Increased autoantibodies against incretin indicate poor prognosis in patients with diabetes

Increased autoantibodies against incretin indicate poor prognosis in patients with diabetes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Increased autoantibodies against incretin indicate poor prognosis in patients with diabetes Minoru Takemoto, Bo-Shi Zhang, Aiko Hayashi, Hiroki Yamagata, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6663426/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract This retrospective cohort study aimed to elucidate the clinical significance of measuring autoantibodies against incretins in diabetes. We enrolled 274 patients with diabetes (mean age ± standard deviation: 63.1 ± 12.1 years) and 109 healthy controls (mean age: 58.0 ± 5.8 years). Titers of autoantibodies against incretins (glucose-dependent insulinotropic peptide and glucagon-like peptide-1) were measured using an amplified luminescent proximity homogeneous assay-linked immunosorbent assay. Both incretin antibody titers were significantly higher in patients with diabetes versus healthy controls (both P < 0.01). A mean 4.9-year (maximum 10-year) follow-up study revealed that patients who tested positive for glucose-dependent insulinotropic peptide antibodies had significantly worse prognoses than those who tested negative (P = 0.0072). Patients who tested positive for glucagon-like peptide-1 antibodies also tended to have worse prognoses (P = 0.06). Autoantibodies against incretins may serve as potential biomarkers for diabetes prognosis. Biological sciences/Biochemistry Biological sciences/Biotechnology Health sciences/Endocrinology Health sciences/Medical research Health sciences/Risk factors Autoantibodies Incretin Glucagon-like peptide-1 Glucose-dependent insulinotropic peptide Figures Figure 1 Figure 2 1. Introduction Autoantibodies are produced under specific conditions, such as autoimmune diseases and cancer. With advancements in detection techniques, antibodies against all proteins in the body have been identified, and they can serve as novel biomarkers [ 1 ]. We previously reported that antibodies against phosphoenolpyruvate carboxykinase and proprotein convertase subtilisin/kexin type 9 are increased in patients with diabetes and associated with poor prognoses [ 2 , 3 ]. Here, we measured autoantibodies against incretins and examined their clinical significance. 2. Materials and Methods 2.1 Collection of serum samples This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. The study protocol was approved by the Ethics Committees of the International University of Health and Welfare (Approval No. 21-Im-037) and Chiba University (Approval Nos. 2017 − 251, 2018 − 320, and 2020 − 1129). All participants provided written informed consent. The inclusion criterion for both patients with diabetes and HCs was obtaining informed consent during the study period. Serum samples from patients with diabetes were obtained from Chiba University Hospital, whereas those of healthy controls (HCs) were collected at Port Square Kashiwado Clinic. The presence or absence of diabetic complications among patients with diabetes is reported based on information from electronic medical records documented by diabetes specialists. All serum samples were stored at -80℃ until use. 2.2 Preparation and purification of incretin proteins The full-length glucose-dependent insulinotropic peptide (GIP) and glucagon-like peptide-1 (GLP-1) cDNAs were recombined into the prokaryotic expression plasmid pGEX-4T-1. ECOSTM competent Escherichia coli BL-21 cells (Nippon Gene; Tokyo, Japan) were transformed with pGEX-4T-1, pGEX-4T-1-GIP, and pGEX-4T-1-GLP-1 and cultured for 3 h in 200 mL Luria broth containing 0.1 mM isopropyl β-D-thiogalactopyranoside (Wako Pure Chemicals, Osaka, Japan). The cells were lysed using sonication in BugBuster Protein Extraction Reagent (Merck Millipore, Darmstadt, Germany), and GST, GST-GIP, and GST-GLP-1 proteins were purified as previously described [ 4 ]. 2.3 Measurement of serum antibody levels Serum levels of antibodies against GIP and GLP-1 (GIP-Abs and GLP-1-Abs, respectively) were measured using the amplified luminescent proximity homogeneous assay-linked immunosorbent assay (AlphaLISA) using 384-well microtiter plates as previously described [ 5 , 6 ]. Specific reactions were calculated by subtracting the alpha photon counts of the GST control from those of the GST fusion proteins. 2.4 Statistical analysis Continuous data of the groups were compared using the Mann–Whitney U test. Correlations were examined using Spearman’s correlation analysis. The cutoff value for detecting diabetes was determined using Youden’s index derived from the receiver operating characteristic (ROC) curve analysis. X-tile software (Yale University, New Haven, CT) 7 was used to determine the best cutoff level in distinguishing survival and mortality cases. The survival curves were represented using Kaplan–Meier plots. The log-rank test was used to compare the univariate analysis results. Statistical significance was defined as P < 0.05. 3. Results 3.1 Elevated incretin antibody levels in patients with diabetes We examined 274 patients with diabetes (77.8% with type 2 diabetes, mean age ± standard deviation: 63.1 ± 12.1 years) and 109 HCs (mean age: 58.0 ± 5.8 years) (Table 1 , upper panel). Characteristics of patients included in the analysis are shown in supplemental table 1 . Both anti-GIP-Ab and anti-GLP-1-Ab levels in patients with diabetes were significantly higher than those in HCs (P = 0.0002 and P = 0.0036, respectively) (Fig. 1 a, c; Table 1 , lower panel). When the cutoff values were determined as the HC value + 2 standard deviations (SDs), the positive rates of GIP-Abs and GLP-1-Abs were 12.5% and 10.3%, respectively (Table 1 ). The areas under the ROC curve of GIP-Abs and GLP-1-Abs were 0.623 and 0.595, respectively (Fig. 1 b, d). Using the cutoff values determined by Youden’s index, the sensitivity and specificity for GIP-Abs were 46.3% and 79.8%, respectively, and those for GLP-1-Abs were 56.6% and 63.3%, respectively (Table 2 ). Given that both anti-GIP-Ab and anti-GLP-1-Ab levels were elevated in patients with diabetes compared with HCs, we analyzed the associations between these antibodies and clinical laboratory findings specifically within those with diabetes. No association with HbA1c levels was observed (Supplemental Table 2). Table 1 Comparison of glucose-dependent insulinotropic peptide (GIP)-antibody (Ab) and glucagon-like peptide-1 ( GLP-1)-Ab levels between healthy controls (HCs) and patients with diabetes Sample information HCs Diabetes Total sample number 109 274 Male/Female 61/48 153/121 Age (average ± SD) 58.0 ± 5.8 63.1 ± 12.1 GIP-Ab GLP-1-Ab HCs Average 2,508 807 SD 1,309 759 Cutoff values 5,126 2,325 Positive number 3 7 Positive (%) 2.80% 6.40% Diabetes Average 3,404 1,170 SD 2,260 1,090 Positive number 34 28 Positive (%) 12.5% 10.3% P value (vs. HCs) 0.0002 0.0036 The upper panel indicates the numbers of total samples, samples from male and female participants, and ages (average ± standard deviation [SD]). The lower panel summarizes the serum antibody levels (alpha photon counts) examined by AlphaLISA using purified GIP and GLP-1 proteins as antigens. Cutoff values were determined as the average HC values plus two SD, and the positive samples higher than the cutoff value were scored. P values were calculated using the Mann–Whitney U test. P values 10% are marked in bold text. Table 2 Results of receiver operating characteristic (ROC) analysis GIP-Ab GLP-1-Ab AUC value 0.623 0.595 Cutoff value > 3187 > 815.5 Sensitivity (%) 46.3 56.62 Specificity (%) 79.8 63.3 95% CI 0.5643–0.6816 0.5341–0.6567 Area under the curve (AUC), cutoff value, sensitivity (%), specificity (%), and 95% confidence interval (CI) of the ROC analysis are shown. Purified GIP and GLP-1 proteins were used as antigens. 3.2 Group comparisons GIP-Ab and GLP-1-Ab levels were compared between men and women; patients with type 1 and type 2 diabetes mellitus; patients with and without complications; and patients who habitually smoked or consumed alcohol and those who abstained from these habits. None of the comparisons revealed significant differences in incretin levels, although GIP-Abs levels tended to correlate with dyslipidemia complications (P = 0.0721) (Fig. S1 ). Regarding types of anti-hypoglycemic drugs, GIP-Ab and GLP-1-Ab levels were not correlated with insulin, GLP-1 receptor agonists (GLP-1 RAs), DPP IV inhibitors, metformin, or thiazolidinediones. Anti-GIP Ab levels were higher in patients using glinides (anti-GIP Abs with [n = 21] vs. without [n = 251] glinide: 4345.9 ± 3776.9 vs. 3324.8 ± 2077; P = 0.01) or α-glucosidase inhibitors (αGIs) (anti-GIP Abs: with [n = 66] vs. without [n = 206] αGI: 3751.7 ± 2941.1 vs. 3292.2 ± 1989.1; P = 0.013). Anti-GLP-1 Ab levels were higher in patients using only glinides (3751.7 ± 2941.1 vs. 3292.2 ± 1989.1) (Supplemental Table 3). 3.3 Survival analysis We subsequently examined whether GIP-Ab and GLP-1-Ab levels were related to survival in patients with diabetes in the mean 4.9-year (maximum 10-year). GIP-Ab and GLP-1-Ab levels were categorized into positive and negative groups using the cutoff values obtained using the X‑tile software [ 7 ]. The GIP‑Ab‑positive group presented a significantly more unfavorable prognosis than did the GIP‑Ab‑negative group (Fig. 2 a; P = 0.0072). The GLP-1-Ab-positive and -negative groups showed a similar tendency, with no statistical significance (Fig. 2 b; P = 0.0640). We also categorized participants into two groups according to age (under 65 and 65 or older), HbA1c level (HbA1c below 7% and HbA1c 7% or above), presence or absence of complications such as retinopathy and nephropathy, and smoking to draw survival curves for each group. Consequently, significant differences were obtained for age and smoking. This implies that incretin antibodies can predict a prognosis where it cannot be predicted by HbA1c or the presence or absence of complications, as shown in Fig. S2 . 4. Discussion To our knowledge, this is the first study to examine anti-incretin antibodies in the sera of patients with diabetes. GIP-Abs and GLP-1-Abs were detected in both HCs and patients with diabetes; however, their levels were significantly higher in patients with diabetes. Furthermore, a mean 4.9-year (maximum 10-year) follow-up study revealed that patients who tested positive for GIP-Abs had a significantly worse prognosis than did those who tested negative for GIP-Abs. Those who tested positive for GLP-1-Abs also tended to have a worse prognosis. Incretin promotes insulin secretion through receptors on pancreatic β cells [ 8 , 9 ]. Clinical trials have shown the glucose-lowering, cardioprotective, and renoprotective effects of GLP-1[ 10 , 11 ]. Tirzepatide, a dual GIP and GLP-1R activator, has both glucose-lowering and weight-reducing effects [ 12 ]. Our findings suggest that incretin Ab titers are higher in patients with diabetes than in healthy individuals and that the inhibition of incretin effects may contribute to the development of diabetes. However, this cross-sectional study is limited in establishing causality. The lack of correlation between antibody titers and blood glucose levels or glycated hemoglobin reflects these limitations. Regarding the tendency of lower GIP-Abs levels among patients with dyslipidemia, when GIP receptor knockout mice were fed a high-fat diet, they showed resistance to obesity compared with wild-type mice, and dietary lipids were more efficiently accumulated in adipocytes [ 13 ], indicating that inhibiting endogenous GIP function may have positively affected lipid metabolism. However, as only a trend was observed without significant differences, future analysis with a larger sample size may be necessary. Regarding the relationship with drugs, αGIs are known to increase incretin expression by suppressing glucose absorption. Increased incretin levels may have enhanced antibody production. In contrast, αGIs are known to prevent the development of diabetes; therefore, the significance of high GIP antibody titers in individuals taking αGIs remains unclear, along with the relationship between glinide and incretin antibody titers. The mechanism by which the body produces antibodies against incretin is unclear. It is speculated that these antibodies generated in response to previous infections may have cross-reacted with incretin, leading to the formation of autoantibodies against it. This suggests that individuals who develop these autoantibodies may be prone to developing diabetes owing to the reduced effectiveness of incretin. Moreover, as the disease progresses, they may experience poorer outcomes. Nevertheless, the relationship with prognosis is interesting. This study revealed that the overall mortality rate was associated with high levels of incretin antibodies; however, the frequency of malignant tumors and cardiovascular events as causes of death was too low to perform statistical analyses. It has been clinically demonstrated that the activation of the GLP-1R suppresses cardiovascular and renal events and that the suppression of the endogenous action of incretin by antibodies may have increased the occurrence of these events as well as the associated mortality. The study has some limitations. Selection bias may have arisen due to participants’ recruitment based on informed consent. This study did not evaluate the relationship between blood incretin concentrations and incretin antibodies or the association between incretin antibody levels and blood insulin and glucagon levels. Additionally, the clinical significance of the antibodies was insufficiently analyzed, which is a subject for future study. The small sample size and cross-sectional design further limited the study’s ability to establish causality. Further studies with larger sample sizes and longer follow-up periods are needed. In conclusion, this study’s results indicate that incretin antibodies may serve as novel prognostic markers for diabetes. Declarations Acknowledgments: The authors would like to thank the physicians and staff who assisted with the care of the patients and the patients and healthy controls themselves. Funding: This study was supported, in part, by research grants from the Japan Science and Technology Agency [Grant Numbers: 23K06889, 24K10559]. Author contributions: B-S.Z., A.H., H.Y., Y.Y., M.K., S.O., M.Y., T.Y., T.H., S.O., T.I., and H.Y. researched the data. T.H. researched the data and discussion and reviewed/edited the manuscript. M.T. is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors have contributed significantly, and that all authors are in agreement with the content of the manuscript. Data availability: The data that support the findings of this study are available from the corresponding author, M.T, upon reasonable request. Disclosure Statement: The authors have no competing interests to declare. References Hiwasa, T. et al. Serum anti-DIDO1, anti-CPSF2, and anti-FOXJ2 antibodies as predictive risk markers for acute ischemic stroke. BMC Med. 19 , 131 (2021). Namiki, T. et al. Serum anti-PCK1 antibody levels are a prognostic factor for patients with diabetes mellitus. BMC Endocr. Disord . 23 , 239 (2023). Yamagata, H. et al. Association of high proprotein convertase subtilisin/kexin type 9 antibody level with poor prognosis in patients with diabetes: a prospective study. Sci. Rep. 13 , 5391 (2023). Li, S. Y. et al. Utility of atherosclerosis-associated serum antibodies against colony-stimulating factor 2 in predicting the onset of acute ischemic stroke and prognosis of colorectal cancer. Front. Cardiovasc. Med. 10 , 1042272 (2023). Bielefeld-Sevigny, M. AlphaLISA immunoassay platform- the no-wash high-throughput alternative to ELISA. Assay. Drug Dev. Technol. 7 , 90–92. 10.1089/adt.2009.9996 (2009). Zhang, B. S. et al. JMJD6 Autoantibodies as a potential biomarker for inflammation-related diseases. Int. J. Mol. Sci. 25 , 4935 (2024). Camp, R. L., Dolled-Filhart, M. & Rimm, D. L. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin. Cancer Res. 10 , 7252–7259 (2004). Rouille, Y., Martin, S. & Steiner, D. F. Differential processing of proglucagon by the subtilisin-like prohormone convertases PC2 and PC3 to generate either glucagon or glucagon-like peptide. J. Biol. Chem. 270 , 26488–26496 (1995). Dillon, J. S. et al. Cloning and functional expression of the human glucagon-like peptide-1 (GLP-1) receptor. Endocrinology 133 , 1907–1910 (1993). Kristensen, S. L. et al. Cardiovascular, mortality, and kidney outcomes with GLP-1 receptor agonists in patients with type 2 diabetes: a systematic review and meta-analysis of cardiovascular outcome trials. Lancet Diabetes Endocrinol. 7 , 776–785 (2019). Perkovic, V. et al. Effects of semaglutide on chronic kidney disease in patients with type 2 diabetes. N Engl. J. Med. 391 , 109–121 (2024). Frias, J. P. et al. Tirzepatide versus semaglutide once weekly in patients with type 2 diabetes. N Engl. J. Med. 385 , 503–515. 10.1056/nejmoa2107519 (2021). Boer, G. A., Keenan, S. N., Miotto, P. M., Holst, J. J. & Watt, M. J. GIP receptor deletion in mice confers resistance to high-fat diet-induced obesity via alterations in energy expenditure and adipose tissue lipid metabolism. Am J. Physiol. Endocrinol Metab . 320 , E835–E845 (2021). Additional Declarations No competing interests reported. Supplementary Files SupplementalFiguresTakemotoMetal.pptx SupplementalTable1TakemotoMetal.docx Cite Share Download PDF Status: Published Journal Publication published 03 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 Sep, 2025 Reviews received at journal 17 Aug, 2025 Reviewers agreed at journal 17 Aug, 2025 Reviews received at journal 07 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers invited by journal 05 Jun, 2025 Editor invited by journal 30 May, 2025 Editor assigned by journal 19 May, 2025 Submission checks completed at journal 17 May, 2025 First submitted to journal 14 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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University","correspondingAuthor":false,"prefix":"","firstName":"Takaki","middleName":"","lastName":"Hiwasa","suffix":""}],"badges":[],"createdAt":"2025-05-14 10:53:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6663426/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6663426/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-22337-z","type":"published","date":"2025-11-03T15:57:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84340366,"identity":"1997232e-18bc-4d3d-b9ac-2340aaae7a44","added_by":"auto","created_at":"2025-06-10 18:24:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42724,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncreased levels of serum anti-glucose-dependent insulinotropic peptide antibodies (GIP-Abs) and anti-glucagon-like peptide antibodies (GLP-1-Abs) in patients with diabetes compared with healthy controls (HCs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum GIP-Abs and GLP-1-Abs were measured using an amplified luminescent proximity homogeneous assay-linked immunosorbent assay (AlphaLISA) and are shown in scatter dot plots (a and c). The bars represent the average and average ± SD. P values were calculated using the Mann–Whitney U test. **P\u0026lt;0.01; ***P\u0026lt;0.001 vs. HC specimens. The abilities of using GIP-Abs and GLP-1-Abs to detect diabetes were evaluated using receiver operating characteristic curve (ROC) analysis (b and d).\u003c/p\u003e","description":"","filename":"Slide1.png","url":"https://assets-eu.researchsquare.com/files/rs-6663426/v1/75a6cf37dc14c3fb23dd23b4.png"},{"id":84340365,"identity":"f8a3ad11-529a-4894-976b-e372a255c6a4","added_by":"auto","created_at":"2025-06-10 18:24:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":27679,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival analysis of patients with diabetes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparison of overall survival of the patients with diabetes according to glucose-dependent insulinotropic peptide antibody (GIP-Ab)-positive (GIP-Ab+) and -negative (GIP-Ab-) groups (a) and GLP-1-Ab-positive (GLP-1-Ab+) and -negative (GLP-1-Ab-) groups (b) are shown in Kaplan–Meier plots. Cutoff values were determined using X-tile software. Statistical analyses were performed using the log-rank test.\u003c/p\u003e","description":"","filename":"Slide2.png","url":"https://assets-eu.researchsquare.com/files/rs-6663426/v1/da3aacdead2ac9267de53b5e.png"},{"id":95564701,"identity":"14f6bb26-cd23-4138-9766-e3c4c8a8e7e0","added_by":"auto","created_at":"2025-11-10 16:10:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":839759,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6663426/v1/99781de7-f5d4-4a73-b6e3-58d4dc83ceb7.pdf"},{"id":84340375,"identity":"16e5ccbb-d447-4325-9779-a9096564ba71","added_by":"auto","created_at":"2025-06-10 18:24:59","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":286790,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFiguresTakemotoMetal.pptx","url":"https://assets-eu.researchsquare.com/files/rs-6663426/v1/ccf8b5c490147b7c8c808128.pptx"},{"id":84341285,"identity":"d8f90aa6-64d0-4f4c-b9fa-b8dbc4286058","added_by":"auto","created_at":"2025-06-10 18:32:59","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21757,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable1TakemotoMetal.docx","url":"https://assets-eu.researchsquare.com/files/rs-6663426/v1/385f2f15d02049fef659769f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Increased autoantibodies against incretin indicate poor prognosis in patients with diabetes","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAutoantibodies are produced under specific conditions, such as autoimmune diseases and cancer. With advancements in detection techniques, antibodies against all proteins in the body have been identified, and they can serve as novel biomarkers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe previously reported that antibodies against phosphoenolpyruvate carboxykinase and proprotein convertase subtilisin/kexin type 9 are increased in patients with diabetes and associated with poor prognoses [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Here, we measured autoantibodies against incretins and examined their clinical significance.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Collection of serum samples\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. The study protocol was approved by the Ethics Committees of the International University of Health and Welfare (Approval No. 21-Im-037) and Chiba University (Approval Nos. 2017\u0026thinsp;\u0026minus;\u0026thinsp;251, 2018\u0026thinsp;\u0026minus;\u0026thinsp;320, and 2020\u0026thinsp;\u0026minus;\u0026thinsp;1129). All participants provided written informed consent.\u003c/p\u003e \u003cp\u003eThe inclusion criterion for both patients with diabetes and HCs was obtaining informed consent during the study period. Serum samples from patients with diabetes were obtained from Chiba University Hospital, whereas those of healthy controls (HCs) were collected at Port Square Kashiwado Clinic. The presence or absence of diabetic complications among patients with diabetes is reported based on information from electronic medical records documented by diabetes specialists. All serum samples were stored at -80℃ until use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Preparation and purification of incretin proteins\u003c/h2\u003e \u003cp\u003eThe full-length glucose-dependent insulinotropic peptide (GIP) and glucagon-like peptide-1 (GLP-1) cDNAs were recombined into the prokaryotic expression plasmid pGEX-4T-1. ECOSTM competent Escherichia coli BL-21 cells (Nippon Gene; Tokyo, Japan) were transformed with pGEX-4T-1, pGEX-4T-1-GIP, and pGEX-4T-1-GLP-1 and cultured for 3 h in 200 mL Luria broth containing 0.1 mM isopropyl β-D-thiogalactopyranoside (Wako Pure Chemicals, Osaka, Japan). The cells were lysed using sonication in BugBuster Protein Extraction Reagent (Merck Millipore, Darmstadt, Germany), and GST, GST-GIP, and GST-GLP-1 proteins were purified as previously described [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Measurement of serum antibody levels\u003c/h2\u003e \u003cp\u003eSerum levels of antibodies against GIP and GLP-1 (GIP-Abs and GLP-1-Abs, respectively) were measured using the amplified luminescent proximity homogeneous assay-linked immunosorbent assay (AlphaLISA) using 384-well microtiter plates as previously described [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Specific reactions were calculated by subtracting the alpha photon counts of the GST control from those of the GST fusion proteins.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eContinuous data of the groups were compared using the Mann\u0026ndash;Whitney U test. Correlations were examined using Spearman\u0026rsquo;s correlation analysis. The cutoff value for detecting diabetes was determined using Youden\u0026rsquo;s index derived from the receiver operating characteristic (ROC) curve analysis. X-tile software (Yale University, New Haven, CT) \u003csup\u003e7\u003c/sup\u003e was used to determine the best cutoff level in distinguishing survival and mortality cases. The survival curves were represented using Kaplan\u0026ndash;Meier plots. The log-rank test was used to compare the univariate analysis results. Statistical significance was defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Elevated incretin antibody levels in patients with diabetes\u003c/h2\u003e \u003cp\u003eWe examined 274 patients with diabetes (77.8% with type 2 diabetes, mean age\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation: 63.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1 years) and 109 HCs (mean age: 58.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 years) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, upper panel). Characteristics of patients included in the analysis are shown in supplemental table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Both anti-GIP-Ab and anti-GLP-1-Ab levels in patients with diabetes were significantly higher than those in HCs (P\u0026thinsp;=\u0026thinsp;0.0002 and P\u0026thinsp;=\u0026thinsp;0.0036, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, c; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, lower panel). When the cutoff values were determined as the HC value\u0026thinsp;+\u0026thinsp;2 standard deviations (SDs), the positive rates of GIP-Abs and GLP-1-Abs were 12.5% and 10.3%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The areas under the ROC curve of GIP-Abs and GLP-1-Abs were 0.623 and 0.595, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, d). Using the cutoff values determined by Youden\u0026rsquo;s index, the sensitivity and specificity for GIP-Abs were 46.3% and 79.8%, respectively, and those for GLP-1-Abs were 56.6% and 63.3%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Given that both anti-GIP-Ab and anti-GLP-1-Ab levels were elevated in patients with diabetes compared with HCs, we analyzed the associations between these antibodies and clinical laboratory findings specifically within those with diabetes. No association with HbA1c levels was observed (Supplemental Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eComparison of glucose-dependent insulinotropic peptide (GIP)-antibody (Ab) and glucagon-like peptide-1\u003c/b\u003e (\u003cb\u003eGLP-1)-Ab levels between healthy controls (HCs) and patients with diabetes\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSample information\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHCs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal sample number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale/Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61/48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153/121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (average\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGIP-Ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGLP-1-Ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCutoff values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.40%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e12.5%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e10.3%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value (vs. HCs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe upper panel indicates the numbers of total samples, samples from male and female participants, and ages (average\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation [SD]). The lower panel summarizes the serum antibody levels (alpha photon counts) examined by AlphaLISA using purified GIP and GLP-1 proteins as antigens. Cutoff values were determined as the average HC values plus two SD, and the positive samples higher than the cutoff value were scored. \u003cem\u003eP\u003c/em\u003e values were calculated using the Mann\u0026ndash;Whitney U test. \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and positive rates\u0026thinsp;\u0026gt;\u0026thinsp;10% are marked in bold text.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of receiver operating characteristic (ROC) analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGIP-Ab\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGLP-1-Ab\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCutoff value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;815.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5643\u0026ndash;0.6816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5341\u0026ndash;0.6567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eArea under the curve (AUC), cutoff value, sensitivity (%), specificity (%), and 95% confidence interval (CI) of the ROC analysis are shown. Purified GIP and GLP-1 proteins were used as antigens.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Group comparisons\u003c/h2\u003e \u003cp\u003eGIP-Ab and GLP-1-Ab levels were compared between men and women; patients with type 1 and type 2 diabetes mellitus; patients with and without complications; and patients who habitually smoked or consumed alcohol and those who abstained from these habits. None of the comparisons revealed significant differences in incretin levels, although GIP-Abs levels tended to correlate with dyslipidemia complications (P\u0026thinsp;=\u0026thinsp;0.0721) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding types of anti-hypoglycemic drugs, GIP-Ab and GLP-1-Ab levels were not correlated with insulin, GLP-1 receptor agonists (GLP-1 RAs), DPP IV inhibitors, metformin, or thiazolidinediones. Anti-GIP Ab levels were higher in patients using glinides (anti-GIP Abs with [n\u0026thinsp;=\u0026thinsp;21] vs. without [n\u0026thinsp;=\u0026thinsp;251] glinide: 4345.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3776.9 vs. 3324.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2077; P\u0026thinsp;=\u0026thinsp;0.01) or α-glucosidase inhibitors (αGIs) (anti-GIP Abs: with [n\u0026thinsp;=\u0026thinsp;66] vs. without [n\u0026thinsp;=\u0026thinsp;206] αGI: 3751.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2941.1 vs. 3292.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1989.1; P\u0026thinsp;=\u0026thinsp;0.013). Anti-GLP-1 Ab levels were higher in patients using only glinides (3751.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2941.1 vs. 3292.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1989.1) (Supplemental Table\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Survival analysis\u003c/h2\u003e \u003cp\u003eWe subsequently examined whether GIP-Ab and GLP-1-Ab levels were related to survival in patients with diabetes in the mean 4.9-year (maximum 10-year). GIP-Ab and GLP-1-Ab levels were categorized into positive and negative groups using the cutoff values obtained using the X‑tile software [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The GIP‑Ab‑positive group presented a significantly more unfavorable prognosis than did the GIP‑Ab‑negative group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea; P\u0026thinsp;=\u0026thinsp;0.0072). The GLP-1-Ab-positive and -negative groups showed a similar tendency, with no statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb; P\u0026thinsp;=\u0026thinsp;0.0640). We also categorized participants into two groups according to age (under 65 and 65 or older), HbA1c level (HbA1c below 7% and HbA1c 7% or above), presence or absence of complications such as retinopathy and nephropathy, and smoking to draw survival curves for each group. Consequently, significant differences were obtained for age and smoking. This implies that incretin antibodies can predict a prognosis where it cannot be predicted by HbA1c or the presence or absence of complications, as shown in Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to examine anti-incretin antibodies in the sera of patients with diabetes. GIP-Abs and GLP-1-Abs were detected in both HCs and patients with diabetes; however, their levels were significantly higher in patients with diabetes. Furthermore, a mean 4.9-year (maximum 10-year) follow-up study revealed that patients who tested positive for GIP-Abs had a significantly worse prognosis than did those who tested negative for GIP-Abs. Those who tested positive for GLP-1-Abs also tended to have a worse prognosis.\u003c/p\u003e \u003cp\u003eIncretin promotes insulin secretion through receptors on pancreatic β cells [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Clinical trials have shown the glucose-lowering, cardioprotective, and renoprotective effects of GLP-1[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Tirzepatide, a dual GIP and GLP-1R activator, has both glucose-lowering and weight-reducing effects [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Our findings suggest that incretin Ab titers are higher in patients with diabetes than in healthy individuals and that the inhibition of incretin effects may contribute to the development of diabetes. However, this cross-sectional study is limited in establishing causality. The lack of correlation between antibody titers and blood glucose levels or glycated hemoglobin reflects these limitations. Regarding the tendency of lower GIP-Abs levels among patients with dyslipidemia, when GIP receptor knockout mice were fed a high-fat diet, they showed resistance to obesity compared with wild-type mice, and dietary lipids were more efficiently accumulated in adipocytes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], indicating that inhibiting endogenous GIP function may have positively affected lipid metabolism. However, as only a trend was observed without significant differences, future analysis with a larger sample size may be necessary. Regarding the relationship with drugs, αGIs are known to increase incretin expression by suppressing glucose absorption. Increased incretin levels may have enhanced antibody production. In contrast, αGIs are known to prevent the development of diabetes; therefore, the significance of high GIP antibody titers in individuals taking αGIs remains unclear, along with the relationship between glinide and incretin antibody titers.\u003c/p\u003e \u003cp\u003eThe mechanism by which the body produces antibodies against incretin is unclear. It is speculated that these antibodies generated in response to previous infections may have cross-reacted with incretin, leading to the formation of autoantibodies against it. This suggests that individuals who develop these autoantibodies may be prone to developing diabetes owing to the reduced effectiveness of incretin. Moreover, as the disease progresses, they may experience poorer outcomes.\u003c/p\u003e \u003cp\u003eNevertheless, the relationship with prognosis is interesting. This study revealed that the overall mortality rate was associated with high levels of incretin antibodies; however, the frequency of malignant tumors and cardiovascular events as causes of death was too low to perform statistical analyses. It has been clinically demonstrated that the activation of the GLP-1R suppresses cardiovascular and renal events and that the suppression of the endogenous action of incretin by antibodies may have increased the occurrence of these events as well as the associated mortality.\u003c/p\u003e \u003cp\u003eThe study has some limitations. Selection bias may have arisen due to participants\u0026rsquo; recruitment based on informed consent. This study did not evaluate the relationship between blood incretin concentrations and incretin antibodies or the association between incretin antibody levels and blood insulin and glucagon levels. Additionally, the clinical significance of the antibodies was insufficiently analyzed, which is a subject for future study. The small sample size and cross-sectional design further limited the study\u0026rsquo;s ability to establish causality. Further studies with larger sample sizes and longer follow-up periods are needed.\u003c/p\u003e \u003cp\u003eIn conclusion, this study\u0026rsquo;s results indicate that incretin antibodies may serve as novel prognostic markers for diabetes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors would like to thank the physicians and staff who assisted with the care of the patients and the patients and healthy controls themselves.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was supported, in part, by research grants from the Japan Science and Technology Agency [Grant Numbers: 23K06889, 24K10559].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eB-S.Z., A.H., H.Y., Y.Y., M.K., S.O., M.Y., T.Y., T.H., S.O., T.I., and H.Y. researched the data. T.H. researched the data and discussion and reviewed/edited the manuscript. M.T. is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors have contributed significantly, and that all authors are in agreement with the content of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe data that support the findings of this study are available from the corresponding author, M.T,\u0026nbsp;upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure Statement:\u003c/strong\u003e The authors have no competing interests to declare.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHiwasa, T. et al. 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Endocrinol Metab\u003c/em\u003e. \u003cb\u003e320\u003c/b\u003e, E835\u0026ndash;E845 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Autoantibodies, Incretin, Glucagon-like peptide-1, Glucose-dependent insulinotropic peptide","lastPublishedDoi":"10.21203/rs.3.rs-6663426/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6663426/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis retrospective cohort study aimed to elucidate the clinical significance of measuring autoantibodies against incretins in diabetes. We enrolled 274 patients with diabetes (mean age\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation: 63.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1 years) and 109 healthy controls (mean age: 58.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 years). Titers of autoantibodies against incretins (glucose-dependent insulinotropic peptide and glucagon-like peptide-1) were measured using an amplified luminescent proximity homogeneous assay-linked immunosorbent assay. Both incretin antibody titers were significantly higher in patients with diabetes versus healthy controls (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). A mean 4.9-year (maximum 10-year) follow-up study revealed that patients who tested positive for glucose-dependent insulinotropic peptide antibodies had significantly worse prognoses than those who tested negative (P\u0026thinsp;=\u0026thinsp;0.0072). Patients who tested positive for glucagon-like peptide-1 antibodies also tended to have worse prognoses (P\u0026thinsp;=\u0026thinsp;0.06). Autoantibodies against incretins may serve as potential biomarkers for diabetes prognosis.\u003c/p\u003e","manuscriptTitle":"Increased autoantibodies against incretin indicate poor prognosis in patients with diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 18:24:55","doi":"10.21203/rs.3.rs-6663426/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-05T10:55:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T03:16:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309856865095515967594689263757674252707","date":"2025-08-17T10:15:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-07T14:55:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52633373342831167302134367401717641340","date":"2025-08-04T12:26:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-05T11:48:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-30T06:45:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-19T08:52:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-17T06:14:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-14T10:43:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1de46f44-e5ff-4a65-9944-9ea4acc9d014","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49683093,"name":"Biological sciences/Biochemistry"},{"id":49683094,"name":"Biological sciences/Biotechnology"},{"id":49683095,"name":"Health sciences/Endocrinology"},{"id":49683096,"name":"Health sciences/Medical research"},{"id":49683097,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-11-10T16:09:02+00:00","versionOfRecord":{"articleIdentity":"rs-6663426","link":"https://doi.org/10.1038/s41598-025-22337-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-03 15:57:46","publishedOnDateReadable":"November 3rd, 2025"},"versionCreatedAt":"2025-06-10 18:24:55","video":"","vorDoi":"10.1038/s41598-025-22337-z","vorDoiUrl":"https://doi.org/10.1038/s41598-025-22337-z","workflowStages":[]},"version":"v1","identity":"rs-6663426","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6663426","identity":"rs-6663426","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}