BACKGROUND:While antihypertensive treatment prevents cardiovascular events, it may also increase risks such as falls in patients with frailty. The Optimising Treatment for Mild Systolic Hypertension in the Elderly (OPTiMISE) trial found that deprescribing one antihypertensive drug did not result in worse short-term blood pressure control and long-term follow-up showed no observed harm, but its generalisability to routine clinical practice remains uncertain. OBJECTIVE:This study aimed to calibrate the OPTiMISE effect to a representative primary care population in England. METHODS:We calibrated the OPTiMISE treatment effect using inverse probability weighting (IPW) based on trial inclusion likelihood. The trial enrolled 569 adults aged ≥80 years with controlled blood pressure on ≥2 antihypertensive drugs. A target population was reconstructed from electronic health records of 24 participating practices and extrapolated to all English adults aged ≥80 years, prescribed ≥2 antihypertensive drugs, using NHS Digital data. The primary outcome was all-cause hospitalisation or death. Weighted Cox models estimated calibrated hazard ratios (HRs). RESULTS:The target population included 798 179 individuals (median age 84 years [81-87], 48% female). Compared with OPTiMISE participants, a higher proportion of individuals in target population were overtly frail (25% vs. 11%). After calibration using IPW, deprescribing was not associated with higher risk of hospitalisation or death [calibrated HR 0.94 (95% CI: 0.73-1.22)], similar to the long-term follow-up of OPTiMISE [HR 0.93 (95% CI: 0.76-1.12)], although with a slightly wider confidence interval. CONCLUSIONS:Calibrating OPTiMISE findings to a representative primary care population frailer than the original participants suggests that the original trial findings could be translated into a real-world population.

Background Lipid‐lowering therapy (LLT) is important for risk reduction for patients after coronary artery bypass grafting. Cost limits the wider use of PCSK9 (proprotein convertase subtilsin/kexin type 9) inhibitors (PCSK9is) and newer drugs. Hence, mathematical modeling was performed on a nationwide cohort of US veterans post coronary artery bypass grafting to understand the need for expensive drugs like PCSK9is and the cost effectiveness of this model was evaluated. Methods First, Monte Carlo simulations were used to model the stepwise initiation of high‐intensity statins, ezetimibe, and PCSK9i therapy for each patient to lower their low‐density lipoprotein cholesterol levels to recommended targets. Next, a lifetime Markov model was constructed with stroke, myocardial infarction, repeat revascularization, and mortality rates obtained our study cohort of nationwide US veterans post coronary artery bypass grafting. LLT treatment costs and quality adjusted life years were used to determine the cost effectiveness of this stepwise LLT approach versus observed clinical practice from the health care perspective. Probabilistic models were fit and results for cost effectiveness reported using incremental cost‐effectiveness ratio per quality adjusted life year gained over the lifetime. Results For 27 443 US veterans post‐ coronary artery bypass grafting (mean age 66 years, 10% Black) with a median low‐density lipoprotein cholesterol 129 (interquartile range, 95.2–180) mg/dL, the 42% and 37% reached target LLT by statin intensification only and including ezetimibe, respectively. Only 6% required bempedoic acid or PCSK9is after the preceding steps. Such stepwise LLT reduction projected 0.8 life years gained over the 30‐year period. With a median incremental cost‐effectiveness ratio of percent 15 232 (interquartile range, 13 520–17 769) per quality adjusted life year gained, this stepwise approach reached 100% probability for being cost effective (compared with observed clinical practice) at willingness‐to‐pay thresholds >$20 000. Conclusions Few patients needed bempedoic acid or PCSK9is when simulating stepwise LLT with high‐intensity statins and ezetimibe. Such an approach was observed as cost effective at very low willingness‐to‐pay thresholds.

Evidence for the net benefit of aspirin for primary prevention of cardiovascular disease (CVD) is finely balanced, leading to variation in guideline recommendations internationally. External validity of randomised clinical trial (RCT) evidence may therefore be of particular importance. The aim of this study is to characterise real-world patients according to their eligibility for guideline-cited aspirin RCTs for primary CVD prevention. Eligibility criteria from 14 RCTs were applied to a linked primary care/hospital discharge dataset of people ≥ 40 years without CVD. Proportions eligible for each trial were calculated, and characteristics of eligible and ineligible patients compared for each trial, including Cox regression analysis of event rates for major adverse cardiovascular events (MACE), major bleeding events, and non-cardiovascular mortality. Of 570,211 included patients (300,500 [52.7

Abstract Background Identifying clusters of multiple long-term conditions (MLTCs), also known as multimorbidity, and their associated burden may facilitate the development of effective and cost-effective targeted healthcare strategies. This study aimed to identify clusters of MLTCs and their associations with long-term health-related quality of life (HRQoL) in two UK population-based cohorts. Methods Age-stratified clusters of MLTCs were identified at baseline in UK Biobank (n = 502,363, 54.6% female) and UKHLS (n = 49,186, 54.8% female) using latent class analysis (LCA). LCA was applied to people who self-reported ≥ 2 LTCs (from n = 43 LTCs [UK Biobank], n = 13 LTCs [UKHLS]) at baseline, across four age-strata: 18–36, 37–54, 55–73, and 74 + years. Associations between MLTC clusters and HRQoL were investigated using tobit regression and compared to associations between MLTC counts and HRQoL. For HRQoL, we extracted EQ-5D index data from UK Biobank. In UKHLS, SF-12 data were extracted and mapped to EQ-5D index scores using a standard preference-based algorithm. HRQoL data were collected at median 5 (UKHLS) and 10 (UK Biobank) years follow-up. Analyses were adjusted for available sociodemographic and lifestyle covariates. Results LCA identified 9 MLTC clusters in UK Biobank and 15 MLTC clusters in UKHLS. Clusters centred around pulmonary and cardiometabolic LTCs were common across all age groups. Hypertension was prominent across clusters in all ages, while depression featured in younger groups and painful conditions/arthritis were common in clusters from middle-age onwards. MLTC clusters showed different associations with HRQoL. In UK Biobank, clusters with high prevalence of painful conditions were consistently associated with the largest deficits in HRQoL. In UKHLS, clusters of cardiometabolic disease had the lowest HRQoL. Notably, negative associations between MLTC clusters containing painful conditions and HRQoL remained significant even after adjusting for number of LTCs. Conclusions While higher LTC counts remain important, we have shown that MLTC cluster types also have an impact on HRQoL. Health service delivery planning and future intervention design and risk assessment of people with MLTCs should consider both LTC counts and MLTC clusters to better meet the needs of specific populations.

Background Trial attrition poses several risks for the validity of randomised controlled trials (RCTs). To better understand attrition, studies have explored and identified predictors among participant and trial characteristics. Reviews of these have so far been limited to single conditions. We performed an umbrella review to explore which participant and trial characteristics are reported in predictive analyses of trial attrition in systematic reviews of RCTs across multiple conditions. Methods We searched MEDLINE, Embase, Web of Science and the Online Resource for Research in Clinical TriAls for systematic reviews of RCTs that evaluated associations between participant/trial characteristics and attrition. We included quantitative systematic reviews of adult populations that evaluated any participant/trial characteristic and any attrition outcome. Review quality was appraised using R-AMSTAR. A review-level narrative synthesis was conducted. Results We identified 88 reviews of RCTs evaluating characteristics associated with attrition. Included reviews encompassed 33 different conditions. Over half (50/88, 56.8%) were of RCTs for psychological conditions. All but one examined trial characteristics (87/88, 98.9%) and fewer than half (42/88, 47.7%) evaluated participant characteristics. Reviews typically reported on participant age (33/42, 78.6%), sex (29/42, 69.1%) and the type (13/42, 31%) or severity (10/42, 23.8%) of an index condition. Trial characteristics typically reported on were intervention type (56/87, 64.4%), intervention frequency/intensity (29/87, 33.3%), intervention delivery/format (26/87, 29.9%), trial duration (16/87, 18.4%), publication/reporting year (15/87, 17.2%) and sample size (15/87, 31.9%). Retention strategies were rarely reported (2/87, 2.3%). No characteristic was examined for every condition. Some reviews of certain conditions found that age (12/33, 36.4%), intervention type (29/56, 51.8%) and trial duration (9/16, 56.3%) were associated with attrition, but no characteristic was reportedly associated across multiple conditions. Conclusions Across conditions, reviews conducting predictive analyses of attrition in RCTs typically report on several characteristics. These are participant age, sex and the type or severity of index condition, as well as the type, frequency or intensity and delivery or format of a trial intervention, trial duration, publication/reporting year and sample size. Future studies should consider exploring these characteristics as a core set when evaluating predictive factors of attrition in RCTs across multiple conditions. Registration PROSPERO: CRD42023398276

BACKGROUND:The electronic frailty index (eFI) was developed in older adults (aged ≥65 years). There are currently no validated frailty scores in clinical practice for younger adults (aged 18-64 years). The aim of this study was to examine whether the eFI score in younger adults had similar or different associations with adverse health outcomes compared with older adults. METHODS:In this population-based cohort study, electronic health records from the UK Clinical Practice Research Datalink GOLD database were analysed. We used a cross-section of patients on Nov 30, 2015, who were alive and had been registered with a general practice for at least 2 years before data capture. Patients were stratified into younger adults (aged 18-64 years, n=708 235; 49·4% female) and older adults (aged 65-95 years, n=231 819; 54·3% female). For all included patients, eFI score, prevalence of individual eFI deficits, and eFI frailty category were calculated. For the main outcomes, crude and age-sex adjusted hazard ratios (HRs) were calculated for 1-year and 3-year mortality and emergency hospitalisation for each group compared with adults defined by the eFI as fit. FINDINGS:The prevalence of eFI-defined frailty was higher in older adults than younger adults. Specifically, in older adults, 77 290 (33·3%) of 231 819 had mild frailty, 44 523 (19·2%) had moderate frailty, and 22 572 (9·7%) had severe frailty. For younger adults, 76 991 (10·9%) of 708 235 had mild frailty, 12 552 (1·8%) had moderate frailty, and 2088 (0·3%) had severe frailty. Adjusted HRs for both 1-year mortality and 1-year emergency hospitalisation in younger adults with mild, moderate, and severe frailty were greater than in older adults with equivalent frailty categorisation. Specifically, compared with fit older adults, age-sex adjusted 1-year mortality HRs were 1·94 (95% CI 1·80-2·09) in older adults with mild frailty, 2·99 (2·77-3·22) with moderate frailty, and 4·03 (3·72-4·36) with severe frailty. Compared with fit younger adults, age-sex adjusted 1-year mortality HRs were 3·15 (2·80-3·55) in younger adults with mild frailty, 5·88 (4·95-6·98) with moderate frailty, and 12·61 (9·76-16·30) with severe frailty (Z score p<0·001 for all comparisons). Compared with fit older adults, age-sex adjusted HRs for 1-year emergency hospitalisation were 2·30 (2·22-2·39) in older adults with mild frailty, 4·09 (3·94-4·25) with moderate frailty, and 6·76 (6·50-7·03) with severe frailty. Compared with fit younger adults, age-sex adjusted HRs for 1-year emergency hospitalisation were 3·16 (3·07-3·25) in younger adults with mild frailty, 6·64 (6·34-6·94) with moderate frailty, and 13·02 (12·04-14·09) with severe frailty (Z score p<0·001 for all comparisons). Similar associations were observed for 3-year mortality and emergency hospitalisation. INTERPRETATION:Similarly to older adults, the eFI identifies younger adults with frailty at high risk of mortality and emergency hospital admission. The eFI might be a tool to identify individuals for further assessment and intervention. FUNDING:Wellcome Trust and Chief Scientist Office.

Background Randomized clinical trials provide the highest standard of evidence about vaccine efficacy. Modelling exercises such as in evidence synthesis and health economic models where efficacy estimates are combined with other data to obtain effectiveness and cost-effectiveness estimates help inform policy decisions. The main challenge with such sensitivity analyses is in deciding on which assumptions to model. Purpose To identify plausible ranges for differential vaccine efficacy across high- and low-income settings. Data Sources and Study Selection MEDLINE, EMBASE, clinicaltrials.gov, and the World Health Organization International Clinical Trials Registry Platform (WHO- ICTRP) were searched for multi-site randomized clinical trials of bacterial and viral vaccines. Articles were restricted to those where at least one trial had included a low- or lower-middle-income setting, published in English, and conducted in humans. Methods A Bayesian random-effects meta-analysis was used to estimate the difference in vaccine efficacy in high- (high or upper middle) and low- (low or lower middle) income settings. A single hierarchical model that included all trials was used so that the degree to which estimates of vaccine efficacy against different diseases influenced one another was estimated from the observed data. Results Across 65 eligible trials (37 high-income, 21 low-income, and 7 both) covering 7 pathogens, only one trial reported efficacy estimates stratified by setting. Trials were similar in terms of design across settings. There was evidence of heterogeneity by vaccine target, typhoid vaccine demonstrated higher vaccine efficacy in low-income settings than in high-income settings but for all other vaccines, the point estimates indicated efficacy was lower in low-income settings; however, all credible intervals crossed the null. Conclusions The percentage of trials in low-income settings poorly reflects the burden of disease experienced in low-income settings. While there is evidence of lower vaccine efficacy in low-income settings relative to high-income settings, the credible intervals were very wide. Vaccine efficacy trials should report treatment effects stratified by settings. Keywords Bayesian analysis, illustrative evidence synthesis, vaccine efficacy, policy. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by an Intermediate Clinical Fellowship and Beit Fellowship from the Wellcome Trust 201492/Z/16/Z. This project was funded via the linked Beit Award. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online at https://zenodo.org/record/7956407

Background Clinical guideline development preferentially relies on evidence from randomized controlled trials (RCTs). RCTs are gold-standard methods to evaluate the efficacy of treatments with the highest internal validity but limited external validity, in the sense that their findings may not always be applicable to or generalizable to clinical populations or population characteristics. The external validity of RCTs for the clinical population is constrained by the lack of tailored epidemiological data analysis designed for this purpose due to data governance, consistency of disease or condition definitions, and reduplicated effort in analysis code. Objective This study aims to develop a digital tool that characterizes the overall population and differences between clinical trial eligible and ineligible populations from the clinical populations of a disease or condition regarding demography (eg, age, gender, ethnicity), comorbidity, coprescription, hospitalization, and mortality. Currently, the process is complex, onerous, and time-consuming, whereas a real-time tool may be used to rapidly inform a guideline developer’s judgment about the applicability of evidence. Methods The National Institute for Health and Care Excellence—particularly the gout guideline development group—and the Scottish Intercollegiate Guidelines Network guideline developers were consulted to gather their requirements and evidential data needs when developing guidelines. An R Shiny (R Foundation for Statistical Computing) tool was designed and developed using electronic primary health care data linked with hospitalization and mortality data built upon an optimized data architecture. Disclosure control mechanisms were built into the tool to ensure data confidentiality. The tool was deployed within a Trusted Research Environment, allowing only trusted preapproved researchers to conduct analysis. Results The tool supports 128 chronic health conditions as index conditions and 161 conditions as comorbidities (33 in addition to the 128 index conditions). It enables 2 types of analyses via the graphic interface: overall population and stratified by user-defined eligibility criteria. The analyses produce an overview of statistical tables (eg, age, gender) of the index condition population and, within the overview groupings, produce details on, for example, electronic frailty index, comorbidities, and coprescriptions. The disclosure control mechanism is integral to the tool, limiting tabular counts to meet local governance needs. An exemplary result for gout as an index condition is presented to demonstrate the tool’s functionality. Guideline developers from the National Institute for Health and Care Excellence and the Scottish Intercollegiate Guidelines Network provided positive feedback on the tool. Conclusions The tool is a proof-of-concept, and the user feedback has demonstrated that this is a step toward computer-interpretable guideline development. Using the digital tool can potentially improve evidence-driven guideline development through the availability of real-world data in real time.

BACKGROUND:Cumulative deficit frailty indices from randomised controlled trials (RCT) are increasingly used to assess whether trial findings are applicable to people living with frailty. The aim of this paper was to examine the range and type of deficits included in these frailty indices and compare these to those from cohort studies. METHODS:We identified RCTs assessing treatment effect modification using the cumulative deficit frailty index, as well as cohort studies assessing mortality risk associated with frailty, from recent systematic reviews. We extracted the deficits included in the frailty index from each RCT and cohort study. We compared the number of deficits, data sources (e.g. medical history, physical measurements, questionnaires, etc.) and physiological domain (e.g. cardiometabolic, neuro-cognitive, physical function, etc.) of the deficits from each source. RESULTS:The number of deficits was similar between RCT frailty indices (median 41 deficits, interquartile range [IQR] 35-50) and cohort studies (median 35, IQR 31-45). Broadly similar data sources were used to identify deficits. However, in RCTs of cardiovascular conditions, cardiometabolic deficits made up a greater proportion of deficits (median 47% of included deficits, IQR 38%-51%, compared to 19%, 14%-24%, in cohort studies). Cardiovascular RCTs included fewer physical function measures (median 4% [3%-9%], compared to 16% in other RCTs of other conditions [13%-17%], 17% in cohort [13%-23%]). CONCLUSION:In many cardiovascular RCTs, frailty indices focus on cardiometabolic deficits rather than measures of function. These frailty indices need to be validated against outcomes important to people living with frailty before being used to inform treatment. Until then, we would emphasise caution.

BACKGROUND:The representation of frailty in type 2 diabetes trials is unclear. This study used individual participant data from trials of newer glucose-lowering therapies to quantify frailty and assess the association between frailty and efficacy and adverse events. METHODS AND FINDINGS:We analysed IPD from 34 trials of sodium-glucose cotransporter 2 (SGLT2) inhibitors, glucagon-like peptide-1 (GLP1) receptor agonists, and dipeptidyl peptidase 4 (DDP4) inhibitors. Frailty was quantified using a cumulative deficit frailty index (FI). For each trial, we quantified the distribution of frailty; assessed interactions between frailty and treatment efficacy (HbA1c and major adverse cardiovascular events [MACE], pooled using random-effects network meta-analysis); and associations between frailty and withdrawal, adverse events, and hypoglycaemic episodes. Trial participants numbered 25,208. Mean age across the included trials ranged from 53.8 to 74.2 years. Using a cut-off of FI > 0.2 to indicate frailty, median prevalence was 9.5% (IQR 2.4%-15.4%). Applying a higher threshold of FI > 0.3, median prevalence was 0.5% (IQR 0.1%-1.5%). Prevalence was higher in trials of older people and people with renal impairment however, even in these higher risk populations, people with FI > 0.4 were generally absent. For SGLT2 inhibitors and GLP1 receptor agonists, there was a small attenuation in efficacy on HbA1c with increasing frailty (0.08%-point and 0.14%-point smaller reduction, respectively, per 0.1-point increase in FI), below the level of clinical significance. Findings for the effect of treatment on MACE (and whether this varied by frailty) had high uncertainty, with few events occurring in trial follow-up. A 0.1-point increase in the FI was associated with more all-cause adverse events regardless of treatment allocation (incidence rate ratio, IRR 1.44, 95% CI 1.35-1.54, p < 0.0001), adverse events judged to the possibly or probably related to treatment (1.36, 1.23, to 1.49, p < 0.0001), serious adverse events (2.09, 1.85, to 2.36, p < 0.0001), hypoglycaemia (1.21, 1.06, to 1.38, p = 0.012), baseline risk of MACE (hazard ratio 3.01, 2.48, to 3.67, p < 0.0001) and with withdrawal from the trial (odds ratio 1.41, 1.27, to 1.57, p < 0.0001). The main limitation was that the large cardiovascular outcome trials did not include data on functional status and so we were unable to assess frailty in these larger trials. CONCLUSIONS:Frailty was uncommon in these trials, and participants with a high degree of frailty were rarely included. Frailty is associated very modest attenuation of treatment efficacy for glycaemic outcomes and with greater incidence of both adverse events and MACE independent of treatment allocation. While these findings are compatible with calls to relax HbA1c-based targets in people living with frailty, they also highlight the need for inclusion of people living with frailty in trials. This would require changes to trial processes to facilitate the explicit assessment of frailty and support the participation of people living with frailty. Such changes are important as the absolute balance of risks and benefits remains uncertain among those with higher degrees of frailty, who are largely excluded from trials.

AIMS:This study assesses national trends and, socio-demographic and clinical factors associated with polypharmacy and potentially in appropriate prescribing among people with type 2 diabetes in Scotland from 2012 to 2022. METHODS:Retrospective cohort study using nationwide data from the Scottish Care Information - Diabetes database. Individuals aged ≥40 years with type 2 diabetes were included. Medication counts were based on unique medications dispensed per calendar year. Potentially inappropriate medications were based on the 2023 Beers criteria and applied to people aged over 65 years. A Poisson mixed-effects model with individual-level random intercepts assessed the relationship between the number of drug classes dispensed and year, gender, age group and socio-economic status, Elixhauser comorbidity index and the hospital frailty risk score. RESULTS:387,338 people were included. The median number of medications dispensed per person was 9 (interquartile range 5-13). Adjusted medication counts were modestly higher in older people (rate ratio [RR] 1.06, 95% confidence interval [CI] 1.06-1.06 at age 80+ compared to 40-59), higher in women (1.14, 1.13-1.14), in more deprived areas (1.24, 1.23-1.24 in the most deprived vs. the most affluent quintile) and in those with higher comorbidity (1.12, 1.12-1.13 in 4+ vs. 0 comorbidities) but not with high frailty risk (1.00, 1.00-1.00). People over 65 were dispensed a median of 2 (IQR 1-3) potentially inappropriate medications. Potentially inappropriate medication showed a stronger association with comorbidity (1.24, 1.23-1.25) and a positive association with high frailty risk (1.24, 1.23-1.25). CONCLUSIONS:The degree of polypharmacy highlights the need for regular formal medication reviews in this population.

Background:Cancer incidence in people with chronic kidney disease (CKD) who do not require kidney replacement therapy remains inadequately characterized. This systematic review aimed to establish whether there is an elevated incidence of cancer in people with CKD. Methods:A systematic search of three online bibliographic databases until 17 January 2023 identified studies reporting cancer incidence in CKD cohorts (PROSPERO CRD42022359690). Meta-analyses using inverse variance method compared incidence rates in individuals with low estimated glomerular filtration rate (eGFR) (<60 mL/min/1.73 m2) with available cohorts with normal eGFR (≥60 mL/min/1.73 m2 or both 60-89 and ≥90 mL/min/1.73 m2) for all cancers and site-specific cancers. Multiple meta-regression analyses explored associations of eGFR and age. Results:In 27 studies (5 519 778 people with CKD), from 10 countries spanning 2009-2022, incidence rates of cancer were associated with worse CKD severity. Incidence rate ratio (IRR) comparing people with an eGFR <60 mL/min/1.73 m2 vs ≥60 mL/min/1.73 m2 was 1.35 [95% confidence interval (CI) 1.12-1.63, P = .002, I2 = 99.9%]. People with eGFR <60 mL/min/1.73 m2 were at an elevated rate of cancer compared with eGFR ≥90 mL/min/1.73 m2 [IRR 1.48 (95% CI 1.04-2.10, P = .03, I2 = 100%)] and those with eGFR 60-89 mL/min/1.73 m2 [IRR 1.21 (95% CI 1.11-1.33, P < .01, I2 = 92%)]. Age was associated with increased cancer incidence (β = 0.31, P = .02) on multiple meta-regression analysis. There was no association between site-specific cancer incidence in CKD patients, but these had wide confidence intervals. Conclusion:Individuals with CKD have an elevated incidence of cancer, with increasing age contributing to this association. These findings emphasize the importance of investigating whether CKD independently elevates cancer risk, building evidence for tailored cancer screening into CKD patient care.

Importance:Sodium-glucose cotransporter 2 (SGLT2) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, and dipeptidyl peptidase 4 (DPP4) inhibitors improve hyperglycemia, and SGLT2 inhibitors and GLP-1 receptor agonists reduce the risk of major adverse cardiovascular events (MACEs) among individuals with type 2 diabetes. It is not clear whether efficacy varies by age or sex. Objective:To assess whether age or sex are associated with differences in the efficacy of SGLT2 inhibitors, GLP-1 receptor agonists, and DPP4 inhibitors. Data Sources and Study Selection:The MEDLINE and Embase databases and US and Chinese clinical trial registries were searched for articles published from inception to November 2022; in August 2024, the search was updated to capture the trial results. Two reviewers screened for randomized clinical trials of SGLT2 inhibitors, GLP-1 receptor agonists, or DPP4 inhibitors vs a placebo or active comparator in adults with type 2 diabetes. Data Extraction and Synthesis:Individual participant data and aggregate data were used to estimate age × treatment interactions and sex × treatment interactions in multilevel network meta-regression models. Main Outcome and Measures:Hemoglobin A1c (HbA1c) and MACEs. Results:Of the 601 eligible trials identified (592 trials with 309 503 participants reported HbA1c; mean age, 58.9 [SD, 10.8] years; 42.3% were female and 23 trials with 168 489 participants reported MACEs; mean age, 64.0 [SD, 8.6] years; 35.3% were female), individual participant data were obtained for 103 trials (103 reported HbA1c and 6 reported MACEs). The use of SGLT2 inhibitors (vs placebo) was associated with less HbA1c lowering with increasing age for monotherapy (absolute reduction [AR], 0.24% [95% credible interval {CrI}, 0.10% to 0.38%] per 30-year increment in age), for dual therapy (AR, 0.17% [95% CrI, 0.10% to 0.24%]), and for triple therapy (AR, 0.25% [95% CrI, 0.20% to 0.30%]). The use of GLP-1 receptor agonists was associated with greater HbA1c lowering with increasing age for monotherapy (AR, -0.18% [95% CrI, -0.31% to -0.05%] per 30-year increment in age) and for dual therapy (AR, -0.24% [95% CrI, -0.40% to -0.07%]), but not for triple therapy (AR, 0.04% [95% CrI, -0.02% to 0.11%]). The use of DPP4 inhibitors was associated with slightly better HbA1c lowering in older people for dual therapy (AR, -0.09% [95% CrI, -0.15% to -0.03%] per 30-year increment in age), but not for monotherapy (AR, -0.08% [95% CrI, -0.18% to 0.01%]) or triple therapy (AR, -0.01% [95% CrI, -0.06% to 0.05%]). The relative reduction in MACEs with use of SGLT2 inhibitors was greater in older vs younger participants per 30-year increment in age (hazard ratio, 0.76 [95% CrI, 0.62 to 0.93]), and the relative reduction in MACEs with use of GLP-1 receptor agonists was less in older vs younger participants (hazard ratio, 1.47 [95% CrI, 1.07 to 2.02]). There was no consistent evidence for sex × treatment interactions with use of SGLT2 inhibitors and GLP-1 receptor agonists. Conclusions and Relevance:The SGLT2 inhibitors and GLP-1 receptor agonists were associated with lower risk of MACEs. Analysis of age × treatment interactions suggested that SGLT2 inhibitors were more cardioprotective in older than in younger people despite smaller reductions in HbA1c; GLP-1 receptor agonists were more cardioprotective in younger people.

Importance Sodium glucose cotransporter 2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor analogues (GLP1ra) and dipeptidyl peptidase-4 inhibitors (DPP4i) improve hyperglycaemia and, in the case of SGLT2i and GLP1ra, reduce the risk of major adverse cardiovascular events (MACE) in type 2 diabetes. It is not clear whether efficacy varies by age or sex. Objective Assess whether age or sex are associated with differences in efficacy of SGL2i, GLP1ra and DPP4i. Data sources Medline, Embase and clinical trial registries. Study selection Two independent reviewers screened for randomised controlled trials of SGLT2i/GLP1ra/DPP4i, compared to placebo/active comparator, in adults with type 2 diabetes. Data extraction and synthesis We sought individual participant data (IPD) all eligible studies. Where IPD were available, we modelled age- and sex-treatment interactions for each trial. Otherwise, we assessed age-sex distributions along with results from aggregate trial data. IPD and aggregate findings were combined in a Bayesian network meta-analysis. Main outcome measures HbA1c and MACE. Results We identified 616 eligible trials (604 reporting HbA1c, 23 reporting MACE) and obtained IPD for 75 trials (6 reporting MACE). Mean age was 59.0 (10.7) years and 64.0 (8.6) in HbA1c and MACE trials, respectively. Proportions of female were 43.1% and 44.0% in HbA1c and MACE trials, respectively. SGLT2i reduced HbA1c by 0.5-1.0% overall compared to placebo. This reduction versus placebo was attenuated in older participants (change in HbA1c 0.25 percentage-points less for 75-year-olds compared to 45-year-olds). SGLT2i showed greater relative efficacy in MACE risk reduction among older than younger people. This finding was sensitive to the exclusion of one of the IPD MACE trials, however, in all sensitivity analyses, SGLT2i were either as efficacious or more efficacious in older participants. There was no consistently significant difference in efficacy by age for GLP1ra or DPP4i for HbA1c or MACE, nor were there consistent significant sex differences for any class. Conclusion Newer glucose-lowering drugs are efficacious across age and sex groups. SGLT2i are more cardioprotective in older than younger people despite smaller HbA1c reductions. Age alone should not be a barrier to treatments with proven cardiovascular benefit providing they are well tolerated align with patient priorities. ### Competing Interest Statement John Petrie reports personal fees (via his employing institution) from Merck KGaA (Lectures), research support from Merck KGaA (Grant), personal fees from Novo Nordisk (Lectures/ Advisory) and personal fees from IQVIA (Boehringer Ingelheim Adjudication Committees) - all outside the submitted work. Dr Petrie has received non-financial support as co-CI of a JDRF-funded trial ([NCT03899402][1]) from Astra Zeneca (donation of investigational medicinal product to US site only) and Novo Nordisk - donation of investigational medicinal product to UK site only; supplementary financial support (to mitigate a budget cut during the COVID-19 pandemic). Robert Lindsey reports Event registration paid for by Novo Nordisk 2021, no personal fees. And is current local PI for SOUL study (Novo Nordisk)- no personal fees. Amanda Adlers trials unit is undertaking a trial funded by NovoNordisk. The indication is not diabetes. Naveed Sattar declares grant funding from AstraZeneca, Boehringer Ingelheim, Novartis, and Roche Diagnostics; consulting fees from Abbott Laboratories, AbbVie, Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hanmi Pharmaceuticals, Janssen, Menarini-Ricerche, Novartis, Novo Nordisk, Pfizer, Roche Diagnostics, and Sanofi; payment for lectures or presentations from Abbott Laboratories, AbbVie, AstraZeneca, Boeringer Ingelheim, Eli Lilly, Janssen, Novo Nordisk, and Sanofi. All work was unrelated to this manuscript. All other authors declare no conflicts of interest. ### Funding Statement This study was funded by the Medical Research Council (Grant reference MR/T017112/1). The funder had no role in the design, conduct or interpretation of the analysis. The pharmaceutical companies that provided the data did not provide any funding or support to the study and had no role in the design, conduct or interpretation of the analysis. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethical approval for IPD use was obtained from the University of Glasgow MVLS College Ethics Committee (Project: 200160070). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Individual-level participant data was obtained through the Vivli project, subject to a data sharing agreement. Data are available on application to the data holder via Vivlis application process. All aggregate data, as well as summary data from all analyses of individual participant data, are available at https://github.com/Type2DiabetesSystematicReview/nma\_agesex\_public, along with analysis code for all the analyses presented in the manuscript and supplementary appendices. [https://github.com/Type2DiabetesSystematicReview/nma\_agesex\_public][2] [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT03899402&atom=%2Fmedrxiv%2Fearly%2F2024%2F06%2F24%2F2024.06.23.24309242.atom [2]: https://github.com/Type2DiabetesSystematicReview/nma_agesex_public

Routinely collected health data (RCD) including electronic health records, disease registries, health administrative data and wearables data are not specifically collected for research purposes. Analysis of these data poses unique methodological challenges that must be addressed when conducting research, particularly as availability and use increase. This scoping review aimed to identify methodological challenges in research using RCD from existing literature (registered protocol: https://doi.org/10.17605/OSF.IO/EBM4D). We searched 6 electronic databases, including medical, health economics, nursing and psychology research databases, between Jan 2015 and Jan 2023, combining multiple “RCD” and “research” search terms (e.g., epidemiologic, informatics, pharmaceutical research). After screening abstracts and full-texts, we doubly extracted methodological themes, categorizing them into different study stages. We screened more than 23,000 records and included 430 papers. Bias and confounding were the most common methodological issues identified, discussed in relation to both study design and data analysis. Data quality, including data accuracy, validation, completeness, timeliness and cleaning, also posed substantial challenges, particularly during data processing stage. Record linkage and conducting analyses using distributed health networks also pose unique methodological challenges. Heterogeneity, incorporating social determinants of health and statistical models that address methodological challenges are also described in the literature. External validity and reporting are important considerations for RCD research. Our review identified several methodological challenges facing researchers using RCD. These issues should be addressed to ensure methodologically sound research. These findings will inform the development of a standardized protocol template and accompanying educational platform aimed at enhancing methodological quality and transparency when conducting research using RCD.

Background It remains unclear how to meaningfully classify people living with multimorbidity (multiple long-term conditions (MLTCs)), beyond counting the number of conditions. This paper aims to identify clusters of MLTCs in different age groups and associated risks of adverse health outcomes and service use. Methods Latent class analysis was used to identify MLTCs clusters in different age groups in three cohorts: Secure Anonymised Information Linkage Databank (SAIL) (n = 1,825,289), UK Biobank (n = 502,363), and the UK Household Longitudinal Study (UKHLS) (n = 49,186). Incidence rate ratios (IRR) for MLTC clusters were computed for: all-cause mortality, hospitalisations, and general practice (GP) use over 10 years, using <2 MLTCs as reference. Information on health outcomes and service use were extracted for a ten year follow up period (between 01 st Jan 2010 and 31st Dec 2019 for UK Biobank and UKHLS, and between 01 st Jan 2011 and 31st Dec 2020 for SAIL). Findings Clustering MLTCs produced largely similar results across different age groups and cohorts. MLTC clusters had distinct associations with health outcomes and service use after accounting for LTC counts, in fully adjusted models. The largest associations with mortality, hospitalisations and GP use in SAIL were observed for the " Pain+ " cluster in the age-group 18 - 36 years (mortality IRR = 4.47, hospitalisation IRR = 1.84; GP use IRR = 2.87) and the " Hypertension, Diabetes & Heart disease " cluster in the age-group 37 - 54 years (mortality IRR = 4.52, hospitalisation IRR = 1.53, GP use IRR = 2.36). In UK Biobank, the " Cancer, Thyroid disease & Rheumatoid arthritis " cluster in the age group 37 - 54 years had the largest association with mortality (IRR = 2.47). Cardiometabolic clusters across all age groups, pain/mental health clusters in younger groups, and cancer and pulmonary related clusters in older age groups had higher risk for all outcomes. In UKHLS, MLTC clusters were not signi fi cantly associated with higher risk of adverse outcomes, except for the hospitalisation in the age -group 18 - 36 years. Interpretation Personalising care around MLTC clusters that have higher risk of adverse outcomes may have important implications for practice (in relation to secondary prevention), policy (with allocation of health care resources), and research (intervention development and targeting), for people living with MLTCs. Funding This study was funded by the National Institute for Health and Care Research (NIHR; Personalised ExerciseRehabilitation FOR people with Multiple long-term conditions (multimorbidity) - NIHR202020). Copyright (c) 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

BACKGROUND:People with chronic kidney disease (CKD) have increased incidence and mortality of most cancer types. We hypothesized that the odds of presenting with advanced cancer may vary according to differences in estimated glomerular filtration rate (eGFR), that this could contribute to increased all-cause mortality and that sex differences may exist. METHODS:Data were from Secure Anonymised Information Linkage Databank, including people with de novo cancer diagnosis (2011-17) and two kidney function tests within 2 years prior to diagnosis to determine baseline eGFR (mL/min/1.73 m2). Logistic regression models determined the odds of presenting with advanced cancer by baseline eGFR. Cox proportional hazards models tested associations between baseline eGFRCr and all-cause mortality. RESULTS:eGFR <30 was associated with higher odds of presenting with advanced cancer of prostate, breast and female genital organs, but not other cancer sites. Compared with eGFR >75-90, eGFR <30 was associated with greater hazards of all-cause mortality in both sexes, but the association was stronger in females [female: hazard ratio (HR) 1.71, 95% confidence interval (CI) 1.56-1.88; male versus female comparison: HR 0.88, 95% CI 0.78-0.99]. CONCLUSIONS:Lower or higher eGFR was not associated with substantially higher odds of presenting with advanced cancer across most cancer sites, but was associated with reduced survival. A stronger association with all-cause mortality in females compared with males with eGFR <30 is concerning and warrants further scrutiny.