ARTICLE Treatment effect heterogeneity following type 2 diabetes treatment with GLP1-receptor agonists and SGLT2-inhibitors: a systematic review Katherine G. Young1,201, Eram Haider McInnes2,201, Robert J. Massey2,201, Anna R. Kahkoska3, Scott J. Pilla4, Sridharan Raghavan5, Maggie A. Stanislawski 6, Deirdre K. Tobias7,8, Andrew P. McGovern1, Adem Y. Dawed2, Angus G. Jones1, Ewan R. Pearson 2✉, John M. Dennis 1✉ & ADA/EASD PDMI* Abstract Background A precision medicine approach in type 2 diabetes requires the identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy. Methods We performed a pre-registered systematic review of meta-analysis studies, ran- domized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes. After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. Results Here we show that the majority of included papers have methodological limitations precluding robust assessment of treatment effect heterogeneity. For SGLT2-inhibitors, mul- tiple observational studies suggest lower renal function as a predictor of lesser glycaemic response, while markers of reduced insulin secretion predict lesser glycaemic response with GLP1-receptor agonists. For both therapies, multiple post-hoc analyses of randomized control trials (including trial meta-analysis) identify minimal clinically relevant treatment effect heterogeneity for cardiovascular and renal outcomes. Conclusions Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care. https://doi.org/10.1038/s43856-023-00359-w OPEN A full list of author affiliations appears at the end of the paper. Plain language summary This study reviews the available evi- dence on which patient features (such as age, sex, and blood test results) are associated with different outcomes for two recently introduced type 2 dia- betes medications: SGLT2-inhibitors and GLP1-receptor agonists. Under- standing what individual character- istics are associated with different response patterns may help clinical providers and people living with dia- betes make more informed decisions about which type 2 diabetes treat- ments will work best for an individual. We focus on three outcomes: blood glucose levels (raised blood glucose is the primary symptom of diabetes and a primary aim of diabetes treatment is to lower this), heart disease, and kid- ney disease. We identified some potential factors that reduce effects on blood glucose levels, including poorer kidney function for SGLT2-inhibitors and lower production of the glucose- lowering hormone insulin for GLP1- receptor agonists. We did not identify clear factors that alter heart and kid- ney disease outcomes for either med- ication. Most of the studies had limitations, meaning more research is needed to fully understand the factors that influence treatment outcomes in type 2 diabetes. COMMUNICATIONS MEDICINE | (2023) 3:131 | https://doi.org/10.1038/s43856-023-00359-w |www.nature.com/commsmed 1 12 34 56 78 9 0 () :,; http://crossmark.crossref.org/dialog/?doi=10.1038/s43856-023-00359-w&domain=pdf http://crossmark.crossref.org/dialog/?doi=10.1038/s43856-023-00359-w&domain=pdf http://crossmark.crossref.org/dialog/?doi=10.1038/s43856-023-00359-w&domain=pdf http://crossmark.crossref.org/dialog/?doi=10.1038/s43856-023-00359-w&domain=pdf http://orcid.org/0000-0001-7768-8868 http://orcid.org/0000-0001-7768-8868 http://orcid.org/0000-0001-7768-8868 http://orcid.org/0000-0001-7768-8868 http://orcid.org/0000-0001-7768-8868 http://orcid.org/0000-0001-9237-8585 http://orcid.org/0000-0001-9237-8585 http://orcid.org/0000-0001-9237-8585 http://orcid.org/0000-0001-9237-8585 http://orcid.org/0000-0001-9237-8585 http://orcid.org/0000-0002-7171-732X http://orcid.org/0000-0002-7171-732X http://orcid.org/0000-0002-7171-732X http://orcid.org/0000-0002-7171-732X http://orcid.org/0000-0002-7171-732X www.nature.com/commsmed www.nature.com/commsmed Two of the most recently introduced anti-hyperglycaemic drug classes, SGLT2-inhibitors (SGLT2i) and GLP1- receptor agonists (GLP1-RA), have been shown in rando- mized clinical trials not only to reduce glycaemia1 but also to lower the risk of renal and cardiovascular disease (CVD) out- comes among high-risk individuals with type 2 diabetes (T2D)2–5. Based on average treatment effects reported in placebo-controlled trials, current T2D clinical consensus guidelines recommend a stratified approach to treatment selection, preferentially recom- mending these drug classes independent of their glucose lowering effect for individuals with cardiovascular or renal comorbidity. Specifically, people with heart failure and/or chronic kidney dis- ease are recommended to initiate SGLT2i and people with prior CVD or high risk for CVD are recommended to initiate either an SGLT2i or a GLP1-RA. In addition, these drugs are recom- mended as second-line glucose lowering medications to be added after metformin6. A limitation of the current stratified approach to SGLT2i and GLP1-RA treatment in clinical guidelines is that it is informed by selective trial recruitment strategies, and consequential accumu- lation of evidence of treatment benefits only for specific sub- groups with or at high risk of cardiorenal disease, rather than from an understanding of how the benefits and risks of each drug class vary across the whole spectrum of T2D. A more compre- hensive approach to treatment selection would require recogni- tion of the extreme heterogeneity in the demographic, clinical, and biological features of people with T2D, and the impact of this heterogeneity on drug-specific clinical outcomes. Identification of robust and reproducible patterns of heterogenous treatment effects is plausible as, at the individual patient level, responses to the same drug treatment appear to vary greatly7. A greater understanding of population-wide heterogenous treatment effects and enhanced capacity to predict individual treatment responses is needed to advance towards the central goal of precision type 2 diabetes medicine—using demographic, clinical, biological, or other patient-level features to match individuals to their optimal anti-hyperglycaemic regimen as part of routine T2D clinical care. To assess the evidence base for treatment effect heterogeneity for SGLT2i and GLP1-RA, we undertook a systematic literature review to summarize key findings from studies that specifically examined interactions between individual-level biomarkers and the effects of these drug classes on clinical outcomes. Although biomarkers may connote laboratory-based measurements in tra- ditional contexts, herein we broadly conceptualized biomarkers as individual-level demographic, clinical, and biological features, including both laboratory measures as well as genetic and geno- mic markers. We focused on three categories of outcomes rele- vant to T2D care: (1) glycaemic response (as measured by hemoglobin A1c; HbA1c); (2) CVD outcomes; and (3) renal outcomes. Our review was guided by the following research question: In a population with T2D, treated with SGLT2i or GLP1-RA, what are the biomarkers associated with heterogenous treatment effects in glycaemic, CVD, and renal outcomes? Each of the three outcomes were evaluated separately for each of the two drug classes for a total of 6 sub-studies. Given the heterogeneity of the T2D population, we anticipated that we would find one or more biomarkers modifying the effects of SGLT2i and GLP1-RA. The Precision Medicine in Diabetes Initiative (PMDI) was established in 2018 by the American Diabetes Association (ADA) in partnership with the European Association for the Study of Diabetes (EASD). The ADA/EASD PMDI includes global thought leaders in precision diabetes medicine who are working to address the burgeoning need for higher quality, individualized diabetes prevention and care through precision medicine8. This systematic review is written on behalf of the ADA/EASD PMDI as part of a comprehensive evidence evaluation in support of the 2nd Inter- national Consensus Report on Precision Diabetes Medicine9. We find that a majority of the papers identified by our review have methodological limitations precluding robust assessment of treatment effect heterogeneity. For SGLT2-inhibitors, multiple observational studies suggest lower renal function as a predictor of lesser glycaemic response, while markers of reduced insulin secretion predict lesser glycaemic response with GLP1-receptor agonists. For both therapies, multiple post-hoc analyses of ran- domized control trials (including trial meta-analysis) identify minimal clinically relevant treatment effect heterogeneity for cardiovascular and renal outcomes. Methods We conducted a systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines10. The protocol was pre-registered (PROSPERO registration number: CRD42022303236). As above, our review was guided by the following research question: In a population with T2D, treated with SGLT2i and GLP1-RA, what are the biomarkers associated with heterogenous treatment effects in glycaemic, CVD, and renal outcomes? Search strategy. The search strategy for this review was devel- oped for each drug class (SGLT2i and GLP1-RA) and outcome (glycaemic, cardiovascular, and renal) to capture studies specifi- cally evaluating treatment effect heterogeneity associated with demographic, clinical, and biological features in people with type 2 diabetes. Terms for drug class (SGLT2i or GLP1-RA) and individual generic names of licensed drugs within each class (e.g. ‘empagliflozin’) were included. Potential effect modifiers of interest comprised age, sex, ethnicity, clinical features, routine blood tests, metabolic markers, and genetics; all search terms were based on medical subject sub-headings (MeSH) terms and are reported in Supplementary Note 1. SGLT2i and GLP1-RA were evaluated at drug class level, and we did not aim to identify within-class heterogeneity in treatment effects. Electronic sear- ches were performed in PubMed and Embase by two independent academic librarians in February 2022. Forwards and backwards citation searching was conducted but grey literature and white papers were not searched. Inclusion criteria. To be included, studies were required to meet the following criteria: full-text English-language publications of RCTs, meta-analyses, post-hoc analyses of RCTs, pooled cohort analyses, prospective and retrospective observational analyses published in peer-reviewed journals; adult populations with type 2 diabetes taking at least one of either SGLT2i or GLP1-RA with sample size >100 for the active drug of interest; at least a 4-month potential follow up period (chosen pragmatically as a suitable time length over which changes in glycaemic response could be assessed) after initiation of the drug class of interest; randomized control trials (RCTs) required a comparison against placebo or an active comparator anti-hyperglycaemic drug (observational stu- dies did not require a comparator group); a pre-specified aim of the study must be to examine heterogeneity in treatment out- come, such as biomarker-treatment interactions, stratified ana- lyses, or heterogeneity-focused machine learning approaches; and the study must report differential effects of the drug class on an outcome of interest (see Outcomes section below) with respect to a biomarker. All individual trial or observational cohorts included in a meta-analysis or pooled cohort analysis must have met the inclusion criteria stated above. We further excluded studies based on the following criteria: studied type 1 or other forms of non-type 2 diabetes; included ARTICLE COMMUNICATIONS MEDICINE | https://doi.org/10.1038/s43856-023-00359-w 2 COMMUNICATIONS MEDICINE | (2023) 3:131 | https://doi.org/10.1038/s43856-023-00359-w |www.nature.com/commsmed www.nature.com/commsmed children/minors; inpatient studies; conference proceeding abstracts, editorials, opinions papers, book chapters, clinical trial registries, case reports, commentaries, narrative reviews, or non- peer reviewed studies; did not adequately adjust for confounders (individual RCTs and observational studies only, this criteria was not applied for meta-analyses and pooled cohort analyses); did not address the question of treatment response heterogeneity for biomarkers of interest. Titles and abstracts were independently screened by pairs of research team members to identify potentially eligible studies; these were then independently evaluated for inclusion in the full- text review. Any discrepancies were discussed with a third author until reaching consensus. Discrepancies were discussed as part of larger group meetings to ensure consistency in decisions across reviewer pairs. Data extraction and quality assessment. Pairs of authors inde- pendently reviewed the main reports and supplementary mate- rials and extracted the following data for each of the included papers: publication (PMID, journal, publication year, first author, title, study type); study (setting and region, study time period, follow up period); population (overall characteristics, ethnicity); intervention (drug class, specific therapies, treatment/comparator arm sizes); statistical analysis (outcome, outcome measurement, subgroups/predictors analysed with respect to biomarkers, sta- tistical model, covariate set); and results (relevant figures and tables, main findings, methodology, quality). Covidence sys- tematic review software11 was used for data extraction. After data were extracted, information was synthesized by drug class and outcome and further examined by biomarkers or subgroups analyzed within each study. Results were extracted within these subsections and summarized for each paper, where general trends in results for each subsection were outlined. Risk of bias evaluations were conducted alongside the data extraction by each pair of authors, using the Joanna Briggs Institute (JBI) Critical Appraisal Tool for Cohort Studies12 for all included research papers. This was used to determine the extent of bias within the study’s design, execution, and analysis, specifically within the population, outcome measurements, and statistical modelling. The Cohort studies tool was applied for all studies as we did not identify any individual RCTs designed to specifically examine treatment effect heterogeneity, and all included RCT meta-analyses represent post-hoc rather than pre-specified analyses. Further detail on the risk of bias can be seen in Supplementary Figs. 1 and 2. Additionally, the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) framework13,14 was applied at the outcome level for each drug class to determine the quality of evidence and certainty of effects for these subsections; an overall GRADE evaluation for all evidence was also provided. Outcomes. Three outcome categories were assessed in the included studies: (1) changes in HbA1c from baseline associated with treatment; (2) CVD outcomes limited to cardiovascular (CV)-related death, non-fatal myocardial infarction, non-fatal stroke, hospitalization for angina, coronary artery bypass graft, percutaneous coronary intervention, hospitalization for heart failure, carotid endarterectomy, and peripheral vascular disease; and (3) renal outcomes including development of chronic kidney disease (including end-stage renal disease, ESRD), and long- itudinal changes in markers of renal function including eGFR/ creatinine and albuminuria. Specific measurements and proce- dures for each category of outcome varied across the included studies. Summaries of the included papers assessing each outcome for each drug class are reported in Supplementary Tables 1-8. Reporting summary. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Results Literature search and screening results. Figures 1 and 2 depict the outcomes of the study screening processes for SGLT2i (Fig. 1) and GLP1-RA (Fig. 2). For SGLT2i, a total of 3415 unique citations underwent title and abstract screening. A total of 3076 were determined to not meet the pre-defined eligibility criteria. The remaining 339 full- text articles were screened, through which process 238 articles were excluded. The most common reasons for exclusion were: studies did not report on the heterogeneity of treatment response (126 studies), studies reported only univariate or unadjusted associations (41 studies), and studies did not meet inclusion criteria (64 studies). In total, 101 studies were identified for inclusion based on the systematic search. For GLP1-RA, a total of 2270 unique citations underwent title and abstract screening. 2109 were determined to not meet the pre-defined eligibility criteria. The remaining 161 full-text articles were screened, through which process 86 articles were excluded. The most common reasons for exclusion were: studies did not meet inclusion criteria (39 studies), studies reported only univariate or unadjusted associations (26 studies), and studies did not report on the heterogeneity of treatment response (17 studies). In total, 75 studies were identified for inclusion. Description of included studies. Included studies for CVD and renal outcomes were predominantly secondary analyses of industry-funded placebo-controlled trials (RCT), or meta- analyses of these trials, with a smaller number of observational studies. For glycaemic outcomes, most studies were observational. Supplementary Tables 1-8 show all included studies for GLP1-RA and SGLT2i, split by glycaemic, CVD, and renal outcomes, and 3417 studies imported for screening 2 duplicates removed 3415 studies screened 339 full-text studies assessed for eligibility 101 studies included 3075 studies irrelevant 238 studies excluded 126 Did not report on treatment heterogeneity response/differen�al effect by biomarker 41 Only reported univariate/unadjusted results 64 Did not meet inclusion criteria 7 Full text Unavailable Fig. 1 Study screening and attrition flow diagram (PRISMA) for SGLT2- inhibitor studies. Study screening and attrition flow diagram (PRISMA) for SGLT2-inhibitor studies. COMMUNICATIONS MEDICINE | https://doi.org/10.1038/s43856-023-00359-w ARTICLE COMMUNICATIONS MEDICINE | (2023) 3:131 | https://doi.org/10.1038/s43856-023-00359-w |www.nature.com/commsmed 3 www.nature.com/commsmed www.nature.com/commsmed including information on study population size, examined bio- markers, and notable findings. Summaries of the major individual RCTs that were included in meta-analyses are detailed in Sup- plementary Tables 9 and 10. SGLT2i, GLP1-RA, and glycaemic outcomes. Study quality for assessment of heterogenous treatment effects of both drug classes was variable with strong methodological limitations for the study of predictors of glycaemia treatment response common. A core weakness with many studies was a lack of head-to-head com- parisons between therapies, which is required to separate broader prognostic factors (that predict response to any glucose-lowering therapy) from drug-specific factors that are associated with dif- ferential treatment response. Put otherwise, even when data suggested that a biomarker was associated with glycaemic response, it was not clear if this factor was helpful for choosing between therapies due to the lack of an active comparator. Other common methodological weaknesses included the use of arbitrary subgroups (rather than the assessment of continuous predictors), small numbers in comparator subgroups that limited statistical power, dichotomized outcomes (responder analysis), multiple testing, and lack of adjustment for key potential confounders. SGLT2i. Of 27 studies that met our inclusion/exclusion criteria, 9 observational studies (usually retrospective analysis of healthcare records), 5 post-hoc analysis of individual RCTs, 10 pooled analyses of individual data from multiple RCTs, and 3 RCT meta- analyses were included (Supplementary Table 7). All included studies assessed routine clinical characteristics and routinely measured clinical biomarkers (Table 1). No pharmacogenetic, or, with the exception of one study of HOMA-B15, non-routine biomarker studies were identified. A key finding across multiple studies including appropriately adjusted analysis of RCT and observational data was that HbA1c reduction with SGLT2i is substantially reduced with lower eGFR16–22. In pooled RCT data for canagliflozin 300 mg, 6-month HbA1c reduction was estimated to be 11.0 mmol/mol for participants with eGFR ≥90 mL/min/1.73 m2, compared to 6.7 mmol/mol for those with eGFR 45-6022. With empagliflozin 25 mg, 6-month HbA1c reduction was 9.6 mmol/mol at eGFR ≥90, and 4.3 mmol/mol at eGFR 30-6019. A further finding confirmed by multiple robust studies is that in keeping with other glucose-lowering agents, higher baseline HbA1c is associated with greater HbA1c lowering with SGLT2 inhibitors, including verses placebo15,21,23–27. Active comparator studies suggested that higher baseline HbA1c may predict greater relative HbA1c response to SGLT2i therapy in comparison to DPP4i and sulfonylurea therapy15,25,26. Notably, an individual participant data meta-analysis of two RCTs showed greater improvement with empagliflozin (6-month HbA1c decline per unit higher baseline HbA1c [HbA1c slope] −0.49% [95%CI −0.62, −0.37] compared to sitagliptin (6-month HbA1c slope −0.29% [95%CI −0.42, −0.15]) and glimepiride (12-month HbA1c slope: empagliflozin -0.52% [95%CI −0.59, −0.44]; glimepiride −0.32% [95%CI −0.39, −0.25])25. A number of studies assessing differences in glycaemic response to SGLT2i by ethnicity suggest that initial glycaemic response to this medication class does not vary by ethnicity28–32. Similarly, many studies also showed that response did not vary meaningfully by sex. Some studies suggested older age may be associated with reduced glycaemia response; however, analyses usually did not adjust for eGFR which may confound this association, as eGFR declines with age17,23,32–35. GLP1-RA. Of 49 studies that met our inclusion/exclusion criteria, 24 observational studies, 6 post-hoc analysis of individual RCTs, and 19 meta-analyses were included (Supplementary Table 8). The majority of included studies assessed routine clinical char- acteristics and routinely measured clinical biomarkers, although 3 studies evaluated genetic variants, and 15 studies evaluated non- routine biomarkers (Table 1). Studies consistently identified baseline HbA1c as a predictor of greater HbA1c response. For other clinical features, the strongest evidence was that, in many observational studies, markers of lower insulin secretion (including longer diabetes duration [or proxies such as insulin treatment], lower fasting C-peptide, lower urine C-peptide-to-creatinine ratio, and positive glutamic acid decarboxylase (GAD) or islet antigen 2 (IA2) islet autoantibodies) were associated with lesser glycaemic response to GLP1-RA36–49. One large prospective study (n=620) observed clinically relevant reductions in HbA1c response with GLP1-RA in individuals with GAD or IA2 autoantibodies (mean HbA1c reduction −5.2 vs. −15.2 mmol/mol without autoantibodies) or C-peptide <0.25 nmol/L (mean HbA1c reduction −2.1 vs. −15.3 mmol/mol with C-peptide >0.25 nmol/L). In contrast, post-hoc RCT analyses have found T2D duration50 and beta-cell function51,52 do not modify glycaemic outcomes. This may reflect trial inclusion criteria as included participants had relatively higher beta-cell function, and were less-commonly insulin-treated, compared with the observational cohorts51. Few studies contrasted HbA1c outcome for GLP1-RA versus a comparator drug. One meta-analysis showed a greater HbA1c reduction with the GLP1-RA liraglutide compared to other antidiabetic drugs (sitagliptin, glimepiride, rosiglitazone, exena- tide, and insulin glargine) across all baseline HbA1c categories (n= 1804)53, a finding supported for the GLP1-RA dulaglutide compared to glimepiride and insulin glargine54. Overall, there was no consistent evidence for effect modifica- tion by body mass index (BMI), sex, age or kidney function, with studies reporting contrasting, or null, associations for these clinical features39,40,44–46,50,54–64. In comparative analysis, one large observational study found that markers of insulin resistance (including higher HOMA-IR, BMI, fasting triglycerides, and 2271 studies imported for screening 1 duplicates removed 2270 studies screened 161 full-text studies assessed for eligibility 75 studies included 2109 studies irrelevant 86 studies excluded 17 Did not report on treatment heterogeneity response/differen�al effect by biomarker 26 Only reported univariate/unadjusted results 39 Did not meet inclusion criteria 4 Full text Unavailable Fig. 2 Study screening and attrition flow diagram (PRISMA) for GLP1- receptor agonist studies. Study screening and attrition flow diagram (PRISMA) for GLP1-receptor agonist studies. ARTICLE COMMUNICATIONS MEDICINE | https://doi.org/10.1038/s43856-023-00359-w 4 COMMUNICATIONS MEDICINE | (2023) 3:131 | https://doi.org/10.1038/s43856-023-00359-w |www.nature.com/commsmed www.nature.com/commsmed T ab le 1 S um m ar y of ev id en ce fo r tr ea tm en t ef fe ct he te ro ge ne it y fo r S G LT 2- in hi bi to r an d G LP 1- re ce pt or ag on is t th er ap ie s fo r gl yc ae m ic ou tc om es . G LP 1- R A S G LT 2i P la in la ng ua ge su m m ar y G ly ca em ic ou tc om es G R A D E EV ID EN C E C G R A D E EV ID EN C E B B io m ar ke r N (o bs er va ti on al ) N (R C T ) N (M et a- an al ys is an d po ol ed R C T ) N (o bs er va ti on al ) N (R C T ) N (M et a- an al ys is an d po ol ed R C T ) G ly ca em ia Bi om ar ke rs 18 37 –4 1, 4 5, 4 7, 4 8 ,5 4 ,5 5, 6 1, 6 2, 14 0 –1 4 5 11 32 ,4 4 ,4 6 ,5 0 ,5 6 ,5 9 ,6 3, 14 6 –1 4 9 15 3 4 24 ,1 50 –1 52 22 7, 32 31 5, 25 ,2 6 G re at er gl yc ae m ic re sp on se fo r bo th SG LT 2i an d G LP 1- R A is se en in in di vi du al s w ith hi gh er ba se lin e H bA 1c , N o st ud ie s ar e av ai la bl e to co m pa ri ng th e re la tiv e ef fi ca cy of SG LT 2i to G LP 1- R A at di ff er en t ba se lin e H bA 1c le ve ls . R en al Fu nc tio n 6 39 ,4 0 ,4 5, 54 ,6 2, 14 2 4 32 ,4 4 ,5 9 ,6 3 16 4 21 51 ,1 52 31 6 ,1 8 ,3 2 4 19 ,2 0 ,2 2, 26 G LP 1- R A : no ev id en ce th at re na l fu nc tio n al te rs gl yc ae m ic re sp on se . SG LT 2i : Le ss er gl yc ae m ic re sp on se fo r re na lly im pa ir ed pa tie nt s an d th os e w ith lo w er ba se lin e ki dn ey fu nc tio n (e G FR ). BM I 15 37 –4 1, 4 5, 4 8 ,5 4 ,5 7, 58 ,6 1, 6 2, 14 0 ,1 4 2, 14 3 11 32 ,4 4 ,4 6 ,5 6 ,5 9 ,6 0 ,6 3, 14 7, 14 8 ,1 53 ,1 54 0 4 21 ,2 4 ,1 50 ,1 51 22 7, 32 22 2, 26 N o co ns is te nt ev id en ce of si gn ifi ca nt m od ify in g ef fe ct s of BM I on gl yc ae m ic re sp on se fo r ei th er SG LT 2i or G LP 1- R A . A ge 13 37 –4 1, 4 5, 4 7, 54 ,6 1, 6 2, 14 0 ,1 4 2, 14 3 73 2, 4 4 ,4 6 ,5 9 ,6 3, 14 8 ,1 55 0 4 17 ,2 4 ,1 50 ,1 51 13 2 6 22 ,2 6 ,3 3– 35 ,1 56 G LP 1- R A : N o ev id en ce th at ag e al te rs gl yc ae m ic re sp on se w ith G LP 1- R A . SG LT 2i :s om e st ud ie s su gg es t ol de r ag e m ay be as so ci at ed w ith re du ce d gl yc ae m ia re sp on se , ho w ev er , an al ys es us ua lly di d no t ad ju st fo r eG FR w hi ch m ay co nf ou nd th is as so ci at io n as eG FR de cl in es w ith ag e. D ia be te s du ra tio n 15 37 –4 2, 4 5, 4 7, 4 8 ,5 4 ,6 1, 6 2, 14 0 ,1 4 2, 14 4 53 2, 4 6 ,5 0 ,5 9 ,6 3 14 9 31 50 –1 52 13 2 12 6 SG LT 2i : N o co ns is te nt ef fe ct of di ab et es du ra tio n on gl yc ae m ic re sp on se . G LP 1- R A : Lo ng er di ab et es du ra tio n (o r pr ox ie s su ch as in su lin tr ea tm en t) as so ci at ed w ith le ss er gl yc ae m ic re sp on se . Se x 13 37 –4 1, 4 5, 4 8 ,5 4 ,5 5, 6 2, 14 0 ,1 4 2, 14 3 6 32 ,4 6 ,5 0 ,5 6 ,5 9 ,6 3 0 4 24 ,3 0 ,1 50 ,1 51 13 2 32 2, 26 ,1 56 N o co ns is te nt ev id en ce of si gn ifi ca nt m od ify in g ef fe ct s of se x on gl yc ae m ic re sp on se fo r ei th er SG LT 2i or G LP 1- R A . Et hn ic ity 24 0 ,6 7 53 2, 59 ,6 3, 6 5, 6 8 16 6 13 0 13 2 4 15 ,2 6 ,2 8 ,2 9 N o co ns is te nt ev id en ce of di ff er en ce s in gl yc ae m ic re sp on se ac ro ss et hn ic gr ou ps fo r ei th er SG LT 2i or G LP 1- R A . G en et ic s 24 3, 14 0 16 9 0 0 0 0 SG LT 2i :n o st ud ie s ex am in ed ge ne tic fa ct or s. G LP 1- R A :T w o sm al ls tu di es su gg es t va ri an ts rs 16 31 8 4 an d rs 10 30 54 20 m ay be as so ci at ed w ith le ss er re sp on se in in di vi du al s of C hi ne se et hn ic ity . N on -r ou tin e bi om ar ke rs 12 37 ,3 8 ,4 0 ,4 1, 4 5, 4 8 ,5 4 ,5 7, 6 2, 14 2, 14 3, 15 7 34 4 ,6 3, 15 8 0 0 11 59 11 5 SG LT 2i : N o ev id en ce of he te ro ge ne ity in tr ea tm en t re sp on se fo r m ea su re s of in su lin se cr et io n an d in su lin re si st an ce , or fo r pa tie nt s w ith ob st ru ct iv e sl ee p ap ne a. G LP 1- R A : O bs er va tio na l st ud ie s su gg es t m ar ke rs of lo w er in su lin se cr et io n in cl ud in g lo w er fa st in g C -p ep tid e, lo w er ur in e C - pe pt id e- to -c re at in in e ra tio , an d po si tiv e G A D or IA 2 is le t au to an tib od ie s ar e as so ci at ed w ith le ss er gl yc ae m ic re sp on se . In co nt ra st , po st -h oc R C T an al ys es fo un d in su lin se cr et io n do es no t m od ify gl yc ae m ic ou tc om e. T hi s m ay re fl ec t tr ia l in cl us io n cr ite ri a as pa rt ic ip an ts ha d re la tiv el y hi gh er be ta -c el l fu nc tio n co m pa re d w ith th e ob se rv at io na l co ho rt s. COMMUNICATIONS MEDICINE | https://doi.org/10.1038/s43856-023-00359-w ARTICLE COMMUNICATIONS MEDICINE | (2023) 3:131 | https://doi.org/10.1038/s43856-023-00359-w |www.nature.com/commsmed 5 www.nature.com/commsmed www.nature.com/commsmed HDL) do not alter GLP1-RA response, but are associated with lesser DPP4-inhibitor response57. There was limited evidence for differences by ethnicity. One large pooled RCT analysis (N= 2355) suggested greater HbA1c response in Asian participants compared to those of other ethnicities, but other studies have not identified differences in response across ethnic groups65–68. Similarly, limited studies evaluated pharmacogenetics, although two small studies suggest variants rs163184 and rs10305420, but not rs3765467, may be associated with lesser response in Chinese patients43,69. SGLT2i, GLP1-RA and cardiovascular outcomes SGLT2i: Evidence from clinical trials. Of 65 studies, 58 were post- hoc meta-analysis of RCTs or meta-analysis of multiple RCTs. Heart failure was common as a secondary outcome. The majority of studies were derived from EMPA-REG70 and the CANVAS program71, although more recent meta-analyses included up to 12 cardiovascular outcome trials (CVOTs) with different inclu- sion criteria, treatments, primary outcomes, and follow-up duration (Supplementary Table 9). Most studies included only participants with established CVD or elevated cardiovascular risk, although some studies were restricted to patients with pre- existing heart failure or chronic kidney disease. While most CVOTs and meta-analyses included only patients with type 2 diabetes, some meta-analyses also included data from patients without diabetes in the EMPEROR-P72, EMPEROR-R73, DAPA- HF74 and DAPA-CKD75 RCTs. Studies primarily focused on relative rather than absolute treatment effects and one of two primary outcomes: 3-point MACE which was a composite of cardiovascular death, non-fatal MI, and non-fatal stroke; or composite heart failure outcomes including hospitalized heart failure and cardiovascular death. The longest duration of follow- up was in the CANVAS CVOT with a median follow-up of 5.7 years, while most other included CVOTs had durations of 1 to 4 years. On average, in relative terms, SGLT2i reduce the risk of cardiovascular disease (MACE) by 10% (HR 0.90 [95%CI 0.85, 0.95]), and heart failure hospitalization by 32% (HR 0.68 [95%CI 0.61, 0.76]) in individuals with or at high-risk of CVD2. The majority of meta-analyses of CVOTs found no significant interactions for MACE or heart failure outcomes across a variety of biomarkers (Table 2; Supplementary Table 1). Several meta- analyses found no interactions by age, sex, and adiposity for MACE or heart failure outcomes. Four meta-analyses examined interactions by race for MACE outcomes and found no interactions. Three meta-analyses consistently identified a greater relative heart failure benefit of SGLT2i in people of Black and Asian ethnicity76–78 (HR SGLT2i versus placebo 0.60 [95% CI 0.47, 0.74]) compared to White individuals (HR 0.82 [95% CI 0.73, 0.92])76, however, one meta-analysis reported no difference between White and non-White individuals79. Contemporary meta-analysis incorporating the CREDENCE and VERTIS-CV trials alongside EMPA-REG, CANVAS, and DECLARE suggests history of CVD does not modify the efficacy of SGLT2i for MACE2,80. One meta-analysis suggests heart failure severity modifies the efficacy of SGLT2i’s for heart failure outcome (composite outcome of cardiovascular death or hospitalization for heart failure) with greater efficacy in patients with NYHA heart failure class II (HR SGLT2i versus placebo 0.66 [95%CI 0.59, 0.74]) than class III or IV (HR 0.86 [95%CI 0.75, 0.99])77. Other meta-analyses that examined treatment effect heterogeneity using heart failure history as a binary predictor did not find significant interactions2,81. A recent meta-analysis82 that included 6 CVOTs of patients with diabetes and 4 CVOTs of patients with and without diabetes found that eGFR did not alter the relative benefit of SGLT2 inhibitors for MACE and heart failure outcomes;2,77,81,83–85 however, a greater relative benefit was reported for MACE in those with higher baseline albuminuria (ACR>300 mg/g HR 0.74 [95%CI 0.66, 0.84]; ACR 30-300 mg/g HR 0.95 [95%CI 0.82, 1.10]) ACR<30 mg/g HR 0.87 [95%CI 0.77, 0.98]). We identified many secondary analyses of single CVOTs, which largely found no interactions by biomarkers (Supplemen- tary Table 1). Single studies identified potential effect modifica- tion for MACE by history of CVD86, and obesity87, and history of heart failure for heart failure outcome88, but these associations were not replicated across the other studies or in multi-RCT meta-analyses. In a secondary analysis of CANVAS, participants with higher levels of biomarkers of cardiovascular stress (high- sensitivity cardiac troponin T (hs-cTnT), soluble suppression of tumorigenesis-2 (sST2), and insulin-like growth factor binding protein 7 (IGFBP7)) had greater relative benefit for MACE; for a multimarker score summing high levels of these 3 biomarkers, the relative benefit of SGLT2i for no abnormal biomarkers was HR: 0.99 [95% CI: 0.66–1.49], 1 abnormal biomarker HR: 1.34 [95% CI: 0.94–1.89), 2 abnormal biomarkers HR: 0.61 [95% CI: 0.45–0.82]), and 3 abnormal biomarkers HR: 0.46 [95% CI:0.18–1.17]; Pinteraction trend =0.005)89. Unlike meta-analyses, studies based on single RCTs typically performed multivariable adjustment for potential confounders. GLP1-RA: Evidence from clinical trials. Of the 35 studies that investigated heterogeneity in the effect of GLP1-RAs on cardio- vascular health and met our inclusion criteria, 15 were meta- analyses of RCTs or pooled analyses of multiple RCTs, 15 were post-hoc analyses of RCTs, and 5 were observational studies (Supplementary Table 2). Most studies used data collected from the LEADER, SUSTAIN 6, and EXSCEL trials, however in total the data from 7 CVOTs were used (Supplementary Table 10). The majority of these CVOTs investigated the effect of us CVD on the cardiovascular efficacy of GLP1-RAs using 3-point MACE as a primary outcome, and with heart failure being a common sec- ondary outcome, focusing on relative rather than absolute benefit. The population of 6 of the 7 CVOTs had established CVD or high CVD risk. The CVOT with the longest median follow-up was REWIND with a median follow-up of 5.4 years, and the median follow-up of the other CVOTs ranged from 1 to 4 years. Contemporary meta-analysis data suggests GLP1-RA reduces the relative risk of cardiovascular disease (MACE) by 14% (HR 0.86 [95%CI 0.80-0.93]), and heart failure hospitalization by 11% (HR 0.89 [95%CI 0.82, 0.98]) compared to placebo3. Several large meta-analyses examining heterogenous treatment effects in placebo-controlled CVOTs have been conducted for GLP1- RA76,83,84,90–97, with the majority of studies focusing on whether prior established CVD modifies the relative effect of GLP1-RA on MACE and/or heart failure. Two meta-analyses reported the relative MACE benefit of GLP-RA may be restricted to those with established CVD83,90, the largest of which included 7 RCTs and reported a 14% relative risk reduction with GLP1-RA specific to individuals with established CVD (with CVD: HR 0.86 [95%CI 0.80, 0.93]; at high-risk of CVD: HR 0.94 [95% CI 0.82, 1.07])83. However, this risk difference is not conclusive and has not been replicated in other meta-analyses and pooled RCT analyses91–93,98,99, including an individual participant level re- analysis of the SUSTAIN and PIONEER RCTs which evaluated baseline CVD risk as a continuous rather than subgroup-level variable100. Differential relative effects of GLP1-RAs on MACE have been reported by ethnicity in two out of three meta-analyses:76,83,90 one showed a benefit of GLP1-RA treatment compared to placebo in Asian (HR 0.76 [95%CI 0.61, 0.96]) and Black (HR 0.77 [95% ARTICLE COMMUNICATIONS MEDICINE | https://doi.org/10.1038/s43856-023-00359-w 6 COMMUNICATIONS MEDICINE | (2023) 3:131 | https://doi.org/10.1038/s43856-023-00359-w |www.nature.com/commsmed www.nature.com/commsmed T ab le 2 S um m ar y of ev id en ce fo r tr ea tm en t ef fe ct he te ro ge ne it y fo r S G LT 2- in hi bi to r an d G LP 1- re ce pt or ag on is t th er ap ie s fo r ca rd io va sc ul ar ou tc om es (i nc lu di ng he ar t fa ilu re ). G LP 1- R A S G LT 2i P la in la ng ua ge su m m ar y C ar di ov as cu la r di se as e (C V D ) G R A D E EV ID EN C E B G R A D E EV ID EN C E B B io m ar ke r N (o bs er va ti on al ) N (R C T ) N (M et a- an al ys is an d po ol ed R C T ) N (o bs er va ti on al ) N (R C T ) N (M et a- an al ys is an d po ol ed R C T ) R ac e/ et hn ic ity 0 0 37 6 ,8 3, 9 0 0 21 6 0 ,1 6 1 4 76 ,7 8 ,8 3, 11 0 N o he te ro ge ne ity by ra ce /e th ni ci ty fo r SG LT 2i s; Po te nt ia li nc re as ed ca rd io va sc ul ar be ne fi t in A si an s as so ci at ed w ith G LP 1- R A us e, bu t re su lts ar e in co ns is te nt . H is to ry of C V D 31 0 5– 10 7 8 12 5, 16 2– 16 7 78 3, 9 0 –9 5, 9 8 ,9 9 4 10 4 –1 0 6 ,1 0 9 38 6 ,1 6 8 ,1 6 9 52 ,8 0 ,8 3, 9 5, 11 2 N o co ns is te nt im pa ct on SG LT 2i or G LP 1- R A ou tc om es . A ge 21 0 7, 10 8 31 21 ,1 6 5, 17 0 38 3, 9 2, 9 4 21 0 4 ,1 0 9 11 71 4 78 –8 0 ,8 3 N o co ns is te nt he te ro ge ne ity by ag e fo r SG LT 2i s; N o ev id en ce of ag e ef fe ct fo r G LP 1- R A s. Se x 31 0 2, 10 7, 10 8 11 6 5 58 3, 9 0 ,9 2, 9 4 ,9 6 ,9 9 4 10 2, 10 4 ,1 0 9 ,1 10 31 71 –1 73 4 79 ,8 3, 17 4 ,1 75 N o co ns is te nt he te ro ge ne ity by se x fo r SG LT 2i s or G LP 1- R A s. R en al fu nc tio n 11 0 8 21 27 ,1 76 58 3, 8 4 ,9 0 ,9 2, 9 4 21 0 4 ,1 0 9 6 16 ,1 17 –1 19 ,1 71 ,1 77 52 ,8 2– 8 4 ,9 5 N o co ns is te nt he te ro ge ne ity by re na l fu nc tio n on ca rd io va sc ul ar ou tc om es fo r SG LT 2i s; N o ev id en ce of he te ro ge ne ity by re na l fu nc tio n on ca rd io va sc ul ar ou tc om es fo r G LP 1- R A s. BM I 0 11 26 58 3, 9 0 ,9 2, 9 4 ,9 7 11 10 28 7, 17 1 37 9 ,8 3, 9 7 N o co ns is te nt he te ro ge ne ity by BM I fo r SG LT 2i s; So m e in co ns is te nt ev id en ce su gg es ts th at hi gh er ba se lin e BM I m ay im pr ov e ca rd io va sc ul ar ef fi ca cy of G LP 1- R A s. G en et ic s 0 0 0 0 0 0 N on -r ou tin e bi om ar ke rs 0 0 0 0 58 9 ,1 78 –1 8 1 0 T he gr ea te r be ne fi t of SG LT 2i in th os e w ith hi gh le ve ls of 3 bi om ar ke rs :h s C ar di ac T ro po ni n T ,s ol ub le su pp re ss io n of tu m or ig en es is -2 (s ST 2) ,a nd in su lin -l ik e gr ow th fa ct or bi nd in g pr ot ei n 7 (I G FB P7 ) le ve ls H ea rt Fa ilu re (H F) G R A D E EV ID EN C E B G R A D E EV ID EN C E B Et hn ic ity 0 11 8 2 17 6 0 4 16 0 ,1 6 1, 18 3, 18 4 4 76 –7 9 Po ss ib ly gr ea te r re la tiv e be ne fi t of SG LT 2i in A si an an d Bl ac k co m pa re d to w hi te et hn ic ity ; Po te nt ia l in cr ea se d ef fi ca cy of G LP 1- R A s in A si an et hn ic ity . A ge 0 21 6 5, 17 0 19 4 11 0 4 11 8 3 37 9 ,8 1 N o he te ro ge ne ity by ag e fo r SG LT 2i s or G LP 1- R A s. Se x 0 21 8 2 19 4 21 0 2, 10 4 31 72 ,1 73 ,1 8 3 37 9 ,8 1, 17 5 N o he te ro ge ne ity by se x fo r SG LT 2i s or G LP 1- R A s. BM I 0 0 19 4 0 38 7, 18 3, 18 4 27 9 ,8 1 N o co ns is te nt he te ro ge ne ity by BM I fo r SG LT 2i s or G LP 1- R A s. H is to ry of C V D 21 0 5, 10 6 31 6 5, 16 6 ,1 8 2 39 3– 9 5 4 10 1, 10 4 –1 0 6 4 8 6 ,1 6 8 ,1 6 9 ,1 8 5 4 2, 77 ,8 1, 8 5 N o co ns is te nt he te ro ge ne ity by C V D hi st or y fo r SG LT 2i s or G LP 1- R A s. H is to ry of H F 0 0 0 11 0 6 18 8 4 2, 77 ,8 1, 8 5 N o co ns is te nt he te ro ge ne ity by H F hi st or y fo r SG LT 2i s; N o an al ys is on he te ro ge ne ity by H F hi st or y w as pe rf or m ed fo r G LP 1- R A s. H F se ve ri ty /s co re 0 0 0 11 0 3 31 6 3, 18 6 ,1 8 7 17 7 G re at er re la tiv e be ne fi t of SG LT 2i in th os e w ith N Y H A cl as s II vs cl as s III /I V in on e m et a- an al ys is ; N o an al ys is of he te ro ge ne ity by H F se ve ri ty /s co re pe rf or m ed fo r G LP 1- R A s. R en al fu nc tio n 0 0 19 4 21 0 3, 10 4 6 16 ,1 17 –1 19 ,1 77 ,1 8 3 52 ,7 7, 8 1, 8 2, 8 5 N o co ns is te nt he te ro ge ne ity in re na l fu nc tio n. A si ng le m et a- an al ys is sh ow ed gr ea te r SG LT -2 be ne fi t w ith lo w er eG FR an d hi gh er A C R ; N o ev id en ce fo r he te ro ge ne ity by re na l fu nc tio n fo r G LP 1- R A s. G en et ic s 0 0 0 0 0 0 N on -r ou tin e bi om ar ke rs 0 0 0 0 6 8 9 ,1 59 ,1 78 –1 8 0 ,1 8 8 0 N o he te ro ge ne ity ac ro ss a va ri et y of no n- ro ut in e bi om ar ke rs . N o an al ys is of he te ro ge ne ity by no n- ro ut in e bi om ar ke rs w as pe rf or m ed fo r G LP 1- R A s. COMMUNICATIONS MEDICINE | https://doi.org/10.1038/s43856-023-00359-w ARTICLE COMMUNICATIONS MEDICINE | (2023) 3:131 | https://doi.org/10.1038/s43856-023-00359-w |www.nature.com/commsmed 7 www.nature.com/commsmed www.nature.com/commsmed CI 0.59, 0.99]) individuals, but not in White individuals (HR 0.95 [95%CI 0.88, 1.02]);90 the second showed a significantly greater benefit of GLP1-RA for MACE in Asian compared to White individuals (HR Asian 0.68 [95%CI 0.53, 0.84]; White 0.87 [95% 0.81, 0.94])76. For other clinical features including sex, BMI/ obesity, baseline kidney disease, duration of diabetes, baseline HbA1c, background glucose lowering medications, and prior history of microvascular disease, the overall body of evidence from meta-analyses does not provide robust evidence to support differential effects of GLP1-RA on CVD outcomes (Table 2). SGLT2i and GLP1-RA: Evidence from observational studies. 10 observational studies met our inclusion criteria, with studies primarily reporting relative rather than absolute risk differences101–110. These studies comparing SGLT2i and GLP1- RA individually with other oral therapies (predominantly DPP4i) generally reported average relative benefits for CVD and heart failure outcomes in-line with placebo-controlled trials, with no consistent pattern of subgroup level differences across studies (Supplementary Tables 1 and 2). A few observational studies compared SGLT2i and GLP1-RA CVD outcomes. In a US claims-based study with follow-up to two years (n= 47,343), Htoo et al. 106 reported a higher relative risk of MACE with SGLT2i compared to GLP1-RA specific to individuals without CVD and heart failure (Relative risk [RR] 1.31 [95% CI 1.09, 1.56]), and a higher risk of stroke with SGLT2i versus GLP1-RA specific to individuals without CVD (No CVD without heart failure: RR 1.62 [95%CI 1.10, 2.38]; No CVD with heart failure: RR 3.30 [95%CI 1.22, 8.97]). In contrast, over a median follow-up of 7 months, Patorno et al. 105 reported a lower relative risk of myocardial infarction with SGLT2i compared to GLP1-RA in US claims data specific to individuals with a history of CVD (n=156,825; HR 0.83 [95%CI 0.74, 0.93] with history of CVD; HR 1.13 [95%CI 1.00, 1.28] without history of CVD), with no differences in stroke outcomes irrespective of CVD status. Both studies reported a consistent benefit of SGLT2i over GLP1-RA for heart failure. Raparelli et al. 102 identified potential differences by sex in the Truven Health MarketScan database (n=167,341): compared to sulfonylureas and over a median follow-up of 4.5 years, there was a greater relative reduction with GLP1-RA for females (HR 0.57 [95%CI 0.48, 0.68]) compared to males (HR 0.82 [95%CI 0.71, 0.95]), but a similar benefit for both sexes with SGLT2i (females HR 0.58 [95%CI 0.57, 0.83]; males HR 0.69 [95%CI 0.57, 0.83]). SGLT2i, GLP1-RA, and renal outcomes SGLT2i: Evidence from clinical trials. A total of 29 studies met our inclusion criteria. These included 20 post-hoc analyses of indi- vidual RCTs, 7 trial meta-analyses (Supplementary Table 4), and 2 analyses of observational data. All of the post-hoc RCT analyses and all but 1 of the meta-analyses used only data from the 12 SGLT2i cardiovascular/renal RCTs shown in Supplementary Table 9, which therefore provided most of the evidence in our review. These trials included people with type 2 diabetes with and without pre-existing cardiovascular disease, and had composite renal endpoints incorporating two or more of the following (which differed between trials): changes in eGFR/serum creati- nine, end-stage renal disease, changes in urine albumin:creatinine ratio (ACR), and/or death from renal causes. Most studies assessed routine clinical characteristics, especially renal function as measured by eGFR or urine ACR or a combination of both. In addition, 4 post-hoc RCT analyses examined non-routine plasma biomarkers. We found no genetic studies (Table 3). On average, SGLT2i have a relative benefit for a number of renal outcomes including kidney disease progression (HR 0.63, 95%CI 0.58,0.69) and acute kidney injury (HR 0.77, 95%CI 0.70, 0.84)4. Placebo-controlled trial meta-analyses of subgroups found no evidence for heterogeneity of SGLT2i treatment effects on relative renal outcomes by age79, use of other glucose-lowering drugs79, use of blood pressure/cardiovascular medications79,111, blood pressure79, BMI79, diabetes duration79, White race79, history of cardiovascular disease or heart failure2,80 or sex79. For baseline eGFR, an early meta-analysis that included EMPA-REG, CANVAS, and DECLARE reported greater effect of SGLT2i on renal outcomes in those with higher eGFR112 but both a later meta-analysis that added CREDENCE111 and a recent meta-analysis that added two further studies (SCORED and DAPA-CKD, including some participants without diabetes)82 showed no effect of baseline eGFR on renal outcomes with SGLT2i. For urine ACR, meta-analyses of subgroups found no evidence for greater SGLT2i effect with higher UACR2,82,111,113. Single RCTs found no heterogeneity of treatment effect by eGFR and UACR, or subgroups defined by the combination of these two114–118, with the exception of Neuen et al. 119 which showed a greater SGLT2i effect in preventing eGFR decline relative to placebo for those with higher UACR, and heterogeneity in a composite renal outcome by UACR. Overall, there was limited or no evidence to support modifying effects of baseline eGFR or UACR on the effect of SGTL2i on renal function outcomes. A few post-hoc analyses of the CANVAS RCT considered non- routine biomarkers, with most showing no interaction with SGLT2i treatment and renal outcomes. Two RCTs studied the effect of SGLT2i on renal outcomes at differing plasma IGFBP7 levels. One study reported an interaction of IGFBP7 with SGLT2i treatment for progression of albuminuria (>96.5 ng/ml HR 0.64; <=96.5 ng/ml HR 0.95, Pinteraction = 0.003)120 but no effect was seen for the composite renal endpoint in two studies89,120. The biomarker panel (sST2, IGFBP7, hs-cTnT) that showed a strong interaction with SGLT2i for MACE outcomes (see above) did not show any interaction for renal outcomes89. GLP1-RA: Evidence from clinical trials. 7 studies met our inclu- sion criteria: all post-hoc RCT analyses, 6 of individual trials (or multiple trials analysed separately) and 1 pooled analysis of two RCTS (Supplementary Table 5). These studies used data from 5 of the 7 GLP1-RA cardiovascular outcome trials shown in Supple- mentary Table 10, with renal outcomes only a secondary end- point. Most of these trials had composite renal endpoints as per the SGLT2i cardiovascular/renal trials, while some examined changes in either eGFR or urine ACR only. All studies assessed routine clinical characteristics, especially renal function as mea- sured by eGFR or urine ACR. No studies of genetics or non- routine biomarkers were identified (Table 3). The overall sample sizes were small and subgroup analyses underpowered to show a subgroup by treatment interaction for renal outcomes. Overall, GLP1-RA reduce the relative risk of albuminuria over 2 years by 24% versus placebo (HR 0.76 [95% CI 0.73-0.80; P < 0.001), and similarly reduce the relative risk of a 40% reduction in eGFR (HR, 0.86 [95% CI 0.75-0.99]; P= 0.039)5. Studies found no heterogeneity of GLP1-RA relative treatment effect by age121, blood pressure122,123, diabetes duration124, history of cardiovascular disease/heart failure122,125 or use of RAS inhibitors122. For BMI, a post-hoc analysis of EXSCEL (Exenatide) found a greater GLP1-RA effect on reducing rate of eGFR decline in those with lower BMI (BMI ≤ 30 kg/m2 treatment difference 0.26mL/min/1.73m2/year [95% CI 0.04, 0.48] vs BMI > 30 kg/m2 −0.12 [-0.26, 0.03], Pinteraction= 0.005)122. However, Verma et al.126 found no significant interaction by BMI subgroup with GLP1-RA treatment for a composite renal outcome in LEADER (Liraglutide) or SUSTAIN 6 (Semaglutide). For baseline eGFR, a pooled analysis of LEADER and SUSTAIN-6 reported a significant interaction, with lower eGFR ARTICLE COMMUNICATIONS MEDICINE | https://doi.org/10.1038/s43856-023-00359-w 8 COMMUNICATIONS MEDICINE | (2023) 3:131 | https://doi.org/10.1038/s43856-023-00359-w |www.nature.com/commsmed www.nature.com/commsmed T ab le 3 S um m ar y of ev id en ce fo r tr ea tm en t ef fe ct he te ro ge ne it y fo r S G LT 2- in hi bi to r an d G LP 1- re ce pt or ag on is t th er ap ie s fo r re na l ou tc om es . G LP 1- R A S G LT 2i P la in la ng ua ge su m m ar y R en al (e G FR ch an ge s/ C K D pr og re ss io n/ co m po si te ou tc om es of th es e w it h or w it ho ut A C R ch an ge s) G R A D E EV ID EN C E B G R A D E EV ID EN C E B B io m ar ke r N (o bs er va ti on al ) N (R C T ) N (M et a- an al ys is an d po ol ed R C T ) N (o bs er va ti on al ) N (R C T ) N (M et a- an al ys is an d po ol ed R C T ) Ba se lin e H bA 1c 0 0 0 0 0 0 R en al Fu nc tio n 0 31 22 ,1 27 ,1 28 15 21 29 ,1 30 6 11 4 ,1 15 ,1 17 –1 19 ,1 27 52 ,8 2, 11 1– 11 3 G en er al ly no re la tio ns hi p be tw ee n ei th er eG FR or A C R an d G LP 1- R A be ne fi t. G re at er re la tiv e be ne fi t of SG LT 2i in th os e w ith hi gh er eG FR (a lth ou gh in co ns is te nt re su lts w ith so m e st ud ie s sh ow in g no im pa ct an d 1 ob se rv at io na ls tu dy fi nd in g th e op po si te re la tio ns hi p) .G en er al ly no re la tio ns hi p be tw ee n A C R /p ro te in ur ia an d SG LT 2i be ne fi t. BM I 0 21 22 ,1 26 0 11 29 28 7, 18 4 17 9 G re at er G LP 1- R A be ne fi t w ith lo w er BM I bu t no t se en co ns is te nt ly .G en er al ly no ef fe ct on SG LT 2i be ne fi t A ge 0 11 21 0 21 29 ,1 30 0 17 9 N o ef fe ct on G LP 1- R A or SG LT 2i be ne fi t D ia be te s du ra tio n 0 11 24 0 0 0 17 9 N o ef fe ct on G LP 1- R A or SG LT 2i be ne fi t Se x 0 0 0 11 29 11 73 17 9 N o ef fe ct on SG LT 2i be ne fi t Et hn ic ity 0 0 0 0 21 8 4 ,1 8 9 17 9 N o ef fe ct on SG LT 2i be ne fi t G en et ic s 0 0 0 0 0 0 N on -r ou tin e bi om ar ke rs 0 0 0 0 58 9 ,1 20 ,1 78 –1 8 0 0 N o ef fe ct on SG LT 2i be ne fi t Bl oo d pr es su re /h yp er te ns io n 0 21 22 ,1 23 0 11 29 0 17 9 N o ef fe ct on G LP 1- R A or SG LT 2i be ne fi t H is to ry of C V D /H F 0 21 22 ,1 25 0 11 29 31 6 8 ,1 6 9 ,1 9 0 32 ,8 0 ,1 12 N o ef fe ct on G LP 1- R A or SG LT 2i be ne fi t R en al (a lb um in ur ia ch an ge s) G R A D E EV ID EN C E B G R A D E EV ID EN C E B Ba se lin e H bA 1c 0 0 0 0 0 0 R en al Fu nc tio n 0 21 22 ,1 28 15 0 0 11 13 G re at er G LP 1- R A be ne fi t w ith hi gh er A C R al th ou gh no t se en co ns is te nt ly .N o re la tio ns hi p be tw ee n eG FR an d G LP 1- R A . N o ef fe ct on SG LT 2i be ne fi t BM I 0 11 22 0 0 0 0 N o ef fe ct on G LP 1- R A be ne fi t A ge 0 0 0 0 0 0 D ia be te s du ra tio n 0 0 0 0 0 0 Se x 0 0 0 0 0 0 Et hn ic ity 0 0 0 0 11 8 9 0 N o ef fe ct on SG LT 2i be ne fi t G en et ic s 0 0 0 0 0 0 N on -r ou tin e bi om ar ke rs 0 0 0 0 11 20 0 Si ng le tr ia l fo un d gr ea te r SG LT 2i be ne fi t at hi gh er IG FB P7 Bl oo d pr es su re /h yp er te ns io n 0 11 22 0 0 0 0 N o ef fe ct on G LP 1- R A be ne fi t H is to ry of C V D /H F 0 11 22 0 0 0 0 N o ef fe ct on G LP 1- R A be ne fi t COMMUNICATIONS MEDICINE | https://doi.org/10.1038/s43856-023-00359-w ARTICLE COMMUNICATIONS MEDICINE | (2023) 3:131 | https://doi.org/10.1038/s43856-023-00359-w |www.nature.com/commsmed 9 www.nature.com/commsmed www.nature.com/commsmed associated with greater GLP1-RA effect in reducing eGFR decline: Semaglutide 1.0 mg vs placebo, eGFR < 60 difference in decline 1.62 ml/min/1.73m2/year vs eGFR>= 60 difference in decline 0.64 ml/min/1.73 m2/year, Pinteraction= 0.057; Liraglutide 1.8 mg vs placebo, eGFR < 60 difference in decline 0.67 ml/min/1.73m2/ year vs 0.15 ml/min/1.73 m2/year, Pinteraction= 0.008)5. However, a study of Exenatide LAR found no treatment heterogeneity for this same outcome by eGFR category122, and in a further analysis of LEADER, the renal composite endpoint was used with no interaction reported by baseline eGFR category127. The overall evidence does not support an effect of baseline eGFR on the relative renal benefit for GLP1-RA as an overall drug class. For baseline UACR, a pooled analysis of LEADER and SUSTAIN-65 and EXSCEL122 showed a greater benefit of GLP1-RA on eGFR reduction or eGFR slope with higher UACR; however, there was either no significant interaction5 or no formal interaction test was reported122. For ELIXA, Muskiet et al. 128 did not find a significant interaction of UACR category on eGFR decline. A further study found no association between UACR and GLP1-RA effect on reducing a composite renal outcome127. Two studies found that GLP1-RAs more effectively reduced UACR in those with higher UACR. In a pooled analysis of LEADER and SUSTAIN-6, those with normal albuminuria had a 20% (95%CI 15%, 25%) reduction in UACR compared to placebo; those with microalbuminuria had a 31% (95%CI 25–37%) reduction; those with macroalbuminuria had a 19% (95%CI 7–30%); Pinteraction= 0.0215. In ELIXA, least-squares mean percentage change in UACR was –1·69% (SE 5·10; 95% CI –11·69 to 8·30; p= 0·7398) in participants with normoalbumi- nuria, –21.10% (10.79; –42.25 to 0·04; p= 0.0502) in participants with microalbuminuria, and –39·18% (14·97; –68·53 to –9·84; p= 0·0070) in participants with macroalbuminuria in favour of lixisenatide; a formal test for interaction was not reported128. A third study found no treatment heterogeneity for this same outcome122. In summary, the included studies showed conflicting results for renal outcomes of GLP1-RA, though the majority were under- powered to detect heterogenous treatment effects. The most consistent finding was that a higher UACR is associated with greater GLP1-RA reduction in UACR relative to placebo, but this does not translate to benefits in eGFR-defined measures of renal function. There were no other biomarkers that robustly predicted benefit from GLP1-RA for the renal outcomes examined. SGLT2i and GLP1-RA: Evidence from observational studies. There were no observational studies for GLP1-RA and renal outcomes included, and no comparison studies between people treated with GLP1-RA and SGLT2i. Observational studies comparing SGLT2i to other glucose-lowering drugs confirmed the lack of treatment effect heterogeneity associated with age129,130, use of blood pressure/cardiovascular medications127, blood pressure (Koh 2021), history of cardiovascular disease129 and sex129, but one study in a Korean population found greater SGLT2i benefit on progression to end stage renal impairment with higher BMI (BMI < 25 kg/m2, HR 0.80 (95%CI 0.51, 1.25); BMI ≥ 25 kg/m2 HR 0.27 (0.16, 0.44), Pinteraction= 0.002) and with abdominal obesity compared to without129. This is not consistent with results from meta-analysis of RCTs. Summary of quality assessment To evaluate risk of bias, we used the JBI critical appraisal tool for cohort studies as the best flexible tool for the range of studies included. Due to our screening criteria, no manuscripts that passed full text screening were excluded due to risk of bias. The checklist results for the 11 points in the appraisal checklist are shown as a heatmap in Supplementary Figure 1 (SGLT2i) and 2 (GLP1-RA). Additionally, the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) framework was applied at the outcome level for each drug class to determine the quality of evidence and certainty of effects (Table 4)13. Overall certainty of evidence was rated as moderate for all outcomes except gly- caemia with GLP1-RA which was rated low certainty. This reflects that a larger proportion of the studies included for eva- luation of GLP1-RA glycaemia outcomes were observational (24/49). By contrast, for SGLT2i glycaemia outcomes there were 18 RCT/meta-analyses and 9 observational studies. For CVD and renal outcomes, observational studies were limited and the majority of evidence came from industry-funded CVOTs (RCT designs), including post-hoc analyses of individual trials as well as meta-analyses. Discussion This systematic review provides a comprehensive review of observational and RCT-based studies of people with type 2 dia- betes, specifically examining heterogenous treatment effects for SGLT2i and GLP1-RA therapies on glycaemic, cardiovascular, and renal outcomes. We assessed evidence for treatment effect modification for a wide range of demographic, clinical and bio- logical features, including pharmacogenetic markers. Each of the three clinical outcomes were evaluated separately for each drug class for a total of 6 sub-studies. Overall, our review identified limited evidence for treatment effect heterogeneity for glycaemia, cardiovascular, and renal outcomes for the two drug classes. We summarize the key findings below. For glycaemic response, there was high certainty that reduced renal function is associated with lower efficacy of SGLT2i. For GLP1-RA there was moderate certainty that markers of reduced insulin secretion, either directly measured (e.g. c-peptide or HOMA-B) or proxy measures, such as diabetes duration, were associated with reduced glycaemic response to GLP1-RA, although the majority of evidence was from observational studies. As with other glucose-lowering drug classes, a greater glycaemic response with both SGLT2i and GLP1-RA was seen at higher baseline HbA1c. We did not identify any studies examining whether the relative efficacy of SGLT2i compared to GLP1-RA is altered by baseline HbA1c levels. Of note, many of the included studies for HbA1c outcome were observational, meaning findings could potentially reflect biases from differential prescribing behaviour, or regression to the mean, although we did attempt to account for the latter by including adjustment for baseline HbA1c as one of our study inclusion criteria. For both CVD and heart failure outcomes, RCT meta-analyses do not support differences in the relative efficacy of either GLP1-RA or SGLT2i based on an individuals’ prior CVD status. However, this finding should be interpreted cautiously as all RCTs to-date have predominantly included participants with, or at high-risk of, CVD, thereby excluding the majority of the wider T2D population at lower risk. However, meta-analyses suggest (with moderate cer- tainty) that the relative effects of both drug classes may be greater in people of non-White ethnicity. In particular, those of Asian and African ethnicity (compared to Whites) have been shown to have a greater relative benefit for hospitalization for heart failure/CV death (but not MACE) with SGLT2i, and MACE for GLP1-RA. When evaluating renal outcomes, there was no consistent evidence of treatment heterogeneity for SGLT2i, but for GLP1- RA, there was greater reduction in proteinuria in those with higher baseline proteinuria. This limited evidence could reflect a true lack of heterogenous treatment effects, but it more likely reflects an absence of clinical ARTICLE COMMUNICATIONS MEDICINE | https://doi.org/10.1038/s43856-023-00359-w 10 COMMUNICATIONS MEDICINE | (2023) 3:131 | https://doi.org/10.1038/s43856-023-00359-w |www.nature.com/commsmed www.nature.com/commsmed studies that were well designed or sufficiently powered to robustly identify and characterise treatment effect heterogeneity. Although five of the six sub-studies we evaluated were evaluated at GRADE B, there were methodological concerns with many of the included studies. As individual RCTs are by design powered only for the main effect of treatment131, our primary focus when reporting were meta-analyses of post-hoc subgroup analyses of RCTs. However, we found the subgroup analyses in these studies pri- marily focused on stratification by baseline risk for the outcome in question e.g. baseline HbA1c on glycaemic response, CKD stage or albuminuria on renal outcomes, and CVD risk or established CVD for CVD outcomes. Other common sub- groups included those defined by BMI, age, sex or other routinely collected clinical characteristics, with very few studies evaluating non-routine biomarkers or pharmacogenetic markers (as highlighted in Tables 1–3). A major limitation was that studies predominantly focused on conventional approaches to subgroup analysis, with very few studies assessing continuous features (such as BMI) on a continuous scale which is required to maximize power to detect treatment effect heterogeneity131,132. It is also important to recognize that almost all the studies evaluating cardiovascular and renal endpoints included in our systematic review focused on the relative effect of a biomarker/ stratifier on the outcome, as most studies reported a hazard ratio compared with a placebo arm for the outcome of interest (e.g. MACE, incident renal disease). This does not recognize that baseline absolute risk of these endpoints is likely to differ sub- stantially across these strata; so although, for example, there was no difference in relative benefit of an SGLT2i by age, this means that on the absolute scale, benefit will increase with age (as underlying absolute risk increases), and it is this absolute benefit that should be considered when deciding on whether to initiate SGLT2i treatment. An important finding of our review is the lack of robust comparative effectiveness studies directly examining treatment effect heterogeneity for these two major drug classes, either head- to-head or compared with other major anti-hyperglycaemic therapies. Insight into effect modification for a single drug class is not sufficient to support the clinical translation of a precision medicine approach. The lack of direct comparisons between therapies obscures the interpretation of biomarkers with regards to whether they function as broad prognostic factors, which may be relevant to any (or at least multiple) drug class, or as markers of heterogenous treatment effects specific to a particular drug class. An evidence base that includes more high-quality studies on heterogeneity in the comparative effectiveness of SGLT2i, GLP1- RA, and other drug classes is needed to advance the field towards clinically useful precision diabetes medicine. For cardiovascular and renal outcomes, these studies need to incorporate both absolute outcome risk and relative estimates of treatment effects in order to usefully inform clinical decision-making. Only when this evidence is available can precision medicine support more individualised treatment decisions, allowing providers to select an optimal therapy from a set of multiple options informed by each medication’s risk/benefit profile specific to the characteristics of an individual patient. We identified the following additional, high-level evidence gaps in our review: (1) Limited head-to-head comparative effectiveness studies examining treatment effect heterogeneity; (2) A lack of robust studies integrating multiple clinical features and bio- markers. The majority of studies only tested single biomarkers one at a time in subgroup analysis; (3) Few studies focused on pharmacogenetics or non-routine biomarkers; (4) Few studies Table 4 Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) framework summary of findings. Drug class Outcome Overall certainty of evidence Elaboration on evidence certainty Evidence for specific biomarkers SGLT2i CVD Moderate Majority of evidence from post-hoc analysis of RCTs and RCT-based meta- analysis - History of prior cardiovascular disease probably does not alter relative benefit (no effect, moderate certainty) - Ethnicity probably does alter relative benefit, with a greater relative heart failure benefit in people of black and Asian ethnicity compared to those of white ethnicity (moderate effect, moderate certainty) - Other biomarkers may not be associated with treatment effect heterogeneity (no effect, low certainty) Renal Moderate Majority of evidence from post-hoc analysis of RCTs and RCT-based meta- analysis - Biomarkers may not be associated with treatment effect heterogeneity (no effect, low certainty) Glycaemia Moderate Majority of evidence from post-hoc analysis of RCTs and RCT-based meta- analysis - Lower renal function results in lesser glycaemic response (moderate effect, high certainty) - Other biomarkers may not be associated with treatment effect heterogeneity (no effect, low certainty) GLP1-RA CVD Moderate Majority of evidence from post-hoc analysis of RCTs and RCT-based meta- analysis - History of prior cardiovascular disease probably does not alter relative benefit (no effect, moderate certainty) - Ethnicity probably does alter relative benefit, with a greater relative CVD benefit in people of black and Asian ethnicity compared to those of white ethnicity (moderate effect, moderate certainty) - Other biomarkers may not be associated with treatment effect heterogeneity (no effect, low certainty) Renal Moderate Majority of evidence from post-hoc analysis of RCTs and RCT-based meta- analysis - Biomarkers may not be associated with treatment effect heterogeneity (no effect, low certainty) Glycaemia Low Majority of evidence from observational studies - Lower insulin secretion probably results in lesser glycaemic response (moderate effect, moderate certainty) - Other biomarkers may not be associated with treatment effect heterogeneity (no effect, low certainty) COMMUNICATIONS MEDICINE | https://doi.org/10.1038/s43856-023-00359-w ARTICLE COMMUNICATIONS MEDICINE | (2023) 3:131 | https://doi.org/10.1038/s43856-023-00359-w |www.nature.com/commsmed 11 www.nature.com/commsmed www.nature.com/commsmed conducted in low-middle income countries, required for an equitable global approach to precision type 2 diabetes medicine; (5) Few RCT meta-analyses based on individual-level participant data, precluding robust evaluation of between-trial heterogeneity and individual-level confounders; (6) An absence of confirmatory studies. We identified no prospective studies testing a priori hypotheses of potential treatment effect modifiers, or studies conducting independent validation of previously described het- erogenous treatment effects; (7) A lack of population-based data representing individuals treated in routine care. As cardiovascular and renal trials have focused on high-risk participants, the ben- efits of SGLT2i and GLP1-RA for primary prevention is a major unanswered question; (8) Few cardiovascular and renal outcome studies considering treatment effect modification on the absolute as well as relative risk scale; (9) A focus on short-term glycaemic outcomes, with limited studies investigating durability of gly- caemic response or time to glycaemic failure. Of note, several studies published since our data extraction was completed in February 2022 which fill some of the evidence gaps identified in our review, and highlight the clear potential for a precision medicine approach to T2D treatment: the TriMaster study—a precision medicine RCT of SGLT2i, DPP4i and thia- zolidinediones (TZD) that established that individuals with higher renal function (eGFR >90ml/min/1.73 m2) have a greater HbA1c response with SGLT2i vs DPP4i relative to those with eGFR 60–90 ml/min/1.73 m2 133, a result concordant with our finding that reduced renal function is associated with lower effi- cacy of SGLT2i; a similarly designed two-way crossover trial in New Zealand which identified a greater relative benefit of TZD therapy compared to DPP4i in people with obesity and/or hypertriglyceridemia;134 a study using large-scale observational data and post-hoc analysis of individual participant-level data from 14 RCTs that specifically investigated differential treatment effects with SGLT2i and DPP4i, and developed a treatment selection model to predict HbA1c response on the two therapies based on an individuals’ routine clinical characteristics;135 and a robust study across observational and multiple RCTs identifying pharmacogenetic markers of differential glycaemic response to GLP1-RA136. In addition, three large trials (AMPLITUDE-O investigating cardiovascular and renal outcomes in 4076 partici- pants with T2D for the GLP-RA efpeglenatide137, DELIVER investigating worsening heart failure or cardiovascular death in 3131 participants [45% with T2D] for the SGLT2i Dapagliflozin138, and EMPA-KIDNEY investigating progression of kidney disease or cardiovascular death in 6609 participants [44% with T2D]139) have recently been published. Although all three are primary RCTs examining average treatment effects rather than treatment effect heterogeneity, and thus would have been ineligible for our review, future meta-analysis studies inte- grating the results of these and other ongoing SGLT2i and GLP1- RA trials may add to the evidence we have presented. As our aim was to provide a comprehensive review of these treatments, we did not conduct quantitative analysis of specific biomarkers due to the range of different biomarkers, methodol- ogies, and outcomes evaluated in the included studies. However, this review provides guidance for where future targeted quanti- tative meta-analysis could be most insightful. In addition, dif- ferent methods for synthesising the current available evidence, such as conducting an umbrella review, may offer further insights into the current state-of-play of precision Type 2 diabetes treatment. This review highlights the need for several research priorities to advance our limited understanding of heterogenous treatment effects among individuals with type 2 diabetes. We outline prio- rities for research to advance the field towards a translational model of evidence-based, empirical precision diabetes medicine (Fig. 3), and highlight the recent Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement to guide this research132. In the future, with a greater understanding of het- erogenous treatment effects and enhanced capacity to predict individual treatment responses, precision treatment in type 2 diabetes may be able to integrate demographic, clinical, biological, or other patient-level features to match individuals to their optimal anti-hyperglycaemic regimen. Conclusions There is limited evidence of treatment effect heterogeneity with SGLT2i and GLP1-RA for glycaemic, cardiovascular, and renal outcomes in people with type 2 diabetes. This lack of evidence likely reflects the methodological limitations of the current evi- dence base. Robust future studies to fill the research gaps iden- tified in this review are required for precision medicine in type 2 diabetes to translate to clinical care. Data availability Template data collection forms and the data extracted from included studies are available upon request. All studies identified by our search protocol are detailed in Supplementary Tables 1–8. Received: 10 May 2023; Accepted: 15 September 2023; References 1. Tsapas, A. et al. Comparative effectiveness of glucose-lowering drugs for type 2 diabetes: a systematic review and network meta-analysis. Ann. Int. Med. 173, https://doi.org/10.7326/M20-0864 (2020). 2. McGuire, D. K. et al. Association of SGLT2 inhibitors with cardiovascular and kidney outcomes in patients with type 2 diabetes: a meta-analysis. JAMA Cardiol. 6, 148–158 (2021). 3. Sattar, N. et al. 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Acknowledgements The ADA/EASD Precision Diabetes Medicine Initiative, within which this work was conducted, has received the following support: The Covidence license was funded by Lund University (Sweden) for which technical support was provided by Maria Björklund and Krister Aronsson (Faculty of Medicine Library, Lund University, Sweden). Administrative support was provided by Lund University (Malmö, Sweden), University of Chicago (IL, USA), and the American Diabetes Association (Washington D.C., USA). The Novo Nordisk Foundation (Hellerup, Denmark) provided grant support for in- person writing group meetings (PI: L Phillipson, University of Chicago, IL, USA). This study was supported by the National Institute for Health and Care Research Exeter Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. K.G.Y. and J.M.D. are supported by Research England’s Expanding Excellence in England (E3) fund. A.R.K. is supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant KL2TR002490. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. S.R. is funded by a US Department of Veterans Affairs Award IK2-CX001907, and a Webb-Waring Biomedical Research Award from the Boettcher Foundation. J.M.D. is funded by the EFSD Rising Star Fellowship Programme, the UK Medical Research Council (MR/N00633X/1), and a BHF-Turing Cardiovascular Data Science Award (SP/ 19/6/34809). M.S. is funded by the National Institutes of Health, K01HL157658. The funders had no role in the study design, data extraction or interpretation, writing of the article, or the decision to submit for publication. ARTICLE COMMUNICATIONS MEDICINE | https://doi.org/10.1038/s43856-023-00359-w 16 COMMUNICATIONS MEDICINE | (2023) 3:131 | https://doi.org/10.1038/s43856-023-00359-w |www.nature.com/commsmed https://doi.org/10.1186/s13098-015-0104-6 https://doi.org/10.1016/j.ahj.2021.03.013 www.nature.com/commsmed Author contributions K.G.Y., E.H.M., R.J.M., A.R.K., S.J.P., S.R., M.A.S., D.K.T., A.P.M., A.Y.D., A.G.J., E.R.P., and J.M.D. designed the study. K.G.Y., E.H.M., R.J.M., A.R.K., S.J.P., S.R., M.A.S., A.P.M., A.Y.D., A.G.J., E.R.P., and J.M.D. implemented the systematic review and contributed to full-text data extraction. K.G.Y., E.H.M., R.J.M., A.R.K., S.J.P., S.R., M.A.S., A.Y.D., A.G.J., E.R.P., and J.M.D. synthesized the data and drafted the article. All authors cri- tically revised the article and approved the final article. E.R.P. and J.M.D. jointly supervised this work. E.R.P. and J.M.D. attest that all listed authors meet authorship criteria, and that no others meeting the criteria have been omitted. E.R.P. and J.M.D. were responsible for the decision to submit for publication. Competing interests The authors declare the following competing interests: A.P.M. declares previous research funding from Eli Lilly and Company, Pfizer, and AstraZeneca. A.G.J. has received research funding from the Novo Nordisk foundation. E.R.P. has received honoraria for speaking from Lilly, Novo Nordisk, and Illumina. All other authors declare no competing interest. Additional information Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s43856-023-00359-w. Correspondence and requests for materials should be addressed to Ewan R. Pearson or John M. Dennis. 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To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/. © The Author(s) 2023 1Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Exeter, UK. 2Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK. 3Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 4Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. 5Section of Academic Primary Care, US Department of Veterans Affairs Eastern Colorado Health Care System, Aurora, CO, USA. 6Department of Biomedical Informatics, School of Medicine, University of Colorado, Aurora, USA. 7Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 8Department of Medicine, Brigham andWomen’s Hospital, Harvard Medical School, Boston, MA, USA. 201These authors contributed equally: Katherine G. Young, Eram Haider McInnes, Robert J. Massey. *A list of authors and their affiliations appears at the end of the paper. ✉email: e.z.pearson@dundee.ac.uk; j.dennis@exeter.ac.uk ADA/EASD PDMI Deirdre K. Tobias7,9, Jordi Merino10,11,12, Abrar Ahmad13, Catherine Aiken14,15, Jamie L. Benham16,198, Dhanasekaran Bodhini17,198, Amy L. Clark18, Kevin Colclough19, Rosa Corcoy20,21,22, Sara J. Cromer11,23,24, Daisy Duan25, Jamie L. Felton26,27,28, Ellen C. Francis29, Pieter Gillard30, Véronique Gingras31,32, Romy Gaillard33, Eram Haider34, Alice Hughes19, Jennifer M. Ikle35,36, Laura M. Jacobsen37, Anna R. Kahkoska3, Jarno L. T. Kettunen38,39,40, Raymond J. Kreienkamp11,12,23,41, Lee-Ling Lim42,43,44, Jonna M. E. Männistö45,46, Robert Massey34, Niamh-Maire Mclennan47, Rachel G. Miller48, Mario Luca Morieri49,50, Jasper Most51, Rochelle N. Naylor52, Bige Ozkan53,54, Kashyap Amratlal Patel19, Scott J. Pilla55,56, Katsiaryna Prystupa57,58, Sridaran Raghaven59,60, Mary R. Rooney53,61, Martin Schön57,58,62, Zhila Semnani-Azad7, Magdalena Sevilla- Gonzalez23,24,63, Pernille Svalastoga64,65, Wubet Worku Takele66, Claudia Ha-ting Tam44,67,68, Anne Cathrine B. Thuesen10, Mustafa Tosur69,70,71, Amelia S. Wallace53,61, Caroline C. Wang61, Jessie J. Wong72, Jennifer M. Yamamoto73, Katherine Young19, Chloé Amouyal74,75, Mette K. Andersen10, Maxine P. Bonham76, Mingling Chen77, Feifei Cheng78, Tinashe Chikowore24,79,80,81, Sian C. Chivers82, Christoffer Clemmensen10, Dana Dabelea83, Adem Y. Dawed34, Aaron J. Deutsch12,23,24, Laura T. Dickens84, Linda A. DiMeglio26,27,28,85, Monika Dudenhöffer-Pfeifer13, Carmella Evans-Molina26,27,28,86, María Mercè Fernández-Balsells87,88, Hugo Fitipaldi13, Stephanie L. Fitzpatrick89, Stephen E. Gitelman90, Mark O. Goodarzi91,92, Jessica A. Grieger93,94, Marta Guasch-Ferré7,95, Nahal Habibi93,94, Torben Hansen10, Chuiguo Huang44,67, Arianna Harris-Kawano26,27,28, Heba M. Ismail26,27,28, Benjamin Hoag96,97, Randi K. Johnson98,99, Angus G. Jones19,100, Robert W. Koivula101, Aaron Leong11,24,102, Gloria K. W. Leung76, Ingrid M. Libman103, Kai Liu93, S. Alice Long104, William L. Lowe Jr.105, Robert W. Morton106,107,108, COMMUNICATIONS MEDICINE | https://doi.org/10.1038/s43856-023-00359-w ARTICLE COMMUNICATIONS MEDICINE | (2023) 3:131 | https://doi.org/10.1038/s43856-023-00359-w |www.nature.com/commsmed 17 https://doi.org/10.1038/s43856-023-00359-w http://www.nature.com/reprints http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ mailto:e.z.pearson@dundee.ac.uk mailto:j.dennis@exeter.ac.uk www.nature.com/commsmed www.nature.com/commsmed Ayesha A. Motala109, Suna Onengut-Gumuscu110, James S. Pankow111, Maleesa Pathirana93,94, Sofia Pazmino112, Dianna Perez26,27,28, John R. Petrie113, Camille E. 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Meigs24,102,180, Shivani Misra181,182, Viswanathan Mohan183, Rinki Murphy184,185,186, Richard Oram19,100, Katharine R. Owen101,187, Susan E. Ozanne188, Ewan R. Pearson34, Wei Perng83, Toni I. Pollin148,189, Rodica Pop-Busui190, Richard E. Pratley191, Leanne M. Redman192, Maria J. Redondo69,70, Rebecca M. Reynolds47, Robert K. Semple47,193, Jennifer L. Sherr194, Emily K. Sims26,27,28, Arianne Sweeting195,196, Tiinamaija Tuomi38,139,40, Miriam S. Udler11,12,23,24, Kimberly K. Vesco197, Tina Vilsbøll198,199, Robert Wagner57,58,200, Stephen S. Rich110 & Paul W. Franks7,13,101,108 9Division of Preventative Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA. 10Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. 11Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA. 12Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. 13Department of Clinical Sciences, Lund University Diabetes Centre, Lund University Malmö, Sweden. 14Department of Obstetrics and Gynaecology, the Rosie Hospital, Cambridge, UK. 15NIHR Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK. 16Departments of Medicine and Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. 17Department of Molecular Genetics, Madras Diabetes Research Foundation, Chennai, India. 18Division of Pediatric Endocrinology, Department of Pediatrics, Saint Louis University School of Medicine, SSM Health Cardinal Glennon Children’s Hospital, St. Louis, MO, USA. 19Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK. 20CIBER-BBN, ISCIII, Madrid, Spain. 21Institut d’Investigació Biomèdica Sant Pau (IIB SANT PAU), Barcelona, Spain. 22Departament de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain. 23Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA. 24Department of Medicine, Harvard Medical School, Boston, MA, USA. 25Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA. 26Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA. 27Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA. 28Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA. 29Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ, USA. 30University Hospital Leuven, Leuven, Belgium. 31Department of Nutrition, Université de Montréal, Montreal, Quebec, Canada. 32Research Center, Sainte-Justine University Hospital Center, Montreal, Quebec, Canada. 33Department of Pediatrics, Erasmus Medical Center, Rotterdam, The Netherlands. 34Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK. 35Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA. 36Stanford Diabetes Research Center, Stanford School of Medicine, Stanford University, Stanford, CA, USA. 37University of Florida, Gainesville, FL, USA. 38Helsinki University Hospital, Abdominal Centre/Endocrinology, Helsinki, Finland. 39Folkhalsan Research Center, Helsinki, Finland. 40Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland. 41Department of Pediatrics, Division of Endocrinology, Boston Children’s Hospital, Boston, MA, USA. 42Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia. 43Asia Diabetes Foundation, Hong Kong SAR, China. 44Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China. 45Departments of Pediatrics and Clinical Genetics, Kuopio University Hospital, Kuopio, Finland. 46Department of Medicine, University of Eastern Finland, Kuopio, Finland. 47Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK. 48Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA. 49Metabolic Disease Unit, University Hospital of Padova, Padova, Italy. 50Department of Medicine, University of Padova, Padova, Italy. 51Department of Orthopedics, Zuyderland Medical Center, Sittard-Geleen, The Netherlands. 52Departments of Pediatrics and Medicine, University of Chicago, Chicago, IL, USA. 53Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public ARTICLE COMMUNICATIONS MEDICINE | https://doi.org/10.1038/s43856-023-00359-w 18 COMMUNICATIONS MEDICINE | (2023) 3:131 | https://doi.org/10.1038/s43856-023-00359-w |www.nature.com/commsmed www.nature.com/commsmed Health, Baltimore, MD, USA. 54Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins School of Medicine, Baltimore, MD, USA. 55Department of Medicine, Johns Hopkins University, Baltimore, MD, USA. 56Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA. 57Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany. 58German Center for Diabetes Research (DZD), Neuherberg, Germany. 59Section of Academic Primary Care, US Department of Veterans Affairs Eastern Colorado Health Care System, Aurora, CO, USA. 60Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA. 61Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 62Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia. 63Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA. 64Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway. 65Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway. 66Eastern Health Clinical School, Monash University, Melbourne, VIC, Australia. 67Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China. 68Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China. 69Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA. 70Division of Pediatric Diabetes and Endocrinology, Texas Children’s Hospital, Houston, TX, USA. 71Children’s Nutrition Research Center, USDA/ARS, Houston, TX, USA. 72Stanford University School of Medicine, Stanford, CA, USA. 73Internal Medicine, University of Manitoba, Winnipeg, MB, Canada. 74Department of Diabetology, APHP, Paris, France. 75Sorbonne Université, INSERM, NutriOmic team, Paris, France. 76Department of Nutrition, Dietetics and Food, Monash University, Melbourne, VIC, Australia. 77Monash Centre for Health Research and Implementation, Monash University, Clayton, VIC, Australia. 78Health Management Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China. 79MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. 80Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA. 81Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. 82Department of Women and Children’s health, King’s College London, London, UK. 83Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. 84Section of Adult and Pediatric Endocrinology, Diabetes and Metabolism, Kovler Diabetes Center, University of Chicago, Chicago, USA. 85Department of Pediatrics, Riley Hospital for Children, Indiana University School of Medicine, Indianapolis, IN, USA. 86Richard L. Roudebush VAMC, Indianapolis, IN, USA. 87Biomedical Research Institute Girona, IdIBGi, Girona, Spain. 88Diabetes, Endocrinology and Nutrition Unit Girona, University Hospital Dr Josep Trueta, Girona, Spain. 89Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA. 90University of California at San Francisco, Department of Pediatrics, Diabetes Center, San Francisco, CA, USA. 91Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA. 92Department of Medicine, Cedars- Sinai Medical Center, Los Angeles, CA, USA. 93Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia. 94Robinson Research Institute, The University of Adelaide, Adelaide, Australia. 95Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 1014 Copenhagen, Denmark. 96Division of Endocrinology and Diabetes, Department of Pediatrics, Sanford Children’s Hospital, Sioux Falls, SD, USA. 97University of South Dakota School of Medicine, Vermillion, SD, USA. 98Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. 99Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA. 100Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK. 101Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK. 102Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA. 103UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA. 104Center for Translational Immunology, Benaroya Research Institute, Seattle, WA, USA. 105Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 106Department of Pathology & Molecular Medicine, McMaster University, Hamilton, Canada. 107Population Health Research Institute, Hamilton, Canada. 108Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark. 109Department of Diabetes and Endocrinology, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa. 110Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA. 111Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA. 112Department of Chronic Diseases and Metabolism, Clinical and Experimental Endocrinology, KU Leuven, Leuven, Belgium. 113School of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK. 114Department of Obstetrics, Gynecology, and Reproductive Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. 115Sanford Children’s Specialty Clinic, Sioux Falls, SD, USA. 116Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA. 117Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 118Centre for Physical Activity Research, Rigshospitalet, Copenhagen, Denmark. 119Institute for Sports and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark. 120Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Indiana University School of Medicine, Indianapolis, IN, USA. 121AMAN Hospital, Doha, Qatar. 122Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 123Institute of Molecular and Genomic Medicine, National Health Research Institutes, Taipei City, Taiwan. 124Divsion of Endocrinology and Metabolism, Taichung Veterans General Hospital, Taichung, Taiwan. 125Division of Endocrinology and Metabolism, Taipei Veterans General Hospital, Taipei, Taiwan. 126Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA, USA. 127Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. 128University Hospital of Tübingen, Tübingen, Germany. 129Institute of Diabetes Research and Metabolic Diseases (IDM), Helmholtz Center Munich, Neuherberg, Germany. 130Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark. 131University of Newcastle, Newcastle upon Tyne, UK. 132Sections on Genetics and Epidemiology, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA. 133Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, Groningen, The Netherlands. 134Gastroenterology, Baylor College of Medicine, Houston, TX, USA. 135Department of Endocrinology, University Hospitals Leuven, Leuven, Belgium. 136Sorbonne University, Inserm U938, Saint-Antoine Research Centre, Institute of Cardiometabolism and Nutrition, Paris, France. 137Department of Endocrinology, Diabetology and Reproductive Endocrinology, Assistance Publique-Hôpitaux de Paris, Saint-Antoine University Hospital, National Reference Center for Rare Diseases of Insulin Secretion and Insulin Sensitivity (PRISIS), Paris, France. 138Royal Melbourne Hospital Department of Diabetes and Endocrinology, Parkville, VIC, Australia. 139Walter and Eliza Hall Institute, Parkville, VIC, Australia. 140University of Melbourne Department of Medicine, Parkville, VIC, Australia. 141Deakin University, Melbourne, Australia. 142Department of Epidemiology, Madras Diabetes Research Foundation, Chennai, India. 143Department of Diabetes and Endocrinology, Guy’s and St Thomas’ Hospitals NHS Foundation Trust, London, UK. 144School of Agriculture, Food and Wine, University of Adelaide, Adelaide, Australia. 145Institut Cochin, Paris, France. 146Pediatric endocrinology and diabetes, Hopital Necker Enfants Malades, APHP Centre, université de Paris, Paris, France. 147Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway. 148Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA. 149Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA. 150Nephrology, Dialysis and Renal Transplant Unit, IRCCS—Azienda Ospedaliero-Universitaria di COMMUNICATIONS MEDICINE | https://doi.org/10.1038/s43856-023-00359-w ARTICLE COMMUNICATIONS MEDICINE | (2023) 3:131 | https://doi.org/10.1038/s43856-023-00359-w |www.nature.com/commsmed 19 www.nature.com/commsmed www.nature.com/commsmed Bologna, Alma Mater Studiorum University of Bologna, Bologna, Italy. 151Department of Medical Genetics, AP-HP Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France. 152Global Center for Asian Women’s Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. 153Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. 154Kaiser Permanente Northern California Division of Research, Oakland, CA, USA. 155Department of Epidemiology and Biostatistics, University of California San Francisco, California, USA. 156National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA. 157Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada. 158Ann & Robert H. Lurie Children’s Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 159Department of Clinical and Organizational Development, Chicago, IL, USA. 160American Diabetes Association, Arlington, Virginia, USA. 161College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia. 162Global Health Institute, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium. 163Department of Medicine and Kovler Diabetes Center, University of Chicago, Chicago, IL, USA. 164School of Nursing, Faculty of Health Sciences, McMaster University, Hamilton, Canada. 165Division of Endocrinology, Metabolism, Diabetes, University of Colorado, Boulder, CO, USA. 166Department of Clinical Medicine, School of Medicine, Trinity College Dublin, Dublin, Ireland. 167Department of Endocrinology, Wexford General Hospital, Wexford, Ireland. 168Division of Endocrinology, NorthShore University HealthSystem, Skokie, IL, USA. 169Department of Medicine, Prtizker School of Medicine, University of Chicago, Chicago, IL, USA. 170Department of Genetics, Stanford School of Medicine, Stanford University, Stanford, CA, USA. 171Faculty of Health, Aarhus University, Aarhus, Denmark. 172Departments of Pediatrics and Medicine and Kovler Diabetes Center, University of Chicago, Chicago, USA. 173Sanford Research, Sioux Falls, SD, USA. 174University of Washington School of Medicine, Seattle, WA, USA. 175Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA. 176Department of Medicine, Universite de Sherbrooke, Sherbrooke, QC, Canada. 177Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea. 178Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA. 179Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 180Broad Institute, Cambridge, MA, USA. 181Division of Metabolism, Digestion and Reproduction, Imperial College London, London, UK. 182Department of Diabetes & Endocrinology, Imperial College Healthcare NHS Trust, London, UK. 183Department of Diabetology, Madras Diabetes Research Foundation & Dr. Mohan’s Diabetes Specialities Centre, Chennai, India. 184Department of Medicine, Faculty of Medicine and Health Sciences, University of Auckland, Auckland, New Zealand. 185Auckland Diabetes Centre, Te Whatu Ora Health New Zealand, Auckland, New Zealand. 186Medical Bariatric Service, Te Whatu Ora Counties, Health New Zealand, Auckland, New Zealand. 187Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK. 188University of Cambridge, Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, Cambridge, UK. 189Department of Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA. 190Department of Internal Medicine, Division of Metabolism, Endocrinology and Diabetes, University of Michigan, Ann Arbor, MI, USA. 191AdventHealth Translational Research Institute, Orlando, FL, USA. 192Pennington Biomedical Research Center, Baton Rouge, LA, USA. 193MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK. 194Yale School of Medicine, New Haven, CT, USA. 195Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia. 196Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, NSW, Australia. 197Kaiser Permanente Northwest, Kaiser Permanente Center for Health Research, Portland, OR, USA. 198Clinial Research, Steno Diabetes Center Copenhagen, Herlev, Denmark. 199Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. 200Department of Endocrinology and Diabetology, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. ARTICLE COMMUNICATIONS MEDICINE | https://doi.org/10.1038/s43856-023-00359-w 20 COMMUNICATIONS MEDICINE | (2023) 3:131 | https://doi.org/10.1038/s43856-023-00359-w |www.nature.com/commsmed www.nature.com/commsmed Treatment effect heterogeneity following type 2 diabetes treatment with GLP1-receptor agonists and SGLT2-inhibitors: a systematic review Methods Search strategy Inclusion criteria Data extraction and quality assessment Outcomes Reporting summary Results Literature search and screening results Description of included studies SGLT2i, GLP1-RA, and glycaemic outcomes SGLT2i GLP1-RA SGLT2i, GLP1-RA and cardiovascular outcomes SGLT2i: Evidence from clinical trials GLP1-RA: Evidence from clinical trials SGLT2i and GLP1-RA: Evidence from observational studies SGLT2i, GLP1-RA, and renal outcomes SGLT2i: Evidence from clinical trials GLP1-RA: Evidence from clinical trials SGLT2i and GLP1-RA: Evidence from observational studies Summary of quality assessment Discussion Conclusions Data availability References References Acknowledgements Author contributions Competing interests Additional information