A roadmap to achieve pharmacological precision medicine in diabetes

Current pharmacological treatment of diabetes is largely algorithmic. Other than for cardiovascular disease or renal disease, where sodium–glucose cotransporter 2 inhibitors and/or glucagon-like peptide-1 receptor agonists are indicated, the choice of treatment is based upon overall risks of harm or side effect and cost, and not on probable benefit. Here we argue that a more precise approach to treatment choice is necessary to maximise benefit and minimise harm from existing diabetes therapies. We propose a roadmap to achieve precision medicine as standard of care, to discuss current progress in relation to monogenic diabetes and type 2 diabetes, and to determine what additional work is required. The first step is to identify robust and reliable genetic predictors of response, recognising that genotype is static over time and provides the skeleton upon which modifiers such as clinical phenotype and metabolic biomarkers can be overlaid. The second step is to identify these metabolic biomarkers (e.g. beta cell function, insulin sensitivity, BMI, liver fat, metabolite profile), which capture the metabolic state at the point of prescribing and may have a large impact on drug response. Third, we need to show that predictions that utilise these genetic and metabolic biomarkers improve therapeutic outcomes for patients, and fourth, that this is cost-effective. Finally, these biomarkers and prediction models need to be embedded in clinical care systems to enable effective and equitable clinical implementation. Whilst this roadmap is largely complete for monogenic diabetes, we still have considerable work to do to implement this for type 2 diabetes. Increasing collaborations, including with industry, and access to clinical trial data should enable progress to implementation of precision treatment in type 2 diabetes in the near future. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains a slideset of the figures for download, which is available at 10.1007/s00125-022-05732-3

On Attitudes Toward Spanish Varieties: A Bilingual Perspective

This study explores the attitudes of 25 English-Spanish bilingual speakers from Tucson (Arizona) towards their own variety and compares them with their attitudes toward monolingual varieties of Mexican (from Hermosillo) and Peninsular Spanish (from Murcia and Madrid). Our analysis points to a clear influence of the standard language ideology (MILROY, 2001) on shaping these attitudes, escalated by a tendency among bilinguals in diglossic societies to feel insecure about their own variety as a minority language, or towards a feeling of linguistic self-hatred. - Este estudo explora as atitudes de 25 falantes bilíngues de espanhol e inglês de Tucson (Arizona) com relação à sua própria variedade linguística, comparando essas atitudes em relação ao espanhol de Hermosillo (México) e a duas variedades peninsulares: a espanhola de Múrcia e a de Madrid. Nossa análise mostra uma clara influência da ideologia da língua padrão (MILROY, 2001) em determinar essas atitudes, agravada pela tendência de os falantes bilíngues de sociedades diglóssicas se sentirem inseguros quanto à sua própria variedade como uma língua minoritária, ou relacionada a um sentimento de auto-ódio linguístico

Genome-Wide Association with Diabetes-Related Traits in the Framingham Heart Study

BACKGROUND: Susceptibility to type 2 diabetes may be conferred by genetic variants having modest effects on risk. Genome-wide fixed marker arrays offer a novel approach to detect these variants. METHODS: We used the Affymetrix 100K SNP array in 1,087 Framingham Offspring Study family members to examine genetic associations with three diabetes-related quantitative glucose traits (fasting plasma glucose (FPG), hemoglobin A1c, 28-yr time-averaged FPG (tFPG)), three insulin traits (fasting insulin, HOMA-insulin resistance, and 0–120 min insulin sensitivity index); and with risk for diabetes. We used additive generalized estimating equations (GEE) and family-based association test (FBAT) models to test associations of SNP genotypes with sex-age-age2-adjusted residual trait values, and Cox survival models to test incident diabetes. RESULTS: We found 415 SNPs associated (at p 1%) 100K SNPs in LD (r2 > 0.05) with ABCC8 A1369S (rs757110), KCNJ11 E23K (rs5219), or SNPs in CAPN10 or HNFa. PPARG P12A (rs1801282) was not significantly associated with diabetes or related traits. CONCLUSION: Framingham 100K SNP data is a resource for association tests of known and novel genes with diabetes and related traits posted at. Framingham 100K data replicate the TCF7L2 association with diabetes.National Heart, Lung, and Blood Institute's Framingham Heart Study (N01-HC-25195); National Institutes of Health National Center for Research Resources Shared Instrumentation grant (1S10RR163736-01A1); National Center for Research Resources General Clinical Research Center (M01-RR-01066); American Diabetes Association Career Developement Award; GlaxoSmithKline; Merck; Lilly; National Institutes of Health Research Career Award (K23 DK659678-03

Teaching Scientific Writing in the Era of ChatGPT

Generative AI (gen-AI) tools such as ChatGPT have very quickly become widely accessible as well as embedded into a wide range of existing software. The early iterations of these technologies so far have produced impressive outcomes in terms of their ability to produce generic writing, leading to the question - will the teaching of writing skills become obsolete? A number of studies show that there are niche areas within engineering that gen-AI tools have not garnered enough specificity in information to produce written reports that are technically accurate enough for students to pass off as their own work. In spite of this, research shows the growing trend of students using gen-AI tools to complete their assignments without understanding the shortcomings of the technology when applied to their engineering discipline, particularly problematic with first-year engineering students. The Integrated Engineering Programme (IEP) was introduced to University College London in 2013/14 as a means of embedding transversal skills-based education into the curriculum. This presentation aims to outline the steps taken on the IEP to maintain the standards of writing competence and how gen-AI tools have shaped how we teach scientific writing and best practice when embedding their use into the curriculum

Co-Inoculation of Plant-Growth-Promoting Bacteria Modulates Physiological and Biochemical Responses of Perennial Ryegrass to Water Deficit

Perennial ryegrass is a forage commonly used in temperate regions for livestock feeding; however, its yield is affected by reduced biomass production under water deficit. In a previous study, three co-inoculations of beneficial bacteria were selected based on their ability to promote plant growth under reduced water availability. The aim of this work was to elucidate some mechanisms by which the selected bacteria can help improve the response of perennial ryegrass to water deficit. Ryegrass plants were inoculated with each of the co-inoculations (Herbaspirillum sp. AP02 Herbaspirillum sp. AP21; Herbaspirillum sp. AP02–Pseudomonas sp. N7; Herbaspirillum sp. AP21–Azospirillum brasilense D7) and subjected to water deficit for 10 days. Physiological and biochemical measurements were taken 10 days after stress and shortly after rehydration. The results showed that bacteria had a positive effect on shoot biomass production, dissipation of excess energy, and proline and chlorophyll pigments during the days of water deficit (p < 0.05). The leaf water status of the inoculated plants was 12% higher than that of the uninoculated control after rehydration. Two Herbaspirillum strains showed greater potential for use as biofertilizers that help ameliorate the effects of water deficit