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Metformin is the first-line drug treatment for type 2 diabetes. Globally, over 100 million patients are prescribed this drug annually. Metformin was discovered before the era of target-based drug discovery and its molecular mechanism of action remains an area of vigorous diabetes research. An improvement in our understanding of metformin’s molecular targets is likely to enable target-based identification of second-generation drugs with similar properties, a development that has been impossible up to now. The notion that 5' AMP-activated protein kinase (AMPK) mediates the anti-hyperglycaemic action of metformin has recently been challenged by genetic loss-of-function studies, thrusting the AMPK-independent effects of the drug into the spotlight for the first time in more than a decade. Key AMPK-independent effects of the drug include the mitochondrial actions that have been known for many years and which are still thought to be the primary site of action of metformin. Coupled with recent evidence of AMPK-independent effects on the counter-regulatory hormone glucagon, new paradigms of AMPK-independent drug action are beginning to take shape. In this review we summarise the recent research developments on the molecular action of metformin.

Type 2 diabetes is a complex disease usually diagnosed with little regard to aetiology. In the broader sense, it is a mix of different clearly defined aetiologies, such as monogenic diabetes, that we need to be better at identifying as this has major implications for treatment and patient management. Beyond this, however, type 2 diabetes is a highly heterogeneous polygenic disease. This review outlines the recent developments that recognise this heterogeneity by deconvoluting the aetiology of type 2 diabetes into pathophysiological processes, either by measuring physiological variables (such as beta cell function or insulin resistance) or using partitioned polygenic scores, and addresses recent work that clusters type 2 diabetes into distinct subgroups. Increasing evidence suggests that considering the aetiological components of type 2 diabetes matters, in terms of progression rates, treatment response and complications. In other words, clinicians need to recognise that type 2 diabetes is multifaceted and that its characteristics are important for how patients are managed.

Precision medicine aims to treat an individual based on their clinical characteristics. A differential drug response, critical to using these features for therapy selection, has never been examined directly in type 2 diabetes. In this study, we tested two hypotheses: (1) individuals with body mass index (BMI) > 30 kg/m 2 , compared to BMI ≤ 30 kg/m 2 , have greater glucose lowering with thiazolidinediones than with DPP4 inhibitors, and (2) individuals with estimated glomerular filtration rate (eGFR) 60–90 ml/min/1.73 m 2 , compared to eGFR >90 ml/min/1.73 m 2 , have greater glucose lowering with DPP4 inhibitors than with SGLT2 inhibitors. The primary endpoint for both hypotheses was the achieved HbA1c difference between strata for the two drugs. In total, 525 people with type 2 diabetes participated in this UK-based randomized, double-blind, three-way crossover trial of 16 weeks of treatment with each of sitagliptin 100 mg once daily, canagliflozin 100 mg once daily and pioglitazone 30 mg once daily added to metformin alone or metformin plus sulfonylurea. Overall, the achieved HbA1c was similar for the three drugs: pioglitazone 59.6 mmol/mol, sitagliptin 60.0 mmol/mol and canagliflozin 60.6 mmol/mol ( P  = 0.2). Participants with BMI > 30 kg/m 2 , compared to BMI ≤ 30 kg/m 2 , had a 2.88 mmol/mol (95% confidence interval (CI): 0.98, 4.79) lower HbA1c on pioglitazone than on sitagliptin ( n  = 356, P  = 0.003). Participants with eGFR 60–90 ml/min/1.73 m 2 , compared to eGFR >90 ml/min/1.73 m 2 , had a 2.90 mmol/mol (95% CI: 1.19, 4.61) lower HbA1c on sitagliptin than on canagliflozin ( n  = 342, P  = 0.001). There were 2,201 adverse events reported, and 447/525 (85%) randomized participants experienced an adverse event on at least one of the study drugs. In this precision medicine trial in type 2 diabetes, our findings support the use of simple, routinely available clinical measures to identify the drug class most likely to deliver the greatest glycemic reduction for a given patient. (ClinicalTrials.gov registration: NCT02653209 ; ISRCTN registration: 12039221 .) Using a randomized three-way crossover design and stratification approaches based on obesity and renal function in people with type 2 diabetes, the TriMaster study demonstrated that patients with obesity were more likely to have greater glycemic control with pioglitazone (a thiazolidinedione) than with sitagliptin (a DPP4 inhibitor) and that patients with eGFR 60–90 ml/min/1.73 m 2 were more likely to achieve lower HbA1c levels on sitagliptin than on canagliflozin (an SGLT2 inhibitor).

The association between body weight variability and the risk of cardiovascular disease (CVD) has been investigated previously with mixed findings. However, there has been no extensive study which systematically evaluates the current evidence. Furthermore, the impact of ethnicity and type 2 diabetes on this phenomena has not yet been investigated. Therefore, the aim of this study was to comprehensively evaluate the effect of weight variability on risk of CVD (any cardiovascular (CV) event, composite CV outcome, CV death, Stroke, Myocardial Infarction) and the influence of ethnicity and type 2 diabetes status on the observed association. A systematic review and meta-analysis was performed according to the meta-analyses of observational studies in epidemiology (MOOSE) guidelines. The electronic databases PubMed, Web of Science, and the Cochrane Library were searched for studies that investigated the relationship between body weight or BMI variability and CV diseases using Medical Subject Headings (MeSH) terms and keywords. The relative risks (RRs) for the outcomes were collected from studies, pooled, and analysed using a random-effects model to estimate the overall relative risk. Of 5645 articles screened, 23 studies with a total population of 15,382,537 fulfilled the prespecified criteria and were included. Individuals in the highest strata of body weight variability were found to have significantly increased risk of any CV event (RR = 1.27; 95% Confidence Interval (CI) 1.17–1.38; P < 0.0001; I 2  = 97.28%), cardiovascular death (RR = 1.29; 95% CI 1.03–1.60; P < 0.0001; I 2  = 55.16%), myocardial infarction (RR = 1.32; 95% CI 1.09–1.59; P = 0.0037; I 2  = 97.14%), stroke (RR = 1.21; 95% CI 1.19–1.24; P < 0.0001; I 2  = 0.06%), and compound CVD outcomes (RR = 1.36; 95% CI 1.08–1.73; P = 0.01; I 2  = 92.41%). Similar RRs were observed regarding BMI variability and per unit standard deviation (SD) increase in body weight variability. Comparable effects were seen in people with and without diabetes, in White Europeans and Asians. In conclusion, body weight variability is associated with increased risk of CV diseases regardless of ethnicity or diabetes status. Future research is needed to prove a causative link between weight variability and CVD risk, as appropriate interventions to maintain stable weight could positively influence CVD.

Most biomedical knowledge is published as text, making it challenging to analyse using traditional statistical methods. In contrast, machine-interpretable data primarily comes from structured property databases, which represent only a fraction of the knowledge present in the biomedical literature. Crucial insights and inferences can be drawn from these publications by the scientific community. We trained language models on literature from different time periods to evaluate their ranking of prospective gene-disease associations and protein–protein interactions. Using 28 distinct historical text corpora of abstracts published between 1995 and 2022, we trained independent Word2Vec models to prioritise associations that were likely to be reported in future years. This study demonstrates that biomedical knowledge can be encoded as word embeddings without the need for human labelling or supervision. Language models effectively capture drug discovery concepts such as clinical tractability, disease associations, and biochemical pathways. Additionally, these models can prioritise hypotheses years before their initial reporting. Our findings underscore the potential for extracting yet-to-be-discovered relationships through data-driven approaches, leading to generalised biomedical literature mining for potential therapeutic drug targets. The Publication-Wide Association Study (PWAS) enables the prioritisation of under-explored targets and provides a scalable system for accelerating early-stage target ranking, irrespective of the specific disease of interest.

Abstract Aims/Hypothesis Studies in permanent neonatal diabetes suggest that sulphonylureas lower blood glucose without causing hypoglycemia, in part by augmenting the incretin effect. This mechanism has not previously been attributed to sulphonylureas in patients with type 2 diabetes (T2DM). We therefore aimed to evaluate the impact of low-dose gliclazide on beta-cell function and incretin action in patients with T2DM. Methods Paired oral glucose tolerance tests and isoglycemic infusions were performed to evaluate the difference in the classical incretin effect in the presence and absence of low-dose gliclazide in 16 subjects with T2DM (hemoglobin A1c < 64 mmol/mol, 8.0%) treated with diet or metformin monotherapy. Beta-cell function modeling was undertaken to describe the relationship between insulin secretion and glucose concentration. Results A single dose of 20 mg gliclazide reduced mean glucose during the oral glucose tolerance test from 12.01 ± 0.56 to 10.82 ± 0.5mmol/l [P = 0.0006; mean ± standard error of the mean (SEM)]. The classical incretin effect was augmented by 20 mg gliclazide, from 35.5% (lower quartile 27.3, upper quartile 61.2) to 54.99% (34.8, 72.8; P = 0.049). Gliclazide increased beta-cell glucose sensitivity by 46% [control 22.61 ± 3.94, gliclazide 33.11 ± 7.83 (P = 0.01)] as well as late-phase incretin potentiation [control 0.92 ± 0.05, gliclazide 1.285 ± 0.14 (P = 0.038)]. Conclusions/Interpretation Low-dose gliclazide reduces plasma glucose in response to oral glucose load, with concomitant augmentation of the classical incretin effect. Beta-cell modeling shows that low plasma concentrations of gliclazide potentiate late-phase insulin secretion and increase glucose sensitivity by 50%. Further studies are merited to explore whether low-dose gliclazide, by enhancing incretin action, could effectively lower blood glucose without risk of hypoglycemia.

Metformin is a widely-used drug that results in clear benefits in relation to glucose metabolism and diabetes-related complications. The mechanisms underlying these benefits are complex and still not fully understood. Physiologically, metformin has been shown to reduce hepatic glucose production, yet not all of its effects can be explained by this mechanism and there is increasing evidence of a key role for the gut. At the molecular level the findings vary depending on the doses of metformin used and duration of treatment, with clear differences between acute and chronic administration. Metformin has been shown to act via both AMP-activated protein kinase (AMPK)-dependent and AMPK-independent mechanisms; by inhibition of mitochondrial respiration but also perhaps by inhibition of mitochondrial glycerophosphate dehydrogenase, and a mechanism involving the lysosome. In the last 10 years, we have moved from a simple picture, that metformin improves glycaemia by acting on the liver via AMPK activation, to a much more complex picture reflecting its multiple modes of action. More work is required to truly understand how this drug works in its target population: individuals with type 2 diabetes.

Aim To investigate the impact of tamoxifen dose, CYP2D6 inhibitors, CYP2D6*4 genotype, and non-genetic parameters on the outcomes of tamoxifen treated female breast cancer patients. Method We retrospectively included 3218 female patients who initiated tamoxifen following a diagnosis of breast cancer with long-term follow-up (median 7.5 years). A subgroup analysis of 303 genotyped patients with a median follow-up of 9.7 years was also conducted. The outcomes of interest were overall survival (OS) and breast-cancer-specific survival (BCS). Results In the whole cohort, an additional 20 mg of tamoxifen during six-month duration was associated with a 1.6% reduction in all-cause mortality (HR: 0.984, 95% CI: 0.982–0.985, P  < 0.001) and a 1.9% decrease in breast cancer mortality (HR: 0.981, 95% CI: 0.979–0.984, P  < 0.001). In the genotyped subgroup, CYP2D6*4 heterozygotes had a 76% greater risk of all-cause mortality than *4 non-carriers (HR: 1.76, 95% CI: 1.07–2.9, P  = 0.025). For breast cancer-specific mortality, CYP2D6*4 heterozygotes and homozygotes had increased risk by 3.7-fold (HR: 3.7, 95% CI: 1.32–10.6, P  = 0.01) and 11.6-fold (HR: 11.6, 95% CI: 1.3–103.5, P  = 0.03), respectively. Conclusion Our study demonstrates that carriers of CYP2D6*4 have a higher risk of both all-cause and breast cancer-specific mortality and indicates that longer follow-up time may be crucial to determining impact. The shorter follow-up in previous studies may be a key reason for the conflicting results. A large real-world pharmacogenomic study with long-term follow-up is warranted to determine the impact of CYP2D6 genotyping and its implications for clinical decision making.

This study estimated to what extent the number of measurements of cardiometabolic risk factors (e.g., blood pressure, cholesterol, glycated haemoglobin) were impacted by the COVID-19 pandemic and whether these have recovered to expected levels. A cohort of individuals aged ≥18 years in England with records in the primary care-COVID-19 General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR) were identified. Their records of 12 risk factor measurements were extracted between November 2018 and March 2024. Number of measurements per 1,000 individuals were calculated by age group, sex, ethnicity, and area deprivation quintile. The observed number of measurements were compared to a composite expectation band, derived as the union of the 95% confidence intervals of 2 estimates: (1) a projected trend based on data prior to the COVID-19 pandemic; and (2) an assumed stable trend from before pandemic. Point estimates were calculated as the mid-point of the expectation band. A cohort of 49,303,410 individuals aged ≥18 years were included. There was sharp drop in all measurements in March 2020 to February 2022, but overall recovered to the expected levels during March 2022 to February 2023 except for blood pressure, which had prolonged recovery. In March 2023 to March 2024, blood pressure measurements were below expectation by 16% (-19 per 1,000) overall, in people aged 18 to 39 (-23%; -18 per 1,000), 60 to 79 (-17%; -27 per 1,000), and ≥80 (-31%; -57 per 1,000). There was suggestion that recovery in blood pressure measurements was socioeconomically patterned. The second most deprived quintile had the highest deviation (-20%; -23 per 1,000) from expectation compared to least deprived quintile (-13%; -15 per 1,000). There was a substantial reduction in routine measurements of cardiometabolic risk factors following the COVID-19 pandemic, with variable recovery. The implications for missed diagnoses, worse prognosis, and health inequality are a concern.