Abstract
Type 1 diabetes (T1D) is classified into three subtypes: acute-onset, slowly progressive, and fulminant T1D, according to the heterogeneity of clinical course in Japan. Although several cross-sectional databases of T1D have been reported, prospective longitudinal databases to investigate clinical outcomes are lacking in our country. Therefore, we herein construct multi-center prospective longitudinal database of the three subtypes of T1D, accompanied with genetic information and biobanking, which is named Japanese Type 1 Diabetes Database Study (TIDE-J). Inclusion criteria of this study are as follows: (1) the duration of T1D was less than 5 years, (2) the patients had one or more islet-related autoantibodies and/or fasting serum C-peptide levels were less than 1.0 ng/mL, (3) the patients could clearly understand the study consent in writing. In the TIDE-J, clinical data, including glycemic control, endogenous insulin secretion, islet-related autoantibodies, diabetic complications, and treatment, are collected annually using electric data collection system, which is named REDCap. Furthermore, HLA genotypes of each participant were analyzed at entry and the blood samples were stored for assessing exploratory markers and further genetic analysis annually. The TIDE-J certainly helps in revealing distinct clinical course of each T1D subtype. Moreover, this database may help in identifying novel markers for diagnosing each subtype of T1D and predicting clinical outcomes (including pancreatic beta cell function and disease severity) in patients.
Keywords: Type 1 diabetes, Database, Multi-center, Electric data collection
Introduction
Type 1 diabetes (T1D) is thought to result mainly from autoimmune destruction of insulin-producing islet beta cells [1, 2]. Islet antigen-reactive immune cells, such as T cells, play a major role in its pathogenesis [3, 4]. In the west, several population-based databases for T1D are used to reveal the clinical course of the disease. For instance, the EURODIAB is a population-based cohort with nearly 30,000 registered T1D patients from 12 European countries to investigate clinical outcomes, including mortality and diabetic complications [5, 6]. The Diabetes Autoimmunity Study in the Young (DAISY) is a prospective cohort in the United States that follows nearly 2000 children with T1D. It primarily predicts autoimmunity, which is evaluated by the emergence of islet-related auto-antibodies. Further, it investigates the association between genetic factors [including the genotypes of human leukocyte antigen (HLA)] and autoimmunity in T1D [7, 8]. In Japan, several cross-sectional T1D databases including factors, such as clinical information and HLA genotypes, have been established [9, 10]. However, prospective longitudinal databases of T1D to investigate clinical outcomes are lacking in Japan.
Although the prevalence of T1D is lower in Japan compared to the West [11], the clinical course of Japanese patients is more heterogeneous. According to the heterogeneity, T1D is classified into three subtypes: acute onset (AT1D), slowly progressive (SP1D), and fulminant type 1 diabetes (FT1D), based on the mode of onset, presence of ketosis, and the rapidity of disease progression in Japan [12–14]. AT1D (broadly recognized as “T1D” in western countries) is the typical insulin-dependent T1D characterized by diminished endogenous insulin secretion and positive islet-related autoantibodies [12]. SP1D is a relatively mild type of autoimmune diabetes wherein pancreatic beta-cell function is preserved at the onset and positive islet-related autoantibodies [12, 15] and later progressive beta cell dysfunction are found [13, 15]. FT1D shows a relatively low level of HbA1c (< 8.7%) because of remarkably abrupt onset of the disease. The secretion of endogenous insulin is almost completely abolished, and islet-related autoantibodies are mostly negative [14, 16, 17]. According to heterogeneity and classification, it is important to construct a database consisting of data from each subtype of T1D patients.
Based on the situation in Japan, we started to construct a prospective longitudinal (annual) database of the three T1D subtypes. It included clinical information, including glycemic control, endogenous insulin secretion, islet autoimmunity, diabetic complications and treatments, and genetic information (e.g., HLA genotypes). Moreover, serum and DNA samples isolated from patients’ blood were stored for assessing exploratory markers and further genetic analysis. Serum samples were stored annually. This integrated database supplemented with biobanking was the Japanese Type 1 Diabetes Database Study (TIDE-J).
Research design and methods
Study design and ethics
TIDE-J was a prospective, multi-center, and registry study designed using clinical data and blood samples of T1D patients. This study was initiated in October 2010 and is currently ongoing. All procedures performed were in accordance with the 1964 Helsinki Declaration and its later amendments and the “ethical guidelines for human genome/gene analysis research” published by the Ministry of Health, Labor, and Welfare of Japan. The study protocol was approved by the ethical committee of the National Center for Global Health and Medicine and other collaborating institutes (ID: NCGM-A-000138-13). Written informed consent was obtained from all participants.
Study participants
The hospitals where T1D patients (registered in this study) were treated are shown in Table 1. Each subtype of T1D, AT1D, SP1D, and FT1D was diagnosed according to the diagnostic criteria published by the Japan Diabetes Society Committee on Type 1 Diabetes Research [12–14]. The inclusion criteria were as follows: (1) duration of T1D less than 5 years; (2) patients had one or more islet-related autoantibodies and/or fasting serum C-peptide levels less than 1.0 ng/mL; and (3) patients could clearly understand the study consent in writing. In this study, both the patients diagnosed as T1D before initiating the study and the patients newly diagnosed during the study are registered.
Table 1.
Hospitals participated in the registration of T1D patients for the TIDE-J
| Hospital name | Location (in Japan) |
|---|---|
| Iwate Medical University Hospital | Shiwa, Iwate |
| International University of Health and Welfare Hospital | Nasu-Shiobara, Tochigi |
| Saitama Medical University Hospital | Iruma, Saitama |
| National Center for Global Health and Medicine | Shinjuku, Tokyo |
| Tokyo Saiseikai Central Hospital | Minato, Tokyo |
| Tachikawa Hospital | Tachikawa, Tokyo |
| Showa University Hospital | Shinagawa, Tokyo |
| University of Yamanashi Hospital | Chuo, Yamanashi |
| Toyama University Hospital | Toyama, Toyama |
| Osaka Medical and Pharmaceutical University Hospital | Takatsuki, Osaka |
| Osaka University Hospital | Suita, Osaka |
| Kindai University Hospital | Osaka-Sayama, Osaka |
| Kobe University Hospital | Kobe, Hyogo |
| Ehime University Hospital | Toon, Ehime |
| Takanoko Hospital | Matsuyama, Ehime |
| Matsuyama Red Cross Hospital | Matsuyama, Ehime |
| Takamatsu Hospital | Takamatsu, Kagawa |
| Shin-Koga Hospital | Kurume, Fukuoka |
| Nagasaki University Hospital | Nagasaki, Nagasaki |
Clinical data collection
Clinical data were collected at the onset or diagnosis of T1D, during registration to the study, and annually after the registration using an electric data collection (EDC) system called REDCap (end user license agreement with Vanderbilt University). The variables at each time point are listed in Table 2. Representative researchers at each hospital had user IDs and passwords to access the EDC system and directly register diabetes-related clinical data online. Registered data from each hospital were aggregated and stored in the server of the Joint Center for Researchers, Associates and Clinicians (JCRAC) data center at the National Center for Global Health and Medicine.
Table 2.
The variables collected in TIDE-J
| Variables | Onset/diagnosis | At the registration | Annually |
|---|---|---|---|
| Birth date | x | ||
| Age | x | ||
| Sex | x | ||
| Date of T1D onset | x | ||
| T1D duration | x | ||
| T1D subtypes | x | ||
| Body height | x | ||
| Body weight | x | x | x |
| BMI | x | x | x |
| Blood pressure | x | x | x |
| Family history of diabetes | x | ||
| Hyperglycemic symptom | x | ||
| Past disease history | x | ||
| History of autoimmune disease | x | ||
| History of pregnancy | x | ||
| FPG | x | x | x |
| HbA1c | x | x | x |
| Glycated albumin | x | x | x |
| Fasting C-peptide | x | x | x |
| Total ketone body | x | ||
| Acetoacetic acid | x | ||
| 3-hydroxybutyric acid | x | ||
| Urinary ketone body | x | ||
| Arterial blood gas pH | x | ||
| Amylase | x | ||
| Elastase-I | x | ||
| Lipase | x | ||
| eGFR | x | ||
| Urinary protein | x | ||
| Urinary albumin | x | ||
| GAD-Ab | x | x | x |
| IA-2-Ab | x | x | x |
| ZnT8-Ab | x | x | x |
| IAA* | x | x | |
| Tg-Ab# | x | x | |
| TPO-Ab# | x | x | |
| TRAb# | x | x | |
| HLA genotyping | x | ||
| Abdominal imaging (screening of pancreatic abnormality) | x | ||
| Details of insulin therapy | x | x | |
| Usage of OHA | x | x | |
| Diabetic retinopathy | x | x | |
| Diabetic nephropathy | x | x | |
| Diabetic neuropathy | x | x | |
| Macroangiopathy | x | x | |
| Other complications | x | x |
T1D type 1 diabetes, BMI body mass index, FPG fasting plasma glucose, HbA1c glycated hemoglobin, eGFR estimated glomerular filtration rate, GAD-Ab anti-glutamic acid decarboxylase antibody, IA-2-Ab anti-insulinoma-associated antigen-2 antibody, ZnT8-Ab anti-zinc transporter-8 antibody, IAA insulin auto-antibody, Tg-Ab anti-thyroglobulin antibody, TPO-Ab anti-thyroid peroxidase antibody, TRAb anti-thyroid stimulating hormone receptor antibody, HLA human leukocyte antigen, OHA oral hypoglycemic agent. *IAA was measured if the period of insulin treatment was within 2 weeks. #Optional
Measuring islet-related autoantibody titers and genotyping human leukocyte antigen (HLA)
In addition to clinical data from the electronic health record used for regular clinical practice, levels of fasting serum C-peptide (F-CPR), anti-glutamic acid dehydrogenase antibody (GAD-Ab), anti-insulinoma-associated antigen-2 antibody (IA-2-Ab), and anti-zinc transporter-8 antibody (ZnT8-Ab) were centrally measured at each time point by the SRL, Inc. (Tokyo, Japan) (Table 2). F-CPR levels were measured using chemiluminescent enzyme immunoassay (CLEIA). GAD-Ab titers were measured by radioimmunoassay (RIA) from the start of the study till December 2015 and by enzyme-linked immunosorbent assay (ELISA) since January 2016. The titers of IA-2-Ab were measured by RIA from the start of the study till December 2018 and by ELISA since January 2019. The titers of ZnT8-Ab were measured by ELISA throughout the study period. In addition, the titers of insulin auto-antibody (IAA) were measured by RIA if the period of insulin treatment was within 2 weeks. These data were also registered in the EDC system. Further, the genotypes of HLA were centrally analyzed using next-generation sequencing at the HLA Laboratory (Kyoto, Japan).
Blood sample storage
Residual serum and DNA samples after measuring antibody titers and HLA genotyping, respectively, were transferred to the Department of Metabolic Disorder laboratory at the National Center for Global Health and Medicine (Tokyo, Japan). They were stored at − 80 °C for future studies.
Data management
Data managers at the JCRAC data center regularly checked and cleaned the registered data. The researchers were informed monthly about the status of data registration at each institute to minimize missing data. When necessary, queries were performed to avoid registration of incorrect data. Furthermore, data monitoring reports were published semi-annually to share the progress of the study. When a researcher uses stored data to consider and confirm the plan for analyses, the data center provides a data set with a comma separated value (csv) file with permission from the principal investigator of the study. Statistical analyses will be conducted by biostatisticians at the Department of Date Science, National Center for Global Health and Medicine. The methods for the analyses will depend on the objectives of the analyses.
Future study plans
Using the collected data, we will analyze the clinical course, including glycemic control, presence of islet-related autoantibodies, changes in endogenous insulin secretion, and progression of diabetic complications. We will also try to identify the clinical and genetic factors causing the endogenous insulin secretion to decline. Additionally, we will analyze how GAD-Ab and IA-2-Ab (measured by RIA and ELISA, respectively) reflect the clinical features of each T1D subtype. Stored serum and DNA samples will be used to discover novel diagnostic markers to predict the onset and progression of T1D.
Results
Overview of data registration and sample collection
At the end of March 2021, data from 315 T1D patients were registered in the EDC system and serum and DNA samples from all the participants were stored. The number of patients with subtypes of T1D, i.e., AT1D, SP1D, and FT1D were 163 (51.7%), 110 (34.9%), and 42 (13.4%), respectively. The percentages of completing date registration form at each time point, i.e., at the onset/diagnosis of T1D, at the registration to the study, 1 year, 2 years, 3 years, 4 years, and 5 years after registration were 100%, 100%, 98.7%, 97.8%, 90.3%, 85.5%, and 84.6%, respectively.
Currently, study participants are still being recruited.
In the TIDE-J, clinical data including glycemic control, endogenous insulin secretion, islet-related autoantibodies, and diabetic complications and treatments are collected annually using REDCap. Further, the HLA genotype, serum, and DNA sample of each participant were analyzed annually and the blood samples were stored for assessing exploratory markers and further genetic analysis. The TIDE-J certainly helps in revealing the clinical course of each T1D subtype. It may also help in identifying novel markers for diagnosing each subtype of T1D and predicting clinical outcomes (including pancreatic beta cell function and disease severity) in patients.
Discussion
To our knowledge, this is the first study that used a multi-center prospective longitudinal (annual) database of the three subtypes of T1D, including adult-onset T1D supplemented with genetic information and biobanking. Thus far, two important cross-sectional databases of T1D have been reported in Japan. Ehime study, which was established in 1998 with nearly 100 T1D patients, had several findings including clinical characteristics of patients with islet-related autoantibodies [9] and contribution of HLA haplotypes to T1D susceptibility [18]. Additionally, the Japanese Study Group of Insulin Therapy for Childhood and Adolescent Diabetes, which was established in 1996 with more than 1000 children with T1D, reported clinical characteristics of the patients [10, 19–21] and results of genetic analysis in children with T1D. In the TIDE-J, annual data are to be collected prospectively after the registration of baseline clinical characteristics, including islet-related autoantibodies and HLA genotypes. Therefore, it would be possible to investigate the clinical course of each T1D subtype and identify clinical or genetic markers to predict disease severity, including changes in serum C-peptide levels indicating endogenous insulin secretion, incidence of ketoacidosis or severe hypoglycemia, and diabetic complications.
The TIDE-J may help in overcoming issues related to the diagnosis of T1D. For instance, since the assays to measure GAD-Ab and IA-2-Ab changed from RIA to ELISA in December 2015 and December 2018, respectively, physicians might have difficulty in diagnosing T1D when the patients show discrepancy between the results of antibody positivity as per RIA and ELISA. This was especially seen during the diagnosis of SP1D because 33% of SP1D patients were negative for GAD-Ab measured by ELISA despite positive GAD-Ab results as per RIA [22]. If the titers of GAD-Ab and IA-2-Ab could be measured by both assays using stored serum samples in the TIDE-J, the significance of each assay for diagnosing T1D might be revealed through in-depth analysis along with clinical data and HLA genotypes. This longitudinal database may also help in classifying “severe T1D” based on glycemic stability (which is affected by endogenous insulin secretion) and acute and chronic diabetic complications.
The prevalence and incidence of T1D differs among countries and races. For instance, the incidence of T1D is over 40 per 100,000 people per year in Finland, whereas it is around 2.0 per 100,000 people per year in Japan [11]. This could be due to differences in genetic background (including the prevalence of HLA class II genotypes) among races and environmental factors among countries. Considering HLA class II haplotypes, while HLA DRB1*04:05-DQB1*04:01 and DRB1*09:01-DQB1*03:03 are susceptible to T1D in the Japanese population, the frequencies of these haplotypes are less than 1.0%, and T1D susceptibility is unknown in the Caucasian population. Further, HLA DRB1*03:01-DQB1*02:01 and DRB1*04:01-DQB1*03:02 haplotypes in the Caucasian population are susceptible to T1D, whereas the frequencies of these haplotypes are less than 1.0%, and T1D susceptibility is unknown in the Japanese population [23]. Additionally, a genome-wide association study of FT1D revealed novel gene variants, CASD/lnc-ITGB7-1 on chromosome 12q13.3 in susceptible Japanese individuals [24]. Genetic susceptibility to T1D in the Japanese population might affect the prevalence and heterogeneity of the disease. This could have led to the classification of T1D originally in Japan. Considering changes in pancreatic beta cell function after the onset of T1D, Shields et al. reported that T1D patients in the United Kingdom displayed two clear phases of C-peptide decline after the onset, an initial exponential fall over 7 years followed by a stable period thereafter [25]. Furthermore, Steck et al. demonstrated that younger age, higher BMI, female sex, increased number of islet autoantibodies, and IA-2-Ab or ZnT8-Ab positivity were associated with higher rate of C-peptide loss in T1D patients from the United States and European countries [26]. Taken together, TIDE-J may contribute to the identification of novel clinical and genetic markers to predict the deterioration of pancreatic beta cell function in each subtype of T1D originally in Japan.
In the TIDE-J, REDCap is used for data collection. REDCap is a web-based tool for creating secure online forms for data collection and management. It enables researchers to directly register data in the system server online and avoids multiple steps for transcribing the data, preventing misentry [27, 28]. Moreover, the quality of data is well controlled through data management, which encompasses data cleaning, query to researchers, and publishing monitoring reports by data managers. Thus, findings from the TIDE-J are highly reliable.
Our study had several limitations. First, the number of study participants was relatively low when they were divided into the three T1D subtypes. To overcome this, we will continue recruiting participants, especially those with rare phenotypes, such as FT1D. Second, in the patients diagnosed as T1D before initiating the study, blood samples to investigate biomarkers at the diagnosis could not be collected. Third, data collection from participants might be discontinued if they changed hospitals. Minimizing this discontinuity needs to be considered. We need to construct a system wherein researchers (physicians) are able to follow clinical data with the help of physicians at the respective hospitals.
In conclusion, the TIDE-J is the first study that used a multi-center prospective longitudinal (annual) database of the three subtypes of T1D. It included adult-onset T1D, and was supplemented with genetic information and biobanking. This database may contribute to the identification of novel markers for diagnosing each subtype of T1D. It may also help in predicting clinical outcomes of patients. Furthermore, the TIDE-J might help in the development of next-generation therapies, such as immune intervention and cell transplantation.
Acknowledgements
The authors would like to thank the following researchers for their support during clinical data collection: Nobuyuki Takahashi, Keisuke Ueno, Aiko Terakawa, Noriko Kodani, Hidekatsu Yanai, Hisayuki, Katsuyama, and Akiko Shima from National Center for Global Health and Medicine; Jungo Terasaki, Yuko Mishiba, Norio Kanatsuna, and Akiko Irie from Osaka Medical and Pharmaceutical University; Shinsuke Noso, Junko Toma, and Yayoi Kibayashi from Kindai University; Hiromi Iwahashi, Sho Yoneda, Harutoshi Ozawa, and Shingo Fujita from Osaka University; Susumu Kurihara from Saitama Medical University; Ryoichi Kawamura and Hiroshi Onuma from Ehime University; Satoshi Akazawa and Ichiro Horie from Nagasaki University; Shoichiro Tanaka, Masahiro Kaneshige, Soichi Takizawa from Yamanashi University; Ken Yajima from Tachikawa Hospital; Yasuhisa Fujii from Takanoko Hospital; Shiori Kondo from Matsuyama Red Cross Hospital; Satoshi Murao from Takamatsu Hospital; Kyoko Kohashi from Showa University; Aira Uchida from Shin-Koga Hospital. We would also like to thank Tomoko Iwamoto from the National Center for Global Health and Medicine for the management of the collected data.
Funding
This study was supported by a grant from the National Center for Global Health and Medicine (19A1008).
Data availability
The datasets generated during this study are available from the corresponding author upon reasonable request.
Declarations
Conflict of interest
Daisuke Chujo received honorarium for lectures from Eli Lilly and Company; research funding from Novo Nordisk Pharma Ltd., and Sanofi K.K.; Akihisa Imagawa received honorarium for lectures from Astellas Pharma Inc.; clinical commissioned/joint research grant from Astra Zeneca, Soiken Inc., Taiho Pharmaceutical Co., Ltd., Daiichi Sankyo Co., Ltd., Merck KGaA, and Parexel International Inc.; research grant from Shionogi Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd., Takeda Pharmaceutical Company, and Ono Pharmaceutical Co., Ltd.; Norio Abiru received honorarium for lectures from Novo Nordisk Pharma Ltd., Astellas Pharma Inc., and Eli Lilly and Company; research funding from Ono Pharmaceutical Co. Ltd., Bristol Myers Squibb, Taisho Pharmaceutical Co., Ltd., and Astellas Pharma Inc.; Takuya Awata received honorarium for lectures from Astellas Pharma Inc.; Hiroshi Ikegami received honorarium for lectures from Astellas Pharma Inc., Eli Lilly and Company, MSD K.K., Novo Nordisk Pharma Ltd., Novartis Pharma K.K., Sumitomo Dainippon Pharma Co., Ltd., and Terumo Corporation; donations from Abbott Japan Co., Ltd., LifeScan Japan K.K., Mitsubishi Tanabe Pharma Corporation, Novo Nordisk Pharma Ltd., Otsuka Pharmaceutical Co., Ltd., Sanofi K.K., Sumitomo Dainippon Pharma Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company. Akira Shimada received honorarium for lectures from Novo Nordisk Pharma Ltd., Eli Lilly and Company, and Sanofi K.K.; Other authors have no conflict of interest to declare.
Human rights statement and informed consent
All procedures performed were in accordance with the 1964 Helsinki Declaration and its later amendments and the “ethical guidelines for human genome/gene analysis research” published by the Ministry of Health, Labor, and Welfare of Japan. The study protocol was approved by the ethical committee of the National Center for Global Health and Medicine and other collaborating institutes (ID: NCGM-A-000138-13). Written informed consent was obtained from all participants.
Footnotes
Publisher's Note
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated during this study are available from the corresponding author upon reasonable request.