Body Mass Index as a Dominant Risk Factor for Metabolic Syndrome among Indonesian Adults: A 6-year Prospective Cohort Study of Non-Communicable Diseases

doi:10.21203/rs.3.rs-3117722/v1

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Research Article

Body Mass Index as a Dominant Risk Factor for Metabolic Syndrome among Indonesian Adults: A 6-year Prospective Cohort Study of Non-Communicable Diseases

https://doi.org/10.21203/rs.3.rs-3117722/v1

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published 04 Mar, 2024

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Background

Non-communicable diseases (NCDs) are the leading cause of death globally. Metabolic syndrome (MetS) refers to a cluster of conditions that significantly increase the risk of some NCDs, in particular cardiovascular disease and type 2 diabetes mellitus. MetS risk factors have been extensively researched using cross-sectional and longitudinal study designs, however, few studies have tried to understand the course of the disease based on established risk factors. This study aimed to track changes in adult MetS risk in a cohort based in Bogor District, one of the most populated areas of Indonesia.

Methods

This prospective open cohort study analysed secondary data from the Special Research - Cohort Study of Non-Communicable Diseases by the Ministry of Health, Republic of Indonesia from 2011 to 2018. The final sample was 1,376 Indonesian adult participants, all residents of Bogor District. MetS outcome, dietary assessment, physical activity, and biomarkers were analysed every two consecutive years.

Results

The risk of overweight and obese participants developing MetS was 2.4 and 4.4 times higher, respectively (p < 0.001, 95% CI: 1.176–3.320 and 3.345–5.740) than those with body mass index (BMI) in the normal range. Participants who reported less intentional physical exercise had a MetS risk 1.5 times higher (p = 0.032, 95% CI: 1.034–2.109) than those with more intentional physical exercise. The role of diet is also significant, evidenced by a 30% reduction in MetS risk for people with fat intakes in the 2nd quartile compared to the 1st quartile (p-value = 0.033, 95% CI: 0.505–0.972). Meanwhile, a carbohydrate intake in the 2nd quartile increased the risk of MetS 1.5 times (p = 0.023, 95% CI: 1.063–2.241) in comparison with the 1st quartile.

Conclusions

After controlling for confounding factors, overweight and obesity, sedentary lifestyle, and a higher quartile of carbohydrate consumption were observed to increase MetS risk. The highest cumulative survival of MetS was recorded for participants with underweight BMI, and the lowest cumulative survival was recorded for participants with obese BMI. These findings indicate immediate strategic actions are required to improve an existing early detection and NCD monitoring programme that promotes a community-based healthy lifestyle in Bogor District, Indonesia.

Metabolic Syndrome (MetS)

Body Mass Index (BMI)

Risk Factors

Prospective Cohort Study

Non-communicable Diseases

Indonesian Adults

The third of the UN’s Sustainable Development Goals (SDGs) is to “Ensure healthy lives and promote well-being for all at all ages”. Encompassed within this is Target 3.4 which aims to reduce premature mortality from non-communicable diseases (NCDs) by one-third by 2030 through prevention and treatment [1]. Despite the implementation of public health policies around the world to address the issue, the prevalence and burden of NCD including diabetes mellitus, obesity and stroke, continues to rise [2, 3]. Forty-one million deaths per year (equivalent to 71% of all deaths globally) are attributed to NCDs, of which 85% are premature deaths that occur in low- and middle-income countries, including Indonesia [4].

Although NCDs that occur singularly represent a significant public health challenge, they often occur in combination, increasing the burden on healthcare services. Metabolic syndrome (MetS) refers to a group of risk factors that collectively increase the risk of cardiovascular disease, insulin resistance and diabetes mellitus, and vascular and neurological complications such as cerebrovascular diseases [5–7]. Metabolic disorders are categorised as MetS if an individual has at least three of the following conditions: (1) abdominal obesity, (2) high triglyceride levels, (3) low levels of high-density lipoprotein cholesterol (HDL), (4) elevated fasting glucose levels, (5) hypertension [8, 9].

Studies suggest that populations with an imbalanced eating pattern [10–12], individuals with low levels of physical inactivity [13, 14], certain racial and ethnic groups [9], those with a family history of diabetes (sibling or parent with diabetes), women with a history of polycystic ovarian syndrome, and older adults [15] are more likely to suffer from MetS [9].

In Indonesia, MetS is also an alarming issue. The prevalence is significantly higher compared to other countries (39%, 29.2%, and 30% in Indonesia, Dutch, and Indian adults, respectively) [6, 16], ranging from 0 to 50% of provincial and ethnic prevalence [5] that may be partially attributed to the susceptibility of Asian populations to metabolic disorders [17]. Without proper management and prevention, MetS can exacerbate the national burden of diseases and healthcare costs, which is particularly relevant to Indonesia following implementation of the National Health Insurance scheme (JKN). NCDs not only exhaust the resources for provision of healthcare services due to their direct cost but are additionally associated with significant indirect costs to society such as productivity losses [18–21].

In order to develop effective prevention programs, several cohort studies have been conducted to better understand the risk factors for MetS and examine MetS components [22–24], yet this prior research has limitations including relatively short follow-up times (one to four years), variation in diagnostic criteria for MetS, or studies on populations of different ethnicities, which may affect generalisability to the Indonesian context [25, 26]. Due to these limitations, there is still a requirement to understand the dynamics of MetS among the Indonesian population and gain a better understanding of the aspects of prevention and promotion. This is highly relevant for Indonesia given that healthcare costs are borne by the government following the implementation of JKN.

To provide better quality evidence based on a longer cohort, this study determined MetS risk factors based on data from the Non-Communicable Disease Cohort, a study conducted from 2011 to 2018 in Bogor District Residence, West Java, Indonesia. The dataset is presumed to display smaller diversity of ethnicity and uniform diagnostic criteria, as well as continuity in the medical tests used. This study aims to understand the changes in MetS risk among Indonesian adults with regard to specific risk factors. The results of this study could provide evidence for regional and national health policymakers to design the most appropriate intervention strategy.

Data collection and participants

This study analysed secondary data from the Special Research - Cohort Study of Non-Communicable Diseases (NCD) by the Ministry of Health, Republic of Indonesia. This population-based prospective cohort study began in 2011 and involved around 5,000 participants from 3,441 households resident in five out of eleven villages in Bogor Tengah Sub-district, Bogor City, West Java Provinces, Indonesia. The study employed total population sampling of participants aged 25 years and older who were willing to have routine medical checkups. Additional participants could be recruited at any point during the study (open cohort). Data collectors were local healthcare professionals in collaboration with the Primary Health Care Centre (Puskesmas) and community health cadres, under the supervision of the Health Research Board, Ministry of Health. Participants were monitored three times per year and had a comprehensive medical checkup every two years. As an open cohort population-based prospective study, it is considered a dynamic cohort that can identify the incidence of diseases [27].

In this study, the initial number of participants was 5,690 adults aged 25 years and above. The eligibility criteria were to have complete biomarkers data from baseline (2011) to endline (2018) with no history of MetS or medication regimen related to MetS risk factors (e.g. antihypertensives). We also excluded participants with incomplete independent variables data, including smoking history. The selection process left 1,376 participants to be included in this study (Fig. 1).

Study variables

The observed outcome, MetS, was defined using criteria modified from the National Cholesterol Education Program’s Adult Treatment Panel (NCEP ATP) III criteria, which states an individual must experience at least three of five conditions as follows: 1) abdominal obesity (waist circumference ≥ 90 cm for men or ≥ 80 cm for women; 2) triglycerides ≥ 150mg/dL; 3) high-density lipoprotein cholesterol (HDL) < 40 mg/dL for men or < 50 mg/dL for women; 4) fasting blood glucose ≥ 100 mg/dL; and 5) hypertension (systolic blood pressure ≥ 130 mmHg and/or diastolic ≥ 85 mmHg).

Anthropometric measurements, clinical measurements, dietary assessment, sociodemographic profiles, and other risk factors were measured and observed by trained operators and standardised tools. Anthropometric measurements include weight, height, and abdominal circumference. Body mass index (BMI) was calculated from weight and height, then classified based on Asia-Pacific BMI as follows: underweight (< 18.50 kg/m²); normal (18.50–22.99 kg/m²); overweight (23.00–24.99 kg/m²); and obese (≥ 25.0 kg/m²). Clinical measurements included blood glucose and blood lipid profiles (LDL, HDL, triglyceride, total cholesterol).

Food intake was recorded using 24-hour dietary recall. The dietary information was then assessed for energy, carbohydrate, protein, fat, and sodium composition using Nutrisurvey™. The values of energy, carbohydrate, protein, fat, and sodium intake per day were classified into quartiles, with the lowest quartile (Quartile-1) representing the 25% of participants with the lowest consumption in the study population.

Sociodemographic profiles include education, occupation, and the availability of health insurance. Other identified risk factors include smoking, stress, and physical activity. Physical activity was assessed based on the number of minutes spent on low, moderate, and vigorous-intensity activities daily, self-reported by participants. The number of minutes was converted to metabolic equivalent of task (MET) where: 1) walking MET-minutes/week = 3.3 x walking minutes x walking days, 2) moderate MET-minutes/week = 4.0 x moderate-intensity activity minutes x moderate days, and 3) vigorous MET-minutes/week = 8.0 x vigorous-intensity activity minutes x vigorous-intensity days. The result was subsequently further classified into sufficient physical activity levels (≥ 600 MET-minutes per week) and insufficient physical activity levels (< 600 MET-minutes per week) [28]. A sub-variable for intentional physical exercise was also based on the self-reported number of minutes spent on moderate and vigorous intensity physical exercise and recreational sport. The number of minutes was then classified based on the WHO recommendation for physical activity into sufficient (at least 75 minutes of vigorous or 150 minutes of moderate activities per week) and insufficient (less than 75 minutes of vigorous or 150 minutes of moderate activities per week) [29]. To adjust for variation in exercise intensity, the reporting of vigorous activity was counted as twice that of the moderate activities, and the cut-off for the grouping was set at 150 minutes of activities. We did the stratification because the type of the occupation in this population was dominated by manual labour (blue collar worker) which may cause bias in the physical activity estimation, as it does not necessarily mean that they have intentional physical exercise.MetS outcome, dietary assessment, and biomarkers were analysed every two consecutive years based on the availability of the data. Several measurements were recorded, including at baseline in two phases: the year 2011 and 2012, and three follow-up measurements in 2013/2014 (2nd year), 2015/2016 (4th year), and 2017/2018 (6th year) were chosen based on the availability of comprehensive data. Meanwhile, the remaining variables: age, physical activity, smoking status, smoking intensity, stress, education level, occupation, health insurance, family history of diabetes mellitus and heart attack (mortality and morbidity experienced by participants’ parents and grandparents), pregnancy and menopausal status, and delivery of a macrosomic new-born (≥ 4,000gr) were measured once at baseline.

Data analysis

Descriptive statistics were used to examine all variables. Mean and standard deviation (SD) were used to describe numerical values. Association between outcome variables and potential risk factors are estimated using Cox Regression with a 5% significance level. We obtained adjusted Hazard Ratios (HR) and 95% confidence intervals (95% CIs) for all predictors retained in the final model. Statistical analysis of the data was performed using Stata 17 and SPSS Software version 26.

Data from a total of 1,521 participants were included in the first year of the observation. However, 145 participants were identified to meet MetS criteria determined at the initial screening and were therefore excluded, leaving 1,376 participants for the subsequent six years of analysis. During six years of observation, 381 (28.6%) participants developed MetS. The characteristics of the participants are presented in Table 1. Most of the participants had a normal BMI at baseline (39.6%), whilst 18.7% and 33.3% were overweight and obese, respectively. At baseline, the majority of the participants were female (66.3%), aged younger than 50 years old (78.5%), were current or ex-smokers (50.9%), experienced no stress (58.0%) and reported insufficient intention to exercise, namely that although they may be physically active to some degree based on their occupation, they did not report intention to take part in sports or recreational exercise (88.5%).

In the second year of observation, 244 (17.7%) people suffered from MetS. This number significantly increased by 48 and 102, respectively in the fourth and sixth years (p-value < 0.001), representing an increase in prevalence to 21.2% and then 28.6%. From baseline to endline, it was observed that the majority of MetS components tended to increase significantly (p-value < 0.001), particularly waist circumference, triglycerides, and fasting blood glucose level, while low HDL and hypertension were observed to fluctuate. In addition, a substantial increase in the prevalence of obesity was observed (from 33.3–44.9%), while the number of people in the normal and underweight BMI range reduced (Table 2). In the second year of observation, most participants were obese (38.2%), and the proportion with normal BMI lessened (36.0%). The mean and SD of energy, carbohydrate, protein, fat, and sodium intake are also presented in Table 2. With the exception of carbohydrate, the means of all measured dietary intakes (energy, protein, fat, and sodium) increased from baseline to endline. However, energy, protein, and sodium intakes in year 2 were not significantly different to baseline.

Further analysis of the combination of variables for the survival rate of MetS at endline is presented in Fig. 2 (a-l). Overweight and obesity, sedentary activity levels and higher carbohydrate consumption led to a higher risk of developing MetS compared to normal BMI, have intention physical exercise, and low carbohydrate consumption (quartile 1). The highest cumulative survival (94%) was recorded in participants with underweight BMI at endline (sixth year). The lowest cumulative survival was recorded in participants with obese BMI, 66.4% at baseline and 49.3% at the endline. Cumulative survival differs substantially between age groups: the younger (< 50 years) group had a lower survival rate compared to the older ( > = 50 years) group. Female participants had lower cumulative survival (68.3%) compared to males (77.4%) at endline. Participants that smoked more than 20 cigarettes per day had lower cumulative survival at endline (76.7%) compared to those with lower smoking intensity (77.0% for one to nine cigarettes per day and 78.1% for ten to 19 cigarettes per day). However, participants that were ex-smokers or had never smoked also had lower cumulative survival (70.5% and 67.8%, respectively) compared to smoking participants (77.0%). Participants who reported stress had 70.9% endline cumulative survival, slightly lower than non-stressed participants at 71.7%. The cumulative survival was apparently higher among participants with insufficient levels of physical activity, 80.7% at baseline, compared to those who reported sufficient levels of physical activity, at 70.4%. However, a different result was obtained when physical activity was categorised by intention to exercise (i.e. recreational exercise such as playing sports rather than daily activity). People who reported their activity levels included intentional physical exercise (e.g. playing sports or recreational exercise) had a cumulative survival approximately 8% higher than those who did not. In terms of dietary intake, with the exception of sodium and energy, the measured nutrients showed a fluctuating trend of survival from baseline to endline. However, the highest quartile of energy consumption had the lowest cumulative survival (69.2% in the sixth year) compared to the lower quartiles. For sodium consumption by quartile, the greater the intake, the lower the rate of cumulative survival, from 74.7% (Q1) to 68.0% (Q4).

Table 1

Participants’ characteristics from baseline to endline (N = 1,376)
Characteristics	n (%)
MetS^a
No	982 (71.4)
Yes	394 (28.6)
Body Mass Index (BMI)^b
Underweight	116 (8.4)
Normal	545 (39.6)
Overweight	257 (18.7)
Obese	458 (33.3)
Age^b
< 50	1080 (78.5)
≥ 50	296 (21.5)
Sex^b
Male	464 (33.7)
Female	912 (66.3)
Smoking habit ^b
Never smoked	676 (49.1)
Ex-smoker	227 (16.5)
Smoke	473 (34.4)
Smoking intensity ^b
Not smoking	960 (69.8)
1–9 cigarette(s) per day	226 (16.4)
10–19 cigarettes per day	160 (11.6)
≥ 20 cigarettes per day	30 (2.18)
Stress ^b
No	798 (58.0)
Yes	578 (42.0)
Physical activity ^b
Overall physical activity
Sufficient	1246 (90.5)
Insufficient	130 (9.5)
Intention to physical exercise
Sufficient	158 (11.5)
Insufficient	1218 (88.5)
^a presented from baseline to endline ^b presented at baseline

Table 2

Distribution of Components of Metabolic Syndrome and Dietary Intake based on Cohort Periods
Characteristics	n (%) N = 1,376	Cohort Periods (in year)
Characteristics	0 (2011/2012)	p-value (year 2 vs 0)	2 (2013/2014)	p-value (year 4 vs 2)	4 (2015/2016)	p-value (year 6 vs 4)	6 (2017/2018)
Metabolic Syndrome Prevalence
Yes	0 (0)	< 0.001*	244 (17.7)	< 0.001*	292 (21.2)	< 0.001*	394 (28.6)
No	1,376 (100)	< 0.001*	1,132 (82.3)	< 0.001*	1,084 (78.8)	< 0.001*	982 (71.4)
Waist Circumference	76.6 ± 10.0 (50–115)	< 0.001**	79. 8 ± 10.1 (49–120)	< 0.001**	82.3 ± 9.8 (56–121)	< 0.001**	83.6 ± 10.0 (54–127)
Abdominal Obesity
Yes	396 (28.8)	< 0.001*	509 (37.0)	< 0.001*	659 (47.9)	< 0.001*	709 (51.5)
No	980 (71.2)	< 0.001*	867 (63.0)	< 0.001*	717 (52.1)	< 0.001*	667 (48.5)
Triglycerides Level^c	92.9 ± 45.6 (30–787)	< 0.001**	101.8 ± 63.3 (30–1305)	0.544**	102.0 ± 57.2 (32–1114)	< 0.001**	112.9 ± 65.59 (32.45–894)
Normal	1,270 (92.3)	< 0.001*	1,220 (88.7)	0.243*	1,205 (87.6)	< 0.001*	1,125 (81.8)
High	106 (7.7)	< 0.001*	156 (11.3)	0.243*	171 (12.4)	< 0.001*	251 (18.2)
HDL Level^c	51.5 ± 10.8 (24–98)	0.446**	51.6 ± 11.7 (26–173)	< 0.001**	53.0 ± 10.9 (22–96)	< 0.001**	50.6 ± 11.1 (26–101.5)
Normal	961 (69.8)	0.085*	931 (67.7)	< 0.001*	1,025 (74.5)	< 0.001*	876 (63.7)
Low	415 (30.2)	0.085*	445 (32.3)	< 0.001*	351 (25.5)	< 0.001*	500 (36.3)
Fasting Blood Glucose Level^c	83.3 ± 9.2 (56–134)	< 0.001**	86.3 ± 10.6 (58–151)	< 0.001**	87.6 ± 9.3 (63–202)	< 0.001**	97.6 ± 18.4 (59–349)
Normal	1,371 (99.6)	0.763*	1,370 (99.6)	0.782*	1,369 (99.5)	< 0.001*	1,311 (95.3)
Diabetes Mellitus	5 (0.4)	0.763*	6 (0.4)	0.782*	7 (0.5)	< 0.001*	65 (4.7)
Systolic Blood Pressure^c	115.2 ± 12.1 (81–163)	0.143**	115.7 ± 12.4 (86–188)	0.009**	116.4 ± 12.7 (84–185)	< 0.001**	120.5 ± 13.8 (83–186)
Diastolic Blood Pressure^c	74.1 ± 8.5 (45–103)	< 0.001**	92.5 ± 10.2 (64–142)	< 0.001**	75.2 ± 8.6 (53–103)	< 0.001**	79.2 ± 9.1 (51–121)
Hypertension^d
Yes	211 (15.3)	< 0.001*	1,075 (78.1)	< 0.001*	280 (20.3)	< 0.001*	456 (33.1)
No	1,165 (84.7)	< 0.001*	301 (21.9)	< 0.001*	1,096 (79.7)	< 0.001*	920 (66.9)
Body Mass Index (BMI)
Underweight (< 18.5 kg/m²)	116 (8.4)	< 0.001***	104 (7.6)	0.001***	100 (7.3)	< 0.001***	86 (6.3)
Normal (18.5–22.9 kg/m²)	545 (39.6)		496 (36.0)		460 (33.4)		423 (30.7)
Overweight (23–24.9 kg/m²)	257 (18.7)		251 (18.2)		265 (19.3)		249 (18.1)
Obese (≥ 25.0 kg/m²)	458 (33.3)		525 (38.2)		551 (40.0)		618 (44.9)
Dietary Intake^c
Energy (kcal)	1721.8 ± 646.7 (185.6–6352.5)	0.992**	1717.3 ± 629.8 (248.5–4838.0)	< 0.001**	1891.8 ± 694.7 (36.0–5537.7)	< 0.001**	2036.3 ± 784.8 (248.8–5733.1)
Carbohydrate (gr)	259.0 ± 102.5 (40.7–1097.3)	0.001**	247.0 ± 94.9 (36.6–839.4)	< 0.001**	263.7 ± 102.0 (7.45–814.4)	< 0.001**	154.8 ± 133.2 (10.22–619.2)
Protein (gr)	57.0 ± 23.2 (3.0–186.3)	0.091**	55.6 ± 23.0 (4.1–265.8)	< 0.001**	61.7 ± 27.8 (0.8–417.7)	< 0.001**	180.9 ± 129.5 (15.3–788.1)
Fat (gr)	52.4 ± 27.3 (0.9–182.9)	< 0.001**	58.4 ± 33.1 (1.5–400.0)	< 0.001**	70.3 ± 34.8 (0.23–295.0)	< 0.001**	80.5 ± 38.7 (7.7–278.76)
Sodium (mg)	1652.8 ± 1605.6 (13.9–15135.5)	0.153**	1508.5 ± 1021.4 (66.5–8264.5)	< 0.001**	1862.2 ± 2851.0 (1.1–77877.3)	< 0.001**	2306.8 ± 1317.1 (173.6–8473.2)
*McNemar Test
**Wilcoxon mean-rank Test
***Friedman Test
^c Presented in mean ± SD (min-max)
^d Based on systolic and diastolic blood pressure measurement, not based on clinical diagnosis of hypertension

Table 3

The Model of the Cox Regression for the 6-Year Survival Rate of MetS
Risk Factors	HR	95% CI	P-Value
Age (< 50 Years Old)
>= 50 Years Old	0.908	0.703–1.174	0.462
Sex (Male)
Female	0.940	0.680–1.302	0.712
BMI (Normal)
Underweight	0.422	0.194–0.919	0.030*
Overweight	2.415	1.176–3.320	< 0.001**
Obese	4.381	3.345–5.740	< 0.001**
Smoking (No)
Ever	0.972	0.727–1.300	0.847
Yes	0.938	0.622–1.415	0.760
Smoking Intensity (Not at all)
1–9 cigarettes/day	1.007	0.642–1.580	0.977
10–19 cigarettes/day	0.996	0.597–1.661	0.988
>= 20 cigarettes/day	0.826	0.370–1.846	0.641
Stress (No)
Yes	1.053	0.859–1.292	0.619
Intended Physical Exercise (Active)
Light/Sedentary	1.476	1.034–2.109	0.032*
Energy (Q-1)
Q-2	0.649	0.408–1.033	0.068
Q-3	0.589	0.307–1.131	0.112
Q-4	0.610	0.256–1.453	0.264
Protein (Q-1)
Q-2	1.179	0.824–1.686	0.368
Q-3	1.201	0.783–1.845	0.401
Q-4	1.210	0.729–2.009	0.461
Fat (Q-1)
Q-2	0.701	0.505–0.972	0.033*
Q-3	0.943	0.643–2.570	0.764
Q-4	0.836	0.524–1.333	0.451
Carbohydrate (Q-1)
Q-2	1.543	1.063–2.241	0.023*
Q-3	1.560	0.947–2.569	0.081
Q-4	1.735	0.933–3.229	0.082
Sodium (Q-1)
Q-2	1.082	0.806–1.453	0.601
Q-3	1.161	0.868–1.553	0.313
Q-4	1.196	0.884–1.618	0.246

HR = Hazard Ratio; * significance p < 0.05; ** p < 0.01.

Multivariate analysis showed that factors influencing MetS survival were BMI, intended exercise and fat and carbohydrate intakes. Participants with underweight BMI had a lower risk of developing MetS (HR = 0.4, p-value 0.030 95% CI 0.194–0.919), while overweight and obese participants were 2.4 and 4.4 times more likely to develop MetS (p-value < 0.001 with 95% CI 1.176–3.320 and 3.345–5.740, respectively) compared to those with normal BMI. In addition, participants who reported less intention to exercise had a MetS risk 1.5 times higher (p-value 0.032 95% CI 1.034–2.109) than those who were physically active. Dietary fat intakes are also important, evident by an around 30% reduction in MetS risk for fat consumption in the 2nd quartile compared to the 1st quartile On the other hand, carbohydrate intakes in the 2nd quartile increased the risk of MetS 1.5 times (p-value 0.023 95% CI 1.063–2.241) in comparison to the 1st quartile, and the risk tended to increase in line with higher consumption. Age, sex, smoking habit and intensity, stress, and energy, protein and sodium intake were considered to be confounders (Table 3).

MetS and its components are risk factors that can elevate the prevalence and severity of leading causes of death such as cardiovascular and cerebrovascular diseases and type 2 diabetes mellitus [30, 31]. The adverse effects of living with the condition are not limited to the affected individuals but also have serious ramifications at the societal level. In the case of Indonesia, all citizens are covered by National Health Insurance (JKN) and treatment costs to the state can represent millions of US dollars each year. It is widely known that BMI, physical activity, and eating habits play critical roles in the development of MetS. This study identified the risk factors for MetS based on six years of follow-up in a cohort of individuals who were not affected by the condition at baseline.

In this population-based cohort study, we investigated the association between risk factors and the survival rate for MetS, defined as the probability of living free from MetS. We demonstrated several principal findings, which are summarised as follows: during the 6-year follow-up period (1) the risk of developing MetS rose with increasing BMI. Obesity reduced the probability of living free from MetS by more than 50% and led to a 4.4-fold increased risk of developing MetS, (2) a sedentary lifestyle significantly increased MetS risk by 1.5 times and reduced the probability of living free from MetS by around 20%, (3) we observed that low and moderate fat intake had a protective effect against MetS and carbohydrate intake was a risk factor for developing MetS. However, this result needs to be carefully interpreted as the lower consumption of fat may be a consequence of having higher consumption of carbohydrate.

Previous studies have consistently reported higher BMI as predictor for MetS [32–34]. Other variables that have been reported in addition to BMI include sedentary lifestyle [35, 36], waist-to-hip ratio [37], older age, being female, and lower education level [34]. A population cohort in Korean adults suggested that dietary aspects, such as dietary diversity, was associated with the risk of MetS [38]. Diet and MetS association also supported by a cross-sectional study reported low fruit and vegetable intakes and high alcohol consumption as dietary factors that contribute to MetS risk, alongside high BMI and a sedentary lifestyle [35]. The findings of the present study are, in general, aligned with previous findings and suggested theories. While overweight-obesity and sedentary lifestyle were consistently reported in another population study, this study’s findings in relation to carbohydrate and fat intake as predictors of the future risk of MetS have not been widely reported in other studies. Different dietary patterns and types of carbohydrate and fat consumed in various populations allow diverse outcomes in the risk of MetS [35].

Previous observations related to fat intake and MetS were inconclusive. While some study proposed that high dietary fat intakes are associated with higher risk of MetS [39, 40], some other studies suggested the opposite [41, 42]. Based on a systematic review, the difference was due to the type of fat consumed, as higher polyunsaturated fatty acids (PUFA) was reported to be inversely associated with adiposity and obesity, as well as lower triglyceride levels, increased HDL, and an overall lower risk of MetS [40]. The current analysis shows that low and moderate fat intake lowers the risk of developing MetS. However, this result needs to be carefully interpreted as it could be depending to the type of fat (fatty acids) consumed, which this study did not measure, or considering the overall energy intake proportion, the protective effect of fat may be a consequence of having higher consumption of carbohydrate.

A dose–response meta-analysis of observational studies suggested that carbohydrate intake had a linear association with MetS risk, with a 2.6% increase in the risk of MetS per 5% energy from carbohydrate intake [43]. In addition, carbohydrate intake from starchy foods with a high glycaemic index (GI > 65) has been shown to contribute more strongly to metabolic disorders and hyperlipidaemia [44]. High consumption of carbohydrates has been consistently associated with a reduced HDL cholesterol level and increased plasma triglyceride levels [45, 46] mainly due to a higher triglyceride content in very low-density lipoprotein (VLDL) particles and overproduction of VLDL particles [47]. In addition to triglyceride and HDL levels, abdominal obesity is also strongly associated with carbohydrate intake [46]. This condition was likely relevant to participants in this study, who were, due to the prevalence of overweight and obesity, already at greater risk of abdominal obesity.

The abdominal obesity component was the most prevalent feature of MetS in this population and another reported MetS population [48]. It is representative of visceral adiposity which is a known risk factor for cardiovascular diseases. Although BMI does not represent adiposity, in the general population, overweight and obese BMI were correlated with increased levels of body fat [49]. Therefore, an individual with overweight-obesity was already at higher risk of abdominal obesity. Furthermore, increased adiposity has been theoretically linked to increased inflammation and metabolism suppression. The biochemical cascade was linked to glucose intolerance and hyperinsulinemia, as well as endothelial dysfunction, which is the predisposing factor for dyslipidaemia and vascular diseases [50, 51].

The present study showed that inadequate exercise had unfavourable effects on MetS risk. Although on the basis of their self-reported data, most participants had sufficient physical activity levels due to their employment as household assistants, when we clustered activity based on intentional physical exercise (playing sports or recreational exercise rather than activity associated with their employment), most participants did not meet recommended levels. Increased sedentary behaviour is associated with an increased risk of high levels of triglycerides, HDL-cholesterol, and fasting glucose [52, 53]. Conversely, around 30 minutes of moderate-intensity daily exercise improves factors related to MetS [54]. Men who have more than 4 times per week of vigorous, moderate, and strength exercise and more than 6 times per week of walking had a triglyceride level lower than 150 mg/dL [55, 56]. Furthermore, diastolic blood pressure [57] and fasting blood glucose [55] were reported to improve as a result of moderate exercise, particularly aerobic exercise [57].

In observing the development of MetS, we worked on several variables to avoid biases in measurement and analysis. We excluded participants who had limited or incomplete data on blood pressure, fasting blood glucose, HDL, and triglycerides and who were taking antihypertensive, antidiabetic, and cholesterol-lowering medications. We also excluded participants who did not fast prior to blood glucose testing. By doing this, we expected to better understand the incidence of MetS during six years of follow-up with observations every two years. However, we tried to involve as many relevant variables as possible to obtain a more accurate model.

The retrospective study design and the use of secondary data were among the limitations of this study. There is some potential that the data is inaccurate due to the self-reporting of some information or human error in data input. However, we used a cleaning procedure to exclude missing information from the MetS criteria, such as instances, when a number was either too small or too excessive, as was the case with blood glucose levels, waist circumference, lipid profiles, and blood pressure, the smaller sample sizes were a result of the stringent data inclusion criteria. The strengths of the present study are the large number of study subjects, and adjustment for potential confounding factors, including dietary intake and physical activity. However, the inclusion of data on changing levels of physical activity during the study period would have been beneficial. The anthropometric and clinical measurements were recorded by trained operators and are more accurate than self-reported measurements. After weighted adjustment, this study is a sound reflection of the epidemic characteristics of MetS among residents in Bogor District, Indonesia.

Between 2011 and 2018, more than a quarter of the sample developed MetS. Overweight and obesity, insufficient exercise, and a higher quartile of carbohydrate consumption were observed as factors increasing the risk of MetS, even after controlling for several confounding factors. The highest cumulative survival of MetS was reported for participants with underweight BMI and the lowest cumulative survival was reported for participants with obese BMI. Given the importance of these findings, immediate strategic actions are needed to improve the existing early detection and monitoring programmes of NCD that promote community-based healthy lifestyle initiatives and is run by both the government and non-government organisations in Bogor District, Indonesia.

BMI: Body Mass Index

HDL: High-Density Lipoprotein

MET: Metabolic Equivalent of Task

MetS: Metabolic Syndrome

MUFA: Monounsaturated Fatty Acid

NCEP ATP: National Cholesterol Education Program’s Adult Treatment Panel

NIHRD: National Institute of Health Research and Development

NCDs: Non-Communicable Diseases

PUFA: Polyunsaturated Fatty Acid

SFA: Saturated Fatty Acid

SDGs: Sustainable Development Goals

VLDL: Very Low-Density Lipoprotein

Acknowledgements

The authors thank the National Institute of Health Research and Development, Ministry of Health, Republic of Indonesia that provided the access to the data for this study and the Directorate of Research and Community Engagement of Universitas Indonesia as the funder. The funder was not involved in the manuscript’s study, drafting, evaluation, or approval. We also convey our gratitude to Doni Hikmat Ramdhan for his thorough review and feedback to authors.

Authors’ Contributions

NDR had primary responsibility for writing of the article. FW and KAKT carried out data analyses. NDR, HA, FW and LF conceived the idea and initiated the analysis plan for the current study. HA and AW supervised the current study. FW and LF reviewed and commented on the draft. All authors coordinated the study and provided comments on drafts of the manuscript and have revised and approved the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by the Directorate of Research and Community Engagement of Universitas Indonesia, grant number NKB-625/UN2.RST/HKP.05.00/2022.

Availability of Data and Materials

The data that support the findings of this study are available from The Ministry of Health, Republic of Indonesia, under the Basic Health Research data, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available with prior officially written permission of the Data Management Laboratory of NIHRD, Ministry of Health, Republic of Indonesia ([email protected]).

Competing Interests

The authors declare no competing interests.

Ethics Approval and Consent to Participate

Ethical approval was waived as we used secondary data. As for the primary data of the Special Study of Non-Communicable Disease, Komisi Etik Penelitian Kesehatan, Badan Penelitian dan Pengembangan Kesehatan (Ethical Committee of Health Research, National Institute of Health Research and Development (NIHRD), Ministry of Health, Republic of Indonesia) had given their approval with the reference number LB.02.01/2/KE.076/2018. All methods were carried out in accordance with relevant guidelines and regulations (Declaration of Helsinki). Additional ethical review board approval was obtained from the Komisi Etik Riset dan Pengabdian Kesehatan Masyarakat Fakultas Kesehatan Masyarakat Universitas Indonesia (The Research and Community Engagement Ethical Committee, Faculty of Public Health Universitas Indonesia) with reference number 534/UN2.F10.D11/PPM.00.02/2022. All participants aged 15 years and above, or a parent and/or legal guardian (in the case of children under 16) had given their written informed consent prior to participating in the 2011-2018 Non-Communicable Disease Cohort Study. Participants’ willingness to participate and written informed consent was reassessed before on-site laboratory testing and blood sample collection.

Consent for Publication

Not applicable.

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Body Mass Index as a Dominant Risk Factor for Metabolic Syndrome among Indonesian Adults: A 6-year Prospective Cohort Study of Non-Communicable Diseases

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