Characteristics of the Gut Microbiota and Metabolism in Patients with Unclassified Diabetes in Adults: A Case‒Control Study

Characteristics of the Gut Microbiota and Metabolism in Patients with Unclassified Diabetes in Adults: A Case‒Control Study | 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 Research Article Characteristics of the Gut Microbiota and Metabolism in Patients with Unclassified Diabetes in Adults: A Case‒Control Study juan zhang, lei wu, zhongyun zhang, Danjie Li, Rulai Han, Lei Ye, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4200061/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The classification of diabetes has become increasingly intricate. In 2019, the World Health Organization (WHO) introduced a new category called "unclassified diabetes" to address this complexity. Our study, employing a multiomics approach, aimed to delineate the distinct gut microbiota and metabolic characteristics in individuals under the age of 30 with unclassified diabetes, thus shedding light on the underlying pathophysiological mechanisms involved. Methods This age- and sex-matched case‒control study involved 18 patients with unclassified diabetes, 18 patients with classic type 1 diabetes, 13 patients with type 2 diabetes, and 18 healthy individuals. Metagenomics facilitated the profiling of the gut microbiota, while untargeted liquid chromatography‒mass spectrometry was used to quantify the serum lipids and metabolites. Results Our findings revealed a unique gut microbiota composition in unclassified diabetes patients, marked by a depletion of Butyrivibrio proteoclasticus and Clostridium and an increase in Ruminococcus torques and Lachnospiraceae bacterium 8_1_57FAA . Comparative analysis identified exclusive bacteria, serum metabolites, and clinical parameter modules within the unclassified diabetes cohort. Notably, the gut microbiota structure of patients with unclassified diabetes resembled that of type 2 diabetes patients, especially in terms of disrupted lipid and branched-chain amino acid metabolism. Conclusions Despite sharing certain metabolic features with type 2 diabetes, unclassified diabetes presents unique features. The distinct microbiota and metabolites in unclassified diabetes patients suggest a significant role in modulating glucose, lipid, and amino acid metabolism, potentially influencing disease progression. Further longitudinal studies are essential to explore therapeutic strategies targeting the gut microbiota and metabolites to modify the disease trajectory. Diabetes mellitus Metagenome Gut microbiome Metabolites analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION The global incidence of diabetes mellitus has risen dramatically, signifying a major public health dilemma in the twenty-first century [ 1 ]. Diabetes is characterized by increasing heterogeneity, resulting in a broad spectrum of clinical manifestations and a more diverse range of diabetic subgroups. The increasing incidence of overweight and obesity in individuals with type 1 diabetes mellitus (T1DM) [ 2 ], coupled with the occurrence of ketosis or ketoacidosis in types beyond T1DM, further complicates the classification process, especially at the initial diagnosis stage. Consequently, the classification and management of diabetes will become increasingly challenging [ 3 ]. To underscore this complexity, the World Health Organization (WHO) introduced the category of unclassified diabetes (UNC) in 2019 [ 4 ]. Nevertheless, the distinctive characteristics and etiological factors of UNC have not been fully elucidated. Emerging evidence indicates a distinct imbalance in the gut microbiota of patients with childhood-onset T1DM and type 2 diabetes mellitus (T2DM) [ 5 , 6 ]. In childhood-onset T1DM, a reduced Firmicutes -to- Bacteroides ratio is common, as is an increased prevalence of Bacteroides and Blautia [ 7 , 8 ]. Research in T1DM animal models suggests that the gut microbiota may influence the autoimmune destruction of pancreatic beta cells by modulating toll-like receptor 2/4 signaling, Th17 cells in the intestinal mucosa, sex hormone levels, and the secretion of pancreatic antibacterial peptides [ 9 – 11 ]. Conversely, T2DM patients often exhibit a decrease in butyrate-producing bacteria, notably Akkermansia muciniphila , and an increase in bacteria such as Prevotella copri and Bacteroides vulgatus , which can synthesize branched-chain amino acids (BCAAs), potentially exacerbating insulin resistance [ 12 – 14 ]. However, the relationships among the gut microbiota, metabolic profiles, and unclassified diabetes status remain unexplored, emphasizing the necessity for additional research in this population. In this investigation, we compared the gut microbiota and metabolic profiles among individuals with UNC, T1DM, T2DM, and healthy controls (HCs), elucidating the intricate relationships between the gut microbiota composition, metabolite modules, and clinical phenotypes across these groups. This comprehensive analysis is intended to identify distinctive features of UNC and unravel potential pathogenic mechanisms, contributing to a more nuanced understanding of diabetes subtypes. RESEARCH DESIGN AND METHODS Study Participants and Recruitment This case‒control study included 18 patients with T1DM, 18 patients with UNC, 13 patients with T2DM, and 18 healthy controls (HCs), all of Han descent and under 30 years. The patients were diagnosed according to the World Health Organization guidelines. T1DM was diagnosed based on the presence of acute ketosis or ketoacidosis, the course of insulin replacement therapy, impaired islet function, or positivity for at least one autoantibody (glutamic acid decarboxylase autoantibodies [GADA], insulinoma-associated antigen-2 autoantibodies [IA-2A], or islet cell antibody [ICA]). T2DM was diagnosed based on a typical history of hyperglycemia, no immediate requirement for insulin treatment, and negativity for islet autoantibodies. Unclassified diabetes was diagnosed based on the exclusion of monogenic diabetes through genetic diagnosis, elimination of infections, pancreas and other conditions that could lead to diabetes, and the patient's clinical characteristics not meeting the standards of T1DM and T2DM. All healthy subjects and patients with T2DM tested negative for GADA, IA-2A, and ICA. Additionally, all healthy subjects underwent a standard 75-g oral glucose tolerance test (OGTT) to confirm their normal blood glucose levels. The exclusion criteria for this study included secondary diabetes, acute or chronic inflammatory diseases, infectious diseases, pregnancy, malignant tumors, a history of steroid or immunosuppressive drug use for more than 7 days, a history of treatment with prebiotics, probiotics, antibiotics, or any other medication that could influence the gut microbiota for more than 3 days within the previous 3 months, gastrointestinal diseases, a history of gastrointestinal surgery within the previous year, and hepatic and renal dysfunction. The collected demographic and clinical data included age, sex, diabetes duration, height, weight, body mass index (BMI), systolic blood pressure, and diastolic blood pressure. Additionally, biochemical data such as the 75-g OGTT, C-peptide release test, HbA1c, fasting plasma glucose (FPG), lipid profile, and renal function were collected. All participants provided written informed consent, and this study was approved by the Ruijin Hospital ethics committee. Metagenomic analysis of the human gut microbiome Metagenomic sequencing was utilized to investigate the gut microbiome of the four groups in this study. Fecal samples were collected, and total genomic DNA was extracted using the QIAamp Fast DNA Stool Mini Kit from Qiagen, Germany. Paired-end sequencing was performed on the NovaSeq 6000 platform from Illumina, Inc., in San Diego, CA, USA, at Majorbio BioPharm Technology Co., Ltd., in Shanghai, China. Reads that had adapter sequences or low quality, with a length shorter than 50 bp or a quality value lower than 20, were discarded. The remaining reads were aligned to the Homo sapiens genome using the NCBI database to remove host DNA. The short reads were assembled using Megahit. SOAPaligner was used to map high-quality reads with 95% identity to representative genes, and the gene abundance in each sample was evaluated. For taxonomic annotations, the nonredundant gene catalogs were aligned against the NCBI NR database using BLASTP (version 2.2.28+) with an e-value cutoff of 1×10 − 5 . Nontargeted lipidomic analysis Human serum samples were analyzed using an ultrahigh-performance liquid chromatography high-resolution mass spectrometry/mass spectrometry (UHPLC-HRMS/MS)-based nontargeted lipidomics platform. Lipidomic analysis was performed using a ThermoFisher Ultimate 3000 UHPLC system coupled to a Q Exactive Orbitrap Mass Spectrometry with a Heated Electrospray Ionization Source. The raw UHPLC-HRMS/MS data were processed using Compound Discoverer (version 3.3, Thermo Fisher) with a lipidomics workflow template. This included retention time alignment, compound detection, and compound group and structural identification of lipids using the LipidBlast library (version 68). Serum metabolites analysis The quantification of human serum was performed using the UHPLC-MS/MS platform, which involved several steps, including sample preparation, UHPLC-MS/MS analysis, raw data preprocessing, and the calculation of relative quantification of target metabolites. The metabolites were analyzed using a Thermo Fisher Ultimate 3000 UHPLC system coupled to a Q Exactive Orbitrap Mass Spectrometry in Heated Electrospray Ionization Source with positive and negative modes. The raw data were processed using Xcalibur Software (version 4.0, Thermo Fisher Scientific). In this step, the target metabolites and internal standards were identified, and the integral areas were exported. The relative quantification results were obtained by normalizing the peak areas of the target metabolites to that of the corresponding internal standard. Statistical analyses Differences in clinical parameters were analyzed using the chi-square test or Kruskal‒Wallis test, and multiple comparisons were corrected using false discovery rate (FDR) post hoc tests. To compare metabolite and lipid profiles, orthogonal projections to latent structures discriminant analysis (OPLS-DA) algorithms were used. Variable importance for the projection (VIP) scores were obtained from the OPLS-DA. A P fdr < 0.1 was considered statistically significant for metabolites, and a P 1, and fold change > 1.5 were considered statistically significant for lipids. The Kruskal‒Wallis rank-sum test was applied to assess the differences in microbial alpha diversity. Permutational multivariate analysis of variance (PERMANOVA) was used to compare microbiota beta diversity, and redundancy analysis (RDA) was used to evaluate the effects of demographic variables on microbiota community variation. Significant differences in the relative abundances of taxa were identified using linear discriminant analysis (LDA) effect size (LEfSe) analysis, and P values were corrected using the Benjamini and Hochberg FDR. Taxa with LDA values > 2.0 and P < 0.05 were considered to be differentially abundant, and taxa with P fdr < 0.1 were considered to be significantly different[ 15 ]. These analyses were conducted using the R package vegan. Random forest models were built using the microbial features, metabolic features, and a combination of the two types of data to differentiate different groups. These models were built using the randomForest package in R. The data were analyzed using the Majorbio Cloud Platform ( https://cloud.majorbio.com/page/tools/ ) [ 16 ]. RESULTS Anthropometric and biochemical measurements of different types of diabetes The study design is illustrated in Fig. 1 . In this study, participants with T1DM, T2DM, UNC, and HCs under the age of 30 were recruited following a stringent pathological diagnostic and exclusion methodology. The delineation of unclassified diabetes was based on the World Health Organization (WHO) criteria, excluding known types such as T1DM, T2DM, and hybrid diabetes and specific types such as monogenic diabetes, diseases of the exocrine pancreas, endocrine disorders, drug- or chemical-induced diabetes, infections, uncommon specific forms of immune-mediated diabetes, and other conditions discerned through genetic, clinical, and laboratory evaluations. There were no significant differences between the groups at baseline in terms of age or sex. The biochemical characteristics of the study groups are presented in Table 1 . Elevated fasting plasma glucose (FPG) and hemoglobin A1C (HbA1c) levels were observed across all patient groups relative to those of HCs. Compared with the T1DM group, the UNC group exhibited increased BMI, FPG, and HbA1c, as well as notably increased uric acid (UA) levels. Furthermore, the homeostatic model assessment for insulin resistance (HOMA-IR) was significantly higher in the UNC group than in the HCs and T1DM groups but remained below the T2DM level. Table 1 Baseline anthropometric and biochemical variables Healthy controls (n = 18) Unclassified diabetes (n = 18) T1DM(n = 18) T2DM(n = 13) P value betweeen all groups Age (years) 23.00(21.00–24.00) 22.00(16.50–27.00) # 22.00(16.00-26.50) 20.00(15.00–22.00) 0.963 Diabetes duration (months) / 1.50 (0.87–10.75) 2.00(0.75-12.00) 24.00(4.00–24.00) 0.156 Male/Female (n) 8/10 10/8 6/12 7/6 0.54 BMI (kg/m 2 ) 19.85(19.27–21.55) 26.95(24.48–30.26) * 21.06(19.28–25.34) 26.37(23.38–28.45)* < 0.001 FBG (mmol/L) 4.73(4.46–4.96) 7.52 (6.19–10.30) * 6.04(5.49–11.64)* 7.20(6.94-12.00)* < 0.001 PBG (mmpl/L) 5.85(4.60–6.81) 15.97(8.78–18.78) * 16.44(8.67–25.66)* 14.18(7.14–17.51)* < 0.001 HbA1c (%) 5.05(4.67–5.12) 9.65 (7.72–10.47) *^ 8.40(6.70–10.60)* 6.00(5.20–9.80)* < 0.001 FCP (ng/ml) 1.61(1.41–2.33) 1.90 (1.68–2.57) #^ 0.11(0.01–0.49)* 3.83(1.97–3.92)* < 0.001 PCP (ng/ml) 6.92(5.54–10.89) 4.53 (2.98–5.28) *#^ 0.14(0.01–2.44)* 10.12(5.98–39.82) < 0.001 TC (mg/dL) 3.90(3.32–4.38) 4.75 (3.74–5.21) * 4.32(3.96–4.59) 5.17 (3.67–5.49) 0.054 TG (mg/dL) 0.62(0.56–0.99) 1.40 (0.80–1.89) *# 0.85(0.69–1.05) 2.08 (1.29–2.55) < 0.001 HDL (mg/dL) 1.38(1.30–1.57) 1.12 (0.90–1.46) *# 1.55(1.23–1.71) 1.02(0.91–1.07)* < 0.001 LDL (mg/dL) 1.98(1.68–2.51) 2.66 (1.99–3.75) * 2.52(2.12–2.90)* 3.19(2.80–3.56)* < 0.001 UA (µmol/L) 313.00(255.75–369.00) 362.50(274.00-445.00) # 286.00(230.00-345.50) 374.00(362.00-481.00) 0.025 HOMA-IR 1.33(0.93–1.65) 2.63 (2.13–5.91) *#^ 0.41(0.11–0.86)* 4.94(3.10–7.90)* < 0.001 HOMA-β 101.72(90.18-119.19) 58.19(29.98–85.25) *# 6.81(1.46–15.21)* 60.46(23.88-133.56) < 0.001 The data are presented as the median (25th–75th percentile). *versus healthy controls, P < 0.05; #versus T1DM patients, P < 0.05; ^versus T2DM patients, P < 0.05. Abbreviations: T1DM, type 1 diabetes mellitus; T2DM, type 2 diabetes mellitus; BMI, body mass index; FBG, fasting blood glucose; PBG, postprandial blood glucose; HbA1c, hemoglobin A1c; FCP, fasting C-peptide; PCP, postprandial C-peptide; TC, cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; UA, uric acid; HOMA-IR, homeostasis model assessment for insulin resistance; HOMA-β, homeostasis model assessment-β. Structural modulation of the gut microbiota in the four groups First, we analyzed the microbial diversity of the four groups. The Chao index, as well as the Shannon and Simpson indices, which indicate community richness, suggested no significant difference in bacterial richness across the groups (healthy controls: 4118 ± 504.9; T1DM patients: 3825 ± 828.7; T2DM patients: 4225 ± 828.1; UNC patients: 3847 ± 688.9; P > 0.05) (Fig. 2 A and Figure S1 ). Principal coordinate analysis (PCoA) based on the Bray–Curtis distance revealed significant differences in the overall microbial features across the four groups (PERMANOVA, P = 0.001) (Fig. 2 B). The bacterial community structure in adult-onset UNC patients was significantly distinct from that in HC and T1DM patients (PERMANOVA, T1DM vs HC: P = 0.005; T2DM vs HC: P = 0.005; UNC vs HC: P = 0.001; T1DM vs UNC: P = 0.019), underscoring the unique microbial composition associated with UNC. Taxonomic changes in microbial composition in adult-onset UNC patients Next, we analyzed the microbial composition at different taxonomic levels, with the phylum and family compositions shown in Fig. 2 C and 2 D. Bacteroidetes and Firmicutes dominated across all groups, followed by Proteobacteria and Actinobacteria . Significantly, unclassified diabetes patients had increased levels of Actinobacteria (0.5636% in healthy controls vs. 3.331% in unclassified diabetes patients; P = 0.0005314, FDR-adjusted = 0.03154) and Proteobacteria (3.057% in healthy controls vs. 6.869% in unclassified diabetes patients; P = 0.001644, FDR-adjusted = 0.0515) compared with healthy controls. LEfSe analysis was employed to discern differentially abundant microbial species among HCs, T1DM patients, T2DM patients, and UNC patients. A total of 205, 198, and 190 species were differentially abundant between adult-onset T1DM patients and HCs, between T2DM patients and HCs, and between UNC patients and HCs, respectively (LDA value > 2, P < 0.05) (Tables S1-3). To determine the potential influence of host factors on microbial composition, we conducted a redundancy analysis (RDA) to ascertain potential confounders within these groups. Key host factors, including age, sex, BMI, and diabetes duration, were integrated into the RDA model. Our analysis revealed that, even after adjusting for these confounding factors, 21 taxa in adult-onset UNC patients exhibited significant differential abundance compared to healthy controls (HCs) (LDA value > 2, P fdr < 0.1). Among these, 6 taxa were particularly enriched in UNC patients, including Lachnospiraceae and Enterobacteriaceae . These taxa are associated with metabolites involved in carbohydrate and amino acid metabolism, suggesting a disturbed gut microbiome's involvement in carbohydrate and amino acid metabolic pathways, potentially contributing to GDM [ 17 ], while there was a notable depletion of 15 species, such as Flintibacter, Butyrivibrio_proteoclasticus , s_Clostridium_sp_AF27_2AA and s_Clostridium_sp_AM33_3 [ 18 – 20 ] (Fig. 2 E). Additionally, we identified critical functional alterations in the gut microbiota of adult-onset UNC patients. There was a significant enrichment of carbon metabolism pathways in these individuals compared to healthy controls, indicating a distinctive metabolic signature. Moreover, the amino sugar and nucleotide sugar metabolism pathways were also significantly enriched in UNC patients compared to those in T1DM and T2DM patients. These findings suggest that the gut microbiota is involved in UNC pathogenesis and shed light on metabolic dysregulation in this disease (Fig. 2 ). Associations of the microbiota with serum metabolites and lipids We observed significant differences in serum metabolites between patients with diabetes and HCs (Figure S3). Specifically, the numbers of enriched differentially abundant metabolites in the UNC, T1DM, and T2DM groups compared to those in the HC group were 9, 12, and 18, respectively ( P fdr <0.1) (Fig. 3 A). Notably, metabolites such as indolelactate, 3-hydroxyisovalerate, acetylcamitine, and 2-hydroxyisocaproate were more abundant in the UNC and T2DM groups than in the HC group. These metabolites are positively associated with the risk of developing T2DM [ 21 – 23 ]. Additionally, we conducted an analysis of serum lipids and revealed significant differences across the groups (Figure S4), highlighting the metabolic distinctions inherent to diabetes. In patients with UNC, we identified 50 differential lipids, predominantly triglycerides (TGs), which were increased (Fig. 3 C). Subsequent correlation analysis explored the relationships between differentially abundant bacteria and metabolites. This revealed that bacteria enriched in HCs had a strong positive correlation with HC-enriched metabolites but exhibited a negative correlation with diabetes-enriched metabolites (Fig. 3 B). Notably, a decrease in carnitine and its derivatives, such as valerylcarnitine and lauroylcarnitine, was observed in UNC patients. These compounds are known to enhance glucose utilization and improve lipid parameters and oxidative stress markers, suggesting their potential protective role against metabolic disruptions in UNC (Fig. 3 B) [ 24 ]. In individuals with adult-onset UNC, an increase in specific metabolic markers, including 3-hydroxybutyric acid, BCAAs, and their catabolic intermediates, was noted. These markers have been associated with an increased risk of transitioning from normoalbuminuria to macroalbuminuria and CKD [ 25 – 27 ]. The abundances of bacteria such as s_Ruminococcus_torques and s_Lachnospiraceae_bacterium_8_1_57FAA were positively correlated with the abundances of metabolites such as 3-hydroxyisovalerate and 3-hydroxybutyric acid, suggesting an increased likelihood of complex diabetic nephropathy in UNC patients. TG and PE were enriched in the UNC and T2DM groups. Additionally, a strong positive correlation between bacteria and lipids (TG and PE) in the UNC and T2DM groups indicates potential parallels in their pathogenic processes (Fig. 3 D). Associations of the altered microbes and metabolites with clinical parameters To understand the role of the gut microbiota in the progression of diabetes, we analyzed the associations between clinical parameters and differentially abundant bacteria or metabolites in the four groups. We discovered that certain taxa related to adult-onset T2DM, including Streptococcaceae [ 28 ] and Actinomycetaceae [ 29 ], were significantly correlated with glucose metabolism and pancreatic beta cell function, corroborating previous findings. These taxa had a positive correlation with PCP and FCP (Fig. 3 E). In the UNC cohort, bacteria such as Ruminococcus__torques , Lachnospiraceae_bacterium_8_1_57FAA , and Nitrobacteraceae were positively associated with PBG and FCP. Furthermore, we discovered novel associations between adult-onset UNC and increased metabolites such as 2-hydroxy-3-methylbutyrate, 2-hydroxyisobutyraten, and 3-hydroxyisovalerate, which are all positively correlated with PBG, FBG, and FCP. Particularly in UNC, high levels of 3-hydroxyisovalerate, 3-hydroxyisobutyrate, and phenylalanine were strongly related to blood uric acid, indicating their potential role in renal function. In UNC, we observed an enrichment of metabolites integral to amino acid metabolism, including 2-hydroxy-3-methylbutyric acid, cysteine, and phenylalanine. This enrichment aligns with an increase in bacterial pathways for amino sugar metabolites, providing insight into the metabolic landscape of UNC. In T2DM patients, elevated levels of D-fructose, D-glucose, and D-mannose were linked to key glucose and lipid metabolism parameters (Figure S5A). Moreover, TG, which was significantly elevated in T2DM patients, correlated strongly with glucose metabolism (Figure S5B). These findings indicate potential links between the gut microbiota, metabolites, and pancreatic beta cell autoimmunity in UNC (Figure S6), suggesting a heightened risk of diabetic nephropathy in UNC patients and shared pathogenetic elements between UNC and T2DM. Multiomic classifier discriminating patients with adult-onset UNC from patients in the other three groups To ascertain the potential of the gut microbiota and metabolites as biomarkers for the differential diagnosis of diabetes, we constructed random forest models based on changes in fecal taxonomic or metabolic features between HCs and UNC patients (Supplementary Table S4). The model revealed a bacterial signature of 5 distinct species that could differentiate UNC patients from HCs, with an area under the curve (AUC) of 0.66 (95% CI 0.45–0.87) (Fig. 4 A). An additional random forest model was assessed for its diagnostic efficacy utilizing a combination of 7 serum biomarkers, including 3 metabolites and 4 lipids. Notably, this model produced an AUC of 0.73 (95% CI 0.53–0.94) for distinguishing patients with adult-onset UNC from HCs (Fig. 4 B). Further enhancement of the model with a panel of five bacterial species, seven serum biomarkers, and three clinical parameters increased its discriminative power, yielding an AUC of 0.94 (95% CI 0.85–1) in differentiating UNC patients from HCs (Fig. 4 C), demonstrating the potential of this comprehensive approach for accurate diagnosis. DISCUSSION Eason RJ et al [ 30 ] demonstrated that adults diagnosed with type 1 diabetes who are negative for islet antibodies have genetic and C-peptide characteristics that are intermediate between those of type 1 and type 2 diabetes. This suggests a significant misclassification within this cohort, potentially including individuals with islet antibody-negative autoimmune (type 1) diabetes as well as those with nonautoimmune (predominantly type 2) diabetes who have been erroneously classified. Such misclassification can lead to inappropriate treatment regimens, including unnecessary lifelong insulin therapy, and hinder access to effective type 2 diabetes treatments. Currently, the high prevalence of type 2 diabetes in adults makes robustly discriminating true type 1 diabetes from atypical presentations of type 2 diabetes challenging. Some reported characteristics of type 1 diabetes in older adults, such as low islet autoantibody prevalence, may reflect the inadvertent study of those with and without autoimmune diabetes, and some research in this area suggests a need to combine clinical diagnosis with gut microbiota and metabolite profile tests in this setting [ 31 – 33 ]. The World Health Organization (WHO) introduced UNC in 2019 when there was no clear diagnostic category [ 4 ]. In this study, we revealed that unclassified diabetes patients have different gut microbiota and metabolite profiles than healthy individuals as well as classic T1DM and T2DM patients. Remarkably, the gut microbiota of unclassified diabetes patients displayed distinctive characteristics, with significantly increased abundances of s___Ruminococcus__torquess and Lachnospiraceae_bacterium_8_1_57FAA and decreased abundances of s__unclassified_g__Clostridium , s__Clostridium_sp__AF27_2AA and s__Clostridium_sp__AM33_3 compared with those in the other groups. There was a clear correlation among the gut microbiota, serum metabolites, and clinical phenotypes. Furthermore, the gut bacterial pathway of “Amino sugar and nucleotide sugar metabolism” was significantly enriched in adult-onset UNC patients, differentiating them from T1DM and T2DM patients and suggesting that unique metabolic processes are involved in UNC. In patients with unclassified diabetes, we detected an enrichment of branched-chain amino acids (BCAAs) and their derivatives in the blood, which correlated with glucose and lipid metabolism. Large human population studies have shown that a high intake of dietary BCAAs increases the risk of T2DM[ 34 ]. In our study, BCAAs and their derivatives might affect glucose metabolism and sensitivity in patients with unclassified diabetes, which was consistent with the functional differences in the bacteria. We found that, serologically, UNC was more similar to T2DM, but T2DM was dominated by TG enrichment and UNC by amino acid derivatives. Moreover, high levels of 3-hydroxyisovalerate and 3-hydroxyisobutyrate were strongly related to blood uric acid in the UNC group, which could suggest that unclassified diabetes patients had poor renal function in the subsequent course. Therefore, this finding suggests that patients with unclassified diabetes mellitus need to pay attention to changes in renal function in later follow-up. Importantly, we developed a prediction model for UNC based on gut microbial signatures and metabolic features, which demonstrated high accuracy in distinguishing patients with this disease from HCs. Furthermore, we have shown that the predictive power of the model can be enhanced by incorporating metabolites, and the utilization of the "5 + 7 + 3" model enables simultaneous differentiation of patients with UNC from HCs. The metabolic composition of the "5 + 7 + 3" model in UNC is similar to that of T2DM. However, the increasing prevalence of obesity among patients with T1DM due to environmental and lifestyle factors, the presence of ketosis-prone individuals in patients with T2DM and idiopathic T1DM, and the unavailability of autoantibody detection facilities in certain clinics pose challenges in accurately classifying different types of diabetes. In this regard, comprehending the metabolic and microbiota characteristics of unclassified diabetes mellitus patients is crucial for gaining insights into disease pathogenesis and prognosis. Although our study provides valuable insights into unclassified diabetes, it has several limitations that should be considered. First, the cross-sectional design of our study cannot establish a causal relationship between the identified gut microbiota and adult-onset unclassified diabetes. Additionally, the relatively small sample size and the restriction of subjects to a specific ethnic population and geographic region may limit the generalizability of our results. Finally, despite our efforts to address confounding factors when comparing the three groups (sex- and age-matched patients with comparable demographic characteristics, antibiotic exposure and comorbidities), our findings could be influenced by other confounders, such as disease duration and dietary intake. Consequently, the significance of these findings should be confirmed through larger prospective follow-up studies involving more diverse ethnic populations and geographic regions. In summary, our study revealed distinct characteristics of the gut microbiota and metabolic profiles in patients with unclassified diabetes, distinguishing them from healthy individuals. Additionally, we observed correlations between these profiles and aspects of glucose metabolism and islet function, suggesting their potential involvement in the development and progression of unclassified diabetes. Importantly, we also found that patients with unclassified diabetes may experience impaired renal function in the future, highlighting the need for careful monitoring. Overall, the findings from this study provide valuable insights that could contribute to the classification and comprehension of diabetes through the identification of novel pathways. Declarations Acknowledgements The authors thank all the participants. Authors’ contributions Z. conceived the study, recruited volunteers, performed the statistical analysis and data interpretation, wrote the manuscript, and made key modifications. L.W. participated in the selection of the samples used, analyzed the microbiota and metabolomics data, and modified the manuscript. Z.Z. participated in the study design, recruited volunteers, retained samples, extracted data, and participated in the writing and modification of the manuscript. D.L. recruited and supervised the participants and performed all clinical procedures. R.H. and L.Y. participated in the recruitment of volunteers and provided the samples used in this study. W.Gu. conceived the study, developed the experimental design, wrote the manuscript, and provided critical revision. All the authors have read and approved the final version of the manuscript. W.Gu. is the guarantor of this work and, as such, had 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. Funding This study was supported by grant 82070864 from the National Natural Science Foundation of China and grant 22Y11904800 from the Shanghai Municipal Science and Technology Major Projects. Data Availability BioSample accession number (NCBI) of metagenomic data is PRJNA1099928. Declarations Ethics approval and consent to participate All experiments were performed in accordance with the Declaration of Helsinki principles. The study was approved by the Medical Ethics Committee of Ruijin Hospital of Shanghai jiaotong University (NO. 2013, 050). The children’s feces were collected only after their parents signed an informed consent to participate in the study. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Tomic D, Shaw JE, Magliano DJ: The burden and risks of emerging complications of diabetes mellitus . Nat Rev Endocrinol 2022, 18 (9):525-539. Low S, Chin MC, Deurenberg-Yap M: Review on Epidemic of Obesity . Annals of the Academy of Medicine, Singapore 2009, 38 (1):57-65. Tuomi T, Santoro N, Caprio S, Cai M, Weng J, Groop L: The many faces of diabetes: a disease with increasing heterogeneity . The Lancet 2014, 383 (9922):1084-1094. Classification of diabetes mellitus. Geneva: World Health Organization . Licence: CC BY-NC-SA 30 IGO 2019. Vatanen T, Franzosa EA, Schwager R, Tripathi S, Arthur TD, Vehik K, Lernmark Å, Hagopian WA, Rewers MJ, She J-X et al : The human gut microbiome in early-onset type 1 diabetes from the TEDDY study . Nature 2018, 562 (7728):589-594. Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, Liang S, Zhang W, Guan Y, Shen D et al : A metagenome-wide association study of gut microbiota in type 2 diabetes . Nature 2012, 490 (7418):55-60. Murri M, Leiva I, Gomez-Zumaquero JM, Tinahones FJ, Cardona F, Soriguer F, Queipo-Ortuno MI: Gut microbiota in children with type 1 diabetes differs from that in healthy children: a case-control study . BMC Med 2013, 11 :46. Huang Y, Li S-C, Hu J, Ruan H-B, Guo H-M, Zhang H-H, Wang X, Pei Y-F, Pan Y, Fang C: Gut microbiota profiling in Han Chinese with type 1 diabetes . Diabetes Research and Clinical Practice 2018, 141 :256-263. Yang Y, Torchinsky MB, Gobert M, Xiong H, Xu M, Linehan JL, Alonzo F, Ng C, Chen A, Lin X et al : Focused specificity of intestinal TH17 cells towards commensal bacterial antigens . Nature 2014, 510 (7503):152-156. Knip M, Siljander H: The role of the intestinal microbiota in type 1 diabetes mellitus . Nature Reviews Endocrinology 2016, 12 (3):154-167. Thomas RM, Jobin C: Microbiota in pancreatic health and disease: the next frontier in microbiome research . Nature Reviews Gastroenterology & Hepatology 2019, 17 (1):53-64. Zhang J, Ni Y, Qian L, Fang Q, Zheng T, Zhang M, Gao Q, Zhang Y, Ni J, Hou X et al : Decreased Abundance of Akkermansia muciniphila Leads to the Impairment of Insulin Secretion and Glucose Homeostasis in Lean Type 2 Diabetes . Advanced Science 2021, 8 (16). Pedersen HK, Gudmundsdottir V, Nielsen HB, Hyotylainen T, Nielsen T, Jensen BAH, Forslund K, Hildebrand F, Prifti E, Falony G et al : Human gut microbes impact host serum metabolome and insulin sensitivity . Nature 2016, 535 (7612):376-381. Canfora EE, Meex RCR, Venema K, Blaak EE: Gut microbial metabolites in obesity, NAFLD and T2DM . Nature Reviews Endocrinology 2019, 15 (5):261-273. Hu J, Ding J, Li X, Li J, Zheng T, Xie L, Li C, Tang Y, Guo K, Huang J et al : Distinct signatures of gut microbiota and metabolites in different types of diabetes: a population-based cross-sectional study . eClinicalMedicine 2023, 62 . Ren Y, Yu G, Shi C, Liu L, Guo Q, Han C, Zhang D, Zhang L, Liu B, Gao H et al : Majorbio Cloud: A one ‐stop, comprehensive bioinformatic platform for multiomics analyses . iMeta 2022, 1 (2). Wang X, Liu H, Li Y, Huang S, Zhang L, Cao C, Baker PN, Tong C, Zheng P, Qi H: Altered gut bacterial and metabolic signatures and their interaction in gestational diabetes mellitus . Gut Microbes 2020, 12 (1). Bond JJ, Dunne JC, Kwan FYS, Li D, Zhang K, Leahy SC, Kelly WJ, Attwood GT, Jordan TW: Carbohydrate transporting membrane proteins of the rumen bacterium, Butyrivibrio proteoclasticus . Journal of Proteomics 2012, 75 (11):3138-3144. LeBlanc JG, Aubry C, Cortes-Perez NG, de Moreno de LeBlanc A, Vergnolle N, Langella P, Azevedo V, Chatel J-M, Miyoshi A, Bermúdez-Humarán LG: Mucosal targeting of therapeutic molecules using genetically modified lactic acid bacteria: an update . FEMS Microbiology Letters 2013, 344 (1):1-9. Yoon HS, Cho CH, Yun MS, Jang SJ, You HJ, Kim J-h, Han D, Cha KH, Moon SH, Lee K et al : Akkermansia muciniphila secretes a glucagon-like peptide-1-inducing protein that improves glucose homeostasis and ameliorates metabolic disease in mice . Nature Microbiology 2021, 6 (5):563-573. Zhao S, Liu M-L, Huang B, Zhao F-R, Li Y, Cui X-T, Lin R: Acetylcarnitine Is Associated With Cardiovascular Disease Risk in Type 2 Diabetes Mellitus . Frontiers in Endocrinology 2021, 12 . Morze J, Wittenbecher C, Schwingshackl L, Danielewicz A, Rynkiewicz A, Hu FB, Guasch-Ferré M: Metabolomics and Type 2 Diabetes Risk: An Updated Systematic Review and Meta-analysis of Prospective Cohort Studies . Diabetes Care 2022, 45 (4):1013-1024. Qi Q, Li J, Yu B, Moon JY, Chai JC, Merino J, Hu J, Ruiz-Canela M, Rebholz C, Wang Z et al : Host and gut microbial tryptophan metabolism and type 2 diabetes: an integrative analysis of host genetics, diet, gut microbiome and circulating metabolites in cohort studies . Gut 2022, 71 (6):1095-1105. Bene J, Hadzsiev K, Melegh B: Role of carnitine and its derivatives in the development and management of type 2 diabetes . Nutrition & Diabetes 2018, 8 (1). Mutter S, Valo E, Aittomäki V, Nybo K, Raivonen L, Thorn LM, Forsblom C, Sandholm N, Würtz P, Groop P-H: 2020. Sharma K, Karl B, Mathew AV, Gangoiti JA, Wassel CL, Saito R, Pu M, Sharma S, You Y-H, Wang L et al : Metabolomics Reveals Signature of Mitochondrial Dysfunction in Diabetic Kidney Disease . Journal of the American Society of Nephrology 2013, 24 (11):1901-1912. Saulnier P-J, Darshi M, Wheelock KM, Looker HC, Fufaa GD, Knowler WC, Weil EJ, Tanamas SK, Lemley KV, Saito R et al : Urine metabolites are associated with glomerular lesions in type 2 diabetes . Metabolomics 2018, 14 (6). Xiang K, Zhang J-J, Xu Y-Y, Zhong X, Ni J, Pan H-F: Genetically Predicted Causality of 28 Gut Microbiome Families and Type 2 Diabetes Mellitus Risk . Frontiers in Endocrinology 2022, 13 . Bonnefond S, Catroux M, Melenotte C, Karkowski L, Rolland L, Trouillier S, Raffray L: Clinical features of actinomycosis . Medicine 2016, 95 (24). Eason RJ, Thomas NJ, Hill AV, Knight BA, Carr A, Hattersley AT, McDonald TJ, Shields BM, Jones AG, Simon G et al : Routine Islet Autoantibody Testing in Clinically Diagnosed Adult-Onset Type 1 Diabetes Can Help Identify Misclassification and the Possibility of Successful Insulin Cessation . Diabetes Care 2022, 45 (12):2844-2851. Leslie RD, Evans-Molina C, Freund-Brown J, Buzzetti R, Dabelea D, Gillespie KM, Goland R, Jones AG, Kacher M, Phillips LS et al : Adult-Onset Type 1 Diabetes: Current Understanding and Challenges . Diabetes Care 2021, 44 (11):2449-2456. Bravis V, Kaur A, Walkey HC, Godsland IF, Misra S, Bingley PJ, Williams AJK, Dunger DB, Dayan CM, Peakman M et al : Relationship between islet autoantibody status and the clinical characteristics of children and adults with incident type 1 diabetes in a UK cohort . BMJ Open 2018, 8 (4). Foteinopoulou E, Clarke CAL, Pattenden RJ, Ritchie SA, McMurray EM, Reynolds RM, Arunagirinathan G, Gibb FW, McKnight JA, Strachan MWJ: Impact of routine clinic measurement of serum C ‐peptide in people with a clinician ‐diagnosis of type 1 diabetes . Diabetic Medicine 2020, 38 (7). Zheng Y, Li Y, Qi Q, Hruby A, Manson JE, Willett WC, Wolpin BM, Hu FB, Qi L: Cumulative consumption of branched-chain amino acids and incidence of type 2 diabetes . International Journal of Epidemiology 2016, 45 (5):1482-1492. Additional Declarations No competing interests reported. Supplementary Files Supplementalmaterial.docx Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4200061","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292072509,"identity":"fd606c2b-0bf3-4985-9858-25212981ba2d","order_by":0,"name":"juan zhang","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"juan","middleName":"","lastName":"zhang","suffix":""},{"id":292072510,"identity":"566b2efa-2c68-4537-8280-7c9fb8f08eb1","order_by":1,"name":"lei wu","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"lei","middleName":"","lastName":"wu","suffix":""},{"id":292072511,"identity":"ee6dbe24-5a7b-4d1b-9232-78ac0e3e6061","order_by":2,"name":"zhongyun zhang","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"zhongyun","middleName":"","lastName":"zhang","suffix":""},{"id":292072512,"identity":"2fac92a2-848d-4348-8846-859d8d2199f2","order_by":3,"name":"Danjie Li","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Danjie","middleName":"","lastName":"Li","suffix":""},{"id":292072513,"identity":"0064b451-d0e7-4341-8381-7edcb96697ca","order_by":4,"name":"Rulai Han","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Rulai","middleName":"","lastName":"Han","suffix":""},{"id":292072514,"identity":"432caf0b-b72c-4858-abcf-39072f776d9d","order_by":5,"name":"Lei Ye","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Ye","suffix":""},{"id":292072515,"identity":"35c4331d-b323-472a-8313-a269bde0c7a2","order_by":6,"name":"Weiqiong Gu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYFACxgaGBDCD+cCBDz9I08KWeHBmD2nW8Rgf5mAjQp3B8ebGGw8q7tjNj8j5cJiBh0GeX+wAAS1nDjZbJJx5lrzxRu6GwwUWDIYzZyfg12J2I7FNIrHtcLLhDKCWGTwMCQa3CWm5/xCmJefBYR42YrTcYARrsZOXyGEgTov9mUSQXw4nGPA8MwAGsgRhv0i2H39480fFYXv59uTHHz78sJHnlyagBQQkgDhxwwE4mwgAUmYv30Cc4lEwCkbBKBiBAADRIE3XKrRCmAAAAABJRU5ErkJggg==","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Weiqiong","middleName":"","lastName":"Gu","suffix":""}],"badges":[],"createdAt":"2024-04-01 10:44:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4200061/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4200061/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55251776,"identity":"58899bf2-dd64-4a55-a4bc-29be88eca19c","added_by":"auto","created_at":"2024-04-24 17:43:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":158767,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of the study design\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4200061/v1/c5fae8bc2ba876b28f1f23fb.png"},{"id":55251777,"identity":"f838e1a7-915b-42e1-a95b-0e8af74cd400","added_by":"auto","created_at":"2024-04-24 17:43:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":86284,"visible":true,"origin":"","legend":"\u003cp\u003eThe structural shifts and signatures of the gut microbiota in the four groups.\u003c/p\u003e\n\u003cp\u003eA. Microbial community richness and diversity (Chao 1 index; \u003cem\u003eP\u003c/em\u003e = 0.3967)\u003c/p\u003e\n\u003cp\u003eB. Principal coordinate analysis (PCoA) analysis based on PERMANOVA (\u003cem\u003eP\u003c/em\u003e = 0.001).\u003c/p\u003e\n\u003cp\u003eC-D. The relative abundance of microbial taxa at the phylum and family levels; phyla or genera with a relative abundance \u0026lt;1% in each sample were merged into others.\u003c/p\u003e\n\u003cp\u003eE. Bar charts showing the relative abundance of taxa that were exclusively altered in patients with adult-onset UNC compared with HCs.\u003c/p\u003e\n\u003cp\u003eAbbreviations: HCs, healthy controls; T1D, type 1 diabetes; T2D, type 2 diabetes; UNC, unclassified diabetes; PCoA, principal coordinate analysis; PERMANOVA, permutational multivariate analysis of variance. Bar charts show the mean ± SD. *\u003cem\u003eP\u003c/em\u003e\u003csub\u003efdr\u003c/sub\u003e \u0026lt; 0.1.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4200061/v1/4fe6abb95aaf2a99c34911e5.png"},{"id":55251779,"identity":"d34fa291-c7fe-4704-9368-925bf6b25961","added_by":"auto","created_at":"2024-04-24 17:43:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":179346,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially abundant serum metabolites and their associations with gut bacteria.\u003c/p\u003e\n\u003cp\u003eA. Distribution of serum-enriched differentially abundant metabolites between the UNC group and the HC, T1DM or T2DM group. Variable importance for the projection (VIP) scores were obtained viaOPLS-DA.\u003c/p\u003e\n\u003cp\u003eB. Associations of representative bacteria and serum metabolites that were altered in UNC patients, adult-onset T1D patients, T2D patients or both compared with HCs were assessed by Spearman’s correlation analysis.\u003c/p\u003e\n\u003cp\u003eC. Volcano plots demonstrating differential lipids between HCs and UNC, T1DM or T2DM patients.\u003c/p\u003e\n\u003cp\u003eD. Associations of representative bacteria and serum lipids that were altered in UNC patients, adult-onset T1D patients, T2D patients or both compared with HCs were assessed by Spearman’s correlation analysis.\u003c/p\u003e\n\u003cp\u003eE. Associations of differentially abundant taxa and clinical parameters in patients.\u003c/p\u003e\n\u003cp\u003eAbbreviations: FC, fold change; OPLS-DA, orthogonal partial least squares discriminant analysis; VIP, variable influence on projection; HC, healthy controls; T1D, type 1 diabetes; T2D, type 2 diabetes; UNC, unclassified diabetes.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4200061/v1/5b1cb746354e9e1a4eccad8e.png"},{"id":55251778,"identity":"8ecfffa8-879c-4afd-9489-6ee0a4fa3d55","added_by":"auto","created_at":"2024-04-24 17:43:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51900,"visible":true,"origin":"","legend":"\u003cp\u003eDisease classification based on the signatures of the gut microbiome and metabolome.\u003c/p\u003e\n\u003cp\u003eRandom forest classifiers composed of bacteria (A), combinations ofmetabolites (B) and clinical parameters (C) were constructed to discriminate patients with UNC from HCs.\u003c/p\u003e\n\u003cp\u003eAbbreviations: HC, healthy controls; UNC, unclassified diabetes; AUC, area under the curve.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4200061/v1/b52026a890f2ca138f6f53f6.png"},{"id":55252807,"identity":"2842f27e-3ca1-442a-84c4-742a46541b60","added_by":"auto","created_at":"2024-04-24 17:59:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2372350,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4200061/v1/76dfcc59-7fe7-49e4-ad49-7497e955838f.pdf"},{"id":55251780,"identity":"92c2caca-8e3d-4cf3-97bd-6e8bb2c9d22a","added_by":"auto","created_at":"2024-04-24 17:43:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1000412,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4200061/v1/83fac10a1ad9e9c29b0d598c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characteristics of the Gut Microbiota and Metabolism in Patients with Unclassified Diabetes in Adults: A Case‒Control Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe global incidence of diabetes mellitus has risen dramatically, signifying a major public health dilemma in the twenty-first century [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Diabetes is characterized by increasing heterogeneity, resulting in a broad spectrum of clinical manifestations and a more diverse range of diabetic subgroups. The increasing incidence of overweight and obesity in individuals with type 1 diabetes mellitus (T1DM) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], coupled with the occurrence of ketosis or ketoacidosis in types beyond T1DM, further complicates the classification process, especially at the initial diagnosis stage. Consequently, the classification and management of diabetes will become increasingly challenging [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. To underscore this complexity, the World Health Organization (WHO) introduced the category of unclassified diabetes (UNC) in 2019 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Nevertheless, the distinctive characteristics and etiological factors of UNC have not been fully elucidated.\u003c/p\u003e \u003cp\u003eEmerging evidence indicates a distinct imbalance in the gut microbiota of patients with childhood-onset T1DM and type 2 diabetes mellitus (T2DM) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In childhood-onset T1DM, a reduced \u003cem\u003eFirmicutes\u003c/em\u003e-to-\u003cem\u003eBacteroides\u003c/em\u003e ratio is common, as is an increased prevalence of \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eBlautia\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Research in T1DM animal models suggests that the gut microbiota may influence the autoimmune destruction of pancreatic beta cells by modulating toll-like receptor 2/4 signaling, Th17 cells in the intestinal mucosa, sex hormone levels, and the secretion of pancreatic antibacterial peptides [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Conversely, T2DM patients often exhibit a decrease in butyrate-producing bacteria, notably \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e, and an increase in bacteria such as \u003cem\u003ePrevotella copri\u003c/em\u003e and \u003cem\u003eBacteroides vulgatus\u003c/em\u003e, which can synthesize branched-chain amino acids (BCAAs), potentially exacerbating insulin resistance [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the relationships among the gut microbiota, metabolic profiles, and unclassified diabetes status remain unexplored, emphasizing the necessity for additional research in this population.\u003c/p\u003e \u003cp\u003eIn this investigation, we compared the gut microbiota and metabolic profiles among individuals with UNC, T1DM, T2DM, and healthy controls (HCs), elucidating the intricate relationships between the gut microbiota composition, metabolite modules, and clinical phenotypes across these groups. This comprehensive analysis is intended to identify distinctive features of UNC and unravel potential pathogenic mechanisms, contributing to a more nuanced understanding of diabetes subtypes.\u003c/p\u003e"},{"header":"RESEARCH DESIGN AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants and Recruitment\u003c/h2\u003e \u003cp\u003eThis case‒control study included 18 patients with T1DM, 18 patients with UNC, 13 patients with T2DM, and 18 healthy controls (HCs), all of Han descent and under 30 years. The patients were diagnosed according to the World Health Organization guidelines. T1DM was diagnosed based on the presence of acute ketosis or ketoacidosis, the course of insulin replacement therapy, impaired islet function, or positivity for at least one autoantibody (glutamic acid decarboxylase autoantibodies [GADA], insulinoma-associated antigen-2 autoantibodies [IA-2A], or islet cell antibody [ICA]). T2DM was diagnosed based on a typical history of hyperglycemia, no immediate requirement for insulin treatment, and negativity for islet autoantibodies. Unclassified diabetes was diagnosed based on the exclusion of monogenic diabetes through genetic diagnosis, elimination of infections, pancreas and other conditions that could lead to diabetes, and the patient's clinical characteristics not meeting the standards of T1DM and T2DM. All healthy subjects and patients with T2DM tested negative for GADA, IA-2A, and ICA. Additionally, all healthy subjects underwent a standard 75-g oral glucose tolerance test (OGTT) to confirm their normal blood glucose levels. The exclusion criteria for this study included secondary diabetes, acute or chronic inflammatory diseases, infectious diseases, pregnancy, malignant tumors, a history of steroid or immunosuppressive drug use for more than 7 days, a history of treatment with prebiotics, probiotics, antibiotics, or any other medication that could influence the gut microbiota for more than 3 days within the previous 3 months, gastrointestinal diseases, a history of gastrointestinal surgery within the previous year, and hepatic and renal dysfunction. The collected demographic and clinical data included age, sex, diabetes duration, height, weight, body mass index (BMI), systolic blood pressure, and diastolic blood pressure. Additionally, biochemical data such as the 75-g OGTT, C-peptide release test, HbA1c, fasting plasma glucose (FPG), lipid profile, and renal function were collected. All participants provided written informed consent, and this study was approved by the Ruijin Hospital ethics committee.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMetagenomic analysis of the human gut microbiome\u003c/h2\u003e \u003cp\u003eMetagenomic sequencing was utilized to investigate the gut microbiome of the four groups in this study. Fecal samples were collected, and total genomic DNA was extracted using the QIAamp Fast DNA Stool Mini Kit from Qiagen, Germany. Paired-end sequencing was performed on the NovaSeq 6000 platform from Illumina, Inc., in San Diego, CA, USA, at Majorbio BioPharm Technology Co., Ltd., in Shanghai, China. Reads that had adapter sequences or low quality, with a length shorter than 50 bp or a quality value lower than 20, were discarded. The remaining reads were aligned to the \u003cem\u003eHomo sapiens\u003c/em\u003e genome using the NCBI database to remove host DNA. The short reads were assembled using Megahit. SOAPaligner was used to map high-quality reads with 95% identity to representative genes, and the gene abundance in each sample was evaluated. For taxonomic annotations, the nonredundant gene catalogs were aligned against the NCBI NR database using BLASTP (version 2.2.28+) with an e-value cutoff of 1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eNontargeted lipidomic analysis\u003c/h2\u003e \u003cp\u003eHuman serum samples were analyzed using an ultrahigh-performance liquid chromatography high-resolution mass spectrometry/mass spectrometry (UHPLC-HRMS/MS)-based nontargeted lipidomics platform. Lipidomic analysis was performed using a ThermoFisher Ultimate 3000 UHPLC system coupled to a Q Exactive Orbitrap Mass Spectrometry with a Heated Electrospray Ionization Source. The raw UHPLC-HRMS/MS data were processed using Compound Discoverer (version 3.3, Thermo Fisher) with a lipidomics workflow template. This included retention time alignment, compound detection, and compound group and structural identification of lipids using the LipidBlast library (version 68).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSerum metabolites analysis\u003c/h2\u003e \u003cp\u003eThe quantification of human serum was performed using the UHPLC-MS/MS platform, which involved several steps, including sample preparation, UHPLC-MS/MS analysis, raw data preprocessing, and the calculation of relative quantification of target metabolites. The metabolites were analyzed using a Thermo Fisher Ultimate 3000 UHPLC system coupled to a Q Exactive Orbitrap Mass Spectrometry in Heated Electrospray Ionization Source with positive and negative modes. The raw data were processed using Xcalibur Software (version 4.0, Thermo Fisher Scientific). In this step, the target metabolites and internal standards were identified, and the integral areas were exported. The relative quantification results were obtained by normalizing the peak areas of the target metabolites to that of the corresponding internal standard.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eDifferences in clinical parameters were analyzed using the chi-square test or Kruskal‒Wallis test, and multiple comparisons were corrected using false discovery rate (FDR) post hoc tests. To compare metabolite and lipid profiles, orthogonal projections to latent structures discriminant analysis (OPLS-DA) algorithms were used. Variable importance for the projection (VIP) scores were obtained from the OPLS-DA. A \u003cem\u003eP\u003c/em\u003e\u003csub\u003efdr\u003c/sub\u003e \u0026lt; 0.1 was considered statistically significant for metabolites, and a \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, VIP\u0026thinsp;\u0026gt;\u0026thinsp;1, and fold change\u0026thinsp;\u0026gt;\u0026thinsp;1.5 were considered statistically significant for lipids. The Kruskal‒Wallis rank-sum test was applied to assess the differences in microbial alpha diversity. Permutational multivariate analysis of variance (PERMANOVA) was used to compare microbiota beta diversity, and redundancy analysis (RDA) was used to evaluate the effects of demographic variables on microbiota community variation. Significant differences in the relative abundances of taxa were identified using linear discriminant analysis (LDA) effect size (LEfSe) analysis, and \u003cem\u003eP\u003c/em\u003e values were corrected using the Benjamini and Hochberg FDR. Taxa with LDA values\u0026thinsp;\u0026gt;\u0026thinsp;2.0 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to be differentially abundant, and taxa with \u003cem\u003eP\u003c/em\u003e\u003csub\u003efdr\u003c/sub\u003e \u0026lt; 0.1 were considered to be significantly different[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These analyses were conducted using the R package vegan. Random forest models were built using the microbial features, metabolic features, and a combination of the two types of data to differentiate different groups. These models were built using the randomForest package in R. The data were analyzed using the Majorbio Cloud Platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cloud.majorbio.com/page/tools/\u003c/span\u003e\u003cspan address=\"https://cloud.majorbio.com/page/tools/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eAnthropometric and biochemical measurements of different types of diabetes\u003c/h2\u003e\n \u003cp\u003eThe study design is illustrated in Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e. In this study, participants with T1DM, T2DM, UNC, and HCs under the age of 30 were recruited following a stringent pathological diagnostic and exclusion methodology. The delineation of unclassified diabetes was based on the World Health Organization (WHO) criteria, excluding known types such as T1DM, T2DM, and hybrid diabetes and specific types such as monogenic diabetes, diseases of the exocrine pancreas, endocrine disorders, drug- or chemical-induced diabetes, infections, uncommon specific forms of immune-mediated diabetes, and other conditions discerned through genetic, clinical, and laboratory evaluations. There were no significant differences between the groups at baseline in terms of age or sex. The biochemical characteristics of the study groups are presented in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e. Elevated fasting plasma glucose (FPG) and hemoglobin A1C (HbA1c) levels were observed across all patient groups relative to those of HCs. Compared with the T1DM group, the UNC group exhibited increased BMI, FPG, and HbA1c, as well as notably increased uric acid (UA) levels. Furthermore, the homeostatic model assessment for insulin resistance (HOMA-IR) was significantly higher in the UNC group than in the HCs and T1DM groups but remained below the T2DM level.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBaseline anthropometric and biochemical variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHealthy controls (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnclassified diabetes (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT1DM(n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eT2DM(n\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value betweeen all groups\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.00(21.00\u0026ndash;24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.00(16.50\u0026ndash;27.00)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.00(16.00-26.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e20.00(15.00\u0026ndash;22.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes duration (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50 (0.87\u0026ndash;10.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00(0.75-12.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e24.00(4.00\u0026ndash;24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale/Female (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6/12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7/6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.85(19.27\u0026ndash;21.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.95(24.48\u0026ndash;30.26)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.06(19.28\u0026ndash;25.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e26.37(23.38\u0026ndash;28.45)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFBG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.73(4.46\u0026ndash;4.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.52 (6.19\u0026ndash;10.30)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.04(5.49\u0026ndash;11.64)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.20(6.94-12.00)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePBG (mmpl/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.85(4.60\u0026ndash;6.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.97(8.78\u0026ndash;18.78)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.44(8.67\u0026ndash;25.66)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.18(7.14\u0026ndash;17.51)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.05(4.67\u0026ndash;5.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.65 (7.72\u0026ndash;10.47)\u003csup\u003e*^\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.40(6.70\u0026ndash;10.60)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.00(5.20\u0026ndash;9.80)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFCP (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61(1.41\u0026ndash;2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90 (1.68\u0026ndash;2.57)\u003csup\u003e#^\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11(0.01\u0026ndash;0.49)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.83(1.97\u0026ndash;3.92)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCP (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.92(5.54\u0026ndash;10.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.53 (2.98\u0026ndash;5.28)\u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14(0.01\u0026ndash;2.44)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.12(5.98\u0026ndash;39.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.90(3.32\u0026ndash;4.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.75 (3.74\u0026ndash;5.21)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.32(3.96\u0026ndash;4.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.17 (3.67\u0026ndash;5.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62(0.56\u0026ndash;0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40 (0.80\u0026ndash;1.89)\u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85(0.69\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.08 (1.29\u0026ndash;2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38(1.30\u0026ndash;1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12 (0.90\u0026ndash;1.46)\u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55(1.23\u0026ndash;1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02(0.91\u0026ndash;1.07)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.98(1.68\u0026ndash;2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.66 (1.99\u0026ndash;3.75)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.52(2.12\u0026ndash;2.90)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.19(2.80\u0026ndash;3.56)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUA (\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e313.00(255.75\u0026ndash;369.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e362.50(274.00-445.00)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286.00(230.00-345.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e374.00(362.00-481.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33(0.93\u0026ndash;1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.63 (2.13\u0026ndash;5.91)\u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41(0.11\u0026ndash;0.86)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.94(3.10\u0026ndash;7.90)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMA-\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.72(90.18-119.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.19(29.98\u0026ndash;85.25)\u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.81(1.46\u0026ndash;15.21)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.46(23.88-133.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eThe data are presented as the median (25th\u0026ndash;75th percentile). *versus healthy controls, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; #versus T1DM patients, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ^versus T2DM patients, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Abbreviations: T1DM, type 1 diabetes mellitus; T2DM, type 2 diabetes mellitus; BMI, body mass index; FBG, fasting blood glucose; PBG, postprandial blood glucose; HbA1c, hemoglobin A1c; FCP, fasting C-peptide; PCP, postprandial C-peptide; TC, cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; UA, uric acid; HOMA-IR, homeostasis model assessment for insulin resistance; HOMA-\u0026beta;, homeostasis model assessment-\u0026beta;.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eStructural modulation of the gut microbiota in the four groups\u003c/h2\u003e\n \u003cp\u003eFirst, we analyzed the microbial diversity of the four groups. The Chao index, as well as the Shannon and Simpson indices, which indicate community richness, suggested no significant difference in bacterial richness across the groups (healthy controls: 4118\u0026thinsp;\u0026plusmn;\u0026thinsp;504.9; T1DM patients: 3825\u0026thinsp;\u0026plusmn;\u0026thinsp;828.7; T2DM patients: 4225\u0026thinsp;\u0026plusmn;\u0026thinsp;828.1; UNC patients: 3847\u0026thinsp;\u0026plusmn;\u0026thinsp;688.9; P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eA and Figure \u003cspan\u003eS1\u003c/span\u003e). Principal coordinate analysis (PCoA) based on the Bray\u0026ndash;Curtis distance revealed significant differences in the overall microbial features across the four groups (PERMANOVA, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eB). The bacterial community structure in adult-onset UNC patients was significantly distinct from that in HC and T1DM patients (PERMANOVA, T1DM vs HC: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; T2DM vs HC: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; UNC vs HC: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; T1DM vs UNC: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019), underscoring the unique microbial composition associated with UNC.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eTaxonomic changes in microbial composition in adult-onset UNC patients\u003c/h2\u003e\n \u003cp\u003eNext, we analyzed the microbial composition at different taxonomic levels, with the phylum and family compositions shown in Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eC and \u003cspan\u003e2\u003c/span\u003eD. \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e dominated across all groups, followed by \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e. Significantly, unclassified diabetes patients had increased levels of \u003cem\u003eActinobacteria\u003c/em\u003e (0.5636% in healthy controls vs. 3.331% in unclassified diabetes patients; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0005314, FDR-adjusted\u0026thinsp;=\u0026thinsp;0.03154) and \u003cem\u003eProteobacteria\u003c/em\u003e (3.057% in healthy controls vs. 6.869% in unclassified diabetes patients; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001644, FDR-adjusted\u0026thinsp;=\u0026thinsp;0.0515) compared with healthy controls.\u003c/p\u003e\n \u003cp\u003eLEfSe analysis was employed to discern differentially abundant microbial species among HCs, T1DM patients, T2DM patients, and UNC patients. A total of 205, 198, and 190 species were differentially abundant between adult-onset T1DM patients and HCs, between T2DM patients and HCs, and between UNC patients and HCs, respectively (LDA value\u0026thinsp;\u0026gt;\u0026thinsp;2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Tables S1-3). To determine the potential influence of host factors on microbial composition, we conducted a redundancy analysis (RDA) to ascertain potential confounders within these groups. Key host factors, including age, sex, BMI, and diabetes duration, were integrated into the RDA model. Our analysis revealed that, even after adjusting for these confounding factors, 21 taxa in adult-onset UNC patients exhibited significant differential abundance compared to healthy controls (HCs) (LDA value\u0026thinsp;\u0026gt;\u0026thinsp;2, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003efdr\u003c/em\u003e\u003c/sub\u003e \u0026lt; 0.1). Among these, 6 taxa were particularly enriched in UNC patients, including \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eEnterobacteriaceae\u003c/em\u003e. These taxa are associated with metabolites involved in carbohydrate and amino acid metabolism, suggesting a disturbed gut microbiome\u0026apos;s involvement in carbohydrate and amino acid metabolic pathways, potentially contributing to GDM [\u003cspan\u003e17\u003c/span\u003e], while there was a notable depletion of 15 species, such as \u003cem\u003eFlintibacter, Butyrivibrio_proteoclasticus\u003c/em\u003e, \u003cem\u003es_Clostridium_sp_AF27_2AA\u003c/em\u003e and \u003cem\u003es_Clostridium_sp_AM33_3\u003c/em\u003e [\u003cspan\u003e18\u003c/span\u003e\u0026ndash;\u003cspan\u003e20\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eE). Additionally, we identified critical functional alterations in the gut microbiota of adult-onset UNC patients. There was a significant enrichment of carbon metabolism pathways in these individuals compared to healthy controls, indicating a distinctive metabolic signature. Moreover, the amino sugar and nucleotide sugar metabolism pathways were also significantly enriched in UNC patients compared to those in T1DM and T2DM patients. These findings suggest that the gut microbiota is involved in UNC pathogenesis and shed light on metabolic dysregulation in this disease (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eAssociations of the microbiota with serum metabolites and lipids\u003c/h2\u003e\n \u003cp\u003eWe observed significant differences in serum metabolites between patients with diabetes and HCs (Figure S3). Specifically, the numbers of enriched differentially abundant metabolites in the UNC, T1DM, and T2DM groups compared to those in the HC group were 9, 12, and 18, respectively (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003efdr\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.1) (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eA). Notably, metabolites such as indolelactate, 3-hydroxyisovalerate, acetylcamitine, and 2-hydroxyisocaproate were more abundant in the UNC and T2DM groups than in the HC group. These metabolites are positively associated with the risk of developing T2DM [\u003cspan\u003e21\u003c/span\u003e\u0026ndash;\u003cspan\u003e23\u003c/span\u003e]. Additionally, we conducted an analysis of serum lipids and revealed significant differences across the groups (Figure S4), highlighting the metabolic distinctions inherent to diabetes. In patients with UNC, we identified 50 differential lipids, predominantly triglycerides (TGs), which were increased (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eC). Subsequent correlation analysis explored the relationships between differentially abundant bacteria and metabolites. This revealed that bacteria enriched in HCs had a strong positive correlation with HC-enriched metabolites but exhibited a negative correlation with diabetes-enriched metabolites (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eB). Notably, a decrease in carnitine and its derivatives, such as valerylcarnitine and lauroylcarnitine, was observed in UNC patients. These compounds are known to enhance glucose utilization and improve lipid parameters and oxidative stress markers, suggesting their potential protective role against metabolic disruptions in UNC (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eB) [\u003cspan\u003e24\u003c/span\u003e]. In individuals with adult-onset UNC, an increase in specific metabolic markers, including 3-hydroxybutyric acid, BCAAs, and their catabolic intermediates, was noted. These markers have been associated with an increased risk of transitioning from normoalbuminuria to macroalbuminuria and CKD [\u003cspan\u003e25\u003c/span\u003e\u0026ndash;\u003cspan\u003e27\u003c/span\u003e]. The abundances of bacteria such as \u003cem\u003es_Ruminococcus_torques\u003c/em\u003e and \u003cem\u003es_Lachnospiraceae_bacterium_8_1_57FAA\u003c/em\u003e were positively correlated with the abundances of metabolites such as 3-hydroxyisovalerate and 3-hydroxybutyric acid, suggesting an increased likelihood of complex diabetic nephropathy in UNC patients. TG and PE were enriched in the UNC and T2DM groups. Additionally, a strong positive correlation between bacteria and lipids (TG and PE) in the UNC and T2DM groups indicates potential parallels in their pathogenic processes (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eAssociations of the altered microbes and metabolites with clinical parameters\u003c/h2\u003e\n \u003cp\u003eTo understand the role of the gut microbiota in the progression of diabetes, we analyzed the associations between clinical parameters and differentially abundant bacteria or metabolites in the four groups. We discovered that certain taxa related to adult-onset T2DM, including \u003cem\u003eStreptococcaceae\u003c/em\u003e [\u003cspan\u003e28\u003c/span\u003e] and \u003cem\u003eActinomycetaceae\u003c/em\u003e [\u003cspan\u003e29\u003c/span\u003e], were significantly correlated with glucose metabolism and pancreatic beta cell function, corroborating previous findings. These taxa had a positive correlation with PCP and FCP (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eE). In the UNC cohort, bacteria such as \u003cem\u003eRuminococcus__torques\u003c/em\u003e, \u003cem\u003eLachnospiraceae_bacterium_8_1_57FAA\u003c/em\u003e, and Nitrobacteraceae were positively associated with PBG and FCP. Furthermore, we discovered novel associations between adult-onset UNC and increased metabolites such as 2-hydroxy-3-methylbutyrate, 2-hydroxyisobutyraten, and 3-hydroxyisovalerate, which are all positively correlated with PBG, FBG, and FCP. Particularly in UNC, high levels of 3-hydroxyisovalerate, 3-hydroxyisobutyrate, and phenylalanine were strongly related to blood uric acid, indicating their potential role in renal function. In UNC, we observed an enrichment of metabolites integral to amino acid metabolism, including 2-hydroxy-3-methylbutyric acid, cysteine, and phenylalanine. This enrichment aligns with an increase in bacterial pathways for amino sugar metabolites, providing insight into the metabolic landscape of UNC. In T2DM patients, elevated levels of D-fructose, D-glucose, and D-mannose were linked to key glucose and lipid metabolism parameters (Figure S5A). Moreover, TG, which was significantly elevated in T2DM patients, correlated strongly with glucose metabolism (Figure S5B). These findings indicate potential links between the gut microbiota, metabolites, and pancreatic beta cell autoimmunity in UNC (Figure S6), suggesting a heightened risk of diabetic nephropathy in UNC patients and shared pathogenetic elements between UNC and T2DM.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eMultiomic classifier discriminating patients with adult-onset UNC from patients in the other three groups\u003c/h2\u003e\n \u003cp\u003eTo ascertain the potential of the gut microbiota and metabolites as biomarkers for the differential diagnosis of diabetes, we constructed random forest models based on changes in fecal taxonomic or metabolic features between HCs and UNC patients (Supplementary Table S4). The model revealed a bacterial signature of 5 distinct species that could differentiate UNC patients from HCs, with an area under the curve (AUC) of 0.66 (95% CI 0.45\u0026ndash;0.87) (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eA). An additional random forest model was assessed for its diagnostic efficacy utilizing a combination of 7 serum biomarkers, including 3 metabolites and 4 lipids. Notably, this model produced an AUC of 0.73 (95% CI 0.53\u0026ndash;0.94) for distinguishing patients with adult-onset UNC from HCs (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eB). Further enhancement of the model with a panel of five bacterial species, seven serum biomarkers, and three clinical parameters increased its discriminative power, yielding an AUC of 0.94 (95% CI 0.85\u0026ndash;1) in differentiating UNC patients from HCs (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eC), demonstrating the potential of this comprehensive approach for accurate diagnosis.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eEason RJ \u003cem\u003eet al\u003c/em\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] demonstrated that adults diagnosed with type 1 diabetes who are negative for islet antibodies have genetic and C-peptide characteristics that are intermediate between those of type 1 and type 2 diabetes. This suggests a significant misclassification within this cohort, potentially including individuals with islet antibody-negative autoimmune (type 1) diabetes as well as those with nonautoimmune (predominantly type 2) diabetes who have been erroneously classified. Such misclassification can lead to inappropriate treatment regimens, including unnecessary lifelong insulin therapy, and hinder access to effective type 2 diabetes treatments.\u003c/p\u003e \u003cp\u003eCurrently, the high prevalence of type 2 diabetes in adults makes robustly discriminating true type 1 diabetes from atypical presentations of type 2 diabetes challenging. Some reported characteristics of type 1 diabetes in older adults, such as low islet autoantibody prevalence, may reflect the inadvertent study of those with and without autoimmune diabetes, and some research in this area suggests a need to combine clinical diagnosis with gut microbiota and metabolite profile tests in this setting [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe World Health Organization (WHO) introduced UNC in 2019 when there was no clear diagnostic category [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In this study, we revealed that unclassified diabetes patients have different gut microbiota and metabolite profiles than healthy individuals as well as classic T1DM and T2DM patients. Remarkably, the gut microbiota of unclassified diabetes patients displayed distinctive characteristics, with significantly increased abundances of \u003cem\u003es___Ruminococcus__torquess\u003c/em\u003e and \u003cem\u003eLachnospiraceae_bacterium_8_1_57FAA\u003c/em\u003e and decreased abundances of \u003cem\u003es__unclassified_g__Clostridium\u003c/em\u003e, \u003cem\u003es__Clostridium_sp__AF27_2AA\u003c/em\u003e and \u003cem\u003es__Clostridium_sp__AM33_3\u003c/em\u003e compared with those in the other groups. There was a clear correlation among the gut microbiota, serum metabolites, and clinical phenotypes. Furthermore, the gut bacterial pathway of \u0026ldquo;Amino sugar and nucleotide sugar metabolism\u0026rdquo; was significantly enriched in adult-onset UNC patients, differentiating them from T1DM and T2DM patients and suggesting that unique metabolic processes are involved in UNC.\u003c/p\u003e \u003cp\u003eIn patients with unclassified diabetes, we detected an enrichment of branched-chain amino acids (BCAAs) and their derivatives in the blood, which correlated with glucose and lipid metabolism. Large human population studies have shown that a high intake of dietary BCAAs increases the risk of T2DM[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In our study, BCAAs and their derivatives might affect glucose metabolism and sensitivity in patients with unclassified diabetes, which was consistent with the functional differences in the bacteria. We found that, serologically, UNC was more similar to T2DM, but T2DM was dominated by TG enrichment and UNC by amino acid derivatives. Moreover, high levels of 3-hydroxyisovalerate and 3-hydroxyisobutyrate were strongly related to blood uric acid in the UNC group, which could suggest that unclassified diabetes patients had poor renal function in the subsequent course. Therefore, this finding suggests that patients with unclassified diabetes mellitus need to pay attention to changes in renal function in later follow-up.\u003c/p\u003e \u003cp\u003eImportantly, we developed a prediction model for UNC based on gut microbial signatures and metabolic features, which demonstrated high accuracy in distinguishing patients with this disease from HCs. Furthermore, we have shown that the predictive power of the model can be enhanced by incorporating metabolites, and the utilization of the \"5\u0026thinsp;+\u0026thinsp;7\u0026thinsp;+\u0026thinsp;3\" model enables simultaneous differentiation of patients with UNC from HCs. The metabolic composition of the \"5\u0026thinsp;+\u0026thinsp;7\u0026thinsp;+\u0026thinsp;3\" model in UNC is similar to that of T2DM. However, the increasing prevalence of obesity among patients with T1DM due to environmental and lifestyle factors, the presence of ketosis-prone individuals in patients with T2DM and idiopathic T1DM, and the unavailability of autoantibody detection facilities in certain clinics pose challenges in accurately classifying different types of diabetes. In this regard, comprehending the metabolic and microbiota characteristics of unclassified diabetes mellitus patients is crucial for gaining insights into disease pathogenesis and prognosis.\u003c/p\u003e \u003cp\u003eAlthough our study provides valuable insights into unclassified diabetes, it has several limitations that should be considered. First, the cross-sectional design of our study cannot establish a causal relationship between the identified gut microbiota and adult-onset unclassified diabetes. Additionally, the relatively small sample size and the restriction of subjects to a specific ethnic population and geographic region may limit the generalizability of our results. Finally, despite our efforts to address confounding factors when comparing the three groups (sex- and age-matched patients with comparable demographic characteristics, antibiotic exposure and comorbidities), our findings could be influenced by other confounders, such as disease duration and dietary intake. Consequently, the significance of these findings should be confirmed through larger prospective follow-up studies involving more diverse ethnic populations and geographic regions.\u003c/p\u003e \u003cp\u003eIn summary, our study revealed distinct characteristics of the gut microbiota and metabolic profiles in patients with unclassified diabetes, distinguishing them from healthy individuals. Additionally, we observed correlations between these profiles and aspects of glucose metabolism and islet function, suggesting their potential involvement in the development and progression of unclassified diabetes. Importantly, we also found that patients with unclassified diabetes may experience impaired renal function in the future, highlighting the need for careful monitoring. Overall, the findings from this study provide valuable insights that could contribute to the classification and comprehension of diabetes through the identification of novel pathways.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZ.\u0026nbsp;conceived the study, recruited volunteers, performed the statistical analysis and data interpretation, wrote the manuscript, and made key modifications. L.W. participated in the\u0026nbsp;selection of\u0026nbsp;the samples used, analyzed the microbiota and metabolomics data, and modified the manuscript. Z.Z. participated in the study design, recruited volunteers, retained samples, extracted data,\u0026nbsp;and participated in the writing and modification of the manuscript. D.L.\u0026nbsp;recruited and supervised the participants and performed all clinical procedures. R.H. and L.Y. participated in the recruitment of volunteers and provided the samples used in this study. W.Gu. conceived the study, developed the experimental design, wrote the manuscript, and provided critical revision. All the authors have read and approved the final version of the manuscript. W.Gu. is the guarantor of this work and, as such, had 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by\u0026nbsp;grant\u0026nbsp;82070864 from the National Natural Science Foundation of China and grant 22Y11904800 from the Shanghai Municipal Science and Technology Major Projects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBioSample accession number (NCBI) of metagenomic data is PRJNA1099928.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experiments were performed in accordance with the Declaration of Helsinki principles. The study was approved by the Medical Ethics Committee of Ruijin Hospital of Shanghai jiaotong University (NO. 2013, 050). The children\u0026rsquo;s feces were collected only after their parents signed an informed consent to participate in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTomic D, Shaw JE, Magliano DJ: \u003cstrong\u003eThe burden and risks of emerging complications of diabetes mellitus\u003c/strong\u003e. \u003cem\u003eNat Rev Endocrinol \u003c/em\u003e2022, \u003cstrong\u003e18\u003c/strong\u003e(9):525-539.\u003c/li\u003e\n\u003cli\u003eLow S, Chin MC, Deurenberg-Yap M: \u003cstrong\u003eReview on Epidemic of Obesity\u003c/strong\u003e. \u003cem\u003eAnnals of the Academy of Medicine, Singapore \u003c/em\u003e2009, \u003cstrong\u003e38\u003c/strong\u003e(1):57-65.\u003c/li\u003e\n\u003cli\u003eTuomi T, Santoro N, Caprio S, Cai M, Weng J, Groop L: \u003cstrong\u003eThe many faces of diabetes: a disease with increasing heterogeneity\u003c/strong\u003e. \u003cem\u003eThe Lancet \u003c/em\u003e2014, \u003cstrong\u003e383\u003c/strong\u003e(9922):1084-1094.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eClassification of diabetes mellitus. 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\u003cstrong\u003e38\u003c/strong\u003e(7).\u003c/li\u003e\n\u003cli\u003eZheng Y, Li Y, Qi Q, Hruby A, Manson JE, Willett WC, Wolpin BM, Hu FB, Qi L: \u003cstrong\u003eCumulative consumption of branched-chain amino acids and incidence of type 2 diabetes\u003c/strong\u003e. \u003cem\u003eInternational Journal of Epidemiology \u003c/em\u003e2016, \u003cstrong\u003e45\u003c/strong\u003e(5):1482-1492.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diabetes mellitus, Metagenome, Gut microbiome, Metabolites analysis","lastPublishedDoi":"10.21203/rs.3.rs-4200061/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4200061/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe classification of diabetes has become increasingly intricate. In 2019, the World Health Organization (WHO) introduced a new category called \"unclassified diabetes\" to address this complexity. Our study, employing a multiomics approach, aimed to delineate the distinct gut microbiota and metabolic characteristics in individuals under the age of 30 with unclassified diabetes, thus shedding light on the underlying pathophysiological mechanisms involved.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis age- and sex-matched case‒control study involved 18 patients with unclassified diabetes, 18 patients with classic type 1 diabetes, 13 patients with type 2 diabetes, and 18 healthy individuals. Metagenomics facilitated the profiling of the gut microbiota, while untargeted liquid chromatography‒mass spectrometry was used to quantify the serum lipids and metabolites.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur findings revealed a unique gut microbiota composition in unclassified diabetes patients, marked by a depletion of \u003cem\u003eButyrivibrio proteoclasticus\u003c/em\u003e and \u003cem\u003eClostridium\u003c/em\u003e and an increase in \u003cem\u003eRuminococcus torques\u003c/em\u003e and \u003cem\u003eLachnospiraceae bacterium 8_1_57FAA\u003c/em\u003e. Comparative analysis identified exclusive bacteria, serum metabolites, and clinical parameter modules within the unclassified diabetes cohort. Notably, the gut microbiota structure of patients with unclassified diabetes resembled that of type 2 diabetes patients, especially in terms of disrupted lipid and branched-chain amino acid metabolism.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDespite sharing certain metabolic features with type 2 diabetes, unclassified diabetes presents unique features. The distinct microbiota and metabolites in unclassified diabetes patients suggest a significant role in modulating glucose, lipid, and amino acid metabolism, potentially influencing disease progression. Further longitudinal studies are essential to explore therapeutic strategies targeting the gut microbiota and metabolites to modify the disease trajectory.\u003c/p\u003e","manuscriptTitle":"Characteristics of the Gut Microbiota and Metabolism in Patients with Unclassified Diabetes in Adults: A Case‒Control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-24 17:43:07","doi":"10.21203/rs.3.rs-4200061/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"58d0017e-ed1d-444b-b4d0-9ffa39f5413e","owner":[],"postedDate":"April 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-24T17:43:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-24 17:43:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4200061","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4200061","identity":"rs-4200061","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}