Functional data analysis to characterize disease patterns in frequent longitudinal data: application to bacterial vaginal microbiota patterns using weekly Nugent scores and identification of pattern-specific risk factors.
Functional data analysis to characterize disease patterns in frequent longitudinal data: application to bacterial vaginal microbiota patterns using weekly Nugent scores and identification of pattern-specific risk factors.
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功能数据分析以表征频繁纵向数据中的疾病模式：使用每周的nugent评分和鉴定模式特异性风险因素应用于细菌阴道菌群模式。
DOI：
10.1186/s12874-023-02063-8
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发表时间：
2023-10-26
影响因子：
4
通讯作者：
中科院分区：
文献类型：
--
作者：
技术进步允许更频繁地监测生物标志物。由此产生的数据结构需要更频繁的后续行动相比，传统的纵向研究的后续行动的数量往往很少。这些数据允许探索人内变异在理解疾病病因和表征疾病过程中的作用。一个具体的例子是使用在乌干达Rakai的月经后妇女中收集的2年内每周阴道微生物群Nugent测定评分来表征细菌性阴道病（BV）的发病机制，并确定每种阴道微生物群模式的风险因素，以告知对BV发病机制的流行病学和病因学理解。我们使用完全数据驱动的方法来表征阴道微生物群的纵向模式，方法是将密集采样的Nugent分数视为随时间变化的随机函数，并通过功能主成分进行降维。扩展当前的功能数据聚类方法，我们使用考虑多个数据特征的分层功能聚类框架来帮助识别阴道微生物群波动的临床有意义的模式。此外，使用多项逻辑回归来识别每种阴道微生物群模式的风险因素，以提供对BV发病机制的流行病学和病因学了解。使用Rakai的211名性活跃和月经初潮后女性的2年内每周Nugent评分，确定了阴道微生物群变化的四种模式：BV状态持续（高Nugent评分），正常范围Nugent评分持续，Nugent评分大幅波动，但主要处于BV状态; Nugent评分大幅波动，但主要处于正常状态。间隔开始时较高的Nugent评分、年龄小于20岁的年轻组、未受保护的洗澡水来源、女性伴侣未行包皮环切术、使用注射/Norplant激素避孕药进行计划生育与女性持续性BV的几率较高相关。分层功能数据聚类方法可用于密集采样纵向数据的完全数据驱动的无监督聚类，以识别临床信息聚类和与每个聚类相关的风险因素。
Technology advancement has allowed more frequent monitoring of biomarkers. The resulting data structure entails more frequent follow-ups compared to traditional longitudinal studies where the number of follow-up is often small. Such data allow explorations of the role of intra-person variability in understanding disease etiology and characterizing disease processes. A specific example was to characterize pathogenesis of bacterial vaginosis (BV) using weekly vaginal microbiota Nugent assay scores collected over 2 years in post-menarcheeal women from Rakai, Uganda, and to identify risk factors for each vaginal microbiota pattern to inform epidemiological and etiological understanding of the pathogenesis of BV. We use a fully data-driven approach to characterize the longitudinal patters of vaginal microbiota by considering the densely sampled Nugent scores to be random functions over time and performing dimension reduction by functional principal components. Extending a current functional data clustering method, we use a hierarchical functional clustering framework considering multiple data features to help identify clinically meaningful patterns of vaginal microbiota fluctuations. Additionally, multinomial logistic regression was used to identify risk factors for each vaginal microbiota pattern to inform epidemiological and etiological understanding of the pathogenesis of BV. Using weekly Nugent scores over 2 years of 211 sexually active and post-menarcheal women in Rakai, four patterns of vaginal microbiota variation were identified: persistent with a BV state (high Nugent scores), persistent with normal ranged Nugent scores, large fluctuation of Nugent scores which however are predominantly in the BV state; large fluctuation of Nugent scores but predominantly the scores are in the normal state. Higher Nugent score at the start of an interval, younger age group of less than 20 years, unprotected source for bathing water, a woman’s partner’s being not circumcised, use of injectable/Norplant hormonal contraceptives for family planning were associated with higher odds of persistent BV in women. The hierarchical functional data clustering method can be used for fully data driven unsupervised clustering of densely sampled longitudinal data to identify clinically informative clusters and risk-factors associated with each cluster.
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影响因子：
15.5
作者：
Munoz A;Hayward MR;Bloom SM;Rocafort M;Ngcapu S;Mafunda NA;Xu J;Xulu N;Dong M;Dong KL;Ismail N;Ndung'u T;Ghebremichael MS;Kwon DS
通讯作者：
Kwon DS
影响因子：
3.1
作者：
Marrazzo JM;Martin DH;Watts DH;Schulte J;Sobel JD;Hillier SL;Deal C;Fredricks DN
通讯作者：
Fredricks DN
影响因子：
17.1
作者：
Gajer P;Brotman RM;Bai G;Sakamoto J;Schütte UM;Zhong X;Koenig SS;Fu L;Ma ZS;Zhou X;Abdo Z;Forney LJ;Ravel J
通讯作者：
Ravel J
影响因子：
4.5
作者：
Hall, Peter;Mueller, Hans-Georg;Wang, Jane-Ling
通讯作者：
Wang, Jane-Ling
影响因子：
1.6
作者：
Gao, Yuan;Shang, Han Lin;Yang, Yanrong
通讯作者：
Yang, Yanrong