Nutrition, Inflammation and Insulin Resistance in End-Stage Renal Disease
Phase: PHASE2
Conditions: ESRD
Sponsor: VA Office of Research and Development

Protocol Date: 12/16/2015 
 
 
 
 
 
 
 
 
Nutrition, Inflammation and Insulin Resistance in 
End Stage Renal Disease—Aim 2 
 
 
T. Alp Ikizler, MD, Professor 
Division of Nephrology 
Vanderbilt University Medical Center 
S-3223 Medical Center North 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
2 
Table of Contents: 
 
Study Schema 
1.0 
Background 
2.0 
Rationale and Specific Aims 
3.0 
Animal Studies and Previous Human Studies 
4.0 
Inclusion/Exclusion Criteria 
5.0 
Enrollment/Randomization 
6.0 
Study Procedures 
7.0 
Risks of Investigational Agents/Devices (side effects) 
8.0 
Reporting of Adverse Events or Unanticipated Problems involving Risk to 
Participants or Others 
9.0 
Study Withdrawal/Discontinuation 
10.0 Statistical Considerations 
11.0 Privacy/Confidentiality Issues 
12.0 Follow-up and Record Retention 
 
Appendix 
References

 
 
3 
Uremic 
Wasting
Inflammation
Insulin 
resistance
?
?
Figure A.1. A schematic diagram of the proposed mechanism of uremic wasting. 
Dashed lines indicate the hypotheses that will be tested in this application.
?
1.0 
Background 
 
There are more than 350,000 patients receiving chronic hemodialysis therapy in the United 
States, which is estimated to rise to over 500,000 patients by 2015. Of those current patients, 
an estimated 11,612 veterans receive hemodialysis through Veterans Administration. Over the 
last decade, there have been no therapies proven to lower the mortality and morbidity risk for 
these patients. One of the most important determinants of this poor clinical outcome is protein 
energy wasting, a unique and highly prevalent nutritional and metabolic abnormality primarily 
characterized by increased protein breakdown in the skeletal muscle compartment. Although 
the etiology and mechanisms leading to increased protein energy wasting in hemodialysis 
patients are complex and mostly ill-defined, two well-recognized and interrelated metabolic 
abnormalities, insulin resistance and chronic inflammation, are likely to play a critical role in the 
pathogenesis of this condition.  
Multiple in vitro and in vivo studies demonstrate the anabolic effects of insulin that extend 
beyond simple carbohydrate metabolism (1). Chevalier et al demonstrated that obese women, a 
well-defined insulin resistant state, had greater degrees of whole-body protein breakdown rates 
compared to their lean counterparts. (2) In a seminal series of observations, DeFronzo and 
colleagues characterized insulin resistance in hemodialysis patients using euglycemic insulin 
clamp techniques (3). In this context, studies indicate the critical importance of insulin resistance 
as the primary mediator of accelerated protein breakdown in the catabolic condition found in 
advanced kidney disease (4). Our laboratory has shown that there is significant association 
between the extent of insulin resistance and muscle protein breakdown in chronic hemodialysis 
patients including ones with diabetes mellitus (5). Consistent with this hypothesis, recent in vivo 
data indicate that treatment with an insulin sensitizer (PPARγ agonist, thiazolidinedione – TZD) 
suppressed muscle proteolysis in insulin resistant mice. We have recently shown that use of 
TZD treatment was associated with significantly lower all-cause mortality among insulin-free, but 
not insulin-requiring, diabetic hemodialysis patients (6). This was accompanied with a 
significantly higher serum albumin concentration in TZD users. Overall, these data support the 
administration of TZD as a potential strategy for examining the mechanisms leading protein 
energy wasting observed in chronic hemodialysis patients as well as its potential treatment. 
Chronic inflammation, a condition known to cause muscle catabolism in animal models, has a 
strong association with advanced kidney disease in epidemiological studies. Chronic 
inflammation is also known to induce insulin resistance, primarily by the induction of pro-
inflammatory cytokines. Similar to insulin resistance, the catabolic effects of inflammation 
involve activation of certain catabolic pathways (such as the Ubiquitin-Proteasome Pathway). 
Thus, it is reasonable to speculate that chronic inflammation of advanced kidney disease 
mediates its protein catabolic effects by inducing insulin resistance of protein metabolism at 
both the physiological and cellular levels (Figure A1). Interleukin1 receptor antagonist – 
Anakinra (IL-1Ra) – is a potent anti-inflammatory medication shown to block the inflammatory 
process in the joints of rheumatoid arthritis patients. A recent study indicates that Anakinra also 
effectively improves insulin sensitivity and inflammatory response in Type 2 DM patients. Given 
these robust effects, it is possible that the beneficial effects of IL-1Ra will extend to the catabolic 
consequence associated of these conditions, as observed in hemodialysis patients. 
 

 
 
4 
1.1 Abbreviations and terms used in this protocol.  
CHD 
Chronic hemodialysis 
ESRD 
End-stage renal disease 
CKD 
Chronic Kidney Disease 
PEW 
Protein-energy wasting 
TZD 
Thiazolidinedione 
IL1-Ra 
Inetrleukin-1 receptor antagonist 
 
1.2. The mortality rate of treated chronic hemodialysis patients remains high in the U.S. 
The life-expectancy of hemodialysis patients is 20-25 years less than the normal age-sex-race 
matched U.S. population over the age of 45, worse than what is observed in patients with newly 
diagnosed colon cancer (7). The recent advances in our understanding of the uremic state, as 
well as the improvements in the science and technology of renal replacement therapy, have not 
significantly impacted these outcomes. Moreover, the health care cost of treating the U.S. end-
stage renal disease program exceeds $17 billion dollars annually (7). Therefore, it is vital to 
develop and implement new and effective strategies to reduce these unacceptably high death 
and hospitalization rates. 
1.3. ESRD is an important health problem within Veterans in the U. S. Due to the increasing 
number of patients with diabetes and obesity, it is likely that this cohort will expand within the VA 
patient population in the future. By 2030 an estimated 2 million people in the US will need 
dialysis or transplantation for advanced kidney failure. In terms of the DVA Health care system, 
there are an estimated 11,612 veterans receiving hemodialysis through VA, of which 5,147 
enrolled veterans were receiving HD at VA facilities in FY 2008. An even more disturbing 
statistic is that mortality in ESRD is six times higher than in the general Medicare population 
with adjustment for age, gender and ethnicity.  
1.4. Chronic hemodialysis patients display multiple metabolic and nutritional 
derangements, which can be collectively termed as protein energy wasting of kidney 
disease. Despite vigorous attempts to prevent these derangements and their consequences, 
protein energy wasting is present in approximately 20 - 50% of patients on chronic 
hemodialysis. (8-14).  Protein energy wasting is primarily characterized by insidious loss of 
somatic protein stores (reflected by changes in lean body mass and serum creatinine) and 
decreased visceral protein concentrations (reflected by changes in serum albumin and 
prealbumin). Several studies have demonstrated that the presence of protein energy wasting, 
especially the degree of loss of muscle mass, sharply increases mortality and hospitalization 
rate in chronic hemodialysis patients (15-21).  
1.5. Protein energy wasting is common in multiple chronic disease states and is a 
significant risk factor for mortality. Chronic hemodialysis is not the only condition where a 
similar nutritional derangement can be observed. Specifically, in many chronic disease states 
such as advanced cancer, congestive heart failure, chronic infections and chronic lung disease, 
a comparable loss of visceral and somatic protein stores occur. Usually, serum albumin is 
decreased and somatic protein stores are either depleted or non-functional in these patients. 
There seems to be several shared pathways leading to PEW in these patients. For example, 
decreased appetite, inappropriately increased resting energy expenditure, and elevated 
concentrations of inflammatory cytokines are observed in most, if not all, of these chronic 
disease states. A common observation that is also applicable to ESRD is that the extent of 
protein energy wasting, regardless of its etiology or disease state, is highly associated with 
hospitalization and death risk. 

 
 
5 
Dietary Nutrient 
Intake
(-)
Dialysis-Associated 
Catabolism
(-)
Co-Morbid Conditions
(Diabetes, Inflammation)
(-)
(+)
(+)
Substrate 
Deficiency/Misutilization
(Carnitine, Phosphocreatine)
(-)
Metabolic Derangements
(HPT, Metabolic Acidosis, 
GH/IGF-1 Resistance)
(-)
Figure B.1: A simplified schematic representation of 
proposed mechanisms leading to muscle wasting in 
advanced kidney disease. (HPT: Hyperparathyroidism; 
GH: Growth hormone; IGF-1: Insulin-like growth factor 1).
Uremic 
Wasting
1.6. Despite its high prevalence and clinical significance, the exact mechanisms leading 
to protein energy wasting in chronic hemodialysis patients are not fully elucidated. 
Protein energy wasting in CHD patients cannot be attributed to any single common factor such 
as anorexia, substrate deficiency or decreased nutrient intake (22, 23).  There are also several 
disease specific factors such as resistance to anabolic hormones, increase in hydrogen ion 
concentrations, 
inefficient 
nutrient 
utilization, or increased metabolic activity. 
Others play important, but not necessarily 
predominant roles and these include 
metabolic 
acidosis, 
growth 
hormone/Insulin 
like 
growth 
factor1 
resistance, 
and 
the 
hemodialysis 
procedure 
per 
se 
(Figure 
B.1). 
Irrespective of the shared or disease-
specific etiologic mechanisms, it appears 
that the common pathway for all the 
metabolic 
derangements 
leading 
to 
protein energy wasting is related to 
exaggerated protein degradation relative 
to protein synthesis (24). Of particular 
note, some of these factors have been 
identified 
and 
appropriately 
treated. 
Despite these preventive strategies, PEW 
in 
CHD 
patients 
cannot 
be 
fully 
overcome.  Accordingly, new strategies 
are necessary to improve the overall well-
being of chronic hemodialysis patients. 
1.7. End-stage renal disease is associated with a state of insulin resistance.  Alterations in 
insulin function associated with kidney disease were reported as early as 1951 (25, 26) and the 
effects of kidney disease on renal uptake and excretion of insulin was reported as early as 1970 
(27). In a seminal series of observations, DeFronzo and colleagues characterized insulin 
resistance in chronic hemodialysis patients using the euglycemic insulin clamp techniques (3, 
28, 29) These investigators demonstrated alterations in glucose metabolism in the face of 
hyperinsulinemia and diminished tissue sensitivity to insulin which were partially correctable by 
hemodialysis. Further studies demonstrated that altered insulin sensitivity was primarily due to a 
post-receptor defect altering primarily skeletal muscle rather than hepatic glucose uptake. 
Despite the well-defined information on the pathophysiology of “uremic insulin resistance”, only 
a few investigations have centered on understanding the effects of insulin resistance on protein 
metabolism and nutrition in chronic hemodialysis patients.  
1.8. In vitro and in vivo data examining muscle protein homeostasis in diabetes mellitus 
and kidney disease indicate abnormally increased skeletal muscle breakdown (4, 30). 
Multiple in vitro and in vivo studies have demonstrated the anabolic effects of insulin beyond 
simple carbohydrate metabolism. Insulin deprivation elevates protein breakdown, while insulin 
elevation blunts protein breakdown (1). Mitch et al have elegantly shown that in insulin-deprived 
animals, muscle protein breakdown is significantly increased, a process that is mediated by the 
proteasome-ubiquitin pathway (31, 32).  There are only limited studies in humans exploring the 
mechanism(s) leading to increased muscle protein breakdown as a function of insulin 
resistance, especially in chronic hemodialysis patients. Our laboratory recently reported that 
type 2 diabetes mellitus patients on chronic hemodialysis had significantly increased skeletal 
muscle protein breakdown and loss of lean body mass compared to matched non-diabetic 
chronic hemodialysis patients (33, 34). We have subsequently shown that there is significant 
association between the extent of insulin resistance and muscle protein breakdown in chronic 
hemodialysis patients who are not diagnosed with diabetes mellitus (5). Consistent with our 
observations, Chevalier et al demonstrated that obese women, another insulin resistant state, 

 
 
6 
had greater degrees of whole-body protein breakdown rates after overnight fast compared to 
their lean counterparts, and also had suppressed anabolic responses to insulin as measured by 
hyperinsulinemic, euglycemic, isoaminoacidemic clamp studies (2). Overall, these data indicate 
the critical importance of insulin resistance as a significant mediator of the accelerated protein 
breakdown in conditions such as 
diabetes mellitus and end-stage renal 
disease (Figure B.2) (31).  
1.9. Thiazolidinediones (TZDs) are 
commonly used insulin sensitizing 
agents with intriguing pleotrophic 
effects.  PPARs belong to a family of 
ligand-activated nuclear transcription 
factors and are involved in a variety of 
human diseases including diabetes 
mellitus, 
obesity, 
hypertension, 
inflammation, 
atherosclerosis, 
and 
cancer.  Three isoforms (PPARα, 
PPARβ/δ, and PPARγ) have been 
cloned and characterized by their 
distinct expression patterns, different 
ligand 
binding 
specificities, 
and 
metabolic 
functions. 
 
Antidiabetic 
thiazolidinediones 
(TZDs) 
promote 
adipocyte 
differentiation 
through 
activation of PPARγ and sensitize the 
cells 
to 
the 
effects 
of 
insulin, 
underlying 
their 
success 
in 
the 
treatment of patients with type II 
diabetes. PPARγ is also found in cells 
of the immune system and in human 
atheroma 
(localized 
in 
monocyte-
derived macrophages, foam cells, and 
smooth muscle cells) where they exert overall atheroprotective effects through regulation of 
monocytes as well as their response to inflammatory stimuli within the arterial wall.  PPARγ has 
also been shown to inhibit the production of the pro-inflammatory mediators TNF-α, IL-1β, and 
IL-6 from activated human macrophages at the transcriptional level. [6]. 
1.10. Thiazolidinediones (TZDs) represent a novel intervention strategy to improve 
protein energy wasting in non-diabetic populations, such as chronic hemodialysis 
patients. In a recent study, Wang et al reported that in muscle of db/db mice, a model for insulin 
resistance, protein degradation and activities of the major proteolytic systems, caspase-3 and 
the proteasome, were increased. Treatment of these mice with rosiglitazone improved indices of 
insulin resistance and of suppression of proteolysis. We recently studied the effect of 
thiazolidinedione use on survival among chronic hemodialysis patients in a national cohort of 
5290 incident dialysis patients with diabetes (35). Thiazolidinedione use was assessed 
according to prescription data, and the analyses were stratified based on insulin use due to 
observed interaction. In the primary analysis, thiazolidinedione treatment was associated with 
significantly lower all- cause mortality among insulin-free but not insulin- requiring subjects, with 
adjusted hazards ratios of 0.53 (0.31-0.89) and 0.82 (0.46-1.47) respectively.  These 
encouraging data provide a strong rationale to utilize TZDs as a therapeutic strategy to 
elucidate the mechanisms involved in protein catabolism associated with wasting, especially in 
the setting of insulin resistance. (36) 
1.11. The presence and the extent of chronic inflammation is a potential catabolic factor 
that worsens the nutritional status of ESRD patients. Inflammation, more correctly termed 
Figure B.2. A proposed mechanism depicting 
mechanisms leading to muscle wasting in 
catabolic diseases. Ubiquitin E3 enzymes 
atrogin-1 and muscle ring finger-1 (MuRF-1) 
seem to play a important role in protein 
degradation based on the originating stimuli. 
Adapted from Lecker JASN 2006
Figure B.2. A proposed mechanism depicting 
mechanisms leading to muscle wasting in 
catabolic diseases. Ubiquitin E3 enzymes 
atrogin-1 and muscle ring finger-1 (MuRF-1) 
seem to play a important role in protein 
degradation based on the originating stimuli. 
Adapted from Lecker JASN 2006

 
 
7 
systemic inflammatory response syndrome, is a complex combination of physiological, 
immunological and metabolic effects occurring in response to a variety of stimulators resulting 
from tissue injury or disease processes. Cytokines, such as IL-1, IL-6 and TNF-α are the 
primary mediators of these catabolic effects. Advanced kidney disease, similar to other chronic 
diseases, is characterized by ineffectual anti-inflammatory responses associated with repetitive 
cytokine releases resulting in significant adverse effects to the host (37). In the case of 
advanced kidney disease, there is decreased clearance of uremic toxins (middle molecules, 
iPTH, leptin) leading to cytokine release and activation. Contributing factors in addition to 
decreased renal function include, but are not limited to, activation of complement factors in 
response to certain dialytic factors such as hemodialysis membrane, endotoxin transfer, 
peritoneal dialysate bio-incompatibility and vascular access (PTFE grafts, catheters). The 
metabolic influence of such factors are worsened by the presence of co-morbidities such as 
chronic infections (periodontal, peritonitis), diabetes mellitus, atherosclerosis and congestive 
heart failure.(38-40)  
1.12. Pro-inflammatory cytokines play an integral role in muscle catabolism in models of 
inflammatory diseases.  The metabolic and nutritional effects of chronic inflammation are 
many and include anorexia, increased skeletal muscle protein breakdown, increased whole 
body protein catabolism, cytokine-mediated hypermetabolism and disruption of the growth 
hormone and IGF-1 axis leading to decreased anabolism (38, 41). IL-1, IL-6 and TNF-α are the 
primary mediators of the metabolic effects of the inflammatory response in animal models (37, 
41-43). Several studies have shown the specific involvement of the pro-inflammatory cytokine 
network in the signaling cascade of Ub-Prot proteolytic pathway. E3α-II is the pro-inflammatory 
cytokine-inducible form of the E3α family (44). These findings lead us to believe that chronic 
inflammation could be a major (if not causative) factor for the poor nutritional status observed in 
these patients (Figure B.2). No study to date has examined the involvement of these proteins, 
in terms of both expression and function, in the development of protein energy wasting of kidney 
disease.  
1.13. Interleukin-1 (IL-1) is associated with development of protein energy wasting in 
chronic hemodialysis patients. The adverse metabolic and nutritional effects of IL1 ß are well 
established (45, 46). IL-1 suppresses dietary nutrient intake and increases resting energy 
expenditure. It is also associated with net negative protein balance in animal models and 
surgical patients. In a longitudinal study of 54 CHD patients, Johansen et al showed that higher 
IL-1ß concentrations were associated with a narrower phase angle (P = 0.004) and with a more 
rapid decline in phase angle with time (time x log IL-1ß interaction, P = 0.01), suggesting that IL 
1 ß beta was associated with the reduction in muscle mass in CHD patients. Kalantar-Zadeh 
showed that appetite is strongly associated with inflammatory cytokine concentrations, including 
IL-1. They also showed a strong relationship between IL-1 concentrations and mortality in CHD 
patients. 
1.14. Anti-inflammatory strategies to improve protein energy wasting in chronic 
hemodialysis patients are limited and IL-1 receptor antagonist, Anakinra, represents a 
promising strategy. Studies in patients with rheumatoid arthritis have shown that 
administration of IL-1Ra blocks the inflammatory process in the joints and prevents destructive 
process associated with IL-1. In a recent study in Type 2 diabetes mellitus patients, 14 weeks of 
IL-1Ra administration resulted in significant improvements in inflammatory state and was 
accompanied by robust improvement in insulin sensitivity as well. Given its strong effects on 
inflammation and insulin activity, it is likely that administration of IL-1Ra in chronic hemodialysis 
patients might lead to blockade of protein catabolic consequences of these conditions. To our 
knowledge, there are no studies examining the nutritional effects of IL-1Ra (other than the 
preliminary studies performed by our laboratory). Based on these observations, we propose to 
study the short-term effects of IL-Ra on the inflammatory state and protein and energy 
metabolism in chronically inflamed CHD patients. It is also clear that there is significant overlap 
in the mechanisms by which chronic inflammation and insulin resistance exert their adverse 
nutritional effects, namely exaggerated protein breakdown. Thus the need for more meticulous 

 
 
8 
examination of the relative contribution of these particular metabolic derangements to protein 
energy wasting of advanced uremia. 
 
 
2.0 
Rationale and Specific Aims 
 
The overall goal is to elucidate the mechanisms by which chronic inflammation and insulin 
resistance influence the development of protein energy wasting in hemodialysis patients.  
Specific Aim: To test the hypothesis that inhibiting inflammatory response by administration of 
an IL-1r antagonist (Anakinra) or increasing insulin sensitivity by administration of a PPARγ 
agonist (Actos) will improve net protein metabolism. 
Hypothesis: The chronic inflammatory component of PEW observed in hemodialysis patients is, 
at least in part, mediated by insulin resistance. 
ESRD represents an important health problem in the VA patient population. Due to the 
increasing number of patients with diabetes and obesity, it is likely that this cohort will expand in 
the future, which renders this project highly relevant to the VA patient population. By 2030 an 
estimated 2 million people in the US will need dialysis or transplantation for advanced kidney 
failure. An even more disturbing statistic is that mortality in ESRD is six times higher than in the 
general Medicare population with adjustment for age, gender and ethnicity. Protein energy 
wasting is highly prevalent in these patients and is one of the most important determinants of 
their poor clinical outcome.  
Despite its well-recognized occurrence, the etiology and the mechanisms leading to protein 
energy wasting observed in chronic hemodialysis patients cannot be attributed to any single 
factor. However, irrespective of the specific etiologic mechanisms, it appears that the common 
pathway for all the metabolic derangements is related to exaggerated protein degradation 
relative to protein synthesis (47).  
Two well-recognized and presumably interrelated metabolic abnormalities, insulin resistance 
and chronic inflammation, may be the major determinants of protein catabolism in CHD patients. 
There are no studies examining the effects of anti-inflammatory interventions and/or insulin 
sensitizers on protein homeostasis in CHD. Due to their established anti-inflammatory and other 
pleotrophic effects, Interleukin-1 receptor antagonist Anakinra and insulin sensitizer PPARγ 
agonist Actos represent two such promising interventions. By modulating inflammatory 
response and insulin signaling through two pharmacological interventions, we will have the 
unique opportunity to clarify mechanisms contributing of these two particular metabolic 
derangements in the development of protein energy wasting observed in chronic hemodialysis 
patients. 
Assessment of protein and energy turnover by stable isotope tracer techniques in CHD patients 
with different levels of inflammatory response and insulin resistance can elucidate the influence 
of these metabolic derangements on protein homeostasis, as well as substrate metabolism and 
oxidation. The combined dual glucose and amino acid (AA) clamp technique, first developed by 
Abumrad et al (65) was devised to compensate for the confounding reduction in plasma amino 
acids with insulin administration due to its suppressive effect on protein breakdown, providing 
the most precise estimation of components of protein homeostasis. 
If successful, our proposed studies have great potential to influence clinical practices in chronic 
hemodialysis patients because the proposed intervention protocol could be easily accessible 
and can ultimately lead to improvements in the hospitalization and death rates. Further, since 
insulin resistance and inflammation are common in other chronic disease states associated with 
protein energy wasting, the application of these medications could be more generalized to those 
conditions. 
The proposed study will therefore represent an important step in identifying predictors of 
adverse renal outcomes and potential intervention strategies. Hence, it is expected that the 

 
 
9 
results of this proposal will have a great impact on Veterans Health Care and make important 
contributions to the research mission of the DVA, providing new knowledge in metabolic and 
nutritional aspects of ESRD patients. 
 
 
3.0 
Animal Studies and Previous Human Studies 
 
The primary focus of the PI’s laboratory is to study the nutritional and metabolic consequences 
of chronic kidney disease, especially in chronic hemodialysis patients. Around that theme, we 
have published a number of studies on this area providing important observations (11, 18, 48-
58) related to the adverse nutritional and metabolic effects of the hemodialysis procedure as a 
model for protein catabolism; etiology, extent and adverse consequences of chronic 
inflammation; and the influence of diabetes mellitus and insulin resistance on protein 
metabolism in advanced kidney disease as preliminary data applicable to this grant proposal. 
The hypothesis put forward in this grant application is directly based on these data and the 
proposed experiments are logical extension of these studies.  
3.1. Studies focused on inflammation 
3.1.1. The extent inflammation in chronic hemodialysis patients 
 As a part of ongoing research efforts on this area, we have recently completed the most 
comprehensive assessment of chronic inflammatory patterns in CHD patients. In brief, we 
measured monthly CRP levels in 128 prevalent CHD patients [56.6 yrs (range 19-90), 68% 
African Americans, 39% diabetics (DM)] over 2 
years. There were a total of 2405 CRP 
measurements (median 5.7 mg/L; IQR 2.4-16.6 
mg/L). We defined persistent inflammation as 3 
consecutive measurements greater than pre-
determined cutoff levels at least once during 
the study. Accordingly, 77% of the patients had 
CRP > 3mg/L for 3 consecutive measurements 
(Figure C.1) Using univariate linear mixed 
effect model we identified (a) the presence of 
hemodialysis catheter (p<0.0001), (b) diabetes 
mellitus 
(p<0.0001), 
(c) 
elevated 
WBC 
(p<0.0001), (d) older age (p=0.04), and (d) 
reduced 
serum 
albumin 
(p<0.02) 
as 
independent predictors of elevated CRP.  CRP 
was not associated with gender, race, dialysis 
vintage, hemoglobin, calcium or creatinine. 
These results are published (59). 
3.1.2. Etiology of high prevalence of chronic inflammation in CHD patients  
3.1.2.a. Loss of renal function leads to increased prevalence of chronic inflammation in renal 
disease: In an attempt to understand the etiology of increased prevalence of inflammation in 
renal failure patients, we studied patients with varying degrees of kidney function. We examined 
a group of patients with chronic kidney disease (n = 60), not requiring dialysis, extending over a 
lower range of kidney function (5–60 ml/min). We measured plasma CRP and IL-6 
concentrations as markers of the inflammatory state. The plasma CRP and IL-6 concentrations 
differed significantly between patients with advanced chronic kidney disease and healthy 
subjects. This study suggests that chronic inflammation is prevalent in patients with chronic 
kidney disease well before the initiation of renal replacement therapy and demonstrates a high 
prevalence of this condition. 
3.1.2.b Chronic hemodialysis therapy is ineffective in controlling chronic inflammation and leads 
to an incremental worsening of chronic inflammatory state overtime:  We performed a 
 
0
5
10
15
20
25
30
1
3
5
7
9
11
13
15
17
19
21
23
25
 Month of Follow up
C Reative Protein, mg/L
25th
50th
75th
Figure C.1. Median, 25th, and 75th percentiles of 
2405 CRP measurements obtained every month 
over 26 months in a cohort 128 prevalent CHD 
patients
 
0
5
10
15
20
25
30
1
3
5
7
9
11
13
15
17
19
21
23
25
 Month of Follow up
C Reative Protein, mg/L
25th
50th
75th
Figure C.1. Median, 25th, and 75th percentiles of 
2405 CRP measurements obtained every month 
over 26 months in a cohort 128 prevalent CHD 
patients

 
 
10 
prospective observational study to examine the influence of maintenance hemodialysis therapy 
(with biocompatible membranes and pure dialysate) on inflammatory markers. We measured 
CRP, IL-6, IL-10 before the first hemodialysis treatment at the start of maintenance 
hemodialysis therapy, and every 3 months over a period of 12 months in 50 CHD patients. 
While there was no significant trend in mean IL-6 concentrations over the 12 months, CHD 
patients with IL-6 concentrations at the lowest 2 quartiles displayed significant increases 
overtime. Similar patterns were observed for CRP concentrations. This observation suggests 
hemodialysis is not only ineffective in controlling inflammation in advanced uremia, but also 
leads to an incremental worsening of the chronic inflammatory state over time. This latter 
phenomenon is observed in patients who initiate maintenance HD at lower concentrations of 
inflammatory markers.  
3.1.2.c. The hemodialysis procedure leads to acute inflammatory response in dialysis patients: 
We examined the effects of a single hemodialysis session on inflammatory markers in 7 
hemodialysis patients before, during and 2 hours after a 
hemodialysis treatment by measurements of serum IL-6 
concentrations 
and 
fractional 
synthetic 
rate 
of 
fibrinogen. There was a 70% increase in IL-6 
concentrations during the 2-hour post-dialysis period (P 
< 0.01 compared to pre-dialysis period, Figure C.2) 
There was also a comparable significant increase in 
fibrinogen fractional synthetic rate during the two-hour 
post dialysis period. These results suggest activation of 
an inflammatory response during a single hemodialysis 
session and provide evidence of an independent 
contribution of hemodialysis to the chronic inflammatory 
state in hemodialysis patients. 
3.1.3. Relationship between chronic inflammation 
and protein catabolism  
3.1.3.a. Simultaneous activation of the inflammatory 
response and muscle proteolysis during hemodialysis:  The fact that CHD patients suffer from 
uremic wasting in spite of efforts to improve dietary nutrient intake led us to hypothesize that 
certain characteristics of the hemodialysis procedure predispose patients to increased 
catabolism. In order to test this hypothesis, we studied 11 hemodialysis patients, 2 hours before 
hemodialysis, during hemodialysis and 2 hours following hemodialysis using primed-constant 
infusion of stable isotopes (54). Our results showed that during the hemodialysis period, there is 
increased whole-body proteolysis (10%) and decreased whole-body protein synthesis resulting 
in significant increase in net whole-body protein catabolism (96%). Simultaneously, there was 
133% increase forearm muscle protein degradation during hemodialysis. Interestingly, the 
increase in muscle protein breakdown continued during the post-dialysis period, which 
coincided with significant increases in IL-6 concentrations and fibrinogen fractional synthetic 
rate (Figure C.2). Collectively, these results indicated that the inflammatory effects 
characteristic of the hemodialysis procedure could 
be one of the prominent causes of hemodialysis-
associated proteolysis.  
3.1.3.b. Increased muscle protein breakdown in 
chronically inflamed hemodialysis patients: In an 
analysis of 16 CHD patients (mean (± SD) age 41.8 
±11, 80%African American, 80% female) who 
completed substrate kinetic studies at the same 
conditions as proposed in this grant application, we 
examined the rate of muscle protein breakdown 
according to the inflammatory response status of the 
patients (5 subjects with high and 11 subjects with 
99
77
0
20
40
60
80
100
120
High CRP
Low CRP
ug/100ml/min
Figure C.3: Muscle protein breakdown 
rates in 5 patients with high versus 11 
patients with low inflammation
99
77
0
20
40
60
80
100
120
High CRP
Low CRP
ug/100ml/min
Figure C.3: Muscle protein breakdown 
rates in 5 patients with high versus 11 
patients with low inflammation
Figure C.2. There is simultaneous increase 
in IL-6, Fibrinogen FSR and skeletal muscle 
protein breakdown during HD.
Figure C.2. There is simultaneous increase 
in IL-6, Fibrinogen FSR and skeletal muscle 
protein breakdown during HD.

 
 
11 
low inflammation). Our preliminary data showed a numerical difference between the muscle 
protein breakdown rates supportive of our hypothesis as depicted in Figure C.3 (98.8 ± 44.27 
vs. 77 ± 36.0 ug/100ml/min (54)). While these experiments are preliminary in nature, they 
provide reasonable rationale supporting our hypothesis.  
3.2. Studies focused on insulin resistance 
3.2.1. Uremia is a state of Insulin resistance. Earlier studies by De Fronzo indicated that 
advanced kidney disease is associated with insulin resistance. In an initial attempt to confirm 
these findings in our patient population, we examined the degree of insulin resistance by 
practical measures in 33 non-diabetic (by ADA criteria) patients on CHD (mean (± SD) age 
46.2±1 2.3, 78.8% African American, 39% females; BMI 27.4±6.3; albumin 3.6±0.35; CRP 
7.7±10.5; CO2 26.4±3.9; Calcium 8.4±1.5).Fasting blood samples were taken to examine 
concentrations of glucose and insulin. We calculated insulin resistance using Homeostasis 
model assessment of insulin resistance (HOMA). Mean HOMA was 2.66±2.61 and median 
(interquartile range) was 1.6 (0.99–3.6). HOMA was associated with BUN, CO2 and BMI by 
spearman correlations. In the multivariate analysis, the only significant determinant of HOMA 
levels was BMI. 
3.2.2. Measures of insulin sensitivity in chronic hemodialysis patients. Hyperinsulinemic 
euglycemic glucose clamp method (HEGC) is the gold standard to determine insulin sensitivity 
(IS) during a stimulated state.  Insulin sensitivity can also be measured indirectly during a non-
stimulated state using HOMA, QUICKI, and HOMA-AD (HOMA corrected by adiponectin).  The 
comparison of the various indirect methods of measuring insulin resistance in CHD patients has 
not been properly studied in ESRD, nor have the extent and the determinants of insulin 
resistance as measured by HEGC in this population. In an attempt to explore this issue, 12 
African American chronic hemodialysis patients (50±9 years old, 33% females, 40% with type 2 
Diabetes Mellitus) were studied 3 consecutive times, 8 weeks apart. Laboratory metabolic 
parameters were obtained after 12 hours of fasting at each time point. Body composition was 
measured by DEXA at baseline.  Insulin resistance was assessed by HEGC (glucose disposal 
rate - GDR), HOMA, HOMA-AD (corrected by adiponectin), QUICKI, and the McAuley’s index 
(triglyceride based). 
 
 
In this cohort, the mean (±SD) values were: BMI 34.4±7.6 kg/m2, HA1c 5.48±0.52, adiponectin 
19.4 ±8.6 μg/mL and median IL-6 was 9.51(5.09,18.80) pg/mL. The mean (±SD) values for 
measures of IR were: GDR 5.22 (4.33, 6.23) mg/kg/min, HOMA 4.7±3.4, QUICKI 0.31±0.02, 
HOMA-AD 115±97, and McAuley’s 1.51±0.33. Table C.1. depicts the correlation matrix between 
these indices. Using GEE model, the best predictor of HEGC was HOMA-AD (p=0.001), 
followed by HOMA (p=0.008) and QUICKI (p=0.03).  Truncal fat % by DEXA, interleukin-6 and 
leptin were strongly associated with GDR by HEGC.   These data indicated that 1) Insulin 
resistance is common in CHD patients; 2) HOMA corrected by adiponectin (HOMA-AD) is the 
best correlate of IR measured by the gold standard HEGC method; 3) While the etiology of IR is 
complex in CHD patients, central obesity and inflammation seem to play important roles. 
 
Table C1 
HEGC 
HOMA-AD 
HOMA-IR 
QUICKI 
McAuley's 
HEGC 
1 
 
 
 
 
HOMA-AD 
-0.671 
*P<0.001 
1 
 
 
 
HOMA-IR 
-0.584 
*P=0.002 
0.88 
*P<0.001 
1 
 
 
QUICKI 
0.584 
*P=0.004 
-0.88 
*P<0.001 
-0.996 
*P<0.001 
1 
 
McAuley's 
0.49 
*P=0.03 
-0.47 
*P=0.06 
-0.851 
*P<0.001 
0.845 
*P<0.001 
1 

 
 
12 
3.2.3. High dose insulin administration leads to a decrease in plasma amino acid 
concentrations. An important aspect of our study 
protocol is utilization of dual clamp technique to 
assess insulin resistance of protein metabolism. 
In order to provide rationale for this approach, we 
studied 
plasma 
amino 
acid 
concentrations 
consecutively during 12 HEGC studies. As can be 
seen 
in 
Figure 
C.4., 
high 
dose 
insulin 
administration led to an average of 25% decrease 
in all plasma total amino acid concentrations. The 
decrease was in the range of 6% to 41%. Similar 
observations were seen in essential, branched-
chain 
and 
non-essential 
amino 
acid 
concentrations, albeit it was more prominent for 
essential amino acid levels. These results indicate 
the need to develop euaminoacidemia in order to 
appropriately 
assess 
the 
extent 
of 
insulin 
resistance of protein metabolism. 
 
3.2.4. Insulin resistance is associated with skeletal muscle protein breakdown in non-
diabetic chronic hemodialysis patients. (5) Based on the data indicating that insulin 
resistance might be a modulator of protein metabolism in chronic dialysis patients (33, 34), we 
hypothesized that resistance to the anti-catabolic effects of insulin as a possible mechanism 
underlying the muscle wasting seen in chronic dialysis patients who have not been diagnosed 
by overt DM. In order to test this hypothesis, we examined the relationship between HOMA, a 
measure of insulin resistance, and fasting whole body and skeletal muscle protein metabolism 
in 18 non-diabetic CHD patients with BMI<35 using 
primed-constant infusions of L-(1-13C) leucine and L-
(ring-2H5) phenylalanine.  Mean (±SD) fasting glucose 
and BMI were 80.6±9.8 mg/dl and 25.4±4.4 kg/m2.  
Median [IQR] HOMA was 1.6 [1.4, 3.9].  Mean skeletal 
muscle protein synthesis, breakdown, and net balance 
were 89.6±11.7, 97±13.3, and -7.4±7.1 μg/100 ml/min, 
respectively.  Using simple linear regression, we 
observed (a) a positive correlation between HOMA and 
skeletal muscle protein synthesis (R2 =0.28; p=0.024), (b) 
a positive correlation between HOMA and protein 
breakdown (R2 = 0.49; p=0.001; Figure C.5); and (c) an 
inverse association between net skeletal muscle protein 
balance and HOMA (R2 = 0.20; p = 0.066). The results of 
this study demonstrated evidence of insulin resistance in 
the dialysis population and this is associated with 
enhanced skeletal muscle protein breakdown. 
These 
findings 
underscore 
the 
therapeutic 
implication for targeting insulin resistance in patients 
with uremia to prevent wasting. 
3.2.5. Increased muscle protein breakdown in 
CHD 
patients 
with 
uncontrolled 
diabetes 
mellitus: We next examined whole-body and 
skeletal muscle protein metabolism in six CHD 
patients with Type 2 DM (5 Male, 44.4 ± 6.1 years 
old, 2 White/4 AA HbA1C=9.5 ± 1.1%), and 6 non-
diabetic CHD patients (5 Male, 43.3 ± 6.7 years old, 
2 W/4 AA) in a fasted state, using same 
methodology, in order to examine whether the 
Figure C.6. Forearm muscle protein components comparing DM with 
non-DM patients. *Denotes significant difference between T2DM versus
non-DM. 
Figure C.6. Forearm muscle protein components comparing DM with 
non-DM patients. *Denotes significant difference between T2DM versus
non-DM. 
1200
1600
2000
2400
2800
0
135
170
190
210
240
260
Time (min)
Total [AA] (mM)
Figure C.4. Changes in mean Total Plasma Amino Acid 
concentrations during Hyperinsulinemic Euglycemic Glucose 
Clamp. There is an average 25% decrease in plasma TAA 
concentrations during the steady state. Data are obtained from 
32 clamp studies in 12 CHD patients.
1200
1600
2000
2400
2800
0
135
170
190
210
240
260
Time (min)
Total [AA] (mM)
Figure C.4. Changes in mean Total Plasma Amino Acid 
concentrations during Hyperinsulinemic Euglycemic Glucose 
Clamp. There is an average 25% decrease in plasma TAA 
concentrations during the steady state. Data are obtained from 
32 clamp studies in 12 CHD patients.
R
2 = 0.49
0
50
100
150
200
250
0
1
2
3
4
5
6
HOMA
Muscle Protein Breakdown 
µg/100 ml/min
.
p   = 0.001
Figure C.5 Simple linear regression revealed a 
direct relationship between HOMA and skeletal 
muscle protein breakdown (p=0.001).
R
2 = 0.49
0
50
100
150
200
250
0
1
2
3
4
5
6
HOMA
Muscle Protein Breakdown 
µg/100 ml/min
.
p   = 0.001
Figure C.5 Simple linear regression revealed a 
direct relationship between HOMA and skeletal 
muscle protein breakdown (p=0.001).

 
 
13 
aforementioned findings extend to worse insulin resistance/deficiency states, i.e. overt diabetes 
mellitus (Figure C.6). Our results showed that CHD patients with Type2 DM had significantly 
increased (83%) skeletal muscle protein loss compared to non-diabetic CHD patients. There 
was no significant difference in muscle protein synthesis between groups, resulting in 
significantly negative net protein balance in the muscle compartment in the DM group. Plasma 
insulin levels were elevated in DM patients (25.3 ± 9.6 vs. 7.3 ± 1.0 uU/ml, for DM vs. non-DM, 
respectively, P<0.05).  
3.2.6. Presence of diabetes is associated with accelerated lean body mass loss in chronic 
dialysis patients: In order to complement our findings in the physiological level, we extended 
our studies to a prospective cohort with the 
hypothesis that patients with DM are more likely 
to lose LBM over extended period of time 
compared to non-diabetic CHD patients.  We 
examined changes in lean body mass (LBM) by 
DEXA in 142 incident dialysis patients (91 males, 
52.8 ± 1 years, 74.2 ± 1.2 kg body weight) among 
which 34 had DM. We measured LBM at the time 
of initiation of chronic dialysis and 12 months 
after. Our results (Figure C.7) showed that 
chronic dialysis patients with DM had significantly 
higher LBM loss compared to non-diabetic chronic 
dialysis 
patients 
during 
the 
first 
year 
of 
maintenance dialysis, suggestive of accelerated 
loss 
of 
protein 
stores. 
Multivariate 
linear 
regression analysis revealed that the presence of 
DM was the strongest predictor of LBM loss independent of several clinically-relevant variables 
such as age, gender, serum albumin, presence of malnutrition and/or inflammation, and dialysis 
modality.  
3.2.7. Leucine Disposal Rate (LDR) in CHD patients and matched controls. To date, we 
studied 6 CHD patients and 4 matched controls using the dual-clamp protocol described in 
approach section. Our results showed that the mean LDR in CHD patients was 0.75 ± 0.16 
(range 1.05 – 0.6) ug/kg body/weight/min. In matched controls, LDR was 0.96 ± 0.23 (range 0.6 
– 1.21) ug/kg body weight/min. 
 
 
 
 
Figure C.7. Scatter plots grouped by nondiabetic (non-DM) and diabetic 
(DM) patients, showing individual lean body mass (LBM) loss in kg during 
1 year of renal replacement therapy. We observed significant LBM loss in 
both groups but at a much greater extent in diabetes mellitus patients (1.1  
0.2 kg for nondiabetes mellitus patients and 3.4  0.6 kg for diabetes 
mellitus patients) (P < 0.05)
Figure C.7. Scatter plots grouped by nondiabetic (non-DM) and diabetic 
(DM) patients, showing individual lean body mass (LBM) loss in kg during 
1 year of renal replacement therapy. We observed significant LBM loss in 
both groups but at a much greater extent in diabetes mellitus patients (1.1  
0.2 kg for nondiabetes mellitus patients and 3.4  0.6 kg for diabetes 
mellitus patients) (P < 0.05)

 
 
14 
4.0 
Inclusion/Exclusion Criteria 
 
Human Subjects Involvement and Characteristics: We propose to study 45 CHD subjects for 
this specific aim. The study subjects will be primarily selected from Vanderbilt’s outpatient 
dialysis center and the Nashville VA outpatient dialysis unit. There will be no restriction on 
gender (except pregnant women), race, and age (except less than 21 years old) for subject 
selection.  
Inclusion Criteria: 
1. 
Patients on CHD undergoing three time a week therapy for more than 6 months; 
2. 
Age ≥ 21 years old; 
3. 
Acceptable dialysis adequacy (spKt/V > 1.2); 
4. 
A patent, well-functioning, arterio-venous dialysis access; 
5. 
Ability to give informed consent; 
6. 
Life expectancy greater than 6 months; 
7. 
BMI ≥ 20 and ≤ 45. 
 
Exclusion Criteria:  
1. 
Pregnancy; 
2. 
Intolerance or allergy to the study medication (including the metabolic clamp studies); 
3. 
Severe, unstable, active inflammatory disease (active infection, active connective 
tissue disorder), active cancer or cancer history in the prior 5 years except skin cancer, 
AIDS-HIV, active or history of liver disease (including HBV and HCV);  
4. 
Hospitalization or infection within 1 month prior to the study; 
5. 
Patients receiving steroids and/or other immunosuppressive agents (Prednisone > 5 
mg/day; excluding inhaled and topical steroids); 
6. 
Diabetes Mellitus on insulin therapy; 
7. 
Previous history of TB with or without documented adequate therapy; 
8. 
Patients with recent close exposure to an individual with active TB; 
9. 
Females using oral contraceptives; 
10. Patients with NYHA Class III or IV heart failure; 
11. Patients with a history of angina, myocardial infarction, transient ischemic attacks, or 
strokes within the last 6 months. 
 
Stopping criteria: If subjects have any signs or symptoms of an infectious process 
(pneumonia, cellulitis, urinary tract infection, etc.) they will be dropped from the study. If a 
subject starts inhaled steroids or high potency topical steroids (with the exception of over the 
counter hydrocortisone cream) the subject may be dropped.  In addition, if a subject has any 
signs or symptoms of decline or deterioration of their health status, the subject will be dropped. 
Additional stopping criteria are detailed in Section 7.0 under the risks associated with the two 
study drugs. 
 
 
 
 

 
 
15 
5.0 
Enrollment/Randomization 
 
All studies will be conducted at the Vanderbilt University Medical Center General Clinical 
Research Center (GCRC). There will be no restriction on sex, race, age (≥ 21 years) or disease 
etiology (except as stated for each protocol) for subject selection or exclusion. Institutional 
review board approval and written informed consent will be obtained from all study subjects. We 
propose to study a total of 45 hemodialysis subjects. We expect 60% of subjects to be African 
Americans. The most common causes of ESRD in our clinic are diabetes (40%), hypertension 
(24%), and glomerulonephritis (24%).  
Recruitment strategies: The study subjects will be primarily selected from Vanderbilt’s outpatient 
dialysis center and the Nashville VA outpatient dialysis unit. These units serve a total of 360 
CHD patients (240 VU affiliated and 160 VA affiliated). The growth rate (incident patients) for 
our patient pool is 5-7% per year, providing a total of approximately 450 subjects over 4 years 
and approximately 300 subjects that would be predicted to be eligible for our study. Accordingly, 
we need 15-20% recruitment rate for sample size proposed for Specific Aim 2. 
Diabetes Mellitus: In this study, we have elected to include subjects with the known diagnosis of 
diabetes mellitus. We will exclude subjects who are receiving insulin. Our preliminary data is 
highly suggestive that this patient cohort is specifically influenced by these abnormalities and 
represent appropriate target for intervention.  
Randomization: Subjects will be randomly assigned to one of the study arms (Anakinra versus 
Actos versus Placebo).  Randomization will be determined by a computer-generated sequence 
and will be stratified by diabetes status.  
Standard follow-up measures for study subjects: All preventive measures such as optimal doses 
of dialysis, provision of adequate dietary protein and energy intake and appropriate control of 
metabolic abnormalities, such as metabolic acidosis, will be employed (11, 60). Further, 
treatment of metabolic acidosis improves nutritional markers in dialysis patients (72). Additional 
bicarbonate will be prescribed to keep pre-dialysis total bicarbonate levels above 22 mmol/L 
(above 24 mmol/L post-dialysis). Patients’ calcium, phosphorus, and hematocrit levels will also 
be followed closely and kept within accepted ranges. Patients will be prescribed phosphate 
binders to control hyperphosphatemia to levels below 6.0 mg/dl. Hematocrit will be followed 
more closely (twice per month) and erythropoietin will be prescribed to keep hematocrit within 
the range of 33 - 36% per recommended clinical practice guidelines.  Also, all participants will 
have a baseline assessment, as well as on-going monitoring, of their health status as 
documented in their medical record.  The study duration is approximately 4 months (2-4 weeks 
of baseline assessment followed by 3 months of treatment and 2 weeks post study follow up).  
 
 
6.0 
Study Procedures 
 
The design is randomized, double-blind, placebo-controlled (Figure 1). Once the subject is 
determined to be eligible for the study, we will randomly assign him/her to one of the study arms 
(Anakinra versus Actos versus Placebo). Each subject will be treated for 3 months. During the 
study, subjects will receive both subcutaneous injection of Anakinra or matching placebo and 
oral administration of Actos or matching placebo. The investigators and the study subjects will 
be blinded to the treatment. Subjects will be assessed for body composition and biochemical 
parameters at the beginning, at 6 weeks and at the end of the study.  A dual-clamp metabolic 
study will be performed at the beginning and at the end of the study.  
 

 
 
16 
At the initiation of the study (prior to the first dose) and at the completion of 3 months of study 
medication subjects will complete all of the procedures detailed below. Once the initial studies 
are completed, we will start the subjects on the randomly assigned protocol. The 3-month 
interventional protocol will be one of the following: (a) a subcutaneous injection of Anakinra 3 
times a week during hemodialysis and 1 pill QD of placebo; (b) 1 pill QD of 30 mg Actos and a 
subcutaneous injection of placebo 3 times a week during hemodialysis; or (c) 1 pill QD of 
placebo and a subcutaneous injection of placebo 3 times a week during hemodialysis. All 
medications and matching placebo will be prepared and dispensed by either Vanderbilt’s 
Investigational Drug Services (IDS) or the VA pharmacy. Note that non-VA subjects will be 
treated at Vanderbilt (IDS will dispense study drug) and VA subjects will be treated at the VA 
(VA pharmacy will dispense study drug).  The subjects will have the option to undergo 2 muscle 
biopsies at the first and last study visits.  
 
6.1 Screening Visit: Within about two weeks up to the start of the Pre-Study assessments, 
and after the subject has provided informed consent, demographics and concomitant 
medications information will be collected.  In addition, the following tests will be performed: 
• 
PPD (tuberculosis skin test) will be placed unless there is a negative documented 
PPD within the last 3 months. 
• 
HIV, Hep B and Hep C (if not tested within 1 month) 
 
6.2 Pre-Study Assessments: Within two weeks of each of the three study visits, subjects will 
undergo dual-energy x-ray absorptiometry (DEXA) to estimate lean and fat body masses, as 
well as perform a hand grip test. Serum pregnancy test will be done on females of childbearing 
potential to confirm non-pregnancy status. For the two metabolic studies, vascular access blood 
flow and recirculation will be checked in every CHD subject prior to the metabolic study to 
assure patency of the vascular access and that a pure arterial sample can be obtained.  

 
 
17 
6.3 Pre-Study Diet: Within two weeks prior to each metabolic study, subjects will be interviewed 
using the NDS-R system for 2 random food recalls. For 2 weeks prior to each metabolic study, 
subjects will be asked to eat a diet based on recommended dietary protein and calorie intakes 
for their weights (1.2 g/kg BW/day of protein, 30 kcal/kg BW/day energy, and low Na) [47]. This 
is critically important as changes in nutrient intake can alter the responses measured in the 
metabolic studies. The subjects will be fed a meal at the CRC the evening before the study.  
 
6.4 Relation to Dialysis Procedure: Subjects will be studied on a non-dialysis day. 
 
6.5 Metabolic Clamp Study: 
Day 1: Subjects will be admitted to the GCRC the night prior to the study.  The last meal will be 
about 8 hours prior to the study but ad lib water consumption will be allowed (post-absorptive 
state).   
 
Day 2: All hemodialysis patients will have either a forearm arterio-venous (AV) shunt created for 
the dialysis treatment, either in the form of AV fistula or synthetic graft or AV shunt in the thigh. 
A needle will be placed at the venous site of the AV access to collect a baseline blood sample 
(for isotopic backgrounds) and once the baseline sampling is completed, to initiate isotopes 
infusion. The arterial vascular access obtained at the arterial side of the AV access will be used 
to sample arterial blood. If possible, another catheter will be placed into the contra lateral 
forearm vein (preferably in a retrograde fashion) to obtain samples of venous blood.  The 
hematocrit will be checked prior to starting the study. If pre-study hematocrit is less than 30%, 
then a concentrated blood draw schedule will be employed (70 ml blood draw maximum). If the 
pre-study hematocrit is less than 27%, the study will be postponed and the subject’s primary 
nephrologist will be informed.  Also prior to starting the study, metabolic cart will be performed to 
measure simultaneous energy expenditure and respiratory quotient over 20 minutes, and arm 
(or leg) blood flow will be estimated using capacitance plethysmography. 
A schematic diagram of the metabolic clamp study is depicted in Figure 2. In brief, at time zero 
(T=0) baseline blood sampling will be done, baseline amino acid levels will be checked by 
bedside HPLC, and a baseline breath sample will be obtained.  Subjects will then receive a 
bolus injection of NaH13CO3 (0.12 mg/kg), L-(1-13C) Leucine (7.2 μmol/kg), L-(ring-2H5) 
Phenylalanine (3.6 μmol/kg), and 
6,6-2H2 Glucose (3.6 mg/kg) to prime 
the CO2, Leucine, Phenylalanine, 
and Glucose pools, respectively.  A 
continuous 
infusion 
of 
labeled 
Leucine 
(0.12 
μmol/kg/min), 
Phenylalanine (0.06 μmol/kg/min) 
and Glucose (0.06 mg/kg/min) will 
then be started and continued 
throughout the remainder of the 
study. The initial 2 hours will be for 
tracer equilibration.  During this 
period, arm (or leg) blood flow will be 
estimated again using capacitance 
plethysmography. 
After two hours (T=120), we will 
obtain samples every 10 minutes 
over 30 minutes for determination 
of protein turnover. We will also 
obtain 2 breath samples. At time 150 minutes, subjects will receive insulin infusion at the 
concentration of 60 mU/m2/min. Blood glucose will be checked every 5 minutes to achieve 
normoglycemia (95-105 mg/dl) using variable infusion of 20% dextrose, and also amino acid 
levels will be checked every 10 minutes using rapid beside HPLC methodology [48] to achieve 
Figure 2: Schematic diagram of the metabolic study day protocol. Metabolic 
cart and plethysmography measurements will be performed during the 
equilibration phase. A primed constant infusion of L-(1-13C) leucine, L-(ring-
2H5) phenylalanine and 6,6-2H2 glucose will be maintained throughout the 
entire study (270 minutes). 

 
 
18 
baseline amino acid concentrations of leucine and phenylalanine using variable infusion of 
amino acid supplementation, the start of which may be delayed to allow achieving an interim 
state of normoglycemia (per PI discretion).  It is anticipated that normoglycemia and normo-
amino acidemia will be achieved within 90-180 minutes. At time 240 minutes, we will obtain 
additional blood samples every 10 minutes over 30 minutes for measurement of protein 
turnover. We will also obtain 2 breath samples. Blood samples obtained every 5 minutes for 
glucose and every 10 minutes for amino acid concentrations over the same 30 minute period 
will allow determination of insulin sensitivity, as well as carbohydrate and protein metabolism, 
respectively.  A similar set of samples will also be collected if the interim normoglycemia state is 
achieved. 
We will also monitor serum potassium levels every 30 minutes and infuse potassium chloride to 
maintain eukalemia. 
Optional muscle biopsy may be obtained before and after the study.  Also, if the subject is able 
to produce urine, we will collect a sample. 
Once the study is completed, the stable isotope, amino acids and insulin infusions will be 
discontinued and subjects will be fed.  The dextrose infusion will be gradually decreased over 
time with blood glucose being checked every 15 minutes. Once the blood glucose level has 
stabilized, the dextrose infusion will be discontinued and the blood sugar will be monitored every 
15 minutes for about another hour. At this point, depending on the clinical status of the 
hemodialysis subjects, we may perform a post-study dialysis procedure either at the GCRC or 
at one of the outpatient dialysis clinics.  Standard discharge instructions will be provided to the 
subject by the GCRC including contact information. After the baseline study visit subject will be 
followed up with at dialysis for first injection. After the end of study (Month 3) we will follow up 
within about 2 weeks at their dialysis session. 
 
6.6 Other assessments: We will assess the following parameters at baseline, week 6 and end 
of study. 
 
Biochemical parameters: comprehensive metabolic panel, phosphorous, prealbumin, 
lipid panel, transferrin, HbA1c 
 
Inflammatory response:  CRP and plasma cytokines (IL-1, IL-6, IL-10, IL-12, TNFα) 
 
Metabolic hormones: insulin, adiponectin, resistin, leptin 
 
Oxidative stress markers: F2-isoprostane, ADMA, cysteine, homocysteine, and 
glutathione 
 
Plasma amino acids and free fatty acids 
 
Body composition assessment: DEXA 
 
Hand grip test 
 
6.7 Methods: 
All methods used in our studies have been standardized in our and co-investigators’ 
laboratories [23, 35, 49, 50]. 
Standard techniques 
Nutritional Assessment: We will use standard and ESRD modified techniques to assess 
nutritional status of the patients. These will include visceral protein concentrations (serum 
albumin, prealbumin, cholesterol, and transferrin) and somatic protein stores (anthropometrics 
and body composition).  
Body composition analysis: We will primarily use DEXA for body composition analysis. The 
studies will be performed within 2 weeks of each of the three study visits. Our group has 
extensive experience with these methodologies in CHD patients and published a manuscript 
regarding their utility (91). While these methodologies have their inherent problems, the 
repeated measures in the same patients will minimize the variability.  

 
 
19 
Dietary Protein and Energy Intake: Prior to each metabolic clamp study, the subjects will be 
interviewed using the NDS-R system for 2-day random food recalls during a two-week period. 
This software is available at the GCRC. 
Metabolic cart: A metabolic cart will be used to measure simultaneous resting energy 
expenditure and respiratory quotient. 
Breath sampling: During the metabolic study we will collect breath samples several times from 
the subject via a Douglas bag with duplicate 25 ml samples placed into non-siliconized glass 
vacutainer tubes for measurement of breath 13CO2 enrichment.  Additional samples may be 
collected if an interim normoglycemia state is achieved. 
Blood flow measurements: Blood flow measurements will be obtained twice—once before the 
metabolic study begins and the second during the equilibration phase.  Arm (or leg) blood flow 
will be estimated using capacitance plethysmography.   
 
 
7.0 
Risks of Investigational Agents/Devices (side effects) 
 
Chronic hemodialysis patients are at high risk of morbidity and mortality due to the co-
morbidities associated with the chronic kidney disease.  
• 
Inconvenience of reporting to the GCRC and staying one night for each metabolic clamp 
study. 
• 
The insulin infusion can lower blood potassium levels. If this drops too low, your heart 
rhythm may be affected.  We will closely monitor potassium levels (every 30 minutes), 
and place the subject on a machine that monitors the heart to decrease this risk and to 
observe any complications. If a decrease in potassium occurs, it will be replaced 
intravenously.  
• 
Replacing the potassium may cause slight burning or irritation and the subject’s heart 
rhythm may be affected.  Again, we will monitor the subject’s heart to decrease this risk 
and adjust the rate of replacement accordingly. 
• 
There is a small risk of low blood sugar. If this occurs, the subject may experience any of 
these symptoms: feeling lightheaded, headache, fast heartbeat, sweating, blurred vision, 
hunger, and rarely seizures.  We will check blood sugar every 5 minutes.  If the blood 
sugar level drops below normal, it will be restored intravenously.  
• 
Inconvenience of having gelled electrodes placed on the chest and being connected to a 
heart monitor. The gelled electrodes used for ECG monitoring may cause skin irritation. 
• 
Having to lie still for 10-15 minutes during the DEXA scan may be uncomfortable. 
• 
The DEXA scan uses x-rays (or radioactivity).  You will be exposed to a small amount of 
radiation. The radiation exposure that you would likely receive from a total of three 
DEXA scans is approximately equal to one month of exposure from natural background 
sources.  If you are a female of childbearing potential, we will do a pregnancy test before 
the DEXA to make sure you are not pregnant. 
• 
The IV placed in the forearm may cause a slight bruise and carries a risk of infection 
which is rare. 
• 
Eating a recommended or stable diet for 2 weeks and providing 2 dietary recalls may be 
inconvenient. 
• 
Breathing through a mouthpiece may be inconvenient for the subject and may make the 
subject feel lightheaded or dizzy. 

 
 
20 
• 
The blood pressure cuffs placed on the forearm/leg may be uncomfortable when inflated.  
For each blood flow test, one cuff will remain inflated up to 2 minutes.  Another cuff will 
be inflated/deflated about 9 times over 2 minutes.  The third cuff will remain uninflated. 
• 
Inhaling and exhaling into a 1-liter Douglas bag may be an inconvenience for the 
subject, but it does not pose any risk. 
• 
The isotopes used in the metabolic studies are not radioactive and do not present any 
additional risk to the subject.   
• 
The fistula needles placed in the subject’s arm or leg carry a risk of infection but it is 
rare. All necessary aseptic procedures will be taken for needle insertion. 
• 
The lidocaine used for local anesthetic prior to IV insertion or muscle biopsy may cause 
numbness, burning and/or local rash or irritation, if the subject is allergic. There is a risk 
that lidocaine may cause problems with heart rhythm, but this is unlikely given the 
amount the subject will receive. 
• 
Subjects will be asked not to take birth control pills during the study due to possible drug 
interactions. 
• 
Subjects should avoid getting flu shots or other vaccines during the study period. 
• 
The pills used in this study (Actos) can cause or make congestive heart failure worse in 
some patients. There have been reports of liver toxicity, anemia and bone fractures. 
Subjects will be monitored closely for signs of any of these adverse events. Subjects 
may be withdrawn from the study if they experience any of these events. 
• 
The injection used in this study (Anakinra) has some adverse effects such headache and 
redness/pain at the injection site. If these effects persist or worsen, subjects will be 
asked to notify the study doctor promptly. Other less common side effects include 
nausea, diarrhea, abdominal pain, sinus problems, and/or flu-like symptoms. If these 
effects occur, subjects will be instructed to contact the study team immediately. This 
drug may rarely cause a decrease in the white blood cell (WBC) count, which could be 
life threatening. We will monitor WBC count on a weekly basis to make sure the WBC is 
within normal limits.  Subjects may be withdrawn from the study if they experience any of 
these events. 
• 
Risk of injected placebo includes redness/pain at the injection site. 
• 
The PPD tuberculosis skin test may cause mild pain at the injection site. 
• 
Taking the placebo pills may be an inconvenience. 
• 
Optional Muscle Biopsy. The muscle biopsy may cause muscle soreness, bruising, 
infection or a small blood filled bump (hematoma) at the biopsy site. There is a small 
chance that some slight bleeding may also occur. The soreness may last as long as 48 
hours. 
 
As with all research studies, there are unidentifiable and unforeseeable risks that may occur 
during this study. All women of child-bearing potential will have a serum pregnancy test prior to 
continuing in the study. 
 
 
8.0 
Reporting of Adverse Events or Unanticipated Problems involving Risk to 
Participants or Others 
 
Safety information will be assessed by subject interview. As these results are collected, all 
adverse events (AE) will be identified and reported to the principle investigator within fourteen 

 
 
21 
(14) days. Adverse events will be reported as described below. The principal investigator (PI) is 
responsible for evaluating each AE and for determining whether the AE affects the risk/benefit 
ratio of the study and whether modifications to the protocol and consent form are required. 
 
Treatment will be discontinued for any subject who develops conditions during the course of the 
study that are felt to significantly alter the risk/benefit profile for the individual subject (including 
but not limited to the development of angina, myocardial infarction, stroke, TIA, ulcer disease, 
gastrointestinal bleeding, worsening liver function, or signs/symptoms of increasing extracellular 
fluid volume overload). 
 
Adverse event grading.  All adverse events will be graded as follows: 
Severity 
0 = No adverse event or within normal limits 
1 = Mild—did not require treatment 
2 = Moderate—resolved with treatment 
3 = Severe—required professional medical attention 
4 = Life-threatening or disabling 
5 = Death 
Related to study drug (there is a reasonable possibility that the experience may have 
been caused by the drug) 
0 = Unrelated 
1 = Unknown 
2 = Related 
Unexpected event (an AE with specificity or severity not consistent with the risk 
information in the protocol/application or an AE that has not been previously observed) 
0 = No 
1 = Yes 
Serious (any AE occurring at any dose that results in death; a life-threatening adverse 
drug experience; inpatient hospitalization or prolongation of existing hospitalization; a 
persistent or significant disability/incapacity; a congenital anomaly/birth defect; or any 
important medical event that, based on medical judgment, jeopardizes the subject and 
may require medical or surgical intervention to prevent one of the above outcomes) 
0 = No 
1 = Yes 
 
Adverse event reporting.  Any adverse experience associated with the use of drug that is 
serious, unexpected and possibly or definitely study related will be reported in writing to the IRB 
within 10 working days of the PI’s first knowledge of the occurrence.  The PI will also notify DVA 
by telephone or by FAX of any unexpected fatal or life-threatening experience associated with 
the use of the drug within 5 business days after initial receipt of the information.  The PI will 
review all expedited adverse event reports.  In addition, any deviations from protocol will be 
reported to the IRB. 
The annual summary of all adverse events and any audit reports will be sent to the IRB and to 
the study DSMB.  A written annual report will also be submitted to the DVA. 
 
Data Safety and Monitoring Board (DSMB).  We will establish a DSMB for the purposes of 
this study. The board will consist of the following members from the Vanderbilt University 
Medical Center: 
Naji Abumrad, MD (Department of Surgery); Chair 
Satish Raj, MD (Division of Clinical Pharmacology) 
Chang Yu, PhD (Department of Biostatistics) 

 
 
22 
The members of the DSMB will meet after enrollment of 15, 30 and 45 subjects or annually 
(whichever comes first) via conference call, email, or face-to-face. The DSMB will review study 
progress, adverse event logs and any preliminary data. DSMB summary reports will be provided 
to the IRB according to the IRB’s policies and procedures, as well as annually to the DVA. 
 
9.0 
Study Withdrawal/Discontinuation 
 
Patients who are non-compliant with the study protocol or no longer wish to participate in the 
study will be withdrawn from the study.  
 
10.0 Statistical Considerations 
 
10.1. Power Calculations 
Sample size computation was based on the following matched group comparisons using 
independent t-tests between an insulin sensitizer vs. placebo. We also anticipate similar effects 
with anti-inflammatory agent vs. placebo. For each comparison, sample size of 15 was used. 
Specific Aim: To test the hypothesis that inhibiting inflammatory response by administration of 
an IL-1r antagonist (Anakinra) or increasing insulin sensitivity by administration of a PPARγ 
agonist (Actos) will improve net protein metabolism. To compare the chronic effects of an insulin 
sensitizer versus placebo on the rate of amino acid (Leucine) disposal rate (LDR) in dialysis 
subjects.  
We are planning a study with 15 pairs of subjects.  Prior data indicate that LDR is normally 
distributed with standard deviation ranging from 0.16 to 0.23 in CHD and control patients, 
respectively. If the true difference in the experimental and control means is 0.21, we will be able 
to reject the null hypothesis that the population means of the experimental and control groups 
are equal with probability (power) 0.933. The Type I error probability associated with this test of 
this null hypothesis is 0.05. These differences are biologically plausible since our preliminary 
data in healthy controls showed LDR of 9.6 ± 0.23 ug/kg/min, an absolute difference of 0.21 
ug/kg/min. When we used the SD of 0.23 considering the true difference in the experimental 
and control means of 0.21, we will be able to reject the null hypothesis that the population 
means of the experimental and control groups are equal with probability (power) 0.673. 
 
10.2. Data analysis plan 
Baseline characteristics will be compared using one-way analysis of variance (ANOVA) or 
Kruskal-Wallis test for continuous variables according to their distributions and chi-square or 
Fisher’s exact test for categorical variables in order to assess the success of randomization. In 
order to assess improvement by the intervention, independent sample t-test or Wilcoxon rank 
sum test will be used to compare rate of change “before” and “after“ leucine disposal rate 
separately between placebo to CHD subjects who are randomized to either anti-inflammatory or 
to insulin sensitizer agent. We will also perform ANCOVA (as described under Data analysis 
plan for the aim 1) analysis using multiple linear regression adjusting baseline values to assess 
difference in % of improvement from baseline between groups.  
When distribution of baseline characteristics variables is not equally distributed nevertheless of 
randomization, we will adjust the un-evenly distributed variables in a regression analysis as 
potential confounders.  For the data with 45 subjects, we will include up to 4 covariates such as 
BMI, age, gender and inflammatory state at entry if they are distributed un-evenly between arms 
plus two dummy variables for intervention groups to avoid over-fitting.  Normality in residuals in 
the multiple regression model will be assessed, and we will attempt transformation of outcome 
variable to achieve normality.  Changes during experimental protocols within and inter-groups 
involving more than 2 time-point measures of outcomes will be analyzed by linear mixed effect 
regression models. 
 
 

 
 
23 
11.0 Privacy/Confidentiality Issues 
 
All efforts, within reason, will be made to keep personal information in the research record 
confidential but total confidentiality cannot be guaranteed. Each participant will be assigned a 
unique study identification number and will be referred to by this number to protect their identity 
over the course of the study. The numbers will be assigned sequentially.  Only key study 
personnel will have access to these numbers. The codes will be kept in a password protected 
database on the VU server. No information will be given to anyone without permission from the 
subject.  All data will be identified with the assigned study identification number unique to the 
subject.   
All access to protected health information (PHI) as defined by current and future federal 
standards will be carefully managed.  Best effort will be made to de-identify PHI in all case 
report forms.  All physical records (including case report forms that contain PHI) are stored in a 
locked office and only study personnel will have access.  All computers containing patient 
information are behind locked doors in rooms with limited access.  
The database is housed on the Vanderbilt University secured computer network and is 
password protected. Only members of the study team will have access to this database.   
Adverse event reports and annual summaries will not include subject-identifiable material but 
only the assigned study identification numbers. 
 
 
12.0 Follow-up and Record Retention 
 
The study will be conducted over the course of three years. Records will be retained indefinitely.  
 
Data will be stored on the Vanderbilt University computer network in a password protected 
database. Only members of the study team will have access. Only Key Study Personnel will 
have access to the subject’s code. The code will be destroyed after study closure. Paper case 
report forms and other pertinent paper documentation will be kept in a locked office and only 
study personnel will have access. 
 
Only personnel directly involved with the study will have access to source data and the 
electronic database. All research data will be maintained by the PI after study closure. The PI 
will maintain the research data indefinitely. 
 
 
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