What is the role of puberty in the development of islet autoimmunity and progression to type 1 diabetes?

Participants

The data originated from the Finnish prospective population-based DIPP Study [22]. Participants in the DIPP birth cohort were born at the Turku (1994–), Oulu (1995–), and Tampere (1997–) University Hospitals. Newborn infants were screened for HLA-conferred genetic susceptibility to T1D using cord blood samples. HLA class II genes are the strongest contributors to the genetic disease susceptibility, being responsible for approximately 50% of the heritability [23]. In addition, also other genes with more than 70 identified contribute to T1D development [24].

Originally, children carrying the genotypes HLA-DQB1*02/*03:02 and HLA-DQB1*03:02/x (x ≠ *02, *03:01, or *06:02/3 until March 1997, and x ≠ *02, *03:01, or *06:02 thereafter) were selected for follow-up [22]. Later on, more DQB1 and DQA1 alleles and subtypes of DRB1*04 alleles were included in the screening to increase the specificity and sensitivity of the procedure [25]. Siblings of the participants were also tested for HLA-conferred susceptibility and invited for follow-up if an increased risk was detected.

The original data included all DIPP participants born before 2020 and their genetically susceptible siblings who participated in the follow-up. Data from 7 years of age were used in the analyses, and participants who had at least four height measurements were included in the assessment of pubertal onset (ngirls = 3118, nboys = 3803). Of these, all who had not developed T1D before 7 years of age were included in the final analyses (n = 6920, Fig. 1). Children who tested positive for IA by 7 years of age contributed only to the analysis of the progression hazards.

Fig. 1figure 1

Flow chart of the compilation of the study population for analysis

Background characteristics

Information on sex was collected as part of a structured questionnaire completed in the hospital after delivery. The child’s body mass index (BMI) at different ages was calculated using the available height and weight measurements obtained during follow-up. To assess BMI at 7 years of age, a polynomial spline mixed-effects model with random intercept and random slopes was fitted to BMI values by age, and each child’s BMI at 7 years was obtained from the individual BMI–age trajectory. For the analyses, the participants were classified into two categories based on the derived BMI value as follows: overweight (ISO-BMI > 25, including overweight and obese) and other (including normal weight and underweight) children, based on the Finnish ISO-BMI classification for children [26]. The ISO-BMI classification defines age-specific BMI cut-off points for underweight, overweight, and obesity for ages 2 to 18 years among Finnish children.

Follow-up characteristics

Participants were monitored for T1D-associated autoantibodies, growth, and weight up to the age of 15 years or until diagnosed with T1D. If autoantibodies were detected before the age of 15 years but the clinical disease was not diagnosed by that point, monitoring would continue. For children born until 2010, antibodies were analyzed at the ages of 3, 6, 12, 18, and 24 months and then annually at the Tampere and Oulu centers and at 3-month intervals until the age of 2 years and then semiannually at the Turku center. For children born since 2010, antibodies were analyzed at the ages of 3, 6, 9, 12, and 24 months and then annually at all centers. After the possible detection of autoantibodies, the participants were followed up at 3-month intervals. Data for T1D diagnosis were obtained from the Finnish Pediatric Diabetes Registry [4] or from the study clinics.

Immunological methods

Four T1D -associated autoantibodies were analyzed, with islet cell antibodies (ICA) used as the primary screening tool for children born before the end of 2002. If a child tested positive for ICA, all the child’s preceding (starting from birth) and subsequent samples were analyzed for three biochemical autoantibodies: insulin autoantibodies (IAA), glutamic acid decarboxylase antibodies (GADA), and islet antigen 2 antibodies (IA-2A). For children born from 2003 onwards, all four autoantibodies from all their samples were regularly analyzed. ICAs were quantified using a standard indirect immunofluorescence method and IAAs, GADAs, and IA-2As using specific radio-binding assays, as described previously [27]. The cut-off for autoantibody positivity was 2.5 JDFU (Juvenile Diabetes Foundation units) for ICA, 3.48 relative units (RU) for IAA, 5.36 RU for GADA and 0.43 RU for IA-2A. Transplacentally transferred autoantibodies were monitored from the maternal samples and from the children’s early samples to exclude those from the analyses.

Definition of outcomes

IA and clinical T1D diagnosis observed after the age of 7 years were used as the outcomes of the study. Because IA is not an unambiguous state, we used two different definitions: 1) repeated positivity for ICA together with at least one biochemical autoantibody (ICA + 1), and 2) repeated positivity for at least one biochemical autoantibody (BC1). As the risk of progression increases significantly with increasing numbers of detected autoantibodies [2], BC1 can be considered a less stringent definition for IA than ICA + 1.

Determination of age at pubertal onset

Age at pubertal onset was assessed based on individual growth characteristics—peak height velocity (cm per year) and age at peak height velocity (years)—and on overweight status (based on ISO-BMI [26]) before the pubertal growth spurt. The assessment was carried out using a previously constructed and validated time-to-pubertal onset model [28]. The children were also divided (at the 33rd and 67th percentiles) into three equally sized groups based on age at pubertal onset; the age cut-off points for the groups were 10.0 and 11.0 years for girls and 11.3 and 11.8 years for boys.

Ethics

Parents gave their written informed consent to genetic testing of their newborn infant and for participation in the follow-up. The study adheres to the Declaration of Helsinki, and the ethics committee of Northern Ostrobothnia Hospital District approved the study protocol. Data were pseudonymized before the corresponding author accessed them for analysis.

Statistical analysisThe three-state survival model

Progressive three-state survival models [29] for continuous time data were used to study the association between puberty and the development of IA and progression to T1D. The hazards were assumed to follow a Weibull distribution. We defined state 1 as being negative for IA without T1D, state 2 as having IA without T1D, and state 3 as being diagnosed with clinical T1D. Time to IA was considered to be interval censored between the last negative IA measurement and the first positive IA measurement, meaning that the true transition time was unknown, although it was known to lie within a specific time interval [30]. T1D was the absorbing state—backward transition from this state was not considered possible, and entry into this state ended the follow-up. Three irreversible transitions were therefore possible: 1 → 2 (IA), 1 → 3 (direct transition to T1D), and 2 → 3 (progression). A few 1 → 3 transitions were considered to be unobserved 1 → 2 → 3 paths based on the rarity of the T1D diagnosis without detected autoantibodies [6]. This three-state model is illustrated in Supplementary Fig. 1.

In addition to the observed transitions, the possibility of unobserved transitions was accounted for in the models. Unobserved transitions for IA represented children under follow-up who had no measurements to assess their status at 7 years (potential IA at 7 years) followed by an observed IA (ICA + 1: n = 16; BC1: n = 27) together with those with a T1D diagnosis but without initial observation of IA (n = 3). The latter were also considered to be unobserved transitions from IA to T1D (progression).

The models were adjusted for the sex of the child (girl or boy) and overweight status (underweight/normal weight or overweight/obese) at 7 years. The potential modifying effect of the actual timing of pubertal onset (divided into three categories by tertiles) on the association between puberty and the transition-specific risks was investigated by adding the corresponding interaction term to the model. Additional analyses for a modifying effect of the pubertal timing were implemented with cut-off points for early and late puberty defined by the age 9 and 12 years for girls, and 10 and 13 years for boys, respectively. Type I errors due to multiple testing were controlled for using the false discovery rate (FDR) method [31] for the adjusted results (four family-wise comparisons were accounted for). The FDR was set at 0.05, and a result was considered a discovery if the p-value was smaller than the corresponding FDR critical value.

The pubertal effect function

Puberty was added to the three-state model as a novel time-dependent function. Different origins, durations, and shapes for the effect were considered as alternative forms of pubertal influence. In the function definition, origin was defined as the initiation of the effect—whether the pubertal effect starts at the estimated onset or before it; duration was defined as the time period of the pubertal effect; and the different shapes of the pubertal effect consisted of ramping-up, steady, and fading-off periods, each accounting for different proportions of the overall duration of the effect but with equal durations for ramping-up and fading-off. The functional form of the pubertal effect is illustrated in detail in the Supplementary Methods.

We considered a range of alternative functional forms. The origin was assumed to occur at onset of puberty or 1 year before; the duration ranged from 1 to 4 years, at 1-year intervals; and the shape was expressed as a percentage of the entire duration at 10% intervals (i.e., 11 options), with ramping-up and fading-off periods being equally long. This led to close to one hundred presumed possible forms of the pubertal effect. It was assumed that the same origin, duration, and shape applied to both transitions in each model, but their regression coefficients could be different.

The pubertal effect function used was chosen in a data-driven manner based on the Akaike information criterion (AIC) by choosing the smallest average AIC over the different IA definitions. The final multi-state models included a pubertal effect with an origin at 1 year before the estimated onset of puberty, a duration of 3 years, and a shape comprising a 0.3-year (10%) ramping-up, a 2.4-year (80%) steady, and a 0.3-year (10%) fading-off period. We verified the performance of the data-driven model selection by implementing a small simulation study, which demonstrated probability of approximately 80% for detecting a pubertal effect as well as the ability to correctly estimate the relative risk even under a slight misspecification of the functional form (see Supplementary Methods).

Sensitivity analysis

We investigated the sensitivity of the results in several ways. The possibility of an error in the growth-based estimation of pubertal onset was evaluated by fitting the same model to ten datasets, with the timing of onset randomly drawn from a normal distribution corresponding to the individual’s pubertal onset prediction interval obtained from the time-to-pubertal onset model [28]. Results from the models were combined using Rubin’s Rules [32].

To verify that the significant findings were not specific to the choice of a particular functional form, we tested a range of models with different origins, durations, and/or shapes and compared them to the results using the chosen functional form.

Finally, additional analyses defining IA as repeated positivity for at least two biochemical autoantibodies (BC2) were performed to determine whether the definition of IA affected the results. In addition, a Cox regression model on progression to T1D was fitted as a sensitivity analysis. The analysis included individuals being positive for IA and the time origin was at 7 years. In addition to the adjustment for the confounding factors, the age at IA was adjusted for because early seroconversion to islet autoantibodies is known to be associated with a faster rate of progression to T1D [6]. Puberty was used as a time dependent covariate lasting from 1 year before onset until 2 years after.

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