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      The presence and impact of multimorbidity clusters on adverse outcomes across the spectrum of kidney function

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          Abstract

          Background

          Multimorbidity (the presence of two or more chronic conditions) is common amongst people with chronic kidney disease, but it is unclear which conditions cluster together and if this changes as kidney function declines. We explored which clusters of conditions are associated with different estimated glomerular filtration rates (eGFRs) and studied associations between these clusters and adverse outcomes.

          Methods

          Two population-based cohort studies were used: the Stockholm Creatinine Measurements project (SCREAM, Sweden, 2006–2018) and the Secure Anonymised Information Linkage Databank (SAIL, Wales, 2006–2021). We studied participants in SCREAM (404,681 adults) and SAIL (533,362) whose eGFR declined lower than thresholds (90, 75, 60, 45, 30 and 15 mL/min/1.73m 2). Clusters based on 27 chronic conditions were identified. We described the most common chronic condition(s) in each cluster and studied their association with adverse outcomes using Cox proportional hazards models (all-cause mortality (ACM) and major adverse cardiovascular events (MACE)).

          Results

          Chronic conditions became more common and clustered differently across lower eGFR categories. At eGFR 90, 75, and 60 mL/min/1.73m 2, most participants were in large clusters with no prominent conditions. At eGFR 15 and 30 mL/min/1.73m 2, clusters involving cardiovascular conditions were larger and were at the highest risk of adverse outcomes. At eGFR 30 mL/min/1.73m 2, in the heart failure, peripheral vascular disease and diabetes cluster in SCREAM, ACM hazard ratio (HR) is 2.66 (95% confidence interval (CI) 2.31–3.07) and MACE HR is 4.18 (CI 3.65–4.78); in the heart failure and atrial fibrillation cluster in SAIL, ACM HR is 2.23 (CI 2.04 to 2.44) and MACE HR is 3.43 (CI 3.22–3.64). Chronic pain and depression were common and associated with adverse outcomes when combined with physical conditions. At eGFR 30 mL/min/1.73m 2, in the chronic pain, heart failure and myocardial infarction cluster in SCREAM, ACM HR is 2.00 (CI 1.62–2.46) and MACE HR is 4.09 (CI 3.39–4.93); in the depression, chronic pain and stroke cluster in SAIL, ACM HR is 1.38 (CI 1.18–1.61) and MACE HR is 1.58 (CI 1.42–1.76).

          Conclusions

          Patterns of multimorbidity and corresponding risk of adverse outcomes varied with declining eGFR. While diabetes and cardiovascular disease are known high-risk conditions, chronic pain and depression emerged as important conditions and associated with adverse outcomes when combined with physical conditions.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12916-022-02628-2.

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          Most cited references39

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          A new equation to estimate glomerular filtration rate.

          Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values. To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates. Research studies and clinical populations ("studies") with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006. 8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16,032 participants in NHANES. GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age. In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m(2)), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m(2)), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m(2) (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m(2), and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%). The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR. The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use. National Institute of Diabetes and Digestive and Kidney Diseases.
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            Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy

            Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium-glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes.
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              Dapagliflozin in Patients with Chronic Kidney Disease

              Patients with chronic kidney disease have a high risk of adverse kidney and cardiovascular outcomes. The effect of dapagliflozin in patients with chronic kidney disease, with or without type 2 diabetes, is not known.
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                Author and article information

                Contributors
                Michael.sullivan@glasgow.ac.uk
                Journal
                BMC Med
                BMC Med
                BMC Medicine
                BioMed Central (London )
                1741-7015
                1 November 2022
                1 November 2022
                2022
                : 20
                : 420
                Affiliations
                [1 ]GRID grid.8756.c, ISNI 0000 0001 2193 314X, BHF Glasgow Cardiovascular Research Centre, , Institute of Cardiovascular and Medical Sciences, University of Glasgow, ; 126 University Place, Glasgow, G12 8TA UK
                [2 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Medical Epidemiology and Biostatistics, , Karolinska Institutet, ; Stockholm, Sweden
                [3 ]GRID grid.8756.c, ISNI 0000 0001 2193 314X, General Practice and Primary Care, , Institute of Health and Wellbeing, University of Glasgow, ; Glasgow, UK
                [4 ]GRID grid.8756.c, ISNI 0000 0001 2193 314X, School of Mathematics and Statistics, , University of Glasgow, ; Glasgow, UK
                [5 ]GRID grid.8756.c, ISNI 0000 0001 2193 314X, Robertson Centre for Biostatistics, , Institute of Health and Wellbeing, University of Glasgow, ; Glasgow, UK
                [6 ]GRID grid.8991.9, ISNI 0000 0004 0425 469X, Faculty of Epidemiology and Population Health, , London School of Hygiene & Tropical Medicine, ; London, UK
                [7 ]GRID grid.8756.c, ISNI 0000 0001 2193 314X, Public Health, , Institute of Health and Wellbeing, University of Glasgow, ; Glasgow, UK
                Author information
                http://orcid.org/0000-0002-3800-2330
                Article
                2628
                10.1186/s12916-022-02628-2
                9623942
                36320059
                e19a24cc-3659-4f5d-9ad9-aa2731786205
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 10 October 2022
                : 24 October 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MR/V001671/1
                Award ID: MR/S021949/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;
                Award ID: 2019-01059
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2022

                Medicine
                multimorbidity,chronic conditions,chronic kidney disease,clustering analysis
                Medicine
                multimorbidity, chronic conditions, chronic kidney disease, clustering analysis

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