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

doi:10.21203/rs.3.rs-4200061/v1

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

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.

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 < 0.05, VIP > 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].

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/m²)	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. 2A 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. 2B). 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. 2C and 2D. 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. 2E). 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. 3A). 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. 3C). 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. 3B). 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. 3B) [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. 3D).

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. 3E). 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. 4A). 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. 4B). 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. 4C), demonstrating the potential of this comprehensive approach for accurate diagnosis.

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.

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.

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No competing interests reported.

Supplementalmaterial.docx

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

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

INTRODUCTION

RESEARCH DESIGN AND METHODS

Study Participants and Recruitment

Metagenomic analysis of the human gut microbiome

Nontargeted lipidomic analysis

Serum metabolites analysis

Statistical analyses

RESULTS

Anthropometric and biochemical measurements of different types of diabetes

Structural modulation of the gut microbiota in the four groups

Taxonomic changes in microbial composition in adult-onset UNC patients

Associations of the microbiota with serum metabolites and lipids

Associations of the altered microbes and metabolites with clinical parameters

Multiomic classifier discriminating patients with adult-onset UNC from patients in the other three groups

DISCUSSION

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1