Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors

 Istead of this fairly useless prediction model, researchers should create EXACT DEMENTIA PREVENTION PROTOCOLS! Useless!

Development of an individualized  model using deep learning survival analysis incorporating genetic and environmental factors

• Shiqi Yuan,
• Qing Liu,
• Xiaxuan Huang,
• Shanyuan Tan,
• Zihong Bai,
• Juan Yu,
• Fazhen Lei,
• Huan Le,
• Qingqing Ye,
• Xiaoxue Peng,
• Juying Yang,
• Yitong Ling &
• Jun Lyu 

Alzheimer's Research & Therapy volume 16, Article number: 278 (2024) Cite this article

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Abstract

Background

Dementia is a major public health challenge in modern society. Early detection of high-risk dementia patients and timely intervention or treatment are of significant clinical importance. Neural network survival analysis represents the most advanced technology for survival analysis to date. However, there is a lack of deep learning-based survival analysis models that integrate both genetic and clinical factors to develop and validate individualized dynamic dementia risk prediction models.

Methods and results

This study is based on a large prospective cohort from the UK Biobank, which includes a total of 41,484 participants with an average follow-up period of 12.6 years. Initially, 364 candidate features (predictor variables) were screened. The top 30 key features were then identified by ranking the importance of each predictor variable using the Gradient Boosting Machine (GBM) model. A multi-model comparison strategy was employed to evaluate the predictive performance of four survival analysis models: DeepSurv, DeepHit, Kaplan–Meier estimation, and the Cox proportional hazards model (CoxPH). The results showed that the average Harrell's C-index for the DeepSurv model was 0.743, for the DeepHit model it was 0.633, for the CoxPH model it was 0.749, and for the Kaplan–Meier estimator model it was 0.500. In addition, the average D-Calibration Survival Measure was 6.014, 4408.086, 32274.743, and 1.508, respectively. The Brier score (BS) was used to assess the importance of features for the DeepSurv dementia prediction model, and the relationship between features and dementia was visualized using a partial dependence plot (PDP). To facilitate further research, the team deployed the DeepSurv dementia prediction model on AliCloud servers and designated it as the UKB-DementiaPre Tool.

Conclusion

This study successfully developed and validated the DeepSurv dementia prediction model for individuals aged 60 years and above, integrating both genetic and clinical data. The model was then deployed on AliCloud servers to promote its clinical translation. It is anticipated that this prediction model will provide more accurate decision support for clinical treatment and will serve as a valuable tool for the primary prevention of dementia.

Background

Dementia is a general term used to describe a range of progressive cognitive declines, characterized by the gradual loss of previously acquired cognitive abilities. The primary symptom is the progressive impairment of multiple cognitive functions, including memory, reasoning, judgment, and language [1]. According to estimates by the World Health Organization, between 5 and 8% of individuals aged 60 and above worldwide are affected by dementia. It is estimated that by 2030, the total number of dementia patients worldwide will reach 82 million, and by 2050, it will increase to 152 million [2]. Dementia encompasses more than 100 distinct diseases and conditions, with Alzheimer's disease (AD) representing the most prevalent form, accounting for 60–70% of cases [3].

Although the number of people with dementia is rising, studies have shown that the risk of dementia in some age groups in high-income countries may have actually declined over the past 25 years. This decline is likely due to improved education levels and better control of major cardiovascular risk factors, such as hypertension, diabetes, and hypercholesterolemia [4, 5]. These studies suggest that AD and other forms of dementia are not necessarily an inevitable consequence of aging. It is possible that some individuals may be able to prevent or delay the onset and progression of dementia by modifying their exposure to specific risk factors, such as hypertension, smoking, obesity, and diabetes.

Nevertheless, the current pharmacological treatments for dementia, particularly AD, are not optimal. Although some drugs can improve the symptoms of dementia or AD, they cannot completely halt the progression of the disease [6]. Consequently, the timely identification of individuals at high risk for dementia, along with the implementation of targeted interventions or treatments at an early stage, is crucial. Such approaches are expected to delay the onset of dementia, improve the prognosis for patients, reduce the overall mortality rate, and mitigate the social and familial impacts of the disease.

As research on dementia continues, an increasing number of risk factors associated with the disease have been identified. In recent years, there has been growing interest in developing new models for predicting dementia. In addition to traditional methods, such as logistic regression and the Cox proportional hazards regression model (CoxPH) for establishing dementia risk prediction models [7,8,9], the advancement of artificial intelligence has led to the application of machine learning techniques for the detection and prediction of dementia. These techniques hold great potential for enhancing our understanding of the disease and advancing the fields of psychiatry and neurology [10].

The CoxPH model is a standard survival analysis model, which is semiparametric and is used to quantify the influence of observed covariates on the risk of an event, such as mortality. The model assumes that the patient's risk of an event is a linear combination of the patient's covariates—an assumption known as the proportional hazards’ assumption [11]. However, in many applications, including the provision of personalized treatment recommendations, the assumption that the log-risk function is linear may be overly simplistic. Therefore, a more comprehensive set of survival models is required to more accurately reflect the nonlinear log-risk functions observed in survival data [12].

Neural network survival analysis represents the most advanced technology currently available for survival analysis [13]. Notable examples of this include DeepSurv, DeepHit, Logistic-Hazard, and others. The DeepSurv model employs deep learning to express the risk function of sensitive factors as a multilayer perceptron. This approach incorporates additional nonlinear activation functions and dropout techniques, which enhance the model's ability to capture the relationships between variables [12]. The complexity of the model increases when applied to real-world medical data. By considering the interactions between multi-gene information and clinical parameters, the integration of genetic data can be promoted, thereby providing insights for the primary prevention of dementia. Nevertheless, there is a lack of dynamic, personalized dementia risk prediction models that integrate genetic and clinical factors using deep learning survival analysis.

The objective of this study was to construct and validate a dynamic, personalized dementia risk prediction model based on the UK Biobank database, which contains large-scale population genetic and clinical data, using the DeepSurv model. This model can assist medical practitioners and clinical teams in more accurately assessing the risk of dementia in patients, thereby facilitating the development of more personalized prevention and treatment plans and providing a reference for early dementia prevention.

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