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Prognostic models for cardiovascular and kidney outcomes in people with type 2 diabetes: living systematic review and meta-analysis of observational studies

Abstract Objective To summarise available evidence regarding the performance metrics of validated prognostic models on cardiovascular and kidney outcomes in adults with type 2 diabetes mellitus. Design Living systematic review and meta-analysis of observational studies. Data sources Medline, Embase, Central, and the Cochrane Database of Systematic Reviews from 1 January 2020 to 17 January 2024. Eligibility criteria for selecting studies Studies validating prognostic models that predicted all cause and cardiovascular mortality, admission to hospital for heart failure, kidney failure, myocardial infarction, or ischaemic stroke in adults with type 2 diabetes mellitus, including people with established cardiovascular disease or chronic kidney disease, or both. Risk models evaluating composite outcomes were not eligible. Data synthesis For each model and outcome, using a random effects model, the reported discrimination measures were pooled, reported as c statistics. Furthermore, when available, calibration plots were reconstructed and interpreted narratively. The Prediction Model Risk of Bias Assessment (PROBAST) tool was used to assess the risk of bias of each analysed study cohort and the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach to evaluate our certainty in the evidence. Results 6529 publications were identified, of which 35 studies reporting on 13 models were included, all of which were developed for general populations with type 2 diabetes but no established cardiovascular disease or chronic kidney disease. Among the identified models, the Risk Equations for Complications of Type 2 Diabetes (RECODe) and the UK Prospective Diabetes Study Outcomes Model 2 (UKPDS-OM2) evaluated all outcomes except for admission to hospital for heart failure. Relative to a threshold c statistic of 0.7, RECODe had an acceptable discrimination for cardiovascular mortality (0.79, high certainty), probably has an acceptable discrimination for myocardial infarction (0.72, moderate certainty) and stroke (0.71, moderate certainty), and may have an acceptable discrimination for kidney failure (0.76, low certainty). High certainty evidence suggests that UKPDS-OM2 has unacceptable discrimination for myocardial infarction (0.64) and stroke (0.65). RECODe showed acceptable calibration for cardiovascular mortality (high certainty), myocardial infarction (high certainty), and kidney failure (moderate certainty) but had unacceptable calibration for stroke (moderate certainty). UKPDS-OM2 showed acceptable calibration for cardiovascular mortality (moderate certainty), stroke (moderate certainty), and kidney failure (low certainty), but may have unacceptable calibration for myocardial infarction (moderate certainty). Conclusion 13 unique models were identified that evaluated cardiovascular and kidney outcomes in patients with type 2 diabetes. Two models, RECODe and UKPDS-OM2, evaluated all outcomes except for admission to hospital for heart failure. Of all the appraised prognostic models, RECODe had acceptable discrimination and calibration in validation studies for most outcomes; although, additional studies directly comparing models are needed. Study registration number PROSPERO, CRD42023423075. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is the original article. What is already known on this topic Several risk prediction models incorporating multiple risk factors have been developed and validated for people with type 2 diabetes mellitus Risk stratification of key patient groups, through the use of risk prediction models, is a core component in the development of clinical practice guidelines What this study adds Although several prediction models for cardiovascular and kidney outcomes in people with type 2 diabetes have been validated, only two models, the Risk Equations for Complications of Type 2 Diabetes (RECODe) and the UK Prospective Diabetes Study Outcomes Model 2 (UKPDS-OM2), assessed most of the patient important outcomes RECODe had acceptable discrimination and calibration in validation studies for most outcomes, and UKPDS-OM2 had variable discrimination and calibration across outcomes How this study might affect research, practice or policy When risk stratifying their patients with type 2 diabetes, clinicians should consider patients' individual anticipated risk of cardiovascular and kidney related outcomes using validated risk prediction models Introduction Type 2 diabetes mellitus affects approximately 537 million adults worldwide, and this number is projected to rise to 643 million by 2030, and 783 million by 2045.1 Diabetes related healthcare expenditures represent a significant economic burden, costing US$966 billion annually worldwide.1 In 2021, diabetes was the cause of 6.7 million deaths globally, accounting for 12.2% of all deaths in individuals aged 20-79 years old.1 Furthermore, individuals living with diabetes have high rates of comorbidity, with approximately 32% of people also having cardiovascular disease and 27% having chronic kidney disease.2–4 However, substantial variation exists in prognoses across patients living with type 2 diabetes mellitus; factors such as age, sex, glycaemic control, obesity, and pre-existing cardiovascular and kidney diseases affect an individuals' risk of future cardiovascular and kidney outcomes. To account for the impact of these variables, many risk prediction models incorporating multiple risk factors have been developed and validated for people with type 2 diabetes mellitus.5–7 These models show great promise: clinicians can use them to prognosticate patients and in clinical decision making, or they can be used by researchers and policy makers to better understand the risk of events across different groups of patients. However, these prognostic models need to yield valid and reliable risk estimates to inform decision making. A plethora of randomised controlled trials have also shown benefits of several novel antidiabetic treatments, including sodium glucose cotransporter-2 inhibitors (SGLT2-i), glucagon-like peptide-1 receptor agonists (GLP-1RA), and non-steroidal mineralocorticoid receptor antagonists, in reducing the risk of cardiovascular and kidney outcomes in adults with diabetes, with weight loss as another outcome of global interest.8 A continued flow of new randomised controlled trials as well as new medications, combined with the rising prevalence of diabetes worldwide, prompted an update to a previous clinical practice guideline (BMJ Rapid Recommendations) on antidiabetic treatments, to become a living guideline.2 Furthermore to that living clinical practice guideline, the panel confirmed that individuals living with diabetes with various levels of risk (eg, high v low) for cardiovascular and kidney outcomes experience different absolute magnitudes of benefit. These variations warrant potentially different recommendations depending on risk strata. As for the first version of the guideline, the panel identified the need to determine the most trustworthy and best performing prognostic models assessing individual outcomes to obtain the most credible baseline risks to inform the development of recommendations. Therefore, to inform the living guideline—as well as other guidelines—on drugs for type 2 diabetes mellitus, we conducted this living systematic review and meta-analysis to identify, critically appraise, and summarise the available evidence regarding the performance of validated prognostic models on cardiovascular and kidney outcomes in people with type 2 diabetes mellitus (box 1). Box 1: Linked articles in this BMJ Rapid Recommendation cluster Practice article: Agarwal A, Mustafa R, Manja V, et al. Cardiovascular, kidney-related, and weight loss effects of therapeutics for type 2 diabetes: a living clinical practice guideline. BMJ 2025;390:e082071. doi:10.1136/bmj-2024-082071 Research article: Nong K, Jeppesen BT, Shi Q, et al. Medications for adults with type 2 diabetes: a living systematic review and network meta-analysis. BMJ 2025;390:e083039. doi:10.1136/bmj-2024-083039 Methods This living systematic review and meta-analysis follows Grading of Recommendations, Assessment, Development and Evaluation (GRADE) guidance and established guidance on prognostic model reviews.9,10 11 We report our systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement12 and the Meta-analyses Of Observational Studies in Epidemiology (MOOSE) checklist13 (see online supplemental appendix 1 for the completed MOOSE checklist). We prospectively registered our protocol on PROSPERO (CRD42023423075). Search strategy and selection criteria A previous systematic review and meta-analysis of prognostic models in adults with type 2 diabetes mellitus identified 15 observational studies reporting on seven risk models, of which one showed adequate calibration and discrimination.14 We updated this systematic review and meta-analysis and transitioned to a living evidence model. Electronic database searches using Medline, Embase, Central, and the Cochrane Database of Systematic Reviews were conducted; this iteration incorporates a search update from 1 January 2020 to 17 January 2024 (see online supplemental appendix 2 for search strategies). Relevant search terms included “diabetes mellitus”, “cardiovascular”, “MACE”, “kidney failure”, “mortality”, and “admission” as well as search terms for clinical prediction guides (“prognosis”, “diagnosed”, “cohort”, and “predictor”). We included observational studies and post-hoc analyses of randomised controlled trials that enroled ambulatory adults (≥18 years) with type 2 diabetes mellitus (with or without established cardiovascular disease or chronic kidney disease) and assessed prognostic models with at least two predictors. Specifically, we included validation (internal and external) studies that assessed the performance of models for all cause mortality, cardiovascular mortality, admission to hospital for heart failure, kidney failure, myocardial infarction or stroke, and reported model discrimination or calibration measures. Studies validating prognostic models used to predict composite outcomes were not eligible for inclusion; for example, studies evaluating the SCORE2-Diabetes model15 evaluated a composite outcome of cardiovascular disease events including cardiovascular mortality, non-fatal myocardial infarction and non-fatal stroke, making them ineligible for inclusion. Furthermore, we restricted our included studies to those reporting prognostic models validated in three or more cohorts. To identify additional studies, we searched the reference lists of included studies and consulted clinical experts and methodologists participating on the guideline panel for the linked living BMJ Rapid Recommendation on medications for type 2 diabetes mellitus. Study selection and data extraction Pairs of calibrated reviewers (DGR, DS, SD, DG, and JZXC) independently assessed titles and abstracts of identified citations as well as full texts of articles that were deemed potentially eligible using Covidence (Veritas Health Innovation, Melbourne, Australia). Pairs of reviewers (DGR, DS, SD, DG, and JZXC) independently extracted data using prepiloted, structured Excel forms. Reviewers resolved conflicts through discussion or, if necessary, through adjudication by a third reviewer (FF). Reviewers collected information related to the data source, time frame of recruitment, duration of patient follow-up, characteristics of the validation cohorts (eg, age, sex, body mass index, comorbidities, laboratory values, etc), details of the prognostic model assessed (eg, predictors included, and definition and measurement of the outcome) and measures of model performance (model discrimination (ie, c statistics or areas under the curve) and calibration (ie, calibration plots)). When studies do not report relevant data in corresponding tabular or narrative formats, we use WebPlotDigitizer v4.7 (Pacifica, CA, USA) to extract values from figures and graphs. If one or more publications reported on the validation of the same model using the same cohort, we included the publication with the largest analytical sample size. Risk of bias assessment Pairs of reviewers (DGR, DS, SD, DG, and JZXC) independently used the Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of the individual cohorts at the outcome level.16 Disagreements were resolved through discussion between reviewers or, if necessary, adjudication by a third reviewer (FF). PROBAST considers four domains: participants, predictors, outcomes, and analysis. Domains were rated as low, high, or unclear risk of bias. We categorised a study as having an overall high risk of bias if reviewers judged one or more domains to be at high risk of bias, or two or more to be at an unclear risk of bias, otherwise, the study was classified as having a low risk of bias. Data synthesis and subgroup analyses STATA SE (v18) was used to perform all analyses. We considered a two sided P value of 0.05 or less statistically significant. Using the “metan” function,17 we pooled estimates and 95% confidence intervals (CIs) of discrimination statistics (eg, c statistics) for prognostic models validated in three or more cohorts, using restricted maximum likelihood random effects models with Hartung-Knapp-Sidik-Jonkman corrections.10 11 We followed guidance from Debray et aland colleagues to estimate the standard error for discrimination statistics for studies in which the authors did not report the 95% CI.10 Extracted data from all calibration plots assessing the same model on the same outcome were re-plotted and calibration was assessed through visual inspection of these reconstructed plots, considering people at low and high risk. We did not use statistical measures (eg, observed to expected ratios or Hosmer-Lemeshow χ2 tests) to assess model calibration, as these measures were not optimal when calculated for the entire cohort without regard to varying risk in half of the patients (who may be high risk) the model underestimates the true risk and in the other half (who may be low risk) the model overestimates the true risk. In a meta-analysis of observed to expected ratios where half the studies reported a ratio of less than 1.0 and the other half reported more than 1.0, the pooled ratio may incorrectly suggest perfect calibration (observed to expected ratio=1.0).18 Likewise, Hosmer-Lemeshow χ2 tests have several drawbacks, including low statistical power and a lack of information regarding the type or extent of miscalibration.19 We assessed heterogeneity through visual inspection of the individual point estimates and their 95% CIs. To explore the observed heterogeneity, we conducted prespecified subgroup analyses to evaluate the effect of high versus low risk of bias on model performance and relied on studies at low risk of bias if a significant difference was observed. Certainty of the evidence In order to assess the credibility of the risk prediction models, we evaluated the certainty of the evidence using GRADE.18 20 We rated certainty in relation to an acceptable discrimination threshold informed by clinician intuition. We selected a threshold of 0.7 to represent clinician intuition, based on a previous systematic review of studies evaluating discrimination of clinicians in disease areas similar to diabetes.21 GRADE rates certainty drawn on the performance of prognostic models, starting as high for a body of evidence informed by observational studies. Certainty may be decreased due to issues related to risk of bias, imprecision, inconsistency, indirectness, and publication bias. When appropriate, we assessed publication bias through visual inspection of funnel plots. Living model of evidence synthesis To iteratively incorporate new evidence regarding performance metrics of prognostic models and newly available prognostic models for adults with type 2 diabetes mellitus, we commit to a living systematic review model. The systematic review is planned for updates if practice changing evidence is made available. Our dynamically updated systematic review will directly inform the linked living BMJ Rapid Recommendation on medications for type 2 diabetes mellitus, planned for update every six months, and other international practice guideline development endeavours addressing type 2 diabetes mellitus management. We will collaborate with members of the linked living guideline to monitor for practice changing evidence and to determine whether an update is warranted. The search strategy and core team of methodologists informing the development and conduct of the living systematic review will remain consistent and convene on at least an annual basis to review the scope and methods of the review and to determine if and when the living review should be retired. We aim to publish major updates of this review on prognostic models in a scientific journal with minor or more frequent updates available through the living guideline as published in the open access authoring and publication platform MAGICapp.22 Patient and public involvement Patient partners participated as part of the living BMJ Rapid Recommendation guideline panel informing the scope and prioritised clinical outcomes for this living review. This study had no public participation. On publication, the study findings will be disseminated to related patients and the public as linked evidence for the paralleled BMJ Rapid Recommendation (https://www.bmj.com/rapid-recommendations) on the use of antidiabetic treatments in people with type 2 diabetes mellitus. Results The systematic search yielded 6529 unique citations and 224 potentially relevant full texts. Ultimately, 35 studies were eligible by reporting on the internal or external validation of 13 prognostic models across 52 validation cohorts (figure 1).23–57 Three models predicted all cause mortality, four predicted cardiovascular mortality, four predicted kidney failure, two predicted myocardial infarction, three predicted stroke, and five predicted admission to hospital for heart failure. Two identified models, RECODe and UKPDS-OM2, evaluated all outcomes except for admission to hospital for heart failure. All studies reported the validation of prognostic models in people with type 2 diabetes mellitus with the presence of various risk factors for the development of cardiovascular and kidney outcomes; none reported validation of models specifically for patients with established cardiovascular disease and/or chronic kidney disease on our outcomes of interest. PRISMA flowchart for study selection Characteristics of synthesised studies The cohorts included a median of 4329 patients (range 125-94 946). The median of mean ages was 61.4 years (range of means 50.9-70.7), median of mean HbA1C was 7.4% (range of means 6.1% to 8.7%) and a median of 54% of participants were male (range 0-100%). Among the cohorts enrolling participants from a single country, the most common countries were the United States (n=9), Italy (n=8), and China (n=7) (Online supplemental appendix 3). Risk of bias of individual studies Online supplemental appendix 4 presents the risk of bias assessments for each validation cohort and respective outcome across the 35 synthesised studies, leading to 119 separate risk of bias assessments. We found 47 assessments to be at a low risk of bias, and 72 to be at a high risk of bias. Common study limitations included inappropriate methods for handling missing data (eg, complete case analysis and use of a separate category to capture missing data), an inadequate number of events observed, and use of administrative databases and codes from International Classification of Diseases (ICD) 9th edition for identifying outcomes, which may face high rates of missing data and poor specificity. All cause mortality Three models predicted all cause mortality in people with type 2 diabetes mellitus: estimation of mortality risk in type 2 diabetic patients (ENFORCE), risk equations for complications of type 2 diabetes (RECODe), and UK prospective diabetes study outcomes model (UKPDS-OM)-2 (table 1; online supplemental appendix 5). Compared with a threshold c statistic of 0.7, high certainty evidence indicates that RECODe has acceptable discrimination for all cause mortality. Moderate certainty evidence suggests that ENFORCE and UKPDS-OM2 probably have acceptable discrimination. Table 1 Summary of findings table for prediction model discrimination With regards to model calibration, high certainty evidence showed that UKPDS-OM2 overestimates risk for all cause mortality. Moderate certainty evidence suggests that ENFORCE probably underestimates risk, and that RECODe probably has acceptable calibration (table 2; online supplemental appendix 6). Table 2 Summary of findings table for prediction model calibration Cardiovascular mortality Four models predicted cardiovascular mortality in people with type 2 diabetes mellitus: Framingham score (FHS), RECODe, systematic coronary risk evaluation (SCORE), and UKPDS-OM2 (table 1; online supplemental appendix 5). High certainty evidence indicates that RECODe has acceptable discrimination for cardiovascular mortality. FHS may have acceptable discrimination, and UKPDS-OM2 may have unacceptable discrimination (both low certainty). We are very uncertain about the discriminatory capability of SCORE (very low certainty). High certainty evidence shows that RECODe has acceptable calibration. Moderate certainty evidence suggests that both SCORE and UKPDS-OM2 probably have acceptable calibration. No studies evaluated the calibration of FHS through calibration plots (table 2; online supplemental appendix 6). Kidney failure Four models predicted kidney failure in individuals with type 2 diabetes mellitus: ADVANCE (action in diabetes and vascular disease: preterax and diamicron MR controlled evaluation), the New Zealand model, RECODe, and UKPDS-OM2 (table 1; online supplemental appendix 5). Moderate certainty evidence indicates that ADVANCE and the New Zealand model probably have acceptable discrimination. RECODe may have acceptable discrimination, and UKPDS-OM2 may have unacceptable discrimination (low certainty). Moderate certainty evidence suggests that ADVANCE and the New Zealand model probably underestimate the risk of kidney failure. RECODe probably has acceptable calibration (moderate certainty), while UKPDS-OM2 may have acceptable calibration for predicting kidney failure (low certainty) (table 2; online supplemental appendix 6). Myocardial infarction Two models predicted myocardial infarction in people with type 2 diabetes mellitus: RECODe and UKPDS-OM2. High certainty evidence indicates that UKPDS-OM2 has unacceptable discrimination for myocardial infarction. Meanwhile, RECODe probably has acceptable discrimination (moderate certainty) (table 1; online supplemental appendix 5). High certainty evidence shows that RECODe has acceptable calibration for myocardial infarction, and moderate certainty evidence suggests that UKPDS-OM2 probably overestimates risk (table 1; online supplemental appendix 6). Stroke Three models predicted stroke in individuals with type 2 diabetes mellitus: RECODe, UKPDS-OM1, and UKPDS-OM2. High certainty evidence indicates that UKPDS-OM2 has unacceptable discrimination for stroke. Moderate certainty evidence suggests that RECODe probably has acceptable discrimination and UKPDS-OM1 probably has unacceptable discrimination for stroke (table 1; online supplemental appendix 5). Moderate certainty evidence suggests that RECODe probably overestimates the risk of stroke, and that both UKPDS-OM1 and UKPDS-OM2 probably have acceptable calibration (table 2; online supplemental appendix 6). Admission to hospital with heart failure Five models predicted admission to hospital with heart failure in people with type 2 diabetes mellitus: DM-CURE (socio-demographic variables, metabolic, diabetes-related complication factors, and health care utilization for risk evaluation), TRS-HFDM (thrombolysis in myocardial infarction risk score for heart failure in diabetes), and three WATCH-DM models (machine learning, regression, and integer based). Moderate certainty evidence suggests that DM-CURE and TRS-HFDM probably have acceptable discrimination for admission to hospital with heart failure. The machine learning and regression based WATCH-DM models may have acceptable discrimination, but the integer based WATCH-DM model may have unacceptable discrimination (all low certainty). No studies evaluated the calibration of DM-CURE using calibration plots. High certainty evidence showed that all other models for admission to hospital with heart failure had acceptable calibration. Other analyses We did not identify any significant effect modification on model discrimination (online supplemental appendix 7) or model calibration (online supplemental appendix 8) based on overall risk of bias. Similarly, we did not identify any evidence of publication bias (online supplemental appendix 9). Discussion Principle findings This systematic review summarised the discrimination and calibration of prognostic models for adults with type 2 diabetes mellitus validated in three or more cohorts. We compared the discriminatory performance of each model to our best estimate of clinician intuition (c statistic=0.7) in predicting mortality (all cause and cardiovascular related), kidney failure, myocardial infarction, stroke, and admission to hospital with heart failure. Among the 13 identified prognostic models, RECODe has the most acceptable discriminatory performance and calibration across the evaluated outcomes; this finding aligns with a previous systematic review.14 Strengths and limitations Strengths of this review include the use of rigorous and comprehensive methods for systematic reviews and meta-analyses of prognostic models10 11 and the use of formal GRADE guidance to assess certainty of evidence for discrimination and calibration.18 20 The GRADE approach allowed us to contextualise our findings in relation to the average discriminatory performance of clinicians, enabling our findings to have direct relevance to clinical practice. Furthermore, this updated review continues to be linked to a multidisciplinary BMJ Rapid Recommendation panel composed of clinical experts, methodologists, and patient partners. The panel has prespecified patient important outcomes of interest and has been consulted to ensure the comprehensiveness of our systematic literature search, ensuring that our review's findings are directly relevant to clinical practice. Potential limitations of this systematic review stem from current limitations of prognosis literature. Firstly, although the discriminatory performance of prognostic models can be quantitatively assessed by pooling the reported c statistics in each study, the methods used to assess the calibration of each model vary substantially. Given the limitations of statistical measures of calibration, including observed to expected ratios and Hosmer-Lemeshow χ2 tests,18 19 we only assessed calibration through the studies' reported calibration plots. This approach involved a visual assessment of reconstructed calibration plots, and a narrative summary of each model's calibration, resulting in the assessment of calibration being more subjective. Secondly, the included studies assessed model calibration among patients with relatively low cardiovascular risk, limiting assessment of model calibration across the full spectrum of risk including among those with established cardiovascular disease or chronic kidney disease. In the absence of credible risk prediction models for this large population with type 2 diabetes mellitus, the linked guideline had to use their clinical experience and perform pragmatic modelling to estimate risk for cardiovascular and kidney outcomes for people at moderate to high risk; patients who will benefit most from medications such as SGLT2-inhibitors and GLP1-RA (unpublished). Thirdly, we were unable to assess on the predictive model performance the influence of several potential sources of heterogeneity, including ascertainment of outcomes across studies, the time periods in which they were conducted and available antidiabetic treatments available during these periods, and diversities in healthcare systems and health outcomes across geographical regions. Fourthly, our review excluded prognostic models solely evaluating composite outcomes, such as major adverse cardiovascular events, as our linked guideline focused on individual cardiovascular and kidney outcomes. As a result, several robust risk prediction models for type 2 diabetes mellitus, such as SCORE2-Diabetes,15 which may assist clinicians and patients with risk stratification in clinical practice, were not assessed in our review. Finally, our review assessed model performance relative to a threshold inferred by clinician intuition and did not directly compare performance between different models. Given the potential intransitivity between validation cohorts used to assess each model, future research is needed to directly compare promising models, such as those that predict multiple patient important outcomes (eg, RECODe, UKPDS-OM2), using the same validation cohort. Implications for current practice and research Models identified by our systematic review, such as RECODe, should help the development and updating of clinical practice guidelines, health technology assessments, and subsequently clinicians, patients, policy makers and payers in informing individual decision making. These models enable appropriate risk stratification for patients with type 2 diabetes mellitus, enable identification of risk stratified baseline risks (ie, likelihood of events occurring without treatment) across patient important outcomes, and facilitate estimation of risk stratified absolute effect estimates for treatments (applying the relative effects anticipated with treatments to baseline risks) and cost-effectiveness analyses. The models also allow clinicians and adults with type 2 diabetes mellitus to define a given individual's risk profile and individualised estimates of benefits or harms with treatment, facilitating evidence informed shared decision making. Additionally, previous clinical practice guidelines on the management of type 2 diabetes mellitus, including those from the American Diabetes Association58 and the American Association of Clinical Endocrinology/American College of Endocrinology,59 recommend the use of prognostic models developed and validated in non-diabetic populations, such as the Pooled Cohort Equation and the FHS. Our findings may enable the adoption of diabetes specific prognostic models in the development of future clinical practice guidelines for diabetes management. Our systematic review identified several areas for future research on prognostic models for adults with type 2 diabetes mellitus. Firstly, our review compared the discriminatory performance of identified prognostic models to a threshold informed by clinician intuition alone (c statistic=0.7).21 In clinical practice, clinicians may use prognostic models in addition to other clinical factors to inform their risk estimation and decision making. One systematic review suggested that clinician intuition enhanced by prognostic models may be superior to clinician intuition alone.21 Future research should investigate the usefulness of adding trustworthy prognostic models for diabetes, such as RECODe, to routine clinical practice. Secondly, most models had limited data assessing their calibration for patients at higher risk. Future research should focus on validating identified prognostic models in cohorts of adults at higher risk. Finally, our review was unable to assess the clinical usefulness of using risk stratification, by leveraging these prognostic models, to guide treatment of type 2 diabetes mellitus. Future research should evaluate the benefit of these identified prognostic models in clinical practice and their impact on patient important outcomes. Conclusion We identified 13 unique prognostic models evaluating cardiovascular and kidney outcomes in patients with type 2 diabetes mellitus, with no models explicitly reporting validation of patients with established cardiovascular disease or chronic kidney disease. We identified two models, RECODe and UKPDS-OM2, which evaluated all outcomes except for admission to hospital with heart failure. Of all the identified prognostic models, RECODe showed acceptable discrimination and calibration in validation studies for most outcomes. Ethics approval Not applicable. Contributors: All authors were involved in the design of the study. FF conceived the idea for this systematic review. DGR, DS, S-CD, DG, and JZXC screened records. DGR, DS, S-CD, DG, and JZXC completed data extraction and assessed risk of bias and certainty. DGR conducted data analysis and presented findings. DGR and FF drafted the initial manuscript. All authors provided critical revisions of the manuscript based on important intellectual content, and FF supervised the project. DGR and FF are the guarantors. Transparency: The lead author (the guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained. Funding: We do not declare a specific grant for this research from any funding agency in the public, commercial, or not-for-profit sectors. Declaration of AI: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. Provenance and peer review: Not commissioned; externally peer reviewed. Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise. Data availability statement All data relevant to the study are included in the article or uploaded as supplementary information. Sun H, Saeedi P, Karuranga S, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract 2022; 183: 109119. Shi Q, Nong K, Vandvik PO, et al. Benefits and harms of drug treatment for type 2 diabetes: systematic review and network meta-analysis of randomised controlled trials. BMJ 2023; 381. Einarson TR, Acs A, Ludwig C, et al. 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Coleman RL, Stevens RJ, Retnakaran R, et al. Framingham, SCORE, and DECODE risk equations do not provide reliable cardiovascular risk estimates in type 2 diabetes. Diabetes Care 2007; 30: 1292–3. Copetti M, Shah H, Fontana A, et al. Estimation of Mortality Risk in Type 2 Diabetic Patients (ENFORCE): An Inexpensive and Parsimonious Prediction Model. J Clin Endocrinol Metab 2019; 104: 4900–8. Copetti M, Biancalana E, Fontana A, et al. All-cause mortality prediction models in type 2 diabetes: applicability in the early stage of disease. Acta Diabetol 2021; 58: 1425–8. Copetti M, Baroni MG, Buzzetti R, et al. Validation in type 2 diabetes of a metabolomic signature of all-cause mortality. Diabetes Metab Res Rev 2024; 40. Davis WA, Colagiuri S, Davis TME, et al. Comparison of the Framingham and United Kingdom Prospective Diabetes Study cardiovascular risk equations in Australian patients with type 2 diabetes from the Fremantle Diabetes Study. Med J Aust 2009; 190: 180–4. Elley CR, Robinson T, Moyes SA, et al. Derivation and validation of a renal risk score for people with type 2 diabetes. Diabetes Care 2013; 36: 3113–20. Jin Q, Lau ESH, Luk AO, et al. High-density lipoprotein subclasses and cardiovascular disease and mortality in type 2 diabetes: analysis from the Hong Kong Diabetes Biobank. Cardiovasc Diabetol 2022; 21. Keng MJ, Leal J, Mafham M, et al. Performance of the UK Prospective Diabetes Study Outcomes Model 2 in a Contemporary UK Type 2 Diabetes Trial Cohort. Value Health 2022; 25: 435–42. Laxy M, Schöning VM, Kurz C, et al. Performance of the UKPDS Outcomes Model 2 for Predicting Death and Cardiovascular Events in Patients with Type 2 Diabetes Mellitus from a German Population-Based Cohort. Pharmacoeconomics 2019; 37: 1485–94. Li T-C, Wang H-C, Li C-I, et al. Establishment and validation of a prediction model for ischemic stroke risks in patients with type 2 diabetes. Diabetes Res Clin Pract 2018; 138: 220–8. Lin Y, Shao H, Shi L, et al. Predicting incident heart failure among patients with type 2 diabetes mellitus: The DM-CURE risk score. Diabetes Obes Metab 2022; 24: 2203–11. Pagano E, Konings SRA, Di Cuonzo D, et al. Prediction of mortality and major cardiovascular complications in type 2 diabetes: External validation of UK Prospective Diabetes Study outcomes model version 2 in two European observational cohorts. Diabetes Obes Metab 2021; 23: 1084–91. Prausmüller S, Resl M, Arfsten H, et al. Performance of the recommended ESC/EASD cardiovascular risk stratification model in comparison to SCORE and NT-proBNP as a single biomarker for risk prediction in type 2 diabetes mellitus. Cardiovasc Diabetol 2021; 20. Quan J, Ng CS, Kwok HHY, et al. Development and validation of the CHIME simulation model to assess lifetime health outcomes of prediabetes and type 2 diabetes in Chinese populations: A modeling study. PLoS Med 2021; 18. Razaghizad A, Sharma A, Ni J, et al. External validation and extension of the TIMI risk score for heart failure in diabetes for patients with recent acute coronary syndrome: An analysis of the EXAMINE trial. Diabetes Obes Metab 2023; 25: 229–37. Scarale MG, Copetti M, Garofolo M, et al. The Synergic Association of hs-CRP and Serum Amyloid P Component in Predicting All-Cause Mortality in Patients With Type 2 Diabetes. Diabetes Care 2020; 43: 1025–32. Scarale MG, Mastroianno M, Prehn C, et al. Circulating Metabolites Associate With and Improve the Prediction of All-Cause Mortality in Type 2 Diabetes. Diabetes 2022; 71: 1363–70. Segar MW, Patel KV, Hellkamp AS, et al. Validation of the WATCH-DM and TRS-HFDM Risk Scores to Predict the Risk of Incident Hospitalization for Heart Failure Among Adults With Type 2 Diabetes: A Multicohort Analysis. J Am Heart Assoc 2022; 11. Tanaka S, Tanaka S, Iimuro S, et al. Predicting macro- and microvascular complications in type 2 diabetes: the Japan Diabetes Complications Study/the Japanese Elderly Diabetes Intervention Trial risk engine. Diabetes Care 2013; 36: 1193–9. Tao L, Wilson ECF, Griffin SJ, et al. Performance of the UKPDS outcomes model for prediction of myocardial infarction and stroke in the ADDITION-Europe trial cohort. Value Health 2013; 16: 1074–80. van der Heijden AAWA, Ortegon MM, Niessen LW, et al. Prediction of coronary heart disease risk in a general, pre-diabetic, and diabetic population during 10 years of follow-up: accuracy of the Framingham, SCORE, and UKPDS risk functions: The Hoorn Study. Diabetes Care 2009; 32: 2094–8. Wan EYF, Fong DYT, Fung CSC, et al. Prediction of new onset of end stage renal disease in Chinese patients with type 2 diabetes mellitus - a population-based retrospective cohort study. BMC Nephrol 2017; 18. Willis M, Asseburg C, Slee A, et al. Macrovascular Risk Equations Based on the CANVAS Program. Pharmacoeconomics 2021; 39: 447–61. Xiong K, Zhang S, Zhong P, et al. Serum cystatin C for risk stratification of prediabetes and diabetes populations. Diabetes Metab Syndr 2023; 17: 102882. Yang X, So W-Y, Kong APS, et al. Development and validation of stroke risk equation for Hong Kong Chinese patients with type 2 diabetes: the Hong Kong Diabetes Registry. Diabetes Care 2007; 30: 65–70. Yew SQ, Chia YC, Theodorakis M, et al. Assessing 10-Year Cardiovascular Disease Risk in Malaysians With Type 2 Diabetes Mellitus: Framingham Cardiovascular Versus United Kingdom Prospective Diabetes Study Equations. Asia Pac J Public Health 2019; 31: 622–32. Zhang X, Lv X, Wang N, et al. WATCH-DM risk score predicts the prognosis of diabetic phenotype patients with heart failure and preserved ejection fraction. Int J Cardiol 2023; 385: 34–40. Zhuo X, Melzer Cohen C, Chen J, et al. Validating the UK prospective diabetes study outcome model 2 using data of 94,946 Israeli patients with type 2 diabetes. J Diabetes Complications 2022; 36: 108086. American Diabetes Association Professional Practice Committee. 10. Cardiovascular Disease and Risk Management: Standards of Care in Diabetes-2024. Diabetes Care 2024; 47: S179–218. Garber AJ, Handelsman Y, Grunberger G, et al. CONSENSUS STATEMENT BY THE AMERICAN ASSOCIATION OF CLINICAL ENDOCRINOLOGISTS AND AMERICAN COLLEGE OF ENDOCRINOLOGY ON THE COMPREHENSIVE TYPE 2 DIABETES MANAGEMENT ALGORITHM - 2020 EXECUTIVE SUMMARY. Endocr Pract 2020; 26: 107–39. Received: 20 January 2025 Accepted: 7 May 2025 First published: 14 August 2025

Association between gout and cardiovascular outcomes in adults with no history of cardiovascular disease: large data linkage study in New Zealand

Source data and participants Aotearoa New Zealand residents aged 20-79 years who were in contact with publicly funded health services in 2011, were still alive on 31 December 2011 and lived in the Auckland/Northland region were identified through anonymised, individual level linkage of national and regional health databases. National databases included hospital admissions, mortality, outpatient visits, primary care enrolment, primary care reimbursement (to capture primary care visits by non-enrolled patients), and community pharmaceutical dispensing. These databases were linked to the Auckland/Northland regional repository of all public, private, hospital, and community laboratory results, because no national repository of laboratory results currently exists. About a third of all New Zealanders live in this region, which includes Aotearoa New Zealand’s largest city (Auckland) as well as several towns and rural areas. The study data were anonymised by the encryption of the National Health Index (NHI) number that is assigned to every person who uses the publicly funded health system and uniquely identifies over 95% of the population. We subsequently excluded individuals if they had been admitted to hospital previously for cardiovascular disease or heart failure between 1 January 1988 and 31 December 2011; had been dispensed loop diuretics or metolozone on at least one occasion as recorded in the national dispensing data between 1 July 2006 and 31 December 2011 to capture individuals who had heart failure but had not been admitted to hospital for it; had a primary residence outside of the Auckland/Northland region for the past three years; and had missing predictor data required to estimate cardiovascular risk (0.06% of the total eligible cohort). Further details regarding the first two criteria are provided in online supplemental appendix 1. We used a validated national health data definition of gout to identify participants with gout: a discharge diagnosis of gout (ICD-9 (international classification of diseases, 9th revision) code 274, ICD-10-AM (Australian modification) code M10) from a public hospital admission between 1 January 1988 and 31 December 2011 or dispensed drug treatments for gout (allopurinol or colchicine) between 2007 and 2011.10 For individuals who had been diagnosed with leukaemia or lymphoma (ICD-10-AM C80-C96), dispensing of allopurinol was excluded as an indicator of gout. In Aotearoa New Zealand, allopurinol is not recommended for the treatment of asymptomatic hyperuricaemia. Benzbromarone was not funded in Aotearoa New Zealand until April 2013 and febuxostat was not funded until July 2014. We did not include probenecid use in the definition because it is not used frequently in Aotearoa New Zealand for gout treatment and is often used to increase antibiotic blood levels for the treatment of bacterial infections in primary care. Serum urate values were identified from the Auckland/Northland regional repository of laboratory results. In participants with multiple serum urate values, the value closest to the index date of 1 January 2012 was selected to most accurately reflect serum urate levels prior to enrolment. Based on recommendations for target serum urate for management of gout and as a surrogate for people with treated or untreated gout, we further categorised serum urate into those with a level below or greater than or equal to 0.36 mmol/L (6 mg/dL).18 Predictors of cardiovascular disease The predictors for inclusion in the multivariable models (described below) were based on previously validated, sex specific prediction equations for the risk of cardiovascular disease. These equations are derived from national administrative health data,17 which allow the estimation of risk of a fatal or non-fatal cardiovascular event occurring over the following five years. Included predictors were age, ethnic origin, level of socioeconomic deprivation, diabetes status, previous admission to hospital for atrial fibrillation, and dispensing at baseline of blood-pressure lowering, lipid lowering, or antiplatelet or anticoagulant drug treatments at least once in the six months before 1 January 2012. Ethnic origin was categorised according to r the Health and Disability Sector. For individuals with more than one ethnic group recorded in the NHI dataset, a single ethnic group was assigned using the following prioritisation order: Māori, Pacific Island people, Indian, Chinese, other, New Zealand European. In Aotearoa New Zealand, ethnic origin recording in health data currently enables identification of Indians (who comprise about 90% of South Asians in Aotearoa New Zealand) but not other South Asian groups such as Pakistanis. People who were not in the most common sian, Middle Eastern, Latin American, African, and unspecified. Socioeconomic deprivation was determined from the New Zealand Index of Deprivation (NZDep, 2006),19 which is a small, area based measure assigned to people living in a defined geographical area (mesh block). NZDep is based on nine variables from the national census—income, employment, access to car or telephone, household living space, educational qualifications, and social support. For these analyses, NZDep was grouped by quintiles into five groups, with group 1 being the least deprived and group 5 being the most deprived. Statistical analysis We used multivariable Cox proportional hazards regression using time from 1 January 2012 as the time scale to compare the risk of total cardiovascular events over five years in women and men with and without gout. Models for women and men were constructed to assess predictors included in the sex specific equations as well as the addition of gout (yes/no) (model 1), regular dispensing of allopurinol (model 2), serum urate level (model 3) and dispensing of colchicine (model 4). These models included adjustment for age; ethnic origin; level of socioeconomic deprivation; diabetes status; previous admisstion to hospital for atrial fibrillation; and dispensing at baseline of blood pressure lowering, lipid lowering, or antiplatelet or anticoagulant drug treatments at least once in the six months before 1 January 2012. For model 2, we defined regular dispensing as allopurinol dispensed from a community pharmacy in at least three of four quarters of 2012. For model 3, serum urate levels in indivdiuals with gout was categorised by recent concentrations of <0.36 mmol/L or ≥0.36 mmol/L, or in those did not have serum urate testing in the previous three years. For model 4, colchicine dispensing for individuals with gout were categorised by those who were dispensed colchicine at least once in the previous 12 months compared with those who were not dispensed colchicine. Given the ethnic differences in gout prevalence,10 the interaction between gout and ethnic origin was examined for model 1. Unlike in model 1, where the reference group was people without gout, the reference groups in models 2-4 changed to the preferred or typical scenario for people with gout (model 2, people with gout who were dispensed allopurinol in three of four quarters of 2012; model 3, people with gout who had a recent serum urate level of <0.36 mmol/L; model 4, people with gout who were dispensed colchicine in the previous 12 months). These reference groups were selected to investigate the potential associations between urate lowering treatment, colchicine, and cardiovascular disease. Validity of the proportional hazards assumption was assessed through visual inspection of scaled Schoenfeld residual plots and a score test that the slope of residuals over time is zero. Only age group among men might have had a non-proportional hazard—the relation over time was consistent across models, and on balance, we decided to continue because the factor's inclusion would not materially affect the findings of this study. All variables were categorical, including age, and follow-up was censored when a non-cardiovascular death occurred. We have previously undertaken competing risk analyses using this cohort and found it was unnecessary owing to the short follow-up. We used R statistical software version 3.6.0 (2019-04-26) for all analyses. Multivariable Cox proportional hazards analyses After adjustment for the cardiovascular predictors included in the sex specific models, gout was associated with an increased risk of a person experiencing a cardiovascular event over five years (adjusted hazard ratio 1.34 (95% confidence interval 1.23 to 1.45) in women; 1.18 (1.12 to 1.24) in men; table 2, model 1). The multivariable models without adjustment for gout or gout associated predictors are presented in online supplemental table 1. Significant interactions were identified between gout and ethnic origin in model 1 (online supplemental table 2A,B). The increased cardiovascular risk associated with gout was proportionally greater for men of Chinese, Indian, or other ethnic origins than for men of European, Māori, or Pacific Island ethnic origins. By contrast, although Māori women remain at highest risk of cardiovascular compared with other women, the increase in risk associated with gout was proportionally less than that for women of other ethnic groups. A significant interaction was also found between gout and sex in model 1 (adjusted hazard ratio 1.36 (95% confidence interval 1.24 to 1.48)). Table 2 Adjusted hazard ratios for fatal or first non-fatal cardiovascular events within five years in study population, according to sex. Data are adjusted hazard ratio (95% confidence interval) Table 3 shows sex specific adjusted hazard ratios for regular dispensing of allopurinol (defined as allopurinol dispensed in three of four quarters of 2012; model 2); serum urate level (model 3); and dispensing of colchicine (model 4). The coefficients for established cardiovascular risk factors in multivariable models of time to first fatal or non-fatal cardiovascular event are not shown because they did not change measurably from model 1, with the addition of drug treatments for gout and serum urate level. Table 3 Effect of regular allopurinol dispensing, serum urate, and colchicine dispensing on adjusted hazard ratios for time to fatal or first non-fatal cardiovascular event within five years in study population, according to sex and analysis model. Data are adjusted hazard ratio (95% confidence interval) We saw an increase in risk of cardiovascular events among men with gout who were not dispensed allopurinol in at least three of four quarters in the year after study entry compared with men with gout who were dispensed allopurinol (adjusted hazard ratio 1.15 (95% confidence interval 1.05 to 1.25); table 3, model 2). This effect was not observed among women. Among men with gout, we also saw a higher risk of cardiovascular disease associated with serum urate ≥0.36 mmol/L compared with serum urate <0.36 mmol/L (1.16 (1.04 to 1.30); table 3, model 3). This effect was also not observed in women. Men with gout who were not dispensed colchicine at least once in the previous 12 months were found to be at lower risk of cardiovascular events than men with gout who were dispensed colchicine (adjusted hazard ratio 0.84 (95% confidence interval 0.77 to 0.92); table 3, model 4). In women with gout, we saw no difference in risk of cardiovascular events according to colchicine dispensing. Comparison of findings with other studies Our findings are consistent with the previous studies that have observed an association between gout and cardiovascular events.1–5 20 In the US Health Professionals Follow-Up Study, the relative risk of fatal cardiovascular disease was increased in men with gout compared with men without gout, after adjustment for other cardiovascular risk factors (adjusted relative risk 1.38 (95% confidence interval 1.15 to 1.66)).1 This study population was also a primary prevention cohort, but the cohort comprised well educated, predominantly white men (>90%) and might not be representative of the general population. An analysis using the All England National Linked Dataset from 1999 to 2011 found that the relative risk of myocardial infarction and stroke was increased in men and women with gout (in men: adjusted relative risk 1.73 (95% confidence interval 1.69 to 1.77) for myocardial infarction and 1.73 (1.69 to 1.78) for stroke; in women: 2.08 (2.01 to 2.16) for myocardial infarction and 1.71 (1.65 to 1.77) for stroke).20 However, this study was unable to adjust for dispensing of blood pressure lowering, lipid lowering, antiplatelet or anticoagulant, or gout drug treatments. Recently, a study investigating the Skåne Health Register in Sweden found that people with newly diagnosed gout had an increased risk of cardiovascular disease (adjusted hazard ratio 1.76 (95% confidence interval 1.69 to 1.84)).4 The authors included people with a history of cardiovascular disease, and found that gout was also associated with an increased risk of other conditions including renal disease, digestive diseases, infections, and dementia. We found that gout modified the association between ethnic origin and cardiovascular events in both men and women. Men with gout and of Māori, Pacific Island, or Indian ethnic origin had a significantly higher risk of cardiovascular disease than men with gout from other ethnic groups. After accounting for this differential effect, Māori men with gout still remained at highest risk of cardiovascular disease, followed by Indian and Pacific Island men. A similar differential effect was observed among women. The risk of cardiovascular disease remains highest among Māori and Pacific Island people with gout. In Aotearoa New Zealand, Māori and Pacific Island people have the highest prevalence of gout,10 are least likely to be taking regular allopurinol,21 and have the highest burden of cardiovascular risk factors.22 With the additive risk factor of gout, our findings highlight the importance of resolving inequities in these population groups through access to timely and culturally safe care.23 In men with gout with no history of cardiovascular disease, we observed a reduction in cardiovascular risk in those individuals with regular allopurinol dispensing (dispensed in at least three of four quarters of 2012) and serum urate at target levels (<0.36 mmol/L), which suggests a possible cardiovascular benefit of appropriate gout management. These findings are similar to a study of the Multi-Payer Claims Database in the US, which found that in people with gout and diabetes, current allopurinol users had a significantly lower adjusted hazard of incident stroke or myocardial infarction than previous allopurinol users, (hazard ratio 0.67 (95% confidence interval 0.53 to 0.84)).14 Furthermore, in a cohort study of almost 1200 people with gout (92% men), researchers found that serum urate above the treatment target (≥0.36 mmol/L) was associated with an increased risk of cardiovascular disease and overall mortality.24 A meta-analysis of 32 studies (1 134 073 people) found a significant positive association between hyperuricaemia and risk of cardiovascular disease (adjusted hazard ratio 1.45 (95% confidence interval 1.33 to 1.58)), although heterogeneity between studies was high.25 By contrast, some earlier studies have not shown an association between hyperuricaemia and cardiovascular disease after adjustment for known cardiovascular risk factors.6–9 We are unaware of any published studies that have not shown an association between gout and cardiovascular disease. The potential cardiovascular benefit of appropriate gout management (regular allopurinol dispensing and serum urate <0.36 mmol/L) among men in our cohort was not observed among women. Potentially in women, a lower target serum urate level could be required for cardiovascular benefit, with a study finding that the optimal discriminator level of serum urate for cardiovascular mortality was 0.30 mmol/L in women compared with 0.33 mmol/L in men.26 Non-linear modelling to explore the association between serum urate and cardiovascular disease in men and women will be undertaken in future studies. Our study found that among people with no history of cardiovascular disease, colchicine dispensing (at least once in the past 12 months) was associated with an increased risk of cardiovascular events in men with gout. Recent large randomised controlled trials have shown that colchicine is beneficial for secondary prevention of cardiovascular disease (ie, in people with a history of cardiovascular disease).15 16 In the COLCOT trial, researchers found that patients randomised to receive colchicine 0.5 mg daily within 30 days after a myocardial infarction had a significantly lower risk of cardiovascular death, resuscitated cardiac arrest, myocardial infarction, stroke, or urgent hospital admission for angina leading to coronary revascularisation (adjusted hazard ratio 0.77 (95% confidence interval 0.61 to 0.96)).16 Similar findings were seen in the LoDoCo2 trial.15 The impact of colchicine has not previously been explored in people with no history of cardiovascular disease in randomised trials. In gout, colchicine is typically prescribed for a short period of time for the prevention or treatment of gout flares. People with gout who were dispensed colchicine are more likely to experience gout flares, which is associated with higher levels of systemic inflammation.27 This inflammation might mitigate the protective effects of colchicine in the setting of gout management. However, these findings need to be interpreted with caution, because the national dispensing database does not discriminate between the indication for colchicine dispensing (ie, short term use for gout flare treatment v longer term use for gout flare prophylaxis) and we did not observe the same association with colchicine dispensing and cardiovascular events in women. Strengths and limitations of the study This study of more than 940 000 participants explores the relation between gout and cardiovascular disease in a nationally representative population; our cohort identified almost all adults without cardiovascular disease in a geographical region that accounts for about a third of the total population in Aotearoa New Zealand. Strengths of our study included our use of predictors from prediction equations of cardiovascular risk developed from routinely collected administrative data, which have been validated within the adult population of Aotearoa New Zealand,17 to adjust our analyses. We also were able to analyse dispensing of gout specific drug treatments (allopurinol and colchicine) and serum urate levels through linkage to the national community pharmaceutical dispensing database and the Auckland/Northland regional repository of laboratory results. This linkage allowed us to use multiple surrogates for regular use of urate lowering treatment (dispensing of allopurinol in three of four quarters of 2012 and serum urate <0.36 mmol/L) to explore the relation between this treatment and cardiovascular risk. Similar studies using linkage of administrative data have been conducted in the UK and in Sweden.4 20 However, these previous studies did not account for cardiovascular risk factors, drug dispensing, or serum urate levels at baseline. Furthermore, in the Swedish study,20 the cohort included people with previous cardiovascular disease. Our study used ICD coded diagnoses to identify and exclude those individuals with a history of cardiovascular disease over an extended period (1988-2011). This approach has been validated in European countries28–30 and previous national cardiovascular studies in Aotearoa New Zealand.17 31 We used a broad definition for cardiovascular disease, reducing the likelihood that relevant diagnoses were missed. The clinical coding is performed by qualified medical coders within each District Health Board of Aotearoa New Zealand, leading to improved consistency compared with systems where coding data is entered ad hoc by clinicians. Although individuals receiving a diagnosis of cardiovascular disease solely in primary care or the private healthcare sector would not have been captured by this definition, private hospital admissions account for less than 2% of admissions for acute ischaemic heart disease in Aotearoa New Zealand.31 Our definition of cardiovascular disease identified diagnoses that were severe enough to warrant hospital admission or result in death, which accounts for the most definitive diagnoses. Furthermore, patients with cardiovascular disease who used private healthcare are likely, at some stage, to have contact with acute, publicly funded, hospital cardiac services where diagnostic data would be captured. The methodology for defining gout used in our study has been validated against the primary care diagnosis of gout10 as well as microscopic crystal identification.32 The definition has been routinely used to evaluate the prevalence of gout in Aotearoa New Zealand over the past decade,10 21 33 and in an insured population in the US.34 The choice between non-steroidal anti-inflammatory drugs (NSAIDs), corticosteroids, or colchicine for the treatment of gout flares depends on patient preference, comorbidities (renal or liver impairment, previous cardiovascular disease, peptic ulcer disease, diabetes, infection, fluid overload), and concomitant drug treatments (anticoagulants, P glycoprotein inhibitors, CYP3A4 inhibitors).35 Although we could have excluded people with gout who were only treated with NSAIDs or corticosteroids, these drugs were not included in the gout definition because they are not specific to gout and could have been dispensed in people with gout for other non-gout indications. How this definition might have affected our study findings is unclear because we are unable to ascertain the rationale for the dispensing of NSAIDs, corticosteroids, or colchicine from the national database of community pharmaceutical dispensing. We used this approach to ensure that we were capturing only people with gout and to minimise potential confounding from comorbidities and concomitant drug treatments that we were unable to adjust for. This approach has been estimated to undercapture up to 20% of gout cases in Aotearoa New Zealand.36 Time related biases have been recognised in previous observational studies of allopurinol on mortality.37 In our study, people with defined gout were identified before the start of follow-up (before 1 January 2012) to avoid immortal time bias and we minimised immeasurable time bias, by using a surrogate of allopurinol adherence (dispensed in three of four quarters of one year) rather than once-off dispensing of allopurinol. We were unable to adjust our analyses for some known modifiable risk factors for cardiovascular disease, such as blood pressure, lipid levels, body mass index, and smoking status, which are not recorded in the national, routinely collected, health administrative data. However, consistent with the risk equations for cardiovascular disease developed from Aotearoa New Zealand national data, we used dispensing of blood pressure lowering and lipid lowering drugs as surrogates for blood pressure and lipid levels.17 Age also acts a proxy for the length of exposure to multiple cardiovascular risk factors.38 We were also unable to adjust for chronic kidney disease, which has been shown to be associated with increased risk of gout, particularly in women.39 We used regular dispensing of allopurinol in three of four quarters of 2012 as a surrogate for regular use of drug treatment but cannot be certain that people with gout who were dispensed allopurinol might have actually taken this drug or continued taking it during follow-up. Similar approaches to assess adherence from community pharmaceutical dispensing data have been used for metformin,40 statins, and antihypertensive drugs.41 Nevertheless, the findings of our study demonstrate an association between gout and cardiovascular disease. The potential causal mechanisms of these associations require further exploration including casual inference modelling in future studies.

Reproductive outcomes in women and men conceived by assisted reproductive technologies

Men and women who were conceived by assisted reproductive technology and become parents do not appear to be at increased risk of adverse pregnancy outcomes Assisted reproduction technologies (ART) include any treatments related to fertility in which eggs or embryos are manipulated to attain a pregnancy, including in-vitro fertilisation and intracytoplasmic sperm injection. 1 The first baby born by in-vitro fertilisation was in 1978, which was the starting point of ART worldwide. More than 10 million children have since been born in the past 40 years. The use of ART is rapidly increasing, with the total number of ART cycles exceeding three million annually and resulting in almost 500 000 babies born every year.2 The proportion of babies born from in-vitro fertilisation varies from 2-4%, with the highest percentages in Australia and New Zealand.3 A substantial proportion of people conceived by ART are now of reproductive age and are becoming parents, which raises the question as to whether they have an increased risk of adverse reproductive and pregnancy outcomes. Even though ART has been associated with adverse obstetric outcomes and perinatal complications,4 the long term health outcomes and reproductive potentials of children born by use of ART are little known.5 6 Children born with ART appear to have an increased risk of developing cardiovascular diseases in the future but further evidence is needed.6 7 The findings for neurobehavioural disorders are ambiguous but the data for cancer risk are reassuring.8 Despite concerns regarding the social development of children conceived by ART, a large longitudinal study showed that these children do not suffer from worse psychosocial health compared with naturally conceived children.9 Strong evidence links ART with epigenetic changes in the newborn baby that suggest impacts on later health outcomes; however, larger studies in adulthood are required to test this hypothesis.10 The pubertal development, measured by Tanner’s classification, age at menarche, menstrual cycle characteristics, bone age, and concentrations of sex hormone, appear to be reassuringly similar between in-vitro fertilisation and children in the control group.11 Yet, the association of ART with the reproductive outcomes of individuals born by ART methods is not yet fully described. To address this gap, Carlsen and colleagues in the linked article by BMJ Medicine report the findings of the first study to examine this association.1 Carlsen and colleagues analysed data from the Norwegian Medical Birth Registry and included all women and men born in Norway from 1984 (which was the year of the first baby born who was conceived by ART in Norway) to 2002, which accounted for just more than one million people. The authors then followed up this population up to 2021 and identified all registered pregnancies linked to these women and men. They included all pregnancies with live born babies, miscarriages (defined as fetal loss between 12 and 22 weeks of gestation), and abortions undertaken at 12-22 weeks of gestation. Pregnancy and perinatal outcomes of these pregnancies were examined and compared between individuals who were conceived by ART and those conceived naturally. Almost 1% of the girls and boys born in Norway between 1984 and 2002 were conceived by ART. The likelihood of people who were conceived by ART of becoming parents was lower compared with people who were naturally conceived (hazard ratio 0.88 (95% confidence interval) 0.81 to 0.96) for women and 0.91 (0.83 to 1.01) for men); however, they did not account for social and medical factors that might alter the decision for reproduction. Obstetric and perinatal outcomes, including hypertensive disorders, lower birth weight, earlier gestational age at birth, congenital malformations, and admission to neonatal intensive care unit, were similar between parents conceived by ART or naturally and the results were adjusted for parental age at conception. However, a slightly increased risk of lower 5 min Apgar score for newborn babies born by women conceived by use of ART was noted. One of the main strengths of this study is the comprehensive inclusion of all births in Norway between the first year of a child born from use of ART from 1984 to 2002 and all registered pregnancies and birth of this population up until the end of 2021. Another strength is the clear definition of ART, which included any use of ART (fresh and frozen embryo transfer), and in-vitro fertilisation with and without intracytoplasmic sperm injection. They excluded intrauterine inseminations or people with a history of isolated incidences of subfertility. Potential weaknesses include the small number of pregnancies by individuals conceived by ART and that the results might not be generalisable to other more diverse populations. Another limitation was that the people in this study were on average younger compared with the mean age at first pregnancy for Norwegians. Therefore, when interpreting the findings of this study, the reader should consider that poor perinatal outcomes are more common with increased age.12 Early miscarriages before 12 weeks of gestation were not measured, which could explain the smaller rate of first time register pregnancy for people conceived by ART. Additionally, socioeconomic factors and chronic conditions were not accounted for, which could affect an individual's decisions and ability to conceive. What can be learnt from this study? Evidence suggests that individuals conceived by ART are not associated with an increased risk of worse pregnancy or perinatal outcomes compared with naturally conceived people. Those conceived by ART appear to conceive less often; however, when factoring in their backgrounds, this association weakens, whereas social and medical or other residual factors were not measured, which might weaken further this association by altering the fertility wishes of individuals. Larger studies with longer follow-ups are needed of women and men throughout their reproductive period to offer adequate reassurance that the parental mode of conception is not associated with fertility challenges or adverse pregnancy and perinatal outcomes. Contributors: TD interpreted the results and wrote the first draft. SI provided important insight and critically revised the manuscript. All authors have contributed to data interpretation, editing of the manuscript, and final approval of the manuscript. Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors. Competing interests: We have read and understood the BMJ policy on declaration of interests and declare the following interests: none. Provenance and peer review: Commissioned; not externally peer reviewed. Data availability statement Data sharing not applicable as no datasets generated and/or analysed for this study.