Objective: Construction of risk prediction model for Alzheimer’s disease based on Meta-analysis. Method: Documenting such as CNKI, WanFang database and VIP database, which were published in China from 2010 to 2018 and had data on risk factors for Alzheimer’s disease. Meta-analysis of standards compliant literature using RevMan5.3 software and then combine the results to obtain a comprehensive risk for each risk factor, the risk prediction model is established based on the natural logarithmic trans-formation value of the comprehensive risk. Result: A total of 28 articles were included in the literature. There are 7 risk factors entering the model through data screening and they were family history of dementia, ApoE, education level, brain trauma, drinking history, family history of dementia and gender and smoking history, a total of 2,229,980 patients were included. Conclusion: Ultimately the model will be used to effectively control risk factors and slow the progression of the disease.
Alzheimer’s disease (AD) is the most common type of senile dementia. It is a central nervous system degenerative characterized by progressive cognitive dysfunction and behavioral damage in the elderly and early senile. Clinical manifestations are memory impairment, aphasia, abstract thinking and computational power impairing personality and behavioral changes [
The binary classification logistic regression analysis is a generalized linear regression analysis model, which is often used for automatic diagnosis of diseases, exploring risk factors for causing diseases and predicting the probability of disease occurrence according to risk factors, and the results are accurate and stable [
The literature search uses “Alzheimer’s disease, risk factors”, as the title or keyword, A total of 1586 articles related to Alzheimer’s disease were retrieved from 2010 to 2018, published in three major databases, CNKI, VIP Database and WanFang Database.
Patients should be selected to meet the diagnostic criteria for Alzheimer’s disease. A parallel randomized controlled clinical trial of the treatment group and the control group was provided; Data on risk factors for Alzheimer’s disease can be extracted from the literature.
Only the literature of the control group was not set up in the self-experiment; patients with Alzheimer’s disease also had other diseases that may cause dementia or congenital cognitive disorders and other types of dementia; Data could not be converted into odds ratio (Odd Ratio) and 95% (CI) confidence interval literature; Repeated inclusion of literature; Poor quality literature, including NOS (Newcastle-Ottawa Scale) scale [
Based on the topic of “Alzheimer’s disease, Alzheimer’s disease, risk factors”, the related literatures were searched, and 1586 articles were initially searched. According to the inclusion and exclusion criteria of the literature, 1557 articles that did not meet the criteria were excluded. 28 papers were analyzed and the literature screening was shown in
Meta-analysis of selected literature using evidence-based medicine software RevMan5.3, Measurement data were expressed as relative risk (OR) and 95% confidence interval (CI). First, the heterogeneity test is carried out on the included documents. When there is no heterogeneity in each of the trials included in the analysis ( p ≥ 0.1 , I 2 ≤ 50 % ) , so using a fixed effect model for analysis. When there is heterogeneity between trials included in the analysis ( p < 0.1 , I 2 ≥ 50 % ) , so using a random effects model for analysis.
| Risk Factor | Number of Patients with AD | Total People | |
|---|---|---|---|
| ApoE | ε2, ε3 | 414 | 1648 |
| ε4 | 161 | 1606 | |
| Educational level | Junior high school or above | 367 | 10,522 |
| Illiterate and junior high school or below | 1204 | 13,415 | |
| Gender | Male | 647 | 10,307 |
| Female | 869 | 10,471 | |
| History of smoking | Never smoke | 446 | 2977 |
| Smoking over three weeks | 1240 | 9278 | |
| Drinking history | Never drink | 438 | 1272 |
| Drinking over three weeks | 3680 | 13,346 | |
| Negative life event | No negative life events | 25 | 115 |
| Negative life event | 53 | 111 | |
| Family history of dementia | No history of dementia in the family | 138 | 1232 |
| Dementia patients in immediate family members | 81 | 10,020 | |
| Brain trauma | No brain trauma | 13,340 | 1,970,599 |
| The head has been hit hard | 5909 | 166,054 | |
| Diabetes | No diabetes | 418 | 3588 |
| Have diabetes | 490 | 3419 |
| The First Author | Research Object Select | Between Groups Comparability | Exposure Factor Measurement | Total Score |
|---|---|---|---|---|
| Zhang Ting [ | 3 | 1 | 3 | 7 |
| Mai Yicheng [ | 3 | 1 | 4 | 8 |
| Cai Haiyan | 3 | 1 | 3 | 7 |
| Dong Xiang | 3 | 2 | 3 | 8 |
| Guo Hongmin | 3 | 1 | 4 | 7 |
| Zhu Weiming | 4 | 1 | 2 | 7 |
| Li Lei | 3 | 1 | 4 | 8 |
| Guan Junhua | 3 | 1 | 4 | 8 |
| Hu Chengping | 4 | 1 | 2 | 7 |
| Huang Heming | 3 | 0 | 3 | 6 |
| Liao Jun | 4 | 0 | 3 | 7 |
| Sun Zhenghai | 3 | 2 | 3 | 8 |
| Ye Guoying | 2 | 1 | 2 | 5 |
| Zhan Changqing | 4 | 0 | 4 | 8 |
| Cui Bo | 2 | 0 | 3 | 5 |
| Zheng Feng | 3 | 2 | 3 | 8 |
By calculating the I2 value and p value of each risk factor,
Gender, ApoE, drinking, smoking, family history of dementia, education, diabetes, negative life events, there is no publication bias (p > 0.05). There is a certain publication bias in brain trauma (p < 0.05). On the whole, the bias of this study included in the literature was small.
A total of 9 risk factors were evaluated in this study. Among all the risk factors evaluated, the OR value of diabetes was between 0 and 1 (indicating that the statistical results of this risk factor were not statistically significant), and negative life events were included. There are few literatures, so no risk factors are included in the model construction. Only 7 items are considered as independent risk factors for Alzheimer’s disease, including: ApoE, brain trauma, family history of dementia, education level, smoking history, drinking History and gender (
The logistic regression model refers to the calculation of the OR value of risk factors based on prospective or retrospective data to evaluate the contribution of
| Risk Factor | t Value | p Value |
|---|---|---|
| Gender | 0.15 | 0.88 |
| ApoE | 11.4 | 0.24 |
| Drinking history | 1.62 | 0.11 |
| Smoking history | 0.72 | 0.98 |
| Brain trauma | 5.52 | p < 0.00001 |
| Family history of dementia | 0.17 | 0.87 |
| Education level | 12.49 | 0.29 |
| Diabetes | 0.03 | 0.98 |
| Negative life events | 4.04 | 0.16 |
| Project | Number of Projects | t | I2/% | Merge Model | Meta Analysis Results | ||
|---|---|---|---|---|---|---|---|
| OR | 95%CI | p | |||||
| ApoE | 11 | 11.4 | 21 | fixed | 3.42 | 2.75 - 4.26 | 0.24 |
| Gender | 10 | 0.15 | 96 | random | 2.48 | 0.54 - 2.04 | 0.88 |
| Education level | 8 | 12.49 | 18 | fixed | 1.66 | 2.15 - 2.86 | 0.29 |
| Smoking history | 11 | 0.72 | 87 | random | 1.17 | 0.77 - 1.78 | 0.98 |
| Drinking history | 11 | 1.62 | 91 | random | 1.52 | 0.92 - 2.53 | 0.11 |
| Diabetes | 7 | 0.03 | 82 | random | 0.99 | 0.60 - 1.63 | 0.98 |
| Brain trauma | 6 | 5.52 | 98 | random | 2.63 | 1.86 - 3.70 | p < 0.00001 |
| Family history of dementia | 5 | 0.17 | 97 | random | 3.37 | 0.13 - 11.54 | 0.87 |
| Negative life events | 2 | 4.04 | 50 | fixed | 3.49 | 1.90 - 6.42 | 0.16 |
risk factors in the risk of specific diseases, and can also be used to predict the risk of disease.
In this study, Meta-analysis was used to calculate the comprehensive risk (OR value) of each risk factor associated with the formation of Alzheimer’s disease, and then the corresponding risk prediction model was established based on the natural logarithmic transformation value of the comprehensive risk. This method accumulates the sample size of the current research, overcomes the defects of the single research sample, and ensures the scientificity, reliability and accuracy of the prediction results of the established risk prediction model. The theoretical prediction model for the risk of Alzheimer’s disease formation based on the logistic regression model is as follows:
logit ( p ) = ln ( p 1 − p ) = α + β 1 X 1 + β 2 X 2 + β 3 X 3 + β 4 X 4 + β 5 X 5 + β 6 X 6 + β 7 X 7 (1)
From the above model, the risk probability of Alzheimer’s disease patients can be calculated:
p = e α + β 1 X 1 + β 2 X 2 + ⋯ + β i X i + ⋯ + β n X n 1 + e α + β 1 X 1 + β 2 X 2 + ⋯ + β i X i + ⋯ + β n X n (2)
In the above formula “ X 1 , X 2 , ⋯ , X i , ⋯ , X n ” express 1 , 2 , ⋯ , i , ⋯ , n risk factors, “ β i ” is the regression coefficient for each hazard element can be calculated using the natural logarithmic conversion value of the comprehensive risk, calculated as follows:
β i = ln ( O R i ) (3)
“ α ” is the constant term, from the theoretical model, the constant term can be calculated by the following formula:
α = ln ( p o j 1 − p o j ) − β 1 X ¯ 1 − β 2 X ¯ 2 − ⋯ − β i X ¯ i − ⋯ − β n X ¯ n (4)
among them p o is the prevalence, “ X ¯ 1 , X ¯ 2 , ⋯ , X ¯ i , ⋯ , X ¯ n ” express 1 , 2 , ⋯ , i , ⋯ , n average per capita of risk factors, that is, the average exposure rate of the event. This value can be estimated by the prevalence and incidence of Alzheimer’s disease.
Based on the results of the meta-analysis and the basis for the selection of the overall risk, this study first removed the factors that could not be considered to have an impact on the formation of Alzheimer’s disease, and then established a risk prediction model based on the combined results of independent risk factors. In this study, a total of seven risk factors were eventually included in the predictive model. The risk prediction model constructed is as follows:
logit ( p ) = α + 1.23 X 1 + 0.97 X 2 + 0.42 X 3 + 0.91 X 4 + 0.50 X 5 + 0.16 X 6 + 1.21 X 7 (5)
However, in this practical application, in addition to estimating the prevalence and incidence of Alzheimer’s disease in the study area, it is also necessary to estimate the risk of each risk factor covered in the risk prediction model of the area. The average value, but the correct calculation of the average of the individual risk levels is extremely difficult, so it is often recommended in practice to ignore the correction of the constant term in the prediction model, and by the formula:
α = ln ( p o j 1 − p o j ) (6)
to calculate. However, it must be noted in the above formula that although the simplified estimation of the constant term increases the actual operability of the risk prediction model, it also has a higher positive rate or a risk factor due to the risk factors of the target population being assessed. In the case of greater risk, the risk of disease is overestimated [
Meta-analysis is a method of consulting medicine that combines multiple homogeneous studies to provide true and reliable scientific evidence for clinical practice and health decision-making [
An example of a risk prediction model for Alzheimer’s disease based on this study is as follows: First, assuming that the incidence rate of Alzheimer’s disease patients in the target hospital is 14.91%, and the local prevalence rate is twice that of the incidence rate, then a single calculation is performed. The risk of Alzheimer’s disease in patients is as follows: In a local hospital, Alzheimer’s disease, the examination report shows that the patient is female, smoking, drinking, and the degree of education is primary school graduation, according to the Alzheimer’s disease risk prediction model, Calculate the constant term:
α = ln ( 14.91 × 2 / 100 1 − 14.91 × 2 / 100 ) = 0.42 (7)
Combined with the risk factors of this patient, the risk of the disease is:
logit ( p ) = ln ( p 1 − p ) = 0.42 + 0.42 × 1 + 0.91 × 1 + 0.50 × 1 + 0.16 × 1 = 2.41 (8)
p = e 2.41 / ( 1 + e 2.41 ) = 92 % (9)
Then the patient’s risk is three times that of other patients in the area ( 92 % 14.91 % × 2 = 3.09 ). Therefore, according to the predicted results, the doctor can
appropriately recommend the patient to reduce the frequency of smoking and drinking when the patient’s gender cannot be changed and the degree of education cannot be improved for a short period of time.
Although this study based on the meta-analysis, a comprehensive analysis of the included research data to establish a Logistic risk prediction model for Alzheimer’s disease provides a predictive tool for the clinical practice of Alzheimer’s disease. However, because the Meta-analysis is a secondary analysis based on the original research, the inadequacy of this study cannot be avoided: Because the selected risk factors related to Alzheimer’s disease are not analyzed, it may lead to the incomplete meta-analysis, so the constructed risk prediction model has certain defects, so the researcher should find Potential risk factors for Alzheimer’s disease and incorporating them into new research to merge to make the risk prediction model more complete; Although this study established a risk prediction model for Alzheimer’s disease, in order to ensure that the final constructed risk prediction model is practical, the model should be validated, so the researcher will continue to verify the model in the follow-up work.
Based on a large amount of literature data, this paper establishes a risk prediction model for Alzheimer’s disease based on the Meta-analysis combined with the mathematical modeling method of logistic regression model, which can be used to predict the risk of Alzheimer’s disease. In addition, in addition to the practical significance of the prediction of Alzheimer’s disease, this model can provide new ideas for the next step to establish a risk prediction model for other diseases.
The authors declare no conflicts of interest regarding the publication of this paper.
Jiang, S. and Jin, Z.Z. (2019) Construction of Risk Prediction Model for Alzheimer’s Disease Based on Meta-Analysis. Open Access Library Journal, 6: e5731. https://doi.org/10.4236/oalib.1105731