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Research Article
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Association between Sago Consumption and NCDs’ Clinical Signs among Those Consuming Sago in Kepulauan Meranti Regency, Riau Province, Indonesia

Syartiwidya S, Martianto D, Tanziha I, Sulaeman A , Rimbawan R
Journal of Food and Nutrition Research. 2019, 7(6), 476-484. DOI: 10.12691/jfnr-7-6-10
Received January 22, 2019; Revised March 18, 2019; Accepted June 24, 2019

Abstract

Non-communicable diseases (NCDs) remain a public health disease worldwide, including Indonesia. Clinical signs of NCDs can be caused by many factors. These diseases may arise from a combination of underlying, modifiable, non-modifiable, and intermediate risk factors. Food consumption can be one of the factors that can prevent NCDs. This study aimed to identify the association between sago consumption and NCDs’ clinical signs among those consuming sago in Kepulauan Meranti Regency, Riau Province. A total of 181 subjects were recruited in this cross-sectional study using purposive sampling. Questionnaire and direct measurement were used to collect the data. Sociodemographic, anthropometric, lifestyle, family history of DM, random capillary blood glucose (RCBG), cholesterol levels, systolic and diastolic blood pressure (SBP & DBP), and waist circumference (WC) data were collected. STATA 22.0 was used for descriptive statistics and comparing the two groups. The majority of subjects who rarely and often consumed sago had RCBG of <140 mg/dL (91.2%) and normal body mass index (65.2%). Sociodemographic characteristics, lifestyle (smoking and physical activity), family history of DM, and clinical signs (cholesterol levels, SBP, DBP, and WC) were not significantly different between subjects who rarely and often consumed sago in the group whose RCBG was 140-200 mg/dL. However, the education level, family income, family history of DM, SBP and DBP were significantly different in the group whose RCBG was <140 mg/dL. Sago consumption had a significant association with cholesterol levels and WC. Sago might have the potential as an alternative food to prevent NCDs.

1. Introduction

Non-communicable diseases (NCDs) are the leading cause of mortality worldwide with 41 million deaths annually, equivalent to 71% of all deaths globally. Fifteen million people aged between 30 and 69 years die from NCDs each year. More than 85% of these "premature" deaths occur in low- and middle-income countries. Cardiovascular diseases account for most NCDs deaths (17.9 million people/year), followed by cancer (9.0 million), respiratory diseases (3.9 million), and diabetes mellitus/DM (1.6 million). A combination of genetic, physiological, lifestyle and environmental factors can cause these diseases. Some risk factors for NCDs are unhealthy diets, lack of physical activity, active and passive smoking, and excessive alcohol consumption 1.

DM is one of NCDs. The individuals with an prediabetic condition (5-10%) can be at risk of developing DM, which is a heterogeneous metabolic disorder characterized by long-term impairment of insulin secretion and damage 2, 3. Prediabetes is a condition in which the blood glucose level is higher than an normal but lower than the cut-off points for DM 3, 4. The prevalence of DM in the world continues to increase, and Indonesia ranks sixth globally as the country with the highest prevalence of DM 5. According to the data of the Indonesian Basic Health Research 6, the prevalence of DM among people under the age of 15 has doubled (2.1%).

The prevalence of DM in Riau Province was 1.0% while the prevalence in Kepulauan Meranti Regency was 0.6% 6. Type 2 diabetes mellitus (T2DM) is the most common form of DM found in adults. Two major groups of factors influence the T2DM, namely unchangeable factors (e.g., sex, age, and family history of DM) and changeable factors (e.g., smoking, alcohol consumption, physical activity, obesity, hypertension, cholesterol levels, and sedentary lifestyle) 5, 7, 8, 9.

Decreased sensitivity of target tissues to the metabolic effects of insulin, known as insulin resistance, is one of the causes of T2DM. Obesity is one of the risk factors for T2DM which can be measured through waist circumference (WC) and body mass index (BMI). The prevention of T2DM can be carried out by controlling the risk factors 6, 7. Some studies showed that traditional food could be an alternative to prevent an RCBG level of 140-200 mg/dL from becoming T2DM by reducing hyperglycemia, insulin resistance, and obesity 10.

Besides rice, sago is a traditional source of carbohydrates which is widely available in Indonesia (5.2 million hectares or ± 50% of the sago area worldwide). It is spread in several provinces, including Riau Province with Kepulauan Meranti Regency as a sago producer 11. Sago is still consumed by most people even though it is not considered as a staple food. It has several advantages over other sources of carbohydrates. Sago has a low glycemic index (GI); i.e., around 27. It also contains resistant starch, and it is high in starch, amylose, and antioxidant 12, 13, 14. These nutrients play a role in maintaining normal blood glucose by increasing the number of beta cells producing insulin, reducing hypoglycemia, and improving lipid metabolism 6, 15. However, rice -- as the main source of carbohydrate in Asia -- has a relatively higher GI (64-93), and several studies have shown that it can increase the risk of T2DM 15, 16, 18. On the other hand, low-GI food may be useful in ameliorating hyperglycemia and glucose overload associated with diabetic conditions 19.

Studies have shown that consuming sago is good for health because it can reduce blood glucose, LDL cholesterol levels, and triglycerides levels 20. Thus, it may reduce the risk of T2DM and heart disease 13, 20, 21. Although Kepulauan Meranti Regency is a sago-producing region, NCDs’ signs are still found in the area. Therefore, we were interested in identifying the association between sago consumption and NCDs’ clinical sign among the sago-based agricultural community in Kepulauan Meranti Regency, Riau Province, Indonesia.

2. Methods

2.1. Study Design and Recruitment of Subjects

This study used a cross-sectional design, and it was conducted in Kepulauan Meranti Regency, Riau Province. The research protocol was approved by the Human Research Ethics Committee of Bogor Agricultural University Number 031/IT3.KEPMSM-IPB/SK/2017. The study involved 181 subjects living in several villages, namely Sungai Tohor, Tanjung Sari, Nipah Sendanu, Batin Suir and Lalang Tanjung. The subjects were selected purposively. The subjects were provided with an information sheet regarding the study and informed consent stating that they could withdraw without prejudice from the study at any time. They were then asked to complete a form regarding the demographic data (age, sex, education level, occupation, and family income).

The inclusion criteria were as follows: 1) age between 35 and 80 years, 2) not hospitalized, and 3) not suffering from serious illness or chronic drug consumption. The subjects recruited in this study were those without complications of co-infected patients. The subjects were divided into two groups; 1) a group that rarely consumed sago (<140 g/day), and 2) a group that often consumed sago (≥140 g/day). The grouping of subjects was based on the research by Hariyanto et al. 16 stating that sago consumption of 140 g/day could reduce blood glucose, cholesterol and triglyceride levels in diabetic patients. Therefore, blood glucose levels are used to describe one of the clinical signs of NCDs, besides WC and blood pressure.

2.2. Data Collection Procedure

The interviewer used questionnaires to collect data on subjects’ characteristics (age, sex, education level, occupation, and family income). Anthropometric measurements were used to describe the BMI which consisted of weight and height using a Seca scale (capacity of 100 kg and an accuracy of 0.1 kg) and microtoise or stature meter (an accuracy of 0.1 cm). The BMI was calculated by dividing the weight (kg) with the square of height (m2). WC was measured using a measuring tape with an accuracy of 0.1 cm. RCBG and cholesterol levels were measured using the Easy Touch GCU monitoring tool made in Taiwan with glucose and cholesterol test strips. Sphygmomanometer was used to determine SBP and DBP.

The family history of DM was obtained from questionnaires. The physical activity was recorded based on the type of physical activity or daily activities in minutes monitored for 24 hours. All values were expressed as a multiple of BMR which was called metabolic rate (MR). The results of the MR calculation were categorized according to the assessment of physical activity levels. The results were classified into vigorous (MR >2.09), moderate (MR <1.76-2.09), and light (MR <1.76). Smoking exposure was obtained from the questionnaire.

The calculation of the minimum sample size was based on the standard deviation of WC from the previous study (7.9) 22, and the values of the degree of precision and degree of freedom used were 1 and 64, respectively.

2.3. Measurement of Dietary Intake

The data regarding the subjects’ consumption of sago processed products were collected through questionnaires and direct interview by enumerators. Sago consumption data were assessed using the semi-quantitative food frequency questionnaire. The amount of sago was summed in a week based on meal frequency and divided by seven to get the daily consumption. This study examined the intake levels of several kinds of sago product to assess the subjects’ sago consumption. The sago products were sago noodle, sago vermicelli, lempeng sagu (sago pancake with additional grated coconut that was eaten with fried anchovy), sempolet (sago pulp with added shrimp, snails, squid or shellfish and fiddlehead fern), sago rendang (small-granule sago eaten with bananas), gobak (sago crust made by pan-frying sago and grated coconut without oil), sago mutiara (sago in the form of granules cooked with added sugar and coconut milk), kapurung (sago porridge eaten with fish curry), sesagon (a snack made from sago flour and grated coconut as the main ingredients), and sago lemak (sago in the form of granules made with the addition of coconut milk).

2.4. Statistical Analysis

The analysis was conducted using SPSS version 20 (SPSS Inc., New York, NY, USA). The dietary intake data were processed using Nutrisurvey software. The results of the descriptive statistics of the variables were expressed as mean ± standard deviation (SD). The bivariate analyses were performed using the independent sample t-test to compare the two groups and Pearson correlation analysis to determine the relationship between the factors examined in this study and the RCBG level of 140-200 mg/dL. The results were considered significant if the p-values were less than 0.05 or ≤0.01.

3. Results

3.1. Subjects’ Characteristics

The background information for the subjects is presented in Table 1. The results showed that the number of subjects who often consumed sago (55.25%) was more than those who rarely consumed sago (44.75%). The subjects who often consumed sago were mostly women aged ≥50 years (56%). Most of the subjects in both groups had low education levels, either in the group that rarely consumed sago (81.5%) or the one that often consumed sago (91%). Most of the subjects in both groups had low income, either in the group who rarely consumed sago (86.4%) or the one who often consumed sago (94%).

As shown in Table 2, the mean BMI (23.67±5.09 kg/m2) in most subjects in both groups (65.2%) was normal. These results were supported by data in Table 1 indicating that the mean of physical activity levels was vigorous (66.9%). Generally, the subjects in both groups were active or passive smokers (83.4%) without a family history of DM (92.3%).

The present study showed that 39.22% of subjects often consumed sago (once or more than once a day), and the mean of sago consumption was 173.73±88.27 g/day. Most of the subjects (71.16%) had consumed sago for more than ten years. As presented in Figure 1, the results of RCBG measurement showed that there were more subjects with RCBG levels less than 140 mg/dL in the group that rarely consumed sago (40.88%) and the group that often consumed sago (52.28%) compared to those with RCBG levels of 140-200 mg/dL.

3.2. NCDs’ Clinical Signs

The results showed that the mean RCBG of the subjects was 102.28±27.76 mg/dL. Most of the subjects (91.2%) had RCBG levels that were still within the normal range, while the rest of them (8.8%) had RCBG levels of 140-200 mg/dL. The factors that affected the incidence of RCBG levels of 140-200 mg/dL were the variables in this study (BMI, SBP, DBP, cholesterol levels, and WC) that were commonly categorized as normal (Table 2).

There was no significant difference in all variables among the subjects with RCBG levels of 140-200 mg/dL between the group that rarely consumed sago and the one that often consumed sago. Education levels, family income/month, and family history of DM among the subjects with RCBG levels of less than 140 mg/dL were significantly different between the two groups (Table 3).

Based on the sago consumption, there were no significant differences in clinical signs (BMI, SBP, DBP, cholesterol levels, and WC) among the subjects with RCBG levels of 140-200 mg/dL between the groups that rarely and often consumed sago. SBP and DBP of the subjects with RCBG levels less than 140 mg/dL were significantly different between the groups (Table 4).

Table 5 showed the results of Pearson's x2 and p-values among variables. The results indicated that the association between the variables was extremely significant with p-values less than 0.01 (<1%) and 0.05 (<5%). There were extremely significant associations between SBP and DBP (p=0.000 and x2=59.8), SBP and cholesterol levels (p=0.001 and x2=24.2), and between WC and BMI (p=0.000, x2=75.6). There were also significant associations between sago consumption and cholesterol levels (p=0.026, x2=-16.5), sago consumption and WC (p=0.019, x2=17.5), and RCBG levels and SBP (p=0.013, x2=18.4).

4. Discussion

The present study suggested that the effect of sago consumption could be seen from the results of RCBG measurement in the two groups. Some studies showed that sex, age, and education levels were the significant factors associated with the incidence of RCBG levels of 140-200 mg/dL 23, 24 while occupation and family income were not significantly associated with the incidence of RCBG levels of 140-200 mg/dL 25, 26.

Education determines a person's knowledge of the dietary patterns 9. However, in general, high income and good occupation will lead to the consumption of high-fat and high-carbohydrate food and inadequate dietary fiber intake which can cause a risk of having RCBG levels of 140-200 mg/dL 27. Some studies suggested that a family history of DM was significantly associated with the incidence of T2DM 28.

An association between WC and BMI was found in the present study. Other studies have also demonstrated that obesity is a risk factor for RCBG levels of 140-200 mg/dL 24, 29. Obesity can be determined by BMI. It is usually associated with blood glucose levels in patients with T2DM and can be reduced by consuming low-GI and high-fiber food 30. Sago is a low-GI food. This mechanism occurs because the suspension of food (chyme) reaches the small intestine slowly, the absorption of glucose in the small intestine becomes slow, and the fluctuations in blood glucose levels are also relatively small 31.

Association between sago consumption, cholesterol levels, and WC was supported by a study by Amir et al. 30 which showed that sago consumption patterns could affect LDL levels. Trisnawati et al. 9 showed that cholesterol levels were significantly associated with the incidence of T2DM. Some studies suggested that WC was significantly associated with the incidence of T2DM 17, 32, causing an increase in free fatty acids (FFA) and damaging pancreatic β cells that could produce insulin due to lipotoxicity 6. This mechanism occurs because cholesterol plays a role in pancreatic beta cell dysfunction through an increase in serum cholesterol which increases pancreatic cholesterol and FFA, especially in the case of obesity 33.

In the present study, it was observed that there were associations between RCBG and SBP. Several studies have shown that blood pressure is significantly associated with the incidence of T2DM. The mechanism was related to high sodium intake causing the changes in insulin sensitivity and insulin plasma concentration associated with nitric oxide pathways 9, 25, 34.

Besides rice, the carbohydrate adequacy of the subjects in this study was also fulfilled by sago which had high fiber and amylose content, resulting in more resistant starch which became prebiotics for the intestines and facilitated digestion 35. The high amylose content in sago was due to the presence of α-(1,4)-glycosidic bonds that were not branched with a more crystalline structure and stronger hydrogen bonds, making it difficult to be hydrolyzed by digestive enzymes and resulting in slow digestion. High levels of amylose also slow down the digestion of starch which causes low GI 36. The high content of sago fiber also affected blood sugar 37. The subjects in this study had normal energy and carbohydrate intakes (1,848 kcal and 284.5 g, respectively) and normal energy and carbohydrate adequacy levels (91.9% and 93.6%, respectively). However, there was a mild deficiency in protein intake (48.3 g) and adequacy levels (84.9%) with excessive fat intake.

4.1. Limitations and Strengths of the Study

This study had several benefits. Specifically, it was one of the community nutrition studies aimed to determine the effect of sago consumption among people consuming sago for a long time. Despite these benefits, our study had some limitations. It did not measure the fasting blood glucose or postprandial blood glucose and lipid profile of the subjects.

5. Conclusion and Recommendation

In conclusion, most of the subjects in both groups had blood glucose levels less than 140 mg/dL rather than 140-200 mg/dL with normal BMI. Sago consumption had a significant association with cholesterol levels and waist circumference. The results suggest that sago might be an alternative food to control NCDs.

Acknowledgments

This work was supported by the Ministry of Research, Technology and Higher Education of the Republic of Indonesia under the National Strategy Research grant scheme. The author would like to thank the ten enumerators who volunteered to collect the data. We would also express our gratitude towards the subjects who voluntarily participated in this study.

List of Abbreviations

NCDs: Non-communicable diseases, T2DM: Type 2 Diabetes Mellitus, RCBG: random capillary blood glucose, BMI: Body mass index, SBP: systolic blood pressure, DBP: diastolic blood pressure, WC: waist circumference.

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QUESTIONNAIRE

RESPONDENT CODE:

IDENTITY OF RESPONDENT

Name :

Place and date of birth :

Age :

Home Address :

Mobile Phone Number :

Date of Questionnaire Completion :

Enumerator’s Name : Initials/Signature :

RESPONDENT’S SOCIOECONOMIC STATUS

Education Level:

a. Not attending school e. Associate Degree

b. Elementary School/Islamic Elementary School f. Bachelor’s Degree

c. Junior High School/Islamic Junior High School g. Others ......

d. Senior High School/Islamic Senior High School

Types of Occupation:

a. Personnel of Indonesian National Armed Forcese. Merchant/Service Provider

b. Civil servant f. Farmworker

c. Private employee g. g. Student

d. Farmer h. Housewife

Monthly income:

a. < IDR 500,000 d. IDR 2,500,001- IDR 5,000,000

b. > IDR 500,000 e. > IDR 5,000,000

c. IDR 1,000,000 - IDR 2,500,000

Total household expenditure:

a. Food expenditure : ..................

b. Non-food expenditure : ..................

SEMI-QUANTITATIVE FFQ

Explain in detail the types of sago processed products you consume in the following table. Fill in the Household Size (URT) and the amount (g) for each type of sago processed products and give a check mark (√) for your choice.

Published with license by Science and Education Publishing, Copyright © 2019 Syartiwidya S, Martianto D, Tanziha I, Sulaeman A and Rimbawan R

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Cite this article:

Normal Style
Syartiwidya S, Martianto D, Tanziha I, Sulaeman A, Rimbawan R. Association between Sago Consumption and NCDs’ Clinical Signs among Those Consuming Sago in Kepulauan Meranti Regency, Riau Province, Indonesia. Journal of Food and Nutrition Research. Vol. 7, No. 6, 2019, pp 476-484. http://pubs.sciepub.com/jfnr/7/6/10
MLA Style
S, Syartiwidya, et al. "Association between Sago Consumption and NCDs’ Clinical Signs among Those Consuming Sago in Kepulauan Meranti Regency, Riau Province, Indonesia." Journal of Food and Nutrition Research 7.6 (2019): 476-484.
APA Style
S, S. , D, M. , I, T. , A, S. , & R, R. (2019). Association between Sago Consumption and NCDs’ Clinical Signs among Those Consuming Sago in Kepulauan Meranti Regency, Riau Province, Indonesia. Journal of Food and Nutrition Research, 7(6), 476-484.
Chicago Style
S, Syartiwidya, Martianto D, Tanziha I, Sulaeman A, and Rimbawan R. "Association between Sago Consumption and NCDs’ Clinical Signs among Those Consuming Sago in Kepulauan Meranti Regency, Riau Province, Indonesia." Journal of Food and Nutrition Research 7, no. 6 (2019): 476-484.
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[1]  World Health Organization (WHO). “World Health Statistics 2016: Monitoring health for the SDGs”. World Health Organization. 2016.
In article      
 
[2]  Punthakee Z., Goldenberg R., Katz P. “Definition, Classification, and Diagnosis of Diabetes, prediabetes and Metabolic Syndrome Diabetes Canada Clinical Practice Guidelines Expert Committee”, Can J Diabetes, 42(1). S10-S15. 2018.
In article      View Article  PubMed
 
[3]  World Health Organization (WHO). “Global Report on Diabetes”. WHO Press, Switzerland. 2016.
In article      
 
[4]  Tabak AD., Christian H., Wolfgang R., Eric JB., Miko K. “Prediabetes: a high-risk state for diabetes development”, The Lancet, 379(6). 2279-2286. June 2012.
In article      View Article
 
[5]  International Diabetes Federation. “IDF Diabetes Atlas Seventh Edition”. Karakas Print. New York. 2015.
In article      
 
[6]  Ministry of Health Republic of Indonesia. Basic health research. Jakarta (ID): Health Research and Development Agency. 2013.
In article      
 
[7]  American Diabetes Association. “Classification and diagnosis of diabetes”, Diabetes Care, 39(1). 513-522. 2016.
In article      View Article
 
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