European Journal of Cardiovascular Medicine

Diabetic Peripheral Neuropathy (DPN) is one of the most prevalent complications of Type 2 Diabetes Mellitus, significantly impacting the quality of life and increasing the risk of severe complications such as foot ulcers, infections, and amputations.1 It is characterized by progressive damage to the peripheral nerves due to chronic hyperglycemia, leading to sensory disturbances, pain, and motor dysfunction.2 Despite its high prevalence, DPN often remains underdiagnosed, particularly in its early stages, resulting in delayed intervention and higher morbidity. Early detection and management are crucial to prevent irreversible nerve damage and associated complications.3

Several risk factors contribute to the development and progression of DPN, including prolonged diabetes duration, poor glycemic control, hypertension, obesity, smoking, and dyslipidemia.4 Studies have shown that elevated metabolic markers, such as HbA1c, cholesterol, and uric acid, are strongly linked to the onset of neuropathy.5 Lifestyle factors, such as smoking and alcohol consumption, further exacerbate nerve damage by impairing vascular function and increasing oxidative stress. Identifying these risk factors is essential for implementing preventive strategies, including glycemic optimization, lifestyle modifications, and routine neuropathy screening, which can significantly reduce the disease burden and improve patient outcomes.  Our study aims to assess the prevalence of DPN and identify key risk factors among Type 2 Diabetes Mellitus patients. Using standardized diagnostic tools such as the Michigan Neuropathy Screening Instrument (MNSI) and Vibration Perception Threshold (VPT), we evaluate the extent of neuropathy and its correlation with clinical and biochemical parameters.

This observational, cross-sectional study was conducted at the Department of Medicine, MGM Medical College and Hospital, Navi Mumbai, from March 2021 to December 2023. Ethical clearance was obtained, and informed consent was taken from all 160 consecutive Type 2 Diabetes Mellitus (T2DM) patients enrolled from the Diabetic OPD. The sample size was calculated based on a diabetes prevalence of 11.6%, yielding a target of 158, rounded to 160 for statistical adequacy.

Inclusion criteria were based on ADA guidelines, with patients aged >30 years, having diabetes duration >5 years. Exclusion criteria included other known causes of neuropathy (e.g., Vitamin B12 deficiency, chronic alcoholism, neuropathy-inducing drugs). Demographic and clinical data (age, BMI, comorbidities, BP, etc.) were collected. Neuropathy assessment was done using the Michigan Neuropathy Screening Instrument (MNSI) and Vibration Perception Threshold (VPT) testing via tuning fork and biothesiometer.

Laboratory investigations included HbA1c, fasting and postprandial blood glucose, lipid profile, renal and liver function tests, electrolytes, and urine analysis. All tests were performed in the hospital’s biochemistry lab. Statistical analysis was performed using IBM SPSS. Continuous variables were expressed as mean ± SD, and categorical variables as percentages. The unpaired t-test and Chi-square test were used for comparisons, with p < 0.05 considered statistically significant. Results were illustrated using bar and pie charts where appropriate.

The primary objective of this study was to determine the prevalence of diabetic neuropathy in Type 2 Diabetes Mellitus (T2DM) patients, while the secondary objective was to identify associated risk factors. A total of 160 subjects were included in the study. The majority of participants were in the 51-60 years age group (36.9%), followed by the 41-50 years group (34.4%), with a mean age of 54.99 ± 10.08 years. The study population was nearly balanced in terms of gender distribution, with 53.1% males and 46.9% females, showing no statistically significant difference (p>0.05). Regarding lifestyle factors, 10.6% of subjects reported smoking, while 89.4% were non-smokers. A significant proportion of participants (85.0%) were classified as overweight (p<0.05), highlighting obesity as a potential risk factor for diabetic neuropathy. The duration of diabetes varied among participants, with the majority (75.0%) having diabetes for 5-10 years, followed by 17.5% with a duration of 10-15 years.

Among the 160 study participants, alcohol consumption was reported in only 6 (3.8%) individuals, while the majority (96.3%) did not consume alcohol. The prevalence of diabetic neuropathy was assessed using two diagnostic tools: the Michigan Neuropathy Screening Instrument (MNSI) and the Vibration Perception Threshold (VPT). Based on MNSI, 49 (30.6%) of the participants were diagnosed with diabetic neuropathy, while 111 (69.4%) showed no signs of neuropathy. VPT assessment further classified neuropathy severity, revealing that 36.9% of the participants had diabetic neuropathy, with 12 (7.5%) classified as mild cases and 47 (29.4%) as moderate cases.

Age-wise analysis of neuropathy prevalence based on MNSI showed variations across different age groups. The highest prevalence was observed in the 31-40 years age group, where 55.6% of participants had diabetic neuropathy. In the 41-50 and 51-60 years age groups, the prevalence was 34.5% and 30.5%, respectively. Lower prevalence rates were seen in older participants, with 17.4% in the 61-70 years group and 21.4% in individuals above 70 years. Overall, the study found that 30.6% of Type 2 Diabetes Mellitus patients had diabetic neuropathy, emphasizing the need for early detection and intervention, particularly in younger diabetic individuals who may be at higher risk. Additionally, hypertension was present in 35.0% of the study population, indicating a potential correlation with the development of diabetic neuropathy.

The study revealed significant associations between diabetic neuropathy and various clinical parameters. Smoking, hypertension, and alcohol consumption were strongly linked to a higher prevalence of diabetic neuropathy, with smokers (76.5%, p<0.001) and alcohol consumers (66.7%, p<0.001) showing markedly increased risks. Obesity also played a crucial role, with overweight individuals having a significantly higher prevalence of neuropathy compared to those with normal BMI (p<0.001). Additionally, longer diabetes duration was associated with increased neuropathy risk, with 50% of individuals having diabetes for 10-15 years developing neuropathy (p<0.005).

Table 1. Association of Diabetic neuropathy (using MNSI) with study parameters

Metabolic parameters such as HbA1c and fasting blood sugar (FBS) showed strong associations with neuropathy. Patients with higher HbA1c levels had a significantly increased risk, with 33.8% of diabetic patients (p<0.001) diagnosed with neuropathy using MNSI. Similarly, VPT assessments revealed that 31.7% of diabetic individuals with high FBS levels had moderate neuropathy (p<0.001).

Table 2. Association of Diabetic neuropathy (using VPT) with study parameters

The study revealed significant associations between diabetic neuropathy and multiple biochemical parameters. Patients with neuropathy had significantly higher postprandial blood sugar (PLBS) levels (371.86 ± 93.67 vs. 310.39 ± 83.80, p<0.001), indicating poor glycemic control as a strong contributing factor. Similarly, uric acid levels were notably elevated in neuropathy patients (8.39 ± 2.74 vs. 5.98 ± 2.01, p<0.001), suggesting a potential link between hyperuricemia and neuropathic complications. Liver enzymes, including SGOT (39.90 ± 69.52 vs. 20.97 ± 10.56, p=0.006) and SGPT (34.27 ± 61.73 vs. 19.76 ± 8.86, p=0.016), were also significantly higher among neuropathy patients, indicating possible hepatic involvement in disease progression.

Lipid profile analysis showed that total cholesterol (226.65 ± 38.33 vs. 199.87 ± 35.43, p<0.001) and triglycerides (191.53 ± 39.47 vs. 170.14 ± 53.57, p=0.013) were significantly higher in patients with neuropathy, further highlighting dyslipidemia as a major risk factor. However, there was no significant difference in LDL (p=0.506) and HDL (p=0.857) levels between the two groups. The comparison of study parameters based on VPT assessment also showed similar trends, with higher PLBS, uric acid, liver enzymes, and cholesterol levels correlating with increased severity of neuropathy (p<0.001). The association between MNSI and VPT assessments revealed that 30.6% of study subjects had neuropathy based on MNSI, whereas 36.87% were identified as neuropathic using VPT, suggesting that VPT might be more sensitive in detecting early neuropathic changes.

The study found significant differences in metabolic parameters between diabetic patients with and without neuropathy. PLBS (p<0.001), uric acid (p<0.001), SGOT (p=0.006), SGPT (p=0.016), cholesterol (p<0.001), and triglycerides (p=0.013) were significantly higher in neuropathy patients, indicating poor metabolic control as a key risk factor. In contrast, creatinine (p=0.932), LDL (p=0.506), and HDL (p=0.857) showed no significant differences, emphasizing the importance of glycemic and lipid regulation in neuropathy prevention. (Table 3)

Table 3: Comparison of Study Parameters Based on Diabetic Neuropathy

The diagnostic performance of the Michigan Neuropathy Screening Instrument (MNSI) was evaluated for its reliability in detecting diabetic neuropathy. The test demonstrated high sensitivity (88.12%, 95% CI: 80.17%-93.71%), indicating its strong ability to correctly identify patients with neuropathy. However, the specificity was moderate (62.71%, 95% CI: 49.15%-74.96%), suggesting some false positives. The positive predictive value (PPV) was 80.18%, meaning that when the test was positive, there was an 80.18% probability that the patient truly had neuropathy. The negative predictive value (NPV) was 75.51%, indicating that a negative result had a 75.51% probability of correctly ruling out the disease. The test achieved an overall accuracy of 78.75%, reinforcing its effectiveness in screening for diabetic neuropathyand patient education to improve adherence to evidence-based practices [Table 5].

The present study aimed to estimate the prevalence of diabetic neuropathy (DPN) in Type 2 Diabetes Mellitus (T2DM) patients using the Michigan Neuropathy Screening Instrument (MNSI) and Vibration Perception Threshold (VPT), as well as to examine the associated risk factors. The findings revealed a DPN prevalence of 30.6% (MNSI) and 36.9% (VPT), aligning with previous studies such as Al-Ayed et al.⁶ (2021), who reported a DPN prevalence of 26.71% in T2DM patients. Other studies have documented varying prevalence rates, ranging from 16.6% (Nang et al.⁷, 2018) to 61.8% (Yang et al.⁸, 2019, China), with differences attributed to variations in screening methods, diagnostic criteria, and population characteristics. The Chennai Urban Rural Epidemiology Study (CURES) (Mohan et al.⁹, 2015) reported a 26% prevalence, which closely aligns with our findings, reinforcing the notion that ethnic and regional factors influence DPN prevalence.

Comparative analyses with international studies indicate that socioeconomic status, healthcare access, and cultural behaviors significantly impact DPN prevalence. For instance, Tesfaye et al.¹⁰ reported a DPN prevalence of 29.2% in a tertiary care setting in North India, which is comparable to our study. However, a meta-analysis by Pop-Busui et al.¹¹ found a pooled prevalence of 31.5%, with rates as low as 2.4% in the UK (Tesfaye et al.¹⁰). These variations may be due to differences in glycemic control, lifestyle factors, and the availability of routine neuropathy screening in healthcare systems across countries.

The study identified several key risk factors significantly associated with diabetic neuropathy, including smoking (p<0.001), high BMI (p<0.001), prolonged diabetes duration (p<0.005), hypertension (p<0.001), alcohol consumption (p<0.001), and poor glycemic control (HbA1c, p<0.001). These findings are consistent with Forrest et al.¹², who reported that hypertension increases DPN risk fourfold. Similarly, Darivemula et al.¹³ found that patients with diabetes for over 10 years had a significantly higher neuropathy prevalence, reinforcing our study’s observation that DPN risk increases with prolonged exposure to hyperglycemia. In contrast, a UK study (Young et al.¹⁴) found only a modest association between BMI and DPN, while Yang et al.⁸ reported a stronger correlation, particularly in obese patients with metabolic syndrome. These discrepancies highlight the complex interplay between metabolic, genetic, and environmental factors in DPN development.

The diagnostic accuracy of MNSI compared to VPT was assessed, with MNSI demonstrating high sensitivity (88.12%) but moderate specificity (62.71%), making it a useful screening tool in outpatient settings. Our findings align with Perveen et al.¹, who validated MNSI as a reliable clinical tool for detecting distal symmetrical peripheral neuropathy. While VPT remains the gold standard, requiring specialized equipment and trained personnel (Dyck et al.)¹⁵, MNSI offers a cost-effective, easily administrable alternative, particularly in resource-limited settings. Additionally, Herman et al.¹⁶ emphasized that early identification of neuropathy through simple screening tools, lifestyle modifications, and patient education can significantly reduce complications such as diabetic foot ulcers (DFUs).

Despite its strengths, our study has limitations, including its cross-sectional design, which prevents the determination of causality. Additionally, as the study was conducted in a single tertiary care hospital with a mix of rural and urban patients, the results may not be fully generalizable to other populations. Similar concerns were raised by Tesfaye et al.¹⁰ and Pop-Busui et al.¹¹, who noted that hospital-based studies often include patients with more advanced disease. However, our findings reinforce the need for regular diabetic foot screenings, patient education, and strict metabolic control to reduce the burden of diabetic neuropathy. Given its simplicity and effectiveness, MNSI can serve as a reliable screening tool for DPN in routine clinical practice, especially in primary care settings.

This study found that 36.9% of Type 2 Diabetes patients developed diabetic peripheral neuropathy (DPN), with major risk factors being long disease duration, poor glycemic control, hypertension, smoking, obesity, and alcohol use. The Michigan Neuropathy Screening Instrument (MNSI) proved to be a simple, sensitive (88.12%), and effective tool for early DPN detection, especially in resource-limited settings. Early screening, better metabolic control, and patient education are key to preventing severe complications like foot ulcers and amputations.

Conflict of Interest: The authors declare no conflict of interest.

Financial Support: No financial assistance or funding was received for the conduct of this study.

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