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Sci Rep
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Scientific Reports
2045-2322
Nature Publishing Group UK London

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10.1038/s41598-024-72602-w
Article
Dietary phytochemical index and its relationship with diabetic sensorimotor polyneuropathy: a case-control study
Asadi Sasan 1
Shiva Fahimeh 2
Mohtashamian Abbas 3
Fallah Melika 4
Nourimajd Saeedeh 4
Aminianfar Azadeh aaminianfar@gmail.com

5
Asadi Sara sr.asadi11@yahoo.com

46
1 https://ror.org/01ntx4j68 grid.484406.a 0000 0004 0417 6812 Department of Community Medicine, School of Medicine, Kurdistan University of Medical Sciences, Sanandaj, Kurdistan Province Iran
2 https://ror.org/04waqzz56 grid.411036.1 0000 0001 1498 685X Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Isfahan University of Medical Sciences, Isfahan, Iran
3 https://ror.org/04sfka033 grid.411583.a 0000 0001 2198 6209 Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
4 https://ror.org/01c4pz451 grid.411705.6 0000 0001 0166 0922 Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran
5 https://ror.org/03dc0dy65 grid.444768.d 0000 0004 0612 1049 Research Center for Biochemistry and Nutrition in Metabolic Diseases, Kashan University of Medical Sciences, Kashan, Iran
6 https://ror.org/03r8z3t63 grid.1005.4 0000 0004 4902 0432 Department of Exercise Physiology, School of Health Sciences, Faculty of Medicine and Health, UNSW, Sydney, Australia
17 9 2024
17 9 2024
2024
14 2168820 8 2023
9 9 2024
© The Author(s) 2024
2024
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Phytochemicals are compounds found in fruits, vegetables, whole grains, nuts and legumes that are non-nutritive but have bioactive properties. A high intake of these compounds is essential for optimal health and disease prevention. No study has investigated the association between Dietary Phytochemical Index (DPI) and polyneuropathy in patients with diabetes. This study aimed to examine the association between DPI and Diabetic Sensory-motor Polyneuropathy (DSPN) in a case-control study. In this case-control study, a total of 185 diabetic patients with DSPN (case group) and 185 sex- and age-matched diabetic patients without neuropathy (control group) were enrolled in this study. Participants were 30–60 years old. A validated food frequency questionnaire was used to measure the dietary intake of all participants. Daily energy derived from phytochemical-rich foods was used to calculate the DPI score. Toronto clinical neuropathy score was applied to define DSPN. Anthropometric data and fasting blood glucose levels were measured using standard methods. The Binary logistic regression was used to estimate Crude and multivariable-adjusted OR (95% CI) for DSPN across tertiles of DPI for the whole population. In the crude model, there was a significant trend across the tertile of DPI (OR highest vs. lowest tertile of DPI = 0.33; 95%CI 0.18, 0.52; P-trend < 0.001). After controlling for age, sex, and energy, a significant reverse association was observed between DPI and DSPN (OR highest vs. lowest tertile of DPI = 0.27; 95%CI 0.15, 0·48; P-trend < 0.001). Moreover, after adjusting for a wide range of confounding variables such as energy intake, physical activity, education, smoking status, and HbA1c, participants in the third tertile of DPI had 75% reduced odds for DSPN (95%CI 0.14, 0.45; P-trend < 0.001). Finally in the full adjusted model, after further adjustment for BMI, observed significant association was remained (OR highest vs. lowest tertile of DPI: 0.24; 95% CI 0.13, 0.14; P-trend < 0.001). Higher intakes of phytochemical-rich foods are associated with lower odds of DSPN.

Keywords

Dietary phytochemicals
Antioxidants
Diabetic sensory-motor polyneuropathy
Type 2 diabetes
Subject terms

Diseases
Endocrinology
Neurology
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Diabetes is one of the most common chronic diseases with many implications for health and quality of life1. Their complications include cardiovascular disease, diabetic nephropathy, diabetic retinopathy, cancers, and diabetic neuropathy2. Diabetes sensorimotor polyneuropathy (DSPN) is a common and debilitating disorder generally characterized by peripheral nervous system degeneration3,4. The global prevalence of diabetes among people aged 20–79 was 6.4% in 2010, which is predicted to reach 20.79% by 20305. DSPN occurs in more than 50% of persons with diabetes and is the most common cause of neuropathy worldwide6,7. The prevalence of neuropathy in type 2 diabetic patients in Iran was estimated at 56.5%5.

The most important risk factors for T2DM include genetics, another chronic disease such as obesity, sedentary lifestyle, and diet8. Previous studies have investigated the relationship between diet and diabetic neuropathy. Documents have shown that some dietary indexes, such as Dietary Inflammatory Index (DII)9, and Dietary Acid Load (DAL)10, could be related to DSPN. The dietary phytochemical index (DPI) is a standard index representing the whole diet's phytochemical content. Because quantifying phytochemicals in food sources is expensive and impractical for large epidemiological studies, McCarthyis et al. developed this simple and practical tool11. DPI is the percentage of energy intake from foods rich in phytochemicals11–13. Although DPI was previously related to several health and disease conditions, such as glioma14, benign breast diseases (BBD)15, and breast cancer16, the relationship between DPI and DSPN has not been investigated yet. Some studies assayed the relationship between foods rich in phytochemicals and diabetic neuropathy or T2DM and reached conflicting results. For example, a cohort study saw an inverse and significant correlation between adherence to the Mediterranean diet and the incidence of diabetic neuropathy17. In another study, intake of some food groups, such as whole grains, which are also rich in phytochemicals, was inversely associated with the incidence of diabetes. However, no significant correlation was observed between the intake of fruits, vegetables and legumes, good dietary sources of phytochemicals, with the incidence of T2DM in another study18. Pain is most important complication of neuropathy, and alleviation of the pain but not glucose control have been reported in group consuming phytochemical-rich foods19. One study reported that those they follow vegetarian diet experience severe neuropathy20. Inflammation and oxidative stress play a key role in the pathophysiology of DSPN. So, phytochemicals can play a role in preventing and managing DSPN due to their antioxidant and anti-inflammatory properties21–24. To the best of our knowledge, no studies have investigated the association between DPI and DSPN. In addition, limited and controversial data are available regards to the relationship between diet and DSPN worldwide, especially in Middle-east countries. It must be kept in mind that diets in Middle-east areas are different from all over the world which they consume a higher intake of refined grains: a poor source of dietary phytochemicals25. Considering above reasons and the high prevalence of diabetic neuropathy in Iran5, we aimed to investigate the linkage between DPI and odds of DSPN in an adult population of T2DM patients in Iran.

Materials and methods

This study was a secondary analysis of a previous case-control study that aimed to investigate the association between DII and DSPN in patients with type 2 diabetes mellitus (T2DM). The details of the study method have already been explained9. This study was conducted at the Diabetes Research Center, Institute of Clinical Sciences of Endocrine and Metabolism, Kermanshah, Iran, between April 2019 and August 2019. The Toronto Clinical Neuropathy Score (TCNS) was applied to all participants in the present study. In this study, in the case group, 185 patients with non-insulin-dependent diabetes mellitus (NIDDM) and a confirmed DSPN (with TCNS ≥ 6) participated. Other inclusion criteria include: (1) Age range between 30 to 60 years old, and (2) Having body mass index between 25 to 39.9 kg/m2. Also, in this study, 185 age-matched (± 10y) and sex-matched NIDDM patients without DSPN (TCNS < 6) participated in the control group. The people in each studied group were selected based on simple sampling. Exclusion criteria in the present study include having malignancies, cancer diseases, liver disease, kidney disease, thyroid disease, cardiovascular disease, nervous diseases, patients with neuropathy due to other conditions, and pregnant or lactating women. In addition, if participants in the current study had unexplained total energy intake (< 800 kcal/day or > 4200 kcal/day), we excluded them from the analysis. Informed consent was obtained from all participants in the study. Ethical approval for the original study was given by the Tehran University of Medical Science Ethics Committee. TUMS.VCR.REC. 1399.269.

The sample size of the study was calculated based on the following equation:N=Z1-α2+Z1-β2p11-p1p21-p2p2-p12

Certainly, for this case-control study, we factored in a desired statistical power of 0.8 when determining the sample size. Despite this consideration, it's important to recognize that formal power calculations in exploratory research, especially in areas lacking prior studies, can be inherently challenging. Assuming α = 0.05 and statistical power = 1-β = 0.8 and also the prevalence of diabetes in the least anti-inflammatory diet group (e .g. those with the lowest Mediterranean-DASH diet intervention for Neurodegenerative Delay scores) to be equal to 6.4 and in the most anti-inflammatory diet group (e .g. those with the highest MIND scores) to be similar to 22.8, 185 participants in each group will be needed26.

Dietary intake assessment and definition of dietary phytochemical index

Data collection about the usual dietary intake of each subject during the last year was done through face-to-face interviews and using a valid and reliable semi-quantitative 168-item food frequency questionnaire (FFQ)27. To determine their average dietary intake over the last year, all participants were asked about their average nutritional intake on a daily, weekly, and monthly basis. The Nutritionist software version 4 (First Data Bank, San Bruno, CA) was modified to Iranian food converts and analyze daily nutrient intakes and energy consumption data from the questionnaire. The FFQs were all completed by a trained nutritionist. The FFQ consisted of a list of foods with a standard serving size commonly consumed by Iranians. The dietitian asked participants to report their frequency of consumption of a given serving of each food item during the previous year. Participants were allowed to report their consumption frequency on a daily, weekly, or monthly basis. All reported frequencies for food items were converted to g/d considering the weight of portion sizes specified for each item28 A previous validation study29 of this FFQ among 132 randomly chosen participants revealed correlations between dietary intakes assessed by similar FFQ and multiple days of 24-h food recalls completed during the year (r = 0.3–0.8; P ˂ 0.05).

To calculate DPI, the method developed by McCarthy was used as follows. Dietary energy is derived from foods rich in phytochemicals (kcal)/total daily energy intake (kcal) × 100. Phytochemical-rich foods are non-nutritive but have bioactive properties. A high intake of these compounds is essential for optimal health and disease prevention. In DPI score fruits and vegetables, legumes, whole grains, seeds, nuts, natural juices, olive oil, and soy products are considered as phytochemical-rich foods. However, potatoes, pickled and powdered vegetables were not considered in the calculations because of their low phytochemical content30. After calculating the DPI scores, people were categorized into tertiles based on DPI score, in which participants in the top tertile of DPI have the highest score compared to those in the bottom tertile.

Assessment of DSPN

Presence and severity of DSPN was diagnosed by an expert neurologist using a valid and reliable Toronto Clinical Neuropathy Score (TCNS) questionnaire, the study assessed the validation of the Toronto Clinical Scoring System for Diabetic Polyneuropathy showed correlation coefficient (0.83)31,32. TCNS scoring has relatively high sensitivity and specificity. It has also been validated in Iran33. Based on a valid and reliable Toronto Clinical Neuropathy Score (TCNS) questionnaire33,34, we diagnosed and calculated the presence and severity of DSPN. TCNS consists of three parts. Part 1; symptoms scores (absence = 0; presence = 1): consist of foot pain, numbness, tingling, and weakness in the feet, similar upper-limb symptoms, and ataxia. Part 2: sensory score (absence = 0; presence = 1): consist of sensory test scores including the absence or presence of pinprick, temperature, light touch, vibration, and position sense. Finally part 3: reflexes scores (normal = 0; reduced = 1; absence = 2): consist of knee reflexes and ankle reflexes. The neurologist evaluated the subjects based on three parts and gave each one a score. The scores of these parts were gathered and a total score of 0 (minimum) to 19 (maximum) was given to each person. Participants was classified into three categories according to the TCNS result, including: (1) no neuropathy: 0–5, (2) mild neuropathy: 6–8, (3) moderate neuropathy: 9–11, and (4) severe neuropathy: > 12.

A trained assistant performed anthropometric measurements according to the protocol prescribed by the World Health Organization. To perform anthropometric measurements, the participants were asked to wear a minimum of clothes and without shoes. In the present study, to measure participants’ body weight, we used a calibrated digital scale to determine the weight to the nearest 50 g. Also, to measuring the height, we used non-elastic tape, with an accuracy of 0.5 cm in normal conditions and a standing position beside the wall. BMI was calculated by dividing weight (kg) by the square of height (m2).

Assessment of other variables

All blood samples were taken intravenously after 10–12 h overnight fasting and early in the morning (8–10 A.M.). The collected blood samples were centrifuged (at 3000 rpm for 10 min at four °C) for 15 min to obtain serum and stored at − 80 °C. Fasting blood sugar (FBS) and blood sugar after 2 h (Bs2hp) were measured through an auto-analyzer instrument (ERBA) and using commercial kits (Pars Azmoon, Iran). Glycated hemoglobin (HbA1c) was also evaluated using a High-Performance Liquid Chromatography (HPLC) (Advance scientific instrument, Germany). This study used the international physical activity questionnaire-short form (IPAQ-SF) to evaluate physical activity. The IPAQ-SF is a seven-item validated questionnaire that includes the time and frequency of normal activities each week over the past year. To expression the amount of physical activity of participants' Metabolic equivalent hours per week (METs-h/week) is used35.

Statistical analysis

All statistical analyses in the present study were performed using IBM SPSS software version 22.0 (SPSS, Chicago, IL, USA). For all tests conducted, values less than 0.05 are considered significant. First of all, subjects based on tertiles of DPI (T1: < 32.9; T2: 32.9, 45.5; T3: > 45.5) were categorized. Then, the mean and distribution of continuous variables were compared using independent-sample t-test and the comparison of qualitative variables was evaluated using the chi-square test. Also, one-way ANOVA and Chi-square tests were used to assess the differences across tertile of DPI. Across tertile of DPI, dietary energy intake was adjusted for age and sex and food and nutrient intakes were adjusted for age, sex, and energy by using ANCOVA. The association between DPI was assessed by binary logistic regression adjusted for total calorie intake (kcal), age (year), and sex in model 1, adjusted for physical activity, education, HbA1c, smoking in model 2, and adjusted for BMI in model 3. To estimate odds ratios and 95% confidence intervals for the other tertile, we defined the first tertile of DPI as the reference. Authors: Thank you very much. All these potential confounders relate to DSPN and DPI.

The results ​​reported in Table 1 for HbA1c, BMI and smoking status showed a significant difference between the case and control groups, due to this significant difference, these items should be considered as confounders in the regression models (models 1, 2, and 3). Also, previous studies have considered items such as age, sex, education, physical activity and energy intake as confounding factors so in addition to previous items these items should be included in the models to be adjusted36–39. Table 1 General participant characteristics.

	DPI		
	Cases (n = 185)	Controls (n = 185)	P	T1(n = 123) < 32.9	T2(n = 124) 32.9–45.5	T3(n = 123) > 45.5	P	
Age(years)	50.4 ± 8.2	50.7 ± 7.3	0.9a	51.4 ± 8.2	49.6 ± 8.02	51.2 ± 7.11	0.1b	
Diabetes duration(years)	7.08 ± 2.1	7.03 ± 2.08	0.8a	6.86 ± 2.04	7.03 ± 2.15	7.28 ± 2.15	0.2b	
BMI (kg/m2)	28.9 ± 2.3	28.2 ± 2.3	0.002a	28.5 ± 2.37	28.6 ± 2.18	28.5 ± 2.56	0.9b	
FBS (mg/dl)	161.7 ± 58.2	165.3 ± 53.4	0.5a	162.6 ± 57.4	167 ± 58.9	160.8 ± 51.1	0.6b	
BS2hp(mg/dl)	242.9 ± 86.4	226 ± 80.7	0.5a	234 ± 80.9	239.3 ± 96.9	236.1 ± 84.2	0.8b	
HbA1c%	8.2 ± 1.5	7.9 ± 1.4	0.03a	8.02 ± 1.53	8 ± 1.42	8.21 ± 1.5	0.4b	
Physical activity (MET-min

/week)d

								
Low intensity (%)	61.6	67.6	0.3c	28.9	34.3	36.8	0.1c	
Moderate (%)	30.3	27		41.5	32.1	26.4		
High intensity (%)	8.1	5.4		40	32	28		
Females (%)	63.8	63.8	0.5c	29.2	36.4	34.3	0.08c	
Married (%)	99.5	100	0.6c	33.1	33.6	33.3	0.3c	
Education (%)								
Literate (%)	94.1	94.6	0.5c	33	33.5	33.5	0.8c	
Illiterate (%)	5.9	5.4		38.1	33.3	28.6		
Current smoker (%)	10.3	4.3	0.02c	37	29.6	33.3	0.8c	
Job								
Working (%)	43.8	48.6	0.2c	36.8	30.4	32.7	0.3c	
Housewife (%)	56.2	51.4		30.2	36.2	33.7		
The results are described as mean ± standard deviation or (%). D.

PRAL: potential renal acid load, NEAP: net endogenous acid production, BS2hP: Blood Sugar after 2 h, BMI: body mass index, FBS: fasting blood sugar, HbA1C: glycated hemoglobin A1c, MET: metabolic equivalent of task.

aIndependent sample t-test.

bANOVA test.

cChi‐square test.

dLow physical activity: lower than 600 MET‐min/week. Moderate: 600–3,000 MET‐min/week. High intensity: more than 3,000 MET‐min/week.

Results

Total of 185 subjects with DSPN and 185 sex- and age-matched people in the control group were included in the analysis. The comparison of general characteristics of participants between cases and controls across tertiles of DPI are provided in Table 1. The mean of BMI (p = 0.002), HbA1c (p = 0.03), and current smoker (p = 0.02) were significantly higher in cases. No other significant differences were observed between case and controls in terms of age, diabetic duration, FBS, BS2hp, physical activity level, gender, marriage status, education status and job (p > 0.05).

In regard to DPI, No significant differences were also seen in regard of other variables including age, diabetes duration, BMI, FBS, BS2hp, HbA1c, physical activity level, female, marriage status, education status, current smoker and job across tertiles of DPI (p > 0.05).

Dietary intakes of study participants across tertiles of DPI are presented in Table 2. Compared to the lowest tertile of DPI, participants in highest tertile, had higher energy intakes (p = 0.0001), lower carbohydrate intake (p = 0.006), higher potassium intake (p = 0.001), higher calcium intake (p = 0.01), lower magnesium intake (p = 0.0001), higher dietary fiber (p = 0.0001), lower whole grains (p = 0.0001). Also, there was a significant difference in fruits intakes (p = 0.0001) across the tertile of DPI. Table 2 Energy-adjusted dietary intakes of patients across tertiles of the dietary phytochemical index.

	DPI		
	T1(n = 123) < 32.9	T2(n = 124) 32.9–45.5	T3(n = 123) > 45.5	P*	
	Mean ± SE	Mean ± SE	Mean ± SE		
Energy (kcal/d)	2658 ± 34.2	2498 ± 34.1	2333 ± 34.1	< 0.001	
Carbohydrates (g/d)	390.6 ± 6.35	394.6 ± 6.15	418.1 ± 6.33	0.006	
Proteins (g/d)	98.45 ± 19.37	120.5 ± 18.73	140.9 ± 19.3	0.3	
Total fats (g/d)	78.9 ± 3.66	79.9 ± 3.54	82.2 ± 3.65	0.8	
SFA (g/d)	20.73 ± 1.23	23.22 ± 1.19	22.79 ± 1.22	0.3	
MUFA (g/d)	24.23 ± 0.39	25.30 ± 0.38	25.91 ± 0.39	0.01	
PUFA(g/d)	17.92 ± 0.28	17.66 ± 0.27	17.77 ± 0.28	0.8	
Vitamin A(RE/d)	556.8 ± 40.6	616.9 ± 39.2	664.6 ± 40.4	0.1	
Vitamin C(mg/d)	89.17 ± 26.56	139.9 ± 25.6	135.2 ± 26.4	0.3	
Vitamin E(mg/d)	13.48 ± 0.32	13.79 ± 0.31	14.02 ± 0.32	0.5	
Vitamin D(mg/d)	1.87 ± 0.21	2 ± 0.20	2.32 ± 0.21	0.3	
Phosphorus(mg/d)	1362 ± 52.25	1384 ± 50.70	1416 ± 52.04	0.7	
Potassium(mg/d)	3353 ± 70.8	3461 ± 68.4	3729 ± 70.5	0.001	
Calcium(mg/d)	1244 ± 24.5	1224 ± 23.71	1325 ± 24.43	0.01	
Magnesium(mg/d)	477.9 ± 9.67	535.6 ± 9.38	641.6 ± 9.68	< 0.001	
Fe(mg/d)	22.32 ± 9.6	33.45 ± 9.29	41.15 ± 9.57	0.3	
Dietary Fiber(g/d)	82.73 ± 2.01	74.01 ± 1.95	70.65 ± 2.01	< 0.001	
Food groups					
Refine grains(g/d)	410.2 ± 12.9	394.1 ± 12.53	414.4 ± 12.9	0.4	
Whole grains(g/d)	121.8 ± 6.89	228.1 ± 6.66	374.5 ± 6.66	< 0.001	
Fruits(g/d)	161.4 ± 4.88	192.9 ± 4.72	177.5 ± 4.86	< 0.001	
Vegetables(g/d)	392.2 ± 10.96	408.7 ± 10.6	404.7 ± 10.93	0.5	
Dairy Products(g/d)	342.1 ± 12.63	335.7 ± 12.22	352.5 ± 12.59	0.6	
*All values were adjusted for age, sex and energy, except for dietary energy intake, which was only adjusted for age and sex using ANCOVA.

Crude and multivariable-adjusted OR (95% CI) for DSPN across tertiles of DPI for the whole population are provided in Table 3. Compared to the first tertile of DPI, participants in second tertile had lower prevalence of DSPN (OR 0.68, 95% CI 0.4, 1.1) in crude model. Adjustment of age, sex, and energy in model 1 (OR 0.64, 95% CI 0.38, 1) even adjustment of other confounding variables such as energy intake, physical activity, education, smoking status, and HbA1c in model 2 (OR 0.63; 95%CI 0.37, 1) and adjustment of BMI in model 3 (OR 0.62, 95% CI 0.36, 1) showed lower prevalence of DSPN in subjects in the second tertile to the first tertile of DPI. Table 3 Odds ratios (ORs) and 95% confidence intervals (95% CIs) of DSPN according to tertiles of the dietary phytochemical index.

		DPI			
	T1(n = 123) < 32.9	T2(n = 124) 32.9; 45.5	T3(n = 123) > 45.5	P-trend*	
		OR	95%CI	OR	95%CI		
Crude	1	0.68	0.4; 1.1	0.31	0.18; 0.52	 < 0.001	
Model 1	1	0.64	0.38; 1	0.27	0.15;48	 < 0.001	
Model 2	1	0.63	0.37; 1	0.25	0.14; 0.45	 < 0.001	
Model 3	1	0.62	0.36; 1	0.24	0.13; 0.44	 < 0.001	
*Binary logistic regression was used to obtain OR and 95% CI. The overall trend of OR across increasing tertiles was examined by considering the median score in each category as a continuous variable.

Model 1: adjusted for age (continuous), sex (male/female) and energy intake (kJ/d or kcal/d).

Model 2: further adjustments were made for physical activity (low intensity, moderate, high intensity), education (literate, illiterate), smoking status (yes, no), and HbA1c(continuous).

Model 3: additionally adjusted for BMI (continuous).

Compared to participants in first tertile, the third tertile in crude model showed lower prevalence of DSPN across the tertile of DPI (OR 0.31; 95%CI 0.18, 0.52; p = 0.0001). After controlling for age, sex, and energy in model 1, a significant association was observed between DPI and DSPN (OR 0·27; 95%CI 0·15, 0·48; P < 0·001). Moreover, adjusting for a wide range of confounding variables such as energy intake, physical activity, education, smoking status, and HbA1c in model 2 (OR 0.25; 95%CI 0.14, 0.45, p = 0.0001) and adjusting for BMI in model 3 (OR 0.24, 95% CI 0.13, 0.14, p = 0.0001) strengthened the association.

Discussion

This case-control study showed that the highest DPI could be associated with decreased odds of risk of DSPN in patients with type 2 diabetes mellitus (T2DM). To the best of our knowledge, no study has yet explored the relationship between the DPI score and the risk of DSPN.

A higher DPI score might be associated with an improved overall diet quality; dietary intakes of antioxidants such as vitamins A, C, and E and dietary fiber also increased. In our study, a DPI might be associated with odds of DSPN. In line with our findings, it has been found that consuming phytochemical-rich foods is associated with a lower odds of abdominal obesity and hypertriglyceridemia, the main risk factors for cardio metabolic disease40,41. According to a cross-sectional study, in healthy young adults, DPI is inversely associated with adiposity and oxidative stress and is responsive to body weight changes42. In addition, investigating the association between the DII and pre-diabetes revealed that patients with higher DPI scores have a lower pre diabetes OR43. Alleviation of pain but not glucose control have been reported in group consuming phytochemical-rich foods19. Another study reported that Vegetarian diet with high DPI score can worsen neuropathy maybe because the role of vitamin B12 in myelination20. According to previous studies to reach a nude relationship between DPI and DSPN, various demographic and dietary confounders such as age, sex, energy intake, physical activity, education, smoking status, HbA1c, and BMI were controlled in this analysis. Adjusting the results for these variables was accompanied by further decrease in prevalence of DSPN in diabetic patients. Several studies within the last decade has been reported that higher BMI and HbA1c, old age, smoking and male gender are factors that can increase the severity of neuropathy44–48. In addition getting more energy49 and less physical activity50 is accompanied by increased insulin resistance and obesity, and as a result can develop diabetes and diabetic neuropathy. Also education can also affect diabetes and diabetic neuropathy by changing the level of awareness and lifestyle51.

Several mechanisms may explain inverse associations between DSPN and DPI. A potential cause of DSPN is oxidative stress because of chronic hyperglycemia, which occurs due to an imbalance between the production of reactive oxygen species and antioxidant defenses. Oxidative stress may be reduced by eating foods high in phytochemicals52. Research suggests that the consumption of plant-based foods high in phytochemicals, especially polyphenols, may help reduce insulin resistance52. In addition, the polyphenols specifically regulate insulin secretion and protect pancreatic beta-cells from oxidative damage53. In chronic hyperglycemia, several pathways are activated, including the protein kinase C pathway, the polyol pathway, glucose oxidation, and hexosamine flux54. Therefore, hyperglycemia through these pathways activate pro-inflammatory cytokines such as interleukin 1, interleukin 6, and TNF-α54. The anti-inflammatory properties of phytochemicals are demonstrated by their ability to decrease biomarkers of inflammation, including TNF-α and IL-652. Phytochemicals mediate their effects through the modulation of signal transduction processes by transcription factors, growth factors, inhibition of inflammatory cytokines expression, regulation of enzymes, such phospholipases, cyclooxygenases, protein kinases, and protein phosphatases55. Phytochemicals also mediate their effects through the modulation of immune function because many major antioxidant defense mechanisms are localized in astrocytes56. Aging, genetic disposition, and environmental factors induce oxidative stress, and iron homeostasis leading to symptoms of neurological disorders. Consumption of phytochemicals results in the expression of stress resistance genes responsible for encoding antioxidant enzymes, protein chaperones, and neurotrophic factor (BDNF)57. Although phytochemicals have no effects on positive family history and gender, their long-term use delaying or slowing the onset of stroke, AD, PD, and other neurodegenerative diseases.58. There are some strengths in this study. First, the present study is the first to investigate the association between DPI and DSPN. Second, to reduce the bias caused by changed dietary habits in participants, new cases of DSPN were enrolled. Third, diet and physical activity data were collected using validated questionnaires. Forth, different demographic and dietary confounders as age, sex, energy intake, physical activity, education, smoking status, HbA1c, and BMI were controlled in this analysis.

However, our study had some limitation and the study limitations should be considered when interpreting study results. first, as our study is based on observation, we cannot infer causal relationships from our findings. As a result, any potential association should be tested directly in future studies. Second, participants were exclusively sourced from a singular center in Kermanshah, Iran, but for greater generalizability and for considering other factors that could affect the external validity of the results, we selected center (Diabetes Research Center) which is the most important specialized center for diabetic patients in the capital city of Kermanshah province, many of the clients who are referred to this center are from different areas of Kermanshah city and even the surrounding cities. Selecting this center provide us select our included participants from various region with different genetic, demographic, cultural, or dietary characteristics. Third, FFQ was used for assessing food intake, which could result in recall bias or misclassification. To reduce the recall bias, we got the help of one expert nutritionist and face-to-face interview. Also, using FFQ questionnaire is very common in nutrition studies which its validity and reliability have been confirmed in past studies.

Forth, despite our efforts to control for potential confounding factors within the study, it's crucial to acknowledge the possibility of unmeasured or residual confounding variables that might impact the observed associations. Fifth, a wider CI suggests greater uncertainty surrounding the association between dietary patterns index (DPI) and diabetic sensorimotor polyneuropathy (DSPN). This uncertainty could stem from various sources, including sample size limitations, measurement errors, or unmeasured confounding variables. While wider CIs may not necessarily imply weaker associations, they do reflect the degree of uncertainty in the estimates. Therefore, it's important to consider the width of the CIs when interpreting the findings, as it provides insights into the reliability and robustness of the observed associations. Sixth, using case control design, resulting lack of controlling selection bias and recall bias affecting the validity of the result. To reduce the selection bias, we selected the case and control groups from the same center. In addition, strict inclusion criteria for entering our study subjects caused lower number of subjects available for sampling, we used simple sampling method and all eligible people were included in the study. Also, to reduce the recall bias, we got the help of an expert nutritionist to help subjects remember their information in a specialized and consistent way. Seventh, the generalizability of the study results to the population, we excluded patients who had BMI < 25 and those who used insulin or had other chronic diseases, so it is possible to generalize the results to this same population. Eighth: by tertiling continuous DPI index, the statistical power of the study may decrease, however, we used this method to make the interpretation of the results easier. Also, because tertiling DPI has been a routine method in previous nutritional studies, it is possible to compare the results of this study with other studies. Ninth: multiple comparisons across different models (crude model, model 1, model 2, and model 3) without adjusting for multiple testing increases the likelihood of Type I errors. Tenth: limitations of relying solely on p-values to determine statistical significance, and highlighting the importance of considering effect sizes, confidence intervals, and the overall reproducibility of the results.

In conclusion, we found that higher DPI was associated with a lower odds of DSPN in patients with type 2 diabetes. Compared to highest tertile of DPI, individuals with lowest tertile of DPI had a lower chance of diabetic neuropathy (OR 0.24, 95% CI 0.13, 0.14, p = 0.0001). DPI is the percentage of energy intake from foods rich in phytochemicals such as fruits and vegetables, legumes, whole grains, seeds, nuts, natural juices, olive oil, and soy products. It seems that food choice with higher DPI would be more effective in improving DSPN. According to diabetic neuropathy prevalence in Iran, it seems that 76% reduction in DSPN prevalence across tertile 3 to tertile 1 of DPI is clinically significant. Ideally, these preliminary findings should be confirmed by prospective studies or studies with clinical trial design. The population should be selected in such a way that the generalization of the results to a wider range of that population would be more possible.

Acknowledgements

This study was conducted in the Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran. We thank the authorities who helped us and all patients who participated in this research.

Author contributions

S.A. and S.A. and F.S. contributed to the conception of the study, design, statistical analyses, data interpretation and drafting of this manuscript. A.M., and S.N. contributed to the design and interpretation of the results. M.F. contributed in editing the revision form. A.A. supervised the study.

Funding

This study was supported financially by Tehran University of Medical Sciences.

Data availability

Data and material are available and it can be available by emailing to the correspond of the study.

Competing interests

The authors declare no competing interests.

Ethical approval

Ethical approval for the present study was given by the Tehran University of Medical Science Ethics Committee. TUMS.VCR.REC. 1399.269.

Consent to participate

Informed consent was obtained from all participants in the study and all methods were conducted in accordance with relevant guidelines and regulations.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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59. Table1 General participant characteristics
