OBJECTIVE: To use statistical techniques to identify underlying subtypes of juvenile idiopathic arthritis (JIA) that best explain the observed relationships of clinical and laboratory variables, and to compare the statistically derived subtypes with those defined by the International League of Associations for Rheumatology (ILAR) criteria and examine them for HLA associations. METHODS: Information on 572 patients diagnosed as having JIA was summarized by 10 clinical and laboratory categorical variables (age at onset, large joint involvement, small joint involvement, polyarthritis, symmetric arthritis, spinal pain, fever, psoriasis, antinuclear antibodies [ANA], and rheumatoid factor). Latent class analysis (LCA) was used to identify underlying ("latent") classes that explained the relationships among the observed variables. Statistical models incorporating 5-8 latent classes were applied to the data. RESULTS: The 7-class model was the most appropriate. Patterns of joint involvement and the presence of ANA were influential in determining latent classes. There was some correspondence between the latent classes and the ILAR categories, but they did not coincide completely. Significant differences between the latent classes were seen for 3 HLA haplotypes (DRB1*04-DQA1*03-DQB1*03, DRB1*13-DQA1*01-DQB1*06, and DRB1*08-DQA1*0401-DQB1*0402). CONCLUSION: LCA provides a novel approach to the task of identifying homogeneous subtypes within the umbrella of JIA. In further work, the identified latent classes will be examined for associations with other candidate genes and for differences in outcome.
OBJECTIVE: To use statistical techniques to identify underlying subtypes of juvenile idiopathic arthritis (JIA) that best explain the observed relationships of clinical and laboratory variables, and to compare the statistically derived subtypes with those defined by the International League of Associations for Rheumatology (ILAR) criteria and examine them for HLA associations. METHODS: Information on 572 patients diagnosed as having JIA was summarized by 10 clinical and laboratory categorical variables (age at onset, large joint involvement, small joint involvement, polyarthritis, symmetric arthritis, spinal pain, fever, psoriasis, antinuclear antibodies [ANA], and rheumatoid factor). Latent class analysis (LCA) was used to identify underlying ("latent") classes that explained the relationships among the observed variables. Statistical models incorporating 5-8 latent classes were applied to the data. RESULTS: The 7-class model was the most appropriate. Patterns of joint involvement and the presence of ANA were influential in determining latent classes. There was some correspondence between the latent classes and the ILAR categories, but they did not coincide completely. Significant differences between the latent classes were seen for 3 HLA haplotypes (DRB1*04-DQA1*03-DQB1*03, DRB1*13-DQA1*01-DQB1*06, and DRB1*08-DQA1*0401-DQB1*0402). CONCLUSION: LCA provides a novel approach to the task of identifying homogeneous subtypes within the umbrella of JIA. In further work, the identified latent classes will be examined for associations with other candidate genes and for differences in outcome.
Authors: Roberta Gonçalves Marangoni; Andre L Hayata; Eduardo F Borba; Pedro M Azevedo; Eloisa Bonfá; Claudia Goldenstein Schainberg Journal: Clinics (Sao Paulo) Date: 2011 Impact factor: 2.365
Authors: Jessica Feldt; Martin Schicht; Jessica Welss; Kolja Gelse; Stefan Sesselmann; Michael Tsokos; Eileen Socher; Fabian Garreis; Thomas Müller; Friedrich Paulsen Journal: Biomedicines Date: 2022-03-21