Study design and subjects
This case-control study was carried out in the Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Kermanshah, Iran between September 2019 and April 2020. The present study included 185 cases with Non–Insulin-Dependent Diabetes Mellitus (NIDDM), who are aged 30-60 years old with Body Mass Index (BMI) between 25 to 39.9 kg/m2 and diagnosed with DSPN caused by diabetes and 185 age-(±10), sex-matched controls. Patients were excluded based on the following criteria; 1) those diagnosed with neuropathy due to other diseases, 2) patients with a history of diseases such as cancer, liver, kidney, autoimmune diseases, and inflammatory, thyroid and nervous and cardiovascular diseases, 3) pregnancy or lactation. Patients were also excluded if they had a particular diet during the recent last two months. The study protocol was approved by the Ethics Committee of Tehran University of Medical Science. TUMS.VCR.REC. 1399.269. All participants were required to complete written informed consent.
The following equation was used to calculate the sample size of the study:
Where p1 is the proportion exposed to DII in the control group while p2 is the proportion exposed to DII in the case group. Assuming α= 0.05 and β= 0.2 and prevalence of diabetes in the first quartile or most anti-inflammatory diet group and the fourth quartile or least anti-inflammatory diet group to be equal to 6.4 and 22.8 respectively, we needed 185 participants in each group.
Dietary assessment and Dietary inflammatory Index calculation
Dietary intakes were assessed using a valid and reliable semi-quantitative Food Frequency Questionnaire (FFQ) (168 food items, with standard serving sizes as commonly consumed by Iranians). One trained expert completed all FFQ questionnaires. All participants were asked about their average dietary intake on a daily, weekly, and monthly basis during the last year. Daily nutrients and energy intakes of foods and beverages were analyzed by Nutritionist software version 4(First Data Bank, San Bruno. CA) modified for Iranian foods.
DII was estimated by using FFQ and Shivappa et al . In this regard, we included 29 parameters to calculate DII which were energy, carbohydrate, fat, protein, fiber, cholesterol, mono-unsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs), saturated fats (SFAs), cobalamin, pyridoxine, folic acid, niacin, riboflavin, thiamin, vitamin A, C, D, E, b-carotene, zinc, selenium, magnesium, iron, caffeine, garlic, onion, pepper, and green/black tea. First, we calculated the Z score for all 29 parameters by the ratio of the standard global mean from the quantity of food parameters consumed by each participant to the global standard deviation. We obtained global mean and SD from Shivappa et al . This value was converted to the percentile score. Then, the resulting of this value was multiplied by the effect score for all of the food parameters gained from Shivappa et al. Eventually, the DII score from all the foods was summed to compute an overall DII score for each subject.
Toronto clinical neuropathy score
We used the Toronto Clinical Neuropathy Score (TCNS) to diagnose and compute the severity of neuropathy. It has been proved that TCNS is a valid and reliable questionnaire in Iran and other countries [19, 20]. TCNS contains three sections; first, symptoms scores include the absence or presence of foot pain, numbness, tingling, and weakness in the feet, similar upper-limb symptoms, and ataxia (six points). Symptom scores rated as absence = 0or presence = 1. Second, sensory test scores contain the absence or presence of pinprick, temperature, light touch, vibration, position sense that were performed at the first toe five points). Scores rated as absence = 0 or presence = 1. Third, Reflexes scores (knee reflexes, Ankle reflexes) which were rated as normal = 0, reduced = 1 or absence = 2 (eight points). TCNS scores range from a minimum of 0 (without neuropathy) to a maximum of 19 points. Based on the result of TCNS, subjects were graded into classes of no neuropathy: 0-5; mild neuropathy: 6-8; moderate neuropathy: 9-11; and severe neuropathy >12. Clinical examinations were conducted by a neurologist and the results were recorded on a special form.
Assessment of other variables
A digital scale was used to determine the weight to the nearest 50 g. height was measured to the nearest 0.5 cm. Participants were asked to no wear shoes. BMI was computed through Weight (kg) divided by height squared (m2). A non‐elastic tape with an accuracy of 0.5 cm was used to measure Waist circumference (WC) at a point midway between the iliac crest and lower rib margin. The short form of the International Physical Activity Questionnaire (IPAQ) was used to estimate physical activity during the last week. IPAQ is a validated self-reported seven-item measure of physical activity during the last week . The anthropometric measurements were performed by a dietitian trained in anthropometry to reduce individual error.
We took fasted venous blood samples from all participants. We separated blood sample by centrifugation (at 3000 rpm for 10 min at 4 °C) to acquire serum. Then they were stored at −80 °C until biochemical analyses performed. An auto-analyzer instrument (ERBA), using commercial kits (Pars Azmoon, Iran) were used to measure Fasting Blood Sugar(FBS) and Blood Sugar after 2 hours (Bs2hp). Also, using a high-Performance Liquid Chromatography (HPLC) (Advance scientific instrument, Germany) to estimate Glycated hemoglobin (HbA1c).
Data were analyzed using SPSS 16 for Windows (SPSS Inc., Chicago, IL). First of all, quartile cut-off points for the DII score was defined. All patients were classified based on these cut-offs: q1<-1.03; q2 (-1.03)-(-0.01); q3 (-0.03)-(0.844); and q4 0.844<. To compare the means and distribution of continuous and categorical variables, independent-sample t-test and analysis of Chi-square test were used, respectively. Differences across quartiles of DII were compared using one-way ANOVA and Chi- square tests. Food and nutrient intakes were adjusted for age, sex and energy, except for dietary energy intake, which was only adjusted for age and sex using ANCOVA by quartiles of the DII. Using Binary logistic regression, to investigate associations between the DII and DSPN. Three regression models were used in our analysis. In model, it was adjusted for age, sex and energy intake. In model2, we adjusted further for physical activity, education, economic status, smoking, FBS, A1C, BS2hP. Moreover, in model 3 it was adjusted for BMI to perceive the relation when in-dependent of obesity. We defined the first quartile of DII as the reference category and odds ratios and 95% CIs for the other quartiles were estimated. P values<0 05 were defined as statistically significant.