Abstract
Context:
Cardiovascular risk factors are well-known predictors of the development of diabetic peripheral neuropathy (DPN), which has traditionally been considered as a manifestation of diabetes-associated microangiopathy. Because endothelial dysfunction is strongly associated with all cardiovascular risk factors, we hypothesized that it may be a link between cardiovascular risk factors and DPN.
Objective:
The primary objective of this study was to test whether endothelial dysfunction is a predictor of DPN.
Design and Setting:
This is a cross-sectional analysis of a cohort composed of patients followed at the Microcirculatory Laboratory, Beth Israel Deaconess Medical Center.
Patients:
Participants with diabetes without DPN (n = 192) and with DPN (n = 166), subjects with prediabetes (n = 75), and nondiabetic controls (n = 59) were included.
Interventions:
Endothelial function was assessed with flow-mediated dilation (FMD) of the brachial artery. Inflammatory cytokines and biomarkers of endothelial function (soluble intercellular and vascular cell adhesion molecules) were quantified using a multiplex bead-based immunoassay. Neurological assessment included the neuropathy disability score (NDS).
Main Outcome Measure:
The relationship between FMD and NDS assessed using multiple linear regression.
Results:
In addition to already known risk factors of DPN, FMD was strongly associated with NDS (β = −0.24; P < .001). Sensitivity analysis that removed FMD from the model provided similar results for soluble intercellular cell adhesion molecule-1, another biomarker of endothelial function. Confirmatory factor analysis further showed that endothelial dysfunction is a significant mediator between glycosylated hemoglobin and diabetes duration and diabetic complications.
Conclusions:
This study shows that endothelial dysfunction occurs early in the pathophysiology of diabetes and is a link between cardiovascular risk factors and DPN.
Decreased arterial flow-mediated dilation, a marker of endothelial dysfunction, occurs early in the pathophysiology of diabetes and is a link between cardiovascular risk factors and DPN.
Diabetic peripheral neuropathy (DPN) is a common complication of diabetes that manifests with various symptoms such as pain, numbness, or tingling (1). Moreover, DPN has been shown to be a major risk factor for diabetic foot ulcers (2), associated with significant morbidity and an important economic burden (3). Considering the limited therapeutic options, early identification of individuals at risk of developing DPN is important.
Although the duration of diabetes and the level of hyperglycemia are key determinants of DPN, the EURODIAB Prospective Complications Study further showed that cardiovascular risk factors were predictors of the development of DPN in patients with type 1 diabetes (4). More recently, similar findings were observed in patients with type 2 diabetes (5). In contrast, DPN was recently shown to be associated with incident cardiovascular events in diabetic patients (6). This apparent contradiction confirms that there is a relationship between DPN and the risk of cardiovascular disease, but that the mechanistic link remains largely unknown (7).
Systemic endothelial dysfunction has been strongly associated with all cardiovascular risk factors, including arterial hypertension, smoking, dyslipidemia, aging, and obesity (8). Moreover, endothelial dysfunction is an early complication of type 1 and type 2 diabetes (9, 10) that has also been observed in subjects with impaired glucose tolerance and in normoglycemic individuals with a parental history of diabetes (11). The relationship between endothelial dysfunction and DPN is complex and has not been fully elucidated. Although DPN has been traditionally considered as a manifestation of diabetes-associated microangiopathy, insights into the underlying cellular abnormalities that occur in both endothelial cells and neurons show similarities, suggesting that concomitant mechanisms are involved (12, 13). Interestingly, experimental data have recently shown that endothelial dysfunction per se is sufficient to cause neuropathy (14).
Altogether, these data suggest that systemic endothelial dysfunction may be the link between cardiovascular risk factors and increased incidence of DPN. However, to our knowledge, this has never been confirmed. The primary objective of this study was therefore to test whether peripheral vascular dysfunction assessed with flow-mediated dilation (FMD), a marker of endothelial dysfunction, was associated with peripheral neuropathy in a cohort of prediabetic, diabetic, and nondiabetic subjects. Factor analysis was performed as a secondary objective to further explore the relationship between biomarkers of inflammation, endothelial function, cardiovascular risk factors, and DPN.
Patients and Methods
Study design
This is a cross-sectional analysis of a cohort composed of patients included in different studies, all conducted at the Microcirculatory Laboratory, Beth Israel Deaconess Medical Center. All the procedures were standardized and identical across the different studies. Participants were included if they were between 21 and 80 years of age and, for patients, if they had a current diagnosis of type 1 or type 2 diabetes according to the criteria of the American Diabetes Association (ADA) (15). Participants without diabetes were included as controls; those with prediabetes were identified based on the criteria of the ADA. Exclusion criteria included the presence of foot ulceration, peripheral arterial occlusive disease requiring surgical bypass surgery, end-stage renal failure, chronic heart failure with severe peripheral edema, transient ischemic attack within the past 6 months, stroke with residual neurological damage, uncontrolled hypertension, and pregnancy. The studies from which these data were extracted were all approved by the Institutional Review Board of the Beth Israel Deaconess Medical Center. All participants gave written informed consent.
Endothelial function assessment
Endothelial function was assessed by measuring FMD in accordance with published guidelines (16). Briefly, brachial artery diameter was measured before and during reactive hyperemia using a high-resolution ultrasound with a 10.0 MHz linear array transducer and an Aloka Prosound α7 system (Hitachi Aloka Medical Ltd). Reactive hyperemia was induced by inflating a pneumatic tourniquet distal to the brachial artery to 50 mm Hg above systolic blood pressure for 5 minutes and then deflating it rapidly. FMD was expressed as the percentage change between baseline and postocclusive artery diameter.
Biochemical markers
Inflammatory cytokines including TNFα, interferon γ, IL-6, and IL-8, as well as vascular endothelium growth factor (VEGF), matrix metallopeptidase-9, and biochemical markers of endothelial function (soluble intercellular adhesion molecule-1 [ICAM] and vascular cell adhesion molecule-1) were quantified using a Luminex 200 apparatus (Luminex Corp.) and Millipore multiplex immunoassay panels (Millipore).
Neurological assessment
Physical examinations were quantified using the neuropathy disability score (NDS), which grades neuropathy in scores from 0 (no neuropathy) to 28 (severe neuropathy) (17). The neuropathic symptoms were assessed by using a modified neuropathy symptom score (2). Sensory testing was performed with the vibration perception threshold (VPT) on the great toe, as previously described (17), and with a Semmes-Weinstein (SW) monofilament. A combination of at least two of the following defined DPN: NDS ≥5, neuropathy symptom score ≥3, vibration detected at a voltage of ≥25 V for VPT, and the inability to feel a 5.07 SW monofilament (10 g of pressure) (2).
Statistical analysis
Continuous data were expressed as mean ± standard deviation, or as median (interquartile range) when the distribution was not normal. Between-group comparisons were analyzed by one-way ANOVA. Post hoc comparisons were performed using the Tukey test. When application conditions were not met (Levene's test was used to test the homogeneity of variance), nonparametric tests were used. Categorical data were expressed as frequencies and percentages and compared with the χ2 test. Bivariate Pearson correlations were used to assess the relationship between NDS, VPT, and other continuous variables. We subsequently used multiple linear regressions to identify independent predictors of DPN. For the primary outcome measure (ie, FMD as a predictor of NDS), the model consisted of a two-step regression, first entering known predictors (duration of diabetes, glycosylated hemoglobin [HbA1c], triglycerides, history of smoking, waist circumference, and blood pressure) (4); and in block 2, additional variables correlated with a P value <.1 (Pearson) were included using the stepwise method. We used the general linear model (GLM) to compare FMD between groups, adjusting for possible confounders, ie, age, body mass index (BMI), mean arterial pressure, and HbA1c, and the initial study patients were included (to take into account interoperator variability). The influence of treatments such as angiotensin-converting enzyme inhibitors or angiotensin II receptor-1 blockers, statins, and vitamin D as possible confounders was also tested using GLM.
Confirmatory factor analysis (CFA) was performed to further explore the relationship between endothelial function, inflammation, cardiovascular risk factors, and DPN, expressed as latent variables. In addition, the duration of diabetes and HbA1c were included as manifests in the hypothesized model (Figure 1). We used the maximum likelihood method to estimate parameters in the CFA. The structure of the factor “endothelial function” was prespecified and included FMD and ICAM. The factor “cardiovascular risk” includes hypertension, defined as arterial systolic blood pressure >140 mm Hg or diastolic blood pressure >90 mm Hg, or as taking at least one antihypertensive medication; statin therapy or low-density lipoprotein-cholesterol ≥100 mg/dL was used to define dyslipidemia in patients. Smoking, BMI, and waist circumference were removed because of poor loadings. For the same reason, NDS, VPT, and SW were included in the latent variable “DPN,” and TNFα and IL-8 were the manifests included in the “inflammation” factor. The comparative fit index (CFI) and the root mean-square error of approximation (RMSEA) were used as fit indexes. A CFI of 1 and RMSEA of 0 indicate a perfect fit, with values >0.9 and <0.05, respectively, indicating a good fit (18).
Figure 1.
CFA assessing the relationship between observed variables (rectangles) and latent variables (circles). Loading factors for latent variables are represented in italics. Standardized regression weights and correlations appear along single and double-headed arrows, respectively. Residual errors are not shown for clarity. CV, cardiovascular; DM, diabetes mellitus. *, P < .05; **, P < .001; §, log transformation was used.
P values <.05 were considered as significant. Statistical analysis was performed using SPSS and SPSS Amos software (version 22.0, IBM Corp.).
Results
We included 492 participants in this study. The characteristics of the different groups are summarized in Table 1. Endothelial function assessed with FMD was significantly impaired in participants with prediabetes, but we observed no significant difference between prediabetes and diabetes without DPN (Table 1). Similar between-group differences in FMD were observed after adjustments (Figure 2), suggesting early impairment of endothelial function, whereas neurological assessment does not show any evidence of DPN in participants with prediabetes. The use of vitamin D supplement was associated with lower FMD (5.79 ± 2.89 vs 6.68 ± 2.81%; P = .01 in the GLM), but the interaction with the group was not significant (P = .3). The other treatments were not associated with significant changes in FMD.
Table 1.
Characteristics of the Population
| No Diabetes | Prediabetes | DM Without DPN | DM With DPN | P Value | |
|---|---|---|---|---|---|
| n | 59 | 75 | 192 | 166 | |
| Age, y | 51.9 ± 12.9 | 55.2 ± 10.5 | 56.5 ± 11.8a | 59.3 ± 9.2a,b | .001 |
| Females | 22 (37.3) | 33 (44) | 93 (48.4) | 59 (35.5) | .54 |
| Type 2 diabetes | 161 (83.8) | 119 (71.7) | .005 | ||
| Duration of diabetes, y | 9 (12.5) | 18 (18.5) | <.001 | ||
| Waist circumference, cm | 94.4 ± 17.3 | 103.5 ± 17.7a | 108.4 ± 17.2a | 113.1 ± 20.8a,b | <.001 |
| BMI, kg/m2 | 28.1 ± 6.9 | 30. 4 ± 7.0 | 32.7 ± 7.4a | 32.8 ± 7.1a | <.001 |
| Mean arterial pressure, mm Hg | 89.6 ± 13.0 | 92.1 ± 10.9 | 93.3 ± 11.3 | 95.2 ± 11.4a | .01 |
| History of smoking | 15 (25.4) | 34 (45.3) | 88 (45.8) | 88 (53.0) | .001 |
| NDS | 0.7 (2) | 11 (10) | <.001 | ||
| VPT (V) | 8 (4.7) | 7 (6.0) | 10.5 (8.5)a,b | 32.5 (23.5)a,b,c | <.001 |
| FMD (% increase) | 8.07 ± 3.17 | 6.29 ± 2.78a | 6.20 ± 2.15a | 5.36 ± 2.27a,b,c | <.001 |
| Fasting plasma glucose, mg/dL | 86 (13) | 93 (14)a | 120 (47)a,b | 128 (80)a,b | <.001 |
| ICAM, ng/mL | 96 (60) | 110 (65) | 105 (56) | 118 (82)a | .008 |
| VCAM, ng/mL | 866 (441) | 883 (546) | 877 (572) | 912 (558) | .50 |
| HbA1c, % | 5.4 (0.15) | 5.9 (0.20)a | 7.1 (1.30)a,b | 7.7 (1.90)a,b | <.001 |
| Total cholesterol, mg/dL | 189 (46) | 197 (47) | 163 (44)a,b | 161 (49)a,b | <.001 |
| Triglycerides, mg/dL | 107 (84) | 124 (98) | 117 (94) | 131 (112) | .08 |
| Creatinine, mg/dL | 0.90 (0.23) | 0.87 (0.30) | 0.90 (0.36) | 1.00 (0.39)a,b,c | <.001 |
| Nephropathy | 0 (0) | 1 (1.3) | 10 (5) | 26 (15.7) | <.001 |
| Retinopathy | 0 (0) | 0 (0) | 28 (14.6) | 65 (39.1) | <.001 |
| Hypertension | 7 (11.9) | 13 (17.3) | 82 (42.7) | 96 (57.8) | <.001 |
| Coronary artery disease | 0 (0) | 1 (1.3) | 15 (7.8) | 33 (19.9) | <.001 |
| Peripheral vascular disease | 0 (0) | 0 (0) | 1 (0.5) | 14 (8.4) | <.001 |
| ACE inhibitor | 3 (5.1) | 8 (10.7) | 84 (43.8) | 84 (50.6) | <.001 |
| AT-1 antagonist | 1 (1.7) | 4 (5.3) | 20 (10.4) | 41 (24.7) | <.001 |
| β-blocker | 6 (10.2) | 7 (9.3) | 41 (21.4) | 57 (34.3) | <.001 |
| Vitamin D | 17 (28.8) | 12 (16) | 41 (21.4) | 42 (25.3) | .29 |
| Statin | 5 (8.5) | 12 (16.0) | 109 (56.8) | 102 (61.4) | <.001 |
| Insulin | 64 (33.3) | 97 (58.4) | <.001 | ||
| Sulfonylurea | 51 (26.6) | 43 (25.9) | .9 | ||
| Metformin | 115 (59.9) | 71 (42.8) | .002 |
Abbreviations: ACE, angiotensin-converting enzyme; AT-1, angiotensin II receptor-1; DM, diabetes mellitus; VCAM, soluble vascular cell adhesion molecule-1; VPT, vibration perception threshold (in volts). Continuous variables are expressed as mean ± standard deviation or as median (interquartile range) when the distribution was not normal. Categorical variables are expressed as frequency (%). Post hoc Tukey test was used for multiple comparisons, or Mann-Whitney test when appropriate.
P < .05 vs healthy controls.
P < .05 vs prediabetes.
P < .05 vs DM without DPN.
Figure 2.
Comparison of marginal means ± SEM for FMD when adjusting for age, BMI, mean arterial pressure, HbA1c, and the initial study in which patients were included (to take into account interoperator variability). *, P < .05 vs healthy controls; †, P < .05 vs prediabetes; ‡, P < .05 vs DM without DPN. DM, diabetes mellitus.
Multivariate analyses to identify predictors of NDS included 318 subjects. Besides duration of diabetes, HbA1c, and traditional cardiovascular risk factors, FMD was a strong predictor of NDS (Table 2, model 1). Of note, an elevated plasma level of VEGF was also significantly associated with a higher NDS. Sensitivity analysis removing FMD from the list of independent variables yielded to similar conclusions for ICAM, another biomarker of endothelial dysfunction, although the correlation was weaker (Table 2, model 2).
Table 2.
Multiple Regression Analyses Assessing the Variables Predicting DPN Expressed as the NDS
| Model 1 (Adjusted R2 = 0.40) |
Model 2 (Adjusted R2 = 0.37) |
|||
|---|---|---|---|---|
| Standardized Coefficient (β) | P Value | Standardized Coefficient (β) | P Value | |
| Duration of diabetes | 0.31 | .001 | 0.32 | .001 |
| FMDa | −0.24 | .001 | ||
| ICAMb | 0.10 | .047 | ||
| HbA1c | 0.20 | .001 | 0.21 | .001 |
| Hypertension | 0.20 | .001 | 0.19 | .001 |
| Waist circumference | 0.20 | .002 | 0.22 | .001 |
| Triglycerides | 0.17 | .001 | 0.17 | .001 |
| VEGFb | 0.14 | .01 | 0.13 | .01 |
| History of smoking | 0.10 | .056 | 0.10 | .058 |
Variables shown in italics were forced into the model (step 1). Other variables were added at block 2 using the stepwise method.
Removed from the list of independent variables in model 2.
Log transformation was used.
Multiple regression was also conducted using VPT (n = 193) as a measure of large-fiber function. The best model (adjusted R2 = 0.45) also included FMD as a strong predictor of VPT (β = −34; P < .001). In the latter, diabetes duration, hypertension, waist circumference, and VEGF were all positively and significantly correlated to VPT.
The CFA included 280 patients. The hypothesized model fit our data very well (RMSEA = 0.038; CFI = 0.976). Endothelial dysfunction, but not inflammation, significantly mediated the effects of duration of diabetes and HbA1c on both cardiovascular risk factors and DPN (Figure 1). Overall, the model accounts for 68% of the variance of the DPN factor. Alternative models directly linking diabetes duration and HbA1c to cardiovascular risk factors and DPN, or linking cardiovascular risk factors to DPN, whatever the direction of the association, had lower fit.
Discussion
The present study shows that endothelial dysfunction assessed with FMD is strongly associated with DPN. Other predictors of DPN include duration of diabetes and HbA1c, as well as cardiovascular risk factors such as hypertension, waist circumference, triglycerides, or history of smoking, as previously reported. Indeed, the study of large cohorts showed that the incidence of neuropathy was associated with cardiovascular risk factors in patients with both type 1 (4) or type 2 (5) diabetes. In contrast, in patients with type 2 diabetes free of cardiovascular disease, DPN at baseline was associated with a higher risk of cardiovascular events (6). This question about causality (ie, DPN causing cardiovascular disease or cardiovascular risk factors leading to DPN) may highlight differences in inclusion criteria between those studies; in the latter case, patients free of CVD at baseline are included, whereas in the former the focus is made on patients without DPN at baseline. In any case, there is a very close relationship between DPN and cardiovascular risk factors. Although the cross-sectional design of our study does not allow us to formally assess the direction of the relationship between endothelial dysfunction and DPN, it is likely that endothelial dysfunction occurs (or, is detected) earlier. Indeed, FMD is a known predictor of incident cardiovascular events (19), and it is also impaired early in the pathophysiology of both type 1 and type 2 diabetes, within a few years (9, 10). Decreased FMD has also been observed in patients with prediabetes (11), which is further confirmed by our results. The CFA model hypothesizing that endothelial dysfunction is the common mechanism underlying both cardiovascular risk factors and DPN showed an excellent fit to our data and explained 68% of the variance of DPN. It suggests that endothelial dysfunction, assessed using FMD and ICAM, is a significant mediator between diabetes duration and HbA1c, and cardiovascular risk factors and DPN.
From a mechanistic perspective, peripheral neuropathy and endothelial dysfunction share similar pathophysiological pathways in diabetes. For example, hyperglycemia induces the formation of advanced glycation end-products and increased generation of reactive oxygen species, leading to cellular injury both in the vascular endothelium and in neurons (12, 13). One can therefore imagine that they occur simultaneously and potentiate each other; microvascular impairment contributes to DPN by decreasing neuronal perfusion, whereas DPN reduces vascular reactivity (eg, impaired axon-reflex vasodilation). We demonstrated in the present study that endothelial dysfunction assessed with FMD is detected earlier than neuropathy. However, we cannot rule out that detection of early endothelial dysfunction in our cohort translates earlier detection of asymptomatic decrease in FMD, whereas neuropathy still evolves silently. A limitation of our study is that we could not include a group of prediabetic subjects with neuropathy. Indeed, despite the higher risk of symptomatic neuropathy in these patients (20), none of the 134 nondiabetic participants in our cohort had neuropathy as defined by the criteria chosen in this study. Abnormal conduction velocity in the distal segment of the sural nerve has been reported in subjects with impaired glucose tolerance (IGT), but without clinical neuropathy (21). Moreover, recent findings have shown that subjects with IGT have a progressive decrease in intraepidermal nerve fiber density over time (22). Interestingly, in that study, subjects with IGT had significantly higher NDS and VPT as compared with matched controls at baseline (22), whereas in our study we found no significant difference between prediabetic subjects and controls, highlighting differences in study populations. Because our study does not permit us to address the role of a potential asymptomatic neuropathy in prediabetic subjects, further mechanistic studies specifically designed to address this issue are needed.
Recent experimental data provide further insight into the complex relationship between endothelial dysfunction and neuropathy, suggesting that endothelium impairment is sufficient to cause neuropathy through the involvement of the Desert Hedgehog pathway (14). Interestingly, VEGF further enhanced blood nerve barrier permeability induced by Desert Hedgehog deficiency, suggesting a deleterious role of VEGF in the physiopathology of diabetic neuropathy (14). This is consistent with the present study showing that circulating VEGF levels are positively correlated with both NDS and VPT. However, considering the complexity of the mechanistic link between neuropathy and endothelial function, all potential confounders have not been included in our analysis. Vitamin D is one such confounder that has been shown to be associated with both increased cardiovascular risk and diabetes complications, especially neuropathy (23). Moreover, in asymptomatic vitamin D-deficient subjects, there was a significant relationship between vitamin D levels and endothelial function (24). In a recent meta-analysis of randomized, placebo-controlled trials, vitamin D replacement significantly improved endothelial function assessed with FMD in a subgroup of patients with diabetes (25). Unfortunately, we did not quantify vitamin D levels in our cohort, and therefore we were not able to include this parameter into our analysis. However, we assessed the influence of the use of vitamin D supplement on endothelial function and inflammation. Our results suggest that across all groups, patients receiving vitamin D supplement have lower FMD, which might indicate insufficient supplementation. However, the use of vitamin D supplement did not affect between-group comparisons, and therefore it does not bias our main conclusions. Also, it had no impact on inflammatory cytokines and growth factors and was not significantly associated with the NDS (data not shown for clarity).
In this study, we chose the NDS as a primary outcome in the regression analysis, considered as a “gold standard” to assess DPN (17). The VPT on the great toe also showed a satisfactory discriminating power in DPN detection (17), and both NDS and VPT are strongly associated with the risk of developing an ulcer (2). Other markers of neuropathy, such as the axon reflex-mediated vasodilation assessed with laser Doppler flowmetry (LDF), showed a high sensitivity to detect DPN progression during a 3-year follow-up of a cohort of diabetic patients (26). However, we did not use this marker because the LDF signal is closely related to microvascular function and provides an indicator of the complex neurovascular mechanisms involved in cutaneous microvascular reactivity (27). Therefore, the fact that axon reflex vasodilation decreases more markedly than other neuropathy tests may also reflect the fact that evolving microvascular dysfunction contributes to this impairment.
Nonetheless, it would be misleading to consider FMD only as a test for nitric oxide (NO)-related vasodilation of large vessels, due to more complex underlying mechanisms. A pioneer study showed that the NO pathway was involved, whereas the role of prostacyclin seems marginal (28). More recently, cytochrome P450 metabolites, and putatively other hyperpolarizing factors, have been suggested to participate in the response interacting with the NO pathway (29). Finally, FMD assesses not only conduit artery vascular function but also, indirectly, the limb microcirculation because the stimulus (ie, reactive hyperemia) is highly dependent on maximal forearm resistance (8). Therefore, it may be more accurate to consider FMD as a test for systemic endothelial function, rather than a test for NO function in conductance arteries, although the role of the microcirculation is difficult to quantify (and likely less important than in axon reflex vasodilation assessed with LDF). Although the association between ICAM and NDS was weaker, the use of another marker of endothelial function strengthens our results and confirms previous findings (30).
Besides the issue of causality discussed above, another limitation of the cross-sectional design is that we could not include a change in HbA1c, which has been shown to be an independent predictor of neuropathy (4). Another weakness of this study design is that the analyses were not planned before including the patients. Therefore, all patients did not undergo all explorations, and this is the reason why multivariate analyses were not performed on the whole cohort, thereby reducing the power of multiple regressions. However, in all cases we had more than 20 participants per variable (and almost 30 for NDS), higher than the rule of thumb of a minimum of 10 participants per variable. This allowed adjusting the results for potential confounders and including all the variables of interest in the multivariate models for NDS. However, although our results show a strong association between FMD and VPT, the best-fitted model does not include all the cardiovascular risk factors described previously (31). This may be explained by weaker associations between VPT and triglycerides or history of smoking, and by the smaller sample size for VPT assessment in our cohort. Finally, the CFA is not free of limitation. The objective of such analysis is to test the hypothesis that there is a relationship between observed variables and underlying latent variables. We therefore postulated the hypothesized relationships a priori, and we cannot rule out that other models would also fit our data. The CFA was a secondary objective of this study, and the robustness of the hypothesized model should now be validated with other datasets.
In conclusion, this study confirms that endothelial dysfunction occurs early in the pathophysiology of diabetes and shows that FMD is a strong, independent predictor of peripheral diabetic neuropathy. It further suggests that endothelial dysfunction mediates the deleterious effects of diabetes on cardiovascular risk and DPN. These results need to be confirmed in a longitudinal study before emphasizing the need for systematic and reliable evaluation of endothelial function in patients at risk.
Acknowledgments
We thank Pr Marc Hommel for his advice on the confirmatory factor analysis.
This work was supported by National Institutes of Health Grants 1R01DK091949 (to A.V.), 1R01NS066205 (to A.V.) 1R01DK076937 (to A.V.), 1R01NS046710 (to A.V.), and R01HL110350 (to A.V.). A.V. received funding from the National Rongxiang Xu Foundation. M.R. received funding from the Fondation AGIR pour les Maladies Chroniques. J.L. received funding from the National Health and Medical Research Council of Australia.
Disclosure Summary: The authors have nothing to disclose.
Footnotes
- BMI
- body mass index
- CFA
- confirmatory factor analysis
- CFI
- comparative fit index
- DPN
- diabetic peripheral neuropathy
- FMD
- flow-mediated dilation
- GLM
- general linear model
- HbA1c
- glycosylated hemoglobin
- ICAM
- soluble intercellular adhesion molecule-1
- IGT
- impaired glucose tolerance
- LDF
- laser Doppler flowmetry
- NDS
- neuropathy disability score
- NO
- nitric oxide
- RMSEA
- root mean-square error of approximation
- SW
- Semmes-Weinstein
- VEGF
- vascular endothelium growth factor
- VPT
- vibration perception threshold.
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