Dietary Patterns Are Associated With Hyperhomocysteinemia Biology Essay

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Little attention has been given to the association of dietary patterns with plasma homocysteine. The objective of the present study was to identify major dietary patterns and investigate their association with plasma homocysteine. In a cross-sectional survey, 872 healthy adults (355 males, 517 females; aged 18-60 years) were enrolled from an urban population in Karachi. Dietary intake was assessed by a food frequency questionnaire. We used factor analysis to define major dietary patterns. Fasting levels of plasma or serum homocysteine, folate, pyridoxal-phosphate (PLP; coenzymic form of vitamin B-6) and vitamin B-12 were determined. Three major dietary patterns were identified and labeled as "prudent diet", "high animal-protein diet" and "high plant-protein diet". We observed the protective effect of a prudent dietary pattern for the highest quartile of intake in comparison with the lowest quartile on hyperhomocysteinemia when the model was adjusted for age, gender, household income, BMI, tobacco chewing and smoking; OR = 0.52 (95% CI = 0.30 - 0.90; P = 0.01). The high plant-protein diet pattern was inversely related to hyperhomocysteinemia with a higher intake being protective. Compared with the first quartile, the adjusted OR was 0.42 (95% CI = 0.25 - 0.69; P = 0.001) for the fourth quartile. The high animal-protein diet showed a positive association with hyperhomocysteinemia with participants in the highest quartile of intake having the highest increase in risk; OR = 2.10 (95% CI = 1.22 - 3.60; P = 0.007). Plasma homocysteine concentrations appeared to correlate more with plasma or serum folate (r = -0.25) rather than PLP (r = -0.02) and vitamin B-12 (r = -0.16). A diet rich in fruits and uncooked-vegetables decreased the risk of hyperhomocysteinemia, while diets rich in red meat, chicken and tea with milk were found to be positively associated with hyperhomocysteinemia.

Introduction

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The association between plasma homocysteine levels and the development of various chronic diseases is gaining increasing recognition. Results pertaining to the association of homocysteine with cardiovascular disease (CVD) are still controversial. There are published reports which show no association between homocysteine and major cardiac events (1-3). However, several other studies have reported homocysteine as an independent risk factor for CVD (4,5). A meta-analysis revealing homocysteine as a modest predictor of ischemic heart disease adds further support (6). It is recommended that levels of homocysteine should be less than 15 umol/L to avert several disease conditions (7).

Hyperhomocysteinemia has been reported to be quite prevalent among South Asians, especially in apparently healthy Pakistani individuals (8-11). There is plenty of evidence to suggest that high levels of plasma homocysteine are due to low serum levels of folate, vitamin B-12 and vitamin B-6 (12,13). Industrialization and urbanization have led to an emergence of dietary patterns in the population which may lead to the development of chronic diseases. It has been reported that the Western diet patterns (high in fried foods, salty snacks, eggs and meat) are strong predictors of cardiovascular events (14,15). On the other hand, the prudent dietary pattern (known to be rich in grains, legumes, vegetables, fruits and fish and poor in red meat and animal products) showed protective effects for chronic diseases or their determinants, such as myocardial infarction (15). Despite such protective effects of a prudent dietary pattern which is commonly consumed in South Asia, CVD and other chronic diseases are becoming a serious problem throughout the region including Pakistan. One particular reason of such findings could be the major modifications in diet (both in developed and developing countries like Pakistan), such as the transition from plant-based dietary products to animal-based food products. Therefore, the understanding of patterns of dietary intake in countries like Pakistan and their association (if any) with some known risk factors for CVD would be helpful in developing prevention programs for overcoming such health issues. It has been demonstrated that the Indo-Mediterranean diet, rich in α-linolenic acid (a constituent of whole grains, fruits, vegetables, walnuts and almonds), is responsible for significant reduction in coronary artery disease (CAD) morbidity and mortality in this region, when compared to step I National Cholesterol Education Program prudent diet (16).

Because of the marked variations in diet between developed and developing countries, identification of dietary patterns which could be related to low homocysteine levels and high B vitamin status would be helpful in making dietary recommendations for this region. This approach is in line with the recommendations from the American Heart Association and other study groups that a Mediterranean-style diet has a preventive effect on the development of CVD and, therefore, could be beneficial (17,18). As a result, appropriate selection of diet might provide an economically feasible approach to reducing CAD in regions like South Asia.

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A numbers of studies have been carried out in developed countries to determine the association of hyperhomocysteinemia with dietary patterns (19-21). However, there are very few reports from developing countries and, to the best of our knowledge, there is no South Asian study reporting the relationship of dietary patterns with hyperhomocysteinemia. The determination of dietary patterns would therefore be of help in identifying any specific patterns in the diet of Pakistanis which could be associated with the risk of hyperhomocysteinemia in this population. The purpose of this study was to identify major dietary patterns of food intake in a Pakistani population residing in Sultanabad, which is a low income neighborhood in Karachi, and to investigate the association of dietary patterns with plasma homocysteine levels.

Materials and Methods

The participants were 872 healthy adults (355 males and 517 females; age range 18-60 years) living in a low income urban area of Karachi, Pakistan. In this cross-sectional survey, systematic random sampling was used to select houses and one individual was selected from each house. Every fourth house was selected from a total frame of 4000 houses available in the target locality.

Healthy individuals who consented to participate in the study were screened using a questionnaire. Anthropometric measurements and fasting blood samples were obtained to measure study outcome variables and their determinants. Socio-demographic characteristics such as age, gender, household income, education, smoking habits and tobacco chewing were recorded. We assessed eating habits using a simple 15-item food group frequency questionnaire. Major food sources of B complex vitamins were selected as the food items on the questionnaire. Information on the number of times each food item was consumed per month, per week or per day was recorded. Frequency of each food item was then converted to per day consumption. For example, a response of 5 servings per month was converted to 0.16 serving per day or a response of 2 servings per week was converted to 0.29 per day. The questionnaire used in this study was not validated, however; it has face validity because similar kinds of questionnaires have been used previously (15,22). Moreover, some of the food items used in this questionnaire have been previously studied for their association with CVD, B vitamins and homocysteine (20-21).

Plasma and serum were separated and kept frozen at -70oC within 4 hours after collection of blood. Serum lipids were measured within 48 hours of collection of samples. Calorimetric kit methods (RANDOX Laboratories Ltd., United Kingdom) were used for the estimation of total cholesterol, HDL-cholesterol, triglycerides and creatinine in serum. LDL-cholesterol was calculated using Friedewald's formula (23). Serum samples were also analyzed for folate and vitamin B-12 using radioassays (24,25). Plasma homocysteine was measured using an immunoassay based kit (Abbott Laboratories Ltd., Pakistan). Hyperhomocysteinemia was defined as value >15 umol/L (7). Pyridoxal phosphate (PLP, a co-enzymic form of vitamin B-6) was chosen as a standard for vitamin B-6 status (26). For determination of PLP in plasma, a modification of the method by Camp et al. (27), as described previously, was used (28). Intra-assay coefficients of variation assessed for PLP and homocysteine in plasma were <9%. Moreover, intra-assay precision for folate and vitamin B-12 in serum was < 15%. The minimum limits of detection for serum folate, serum vitamin B-12, plasma PLP and plasma homocysteine were 1.13 nmol/L, 36.9 pmol/L, 3.2 nmol/L and 4 umol/L, respectively.

All individuals who reported use of alcohol, oral contraceptives and antiepileptics, pregnancy, diabetes mellitus, renal disease (assessed by serum creatinine levels above 115 umol/L) and intake of B vitamin supplements during the last 6 months were excluded from the analysis as these conditions have been known to influence levels of homocysteine. Out of 872 participants, 57 were not included in the final analysis because of incomplete questionnaires or refusal to give blood for biochemical analysis. Our final sample size was 815 participants for this analysis. Ethical clearance of the study was obtained from the Ethics Review Committee of the Aga Khan University, and all the participants provided informed consent.

Statistical Analysis

This analysis had 2 major phases; developing dietary patterns using factor analysis and then logistic regression analysis to predict the association of hyperhomocysteinemia with dietary patterns. Factor analysis was used to identify common underlying dimensions (factors or patterns) of the dietary data. In order to generate uncorrelated factors, factors were rotated orthogonally. The number of factors to be retained in the model was determined on the basis of eigenvalue (> 1), scree plot and factor interpretability (29). Initially we obtained 6 uncorrelated factors from this analysis; however, we decided to use the first three factors in our prediction models because the other three factors had high loading on very few food groups. The analyses were conducted with the data reduction procedure in SPSS 16 (Statistical Package for Social Sciences, version 16 for Windows® Apache Software Foundation, USA).

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Quartiles of all 3 dietary patterns were generated. Continuous variables including age, homocysteine, folate, PLP, vitamin B-12, cholesterol, LDL, HDL, triglycerides, BMI and waist to hip ratio (WHR) were expressed as mean ± SD. Categorical variables, such as gender, educational status, household income, tobacco chewing and smoking were expressed as n, (%).

Level of significance (P-value) was estimated, using Chi-square to compare the frequency of the above mentioned categorical variables across the quartiles of each dietary pattern. For continuous variables, ANOVA was used to compare the mean values across the quartiles of each dietary pattern. All P-values were 2 sided at α = 0.05.

Logistic regression analysis was used to examine the association between quartiles of three dietary patterns (independent variables) and hyperhomocysteinemia (dependent variable) and each model was also adjusted for age, gender, household income, BMI, tobacco chewing and smoking status. Values are expressed as OR (95 % CI; P-value).

Results

Three major dietary patterns were identified by using factor analysis, and subjectively labeled as "prudent diet", "high animal-protein diet" and "high plant-protein diet". The prudent diet was characterized by a high intake of eggs, fish, uncooked vegetables, juices, banana and other fruits. The high animal-protein diet pattern was characterized by a high intake of meat, chicken, wheat, banana and tea with milk, whereas high plant-protein diet was characterized by a large intake of cooked vegetables and legumes and a small intake of meat (Supplemental Table 1).

Each dietary pattern was classified into quartiles. Descriptive information for each of the dietary patterns by quartiles of intake is presented in Table 1. Mean ± SD age of participants in the highest quartile of prudent diet pattern was significantly less than for participants in the lowest quartile of the same dietary pattern. A lower proportion of males was observed in the lowest quartile of prudent diet as compared to the highest quartile of this diet (P = 0.009). Similar findings were obtained for the proportion of males in highest and lowest quartiles of the high animal-protein diet pattern (P < 0.001). In comparison, women scored higher on the first and second quartiles of prudent and high animal-protein diet patterns and on the third and fourth quartiles of the high plant-protein diet pattern. A high percentage of individuals with no education were in the lowest quartile of the prudent diet compared to the highest quartile of the prudent diet. Similarly, a significantly high proportion of individuals with education level of 12th grade and above were found in the highest quartile of the prudent diet pattern as compared to the lowest quartile of this diet pattern. A similar significant trend was observed for education status in the quartiles of high animal-protein diet pattern. Greater numbers of individuals having a monthly household income of less than 5000 rupees were found in the lowest quartile of the high animal-protein diet pattern compared to the highest quartile of the same dietary pattern. Moreover, significantly greater numbers of individuals with a monthly household income greater than 10,000 rupees were in the fourth quartile of the high animal-protein diet pattern compared to the first quartile. Such a trend was also observed with regard to household income in the quartiles of the high plant-protein diet pattern (P for trend = 0.005). This indicates a relationship between household income and consumption of diets which are high in animal-protein or high in plant-protein.

The mean ± SD values of homocysteine and various biomarkers have been shown across the quartiles of dietary pattern factor scores in Table 2. Plasma homocysteine was higher in the highest quartile of high animal-protein diet as compared to the lowest quartile of this diet pattern (P < 0.001). In contrast, plasma homocysteine was significantly lower in the highest quartile of the high plant-protein diet pattern as compared to the lowest quartile. Serum folate had significantly higher scores on the fourth quartiles of prudent diet and high plant-protein diet patterns when compared with the first quartiles of their respective dietary patterns (P = 0.01 and P < 0.001, respectively). The plasma PLP was significantly higher in the highest quartile of the prudent diet compared to the lowest quartile. No significant differences were observed in vitamin B-12 concentrations over the quartiles of different dietary patterns. Correlation analysis, however, revealed a very weak relationship of PLP with homocysteine (r = -0.02). This is suggestive that homocysteine concentrations correlated more to folate (r = -0.25) than to PLP (r = -0.02) and vitamin-B12 (r = -0.16) among consumers of prudent and high plant-protein diet patterns.

HDL cholesterol was significantly higher in individuals in the highest quartile of the prudent diet pattern compared to the lowest quartile of the prudent diet. Mean BMI was on the higher side in the lowest quartile of the prudent dietary pattern in comparison to the highest quartile of the prudent diet pattern, even when the model was adjusted for age and gender. A higher WHR was observed with the highest quartile of the high animal-protein diet when compared with the lowest quartile of the high animal-protein diet pattern.

We observed no association between the non-adjusted prudent diet pattern and hyperhomocysteinemia (Table 3). However, when the model was adjusted for age, gender, household income, BMI, tobacco chewing and smoking, the highest quartile of the prudent diet pattern showed 48% (95% CI = 0.30 - 0.90) protective effect for hyperhomocysteinemia compared to the lowest quartile of intake.

The highest quartile of the high animal-protein diet pattern was positively associated with hyperhomocysteinemia. Compared with the lowest quartile of intake, the adjusted odds were 2.1 (95% CI = 1.22 - 3.60) for the fourth quartile.

An inverse association between the high plant-protein diet pattern and hyperhomocysteinemia was found in the study population. Compared with the reference group, protection for hyperhomocysteinemia with the high plant-protein diet pattern was 34% (95% CI = 0.40 - 1.07) for the second quartile, 49% (95% CI = 0.31 - 0.85) for the third quartile and 58% (95% CI = 0.25 - 0.69) for the fourth quartile when the model was adjusted for age, gender, household income, BMI, tobacco chewing and smoking.

Discussion

The association of single food items with different diseases or biomarkers, such as homocysteine has been reported previously (30-34). Such analyses are important but are not without limitations because people do not eat individual nutrients and food items alone; rather, these are consumed in combination with other food items. Keeping in view how different foods and nutrients are consumed in combinations, hypotheses have been put forward to investigate the effect of overall diet on the disease of interest (35,36). As a result, dietary pattern analysis has emerged and, factor analysis is commonly used to explore dietary patterns (15,37,38).

Using factor analysis, we were able to define three dietary patterns in our study population. These dietary patterns were labeled as prudent diet, high animal-protein diet and high plant-protein diet. The high animal-protein diet pattern as defined by us was quite similar to the pattern defined earlier by Zobairi et al (39).

South Asian diets are known to be rich in grains, legumes, vegetables, fruit and fish and low in red meat and animal products, similar to the third pattern that we identified. In other words, these patterns are culturally and ethnically specific; however, they are not much different in the context of food items and patterns from those identified in other parts of the world. The "prudent" dietary patterns generated from the INTERHEART, Health Professional Follow up Study and the Nurses' Health Cohort using factor analysis also appear to be similar to the pattern identified in this study (15,20,22). However, the high loading of eggs in the prudent diet identified in this study was different from the above mentioned studies, as the grouping of eggs was reported mostly as part of Western dietary patterns.

The protective role of the high plant-protein diet pattern and the prudent diet pattern against hyperhomocysteinemia is similar to the findings of some other reports (20,21,40), indicating that such dietary patterns were positively associated with serum folate levels, and inversely associated with plasma homocysteine. The relationship of increased intake of the high plant-protein diet with serum folate appears to be stronger compared to the association between increased consumption of prudent diet and folate. This may explain why we have observed low levels of homocysteine in individuals consuming more high plant-protein diet. The high plant-protein diet pattern was bipolar with negative loading for meat (rich in methionine) and positive loadings for cooked vegetables and legumes. We postulate that the bipolar nature of this pattern may be associated with increased levels of folate, thereby providing protection against hyperhomocysteinemia in individuals in the highest quartile. High loading of eggs as part of the prudent diet in the present study merits some discussion. The inverse relationship of the prudent diet with homocysteine may be due in part to the higher consumption of eggs which contain large amounts of the methyl donor choline (41). Specifically, the choline metabolite betaine can be used as an alternate of folate for the remethylation of homocysteine to methionine. The inverse relationship of high plant-protein diet and prudent diet patterns with plasma homocysteine and the positive association of these diets with serum folate indicate that adequate consumption of fresh fruits and vegetables could be beneficial in reducing the risk of hyperhomocysteinemia. Some of the components of the high plant-protein diet pattern and the prudent diet pattern are also part of the "prudent" patterns identified to be protective for coronary heart disease in other studies (14,15,22).

The adverse role of high consumption of the high animal-protein diet with hyperhomocysteinemia could be due to an increased intake of red meat and chicken (rich sources of methionine). In South Asian diets, meat and chicken are not only rich sources of saturated fat but their cooking is also generally carried out in oil or "vanaspati ghee" (hydrogenated oil). Recently, a positive association between dietary saturated fat intake and plasma homocysteine has been reported (42). This may explain why high animal-protein dietary pattern with high loadings for meat, chicken and tea with milk is associated with an increased risk of hyperhomocysteinemia in the present study. The other possible reason could be the frequent use of tea with milk which is known to be positively associated with hyperhomocysteinemia (40,43,44). Although we think that the effect of tea in terms of polyphenol concentration may not be very significant (45), it could be adding to the homocysteine levels by decreasing the bioavailability of folate, and hence, affect the homocysteine remethylation cycle (46).

We observed association of the prudent diet and high animal-protein dietary patterns with physical measures of obesity (BMI and WHR). After adjusting for age and gender, the protective role of, the prudent diet on BMI indicates that a balanced diet such as the prudent diet is less likely to lead to weight gain. The adverse impact of high intake of high animal-protein diet with WHR may lie with the extensive use of cooking oil in the preparation of dishes with red meat and chicken in this part of the world.

The present analysis is one of the few large epidemiological studies that have taken place in this region and, perhaps, the first in South Asia highlighting the impact of dietary patterns on hyperhomocysteinemia. The three dietary patterns observed in this study, we contend, represent the most common types of dietary intake patterns of the low-income urban population of Pakistan. The dietetic connotations of these patterns are helpful in that they are commonly explicable and could offer clear public health direction. Increased consumption of the high plant-protein diet and reduced intake of high animal-protein diet turn out to be the recommended dietary patterns to keep the levels of homocysteine within acceptable limits. Furthermore, the protective effect of vegetables and fruits and some undesirable effects of tea with milk and red meat observed in this study population were in line with the results reported for Western populations (47). As mentioned above, Pakistanis are among those populations having high levels of plasma homocysteine and a high rate of CAD (8,9). An association of hyperhomocysteinemia with some of the common dietary patterns suggests that diet may have a major role in high prevalence of CAD in this population. The maximum level of protection from hyperhomocysteinemia observed in participants in the highest quartile of high plant-protein diet strengthens the notion that the use of fresh fruit and vegetables would be protective against the development of high levels of homocysteine. However, further large scale cohort studies involving both the rural and urban Pakistani population need to be conducted for validation of our findings as this was a small, cross-sectional study conducted in a low income community in Karachi. We used a 15-item food group frequency questionnaire. Though lack of validation of the dietary questionnaire could be regarded as a limitation of the study, we still believe that we were able to gauge the usual dietary intake of participants. Another point to mention is that we were not able to adjust our models for total energy intake as we did not have sufficient information for nutrient analysis. However, an alternative adjustment has been done by adjusting the regression model for BMI as previously recommended by Walter Willet (48). Since older subjects and women had higher scores on the first quartile of prudent diet, while younger participants and men had higher scores on the fourth quartile of this dietary pattern, it is suggestive that menopausal status might have an impact on these associations. We conjecture that, in future studies, the use of a detailed dietary questionnaire, inclusive of sugar and sugar products, milk and dairy food and information regarding menopausal status and physical activity would be of help to ascertain the relationship between dietary patterns and homocysteine while adjusting for total energy intake.

In conclusion, of the three dietary patterns observed among the Karachi urban population, the prudent diet (with the highest quartile of intake) shows a protective effect towards the development of hyperhomocysteinemia. Moreover, increased consumption of high plant-protein diet decreases the risk of hyperhomocysteinemia by about 50%. Furthermore, we observed that the use of high animal-protein diet in this population could, perhaps, be one of the reasons for hyperhomocysteinemia.

Acknowledgements

M.Y. and M.P.I. designed and conducted the research; R.I. and M.Y. analyzed the data; M.Y., M.P.I., and R.I., were involved in writing the manuscript; M.Y., and M.P.I. had primary responsibility for final contents.

All authors have read and approved the final manuscript.

We thank Professor Graeme Cane, Head of the Center of English Language, Aga Khan University for editing the manuscript.

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46. Alemdaroglu NC, Dietz U, Wolffram S, Spahn-Langguth H, Langguth P. Influence of green and black tea on folic acid pharmacokinetics in healthy volunteers: potential risk of diminished folic acid bioavailability. Biopharm Drug Dispos. 2008; 29:335-48.

47. Tucker KL, Selhub J, Wilson PW, Rosenberg IH. Dietary intake pattern relates to plasma folate and homocysteine concentrations in the Framingham Heart Study. J Nutr. 1996; 126:3025-31.

48. Willet W. Nutritional Epidemiology. New York: Oxford University Press; 1998.

TABLE 1 Characteristics of study population in lowest quartile to highest quartile of different dietary pattern scores

Prudent diet

High animal-protein diet

High plant-protein diet

Characteristics

Q1

n= 204

Q2

n= 204

Q3

n= 203

Q4

n= 204

Pfor trend4

Q1

n= 204

Q2

n= 204

Q3

n= 203

Q4

n= 204

Pfor trend4

Q1

n= 204

Q2

n= 204

Q3

n= 203

Q4

n= 204

Pfor trend4

Age1, y

35 ± 11

33 ± 11

31 ± 10

30 ± 10

<0.001

33 ± 10

32 ± 11

32 ± 11

33 ± 11

0.8

31 ± 10

33 ± 11

33 ± 10

33 ± 10

0.07

Gender2

Male

66

(19)

86

(25)

104

(30)

90

(26)

0.009

74

(21)

63

(18)

89

(26)

120 (35)

<0.001

101

(29)

86

(25)

75

(22)

84

(24)

0.19

Female

138

(30)

118

(25)

99

(21)

114

(24)

0.01

130

(28)

141

(30)

114

(24)

84

(18)

<0.001

103

(22)

118

(25)

128

(27)

120

(26)

0.3

Educational Status2

Nil

113

(55)

81

(40)

68

(33)

49

(24)

<0.001

88

(43)

93

(46)

74

(36)

56

(27)

0.004

66

(32)

72

(35)

89

(44)

84

(41)

0.5

Primary

41

(20)

48

(23)

41

(20)

49

(24)

46

(23)

33

(16)

48

(24)

52

(25)

46

(23)

49

(24)

41

(20)

43

(21)

Matriculate

34

(17)

45

(22)

56

(28)

54

(26)

44

(21)

51

(25)

45

(22)

49

(24)

55

(27)

46

(23)

44

(22)

44

(22)

12thgrade &Above

16

(8)

30

(15)

38

(19)

52

(25)

26

(13)

27

(13)

36

(18)

47

(23)

37

(18)

37

(18)

29

(14)

33

(16)

Household Income2,3

<5000 Rs.

94

(46)

96

(47)

104

(51)

98

(48)

0.8

138

(68)

103

(50)

94

(46)

57

(28)

<0.001

106 (52)

113

(55)

95

(47)

78

(38)

0.005

5000-10000 Rs

88

(43)

83

(41)

73

(36)

85

(42)

62

(30)

82

(40)

85

(42)

100 (49)

80

(39)

76

(37)

81

(40)

92

(45)

>10000

22

(11)

25

(12)

26

(13)

21

(10)

4

(2)

19

(10)

24

(12)

47

(23)

18

(9)

15

(7)

27

(13)

34

(17)

Tobacco Chewing2

Never

177

(87)

176

(86)

175

(86)

184

(90)

0.5

180

(88)

187

(92)

183

(90)

162

(79)

0.004

171

(84)

184

(90)

176

(87)

181

(89)

0.03

Only Tobacco

2

(1)

7

(4)

5

(3)

4

(2)

3

(2)

2

(1)

3

(2)

10

(5)

8

(4)

2

(1)

8

(4)

0

(0)

Tobacco with Betel Nut

25

(12)

21

(10)

23

(11)

16

(8)

21

(10)

15

(7)

17

(8)

32

(16)

25

(12)

18

(9)

19

(9)

23

(11)

Smoking2

No

185

(91)

183

(90)

181

(89)

190

(93)

0.5

185

(91)

191

(94)

188

(93)

175

(86)

0.03

179

88)

190

(93)

183

(90)

187

(92)

0.2

Yes

19

(9)

21

(10)

22

(11)

14

(7)

19

(9)

13

(6)

15

(7)

29

(14)

25

(12)

14

(7)

20

(10)

17

(8)

1 Values are means ± SD

2 Numbers represent n (%)

3 1 Rs (rupee) = 0.012 US$

4 P for trend value was based on ANOVA when row variable was continuous (age) and χ2 test when row variable was categorical.

TABLE 2 Concentrations of biomarkers and anthropometric measures by quartiles of dietary patterns1

Prudent diet

High animal-protein diet

High plant-protein diet

Characteristics

Q12

n= 204

Q4

n= 204

P3-4

Q12

n= 204

Q4

n= 204

P3-4

Q12

n= 204

Q4

n= 204

P3-4

Homocysteine, umol/L

15.78 ± 12.5

13.97 ± 7.7

0.26

13.29 ± 6.4

18.58 ± 13.3

<0.001

18.40 ± 13

12.50 ± 5

<0.001

Folate, nmol/L

13 ± 8.61

15.95 ± 10.65

0.01

14.84 ± 9.97

14.04 ± 11.33

0.88

10.87 ± 7.7

17.13 ± 10.65

<0.001

PLP, nmol/L

30.82 ± 40.1

39.32 ± 43.1

0.04

32.91 ± 34.2

32.72 ± 25.2

0.05

33.71 ± 30.5

36.86 ± 40.7

0.39

Vitamin B-12, pmol/L

317 ± 162

322 ± 175

0.85

312 ± 171

335 ± 179

0.56

326 ± 165

326 ± 177

0.8

Cholesterol, mmol/L

4.14 ± 0.88

4.08 ± 0.91

0.9

4.19 ± 0.89

4.11 ± 0.91

0.65

4.19 ± 0.89

4.06 ± 0.91

0.42

LDL, mmol/L

2.48 ± 0.82

2.3 ± 0.8

0.16

2.46 ± 0.77

2.40 ± 0.93

0.69

2.5 ± 0.8

2.32 ± 0.92

0.17

Triglyceride, mmol/L

1.47 ± 0.21

1.56 ± 1.08

0.33

1.05 ± 0.23

1.03 ± 0.21

0.77

1.63 ± 1.19

1.6 ± 1.28

0.71

HDL, mmol/L

1.01 ± 0.21

1.08 ± 0.25

0.01

1.05 ± 0.23

1.03 ± 0.21

0.73

1.05 ± 0.23

1.04 ± 0.22

0.72

BMI, kg/m2

25.50 ± 5.5

23.13 ± 5.4

0.008

24.20 ± 6

24.50 ± 5.6

0.15

23.84 ± 5.4

24.03 ± 5.6

0.46

Waist Hip Ratio

0.85 ± 0.07

0.83 ± 0.08

0.18

0.83 ± 0.07

0.85 ± 0.09

0.05

0.84 ± 0.07

0.84 ± 0.08

0.89

1 Values are means ± SD

2 Lowest quartile.

3 P-value was based on ANOVA comparing mean values among lowest to highest quartiles.

4 For BMI, P-value was based on ANOVA comparing mean values, among lowest to highest quartiles adjusted for age (y) and gender.

TABLE 3 OR and 95% CI of hyperhomocysteinemia1 by quartiles of different dietary patterns

Q1

n= 204

Q2

n= 204

Q3

n= 203

Q4

n= 204

Prudent diet

Crude2

1

1.16 (0.77 - 1.74)

1.09 (0.73 - 1.65)

0.79 (0.52 - 1.21)

Adjusted3

1

0.86 (0.52 - 1.43)

0.72 (0.43 - 1.21)

0.52 (0.30 - 0.90)*

High animal-protein diet

Crude2

1

1.02 (0.66 - 1.59)

1.43 (0.93 - 2.18)

2.42 (1.59 - 3.66 )**

Adjusted3

1

1.18 (0.69 - 2.01)

1.46 (0.87 - 2.45)

2.1 (1.22 - 3.6)*

High plant-protein diet

Crude2

1

0.64 (0.43 - 0.96)*

0.48 (0.32 - 0.73)*

0.45 (0.30 - 0.69)**

Adjusted3

1

0.66 (0.40- 1.07)

0.51 (0.31 - 0.85)*

0.42 ( 0.25 - 0.69)*

1 Plasma homocysteine > 15 umol/L

2 Values are OR (95% CI) from logistic regression, *P < 0.05, ** P < 0.001

3 Values are OR (95% CI) from logistic regression adjusted for age (y), gender, household income (< 5000 rupees, 5000-10000 rupees, > 10000 rupees), BMI (kg/m2), tobacco chewing (never, only tobacco, tobacco with betel nut), and smoking (yes, no), *P < 0.05.

Supplemental TABLE 1 Factor Loadings for Varimax Rotated Factors1

Food Items

Mean Intake Per Day ± SD

Prudent diet

High animal-protein diet

High plant-protein diet

Meat

0.23 ± 0.24

….

0.47

-0.51

Egg

0.21 ± 0.30

0.48

….

….

Fish

0.07 ± 0.13

0.41

….

….

Chicken

0.21 ± 0.19

….

0.53

….

Cooked Vegetables

0.57 ± 0.34

….

….

0.66

Uncooked Vegetables

0.76 ± 0.55

0.41

….

….

Legumes

0.33 ± 0.24

….

….

0.52

Wheat

2.52 ± 1.04

….

0.52

….

Banana

0.38 ± 0.44

0.63

0.43

….

Other Fruits

0.55 ± 0.51

0.64

….

….

Tea with Milk

2.3 ± 1.44

….

0.52

….

Tea without Milk

0.07 ± 0.52

….

….

….

Green Tea

0.16 ± 0.46

….

….

….

Coffee

0.003 ± 0.05

….

….

….

Juices

0.05 ± 0.13

0.46

….

….

Eigenvalue

1.76

1.55

1.47

% of variance

12.8

10.3

8.6

1Factor Loadings less than 0.3 are not shown