Subtyping of common complex diseases and disorders by integrating heterogeneous data. Identifying clusters among women with lower urinary tract symptoms in the LURN study.

Andreev, Victor P

Helmuth, Margaret E

Liu, Gang

Smith, Abigail R

Merion, Robert M

Yang, Claire C

Cameron, Anne P

Jelovsek, J Eric

Amundsen, Cindy L

Helfand, Brian T

Bradley, Catherine S

DeLancey, John OL

Griffith, James W

Glaser, Alexander P

Gillespie, Brenda W

Clemens, J Quentin

Lai, H Henry

LURN Study Group

Rosier, Peter FWM

2023-06-01T13:28:17Z

2023-06-01T13:28:17Z

2022-01

2023-06-01T13:28:16Z

We present a methodology for subtyping of persons with a common clinical symptom complex by integrating heterogeneous continuous and categorical data. We illustrate it by clustering women with lower urinary tract symptoms (LUTS), who represent a heterogeneous cohort with overlapping symptoms and multifactorial etiology. Data collected in the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN), a multi-center observational study, included self-reported urinary and non-urinary symptoms, bladder diaries, and physical examination data for 545 women. Heterogeneity in these multidimensional data required thorough and non-trivial preprocessing, including scaling by controls and weighting to mitigate data redundancy, while the various data types (continuous and categorical) required novel methodology using a weighted Tanimoto indices approach. Data domains only available on a subset of the cohort were integrated using a semi-supervised clustering approach. Novel contrast criterion for determination of the optimal number of clusters in consensus clustering was introduced and compared with existing criteria. Distinctiveness of the clusters was confirmed by using multiple criteria for cluster quality, and by testing for significantly different variables in pairwise comparisons of the clusters. Cluster dynamics were explored by analyzing longitudinal data at 3- and 12-month follow-up. Five clusters of women with LUTS were identified using the developed methodology. None of the clusters could be characterized by a single symptom, but rather by a distinct combination of symptoms with various levels of severity. Targeted proteomics of serum samples demonstrated that differentially abundant proteins and affected pathways are different across the clusters. The clinical relevance of the identified clusters is discussed and compared with the current conventional approaches to the evaluation of LUTS patients. The rationale and thought process are described for the selection of procedures for data preprocessing, clustering, and cluster evaluation. Suggestions are provided for minimum reporting requirements in publications utilizing clustering methodology with multiple heterogeneous data domains.

PONE-D-21-31683

1932-6203

1932-6203

https://hdl.handle.net/10161/27464

eng

Public Library of Science (PLoS)

PloS one

10.1371/journal.pone.0268547

LURN Study Group

Humans

Cluster Analysis

Cohort Studies

Proteomics

Female

Urinary Bladder

Lower Urinary Tract Symptoms

Subtyping of common complex diseases and disorders by integrating heterogeneous data. Identifying clusters among women with lower urinary tract symptoms in the LURN study.

Journal article

Jelovsek, J Eric|0000-0002-7196-817X

e0268547

6

Duke

School of Medicine

Clinical Science Departments

Obstetrics and Gynecology

Surgery

Obstetrics and Gynecology, Urogynecology

Surgery, Urology

Published

17