Physical, Psychological and Socioeconomic Effects of Childhood Onset Diabetes in Sweden - Longitudinal Register Based Studies
Conditions: Type 1 Diabetes Mellitus (T1DM), End-Stage Renal Disease
Sponsor: Umeå University

ClinicalTrial.gov identiﬁer NCT06883318 
 
18 March 2025 
1 
 
 
Physical, psychological and socioeconomic effects of childhood-onset diabetes in Sweden 
– longitudinal, register-based studies 
________________________________________________________________________________________________ 
Torbjörn Lind (TL), MD, PhD and Anna Möllsten (AM), MD, PhD, Department of Clinical Sciences, 
Paediatrics, Umeå University, Ingeborg Waernbaum (IW), PhD, Department of Statistics, Uppsala 
University, Emma Persson (EP), PhD, Department of Statistics, Umeå University and Katarina Steen 
Carlsson (KSC), PhD, Department of Clinical Sciences, Malmö; Health Economics, Lund 
Purpose and Aims 
The overarching purpose of the suggested research is to investigate the incidence and risk fac-
tors as well as long-term complications and consequences of childhood-onset type 1 diabetes 
(T1D), with an emphasis on understanding the multifactorial aetiology, socioeconomic dispari-
ties and gender differences associated with the disease. Through a population-based approach, 
this research aims to provide evidence for more effective prevention, management, and long-
term care strategies for individuals with T1D, while at the same time advancing research meth-
odologies for this and similar chronic conditions. We focus on ﬁve main research areas with the 
following speciﬁc aims: 
1. Time trends in childhood-onset type 1 diabetes: 
• 
To examine time, age and cohort effects on the incidence of childhood-onset T1D in Sweden. 
• 
To identify environmental and lifestyle-related risk factors contributing to the increasing inci-
dence of T1D. 
• 
To assess the potential impact of early-life exposures, including pre- and perinatal factors 
and early childhood infections, on the development of islet autoimmunity and T1D. 
2. Diabetic nephropathy and end-stage renal disease: 
• 
To investigate the incidence, survival and associated risk factors for diabetic nephropathy 
and end-stage renal disease (ESRD) among individuals with childhood-onset T1D. 
• 
To explore the impact of diabetes management improvements on the declining incidence of 
ESRD. 
• 
To evaluate sex-speciﬁc differences and the inﬂuence of pregnancy on the progression of mi-
crovascular complications in women with T1D. 
3. Socioeconomic circumstances and health outcomes: 
• 
To examine the impact of familial socioeconomic stress on metabolic control and risk of 
complications. 
• 
To investigate the long-term health and social outcomes of siblings of individuals with T1D 
compared to matched controls 
4. Health economics and type 1 diabetes: 
• 
To estimate risk-equations for T1D-related complications and outcomes adapted for use in 
health-economic models for children, young adults and middle-aged people. 
• 
To analyse consequences of T1D onset in a child on socioeconomic and health-related out-
comes for parents. 
5. Target trial emulation, case-control studies and time-zero implementation:  
• 
To develop statistical methods for cohort and case-control designs for setting of “time zero” 
in target trial emulation for the purpose of assessing causal structures using T1D and various 
complications as empiric models. 

ClinicalTrial.gov identiﬁer NCT06883318 
 
18 March 2025 
2 
 
Survey of the Field 
This is a follow up of a previous grant from the Research Council. Last code: 2018-02565, 
principal investigator Torbjörn Lind. 
Time Trends in Childhood-onset Type 1 Diabetes 
The aetiology of childhood-onset type 1 diabetes (T1D) is multifactorial, arising from a complex 
interaction between genetic predisposition and environmental risk factors (1,2). In contrast to 
other autoimmune diseases, T1D predominantly affects younger populations (3). Between 1978 
and 2008, the incidence of T1D among children below 15 years in Sweden more than doubled 
(4). Consequently, alongside asthma, T1D has become one of the most prevalent chronic dis-
eases among children in Sweden. Similar upward trends in incidence have been documented in 
numerous other countries (5). Over the past decade, the incidence has remained high but stable 
(Figure 1) (6). 
 
Figure 1: The incidence childhood-onset type 1 diabetes in Swedish, foreign and total population 0-15 
years divided on boys (a) and girls (b), dots being incidence and lines being predicted incidence from a 
general additive model (GAM) for Poisson response (6). 
The rapid increase in the incidence of childhood-onset T1D can only be attributed to a rise in 
non-genetic, lifestyle-related risk factors (7). The peak incidence of autoimmunity against insu-
lin-producing β-cells occurs in the second year of life (8), suggesting that environmental triggers 
likely operate very early, either in utero or during early childhood. A wide range of environmental 
risk factors have been investigated (2), with several pre- and perinatal factors consistently asso-
ciated with T1D risk, including maternal obesity, preterm birth, and postnatal growth (9,10). In-
fections, particularly viral infections, have also been implicated in T1D pathogenesis (11), alt-
hough their population-level impact is harder to study due to the frequent lack of diagnosis or 
aetiological identiﬁcation of childhood infections. Some prospective studies have found associ-
ations between respiratory infections and islet autoimmunity (12), whereas others have not (13). 
During the COVID-19 pandemic, reports indicated an initial rise in T1D incidence followed by a 
subsequent decline, a trend less pronounced in younger children (14,15). Preliminary data from 
Sweden suggest similar patterns, though these observations require conﬁrmation through ex-
tended follow-up and analysis. A whole-population study in Denmark (16) found no association 
between SARS-CoV-2 infection and subsequent T1D risk in children, whereas a preliminary re-
port from Norway described a 60% risk increase (17). 

ClinicalTrial.gov identiﬁer NCT06883318 
 
18 March 2025 
3 
 
Signiﬁcance and Scientiﬁc Novelty 
Given the chronic nature of T1D, its severe long-term complications, and the necessity for life-
long healthcare, the disease demands increased attention. Continued surveillance of T1D inci-
dence trends is crucial for generating hypotheses on disease aetiology and providing a founda-
tion for future diabetes care and research planning. To better understand the drivers of changing 
incidence rates, it is essential to link individual-level microdata to population-based variables 
that could accelerate the pre-existing autoimmune process of T1D. Such variables may be ma-
ternal weight, gestational age and postnatal growth. 
Diabetic Nephropathy and End-Stage Renal Disease 
Diabetic nephropathy is the most severe long-term complication of T1D, leading to glomerulo-
sclerosis, progressive glomerular dysfunction, and ultimately ESRD. This life-threatening condi-
tion necessitates renal replacement therapy (RRT) via dialysis or transplantation. Diabetic 
nephropathy is also a key contributor to premature mortality in T1D, driving both renal failure 
and cardiovascular disease risk (18). A meta-analysis demonstrated increased cardiovascular 
and all-cause mortality in diabetes patients, although mortality risk among those with chronic 
kidney disease was comparable irrespective of diabetes status (24), which emphasizes the im-
portance of renal disease. Our research group previously reported that T1D accounted for 10% 
of ESRD cases, later decreasing to 5.6%, potentially reﬂecting improved diabetes management, 
hypertension, and dyslipidaemia control (19,20). Finnish data also indicated a declining ESRD 
incidence, with onset typically 15 years post-T1D diagnosis, peaking at 25 years before plat-
eauing (21). However, it remains unclear whether current interventions genuinely reduce risk or 
merely delay nephropathy progression. 
Epidemiological studies show varying nephropathy incidence across populations (22), dispro-
portionately affecting socioeconomically disadvantaged individuals (23,24). Survival after RRT 
onset depends on treatment modality and socioeconomic status, highlighting disparities in 
healthcare access and outcomes (25). 
Sex differences in nephropathy risk remain inconsistent. While men appear at higher risk than 
premenopausal women, postmenopausal women show increased susceptibility. Age at diabe-
tes onset may further inﬂuence this risk (26). Among women with T1D, pregnancy outcomes are 
closely linked to glycaemic control, with poor metabolic control increasing maternal and foetal 
morbidity and mortality; however, the long-term impact of pregnancy on complications such as 
nephropathy progression remains understudied, despite that evidence-based guidance could 
have a signiﬁcant impact on pregnancy strategies for all T1D women (27).  
Signiﬁcance and Scientiﬁc Novelty 
Continuous monitoring of ESRD trends concerning socioeconomic factors, comorbidities, and 
RRT modalities is vital to understanding this serious complication. Exploring pregnancy as a 
novel risk factor for diabetic nephropathy will enhance knowledge of gender differences in T1D 
outcomes. 
Socioeconomic Circumstances and Health Outcomes  
Type 1 diabetes management requires strict adherence to insulin therapy and blood glucose 
monitoring (28). Socioeconomic stress, particularly in families with low income or parental edu-

ClinicalTrial.gov identiﬁer NCT06883318 
 
18 March 2025 
4 
 
cation, has been linked to increased risks of long-term complications, including nephropathy 
and premature mortality in adulthood (29,30). Socioeconomically disadvantaged children with 
T1D also exhibit poorer metabolic control (31); however, whether this persists into adulthood re-
mains debated, as systematic reviews report conﬂicting results (32).  
A T1D diagnosis affects the entire family, inducing distress in parents and siblings (33). While 
siblings develop coping mechanisms, their emotional needs may be underestimated, leading to 
increased rates of depression and anxiety (34). Previous research has primarily examined the im-
mediate post-diagnosis period, often with small sample sizes and short follow-up durations 
(35). Our prior ﬁndings indicate that brothers of individuals with T1D, but not sisters, have lower 
annual earnings compared to their peers (36), yet the long-term health consequences for sib-
lings are largely unexplored. 
Signiﬁcance and Scientiﬁc Novelty 
Understanding T1D in a life course perspective requires examining the relationship between 
childhood socioeconomic status and long-term glycaemic control. Further research into mortal-
ity, hospitalisations, and mental health outcomes among T1D siblings compared to matched 
controls is essential for comprehensively addressing the broader impacts of T1D on affected 
families. 
Health Economics and Type 1 Diabetes 
Health-economic evaluation models in diabetes, typically microsimulations or Markov models 
(37), rely on risk equations that capture short- and long-term outcomes inﬂuenced by interven-
tions such as improved diagnostics and treatment (38–40). These models link health outcomes 
to costs and patient beneﬁts, aiding healthcare decision-making and guideline optimisation. 
Traditionally, models for T1D have been based on the American Diabetes Control and Complica-
tions Trial (DCCT) (41), and its follow-up, the Epidemiology of Diabetes Interventions and Com-
plications (EDIC) study (42), alongside frameworks borrowed from Type 2 Diabetes, despite fun-
damental differences in aetiology, onset and risk factors.  
There is a growing need to reﬁne models for T1D evaluation (39,40), including the integration of 
risk equations derived from more recent patient cohorts, such as Swedish observational data 
(40). Existing health-economic models predominantly focus on adult populations and long-term 
vascular complications, with limited data available on children and adolescents. Additionally, 
mental health effects (43) and other non-vascular complications remain underrepresented. 
While international research on T1D complications is expanding, it tends to be clinically focused 
and lacks straightforward applicability to health-economic models. 
Understanding the short- and long-term consequences of T1D across the lifespan is increasingly 
important due to advances in early risk identiﬁcation, interventions to reduce incidence (44) 
(GPPAD-POInT at ClinicalTrials.gov IDNCT03364868), and discussions on newborn screening. In 
this context, health-economic models will support decision-makers in assessing the value of 
delaying T1D onset in terms of patient outcomes and cost savings. 
Signiﬁcance and Scientiﬁc Novelty 
There is a critical need for updated risk estimates of T1D complications that can be integrated 
into health-economic simulation models. Such models are essential for researchers to assess 

ClinicalTrial.gov identiﬁer NCT06883318 
 
18 March 2025 
5 
 
new interventions and for organisations that evaluate treatments and diagnostics, including 
Health-Technology Assessment agencies and the life sciences industry. While existing models 
primarily address micro- and macrovascular complications that typically arise after age 50, fu-
ture models should incorporate risk estimates for T1D-related outcomes affecting children, ado-
lescents, and younger adults, as well as the caregiving burden on parents of children with T1D. 
Target Trial Emulation, Case-Control Studies and Time-zero Implementation 
Observational studies are increasingly designed to emulate randomised controlled trials (RCTs) 
to strengthen causal inference, a concept known as Target Trial Emulation (TTE) (45). RCTs re-
main the gold standard for causal effect estimation due to their ability to eliminate confounding 
and simplify analyses. However, well-structured observational studies can provide valuable in-
sights by incorporating key RCT elements such as eligibility criteria, treatment assignment, ran-
domisation, and clearly deﬁned "time zero" for follow-up. In Hernán et al. (46) four examples are 
given of failures to emulate a target trial using observational data (Figure 2).  
 
Figure 2. Four examples of failures of emulation of a target trial using observational data. Here, T0, is time 
zero, E is eligibility, and A is a period under which treatments are assigned (46). 
Signiﬁcance and Scientiﬁc Novelty 
The signiﬁcance of TTE continues to grow within epidemiological and statistical research. Ad-
vancing this framework necessitates new case-control study designs that can be practically ap-
plied by data analysts. Further methodological development is required to reﬁne observational 
study designs to more closely resemble RCTs.  
Preliminary and Previous Results 
Time Trends in Childhood-onset Type 1 Diabetes 
The research group has followed childhood-onset T1D incidence since the early 1980s (47). 
More recently, we have considered trends for a levelling off of the incidence (4) and the impact of 
demographic dynamics on the incidence trends (6). We have also explored various aetiological 
risk factors, showing among other things that higher birthweight, moderate prematurity and ma-
ternal obesity are independent risk factors for T1D (10).  

ClinicalTrial.gov identiﬁer NCT06883318 
 
18 March 2025 
6 
 
Diabetic Nephropathy and End-Stage Renal Disease 
The group has published extensively on various aspects of diabetic nephropathy and ESRD 
(19,20,29,48,49). Recently, we have investigated the risk of renal failure and death in individuals 
with parental T1D, i.e., having parents with the same disease, ﬁnding slightly higher mean gly-
cated haemoglobin and a higher risk of renal failure. Also, women with parental T1D, but not 
men had higher risk of premature death (50).  
Socioeconomic Circumstances and Health Outcomes 
We have researched socioeconomic disparities between individuals with and without T1D both 
in aetiological and prognostic studies, see (29,30,49,51,52) for some examples. Most recently, 
we have investigated socioeconomic exposure during childhood and impact on glycaemic con-
trol in the individual as an adult. Preliminary data show that HbA1c is higher if the individual lived 
in household that received income support (effect size 0.45 SD), unemployment beneﬁts (effect 
size 0.19 SD) or if the parents had less than 12 years of education (effect size 0.46 SD). 
Health Economics and Type 1 Diabetes 
Research from our group and its collaborators has demonstrated positive trends in T1D compli-
cations, including a decline in ESRD incidence (20,49), Additionally, we have explored the socio-
economic consequences of T1D (52,53), developed risk equations for Type 2 Diabetes (54) and 
validated health-economic models (55). Ongoing work at the Swedish Institute for Health Eco-
nomics (secondary affiliation of co-applicant Steen-Carlsson) includes the development of a 
health-economic model for T1D, initially focusing on adults (40) but with plans to incorporate 
childhood and adolescent risks for a full lifespan perspective. Preliminary data indicate that T1D 
in children has lasting effects on parental labour-market outcomes, suggesting a need to inte-
grate a family impact module into health-economic models. 
Case-Control and Cohort Registers as a Part of Statistical Method Development 
Our group has extensive experience in developing statistical methods for observational studies. 
Since 2009, our team has advanced theoretical frameworks related to causal inference in dis-
ease incidence data, see (49,56–59) for some examples. Research has alternated between ap-
plied and theoretical studies, with particular focus on the design and methodologies used in the 
SCDR. 
Equipment 
The Swedish Childhood Diabetes Register (SCDR) started for research purposes and records vir-
tually all incident cases of childhood-onset T1D (0-14 years) since 1 July 1977 (47). The SCDR in-
cludes more than 24 000 cases and for each case there are four controls matched for age and 
municipality at the time of T1D diagnosis. In 2008, the SCDR was linked to several national regis-
ters to create a comprehensive database. These registers include the Swedish Renal Register 
(SRR), the Medical Birth Register (MBR), the National Patient Register (NPR), the Cause of Death 
Register (CDR), the Prescribed Drug Register (PDR), the Cancer Register (CR), the Integrated Da-
tabase for Labour Market Research (Swedish acronym LISA) and the Multiple Generation Regis-
ter (MGR). The SCDR is also collaborating with the National Diabetes Register (NDR). For this 
project, particularly hypothesis 1, we will link SCDR to the Child Health Services Register (CHS). 

ClinicalTrial.gov identiﬁer NCT06883318 
 
18 March 2025 
7 
 
The SCDR database has been updated with longitudinal data several times, most recently in 
2023. It is stored and administered at the Umeå University SIMSAM-laboratory. 
Project Description 
The SCDR, initially established through collaboration with all Swedish paediatric diabetes clin-
ics, collected clinical data until 2010 (4). Since then, the National Board of Health and Welfare 
has provided incident data via the Prescribed Drug Register (PDR) (6). An interdisciplinary team 
has long been engaged in SCDR research, encompassing expertise in paediatric diabetes medi-
cine, general medicine, molecular biology, endocrinology, nephrology, statistics, and health 
economics. PhD students from multiple institutions contribute to this work. The SCDR research 
project is registered at ClinicalTrials.gov with identiﬁer NCT06883318. 
Hypothesis 1: The incidence of childhood-onset type 1 diabetes will peak in Sweden during 
the early 2020s.  
To deepen our knowledge on incidence trends and understand more of age-period and cohort 
effects in T1D incidence trends, we will make use of hierarchical Age-Period-Cohort (APC) analy-
sis with covariates, see e.g. (60) for a comprehensive overview. Incident cases come from the 
PDR and covariates will come from the MBR concerning early pregnancy weight and gestational 
age and from the CHS regarding child growth (61). We will expand our previous analysis using 
non-linear semiparametric generalized additive models for count data (4,6). The time related 
changes we study are age effects (A), period effects (P) and shared life events for a set of individ-
uals, i.e., cohort effects (C). The ﬁtted APC models can be evaluated with model diagnostics and 
give (i) the magnitude of the individual APC effects. For example, the minimum and maximum 
values of the marginal APC effects and the overall size of each effect, (ii) an assessment of one-
dimensional covariate smooth effects, for example for child weight or mother’s weight respec-
tively, and (iii) a table with full model results. Either a list of several models or for a single model, 
here both linear coefficients and all nonlinear estimates are returned. The work will resume dur-
ing the latter part of the period, with IW and TL leading. 
Hypothesis 2: The incidence of ESRD, caused by T1D, will stabilize at a low level but 
we will see a delayed peek incidence in ESRD, with pregnancy as a signiﬁcant risk 
factor in women. 
Both socioeconomic background and sex are important mediators of risk to develop ESRD and 
can also be important for survival in RRT. Another important determinant of survival in RRT is the 
mode of treatment, where early renal transplantation is associated with increased survival (25). 
The SCDR is regularly linked to the SRR, allowing analyses of ESRD incidence by duration of dia-
betes, age and gender. In the coming years, part of our population will have reached post-meno-
pausal age, and we have the possibility to follow-up on renal failure and ESRD with respect to 
differences between men and women in different ages. Linkage to LISA allows further analyses 
on socioeconomic risk determinants and region of living, linkage to the PDR will allow us to 
study effects of antihypertensive medication and linkage to the MBR with give data on pregnancy 
circumstances. We will use conventional life table and Cox regression but also more recent pro-
posals from causal survival analysis. These studies will be performed in collaboration with Ce-

ClinicalTrial.gov identiﬁer NCT06883318 
 
18 March 2025 
8 
 
cilia Toppe and Staffan Schön at SRR, Jönköping. In 2024, the most recent SRR data was added 
to the SCDR. Analytic work can commence in early 2026. 
Hypothesis 3: Glycaemic control is on the causal pathway linking socioeconomic status 
during childhood and long-term complications in adults with childhood-onset T1D. Family 
health shocks such as a child with T1D negatively affects the health of siblings. 
In these cohort studies, we identify parents and siblings through the MGR. Exposure data on par-
ents’ educational level, income, well-fare support and other socioeconomic circumstances 
come from LISA. Outcome data on glycaemic control and other clinical parameters (blood pres-
sure, weight, urine-albumin, etc.), come from the NDR; we take data on pharmacologic treat-
ment from the PDR and data on long-term complications from the NPR and the SRR. To account 
for repeated measures of glycaemic control and also perform variable selection, we will use a 
penalized generalized linear mixed model approach (62,63).  
In the analysis on the siblings, we use pharmacological information from the PDR, speciﬁcally 
data on antidepressants and compare them to non-related controls. We will retrieve health data 
from the NPR and the CDR and use LISA for socioeconomic variables. We will apply a non-para-
metric approach, focusing on population effects with analyses based on Persson et al (58). Both 
these studies will involve PhD-student Marie Fredriksson. 
Hypothesis 4: Health-economic evaluation models require risk equations tailored for inte-
gration into Markov and microsimulation models. These equations must capture T1D-re-
lated complications and outcomes from childhood through middle age, reﬂecting current 
diabetes management practices. 
Modern T1D management has improved outcomes for individuals diagnosed in childhood, as 
well as their parents. We will use NPR data to develop a comprehensive set of risk equations 
predicting a wide range of T1D-related outcomes, including micro- and macrovascular compli-
cations such as myocardial infarction, stroke, heart failure, peripheral vascular disease, hypogly-
caemia, hyperglycaemia, amputation, end-stage renal disease, and retinopathy. These analyses 
will incorporate time-to-event methods and assess excess risks for additional comorbidities, in-
cluding psychiatric conditions and all-cause hospitalisations. Mortality risk estimates will also 
be included.  
Risk factors such as age, diabetes duration, and clinical variables (e.g., HbA1c) will be consid-
ered. Sensitivity analyses will evaluate changes in risk estimates over time in response to evolv-
ing treatment recommendations. Additionally, the impact of T1D onset on parents will be exam-
ined using hospitalisation data (NPR) and employment-related records (LISA). We will compare 
T1D parents to control parents, adjusting for confounders such as sibling number, age, employ-
ment, and education using panel-data regression methods. This work, led by Steen-Carlsson, 
will involve a PhD student based at Lund University with proximity to modelling expertise at the 
Swedish Institute for Health Economics (https://ihe.se/). 
Hypothesis 5: Target trial protocols can be speciﬁed for research questions lacking a pre-
deﬁned time zero, allowing for both cohort and case-control study designs in the SCDR. For 
research questions related to disease-free controls, a case-control sample analogue can 
be given. 

ClinicalTrial.gov identiﬁer NCT06883318 
 
18 March 2025 
9 
 
This project will address methodological challenges in deﬁning time zero when emulating a tar-
get trial. Key challenges include specifying eligibility criteria, treatment strategies, and treatment 
assignment while ensuring appropriate follow-up. We will deﬁne causal contrasts (intention-to-
treat and per-protocol effects) and outline statistical analyses suited to target trial, cohort, and 
case-control studies.  
We will follow the TTE framework (46) and focus on Emulation Failure 1 (Figure 2). Statistical 
methodology will be developed using standard inference, causal inference, and epidemiological 
methods adapted for TTE applications. Research will progress in three phases: First, analytical 
methods will be developed. Second, these methods will be integrated into computer software, 
followed by testing with simulated data. Finally, the methods will be applied to the SCDR data. In 
the ﬁnal phase, interdisciplinary collaboration will extend beyond epidemiology and statistics to 
incorporate expertise from education and health sciences, addressing real-world challenges. 
The project will involve IW and EP in phases one and two, with TL and AM contributing to the ﬁnal 
phase to contextualise the empirical ﬁndings.  
International and National Collaboration 
The research group itself is a nation-wide collaboration working on data from national registers. 
Since 2015, IW is part of an international initiative for experts on observational data analysis, 
Strengthening Analytical Thinking for Observational Studies (STRATOS), (http://www.stratos-ini-
tiative.org). IW is co-chair of the STRATOS Topic Group 7: Causal Inference. 
Since 2009, KSC is part of the Swedish Institute for Health Economics, an institute that during 
more than two decades has developed health-economic models for the evaluation of diabetes 
interventions (https://ihe.se/). The institute is a driving contributor to the Mount Hood Diabetes 
Challenge Network (www.mthooddiabeteschallenge.com), an international collaboration with 
regular conferences focusing on comparing and exploring validations of health-economic simu-
lation models in diabetes. 
References 
* Publications by former and current members of the SCDR research group. 
1. 
Steck AK, Rewers MJ. Clin Chem. 2011;57:176–85.  
2. 
Stene LC, Norris JM, Rewers MJ. Risk Factors for Type 1 Diabetes. In: Lawrence JM, Casagrande SS, 
Herman WH, Wexler DJ, Cefalu WT, editors. Diabetes in America. Bethesda (MD): National Institute of 
Diabetes and Digestive and Kidney Diseases; 2023. 
3. 
Conrad N, et al. The Lancet. 2023;401:1878–90.  
4. 
*Berhan Y, Waernbaum I, Lind T, Möllsten A, Dahlquist G. Diabetes. 2011;60:577–81.  
5. 
*Patterson CC, et al. Diabetologia. 2019;62:408–17.  
6. 
*Waernbaum I, Lind T, Möllsten A, Dahlquist G. Diabetologia. 2023;66:346–53.  
7. 
Egro FM. J Mol Endocrinol. 2013;51:R1–13.  
8. 
Krischer JP, et al. Diabetologia. 2015;58:980–7.  
9. 
Magnus MC, et al. Int J Epidemiol. 2018;47(2):417–26.  
10. *Waernbaum I, Dahlquist G, Lind T. Diabetologia. 2019;62:1173–84.  
11. Nekoua MP, Alidjinou EK, Hober D. Nat Rev Endocrinol. 2022;18:503–16.  
12. Lönnrot M et al. Diabetologia. 2017;60:1931–40.  
13. Tapia G, et al. Int J Epidemiol. 2018;47:1538–48.  
14. D’Souza D, et al. JAMA Netw Open. 2023;6:e2321281.  
15. Stahl-Pehe A, et al. Diabetes Metab. 2024;50:101567.  

ClinicalTrial.gov identiﬁer NCT06883318 
 
18 March 2025 
10 
 
16. Noorzae R, Junker TG, Hviid AP, Wohlfahrt J, Olsen SF. Diabetes Care. 2023;46:1261–4.  
17. Gulseth HL, et al. SARS-CoV-2 infection and subsequent risk of type 1 diabetes in 1.2 million children. 
Abstract 58th EASD in Stockholm, Sweden; 2022. Available from: https://www.easd.org. 
18. Saeed M, et al. Ann Epidemiol. 2022;76:181–7.  
19. *Möllsten A, et al. Diabetes. 2010;59:1803–8.  
20. *Toppe C, et al. Diabetes Care. 2019;42:27–31.  
21. Helve J, et al. Diabetes Care. 2018;41:434–9.  
22. Young BA, Maynard C, Boyko EJ. Diabetes Care. 2003;26:2392–9.  
23. Casey C, et al. J Diabetes Investig. 2024;15:541–56.  
24. Fox CS, et al. The Lancet. 2012;380:1662–73.  
25. *Olarte Parra C, Waernbaum I, Schön S, Goetghebeur E. Stat Med. 2022;41:4176–99.  
26. Piani F, et al. J Diabetes Complications. 2021;35:107841.  
27. Taylor R, Davison JM. BMJ. 2007;334(7596):742–5.  
28. Lind M, et al. N Engl J Med. 2014;371:1972–82.  
29. *Toppe C, Möllsten A, Schön S, Dahlquist G. Diabet Med. 2017;34:676–82.  
30. *Berhan YT, Eliasson M, Möllsten A, Waernbaum I, Dahlquist G. Diabetes Care. 2015;38:827–32.  
31. Mönkemöller K, et al. Pediatr Diabetes. 2019;20:637–44.  
32. Lindner LME, Rathmann W, Rosenbauer J. Diabet Med. 2018;35:12–32.  
33. Kimbell B, Lawton J, Boughton C, Hovorka R, Rankin D. BMC Pediatr. 2021;21:160.  
34. Deavin A, Greasley P, Dixon C. Pediatrics. 2018;142:e20174151.  
35. Chan KKL, Shorey S. J Pediatr Nurs. 2022;63:1–8.  
36. *Lovén I. Soc Sci Med. 2017;178:1–10.  
37. *Palmer AJ, et al. Value Health. 2013;16:670–85.  
38. Caro JJ, et al. Value Health. 2012;15:796–803.  
39. Valentine WJ, Pollock RF, Saunders R, Bae J, Norrbacka K, Boye K. Value Health. 2017;20:985–91.  
40. Tran-Duy A, et al. Diabetes Care. 2020;43:1741–9.  
41. The Diabetes Control and Complications Trial Research Group. N Engl J Med. 1993;329:977–86.  
42. Nathan DM, Lachin JM. Diabetes Care. 2024;47:1511–7.  
43. Liu S, et al. Diabetes Care. 2022;45:1987–93.  
44. Lernmark Å, et al. Nat Rev Endocrinol. 2025;21:154–65.  
45. Hernán MA, Sterne JAC, Higgins JPT, Shrier I, Hernández-Díaz S. Epidemiology. 2025;36:107–14.  
46. Hernán MA, Sauer BC, Hernández-Díaz S, Platt R, Shrier I. J Clin Epidemiol. 2016;79:70–5.  
47. *Dahlquist G, et al. Acta Paediatr Scand. 1982;71:7–14.  
48. *Toppe C, Möllsten A, Schön S, Jönsson A, Dahlquist G. J Diabetes Complications. 2014;28:152–5.  
49. *Pazzagli L, Möllsten A, Waernbaum I. Ann Epidemiol. 2017;27:479–84.  
50. *Fredriksson M, Persson E, Möllsten A, Lind T. BMJ Open Diabetes Res Care. 2025;13:e004709.  
51. *Lind T, Waernbaum I, Berhan Y, Dahlquist G. Diabetologia. 2012;55:617–24.  
52. *Persson S, et al. Diabetologia. 2018;61:342–53.  
53. *Toresson Grip E et al. Diabetes Care. 2019;42:545–52.  
54. *Kiadaliri AA, et al. PLOS ONE. 2013;8:e62650.  
55. *Lundqvist A, Carlsson KS, Johansen P, Andersson E, Willis M. PLOS ONE. 2014;9:e110235.  
56. *Zetterström S, Waernbaum I. Epidemiol Methods. 2022;11:220108.  
57. *Persson E, Waernbaum I. Stat Med. 2013;32:2500–12.  
58. *Persson E, Waernbaum I, Lind T. Stat Med. 2017;36:2404–19.  
59. *Waernbaum I, Pazzagli L. Biom J. 2023;65:2100118.  
60. Yang Y, Land KC. Age-Period-Cohort Analysis: New Models, Methods, and Empirical Applications. 1st 
ed. New York: Chapman and Hall/CRC; 2016. 
61. Wennergren M, et al. BMJ Paediatr Open. 2023;7:e001805.  
62. Rashid NU, Li Q, Yeh JJ, Ibrahim JG. J Am Stat Assoc. 2020;115:1125–38.  
63. Heiling HM, Rashid NU, Li Q, Ibrahim JG. R Journal. 2023;15:106–28.  
64. *Persson S, et al. Diabetes Obes Metab. 2020;22:1586–97.