Predictive Model of Diabetic Polyneuropathy Severity Based on Vitamin D Level

Abstract

BACKGROUND: Type 2 Diabetes Mellitus is one of the most common metabolic diseases worldwide. The most common complication of DM is diabetic neuropathy (DN), especially diabetic polyneuropathy (DPN). Vitamin D plays an important role in the pathogenesis of DN, thus affecting its severity which can be assessed using nerve conduction study (NCS).

AIM: This study aimed to develop a predictive model of DPN severity based on vitamin D level.

METHODS: This was a prospective cohort study involving 50 subjects with DM which was conducted in Haji Adam Malik General Hospital Medan. All subjects were fulfilling inclusion criteria underwent laboratory examination to determine HbA1c and 25 (OH) D levels. Predictive variables were sex, age, duration of DM, smoking status, type and number of anti-diabetic drugs, the presence of metabolic syndrome, HbA1c and vitamin D levels. A scoring system was developed to determine a predictive model. The DPN severity was assessed using NCS and was re-evaluated after 3 months.

RESULTS: Most of the subjects were female (60%), belonged to ≥ 50 years old age-group (88%), with DM duration < 5 years (56%), were non-smoker (90%), we're using one anti-diabetic drug (60%), were using insulin (50%), had metabolic syndrome (68%), had HbA1c level > 6.5% (94%), and had vitamin D level < 20 ng/ml (56%). A score of > 4 on this predictive model of DPN severity had a relative risk (RR) of 2.70. The predictive model had a sensitivity of 82.8% and specificity of 61.9%.

CONCLUSION: A score of higher than 4 on this predictive model showed a 2.7 times higher risk of severe DPN. A predictive model of DPN severity based on vitamin D level had high sensitivity and specificity.