Assessing corneal dendritic cells in glucose dysregulation small‐fibre neuropathy

Juan Francisco Idiaquez; Carolina Barnett‐Tapia; Bruce A Perkins; Vera Bril

doi:10.1111/jns.12671

. 2024 Nov 12;29(4):400–405. doi: 10.1111/jns.12671

Assessing corneal dendritic cells in glucose dysregulation small‐fibre neuropathy

Juan Francisco Idiaquez ¹, Carolina Barnett‐Tapia ¹, Bruce A Perkins ², Vera Bril ^1,^✉

PMCID: PMC11625976 PMID: 39532698

Abstract

Background and Aims

Small‐fibre neuropathy (SFN) is associated with glucose dysregulation, including impaired glucose tolerance (IGT) and type 2 diabetes (T2D). Corneal confocal microscopy (CCM) offers a non‐invasive tool to assess corneal nerve damage and dendritic cell density (DCD). In this study, we investigated corneal DCD in patients with SFN and glucose dysregulation, defined as IGT or T2D.

Methods

We enrolled 38 patients with SFN + glucose dysregulation, 51 with SFN + non‐glucose dysregulation and 20 healthy controls. All participants underwent neurological examination, neurophysiology and CCM.

Results

Individuals with SFN and glucose dysregulation had higher DCD compared with healthy controls (p = .01), and mature DCD was higher in IGT SFN patients than in T2D patients.

Interpretation

Higher DCD in IGT compared with controls and patients with established T2D may suggest that DCD is a biomarker of early neuropathy.

Keywords: corneal confocal microscopy, corneal dendritic cells, impaired glucose tolerance, small‐fibre neuropathy

1. INTRODUCTION

Impaired glucose tolerance (IGT) represents an early stage of glucose dysregulation and is associated with small‐fibre neuropathy (SFN). ¹ , ² SFN can also manifest in patients with recently diagnosed type 2 diabetes, ³ often preceding diabetic complications. Corneal confocal microscopy (CCM) is a high‐resolution imaging technique that is particularly useful in diagnosing SFN by measuring corneal nerve fibre length (CNFL) and also allows for the quantification of dendritic cell density (DCD). ⁴ CCM is a non‐invasive and rapid technique that provides structural insights into nerve fibre loss, with diagnostic properties comparable to intraepidermal nerve fibre density assessments. ⁵ , ⁶

Chronic hyperglycaemia negatively impacts corneal nerve function and epithelial cell repair. ⁷ The role and pathogenesis of DC accumulation in diabetic neuropathy are not well elucidated. Both animal and clinical studies have reported increased DCD in subjects with type 1 and type 2 diabetes. ⁸ , ⁹ , ¹⁰ Increased DCD in diabetic patients, particularly in the earlier phases of corneal nerve damage, suggests an immune‐mediated contribution to corneal nerve damage in diabetes. ¹⁰ Additionally, studies show that DCD correlates with corneal nerve loss in various diabetes types, including type 1, type 2 and latent autoimmune diabetes in adults. ¹¹

Patients with diabetic SFN and neuropathic pain had increased DCs in the epidermis, potentially aiding nerve repair. ¹² , ¹³ A significant reduction in cutaneous DCD has been observed in newly diagnosed type 2 diabetes, potentially contributing to a metabolically associated, local imbalance in immune cells in peripheral nerves and increasing the risk of complications like polyneuropathy and foot ulcers. ¹⁴ Research comparing SFN patients with healthy controls found a significant increase in DCD in SFN patients, suggesting that DCs may serve as inflammatory markers in various forms of small‐fibre polyneuropathy. ¹⁵

Given these findings, the role of DCs in SFN, particularly in the context of glucose dysregulation, is of significant interest. While previous research has established that DCD is higher in SFN patients compared with healthy controls, it remains unclear whether DCD differs between forms of SFN—specifically those associated with glucose dysregulation (IGT and type 2 diabetes) and those occurring without glucose dysregulation. In this study, we aimed to examine and compare corneal DCs in patients with SFN related to IGT and type 2 diabetes, as well as in SFN patients without glucose dysregulation, alongside healthy controls.

2. PATIENTS AND METHODS

2.1. Study participants

We performed a retrospective cross‐sectional single‐centre study of patients with established SFN who attended the Prosserman Family Neuromuscular Clinic at Toronto General Hospital between 2018 and 2021. The study protocol was approved by the Research Ethics Board of the University Health Network, who waived informed consent.

Participants included individuals aged ≥18 years diagnosed with SFN based on consistent clinical symptoms and/or signs, plus normal nerve conduction studies and two out of three abnormal small nerve fibre tests (CCM, Laser Doppler Imaging flare and Quantitative Thermal Thresholds). ¹⁶ Additionally, patients with documented type 2 diabetes or IGT were included based on medical records and verified using American Diabetes Association criteria for plasma glucose levels, including haemoglobin A1c (HbA1c), fasting plasma glucose, random elevated glucose with symptoms or abnormal 2‐h oral glucose tolerance test. ¹⁷ SFN participants were categorized into those with glucose dysregulation (IGT and type 2 diabetes) and non‐glucose dysregulation groups.

2.2. Data collection

Medical records were reviewed to extract demographic and clinical data, including age, gender, medical history, nerve conduction study results, Toronto Clinical Neuropathy Score (TCNS), modified TCNS, ¹⁸ , ¹⁹ Overall Neuropathy Limitation Scale (ONLS) ²⁰ and Rasch‐built Overall Disability Scale (RODS). ²¹ CCM was used to assess SFN. A cohort of 20 healthy controls with normative CCM data from prior study was used for comparison. ²² SFN patients were categorized into severity groups based on the TCNS score: mild neuropathy (scores 6–8), moderate neuropathy (scores 9–11) and severe neuropathy (scores ≥12). ¹⁸ , ¹⁹

2.3. Nerve conduction studies (NCS)

NCS were conducted using the Sierra Wave instrument (Cadwell Laboratories Inc., Kennewick, WA, USA) using age‐ and height‐adjusted reference values from the Toronto General Hospital electrophysiology laboratory.

2.4. CCM image acquisition

Corneal examination was performed using the Rostock Cornea Module of the Heidelberg Tomograph II (Heidelberg Engineering, Smithfield, Rhode Island, United States). Patients received topical anaesthetic and tear gel to optimize corneal contact with a disposable sterile cap on the lens. Automated imaging captured 40 contiguous 0.3‐mm² digital pictures per cornea, recorded at 1.3 μm increments to a total depth of 50 μm. This process was repeated twice per eye to ensure precise data acquisition. A trained examiner selected six high‐quality images (three per eye) from the central cornea for CNFL and DCD analysis as part of a quality control protocol. These images were then evaluated in a masked fashion to eliminate bias, and the image with the highest fibre density was chosen for final analysis.

2.5. Dendritic cell quantification

ImageJ software (version 1.41, National Institutes of Health, USA) with a cell‐count plugin was used for blinded quantification of CNFL and DCD. The presence or absence of highly reflective cells was assessed. Then, the number of highly reflective cells DC (Figure 1) was categorized as immature (less than 50 μm without dendritic structures) or mature (with dendritic structures). ²³ , ²⁴ The reliability of these DCD measurements in the corneal sub‐basal epithelium has been confirmed. ²³ , ²⁴

Representative in vivo confocal microscopy images of the central cornea showing highly reflective dendritic cells (DCs). Mature DCs are indicated by blue arrows, and an immature DC by a red “X.” (A) Control subject, (B) patient with SFN related to glucose dysregulation and (C) patient with non‐glucose dysregulation SFN.

2.6. Statistical analysis

Statistical analysis was performed using R (R Foundation for Statistical Computing version 4.0.4). Descriptive statistics such as mean ± standard deviation or median with interquartile range were used to summarize demographic and clinical characteristics. For the primary analysis, we compared corneal DCD between three groups: SFN with glucose dysregulation, SFN without glucose dysregulation and healthy controls. Analysis of variance (ANOVA) or non‐parametric equivalents were used to compare DCD among these groups. Spearman's rank correlation coefficient was used for correlation analysis. In our cohort of subjects with SFN related to glucose dysregulation, we estimated 80% power to detect a significant difference in DCD between groups. This estimate was based on α = .05, assuming an anticipated 70% DCD in the SFN with glucose dysregulation compared with healthy controls, derived from a report of a comparably aged diabetic neuropathy cohort. ¹⁰

3. RESULTS

We included 89 individuals with SFN, of which 38 had glucose dysregulation (18 IGT, 20 type 2 diabetes). The non‐glucose dysregulation group had 51 individuals, 43 with idiopathic SFN and 8 with SFN due to other causes: monoclonal gammopathy of undetermined significance (n = 2), toxic (n = 3), vitamin B12 deficiency (n = 2) and human immunodeficiency virus (n = 1). We also included 20 controls.

Table 1 presents demographic data and clinical measures, including age, sex distribution, HbA1C levels, and sensory and neuropathic indices (RODS, ONLS, MTCNS, TCNS, CNFL, DCD), comparing individuals with glucose dysregulation SFN (n = 38), non‐glucose dysregulation SFN (n = 51) and healthy controls (n = 20).

TABLE 1.

Clinical characteristics stratified by glucose dysregulation classification.

Variable (mean (SD))	Glucose dysregulation SFN (n = 38)	Non‐glucose dysregulation SFN (n = 51)	Healthy control (n = 20)	p value (GD vs. NGD)	p value (GD vs. healthy control)	p value (NGD vs. healthy control)	p value (ANOVA)
Sex, (Male)	17 (44.74)	21 (41.18)	10 (50%)
Age (years)	59.45 (10.40)	55.98 (11.82)	41.3 (17.3)	.154			<.56
HbA1C	6.46 (0.90)	5.45 (0.26)	NaN (NA)	<.001	‐	‐	<.001
RODS	42.26 (7.81)	41.24 (6.73)	NaN (NA)	.567	‐	‐	.567
ONLS	1.16 (1.49)	1.05 (1.08)	NaN (NA)	.732	‐	‐	.732
MTCNS	10.39 (7.25)	12.66 (6.44)	NaN (NA)	.239	‐	‐	.239
TCNS	7.41 (4.03)	7.93 (3.95)	NaN (NA)	.552	‐	‐	.552
CNFL (mm/mm²)	5.21 (1.85)	4.58 (1.79)	12.53 (2.87)	.111	<.001	<.001	.105
DCDM (cells/mm²)	10.40 (8.45)	8.11 (8.47)	1.47 (1.90)	.211	<.001	<.001	<.001
DCDI (cells/mm²)	12.41 (13.42)	9.65 (11.74)	2.17 (3.35)	.306	<.001	.003	.002
DCDT (cells/mm²)	11.40 (9.39)	8.88 (9.00)	1.82 (2.16)	.203	<.001	<.001	<.001

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Abbreviations: CNFL, Corneal Nerve Fibre Length; DCDI, Dendritic Cell Density Immature; DCDM, Dendritic Cell Density Mature; DCDT, Dendritic Cell Density Total; GD, Glucose dysregulation; HbA1C, Haemoglobin A1C; LDI, Laser Doppler Imaging; MTCNS, Modified Toronto Clinical Neuropathy Score; NGD, non‐Glucose dysregulation; ONLS, Overall Neuropathy Limitations Scale; RODS, Rasch‐built Overall Disability Scale; TCNS, Toronto Clinical Neuropathy Score.

Both groups—SFN with and without glucose dysregulation—had significantly higher DCD than healthy controls (p‐value = <.001). There was no significant difference in DCD between the GD and no GD groups (p = .081, Figure 2).

Comparison of dendritic cell density (DCD) (cells/mm²) across glucose dysregulation classifications. Boxplots show median and interquartile ranges for subjects with SFN with glucose dysregulation (GD), SFN without glucose dysregulation and healthy controls. Outliers are represented by individual data points.

Table 2 shows significant differences in HbA1C levels between healthy controls and patients with IGT or type 2 diabetes, while other characteristics like age and sex did not differ significantly between the groups. In particular, individuals with IGT have significantly higher mature DCD levels compared with those with type 2 diabetes (p‐value = .045) (Figure 3). HbA1c showed a weak positive correlation with an r coefficient of 0.21, with mature DCD and total DCD (p‐values <.0001), but no significant correlation with other variables (RODS, ONLS, TCNS, mTCNS, CNFD, CNFL).

TABLE 2.

Clinical characteristics stratified by aetiology classification.

Variable (mean (SD))	Healthy control (n = 20)	IGT (n = 18)	Type 2 diabetes (n = 20)	Idiopathic (n = 43)	Other (n = 8)	IGT vs. type 2 diabetes	p‐value (ANOVA)
Sex, (Male)	10 (40%)	7 (38.9%)	10 (50%)	17 (39.53)	4 (50%)
Age (years)	41.3 (17.3)	59.11 (11.64)	59.75 (9.45)	54.72 (12.25)	62.75 (5.95)	0.948	.139
HbA1C (mean (SD)	5.5 (0.4)	5.88 (0.35)	6.97 (0.92)	5.43 (0.28)	5.58 (0.13)	<0.001	<.001
RODS	NaN (NA)	43.94 (8.41)	40.21 (6.76)	40.58 (6.83)	44.67 (5.43)	0.191	.264
ONLS	NaN (NA)	0.65 (0.79)	1.79 (1.89)	1.13 (1.12)	0.67 (0.82)	0.031	.070
mTCNS	NaN (NA)	9.64 (5.63)	11.08 (8.67)	13.00 (6.66)	10.50 (5.00)	0.643	.559
TCNS	NaN (NA)	6.59 (3.08)	8.10 (4.66)	7.92 (4.26)	8.00 (2.14)	0.343	.646
CNFL (mm/mm²)	12.53 (2.87)	4.91 (1.70)	5.49 (1.97)	4.59 (1.83)	4.52 (1.64)	0.206	.310
DCDM (cells/mm²)	1.47 (1.90)	13.28 (9.86)	7.81 (6.09)	8.07 (8.19)	8.35 (10.47)	0.045	.136
DCDI (cells/mm²)	2.17 (3.35)	13.69 (12.86)	11.25 (14.13)	10.06 (12.37)	7.46 (7.74)	0.496	.641
DCDT (cells/mm²)	1.82 (2.16)	13.49 (9.80)	9.53 (8.83)	9.06 (9.15)	7.91 (8.66)	0.168	.325

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Abbreviations: CNFL, Corneal Nerve Fibre Length; DCDI, Dendritic Cell Density Immature; DCDM, Dendritic Cell Density Mature; DCDT, Dendritic Cell Density Total; HbA1C, Haemoglobin A1C; LDI, Laser Doppler Imaging; MTCNS, Modified Toronto Clinical Neuropathy Score; ONLS, Overall Neuropathy Limitations Scale; RODS, Rasch‐built Overall Disability Scale; TCNS, Toronto Clinical Neuropathy Score.

Boxplots comparing dendritic cell density (DCD) (cells/mm²) across aetiology groups: IGT and Type 2 Diabetes. DCDM represents the density of mature dendritic cells measured in the study participants. Outliers are represented by individual data points.

There was no significant difference in mean DCD differences across the mild, moderate and severe neuropathy severity groups, as indicated by p‐values of .48, .19 and .19 for each comparison.

4. DISCUSSION

The main finding of our study is that patients with SFN and glucose dysregulation exhibited a significant increase in corneal DCD compared with healthy controls. Additionally, other acquired types of SFN also demonstrated elevated corneal DCD. Importantly, there was no difference in DCD between SFN patients with and without glucose dysregulation. Notably, patients with IGT have higher levels of mature DCs than those with type 2 diabetes and healthy individuals.

Our study demonstrated that increased DCD is a common phenomenon across various acquired causes of SFN and is significantly higher compared with healthy controls. This finding aligns with the idea that the presence of DCD is an immune response to nerve damage, independent of its cause. Previous studies have also demonstrated the presence of DCD in infections and immune neuropathies. ²⁵ , ²⁶

We described elevated DCD in patients who met diagnostic criteria for SFN and glucose dysregulation. Our findings are consistent with other studies in diabetic patients without confirmed neuropathy, which also showed increased corneal DCD compared with healthy controls. ⁹ , ¹⁰ , ¹¹ Our finding of increased DCD in IGT and SFN contrasts with a study on pre‐diabetes that did not show an increase in DCD. ²⁷ This discrepancy may be because we defined IGT status using a 2‐hour GTT, whereas the previous study based its subject grouping on HbA1c testing, which is less sensitive.

We did not find a correlation between the clinical severity scoring (TCNS, mTCNS, ONLS, RODS) and DCD. However, there was a weak positive correlation between glycaemic control (HbA1c) and DCD. This lack of correlation between neuropathy measures and DC parameters might suggest that DCs are an epiphenomenon related to early nerve regeneration rather than nerve damage. Alternatively, the number of patients may not have allowed the discovery of any relationship. Future studies with larger cohorts are recommended to explore these relationships further.

We hypothesize that corneal DCs may play a role in nerve repair during the early stages of dysglycaemia, such as IGT, by modulating neuroinflammation. In this phase, an increased presence of mature DCs likely supports nerve repair mechanisms. ²⁸ However, as neuronal damage progresses, a reduction in DC numbers may impair the corneal nerve repair process, potentially exacerbating neuropathy due to diminished regenerative capacity, as observed in animal models. ²⁹

Our study has limitations, including sample size and measurement error. Elevated DCD can be influenced by other factors, such as allergic conjunctivitis, keratitis, dry eyes and contact lens wear. ²⁸ , ³⁰ , ³¹ As our study is retrospective, we cannot account for these potential confounding factors, which should be considered in future research.

Future research should focus on large‐scale retrospective, observational and prospective studies to precisely characterize corneal DCs in humans, examining their density, distribution and correlation with nerve damage across various stages of dysglycaemia (IGT, early diabetes, advanced diabetes). These studies should also account for other co‐variables that may impact outcomes, including medication use, ³² aging ³³ and kidney function. ³⁴ Collectively, these factors would provide crucial insights into the specific role of DCs in SFN. Subsequently, clinical trials could investigate the potential of using corneal DCs as biomarkers to identify patients who may benefit from disease‐modifying therapies aimed at enhancing nerve repair. ³⁵

In conclusion, elevated DCD potentially plays a significant role in IGT and early stages of neuropathy, serving as a potential biomarker for early neuropathic phases with neuroprotective or therapy‐responsive implications. Our data indicate that higher mature DCD levels are significant in SFN with glucose dysregulation, suggesting a common immune‐mediated mechanism for neuropathy in these individuals.

CONFLICT OF INTEREST STATEMENT

Juan Francisco Idiaquez Rios has no conflicts of interest to declare. Carolina Barnett has received honoraria for consulting and/or advisory boards for Sanofi, Alexion and Argenx. She has received research grants from Grifols and Octapharma, not related to this work. BAP has received honoraria for educational events from Medtronic, Novo Nordisk, Sanofi, Insulet and Abbott. His research institute has received funding from BMO Bank of Montreal and Novo Nordisk for research support. He has served as an advisor to Boehringer Ingelheim, Sanofi, Insulet, Abbott, Nephris and Vertex. Vera Bril has received fees for consultancy from Grifols, CSL, UCB, Argenx, Takeda, Alnylam Octapharma, Pfizer, Powell Mansfield Inc., Akcea, Ionis Immunovant, Sanofi, Momenta (J&J), Roche, Janssen, AZ‐Alexion, NovoNordisk and research support: AZ‐Alexion, Grifols, CSL, UCB, Argenx, Takeda, Octapharma, Akcea, Momenta (J&J), Immunovant, Ionis. No funding was received for the publication of this article.

Idiaquez JF, Barnett‐Tapia C, Perkins BA, Bril V. Assessing corneal dendritic cells in glucose dysregulation small‐fibre neuropathy. J Peripher Nerv Syst. 2024;29(4):400‐405. doi: 10.1111/jns.12671

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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35. Ponirakis G, Al‐Janahi I, Elgassim E, et al. Glucose‐lowering medication associated with weight loss may limit the progression of diabetic neuropathy in type 2 diabetes. J Peripher Nerv Syst. 2024;29(4):406‐414. doi: 10.1111/jns.12664 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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PERMALINK

Assessing corneal dendritic cells in glucose dysregulation small‐fibre neuropathy

Juan Francisco Idiaquez

Carolina Barnett‐Tapia

Bruce A Perkins

Vera Bril