Insulin resistance and chronic kidney disease progression, cardiovascular events, and death: Findings from the Chronic Renal Insufficiency Cohort Study

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

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

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.https://deepblue.lib.umich.edu/bitstream/2027.42/148132/1/12882_2019_Article_1220.pd

Identification of Distinct Clinical Subphenotypes in Critically Ill Patients With COVID-19.

BackgroundSubphenotypes have been identified in patients with sepsis and ARDS and are associated with different outcomes and responses to therapies.Research questionCan unique subphenotypes be identified among critically ill patients with COVID-19?Study design and methodsUsing data from a multicenter cohort study that enrolled critically ill patients with COVID-19 from 67 hospitals across the United States, we randomly divided centers into discovery and replication cohorts. We used latent class analysis independently in each cohort to identify subphenotypes based on clinical and laboratory variables. We then analyzed the associations of subphenotypes with 28-day mortality.ResultsLatent class analysis identified four subphenotypes (SP) with consistent characteristics across the discovery (45 centers; n = 2,188) and replication (22 centers; n = 1,112) cohorts. SP1 was characterized by shock, acidemia, and multiorgan dysfunction, including acute kidney injury treated with renal replacement therapy. SP2 was characterized by high C-reactive protein, early need for mechanical ventilation, and the highest rate of ARDS. SP3 showed the highest burden of chronic diseases, whereas SP4 demonstrated limited chronic disease burden and mild physiologic abnormalities. Twenty-eight-day mortality in the discovery cohort ranged from 20.6% (SP4) to 52.9% (SP1). Mortality across subphenotypes remained different after adjustment for demographics, comorbidities, organ dysfunction and illness severity, regional and hospital factors. Compared with SP4, the relative risks were as follows: SP1, 1.67 (95% CI, 1.36-2.03); SP2, 1.39 (95% CI, 1.17-1.65); and SP3, 1.39 (95% CI, 1.15-1.67). Findings were similar in the replication cohort.InterpretationWe identified four subphenotypes of COVID-19 critical illness with distinct patterns of clinical and laboratory characteristics, comorbidity burden, and mortality

Identification of distinct clinical subphenotypes in critically ill patients with COVID-19

Subphenotypes have been identified in patients with sepsis and acute respiratory distress syndrome (ARDS), and are associated with different outcomes and response to therapies. Can unique subphenotypes be identified among critically ill patients with coronavirus disease 2019 (COVID-19)? & Methods: Using data from a multicenter cohort study that enrolled critically ill patients with COVID-19 from 67 hospitals across the United States, we randomly divided centers into Discovery and Replication cohorts. We utilized latent class analysis independently in each cohort to identify subphenotypes based on clinical and laboratory variables. We then analyzed the associations of subphenotypes with 28-day mortality. Latent class analysis identified four subphenotypes (SP) with consistent characteristics across Discovery (45 centers, n=2,188) and Replication (22 centers, n=1,112) cohorts. SP1 was characterized by shock, acidemia, and multi-organ dysfunction, including acute kidney injury treated with renal replacement therapy. SP2 was characterized by high C-reactive protein, early need for mechanical ventilation, and the highest rate of ARDS. SP3 had the highest burden of chronic diseases, while SP4 had limited chronic disease burden and mild physiologic abnormalities. 28-day mortality in the Discovery cohort ranged from 20.6% (SP4) to 52.9% (SP1). Mortality across subphenotypes remained different after adjustment for demographics, comorbidities, organ dysfunction and illness severity, regional and hospital factors: compared with SP4, SP1 relative risk (RR) 1.67 (95% CI 1.36-2.03); SP2 RR 1.39 (1.17-1.65); SP3 RR 1.39 (1.15-1.67). Findings were similar in the Replication cohort. We identified four subphenotypes of COVID-19 critical illness with distinct patterns of clinical and laboratory characteristics, comorbidity burden, and mortality

Urine Biomarkers of Kidney Tubule Health and Risk of Incident CKD in Persons Without Diabetes: The ARIC, MESA, and REGARDS Studies

Rationale & objectiveTubulointerstitial damage is a feature of early chronic kidney disease (CKD), but current clinical tests capture it poorly. Urine biomarkers of tubulointerstitial health may identify risk of CKD.Study designProspective cohort (Atherosclerosis Risk in Communities [ARIC]) and case-cohort (Multi-Ethnic Study of Atherosclerosis [MESA] and Reasons for Geographic and Racial Differences in Stroke [REGARDS]).Setting & participantsAdults with estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2 and without diabetes in the ARIC, REGARDS, and MESA studies.ExposuresBaseline urine monocyte chemoattractant protein-1 (MCP-1), alpha-1-microglobulin (α1m), kidney injury molecule-1, epidermal growth factor, and chitinase-3-like protein 1.OutcomeIncident CKD or end-stage kidney disease.Analytical approachMultivariable Cox proportional hazards regression for each cohort; meta-analysis of results from all 3 cohorts.Results872 ARIC participants (444 cases of incident CKD), 636 MESA participants (158 cases), and 924 REGARDS participants (488 cases) were sampled. Across cohorts, mean age ranged from 60 ± 10 to 63 ± 8 years, and baseline eGFR ranged from 88 ± 13 to 91 ± 14 mL/min/1.73 m2. In ARIC, higher concentrations of urine MCP-1, α1m, and kidney injury molecule-1 were associated with incident CKD. In MESA, higher concentration of urine MCP-1 and lower concentration of epidermal growth factor were each associated with incident CKD. In REGARDS, none of the biomarkers were associated with incident CKD. In meta-analysis of all 3 cohorts, each 2-fold increase α1m concentration was associated with incident CKD (HR, 1.19; 95% CI, 1.08-1.31).LimitationsObservational design susceptible to confounding; competing risks during long follow-up period; meta-analysis limited to 3 cohorts.ConclusionsIn 3 combined cohorts of adults without prevalent CKD or diabetes, higher urine α1m concentration was independently associated with incident CKD. 4 biomarkers were associated with incident CKD in at least 1 of the cohorts when analyzed individually. Kidney tubule health markers might inform CKD risk independent of eGFR and albuminuria

Clinical events and patient-reported outcome measures during CKD progression: findings from the Chronic Renal Insufficiency Cohort Study.

BackgroundPatients with chronic kidney disease (CKD) face risks of not only end-stage kidney disease (ESKD), cardiovascular disease (CVD) and death, but also decline in kidney function, quality of life (QOL) and mental and physical well-being. This study describes the multidimensional trajectories of CKD using clinical events, kidney function and patient-reported outcome measures (PROMs). We hypothesized that more advanced CKD stages would associate with more rapid decline in each outcome.MethodsAmong 3939 participants enrolled in the Chronic Renal Insufficiency Cohort (CRIC) Study, we evaluated multidimensional disease trajectories by G- and A-stages of enrollment estimated glomerular filtration rate (eGFR) and albuminuria, respectively. These trajectories included clinical events (ESKD, CVD, heart failure and death), eGFR decline and PROMs [kidney disease QOL (KDQOL) burden, effects and symptoms questionnaires, as well as the 12-item short form mental and physical component summaries]. We also evaluated a group-based multitrajectory model to group participants on the basis of longitudinal PROMs and compared group assignments by enrollment G- and A-stage.ResultsThe mean participant age was 58 years, 45% were women, mean baseline eGFR was 44 mL/min/1.73 m2 and median urine albumin:creatinine ratio was 52 mg/g. The incidence of all clinical events was greater and eGFR decline was faster with more advanced G- and A-stages. While baseline KDQOL and physical component measures were lower with more advanced G- and A-stage of CKD, changes in PROMs were inconsistently related to the baseline CKD stage. Groups formed on PROM trajectories were fairly distinct from existing CKD staging (observed agreement 60.6%) and were associated with the risk of ESKD, CVD, heart failure and death.ConclusionsMore advanced baseline CKD stage was associated with a higher risk of clinical events and faster eGFR decline, and was only weakly related to changes in patient-reported metrics over time

Health Behaviors in Younger and Older Adults With CKD: Results From the CRIC Study

Introduction: A cornerstone of kidney disease management is participation in guideline-recommended health behaviors. However, the relationship of these health behaviors with outcomes, and the identification of barriers to health behavior engagement, have not been described among younger and older adults with chronic kidney disease. Methods: Data from a cohort study of 5499 individuals with chronic kidney disease was used to identify health behavior patterns with latent class analysis stratified by age <65 and ≥65 years. Cox models, stratified by diabetes, assessed the association of health behavior patterns with chronic kidney disease (CKD) progression, atherosclerotic events, and death. Logistic regression was used to assess for barriers to health behavior engagement. Results: Three health behavior patterns were identified: 1 “healthy” pattern, and 2 “less healthy” patterns comprising 1 pattern with more obesity and sedentary activity and 1 with more smoking and less obesity. Less healthy patterns were associated with an increased hazard of poor outcomes. Among participants <65 years of age, the less healthy patterns (vs. healthy pattern) was associated with an increased hazard of death in diabetic individuals (hazard ratio [HR] = 2.17, 95% confidence interval [CI] = 1.09–4.29; and HR = 2.50, 95% CI = 1.39–4.50) and cardiovascular events among nondiabetic individuals (HR = 1.49, 95% CI = 1.04–2.43; and HR = 2.97, 95% CI = 1.49–5.90). Individuals with the more obese/sedentary pattern had an increased risk of CKD progression in those who were diabetic (HR = 1.34, 95% CI = 1.13–1.59). Among older adults, the less healthy patterns were associated with increased risk of death (HR = 2.97, 95% CI = 1.43–6.19; and HR = 3.47, 95% CI = 1.48–8.11) in those who were nondiabetic. Potential barriers to recommended health behaviors include lower health literacy and self-efficacy. Conclusion: Identifying health behavior patterns and barriers may help target high-risk groups for strategies to increase participation in health behaviors. Keywords: all-cause death cardiovascular outcomes, chronic renal failure, chronic renal insufficiency, CKD progression, health behavior, self-managemen

Hospitalization Trajectories and Risks of ESKD and Death in Individuals With CKD

Introduction: Management of chronic kidney disease (CKD) entails high medical complexity and often results in high hospitalization burden. There are limited data on the associations of longitudinal hospital utilization patterns with adverse clinical outcomes in individuals with CKD. Methods: We derived cumulative all-cause hospitalization trajectory groups using latent class trajectory analysis in 3012 participants of the Chronic Renal Insufficiency Cohort (CRIC) Study who were alive and did not reach end-stage kidney disease (ESKD) within 4 years of study entry. Cox proportional hazards models tested the associations between hospitalization trajectory groups and risks of ESKD and death prior to the onset of ESKD (ESKD-censored death). Results: Within 4 years of study entry, there were 5658 hospitalizations among 3012 participants. We identified 3 distinct subgroups of individuals with CKD based on cumulative all-cause hospitalization trajectories over 4 years: low-utilizer (n = 1066), intermediate-utilizer (n = 1802), and high-utilizer (n = 144). High-utilizers represented a patient population of lower socioeconomic status who had a greater prevalence of comorbid conditions and lower kidney function compared with intermediate- and low-utilizers. After the 4-year ascertainment period to form the trajectory subgroups, there were 544 ESKD events and 437 ESKD-censored deaths during a median follow-up time of 5.1 years. Compared with low-utilizers, intermediate-utilizers and high-utilizers were at 1.49-fold (95% confidence interval [CI] 1.22–1.84) and 1.75-fold (95% CI 1.20–2.56) higher risk of ESKD in adjusted analyses, respectively. Compared with low-utilizers, intermediate-utilizers and high-utilizers were at 1.48-fold (95% CI 1.17–1.87) and 2.58-fold (95% CI 1.74–3.83) higher risk of ESKD-censored death in adjusted analyses, respectively. Conclusions: Trajectories of cumulative all-cause hospitalization identify subgroups of individuals with CKD who are at high risk of ESKD and death