Prediabetes and type 2 diabetes (T2D) are metabolic disorders characterized by insulin resistance and β-cell dysfunction. To understand the molecular mechanisms driving the transition from prediabetes to T2D, we performed a longitudinal proteogenomic analysis on 458 participants from the Prediabetes Lifestyle Intervention Study (PLIS). We identified 185 plasma proteins to be differentially expressed between conditions, 36 of which predict future T2D-onset. Integrating genetic data from 321 individuals, we generated a genome-wide protein quantitative trait loci (pQTL) map, identifying 86 differential and 700 shared cis-pQTLs between prediabetes and T2D. Mediation analysis revealed 60 putative causal links connecting allele-driven plasma protein expression to clinical traits, identifying body fat distribution, insulin resistance, and β-cell function as central drivers of pathogenesis. Collectively, these findings highlight specific proteins underlying disease progression and substantiate the view that prediabetes and T2D are not distinct conditions, but rather stages on a unified metabolic spectrum. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Trial NCT01947595 ### Funding Statement Archit Singh, Dr Mauro Tutino and Dr Ozvan Bocher have received funding from the European Union's Horizon 2020 research and innovation program under Grant Agreement No 101017802 (OPTOMICS). PLIS and this post hoc analysis were supported by the German Center for Diabetes Research, which is funded by the German Federal Ministry for Education and Research and the German states where its partner institutions are located (01GI0925). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study protocol was approved by the ethics committee of the University Clinic of Tübingen (Tübingen, 55/2012; ClinicalTrials.gov registration: [NCT01947595][1]). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors. [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT01947595&atom=%2Fmedrxiv%2Fearly%2F2026%2F02%2F16%2F2026.02.13.26346161.atom

Clinical practice guidelines recommend defined weight loss goals for the prevention of type 2 diabetes (T2D) in those individuals with increased risk, such as prediabetes. However, achieving prediabetes remission, that is, reaching normal glucose regulation according to American Diabetes Association criteria, is more efficient in preventing T2D than solely reaching weight loss goals. Here we present a post hoc analysis of the large, multicenter, randomized, controlled Prediabetes Lifestyle Intervention Study (PLIS), demonstrating that prediabetes remission is achievable without weight loss or even weight gain, and that it also protects against incident T2D. The underlying mechanisms include improved insulin sensitivity, β-cell function and increments in β-cell-GLP-1 sensitivity. Weight gain was similar in those achieving prediabetes remission (responders) compared with nonresponders; however, adipose tissue was differentially redistributed in responders and nonresponders when compared against each other-while nonresponders increased visceral adipose tissue mass, responders increased adipose tissue in subcutaneous depots. The findings were reproduced in the US Diabetes Prevention Program. These data uncover essential pathways for prediabetes remission without weight loss and emphasize the need to include glycemic targets in current clinical practice guidelines to improve T2D prevention.

Introduction and Objective: Prediabetes remission has beneficial effects for type 2 diabetes (T2D) prevention. However, it is unknown whether early remission is superior to late remission. Thus, we investigated if reaching prediabetes remission early during a lifestyle intervention is associated with lower T2D risk compared to reaching remission later on. Methods: We studied 865 individuals with prediabetes from the German multi-center Prediabetes Lifestyle Intervention Study (PLIS) who could be classified into early remission at 6 months of a lifestyle intervention (ER, n = 217), late remission at 12 months (LR, n = 110), or no remission (NR, n = 538). Prediabetes remission was defined as return to normal glucose regulation and normalized HbA1c according to ADA criteria. Cox regression models were fit with age, sex, intervention intensity, T2D risk and weight loss as covariates. Results: The ER group (n=217) was comparable in age (p=0.24), sex distribution (p = 0.07), BMI (p > 0.99), insulin sensitivity (p = 0.2) and insulin secretion (p = 0.48) vs the LR group (n=110). Fasting glucose (5.70 ±0.43 mmol/L vs 5.83 ±0.44, p = 0.016) and HbA1c (5.53 ±0.29 % vs 5.68 ±0.31, p < 0.001) was slightly lower in ER compared to LR, while 2h glucose was similar (p = 0.77). Overall, T2D risk was lower in both ER and LR compared to NR (n=538; RR 0.15 [95% CI: 0.07-0.31], p < 0.001 and 0.44 [0.23-0.82], p = 0.009, respectively). However, ER provided a more pronounced T2D risk reduction than LR (0.31 [0.12-0.79], p = 0.01). Conclusion: Achieving early remission of prediabetes to NGR during lifestyle intervention may provide additional benefits for T2D prevention compared with late remission. A. Sandforth: None. L. Sandforth: None. S. Katzenstein: None. J. Seissler: None. N. Perakakis: Other Relationship; Novo Nordisk, Lilly Diabetes. Advisory Panel; Bayer Pharmaceuticals, Inc. Other Relationship; APOGEPHA, Transmedac Innovations AG, GWT-TUD, Elbe-Gesundsheintszentrum GmbH, Open Exploration. R. Wagner: Speaker's Bureau; Boehringer-Ingelheim, Novo Nordisk. Advisory Panel; Sanofi. Speaker's Bureau; Sanofi. Advisory Panel; Lilly Diabetes. A. Peter: None. R. Lehmann: None. H. Preissl: None. I. Yurchenko: None. J. Szendroedi: Advisory Panel; Novo Nordisk, Lilly Diabetes, Novartis AG, Boehringer-Ingelheim. M. Blüher: Advisory Panel; AstraZeneca. Speaker's Bureau; Amgen Inc. Advisory Panel; Bayer Pharmaceuticals, Inc, Boehringer-Ingelheim. Speaker's Bureau; Daiichi Sankyo. Advisory Panel; Eli Lilly and Company, Novo Nordisk, Nestlé Health Science, Sanofi-Aventis Deutschland GmbH. A. Schürmann: None. S. Kabisch: Research Support; Almond Board California, California Walnut Commission. Other Relationship; JuZo-Akademie, Boehringer-Ingelheim. Research Support; J. Rettenmaier & Söhne. Other Relationship; Lilly Diabetes. Research Support; Wilhelm-Doerenkamp-Foundation. K. Mai: None. P.E. Schwarz: None. M. Heni: Advisory Panel; Amryt Pharma. Speaker's Bureau; Amryt Pharma, AstraZeneca, Boehringer-Ingelheim. Advisory Panel; Boehringer-Ingelheim. Speaker's Bureau; Lilly Diabetes, Novartis AG, Novo Nordisk, Sanofi. M. Roden: Research Support; Boehringer-Ingelheim. Advisory Panel; Echosens. Speaker's Bureau; Madrigal Pharmaceuticals, Inc. Advisory Panel; MSD Life Science Foundation. Board Member; Novo Nordisk. Advisory Panel; TARGET PharmaSolutions, Inc. N. Stefan: Speaker's Bureau; AstraZeneca, Boehringer-Ingelheim. Consultant; Lilly Diabetes. Speaker's Bureau; Lilly Diabetes. Consultant; Pfizer Inc. Speaker's Bureau; Sanofi. Research Support; Sanofi. Speaker's Bureau; Novo Nordisk, GlaxoSmithKline plc. Consultant; GlaxoSmithKline plc. A. Fritsche: Advisory Panel; Abbott. Speaker's Bureau; AstraZeneca. A.L. Birkenfeld: None. R. Jumpertz von Schwartzenberg: None.

Introduction and Objective: Current guidelines recommend weight loss targets for individuals at risk for type 2 diabetes (T2D). Prediabetes is a high-risk state for T2D, and remission of prediabetes during weight loss has additional benefits for T2D prevention. Thus, we hypothesized that reaching glycemic targets is a more effective strategy for T2D prevention than weight loss targets. Methods: We studied 903 individuals with prediabetes from the German Prediabetes Lifestyle Intervention Study for whom data for weight loss and glycemic category classification was available. Glucose regulation was assessed by a 75 g oral glucose tolerance test. Prediabetes remission was defined as return to normal glucose regulation and normalized HbA1c according to ADA criteria. T2D risk was compared between responders and non-responders (R and NR) who lost weight (WL, n=298; < -5% of initial body weight), remained weight stable (WS, n=371; -5-0%) and gained weight (WG, n=234; >0%). Cox regression models were fit with age, sex and intervention intensity as covariates. Results: At baseline, age (p=0.11), fasting glucose (p=0.09), 2-hour glucose (p=0.98) and beta cell function were comparable between all three responder groups. WL-, WS- and WG-response was similarly protective from developing future T2D (HR for WL R vs. WL NR 0.11 [95 CI: 0.03-0.36], p = 0.00026, HR for WS R vs. WS NR 0.40 [95 CI: 0.17-0.93], p = 0.033, HR for WG R vs. WG NR 0.25 [95 CI: 0.09 -0.69], p = 0.0072,). T2D risk did not differ between weight loss strata (HR 0.82 [95 CI: 0.54-1.25], p = 0.36 for WS-R vs WG-R; and HR 0.91 [0.64-1.30], p = 0.61 for WL-R vs WG-R). Conclusion: Prediabetes remission, i.e. glycemic targets rather than weight loss targets, should be the primary treatment goal for T2D prevention. A. Sandforth: None. L. Sandforth: None. S. Katzenstein: None. J. Seissler: None. N. Perakakis: Other Relationship; Novo Nordisk, Lilly Diabetes. Advisory Panel; Bayer Pharmaceuticals, Inc. Other Relationship; APOGEPHA, Transmedac Innovations AG, GWT-TUD, Elbe-Gesundsheintszentrum GmbH, Open Exploration. R. Wagner: Speaker's Bureau; Boehringer-Ingelheim, Novo Nordisk. Advisory Panel; Sanofi. Speaker's Bureau; Sanofi. Advisory Panel; Lilly Diabetes. A. Peter: None. R. Lehmann: None. H. Preissl: None. I. Yurchenko: None. J. Szendroedi: Advisory Panel; Novo Nordisk, Lilly Diabetes, Novartis AG, Boehringer-Ingelheim. M. Blüher: Advisory Panel; AstraZeneca. Speaker's Bureau; Amgen Inc. Advisory Panel; Bayer Pharmaceuticals, Inc, Boehringer-Ingelheim. Speaker's Bureau; Daiichi Sankyo. Advisory Panel; Eli Lilly and Company, Novo Nordisk, Nestlé Health Science, Sanofi-Aventis Deutschland GmbH. A. Schürmann: None. S. Kabisch: Research Support; Almond Board California, California Walnut Commission. Other Relationship; JuZo-Akademie, Boehringer-Ingelheim. Research Support; J. Rettenmaier & Söhne. Other Relationship; Lilly Diabetes. Research Support; Wilhelm-Doerenkamp-Foundation. K. Mai: None. P.E. Schwarz: None. M. Heni: Advisory Panel; Amryt Pharma. Speaker's Bureau; Amryt Pharma, AstraZeneca, Boehringer-Ingelheim. Advisory Panel; Boehringer-Ingelheim. Speaker's Bureau; Lilly Diabetes, Novartis AG, Novo Nordisk, Sanofi. M. Roden: Research Support; Boehringer-Ingelheim. Advisory Panel; Echosens. Speaker's Bureau; Madrigal Pharmaceuticals, Inc. Advisory Panel; MSD Life Science Foundation. Board Member; Novo Nordisk. Advisory Panel; TARGET PharmaSolutions, Inc. N. Stefan: Speaker's Bureau; AstraZeneca, Boehringer-Ingelheim. Consultant; Lilly Diabetes. Speaker's Bureau; Lilly Diabetes. Consultant; Pfizer Inc. Speaker's Bureau; Sanofi. Research Support; Sanofi. Speaker's Bureau; Novo Nordisk, GlaxoSmithKline plc. Consultant; GlaxoSmithKline plc. A. Fritsche: Advisory Panel; Abbott. Speaker's Bureau; AstraZeneca. R. Jumpertz von Schwartzenberg: None. A.L. Birkenfeld: None.

Objective Bariatric surgery (BS) is an effective treatment option for individuals with obesity and type 2 diabetes (T2D). However, whether outcomes in subtypes of individuals at risk for T2D and/or comorbidities (Tübingen Clusters) differ, is unknown. Of these, clusters 5 and 6 (C5, C6) are high-risk clusters for developing T2D and/or comorbidities, while cluster 4 (C4) is a low-risk cluster. We investigated BS outcomes, hypothesizing high-risk clusters benefit most due to great potential for metabolic improvement. Research Design and Methods We allocated participants without T2D but at risk for T2D, defined by elevated BMI, to the Tübingen Clusters. Participants had normal glucose regulation or prediabetes according to American Diabetes Association criteria. Two cohorts underwent BS: A discovery (Lille, France) and a replication cohort (Rome, Italy). A control cohort (Tübingen, Germany) received behavioral modification counseling. Main outcomes included glucose regulation and prediabetes remission. Results In the discovery cohort, 15.0% of participants (n=121) were allocated to C4, 22.3% (n=180) to C5, and 62.4% (n=503) to C6. Relative body weight loss was similar between all clusters, however C5 most strongly reduced insulin resistance and improved beta-cell function. Prediabetes remission rate was lowest in low-risk C4 and highest in high-risk C5. Individuals from high-risk clusters changed to low-risk clusters in all BS cohorts but not in the control cohort. Conclusions Participants in C5 had the highest benefit from BS in terms of improvement in insulin resistance, beta-cell function and prediabetes remission. This novel classification might help identify individuals who will benefit specifically from BS.

Introduction and Objective: Prediabetes is a high-risk state for the development of type 2 diabetes (T2D), with large heterogeneity for the risk of progression and complications and additional variation imposed by ethnicity. This study aimed to develop an unified data-driven approach to characterize individuals with prediabetes and assess differences in risk profiles of European and South Asian (Indian) populations. Methods: Data from Central Europeans (n = 1628) and South Asians (n = 5171), who underwent a 75 g oral glucose tolerance test, were analyzed. Prediabetes was defined by ADA criteria. We selected nine common clinical traits in each cohort. Partitioning around medoids algorithm was employed and cluster number was determined by evaluating Jaccard stability. We determined risk for adverse events by calculating Hazard ratios (HR) corrected for age and sex. Results: The Asian Indian cohort was younger (48.4 years ± 10.9 vs 52.2 ± 13.5, p < 0.01) and had a lower body mass index (27.8 kg/m2 ± 4.5 vs 32.9 ± 8.9, p < 0.01), however had higher HbA1c, fasting and post-challenge glucose. Consensus clustering yielded 5 stable clusters: Young/Overweight, Isolated IFG, Age-related, High Glycemia and Obesity-related. The High Glycemia Cluster had the highest risk to develop T2D and also Age-related and Obesity-related Clusters had increased T2D risk in both cohorts. However, the Young/Overweight Cluster had elevated T2D risk only in the Asian Indian Cohort. On the contrary, future nephropathy risk was elevated in the Age-related and High Glycemia Clusters among Central Europeans but not South Asians. Conclusion: This study establishes an overarching sub-phenotyping framework for individuals with prediabetes and its complications, revealing ethnic differences and commonalities in risk profiles. This approach facilitates the unprecedented undertaking to compare disease trajectories and, in future, treatment responses in individuals from diverse ethnic communities. V. Baskar: None. M. Ganslmeier: None. A. Vignesh: None. J. S: None. H. Preissl: None. R. Wagner: Speaker's Bureau; Boehringer-Ingelheim, Novo Nordisk. Advisory Panel; Sanofi. Speaker's Bureau; Sanofi. Advisory Panel; Lilly Diabetes. A. Peter: None. R. Lehmann: None. N. Perakakis: Other Relationship; Novo Nordisk, Lilly Diabetes. Advisory Panel; Bayer Pharmaceuticals, Inc. Other Relationship; APOGEPHA, Transmedac Innovations AG, GWT-TUD, Elbe-Gesundsheintszentrum GmbH, Open Exploration. K. Mai: None. S. Kabisch: Research Support; Almond Board California, California Walnut Commission. Other Relationship; JuZo-Akademie, Boehringer-Ingelheim. Research Support; J. Rettenmaier & Söhne. Other Relationship; Lilly Diabetes. Research Support; Wilhelm-Doerenkamp-Foundation. M. Blüher: Advisory Panel; AstraZeneca. Speaker's Bureau; Amgen Inc. Advisory Panel; Bayer Pharmaceuticals, Inc, Boehringer-Ingelheim. Speaker's Bureau; Daiichi Sankyo. Advisory Panel; Eli Lilly and Company, Novo Nordisk, Nestlé Health Science, Sanofi-Aventis Deutschland GmbH. P.E. Schwarz: None. J. Seissler: None. I. Yurchenko: None. M. Heni: Advisory Panel; Amryt Pharma. Speaker's Bureau; Amryt Pharma, AstraZeneca, Boehringer-Ingelheim. Advisory Panel; Boehringer-Ingelheim. Speaker's Bureau; Lilly Diabetes, Novartis AG, Novo Nordisk, Sanofi. J. Szendroedi: Advisory Panel; Novo Nordisk, Lilly Diabetes, Novartis AG, Boehringer-Ingelheim. A. Schürmann: None. M. Roden: Research Support; Boehringer-Ingelheim. Advisory Panel; Echosens. Speaker's Bureau; Madrigal Pharmaceuticals, Inc. Advisory Panel; MSD Life Science Foundation. Board Member; Novo Nordisk. Advisory Panel; TARGET PharmaSolutions, Inc. N. Stefan: Speaker's Bureau; AstraZeneca, Boehringer-Ingelheim. Consultant; Lilly Diabetes. Speaker's Bureau; Lilly Diabetes. Consultant; Pfizer Inc. Speaker's Bureau; Sanofi. Research Support; Sanofi. Speaker's Bureau; Novo Nordisk, GlaxoSmithKline plc. Consultant; GlaxoSmithKline plc. A. Fritsche: Advisory Panel; Abbott. Speaker's Bureau; AstraZeneca. A. Daniel: None. R. Anjana: None. A.L. Birkenfeld: None. V. Mohan: Speaker's Bureau; Novo Nordisk. Advisory Panel; Abbott. Research Support; Servier Laboratories. Speaker's Bureau; USV Private Limited, Sanofi, Medtronic, Eli Lilly and Company. R. Jumpertz von Schwartzenberg: None. German Center for Diabetes Research (DZD)

Signaling lipids are key players in cellular processes. Despite their importance, no method currently allows their comprehensive monitoring in one analytical run. Challenges include a wide dynamic range, isomeric and isobaric species, and unwanted interaction along the separation path. Herein, we present a sensitive and robust targeted liquid chromatography-mass spectrometry (LC-MS/MS) approach to overcome these challenges, covering a broad panel of 17 different signaling lipid classes. It involves a simple one-phase sample extraction and lipid analysis using bioinert reversed-phase liquid chromatography coupled to targeted mass spectrometry. The workflow shows excellent sensitivity and repeatability in different biological matrices, enabling the sensitive and robust monitoring of 388 lipids in a single run of only 20 min. To benchmark our workflow, we characterized the human plasma signaling lipidome, quantifying 307 endogenous molecular lipid species. Furthermore, we investigated the signaling lipidome during platelet activation, identifying numerous regulations along important lipid signaling pathways. This highlights the potential of the presented method to investigate signaling lipids in complex biological systems, enabling unprecedentedly comprehensive analysis and direct insight into signaling pathways.

The rapid increase in lipidomic studies has led to a collaborative effort within the community to establish standards and criteria for producing, documenting, and disseminating data. Creating a dynamic easy-to-use checklist that condenses key information about lipidomic experiments into common terminology will enhance the field's consistency, comparability, and repeatability. Here, we describe the structure and rationale of the established Lipidomics Minimal Reporting Checklist to increase transparency in lipidomics research.

Aims/hypothesis While physical activity is clearly beneficial in combating type 2 diabetes, the underlying molecular mechanisms are incompletely understood. Moreover, there is a considerable degree of variability in the individual response to exercise-based lifestyle interventions that remains to be explained. We aimed to identify novel exercise-induced metabolites that could mediate the improvement in glycemic control and reduction of obesity and contribute to individual differences in the response to exercise interventions. Methods We studied acute exercise- and training-induced changes in plasma metabolites in sedentary subjects with overweight (8 male, 14 female) participating in an eight-week supervised training program flanked by two acute endurance exercise sessions. Plasma metabolites were quantified using LC- and CE-MS. In a separate study (n=9 lean males), we assessed metabolite fluxes over the leg using arterial and venous catheters. Functional analyses were performed in primary blood mononuclear cells (PBMCs) stimulated with lipopolysaccharide (LPS) or the saturated fatty acid palmitate. Results The amino acid breakdown products 3-phenyllactic acid (PLA), 4-hydroxyphenyllactic acid and indolelactic acid were increased after both acute exercise and training. All three aromatic lactic acids, which so far mainly received attention as bacterial metabolites, exhibited an efflux from the leg. PLA showed the largest increase after both acute exercise and training, of 57% and 20% respectively. The magnitude of the acute exercise-induced increase in PLA correlated with a decrease in subcutaneous adipose tissue volume and an improvement in insulin sensitivity over the course of the intervention. Furthermore, both isomers, D- and L-PLA, counteracted inflammatory cytokine production in PBMCs. Conclusions/interpretation Our findings indicate that PLA is physiologically released from skeletal muscle and can contribute to the anti-inflammatory effects of exercise as well as to individual difference in the response to lifestyle interventions in humans. PLA and potentially, aromatic lactic acids in general may be particularly relevant metabolic regulators because they can be produced both endogenously and by the microbiome. Trial registration ClinicalTrials.gov NCT03151590 ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Trial NCT03151590 ### Funding Statement This study was supported in part by grants from the Mobility Programme of the Sino-German Center for Research Promotion (M-0257), the Strategic Leading Science and Technology Project B of the Chinese Academy of Sciences (XDB38020200), the German Federal Ministry of Education and Research (BMBF) to the German Centre for Diabetes Research (DZD e.V., 01GI0925) and by grants from the German Diabetes Association (DDG) to MH and AM. AM is currently funded by a clinician scientist program from the medical faculty of the University of Tuebingen. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Written consent was obtained from all participants and the study was approved by the ethics committee of the University of Tuebingen and registered at Clinicaltrials.gov. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes

BACKGROUND:Remission of type 2 diabetes can occur as a result of weight loss and is characterised by liver fat and pancreas fat reduction and recovered insulin secretion. In this analysis, we aimed to investigate the mechanisms of weight loss- induced remission in people with prediabetes. METHODS:In this prespecified post-hoc analysis, weight loss-induced resolution of prediabetes in the randomised, controlled, multicentre Prediabetes Lifestyle Intervention Study (PLIS) was assessed, and the results were validated against participants from the Diabetes Prevention Program (DPP) study. For PLIS, between March 1, 2012, and Aug 31, 2016, participants were recruited from eight clinical study centres (including seven university hospitals) in Germany and randomly assigned to receive either a control intervention, a standard lifestyle intervention (ie, DPP-based intervention), or an intensified lifestyle intervention for 12 months. For DPP, participants were recruited from 23 clinical study centres in the USA between July 31, 1996, and May 18, 1999, and randomly assigned to receive either a standard lifestyle intervention, metformin, or placebo. In both PLIS and DPP, only participants who were randomly assigned to receive lifestyle intervention or placebo and who lost at least 5% of their bodyweight were included in this analysis. Responders were defined as people who returned to normal fasting plasma glucose (FPG; <5·6 mmol/L), normal glucose tolerance (<7·8 mmol/L), and HbA1c less than 39 mmol/mol after 12 months of lifestyle intervention or placebo or control intervention. Non-responders were defined as people who had FPG, 2 h glucose, or HbA1c more than these thresholds. The main outcomes for this analysis were insulin sensitivity, insulin secretion, visceral adipose tissue (VAT), and intrahepatic lipid content (IHL) and were evaluated via linear mixed models. FINDINGS:Of 1160 participants recruited to PLIS, 298 (25·7%) had weight loss of 5% or more of their bodyweight at baseline. 128 (43%) of 298 participants were responders and 170 (57%) were non-responders. Responders were younger than non-responders (mean age 55·6 years [SD 9·9] vs 60·4 years [8·6]; p<0·0001). The DPP validation cohort included 683 participants who lost at least 5% of their bodyweight at baseline. Of these, 132 (19%) were responders and 551 (81%) were non-responders. In PLIS, BMI reduction was similar between responders and non-responders (responders mean at baseline 32·4 kg/m2 [SD 5·6] to mean at 12 months 29·0 kg/m2 [4·9] vs non-responders 32·1 kg/m2 [5·9] to 29·2 kg/m2 [5·4]; p=0·86). However, whole-body insulin sensitivity increased more in responders than in non-responders (mean at baseline 291 mL/[min × m2], SD 60 to mean at 12 months 378 mL/[min × m2], 56 vs 278 mL/[min × m2], 62, to 323 mL/[min × m2], 66; p<0·0001), whereas insulin secretion did not differ within groups over time or between groups (responders mean at baseline 175 pmol/mmol [SD 64] to mean at 12 months 163·7 pmol/mmol [60·6] vs non-responders 158·0 pmol/mmol [55·6] to 154·1 pmol/mmol [56·2]; p=0·46). IHL decreased in both groups, without a difference between groups (responders mean at baseline 10·1% [SD 8·7] to mean at 12 months 3·5% [3·9] vs non-responders 10·3% [8·1] to 4·2% [4·2]; p=0·34); however, VAT decreased more in responders than in non-responders (mean at baseline 6·2 L [SD 2·9] to mean at 12 months 4·1 L [2·3] vs 5·7 L [2·3] to 4·5 L [2·2]; p=0·0003). Responders had a 73% lower risk of developing type 2 diabetes than non-responders in the 2 years after the intervention ended. INTERPRETATION:By contrast to remission of type 2 diabetes, resolution of prediabetes was characterised by an improvement in insulin sensitivity and reduced VAT. Because return to normal glucose regulation (NGR) prevents development of type 2 diabetes, we propose the concept of remission of prediabetes in analogy to type 2 diabetes. We suggest that remission of prediabetes should be the primary therapeutic aim in individuals with prediabetes. FUNDING:German Federal Ministry for Education and Research via the German Center for Diabetes Research; the Ministry of Science, Research and the Arts Baden-Württemberg; the Helmholtz Association and Helmholtz Munich; the Cluster of Excellence Controlling Microbes to Fight Infections; and the German Research Foundation.

Metformin-induced glycolysis and lactate production can lead to acidosis as a life-threatening side effect, but slight increases in blood lactate levels in a physiological range were also reported in metformin-treated patients. However, how metformin increases systemic lactate concentrations is only partly understood. Because human skeletal muscle has a high capacity to produce lactate, the aim was to elucidate the dose-dependent regulation of metformin-induced lactate production and the potential contribution of skeletal muscle to blood lactate levels under metformin treatment. This was examined by using metformin treatment (16-776 μM) of primary human myotubes and by 17 days of metformin treatment in humans. As from 78 µM, metformin induced lactate production and secretion and glucose consumption. Investigating the cellular redox state by mitochondrial respirometry, we found metformin to inhibit the respiratory chain complex I (776 µM, P < 0.01) along with decreasing the [NAD+]:[NADH] ratio (776 µM, P < 0.001). RNA sequencing and phospho-immunoblot data indicate inhibition of pyruvate oxidation mediated through phosphorylation of the pyruvate dehydrogenase (PDH) complex (39 µM, P < 0.01). On the other hand, in human skeletal muscle, phosphorylation of PDH was not altered by metformin. Nonetheless, blood lactate levels were increased under metformin treatment (P < 0.05). In conclusion, the findings suggest that metformin-induced inhibition of pyruvate oxidation combined with altered cellular redox state shifts the equilibrium of the lactate dehydrogenase (LDH) reaction leading to a dose-dependent lactate production in primary human myotubes.NEW & NOTEWORTHY Metformin shifts the equilibrium of lactate dehydrogenase (LDH) reaction by low dose-induced phosphorylation of pyruvate dehydrogenase (PDH) resulting in inhibition of pyruvate oxidation and high dose-induced increase in NADH, which explains the dose-dependent lactate production of differentiated human skeletal muscle cells.

Background: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a sensitive method with high specificity. However, its routine use in the clinical laboratory is hampered by its high complexity and lack of automation. Studies demonstrate excellent analytical performance using the first fully automated LC-MS/MS for 25-hydroxy vitamin D and immunosuppressant drugs (ISD) in hospital routine laboratories.Objectives: Our objectives were (1) to verify the suitability of an automated LC-MS/MS in a commercial labo-ratory, which differs from the needs of hospital laboratories, and (2) examine its usability among operators with various professional backgrounds.Methods: We assessed the analytical assay performance for vitamin D and the ISDs cyclosporine A and tacrolimus over five months. The assays were compared to an identical analyzer in a hospital laboratory, to in-house LC-MS/ MS methods, and to chemiluminescent microparticle immunoassays (CMIA). Nine operators evaluated the us-ability of the fully automated LC-MS/MS system by means of a structured questionnaire.Results: The automated system exhibited a high precision (CV < 8%), accuracy (bias < 7%) and good agreement with concentrations of external quality assessment (EQA) samples. Comparable results were obtained with an identical analyzer in a hospital routine laboratory. Acceptable median deviations of results versus an in-house LC-MS/MS were observed for 25-OH vitamin D3 (-10.6%), cyclosporine A (-4.3%) and tacrolimus (-6.6%). The median bias between the automated system and immunoassays was only acceptable for 25-OH vitamin D3 (6.6%). All users stated that they had had a good experience with the fully automated LC-MS/MS system.Conclusions: A fully automated LC-MS/MS can be easily integrated for routine diagnostics in a commercial laboratory.

The gut microbiome is of tremendous relevance to human health and disease, so it is a hot topic of omics-driven biomedical research. However, a valid identification of gut microbiota-associated molecules in human blood or urine is difficult to achieve. We hypothesize that bowel evacuation is an easy-to-use approach to reveal such metabolites. A non-targeted and modifying group-assisted metabolomics approach (covering 40 types of modifications) was applied to investigate urine samples collected in two independent experiments at various time points before and after laxative use. Fasting over the same time period served as the control condition. As a result, depletion of the fecal microbiome significantly affected the levels of 331 metabolite ions in urine, including 100 modified metabolites. Dominating modifications were glucuronidations, carboxylations, sulfations, adenine conjugations, butyrylations, malonylations, and acetylations. A total of 32 compounds, including common, but also unexpected fecal microbiota-associated metabolites, were annotated. The applied strategy has potential to generate a microbiome-associated metabolite map (M3) of urine from healthy humans, and presumably also other body fluids. Comparative analyses of M3 vs. disease-related metabolite profiles, or therapy-dependent changes may open promising perspectives for human gut microbiome research and diagnostics beyond analyzing feces.

Background Physical exercise causes pronounced changes in circulating metabolites that could act as “exerkines” mediating its well-known beneficial effects, such as improvement of chronic low-grade inflammation, glucose homeostasis and insulin sensitivity.

Reliability, robustness, and interlaboratory comparability of quantitative measurements is critical for clinical lipidomics studies. Lipids' different ex vivo stability in blood bears the risk of misinterpretation of data. Clear recommendations for the process of blood sample collection are required. We studied by UHPLC-high resolution mass spectrometry, as part of the "Preanalytics interest group" of the International Lipidomics Society, the stability of 417 lipid species in EDTA whole blood after exposure to either 4°C, 21°C, or 30°C at six different time points (0.5 h–24 h) to cover common daily routine conditions in clinical settings. In total, >800 samples were analyzed. 325 and 288 robust lipid species resisted 24 h exposure of EDTA whole blood to 21°C or 30°C, respectively. Most significant instabilities were detected for FA, LPE, and LPC. Based on our data, we recommend cooling whole blood at once and permanent. Plasma should be separated within 4 h, unless the focus is solely on robust lipids. Lists are provided to check the ex vivo (in)stability of distinct lipids and potential biomarkers of interest in whole blood. To conclude, our results contribute to the international efforts towards reliable and comparable clinical lipidomics data paving the way to the proper diagnostic application of distinct lipid patterns or lipid profiles in the future.

We present an approach for the visual analysis of multi‐omics data obtained using high‐throughput methods. The term “omics” denotes measurements of different types of biologically relevant molecules like the products of gene transcription (transcriptomics) or the abundance of proteins (proteomics). Current popular visualization approaches often only support analyzing each of these omics separately. This, however, disregards the interconnectedness of different biologically relevant molecules and processes. Consequently, it describes the actual events in the organism suboptimally or only partially. Our visual analytics approach for multi‐omics data provides a comprehensive overview and details‐on‐demand by integrating the different omics types in multiple linked views. To give an overview, we map the measurements to known biological pathways and use a combination of a clustered network visualization, glyphs, and interactive filtering. To ensure the effectiveness and utility of our approach, we designed it in close collaboration with domain experts and assessed it using an exemplary workflow with real‐world transcriptomics, proteomics, and lipidomics measurements from mice.

Quantitative sphingolipid analysis is crucial for understanding the roles of these bioactive molecules in various physiological and pathological contexts. Molecular sphingolipid species are typically quantified using sphingoid base-derived fragments relative to a class-specific internal standard. However, the commonly employed "one standard per class" strategy fails to account for fragmentation differences presented by the structural diversity of sphingolipids. To address this limitation, we developed a novel approach for quantitative sphingolipid analysis. This approach utilizes fragmentation models to correct for structural differences and thus overcomes the limitations associated with using a limited number of standards for quantification. Importantly, our method is independent of the internal standard, instrumental setup, and collision energy. Furthermore, we integrated this method into a user-friendly KNIME workflow. The validation results illustrate the effectiveness of our approach in accurately quantifying ceramide subclasses from various biological matrices. This breakthrough opens up new avenues for exploring sphingolipid metabolism and gaining insights into its implications.