Increased genetic risk for ß-cell failure is associated with ß-cell function decline in people with prediabetes.

Partitioned polygenic scores (pPS) have been developed to capture pathophysiologic processes underlying type 2 diabetes (T2D). We investigated the influence of T2D pPS on diabetes-related traits and T2D incidence in the Diabetes Prevention Program. We generated five T2D pPS (ß-cell, proinsulin, liver/lipid, obesity, lipodystrophy) in 2,647 participants randomized to intensive lifestyle, metformin or placebo arms. Associations were tested using general linear models and Cox regression adjusted for age, sex, and principal components. Sensitivity analyses included adjustment for BMI. Higher ß-cell pPS was associated with lower insulinogenic index and corrected insulin response at one year follow-up adjusted for baseline measures (effect per pPS standard deviation (SD) -0.04, P=9.6 x 10-7; -8.45 uU/mg, P=5.6 x 10-6, respectively) and with increased diabetes incidence adjusted for BMI at nominal significance (HR 1.10 per SD, P=0.035). The liver/lipid pPS was associated with reduced one-year baseline-adjusted triglyceride levels (effect per SD -4.37, P=0.001). There was no significant interaction between T2D pPS and randomized groups. The remaining pPS were associated with baseline measures only. We conclude that despite interventions for diabetes prevention, participants with a high genetic burden of the ß-cell cluster pPS had worsening in measures of ß-cell function.

Precision subclassification of type 2 diabetes: a systematic review.

BACKGROUND: Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients. METHODS: We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches. RESULTS: Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes. CONCLUSION: Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

In people with type 2 diabetes there may be differences in the way people present, including for example, their symptoms, body weight or how much insulin they make. We looked at recent publications describing research in this area to see whether it is possible to separate people with type 2 diabetes into different subgroups and, if so, whether these groupings were useful for patients. We found that it is possible to group people with type 2 diabetes into different subgroups and being in one subgroup can be more strongly linked to the likelihood of developing complications over others. This might mean that in the future we can treat people in different subgroups differently in ways that improves their treatment and their health but it requires further study.

The use of precision diagnostics for monogenic diabetes: a systematic review and expert opinion.

BACKGROUND: Monogenic diabetes presents opportunities for precision medicine but is underdiagnosed. This review systematically assessed the evidence for (1) clinical criteria and (2) methods for genetic testing for monogenic diabetes, summarized resources for (3) considering a gene or (4) variant as causal for monogenic diabetes, provided expert recommendations for (5) reporting of results; and reviewed (6) next steps after monogenic diabetes diagnosis and (7) challenges in precision medicine field. METHODS: Pubmed and Embase databases were searched (1990-2022) using inclusion/exclusion criteria for studies that sequenced one or more monogenic diabetes genes in at least 100 probands (Question 1), evaluated a non-obsolete genetic testing method to diagnose monogenic diabetes (Question 2). The risk of bias was assessed using the revised QUADAS-2 tool. Existing guidelines were summarized for questions 3-5, and review of studies for questions 6-7, supplemented by expert recommendations. Results were summarized in tables and informed recommendations for clinical practice. RESULTS: There are 100, 32, 36, and 14 studies included for questions 1, 2, 6, and 7 respectively. On this basis, four recommendations for who to test and five on how to test for monogenic diabetes are provided. Existing guidelines for variant curation and gene-disease validity curation are summarized. Reporting by gene names is recommended as an alternative to the term MODY. Key steps after making a genetic diagnosis and major gaps in our current knowledge are highlighted. CONCLUSIONS: We provide a synthesis of current evidence and expert opinion on how to use precision diagnostics to identify individuals with monogenic diabetes.

Some diabetes types, called monogenic diabetes, are caused by changes in a single gene. It is important to know who has this kind of diabetes because treatment can differ from that of other types of diabetes. Some treatments also work better than others for specific types, and some people can for example change from insulin injections to tablets. In addition, relatives can be offered a test to see if they are at risk. Genetic testing is needed to diagnose monogenic diabetes but is expensive, so it's not possible to test every person with diabetes for it. We evaluated published research on who should be tested and what test to use. Based on this, we provide recommendations for doctors and health care providers on how to implement genetic testing for monogenic diabetes.

Utility of Polygenic Scores for Differentiating Diabetes Diagnosis Among Patients With Atypical Phenotypes of Diabetes.

CONTEXT: Misclassification of diabetes type occurs in people with atypical presentations of type 1 diabetes (T1D) or type 2 diabetes (T2D). Although current clinical guidelines suggest clinical variables and treatment response as ways to help differentiate diabetes type, they remain insufficient for people with atypical presentations. OBJECTIVE: This work aimed to assess the clinical utility of 2 polygenic scores (PGSs) in differentiating between T1D and T2D. METHODS: Patients diagnosed with diabetes in the UK Biobank were studied (N = 41 787), including 464 (1%) and 15 923 (38%) who met the criteria for classic T1D and T2D, respectively, and 25 400 (61%) atypical diabetes. The validity of 2 published PGSs for T1D (PGST1D) and T2D (PGST2D) in differentiating classic T1D or T2D was assessed using C statistic. The utility of genetic probability for T1D based on PGSs (GenProb-T1D) was evaluated in atypical diabetes patients. RESULTS: The joint performance of PGST1D and PGST2D for differentiating classic T1D or T2D was outstanding (C statistic = 0.91), significantly higher than that of PGST1D alone (0.88) and PGST2D alone (0.70), both P less than .001. Using an optimal cutoff of GenProb-T1D, 23% of patients with atypical diabetes had a higher probability of T1D and its validity was independently supported by clinical presentations that are characteristic of T1D. CONCLUSION: PGST1D and PGST2D can be used to discriminate classic T1D and T2D and have potential clinical utility for differentiating these 2 types of diseases among patients with atypical diabetes.

The impact of obesity on cardiovascular and kidney outcomes in patients with chronic kidney disease and type 2 diabetes treated with finerenone: Post hoc analysis of the FIDELITY study.

AIM: To assess the effect of finerenone on the risk of cardiovascular and kidney outcomes in patients with chronic kidney disease and type 2 diabetes, with and without obesity. MATERIALS AND METHODS: A post hoc analysis of the prespecified pooled FIDELITY dataset assessed the association between waist circumference (WC), composite cardiovascular and kidney outcomes, and the effects of finerenone. Participants were stratified by WC risk groups (representing visceral obesity) as low-risk or high-very high-risk (H-/VH-risk). RESULTS: Of 12 986 patients analysed, 90.8% occupied the H-/VH-risk WC group. Incidence of the composite cardiovascular outcome was similar between finerenone and placebo in the low-risk WC group (hazard ratio [HR] 1.03; 95% confidence interval [CI], 0.72-1.47); finerenone reduced the risk in the H-/VH-risk WC group (HR 0.85; 95% CI, 0.77-0.93). For the kidney outcome, the risk was similar in the low-risk WC group (HR 0.98; 95% CI, 0.66-1.46) and reduced within the H-/VH-risk WC group (HR 0.75; 95% CI, 0.65-0.87) with finerenone versus placebo. There was no significant heterogeneity between the low-risk and H-/VH-risk WC groups for cardiovascular and kidney composite outcomes (P interaction = .26 and .34, respectively). The apparent greater benefit of finerenone on cardiorenal outcomes but lack of significant heterogeneity observed in H-/VH-risk WC patients may be because of the small size of the low-risk group. Adverse events were consistent across WC groups. CONCLUSION: In FIDELITY, benefits of finerenone in lowering the risk of cardiovascular and kidney outcomes were not significantly modified by patient obesity.

Cancer-associated thrombosis by cancer sites and inherited factors in a prospective population-based cohort.

Cancer-associated thrombosis (CAT) is common and associated with mortality. We estimated CAT rate by cancer sites and inherited factors among cancer patients from the UK Biobank (N =70,406). The 12-month CAT rate after cancer diagnosis was 2.37% overall but varied considerably among cancer sites. Among the 10 cancer sites classified as 'high-risk' of CAT by the National Comprehensive Cancer Network guidelines, 6 had CAT rate <5%. In contrast, 5 cancer sites classified as 'average-risk' by the guidelines had CAT rate >5%. For inherited risk factors, both known mutation carriers in two genes (F5/F2) and polygenic score for venous thromboembolism (VTE) (PGSVTE) were independently associated with increased CAT risk. While F5/F2 identified 6% patients with high genetic-risk for CAT, adding PGSVTE identified 13 % patients at equivalent/higher genetic-risk to CAT than that of F5/F2 mutations. Findings from this large prospective study, if confirmed, provide critical data to update guidelines for CAT risk assessment.

A Systematic Review of the use of Precision Diagnostics in Monogenic Diabetes.

Monogenic forms of diabetes present opportunities for precision medicine as identification of the underlying genetic cause has implications for treatment and prognosis. However, genetic testing remains inconsistent across countries and health providers, often resulting in both missed diagnosis and misclassification of diabetes type. One of the barriers to deploying genetic testing is uncertainty over whom to test as the clinical features for monogenic diabetes overlap with those for both type 1 and type 2 diabetes. In this review, we perform a systematic evaluation of the evidence for the clinical and biochemical criteria used to guide selection of individuals with diabetes for genetic testing and review the evidence for the optimal methods for variant detection in genes involved in monogenic diabetes. In parallel we revisit the current clinical guidelines for genetic testing for monogenic diabetes and provide expert opinion on the interpretation and reporting of genetic tests. We provide a series of recommendations for the field informed by our systematic review, synthesizing evidence, and expert opinion. Finally, we identify major challenges for the field and highlight areas for future research and investment to support wider implementation of precision diagnostics for monogenic diabetes.

Systematic review of precision subclassification of type 2 diabetes.

Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed 'simple subclassification' using clinical features, biomarkers, imaging or other routinely available parameters or 'complex subclassification' approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.

Effect of a Type 2 Diabetes-Focused Visit Improvement Initiative on Therapeutic Inertia and Glycemic Control in Primary Care.

Quality Improvement Success Stories are published by the American Diabetes Association in collaboration with the American College of Physicians and the National Diabetes Education Program. This series is intended to highlight best practices and strategies from programs and clinics that have successfully improved the quality of care for people with diabetes or related conditions. Each article in the series is reviewed and follows a standard format developed by the editors of Clinical Diabetes. The following article describes a multicomponent quality improvement initiative in the Chicago, IL, area that used a diabetes-focused clinic visit to overcome barriers that lead to clinical inertia for type 2 diabetes.

Correlation Between Time in Range and HbA1c in People with Type 2 Diabetes on Basal Insulin: Post Hoc Analysis of the SWITCH PRO Study.

INTRODUCTION: Use of continuous glucose monitoring (CGM) in people with diabetes may provide a more complete picture of glycemic control than glycated hemoglobin (HbA1c) measurements, which do not capture day-to-day fluctuations in blood glucose levels. The randomized, crossover, phase IV SWITCH PRO study assessed time in range (TIR), derived from CGM, following treatment with insulin degludec or insulin glargine U100 in patients with type 2 diabetes at risk for hypoglycemia. This post hoc analysis evaluated the relationship between TIR and HbA1c, following treatment intensification during the SWITCH PRO study. METHODS: Correlation between absolute values for TIR (assessed over 2-week intervals) and HbA1c, at baseline and at the end of maintenance period 1 (M1; week 18) or maintenance period 2 (M2; week 36), were assessed by linear regression and using the Spearman correlation coefficient (rs). These methods were also used to assess correlation between change in TIR and change in HbA1c from baseline to the end of M1, both in the full cohort and in subgroups stratified by baseline median HbA1c (≥ 7.5% [≥ 58.5 mmol/mol] or < 7.5% [< 58.5 mmol/mol]). RESULTS: A total of 419 participants were included in the analysis. A moderate inverse linear correlation was observed between TIR and HbA1c at baseline (rs -0.54), becoming stronger following treatment intensification during maintenance periods M1 (weeks 17-18: rs -0.59) and M2 (weeks 35-36: rs -0.60). Changes in TIR and HbA1c from baseline to end of M1 were also linearly inversely correlated in the full cohort (rs -0.40) and the subgroup with baseline HbA1c ≥ 7.5% (rs -0.43). This was less apparent in the subgroup with baseline HbA1c < 7.5% (rs -0.17) (p-interaction = 0.07). CONCLUSION: Results from this post hoc analysis of data from SWITCH PRO, one of the first large interventional clinical studies to use TIR as the primary outcome, further support TIR as a valid clinical indicator of glycemic control. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT03687827.

Higher Derived Time in Range With IDegLira Versus Insulin Glargine U100 in People With Type 2 Diabetes.

BACKGROUND: Derived time in range (dTIR), calculated from self-monitored blood glucose (SMBG-dTIR) profiles, has demonstrated correlation with risk of cardiovascular and microvascular complications. This post hoc analysis of the DUAL V and DUAL VIII trials aimed to compare dTIR with an insulin degludec/liraglutide fixed-ratio combination (IDegLira) versus insulin glargine 100 units/mL (glargine U100) in people with type 2 diabetes (T2D). MATERIALS AND METHODS: Nine-point SMBG profiles were taken more than 24 hours at baseline and end of trial (EOT: 26 weeks [DUAL V] and 104 weeks [DUAL VIII]) and used to derive the percentage of readings within target range (70-180 mg/dL). Estimated treatment differences (ETDs, IDegLira-glargine U100) were analyzed using analysis of covariance, with treatment as fixed effects and baseline response as a covariate. RESULTS: ETDs for change from baseline to EOT in dTIR were significantly greater with IDegLira versus glargine U100 in DUAL V (4.18%, P = .027) and DUAL VIII (5.17%, P = .001). The proportions of people achieving ≥70% dTIR at EOT with IDegLira and glargine U100, respectively, were 62% and 60% in DUAL V (P = .7541), and 50% and 26% in DUAL VIII (P < .0001). The proportion achieving a ≥5% increase in dTIR from baseline to EOT with IDegLira and glargine U100 was 63% in both groups in DUAL V (P = .9043), and 44% and 25%, respectively, in DUAL VIII (P < .0001). CONCLUSIONS: IDegLira was associated with significantly greater increases in dTIR versus basal insulin alone in people with T2D. TRIAL ID(S): ClinicalTrials.gov, NCT01952145 (DUAL V); ClinicalTrials.gov, NCT02501161 (DUAL VIII).

Living with Chronic Kidney Disease and Type 2 Diabetes Mellitus: The Patient and Clinician Perspective.

Comorbid type 2 diabetes mellitus (T2D) and chronic kidney disease (CKD) is associated with poor health outcomes and a high economic burden. Management of these conditions remains a significant challenge for current healthcare systems. The objective of this article is to describe the experiences of patients living with T2D and CKD and their thoughts on how communication between patients and their clinicians could be improved despite the multiple comorbidities that need to be addressed. We present the individual perspectives of three patient authors, followed by relevant discussion around the management of CKD in patients with T2D by clinician authors.Audio abstract available for this article. Audio Abstract. In this audio introduction, the authors Patrick Gee (a patient author) and Eugene Wright (a clinician author) provide a brief overview and discuss the key findings of their article titled "Living with Chronic Kidney Disease and Type 2 Diabetes Mellitus: The Patient and Clinician Perspective".

People living with type 2 diabetes mellitus (T2D for short) and chronic kidney disease (CKD for short) may have worse health over time. Managing long-term health conditions can be expensive for those living with the conditions and for healthcare systems. To optimize their quality of life, people with T2D and CKD need the necessary resources to better manage their conditions. Healthcare professionals desire the best outcomes for their patients. Currently, communication between healthcare professionals and their patients is suboptimal, and ineffective communication creates a barrier to effective optimal care. The aim of this article is to describe the experiences of three people living with T2D and CKD (patients), who are also authors of the article. They outline their thoughts on how communication between patients and healthcare professionals might be improved when managing multiple conditions. We also present responses from three healthcare professionals (clinicians), who are co-authors of this article, to the points made by the patients, as well as their views on how to manage these long-term conditions.

Statistical evidence for high-penetrance MODY-causing genes in a large population-based cohort.

AIMS: Numerous genes have been proposed as causal for maturity-onset diabetes of the young (MODY). Scoring systems to annotate mutation pathogenicity have been widely used; however, statistical evidence for being a highly penetrant MODY gene has not been well-established. METHODS: Participants were from the UK Biobank with whole-exome sequencing data, including 14,622 with and 185,509 without diagnosis of diabetes. Pathogenic/likely pathogenic (P/LP) mutations in 14 reported and 3 possible MODY genes were annotated using American College of Medical Genetics criteria. Evidence for being a high-penetrant MODY gene used two statistical criteria: frequency of aggregate P/LP mutations in each gene are (1) significantly more common in participants with a diagnosis of diabetes than without using the SKAT-O (p < .05) and (2) lower than the maximum credible frequency in the general population. RESULTS: Among the 17 genes, 6 (GCK, HNF1A, HNF4A, NEUROD1, KCNJ11 and HNF1B) met both criteria, 7 (ABCC8, KLF11, RFX6, PCBD1, WFS1, INS and PDX1) met only one criterion, and the remaining 4 (CEL, BLK, APPL1 and PAX4) failed both criteria, and were classified as 'consistent', 'inconclusive' and 'inconsistent' for being highly penetrant diabetes genes, respectively. Diabetes participants with mutations in the 'consistent' genes had clinical presentations that were most consistent with MODY. In contrast, the 'inconclusive' and 'inconsistent' genes did not differ clinically from non-carriers in diabetes-related characteristics. CONCLUSIONS: Data from a large population-based study provided novel statistical evidence to identify 6 MODY genes as consistent with being highly penetrant. These results have potential implications for interpreting genetic testing results and clinical diagnosis of MODY.

Transitioning from basal-bolus or premix insulin therapy to a combination of basal insulin and glucagon-like peptide-1 receptor agonist in people with type 2 diabetes.

AIMS: Two fixed-ratio combinations (FRCs) of basal insulin and glucagon-like peptide-1 receptor agonist (GLP-1RA) are available for once-daily use in adults with type 2 diabetes. We aimed to review the clinical evidence for the efficacy and safety of changing treatment from a basal-bolus insulin (BBI) regimen or a premix insulin to these combination treatments (fixed-ratio or loose) and provide expert opinion on the practicalities of making such a change. METHODS: Relevant clinical and trial evidence and general review articles were identified through a literature review of ProQuest (comprising BIOSIS Previews®, Current Contents® Search, Embase® and MEDLINE®) for articles published between 2009 and 2021. RESULTS: We identified nine articles reporting the results of FRCs, and seven articles reporting results of loose combinations of basal insulin and GLP-1RAs, in people who transitioned treatment from BBI or premix regimens. In most trials, combination treatment led to improved or equivalent glycaemic control, and a reduction in body weight or BMI, versus the original regimens. Some trials reported a reduction in total insulin dose. A few trials reported reduced or unchanged hypoglycaemia rates, or increased patient satisfaction, with combination therapy where these endpoints were examined. We provide guidance on transitioning of treatment and the patient types most likely to benefit. CONCLUSIONS: In people not achieving glycaemic control with BBI or premix insulin regimens, an FRC or loose combination of basal insulin and GLP-1RA may improve control, decrease the risk of body weight gain or hypoglycaemia and reduce the complexity of treatment.

Effect of insulin degludec versus insulin glargine U100 on time in range: SWITCH PRO, a crossover study of basal insulin-treated adults with type 2 diabetes and risk factors for hypoglycaemia.

AIMS: To compare time in range (TIR) with use of insulin degludec U100 (degludec) versus insulin glargine U100 (glargine U100) in people with type 2 diabetes. MATERIALS AND METHODS: We conducted a randomized, crossover, multicentre trial comparing degludec and glargine U100 in basal insulin-treated adults with type 2 diabetes and ≥1 hypoglycaemia risk factor. There were two treatment periods, each with 16-week titration and 2-week maintenance phases (with evaluation of glucose using blinded professional continuous glucose monitoring). The once-weekly titration (target: 3.9-5.0 mmol/L) was based on pre-breakfast self-measured blood glucose. The primary endpoint was percentage of TIR (3.9─10.0 mmol/L). Secondary endpoints included overall and nocturnal percentage of time in tight glycaemic range (3.9-7.8 mmol/L), and mean glycated haemoglobin (HbA1c) and glucose levels. RESULTS: At baseline, participants (n = 498) had a mean (SD) age of 62.8 (9.8) years, a diabetes duration of 15.1 (7.7) years and an HbA1c level of 59.6 (11.0) mmol/mol (7.6 [1.0]%). Noninferiority and superiority were confirmed for degludec versus glargine U100 for the primary endpoint, with a mean TIR of 72.1% for degludec versus 70.7% for glargine U100 (estimated treatment difference [ETD] 1.43% [95% confidence interval (CI): 0.12, 2.74; P = 0.03] or 20.6 min/d). Overall time in tight glycaemic range favoured degludec versus glargine U100 (ETD 1.5% [95% CI: 0.15, 2.89] or 21.9 min/d). Degludec also reduced nocturnal time below range (TBR; <3.9 mmol/L) compared with glargine U100 (ETD -0.88% [95% CI: -1.34, -0.42] or 12.7 min/night; post hoc) and significantly fewer nocturnal hypoglycaemic episodes of <3.0 mmol/L were observed. CONCLUSIONS: Degludec, compared with glargine U100, provided more TIR and time in tight glycaemic range, and reduced nocturnal TBR in insulin-treated people with type 2 diabetes.

Leveraging advances in diabetes technologies in primary care: a narrative review.

Primary care providers (PCPs) play an important role in providing medical care for patients with type 2 diabetes. Advancements in diabetes technologies can assist PCPs in providing personalised care that addresses each patient's individual needs. Diabetes technologies fall into two major categories: devices for glycaemic self-monitoring and insulin delivery systems. Monitoring technologies encompass self-measured blood glucose (SMBG), where blood glucose is intermittently measured by a finger prick blood sample, and continuous glucose monitoring (CGM) devices, which use an interstitial sensor and are capable of giving real-time information. Studies show people using real-time CGM have better glucose control compared to SMBG. CGM allows for new parameters including time in range (the time spent within the desired target glucose range), which is an increasingly relevant real-time metric of glycaemic control. Insulin pens have increased the ease of administration of insulin and connected pens that can calculate and capture data on dosing are becoming available. There are a number of websites, software programs, and applications that can help PCPs and patients to integrate diabetes technology into their diabetes management schedules. In this article, we summarise these technologies and provide practical information to inform PCPs about utility in their clinical practice. The guiding principle is that use of technology should be individualised based on a patient's needs, desires, and availability of devices. Diabetes technology can help patients improve their clinical outcomes and achieve the quality of life they desire by decreasing disease burden.KEY MESSAGESIt is important to understand the role that diabetes technologies can play in primary care to help deliver high-quality care, taking into account patient and community resources. Diabetes technologies fall into two major categories: devices for glycaemic self-monitoring and insulin delivery systems. Modern self-measured blood glucose devices are simple to use and can help guide decision making for self-management plans to improve clinical outcomes, but cannot provide "live" data and may under- or overestimate blood glucose; patients' monitoring technique and compliance should be reviewed regularly. Importantly, before a patient is provided with monitoring technology, they must receive suitably structured education in its use and interpretation.Continuous glucose monitoring (CGM) is now standard of care for people with type 1 diabetes and people with type 2 diabetes on meal-time (prandial) insulin. Real-time CGM can tell both the patient and the healthcare provider when glucose is in the normal range, and when they are experiencing hyper- or hypoglycaemia. Using CGM data, changes in lifestyle, eating habits, and medications, including insulin, can help the patient to stay in a normal glycaemic range (70-180 mg/dL). Real-time CGM allows for creation of an ambulatory glucose profile and monitoring of time in range (the time spent within target blood glucose of 70-180 mg/dL), which ideally should be at least 70%; avoiding time above range (>180 mg/dL) is associated with reduced diabetes complications and avoiding time below range (<70 mg/dL) will prevent hypoglycaemia. Insulin pens are simpler to use than syringes, and connected pens capture information on insulin dose and injection timing.There are a number of websites, software programs and applications that can help primary care providers and patients to integrate diabetes technology into their diabetes management schedules. The guiding principle is that use of technology should be individualised based on a patient's needs, desires, skill level, and availability of devices.

Association of Sickle Cell Trait with Risk and Mortality of COVID-19: Results from the United Kingdom Biobank.

Sickle cell trait (SCT) carriers inherit one copy of the Glu6Val mutation in the hemoglobin gene and is particularly common in Black individuals (5-10%). Considering the roles of hemoglobin in immune responses and the higher risk for coronavirus disease (COVID-19) among Black individuals, we tested whether Black SCT carriers were at increased risk for COVID-19 infection and mortality according to the United Kingdom Biobank. Among Black individuals who were tested for COVID-19, we found similar infection rates among SCT carriers (14/72; 19.7%) and noncarriers (167/791; 21.1%), but higher COVID-19 mortality rates among SCT carriers (4/14; 28.6%) than among noncarriers (21/167; 12.6%) (odds ratio [OR], 3.04; 95% confidence interval [CI], 0.69-11.82; P = 0.12). Notably, SCT carriers with preexisting diabetes had significantly higher COVID-19 mortality (4/4; 100%) than those without diabetes (0/10; 0%; (OR, 90.71; 95% CI, 5.66-infinite; P = 0.0005). These findings suggest that Black SCT carriers with preexisting diabetes are at disproportionally higher risk for COVID-19 mortality. Confirmation by larger studies is warranted.

Effect of Subcutaneous Semaglutide vs Placebo as an Adjunct to Intensive Behavioral Therapy on Body Weight in Adults With Overweight or Obesity: The STEP 3 Randomized Clinical Trial.

Importance: Weight loss improves cardiometabolic risk factors in people with overweight or obesity. Intensive lifestyle intervention and pharmacotherapy are the most effective noninvasive weight loss approaches. Objective: To compare the effects of once-weekly subcutaneous semaglutide, 2.4 mg vs placebo for weight management as an adjunct to intensive behavioral therapy with initial low-calorie diet in adults with overweight or obesity. Design, Setting, and Participants: Randomized, double-blind, parallel-group, 68-week, phase 3a study (STEP 3) conducted at 41 sites in the US from August 2018 to April 2020 in adults without diabetes (N = 611) and with either overweight (body mass index ≥27) plus at least 1 comorbidity or obesity (body mass index ≥30). Interventions: Participants were randomized (2:1) to semaglutide, 2.4 mg (n = 407) or placebo (n = 204), both combined with a low-calorie diet for the first 8 weeks and intensive behavioral therapy (ie, 30 counseling visits) during 68 weeks. Main Outcomes and Measures: The co-primary end points were percentage change in body weight and the loss of 5% or more of baseline weight by week 68. Confirmatory secondary end points included losses of at least 10% or 15% of baseline weight. Results: Of 611 randomized participants (495 women [81.0%], mean age 46 years [SD, 13], body weight 105.8 kg [SD, 22.9], and body mass index 38.0 [SD, 6.7]), 567 (92.8%) completed the trial, and 505 (82.7%) were receiving treatment at trial end. At week 68, the estimated mean body weight change from baseline was -16.0% for semaglutide vs -5.7% for placebo (difference, -10.3 percentage points [95% CI, -12.0 to -8.6]; P < .001). More participants treated with semaglutide vs placebo lost at least 5% of baseline body weight (86.6% vs 47.6%, respectively; P < .001). A higher proportion of participants in the semaglutide vs placebo group achieved weight losses of at least 10% or 15% (75.3% vs 27.0% and 55.8% vs 13.2%, respectively; P < .001). Gastrointestinal adverse events were more frequent with semaglutide (82.8%) vs placebo (63.2%). Treatment was discontinued owing to these events in 3.4% of semaglutide participants vs 0% of placebo participants. Conclusions and Relevance: Among adults with overweight or obesity, once-weekly subcutaneous semaglutide compared with placebo, used as an adjunct to intensive behavioral therapy and initial low-calorie diet, resulted in significantly greater weight loss during 68 weeks. Further research is needed to assess the durability of these findings. Trial Registration: ClinicalTrials.gov Identifier: NCT03611582.