Insulin resistance and chronic kidney disease progression, cardiovascular events, and death: findings from the chronic renal insufficiency cohort study

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Abstract

Background

Insulin resistance contributes to the metabolic syndrome, which is associated with the development of kidney disease. However, it is unclear if insulin resistance independently contributes to an increased risk of chronic kidney disease (CKD) progression or CKD complications. Additionally, predisposing factors responsible for insulin resistance in the absence of diabetes in CKD are not well described. This study aimed to describe factors associated with insulin resistance and characterize the relationship of insulin resistance to CKD progression, cardiovascular events and death among a cohort of non-diabetics with CKD.

Methods

Data was utilized from Chronic Renal Insufficiency Cohort Study participants without diabetes ( N = 1883). Linear regression was used to assess associations with insulin resistance, defined using the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR). The relationship of HOMA-IR, fasting glucose, hemoglobin A1c (HbA1c), and C-peptide with CKD progression, cardiovascular events, and all-cause mortality was examined with Cox proportional hazards models.

Results

Novel positive associations with HOMA-IR included serum albumin, uric acid, and hemoglobin A1c. After adjustment, HOMA-IR was not associated with CKD progression, cardiovascular events, or all-cause mortality. There was a notable positive association of one standard deviation increase in HbA1c with the cardiovascular endpoint (HR 1.16, 95% CI: 1.00–1.34).

Conclusion

We describe potential determinants of HOMA-IR among a cohort of non-diabetics with mild-moderate CKD. HOMA-IR was not associated with renal or cardiovascular events, or all-cause mortality, which adds to the growing literature describing an inconsistent relationship of insulin resistance with CKD-related outcomes.

Electronic supplementary material

The online version of this article (10.1186/s12882-019-1220-6) contains supplementary material, which is available to authorized users.

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Most cited references 40

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Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage.

Ranganath Muniyappa, Sihoon Lee, Hui Chen … (2008)

Insulin resistance contributes to the pathophysiology of diabetes and is a hallmark of obesity, metabolic syndrome, and many cardiovascular diseases. Therefore, quantifying insulin sensitivity/resistance in humans and animal models is of great importance for epidemiological studies, clinical and basic science investigations, and eventual use in clinical practice. Direct and indirect methods of varying complexity are currently employed for these purposes. Some methods rely on steady-state analysis of glucose and insulin, whereas others rely on dynamic testing. Each of these methods has distinct advantages and limitations. Thus, optimal choice and employment of a specific method depends on the nature of the studies being performed. Established direct methods for measuring insulin sensitivity in vivo are relatively complex. The hyperinsulinemic euglycemic glucose clamp and the insulin suppression test directly assess insulin-mediated glucose utilization under steady-state conditions that are both labor and time intensive. A slightly less complex indirect method relies on minimal model analysis of a frequently sampled intravenous glucose tolerance test. Finally, simple surrogate indexes for insulin sensitivity/resistance are available (e.g., QUICKI, HOMA, 1/insulin, Matusda index) that are derived from blood insulin and glucose concentrations under fasting conditions (steady state) or after an oral glucose load (dynamic). In particular, the quantitative insulin sensitivity check index (QUICKI) has been validated extensively against the reference standard glucose clamp method. QUICKI is a simple, robust, accurate, reproducible method that appropriately predicts changes in insulin sensitivity after therapeutic interventions as well as the onset of diabetes. In this Frontiers article, we highlight merits, limitations, and appropriate use of current in vivo measures of insulin sensitivity/resistance.

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Sugar, Uric Acid, and the Etiology of Diabetes and Obesity

Richard J. Johnson, Takahiko Nakagawa, L Sanchez-Lozada … (2013)

The intake of added sugars, such as from table sugar (sucrose) and high-fructose corn syrup has increased dramatically in the last hundred years and correlates closely with the rise in obesity, metabolic syndrome, and diabetes. Fructose is a major component of added sugars and is distinct from other sugars in its ability to cause intracellular ATP depletion, nucleotide turnover, and the generation of uric acid. In this article, we revisit the hypothesis that it is this unique aspect of fructose metabolism that accounts for why fructose intake increases the risk for metabolic syndrome. Recent studies show that fructose-induced uric acid generation causes mitochondrial oxidative stress that stimulates fat accumulation independent of excessive caloric intake. These studies challenge the long-standing dogma that “a calorie is just a calorie” and suggest that the metabolic effects of food may matter as much as its energy content. The discovery that fructose-mediated generation of uric acid may have a causal role in diabetes and obesity provides new insights into pathogenesis and therapies for this important disease.

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Chronic Renal Insufficiency Cohort (CRIC) Study: baseline characteristics and associations with kidney function.

John W. Kusek, Denise Cifelli, Jeffrey C. Fink … (2009)

The Chronic Renal Insufficiency Cohort (CRIC) Study was established to examine risk factors for the progression of chronic kidney disease (CKD) and cardiovascular disease (CVD) in patients with CKD. We examined baseline demographic and clinical characteristics. Seven clinical centers recruited adults who were aged 21 to 74 yr and had CKD using age-based estimated GFR (eGFR) inclusion criteria. At baseline, blood and urine specimens were collected and information regarding health behaviors, diet, quality of life, and functional status was obtained. GFR was measured using radiolabeled iothalamate in one third of participants. A total of 3612 participants were enrolled with mean age +/- SD of 58.2 +/- 11.0 yr; 46% were women, and 47% had diabetes. Overall, 45% were non-Hispanic white, 46% were non-Hispanic black, and 5% were Hispanic. Eighty-six percent reported hypertension, 22% coronary disease, and 10% heart failure. Mean body mass index was 32.1 +/- 7.9 kg/m(2), and 47% had a BP >130/80 mmHg. Mean eGFR was 43.4 +/- 13.5 ml/min per 1.73 m(2), and median (interquartile range) protein excretion was 0.17 g/24 h (0.07 to 0.81 g/24 h). Lower eGFR was associated with older age, lower socioeconomic and educational level, cigarette smoking, self-reported CVD, peripheral arterial disease, and elevated BP. Lower level of eGFR was associated with a greater burden of CVD as well as lower socioeconomic and educational status. Long-term follow-up of participants will provide critical insights into the epidemiology of CKD and its relationship to adverse outcomes.

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Author and article information

Contributors

Sarah J. Schrauben:

ORCID: http://orcid.org/0000-0003-2557-5161

267-251-7669 , Sarah.Schrauben@uphs.upenn.edu

Christopher Jepson: cjepson@pennmedicine.upenn.edu

Jesse Y. Hsu: Jesse.Hsu@pennmedicine.upenn.edu

F. Perry Wilson: francis.p.wilson@yale.edu

Xiaoming Zhang: xzh@pennmedicine.upenn.edu

James P. Lash: jplash@uic.edu

Bruce M. Robinson: bruce.robinson@arborresearch.org

Raymond R. Townsend: townsend@upenn.edu

Jing Chen: jchen@tulane.edu

Leon Fogelfeld: lfogelfe@cchil.org

Patricia Kao: kaop@wustl.edu

J. Richard Landis: jrlandis@pennmedicine.upenn.edu

Daniel J. Rader: rader@pennmedicine.upenn.edu

L. Lee Hamm: lhamm@tulane.edu

Amanda H. Anderson: aanderson5@tulane.edu

Harold I. Feldman: feldman@pennmedicine.upenn.edu

Journal

Journal ID (nlm-ta): BMC Nephrol

Journal ID (iso-abbrev): BMC Nephrol

Title: BMC Nephrology

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2369

Publication date (Electronic): 20 February 2019

Publication date PMC-release: 20 February 2019

Publication date Collection: 2019

Volume: 20

Electronic Location Identifier: 60

Affiliations

[1 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Division of Renal-Electrolyte and Hypertension, Department of Medicine, , Perelman School of Medicine at the University of Pennsylvania, ; Philadelphia, 19103 PA USA

[2 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Center for Clinical Epidemiology and Biostatistics, , Perelman School of Medicine at the University of Pennsylvania, ; Philadelphia, PA 19103 USA

[3 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Biostatistics, Epidemiology, and Informatics, , Perelman School of Medicine at the University of Pennsylvania, ; Philadelphia, PA USA

[4 ]ISNI 0000000419368710, GRID grid.47100.32, Department of Medicine, , Yale School of Medicine, ; New Haven, CT USA

[5 ]ISNI 0000 0001 2175 0319, GRID grid.185648.6, Department of Medicine, , University of Illinois at Chicago College of Medicine, ; Chicago, IL USA

[6 ]ISNI 0000000086837370, GRID grid.214458.e, Department of Internal Medicine, , University of Michigan Medical School, Ann Arbor, MI; Arbor Research Collaborative for Health, ; Ann Arbor, MI USA

[7 ]ISNI 0000 0001 2217 8588, GRID grid.265219.b, Department of Medicine, , Tulane University School of Medicine, ; New Orleans, Lousiana USA

[8 ]ISNI 0000 0001 0705 3621, GRID grid.240684.c, Department of Medicine, , Rush University Medical Center, ; Chicago, IL USA

[9 ]ISNI 0000 0001 2355 7002, GRID grid.4367.6, Deparment of Medicine, , Washington University School of Medicine in Saint Louis, ; St. Louis, MO USA

[10 ]ISNI 0000 0001 2217 8588, GRID grid.265219.b, Tulane University, School of Public Health and Tropical Medicine, ; New Orleans, LA USA

Author information

Sarah J. Schrauben http://orcid.org/0000-0003-2557-5161

Article

Publisher ID: 1220

DOI: 10.1186/s12882-019-1220-6

PMC ID: 6383235

PubMed ID: 30786864

SO-VID: d83e6429-3357-4eec-913b-582acf0fe857

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

History

Date received : 8 November 2018

Date accepted : 17 January 2019

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100000062, National Institute of Diabetes and Digestive and Kidney Diseases;

Award ID: Multiple

Award ID: R01DK107566 and R01DK104730

Award ID: R01 DK113191 and K23 DK097201

Award Recipient : Amanda H. Anderson Harold I. Feldman

Custom metadata

ScienceOpen disciplines: Nephrology

Keywords: insulin resistance,chronic kidney disease,chronic renal insufficiency,cardiovascular disease,mortality

Data availability:

ScienceOpen disciplines: Nephrology

Keywords: insulin resistance, chronic kidney disease, chronic renal insufficiency, cardiovascular disease, mortality

Insulin resistance and chronic kidney disease progression, cardiovascular events, and death: findings from the chronic renal insufficiency cohort study

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Results

Conclusion

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Karger: Cardiovascular System

Most cited references 40

Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage.

Sugar, Uric Acid, and the Etiology of Diabetes and Obesity

Chronic Renal Insufficiency Cohort (CRIC) Study: baseline characteristics and associations with kidney function.

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