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      string(188) "NCT01989754 - A Randomized, Multicenter, Double-Blind, Parallel, Placebo-Controlled Study of the Effects of Canagliflozin on Renal Endpoints in Adult Subjects With Type 2 Diabetes Mellitus"
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      string(133) "NCT04252287 - A Study on Impact of Canagliflozin on Health Status, Quality of Life, and Functional Status in Heart Failure (CHIEF-HF)"
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  ["project_title"]=>
  string(140) "Agreement and Randomized Responsiveness of Candidate Hepatic Measures for Cardiovascular-Kidney-Metabolic Staging: Four Canagliflozin Trials"
  ["project_narrative_summary"]=>
  string(857) "Doctors increasingly treat heart disease, kidney disease, diabetes and obesity as one condition, called cardiovascular-kidney-metabolic (CKM) syndrome, and experts have proposed adding the liver to it. But first the liver has to be measured. Several scores from ordinary blood tests estimate liver fat and scarring, yet may not point to the same patients.

Three completed trials of canagliflozin, a diabetes medicine, repeated liver blood tests over several years in more than 14,000 people. We will ask whether these scores agree, whether they move together when people receive an effective treatment, and whether they predict heart and kidney events. A fourth trial of the same drug in heart failure was run entirely remotely and drew no blood. Comparing all four shows how the information needed to judge the liver is vanishing from modern trials."
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  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(4) "Peng"
    ["last_name"]=>
    string(2) "Li"
    ["degree"]=>
    string(2) "MD"
    ["primary_affiliation"]=>
    string(16) "Beijing Hospital"
    ["email"]=>
    string(20) "lipeng7706@bjhmoh.cn"
    ["state_or_province"]=>
    string(7) "Beijing"
    ["country"]=>
    string(5) "China"
  }
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    ["label"]=>
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  ["property_scientific_abstract"]=>
  string(1631) "Background: Extension of the cardiovascular-kidney-metabolic (CKM) construct to the liver (CKLM) is proposed, but which measure should occupy the hepatic axis is undefined; candidate blood-based indices use different inputs and may not be interchangeable.

Objective: To test whether candidate hepatic-axis measures identify the same people, respond concordantly to randomization and carry the same prognostic weight, and to inventory what four trials of one drug recorded.

Study Design: Pooled participant-level secondary analysis of four randomized placebo-controlled canagliflozin trials, covering agreement, randomized responsiveness, prognosis and measurement availability.

Participants: All randomized participants of CANVAS, CANVAS-R and CREDENCE with a computable baseline index; CHIEF-HF, which collected no laboratory data, enters the inventory only.

Primary and Secondary Outcome Measure(s): Primary, joint classification of each participant by every index into rule-in, indeterminate and rule-out categories at baseline, and standardized change in each index at weeks 52 and 156. Secondary, each trial's adjudicated cardiovascular, heart failure, kidney and mortality outcomes, and per-trial availability of each axis component.

Statistical Analysis: Cross-classification with disjoint-region decomposition and prevalence-adjusted agreement; correlation of each index with its inputs; mixed models for repeated measures testing heterogeneity of standardized treatment effects across indices; Cox and Fine-Gray models stratified by trial with change in c-statistic and reclassification."
  ["project_brief_bg"]=>
  string(3235) "The American Heart Association's cardiovascular-kidney-metabolic (CKM) syndrome construct has been rapidly adopted into risk assessment and a large literature. A 2026 roadmap in the Journal of the American College of Cardiology proposes extending it to the liver, arguing liver endpoints belong in cardiovascular trials as kidney endpoints do.

An organ cannot be an axis of a staging system before it is a measurement. Outside specialist hepatology there is no imaging or elastography, so a hepatic axis must be built from routine blood tests. Several exist, use different inputs, and are not obviously interchangeable: steatosis indices are driven by adiposity and the aminotransferase ratio, fibrosis indices by age and platelet count. In our earlier work in a community cohort and in cardiovascular trials, the fibrosis-4 index correlated near zero with alanine aminotransferase, the only liver-specific enzyme in its own formula, while correlating -0.69 to -0.77 with platelet count, and steatosis and fibrosis indices flagged largely non-overlapping people; being observational, they could not test responsiveness.

CANVAS, CANVAS-R and CREDENCE are the only large resource we know of that can. They combine a complete hepatic panel with the platelet count, repeated over several years; randomized allocation to an agent shown here to lower aminotransferases and gamma-glutamyl transferase and reduce weight; adjudicated cardiovascular and kidney endpoints; and two contrasting CKM populations, one at high cardiovascular risk, one with albuminuric kidney disease.

CHIEF-HF is requested for a complementary reason. It tests the same drug under the same sponsor but ran entirely remotely: no in-person visits, no case report forms, no biomarker collection. It marks one end of a measurement gradient across one programme, 2009 to 2020, and is the design the field is urged to adopt. A framework asking trials to report liver endpoints must be judged against the trials that will be run.

Published secondary analyses confirm these variables exist and report treatment effects on liver biochemistry and fibrosis scores, reading index change as a treatment effect. Our question is prior, and is a measurement rather than an efficacy question: do these indices measure the same thing? Published marginals suggest not: the proportion classified with advanced fibrosis at baseline differs by more than an order of magnitude across indices in the same participants, and one widely used index appears unmoved by an intervention that improves its clinical correlates. No published analysis has cross-classified individuals, quantified agreement, or tested concordance of response.

The significance is operational. If the hepatic axis is not identified, a CKLM stage is not well defined, hepatic-involvement estimates in CKM populations are not comparable, and a trial enriching on one index enrols a different population from one using another. Randomization gives a test of convergent validity no observational dataset can. The deliverable is a recommendation on which measure, if any, is fit to be the hepatic axis, and a quantification of how far a CKLM stage depends on that choice and on trial design."
  ["project_specific_aims"]=>
  string(1553) "Aim 1. Quantify agreement among candidate hepatic-axis measures at baseline. Hypothesis: the proportion classified with advanced fibrosis differs several-fold across indices, individual-level agreement is low once prevalence is accounted for, and each index correlates more strongly with one of its own inputs than with the other indices.

Aim 2. Test randomized responsiveness. Hypothesis: standardized change from baseline differs across indices for the same randomized comparison, and indices in which alanine aminotransferase enters the numerator move opposite to those in which it enters the denominator.

Aim 3. Determine the endpoint-specific incremental prognostic value of each hepatic measure beyond baseline CKM stage and conventional risk factors, for cardiovascular and for kidney endpoints separately. Hypothesis: any increment is small, endpoint-specific, and not delivered by the index that is treatment-responsive.

Aim 4. Quantify how many participants are reclassified when a hepatic axis is added to CKM staging, how stable the resulting stage is, and what fraction of observed change is attributable to within-person measurement variability estimated in the placebo arm.

Aim 5. Inventory, from the delivered datasets themselves, which CKM and hepatic axis components are constructible in each of the four trials. Hypothesis: availability declines across the programme and is minimal in the fully decentralized trial, so that the feasibility of CKLM staging is set by trial design rather than by the disease."
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      ["label"]=>
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      ["label"]=>
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      ["value"]=>
      string(76) "confirm_or_validate previously_conducted_research_on_treatment_effectiveness"
      ["label"]=>
      string(76) "Confirm or validate previously conducted research on treatment effectiveness"
    }
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      ["value"]=>
      string(69) "confirm_or_validate previously_conducted_research_on_treatment_safety"
      ["label"]=>
      string(69) "Confirm or validate previously conducted research on treatment safety"
    }
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  string(1878) "(NCT02065791) and CHIEF-HF (NCT04252287), participant-level data.

Inclusion: all randomized participants of the four trials. Analysis populations are defined per aim. For Aims 1 to 4 the trials are CANVAS, CANVAS-R and CREDENCE: the agreement population comprises participants with at least two hepatic indices computable at baseline; the responsiveness population, those with a baseline and at least one post-baseline value of the index concerned; the prognostic population, all randomized participants with a computable baseline index and any follow-up.

CHIEF-HF contributes to Aim 5 only. It was conducted without in-person visits and without case report forms, and no laboratory or biomarker data were collected, so no hepatic index and no complete CKM stage can be constructed in it. It is requested because a claim about what a trial did not measure must be verified against the released datasets rather than inferred from publications, and because it defines the endpoint of the measurement gradient the inventory describes. We state this explicitly rather than implying that CHIEF-HF contributes participants to the analytic aims.

Exclusions: none applied at the participant level. Participants with elevated aminotransferases, recorded hepatobiliary history or hepatotoxic concomitant medication are retained and handled as prespecified subgroups, because excluding them would remove precisely those in whom the indices are meant to discriminate. Participants missing an index input contribute to the indices that remain computable, and the missingness pattern is itself reported.

No participant-level data from outside the YODA Project will be pooled, and all individual participant data analysis will be conducted within the YODA secure platform. Published aggregate estimates will be used only for contextual comparison in the discussion."
  ["project_main_outcome_measure"]=>
  string(1880) "Measurement outcomes (Aims 1, 2, 4 and 5). Primary: joint classification of each participant by every computable hepatic index at baseline into rule-in, indeterminate and rule-out categories at published thresholds (fibrosis-4 1.30 and 2.67, and 2.0 at age 65 years or older; NAFLD fibrosis score -1.455 and 0.675; AST-to-platelet ratio index 0.5 and 1.5; fibrotic NASH index 0.10 and 0.33; hepatic steatosis index 30 and 36). Secondary: change from baseline in each index in native units and in baseline standard-deviation units at weeks 13, 26, 52 and 156, and the proportion crossing a threshold in either direction; and, for Aim 5, availability of every CKM and hepatic axis component in each trial, expressed as the number and proportion of participants with a non-missing value at each scheduled visit.

Clinical outcomes (Aim 3), taken as adjudicated by each trial's central endpoint committee and analysed as time to first event: (i) major adverse cardiovascular events, defined as cardiovascular death, non-fatal myocardial infarction or non-fatal stroke; (ii) hospitalization for heart failure; (iii) the kidney composite as defined in each trial, with end-stage kidney disease, doubling of serum creatinine and renal death as the common core; (iv) cardiovascular death; (v) all-cause death. CHIEF-HF is not included in the clinical outcome analyses: it ran for 12 weeks, was not powered for events, and captured serious adverse events through claims data rather than adjudication.

Because the kidney composite definitions are not identical across trials, each clinical outcome is analysed within trial and then pooled with trial as a stratification factor; the harmonized definition applied and any component that could not be harmonized will be tabulated explicitly. Any change to an outcome definition made after data inspection will be reported as such."
  ["project_main_predictor_indep"]=>
  string(2032) "The main independent variables are the candidate hepatic-axis measures, each computed at every visit at which its inputs are available, using the published formula and without recalibration.

Steatosis axis: hepatic steatosis index, calculated as 8 times the ratio of alanine to aspartate aminotransferase plus body mass index, with 2 added for female sex and 2 for diabetes; and, if waist circumference is present in the delivered data, the fatty liver index, which additionally requires triglycerides and gamma-glutamyl transferase.

Fibrosis axis: fibrosis-4 index, calculated as age times aspartate aminotransferase divided by the product of platelet count and the square root of alanine aminotransferase; the NAFLD fibrosis score, which additionally uses albumin and glycaemic status; the AST-to-platelet ratio index; and the fibrotic NASH index, which uses aspartate aminotransferase, high-density lipoprotein cholesterol and glycated haemoglobin.

Each measure is analysed in three parameterizations: continuous per baseline standard deviation, ordinal in the three published categories, and binary as a rule-in flag. All parameterizations are reported for every measure, so that no threshold is selected after inspecting results.

For Aim 2 the independent variable is randomized allocation (canagliflozin versus placebo, pooling the 100 mg and 300 mg arms of CANVAS, with dose examined separately in sensitivity analysis) and the hepatic index is the dependent variable. For Aim 5 the independent variable is the trial itself.

CKM stage is constructed at baseline following the American Heart Association advisory. In CANVAS, CANVAS-R and CREDENCE every participant has type 2 diabetes and is therefore at stage 2 or above; stage 3 versus stage 4 is assigned from estimated glomerular filtration rate, urinary albumin-to-creatinine ratio and documented clinical cardiovascular disease or heart failure. The stage distribution within and across trials is reported as a result in its own right."
  ["project_other_variables_interest"]=>
  string(1977) "Characterization and adjustment variables, at baseline unless stated: age, sex, race or ethnicity and region as released; body mass index, weight and waist circumference if present; smoking; alcohol intake if collected; duration of diabetes; glycated haemoglobin; blood pressure; total, low-density and high-density lipoprotein cholesterol and triglycerides; estimated glomerular filtration rate; urinary albumin-to-creatinine ratio; haemoglobin and haematocrit; the full hepatic panel (alanine and aspartate aminotransferase, gamma-glutamyl transferase, alkaline phosphatase, total bilirubin, albumin) and platelet count; history of coronary, cerebrovascular and peripheral arterial disease, heart failure and diabetic microvascular complications; recorded hepatobiliary history; background therapy including metformin, insulin, renin-angiotensin system blockade, statins and diuretics; hepatotoxic concomitant medication; and randomized dose, treatment duration and permanent discontinuation. Repeated measurements of the hepatic panel, platelet count, weight, glycated haemoglobin, blood pressure, renal function and albuminuria are used in the longitudinal components. In CHIEF-HF only the variables that exist are inventoried, including ejection fraction category, diabetes status and the Kansas City Cardiomyopathy Questionnaire.

Two features of the anonymization applied to these datasets are accommodated by design. Site identifiers are suppressed, so no site-level clustering will be attempted and the trial is the stratification unit. Participant dates are shifted by a per-participant offset, which preserves within-participant intervals; time-to-event and repeated-measures analyses are therefore unaffected, and no calendar-time analysis is planned. If age is released only in bands, indices requiring age are computed at band midpoints and the resulting non-differential misclassification is quantified in sensitivity analysis and declared as a limitation."
  ["project_stat_analysis_plan"]=>
  string(5056) "General. Analyses use pooled participant-level data with trial as a stratification factor; trial-specific estimates are given beside pooled ones. Tests are two-sided at 0.05, and unadjusted and adjusted estimates are given for every model.

Aim 1, agreement. Each participant is cross-classified by all computable indices. We report the contingency of rule-in status and a decomposition into disjoint regions, making explicit the number flagged by one index alone, by each pair, and by all. Because kappa is bounded when marginal prevalences differ, agreement is summarized with Cohen and Fleiss kappa plus prevalence-adjusted bias-adjusted kappa, positive and negative agreement, and each index's prevalence. Each index is then correlated with its own inputs (age, platelets, aminotransferases, body mass index, albumin, glycated haemoglobin); the pre-specified comparison is whether an index correlates more strongly with one input than with the other indices. Analyses are repeated within trial, CKM stage and age band.

Aim 2, randomized responsiveness. Change from baseline in each index is modelled with a mixed model for repeated measures with fixed effects for treatment, visit, treatment-by-visit, baseline value and trial, an unstructured covariance and restricted maximum likelihood; between-group differences are given at weeks 52 and 156. Each index is standardized to its baseline standard deviation, and the pre-specified primary test of Aim 2 is a single test of heterogeneity of the standardized treatment effect across indices, fitted over the stacked standardized outcomes with an index-by-treatment interaction and a participant random effect. Change in each index is then decomposed into the contributions of its inputs by the delta method, making the arithmetic reason for any discordant response explicit.

Aim 3, prognostic value. Cox models stratified by trial estimate the association of each hepatic measure with each clinical outcome, adjusted for age, sex, race, body mass index, glycated haemoglobin, systolic blood pressure, LDL cholesterol, estimated glomerular filtration rate, albuminuria, smoking, prior cardiovascular disease and randomized treatment. Non-fatal endpoints are also analysed with Fine-Gray models treating death as a competing risk, and proportional hazards are checked with scaled Schoenfeld residuals. Increment beyond CKM stage and the covariates is assessed by change in Harrell's c-statistic with bootstrap intervals, three-year time-dependent area under the curve, category-free net reclassification improvement and calibration; a change of 0.005 or less is pre-specified as not clinically meaningful irrespective of the p-value. Continuous associations use restricted cubic splines before any threshold is applied. Treatment-by-measure interactions are reported with p-values and subgroup hazard ratios, designated exploratory.

Aim 4, staging and measurement error. Participants are cross-classified by CKM stage and hepatic rule-in status under each index, and the number reclassified into a putative higher CKLM stage is tabulated, estimating how far staging depends on the instrument. Transitions from baseline to weeks 52 and 156 are tabulated by arm. Variance components from serial placebo-arm measurements yield the within-participant standard deviation, intraclass correlation and smallest detectable change for each index; the proportion observed to change category is compared with that expected from measurement variability alone.

Aim 5, measurement inventory. For each trial we enumerate every variable name and label in every delivered dataset without prefix assumptions, grouping long-format laboratory files by test code, and classify each as an input to a CKM or hepatic axis component. Every judgement of absence rests on full enumeration rather than a targeted search, since one missed variable falsifies a claim about what a trial did not measure. Coverage is reported per component per visit, with denominators stated.

Missing data. Missingness by variable, visit and arm is tabulated first. The mixed models are valid under missing-at-random; multiple imputation by chained equations and a tipping-point analysis are sensitivity analyses, with complete-case results shown alongside.

Multiplicity and reporting. Aims 1 and 5 are descriptive; Aim 2 has one pre-specified primary contrast; Aim 3 outcomes are ordered hierarchically with cardiovascular events first; all else is exploratory. Every index and threshold examined will appear in the published tables regardless of direction or significance, and the number of models fitted stated. The analysis plan is finalized and dated before any outcome model is fitted.

Software. R and RStudio within the YODA secure platform, with Stata for cross-checking. Index code is written from the published formula, unit-checked against the laboratory units in the delivered datasets, and verified by reproducing a published summary statistic from these trials before any new analysis."
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  ["project_timeline"]=>
  string(1137) "Month 0: data access granted and platform onboarding, as soon as approval and the Data Use Agreement permit.

Months 0 to 2: data familiarization, variable derivation, unit checking, construction of all hepatic indices and CKM stage, and reproduction of a previously published summary statistic from these trials as a quality control step. The Aim 5 enumeration is run in this window, since it also serves as the feasibility check for the other aims. Analysis plan finalized and dated at the end of month 2.

Months 2 to 4: Aim 1, agreement analyses complete.

Months 4 to 6: Aim 2, randomized responsiveness analyses complete.

Months 6 to 8: Aim 3, prognostic analyses complete.

Months 8 to 9: Aims 4 and 5 complete; analysis frozen.

Month 10: full manuscript drafted and internally reviewed.

Month 11: manuscript submitted for peer review.

Month 12: results reported back to the YODA Project, within the 12-month access period. An extension will be requested in advance if the analyses or peer review require it, and any publication will be reported to the YODA Project as required."
  ["project_dissemination_plan"]=>
  string(1450) "The anticipated product is one primary manuscript reporting all five aims, with a supplementary appendix containing every index, threshold and model examined, and the analysis code deposited in a public repository at the time of publication.

Target audiences are clinical trialists designing multi-organ cardiovascular, kidney and metabolic trials; guideline and advisory writing groups working on cardiovascular-kidney-metabolic staging; and hepatologists, cardiologists, nephrologists and diabetologists who apply non-invasive liver indices to metabolic populations.

Journals considered suitable, in order of intended submission: Journal of Hepatology; Clinical Gastroenterology and Hepatology; Diabetes Care; Circulation: Cardiovascular Quality and Outcomes; JACC: Advances; Diabetes, Obesity and Metabolism; and Journal of Clinical Epidemiology. Abstracts will be submitted to the EASL Congress, the American Heart Association Scientific Sessions and the American Diabetes Association Scientific Sessions.

The work will acknowledge Yale University and Johnson & Johnson as the source of the data, will cite the YODA Project and the data request identification number, and will state that the analyses and conclusions are those of the authors. The research is non-commercial, will not be used in pursuit of litigation, and no press release will be issued other than under embargo in accordance with the Data Use Agreement."
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Ndumele CE, Rangaswami J, Chow SL, et al. Cardiovascular-kidney-metabolic health: a presidential advisory from the American Heart Association. Circulation. 2023;148(20):1606-1635. doi:10.1161/CIR.0000000000001184
Ndumele CE, Neeland IJ, Tuttle KR, et al. A synopsis of the evidence for the science and clinical management of cardiovascular-kidney-metabolic (CKM) syndrome: a scientific statement from the American Heart Association. Circulation. 2023;148(20):1636-1664. doi:10.1161/CIR.0000000000001186
Zannad F, Khan MS, Bansal N, et al. MASLD, MASH, and the cardiovascular-kidney-metabolic spectrum: a roadmap for multiorgan clinical trial design. J Am Coll Cardiol. 2026;87(15):2006-2033. doi:10.1016/j.jacc.2025.12.015
Khan SS, Matsushita K, Sang Y, et al. Development and validation of the American Heart Association’s PREVENT equations. Circulation. 2023;148(24):1982-2004. doi:10.1161/CIRCULATIONAHA.123.067626
Neal B, Perkovic V, Mahaffey KW, et al. Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med. 2017;377(7):644-657. doi:10.1056/NEJMoa1611925
Perkovic V, Jardine MJ, Neal B, et al. Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. N Engl J Med. 2019;380(24):2295-2306. doi:10.1056/NEJMoa1811744
Spertus JA, Birmingham MC, Nassif M, et al. The SGLT2 inhibitor canagliflozin in heart failure: the CHIEF-HF remote, patient-centered randomized trial. Nat Med. 2022;28(4):809-813. doi:10.1038/s41591-022-01703-8
Spertus JA, Birmingham MC, Butler J, et al. Novel trial design: CHIEF-HF. Circ Heart Fail. 2021;14(3):e007767. doi:10.1161/CIRCHEARTFAILURE.120.007767
Borisov AN, Kutz A, Christ ER, Heim MH, Ebrahimi F. Canagliflozin and metabolic associated fatty liver disease in patients with diabetes mellitus: new insights from CANVAS. J Clin Endocrinol Metab. 2023;108(11):2940-2949. doi:10.1210/clinem/dgad249
Ferrannini G, Rosenthal N, Hansen MK, Ferrannini E. Liver function markers predict cardiovascular and renal outcomes in the CANVAS Program. Cardiovasc Diabetol. 2022;21(1):127. doi:10.1186/s12933-022-01558-w
Koshino A, Oshima M, Arnott C, et al. Effects of canagliflozin on liver steatosis and fibrosis markers in patients with type 2 diabetes and chronic kidney disease: a post hoc analysis of the CREDENCE trial. Diabetes Obes Metab. 2023;25(5):1413-1418. doi:10.1111/dom.14978
Sterling RK, Lissen E, Clumeck N, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology. 2006;43(6):1317-1325. doi:10.1002/hep.21178
Lee JH, Kim D, Kim HJ, et al. Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease. Dig Liver Dis. 2010;42(7):503-508. doi:10.1016/j.dld.2009.08.002
Angulo P, Hui JM, Marchesini G, et al. The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology. 2007;45(4):846-854. doi:10.1002/hep.21496
Wai CT, Greenson JK, Fontana RJ, et al. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology. 2003;38(2):518-526. doi:10.1053/jhep.2003.50346
Tavaglione F, Jamialahmadi O, De Vincentis A, et al. Development and validation of a score for fibrotic nonalcoholic steatohepatitis. Clin Gastroenterol Hepatol. 2023;21(6):1523-1532.e1. doi:10.1016/j.cgh.2022.03.044
Bedogni G, Bellentani S, Miglioli L, et al. The fatty liver index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. 2006;6:33. doi:10.1186/1471-230X-6-33
McPherson S, Hardy T, Dufour JF, et al. Age as a confounding factor for the accurate non-invasive diagnosis of advanced NAFLD fibrosis. Am J Gastroenterol. 2017;112(5):740-751. doi:10.1038/ajg.2016.453
Rinella ME, Neuschwander-Tetri BA, Siddiqui MS, et al. AASLD practice guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology. 2023;77(5):1797-1835. doi:10.1097/HEP.0000000000000323
Harrison SA, Bedossa P, Guy CD, et al. A phase 3, randomized, controlled trial of resmetirom in NASH with liver fibrosis. N Engl J Med. 2024;390(6):497-509. doi:10.1056/NEJMoa2309000
Newsome PN, Sanyal AJ, Engebretsen KA, et al. Semaglutide 2.4 mg in participants with metabolic dysfunction-associated steatohepatitis: baseline characteristics and design of the phase 3 ESSENCE trial. Aliment Pharmacol Ther. 2024;60(11-12):1525-1533. doi:10.1111/apt.18331
Ferreira JP, Marques P, Anker SD, et al. Sodium-glucose co-transporter 2 inhibitors in severe estimated glomerular filtration rate deterioration across cardiovascular-kidney-metabolic conditions: a pooled analysis of randomized trials. Eur J Heart Fail. 2025;27(11):2433-2441. doi:10.1002/ejhf.70093
von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453-1457. doi:10.1016/S0140-6736(07)61602-X

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