Am J Physiol Endocrinol Metab 312: E175–E182, 2017. First published January 10, 2017; doi:10.1152/ajpendo.00394.2016.
RESEARCH ARTICLE
Translational Physiology
Chronic kidney disease and obesity bias surrogate estimates of insulin sensitivity compared with the hyperinsulinemic euglycemic clamp Iram Ahmad,1,2 Leila R. Zelnick,2 Nicole R. Robinson,2 Adriana M. Hung,3 Bryan Kestenbaum,2 Kristina M. Utzschneider,1,4 Steven E. Kahn,1,4 and Ian. H. de Boer2,4 1
Submitted 2 November 2016; accepted in final form 2 January 2017
Ahmad I, Zelnick LR, Robinson NR, Hung AM, Kestenbaum B, Utzschneider KM, Kahn SE, de Boer IH. Chronic kidney disease and obesity bias surrogate estimates of insulin sensitivity compared with the hyperinsulinemic euglycemic clamp. Am J Physiol Endocrinol Metab 312: E175–E182, 2017. First published January 10, 2017; doi:10.1152/ajpendo.00394.2016.—Insulin sensitivity can be measured by procedures such as the hyperinsulinemic euglycemic clamp or by using surrogate indices. Chronic kidney disease (CKD) and obesity may differentially affect these measurements because of changes in insulin kinetics and organ-specific effects on insulin sensitivity. In a cross-sectional study of 59 subjects with nondiabetic CKD [estimated glomerular filtration rate: (GFR) ⬍60 ml·min⫺1·1.73 m2] and 39 matched healthy controls, we quantified insulin sensitivity by clamp (SIclamp), oral glucose tolerance test, and fasting glucose and insulin. We compared surrogate insulin sensitivity indices to SIclamp using descriptive statistics, graphical analyses, correlation coefficients, and linear regression. Mean age was 62.6 yr; 48% of the participants were female, and 77% were Caucasian. Insulin sensitivity indices were 8 –38% lower in participants with vs. without CKD and 13–59% lower in obese compared with nonobese participants. Correlations of surrogate indices with SIclamp did not differ significantly by CKD or obesity status. Adjusting for SIclamp in addition to demographic factors, Matsuda index was 15% lower in participants with vs. without CKD (P ⫽ 0.09) and 36% lower in participants with vs. without obesity (P ⫽ 0.0001), whereas 1/HOMA-IR was 23% lower in participants with vs. without CKD (P ⫽ 0.02) and 46% lower in participants with vs. without obesity (P ⬍ 0.0001). We conclude that CKD and obesity do not significantly alter correlations of surrogate insulin sensitivity indices with SIclamp, but they do bias surrogate measurements of insulin sensitivity toward lower values. This bias may be due to differences in insulin kinetics or organ-specific responses to insulin.
(i.e., insulin resistance) is an important risk factor for type 2 diabetes, cardiovascular disease, and mortality (33). To determine the causes and consequences of insulin resistance and evaluate treatments for it, accurate measurements of insulin sensitivity are needed. The classic hyperinsulinemic euglycemic clamp directly measures the ability of insulin to stimulate whole body glucose uptake, which occurs predominantly in peripheral tissues, such as skeletal muscle, during high-dose exogenous
REDUCED SENSITIVITY TO THE METABOLIC ACTIONS OF INSULIN
Address for reprint requests and other correspondence: I. Ahmad, Kidney Research Institute, 325 9th Ave., Box 359606, Seattle, WA 98104 (e-mail: [email protected]). http://www.ajpendo.org
insulin infusion (8). The trained staff required for administration of the continuous infusions coupled with frequent blood sampling typically restrict application of the clamp to small research protocols. Surrogate insulin sensitivity indices based on insulin and glucose concentrations obtained from fasting samples or the oral glucose tolerance test (OGTT) have been developed to apply in a wider variety of settings. Insulin sensitivity indices derived from these other approaches generally correlate well with results from the hyperinsulinemic euglycemic clamp (19, 21, 23, 28), but they may be differentially affected in some pathophysiological conditions. Fasting insulin and glucose concentrations largely reflect hepatic insulin sensitivity and are influenced by insulin clearance in addition to insulin sensitivity (8, 13). The OGTT includes insulin and glucose measurements made while fasting and after endogenous insulin secretion, which are also affected by insulin clearance and may reflect a combination of hepatic and skeletal muscle insulin sensitivity. Chronic kidney disease (CKD) is a common condition associated with decreased insulin sensitivity (6, 11, 29). Because skeletal muscle is an important site of impaired insulin signaling in CKD, differences in insulin sensitivity between people with and without CKD could potentially be more pronounced using the clamp compared with surrogate indices (30). On the other hand, because insulin clearance is decreased in CKD, surrogate indices based on fasting insulin or OGTT results may yield lower insulin sensitivity values than those measured with the clamp (5, 6). Obesity is strongly coupled with hepatic insulin resistance (35) and also decreases insulin clearance (26), which may lead to larger effects on surrogate indices of insulin sensitivity than when measured by clamp. These effects of CKD and obesity could potentially reduce the precision of insulin sensitivity indices compared with the clamp or bias insulin sensitivity indices compared with the clamp. The purpose of our study was to evaluate the performance of surrogate insulin sensitivity indices compared with insulin sensitivity measured using the hyperinsulinemic euglycemic clamp among nondiabetic subjects with and without moderate to severe CKD. We hypothesized that, comparing participants with CKD vs. without CKD, surrogate insulin sensitivity indices would correlate less strongly with insulin sensitivity measured by the clamp and would overestimate the degree of insulin sensitivity in comparison with the E175
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Division of Metabolism, Endocrinology, and Nutrition, Department of Medicine, University of Washington, Seattle, Washington; 2Division of Nephrology, Department of Medicine, Kidney Research Institute, University of Washington, Seattle, Washington; 3Division of Nephrology and Hypertension, Vanderbilt University School of Medicine, Nashville, Tennessee; and 4 Veterans Affairs Puget Sound Health Care System, Seattle, Washington
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clamp. We evaluated the impact of obesity on the performance of insulin sensitivity indices compared with the clamp as a secondary aim. METHODS
SIclamp ⫽
glucose infusion rate ⫻ concentration of infused glucose
共insulin concentration at steady state ⫺ fasting insulin concentration兲 ⫻ lean body mass
The inclusion of insulin concentration in the denominator of SI accounts for variability in achieved insulin concentrations and is supported by the linear relationship between insulin concentration and glucose disposal rate within individuals in our dose-ranging study (6). For primary analyses, we normalized SIclamp to lean body mass measured by DEXA because lean mass tissues (primarily liver and particularly skeletal muscle) are the primary sites for insulin-mediated glucose disposal, and most published studies take this approach (27). Insulin clearance was calculated as the insulin infusion rate divided by steady-state insulin concentration (6). A standard 75-g OGTT was performed approximately 1 wk after the clamp, and insulin and glucose values before and during the OGTT were used to derive the Matsuda index and 1/HOMA-IR (homeostatic model assessment of insulin resistance), two of the more common insulin sensitivity indices. Basal blood samples for glucose and insulin measurements were taken as the average of measurements from ⫺10, ⫺5, and 0 min; plasma glucose and insulin drawn again at 30, 60, 90, and 120 min after the start of glucose ingestion. Plasma free fatty acid levels were measured during the OGTT at ⫺10, ⫺5, 30, 60, and 120 min, and baseline values were the average of the levels at ⫺10 and ⫺5 min. For secondary analysis, we also calculated the Stumvoll, Gutt, McAuley, quantitative insulin sensitivity check index (QUICKI), simplified Belfiore index, and nonesterified fatty acidinsulin sensitivity index (NEFA-ISI) (2, 14, 20, 24, 25, 36, 37). For the simplified Belfiore index calculations, we used the free fatty acid concentrations of our nonobese, non-CKD subgroup to derive mean normal values. There were six negative values of Stumvoll for unknown reasons, which were truncated at the lowest positive value of the Stumvoll index. Six participants had an OGTT free fatty acid
(NEFA) measurement of zero at 120 min, and the NEFA level at 120 min was set to the lowest non-zero value observed (0.01) in calculating the NEFA index. Laboratory analysis. Blood samples were drawn and placed on ice slurry before centrifugation at 4°C. Samples were aliquoted and frozen at ⫺80°C within 1 h of drawing. NEFAs were drawn into Vacutainer tubes containing orlistat. Blood glucose (glucose hexokinase method; Roche Module P Chemistry autoanalyzer; Roche, Basel, Switzerland) and insulin (2-site immune-enzymometric assay; Tosoh 2000 Autoanalyzer) concentrations, NEFA levels, and dextrose infusate concentrations were measured at the Northwest Lipid Research Laboratories (Seattle, WA). Statistical analysis. All analyses were performed using R 3.2.2 (R Foundation for Statistical Computing, Vienna, Austria; http://www. R-project.org/). Bivariate relationships of continuous variables were evaluated using scatterplots and Pearson correlation coefficients. Bestfit lines were generated using linear regression. Multivariable relationships were evaluated using linear regression with Huber-White standard errors (38), and inferential comparisons of correlation coefficients were made using Fisher’s r-to-z transformation (10). Approximately 5% of participants had missing covariates or outcomes. For regression and correlation analyses, these values were multiply imputed using chained equations, and imputations were combined using Rubin’s rules (34). Insulin sensitivity index measurements were log transformed to decrease skew and improve interpretation. To put our results into context, we abstracted published data evaluating the correlations of Matsuda Index or HOMA-IR with clamp insulin sensitivity (28), plotted correlation coefficient magnitudes vs. clamp insulin dose, restricting ourselves to studies that used dosing per body
AJP-Endocrinol Metab • doi:10.1152/ajpendo.00394.2016 • www.ajpendo.org
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Study subjects. We performed the Study of Glucose and Insulin in Renal Disease (SUGAR) to evaluate insulin sensitivity among people with a broad range of glomerular filtration rate (GFR) and to test the performance of insulin sensitivity indices among people with moderate to severe CKD (6). Subjects were recruited from nephrology and primary care clinics affiliated with the University of Washington and nearby institutions in Seattle, WA. Exclusion criteria for both groups included age ⬍18 yr, a clinical diagnosis of diabetes, maintenance dialysis or fistula in place, history of kidney transplantation, use of medications known to reduce insulin sensitivity (including corticosteroids and immunosuppressants), fasting serum glucose ⱖ126 mg/dl, and hemoglobin ⬍10 g/dl. This study was approved by the University of Washington Human Subjects Division Institutional Review Board, and informed written consent was obtained from all subjects before their participation. Estimated GFR and body composition. Serum creatinine and cystatin C were measured in fasting serum using a Beckman DxC automated chemistry analyzer. Creatinine and cystatin C concentrations were traceable to isotope dilution mass spectrometry and ERM-DA471/IFCC, respectively. Interassay coefficients of variation were 1.5–3.0%. GFR was estimated from creatinine and cystatin C concentrations using the CKD-EPI formula (18). Fat and lean masses were measured by dual-energy X-ray absorptiometry (DEXA; GE Prodigy and/or GE Lunar iDEXA with enCORE 14.1 software; GE Healthcare, Waukesha, WI) by an experienced operator. Seventeen participants who completed the iDEXA alone
had their values calibrated to the Prodigy by single imputation using regression models from participants who had both the GE Prodigy and Lunar iDEXA, and the values from or calibrated to the Prodigy were used for analysis. Liver and spleen densities were measured by single-slice computed tomography, and the ratio of liver density to spleen density was used to account for differences in technique (31). Measurements of glucose and insulin homeostasis. Insulin sensitivity was measured using the hyperinsulinemic euglycemic clamp technique, as described previously (6), adapted from the method of DeFronzo, et. al. (8). Each participant was admitted to the University of Washington Clinical Research Center after an overnight fast. Intravenous catheters were placed in peripheral veins in each upper extremity and kept patent with a slow infusion of normal saline. One arm was warmed to allow for the sampling of “arterialized” blood. Three baseline blood samples were drawn 5 min apart. At time zero, an insulin infusion was initiated as a prime (160 mU·m2·min⫺1 for 5 min) followed by a constant rate (80 mU·m2·min⫺1). This insulin dose was chosen from a dose-ranging study because it effectively suppressed endogenous glucose production, fell within a range demonstrating a linear dose-response relationship with glucose uptake, and yielded a submaximal rate of glucose disposal (6). A variable rate infusion of unlabeled 20% dextrose was administered to maintain blood glucose (measured every 5 min) at ~90 mg/dl. Beginning 120 –150 min after initiation of the insulin infusion, the dextrose infusion rate was held constant for 30 min, over which time three blood samples were obtained 15 min apart to calculate the glucose disposal rate. The glucose infusion rate was calculated as the glucose infusion rate during the last 30 min of the clamp, adjusted for the drift in plasma glucose concentration using Steels’ non-steady-state equations. Insulin sensitivity (SIclamp) was calculated as
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INSULIN SENSITIVITY IN CKD AND OBESITY
surface area (similar to our study) and converting to mU·m2·min⫺1 when necessary. RESULTS
Table 1. Characteristics of participants in the Study of Glucose and Renal Disease Estimated GFR All (n ⫽ 98)
Age (yr), means (SD) Female sex, n (%) Race, n (%) White Black Asian/Pacific Islander Height, cm Weight, kg BMI, kg/m2 Lean mass, kg Fat mass, kg Serum creatinine (mg/dl), median (IQR) Serum cystatin (mg/l), median (IQR) Estimated GFR (ml·min⫺1·1.73 m2), means (IQR) Fasting glucose, OGTT, mg/dl 2-h Glucose, OGTT, mg/dl Fasting insulin, clamp, U/ml Steady-state insulin, clamp, U/ml
ⱖ60 ml·min·1.73 m2 (n ⫽ 39)
62.6 (13.3) 47 (48)
61.0 (12.4) 17 (44)
75 (77) 17 (17) 6 (6)
34 (87) 4 (10) 1 (3)
170.7 (0.1) 85.5 (20.2) 29.3 (6.4) 51.7 (11.8) 30.2 (12.7) 1.4 (0.9–1.8) 1.3 (0.96–1.7) 49.9 (36.6–81.1) 99.8 (8.9) 150.6 (39.2) 9.0 (5.4) 178.8 (39.8)
⬍60 ml·min·1.73 m2 (n ⫽ 59)
Demographics 63.6 (13.9) 30 (51)
Body Mass Index ⬍30 kg/m2 (n ⫽ 58)
62.4 (14.1) 26 (45)
41 (69) 46 (79) 13 (22) 6 (10) 5 (8) 6 (10) Physical characteristics, means (SD) 172.9 (10.7) 170.4 (10.3) 172.0 (10.9) 82.6 (20.6) 88.1 (19.8) 74.4 (13.8) 27.9 (6.6) 30.3 (6.2) 25.0 (3.0) 53.1 (12.6) 50.7 (11.2) 48.9 (10.9) 27.7 (14.0) 31.9 (11.6) 22.4 (8.0) Laboratory data 0.9 (0.8–1.0) 1.7 (1.5–2.1) 1.2 (0.9–1.7) 0.9 (0.7–1.0) 1.6 (1.4–2.0) 1.2 (0.8–1.6) 85.3 (76.0–102.6) 98.4 (9.2) 149.1 (44.6) 7.3 (5.2) 162.0 (34.2)
39.1 (27.2–47.6) 100.8 (8.6) 151.6 (35.5) 10.1 (5.3) 190.0 (39.5)
56.9 (39.4–86.8) 97.9 (9.4) 147.1 (41.2) 6.3 (3.8) 167.2 (36.1)
ⱖ30 kg/m2 (n ⫽ 40)
62.8 (12.2) 21 (53) 29 (73) 11 (28) 0 (0) 170.5 (10.0) 102.5 (16.1) 35.2 (4.4) 55.6 (12.0) 41.5 (9.4) 1.6 (1.2–1.8) 1.4 (1.1–1.9) 42.8 (36.0–68.2) 102.7 (7.3) 155.6 (36.0) 13.0 (4.9) 195.7 (39.1)
GFR, glomerular filtration rate; BMI, body mass index. IQR, interquartile range; OGTT, oral glucose tolerance test. Data were missing for fat-free mass and fat mass for 5 participants. AJP-Endocrinol Metab • doi:10.1152/ajpendo.00394.2016 • www.ajpendo.org
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Of the 98 participants included in SUGAR, 59 (60%) had CKD, 38 (39%) were obese, 47 (48%) were women, and 75 (77%) were white (Table 1). Mean age was 62.6 yr, and mean BMI was 29.3 kg/m2. For the participants with CKD, CKD was most often attributed by supervising clinicians to hypertension. BMI was similar when comparing participants with CKD vs. those without; obese participants tended to have lower estimated GFR (eGFR) than those without obesity. Ten (25.6%) participants without CKD were obese, and 28 (47.5%) with CKD were obese. CKD and insulin sensitivity. Insulin sensitivity was significantly lower among participants with CKD vs. without CKD for eight of 10 methods used to measure insulin sensitivity, the exceptions being the Gutt and Stumvoll indices, which were nonsignificantly lower (Table 2). Comparing participants with CKD with those without CKD, geometric mean SIclamp was 19% lower, and mean values of the insulin sensitivity indices ranged from 8 to 38% lower. Results were similar repeating analyses using SIclamp not indexed to lean mass or using fasting insulin and glucose measurements obtained before the clamp rather than before the OGTT. Mean (SD) insulin clearance was 998.3 (212.1) ml/min in the non-CKD group and 876.1 (225.7) ml/min in the CKD group (P ⬍ 0.01). Correlation coefficients comparing insulin sensitivity indices to SIclamp were lower among participants with vs. without CKD for eight of nine indices (and equal for the Gutt index), but none of the differences in correlation were statistically significant (Table 2). Results were similar repeating analyses using SIclamp not indexed to lean mass. Correlation coefficients
were within the broad ranges reported previously, particularly considering that correlations tended to be lower with higher doses of insulin infused during the clamp. At most values of SIclamp, particularly higher values of SIclamp, participants with CKD tended to have Matsuda index and 1/HOMA-IR values that were lower than participants without CKD (Fig. 1). Results were similar when using SIclamp not indexed to lean mass. In a multivariable model adjusted for SIclamp in addition to age, sex, race, and BMI status, participants with CKD had a mean Matsuda index that was 15% lower than those without CKD (P ⫽ 0.09, Table 3). In a parallel multivariable model adjusting for age, sex, race, and BMI status, participants with CKD had a mean 1/HOMA-IR that was 23% lower than those without CKD (P ⫽ 0.02). Further adjusting for liver density and liver enzyme tests did not substantially affect these results. Obesity and insulin sensitivity. Insulin sensitivity was significantly lower among obese (BMI ⱖ30 kg/m2) participants than among the nonobese (BMI ⬍30 kg/m2) for all methods used to measure insulin sensitivity (Table 2). Comparing obese participants with nonobese participants, mean SIclamp was 31% lower, and mean values of the insulin sensitivity indices ranged from 13 to 59% lower. Results were similar when analyses were repeated without correcting SIclamp for lean mass. Mean (SD) insulin clearance was 934.0 (221.0) ml/min in the nonobese group and 910.1 (238.3) ml/min in the obese group (P ⫽ 0.06). Correlation coefficients comparing insulin sensitivity indices to SIclamp were lower among participants with vs. without obesity for eight of nine indices (except for the McAuley index), but none of the differences in correlation were statistically significant (Table 2). Results were similar repeating
AJP-Endocrinol Metab • doi:10.1152/ajpendo.00394.2016 • www.ajpendo.org 0.48 0.61 0.38 0.57 0.23 0.052
0.57 0.68 0.40 0.63 0.43 0.19
0.08 (1.76) 0.04 (1.97) ⫺55 (⫺65, ⫺41) ⬍0.0001 0.56
5.5 (1.8) 2.7 (1.7) ⫺50 (⫺60, ⫺38) ⬍0.0001
0.09 (1.60) 0.06 (1.54) ⫺31 (⫺42, ⫺16) 0.0002
0.05 (2.14) 0.07 (1.89) ⫺25 (⫺43, 0) 0.052
0.06 (2.06)
Stumvoll
0.64
3.5 (1.8) 5.3 (2.0) ⫺33 (⫺49, ⫺12) 0.005
4.2 (1.9)
0.07 (1.63) 0.09 (1.58) ⫺19 (⫺34, ⫺1) 0.04
0.08 (1.62)
Matsuda
0.66 0.34 0.04
0.61 0.61 ⬎0.99
0.61
1.65 (1.44) 1.28 (1.26) ⫺22 (⫺31, ⫺12) ⬍0.0001
1.41 (1.33) 1.61 (1.48) ⫺13 (⫺25, 1) 0.07
1.48 (1.41)
Gutt
NEFA index
5.02 (1.61) 2.41 (1.38) ⫺52 (⫺59, ⫺43) ⬍0.0001
3.21 (1.64) 4.64 (1.77) ⫺31 (⫺45, ⫺14) 0.002
0.49 0.31 0.31
0.44 0.56 0.45
0.65 0.46 0.19
0.56 0.72 0.20
Correlation with SI clamp 0.50 0.65
0.96 (1.50) 0.69 (1.43) ⫺28 (⫺38, ⫺16) ⬍0.0001
0.78 (1.56) 0.93 (1.43) ⫺17 (⫺29, ⫺2) 0.03
Mean values* 0.84 (1.52) 3.73 (1.74)
Belfiore
0.60 0.36 0.14
0.52 0.67 0.27
0.60
0.78 (1.93) 0.32 (1.52) ⫺59 (⫺67, ⫺49) ⬍0.0001
0.45 (1.77) 0.72 (2.28) ⫺38 (⫺54, ⫺16) 0.002
0.54 (2.06)
1/HOMA-IR ([IU/ mmol]/ [ml/l])
1.66 (1.33) 2.16 (1.40) ⫺23 (⫺32, ⫺12) 0.0002 2.14 (1.35) 1.48 (1.27) ⫺31 (⫺38, ⫺23) ⬍0.0001 0.60 0.50 0.68 0.19 0.51 0.55 0.79
0.15 (1.09) 0.16 (1.14) ⫺8 (⫺12, ⫺3) 0.002 0.16 (1.11) 0.14 (1.06) ⫺13 (⫺16, ⫺10) ⬍0.0001 0.60 0.52 0.66 0.31 0.59 0.37 0.17
McAuley 1.85 (1.39)
QUICKI
Fasting
0.15 (1.12)
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0.57 0.42 0.35
0.52 0.66 0.31
0.60
0.19 (1.84) 0.08 (1.51) ⫺56 (⫺64, ⫺46) ⬍0.0001
0.11 (1.70) 0.18 (2.16) ⫺37 (⫺52, ⫺16) 0.002
0.14 (1.95)
1/fasting insulin, l/pmol
OGTT, oral glucose tolerances test; HOMA-IR, homeostatic model assessment of insulin resistance; BMI, body mass index in kg/m2; NEFA index nonesterified fatty acid-insulin sensitivity index; QUICKI, quantitative insulin sensitivity check index. Units for SIclamp/lean mass are in (mg/min)/(kg·U⫺1·ml⫺1). *Geometric means (SD) presented for all insulin sensitivity indices except Stumvoll, which presents absolute difference IQR); †%difference compares participants with to without CKD or with to without obesity, and corresponding P values come from comparison of geometric means; ‡for %difference between participants with to without CKD or with to without obesity; §for difference in correlation coefficients between participants with to without CKD or with to without obesity. All correlations are on log-scale. Pearson’s correlation coefficients were calculated using log-transformed values of each insulin sensitivity measurement.
All By estimated GFR ⬍60 ml·min- 1·1.73 m2 ⱖ60 ml·min⫺1·1.73 m2 P value§ By body mass index ⬍30 kg/m2 ⱖ30 kg/m2 P value§
All By estimated GFR ⬍60 ml·min⫺1·1.73 m2 ⱖ60 ml·min⫺1·1.73 m2 %Difference† P value‡ By BMI ⬍30 kg/m2 ⱖ30 kg/m2 %Difference† P value‡
SIclamp ([mg/min)]/ [kg· U⫺1·ml⫺1])
OGTT Based
Table 2. Insulin sensitivity indices by CKD status and BMI and correlation of insulin sensitivity indices with insulin sensitivity measured by hyperinsulinemic euglycemic clamp
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By Body Size
10 5 1
1
0.1
0.2
0.05
0.2
5
SIclamp/lean mass (mg/min)/(kg x µU/mL)
Obese Not obese
Fig. 1. Correlations by chronic kidney disease (CKD) and body size of Matsuda index and 1/HOMA-IR (homeostatic model assessment of insulin resistance) with insulin sensitivity measured by hyperinsulinemic euglycemic clamp. SIclamp, insulin sensitivity as measured by clamp indexed to lean mass.
0.2
0.2
0.5
1/HOMA-IR
1 0.5
1
2
CKD Control
2
5
SIclamp/lean mass (mg/min)/(kg x µU/mL)
0.1
0.05
0.1
0.2
0.05
SIclamp/lean mass (mg/min)/(kg x µU/mL)
0.1
0.2
SIclamp/lean mass (mg/min)/(kg x µU/mL)
analyses using SIclamp not indexed to lean mass. There was no interaction seen between CKD and obesity. At any SIclamp, obese participants tended to have Matsuda index and 1/HOMA-IR values that were lower than participants who were not obese (Fig. 1). In a multivariable model adjusted for SIclamp in addition to age, sex, race, and CKD, obese participants had a mean Matsuda index that was 36% lower than those who were not obese (Table 3). In a parallel multivariable model adjusting for SIclamp, age, sex, race, and
CKD, obese participants had a mean value of 1/HOMA-IR that was 46% lower than those who were not obese. Further adjusting for liver density and liver enzyme tests did not substantially affect these results. Insulin sensitivity was negatively correlated with adiposity, measured as BMI or fat mass, but the strength of correlations was stronger when insulin sensitivity was ascertained as the Matsuda index or 1/HOMA-IR than as SIclamp.
Table 3. Bias comparing Matsuda index and 1/HOMA-IR to insulin sensitivity derived from the hyperinsulinemic euglycemic clamp, adjusting for selected covariates Matsuda Model 1
CKD %Difference (95% CI)* P value BMI %Difference (95% CI)* P value
1/HOMA-IR Model 2
Model 1
Model 2
⫺15 (⫺30, 3) 0.09
⫺16 (⫺31, 1) 0.06
⫺23 (⫺37, ⫺5) 0.02
⫺24 (⫺38, ⫺6) 0.01
⫺36 (⫺49, ⫺19) 0.0002
⫺34 (⫺47, ⫺17) 0.0003
⫺46 (⫺58, ⫺31) ⬍0.0001
⫺44 (⫺56, ⫺29) ⬍0.0001
CKD, chronic kidney disease; CI, confidence interval. *Cell contents are %differences in surrogate insulin sensitivity indices (Matsuda index or 1/HOMA-IR, modeled separately as parallel log-transformed-dependent variables) associated with CKD (estimated GFR: ⬍60 ml·min⫺1·1.73m2) vs. no CKD or obesity (BMI ⱖ30 kg/m2) vs. no obesity. Estimates were derived from multivariable regression models that included SIclamp, age, sex, race, CKD, and obesity as independent variables (model 1) or these independent variables plus liver/spleen density and aspartate aminotransferase and alanine aminotransferase levels (model 2). Thus, in model 1 assessing the Matsuda index, participants with CKD were estimated to have a mean Matsuda index that was 15% lower than those without CKD at the same SIclamp, age, sex, race, and BMI status. AJP-Endocrinol Metab • doi:10.1152/ajpendo.00394.2016 • www.ajpendo.org
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0.05
1/HOMA-IR
Obese Not obese
2
5
Matsuda index
10
20
CKD Control
2
Matsuda index
20
By CKD
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DISCUSSION
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In our study of moderate to severe CKD, surrogate insulin sensitivity indices correlated moderately with SIclamp, and there were no significant differences in correlation comparing participants with vs. without CKD or with vs. without obesity. However, CKD and obesity did bias insulin sensitivity indices compared with clamp values. Specifically, at the same SIclamp, participants with CKD (or obesity) tended to have significantly lower values of insulin sensitivity indices than participants without CKD (or obesity). These biases were independent of demographic factors and were additive to each other. CKD had the smallest effect on QUICKI and Gutt (with participants with CKD having on average 8 and 13% lower measurement of insulin sensitivity than those without CKD). Obesity had the smallest effect on QUICKI, with obese participants having on average 13% lower insulin sensitivity index reading in comparison with nonobese participants (Table 2). Our observed correlations of the Matsuda index and 1/HOMA-IR with SIclamp were within the broad ranges reported in a meta-analysis of 120 published studies (28). In this meta-analysis, stronger correlations were seen when lower doses of insulin were infused during the clamp. At lower doses of infused insulin, insulin-resistant individuals may not fully suppress hepatic glucose production, and skeletal muscle glucose uptake is not as vigorously stimulated, so hepatic insulin sensitivity may contribute more to SIclamp. Similar to our study, correlations of surrogate insulin sensitivity indices with SIclamp were slightly weaker among older Swedish men with CKD compared with those without CKD, but differences in correlation were not significant (19). In a study of 12 African-American participants with end-stage renal disease treated with hemodialysis, correlations of surrogate insulin sensitivity indices with SIclamp were similar to ours (16). Taken together, these data suggest that correlations of insulin sensitivity indices with SIclamp may be slightly weaker in CKD compared with populations with normal kidney function but not markedly different. Our observed correlations of insulin sensitivity indices with SIclamp also tended to be lower among participants with vs. without obesity, which was perhaps due to the more limited range of insulin sensitivity among the obese, but differences were not statistically significant. In contrast, prior studies show stronger correlations of surrogate insulin sensitivity indices to the clamp in obese participants (23) or a more variable relationship between correlations and BMI status (21). Our key finding is that surrogate insulin sensitivity indices yield insulin sensitivity values that are systematically lower than values measured by hyperinsulinemic euglycemic clamp in CKD and obesity. We are not aware of a prior study that has explicitly addressed this issue. However, Stumvoll, et. al. (36) and Wagner, et. al. (37) used a best-fit approach to estimate insulin sensitivity, and the resulting empiric equations include BMI as an independent variable, implying that adiposity does confer a bias. Decreased insulin clearance is one potential reason for bias of insulin sensitivity indices by CKD and obesity. We and others demonstrated previously that insulin clearance is reduced in CKD, which is probably due to reduced glomerular insulin filtration as well as reduced insulin extraction from renal peritubular capillaries (6, 11). Insulin clearance is also
reduced in obesity (26, 32). Goodarzi et. al. (13) and Lee et al. (22) showed the consequences of this in vivo and demonstrated that both insulin clearance and insulin sensitivity account for variation in fasting insulin levels. Insulin sensitivity indices are calculated directly from insulin concentrations, including fasting insulin concentrations, and therefore, they are influenced by insulin clearance. Differential effects on various sites of insulin action constitute a second potential explanation for bias of insulin sensitivity indices by CKD and obesity. During the hyperinsulinemic euglycemic clamp, exogenous insulin escapes first-pass metabolism and reaches high peripheral concentrations, particularly with high insulin infusion rates. Endogenous hepatic glucose production is completely suppressed, and glucose is taken up primarily by skeletal muscle (7). As a result, SIclamp largely reflects skeletal muscle insulin sensitivity. Fasting glucose concentrations are maintained predominantly through hepatic gluconeogenesis and glycogenolysis, which are determined by the hepatic response to insulin secreted by the pancreas into the portal circulation. The OGTT reflects a combination of the fasting state at baseline, the ability of endogenous insulin to suppress hepatic glucose production during early time points, and the ability of endogenous insulin to stimulate glucose uptake into skeletal muscle during later time points (1, 7). Obesity preferentially reduces hepatic insulin sensitivity due to high insulin levels, inducing lipolysis, which leads to increasing nonesterified fatty acid levels in the blood and in which adipocyte-associated signaling molecules are involved (3, 35). Although it is thought that in humans sustained hyperinsulinemia induces insulin resistance in the peripheral tissues but not the liver (9), more recent studies in mice demonstrate that hyperinsulinemia induces hepatic CD36 expression, which leads to the development of hepatic insulin resistance and hepatosteatosis. Obesity is also correlated with skeletal muscle insulin resistance, but this link may be weaker than the link with hepatic insulin resistance. A tighter link of CKD with hepatic vs. skeletal muscle insulin resistance would run counter to current theory, since uremia is known to inhibit postreceptor insulin signaling in muscle via the phosphatidylinositol-4,5-bisphosphate 3-kinase pathway, and seminal studies demonstrated that insulin resistance in end-stage renal disease was specifically localized to skeletal muscle (12). However, subsequent studies of end-stage renal disease have identified defects in the incretin system and hyperglucagonemia, which may affect hepatic insulin sensitivity (17), and additional studies of hepatic insulin action in contemporary, frequently obese CKD patients may be warranted. One strength of our study is that we were able to correlate insulin sensitivity indices with the hyperinsulinemic euglycemic clamp, which is considered the classic standard for measuring insulin sensitivity. In addition, we evaluated bias by CKD and obesity in addition to correlation. We included a broad range of eGFR, including severe CKD, as well as a broad range of adiposity, and we used DEXA to quantify body composition in addition to BMI. However, our study was cross-sectional, so we cannot determine whether the biases we observed are attributable to CKD and obesity or from other unmeasured characteristics. It should be noted that endogenous insulin secretion may also bias clamp-based clearance estimates in our nondiabetic participants whose insulin secretion
INSULIN SENSITIVITY IN CKD AND OBESITY
5. 6.
7.
8.
9.
10.
11.
ACKNOWLEDGMENTS We thank Cassianne Robinson-Cohen for guidance on study procedures, Connor Henry for contributions to data collection, Tamara Chin and Alexandra Kozedub for their work on the clamps, and John Ruzinski, Denise Rock, and Charles Ellis for their work in the laboratory.
12.
13.
GRANTS The SUGAR study was funded primarily by Grant no. R01-DK-087726 from the National Institute of Diabetes and Digestive and Kidney Diseases. Additional support came from the Department of Veterans Affairs, National Institutes of Health Grants UL1-TR-000423, P01-DK-017047, P30-DK035816, R01-DK-088762, R01-DK-099199, and T32-DK-007247, and an unrestricted fund from the Northwest Kidney Center.
14.
16.
DISCLOSURES I. H. de Boer received research support from Abbvie and consulted for Amgen (Thousand Oaks, CA), Bayer HealthCare (Whippany, NJ), Boehringer Ingelheim (Mannheim, Germany), Ironwood (Cambridge, MA), and Janssen Biotech (Horsham, PA). S. E. Kahn received research support from Eli Lilly and consulted for Astra Zeneca, Boehringer Ingelheim, GlaxoSmithKline, Intarica Therapeutics, Janssen, Merck, Novo Nordisk, and Receptos. B. Kestenbaum received an honorarium from Keryx Biopharmaceuticals.
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AUTHOR CONTRIBUTIONS I.A., L.R.Z., N.R.R., and I.H.d.B. analyzed data; I.A., L.R.Z., B.K., K.M.U., S.E.K., and I.H.d.B. interpreted results of experiments; I.A. drafted manuscript; I.A., L.R.Z., N.R.R., A.M.H., B.K., K.M.U., S.E.K., and I.H.d.B. edited and revised manuscript; I.A., L.R.Z., N.R.R., A.M.H., B.K., K.M.U., S.E.K., and I.H.d.B. approved final version of manuscript; L.R.Z. and N.R.R. prepared figures; A.M.H. and K.M.U. performed experiments; B.K., K.M.U., S.E.K., and I.H.d.B. conceived and designed research. REFERENCES
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may not have been completely suppressed during the hyperinsulinemic euglycemic clamp, but we did not perform measurements of C-peptide during the clamp to use as a surrogate of endogenous insulin secretion. In addition, we did not perform an intravenous glucose tolerance test to measure insulin sensitivity using the minimal model approach (4). We were also unable to measure specific sites of insulin sensitivity. In conclusion, CKD and obesity do not significantly alter correlations of insulin sensitivity indices with insulin sensitivity measured by the hyperinsulinemic euglycemic clamp, but they do bias the results of insulin sensitivity indices compared with the clamp, generating systematically lower values. Our results suggest that insulin sensitivity indices derived from fasting or OGTT measurements may reflect aspects of insulin action different from those reflected by the hyperinsulinemic euglycemic clamp and that CKD and obesity may differentially affect these aspects of insulin sensitivity. Future studies of insulin sensitivity should carefully consider the sites and mechanisms of insulin resistance to be studied when choosing a method of quantifying insulin sensitivity.
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24. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and betacell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28: 412–419, 1985. doi:10.1007/BF00280883. 25. McAuley KA, Williams SM, Mann JI, Walker RJ, Lewis-Barned NJ, Temple LA, Duncan AW. Diagnosing insulin resistance in the general population. Diabetes Care 24: 460 –464, 2001. doi:10.2337/diacare.24.3. 460. 26. Meistas MT, Margolis S, Kowarski AA. Hyperinsulinemia of obesity is due to decreased clearance of insulin. Am J Physiol Endocrinol Metab 245: E155–E159, 1983. 27. Muniyappa R, Lee S, Chen H, Quon MJ. Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab 294: E15– E26, 2008. doi:10.1152/ajpendo.00645.2007. 28. Otten J, Ahrén B, Olsson T. Surrogate measures of insulin sensitivity vs the hyperinsulinaemic-euglycaemic clamp: a meta-analysis. Diabetologia 57: 1781–1788, 2014. doi:10.1007/s00125-014-3285-x. 29. Pham H, Robinson-Cohen C, Biggs ML, Ix JH, Mukamal KJ, Fried LF, Kestenbaum B, Siscovick DS, de Boer IH. Chronic kidney disease, insulin resistance, and incident diabetes in older adults. Clin J Am Soc Nephrol 7: 588 –594, 2012. doi:10.2215/CJN.11861111. 30. Pham H, Utzschneider KM, de Boer IH. Measurement of insulin resistance in chronic kidney disease. Curr Opin Nephrol Hypertens 20: 640 –646, 2011. doi:10.1097/MNH.0b013e32834b23c1. 31. Piekarski J, Goldberg HI, Royal SA, Axel L, Moss AA. Difference between liver and spleen CT numbers in the normal adult: its usefulness
RESEARCH ARTICLE
Translational Physiology
Chronic kidney disease and obesity bias surrogate estimates of insulin sensitivity compared with the hyperinsulinemic euglycemic clamp Iram Ahmad,1,2 Leila R. Zelnick,2 Nicole R. Robinson,2 Adriana M. Hung,3 Bryan Kestenbaum,2 Kristina M. Utzschneider,1,4 Steven E. Kahn,1,4 and Ian. H. de Boer2,4 1
Submitted 2 November 2016; accepted in final form 2 January 2017
Ahmad I, Zelnick LR, Robinson NR, Hung AM, Kestenbaum B, Utzschneider KM, Kahn SE, de Boer IH. Chronic kidney disease and obesity bias surrogate estimates of insulin sensitivity compared with the hyperinsulinemic euglycemic clamp. Am J Physiol Endocrinol Metab 312: E175–E182, 2017. First published January 10, 2017; doi:10.1152/ajpendo.00394.2016.—Insulin sensitivity can be measured by procedures such as the hyperinsulinemic euglycemic clamp or by using surrogate indices. Chronic kidney disease (CKD) and obesity may differentially affect these measurements because of changes in insulin kinetics and organ-specific effects on insulin sensitivity. In a cross-sectional study of 59 subjects with nondiabetic CKD [estimated glomerular filtration rate: (GFR) ⬍60 ml·min⫺1·1.73 m2] and 39 matched healthy controls, we quantified insulin sensitivity by clamp (SIclamp), oral glucose tolerance test, and fasting glucose and insulin. We compared surrogate insulin sensitivity indices to SIclamp using descriptive statistics, graphical analyses, correlation coefficients, and linear regression. Mean age was 62.6 yr; 48% of the participants were female, and 77% were Caucasian. Insulin sensitivity indices were 8 –38% lower in participants with vs. without CKD and 13–59% lower in obese compared with nonobese participants. Correlations of surrogate indices with SIclamp did not differ significantly by CKD or obesity status. Adjusting for SIclamp in addition to demographic factors, Matsuda index was 15% lower in participants with vs. without CKD (P ⫽ 0.09) and 36% lower in participants with vs. without obesity (P ⫽ 0.0001), whereas 1/HOMA-IR was 23% lower in participants with vs. without CKD (P ⫽ 0.02) and 46% lower in participants with vs. without obesity (P ⬍ 0.0001). We conclude that CKD and obesity do not significantly alter correlations of surrogate insulin sensitivity indices with SIclamp, but they do bias surrogate measurements of insulin sensitivity toward lower values. This bias may be due to differences in insulin kinetics or organ-specific responses to insulin.
(i.e., insulin resistance) is an important risk factor for type 2 diabetes, cardiovascular disease, and mortality (33). To determine the causes and consequences of insulin resistance and evaluate treatments for it, accurate measurements of insulin sensitivity are needed. The classic hyperinsulinemic euglycemic clamp directly measures the ability of insulin to stimulate whole body glucose uptake, which occurs predominantly in peripheral tissues, such as skeletal muscle, during high-dose exogenous
REDUCED SENSITIVITY TO THE METABOLIC ACTIONS OF INSULIN
Address for reprint requests and other correspondence: I. Ahmad, Kidney Research Institute, 325 9th Ave., Box 359606, Seattle, WA 98104 (e-mail: [email protected]). http://www.ajpendo.org
insulin infusion (8). The trained staff required for administration of the continuous infusions coupled with frequent blood sampling typically restrict application of the clamp to small research protocols. Surrogate insulin sensitivity indices based on insulin and glucose concentrations obtained from fasting samples or the oral glucose tolerance test (OGTT) have been developed to apply in a wider variety of settings. Insulin sensitivity indices derived from these other approaches generally correlate well with results from the hyperinsulinemic euglycemic clamp (19, 21, 23, 28), but they may be differentially affected in some pathophysiological conditions. Fasting insulin and glucose concentrations largely reflect hepatic insulin sensitivity and are influenced by insulin clearance in addition to insulin sensitivity (8, 13). The OGTT includes insulin and glucose measurements made while fasting and after endogenous insulin secretion, which are also affected by insulin clearance and may reflect a combination of hepatic and skeletal muscle insulin sensitivity. Chronic kidney disease (CKD) is a common condition associated with decreased insulin sensitivity (6, 11, 29). Because skeletal muscle is an important site of impaired insulin signaling in CKD, differences in insulin sensitivity between people with and without CKD could potentially be more pronounced using the clamp compared with surrogate indices (30). On the other hand, because insulin clearance is decreased in CKD, surrogate indices based on fasting insulin or OGTT results may yield lower insulin sensitivity values than those measured with the clamp (5, 6). Obesity is strongly coupled with hepatic insulin resistance (35) and also decreases insulin clearance (26), which may lead to larger effects on surrogate indices of insulin sensitivity than when measured by clamp. These effects of CKD and obesity could potentially reduce the precision of insulin sensitivity indices compared with the clamp or bias insulin sensitivity indices compared with the clamp. The purpose of our study was to evaluate the performance of surrogate insulin sensitivity indices compared with insulin sensitivity measured using the hyperinsulinemic euglycemic clamp among nondiabetic subjects with and without moderate to severe CKD. We hypothesized that, comparing participants with CKD vs. without CKD, surrogate insulin sensitivity indices would correlate less strongly with insulin sensitivity measured by the clamp and would overestimate the degree of insulin sensitivity in comparison with the E175
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Division of Metabolism, Endocrinology, and Nutrition, Department of Medicine, University of Washington, Seattle, Washington; 2Division of Nephrology, Department of Medicine, Kidney Research Institute, University of Washington, Seattle, Washington; 3Division of Nephrology and Hypertension, Vanderbilt University School of Medicine, Nashville, Tennessee; and 4 Veterans Affairs Puget Sound Health Care System, Seattle, Washington
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clamp. We evaluated the impact of obesity on the performance of insulin sensitivity indices compared with the clamp as a secondary aim. METHODS
SIclamp ⫽
glucose infusion rate ⫻ concentration of infused glucose
共insulin concentration at steady state ⫺ fasting insulin concentration兲 ⫻ lean body mass
The inclusion of insulin concentration in the denominator of SI accounts for variability in achieved insulin concentrations and is supported by the linear relationship between insulin concentration and glucose disposal rate within individuals in our dose-ranging study (6). For primary analyses, we normalized SIclamp to lean body mass measured by DEXA because lean mass tissues (primarily liver and particularly skeletal muscle) are the primary sites for insulin-mediated glucose disposal, and most published studies take this approach (27). Insulin clearance was calculated as the insulin infusion rate divided by steady-state insulin concentration (6). A standard 75-g OGTT was performed approximately 1 wk after the clamp, and insulin and glucose values before and during the OGTT were used to derive the Matsuda index and 1/HOMA-IR (homeostatic model assessment of insulin resistance), two of the more common insulin sensitivity indices. Basal blood samples for glucose and insulin measurements were taken as the average of measurements from ⫺10, ⫺5, and 0 min; plasma glucose and insulin drawn again at 30, 60, 90, and 120 min after the start of glucose ingestion. Plasma free fatty acid levels were measured during the OGTT at ⫺10, ⫺5, 30, 60, and 120 min, and baseline values were the average of the levels at ⫺10 and ⫺5 min. For secondary analysis, we also calculated the Stumvoll, Gutt, McAuley, quantitative insulin sensitivity check index (QUICKI), simplified Belfiore index, and nonesterified fatty acidinsulin sensitivity index (NEFA-ISI) (2, 14, 20, 24, 25, 36, 37). For the simplified Belfiore index calculations, we used the free fatty acid concentrations of our nonobese, non-CKD subgroup to derive mean normal values. There were six negative values of Stumvoll for unknown reasons, which were truncated at the lowest positive value of the Stumvoll index. Six participants had an OGTT free fatty acid
(NEFA) measurement of zero at 120 min, and the NEFA level at 120 min was set to the lowest non-zero value observed (0.01) in calculating the NEFA index. Laboratory analysis. Blood samples were drawn and placed on ice slurry before centrifugation at 4°C. Samples were aliquoted and frozen at ⫺80°C within 1 h of drawing. NEFAs were drawn into Vacutainer tubes containing orlistat. Blood glucose (glucose hexokinase method; Roche Module P Chemistry autoanalyzer; Roche, Basel, Switzerland) and insulin (2-site immune-enzymometric assay; Tosoh 2000 Autoanalyzer) concentrations, NEFA levels, and dextrose infusate concentrations were measured at the Northwest Lipid Research Laboratories (Seattle, WA). Statistical analysis. All analyses were performed using R 3.2.2 (R Foundation for Statistical Computing, Vienna, Austria; http://www. R-project.org/). Bivariate relationships of continuous variables were evaluated using scatterplots and Pearson correlation coefficients. Bestfit lines were generated using linear regression. Multivariable relationships were evaluated using linear regression with Huber-White standard errors (38), and inferential comparisons of correlation coefficients were made using Fisher’s r-to-z transformation (10). Approximately 5% of participants had missing covariates or outcomes. For regression and correlation analyses, these values were multiply imputed using chained equations, and imputations were combined using Rubin’s rules (34). Insulin sensitivity index measurements were log transformed to decrease skew and improve interpretation. To put our results into context, we abstracted published data evaluating the correlations of Matsuda Index or HOMA-IR with clamp insulin sensitivity (28), plotted correlation coefficient magnitudes vs. clamp insulin dose, restricting ourselves to studies that used dosing per body
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Study subjects. We performed the Study of Glucose and Insulin in Renal Disease (SUGAR) to evaluate insulin sensitivity among people with a broad range of glomerular filtration rate (GFR) and to test the performance of insulin sensitivity indices among people with moderate to severe CKD (6). Subjects were recruited from nephrology and primary care clinics affiliated with the University of Washington and nearby institutions in Seattle, WA. Exclusion criteria for both groups included age ⬍18 yr, a clinical diagnosis of diabetes, maintenance dialysis or fistula in place, history of kidney transplantation, use of medications known to reduce insulin sensitivity (including corticosteroids and immunosuppressants), fasting serum glucose ⱖ126 mg/dl, and hemoglobin ⬍10 g/dl. This study was approved by the University of Washington Human Subjects Division Institutional Review Board, and informed written consent was obtained from all subjects before their participation. Estimated GFR and body composition. Serum creatinine and cystatin C were measured in fasting serum using a Beckman DxC automated chemistry analyzer. Creatinine and cystatin C concentrations were traceable to isotope dilution mass spectrometry and ERM-DA471/IFCC, respectively. Interassay coefficients of variation were 1.5–3.0%. GFR was estimated from creatinine and cystatin C concentrations using the CKD-EPI formula (18). Fat and lean masses were measured by dual-energy X-ray absorptiometry (DEXA; GE Prodigy and/or GE Lunar iDEXA with enCORE 14.1 software; GE Healthcare, Waukesha, WI) by an experienced operator. Seventeen participants who completed the iDEXA alone
had their values calibrated to the Prodigy by single imputation using regression models from participants who had both the GE Prodigy and Lunar iDEXA, and the values from or calibrated to the Prodigy were used for analysis. Liver and spleen densities were measured by single-slice computed tomography, and the ratio of liver density to spleen density was used to account for differences in technique (31). Measurements of glucose and insulin homeostasis. Insulin sensitivity was measured using the hyperinsulinemic euglycemic clamp technique, as described previously (6), adapted from the method of DeFronzo, et. al. (8). Each participant was admitted to the University of Washington Clinical Research Center after an overnight fast. Intravenous catheters were placed in peripheral veins in each upper extremity and kept patent with a slow infusion of normal saline. One arm was warmed to allow for the sampling of “arterialized” blood. Three baseline blood samples were drawn 5 min apart. At time zero, an insulin infusion was initiated as a prime (160 mU·m2·min⫺1 for 5 min) followed by a constant rate (80 mU·m2·min⫺1). This insulin dose was chosen from a dose-ranging study because it effectively suppressed endogenous glucose production, fell within a range demonstrating a linear dose-response relationship with glucose uptake, and yielded a submaximal rate of glucose disposal (6). A variable rate infusion of unlabeled 20% dextrose was administered to maintain blood glucose (measured every 5 min) at ~90 mg/dl. Beginning 120 –150 min after initiation of the insulin infusion, the dextrose infusion rate was held constant for 30 min, over which time three blood samples were obtained 15 min apart to calculate the glucose disposal rate. The glucose infusion rate was calculated as the glucose infusion rate during the last 30 min of the clamp, adjusted for the drift in plasma glucose concentration using Steels’ non-steady-state equations. Insulin sensitivity (SIclamp) was calculated as
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surface area (similar to our study) and converting to mU·m2·min⫺1 when necessary. RESULTS
Table 1. Characteristics of participants in the Study of Glucose and Renal Disease Estimated GFR All (n ⫽ 98)
Age (yr), means (SD) Female sex, n (%) Race, n (%) White Black Asian/Pacific Islander Height, cm Weight, kg BMI, kg/m2 Lean mass, kg Fat mass, kg Serum creatinine (mg/dl), median (IQR) Serum cystatin (mg/l), median (IQR) Estimated GFR (ml·min⫺1·1.73 m2), means (IQR) Fasting glucose, OGTT, mg/dl 2-h Glucose, OGTT, mg/dl Fasting insulin, clamp, U/ml Steady-state insulin, clamp, U/ml
ⱖ60 ml·min·1.73 m2 (n ⫽ 39)
62.6 (13.3) 47 (48)
61.0 (12.4) 17 (44)
75 (77) 17 (17) 6 (6)
34 (87) 4 (10) 1 (3)
170.7 (0.1) 85.5 (20.2) 29.3 (6.4) 51.7 (11.8) 30.2 (12.7) 1.4 (0.9–1.8) 1.3 (0.96–1.7) 49.9 (36.6–81.1) 99.8 (8.9) 150.6 (39.2) 9.0 (5.4) 178.8 (39.8)
⬍60 ml·min·1.73 m2 (n ⫽ 59)
Demographics 63.6 (13.9) 30 (51)
Body Mass Index ⬍30 kg/m2 (n ⫽ 58)
62.4 (14.1) 26 (45)
41 (69) 46 (79) 13 (22) 6 (10) 5 (8) 6 (10) Physical characteristics, means (SD) 172.9 (10.7) 170.4 (10.3) 172.0 (10.9) 82.6 (20.6) 88.1 (19.8) 74.4 (13.8) 27.9 (6.6) 30.3 (6.2) 25.0 (3.0) 53.1 (12.6) 50.7 (11.2) 48.9 (10.9) 27.7 (14.0) 31.9 (11.6) 22.4 (8.0) Laboratory data 0.9 (0.8–1.0) 1.7 (1.5–2.1) 1.2 (0.9–1.7) 0.9 (0.7–1.0) 1.6 (1.4–2.0) 1.2 (0.8–1.6) 85.3 (76.0–102.6) 98.4 (9.2) 149.1 (44.6) 7.3 (5.2) 162.0 (34.2)
39.1 (27.2–47.6) 100.8 (8.6) 151.6 (35.5) 10.1 (5.3) 190.0 (39.5)
56.9 (39.4–86.8) 97.9 (9.4) 147.1 (41.2) 6.3 (3.8) 167.2 (36.1)
ⱖ30 kg/m2 (n ⫽ 40)
62.8 (12.2) 21 (53) 29 (73) 11 (28) 0 (0) 170.5 (10.0) 102.5 (16.1) 35.2 (4.4) 55.6 (12.0) 41.5 (9.4) 1.6 (1.2–1.8) 1.4 (1.1–1.9) 42.8 (36.0–68.2) 102.7 (7.3) 155.6 (36.0) 13.0 (4.9) 195.7 (39.1)
GFR, glomerular filtration rate; BMI, body mass index. IQR, interquartile range; OGTT, oral glucose tolerance test. Data were missing for fat-free mass and fat mass for 5 participants. AJP-Endocrinol Metab • doi:10.1152/ajpendo.00394.2016 • www.ajpendo.org
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Of the 98 participants included in SUGAR, 59 (60%) had CKD, 38 (39%) were obese, 47 (48%) were women, and 75 (77%) were white (Table 1). Mean age was 62.6 yr, and mean BMI was 29.3 kg/m2. For the participants with CKD, CKD was most often attributed by supervising clinicians to hypertension. BMI was similar when comparing participants with CKD vs. those without; obese participants tended to have lower estimated GFR (eGFR) than those without obesity. Ten (25.6%) participants without CKD were obese, and 28 (47.5%) with CKD were obese. CKD and insulin sensitivity. Insulin sensitivity was significantly lower among participants with CKD vs. without CKD for eight of 10 methods used to measure insulin sensitivity, the exceptions being the Gutt and Stumvoll indices, which were nonsignificantly lower (Table 2). Comparing participants with CKD with those without CKD, geometric mean SIclamp was 19% lower, and mean values of the insulin sensitivity indices ranged from 8 to 38% lower. Results were similar repeating analyses using SIclamp not indexed to lean mass or using fasting insulin and glucose measurements obtained before the clamp rather than before the OGTT. Mean (SD) insulin clearance was 998.3 (212.1) ml/min in the non-CKD group and 876.1 (225.7) ml/min in the CKD group (P ⬍ 0.01). Correlation coefficients comparing insulin sensitivity indices to SIclamp were lower among participants with vs. without CKD for eight of nine indices (and equal for the Gutt index), but none of the differences in correlation were statistically significant (Table 2). Results were similar repeating analyses using SIclamp not indexed to lean mass. Correlation coefficients
were within the broad ranges reported previously, particularly considering that correlations tended to be lower with higher doses of insulin infused during the clamp. At most values of SIclamp, particularly higher values of SIclamp, participants with CKD tended to have Matsuda index and 1/HOMA-IR values that were lower than participants without CKD (Fig. 1). Results were similar when using SIclamp not indexed to lean mass. In a multivariable model adjusted for SIclamp in addition to age, sex, race, and BMI status, participants with CKD had a mean Matsuda index that was 15% lower than those without CKD (P ⫽ 0.09, Table 3). In a parallel multivariable model adjusting for age, sex, race, and BMI status, participants with CKD had a mean 1/HOMA-IR that was 23% lower than those without CKD (P ⫽ 0.02). Further adjusting for liver density and liver enzyme tests did not substantially affect these results. Obesity and insulin sensitivity. Insulin sensitivity was significantly lower among obese (BMI ⱖ30 kg/m2) participants than among the nonobese (BMI ⬍30 kg/m2) for all methods used to measure insulin sensitivity (Table 2). Comparing obese participants with nonobese participants, mean SIclamp was 31% lower, and mean values of the insulin sensitivity indices ranged from 13 to 59% lower. Results were similar when analyses were repeated without correcting SIclamp for lean mass. Mean (SD) insulin clearance was 934.0 (221.0) ml/min in the nonobese group and 910.1 (238.3) ml/min in the obese group (P ⫽ 0.06). Correlation coefficients comparing insulin sensitivity indices to SIclamp were lower among participants with vs. without obesity for eight of nine indices (except for the McAuley index), but none of the differences in correlation were statistically significant (Table 2). Results were similar repeating
AJP-Endocrinol Metab • doi:10.1152/ajpendo.00394.2016 • www.ajpendo.org 0.48 0.61 0.38 0.57 0.23 0.052
0.57 0.68 0.40 0.63 0.43 0.19
0.08 (1.76) 0.04 (1.97) ⫺55 (⫺65, ⫺41) ⬍0.0001 0.56
5.5 (1.8) 2.7 (1.7) ⫺50 (⫺60, ⫺38) ⬍0.0001
0.09 (1.60) 0.06 (1.54) ⫺31 (⫺42, ⫺16) 0.0002
0.05 (2.14) 0.07 (1.89) ⫺25 (⫺43, 0) 0.052
0.06 (2.06)
Stumvoll
0.64
3.5 (1.8) 5.3 (2.0) ⫺33 (⫺49, ⫺12) 0.005
4.2 (1.9)
0.07 (1.63) 0.09 (1.58) ⫺19 (⫺34, ⫺1) 0.04
0.08 (1.62)
Matsuda
0.66 0.34 0.04
0.61 0.61 ⬎0.99
0.61
1.65 (1.44) 1.28 (1.26) ⫺22 (⫺31, ⫺12) ⬍0.0001
1.41 (1.33) 1.61 (1.48) ⫺13 (⫺25, 1) 0.07
1.48 (1.41)
Gutt
NEFA index
5.02 (1.61) 2.41 (1.38) ⫺52 (⫺59, ⫺43) ⬍0.0001
3.21 (1.64) 4.64 (1.77) ⫺31 (⫺45, ⫺14) 0.002
0.49 0.31 0.31
0.44 0.56 0.45
0.65 0.46 0.19
0.56 0.72 0.20
Correlation with SI clamp 0.50 0.65
0.96 (1.50) 0.69 (1.43) ⫺28 (⫺38, ⫺16) ⬍0.0001
0.78 (1.56) 0.93 (1.43) ⫺17 (⫺29, ⫺2) 0.03
Mean values* 0.84 (1.52) 3.73 (1.74)
Belfiore
0.60 0.36 0.14
0.52 0.67 0.27
0.60
0.78 (1.93) 0.32 (1.52) ⫺59 (⫺67, ⫺49) ⬍0.0001
0.45 (1.77) 0.72 (2.28) ⫺38 (⫺54, ⫺16) 0.002
0.54 (2.06)
1/HOMA-IR ([IU/ mmol]/ [ml/l])
1.66 (1.33) 2.16 (1.40) ⫺23 (⫺32, ⫺12) 0.0002 2.14 (1.35) 1.48 (1.27) ⫺31 (⫺38, ⫺23) ⬍0.0001 0.60 0.50 0.68 0.19 0.51 0.55 0.79
0.15 (1.09) 0.16 (1.14) ⫺8 (⫺12, ⫺3) 0.002 0.16 (1.11) 0.14 (1.06) ⫺13 (⫺16, ⫺10) ⬍0.0001 0.60 0.52 0.66 0.31 0.59 0.37 0.17
McAuley 1.85 (1.39)
QUICKI
Fasting
0.15 (1.12)
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0.57 0.42 0.35
0.52 0.66 0.31
0.60
0.19 (1.84) 0.08 (1.51) ⫺56 (⫺64, ⫺46) ⬍0.0001
0.11 (1.70) 0.18 (2.16) ⫺37 (⫺52, ⫺16) 0.002
0.14 (1.95)
1/fasting insulin, l/pmol
OGTT, oral glucose tolerances test; HOMA-IR, homeostatic model assessment of insulin resistance; BMI, body mass index in kg/m2; NEFA index nonesterified fatty acid-insulin sensitivity index; QUICKI, quantitative insulin sensitivity check index. Units for SIclamp/lean mass are in (mg/min)/(kg·U⫺1·ml⫺1). *Geometric means (SD) presented for all insulin sensitivity indices except Stumvoll, which presents absolute difference IQR); †%difference compares participants with to without CKD or with to without obesity, and corresponding P values come from comparison of geometric means; ‡for %difference between participants with to without CKD or with to without obesity; §for difference in correlation coefficients between participants with to without CKD or with to without obesity. All correlations are on log-scale. Pearson’s correlation coefficients were calculated using log-transformed values of each insulin sensitivity measurement.
All By estimated GFR ⬍60 ml·min- 1·1.73 m2 ⱖ60 ml·min⫺1·1.73 m2 P value§ By body mass index ⬍30 kg/m2 ⱖ30 kg/m2 P value§
All By estimated GFR ⬍60 ml·min⫺1·1.73 m2 ⱖ60 ml·min⫺1·1.73 m2 %Difference† P value‡ By BMI ⬍30 kg/m2 ⱖ30 kg/m2 %Difference† P value‡
SIclamp ([mg/min)]/ [kg· U⫺1·ml⫺1])
OGTT Based
Table 2. Insulin sensitivity indices by CKD status and BMI and correlation of insulin sensitivity indices with insulin sensitivity measured by hyperinsulinemic euglycemic clamp
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INSULIN SENSITIVITY IN CKD AND OBESITY
By Body Size
10 5 1
1
0.1
0.2
0.05
0.2
5
SIclamp/lean mass (mg/min)/(kg x µU/mL)
Obese Not obese
Fig. 1. Correlations by chronic kidney disease (CKD) and body size of Matsuda index and 1/HOMA-IR (homeostatic model assessment of insulin resistance) with insulin sensitivity measured by hyperinsulinemic euglycemic clamp. SIclamp, insulin sensitivity as measured by clamp indexed to lean mass.
0.2
0.2
0.5
1/HOMA-IR
1 0.5
1
2
CKD Control
2
5
SIclamp/lean mass (mg/min)/(kg x µU/mL)
0.1
0.05
0.1
0.2
0.05
SIclamp/lean mass (mg/min)/(kg x µU/mL)
0.1
0.2
SIclamp/lean mass (mg/min)/(kg x µU/mL)
analyses using SIclamp not indexed to lean mass. There was no interaction seen between CKD and obesity. At any SIclamp, obese participants tended to have Matsuda index and 1/HOMA-IR values that were lower than participants who were not obese (Fig. 1). In a multivariable model adjusted for SIclamp in addition to age, sex, race, and CKD, obese participants had a mean Matsuda index that was 36% lower than those who were not obese (Table 3). In a parallel multivariable model adjusting for SIclamp, age, sex, race, and
CKD, obese participants had a mean value of 1/HOMA-IR that was 46% lower than those who were not obese. Further adjusting for liver density and liver enzyme tests did not substantially affect these results. Insulin sensitivity was negatively correlated with adiposity, measured as BMI or fat mass, but the strength of correlations was stronger when insulin sensitivity was ascertained as the Matsuda index or 1/HOMA-IR than as SIclamp.
Table 3. Bias comparing Matsuda index and 1/HOMA-IR to insulin sensitivity derived from the hyperinsulinemic euglycemic clamp, adjusting for selected covariates Matsuda Model 1
CKD %Difference (95% CI)* P value BMI %Difference (95% CI)* P value
1/HOMA-IR Model 2
Model 1
Model 2
⫺15 (⫺30, 3) 0.09
⫺16 (⫺31, 1) 0.06
⫺23 (⫺37, ⫺5) 0.02
⫺24 (⫺38, ⫺6) 0.01
⫺36 (⫺49, ⫺19) 0.0002
⫺34 (⫺47, ⫺17) 0.0003
⫺46 (⫺58, ⫺31) ⬍0.0001
⫺44 (⫺56, ⫺29) ⬍0.0001
CKD, chronic kidney disease; CI, confidence interval. *Cell contents are %differences in surrogate insulin sensitivity indices (Matsuda index or 1/HOMA-IR, modeled separately as parallel log-transformed-dependent variables) associated with CKD (estimated GFR: ⬍60 ml·min⫺1·1.73m2) vs. no CKD or obesity (BMI ⱖ30 kg/m2) vs. no obesity. Estimates were derived from multivariable regression models that included SIclamp, age, sex, race, CKD, and obesity as independent variables (model 1) or these independent variables plus liver/spleen density and aspartate aminotransferase and alanine aminotransferase levels (model 2). Thus, in model 1 assessing the Matsuda index, participants with CKD were estimated to have a mean Matsuda index that was 15% lower than those without CKD at the same SIclamp, age, sex, race, and BMI status. AJP-Endocrinol Metab • doi:10.1152/ajpendo.00394.2016 • www.ajpendo.org
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0.05
1/HOMA-IR
Obese Not obese
2
5
Matsuda index
10
20
CKD Control
2
Matsuda index
20
By CKD
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In our study of moderate to severe CKD, surrogate insulin sensitivity indices correlated moderately with SIclamp, and there were no significant differences in correlation comparing participants with vs. without CKD or with vs. without obesity. However, CKD and obesity did bias insulin sensitivity indices compared with clamp values. Specifically, at the same SIclamp, participants with CKD (or obesity) tended to have significantly lower values of insulin sensitivity indices than participants without CKD (or obesity). These biases were independent of demographic factors and were additive to each other. CKD had the smallest effect on QUICKI and Gutt (with participants with CKD having on average 8 and 13% lower measurement of insulin sensitivity than those without CKD). Obesity had the smallest effect on QUICKI, with obese participants having on average 13% lower insulin sensitivity index reading in comparison with nonobese participants (Table 2). Our observed correlations of the Matsuda index and 1/HOMA-IR with SIclamp were within the broad ranges reported in a meta-analysis of 120 published studies (28). In this meta-analysis, stronger correlations were seen when lower doses of insulin were infused during the clamp. At lower doses of infused insulin, insulin-resistant individuals may not fully suppress hepatic glucose production, and skeletal muscle glucose uptake is not as vigorously stimulated, so hepatic insulin sensitivity may contribute more to SIclamp. Similar to our study, correlations of surrogate insulin sensitivity indices with SIclamp were slightly weaker among older Swedish men with CKD compared with those without CKD, but differences in correlation were not significant (19). In a study of 12 African-American participants with end-stage renal disease treated with hemodialysis, correlations of surrogate insulin sensitivity indices with SIclamp were similar to ours (16). Taken together, these data suggest that correlations of insulin sensitivity indices with SIclamp may be slightly weaker in CKD compared with populations with normal kidney function but not markedly different. Our observed correlations of insulin sensitivity indices with SIclamp also tended to be lower among participants with vs. without obesity, which was perhaps due to the more limited range of insulin sensitivity among the obese, but differences were not statistically significant. In contrast, prior studies show stronger correlations of surrogate insulin sensitivity indices to the clamp in obese participants (23) or a more variable relationship between correlations and BMI status (21). Our key finding is that surrogate insulin sensitivity indices yield insulin sensitivity values that are systematically lower than values measured by hyperinsulinemic euglycemic clamp in CKD and obesity. We are not aware of a prior study that has explicitly addressed this issue. However, Stumvoll, et. al. (36) and Wagner, et. al. (37) used a best-fit approach to estimate insulin sensitivity, and the resulting empiric equations include BMI as an independent variable, implying that adiposity does confer a bias. Decreased insulin clearance is one potential reason for bias of insulin sensitivity indices by CKD and obesity. We and others demonstrated previously that insulin clearance is reduced in CKD, which is probably due to reduced glomerular insulin filtration as well as reduced insulin extraction from renal peritubular capillaries (6, 11). Insulin clearance is also
reduced in obesity (26, 32). Goodarzi et. al. (13) and Lee et al. (22) showed the consequences of this in vivo and demonstrated that both insulin clearance and insulin sensitivity account for variation in fasting insulin levels. Insulin sensitivity indices are calculated directly from insulin concentrations, including fasting insulin concentrations, and therefore, they are influenced by insulin clearance. Differential effects on various sites of insulin action constitute a second potential explanation for bias of insulin sensitivity indices by CKD and obesity. During the hyperinsulinemic euglycemic clamp, exogenous insulin escapes first-pass metabolism and reaches high peripheral concentrations, particularly with high insulin infusion rates. Endogenous hepatic glucose production is completely suppressed, and glucose is taken up primarily by skeletal muscle (7). As a result, SIclamp largely reflects skeletal muscle insulin sensitivity. Fasting glucose concentrations are maintained predominantly through hepatic gluconeogenesis and glycogenolysis, which are determined by the hepatic response to insulin secreted by the pancreas into the portal circulation. The OGTT reflects a combination of the fasting state at baseline, the ability of endogenous insulin to suppress hepatic glucose production during early time points, and the ability of endogenous insulin to stimulate glucose uptake into skeletal muscle during later time points (1, 7). Obesity preferentially reduces hepatic insulin sensitivity due to high insulin levels, inducing lipolysis, which leads to increasing nonesterified fatty acid levels in the blood and in which adipocyte-associated signaling molecules are involved (3, 35). Although it is thought that in humans sustained hyperinsulinemia induces insulin resistance in the peripheral tissues but not the liver (9), more recent studies in mice demonstrate that hyperinsulinemia induces hepatic CD36 expression, which leads to the development of hepatic insulin resistance and hepatosteatosis. Obesity is also correlated with skeletal muscle insulin resistance, but this link may be weaker than the link with hepatic insulin resistance. A tighter link of CKD with hepatic vs. skeletal muscle insulin resistance would run counter to current theory, since uremia is known to inhibit postreceptor insulin signaling in muscle via the phosphatidylinositol-4,5-bisphosphate 3-kinase pathway, and seminal studies demonstrated that insulin resistance in end-stage renal disease was specifically localized to skeletal muscle (12). However, subsequent studies of end-stage renal disease have identified defects in the incretin system and hyperglucagonemia, which may affect hepatic insulin sensitivity (17), and additional studies of hepatic insulin action in contemporary, frequently obese CKD patients may be warranted. One strength of our study is that we were able to correlate insulin sensitivity indices with the hyperinsulinemic euglycemic clamp, which is considered the classic standard for measuring insulin sensitivity. In addition, we evaluated bias by CKD and obesity in addition to correlation. We included a broad range of eGFR, including severe CKD, as well as a broad range of adiposity, and we used DEXA to quantify body composition in addition to BMI. However, our study was cross-sectional, so we cannot determine whether the biases we observed are attributable to CKD and obesity or from other unmeasured characteristics. It should be noted that endogenous insulin secretion may also bias clamp-based clearance estimates in our nondiabetic participants whose insulin secretion
INSULIN SENSITIVITY IN CKD AND OBESITY
5. 6.
7.
8.
9.
10.
11.
ACKNOWLEDGMENTS We thank Cassianne Robinson-Cohen for guidance on study procedures, Connor Henry for contributions to data collection, Tamara Chin and Alexandra Kozedub for their work on the clamps, and John Ruzinski, Denise Rock, and Charles Ellis for their work in the laboratory.
12.
13.
GRANTS The SUGAR study was funded primarily by Grant no. R01-DK-087726 from the National Institute of Diabetes and Digestive and Kidney Diseases. Additional support came from the Department of Veterans Affairs, National Institutes of Health Grants UL1-TR-000423, P01-DK-017047, P30-DK035816, R01-DK-088762, R01-DK-099199, and T32-DK-007247, and an unrestricted fund from the Northwest Kidney Center.
14.
16.
DISCLOSURES I. H. de Boer received research support from Abbvie and consulted for Amgen (Thousand Oaks, CA), Bayer HealthCare (Whippany, NJ), Boehringer Ingelheim (Mannheim, Germany), Ironwood (Cambridge, MA), and Janssen Biotech (Horsham, PA). S. E. Kahn received research support from Eli Lilly and consulted for Astra Zeneca, Boehringer Ingelheim, GlaxoSmithKline, Intarica Therapeutics, Janssen, Merck, Novo Nordisk, and Receptos. B. Kestenbaum received an honorarium from Keryx Biopharmaceuticals.
17.
18.
AUTHOR CONTRIBUTIONS I.A., L.R.Z., N.R.R., and I.H.d.B. analyzed data; I.A., L.R.Z., B.K., K.M.U., S.E.K., and I.H.d.B. interpreted results of experiments; I.A. drafted manuscript; I.A., L.R.Z., N.R.R., A.M.H., B.K., K.M.U., S.E.K., and I.H.d.B. edited and revised manuscript; I.A., L.R.Z., N.R.R., A.M.H., B.K., K.M.U., S.E.K., and I.H.d.B. approved final version of manuscript; L.R.Z. and N.R.R. prepared figures; A.M.H. and K.M.U. performed experiments; B.K., K.M.U., S.E.K., and I.H.d.B. conceived and designed research. REFERENCES
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may not have been completely suppressed during the hyperinsulinemic euglycemic clamp, but we did not perform measurements of C-peptide during the clamp to use as a surrogate of endogenous insulin secretion. In addition, we did not perform an intravenous glucose tolerance test to measure insulin sensitivity using the minimal model approach (4). We were also unable to measure specific sites of insulin sensitivity. In conclusion, CKD and obesity do not significantly alter correlations of insulin sensitivity indices with insulin sensitivity measured by the hyperinsulinemic euglycemic clamp, but they do bias the results of insulin sensitivity indices compared with the clamp, generating systematically lower values. Our results suggest that insulin sensitivity indices derived from fasting or OGTT measurements may reflect aspects of insulin action different from those reflected by the hyperinsulinemic euglycemic clamp and that CKD and obesity may differentially affect these aspects of insulin sensitivity. Future studies of insulin sensitivity should carefully consider the sites and mechanisms of insulin resistance to be studied when choosing a method of quantifying insulin sensitivity.
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