Bidirectional modulation of TCA cycle metabolites and anaplerosis by metformin and its combination with SGLT2i

Bidirectional modulation of TCA cycle metabolites and anaplerosis by metformin and its combination with SGLT2i | 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 Bidirectional modulation of TCA cycle metabolites and anaplerosis by metformin and its combination with SGLT2i Jonathan Adam, Makoto Harada, Marcela Covic, Stefan Brandmaier, and 24 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3931333/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Metformin and sodium-glucose-cotransporter-2 inhibitor (SGLT2i) are cornerstone therapies for managing hyperglycemia in diabetes, yet their nuanced impacts on metabolic processes, particularly in the citric acid (TCA) cycle and its anaplerotic pathways, are not fully delineated. This study aims to investigate the tissue-specific metabolic effects of metformin, both as a monotherapy and in combination with SGLT2i, on the TCA cycle and associated anaplerotic reactions. Methods Our study employed a three-pronged approach: first, comparing metformin-treated diabetic mice (MET) with vehicle-treated controls (VG) and non-diabetic wild types (WT) to identify metformin-specific metabolic changes; second, assessing these changes in human cohorts (KORA and QBB) and a longitudinal KORA study of metformin-naïve patients; third, contrasting MET with those on combination therapy (SGLT2i + MET). Metabolic profiling was conducted on 716 metabolites from plasma, liver, and kidney tissues post-treatment. Linear regression analysis and Bonferroni correction were used for rigorous statistical evaluation across all comparisons, complemented by pathway analyses to elucidate the pathophysiological implications of the metabolites involved. Results Metformin monotherapy was significantly associated with upregulation of TCA cycle intermediates, such as malate, fumarate, and α-ketoglutarate (α-KG), in plasma, along with anaplerotic substrates including hepatic glutamate and renal 2-hydroxyglutarate (2-HG) in diabetic mice. Conversely, downregulated hepatic taurine was observed. However, the addition of SGLT2i reversed these metabolic effects, indicating a complex interplay between these antidiabetic drugs in regulating the central energy metabolism. Human T2D subjects on metformin therapy exhibited significant systemic alterations in metabolites, including increased malate but decreased citrulline. The drugs' bidirectional modulation of TCA cycle intermediates appeared to influence four key anaplerotic pathways linked to glutaminolysis, tumorigenesis, immune regulation, and antioxidative responses. Conclusion This study elucidates the specific metabolic consequences of metformin and SGLT2i on the TCA cycle and beyond, reflecting potential impacts on the immune system. Metformin shows promise for its anti-inflammatory properties, while the addition of SGLT2i may provide liver protection in conditions like non-alcoholic fatty liver disease (NAFLD). These observations highlight the potential for repurposing these drugs for broader therapeutic applications and underscore the importance of personalized treatment strategies. Pharmacometabolomics metformin SGLT2 inhibitors TCA cycle anaplerosis glutaminolysis anti-inflammatory effects antioxidant responses renal metabolism non-alcoholic fatty liver disease (NAFLD) type 2 diabetes personalized medicine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Metformin is widely recognized as a primary treatment option for non-insulin-dependent type 2 diabetes (T2D) [ 1 ]. Beyond its established benefits in blood sugar control and weight management, recent research has unveiled the pleiotropic effects of metformin, including potential anti-cancer effects, such as a decreased proliferation of cancer cells [ 2 , 3 ], cardiovascular benefits such as lowered low-density lipoprotein cholesterol [ 4 ], and reduced inflammation and fibrosis [ 5 , 6 ]. However, it is important to acknowledge that metformin is not without potential side effects, including gastrointestinal issues and, in rare cases, lactic acidosis [ 7 ]. Clinical guidelines now recommend the use of sodium-glucose-cotransporter-2 inhibitors (SGLT2i) as add-on therapy with metformin to enhance glycemic control [ 8 , 9 ]. However, the effects of metformin monotherapy and its combination with SGLT2i on tissue-specific metabolism, particularly the citric acid, also known as tricarboxylic acid (TCA) cycle and anaplerosis in the liver and kidneys, have not been extensively investigated. Both the liver and kidneys are integral to metformin's pharmacokinetics: the liver primarily processes the drug post-absorption, and the kidneys subsequently handle its clearance [ 10 ]. SGLT2 inhibitors, on the other hand, impede renal glucose reabsorption, promoting its urinary excretion. Appreciating the tissue-specific actions of metformin and its interactions with SGLT2i is essential to refine treatment modalities for T2D and to fully understand the metabolic shifts induced by these drugs. Pharmacometabolomics studies in animals and humans have provided further insights into the comprehensive effects of metformin, highlighting its significant influence on nitric oxide (NO) and urea cycles [ 11 – 13 ]. Non-targeted metabolomics has played a pivotal role in the research and development of biomarkers and screening assays for T2D [ 14 – 16 ]. Previous studies utilizing this approach have observed significantly lower citrulline levels in diabetic and non-diabetic individuals treated with metformin, as well as in peripheral tissues of metformin-treated diabetic mice [ 12 , 13 ]. These findings suggest that metformin treatment alters urea and NO production by activating the endothelial NO synthase (eNOS) and NO biosynthesis, which could mediate its benefits in reducing cardiovascular sequels of T2D [ 17 ]. This non-targeted metabolomics study aims to examine the tissue-specific effects of metformin, both as a monotherapy and in combination with SGLT2i, on the plasma, hepatic and renal metabolite profiles in obese diabetic mice. We seek to corroborate our findings through the analysis of serum and plasma from T2D patients undergoing metformin treatment in the longitudinal KORA (Cooperative Health Research in the Region of Augsburg) study and the cross-sectional QBB study (Qatar Biobank). Our research endeavors to offer clinically relevant insights that could pave the way for personalized diabetes management approaches. Methods Study design Our study comprised three interconnected components to investigate the metabolic effects of metformin monotherapy and its combination with SGLT2i in both animal models and human subjects. First, we assessed the metabolic alterations in diabetic mice treated with metformin (MET) compared to vehicle-gavaged diabetic mice (VG), focusing on the analysis of significant tissue-specific metabolite differences (plasma, liver, kidney) between the MET and VG groups. Additionally, we evaluated whether the metabolic changes attributed to metformin were independent of leptin receptor (Lepr) deficiency by comparing the VG group to wild type (WT) mice. Second, we further examined the metformin-associated metabolites, initially identified in murine plasma, in two human cross-sectional studies: KORA and QBB. In these studies, we compared metformin-treated type 2 diabetes patients (mt-T2D) with non-antidiabetic drug-treated patients (ndt-T2D). Metabolites replicated in these cross-sectional cohorts were further validated in a longitudinal KORA study, focusing on metformin-naïve participants at baseline. Third, we compared the metabolic profiles of mice treated with combination therapy (SGLT2i + MET) versus those on metformin monotherapy (MET) across the three tissues. For each of these comparisons, we performed linear regression analysis for pairwise comparisons and applied Bonferroni correction to address multiple testing concerns. Lastly, we conducted tissue-specific pathway analyses to elucidate the biological relevance of the identified metabolites, thereby enhancing our understanding of the metabolic effects of these antidiabetic therapies. Metformin and SGLT2i intervention study Pharmacological studies were conducted in compliance with FELASA protocols using 40 male mice including 10 wild-type (WT) and 30 db/db (BKS.Cg Dock7m+/+ Leprdb/J) mice. Starting from 3 weeks of age, all db/db mice were fed a high-fat diet (HFD) (S0372 E010, ssniff Spezialdiäten, Soest, Germany) [ 18 ]. After 3 weeks of being on the HFD, the 6-week-old animals were treated for 2 weeks via gavage once a day between 5:00–6:00 p.m. before the onset of the dark phase (6:00 p.m.). The treatment groups consisted of 10 vehicle-gavaged (VG) diabetic mice that were vehicle-gavaged with a solution of 5% solutol and 95% hydroxyethylcellulose, 10 MET mice treated with 300 mg/kg metformin (Sigma Aldrich, Taufkirchen, Germany), and 10 SGLT2i + MET mice treated with 30 mg/kg SGLT2i (AVE2268, Sanofi AG, Frankfurt, Germany) and 300 mg/kg metformin (Sigma Aldrich, Taufkirchen, Germany) [ 19 ]. After the completion of the treatment period, which lasted for 2 weeks, the 8-week-old mice (± 3 days) underwent a fasting period of four hours before being sacrificed. An overdose of isoflurane was used for euthanasia, and immediate blood and organ collection were performed as previously reported [ 18 , 20 ]. Murine plasma was prepared from whole blood by centrifugation at 4°C, and tissues were freeze-clamped. All samples were stored at -80°C until further analyses. Characteristics of mouse studies In the mouse study, compared to WT mice, all three groups of diabetic mice (VG, MET, SGLT2i + MET) exhibited characteristic features of obesity, including higher body and liver weights (Table 1 ). They also displayed hyperglycemia, as evidenced by elevated blood glucose and insulin levels. Dyslipidemia was observed with elevated cholesterol and triglyceride levels, and systemic inflammation was evident with elevated C-reactive protein levels, as shown in Table 1 . Following two weeks of daily monotherapy with metformin or combination therapy in diabetic mice, significant reductions in blood glucose levels were observed. The MET mice showed a 29.1% reduction in blood glucose levels, while the SGLT2i + MET mice exhibited a substantial 70.0% reduction. In contrast, the VG and WT mice experienced modest reductions of 4.7% and 1.9%, respectively (Table 1 ). Furthermore, when comparing mice treated with monotherapy metformin, the SGLT2i + MET mice displayed lower body and liver weights, insulin levels, and triglyceride levels. However, they exhibited higher cholesterol and C-reactive protein levels (Table 1 ). These observations suggest that the combination therapy had additional metabolic effects compared to monotherapy with metformin alone. Table 1 Characteristics of the murine samples. Means (standard deviation) of clinical variables in four mice groups. Clinical parameters WT (n = 10) VG (n = 10) MET (n = 10) SGLT2i + MET (n = 10) Body weight, g 22.0 (0.6) 47.9 (2.4) 47.8 (2.1) 46.8 (1.7) Liver weight, g 1.02 (0.09) 2.56 (0.29) 2.61 (0.09) 2.36 (0.19) Kidney weight, g 0.16 (0.02) 0.20 (0.02) 0.21 (0.02) 0.21 (0.02) Blood glucose † , mg/dL 108.8 (14.3) 442.5 (65.1) 454.8 (60.2) 439.1 (62.0) Blood glucose, mg/dL 106.7 (16.8) 421.6 (41.2) 322.6 (92.7) 129.9 (46.3) Changed glucose (%) 1.9 4.7 29.1 70.1 Insulin, µg/l 1.03 (0.4) 7.76 (2.3) 7.86 (1.7) 6.78 (2.4) HDL cholesterol, mg/dL 84.3 (8.6) 125.3 (13.1) 135.5 (9.8) 156.0 (22.9) LDL cholesterol, mg/dL 14.5 (2.1) 18.76 (3.7) 19.49 (2.6) 25.19 (7.6) Total cholesterol, mg/dL 100.6 (12.2) 153.2 (16.1) 164.5 (12.6) 188.8 (29.9) Triglycerides, mg/dL 122.2 (24.5) 224.8 (106.5) 262.4 (63.4) 199.9 (46.7) C-reactive protein, mg/l 5.4 (1.1) 13.1 (3.3) 14.0 (4.1) 17.1 (3.7) Abbreviations: WT, wild type mice; VG, vehicle‑gavaged diabetic mice; MET, metformin-treated diabetic mice; SGLT2i+MET, Sodium-glucose-cotransporter-2-inhibitor and metformin-treated diabetic mice; HDL, high-density lipoprotein; LDL, low-density lipoprotein. † 6 weeks. KORA and QBB human studies KORA is a population-based cohort conducted in southern Germany [21]. The baseline survey, Survey 4 (S4) included 4,261 individuals who were examined between 1999 and 2001. The follow-up survey (F4), took place from 2006 to 2008 and involved 3,080 individuals [22]. For the present analysis, only participants with non-targeted metabolite profiles were included. Additionally, individuals who did not undergo overnight fasting, had type 1 diabetes or drug-induced diabetes, were treated with insulin or both insulin and metformin, took glucose-lowering oral medication other than metformin, or had missing covariates in the fully adjusted model were excluded. In the cross-sectional F4 investigation, patients with T2D were included. For the longitudinal analysis (KORA S4 to F4), participants who were naïve to metformin at baseline S4 and had non-targeted metabolite profiles at both the S4 and F4 surveys were included. QBB is a population-based study conducted in Qatar, which was established in 2012 [23,24]. Characteristics of cross-sectional human studies Cross-sectional validation of our mouse findings was conducted in the KORA and QBB human studies, which involved a total of 478 patients with T2D and available metabolite profiles. In the KORA study, we examined 184 participants from the KORA F4 study who had T2D, including 70 individuals who underwent metformin therapy (mt-T2D), as shown in Table 2. The QBB study comprised a sample set of 294 individuals, including 146 with mt-T2D (Table 2). Comparing the mt-T2D patients to metformin-naïve T2D patients in both studies, we observed that the mt-T2D patients were older and exhibited higher levels of HbA1c and fasting glucose. These observations suggest that the mt-T2D patients had more advanced dysglycemia compared to the metformin-naïve T2D patients (Table 2). Table 2. Characteristics of the KORA F4 and QBB cross-sectional study samples (N = 478). Percentages of individuals or means (standard deviation) are shown for each variable and each group. KORA F4 study QBB study Clinical parameters ndt-T2D (N = 114) mt-T2D (N = 70) ndt-T2D (N = 148) mt-T2D (N = 146) Age, years 65 (7.1) 66.1 (7.7) 46.3 (10.1) 52.5 (9.2) Male, % 61 59 51,4 50,7 BMI, kg/m 2 31.0 (4.8) 32.0 (5.6) 31.7 (6.3) 31.3 (6.1) Physical active, % > 1 hour per week 48 37 - - High alcohol intake ‡ , % 25 19 - - Smoker, % 12 13 19,6 16,4 Systolic BP, mmHg 134.6 (19.1) 130.2 (18.1) 121.2 (16.6) 125.5 (15.5) Diastolic BP, mmHg 77.9 (10.5) 74.8 (9.8) 78.2 (11.8) 76.5 (10.2) HDL cholesterol, mg/dL 49.4 (11.5) 50.2 (9.7) 48.6 (13.6) 46.3 (11.0) LDL cholesterol, mg/dL 136.6 (36.1) 123.2 (27.2) 121.5 (36.8) 102.8 (36.1) Total cholesterol, mg/dL 214.4 (37.0) 201.8 (34.7) 201.1 (41.4) 181.2 (40.0) Triglycerides, mg/dL 172.2 (129.3) 174.1 (141.0) 157.3 (88.4) 153.8 (65.6) HbA 1c , % 6.3 (0.9) 6.8 (1.1) 6.6 (1.3) 7.6 (1.6) Fasting glucose, mg/dL 126.8 (30.5) 140.5 (34.9) - - Metformin usage, % 0 100 0 100 Fasting (min. 8 h) before blood draw, % ddddddddd ddddddddddd 100 100 27.7 27.4 Abbreviations: ndt-T2D, non-anti-diabetic drug-treated type 2 diabetes; mt-T2D, metformin treated type 2 diabetes; BMI, body mass index; BP, blood pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein. ‡ ≥ 20 g/day for women; ≥ 40 g/day for men Characteristics of longitudinal human studies Longitudinal validation was conducted in the prospective KORA study, spanning from the baseline survey (S4) to the 7-year follow-up (F4). In this analysis, we compared 34 patients with T2D who initiated metformin therapy after the S4 examination to a group of 628 metformin-naïve individuals at each time point. The characteristics of these two groups were assessed at the S4 and F4 surveys. As shown in Table 3, the mt-T2D patients were found to be older, more sedentary, and had a higher prevalence of obesity compared to the group of 628 metformin-naïve individuals. Additionally, mt-T2D patients displayed higher blood pressure, triglyceride levels, and glycemic parameters at both the S4 and F4 surveys (Table 3). It's important to note that during the longitudinal analysis, adjustments were made for various potential confounding factors, including age, body mass index, physical activity, systolic blood pressure, HbA1c, fasting glucose levels, high-density lipoprotein cholesterol, triglycerides among others. Table 3. Characteristics of the KORA S4 à F4 prospective study samples (N = 662). Percentages or means (standard deviation) are shown for each variable and each group. Clinical parameters Baseline S4 Follow-up F4 w/o metformin w/o metformin w/o metformin mt-T2D N 628 34 628 34 Age, years 61.4 (4.3) 63.5 (3.8) 68.5 (4.3) 70.6 (3.9) Male, % 51 50 51 50 BMI, kg/m 2 27.9 (3.9) 32.4 (4.1) 28.2 (4.2) 31.8 (4.3) Physical active † , % 48 32 57 44 High alcohol intake ‡ , % 16 21 13 12 Smoker, % 13 9 8 3 Systolic BP, mmHg 132 (18.7) 145.5 (19.9) 128.2 (19.6) 131.5 (18.5) Diastolic BP, mmHg 80 (10.1) 83.6 (9.8) 75.1 (9.9) 74.8 (9.9) HDL cholesterol, mg/dL 59 (16.2) 53.9 (11.9) 56.7 (14.1) 52.3 (7.7) LDL cholesterol, mg/dL 155.1 (40.9) 145.5 (38.4) 142.5 (36.5) 124.4 (23.6) Total cholesterol, mg/dL 245.9 (42.0) 236.2 (41.5) 225.3 (40.4) 202.6 (33.8) Triglyceride, mg/dL 130.3 (76.6) 170.2 (175.2) 132.4 (83.2) 148.3 (164.6) HbA 1C , % 5.6 (0.3) 6.3 (0.9) 5.6 (0.5) 6.5 (0.7) Fasting glucose, mg/dL 99.7 (10.9) 127.6 (30.9) 100.8 (17.8) 126 (28.4) Metformin usage, % 0 0 0 100 Fasting 100 100 100 100 Abbreviations: w/o, without; BMI, body mass index; BP, blood pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein. † > 1 hour per week ‡ > 40 g/day in men; > 20 g/day in women Non-targeted metabolite profiling Non-targeted metabolite profiling was performed using samples from various murine tissues, KORA serum, and QBB plasma, using the Metabolon analytical system (Metabolon Inc., Durham, North Carolina, USA). Metabolon employed a non-targeted semi-quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) and gas chromatography mass spectrometry (GC-MS) platform for the identification of both structurally named and unknown molecules [16]. In this study, the same quality control (QC) criteria were applied [12]. Specifically, metabolites with more than 20% missing values were excluded for each tissue, as were samples and running days for metabolites with more than 10% missing values [12]. All relative ion counts were initially normalized for each tissue by each running day and then natural log transformed. After QC, a total of 716 metabolites were used, including 351 in plasma, 391 in the liver, and 447 in the kidney (Supplementary Table 1, Additional File 1). Among these three murine tissues, 136 metabolites overlapped, and approximately 86% of them were structurally named. Furthermore, there were 118 circulating-specific metabolites, 119 renal-specific metabolites, and 132 hepatic-specific metabolites (Supplementary Table 1, Additional File 1). Statistical analysis In the mouse study, three pairwise comparisons were conducted to assess the effects of mono- and combination therapy in diabetic mice (MET versus VG and SGLT2i+MET versus MET) and the impact of the diabetes-prone genetic background (VG versus WT mice). Linear regression analysis was used, with the relative metabolite concentration as the outcome variable and the animal grouping as the predictor variable. Metabolites were evaluated individually and separately for each tissue. All measured metabolite values were standardized (average = 0, standard deviation = 1). To account for multiple testing in the linear models, Bonferroni correction was applied. Only metabolites with a P -value below the cutoff of P = 0.05 / ’Number of metabolites after QC’ for each tissue were considered statistically different. This resulted in a cutoff of P < 1.42x10 ‑4 for plasma ( P < 0.05 / 351), P < 1.28x10 -4 for liver ( P < 0.05 / 391), P < 1.11x10 -4 for kidney ( P < 0.05 / 447). Please note that Bonferroni correction was applied to determine the significance of the observed associations, and nominal significance levels ( P < 0.05) were also considered for additional metabolites. For the human studies, potential risk factors and confounding parameters known to affect metabolite profiles were taken into account [12]. The basic model was adjusted for age and sex, while the full model included additional adjustments for body mass index, physical activity, high alcohol intake, smoking status, systolic blood pressure, HbA1c, fasting glucose levels, high-density lipoprotein cholesterol, triglycerides. Similar to the mouse study, Bonferroni correction was applied to account for multiple testing, and associations with a P -value below the cutoff of P < 0.05 / ’Number of validated metabolites’ were considered statistically significant. In the longitudinal study (S4 to F4), generalized estimating equations were used to validate the significant associations in both the basic and fully adjusted models. All R Core Team, 2022 were performed using R (version 4.0.3). Results Metformin effects on the blood metabolites in mouse and human studies In the mouse study, among the 351 metabolites analyzed, plasma levels of seven metabolites were significantly altered following two weeks of metformin treatment in db/db mice. These results were determined to be statistically significant after applying Bonferroni correction (Figure 1A, Supplementary Table 2, Additional File 1). Out of the seven metformin-associated metabolites, three were identified as intermediates of the TCA cycle, namely fumarate, malate, and α-ketoglutarate (α KG). Of these, six metabolites showed upregulation in response to metformin treatment (e.g., fumarate displayed a positive β-estimate in the linear regression analysis when comparing MET with VG mice as shown in Figure 1B, and the relative concentration in MET mice was higher than that of VG mice as displayed with boxplots in Figure 1C). Figure 1 Effects of metformin and leptin receptor mutation in murine plasma. A: Volcano plots of linear regression analysis results (β-estimates and P-values) for 351 plasma metabolites in two pairwise comparisons of MET with VG diabetic mice and VG with WT mice. The upper and lower dashed lines represent Bonferroni-corrected and uncorrected (P = 0.05) significance levels, respectively. B: Seven metformin-associated metabolites are shown. C: Boxplots of seven metabolites. Abbreviations: WT, wild type mice; VG, vehicle gavaged diabetic mice; MET, metformin-treated diabetic mice; 2-AB, 2-aminobutyrate; 4-HB, 4-hydroxybutyrate; α-KG, α-ketoglutarate. See also Supplementary Table 2, Additional File 1. In the comparison of VG with WT mice, none of the seven metabolites exhibited a significant difference after applying Bonferroni correction. However, the values of four of these metabolites (fumarate, malate, 4-hydroxybutyrate [4-HB], and uracil) were nominally affected ( P < 0.05) by the genetic background of the mice as well as HFD (Supplementary Table 2, Additional File 1). Interestingly, three metabolites (α‑KG, citrulline, and 2-aminobutyrate [2-AB]) did not show a significant difference between VG and WT mice. This suggests that the changes induced by metformin in these three metabolites in db/db mice were independent of the physiological consequences of the leptin receptor mutation besides the HFD. Figure 2 Cross-sectional and longitudinal analyses reveal specific pattern of metformin action in human serum and plasma. A and B: β-estimates and confidence intervals of three metabolites in KORA and QBB cross-sectional human studies. C: Mean relative residue of two metabolites in longitudinal KORA study based on the fully adjusted model (age, sex, BMI, physical activity, high alcohol intake, smoking status, systolic blood pressure, HbA1c, fasting glucose levels, high density lipoprotein cholesterol, triglycerides as well as the use of statins, beta blockers, angiotensin-converting-enzyme inhibitors, and angiotensin receptor blockers). Abbreviation: 2-AB, 2 aminobutyrate. See also Supplementary Tables 3,4, Additional File 1. In the human studies, three of the seven metformin-associated metabolites identified in murine plasma (malate, citrulline, and 2-AB) were measured in serum samples obtained from the KORA participants at both the S4 and F4 surveys. The analysis included a comparison between 70 individuals with mt-T2D and 114 individuals with ndt-T2D patients. Using a Bonferroni cutoff for significance ( P < 0.017) for the three analyzed metabolites, two of them, citrulline and malate, were found to be significantly different in the fully adjusted model (Figure 2A, Supplementary Table 3, Additional File 1). These findings were independently replicated in the plasma samples of patients from the QBB study, comparing 146 mt-T2D with 148 ndt-T2D patients (Figure 2B, Supplementary Table 3, Additional File 1). However, no significant correlation was observed for 2-AB in any of the comparisons (Figures 2A, 2B, Supplementary Table 3, Additional File 1). In the prospective KORA S4 to F4 study, the two metabolites that showed significant associations with metformin in the cross-sectional studies, namely malate and citrulline, were further investigated. The metformin-specific upregulation of malate and downregulation of citrulline were prospectively validated in serum samples obtained from patients with T2D who started metformin therapy after the baseline survey S4 (Figure 2C). This validation was performed using both basic and full models (Supplementary Table 4, Additional File 1). For example, the relative concentrations of malate were significantly increased in individuals who started metformin therapy after the S4 timepoint. This increase remained significant in both the crude model (β = 0.39, P = 3.31x10 -4 ) and the full model (β = 0.25, P = 0.043), when comparing 34 T2D participants who started metformin therapy during the follow-up period with 628 metformin-naïve participants (Supplementary Table 4, Additional File 1). Metformin’s effects on the hepatic and renal metabolites Among the 391 analyzed metabolites in the liver, two metabolites, glutamate and taurine, showed Bonferroni-significant associations with metformin (Figure 3A, Supplementary Table 2, Additional File 1). Metformin treatment resulted in an upregulation of glutamate and a downregulation of taurine compared to VG mice. Additionally, the values of fumarate and malate in the db/db liver were upregulated by metformin (with unadjusted P < 0.05), although they did not reach Bonferroni-significance levels (Supplementary Table 2, Additional File 1). In the comparison between VG and WT mice, glutamate, fumarate, and malate were downregulated at a nominal significant level (Figure 3A, Supplementary Table 2, Additional File 1), suggesting that these changes may be associated with the genetic background and the HFD. Notably, the observed upregulation of these metabolites (glutamate, fumarate, and malate) in the MET mice compared to VG ones may indicate beneficial effects of metformin in the liver of the db/db mice. Figure 3 Hepatic and renal effects of metformin.­­ Volcano plots of linear regression results for 391 hepatic (A) and 447 renal (B) metabolites for the comparison between MET and VG are shown. The upper and lower dashed lines represent Bonferroni-corrected and uncorrected (P = 0.05) significance levels, respectively. Boxplots of selected metformin-associated metabolites in WT, VG and MET mice are shown. Abbreviations: MET, metformin-treated leptin receptor-deficient diabetic; VG, vehicle-gavaged db/db mice; WT, wild type mice; 2-AB, 2-aminobutyrate; 2-HG, 2-hydroxyglutarate. See also Supplementary Table 2, Additional File 1. Moving to the kidneys of db/db mice, among the 447 analyzed metabolites, three metabolites (2-hydroxyglutarate [2-HG], 2-AB, and 5,6-dihydrouracil) exhibited Bonferroni-significant upregulation due to metformin treatment (Figure 3B, Supplementary Table 2, Additional File 1). Additionally, malate values in the db/db kidneys were altered by metformin at a nominal significant level (Figure 3B, Supplementary Table 2, Additional File 1). In the comparison between VG and WT mice, 2-HG had a Bonferroni-significant downregulation, malate showed an upregulation at a nominal significant level, while comparable levels of 2-AB and 5,6-dihydrouracil were observed between VG and WT mice (Figure 3B, Supplementary Table 2, Additional File 1). Metabolic effects of adding SGLT2i to metformin Of 716 analyzed metabolites in the three tissues, three (butyrylglycine, N-acetyl glycine, and indole lactate) in plasma and two (choline and X-10460) in the liver were found to have Bonferroni-significant associations with the combination therapy when comparing SGLT2i+MET with MET mice (Figures 4A, 4B, Supplementary Table 5, Additional File 1). Except for X-10460, all four identified metabolites were upregulated in the combination therapy group. However, none of the 447 analyzed renal metabolites showed Bonferroni-significant differences in the pairwise comparison between SGLT2i+MET and MET mice (Figure 4C). Figure 4 Metabolic effects of combination therapy in the three murine tissues Volcano plots in plasma (A), liver (B) and kidney (C) when compare SGLT2i+MET with MET mice. The upper and the lower dashed lines represent Bonferroni-corrected and uncorrecte­d (P = 0.05) significance levels, respectively. Boxplots of selected metabolites in MET and SGLT2i+MET mice are shown. Abbreviations: MET, metformin-treated diabetic mice; SGLT2i+MET, SGLT2i and metformin treated db/db mice. See also Supplementary Table 5, Additional File 1. In addition to the Bonferroni-significant metabolites associated with the combination therapy, several metabolites related to the TCA cycle showed nominal significant alterations (see Supplementary Table 5, Additional File 1). In the pairwise comparison between SGLT2i+MET and MET mice, the levels of malate, α-KG, and pyruvate in plasma, malate and glutamate in the liver, and 2-HG in the kidneys were downregulated. On the other hand, taurine and citrulline in the liver were upregulated (Figures 4A, 4B, Supplementary Table 5, Additional File 1). Tissue- and drug-specific effects of TCA cycle metabolites and its anaplerosis Collectively, we observed different tissue-dependent responses to metformin and its combination with SGLT2i for intermediates of TCA cycle and its anaplerosis. Specifically, circulating malate levels in all three db/db mice (VG, MET and SGLT2i+MET) were higher than WT ones, whereas their hepatic values were comparable between WT and MET mice, but lowered in both VG and SGLT2i+MET mice (Figure 5A). These observations may indicate that the TCA cycle activity in leukocytes (erythrocytes lack mitochondria and functional TCA cycle) and hepatocytes responds differently to metformin. Moreover, in the three group of db/db mice, similar patterns for circulating malate and α-KG, hepatic malate and glutamate, and renal 2-HG, were observed (e.g., highest levels were observed in MET mice). Whereas, taurine levels were lowest in the MET mice in the liver (Figure 5A). These results may suggest that add-on SGLT2i to metformin reversed abbudances of these metabolites, thereby suggesting a bidirectional modulation of TCA cycle metabolites and anaplerosis. Figure 5 Alteraration of TCA cycle metabolites and related pathways. Boxplots of selected metabolites in four groups (A). Measured and altered TCA cycle related metabolites by monotherapy of metformin (B) and combination therapy of SGLT2i with metformin (C) in the three tissues of diabetic mice and serum/plasma in humans. Abbreviations: 2-HG, 2-hydroxyglutarate; α-KG, α-ketoglutarate; TCA, citric acid; NO, nitric oxide. Discussion In this study, we conducted a systematic examination of how metformin and SGLT2i modulate 716 distinct metabolites across liver, kidneys, and plasma, integrating comparative analyses from both animal models and human subjects. Our findings highlight metformin's predominant influence on metabolites associated with the TCA cycle, with its effects being nuanced by the addition of SGLT2i, resulting in a bidirectional 'reversal' of its core impacts on energy metabolism, as illustrated in Figures 5B and 5C. This suggests a complex interplay between the two drugs that warrants further exploration. To establish clinical relevance, we corroborated our results with patient samples from the German KORA and South Arabian QBB studies, identifying two consistent systemic changes across ethnicities: increased malate and decreased citrulline levels. These shifts in TCA cycle metabolites and anaplerotic processes, induced by metformin treatment, not only corroborate the translational success from mice to humans but also carry implications for refining T2D management strategies. The TCA cycle, an essential component of cellular metabolism, relies on intermediate metabolites like α-KG, fumarate, and malate, which are consumed during energy production and replenished by anaplerotic pathways [25]. Our analysis encompasses four of these pathways, depicted in Figures 5B and 5C, including the pivotal glutaminolysis process that transforms glutamine into glutamate and ultimately into α-KG. Beyond its TCA cycle role, α-KG serves as a metabolic hub influencing epigenetic modifications, the cellular response to hypoxia, and immune regulation, particularly in macrophages, where it influences inflammatory and anti-inflammatory pathways [25]. The equilibrium of α-KG and its competitors (e.g., succinate and 2-HG) is crucial, as it regulates the activity of α-KG-dependent enzymes and maintains cellular homeostasis. In our investigation into the metabolic impacts of antidiabetic agents, we assessed the influence of metformin and SGLT2i on succinate and 2-HG. Our findings reveal that these therapeutic interventions do not alter succinate levels, implying that they do not disrupt succinate's role in cellular signaling. On the other hand, metformin alone caused an raised 2-HG levels within the kidneys of diabetic mice, aligning with levels observed in WT mice, hinting at a possible protective effect against diabetic kidney disease (DKD). Nonetheless, this metabolite also exerts significant influence over immune responses, notably by inhibiting key cellular pathways such as ATP synthase and mTOR, demonstrating its multifaceted nature. 2-HG, a structural analog of α-KG, has been termed an "oncometabolite" for its association with cancer progression through the disruption of epigenetic landscapes and DNA repair mechanisms [26–29]. Nonetheless, this metabolite also exerts significant influence over immune responses, notably by inhibiting key cellular pathways such as ATP synthase and mTOR, demonstrating its multifaceted nature [30]. In the context of diabetes and kidney function, 2-HG's role is particularly compelling. It acts as an epigenetic modulator with the potential to alter gene expression related to fibrosis and inflammation, two key processes in the pathogenesis of DKD. Studies, including those in diabetic db/db mice which are established models for this condition, have linked metformin treatment to improved renal function [31]. This improvement correlates with enhanced renal retention and reduced urinary excretion of 2-HG, suggesting that metformin's renal benefits might be partially conveyed through modulation of 2-HG levels. Such modulation could mitigate inflammation and fibrosis within the kidneys. The intricacy of 2-HG's influence, however, is underscored by the existence of its isomers, L-2-HG and D-2-HG, each with unique biological functions [25]. Continued research is imperative to fully decipher 2-HG's effects on DKD and to harness its therapeutic potential. Our study highlights a lesser-known entry point into the TCA cycle: fumarate. This metabolite can be derived from the urea/nitric oxide (NO) cycle occurring in the cytosol, as depicted in Figure 5B. Notably, we observed a significant reduction in circulating citrulline levels in both animal models and human subjects with diabetes, which was not mirrored in the hepatic tissue of db/db mice. This finding aligns with previous research indicating that metformin therapy decreases citrulline levels in the bloodstream of both diabetic and non-diabetic individuals [12,13], suggesting a possible influence of metformin on the urea/NO cycle and, consequently, on the TCA cycle. In our analysis of metformin's metabolic impact, hepatic taurine emerged as the metabolite most significantly downregulated following metformin monotherapy (Figures 5A and 5B). Taurine, a compound recognized for its antioxidant properties, is commonly found in various foods and is a prevalent ingredient in energy drinks [32]. Research in rat models has demonstrated that taurine can modulate the activity of the pyruvate dehydrogenase complex, thereby influencing the rate of pyruvate's entry, or anaplerosis, into the TCA cycle [33]. The decrease in taurine levels induced by metformin might initially appear paradoxical. This is because taurine is known to bolster the antioxidative actions of metformin in diabetic rats [34], and taurine supplementation has been recommended for conditions such as congestive heart failure [35]. This discrepancy suggests a complex interplay between metformin's therapeutic effects and its influence on metabolic pathways, warranting further investigation to elucidate the specific mechanisms at play. Collectively, our analysis suggests that metformin monotherapy might regulate the replenishment of TCA cycle intermediates, an effect known as anaplerosis, potentially by stimulating hepatic glutaminolysis, which could augment glutamate production, leading to increased levels of α-KG. Additionally, metformin therapy's association with elevated renal 2-HG levels, a metabolite implicated in both tumor development and immune system regulation, indicates that metformin may have a broader impact on these biological processes. The observed increase in fumarate and subsequent rise in malate levels may be influenced by the dampening of both the urea and NO cycles. Concurrently, the observed hepatic taurine depletion points towards a possible reduction in antioxidant defense mechanisms and regulatory influence on the TCA cycle. These metabolic adjustments may be closely linked with the multifaceted therapeutic effects of metformin, particularly in the management of diabetic hepatic and renal pathologies. Metformin's capacity to normalize hepatic glutamate and renal 2-HG levels to those observed in non-diabetic WT mice indicates a potential rebalancing of metabolic disturbances caused by diabetes. This homeostatic effect could represent one of several mechanisms by which metformin exerts its therapeutic action. Such mechanisms include the improvement of hepatic steatosis, decrease in lipotoxicity, and mitigation of inflammation, all of which contribute to the drug’s broad spectrum of benefits in the context of diabetic liver and kidney disease management. The administration of metformin in conjunction with SGLT2i painted a contrasting metabolic landscape. This combination therapy resulted in a notable reduction in plasma α-KG, hepatic glutamate, renal 2-HG, and both plasma and hepatic malate levels, while hepatic taurine values increased in diabetic mice (Figures 5B, 5C). These findings suggest that SGLT2i may counterbalance some of metformin's metabolic actions, prompting changes in key metabolites that could reshape the anaplerotic flux and the physiological processes it governs. The notable decrease in renal 2-HG prompted by the combined therapy could hint at a subdued immune response. This postulation is particularly intriguing when considering the elevated levels of C-reactive proteins observed in mice receiving both SGLT2i and metformin compared to those on metformin monotherapy (refer to Table 1). The increased C-reactive protein levels in the dual therapy group might represent a compensatory response to the suggested attenuation of immune activity inferred from the lower 2-HG values. Alternatively, it could signal an independent effect of the drug combination that has yet to be identified. These findings underscore the need for further research to unravel the complex interactions between metabolic modulation, pharmacological treatment, and immune responses in the management of diabetic kidney disease, as well as to fully comprehend the implications for patient care strategies. Adding SGLT2i to metformin, an increase in hepatic, but not plasma, citrulline levels was observed. This elevation could potentially result in higher fumarate levels within the liver. However, as previously discussed, the combination therapy led to lower levels of hepatic glutamate, renal 2-HG, and circulating α-KG, which might then precipitate a downstream decrease in TCA cycle intermediates, including fumarate and malate. Consequently, considering the opposing effects of these three entry points into the TCA cycle, the net effect on fumarate levels was that they remained similar between the SGLT2i+MET and MET treatment groups across all examined tissues (plasma, liver, and kidneys, Figure 5C). This balance suggests a complex interaction of the combined therapy on TCA cycle dynamics, wherein the influence of one drug may modulate or offset the effects of the other, resulting in a metabolic equilibrium of certain intermediates within the cycle. Our research also revealed that combination therapy restores hepatic taurine levels, which were reduced by metformin alone, suggesting a compensatory mechanism at play. Given taurine’s well-established antioxidative capabilities, its upregulation in the liver under combination therapy may imply an intensified defense against oxidative stress. This antioxidative adjustment could be especially advantageous for diabetic patients, where oxidative stress significantly contributes to the advancement of liver disease and complicates other diabetes-related conditions. Mechanistically, a previous study found that monotherapy with SGLT2 inhibitors (SGLT2i) suppresses the activity of glutamate dehydrogenase (GDH) [36]. This suppression, when combined with other therapies, impacts a series of enzymatic reactions that include GDH, consequently affecting the production of nicotinamide adenine dinucleotide reduced form (NADH). NADH molecules are essential for transferring electrons to the mitochondrial electron transport chain (ETC). As electrons are transported through the complexes in the inner mitochondrial membrane, a functional ETC generates a mitochondrial membrane potential, which is utilized to produce ATP through a process requiring oxygen, known as oxidative phosphorylation (OXPHOS). Mitochondrial complex I in the ETC replenishes nicotinamide adenine dinucleotide (NAD+), allowing the oxidative TCA cycle to continue [25]. Furthermore, the inhibition of GDH activity by SGLT2i is thought to increase the AMP/ATP ratio, thereby activating AMP-activated protein kinase (AMPK), which is a well-known target of metformin [36]. Activation of AMPK leads to the inhibition of lipogenesis and the promotion of fatty acid oxidation, which facilitates the breakdown of fatty acids for energy production [37]. Therefore, AMPK activation in the liver has been associated with beneficial effects on liver diseases, particularly in the context of metabolic hepatic disorders like non-alcoholic fatty liver disease (NAFLD). Excessively elevated hepatic mitochondrial TCA cycle activity has been observed in HFD-induced fatty liver and in patients with NAFLD [38,39]. In these cases, oxidative substrates are produced, leading to oxidative stress and tissue damage in the liver. However, SGLT2i may exert antioxidative effects on the liver by suppressing the TCA cycle. Indeed, similar effects have been observed in the kidneys of diabetic mice, where SGLT2i suppresses the accumulation of TCA cycle-associated metabolites and the increase of oxidative stress [40]. Therefore, adding SGLT2i to metformin might protect the liver in patients with NAFLD by suppressing an overly activated TCA cycle. To reliably identify true drug effects, we additionally examined metabolites associated with drug treatment through pairwise comparisons between VG and WT mice. The db/db mice used in our study have a distinct genetic background and were subjected to a HFD, resulting in the VG mice exhibiting characteristics of diabesity, dyslipidemia, and inflammation, as detailed in Table 1. The metformin-induced upregulation of malate, fumarate, and α-KG observed in the plasma of diabetic mice, as well as the increase of malate in the blood of T2D patients, might be attributed to the non-TCA cycle related roles of these metabolites in immunometabolism. For instance, fumarate and α-KG are known to promote anti-inflammatory phenotypes in immune cells and extend lifespan in Caenorhabditis elegans [25]. Similarly, metformin is recognized for its anti-inflammatory and anti-aging properties [41]. Furthermore, adequate nutrient availability can stimulate the reverse flux in the TCA cycle through glutaminolysis, leading to increased production of malate and fumarate in cancer cells treated with metformin [42]. Reductive carboxylation in the TCA cycle and glutaminolysis are also essential for glucose-stimulated insulin secretion in the pancreas [43] and for modulating immune cell activity [44]. Investigating whether a similar shift in TCA cycle dynamics and anaplerotic processes occurs in the leukocytes and hepatocytes of metformin-treated mice warrants further research. Limitations This study conducted a comprehensive analysis of 716 metabolites across three murine tissues, yet a limitation of our non-targeted metabolomics approach was the presence of some structurally unknown metabolites, such as X-10460. This particular metabolite was found to differ significantly in the comparison between the SGLT2i+MET and MET groups. Despite this finding, we could not ascertain the biomedical relevance of the combination therapy based on this metabolite alone due to its unknown nature. While the effects of metformin observed in mice were confirmed in the blood samples of two ethnically diverse human cohorts, our human studies did not involve patients treated with SGLT2 inhibitors. This was because the approval of SGLT2i occurred subsequent to the commencement of our examinations in the KORA studies (S4 between 1999 and 2001, and F4 from 2006 to 2008). Furthermore, our mouse intervention study was confined to male subjects, which limits the generalizability of our findings and prevents validation in human tissues beyond blood samples. Additionally, our study was conducted using a steady-state metabolomics approach, which only allows for single metabolite analysis. Consequently, this methodology does not yield information regarding dynamic inter-organ metabolic pathways, such as the TCA cycle flux, the directionality of this cycle (whether oxidative or reductive), or the anaplerotic processes fueling it. Future research should aim to address these limitations by incorporating targeted analyses that can capture the complexity and dynamism of metabolic pathways. Conclusion Our study reveals the complex interactions between the antidiabetic drugs, metformin and SGLT2i, on central metabolic pathways, with implications extending beyond diabetes treatment. We observed that these medications bidirectionally modulate TCA cycle intermediates, with metformin potentially offering anti-inflammatory benefits, although these benefits may be diminished when combined with SGLT2i. Notably, SGLT2i may counteract an overactive TCA cycle, offering hepatic protection, which is particularly relevant for NAFLD patients. Additionally, the metabolites we have examined may play roles in immune cell regulation, pointing to a wider therapeutic potential for disorders that are influenced by α-KG-dependent pathways, including cancer and immune dysfunction. Our results emphasize the necessity for further research to understand how these drugs modulate metabolism and the potential for creating targeted treatments that leverage these metabolic alterations. Moreover, these insights could pave the way for personalized treatment strategies, optimizing therapeutic outcomes by accounting for individual metabolic responses. In conclusion, our research not only underscores the complex metabolic effects of widely used diabetes medications but also suggests the exciting potential for their repurposing in the treatment of intricate metabolic and immune-mediated disorders, underlining the evolving nature of drug use in modern medicine. Abbreviations 2-AB : 2-aminobutyrate 2-HG : 2-hydroxyglutarate 4-HB : 4-hydroxybutyrate α-KG : α-ketoglutarate AMP : adenosine monophosphate AMPK : AMP-activated protein kinase ATP : adenosine triphosphate BMI : body mass index BP : blood pressure DKD : diabetic kidney disease eNOS : endothelial nitric oxide synthase ETC : electron transport chain GDH : glutamate dehydrogenase HDL : high-density lipoprotein HFD : high-fat diet LDL : low-density lipoprotein MET : metformin-treated diabetic mice mTOR : mammalian target of rapamycin mt-T2D : metformin treated type 2 diabetes NAD : nicotinamide adenine dinucleotide NADH : nicotinamide adenine dinucleotide reduced form NAFLD : non-alcoholic fatty liver disease ndt-T2D : non-anti-diabetic drug-treated type 2 diabetes NO : nitric oxide OXPHOS : oxidative phosphorylation QC : quality control SGLT2i : sodium-glucose-cotransporter-2 inhibitors SGLT2i+MET : Sodium-glucose-cotransporter-2-inhibitor and metformin-treated diabetic mice T2D : type 2 diabetes TCA : citric acid, also known as tricarboxylic acid w/o : without WT : wild type mice VG : vehicle gavaged diabetic mice Declarations Ethics approval and consent to participate All animal studies were conducted in accordance with FELASA protocols and approved by the District Government of Upper Bavaria, Germany (Regierung von Oberbayern, Gz.55.2 1 54 2531 70 07, 55.2 1 2532 153 11). KORA S4 and F4 studies were approved by the Ethics Committees of the Bavarian Medical Association in Munich, Germany. All protocols of the QBB study were approved by the Hamad Medical Corporation Ethics Committee. All study participants provided written informed consent. Consent for publication Not applicable. Availability of data and materials The KORA S4/F4 data sets are not publicly available because of data protection agreements but can be provided upon request through the KORA-PASST (Project application self-service tool, www.helmholtz-muenchen.de/kora-gen). The QBB data can be obtained to the researcher to submit access application forms online (https://researchportal.qatarbiobank.org.qa/). Declaration of interests: M.F.S. was employed at Helmholtz Munich during the execution of this study. He is currently an employee of the Global Medical Affairs and Pharmacovigilance Department of BAYER AG Pharmaceuticals (Berlin, Germany), however, the company was not involved in work related to data generation and manuscript generation. S.N. was employed by the Helmholtz Munich during the execution of this study. She is currently an employee of Sanofi Aventis Deutschland GmbH; however, the company was not involved in work related to data and manuscript generation. Funding This Mouse200 was funded in part by grants from the German Federal Ministry of Education and Research to the German Center for Diabetes Research and to the Research Consortium "Systems Biology of Metabotypes" (SysMBo grant 0315494A) and by the Helmholtz Alliance ICEMED (Imaging and Curing Environmental Metabolic Diseases), through the Initiative and Network Fund of the Helmholtz Association and supported by the Helmholtz Portfolio Theme "Metabolic Dysfunction and Disease." The KORA study was initiated and financed by the Helmholtz Zentrum München – German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC‑Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ. The German Diabetes Center is funded by the German Federal Ministry of Health (Berlin, Germany) and the Ministry of Innovation, Science and Research of the State of North Rhine-Westphalia (Dusseldorf, Germany). This study was supported in part by a grant from the German Federal Ministry of Education and Research to the German Center for Diabetes Research (DZD). The diabetes part of the KORA F4 study was funded by a grant from the German Research Foundation (DFG; RA 459/3‑1). Part of this study was supported by EU FP7 grants HEALTH‑2013‑2.4.2‑1/602936 (Project CarTarDis). Part of this study was supported by the funding from the European Union’s Horizon 2020 research and innovation programmes: The DeTecT2D & iPDM-GO EIT Health Innovation Project supported by the European Institute of Innovation and Technology (EIT), a body of the European Union; This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 821508 (CARDIATEAM). The JU receives support from the European Union's Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA). K.Suh. was supported by 'Biomedical Research Program' funds at Weill Cornell Medicine in Qatar, a program funded by the Qatar Foundation and is supported by Qatar National Research Fund (QNRF) grant NPRP11C-0115-180010. Authors' contributions J.Adam, M.H, R.W-S. conceived and designed the current study. J.Adam, M.H., M.C., S.B., C.M., J.H., S.H., M.Rom., M.R., S.M., J.K., K.S., R.W-S. analyzed the data and interpreted the results. M.H., R.P.M., G.K., W.R., S.Z., M.F.S., S.N., J.A., C.G., A.P., M.RdA., K.Suh. performed the experiments, including metabolic profiling. Z.Z., D.P.A., T.M., assisted in manuscript generation. J.Adam, M.C., S.B., T.L.A., R.W-S. wrote the manuscript. All authors approved the final version of manuscript. Acknowledgements We thank the people of the Institute of Diabetes and Regeneration Research (Anett Seelig, Jürgen Schultheiß), Institute of Experimental Genetics (Moya Wu), and the animal caretaker staff of the German Mouse Clinic for excellent technical assistance. Moreover, we thank the Mouse200 project team Babara Pfitzner, Alesia Walker and Gabriele Zieglmeier. We express our appreciation to all KORA study participants for donating their blood and time. We thank the field staff in Augsburg conducting the KORA studies. 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Additional Declarations Competing interest reported. • Markus F. Scheerer was affiliated with Helmholtz Munich during the study and has since joined the Global Medical Affairs and Pharmacovigilance Department of BAYER AG Pharmaceuticals (Berlin, Germany). BAYER AG had no role in the study design, data collection, or manuscript preparation. • Susanne Neschen was also associated with Helmholtz Munich for the duration of the study and is now with Sanofi Aventis Deutschland GmbH, which similarly had no involvement in the study. Supplementary Files SOMCD.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Feb, 2024 Reviews received at journal 20 Feb, 2024 Reviewers agreed at journal 08 Feb, 2024 Reviewers invited by journal 07 Feb, 2024 Editor assigned by journal 07 Feb, 2024 Submission checks completed at journal 07 Feb, 2024 First submitted to journal 05 Feb, 2024 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3931333","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271547608,"identity":"42d796aa-dab6-4f91-a4da-0e02134116cb","order_by":0,"name":"Jonathan Adam","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Adam","suffix":""},{"id":271547609,"identity":"3f7f1ee2-dd03-44f9-a362-8e724df69104","order_by":1,"name":"Makoto Harada","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Makoto","middleName":"","lastName":"Harada","suffix":""},{"id":271547610,"identity":"bc4f24a2-7c84-4ced-b41c-ac6d4610f985","order_by":2,"name":"Marcela Covic","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marcela","middleName":"","lastName":"Covic","suffix":""},{"id":271547611,"identity":"9bb4d30c-0c58-4e69-8cc4-984ee4a9feac","order_by":3,"name":"Stefan Brandmaier","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Brandmaier","suffix":""},{"id":271547612,"identity":"271c1a2a-a1b3-410b-a666-6ba556718db2","order_by":4,"name":"Caroline Muschet","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Caroline","middleName":"","lastName":"Muschet","suffix":""},{"id":271547613,"identity":"257e02fb-1f9b-4126-bd3c-bada85ea7a86","order_by":5,"name":"Jialing Huang","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jialing","middleName":"","lastName":"Huang","suffix":""},{"id":271547614,"identity":"d3bd8e48-7895-4505-b2bd-75a87488fe4d","order_by":6,"name":"Siyu Han","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Han","suffix":""},{"id":271547615,"identity":"ebb1c487-f0bb-441d-ae2f-939b2cd70e94","order_by":7,"name":"Jianhong Ge","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jianhong","middleName":"","lastName":"Ge","suffix":""},{"id":271547616,"identity":"d1be4132-8ad7-47f1-a23b-656d3b302afa","order_by":8,"name":"Martina Rommel","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Martina","middleName":"","lastName":"Rommel","suffix":""},{"id":271547617,"identity":"27800175-ec3d-424a-a32b-24a4f3afdf32","order_by":9,"name":"Markus Rotter","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Markus","middleName":"","lastName":"Rotter","suffix":""},{"id":271547618,"identity":"20bb09d6-9368-4676-b2a6-79b5c6e9c3e3","order_by":10,"name":"Margit Heier","email":"","orcid":"","institution":"University Hospital of Augsburg","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Margit","middleName":"","lastName":"Heier","suffix":""},{"id":271547619,"identity":"dd072609-14cf-407f-9c0b-9f183c09e1bb","order_by":11,"name":"Robert P. Mohney","email":"","orcid":"","institution":"Metabolon, Inc","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Robert","middleName":"P.","lastName":"Mohney","suffix":""},{"id":271547620,"identity":"2da6cc21-e02c-47a3-b15b-3104ed7334c2","order_by":12,"name":"Jan Krumsiek","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Krumsiek","suffix":""},{"id":271547621,"identity":"d59b6e06-14dc-49d1-ba91-dcb1d31d49ba","order_by":13,"name":"Gabi Kastenmüller","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gabi","middleName":"","lastName":"Kastenmüller","suffix":""},{"id":271547622,"identity":"8491ad87-49b4-43a9-82f8-ffedcbdbf513","order_by":14,"name":"Wolfgang Rathmann","email":"","orcid":"","institution":"German Center for Diabetes Research (DZD)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wolfgang","middleName":"","lastName":"Rathmann","suffix":""},{"id":271547623,"identity":"9b05e99c-8361-4fed-9e3b-31888898518d","order_by":15,"name":"Zhongmei Zou","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhongmei","middleName":"","lastName":"Zou","suffix":""},{"id":271547624,"identity":"faea5309-e347-407a-8e42-7c0a1aa8f374","order_by":16,"name":"Sven Zukunft","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sven","middleName":"","lastName":"Zukunft","suffix":""},{"id":271547625,"identity":"123343ab-f85e-4826-ac1f-9f148e2e6fe4","order_by":17,"name":"Markus F. Scheerer","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Markus","middleName":"F.","lastName":"Scheerer","suffix":""},{"id":271547626,"identity":"dc57405a-2e22-4c50-b5b7-f072d44b00a3","order_by":18,"name":"Susanne Neschen","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Susanne","middleName":"","lastName":"Neschen","suffix":""},{"id":271547627,"identity":"26c3661c-b005-403f-ada6-8a3a8314dab5","order_by":19,"name":"Jerzy Adamski","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jerzy","middleName":"","lastName":"Adamski","suffix":""},{"id":271547628,"identity":"449caf9e-8a9c-41c4-8efd-9d104f940308","order_by":20,"name":"Christian Gieger","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Gieger","suffix":""},{"id":271547629,"identity":"1aed5aa8-4029-4ade-b84a-b0461bc0056c","order_by":21,"name":"Annette Peters","email":"","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Annette","middleName":"","lastName":"Peters","suffix":""},{"id":271547630,"identity":"d522a829-cd0d-430b-a1f6-5955d498dfb0","order_by":22,"name":"Donna P. Ankerst","email":"","orcid":"","institution":"Technical University of Munich (TUM)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Donna","middleName":"P.","lastName":"Ankerst","suffix":""},{"id":271547631,"identity":"e693db51-e0a3-4344-a8bb-100925b3b814","order_by":23,"name":"Thomas Meitinger","email":"","orcid":"","institution":"Klinikum rechts der Isar, TUM","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Meitinger","suffix":""},{"id":271547632,"identity":"7e0d264b-d842-4b89-8291-cc7e734d8075","order_by":24,"name":"Tanya L. Alderete","email":"","orcid":"","institution":"University of Colorado Boulder","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tanya","middleName":"L.","lastName":"Alderete","suffix":""},{"id":271547634,"identity":"efda63a0-de76-499a-a780-4e1a82dda29f","order_by":25,"name":"Martin Hrabe Angelis","email":"","orcid":"","institution":"German Center for Diabetes Research (DZD)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Martin","middleName":"Hrabe","lastName":"Angelis","suffix":""},{"id":271547635,"identity":"db4707e6-9dc0-4a8c-9454-3bb817db9c39","order_by":26,"name":"Karsten Suhre","email":"","orcid":"","institution":"Weill Cornell Medicine - Qatar, Education City - Qatar Foundation","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Karsten","middleName":"","lastName":"Suhre","suffix":""},{"id":271547638,"identity":"c86b7c21-3820-44f3-9ef8-5a8a53250a57","order_by":27,"name":"Rui Wang-Sattler","email":"data:image/png;base64,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","orcid":"","institution":"Helmholtz Zentrum München, German Research Center for Environmental Health","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Wang-Sattler","suffix":""}],"badges":[],"createdAt":"2024-02-05 15:19:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3931333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3931333/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50936621,"identity":"c8a48a74-b7a0-4841-9763-2d6f86d2ebf2","added_by":"auto","created_at":"2024-02-09 21:09:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":196759,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of metformin and leptin receptor mutation in murine plasma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: Volcano plots of linear regression analysis results (β-estimates and P-values) for 351 plasma metabolites in two pairwise comparisons of MET with VG diabetic mice and VG with WT mice. The upper and lower dashed lines represent Bonferroni-corrected and uncorrected (P = 0.05) significance levels, respectively. B: Seven metformin-associated metabolites are shown. C: Boxplots of seven metabolites. Abbreviations: WT, wild type mice; VG, vehicle gavaged diabetic mice; MET, metformin-treated diabetic mice; 2-AB, 2-aminobutyrate; 4-HB, 4-hydroxybutyrate; α-KG, α-ketoglutarate. See also Supplementary Table 2, Additional File 1.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3931333/v1/ec06396b32a49e3ef5d80066.png"},{"id":50936619,"identity":"d2994a47-f6f1-4050-8e48-2b212b561be1","added_by":"auto","created_at":"2024-02-09 21:09:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":146834,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCross-sectional and longitudinal analyses reveal specific pattern of metformin action in human serum and plasma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA and B: β-estimates and confidence intervals of three metabolites in KORA and QBB cross-sectional human studies. C: Mean relative residue of two metabolites in longitudinal KORA study based on the fully adjusted model (age, sex, BMI, physical activity, high alcohol intake, smoking status, systolic blood pressure, HbA1c, fasting glucose levels, high density lipoprotein cholesterol, triglycerides as well as the use of statins, beta blockers, angiotensin-converting-enzyme inhibitors, and angiotensin receptor blockers). Abbreviation: 2-AB, 2 aminobutyrate. See also Supplementary Tables 3,4, Additional File 1.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3931333/v1/42e856ba1440bb5a90a011eb.png"},{"id":50936620,"identity":"08bbdba2-6470-4243-99f1-3da080c4e406","added_by":"auto","created_at":"2024-02-09 21:09:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":218453,"visible":true,"origin":"","legend":"\u003cp\u003eHepatic and renal effects of metformin.­­\u003c/p\u003e\n\u003cp\u003eVolcano plots of linear regression results for 391 hepatic (A) and 447 renal (B) metabolites for the comparison between MET and VG are shown. The upper and lower dashed lines represent Bonferroni-corrected and uncorrected (P = 0.05) significance levels, respectively. Boxplots of selected metformin-associated metabolites in WT, VG and MET mice are shown. Abbreviations: MET, metformin-treated leptin receptor-deficient diabetic; VG, vehicle-gavaged db/db mice; WT, wild type mice; 2-AB, 2-aminobutyrate; 2-HG, 2-hydroxyglutarate. See also Supplementary Table 2, Additional File 1.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3931333/v1/161b07bb59dc0bebede5db96.png"},{"id":50937307,"identity":"ae8901c9-2755-47ec-9dcf-ed74ca4dc216","added_by":"auto","created_at":"2024-02-09 21:17:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":309833,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolic effects of combination therapy in the three murine tissues\u003c/p\u003e\n\u003cp\u003eVolcano plots in plasma (A), liver (B) and kidney (C) when compare SGLT2i+MET with MET mice. The upper and the lower dashed lines represent Bonferroni-corrected and uncorrecte­d (P = 0.05) significance levels, respectively. Boxplots of selected metabolites in MET and SGLT2i+MET mice are shown. Abbreviations: MET, metformin-treated diabetic mice; SGLT2i+MET, SGLT2i and metformin treated db/db mice. See also Supplementary Table 5, Additional File 1.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3931333/v1/fcff4baae3f7341a6374dead.png"},{"id":50936623,"identity":"ebbaf1e3-2262-4f91-a5ba-4f1b32fd5c3a","added_by":"auto","created_at":"2024-02-09 21:09:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":574846,"visible":true,"origin":"","legend":"\u003cp\u003eAlteraration of TCA cycle metabolites and related pathways.\u003c/p\u003e\n\u003cp\u003eBoxplots of selected metabolites in four groups (A). Measured and altered TCA cycle related metabolites by monotherapy of metformin (B) and combination therapy of SGLT2i with metformin (C) in the three tissues of diabetic mice and serum/plasma in humans. Abbreviations: 2-HG, 2-hydroxyglutarate; α-KG, α-ketoglutarate; TCA, citric acid; NO, nitric oxide.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3931333/v1/5846eb17bddaf8dede64f750.png"},{"id":50938054,"identity":"bbb9b60d-bc97-439c-a5ce-425da9b6dd5b","added_by":"auto","created_at":"2024-02-09 21:25:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2035722,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3931333/v1/ccd23cd5-d122-4c20-832a-90dbdbde16cf.pdf"},{"id":50936622,"identity":"c7e280bd-965d-467a-a50b-55e6ba063def","added_by":"auto","created_at":"2024-02-09 21:09:22","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":103762,"visible":true,"origin":"","legend":"","description":"","filename":"SOMCD.docx","url":"https://assets-eu.researchsquare.com/files/rs-3931333/v1/9476e2318a91dfbc866244f9.docx"}],"financialInterests":"Competing interest reported. •\tMarkus F. Scheerer was affiliated with Helmholtz Munich during the study and has since joined the Global Medical Affairs and Pharmacovigilance Department of BAYER AG Pharmaceuticals (Berlin, Germany). BAYER AG had no role in the study design, data collection, or manuscript preparation.\n\n•\tSusanne Neschen was also associated with Helmholtz Munich for the duration of the study and is now with Sanofi Aventis Deutschland GmbH, which similarly had no involvement in the study.","formattedTitle":"Bidirectional modulation of TCA cycle metabolites and anaplerosis by metformin and its combination with SGLT2i","fulltext":[{"header":"Background","content":"\u003cp\u003eMetformin is widely recognized as a primary treatment option for non-insulin-dependent type 2 diabetes (T2D) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Beyond its established benefits in blood sugar control and weight management, recent research has unveiled the pleiotropic effects of metformin, including potential anti-cancer effects, such as a decreased proliferation of cancer cells [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], cardiovascular benefits such as lowered low-density lipoprotein cholesterol [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and reduced inflammation and fibrosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, it is important to acknowledge that metformin is not without potential side effects, including gastrointestinal issues and, in rare cases, lactic acidosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eClinical guidelines now recommend the use of sodium-glucose-cotransporter-2 inhibitors (SGLT2i) as add-on therapy with metformin to enhance glycemic control [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the effects of metformin monotherapy and its combination with SGLT2i on tissue-specific metabolism, particularly the citric acid, also known as tricarboxylic acid (TCA) cycle and anaplerosis in the liver and kidneys, have not been extensively investigated.\u003c/p\u003e \u003cp\u003eBoth the liver and kidneys are integral to metformin's pharmacokinetics: the liver primarily processes the drug post-absorption, and the kidneys subsequently handle its clearance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. SGLT2 inhibitors, on the other hand, impede renal glucose reabsorption, promoting its urinary excretion. Appreciating the tissue-specific actions of metformin and its interactions with SGLT2i is essential to refine treatment modalities for T2D and to fully understand the metabolic shifts induced by these drugs.\u003c/p\u003e \u003cp\u003ePharmacometabolomics studies in animals and humans have provided further insights into the comprehensive effects of metformin, highlighting its significant influence on nitric oxide (NO) and urea cycles [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Non-targeted metabolomics has played a pivotal role in the research and development of biomarkers and screening assays for T2D [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Previous studies utilizing this approach have observed significantly lower citrulline levels in diabetic and non-diabetic individuals treated with metformin, as well as in peripheral tissues of metformin-treated diabetic mice [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These findings suggest that metformin treatment alters urea and NO production by activating the endothelial NO synthase (eNOS) and NO biosynthesis, which could mediate its benefits in reducing cardiovascular sequels of T2D [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis non-targeted metabolomics study aims to examine the tissue-specific effects of metformin, both as a monotherapy and in combination with SGLT2i, on the plasma, hepatic and renal metabolite profiles in obese diabetic mice. We seek to corroborate our findings through the analysis of serum and plasma from T2D patients undergoing metformin treatment in the longitudinal KORA (Cooperative Health Research in the Region of Augsburg) study and the cross-sectional QBB study (Qatar Biobank). Our research endeavors to offer clinically relevant insights that could pave the way for personalized diabetes management approaches.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eOur study comprised three interconnected components to investigate the metabolic effects of metformin monotherapy and its combination with SGLT2i in both animal models and human subjects. First, we assessed the metabolic alterations in diabetic mice treated with metformin (MET) compared to vehicle-gavaged diabetic mice (VG), focusing on the analysis of significant tissue-specific metabolite differences (plasma, liver, kidney) between the MET and VG groups. Additionally, we evaluated whether the metabolic changes attributed to metformin were independent of leptin receptor (Lepr) deficiency by comparing the VG group to wild type (WT) mice. Second, we further examined the metformin-associated metabolites, initially identified in murine plasma, in two human cross-sectional studies: KORA and QBB. In these studies, we compared metformin-treated type 2 diabetes patients (mt-T2D) with non-antidiabetic drug-treated patients (ndt-T2D). Metabolites replicated in these cross-sectional cohorts were further validated in a longitudinal KORA study, focusing on metformin-na\u0026iuml;ve participants at baseline. Third, we compared the metabolic profiles of mice treated with combination therapy (SGLT2i\u0026thinsp;+\u0026thinsp;MET) versus those on metformin monotherapy (MET) across the three tissues. For each of these comparisons, we performed linear regression analysis for pairwise comparisons and applied Bonferroni correction to address multiple testing concerns. Lastly, we conducted tissue-specific pathway analyses to elucidate the biological relevance of the identified metabolites, thereby enhancing our understanding of the metabolic effects of these antidiabetic therapies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMetformin and SGLT2i intervention study\u003c/h2\u003e \u003cp\u003ePharmacological studies were conducted in compliance with FELASA protocols using 40 male mice including 10 wild-type (WT) and 30 db/db (BKS.Cg Dock7m+/+ Leprdb/J) mice. Starting from 3 weeks of age, all db/db mice were fed a high-fat diet (HFD) (S0372 E010, ssniff Spezialdi\u0026auml;ten, Soest, Germany) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. After 3 weeks of being on the HFD, the 6-week-old animals were treated for 2 weeks via gavage once a day between 5:00\u0026ndash;6:00 p.m. before the onset of the dark phase (6:00 p.m.). The treatment groups consisted of 10 vehicle-gavaged (VG) diabetic mice that were vehicle-gavaged with a solution of 5% solutol and 95% hydroxyethylcellulose, 10 MET mice treated with 300 mg/kg metformin (Sigma Aldrich, Taufkirchen, Germany), and 10 SGLT2i\u0026thinsp;+\u0026thinsp;MET mice treated with 30 mg/kg SGLT2i (AVE2268, Sanofi AG, Frankfurt, Germany) and 300 mg/kg metformin (Sigma Aldrich, Taufkirchen, Germany) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAfter the completion of the treatment period, which lasted for 2 weeks, the 8-week-old mice (\u0026plusmn;\u0026thinsp;3 days) underwent a fasting period of four hours before being sacrificed. An overdose of isoflurane was used for euthanasia, and immediate blood and organ collection were performed as previously reported [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Murine plasma was prepared from whole blood by centrifugation at 4\u0026deg;C, and tissues were freeze-clamped. All samples were stored at -80\u0026deg;C until further analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of mouse studies\u003c/h2\u003e \u003cp\u003eIn the mouse study, compared to WT mice, all three groups of diabetic mice (VG, MET, SGLT2i\u0026thinsp;+\u0026thinsp;MET) exhibited characteristic features of obesity, including higher body and liver weights (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). They also displayed hyperglycemia, as evidenced by elevated blood glucose and insulin levels. Dyslipidemia was observed with elevated cholesterol and triglyceride levels, and systemic inflammation was evident with elevated C-reactive protein levels, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFollowing two weeks of daily monotherapy with metformin or combination therapy in diabetic mice, significant reductions in blood glucose levels were observed. The MET mice showed a 29.1% reduction in blood glucose levels, while the SGLT2i\u0026thinsp;+\u0026thinsp;MET mice exhibited a substantial 70.0% reduction. In contrast, the VG and WT mice experienced modest reductions of 4.7% and 1.9%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, when comparing mice treated with monotherapy metformin, the SGLT2i\u0026thinsp;+\u0026thinsp;MET mice displayed lower body and liver weights, insulin levels, and triglyceride levels. However, they exhibited higher cholesterol and C-reactive protein levels (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These observations suggest that the combination therapy had additional metabolic effects compared to monotherapy with metformin alone.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the murine samples. Means (standard deviation) of clinical variables in four mice groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWT\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVG\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMET\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSGLT2i\u0026thinsp;+\u0026thinsp;MET\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody weight, g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.0 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.9 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.8 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.8 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver weight, g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.56 (0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.61 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.36 (0.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney weight, g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21 (0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood glucose\u003csup\u003e\u0026dagger;\u003c/sup\u003e, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108.8 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e442.5 (65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e454.8 (60.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e439.1 (62.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood glucose, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106.7 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e421.6 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e322.6 (92.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e129.9 (46.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChanged glucose (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin, \u0026micro;g/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.76 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.86 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.78 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.3 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125.3 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e135.5 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e156.0 (22.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.5 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.76 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.49 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.19 (7.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100.6 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e153.2 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e164.5 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e188.8 (29.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122.2 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e224.8 (106.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e262.4 (63.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e199.9 (46.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein, mg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.4 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.1 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.0 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.1 (3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e WT, wild type mice; VG, vehicle‑gavaged diabetic mice; MET, metformin-treated diabetic mice; SGLT2i+MET, Sodium-glucose-cotransporter-2-inhibitor and metformin-treated diabetic mice;\u0026nbsp;HDL, high-density lipoprotein; LDL, low-density lipoprotein.\u0026nbsp;\u003csup\u003e\u0026dagger;\u003c/sup\u003e6 weeks.\u003c/p\u003e\n\u003ch3\u003eKORA and QBB human studies\u003c/h3\u003e\n\u003cp\u003eKORA is a population-based cohort conducted in southern Germany [21]. The baseline survey, Survey 4 (S4)\u0026nbsp;included 4,261 individuals who were examined between 1999 and 2001.\u0026nbsp;The follow-up survey (F4), took place from 2006 to 2008 and involved 3,080 individuals\u0026nbsp;[22]. For the present analysis, only participants with non-targeted metabolite profiles were included.\u0026nbsp;Additionally, individuals who did not undergo overnight fasting, had type 1 diabetes or drug-induced diabetes, were treated with insulin or both insulin and metformin, took glucose-lowering oral medication other than metformin, or had missing covariates in the fully adjusted model were excluded.\u0026nbsp;In the cross-sectional F4 investigation,\u0026nbsp;patients with T2D were included.\u0026nbsp;For the longitudinal analysis (KORA S4 to F4), participants who were na\u0026iuml;ve to metformin at baseline S4 and had non-targeted metabolite profiles at both the S4 and F4 surveys were included.\u003c/p\u003e\n\u003cp\u003eQBB is a population-based study conducted in Qatar, which was established in 2012\u0026nbsp;[23,24].\u003c/p\u003e\n\u003ch3\u003eCharacteristics of cross-sectional human studies\u003c/h3\u003e\n\u003cp\u003eCross-sectional validation of our mouse findings was conducted in the KORA and QBB human studies, which involved a total of 478 patients with T2D and available metabolite profiles. In the KORA study, we examined 184 participants from the KORA F4 study who had T2D, including 70 individuals who underwent metformin therapy (mt-T2D), as shown in Table 2. The QBB study comprised a sample set of 294 individuals, including 146 with mt-T2D (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComparing the mt-T2D patients to metformin-na\u0026iuml;ve T2D patients in both studies, we observed that the mt-T2D patients were older and exhibited higher levels of HbA1c and fasting glucose. These observations suggest that the mt-T2D patients had more advanced dysglycemia compared to the metformin-na\u0026iuml;ve T2D patients (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Characteristics of the KORA F4 and QBB cross-sectional study samples (N = 478). Percentages of individuals or means (standard deviation) are shown for each variable and each group.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"98%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eKORA F4 study\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eQBB study\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eClinical parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003endt-T2D\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N = 114)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003emt-T2D\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N = 70)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003endt-T2D\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N = 148)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003emt-T2D\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N = 146)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.1 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.3 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.5 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50,7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.0 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.0 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.7 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.3 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePhysical active, % \u0026gt; 1 hour per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh alcohol intake\u003csup\u003e\u0026Dagger;\u003c/sup\u003e, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSmoker, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSystolic BP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e134.6 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e130.2 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e121.2 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e125.5 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiastolic BP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.9 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.8 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78.2 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76.5 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHDL\u0026nbsp;cholesterol, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49.4 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.2 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48.6 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.3 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLDL\u0026nbsp;cholesterol, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e136.6 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e123.2 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e121.5 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e102.8 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal cholesterol, mg/dL\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e214.4 (37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e201.8 (34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e201.1 (41.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e181.2 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTriglycerides, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e172.2 (129.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e174.1 (141.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e157.3 (88.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e153.8 (65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHbA\u003csub\u003e1c\u003c/sub\u003e, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.3 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.8 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.6 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.6 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFasting glucose, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e126.8 (30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e140.5 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMetformin usage, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFasting (min. 8 h) before blood draw, %\u003c/p\u003e\n \u003cp\u003eddddddddd\u003c/p\u003e\n \u003cp\u003eddddddddddd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e ndt-T2D, non-anti-diabetic drug-treated type 2 diabetes; mt-T2D, metformin treated type 2 diabetes; BMI, body mass index; BP, blood pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026Dagger;\u0026nbsp;\u003c/sup\u003e\u0026ge; 20 g/day for women; \u0026ge; 40 g/day for men\u003c/p\u003e\n\u003ch3\u003eCharacteristics of longitudinal human studies\u003c/h3\u003e\n\u003cp\u003eLongitudinal validation was conducted in the prospective KORA study, spanning from the baseline survey (S4) to the 7-year follow-up (F4). In this analysis, we compared 34 patients with T2D who initiated metformin therapy after the S4 examination to a group of 628 metformin-na\u0026iuml;ve individuals at each time point. The characteristics of these two groups were assessed at the S4 and F4 surveys. As shown in Table 3, the mt-T2D patients were found to be older, more sedentary, and had a higher prevalence of obesity compared to the group of 628 metformin-na\u0026iuml;ve individuals. Additionally, mt-T2D patients displayed higher blood pressure, triglyceride levels, and glycemic parameters at both the S4 and F4 surveys (Table 3). It\u0026apos;s important to note that during the longitudinal analysis, adjustments were made for various potential confounding factors, including age, body mass index, physical activity, systolic blood pressure, HbA1c, fasting glucose levels, high-density lipoprotein cholesterol, triglycerides among others.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Characteristics of the KORA S4\u0026nbsp;\u0026agrave;\u0026nbsp;F4 prospective study samples (N = 662). Percentages or means (standard deviation) are shown for each variable and each group.\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.8671096345515%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline S4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.70431893687708%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up F4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.628691983122362%\"\u003e\n \u003cp\u003e\u003cstrong\u003ew/o metformin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\"\u003e\n \u003cp\u003e\u003cstrong\u003ew/o metformin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.839662447257385%\"\u003e\n \u003cp\u003e\u003cstrong\u003ew/o metformin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.31645569620253%\"\u003e\n \u003cp\u003e\u003cstrong\u003emt-T2D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e61.4 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e63.5 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e68.5 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e70.6 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003eMale, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e27.9 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e32.4 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e28.2 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e31.8 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003ePhysical active\u003csup\u003e\u0026dagger;\u003c/sup\u003e, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003eHigh alcohol intake\u003csup\u003e\u0026Dagger;\u003c/sup\u003e, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003eSmoker, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003eSystolic BP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e132 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e145.5 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e128.2 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e131.5 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003eDiastolic BP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e80 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e83.6 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e75.1 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e74.8 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003eHDL cholesterol, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e59 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e53.9 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e56.7 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e52.3 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003eLDL cholesterol, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e155.1 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e145.5 (38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e142.5 (36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e124.4 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003eTotal cholesterol, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e245.9 (42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e236.2 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e225.3 (40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e202.6 (33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003eTriglyceride, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e130.3 (76.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e170.2 (175.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e132.4 (83.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e148.3 (164.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003eHbA\u003csub\u003e1C\u003c/sub\u003e, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e5.6 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e6.3 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e5.6 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e6.5 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003eFasting glucose, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e99.7 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e127.6 (30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e100.8 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e126 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003eMetformin usage, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003eFasting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.57379767827529%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.393034825870647%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.739635157545607%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e w/o, without; BMI, body mass index; BP, blood pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e \u0026gt; 1 hour per week\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026Dagger;\u0026nbsp;\u003c/sup\u003e\u0026gt; 40 g/day in men; \u0026gt; 20 g/day in women\u003c/p\u003e\n\u003ch3\u003eNon-targeted metabolite profiling\u003c/h3\u003e\n\u003cp\u003eNon-targeted metabolite profiling was performed using samples from various murine tissues, KORA serum, and QBB plasma, using the Metabolon analytical system (Metabolon Inc., Durham, North Carolina, USA). Metabolon employed a non-targeted semi-quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) and gas chromatography mass spectrometry (GC-MS) platform for the identification of both structurally named and unknown molecules\u0026nbsp;[16].\u003c/p\u003e\n\u003cp\u003eIn this study, the same quality control (QC) criteria were applied\u0026nbsp;[12]. Specifically, metabolites with more than 20% missing values were excluded for each tissue, as were samples and running days for metabolites with more than 10% missing values\u0026nbsp;[12]. All relative ion counts were initially normalized for each tissue by each running day and then natural log transformed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter QC, a total of 716 metabolites were used, including 351 in plasma, 391 in the liver, and 447 in the kidney (Supplementary Table 1, Additional File 1). Among these three murine tissues, 136 metabolites overlapped, and approximately 86% of them were structurally named. Furthermore, there were 118 circulating-specific metabolites, 119 renal-specific metabolites, and 132 hepatic-specific metabolites (Supplementary Table 1, Additional File 1).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eStatistical analysis\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eIn the mouse study, three pairwise comparisons were conducted to assess the effects of mono- and combination therapy in diabetic mice (MET versus VG and SGLT2i+MET versus MET) and the impact of the diabetes-prone genetic background (VG versus WT mice). Linear regression analysis was used, with the relative metabolite concentration as the outcome variable and the animal grouping as the predictor variable. Metabolites were evaluated individually and separately for each tissue. All measured metabolite values were standardized (average = 0, standard deviation = 1). To account for multiple testing in the linear models, Bonferroni correction was applied. Only metabolites with a \u003cem\u003eP\u003c/em\u003e-value below the cutoff of \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.05 / \u0026rsquo;Number of metabolites after QC\u0026rsquo; for each tissue were considered statistically different. This resulted in a cutoff of \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; \u0026nbsp;1.42x10\u003csup\u003e‑4\u003c/sup\u003e for plasma (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; \u0026nbsp;0.05 / 351), \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;\u0026nbsp;1.28x10\u003csup\u003e-4\u003c/sup\u003e for liver (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05 / 391), \u003cem\u003eP\u003c/em\u003e \u0026lt; 1.11x10\u003csup\u003e-4\u003c/sup\u003e for\u0026nbsp;kidney (\u003cem\u003eP\u003c/em\u003e \u0026lt;\u0026nbsp;0.05 / 447). Please note that Bonferroni correction was applied to determine the significance of the observed associations, and nominal significance levels (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;\u0026nbsp;0.05) were also considered for additional metabolites.\u003c/p\u003e\n\u003cp\u003eFor the human studies, potential risk factors and confounding parameters known to affect metabolite profiles were taken into account [12]. The basic model was adjusted for age and sex, while the full model included additional adjustments for body mass index, physical activity, high alcohol intake, smoking status, systolic blood pressure, HbA1c, fasting glucose levels, high-density lipoprotein cholesterol, triglycerides. Similar to the mouse study, Bonferroni correction was applied to account for multiple testing, and associations with a \u003cem\u003eP\u003c/em\u003e-value below the cutoff of P \u0026lt; 0.05 / \u0026rsquo;Number of validated metabolites\u0026rsquo; were considered statistically significant.\u003c/p\u003e\n\u003cp\u003eIn the longitudinal study (S4 to F4), generalized estimating equations were used to validate the significant associations in both the basic and fully adjusted models.\u003c/p\u003e\n\u003cp\u003eAll R Core Team, 2022 were performed using R (version 4.0.3).\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eMetformin effects on the blood metabolites in mouse and human studies\u003c/h3\u003e\n\u003cp\u003eIn the mouse study, among the 351 metabolites analyzed, plasma levels of seven metabolites were significantly altered following two weeks of metformin treatment in db/db mice. These results were determined to be statistically significant after applying Bonferroni correction (Figure 1A, Supplementary Table 2, Additional File 1). Out of the seven metformin-associated metabolites, three were identified as intermediates of the TCA cycle, namely fumarate, malate, and \u0026alpha;-ketoglutarate (\u0026alpha; KG). Of these, six metabolites showed upregulation in response to metformin treatment (e.g., fumarate displayed a positive \u0026beta;-estimate in the linear regression analysis when comparing MET with VG mice as shown in Figure 1B, and the relative concentration in MET mice was higher than that of VG mice as displayed with boxplots in Figure 1C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure \u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e Effects of metformin and leptin receptor mutation in murine plasma. \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: Volcano plots of linear regression analysis results (\u0026beta;-estimates and P-values) for 351 plasma metabolites in two pairwise comparisons of MET with VG diabetic mice and VG with WT mice. The upper and lower dashed lines represent Bonferroni-corrected and uncorrected (P = 0.05) significance levels, respectively. B: Seven metformin-associated metabolites are shown. C: Boxplots of seven metabolites. Abbreviations: WT, wild type mice; VG, vehicle gavaged diabetic mice; MET, metformin-treated diabetic mice; 2-AB, 2-aminobutyrate; 4-HB, 4-hydroxybutyrate; \u0026alpha;-KG, \u0026alpha;-ketoglutarate. See also Supplementary Table 2, Additional File 1.\u003c/p\u003e\n\u003cp\u003eIn the comparison of VG with WT mice, none of the seven metabolites exhibited a significant difference after applying Bonferroni correction. However, the values of four of these metabolites (fumarate, malate, 4-hydroxybutyrate [4-HB], and uracil) were nominally affected (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) by the genetic background of the mice as well as HFD (Supplementary Table 2, Additional File 1). Interestingly, three metabolites (\u0026alpha;‑KG, citrulline, and 2-aminobutyrate [2-AB]) did not show a significant difference between VG and WT mice. This suggests that the changes induced by metformin in these three metabolites in db/db mice were independent of the physiological consequences of the leptin receptor mutation besides the HFD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure \u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e Cross-sectional and longitudinal analyses reveal specific pattern of metformin action in human serum and plasma. \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA and B: \u0026beta;-estimates and confidence intervals of three metabolites in KORA and QBB cross-sectional human studies. C: Mean relative residue of two metabolites in longitudinal KORA study based on the fully adjusted model (age, sex, BMI, physical activity, high alcohol intake, smoking status, systolic blood pressure, HbA1c, fasting glucose levels, high density lipoprotein cholesterol, triglycerides as well as the use of statins, beta blockers, angiotensin-converting-enzyme inhibitors, and angiotensin receptor blockers). Abbreviation: 2-AB, 2 aminobutyrate. See also Supplementary Tables 3,4, Additional File 1.\u003c/p\u003e\n\u003cp\u003eIn the human studies, three of the seven metformin-associated metabolites identified in murine plasma (malate, citrulline, and 2-AB) were measured in serum samples obtained from the KORA participants at both the S4 and F4 surveys. The analysis included a comparison between 70 individuals with mt-T2D and 114 individuals with ndt-T2D patients. Using a Bonferroni cutoff for significance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.017) for the three analyzed metabolites, two of them, citrulline and malate, were found to be significantly different in the fully adjusted model (Figure 2A, Supplementary Table 3, Additional File 1). These findings were independently replicated in the plasma samples of patients from the QBB study, comparing 146 mt-T2D with 148 ndt-T2D patients (Figure 2B, Supplementary Table 3, Additional File 1). However, no significant correlation was observed for 2-AB in any of the comparisons (Figures 2A, 2B, Supplementary Table 3, Additional File 1).\u003c/p\u003e\n\u003cp\u003eIn the prospective KORA S4 to F4 study, the two metabolites that showed significant associations with metformin in the cross-sectional studies, namely malate and citrulline, were further investigated. The metformin-specific upregulation of malate and downregulation of citrulline were prospectively validated in serum samples obtained from patients with T2D who started metformin therapy after the baseline survey S4 (Figure 2C). This validation was performed using both basic and full models (Supplementary Table 4, Additional File 1). For example, the relative concentrations of malate were significantly increased in individuals who started metformin therapy after the S4 timepoint. This increase remained significant in both the crude model (\u0026beta;\u0026nbsp;=\u0026nbsp;0.39, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e=\u0026nbsp;3.31x10\u003csup\u003e-4\u003c/sup\u003e) and the full model (\u0026beta;\u0026nbsp;=\u0026nbsp;0.25, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e=\u0026nbsp;0.043), when comparing 34 T2D participants who started metformin therapy during the follow-up period with 628 metformin-na\u0026iuml;ve participants (Supplementary Table 4, Additional File 1).\u003c/p\u003e\n\u003ch3\u003eMetformin\u0026rsquo;s effects on the hepatic and renal metabolites\u003c/h3\u003e\n\u003cp\u003eAmong the 391 analyzed metabolites in the liver, two metabolites, glutamate and taurine, showed Bonferroni-significant associations with metformin (Figure 3A, Supplementary Table 2, Additional File 1). Metformin treatment resulted in an upregulation of glutamate and a downregulation of taurine compared to VG mice. Additionally, the values of fumarate and malate in the db/db liver were upregulated by metformin (with unadjusted P \u0026lt; 0.05), although they did not reach Bonferroni-significance levels (Supplementary Table 2, Additional File 1). In the comparison between VG and WT mice, glutamate, fumarate, and malate were downregulated at a nominal significant level (Figure 3A, Supplementary Table 2, Additional File 1), suggesting that these changes may be associated with the genetic background and the HFD. Notably, the observed upregulation of these metabolites (glutamate, fumarate, and malate) in the MET mice compared to VG ones may indicate beneficial effects of metformin in the liver of the db/db mice.\u003c/p\u003e\n\u003ch3\u003eFigure 3 Hepatic and renal effects of metformin.\u0026shy;\u0026shy;\u003c/h3\u003e\n\u003cp\u003eVolcano plots of linear regression results for 391 hepatic (A) and 447 renal (B) metabolites for the comparison between MET and VG are shown. The upper and lower dashed lines represent Bonferroni-corrected and uncorrected (P = 0.05) significance levels, respectively. Boxplots of selected metformin-associated metabolites in WT, VG and MET mice are shown. Abbreviations: MET, metformin-treated leptin receptor-deficient diabetic; VG, vehicle-gavaged db/db mice; WT, wild type mice; 2-AB, 2-aminobutyrate; 2-HG, 2-hydroxyglutarate. See also Supplementary Table 2, Additional File 1.\u003c/p\u003e\n\u003cp\u003eMoving to the kidneys of db/db mice, among the 447 analyzed metabolites, three metabolites (2-hydroxyglutarate [2-HG], 2-AB, and 5,6-dihydrouracil) exhibited Bonferroni-significant upregulation due to metformin treatment (Figure 3B, Supplementary Table 2, Additional File 1). Additionally, malate values in the db/db kidneys were altered by metformin at a nominal significant level (Figure 3B, Supplementary Table 2, Additional File 1). In the comparison between VG and WT mice, 2-HG had a Bonferroni-significant downregulation, malate showed an upregulation at a nominal significant level, while comparable levels of 2-AB and 5,6-dihydrouracil were observed between VG and WT mice (Figure 3B, Supplementary Table 2, Additional File 1).\u003c/p\u003e\n\u003ch3\u003eMetabolic effects of adding SGLT2i to metformin\u003c/h3\u003e\n\u003cp\u003eOf 716 analyzed metabolites in the three tissues, three (butyrylglycine, N-acetyl glycine, and indole lactate) in plasma and two (choline and X-10460) in the liver were found to have Bonferroni-significant associations with the combination therapy when comparing SGLT2i+MET with MET mice (Figures 4A, 4B, Supplementary Table 5, Additional File 1). Except for X-10460, all four identified metabolites were upregulated in the combination therapy group. However, none of the 447 analyzed renal metabolites showed Bonferroni-significant differences in the pairwise comparison between SGLT2i+MET and MET mice (Figure 4C).\u003c/p\u003e\n\u003ch3\u003eFigure 4 Metabolic effects of combination therapy in the three murine tissues\u003c/h3\u003e\n\u003cp\u003eVolcano plots in plasma (A), liver (B) and kidney (C) when compare SGLT2i+MET with MET mice. The upper and the lower dashed lines represent Bonferroni-corrected and uncorrecte\u0026shy;d (P = 0.05) significance levels, respectively. Boxplots of selected metabolites in MET and SGLT2i+MET mice are shown. Abbreviations: MET, metformin-treated diabetic mice; SGLT2i+MET, SGLT2i and metformin treated db/db mice. See also Supplementary Table 5, Additional File 1.\u003c/p\u003e\n\u003cp\u003eIn addition to the Bonferroni-significant metabolites associated with the combination therapy, several metabolites related to the TCA cycle showed nominal significant alterations (see Supplementary Table 5, Additional File 1). In the pairwise comparison between SGLT2i+MET and MET mice, the levels of malate, \u0026alpha;-KG, and pyruvate in plasma, malate and glutamate in the liver, and 2-HG in the kidneys were downregulated. On the other hand, taurine and citrulline in the liver were upregulated (Figures 4A, 4B, Supplementary Table 5, Additional File 1).\u003c/p\u003e\n\u003ch3\u003eTissue- and drug-specific effects of TCA cycle metabolites and its anaplerosis\u003c/h3\u003e\n\u003cp\u003eCollectively, we observed different tissue-dependent responses to metformin and its combination with SGLT2i for intermediates of TCA cycle and its anaplerosis. Specifically, circulating malate levels in all three db/db mice (VG, MET and SGLT2i+MET) were higher than WT ones, whereas their hepatic values were comparable between WT and MET mice, but lowered in both VG and SGLT2i+MET mice (Figure 5A). These observations may indicate that the TCA cycle activity in leukocytes (erythrocytes lack mitochondria and functional TCA cycle) and hepatocytes responds differently to metformin. Moreover, in the three group of db/db mice, similar patterns for circulating malate and \u0026alpha;-KG, hepatic malate and glutamate, and renal 2-HG, were observed (e.g., highest levels were observed in MET mice). Whereas, taurine levels were lowest in the MET mice in the liver (Figure 5A). These results may suggest that add-on SGLT2i to metformin reversed abbudances of these metabolites, thereby suggesting a bidirectional modulation of TCA cycle metabolites and anaplerosis.\u003c/p\u003e\n\u003ch3\u003eFigure 5 Alteraration of TCA cycle metabolites and related pathways.\u003c/h3\u003e\n\u003cp\u003eBoxplots of selected metabolites in four groups (A). Measured and altered TCA cycle related metabolites by monotherapy of metformin (B) and combination therapy of SGLT2i with metformin (C) in the three tissues of diabetic mice and serum/plasma in humans. Abbreviations: 2-HG, 2-hydroxyglutarate; \u0026alpha;-KG, \u0026alpha;-ketoglutarate; TCA, citric acid; NO, nitric oxide.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we conducted a systematic examination of how metformin and SGLT2i modulate 716 distinct metabolites across liver, kidneys, and plasma, integrating comparative analyses from both animal models and human subjects. Our findings highlight metformin\u0026apos;s predominant influence on metabolites associated with the TCA cycle, with its effects being nuanced by the addition of SGLT2i, resulting in a bidirectional \u0026apos;reversal\u0026apos; of its core impacts on energy metabolism, as illustrated in Figures 5B and 5C. This suggests a complex interplay between the two drugs that warrants further exploration. To establish clinical relevance, we corroborated our results with patient samples from the German KORA and South Arabian QBB studies, identifying two consistent systemic changes across ethnicities: increased malate and decreased citrulline levels. These shifts in TCA cycle metabolites and anaplerotic processes, induced by metformin treatment, not only corroborate the translational success from mice to humans but also carry implications for refining T2D management strategies.\u003c/p\u003e\n\u003cp\u003eThe TCA cycle, an essential component of cellular metabolism, relies on intermediate metabolites like \u0026alpha;-KG, fumarate, and malate, which are consumed during energy production and replenished by anaplerotic pathways [25]. Our analysis encompasses four of these pathways, depicted in Figures 5B and 5C, including the pivotal glutaminolysis process that transforms glutamine into glutamate and ultimately into \u0026alpha;-KG. Beyond its TCA cycle role, \u0026alpha;-KG serves as a metabolic hub influencing epigenetic modifications, the cellular response to hypoxia, and immune regulation, particularly in macrophages, where\u0026nbsp;it influences inflammatory and anti-inflammatory pathways\u0026nbsp;[25]. The equilibrium of \u0026alpha;-KG and its competitors\u0026nbsp;(e.g., succinate and 2-HG)\u0026nbsp;is crucial, as it regulates the activity of \u0026alpha;-KG-dependent enzymes and maintains cellular homeostasis.\u003c/p\u003e\n\u003cp\u003eIn our investigation into the metabolic impacts of antidiabetic agents, we assessed the influence of metformin and SGLT2i on succinate and 2-HG. Our findings reveal that these therapeutic interventions do not alter succinate levels, implying that they do not disrupt succinate\u0026apos;s role in cellular signaling. On the other hand, metformin alone caused an raised 2-HG levels within the kidneys of diabetic mice, aligning with levels observed in WT mice, hinting at a possible protective effect against diabetic kidney disease (DKD). Nonetheless, this metabolite also exerts significant influence over immune responses, notably by inhibiting key cellular pathways such as ATP synthase and mTOR, demonstrating its multifaceted nature.\u003c/p\u003e\n\u003cp\u003e2-HG, a structural analog of \u0026alpha;-KG, has been termed an \u0026quot;oncometabolite\u0026quot; for its association with cancer progression through the disruption of epigenetic landscapes and DNA repair mechanisms [26\u0026ndash;29]. Nonetheless, this metabolite also exerts significant influence over immune responses, notably by inhibiting key cellular pathways such as ATP synthase and mTOR, demonstrating its multifaceted nature [30].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the context of diabetes and kidney function, 2-HG\u0026apos;s role is particularly compelling. It acts as an epigenetic modulator with the potential to alter gene expression related to fibrosis and inflammation, two key processes in the pathogenesis of DKD. Studies, including those in diabetic db/db mice which are established models for this condition, have linked metformin treatment to improved renal function [31]. This improvement correlates with enhanced renal retention and reduced urinary excretion of 2-HG, suggesting that metformin\u0026apos;s renal benefits might be partially conveyed through modulation of 2-HG levels. Such modulation could mitigate inflammation and fibrosis within the kidneys. The intricacy of 2-HG\u0026apos;s influence, however, is underscored by the existence of its isomers, L-2-HG and D-2-HG, each with unique biological functions [25]. Continued research is imperative to fully decipher 2-HG\u0026apos;s effects on DKD and to harness its therapeutic potential.\u003c/p\u003e\n\u003cp\u003eOur study highlights a lesser-known entry point into the TCA cycle: fumarate. This metabolite can be derived from the urea/nitric oxide (NO) cycle occurring in the cytosol, as depicted in Figure 5B. Notably, we observed a significant reduction in circulating citrulline levels in both animal models and human subjects with diabetes, which was not mirrored in the hepatic tissue of db/db mice. This finding aligns with previous research indicating that metformin therapy decreases citrulline levels in the bloodstream of both diabetic and non-diabetic individuals [12,13], suggesting a possible influence of metformin on the urea/NO cycle and, consequently, on the TCA cycle.\u003c/p\u003e\n\u003cp\u003eIn our analysis of metformin\u0026apos;s metabolic impact, hepatic taurine emerged as the metabolite most significantly downregulated following metformin monotherapy (Figures 5A and 5B). Taurine, a compound recognized for its antioxidant properties, is commonly found in various foods and is a prevalent ingredient in energy drinks [32]. Research in rat models has demonstrated that taurine can modulate the activity of the pyruvate dehydrogenase complex, thereby influencing the rate of pyruvate\u0026apos;s entry, or anaplerosis, into the TCA cycle [33]. The decrease in taurine levels induced by metformin might initially appear paradoxical. This is because taurine is known to bolster the antioxidative actions of metformin in diabetic rats [34], and taurine supplementation has been recommended for conditions such as congestive heart failure [35]. This discrepancy suggests a complex interplay between metformin\u0026apos;s therapeutic effects and its influence on metabolic pathways, warranting further investigation to elucidate the specific mechanisms at play.\u003c/p\u003e\n\u003cp\u003eCollectively, our analysis suggests that metformin monotherapy might regulate the replenishment of TCA cycle intermediates, an effect known as anaplerosis, potentially by stimulating hepatic glutaminolysis, which could augment glutamate production, leading to increased levels of \u0026alpha;-KG. Additionally, metformin therapy\u0026apos;s association with elevated renal 2-HG levels, a metabolite implicated in both tumor development and immune system regulation, indicates that metformin may have a broader impact on these biological processes.\u0026nbsp;The observed increase in fumarate and subsequent rise in malate levels may be influenced by the dampening of both the urea and NO cycles. Concurrently, the observed hepatic taurine depletion points towards a possible reduction in antioxidant defense mechanisms and regulatory influence on the TCA cycle. These metabolic adjustments may be closely linked with the multifaceted therapeutic effects of metformin, particularly in the management of diabetic hepatic and renal pathologies.\u003c/p\u003e\n\u003cp\u003eMetformin\u0026apos;s capacity to normalize hepatic glutamate and renal 2-HG levels to those observed in non-diabetic WT mice indicates a potential rebalancing of metabolic disturbances caused by diabetes. This homeostatic effect could represent one of several mechanisms by which metformin exerts its therapeutic action. Such mechanisms include the improvement of hepatic steatosis, decrease in lipotoxicity, and mitigation of inflammation, all of which contribute to the drug\u0026rsquo;s broad spectrum of benefits in the context of diabetic liver and kidney disease management.\u003c/p\u003e\n\u003cp\u003eThe administration of metformin in conjunction with SGLT2i painted a contrasting metabolic landscape. This combination therapy resulted in a notable reduction in plasma \u0026alpha;-KG, hepatic glutamate, renal 2-HG, and both plasma and hepatic\u0026nbsp;malate levels, while hepatic taurine values increased in diabetic mice (Figures 5B, 5C). These findings suggest that SGLT2i may counterbalance some of metformin\u0026apos;s metabolic actions, prompting changes in key metabolites that could reshape the anaplerotic flux and the physiological processes it governs.\u003c/p\u003e\n\u003cp\u003eThe notable\u0026nbsp;decrease\u0026nbsp;in renal 2-HG prompted by the combined therapy could hint at a subdued immune response. This postulation is particularly intriguing when considering the elevated levels of C-reactive proteins observed in mice receiving both SGLT2i and metformin compared to those on metformin monotherapy (refer to Table 1). The increased C-reactive protein levels in the dual therapy group might represent a compensatory response to the suggested attenuation of immune activity inferred from the lower 2-HG values. Alternatively, it could signal an independent effect of the drug combination that has yet to be identified. These findings underscore the need for further research to unravel the complex interactions between metabolic modulation, pharmacological treatment, and immune responses in the management of diabetic kidney disease, as well as to fully comprehend the implications for patient care strategies.\u003c/p\u003e\n\u003cp\u003eAdding SGLT2i to metformin, an increase in hepatic, but not plasma, citrulline levels was observed. This elevation could potentially result in higher fumarate levels within the liver. However, as previously discussed, the combination therapy led to lower levels of hepatic glutamate, renal 2-HG, and circulating \u0026alpha;-KG, which might then precipitate a downstream decrease in TCA cycle intermediates, including fumarate and malate. Consequently, considering the opposing effects of these three entry points into the TCA cycle, the net effect on fumarate levels was that they remained similar between the SGLT2i+MET and MET treatment groups across all examined tissues (plasma, liver, and kidneys, Figure 5C). This balance suggests a complex interaction of the combined therapy on TCA cycle dynamics, wherein the influence of one drug may modulate or offset the effects of the other, resulting in a metabolic equilibrium of certain intermediates within the cycle.\u003c/p\u003e\n\u003cp\u003eOur research also revealed that combination therapy restores hepatic taurine levels, which were reduced by metformin alone, suggesting a compensatory mechanism at play. Given taurine\u0026rsquo;s well-established antioxidative capabilities, its upregulation in the liver under combination therapy may imply an intensified defense against oxidative stress. This antioxidative adjustment could be especially advantageous for diabetic patients, where oxidative stress significantly contributes to the advancement of liver disease and complicates other diabetes-related conditions.\u003c/p\u003e\n\u003cp\u003eMechanistically, a previous study found that monotherapy with SGLT2 inhibitors (SGLT2i) suppresses the activity of glutamate dehydrogenase (GDH) [36]. This suppression, when combined with other therapies, impacts a series of enzymatic reactions that include GDH, consequently affecting the production of nicotinamide adenine dinucleotide reduced form (NADH). NADH molecules are essential for transferring electrons to the mitochondrial electron transport chain (ETC). As electrons are transported through the complexes in the inner mitochondrial membrane, a functional ETC generates a mitochondrial membrane potential, which is utilized to produce ATP through a process requiring oxygen, known as oxidative phosphorylation (OXPHOS). Mitochondrial complex I in the ETC replenishes nicotinamide adenine dinucleotide (NAD+), allowing the oxidative TCA cycle to continue [25].\u003c/p\u003e\n\u003cp\u003eFurthermore, the inhibition of GDH activity by SGLT2i is thought to increase the AMP/ATP ratio, thereby activating AMP-activated protein kinase (AMPK), which is a well-known target of metformin [36]. Activation of AMPK leads to the inhibition of lipogenesis and the promotion of fatty acid oxidation, which facilitates the breakdown of fatty acids for energy production [37]. Therefore, AMPK activation in the liver has been associated with beneficial effects on liver diseases, particularly in the context of metabolic hepatic disorders like non-alcoholic fatty liver disease (NAFLD).\u003c/p\u003e\n\u003cp\u003eExcessively elevated hepatic mitochondrial TCA cycle activity has been observed in HFD-induced fatty liver and in patients with NAFLD [38,39]. In these cases, oxidative substrates are produced, leading to oxidative stress and tissue damage in the liver. However, SGLT2i may exert antioxidative effects on the liver by suppressing the TCA cycle. Indeed, similar effects have been observed in the kidneys of diabetic mice, where SGLT2i suppresses the accumulation of TCA cycle-associated metabolites and the increase of oxidative stress [40]. Therefore, adding SGLT2i to metformin might protect the liver in patients with NAFLD by suppressing an overly activated TCA cycle.\u003c/p\u003e\n\u003cp\u003eTo reliably identify true drug effects, we additionally examined metabolites associated with drug treatment through pairwise comparisons between VG and WT mice. The db/db mice used in our study have a distinct genetic background and were subjected to a HFD, resulting in the VG mice exhibiting characteristics of diabesity, dyslipidemia, and inflammation, as detailed in Table 1. The metformin-induced upregulation of malate, fumarate, and \u0026alpha;-KG observed in the plasma of diabetic mice, as well as the increase of malate in the blood of T2D patients, might be attributed to the non-TCA cycle related roles of these metabolites in immunometabolism. For instance, fumarate and \u0026alpha;-KG are known to promote anti-inflammatory phenotypes in immune cells and extend lifespan in Caenorhabditis elegans [25]. Similarly, metformin is recognized for its anti-inflammatory and anti-aging properties [41].\u003c/p\u003e\n\u003cp\u003eFurthermore, adequate nutrient availability can stimulate the reverse flux in the TCA cycle through glutaminolysis, leading to increased production of malate and fumarate in cancer cells treated with metformin [42]. Reductive carboxylation in the TCA cycle and glutaminolysis are also essential for glucose-stimulated insulin secretion in the pancreas [43] and for modulating immune cell activity [44]. Investigating whether a similar shift in TCA cycle dynamics and anaplerotic processes occurs in the leukocytes and hepatocytes of metformin-treated mice warrants further research.\u003c/p\u003e\n\u003ch1\u003eLimitations\u0026nbsp;\u003c/h1\u003e\n\u003cp\u003eThis study conducted a comprehensive analysis of 716 metabolites across three murine tissues, yet a limitation of our non-targeted metabolomics approach was the presence of some structurally unknown metabolites, such as X-10460. This particular metabolite was found to differ significantly in the comparison between the SGLT2i+MET and MET groups. Despite this finding, we could not ascertain the biomedical relevance of the combination therapy based on this metabolite alone due to its unknown nature. While the effects of metformin observed in mice were confirmed in the blood samples of two ethnically diverse human cohorts, our human studies did not involve patients treated with SGLT2 inhibitors. This was because the approval of SGLT2i occurred subsequent to the commencement of our examinations in the KORA studies (S4 between 1999 and 2001, and F4 from 2006 to 2008). Furthermore, our mouse intervention study was confined to male subjects, which limits the generalizability of our findings and prevents validation in human tissues beyond blood samples. Additionally, our study was conducted using a steady-state metabolomics approach, which only allows for single metabolite analysis. Consequently, this methodology does not yield information regarding dynamic inter-organ metabolic pathways, such as the TCA cycle flux, the directionality of this cycle (whether oxidative or reductive), or the anaplerotic processes fueling it. Future research should aim to address these limitations by incorporating targeted analyses that can capture the complexity and dynamism of metabolic pathways.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study reveals the complex interactions between the antidiabetic drugs, metformin and SGLT2i, on central metabolic pathways, with implications extending beyond diabetes treatment. We observed that these medications bidirectionally modulate TCA cycle intermediates, with metformin potentially offering anti-inflammatory benefits, although these benefits may be diminished when combined with SGLT2i. Notably, SGLT2i may counteract an overactive TCA cycle, offering hepatic protection, which is particularly relevant for NAFLD patients.\u003c/p\u003e\n\u003cp\u003eAdditionally, the metabolites we have examined may play roles in immune cell regulation, pointing to a wider therapeutic potential for disorders that are influenced by \u0026alpha;-KG-dependent pathways, including cancer and immune dysfunction. Our results emphasize the necessity for further research to understand how these drugs modulate metabolism and the potential for creating targeted treatments that leverage these metabolic alterations. Moreover, these insights could pave the way for personalized treatment strategies, optimizing therapeutic outcomes by accounting for individual metabolic responses.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our research not only underscores the complex metabolic effects of widely used diabetes medications but also suggests the exciting potential for their repurposing in the treatment of intricate metabolic and immune-mediated disorders, underlining the evolving nature of drug use in modern medicine.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2-AB\u003c/em\u003e\u003c/strong\u003e: 2-aminobutyrate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2-HG\u003c/em\u003e\u003c/strong\u003e: 2-hydroxyglutarate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4-HB\u003c/em\u003e\u003c/strong\u003e: 4-hydroxybutyrate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026alpha;-KG\u003c/em\u003e\u003c/strong\u003e: \u0026alpha;-ketoglutarate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAMP\u003c/em\u003e\u003c/strong\u003e: adenosine monophosphate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAMPK\u003c/em\u003e\u003c/strong\u003e: AMP-activated protein kinase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eATP\u003c/em\u003e\u003c/strong\u003e: adenosine triphosphate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBMI\u003c/em\u003e\u003c/strong\u003e: body mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBP\u003c/em\u003e\u003c/strong\u003e: blood pressure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDKD\u003c/em\u003e\u003c/strong\u003e: diabetic kidney disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eeNOS\u003c/em\u003e\u003c/strong\u003e: endothelial nitric oxide synthase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eETC\u003c/em\u003e\u003c/strong\u003e: electron transport chain\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGDH\u003c/em\u003e\u003c/strong\u003e: glutamate dehydrogenase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHDL\u003c/em\u003e\u003c/strong\u003e: high-density lipoprotein\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHFD\u003c/em\u003e\u003c/strong\u003e: high-fat diet\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLDL\u003c/em\u003e\u003c/strong\u003e: low-density lipoprotein\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMET\u003c/em\u003e\u003c/strong\u003e: metformin-treated diabetic mice\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003emTOR\u003c/em\u003e\u003c/strong\u003e: mammalian target of rapamycin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003emt-T2D\u003c/em\u003e\u003c/strong\u003e: metformin treated type 2 diabetes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNAD\u003c/em\u003e\u003c/strong\u003e:\u0026nbsp;nicotinamide adenine dinucleotide\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNADH\u003c/em\u003e\u003c/strong\u003e:\u0026nbsp;nicotinamide adenine dinucleotide reduced form\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNAFLD\u003c/em\u003e\u003c/strong\u003e:\u0026nbsp;non-alcoholic fatty liver disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003endt-T2D\u003c/em\u003e\u003c/strong\u003e: non-anti-diabetic drug-treated type 2 diabetes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNO\u003c/em\u003e\u003c/strong\u003e: nitric oxide\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOXPHOS\u003c/em\u003e\u003c/strong\u003e: oxidative phosphorylation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eQC\u003c/em\u003e\u003c/strong\u003e: quality control\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSGLT2i\u003c/em\u003e\u003c/strong\u003e: sodium-glucose-cotransporter-2 inhibitors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSGLT2i+MET\u003c/em\u003e\u003c/strong\u003e: Sodium-glucose-cotransporter-2-inhibitor and metformin-treated diabetic mice\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eT2D\u003c/em\u003e\u003c/strong\u003e: type 2 diabetes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTCA\u003c/em\u003e\u003c/strong\u003e: citric acid, also known as\u0026nbsp;tricarboxylic acid\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ew/o\u003c/em\u003e\u003c/strong\u003e: without\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWT\u003c/em\u003e\u003c/strong\u003e: wild type mice\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eVG\u003c/em\u003e\u003c/strong\u003e: vehicle gavaged diabetic mice\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eAll animal studies were conducted in accordance with FELASA protocols and approved by the District Government of Upper Bavaria, Germany (Regierung von Oberbayern, Gz.55.2 1 54 2531 70 07, 55.2 1 2532 153 11).\u003c/p\u003e\n\u003cp\u003eKORA S4 and F4 studies were approved by the Ethics Committees of the Bavarian Medical Association in Munich, Germany.\u0026nbsp;All protocols of the QBB study were approved by the Hamad Medical Corporation Ethics Committee. All study participants provided written informed consent.\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eThe KORA S4/F4 data sets are not publicly available because of data protection agreements but can be provided upon request through the KORA-PASST (Project application self-service tool, www.helmholtz-muenchen.de/kora-gen). The QBB data can be obtained to the researcher to submit access application forms online (https://researchportal.qatarbiobank.org.qa/).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eDeclaration of interests:\u003c/h3\u003e\n\u003cp\u003eM.F.S. was employed at Helmholtz Munich during the execution of this study. He is currently an employee of the Global Medical Affairs and Pharmacovigilance Department of BAYER AG Pharmaceuticals (Berlin, Germany), however, the company was not involved in work related to data generation and manuscript generation. S.N. was employed by the Helmholtz Munich during the execution of this study. She is currently an employee of Sanofi Aventis Deutschland GmbH; however, the company was not involved in work related to data and manuscript generation.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis Mouse200 was funded in part by grants from the German Federal Ministry of Education and Research to the German Center for Diabetes Research and to the Research Consortium \u0026quot;Systems Biology of Metabotypes\u0026quot; (SysMBo grant 0315494A) and by the Helmholtz Alliance ICEMED (Imaging and Curing Environmental Metabolic Diseases), through the Initiative and Network Fund of the Helmholtz Association and supported by the Helmholtz Portfolio Theme \u0026quot;Metabolic Dysfunction and Disease.\u0026quot;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe KORA study was initiated and financed by the Helmholtz Zentrum M\u0026uuml;nchen \u0026ndash; German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC‑Health), Ludwig-Maximilians-Universit\u0026auml;t, as part of LMUinnovativ.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe German Diabetes Center is funded by the German Federal Ministry of Health (Berlin, Germany) and the Ministry of Innovation, Science and Research of the State of North Rhine-Westphalia (Dusseldorf, Germany). This study was supported in part by a grant from the German Federal Ministry of Education and Research to the German Center for Diabetes Research (DZD). The diabetes part of the KORA\u0026nbsp;F4 study was funded by a grant from the German Research Foundation (DFG; RA\u0026nbsp;459/3‑1).\u003c/p\u003e\n\u003cp\u003ePart of this study was supported by EU\u0026nbsp;FP7 grants HEALTH‑2013‑2.4.2‑1/602936 (Project CarTarDis). Part of this study was supported by the funding from the European Union\u0026rsquo;s Horizon 2020 research and innovation programmes: The DeTecT2D \u0026amp; iPDM-GO EIT Health Innovation Project supported by the European Institute of Innovation and Technology (EIT), a body of the European Union; This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 821508 (CARDIATEAM). The JU receives support from the European Union\u0026apos;s Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA).\u003c/p\u003e\n\u003cp\u003eK.Suh. was supported by \u0026apos;Biomedical Research Program\u0026apos; funds at Weill Cornell Medicine in Qatar, a program funded by the Qatar Foundation and\u0026nbsp;is supported by Qatar National Research Fund (QNRF) grant NPRP11C-0115-180010.\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026apos; contributions\u003c/h3\u003e\n\u003cp\u003eJ.Adam, M.H, R.W-S. conceived and designed the current study. J.Adam, M.H., M.C., S.B., C.M., J.H., S.H., M.Rom., M.R., S.M., J.K., K.S., R.W-S. analyzed the data and interpreted the results. M.H., R.P.M., G.K., W.R., S.Z., M.F.S., S.N., J.A., C.G., A.P., M.RdA., K.Suh. performed the experiments, including metabolic profiling. Z.Z., D.P.A., T.M., assisted in manuscript generation. J.Adam, M.C., S.B., T.L.A., R.W-S. wrote the manuscript. All authors approved the final version of manuscript.\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003ch3\u003eWe thank the people of the Institute of Diabetes and Regeneration Research (Anett\u0026nbsp;Seelig, J\u0026uuml;rgen\u0026nbsp;Schulthei\u0026szlig;), Institute of Experimental Genetics (Moya\u0026nbsp;Wu), and the animal caretaker staff of the German Mouse Clinic for excellent technical assistance. Moreover, we thank the Mouse200 project team Babara\u0026nbsp;Pfitzner, Alesia\u0026nbsp;Walker and Gabriele\u0026nbsp;Zieglmeier.\u003c/h3\u003e\n\u003cp\u003eWe express our appreciation to all KORA study participants for donating their blood and time. We thank the field staff in Augsburg conducting the KORA studies. We are grateful to the staff (Sophie Molnos, Annika Wahl, Mohamed Elhadad) from the Institute of Epidemiology, and from Genome Analysis Center Metabolomics Platform, at the Helmholtz Zentrum M\u0026uuml;nchen, who helped in the sample/data logistics, and metabolomic measurements.\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the contribution of OpenAI\u0026apos;s ChatGPT language model in providing assistance during the preparation of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLin H, Ao H, Guo G, Liu M. The Role and Mechanism of Metformin in Inflammatory Diseases. J Inflamm Res. 2023;16:5545\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriss T, Vincent EE, Egnatchik R, Chen J, Ma EH, Faubert B, et al. Metformin Antagonizes Cancer Cell Proliferation by Suppressing Mitochondrial-Dependent Biosynthesis. 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Science. 2013;342:1242454.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pharmacometabolomics, metformin, SGLT2 inhibitors, TCA cycle, anaplerosis, glutaminolysis, anti-inflammatory effects, antioxidant responses, renal metabolism, non-alcoholic fatty liver disease (NAFLD), type 2 diabetes, personalized medicine","lastPublishedDoi":"10.21203/rs.3.rs-3931333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3931333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMetformin and sodium-glucose-cotransporter-2 inhibitor (SGLT2i) are cornerstone therapies for managing hyperglycemia in diabetes, yet their nuanced impacts on metabolic processes, particularly in the citric acid (TCA) cycle and its anaplerotic pathways, are not fully delineated. This study aims to investigate the tissue-specific metabolic effects of metformin, both as a monotherapy and in combination with SGLT2i, on the TCA cycle and associated anaplerotic reactions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eOur study employed a three-pronged approach: first, comparing metformin-treated diabetic mice (MET) with vehicle-treated controls (VG) and non-diabetic wild types (WT) to identify metformin-specific metabolic changes; second, assessing these changes in human cohorts (KORA and QBB) and a longitudinal KORA study of metformin-na\u0026iuml;ve patients; third, contrasting MET with those on combination therapy (SGLT2i\u0026thinsp;+\u0026thinsp;MET). Metabolic profiling was conducted on 716 metabolites from plasma, liver, and kidney tissues post-treatment. Linear regression analysis and Bonferroni correction were used for rigorous statistical evaluation across all comparisons, complemented by pathway analyses to elucidate the pathophysiological implications of the metabolites involved.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMetformin monotherapy was significantly associated with upregulation of TCA cycle intermediates, such as malate, fumarate, and α-ketoglutarate (α-KG), in plasma, along with anaplerotic substrates including hepatic glutamate and renal 2-hydroxyglutarate (2-HG) in diabetic mice. Conversely, downregulated hepatic taurine was observed. However, the addition of SGLT2i reversed these metabolic effects, indicating a complex interplay between these antidiabetic drugs in regulating the central energy metabolism. Human T2D subjects on metformin therapy exhibited significant systemic alterations in metabolites, including increased malate but decreased citrulline. The drugs' bidirectional modulation of TCA cycle intermediates appeared to influence four key anaplerotic pathways linked to glutaminolysis, tumorigenesis, immune regulation, and antioxidative responses.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study elucidates the specific metabolic consequences of metformin and SGLT2i on the TCA cycle and beyond, reflecting potential impacts on the immune system. Metformin shows promise for its anti-inflammatory properties, while the addition of SGLT2i may provide liver protection in conditions like non-alcoholic fatty liver disease (NAFLD). These observations highlight the potential for repurposing these drugs for broader therapeutic applications and underscore the importance of personalized treatment strategies.\u003c/p\u003e","manuscriptTitle":"Bidirectional modulation of TCA cycle metabolites and anaplerosis by metformin and its combination with SGLT2i","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-09 21:09:18","doi":"10.21203/rs.3.rs-3931333/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-02-29T12:06:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-21T00:13:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136da14d-893a-4472-93ab-78162bf848ac","date":"2024-02-09T00:40:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-07T08:40:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-07T08:25:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-07T06:00:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2024-02-05T15:16:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"09db9318-a8fa-45c1-a25b-350b2795b598","owner":[],"postedDate":"February 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-04T16:39:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-09 21:09:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3931333","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3931333","identity":"rs-3931333","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}