Prediction of Diabetic Neuropathy in Patients with Type 2 Diabetes Mellitus by Using Monocyte to High-Density Lipoprotein Cholesterol Ratio

Abstract

Background:

Diabetic peripheral neuropathy (DPN) is one of the most common and debilitating microvascular complications of type 2 diabetes mellitus (T2DM) with complex and multifactorial pathogenesis involving chronic hyperglycemia, oxidative stress, and sustained low-grade inflammation. Traditional biochemical markers often fall short in early identification of DPN. Emerging evidences suggest that inflammatory markers such as the monocyte-to-high-density lipoprotein cholesterol ratio (MHR) may serve as a novel biomarker reflecting systemic inflammation and oxidative stress.

Methods:

This was a case–control study with 600 patients with T2DM divided into two groups: 300 cases with diabetic neuropathy and 300 diabetic controls without neuropathy. Inclusion criteria were age between 25 and 75 years, diagnosis of T2DM for at least 5 years, and confirmed neuropathy for cases. Exclusion criteria included age outside the defined range, diagnosis of type 1 DM, T2DM duration under 5 years, patients with active infections, and neuropathy due to secondary causes. The demographic as well as laboratory data and inflammatory ratios such as MHR, monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR) were analyzed and compared between the two groups.

Results:

Patients with DPN had significantly higher MHR (10.89 ± 7.17) and MLR (0.21 ± 0.11) compared with controls (MHR: 7.06 ± 4.30; MLR: 0.14 ± 0.08; P < 0.001). No significant differences were observed in NLR and PLR. Insulin and statin use were more frequent in the neuropathy group, likely reflecting longer disease duration or poorer glycemic control. Age was an important risk factor, consistent with cumulative metabolic and vascular damage contributing to neuropathy development. Traditional markers such as low-density lipoprotein cholesterol (LDL-C) and glycated hemoglobin (HbA1c) did not differ significantly between groups.

Conclusions:

The significant elevation of MHR and MLR in patients with neuropathy, in contrast to the lack of significant difference in more conventional biochemical markers such as LDL-C and HbA1c, underscores a shift toward recognizing immune-inflammatory dysregulation as a central mechanism in neuropathic progression. These markers may complement existing tools to improve early detection and risk stratification of DPN, especially in resource-limited settings.

Keywords

diabetes mellitus diabetic peripheral neuropathy biochemical markers monocyte-to-HDL cholesterol ratio monocyte-to-lymphocyte ratio neutrophil-to-lymphocyte ratio

Introduction

Diabetes has emerged as a global health crisis in the 21st century, ranking among the top 10 leading causes of death along with cardiovascular diseases (CVD), respiratory illnesses, and cancer.¹ Type 2 diabetes mellitus (T2DM) is marked by high blood glucose levels with decrease in responsiveness to insulin and a subsequent decline in insulin production. The pathophysiology of this disease is complexly characterized and is yet to be understood completely. It is influenced by both genetic variables that affect insulin secretion and response and environmental factors such as obesity and lifestyle. As stated in the 11th edition of International Diabetes Federation Diabetes Atlas, approximately 588.7 million adults worldwide were living with diabetes in 2024 and the number is expected to rise to 852.5 million by 2050.² In the Southeast Asian region, 106.9 million people have diabetes, with 89.8 million living in India alone.² T2DM is a chronic condition that progressively worsens and affects an individual’s well-being negatively. The disease progression occurs due to development of associated micro- and macrovascular complications. Diabetic neuropathy is a chronic microvascular complication of diabetes. The severity of symptoms is closely linked with the degree of glycemic control, with poorly controlled levels leading to earlier onset of disease symptoms. It is proposed that of the projected 9.7 billion people living in 2050, nearly a third of the population will have diabetes, with half of them experiencing neuropathy.³ The research on diabetes-related complications has gathered considerable interest and diabetic neuropathy, being the most commonly occurring complication worldwide, necessitates public health intervention to effectively mitigate the growing burden. As per the Indian Council of Medical Research- India Diabetes (ICMR-INDIAB) national cross-sectional study, conducted between October 18, 2008, and December 17, 2020 (ICMR-INDIAB-17), the weighted prevalence of diabetes in India was 11.4% and that of prediabetes was 15.3%.⁴ The prevalence of diabetic neuropathy is anywhere from 18.8% to 61.9% in India and 6% to 51% in the United States and Europe depending on the population studied.^5,6 Diabetic neuropathy is described as the occurrence of neuropathic symptoms and signs in a person with diabetes, after excluding all other potential causes of neuropathy. Distal symmetric neuropathy is the most common type of neuropathy accounting for 75% of total diabetic neuropathy cases.⁷ Treatment of diabetic neuropathy focuses on optimizing high blood sugar levels as well as addressing associated hypertension and dyslipidemia along with lifestyle modification (exercise and diet), analgesics, antidepressants, and anticonvulsants.

The monocyte-to-high-density lipoprotein cholesterol (MHR) ratio serves as a marker of inflammation and oxidative stress that highlights the proinflammatory activity of monocytes and anti-inflammatory and antioxidant properties of high-density lipoprotein cholesterol (HDL-C).⁸ MHR is linked to the development and prognostication of CVD, diabetic nephropathy, and diabetic retinopathy. Numerous studies have used this marker to investigate the role of inflammation and atherosclerosis in the development, onset, and progression of CVD and cerebrovascular disease.⁹ However, the role of MHR has not yet been fully investigated in the pathogenesis of diabetic peripheral neuropathy (DPN) despite ischemia and inflammation being widely recognized as contributing factors in its development. MHR reflects inflammation and oxidative stress, based on the proinflammatory role of monocytes and the anti-inflammatory and antioxidant effects of HDL-C.⁹ Monocytes arise from precursor cells in the bone marrow that are further released into the bloodstream from where they can migrate to various tissues and produce proinflammatory cytokines at the sites of inflammation.¹⁰ This process affects the severity of inflammation, and therefore, MHR serves as a useful marker of systemic inflammation and is associated with the progression of chronic inflammatory diseases.¹¹ However, to date, only a limited number of studies have explored the association between MHR and DPN. These studies have produced conflicting results, thus leaving this sphere inconclusive warranting further research. Hence, we planned this study to see the association between MHR and diabetic neuropathy.

Materials and Methods

This hospital-based observational case–control study was conducted over 1 year at a tertiary care teaching hospital in north India following approval from the Institutional Ethics Committee (Letter No-SNMC/IEC/2024/237). Written and informed consent was obtained from all participants, and all personal data were kept confidential. The study included 600 participants: 300 patients with T2DM with confirmed diabetic neuropathy as cases and 300 patients with T2DM without neuropathy as controls, selected based on specific inclusion and exclusion criteria. We diagnosed diabetic neuropathy based on a combination of clinical symptoms and signs suggestive of peripheral nerve involvement. The assessment included evaluation of sensory disturbances (such as numbness, tingling, or burning sensation), diminished or absent ankle reflexes, and reduced vibration perception. Standardized diagnostic tools such as the Michigan Neuropathy Screening Instrument or biothesiometry were not employed in this study due to resource and logistical constraints. The diagnosis was made through clinical examination and relevant patient history, as routinely practiced in our setting. Inclusion criteria were age between 25 and 75 years, diagnosis of T2DM for at least 5 years, and confirmed neuropathy for cases. Exclusion criteria included age outside the defined range, diagnosis of type 1 DM, T2DM duration under 5 years, neuropathy due to secondary causes (e.g., known neurological condition, neuropathy due to drugs such as linezolid, isoniazid, dapsone, metronidazole, amiodarone, cisplatin; alcohol; tobacco; heavy metals such as arsenic, lead, mercury), active infections, and refusal to give consent. Data collected included age, diabetes duration, medication status (including insulin and statin use), comorbidities such as hypertension, and neuropathy symptoms. Laboratory evaluations were performed for all participants, including complete blood count using a five-part hematology analyzer (Medonic M51) and other tests such as lipid profile (total cholesterol, HDL, LDL, and triglycerides), glycated hemoglobin (HbA1c) (measured via ion exchange high-performance liquid chromatography), and serum creatinine on the Beckman Coulter D×C 700 AU analyzer. Inflammatory markers such as the MHR, monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR) were calculated and compared between the two groups.

Statistical analysis

We analyzed the data using the statistical package Statistical Package for the Social Sciences, IBM version 29.0. Continuous variables were tested for normality using the Kolmogorov–Smirnov test. We calculated the descriptive measures such as mean and standard deviation with 95% confidence limits for normally distributed data. As appropriate, we compared mean values using the Student’s t-independent test or o-way analysis of variance test. Categorical data were presented as frequency and percentage values. We used the Chi-square test to compare the frequency data across the categories. A bivariate logistic regression analysis was carried out to assess the significant variables that will predict the outcome. The odds ratio with 95% confidence limits was calculated for the statistically significant variables. To determine the cutoff value for the significant predictor, receiver operating characteristic (ROC) analysis was performed, and using the Yuden index criteria, the cutoff value was determined for an optimum sensitivity and specificity level. A two-tailed probability of P < 0.05 was considered statistically significant for all the statistical tests.

Results

The study enrolled 600 patients with T2DM divided into two groups: 300 cases with diabetic neuropathy and 300 controls without neuropathy. The demographic as well as laboratory data and inflammatory ratios such as MHR, MLR, PLR, and NLR were analyzed and compared between the two groups as discussed below.

Gender and age distribution

Out of a total of 600 patients evaluated in the study, the distribution of neuropathy and non-neuropathy cases across gender revealed a marginal difference. Among the cases, 51.0% were male (n = 153) and 49.0% were female (n = 147). Conversely, in the control group, a higher proportion of males (59.0%, n = 177) was observed compared with females (41.0%, n = 123). The distribution of age among the cases and controls revealed a clear age-related trend (Fig. 1). The majority of neuropathy cases (56.0%) were observed in patients aged above 55 years, followed by 37.3% in the 41–55 years group. In contrast, among non-neuropathy patients (controls), the highest proportion belonged to the 41–55 years group (40.7%), and a notably lower proportion (39.7%) belonged to the >55 years category. Interestingly, only 6.3% of neuropathy cases occurred in patients aged 26–40 years. Patients under 25 years comprised a very small fraction in both groups.

FIG. 1.

Age distribution among cases and controls.

Hypertension

In our study, 140 people in the case group and 136 in the control group had hypertension with a mean duration of 3.5 years among cases and 3.4 years among controls. The prevalence and mean duration of diabetes were comparable between both the groups without significant statistical difference.

Comparison of duration of diabetes

A statistically significant difference in the mean duration of diabetes was observed between patients with and without diabetic neuropathy (10.16 ± 2.47 years vs. 7.74 ± 1.46 years, P < 0.001) (Table 1). The independent samples t-test (t = 14.565) indicated that patients with neuropathy had a longer duration of diabetes compared with those without neuropathy.

Table 1.

Mean Duration of Diabetes Between Cases and Controls

Group	N	Mean (years)	Standard deviation	t-Test	P-value
Neuropathy	300	10.16	2.47	14.565	<0.001
Non-neuropathy	300	7.74	1.46

Insulin and statin usage

Among the 600 patients evaluated, insulin and statin use were more common in patients with diabetic neuropathy compared with those without (Table 2). Specifically, 17.7% of patients with neuropathy were on insulin therapy, while only 9.3% of non-neuropathy patients reported insulin use. Statin use was more frequently observed among patients with diabetic neuropathy (22.3%) compared with those without neuropathy (15.3%). Conversely, a higher proportion of patients not on statin therapy was seen in the non-neuropathy group (84.7%) relative to the neuropathy group (77.7%).

Table 2.

Insulin and Statin Usage

	Group
	Neuropathy		Non-neuropathy
	Count	%	Count	%
Insulin
Yes	53	17.7	28	9.3
No	247	82.3	272	90.7
Statin
Yes	67	22.3	46	15.3
No	233	77.7	254	84.7

Comparison of biochemical parameters

Hemoglobin and total leukocyte count

The mean hemoglobin level in the neuropathy group was 12.65 ± 7.19 g/dL, while in the non-neuropathy group it was 12.96 ± 7.23 g/dL. The independent samples t-test showed no statistically significant difference between the groups (t = −0.514, P = 0.607). For total leukocyte count, the mean value in the neuropathy group was 7696.20 ± 2361.34/µL, compared with 7634.83 ± 2499.84/µL in the non-neuropathy group. Again, the difference was not statistically significant (t = 0.309, P = 0.757) (Table 3).

Table 3.

Comparison of Biochemical Parameters Between Cases and Controls

Parameter	Group	N	Mean	Standard deviation	t-Test	P-value
Hemoglobin (g/dL)	Neuropathy	300	12.65	7.19	−0.514	0.607
	Non-neuropathy	299	12.96	7.23
Total leukocyte count	Neuropathy	300	7696.20	2361.34	0.309	0.757
	Non-neuropathy	300	7634.83	2499.84
Monocyte count (/µL)	Neuropathy	300	433.30	216.96	8.076	<0.0001
	Non-neuropathy	300	303.81	173.33
Neutrophil count (/µL)	Neuropathy	300	4815.98	1883.86	−0.407	0.684
	Non-neuropathy	300	4884.04	2195.38
Lymphocyte count (/µL)	Neuropathy	300	2209.92	806.94	−0.565	0.572
	Non-neuropathy	300	2247.14	806.23
Platelet count (/µL)	Neuropathy	300	228170.00	95653.30	1.374	0.170
	Non-neuropathy	300	217743.33	90107.09
HbA1c (%)	Neuropathy	300	8.43	1.93	1.905	0.057
	Non-neuropathy	300	8.14	1.84
Creatinine (mg/dL)	Neuropathy	300	0.97	0.40	−0.658	0.511
	Non-neuropathy	300	1.12	4.12
Total cholesterol (mg/dL)	Neuropathy	300	189.82	72.46	0.982	0.326
	Non-neuropathy	300	184.81	50.55
Triglycerides (mg/dL)	Neuropathy	300	190.76	121.29	−1.163	0.245
	Non-neuropathy	300	202.85	132.97
HDL (mg/dL)	Neuropathy	300	43.68	11.27	−1.556	0.120
	Non-neuropathy	300	45.11	11.13
LDL (mg/dL)	Neuropathy	300	112.42	42.09	−0.169	0.866
	Non-neuropathy	300	113.05	48.83

HbA1c, glycated hemoglobin; HDL, high-density lipoprotein; LDL, low-density lipoprotein.

Comparison of inflammatory cell counts

The mean monocyte count was significantly higher in the neuropathy group (433.30 ± 216.96) compared with the non-neuropathy group (303.81 ± 173.33), with a statistically significant difference (t = 8.076, P < 0.001). In contrast, no statistically significant differences were observed in neutrophil counts, lymphocyte counts, or platelet counts between the two groups. The mean neutrophil count in the neuropathy group was 4815.98 ± 1883.86, while it was 4884.04 ± 2195.38 in the non-neuropathy group (t = −0.407, P = 0.684). Similarly, lymphocyte counts were comparable between groups (2209.92 ± 806.94 vs. 2247.14 ± 806.23, t = −0.565, P = 0.572). The mean platelet count was slightly higher in the neuropathy group (228,170 ± 95653.30) compared with the non-neuropathy group (217743.33 ± 90107.09), but the difference was not statistically significant (P = 0.170) (Table 3).

Comparison of HbA1c and lipid profile

The mean HbA1c in the neuropathy group was 8.43 ± 1.93, while in the non-neuropathy group it was 8.14 ± 1.84. This difference approached statistical significance (t = 1.905, P = 0.057) but did not cross the conventional threshold of 0.05. Serum creatinine levels were comparable between groups, showing no significant difference (P = 0.511). No statistically significant differences were observed in total cholesterol, triglycerides, HDL, or LDL levels between the neuropathy and non-neuropathy groups (all P-values >0.1) (Table 3).

Comparison of inflammatory ratios: MHR, MLR, PLR, and NLR

The mean MHR in patients with diabetic neuropathy was 10.89 ± 7.17, compared with 7.06 ± 4.30 in the non-neuropathy group (Fig. 2a). This difference was found to be statistically significant with a t-value of 7.937 and a P-value of <0.001. The data indicate a higher MHR among patients with neuropathy, reflecting a marked variation between the two groups in terms of this inflammatory and oxidative stress marker. The mean MLR in patients with neuropathy was 0.21 ± 0.11, while the non-neuropathy group had a mean MLR of 0.14 ± 0.08 (Fig. 2b). The difference between the groups was statistically significant, with a t-value of 8.154 and a P-value of <0.001. The PLR was assessed across both study groups. In the neuropathy group, the mean PLR was 113.73 ± 62.52, while the non-neuropathy group recorded a mean of 111.76 ± 83.35 (Fig. 2c). The difference between the two groups was not statistically significant, as indicated by a t-value of 0.329 and a P-value of 0.742. Although the mean NLR was slightly higher in the case group, it showed no statistically significant difference between neuropathy (mean = 2.44 ± 1.38) and non-neuropathy (mean = 2.38 ± 1.25) groups (P = 0.616) (Figure 2d). These findings are summarized in Table 4.

FIG. 2.

(a, b, c, and d) Box plots with mean and standard deviation of inflammatory markers.

Table 4.

Summary of Comparison of Inflammatory Markers

Parameter	Group	N	Mean	Standard deviation	t-test	P-value
MHR	Neuropathy	300	10.89	7.17	7.937	0
MHR	Non-neuropathy	300	7.06	4.3
MLR	Neuropathy	300	0.21	0.11	8.154	0
MLR	Non-neuropathy	300	0.14	0.08
PLR	Neuropathy	300	113.73	62.52	0.329	0.742
PLR	Non-neuropathy	300	111.76	83.35
NLR	Neuropathy	300	2.44	1.38	0.502	0.616
NLR	Non-neuropathy	300	2.38	1.25

As per the analysis described above, we observed a significant association of higher age and use of insulin and statins with diabetic neuropathy. A positive association between MHR and diabetic neuropathy was observed, which was the primary aim of the study. Also, another inflammatory marker MLR showed a positive association with neuropathy in patients with T2DM; however, markers such as PLR and NLR showed no statistically significant association with neuropathy in T2DM.

Discussion

We did a literature search of various studies to determine the association between MHR and the chronic complications of diabetes, which is summarized in Table 5 and discussed subsequently. The objectives of our study were to determine the potential role of hematological inflammatory markers with a particular focus on the MHR, in predicting DPN among patients with T2DM. A total of 600 patients were evaluated, of which 300 were clinically diagnosed with DPN taken as cases and 300 diabetics without neuropathy as controls.

Table 5.

Summary of Studies to Determine Association Between MHR and the Chronic Complications of Diabetes

Author (year)	Study focus	Population	Key findings	Conclusion
Canpolat et al. (2019)⁸	MHR and DPN	180 patients with T2DM	No significant MHR difference between groups (P = 0.447); CRP and diabetes duration were predictors.⁸	MHR not predictive of DPN.
Onalan et al. (2018)⁹	MHR and diabetic nephropathy	262 T2DM (60 with, 202 w/o nephropathy), 50 controls	MHR significantly higher in patients with nephropathy; negative correlation with HDL.⁹	MHR may be a low-cost marker for diabetic nephropathy.
Vural et al. (2018)¹²	MHR and diabetic polyneuropathy	67 healthy, 92 T2DM w/o DPN, 75 non-DPN, 90 with DPN	MHR significantly higher in DPN; inversely related to CMAP amplitudes.¹²	High MHR may predict DPN risk.
Tang et al. (2021)¹³	MHR and diabetic retinopathy (DR)	771 T2DM, 607 healthy controls	MHR higher in patients with DR; independent predictor of DR, unaffected by severity.¹³	MHR may serve as a biomarker for DR.
Mayasari et al. (2021)¹⁴	MHR and arterial stiffness (CAVI)	81 patients with T2DM	Positive correlation between MHR and CAVI ≥9; MHR cutoff ≥13 predictive of stiffness.¹⁴	MHR independently associated with arterial stiffness in diabetes.
Li et al. (2021)¹⁵	MHR in T2DM with NSTE-ACS undergoing PCI	1045 patients with T2DM	High MHR associated with increased MACE, bleeding, and mortality; MHR >0.022 predictive for MACE.¹⁵	MHR is a strong prognostic marker for adverse outcomes post-PCI.
Amouzegar et al. (2022)¹⁶	MHR and carotid intima-media thickness (CIMT)	118 T2DM, 126 non-diabetics	MHR associated with CIMT only in diabetic males; no groupwide significance.¹⁶	Gender-specific association between MHR and CIMT in T2DM.
Calli et al. (2022)¹⁷	MHR and DR	45 DR, 45 T2DM w/o DR, 45 healthy	Higher MHR in DR group; significant difference from controls.¹⁷	MHR may act as inflammatory biomarker in DR.
Wu et al. (2022)¹⁸	Cumulative MHR (CumMHR) and T2DM risk	40,813 non-diabetics (Kailuan cohort)	High CumMHR predicted T2DM risk, especially with low hsCRP; effect modified by lifestyle and metabolic factors.¹⁸	CumMHR may aid in early risk identification and prevention of T2DM.

CRP, C-reactive protein; hsCRP, high sensitive CRP; DPN, diabetic peripheral neuropathy; MHR, monocyte-to-high-density lipoprotein ratio; T2DM, type 2 diabetes mellitus; CAVE, cardio-ankle vascular index; PCI, percutaneous coronary intervention; CMAP, compound muscle action potential; MACE, major adverse cardiac event.

Gender and age distribution

The present study observed a relatively balanced gender distribution among patients with diabetic neuropathy, with a slight male predominance (51%). The majority of neuropathy cases (56.0%) were observed in patients aged above 55 years. Age correlates positively with markers of systemic inflammation and immuno-senescence, which could amplify the inflammatory processes underlying DPN. As demonstrated by Wu et al., the cumulative MHR increases with age and predicts future T2DM risk, suggesting that inflammatory markers such as MHR may also accumulate and exert neurotoxic effects over time.¹⁸

Duration of diabetes and diabetic neuropathy

In the present study, a statistically significant difference was found in the mean duration of diabetes between patients with and without diabetic neuropathy. Patients with neuropathy had a longer mean disease duration (10.16 ± 2.47 years) than those without neuropathy (7.74 ± 1.46 years), with a P-value <0.001. This result affirms the well-established association between prolonged hyperglycemia and the development of DPN, a relationship consistently highlighted in the literature; for example, a study by Rani et al., a large-scale population-based study involving 1401 individuals with T2DM, demonstrated a prevalence of 18.84% for diabetic neuropathy, with increasing age and duration of diabetes identified as significant risk factors for all severity categories of neuropathy.¹⁹ The relationship between disease duration and neuropathy was also substantiated by Mishra et al., who reported that patients with neuropathy had a mean diabetes duration of 10.9 years compared with 5.6 years among those without neuropathy.²⁰ At the molecular level, chronic hyperglycemia is known to trigger the production of advanced glycation end-products, reactive oxygen species, and proinflammatory cytokines, leading to endothelial dysfunction, ischemia, and direct neuronal toxicity.²¹ Furthermore, the microvascular complications of diabetes, including neuropathy, retinopathy, and nephropathy, often co-evolve, highlighting the shared inflammatory and oxidative pathways underpinning their pathogenesis.^22,23

Insulin and statin use

In this study, insulin use was more frequent among patients with diabetic neuropathy compared with those without, suggesting a possible link between insulin therapy and neuropathy risk. While insulin itself is not directly neurotoxic, its use often reflects longer disease duration or poorer glycemic control, both of which are established contributors to microvascular complications. This is indirectly supported by Zhang et al., who emphasized the mediating role of HbA1c in the relationship between LDL-C and diabetic neuropathy.²⁴ Their findings highlight the cumulative impact of prolonged hyperglycemia on nerve health, and the higher proportion of insulin users in the neuropathy group may be a surrogate for such chronic metabolic stress. Furthermore, the lack of a statistically significant difference in HbA1c in our study, despite this trend, suggests that other inflammatory factors such as MHR may operate independently of glycemic status.

With respect to statin use, a higher proportion of patients with neuropathy were on statin therapy compared with the non-neuropathy group. Although statins are primarily prescribed to manage dyslipidemia and reduce cardiovascular risk, their relationship with neuropathy remains complex. In the context of present study, the increased use of statins may reflect a clinical attempt to manage dyslipidemia and higher cardiovascular risk rather than a causal factor in neuropathy development.

Insulin resistance in individuals with T2DM has shown to raise levels of inflammatory markers such as MHR, NLR, MLR, and PLR.^25,26 By improving glycemic control and lipid metabolism, exogenous insulin may help reduce inflammation, lowering MHR and other inflammatory markers. Statins are known for their pleiotropic effects, including anti-inflammatory, antioxidant, and endothelial-healing properties.²⁷ In clinical practice, reductions in inflammatory markers such as NLR and MHR may indirectly reflect these effects. While some studies, for example, by Gungoren et al., found statins to be ineffective in lowering NLR, others, such as by Akın et al. and Tuncez, observed significant reductions in NLR with statin use in hypercholesterolemic patients.^27–29 Statins have also shown to increase HDL-C and decrease MHR as reported by Nissen et al. and Tuncez; however, they were ineffective on PLR level.^27,30 This suggests that the use of insulin and statins may prove beneficial for patients with DPN in the long run.

Hematological parameters: Hemoglobin, total leukocyte count, and platelet count

Hemoglobin levels, total leukocyte counts, and platelet counts did not show statistically significant differences between neuropathy and non-neuropathy groups. These findings suggest that generalized anemia or total systemic leukocyte burden may not be directly associated with the development of diabetic neuropathy. Instead, more specific inflammatory indices, such as cell-based ratios, may offer better discriminatory value in microvascular complication risk stratification. This is consistent with observations by Wu et al., who reported that MHR was a more sensitive marker of early inflammatory burden in patients with developing metabolic disease, even in the absence of overt elevations in common inflammatory parameters such as hsCRP or total leukocyte count.¹⁸

Biochemical parameters: HbA1c, serum creatinine, and lipid profile

Although HbA1c levels were higher in the neuropathy group, the difference did not reach statistical significance. Similarly, neither LDL-C nor other lipid profile components including total cholesterol, triglycerides, and HDL-C were significantly different between the neuropathy and non-neuropathy groups. Serum creatinine values were also comparable between groups. While creatinine is often elevated in diabetic nephropathy, it is less directly related to peripheral neuropathy, and the lack of difference here is not unexpected.

Inflammatory cell counts and ratios: Monocytes, MHR, MLR, PLR, and NLR

Monocyte count was significantly elevated in patients with diabetic neuropathy, supporting the role of innate immune activation in the pathogenesis of microvascular complications. Monocytes contribute to chronic inflammation through the release of proinflammatory cytokines, which can impair endothelial function and promote oxidative stress within peripheral nerves. In contrast, no statistically significant differences were observed in neutrophil counts or lymphocyte counts between the two groups.

In this study, the MHR was significantly higher in the neuropathy group, reinforcing its association with diabetic microvascular complications. This finding is consistent with the work of Tang et al., who found that MHR was significantly elevated in patients with diabetic retinopathy and remained an independent predictor even after adjusting for glycemic control, lipid profile, and duration of diabetes.¹³ Similarly, Erdem and Kaya demonstrated that MHR was significantly higher in diabetic patients with retinopathy compared with those without and reported a threshold MHR value above which the risk of retinopathy sharply increased.³¹ Notably, the mean MHR observed in this study closely matches the cutoff values suggested in their ROC analysis, lending further support to the clinical applicability of MHR in neuropathy risk stratification. The elevation of MHR was also supported by findings from Onalan et al., who reported significantly higher MHR values in patients with diabetic nephropathy compared with those without.⁹ Taken together, these studies confirm that MHR serves as a unifying biomarker across multiple microvascular complications, including retinopathy, nephropathy, and neuropathy, reflecting a systemic proinflammatory state that contributes to progressive tissue damage.

The MLR also showed a significant increase in the neuropathy group, further highlighting the imbalance between proinflammatory monocytes and regulatory lymphocytes. This ratio has not been as extensively studied as MHR in diabetic neuropathy; however, its significance in this context may reflect heightened immune activation and relative immunosuppression, both of which have been implicated in microvascular dysfunction. While MLR was not directly addressed in the reviewed literature, its mechanistic basis is consistent with the inflammatory profiles described in the studies in which innate immunity and endothelial injury played central roles.

In contrast, the PLR did not differ significantly between groups. This finding is consistent with the results of Gokçay Canpolat et al., who also found no significant association between PLR and DPN.⁸ The lack of statistical significance in PLR may reflect its lower specificity for microvascular inflammation in the diabetic context or population-related variability in platelet dynamics and lymphocyte counts. The NLR was evaluated as a potential inflammatory marker for DPN. Although the mean NLR was marginally higher in the neuropathy group (2.44 ± 1.38) compared with the non-neuropathy group (2.38 ± 1.25), this difference was not statistically significant (P = 0.616). These findings suggest that, unlike other inflammatory ratios such as the MHR or MLR, the NLR may not be a reliable indicator of neuropathy in patients with T2DM within this cohort. This result diverges from the broader trend in literature, where NLR has been widely recognized as a nonspecific marker of systemic inflammation and has shown prognostic value in various chronic diseases, including cardiovascular disorders, malignancies, and diabetic complications such as nephropathy and retinopathy.^32,33

The absence of a significant association in the present study is consistent with findings by Gokçay Canpolatet al., who reported that while MHR and MLR were elevated in diabetic patients with neuropathy, NLR and PLR did not consistently correlate with the presence of neuropathy.⁸ Similarly, Tang et al. and Vural et al. emphasized the superior predictive utility of MHR over NLR, attributing this to the dual role of monocytes in oxidative stress and microvascular injury, whereas neutrophils are more indicative of acute-phase reactions rather than chronic inflammation.^12,13

Summary of key discussion points

MHR and MLR were significantly higher in patients with diabetic neuropathy, indicating a strong association between systemic inflammation and neuropathic complications.

Age was an important risk factor, consistent with cumulative metabolic and vascular damage contributing to neuropathy development.

Insulin use was more common in neuropathy patients, likely reflecting longer disease duration or poorer glycemic control.

Traditional biochemical markers, including HbA1c and LDL-C, were not significantly different between groups, emphasizing the superior sensitivity of inflammatory cell-based ratios.

Findings are strongly supported by multiple studies (Tang et al., Wu et al., Onalan et al., Erdem & Kaya, Zhang et al.) linking MHR to diabetic microvascular complications.

MHR and MLR offer potential as cost-effective, easily accessible biomarkers for early identification of patients at high risk for neuropathy, particularly in resource-limited settings.

Clinical implications

The findings of this study reinforce the evolving understanding of DPN as not merely a consequence of hyperglycemia but as a chronic inflammatory and microvascular disorder. The significant elevation of MHR and MLR in patients with neuropathy reflects a systemic inflammatory milieu, which plays a pivotal role in the initiation and progression of neural damage in diabetes. Monocytes are known to infiltrate vascular endothelium and peripheral nerve tissue, releasing proinflammatory cytokines such as TNF-α and IL-6, which contribute to oxidative stress and endothelial dysfunction.⁸ At the same time, HDL-C, which normally exhibits anti-inflammatory, antioxidant, and vasoprotective properties, is often reduced in both quantity and functionality in diabetic patients. The MHR thus serves as a composite marker of proinflammatory burden versus anti-inflammatory capacity, capturing a dynamic imbalance that contributes to microvascular injury and neural ischemia.

From a clinical standpoint, both MHR and MLR can be derived from routine, inexpensive blood tests, making them highly accessible tools for early identification of patients at risk for DPN. This is particularly valuable in settings where specialized diagnostic modalities such as nerve conduction studies are not readily available. Moreover, their utility appears to be independent of glycemic control, as suggested by the absence of a statistically significant difference in HbA1c levels in this study and by the findings of Tang et al. and Erdem and Kaya, who observed that MHR retained predictive power even after adjusting for HbA1c and lipid levels.^13,25

The integration of these inflammatory ratios into clinical practice may allow for risk stratification and targeted preventive strategies. Patients with elevated MHR or MLR could be prioritized for closer monitoring, lifestyle interventions, or early pharmacologic management to mitigate neuropathic progression.

Study limitations

While the present study provides valuable insight into the inflammatory basis of DPN and the potential role of hematological inflammatory ratios as predictive markers, there are several limitations as mentioned below.

First, the case–control design limits the ability to infer causality. Although significant associations were observed between MHR, MLR, and neuropathy status, longitudinal studies are necessary to determine whether elevated inflammatory ratios precede the development of neuropathy or simply reflect established disease. Second, neuropathy diagnosis in this study was based on clinical assessment rather than nerve conduction studies. While widely used in clinical practice, symptom-based tools may be less sensitive for detecting subclinical or early-stage neuropathy. Third, potential confounders such as duration of diabetes, body mass index, smoking status, physical activity, and concurrent medication use were not fully stratified in subgroup analysis, which may have influenced inflammatory markers. We recognized this as a limitation, and future studies with a larger sample size and more comprehensive data collection will aim to incorporate these variables to better control for their potential influence. Lastly, while MHR and MLR are promising indicators, their variability across populations and lack of standardized cutoff values limit their immediate integration into routine clinical protocols without further validation. Future prospective studies with larger and more diverse cohorts are warranted to validate these findings. Such studies should aim to establish clinically useful thresholds for MHR and MLR. Additionally, longitudinal follow-up would help determine whether changes in these inflammatory markers correlate with the onset or progression of neuropathy, thereby enhancing their potential utility as prognostic biomarkers.

Conclusions

This study highlights the potential role of hematological inflammatory indices, particularly MHR and MLR, as independent and clinically relevant biomarkers for the presence of diabetic neuropathy. The significant elevation of MHR and MLR in patients with neuropathy, in contrast to the lack of significant difference in more conventional biochemical markers such as LDL-C and HbA1c, underscores a shift toward recognizing immune-inflammatory dysregulation as a central mechanism in neuropathic progression. The use of MHR and MLR offers a practical advantage due to their derivation from routine complete blood counts and lipid profiles, making them cost-effective and easily implementable in diverse health care settings. Their integration into standard diabetic assessments may enhance early detection and facilitate timely interventions, especially in resource-constrained environments where access to electrophysiological diagnostics is limited.

While these findings are promising, they also call for further prospective studies to validate the predictive utility of MHR and MLR, define standardized reference thresholds, and evaluate their role in monitoring therapeutic response. The incorporation of these markers into comprehensive risk stratification models may represent a meaningful advancement in the prevention and management of DPN.

Authors’ Contributions

P.K.A.: Conceptualization, methodology, investigation, validation, writing—original draft, writing—review and editing, project administration, and supervision. S.K.P.: Conceptualization, methodology, investigation, validation, writing—original draft, writing—review and editing, formal analysis, and project administration. K.S.: Methodology, writing—review and editing, and formal analysis. S.S.Y.: Methodology, writing—review and editing, and formal analysis. S.D.: Writing-review and editing and resources. G.G.: Formal analysis and resources. R.G.: Formal analysis and project administration. A.G.: Formal analysis and project administration.

Footnotes

Author Disclosure Statement

The authors declare that they have no conflicts of interest to disclose.

Funding Information

This study was not funded or sponsored from any source.

Declaration by Authors

All the authors declare that the article has been read and approved by all the authors, that the requirements for authorship as stated earlier in this document have been met, and that each author believes that the article represents honest work. They confirm that the article has not been presented in any meeting. They confirm that this work is original and has not been published or submitted elsewhere neither as a whole nor as any redundant or similar form.

Data Availability

Data will be available upon request from the corresponding author.

Ethical Aspects

The study received Ethical approval from the Institutional Ethics Committee, Sarojini Naidu Medical College, Agra (Reg No-ECR/1409/Inst/UP/2020). The approval letter number is SNMC/IEC/2024/237, dated March 28, 2024. Written informed consent was obtained from all participants for participation in the study and use of their data for research and educational purpose. The study was conducted in accordance with the Declaration of Helsinki as revised in 2013.

Abbreviations

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