School of Psychology, University of New South Wales, Kensington, NSW, Australia.
Neuroscience Research Australia, Randwick, NSW, Australia.
BMC Med. 2024 Oct 31;22(1):501. doi: 10.1186/s12916-024-03711-6.
We aimed to develop risk tools for dementia, stroke, myocardial infarction (MI), and diabetes, for adults aged ≥ 65 years using shared risk factors.
Data were obtained from 10 population-based cohorts (N = 41,755) with median follow-up time (years) for dementia, stroke, MI, and diabetes of 6.2, 7.0, 6.8, and 7.4, respectively. Disease-free participants at baseline were included, and 22 risk factors (sociodemographic, medical, lifestyle, laboratory biomarkers) were evaluated. Two risk tools (DemNCD and DemNCD-LR based on Fine and Gray sub-distribution and logistic regression [LR], respectively) were developed and validated. Predictive accuracies of these risk tools were assessed using Harrel's C-statistics and area under the curve (AUC) and 95% confidence interval (CI). Model calibration was conducted using Hosmer-Lemeshow goodness of fit test along calibration plots.
Both the DemNCD and DemNCD-LR resulted in similar predictive accuracy for each outcome. The overall AUC (95% CI) for dementia, stroke, MI, and diabetes risk tool were 0·68 (0·65, 0·70), 0·58 (0·54, 0·61), 0·65 (0·61, 0·68), and 0·68 (0·64, 0·72), respectively, for males. For females, these figures were 0·65 (0·63, 0·67), 0·55 (0·52, 0·57), 0·65 (0·62, 0·68), and 0·61 (0·57, 0·65).
The DemNCD is the first tool to predict both dementia and multiple cardio-metabolic diseases using comprehensive risk factors and provided similar predictive accuracy to existing risk tools. It has similar predictive accuracy as tools designed for single outcomes in this age-group. DemNCD has the potential to be used in community and clinical settings as it includes self-reported and routinely available clinical measures.
我们旨在开发适用于≥65 岁成年人的痴呆、卒、心肌梗死（MI）和糖尿病风险工具，这些工具基于共享的风险因素。
本研究数据来自 10 个人群为基础的队列（N=41755），痴呆、卒、MI 和糖尿病的中位随访时间（年）分别为 6.2、7.0、6.8 和 7.4。基线时无疾病的参与者被纳入研究，共评估了 22 个风险因素（社会人口学、医学、生活方式、实验室生物标志物）。分别使用 Fine 和 Gray 亚分布和逻辑回归（LR）开发和验证了两个风险工具（DemNCD 和 DemNCD-LR）。使用 Harrell 的 C 统计量和曲线下面积（AUC）及其 95%置信区间（CI）评估这些风险工具的预测准确性。使用 Hosmer-Lemeshow 拟合优度检验和校准图进行模型校准。
DemNCD 和 DemNCD-LR 对每个结局的预测准确性相似。痴呆、卒、MI 和糖尿病风险工具的整体 AUC（95%CI）男性分别为 0.68（0.65，0.70）、0.58（0.54，0.61）、0.65（0.61，0.68）和 0.68（0.64，0.72），女性分别为 0.65（0.63，0.67）、0.55（0.52，0.57）、0.65（0.62，0.68）和 0.61（0.57，0.65）。
DemNCD 是首个使用综合风险因素预测痴呆和多种心血管代谢疾病的工具，其预测准确性与现有风险工具相似。它在该年龄组中与专门用于单一结局的工具具有相似的预测准确性。DemNCD 具有在社区和临床环境中使用的潜力，因为它包含自我报告和常规可用的临床指标。
