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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Neurol.</journal-id>
<journal-title>Frontiers in Neurology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Neurol.</abbrev-journal-title>
<issn pub-type="epub">1664-2295</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fneur.2023.1125842</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Neurology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Type 2 diabetes mellitus as a possible risk factor for myasthenia gravis: a case&#x02013;control study</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" equal-contrib="yes">
<name><surname>Liu</surname> <given-names>Yu-Dong</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn002"><sup>&#x02020;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1609594/overview"/>
</contrib>
<contrib contrib-type="author" equal-contrib="yes">
<name><surname>Tang</surname> <given-names>Fang</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="author-notes" rid="fn002"><sup>&#x02020;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1021981/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Li</surname> <given-names>Xiao-Li</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1436378/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Liu</surname> <given-names>Ya-Fei</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/2198137/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Zhang</surname> <given-names>Peng</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Yang</surname> <given-names>Chun-Lin</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1978465/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Du</surname> <given-names>Tong</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/863484/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Li</surname> <given-names>Heng</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1684134/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Wang</surname> <given-names>Cong-Cong</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Liu</surname> <given-names>Ying</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Yang</surname> <given-names>Bing</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Duan</surname> <given-names>Rui-Sheng</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x0002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/251713/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Department of Neurology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital</institution>, <addr-line>Jinan</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital</institution>, <addr-line>Jinan</addr-line>, <country>China</country></aff>
<aff id="aff3"><sup>3</sup><institution>Shandong Institute of Neuroimmunology</institution>, <addr-line>Jinan</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Hai-Feng Li, Capital Medical University, China</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Honghao Wang, Guangzhou First People&#x00027;s Hospital, China; Yuzhou Guan, Peking Union Medical College Hospital (CAMS), China</p></fn>
<corresp id="c001">&#x0002A;Correspondence: Rui-Sheng Duan <email>ruisheng_duan&#x00040;163.com</email></corresp>
<fn fn-type="other" id="fn001"><p>This article was submitted to Neuromuscular Disorders and Peripheral Neuropathies, a section of the journal Frontiers in Neurology</p></fn>
<fn fn-type="equal" id="fn002"><p>&#x02020;These authors have contributed equally to this work and share first authorship</p></fn></author-notes>
<pub-date pub-type="epub">
<day>17</day>
<month>04</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>14</volume>
<elocation-id>1125842</elocation-id>
<history>
<date date-type="received">
<day>16</day>
<month>12</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>21</day>
<month>03</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2023 Liu, Tang, Li, Liu, Zhang, Yang, Du, Li, Wang, Liu, Yang and Duan.</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Liu, Tang, Li, Liu, Zhang, Yang, Du, Li, Wang, Liu, Yang and Duan</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license></permissions>
<abstract>
<sec>
<title>Background</title>
<p>A certain number of myasthenia gravis (MG) patients clinically had type 2 diabetes mellitus (T2DM) prior to MG onset, which suggests that the onset of MG may correlate with the history of T2DM. This study aimed to examine the correlation between MG and T2DM.</p>
</sec>
<sec>
<title>Methods</title>
<p>In a single-center, retrospective, 1:5 matched case&#x02013;control study, all 118 hospitalized patients with a diagnosis of MG from 8 August 2014 to 22 January 2019 were enrolled. In total, four datasets with different sources of the control group were retrieved from the electronic medical records (EMRs). Data were collected at the individual level. A conditional logistic regression analysis was used to test the risk of MG associated with T2DM.</p>
</sec>
<sec>
<title>Findings</title>
<p>The risk of MG was significantly associated with T2DM, and there were notable differences by sex and age. Whether compared to the general population, general hospitalized patients without autoimmune diseases (AIDs), or patients with other AIDs except MG, women aged over 50 years with T2DM had an increased risk of MG. The mean onset age of diabetic MG patients was more than that of the non-diabetic MG patients.</p>
</sec>
<sec>
<title>Interpretation</title>
<p>This study demonstrates that T2DM is strongly associated with the subsequent risk of MG and varies significantly by sex and age. It reveals that diabetic MG may be a unique subtype that is different from the conventional MG subgroup classification. More clinical and immunological features of diabetic MG patients need to be explored in further studies.</p>
</sec></abstract>
<kwd-group>
<kwd>myasthenia gravis</kwd>
<kwd>type 2 diabetes mellitus</kwd>
<kwd>case control study</kwd>
<kwd>diabetic myasthenia gravis</kwd>
<kwd>advanced glycation end products</kwd>
</kwd-group>
<contract-sponsor id="cn001">Natural Science Foundation of Shandong Province<named-content content-type="fundref-id">10.13039/501100007129</named-content></contract-sponsor>
<contract-sponsor id="cn002">National Natural Science Foundation of China<named-content content-type="fundref-id">10.13039/501100001809</named-content></contract-sponsor>
<counts>
<fig-count count="3"/>
<table-count count="5"/>
<equation-count count="0"/>
<ref-count count="33"/>
<page-count count="9"/>
<word-count count="5684"/>
</counts>
</article-meta>
</front>
<body>
<sec id="s1">
<title>1. Introduction</title>
<p>Myasthenia gravis (MG) is a B-cell-driven, T-cell-dependent autoimmune disease (AID), which results from the transmission disorder in the post-synaptic neuromuscular junction caused by the production of antibodies for acetylcholine receptors (AChRs) (<xref ref-type="bibr" rid="B1">1</xref>, <xref ref-type="bibr" rid="B2">2</xref>) or other proteins, such as muscle-specific kinase (MuSK) (<xref ref-type="bibr" rid="B3">3</xref>). Low-density lipoprotein receptor-4 (LRP-4) and agrin were also considered indicative of MG. The main clinical manifestations of MG include muscle weakness and abnormal fatigability which seriously affect the quality of patients&#x00027; daily life. Generally, MG can be divided into different clinical subtypes. According to the different distributions of muscle weakness, MG can be divided into ocular MG (OMG) and generalized MG (GMG) (<xref ref-type="bibr" rid="B4">4</xref>). Grouped from the age of onset, MG patients also can be divided into early-onset (EOMG) and late-onset (LOMG) forms by the age of 50 years (<xref ref-type="bibr" rid="B5">5</xref>). It has been confirmed that the incidence of MG in the elderly of both sexes is increasing (<xref ref-type="bibr" rid="B5">5</xref>). Incredibly, very late-onset MG patients (onset age: 65 years or older) are more prone to severity (<xref ref-type="bibr" rid="B6">6</xref>). Thymoma-associated myasthenia gravis (TAMG) was considered a different subset from EOMG/LOMG or OMG/GMG clinical classification because of its specific characteristics in pathogenesis and clinical features (<xref ref-type="bibr" rid="B7">7</xref>). The traditional therapies of MG are acetylcholinesterase inhibitors (AChEIs), immunomodulatory therapies, thymectomy, and so on (<xref ref-type="bibr" rid="B8">8</xref>).</p>
<p>Type 2 diabetes mellitus (T2DM) is a common chronic disease, characterized by elevated blood glucose and attenuated insulin action. The incidence of T2DM has been rapidly increasing over recent decades, which has become a significant public health problem in the general population of China (<xref ref-type="bibr" rid="B9">9</xref>). Although the traditional view is that the correlation between MG and DM might be related to the adverse effect of corticosteroid and aggressive therapy (<xref ref-type="bibr" rid="B10">10</xref>), a 35-year retrospective study in 2007 raised doubts that it remained unclear whether prednisolone administration directly induced DM or if it merely accelerated disease onset (<xref ref-type="bibr" rid="B11">11</xref>). We found that quite a lot of MG patients had T2DM prior to the onset of MG. Traditional views cannot explain the phenomenon we found in the clinical work and may even obscure the true relationship between T2DM and MG.</p>
<p>In the present study, we propose a preliminary hypothesis: T2DM may be associated with MG. A matched case&#x02013;control study was designed to systematically test the hypothesis. The chronological order of the onset of T2DM and MG was also considered. Furthermore, the correlation between T2DM and MG, potential clinical features, and possible molecular mechanisms were elaborated. This discovery will provide innovative ideas for MG diagnosis and treatment.</p>
</sec>
<sec id="s2">
<title>2. Materials and methods</title>
<sec>
<title>2.1. Data sources</title>
<p>Data at the individual level were retrieved from electronic medical records (EMRs) in the First Affiliated Hospital of Shandong First Medical University between 8 August 2014 and 22 January 2019. The time of onset of MG and T2DM and the history of medicine compliance, such as glucocorticoid, were collected and recorded by tracking the patient&#x00027;s progress through EMRs and regular follow-up visits. Patients with ambiguous information were not included in the study.</p>
</sec>
<sec>
<title>2.2. Criteria of participants</title>
<p>A total of 141 inpatients (74 male patients and 67 female patients) with a diagnosis of MG were retrieved from EMR. The diagnosis of MG was made according to the following parameters (<xref ref-type="bibr" rid="B12">12</xref>): (1) the definite typical history and symptom of fluctuating weakness as well as fatigability of voluntary muscles; (2) the presence of serum anti-AChR antibody or other MG-associated antibodies (MUSK-Ab); (3) positive response to the neostigmine test; and (4) low-frequency repetitive nerve stimulation (RNS) reveals that the amplitude decreased by more than 10% (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B14">14</xref>). Collected data included demographics, comorbidities (including other discharge diagnoses except MG), clinical type of MG, results of antibody testing, laboratory results, and electrophysiological data. The boundary age between EOMG and LOMG is 50 years. The diagnosis of TAMG is based on the patient&#x00027;s pathological diagnosis.</p>
<p>Participants were diagnosed with T2DM according to the 1999 World Health Organization criteria (<xref ref-type="bibr" rid="B15">15</xref>). The diagnostic information of MG or T2DM must be consistent with the records of discharge diagnosis or past medical history from EMR.</p>
<p>The inclusion criteria for MG patients included a clinical diagnosis of MG: (1) MG must be conclusively diagnosed based on the diagnostic criteria and (2) the onset age must be at least 18 years old. Meanwhile, the following were the exclusion criteria: (1) participants with other AIDs except MG and (2) diabetic MG patients who took glucocorticoids before the onset of DM, considering the possible effect of glucocorticoids on T2DM pathogenesis. The Global Autoimmune Institute (GAI) made a list of recognized autoimmune diseases available.</p>
<p>Overall, 23 patients were not included in the study due to the following reasons: one patient had an unclear diagnosis of DM; one patient&#x00027;s information was partially missing; 10 patients took glucocorticoids before the onset of DM; six patients had other AIDs; and five patients were &#x0003C;18 years old at onset. Finally, a total of 118 patients were included in the study.</p>
</sec>
<sec>
<title>2.3. Criteria of matched controls</title>
<p>Up to five controls per MG patient, matched for sex and date of birth, were randomly drawn from people of the same period in the same hospital. A total of four datasets were analyzed (<xref ref-type="fig" rid="F1">Figure 1</xref>): (1) Dataset A: 118 MG patients and 590 controls, which were randomly selected from a population without AIDs of the health examination system. (2) Dataset B: 113 MG patients and 565 controls, who were diabetic MG patients with a T2DM onset time after MG or an uncertain onset time of diabetes, and their controls were excluded from dataset A. (3) Dataset C: 118 MG patients and 590 controls, which were randomly selected from all hospitalized patients without AIDs. (4) Dataset D: 117 MG patients and 573 controls, which were randomly selected from inpatients diagnosed with AIDs except MG. In total, one MG patient was removed from the patient group of dataset D because she was too old (89 years old) to match the eligible controls.</p>
<fig id="F1" position="float">
<label>Figure 1</label>
<caption><p>Flowchart of MG patients and controls inclusion (PG, patient group; CG, control group).</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fneur-14-1125842-g0001.tif"/>
</fig>
</sec>
<sec>
<title>2.4. Statistical analysis</title>
<p>Descriptive analyses were used for the distribution of variables of interest in the study population. Categorical variables were compared by the &#x003C7;<sup>2</sup> test. Continuous variables were compared by the <italic>t-</italic>test or non-parametric test. A conditional logistic regression analysis was used to test the risk of MG onset associated with T2DM. We stratify the particular MG case based on the onset age and sex in different MG clinical subtypes to eliminate the interference of confounding factors.</p>
</sec>
<sec>
<title>2.5. Ethics approval</title>
<p>All clinical information was obtained after the patients had given their written informed consent. The research received approval from the Research Ethics Committee of the First Affiliated Hospital of Shandong First Medical University (No: S030).</p>
</sec>
</sec>
<sec id="s3">
<title>3. Results</title>
<p>Our study showed that the top eight comorbidities of MG with prevalence were hypertension (HTN) (34.04%), thymoma (27.66%), T2DM (23.40%), coronary heart disease (18.44%), pulmonary infection (16.31%), cerebral infarction (16.31%), pneumonia (7.09%), and cataract (7.09%). Most comorbidities, except for thymoma, were related to geriatric chronic disease or infection. The correlation between thymoma and MG is already acknowledged, and the other top two comorbidities (HTN and T2DM) were focused on. The prevalence of HTN and T2DM in the MG inpatients and the general population was separately collected and analyzed. In terms of the prevalence of HTN, there was no statistical difference between controls and MG patients (33.77% vs. 34.56%, <italic>P</italic> &#x0003E; 0.05), while the T2DM prevalence of MG patients was higher than that of controls (24.26% vs. 9.54%, <italic>P</italic> &#x0003C; 0.001) (<xref ref-type="fig" rid="F2">Figure 2</xref>).</p>
<fig id="F2" position="float">
<label>Figure 2</label>
<caption><p>HTN and T2DM prevalence <bold>(A, B)</bold> between 676 control people and 136 MG patients (the controls of the same sex and age in the same period were randomly sampled from the physical examination system to match MG inpatients in the same hospital; <italic>t</italic>-test, &#x0002A;&#x0002A;&#x0002A;<italic>P</italic> &#x0003C; 0.001).</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fneur-14-1125842-g0002.tif"/>
</fig>
<sec>
<title>3.1. Prevalence of MG patients</title>
<p>In this study, the mean onset age of 118 MG patients was 55.14 &#x000B1; 17.49 years, and 44.07% were women (<xref ref-type="table" rid="T1">Table 1</xref>). The incidence was highest in male patients aged 40 to 60 and 70 to 80 and in female patients aged 20 to 30 and 60 to 70 (<xref ref-type="fig" rid="F3">Figure 3</xref>). Myasthenia gravis was more common in the elderly and young women. Of these patients, 33.90% of them (<italic>n</italic> = 40) were diagnosed to be EOMG, 34.75% of them (<italic>n</italic> = 41) were diagnosed to be OMG, and 30.51% of them (<italic>n</italic> = 36) were diagnosed to be TAMG (<xref ref-type="table" rid="T1">Table 1</xref>). In total, 18.64% of MG patients (<italic>n</italic> = 22) had T2DM.</p>
<table-wrap position="float" id="T1">
<label>Table 1</label>
<caption><p>Demographic characteristics of MG patients.</p></caption> 
<table frame="box" rules="all">
<thead>
<tr style="background-color:&#x00023;919498;color:&#x00023;ffffff">
<th valign="top" align="left"><bold>Characteristics</bold></th>
<th valign="top" align="center"><bold>Cases (<italic>n</italic> = 118)</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Male: Females</td>
<td valign="top" align="center">66: 52</td>
</tr> <tr>
<td valign="top" align="left">Age of onset (years: mean &#x000B1; sd)</td>
<td valign="top" align="center">55.14 &#x000B1; 17.49</td>
</tr> <tr style="background-color:#dee1e1;">
<td valign="top" align="left" colspan="2"><bold>EOMG/LOMG</bold></td>
</tr> <tr>
<td valign="top" align="left">EOMG</td>
<td valign="top" align="center">40 (33.90%)</td>
</tr> <tr>
<td valign="top" align="left">LOMG</td>
<td valign="top" align="center">78 (66.10%)</td>
</tr> <tr style="background-color:#dee1e1;">
<td valign="top" align="left" colspan="2"><bold>OMG/GMG</bold></td>
</tr> <tr>
<td valign="top" align="left">OMG</td>
<td valign="top" align="center">41 (34.75%)</td>
</tr> <tr>
<td valign="top" align="left">GMG</td>
<td valign="top" align="center">71 (60.17%)</td>
</tr> <tr>
<td valign="top" align="left">Not available</td>
<td valign="top" align="center">6 (5.08%)</td>
</tr> <tr style="background-color:#dee1e1;">
<td valign="top" align="left" colspan="2"><bold>TAMG/NTAMG</bold></td>
</tr> <tr>
<td valign="top" align="left">TAMG</td>
<td valign="top" align="center">36 (30.51%)</td>
</tr>
<tr>
<td valign="top" align="left">NTAMG</td>
<td valign="top" align="center">82 (69.49%)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Demographic characteristics of 118 MG patients.</p>
</table-wrap-foot>
</table-wrap>
<fig id="F3" position="float">
<label>Figure 3</label>
<caption><p>Presence of MG by age of onset and sex (<italic>n</italic> = 118).</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fneur-14-1125842-g0003.tif"/>
</fig>
</sec>
<sec>
<title>3.2. Risk of MG associated with T2DM</title>
<p>In this study, the objective was to study the effects of T2MD exposure on MG. The risk of MG was higher among T2DM patients than those without T2DM (OR = 1.92, 95% CI: 1.12&#x02013;3.29, <xref ref-type="table" rid="T2">Table 2</xref>) in the health examination population. Stratified by sex and age, female and elderly diabetic patients were at greater risk of MG. Compared with women without T2DM, the risk of MG was significantly higher for women with T2DM (OR = 4.25, 95% CI: 1.99&#x02013;9.05, <xref ref-type="table" rid="T2">Table 2</xref>). Of all the 22 diabetic MG patients, 17 fell ill with MG several years after the onset of diabetes. The remaining five were diagnosed with diabetes for the first time while hospitalized. In dataset B, diabetic MG patients with T2DM onset time after MG or uncertain onset time of diabetes and their controls were excluded from dataset A. In dataset B, T2DM remained significantly associated with the risk of MG among the female patient group (OR = 2.68, 95% CI: 1.13&#x02013;6.33, <xref ref-type="table" rid="T2">Table 2</xref>). Women with diabetes have a 2.68 times higher risk of developing MG than the general population, while the results of other subgroups were not statistically significant.</p>
<table-wrap position="float" id="T2">
<label>Table 2</label>
<caption><p>Distribution of T2DM and associated risk for MG in datasets A and B.</p></caption> 
<table frame="box" rules="all">
<thead>
<tr style="background-color:&#x00023;919498;color:&#x00023;ffffff">
<th/>
<th valign="top" align="left"><bold>Diagnosed with T2DM</bold></th>
<th valign="top" align="center" colspan="2"><bold>Distribution</bold>, <italic><bold>n</bold></italic> <bold>(%)</bold></th>
<th valign="top" align="center"><bold>Risk for MG, OR (95%CI)</bold></th>
<th valign="top" align="center"><bold><italic>P</italic> value</bold></th>
</tr>
</thead>
<tbody>
<tr style="background-color:&#x00023;919498;color:&#x00023;ffffff">
<td/>
<td/>
<td valign="top" align="center"><bold>MG patients</bold></td>
<td valign="top" align="center"><bold>Matched controls</bold></td>
<td/>
<td/>
</tr>
 <tr>
<td/>
<td valign="top" align="left">No</td>
<td valign="top" align="center">96 (81.36)</td>
<td valign="top" align="center">526 (89.15)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.018</td>
</tr>
 <tr>
<td/>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">22 (18.64)</td>
<td valign="top" align="center">64 (10.85)</td>
<td valign="top" align="center">1.92 (1.12&#x02013;3.29)</td>
<td/>
</tr> <tr style="background-color:#dee1e1;">
<td valign="top" align="left"><bold>Dataset A</bold></td>
<td valign="top" align="left" colspan="5">Age &#x0003C; 50 yr</td>
</tr>
 <tr>
<td valign="top" align="left" rowspan="11"></td>
<td valign="top" align="left">No</td>
<td valign="top" align="center">39 (97.5)</td>
<td valign="top" align="center">196 (98)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.84</td>
</tr>
 <tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">1 (2.50)</td>
<td valign="top" align="center">4 (2.00)</td>
<td valign="top" align="center">1.25(0.14&#x02013;11.18)</td>
<td/>
</tr>
 <tr>
<td valign="top" align="left" colspan="5" style="background-color:#dee1e1;">Age &#x02265; 50 yr</td>
</tr>
 <tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">57 (73.08)</td>
<td valign="top" align="center">330 (84.62)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.02</td>
</tr>
 <tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">21 (26.92)</td>
<td valign="top" align="center">60 (15.38)</td>
<td valign="top" align="center">1.98 (1.13&#x02013;3.46)</td>
<td/>
</tr>
 <tr>
<td valign="top" align="left" colspan="5" style="background-color:#dee1e1;">Males</td>
</tr>
 <tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">58 (87.88)</td>
<td valign="top" align="center">286 (86.67)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.79</td>
</tr>
 <tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">8 (12.12)</td>
<td valign="top" align="center">44 (13.33)</td>
<td valign="top" align="center">0.89 (0.39&#x02013;2.03)</td>
<td/>
</tr>
 <tr>
<td valign="top" align="left" colspan="5" style="background-color:#dee1e1;">Females</td>
</tr>
 <tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">38 (73.08)</td>
<td valign="top" align="center">240 (92.31)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.0002</td>
</tr>
 <tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">14 (26.92)</td>
<td valign="top" align="center">20 (7.69)</td>
<td valign="top" align="center">4.25 (1.99&#x02013;9.05)</td>
<td/>
</tr> <tr style="background-color:#dee1e1;">
<td valign="top" align="left"><bold>Dataset B</bold></td>
<td valign="top" align="left" colspan="5">Age &#x0003C; 50 yr</td>
</tr>
 <tr>
<td valign="top" align="left" rowspan="11"></td>
<td valign="top" align="left">No</td>
<td valign="top" align="center">39 (97.5)</td>
<td valign="top" align="center">196 (98)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.84</td>
</tr>
 <tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">1 (2.50)</td>
<td valign="top" align="center">4 (2.00)</td>
<td valign="top" align="center">1.250 (0.140&#x02013;11.184)</td>
<td/>
</tr>
 <tr>
<td valign="top" align="left" colspan="5" style="background-color:#dee1e1;">Age &#x02265; 50 yr</td>
</tr>
 <tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">57 (78.08)</td>
<td valign="top" align="center">306 (83.84)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.24</td>
</tr>
 <tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">16 (21.92)</td>
<td valign="top" align="center">59 (16.16)</td>
<td valign="top" align="center">1.444 (0.782&#x02013;2.666)</td>
<td/>
</tr>
 <tr>
<td valign="top" align="left" colspan="5" style="background-color:#dee1e1;">Males</td>
</tr>
 <tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">58 (87.88)</td>
<td valign="top" align="center">286 (86.67)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.79</td>
</tr>
 <tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">8 (12.12)</td>
<td valign="top" align="center">44 (13.33)</td>
<td valign="top" align="center">0.89 (0.39&#x02013;2.03)</td>
<td/>
</tr>
 <tr>
<td valign="top" align="left" colspan="5" style="background-color:#dee1e1;">Females</td>
</tr>
 <tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">38 (80.85)</td>
<td valign="top" align="center">216 (91.91)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.03</td>
</tr>
<tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">9 (19.15)</td>
<td valign="top" align="center">19 (8.09)</td>
<td valign="top" align="center">2.68 (1.13&#x02013;6.33)</td>
<td/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Distribution of T2DM and associated risk for MG stratified by sex and age in datasets A and B (The controls were randomly drawn from a population without any AID of the health examination system. Data were analyzed by conditional logistic regression analysis).</p>
</table-wrap-foot>
</table-wrap>
<p>Further analysis was made in datasets C and D. Dataset C represented the comparison of MG inpatients and inpatients without any AID. The results of dataset C showed that the risk of MG in female T2DM patients was higher than that of non-diabetic hospitalized patients (OR = 2.07, 95% CI: 1.01&#x02013;4.25, <xref ref-type="table" rid="T3">Table 3</xref>). Dataset D represented the comparison of MG inpatients and AID inpatients. The results of dataset D suggested that T2DM patients were at higher risk for MG than those with other AIDs in female patients and the elderly (females: OR = 2.40, 95% CI: 1.19&#x02013;4.86; elderly: OR = 1.91, 95% CI: 1.09&#x02013;3.34, <xref ref-type="table" rid="T3">Table 3</xref>). In a word, diabetic patients had a higher risk of MG, whether compared with ordinary hospitalized patients or patients with other AIDs.</p>
<table-wrap position="float" id="T3">
<label>Table 3</label>
<caption><p>Distribution of T2DM and associated risk for MG in datasets C and D.</p></caption> 
<table frame="box" rules="all">
<thead>
<tr style="background-color:&#x00023;919498;color:&#x00023;ffffff">
<th/>
<th valign="top" align="left"><bold>Diagnosed with T2DM</bold></th>
<th valign="top" align="center" colspan="2"><bold>Distribution</bold>, <italic><bold>n</bold></italic> <bold>(%)</bold></th>
<th valign="top" align="center"><bold>Risk for MG, OR (95%CI)</bold></th>
<th valign="top" align="center"><bold><italic>P</italic> value</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td/>
<td/>
<td valign="top" align="center"><bold>MG patients</bold></td>
<td valign="top" align="center"><bold>Matched controls</bold></td>
<td/>
<td/>
</tr> <tr style="background-color:#dee1e1;">
<td valign="top" align="left"><bold>Dataset C</bold></td>
<td valign="top" align="left" colspan="5">Age &#x0003C; 50 yr</td>
</tr>
 <tr>
<td valign="top" align="left" rowspan="11"></td>
<td valign="top" align="left">No</td>
<td valign="top" align="center">39 (97.5)</td>
<td valign="top" align="center">189 (94.5)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.44</td>
</tr>
 <tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">1 (2.50)</td>
<td valign="top" align="center">11 (5.50)</td>
<td valign="top" align="center">0.44 (0.05&#x02013;3.51)</td>
<td/>
</tr>
 <tr>
<td valign="top" align="left" colspan="5" style="background-color:#dee1e1;">Age &#x02265; 50 yr</td>
</tr>
 <tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">57 (73.08)</td>
<td valign="top" align="center">320 (82.05)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.07</td>
</tr>
 <tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">21 (26.92)</td>
<td valign="top" align="center">70 (17.95)</td>
<td valign="top" align="center">1.66 (0.95&#x02013;2.89)</td>
<td/>
</tr>
 <tr>
<td valign="top" align="left" colspan="5" style="background-color:#dee1e1;">Males</td>
</tr>
 <tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">58 (87.88)</td>
<td valign="top" align="center">289 (87.58)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.95</td>
</tr>
 <tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">8 (12.12)</td>
<td valign="top" align="center">41 (12.42)</td>
<td valign="top" align="center">0.97 (0.43&#x02013;2.19)</td>
<td/>
</tr>
 <tr>
<td valign="top" align="left" colspan="5" style="background-color:#dee1e1;">Females</td>
</tr>
 <tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">38 (73.08)</td>
<td valign="top" align="center">220 (84.62)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.047</td>
</tr>
 <tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">14 (26.92)</td>
<td valign="top" align="center">40 (15.38)</td>
<td valign="top" align="center">2.07 (1.01&#x02013;4.25)</td>
<td/>
</tr> <tr style="background-color:#dee1e1;">
<td valign="top" align="left"><bold>Dataset D</bold></td>
<td valign="top" align="left" colspan="5">Age &#x0003C; 50 yr</td>
</tr>
 <tr>
<td valign="top" align="left" rowspan="11"></td>
<td valign="top" align="left">No</td>
<td valign="top" align="center">38 (97.44)</td>
<td valign="top" align="center">171 (91.44)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.23</td>
</tr>
 <tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">1 (2.56)</td>
<td valign="top" align="center">16 (8.56)</td>
<td valign="top" align="center">0.29 (0.04&#x02013;2.21)</td>
<td/>
</tr>
 <tr>
<td valign="top" align="left" colspan="5" style="background-color:#dee1e1;">Age &#x02265; 50 yr</td>
</tr>
 <tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">57 (73.08)</td>
<td valign="top" align="center">324 (83.94)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.02</td>
</tr>
 <tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">21 (26.92)</td>
<td valign="top" align="center">62 (16.06)</td>
<td valign="top" align="center">1.91 (1.09&#x02013;3.34)</td>
<td/>
</tr>
 <tr>
<td valign="top" align="left" colspan="5" style="background-color:#dee1e1;">Males</td>
</tr>
 <tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">57 (87.69)</td>
<td valign="top" align="center">277 (86.29)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.76</td>
</tr>
 <tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">8 (12.31)</td>
<td valign="top" align="center">44 (13.71)</td>
<td valign="top" align="center">0.88 (0.40&#x02013;1.98)</td>
<td/>
</tr>
 <tr>
<td valign="top" align="left" colspan="5" style="background-color:#dee1e1;">Females</td>
</tr>
 <tr>
<td valign="top" align="left">No</td>
<td valign="top" align="center">38 (73.08)</td>
<td valign="top" align="center">218 (86.51)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.02</td>
</tr>
<tr>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">14 (26.92)</td>
<td valign="top" align="center">34 (13.49)</td>
<td valign="top" align="center">2.40 (1.19&#x02013;4.86)</td>
<td/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Distribution of T2DM and associated risk for MG stratified by sex and age in datasets C and D (the controls of dataset C were randomly drawn from all hospitalized patients without any AID. The controls of dataset D were randomly drawn from inpatients diagnosed with AIDs except MG. Data were analyzed by conditional logistic regression analysis).</p>
</table-wrap-foot>
</table-wrap>
<p>In all four datasets, only the results of the female group were consistently positive. The greater risk of MG associated with the prevalence of T2DM was more noteworthy in female patients. The female population was stratified by the age of 50. As shown in <xref ref-type="table" rid="T4">Table 4</xref>, elderly women with diabetes were at a higher risk of MG compared with four different sources of the control group.</p>
<table-wrap position="float" id="T4">
<label>Table 4</label>
<caption><p>Distribution of T2DM and associated risk for MG in female patients.</p></caption> 
<table frame="box" rules="all">
<thead>
<tr style="background-color:&#x00023;919498;color:&#x00023;ffffff">
<th valign="top" align="left"><bold>Females Age &#x02265;50 yr</bold></th>
<th valign="top" align="left"><bold>Diagnosed with T2DM</bold></th>
<th valign="top" align="center" colspan="2"><bold>Distribution</bold>, <italic><bold>n</bold></italic> <bold>(%)</bold></th>
<th valign="top" align="center"><bold>Risk for MG, OR (95%CI)</bold></th>
<th valign="top" align="center"><bold><italic>P</italic> value</bold></th>
</tr>
</thead>
<tbody>
<tr style="background-color:&#x00023;919498;color:&#x00023;ffffff">
<td/>
<td/>
<td valign="top" align="center"><bold>MG patients</bold></td>
<td valign="top" align="center"><bold>Matched controls</bold></td>
<td/>
<td/>
</tr> <tr>
<td valign="top" align="left">Dataset A</td>
<td valign="top" align="left">No</td>
<td valign="top" align="center">21 (60)</td>
<td valign="top" align="center">156 (89.14)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.0001</td>
</tr> <tr>
<td/>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">14 (40)</td>
<td valign="top" align="center">19 (10.86)</td>
<td valign="top" align="center">4.56 (2.11&#x02013;9.86)</td>
<td/>
</tr> <tr>
<td valign="top" align="left">Dataset B</td>
<td valign="top" align="left">No</td>
<td valign="top" align="center">21 (70)</td>
<td valign="top" align="center">132 (88)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.02</td>
</tr> <tr>
<td/>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">9 (30)</td>
<td valign="top" align="center">18 (12)</td>
<td valign="top" align="center">2.87 (1.20&#x02013;6.8787)</td>
<td/>
</tr> <tr>
<td valign="top" align="left">Dataset C</td>
<td valign="top" align="left">No</td>
<td valign="top" align="center">21 (60)</td>
<td valign="top" align="center">137 (78.29)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.03</td>
</tr> <tr>
<td/>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">14 (40)</td>
<td valign="top" align="center">38 (21.71)</td>
<td valign="top" align="center">2.25 (1.08&#x02013;4.70)</td>
<td/>
</tr> <tr>
<td valign="top" align="left">Dataset D</td>
<td valign="top" align="left">No</td>
<td valign="top" align="center">21 (60)</td>
<td valign="top" align="center">141 (82.46)</td>
<td valign="top" align="center">1 (Reference)</td>
<td valign="top" align="center">0.005</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Yes</td>
<td valign="top" align="center">14 (40)</td>
<td valign="top" align="center">30 (17.54)</td>
<td valign="top" align="center">2.90 (1.38&#x02013;6.08)</td>
<td/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Distribution of T2DM and associated risk for MG in female patients aged over 50 years (analyzed by conditional logistic regression analysis).</p>
</table-wrap-foot>
</table-wrap>
<p>Furthermore, the characteristics of diabetic and non-diabetic MG patients were observed. The average course of DM in MG patients was 7.64 years by the time of admission. The mean onset age of diabetic MG patients was higher than that of the non-diabetic MG patients (68.23 &#x000B1; 10.11 vs. 52.15 &#x000B1; 17.48, <italic>P</italic> &#x0003C; 0.001, <xref ref-type="table" rid="T5">Table 5</xref>). There was a higher proportion of LOMG clinical type in diabetic MG patients than in non-diabetic MG patients (95.45% vs. 59.38%, <italic>P</italic> &#x0003C; 0.001, <xref ref-type="table" rid="T5">Table 5</xref>). Of the 17 diabetic MG patients with T2DM onset time before MG, six patients had been tested for antibodies. The AChR antibody was found in 66.67% (<italic>n</italic> = 4) of them, which was not statistically different from that in MG patients without T2DM (87.10%).</p>
<table-wrap position="float" id="T5">
<label>Table 5</label>
<caption><p>Different characteristics between non-diabetic and diabetic MG patients.</p></caption> 
<table frame="box" rules="all">
<thead>
<tr style="background-color:&#x00023;919498;color:&#x00023;ffffff">
<th/>
<th valign="top" align="center"><bold>MG without T2DM</bold></th>
<th valign="top" align="center"><bold>MG with T2DM</bold></th>
<th valign="top" align="center"><bold>Available number</bold></th>
<th valign="top" align="center"><bold><italic>P</italic></bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">N</td>
<td valign="top" align="center">96</td>
<td valign="top" align="center">22</td>
<td valign="top" align="center">118</td>
<td/>
</tr> <tr>
<td valign="top" align="left">Gender, females (%)</td>
<td valign="top" align="center">38 (39.58)</td>
<td valign="top" align="center">14 (63.64)</td>
<td valign="top" align="center">118</td>
<td valign="top" align="center">0.056</td>
</tr> <tr>
<td valign="top" align="left">Onset age (mean &#x000B1; sd)</td>
<td valign="top" align="center">52.15 &#x000B1; 17.48</td>
<td valign="top" align="center">68.23 &#x000B1; 10.11<sup>&#x0002A;&#x0002A;&#x0002A;</sup></td>
<td valign="top" align="center">118</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr> <tr>
<td valign="top" align="left">LOMG (%)</td>
<td valign="top" align="center">57 (59.38)</td>
<td valign="top" align="center">21 (95.45)<sup>&#x0002A;&#x0002A;&#x0002A;</sup></td>
<td valign="top" align="center">118</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr> <tr>
<td valign="top" align="left">OMG (%)</td>
<td valign="top" align="center">36 (39.13)</td>
<td valign="top" align="center">5 (25.00)</td>
<td valign="top" align="center">112</td>
<td valign="top" align="center">0.31</td>
</tr> <tr>
<td valign="top" align="left">TAMG (%)</td>
<td valign="top" align="center">33 (34.38)</td>
<td valign="top" align="center">3 (13.64)</td>
<td valign="top" align="center">118</td>
<td valign="top" align="center">0.07</td>
</tr> <tr>
<td valign="top" align="left">Thymoma WHO classification (%)</td>
<td/>
<td/>
<td valign="top" align="center">22</td>
<td valign="top" align="center">1</td>
</tr> <tr>
<td valign="top" align="left">A</td>
<td valign="top" align="center">1 (4.76)</td>
<td valign="top" align="center">0 (0.00)</td>
<td/>
<td/>
</tr> <tr>
<td valign="top" align="left">AB</td>
<td valign="top" align="center">3 (14.29)</td>
<td valign="top" align="center">0 (0.00)</td>
<td/>
<td/>
</tr> <tr>
<td valign="top" align="left">B1</td>
<td valign="top" align="center">5 (22.81)</td>
<td valign="top" align="center">1 (100.00)</td>
<td/>
<td/>
</tr> <tr>
<td valign="top" align="left">B2</td>
<td valign="top" align="center">6 (28.57)</td>
<td valign="top" align="center">0 (0.00)</td>
<td/>
<td/>
</tr> <tr>
<td valign="top" align="left">B3</td>
<td valign="top" align="center">3 (14.29)</td>
<td valign="top" align="center">0 (0.00)</td>
<td/>
<td/>
</tr> <tr>
<td valign="top" align="left">B2 &#x0002B; B3</td>
<td valign="top" align="center">3 (14.29)</td>
<td valign="top" align="center">0 (0.00)</td>
<td/>
<td/>
</tr> <tr>
<td valign="top" align="left">Serology (%)</td>
<td/>
<td/>
<td valign="top" align="center">39</td>
<td valign="top" align="center">0.59</td>
</tr> <tr>
<td valign="top" align="left">AchR-Pos</td>
<td valign="top" align="center">27 (87.10)</td>
<td valign="top" align="center">6 (0.75)</td>
<td/>
<td/>
</tr> <tr>
<td valign="top" align="left">AchR-Neg</td>
<td valign="top" align="center">4 (12.90)</td>
<td valign="top" align="center">2 (0.25)</td>
<td/>
<td/>
</tr> <tr>
<td valign="top" align="left">Average course of DM</td>
<td valign="top" align="center">/</td>
<td valign="top" align="center">7.64 years</td>
<td/>
<td/>
</tr> <tr style="background-color:#dee1e1;">
<td valign="top" align="left" colspan="5"><bold>Laboratory indexes</bold></td>
</tr> <tr>
<td valign="top" align="left">Fasting blood glucose (mean &#x000B1; sd)</td>
<td valign="top" align="center">5.21 &#x000B1; 1.08</td>
<td valign="top" align="center">7.36 &#x000B1; 4.21<sup>&#x0002A;&#x0002A;&#x0002A;</sup></td>
<td valign="top" align="center">91</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">Glycated hemoglobin (mean &#x000B1; sd)</td>
<td valign="top" align="center">5.70 &#x000B1; 1.02</td>
<td valign="top" align="center">6.92 &#x000B1; 0.99<sup>&#x0002A;&#x0002A;&#x0002A;</sup></td>
<td valign="top" align="center">58</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Onset age and laboratory indexes were compared by Mann-Whitney U test. Dichotomous variables were compared by the &#x003C7;<sup>2</sup> test. Polytomous variables (Thymoma WHO classification) were compared by Fisher exact probability test. <italic>n</italic> = 118; <sup>&#x0002A;&#x0002A;&#x0002A;</sup><italic>P</italic> &#x0003C; 0.001.</p>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec id="s4">
<title>4. Discussion</title>
<p>Research has confirmed that a high proportion of abnormalities in glycolipid metabolism has been present in MG patients before treatment with glucocorticoids (<xref ref-type="bibr" rid="B16">16</xref>). According to the International Diabetes Federation (IDF) Diabetes Atlas (eighth edition 2017) and the latest Chinese diabetes epidemiological survey, the prevalence of diabetes (20&#x02013;79 years old) in China was 10.9% (<xref ref-type="bibr" rid="B17">17</xref>), which was far lower than that of MG hospitalized patients in this study (24.26%). The probability of MG patients complicated with diabetes was higher than that of the general population. The risk of MG in association with T2DM exposure was explored through an EMR-based, 1:5 matched case&#x02013;control study. This study illustrated distinct age- and sex-specific associations between T2DM and MG.</p>
<p>In this study, the results of four datasets showed that patients with T2DM had a higher risk of MG than those without T2DM. The sources of the control population of the four datasets were different. Datasets A and B represented the comparison of MG patients and the general population settled in the area. The findings can be extrapolated to the whole population more reliably by selecting controls from the local population without MG. The result of dataset A could only reveal a statistical association between T2DM exposure and MG but not a causal association. For chronic diseases, the time interval between exposure and illness still needs to be considered to determine the cause and effect of this association. In the MG patients of dataset B, the onset of diabetes preceded MG, which is consistent with the sequence of a causal association. The results of dataset B further proved that T2DM might be one of the causative factors of MG. Datasets C and D represented the comparison of MG inpatients and other inpatients, which made better homology and comparability. The results of dataset C demonstrated that MG is associated with diabetes in the female hospitalized population. The results of dataset D further extended the significance of this study. The controls of dataset D were other AID inpatients. The result of dataset D illustrated that MG, but not any other autoimmune disease, was strongly associated with diabetes.</p>
<p>The female population of four different datasets was further stratified by age. The results suggested that T2DM remained significantly associated with the risk of MG in any dataset. Age may be the deciding determinant among female patients as well. As it is well known, the significant asymmetry of AIDs between sexes is thought to be related to sex hormones, sex chromosomes, gut microbiota, and so on (<xref ref-type="bibr" rid="B18">18</xref>). There may be differences in etiology according to sex and aging that affect the progression of MG in diabetic patients. Research on related molecular mechanisms is urgently needed.</p>
<p>The mean onset age of diabetic MG patients was significantly higher than that of non-diabetic MG patients, illustrating that the clinical types of diabetic MG patients were mostly LOMG (<xref ref-type="table" rid="T5">Table 5</xref>). It has been reported that diabetes was more prevalent in LOMG when compared to EOMG (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B20">20</xref>). There are no differences in different autoantibodies in MG with type 2 diabetes, compared with patients without T2DM. The AChR antibody still takes the overwhelming majority when tested in Chinese patients (<xref ref-type="bibr" rid="B21">21</xref>). The lack of positive results may be related to data deficiencies.</p>
<p>A verdict of causation can be safely made only on a sufficient totality of evidence. Several positive criteria support a judgment of causality, including (<xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B23">23</xref>) (1) strength; (2) consistency; (3) specificity; (4) temporality; (5) biological gradient; (6) plausibility; (7) coherence; (8) experiment; and (9) analogy. In our study, the risk for MG in T2DM patients was 1.91 to 4.56 times higher than that of groups without T2DM. After identifying the temporal sequence of DM and MG, the results were still positive. The same answer has been reached by different populations with different circumstances. This study should be viewed in the context of its limitations. First, dose&#x02013;response relationship analyses about the severity of illness and more clinical subgroup analyses, except for age and sex, were not attempted. Second, there may be other unknown confounding factors or mediating variables related to both MG and T2DM, obscuring or exaggerating the relationship between MG and T2DM.</p>
<p>This new finding has led to our further thinking. The concept of diabetic MG may need to be paid more attention to. It may have unique clinical electrophysiology and also clinical and immunological phenotypes distinct from previous subgroup classifications. Previous studies could not explain the relationship between T2DM and the subsequent onset of MG. A protein named advanced glycation end products (AGEs), which are greatly augmented in the early stage of diabetes, might play an important role in this process. As the main ligand of the receptor for AGEs (RAGE) (<xref ref-type="bibr" rid="B24">24</xref>), AGEs stimulate inflammatory signal amplification, produce several cytokines and growth factors (<xref ref-type="bibr" rid="B25">25</xref>), enhance the proliferation response of CD4(&#x0002B;)CD28(-) T cells (<xref ref-type="bibr" rid="B26">26</xref>), and further act in AIDs (<xref ref-type="bibr" rid="B27">27</xref>, <xref ref-type="bibr" rid="B28">28</xref>). RAGE mainly acts in pro-inflammatory cellular responses and contributes to the failure of self-tolerance mechanisms (<xref ref-type="bibr" rid="B29">29</xref>). MG patients with strong RAGE expression in the thymus tissue had higher levels of AChR-Ab, especially at the disease onset of TAMG (<xref ref-type="bibr" rid="B30">30</xref>). In experimental autoimmune MG (EAMG) animal models, increased expression of RAGE and its ligand S100B enhanced T-cell pro-inflammatory responses, affected the Th1/Th2/Th17/Treg cell equilibrium, and upregulated AChR-specific T-cell proliferation (<xref ref-type="bibr" rid="B31">31</xref>). S100B-RAGE interaction promotes the production of antibodies against AchRs from the spleen of EAMG (<xref ref-type="bibr" rid="B32">32</xref>). The clinical importance of AGEs-RAGE signaling in diabetic MG pathophysiology should be emphasized.</p>
<p>Recently, our group has confirmed that DM aggravated the aberrant humoral immunity in MG patients by promoting differentiation and activation of circulating follicular helper T (Tfh) cells (<xref ref-type="bibr" rid="B33">33</xref>). <italic>In vitro</italic>, hyperglycemia increased the expression of inducible costimulatory (ICOS) in Tfh cells <italic>via</italic> the mechanistic target of the rapamycin (mTOR) signaling pathway. This process promoted the differentiation of plasmablasts and IgG secretion. Monitoring humoral immune indicators may be beneficial to patients. The possible immunologic link between MG and T2DM will provide a new strategy for the prevention and treatment of MG.</p>
</sec>
<sec id="s5">
<title>5. Conclusion</title>
<p>Overall, this study has shown a significant association between T2DM and MG, suggesting T2DM may be a risk factor for the onset of MG. Basic experiments to explore relevant molecular mechanisms are needed, but there is no denying that the relationship between T2DM and MG has come to our attention. We hypothesize that a diabetic MG subtype may exist in LOMG patients. Further personalized diagnosis, treatment, and management of diabetic MG patients should be on the agenda.</p>
</sec>
<sec sec-type="data-availability" id="s6">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec sec-type="ethics-statement" id="s7">
<title>Ethics statement</title>
<p>The studies involving human participants were reviewed and approved by Research Ethics Committee of the First Affiliated Hospital of Shandong First Medical University (No: S030). The patients/participants provided their written informed consent to participate in this study.</p>
</sec>
<sec sec-type="author-contributions" id="s8">
<title>Author contributions</title>
<p>R-SD, Y-DL, and FT contributed to the conception and design of this study. X-LL, HL, C-CW, and BY contributed to data acquisition. Y-DL and X-LL organized the database. PZ, C-LY, and TD contributed to the literature search. Y-DL and Y-FL contributed to data analysis and interpretation and verified the underlying data. Y-DL, FT, YL, and R-SD contributed to drafting the manuscript and figures. All authors contributed to the manuscript revision and read and approved the submitted version of the manuscript.</p>
</sec>
</body>
<back>
<sec sec-type="funding-information" id="s9">
<title>Funding</title>
<p>This study was funded by the Academic Promotion Programme of Shandong First Medical University (2019QL013), the Shandong Provincial Natural Science Foundation, China (ZR2020MH142), and the National Natural Science Foundation of China (81801247 and 81801198).</p>
</sec>
<sec sec-type="COI-statement" id="conf1">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s10">
<title>Publisher&#x00027;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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