Introduction: In randomized controlled trials (RCTs), treatment effects are commonly reported as hazard ratio, a measure often misinterpreted as a relative risk reduction. The acceleration factor (AF) indicates the extent to which a treatment increases/decreases the time before the occurrence of an outcome and gives useful insights in the interpretation of trials' results. Methods: Using individual time-to-event data reconstructed from Kaplan-Meier plots, we estimated AFs for the primary outcomes (POs) and all-cause mortality in glucagon-like peptide-1 receptor agonists (GLP1-RAs) or sodium-glucose cotransporter-2 inhibitors (SGLT2-is) cardiorenal outcome trials in subjects with type 2 diabetes. Results: AFs were estimated from 28 Kaplan-Meier plots of 19 RCTs. Compared to placebo, most GLP1-RAs increased the time before the onset of POs (from 9 % to 59 %) and all-cause mortality (from 8 to 13 %). Similarly, SGLT2-is increased time before the onset of POs (from 19 % to 87 %) and all-cause mortality (from 13 % to 42 %). Conclusions: The AFs provide a complementary and easier-to-interpret measure of treatment effect that could be useful to improve the shared decision-making.

BACKGROUND AND AIMS:In a randomised controlled trial (RCT), the between-arm difference in the average probability of an event per unit of time (i.e., yearly incidence risk difference, YIRD) is an easy-to-interpret treatment effect metric. We aimed to quantify the YIRD in cardiorenal RCTs of GLP-1RAs or SGLT-2is. METHODS AND RESULTS:We digitally searched for RCTs published up to March 1st, 2023, including subjects with type 2 diabetes randomised to GLP-1RAs or SGLT-2is and investigating cardiorenal outcomes or death. We extracted information from Kaplan-Meier (KM) plots to obtain time-to-event individual data and estimate within-arm yearly incidence risk and YIRD. Data from 19 RCTs (28 kM plots) were analysed: comparing treatment to placebo, in GLP-1RA RCTs the YIRD ranged from 0.2 % (95 % CI: -0.7 %, 1.1 %) to -1.9 % (-3.1, -0.7), for primary outcome; and from -0.2 % (-0.5, 0.2) to -0.4 % (-0.7 %, -0.0 %), for mortality. With the exception of SOLOIST-WHF (YIRD 11.9 % for primary outcome), corresponding estimates in SGLT-2is RCTs were: from -0.1 % (-0.4, 0.1) to -5.0 % (-7.7, -2.6), for primary outcome; and from -0.1 % (-0.2, 0.1) to -1.9 % (-4.4 %, 0.6 %), for mortality. CONCLUSION:The YIRD metric complements other relative treatment effect estimates and helps quantify the absolute benefit of GLP-1RAs and SGLT-2is.

AIMS:While cardiovascular disease in patients with type 2 diabetes commonly progresses with the occurrence of repeated events, most trials consider the effect of glucose-lowering strategies only on the first event. We examined the Action to Control Cardiovascular Risk in Diabetes trial and its observational follow-up study (ACCORDION) to investigate the effect of intensive glucose control on multiple events and further identify any subgroup effects.MATERIALS AND METHODS:A recurrent events analysis, using a negative binomial regression model, was applied to estimate the treatment effect on different consecutive cardiovascular disease events, including non-fatal myocardial infarction, non-fatal stroke, hospitalisation from heart failure, and cardiovascular death. Interaction terms were used to identify potential effect modifiers. The robustness of the results was confirmed in sensitivity analyses using alternative models.RESULTS:The median duration of follow-up was 7.7 years. Of the 5128 participants in the intensive and 5123 in the standard glucose control arm, respectively, 822 (16.0%) and 840 (16.4%) participants experienced a single event; 189 (3.7%) and 214 (4.2%) participants experienced two events; 52 (1.0%) and 40 (0.8%) experienced three events; and 1 (0.02%) and 1 (0.02%) experienced four events. There was no evidence of a treatment effect, with a rate difference of 0.0 (-0.3, 0.3) per 100 person-years comparing intensive versus standard intervention, although with non-significantly lower event rates in younger patients with HbA1c < 7% and higher event rates in older patients with HbA1c ≥ 9%.DISCUSSION:Intensive glucose control may not affect cardiovascular disease progression except in select subgroups. Since time-to-first event analysis may miss beneficial or harmful effects of glucose control on the risk of cardiovascular disease, recurrent events analysis should be routinely analysed in cardiovascular outcome trials, particularly when investigating long-term treatment effects.CLINICAL TRIAL REG NO:NCT00000620, clinicaltrials.gov.

BACKGROUND:Restrictions implemented by governments during the coronavirus disease 2019 (COVID-19) pandemic affected people's eating habits and physical activity. We investigated the effect of COVID-19 lockdowns and restrictions on body mass index (BMI) and weight in a UK population, according to BMI class, sex, age and ethnicity. METHODS:This retrospective observational cohort study used the Clinical Practice Research Datalink AURUM database. Baseline spanned from 22 March 2017-22 March 2020, and the follow-up lockdown period was from 23 March 2020 (start of the lockdown in the UK) to 13 March 2021. The descriptive analysis included individuals with ≥ 1 valid BMI/weight measurements during both the baseline and follow-up periods, while the model-based analysis comprised individuals with ≥ 1 valid measurement(s) during baseline. Results were stratified by baseline BMI category, sex, age and ethnicity. RESULTS:In the descriptive analysis (n = 273,529), most individuals did not change BMI category post-lockdown (66.4-83.3%). A greater proportion of women (12.6%) than men (9.5%) moved up BMI categories post-lockdown. Compared with older groups, a higher proportion of individuals < 45 years old increased post-lockdown BMI category. The model-based analysis (n = 938,150) revealed consistent trends, where changes in body weight and BMI trajectories pre- and post-lockdown were observed for women and for individuals < 45 years. CONCLUSION:During COVID-19 restrictions, women and young individuals were more likely than other groups to increase BMI category and weight post-lockdown.

Most trials leading to the approval of glucagon‐like peptide receptor agonists (GLP‐1RAs) and sodium‐glucose co‐transporter‐2 inhibitors (SGLT2is) were primarily designed to confirm their non‐inferiority to placebo (commonly using an upper 95% confidence limit threshold of 1.3) and, if confirmed, superiority (threshold 1): this asymmetry of margins (1 vs. 1.3) favours the active intervention. We aimed to quantify the probability of clinical superiority of the active treatment by applying the same threshold used to claim non‐inferiority.

Funder: National Institute for Health Research; Grant(s): Biomedical Research Centre Cambridge: Nutrition, Diet, and Lifestyle Research Theme (IS-BRC-1215-20014), NIHR Applied Research Collaboration East Midlands (ARC EM), NIHR Leicester Biomedical Research Centre

Background Pre-existing comorbidities have been linked to SARS-CoV-2 infection but evidence is sparse on the importance and pattern of multimorbidity (2 or more conditions) and severity of infection indicated by hospitalisation or mortality. We aimed to use a multimorbidity index developed specifically for COVID-19 to investigate the association between multimorbidity and risk of severe SARS-CoV-2 infection. Methods We used data from the UK Biobank linked to laboratory confirmed test results for SARS-CoV-2 infection and mortality data from Public Health England between March 16 and July 26, 2020. By reviewing the current literature on COVID-19 we derived a multimorbidity index including: (1) angina; (2) asthma; (3) atrial fibrillation; (4) cancer; (5) chronic kidney disease; (6) chronic obstructive pulmonary disease; (7) diabetes mellitus; (8) heart failure; (9) hypertension; (10) myocardial infarction; (11) peripheral vascular disease; (12) stroke. Adjusted logistic regression models were used to assess the association between multimorbidity and risk of severe SARS-CoV-2 infection (hospitalisation/death). Potential effect modifiers of the association were assessed: age, sex, ethnicity, deprivation, smoking status, body mass index, air pollution, 25-hydroxyvitamin D, cardiorespiratory fitness, high sensitivity C-reactive protein. Results Among 360,283 participants, the median age was 68 [range 48-85] years, most were White (94.5%), and 1706 had severe SARS-CoV-2 infection. The prevalence of multimorbidity was more than double in those with severe SARS-CoV-2 infection (25%) compared to those without (11%), and clusters of several multimorbidities were more common in those with severe SARS-CoV-2 infection. The most common clusters with severe SARS-CoV-2 infection were stroke with hypertension (79% of those with stroke had hypertension); diabetes and hypertension (72%); and chronic kidney disease and hypertension (68%). Multimorbidity was independently associated with a greater risk of severe SARS-CoV-2 infection (adjusted odds ratio 1.91 [95% confidence interval 1.70, 2.15] compared to no multimorbidity). The risk remained consistent across potential effect modifiers, except for greater risk among older age. The highest risk of severe infection was strongly evidenced in those with CKD and diabetes (4.93 [95% CI 3.36, 7.22]). Conclusion The multimorbidity index may help identify individuals at higher risk for severe COVID-19 outcomes and provide guidance for tailoring effective treatment.

Objective: To assess whether the presence of microvascular complications modifies the effect of intensive glucose reduction on long-term outcomes in patients with type 2 diabetes. Patients and Methods: Using ACCORD and ACCORDION study data, we investigated the risk of the primary outcome (nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death) or death in relation to the prerandomization type and extent of microvascular complications. Interaction terms were fitted in survival models to estimate the risk of both outcomes across levels of an overall microvascular disease score (range 0 to 100) and its individual components: diabetic nephropathy, retinopathy, and neuropathy. Results: During a mean follow-up of 7.7 years, 1685 primary outcomes and 1806 deaths occurred in 9405 participants. The outcome-specific microvascular score was <30 in 97.9% of subjects for the primary outcome and in 98.5% for death. For participants with scores of 0 and 30, respectively, the 10year absolute risk difference between intensive glucose control and standard treatment ranged from -0.8% (95% CI, -2.6, 1.1) to -3.0% -7.1, 1.1) for the primary outcome and from -0.5% (-2.1, 1.1) to 0.7% (-4.2, 5.6) for mortality. Retinopathy was associated with the largest effects, with a 10year absolute risk difference of -6.5% (-11.1 to -2.0) for the primary outcome and -3.9% (-7.8 to 0.1) for mortality. Conclusion: This hypothesis-generating study identifies diabetic retinopathy as predictor of the beneficial effect of intensive glucose control on the risk of cardiovascular disease and possibly death. Further long-term studies are required to confirm these findings. (c) 2020 Mayo Foundation for Medical Education and Research center dot Mayo Clin Proc. 2021;96(6):1458-1469

Background: People from South Asian and black minority ethnic groups are disproportionately affected by the COVID-19 pandemic. It is unknown whether deprivation mediates this excess ethnic risk. Methods: We used UK Biobank with linked COVID-19 outcomes occurring between 16th March 2020 and 24th August 2020. A four-way decomposition mediation analysis was used to model the extent to which the excess risk of testing positive, severe disease and mortality for COVID-19 in South Asian and black individuals, relative to white individuals, would be eliminated if levels of high material deprivation were reduced within the population. Results: We included 15 044 (53.0% women) South Asian and black and 392 786 (55.2% women) white individuals. There were 151 (1.0%) positive tests, 91 (0.6%) severe cases and 31 (0.2%) deaths due to COVID-19 in South Asian and black individuals compared with 1471 (0.4%), 895 (0.2%) and 313 (0.1%), respectively, in white individuals. Compared with white individuals, the relative risk of testing positive for COVID-19, developing severe disease and COVID-19 mortality in South Asian and black individuals were 2.73 (95% CI: 2.26, 3.19), 2.96 (2.31, 3.61) and 4.04 (2.54, 5.55), respectively. A hypothetical intervention moving the 25% most deprived in the population out of deprivation was modelled to eliminate between 40 and 50% of the excess risk of all COVID-19 outcomes in South Asian and black populations, whereas moving the 50% most deprived out of deprivation would eliminate over 80% of the excess risk of COVID-19 outcomes. Conclusions: The excess risk of COVID-19 outcomes in South Asian and black communities could be substantially reduced with population level policies targeting material deprivation.

Despite growing awareness of the dangers of a dichotomous interpretation of trial results based on the ‘statistical significance’ of a treatment effect, the uptake of new approaches has been slow in diabetes medicine. We showcase a number of ways to interpret the evidence for a treatment effect applied to the cardiovascular outcome trials of glucagon‐like peptide‐1 receptor agonists (GLP‐1RAs) and sodium‐glucose co‐transporter‐2 inhibitors (SGLT‐2is): the P value function (or confidence curves), which depicts the treatment effect across the whole spectrum of confidence levels; the counternull value, which is the hazard ratio (i.e. treatment effect size) supported by the same amount of evidence as the null value (i.e. no treatment effect); and the S value, which quantifies the strength of the evidence against the null hypothesis in terms of the number of coin tosses yielding the same side. We show how this approach identifies potential treatment effects, highlights similarities among trials straddling the threshold of statistical significance, and quantifies differences in the strength of the evidence from trials reporting statistically significant results. For example, while REWIND, CANVAS and CREDENCE failed to reach statistical significance at the .05 level for all‐cause mortality, their counternull values indicate that reduced death rates by 19%, 24% and 31%, respectively, are supported by the same amount of evidence as that indicating no treatment effect. Moreover, similarities among results emerge in trials of GLP‐1RAs (REWIND, EXSCEL and LEADER) lying closely around the threshold of ‘statistical significance’. Lastly, several S values, such as for the primary outcome in HARMONY Outcomes (S value 10.9) and all‐cause death in EMPAREG‐OUTCOME (S value 15.0), stand out compared with values for other outcomes and other trials, suggesting much larger differences in the evidence between these studies and several others that cluster around the .05 significance threshold. P value functions, counternull values and S values should complement the standard reporting of the treatment effect to help interpret clinical trials and make decisions among competing glucose‐lowering medications.

Over the last two decades, the large majority of clinical guidelines on the treatment of hyperglycemia in subjects with type 2 diabetes have suggested metformin as the first-line glucose-lowering treatment alongside lifestyle changes to reach personalized glycemic targets. Recently, the European Society of Cardiology recommended using glucagon-like peptide 1 receptor agonists (GLP-1RA) or sodium–glucose cotransporter 2 inhibitors (SGLT-2i) as first-line glucose-lowering therapy in subjects with type 2 diabetes at high or very high risk of cardiovascular disease, ahead of metformin treatment, to reduce cardiovascular events (1). Following the European Society of Cardiology guidelines, several analyses have investigated whether the cardiovascular effects of GLP-1RA or SGLT-2i would differ in relation to the use of metformin. Some of these studies reported a “statistically significant” difference (i.e., interaction) in the cardiovascular effects of SGLT-2is, whereby subjects with metformin have a lower cardiovascular protection from SGLT-2i, leading to several hypotheses about the possible pharmacological mechanisms. Interpreting interaction results, however, may be difficult, as they suffer from well-known drawbacks, including limited statistical power (2). For overcoming this problem and identifying who may be most likely to benefit from a specific treatment, trial-specific interactions may be combined with a meta-analytical approach (3). In this study, we systematically investigated the differences in the treatment effect of incretins (GLP-1RA and dipeptidyl peptidase 4 inhibitors [DPP-4i]) and SGLT-2i on cardiovascular outcomes according to metformin use. We included DPP-4i given their overlapping pharmacodynamics with GLP-1RAs and the previous evidence of interactions with metformin (4). On 5 October 2020, we searched for randomized controlled trials (RCTs) in adults with type 2 diabetes reporting incretin or SGLT-2i treatment effect for the primary cardiovascular outcome (major adverse cardiovascular event [MACE]) stratified by baseline metformin use; details of the search, included trials, and risk of bias are available on request. We …

Objective: To quantify the association between accelerometer-assessed physical activity and coronavirus disease 2019 (COVID-19) outcomes. Methods: Data from 82,253 UK Biobank participants with accelerometer data (measured 2013-2015), complete covariate data, and linked COVID-19 data from March 16, 2020, to March 16, 2021, were included. Two outcomes were investigated: severe COVID-19 (positive test result from in-hospital setting or COVID-19 as primary cause of death) and nonsevere COVID-19 (positive test result from community setting). Logistic regressions were used to assess associations with moderate to vigorous physical activity (MVPA), total activity, and intensity gradient. A higher intensity gradient indicates a higher proportion of vigorous activity. Results: Average MVPA was 48.1 (32.7) min/d. Physical activity was associated with lower odds of severe COVID-19 (adjusted odds ratio per standard deviation increase: MVPA, 0.75 [95% CI, 0.67 to 0.85]; total, 0.83 [0.74 to 0.92]; intensity, 0.77 [0.70 to 0.86]), with stronger associations in women (MVPA, 0.63 [0.52 to 0.77]; total, 0.76 [0.64 to 0.90]; intensity, 0.63 [0.53 to 0.74]) than in men (MVPA, 0.84 [0.73 to 0.97]; total, 0.88 [0.77 to 1.01]; intensity, 0.88 [0.77 to 1.00]). In contrast, when mutually adjusted, total activity was associated with higher odds of a nonsevere infection (1.10 [1.04 to 1.16]), whereas the intensity gradient was associated with lower odds (0.91 [0.86 to 0.97]). Conclusion: Odds of severe COVID-19 were approximately 25% lower per standard deviation (∼30 min/d) MVPA. A greater proportion of vigorous activity was associated with lower odds of severe and nonsevere infections. The association between total activity and higher odds of a nonsevere infection may be through greater community engagement and thus more exposure to the virus. Results support calls for public health messaging highlighting the potential of MVPA for reducing the odds of severe COVID-19.

The global coronavirus pandemic has precipitated a rapid unprecedented research response, including investigations into risk factors for COVID-19 infection, severity, or death. However, results from this research have produced heterogeneous findings, including articles published in Wellcome Open Research. Here, we use ethnicity, obesity, and smoking as illustrative examples to demonstrate how a research question can produce very different answers depending on how it is framed. For example, these factors can be both strongly associated or have a null association with death due to COVID-19, even when using the same dataset and statistical modelling. Highlighting the reasons underpinning this apparent paradox provides an important framework for reporting and interpreting ongoing COVID-19 research.

Behavioral lifestyle factors are associated with cardiometabolic disease and obesity, which are risk factors for coronavirus disease 2019 (COVID-19). We aimed to investigate whether physical activity, and the timing and balance of physical activity and sleep/rest, were associated with SARS-CoV-2 positivity and COVID-19 severity. Data from 91,248 UK Biobank participants with accelerometer data and complete covariate and linked COVID-19 data to July 19, 2020, were included. The risk of SARS-CoV-2 positivity and COVID-19 severity-in relation to overall physical activity, moderate-to-vigorous physical activity (MVPA), balance between activity and sleep/rest, and variability in timing of sleep/rest-was assessed with adjusted logistic regression. Of 207 individuals with a positive test result, 124 were classified as having a severe infection. Overall physical activity and MVPA were not associated with severe COVID-19, whereas a poor balance between activity and sleep/rest was (odds ratio [OR] per standard deviation: 0.71; 95% confidence interval [CI], 0.62 to 0.81]). This finding was related to higher daytime activity being associated with lower risk (OR, 0.75; 95% CI, 0.61 to 0.93) but higher movement during sleep/rest being associated with higher risk (OR, 1.26; 95% CI, 1.12 to 1.42) of severe infection. Greater variability in timing of sleep/rest was also associated with increased risk (OR, 1.21; 95% CI, 1.08 to 1.35). Results for testing positive were broadly consistent. In conclusion, these results highlight the importance of not just physical activity, but also quality sleep/rest and regular sleep/rest patterns, on risk of COVID-19. Our findings indicate the risk of COVID-19 was consistently approximately 1.2-fold greater per approximately 40-minute increase in variability in timing of proxy measures of sleep, indicative of irregular sleeping patterns.

The global coronavirus pandemic has precipitated a rapid unprecedented research response, including investigations into risk factors for COVID-19 infection, severity, or death However, results from this research have produced heterogeneous findings, including articles published in Wellcome Open Research Here, we use ethnicity, obesity, and smoking as illustrative examples to demonstrate how a research question can produce very different answers depending on how it is framed For example, these factors can be both strongly associated or have a null association with death due to COVID-19, even when using the same dataset and statistical modelling Highlighting the reasons underpinning this apparent paradox provides an important framework for reporting and interpreting ongoing COVID-19 research

The global coronavirus pandemic has precipitated a rapid unprecedented research response, including investigations into risk factors for COVID-19 infection, severity, or death. However, results from this research have produced heterogeneous findings, including articles published in Wellcome Open Research. Here, we use ethnicity, obesity, and smoking as illustrative examples to demonstrate how a research question can produce very different answers depending on how it is framed. For example, these factors can be both strongly associated or have a null association with death due to COVID-19, even when using the same dataset and statistical modelling. Highlighting the reasons underpinning this apparent paradox provides an important framework for reporting and interpreting ongoing COVID-19 research.