Abstract
The prevalence of metabolic syndrome has reached epidemic levels in the Samoan Islands. In this cross-sectional study conducted in 2002–2003, dietary patterns were described among American Samoan (n = 723) and Samoan (n = 785) adults (≥18 y) to identify neo-traditional and modern eating patterns and to relate these patterns to the presence of metabolic syndrome using Adult Treatment Panel III criteria. The neo-traditional dietary pattern, similar across both polities, was characterized by high intake of local foods, including crab/lobster, coconut products, and taro, and low intake of processed foods, including potato chips and soda. The modern pattern, also similar across both polities, was characterized by high intake of processed foods such as rice, potato chips, cake, and pancakes and low intake of local foods. The neo-traditional dietary pattern was associated with significantly higher serum HDL-cholesterol in American Samoa (P-trend = 0.05) and a decrease in abdominal circumference in American Samoa and Samoa (P-trend = 0.004 and 0.01, respectively). An inverse association was found with metabolic syndrome, although it did not reach significance (P = 0.23 in American Samoa; P = 0.13 in Samoa). The modern pattern was significantly positively associated with metabolic syndrome in Samoa (prevalence ratio = 1.21 for the fifth compared with first quintile; 95% CI: 0.93.1.57; P-trend = 0.05) and with increased serum triglyceride levels in both polities (P < 0.05). Reduced intake of processed foods high in refined grains and adherence to a neo-traditional eating pattern characterized by plant-based fiber, seafood, and coconut products may help to prevent growth in the prevalence of metabolic syndrome in the Samoan islands.
Introduction
Metabolic syndrome is related to the basic pathophysiologic processes underlying atherosclerotic cardiovascular disease (CVD)8 and type 2 diabetes and is an important epidemiological measure allowing for the identification of individuals at high risk for developing these conditions (1). The prevalence of CVD risk factors related to metabolic syndrome has reached epidemic levels in the Samoan Islands (Samoa and American Samoa), which have undergone major health and dietary transitions over the last 3 decades as a result of rapid modernization (2–6). Currently, based upon Polynesian-specific obesity definitions, ∼71% of women and 61% of men in American Samoa are obese. Although the prevalence of obesity and other metabolic disorders has not reached the same high levels in Samoa as in American Samoa, almost 30% of men and over 50% of women in Samoa are obese (7). This ecological difference in the proportion of adults with obesity can be related to the different levels of modernization present across the Samoan islands. American Samoa has modernized more rapidly than Samoa due to U.S. investment in its infrastructure. However, Samoa will likely follow in the footsteps of its neighbor as it continues to experience economic development. Changes in lifestyle behaviors due to the varying effects of modernization across the Samoan islands can be seen in the dietary habits of this population.
The neo-traditional diet of the Samoan Islands studied several hundred years after contact with outside populations consists of starchy vegetables (yam, taro, banana, and breadfruit), seafood, coconut, and domesticated pig (7–11).
Between 1961 and 2005 food imports increased ∼5-fold (12), leading to a presumed greater intake of processed and fast foods. In the mid-1970s in American Samoa and early 1980s in Samoa, dietary diversity measured at the individual level with 24 -h recalls and food-weighing methods reflected this modernization of the diet throughout the archipelago (8–10). In addition total caloric intakes were lower among those living the most modern way of life (8). In the 1990s our results indicated that the American Samoan diet had been more affected by modernization than the Samoan diet as evidenced by increased intake of protein, cholesterol, and sodium in American Samoa relative to Samoa (11). From 1990 to 2002–03 there was a significant decrease in the intake of green leafy vegetables in American Samoa and in papaya in both Samoa and American Samoa. Fish consumption has also decreased considerably while the intake of processed foods such as potato chips, pancakes, coffee, beer, and soda has increased over time (7). While the prevalence of risk factors for CVD has increased along with changes in the Samoan diet, to our know knowledge, no prior studies have characterized dietary patterns in these polities and related these patterns to the presence of metabolic syndrome.
The neo-traditional Samoan Islands diet is high in saturated fat due to the relatively high intake of coconut and coconut products and rich in cardioprotective fiber from fruit and vegetable intake (11). Therefore, it is unclear how the traditional eating habits of Samoan Islanders are associated with CVD risk factors. Dietary pattern analysis will allow for the examination of the combined effects of these correlated dietary exposures (13) and may be useful in identifying neo-traditional and more modern eating patterns in the Samoan Islands related to the prevalence of metabolic syndrome.
Although most research groups have used principal components analysis (PCA) and cluster analysis to derive dietary patterns (14), partial least squares regression (PLS), an innovative methodology in nutritional epidemiology that has shown promise in identifying dietary patterns associated with disease risk (15), has the advantage of summarizing variability in intermediate response variables as well as food items, thereby allowing the investigator to test specific hypotheses concerning nutrient-disease relationships while at the same time allowing for the discovery of novel diet-disease relationships (15). PLS has not been used previously in a study of dietary patterns and metabolic syndrome; however, PCA has been used in previous studies across a wide variety of populations relating dietary patterns to metabolic syndrome (16–19) or specific components of this condition (14,20–35). Of the 4 identified studies examining associations between dietary patterns and metabolic syndrome as a whole, 3 of them calculated measures of effect (16,18,19). Esmaillzadeh et al. (16) derived 3 dietary patterns among Iranian women (western, healthy, and traditional). Similarly, in the Atherosclerosis Risk in Communities study among U.S. adults aged 45–64 y, healthy and western patterns were derived. The healthy pattern in both populations was characterized by high intakes of fruits and vegetables and whole grains and was inversely related to the occurrence of metabolic syndrome among Iranian women, whereas the western pattern characterized by high intakes of refined grains, meat, high-fat dairy products, sweets, and potatoes was directly related to metabolic syndrome (16,18). The traditional pattern found among Iranian women was not associated with metabolic syndrome. Three dietary patterns resulted from PCA analyses among Puerto Rican adults (meat and French fries, traditional, and sweets). The traditional pattern associated with high intakes of oils and rice and beans was related to increased risk of metabolic syndrome (19).
The purposes of this investigation were to derive dietary patterns in the Samoan Islands population using PLS and to relate these patterns to the presence of metabolic syndrome. We hypothesized that a traditional and more modern eating pattern would emerge, with the traditional and modern patterns related to decreased and increased occurrence of metabolic syndrome, respectively.
Materials and Methods
Study population.
The study population was derived from 2 polities, American Samoa and Samoa. In 1878 the United States annexed the Pacific islands of Tutuila, Aunu'u, Ofu, Olesega, and Ta'u, which became the U.S. territory of American Samoa. Germany gained control of Upolu, Savai'i, and the small surrounding islands in 1900 as Western Samoa and developed it predominately for agriculture (7). After World War I, New Zealand oversaw Western Samoa as a League of Nations protectorate and in 1962, the country became independent, later changing its name to Samoa (36). Substantial exposure to outside influences occurred during World War II and afterwards with migration and establishment of Samoan enclaves in the US and New Zealand. In the early 1960s, the U.S. federal government invested substantially in infrastructure (roads, sewer systems, education, etc.) in American Samoa. The 2000 census population in American Samoa was 57,291, 88.2% of which were ethnic Samoans (37). The 2001 census population in Samoa was 177,714, with 92.6% ethnic Samoans (38). American Samoa has higher education levels, a higher proportion of adults in wage and salary occupations, and higher economic and material lifestyle indicators than Samoa (2,3,11,39). For example, the per capita income in U.S. dollars for American Samoa has risen from $596 in 1969 to $3039 in 1989 and $5800 in 2005 (40), whereas the per capita income in Samoa in 2007 was $2020.
All study participants were ≥18 y of age and took part in the Samoan Family Study of Overweight and Diabetes in 2002–03, a cross-sectional genetic study using extended pedigrees designed to identify chromosomal regions with susceptibility genes associated with several adiposity and related phenotypes and to assess how these genomic regions may differ under varying environmental influences (41–43). Recruitment of families in American Samoa in 2002 was based on random selection of individuals participating in the 1990–94 cohort study in American Samoa and the presence of at least 2 adult siblings alive and residing in American Samoa (3). Initial recruitment of families in Samoa in 2003 was based on finding individuals who were members of American Samoan pedigrees. Later recruitment occurred in villages selected to represent geographic and socioeconomic diversity across the polity. Sampling in both of the Samoan polities was designed to maximize the size of pedigrees. Participants were not selected based on obesity or related morbidities.
Comparison of the education and occupation distribution by age and gender of study participants with the 2000 census of American Samoa and the 2001 census of Samoa had high similarity.
Measurements.
Physical measurements and biological specimens were obtained in participants' homes by phlebotomists, nurses, and trained field workers. Measurements of stature and weight were taken with participants wearing light tropical clothing without shoes. Abdominal circumference was measured twice at the level of the umbilicus with a metal tape and the mean of 2 measurements was taken. Blood was collected in the early morning from participants after a minimum 10-h fast in the field, generally in participants' homes. Serum was shipped on dry ice and serum glucose was assayed using a Beckman CX4 automatic analyzer. Total cholesterol and triglycerides were measured by enzymatic assays on a Gilford Impact 400 computer directed analyzer. HDL cholesterol was measured after precipitation of VLDL and LDL with heparin-Mn2+ reagent (44). Blood pressure was measured 3 times after participants were seated and resting for 5 min and the mean of the 3 measurements was calculated and used in all analyses.
Metabolic syndrome was defined using the Adult Treatment Panel III definition, where participants must have ≥3 of the following criteria to be classified as having metabolic syndrome: abdominal obesity (abdominal circumference >102 cm in men or >88 cm in women), hypertriglyceridemia (serum triglycerides ≥1.7 mmol/L), low HDL cholesterol (serum HDL cholesterol <1.0 mmol/L in men or <1.3 mmol/L in women), high blood pressure (blood pressure ≥130/85 mm Hg), or impaired fasting glucose (glucose ≥5.5mmol/L) (1). Participants reporting the use of antihypertensive or diabetic medication were considered to meet the criteria for high blood pressure or impaired fasting glucose, respectively. Information about HDL and triglyceride medications was not obtained. However, due to the low availability of these drugs in the study locations during the study periods, we do not expect that many participants were taking these types of medications (44). The fasting glucose cutoff was lowered to 5.5 mmol/L to reflect revised guidelines for impaired fasting glucose (45).
Dietary intake was assessed during interviews with trained fieldworkers in participants' homes using a 42-item FFQ updated for new foods based upon the 1995 version of the FFQ used in both Samoan polities. In Samoa, 13 food items specific to this locale were added to the FFQ bringing the total number of foods on the questionnaire to 55. The frequency of intake was measured across 7 categories on the FFQ ranging from 0 to 7 d/wk. Additionally, information on the portion size of foods consumed was obtained. Intake of energy and nutrients was computed by multiplying the consumption frequency of each food by the nutrient content of the specific portion. Energy and nutrient intakes were estimated from the Pacific Island food composition tables and complemented with the USDA food composition tables (46,47).
FFQ validity and reliability.
FFQ (n = 450), 24-h recalls (n = 230), and overnight excretion of urinary potassium (n = 262) were collected in 1990–1991 in American Samoa. A validation study using the method of triads was conducted using these measurements. This method estimates the correlation between the dietary assessment method and a person's “true” long-term intake [referred to as the validity coefficient (VC)] from 3 pairwise correlations between the FFQ, the 24-h recall, and the biomarker and corrects for bias due to correlated errors in the measurements from dietary assessments (the FFQ and 24-h recall) (48). Correlations between urinary potassium measures and potassium levels from the dietary assessment were adjusted for random within-person variation using ANOVA to estimate within- and between-subject variation (49). The ratio of the 2 variances was then used to correct correlations for day-to-day variation using the formula suggested by Willett (49). VC were calculated for 24-h recall compared with “true intake,” FFQ compared with “true intake,” and urinary potassium compared with “true intake.” The VC for the 24-h recall, FFQ, and urinary potassium measures were 0.55, 0.66, and 0.94, respectively.
As part of a reliability substudy, the FFQ was administered to the same subsample of participants in American Samoa (n = 45) ∼6 mo apart in 1994–1995. Correlations between the energy-adjusted intake of potassium, saturated fat, and fiber (the 2 response variables used in this study plus potassium used in the validation study) derived from the repeat FFQ were calculated. The correlations between potassium, saturated fat, and fiber measurements and corresponding P-values were 0.25 (P = 0.10), 0.21 (P = 0.16), and 0.25 (P = 0.10), respectively. Correlations in the range of 0.2–0.3 are usually seen in nutritional epidemiology and are considered reasonable due to the error inherent in measuring diet on the population level, particularly when small sample sizes are assessed (49).
Health history, information concerning socioeconomic status (SES), and physical activity were collected during interviews with trained fieldworkers in participants' homes. Sociodemographic and health information included questions about smoking, history of prior diabetes and hypertension, duration of diabetes, and current treatment for these conditions. A material lifestyle score was calculated for each participant based upon a 10-point summary index, which assessed domestic flooring type, bathroom fixture, water supply, cooking facilities and electrification, and possession of a refrigerator, stereo, television, VCR, and motor vehicle (11). This type of index is a sensitive measure of SES in modernizing societies, because it includes the impact of migrant remittances on household income and wealth (50). Hours per week of physical activity were obtained by interview questions about the type and duration of physical activity in wage labor occupations, recreational sports, and farming and fishing activities. All physical activities were coded for intensity of effort using CDC guidelines (51) and categorized into hours per week of high (20.5 J/min–50.2 J/min), moderate (10.5 J/min–20.4 J/min), and low (≤10.4 J/min) intensity activity for each participant.
Protocols for this study were approved by the Brown University and American Samoan Institutional Review Board and the Government of Samoa, Ministry of Health, Health Research Committee. Written informed consent was obtained from all participants.
Statistical methods.
We excluded participants who were missing the majority of the items on the FFQ (in American Samoa, 1 participant was removed from analysis because of missing information for 98% of the items on the FFQ and in Samoa no participants were removed) or had missing data for potential confounders, leaving a total of 723 adults residing in American Samoa. In Samoa, as data collection progressed, it was noted that several food items commonly consumed in this polity were not included on the FFQ. These items were added to the FFQ to ensure completeness and accuracy in the ascertainment of dietary information in Samoa. Participants (n = 106) given the shorter version of the FFQ were excluded from analyses in Samoa to allow for consistency in the foods used to derive dietary patterns, leaving a total sample for analysis of 785 adults living in Samoa. The excluded participants were similar to those included in analyses with respect to the prevalence of metabolic syndrome, demographics, and potential confounding factors.
To test for differences in means or distribution of demographic and lifestyle variables between those with and without metabolic syndrome, we used chi-square tests for categorical variables and t tests for continuous variables. All reported P-values are 2-sided and P-values < 0.05 were considered significant.
Daily frequencies of each food item on the FFQ were multiplied by corresponding portion sizes, transformed where appropriate to improve normality, and adjusted for total energy intake by the residual method (52) before being used in dietary pattern analyses. If the frequency of consumption of an item on the FFQ was indicated and portion size was missing for this item, portion size was imputed based on the mean portion size within strata of age (10-y intervals), sex, and polity (American Samoa, Samoa).
Intake of saturated fat and fiber were selected as response variables in this study based on previous research demonstrating a decrease in the consumption of fruits and vegetables rich in fiber and fish low in saturated fat, as well as an increase in the consumption of processed foods high in refined carbohydrates in this population (7). These types of dietary changes characteristic of developing countries have been associated with increased body weight, blood pressure, and cholesterol levels, some of the components of metabolic syndrome (53,54). For example, in Costa Rica, like many Latin American countries, the nutrition transition has affected more modernized, urban populations. The urban diet is characterized by higher saturated fat and lower carbohydrate intake than that of rural residents (55). General and abdominal obesity, as well as diastolic blood pressure, are higher in urban than in rural areas of Costa Rica (56). Similar dietary changes and associations with obesity and CVD risk factors have been observed in China and other Asian countries (53). We hypothesized that these changes in diet would also be related to metabolic syndrome among Samoan islanders.
Dietary patterns were derived using PLS such that the patterns represented linear combinations of foods that maximized the explained variance in the response variables and in food items from the FFQ (57). Additionally, random sample cross validation and van der Voet's test (58) were used as a guide in selecting the number of factors to retain in PLS analyses. The final number of factors selected represents the most parsimonious model with residuals that were insignificantly larger than the model with minimum predicted residual sum of squares (59). Using van der Voet's criteria, a large number of factors were retained (n = 14), with many of the factors explaining a small percentage of predictor and response variation. Thus, we also considered interpretability of the factors, because those explaining little predictor variation were unlikely to represent major dietary patterns in our population. We chose to retain 3 factors in both American Samoa and Samoa (collectively explaining 92 and 91% of the variability in saturated fat and 86 and 88% of the variability in fiber in American Samoa and Samoa, respectively) based on these criteria. To examine potential variation in dietary patterns and in the relationships of these patterns with metabolic syndrome, we stratified our sample by gender within each polity and rederived dietary patterns. There were no major differences in the gender-specific dietary patterns.
The continuous factor scores produced by PLS for all retained factors were then divided into quintiles. Metabolic syndrome status was regressed on these categorized PLS scores using generalized estimating equations accounting for clustering among individuals by household. The log link was used to produce prevalence ratios (PR) and 95% CI while adjusting for age, sex, modern lifestyle score, current smoking status, physical activity, and total energy intake. To test for trends across quintiles of dietary patterns, the median intake of each quintile was assigned to each subject in the same quintile and treated as a continuous variable in regression analyses. Data were analyzed using SAS software version 9.1 (SAS Institute).
Results
Study sample characteristics.
The proportion of females in American Samoa and Samoa was 56.5 and 54.5%, respectively, with Samoan women significantly more likely to meet the criteria for metabolic syndrome than Samoan men. The prevalence of metabolic syndrome was 49.4 and 30.6% in the American Samoa and Samoa study samples, respectively. Those with metabolic syndrome were significantly older; had a higher SES index, BMI, and abdominal circumferences; and had lower levels of physical activity than those without metabolic syndrome. The daily saturated fat and fiber intakes did not significantly differ according to metabolic syndrome status across both polities (Table 1).
TABLE 1.
Characteristics of the Samoan Islands sample by metabolic syndrome status1
| Metabolic syndrome2
|
||||||
|---|---|---|---|---|---|---|
| American Samoa
|
Samoa
|
|||||
| Yes | No | P | Yes | No | P | |
| n | 357 | 366 | 240 | 545 | ||
| Characteristics | ||||||
| Age, y | 46 ± 14 | 38 ± 16 | <0.001 | 51 ± 14 | 39 ± 16 | <0.001 |
| BMI, kg/m2 | 37.5 ± 7.3 | 32.7 ± 7.5 | <0.001 | 35.4 ± 6.1 | 29.6 ± 5.8 | <0.001 |
| Abdominal circumference, cm | 115.3 ± 13.3 | 103.1 ± 16.6 | <0.001 | 113.5 ± 12.5 | 97.6 ± 14.9 | <0.001 |
| Physical activity,3h/wk | 2.8 ± 4.8 | 3.1 ± 5.5 | 0.31 | 3.7 ± 7.3 | 6.9 ± 13.9 | 0.0004 |
| Material lifestyle score4 | 7.8 ± 1.4 | 7.6 ± 1.4 | 0.04 | 6.8 ± 2.1 | 6.5 ± 2.1 | 0.03 |
| Saturated fat, % energy | 23.3 ± 7.6 | 23.5 ± 7.3 | 0.75 | 36.6 ± 7.9 | 37.0 ± 8.3 | 0.48 |
| Fiber,5g/d | 22.4 ± 7.7 | 21.6 ± 7.4 | 0.17 | 46.9 ± 9.4 | 46.5 ± 10.2 | 0.61 |
| Gender, female, % | 56.0 | 57.0 | 0.77 | 63.8 | 50.5 | 0.0006 |
| Hypertension,6% | 29.0 | 7.1 | <0.001 | 20.0 | 2.9 | <0.001 |
| Diabetes,6% | 28.8 | 5.3 | <0.001 | 14.2 | 1.5 | <0.001 |
| Current smoker,7% | 26.5 | 28.8 | 0.48 | 24.7 | 30.7 | 0.10 |
| Components of metabolic syndrome,2% | ||||||
| Abdominal obesity | 94.7 | 63.4 | <0.001 | 97.5 | 51.7 | <0.001 |
| Hypertriglyceridemia | 59.9 | 6.8 | <0.001 | 51.7 | 7.7 | <0.001 |
| Low HDL cholesterol | 89.6 | 54.1 | <0.001 | 82.9 | 27.1 | <0.001 |
| High blood pressure | 63.0 | 11.8 | <0.001 | 62.1 | 10.3 | <0.001 |
| Elevated fasting glucose | 60.5 | 8.2 | <0.001 | 65.4 | 9.4 | <0.001 |
Values are mean ± SD or %.
Metabolic syndrome defined using the National Cholesterol Education Program-Adult Treatment Panel III R III definition (1).
Physical activity was measured as h/wk spent in wage labor occupations, recreational sports, and farming and fishing activities.
Material lifestyle score was calculated based on a 10-point summary index of household possessions, including domestic flooring type, bathroom fixture, water supply, cooking facilities and electrification, and possession of a refrigerator, stereo, television, VCR, and motor vehicle.
Fiber intake was adjusted for total energy intake using the residual method.
Based upon self-reported history of this medical condition.
There were 40 and 98 missing values for current smoking status in American Samoa and Samoa, respectively.
Dietary patterns.
The main factor loadings (correlations of the food groups with the dietary patterns) for patterns significantly associated with metabolic syndrome or individual components of the syndrome are shown in Table 2.
TABLE 2.
Factor loadings for modern and neo-traditional dietary patterns in American Samoa and Samoa1
| Modern pattern
|
Neo-traditional pattern
|
|||
|---|---|---|---|---|
| Food item | Factor loading AS | Factor loading S | Factor loading AS | Factor loading S |
| Sausage | 0.24 | 0.10 | −0.11 | −0.19 |
| Fish | −0.12 | 0.03 | 0.22 | −0.03 |
| Crab and lobster | −0.12 | 0.04 | 0.19 | 0.12 |
| Egg | 0.27 | 0.11 | −0.06 | −0.16 |
| Milk | 0.16 | −0.02 | 0.01 | −0.06 |
| Cheese | 0.17 | 0.06 | 0.01 | −0.12 |
| Butter and margarine | 0.26 | 0.07 | −0.11 | −0.03 |
| Coconut milk | 0.13 | −0.07 | 0.22 | 0.07 |
| Coconut cream with water | 0.13 | −0.08 | 0.23 | 0.31 |
| Coconut cream | 0.32 | −0.05 | 0.18 | 0.19 |
| Coconut cream and tinned fish2 | n/a | −0.10 | n/a | 0.14 |
| Coconut cream in taro leaves | 0.12 | −0.06 | 0.15 | 0.21 |
| Coconut cream in banana leaves2 | n/a | −0.01 | n/a | 0.23 |
| Coconut cream and banana | 0.04 | 0.01 | 0.22 | 0.12 |
| Coconut cream and taro | 0.05 | −0.09 | 0.23 | −0.05 |
| Coconut porridge | −0.08 | 0.04 | 0.30 | 0.14 |
| Papaya soup | −0.10 | 0.05 | 0.29 | 0.13 |
| Ripe coconut2 | n/a | 0.44 | n/a | 0.32 |
| Taro | 0.01 | −0.21 | 0.17 | 0.10 |
| Breadfruit | −0.2 | 0.09 | 0.15 | 0.08 |
| Yam | −0.04 | 0.08 | 0.20 | −0.04 |
| Papaya | −0.02 | 0.20 | 0.22 | −0.08 |
| Rice | 0.26 | 0.16 | −0.25 | −0.15 |
| Rice dishes2 | n/a | 0.23 | n/a | −0.10 |
| Instant noodle soup | 0.19 | 0.18 | −0.15 | −0.15 |
| Soup with vegetables2 | n/a | 0.31 | n/a | −0.15 |
| Chop suey2 | n/a | 0.22 | n/a | −0.16 |
| Bread | 0.30 | −0.09 | −0.13 | 0.08 |
| Pancakes | 0.15 | 0.18 | 0.01 | −0.10 |
| Cereal | 0.22 | 0.13 | −0.03 | −0.02 |
| Cake | 0.13 | 0.11 | −0.06 | −0.13 |
| Potato chips | 0.20 | 0.23 | −0.19 | −0.16 |
| Crackers2 | n/a | 0.25 | n/a | −0.08 |
| Coca Cola | −0.02 | 0.02 | −0.22 | −0.10 |
Values are factor loadings or correlations of each food item with the given dietary pattern.
Foods items not included on the American Samoan FFQ.
The first factor derived (neo-traditional pattern) was similar across American Samoa and Samoa and was characterized by similar foods in each polity. The neo-traditional pattern in American Samoa was primarily characterized by high intakes of crab and lobster, fish, coconut cream dishes, papaya soup, coconut milk, papaya, and taro, and low intakes of sausage, potato chips, Coca Cola, rice, and instant noodle soup. In Samoa, the neo-traditional pattern was associated with high intakes of crab and lobster, ripe coconut, coconut cream and coconut cream dishes, and papaya soup, and low intakes of sausage, eggs, cheese, potato chips, cake, rice, and instant noodle soup.
Factor 2 in American Samoa and factor 3 in Samoa were not significantly associated with metabolic syndrome or the components of the syndrome and consisted of a mix of meat and coconut products such as coconut cream dishes and lamb (data not shown). Factor 3 in American Samoa and factor 2 in Samoa (modern patterns) were associated with similar foods. In American Samoa, the modern pattern was mainly characterized by high intakes of sausage, eggs, milk, cheese, coconut cream, rice, instant noodle soup, bread, pancakes, cereal, butter/margarine, cake, and potato chips and low intakes of fish, crab, lobster, and bread fruit. The modern pattern in Samoa was associated with high intakes of sausage, eggs, rice, instant noodle soup, pancakes, cereal, papaya, cake, potato chips, ripe coconut, chop suey, rice dishes, crackers, and soup with vegetables, and low intakes of coconut cream dishes and taro.
The distribution of potential confounders by quintiles of the derived dietary patterns is shown in Table 3. Among American Samoans, age, physical activity, and saturated fat and fiber intakes increased across quintiles of the neo-traditional pattern and age decreased across increasing quintiles of the modern pattern. Samoans in higher quintiles of the neo-traditional pattern were older, had smaller abdominal circumferences and lower material lifestyle scores, were more active, and had higher saturated fat and fiber intakes, and those in higher quintiles of the modern pattern were younger, less likely to smoke, and had a higher fiber intake than those in lower quintiles of these patterns.
TABLE 3.
Distribution of potential confounders among those without metabolic syndrome by quintile of dietary pattern for the Samoan Islands sample1
| Quintiles of factors scores
|
|||||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Q5 | |
| American Samoa | |||||
| n | 73 | 73 | 74 | 72 | 74 |
| Neo-traditional pattern | |||||
| Age, y | 28.7 | 37.8 | 39.6 | 41.3 | 41.5 |
| Gender, female, % | 56.1 | 62.4 | 61.1 | 47.4 | 40.5 |
| BMI, kg/m2 | 32.2 | 33.6 | 33.2 | 30.6 | 31.2 |
| Abdominal circumference, cm | 98.4 | 105.4 | 104.6 | 98.3 | 100.0 |
| Physical activity,2h/wk | 2.0 | 2.0 | 2.8 | 3.0 | 6.1 |
| Material lifestyle score3 | 7.3 | 7.6 | 7.5 | 7.3 | 7.4 |
| Current smoker, % | 24.2 | 33.4 | 22.5 | 22.9 | 33.0 |
| Saturated fat, % energy | 17.7 | 19.9 | 23.8 | 25.8 | 32.3 |
| Fiber,4g/d | 16.7 | 24.2 | 25.0 | 26.8 | 30.7 |
| Factor 2 | |||||
| Age, y | 39.4 | 35.3 | 35.9 | 40.0 | 37.4 |
| Gender, female, % | 57.7 | 59.3 | 63.5 | 57.9 | 34.4 |
| BMI, kg/m2 | 31.6 | 31.8 | 33.0 | 33.0 | 32.9 |
| Abdominal circumference, cm | 100.2 | 101.4 | 103.6 | 104.0 | 104.4 |
| Physical activity,2h/wk | 3.5 | 2.8 | 3.2 | 2.4 | 3.8 |
| Material lifestyle score3 | 7.8 | 7.8 | 7.6 | 7.7 | 7.4 |
| Current smoker, % | 34.8 | 21.6 | 21.7 | 40.0 | 31.7 |
| Saturated fat, % energy | 18.7 | 19.9 | 22.3 | 24.8 | 32.2 |
| Fiber,4g/d | 31.7 | 25.6 | 23.9 | 22.6 | 21.1 |
| Modern pattern | |||||
| Age, y | 41.5 | 39.0 | 39.8 | 36.1 | 31.9 |
| Gender, female | 46.0 | 48.8 | 66.4 | 64.2 | 58.0 |
| BMI, kg/m2 | 32.2 | 31.1 | 34.3 | 33.2 | 31.3 |
| Abdominal circumference, cm | 101.5 | 100.0 | 107.0 | 102.9 | 98.7 |
| Physical activity,2h/wk | 3.6 | 4.5 | 2.0 | 2.0 | 2.9 |
| Material lifestyle score3 | 7.3 | 7.5 | 7.5 | 7.7 | 7.4 |
| Current smoker, % | 34.3 | 35.7 | 24.8 | 30.1 | 22.5 |
| Saturated fat, % energy | 21.9 | 22.6 | 23.4 | 24.2 | 24.2 |
| Fiber,4g/d | 24.6 | 25.3 | 25.2 | 24.0 | 23.3 |
| Samoa | |||||
| n | 109 | 109 | 109 | 109 | 109 |
| Neo-traditional pattern | |||||
| Age, y | 32.2 | 39.9 | 40.8 | 40.1 | 42.3 |
| Gender, female, % | 68.1 | 47.5 | 39.4 | 46.1 | 51.7 |
| BMI, kg/m2 | 30.9 | 29.4 | 29.4 | 29.1 | 29.4 |
| Abdominal circumference, cm | 99.9 | 97.5 | 97.6 | 96.7 | 96.6 |
| Physical activity,2h/wk | 6.6 | 5.7 | 4.7 | 5.7 | 11.6 |
| Material lifestyle score3 | 7.5 | 6.8 | 6.4 | 6.0 | 5.9 |
| Current smoker, % | 22.7 | 30.0 | 32.3 | 34.8 | 29.4 |
| Saturated fat, % energy | 30.5 | 33.8 | 35.7 | 38.8 | 46.4 |
| Fiber,4g/d | 37.0 | 43.7 | 46.2 | 50.5 | 55.5 |
| Modern pattern | |||||
| Age, y | 39.7 | 41.8 | 38.6 | 38.7 | 36.6 |
| Gender, female, % | 38.1 | 40.7 | 56.3 | 61.0 | 57.9 |
| BMI, kg/m2 | 29.5 | 29.1 | 29.0 | 30.7 | 30.2 |
| Abdominal circumference, cm | 97.6 | 96.8 | 95.6 | 100.1 | 99.0 |
| Physical activity,2h/wk | 9.4 | 4.9 | 4.7 | 9.3 | 6.7 |
| Material lifestyle score3 | 6.4 | 6.5 | 6.2 | 6.7 | 6.5 |
| Current smoker, % | 48.7 | 33.4 | 32.9 | 13.2 | 25.6 |
| Saturated fat, % energy | 37.7 | 36.3 | 37.6 | 36.3 | 36.5 |
| Fiber,4g/d | 40.4 | 45.3 | 48.1 | 47.7 | 52.0 |
| Factor 3 | |||||
| Age, y | 44.8 | 39.2 | 37.7 | 38.0 | 36.4 |
| Gender, female, % | 56.9 | 55.1 | 47.9 | 42.1 | 45.2 |
| BMI, kg/m2 | 29.7 | 29.7 | 30.5 | 29.7 | 28.6 |
| Abdominal circumference, cm | 97.5 | 98.1 | 100.3 | 98.1 | 95.0 |
| Physical activity,2h/wk | 11.0 | 7.3 | 7.5 | 6.3 | 5.4 |
| Material lifestyle score3 | 6.1 | 6.4 | 6.6 | 6.3 | 6.8 |
| Current smoker, % | 23.6 | 29.0 | 36.1 | 23.2 | 36.9 |
| Saturated fat, % energy | 33.0 | 36.2 | 37.1 | 38.1 | 40.0 |
| Fiber,4g/d | 51.0 | 49.2 | 47.7 | 45.7 | 41.7 |
Values are age-adjusted means and percents or %.
Physical activity was measured as h/wk spent in wage labor occupations, recreational sports, and farming and fishing activities.
Material lifestyle score was calculated based on a 10-point summary index of household possessions, including domestic flooring type, bathroom fixture, water supply, cooking facilities and electrification, and possession of a refrigerator, stereo, television, VCR, and motor vehicle.
Fiber intake was adjusted for total energy intake using the residual method.
Associations between dietary patterns and metabolic syndrome.
In examining the adjusted PR across quintiles of the dietary patterns significantly associated with metabolic syndrome, or the individual components of the syndrome, factor 1 (neo-traditional pattern) showed a trend toward decreased prevalence of metabolic syndrome across increasing quintiles of the pattern. There was an increased prevalence of metabolic syndrome across increasing quintiles of the modern pattern in American Samoa and Samoa and this trend reached significance in Samoa (Table 4).
TABLE 4.
PR and 95% CI for metabolic syndrome according to quintiles of dietary for the Samoan Islands sample1
| Quintiles of dietary patterns
|
||||||
|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Q5 | P2 | |
| American Samoa | ||||||
| n | 145 | 145 | 145 | 144 | 144 | |
| Neo-traditional pattern | 1.0 | 0.87 (0.72,1.05) | 0.78 (0.62,0.97) | 0.84 (0.69,1.03) | 0.89 (0.72,1.06) | 0.23 |
| Factor 2 | 1.0 | 1.04 (0.87,1.24) | 0.95 (0.78,1.14) | 0.93 (0.76,1.15) | 0.99 (0.81,1.23) | 0.64 |
| Modern pattern | 1.0 | 0.98 (0.79,1.22) | 1.04 (0.84,1.28) | 1.22 (1.00,1.50) | 1.13 (0.93,1.38) | 0.08 |
| Samoa | ||||||
| n | 157 | 157 | 157 | 157 | 157 | |
| Neo-traditional pattern | 1.0 | 0.83 (0.61,1.12) | 0.95 (0.74,1.21) | 0.94 (0.73,1.20) | 0.74 (0.54,1.01) | 0.13 |
| Modern pattern | 1.0 | 0.91 (0.67,1.22) | 0.98 (0.72,1.34) | 1.12 (0.80,1.56) | 1.21 (0.93,1.57) | 0.05 |
| Factor 3 | 1.0 | 0.95 (0.70,1.28) | 1.26 (0.97,1.64) | 0.97 (0.73,1.29) | 0.98 (0.71,1.35) | 0.99 |
Values are PR and 95% CI. PR are adjusted for age (in 10-y intervals), sex, modern lifestyle score, current smoking status (with an indicator for missing data), physical activity (total weekly hours), and total energy intake. Adjustment for dietary patterns did not appreciably alter the results
To test for trends across quintiles of dietary patterns, the median intake of each quintile was set to each subject in the same quintile and treated as a continuous variable in regression analyses.
There were significant trends in means and PR for the components of metabolic syndrome across increasing quintiles of the modern and neo-traditional dietary patterns in American Samoa and Samoa. The neo-traditional dietary pattern was associated with an increase in serum concentrations of HDL cholesterol in American Samoa and a decrease in abdominal circumference across both polities. The modern dietary pattern was associated with an increase in serum triglyceride levels in both American Samoa and Samoa (Tables 5 and 6).
TABLE 5.
PR and 95% CI for the components of metabolic syndrome according to quintiles of dietary patterns for the Samoan Islands sample1
| Quintiles of dietary patterns
|
||||||
|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Q5 | P | |
| American Samoa | ||||||
| n | 145 | 145 | 145 | 144 | 144 | |
| Abdominal circumference, cm | ||||||
| Neo-traditional pattern | 1.0 | 0.98 (0.88,1.10) | 0.99 (0.90,1.10) | 0.86 (0.76,0.96) | 0.90 (0.80,1.02) | 0.03 |
| Factor 2 | 1.0 | 1.01 (0.90,1.14) | 0.98 (0.88,1.09) | 1.05 (0.95,1.17) | 1.01 (0.91,1.13) | 0.65 |
| Modern pattern | 1.0 | 0.96 (0.85,1.10) | 1.05 (0.94,1.17) | 1.04 (0.92,1.18) | 1.02 (0.91,1.15) | 0.44 |
| Serum glucose,2mmol/L | ||||||
| Neo-traditional pattern | 1.0 | 0.94 (0.71,1.25) | 1.23 (0.91,1.68) | 1.02 (0.76,1.38) | 1.05 (0.79,1.39) | 0.63 |
| Factor 2 | 1.0 | 1.20 (0.96,1.51) | 1.01 (0.75,1.37) | 1.02 (0.76,1.35) | 1.03 (0.77,1.37) | 0.85 |
| Modern pattern | 1.0 | 0.82 (0.62,1.07) | 0.93 (0.70,1.22) | 0.87 (0.67,1.14) | 0.99 (0.77,1.29) | 0.91 |
| Serum HDL cholesterol, mmol/L | ||||||
| Neo-traditional pattern | 1.0 | 1.0 (0.88,1.15) | 0.99 (0.86,1.14) | 0.91 (0.78,1.06) | 0.83 (0.70,0.98) | 0.02 |
| Factor 2 | 1.0 | 1.05 (0.91,1.21) | 1.12 (0.97,1.30) | 1.10 (0.96,1.26) | 1.12 (0.97,1.29) | 0.09 |
| Modern pattern | 1.0 | 0.96 (0.84,1.10) | 0.92 (0.79,1.08) | 0.98 (0.85,1.13) | 0.98 (0.84,1.13) | 0.93 |
| Serum triglycerides, mmol/L | ||||||
| Neo-traditional pattern | 1.0 | 1.01 (0.73,1.39) | 0.92 (0.67,1.28) | 0.87 (0.62,1.22) | 0.82 (0.57,1.18) | 0.19 |
| Factor 2 | 1.0 | 0.84 (0.61,1.15) | 0.98 (0.72,1.33) | 1.06 (0.80,1.41) | 0.91 (0.67,1.25) | 0.94 |
| Modern pattern | 1.0 | 0.92 (0.66,1.28) | 1.21 (0.85,1.72) | 1.50 (1.09,2.06) | 1.27 (0.88,1.84) | 0.04 |
| Blood pressure, mm Hg3 | ||||||
| Neo-traditional pattern | 1.0 | 1.0 (0.73,1.36) | 0.97 (0.72,1.31) | 0.88 (0.65,1.18) | 1.08 (0.81,1.44) | 0.68 |
| Factor 2 | 1.0 | 0.87 (0.71,1.08) | 0.86 (0.68,1.09) | 0.89 (0.67,1.18) | 1.04 (0.80,1.36) | 0.86 |
| Modern pattern | 1.0 | 0.82 (0.69,1.07) | 1.01 (0.79,1.29) | 1.05 (0.81,1.35) | 0.89 (0.66,1.15) | 0.78 |
| Samoa | Q1 | Q2 | Q3 | Q4 | Q5 | P |
| n | 157 | 157 | 157 | 157 | 157 | |
| Abdominal circumference, cm | ||||||
| Neo-traditional pattern | 1.0 | 0.97 (0.85,1.11) | 0.88 (0.76,1.01) | 0.87 (0.76,1.00) | 0.87 (0.76,1.00) | 0.03 |
| Modern pattern | 1.0 | 0.93 (0.82,1.06) | 0.88 (0.75,1.03) | 0.96 (0.84,1.10) | 0.89 (0.77,1.02) | 0.19 |
| Factor 3 | 1.0 | 1.03 (0.92,1.15) | 1.12 (0.99,1.27) | 1.00 (0.89,1.13) | 0.99 (0.86,1.14) | 0.94 |
| Serum glucose,2mmol/L | ||||||
| Neo-traditional pattern | 1.0 | 0.96 (0.68,1.35) | 0.97 (0.68,1.38) | 0.86 (0.61,1.20) | 0.74 (0.51,1.06) | 0.08 |
| Modern pattern | 1.0 | 0.97 (0.70,1.34) | 0.95 (0.68,1.33) | 0.99 (0.69,1.43) | 1.08 (0.77,1.51) | 0.62 |
| Factor 3 | 1.0 | 1.11 (0.78,1.58) | 1.29 (0.93,1.78) | 0.89 (0.61,1.30) | 1.15 (0.76,1.74) | 0.82 |
| Serum HDL cholesterol, mmol/L | ||||||
| Neo-traditional pattern | 1.0 | 0.94 (0.73,1.20) | 0.92 (0.72,1.19) | 0.90 (0.69,1.17) | 0.81 (0.62,1.06) | 0.15 |
| Modern pattern | 1.0 | 0.91 (0.72,1.15) | 0.92 (0.73,1.17) | 0.96 (0.77,1.20) | 1.02 (0.80,1.29) | 0.72 |
| Factor 3 | 1.0 | 0.94 (0.75,1.18) | 1.07 (0.84,1.36) | 1.08 (0.86,1.36) | 0.90 (0.70,1.16) | 0.84 |
| Serum triglycerides, mmol/L | ||||||
| Neo-traditional pattern | 1.0 | 0.98 (0.64,1.50) | 0.96 (0.64,1.43) | 1.20 (0.80,1.78) | 1.13 (0.73,1.76) | 0.39 |
| Modern pattern | 1.0 | 1.39 (0.86,2.25) | 1.55 (1.01,2.39) | 1.74 (1.14,2.67) | 1.41 (0.94,2.13) | 0.06 |
| Factor 3 | 1.0 | 0.76 (0.49,1.17) | 1.02 (0.70,1.50) | 1.03 (0.66,1.60) | 0.89 (0.59,1.35) | 0.97 |
| Blood pressure,3mm Hg | ||||||
| Neo-traditional pattern | 1.0 | 1.09 (0.74,1.61) | 1.31 (0.88,1.96) | 1.22 (0.81,1.83) | 0.98 (0.63,1.50) | 0.90 |
| Modern pattern | 1.0 | 0.96 (0.64,1.43) | 0.97 (0.68,1.38) | 1.07 (0.73,1.58) | 1.14 (0.80,1.63) | 0.31 |
| Factor 3 | 1.0 | 0.83 (0.59,1.16) | 1.09 (0.81,1.47) | 0.90 (0.64,1.28) | 1.05 (0.74,1.51) | 0.74 |
Values are PR and 95% CI adjusted for age (in 10- y intervals), sex, material lifestyle score, current smoking status (with an indicator for missing data), physical activity (total weekly hours), and total energy intake.
Adjusted for all previous factors and diabetes medication use.
Adjusted for all previous factors and hypertension medication use.
TABLE 6.
Serum glucose, HDL cholesterol, and triglyceride concentrations, abdominal circumference, and blood pressure by quintile of dietary pattern in the Samoan Islands sample1
| Quintiles of dietary patterns
|
||||||
|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Q5 | P | |
| American Samoa | ||||||
| n | 145 | 145 | 145 | 144 | 144 | |
| Abdominal circumference,2cm | ||||||
| Neo-traditional pattern | 108.8 ± 1.6 | 108.0 ± 1.8 | 106.7 ± 1.6 | 103.3 ± 1.5 | 103.9 ± 1.6 | 0.004 |
| Factor 2 | 106.2 ± 1.8 | 105.1 ± 1.8 | 104.5 ± 1.4 | 107.2 ± 1.5 | 107.3 ± 1.7 | 0.42 |
| Modern pattern | 105.4 ± 1.5 | 104.6 ± 1.5 | 109.4 ± 1.8 | 105.7 ± 1.7 | 105.5 ± 1.8 | 0.91 |
| Glucose,3mmol/L | ||||||
| Neo-traditional pattern | 7.2 ± 0.3 | 7.0 ± 0.3 | 7.0 ± 0.3 | 6.9 ± 0.4 | 6.8 ± 0.3 | 0.26 |
| Factor 2 | 6.9 ± 0.3 | 7.1 ± 0.3 | 7.0 ± 0.3 | 7.1 ± 0.3 | 6.8 ± 0.3 | 0.73 |
| Modern pattern | 6.8 ± 0.3 | 6.9 ± 0.3 | 6.9 ± 0.5 | 7.4 ± 0.3 | 7.0 ± 0.3 | 0.21 |
| HDL cholesterol, mmol/L | ||||||
| Neo-traditional pattern | 1.02 ± 0.02 | 1.05 ± 0.02 | 1.04 ± 0.02 | 1.07 ± 0.02 | 1.07 ± 0.02 | 0.05 |
| Factor 2 | 1.06 ± 0.02 | 1.06 ± 0.02 | 1.05 ± 0.02 | 1.03 ± 0.02 | 1.05 ± 0.02 | 0.44 |
| Modern pattern | 1.05 ± 0.02 | 1.05 ± 0.02 | 1.08 ± 0.02 | 1.05 ± 0.02 | 1.04 ± 0.02 | 0.69 |
| Triglycerides, mmol/L | ||||||
| Neo-traditional pattern | 1.80 ± 0.14 | 1.63 ± 0.14 | 1.81 ± 0.17 | 1.62 ± 0.13 | 1.73 ± 0.20 | 0.81 |
| Factor 2 | 1.80 ± 0.19 | 1.59 ± 0.11 | 1.64 ± 0.12 | 1.83 ± 0.16 | 1.64 ± 0.16 | 0.77 |
| Modern pattern | 1.51 ± 0.11 | 1.64 ± 0.16 | 1.74 ± 0.17 | 1.97 ± 0.17 | 1.87 ± 0.17 | 0.03 |
| Systolic blood pressure,4mm Hg | ||||||
| Neo-traditional pattern | 134.7 ± 1.7 | 133.7 ± 1.7 | 133.6 ± 1.9 | 131.6 ± 2.1 | 133.5 ± 1.9 | 0.32 |
| Factor 2 | 134.4 ± 2.0 | 133.2 ± 1.8 | 133.1 ± 1.8 | 131.9 ± 1.8 | 133.5 ± 1.8 | 0.47 |
| Modern pattern | 135.8 ± 2.0 | 130.6 ± 1.8 | 134.7 ± 1.7 | 134.7 ± 2.2 | 131.4 ± 1.8 | 0.19 |
| Diastolic blood pressure,4mm Hg | ||||||
| Neo-traditional pattern | 87.2 ± 1.2 | 85.4 ± 1.2 | 85.6 ± 1.4 | 83.2 ± 1.2 | 85.2 ± 1.4 | 0.06 |
| Factor 2 | 87.1 ± 1.3 | 85.1 ± 1.1 | 83.9 ± 1.4 | 84.2 ± 1.3 | 85.0 ± 1.2 | 0.07 |
| Modern pattern | 86.8 ± 1.2 | 82.5 ± 1.1 | 87.5 ± 1.3 | 85.7 ± 1.4 | 84.1 ± 1.3 | 0.32 |
| Samoa | ||||||
| n | 157 | 157 | 157 | 157 | 157 | |
| Abdominal circumference,2cm | ||||||
| Neo-traditional pattern | 103.9 ± 1.4 | 101.2 ± 1.3 | 103.4 ± 1.1 | 101.0 ± 1.1 | 99.2 ± 1.0 | 0.01 |
| Modern pattern | 101.9 ± 1.2 | 101.6 ± 1.1 | 99.5 ± 1.2 | 103.4 ± 1.0 | 102.3 ± 1.1 | 0.48 |
| Factor 3 | 100.5 ± 1.0 | 101.2 ± 1.1 | 104.2 ± 1.1 | 102.0 ± 1.1 | 100.5 ± 1.3 | 0.77 |
| Glucose,34mmol/L | ||||||
| Neo-traditional pattern | 7.8 ± 0.4 | 7.8 ± 0.5 | 8.2 ± 0.5 | 7.9 ± 0.4 | 7.9 ± 0.4 | 0.45 |
| Modern pattern | 8.1 ± 0.5 | 7.8 ± 0.5 | 8.0 ± 0.5 | 7.8 ± 0.5 | 7.7 ± 0.4 | 0.19 |
| Factor 3 | 8.0 ± 0.5 | 7.9 ± 0.4 | 7.9 ± 0.4 | 7.9 ± 0.5 | 7.9 ± 0.5 | 0.79 |
| HDL cholesterol, mmol/L | ||||||
| Neo-traditional pattern | 1.19 ± 0.03 | 1.20 ± 0.02 | 1.20 ± 0.02 | 1.22 ± 0.03 | 1.22 ± 0.03 | 0.40 |
| Modern pattern | 1.23 ± 0.03 | 1.22 ± 0.02 | 1.22 ± 0.03 | 1.17 ± 0.03 | 1.17 ± 0.03 | 0.08 |
| Factor 3 | 1.20 ± 0.03 | 1.20 ± 0.02 | 1.18 ± 0.02 | 1.19 ± 0.03 | 1.25 ± 0.03 | 0.41 |
| Triglycerides, mmol/L | ||||||
| Neo-traditional pattern | 1.30 ± 0.07 | 1.22 ± 0.06 | 1.36 ± 0.08 | 1.37 ± 0.07 | 1.28 ± 0.07 | 0.68 |
| Modern pattern | 1.17 ± 0.05 | 1.25 ± 0.07 | 1.31 ± 0.06 | 1.49 ± 0.09 | 1.39 ± 0.08 | 0.002 |
| Factor 3 | 1.38 ± 0.09 | 1.25 ± 0.06 | 1.38 ± 0.06 | 1.30 ± 0.07 | 1.21 ± 0.06 | 0.20 |
| Systolic blood pressure,5mm Hg | ||||||
| Neo-traditional pattern | 132.3 ± 6.6 | 134.1 ± 2.9 | 135.7 ± 2.5 | 133.1 ± 2.6 | 132.6 ± 2.3 | 0.94 |
| Modern pattern | 133.1 ± 2.9 | 131.3 ± 2.6 | 133.8 ± 2.9 | 134.2 ± 2.2 | 135.0 ± 2.4 | 0.24 |
| Factor 3 | 132.2 ± 2.8 | 131.9 ± 2.3 | 135.6 ± 2.6 | 133.6 ± 2.4 | 134.7 ± 2.5 | 0.14 |
| Diastolic blood pressure,5mm Hg | ||||||
| Neo-traditional pattern | 84.0 ± 1.7 | 84.0 ± 1.7 | 85.8 ± 1.4 | 83.4 ± 1.6 | 82.5 ± 1.5 | 0.24 |
| Modern pattern | 83.7 ± 1.7 | 82.8 ± 1.5 | 83.7 ± 1.8 | 84.8 ± 1.4 | 84.6 ± 1.7 | 0.34 |
| Factor 3 | 84.5 ± 1.6 | 82.4 ± 1.5 | 85.3 ± 1.7 | 83.5 ± 1.4 | 83.6 ± 1.7 | 0.72 |
Values are adjusted mean ± SE.
Adjusted for age (in 10-y intervals), sex, material lifestyle score, current smoking status (with an indicator for missing data), physical activity (total weekly hours), and total energy intake (in quintiles).
Adjusted for all previous factors and diabetes medication use.
Measured in samples from fasting participants
Adjusted for all previous factors and hypertension medication use.
Discussion
We identified neo-traditional and modern dietary patterns in American Samoa and Samoa that were associated with the presence of metabolic syndrome. The neo-traditional dietary pattern showed a trend toward decreased prevalence of metabolic syndrome across both polities although this relationship did not reach statistical significance. In American Samoa and Samoa, the neo-traditional dietary pattern was significantly associated with decreased abdominal circumference, whereas in residents of American Samoa, it was associated with increased serum concentrations of HDL cholesterol. The neo-traditional pattern was primarily characterized by high intakes of seafood and coconut products and low intake of processed foods, including potato chips, rice, and soft drinks; it was also positively associated with fiber and saturated fat intake. Interestingly, this pattern did not increase the risk of metabolic syndrome in this population, although increased consumption of saturated fat has been associated with both an increase in insulin resistance and development of CVD (60,61). The majority of saturated fat intake associated with the neo-traditional pattern is derived from coconut cream and coconut intake. A previous study among the Minangkabau of West Sumatra, Indonesia known for their high consumption of coconut products, found that the intake of saturated fat from coconut was not predictive of heart disease (62). On the other hand, the intake of several other dietary factors, including animal products, and consumption of fewer plant-derived carbohydrates predicted the occurrence of coronary heart disease among the Minangkabau (62). Lauric acid found in coconut oil has been linked to increased levels of HDL cholesterol (63,64). This association may help explain the lack of association between high intake of coconut products and heart disease, as well as the positive association between the neo-traditional pattern and increased levels of HDL cholesterol seen in this study. Furthermore, the consumption of coconut products is associated with an overall dietary pattern high in fiber and seafood intakes and relatively low in meat products; the combined effects of the dietary exposures in this pattern may be protective for the occurrence of metabolic syndrome.
Papaya and papaya soup, both positively correlated with the neo-traditional dietary pattern, represent an important source of fiber in the Samoan Islands diet. High-fiber diets have been associated with decreased insulin resistance and abdominal circumference (65,66), potentially accounting for some of the association between the neo-traditional dietary pattern, decreased abdominal circumference, and the prevalence of metabolic syndrome observed in this study.
The modern dietary pattern primarily characterized by high intake of sausage and eggs and processed foods rich in refined grains such as rice, potato chips, instant noodle soup, and pancakes was associated with increased prevalence of metabolic syndrome in both Samoa and American Samoa (P = 0.05 and 0.08, respectively). In both polities, the modern pattern was significantly associated with increased levels of serum triglycerides. However, unlike the neo-traditional dietary pattern, fiber and saturated fat intakes remained relatively constant across increasing quintiles of this pattern. While these dietary exposures may have partially mediated the relationship between the prevalence of metabolic syndrome and diet in the Samoan Islands, this analysis suggests an important role for refined grains in the pathogenesis of this CVD syndrome in these polities. Diets rich in refined grains have been associated with an increased risk of hypertriglyceridemia and metabolic syndrome (67,68).
Many observational studies (69) and some clinical trials (70) have shown an inverse relationship between exercise and levels of serum triglycerides. Because the modern dietary pattern was related to higher triglyceride levels, we also examined the potential modification of this pattern by physical activity by examining trends in mean levels of triglycerides across tertiles of hours of physical activity per week and entering interaction terms in our regression models. No significant relationships were found (results not shown).
It is noteworthy that the neo-traditional dietary pattern was associated with older age in both polities and that the associations with metabolic syndrome and its components persisted despite this age relationship. This suggests cohort effects on dietary intake or the maintenance of a less modern diet among older adults. Future work on community and individual level dietary interventions in both Samoan polities should be aware that adherence to a potentially more healthful diet still exists as they develop primary and secondary programs to improve Samoan population health.
Although the diet of Samoan Islanders is quite specific to these polities, some parallels can be drawn between the patterns derived in this study and those found in previous investigations. A western dietary pattern was derived in 2 studies among Iranian women and U.S. adults, respectively, that was also characterized by high intakes of refined grains (among other dietary exposures) and was associated with an increased risk of metabolic syndrome (16,18). Dietary patterns among Greek adults, which were differentially associated with metabolic syndrome or its individual components, were also found to have some similarities to those observed in the present study. In the study of Panagiotakos et al. (71), 2 dietary patterns were noted that were characterized by the consumption of foods high in fiber such as cereals, legumes, vegetables, and fruits, whereas a second pattern was characterized by the intake of potatoes, a high-glycemic food, and meat. The first pattern was inversely associated with waist circumference, positively associated with increased serum concentrations of HDL cholesterol, and had an inverse relationship with metabolic syndrome. On the other hand, the second dietary pattern was positively correlated with serum triglycerides and metabolic syndrome (71).
Strengths of this study include the representativeness of the study sample with respect to the populations of American Samoa and Samoa and the use of dietary patterns to capture holistic measures of diet. PLS was able to identify dietary patterns characterized by saturated fat and fiber as well as dietary exposures that were not specified as response variables in the analysis. This dietary pattern approach allowed for the consideration of the combined effect of many different dietary exposures that may act synergistically to affect the risk of disease in this population.
There are some potential limitations to these analyses that should be considered. First, this study was cross-sectional and therefore the results should be confirmed using a prospective study design. Second, some of the effects of the dietary patterns in this study may be due to nutrients correlated with the chosen response variables and not to the responses themselves. Clinical trials are needed to separate the effects of particular nutrients from the overall dietary patterns derived in this study. Third, PLS can produce chance associations with response variables that do not generalize beyond the data set used to derive dietary patterns. This risk was minimized by using cross validation to choose the patterns to investigate. Fourth, there may be some error in classifying participants using the ATP-III metabolic syndrome definition due to measurement error of the individual components of the syndrome (1). Finally, there is the possibility of residual confounding by nondietary lifestyle factors.
In conclusion, the results of this study provide evidence for the potential protective effect on an important precursor to the development of CVD of a neo-traditional eating pattern in American Samoa and Samoa. This dietary profile was characterized by high intake of coconut products and seafood and low intake of processed foods, including potato chips, rice, and soft drinks. On the other hand, a more modern dietary pattern associated with the intake of processed foods high in refined grains, including rice, potato chips, and pancakes was positively associated with the presence of metabolic syndrome. Reduced intake of more modern processed foods, especially those with high content of refined grains, and adherence to a neo-traditional eating pattern characterized by high intake of plant-based fiber, seafood, and coconut products may help to prevent the continued growth in the prevalence of metabolic syndrome in American Samoa and Samoa.
Acknowledgments
A.B., J.R.D., P.K., R.G., S.T.M., and H.C. designed the research; S.T.M., C.Q., and T.S.L. conducted the research; J.R.D. analyzed the data; J.R.D., A.B., and S.T.M wrote the paper; J.R.D., A.B., and S.T.M had primary responsibility for final content. All authors read and approved the final manuscript.
Supported by NIH grants HL081549, DK59642, and DK075371.
Author disclosures: J. R. DiBello, S. T. McGarvey, P. Kraft, R. Goldberg, H. Campos, C. Quested, T. Salamo Laumoli, and A. Baylin, no conflicts of interest.
Abbreviations used: CVD, cardiovascular disease, PLS, partial least squares regression; PCA, principal components analysis; VC, validity coefficient.
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