Pharmacogenomics, Race, and Treatment Outcome in Pediatric Acute Myeloid Leukemia.

Importance: Disparities in outcomes exist between Black and White patients with acute myeloid leukemia (AML), with Black patients experiencing poorer prognosis compared with their White counterparts. Objective: To assess whether varying intensity of induction therapy to treat pediatric AML is associated with reduced disparities in treatment outcome by race. Design, Setting, and Participants: A comparative effectiveness analysis was conducted of 86 Black and 359 White patients with newly diagnosed AML who were enrolled in the AML02 trial from 2002 to 2008 or the AML08 trial from 2008 to 2017. Statistical analysis was conducted from July 2023 through January 2024. Interventions: Patients in AML02 were randomly assigned to receive standard low-dose cytarabine-based induction therapy or augmented high-dose cytarabine-based induction therapy, whereas patients in AML08 received high-dose cytarabine-based therapy. Main Outcomes and Measures: Cytarabine pharmacogenomic 10-single-nucleotide variant (ACS10) scores were evaluated for association with outcome according to race and treatment arm. Results: This analysis included 86 Black patients (mean [SD] age, 8.8 [6.5] years; 54 boys [62.8%]; mean [SD] leukocyte count, 52 600 [74 000] cells/µL) and 359 White patients (mean [SD] age, 9.1 [6.2] years; 189 boys [52.6%]; mean [SD] leukocyte count, 54 500 [91 800] cells/µL); 70 individuals with other or unknown racial and ethnic backgrounds were not included. Among all patients without core binding factor AML who received standard induction therapy, Black patients had significantly worse outcomes compared with White patients (5-year event-free survival rate, 25% [95% CI, 9%-67%] compared with 56% [95% CI, 46%-70%]; P = .03). By contrast, among all patients who received augmented induction therapy, there were no differences in outcome according to race (5-year event-free survival rate, Black patients, 50% [95% CI, 38%-67%]; White patients, 48% [95% CI, 42%-55%]; P = .78). Among patients who received standard induction therapy, those with low ACS10 scores had a significantly worse 5-year event-free survival rate compared with those with high scores (42.4% [95% CI, 25.6%-59.3%] and 70.0% [95% CI, 56.6%-83.1%]; P = .004); however, among patients who received augmented induction therapy, there were no differences in 5-year event-free survival rates according to ACS10 score (low score, 60.6% [95% CI, 50.9%-70.2%] and high score, 54.8% [95% CI, 47.1%-62.5%]; P = .43). Conclusions and Relevance: In this comparative effectiveness study of pediatric patients with AML treated in 2 consecutive clinical trials, Black patients had worse outcomes compared with White patients after treatment with standard induction therapy, but this disparity was eliminated by treatment with augmented induction therapy. When accounting for ACS10 scores, no outcome disparities were seen between Black and White patients. Our results suggest that using pharmacogenomics parameters to tailor induction regimens for both Black and White patients may narrow the racial disparity gap in patients with AML.

AZD1222 effectiveness against severe COVID-19 in individuals with comorbidity or frailty: The RAVEN cohort study.

OBJECTIVES: Despite being prioritized during initial COVID-19 vaccine rollout, vulnerable individuals at high risk of severe COVID-19 (hospitalization, intensive care unit admission, or death) remain underrepresented in vaccine effectiveness (VE) studies. The RAVEN cohort study (NCT05047822) assessed AZD1222 (ChAdOx1 nCov-19) two-dose primary series VE in vulnerable populations. METHODS: Using the Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub, linked to secondary care, death registration, and COVID-19 datasets in England, COVID-19 outcomes in 2021 were compared in vaccinated and unvaccinated individuals matched on age, sex, region, and multimorbidity. RESULTS: Over 4.5 million AZD1222 recipients were matched (mean follow-up ∼5 months); 68% were ≥50 years, 57% had high multimorbidity. Overall, high VE against severe COVID-19 was demonstrated, with lower VE observed in vulnerable populations. VE against hospitalization was higher in the lowest multimorbidity quartile (91.1%; 95% CI: 90.1, 92.0) than the highest quartile (80.4%; 79.7, 81.1), and among individuals ≥65 years, higher in the 'fit' (86.2%; 84.5, 87.6) than the frailest (71.8%; 69.3, 74.2). VE against hospitalization was lowest in immunosuppressed individuals (64.6%; 60.7, 68.1). CONCLUSIONS: Based on integrated and comprehensive UK health data, overall population-level VE with AZD1222 was high. VEs were notably lower in vulnerable groups, particularly the immunosuppressed.

Clinical and biological landscape of constitutional mismatch-repair deficiency syndrome: an International Replication Repair Deficiency Consortium cohort study.

BACKGROUND: Constitutional mismatch repair deficiency (CMMRD) syndrome is a rare and aggressive cancer predisposition syndrome. Because a scarcity of data on this condition contributes to management challenges and poor outcomes, we aimed to describe the clinical spectrum, cancer biology, and impact of genetics on patient survival in CMMRD. METHODS: In this cohort study, we collected cross-sectional and longitudinal data on all patients with CMMRD, with no age limits, registered with the International Replication Repair Deficiency Consortium (IRRDC) across more than 50 countries. Clinical data were extracted from the IRRDC database, medical records, and physician-completed case record forms. The primary objective was to describe the clinical features, cancer spectrum, and biology of the condition. Secondary objectives included estimations of cancer incidence and of the impact of the specific mismatch-repair gene and genotype on cancer onset and survival, including after cancer surveillance and immunotherapy interventions. FINDINGS: We analysed data from 201 patients (103 males, 98 females) enrolled between June 5, 2007 and Sept 9, 2022. Median age at diagnosis of CMMRD or a related cancer was 8·9 years (IQR 5·9-12·6), and median follow-up from diagnosis was 7·2 years (3·6-14·8). Endogamy among minorities and closed communities contributed to high homozygosity within countries with low consanguinity. Frequent dermatological manifestations (117 [93%] of 126 patients with complete data) led to a clinical overlap with neurofibromatosis type 1 (35 [28%] of 126). 339 cancers were reported in 194 (97%) of 201 patients. The cumulative cancer incidence by age 18 years was 90% (95% CI 80-99). Median time between cancer diagnoses for patients with more than one cancer was 1·9 years (IQR 0·8-3·9). Neoplasms developed in 15 organs and included early-onset adult cancers. CNS tumours were the most frequent (173 [51%] cancers), followed by gastrointestinal (75 [22%]), haematological (61 [18%]), and other cancer types (30 [9%]). Patients with CNS tumours had the poorest overall survival rates (39% [95% CI 30-52] at 10 years from diagnosis; log-rank p<0·0001 across four cancer types), followed by those with haematological cancers (67% [55-82]), gastrointestinal cancers (89% [81-97]), and other solid tumours (96% [88-100]). All cancers showed high mutation and microsatellite indel burdens, and pathognomonic mutational signatures. MLH1 or MSH2 variants caused earlier cancer onset than PMS2 or MSH6 variants, and inferior survival (overall survival at age 15 years 63% [95% CI 55-73] for PMS2, 49% [35-68] for MSH6, 19% [6-66] for MLH1, and 0% for MSH2; p<0·0001). Frameshift or truncating variants within the same gene caused earlier cancers and inferior outcomes compared with missense variants (p<0·0001). The greater deleterious effects of MLH1 and MSH2 variants as compared with PMS2 and MSH6 variants persisted despite overall improvements in survival after surveillance or immune checkpoint inhibitor interventions. INTERPRETATION: The very high cancer burden and unique genomic landscape of CMMRD highlight the benefit of comprehensive assays in timely diagnosis and precision approaches toward surveillance and immunotherapy. These data will guide the clinical management of children and patients who survive into adulthood with CMMRD. FUNDING: The Canadian Institutes for Health Research, Stand Up to Cancer, Children's Oncology Group National Cancer Institute Community Oncology Research Program, Canadian Cancer Society, Brain Canada, The V Foundation for Cancer Research, BioCanRx, Harry and Agnieszka Hall, Meagan's Walk, BRAINchild Canada, The LivWise Foundation, St Baldrick Foundation, Hold'em for Life, and Garron Family Cancer Center.

Phenotype-based targeted treatment of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes.

AIMS/HYPOTHESIS: A precision medicine approach in type 2 diabetes could enhance targeting specific glucose-lowering therapies to individual patients most likely to benefit. We aimed to use the recently developed Bayesian causal forest (BCF) method to develop and validate an individualised treatment selection algorithm for two major type 2 diabetes drug classes, sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1-RA). METHODS: We designed a predictive algorithm using BCF to estimate individual-level conditional average treatment effects for 12-month glycaemic outcome (HbA1c) between SGLT2i and GLP1-RA, based on routine clinical features of 46,394 people with type 2 diabetes in primary care in England (Clinical Practice Research Datalink; 27,319 for model development, 19,075 for hold-out validation), with additional external validation in 2252 people with type 2 diabetes from Scotland (SCI-Diabetes [Tayside & Fife]). Differences in glycaemic outcome with GLP1-RA by sex seen in clinical data were replicated in clinical trial data (HARMONY programme: liraglutide [n=389] and albiglutide [n=1682]). As secondary outcomes, we evaluated the impacts of targeting therapy based on glycaemic response on weight change, tolerability and longer-term risk of new-onset microvascular complications, macrovascular complications and adverse kidney events. RESULTS: Model development identified marked heterogeneity in glycaemic response, with 4787 (17.5%) of the development cohort having a predicted HbA1c benefit >3 mmol/mol (>0.3%) with SGLT2i over GLP1-RA and 5551 (20.3%) having a predicted HbA1c benefit >3 mmol/mol with GLP1-RA over SGLT2i. Calibration was good in hold-back validation, and external validation in an independent Scottish dataset identified clear differences in glycaemic outcomes between those predicted to benefit from each therapy. Sex, with women markedly more responsive to GLP1-RA, was identified as a major treatment effect modifier in both the UK observational datasets and in clinical trial data: HARMONY-7 liraglutide (GLP1-RA): 4.4 mmol/mol (95% credible interval [95% CrI] 2.2, 6.3) (0.4% [95% CrI 0.2, 0.6]) greater response in women than men. Targeting the two therapies based on predicted glycaemic response was also associated with improvements in short-term tolerability and long-term risk of new-onset microvascular complications. CONCLUSIONS/INTERPRETATION: Precision medicine approaches can facilitate effective individualised treatment choice between SGLT2i and GLP1-RA therapies, and the use of routinely collected clinical features for treatment selection could support low-cost deployment in many countries.

MouseVUER: video based open-source system for laboratory mouse home-cage monitoring.

Video monitoring of mice in the home-cage reveals behavior profiles without the disruptions caused by specialized test setups and makes it possible to quantify changes in behavior patterns continually over long time frames. Several commercial home-cage monitoring systems are available with varying costs and capabilities; however there are currently no open-source systems for home-cage monitoring. We present an open-source system for top-down video monitoring of research mice in a slightly modified home-cage. The system is designed for integration with Allentown NexGen ventilated racks and allows unobstructed view of up to three mice, but can also be operated outside the rack. The system has an easy to duplicate and assemble home-cage design along with a video acquisition solution. The system utilizes a depth video camera, and we demonstrate the robustness of depth video for home-cage mice monitoring. For researchers without access to Allentown NexGen ventilated racks, we provide designs and assembly instructions for a standalone non-ventilated rack solution that holds three systems for more compact and efficient housing. We make all the design files, along with detailed assembly and installation instructions, available on the project webpage ( https://github.com/NIH-CIT-OIR-SPIS/MouseVUER ).

Risk factor associations for severe COVID-19, influenza and pneumonia in people with diabetes to inform future pandemic preparations: UK population-based cohort study.

OBJECTIVE: This study aimed to compare clinical and sociodemographic risk factors for severe COVID-19, influenza and pneumonia, in people with diabetes. DESIGN: Population-based cohort study. SETTING: UK primary care records (Clinical Practice Research Datalink) linked to mortality and hospital records. PARTICIPANTS: Individuals with type 1 and type 2 diabetes (COVID-19 cohort: n=43 033 type 1 diabetes and n=584 854 type 2 diabetes, influenza and pneumonia cohort: n=42 488 type 1 diabetes and n=585 289 type 2 diabetes). PRIMARY AND SECONDARY OUTCOME MEASURES: COVID-19 hospitalisation from 1 February 2020 to 31 October 2020 (pre-COVID-19 vaccination roll-out), and influenza and pneumonia hospitalisation from 1 September 2016 to 31 May 2019 (pre-COVID-19 pandemic). Secondary outcomes were COVID-19 and pneumonia mortality. Associations between clinical and sociodemographic risk factors and each outcome were assessed using multivariable Cox proportional hazards models. In people with type 2 diabetes, we explored modifying effects of glycated haemoglobin (HbA1c) and body mass index (BMI) by age, sex and ethnicity. RESULTS: In type 2 diabetes, poor glycaemic control and severe obesity were consistently associated with increased risk of hospitalisation for COVID-19, influenza and pneumonia. The highest HbA1c and BMI-associated relative risks were observed in people aged under 70 years. Sociodemographic-associated risk differed markedly by respiratory infection, particularly for ethnicity. Compared with people of white ethnicity, black and south Asian groups had a greater risk of COVID-19 hospitalisation, but a lesser risk of pneumonia hospitalisation. Risk factor associations for type 1 diabetes and for type 2 diabetes mortality were broadly consistent with the primary analysis. CONCLUSIONS: Clinical risk factors of high HbA1c and severe obesity are consistently associated with severe outcomes from COVID-19, influenza and pneumonia, especially in younger people. In contrast, associations with sociodemographic risk factors differed by type of respiratory infection. This emphasises that risk stratification should be specific to individual respiratory infections.

Malignant A-to-I RNA editing by ADAR1 drives T cell acute lymphoblastic leukemia relapse via attenuating dsRNA sensing.

Leukemia-initiating cells (LICs) are regarded as the origin of leukemia relapse and therapeutic resistance. Identifying direct stemness determinants that fuel LIC self-renewal is critical for developing targeted approaches. Here, we show that the RNA-editing enzyme ADAR1 is a crucial stemness factor that promotes LIC self-renewal by attenuating aberrant double-stranded RNA (dsRNA) sensing. Elevated adenosine-to-inosine editing is a common attribute of relapsed T cell acute lymphoblastic leukemia (T-ALL) regardless of molecular subtype. Consequently, knockdown of ADAR1 severely inhibits LIC self-renewal capacity and prolongs survival in T-ALL patient-derived xenograft models. Mechanistically, ADAR1 directs hyper-editing of immunogenic dsRNA to avoid detection by the innate immune sensor melanoma differentiation-associated protein 5 (MDA5). Moreover, we uncover that the cell-intrinsic level of MDA5 dictates the dependency on the ADAR1-MDA5 axis in T-ALL. Collectively, our results show that ADAR1 functions as a self-renewal factor that limits the sensing of endogenous dsRNA. Thus, targeting ADAR1 presents an effective therapeutic strategy for eliminating T-ALL LICs.

Dirichlet process mixture models to impute missing predictor data in counterfactual prediction models: an application to predict optimal type 2 diabetes therapy.

BACKGROUND: The handling of missing data is a challenge for inference and regression modelling. A particular challenge is dealing with missing predictor information, particularly when trying to build and make predictions from models for use in clinical practice. METHODS: We utilise a flexible Bayesian approach for handling missing predictor information in regression models. This provides practitioners with full posterior predictive distributions for both the missing predictor information (conditional on the observed predictors) and the outcome-of-interest. We apply this approach to a previously proposed counterfactual treatment selection model for type 2 diabetes second-line therapies. Our approach combines a regression model and a Dirichlet process mixture model (DPMM), where the former defines the treatment selection model, and the latter provides a flexible way to model the joint distribution of the predictors. RESULTS: We show that DPMMs can model complex relationships between predictor variables and can provide powerful means of fitting models to incomplete data (under missing-completely-at-random and missing-at-random assumptions). This framework ensures that the posterior distribution for the parameters and the conditional average treatment effect estimates automatically reflect the additional uncertainties associated with missing data due to the hierarchical model structure. We also demonstrate that in the presence of multiple missing predictors, the DPMM model can be used to explore which variable(s), if collected, could provide the most additional information about the likely outcome. CONCLUSIONS: When developing clinical prediction models, DPMMs offer a flexible way to model complex covariate structures and handle missing predictor information. DPMM-based counterfactual prediction models can also provide additional information to support clinical decision-making, including allowing predictions with appropriate uncertainty to be made for individuals with incomplete predictor data.

Recent UK type 2 diabetes treatment guidance represents a near whole population indication for SGLT2-inhibitor therapy.

Recent type 2 diabetes guidance from the UK's National Institute for Health and Care Excellence (NICE) proposes offering SGLT2-inhibitor therapy to people with established atherosclerotic cardiovascular disease (ASCVD) or heart failure, and considering SGLT2-inhibitor therapy for those at high-risk of cardiovascular disease defined as a 10-year cardiovascular risk of > 10% using the QRISK2 algorithm. We used a contemporary population-representative UK cohort of people with type 2 diabetes to assess the implications of this guidance. 93.1% of people currently on anti-hyperglycaemic treatment are now recommended or considered for SGLT2-inhibitor therapy under the new guidance, with the majority (59.6%) eligible on the basis of QRISK2 rather than established ASCVD or heart failure (33.6%). Applying these results to the approximately 2.20 million people in England currently on anti-hyperglycaemic medication suggests 1.75 million people will now be considered for additional SGLT2-inhibitor therapy, taking the total cost of SGLT2-inhibitor therapy in England to over £1 billion per year. Given that older people, those of non-white ethnic groups, and those at lower cardiovascular disease risk were underrepresented in the clinical trials which were used to inform this guidance, careful evaluation of the impact and safety of increased SGLT2-inhibitor prescribing across different populations is urgently required. Evidence of benefit should be weighed against the major cost implications for the UK National Health Service.

Precision medicine for cardiometabolic disease: a framework for clinical translation.

Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic disease.

Treatment effect heterogeneity following type 2 diabetes treatment with GLP1-receptor agonists and SGLT2-inhibitors: a systematic review.

BACKGROUND: A precision medicine approach in type 2 diabetes requires the identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy. METHODS: We performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes. After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. RESULTS: Here we show that the majority of included papers have methodological limitations precluding robust assessment of treatment effect heterogeneity. For SGLT2-inhibitors, multiple observational studies suggest lower renal function as a predictor of lesser glycaemic response, while markers of reduced insulin secretion predict lesser glycaemic response with GLP1-receptor agonists. For both therapies, multiple post-hoc analyses of randomized control trials (including trial meta-analysis) identify minimal clinically relevant treatment effect heterogeneity for cardiovascular and renal outcomes. CONCLUSIONS: Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care.

This study reviews the available evidence on which patient features (such as age, sex, and blood test results) are associated with different outcomes for two recently introduced type 2 diabetes medications: SGLT2-inhibitors and GLP1-receptor agonists. Understanding what individual characteristics are associated with different response patterns may help clinical providers and people living with diabetes make more informed decisions about which type 2 diabetes treatments will work best for an individual. We focus on three outcomes: blood glucose levels (raised blood glucose is the primary symptom of diabetes and a primary aim of diabetes treatment is to lower this), heart disease, and kidney disease. We identified some potential factors that reduce effects on blood glucose levels, including poorer kidney function for SGLT2-inhibitors and lower production of the glucose-lowering hormone insulin for GLP1-receptor agonists. We did not identify clear factors that alter heart and kidney disease outcomes for either medication. Most of the studies had limitations, meaning more research is needed to fully understand the factors that influence treatment outcomes in type 2 diabetes.

Malignant A-to-I RNA editing by ADAR1 drives T-cell acute lymphoblastic leukemia relapse via attenuating dsRNA sensing.

Leukemia initiating cells (LICs) are regarded as the origin of leukemia relapse and therapeutic resistance. Identifying direct stemness determinants that fuel LIC self-renewal is critical for developing targeted approaches to eliminate LICs and prevent relapse. Here, we show that the RNA editing enzyme ADAR1 is a crucial stemness factor that promotes LIC self-renewal by attenuating aberrant double-stranded RNA (dsRNA) sensing. Elevated adenosine-to-inosine (A-to-I) editing is a common attribute of relapsed T-ALL regardless of molecular subtypes. Consequently, knockdown of ADAR1 severely inhibits LIC self-renewal capacity and prolongs survival in T-ALL PDX models. Mechanistically, ADAR1 directs hyper-editing of immunogenic dsRNA and retains unedited nuclear dsRNA to avoid detection by the innate immune sensor MDA5. Moreover, we uncovered that the cell intrinsic level of MDA5 dictates the dependency on ADAR1-MDA5 axis in T-ALL. Collectively, our results show that ADAR1 functions as a self-renewal factor that limits the sensing of endogenous dsRNA. Thus, targeting ADAR1 presents a safe and effective therapeutic strategy for eliminating T-ALL LICs.

Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine.

OBJECTIVE: Precision medicine requires reliable identification of variation in patient-level outcomes with different available treatments, often termed treatment effect heterogeneity. We aimed to evaluate the comparative utility of individualized treatment selection strategies based on predicted individual-level treatment effects from a causal forest machine learning algorithm and a penalized regression model. METHODS: Cohort study characterizing individual-level glucose-lowering response (6 month reduction in HbA1c) in people with type 2 diabetes initiating SGLT2-inhibitor or DPP4-inhibitor therapy. Model development set comprised 1,428 participants in the CANTATA-D and CANTATA-D2 randomised clinical trials of SGLT2-inhibitors versus DPP4-inhibitors. For external validation, calibration of observed versus predicted differences in HbA1c in patient strata defined by size of predicted HbA1c benefit was evaluated in 18,741 patients in UK primary care (Clinical Practice Research Datalink). RESULTS: Heterogeneity in treatment effects was detected in clinical trial participants with both approaches (proportion predicted to have a benefit on SGLT2-inhibitor therapy over DPP4-inhibitor therapy: causal forest: 98.6%; penalized regression: 81.7%). In validation, calibration was good with penalized regression but sub-optimal with causal forest. A strata with an HbA1c benefit > 10 mmol/mol with SGLT2-inhibitors (3.7% of patients, observed benefit 11.0 mmol/mol [95%CI 8.0-14.0]) was identified using penalized regression but not causal forest, and a much larger strata with an HbA1c benefit 5-10 mmol with SGLT2-inhibitors was identified with penalized regression (regression: 20.9% of patients, observed benefit 7.8 mmol/mol (95%CI 6.7-8.9); causal forest 11.6%, observed benefit 8.7 mmol/mol (95%CI 7.4-10.1). CONCLUSIONS: Consistent with recent results for outcome prediction with clinical data, when evaluating treatment effect heterogeneity researchers should not rely on causal forest or other similar machine learning algorithms alone, and must compare outputs with standard regression, which in this evaluation was superior.

Precision medicine in type 2 diabetes: A systematic review of treatment effect heterogeneity for GLP1-receptor agonists and SGLT2-inhibitors.

Background: A precision medicine approach in type 2 diabetes requires identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy. Methods: We performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes. Results: After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. The majority of papers had methodological limitations precluding robust assessment of treatment effect heterogeneity. For glycaemic outcomes, most cohorts were observational, with multiple analyses identifying lower renal function as a predictor of lesser glycaemic response with SGLT2-inhibitors and markers of reduced insulin secretion as predictors of lesser response with GLP1-receptor agonists. For cardiovascular and renal outcomes, the majority of included studies were post-hoc analyses of randomized control trials (including meta-analysis studies) which identified limited clinically relevant treatment effect heterogeneity. Conclusions: Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care. Plain language summary: This review identifies research that helps understand which clinical and biological factors that are associated with different outcomes for specific type 2 diabetes treatments. This information could help clinical providers and patients make better informed personalized decisions about type 2 diabetes treatments. We focused on two common type 2 diabetes treatments: SGLT2-inhibitors and GLP1-receptor agonists, and three outcomes: blood glucose control, heart disease, and kidney disease. We identified some potential factors that are likely to lessen blood glucose control including lower kidney function for SGLT2-inhibitors and lower insulin secretion for GLP1-receptor agonists. We did not identify clear factors that alter heart and renal disease outcomes for either treatment. Most of the studies had limitations, meaning more research is needed to fully understand the factors that influence treatment outcomes in type 2 diabetes.

Data-Resource Profile: United Kingdom Optimum Patient Care Research Database.

Introduction: Electronic medical records (EMRs) maintained in primary care in the UK and collected and stored in EMR databases offer a world-leading resource for observational clinical research. We aimed to profile one such database: the Optimum Patient Care Research Database (OPCRD). Methods and Participants: The OPCRD, incepted in 2010, is a growing primary care EMR database collecting data from 992 general practices within the UK. It covers over 16.6 million patients across all four countries within the UK, and is broadly representative of the UK population in terms of age, sex, ethnicity and socio-economic status. Patients have a mean duration of 11.7 years' follow-up (SD 17.50), with a majority having key summary data from birth to last data entry. Data for the OPCRD are collected incrementally monthly and extracted from all of the major clinical software systems used within the UK and across all four coding systems (Read version 2, Read CTV3, SNOMED DM+D and SNOMED CT codes). Via quality-improvement programmes provided to GP surgeries, the OPCRD also includes patient-reported outcomes from a range of disease-specific validated questionnaires, with over 66,000 patient responses on asthma, COPD, and COVID-19. Further, bespoke data collection is possible by working with GPs to collect new research via patient-reported questionnaires. Findings to Date: The OPCRD has contributed to over 96 peer-reviewed research publications since its inception encompassing a broad range of medical conditions, including COVID-19. Conclusion: The OPCRD represents a unique resource with great potential to support epidemiological research, from retrospective observational studies through to embedded cluster-randomised trials. Advantages of the OPCRD over other EMR databases are its large size, UK-wide geographical coverage, the availability of up-to-date patient data from all major GP software systems, and the unique collection of patient-reported information on respiratory health.

Insight into the Effect of Glycerol on Dielectric Relaxation and Transport Properties of Potassium-Ion-Conducting Solid Biopolymer Electrolytes for Application in Solid-State Electrochemical Double-Layer Capacitor.

The increased interest in the transition from liquid to solid polymer electrolytes (SPEs) has driven enormous research in the area polymer electrolyte technology. Solid biopolymer electrolytes (SBEs) are a special class of SPEs that are obtained from natural polymers. Recently, SBEs have been generating much attention because they are simple, inexpensive, and environmentally friendly. In this work, SBEs based on glycerol-plasticized methylcellulose/pectin/potassium phosphate (MC/PC/K3PO4) are investigated for their potential application in an electrochemical double-layer capacitor (EDLC). The structural, electrical, thermal, dielectric, and energy moduli of the SBEs were analyzed via X-ray diffractometry (XRD), Fourier transforms infrared spectroscopy (FTIR), electrochemical impedance spectroscopy (EIS), transference number measurement (TNM), and linear sweep voltammetry (LSV). The plasticizing effect of glycerol in the MC/PC/K3PO4/glycerol system was confirmed by the change in the intensity of the samples' FTIR absorption bands. The broadening of the XRD peaks demonstrates that the amorphous component of SBEs increases with increasing glycerol concentration, while EIS plots demonstrate an increase in ionic conductivity with increasing plasticizer content owing to the formation of charge-transfer complexes and the expansion of amorphous domains in polymer electrolytes (PEs). The sample containing 50% glycerol has a maximal ionic conductivity of about 7.5 × 10-4 scm-1, a broad potential window of 3.99 V, and a cation transference number of 0.959 at room temperature. Using the cyclic voltammetry (CV) test, the EDLC constructed from the sample with the highest conductivity revealed a capacitive characteristic. At 5 mVs-1, a leaf-shaped profile with a specific capacitance of 57.14 Fg-1 was measured based on the CV data.

A Review of Current Trends on Polyvinyl Alcohol (PVA)-Based Solid Polymer Electrolytes.

Presently, the rising concerns about the fossil fuel crisis and ecological deterioration have greatly affected the world economy and hence have attracted attention to the utilization of renewable energies. Among the renewable energy being developed, supercapacitors hold great promise in broad applications such as electric vehicles. Presently, the main challenge facing supercapacitors is the amount of energy stored. This, however, does not satisfy the increasing demand for higher energy storage devices, and therefore, intensive research is being undertaken to overcome the challenges of low energy density. The purpose of this review is to report on solid polymer electrolytes (SPEs) based on polyvinyl alcohol (PVA). The review discussed the PVA as a host polymer in SPEs followed by a discussion on the influence of conducting salts. The formation of SPEs as well as the ion transport mechanism in PVA SPEs were discussed. The application and development of PVA-based polymer electrolytes on supercapacitors and other energy storage devices were elucidated. The fundamentals of electrochemical characterization for analyzing the mechanism of supercapacitor applications, such as EIS, LSV and dielectric constant, are highlighted. Similarly, thermodynamic transport models of ions and their mechanism about temperature based on Arrhenius and Vogel-Tammann-Fulcher (VTF) are analyzed. Methods for enhancing the electrochemical performance of PVA-based SPEs were reported. Likely challenges facing the current electrolytes are well discussed. Finally, research directions to overcome the present challenges in producing SPEs are proposed. Therefore, this review is expected to be source material for other researchers concerned with the development of PVA-based SPE material.