PAPER Relative influence of diet and physical activity on ... - Nature

International Journal of Obesity (2003) 27, 920–932 & 2003 Nature Publishing Group All rights reserved 0307-0565/03 $25.00 www.nature.com/ijo

PAPER Relative influence of diet and physical activity on cardiovascular risk factors in urban Chinese adults M Yao1, AH Lichtenstein1, SB Roberts1, G Ma2, S Gao2, KL Tucker1 and MA McCrory1* 1

Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA 02111-1524 USA; and 2Institute of Nutrition and Food Safety, Chinese Center for Disease Control and Prevention, Beijing 100050, China

OBJECTIVE: The relative influence of dietary factors vs physical activity on cardiovascular risk factors are poorly understood. We investigated these factors in a population whose traditional diet may have both positive (high plant-based) and negative (high refined carbohydrate) aspects, and whose physical activity levels (PALs) vary widely. DESIGN: Cross-sectional study. SUBJECTS: A total of 130 weight stable adults aged 35–49 y (BMI 18–35 kg/m2) living in urban Beijing, China. MEASUREMENTS: Dietary intake (by food frequency questionnaire), PAL as the ratio of predicted total to resting energy expenditure), percent body fat (by deuterium oxide dilution), and central adiposity (waist circumference and waist to hip ratio) were assessed. Biochemical parameters (total cholesterol, low- and high-density lipoprotein cholesterol (LDL-C; HDL-C), triglyceride (TG), apolipoproteins A-I and B, glucose, insulin, and homocysteine and its related vitamins), blood pressure and presence of the metabolic syndrome (having Z3 risk factors of central adiposity, HDL-C, TG, glucose, blood pressure) were also examined. RESULTS: Mean values for cardiovascular risk factors were relatively low, but 19% of subjects had the metabolic syndrome. Using validated methods for measuring food intake and energy expenditure, we found that an adverse cardiovascular risk profile was associated with a diet high in carbohydrate, low in polyunsaturated fat, and low in fruit and vegetables, independent of body fatness and its distribution. While dietary factors predicted individual cardiovascular risk factors more consistently than PAL, avoidance of low PAL reduced the risk of having the metabolic syndrome. CONCLUSION: These results suggest that, regardless of total body fatness and fat distribution, multiple unfavorable dietary factors and low physical activity independently increase the risk for cardiovascular disease. Avoidance of a sedentary lifestyle additionally reduces the risk of developing the metabolic syndrome. International Journal of Obesity (2003) 27, 920–932. doi:10.1038/sj.ijo.0802308 Keywords: dietary macronutrient composition; dietary patterns; physical activity level; percent body fat; cardiovascular risk factors; metabolic syndrome

Introduction Cardiovascular disease is the leading cause of morbidity and mortality both in Western societies and in many countries undergoing the nutrition transition,1,2 and prevention through risk factor control remains the most effective longterm option for treatment. It is well documented that diet and physical activity contribute to cardiovascular risk factors3–6 and body fatness,7–10 and that obesity itself is

*Correspondence: Dr MA McCrory, Energy Metabolism Laboratory, the Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington St., Boston, MA 02111, USA. Email: [email protected]. Received 3 September 2002; revised 1 February 2003; accepted 3 February 2003

adversely associated with cardiovascular risk factors.11 However, the relative influence of specific dietary components vs physical activity on cardiovascular risk factors independent of body fatness remains uncertain. Considerable disagreement exists regarding the relative importance of different dietary parameters on risk of cardiovascular disease. Of the dietary components examined, saturated fat has shown the most consistent significant adverse association with blood lipid profile, whereas monoand poly-unsaturated fat (PUFA) are associated with a favorable blood lipid profile.6,12,13. The effect of total fat is controversial. Furthermore, low-fat diets may also tend to be high in carbohydrate,14 and several recent epidemiological studies have shown that diets high in refined carbohydrates are adversely associated with cardiovascular risk factors and

Diet, physical activity and cv risk factors M Yao et al

921 positively associated with the risk of cardiovascular disease itself.15,16 In addition, certain dietary patterns, which represent the combined effects of nutrients in food, such as high fruit and vegetable intake, have been shown to beneficially affect cardiovascular risk profile.17,18 However, few of these studies have examined multiple dietary components simultaneously and therefore have not been able to discern which dietary components are most important for reducing cardiovascular risk. Several studies have shown that physical activity favorably affects cardiovascular risk profile,19,20 but very few studies have simultaneously examined both dietary components and physical activity.3,4 Additionally, accurate measures of diet and physical activity are difficult to obtain because of a high degree of dietary under-reporting21 and recently observed physical activity over-reporting.22,23 Therefore, studies using accurate methods for measuring dietary components and physical activity are urgently needed in order to more effectively determine the relative association of these factors with cardiovascular disease risk. The primary purpose of this study was to investigate the relative influence of different dietary components and physical activity on cardiovascular risk factors using validated methods for measuring all primary parameters. We conducted the study in China, where the traditional diet may have both positive (high plant-based) and negative (high refined carbohydrate) aspects, and where physical activity levels (PALs) vary widely among individuals. We anticipated that the data from a population with very different dietary habits and activity patterns from Western populations would help to further elucidate the roles of diet and physical activity in predicting cardiovascular risk factors independent of body fatness and fat distribution.

Subjects from the upper and lower tertiles of the scores for dietary fat intake (% energy) and habitual physical activity index based on the screening questionnaire were considered potentially eligible to participate. The purpose of this stratification was to ensure wide ranges of dietary composition and physical activity within the study population, with approximately equal numbers of subjects having each of four combinations of high or low dietary fat (HF, LF) and high or low physical activity (HPA, LPA). Note that this strategy proved successful, with the number of subjects ranging from 31 to 34 in the four combinations. The reason for choosing tertiles over a median split was to minimize misclassification into fat and physical activity categories. All subjects were required to be free of any known illnesses or medical conditions that might affect energy intake or energy metabolism or prevent them from being physically active, were not taking any medications known to influence energy regulation or blood lipid levels, and were healthy as judged by a normal physical examination and a normal blood hemoglobin concentration (120–150 g/l for men and 105– 135 g/l for women27 ). Additional exclusion criteria included postmenopausal status in women, smoking 420 cigarettes per day, drinking 42 alcoholic drinks per day, weight change of 43 kg over the past year, following a weight-control or vegetarian diet, or self-reported change of eating habits or habitual PALs during the past year. Based on the screening examination, 142 subjects qualified for the study, and 130 were willing to participate. Details of the subjects are given in Table 1. The studies were conducted from the Institute of Nutrition and Food Safety, Beijing, with ethical approval obtained from the Human Investigations Review Committees at the Chinese Center for Disease Control and Prevention and New England Medical Center/Tufts University. Written informed consent was obtained from all subjects prior to the start of the study.

Subjects and methods Subjects The subjects were 130 adults (63 men and 67 women) living in urban areas of Beijing, China. Study participants were recruited from 15 neighborhoods that were widely distributed in three of the four urban districts of Beijing. Basic information on 100–150 randomly selected household residents in each neighborhood was first obtained from archive records of the neighborhood committees. Those individuals who were 35–49 y old, living in the same neighborhood for Z2 y, and willing to participate (45% of the randomly selected residents) were given a screening test. This test included a screening questionnaire by interview, physical examination, and blood screening for hemoglobin concentration. The questionnaire included information on medical history, physical activity (14 scaled questions related to activity during work, transportation, household work, and leisure time),24 and eating habits (including consumption frequency and portion size of 10 food items which are major contributors to dietary fat content).25,26

General protocol The study was conducted over a 9-day period. The subjects were studied in groups of 8–10, and these subjects were equally selected from the four different lifestyle combinations (HF or LF and HPA or LPA) during different study months in order to control for potential confounding by seasonal effects. Throughout the study, the subjects were able and encouraged to pursue their usual lifestyle, and all continued their regular occupations, transportation, and leisure activities. All measurements were conducted at the research unit of the Institute of Nutrition and Food Safety, and subjects usually traveled there by leisurely walking or bicycling (oB8 km). Subjects arrived at the research unit on study day 1 after an overnight fast. A measurement of total energy expenditure (TEE) by doubly labeled water was started (in the first 73 subjects only, due to the worldwide H18 2 O shortage) and anthropometric variables were obtained. In addition, subjects were instructed in wearing an activity monitor at the International Journal of Obesity


922 Table 1

Subject characteristicsa

n Age (y) Body weight (kg) Height (cm) BMI (kg/m2) Body fat (% weight) Waist–hip ratio Waist (cm) PAL Reported physical activity (min/day) Light activity, o 3 METs Moderate activity, 3–6 METs Vigorous activity, >6 METs

questionnaire (FFQ) developed for an urban Chinese population was completed. Information on education level and smoking status was also obtained by questionnaire.

Men

Womenb

63 42.870.5 74.671.5 171.170.7 25.470.4 27.170.7 0.9070.01 87.771.1 1.7970.02

67 42.370.5 63.971.4* 160.170.7* 24.970.5 36.970.6* 0.8170.01* 79.471.1* 1.6670.01*

295717 157713 3574

290719 152711 2775

Mean7s.e.m. BMI, body mass index; PAL, physical activity level. Significantly different from men: * P o 0.001 (independent-samples t-test).

a

b

waist and had a motion detector installed on their bicycles. Subjects were then discharged from the research unit and during study days 2–8 lived a normal life at home, completing activity documentation as described below. In addition, daily timed urine specimens for the doubly labeled water analyses were collected by field staff visiting subjects at home. Subjects returned to the research unit on the morning of day 9 after an overnight fast for measuring body composition, repeating anthropometric measurements, and obtaining fasting blood samples for biochemical determinations. In addition, an interview-administered food frequency Table 2

Dietary intake Usual dietary intake over the preceding 12 months was assessed by using an interview-administered Chinese FFQ developed based on 24 h dietary recall data from the 1992 China Health and Nutrition Survey.28 The major food item contributors to intake from this study were compiled, and some items were deleted or additional items added according to eating practices common in Beijing. The questionnaire lists 170 individual food items classified into 11 food categories: rice and cereals, meat and eggs, vegetables, fruits, nuts, soy products, dairy products, bakery and sweets, condiments, beverages, and Western fast food. Subjects were asked to recall the frequency of consumption of individual food items (number of times per day, per week, per month, or per year), and the estimated portion size determined using local weight units (liang and jin, ie 50 and 500 g, respectively) or natural units (ie small, medium, or large apple). The questionnaire also requested information on the frequency of restaurant food consumption, and included open-ended sections for information on foods and supplements not specified on the questionnaire. Daily energy and macronutrient intakes were calculated by using a Chinese food-composition database.29 Energy density was calculated as the total daily energy intake divided by the total daily

Daily dietary intakes estimated from the food frequency questionnairea

n Energy and macronutrients Energy (MJ) Energy density (kJ/day) Total fat (% of energy) Saturated fat (% of energy) Monounsaturated fat (% of energy) Polyunsaturated fat (% of energy) Cholesterol (mg/MJ) Carbohydrate (% of energy) Protein (% of energy) Fiber (g/MJ) Alcohol (% of energy) Food groupsc Rice, noodles, and cereals Vegetables Fruit Soy products Meat, eggs, and dairy products Nuts Bakery and sweets Noncaloric beverages Caloric beverages a

Men

Womenb

63

67

12.370.7 2.670.1 36.971.0 10.470.3 15.570.5 8.470.3 56.773.3 38.471.4 16.970.3 1.470.1 8.571.0

(5.4–33.7) (1.1–5.4) (17.4–61.6) (4.1–18.5) (6.5–26.5) (4.0–13.9) (18.2–204.8) (17.3–71.2) (11.6–22.1) (0.5–3.4) (0–28.0)

8.370.5 2.670.1 34.571.0 9.770.4 14.070.5 7.870.3 66.473.2 47.871.1 17.470.4 1.870.1 0.670.2

(3.2–22.9)*** (1.0–4.4) (20.9–55.6) (4.7–19.0) (6.4–23.7)* (4.8–18.2) (18.6–140.9)* (27.9–64.6)*** (13.2–29.3)* (0.7–3.4)*** (0–9.3)** *

12.370.9 13.170.8 6.070.6 2.470.3 9.670.6 0.870.1 0.270.1 45.971.9 7.771.0

(2.4–39.5) (2.2–28.7) (0–17.4) (0–13.3) (1.2–22.8) (0–5.2) (0–2.5) (12.7–72.9) (0–36.1)

13.470.9 16.470.9 12.170.9 2.570.3 11.570.6 0.770.1 0.470.1 38.671.9 2.370.4

(3.0–39.6) (4.4–38.1)** (1.3–33.7)*** (0.2–11.3) (1.9–25.1)* (0–4.6) (0–3.7)* (0–70.5)** (0–18.3)***

Mean7s.e.m. (range in parentheses). Significantly different from men: * Po0.05, **Po0.01, *** Po0.001 (independent-samples t-test). c Expressed as (grams from group/total daily grams) 100%. Intakes from condiments, fast food, and seafood groups were o1% on average (data not shown). b

International Journal of Obesity


923 weight of food consumed, including all beverages. Analysis of the accuracy of reported energy intake per day from the FFQ compared with TEE, (see below) determined by the doubly labeled water method showed a significant correlation (r ¼ 0.45, Po0.01 in 73 subjects) and a mean percentage difference of 0.1%75.3% (s.e.m.), and a 95% confidence interval of 11–11%. For the analysis of the relation between food group consumption and the cardiovascular and metabolic outcomes, foods from the FFQ were categorized into 13 major food groups based on common sources of nutrients and potential effects on the outcome studied (see Table 2). Food group intakes were computed as (grams from food group/ total daily grams) 100%. The percentage daily energy intake supplied by selected nutrients within food groups (ie carbohydrate from rice/noodles/cereals, total fat from meat/ eggs/dairy, and dietary fiber from fruit/vegetables) was also calculated. Dietary glycemic index and glycemic load were calculated by using the methods of Liu et al.30 Briefly, we calculated glycemic load by multiplying the carbohydrate content of each food by its estimated glycemic index.31 We then multiplied this value by the frequency of consumption and the estimated weight consumed, and summed these products over all food items to produce the dietary glycemic load. Additionally, the overall dietary glycemic index was created by dividing the dietary glycemic load by the total daily carbohydrate intake in grams.

Measurement of TEE and estimation of physical activity An 8-day doubly labeled water study was conducted to measure TEE in 73 subjects and used to validate determinations of energy intake from FFQ and predicted total energy expenditure (pTEE). A detailed description of the measurement procedure was described elsewhere.32 Briefly, a mixed 2 18 H2 O dose containing 0.10 g/kg body weight of H18 2 O and 0.08 g/kg body weight of 2H2O was given orally on the early morning of study day 1 after subjects had fasted overnight and after the collection of baseline urine specimens. Postdose urine samples were collected at 3, 4, and 5 h after dose administration and on study days 2, 7, and 8 (samples were the second void of day, usually collected under supervision of a field worker). Isotope analyses were performed by using isotope ratio mass spectrometry (PDZ Europa Ltd, Crewe, England) as described elsewhere.32 TEE was calculated using standard equations with a food quotient value of 0.88 determined by 24-h recall in the 1992 China National Nutrition Survey28 as discussed elsewhere.32 Predicted resting energy expenditure (pREE) was predicted using a cross-validated equation for healthy Chinese adults33 as described elsewhere.32 Since we were only able to measure TEE by doubly labeled water in the first 73 subjects, pTEE was also obtained for all subjects from activity monitors, estimations of activity when the monitor was not worn, and supplemental information

on bicycling (since the monitor chosen did not detect this kind of activity). The activity monitor we used (MTI ActiGraph, 709 Anchors St., Fort Walton Beach, FL, USA) is a uniaxial accelerometer designed to measure accelerations in the vertical direction and is worn at the waist along the anterior axillary line. Subjects were instructed to wear the monitor during all waking hours except when conducting activities involving water (eg bathing, swimming) and when getting up at night. In addition, subjects kept a diary of the type and duration of activities performed when the MTI monitor was not worn, and the energy expenditure of these activities was predicted using literature values for METs (an abbreviation for metabolic, representing the predicted ratio of TEE to REE during the activity34).35 Energy expenditure during sleep without hindrance was assumed to equal 90% of pREE.36,37 Since the MTI monitor is insensitive to bicycling, which requires little vertical movement, and bicycling is a common form of transport in China, the average daily distance and speed of bicycling was also determined by connecting a motion detector (Bike Computer Model 800, Sigma Sport, Olney, IL, USA) to the bicycle of each subject. Bicycling duration, distance, and speed were recorded daily. Energy expenditure was then predicted using published MET values for bicycling at different speeds.35 The pTEE of each subject was determined by summing the above described activity components (MTI monitoring, bicycling, and other time periods without the monitor), and a ratio of pTEE to pREE was also calculated to give a PAL. There was a significant association between measured TEE and pTEE (r ¼ 0.81, SEE ¼ 1.233, Po 0.001), suggesting that the prediction approach was valid. Cross-validation of this method in an independent population group is needed in future studies. In addition, a physical activity questionnaire (World Health Organization-Monitoring Trends and Determinants of Cardiovascular Diseases, (WHO-MONICA)) was interviewadministered to all subjects to assess their habitual physical activity during the previous 12 months.38 Reported average daily physical activity was expressed as min/day, and classified by intensity levels using METs.

Body composition Anthropometric measurements (including weight, height, and waist and hip circumferences) were obtained in triplicate on days 1 and 9 using standard methods,39 as described elsewhere.40 Body fat and fat-free mass were determined by deuterium oxide (2H2O) dilution. For this measurement, subjects consumed 0.05 g/kg body weight of 2H2O on day 9 after an overnight fast and collection of a baseline urine specimen. Urine specimens were collected at 3, 4, and 5 h, and abundances of 2H2O were measured as described elsewhere.40 Total body water was calculated as the 2H2O dilution space at 5-h postdose divided by 1.04.41 Fat-free mass was then calculated assuming a fat-free mass hydration International Journal of Obesity


924 coefficient of 0.732.42,43 The appropriateness of applying the standard hydration coefficient to this Chinese study population was confirmed by our recent analysis as reported elsewhere.40 Percent body fat was then computed from body weight and fat-free mass.

Measurement of cardiovascular risk factors Blood pressure was measured on days 1 and 9 by a physician (MY) using a standard mercury sphygmomanometer (Country Technology Inc., Gays Mills, WI, USA). At each visit, three sequential blood pressure measurements were obtained after the subjects rested for 10 min in the seated position. Systolic blood pressure was measured as the point of appearance (phase I) of Korotkoff sounds; diastolic blood pressure was measured as the point of disappearance (phase V). Readings were recorded to the nearest 2 mmHg. The mean of the readings on the 2 days was used in the analysis. A single blood sample of 30 ml was collected after a 12– 14 h overnight fast. Blood (20 ml) was drawn into two 10 ml lavender-top tubes (containing EDTA as an anticoagulant) for preparation of plasma, and 10 ml blood was drawn into one 10 ml red-top tube for preparation of serum. After the blood draw, the lavender-top tubes were gently shaken and then promptly cooled on ice, while the red-top tubes were first placed at room temperature for at least 30 min for blood clotting and then cooled on ice. Within 3 h of the collection, the plasma/serum was separated by centrifugation at 2500 rpm for 20 min at 41C. All plasma/serum samples were aliquoted into airtight storage tubes (Cryos cryogenic vials, Vangard International, Inc., Neptune, NJ, USA) and stored at 801C prior to shipment on dry ice to the Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA. All determinations of lipids, lipoprotein cholesterol, and apolipoprotein concentrations were performed in the Cardiovascular Nutrition Laboratory. Plasma total cholesterol (TC) and triglyceride (TG) were analyzed with a Spectrum CCX bichromatic analyzer (Abbott, Abbott Park, IL, USA) using enzymatic reagents (Technicon, Tarrytown, NY, USA).44 High-density lipoprotein cholesterol (HDL-C) was measured in the supernatant fraction after precipitation of apolipoprotein (apo) B-containing lipoproteins using dextran– magnesium sulfate.45 Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald formula.46 If TG levels were greater than 400 mg/dl LDL, cholesterol was measured directly (Sigma Diagnostics, St Louis, MO, USA). Lipid assays were standardized through the Lipid Standardization Program of the Centers for Disease Control, Atlanta, GA, USA. Apo A-I and apo B were measured by immunoturbidometric assays (INCSTAR Stillwater, MN, USA).47,48 Plasma homocysteine was measured by HPLC with fluorometric detection,49 plasma folate by a 96-well plate microbial (Lactobacillus casei) assay,50 plasma vitamin B12 by a radioassay (Ciba-Corning, Medfield, MA, USA), and plasma pyridoxal-50 -phosphate (PLP, the active circulating form of International Journal of Obesity

vitamin B6) by the tyrosine decarboxylase method.51 Interassay coefficients of variation were 9% for homocysteine, 13% for folate, 7% for vitamin B12, and 16% for PLP. Serum glucose concentration was measured enzymatically using an automated analyzer (Cobas Mira; Roche Diagnostic systems, Indianapolis, IN, USA), and insulin concentration was analyzed using a competitive binding radioimmunoassay (RIA) (Human Insulin Specific RIA Kit, Linco Research Inc., St Charles, MO, USA).

Clinical identification of cardiovascular risk factors and metabolic syndrome As defined by the Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adults Treatment Panel III, or ATP III),52 standard cutoffs to determine high cardiovascular risk were used as follows: waist circumference 4102 cm in men and 488 cm in women; TG concentration Z150 mg/dl; HDL-C concentration o40 mg/dl in men and o50 mg/dl in women; blood pressure of Z130/85 mmHg; or serum glucose concentration Z110 mg/dl; high LDL-C concentration was defined as 4160 mg/dl. In addition, high homocysteine was defined as 412 mmol/l for men and 411 mmol/l for women.53 Subjects having three or more of the above criteria (excluding LDL-C and homocysteine) were considered to have the metabolic syndrome as recently defined by ATP III.52

Statistics Statistical analyses were performed using SPSS 10.0 for Windows (SPSS Inc., Chicago, IL, USA) and SAS 8e for Windows (SAS Institute Inc., Cary, NC, USA). Descriptive data are presented as mean7s.e.m. unless otherwise indicated. Since the distributions of TG, glucose, insulin, homocysteine, folate, PLP, and vitamin B12 concentrations were skewed, these variables were log transformed (natural log) prior to further analyses. Group means (ie by sex) were compared using independent sample t-tests. Multiple regression with general linear models analysis of covariance was performed to examine the associations of dietary variables, physical activity, and total and central adiposity with cardiovascular risk factors. A stepwise manual procedure was followed to examine consistency of the associations. First, we tested the associations of body composition variables, including percent body fat and WHR or waist circumference with individual cardiovascular risk factors (body composition models). Second, we tested the associations of dietary variables, including macronutrient intakes (ie percentage of energy intake from carbohydrate, protein, total fat, saturated fat, mono- and polyunsaturated fat, and alcohol, as well as grams of cholesterol and fiber intakes adjusted for energy intake), percentage amount of intakes from nine food groups, and dietary glycemic index and glycemic load, with cardiovascular risk factors (dietary


925 models). Macronutrient intakes (with the exception of alcohol) and intakes from food groups were not entered in each of the dietary models at the same time because of their high collinearity. In each of the above models, potential confounding variables, including sex, smoking status, alcohol intake and education level were included, and the best body composition or dietary predictors for each dependent variable were determined. Finally, the significant body composition and dietary variables determined from the above two type of models, along with PAL, were included in a stepwise manual multiple regression model as independent variables to determine the best fitting model for predicting individual cardiovascular risk factors. Both main effects and interactions between independent variables were examined, with adjustment for all the confounding variables listed above. Additionally, stepwise manual logistic regression analysis was used to examine the associations of diet and physical activity with metabolic syndrome. The regression procedure considered the influence of both main effects and interactions between dietary variables as described above and PAL quintile after adjustment for confounding variables listed above. The goodness-of-fit of the logistic regression model was examined using Hosmer and Lemeshow’s test (P40.05 for models presented). Statistical significance of main effects was accepted at Po0.05, and of interactions, Po0.01.

Results The mean BMI of men and women in the current study was slightly higher than those of 2328 urban adult residents in

Table 3

Beijing (23.8 kg/m2 for men and 23.9 kg/m2 for women) from the 2000 National Disease Control and Monitoring survey (GH Yang et al, unpublished data).32 Men had a significantly higher waist circumference and waist to hip ratio (WHR), and a lower percent body fat than did women. In addition, PAL was significantly higher for men than for women, while reported daily duration of light, moderate and heavy activities did not differ significantly between sexes, as reported elsewhere when using a 7-day modified version of the WHO-MONICA questionnaire32 (Table 1). Energy and macronutrient intakes, and intakes from the different food groups, are shown in Table 2. For both men and women, the percentage of energy from total fat averaged 35–37%, and less than one-third (28%) of the total fat intake was derived from saturated fat. Women reported significantly higher intakes of most of the other macronutrients, with the exception of alcohol, than did men. Women also reported consuming significantly higher percentage intakes of vegetables, fruit, meat/eggs/dairy, and bakery/sweets, but significantly lower percentage intakes of both caloric and non-caloric beverages. None of the subjects took vitamin supplements. For all nutrients and food groups, intakes varied widely among individuals. Cardiovascular risk factors are presented in Table 3. Men had significantly higher lipid profiles than did women, except for HDL-C and apo A-I where HDL-C was significantly higher in woman than in men, and apo A-I was not significantly different between sexes. Serum glucose and insulin concentrations did not differ significantly between sexes. The plasma homocysteine concentration of men was 50% higher than that of the women, while folate concentra-

Cardiovascular risk factors of the study populationa Men

n TC (mg/dl) LDL-C (mg/dl) HDL-C (mg/dl) TC/HDL-C TG (mg/dl) Apo A-I (mg/dl) Apo B (mg/dl) Glucose (mg/dl) Insulin (mg/dl) Homocysteine (mmol/l) Folate (ng/ml) SBP (mm Hg) DBP (mm Hg)

63 172.673.5 (107.0–241.0) 101.873.2 (47.0–175.0) 40.371.3 (21.0–74.0) 4.570.2 (2.0–7.2) 183.2718.1 (57.0–722.0) [143.0] 133.172.9 (92.0–195.0) 83.672.6 (37.0–123.0) 93.672.5 (68.0–206.0) [92.0] 12.270.6 (3.9–24.1) [11.2] 15.071.2 (4.2–58.0) [11.6] 3.470.2 (1.0–9.2) [3.0] 126.071.6 (98.8–159.2) 83.771.0 (67.2–102.2)

Womenb

154.473.0 89.472.8 46.671.2 3.470.1 98.379.5 140.372.4 64.972.0 91.371.2 14.971.2 10.071.0 3.870.2 117.771.4 78.870.9

67 (109.0–217.0)** * (41.0–139.0)** (28.0–76.0)** (1.8–5.5)*** (28.0–624.0)** [84.0] (105.0–194.0) (34–112)*** (73.0–120.0) [91.0] (4.5–67.4) [11.9] (4.0–54.4)*** [7.2] (1.8–9.4)* [3.5] (88.5–151.3)*** (62.2–100.8)***

a

Mean7s.e.m. (range in parentheses). Median values for variables not normally distributed (TG, glucose, insulin, homocysteine, and folate) are shown in parantheses. TC, total cholesterol; LDL-C, low-density-lipoprotein cholesterol; HDL-C, high-density-lipoprotein cholesterol; TG, triglyceride; SBP, systolic blood pressure; DBP, diastolic blood pressure; apo, apolipoprotein. b Significantly different from men: * P o0.01, ** P o0.001 (independent-samples t-test).



926 tion was significantly lower in men than in women. All means were within the normal ranges, but individual values varied substantially. As a result of this high individual variability, the percentage of individuals with cardiovascular risk factors exceeding established cutoffs were calculated and are presented in Table 4. The percentages of individuals with low HDL-C, and high abdominal obesity (in women), TG (in men), blood pressure, homocysteine, and current smoking (in men) were fairly high in this study group. However, only two men had an LDL-C level 4160 mg/dl. Overall, the percentage of individuals with the metabolic syndrome was 26% for men and 13% for women. It should be noted that the cutoffs used to determine cardiovascular risk were developed in Western populations and may not be applicable to Chinese or other populations. In addition, becasuse of the low HDL concentration in our study subjects, we also considered defining metabolic syndrome without inclusion of this variable. In this case, there was only a slight change of the percentage of men with the metabolic syndrome, from 26 to 22%, and no change for the percentage of women). Table 5 presents the best fitting regression models for the associations of diet and physical activity with cardiovascular risk factors independent of body composition, after adjustment for sex, smoking, alcohol intake, and education level. Dietary intake is presented as either macronutrients or food groups. Regression coefficients are also shown with partial R2 in parentheses, and P-values are given in the right-hand column. Body composition variables significantly predicted blood lipids and fasting glucose. Measures of central

Table 4 Percentage of individuals with elevated cardiovascular disease risk factors and the metabolic syndrome

Abdominal obesity1 (M >102 cm; F >88 cm) High LDL-C (>160 mg/dl) Low HDL-C2 (M o40 mg/dl; F o50 mg/dl) High TG3 (X150 mg/dl) High fasting glucose4 (X110 mg/dl) High BP5 SBP (X130 mm Hg) DBP (X85 mm Hg) High homocysteineb (M >12 mmol/l; F >11 mmol/l) Current smoking Metabolic syndromea

Men n ¼ 63 (%)

Women n ¼ 67 (%)

Total n ¼ 130 (%)

6

21

14

2

0

1

54

61

58

49

8

28

10

8

9

37 44 45

16 19 21

26 31 33

54 26

5 13

29 19

LDL-C, low-density-lipoprotein cholesterol; HDL-C, high-density-lipoprotein cholesterol; TG, triglyceride; SBP, systolic blood pressure; DBP, diastolic blood pressure. a Defined as having three or more of the individual metabolic abnormalities (superscript numbers 1–5).66


adiposity differed in their associations with these risk factors: WHR was the stronger predictor for LDL-C, HDL-C, and glucose; whereas waist circumference was the stronger predictor for insulin and blood pressures. The models in Table 5 also show that, independent of total and central adiposity, dietary variables and physical activity were significantly associated with cardiovascular risk factors. In general, a diet high in carbohydrate or low in PUFA was predictive of an adverse lipid profile, seen as significant effects of high carbohydrate, rice/noodles/cereals, glycemic load, or low PUFA intakes on HDL-C, apo A-I, and TC/HDL-C (data shown for the best fitting models only). In separate models, carbohydrate and PUFA were each significant and contributed similarly to the variance in TC/HDL-C (4% for carbohydrate, 5% for PUFA). In addition, intakes of fruit and/or vegetables were inversely associated with TG, fasting glucose concentrations, and diastolic blood pressure. High alcohol intake was significantly associated with a more favorable cholesterol profile (ie high HDL-C and apo A-I concentrations, and low TC/HCL-C), but it was also associated with higher blood pressures. Plasma homocysteine concentration was strongly inversely related to plasma folate and vitamin B12 concentrations after controlling for all confounding factors and plasma PLP concentration. Although PAL was not associated with most of the cardiovascular risk factors mentioned above, PAL was positively associated with HDL-C and TC and inversely associated with fasting insulin, independent of diet and body composition (Table 5). PAL and carbohydrate had approximately equal but opposite contributions in a model where approximately 18% of the variability in HDL-C was accounted for by a combination of alcohol intake, intakes from rice/noodles/cereals, and PAL, independent of body composition. In another model, fasting insulin concentration was also predicted by PAL, independent of waist circumference. Figure 1 demonstrates the independent effects of PAL and central adiposity (as indicated by waist circumference) on fasting insulin concentration. For the purposes of illustrating this relation, we used the WHO cutoffs for low PAL vs moderate PAL and above54 and the ATP III cutoffs for waist circumference52 instead of using the continuous variables of PAL and waist circumference as shown in the regression model. As seen, a greater waist circumference was associated with higher fasting insulin concentration. However, individuals with higher PALs had lower insulin concentrations at any waist circumference level. Overall, PAL and waist circumference accounted for 23% of the variance in fasting insulin concentration. Figure 2 illustrates the significant and independent associations of PAL and sex with the metabolic syndrome, after adjusting for education, current smoking status, and alcohol intake. Individuals with PAL values in the highest four quintiles (representing mean PAL Z1.70 for men and Z1.59 for women, which approaches WHO defined PAL cutoffs for moderate occupational activity,54) were less likely to have the metabolic syndrome compared to those with PAL

Table 5

Best fitting multiple regression models for predicting cardiovascular risk factorsa Body composition % BF

TC LDL

1.88*** (0.12) F

WHR F

THcy

F

F

SBP

F

F

DBP

F

F

Waist (cm) CHO (% EI) PUFA (% EI) Alcohol (% EI) R/N/Cb

Plasma Fb

F&Vb

CBb

BASWb

Physical activity PAL

Folate (ng/ml) B12 (pg/ml)

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

39.01* (0.04) F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

15.25* (0.05) F

F

0.02* (0.04) F

0.23* (0.04) F

F

F

F

F

F

F

F

F

F

F

F

F

0.09* (0.05) F

0.47*** (0.09) 0.04* (0.04) 0.06*** (0.14) F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

1.09** (0.09) F

0.02* (0.05) 0.51* F (0.03) F F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

0.01* F (0.03) F F

F

0.02*** (0.19) F

0.01* (0.04) F

F

F

F

F

F

F

F

F

F

0.41** (0.10) 0.30*** (0.14)

F

F

F

F

F

F

0.55*** (0.15) F

0.48*** (0.10) F

F

F

0.46* (0.05) 0.22* (0.03)

0.16* (0.04) F

0.62* (0.04) F

F

F

0.17* F (0.05)

F

F

F

F

AdjR2 P

F

F F

0.18 o 0.001 0.09 0.01 0.35 o 0.001 0.53 o 0.001 0.53 o 0.001 0.38 o 0.001 0.14 0.003 0.36 o 0.001 0.12 0.004 0.27 0.009 F 0.41 o 0.001 F 0.24 o 0.001 F 0.29 o 0.001


138.24** (0.07) HDL F 84.58*** (0.17) TC/HDL 0.08*** 5.37** (model 1) (0.16) (0.06) TC/HDL 0.08*** 5.11** (model 2) (0.16) (0.06) TG 0.05*** F (0.20) Apo A-I 0.84* F (0.04) Apo B 1.80*** F (0.23) Glucose F 0.92** (0.10) Insulin F F

Dietary intake

% BF, percentage body fat; WHR, waist-hip ratio; CHO, carbohydrate; PUFA, polyunsaturated fat; PAL, physical activity level; TG, triglyceride; TC, total cholesterol; HDL, high-density-lipoprotein cholesterol; LDL, low-density- lipoprotein cholesterol; tHcy, total homocysteine; SBP, systolic blood pressure; DBP, diastolic blood pressure; apo, apolipoprotein. Regression coefficients for significant predictors in best fitting models are shown (partial R2 in parentheses). Models showing alcohol as a predictor are adjusted for sex, smoking, and education level; models not showing alcohol as a predictor are also adjusted for these variables and further adjusted for alcohol intake. * Po0.05, ** Po0.01, *** Po0.001. b Expressed as (grams from group/daily total grams)100%. R/N/C, rice, noodles, and cereals; F, fruit; V, vegetable; F and V, fruit and vegetable; CB, caloric beverages; BASW, bakery and sweets. a

927



928 values falling into the lowest quintile (odds ratio (OR) ¼ 0.14–0.23, Po 0.05; P ¼ 0.09 for trend). In addition, women were less likely to have the metabolic syndrome than were men (OR ¼ 0.25, P ¼ 0.02). Although dietary variables predicted many of the individual cardiovascular risk factors more consistently than did physical activity, no dietary variables were associated with the risk of having the metabolic syndrome.

Discussion This study showed that although mean values for most cardiovascular risk factors were relatively low in this urban Chinese population aged 35–49 y, the percentage of indivi-

Figure 1

Adjusted mean serum concentrations of fasting insulin (natural log-transformed) according to waist circumference cutoffs (low waist: men r102 cm, women r80 cm; high waist: men 4102 cm, women 480 cm) in urban Chinese adults in two PAL categories: low PAL (men o1.78, women o1.64) and high PAL (men Z1.78, women Z1.64). The general linear model was adjusted for all covariates, including sex, smoking, alcohol intake, and education level (adjusted R2 ¼ 0.30, Po0.001). P ¼ 0.019 for the interaction between PAL and waist circumference.

duals with the metabolic syndrome was unexpectedly high (19%) and comparable to that of the US population (22%55). However, because of the nonrandom sampling characteristics of the present study, the percentage of individuals with cardiovascular risk factors and the metabolic syndrome may not be representative of the entire urban Chinese population. Using validated methods for measuring food intake and energy expenditure, we found that an adverse cardiovascular risk profile was associated with key dietary components including high carbohydrate intake, low PUFA intake, and low fruit and vegetable intake. These dietary factors predicted individual risk factors more consistently than did PAL, but PAL levels in the second quintile and above (mean PAL Z1.70 for men, and Z1.59 for women) were significantly associated with a lower risk of having the multiple simultaneous risk factors defined as the metabolic syndrome. This study, showing that a Chinese diet is not particularly healthful, extends earlier research by demonstrating that an unfavorable diet may be associated with a higher risk for cardiovascular disease independent of the effects of having a sedentary lifestyle. Both nutrient and food group based analyses were conducted in this study using validated methods to examine the associations between dietary factors and cardiovascular risk profile since food groups represent a combination of nutrients in food, and thus may be a more powerful predictor than any single nutrient alone. Our findings, that higher intake of carbohydrate (as percentage of energy), higher intake of foods from the rice/noodles/cereals group, and higher dietary glycemic load were associated with an adverse lipid profile, are consistent with data from metabolic studies56,57 showing that high intake of carbohydrate can reduce HDL-C, and also with studies showing inverse associations between high dietary glycemic load (or dietary glycemic index) and HDL-C.16,30 Approximately 60–70% of

Figure 2 Odds ratios (ORs) with 95% confidence intervals for significant independent associations of quintiles of PAL (Panel A: Po0.05 for quintiles 1 vs 2, 3, 4, or 5; P ¼ 0.09 for trend), and sex (Panel B: P ¼ 0.02 for trend) with metabolic syndrome. Logistic regression model from which ORs were calculated was adjusted for all the other covariates, including smoking, alcohol intake, and education level. Mean PAL values for quintile are in parentheses.



929 the carbohydrate intake of our study population was derived from refined cereal products (ie white rice, waxy rice, noodles, buns and bread made from white flour), starchy root vegetables (ie potatoes, sweet potatoes) and sugarsweetened soft drinks, which are all refined carbohydratedense foods and beverages with high glycemic indexes.31 However, the consistent use of cooking oil (mainly corn) also made PUFA intake quite high for some individuals. In this study dietary carbohydrate and PUFA intakes were inversely related, and a model incorporating PUFA intake (rather than carbohydrate intake) was equally predictive of TC/HDL-C. Hence, an alternative interpretation of these data is that, in these subjects, diets high in refined carbohydrate diet are also likely to be low in PUFA. With our cross-sectional study design, it is impossible to statistically determine which dietary variable is the more important predictor. The hypocholesterolemic effect of PUFA observed in this study is generally concordant with findings from previous observational3,58 and short-term intervention studies.59, 60 Fruit and vegetables are sources of both carbohydrate and fiber with low to very low dietary glycemic indexes. The fruit and vegetable intake in this population was higher than that of US adults of similar age range (9 vs 6% for fruit; 15 vs 8% for vegetables61). High fruit intake was associated with a lower fasting glucose concentration, and a high intake of fruit and vegetables together was also associated with a lower diastolic blood pressure in this population. This finding is broadly consistent with data from the Dietary Approaches to Stop Hypertension (DASH) trial62 showing that both a fruit and vegetable diet and the combination diet rich in fruit, vegetables and low-fat dairy products substantially reduced blood pressure among individuals with and without hypertension. In the present study, a high fruit intake was predictive of a lower fasting TG concentration. While fruit and vegetables have been proposed to have little effect on TG,63 some investigators have suggested that the fiber content of fruit may be an important factor in moderating carbohydrate-induced rise in fasting TG concentration.64,65 We did not, however, find a significant association between total dietary fiber or fiber from fruit and/or vegetables and TG. Additionally, other studies have observed increases in TG despite the use of a diet rich in fiber and complex carbohydrates.66 In contrast to the significant associations of dietary carbohydrate and PUFA with lipid profile, dietary saturated fat or the meat/eggs/dairy products group representing sources of saturated fat did not independently predict any of the individual cardiovascular risk factors in this study. This finding is in agreement with some67,68 but not other studies.69,70 The conflicting findings may be owing to the generally low levels of plasma LDL-C in this (99% o160 mg/ dl) and other Asian populations,5 and the lower percentage of total fat intake from saturated fat (28%) in our subjects compared to a Western population (35% from NHANES III6). Homocysteine, another risk factor for cardiovascular disease,71 exhibited strong independent and inverse associa-

tions with plasma folate and vitamin B12. This finding supports the previously identified role for nutritional status in homocysteine metabolism,18,72 and is consistent with the beneficial effects of folate fortification.73 We further observed that the mean plasma folate concentration for this study population was very low (3.8 and 3.4 ng/ml for men and women, respectively). A level of 3 ng/ml has often been used as a cutoff for inadequate folate status.74 Thus, the surprisingly high percentage of subjects with high homocysteine concentrations (39%) in our study may indicate inadequate folate status, and possibly vitamin B12 status. Their relatively high homocysteine concentrations in comparison to mean values of 10.3 mmol/l for men and 8.8 mmol/l for women from the Framingham Offspring cohort75 may reflect higher cardiovascular disease risk. Our study population could possibly improve their folate status by increasing consumption of local folate sources that are readily available but not widely consumed such as leafy green vegetables, citrus fruit, and unrefined grains (ie, brown rice, and whole wheat bread). The finding that higher physical activity was independently associated with lower fasting insulin concentration after adjusting for waist circumference suggests that increasing physical activity can help improve the metabolic profile across different degrees of central fat distribution. This finding is consistent with exercise intervention studies showing improvement in metabolic profile without changes in body weight or composition.76,77 Fasting insulin concentration can be used as a reliable marker for insulin sensitivity in subjects with varying degrees of glucose tolerance.78 Data from Figure 1 indicate that higher PALs were associated with greater insulin sensitivity (by 11 and 15% at low and high waist circumference levels, respectively). Our findings, combined with those of other studies,79,80 further support recommendations of avoidance of a sedentary lifestyle to promote metabolic fitness and improved health in all individuals, regardless of the degree of central adiposity. Higher PALs were also associated with lower risk of having the metabolic syndrome, and in the same models dietary variables were not significantly related to the metabolic syndrome (although dietary variables predicted many of the individual risk factors included in the metabolic syndrome). The results of the study also indicate that a threshold level of PAL (in the second quintile and above, representing mean PAL Z1.70 for men and Z1.59 for women) is needed for protecting against the development of metabolic syndrome, with no further reduction in risk with more intense PALs. This level approaches the 1985 FAO/WHO/UNU estimates of PAL values for adults with moderate occupations (1.78 for men and 1.64 for women).54 PAL differences from the lowest quintile to this threshold level were equivalent to 950 kcal/ week (using the mean value for REE of this study population), which is in support of the US Surgeon General’s recommendation for moderate amounts of physical activity (1000 kcal/week, achievable by 30 min per day of brisk walking or other moderate-intensity activities80). Thus, these International Journal of Obesity


930 results emphasize the importance of avoiding a sedentary lifestyle rather than only encouraging vigorous physical activity reducing the risk of developing the metabolic syndrome. In summary, these results suggest that a diet high in refined carbohydrates, low in PUFA, and low in fruit and vegetables is associated with a higher risk for cardiovascular disease, independent of body fatness and its distribution. Furthermore, the risks of poor diet and sedentary lifestyle appear to be independent.

Acknowledgements We thank J Selhub for analysis of homocysteine and B vitamins in his laboratory at the HNRCA. We are also grateful to BM Popkin at the University of North Carolina at Chapel Hill, KY Ge at the Institute of Nutrition and Food Hygiene, and F Zheng at the Institute of Parasitic Diseases, Chinese Academy of Preventive Medicine for discussions about the protocol, XQ Hu, YP Li, H Pan and J Song for their technical assistance, PF Jacques for help with interpretation of homocysteine and folate results, and the subjects for participating in the study. Supported by NIH Grants DK53404 and F32-DK09747. Contents of this publication do not necessarily reflect the views or policies of the US Department of Agriculture.

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