A Joint Modeling Approach for Childhood Meat, Fish and Egg Consumption and the Risk of Advanced Islet Autoimmunity
: Essi Syrjälä, Jaakko Nevalainen, Jaakko Peltonen, Hanna-Mari Takkinen, Leena Hakola, Mari Åkerlund, Riitta Veijola, Jorma Ilonen, Jorma Toppari, Mikael Knip, Suvi M. Virtanen
Publisher: NATURE PUBLISHING GROUP
: 2019
: Scientific Reports
: SCIENTIFIC REPORTS
: SCI REP-UK
: ARTN 7760
: 9
: 10
: 2045-2322
DOI: https://doi.org/10.1038/s41598-019-44196-1
: https://research.utu.fi/converis/portal/detail/Publication/40653193
Several dietary factors have been suspected to play a role in the development of advanced islet autoimmunity (IA) and/or type 1 diabetes (T1D), but the evidence is fragmentary. A prospective population-based cohort of 6081 Finnish newborn infants with HLA-DQB1-conferred susceptibility to T1D was followed up to 15 years of age. Diabetes-associated autoantibodies and diet were assessed at 3-to 12-month intervals. We aimed to study the association between consumption of selected foods and the development of advanced IA longitudinally with Cox regression models (CRM), basic joint models (JM) and joint latent class mixed models (JLCMM). The associations of these foods to T1D risk were also studied to investigate consistency between alternative endpoints. The JM showed a marginal association between meat consumption and advanced IA: the hazard ratio adjusted for selected confounding factors was 1.06 (95% CI: 1.00, 1.12). The JLCMM identified two classes in the consumption trajectories of fish and a marginal protective association for high consumers compared to low consumers: the adjusted hazard ratio was 0.68 (0.44, 1.05). Similar findings were obtained for T1D risk with adjusted hazard ratios of 1.13 (1.02, 1.24) for meat and 0.45 (0.23, 0.86) for fish consumption. Estimates from the CRMs were closer to unity and CIs were narrower compared to the JMs. Findings indicate that intake of meat might be directly and fish inversely associated with the development of advanced IA and T1D, and that disease hazards in longitudinal nutritional epidemiology are more appropriately modeled by joint models than with naive approaches.
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