Background
Cardiometabolic syndrome (CMS) is a combination of metabolic dysfunctions mainly characterized by insulin resistance, impaired glucose tolerance, dyslipidemia, hypertension, and central adiposity. People with CMS are two times more likely to die from coronary heart disease and three times more likely to have a heart attack or stroke than those who do not have the syndrome. It is now known that central adiposity is a major contributor to increased cardiometabolic risk [
1]. There are many challenges to bringing CMS risk factors under control. However, cardiometabolic programs and therapeutic strategies exist that combine diet and exercise prescriptions and focus on behavioral change to maximize success in reducing cardiometabolic risk factors. These programs have specific recommendations for calorie intake, nutrition, and ongoing cognitive and psychological assessments of habits and unhealthy behaviors [
2].
In the Philippines, NCD have overtaken communicable diseases as the top cause of mortality wherein it is estimated to account for 67% of all deaths in 2016 [
3]. The five major NCD in the Philippines in proportion to mortality are cardiovascular diseases (35%), cancers (10%), chronic respiratory diseases (6%), diabetes (4%), and other NCD (12%) [
4]. Specifically, diseases of the heart and of the vascular system are the leading cause of mortality in the Philippines [
5]. The National Nutrition Survey (NNS) conducted by the Food and Nutrition Research Institute (FNRI) in 2013 showed a large number of Filipinos at risk of selected cardiometabolic NCD factors. Risk factors assessed in the NNS include hypertension, obesity, high cholesterol, and diabetes [
6]. In 2014, there were 16 for every 1000 Filipino patients admitted due to a medical condition wherein hypertension was possibly the most common etiology factor [
7]. Moreover, in the past decade it has been observed that there is a steady increase in the prevalence of high fasting blood glucose (FBG) from 3.4% in 2003 to 5.6% in 2013, and the prevalence is even higher among Filipino adults residing in urban areas (6.4%).
Food, diet and nutritional status are important determinants of NCD. Poor dietary quality, in particular high salt intake, high saturated and
trans-fatty acid intake, and low fruit and vegetable consumption coupled with sedentary lifestyle and stressful environment are some risk factors of CMS development [
8]. The role of diet in the etiology of most NCDs is extremely important and considered a modifiable risk factor for NCDs [
9]. The Philippines is at a high risk for a rise in NCDs as measured by selected CMS especially among adults since the pattern of consumption among this population group is associated with the consumption of processed food laden with sugar, salt and fat, drinking alcohol, snacking between meals, eating while distracted and sedentary lifestyle [
10]. In addition, it has been recognized that dietary patterns rather than single nutrients are stronger predictors of NCD risks, and should be the focus for NCD prevention.
Limited data exist in the Philippines with regards to the local dietary patterns and their associations with NCD. Thus, this study evaluated the relationship between dietary quality and food patterns of Filipino adults and the rising prevalence of selected cardiometabolic NCD risk factors. Through the use of the Alternative Healthy Eating Index (AHEI-2010), which is based on foods and nutrients predictive of chronic disease risk, we could assess the quality of typical Filipino diet. A data-driven approach was also employed to understand major dietary patterns in the population. Using data collected in NNS 2013, dietary patterns derived from both approaches were studied in association with major NCD biomarkers, with the aim to identify potential protective or detrimental dietary patterns using local data that could guide future dietary intervention strategies appropriate and applicable in the Philippines.
Results
For this study, a total of 19,914 adults aged 20 years and above were included in the analyses (men: n = 10,001 and women: n = 9913), with a mean of age of 45.7 yrs. old.
Mean AHEI-2010 score in the studied Philippines adults population was 19.7 for women and 18.9 for men out of a total possible score of 100 (Table
1). This suggested an overall poor quality of diet in the general population. A mean score of 28.2 even in the highest tertile of AHEI-2010 (Table
3) could barely be considered a healthy eating group of subjects. Such lack of variation in the data limited the potential of this hypothesis-based healthy dietary pattern score to differentiate various subgroups of the population. Correspondingly, most of the demographic characteristics of the study participants did not differ significantly across the three tertiles of AHEI-2010 (Table
3). On the other hand, greater differences were observed across the tertile distribution of the three PCA-derived dietary patterns (Table
3). Respondents consuming a MSB pattern (highest tertile) are more likely to be younger, urban residents, from the rich and richest wealth quintiles, non-smoker, and currently drinking alcohol. The highest tertile of RF pattern are more likely to be younger, males, urban residents, from the rich and richest wealth quintiles, currently smoking and drinking alcohol. Subjects in the highest tertile of the FVS patterns are more likely to be from the richest wealth quintile and less likely to be currently smoking or drinking.
Table 3
Demographic and health characteristics by tertile of the four dietary patternsa
Age (y) | 44.2 ± 0.20 | 46.5 ± 0.20 | 46.6 ± 0.19 | 48.7 ± 0.19 | 47.1 ± 0.20 | 41.5 ± 0.18 | 49.0 ± 0.21 | 45.8 ± 0.19 | 42.5 ± 0.18 | 44.5 ± 0.19 | 46.5 ± 0.19 | 46.4 ± 0.19 |
Gender (%) |
Male | 53.2 | 50.4 | 47.1 | 53.7 | 44.7 | 52.2 | 36.6 | 47.5 | 66.6 | 52.9 | 48.7 | 49.1 |
Female | 46.8 | 49.6 | 52.9 | 46.3 | 55.3 | 47.8 | 63.4 | 52.5 | 33.4 | 47.1 | 51.3 | 50.9 |
Residence (%) |
Rural | 54.2 | 57.9 | 52.4 | 72 | 53.3 | 39.3 | 58.2 | 54.7 | 51.6 | 53.7 | 55.7 | 55.1 |
Urban | 45.8 | 42.1 | 47.6 | 28 | 46.7 | 60.7 | 41.8 | 45.3 | 48.4 | 46.3 | 44.3 | 44.9 |
Wealth status (%) |
Poorest | 19.2 | 21.3 | 21.3 | 19.8 | 21.9 | 20 | 18.6 | 21.5 | 21.6 | 21.3 | 21 | 19.4 |
Poor | 20.8 | 22.1 | 20.5 | 26.7 | 21.7 | 15 | 22.4 | 21.2 | 19.8 | 22 | 22.4 | 19 |
Middle | 20.9 | 21.8 | 17.8 | 33.7 | 18.1 | 8.7 | 24.6 | 18.7 | 17.2 | 21.1 | 19.7 | 19.8 |
Rich | 18.6 | 18.5 | 20.9 | 12 | 21 | 25 | 17.3 | 19.4 | 21.4 | 18.7 | 19.5 | 19.8 |
Richest | 20.5 | 16.4 | 19.5 | 7.8 | 17.3 | 31.3 | 17.1 | 19.2 | 20 | 16.9 | 17.4 | 22 |
Currently smoking (%) | 28.7 | 27.6 | 24.7 | 30.1 | 24.1 | 26.7 | 23.7 | 24.7 | 32.6 | 29.1 | 26.7 | 25.1 |
Currently drinking (%) | 50.2 | 48.6 | 48.5 | 46.7 | 45.4 | 55.2 | 40.9 | 47.1 | 59.3 | 53 | 46.5 | 47.8 |
BMI status (%) |
CED | 12.3 | 12.8 | 10.1 | 14.5 | 11.8 | 8.9 | 15 | 11.8 | 8.6 | 11.8 | 12.2 | 11.3 |
normal | 59.9 | 59.5 | 59.4 | 63.4 | 58.5 | 56.8 | 59.4 | 59.2 | 60.2 | 59 | 60.1 | 59.6 |
overweight | 22.2 | 21.9 | 24.1 | 18.4 | 23.6 | 26.3 | 20.1 | 23 | 25 | 23.2 | 22.1 | 22.9 |
obesity | 5.6 | 5.8 | 6.4 | 3.7 | 6.1 | 8 | 5.5 | 6 | 6.2 | 6 | 5.6 | 6.2 |
Hypertension (%) | 24.4 | 25.5 | 25.8 | 25.8 | 27.3 | 22.6 | 26.7 | 25.5 | 23.5 | 25.4 | 25.6 | 24.8 |
Diabetes (%) | 19.2 | 18.5 | 18.5 | 16.7 | 19.2 | 20.1 | 19.1 | 19 | 18 | 20.2 | 17.5 | 18.5 |
Total cholesterol > 240 mg/dL (%) | 19 | 18.7 | 20.4 | 16.2 | 21.1 | 20.8 | 19.9 | 20 | 18.2 | 18.3 | 19.6 | 20.2 |
LDL-cholesterol > 160 mg/dL (%) | 22.1 | 23.6 | 24.9 | 21.1 | 25.7 | 23.8 | 24.4 | 24.5 | 21.7 | 22.3 | 24 | 24.3 |
HDL-cholesterol < 40 mg/dL (%) | 59.6 | 61.9 | 59.9 | 67.6 | 59.9 | 53.6 | 60.2 | 60.2 | 61 | 61.6 | 60.7 | 59.1 |
Triglycerides > 200 mg/dL (%) | 21.5 | 20.6 | 20.7 | 18.5 | 21.2 | 23.2 | 18.4 | 20.6 | 23.7 | 21.7 | 20.3 | 20.7 |
AHEI-2010 Score | 10.7 ± 0.04 | 19.0 ± 0.02 | 28.2 ± 0.1 | 20.6 ± 0.1 | 21.0 ± 0.1 | 16.4 ± 0.2 | 18.6 ± 0.1 | 19.7 ± 0.1 | 19.7 ± 0.1 | 18.9 ± 0.1 | 19.2 ± 0.1 | 19.8 ± 0.1 |
The prevalence of abnormalities in selected cardiometabolic NCD risk factors did not differ significantly across the tertiles of AHEI-2010 score for most measures. In comparison, the highest tertile of MSB pattern was associated with lower prevalence of chronic energy deficiency, hypertension and low HDL-cholesterol, and higher prevalence of overweight, obesity, diabetes, high cholesterol, high LDL-cholesterol, and high triglycerides. The RF pattern was associated with lower prevalence of chronic energy deficiency, hypertension and high LDL-cholesterol, and higher prevalence of overweight, obesity, and high triglycerides. The FVS pattern was associated with lower prevalence of diabetes (Table
3).
The intake of energy, total fat and sodium in lowest tertile of AHEI pattern were higher than the intake in the highest tertile, while magnesium, potassium and vitamin C intakes were higher in the highest tertile than the intake in lowest tertile (Table
4). The highest tertile of MSB pattern was associated with higher intakes of energy, total fat, saturated fat (SFA), monounsaturated fat (MUFA), polyunsaturated fat (PUFA), protein, sugar, iron and sodium, and a lower average score of AHEI-2010. The intakes of energy, iron, calcium, magnesium, phosphorus, potassium, selenium, and niacin were higher in the highest tertile of the RF pattern than the lowest tertile. For the FVS pattern, the intakes of energy, calcium, fiber, folate, magnesium and potassium were higher than the intakes in the lowest tertile (Table
4).
Table 4
Nutrients intake of Filipino’s adults by tertile of the four dietary patterns
Macronutrients |
Energy intake (kcal) | 1763.1 ± 6.5 | 1652.9 ± 6.1 | 1682.3 ± 5.7 | 1608.7 ± 5.9 | 1578.2 ± 5.6 | 1911.5 ± 6.0 | 1365.2 ± 4.6 | 1659.5 ± 4.5 | 2073.6 ± 5.9 | 1639.8 ± 6.1 | 1642.7 ± 5.9 | 1815.8 ± 6.2 |
Total fat (g/d) | 30.4 ± 0.2 | 25.7 ± 0.1 | 26.6 ± 0.1 | 20.6 ± 0.1 | 25.2 ± 0.1 | 36.9 ± 0.2 | 23.3 ± 0.1 | 26.8 ± 0.1 | 32.6 ± 0.2 | 26.4 ± 0.2 | 26.1 ± 0.1 | 30.2 ± 0.2 |
Saturated fat (g) | 13.1 ± 0.1 | 11.6 ± 0.1 | 12.4 ± 0.1 | 9.6 ± 0.04 | 11.7 ± 0.1 | 15.8 ± 0.1 | 10.3 ± 0.1 | 12.0 ± 0.1 | 14.7 ± 0.1 | 12.0 ± 0.1 | 11.8 ± 0.1 | 13.2 ± 0.1 |
Monounsaturated fatty acids (g) | 9.8 ± 0.1 | 8.5 ± 0.05 | 9.1 ± 0.04 | 7.0 ± 0.04 | 8.4 ± 0.04 | 11.9 ± 0.1 | 8.1 ± 0.05 | 9.0 ± 0.05 | 10.2 ± 0.1 | 8.8 ± 0.05 | 8.8 ± 0.04 | 9.8 ± 0.1 |
Polyunsaturated fatty acids (g) | 4.6 ± 0.02 | 4.1 ± 0.02 | 4.2 ± 0.02 | 3.4 ± 0.02 | 4.0 ± 0.02 | 5.5 ± 0.02 | 3.9 ± 0.02 | 4.2 ± 0.02 | 4.8 ± 0.02 | 4.1 ± 0.02 | 4.1 ± 0.02 | 4.7 ± 0.02 |
Protein (g/d) | 57.0 ± 0.2 | 54.8 ± 0.2 | 55.7 ± 0.2 | 52.4 ± 0.2 | 52.1 ± 0.2 | 63.0 ± 0.2 | 45.4 ± 0.2 | 54.6 ± 0.1 | 67.9 ± 0.2 | 55.7 ± 0.2 | 54.0 ± 0.2 | 57.8 ± 0.2 |
Carbohydrate (g/d) | 307.3 ± 1.2 | 296.3 ± 1.2 | 301.1 ± 1.1 | 301.3 ± 1.2 | 283.7 ± 1.1 | 319.7 ± 1.1 | 240.2 ± 0.8 | 295.3 ± 0.8 | 369.1 ± 1.2 | 288.4 ± 1.1 | 294.1 ± 1.1 | 322.3 ± 1.2 |
Total sugars (g/d) | 25.1 ± 0.1 | 23.2 ± 0.1 | 25.3 ± 0.1 | 23.1 ± 0.1 | 22.3 ± 0.1 | 28.2 ± 0.1 | 23.5 ± 0.1 | 24.5 ± 0.1 | 25.6 ± 0.1 | 19.5 ± 0.1 | 23.3 ± 0.1 | 30.8 ± 0.1 |
Dietary fibre (g/d) | 8.4 ± 0.02 | 8.4 ± 0.03 | 8.9 ± 0.03 | 9.2 ± 0.04 | 8.0 ± 0.03 | 8.6 ± 0.03 | 8.1 ± 0.03 | 8.3 ± 0.03 | 9.3 ± 0.03 | 7.4 ± 0.02 | 8.4 ± 0.03 | 9.9 ± 0.04 |
As percentage of total energy |
Total Fat (%) | 13.1 ± 0.01 | 13.3 ± 0.01 | 13.3 ± 0.01 | 13.1 ± 0.02 | 13.2 ± 0.01 | 13.3 ± 0.01 | 13.1 ± 0.01 | 13.2 ± 0.01 | 13.4 ± 0.01 | 13.5 ± 0.01 | 13.2 ± 0.01 | 13.0 ± 0.01 |
Protein (%) | 71.4 ± 0.1 | 72.6 ± 0.1 | 72.3 ± 0.1 | 75.1 ± 0.1 | 72.5 ± 0.1 | 68.8 ± 0.1 | 72.5 ± 0.1 | 72.3 ± 0.1 | 71.6 ± 0.1 | 71.8 ± 0.1 | 72.6 ± 0.1 | 72.0 ± 0.1 |
Carbohydrates (%) | 307.0 ± 1.1 | 299.6 ± 1.1 | 304.2 ± 1.0 | 308.8 ± 1.1 | 284.2 ± 1.0 | 317.8 ± 1.1 | 269.7 ± 1.0 | 297.0 ± 0.9 | 344.2 ± 1.1 | 281.3 ± 1.1 | 292.1 ± 1.0 | 337.5 ± 1.1 |
Antioxidants |
Vitamin C | 20.9 ± 0.1 | 20.7 ± 0.1 | 22.1 ± 0.1 | 22.0 ± 0.1 | 20.0 ± 0.1 | 21.6 ± 0.1 | 21.3 ± 0.1 | 21.2 ± 0.1 | 21.1 ± 0.1 | 18.2 ± 0.1 | 20.8 ± 0.1 | 24.6 ± 0.1 |
Vitamin E | 2.4 ± 0.01 | 2.2 ± 0.01 | 2.3 ± 0.01 | 2.1 ± 0.01 | 2.2 ± 0.01 | 2.6 ± 0.01 | 2.0 ± 0.01 | 2.2 ± 0.01 | 2.7 ± 0.01 | 2.1 ± 0.01 | 2.2 ± 0.01 | 2.5 ± 0.01 |
B-vitamins |
Thiamine (mg/d) | 0.8 ± 0.01 | 0.7 ± 0.01 | 0.7 ± 0.01 | 0.7 ± 0.01 | 0.7 ± 0.01 | 0.8 ± 0.01 | 0.6 ± 0.01 | 0.7 ± 0.01 | 0.8 ± 0.01 | 0.7 ± 0.01 | 0.7 ± 0.01 | 0.8 ± 0.01 |
Riboflavin (mg/d) | 0.7 ± 0.01 | 0.6 ± 0.01 | 0.7 ± 0.01 | 0.6 ± 0.01 | 0.6 ± 0.01 | 0.8 ± 0.01 | 0.6 ± 0.01 | 0.6 ± 0.01 | 0.7 ± 0.01 | 0.6 ± 0.01 | 0.6 ± 0.01 | 0.7 ± 0.01 |
Niacin (mg/d) | 18.2 ± 0.1 | 18.1 ± 0.1 | 18.7 ± 0.1 | 17.4 ± 0.1 | 17.3 ± 0.1 | 20.2 ± 0.1 | 14.6 ± 0.1 | 18.1 ± 0.04 | 22.3 ± 0.1 | 18.4 ± 0.1 | 17.7 ± 0.1 | 18.8 ± 0.1 |
Vitamin B6 (mg) | 1.5 ± 0.01 | 1.5 ± 0.01 | 1.5 ± 0.01 | 1.4 ± 0.01 | 1.4 ± 0.01 | 1.7 ± 0.01 | 1.2 ± 0.01 | 1.4 ± 0.01 | 1.8 ± 0.01 | 1.4 ± 0.01 | 1.4 ± 0.01 | 1.6 ± 0.01 |
Folate (DFE μg) | 14.9 ± 0.1 | 13.5 ± 0.1 | 13.8 ± 0.1 | 11.3 ± 0.04 | 13.8 ± 0.05 | 17.1 ± 0.1 | 13.9 ± 0.1 | 14.0 ± 0.1 | 14.4 ± 0.1 | 13.9 ± 0.1 | 13.7 ± 0.1 | 14.6 ± 0.1 |
Vitamin B12 (mg) | 3.5 ± 0.01 | 3.6 ± 0.01 | 3.7 ± 0.01 | 3.5 ± 0.01 | 3.5 ± 0.01 | 3.7 ± 0.01 | 3.2 ± 0.01 | 3.6 ± 0.01 | 4.0 ± 0.01 | 3.7 ± 0.01 | 3.5 ± 0.01 | 3.5 ± 0.01 |
Bone-related nutrients |
Calcium (mg/d) | 832.5 ± 3.2 | 815.4 ± 3.0 | 834.6 ± 2.8 | 805.7 ± 3.1 | 775.9 ± 2.8 | 900.8 ± 3.0 | 648.7 ± 2.1 | 809.2 ± 2.0 | 1024.6 ± 2.8 | 813.5 ± 3.0 | 799.6 ± 2.9 | 869.3 ± 3.1 |
Phosphorus (mg/d) | 169.7 ± 0.6 | 168.3 ± 0.6 | 175.2 ± 0.6 | 177 ± 0.6 | 159.4 ± 0.5 | 176.7 ± 0.5 | 147.2 ± 0.5 | 167.1 ± 0.5 | 198.9 ± 0.5 | 157.8 ± 0.5 | 166.5 ± 0.5 | 188.9 ± 0.6 |
Magnesium (mg) | 8.4 ± 0.03 | 7.8 ± 0.03 | 8.0 ± 0.03 | 7.5 ± 0.03 | 7.5 ± 0.03 | 9.3 ± 0.03 | 6.9 ± 0.02 | 8.0 ± 0.02 | 9.4 ± 0.03 | 7.6 ± 0.03 | 7.8 ± 0.02 | 8.9 ± 0.03 |
Vitamin D (mg) | 2.8 ± 0.01 | 2.9 ± 0.01 | 3.0 ± 0.01 | 2.8 ± 0.01 | 2.9 ± 0.01 | 3.1 ± 0.01 | 2.6 ± 0.01 | 2.9 ± 0.01 | 3.3 ± 0.01 | 3.0 ± 0.01 | 2.9 ± 0.01 | 2.9 ± 0.01 |
Other micronutrients |
Vitamin A (μg RE/d) | 433.2 ± 1.5 | 424.6 ± 1.4 | 433.9 ± 1.4 | 430.3 ± 1.5 | 410.3 ± 1.4 | 451.1 ± 1.4 | 411.7 ± 1.5 | 424.4 ± 1.4 | 455.6 ± 1.3 | 404.4 ± 1.4 | 427.2 ± 1.4 | 460.1 ± 1.5 |
Iron (mg/d) | 181.8 ± 0.9 | 172.4 ± 0.9 | 182.8 ± 0.9 | 180.8 ± 1.0 | 166.8 ± 0.8 | 189.4 ± 0.8 | 189.2 ± 1.1 | 167.1 ± 0.7 | 180.7 ± 0.7 | 149.8 ± 0.8 | 181.3 ± 0.9 | 205.9 ± 0.9 |
Zinc (mg) | 5.9 ± 0.03 | 5.4 ± 0.02 | 5.5 ± 0.02 | 5.0 ± 0.02 | 5.2 ± 0.02 | 6.7 ± 0.02 | 4.7 ± 0.02 | 5.5 ± 0.02 | 6.6 ± 0.02 | 5.5 ± 0.02 | 5.4 ± 0.02 | 5.9 ± 0.02 |
Sodium (mg/d) | 854.4 ± 2.9 | 706.1 ± 2.8 | 691.3 ± 2.4 | 656.3 ± 2.8 | 729.8 ± 2.5 | 865.6 ± 2.7 | 704.2 ± 2.8 | 741.7 ± 2.7 | 805.7 ± 2.9 | 723.9 ± 2.7 | 740.1 ± 2.8 | 787.6 ± 3.0 |
Potassium (mg) | 1198.2 ± 3.8 | 1186.4 ± 3.7 | 1262.2 ± 3.7 | 1217.8 ± 3.9 | 1126.5 ± 3.4 | 1302.5 ± 3.6 | 1082.1 ± 3.5 | 1190.8 ± 3.2 | 1373.8 ± 3.6 | 1117.9 ± 3.3 | 1167.0 ± 3.3 | 1361.9 ± 3.9 |
Selenium (mg) | 99.0 ± 0.4 | 93.1 ± 0.3 | 93.9 ± 0.3 | 87.9 ± 0.3 | 90.0 ± 0.3 | 108.2 ± 0.3 | 78.7 ± 0.3 | 93.8 ± 0.3 | 113.6 ± 0.3 | 95.1 ± 0.3 | 92.9 ± 0.3 | 98.0 ± 0.3 |
Logistic regression analyses results of selected cardiometabolic NCD risk factors across tertiles of the 4 dietary patterns are provided in Table
5. After adjustment for various potential confounding factors, the AHEI pattern was associated with higher odds of overweight/obesity [odds ratio for extreme tertile comparison: 1.1, 95% CI: 1.02, 1.21]. The MSB pattern was associated with higher odds of overweight/obesity [1.3, 95% CI: 1.21, 1.47], diabetes [1.20, 95% CI: 1.10, 1.36], high total cholesterol [1.4, 95% CI: 1.29, 1.62], low HDL-cholesterol [1.7, 95% CI: 1.41, 2.10], high LDL-cholesterol [1.30, 95% CI: 1.15, 1.43], and high/very high triglycerides [1.30, 95% CI: 1.16, 1.43]. The RF pattern was associated with higher probability of overweight/obesity [1.20, 95% CI: 1.08, 1.32], high LDL-cholesterol [1.20, 95% CI:1.07, 1.37], and less likelihood of diabetes [0.87, 95% CI: 0.77, 0.98]. The FVS pattern was associated with lower probability of overweight/obesity [0.85, 95% CI: 0.77, 0.92], diabetes [0.88, 95% CI: 0.80, 0.97], high triglycerides [0.90, 95% CI: 0.81, 1.00], and hypertension [0.88, 95% CI: 0.81, 0.96].
Table 5
Multivariate adjusted odds ratio for non-communicable disease biomarkers by tertiles of four dietary patterns
BMI (Overweight/Obese vs Normal) |
Model 1 | Ref | 1.0 (0.92, 1.08) | 1.1 (1.02, 1.19) | 0.012 | ref | 1.5 (1.34, 1.87) | 1.1 (1.05, 1.22) | < 0.001 | ref | 1.1 (1.05, 1.22) | 1.2 (1.11, 1.30) | < 0.001 | ref | 0.93 (0.86, 1.01) | 0.98 (0.91, 1.06) | 0.948 |
Model 2 | Ref | 1.1 (0.97, 1.15) | 1.1 (1.02, 1.21) | 0.016 | ref | 1.3 (1.19, 1.42) | 1.3 (1.21, 1.47) | < 0.001 | ref | 1.1 (1.03, 1.23) | 1.2 (1.08, 1.32) | 0.001 | ref | 0.89 (0.82, 0.97) | 0.85 (0.77, 0.92) | < 0.001 |
FBG (Diabetes vs Normal) |
Model 1 | Ref | 0.96 (0.88, 1.05) | 0.96 (0.88, 1.04) | 0.368 | ref | 1.2 (1.08, 1.30) | 1.3 (1.15, 1.37) | < 0.001 | ref | 0.99 (0.91, 1.08) | 0.93 (0.85, 1.01) | 0.091 | ref | 0.84 (0.77, 0.91) | 0.88 (0.82, 0.98) | 0.074 |
Model 2 | Ref | 0.97 (0.88, 1.07) | 0.95 (0.86, 1.04) | 0.282 | ref | 1.1 (1.03, 1.27) | 1.2 (1.10, 1.36) | < 0.001 | ref | 0.99 (0.90, 1.10) | 0.87 (0.77, 0.98) | 0.016 | ref | 0.84 (0.76, 0.92) | 0.88 (0.80, 0.97) | 0.043 |
Total cholesterol (High vs Desirable) |
Model 1 | Ref | 0.99 (0.89, 1.09) | 1.1 (1.04, 1.25) | 0.006 | ref | 1.5 (1.36, 1.66) | 1.5 (1.33, 1.63) | < 0.001 | ref | 1.0 (0.93, 1.12) | 0.89 (0.82, 0.99) | 0.021 | ref | 1.1 (1.0, 1.21) | 1.1 (1.04, 1.26) | 0.01 |
Model 2 | Ref | 0.96 (0.86, 1.07) | 1.0 (0.93, 1.15) | 0.474 | ref | 1.4 (1.23, 1.53) | 1.4 (1.29, 1.62) | < 0.001 | ref | 1.1 (1.01, 1.25) | 1.1 (0.99, 1.29) | 0.073 | ref | 1.0 (0.94, 1.17) | 1.0 (0.92, 1.13) | 0.85 |
HDL-cholesterol (Low vs Desirable) |
Model 1 | Ref | 0.90 (0.76, 1.07) | 1.1 (0.92, 1.28) | 0.308 | ref | 1.5 (1.22, 1.77) | 2.2 (1.86, 2.63) | < 0.001 | ref | 0.96 (0.81, 1.14) | 0.90 (0.76, 1.07) | 0.245 | ref | 1.0 (0.84, 1.20) | 1.1 (0.96, 1.35) | 0.11 |
Model 2 | Ref | 0.88 (0.74, 1.06) | 1.1 (0.89, 1.27) | 0.408 | ref | 1.2 (1.03, 1.51) | 1.7 (1.41, 2.10) | < 0.001 | ref | 1.0 (0.85, 1.23) | 1.0 (0.81, 1.25) | 0.971 | ref | 0.93 (0.78, 1.11) | 1.1 (0.90, 1.29) | 0.273 |
LDL-cholesterol (High vs Desirable) |
Model 1 | Ref | 1.1 (1.01, 1.21) | 1.2 (1.09, 1.30) | < 0.001 | ref | 1.4 (1.23, 1.48) | 1.2 (1.10, 1.32) | 0.002 | ref | 1.0 (0.92, 1.09) | 0.83 (0.75, 0.90) | < 0.001 | ref | 1.1 (1.03, 1.23) | 1.2 (1.05, 1.26) | 0.006 |
Model 2 | Ref | 1.0 (0.95, 1.16) | 1.1 (0.97, 1.18) | 0.181 | ref | 1.3 (1.15, 1.41) | 1.3 (1.15, 1.43) | < 0.001 | ref | 1.2 (1.05, 1.29) | 1.2 (1.07, 1.37) | 0.002 | ref | 1.1 (0.95, 1.16) | 1.0 (0.93, 1.14) | 0.662 |
Triglycerides (High/Very High vs Desirable) |
Model 1 | Ref | 0.93 (0.84, 1.01) | 0.95 (0.86, 1.04) | 0.297 | ref | 1.2 (1.11, 1.34) | 1.4 (1.28, 1.54) | < 0.001 | ref | 1.2 (1.06, 1.28) | 1.4 (1.31, 1.57) | < 0.001 | ref | 0.91 (0.83, 0.99) | 0.94 (0.86, 1.02) | 0.328 |
Model 2 | Ref | 0.97 (0.88, 1.07) | 0.98 (0.88, 1.07) | 0.626 | ref | 1.3 (1.4, 1.39) | 1.3 (1.16, 1.43) | < 0.001 | ref | 1.0 (0.90, 1.10) | 0.96 (0.85, 1.08) | 0.454 | ref | 0.96 (0.87, 1.06) | 0.90 (0.81, 1.0) | 0.044 |
Blood pressure (Hypertension vs Normal) |
Model 1 | Ref | 1.1 (0.98, 1.14) | 1.1 (0.99, 1.16) | 0.805 | ref | 1.1 (0.99, 1.17) | 0.84 (0.77, 0.91) | < 0.001 | ref | 0.94 (0.86, 1.02) | 0.84 (0.78, 0.91) | < 0.001 | ref | 1.0 (0.93, 1.09) | 0.96 (0.89, 1.05) | 0.356 |
Model 2 | Ref | 0.99 (0.91, 1.09) | 1.0 (0.91, 1.09) | 0.919 | ref | 1.2 (1.10, 1.31) | 1.1 (1.01, 1.22) | 0.082 | ref | 1.0 (0.95, 1.13) | 1.02 (0.92, 1.14) | 0.708 | ref | 0.93 (0.86, 1.02) | 0.88 (0.81, 0.96) | 0.008 |
Discussion
This study evaluated the relationship between dietary quality and food patterns of Filipino adults and the rising prevalence of selected cardiometabolic NCD risk factors. Dietary quality was derived from the national food consumption survey adopting the AHEI-2010 pattern as standard. The respondents in this study reported poor overall diet quality as illustrated by the very low mean score of AHEI-2010 of less than 20 out of 100. This is very low compared with the findings in many other countries: Brazilian population had a mean adapted HEI-2015 of 45.7; among Americans, the mean AHEI-2010 was 52.4 for men and 47.6 for women out of 110; the Chinese had a mean AHEI-2010 of 42.2 for men and 43.8 for women out of 80; and the finding among Singaporeans revealed that the median quintile range of AHEI-2010 was 48.1–51.6 out of 110 total score [
12,
17‐
19]. Very low consumption of vegetables, fruits, and whole grains were the main contributing factors for the poor quality of diet, and these could be due to several reasons: high price, poor availability, low accessibility and possible contamination of pesticides, lack of knowledge on the benefits of these foods, and no time to cook especially among working adults [
20,
21]. In a previous study, better diet quality is seen in women compared with men due to higher awareness and better nutrition knowledge of women than men and several studies also point out that women seek nutrition counselling more frequently than men do [
22]. In this present study only a slight difference in AHEI is seen among women (19.7) and men (18.9). This insignificant difference can be attributed to the varied modes of acquiring information about nutritious diet on different social media platforms.
Due to lack of variability in the studied sample using the hypothesis-based approach, AHEI-2010 score was not associated with many socio-demographic characteristics and the selected CMS. Therefore, we explored dietary patterns which could be potentially more meaningful to the local diet with a data-driven approach, PCA. Three major dietary patterns were identified, a meat and sweetened beverages pattern (MSB), a rice and fish pattern (RF), and a fruits, vegetables and snack pattern (FVS).
Our respondents who consume a MSB and RF patterns (highest tertile) are more likely to be younger, urban residents, and from the rich and richest wealth quintile. This is in conformance with an earlier study which revealed that dietary patterns differ between urban and rural areas due to differences in educational attainment, financial resources, and access to healthier foods [
23,
24]. Furthermore, urban areas have higher accessibility to a wide range of processed and traditional high-sugar, high-fat snack foods and beverages [
25]. The Food and Agriculture Organization statistics also showed that fish consumption in urban areas stood at 14.5 kg per capita per year compared to 11 kg per capita per year in rural areas, this is in line with our finding that the RF pattern are more likely to be consumed by urban residents. Also in our study, respondents who are in the highest tertile of the FVS patterns are more likely to be from the richest wealth quintile. This is in agreement with the study in Korea where fruit consumption is associated with higher income and educational level [
26]. The same findings were seen in Australiaand China [
27,
28].
In terms of association with cardiometabolic NCD risk factors, the MSB pattern were associated with a higher risk of various metabolic disorders including overweight and obesity, diabetes, and dyslipidemia, possibly through higher intakes of energy, fat, sugar and sodium. The RF diet also showed an association with cardiometabolic risks. It has been found that fish and rice are contaminated with methylmercury (MeHg) when produced in polluted areas. The chemical form of MeHg in fish tissue has recently been identified as attached to the thiol group of the cysteine residues in fish protein [
29], which are not removed and destroyed by any cooking or cleaning processes. Similarly rice cultivated in Hg contaminated areas can contain relatively high levels of MeHg [
30‐
34] and the main route of human MeHg exposure is related to frequent rice consumption [
32]. A body of evidence was developed that addresses potential associations between MeHg and a range of cardiovascular effects. These include cardiovascular disease (coronary heart disease, acute myocardial infarction (AMI), ischemic heart disease), blood pressure and hypertension effects, and alterations in heart rate variability [
35,
36]. There are strong evidences for causal associations with cardiovascular disease, particularly AMI in adult men [
37‐
40]. On the contrary, the FVS pattern was associated with lower risk of overweight, obesity, diabetes, dyslipidemia, and hypertension, which could be mediated through higher intakes of various beneficial nutrients including fiber, folate, calcium, potassium and magnesium.
A high consumption of sugar-sweetened beverages is evident in this study. Increased consumption of free sugars is particularly indicated in the form of sugar-sweetened beverages. Sugar-sweetened beverages usually contain added sugar such as sucrose or high fructose corn syrup. Every 330 ml or 12 oz. portion of sugar-sweetened carbonated soft drinks typically contains 35 g (around nine teaspoon) of sugars and provide approximately 140 kcal of energy, but generally with little value of other nutrients [
41]. As part of an unhealthy dietary pattern, this may have an effect on increased blood sugar, LDL-cholesterol and triglycerides. Thus, poor diet contributes to the occurrence of a cluster of disorders known as the metabolic syndrome: abdominal obesity, hypertension, dyslipidemia, and disturbed metabolism of glucose or insulin [
42]. The presence of the metabolic syndrome increases the risk of developing NCDs such as cardiovascular diseases, diabetes, chronic respiratory diseases, and cancer [
43,
44].
The prevalence of cardiometabolic NCD risk factors continues to rise in the Philippines and this is compounded by the practice of unhealthy lifestyle behaviours. In 2013, the prevalence of high fasting blood glucose among adults was 5.6%, and this has increased to 7.9% in 2018 [
45,
46].
Additionally, the prevalence remained high for elevated blood pressure (19.2%) (NNS 2018 data), total cholesterol (18.6%), LDL-cholesterol (21.9%) and triglycerides (17.7%). (NNS 2013 data) The key dietary components that lower cholesterol and triglycerides include increased consumption of fruits, vegetables, and whole grains instead of highly refined ones and plant-based protein [
47,
48]. However, these are consumed in very small amounts in the studied population. Fruit and vegetable consumption of Filipino adults was only at 41 g and 114 g per capita respectively; further, only about 9.9% of the population were consuming whole grains.. In our study, the respondents who consumed a FVS pattern was observed to have an overall lower metabolic risk profile, which further corroborates the importance of promoting higher consumption of fruits, vegetables, and healthy snacks among the Filipino adults. Besides unhealthy diet, the prevalence of current smokers during the study period was 25.4%; binge drinkers was56.2%; and physical inactivity was 45.5%, and these numbers remained high in the latest national survey conducted in 2018. Promoting healthy lifestyle is indeed very much needed.
To our knowledge, our study is the first one to use recent nationally representative data to characterize the dietary patterns of adults in the Philippines. The utilization of both a priori defined index (AHEI-2010) and posteriori derived dietary patterns (PCA) provided complementary and comprehensive assessment of the Filipino dietary quality and food consumption patterns. However, this study has several limitations. Firstly, the dietary data collection using 24-h recalls is subject to measurement errors from the subjects’ recall and estimation of consumption portions. Secondly, the lack of trans-fat information in our food composition database limits our ability to assess trans-fat as a component of AHEI-2010 in association with cardiometabolic risk factors. Lastly, the cross-sectional design of the survey prohibits us from drawing conclusions about the causal relationship between the observed dietary pattern and the cardiometabolic NCD risk factors. Future prospective studies are warranted to corroborate the findings of the present study.
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