{"title":"高空腹血糖导致的全球阿尔茨海默病负担：流行病学趋势和机器学习见解。","authors":"Yixiao Ma, Shuohan Huang, Yahong Dong, Qiguan Jin","doi":"10.2147/RMHP.S506581","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>High fasting plasma glucose (HFPG) is a known risk factor for Alzheimer's disease (AD). This study aims to analyze global trends in AD death rates and disability-adjusted life years (DALYs) rates attributable to HFPG from 1990 to 2021 and assess the potential of glucose-related biomarkers in predicting cognitive impairment.</p><p><strong>Methods: </strong>Data from the Global Burden of Disease 2021 database were used to analyze AD death rates and DALY rates due to HFPG across 204 countries. All rates were age-standardized. Joinpoint regression, age-period-cohort models, and ARIMA were employed to analyze trends and make future predictions. NHANES data were used to build machine learning models (including logistic regression, SVM, random forests, etc). to evaluate glucose-related biomarkers in predicting cognitive impairment.</p><p><strong>Results: </strong>From 1990 to 2019, global AD death rates attributable to HFPG increased from 2.64 (95% UI: 0.11, 8.38) to 3.73 (95% UI: 0.15, 11.84), with the highest increases in high-income North America, North Africa, and Sub-Saharan Africa. DALY rates also rose globally, from 47.07 (95% UI: 2.72, 126.46) to 66.42 (95% UI: 3.83, 178.85). The greatest impact was observed in females, particularly those aged 80 and above. Joinpoint analysis indicated a significant rise in death rates from 1995 to 2000, followed by a slower increase in recent years. ARIMA model predictions indicate a gradual decline in death rates and DALY rates over the next 15 years. Logistic regression models showed the highest accuracy (90.4%) in predicting cognitive impairment, with 2-hour postprandial glucose and fasting plasma glucose being key predictors.</p><p><strong>Conclusion: </strong>From 1990 to 2021, global AD death rates and DALY rates due to HFPG significantly increased, with a greater burden in females and regions with higher socio-demographic development. Machine learning models are effective tools for identifying individuals at high risk of elevated blood glucose leading to cognitive impairment.</p>","PeriodicalId":56009,"journal":{"name":"Risk Management and Healthcare Policy","volume":"18 ","pages":"1291-1307"},"PeriodicalIF":2.7000,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007509/pdf/","citationCount":"0","resultStr":"{\"title\":\"Global Burden of Alzheimer's Disease Attributable to High Fasting Plasma Glucose: Epidemiological Trends and Machine Learning Insights.\",\"authors\":\"Yixiao Ma, Shuohan Huang, Yahong Dong, Qiguan Jin\",\"doi\":\"10.2147/RMHP.S506581\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>High fasting plasma glucose (HFPG) is a known risk factor for Alzheimer's disease (AD). This study aims to analyze global trends in AD death rates and disability-adjusted life years (DALYs) rates attributable to HFPG from 1990 to 2021 and assess the potential of glucose-related biomarkers in predicting cognitive impairment.</p><p><strong>Methods: </strong>Data from the Global Burden of Disease 2021 database were used to analyze AD death rates and DALY rates due to HFPG across 204 countries. All rates were age-standardized. Joinpoint regression, age-period-cohort models, and ARIMA were employed to analyze trends and make future predictions. NHANES data were used to build machine learning models (including logistic regression, SVM, random forests, etc). to evaluate glucose-related biomarkers in predicting cognitive impairment.</p><p><strong>Results: </strong>From 1990 to 2019, global AD death rates attributable to HFPG increased from 2.64 (95% UI: 0.11, 8.38) to 3.73 (95% UI: 0.15, 11.84), with the highest increases in high-income North America, North Africa, and Sub-Saharan Africa. DALY rates also rose globally, from 47.07 (95% UI: 2.72, 126.46) to 66.42 (95% UI: 3.83, 178.85). The greatest impact was observed in females, particularly those aged 80 and above. Joinpoint analysis indicated a significant rise in death rates from 1995 to 2000, followed by a slower increase in recent years. ARIMA model predictions indicate a gradual decline in death rates and DALY rates over the next 15 years. Logistic regression models showed the highest accuracy (90.4%) in predicting cognitive impairment, with 2-hour postprandial glucose and fasting plasma glucose being key predictors.</p><p><strong>Conclusion: </strong>From 1990 to 2021, global AD death rates and DALY rates due to HFPG significantly increased, with a greater burden in females and regions with higher socio-demographic development. Machine learning models are effective tools for identifying individuals at high risk of elevated blood glucose leading to cognitive impairment.</p>\",\"PeriodicalId\":56009,\"journal\":{\"name\":\"Risk Management and Healthcare Policy\",\"volume\":\"18 \",\"pages\":\"1291-1307\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-04-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007509/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Risk Management and Healthcare Policy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/RMHP.S506581\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Risk Management and Healthcare Policy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/RMHP.S506581","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Global Burden of Alzheimer's Disease Attributable to High Fasting Plasma Glucose: Epidemiological Trends and Machine Learning Insights.
Purpose: High fasting plasma glucose (HFPG) is a known risk factor for Alzheimer's disease (AD). This study aims to analyze global trends in AD death rates and disability-adjusted life years (DALYs) rates attributable to HFPG from 1990 to 2021 and assess the potential of glucose-related biomarkers in predicting cognitive impairment.
Methods: Data from the Global Burden of Disease 2021 database were used to analyze AD death rates and DALY rates due to HFPG across 204 countries. All rates were age-standardized. Joinpoint regression, age-period-cohort models, and ARIMA were employed to analyze trends and make future predictions. NHANES data were used to build machine learning models (including logistic regression, SVM, random forests, etc). to evaluate glucose-related biomarkers in predicting cognitive impairment.
Results: From 1990 to 2019, global AD death rates attributable to HFPG increased from 2.64 (95% UI: 0.11, 8.38) to 3.73 (95% UI: 0.15, 11.84), with the highest increases in high-income North America, North Africa, and Sub-Saharan Africa. DALY rates also rose globally, from 47.07 (95% UI: 2.72, 126.46) to 66.42 (95% UI: 3.83, 178.85). The greatest impact was observed in females, particularly those aged 80 and above. Joinpoint analysis indicated a significant rise in death rates from 1995 to 2000, followed by a slower increase in recent years. ARIMA model predictions indicate a gradual decline in death rates and DALY rates over the next 15 years. Logistic regression models showed the highest accuracy (90.4%) in predicting cognitive impairment, with 2-hour postprandial glucose and fasting plasma glucose being key predictors.
Conclusion: From 1990 to 2021, global AD death rates and DALY rates due to HFPG significantly increased, with a greater burden in females and regions with higher socio-demographic development. Machine learning models are effective tools for identifying individuals at high risk of elevated blood glucose leading to cognitive impairment.
期刊介绍：
Risk Management and Healthcare Policy is an international, peer-reviewed, open access journal focusing on all aspects of public health, policy and preventative measures to promote good health and improve morbidity and mortality in the population. Specific topics covered in the journal include:
Public and community health
Policy and law
Preventative and predictive healthcare
Risk and hazard management
Epidemiology, detection and screening
Lifestyle and diet modification
Vaccination and disease transmission/modification programs
Health and safety and occupational health
Healthcare services provision
Health literacy and education
Advertising and promotion of health issues
Health economic evaluations and resource management
Risk Management and Healthcare Policy focuses on human interventional and observational research. The journal welcomes submitted papers covering original research, clinical and epidemiological studies, reviews and evaluations, guidelines, expert opinion and commentary, and extended reports. Case reports will only be considered if they make a valuable and original contribution to the literature. The journal does not accept study protocols, animal-based or cell line-based studies.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
