Estimation of life's essential 8 score with incomplete data of individual metrics.
Estimation of life's essential 8 score with incomplete data of individual metrics.
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DOI：
10.3389/fcvm.2023.1216693
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发表时间：
2023
影响因子：
3.6
通讯作者：
Hu, Hui
中科院分区：
文献类型：
--
作者：
Zheng, Yi;Huang, Tianyi;Guasch-Ferre, Marta;Hart, Jaime;Laden, Francine;Chavarro, Jorge;Rimm, Eric;Coull, Brent;Hu, Hui
美国心脏协会的生命必需8 （LE8）是心血管健康（CVH）的更新结构，包括血压、血脂、葡萄糖、体重指数、尼古丁暴露、饮食、身体活动和睡眠健康。在大多数研究和临床环境中，在多个时间点同时测量所有8个指标是具有挑战性的，这阻碍了使用LE8来评估个体的整体CVH轨迹。我们获得了护士健康研究（NHS， NHSII）和健康Professionaĺs随访研究（HPFS）的5,588名参与者的数据，以及2005-2016年国家健康与营养检查调查（NHANES）的27,194名参与者的数据，所有8个指标均可获得。个体的整体心血管健康（CVH）由LE8评分（0-100）确定。在许多情况下常规收集的cvh相关因素（即人口统计学、BMI、吸烟、高血压、高胆固醇血症和糖尿病）被纳入LE8评分的基础模型作为预测因子，随后的模型进一步纳入较少测量的因素（即体力活动、饮食、血压和睡眠健康）。梯度增强决策树的训练采用交叉验证调优的超参数。使用NHS、NHSII和HPFS训练的基础模型验证了均方根误差（rmse）为8.06（内部）和16.72（外部）。具有额外预测因子的模型进一步提高了性能。在使用NHANES训练的模型中观察到一致的结果。预测的CVH评分可以在关联研究中产生与观察到的CVH评分一致的效应估计。当LE8指标不完整时，在许多情况下常规测量CVH相关因素可用于准确估计个体的整体CVH。
The American Heart Association's Life's Essential 8 (LE8) is an updated construct of cardiovascular health (CVH), including blood pressure, lipids, glucose, body mass index, nicotine exposure, diet, physical activity, and sleep health. It is challenging to simultaneously measure all eight metrics at multiple time points in most research and clinical settings, hindering the use of LE8 to assess individuals' overall CVH trajectories over time. We obtained data from 5,588 participants in the Nurses' Health Studies (NHS, NHSII) and Health Professionaĺs Follow-up Study (HPFS), and 27,194 participants in the 2005–2016 National Health and Nutrition Examination Survey (NHANES) with all eight metrics available. Individuals' overall cardiovascular health (CVH) was determined by LE8 score (0–100). CVH-related factors that are routinely collected in many settings (i.e., demographics, BMI, smoking, hypertension, hypercholesterolemia, and diabetes) were included as predictors in the base models of LE8 score, and subsequent models further included less frequently measured factors (i.e., physical activity, diet, blood pressure, and sleep health). Gradient boosting decision trees were trained with hyper-parameters tuned by cross-validations. The base models trained using NHS, NHSII, and HPFS had validated root mean squared errors (RMSEs) of 8.06 (internal) and 16.72 (external). Models with additional predictors further improved performance. Consistent results were observed in models trained using NHANES. The predicted CVH scores can generate consistent effect estimates in associational studies as the observed CVH scores. CVH-related factors routinely measured in many settings can be used to accurately estimate individuals' overall CVH when LE8 metrics are incomplete.
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影响因子：
16.2
作者：
Fretts AM;Howard BV;McKnight B;Duncan GE;Beresford SA;Mete M;Zhang Y;Siscovick DS
通讯作者：
Siscovick DS
影响因子：
5.4
作者：
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Cushman M
影响因子：
3.7
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König M;Drewelies J;Norman K;Spira D;Buchmann N;Hülür G;Eibich P;Wagner GG;Lindenberger U;Steinhagen-Thiessen E;Gerstorf D;Demuth I
通讯作者：
Demuth I
影响因子：
5.9
作者：
Alonso-Pedrero, Lucia;Ojeda-Rodriguez, Ana;Marti, Amelia
通讯作者：
Marti, Amelia