The Effect of High Intensity Interval Training and Surgical Weight Loss on Distal Symmetric Polyneuropathy Outcomes
Phase: NA
Conditions: Polyneuropathies, Obesity, Bariatric Surgery Candidate
Sponsor: University of Michigan

 
HUM00143541: The effect of high intensity interval training and surgical weight loss on distal 
symmetric polyneuropathy outcomes 
Co- Principal: Brian C. Callaghan 
Co- Investigators:  Jeff Horowitz 
 
Protocol Version Date: July 8, 2020 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

2. SPECIFIC AIMS 
Distal symmetric polyneuropathy (DSP) affects nearly 15% of Americans over the age of 40. The prevalence in 
diabetes, the most common cause, is ~30%, and in the morbidly obese is 23%. Patients with DSP have lower 
quality of life, more pain, and fall more frequently than those without this disease. In diabetes, lower extremity 
amputations are 2.6 times more likely in those with DSP. Despite the substantial morbidity associated with this 
highly prevalent condition, DSP patients have limited therapeutic options. Some medications can reduce pain, 
but they are not disease modifying treatments. For patients with diabetes, better glucose control results in a 
large risk reduction of DSP in type 1 diabetes, but large, randomized trials failed to show a statistically 
significant risk reduction in type 2 diabetes. For obese patients, no proven treatments are available. Therefore, 
there is a critical need to develop disease modifying treatments for DSP patients with diabetes and/or obesity. 
Multiple studies reveal an association between the metabolic syndrome and DSP. We further defined the 
contributions of individual metabolic syndrome components in three separate epidemiologic studies, including 
our ongoing NIH K23 study. While diabetes is the best established risk factor for DSP, our data support obesity 
and pre-diabetes as important metabolic drivers of nerve injury. The most effective therapies for obesity and 
pre-diabetes are exercise and/or weight loss. While the combination of these two vastly different interventions 
is likely optimal, each requires a life-altering change in behavior. Therefore, the primary objective of this 
application is to determine the independent and combined effects of exercise and surgical weight loss on DSP 
outcomes. Our central hypothesis is that exercise and surgical weight loss will both improve DSP outcomes, 
but the effect of exercise will be significantly larger. The rationale for this hypothesis is our preliminary data 
revealing stable DSP outcomes 2 years after diet-induced weight loss (the natural history is worse DSP 
outcomes over time), while other groups show a substantial improvement in DSP outcomes after exercise in 
subjects with minimal weight loss. Limitations of previous work include the lack of a comparison group for the 
intervention. Likewise, the effect of surgical weight loss on DSP has not been rigorously tested. 
We propose to study the effects of a randomized exercise intervention on DSP outcomes (Aim 1). We will 
recruit patients from a Bariatric Surgery clinics. Patients will be recruited after their visit with the physician’s 
assistant and/or surgeon. We will stratify randomization of the exercise intervention by those that do and those 
that do not have bariatric surgery to allow evaluation of the effect of surgical weight loss on DSP outcomes 
(Aim 2). For the exercise intervention, we will employ the innovative high intensity interval training (HIIT) 
regimen, which our group has shown improves metabolic outcomes and has high compliance. For weight loss, 
bariatric surgery results in larger and more sustained weight loss compared to dietary approaches, making this 
an ideal intervention to investigate. Similar to HIIT, surgical weight loss improves metabolic outcomes and has 
high compliance. 
Aim 1: Examine the efficacy of a HIIT exercise regimen on DSP outcomes (randomized). We hypothesize 
that HIIT will improve DSP outcomes compared with routine exercise counseling. To test this hypothesis, we 
will randomize patients approved for bariatric surgery clinic 1:1 to HIIT versus routine exercise counseling. The 
HIIT regimen will consist of 3 sessions/week (2 supervised and 1 unsupervised) for 24 months. Routine 
exercise counseling is the current real world practice in the bariatric surgery clinic. The primary DSP outcome 
will be intraepidermal nerve fiber density (IENFD) of the proximal thigh measured at baseline, 3, 12, and 24 
months. Linear mixed models will determine the longitudinal effect of HIIT on DSP while accounting for 
repeated measures. Importantly, we will capture a sensitive, quantitative DSP measure (IENFD), determine if 
an intervention effect exists, and link the measures to meaningful patient-oriented outcomes such as DSP-
related pain (Short form McGill), function (Berg Balance Scale), and quality of life (NeuroQOL). 
Aim 2: Explore the impact of surgical weight loss on DSP outcomes (non-randomized). We hypothesize 
that bariatric surgery patients will have better DSP outcomes compared to patients without surgery, but the 
effect will be less than the effect of HIIT. To test this hypothesis, we will stratify the HIIT randomization in Aim 1 
1:1 for those that have and those that do not have surgery. This will allow us to explore the effect of surgical 
weight loss on DSP outcomes without requiring an expensive, randomized, surgical intervention trial. Linear 
mixed models will determine the longitudinal effect of surgical weight loss on DSP. We will also assess the 
interaction between HIIT and surgical weight loss to evaluate the combined effect of these two interventions.  
Impact: We will determine whether HIIT and/or surgical weight loss are promising disease modifying 
treatments for DSP. If successful, our phase 2 trial will form the basis of a large, pivotal phase 3 trial of 
exercise and/or weight loss for the treatment of DSP. Either would be the first disease modifying therapy for 
this common, highly morbid condition. 
 

3. RESEARCH STRATEGY  
 
A. Significance 
 
A1. Distal symmetric polyneuropathy (DSP) is common, disabling, and untreatable. Peripheral 
neuropathy affects more than 2% of the population, with DSP representing the leading neuropathy subtype.1 
The prevalence of DSP increases dramatically in older populations to ~15% in those over the age of 40.2 In a 
nationally representative Medicare population, DSP patients reported a significantly lower quality of life, 
increased falls, and more pain than a propensity score-matched control group.3 DSP patients were also more 
likely to have trouble with sleep, activities of daily living, depression, and fatigue. Diabetes is the most common 
cause of DSP, accounting for 32-53% of all cases.4-6 In the U.S., DSP from diabetes is a significant public 
health burden, serving as the leading cause of diabetes-related hospital admissions and non-traumatic 
amputations. Unfortunately, few treatments exist for DSP patients with diabetes and/or obesity. An American 
Academy of Neurology systematic review found that several treatments decrease pain in this population.7 
Disappointingly, none are disease modifying therapies. While improved glucose control is a potential disease 
modifying therapy, the results are quite disparate in patients with type 1 and type 2 diabetes. In type 1 
diabetes, enhanced glucose control dramatically reduces the incidence of DSP (78% relative risk reduction).8, 9 
Conversely, in type 2 diabetes, enhanced glucose control only slightly reduces the risk of developing DSP (5-
9% relative risk reduction), which is not statistically significant in a meta-analysis or any individual study.10, 11 
This discrepancy highlights the difference between these two conditions and emphasizes that many patients 
with type 2 diabetes develop DSP despite adequate glucose control. Therefore, factors besides hyperglycemia 
are likely involved in the pathophysiology of DSP in type 2 diabetes. Our recent work, in conjunction with past 
studies, provides evidence to support the metabolic syndrome (MetS) and its individual components (obesity, 
pre-diabetes or diabetes, low high-density lipoprotein (HDL) cholesterol, elevated triglycerides, and elevated 
blood pressure) as important risk factors for DSP. Given the current absence of disease modifying therapies 
for DSP, there is a critical need to identify new interventions. 
 
 A2. MetS is associated with DSP, with diabetes, pre-diabetes, and obesity representing the primary 
drivers. Multiple studies demonstrate an association between the MetS and DSP.12-16 However, until recently, 
the precise metabolic drivers within this syndrome were unknown. In our ongoing NIH K23 study (NS-079417), 
we performed comprehensive metabolic and DSP phenotyping in 102 obese subjects and 53 lean controls.17 
We found that diabetes and obesity were independently associated with DSP (Table 1). Furthermore, pre-
diabetes approached statistical significance, with an odds ratio 
similar to that of diabetes. The prevalence of DSP increased from 
3.8% in lean controls to 11.1% in normoglycemic obese controls, 
indicating that the effect of obesity alone is substantial. The 
prevalence further increased to 29% and 34.6% in those with pre-
diabetes and diabetes, respectively. The main difference between 
those with diabetes and pre-diabetes was the severity of DSP. 
We also demonstrated that DSP in this obese population resulted 
in worse nerve conduction studies (NCS), lower intraepidermal 
nerve fiber density (IENFD), worse DSP-specific quality of life, 
and higher pain scores compared with obese patients without 
DSP. These findings highlight the severity of DSP in this obese 
population. Even the obese patients without DSP had statistically 
significant lower IENFD than lean controls. Given that these obese participants also had lower DSP-specific 
quality of life and higher pain scores, the lower IENFD is likely clinically relevant even in those not meeting a 
clinical definition of DSP. In addition to this study in middle aged obese individuals, we also addressed similar 
questions in an elderly, population-based cohort (N=2,382) from the Health, Aging, and Body Composition 
Study.18 We found that diabetes and obesity were the metabolic syndrome components most often associated 
with our primary and secondary DSP outcomes. Moreover, we demonstrated that DSP is more common as the 
number of metabolic syndrome components increases, independent of glycemic status. Finally, our 
unpublished data from a Chinese population based cohort (N=4,001) also demonstrates that diabetes and 
obesity are 
independently 
associated with 
DSP. Furthermore, 
the prevalence of 
Table 2: Prevalence of DSP in a Chinese cohort using three different definitions 
DSP measure 
Total 
Normoglycemia 
Pre-diabetes 
Diabetes 
P value 
MNSI Examination 
272 (6.8%) 
48 (3.3%) 
110 (6.3%) 
114 (15.1%) 
<0.01 
MNSI Questionnaire 
93 (2.3%) 
18 (1.2%) 
29 (1.7%) 
46 (6.1%) 
<0.01 
Monofilament 
252 (6.3%) 
55 (3.7%) 
128 (7.3%) 
69 (9.1%) 
<0.01 
Table 1: The association of metabolic 
syndrome components with DSP 
Variable 
OR (95%CI) 
Age 
1.09 (1.02,1.16)* 
Male (reference female) 
0.70 (0.12,4.00) 
Height (5 cm) 
1.12 (0.75,1.69) 
Glycemic status 
Pre-diabetes 
Diabetes  
(reference normal) 
 
3.82 (0.95,15.41) 
4.90 (1.06,22.63)* 
Waist circumference (5 cm) 
1.24 (1.00,1.55)* 
SBP (10 mm Hg) 
0.98 (0.66,1.46) 
Triglycerides (50 mg/dL) 
1.03 (0.72,1.48) 
HDL (10 mg/dL) 
1.31 (0.74,2.32) 

DSP increases significantly in those with pre-diabetes and in those with diabetes (Table 2). In this patient 
population, the number of metabolic syndrome components, independent of glycemic status, is also associated 
with DSP. Another group has reported similar findings in a separate area in China.19, 20 Taking our three 
studies together, the main metabolic drivers of DSP are highly likely to be diabetes, obesity, pre-diabetes, and 
the number of metabolic syndrome components. Proposed interventions should target these metabolic factors.  
 
A3. Weight loss and exercise are the most widely accepted treatments for obesity and pre-diabetes. 
The 2013 American College of Cardiology/American Heart Association guideline for the management of 
overweight and obesity in adults recommends diet, lifestyle intervention including increasing physical activity, 
and bariatric surgery.21 Notably, this guideline does not recommend pharmacologic therapy for obesity.22 While 
pharmacologic therapies reduce weight,23 the discontinuation rates for these medications are quite high, the 
evidence is only for short term outcomes, and most patients regain weight after stopping these therapies.24 
Therefore, diet-induced weight loss, surgical weight loss, and exercise are the most widely accepted 
treatments of obesity. In terms of pre-diabetes, the Diabetes Prevention Program trial demonstrated that an 
intensive lifestyle intervention was superior to metformin and also superior to standard lifestyle 
recommendations in the prevention of diabetes.25 The intensive lifestyle intervention consisted of a diet with a 
goal of 7% weight loss and moderate intensity exercise for 150 min per wk. Therefore, a combination of weight 
loss and exercise is the most widely accepted treatment of pre-diabetes. 
 
For our proposed study, we chose to determine the effect of exercise and surgical weight loss on DSP 
outcomes based on our preliminary data and previously published studies (see C1.2.1). For the exercise 
intervention, we will employ the innovative high intensity interval training (HIIT) regimen because of the high 
compliance rate, particularly in those with obesity (see C1.2.2). In addition to the HIIT intervention, we have the 
opportunity to explore the effect of surgical weight loss on DSP outcomes by recruiting patients from bariatric 
surgery clinics, including those who do and those who do not have surgery (see C2.2.1). We chose to recruit a 
bariatric surgery population rather than a medical weight loss population because surgical weight loss yields 
robust and sustained weight loss, even when following patients for as long as 10 yr after surgery, whereas 
medical weight loss is often temporary.26 
 
A4. Significance. This study is highly significant because: 1) either HIIT or surgical weight loss would provide 
the first disease modifying therapy for DSP other than glucose control for those with diabetes; 2) these 
interventions would be the first available treatments for obese DSP patients without diabetes; 3) the results will 
lead to a phase 3 study to determine the best treatment of DSP so that physicians will no longer have to 
recommend two disparate life altering interventions at once; 4) the study will provide data on the risks and 
benefits of bariatric surgery for DSP; and 5) a positive study would provide further evidence to support 
diabetes, pre-diabetes, and obesity as the main metabolic drivers of DSP. Furthermore, while weight loss and 
exercise are currently recommended for all patients with obesity and/or diabetes, much remains to be learned 
about the specifics of each of these interventions and their effects on DSP. First, few obese subjects 
participate in the recommended diet and exercise regimens; therefore, effective interventions that have high 
compliance are needed. Unfortunately, we do not even know which of these two life altering interventions 
(weight loss and/or exercise) to focus on. Second, we do not know how aggressive to be in pursuing weight 
loss, such as when to recommend bariatric surgery for patients at risk for DSP. Third, the effect of HIIT on DSP 
is unknown, and our results will potentially lead to more specific exercise recommendations. Finally, our 
consultants will be able concentrate on the underlying mechanisms of exercise and/or weight loss on 
preventing DSP depending on which is clinically effective. Importantly, our research will eventually lead to 
more specific recommendations rather than the current broad statements regarding weight loss and exercise. 
 
B. Innovation  
 
The proposed trial is highly innovative and represents a paradigm shift in current treatment strategies for the 
prevention and treatment of DSP for patients with and without diabetes. We strongly contend that the diabetes 
research community must move beyond glucose control alone to more comprehensive treatment approaches 
for diabetic complications. Moreover, a successful trial would bring us one step closer to a desperately needed 
intervention for patients without diabetes, including the large obese population, to prevent and improve DSP. 
The innovation in this proposal is high and includes: 1) an exercise regimen (HIIT) that is time efficient, yields 
improved metabolic health even after short term participation, and is better tolerated with higher compliance in 
an obese population than conventional exercise; 2) a study design that allows comparisons between exercise, 
bariatric surgery, and the combination of these interventions without the need for an expensive, randomized 
surgical intervention phase 2 trial; 3) a main outcome measure (IENFD), which allows sensitive and objective 

quantification of nerve fiber loss and regeneration over time, with true potential to capture early changes of 
DSP; 4) comprehensive patient-oriented outcomes, including multiple measures of DSP-related pain, daily 
function, and DSP-specific quality of life outcomes; 5) the use of a non-invasive measure of DSP, corneal 
confocal microscopy (CCM), to compare with the primary outcome of this trial, IENFD, to inform future clinical 
trials (see C.3.4.2); and 6) an analysis plan that investigates the potential modifying effects of sex on DSP 
outcomes, the differential effect of sleeve gastrectomy versus Rou-en-Y gastric bypass on DSP outcomes, and 
the weight loss independent effects of bariatric surgery by incorporating early outcome measures. 
 
C. Approach  
 
The clinical trial 
design summary for 
Aims 1 and 2 is 
shown in Figure 1. 
All patients will be 
recruited from a 
Bariatric Surgery 
Clinic, including 
those that do and 
those that do not 
have surgery. We 
will randomize 
patients 1:1 to either 
the HIIT exercise 
regimen or routine 
exercise counseling. 
Randomization will 
be stratified 1:1 for 
those that do and 
those that do not have surgery. We will also stratify by glycemic status to limit confounding. Randomization will 
be performed using permuted blocks, with a block size known only to the statistician. We will perform 
longitudinal analyses using linear mixed models to study the evolution of IENFD from baseline to 2 yr (4 
measures in total). We will compare the effect of HIIT, bariatric surgery, and the combination of HIIT and 
surgery on all outcomes. 
 
C1. Aim 1: Examine the efficacy of a HIIT exercise regimen on DSP outcomes (randomized)   
 
C1.1. Introduction: There is an urgent need for a disease modifying therapy for the prevention and treatment 
of DSP. Currently, physicians only have neuropathic pain medications and glucose control to offer patients with 
DSP.7, 27 However, glucose control alone is not effective at preventing DSP and many patients with diabetes 
develop DSP despite good glucose control.27 Similarly, no disease modifying interventions exist for the 
increasing population with pre-diabetes and/or obesity. Thus, it is essential to identify new interventions that 
can prevent and treat DSP. To date, uncontrolled studies support improvement in the natural history of DSP 
decline over time after an exercise intervention.28-30 The next step is to determine whether a randomized, 
intensive, exercise intervention improves DSP outcomes compared to routine exercise counseling. 
 
C1.2. Justification & feasibility:  
C1.2.1 Justification for the hypothesis that exercise is likely to improve DSP outcomes: Three uncontrolled 
studies have shown the potential for exercise to improve DSP outcomes with lifestyle interventions that 
primarily focused on exercise without significant weight loss.28-31 One study followed 32 patients with DSP 
caused by impaired glucose tolerance and measured IENFD in skin biopsies at the proximal thigh after 12 mo 
of a lifestyle intervention.30 The intervention consisted of individualized diet and exercise counseling; however, 
the BMI of participants only decreased by an average of 1.1 kg/m2. Despite the minimal weight loss, IENFD 
levels significantly increased by 1.4 fibers/mm, in spite of the known natural history of decline over time. The 
improvement in IENFD also significantly correlated with decreased neuropathic pain, which indicates that this 
IENFD increase is likely clinically relevant to patients. Similarly, another study followed 36 patients with 
diabetes and/or the metabolic syndrome and determined the cutaneous nerve regenerative capacity 
(measured by IENFD) after 4 months of a lifestyle intervention.29 The intervention was 30-90 min of supervised 
exercise twice weekly that was supplemented with home exercise. Dietary counseling was only provided twice 
Figure 1: Clinical trial design summary for Aims 1 and 2. 
 

and the BMI decreased by an average of only 0.11 kg/m2. Following the exercise intervention, the cutaneous 
nerve regenerative capacity increased from 0.051 to 0.072 fibers/mm/day (p=0.002). Notably, those with 
improvements in more metabolic syndrome components had a greater increase in cutaneous nerve 
regenerative capacity (p<0.012). A third study followed 17 patients with DSP caused by diabetes before and 
after 10 wk of aerobic and strengthening exercise.28 They found that intraepidermal nerve fiber branching at 
the proximal thigh was significantly improved after exercise (0.11 branch nodes/fiber, p=0.008) despite no 
change in BMI. Furthermore, pain and neuropathic symptoms were significantly reduced. IENFD at the 
proximal thigh also improved, although this result did not quite meet statistical significance (1.68 fibers/mm, 
p=0.09). All of these studies show the promise of exercise regimens to improve IENFD outcomes without 
significant weight loss, but importantly none had a control group. Finally, a small randomized trial of a mixture 
of patients with type 1 and type 2 diabetes revealed improvements in some NCS parameters and vibration 
perception thresholds after 4 yr of an aerobic exercise regimen.31 BMI changed only a little over the 4 yr, and 
IENFD was not measured in this study. Limitations of this study include the lack of designated primary and 
secondary outcomes, no blinded outcome assessments, inclusion of type 1 and type 2 diabetes, unequal 
randomization of the population, and no patient oriented outcomes. Of note, exercise interventions in mouse 
and rat models of obesity and diabetes also support the positive effects of exercise on DSP.32, 33  
 
In addition to the above exercise studies on DSP outcomes, our current NIH K23 study is following 130 obese 
participants for 2 yr following a diet-induced weight loss intervention with limited exercise counseling.17 Our 
preliminary data reveal that IENFD increased by only 0.2 fibers/mm at both the distal leg and proximal thigh 
despite an average improvement in BMI of 3.67 kg/m2. This is in contrast to the two previously mentioned 
exercise trials demonstrating IENFD improvements of 1.4 and 1.68 fibers/mm after 1 yr and 10wk 
respectively.30 Taken together, these studies support our hypothesis that exercise is the most likely 
intervention to improve DSP outcomes. Given the natural history of IENFD decline over time, diet-induced 
weight loss is also likely to improve DSP outcomes, but not to the same degree as exercise. Importantly, the 
effect of surgical weight loss on IENFD is unknown.  
 
C1.2.2 Justification for high intensity interval training (HIIT): The ideal exercise regimen would improve 
metabolic outcomes and have high compliance in the short and long term. We believe that HIIT is that 
regimen. Co-investigator Jeff Horowitz, PhD, and consultant Jonathan Little, PhD have been involved in 5 
studies in obese populations that support an improvement in metabolic outcomes and/or high compliance with 
HIIT. Specifically, one study involved obese adults (n=11; body mass index (BMI): 32±1 kg/m2) completing 
either a 2-wk program of HIIT (10 x 1 min @ 90% heart rate (HR) max; n=5) or moderate-intensity continuous 
training (MICT) (50 min @ 60-70% HR max). They measured fasting blood glucose and insulin concentration 3 
d after the last exercise training session to calculate Homeostatic Model Assessment of Insulin Resistance 
(HOMA-IR) as an assessment of whole-body insulin resistance. Three days after the last training session, 
HOMA-IR was lower than pre-training values in all HIIT subjects, but not in the MICT group. As a result, the 
reduction in HOMA-IR was significantly greater in HIIT vs. MICT (p<0.01). These findings agree with several 
previous studies that used more direct assessments of insulin sensitivity (e.g., clamp, glucose tolerance tests 
(GTT)) to demonstrate that, unlike conventional exercise training, HIIT induces a persistent improvement in 
insulin sensitivity.34-37 Similarly, another study enrolled type 2 diabetic patients (n=8; BMI= 32±6 kg/m2; 2h GTT 
>200 mg/dl) in a 2-wk HIIT protocol, with each session identical to the HIIT regimen proposed in this 
application (10 x 1 min at ~90%; 25 min total exercise time).38 All participants completed the entire HIIT 
protocol without any complications. After 2 wk of HIIT (without weight loss and measurements made 3 d after 
exercise), hyperglycemia was reduced by nearly 15% as measured by 24 hr blood glucose concentration. 
Perhaps more importantly, HIIT reduced post-prandial blood glucose concentrations by ~30%, which was 
highlighted by a marked suppression in blood glucose concentration after meals as measured by a continuous 
glucose monitor. A third study randomized 53 participants with diabetes to 2 different dietary interventions or 
placebo.39 All participants underwent 12 weeks of HIIT. The mean BMI in this population was 35±7. Continuous 
glucose monitoring, HA1C, percent body fat, lean mass, VO2peak, and endothelial function were all improved 
over time after HIIT. Importantly, 96.2% of participants completed all HIIT sessions in 12±1 weeks. Another 
study involved 10 overweight or obese individuals performing HIIT and continuous moderate intensity exercise 
seven days apart to compare the effects of these regimens on post-prandial glucose.40 The mean BMI in this 
population was 36±7. HIIT had greater and longer lasting effects on post-prandial glucose compared with 
continuous moderate intensity exercise. All participants were able to complete the HIIT exercise sessions 
without difficulty. These data indicate that HIIT can indeed lead to meaningful improvements in metabolic 
health, and that HIIT is safe and well-tolerated in obese and type 2 diabetic patients. Notably, Dr. Little’s group 

also has data supporting that compliance to HIIT is higher than conventional regimens. Specifically, his group 
randomized 32 obese, pre-diabetic men and women (BMI: 33.1±7.7 kg/m2; HbA1C: 5.7±0.1%) to either HIIT 
(10 x 1 min @ 90% HR max; n=15) or MICT (50 min @ 60-70% HR max; n=17).41 Subjects self-selected their 
mode of training (e.g., walk/run, elliptical machine, stairs, bicycle). After a 2-wk familiarization period of 
supervised training, subjects continued their assigned exercise program on their own, 3 times per wk for 4 wk. 
Compliance was monitored using accelerometers and training log books. They found that compliance in this 
obese, pre-diabetic population was higher in those with HIIT compared with MICT (89±3% vs. 71±8%, p=0.05). 
Along with the metabolic improvements noted above, the improved compliance in those performing HIIT 
compared to MICT is a major reason for choosing this as the exercise intervention. Of note, all five of the 
above studies were well tolerated in obese participants (total N=91) including 34 with BMI>35, 23 of these with 
BMI>40. Furthermore, a recent meta-analysis identified 9 additional studies using HIIT as an intervention in  
obese populations with a total of 112 participants.42 Two of these studies had mean BMIs above 35 (35.7 and 
36.6).43, 44 The lowest compliance in these 9 studies 
was 71% with most reporting 80% compliance or 
better.42, 45 Overall, these data support HIIT as an 
innovative, safe, and well tolerated intervention to 
improve metabolic outcomes with high compliance 
even in those with very high BMI.  
 
 
C1.2.3 Feasibility of the study design: We have 
demonstrated the ability to recruit, retain, and measure 
extensive DSP outcomes in two cohorts of obese 
patients. For our NIH K23 study, we have enrolled 125 
obese patients and 90 age- and gender-matched lean 
controls from the Weight Management Program (WMP) 
at U-M (Tables 3 and 4), which has led to a manuscript 
detailing the metabolic drivers of DSP as detailed in 
A2.17 
 
As part of an ongoing study funded by a 1 yr pilot grant (2P30-DK020572), principal investigator (PI) Brian 
Callaghan, MD, MS, Assistant Professor of 
Neurology, and co-investigator Justin 
Dimick, MD, MPH, the Zuidema Professor 
of Surgery, completed comprehensive 
metabolic and DSP phenotyping on 71 
participants (up from 43 previously) from U-
M’s bariatric surgery clinic (Tables 3 and 
4). They also recruited an additional 28 
participants that are in the process of 
completing study measures. Of the 40 
patients who completed bariatric surgery 
and have been enrolled in the study for 
over 6 mo, 37 (93%) had additional 
testing at 6 mo, demonstrating our ability 
to effectively retain subjects once they 
have entered our study. Notably, the 
metabolic phenotyping and neurologic 
outcomes in these studies are 
comparable to those in the current 
proposal, including skin biopsies to 
measure IENFD at the proximal thigh 
and distal leg (Table 5). We are also 
currently recruiting patients from the U-M 
bariatric surgery clinic who have decided 
not to have surgery. Hence, recruiting the 
non-surgical population is not likely to be 
a barrier given our experience with this 
Table 3: Population Demographics 
Bariatric (n=71) 
WMP (n=125) 
Age, mean (SD) 
45.5 (11.1) 
52.8 (10.9) 
Female 
53 (74.7%) 
68 (55.3%) 
White 
56 (78.9%) 
110 (90.9%) 
Black 
13 (18.3%) 
9 (7.4%) 
BMI, mean (SD) 
45.7 (6.4) 
41.4 (6.4) 
Waist circumference, cm, 
mean (SD) 
123.4 (17.4) 
122.4 (15.5) 
SBP, mean (SD) 
128.0 (15.0) 
130.0 (13.3) 
DBP, mean (SD) 
73.1 (11.4) 
66.5 (10.8) 
Diabetes 
25 (35.2%) 
30 (24.0%) 
Pre-diabetes 
26 (36.6%) 
45 (36.0%) 
Normoglycemia 
20 (28.2%) 
50 (40.0%) 
HDL, mean (SD) 
45.6 (13.6) 
46.0 (11.5) 
Triglycerides, mean (SD) 
120.5 (58.7) 
155.0 (80.8) 
Table 4: Prevalence of DSP 
Normoglycemia 
Pre-diabetes 
Diabetes 
Total 
IWMC 
6 (12.0%) 
11 (24.4%) 
9 (30.0%) 
26 (20.8%) 
Bariatric 
1 (5.0%) 
2 (7.7%) 
7 (28.0%) 
10 (14.1%) 
Total 
7 (10.0%) 
13 (18.3%) 
16 (29.1%) 
36 (18.4%) 
Table 5: Extensive DSP outcome measures in the bariatric surgery cohort 
compared with lean controls 
Variable 
Lean Without 
DSP 
Obese without 
DSP 
Obese with 
DSP 
IENFD leg (fibers/mm) 
14.7 (7.0) 
9.1 (7.3) 
5.4 (4.3) 
IENFD thigh (fibers/mm) 
26.1 (8.1) 
18.2 (12.7) 
15.3 (5.4) 
Sural amplitude (uV) 
21.4 (5.7) 
10.9 (5.5) 
7.1 (4.3) 
Sural PL (ms) 
4.1 (0.4) 
3.8 (0.4) 
4.0 (0.3) 
Peroneal amplitude (mV) 
5.7 (2.5) 
5.6 (2.4) 
3.2 (2.1) 
Peroneal DML (ms) 
5.0 (0.9) 
4.7 (0.7) 
5.6 (1.1) 
Peroneal CV (m/s) 
43.3 (9.2) 
45.7 (6.3) 
39.5 (3.1) 
Peroneal F response (ms) 
65.9 (85.5) 
47.5 (8.2) 
55.3 (5.5) 
Tibial amplitude (mV) 
13.8 (5.7) 
9.2 (4.9) 
4.1 (3.4) 
Tibial DML (ms) 
7.6 (10.2) 
4.6 (0.9) 
5.4 (0.7) 
Tibial F response (ms) 
50.1 (4.9) 
50.4 (5.9) 
58.2 (4.2) 
UENS 
0.2 (0.6) 
1.5 (3.0) 
9.5 (5.2) 
MNSI Questionnaire 
0.4 (0.7) 
2.7 (2.6) 
6.6 (3.4) 
MNSI Examination 
0.1 (0.6) 
0.7 (1.1) 
2.1 (1.4) 
Sudoscan feet 
75.7 (14.3) 
70.5 (12.5) 
54.5 (21.9) 
Sudoscan hands 
64.4 (13.4) 
65.3 (14.9) 
45.8 (18.2) 
SAS 
3.0 (5.1) 
5.6 (4.5) 
10.3 (6.2) 
Neuro-QOL 
1.7 (0.9) 
2.6 (1.1) 
3.1 (0.9) 
McGill Pain score 
1.0 (3.5) 
5.0 (6.6) 
10.8 (7.2) 
VAS Pain score 
5.5 (18.6) 
26.3 (28.7) 
55.0 (28.4) 

population to date. Of note, of the 480 patients that consider bariatric surgery each year at the U-M clinic, 55% 
undergo a surgical operation. Therefore, a large non-surgical population exists as well (N=216). For the 
exercise intervention, Drs. Horowitz and Little have a tremendous history of successful completion of clinical 
research projects including HIIT and other exercise trials.46-50  
 
C2. Aim 2: Explore the impact of surgical weight loss on DSP outcomes (non-randomized) 
 
C2.1. Introduction: Similar to previous data on exercise interventions on DSP outcomes, one uncontrolled 
study revealed improved DSP outcomes after bariatric surgery (Roux-en-Y gastric bypass) compared with the 
known natural history of decline.51 The next step is to investigate the potential impact of bariatric surgery on 
DSP outcomes compared to those without surgery. Our innovative study design allows exploration of the effect 
of surgical weight loss on DSP outcomes without an expensive, randomized, surgical intervention trial. 
 
C2.2. Justification & feasibility:  
C2.2.1 Justification for bariatric surgery: Similar to the ideal exercise regimen, the best weight loss strategy 
would improve metabolic outcomes and have high compliance. Just like HIIT, bariatric surgery meets those 
criteria. Mounting evidence supports bariatric surgery for improvements in metabolic syndrome components 
including diabetes, hypertension, and dyslipidemia. In terms of diabetes, a prospective, longitudinal study of 
28,616 obese diabetic patients undergoing a variety of surgical approaches revealed remission or 
improvement of diabetes in 44-83% of participants.52 These results have been duplicated in four randomized 
controlled trials of surgery compared with medical management or lifestyle interventions.53-56 Most of these 
trials looked at diabetes outcomes at 1-2 yr, but data at 3-5 yr is also promising.57, 58 All four trials revealed 
large effect sizes regardless of the type of surgery employed. Data to support a reduction in hypertension are 
less robust compared with diabetes. A large prospective, longitudinal study found resolution or remission of 
hypertension ranging from 44-79% depending on the surgical procedure.52 However, a randomized trial of 
surgery compared with lifestyle and medical modifications demonstrated similar large reductions in systolic 
blood pressure at 1 year.54 Whether the difference in long term compliance in these groups would result in 
different long term hypertension outcomes is unclear. Data on dyslipidemia are similar to that for hypertension. 
The same large prospective, longitudinal study found remission of dyslipidemia ranging from 33-66% at 1 year 
depending on the surgical procedure performed.52 No randomized data demonstrating a reduction in 
dyslipidemia are available to provide higher levels of evidence. In terms of reducing obesity, bariatric surgery is 
incredibly effective and the effect is long lasting. Patients lose between 45-85% of their excess weight, with 
maximum weight loss between 1-2 yr after the procedure.59, 60 In addition, surgery results in longstanding 
weight loss that is sustained up to 10 yr after the procedure.26 Medical weight loss with dietary or 
pharmacologic interventions is often successful in the short term, but in contrast to surgery, it is difficult to 
sustain. The lack of concern regarding compliance is one of the main reasons why studying a bariatric surgery 
population is preferred to studying a medical weight loss population. In addition to the data supporting the 
effect of bariatric surgery on improving metabolic outcomes and the inherently high compliance, one small 
study supports a possible effect on DSP outcomes. Specifically, a prospective cohort study of 20 patients with 
type 2 diabetes undergoing a Roux-en-Y gastric bypass revealed that the 12 patients with pre-operative DSP 
had significant improvements in DSP outcomes measures 6 mo after surgery. Of note, no control group was 
provided and outcome measures were not masked to the intervention. Despite these limitations in study 
design, these results are encouraging for a possible positive effect on DSP outcomes after bariatric surgery.51 
 
C3. Clinical trial methodology  
 
C3.1. Target population: We will enroll 35 patients into each arm of the study (HIIT/no surgery, HIIT/surgery, 
routine exercise/no surgery, routine exercise/surgery) for a total of 140 patients. 
 
Inclusion criteria:  
1) Attending a bariatric surgery clinic;  
2) At least  40 years of age  
3) BMI > 35 with one comorbid condition present or BMI > 40 without comorbid conditions present;  
4) willing and capable to sign the IRB-approved consent form and cooperate with the medical procedures 
for the study duration;  
5) willing to accept random treatment assignment to HIIT or routine exercise counseling;  
 
Exclusion criteria:  
1) History of DSP from causes other than diabetes and/or the metabolic syndrome as determined through 
medical history, family history, history of medications, occupational history, history of exposure to 
toxins, physical and neurological examinations;  

2) use of warfarin, heparin, or other anticoagulants, which would increase the risk of complications from 
skin biopsy;  
3) contraindication to HIIT participation including a failed exercise stress test;  
4) participation in an experimental medication trial within 3 mo of starting the study;  
5) undergoing therapy for malignant disease other than basal-cell or squamous-cell skin cancer;  
6) medical or psychiatric reason for not being a surgical candidate;  
7) requiring a walking assist device;  
8) currently smoking; 
9) weight over 450 pounds. 
 
Screening Visit:  Patients will complete a medical history review during their screening visit to determine 
subject eligibility.  As outlined by the University of Michigan clinical care guidelines, subjects will complete a 
Covid-19 test prior to the stress test. A results will be provided to the study team. Patients will complete an 
exercise stress test as part of the screening procedures. The %HR the stress test will start with will be the 
patient's resting heart rate, typically below 100. The %HR max will vary per patient. This is calculated by 220-
age. It is possible that this value will not be met but it is also possible that the predicted %max HR will be 
exceed. The Cornell protocol will be utilized for this study. The stress test will be performed on a treadmill. Only 
patients that have a normal exercise stress test will be allowed to participate in this study.  To mitigate the risks 
associated with the exercise stress test, the test will be overseen by medical professionals that are ACLS 
certified including a physician, nurse and/or exercise physiologist. Patients with an abnormal EKG will be 
referred to their primary care physician and not cleared for participation 
 
Justification of the target population: We are focusing on an obese population because we have built a 
collaborative obesity-DSP team with 5 years of experience recruiting and studying this population. Focusing on 
an obese population, rather than a diabetic population, increases the number of people affected by our results. 
Furthermore, our observational data revealing a high prevalence of DSP in obese participants without diabetes 
highlights that DSP is an important issue for all obese patients.61 Limiting recruitment to the U-M’s bariatric 
surgery clinic allows us to evaluate the effect of both HIIT and bariatric surgery on DSP outcomes. Selection 
bias will be reduced by requiring that patients have started the bariatric surgery process by seeing a physician 
assistant or bariatric surgeon.  Patients will also be referred to a dietician. The limited generalizability to other 
populations can be addressed in subsequent studies. 
 
C3.2. Interventions:  
C3.2.1 HIIT: Training sessions during the 24-mo will be supervised 
by exercise physiologists and trainers. Dr. Horowitz, the director of 
the  Human Phenotyping Core (HPC) of the U-M’s Nutrition and 
Obesity Research Center will provide oversight of this aspect of the 
study given his vast expertise on the impact of HIIT and other 
exercise interventions on metabolic outcomes.46-50 The exercise 
modality will be self-selected (e.g., walk/run (outdoors or treadmill), 
elliptical machine, stair climbing, cycling), and subjects will be 
encouraged to regularly vary the exercise modality to reduce risk of 
overuse injury and to enhance the variety of the exercise program. 
Subjects receiving the surgery intervention will start the HIIT regimen no sooner than 4 wk post-operatively to 
provide an adequate time to heal from the procedure. All subjects will first undergo a familiarization program for 
1 week where they all will perform conventional exercise (steady-state at 65% HR max for 25 min/session). 
After this 1-week familiarization period, subjects will begin the HIIT regimen. The first session of the “ramp-up” 
period will entail 19 min of conventional exercise at 65% HR max, followed immediately by 2 x 1 min intervals 
at 90% HR max with a 1 min active recovery period between intervals at 50% HR max, and a 3 min cool down 
at 60% HR max after the second interval. After that first ramp up training session, 1 additional high intensity 
interval will be added to each training session, so by the beginning of week 3 of HIIT training, subjects will 
reach the full HIIT training protocol of a total of 10 x 1 min intervals at 90% HR max (Figure 2). They will 
continue this training protocol, 3 sessions/wk (2 supervised and 1 unsupervised), for 24-mo. Subjects who may 
feel challenged by the rate of increase in the number of intervals during this ramp up period will be allowed to 
increase the number of intervals at a slower pace, but all subjects must reach the full HIIT protocol during week 
4 of HIIT training. Of note, our team regularly has patients with obesity and/or diabetes progress to the full 
protocol HIIT within 3 weeks, demonstrating the feasibility of this training approach in our population.38, 41, 62, 63 
 
Figure 2. HIIT training protocol. 

Participant safety and well-being during the exercise interventions: Caution is warranted when initiating an 
exercise training study in sedentary obese adults, and additional concern is needed when participants are 
required to exercise at high intensity. We take this issue seriously and extreme caution will be taken throughout 
the project to greatly reduce risk associated with the exercise regimen. Patients will be provided medical 
oversight and any injuries, and/or medical concerns that may arise in our patients during the course of this 
project will be address by Dr. Callaghan, the co-investigators, the patient’s  Bariatric Surgery Team, and the 
patient’s primary care physician.  It is important to note, however, that several studies have now been 
published demonstrating that the HIIT protocol in this project can be used safely in patients with obesity, 
cardiovascular disease, and type 2 diabetes.64-66 Furthermore, another study demonstrated the safety of 
exercise interventions in those with DSP from type 2 diabetes with no serious or unanticipated adverse events 
reported.67 Notably, all patients will complete an exercise stress test and exercise will be supervised for the full 
24 mo of the HIIT intervention.  
 
Exercise compliance: In addition to the 2 supervised sessions per week, subjects will be asked to complete 1 
unsupervised session. To improve compliance in both types of sessions, patients will receive monthly phone 
calls from research staff to discuss potential solutions to barriers. Subjects will be provided with a wearable 
physical activity monitor with a visible heart rate display. Importantly, the data from these devices are 
downloaded wirelessly to a computer and/or mobile device, which then automatically updates their physical 
activity profile on their personal web-page. Our research study coordinator will receive permission to access 
each subject’s physical activity profile, enabling us to assess their compliance remotely. The data reported on 
the website includes a 24-hr profile of heart rate and calories expended, allowing us to clearly distinguish 
compliance to the HIIT regimen. We will assess each subjects’ physical activity profile on a weekly basis and 
record how many prescribed exercise sessions were successfully completed. If participants do not have 
access to the appropriate technology, we will make alternative arrangements such as providing them with an 
inexpensive mobile device with the necessary technology, providing a recordable heart rate monitor to wear 
during exercise, or collecting training logs. 
 
C3.2.2 Routine exercise counseling: Patients receive counseling regarding exercise as a routine part of their 
participation in the bariatric surgery clinic. Specifically, they are counseled to participate in 60 min of aerobic 
exercise daily in addition to 2-3 non-consecutive days of strength training workouts every wk. Patients are 
encouraged to contact the bariatric conditioning program, obtain a gym membership, purchase exercise 
equipment, join a walking group, and/or sign up for fitness classes (employer or city parks and recreation). 
 
C3.2.3 Bariatric surgery: Patients will undergo the surgical procedure as recommended by their surgeon as 
part of their routine care at a bariatric clinic. Our preliminary data indicates that 85% of patients had a sleeve 
gastrectomy, 15% had a Rou-en-Y gastric bypass at the University of Michigan, and none had an adjustable 
gastric band. We expect a similar distribution of procedures as part of this proposed study. Patients undergoing 
sleeve gastrectomy and Roux-en-Y gastric bypass have similar weight loss and time to stabilization, which 
limits potential confounding by type of surgical procedure.59, 60 Specifically, weight loss is typically 60-80 
pounds at 6 mo, and 100-120 pounds at one year with stabilization at 1-1.5 yr. In contrast, adjustable gastric 
band procedures result in a 45-55 pound weight loss at 6 mo with stabilization at 2 yr, but this procedure is not 
performed at U-M. Of note, as part of their routine post-operative care, patients are supplemented with a 
multivitamin, sublingual B12, and calcium. Furthermore, patients have laboratory monitoring including 
comprehensive metabolic panel, complete blood count, B12, TSH, ferritin, and vitamin D. The prophylactic 
supplementation and laboratory monitoring mitigate the potential for nutritional deficiencies to lead to nerve 
injury and subsequent DSP. The strength of this approach is that it mirrors current practice in the bariatric 
surgery clinic; therefore increasing generalizability. Furthermore, when neurologic complications occur after 
bariatric surgery, a specific nutritional deficiency other than B12 deficiency is often not found.68 
 
C3.3. Metabolic phenotyping: These tests will aid in the appropriate description of the population, allow for 
adjustment of potential confounding as needed, and may provide further data in support of the metabolic 
factors that improve when IENFD increases over time.  
 
Phenotypic tests will occur at baseline, 3, 12, and 24 mo, with the exception of the GTT which will only be 
performed at baseline. The metabolic phenotyping tests include:  
 
1. 2 hr, 75 gram oral GTT with glucose and insulin levels drawn at 0, 30, 60, 90, and 120 min to allow 
categorization of glycemic status as well as calculation of Oral Glucose Insulin Sensitivity (OGIS). 
Participants with a known diagnosis of diabetes will not complete an OGTT. Participants with a fasting 

glucose >126 mg/dL will not complete the test. Fasting glucose and insulin levels will be drawn at the 3, 
12 and 24 month time points; 
2. Hemoglobin A1C;  
3. Triglyceride, HDL, and low-density lipoprotein (LDL) levels from a fasting lipid panel;  
4. Systolic and diastolic blood pressure;  
5. BMI, waist circumference, and hip to waist ratio. Measurement of leg and thigh circumference at skin 
biopsy sites and circumferences at NCS stimulation sites will also be documented.  
6. Documentation of medications for diabetes, hypertension, hypercholesterolemia, and 
hypertriglyceridemia;  
7. We will obtain plasma, DNA, skin biopsies, subcutaneous adipose tissue and urine samples for 
potential genomic, metabolomics data, and future research. All samples collected will be stored in the 
biorepository. 
 
C3.4. Outcome measures: Dr. Callaghan, and consultants Eva Feldman MD, PhD, the DeJong Professor of 
Neurology and my K23 mentor, and Rodica Busui MD, PhD, Professor of Metabolism, Endocrinology and 
Diabetes, all have extensive experience with the DSP outcome measures proposed for this study. 
 
 
C3.4.1 Primary outcome:  
 
IENFD at the proximal thigh as assessed by skin biopsies obtained at baseline, 3, 12, and 24 mo using 
published protocols will be the primary outcome.30, 69, 70 IENFD use for this trial was based on important lessons 
learned from multiple prior failed clinical trials in DSP71-74 and the Feb 11-12, 2013, FDA-sponsored public 
workshop entitled: “Clinical Development Programs for Disease-Modifying Agents for Peripheral Neuropathy.” 
The expert consensus of this workshop was: A morphological measure, such as IENFD, will most accurately 
assess earlier changes consistent with DSP and of nerve fiber regeneration. Traditionally, clinical trials 
assessed changes in NCS as a primary outcome measure75; however, NCS only assess large myelinated 
nerve fiber function, which in early DSP can be completely normal. Recent data have also described minimal 
worsening71 or improvement in NCS76 in placebo and epidemiological cohorts with little relation to other 
measures of nerve function in diabetic patients. The smallest nerve fibers are likely the earliest to undergo 
damage in the natural history of DSP, but are not measured by NCS. Therefore, using NCS will miss the 
opportunity to identify the earliest phenotypes of disease. 
 
Skin biopsy is a minimally invasive procedure, with less than 1% reporting mild adverse events such as 
bleeding, swelling, or erythema. It allows morphometric quantification of C-fibers in the epidermis (IENFD).77, 78 
Data from several clinical trials show that decreased IENFD is a robust measure of the clinical signs and 
symptoms of DSP77, 79-86 and correlates with a patient’s perception of neuropathic pain.77, 78 Serial 
measurements of IENFD may be performed to assess the therapeutic efficacy of drugs77, 78, 87, 88 and/or lifestyle 
interventions30 in arresting disease progression and providing pain relief, and a large normative database was 
recently created by an international consortium.77, 78, 89 Published guidelines concur that IENFD is the standard 
for the diagnosis of small fiber sensory neuropathy.77, 87, 90, 91 Further support for IENFD as an early biomarker 
is the observation of reduced density in patients with pre-diabetes compared to those with normal glucose 
tolerance.30, 83 Finally, several studies report increased IENFD in response to therapeutic interventions that 
correlate with improvements in the signs and symptoms of DSP.83-85 IENFD is also endorsed by several 
societies and authorities in the field as diagnostically efficient at distinguishing patients with early small fiber 
neuropathy from normal controls, and as a reliable and valid outcome measure to assess DSP in clinical 
trials.77, 79, 87, 90, 92 In summary, IENFD is a sensitive and reliable measure of early DSP, its loss directly 
correlates with increasing DSP severity, is amenable to improvement with appropriate intervention, is safe and 
easy to perform, and to date represents the gold-standard method for evaluating morphological changes in 
small nerve fibers.77, 79, 87, 90, 91 In addition, the PI has used this procedure extensively in two ongoing studies 
with excellent patient compliance (NS-079417 and 2P30-DK020572). Thus, IENFD was selected as the most 
appropriate and informative primary endpoint in the current clinical trial.  
 
C3.4.2 Secondary outcomes: Selecting the most appropriate outcome measures is critical in the design of an 
interventional clinical trial and essential to obtaining clinically relevant information on the true response to the 
intervention. Thus, we will evaluate changes from baseline to 3, 12, and 24 months in several secondary 
outcomes: 
 
1. IENFD at the distal leg: In addition to the IENFD measure at the proximal thigh, we will also measure 

this entity at the distal leg. Previous data support that the proximal thigh measurement is more likely to 
show change over time after an exercise regimen in a population of patients with pre-diabetes.30 
However, IENFD at the distal leg will provide additional data as to the severity and length-dependence 
of the nerve injury. Furthermore, measurement at the distal leg may be more sensitive to change in 
those patients with obesity and normoglycemia, which may have milder nerve injury than those with 
diabetes. 
2. Corneal confocal microscopy (CCM): While IENFD is the current gold-standard for detection of small 
fiber nerve injury, CCM is an innovative approach that also attempts to measure small fiber nerve injury 
in the cornea. This procedure is less invasive than the skin biopsy required for IENFD measurement. 
The benefit of including this secondary outcome measure is that we will be able to compare and 
contrast the diagnostic characteristics of IENFD to CCM and also determine which measure is more 
sensitive to change over time. This data will help determine which of these two measurements should 
be utilized in future intervention studies. Specifically, we will measure nerve fiber density, nerve branch 
density, and nerve fiber length. A recent meta-analysis revealed that all three of these parameters are 
reduced in those with diabetic DSP compared to those with diabetes without DSP and to healthy 
controls.93 Furthermore, studies have revealed similar diagnostic characteristics for CCM and IENFD 
for diabetic DSP.94 CCM parameters also improve after simultaneous pancreas and kidney transplants, 
even when IENFD remains unchanged, indicating that CCM may be a useful early marker of nerve 
regeneration.95 Consultant Roni Shtein, MD, Associate Professor of Ophthalmology and Visual 
Sciences, has extensive experience utilizing CCM as an outcome measure. 
3. Retinopathy measures: 
a) 24-2 FDT - measure of retinal ganglion function. Best corrected visual acuity will also be 
measured. 
b) Retinal Imaging - Retinal Fundus Photography is currently the gold standard for diagnosing and 
staging diabetic retinopathy. 
 
4.  Electrophysiological measures:  
a) NCS of the sural, peroneal, and tibial nerves will be obtained following the DCCT/EDIC 
protocol.96, 97 Briefly, measurements will be done at the dominant limb, using temperature 
control.96, 97 NCS provide a quantitative, reproducible way to evaluate for large fiber function.  
b) Measures of CAN will comprise cardiovascular reflex testing using the DCCT/EDIC protocol (R-
R interval ratio during deep breathing, postural change, and Valsalva) as described.98, 99 These 
tests are recommended by the Toronto Consensus Panel on Diabetic Neuropathy100 as the 
gold-standard for CAN. We will also assess resting HR and HR variability. Consultant Dr. Busui 
has extensive expertise in performing and interpreting CAN outcomes in clinical trials.  
 
5.  Clinical DSP measures:  
a) Structured neurological examination following the DCCT/EDIC protocol96, 97;  
b) Michigan Neuropathy Screening Instrument (MNSI) questionnaire and examination, validated 
instruments to identify diabetic DSP103, 104;  
c) Utah Early Neuropathy Scale (UENS), a validated tool to identify small fiber predominant 
neuropathy such as that found in obese and pre-diabetic individuals105;  
d) Modified Toronto Neuropathy Score (mTNS), a validated instrument for the detection of mild to 
moderate diabetic DSP106; and  
e) Survey of Autonomic Symptoms (SAS), a validated measure of autonomic symptoms107 
f) DNS-score and guidelines, a four-item validated symptom score, with high predictive value to 
screen for polyneuropathy. 
g) DN4 questionnaire, to assess probability of neuropathic pain. 
 
6. DSP-specific pain: Pain will be assessed using the Short Form McGill Pain Questionnaire. This 
questionnaire is a reliable and validated pain questionnaire that utilizes a visual analogue scale, a 6 
point rating scale of pain intensity, and a 4 point rating scale of 15 different neuropathic pain 
descriptors.108 Subjects will also be asked to rate their current pain on a numeric rating scale.  
 
7. Quality of life measures:  
a) We will use the NeuroQOL, a validated measure of quality of life specific to DSP,109 which 
comprises 6 domains (pain, lost/reduced feeling, diffuse sensory-motor symptoms, restrictions 
in activities of daily living, disruptions in social relationships, and emotional distress) along 
with an overall measure of DSP impact on quality of life and general quality of life.109 

b) Questionnaires that ask about the participant’s mood and how weight has impacted 
participant’s physical and emotional health. These questionnaires include the EQ-5D, IWQOL-
Lite, Godin Leisure-Time Exercise, and a brief physical activity questionnaire. 
c) Demographic questionnaire will be administered to participants. 
 
8. Functional scales:  
a) The Berg Balance Scale, measures balance in 14 separate activities of daily living (the 
unipedal stance portion has been shown to be particularly reflective of DSP-related mobility 
loss)110;  
b) The 8 Foot Up and Go Test, a test of functional mobility that assesses the time needed for a 
subject to arise from sitting position, walk 8 ft and turn 180 degrees around a cone, and return 
to sitting111;   
c) The Modified Falls Efficacy Scale, assessing patient’s self-reported ability to perform activities 
of daily living (e.g. get dressed/ undressed, walking, shopping)86, 112, 113 The fall survey will also 
ask “Have you fallen within the last year? If yes, how many falls?” 
 
9. Qualitative sensory testing: 
a) Neurothesiometer- quantitative measure of vibration threshold at both toes. 
 
10. Cognitive impairment: 
a) NIH Toolbox Cognitive Battery – A 30-minute brief computerized battery comprised of tasks 
covering several cognitive domains including language, attention, working memory, episodic 
memory, executive function, and processing speed. The NIH Toolbox Cognitive Battery is a 
reliable and valid instrument of cognitive function48.   
b) The Rey Auditory Verbal Learning Test will be presented in addition to the NIH Toolbox. The 
Rey is presented verbally to the participant and is important as a supplement to the single 
visual-based short memory task in the NIH Toolbox.  The computer format and national 
standardization of this tool will allow for comparisons to future studies.  We will partner with 
Dr. Bruno Giordani who has expertise in these cognitive assessments.  
 
11. Urine: Urine albumin/creatinine ratio and serum creatinine measurements.  Urine samples will be stored 
in the biorepository. 
 
12. Anthropometric measurements: Body measurements at different site circumferences will be measured 
at each study visit including: abdomen, arm, buttocks/hips, calf, forearm, hips/thigh, mid-thigh and 
waist.  
 
13. Orthostatic Hypotension: Blood pressure and heart rate will be assessed after patient is supine for 5 
minutes, standing for 1 minute and at 3 minutes standing. 
 
14. Aerobic Assessment: All participants enrolled in the study will participate in brief graded exercise test 
(bike or treadmill) to determine maximal aerobic capacity (VO2max). Participants randomized to the 
HIIT cohort will also complete a second VO2max test after the training intervention to document the 
change in aerobic fitness.  This exercise test is routinely used in clinical research projects to assess 
aerobic fitness in lean, obese and other clinical populations.   
 
15. Dual Energy X-ray Absorptiometry: Body composition will be measured, including body fat, muscle 
mass and bone density using low-dose x-ray. All participants enrolled in the study will complete a 
DEXA scan at the beginning and end of the study. 
 
16. BodPod: Subjects that exceed the weight limits of the dual energy x-ray absorptiometry may complete 
the BodPod as an alternative in order to determine body composition. Subjects will complete at 
baseline and at the end of the study. 
 
 
 
 
 

C3.5. Visits schedule (Table 6):  
 *Randomization to exercise protocol to occur after baseline visit 
  +Only participants randomized to the HIIT will complete a second time 
  T Participants can consent to have skin biopsy added to biorepository 
 ^Participants may complete the consent and outcomes virtually prior to the upcoming visit to decrease in-person interaction due to Covid-19. If the      
outcome cannot be completed successfully it with be completed during the visit.    
 
Data collected for clinical purposes as part of the Adult Bariatric Surgery Program will be reviewed as part of this study and included in the study records. 
 
C3.6. Timeline and milestones: The planned study duration is 5 yr, with 2 yr of subject accrual, 2 yr of subject 
follow-up, plus 1 yr for analysis and results presentation. 
 
C3.7. Statistical analysis: The four treatment groups (HIIT/no surgery, HIIT/surgery, routine exercise/no 
surgery, routine exercise/surgery) will be compared with respect to demographic and baseline variables (e.g., 
age, sex, glycemic status, OGIS, BMI, etc.). If a significant difference is found in any variable, the models 
described below will be fitted after adjusting for these variables. 
 
C3.7.1 Analysis of primary outcome: The primary outcome is IENFD at the proximal thigh, measured at 
baseline, 3, 12, and 24 mo. Exploratory analysis will be performed to assess the need for any transformation 
Table 6: Study Events Schedule 
Visit Number 
Visit 1 
Visit 2 
Visit 3 
Visit 4 
Visit 5 
Visit Description 
Screening 
Baseline 
Outcomes 
Outcomes 
Outcomes 
Time point 
Before baseline 
Day -70 to 0 
Month 3 
Month 12 
Month 24 
Visit window 
NA 
70 days 
± 14 d 
± 14 d 
± 14 d 
Informed Consent^/study criteria 
  Medical History Review 
  Stress Test 
 
X 
X 
 
 
 
 
Randomized to Exercise Protocol 
 
  X* 
 
 
 
Laboratory Measures 
 
X 
X 
X 
X 
Biorepository Measures 
  Plasma 
  Urine 
  Subcutaneous Adipose Tissue 
  DNA 
 
 
X 
X 
X 
X 
 
X 
 
 
X 
 
 
X 
X 
X 
Skin Biopsies (IENFD)T 
 
X 
X 
X 
X 
  Corneal confocal microscopy 
  FDT-24-2 
  Retinal Imaging 
 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
Electrophysiologic measures 
   NCS 
   Cardiovascular reflex tests 
 
X 
X 
 
 
 
 
 
 
 
 
 
X 
X 
 Clinical measures 
   Neurologic examination 
   MNSI, UENS, mTNS, SAS, DNS, DN4 
 
 
X 
X 
 
 
X 
 
 
X 
 
X 
X 
McGill pain questionnaire, NRS   
 
X 
X 
X 
X 
Qualitative sensory testing 
  Neurotheisiometer   
 
 
 
 
X 
 
 
X 
 
 
X 
 
 
X 
 
Cognitive impairment 
  NIH Toolbox 
  Rey 
 
 
X 
X 
 
 
 
X 
X 
Anthropometric measures^ 
 
X 
X 
X 
X 
Orthostatic Hypotension Assessment^  
 
X 
X 
X 
X 
Quality of Life measures: 
  NeuroQol 
  Questionnaires 
  Demographics 
 
 
X 
X 
X 
 
X 
X 
 
 
X 
X 
 
 
X 
X 
 
Functional Assessments 
   Berg Balance Scale^ 
   8-foot Get Up and Go Test^ 
   Modified Falls Efficacy Scale 
 
 
X 
X 
X 
 
X 
X 
X 
 
X 
X 
X 
 
X 
X 
X 
Aerobic Assessment 
  VO2 max 
X 
 
 
 
 
 
X+ 
Dual Energy X-ray Absorptiometry 
 
 
X 
 
 
 
X 

on the primary outcome variable. The primary exposure variables are HIIT: yes/no (Aim 1), and bariatric 
surgery: yes/no (Aim 2). We will adjust for demographic, clinical, and baseline characteristics as covariates in 
the model. In addition to the main effects of HIIT exercise program and bariatric surgery, we will also examine 
selected interactions based on a priori considerations. In particular, we will focus on the interaction between 
HIIT and bariatric surgery to assess if the effect of HIIT exercise on IENFD is modified by bariatric surgery. 
Linear mixed effects regression modeling will be used to determine the relationship between the primary 
exposure variables and IENFD over time. Longitudinal data analysis approaches such as this offer enhanced 
statistical power, as subjects are able to serve as their own reference. Furthermore, linear mixed effects 
regression offers a flexible framework to handle missing data such as due to attrition. Let Yit denote the IENFD 
of the ith patient at the tth time point. Then Yit can be modeled using the random intercept model.114 The 
random intercept model can be viewed as arising from a two-stage model as follows: 
 
Stage 1: subject-specific trajectory: Yit = 0i + λZ i + θXit + eit 
Stage 2: between-subjects: 0i = β00 + β0i  
Combining the two stages, we can write Yit = β00 + β0i  + θXit  + eit                                  
where β00 is the population-averaged intercept, Z i is the matrix of main effects and interaction corresponding to 
HIIT exercise and bariatric surgery, Xit is the matrix of fixed covariates (demographic and baseline 
characteristics), λ and θ are the vectors of corresponding fixed effects parameters, β0i is the subject-specific 
random (intercept) effect capturing heterogeneity across subjects due to unmeasured confounders, and eit is 
the random error characterizing variation due to sources like within-subject fluctuations and measurement 
error. The random effect β0i is assumed to follow a normal distribution with mean zero and variance σ2sub, and 
the eit’s are assumed to be distributed normally with mean 0 and variance covariance matrix R (non-diagonal). 
Furthermore, we also assume that β0i and eit are independent. Maximum likelihood method will be used to 
estimate the model parameters. We will use Akaike Information Criterion (AIC) and Schwarz Bayesian 
Information Criterion (BIC) to guide the selection of the covariance structure.  Inference for λ (which is of 
primary interest) will be based on Wald tests. We will use PROC MIXED in SAS to fit the above model. To 
explore potential non-linear or threshold effect of treatment exposure on IENFD we will use a nonparametric 
regression model fitted using the framework of generalized additive models (GAM).115 Given the exploratory 
nature of the research proposed here, the GAM models will allow us the flexibility to include a smooth 
functional term instead of restricting to a linear relationship. The smoothing term is fitted by using penalized 
regression splines or locally smoothed loess curves, with the degree of smoothness chosen in a data-adaptive 
way, using cross-validation techniques. We will use the gam function in the mgcv package available in the R 
software to fit these non-parametric regression models. We will perform sensitivity analyses to assess how 
subjects who withdrew affect conclusions of the analysis. An intent-to-treat analysis set (including subjects who 
are randomized, treated, and have a baseline IENFD) will be used to assess treatment effects, with multiple 
imputation methods116 employed to impute outcomes for subjects with missing post-baseline data. Depending 
on the extent and pattern of missingness, other simpler sensitivity analyses may be used: e.g., subjects 
completing the study (completers) may be analyzed using a repeated measures analysis of variance model. 
The model will include the same covariates that are included in the primary analysis. 
 
C3.7.2 Analyses of secondary outcomes: Due to the pitfalls of multiple testing, formal statistical testing won’t 
be performed. For analysis of repeated measures of continuous data (CCM, cardiovascular reflex tests, MNSI, 
UENS, mTNS, SAS, McGill Pain questionnaire, NeuroQOL, and functional assessments), the longitudinal 
approaches described above will be employed. Specifically, each outcome will be analyzed separately using a 
linear mixed effect regression model, with the treatment group as the primary exposure. 
 
C3.7.3 Exploratory analyses: To investigate the potential modifying effects of sex, we will perform an additional 
analysis that includes an interaction term between the intervention groups and sex. Similarly, we will explore 
the differential effect of sleeve gastrectomy versus Rou-en-Y gastric bypass by including separate indicator 
variables for these two different surgical techniques. We will also investigate the potential weight loss 
independent effects of bariatric surgery by evaluating the earliest time point (3 months). In addition to other 
metabolic syndrome components, we will study the effect of change in insulin sensitivity as measured by OGIS 
on our primary outcome. Finally, we will perform an analysis of those who complete more than 80% of 
supervised exercise sessions to assess the effect of exercise compliance. Importantly, these analyses will be 
hypothesis generating for future studies.    
 
C3.8. Power calculation: The purpose of this clinical trial is to provide the preliminary data necessary to 
design a multicenter clinical intervention trial. Therefore, it is not strictly amenable to formal power 

considerations. However, there are two assumptions necessary to justify the multicenter trial. The first is that 
HIIT will cause an increase in IENFD (re-innervation) and the second is that the natural progression of DSP is 
loss of nerve fibers over time. The primary outcome is an assessment of IENFD at the proximal thigh over 4 
time points spanning 24 mo in the four trial arms. To estimate the likely effect sizes of the four interventions, 
we will rely on previous well-conducted studies, including our ongoing NIH K23 project. For the routine 
exercise/no surgery group, we estimate that the IENFD will decrease similar to that observed for those with 
pre-diabetes and DSP. Over 26.7 mo, IENFD at the proximal thigh decreased from 19±5 to 13±4. Given that 
our population includes patients with normoglycemia, we expect a decline of at least 3 fibers/mm over the 2 yr 
of our study. For the HIIT/no surgery group, we estimate that the IENFD will increase by 1.4±2.3 fibers/mm 
based on a study of the effect of exercise by Smith et al.30 They saw this effect after 1 yr, but we will 
conservatively estimate that the effect will plateau after the initial yr after the intervention. For routine 
exercise/surgery group, we estimate that the IENFD will increase by 0.2±7.0 based on our preliminary data on 
patients undergoing diet-induced weight loss for 2 yr. For the HIIT/surgery group, we estimate that the effect 
will be additive compared to the two separate interventions. With our expected sample size of 30 patients 
(allowing for ~15% attrition) in each of the four treatment groups, we will have at least 80% power to detect 
effect sizes between 0.9 and 1.7 as statistically significant based on a two-sided test with alpha=0.05. 
 
C3.9. Data collection and management: Data report forms will be filled out during each visit, verified, and 
entered into RedCap. Validation reports will be run, and checked for omissions, improbable values, and data 
consistency. Distributions of continuous measures will be assessed for symmetry and transformations will be 
applied as needed. Co-investigator Mousumi Banerjee, PhD, Professor of Biostatistics, Evan Reynolds, 
graduate student, and Dr. Callaghan will have data access for monitoring and verification.  
 
C3.10. Recruitment and retention plan: Dr. Callaghan has experience recruiting obese participants for two 
epidemiologic cohorts, his NIH K23 and 2P30-DK020572 grants, that provided the preliminary data for the 
proposed study. He works closely with Emily Villegas-Umana, RN (project coordinator) and Ericka Chant, MPH 
(research coordinator) on these projects. Both coordinators have successfully recruited subjects for these 
studies, developed strategies for retention, and can perform the outcomes measures required for this proposal. 
 
C3.10.1 Recruitment strategies: Patients will be recruited from a bariatric surgery clinic. Recruitment for the 
study begins at the informational session, where flyers are passed out providing information about the study as 
well as contact information for the study team. Patients are then recruited one-on-one during their bariatric 
surgery clinic visits. Patients meet individually with the study coordinator to discuss the study, answer 
questions, and begin the scheduling process. Patients are then followed as they complete the requirements 
prior to surgery, and are emailed at different time points to check for interest in participation. The final face-to 
face interaction for recruitment is during the required nutrition information session. Patients will be consented 
once they have made a decision whether or not to have surgery. This multi-step approach to recruitment has 
been effective for our ongoing bariatric surgery study. Compared with the start of the ongoing project, we have 
added multiple points of face-to-face contact and added a mixed media presentation, which will only increase 
the feasibility of the current proposal. 
 
C3.10.2 Retention strategies: Patients will be emailed and/or called to schedule their 3, 12, and 24 mo visits 
approximately 2-3 mo prior to the date. We will also call patients monthly to address any concerns and discuss 
compliance. This limits the amount of time that the study team is not in contact with the participant, helping to 
increase patient retention. Patients are mailed out a reminder post-card just prior to the 1-yr and 2-yr mark, 
thanking them for their participation and reminding them that we will be in contact with them. This patient 
population has proven to be committed and motivated to participate in our current study, with 94% retention. 
 
C4. Potential problems & alternative approaches for all Aims: We hypothesize that HIIT will improve DSP 
outcomes. We also believe that the effect of HIIT will be greater than the effect of bariatric surgery. However, 
our supportive preliminary data are based on a diet-induced weight loss intervention, and the effect of surgical 
weight loss may be larger. If surgery is as good as or better than HIIT, the results would still lead us toward 
one of the first disease modifying therapies for DSP. Our study design is also able to look at the combined 
effects of HIIT and bariatric surgery. If the severity of DSP is less in our population, the IENFD of the proximal 
thigh may not be the most sensitive DSP outcomes measure; however, we plan to use IENFD of the distal leg 
as a secondary outcome measure. In contrast to our supportive preliminary data, if the HIIT regimen is difficult 
to perform for this obese population, we could switch interventions to a conventional exercise regimen, which 
Dr. Horowitz has extensive experience with in patients with obesity and diabetes. 
  
C5. Biorepository to enable future mechanistic studies: We will obtain fasting plasma DNA, urine, skin 

biopsies, and subcutaneous adipose tissue samples. While beyond the scope of this current proposal, this 
biorepository will allow for future studies to better understand the mechanisms by which HIIT and/or bariatric 
surgery improve DSP outcomes. Which intervention to pursue will be based on which intervention(s) is/are 
successful at improving DSP outcomes in this clinical trial. Drs. Horowitz (co-I, HIIT) and O’Rourke (consultant, 
bariatric surgery) have the expertise to explore mechanisms in humans. As Director of the Obese-Adipose 
Tissue Research Laboratory, Dr. O’Rourke will help obtain and process the subcutaneous adipose tissue 
obtained for this proposal. Dr. Feldman (consultant, murine DSP outcomes), in conjunction with Drs. Seeley 
and Sandoval (consultants, murine bariatric surgery), has the expertise to explore mechanisms in mice. The 
biorepository will allow translational research from mouse to human and from human to mouse. Our 
experienced team of investigators and consultants is uniquely positioned to accomplish this goal.  
 
C6. Future directions: This phase 2 study will provide results that will directly inform which intervention or 
interventions to pursue in a phase 3 study. The phase 3 study will need to be a multi-center study with a 
patient-oriented primary outcome. This proposal will also help decide which secondary outcomes to include in 
the pivotal trial after evaluating the longitudinal changes of IENFD, CCM, and NCS over time. 
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