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Metformin is the most commonly prescribed medication for type 2 diabetes, owing to its glucose-lowering effects, which are mediated through the suppression of hepatic glucose production (reviewed in refs. 1 – 3 ). However, in addition to its effects on the liver, metformin reduces appetite and in preclinical models exerts beneficial effects on ageing and a number of diverse diseases (for example, cognitive disorders, cancer, cardiovascular disease) through mechanisms that are not fully understood 1 – 3 . Given the high concentration of metformin in the liver and its many beneficial effects beyond glycemic control, we reasoned that metformin may increase the secretion of a hepatocyte-derived endocrine factor that communicates with the central nervous system 4 . Here we show, using unbiased transcriptomics of mouse hepatocytes and analysis of proteins in human serum, that metformin induces expression and secretion of growth differentiating factor 15 (GDF15). In primary mouse hepatocytes, metformin stimulates the secretion of GDF15 by increasing the expression of activating transcription factor 4 (ATF4) and C/EBP homologous protein (CHOP; also known as DDIT3). In wild-type mice fed a high-fat diet, oral administration of metformin increases serum GDF15 and reduces food intake, body mass, fasting insulin and glucose intolerance; these effects are eliminated in GDF15 null mice. An increase in serum GDF15 is also associated with weight loss in patients with type 2 diabetes who take metformin. Although further studies will be required to determine the tissue source(s) of GDF15 produced in response to metformin in vivo, our data indicate that the therapeutic benefits of metformin on appetite, body mass and serum insulin depend on GDF15. The anti-diabetic drug metformin is shown to elevate plasma levels of the hormone GDF15. This increase in GDF15 is required for reductions in appetite and body mass, which are known to contribute to the beneficial metabolic effects of the drug.

Here we evaluated the performance of a large set of serum biomarkers for the prediction of rapid progression of chronic kidney disease (CKD) in patients with type 2 diabetes. We used a case–control design nested within a prospective cohort of patients with baseline eGFR 30–60 ml/min per 1.73 m2. Within a 3.5-year period of Go-DARTS study patients, 154 had over a 40% eGFR decline and 153 controls maintained over 95% of baseline eGFR. A total of 207 serum biomarkers were measured and logistic regression was used with forward selection to choose a subset that were maximized on top of clinical variables including age, gender, hemoglobin A1c, eGFR, and albuminuria. Nested cross-validation determined the best number of biomarkers to retain and evaluate for predictive performance. Ultimately, 30 biomarkers showed significant associations with rapid progression and adjusted for clinical characteristics. A panel of 14 biomarkers increased the area under the ROC curve from 0.706 (clinical data alone) to 0.868. Biomarkers selected included fibroblast growth factor-21, the symmetric to asymmetric dimethylarginine ratio, β2-microglobulin, C16-acylcarnitine, and kidney injury molecule-1. Use of more extensive clinical data including prebaseline eGFR slope improved prediction but to a lesser extent than biomarkers (area under the ROC curve of 0.793). Thus we identified several novel associations of biomarkers with CKD progression and the utility of a small panel of biomarkers to improve prediction.

Background Lixisenatide is a glucagon-like peptide-1 analog which stimulates insulin secretion and inhibits glucagon secretion and gastric emptying. We investigated cardioprotective effects of lixisenatide in rodent models reflecting the clinical situation. Methods The acute cardiac effects of lixisenatide were investigated in isolated rat hearts subjected to brief ischemia and reperfusion. Effects of chronic treatment with lixisenatide on cardiac function were assessed in a modified rat heart failure model after only transient coronary occlusion followed by long-term reperfusion. Freshly isolated cardiomyocytes were used to investigate cell-type specific mechanisms of lixisenatide action. Results In the acute setting of ischemia-reperfusion, lixisenatide reduced the infarct-size/area at risk by 36% ratio without changes on coronary flow, left-ventricular pressure and heart rate. Treatment with lixisenatide for 10 weeks, starting after cardiac ischemia and reperfusion, improved left ventricular end-diastolic pressure and relaxation time and prevented lung congestion in comparison to placebo. No anti-fibrotic effect was observed. Gene expression analysis revealed a change in remodeling genes comparable to the ACE inhibitor ramipril. In isolated cardiomyocytes lixisenatide reduced apoptosis and increased fractional shortening. Glucagon-like peptide-1 receptor (GLP1R) mRNA expression could not be detected in rat heart samples or isolated cardiomyocytes. Surprisingly, cardiomyocytes isolated from GLP-1 receptor knockout mice still responded to lixisenatide. Conclusions In rodent models, lixisenatide reduced in an acute setting infarct-size and improved cardiac function when administered long-term after ischemia-reperfusion injury. GLP-1 receptor independent mechanisms contribute to the described cardioprotective effect of lixisenatide. Based in part on these preclinical findings patients with cardiac dysfunction are currently being recruited for a randomized, double-blind, placebo-controlled, multicenter study with lixisenatide. Trial registration (ELIXA, ClinicalTrials.gov Identifier: NCT01147250 )

Mining and exploring databases should provide users with knowledge and new insights. Tiles of data strive to unveil true underlying structure and distinguish valuable information from various kinds of noise. We propose a novel Boolean matrix factorization algorithm to solve the tiling problem, based on recent results from optimization theory. In contrast to existing work, the new algorithm minimizes the description length of the resulting factorization. This approach is well known for model selection and data compression, but not for finding suitable factorizations via numerical optimization. We demonstrate the superior robustness of the new approach in the presence of several kinds of noise and types of underlying structure. Moreover, our general framework can work with any cost measure having a suitable real-valued relaxation. Thereby, no convexity assumptions have to be met. The experimental results on synthetic data and image data show that the new method identifies interpretable patterns which explain the data almost always better than the competing algorithms.

Aims/hypothesisData analyses from Swedish individuals with newly diagnosed diabetes have suggested that diabetes could be classified into five subtypes that differ with respect to the progression of dysglycaemia and the incidence of diabetes consequences. We assessed this classification in a multiethnic cohort of participants with established and newly diagnosed diabetes, randomly allocated to insulin glargine vs standard care.MethodsIn total, 7017 participants from the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial were assigned to the five predefined diabetes subtypes (namely, severe auto-immune diabetes, severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes, mild age-related diabetes) based on the age at diabetes diagnosis, BMI, HbA1c, fasting C-peptide levels and the presence of glutamate decarboxylase antibodies at baseline. Differences between diabetes subtypes in cardiovascular and renal outcomes were investigated using Cox regression models for a median follow-up of 6.2 years. We also compared the effect of glargine vs standard care on hyperglycaemia, defined by having a mean post-randomisation HbA1c ≥6.5%, between subtypes.ResultsThe five diabetes subtypes were replicated in the ORIGIN trial and exhibited similar baseline characteristics in Europeans and Latin Americans, compared with the initially described clusters in the Swedish cohort. We confirmed differences in renal outcomes, with a higher incidence of events in the severe insulin-resistant diabetes subtype compared with the mild age-related diabetes subtype (i.e., chronic kidney disease stage 3A: HR 1.49 [95% CI 1.31, 1.71]; stage 3B: HR 2.25 [1.82, 2.78]; macroalbuminuria: HR 1.56 [1.22, 1.99]). No differences were observed in the incidence of retinopathy and cardiovascular diseases after adjusting for multiple hypothesis testing. Diabetes subtypes also differed in glycaemic response to glargine, with a particular benefit of receiving glargine (vs standard care) in the severe insulin-deficient diabetes subtype compared with the mild age-related diabetes subtype, with a decreased occurrence of hyperglycaemia by 13% (OR 1.36 [1.30, 1.41] on glargine; OR 1.49 [1.43, 1.57] on standard care; p for interaction subtype × intervention = 0.001).Conclusions/interpretationCluster analysis enabled the characterisation of five subtypes of diabetes in a multiethnic cohort. Both the incidence of renal outcomes and the response to insulin varied between diabetes subtypes. These findings reinforce the clinical utility of applying precision medicine to predict comorbidities and treatment responses in individuals with diabetes.Trial registrationORIGIN trial, ClinicalTrials.gov NCT00069784.

Aims/hypothesis Individuals with diabetes can be clustered into five subtypes using up to six routinely measured clinical variables. We hypothesised that circulating protein levels might be used to distinguish between these subtypes. We recently used five of these six variables to categorise 7017 participants from the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial into these subtypes: severe autoimmune diabetes (SAID, n =241), severe insulin-deficient diabetes (SIDD, n =1594), severe insulin-resistant diabetes (SIRD, n =914), mild obesity-related diabetes (MOD, n =1595) and mild age-related diabetes (MARD, n =2673). Methods Forward-selection logistic regression models were used to identify a subset of 233 cardiometabolic protein biomarkers that were independent determinants of one subtype vs the others. We then assessed the performance of adding identified biomarkers (one after one, from the most discriminant to the least) to predict each subtype vs the others using area under the receiver operating characteristic curve (AUC ROC). Models were adjusted for age, sex, ethnicity, C-peptide level, diabetes duration and glucose-lowering medication usage at blood collection. Results A total of 25 biomarkers were independent determinants of subtypes, including 13 for SIDD, 2 for SIRD, 7 for MOD and 11 for MARD (all p <4.3 × 10 −5 ). The performance of the biomarker sets (comprising 1 to 25 biomarkers), assessed through the AUC ROC, ranged from 0.611 to 0.734, 0.723 to 0.861, 0.672 to 0.742, and 0.651 to 0.751, for SIDD, SIRD, MOD and MARD, respectively. No biomarkers other than GAD antibodies were determinants of SAID. Conclusions/interpretation We identified 25 serum biomarkers, as independent determinants of type 2 diabetes subtypes, that could be combined into a diagnostic test for subtyping. Trial registration ORIGIN trial, ClinicalTrials.gov NCT00069784. Graphical abstract

Matrix tri-factorization subject to binary constraints is a versatile and powerful framework for the simultaneous clustering of observations and features, also known as biclustering. Applications for biclustering encompass the clustering of high-dimensional data and explorative data mining, where the selection of the most important features is relevant. Unfortunately, due to the lack of suitable methods for the optimization subject to binary constraints, the powerful framework of biclustering is typically constrained to clusterings which partition the set of observations or features. As a result, overlap between clusters cannot be modelled and every item, even outliers in the data, have to be assigned to exactly one cluster. In this paper we propose Broccoli, an optimization scheme for matrix factorization subject to binary constraints, which is based on the theoretically well-founded optimization scheme of proximal stochastic gradient descent. Thereby, we do not impose any restrictions on the obtained clusters. Our experimental evaluation, performed on both synthetic and real-world data, and against 6 competitor algorithms, show reliable and competitive performance, even in presence of a high amount of noise in the data. Moreover, a qualitative analysis of the identified clusters shows that Broccoli may provide meaningful and interpretable clustering structures.

Aims/hypothesisAs part of the Surrogate Markers for Micro- and Macrovascular Hard Endpoints for Innovative Diabetes Tools (SUMMIT) programme we previously reported that large panels of biomarkers derived from three analytical platforms maximised prediction of progression of renal decline in type 2 diabetes. Here, we hypothesised that smaller (n ≤ 5), platform-specific combinations of biomarkers selected from these larger panels might achieve similar prediction performance when tested in three additional type 2 diabetes cohorts.MethodsWe used 657 serum samples, held under differing storage conditions, from the Scania Diabetes Registry (SDR) and Genetics of Diabetes Audit and Research Tayside (GoDARTS), and a further 183 nested case–control sample set from the Collaborative Atorvastatin in Diabetes Study (CARDS). We analysed 42 biomarkers measured on the SDR and GoDARTS samples by a variety of methods including standard ELISA, multiplexed ELISA (Luminex) and mass spectrometry. The subset of 21 Luminex biomarkers was also measured on the CARDS samples. We used the event definition of loss of >20% of baseline eGFR during follow-up from a baseline eGFR of 30–75 ml min−1 [1.73 m]−2. A total of 403 individuals experienced an event during a median follow-up of 7 years. We used discrete-time logistic regression models with tenfold cross-validation to assess association of biomarker panels with loss of kidney function.ResultsTwelve biomarkers showed significant association with eGFR decline adjusted for covariates in one or more of the sample sets when evaluated singly. Kidney injury molecule 1 (KIM-1) and β2-microglobulin (B2M) showed the most consistent effects, with standardised odds ratios for progression of at least 1.4 (p < 0.0003) in all cohorts. A combination of B2M and KIM-1 added to clinical covariates, including baseline eGFR and albuminuria, modestly improved prediction, increasing the area under the curve in the SDR, Go-DARTS and CARDS by 0.079, 0.073 and 0.239, respectively. Neither the inclusion of additional Luminex biomarkers on top of B2M and KIM-1 nor a sparse mass spectrometry panel, nor the larger multiplatform panels previously identified, consistently improved prediction further across all validation sets.Conclusions/interpretationSerum KIM-1 and B2M independently improve prediction of renal decline from an eGFR of 30–75 ml min−1 [1.73 m]−2 in type 2 diabetes beyond clinical factors and prior eGFR and are robust to varying sample storage conditions. Larger panels of biomarkers did not improve prediction beyond these two biomarkers.

Analyses of stored blood from the Outcomes Reduction with an Initial Glargine Intervention (ORIGIN) trial identified biomarkers that supplemented clinical risk factors for cardiovascular (CV) events or death. Their performance in participants with diabetes in the Heart Outcomes Prevention Evaluation (HOPE) study and the incremental value of adding high-sensitivity assays of serum troponin I (hsTnI) in the ORIGIN study were assessed. Levels of the 10 ORIGIN biomarkers for the composite CV outcome of myocardial infarction, stroke, or CV death were measured in 350 HOPE study participants with diabetes and stored serum that included all 77 who experienced this outcome. The effect of adding hsTnI levels to this panel, and the previously identified ORIGIN biomarkers for this composite outcome, this outcome, or revascularization or heart failure, and for mortality was also analyzed. Within the HOPE cohort, the ORIGIN biomarker panel increased the C statistic from 0.63 for clinical risk factors alone to 0.67 with the addition of the 10 biomarkers, and the net reclassification improvement was 0.14 (95% confidence interval, 0.01, 0.28). Within the ORIGIN cohort, hsTnI levels predicted all three outcomes during follow-up both alone, and independently of the other biomarkers, which all remained independent predictors of outcomes after inclusion of the hsTnI levels. The hsTnI level interacted with follow-up time such that it was a stronger predictor of earlier vs later events. The ORIGIN biomarkers predicted CV outcomes in the independent HOPE cohort. Adding hsTnI levels to the previously identified models in ORIGIN modestly improved their performance.

In patients with diabetes and cardiovascular or renal comorbidities, circulating levels of the N-terminal fragment of prohormone B-type natriuretic peptide (NT-proBNP) have similar discriminatory ability as multivariate models for prediction of cardiovascular events or death. We validated this finding in patients with dysglycemia not selected for co-existing cardiorenal diseases. (c) 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).