCA) will be used to visualise the data distribution of samples across different groups, and Partial Least Squares Discriminant Analysis (PLSDA) will be employed to observe the differential data distribution among sample groups, screening out metabolites, proteins, microbial features, and imaging phenotypes significantly associated with MCI combined with CHD in the elderly. Functional enrichment analysis, such as GO and KEGG pathway analysis, will be conducted to preliminarily understand the biological significance of each omics data. Spearman correlation will be used to evaluate the co-expression patterns between different types of omics data features and clinical phenotypes in each sample group. Based on the results of correlation analysis, we will apply network association analysis to construct an association network diagram between multi-omics features and clinical phenotypes. Finally, Cytoscape software will be used to visualise the generated network diagram, intuitively displaying the complex relationship between each omics feature and clinical phenotype.

Study data will be collected by the researchers and recorded in a standardised medical record report form. The names of participating patients will be replaced with ID numbers and medical record numbers to protect patient privacy. Data entry will be performed using an electronic data collection (EDC) system (http://121.229.17.89:9080/NewXYEDC/), which is constructed by the National Drug Clinical Trial Facility (GCP Centre) of Xiyuan Hospital, China Academy of Chinese Medical Sciences. After data collection and entry are completed, the GCP Centre staff and the Principal Investigator will conduct data review and cleaning to ensure data completeness and accuracy.