Characterising the age-dependent effects of risk factors on type 1 diabetes progression

Abstract

Aims/hypothesis Age is known to be one of the most important stratifiers of disease progression in type 1 diabetes. However, what drives the difference in rate of progression between adults and children is poorly understood. Evidence suggests that many type 1 diabetes disease predictors do not have the same effect across the age spectrum. Without a comprehensive analysis describing the varying risk profiles of predictors over the age continuum, researchers and clinicians are susceptible to inappropriate assessment of risk when examining populations of differing ages. We aimed to systematically assess and characterise how the effect of key type 1 diabetes risk predictors changes with age. Methods Using longitudinal data from single-and multiple-autoantibody-positive at-risk individuals recruited between the ages of 1 and 45 years in TrialNet's Pathway to Prevention Study, we assessed and visually characterised the age-varying effect of key demographic, immune and metabolic predictors of type 1 diabetes by employing a flexible spline model. Two progression outcomes were defined: participants with single autoantibodies (n=4893) were analysed for progression to multiple autoantibodies or type 1 diabetes, and participants with multiple autoantibodies were analysed (n=3856) for progression to type 1 diabetes. Results Several predictors exhibited significant age-varying effects on disease progression. Amongst single-autoantibody participants, HLA-DR3 (p=0.007), GAD65 autoantibody positivity (p=0.008), elevated BMI (p=0.007) and HOMA-IR (p=0.002) showed a significant increase in effect on disease progression with increasing age. Insulin autoantibody positivity had a diminishing effect with older age in single-autoantibody-positive participants (p<0.001). Amongst multiple-autoantibodypositive participants, male sex (p=0.002) was associated with an increase in risk for progression, and HLA DR3/4 (p=0.05) showed a decreased effect on disease progression with older age. In both single-and multiple-autoantibody-positive individuals, significant changes in HR with age were seen for multiple measures of islet function. Risk estimation using prediction risk score Index60 was found to be better at a younger age for both single-and multiple-autoantibody-positive individuals (p=0.007 and p<0.001, respectively). No age-varying effect was seen for prediction risk score DPTRS (p=0.861 and p=0.178, respectively). Multivariable analyses suggested that incorporating the age-varying effect of the individual components of these validated risk scores has the potential to enhance the risk estimate. Conclusions/interpretation Analysing the age-varying effect of disease predictors improves understanding and prediction of type 1 diabetes disease progression, and should be leveraged to refine prediction models and guide mechanistic studies.Michelle So and Colin O'Rourke contributed equally to this work.

Type 1 Diabetes Prevention: a systematic review of studies testing disease-modifying therapies and features linked to treatment response

“…For example, younger age was linked to an improved treatment response in several new-onset trials using CD3-based agents 49,53,56,82 . The association of age with treatment response is in keeping with the strong associations of age to features of T1D in many observational and natural history studies, before and after clinical diagnosis 10,[96][97][98] . Differences in pancreas histology have been identified in donors with younger age of diagnosis 99,100 .…”

Maternal depression or anxiety during pregnancy and offspring type 1 diabetes: a population-based family-design cohort study

“…This is in line with the growing body of current research on disease heterogeneity in type 1 diabetes and the concept of endotypes with different underlying disease pathways 29. Age-varying differences in risk factors for the progression from autoantibody positivity to clinical disease as well as in characteristics at diagnosis of type 1 diabetes have been shown 30–32…”

“… 29 Age-varying differences in risk factors for the progression from autoantibody positivity to clinical disease as well as in characteristics at diagnosis of type 1 diabetes have been shown. 30–32 …”

A classification and regression tree analysis identifies subgroups of childhood type 1 diabetes

“…Feature of type 2 diabetes such as BMI, glycaemic index, and obesity have been previously associated with progression to type 1 diabetes in people with islet autoantibodies. 55 , 56 Finally, patients in the N3 subgroup may also benefit from therapies for type 2 diabetes such as metformin, glucagon-like peptide-1 receptor analogues, and sodium-glucose cotransporter-2 inhibitors rather than insulin.…”