Skip to main content
Download PDF

Human Leukocyte Antigens class II (HLA II) gene profile from an admixed population of patients with type 1 diabetes with severe diabetic retinopathy: a nested case-control study in Brazil

Diabetology & Metabolic Syndrome volume 13, Article number: 83 (2021) Cite this article

Abstract

Background

Although the well-established role of the HLA genes on the predisposition of type 1 diabetes (T1D), its contribution to the development and progression of diabetic retinopathy is still unclear, especially in admixed populations. We aimed to study the relationship between HLA alleles and severe diabetic retinopathy in a highly admixed population of T1D patients.

Methods

This was a nested case-control study based on a cross-sectional, nationwide survey conducted in Brazil. We included 117 patients with severe diabetic retinopathy and 117 random controls composed of T1D patients without retinopathy, matched for diabetes duration. HLA-class II genes (HLA-DRB1, -DQA1, and -DQB1) were genotyped using the SSO and NGS methods.

Results

Haplotypes HLA-DRB1*04:05 ~ DQA1*03:01 g ~ DQB1*03:02 (OR 1.75, CI 0.97–3.16, p value 0.058) and HLA-DRB1*13:02 ~ DQA1*01:02 ~ DQB1*06:04 (OR 5.18, CI 1.12–23.09, p value 0.019) were more prevalent on the severe DR group but they did not present statistically difference after Bonferroni correction. The most frequent haplotype on both groups was HLA-DRB1*03:01 ~ DQA1*05:01 g ~ DQB1*02:01 (29.6% on severe DR and 33.33% on the control group).

Conclusions

Our study showed no influence of HLA genes on the development of DR. Further longitudinal data is needed to better understand the role of genetic factors on this multifactorial significant microvascular complication.

Background

Type 1 diabetes (T1D) is a chronic disease that arises from multiple genetic and environmental factors [1]. Although several different loci influence the expression of T1D, the HLA region on chromosome 6p21 responds to almost 50% of the risk [2].

A prevalence of 30% of microvascular complications is observed in T1D patients from different populations [3]. Diabetic retinopathy (DR) is one of the most frequent diabetes-related chronic complications, with an estimated global prevalence of 35%. DR is considered the leading cause of blindness in the adult population resulting in a significant social and financial burden [4]. The diabetes control and complications trial (DCCT) and epidemiology of diabetes interventions and complications (EDIC) studies [5] showed that adequate glycemic control is essential to avoid or postpone diabetes-related chronic complications, including DR.

Besides glycemic control and diabetes duration, HLA-DRB1*04 and HLA-DRB1*03 have been linked to the development and progression of DR with controversial results [6,7,8,9,10,11,12]. The WESDR cohort with T1D Caucasian patients showed a positive association of HLA-DRB1*04 with DR in its first cross-sectional analysis with no adjustment for disease duration [13]. In contrast, in its 14-years-follow-up report, no differences were found [6]. Another study conducted in the Caucasian population showed a protective effect of HLA-DRB1*03 and a risk effect of HLA-DRB1*04 [14].

The controversial results found in previous studies could be even more controversial in a highly admixed population, such as the Brazilian. A previous HLA study in Brazilian type 1 diabetes patients showed similar haplotypes frequencies for HLA-DRB1*03 and HLA-DRB1*04 as described in the Caucasian population. However, it also demonstrated the association of the HLA-DRB1*09 allele with T1D, mainly expressed in the African-American population [15]. The heterogeneous Brazilian population is composed of a highly admixed combination of three principal ancestral roots: Native Amerindians (NAM), Europeans (EUR), and Africans (AFR). The process of population admixture started with European colonization (particularly by the Portuguese) in the coastal region, eventually spreading to the country’s interior, which was populated by Native Amerindians. Afterward, with the heavy slavery traffic (particularly from Africa), even more migration and admixture have occurred, explaining the substantial genetic diversity in Brazil.

We aimed to study the relationship between HLA alleles and severe diabetic retinopathy in a highly admixed population of type 1 diabetes patients.

Methods and subjects

Study design and population

The present study was a nested case-control study based on a cross-sectional, nationwide survey conducted in Brazil’s five geographic regions between August 2011 and August 2014. The original study included 1,760 patients with T1D, and the methods have been described previously [16]. Briefly, all patients received health care from the Brazilian National Health Care System (SUS). The diagnosis of type 1 diabetes was made based on typical clinical presentation (hyperglycemia, weight loss, polyuria, polydipsia, polyphagia, and the need for continuous insulin use since the diagnosis). Inclusion criteria were: at least 13 years of age and minimum follow-up at each center of at least 6 months.

For the present study, we included 117 cases, defined as patients with severe diabetic retinopathy (severe nonproliferative or proliferative diabetic retinopathy) and 117 random controls defined as patients from the same cohort without retinopathy, matched for diabetes duration by a range of 5 years. The Ethics Committee of the University Hospital of Pedro Ernesto approved the study. Written informed consent was obtained from all participants or their representatives.

Participants responded to a standardized questionnaire during a clinical visit to evaluate clinical and demographic data such as current age, age at diagnosis, gender, duration of diabetes, years of formal education and socio-economic status, and self-reported color/race. They were also submitted to clinical evaluation to assess the presence of complications and the measurement of clinical data. Hypertension was considered if self-reported or in the presence of a health practitioner’s previous diagnosis on at least two separate occasions. Glomerular Filtration Rate (GFR) was estimated by the CKD-EPI equation [17] in adults and by the Schwartz formula [18] in adolescents and was expressed in milliliters per minute per 1.73 m2 (ml/min). Blood sampling was collected to determine HbA1c levels (high-performance liquid chromatography-HPLC, Bio-Rad Laboratories, Hercules, California, USA) and genetic analysis.

Evaluation of diabetic retinopathy

Mydriatic binocular indirect ophthalmoscopy with an Eyetec Ophthalmoscope (Eyetec, São Carlos-SP, Brazil) and a 20-diopter lens (Volk Optical, Mentor, OH, USA) was performed by an experienced retinal specialist in each center. Patients had both eyes examined. Mydriasis was obtained with 1% tropicamide drops. All ophthalmologists who performed the fundoscopy were trained by the same retinal specialist and followed the same protocol. For patient classification, we considered the eye with the most severe classification of DR (absent, mild nonproliferative, moderate nonproliferative, severe nonproliferative, and proliferative DR) according to the American Academy of Ophthalmology guidelines [19].

DNA extraction and HLA genotyping

Genomic DNA was extracted from peripheral blood with the commercial kit SP QIA symphony by automation with QIA symphony equipment, following the manufacturer’s instructions (Qiagen, USA).

HLA-class II genes (HLA-DRB1, HLA-DQA1, and HLA-DQB1) were genotyped either by Next Generation Sequencing (NGS) or Medium to high-resolution PCR-RSSO (LabType SSO, One lambda Inc. West Hills, USA) method. Detailed information on genotyping of our studied population was described previously [20]. Briefly, in this present analysis, 79 (34%) individuals were genotyped using PCR- RSSO (LabType SSO2B1 High resolution, One Lambda Inc., West Hills, USA) and 155 (66%) patients by next-generation sequencing (NGSgo.v2, GenDx, Utrecht, the Netherlands). We used the Common and Well Documented version 2.0 (CWD2) to assign HLA-DRB1 and HLA-DQB1 genes ambiguities. Three-locus haplotype frequencies (HLA-DRB1 ~ HLA-DQA1 ~ HLA-DQB1) were estimated using the expectation-maximization (EM) algorithm [21, 22]. Deviations from Hardy–Weinberg equilibrium (HWE) were assessed at the allele-family level (first nomenclature field) using a modified version of the Guo and Thompson algorithm [23] as implemented in the software Arlequin v.3.5 [24].

Ambiguous HLA class II alleles within G group (i.e., groups of alleles that have identical nucleotide sequences across the exons encoding the peptide binding domains) were designated by a lower case ‘g’ (HLA-DRB1*12:01 g  =  12:01/12:10; HLA-DQA1*01:01 g  =  01:01/01:04/01:05; HLA-DQA1*03:01 g  =  03:01/03:02/03:03; HLA-DQA1*05:01 g  =  05:05/05:09; HLA-DQB1*03:01 g  =  0.03:01/03:09/03:19).

Statistical analysis

Categorical variables were presented as frequency (percentage). All normally distributed values were given as the mean  ±  standard deviation (SD), and all other values were given as the median (IQR). We used Chi-squared and Fisher’s tests to compare categorical data; the Student t test and analysis of variance (ANOVA) were used for comparisons between groups with numeric variables when indicated. Samples were divided into two groups (patients with T1D and severe DR and patients with T1D without DR) for population comparison testing. Arlequin software was used to calculate FST genetic distance, and the exact test for population differentiation test results was performed via allele frequencies extrapolations [24]. Bonferroni correction was applied for multiple tests. We used the Statistical Program for Social Sciences version 17.0 (SPSS, Inc., Chicago, Illinois). A two-sided p value of less than 0.05 was considered significant. Haplotype frequencies in cases and controls were compared using a Pearson X2 test. Odds ratios (ORs) and 95% CIs were computed.

Results

Population characteristics

Table 1 shows the study population characteristics. Patients with severe DR were older, with lower GFR, higher levels of HbA1c, and were more prone to hypertension than patients without DR.

Table 1 Characteristics of the study participants
Full size table

Overview of the risk and protective alleles and/or haplotypes of the HLA system in patients with severe DR and controls

Allele frequencies and distribution between groups are described in Table 2. The HLA-DRB1*13:02 allele was more frequent in patients with severe DR (6.41 vs. 1.7, p  =  0.01, OR  =  3.94). HLA-DQB1*03:01 was also more frequent in patients with severe DR (9.40 vs. 4.70, OR  =  2.10, p  =  0.047). None of the HLA-DRB1*, HLA-DQA1*, and HLA-DQB1* genes showed statistically significant differences between groups after Bonferroni correction for multiple tests.

Table 2 HLA-DRB1*, -DQB1*, and -DQA1* alleles distribution in patients with severe DR and patients without DR
Full size table

Table 3 shows the distribution of the HLA-DRB1 ~ DQA1 ~ DQB1 haplotypes in patients with severe DR and patients without DR. The most frequent haplotype on both groups was HLA-DRB1*03:01 ~ DQA1*05:01 g ~ DQB1*02:01 (29.6% on severe DR and 33.33% on patients without DR group). Haplotypes HLA-DRB1*04:05 ~ DQA1*03:01 g ~ DQB1*03:02 (OR 1.75, CI 0.97–3.16, p value 0.058) and HLA-DRB1*13:02 ~ DQA1*01:02 ~ DQB1*06:04 (OR 5.18, CI 1.12–23.09, p value 0.019) were more prevalent on the severe DR group but they did not present statistically difference after Bonferroni correction.

Table 3 Distribution of the HLA-DRB1 ~ DQA1 ~ DQB1 haplotypes in patients with severe DR and patients without DR
Full size table

HLA-DRB1/HLA-DRB1 genotypes are demonstrated in Table 4. The most frequent genotype on both groups was HLA-DRB1*03/HLA-DRB1*04 (26.5% on each group). The HLA-DRB1*13/X was more prevalent in the severe DR group (OR 5.37, CI 1.15–25.09, p value 0.019). Although HLA-DRB1*09/X and HLA-DRB1*03/X were more frequent in the group of patients without DR, this difference was not statistically significant.

Table 4 HLA-DRB1*/HLA-DRB1* genotypes distribution in patients with severe DR and patients without DR
Full size table

Discussion

Our study, conducted in a highly admixed population with T1D, showed no influence of HLA genes on the development of DR. All the possible risk associations such as those with the HLA-DRB1*13 allele and with the haplotype HLA-DRB1*13:02 ~ DQA1*01:02 ~ DQB1*06:04 as well the protection association such as that with the haplotype HLA-DRB1*04:01 ~ DQA1*03:01 g ~ DQB1*03:02 were no longer significant after Bonferroni correction. Independent of the retinopathy status, HLA-DRB1*03 and HLA-DRB1*04 were the most prevalent alleles.

Although HLA's role in the predisposition to T1D has been studied for decades [25,26,27,28], whether the HLA region influences the development of microvascular complications, especially retinopathy, is a matter of some controversy [6,7,8,9, 12,13,14, 29]. Several studies from different populations, including Brazilians, showed that HLA-DRB1*03 and HLA-DRB1*04 are the most frequent alleles in patients with type 1 diabetes [15, 28]. A recently published paper from our group using a large number of patients from Brazil also found those alleles as the most prevalent in the T1D group [20]. The present study and other studies in diabetic retinopathy demonstrated the HLA-DRB1*03 and HLA-DRB1*04 alleles as the most prevalent. Although some studies found the HLA-DRB1*04 allele associated with the risk of developing DR and the HLA-DRB1*03 allele to be related to protection [6], others found no association when comparing patients with severe DR and patients without severe DR [11,12,13]. Our study found no association between groups after Bonferroni corrections. It is important to note that most of the previous studies did not adjust for critical classic factors associated with DR, such as duration of diabetes, and were conducted in Caucasian populations.

Genetic predisposition to DR might be influenced by the differences in ancestry profiles of the studied populations. A recent study from our group with a similar group of patients and controls showed a difference between groups' ancestry profiles [30]. African genomic ancestry was associated with DR even after corrections. This could indicate that genetic predisposition might be related to other non-HLA genes.

In addition, the lack of association of the HLA genes and the development of DR might rely on the basis of DR’s pathogenesis. The pathogenesis of DR is multifactorial and still not yet completely understood. One well-established mechanism is the endothelial lesion mediated by hyperglycemia or hypoxia. High glucose levels induce oxidative stress mediated by the reduced effect of NO and activation of macrophages and the productions of inflammatory cytokines [31]. The TNF alfa has its gene located in the same region of HLA and has been implicated in the pathogenesis of DR [32], but genetic studies of its relation with DR predisposition are yet to be proved [33, 34]. Different genes have been studied in search of DR's genetic predisposition, but the only ones that showed relevant results were the polymorphisms of aldose reductase (AKR1B1) and VEGF genes [34]. Recent studies have demonstrated neurodegeneration as an early factor in the pathogenesis of DR, and some other studies have concentrated on the role of epigenetics, especially in mitochondrial DNA [35]. Despite several treatment options for severe DR, some patients do not achieve a satisfactory response to treatment. Therefore, further research is needed to better elucidate novel mechanisms related to this multifactorial disease.

Duration of diabetes is a critical factor in the development of DR, as demonstrated in previous data from several studies, including a survey from our group [36]. It is hypothesized that HLA-related influence on DR might be significant only in patients that develop DR in the early course of T1D [12]. In our study, as we matched patients and controls by the duration of diabetes in a 5-years range, therefore, excluding this potential confounder from our analysis, this time-influenced association might be diminished. However, this hypothesis is yet to be proved by further extensive longitudinal data.

Our study has strengths and limitations. One particular strength is the population-based ascertainment of diabetes cases in a highly admixed population. Also, we adjusted for the duration of diabetes, one of the most important risk factors for DR, at selection, matching controls by a range of five years. Our study was based only on patients assisted by the public health system in urban areas. Although this could have led to some selection bias, most T1D patients are treated on the public health system or both public and private health care in Brazil. Another limitation is that autoantibodies and C-peptide levels were not measured. This misdiagnosis bias might be mitigated by the fact that 96.5% of the patients included were diagnosed before 30 years of age. Also, the diagnosis of DR was assessed by binocular indirect ophthalmoscopy for all participants. Despite the inherent limitations of this method, a previous study from our group showed a substantial agreement between binocular indirect ophthalmoscopy and digital retinography [37].

In conclusion, our study, performed in a highly admixed population comparing patients with severe DR to T1D without DR matched for diabetes duration, found no association between HLA-Class II genes and severe DR. Further longitudinal data is needed to better understand the role of genetic factors on this multifactorial significant microvascular complication.

Availability of data and materials

Data will be made available upon request.

Code availability

Not applicable.

Abbreviations

TID:

Type 1 diabetes

HLA:

Human leukocyte antigen

DCCT:

Diabetes control and complication trial

EDIC:

Epidemiology of diabetes interventions and complications

DR:

Diabetic retinopathy

NAM:

Native Amerindian

EUR:

European

AFR:

African

GFR:

Glomerular filtration rate

NGS:

Next generation sequencing

SD:

Standart deviation

IQR:

Inter quartil range

NO:

Nitric oxide

TNF:

Tumor necrosis factor

VEGF:

Vascular endothelial growth factor

References

  1. Redondo MJ, Steck AK, Pugliese A. Genetics of type 1 diabetes. Pediatr Diabetes. 2018;19:346–53.

    Article  Google Scholar 

  2. Cudworth AG, Woodrow JC. HL-A antigens and diabetes mellitus. Lancet. 1974;304:1153.

    Article  Google Scholar 

  3. Esteves JF, Kramer CK, Azevedo MJD, Stolz AP, Roggia MF, Larangeira A, et al. Prevalence of diabetic retinopathy in patients with type 1 diabetes mellitus. Rev Assoc Méd Bras. 2009;55:268–73.

    Article  Google Scholar 

  4. Leasher JL, Bourne RRA, Flaxman SR, Jonas JB, Keeffe J, Naidoo K, et al. Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 1990 to 2010. Diabetes Care. 2016;39:1643–9.

    Article  Google Scholar 

  5. Nathan DM. The diabetes control and complications trial/epidemiology of diabetes interventions and complications study at 30 years: overview. Diabetes Care. 2014;37:9–16.

    Article  CAS  Google Scholar 

  6. Wong TY, Cruickshank KJ, Klein R, Klein BEK, Moss SE, Palta M, et al. HLA-DR3 and DR4 and their relation to the incidence and progression of diabetic retinopathy. Ophthalmology. 2002;109:275–81.

    Article  Google Scholar 

  7. Dornan TL, Ting A, McPherson CK, Peckar CO, Mann JI, Turner RC, et al. Genetic susceptibility to the development of retinopathy in insulin-dependent diabetics. Diabetes. 1982;31:226–31.

    Article  CAS  Google Scholar 

  8. Baker RS, Rand LI, Krolewski AS, Maki T, Warram JH, Aiello LM. Influence of HLA-DR phenotype and myopia on the risk of nonproliferative and proliferative diabetic retinopathy. Am J Ophthalmol. 1986;102:693–700.

    Article  CAS  Google Scholar 

  9. Agardh D, Gaur LK, Agardh E, Landin-Olsson M, Agardh CD, Lernmark Å. HLA-DQB1*0201/0302 is associated with severe retinopathy in patients with IDDM. Diabetologia. 1996;39:1313–7.

    Article  CAS  Google Scholar 

  10. Middleton D, Johnston P, Gillespie E. HLA-DR antigen association with proliferative diabetic retinopathy. Int Ophthalmol. 1985;8:33–5.

    Article  CAS  Google Scholar 

  11. Stewart LL, Field LL, Ross S, McArthur RG. Genetic risk factors in diabetic retinopathy. Diabetologia. 1993;36:1293–8.

    Article  CAS  Google Scholar 

  12. Falck AA, Knip JM, Ilonen JS, Laatikainen LT. Genetic markers in early diabetic retinopathy of adolescents with type I diabetes. J Diabetes Complicat. 1997;11:203–7.

    Article  CAS  Google Scholar 

  13. Cruickshanks KJ, Vadheim CM, Moss SE, Roth MP, Riley WJ, MacLaren NK, et al. Genetic marker associations with proliferative retinopathy in persons diagnosed with diabetes before 30 yr of age. Diabetes. 1992;41:879–85.

    Article  CAS  Google Scholar 

  14. Lipner EM, Tomer Y, Noble JA, Monti MC, Lonsdale JT, Corso B, et al. Linkage analysis of genomic regions contributing to the expression of type 1 diabetes microvascular complications and interaction with HLA. J Diabetes Res. 2015. https://doi.org/10.1155/2015/694107.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Gomes KFB, Santos AS, Semzezem C, Correia MR, Brito LA, Ruiz MO, et al. The influence of population stratification on genetic markers associated with type 1 diabetes. Sci Rep. 2017. https://doi.org/10.1038/srep43513.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Gomes MB, Rodacki M, Pavin EJ, Cobas RA, Felicio JS, Zajdenverg L, et al. The impact of ethnicity, educational and economic status on the prescription of insulin therapeutic regimens and on glycemic control in patients with type 1 diabetes. A nationwide study in Brazil. Diabetes Res Clin Pract. 2017;134:44–52.

    Article  Google Scholar 

  17. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604–12.

    Article  Google Scholar 

  18. Schwartz GJ, Work DF. Measurement and estimation of GFR in children and adolescents. Clin J Am Soc Nephrol. 2009;4:1832–43.

    Article  Google Scholar 

  19. Wilkinson CP, Ferris FL, Klein RE, Lee PP, Agardh CD, Davis M, et al. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology. 2003;110:1677–82.

    Article  CAS  Google Scholar 

  20. Santos DC, Porto LC, Oliveira RV, Secco D, Hanhoerderster L, Pizarro MH, et al. HLA class II genotyping of admixed Brazilian patients with type 1 diabetes according to self-reported color/race in a nationwide study. Sci Rep. 2020;10:1–10.

    Article  Google Scholar 

  21. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J Roy Stat Soc Ser B. 1977;39:1–22.

    Google Scholar 

  22. Excoffier L, Slatkin M. Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population. Mol Biol Evol. 1995;12:921–7.

    CAS  PubMed  Google Scholar 

  23. Guo SW, Thompson EA. Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics. 1992;48:361–72.

    Article  CAS  Google Scholar 

  24. Excoffier L, Lischer HEL. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Res. 2010;10:564–7.

    Article  Google Scholar 

  25. Risch N. Genetics of IDDM: evidence for complex inheritance with HLA. Genet Epidemiol. 1989;6:143–8.

    Article  CAS  Google Scholar 

  26. Kockum I, Sanjeevi C, Eastman S, Landin-Olsson M, Dahlquist G, Lernmark Å. Complex interaction between HLA DR and DQ in conferring risk for childhood type 1 diabetes. Eur J Immunogenet. 1999;26:361–72.

    Article  CAS  Google Scholar 

  27. Erlich H, Valdes AM, Noble J, Carlson JA, Varney M, Concannon P, et al. HLA DR-DQ haplotypes and genotypes and type 1 diabetes risk analysis of the type 1 diabetes genetics consortium families. Diabetes. 2008;57:1084–92.

    Article  CAS  Google Scholar 

  28. Noble JA, Valdes AM. Genetics of the HLA region in the prediction of type 1 diabetes. Curr DiabRep. 2011;11:533.

    Article  CAS  Google Scholar 

  29. Lipner EM, Tomer Y, Noble JA, Monti MC, Lonsdale JT, Corso B, et al. HLA class I and II alleles are associated with microvascular complications of type 1 diabetes. Hum Immunol. 2013;74:538–44.

    Article  CAS  Google Scholar 

  30. Santos DC, de Melo LGN, Pizarro MH, Barros BS, Negrato CA, Porto LC, et al. Genomic ancestry as a risk factor for diabetic retinopathy in patients with type 1 diabetes from an admixed population: a nested case–control study in Brazil. Acta Diabetologica. 2020;57:1–9.

    Article  Google Scholar 

  31. Pusparajah P, Lee L-H, Abdul KK. Molecular markers of diabetic retinopathy: potential screening tool of the future? Front Physiol. 2016;7:200.

    Article  Google Scholar 

  32. Limb G, Soomro H, Janikoun S, Hollifield R, Shilling J. Evidence for control of tumour necrosis factor-alpha (TNF-α) activity by TNF receptors in patients with proliferative diabetic retinopathy. Clin Exp Immunol. 1999;115:409.

    Article  CAS  Google Scholar 

  33. Grassi MA, Tikhomirov A, Ramalingam S, Below JE, Cox NJ, Nicolae DL. Genome-wide meta-analysis for severe diabetic retinopathy. Hum Mol Genet. 2011;20:2472–81.

    Article  CAS  Google Scholar 

  34. Kuo JZ, Wong TY, Rotter JI. Challenges in elucidating the genetics of diabetic retinopathy. JAMA Ophthalmol. 2014;132:96–107.

    Article  CAS  Google Scholar 

  35. Kowluru RA. Mitochondrial stability in diabetic retinopathy: lessons learned from epigenetics. Diabetes. 2019;68:241–7.

    Article  CAS  Google Scholar 

  36. Melo LGN, Morales PH, Drummond KRG, Santos DC, Pizarro MH, Barros BSV, et al. Current epidemiology of diabetic retinopathy in patients with type 1 diabetes: a national multicenter study in Brazil. BMC Public Health. 2018;18:989.

    Article  Google Scholar 

  37. Malerbi FK, Morales PH, Farah ME, Drummond KRG, Mattos TCL, Pinheiro AA, et al. Comparison between binocular indirect ophthalmoscopy and digital retinography for diabetic retinopathy screening: the multicenter Brazilian Type 1 Diabetes Study. Diabetol Metab Syndr. 2015;7:116.

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

FAPERJ—Fundação do Amparo à Pesquisa do Estado do Rio de Janeiro (Grant number: E-26/110.170/2013) and CNPq-Conselho Nacional de Desenvolvimento Científico e Tecnológico do Brasil (Grant number: 563753/2010-2).

Author information

Authors and Affiliations

  1. Department of Internal Medicine, Diabetes Unit, Rio de Janeiro State University (UERJ), Boulevard 28 de Setembro, 77-3º andar, Vila Isabel, Rio de Janeiro, Rio de Janeiro, CEP 20551-030, Brazil

    Deborah Conte Santos, Marcela Haas Pizarro, Luiza Harcar Muniz, Camila Soares, Lucianne Righeti Monteiro Tannus & Marília Brito Gomes

  2. Histocompatibility and Cryopreservation Laboratory (HLA), Rio de Janeiro State University (UERJ), Rio de Janeiro, Rio de Janeiro, Brazil

    Luís Cristóvão Porto, Romulo Vianna Oliveira & Anna Paula Villela

  3. Department of Ophthalmology, State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Laura Gomes Nunes de Melo

  4. DNA Diagnostic Laboratory (LDD), Rio de Janeiro State University (UERJ), Rio de Janeiro, Rio de Janeiro, Brazil

    Dayse A. Silva

  5. Department of Ophthalmology, Federal University of São Paulo, São Paulo, Brazil

    Karla Rezende Guerra Drummond & Paulo Henrique Morales

  6. Department of Ophthalmology, Regional Hospital of Taguatinga, Brasília, Brazil

    André Araújo Pinheiro

  7. Department of Ophthalmology, Federal University of Rio Grande Do Sul, Porto Alegre, Brazil

    Felipe Mallmann

  8. Department of Ophthalmology, University of Campinas, Campinas, São Paulo, Brazil

    Franz Schubert Lopes Leal

  9. Department of Endocrinology and Ophthalmology, Federal University of São Paulo, São Paulo, Brazil

    Fernando Korn Malerbi

Authors
  1. Deborah Conte Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luís Cristóvão Porto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marcela Haas Pizarro
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Laura Gomes Nunes de Melo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dayse A. Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Romulo Vianna Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Anna Paula Villela
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Luiza Harcar Muniz
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Camila Soares
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Lucianne Righeti Monteiro Tannus
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Karla Rezende Guerra Drummond
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. André Araújo Pinheiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Felipe Mallmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Franz Schubert Lopes Leal
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Fernando Korn Malerbi
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Paulo Henrique Morales
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Marília Brito Gomes
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

DCS, LCP, DAS, MBG, MHP, LRMT and LGNM analyzed and interpreted the data. RVO, APV, CS, and LHM performed HLA laboratory analysis. KRGD, AAP, FM, FSLL, FKM, and PHM performed the eye examination. DCS and MBG wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Deborah Conte Santos.

Ethics declarations

Ethics approval and consent to participate

All procedures performed in the study were in accordance with the ethical standards of the institutional ethics committee of all centers as listed below and with the 1964 Helsinki declaration and its later amendments.

Consent for publication

Not applicable.

Informed consent

Informed consent was obtained from all the study participants included in the study, according to the national guidelines of ethical standards and in keeping with the Helsinki Declaration of 2008 (ICH GCP).

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

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 the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Santos, D.C., Porto, L.C., Pizarro, M.H. et al. Human Leukocyte Antigens class II (HLA II) gene profile from an admixed population of patients with type 1 diabetes with severe diabetic retinopathy: a nested case-control study in Brazil. Diabetol Metab Syndr 13, 83 (2021). https://doi.org/10.1186/s13098-021-00702-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13098-021-00702-y

Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

Contact us