Abstract
Aim
To assess the incidence trends in type 1 diabetes among children and adolescents across Europe during the period from 1994 to 2022 using a systematic methodology.
Materials and Methods
Cross-sectional or follow-up studies reporting population-based incidence rates (IRs) of European children and adolescents diagnosed aged <15 years with type 1 diabetes were included. The Mantel‒Haenszel or DerSimonian and Laird random-effects method was used to compute the pooled IR estimates and their 95% confidence intervals (CIs). Subgroup analyses were conducted by study year, biological sex, age group (0–4, 5−9, and 10–14 years), country, and European regions.
Results
A total of 75 studies (219,331 children and adolescents aged 0–14 years) with data from 32 countries were included. Generally, a high overall rate of increase in type 1 diabetes incidence has been shown in most European countries from 1994 to 2022 in both sexes, with an overall increase from 10.85 (95% CI, 9.62–12.07) per 100,000 person-years from 1994 to 2003 to 20.96 (95% CI, 19.26–22.66) per 100,000 person-years from 2013 to 2022.
Conclusions
There are substantial between-country differences in the current levels and trends of IR in type 1 diabetes in European children and adolescents. Our data suggest a worrying upward trend in most European countries.
1. Introduction
The incidence of newly diagnosed cases of type 1 diabetes worldwide is a concern for health systems, especially in children and adolescents [1]. In 2019, there were approximately 1.5 million individuals younger than 20 years worldwide with type 1 diabetes [1] and 16,300 global deaths due to diabetes (types 1 and 2 combined) occurred in people younger than 25 years and 73.7% (68.3–77.4) were classified as due to type 1 diabetes [2].
Exposure to different risk factors [3, 4, 5] in addition to genetic factors could have different influences depending on the country studied, especially exogenous exposures (e.g., environmental risk factors), and may be responsible for changes in the trend and incidence of type 1 diabetes in children and adolescents around the world [6].
Data from the International Diabetes Federation estimated that around 108,300 children and adolescents under 15 years would be diagnosed with type 1 diabetes in 2021, and this number rises to 149,500 when the age range is extended to under 20 years [7, 8]; besides a recent systematic review and meta-analysis showed that the incidence of type 1 diabetes in Europe from 1980 to 2019 was 15 per 100,000 individuals in the general population [9]. The 2019 report of the Europe and Diabetes Study (EURODIAB), which analyzed the trends of 22 European countries, including data from 1989 to 2013, indicated an overall pooled rate of annual increase of 3.4% (95% CI, 2.8%−3.9%) [10]. Europe is one of the continents where the data seem to be more accurate due to the EURODIAB registry [7, 8].
However, specific data from different studies are not included in the EURODIAB studies, such as incidence studies conducted in other regions [11, 12] or other centers in countries that are included in the EURODIAB Family Study.
Previous studies have explored the incidence of type 1 diabetes among children and adolescents under 15 years of age during different study periods [13, 14] or among individuals below 20 years old in a similar study period [15]. However, to date, no study has examined data on the incidence of type 1 diabetes in children and adolescents in the most European countries and regions with available data during the last three decades. Taking into account during this period, the COVID-19 pandemic phenomenon occurred, with the potential impact on the trend of type 1 diabetes in this population. Monitoring type 1 diabetes incidence trends across most European countries using the latest objectively measured data can also provide a more complete picture of the epidemiological situation in Europe and may help elucidate disparities across the continent. Therefore, the present systematic review and meta-analysis aimed to assess the incidence and trends in type 1 diabetes among European children and adolescents aged 0–14 years in Europe from 1994 to 2022.
2. Materials and Methods
This systematic review and meta-analysis were conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) (Figure 1) [16] and the Cochrane Collaboration Handbook [17]. We have registered and published this review on PROSPERO (registration number: CRD42021239480), and its protocol has been published elsewhere [18].
Figure 1.
PRISMA 2020 flow diagram for new systematic reviews which included searches of databases and registers only.
2.1. Search Strategy
We systematically searched the MEDLINE (via PubMed), Embase (via Scopus), CINAHL, and Web of Science databases for papers published from 1994 to December 2023. The following terms were combined to design the search strategy: (1) population (child, children, childhood, schooler, toddlers, preadolescents, adolescent, infant, pediatr ∗, child ∗, teenag ∗, youth, young, school, school-aged, school-aged); (2) outcome (diabetes mellitus, diabetes mellitus type 1, diabetes mellitus, insulin-resistant, diabetes mellitus, insulin dependent, T1D); (3) study design (incidence, trend, epidemic ∗, observational, cross-sectional, longitudinal, survey); and (4) location (including terms for different European countries). Medical Subject Headings of the National Library of Medicine and free terms were used to perform the MEDLINE search (Table S1).
The literature search, data extraction, and quality assessment were performed independently by two investigators (MG-M and MCR-G), and disagreements were resolved by consensus or involving a third researcher (AD-F).
2.2. Study Selection
The inclusion criteria were as follows: (1) studies reporting the population-based incidence rates (IR) of European children and adolescents aged <15 years diagnosed with type 1 diabetes; (2) observational studies (cross-sectional or follow-up studies (cohort studies)); (3) studies reporting data by year or period; and (4) studies including IR data.
Studies were excluded from the analyses when (1) they were published in languages other than English, Spanish, Italian, or Portuguese; (2) they did not provide details of the sampling method or sample composition; (3) the target population was a specific subgroup; or (4) they were duplicate reports of the same study. When more than one article provided data on the same sample, the article reporting the most detailed results and/or with the largest sample size was retained for data synthesis.
2.3. Search and Data Extraction
The main characteristics of the selected studies are summarized in Table S2 in the supplementary section, including information regarding (1) first author's name and publication year; (2) country; (3) European region; (4) level of representativeness (national/regional data); (5) period of study; (6) study design; (7) characteristics of the included population (age of participants and sample size); and (8) outcomes (mean annual incidence rates of type 1 diabetes by age group and total).
2.4. Quality Assessment
We used the Quality Assessment Tool for Observational Cohorts and Cross-Sectional Studies from the National Heart, Lung, and Blood Institute (NHLBI Cohort and Cross-sectional Studies). This tool evaluates the risk of bias according to 14 criteria: research question, study population, groups recruited from the same population and uniform eligibility criteria, sample size, justification, exposure assessed prior to outcome measurement, sufficient time frame to see an effect, different levels of exposure of interest, exposure measures and assessment, repeated exposure assessment, outcome measures, blinding of outcome assessors, follow-up rate, and statistical analyses. These criteria could be assessed as “yes,” “no,” or “other” (cannot be determined, not applicable, or not reported) [19].
Disagreements in the assessment of the risk of bias were discussed to reach a consensus (AD-F and MCR-G). A third researcher (MG-M) was consulted to come to the final decision when a consensus was not reached.
2.5. Statistical Analysis
The characteristics of the included studies are summarized in an ad hoc table. Data used to estimate the pooled incidence means were obtained from cross-sectional studies as well as from baseline measurements of longitudinal studies. The total incidence was categorized based on age group (0–4, 5−9, and 10–14.9 years old) and sex. Additionally, data were analyzed in different age groups, time periods (1994–2003, 2004−2012, 2013–2022), countries, and regions (Eastern, Northern, Southern, Western). Weighted pooling was used to study the size method in each analysis [20]. The effect size used was the IR per 100,000 person-years; for each country, the combined and stratified results by age group, sex, and period are presented. The Mantel‒Haenszel fixed-effects method [21] was used to compute the pooled incidence estimate and its 95% CI whenever there was no evidence of heterogeneity; otherwise, the DerSimonian and Laird random-effects method [22] was used. The heterogeneity of the results across studies was evaluated with the I2 statistic [23], which was interpreted as not important (0%–40%), moderate heterogeneity (30%–60%), substantial heterogeneity (50%–90%), or large heterogeneity (75%–100%). Considering the overlap between these heterogeneity categories, we decided to use the Mantel‒Haenszel fixed-effects method when I2 was less than 50% and the DerSimonian and Laird random-effects method when I2 was ≥50%.
In the subgroup analyses, we distinguished three time periods: 1994–2003, 2004−2012, and 2013–2022. We also distinguished four European regions: Eastern (Bosnia and Herzegovina, Croatia, Czech Republic, Hungary, Latvia, Lithuania, Montenegro, North Macedonia, Poland, Romania, Serbia, and Slovenia), Northern (Denmark, Estonia, Finland, Norway, and Sweden), Southern (Cyprus, Greece, Italy, Malta, Spain, and Turkey), and Western (Austria, Belgium, France, Germany, Ireland, Luxembourg, Switzerland, The Netherlands, and United Kingdom). Additionally, random-effects meta-regression analyses were used to evaluate whether the incidence estimate differed according to the gross national income per capita based on the purchasing power, parity of each European country, and latitude as an associated geographical factor.
The significance value of the pooled effect size was estimated based on the 95% CI. Two-sided P values of 0.05 or less were considered significant. Statistical analyses were performed using STATA V.15 software.
3. Results
3.1. Study Selection and Characteristics
The PRISMA diagram with the flow of studies through the review is presented in Figure 1. From the 4,187 articles identified, 75 (1.7%) met the inclusion criteria (Table S2) [10, 11, supplementary references (s)1-s73]. Of these, one study displayed data for several European countries (10). Studies were conducted in 32 European countries: Austria (3 reports), Belgium (1), Bosnia and Herzegovina (2), Croatia (3), Cyprus (2), Czech Republic (3), Denmark (1), Estonia (2), Finland (4), France (3), Germany (6), Greece (1), Hungary (2), Ireland (3), Italy (7), Latvia (1), Lithuania (3), Luxembourg (1), Malta (1), Montenegro (3), the Netherlands (2), North Macedonia (1), Norway (3), Poland (4), Romania (3), Serbia (2), Slovenia (2), Spain (16), Sweden (2), Switzerland (1), Turkey (3), and the United Kingdom (9).
A total of 219,331 children and adolescents aged 0–14 years were included in this review, with sample sizes of analyzed studies ranging from 41 to 30,840 participants with type 1 diabetes. In two studies [s17, s29], it was not possible to report the sample size. The reports were published between 1999 and 2023. The designs of the included studies were prospective [11, s1–s3, s6, s7, s9, s10, s12, s14, s16, s21, s27, s28, s31, s34, s36, s40, s49, s50, s52, s55, s57, s58, s60, s62, s63, s66, s70, s73] and prospective based on the capture–recapture method [s8, s19, s22, s23, s25, s29, s33, s43–s45, s48, s51, s56, s59] in 43 studies and retrospective in 15 studies [(s5, s7, s24, s30, s37–s39, s46, s53, s54, s64, s68, s69, s71, s72], and the rest of the studies were based on a mixed prospective–retrospective and capture–recapture method [10, s4, s11, s13, s15, s17, s18, s20, s32, s35, s41, s42, s47, s61, s65, s67]. Furthermore, 35 studies were based on national samples/registries [10, 11, s2, s5–s14, s17, s22, s23, s25–s33, s37, s38, s52, s53, s59, s60, s63, s65, s68, s70], 39 studies were based on regional samples [s1, s3, s4, s15, s16, s18, s19, s21, s24, s34, s35, s36, s39, s40–s51, s54, s58, s61, s62, s64, s66, s67, s69, s71–s73], and only one was based on national and regional samples [s20].
3.2. Study Quality
We evaluated 75 studies (100%) with a low risk of bias by the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies from the NHLBI. None of the included studies showed poor quality according to this scale. Specifically, the items that could not be assessed and were not taken into account for quality assessment using this scale were the following: exposure assessed prior to outcome measurement criteria (Item 6), different levels of exposure of interest criteria (Item 8), exposure measures and assessment criteria (Item 9), repeated exposure assessment criteria (Item 10), blinding of outcome assessors criteria (Item 12), and the potential confounding variables measured and adjusted statistically for their impact on the relationship between exposure(s) and outcome(s) (Item 14) (Table S3).
3.3. Incidence and Trends of Type 1 Diabetes Mellitus
Overall, the IR of type 1 diabetes in European children and adolescents aged 0–14 years increased from 10.85 (95% CI, 9.62–12.07) per 100,000 person-years from 1994 to 2003 to 20.96 (95% CI, 19.26–22.66) per 100,000 person-years from 2013 to 2022.
3.4. Time Trends by Country
Table 1 shows the IR of type 1 diabetes in children and adolescents (aged 0–14 years) by sex in 32 European countries from 1994 to 2022. From 1994 to 2003, the lowest IRs of type 1 diabetes were observed in Bosnia and Herzegovina (4.68; 95% CI, 1.84–7.52) and North Macedonia (4.95; 95% CI, 4.27–5.63), and the highest IRs were in Finland (39.55; 95% CI, 38.44–40.65) and Sweden (31.84; 95% CI, 29.29–34.48). From 2004 to 2012, the lowest IRs of type 1 diabetes were observed in Turkey (7.20; 95% CI, 5.00–9.40) and North Macedonia (7.45; 95% CI, 6.62–8.28), and the highest IRs were in Finland (55.75; 95% CI, 53.20–58.29) and Sweden (41.10; 95% CI, 36.76–45.45). Finally, from 2013 to 2022, data were available from 14 European countries. Thus, the lowest IRs of type 1 diabetes were observed in Romania (10.51; 95% CI, 9.58–11.55) and Turkey (12.77; 95% CI, 5.22–20.33); the highest IRs were in Finland (56.42; 95% CI, 47.80–65.04) and Ireland (32.63; 95% CI, 27.13–38.12). In most analyzed European countries, there is a significant increase in the IR of type 1 diabetes from 1994 to 2022. However, in countries such as the United Kingdom and Spain, there is some stabilization and even a slight decrease in the IR of type 1 diabetes from 2004 to 2022.
Table 1.
Trends in the incidence rates (pool estimate (95% CI) of childhood diabetes type 1 (0–14 years old) by sex for 32 countries of Europe.
| Country | 1994–2003 | 2004–2012 | 2013–2022 | Δ diabetes 1994–2022 |
|---|---|---|---|---|
| Austria | ||||
| Boys | 10.88 (9.28–12.48)# | 17.24 (12.50–22.00)# | — | +6.36a |
| Girls | 10.22 (9.08–11.37)# | 15.81 (12.87–18.75)# | — | +5.59a |
| Overall | 10.53 (9.35–11.71)# | 16.56 (12.74–20.38)# | — | +6.03a |
| Belgium | ||||
| Boys | 16.01 (14.01–18.01) ∗ | 17.75 (15.75–19.75) ∗ | — | +1.75 |
| Girls | 15.16 (13.16–17.16) ∗ | 16.63 (14.63–18.63) ∗ | — | +1.47 |
| Overall | 14.87 (12.84–16.5) ∗ | 17.20 (15.23–19.17) ∗ | — | +2.33 |
| Bosnia and Herzegovina | ||||
| Boys | 3.96 (2.57–5.34)# | 9.50 (7.42–11.58) ∗ | 16.6 (13.9–19.4) ∗ | +12.68a,b,c |
| Girls | 4.30 (1.99–6.62)# | 10.50 (8.29–12.71) ∗ | 14.79 (12.16–17.37) ∗ | +10.49a,b |
| Overall | 4.68 (1.84–7.52)# | 12.10 (10.34–13.86) ∗ | 14.54 (12.83–16.26) ∗ | +9.86a,b |
| Croatia | ||||
| Boys | 11.70 (7.04–16.37)# | 17.20 (16.01–18.40) ∗ | — | +5.50 |
| Girls | 10.05 (7.05–13.04)# | 19.32 (16.06–22.57)# | — | +9.27a |
| Overall | 10.87 (7.06–14.68)# | 17.57 (16.56–18.58) ∗ | — | +6.70a |
| Cyprus | ||||
| Boys | 11.20 (8.20–14.20) ∗ | 13.30 (9.90–16.70) ∗ | — | +2.10 |
| Girls | 9.70 (6.90–12.50) ∗ | 16.50 (12.60–20.40) ∗ | — | +6.80a |
| Overall | 10.48 (8.40–12.56) ∗ | 12.89 (9.20–16.58)# | — | +2.41 |
| Czech Republic | ||||
| Boys | 12.71 (9.19–16.24)# | 20.99 (19.69–22.23)# | — | +8.28a |
| Girls | 13.03 (10.20–15.85)# | 18.99 (17.59–20.40)# | — | +5.96a |
| Overall | 12.83 (9.60–16.06)# | 19.90 (18.58–21.22)# | — | +7.07a |
| Denmark | ||||
| Boys | 18.71 (17.71–19.71) ∗ | 26.38 (25.38–27.38) ∗ | — | +7.67a |
| Girls | 20.13 (19.13–21.13) ∗ | 25.80 (24.80–26.80) ∗ | — | +5.67a |
| Overall | 19.40 (18.42–20.38) ∗ | 26.10 (24.48–27.72) ∗ | — | +6.70a |
| Estonia | ||||
| Boys | 13.71 (10.83–16.59)# | 21.40 (16.35–26.45) ∗ | — | +7.70 |
| Girls | 11.44 (9.96–12.93)# | 20.20 (15.09–25.31) ∗ | — | +8.80a |
| Overall | 14.09 (8.75–19.44)# | — | — | — |
| Finland | ||||
| Boys | 46.91 (35.20–58.62)# | 61.12 (59.06–63.18) ∗ | 63.35 (51.70–75.01)# | +16.44a |
| Girls | 43.76 (34.71–52.80)# | 50.15 (48.27–52.04) ∗ | 48.67 (43.17–54.18)# | +4.91 |
| Overall | 39.55 (38.44–40.65) ∗ | 55.75 (53.20–58.29)# | 56.42 (47.80–65.04)# | +16.87a,b |
| France | ||||
| Boys | 7.06 (6.00–8.26) ∗ | — | 19.60 (18.50–20.70) ∗ | +12.54b |
| Girls | 7.13 (6.03–8.36) ∗ | — | 18.70 (17.5–19.8) ∗ | +12.00b |
| Overall | 10.78 (9.23–12.34)# | 15.90 (14.92–16.88)# | 17.56 (16.48–18.64)# | +6.78a,b |
| Germany | ||||
| Boys | 17.42 (13.48–21.37)# | 23.30 (22.72–23.87) ∗ | 28.37 (25.63–31.11)# | +10.95a,b,c |
| Girls | 16.35 (12.94–19.77)# | 21.63 (20.23–23.04)# | 23.30 (22.38–24.22) ∗ | +6.95a,b |
| Overall | 16.22 (13.86–18.57)# | 21.58 (19.80–23.36)# | 25.30 (22.36–28.20)# | +9.08a,b |
| Greece | ||||
| Boys | 8.89 (6.61–11.17) ∗ | 10.81 (8.52–13.10) ∗ | 16.50 (12.00–22.10) ∗ | +7.61b |
| Girls | 6.50 (4.56–8.44) ∗ | 10.05 (7.65–12.45) ∗ | 15.10 (10.90–20.50) ∗ | +8.60b |
| Overall | 7.56 (5.84–9.28) ∗ | 10.35 (8.52–12.19) ∗ | 15.80 (7.70–18.90) ∗ | +8.24 |
| Hungary | ||||
| Boys | 11.40 (10.80–12.00) ∗ | 20.15 (19.55–20.75) ∗ | — | +8.75a |
| Girls | 11.66 (11.06–12.26) ∗ | 18.18 (17.58–18.78) ∗ | — | +6.52a |
| Overall | 11.50 (10.55–12.45)# | 18.81 (18.18–19.44) ∗ | — | +7.30a |
| Ireland | ||||
| Boys | 16.80 (13.20–21.10) ∗ | 28.75 (27.31–30.19) ∗ | 33.52 (27.77–39.27)# | +16.72a,b |
| Girls | 16.30 (12.60–20.70) ∗ | 26.36 (24.93–27.79) ∗ | 31.90 (27.02–36.78)# | +15.60a,b |
| Overall | 16.60 (13.90–19.50)# | 26.72 (24.45–28.99) ∗ | 32.63 (27.13–38.12)# | +16.03a,b |
| Italy | ||||
| Boys | 13.08 (12.63–13.54) ∗ | 18.39 (4.32–32.45) ∗ | 21.39 (15.83–26.95)# | +8.31b |
| Girls | 11.39 (10.96–11.83) ∗ | 18.60 (7.37–29.57) ∗ | 23.03 (20.26–25.78) ∗ | +11.64b |
| Overall | 12.26 (11.94–12.58) ∗ | 19.04 (17.85–20.23) ∗ | 21.50 (10.19–24.80)# | +9.24a |
| Latvia | ||||
| Boys | 6.70 (5.70–7.70) ∗ | — | — | — |
| Girls | 7.20 (6.10–8.30) ∗ | — | — | — |
| Overall | 6.90 (6.13–7.67) ∗ | — | — | — |
| Lithuania | ||||
| Boys | 7.80 (6.61–8.99)# | 16.51 (15.61–17.41) ∗ | — | +8.70a |
| Girls | 8.91 (7.53–10.28)# | 17.61 (16.71–18.51) ∗ | — | +9.00a |
| Overall | 8.33 (7.11–9.55)# | 17.05 (15.91–18.19) ∗ | — | +8.72a |
| Luxembourg | ||||
| Boys | 13.26 (10.46–16.06) ∗ | 19.51 (16.71–22.31) ∗ | — | +6.24a |
| Girls | 14.80 (12.00–17.60) ∗ | 17.48 (14.68–20.28) ∗ | — | +2.68 |
| Overall | 14.00 (11.43–16.57) ∗ | 18.50 (15.67–21.33) ∗ | — | +4.50 |
| Malta | ||||
| Boys | — | — | — | — |
| Girls | — | — | — | — |
| Overall | — | 24.68 (21.94–27.43) ∗ | — | — |
| Montenegro | ||||
| Boys | 13.46 (12.57–14.34) ∗ | 18.91 (16.90–20.93) ∗ | — | +5.44a |
| Girls | 12.30 (10.39–14.20) ∗ | 16.81 (14.91–18.71) ∗ | — | +4.50a |
| Overall | 11.94 (10.21–13.66) ∗ | 17.55 (15.60–19.49) ∗ | 20.55 (14.62–26.49) ∗ | +8.61a,b |
| North Macedonia | ||||
| Boys | 5.01 (4.21–5.81) ∗ | 7.21 (6.41–8.01) ∗ | — | +2.20a |
| Girls | 4.88 (4.08–5.68) ∗ | 7.13 (6.33–7.93) ∗ | — | +2.25a |
| Overall | 4.95 (4.27–5.63) ∗ | 7.45 (6.62–8.28) ∗ | — | +2.50a |
| Norway | ||||
| Boys | 28.46 (27.46–29.46) ∗ | 34.40 (33.54–35.27) ∗ | — | +5.95a |
| Girls | 26.61 (25.61–27.61) ∗ | 27.54 (20.05–35.04)# | — | +0.93 |
| Overall | 25.54 (22.09–28.99)# | 32.70 (31.50–34.00) ∗ | — | +7.16a |
| Poland | ||||
| Boys | 10.43 (9.54–11.32) ∗ | 17.14 (14.77–19.52)# | 23.15 (21.10–25.20) ∗ | +12.73a,b,c |
| Girls | 10.53 (10.43–10.63) ∗ | 15.01 (12.56–17.46)# | 17.59 (14.85–20.34)# | +7.06a,b |
| Overall | 10.42 (9.77–11.08) ∗ | 17.63 (15.72–19.53)# | 21.49 (20.07–22.91) ∗ | +11.07a,b,c |
| Romania | ||||
| Boys | 5.54 (4.58–6.49)# | 8.68 (7.53–9.82)# | 10.85 (9.98–11.71) ∗ | +5.30a,b,c |
| Girls | 5.65 (5.04–6.27)# | 8.04 (7.17–8.92)# | 10.15 (9.30–10.99)# | +4.50a,b,c |
| Overall | 5.60 (4.93–6.27)# | 8.62 (7.66–9.57)# | 10.51 (9.58–11.55)# | +4.90a,b,c |
| Serbia | ||||
| Boys | 10.60 (9.50–11.80) ∗ | — | — | — |
| Girls | 16.40 (14.00–19.20) ∗ | — | — | — |
| Overall | 10.60 (9.80–11.40) ∗ | — | 13.68 (6.86–20.51)# | +3.08 |
| Slovenia | ||||
| Boys | 9.73 (8.63–10.83) ∗ | 13.98 (12.88–15.08) ∗ | — | +4.25a |
| Girls | 10.48 (9.38–11.58) ∗ | 16.11 (15.01–17.21) ∗ | — | +5.63a |
| Overall | 10.10 (9.00–11.20) ∗ | 15.60 (14.15–17.05) ∗ | — | +5.50a |
| Spain | ||||
| Boys | 12.74 (9.85–15.64)# | 14.39 (12.47–16.31)# | 13.09 (7.80–18.38)# | +0.35 |
| Girls | 11.13 (9.23–13.02)# | 17.08 (13.42–20.74)# | 11.36 (5.98–16.68)# | +0.23a |
| Overall | 15.99 (14.43–17.54)# | 20.83 (16.75–24.91)# | 17.47 (10.35–24.52) ∗ | +1.48 |
| Sweden | ||||
| Boys | 32.80 (30.03–35.57)# | 42.41 (37.92–46.91)# | — | +9.60a |
| Girls | 30.94 (28.15–33.74)# | 39.25 (34.57–43.94)# | — | +8.31a |
| Overall | 31.84 (29.29–34.38)# | 41.10 (36.76–45.45)# | — | +9.26a |
| Switzerland | ||||
| Boys | 10.23 (9.50–10.96) ∗ | 13.61 (12.90–14.32) ∗ | — | +3.39a |
| Girls | 9.00 (8.30–9.70) ∗ | 23.30 (22.66–23.90) ∗ | — | +14.30a |
| Overall | 9.65 (9.08–10.22) ∗ | 13.25 (12.58–13.92) ∗ | — | +3.60a |
| The Netherlands | ||||
| Boys | — | 20.80 (19.22–22.5) ∗ | — | — |
| Girls | — | 22.00 (20.40–23.80) ∗ | — | — |
| Overall | 18.10 (16.60–19.60) ∗ | 23.08 (19.66–26.51)# | — | +5.00a |
| Turkey | ||||
| Boys | — | 5.70 (2.99–8.41) ∗ | 12.76 (5.10–20.41)# | +7.06 |
| Girls | — | 8.70 (5.22–12.18) ∗ | 12.61 (5.28–19.95)# | +3.90 |
| Overall | — | 7.20 (5.00–9.40) ∗ | 12.77 (5.22–20.33)# | +5.57 |
| United Kingdom | ||||
| Boys | 23.99 (21.35–26.62)# | 29.47 (23.81–35.13)# | 22.20 (20.30–24.10) ∗ | −1.7 |
| Girls | 23.46 (20.95–25.96)# | 28.21 (22.02–34.41)# | 20.10 (18.30–21.90) ∗ | −3.36c |
| Overall | 23.18 (20.73–25.64)# | 27.63 (23.64–31.61)# | 21.30 (19.9–22.70) ∗ | −1.88c |
aStatistical significance between 1994 and 2012; bstatistical significance between 1994 and 2022; cstatistical significance between 2004 and 2022; ∗Mantel‒Haenszel fixed-effects method; #DerSimonian and Laird random-effects method.
The analyzed spatial distribution of IR changes in type 1 diabetes in children and adolescents in different European countries is shown in Figure 2.
Figure 2.
Spatial distribution of incidence rate changes in type 1 diabetes in children and adolescents from 1994 to 2022.
Table S4 provides sex-specific IR trends in each group (aged 0–4, 5−9, and 10–14 years) and country. In most European countries, boys presented a slightly higher IR of type 1 diabetes than girls, although these differences were not significant. However, significant differences were observed between the youngest group studied (0–4 years) and the oldest group studied (10–14 years) for both sexes.
3.5. Time Trends by Region
Figure 3 displays trends in the pooled IR estimates of type 1 diabetes in children (aged 0–14 years) from four regions of Europe stratified into three time periods (1994–2003, 2004–2012, and 2013–2022). Overall, from 1994 to 2022, the IR of type 1 diabetes increased in the Northern region from 26.18 (95% CI, 15.35–37.01) to 56.42 (95% CI 47.80–65.04) and also showed a significant increase in Eastern, Southern and Western Europe, where the type 1 diabetes incidence increased from 8.97 (7.38–10.57) to 15.34 (12.49–18.18), from 11.62 (8.89–14.34) to 18.08 (14.32–21.84), and from 14.79 (12.00–17.58) to 23.95 (21.28–26.61), respectively. The results for boys and girls are also shown in Figure 3. A significant increase in the trend is observed in several countries in the Northern European region (Finland, Sweden, or Norway), and for other regions, an increasing but not significant trend is observed. The country-specific increase in the IR of type 1 diabetes by sex for children and adolescents aged 0–14 years is shown in Table S4.
Figure 3.
Pooled estimated (95% CI) and trends in the incidence rates of childhood type 1 diabetes (0–14 years) by sex across European regions. (a) Boys (aged 0–14 years), (b) girls (aged 0–14 years), and (c) overall (aged 0–14 years).
3.6. Meta-Regression
Random-effects meta-regression models showed a positively significant association between the IR of type 1 diabetes and gross national income per capita in European countries (ß = 0.19, 95% CI, 0.18–0.20) (p < 0.001) and latitude as a geographical factor (ß = 0.64, (95% CI, 0.60–0.67) (p < 0.001)) (Figure 4).
Figure 4.
Meta-regression models. GNI PPP: gross national income per capita. (a) Incidence of type 1 diabetes and gross national income per capita in European countries and (b) Incidence of type 1 diabetes and geographical latitude in European countries.
4. Discussion
This systematic review and meta-analysis provide a comprehensive picture of the trends from 1994 to 2022 in the IR of type 1 diabetes in children and adolescents aged 0–14 years in 32 European countries. Overall, our results indicate a significant increase in the IR of type 1 diabetes in Europe from 10.85 per 100,000 person-years from 1994 to 2003 to 20.96 per 100,000 person-years during the period 2013–2022. Substantial differences between countries in both current levels and trends were found. In particular, some Northern countries (Finland, Sweden, or Norway) showed the highest incidence estimates, and Southern and Eastern countries had the lowest estimates. Finally, our data reveal an upward trend that has continued over the last years.
Different reasons could help to explain these changes in trends over the last two decades. First, the lower incidence rates reported during the first period could be related to an underreporting of type 1 diabetes cases. In addition, the increase in type 1 diabetes incidence during the last period may be attributed to improvements in the screening, diagnosis, and notification of true type 1 diabetes cases due to prevention or national programs [24]. Nevertheless, as incidence rates continue to increase in some European countries, especially in countries belonging to the Northern European region, not only genetic susceptibility [6] but also other socioeconomic, environmental, geographic, or lifestyle risk factors may be behind the marked differences in type 1 diabetes incidence [3, 25, 26].
In this regard, it has already been shown that a high body mass index (BMI) can exacerbate islet autoimmunity before clinical type 1 diabetes, particularly in children at lower risk based on age and human leukocyte antigen (HLA) levels [27]. Moreover, it has been observed that the increase in BMI might explain, at least in part, why minority ethnic groups present type 1 diabetes with a worse prognosis [28]. Thus, the increase in overweight and obesity in children could be a determining factor in the recent increase in the incidence of type 1 diabetes.
Socioeconomic status (SES) could be associated with type 1 diabetes. In countries/continents with high gross domestic product (GDP), type 1 diabetes incidence is higher than in countries/continents with low GDP [29]. This could be explained by an improvement in the diagnosis procedures in high GDP (Europe and North America); a decrease in type 1 diabetes incidence was observed in continents with low GDP and the largest child populations (West Indies), where the mortality in children is high before 5 years old due to infectious diseases [30].
Our study showed a significant positive association between the incidence of type 1 diabetes and gross national income per capita in European countries, which is in line with the findings of previous studies [31]. Similarly, another article that examined neighborhood-level risk factors for type 1 diabetes in the United States showed that higher levels of SES were associated with a higher risk of type 1 diabetes, and the proportion of crowded households or poverty was associated with a low risk of type 1 diabetes [31]. A phenomenon to highlight is the significant increase in autoimmune diseases taking place while the incidence of infectious diseases decreased due to vaccination programs, better hygiene conditions, or the use of antibiotics [4].
Geographical and environmental risk factors could also be associated with type 1 diabetes. Some studies have already evaluated the hypothesis of possible migratory phenomena and have shown that the offspring of the transmigratory population had a rising incidence of childhood diabetes, which was approaching that of the indigenous population [32]. Although, in Sweden, where the increase in the incidence of type 1 diabetes in the late twentieth century has been approaching a more stable, albeit high level over the last two decades, the increased immigration from countries with lower incidences of type 1 diabetes did not provide a complete explanation for an observed leveling off [33]. Another geographical risk factor may be latitude because some studies have suggested a positive association between the latitude north of the equator and the risk of type 1 diabetes. This risk increased on average from 3.3% to 3.5% for each degree of latitude away from the equator [33]. These results could be in line with our findings in meta-regression; we have also shown that in some European countries, such as Norway, Sweden, or Finland, the incidence of type 1 diabetes is higher than that in other countries further south. This hypothesis could be explained by the fact that the exposure to ultraviolet radiation (UVR) reaching the Earth's surface varies inversely with latitude and is a prominent latitude-related environmental factor.
Some studies have shown that UVR downregulates cellular immunity, attenuating T-helper (Th)1 T-cell–mediated immune responses [34]. These responses are considered involved in some autoimmune disorders, such as type 1 diabetes. One study found an inverse association between UVR levels in Australia and type 1 diabetes prevalence and incidence [35]. It may be by the fact that UVR exposure could be protective against Th1-mediated disorders such as type 1 diabetes by downregulating Th1 autoimmune responses by several different immunoregulatory mechanisms. One of these mechanisms involves UVR-induced vitamin D.
The protective role of UVR in type 1 diabetes may act through its important role in vitamin D synthesis in the skin. Similarly, a decreased incidence of type 1 diabetes was shown in children supplemented with vitamin D in infancy [36]. One study showed that maternal intake of vitamin D during pregnancy and high doses of vitamin D early in life were protective factors against autoimmunity and type 1 diabetes [36].
Moreover, vitamin D receptor (VDR) polymorphisms have been investigated in type 1 diabetes, and some studies have reported that individual VDR polymorphisms seem not to be associated with type 1 diabetes risk [5]. However, haplotypes could contribute significantly to disease susceptibility, and it is suggested that in type 1 diabetes pathogenesis, VDR polymorphisms interact with each other and with environmental factors, such as the previously mentioned latitude and microbial environment [5].
However, the north–south gradient in Europe, which has been evidenced in some studies [37], is not observed in the case of countries such as Spain, which is at a lower latitude but where the incidence is high. In this case, it could be environmental factors that are intense enough to trigger autoimmune processes and give rise to type 1 diabetes in childhood, including perinatal infections and the type of feeding, including breastfeeding, rapid growth, or weight evolution, among others [6].
Additionally, the role of the gut microbiome is a recent risk factor that could be related to type 1 diabetes [6]. On the other hand, the role of breastfeeding in the risk of T1D remains unclear, and the consequences of initiating or prolonging breastfeeding are still controversial [38]. The Mediterranean diet and the consumption of dietary fiber and/or probiotics are being studied as possible modifiers of the onset and progression of this disease by their role modulators of the intestinal microflora and the reduction of the associated proinflammatory profile relationship to type 1 diabetes [39].
Other prominent environmental factors related to type 1 diabetes incidence in Europe could be the increasing exposure of the population to various food chemicals, as well as to viral infections because of increased mobility. Seasonality is another risk factor studied [6], with a peak of the incidence of type 1 diabetes registered in the cold season that could be explained by the involvement of seasonal viral infections [40]. It is known that viruses are a common environmental risk factor and that early exposure to microbes and other pathogens early in life promotes immune responses and protects against the risk of autoimmunity in diseases such as type 1 diabetes [41]. In this sense, exposure to a persistent cold climate over time could explain why the incidence of type 1 diabetes is higher in countries belonging to the Northern European region.
On the other hand, it is possible to observe the presence of the phenomenon of the COVID-19 pandemic and its effect on chronic pathologies during the last studied period (2013–2022). Our findings are similar to those of another recent meta-analysis that showed an increased incidence rates of type 1 diabetes in children and adolescents during vs. before the COVID-19 pandemic around the world. This annual increase in the incidence was greater than the 3%–4% expected, based on prepandemic temporal trends in Europe [42].
Exploration of potential mechanisms linking SARS-CoV-2 infection to new-onset diabetes includes immune-mediated responses, dysregulated glucose metabolism, and direct viral damage to β cells, thereby hindering compensatory mechanisms and leading to β-cell exhaustion. Additionally, lifestyle changes, shifts in pediatric non-COVID-19 infection patterns, increased stress, and social isolation are considered potential contributors. However, all of these mechanisms are unclear [42]. Furthermore, Rahmati et al. [43] reported a noteworthy increase in childhood new-onset type 1 diabetes incidence during the first year of the COVID-19 pandemic (32.39 per 100,000 children) compared to the pre-COVID-19 period (19.73 per 100,000 children) worldwide, as revealed by a systematic review and meta-analysis.
Finally, in both children and adolescents, our study showed a higher incidence in boys than girls in most European countries, with no statistically significant differences, especially in high-incidence countries. One study showed that the annual rate of type 1 diabetes increased more in boys than in girls in the 10- to 14-year-old age group (3.3% and 2.6% per annum, respectively) in 26 European centers from 1989 to 2013 [8, 10]. This phenomenon has been observed in worldwide studies and in several countries [6]. Possible explanations for sex differences could be that the different effects of environmental risk factors and lifestyle on type 1 diabetes incidence in females and males during childhood onset may have different manifestations in the sexes [44]. Additionally, according to our results, the incidence of type 1 diabetes increases with age up to a peak at approximately 10–14 years, consistent with the findings of worldwide studies, even in low-incidence countries such as China [45].
5. Conclusions
In conclusion, this systematic review and meta-analysis shows that in most countries for which data are available, the IR of type 1 diabetes in children and adolescents has increased in recent decades. Our data suggest an upward trend in some European countries, such as those belonging to the northern region, being a worrisome phenomenon. However, due to the more limited number of studies available, especially in the last period where the COVID-19 pandemic phenomenon was present, our results should be interpreted with caution. Additionally, an interaction between genetic, behavioral, and environmental risk factors and type 1 diabetes has been suggested in this population. Further studies are needed to clarify this interaction. In the meantime, policies to promote healthy behaviors and to control environmental factors that could influence the incidence of some health disorders related to immunity, such as type 1 diabetes, could be of great interest.
Acknowledgments
Arthur E. Mesas is financially supported by a Beatriz Galindo contract (BEAGAL18/00093) from the Spanish Ministry of Education, Culture and Sport.
Data Availability
All data are available upon request to the corresponding author.
Additional Points
Highlights. The incidence rate of type 1 diabetes in European children and adolescents aged 0–14 years increased from 10.85 per 100,000 person-years from 1994 to 2003 to 20.96 per 100,000 person-years from 2013 to 2022. Trends are increasing in most European countries, especially those belonging to the Northern region (such as Finland, Sweden, or Norway). However, in countries such as the United Kingdom and Spain, there is some stabilization and even a slight decrease during last years. Genetic, behavioral, and environmental risk factors related with type 1 diabetes need further research. Limitations. There were some limitations of this meta-analysis that should be acknowledged. Some of these are common to meta-analyses (e.g., selection bias and limited availability of complete information from study reports), but others are particularly important in our study. First, not all studies ensured representative samples of the population, which posed a threat to the validity of their estimates. Second, only studies that reported data by year or periods of time and included incidence rates were included, which limited the comparability with other relevant studies. Third, there were limited data during the 2013–2022 period, due to the low number of studies available in this period; thus, our findings for this period should be taken with caution. Fourth, for our analyses, because of the scarcity of follow-up studies involving the same people over time, the analysis of time series was discarded. Fifth, differences in sample characteristics, number of studies by country, limited number of national-based samples, geographic location, and the quality of the included data may have increased heterogeneity between studies, which may have reduced the quality of evidence on incidence and type 1 diabetes trends.
Disclosure
The corresponding authors will have full access to the data in the study and has final responsibility for the decision to submit for publication.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Authors' Contributions
Marta Carolina Ruiz-Grao designed the study, was involved in the analysis of the data, cowrote the first draft of the manuscript, and was the coprincipal investigator of the study. Miriam Garrido-Miguel and Ana Díez-Fernández compiled and analyzed the data. Vicente Martínez-Vizcaíno, Miriam Garrido-Miguel, and Ana Díez-Fernández contributed to analysis and interpretation, critical review, and editing of the manuscript. Irene Sequí-Domínguez, Fernando Sebastián-Valles, and Arthur E. Mesas contributed to the critical review and editing of the manuscript. All authors contributed to revision of the report and agreed to its publication.
Supplementary Materials
Table S1: search strategy for the MEDLINE database. Table S2: characteristics of studies included in the systematic review and or meta-analysis. Table S3: quality assessment (NHLBI cohort and cross-sectional studies). Table S4: trends in the incidence rates (pool estimate (95% confidence interval) of childhood diabetes type 1 by sex and age group for 32 countries of Europe). References of included studies and PRISMA checklist.
References
- 1.Gregory G. A., Robinson T. I. G., Linklater S. E., et al. Global incidence, prevalence, and mortality of type 1 diabetes in 2021 with projection to 2040: a modelling study. The Lancet Diabetes & Endocrinology . 2022;10(10):741–760. doi: 10.1016/S2213-8587(22)00218-2. [DOI] [PubMed] [Google Scholar]
- 2.Cousin E., Duncan B. B., Stein C., et al. Diabetes mortality and trends before 25 years of age: an analysis of the global burden of disease study 2019. The Lancet Diabetes & Endocrinology . 2022;10(3):177–192. doi: 10.1016/S2213-8587(21)00349-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Knip M., Siljander H. The role of the intestinal microbiota in type 1 diabetes mellitus. Nature Reviews Endocrinology . 2016;12(3):154–167. doi: 10.1038/nrendo.2015.218. [DOI] [PubMed] [Google Scholar]
- 4.Bach J. F. The effect of infections on susceptibility to autoimmune and allergic diseases. New England Journal of Medicine . 2002;347(12):911–920. doi: 10.1056/NEJMra020100. [DOI] [PubMed] [Google Scholar]
- 5.Tizaoui K., Kaabachi W., Hamzaoui A., Hamzaoui K. Contribution of VDR polymorphisms to type 1 diabetes susceptibility: systematic review of case–control studies and meta-analysis. The Journal of Steroid Biochemistry and Molecular Biology . 2014;143:240–249. doi: 10.1016/j.jsbmb.2014.03.011. [DOI] [PubMed] [Google Scholar]
- 6.Norris J. M., Johnson R. K., Stene L. C. Type 1 diabetes—early life origins and changing epidemiology. The Lancet Diabetes & Endocrinology . 2020;8(3):226–238. doi: 10.1016/S2213-8587(19)30412-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.International Diabetes Federation. IDF Diabetes Atlas . 9th. Brussels, Belgium: IDF; 2019. [PubMed] [Google Scholar]
- 8.Patterson C. C., Karuranga S., Salpea P., et al. Worldwide estimates of incidence, prevalence and mortality of type 1 diabetes in children and adolescents: results from the international diabetes federation diabetes atlas, 9th edition. Diabetes Research and Clinical Practice . 2019;157 doi: 10.1016/j.diabres.2019.107842.107842 [DOI] [PubMed] [Google Scholar]
- 9.Mobasseri M., Shirmohammadi M., Amiri T., Vahed N., Hosseini Fard H., Ghojazadeh M. Prevalence and incidence of type 1 diabetes in the world: a systematic review and meta-analysis. Health Promotion Perspectives . 2020;10(2):98–115. doi: 10.34172/hpp.2020.18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Patterson C. C., Harjutsalo V., Rosenbauer J., et al. Trends and cyclical variation in the incidence of childhood type 1 diabetes in 26 European centres in the 25 year period 1989–2013: a multicentre prospective registration study. Diabetologia . 2019;62(3):408–417. doi: 10.1007/s00125-018-4763-3. [DOI] [PubMed] [Google Scholar]
- 11.Piffaretti C., Mandereau-Bruno L., Guilmin-Crepon S., Choleau C., Coutant R., Fosse-Edorh S. Trends in childhood type 1 diabetes incidence in France, 2010–2015. Diabetes Research and Clinical Practice . 2019;149:200–207. doi: 10.1016/j.diabres.2018.11.005. [DOI] [PubMed] [Google Scholar]
- 12.Cardwell C. R., Carson D. J., Patterson C. C. Secular trends, disease maps and ecological analyses of the incidence of childhood onset type 1 diabetes in Northern Ireland, 1989–2003. Diabetic Medicine . 2007;24(3):289–295. doi: 10.1111/j.1464-5491.2007.02080.x. [DOI] [PubMed] [Google Scholar]
- 13.Gomez-Lopera N., Pineda-Trujillo N., Diaz-Valencia P. A. Correlating the global increase in type 1 diabetes incidence across age groups with national economic prosperity: a systematic review. World Journal of Diabetes . 2019;10(12):560–580. doi: 10.4239/wjd.v10.i12.560. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Xia Y., Xie Z., Huang G., Zhou Z. Incidence and trend of type 1 diabetes and the underlying environmental determinants. Diabetes/Metabolism Research and Reviews . 2019;35(1) doi: 10.1002/dmrr.3075.e3075 [DOI] [PubMed] [Google Scholar]
- 15.Hormazábal-Aguayo I., Ezzatvar Y., Huerta-Uribe N., Ramírez-Vélez R., Izquierdo M., García-Hermoso A. Incidence of type 1 diabetes mellitus in children and adolescents under 20 years of age across 55 countries from 2000 to 2022: a systematic review with meta-analysis. Diabetes/Metabolism Research and Reviews . 2024;40(3) doi: 10.1002/dmrr.3749.e3749 [DOI] [PubMed] [Google Scholar]
- 16.Page M. J., McKenzie J. E., Bossuyt P. M., et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ . 2021;372 doi: 10.1136/bmj.n71.n71 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Higgins J. P. T., Thomas J., Chandler J., et al. Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023) Cochrane: Cochrane Training; 2023. [Google Scholar]
- 18.Díez-Fernández A., Ruiz-Grao M. C., Mesas A. E., Martinez-Vizcaino V., Garrido-Miguel M. Type 1 diabetes incidence trends in children and adolescents aged 0–14 years in Europe: a systematic review and meta-analysis protocol. BMJ Open . 2021;11(10) doi: 10.1136/bmjopen-2021-054962.e054962 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.NHLBI. Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies . Bethesda, MD: National Institutes of Health Department of Health and Human Services; 2014. pp. 1–4. [Google Scholar]
- 20.Barendregt J. J., Doi S. A., Lee Y. Y., Norman R. E., Vos T. Meta-analysis of prevalence. Journal of Epidemiology & Community Health . 2013;67(11):974–978. doi: 10.1136/jech-2013-203104. [DOI] [PubMed] [Google Scholar]
- 21.Leonard T., Duffy J. C. A Bayesian fixed effects analysis of the Mantel–Haenszel model applied to meta-analysis. Statistics in Medicine . 2002;21(16):2295–2312. doi: 10.1002/sim.1048. [DOI] [PubMed] [Google Scholar]
- 22.DerSimonian R., Kacker R. Random-effects model for meta-analysis of clinical trials: an update. Contemporary Clinical Trials . 2007;28(2):105–114. doi: 10.1016/j.cct.2006.04.004. [DOI] [PubMed] [Google Scholar]
- 23.Higgins J. P. T., Thompson S. G. Quantifying heterogeneity in a meta-analysis. Statistics in Medicine . 2002;21(11):1539–1558. doi: 10.1002/sim.1186. [DOI] [PubMed] [Google Scholar]
- 24.Uçar A., Saka N., Baş F., et al. Frequency and severity of ketoacidosis at onset of autoimmune type 1 diabetes over the past decade in children referred to a tertiary paediatric care centre: potential impact of a national programme highlighted. Journal of Pediatric Endocrinology and Metabolism . 2013;26(11-12):1059–1065. doi: 10.1515/jpem-2013-0060. [DOI] [PubMed] [Google Scholar]
- 25.Rasoul M. A., Al-Mahdi M., Al-Kandari H., Dhaunsi G. S., Haider M. Z. Low serum vitamin-D status is associated with high prevalence and early onset of type-1 diabetes mellitus in Kuwaiti children. BMC Pediatrics . 2016;16(1) doi: 10.1186/s12887-016-0629-3.95 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Howard S. G. Developmental exposure to endocrine disrupting chemicals and type 1 diabetes mellitus. Frontiers in Endocrinology . 2018;9 doi: 10.3389/fendo.2018.00513.513 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Ferrara-Cook C., Geyer S. M., Evans-Molina C., et al. Excess BMI accelerates islet autoimmunity in older children and adolescents. Diabetes Care . 2020;43(3):580–587. doi: 10.2337/dc19-1167. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Redondo M. J., Libman I., Cheng P., et al. Racial/ethnic minority youth with recent-onset type 1 diabetes have poor prognostic factors. Diabetes Care . 2018;41(5):1017–1024. doi: 10.2337/dc17-2335. [DOI] [PubMed] [Google Scholar]
- 29.Torres-Avilés F., Carrasco E., Icaza G., Pérez-Bravo F. Clustering of cases of type 1 diabetes in high socioeconomic communes in Santiago de Chile: spatio-temporal and geographical analysis. Acta Diabetologica . 2010;47(3):251–257. doi: 10.1007/s00592-010-0189-1. [DOI] [PubMed] [Google Scholar]
- 30.The DIAMOND Project Group. Incidence and trends of childhood type 1 diabetes worldwide 1990–1999. Diabetic Medicine . 2006;23(8):857–866. doi: 10.1111/j.1464-5491.2006.01925.x. [DOI] [PubMed] [Google Scholar]
- 31.Liese A. D., Puett R. C., Lamichhane A. P., et al. Neighborhood level risk factors for type 1 diabetes in youth: the SEARCH case-control study. International Journal of Health Geographics . 2012;11(1) doi: 10.1186/1476-072X-11-1.1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Bodansky H. J., Staines A., Stephenson C., Haigh D., Cartwright R. Evidence for an environmental effect in the aetiology of insulin dependent diabetes in a transmigratory population. BMJ . 1992;304(6833):1020–1022. doi: 10.1136/bmj.304.6833.1020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Ball S. J., Haynes A., Jacoby P., et al. Spatial and temporal variation in type 1 diabetes incidence in Western Australia from 1991 to 2010: increased risk at higher latitudes and over time. Health & Place . 2014;28:194–204. doi: 10.1016/j.healthplace.2014.05.004. [DOI] [PubMed] [Google Scholar]
- 34.McCullough M. L., Wan N., Pezzolesi M. G., et al. Type 1 diabetes incidence among youth in Utah: a geographical analysis. Social Science & Medicine . 2021;278 doi: 10.1016/j.socscimed.2021.113952.113952 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Staples J. A., Ponsonby A.-L., Lim L. L.-Y., McMichael A. J. Ecologic analysis of some immune-related disorders, including type 1 diabetes, in Australia: latitude, regional ultraviolet radiation, and disease prevalence. Environmental Health Perspectives . 2003;111(4):518–523. doi: 10.1289/ehp.5941. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Hyppönen E., Läärä E., Reunanen A., Järvelin M.-R., Virtanen S. M. Intake of vitamin D and risk of type 1 diabetes: a birth-cohort study. The Lancet . 2001;358(9292):1500–1503. doi: 10.1016/S0140-6736(01)06580-1. [DOI] [PubMed] [Google Scholar]
- 37.Forga L., Tamayo I., Berrade S., Ibañez B., Rodríguez Erdozain R. M., Goñi M. J. Incidence of type 1 diabetes in Navarre (2009–2016): higher in the southern regions of the autonomous community. Anales del Sistema Sanitario de Navarra . 2018;41(1):69–74. doi: 10.23938/ASSN.0124. [DOI] [PubMed] [Google Scholar]
- 38.Houeiss P., Luce S., Boitard C. Environmental triggering of type 1 diabetes autoimmunity. Frontiers in Endocrinology . 2022;13 doi: 10.3389/fendo.2022.933965.933965 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Calabrese C. M., Valentini A., Calabrese G. Gut microbiota and type 1 diabetes mellitus: the effect of mediterranean diet. Frontiers in Nutrition . 2021;7 doi: 10.3389/fnut.2020.612773.612773 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Patterson C. C., Dahlquist G. G., Gyürüs E., Green A., Soltész G. Incidence trends for childhood type 1 diabetes in Europe during 1989–2003 and predicted new cases 2005–20: a multicentre prospective registration study. The Lancet . 2009;373(9680):2027–2033. doi: 10.1016/S0140-6736(09)60568-7. [DOI] [PubMed] [Google Scholar]
- 41.Hyöty H., Taylor K. W. The role of viruses in human diabetes. Diabetologia . 2002;45(10):1353–1361. doi: 10.1007/s00125-002-0852-3. [DOI] [PubMed] [Google Scholar]
- 42.D’Souza D., Empringham J., Pechlivanoglou P., Uleryk E. M., Cohen E., Shulman R. Incidence of diabetes in children and adolescents during the COVID-19 pandemic: a systematic review and meta-analysis. JAMA Network Open . 2023;6(6) doi: 10.1001/jamanetworkopen.2023.21281.e2321281 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Rahmati M., Keshvari M., Mirnasuri S., et al. The global impact of COVID-19 pandemic on the incidence of pediatric new-onset type 1 diabetes and ketoacidosis: a systematic review and meta-analysis. Journal of Medical Virology . 2022;94(11):5112–5127. doi: 10.1002/jmv.27996. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Goday A., Lloveras G. Is the incidence of type 1 diabetes increasing in Europe? Endocrinology and Nutrition . 2000;47(9):253–255. [Google Scholar]
- 45.Weng J., Zhou Z., Guo L., et al. Incidence of type 1 diabetes in China, 2010–13: population based study. BMJ . 2018;360 doi: 10.1136/bmj.j5295.j5295 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1: search strategy for the MEDLINE database. Table S2: characteristics of studies included in the systematic review and or meta-analysis. Table S3: quality assessment (NHLBI cohort and cross-sectional studies). Table S4: trends in the incidence rates (pool estimate (95% confidence interval) of childhood diabetes type 1 by sex and age group for 32 countries of Europe). References of included studies and PRISMA checklist.
Data Availability Statement
All data are available upon request to the corresponding author.