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Weng SS, Chien LY. IVF and risk of Type 1 diabetes mellitus: a population-based nested case-control study. Hum Reprod 2024; 39:1816-1822. [PMID: 38852062 DOI: 10.1093/humrep/deae122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 05/15/2024] [Indexed: 06/10/2024] Open
Abstract
STUDY QUESTION Is the mode of conception (natural, subfertility and non-IVF, and IVF) associated with the risk of Type 1 diabetes mellitus among offspring? SUMMARY ANSWER The risk of Type 1 diabetes in offspring does not differ among natural, subfertility and non-IVF, and IVF conceptions. WHAT IS KNOWN ALREADY Evidence has shown that children born through IVF have an increased risk of impaired metabolic function. STUDY DESIGN, SIZE, DURATION A population-based, nested case-control study was carried out, including 769 children with and 3110 children without Type 1 diabetes mellitus within the prospective cohort of 2 228 073 eligible parent-child triads between 1 January 2004 and 31 December 2017. PARTICIPANTS/MATERIALS, SETTING, METHODS Using registry data from Taiwan, the mode of conception was divided into three categories: natural conception, subfertility, and non-IVF (indicating infertility diagnosis but no IVF-facilitated conception), and IVF conception. The diagnosis of Type 1 diabetes mellitus was determined according to the International Classification of Diseases, 9th or 10th Revision, Clinical Modification. Each case was matched to four controls randomly selected after matching for child age and sex, residential township, and calendar date of Type 1 diabetes mellitus occurrence. MAIN RESULTS AND THE ROLE OF CHANCE Based on 14.3 million person-years of follow-up (median, 10 years), the incidence rates of Type 1 diabetes were 5.33, 5.61, and 4.74 per 100 000 person-years for natural, subfertility and non-IVF, and IVF conceptions, respectively. Compared with natural conception, no significant differences in the risk of Type 1 diabetes were observed for subfertility and non-IVF conception (adjusted odds ratio, 1.04 [95% CI, 0.85-1.27]) and IVF conception (adjusted odds ratio, 1.00 [95% CI, 0.50-2.03]). In addition, there were no significant differences in the risk of Type 1 diabetes according to infertility source (male/female/both) and embryo type (fresh/frozen). LIMITATIONS, REASONS FOR CAUTION Although the population-level data from Taiwanese registries was used, a limited number of exposed cases was included. We showed risk of Type 1 diabetes was not associated with infertility source or embryo type; however, caution with interpretation is required owing to the limited number of exposed events after the stratification. The exclusion criterion regarding parents' history of diabetes mellitus was only applicable after 1997, and this might have caused residual confounding. WIDER IMPLICATIONS OF THE FINDINGS It has been reported that children born to parents who conceived through IVF had worse metabolic profiles than those who conceived naturally. Considering the findings of the present and previous studies, poor metabolic profiles may not be sufficient to develop Type 1 diabetes mellitus during childhood. STUDY FUNDING/COMPETING INTEREST(S) This study was supported by grants from Shin Kong Wu Ho-Su Memorial Hospital (No. 109GB006-1). The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication. The authors have no competing interests to disclose. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
- Shiue-Shan Weng
- Institute of Public Health, College of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
| | - Li-Yin Chien
- Institute of Community Health Care, College of Nursing, National Yang Ming Chiao Tung University, Taipei City, Taiwan
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Negrato CA, Martins RLDM, Louro MD, Medeiros GA, Lanzarin JVM, Zajdenverg L, Lopes LCP. Association between perinatal and obstetric factors and early age at diagnosis of type 1 diabetes mellitus: a cohort study. J Pediatr Endocrinol Metab 2024; 0:jpem-2024-0235. [PMID: 39042913 DOI: 10.1515/jpem-2024-0235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Accepted: 07/07/2024] [Indexed: 07/25/2024]
Abstract
OBJECTIVES To evaluate the association between perinatal and obstetric factors as potential triggers for the early onset of T1DM. METHODS This was a retrospective cohort study enrolling 409 patients diagnosed with T1DM, in Bauru, São Paulo, Brazil, from 1981 to 2023. Data were retrieved from medical records, regarding sociodemographic parameters as age, sex, ethnicity, and socioeconomic status. Perinatal and obstetric factors as delivery type, gestational age, filiation order, length of exclusive breastfeeding, maternal age, maternal and fetal blood types, and occurrence of maternal gestational diabetes were also analyzed. An adapted survival analysis was employed to gauge the impact of each assessed variable at the age of T1DM diagnosis. RESULTS The median age of T1DM diagnosis was 10.3 years with an interquartile range between 6.4 and 15.5 years. Delivery type and filiation order were the only factors statistically significantly associated with an early age at T1DM diagnosis. Patients who were born through cesarean section and who were firstborns showed a 28.6 and 18.0 % lower age at T1DM diagnosis, respectively, compared to those born through vaginal delivery and those that were nonfirstborns. CONCLUSIONS Being born by cesarean section and being firstborn showed to be statistically significant factors to determine an early T1DM diagnosis.
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Affiliation(s)
- Carlos A Negrato
- Universidade de São Paulo, Faculdade de Medicina de Bauru, Bauru, SP, Brazil
| | | | - Marina D Louro
- Universidade de São Paulo, Faculdade de Medicina de Bauru, Bauru, SP, Brazil
| | - Gabriel A Medeiros
- Universidade de São Paulo, Faculdade de Medicina de Bauru, Bauru, SP, Brazil
| | - João V M Lanzarin
- Universidade de São Paulo, Faculdade de Medicina de Bauru, Bauru, SP, Brazil
| | - Lenita Zajdenverg
- Internal Medicine Department - Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Lucas C P Lopes
- Universidade de São Paulo, Faculdade de Medicina de Bauru, Bauru, SP, Brazil
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Li J, Zhang M, Zhang S, Wang R, Cai Y, Chen X, Dong Y, Wang P, Shu J, Lv L, Cai C. CTSG polymorphisms in Chinese children with type 1 diabetes mellitus. J Trop Pediatr 2024; 70:fmae017. [PMID: 39122654 DOI: 10.1093/tropej/fmae017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/12/2024]
Abstract
Cathepsin G (CTSG) plays an important role in the regulation of immune processes. Accumulated studies show that CTSG is involved in the onset and development of type 1 diabetes mellitus (T1DM). As the genetic background of T1DM varies widely among populations, we aimed to study the relationship between genetic polymorphisms in CTSG and T1DM susceptibility in Chinese populations. A total of 141 patients with T1DM and 200 healthy controls were enrolled in the study. Serum CTSG expression was detected using enzyme-linked immunosorbent assay (ELISA). Genotyping of two selected single nucleotide polymorphisms (SNPs) (rs2236742 and rs2070697) of CTSG was performed using PCR and Sanger sequencing. CTSG expression in patients with T1DM was significantly higher than in the control group. Alleles C and T of CTSG SNP rs2236742 were increased in T1DM. No significant associations were found for the SNP rs2070697. Our results indicate that the CTSG rs2236742 allele (C/T) is associated with T1DM in Chinese children and may serve as a new biomarker for predicting T1DM susceptibility.
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Affiliation(s)
- Jiaci Li
- Tianjin Children's Hospital (Children's Hospital, Tianjin University), Tianjin, 300074, China
- Tianjin Pediatric Research Institute, Tianjin, 300074, China
- Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, Tianjin, 300074, China
| | - Mingying Zhang
- Tianjin Children's Hospital (Children's Hospital, Tianjin University), Tianjin, 300074, China
- Department of Endocrinology, Tianjin Children's Hospital (Children's Hospital, Tianjin University), Tianjin, 300074, China
| | - Shuyue Zhang
- Tianjin Children's Hospital (Children's Hospital, Tianjin University), Tianjin, 300074, China
- Graduate College of Tianjin Medical University, Tianjin, 300070, China
| | - Rui Wang
- Tianjin Children's Hospital (Children's Hospital, Tianjin University), Tianjin, 300074, China
- Graduate College of Tianjin Medical University, Tianjin, 300070, China
| | - Yingzi Cai
- Tianjin Children's Hospital (Children's Hospital, Tianjin University), Tianjin, 300074, China
- Medical College of Tianjin University, Tianjin, 300072, China
| | - Xiaofang Chen
- Tianjin Children's Hospital (Children's Hospital, Tianjin University), Tianjin, 300074, China
- Graduate College of Tianjin Medical University, Tianjin, 300070, China
| | - Yan Dong
- Tianjin Children's Hospital (Children's Hospital, Tianjin University), Tianjin, 300074, China
- Graduate College of Tianjin Medical University, Tianjin, 300070, China
| | - Ping Wang
- Tianjin Children's Hospital (Children's Hospital, Tianjin University), Tianjin, 300074, China
- Tianjin Pediatric Research Institute, Tianjin, 300074, China
- Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, Tianjin, 300074, China
| | - Jianbo Shu
- Tianjin Children's Hospital (Children's Hospital, Tianjin University), Tianjin, 300074, China
- Tianjin Pediatric Research Institute, Tianjin, 300074, China
- Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, Tianjin, 300074, China
| | - Ling Lv
- Tianjin Children's Hospital (Children's Hospital, Tianjin University), Tianjin, 300074, China
- Department of Endocrinology, Tianjin Children's Hospital (Children's Hospital, Tianjin University), Tianjin, 300074, China
| | - Chunquan Cai
- Tianjin Children's Hospital (Children's Hospital, Tianjin University), Tianjin, 300074, China
- Tianjin Pediatric Research Institute, Tianjin, 300074, China
- Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, Tianjin, 300074, China
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Joglekar MV, Kaur S, Pociot F, Hardikar AA. Prediction of progression to type 1 diabetes with dynamic biomarkers and risk scores. Lancet Diabetes Endocrinol 2024; 12:483-492. [PMID: 38797187 DOI: 10.1016/s2213-8587(24)00103-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 03/31/2024] [Accepted: 04/02/2024] [Indexed: 05/29/2024]
Abstract
Identifying biomarkers of functional β-cell loss is an important step in the risk stratification of type 1 diabetes. Genetic risk scores (GRS), generated by profiling an array of single nucleotide polymorphisms, are a widely used type 1 diabetes risk-prediction tool. Type 1 diabetes screening studies have relied on a combination of biochemical (autoantibody) and GRS screening methodologies for identifying individuals at high-risk of type 1 diabetes. A limitation of these screening tools is that the presence of autoantibodies marks the initiation of β-cell loss, and is therefore not the best biomarker of progression to early-stage type 1 diabetes. GRS, on the other hand, represents a static biomarker offering a single risk score over an individual's lifetime. In this Personal View, we explore the challenges and opportunities of static and dynamic biomarkers in the prediction of progression to type 1 diabetes. We discuss future directions wherein newer dynamic risk scores could be used to predict type 1 diabetes risk, assess the efficacy of new and emerging drugs to retard, or prevent type 1 diabetes, and possibly replace or further enhance the predictive ability offered by static biomarkers, such as GRS.
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Affiliation(s)
- Mugdha V Joglekar
- School of Medicine, Western Sydney University, Sydney, NSW, Australia
| | | | - Flemming Pociot
- Steno Diabetes Center Copenhagen, Herlev, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
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Gomes MB, Dos Santos GC, de Sousa Azulay RS, Santos DC, Silva DA, Carvalho PRVB, Negrato CA, Porto LC. Association between HLA alleles and haplotypes with age at diagnosis of type 1 diabetes in an admixed Brazilian population: A nationwide study. HLA 2024; 104:e15574. [PMID: 38993161 DOI: 10.1111/tan.15574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 05/13/2024] [Accepted: 06/05/2024] [Indexed: 07/13/2024]
Abstract
To investigate the potential relationship between HLA alleles and haplotypes and the age at diagnosis of type 1 diabetes (T1DAgeD) in an admixed Brazilian population. This nationwide study was conducted in public clinics across 12 Brazilian cities. We collected demographic and genetic data from 1,600 patients with T1D. DNA samples were utilised to determine genomic ancestry (GA) and perform HLA typings for DRB1, DQA1 and DQB1. We explored allele and haplotype frequencies and GA in patients grouped by T1DAgeD categories (<6 years, ≥6-<11 years, ≥11-<19 years and ≥19 years) through univariate and multivariate analyses and primary component analyses. Additionally, we considered self-reported colour-race and identified a familiar history of T1D in first-degree relatives. The homozygosity index for DRB1~DQA1~DQB1 haplotypes exhibited the highest variation among T1DAgeD groups, and the percentages of Sub-Saharan African and European ancestries showed opposite trends in principal component analysis (PCA) analyses. Regarding the association of alleles and haplotypes with T1DAgeD, risk alleles such as HLA-DQB1*03:02g, -DQA1*03:01g, -02:01g, DRB1*04:05g and -04:02g were more frequently observed in heterozygosity or homozygosity in T1D patients with an early disease onset. Conversely, alleles such as DRB1*07:01g, -13:03g, DQB1*06:02g and DQA1*02:01 were more prevalent in older T1D patients. The combination DR3/DR4.5 was significantly associated with early disease onset. However, gender, GA, familiar history of T1D and self-reported colour-race identity did not exhibit significant associations with the onset of T1D. It is worth noting that the very common risk haplotype DRB1*03:01g~DQA1*05:01g~DQB1*02:01g did not differentiate between T1DAgeD groups. In the admixed Brazilian population, the high-risk haplotype DRB1*04:05~DQA1*03:01~DQB1*03:02 was more prevalent in individuals diagnosed before 6 years of age. In contrast, the protective alleles DQA1*01:02g, DQB1*06:02g, DRB1*07:01g and DRB1*13:03g and haplotypes DRB1*13:03g~DQA1*05:01g~DQB1*03:01g and DRB1*16:02g~DQA1*01:02g~DQB1*05:02g were more frequently observed in patients diagnosed in adulthood. Notably, these associations were independent of factors such as sex, economic status, GA, familiar history of T1D and region of birth in Brazil. These alleles and haplotypes contribute to our understanding of the disease onset heterogeneity and may have implications for early interventions when detected in association with well-known genomic risk or protection factors for T1D.
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Affiliation(s)
- Marília Brito Gomes
- Department of Internal Medicine, Diabetes Unit, Rio de Janeiro State University (UERJ), Rio de Janeiro, Brazil
| | - Gilson Costa Dos Santos
- Laboratory of Metabolomics (LabMet), Department of Genetics, IBRAG, Rio de Janeiro State University, Rio de Janeiro, Brazil
| | | | - Deborah Conte Santos
- Department of Internal Medicine, Diabetes Unit, Rio de Janeiro State University (UERJ), Rio de Janeiro, Brazil
| | - Dayse Aparecida Silva
- DNA Diagnostic Laboratory (LDD), Rio de Janeiro State University (UERJ), Rio de Janeiro, Brazil
| | | | | | - Luís Cristóvão Porto
- Histocompatibility and Cryopreservation Laboratory (HLA-UERJ), Rio de Janeiro State University (UERJ), Rio de Janeiro, Brazil
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Wei Y, Liu S, Andersson T, Feychting M, Kuja-Halkola R, Carlsson S. Familial aggregation and heritability of childhood-onset and adult-onset type 1 diabetes: a Swedish register-based cohort study. Lancet Diabetes Endocrinol 2024; 12:320-329. [PMID: 38561011 DOI: 10.1016/s2213-8587(24)00068-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/14/2024] [Accepted: 02/23/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND Type 1 diabetes in children is known to be highly heritable, but much less is known about the heritability of adult-onset type 1 diabetes. Thus, our objective was to compare the familial aggregation and heritability of type 1 diabetes in adults and children. METHODS This Swedish nationwide register-based cohort study included individuals born from Jan 1, 1982, to Dec 31, 2010, identified through the Medical Birth Register who were linked to their parents, full siblings, half siblings, and cousins through the Multi-Generation Register (MGR). We excluded multiple births, deaths within the first month of life, individuals who could not be linked to MGR, or individuals with contradictory information on sex. Information on diagnoses of type 1 diabetes was retrieved by linkages to the National Diabetes Register and National Patient Register (1982-2020). Individuals with inconsistent records of diabetes type were excluded. We estimated the cumulative incidence and hazard ratios (HRs) of type 1 diabetes in adults (aged 19-30 years) and children (aged 0-18 years) by family history of type 1 diabetes and the heritability of adult-onset and childhood-onset type 1 diabetes based on tetrachoric correlations, using sibling pairs. FINDINGS 2 943 832 individuals were born in Sweden during the study period, 2 832 755 individuals were included in the analysis of childhood-onset type 1 diabetes and 1 805 826 individuals were included in the analysis of adult-onset type 1 diabetes. 3240 cases of adult-onset type 1 diabetes (median onset age 23·4 years [IQR 21·1-26·2]; 1936 [59·7%] male and 1304 [40·2%] female) and 17 914 cases of childhood-onset type 1 diabetes (median onset age 9·8 years [6·2-13·3]; 9819 [54·8%] male and 8095 [45·2%] female) developed during follow-up. Having a first-degree relative with type 1 diabetes conferred an HR of 7·21 (95% CI 6·28-8·28) for adult-onset type 1 diabetes and 9·92 (9·38-10·50) for childhood-onset type 1 diabetes. The HR of developing type 1 diabetes before the age 30 years was smaller if a first-degree relative developed type 1 diabetes during adulthood (6·68 [6·04-7·39]) rather than during childhood (10·54 [9·92-11·19]). Similar findings were observed for type 1 diabetes in other relatives. Heritability was lower for adult-onset type 1 diabetes (0·56 [0·45-0·66]) than childhood-onset type 1 diabetes (0·81 [0·77-0·85]). INTERPRETATION Adult-onset type 1 diabetes seems to have weaker familial aggregation and lower heritability than childhood-onset type 1 diabetes. This finding suggests a larger contribution of environmental factors to the development of type 1 diabetes in adults than in children and highlights the need to identify and intervene on such factors. FUNDING Swedish Research Council, the Swedish Research Council for Health, Working Life and Welfare, Swedish Diabetes Foundation, and the China Scholarship Council.
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Affiliation(s)
- Yuxia Wei
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
| | - Shengxin Liu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Tomas Andersson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Centre for Occupational and Environmental Medicine, Stockholm County Council, Stockholm, Sweden
| | - Maria Feychting
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Ralf Kuja-Halkola
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Sofia Carlsson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
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Yu G, Tam HCH, Huang C, Shi M, Lim CKP, Chan JCN, Ma RCW. Lessons and Applications of Omics Research in Diabetes Epidemiology. Curr Diab Rep 2024; 24:27-44. [PMID: 38294727 PMCID: PMC10874344 DOI: 10.1007/s11892-024-01533-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/04/2024] [Indexed: 02/01/2024]
Abstract
PURPOSE OF REVIEW Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies. RECENT FINDINGS We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.
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Affiliation(s)
- Gechang Yu
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Henry C H Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Chuiguo Huang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Mai Shi
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Cadmon K P Lim
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Juliana C N Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China.
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China.
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China.
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Minniakhmetov I, Yalaev B, Khusainova R, Bondarenko E, Melnichenko G, Dedov I, Mokrysheva N. Genetic and Epigenetic Aspects of Type 1 Diabetes Mellitus: Modern View on the Problem. Biomedicines 2024; 12:399. [PMID: 38398001 PMCID: PMC10886892 DOI: 10.3390/biomedicines12020399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
Omics technologies accumulated an enormous amount of data that advanced knowledge about the molecular pathogenesis of type 1 diabetes mellitus and identified a number of fundamental problems focused on the transition to personalized diabetology in the future. Among them, the most significant are the following: (1) clinical and genetic heterogeneity of type 1 diabetes mellitus; (2) the prognostic significance of DNA markers beyond the HLA genes; (3) assessment of the contribution of a large number of DNA markers to the polygenic risk of disease progress; (4) the existence of ethnic population differences in the distribution of frequencies of risk alleles and genotypes; (5) the infancy of epigenetic research into type 1 diabetes mellitus. Disclosure of these issues is one of the priorities of fundamental diabetology and practical healthcare. The purpose of this review is the systemization of the results of modern molecular genetic, transcriptomic, and epigenetic investigations of type 1 diabetes mellitus in general, as well as its individual forms. The paper summarizes data on the role of risk HLA haplotypes and a number of other candidate genes and loci, identified through genome-wide association studies, in the development of this disease and in alterations in T cell signaling. In addition, this review assesses the contribution of differential DNA methylation and the role of microRNAs in the formation of the molecular pathogenesis of type 1 diabetes mellitus, as well as discusses the most currently central trends in the context of early diagnosis of type 1 diabetes mellitus.
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Affiliation(s)
- Ildar Minniakhmetov
- Endocrinology Research Centre, Dmitry Ulyanov Street, 11, 117292 Moscow, Russia; (R.K.); (E.B.); (G.M.); (I.D.); (N.M.)
| | - Bulat Yalaev
- Endocrinology Research Centre, Dmitry Ulyanov Street, 11, 117292 Moscow, Russia; (R.K.); (E.B.); (G.M.); (I.D.); (N.M.)
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Ilonen J, Kiviniemi M, El-Amir MI, Nygård L, Härkönen T, Lempainen J, Knip M. Increased Frequency of the HLA-DRB1*04:04-DQA1*03-DQB1*03:02 Haplotype Among HLA-DQB1*06:02-Positive Children With Type 1 Diabetes. Diabetes 2024; 73:306-311. [PMID: 37934957 DOI: 10.2337/db23-0387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 10/20/2023] [Indexed: 11/09/2023]
Abstract
HLA-DR/DQ haplotypes largely define genetic susceptibility to type 1 diabetes (T1D). The DQB1*06:02-positive haplotype (DR15-DQ602) common in individuals of European ancestry is very rare among children with T1D. Among 4,490 children with T1D in the Finnish Pediatric Diabetes Register, 57 (1.3%) case patients with DQB1*06:02 were identified, in comparison with 26.1% of affected family-based association control participants. There were no differences between DQB1*06:02-positive and -negative children with T1D regarding sex, age, islet autoantibody distribution, or autoantibody levels, but significant differences were seen in the frequency of second class II HLA haplotypes. The most prevalent haplotype present with DQB1*06:02 was DRB1*04:04-DQA1*03-DQB1*03:02, which was found in 27 (47.4%) of 57 children, compared with only 797 (18.0%) of 4,433 among DQB1*06:02-negative case patients (P < 0.001 by χ2 test). The other common risk-associated haplotypes, DRB1*04:01-DQA1*03-DQB1*03:02 and (DR3)-DQA1*05-DQB1*02, were less prevalent in DQB1*06:02-positive versus DQB1*06:02-negative children (P < 0.001). HLA-B allele frequencies did not differ by DQB1*06:02 haplotype between children with T1D and control participants or by DRB1*04:04-DQA1*03-DQB1*03:02 haplotype between DQB1*06:02-positive and -negative children with T1D. The increased frequency of the DRB1*04:04 allele among DQB1*06:02-positive case patients may indicate a preferential ability of the DR404 molecule to present islet antigen epitopes despite competition by DQ602. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Jorma Ilonen
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Minna Kiviniemi
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Mostafa I El-Amir
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Medical Microbiology and Immunology, Faculty of Medicine, South Valley University, Qena, Egypt
| | - Lucas Nygård
- Department of Clinical Microbiology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Taina Härkönen
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Johanna Lempainen
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
- Departments of Pediatrics, University of Turku and Turku University Hospital, Turku, Finland
- Clinical Microbiology, Turku University Hospital, Turku, Finland
| | - Mikael Knip
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Pediatric Research Center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
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10
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Song R, Xie L, Ding J, Chen Y, Zou H, Pang H, Peng Y, Xia Y, Xie Z, Li X, Xiao Y, Zhou Z, Hu J. Association of RPS26 gene polymorphism with different types of diabetes in Chinese individuals. J Diabetes Investig 2024; 15:34-43. [PMID: 38041572 PMCID: PMC10759724 DOI: 10.1111/jdi.14117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/07/2023] [Accepted: 11/16/2023] [Indexed: 12/03/2023] Open
Abstract
AIMS/INTRODUCTION Different types of diabetes show distinct genetic characteristics, but the specific genetic susceptibility factors remain unclear. Our study aimed to explore the associations between the ribosomal protein S26 (RPS26) gene rs1131017 polymorphisms and susceptibility to type 1 diabetes mellitus, latent autoimmune diabetes in adults (LADA) and type 2 diabetes mellitus in the Chinese Han population, and their correlations with clinical features. MATERIALS AND METHODS Genotyping of the rs1131017 variant was carried out for 1,006 type 1 diabetes mellitus patients, 210 LADA patients, 642 type 2 diabetes mellitus patients and 2,099 control individuals. RESULTS We found that the rs1131017 C allele was a risk locus for both type 1 diabetes mellitus and LADA (odds ratio [OR] 1.50, 95% confidence interval [CI] 1.33-1.69, P < 0.001; OR 1.31, 95% CI 1.04-1.64, P = 0.021, respectively). Nevertheless, this association was not found for type 2 diabetes mellitus. Carrying the C allele genotype was associated with a lower postprandial C-peptide for type 1 diabetes mellitus (OR 1.41, 95% CI 1.11-1.80, P = 0.006) and lower fasting C-peptide for LADA (OR 1.55, 95% CI 1.01-2.38, P = 0.047). Interestingly, a lower GC frequency was noted for LADA than for type 1 diabetes mellitus, regardless of classification based on age at diagnosis, C-peptide or glutamic acid decarboxylase antibody positivity. CONCLUSIONS The RPS26 polymorphism was associated with susceptibility and clinical characteristics of type 1 diabetes mellitus and LADA in the Chinese population, but was not related to type 2 diabetes mellitus. Thus, it might serve as a novel biomarker for particular types of diabetes.
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Affiliation(s)
- Rong Song
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Lingxiang Xie
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Jin Ding
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Yan Chen
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Hailan Zou
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Haipeng Pang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Yiman Peng
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Ying Xia
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Zhiguo Xie
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Xia Li
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Yang Xiao
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Zhiguang Zhou
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Jingyi Hu
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
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11
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Leslie RD, Ma RCW, Franks PW, Nadeau KJ, Pearson ER, Redondo MJ. Understanding diabetes heterogeneity: key steps towards precision medicine in diabetes. Lancet Diabetes Endocrinol 2023; 11:848-860. [PMID: 37804855 DOI: 10.1016/s2213-8587(23)00159-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/30/2023] [Accepted: 05/27/2023] [Indexed: 10/09/2023]
Abstract
Diabetes is a highly heterogeneous condition; yet, it is diagnosed by measuring a single blood-borne metabolite, glucose, irrespective of aetiology. Although pragmatically helpful, disease classification can become complex and limit advances in research and medical care. Here, we describe diabetes heterogeneity, highlighting recent approaches that could facilitate management by integrating three disease models across all forms of diabetes, namely, the palette model, the threshold model and the gradient model. Once diabetes has developed, further worsening of established diabetes and the subsequent emergence of diabetes complications are kept in check by multiple processes designed to prevent or circumvent metabolic dysfunction. The impact of any given disease risk factor will vary from person-to-person depending on their background, diabetes-related propensity, and environmental exposures. Defining the consequent heterogeneity within diabetes through precision medicine, both in terms of diabetes risk and risk of complications, could improve health outcomes today and shine a light on avenues for novel therapy in the future.
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Affiliation(s)
| | - Ronald Ching Wan Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China; Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China; Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Paul W Franks
- Novo Nordisk Foundation, Hellerup, Denmark; Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmo, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Kristen J Nadeau
- Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Ewan R Pearson
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
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12
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Liao WL, Huang YN, Chang YW, Liu TY, Lu HF, Tiao ZY, Su PH, Wang CH, Tsai FJ. Combining polygenic risk scores and human leukocyte antigen variants for personalized risk assessment of type 1 diabetes in the Taiwanese population. Diabetes Obes Metab 2023; 25:2928-2936. [PMID: 37455666 DOI: 10.1111/dom.15187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 06/06/2023] [Accepted: 06/06/2023] [Indexed: 07/18/2023]
Abstract
AIMS To analyse the genome-wide association study (GWAS) data of patients with type 1 diabetes mellitus (T1D) in order to develop a risk score for the genetic effects on T1D risk and age at diagnosis in the Taiwanese population. MATERIALS AND METHODS We selected 610 patients with T1D and 2511 healthy individuals from an electronic medical record database of more than 300 000 individuals with genetic information, analysed their GWAS data, and developed a polygenic risk score (PRS). RESULTS The PRS, based on 149 selected single-nucleotide polymorphisms, could effectively predict T1D risk. A PRS increase was associated with increased T1D risk (odds ratio [OR] 2.09, 95% confidence interval [CI] 1.72-2.55). Moreover, a 1-unit increase in standardized T1D PRS decreased the age at diagnosis by 0.74 years. Combined PRS and human leukocyte antigen (HLA) DQA1*03:02-DQA1*05:01 genotypes could accurately predict T1D risk. In multivariable models, HLA variants and PRS were independent risk factors for T1D risk (OR 3.76 [95% CI 1.54-9.16] and 1.71 [95% CI 1.37-2.13] for HLA DQA1*03:02-DQA1*05:01 and PRS, respectively). In a limited study population of those aged ≤18 years, PRS remained significantly associated with T1D risk. The association between T1D PRS and age at diagnosis was more obvious among males and patients aged ≤18 years. CONCLUSIONS Polygenic risk score and HLA variations enable personalized risk estimates, enhance newborn screening efficiency for ketoacidosis prevention, and addresses the gap in data on T1D prediction in isolated Asian populations.
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Affiliation(s)
- Wen-Ling Liao
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan
- Center for Personalized Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Yu-Nan Huang
- Division of Genetics and Metabolism, Children's Hospital of China Medical University, Taichung, Taiwan
- Department of Pediatrics, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Ya-Wen Chang
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan
- Center for Personalized Medicine, China Medical University Hospital, Taichung, Taiwan
- Department of Medical Research, Genetic Center, China Medical University Hospital, Taichung, Taiwan
| | - Ting-Yuan Liu
- Center for Precision Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Hsing-Fang Lu
- Center for Precision Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Zih-Yu Tiao
- School of Chinese Medicine, China Medical University, Taichung, Taiwan
| | - Pen-Hua Su
- Department of Pediatrics, Chung Shan Medical University Hospital, Taichung, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Chung-Hsing Wang
- Division of Genetics and Metabolism, Children's Hospital of China Medical University, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
| | - Fuu-Jen Tsai
- Department of Medical Research, Genetic Center, China Medical University Hospital, Taichung, Taiwan
- School of Chinese Medicine, China Medical University, Taichung, Taiwan
- Division of Medical Genetics, China Medical University Children's Hospital, Taichung, Taiwan
- Department of Biotechnology and Bioinformatics, Asia University, Taichung, Taiwan
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13
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Osafehinti D, Mulukutla SN, Hampe CS, Gaba R, Ram N, Weedon MN, Oram RA, Balasubramanyam A. Type 1 Diabetes Genetic Risk Score Differentiates Subgroups of Ketosis-Prone Diabetes. Diabetes Care 2023; 46:1778-1782. [PMID: 37506364 PMCID: PMC10516251 DOI: 10.2337/dc23-0622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023]
Abstract
OBJECTIVE To determine whether genetic risk for type 1 diabetes (T1D) differentiates the four Aβ subgroups of ketosis-prone diabetes (KPD), where A+ and A- define the presence or absence of islet autoantibodies and β+ and β- define the presence or absence of β-cell function. RESEARCH DESIGN AND METHODS We compared T1D genetic risk scores (GRS) of patients with KPD across subgroups, race/ethnicity, β-cell function, and glycemia. RESULTS Among 426 patients with KPD (54% Hispanic, 31% African American, 11% White), rank order of GRS was A+β- > A+β+ = A-β- > A-β+. GRS of A+β- KPD was lower than that of a T1D cohort, and GRS of A-β+ KPD was higher than that of a type 2 diabetes cohort. GRS was lowest among African American patients, with a similar distribution across KPD subgroups. CONCLUSIONS T1D genetic risk delineates etiologic differences among KPD subgroups. Patients with A+β- KPD have the highest and those with A-β+ KPD the lowest GRS.
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Affiliation(s)
- Deborah Osafehinti
- Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, Houston, TX
| | | | | | - Ruchi Gaba
- Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, Houston, TX
| | - Nalini Ram
- Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, Houston, TX
| | - Michael N. Weedon
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, and The Academic Kidney Unit, Royal Devon and Exeter NHS Foundation Trust, Exeter, U.K
| | - Richard A. Oram
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, and The Academic Kidney Unit, Royal Devon and Exeter NHS Foundation Trust, Exeter, U.K
| | - Ashok Balasubramanyam
- Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, Houston, TX
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14
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Lugar M, Eugster A, Achenbach P, von dem Berge T, Berner R, Besser REJ, Casteels K, Elding Larsson H, Gemulla G, Kordonouri O, Lindner A, Lundgren M, Müller D, Oltarzewski M, Rochtus A, Scholz M, Szypowska A, Todd JA, Ziegler AG, Bonifacio E. SARS-CoV-2 Infection and Development of Islet Autoimmunity in Early Childhood. JAMA 2023; 330:1151-1160. [PMID: 37682551 PMCID: PMC10523173 DOI: 10.1001/jama.2023.16348] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/07/2023] [Indexed: 09/09/2023]
Abstract
Importance The incidence of diabetes in childhood has increased during the COVID-19 pandemic. Elucidating whether SARS-CoV-2 infection is associated with islet autoimmunity, which precedes type 1 diabetes onset, is relevant to disease etiology and future childhood diabetes trends. Objective To determine whether there is a temporal relationship between SARS-CoV-2 infection and the development of islet autoimmunity in early childhood. Design, Setting, and Participants Between February 2018 and March 2021, the Primary Oral Insulin Trial, a European multicenter study, enrolled 1050 infants (517 girls) aged 4 to 7 months with a more than 10% genetically defined risk of type 1 diabetes. Children were followed up through September 2022. Exposure SARS-CoV-2 infection identified by SARS-CoV-2 antibody development in follow-up visits conducted at 2- to 6-month intervals until age 2 years from April 2018 through June 2022. Main Outcomes and Measures The development of multiple (≥2) islet autoantibodies in follow-up in consecutive samples or single islet antibodies and type 1 diabetes. Antibody incidence rates and risk of developing islet autoantibodies were analyzed. Results Consent was obtained for 885 (441 girls) children who were included in follow-up antibody measurements from age 6 months. SARS-CoV-2 antibodies developed in 170 children at a median age of 18 months (range, 6-25 months). Islet autoantibodies developed in 60 children. Six of these children tested positive for islet autoantibodies at the same time as they tested positive for SARS-CoV-2 antibodies and 6 at the visit after having tested positive for SARS-CoV-2 antibodies. The sex-, age-, and country-adjusted hazard ratio for developing islet autoantibodies when the children tested positive for SARS-CoV-2 antibodies was 3.5 (95% CI, 1.6-7.7; P = .002). The incidence rate of islet autoantibodies was 3.5 (95% CI, 2.2-5.1) per 100 person-years in children without SARS-CoV-2 antibodies and 7.8 (95% CI, 5.3-19.0) per 100 person-years in children with SARS-CoV-2 antibodies (P = .02). Islet autoantibody risk in children with SARS-CoV-2 antibodies was associated with younger age (<18 months) of SARS-CoV-2 antibody development (HR, 5.3; 95% CI, 1.5-18.3; P = .009). Conclusion and relevance In young children with high genetic risk of type 1 diabetes, SARS-CoV-2 infection was temporally associated with the development of islet autoantibodies.
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Affiliation(s)
- Marija Lugar
- Technische Universität Dresden, Center for Regenerative Therapies Dresden, Dresden, Germany
| | - Anne Eugster
- Technische Universität Dresden, Center for Regenerative Therapies Dresden, Dresden, Germany
| | - Peter Achenbach
- Institute of Diabetes Research, Helmholtz Munich, German Center for Environmental Health, Munich, Germany
- Forschergruppe Diabetes, School of Medicine, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
- Forschergruppe Diabetes e.V. at Helmholtz Munich, German Research Center for Environmental Health, Munich, Germany
| | | | - Reinhard Berner
- Department of Pediatrics, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Rachel E. J. Besser
- Department of Pediatrics, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, Oxford University, Oxford, United Kingdom
| | - Kristina Casteels
- Department of Pediatrics, University Hospitals Leuven, Leuven, Belgium
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Helena Elding Larsson
- Unit for Pediatric Endocrinology, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
- Department of Paediatrics, Skåne University Hospital, Malmö, Sweden
| | - Gita Gemulla
- Technische Universität Dresden, Center for Regenerative Therapies Dresden, Dresden, Germany
- Department of Pediatrics, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Olga Kordonouri
- Kinder-und Jugendkrankenhaus AUF DER BULT, Hannover, Germany
| | - Annett Lindner
- Technische Universität Dresden, Center for Regenerative Therapies Dresden, Dresden, Germany
| | - Markus Lundgren
- Unit for Pediatric Endocrinology, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
- Department of Pediatrics, Kristianstad Hospital, Kristianstad, Sweden
| | - Denise Müller
- Technische Universität Dresden, Center for Regenerative Therapies Dresden, Dresden, Germany
| | | | - Anne Rochtus
- Department of Pediatrics, University Hospitals Leuven, Leuven, Belgium
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Marlon Scholz
- Institute of Diabetes Research, Helmholtz Munich, German Center for Environmental Health, Munich, Germany
- Forschergruppe Diabetes, School of Medicine, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
- Forschergruppe Diabetes e.V. at Helmholtz Munich, German Research Center for Environmental Health, Munich, Germany
| | | | - John A. Todd
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, Oxford University, Oxford, United Kingdom
| | - Anette-Gabriele Ziegler
- Institute of Diabetes Research, Helmholtz Munich, German Center for Environmental Health, Munich, Germany
- Forschergruppe Diabetes, School of Medicine, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
- Forschergruppe Diabetes e.V. at Helmholtz Munich, German Research Center for Environmental Health, Munich, Germany
| | - Ezio Bonifacio
- Technische Universität Dresden, Center for Regenerative Therapies Dresden, Dresden, Germany
- Paul Langerhans Institute Dresden of the Helmholtz Munich at University Hospital Carl Gustav Carus and Faculty of Medicine, Technische Universität Dresden, Germany
- Institute for Diabetes and Obesity, Helmholtz Munich, German Center for Environmental Health, Munich, Germany
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15
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Zöller B, Manderstedt E, Lind‐Halldén C, Halldén C. Bioinformatic and rare-variant collapsing analyses for type 1 and type 2 diabetes in the UK Biobank reveal novel pleiotropic susceptibility loci. J Diabetes 2023; 15:799-802. [PMID: 37528512 PMCID: PMC10509505 DOI: 10.1111/1753-0407.13452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 07/19/2023] [Indexed: 08/03/2023] Open
Affiliation(s)
- Bengt Zöller
- Center for Primary Health Care Research, Department of Clinical SciencesLund University and Region SkåneMalmöSweden
| | - Eric Manderstedt
- Center for Primary Health Care Research, Department of Clinical SciencesLund University and Region SkåneMalmöSweden
| | - Christina Lind‐Halldén
- Department of Environmental Science and BioscienceKristianstad UniversityKristianstadSweden
| | - Christer Halldén
- Center for Primary Health Care Research, Department of Clinical SciencesLund University and Region SkåneMalmöSweden
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16
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Luckett AM, Weedon MN, Hawkes G, Leslie RD, Oram RA, Grant SFA. Utility of genetic risk scores in type 1 diabetes. Diabetologia 2023; 66:1589-1600. [PMID: 37439792 PMCID: PMC10390619 DOI: 10.1007/s00125-023-05955-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 05/23/2023] [Indexed: 07/14/2023]
Abstract
Iterative advances in understanding of the genetics of type 1 diabetes have identified >70 genetic regions associated with risk of the disease, including strong associations across the HLA class II region that account for >50% of heritability. The increased availability of genetic data combined with the decreased costs of generating these data, have facilitated the development of polygenic scores that aggregate risk variants from associated loci into a single number: either a genetic risk score (GRS) or a polygenic risk score (PRS). PRSs incorporate the risk of many possibly correlated variants from across the genome, even if they do not reach genome-wide significance, whereas GRSs estimate the cumulative contribution of a smaller subset of genetic variants that reach genome-wide significance. Type 1 diabetes GRSs have utility in diabetes classification, aiding discrimination between type 1 diabetes, type 2 diabetes and MODY. Type 1 diabetes GRSs are also being used in newborn screening studies to identify infants at risk of future presentation of the disease. Most early studies of type 1 diabetes genetics have been conducted in European ancestry populations, but, to develop accurate GRSs across diverse ancestries, large case-control cohorts from non-European populations are still needed. The current barriers to GRS implementation within healthcare are mainly related to a lack of guidance and knowledge on integration with other biomarkers and clinical variables. Once these limitations are addressed, there is huge potential for 'test and treat' approaches to be used to tailor care for individuals with type 1 diabetes.
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Affiliation(s)
- Amber M Luckett
- University of Exeter College of Medicine and Health, Exeter, UK
| | | | - Gareth Hawkes
- University of Exeter College of Medicine and Health, Exeter, UK
| | - R David Leslie
- Blizard Institute, Queen Mary University of London, London, UK.
| | - Richard A Oram
- University of Exeter College of Medicine and Health, Exeter, UK.
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK.
| | - Struan F A Grant
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Division of Diabetes and Endocrinology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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17
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Abstract
Despite major advances over the past decade, prevention and treatment of type 1 diabetes mellitus (T1DM) remain suboptimal, with large and unexplained variations in individual responses to interventions. The current classification schema for diabetes mellitus does not capture the complexity of this disease or guide clinical management effectively. One of the approaches to achieve the goal of applying precision medicine in diabetes mellitus is to identify endotypes (that is, well-defined subtypes) of the disease each of which has a distinct aetiopathogenesis that might be amenable to specific interventions. Here, we describe epidemiological, clinical, genetic, immunological, histological and metabolic differences within T1DM that, together, suggest heterogeneity in its aetiology and pathogenesis. We then present the emerging endotypes and their impact on T1DM prediction, prevention and treatment.
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Affiliation(s)
- Maria J Redondo
- Paediatric Diabetes & Endocrinology, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA.
| | - Noel G Morgan
- Exeter Centre of Excellence for Diabetes Research (EXCEED), Department of Clinical and Biomedical and Science, University of Exeter Medical School, Exeter, UK
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Grier AE, McGill JB, Lord SM, Speake C, Greenbaum C, Chamberlain CE, German MS, Anderson MS, Hirsch IB. ABCC8-Related Monogenic Diabetes Presenting Like Type 1 Diabetes in an Adolescent. AACE Clin Case Rep 2023; 9:101-103. [PMID: 37520758 PMCID: PMC10382606 DOI: 10.1016/j.aace.2023.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/11/2023] [Accepted: 04/03/2023] [Indexed: 08/01/2023] Open
Abstract
Background Identifying cases of diabetes caused by single gene mutations between the more common type 1 diabetes (T1D) and type 2 diabetes (T2D) is a difficult but important task. We report the diagnosis of ATP-binding cassette transporter sub-family C member 8 (ABCC8)-related monogenic diabetes in a 35-year-old woman with a protective human leukocyte antigen (HLA) allele who was originally diagnosed with T1D at 18 years of age. Case Report Patient A presented with polyuria, polydipsia, and hypertension at the age of 18 years and was found to have a blood glucose > 500 mg/dL (70-199 mg/dL) and an HbA1C (hemoglobin A1C) >14% (4%-5.6%). She had an unmeasurable C-peptide but no urine ketones. She was diagnosed with T1D and started on insulin therapy. Antibody testing was negative. She required low doses of insulin and later had persistence of low but detectable C-peptide. At the age of 35 years, she was found to have a protective HLA allele, and genetic testing revealed a pathogenic mutation in the ABCC8 gene. The patient was then successfully transitioned to sulfonylurea therapy. Discussion Monogenic diabetes diagnosed in adolescence typically presents with mild to moderate hyperglycemia, positive family history and, in some cases, other organ findings or dysfunction. The patient in this report presented with very high blood glucose, prompting the diagnosis of T1D. When she was found to have a protective HLA allele, further investigation revealed the mutation in the sulfonylurea receptor gene, ABCC8. Conclusion Patients suspected of having T1D but with atypical clinical characteristics such as negative autoantibodies, low insulin requirements, and persistence of C-peptide should undergo genetic testing for monogenic diabetes.
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Affiliation(s)
- Alexandra E. Grier
- Department of Pediatrics, St. Louis Children’s Hospital, St. Louis, Missouri
| | - Janet B. McGill
- Division of Endocrinology, Metabolism and Lipid Research, Washington University School of Medicine, St. Louis, Missouri
| | - Sandra M. Lord
- Diabetes Clinical Research Program and Center for Interventional Immunology, Benaroya Research Institute at Virginia Mason, Seattle, Washington
| | - Cate Speake
- Diabetes Clinical Research Program and Center for Interventional Immunology, Benaroya Research Institute at Virginia Mason, Seattle, Washington
| | - Carla Greenbaum
- Diabetes Clinical Research Program and Center for Interventional Immunology, Benaroya Research Institute at Virginia Mason, Seattle, Washington
| | - Chester E. Chamberlain
- Department of Medicine, Diabetes Center, University of California, San Francisco, California
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, California
| | - Michael S. German
- Department of Medicine, Diabetes Center, University of California, San Francisco, California
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, California
| | - Mark S. Anderson
- Department of Medicine, Diabetes Center, University of California, San Francisco, California
| | - Irl B. Hirsch
- Division of Metabolism, Endocrinology, and Nutrition, Department of Medicine, University of Washington School of Medicine, Seattle, Washington
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19
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Eizirik DL, Szymczak F, Mallone R. Why does the immune system destroy pancreatic β-cells but not α-cells in type 1 diabetes? Nat Rev Endocrinol 2023; 19:425-434. [PMID: 37072614 DOI: 10.1038/s41574-023-00826-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/06/2023] [Indexed: 04/20/2023]
Abstract
A perplexing feature of type 1 diabetes (T1D) is that the immune system destroys pancreatic β-cells but not neighbouring α-cells, even though both β-cells and α-cells are dysfunctional. Dysfunction, however, progresses to death only for β-cells. Recent findings indicate important differences between these two cell types. First, expression of BCL2L1, a key antiapoptotic gene, is higher in α-cells than in β-cells. Second, endoplasmic reticulum (ER) stress-related genes are differentially expressed, with higher expression levels of pro-apoptotic CHOP in β-cells than in α-cells and higher expression levels of HSPA5 (which encodes the protective chaperone BiP) in α-cells than in β-cells. Third, expression of viral recognition and innate immune response genes is higher in α-cells than in β-cells, contributing to the enhanced resistance of α-cells to coxsackievirus infection. Fourth, expression of the immune-inhibitory HLA-E molecule is higher in α-cells than in β-cells. Of note, α-cells are less immunogenic than β-cells, and the CD8+ T cells invading the islets in T1D are reactive to pre-proinsulin but not to glucagon. We suggest that this finding is a result of the enhanced capacity of the α-cell to endure viral infections and ER stress, which enables them to better survive early stressors that can cause cell death and consequently amplify antigen presentation to the immune system. Moreover, the processing of the pre-proglucagon precursor in enteroendocrine cells might favour immune tolerance towards this potential self-antigen compared to pre-proinsulin.
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Affiliation(s)
- Decio L Eizirik
- Université Libre de Bruxelles (ULB) Center for Diabetes Research and Welbio, Medical Faculty, Brussels, Belgium.
| | - Florian Szymczak
- Université Libre de Bruxelles (ULB) Center for Diabetes Research and Welbio, Medical Faculty, Brussels, Belgium
| | - Roberto Mallone
- Université Paris Cité, Institut Cochin, CNRS, INSERM, Paris, France
- Assistance Publique Hôpitaux de Paris, Service de Diabétologie et Immunologie Clinique, Cochin Hospital, Paris, France
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20
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Pande AK, Dutta D, Singla R. Prevention of Type 1 Diabetes: Current Perspective. Indian J Endocrinol Metab 2023; 27:277-285. [PMID: 37867976 PMCID: PMC10586562 DOI: 10.4103/ijem.ijem_78_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/17/2023] [Accepted: 05/16/2023] [Indexed: 10/24/2023] Open
Abstract
People living with type 1 Diabetes (T1D) and their families have poor perception of health related quality of life. Therapies for T1D are becoming better with time, but they still involve a lot of effort. Prevention of T1D, if successful, has potential to change lives of millions of families across the globe. Type 1 diabetes is an autoimmune disease with underlying genetic predisposition for autoimmunity against beta cell antigens upon exposure to an environmental trigger. Identifying underlying primary antigen responsible for initiating autoimmune cascade, avoiding environmental trigger and modifying immunity has all been used as strategies for preventing or delaying onset of type 1 diabetes. Primary prevention for type 1 diabetes is hindered by difficulty in identifying at-risk population and also due to lack of effective preventive strategy. Secondary prevention, in children with presence of autoimmunity, has recently received a boost with approval of Teplizumab, an immunity modifying drug by its Anti-CD3 action. Application of preventive strategies would also change based on country specific incidence, prevalence and availability of health resources. In current review, an update on preventive strategies for type 1 diabetes is being discussed as well as their applicability in Indian context.
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Affiliation(s)
- Arun K Pande
- Consultant Endocrinologist, Lucknow Endocrine Diabetes and Thyroid Clinic, Lucknow, Uttar Pradesh, India
| | - Deep Dutta
- Consultant Endocrinologist, CEDAR Superspeciality Healthcare, Dwarka, New Delhi, India
| | - Rajiv Singla
- Consultant Endocrinologist, Kalpavriksh Healthcare, Dwarka, Delhi, India
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21
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Toniolo A. Islet autoimmunity and type 1 diabetes associated with enterovirus infections. Lancet Diabetes Endocrinol 2023:S2213-8587(23)00133-X. [PMID: 37390840 DOI: 10.1016/s2213-8587(23)00133-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 05/07/2023] [Indexed: 07/02/2023]
Affiliation(s)
- Antonio Toniolo
- Global Virus Network, University of Insubria Medical School, 21100 Varese, Italy.
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22
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Gomes MB, Rodrigues V, Santos DC, Bôas PRV, Silva DA, de Sousa Azulay RS, Dib SA, Pavin EJ, Fernandes VO, Montenegro Junior RM, Felicio JS, Réa R, Negrato CA, Porto LC. Association between HLA Class II Alleles/Haplotypes and Genomic Ancestry in Brazilian Patients with Type 1 Diabetes: A Nationwide Exploratory Study. Genes (Basel) 2023; 14:genes14050991. [PMID: 37239351 DOI: 10.3390/genes14050991] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 04/23/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023] Open
Abstract
We aimed to identify HLA-DRB1, -DQA1, and -DQB1 alleles/haplotypes associated with European, African, or Native American genomic ancestry (GA) in admixed Brazilian patients with type 1 diabetes (T1D). This exploratory nationwide study enrolled 1599 participants. GA percentage was inferred using a panel of 46 ancestry informative marker-insertion/deletion. Receiver operating characteristic curve analysis (ROC) was applied to identify HLA class II alleles related to European, African, or Native American GA, and showed significant (p < 0.05) accuracy for identifying HLA risk alleles related to European GA: for DRB1*03:01, the area under the curve was (AUC) 0.533; for DRB1*04:01 AUC = 0.558, for DRB1*04:02 AUC = 0.545. A better accuracy for identifying African GA was observed for the risk allele DRB1*09:01AUC = 0.679 and for the protective alleles DRB1*03:02 AUC = 0.649, DRB1*11:02 AUC = 0.636, and DRB1*15:03 AUC = 0.690. Higher percentage of European GA was observed in patients with risk haplotypes (p < 0.05). African GA percentage was higher in patients with protective haplotypes (p < 0.05). Risk alleles and haplotypes were related to European GA and protective alleles/haplotypes to African GA. Future studies with other ancestry markers are warranted to fill the gap in knowledge regarding the genetic origin of T1D in highly admixed populations such as that found in Brazil.
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Affiliation(s)
- Marília Brito Gomes
- Department of Internal Medicine, Diabetes Unit, Rio de Janeiro State University (UERJ), Rio de Janeiro 20950-003, Brazil
| | - Vandilson Rodrigues
- Research Group in Clinical and Molecular Endocrinology and Metabology (ENDOCLIM), São Luís 65080-805, Brazil
| | - Deborah Conte Santos
- Department of Internal Medicine, Diabetes Unit, Rio de Janeiro State University (UERJ), Rio de Janeiro 20950-003, Brazil
| | - Paulo Ricardo Villas Bôas
- Histocompatibility and Cryopreservation Laboratory (HLA), Rio de Janeiro State University (UERJ), Rio de Janeiro 20950-003, Brazil
| | - Dayse A Silva
- DNA Diagnostic Laboratory (LDD), Rio de Janeiro State University (UERJ), Rio de Janeiro 20550-900, Brazil
| | - Rossana Santiago de Sousa Azulay
- Research Group in Clinical and Molecular Endocrinology and Metabology (ENDOCLIM), São Luís 65080-805, Brazil
- Service of Endocrinology, University Hospital of the Federal University of Maranhão (HUUFMA/EBSERH), São Luís 65020-070, Brazil
| | - Sergio Atala Dib
- Endocrinology Division, Escola Paulista de Medicina, Federal University of São Paulo (UNIFESP), São Paulo 04023-062, Brazil
| | - Elizabeth João Pavin
- Endocrinology Division, School of Medical Sciences, University of Campinas (UNICAMP), São Paulo 13083-970, Brazil
| | - Virgínia Oliveira Fernandes
- Department of Clinical Medicine, Federal University of Ceará (UFC), Fortaleza 60430-275, Brazil
- Department of Community Health, Federal University of Ceará (UFC), Fortaleza 60430-275, Brazil
- Clinical Research Unit, Walter Cantídio University Hospital, Federal University of Ceará (UFC/EBSERH), Fortaleza 60430-372, Brazil
| | - Renan Magalhães Montenegro Junior
- Department of Clinical Medicine, Federal University of Ceará (UFC), Fortaleza 60430-275, Brazil
- Department of Community Health, Federal University of Ceará (UFC), Fortaleza 60430-275, Brazil
- Clinical Research Unit, Walter Cantídio University Hospital, Federal University of Ceará (UFC/EBSERH), Fortaleza 60430-372, Brazil
| | - João Soares Felicio
- Endocrinology Division, João de Barros Barreto University Hospital, Federal University of Pará (UFPA), Belém 66073-000, Brazil
| | - Rosangela Réa
- Endocrinology Unit, Federal University of Paraná (UFPR), Curitiba 80060-900, Brazil
| | - Carlos Antonio Negrato
- Medical Doctor Program, School of Dentistry, University of São Paulo (USP), Bauru 17012-901, Brazil
| | - Luís Cristóvão Porto
- Histocompatibility and Cryopreservation Laboratory (HLA), Rio de Janeiro State University (UERJ), Rio de Janeiro 20950-003, Brazil
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23
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Mancuso G, Bechi Genzano C, Fierabracci A, Fousteri G. Type 1 diabetes and inborn errors of immunity: Complete strangers or 2 sides of the same coin? J Allergy Clin Immunol 2023:S0091-6749(23)00427-X. [PMID: 37097271 DOI: 10.1016/j.jaci.2023.03.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/31/2023] [Accepted: 03/31/2023] [Indexed: 04/26/2023]
Abstract
Type 1 diabetes (T1D) is a polygenic disease and does not follow a mendelian pattern. Inborn errors of immunity (IEIs), on the other hand, are caused by damaging germline variants, suggesting that T1D and IEIs have nothing in common. Some IEIs, resulting from mutations in genes regulating regulatory T-cell homeostasis, are associated with elevated incidence of T1D. The genetic spectrum of IEIs is gradually being unraveled; consequently, molecular pathways underlying human monogenic autoimmunity are being identified. There is an appreciable overlap between some of these pathways and the genetic variants that determine T1D susceptibility, suggesting that after all, IEI and T1D are 2 sides of the same coin. The study of monogenic IEIs with a variable incidence of T1D has the potential to provide crucial insights into the mechanisms leading to T1D. These insights contribute to the definition of T1D endotypes and explain disease heterogeneity. In this review, we discuss the interconnected pathogenic pathways of autoimmunity, β-cell function, and primary immunodeficiency. We also examine the role of environmental factors in disease penetrance as well as the circumstantial evidence of IEI drugs in preventing and curing T1D in individuals with IEIs, suggesting the repositioning of these drugs also for T1D therapy.
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Affiliation(s)
- Gaia Mancuso
- Unit of Immunology, Rheumatology, Allergy and Rare Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Camillo Bechi Genzano
- Columbia Center for Translational Immunology, Department of Medicine, Columbia University Irving Medical Center, New York, NY
| | | | - Georgia Fousteri
- Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy.
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24
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Deutsch AJ, Stalbow L, Majarian TD, Mercader JM, Manning AK, Florez JC, Loos RJ, Udler MS. Polygenic Scores Help Reduce Racial Disparities in Predictive Accuracy of Automated Type 1 Diabetes Classification Algorithms. Diabetes Care 2023; 46:794-800. [PMID: 36745605 PMCID: PMC10090893 DOI: 10.2337/dc22-1833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 01/10/2023] [Indexed: 02/07/2023]
Abstract
OBJECTIVE Automated algorithms to identify individuals with type 1 diabetes using electronic health records are increasingly used in biomedical research. It is not known whether the accuracy of these algorithms differs by self-reported race. We investigated whether polygenic scores improve identification of individuals with type 1 diabetes. RESEARCH DESIGN AND METHODS We investigated two large hospital-based biobanks (Mass General Brigham [MGB] and BioMe) and identified individuals with type 1 diabetes using an established automated algorithm. We performed medical record reviews to validate the diagnosis of type 1 diabetes. We implemented two published polygenic scores for type 1 diabetes (developed in individuals of European or African ancestry). We assessed the classification algorithm before and after incorporating polygenic scores. RESULTS The automated algorithm was more likely to incorrectly assign a diagnosis of type 1 diabetes in self-reported non-White individuals than in self-reported White individuals (odds ratio 3.45; 95% CI 1.54-7.69; P = 0.0026). After incorporating polygenic scores into the MGB Biobank, the positive predictive value of the type 1 diabetes algorithm increased from 70 to 97% for self-reported White individuals (meaning that 97% of those predicted to have type 1 diabetes indeed had type 1 diabetes) and from 53 to 100% for self-reported non-White individuals. Similar results were found in BioMe. CONCLUSIONS Automated phenotyping algorithms may exacerbate health disparities because of an increased risk of misclassification of individuals from underrepresented populations. Polygenic scores may be used to improve the performance of phenotyping algorithms and potentially reduce this disparity.
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Affiliation(s)
- Aaron J. Deutsch
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Lauren Stalbow
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Timothy D. Majarian
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Josep M. Mercader
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Alisa K. Manning
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA
| | - Jose C. Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Ruth J.F. Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Miriam S. Udler
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
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25
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Michalek DA, Onengut-Gumuscu S, Repaske DR, Rich SS. Precision Medicine in Type 1 Diabetes. J Indian Inst Sci 2023; 103:335-351. [PMID: 37538198 PMCID: PMC10393845 DOI: 10.1007/s41745-023-00356-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 01/04/2023] [Indexed: 03/09/2023]
Abstract
Type 1 diabetes is a complex, chronic disease in which the insulin-producing beta cells in the pancreas are sufficiently altered or impaired to result in requirement of exogenous insulin for survival. The development of type 1 diabetes is thought to be an autoimmune process, in which an environmental (unknown) trigger initiates a T cell-mediated immune response in genetically susceptible individuals. The presence of islet autoantibodies in the blood are signs of type 1 diabetes development, and risk of progressing to clinical type 1 diabetes is correlated with the presence of multiple islet autoantibodies. Currently, a "staging" model of type 1 diabetes proposes discrete components consisting of normal blood glucose but at least two islet autoantibodies (Stage 1), abnormal blood glucose with at least two islet autoantibodies (Stage 2), and clinical diagnosis (Stage 3). While these stages may, in fact, not be discrete and vary by individual, the format suggests important applications of precision medicine to diagnosis, prevention, prognosis, treatment and monitoring. In this paper, applications of precision medicine in type 1 diabetes are discussed, with both opportunities and barriers to global implementation highlighted. Several groups have implemented components of precision medicine, yet the integration of the necessary steps to achieve both short- and long-term solutions will need to involve researchers, patients, families, and healthcare providers to fully impact and reduce the burden of type 1 diabetes.
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Affiliation(s)
- Dominika A. Michalek
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA USA
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA USA
| | - David R. Repaske
- Division of Endocrinology, Department of Pediatrics, University of Virginia, Charlottesville, VA USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA USA
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26
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Wang Y, Xia Y, Chen Y, Xu L, Sun X, Li J, Huang G, Li X, Xie Z, Zhou Z. Association analysis between the TLR9 gene polymorphism rs352140 and type 1 diabetes. Front Endocrinol (Lausanne) 2023; 14:1030736. [PMID: 37139337 PMCID: PMC10150994 DOI: 10.3389/fendo.2023.1030736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 03/24/2023] [Indexed: 05/05/2023] Open
Abstract
Background To a great extent, genetic factors contribute to the susceptibility to type 1 diabetes (T1D) development, and by triggering immune imbalance, Toll-like receptor (TLR) 9 is involved in the development of T1D. However, there is a lack of evidence supporting a genetic association between polymorphisms in the TLR9 gene and T1D. Methods In total, 1513 individuals, including T1D patients (n=738) and healthy control individuals (n=775), from the Han Chinese population were recruited for an association analysis of the rs352140 polymorphism of the TLR9 gene and T1D. rs352140 was genotyped by MassARRAY. The allele and genotype distributions of rs352140 in the T1D and healthy groups and those in different T1D subgroups were analyzed by the chi-squared test and binary logistic regression model. The chi-square test and Kruskal-Wallis H test were performed to explore the association between genotype and phenotype in T1D patients. Results The allele and genotype distributions of rs352140 were significantly different in T1D patients and healthy control individuals (p=0.019, p=0.035). Specifically, the T allele and TT genotype of rs352140 conferred a higher risk of T1D (OR=1.194, 95% CI=1.029-1.385, p=0.019, OR=1.535, 95% CI=1.108-2.126, p=0.010). The allele and genotype distributions of rs352140 were not significantly different between childhood-onset and adult-onset T1D and between T1D with a single islet autoantibody and T1D with multiple islet autoantibodies (p=0.603, p=0.743). rs352140 was associated with T1D susceptibility according to the recessive and additive models (p=0.015, p=0.019) but was not associated with T1D susceptibility in the dominant and overdominant models (p=0.117, p=0.928). Moreover, genotype-phenotype association analysis showed that the TT genotype of rs352140 was associated with higher fasting C-peptide levels (p=0.017). Conclusion In the Han Chinese population, the TLR9 polymorphism rs352140 is associated with T1D and is a risk factor for susceptibility to T1D.
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27
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Pilśniak A, Otto-Buczkowska E. Type 1 diabetes - What's new in prevention and therapeutic strategies? Pediatr Endocrinol Diabetes Metab 2023; 29:196-201. [PMID: 38031834 PMCID: PMC10679919 DOI: 10.5114/pedm.2023.132028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 04/10/2023] [Indexed: 12/01/2023]
Abstract
Type 1 diabetes (T1D) is an autoimmune disorder, and insulin deficiency is the result of b-cell dysfunction. Treatment of type 1 diabetes requires constant parenteral insulin administration, which can be very burdensome for the patient. Meticulous use of insulin therapy does not protect the patient against complications. Hence, the search for other methods of treatment as well as ways of preventing the onset of diabetes has been ongoing for a long time. The main obstacle in the implementation of the prevention task is the need to identify people at risk of developing diabetes before the start of autoimmunity. It seems that primary prevention is still unrealistic at the moment, because we do not know all the factors leading to the activation of autoimmunity processes. Research on the use of late secondary prevention in people who develop glucose tolerance disorders or in the early period after the onset of type 1 diabetes are at the most advanced stage. Gene therapy is another attempt at an alternative treatment and prevention of type 1 diabetes and still requires further research. Recent years have brought a lot of information about the nature of type 1 diabetes and the mechanisms leading to its development. However, it has not yet been established what factors decide about the initiation of autoimmunity and what determines the dynamics of these processes.
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Affiliation(s)
- Aleksandra Pilśniak
- Department of Internal Medicine, Autoimmune and Metabolic Diseases, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland
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28
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Limbert C, Lanzinger S, deBeaufort C, Iotova V, Pelicand J, Prieto M, Schiaffini R, Šumnik Z, Pacaud D. Diabetes-related antibody-testing is a valuable screening tool for identifying monogenic diabetes - A survey from the worldwide SWEET registry. Diabetes Res Clin Pract 2022; 192:110110. [PMID: 36183869 DOI: 10.1016/j.diabres.2022.110110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/19/2022] [Accepted: 09/25/2022] [Indexed: 11/26/2022]
Abstract
AIMS To evaluate access to screening tools for monogenic diabetes in paediatric diabetes centres across the world and its impact on diagnosis and clinical outcomes of children and youth with genetic forms of diabetes. METHODS 79 centres from the SWEET diabetes registry including 53,207 children with diabetes participated in a survey on accessibility and use of diabetes related antibodies, c-peptide and genetic testing. RESULTS 73, 63 and 62 participating centres had access to c-peptide, antibody and genetic testing, respectively. Access to antibody testing was associated with higher proportion of patients with rare forms of diabetes identified with monogenic diabetes (54 % versus 17 %, p = 0.01), lower average whole clinic HbA1c (7.7[Q1,Q2: 7.3-8.0]%/61[56-64]mmol/mol versus 9.2[8.6-10.0]%/77[70-86]mmol/mol, p < 0.001) and younger age at onset (8.3 [7.3-8.8] versus 9.7 [8.6-12.7] years p < 0.001). Additional access to c-peptide or genetic testing was not related to differences in age at onset or HbA1c outcome. CONCLUSIONS Clinical suspicion and antibody testing are related to identification of different types of diabetes. Implementing access to comprehensive antibody screening may provide important information for selecting individuals for further genetic evaluation. In addition, worse overall clinical outcomes in centers with limited diagnostic capabilities indicate they may also need support for individualized diabetes management. TRIAL REGISTRATION NCT04427189.
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Affiliation(s)
- Catarina Limbert
- Hospital Dona Estefânia, Unit of Paediatric Endocrinology and Diabetes, Lisbon, Portugal; Nova Medical School, Universidade Nova de Lisboa, Lisbon, Portugal.
| | - Stefanie Lanzinger
- Institute of Epidemiology and Medical Biometry, ZIBMT, University of Ulm, Ulm, Germany; German Centre for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Carine deBeaufort
- Department of Paediatric Diabetes and Endocrinology, Centre Hospitalier Luxembourg, Luxembourg, Luxembourg
| | - Violeta Iotova
- Department of Paediatrics, Medical University of Varna, Varna, Bulgaria
| | - Julie Pelicand
- San Camilo Hospital-Medicine School, Universidad de Valparaíso, San Felipe, Chile
| | - Mariana Prieto
- Servicio de Nutrición, Hospital de Pediatría SAMIC J. P. Garrahan, 1245 Buenos Aieres, Argentina
| | | | - Zdeněk Šumnik
- Department of Paediatrics, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Danièle Pacaud
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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