A Joint Modeling Approach for Childhood Meat, Fish and Egg Consumption and the Risk of Advanced Islet Autoimmunity

Syrjälä, Essi; Nevalainen, Jaakko; Peltonen, Jaakko; Takkinen, Hanna‐Mari; Hakola, Leena; Åkerlund, Mari; Veijola, Riitta; Ilonen, Jorma; Toppari, Jorma; Knip, Mikael; Virtanen, Suvi Μ.

doi:10.1038/s41598-019-44196-1

Cited by 17 publications

(15 citation statements)

References 48 publications

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“…Another advantage of the present study is that we could apply joint model on these data. Joint models are computationally burdensome, but they can take intra-individual variation of the food records into account and are capable of handling incomplete food record data better than the commonly used Cox regression model (21) .…”

Section: Discussionmentioning

confidence: 99%

“…Otherwise, data were right-censored at the age of the last autoantibody measurement. Detailed information on the joint models used has been described previously (21) .…”

Section: Methodsmentioning

confidence: 99%

“…We adjusted the joint models for sex (boy or girl), HLAgenotype (high or moderate risk) and diabetes of the first-degree relative (yes, no or missing value), as these variables have been previously found to be potential confounders (21) . Missing values in diabetes of the first-degree relative were coded as a third level of the factor to enable the inclusion of those children in the model.…”

Section: Methodsmentioning

confidence: 99%

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Consumption of differently processed milk products in infancy and early childhood and the risk of islet autoimmunity

et al. 2020

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Several prospective studies have shown an association between cows’ milk consumption and the risk of islet autoimmunity and/or type 1 diabetes. We wanted to study whether processing of milk plays a role. A population-based birth cohort of 6081 children with HLA-DQB1-conferred risk to type 1 diabetes was followed until the age of 15 years. We included 5545 children in the analyses. Food records were completed at the ages of 3 and 6 months and 1, 2, 3, 4 and 6 years, and diabetes-associated autoantibodies were measured at 3–12-month intervals. For milk products in the food composition database, we used conventional and processing-based classifications. We analysed the data using a joint model for longitudinal and time-to-event data. By the age of 6 years, islet autoimmunity developed in 246 children. Consumption of all cows’ milk products together (energy-adjusted hazard ratio 1·06; 95 % CI 1·02, 1·11; P = 0·003), non-fermented milk products (1·06; 95 % CI 1·01, 1·10; P = 0·011) and fermented milk products (1·35; 95 % CI 1·10, 1·67; P = 0·005) was associated with an increased risk of islet autoimmunity. The early milk consumption was not associated with the risk beyond 6 years. We observed no clear differences based on milk homogenisation and heat treatment. Our results are consistent with the previous studies, which indicate that high milk consumption may cause islet autoimmunity in children at increased genetic risk. The study did not identify any specific type of milk processing that would clearly stand out as a sole risk factor apart from other milk products.

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Section: Discussionmentioning

confidence: 99%

“…Otherwise, data were right-censored at the age of the last autoantibody measurement. Detailed information on the joint models used has been described previously (21) .…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Consumption of differently processed milk products in infancy and early childhood and the risk of islet autoimmunity

et al. 2020

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“…Joint modelling can offer an opportunity to explicitly model the correlations and account for the follow-up time, ensuring efficient and unbiased estimates. Despite the advanced development and evident benefits of joint modelling of multivariate longitudinal data [17][18][19], joint modelling has received limited attention in drug safety assessment.…”

Section: Introductionmentioning

confidence: 99%

Joint modelling of multivariate longitudinal clinical laboratory safety outcomes, concomitant medication and clinical adverse events: Application to artemisinin-based treatment during pregnancy clinical trial

Patson

Mukaka

D’Alessandro

et al. 2021

Preprint

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Background In drug trials, clinical adverse events (AEs), concomitant medication and laboratory safety outcomes are repeatedly collected to support drug safety evidence. Despite the potential correlation of these outcomes, they are typically analysed separately, potentially leading to misinformation and inefficient estimates due to partial assessment of safety data. Using joint modelling, we investigated whether clinical AEs vary by treatment and how laboratory outcomes (alanine amino-transferase, total bilirubin) and concomitant medication are associated with clinical AEs over time following artemisinin-based antimalarial therapy. Methods We used data from a trial of artemisinin-based treatments for malaria during pregnancy that randomized 870 women to receive artemether–lumefantrine (AL), amodiaquine–artesunate (ASAQ) and dihydroartemisinin–piperaquine (DHAPQ). We fitted a joint model containing four sub-models from four outcomes: longitudinal sub-model for alanine aminotransferase, longitudinal sub-model for total bilirubin, Poisson sub-model for concomitant medication and Poisson sub-model for clinical AEs. Since the clinical AEs was our primary outcome, the longitudinal sub-models and concomitant medication sub-model were linked to the clinical AEs sub-model via current value and random effects association structures respectively. We fitted a conventional Poisson model for clinical AEs to assess if the effect of treatment on clinical AEs (i.e. incidence rate ratio (IRR)) estimates differed between the conventional Poisson and the joint models, where AL was reference treatment.Results Out of the 870 women, 564 (65%) experienced at least one AE. Using joint model, AEs were associated with the concomitant medication (log IRR 1.7487; 95% CI: 1.5471, 1.9503; p<0.001) but not the total bilirubin (log IRR: -0.0288; 95% CI: -0.5045, 0.4469; p=0.906) and alanine aminotransferase (log IRR: 0.1153; 95% CI: -0.0889, 0.3194; p=0.269). The Poisson model underestimated the effects of treatment on AE incidence such that log IRR for ASAQ was 0.2118 (95% CI: 0.0082, 0.4154; p=0.041) for joint model compared to 0.1838 (95% CI: 0.0574, 0.3102; p=0.004) for Poisson model.Conclusion We demonstrated that although the AEs did not vary across the treatments, the joint model yielded efficient AE incidence estimates compared to the Poisson model. The joint model showed a positive relationship between the AEs and concomitant medication but not with laboratory outcomes. Trial registration ClinicalTrials.gov: NCT00852423.

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“…Consumption of red meat, especially processed meat, has been associated with an increased risk of type 2 diabetes (T2D) [1] as well as childhood type 1 diabetes (T1D) [2][3][4]. Several compounds in red meat, some of which are particularly abundant in processed meat products, may affect diabetes risk including advanced glycation endproducts (AGEs), sodium, iron, and nitrates, nitrites, and nitrosamines.…”

Section: Introductionmentioning

confidence: 99%

Consumption of red meat, genetic susceptibility, and risk of LADA and type 2 diabetes

et al. 2020

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Purpose Red meat consumption is positively associated with type 1 (T1D) and type 2 (T2D) diabetes. We investigated if red meat consumption increases the risk of latent autoimmune diabetes in adults (LADA) and T2D, and potential interaction with family history of diabetes (FHD), HLA and TCF7L2 genotypes. Methods Analyses were based on Swedish case–control data comprising incident cases of LADA (n = 465) and T2D (n = 1528) with matched, population-based controls (n = 1789; n = 1553 in genetic analyses). Multivariable-adjusted ORs in relation to self-reported processed and unprocessed red meat intake were estimated by conditional logistic regression models. Attributable proportion (AP) due to interaction was used to assess departure from additivity of effects. Results Consumption of processed red meat was associated with increased risk of LADA (per one servings/day OR 1.27, 95% CI 1.07–1.52), whereas no association was observed for unprocessed red meat. For T2D, there was no association with red meat intake once BMI was taken into account. The combination of high (> 0.3 servings/day vs. less) processed red meat intake and high-risk HLA-DQB1 and -DRB1 genotypes yielded OR 8.05 (95% CI 4.86–13.34) for LADA, with indications of significant interaction (AP 0.53, 95% CI 0.32–0.73). Results were similar for the combination of FHD-T1D and processed red meat. No interaction between processed red meat intake and FHD-T2D or risk variants of TCF7L2 was seen in relation to LADA or T2D. Conclusion Consumption of processed but not unprocessed red meat may increase the risk of LADA, especially in individuals with FHD-T1D or high-risk HLA genotypes.

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A Joint Modeling Approach for Childhood Meat, Fish and Egg Consumption and the Risk of Advanced Islet Autoimmunity

Cited by 17 publications

References 48 publications

Consumption of differently processed milk products in infancy and early childhood and the risk of islet autoimmunity

Consumption of differently processed milk products in infancy and early childhood and the risk of islet autoimmunity

Joint modelling of multivariate longitudinal clinical laboratory safety outcomes, concomitant medication and clinical adverse events: Application to artemisinin-based treatment during pregnancy clinical trial

Consumption of red meat, genetic susceptibility, and risk of LADA and type 2 diabetes

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