Abstract
Importance
Past studies revealed an association between the metabolic syndrome and polyneuropathy, but the precise components that drive this association remain unclear.
Objective
We aimed to determine the prevalence of polyneuropathy stratified by glycemic status in well characterized obese and lean participants. We also investigated the association of specific metabolic syndrome components and polyneuropathy.
Design
We performed a cross-sectional, observational study in obese participants from a weight management program and lean controls from a research website. The prevalence of neuropathy, stratified by glycemic status, was determined, and a Mantel–Haenszel chi-square test was used to investigate for a trend. Logistic regression was used to model the primary polyneuropathy outcome as a function of the metabolic syndrome components after adjusting for demographic factors. Secondary outcomes included intraepidermal nerve fiber density and nerve conduction study parameters. Participants also completed quantitative sudomotor axon reflex testing, quantitative sensory testing, quality of life (Neuro-QOL), and pain (short form McGill Pain questionnaire) assessments.
Setting
Tertiary care, weight management program.
Participants
Obese patients were required to have a body mass index ≥ 35 kg/m^2 or ≥ 32 kg/m^2 if they had one or more comorbidity. Lean controls met no metabolic syndrome criteria (modified NCEP/ATPIII definition).
Exposures
Metabolic syndrome components (NCEP/ATPIII definition) including glycemic status (Expert Committee on the diagnosis and classification of diabetes mellitus definition).
Main Outcome
Toronto consensus definition of probable polyneuropathy.
Results
We enrolled 102 obese participants (44.1% normoglycemia, 30.4% pre-diabetes, and 25.5% diabetes) and 53 lean controls. The polyneuropathy prevalence was 3.8% in lean controls, 11.1% in the obese, normoglycemia group, 29.0%, in the obese, pre-diabetes group, and 34.6% in the obese, diabetes group (p<0.01 for trend). Age (OR=1.09, 95% CI 1.02–1.16), diabetes (OR=4.90, 95% CI 1.06–22.63), and waist circumference (OR=1.24, 95% CI 1.00–1.55) were significantly associated with neuropathy in multivariable models. Pre-diabetes (OR=3.82, 95% CI 0.95–15.41) approached, but did not reach, statistical significance.
Conclusions and Relevance
The polyneuropathy prevalence is high in an obese population even in those with normoglycemia. Diabetes, pre-diabetes, and obesity are the likely metabolic drivers of this neuropathy.
Introduction
Polyneuropathy is a prevalent, disabling condition affecting 2–7% of the population,1,2 however, a definitive cause is lacking in 30% of patients.3,4 The most common etiology is diabetes with a similar prevalence in those with type 1 and type 2.5–9 In patients with type 1 diabetes, enhanced glucose control reduces the incidence of polyneuropathy substantially, but in type 2 diabetes the effect does not reach statistical significance.10 Therefore, factors other than glucose control are likely to be involved in the development of type 2 diabetic polyneuropathy.11 These same factors may also explain why there are so many patients diagnosed with idiopathic polyneuropathy. Metabolic syndrome components are potential candidates since these cardiovascular risk factors cluster with hyperglycemia.
Multiple studies reveal an association between the metabolic syndrome and polyneuropathy.12–15 However, investigations of the precise association between individual metabolic syndrome components and polyneuropathy demonstrate conflicting results.7,16–22 Limitations of these studies include definitions of polyneuropathy based on surrogate tests rather than a neurologist’s history and examination using rigorous criteria, and focusing primarily on patients with diabetes.
In obese and lean populations with detailed metabolic and neuropathy phenotyping, we aimed to determine the prevalence of polyneuropathy stratified by glycemic status. We also examined the associations between the individual metabolic syndrome components and a clinical definition of polyneuropathy (primary outcome) as well as intraepidermal nerve fiber density (IENFD) and nerve conduction study parameters (secondary outcomes).
Methods
Population
From November 2010 to December 2014, we recruited obese patients attending the University of Michigan Weight Management Program (prior to starting a diet/exercise regimen). Inclusion criteria included age 18 years or older and a body mass index (BMI) ≥ 35 kg/m^2 or ≥ 32 kg/m^2 if they had one or more comorbidity.23 We recruited lean controls with no metabolic syndrome components (BMI <28 was required rather than a waist circumference cutoff) through a University of Michigan research website (umclinicalstudies.org). Lean controls were excluded if they were taking medications for blood pressure, cholesterol, diabetes, or triglycerides.
This study was approved by the University of Michigan Institutional Review Board.
Metabolic phenotyping
Obese and lean patients underwent glucose tolerance testing (except those with a previous diagnosis of diabetes), and a fasting lipid panel. Patients also had blood pressure, height, weight, waist circumference, and BMI measurements.
Metabolic Syndrome components
Participants were classified as having normoglycemia, pre-diabetes, and diabetes according to the Expert Committee on the diagnosis and classification of diabetes mellitus.24 The updated NCEP/ATPIII criteria were used to define the metabolic syndrome and its individual components.25
Polyneuropathy Definition (primary outcome)
Our primary outcome measure was the Toronto consensus definition of probable polyneuropathy (2 or more of the following: neuropathy symptoms, abnormal sensory examination, and abnormal reflexes) as determined by one of four neuromuscular specialists.26
Secondary neuropathy outcomes
Our secondary outcome measures were IENFD measured at the distal leg and three nerve conduction study parameters (sural, tibial, and ulnar sensory amplitudes). IENFD was evaluated using brightfield immunohistochemistry using an established protocol.27 Nerve conduction studies were performed by a certified nerve conduction study technologist using the CareFusion’s Viking on Nicolet EDX electrodiagnostic system.
Additional neuropathy measures
To further characterize peripheral nerve function, we obtained the IENFD at the proximal thigh, and other nerve conduction study parameters including the sural (peak latency), peroneal (amplitude, distal motor latency, conduction velocity, and F response), tibial (distal motor latency and F response), ulnar sensory (peak latency), median sensory (amplitude and peak latency), and median motor responses (amplitude, distal motor latency, conduction velocity). Quantitative sudomotor axon reflex testing (QSART) measurements were performed at the foot, distal leg, proximal leg, and arm using the WR Medical Electronics Co. Q-Sweat, Quantitative Sweat Measurement System. Quantitative sensory testing (QST) measurements of vibration and cold detection thresholds were performed using the WR Medical Electronics Co. Computer Aided Sensory Evaluator (CASE) IV. The validated Neuro-QOL instrument was utilized to measure neuropathy specific quality of life with higher numbers reflecting a worse quality of life.28 The validated short form McGill Pain questionnaire was employed to measure pain with a visual analogue scale (VAS), a 6 point rating scale of Present Pain Intensity (PPI) score, and 4 point rating scale of 15 different neuropathic pain descriptors (McGill pain score).29
Statistics
Descriptive statistics were used to characterize the obese and lean populations in terms of demographics and neuropathic outcome measures. Chi square or Fisher’s Exact tests were used to compare the two populations in terms of categorical variables and t-tests for continuous variables.
We determined the prevalence of polyneuropathy (primary outcome) stratified by glycemic status. We then applied the Mantel-Haenszel chi-square test to investigate for a trend in the polyneuropathy prevalence in the four groups based on obesity and glycemic status.
For the primary analysis, univariate and multivariable logistic regression was used to model the primary polyneuropathy outcome as a function of the metabolic syndrome components, after adjusting for demographic factors. Sensitivity analyses were performed after excluding patients with conditions known to be associated with neuropathy (N=9),a first-degree relative with idiopathic neuropathy (N=3), or those without a glucose tolerance test, and using symptomatic polyneuropathy as the primary outcome. For the secondary neuropathy outcomes, we fitted multivariable linear regression to analyze them as a function of metabolic syndrome components, adjusting for the same demographic factors. Regression analyses were restricted to the obese population with complete metabolic data. Lean controls were not included in these analyses.
All analyses were performed with SAS 9.3 (Cary, NC).
Results
Population
During the recruitment time period, the University of Michigan Weight Management Program enrolled 532 patients. Of these patients, 394 consented to be contacted about research studies with 102 enrolling in our study. Of 79 lean control participants that consented to participate, 53 met all inclusion and exclusion criteria after metabolic phenotyping and participated in the study. Screen failures consisted of 6 with elevated blood pressure, 2 with BMI greater than 28, 6 with elevated fasting or 2 hour glucose levels, 5 with elevated HDL cholesterol or triglyceride parameters. Of note two participants failed multiple criteria. In addition, we had 4 patients not complete neuropathy testing and 5 withdraw consent.
Demographics and metabolic phenotyping
The obese group was older (mean (SD) 52.9 (10.2) vs. 48.5 (9.9), p=0.01) and differences were observed between the obese and lean populations in terms of race (p=0.02) and marital status (p<0.01) (see Table 1 for differences in metabolic variables). Ten obese patients did not receive a glucose tolerance test, but all had a fasting glucose and/or a hemoblogin A1C to allow assessment of glycemic status. Similarly, four obese patients did not have a lipid panel and one did not have a waist circumference measurement.
Table 1.
Demographics of the lean controls and the obese group
| Variable | Lean Controls | Obese group | P value |
|---|---|---|---|
| Age, mean (SD) | 48.5 (9.9) | 52.9 (10.2) | 0.01 |
| Male, N (%) | 16 (30.2%) | 48 (47.1%) | 0.04 |
| Race, N (%) | 0.02 | ||
| White | 48 (90.6%) | 91 (90.1%) | |
| Black | 1 (1.9%) | 8 (7.9%) | |
| Asian | 3 (5.7%) | 0 (0%) | |
| Other | 1 (1.9%) | 2 (2.0%) | |
| Hispanic, N (%) | 3 (5.8%) | 2 (2.0%) | 0.34 |
| Marital status | <0.01 | ||
| Single | 14 (26.4%) | 19 (19.0%) | |
| Married | 25 (47.2%) | 74 (74.0%) | |
| Divorced | 12 (22.6%) | 6 (6.0%) | |
| Widowed | 2 (3.8%) | 1 (1.0%) | |
| Smoking status | 0.72 | ||
| Current | 3 (5.7%) | 3 (3.0%) | |
| Never | 31 (58.5%) | 61 (60.4%) | |
| Former | 19 (35.9%) | 37 (36.6%) | |
| Education level | 0.60 | ||
| High school | 2 (3.8%) | 2 (2.0%) | |
| Some college | 14 (26.4%) | 20 (19.8%) | |
| College degree | 18 (34.0%) | 43 (42.6%) | |
| Graduate degree | 19 (35.9%) | 36 (35.6 % ) | |
| Height (cm), mean (SD) | 170.3 (10.0) | 172.1 (9.4) | 0.26 |
| Fasting glucose mg/DL, mean (SD) | 85.4 (6.1) | 95.3 (10.1)* | <0.01 |
| 2 hour glucose mg/dL, mean (SD) | 92.8 (19.1) | 116.3 (28.4)* | <0.01 |
| BMI kg/m^2, mean (SD) | 22.9 (2.7) | 41.1 (6.5) | <0.01 |
| Waist circumference (cm), mean (SD) | 80.7 (18.2) | 123.0 (17.3) | <0.01 |
| SBP (mm Hg), mean (SD) | 109.8 (12.0) | 131.1 (13.8) | <0.01 |
| DBP (mm Hg), mean (SD) | 67.3 (9.8) | 66.8 (11.4) | 0.81 |
| Triglycerides mg/dL, mean (SD) | 72.6 (27.4) | 147.4 (73.4) | <0.01 |
| HDL mg/dL, mean (SD) | 70.7 (17.6) | 46.3 (12.3) | <0.01 |
| LDL mg/dL, mean (SD) | 97.7 (27.7) | 101.0 (27.4) | 0.48 |
| Metabolic syndrome, N (%) | 0 (0%) | 77 (75.5%) | <.01 |
only reported for those without diabetes
BMI= body mass index, SBP=systolic blood pressure, DBP=diastolic blood pressure
Polyneuropathy prevalence stratified by glycemic status
The prevalence of polyneuropathy was 3.8% (2 of 53) in the lean control group, 11.1% (5 of 45) in the obese with normoglycemia group, 29.0% (9 of 31) in the obese with pre-diabetes group, and 34.6% (9 of 26) in the obese with diabetes group (p<0.01 for trend).
Neuropathy phenotyping- obese with and without neuropathy
Comparing neuropathy measures between the obese population with and without neuropathy revealed that the IENFD at the leg (2.2 fibers/mm (2.3) vs. 4.5 (2.2), p<0.01) and thigh (7.2 fibers/mm (4.9) vs. 9.5 (4.1), p=0.03) were reduced in those with neuropathy (Table 2). Similarly, all three sensory amplitudes were reduced in those with neuropathy, but no significant changes were observed for the corresponding peak latencies. Six of the seven lower extremity motor nerve conduction study parameters were worse in those with neuropathy with the exception of the tibial distal motor latency, which approached statistical significance. In contrast, the only upper extremity motor nerve conduction parameter that was different between the groups was the median motor conduction velocity. QST for vibration (just noticeable difference (JND) 21.9 (3.3) vs. 19.0 (4.3), p<0.01) and cold (JND 16.6 (4.5) vs. 13.8 (4.1), p<0.01) demonstrated higher thresholds in those with neuropathy than those without. In contrast, none of the four QSART parameters were significantly different between the groups.
Table 2.
A comparison of neuropathy outcome measurements between the lean controls and obese patients with and without neuropathy
| Variable | Lean Without Neuropathy |
Obese without neuropathy |
P value* | Obese with neuropathy |
P value** |
|---|---|---|---|---|---|
| IENFD leg (fibers/mm) | 6.4 (3.8) | 4.5 (2.2) | <0.01 | 2.2 (2.3) | <0.01 |
| IENFD thigh (fibers/mm) |
12.3 (6.5) | 9.5 (4.1) | <0.01 | 7.2 (4.9) | 0.03 |
| Sural amplitude (uV) | 15.1 (10.4) | 13.1 (8.6) | 0.24 | 7.3 (7.0) | <0.01 |
| Sural PL (ms) | 3.8 (0.4) | 3.7 (0.4) | 0.50 | 3.9 (0.4) | 0.15 |
| Peroneal amplitude (mV) |
5.0 (2.4) | 6.0 (2.7) | 0.04 | 4.2 (3.0) | <0.01 |
| Peroneal DML (ms) | 5.0 (0.8) | 4.7 (0.6) | <0.01 | 5.2 (0.7) | <0.01 |
| Peroneal CV (m/s) | 41.7 (6.1) | 44.5 (5.7) | 0.08 | 41.4 (6.0) | 0.03 |
| Peroneal F response (ms) |
49.9 (5.4) | 48.1 (9.5) | 0.17 | 53.6 (7.1) | 0.02 |
| Tibial amplitude (mV) | 11.1 (4.8) | 9.4 (4.7) | 0.05 | 5.5 (5.4) | <0.01 |
| Tibial DML (ms) | 4.7 (0.9) | 4.8 (0.9) | 0.54 | 5.2 (0.7) | 0.07 |
| Tibial F response (ms) | 51.8 (5.8) | 52.2 (5.9) | 0.77 | 57.8 (6.9) | <0.01 |
| Ulnar sensory amplitude (uV) |
29.3 (14.4) | 28.7 (12.1) | 0.79 | 18.2 (9.4) | <0.01 |
| Ulnar sensory PL (ms) | 3.6 (1.1) | 3.5 (1.0) | 0.40 | 3.5 (0.3) | 0.94 |
| Median sensory amplitude (uV) |
30.9 (15.2) | 28.4 (14.4) | 0.35 | 18.9 (12.9) | <0.01 |
| Median sensory PL (ms) |
3.7 (0.8) | 3.9 (0.8) | 0.15 | 4.0 (0.7) | 0.37 |
| Median motor amplitude (mV) |
8.3 (3.4) | 8.0 (3.0) | 0.64 | 8.8 (5.7) | 0.54 |
| Median motor DML (ms) |
3.8 (0.8) | 4.1 (0.8) | 0.01 | 4.4 (1.1) | 0.19 |
| Median motor CV (m/s) |
52.3 (6.8) | 51.5 (6.5) | 0.51 | 47.7 (8.8) | 0.03 |
| QSART foot (uL) | 1.3 (6.0) | 0.5 (0.5) | 0.34 | 0.4 (0.3) | 0.58 |
| QSART distal leg (uL) | 1.4 (7.1) | 0.5 (0.8) | 0.36 | 0.5 (0.5) | 0.81 |
| QSART proximal leg (uL) |
1.8 (10.8) | 0.5 (0.6) | 0.42 | 0.5 (0.5) | 0.90 |
| QSART arm (uL) | 1.7 (5.6) | 1.0 (0.9) | 0.41 | 1.3 (0.9) | 0.18 |
| QST vibration (JND) | 20.2 (11.9) | 19.0 (4.3) | 0.51 | 21.9 (3.3) | <0.01 |
| QST cold (JND) | 15.7 (12.6) | 13.8 (4.1) | 0.23 | 16.6 (4.5) | <0.01 |
comparison is between lean controls and obese participants without neuropathy
comparison is between obese participants with and without neuropathy
IENFD=nerve fiber density, PL=peak latency, DML=distal motor latency, CV=conduction velocity, QSART=quantitative sudomotor axonal reflex testing, QST=quantitative sensory testing, JND=just normal difference
The total Neuro-QOL score was higher (worse quality of life) in the obese population with neuropathy compared to those without (3.4 (2.7) vs. 1.9 (1.3), p=0.02) (Table 3). All 5 subdomain scores were also higher in the neuropathy group, but only the pain (3.5 (2.3) vs. 1.9 (1.2), p<0.01) and reduced sensation score (4.0 (4.0) vs. 1.4 (0.9), p<0.01) were statistically significant. The McGill pain score (8.2 (8.6) vs. 2.8 (3.3), p<0.01) and the VAS score (27.2 mm (26.9) vs. 14.8 (20.1), p=0.02) were higher in those with neuropathy. No significant difference was observed in the PPI score.
Table 3.
A comparison of the patient oriented outcomes between the lean controls and obese patients with and without neuropathy
| Variable | Lean Without Neuropathy |
Obese without neuropathy |
P value* | Obese with neuropathy |
P value** |
|---|---|---|---|---|---|
| Total QOL | 1.4 (0.4) | 1.9 (1.3) | <0.01 | 3.4 (2.7) | 0.02 |
| Pain QOL | 1.3 (0.5) | 1.9 (1.2) | <0.01 | 3.5 (2.3) | <0.01 |
| Reduced sensation QOL |
1.0 (0.1) | 1.4 (0.9) | <0.01 | 4.0 (4.0) | <0.01 |
| Sensory motor QOL |
1.5 (1.4) | 1.9 (1.6) | 0.13 | 2.7 (2.9) | 0.26 |
| ADL QOL | 2.0 (1.3) | 2.5 (2.6) | 0.16 | 3.7 (3.6) | 0.14 |
| Social QOL | 1.7 (0.9) | 1.7 (1.4) | 0.79 | 2.7 (2.8) | 0.13 |
| McGill Pain score |
1.0 (1.9) | 2.8 (3.3) | <0.01 | 8.2 (8.6) | <0.01 |
| VAS | 6.0 (13.3) | 14.8 (20.1) | <0.01 | 27.2 (26.9) | 0.02 |
| PPI score N, (%) | 0.07 | 0.47 | |||
| Discomforting | 0 (0%) | 4 (5.1%) | 3 (13.0%) | ||
| Mild | 5 (10%) | 13 (16.5%) | 3 (13.0%) | ||
| No Pain | 45 (90%) | 61 (77.2%) | 17 (73.9%) |
comparison is between lean controls and obese participants without neuropathy
comparison is between obese participants with and without neuropathy
QOL= quality of life, ADL=activities of daily living, VAS=visual analogue scale, PPI=present pain intensity
Neuropathy phenotyping- obese without neuropathy and lean controls
Comparing neuropathy measures between the obese without neuropathy and lean controls demonstrated that IENFD at the leg (4.5 fibers/mm (2.2) vs. 6.4 (3.8), p<0.01) and thigh (9.5 fibers/mm (4.1) vs. 12.3 (6.5), p<0.01) were reduced in the obese without neuropathy group (Table 2). In contrast, the only lower extremity nerve conduction study that was significantly worse in the obese without neuropathy group was the tibial amplitude (9.4 mV (4.7) vs. 11.1 (4.8), p=0.046). However, the peroneal amplitude was higher (6.0 mV (2.7) vs. 5.0 (2.4), p=0.04) and the peroneal distal motor latency was shorter (4.7 mV (0.6) vs. 5.0 (0.8), p=<0.01) in the obese without neuropathy group compared to lean controls. The total Neuro-QOL score (1.9 (1.3) vs. 1.4 (0.4), p<0.01), McGill pain score (2.8 (3.3) vs. 1.0 (1.9), p<0.01), and VAS score (14.8 mm (20.1) vs. 6.0 (13.3), p<0.01) were higher in the obese without neuropathy group compared to lean controls (Table 3). None of the four QSART parameters or two QST thresholds was significantly different between these two groups.
Logistic regression-primary outcome
In a univariable logistic regression model investigating the individual metabolic syndrome components, age (OR=1.08, 95% CI 1.02–1.14), and diabetes (OR=4.24, 95% CI 1.24–14.51) were significantly associated with the primary neuropathy outcome (Table 4). Pre-diabetes (OR=3.27, 95% CI 0.98–10.98), waist circumference (OR=1.11, 95% CI 0.97–1.26), and height (OR=1.23, 95% CI 0.95–1.58) approached, but did not reach statistical significance. Based on multivariable logistic regression, age (OR=1.09, 95% CI 1.02–1.16), diabetes (OR=4.90, 95% CI 1.06–22.63), and waist circumference (OR=1.24, 95% CI 1.00–1.55) were significantly associated with neuropathy. Systolic blood pressure, triglyceride level, and HDL level were not associated with neuropathy. In the sensitivity analysis where 11 patients were removed because of a history of conditions known to be associated with neuropathy or a first degree relative with neuropathy of unknown cause, the odds ratios for diabetes and pre-diabetes increase to 7.37 (95% CI 1.16–46.86) and 6.29 (95% CI 1.21–32.78) respectively. Sensitivity analyses using the primary outcome of symptomatic polyneuropathy or excluding those patients yielded similar results.
Table 4.
Logistic regression evaluating the association of MetS components and clinical neuropathy (primary outcome) in the obese population
| Variable | Unadjusted OR (95%CI) |
Adjusted OR (95%CI)a |
|---|---|---|
| Age | 1.08 (1.02,1.14)* | 1.09 (1.02,1.16)* |
| Male (reference female) |
1.63 (0.64,4.17) | 0.70 (0.12,4.00) |
| Height unit=5 cm |
1.23 (0.95,1.58) | 1.12 (0.75,1.69) |
|
Glycemic status Pre-diabetes Diabetes (reference normal) |
3.27 (0.98,10.98) 4.24 (1.24,14.51)* |
3.82 (0.95,15.41) 4.90 (1.06,22.63)* |
| Waist Circumference unit=5 cm |
1.11 (0.97,1.26) | 1.24 (1.00,1.55)* |
| SBP unit=10 mm Hg |
1.03 (0.74,1.44) | 0.98 (0.66,1.46) |
| Triglycerides unit=50 mg/dL |
1.06 (0.78,1.45) | 1.03 (0.72,1.48) |
| HDL unit=10 mg/dL |
0.97 (0.66,1.43) | 1.31 (0.74,2.32) |
Adjusted analysis includes all variables listed in the table
p<0.05
SBP=systolic blood pressure
Logistic regression-secondary outcomes
When investigating the association of metabolic syndrome components and the 4 secondary neuropathy outcomes in multivariable models, diabetes was associated with a significant reduction in IENFD (parameter estimate (PE) = −1.39, 95% CI −2.61,-0.17), and non-significant reductions in the three nerve conduction study parameters (Table 5). Waist circumference was associated with a significant reduction in the tibial amplitude (PE= −0.51, 95% CI −0.92,-0.10), and non-significant reductions in the sural and ulnar sensory amplitudes. Higher HDL levels were associated with significant reductions in the IENFD at the leg (PE= −0.56, 95% CI −1.03,-0.10) and sural amplitude (PE= −1.97, 95% CI −3.66,-0.28), and non-significant reductions in the tibial and ulnar sensory amplitudes. Pre-diabetes, systolic blood pressure, and triglyceride levels were not associated with the secondary neuropathy outcomes.
Table 5.
Multivariable linear regression evaluating the association of MetS components and secondary neuropathy outcome measures
| Variable | IENFD leg Parameter estimate (m/s) (95%CI) |
Sural amplitude Parameter estimate (uV) (95%CI) |
Tibial amplitude Parameter estimate (mV) (95%CI) |
Ulnar amplitude Parameter estimate (uV) (95%CI) |
|---|---|---|---|---|
| Age | −0.05 (−0.09,0.001) | −0.17 (−0.34,0.001) | −0.12 (−0.23,−0.02)* | −0.52 (−0.72,−0.32)* |
| Male (reference female) |
−1.89 (−3.35,−0.43)* | 0.84 (−4.46,6.15) | 0.60 (−2.68,3.88) | −9.96 (−16.19,−3.73)* |
| Height unit=5 cm |
−0.04 (−0.39,0.30) | −1.25 (−2.51,0.01) | −0.34 (−1.12,0.44) | −0.49 (−1.97,0.99) |
| Pre-diabetes (reference normal) |
−0.09 (−1.18,0.99) | 2.46 (−1.47,6.4) | −0.92 (−3.35,1.51) | 0.18 (−4.44,4.80) |
| Diabetes (reference normal) |
−1.39 (−2.61,−0.17)* | −3.64 (−8.07, 0.79) | −1.19 (−3.93,1.55) | −4.03 (−9.24,1.17) |
| Waist Circumference unit=5 cm |
0.03 (−0.16,0.21) | −0.54 (−1.21,0.12) | −0.51 (−0.92,−0.10)* | −0.36 (−1.14,0.43) |
| SBP unit=10 mm Hg |
0.09 (−0.25,0.43) | −0.53 (−1.76,0.70) | −0.23 (−0.99,0.53) | 0.07 (−1.38,1.51) |
| Triglycerides unit=50 mg/dL |
−0.23(−0.56,0.10) | 0.04 (−1.15,1.23) | −0.04 (−0.77,0.70) | 0.18 (−1.22,1.58) |
| HDL unit=10 mg/dL |
−0.56 (−1.03,−0.10)* | −1.97 (−3.66,−0.28)* | −0.40 (−1.44,0.65) | −1.71 (−3.7,0.28) |
p<0.05
IENFD= nerve fiber density, SBP=systolic blood pressure
Discussion
In obese and lean control populations that received comprehensive metabolic and neuropathy phenotyping, we found a higher prevalence of neuropathy in obese patients with normoglycemia compared to lean controls. The neuropathy prevalence continued to increase in obese patients with pre-diabetes and diabetes. Diabetes, waist circumference, and likely pre-diabetes were the main metabolic factors associated with neuropathy. In contrast, systolic blood pressure, triglyceride levels, and HDL cholesterol levels were not associated with neuropathy. Future intervention studies are needed to confirm a causal relationship between these metabolic factors and neuropathy.
Diabetes is a well-established risk factor for neuropathy, and our data support diabetes as the largest risk factor for polyneuropathy.10 However, previous studies have shown conflicting results regarding pre-diabetes and neuropathy. Two separate groups have shown a higher pre-diabetes prevalence in patients with idiopathic neuropathy compared to literature based controls.18,19 In addition, subjects with impaired glucose tolerance and neuropathy had an increase in IENFD after an extensive diet and exercise intervention (no control group), which is in stark contrast to historical controls.30 Furthermore, three independent population-based studies (MONICA/KORA, San Luis Valley, and PROMISE) demonstrated a higher neuropathy prevalence in subjects with impaired glucose tolerance compared to normoglycemic subjects.7,31,32 In contrast, Hughes et al. did not find a significant association between impaired glucose tolerance and neuropathy in a case-control study.33 Similarly, Dyck et al found no difference in the neuropathy prevalence in those with impaired glucose tolerance compared to matched controls in a population based study in Olmsted County.34 Two other groups have also failed to find a higher neuropathy prevalence in those with pre-diabetes compared to those without.35,36 However, only one of the population based studies utilized a rigorous definition of polyneuropathy incorporating the neurologic examination, and this study required abnormalities in nerve conduction studies.34 Since nerve conduction studies are often normal in small fiber predominant polyneuropathies, this definition may have improperly categorized those with early polyneuropathy. Furthermore, not all studies required the oral glucose tolerance test to classify glycemic status.36 Our study, utilizing a neuropathy definition based on a neurologist’s history and examination, lends further support to pre-diabetes as a cause of polyneuropathy. The neuropathy prevalence of 29% approached that seen in participants with diabetes. Pre-diabetes also had a large increased odds of polyneuropathy that approached statistical significance in univariable and multivariable logistic regression models. As a result, our data pushes the pendulum towards pre-diabetes as a likely cause of polyneuropathy, and therefore, testing for this common condition should be performed in those with a new diagnosis of polyneuropathy of unknown cause.37 Whether treatment of pre-diabetes improves or prevents neuropathy remains to be determined.
In addition to diabetes and pre-diabetes, the other metabolic component associated with polyneuropathy was obesity. Most previous studies investigating the relationship of obesity and polyneuropathy have also demonstrated a significant relationship. Specifically, three of four cross sectional studies and one longitudinal study support this association.17,20–22,38 In contrast to these studies, our study did not reveal a significant association between other metabolic components, such as hypertension, HDL, and triglycerides, and polyneuropathy. One important difference in our study is that our population consisted of severely obese patients with a mean BMI of 41.1 kg/m2. Furthermore, our study is the first that did not solely focus on patients with diabetes and utilized a rigorous definition of polyneuropathy including a neurologic examination. Our data supports obesity, more than other metabolic factors, as one of the main metabolic drivers of polyneuropathy in addition to hyperglycemia, and provides support for targeting this component in intervention trials designed to prevent or improve polyneuropathy. Specifically, weight loss interventions may be more likely to be successful than efforts to improve hyperlipidemia and hypertension.
Not only is the neuropathy prevalence high in the obese population, but the neuropathy also results in a significant impact on patient oriented outcomes such as neuropathy-specific quality of life and pain. While many previous studies have demonstrated an impact of neuropathy on these two important domains,39–42 our data show that the neuropathy observed in an obese population is similarly impactful even when including those patients with normoglycemia and pre-diabetes. We also observed that obese participants with neuropathy had abnormalities on other neuropathy outcome measures such as IENFD, nerve conduction studies, and QST, which gives further evidence to support the high prevalence of a significant neuropathy in those with obesity.
Our data also suggest that a significant proportion of obese participants without clinical neuropathy, based on a neurologic history and examination, likely have an asymptomatic neuropathy that is small fiber predominant. This is supported by the lower IENFD measurements (small fibers) in this population compared to lean controls without significant changes in nerve conduction studies (large fibers). Another possible explanation is that obese participants have lower IENFD values solely based on larger skin areas. However, this would not account for the worse neuropathy specific quality of life measures, and the higher VAS and McGill pain scores observed in the obese without neuropathy group compared to lean controls. Further evidence against obesity itself reducing IENFD is that the published normative data did not reveal a BMI effect.43 Previous studies have shown that the neuropathy associated with pre-diabetes and early diabetes is a small fiber predominant neuropathy.44,45 Our results support a similar small fiber predominant neuropathy early in the course of metabolic neuropathy.
Limitations of our study include a small sample size, which limits our ability to detect small effects of the metabolic syndrome components. However, we did find statistically significant associations of metabolic syndrome components and neuropathy. We recruited obese participants attending a weight management program at a tertiary referral center, less than twenty percent of these patients agreed to participate, and the population was primarily non-hispanic white. Therefore, how these results generalize to the entire clinic population and other populations is unknown. Lean controls were recruited primarily from a university research website, which may have introduced selection bias. The age difference between obese and lean participants is a potential confounder.
In summary, obese patients have a higher prevalence of neuropathy than lean controls, even in those without diabetes or pre-diabetes. Furthermore, the neuropathy prevalence in those with pre-diabetes is only slightly lower than the prevalence in those with diabetes. The neuropathy in this population is associated with lower quality of life and higher pain scores, indicating that the neuropathy is clinically important. Current clinical practice concentrates on the management of diabetes in those with neuropathy. However, our data supports management of obesity and pre-diabetes as well, more so than other metabolic factors such as hyperlipidemia and hypertension. Future studies are needed to determine the best interventions to prevent and improve neuropathy in the obese population.
Acknowledgments
Brian Callaghan had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
The study sponsor had no role in the design and conduct of the study; collection, management, analysis, interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Study Funding: Drs. Callaghan, Burant and Feldman are supported by the Taubman Medical Institute. Dr. Callaghan is supported by a NIH K23 grant (NS079417). Drs. Pop-Busui, Burant, and Feldman are supported by DP3DK094292. Dr. Pop-Busui is supported by R01DK-107956, and R01HL102334. Dr. Feldman is supported by R24 082841. The Michigan Institute for Clinical & Health Research (UL1TR000433) supported this study.
Dr. Callaghan receives research support from Impeto Medical Inc. He performs medical consultations for Advance Medical, performs medical legal consultations, and consults for a PCORI grant. Dr. Pop-Busui receives research support from Impeto Medical Inc.
Footnotes
Trial Registration: NCT02689661
Author contributions:
Brian Callaghan was involved in the study design, interpretation of the statistical analysis, and wrote the manuscript. Rong Xia, Evan Reynolds, Mousumi Banerjee, Amy Rothberg, Charles Burant, Emily Villegas-Umana, Rodica Pop-Busui, and Eva Feldman were integrally involved in the study design, interpretation of the data, and critical revisions of the manuscript. Rong Xia and Evan Reynolds performed the statistical analyses.
Disclosures:
Mr. Xia, Mr. Reynolds, Dr. Banerjee, Dr. Rothberg, Dr. Burant, Mrs. Villegas-Umana, and Dr. Feldman report no disclosures.
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