Supplementary Material for: Uncovering predictors of low hippocampal volume: Evidence from a large-scale machine-learning-based study in the UK Biobank

Introduction: Hippocampal atrophy is an established biomarker for conversion from the normal ageing process to developing cognitive impairment and dementia. This study used a novel hypothesis-free machine-learning approach, to uncover potential risk factors of lower hippocampal volume using information from the world’s largest brain imaging study. Methods: A combination of machine learning and conventional statistical methods were used to identify predictors of low hippocampal volume. We run gradient boosting decision tree modelling including 2891 input features measured before magnetic resonance imaging assessments (median 9.2 years, range 4.2-13.8 years) using data from 42,152 dementia-free UK Biobank participants. Logistic regression analyses were run on 87 factors identified as important for prediction based on Shapley values. False discovery rate adjusted P-value <0.05 was used to declare statistical significance. Results: Older age, male sex, greater height, and whole-body fat free mass were the main predictors of low hippocampal volume with the model also identifying associations with lung function and lifestyle factors including smoking, physical activity, and coffee intake (corrected P<0.05 for all). Red blood cell count and several red blood cell indices such as haemoglobin concentration, mean corpuscular haemoglobin, mean corpuscular volume, mean reticulocyte volume, mean sphered cell volume, and red blood cell distribution width were among many biomarkers associated with low hippocampal volume. Conclusion: Lifestyles, physical measures, and biomarkers may affect hippocampal volume, with many of the characteristics potentially reflecting oxygen supply to the brain. Further studies are required to establish causality and clinical relevance of these findings.

引言：海马萎缩是公认的生物标志物，可用于区分正常衰老进程与认知障碍及痴呆的发生风险。本研究采用一种新颖的无假设机器学习方法，依托全球规模最大的脑成像研究数据集，探究海马体积降低的潜在危险因素。
研究方法：本研究结合机器学习与传统统计方法，旨在识别海马体积偏低的预测因素。我们以42152名无痴呆史的英国生物银行（UK Biobank）参与者的数据为基础，构建梯度提升决策树（gradient boosting decision tree）模型，纳入磁共振成像（magnetic resonance imaging）评估前采集的2891项输入特征，评估间隔的中位数为9.2年，范围4.2~13.8年。基于夏普利值（Shapley values）筛选出87项具有预测价值的因素后，我们对其开展逻辑回归分析。本研究以错误发现率校正后的P值<0.05作为统计学显著性的判定标准。
研究结果：年龄增长、男性性别、身高较高以及全身去脂体重是海马体积偏低的主要预测因素；模型同时还识别出其与肺功能及吸烟、体力活动、咖啡摄入等生活方式因素存在关联（所有指标校正后P值均<0.05）。红细胞计数及血红蛋白浓度、平均红细胞血红蛋白量、平均红细胞体积、平均网织红细胞体积、平均球形红细胞体积、红细胞分布宽度等多项红细胞指标，均属于与海马体积偏低相关的生物标志物范畴。
结论：生活方式、体格指标及生物标志物可能影响海马体积，其中诸多特征或可反映大脑的氧供状态。未来仍需开展进一步研究，以明确本研究发现的因果关系及临床应用价值。