|Year : 2018 | Volume
| Issue : 2 | Page : 59-64
Association of dietary patterns with glycated haemoglobin among Type 2 diabetics in Karachi, Pakistan
Rubina Hakeem1, Mahwish Shiraz2, Musarrat Riaz3, Asher Fawwad4, Abdul Basit3
1 Department of Nutrition, Taibah University, Madinah Almuawwara, Saudi Arabia; Department of Research, Baqai Institute of Diabetology and Endocrinology, Baqai Medical University, Karachi, Pakistan
2 Department of Diet and Education, Baqai Institute of Diabetology and Endocrinology, Baqai Medical University, Karachi, Pakistan
3 Department of Medicine, Baqai Institute of Diabetology and Endocrinology, Baqai Medical University, Karachi, Pakistan
4 Department of Research, Baqai Institute of Diabetology and Endocrinology, Baqai Medical University; Department of Biochemistry, Baqai Medical University, Karachi, Pakistan
|Date of Web Publication||10-May-2018|
Dr. Asher Fawwad
Baqai Medical University, Karachi; Baqai Institute of Diabetology and Endocrinology, Plot No. 1-2, II-B, Nazimabad No. 2, Karachi 74600
Source of Support: None, Conflict of Interest: None
Background: Dietary habits and sedentary lifestyle are major risk factors for rapidly rising incidence of type 2 diabetes. Aim: This study aims to study the association of dietary patterns with glycated haemoglobin (HbA1c) among type 2 diabetics in Karachi. Setting: Individuals attending outpatient department of Baqai Institute of Diabetology and Endocrinology (BIDE), Karachi, Pakistan. Design: Retrospective observational study. Methodology: A total of 3193 subject's data were available. Demographic, clinical parameters, food and nutrient intake were explored; patients were categorised into groups according to the adequacy of food intake. The nutrition care process at BIDE consists of getting details of 24-h diet recall. Academy of nutrition and dietetic food exchange system was used to estimate the food requirement, energy and macronutrient intakes. Statistical Analysis: Linear regression analysis was performed for establishing relationship of HbA1c. P < 0.05 was statistically significant. SPSS version 17.0 was used for the analysis. Results: Majority of the patients (89.5%) were above the age of 35 years, using oral hypoglycaemic agents (OHA) or insulin and being overweight or obese (88%). Mean HbA1c was significantly higher (P = 0.006) in cluster 1 (high cereal, vegetable and meat) as compared to cluster 2 (moderate cereal, high vegetable and moderate meat) and cluster 3 (low cereal, moderate vegetable and moderate meat). High percentage of dietary energy was found to be significant predictors of higher levels of HbA1c (P < 0.01). Females with type 2 diabetes using OHA or using OHA with Insulin following the prescribed diet pattern were associated with better glycaemic control. Conclusion: Significant association between dietary patterns and level of HbA1c was seen among type 2 diabetics. Dietary energy was found to be significant predictors of higher levels of HbA1c. Females with type 2 diabetes using OHA or using OHA with insulin following the prescribed diet pattern were associated with better glycaemic control.
Keywords: Carbohydrate, dietary pattern, glycated haemoglobin, protein, type 2 diabetes
|How to cite this article:|
Hakeem R, Shiraz M, Riaz M, Fawwad A, Basit A. Association of dietary patterns with glycated haemoglobin among Type 2 diabetics in Karachi, Pakistan. J Diabetol 2018;9:59-64
| Introduction|| |
Diabetes is a disease that is strongly associated with both micro- and macro-vascular complications, resulting in organ and tissue damage in approximately one-third to one-half of the diabetic patients. The incidence of diabetes is seen to be increasing in both the developed and developing countries., According to the International Diabetes Federation, there were 6.7 million cases of diabetes in Pakistan in 2013. The dietary pattern reflects an individual's food consumption and its change during the lifespan.
America's Institute of medicine has been suggesting specific Acceptable Macronutrient Distribution Range for the prevention of chronic diseases. However, based on further research evidence, the recent Diabetes Medical nutrition therapy guidelines by the American Diabetes Association does not suggest any universally ideal deal mix and recommends fulfilling the minimum requirement only, and exact macronutrient proportions should be individualised.
Some studies have shown that inclusion of meats, high-fat dairy products and refined grain sized also termed as Western dietary pattern was affiliated with the risk of increase in type 2 patients; however, the pattern which included more of vegetables, fruits, fish, poultry and whole grains was associated with a reduced risk. An inverse association was seen between the risk of type 2 diabetes and the dietary pattern which included high consumption of fruits and vegetables and low consumption of processed meat and fried foods according to a British study. The Western dietary pattern was seen to be associated with the biomarkers of type 2 diabetes  A cross-sectional study in India observed associations between dietary patterns and cardio-metabolic risk factors. They reported that 'snack and meat' pattern appeared to be positively associated with abdominal adiposity.
A growing burden of diabetes particularly in developing countries has been predicted in a study which also estimates that by 2030, the prevalence of diabetes is expected to increase to 7.7% affecting 439 million adults. In both the developed and developing countries from 2010 to 2030, the increase was expected to be 20% and 69%, respectively.
There is scarcity of data regarding dietary pattern and its impact on glycaemic control from the developing countries; hence, the aim of this study is to evaluate the association of dietary pattern of type 2 diabetic patients with glycated haemoglobin (HbA1c) levels.
| Methodology|| |
The data used for this study were retrieved from the electronic database available at Baqai Institute of Diabetology and Endocrinology (BIDE), Karachi. Approval for the analysis was taken from the institute and data were anonymised before its use for the study.
The nutrition care process at BIDE consists of following steps:
- Interviewing the subject to get information about daily dietary intake on most days by getting details of 24-h diet recall for the previous day or nearest day if the previous day was not usual. The 24-h-recall method was used with probes to identify forgotten item and estimate quantities using techniques similar to that used 'multiple pass method' employed in NHANES surveys., The 24-h-recall method was adapted to assess average weekly intake. Besides asking about food eaten in pervious 24-h by questions about weekly variations in diet were also asked and estimate average weekly intake of number of exchanges from various food groups was estimated. Thus, the dietary information collected from the subjects was not only based on diet of previous 24 h but represented usual food intake are the limitations of the study
- Using academy of nutrition and dietetic food exchange system to estimate the subjects' food, energy and macronutrient intakes ,
- Getting information about daily activities to assess subjects' physical activity level using factorial method 
- Estimating basal energy requirement using Mifflin formula and total energy requirement by adding allowance for physical activity level and stress factors ,
- Estimating the number of exchanges of foods from various food groups required to fulfil the estimated energy and macronutrients needs
- Comparing the intake with requirements for food and nutrients and identifying the gaps
- Estimating the scope and possibilities for minimising the gaps between intake and the requirements and setting priorities in accordance with patient's contemplation, needs and limitations
- Educating the subjects to facilitate the staged and targeted dietary modifications and plan for monitoring and assessment.
For this study, computer coded records of the first visit of all diabetic patients who visited the outpatient department of BIDE from January 2009 to January 2011 were analysed. Minimal confidentiality or ethical issues were involved because names were not disclosed anywhere and the researchers used only the patients' identification codes.
Dietary data were used to assess the differences in dietary patterns and associations between various dietary patterns and glycaemic control (as assessed by HbA1C) were explored. Implication of observations for care and education is discussed.
Chi-square test, ANOVA and independent sample t-tests were used according to the nature of data and number of groups to assess the statistical significance of differences between the groups. Linear regression analysis was performed for establishing a relationship of HbA1c with associated factors. P < 0.05 was considered statistically significant. Statistical Package for Social Sciences version 17.0 (SPSS Inc. 2008) was used for the analysis.
| Results|| |
Characteristics of the sample
Data about diet intake at first visit to dietitians and HbA1c was available for 3193 type 2 diabetic patients (54% males and 46% females). Age ranged from 13 to 85 years. Majority of the patients were above the age of 35 years (89.5%), using oral hypoglycaemic agents (OHA) or insulin most of them had diabetes for more than 5 years (56%) and were using OHA (94%) either exclusively or in combination with Insulin and a vast majority (88%) was either overweight (23–30) or obese (>30) [Table 1]. While categorising the different categories of body mass index (BMI), it was observed that the group with high-risk diet pattern (high cereal, veg and meat) had higher HbA1c level in each subgroup. Patients were categorised on the basis of BMI as <18, 18–22.9, 23–30 and >30 with mean ± standard deviation (SD) of 11.76 ± 4.01, 9.81 ± 2.67, 9.42 ± 2.38 and 9.31 ± 2.17, respectively.
Dietary patterns of subjects
On the basis of cluster analysis, three dietary patterns were identified. The average intake of fruits and milk was same in all the three patterns (1 exchange of each per day). There were minor differences in intake of vegetables and meat (2–3 exchanges for both per day). The three dietary patterns had marked and statistically significant differences in the intake of starchy foods. Average daily intake of number of exchanges of starchy foods was 28 for cluster 1, 17 for cluster 2 and 11 for cluster 3 [Table 2].
The relative proportion of foods from milk, fruit and vegetable group in diet was higher in cluster 3 as compared to cluster 1 and 2 [Figure 1].
|Figure 1: Proportion of various food groups in food intake in three diet cluster|
Click here to view
The proportion of energy from various macronutrients in three dietary patterns was calculated [Table 2]. Percentage of energy from protein was around 15% in all the three patterns. Percentage of energy from carbohydrates was highest in cluster 1 (59%), moderate in cluster 2 (55%) and lowest in cluster3 (49%). Percentage of energy from fats was lowest in cluster 1 (26%), moderate in cluster 2 (29%) and highest in cluster 3 (36%). Caloric intake in relation to individually assessed requirements was highest in cluster 1 (142%) followed by cluster 2 (112%) and lowest in cluster 3 (88%).
Association of dietary patterns with glycaemic control
Mean HbA1c was significantly higher P = 0.006 for cluster 1 (10.06) as compared to cluster 2 (n = 1138) and 3 (n = 1976) (9.56 and 9.44, respectively) [Table 2].
In relation to categories of HbA1c, among those who had diet pattern 1 (n = 109), a significantly higher proportion of subjects (44%) had HbA1c above 11 as compared to the other two groups (31%) who had two other diet patterns P = 0.038 [Table 2]. When seen separately within groups having different medicine regimes the groups with high-risk diet pattern (high cereal, veg and meat) had highest hbA1c. Mean HbA1c according to diet clusters in subgroups having different medicinal treatments were categorised into diet, insulin, OHA and OHA + insulin with mean ± SD of 8.81 ± 2.65, 10.69 ± 3.05, 9.08 ± 2.29 and 10.26 ± 2.34, respectively.
The regression analysis was done to understand the relative role of various aspects of diet (energy balance and macronutrient intake) and a few other factors (age, BMI and activity) in predicting glycaemic control [Table 3]. The proportion of carbohydrates and proteins in diet were found to be strong predictors of HbA1C. Higher percentage of dietary energy from carbohydrates had a significant positive association, and higher percentage of energy from proteins had a significant negative association with HbA1C (P< 0.01 in each case). Energy balance did not predict levels of HbA1c and proportion of energy form fat had a positive but marginally nonsignificant association with HbA1C. Among the non-dietary factors, age and BMI had a positive association with HbA1c and were strong predictors of glycaemic control. The relative contribution of various factors in determining HbA1c was highest for age followed by percentage of calories from proteins.
|Table 3: Regression analysis to identify predictors of glycaemic control coefficients (a)|
Click here to view
Personal predictors of dietary patterns
To explore the type of patients who are likely to have any particular diet pattern, difference in personal characteristics of subjects who were following any particular diet patterns was compared. It was noted that female subjects using OHA or OHA with insulin, having higher age and having longer duration of diabetes had more frequent intake of diet pattern 3 that was associated with better glycaemic control [Figure 2].
|Figure 2: Rates of following various diet patterns among subjects having various personal characteristic|
Click here to view
| Discussion|| |
In this study, we provide evidence of a consistent relation between the dietary pattern of type 2 diabetics and its association with HbA1c. The management of diabetes includes dietary modifications for achieving better metabolic outcomes. Healthy eating patterns usually contain higher intake of wholemeal bread, fruit and vegetables and polyunsaturated margarine besides food rich in fruits, vegetables, poultry, legumes and whole grains and lower consumption of red meat, fruit juices and sweet foods.,
The results showed that all patients had servings from each food group but in different proportions representing the healthy eating patterns of type 2 diabetic patients. More specifically, a meta-analysis study by Wolfram and Ismail-Beigiconfirmed the findings that better diabetes control and more sensitivity to insulin was a result of a healthy diet including high fibre and low fat.
In our study, three dietary patterns were identified. The average intake of fruits and milk was same in all three patterns (one exchange of each per day); however, there were minor differences in intake of vegetables and meat (2–3 exchanges for both per day. The three dietary patterns had marked and statistically significant differences in intake of starchy foods (carbohydrates). This composition of the diet has been observed in some other studies as well., Clusters are not made on the basis of starch intake but generated by computer by conducting the statistical technique 'cluster analysis hierarchal means'. The cluster was created on the basis of similarities in all food/nutrient intake related variables. Energy and macronutrient intake of each cluster was explored after the cluster had been generated and named according to major differences in clusters.
Dietary approaches to the management of type 2 diabetes provides evidence that modifying the amounts of macronutrient can improve glycaemic control, weight and lipids in diabetic patients. Our study results indicate that focus on carbohydrate and energy restriction with permissiveness in allowing 35%–40% of energy from carbohydrates and 30% from fat and 15%–20% from protein could be a more effective strategy for glycaemic control.
Study results revealed that the mean HbA1c (above 11) was significantly elevated among those who had diet pattern 1, which consists of comparatively higher intake of carbohydrates. To assess the role of various aspects of diet in predicting glycaemic control regression analysis was also done. Similar results were reported from another study which shows that high percentage of dietary energy from carbohydrate and lower percentage of energy of proteins were found to be significant predictors of higher levels of HbA1c.
It has been shown in our study that along with increment in calorie intake, among all dietary macronutrients, proportion of dietary carbohydrate and fat intake increased because of personal preferences or limitation in financial ability and the consumption of high protein-containing foods (e.g., meat and dairy products) were not increased.
It has been observed in several studies that a reduction in carbohydrates for type 2 diabetic patients effectively reduces both fasting and post-prandial glucose as well as HbA1c. These effects can be independent of weight loss., Evidence exists that both the quantity and type of carbohydrate in a food influence blood glucose level and total amount of carbohydrate eaten is the primary predictor of glycaemic response., This is also proven in our findings that the increased intake of dietary carbohydrate is associated with elevated level of HbA1c.
In addition, a study has compared a high-protein diet with a high carbohydrate diet. The results did not show any significant differences in weight, glycaemic control and lipids but pooled data showed significantly lower HbA1c concentrations in the high-protein diet group. These findings support our study in which association of HbA1c with carbohydrate intake was positive and negative with protein intake.
In our study, we have also assessed differences in personal characteristics of individuals who were following any particular diet patterns and it was observed that females, those using OHA or using OHA with insulin, having higher age and longer duration of diabetes had more frequent intake of diet pattern 3 which was associated with better glycaemic control. As it is reported in the literatures, it was found that older age groups and people with longer duration of diabetes had better dietary practice.
The limitation of our study is that it was conducted at a tertiary care unit and not a community-based study. Another limitation was that the dietary intakes were measured only once and may not reflect the long-term intake. It is suggested to see the in-depth relationship of different dietary patterns with HbA1c in type 2 diabetic patients in future studies.
We would like to acknowledge the support of Ms. Fakiha Zainab, Ms. Tabassum Zehra and Ms. Safia Mehboob (Dieticians) for the concept and data collection for the paper.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
UK prospective diabetes study (UKPDS). VIII. Study design, progress and performance. Diabetologia 1991;34:877-90.
International Diabetes Federation. Diabetes Atlas. 6th
ed. Brussels, Belgium: International Diabetes Federation (IDF); 2014.
Newby PK, Weismayer C, Akesson A, Tucker KL, Wolk A. Long-term stability of food patterns identified by use of factor analysis among Swedish women. J Nutr 2006;136:626-33.
Williams DE, Prevost AT, Whichelow MJ, Cox BD, Day NE, Wareham NJ, et al.
Across-sectional study of dietary patterns with glucose intolerance and other features of the metabolic syndrome. Br J Nutr 2000;83:257-66.
Gittelsohn J, Wolever TM, Harris SB, Harris-Giraldo R, Hanley AJ, Zinman B, et al.
Specific patterns of food consumption and preparation are associated with diabetes and obesity in a Native Canadian community. J Nutr 1998;128:541-7.
Daniel CR, Prabhakaran D, Kapur K, Graubard BI, Devasenapathy N, Ramakrishnan L, et al.
Across-sectional investigation of regional patterns of diet and cardio-metabolic risk in India. Nutr J 2011;10:12.
Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract 2010;87:4-14.
Bliss RM. Researchers produce innovation in dietary recall. Agric Res 2004;52:10-2.
Raper N, Perloff B, Ingwersen L, Steinfeldt L, Anand J. An overview of USDA's dietary intake data system. J Food Compos Anal 2004;17:545-55.
World Health Organization. Energy and Protein Requirements: Report of a Joint FAO/WHO/UNU Expert Consultation. WHO Technical Report Series 724. Geneva; 1985. Available from: http://www.apps.who.int/iris/handle/10665/39527
. [Last accessed on 2017 Mar 25].
Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO, et al.
Anew predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr 1990;51:241-7.
Esmaillzadeh A, Kimiagar M, Mehrabi Y, Azadbakht L, Hu FB, Willett WC, et al.
Dietary patterns, insulin resistance, and prevalence of the metabolic syndrome in women. Am J Clin Nutr 2007;85:910-8.
Brunner EJ, Mosdøl A, Witte DR, Martikainen P, Stafford M, Shipley MJ, et al.
Dietary patterns and 15-y risks of major coronary events, diabetes, and mortality. Am J Clin Nutr 2008;87:1414-21.
Wolfram T, Ismail-Beigi F. Efficacy of high-fiber diets in the management of type 2 diabetes mellitus. Endocr Pract 2011;17:132-42.
Shirinzadeh M, Shakerhosseini R, Hoshiyar Rad A. Nutritional value assessment and adequacy of dietary intake in type 2 diabetic patients. J Diabetes Metab Disord 2009;11:25-32.
Kant AK. Dietary patterns and health outcomes. J Am Diet Assoc 2004;104:615-35.
Delahanty LM, Nathan DM, Lachin JM, Hu FB, Cleary PA, Ziegler GK, et al.
Association of diet with glycated hemoglobin during intensive treatment of type 1 diabetes in the Diabetes Control and Complications Trial. Am J Clin Nutr 2009;89:518-24.
Gutierrez M, Akhavan M, Jovanovic L, Peterson CM. Utility of a short-term 25% carbohydrate diet on improving glycemic control in type 2 diabetes mellitus. J Am Coll Nutr 1998;17:595-600.
Ajala O, English P, Pinkney J. Systematic review and meta-analysis of different dietary approaches to the management of type 2 diabetes. Am J Clin Nutr 2013;97:505-16.
Boden G, Sargrad K, Homko C, Mozzoli M, Stein TP. Effect of a low-carbohydrate diet on appetite, blood glucose levels, and insulin resistance in obese patients with type 2 diabetes. Ann Intern Med 2005;142:403-11.
Rabasa-Lhoret R, Garon J, Langelier H, Poisson D, Chiasson JL. Effects of meal carbohydrate content on insulin requirements in type 1 diabetic patients treated intensively with the basal-bolus (ultralente-regular) insulin regimen. Diabetes Care 1999;22:667-73.
Wolever TM, Gibbs AL, Mehling C, Chiasson JL, Connelly PW, Josse RG, et al.
The Canadian Trial of Carbohydrates in Diabetes (CCD), a 1-y controlled trial of low-glycemic-index dietary carbohydrate in type 2 diabetes: No effect on glycated hemoglobin but reduction in C-reactive protein. Am J Clin Nutr 2008;87:114-25.
Brazeau AS, Mircescu H, Desjardins K, Leroux C, Strychar I, Ekoé JM, et al.
Carbohydrate counting accuracy and blood glucose variability in adults with type 1 diabetes. Diabetes Res Clin Pract 2013;99:19-23.
Larsen RN, Mann NJ, Maclean E, Shaw JE. The effect of high-protein, low-carbohydrate diets in the treatment of type 2 diabetes: A 12 month randomised controlled trial. Diabetologia 2011;54:731-40.
Tan MY, Magarey J. Self-care practices of Malaysian adults with diabetes and sub-optimal glycaemic control. Patient Educ Couns 2008;72:252-67.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3]