CN113640516A - Application of peripheral blood EPCs (Epiches sinensis) as life time prediction marker for old people - Google Patents

Application of peripheral blood EPCs (Epiches sinensis) as life time prediction marker for old people Download PDF

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CN113640516A
CN113640516A CN202110937711.1A CN202110937711A CN113640516A CN 113640516 A CN113640516 A CN 113640516A CN 202110937711 A CN202110937711 A CN 202110937711A CN 113640516 A CN113640516 A CN 113640516A
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张蔷
孔晓冬
梁昊岳
李岱
张释双
孙宁
刘丽
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Institute of Hematology and Blood Diseases Hospital of CAMS and PUMC
Tianjin Medical University General Hospital
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Abstract

The invention firstly explores the relationship between the quantity of Endothelial Progenitor Cells (EPCs) and a blood-related index model and the survival time in the old and old people, discloses the relationship between the quantity of EPCs, the blood-related index, the age and other related factors and the survival of the old and old people, verifies that the quantity of EPCs in peripheral blood can be used as a prediction marker of the survival time of the old and old people, and lays a solid theoretical foundation for evaluating the health state of the old and old people and improving the life quality of the old and old people.

Description

Application of peripheral blood EPCs (Epiches sinensis) as life time prediction marker for old people
Technical Field
The invention relates to a new application of peripheral blood EPCs, in particular to an application of a peripheral blood EPCs quantity and blood related index model in predicting the survival time of old and old people.
Background
With the increase of the average life of the population in China, the aging problem is increasingly prominent, and how to improve the life quality of the old and predict the life cycle of the old is a problem in front of scientific researchers. Endothelial Progenitor Cells (EPCs) are precursors of mature endothelial cells, mainly derived from the bone marrow. EPCs can differentiate into mature endothelial cells, contribute to the repair of vascular injury, maintain endothelial and vascular barrier homeostasis, and are considered as an evaluation index of the endogenous regenerative capacity of the cardiovascular system, and changes in the status of circulating EPCs represent a marker of endothelial dysfunction and vascular health. EPCs are known to be used as markers of various diseases, such as chronic heart failure, ischemic cardiovascular and cerebrovascular disease events, and also as potential markers of vascular diseases such as hypertension, coronary heart disease, diabetes and the like. Changes in the levels of EPCs may be closely related to aging, with significantly fewer EPCs in the peripheral blood of the elderly than in the young, with significantly impaired in vitro migration and adhesion of EPCs in the elderly, and in vivo re-endothelialization. In addition to aging factors, drugs, exercise, etc. can affect the levels of EPCs in the elderly, where exercise can also affect the re-endothelialization capabilities of EPCs in the elderly. Until the current domestic and foreign researches, the researches are mostly limited to the research of the aged people more than 60 years old, the aged people are not further layered according to the ages, particularly, the research on the relation between relevant indexes of blood of the aged people more than 75 years old and EPCs and whether the level of the EPCs in circulation can influence the survival time of the aged people is not reported. The old and the elderly refer to people with the age of more than or equal to 75 years old, and the people often integrate a plurality of basic diseases, so that the survival time of the people is more difficult to judge, and people hope to find a method which can help people to predict the survival time of the old and the elderly at an early stage.
Disclosure of Invention
The invention explores the relation between the EPCs and the blood-related index and the survival time in the old and the elderly for the first time, discloses the relation between the EPCs and the blood-related index and the survival of the old and the elderly, and lays a solid theoretical foundation for further prolonging the life of the old and the elderly and improving the life quality of the old and the elderly.
The invention firstly verifies that the EPCs in the peripheral blood can be used as the survival time prediction marker for the old and the elderly. EPCs in circulation of old and old people are grouped according to a median grouping method and an average grouping method by an OPLS-DA method, and two groups of data are found to be credible and are basically and completely separated. Through SPSS verification, in blood related indexes, the difference of two groups in the median group and the average group in RBC, HGB, HCT, RDW-CV, RDW-SD, AST, GGT, LDH, UREA and FER has statistical significance, the difference of two groups in the median group in MCV, ALB, ALKP and D-dimer has statistical significance, and the difference of two groups in the average group in BAS has statistical significance. The blood related indexes are related markers reflecting blood state (blood routine), liver function, kidney function, blood coagulation and tumor related factors, and suggest that EPCs in peripheral blood circulation may participate in the progress of various diseases such as anemia, heart, liver and kidney injury, blood coagulation and the like, and the level of EPCs can influence the survival time of old and old people, so that the level of EPCs can be regarded as a marker reflecting the comprehensive level of the health condition of old and old people, and can be used as a powerful index for predicting the survival time of old and old people.
Further, the number of the EPCs in the peripheral blood is a positive correlation marker for the prediction of the survival time of the old and the elderly. On the one hand, in the two groups, the statistically significant difference in survival time, the higher-level EPCs group had lower occurrence of all-cause death events than the lower-level EPCs group (5-year, 7-year survival time in terms of median, 5-year, 6-year, 7-year in terms of mean), indicating that the higher the level of EPCs in peripheral blood, the higher the expected survival time of the elderly. On the other hand, in the index of correlation between the number of circulating EPCs and blood indices, the number of circulating EPCs is positively correlated with RBC, HGB, HCT, the number of EPCs is negatively correlated with MCV, RDW-CV, RDW-SD, AST, GGT, LDH, UREA (P0.008), D-dimer, FER, and there is a substantially uniform correlation difference between the high and low level groups in the two grouping modes (RBC, HGB, HCT, ALB is lower than the high level EPCs group, MCV, RDW-CV, RDW-SD, AST, ALKP, GGT, LDH, UREA, D-dimer, FER is lower than the low level EPCs group, RBC, HGB, BAS, HCT is lower than the high level EPCs group, RBC, HGB, RBC is lower than the high level EPCs group, low level EPCs group, RDW-CV, RDW-SD, GGT, FER is lower than the low level EPCs group, FER is lower than the high level EPCs group, RBC is lower than the high level EPCs group, and the health index of the high level EPCs group is positively correlated with the anemia index of the aged, the negative correlation index is consistent with the deteriorated health conditions of liver and kidney function damage, blood coagulation, tumorigenesis and development and the like, and can also indicate that the higher the level of EPCs in peripheral blood is, the better the health condition of the old and the old is reflected, and the higher the expected survival time of the old and the old is. In the third aspect, the difference of calcium antagonist application by the elderly in the average number group hierarchical card formula has statistical significance (the number of people applying the low-level EPCs group is higher than that of the high-level EPCs group), and the difference of aspirin/clopidogrel application by the elderly has statistical significance (the number of people applying the low-level EPCs group is higher than that of the high-level EPCs group), which indicates that the medical application can reflect that the health condition of the elderly with low-level EPCs is poor, and the expected survival time is lower.
Furthermore, the prediction of the survival time of the old and the elderly by the EPCs can be within 13 years. The survival time prediction of the peripheral blood EPCs quantity index serving as a single prediction factor within 7 years is reliable, and the comparison of the low-level and high-level EPCs group survival curves of the two grouping modes can prove that the difference of two groups of survival rates within 7 years in the recent survival time has statistical significance; when the number of EPCs in peripheral blood is combined with other prediction factors to serve as a common influence factor, the life time can be predicted to be 13 years or more, the other prediction factors comprise RBC, HCT, AST, age and gender which play a decisive role in the EPCs level of the old and the old, and can also comprise HGB, MCV, RDW-CV, RDW-SD, GGT, LDH, UREA, D-dimer, FER and the like which are related to the EPCs level of the old and the old, the age and the HCT can be risk factors of all-cause death events, the EPCs number can be protective factors, a logic regression model constructed by utilizing the age, the EPCs number and the HCT proves that the prediction model constructed by introducing the other prediction factors has the most significance for judging the long-term survival prognosis (more than 10 years) of the old and an experiment proves that the prediction model has statistical significance on the life time of 12-13 years. Among other prediction factors, RBC, HGB and HCT are positive correlation markers for predicting the survival time of the old and the old, and MCV, RDW-CV, RDW-SD, AST, GGT, LDH, UREA, D-dimer, FER and age are negative correlation markers for predicting the survival time of the old and the old.
The invention provides a method for predicting the survival time of old people by adopting EPCs, wherein a prediction model of the survival time of old people is established by taking the number of EPCs in peripheral blood as a single prediction factor or taking the number of EPCs in peripheral blood and other prediction factors, and the actual index of the prediction factor of the old people to be detected is obtained and substituted into the prediction model to obtain the predicted survival time of the old people to be detected.
Other predictors include RBC, HCT, AST, age, gender, which are critical for the levels of EPCs in the elderly as described above, and may also include HGB, MCV, GGT, LDH, UREA, D-dimer, FER, etc., which are correlated with the levels of EPCs in the elderly as described above. The predictive model may be a linear fit equation, a logistic regression model, or other statistical model.
Wherein, the establishment of a prediction model of the survival time of the old and the elderly excludes uncontrolled acute internal medicine diseases such as infection, acid-base balance, electrolyte disorder and the like, and newly-occurred cerebral infarction and myocardial infarction, trauma and operation history in nearly 3 months; patients with malignant tumors and patients with neoplastic disease during follow-up; EPCs in CD34+/CD133+And (6) counting the marks.
Based on the above, the invention can also provide a reagent or a kit for predicting the survival time of the old, which comprises a detection reagent for detecting the quantity of EPCs in peripheral blood, and can further comprise other detection reagents for detecting HGB, MCV, GGT, LDH, UREA, D-dimer, FER and the like related to the level of EPCs in the old. The result obtained by the detection of the reagent or the kit is substituted into the established prediction model, and the predicted life time of the old can be obtained. The kit can also comprise a prediction card which is prepared by standardizing the established prediction model, and the detection result can be directly compared with the prediction card to quickly obtain the prediction result.
Based on the above, the present invention can also provide a method or a medicament for prolonging the life time of the elderly, which is used for increasing the level of EPCs in the peripheral blood of the elderly. The method of the invention has proved that the EPCs level is a marker reflecting the comprehensive health status of the old and the elderly, and the improvement of the EPCs level is beneficial to improving the health status of the old and the elderly, thereby effectively prolonging the life time of the old and the elderly. Further, the method or medicament may have one or more of the following pathways of action, depending on the mechanism by which EPCs influence survival as we discuss: (1) promoting vascular endothelial repair of old and old people; (2) improving endothelial function homeostasis and mediating neovascularization via a variety of signaling pathways; (3) promoting angiogenesis through a microRNA signal pathway; (4) the secreted vasoactive substances are involved in the repair of endothelial function.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1: characterization of circulating EPCs and related cell populations in peripheral blood. (a) Flow cytometry analysis of EPCs, CEPs, and EPC precursors, (b) UMAP analysis of EPCs, CEPs, and EPC precursors, (c) FlowSOM analysis of EPCs, CEPs, and EPC precursors.
FIG. 2: the low-high level EPCs group blood related index data is obtained by a median grouping method; (a) a score map, (b) a variable coefficient map, (c) a linear score map, and (d) a spatial distribution map.
FIG. 3: replacement check chart (upper green: low level EPCs group, lower blue: high level EPCs group), (b) ROC curve;
FIG. 4: the median grouping method includes (a) a loading matrix map, (b) a loading line map, (c) an S line map, (d) an S line map, (e) a scatter map, and (f) VIP.
FIG. 5: the average grouping method is used for low-level and high-level EPCs group blood related index data; (a) a score map, (b) a variable coefficient map, (c) a linear score map, and (d) a spatial distribution map.
FIG. 6: displacement test plots (upper green: low-level EPCs group, lower blue: high-level EPCs group), (b) ROC curve;
FIG. 7: a load matrix map (a), a load line map (b), an S line map (c), an S point map (d), a scatter map (e), and a VIP (f) of the mean number grouping method.
FIG. 8: and (3) comparing two groups of blood related indexes by adopting a median grouping method to obtain a project box diagram with statistical significance. RBC comparison (A), HGB comparison (B), HCT comparison (C), MCV comparison (D), RDW-CV comparison (E), RDW-SD comparison (F) in two blood norms; ALB comparison (G), AST comparison (H), ALKP comparison (I), GGT comparison (J), LDH comparison (K) in two groups of liver functions; UREA comparison (L) in renal function in two groups; d-dimer comparison (M) in two coagulation groups; FER comparison (N) in two groups of tumor markers; the horizontal line shows the median, the rectangular box shows the upper and lower quartiles (Q3-Q1); p <0.001, P <0.01, P < 0.05.
FIG. 9: and (3) comparing two groups of blood related indexes by adopting an average grouping method to obtain a statistically significant item bar chart. RBC comparison (a), HGB comparison (b), BAS comparison (c), HCT comparison (d), RDW-CV comparison (e), RDW-SD comparison (f) in two blood norms; AST comparison (g), GGT comparison (h), LDH comparison (i) in two groups of liver functions; UREA comparisons (j) in renal function in both groups; FER comparison (k) in two groups of tumor markers; the error line of the data conforming to the normal distribution in the bar graph is represented by Mean plus or minus SD, and the error line of the data conforming to the normal distribution is represented by Median plus or minus internqualile range; p <0.001, P <0.01, P < 0.05.
FIG. 10: scatter plot of EPCs versus blood related indices. Normal transformed EPCs (lg EPCs) and their correlation scattergrams with RBC (A), HGB (B), MCV (C), EPCs and HCT (D), RDW-CV (E), RDW-SD (F), AST (G), GGT (H), LDH (I), UREA (J), D-dimer (K) and FER (L).
FIG. 11: scatter plot of EPCs versus time to live correlation.
FIG. 12: two sets of survival curves for the median group were compared. Log Rank 0.070, Breslow 0.036.
FIG. 13: two sets of survival curves for the mean groups were compared. Log Rank 0.084, Breslow 0.043.
FIG. 14: and (4) crossing the results of the OPLS-DA method and the SPSS statistical method.
Detailed Description
For a better understanding of the present invention, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
The research is to explore the relation between the EPCs and the relevant indexes of the blood and the survival time in the old and old people for the first time, and establish the standard and the method of the system for the old and old people survival research based on the EPCs and the blood indexes, thereby laying a solid theoretical foundation for further predicting the life of the old and old people and improving the life quality of the old and old people.
Study object and method
1. Study object
The elderly population who had a visit in the department of geriatrics at the general hospital of Tianjin medical university from month 2008 to month 2008 was collected, of which 81 males and 23 females aged 75-103 years old.
Grouping standard: elderly people aged 75 years or older who have completed peripheral EPCs number determinations at the time of enrollment, and who have agreed to participate in the acquisition of long-term follow-up data for this study.
Exclusion criteria: elderly people under 75 years of age; uncontrolled acute medical conditions such as infection, acid-base balance, electrolyte disorders, and the like; the new cerebral infarction, myocardial infarction, trauma and operation history occur in the last 3 months; patients with malignant tumors and patients with neoplastic disease during follow-up.
The study was initially enrolled in 114 patients, except for neoplastic patients and depressive suicide patients found during follow-up visits, and finally 104 elderly patients were kept for inclusion in the study.
The research accords with the ethical standard of human body tests and is approved by ethical committee of hospitals, the subjects and family members who enter the research group provide written informed consent, and all the research objects are treated in a standardized way according to the medical diagnosis and treatment steps formulated by the aged disease institute in Tianjin.
2. Collecting medical history
After approval, the patient refers to the data related to the medical record room of general Hospital healthcare department (geriatrics department) of Tianjin medical university, and collects the detailed medical history (coronary heart disease, hypertension, diabetes, hyperlipidemia, renal insufficiency, stroke history, myocardial infarction history, atrial fibrillation history, coronary stent operation and coronary bypass operation), smoking history, drinking history and current medical history (calcium antagonist, ACEI/ARB, beta receptor blocker, aspirin, clopidogrel/warfarin, statins, oral hypoglycemic agent, insulin and the like) of the subjects finally entering the study group.
3. Detection method of peripheral blood EPCs
Subjects were enrolled for peripheral blood EPCs determination by the neurology institute of general hospital, tianjin medical university, as follows:
blood is taken from the subject after fasting for 10 hours, 2ml of peripheral blood sample is collected from antecubital vein of each subject, and is placed in EDTA tube for anticoagulation (anticoagulant is 0.5mM EDTA) to separate mononuclear cells, and the surface label is CD34+/CD133+To determine the number of EPCs. The separated cells were labeled with FITC-CD34(BD Pharmingen, San Jose, CA) monoclonal antibody and PE-CD133 monoclonal antibody (Meitianni, Germany) simultaneously, and the mouse-conjugated PE-and FITC-IgG antibodies were incubated at 4 ℃ for 15min as a quality control. Using flow cytometry (BD FACS AriaTMBD usa) were first set to a mononuclear cell gate, and CD34 was collected separately+And CD133+Cells, then double positive cells were collected. EPC number in the order of CD34 per 20 ten thousand monocytes+/CD133+The number of cells occupied is expressed. 4. Collection of blood related index data
After approval, relevant indexes of blood of a group research object are collected in a medical record room of the department of health care and medical treatment (the department of geriatrics) of the general hospital of Tianjin medical university, and the reference standard is the relevant indexes of the blood of clinical, biochemical and special laboratory which are detected simultaneously with the EPCs test, and the relevant indexes specifically comprise blood routine, blood coagulation function, liver function, kidney function, electrolyte and tumor markers. The blood routine includes White Blood Cells (WBC), Red Blood Cells (RBC), Hemoglobin (HGB), Platelets (PLT), the percentage of neutrophils (NEU%), the percentage of lymphocytes (LYMPH%), the percentage of monocytes (MON%), the percentage of eosinophils (EOS%), the percentage of basophils (BAS%), the absolute value of neutrophils (NEU #), the absolute value of lymphocytes (LYM #), the absolute value of monocytes (MON #), the absolute value of eosinophils (EOS #), the absolute value of basophils (BAS #), Hematocrit (HCT), Mean Corpuscular Volume (MCV), mean corpuscular hemoglobin content (MCH), Mean Corpuscular Hemoglobin Concentration (MCHC), coefficient of variation in the distribution width (RDW-CV), standard deviation in the distribution width of erythrocytes (RDW-SD), thrombocyte volume (PCT), Platelet volume distribution width (PDW), Mean Platelet Volume (MPV), large platelet ratio (P-LCR); liver function includes Total Protein (TP), Albumin (ALB), Globulin (GLO), alanine Aminotransferase (ALT), aspartate Aminotransferase (AST), alkaline phosphatase (ALKP), gamma-glutamyl transpeptidase (GGT), Lactate Dehydrogenase (LDH), Total Bilirubin (TBIL), Direct Bilirubin (DBIL), Total Cholesterol (TC), Triglycerides (TG), high density lipoprotein (HDL-C), low density lipoprotein (LDL-C), Glucose (GLU); renal function including UREA (UREA), Creatinine (CREA), Uric Acid (UA), electrolytes including calcium (Ca), potassium (K), sodium (Na), Chlorine (CL), carbon dioxide binding capacity (CO2CP), Anion Gap (AG); the blood coagulation comprises Prothrombin Time (PT), international normalized ratio of prothrombin time (PT-INR), Activated Partial Thromboplastin Time (APTT), Fibrinogen (FIB), and plasma D-dimer (D-dimer); tumor markers include Ferritin (FER), carbohydrate antigen 19-9(CA19-9), carbohydrate antigen 242(CA242), alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA).
5. Information collection of study subject survival time
The modern communication method is used for follow-up visit of all the study objects which are put into the group, the survival time of the study objects is collected, the death of the study objects is taken as a follow-up visit endpoint, and all-cause death events refer to death events caused by cerebrovascular diseases, cardiovascular diseases and death events caused by other reasons (lung infection, urinary infection, bacteremia and the like). The follow-up starting date is 2008, all the surviving study subjects take 12-31 days of 2020 as follow-up end points, the follow-up period is about 12-13 years in total, and the life limit of each study subject during the period is recorded.
Second, the influence of EPCs and related cell populations in peripheral blood circulation on the survival of the elderly
Table 1 and fig. 1a show the results of a classical flow-based analysis of EPC detected by flow cytometry. EPCs have different populations of related cells, including CD34+CD133-Labeled CEPs and more mature CD34-CD133+Labeled EPC precursors. To study the population characteristics of EPCs and related cell populations, UMAP and FlowSOM were used to characterize the relationship between EPCs-related cells. The UMAP analysis results reflect the antigen expression distribution of EPCs, CEPs and EPC recursors (FIG. 1b), and are consistent with the cell population ratio obtained by the classical flow-type two-dimensional step-by-step loop. As shown in fig. 1c, the EPC-associated cell populations were divided into 3 groups by FlowSOM, and the antigen expression profiles of the cells represented by the nodes were characterized by the colors of the nodes and the relative positions between the nodes. UMAP and FlowSOM analyses indicate that EPCs, CEPs and EPC recursors can be divided into different feature populations based on phenotypic features. Statistical analysis results are shown in table 1, differences in CEPs in the low and high level groups are not statistically significant (Z ═ 1.757, P ═ 0.079), and differences in values of EPC precursors (Z ═ 1.203, P ═ 0.229) are not statistically significant. Surface label CD34+CD133+The cells of (a) to determine mature EPCs have statistical significance in the high-low level group. The classification result lays a foundation for researching the relationship among EPC related cells and the influence of EPCs on blood indexes and survival of old patients.
TABLE 1 flow analysis of EPCs in peripheral blood circulation and characterization of related cell populations
Figure BDA0003213448170000051
Third, the relationship between EPCs and blood-related index
3.1OPLS-DA method for determining the index possibly related to EPCs in blood related indexes, and verifying the feasibility of median grouping and average grouping
Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) is a newly developed metabolomics data Analysis method, and the PLS is corrected by integrating an Orthogonal Signal Correction method (OSA) and a Partial Least Squares method (PLS), and R is utilized2X、R2Y and Q2The method is a quality evaluation index for establishing a model, and the discrimination capability of the method is often superior to other mode identification methods such as Principal Component Analysis (PCA) and the like. The degree of optimization of the analytical model is usually represented by R2X parameter, representing the total variation of X, R2Y represents the compilation percentage of the response variable Y, the cross-check parameter Q2Representing the cumulative prediction degree of the model, can be used to predict the authenticity of the result.
The OSC noise filtering can be used for removing the influence of factors such as diet and environment and reducing heterogeneity of clinical samples, the PLS-DA can obviously separate test groups after OSC noise filtering, and aggregation among different individuals in the groups is obvious, so that OPLS-DA pattern recognition analysis is firstly carried out by using SIMCA-P +12.0 software before SPSS data processing is carried out in the research, and potential markers are searched by referring to importance factors (VIP values) of model variables, nonparametric test results and P values.
3.1.1 Using median grouping method results
The median of the overall EPCs level is obtained through calculation, data are divided into low-level EPCs and high-level EPCs according to the median, pattern recognition analysis is carried out through OPLS-DA, and the main results comprise a score map, a variable coefficient map, a linear score map and a space distribution map. The blood-related index data of the low and high EPCs are shown in FIG. 2, in which FIG. 2a is a score chart, FIG. 2b is a variable coefficient chart, and FIG. 2c is a linear score chartFIG. 2d is a spatial distribution diagram. R2X[1]＝0.0307，R2Xo[1]＝0.406，R2Xo[2]0.155, the two groups were substantially separated in different scores and aggregation was more evident between different individuals within the two groups (see fig. 2 a); both sets of data are distributed within the red dashed line, and individual data are distributed between the red and yellow dashed lines, indicating that both sets of data are more reliable (see fig. 2c), and that the sets of low and high levels of EPCs are well distributed spatially (see fig. 2 d). In summary, the median grouping method established in the research can effectively distinguish the difference of two groups of blood related indexes and prompt the feasibility of the median grouping method.
From FIG. 3a, R can be seen2(0.0，0.3)，R2<0.4，Q2(0.0，-1.57)，Q2The intercept on the Y-axis is less than 0.05, so we can consider the model to fit well and no overfitting phenomenon occurs. Fig. 3b is a ROC curve, AUC (1) is 0.896, AUC (2) is 0.896, and AUC (1) and AUC (2) are both greater than 0.5.
As can be seen from FIG. 4, values for VIP greater than 1.0 include PLT (1.003), AST (1.194), CEA (1.206), CREA (1.245), ALKP (1.324), GGT (1.490), LDH (1.642), CA199(1.921), FER (2.208), AFP (2.918), and D-dimer (4.750), which contribute significantly to the median grouping method, and these factors may differ between the low and high EPCs groups. The more relevant variables include RBC (0.711), HCT (0.668), ALB (0.338) and BAS (0.303).
3.1.2 average grouping method results
We calculated the mean of the overall EPCs levels, divided the data into groups of low and high EPCs based on the mean, and performed pattern recognition analysis using OPLS-DA, with the main results including score plots, variable coefficient plots, linear score plots, and spatial distribution plots. The blood-related index data of the low and high EPCs sets are shown in FIG. 5, in which FIG. 5a is a score plot, FIG. 5b is a variable coefficient plot, FIG. 5c is a linear score plot, and FIG. 5d is a spatial distribution plot. R2X[1]＝0.015，R2Xo[1]＝0.426，R2Xo[2]0.169, the two groups were substantially separated in different scores, and different individuals within the two groupsThe aggregation between them is more evident (see fig. 5 a); both sets of data are distributed within the red dashed line, and the individual data are distributed between the red and yellow dashed lines, both sets of data are more reliable (see fig. 5c), while both sets are spatially well distributed (see fig. 5 d). In summary, the mean grouping method established in the research can effectively distinguish the difference of two groups of blood related indexes and prompt the feasibility of the mean grouping method.
From FIG. 6a, R can be seen2(0.0，0275)，R2<0.4，Q2(0.0，-1.01)，Q2The intercept on the Y-axis is less than 0.05, so we can consider the model to fit well and no overfitting phenomenon occurs. Fig. 6b is a ROC curve, AUC (1) is 0.883, AUC (2) is 0.883, and AUC (1) and AUC (2) are both greater than 0.5.
As can be seen from FIG. 7, values for VIP greater than 1.0 are HCT (1.017), UREA (1.148), AST (1.156), ALKP (1.158), LDH (1.163), PLT (1.232), UA (1.336), GGT (1.373), CREA (1.622), CEA (1.671), CA199(2.123), FER (2.259), AFP (3.282), and D-dimer (3.889), respectively, which are factors that contribute significantly to the mean grouping method, and may differ in the low and high EPCs groups. The more relevant variables are RBC (0.551), HCT (0.545) and ALB (0.334).
3.1.3 discussion of results of two grouping methods
The research finds that no matter a median or average grouping method is adopted, two groups of data can be reasonably grouped, and the result is credible. From the VIP values and the correlation coefficients, it can be seen that there are two groups of data in the blood related index that may be different. From the results, it was found that there were data indicating that the EPCs correlation coefficient was positive and data indicating that the EPCs correlation coefficient was negative in the blood-related index. The low and high level EPCs in the median grouping may differ in PLT, AST, CEA, CREA, ALKP, GGT, LDH, CA19-9, FER, AFP, D-dimer, and the variables strongly correlated with the number of EPCs are RBC, HCT, ALB, BAS. The groups of low and high levels of EPCs in the average group may differ from HCT, UREA, AST, ALKP, LDH, PLT, UA, GGT, CREA, CEA, CA19-9, FER, AFP, D-dimer, and the variables that are strongly correlated with the number of EPCs are RBC, HCT, ALB. Whether grouped by median or average, we can see that the two groups may differ in PLT, AST, CEA, CREA, ALKP, GGT, LDH, CA19-9, FER, AFP, D-dimer.
The variables with strong correlation in the two grouping methods are RBC, HCT and ALB, and the variables are focused when SPSS is subsequently applied to data analysis.
3.2SPSS software for determining EPCs and blood-related indexes
Statistical analysis using SPSS 26.0 software, normal distribution of data to
Figure BDA0003213448170000061
The non-normal data are shown as median (P25, P75). And the classified data are compared pairwise by adopting chi-square test, if necessary, Fisher accurate test is adopted for comparison, the hierarchical comparison adopts hierarchical chi-square test, the comparison among normal distribution sample groups adopts independent sample t test, and the comparison among non-normal distribution sample groups adopts rank sum test. The Spearman rank correlation analysis evaluates the correlation between the number of EPCs and the all-cause death event, and the correlation is analyzed by Pearson product moment correlation analysis when the blood monitoring index accords with normal distribution, and the correlation is analyzed by Spearman rank correlation analysis when the blood monitoring index does not accord with normal distribution. The relationship between the all-cause death events and the age, the number of EPCs and the hematocrit is analyzed by two-class Logistic regression, the survival rate is estimated by adopting a Kaplan-Meier method, two groups of survival curves are compared, and the difference P less than 0.05 has statistical significance. The graphic rendering is done using SPSS 26.0 software and GraphPad6 rendering software.
3.2.1 grouping results by median
3.2.1.1 clinical basic data comparison results
To the end of the observation period, except for newly-discovered patients with malignant tumor disease and patients with depression suicide during follow-up, 104 patients who will eventually be included in the group were divided into two groups of low and high-level EPCs according to median, wherein the number of EPCs in the median group was in the range of 5 to 36(/20 ten thousand mononuclear cells) in the low-level group, 52 in the high-level group, and 40 to 204(/20 ten thousand mononuclear cells) in the high-level group.
The statistical results are shown in table 1, the high-level EPCs group is more male than the low-level EPCs group, the low-level EPCs group is more female than the high-level EPCs group, and the difference is statistically significant (P ═ 0.009); the comparative differences of the age, the current medical history (renal insufficiency, coronary stent operation, coronary taffy operation, stroke history, myocardial infarction history and atrial fibrillation), the risk factors (coronary heart disease, hypertension, diabetes, hyperlipidemia, smoking history and drinking history), the medical history (Ca retardant, ACEI/ARB, beta retardant, aspirin/clopidogrel, warfarin, statins, oral hypoglycemic drugs, insulin and the like) of two groups have no statistical significance.
TABLE 2 two sets of basic data (median group)
Figure BDA0003213448170000071
Figure BDA0003213448170000081
Note: when the theoretical number is more than or equal to 5, a Person chi-square P value is adopted, when the theoretical number is less than 5 and the theoretical number is more than or equal to 1, the chi-square P value is corrected by adopting continuity, and when the theoretical number is less than 1, a Fisher accurate value is adopted.
Two groups of data are layered according to ages, the two groups of layered basic data are shown in table 3, the two groups of layered basic data have no statistical significance in comparison of difference of current medical history (renal insufficiency, coronary stent operation, coronary artery and bridge operation, stroke history, myocardial infarction history, atrial fibrillation), risk factors (coronary heart disease, hypertension, diabetes, hyperlipidemia, smoking history and drinking history), drug history (Ca retardant, ACEI/ARB, beta retardant, aspirin, clopidogrel/warfarin, statins, oral hypoglycemic agent, insulin and the like), and layered chi-square value results are shown in table 4.
TABLE 3 two sets of basic data hierarchy (median grouping)
Figure BDA0003213448170000082
TABLE 4 two sets of hierarchical chi-square value comparisons (median group)
Figure BDA0003213448170000083
Figure BDA0003213448170000091
3.2.1.2 comparison of two groups of blood-related indicators
The results of the comparison of the two blood related indices are shown in table 5 and fig. 8. The low and high-level EPCs groups are statistically different in RBC (P ═ 0.000), HGB (P ═ 0.000), HCT (P ═ 0.001), MCV (P ═ 0.010), RDW-CV (P ═ 0.014), RDW-SD (P ═ 0.014), TP (P ═ 0.025), AST (P ═ 0.005), ALKP (P ═ 0.021), GGT (P ═ 0.005), LDH (P ═ 0.007), UREA (P ═ 0.022), D-dimer (P ═ 0.004), FER (P ═ 0.031sd), and the low-level EPCs groups, HGB, HCT, ALB levels are lower than the high-level EPCs group, and the high-level EPCs groups are lower than the high-level EPCs groups, RDW-CV, RDW-akst, akta, ggd, and low-FER.
TABLE 5 two sets of basic data of blood related index (median group)
Figure BDA0003213448170000092
Figure BDA0003213448170000101
Figure BDA0003213448170000111
Note: subject to normally distributed data to
Figure BDA0003213448170000113
The non-normal distribution data are expressed by median (P25-P75)
3.2.2 grouping results on average
3.2.2.1 clinical basic data comparison
The 104 patients who were finally enrolled were divided into two groups of low and high EPCs based on the mean, 63 persons in the low group, 41 persons in the high group, 5-50 EPCs in the low group (/20 ten thousand mononuclear cells), 51-204 EPCs in the high group (/20 ten thousand mononuclear cells), and the results are shown in table 5.
The results of the non-stratified chi-square test are shown in table 6, and the results show that the male and female in the low-level EPCs group are higher than the high-level EPCs group, and the difference is significant (P is 0.007); the number of aspirin/clopidogrel applied to the low-level EPCs group is higher than that of the high-level EPCs group, the difference has significance (P is 0.045), and the comparison difference between the age and the current medical history (renal insufficiency, coronary stent operation, coronary artery and bridge operation, stroke history, myocardial infarction history and atrial fibrillation), the risk factors (coronary heart disease, hypertension, diabetes, hyperlipidemia, smoking history and drinking history) and the medical history (Ca retarder, ACEI/ARB, beta retarder, warfarin, statins, oral hypoglycemic agent, insulin and the like) of the two groups has no statistical significance.
TABLE 6 comparison of two sets of basic data (average number grouping)
Figure BDA0003213448170000112
Figure BDA0003213448170000121
The two groups of data are further layered according to the fact that the aged people are old people with the age of less than 85 years, the aged people with the age of more than OR equal to 85 years are old people, the two groups of basic data after layering are shown in a table 7, and the layering result is shown in a table 8, so that the difference of the number of people using the calcium ion antagonist in the low-level EPCs group and the high-level EPCs group in the old people is statistically significant, the number of people using the calcium ion antagonist in the low-level EPCs group is higher than that in the high-level EPCs group, the OR is 0.133, the 95% CI is 0.024-0.724, and the P is 0.034, and the number of people using the calcium ion antagonist in the low-level EPCs group and the high-level EPCs group in the old people has no statistical significance; in the elderly, the difference of aspirin/clopidogrel number of the low-level and high-level EPCs group is statistically significant, the number of the low-level EPCs group is higher than that of the high-level EPCs group, OR is 0.257, 95% CI is 0.081-0.818, P is 0.018, and in the elderly, the difference of aspirin/clopidogrel number of the low-level and high-level EPCs group is not statistically significant. The comparative difference of the two groups of current medical histories (renal insufficiency, coronary stent operation, coronary taffy operation, stroke history, myocardial infarction history and atrial fibrillation), risk factors (coronary heart disease, hypertension, diabetes, hyperlipidemia, smoking history and drinking history) and medical histories (ACEI/ARB, beta blocker, aspirin, statins, oral hypoglycemic drugs, insulin and the like) has no statistical significance.
TABLE 7 two sets of basic data in layers (average number of groups)
Figure BDA0003213448170000122
Figure BDA0003213448170000131
TABLE 8 two sets of hierarchical chi-square comparisons (average number grouping)
Figure BDA0003213448170000132
3.2.2.2 comparison of two groups of blood-related indicators
The basic data of two groups of blood related indexes and the comparison result are shown in Table 9 and FIG. 9. The low and high EPCs groups are statistically different between RBC (P ═ 0.000), HGB (P ═ 0.000), BAS% (P ═ 0.049), HCT (P ═ 0.001), RDW-CV (P ═ 0.028), RDW-SD (P ═ 0.043), AST (P ═ 0.001), GGT (P ═ 0.022), LDH (P ═ 0.024), UREA (P ═ 0.008), and FER (P ═ 0.036). The low-level EPCs group RBC, HGB, BAS% and HCT are lower than the high-level EPCs group, and the low-level EPCs group RDW-CV, RDW-SD, AST, GGT, LDH, UREA and FER are higher than the high-level EPCs group.
TABLE 9 two groups of basic data of blood related index (mean number group)
Figure BDA0003213448170000141
Figure BDA0003213448170000151
3.3 analysis of the correlation between the number of circulating EPCs and blood indicators and their significance as an indicator of the overall level of health
3.3.1 analysis of correlation between circulating EPCs number and blood index
The results of the correlation between circulating EPCs and the blood indices are shown in table 10, and the number of circulating EPCs is in positive correlation with RBC (P ═ 0.000), HGB (P ═ 0.000), HCT (P ═ 0.000), the number of EPCs is in negative correlation with MCV (P ═ 0.006), RDW-CV (P ═ 0.017), RDW-SD (P ═ 0.005), AST (P ═ 0.013), GGT (P ═ 0.001), LDH (P ═ 0.009), UREA (P ═ 0.008), D-dimer (P ═ 0.001), and FER (P ═ 0.012).
TABLE 10 correlation between circulating EPCs and blood normothermia
Figure BDA0003213448170000152
Figure BDA0003213448170000161
Figure BDA0003213448170000171
Note: performing correlation analysis on the normal distribution data and the EPCs (lg EPCs) subjected to the normality conversion
The results of the scatter diagram are shown in fig. 10, in which it can be seen more intuitively that the orthomorphically transformed epcs (lg epcs) are positively correlated with RBC and HGB and negatively correlated with MCV; EPCs are positively correlated with HCT and negatively correlated with RDW-CV, RDW-SD, AST, GGT, LDH, UREA, D-dimer, and FER.
3.3.1 number of circulating EPCs can be used as an identification factor of the comprehensive level of the health condition of the old and the elderly
The EPCs not only participate in the development and formation of blood vessels in the embryonic period, but also have important significance for the angiogenesis, repair, reconstruction and the like of adults after birth. In the cardiovascular system, EPCs are involved in repair of endarterial damage and in the development and progression of atherosclerosis; in the cerebrovascular system, EPCs participate in maintaining the integrity and function of the cerebral vessels, promoting recovery of neurological function. Various diseases can affect the level of EPCs in circulation, and patients in acute and convalescent phases of stroke are monitored to have the EPCs increased even for 6 months after stroke follow-up; in patients with aortic stenosis, the number of EPCs is significantly elevated; in chronic heart failure patients, EPCs levels are reduced, significantly lower than in healthy subjects. According to the research, old and old people are selected as research objects, the old and old people have more basic diseases, chronic diseases such as hypertension, coronary heart disease, diabetes, hyperlipidemia and the like and have more history of cardiovascular and cerebrovascular diseases, and the inventor finds that the EPCs level of the old and old people is related to blood related indexes of ischemia and hypoxia, is an identification factor reflecting the comprehensive level of the health condition of the old and old people and is possible to predict the survival time of the old and old people. We will now discuss the following subsections:
3.3.1.1 EPCs and hematology
In the research, the RBC, HGB, HCT, RDW-CV and RDW-SD in two groups of the median group and the average group are found to have obvious difference, and the correlation shows that the quantity of EPCs is positively correlated with RBC, HGB and HCT, and the quantity of EPCs is negatively correlated with RDW-CV and RDW-SD. The previous research proves that the quantity of EPCs in peripheral blood vessels of patients suffering from acute coronary syndrome with anemia is reduced, and the anemia is proved to cause the reduction of the level of EPCs. The low-level EPCs group old and old people have reduced RBC, HGB, HCT, and are in a state of mild anemia, which causes ischemia, hypoxia in vivo, which affects angiogenesis and differentiation of EPCs, mainly by HIF (hypoxia inducible factor) mediated associated responses, which in turn respond to hypoxia through angiogenesis. All eukaryotes rely on oxygen to support oxidative phosphorylation, thereby efficiently producing adenosine triphosphate. Therefore, maintaining a constant supply of oxygen to the vascular system of a mammal is critical to tissue development, maintenance of homeostasis and function. For the elderly, the anemia state may cause a decrease in the level of EPCs in the body, and appropriate amelioration of the anemia state in the elderly may improve the level of EPCs in the body.
The previous scholars demonstrated that both the breadth of red blood cell distribution, EPCs, can be used to predict vascular aging and to diagnose/predict age-related degenerative arterial diseases such as ascending aortic aneurysms. The red blood cell distribution width is recently considered as the best prognostic biomarker of cardiovascular diseases, and combined with the research of the scholars and the experimental results of the scholars, the RDW-CV, the RDW-SD level and the circulating EPCs level are inversely related, and RBC, HGB, HCT, RDW-CV and RDW-SD can be used as the markers for predicting the EPCs level in the old and old human bodies.
3.3.1.2EPCs and liver function
EPCs are not only involved in angiogenesis and maintaining endothelial homeostasis, but also migrate to damaged tissues via angiogenic signals and promote endothelial repair and neovascularization, and participate in the repair of liver damage. The number of EPCs increases in alcoholic cirrhosis patients, the levels of EPCs decrease in non-alcoholic fatty liver disease patients, and the tendency of the EPCs to decrease correlates with the severity of liver disease in non-alcoholic fatty liver disease patients. In animal experiments, when EPCs derived from bone marrow of mice were transplanted into mice by tail vein injection, the researchers found that the serum total protein and albumin concentrations were significantly increased and the glutamic-pyruvic transaminase, alanine aminotransferase and total bilirubin were significantly decreased in the group of transplanted EPCs, compared with the control group injected with saline. Previous experiments prove that the periodic activation of bone marrow EPCs is closely related to liver injury, and the research shows that the level of circulating EPCs is negatively related to the existence of AST, GGT and LDH, the increase of AST and GGT is related to the damage of liver cells, and the low level of EPCs is related to the damaged liver function, so that the damaged liver function is possibly related to the lower level of EPCs in the old and old people.
3.3.1.3EPCs and kidney function
Serum UREA is a protein metabolite, and needs to be discharged with urine through glomerular filtration, and when kidney function is damaged, glomerular filtration function is reduced, and the UREA level in serum is increased. In this study we found that there was a difference in the two groups of UREA levels in the median and mean groups, with the low EPCs group having a higher UREA level than the high EPCs group, the difference was statistically significant, and the EPCs levels were negatively correlated with UREA. The in vitro experiment of the prior scholars proves that high level of UREA is related to chronic renal failure, and the UREA can cause the reduction of the quantity and the dysfunction of EPCs, thereby promoting the occurrence of cardiovascular diseases of patients with chronic renal failure. EPCs play an important role in a variety of kidney diseases, including ischemic acute kidney injury, sepsis, chronic kidney disease, glomerulonephritis, acute and chronic rejection in renal transplant patients, and may participate in the development and repair of kidney microvasculature by expressing miR-218. In renal failure patients, EPCs levels decrease, while in renal failure patients, UREA levels increase, and we suspect that in renal failure patients, fewer EPCs fail to complete repair of damaged renal microvasculature due to increased UREA levels leading to decreased levels and function of circulating EPCs, thereby exacerbating renal failure.
3.3.1.4 EPCs and coagulation
D-dimer is the simplest fibrin degradation product, and the level of D-dimer in vivo has great significance for the diagnosis of thrombotic diseases. When deep vein thrombosis occurs, EPCs mobilize and enter the injured vessel and the site of the thrombus, promoting thrombolysis and neovascularization. EPCs also express antithrombotic agents such as NO and prostaglandin I2, and thus have antithrombotic potential. Our study found that in the median cohort, the low-level EPCs group had higher D-dimer levels than the high-level EPCs group, the differences were statistically significant, and EPCs levels were inversely correlated with D-dimer. In the average grouping, the number of aspirin/clopidogrel applied in the low-level EPCs group is higher than that in the high-level EPCs group, and the data are further layered, so that the difference is statistically significant in the old, but not in the old. The rising of the D-dimer prompts that the body is in a high-condensation state, the low-level EPCs group aged people take aspirin/clopidogrel more, the high-condensation state of the low-level group aged people is reflected from the side, when the bodies are in the high-condensation state for a long time, the in-vivo EPCs are in an exhausted state for a long time, the vascular endothelium is not repaired, and the level of the in-vivo EPCs is influenced, so that the in-vivo EPCs cannot be effectively improved even if the low-level EPCs group aged people take aspirin/clopidogrel.
3.3.1.5 EPCs and FER
The FER levels are associated with various diseases, such as iron deficiency anemia, malignancy, acute hepatitis, acute infection, chronic kidney disease, etc., and we observed that the differences in FER levels between the low and high EPCs groups are statistically significant, and that EPCs levels are negatively correlated with FER levels. This study has excluded malignant tumors or older adults who found malignant tumors during follow-up, and FER, a tumor marker, does not completely represent tumor development, so we suspect 1.FER may be involved in the development and progression of other diseases, thereby affecting the levels of EPCs in vivo; people with elevated FER may be in the pre-stage of tumor or other disease until death; 3. the FER levels of the elderly are significantly different as influenced by other factors. Scholars found that the average percentage of circulating EPC within the HSCs subpopulation (EPCs/HSCs%) was significantly increased in patients with hepatocellular carcinoma compared to the control group, the difference was statistically significant, and high levels of EPC were associated with a poorer prognosis in patients with hepatocellular carcinoma. Unlike our previous intrinsic impression of EPCs, high levels of EPCs can lead to poor prognosis and, therefore, in terms of the relationship between tumor and EPCs levels, further studies are warranted.
3.3.1.6 EPCs and sex
In our country, the female population has a higher mortality rate than men, so among the data we have collected, the proportion of men is significantly higher, and the overall EPCs levels in men are higher than in women, the difference is not statistically significant, but men in the high-level EPCs group are more than in the low-level EPCs group, women in the low-level EPCs group are more than in the high-level EPCs group, and the difference is statistically significant. Based on previous studies by scholars, women have significantly lower health levels than men in the elderly population in china, and it is believed that lower health levels and higher mortality rates in women in the elderly may be associated with lower EPCs levels in vivo.
Fourth, EPCs and verification in terms of predicted survival time
4.1 relationship between the number of circulating EPCs in two packets and the time-to-live
4.1.1 grouping the number of two sets of circulating EPCs by median and the relationship between the survival time
Grouped by median and the results of the difference in survival time for the two groups are shown in Table 11. The survival time is 5 years, the occurrence of all-cause death events of the high-level EPCs group is lower than that of the low-level EPCs group, and the difference has statistical significance (P is 0.049); the survival time is 7 years, the occurrence of all-cause death events of the high-level EPCs group is lower than that of the low-level EPCs group, and the difference has statistical significance (P is 0.024);
TABLE 11 two sets of time-to-live differences (median group)
Figure BDA0003213448170000191
Note: in the table, "-" represents that the statistical value could not be calculated
The two groups of data are further layered, the layering results are shown in table 12, the layering bases are that the aged people with the age of less than 85 years old and the aged people with the age of more than or equal to 85 years old are old, and the difference of all-cause death events of the low-level and high-level EPCs groups in the aged people and the aged people is not statistically significant.
TABLE 12 two sets of time-to-live differences (median grouping and layering)
Figure BDA0003213448170000192
Figure BDA0003213448170000201
Note: in the table, "-" represents that the statistical value could not be calculated
4.1.2 grouping the number of two sets of circulating EPCs by average number versus time to live
Grouping according to the average, the difference results of the survival time of the two groups are shown in the table 13, the survival time is 5 years, and the occurrence difference of the all-cause death events of the low-level EPCs and the high-level EPCs has statistical significance (P is 0.005); the survival time is 6 years, and the occurrence difference of all-cause death events of the low-level EPCs and the high-level EPCs has statistical significance (P is 0.033); the survival time is 7 years, and the occurrence difference of all-cause death events of the low-level and high-level EPCs groups has statistical significance (P is 0.023).
TABLE 13 two groups survival time Difference (average group)
Figure BDA0003213448170000202
The two groups of data are further layered, the results after layering are shown in table 14, the layering bases are that the aged people are old people with the age of less than 85 years old, the aged people with the age of more than or equal to 85 years old, and the occurrence difference of all-cause death events of the low-level EPCs and the high-level EPCs in the aged people and the aged people has no statistical significance.
TABLE 14 two groups survival time Difference (average number divided into layers)
Figure BDA0003213448170000203
Figure BDA0003213448170000211
4.2 establishment of model of number of circulating EPCs and survival time
4.2.1 univariate model
The number of EPCs is related to 2-year all-cause death events, P is 0.004, is related to 5-year all-cause death events, P is 0.034, is related to 6-year all-cause death events, P is 0.045, is related to 7-year all-cause death events, P is 0.038, correlation coefficients are negative values, and the number of EPCs is negatively related to occurrence of all-cause death events. Considering the influence of other unpredictable mutation factors, the judgment of the survival time of the circulating EPCs within 7 years can be preliminarily judged with higher accuracy.
TABLE 15 correlation of circulating EPCs with all-cause death events
Figure BDA0003213448170000212
The correlation scatter diagram of EPCs and survival time is shown in FIG. 11, the Spearman correlation coefficient is 0.234, P is 0.017, and the number of EPCs and survival time are positively correlated.
4.2.2 prediction accuracy discussion of univariate models
The survival curve comparison by median group is shown in fig. 12, and it can be seen from the graph that the survival curve of the low-level EPCs group is below, which indicates that the survival rate of the low-level EPCs group is low; the survival curves of the high-level EPCs are above, indicating that the survival rate of the high-level EPCs is higher. By Log-rank and Breslow tests, the Log-rank test P-value is >0.05 and Breslow test P-value is 0.036, we consider that for the near-term survival time, the two groups of differences are statistically significant, and for the long-term survival, the two groups of differences gradually decrease and approach each other.
The comparison of the survival curves grouped according to the average is shown in FIG. 13, and it can be seen from the graph that the survival curve of the low-level EPCs group is lower, which indicates that the survival rate of the low-level EPCs group is lower; the survival curves of the high-level EPCs are above, indicating that the survival rate of the high-level EPCs is higher. By Log-rank and Breslow tests, the Log-rank test P-value is >0.05 and Breslow test P-value is 0.043, we consider that for the near-term survival time, the two groups of differences are statistically significant, and for the long-term survival, the two groups of differences gradually decrease and approach each other.
In conclusion, the single variable model has higher accuracy in the prediction of the recent survival time, and the accuracy within 7 years is considered to be higher, and the accuracy of the long-term survival time needs to be enhanced.
4.2.3 construction of logistic regression model with all-cause death events and age, number of EPCs, and HCT
The number of EPCs, the age and the HCT and the all-cause death events are brought into a logistic regression model, and the difference of variables in the model is found to have statistical significance when the survival time reaches 12 years and 13 years, wherein the age and the HCT are risk factors for increasing the occurrence of the all-cause death events, and the number of EPCs is a protective factor for reducing the occurrence of the all-cause death events. The occurrence probability of the long-term all-cause death events of the old and old people can be predicted by a logistic regression model constructed by the quantity, age and HCT of the EPCs. It can be seen that when the number of EPCs in peripheral blood is combined with other predictors as co-influencing factors, the survival time can be predicted to be as long as 13 years or more, and according to the indicators discussed above that have a greater correlation with the EPCs level of the elderly, the other predictors include RBC, HCT, AST, age, and sex that are critical to the EPCs level of the elderly, and may also include HGB, MCV, RDW-CV, RDW-SD, GGT, LDH, UREA, D-dimer, FER, and the like that are correlated with the EPCs level of the elderly.
TABLE 16 Logistic regression model
Figure BDA0003213448170000221
Figure BDA0003213448170000231
Note: the survival time is 1 year, the variable is constant, and therefore, the survival time cannot be calculated
The results of an OPLS-DA method and an SPSS statistical method are combined, and the results show that three blood indexes of RBC, HCT and AST play a decisive role in determining the level of EPCs and judging the expected survival period (figure 14), and are optional important indexes for establishing a prediction model together with EPCs by using other prediction factors. RBCs, which have the highest number of blood cells in the blood, are the mediators of oxygen transport, and a decrease in their number inevitably leads to hypoxia, which causes a decrease in endothelial function, presumably leading to a decrease in the level of EPCs in the elderly; HCT has essentially the same clinical significance as red blood cell count, often used as a classification indicator for anemia, and HCT levels are considered to be associated with chronic hypoxic disease; AST is mainly distributed in cardiac muscle, the concentration of the AST is closely related to the cell damage level of cardiac muscle, liver, skeletal muscle, kidney and the like, and the research finds that the AST level of a low-level EPCs group is obviously higher than that of a high-level EPCs group and is negatively related to the EPCs level, which may suggest that the cell membrane permeability of old people is increased and is related to the low level of the EPCs.
4.3 mechanisms that EPCs influence blood-related markers and survival time, if any
In the 13 years of follow-up we found that high levels of EPCs in circulation help reduce the incidence of all-cause death events in the elderly, with the possible mechanisms as follows: the EPCs participate in vascular endothelial repair of old and old people: the old and the elderly are often accompanied by arteriosclerosis and poor elasticity of blood vessels. EPCs participate in vascular repair after injury, and the decrease in the number of circulating EPCs leads to endothelial dysfunction, further reduces arterial elasticity, increases the number of EPCs, possibly improves vascular function, reduces the occurrence of adverse vascular events, and thus improves the survival time of the elderly. EPCs improve endothelial function homeostasis and mediate neovascularization via a variety of signaling pathways: EPCs may maintain homeostasis of endothelial function through endothelial nitric oxide synthase (eNOS)/brain-derived neurotrophic factor (BDNF) signaling pathways, or enhance EPCs-mediated neovascularization through activation of ERK and Akt signaling pathways by arbb 2; and the EPCs can promote the regeneration of blood vessels through microRNA signal paths, for example, miR-384-5p, miR-212, miR-137 and miR-381-3p can promote the regeneration capability of blood vessels of the EPCs and participate in the generation and development of vascular diseases. EPCs can secrete various vasoactive substances to participate in endothelial function repair, such as prostacyclin I2，PG I2) Nitric Oxide (NO), vascular growth factors (including VEGF, SDF-1, OGF-1, HGF), neurotrophic factor (GDNF), stromal cell derived factor 1(SDF-1), interleukin 6(IL-6), angiogenin, etc., which activate the angiogenic and cytoprotective response of brain microvascular cells and are critical for brain repair. OverallThe EPCs can participate in the repair process of vascular injury through various ways, thereby participating in the repair process of injury of various diseases of old people.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1. The application of the EPCs in the peripheral blood as the survival time prediction marker of the old and the elderly is provided.
2. The use of claim 1, wherein the number of EPCs in peripheral blood is a positively correlated marker for prediction of survival time in elderly.
3. The use of claim 1, wherein the indicator of the number of EPCs in peripheral blood is used as a single predictor or the number of EPCs in peripheral blood is used as a common predictor in combination with other predictors including RBC, HCT, AST, HGB, MCV, RDW-CV, RDW-SD, GGT, LDH, UREA, D-dimer, FER in age, gender and blood related indicators; among other prediction factors, anemia related indexes RBC, HGB and HCT are positive related markers for predicting the survival time of the old and the elderly, and age, heart, liver and kidney injury and coagulation related indexes MCV, RDW-CV, RDW-SD, AST, GGT, LDH, UREA, D-dimer and FER are negative related markers for predicting the survival time of the old and the elderly;
furthermore, the prediction of the survival time of the old and the elderly is within 13 years, preferably within 7 years;
furthermore, the model constructed by taking the number, age and HCT of the EPCs in the peripheral blood as common prediction factors can be used for predicting the long-term survival prognosis of the old and the elderly for more than 10 years;
wherein the number of EPCs is CD34+/CD133+Is a mark.
4. The method for predicting the survival time of the old and the elderly by adopting the EPCs comprises the steps of establishing a prediction model of the survival time of the old and the elderly by using the number of the EPCs in the peripheral blood as a single prediction factor or using the number of the EPCs in the peripheral blood and other prediction factors, obtaining the actual index of the prediction factor of the old and the elderly to be detected, and substituting the actual index into the prediction model to obtain the predicted survival time of the old and the elderly to be detected.
5. The method of claim 4, wherein said other predictors include age, RBC, HCT, HGB, MCV, RDW-CV, RDW-SD, AST, GGT, LDH, UREA, D-dimer, FER; the predictive model is a linear fit equation, a logistic regression model, or other statistical model.
6. The method of claim 4, wherein the model for prediction of the elderly's survival time excludes (1) uncontrolled acute medical conditions; and (2) a history of newly-occurring cerebral infarction, myocardial infarction, trauma and surgery in nearly 3 months; (3) patients with malignant tumors and patients with neoplastic disease during follow-up;
the number of EPCs is CD34+/CD133+And (6) counting the marks.
7. A reagent or a kit for predicting the survival time of old and old people comprises a detection reagent for detecting the quantity of EPCs in peripheral blood.
8. The reagent or kit of claim 7, further comprising other detection reagents for detecting RBC, HCT, AST, HGB, MCV, RDW-CV, RDW-SD, GGT, LDH, UREA, D-dimer, FER;
wherein the detection reagent for detecting the amount of EPCs in peripheral blood is used for detecting CD34+And CD133+Number of EPCs of double positive markers;
the kit also comprises a prediction card which is made by standardizing the established prediction model and is used for directly comparing the detection result with the prediction card to obtain a prediction result.
9. A method or medicament for prolonging the life time of the elderly, said method or medicament being for increasing the level of EPCs in the peripheral blood of the elderly.
10. The method or medicament of claim 9, wherein the method or medicament may have one or more of the following pathways of action: (1) promoting vascular endothelial repair of old and old people; (2) improving endothelial function homeostasis and mediating neovascularization via a variety of signaling pathways; (3) promoting angiogenesis through a microRNA signal pathway; (4) the secreted vasoactive substances are involved in the repair of endothelial function.
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