• Users Online: 702
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Ahead of print Browse Articles Search Archives Submit article Instructions Subscribe Contacts Login 


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 12  |  Issue : 1  |  Page : 7

Healthy Dietary Pattern is Related to Blood Lipids in Patients with Type 1 Diabetes Mellitus: A Cross-sectional Study from a Developing Country


1 Department of Community Nutrition, Zahra Sajjadpour School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Karaj, Iran
2 Department of Community Nutrition, Fereydoun Siassi School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Karaj, Iran
3 Diabetes Research Center, Ensieh Nasli-Esfahani Endocrinology and Metabolism Clinical Sciences Institue, Tehran University of Medical Sciences, Tehran, Iranian Diabetes Association Asadollah Rajab, Iran
4 Non-communicable Diseases Research Center, Mostafa Qorbani Alborz University of Medical Sciences, Karaj; Department of Community Nutrition, Gity Sotoudeh School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran

Date of Submission10-Dec-2018
Date of Acceptance19-Mar-2019
Date of Web Publication19-Jan-2021

Correspondence Address:
Fereydoun Siassi
Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Hojatdost Street, Naderi Street, Keshavarz Blv., Tehran
Iran
Gity Sotoudeh
Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Hojatdost Street, Naderi Street, Keshavarz Blv., Tehran
Iran
Mostafa Qorbani
Non-communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj
Iran
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijpvm.IJPVM_554_18

Rights and Permissions
  Abstract 


Background: The association between dietary patterns and cardiovascular disease (CVD) risk factors has been investigated in very limited studies in patients with type 1 diabetes mellitus (T1DM). The aim of this study was to determine the relationship between the major dietary patterns and CVD risk factors in these patients. Methods: A cross-sectional study was performed on 169 females of 18--35 years who were diagnosed with T1DM attending Iranian Diabetes Association in Tehran. Anthropometric measures, blood glucose, and lipid levels of all participants were measured. Dietary data was collected using a food frequency questionnaire. Dietary patterns were determined by factor analysis. Using the analysis of covariance (ANCOVA), mean value of the biochemical factors across the tertiles of dietary patterns was compared. Results: Three major dietary patterns were identified: the grain, legume and nut (GLN), the fruits and vegetables (FV), and the high calorie foods, salty snacks, sweet and dessert (HSD). After adjustment for age, body mass index and energy intake, subjects who were in the highest tertile of FV pattern had significantly lower levels of LDL-c (P = 0.01), triglyceride (TG) (P = 0.02), and total cholesterol (P = 0.01). GLN and HSD patterns had no significant relationship with blood glucose and lipids. Conclusions: This study demonstrates that a dietary pattern rich in vegetables and fruits may be inversely associated with dyslipidemia in patients with T1DM. The results can be used for developing interventions that aim to promote healthy eating for the prevention of CVD in these patients.

Keywords: Blood lipids, cardiovascular, dietary pattern, type 1 diabetes


How to cite this article:
Sajjadpour Z, Nasli-Esfahani E, Siassi F, Rajab A, Qorbani M, Sotoudeh G. Healthy Dietary Pattern is Related to Blood Lipids in Patients with Type 1 Diabetes Mellitus: A Cross-sectional Study from a Developing Country. Int J Prev Med 2021;12:7

How to cite this URL:
Sajjadpour Z, Nasli-Esfahani E, Siassi F, Rajab A, Qorbani M, Sotoudeh G. Healthy Dietary Pattern is Related to Blood Lipids in Patients with Type 1 Diabetes Mellitus: A Cross-sectional Study from a Developing Country. Int J Prev Med [serial online] 2021 [cited 2021 Feb 26];12:7. Available from: https://www.ijpvmjournal.net/text.asp?2021/12/1/7/307482


  Introduction Top


There are 425 million individuals with type 1 (T1DM) or type 2 diabetes mellitus (T2DM) around the globe. Five percent of this figure is T1DM. The risk of developing cardiovascular disease (CVD), dyslipidemia including low blood levels of HDL-c and high levels of total cholesterol (TC), triglyceride (TG), LDL-c, and blood pressure in these patients is two to four times more than nondiabetic individuals.[1]

Diet as an important modifiable factor is related to CVD risk factors.[2] Identifying dietary patterns and assessing their relationship with chronic diseases is better than studying the relationships between single nutrients or foods and diseases because the nutrients of different foods may have diminishing or exacerbating effects.[3] There are two main approaches for determination of dietary patterns, priori and posteriori approaches. In a priori approach, dietary patterns are identified based on previous knowledge of healthy foods. On the other hand, in a posteriori approach, statistical methods are applied to the dietary data in order to identify patterns.[3]

American Diabetes Association (ADA) does not recommend a specific percentage of calories from carbohydrate, protein, and fat for diabetic patients. However, it encourages healthy food choices such as vegetables, fruits, whole grains, and monounsaturated and omega-3 fatty acids.[3]

The study of dietary patterns and CVD risk factors such as dyslipidemia and hypertension in T1DM patients is very limited.[4],[5],[6] Some studies conducted among T1DM patients that showed healthy dietary pattern with high intakes of vegetables, fruits, fish, and yoghurt were related to better glycemic control in T1DM patients.[4] On the other hand, the direct relationship of unhealthy dietary pattern, featured with high content of high-fat cakes, and pickled vegetables, with serum LDL-c and glycated hemoglobin (HbA1c) levels in these patients has been reported.[5] In addition, a dietary pattern characterized by high intakes of eggs, sweetened beverages, diet soda, potatoes, high-fat meat, and low intakes of sweets/desserts and low-fat dairy was associated with CVD risk factors including dyslipidemia, hypertension, and waist circumference in T1DM.[6] High content of nutrients and phytochemicals in healthy foods such as fiber, vitamins, minerals, and other compounds may have beneficial effect on lipid profile, blood pressure, and body weight.[7]

It has been argued that dietary patterns are influenced by sex, geographic, social, environmental, and cultural factors. So it is better to do these investigations in diverse population.[8] The aim of this study was to find the association of dietary patterns and some cardiometabolic factors in female adults with T1DM.


  Methods Top


Subjects

The present study is part of a cross-sectional study which aimed to investigate the relationship between dietary intake and CVD risk factors in patients with T1DM. The sample size was calculated using the following equation:



A total of 96 diabetic patients were calculated in each tertile of dietary intake based on total fat intake,[9] with 95% confidence level and 90% power. The total sample size was 288. In practice, 273 patients with T1DM were recruited. As gender differences have been reported in the dietary patterns and their association with the metabolic syndrome,[10] we restricted the present study to only 169 female patients. The participants were recruited from Iranian Diabetes Association and Diabetes Research Center of Tehran University of Medical Sciences located in Tehran, Iran, from January 2016 to March 2018. Patients attending these centers were invited to participate in the study. Inclusion criteria were diagnosed with T1DM for at least 6 months, 18--35 years, and HbA1c less or equal to 8%. The exclusion criteria were pregnancy or lactation, smoking, any other diagnosed diseases such as liver and kidney diseases, CVD, cancer, and using any other medication except insulin. The study was completely explained to the participants and a written consent form was obtained from them. The research has been approved by the Ethics Committee of the Tehran University of Medical Sciences.

Dietary analysis

To evaluate the dietary intake, a 147 item, semiquantitative food frequency questionnaire (FFQ) was used.[11] The patients were requested to mention the consumption of the food items contained in this questionnaire in terms of per day, week, month, and year, and also the amount in each time, keeping in mind their food consumption in the past year. The reliability and validity of the questionnaire was confirmed.[11] The food items were converted to g/d and analyzed for their energy content using the Nutritionist 4 software modified for Iranian foods.

Anthropometry and physical activity assessment

Height was recorded from the medical file of patients and weight was measured with minimal clothing and shoes off, on a digital scale with the accuracy of 100 g. Body mass index (BMI) was calculated as the division of weight by square height. Waist circumference was measured with an accuracy of 0.5 cm, midway between the last rib and the iliac crest. All measurements were done by the first author as a nutrition student.

The short form of international physical activity questionnaire (IPAQ) was used to assess physical activity of participants during the previous 7 days.[12] To compute activities, the duration and number of activities were multiplied by the metabolic equivalent task (MET) value of the activity and summed all activities to gain an estimate of total physical activity.

Laboratory measurements

Due to funding limitations, 58 women, using random sample table, were selected from the participants for biochemical evaluation. After 12 h of fasting, 10 ml of venous blood sample was collected. The blood samples were centrifuged for 10 min at 3,000 rpm, in order to separate the serum. The serum was used to assess fasting blood sugar (FBS), HDL-c, TG, and TC levels. Pars Azmoon kit (manufactured in Iran) was used to assay the latter biochemical factors. TG was measured by enzymatic colorimetry and photometry.[13] FBS and total cholesterol levels were detected with enzymatic colorimetry and single point with photometric method.[13],[14] LDL-c was calculated through Freidewald formula.[15]

Statistical analysis

The SPSS, version 24 (Armonk, NY: IBM Corp), was used for analysis. As it has been suggested, for dealing with under or over reporting of energy intake in women,[16] those who had reported energy intake out of 500 to 3,500 kcal per day were excluded from the study (n = 6).[16] The Kolmogorov–Smirnov test was used to evaluate the normality of the data. Logarithmic transformation was applied to skewed variables. The geometric mean (standard error of mean) was calculated for the transformed data. Dietary patterns were obtained based on factor analysis using the principal component analysis method.[17] First, the foods were categorized into 13 groups in terms of food items similarity and a previous study.[18] The number of factors was chosen by the scree plot, and eigenvalues of over 1.[19] A varimax rotation was carried out to create a simple and definitive component matrix.[19] The factor score for each pattern was calculated by summing intakes of food groups weighted by their factor score matrix. Then we categorized participants into tertiles of dietary patterns scores. To evaluate the relationship between education and dietary supplement intake with dietary patterns, Chi-square test was used. To compare the mean value of the biochemical factors across the tertiles of dietary patterns, a one-way ANOVA test was employed. Further, using the analysis of covariance (ANCOVA), the mean value of the biochemical factors across the tertiles of dietary patterns was compared with adjustment for age, BMI, and energy intake.


  Results Top


In this study, 179 women were invited to participate. Six patients refused to participate and four women were not eligible for the study. In addition, six patients reported energy intake less than 500 kcal and more than 3,500 kcal which were excluded from the analysis. So the final analysis was performed on 163 patients. The mean age of the participants was 25.5 ± 5.1 years, with T1DM duration of 13.1 ± 5.9 years. Their mean serum HbA1c level was 6.9% ± 0.8% [Table 1].
Table 1: Characteristics of patients with type 1 diabetes mellitus

Click here to view


Three major dietary patterns were found: the grain, legume, and nut (GLN), the fruits and vegetables (FV), and the high calorie foods, salty snacks, sweet and dessert (HSD). [Table 2] shows the food groups and [Table 3] shows the factor loading of each of the three obtained dietary patterns. The three presented dietary patterns cover 33.87% of the total variance of all exploratory dietary patterns. The variance for GLN, FV, and HSD patterns were 11.4%, 11.4% and 11.0%, respectively.
Table 2: Food groups and their components

Click here to view
Table 3: Factor loading for major dietary patterns

Click here to view


In the FV dietary pattern, subjects in the third tertile were older than the subjects of the first tertile (P < 0.001). In the GLN dietary pattern, subjects in the third tertile had lower BMI (P = 0.01). In addition, in all three dietary patterns, energy intake in the third tertiles was significantly higher than the first tertiles (P < 0.04) [Table 4]. No relationship was found between dietary patterns and education or dietary supplement intake [Table 5].
Table 4: Mean and standard deviation of variables across dietary patterns tertiles in female adults with type one diabetes (n=163)

Click here to view
Table 5: Demographic variables distribution across dietary patterns tertiles in female adults with type one diabetes (n=163)

Click here to view


After adjustment for age, BMI, and energy intake, subjects in the third tertile of the FV pattern had a significantly lower serum LDL-c (P = 0.01), TG (P = 0.02), and TC (P = 0.01) compared with the subjects in the first tertile. This difference for FBS was close to significant (P = 0.06). Subjects in the third tertile of the GLN pattern had lower serum FBS and some blood lipids. In addition, adherence to HSD pattern was associated to higher serum FBS and higher blood lipids. However, these differences were not significant [Table 6].
Table 6: Mean and standard deviation of cardiovascular disease risk factors across dietary patterns tertiles in female adults with type one diabetes mellitus (n=58)

Click here to view



  Discussion Top


In this cross-sectional study, which is the first study to investigate dietary patterns in Iranian females with T1DM, major dietary patterns have been determined and the association of these dietary patterns and some CVD risk factors was assessed in a subgroup of patients. The results of this study show that the FV dietary pattern is inversely related to serum LDL-c, TG, and TC. However, no significant relationship was found between CVD risk factors and other dietary patterns.

A diet rich in vegetables, fruits, fish, and yogurt was related to better glycemic control in T1DM patients.[4] Another study found that a dietary pattern low in wheat products and high in cakes, beans and pickled vegetable was positively associated with serum LDL-c and HbA1c.[5] In addition, high adherence to a dietary pattern characterized by high intakes of eggs, sweetened beverages, diet soda, potatoes, high-fat meat, and low intakes of sweets/desserts, and low-fat dairy was associated with better lipid profile, blood pressure, and waist circumference in T1DM.[6] Different age of participants, different dietary intake measurements (FFQ or other methods), and different dietary analysis (factor analysis or reduced rank regression methods) may in part explain these differences.

In the present study, the FV dietary pattern was loaded with high fruits and vegetables. In addition, it contains higher amounts of legume, nuts, and liquid oils. This pattern may have a beneficial effect on serum TG, LDL-c, and TC. Fruits and vegetables intake is known to improve glucose and lipid disturbances.[20] Furthermore, consumption of legumes has beneficial effects on CVD risk factors.[21]

In this study, unexpectedly, no relationship was observed between the GLN and HSD patterns and CVD risk factors. The GLN dietary pattern which is rich in grain, legume and nut (GLN) also contains dairy, sweets, desserts, olive, and pickled vegetable. This finding might be due to high consumption of grains in this pattern, which are refined grains. Refined grains contain complex carbohydrate, which increase serum TG and result in dyslipidemia.[22] The HSD dietary pattern is also high in sweets and dessert, high calorie foods, and salty snacks. All of these food groups have higher content of trans-fatty acids, simple carbohydrate, and salt which have adverse effect on blood lipids.[22],[23]

There were limitations to this study. First, collecting dietary data using FFQ rely on participants' memory. Second, factor analysis does not have any special protocol to follow. The food groups are arranged by the researcher based on experience. Third, cross-sectional studies only observe the current situation and no reason or cause is understood completely. Fourth, the findings of the present study might not be generalized to all T1DM patients including male patients. Fifth, residual confounding remains a possible concern for our results, since besides dietary pattern, food consumption behaviors such as timing, number of meals, and ways of preparing the food are also important.[24] However, the present study is one of the few studies that have examined the relationship between dietary patterns and CVD risk factors in T1DM.


  Conclusions Top


We have found that the FV dietary pattern is inversely related to serum LDL-c, TG, and TC in females with T1DM which is implicated in the development of CVD in these patients; hence, female patients can reap the benefits of healthy dietary pattern to reduce the risk of CVD.

Acknowledgements

We would like to thank all the participants who helped us in this research and the two institutes that we gathered our subjects from: Iranian Diabetes Association and Diabetes and Metabolic Disorders Specialty Clinic of Tehran University of Medical Sciences. This research has been supported by International Campus of Tehran University of Medical Sciences. Grant number 94-03-103-30046.

Financial support and sponsorship

This research was granted by International Campus of Tehran University of Medical Sciences. Grant number 94-03-103-30046.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
IDF Diabetes Atlas. 8th ed. 2017, Available from: www.idf.org. [Last accessed on 2019 Jan 12].  Back to cited text no. 1
    
2.
Yusuf S, Hawken S, ™unpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): Case–control study. Lancet 2004;364:937-52.  Back to cited text no. 2
    
3.
Hu FB. Dietary pattern analysis: A new direction in nutritional epidemiology. Curr Opin Lipidol 2002;13:3-9.  Back to cited text no. 3
    
4.
Ahola AJ, Freese R, Makamittila S, Forsblom C, Groop PH. Dietary patterns are associated with various vascular health markers and complication in type 1 diabetes. J Diabetes Complications 2016;30:1144-50.  Back to cited text no. 4
    
5.
Jaacks LM, Crandell J, Mendez MA, Lamicchane AP, Liu W, Ji L, et al. Dietary pattern associated with HbA1c and LDL cholesterol among individuals with type 1 diabetes in China. J Diabetes Complications 2015;29:343-9.  Back to cited text no. 5
    
6.
Lamichhane AP, Liese AD, Urbina EM, Crandell JL, Jaacks LM, Dabelea D, et al. Associations of dietary intake patterns identified using reduced rank regression with markers of arterial stiffness among youth with type 1 diabetes. Eur J Clin Nutr 2014;68:1327-33.  Back to cited text no. 6
    
7.
Slavin JL, Lloyd B. Health benefits of fruit and vegetable. Adv Nutr 2012;3:506-16.  Back to cited text no. 7
    
8.
Freeman MP. Nutrition and psychiatry. Am Psychiatry 2010;167:244-7.  Back to cited text no. 8
    
9.
Homma TK, Endo CM, Saruhashi T, Ivata Mori AP, de Noronha RN, Monte O, et al. Dyslipidemia in young patients with type 1 diabetes mellitus. Arch Endocrinol Metab 2015;59:215-9.  Back to cited text no. 9
    
10.
Xu SH, Qiao N, Huanj JJ, Sun CM, Cui Y, Tian SS, et al. Gender differences in dietary patterns and their association with the prevalence of metabolic syndrome among Chinese: A cross-sectional study. Nutrients 2016;8:180.  Back to cited text no. 10
    
11.
Hosseini Esfahani F, Asghari G, Mirmiran P, Azizi F. Reproducibility and relative validity of food group intake in a food frequency questionnaire developed for the Tehran lipid and glucose study. Int J Epidemiol 2010;20:150-8.  Back to cited text no. 11
    
12.
Lee PH, Macfarlane DJ, Lam TH, Stewart SM. Validity of the international physical activity questionnaire short form (IPAQ-SF): A systematic review. Int J Behav Nutr Phys Act 2011;8:115.  Back to cited text no. 12
    
13.
Rifai N, Bachorik PS, Albers JJ. Lipids, lipoproteins and apolipoproteins. In: Burtis CA, Ashwood ER, editors. Tietz Textbook of Clinical Chemistry. 3rd ed. Philadelphia: W. B Saunders Company; 1999. p. 809-61.  Back to cited text no. 13
    
14.
Barham D, Trinder P. An improved color reagent for the determination of blood glucose by the oxidase system. Analyst 1972;97:142-5.  Back to cited text no. 14
    
15.
Tremblay AJ, Morissette H, Gagne JM, Bergeron J, Gagne C, Couture P. Validation of the Freidewald formula for the determination of low-density lipoprotein cholesterol compared with beta qualification in a large population. Clin Biochem 2004;37:785-90.  Back to cited text no. 15
    
16.
Banna JC, McCrocy MA, Fialkowski MK. Examining plausibility of self-reported energy intake data: Consideration for method selection. Front Nutr 2017;4:45.  Back to cited text no. 16
    
17.
Gleason PM, Boushey CJ, Harris JE, Zoellner J. Publishing nutrition research: A review of multivariate techniques – part 3: Data reduction methods. J Acad Nutr Diet 2015;115:1072-82.  Back to cited text no. 17
    
18.
Zarodi M, Mirmiran P, Fazel A. The association between major dietary pattern and type 2 diabetes. Health Sys Res Nutrition Supplement 2013;679-95.  Back to cited text no. 18
    
19.
Jackson JE. Statistical factor analysis and related methods: Theory and applications. Technometrics 1995;37:226-8.  Back to cited text no. 19
    
20.
Kushi LH, Meyer KA, Jacobs DR. Cereals, legumes, and chronic disease risk reduction: Evidence from epidemiologic studies. Am J Clin Nutr 1999;70:451s-8s.  Back to cited text no. 20
    
21.
Mozaffarian D, Katan MB, Ascherio A, Stampfer MJ, Willet WC. Trans fatty acids and cardiovascular disease. N Engl J Med 2006;354:1601-13.  Back to cited text no. 21
    
22.
Siri-Tarino PW, Sun Q, Hu FB, Krauss RM. Meta-analysis of prospective cohort studies evaluating the association of saturated fat with cardiovascular disease. Am J Clin Nutr 2010;91:535-46.  Back to cited text no. 22
    
23.
Hodges RE, Krehl WA. The role of carbohydrates in lipid metabolism. Am J Clin Nutr 1965;17:334-46.  Back to cited text no. 23
    
24.
Tseng M. Validation of 0dietary patterns assessed with a food-frequency questionnaire. Am J Clin Nutr 1999;70:422.  Back to cited text no. 24
    



 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Methods
Results
Discussion
Conclusions
References
Article Tables

 Article Access Statistics
    Viewed136    
    Printed0    
    Emailed0    
    PDF Downloaded22    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]