Abstract
Introduction
This study aimed to investigate differences in clinical and biochemical characteristics between autoantibody-positive and autoantibody-negative type 1 diabetes mellitus (T1DM) in children, to get a better understanding of autoantibody-negative T1DM.
Methods
This study is a monocenter retrospective cross-sectional cohort study conducted at Diaboss, a Dutch pediatric diabetes clinic at OLVG hospital. Patients diagnosed with T1DM before the age of 18 years were included. Patients tested positive for one or more diabetes-associated autoantibodies (GADA, IA2A, ZnT8, IAA, and/or ICA) were considered autoantibody positive. Patients solely positive for anti-ICA were excluded.
Results
A total of 562 patients were recruited. Eight of 562 (1.4%) patients tested positive for a monogenic cause of diabetes at a later stage and were excluded. A total of 11 of 488 (2.2%) autoantibody-positive patients tested solely positive for anti-ICA and were excluded. In 15 of 66 patients (23%) with autoantibody-negative T1DM, monogenic causes were ruled out by genetic testing. The other 51 patients (77%) were not tested. Autoantibody-negative patients with a positive family history for diabetes were more likely to be tested for monogenic causes (p = 0.002). No significant differences in demographic and clinical characteristics, diabetes presentation, and long-term diabetes regulation were found between autoantibody-positive and autoantibody-negative patients.
Conclusion
In our study, autoantibody-positive and -negative T1DM patients were similar. This implies that both patient groups can be approached in the same way regarding treatment, education, and prognosis. Misdiagnosis of monogenic causes within T1DM could occur; therefore, clinicians should be aware of and perform genetic tests according to international guidelines.
Keywords: Diabetes autoimmunity, Diabetes in children, Diabetes mellitus type 1, Clinical decision making
Introduction
Type 1 diabetes mellitus (T1DM) is an autoimmune disease characterized by gradual destruction of pancreatic beta-cells. This usually leads to absolute insulin deficiency and hyperglycemia, and propensity for diabetic ketoacidosis (DKA) [1–3]. Serum autoantibodies directed against the pancreatic beta-cells reflect the autoimmunity and are considered the “smoke of the fire.” Detection of these autoantibodies confirms the diagnosis T1DM or suggests a high risk of progressing to this condition [4]. The autoantibodies known to be associated with T1DM are glutamic acid decarboxylase-65 (GADA), islet antigen 2 (IA2A), zinc transporter 8 (ZnT8), islet cell (ICA) and insulin (IAA) autoantibodies. To differentiate between T1DM and type 2 diabetes mellitus (T2DM), monogenic diabetes, and other types of diabetes, it is recommended to test for these autoantibodies in children with newly diagnosed diabetes [1]. Precise distinction between the different types of diabetes is essential because some types of diabetes require a different treatment. In the majority of newly diagnosed patients with suspected T1DM, diabetes-associated autoantibodies are detectable. Currently, this is categorized as autoantibody-positive or immune-mediated T1DM [1, 5]. In the remaining 10–20%, autoantibodies are not detectable; this is known as autoantibody-negative or idiopathic T1DM [5]. Still, the pathogenesis of this last subtype is not well understood.
Autoantibody-negative T1DM is thought to and may be a misclassification of unrecognized diabetes subtypes including T2DM or monogenic diabetes. Previous studies revealed some cases with monogenic diabetes in initially diagnosed autoantibody-negative T1DM [6–13]. A large prospective cohort study by Carlson et al. [8] found HbA1c <58 mmol/mol (or <7.5%) and a family history of diabetes as the most discriminatory clinical features to distinguish monogenic diabetes from T1DM. Systematically testing autoantibody-negative T1DM patients based on these features led to identification of 94% of misdiagnosed cases of monogenic diabetes with a detection rate of ∼33%, compared to 74% with a detection rate of ∼45% by testing on clinicians’ request. However, in 4–10% of the autoantibody-negative patients with a clinical presentation similar to autoantibody-positive T1DM the etiology remains uncertain even after genetic testing [6–12].
Several studies comparing autoantibody-positive with autoantibody-negative with T1DM patients have been published. A higher age at diagnosis, lower insulin requirement, lower HbA1c 1 year after diagnosis, and a higher prevalence of a positive family history was reported in autoantibody-negative T1DM [6, 7, 10–16]. However, the results of these studies were not all consistent. Lezzi et al. [11] and Hameed et al. [6] did not find any significant differences between the two groups. Additionally, the study populations comprised children, adolescents, and adults. Consequently, recommendations regarding negative T1DM patients are not uniform yet. By investigating possible differences in clinical and biochemical characteristics between autoantibody-negative and -positive T1DM in a large Dutch pediatric diabetes population, this study aimed to contribute to a better understanding of autoantibody-negative T1DM.
Methods
Study Design and Participants
This study is a monocenter retrospective cross-sectional cohort study. It was conducted at Diaboss, a Dutch pediatric diabetes clinic in the OLVG hospital in Amsterdam, The Netherlands. Data were collected from medical records of patients who were treated by one of the health care professionals of Diaboss, between July 2012 and September 2022.
To be eligible to participate in this study, patients had to meet all of the following inclusion criteria: diagnosis of T1DM before the age of 18 years, tested for diabetes-associated autoantibodies (GADA, IA2A, ZnT8, IAA, and/or ICA) and treated by a health care professional of Diaboss. Patients were classified as having T1DM according to the most recent guidelines of ISPAD [1].
Included patients were divided into two subgroups which were compared in statistical analysis: (1) patients with autoantibody-negative T1DM and (2) patients with autoantibody-positive T1DM, tested positive for one or more diabetes-associated autoantibodies. Patients exclusively positive for anti-ICA were excluded as it is suggested that ICA positivity alone does not point to T1DM [17]. The study was reviewed and approved by the local research and ethics committee of OLVG hospital.
Data Measurements
Patient demographics, including age at diagnosis, gender, and ethnicity, as well as patient characteristics, including the presence of other autoimmune disease and family history of first-degree relatives with T1DM or other types of diabetes, and/or other autoimmune disease were collected. Data collected on diabetes presentation were duration of symptoms, presence of osmotic symptoms (polyuria, polydipsia, and weight loss), presentation with severe DKA, body mass index (BMI), HbA1c, serum glucose, pH, and the level of ketone bodies. To assess long-term diabetes regulation, HbA1c levels and total insulin dose 1 and 5 year(s) after diagnosis were used. Finally, data on other autoimmune autoantibodies and the performance of genetic testing for monogenic causes of diabetes were collected.
Diabetes-Associated Autoantibodies
Since 2016, diagnostic testing for diabetes-associated autoantibodies has been performed in a cascade by an external laboratory. As a first step, GADA is tested, which is followed by IA2A if GADA is <30 U/mL, and then ZnT8 if IA2A is <30 U/mL. These three autoantibodies are thought to be the best predictors of T1DM. ICA is only tested when ordered separately because of the lower prevalence in T1DM patients. Currently, IAA is not tested anymore because of the lack of sensitivity [18]. From 2016, autoantibodies (GADA, IA2A, and ZnT8) were determined by ELISA. ICA was qualitatively determined by immunofluorescence testing, with as reference positive and negative controls. The tests are considered positive for values of >5 U/mL (GADA), >8 U/mL (IA2A), and >15 U/mL (ZnT8).
Due to alterations in diagnostic procedures over time, the procedures performed on the included patients before 2016 differ. Several included patients were transferred to Diaboss after being diagnosed with T1DM in another hospital. Therefore, information on the diabetes autoantibodies as well as the diagnostic procedures of the laboratory involved was not always available.
Testing for Monogenic Diabetes
Diagnostic testing for monogenic diabetes was performed in accordance with the ISPAD Clinical Practice Consensus Guidelines.
Data Collection and Validation
Data were collected retrospectively from the electronic patient file (EPIC), using software program CTcue v4.5.0. Data cleaning, management, and analyses were carried out in IBM® SPSS® Statistics, version 28.0. Ethnicity was based on data obtained by medical history and categorized according to the DPARD register, the National Pediatric and Adult Registry of Diabetes [2]. The categories were defined as “Caucasian,” “North-African,” “African other,” “Turkish- and Caicosislands,” “Hindu,” “Asian other,” “Latin American,” and “Multiple Ancestry.” Other autoimmune diseases that were collected were celiac disease, Hashimoto’s disease, Graves’ disease, vitiligo, juvenile idiopathic arthritis, alopecia areata, and inflammatory bowel disease and were categorized as present or not present according to international guidelines. Presentation with severe DKA was defined as a need for fluid and insulin treatment and a measured pH <7.3 at diagnosis. The BMI pediatric Z-score was calculated using an international calculator from the “Children’s Hospital of Philadelphia research institute” [19]. HbA1c was expressed in mmol/mol and was converted from percentage to mmol/mol if needed, using the conversion formula “mmol/mol = (10.93 × %) − 23.5” [20]. Serum glucose was expressed in mmol/L and was converted from mg/dL to mmol/L if needed, using the conversion formula “mg/dL = mmol/L × 18” [20].
Statistical Analysis
Categorical variables were summarized as percentages. Quantitative variables were expressed as mean with standard deviations or median with interquartile range, as appropriate. Comparisons between the subgroups were performed using Fisher’s exact test or Chi-square test for categorical variables. Comparisons between quantitative variables were performed using unpaired t test (parametric) or Mann-Whitney U test (non-parametric). p values ≤0.05 were considered statistically significant. Missing data or extreme values were coded as missing, the remaining data were analyzed.
Results
Description of the Total Cohort
A total of 562 patients met all criteria for inclusion. Eight of the 562 (1.4%) included patients, however, tested positive for a monogenic cause of diabetes at a later stage, and were excluded from statistical analyses. A total of 11 of 488 (2.2%) autoantibody-positive patients tested exclusively positive for anti-ICA and were excluded from statistical analysis. Table 1 shows an overview of the demographic characteristics of the total cohort of 543 patients. A slight predominance of males (52.5%) was observed. The average age at diagnosis was 8.83 years (SD = 4.47). The majority of the patients was of Caucasian (n = 149, 39.4%) or North African (n = 148, 39.2%) origin. Because of low counts in the other ethnicity groups, this variable was categorized into “Caucasian,” “North African” or “Other” in further analysis. Data collection was not complete for all variables. For every variable, sample sizes are presented separately.
Table 1.
Demographic characteristics of the total cohort
| | N | Total cohort (N = 543) |
|---|---|---|
| Age at onset, years | 542 | 8.83±4.47 |
| Gender | 543 | |
| Male | | 52.5 (285) |
| Female | | 47.5 (258) |
| Ethnicity | 378 | |
| Caucasian | | 39.4 (149) |
| North African | | 39.2 (148) |
| African other | | 6.1 (23) |
| Turkish- and Caico islands | | 4.2 (16) |
| Hindu | | 0.8 (3) |
| Asian other | | 1.3 (5) |
| Latin American | | 1.6 (6) |
| Multiple ancestry | | 7.4 (28) |
Mean ± SD, percentages (n), or medians (IQR) are shown.
Sample sizes (n) are given for each variable.
Autoantibody Distribution in the Antibody-Positive Group
Table 2 shows an overview of autoantibody positivity among the autoantibody-positive group which comprised 477 patients. The majority of patients tested positive for GADA with positivity rates of 84.3%. A substantial number of patients were not tested for ICA, IAA, and ZnT8, resulting in missing data.
Table 2.
Positivity rates for each antibody within the autoantibody-positive group
| | N (total = 477) | Positivity |
|---|---|---|
| GADA | 476 | 84.3 (402) |
| IA2 | 282 | 42.3 (202) |
| IAA | 73 | 9.9 (47) |
| ICA | 67 | 4.6 (22) |
| ZnT8 | 28 | 4.2 (20) |
Percentages (n) are shown.
Sample sizes (N) are given for each autoantibody.
Monogenic Causes of Diabetes Tested in the Autoantibody-Negative Group
The autoantibody-negative group comprised 66 patients. In 15 patients (23%) monogenic causes of diabetes were ruled out by genetic testing. There was a significantly higher percentage of first-degree relatives with type 1 diabetes and other types of diabetes in these 15 patients compared to the patients who were not tested for monogenic causes (retrospectively 50% vs. 17.8%, p = 0.016, and 28.6% vs. 2.2%, p = 0.002). Regarding other demographic and clinical characteristics, no significant differences were found between patients who were tested and patients who were not tested for monogenic causes.
Comparison between Autoantibody-Positive and Autoantibody-Negative T1DM
A total of 477 patients (88%) were assigned to the autoantibody-positive group and 66 patients (12%) to the autoantibody-negative group shown in Figure 1. An overview of the results is shown in Tables 3 and 4. There was no difference in demographic and clinical characteristics between autoantibody-positive and -negative patients. There was a tendency toward significance regarding a higher prevalence of other autoimmune diseases in the autoantibody-positive group compared to the negative group (retrospectively 11.7% vs. 4.5%, p = 0.078) and a higher percentage of first-degree relatives with T1DM in the autoantibody-negative group compared to the positive group (retrospectively 25.4% vs. 15.8%, p = 0.065).
Fig. 1.
Flow diagram showing the inclusion of patients and subgroup distribution.
Table 3.
Comparison between autoantibody-positive and autoantibody-negative T1DM at diagnosis
| | N | AB+ (N = 477) | AB− (N = 66) | p value |
|---|---|---|---|---|
| Demographic characteristics | ||||
| Age at onset, years | 542 (476/66) | 8.88±4.40 | 8.41±4.97 | 0.419a |
| Gender | 543 (477/66) | | | 0.377b |
| Male | | 51.8 (247) | 57.6 (38) | |
| Female | | 48.2 (230) | 42.4 (28) | |
| Ethnicity | 350 (302/48) | | | 0.863b |
| Caucasian | | 43.0 (130) | 39.6 (19) | |
| North African | | 41.7 (126) | 45.8 (22) | |
| Other | | 15.2 (46) | 14.6 (7) | |
| Clinical characteristics | ||||
| Other autoimmune disease | 543 (477/66) | 11.7 (56) | 4.5 (3) | 0.078b |
| First degree family history | 501 (442/59) | | | |
| T1DM | | 15.8 (70) | 25.4 (15) | 0.065b |
| Other DM | | 11.8 (52) | 8.5 (5) | 0.455b |
| Other autoimmune disease | | 9.0 (40) | 10.2 (6) | 0.780b |
| Diabetes presentation | ||||
| Duration of symptoms, weeks | 268 (243/25) | 2 (1–4) | 3 (1.3–6.5) | 0.159c |
| Osmotic symptoms | ||||
| Polyuria | 360 (326/34) | 94.5 (308) | 91.2 (31) | 0.434d |
| Polydipsia | 361 (327/34) | 95.1 (311) | 94.1 (32) | 0.682d |
| Weight loss | 357 (324/33) | 62.0 (201) | 75.8 (25) | 0.119b |
| Severe DKA | 412 (373/39) | 16.4 (61) | 10.3 (4) | 0.320b |
| Measurements at diagnosis | ||||
| BMI (z-score) | 280 (253/27) | −0.46±1.37 | −0.24±1.58 | 0.516a |
| HbA1c, mmol/mol | 293 (264/29) | 99.81±26.82 | 102.79±24.48 | 0.638a |
| Serum glucose, mmol/L | 335 (303/32) | 24.71±11.17 | 24.51±9.19 | 0.342a |
| pH | 235 (215/20) | 7.35 (7.25–7.39) | 7.37 (7.28–7.40) | 0.481c |
| Ketone bodies, mmol/L | 304 (277/27) | 2.10 (0.70–4.30) | 1.80 (0.40–4.90) | 0.976c |
AB+, autoantibody positive; AB−, autoantibody negative; anti-TPO, anti-thyroid peroxidase; anti-TTG, anti-transglutaminase; BMI, body mass index; DM, diabetes mellitus; DKA, diabetic ketoacidosis; HbA1c, hemoglobin A1C.
Mean ± SD, percentages (n) or medians (IQR) are shown.
Sample sizes (N) are given for each variable.
p values are shown within the row of the calculated variable.
aIndependent unpaired t test.
bChi-square test.
cMann-Whitney-U test.
dFisher’s exact test.
Table 4.
Comparison between autoantibody-positive and autoantibody-negative T1DM one and 5 years after diagnosis
| | N | AB+ (N = 477) | AB− (N = 66) | p value |
|---|---|---|---|---|
| 1 year after diagnosis | ||||
| HbA1c, mmol/mol | 311 (276/34) | 61.39±16.14 | 66.68±22.17 | 0.086 |
| Total daily insulin dose, U/kg/day | 205 (190/15) | 0.61±0.33 | 0.56±0.20 | 0.612 |
| 5 years after diagnosis | ||||
| HbA1c, mmol/mol | 233 (209/24) | 66.01±17.41 | 66.24±18.04 | 0.949 |
| Total daily insulin dose, U/kg/day | 318 (290/28) | 0.69±0.33 | 0.71±0.25 | 0.814 |
Mean ± SD are shown.
AB+, autoantibody positive; AB−, autoantibody negative; HbA1c, Hemoglobin A1C.
p values are shown within the row of the calculated variable.
Independent unpaired t test was performed for statistical analysis.
Discussion
In this study, a large group of pediatric patients with T1DM, with and without autoantibodies, was investigated. 12% of the total cohort was autoantibody negative and their clinical and biochemical characteristics did not appear to be significantly different from those of the autoantibody-positive T1DM patients. As mentioned before, previous studies reported conflicting results. By investigating a large multiethnic cohort of children, this study provides more information about the group of T1DM patients without autoantibodies.
One of the characteristics we investigated was the long-term diabetes control. Two previous studies showed better diabetes control in the autoantibody-negative T1DM patients. Pörksen et al. [12] found significantly higher levels of C-peptide after meal-stimulation, lower HbA1c, and less insulin requirement 1 year after diagnosis. Abdel-Karim et al. [7] did also find significantly higher levels of C-peptide and lower insulin requirement. In this study, C-peptide data were not available as part of routine pediatric T1DM care, limiting assessment of residual beta-cell function. Our study did not show any difference between autoantibody-negative and -positive T1DM patients regarding HbA1c level and insulin requirement after 1 and 5 years. However, this outcome should be interpreted with caution due to missing data, particularly within the autoantibody-negative group.
The comparison of the autoantibody status between different ethnic groups did not show any significant differences. Barman et al. [21] and Gall et al. [22] reported a higher frequency of autoantibody-negative forms of diabetes, including malnutrition-related diabetes and ketosis-prone diabetes, in Africa and South Asia in older children, adolescents and young adults. Our study population included only pediatric patients <18 years old, which might explain why we did not find this higher frequency. Furthermore, our data on ethnic distribution may be unreliable due to potential misclassification by clinicians.
As previously mentioned, a possible reason for autoantibody negativity can be a misdiagnosis of another type of diabetes such as monogenic diabetes and T2DM. Previous studies investigated the prevalence of missed monogenic diabetes in initially autoantibody-negative T1DM patients, and showed percentages ranging from 1.4 to 8% [6–10, 12]. In our study 51 of 66 (77%) included autoantibody-negative patients, monogenic causes of diabetes had not been tested yet, but were considered to be autoantibody negative. Some of these patients might have a missed diagnosis of monogenic diabetes. In our patient population, the choice whether to test for monogenic diabetes was made by the responsible clinician and was not necessarily performed in each patient without autoantibodies, as advised by international guidelines. Therefore, some cases of monogenic diabetes may have been missed. Within the autoantibody-negative group, we found a significantly higher percentage of first-degree relatives with type 1 diabetes in patients tested for monogenic causes (p = 0.002). The latter finding suggests that our clinicians based their choice to perform monogenic testing at least partly on a positive family history for diabetes. No other significant differences were found between the tested and not tested patients within the negative group. This suggests that the included patients not tested for monogenic causes were truly autoantibody-negative T1DM. Thus, our findings concur with Carlson et al. [8] and Karaoglan and Nacarkahya [23] suggesting positive family history of diabetes as a significant clue for monogenic diabetes and are in line with the ISPAD guidelines 2022 [24].
We found no evidence to suggest that (some) autoantibody-negative patients in our population had misdiagnosed T2DM. No significant difference in BMI z-scores was found between both groups, and mean BMI z-scores were rather low in both groups (<1 SD). T2DM is a type of diabetes which is becoming more common in children [25]. Hence, it is important to keep paying attention to clues that point to T2DM, including obesity, older age at onset of disease, family history of T2DM, acanthosis nigricans, high-risk ethnic groups and insulin resistance [25, 26].
This study has several limitations. Most patients were not tested for all available autoantibodies. Considering that anti-ZnT8 is highly predictive for T1DM, it is important to note that 34 patients in the autoantibody-negative group were not tested for anti-ZnT8. This limitation is unavoidable due to the retrospective nature of the study. These patients were tested negative for multiple other autoantibodies, and sole anti-ZnT8 positivity is uncommon [27]. Moreover, it is thought that initially elevated autoantibodies can disappear over time, and we did not take the timing of testing of autoantibodies into account when classifying. Given that autoantibody testing is routinely performed at T1DM diagnosis, we consider it unlikely that this significantly affected our study cohort. Some cases of monogenic diabetes may have been missed and misdiagnosed as T1DM in our study population. As a result, the classification of autoantibody-positive or -negative T1DM may not be accurate enough. Nevertheless, 12% of our total cohort were considered autoantibody negative which is consistent with other studies [6, 9–12, 15]. The different sizes of the autoantibody-positive and -negative group may have led to absence of statistically significant outcomes. Another limitation is that the results may have been influenced by missing data, which are related to the retrospective nature of this study. Finally, this study did not adjust for confounders in a multivariable analysis. One of the strengths of this study is the fact that it is the first study that investigated a Dutch population regarding this topic. In addition, this study includes a relatively large multiethnic cohort of children, which makes the results generalizable.
Based on our findings, we conclude that clinical and biochemical characteristics of autoantibody-positive and -negative T1DM patients are similar. This implies, in daily practice, that both patient groups can be approached in the same way with respect to patient prognosis, treatment, and education. Yet, clinicians should always be aware of a possible monogenic cause and perform appropriate genetic tests according to international guidelines. Additionally, a positive family history of diabetes was a significant clue for our clinicians to distinct monogenic diabetes from T1DM. Future studies on autoantibody-negative T1DM should be preferably prospective and include genetic testing in all autoantibody-negative T1DM patients, and collection of long-term c-peptide measurements to investigate residual beta-cell function.
Acknowledgments
I extend my gratitude to the International Society for Pediatric and Adolescent Diabetes (ISPAD) for the opportunity to present my findings at the ISPAD 2023 Congress in Rotterdam, The Netherlands (October 18–21, 2023). The feedback received was instrumental in refining this research.
Statement of Ethics
This study protocol received approval from the Local Research and Ethics Committee of OLVG Hospital (Advisory Committee on Scientific Research, ACWO; Approval No.: WO.22.107). The requirement for written informed consent was waived by the Advisory Committee on Scientific Research, ACWO due to the disproportionate burden associated with the retrospective design of the study, which spanned an extended period.
Conflict of Interest Statement
The authors declare that there is no conflict of interest regarding the publication of this paper.
Funding Sources
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Author Contributions
Roos I. Van Rhijn, MD: conceptualization, data curation, formal analysis, investigation, methodology, project administration, validation, visualization, and writing – original draft, review, and editing; Rosaline Mentink, MD: conceptualization, methodology, validation, supervision, and writing – original draft, review, and editing; A.S. Paul van Trotsenburg, prof.: conceptualization and writing – review and editing.
Funding Statement
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Data Availability Statement
The data that support the findings of this study are not publicly available due to privacy reasons but are available from the corresponding author, R.I. van Rhijn, upon reasonable request.
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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 data that support the findings of this study are not publicly available due to privacy reasons but are available from the corresponding author, R.I. van Rhijn, upon reasonable request.
