(2024) 24:569
Nóvoa‑Medina et al. BMC Pediatrics
https://doi.org/10.1186/s12887-024-04983-w

BMC Pediatrics

RESEARCH

Open Access

Does HLA explain the high incidenceof childhood-onset type 1 diabetes
in the Canary Islands? The role of Asp57 DQB1
molecules
Yeray Nóvoa‑Medina1,2,3* , Itahisa Marcelino‑Rodriguez4,5, Nicolás M. Suárez3, Marta Barreiro‑Bautista3,
Eva Rivas‑García6, Santiago Sánchez‑Alonso6, Gema González‑Martínez6, Sofía Quinteiro‑González1,Ángela Domínguez1, María Cabrera1, Sara López1, Svetlana Pavlovic7, Carlos Flores5,8,9,10, Gran Canarian
Diabetes and Obesity Research Group3,7,11 and Ana M. Wägner3,11

Abstract
The Canary Islands inhabitants, a recently admixed population with significant North African genetic influence,
has the highest incidence of childhood-onset type 1 diabetes (T1D) in Spain and one of the highest in Europe.
HLA accounts for half of the genetic risk of T1D.
Aims To characterize the classical HLA-DRB1 and HLA-DQB1 alleles in children from Gran Canaria with and without
T1D.
Methods We analyzed classic HLA-DRB1 and HLA-DQB1 alleles in childhood-onset T1D patients (n = 309) and control
children without T1D (n = 222) from the island of Gran Canaria. We also analyzed the presence or absence of aspartic
acid at position 57 in the HLA-DQB1 gene and arginine at position 52 in the HLA-DQA1 gene. Genotyping of classical
HLA-DQB1 and HLA-DRB1 alleles was performed at two-digit resolution using Luminex technology. The chi-square test
(or Fisher’s exact test) and odds ratio (OR) were computed to assess differences in allele and genotype frequencies
between patients and controls. Logistic regression analysis was also used.
Results Mean age at diagnosis of T1D was 7.4 ± 3.6 years (46% female). Mean age of the controls was 7.6 ± 1.1 years(55% female). DRB1*03 (OR = 4.2; p = 2.13–13), DRB1*04 (OR = 6.6; p ≤ 2.00–16), DRB1* 07 (OR = 0.37; p = 9.73–06), DRB1*11(OR = 0.17; p = 6.72–09), DRB1*12, DRB1*13 (OR = 0.38; p = 1.21–05), DRB1*14 (OR = 0.0; p = 0.0024), DRB1*15 (OR = 0.13;
p = 7.78–07) and DRB1*16 (OR = 0.21; p = 0.003) exhibited significant differences in frequency between groups. Among
the DQB1* alleles, DQB1*02 (OR: 2.3; p = 5.13–06), DQB1*03 (OR = 1.7; p = 1.89–03), DQB1*05 (OR = 0.64; p = 0.027)
and DQB1*06 (OR = 0.19; p = 6.25–14) exhibited significant differences. A total of 58% of the studied HLA-DQB1 genes
in our control population lacked aspartic acid at position 57.
Conclusions In this population, the overall distributions of the HLA-DRB1 and HLA-DQB1 alleles are similar to thosein other European populations. However, the frequency of the non-Asp-57 HLA-DQB1 molecules is greater than that inother populations with a lower incidence of T1D. Based on genetic, historical and epidemiological data, we propose*Correspondence:
Yeray Nóvoa‑Medina
yeraynm@hotmail.com
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
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Nóvoa‑Medina et al. BMC Pediatrics(2024) 24:569

Page 2 of 12

that a common genetic background might help explain the elevated pediatric T1D incidence in the Canary Islands,
North-Africa and middle eastern countries.
Keywords Genetics, HLA, Pediatrics, Type 1 diabetes

Research in context
What is already known about this subject?
• The Canary Islands have the highest incidence todate of T1D in Spain.
• The HLA region accounts for up to 50% of thegenetic risk for type 1 diabetes (T1D).
• The Canary Islands original inhabitants are genetically related to the current Berber population, andshare a high incidence of pediatric T1D with northAfrican and middle eastern countries.
What is the key question?
• Is there a genetic basis to the increased T1D in thepediatric age group in the Canary Islands?
What are the new findings?
• There is a low prevalence of Asp57 HLA-DQB1 molecules in the population from Gran Canaria without
T1D compared to other regions with a lower T1D
incidence. Our findings support the seemingly protective role of HLA-DQB1 Asp57 molecules in T1D(especially HLA-DQB1*06).
• We hypothesize that a common genetic background,resulting from the migration of Arab and North African populations, and the resulting genetic admixture,
may help explain the increased incidence of T1D in
the pediatric age group in Arab, North African, Sardinian and Canarian populations.
How might this impact on clinical practice in the foreseeable future?
• A deeper understanding of the root causes of T1D
in our population might prove beneficial once anindividualized approach to the treatment of T1D
becomes available.

Introduction
Type 1 diabetes mellitus (T1D) is a multi-factorial disease
resulting from autoimmune destruction of pancreatic β
cells, limiting the body´s ability to produce insulin [1]. It
is the most common type of diabetes in childhood andadolescence [2]. The development of T1D is influenced
by a complex interplay between genetic factors and environmental exposures, which are not fully understood [3].
The Type 1 Diabetes Genetics Consortium (T1DGC)
identified more than 75 different genetic risk regions forT1D [4], with the human leucocyte antigen (HLA) region
accounting for 40–50% of that genetic susceptibility [5].
The HLA region comprises three major coding regions,and the proteins encoded by HLA class I and II are primarily responsible for binding and presenting antigens
to T lymphocytes, playing a crucial role in the development of autoimmune diseases like T1D [6]. However, theinfluence of HLA in the appearance of T1D is not simple.
Numerous studies have reported substantial ethnic differences in the risk and protective effects conferred byvarious HLA alleles and haplotype combinations. Thesefindings underscore the importance of conducting population-specific characterizations of HLA associations
with T1D to accurately assess disease risk and understand the underlying mechanisms.
The incidence of T1D varies worldwide, with the highest current incidence rates occurring in Northern Europe
and Middle Eastern countries [7]. Incidence ranges from52/100,000 in children < 15 years of age in Finland to
1–2/100,000 in Southeast Asia and the Western Pacific[7]. Most countries have experienced a global increase[8], and recent analyses predict even greater increases in
the coming decades [9].
Located nearly 100 km west of Morocco, the CanaryIslands archipelago is the southernmost region of Spain.
With a T1D incidence in the pediatric age group of
30–32/100,000 for the last 15 years in Gran Canaria [10,11] and the period 1993–2007 in the island of La Palma[12], it is the highest described to date in Spain [13] andone of the highest in Europe [14]. The current inhabitants are the result of a historical admixture of Western
Europeans, North Africans, and Sub-Saharan Africans.
The aboriginal population of the Canary Islands originally came from North Africa, giving its inhabitants acommon ancestry with the current Berber population.
In fact, genetic studies of the current population haveshown that > 70% of the influences can be traced to Iberia (mainly Galicia and Portugal [15]), approximately
22% from the populations of Northwest Africa, and asmall proportion is linked to sub-Saharan African influences (3%) [16]. These studies also confirmed that the lastadmixture event occurred approximately 14 generations

Nóvoa‑Medina et al. BMC Pediatrics(2024) 24:569

ago, which is within the time frame of the Spanish conquest of the archipelago [16]. Furthermore, certain
genomic regions of the current population have greaterthan-expected African genetic ancestry and were shown
to be significantly enriched in genes involved in diseases
prevalent in the Canarian population, including diabetes.
Specifically, one of these regions is HLA (16).
In accordance with the relative isolation of the population of the Canary Islands and the evident footprints
of genetic inbreeding on some of the islands [16], a highprevalence of monogenic diseases, such as Wilson´s disease [17], familial hypercholesterolemia [18], congenital
hyperinsulinism [19], and primary hyperoxaluria [20],among others, has been described in this population.
Given the high incidence of T1D in both the CanaryIslands [10] and North African countries [21] and theimportant recent genetic admixture with North African populations, we hypothesized that differences in theprevalence of high-risk or protective variants of HLAclass II alleles between the Canary Islands and other populations could help explain the higher incidences in thepediatric age group.

Methods
Study design

This is a case‒control study with unrelated subjects. The
Strengthening the Reporting of Observational Studies in
Epidemiology (STROBE) guidelines [22] were followed in
the reporting of the study.
Setting

The study was conducted on the population from theisland of Gran Canaria. Peripheral blood samples frompatients with T1D were collected immediately after
onset between 2010 and 2017 as part of the initial evaluation defined in our protocol for the diagnosis of T1D. In
addition, peripheral blood samples were collected fromhealthy children between April and June 2022 as partof the protocol of a randomized, controlled trial on theeffectiveness of an obesity prevention program [23] (clinical trial number 44205, www.​aspre​dicted.​org).
ParticipantsThis study was conducted according to the tenets of
the Declaration of Helsinki after approval by the Ethics
Committee of Hospital Universitario de Gran Canaria
Dr. Negrín (protocol codes 2019–477-1 and 2020–3561, approved on November 28th, 2019, and October 2nd,
2020, respectively). Informed consent was obtained fromall subjects included in the study, as well as from theirparents or guardians.
Patients were defined as having new-onset T1D before
the age of 14 years. The American Diabetes Association

Page 3 of 12(ADA) criteria were used for the diagnosis of T1D [24].
Controls were recruited from healthy schoolchildren
aged 6–9 years. Peripheral blood samples were collected
after parental and child consent were obtained from asample of 13 schools on the island of Gran Canaria participating in an intervention study to evaluate the effectiveness of an obesity prevention program. The schools
were selected with the approval of their school board.
The number of children with T1D who participated in
the study was defined by the number of onsets during theperiod 2010–2017. The number of children in the control
group was defined by the number of children whose parents consented to their participation and blood samplingin the study.
Anti‑islet autoantibodies

T1D-related autoantibodies were measured at disease
onset in our patients by radioimmunoassay (RIA) at Reference Laboratory S.A. (Barcelona, Spain). An islet antigen 2 (IA2) autoantibody RIA kit from RSR (Ldt, Cardiff,UK) with 125I-labeled IA2 was used for the detectionof IA2 antibodies. Glutamic acid decarboxylase (GAD)
autoantibody RIA kit from RSR with 125I-labeled GAD
was used to detect GAD antibodies. The DIAsource
AIA-100 kit was used for the detection of anti-insulin
antibodies.
DNA extraction and HLA genotyping

Peripheral blood samples were preserved in ethylenediaminetetraacetic acid (EDTA) tubes. Genomic
DNA extraction method was an automated magnetic
particle-based method using the commercial Maxwell
DNA Purification Kit from Promega. Genotyping of the
HLA-DQB1 and HLA-DRB1 alleles was performed withsequence-specific oligonucleotides (PCR-SSO) using
Luminex technology. In these assays, oligonucleotide
probes are attached to microspheres that are analyzed
in blocks using a customized cytometer (Luminex) andspecific software. Commercial LabType SSO Class II kits
(One Lambda) were used to genotype the HLA-DQB1
and HLA-DRB1 loci. Characterization was performed up
to a two-digit resolution in all patients. Estimation was
performed up to the fourth digit in a subset of 75 children with T1D (those with T1D onset during a 3 year
period in which our laboratory changed its protocol andperformed HLA 4th digit estimation routinely) and allcontrol participants. The frequency of HLA-DQB1 molecules with aspartic acid at position 57 (Asp57; considered to be a protective molecule [25]) and HLA-DQA1
with arginine at position 52 (Arg52; considered to be arisk molecule [26]) was assessed based on intermediate resolution typing and linkage disequilibrium with
the HLA-DRB1 locus. The HLA-DQB1 molecules with

Nóvoa‑Medina et al. BMC Pediatrics(2024) 24:569

Asp57 considered were *02:03, *03:01, *03:03, *04, *05:03,*06:01, *06:02, and *06:03. The HLA-DQA1 moleculeswith Arg52 considered were *03:01, *04:01, and *05:01. A
high-risk HLA genotype was defined by the presence ofthe HLA-DRB1*03 and/or HLA-DRB1*04 and/or HLADQB1*02 and/or HLA-DQB1*03 alleles [5].
Statistical analysis

Descriptive statistics were used to calculate the frequency
of HLA class II in the population. We examined the odds
ratio (OR) and 95% confidence interval (95% CI) after
comparing the frequency of each allele and genotype
between study groups. The chi-square test (or Fisher’s
exact test when appropriate) and odds ratio (OR) wereused to compare differences in allele and genotype frequencies between controls and T1D patients. To estimate
the risk of T1D, logistic regression models were adjusted
for sex, and were used to derive the ORs and 95% CIs.
The significance threshold was set at p = 2.94E − 3 after
Bonferroni correction based on the number of alleles
tested considering both genes together.
Based on the identification of alleles associated withearlier disease onset, we investigated whether theseeffects varied with age at diagnosis to determine thethreshold at which the allelic effects were most pronounced. To address this issue, we employed a sequential addition approach for the analysis of case‒control
data, applicable in scenarios where a quantitative trait ismeasured solely in cases and where no defined threshold
is known [27]. In summary, individual associations (forHLA-DRB1*03 and *04) were calculated using the same
method as before but iteratively for each subset of cases
formed by including patients with T1D at each incremental increase of 1 year in the age of diagnosis (from 1 to 15
years of age). The P value was subsequently obtained bymultiple logistic regression adjusting by sex. These statistical analyses were performed using the statistical package SPSS v.29.0 (IBM Corp. Released 2022. IBM SPSS
Statistics for Windows, Version 29.0. Armonk, NY: IBM
Corp) and R v.3.6.3 [28].

Results
Participants

A total of 309 children diagnosed with T1D (mean age
at diagnosis: 7.4 ± 3.7 years, 46% female) and 222 healthy
controls (mean age: 7.6 ± 1.1 years, 55% female) were
included in the study.
Pancreatic autoantibodies

Overall, 90.4% of the T1D patients were positive for atleast one autoantibody, the most common being antiGAD (73%), followed by anti-IA2 (66%) and anti-insulin

Page 4 of 12(28.7%). ZnT8 autoantibodies were not measured in ourpatients.
HLA‑DRB1 associations

Among the HLA-DRB1 alleles, the frequency of HLADRB1*03 and HLA-DRB1*04 is higher in patients than incontrols (91% vs 42%; p < 0.00001), with HLA-DRB1*04
presenting the largest OR for T1D. HLA-DRB1* 07, *11,
*13, *14, *15 and *16 had an increased frequency in thecontrol group, with DRB1*13 being the most frequent.
Regarding the genotype frequencies, HLA-DRB1*01/04,*03/03, *03/04 and *04/13 were significantly more frequent in patients, HLA-DRB1*03/03 presented the largest OR, and HLA-DRB1*03/04 was the most common
genotype among T1D patients. HLA-DRB1*03/13, 07/11,
07/13, 07/15, 07/07, 11/13, 11/15, 13/13, and 15/15 weresignificantly more frequent in the controls (Table 1 andSupplementary Table 1).
HLA‑DQB1 associations

Regarding HLA-DQB1, 95.5% of cases carried HLADQB1*02 and/or *03, compared to 83.1% of controls(p < 0.00001). The protective allele HLA-DQB1*06 waspresent in 12.6% of cases (only 0.6% in homozygosis)
and 41% of controls (5% in homozygosis) (p < 0.00001).
HLA-DQB1*02 and HLA-DQB1*03 were more prevalent
in cases, while HLA-DQB1*05 and HLA-DQB1*06 weremore frequent in controls. Regarding genotypes, HLADQB1*02/02 and HLA-DQB1*02/03 were significantly
Table 1 HLA-DRB1 allele frequency in children with T1D andcontrols
AlleleCases (2N)

Controls (2N)

OR

95%CI

P-value

618

444

*01

55

40

0.94

0.05—1.47

0.79

*03

199

52

4.27

2.89—6.28

2.13–13

*04

219

55

6.61

4.45—9.81< 2.00–16

*07

42

72

0.37

0.24—0.57

9.73–06

*08

14

13

0.74

0.34—1.62

0.46

*09

7

7

0.73

0.25—2.12

0.56

*10

4

6

0.47

0.13—1.69

0.25

*11

17

55

0.17

0.09—0.31

6.72–09

*13

48

77

0.39

0.25—0.59

1.21–05

*14

0

7

0

*15

8

41

0.17

0.06—0.30

7.78–07

*16

5

15

0.21

0.07—0.60

3.50–03

0.0024†

Only alleles with ≥ 5 counts when considering cases and controls together were
included in the table
OR Odds ratio, 95% CI 95% Confidence Interval†

Fisher exact test

Nóvoa‑Medina et al. BMC Pediatrics(2024) 24:569

Page 5 of 12

Table 2 HLA-DQB1 allele frequency in children with T1D andcontrols
AlleleCases (2N)

Controls (2N)

OR

95%CI

P-value

618

444

*02

262

124

2.31

1.61—3.30

5.13–06

*03

232

132

1.75

1.23—2.49

1.89–03

*04

12

11

0.74

0.31—1.71

0.48

*05

71

69

0.64

0.43—0.95

0.028

*06

41

108

0.19

0.12—0.29

6.25–14

OR Odds ratio, 95% CI 95% confidence interval

Table 3 Estimation of DRB1 and DQB1 up to the 4th digit inpatients with T1DAllele (N)

4th digit
estimation(%)

4th digit
estimation(%)

DRB1*01 (12)01:01 (58)

01:02 (42)

DRB1*03 (44)03:01 (98)

03:05 (2)DRB1*04 (57)

04:01 (19)04:02 (16)DRB1*07 (6)07:01 (100)

DRB1*08 (3)08:01 (67)

DRB1*10 (2)

10:01

DRB1*11 (3)

11:01 (67)

11:03 (33)

DRB1*13 (13)13:01 (46)

13:02 (31)

DRB1*15 (2)

15:01

DRB1*16 (1)

16:01

4th digit
estimation(%)

04:04 (14)04:05 (51)

08:04 (33)

DQB1*02 (57) 02:01 (86)02:02 (14)DQB1*03 (61) 03:01 (13)03:02 (85)

DQB1*04 (1)4th digit
estimation(%)

13:03 (23)03:03 (2)04:02

DQB1*05 (13) 05:01 (92)

05:02 (8)DQB1*06 (12) 06:02 (8)06:03 (47)06:04 (39)more frequent in cases, while HLA-DQB1*02/06, *03/06,*05/06 and *06/06 were more frequent in controls(Table 2 and Supplementary Table 2).
Combinations of HLA‑DRB1 and HLA‑DQB1

We also examined combinations of these alleles andfound that certain types of alleles were more frequent in
T1D patients than in controls. Regarding the HLA-DRB1DQB1 combinations, *01/04–03/05, *03/03–02/02,*03/04–02/02, and *03/04–02/03 were significantly more
frequent in patients, whereas *01/11–03/05, *11/13–
03/06, *11/15–03/06, *13/16–05/06, *15/15–06/06,*04/13–02/06, *07/11–02/03 and *07/13–02/06 weresignificantly more frequent in controls (Supplementary
Table 3).

Estimation up to the fourth digit of HLA‑DRB1
and HLA‑DQB1

An estimation of a small subset of patients with T1D (75patients [25%]) allowed us to characterize the DRB1* andDQB1* alleles up to the 4th digit resolution (Table 3).
We were able to estimate up to the 4th digit in all control patients (Table 4). The only significant differences in
4-digit characterization were found for the DRB1* 04:03(p = 0,025) (more frequent in controls), 04:05 (p = 0,023)(more frequent in T1D patients), 04:07 (p = 0,025) (morefrequent in controls) and DQB1*02 alleles (with 02:01
being more frequently found in T1D patients, and 02:02
in controls (p = 0,000)). Estimations in patients with T1D
and controls are shown in more detail in Supplementary
Tables 4 and 5.
As expected, the frequency of (1 or 2) HLA-DRB1
risk alleles was greater in patients with T1D (OR = 1.5;
p = 0.02; OR = 12.3; p < 0.00001, respectively). In the caseof HLA-DQB1 alleles, the presence of two risk alleles wasmore frequent in patients (OR = 3.5; p < 0.00001), while
the presence of one risk allele or none was more frequent in controls (OR = 0.48; p = 0.00006 and OR = 0.19;
p < 0.00001, respectively) (Supplementary Tables 6 and 7).
Asp‑57 and non‑Asp‑57 molecules

Focusing on the HLA-DQB1 molecules with Asp57, wefound evident differences in the frequency of homozygous and heterozygous combinations in the population ofGran Canaria (Table 5).
Evaluating all the Asp57 molecules (HLA-DQB1*02:03,*03:01, *03:03, *04, *05:03, *06:01, *06:02, and *06:03), we
found that *02:03 was very rare, while *03:01 and *03:03
represented only 15% of the HLA-DQB1*03 alleles inchildren with T1D (Table 3), and HLA-DQB1*04 and*05 were rare in both patients and controls (Tables 3 andSupplementary Tables 4 and 5). Thus, HLA-DQB1*06(*06:01, *06:02, and *06:03) was the most common Asp57
molecule and carries most of the weight for the protective effect attributed to the Asp57 HLA-DQB1 alleles inthis population.
We performed a correlation analysis using the nonAsp57 gene frequencies published by Dorman et al.
[29] and added the data from this study. The correlation between the absence of Asp57 molecules and T1D
incidence was strong even after including data from
Gran Canaria. The initial correlation analysis showed an
R = 0.98, p = 0.003. The correlation was similar (R = 0.95;
p = 0.003) when the data from this study were added. We
also added data from other publications where the incidence did not seem to correlate so well with the absenceof Asp57 genes (R = 0.76; p = 0.017) (Table 5).

Nóvoa‑Medina et al. BMC Pediatrics(2024) 24:569

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Table 4 Estimation of DRB1 and DQB1 up to the 4th digit in control children
Allele (N)

4th digit
estimation(%)

4th digit
estimation(%)

4th digit
estimation(%)
01:03 (10)

4th digit
estimation(%)

4th digit
estimation(%)

4th digit
estimation(%)

4th digit
estimation(%)

4th digit
estimation(%)

04:05 (29)04:06 (2)04:07 (9)04:10 (2)

13:05 (1)

13:36 (1)

13:4 (1)DRB1*01 (39)01:01 (41)01:02 (49)

DRB1*03 (52)03:01 (96)03:02 (4)DRB1*04 (54)04:01 (19)04:02 (13)04:03 (9)04:04 (17)DRB1*07 (72)

07:01 (100)08:03 (23)08:04 (8)08:02 (8)11:02 (16)11:03 (9)11:04 (16)13:02 (35)

13:03 (12)13:56 (1)DRB1*08 (13)08:01 (61)

DRB1*09 (7)09:01 (100)

DRB1*10 (6)10:01 (100)

DRB1*11 (55)

11:01 (59)

DRB1*12 (4)12:01 (100)

DRB1*13 (77)

13:01 (49)

DRB1*14 (7)14:01 (100)

DRB1*15 (42)

15:01 (88)

DRB1*16 (14)16:01 (86)

15:03 (12)16:02 (14)DQB1*02 (123)02:01 (46)02:02 (54)

DQB1*03 (132)

03:01 (54)03:02 (30)

DQB1*04 (11)04:02 (100)03:03 (10)DQB1*05 (70)05:01 (69)05:02 (23)05:03 (8)DQB1*06 (106)06:02 (34)06:03 (39)06:04 (21)03:19 (6)06:09 (4)06:01 (2)Table 5 Adapted from Dorman et al. [29]. Presence/absence of Asp57 in patients and controls in various population [30–36]
T1D

Controls

T1D incidence

N/N(%)

N/A(%)

A/A(%)N(%)

N/N(%)

N/A(%)

A/A(%)

N(%)

Finland(30)

74

22

3

85

18

57

25

46

52.2/100,000(7)Sardinia

100

0

0

100

38

47

15

62

45/100,000(31)Norway

80

16

4

89

27

51

22

53

32.7/100,000(32)Gran Canaria

78

19

2

88

35

46

19

58

30/100,000(10,11)

US Caucasians

61

39

0

81

20

46

34

43

27.3/100,000(33)US Blacks

73

27

0

87

18

37

45

36

20.8/100,000(33)France(34)

88

12

0

94

23

55

22

51

19.1/100,000(35)Chinese

6

72

22

42

0

8

92

4

3.1/100,000(36)Japan(37)

3

49

48

27

8

34

58

25

2.25/100,000(38)Erlich et al. [5] (Europe, North
America, Australia/New Zealand)

91

52

N/N non-Asp57 homozygosity, N/A non-Asp57 heterozygosity, A/A Asp57 homozygosity, N non-Asp57 gene frequency

Arg52 and non‑Arg52 molecules

Age effect

When assessing HLA-DQA1 molecules with Arg52, somecombinations were evidently different between cases andcontrols (Table 6). We added the presence and combinationof the HLA-DQB1 Arg57 molecules to allow comparison
with the findings of Khalil et al. [26]. The table represents all
possible combinations of susceptible (S) and protective (P)HLA-DQA1 and HLA-DQB1 Arg52 and Asp57 molecules.

We analyzed the effect of the highest risk HLA-DRB1
alleles on the age of T1D onset. By analyzing the risk-documented alleles (HLA-DRB1*03 and *04), we observed thatthe effects were greatest for 2 years of age at diagnosis for
HLA-DRB1*03 (OR, 5.49; 95% CI, 2.75–10.98; P < 0.001)
and for 5 years at diagnosis for HLA-DRB1*04 (OR, 6.67;
95% CI, 4.01–11.09; P < 0.001) (Fig. 1).

Nóvoa‑Medina et al. BMC Pediatrics(2024) 24:569

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Table 6 Frequency of appearance of susceptible DQA1 andDQB1 alleles
Combinations

DQA1*

DQB1*

T1D (N = 69) n(%)

Controls(N = 222)
n(%)

1

S

S

15 (21.7)

9 (4)

S

S

S

S

2 (2.9)

9 (4)

S

P

S

S

20 (29)42 (18.9)

P

S

P

S

14 (20.3)

33 (14.8)

S

P

S

S

P

P

P

S

11 (15.9)30 (13.5)

P

S

S

P

0

0

S

P

P

S

4 (5.8)61 (27.4)

P

P

P

P

2 (2.9)8 (3.6)

S

P

P

P

1 (1.5)30 (13.5)

P

P

2
3
4
5
6
7
8
9
10

S Susceptible chain (DQB1*Asp57 negative; DQA1*Arg52 positive)
P Protective chain (DQB1*Asp57 positive; DQA1*Arg52 negative)
T1D type 1 diabetes

When comparing the presence of risk alleles among different age groups, we did not find significant differences.
However, a trend was found for the presence of HLADRB1*04 among children aged 1–5 years (Table 7).

Discussion
This study investigated the genetic characteristics
of T1D patients from the island of Gran Canaria andyoung children without T1D from the same island. In
general, the HLA profile is similar to that describedfor other populations with childhood-onset T1D. We
found small differences, with similar frequencies of
HLA-DRB1*03 and *04 in our population, while HLADRB1*03 was found more frequently in T1D children
from the rest of Spain. Additionally, for HLA-DQB1
we found a lower prevalence of protective molecules(Asp57-positive) in the control group compared toother non-diabetic populations with a lower incidenceof T1D.
As the genetic region associated with the highest
risk for T1D, class II HLA has been extensively studied. Genetic differences have been described between
European, African, and Asian populations [5, 37, 38](Supplementary Table 8). A lower frequency of HLADRB1*03 and *04 [37, 39], variable presence of HLADQB1 Asp57 [29] and HLA-DQA1 Arg52 molecules [26],and other genetic variation have been used to explain the
differences in incidence between populations.
Risk alleles for T1D

In agreement with the findings of previous reports, ourstudy revealed a predominance of HLA-DRB1*03 and*04, as well as HLA-DQB1*02 and *03, in patients with
T1D, with a non-significant trend suggesting a higher
effect for the HLA-DRB1*04 allele peaking among children aged 1–5 years. Previous studies in populations
from the Canary Islands and other populations frommainland Spain showed that patients diagnosed with
T1D have a greater proportion of HLA-DRB1 risk alleles
than healthy controls. A previous study by our group[40] evaluated the results of Spanish participants in the
T1DGC (142 families, 49 from the Canary Islands), comparing affected and unaffected family members from theCanary Islands with those from the rest of Spain. The
study concluded that the high incidence of childhoodonset T1D in the Canarian population does not seem tobe explained by a higher prevalence of high-risk class II
HLA haplotypes in families with this disease. Our results
here point in the same direction, with the healthy control
group having HLA risk profiles similar to those describedin other populations with a lower incidence of T1D. Santana et al. also evaluated the prevalence of DRB1 risk
alleles and reported a higher frequency of HLA-DRB1*03
than of HLA-DRB1*04 in mainland Spain, whereas in the
Canary Islands population, the frequency of both alleles
was similar. The present study also revealed a similarprevalence of both risk alleles in the Canarian population.
Regarding studies in other Spanish populations, Urrutia et al. [41] compared the number of HLA-DRB1 risk
alleles between 160 T1D patients, 74 patients with monogenic diabetes, and 75 healthy controls. They reported
that 48% and 44.3% of pediatric patients with T1D had 2
and 1 HLA-DRB1 risk alleles, respectively, which was significantly greater than the proportion found in patientswith monogenic diabetes and healthy controls. Our
results in T1D patients are very similar (45% and 46%,
respectively).
When compared to Europeans [5] and North Africans [42–44], the HLA-DRB1 alleles in the T1D
patients and healthy individuals were similar to
those described by other studies. We present differences with African and Asian populations (particularly Chinese and Japanese), were DRB1*09:01 is oneof the main risk alleles (supplementary Table 8). The
similitudes with European populations remain whencomparing results after high-resolution estimation

Nóvoa‑Medina et al. BMC Pediatrics(2024) 24:569

Page 8 of 12

Fig. 1 Effects of HLA-DRB1*03 and *04 according to age at T1D diagnosis

Table 7 Probability of presenting HLA-DRB1*03 and *04 in eachage group (at diagnosis)
1–5 years of age

6–10 years ofage

11–14 yearsof age

OR

1.20

1.12

0.71

95%CI

0.74–1.94

0.71–1.77

0.42–1.18

P value

0.47

0.63

0.19

OR

1.67

0.71

0.88

95%CI

0.99–2.83

0.44–1.14

0.52–1.50

P value

0.054

0.15

0.64

HLA-DRB1*03

HLA-DRB1*04(of up to the fourth digit in patients with T1D). HLADRB1*03:01 was the main *03 allele (98%), while HLADRB1*04:05 (51%) was the main *04 allele in the T1D
population. Compared to our control group, there were
small differences in the 4-digit characterization, with
only DRB1*04 and DQB1*02 presenting differences

between both groups (DRB1*04:03, DRB1*04:05
and DRB1*04:07 were more frequent in controls
while DRB1*04:05 in T1D patients, and DQB1*02:01
being more frequently found in T1D patients, while
DQB1*02:02 in controls).
Protective alleles

In control children, DRB*13 was the most common allele,with DRB*15 presenting the lowest OR. Among the HLADQB1 alleles, HLA-DQB1*02 and *03 were also the mostcommon, followed by HLA-DQB1*06, which had thelowest OR. These results are, in general, similar to thosereported by other authors (Supplementary Table 8).
Of particular interest is the presence/absence of aspartic acid at position 57 in HLA-DQB1 molecules. Several
studies, some dating back to 1987 [45], associated theabsence of the amino acid aspartic acid at residue 57 of
the HLA-DQB1 chain (Asp57-) with T1D susceptibility [29, 46]. The presence of Asp57 appears to be associated with dominant protection [25]. The mechanisms
underlying its effect on T1D susceptibility could be
related to how it affects the binding affinity of the HLA

Nóvoa‑Medina et al. BMC Pediatrics(2024) 24:569

molecule with T cells and its effects on thymic selection
and T-cell receptor responsiveness in the periphery [47].
In 1990, Dorman et al. [29] reported an inverse correlation between the presence of Asp57 and the incidenceof T1D in different populations (Sardinia, Norway, theU.S. Caucasian and African American populations andChina). In 2000, another report [48] compared the frequency of alleles in European, African American, andAsian populations from the DiaMond Molecular Epidemiology Project and the 12th International Histocompatibility Workshop and Conference (aggregating datafrom 20 European populations worldwide). They focusedon the HLA-DQB1*06 allele (the most common Asp57
molecule) and found a greater frequency in controls inall ethnic groups. This difference was more pronounced
in controls from populations with the lowest incidenceof T1D, African Americans and Asians (32% and 33%
vs. 23%, respectively). Our findings are in good agreement with these studies (Table 4), as the low prevalenceof HLA-DQB1 Asp57 could partially explain the highincidence of T1D in the Canarian population. It is worth
mentioning that published results regarding the possible
effects of Asp57 molecules do not fully explain the variability in incidence rates (Table 4). Reijonen et al. [49]studied the presence of HLA-DQB1 Asp57 moleculesin the Finnish population. When comparing its prevalence in the general population with that in other countries, Asp57 molecules did not explain the variation inincidence rates. In 2001, Ronningen et al. investigated
the relationship between HLA genotypes and the incidence of T1D in Europe [50]. While they reported theexistence of a clear correlation between the incidence of
T1D and HLA genes, most of the effects were attributed
to the HLA-DQ2/DQ8 (DQB1*02:01/DQB1*03:02) andHLA-DQ4/DQ8 (DQB1*04:02/DQB1*03:02) genotypes.
They found no correlation between T1D incidence andthe population prevalence of genotypes without Asp57.
A possible explanation for these differences could be theeffect of other HLA and non-HLA genes, as well as environmental factors, on the development of T1D. Khalil
et al. in 1990 investigated the effect of HLA genes on theoccurrence of T1D in France [26]. They studied the effectof HLA-DQA1 alleles and reported a clear relationshipbetween the presence of Arg52 molecules in combination with Asp57- molecules and the risk of T1D. Studying 50 patients with T1D and 73 healthy controls, theyfound that only individuals with the Arg52/Asp57- molecules (both risk molecules) developed T1D. None of theindividuals negative for Arg52 but with Asp57 molecules(both protective molecules) developed T1D. In the population of Gran Canaria, we found controls in which Arg52
was absent but Asp57 was present (both protective)(Table 5), whereas Khalil et al. did not. However, children

Page 9 of 12with Arg52 but not Asp57 (both susceptible molecules)
were much more likely to have T1D (73.9% vs 41.7% of
controls), whereas children with Arg52- but with Asp57
were more likely to be healthy (26.1% T1D vs 58% of controls). These results reflect the importance of HLA alleles
other than HLA-DQB1 and HLA-DRB1 in the susceptibility to T1D. The results published by Aydemir et al. [51]help to reinforce their importance by describing a haplotype of three variants in HLA-DQA1 as modulators of
T1D risk in children homozygous for HLA-DR3.
In other studies in Spanish populations, Escribano et al.
[52] reported a greater frequency of HLA-DQB1*06 in
Cantabrian controls than in patients with T1D. However,we were unable to make a direct comparison between
our data and those of Dorman et al. with the information
provided in their report. When comparing data from theCanary Islands and the rest of Spain, Santana et al. [40]found no difference between controls from the CanaryIslands and the rest of the country. Nevertheless, theyreported a similar prevalence of HLA-DQB1*06 in control subjects from the Canary Islands (6.4% vs 8.6%).
Regarding other protective alleles more frequently
found in the control population, our results are similar
to those reported by other authors, with the exception of
HLA-DRB1*16. This allele had an OR of 0.24 (p < 0.05) in
our sample, while it did not reach significance in Erlich
et al.´s report [5] or other studies in different populations.
African influence on the Canary Islands

In 2018, Guillen-Guio et al. [16] published the resultsof a study analyzing the European and African genomic
influence in the current population of the Canary Islands.
Based on SNP array data and whole-genome sequencing,
they concluded that up to 34% of the Canarian genome
is of recent African descent, with the predominance ofAfrican alleles in some chromosomal regions (where
40–50% of the genome is of African descent), which
include HLA. These genetic findings reflect the importance of past migration from Arab and North African
populations to their Western European neighbors andthe resulting genetic admixture [53]. We hypothesizethat this common genetic background may help explain
the increased incidence of T1D in the pediatric age groupin Arab, North African, and Canarian populations. Notethat Kuwait, Qatar, Algeria, and Saudi Arabia are among
the eight countries with the highest incidence of pediatric T1D worldwide, with values of 41.7, 38.1, 34.8 and31.4/100,000, respectively [7, 10]. Furthermore, the incidence of T1D in Spain shows an increasing North–South
gradient, with the highest incidence rates found in Andalusia [54]. Additionally, the Italian island of Sardinia has
the highest incidence of pediatric T1D in Europe after
Finland (45/100,000) [55]. Thus, the presence of alleles

Nóvoa‑Medina et al. BMC Pediatrics(2024) 24:569

of recent African ancestry in southern Europe may playa role in the increased incidence of T1D in these regions[56].
Some of the strengths of our study are the number ofsubjects included (the greatest to date in our region) andthe use of a control group without T1D. We also acknowledge that this study has several limitations. The limited
sample size (when compared to multicenter or collaborative projects) and young age of our control group (someof whom could develop T1D in the future) are some of
our limitations. Our HLA analysis is limited to two-digit
resolution. Four-digit characterization is important since
it is sometimes the only way to differentiate between risk
and protective alleles (e.g., HLA-DQB1*03:02 is considered a risk allele, whereas *03:01 is considered neutral orprotective [5]). Direct comparisons between studies areoften difficult due to methodological differences in theanalysis and the reporting of the results. Some authors
describe the allelic characteristics of their population,
while others describe the genotype. Our findings regarding the HLA-DQB1 Asp57 molecules support an association with T1D incidence. However, it is important to bearin mind that this does not imply causality.

Conclusion
The risk and protective alleles in patients with T1D and in
the healthy population from the Canary Islands are similar to those described in mainland Spain and other European populations. There is a low prevalence of Asp57
HLA-DQB1 molecules in the population from Gran
Canaria without T1D compared to other regions witha lower T1D incidence. Our findings support the seemingly protective role of HLA-DQB1 Asp57 molecules in
T1D (especially HLA-DQB1*06), and their absence orpresence may help to explain incidence variation acrosspopulations. Larger-scale and more comprehensive studies are needed to confirm the role of these genes in thepathogenesis of T1D. Historical and T1D epidemiologic
data and genetic evidence also support the relationships among Arab, North African, southern Spanish, andCanarian populations.
Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12887-​024-​04983-w.
Supplementary Material 1.
Authors’ contributions
YNM designed the research study, conducted the research, raised the funds,
participated in the data analysis, wrote the first draft and revised subsequentversions of the manuscript. IMR and NMS collaborated in the writing of themanuscript and in the analysis of the results. ERG, SSA and GMG per‑formed the HLA analysis. AMW collaborated in the design of the study andPage 10 of 12interpretation of the results and revised successive versions of the manuscript.
The Gran Canarian diabetes and obesity research group contributed withdata collection and revision of the manuscript. All the authors whose names
appear in the submission 1) made substantial contributions to the concep‑
tion or design of the work; 2) acquired, analyzed, or interpreted the data; 3)
drafted the work or critically reviewed it for important intellectual content; 4)approved the version to be published; and 5) agreed to be accountable for
all aspects of the work in ensuring that questions related to the accuracy or
integrity of any part of the work were appropriately investigated and resolved.
Gran Canarian Diabetes and Obesity Research Group
Carlota Rodriguez-Benitez, Héctor Ageno-Alemán, Cristina Perera-Hernández,
Catalina de Elejabeitia-Cortezo, Nieves Franco-Mateu, AnaMaría Rodríguez
Gonzalez, Victor Manuel Leon-Olmo, Fátima Aitoutouhen-Infante, Sade PérezLópez, Saula del Pino Alonso-Falcón, Acoraida Bolaños-Alzola, Zeltia GarcíaSuárez, Inés Perdomo-Delgado, Sara Ayala-Martínez, Laura Valenzuela-Alvarez,
Elena Caballero-Estupiñán, Celia Rúa-Figueroa, Claudia Travieso-Hermoso,
Yaiza García-Delgado, Pablo Azcoitia, Sara Quintana Arroyo, Carlos Rodríguez,
Yaiza López-Plasencia, Nuria Pérez-Martín, Rosa María Sánchez-Hernández,
María José López-Madrazo, Alejandro Déniz, Rossella Tozzi, Mauro BoronatCortés, Carmen Valverde-Tercedor, Garlene Zamora-Zamorano, Roberto
Jiménez-Monzón, Luisa Hernández-Baeza, Verónica Dávila-Batista, Yaiza Gil, Oli‑
ver Gil Jorge, Romina Soage-Villegas, Sofia Bueno-Montoro, Aitana GuancheSicilia, Brenda Santos-Morán, Jesús Santana-Medina, Sofía Ojeda-Elías, Beatriz
Melian-Cordovez, Marina Corona-Lopez, Marta Macías-Dolz, Saray BetancortAvero, Samuel Rodriguez-Déniz, Ana Puga-Morales, Rose Bowler-Parminter,
María de los Angeles Ferrera Fernandez, Rocio Rodriguez-Sánchez
Funding
This research was funded by Fundación Canaria Instituto de Investigaciones
Sanitarias de Canarias (FCIISC) (project numbers PIFIISC19/30 and 22/08), Fun‑dación DISA (Premios a la Investigación Biomédica OA22/027) and Fundación
Mapfre Guanarteme (project number OA1/129). NMS is funded by a María
Zambrano scholarship (Ministerio de Universidades, Spain). The funders had
no role in the design of the study; in the collection, analyses, or interpretationof the data; in the writing of the manuscript; or in the decision to publish theresults.
Availability of data and materials
The dataset(s) supporting the conclusions of this article are included within
the article (and its additional file(s)).

Declarations
Ethics approval and consent to participate
The study was conducted according to the guidelines of the Declaration of
Helsinki and was approved by the Ethics Committee of Hospital Universitario
de Gran Canaria Dr. Negrín (protocol codes 2019–477-1 and 2020–356-1,
approved on November 28th 2019 and October 2nd 2020). Informed consent
was obtained from all subjects involved in the study. Informed written con‑sent was obtained from a parent or guardian for participants under 16 yearsof age.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Unidad de Endocrinología Pediátrica, Complejo Hospitalario Universitario
Insular Materno Infantil de Las Palmas de Gran Canaria, Las Palmas de Gran
Canaria, Spain. 2 Asociación Canaria para la Investigación Pediátrica (ACIP
canarias), Las Palmas, Spain. 3 Instituto Universitario de Investigaciones
Biomédicas y Sanitarias de la Universidad de Las Palmas de Gran Canaria, Las
Palmas de Gran Canaria, Spain. 4 Preventive Medicine and Public Health Area,
University of La Laguna, Santa Cruz de Tenerife, Spain. 5 Institute of Biomedical
Technologies, University of La Laguna, Santa Cruz de Tenerife, Spain. 6 Servicio
de Inmunología, Complejo Hospitalario Universitario Insular Materno Infantil
de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain. 7 Servicio

Nóvoa‑Medina et al. BMC Pediatrics(2024) 24:569

de Pediatría Complejo Hospitalario Universitario Insular Materno Infantil de
Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain. 8 Genomics
Division, Instituto Tecnológico y de Energías Renovables (ITER), Santa Cruz deTenerife, Spain. 9 CIBER de Enfermedades Respiratorias (CIBERES), Instituto de
Salud Carlos III, Madrid, Spain. 10 Facultad de Ciencias de la Salud, Universidad
Fernando de Pessoa Canarias, Las Palmas de Gran Canaria, Spain. 11 Servicio
de Endocrinología y Nutrición, Complejo Hospitalario Universitario InsularMaterno Infantil de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria,
Spain.
Received: 16 March 2024 Accepted: 29 July 2024

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