1.0Introductionraschfoundation.org/wp-content/uploads/Cornell... · Chen J,Peto R ,Pan W ,Liu B ,Campbell TC .Mo rtality, Biochemistry, Diet, and Lifestyle in Rural China: Geographic

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1.0 Introduction

The China Study, more formally known as the China-‐Cornell-‐Oxford study, has

been described as the most comprehensive study of nutrition ever conducted. 1 The

purpose of the China-‐Cornell-‐Oxford study was to investigate the diet, lifestyle,

anthropometry, blood chemistry, and mortality rates of sixty-‐nine counties in twenty-‐

four different provinces in China. The primary concern of the investigators was to

compare the study areas with every other study area and the uniqueness of this large

epidemiological study revolved around the predominantly plant-‐based diet that is

consumed in rural China. In 2005, T. Colin Campbell, professor emeritus of nutritional

biochemistry at Cornell University and one of the lead investigators of the China-‐

Cornell-‐Oxford study, authored the book The China Study. This book written by Dr.

Campbell and his son, Thomas M. Campbell II, details various findings of his scientific

research and is named after the China-‐Oxford-‐Cornell Diet and Health project – an

epidemiological study in rural China and Taiwan funded by the University of Oxford,

Cornell University, and the Government of China. 1 Based on findings from

experimental animal studies during his graduate studies, the large human study on

dietary patterns and disease in rural China and Taiwan and other published research,

Dr. Colin Campbell claims that the research implies the same conclusion: consumption

of animal-‐based foods is associated with chronic disease while the opposite is true for

consumption of predominantly plant-‐based foods. 1

2.0 Study Design

The China-‐Oxford-‐Cornell project had an ecologic study design – an

epidemiological study that involves comparison of populations rather than

individuals. Therefore, analysis of the results involved calculation of county averages

for diet, lifestyle, and disease characteristics and correlation coefficients were

compared among counties rather then at the individual level. Although using

aggregate data is advantageous for population studies due to its convenience,

limitations of this study design include its high susceptibility to confounding. 2

Associations between mortality rates and diet observed using aggregate data might

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not necessarily be observed when comparisons are made at the individual level.

Therefore, these studies are generally used as hypothesis-‐generating studies to be

further tested in studies using data at the individual level. Furthermore, using county

averages in place of individual data significantly reduced the sample size of the China

Study, thereby reducing the statistical power of the study. Therefore, although

approximately 8,307 adults in sixty-‐nine counties were surveyed, only sixty-‐nine data

points are provided for each variable and mortality rate.

In observational studies such as the China Study, the extent of the

generalizability of the results is important and highly dependent on sample size (to

control for random error) and the sampling strategy employed by the investigator,

which will determine how representative the sample is of the population of interest.

Sampling in the China Study involved random selection of sixty-‐nine counties out of

2400 in rural China, which are fairly representative of rural China and are distributed

throughout China. Within each of these counties, two xiangs were also randomly

selected, followed by random selection of one or two villages in each xiang to be

surveyed. An official registry of residences was used to randomly select 50-‐60

households and one individual per household (age 35-‐64) was then randomly selected

to be interviewed. Approximately equal numbers of males and females were

interviewed. Out of all households randomly selected, half were asked to participate in

the three-‐day dietary survey used to gather information on dietary patterns. Random

sampling is the gold standard for ensuring generalizability. The cluster sampling

approach used by the investigators introduces some error, however, cluster sampling

is very useful for population level studies, especially when the population is widely

dispersed and it would be impractical and very costly to list and sample from the

entire population. Limitations include the fact that no response rate was provided.

The response rate affects the validity of inferring that the sample is representative of

the population since individuals who refuse to participate in the study tend to be

different than those who do agree to participate and this introduces bias.

Furthermore, since several individuals in China refused to provide a vial of blood for

biochemical analyses due to cultural reasons, blood from each study area was pooled

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to provide a large enough sample for analyses to be conducted. Unfortunately, this

introduces the potential of confounding since aggregate data is used here. Although

the pooled blood samples collected were sex-‐specific, the dietary data obtained from

the three-‐day dietary record was not. This also increases the chance of potential

confounding of results due to the fact that dietary and lifestyle patterns among

females may be markedly different from patterns observed among males.

In addition to the possible errors introduced and described above, which affect

the external validity of the study and therefore the ability to make any inferences

about the target population of China from the sample data, bias may also have been

introduced due to measurement error. For example, the three-‐day diet record may not

be representative of the sample population due to the fact that individuals tend to

consciously or subconsciously alter their eating habits during such dietary surveys

which increases the potential for systematic bias of the results. There are methods of

adjusting for possible measurement error in dietary surveys, such as the calculation of

EI: BMR ratios to identify potential underreporting and over-‐reporting of energy

intake. 3 This method may be used if individual data is available but is difficult when

only aggregate data is available. Furthermore, the use of average values to describe

the consumption of foods and nutrients in each study area is calculated through the

use of food composition databases that provide average nutrient values for each food.

As a result, the variation in nutrient composition of foods cannot be taken into

account. However, this is a limitation of population level studies measuring dietary

intake and the logistics of the fieldwork simply do not make it feasible to overcome all

possible measurement error.

Last but not least, the ecologic or correlation study design of the China-‐Cornell-‐

Oxford study means that the analysis of the findings involved calculation of

correlation coefficients between the mortality rates and the various biochemical,

dietary, and behavioural factors. The most important rule to note here is that

correlation does not equal causation; in other words, establishing a correlation

between a dietary variable and disease mortality rate is not a sufficient condition to

establish a causal relationship. The correlation coefficient is a measure of the strength

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of the linear association between the two variables; however, the causes of this

correlation may be indirect due to the presence of some other confounding variable.

Furthermore, some statistically significant correlations may have occurred simply due

to chance. Thus, any claims made based on the existence of an unadjusted correlation

coefficient are unjustifiable and any information obtained from a geographical

correlation study must be interpreted with caution and supported by other scientific

research that demonstrates the biological plausibility of such a relationship. No causal

inference can be made based on the observed relationships due to the observational

nature of the study and especially because the mortality rate data was collected prior

to collection of the dietary data, therefore the outcome and risk factor sequence is out

of order.

3.0 Methods of Secondary Data Analysis

Secondary data analysis was conducted using data collected for the China-‐

Cornell-‐Oxford Project in 1989-‐1990 and published in:

Chen J, Peto R, Pan W, Liu B, Campbell TC. Mortality, Biochemistry, Diet, and

Lifestyle in Rural China: Geographic study of the characteristics of 69 counties in mainland China and 16 rural areas in Taiwan: Oxford University Press; 2006.

3.1 Objectives

The primary objective of the secondary data analysis was to determine the

relationship between a plant-‐based diet versus an animal-‐based diet and chronic

disease mortality. Data collected by the 1989-‐1990 survey was used to evaluate the

association between chronic disease and diet, focusing specifically on the following

diseases and dietary variables:

“Diseases of Affluence”:

• Obesity (Body Mass Index kg/m2 entered as a continuous variable from

measured height and weight)

• Diabetes Mortality Age 35-‐69 (stand.rate/100,000) (ICD9 250)

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• Hypertensive Disease Mortality Age 35-‐69 (stand.rate/100,000)(ICD9 401-‐5)

• Cancer Mortality (all malignant neoplasms) Age 35-‐69

(stand.rate/100,000)(ICD9 140-‐208)

• Lymphoma and Myeloma Mortality Age 35-‐69 (stand.rate/100,000) (ICD9

200-‐3)

Select Dietary Variables*:

• % Energy from Fat (for ref. man 65 kg**)

• % Energy from Carbohydrates (for ref. man 65 kg)

• % Energy from Protein (for ref. man 65 kg)

• % Animal Food Intake (for ref. man 65 kg)

• % Plant Food Intake (for ref. man 65 kg)

• Processed Starch and Sugar (g/day/ref. man 65 kg)

• Fiber (g/day/ref. man 65 kg)

• Legumes (g/day/ref. man 65 kg)

• Light Coloured Vegetable Intake (g/day/ref. man 65 kg, fresh wt.)

• Green Vegetable Intake (g/day/ref. man 65 kg)

• Fish Intake (g/day/ref. man 65 kg)

• Meat Intake (g/day/ref. man 65 kg)

• Milk Intake (g/day/ref. man 65 kg)

• Eggs Intake (g/day/ref. man 65 kg)

• Added Vegetable Oil (for cooking etc.) Intake (g/day/ref. man 65 kg)

• Vitamin A Intake (retinol equivalents/day/ref. man 65 kg)

• Vitamin E Intake (mg/day/ref. man 65 kg)

• Vitamin C (ascorbic acid) Intake (mg/day/ref. man 65 kg)

* Dietary variables were obtained from the household three-‐day weighed food intake

diet survey.

** Food intakes were standardized to intake per ‘reference man’, defined as a male

aged 19-‐59 years old, weighing 65 kg and undertaking very light physical activity. 4

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Select Variables from Laboratory Measurements (Red Blood Cell, Plasma) and Self-‐

Reported Diet Questionnaire Responses:

• RBC Total Lipid n-‐6 Polyunsaturates (% of total fatty acid by wt.)

• RBC Total Lipid n-‐3 Polyunsaturates (% of total fatty acid by wt.)

• RBC Total Lipid Eicosapentaenoic Acid (EPA) (% of total fatty acid by wt.)

• RBC Total Lipid Docosahexaenoic Acid (DHA) (% of total fatty acid by wt.)

• Plasma Total Cholesterol (mg/dL)

• Animal Fat Intake (g/day)

• Vegetable Fat Intake (g/day)

3.2 Statistical Analyses

Linear regression analyses were performed with age-standardized disease mortality

rate the dependent variable and each dietary variable evaluated separately in models as the

independent variable. Multiple linear regression was then performed, adjusting for potential

confounding variables and other dietary variables. All nutrients were adjusted for total

energy using the nutrient density approach or by entering total kilocalories as an additional

covariate. Nutrients that were not normally distributed were evaluated as categorical

variables in linear regression, using a value between the mean and the median as a cut-off

to distinguish between low consumers versus high consumers. Mortality rates from the

1989 survey were used in regression analyses, stratified by sex. For each study area the

causes of death were obtained by a retrospective review undertaken in 1989 and cause of

death was coded according to the International Classification of Diseases version 9.0

(WHO ICD-9). The mortality rates used were age-standardized for particular age ranges,

calculated as the unweighted average of the component five-year mortality rates (i.e., 35-

39, 40-44, …., 65-69 for the age range 35-69). 4 Statistical significance was set at a p-value

of less than 0.05.

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3.4 Study Location

Figure 1. http://webarchive.human.cornell.edu/chinaproject/images/Map.GIF

C Shanxi (!"!"!"!"####) CB Huguan ($%) CC Jiangxian (&') CD Jiexiu (())D Henan (*+#*+#*+#*+#) DA Shangshui (,-) DB Linxian (.') DC Songxian (/')F Jilin (0.#0.#0.#0.#) FA Changling (12)G Heilongjiang (345#345#345#345#) GA Baoqing (67)J Anhui (89#89#89#89#) JA Zongyang (:;) JB Qianshan (<!)M Jiangxi (5"5"5"5"####) MB Lean (=8) MC Nancheng (+>) MD Xiajiang (?5)N Hunan (@+#@+#@+#@+#) NA Linwu (AB) NB Mayang (C;) NC Qiyang (D;) ND Yuanjiang (E5)O Hubei (@F@F@F@F####) OA Zaoyang (G;) OB Echeng (H>)Q Guizhou (IJ#IJ#IJ#IJ#) QA Qingzhen (7K) QB Yinjiang (L5) QC Huishui (M-)R Yunnan (N+#N+#N+#N+#) RA Xuanwei (OP)S Sichuan (QR#QR#QR#QR#) SA Wenjiang (S5) SB Cangxi (TU) SC Quxian (V')T Shaanxi (W"#W"#W"#W"#) TA Shanyang (!;) TC Jiaxian (X') TD Longxian (Y')V Gansu (Z[#Z[#Z[#Z[#) VA Tianzhu (\]) VB Dunhuang (^_) VC Wudu (B`)W Xinjiang (ababababcdecdecdecde) WA Tuoli (fg) WB Xinyuan (ah) WC Tulufan (ijk)X Ningxia (lmlmlmlmcdecdecdecde) XA Yongning (nl) XB Longde (op)Y Neimongol (qrsqrsqrsqrscdecdecdecde) YA Xianghuangqi (tuv)

Coastal Provinces (wxwxwxwx)A Shanghai (yxzyxzyxzyxz) AA Shanghai (yx) AB Qingpu ({|) AC Songjiang (}5)B Hebei (*F#*F#*F#*F#) BA Cixian (~') BB Jingxing (�Ä) BC Huanghua (uÅ)E Liaoning (Çl#Çl#Çl#Çl#) EA Xiuyan (ÉÑ)H Shandong (!Ö#!Ö#!Ö#!Ö#) HA Laoshan (Ü!)I Jiangsu (5á#5á#5á#5á#) IA Shuyang (à;) IB Huaian (â8) IC Yangzhong (;ä) ID Jianhu (ã@)

IE Qidong (åÖ) IF Haimen (xç) IG Taixing (éè)K Zhejiang (ê5ê5ê5ê5####) KB Daishan (ë!) KC Jiashan (íì)L Fujian (îã#îã#îã#îã#) LA Zhangpu (ï|) LB Nanan (+8) LC Changle (1=) LD Huian (M8)P Guangxi (ñ"ñ"ñ"ñ"cdecdecdecde) PA Cangwu (Tó) PC Chongzuo (òô) PD Fusui (öõ) PE Rongxian (ú')U Guangdong (ñÖ#ñÖ#ñÖ#ñÖ#) UA Sihui (Qù) UB Panyu (kû) UC Zhongshan (ä!) UD Wuchuan (üR)

UE Shunde (†p) UF Wuhua (°¢)Taiwan (£§£§£§£§)ZA Taipei City, Kaohsiung City(£F, •¶z) ZB Taichung City, Tainan City(£ä, £+z) ZC Chungho City, Fengshan City(äß, ®!z)ZD Miaoli(©™) ZE Hsinchu(a´) ZF Chiai, Tainan(í¨, £+) ZG Penghu(≠@) ZH Nantou, Hualien(+Æ, Ø∞)ZI Kaohsiung, Taitung(•¶, £Ö) ZJ Pingtung(±Ö) ZK Taipei(£F), Ilan(≤≥) ZL Changhua, Pingtung|(¥µ, ±Ö)ZM Taitung(£Ö) ZN Ilan(≤≥) ZO Changhua(¥µ) ZP Tainan(£+)

AAAB

AC

BA

BBBC

CBCC

CD

DA

DB

DC

EA

FA

GA

HA

IA

IB

IC

ID

IEIFIG

JAJB

KBKC

LALB

LC

LD

MBMCMD

NA

NB

NC

ND

OA

OB

PA

PC PDPE

QA

QB

QCRA

SASB

SC

TA

TC

TD

UAUB

UCUD

UE

UF

VA

VB

VC

WA

WB

WC

XA

XB

YA

TW

Survey areas in 1989 survey

Inland Provinces (q∂q∂q∂q∂)

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4.0 Results

4.1 Dietary Correlates of BMI

Linear regression was conducted to evaluate the association between select

dietary variables and body mass index (BMI), where BMI was the dependent variable

and dietary factors were the independent variables. Analyses were conducted

separately for males and females.

Among men, percent of energy from protein was found to be significantly

positively associated with BMI (B=0.07; SE=0.01; p-‐value<0.001) (Table 1).

Furthermore, it was found that percentage of animal food intake was negatively

associated with BMI in both men and percentage of plant food intake were positively

associated with BMI, however the relationships were not statistically significant.

Intake of fiber (g), adjusted for total energy by entering kilocalories as a covariate,

was positively associated with BMI, however the relationship was only significant

among women (B=0.58; SE=0.27; p-‐value<0.05). For vegetable intake, consumption

of green vegetables was significantly negatively associated with BMI among both men

and women (B=-‐0.004; SE=0.001; p-‐value<0.01), however the relationship was

modest. Fish consumption was also found to be significantly negatively correlated

among both men and women, however the strength of the association decreased with

increasing consumption of fish. Milk consumption was also found to be significantly

positively associated with BMI, however, milk consumption was particularly low

(Mean=2.3g/day; SD=16.9) with most counties having no consumption at all and three

counties having exceptionally high consumption (WA=94.2 g/day; WB=292.2g/day;

YA = 135.2g/day).

In order to determine the association between dietary fat and BMI,

linear regression was also performed where the independent variables were oils and

fats measured by the three day food record as well as erythrocyte fatty acid

composition from the laboratory analyses conducted on pooled blood samples in each

county (Table 2). Vegetable oil (g/day) consumption was found to be significantly

positively associated with BMI in both men and women (B=0.03; SE=0.01; p-‐

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value<0.05). Vegetable fat (g/day) consumption was significantly positively

associated with BMI among both men (B=0.05; SE=0.02; p-‐value<0.05) and women

(B=0.07; SE=0.02; p-‐value<0.01). Percentage of erythrocyte fatty acid content as

omega-‐3 fatty acid was not found to be significantly associated with BMI, and further

analysis of EPA and DHA omega-‐3 fatty acids did not reveal any significant association.

Erythrocyte omega-‐6 fatty acid content was found to be negatively associated with

BMI among women (B=-‐0.09; SE=0.03; p-‐value<0.05).

The inverse association between percentage of kilocalories from animal food

and BMI is likely due to the overall low average intake of animal foods in general

(Mean=7.04; SD=6.89) versus overall plant food intake (Mean=93.0; SD=6.88).

Nevertheless, the association was further adjusted for animal protein intake (to test

Dr. Campbell’s argument that protein, especially animal protein, is linked to chronic

diseases), however the inverse relationship was still maintained. Thus, the indictment

of animal foods as a risk factor for increasing weight gain is not justified based on

these analyses. However, it is important to keep in mind that the prevalence of

overweight, based on averages of each study area, is almost non-‐existent. The mean

BMI for both men (Mean=21.0; SD=1.0) and women (Mean=21.4; SD=1.1) fell into the

normal weight category based on WHO BMI classification. 5 Therefore, the

combination of lack of prevalence of overweight and obesity, as well as low animal

food consumption and a low sample size due to aggregate data makes it more difficult

to detect true associations. On the other hand, we observed protein consumption,

vegetable oil and vegetable fat consumption as having statistically significant positive

associations with BMI. In his book The China Study, Dr. Campbell claims that protein

and fat are implicated in weight gain, however in this case it appears plant foods and

vegetable fats are associated with weight gain. However, a limitation is that dietary

intakes were not adjusted for physical activity levels – a factor which would likely

alter the associations we observe.

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Table 1. Linear Regression Beta Coefficients (SE) for Each Diet Quality Indicator Separately Evaluated for its Association with BMI (kg/m2).

Independent Variables Model 1 MALE

Model 2

FEMALE

Beta Coefficient

(SE) Beta Coefficient (SE)

% E Fat -0.007 0.02 -0.01 0.02 % E Carbohydrate -0.0001 0.02 -0.004 0.02 % E Protein 0.07 0.01*** 0.09 0.02*** % E Animal Food Low (0%-5%) High (6%-27%)

-0.35

0.24

-0.41

0.28

% E Plant Food Low (73%-96%) High (96%-100%)

0.23

0.25

0.24

0.29

% E Processed Starch & Sugar Low (0g-1g) High (2g-22.9g)

0.37

0.25

0.41

0.30

% E Fiber Low (4.8g-11g) High (12g-38.8g)

0.39

0.23#

0.58

0.27*

% E Legumes Low (0g-17g) High (18g-104.6g)

-0.32

0.23

-0.12

0.28

% E Light Colored Vegetables Low (0g-148g) High (149g-510.6g)

0.36

0.23

0.66

0.27*

% E Green Vegetables -0.004 0.001** -0.004 0.001** % E Fish No (referent) Low (1g-14g) High (15g-184.7g)

--1.03 -0.55

0.26*** 0.25*

-0.89 -0.59

0.33* 0.32

% E Meat Low (0g-31g) High (32g-104.4g)

0.16

0.23

-0.0002

0.28

% E Milk No (0g) Yes (1g-292.2g)

1.13

0.26***

1.21

0.32***

% E Eggs Low (0g-3g) High (3g-18g)

-0.11

0.23

0.05

0.28

Note. All nutrients are presented as a percentage of total energy (E) intake or adjusted for total energy by entering total kilocalories as a covariate.

*P<0.05; **P<0.01; ***P<0.001

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Table 2. Linear Regression Beta Coefficients (SE) for RBC Fatty Acids and Intake of Fats and Oils Separately Evaluated for their Association with BMI (kg/m2).

Independent Variables Model 1

MALE Model 22

FEMALE

Beta Coefficient


Added Vegetable Oil (g) 0.03 0.01* 0.03 0.01* RBC omega-6 -0.05 0.03# -0.09 0.03* RBC omega-3 0.05 0.07 0.06 0.08 RBC EPA Low (0.09%-0. 55%) High (0.56%-2.09%)

0.004

0.25

-0.12

0.28

RBC DHA 0.03 0.07 0.06 0.09 Animal Fat (g/day) 1

Low (0.2g-5g) High (6g-23.4g)

-0.57

0.23*

-0.43

0.28

Vegetable Fat (g/day) 1 0.05 0.02* 0.07 0.02** Plasma Cholesterol (mg/dL) 0.008 0.010 0.02 0.01

1 Adjusted for total energy intake by entering kilocalories as an additional covariate.

*P<0.05; **P<0.01; ***P<0.001

Table 3. Linear Regression Beta Coefficients (SE) for Select Micronutrients Separately Evaluated for their Association with BMI (kg/m2).


MALE Model 22

FEMALE

Beta Coefficient


Vitamin A (RE/day/ref. man) -0.0006 0.0003* -0.0008 0.0003* Vitamin C (mg/day/ref. man) -0.005 0.002* -0.007 0.003* Vitamin E (mg/day/ref. man) 0.04 0.01*** 0.05 0.01***

Note. All micronutrient intakes are adjusted for total energy by entering kilocalories as an additional covariate.

*P<0.05; **P<0.01; ***P<0.001

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4.2 Correlates of Diabetes Mortality – Age 35-69 (stand. rate/100, 000) (ICD 9

250)

Linear regression was performed to evaluate the association between risk of

mortality from diabetes at ages 35-‐69 in rural China and select dietary factors

evaluated separately as independent variables. The age-‐standardized mortality rate

(stand. rate/100,00) for the age range 35-‐69 was used as the dependent variable. The

International Classification of Diseases (ICD) version 9 was used to classify mortality

from diabetes. Diabetes mortality rates were very low for both men and women (0.2%

and 0.3%, respectively) and may be due to the low prevalence of obesity in rural

China and potentially due to the fact that classification of death due to diabetes is

difficult and highly variable. For example, the deaths of diabetics due to vascular

disease may be coded as death from diabetes. Analyses were conducted separately for

men and women.

The direction of relationships between numerous nutrients and diabetes

mortality among men did not follow the same pattern among women, leading one to

believe some other underlying potential confounder may be present. Evaluation of

each macronutrient separately as an independent variable revealed statistically

significant relationships among men only, where percent of energy from fat was

significantly negatively associated with diabetes mortality (B=-‐0.22; SE=0.09; p-‐

value<0.05) and percent of energy from carbohydrates had a significant positive

relationship (B=0.20; SE=0.08; p-‐value<0.05) (Table 4). Percentage of intake of animal

foods was inversely associated with the mortality rate, however the relationship was

not statistically significant. Percent of intake of plant foods was significantly positively

associated with diabetes mortality among men only (B=3.48; SE=0.96; p<0.01). No

statistically significant associations between vegetable intake and diabetes mortality

were observed. Fish consumption was negatively associated with diabetes mortality

among both men and women, however the relationship was not statistically

significant.

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Analysis of fats and oils as potential risk factors revealed a statistically

significant relationship between vegetable oil (g) and vegetable fat (g) intake among

women (B=0.14; SE=0.06; p-‐value<0.05 and B=0.27; SE=0.12; p-‐value<0.05,

respectively) (Table 5). Among men, only animal fat intake (g) had a statistically

significant and negative association with diabetes mortality (B=-‐2.36; SE=1.01; p-‐

value<0.05).

Among vitamin A, vitamin C, and vitamin E, only vitamin E had a statistically

significant association among women and it was positive (B=0.19; SE=0.07; p-‐

value<0.01) (Table 6).

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Table 4. Linear Regression Beta Coefficients (SE) for Each Diet Quality Indicator Separately Evaluated for its Association with Diabetes Mortality.


Model 22

FEMALE

Beta Coefficient


% E Fat -0.22 0.09* 0.13 0.12 % E Carbohydrate 0.20 0.08* -0.15 0.11 % E Protein 0.11 0.06 0.12 0.09 % E Animal Food Low (0%-5%) High (6%-27%)

-1.53

1.01

-0.03

1.38


3.48

0.96**

-0.40

1.40


-0.66

1.07

1.21

1.42


1.36

1.00

-0.70

1.35


-1.75

0.99#

0.95

1.35


-0.94

1.01

2.02

1.33

% E Green Vegetables -0.00005 .005 -0.001 0.007 % E Fish No (referent) Low (1g-14g) High (15g-184.7g)

-1.98 -1.33

1.23 1.20

-1.14 1.73

1.64 1.60


-1.89

0.98#

0.57

1.35


0.85

1.27

3.19

1.65#


-1.25

1.00

1.18

1.34


*P<0.05; **P<0.01; ***P<0.001

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Table 5. Linear Regression Beta Coefficients (SE) for RBC Fatty Acids and Intake of Fats and Oils Separately Evaluated for their Association with Diabetes Mortality.


MALE Model 22

FEMALE

Beta Coefficient


Vegetable Oil (g) 0.01 0.05 0.14 0.06* RBC omega-6 -0.004 0.13 -0.13 0.18 RBC omega-3 0.49 0.27# 0.19 0.36 RBC EPA Low (0.09%-0.55%) High (0.56%-2.09%)

0.81

1.05

1.17

1.36

RBC DHA 0.43 0.28 0.13 0.41 Animal Fat (g/day)1

Low (0.2g-5g) High (6g-23.4g)

-2.36

1.01*

-2.20

1.32

Vegetable Fat (g/day)1 0.07 0.09 0.27 0.12* Plasma Cholesterol (mg/dL) -0.01 0.04 0.07 0.06


*P<0.05; **P<0.01; ***P<0.001

Table 6. Linear Regression Beta Coefficients (SE) for Select Micronutrients Separately Evaluated for their Association with Diabetes Mortality.


MALE Model 22

FEMALE

Beta Coefficient


Vitamin A (RE/day/ref. man) -0.002 0.001 0.0008 0.002 Vitamin C (mg/day/ref. man) -0.02 0.009# -0.01 0.01 Vitamin E (mg/day/ref. man) 0.06 0.05 0.19 0.07**


*P<0.05; **P<0.01; ***P<0.001

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4.3 Correlates of Hypertensive Disease Mortality Age 35-69

(stand.rate/100,000)(ICD9 401-5)


mortality from hypertensive disease at ages 35-‐69 in rural China and select dietary

factors evaluated separately as independent variables. The age-‐standardized mortality

rate (stand. rate/100,00) for the age range 35-‐69 was used as the dependent variable.

The International Classification of Diseases (ICD) version 9 was used to classify

mortality from lymphoma and myeloma. Analyses were conducted separately for men

and women. Xinyuan county (WB) was excluded from analyses due to the fact that an

especially high mortality rate was observed in this study area and may be due to

miscoding of ischaemic heart disease as hypertensive heart disease.

Evaluation of macronutrients as a percent of total energy intake as

independent variables revealed statistically significant relationships among women

only, where percent of energy from fat was significantly negatively associated with

mortality rate (B=-‐0.87; 0.41; p-‐value<0.05) and percent of energy from

carbohydrates was significantly positively associated with mortality rate (B=0.88;

SE=0.39; p-‐value<0.05) (Table 7). Percentage of plant food and animal food intake

revealed statistically significant relationships among women again, where animal food

intake had an inverse relationship (B=-‐12.2; SE=4.55; p-‐value<0.01) and plant food

intake had a positive relationship (B=11.5; SE=4.63; p-‐value<0.05). Processed starch

and sugar intake, adjusted for total energy, was found to be significantly negatively

associated with mortality rate among both men (B=-‐17.5; SE=5.94; p-‐value<0.01) and

women (B=-‐13.3; SE=4.65; p-‐value<0.01). Regarding vegetable consumption, only the

intake of legumes (g) had a significant relationship with mortality rate, which was

inverse for both men (B=-‐12.8; SE=5.78; p-‐value<0.05) and women (B=-‐11.1; SE=4.46;

p-‐value<0.05). In addition, fish consumption was significantly and negatively

associated with mortality rate for both men and women, and the relationship became

stronger with increasing fish consumption. Egg consumption was also significantly

negatively associated with mortality rate among both men (B=-‐13.2; SE=5.60; p-‐

value<0.05) and women (B=-‐9.94; SE=4.45; p-‐value<0.05).

17

Evaluation of fats and oils separately as independent variables did not reveal

any significant associations with mortality from hypertensive disease (Table 8) and

neither vitamin A, vitamin C, or vitamin E were significantly associated with mortality

from hypertensive disease (Table 9), although all three were inversely related with

the mortality rate.

A significant claim by Dr. Campbell is that individuals with high cholesterol

levels have a much higher incidence of CHD. 1 His indictment of animal foods stemmed

from the positive correlation between animal foods and cholesterol and the negative

correlation between cholesterol and plant foods, thereby inferring that animal foods

are a risk factor for coronary heart disease. No significant relationships between

cholesterol and hypertensive disease were observed. However, cholesterol did have

an inverse relationship with percent E from plant foods and a positive relationship

with percent E from animal foods and mean cholesterol levels were quite low for both

men (Mean=148 mg/dL; SD=12) and women (Mean=147 mg/dL; SD=12) in rural

China. After adjustment for plasma cotinine levels, a potential risk factor that may be

confounding results, no differences in nutrient associations with hypertensive disease

mortality were observed.

18

Table 7. Linear Regression Beta Coefficients (SE) for Each Diet Quality Indicator Separately Evaluated for its Association with Hypertensive Disease Mortality.


Model 22

FEMALE

Beta Coefficient


% E Fat -0.62 0.54 -0.87 0.41* % E Carbohydrate 0.78 0.50 0.88 0.39* % E Protein -0.01 0.39 0.18 0.31 % E Animal Food Low (0%-5%) High (6%-27%)

-10.3

6.00#

-12.2

4.55**


10.8

6.05#

11.5

4.63*


-17.5

5.94**

-13.3

4.65**


-3.74

5.95

2.67

4.64


-12.8

5.78*

-11.1

4.46*


-1.52

5.99

0.24

4.67

% E Green Vegetables 0.03 0.03 0.02 0.02 % E Fish No (referent) Low (1g-14g) High (15g-184.7g)

-12.4 -19.3

6.96# 6.79**

-9.75 -18.5

5.25# 5.12**


-6.85

5.89

-8.13

4.53


7.44

7.62

5.26

5.95


-13.2

5.69*

-9.94

4.45*

Note. All nutrients are presented as a percentage of total energy (E) intake or adjusted for total energy by entering total kilocalories as a covariate. Xinyuan county (WB) was excluded from analyses due to miscoding of ischemic as hypertensive heart disease.

*P<0.05; **P<0.01; ***P<0.001

19

Table 8. Linear Regression Beta Coefficients (SE) for RBC Fatty Acids and Intake of Fats and Oils Separately Evaluated for their Association with Hypertensive Disease Mortality.


MALE Model 22

FEMALE

Beta Coefficient


Vegetable Oil (g) -0.27 0.28 -0.19 0.22 RBC omega-6 0.40 0.74 -0.73 0.60 RBC omega-3 2.08 1.60 1.13 1.24 RBC EPA Low (0.09%-0.55%) High (0.56%-2.09%)

-1.29

6.19

-8.26

4.65#

RBC DHA 1.77 1.66 2.64 1.36 Animal Fat (g/day)1

Low (0.2g-5g) High (6g-23.4g)

-2.50

6.18

0.76

4.64

Vegetable Fat (g/day)1 -0.39 0.53 -0.08 0.41 Plasma Cholesterol (mg/dL) -0.15 0.25 -0.30 0.19


*P<0.05; **P<0.01; ***P<0.001

Table 9. Linear Regression Beta Coefficients (SE) for Select Micronutrients Separately Evaluated for their Association with Hypertensive Disease Mortality.


MALE Model 22

FEMALE

Beta Coefficient


Vitamin A (RE/day/ref. man) -0.009 0.007 -0.004 0.006 Vitamin C (mg/day/ref. man) -0.06 0.06 -0.04 0.05 Vitamin E (mg/day/ref. man) -0.45 0.30 -0.23 0.24


*P<0.05; **P<0.01; ***P<0.001

20

4.4 Correlates of Cancer Mortality (all malignant neoplasms) Age 35-69

(stand.rate/100,000)(ICD9 140-208)


mortality from cancer at ages 35-‐69 in rural China and select dietary factors evaluated

separately as independent variables. The mortality rate for all malignant neoplasms,

age-‐standardized for the age range 35-‐69, was used as the dependent variable. The

International Classification of Diseases (ICD) version 9 was used to assign codes and

diagnose all cancers. All analyses were conducted separately for males and females.

No statistically significant associations between macronutrients and select

foods and mortality rate for cancer were observed, with the exception of meat (g)

consumption among women, which was significant and inverse (B=-‐0.87; SE=0.37; p-‐

value<0.05) (Table 10). Evaluation of each macronutrient separately as an

independent variable, adjusted for total energy intake, did not reveal any statistically

significant associations with cancer mortality rate. Percentage of animal food intake

was also non-‐significantly positively associated with mortality rate in men, while the

relationship was non-‐significant and negative among women. The association

between percentage of plant food intake and mortality rate was positive and non-‐

significant for both men and women. For vegetables, intake of legumes, light coloured

vegetables, and green vegetables (among women only) was non-‐significantly

negatively associated with cancer mortality among both men and women.

Further evaluation of consumption of fats and oils revealed vegetable oil intake

(g) to be significantly positively associated with cancer mortality among men (B=0.06;

SE=0.03; p-‐value<0.05) (Table 11). Daily animal fat intake (g) was negatively

associated with cancer mortality among both men and women, but relationships were

not statistically significant. Evaluation of daily vegetable fat (g) intake as an

independent variable revealed a statistically positive association with cancer

mortality among men (B=0.12; SE=0.06; p-‐value<0.05) and a non-‐significant positive

association among women. Evaluation of RBC omega-‐3 and omega-‐6 fatty acid content

(evaluated separately as a % of all RBC fatty acid content) revealed a statistically

21

significant and negative association with cancer mortality for omega-‐6 fatty acids

among women only (B=-‐0.11; SE=0.05; p-‐value<0.05).

Evaluation of daily consumption of select micronutrients revealed a

statistically significant and positive relationship between cancer mortality and intake

of vitamin E (mg/day) for both men (B=0.09; SE=0.03; p-‐value<0.01) and women

(B=0.05; SE=0.02; p-‐value<0.01) (Table 12) Relationships between intake of vitamin A

and vitamin E and cancer mortality were inverse for both men and women, however

the relationships were not statistically significant.

The main dietary variables significantly positively associated with mortality

from all cancers were percent E from vegetable fat and added vegetable oil. Entering

these variables as covariates with percent E from plant food did not alter the positive

association with all cancer mortality.

22

Table 10. Linear Regression Beta Coefficients (SE) for Each Diet Quality Indicator Separately Evaluated for its Association with Cancer Mortality.


Model 22

FEMALE

Beta Coefficient


% E Fat -0.04 0.06 -0.05 0.03 % E Carbohydrate 0.03 0.06 0.04 0.03 % E Protein 0.03 0.04 0.03 0.02 % E Animal Food Low (0%-5%) High (6%-27%)

0.07

0.70

-0.44

0.39


0.13

0.69

0.53

0.37


-0.01

0.73

0.08

0.41


0.15

0.69

0.66

0.38#


-0.40

0.69

-0.27

0.39


-0.11

0.69

-0.16

0.39

% E Green Vegetables 0.0003 0.004 -0.001 0.002 % E Fish No (referent) Low (1g-14g) High (15g-184.7g)

-0.19 0.37

0.85 0.83

-0.44 -0.61

0.47 0.46


-0.95

0.68

-0.87

0.37*


0.64

0.86

0.33

0.48


0.79

0.68

-0.07

0.38


*P<0.05; **P<0.01; ***P<0.001

23

Table 11. Linear Regression Beta Coefficients (SE) for RBC Fatty Acids and Intake of Fats and Oils Separately Evaluated for their Association with Cancer Mortality.


MALE Model 22

FEMALE

Beta Coefficient


Vegetable Oil (g/day) 0.06 0.03* 0.02 0.02 RBC omega-6 -0.11 0.09 -0.11 0.05* RBC omega-3 -0.06 0.19 0.06 0.10 RBC EPA Low (0.09%-0.55%) High (0.56%-2.09%)

-0.23

0.72

-0.01

0.39

RBC DHA -0.09 0.19 0.05 0.12 Animal Fat (g/day)1

Low (0.2g-5g) High (6g-23.4g)

-0.44

0.72

-0.41

0.38

Vegetable Fat (g/day)1 0.12 0.06* 0.06 0.03# Plasma Cholesterol (mg/dL) 0.02 0.03 -0.002 0.02


*P<0.05; **P<0.01; ***P<0.001

Table 12. Linear Regression Beta Coefficients (SE) for Select Micronutrients Separately Evaluated for their Association with Cancer Mortality.


MALE Model 22

FEMALE

Beta Coefficient


Vitamin A (RE/day/ref. man) 0.0004 0.0008 -0.00006 0.0005 Vitamin C (mg/day/ref. man) -0.002 0.007 -0.004 0.004 Vitamin E (mg/day/ref. man) 0.09 0.03** 0.05 0.02**


*P<0.05; **P<0.01; ***P<0.001

24

4.5 Correlates of Lymphoma and Myeloma Mortality Age 35-69

(stand.rate/100,000) (ICD9 200-3)


mortality from lymphoma and myeloma at ages 35-‐69 in rural China and select

dietary factors evaluated separately as independent variables. The age-‐standardized

mortality rate (stand. rate/100,00) for the age range 35-‐69 was used as the dependent

variable. The International Classification of Diseases (ICD) version 9 was used to

classify mortality from lymphoma and myeloma. The data indicated much higher

mortality rate for men in most counties, therefore data analysis was focused on male

mortality rate.

No statistically significant relationships between macronutrients and mortality

rate were observed (Table 13). The relationships between percent of energy from

protein and carbohydrates and mortality rate were both negative while the

relationship between percent of energy from fat was positive. Neither percentage of

animal food intake or plant food intake and mortality rate were significantly

associated, however intake of both animal food and plant food was inversely

associated with mortality rate. A statistically significant and positive association was

observed for intake of processed starch and sugars, adjusted for total energy by

entering kcalories as a covariate, and mortality rate (B=2.82; SE=1.26; p-‐value<0.05).

For vegetable intake, only a statistically significant and positive relationship was

observed for intake of legumes (g) and mortality rate (B=3.07; SE=1.18; p-‐

value<0.05). The association between fiber intake and mortality rate was inverse but

not significant. Fish consumption was also found to be significantly and positively

associated with mortality rate, while no significant relationship was observed

between meat (g) intake and mortality rate.

Potential risk factors for lymphoma include hepatitis infection and malaria and

both of these variables were significantly correlated with lymphoma in unadjusted

analyses, therefore, regression analyses were repeated with the addition of the

following two potential confounding variables: plasma hepatitis B surface antigen (%

25

of individual samples that were positive in non-‐pooled analysis in each study area)

and malaria (% of individuals in each study area with a history of malaria diagnosis).

After addition of these potential confounding variables, processed starch and sugar

intake, fish consumption, and legume (g) intake all maintained their positive and

significant relationships with mortality rate.

Evaluation of fats and oils revealed statistically significant and positive

associations for vegetable oil (g) intake (B=0.16; SE=0.006; p-‐value<0.01) and

vegetable fat (g) intake (B=0.29; SE=1.11; p-‐value<0.01) and mortality rate, even after

adjustment for hepatitis infection and history of malaria (Table 14). Erythrocyte

omega-‐3 fatty acid content was also found to be significantly and inversely associated

with mortality rate after adjustment for the potential confounding variables (B=-‐0.65;

SE=0.35; p-‐value<0.05). Further analysis of erythrocyte EPA and DHA content

revealed inverse associations with mortality rate, however the relationship was only

statistically significant for DHA (B=-‐0.77; SE=0.34; p-‐value<0.05).

Evaluation of daily intake of the select micronutrients and their association

with mortality rate after adjustment for hepatitis infection and malaria revealed a

statistically significant and positive association between vitamin E intake and

mortality from lymphoma and myeloma (B=0.14; SE=0.05; p-‐value<0.01) (Table 15).

Although fish consumption was significantly positively associated with

lymphoma mortality, omega-‐3 fatty acid – especially docosahexanaeoic acid – was

significantly inversely associated with lymphoma mortality. However, adjusting for

omega-‐3 fatty acids in the linear regression of fish and lymphoma mortality rate, fish

intake was still significantly negatively associated with mortality rate.

26

Table 13. Linear Regression Beta Coefficients (SE) for Each Diet Quality Indicator Separately Evaluated for its Association with Lymphoma and Myeloma Mortality Rate.

Independent Variables Model 11 Model 22

HBsAg, malaria Beta

Coefficient (SE) Beta Coefficient (SE)

% E Fat 0.11 0.11 0.07 0.11 % E Carbohydrate -0.10 0.11 -0.09 0.10 % E Protein -0.13 0.08 -0.05 0.09 % E Animal Food Low (0%-5%) High (6%-27%)

-0.10

1.27

-0.56

1.26


-2.15

1.26#

-1.18

1.32


2.82

1.26*

3.02

1.23*


-1.64

1.22

-0.35

1.32


3.07

1.18*

2.48

1.22*


0.23

1.24

0.77

1.22

% E Green Vegetables 0.008 0.007 0.003 0.007 % E Fish No (referent) Low (1g-14g) High (15g-184.7g)

4.53 4.09

1.39** 1.36**

3.69 3.46

1.50* 1.46*


0.02

1.23

-0.66

1.25


-1.56

1.54

-0.80

1.52


2.27

1.20

1.70

1.22


*P<0.05; **P<0.01; ***P<0.001

27

Table 14. Linear Regression Beta Coefficients (SE) for RBC Fatty Acids and Intake of Fats and Oils Separately Evaluated for their Association with Lymphoma and Myeloma Mortality.


HBsAg, Malaria Beta


Added Vegetable Oil (g/day) 0.16 0.06** 0.16 0.05** RBC omega-6 -0.22 0.15 -0.21 0.16 RBC omega-3 -0.87 0.32** -0.65 0.35* RBC EPA Low (0.09%-0.55%) High (0.56%-2.09%)

-0.87

1.29

-0.67

1.28

RBC DHA -0.99 0.33** -0.77 0.34* % Animal Fat (g/day)

Low (0.2g-5g) High (6g-21.1g)

-0.02

1.29

-0.74

1.29

% Vegetable Fat (g/day) 0.29 1.11** 0.31 0.10** Plasma Cholesterol (mg/dL) -0.03 0.05 0.0005 0.05


*P<0.05; **P<0.01; ***P<0.001

Table 15. Linear Regression Beta Coefficients (SE) for Select Micronutrients Separately Evaluated for their Association with Lymphoma and Myeloma Mortality.


HBsAg, Malaria Beta


Vitamin A (RE/day/ref. man) 0.003 0.001 0.001 0.002 Vitamin C (mg/day/ref. man) 0.01 0.01 -0.001 0.01 Vitamin E (mg/day/ref. man) 0.13 0.06* 0.14 0.05**


*P<0.05; **P<0.01; ***P<0.001

28

5.0 Conclusion

After performing regression analyses, the data does not justify the indictment

of all animal foods as risk factors for chronic degenerative disease. Limitations of the

analyses include the overall low consumption of animal foods, as a percent of total

energy, compared to the consumption of plant foods and the small sample size, which

may be decreasing the statistical power to detect true associations. Furthermore,

added vegetable oil was consistently identified as a risk factor for the above chronic

diseases, with the exception of hypertensive disease where no significant association

was identified. However, in analyzing nutrition data and the effect of isolated

nutrients on chronic disease it is important to keep in mind that all nutrients are

inter-‐correlated and, therefore, it is quite difficult to detect the subtle effects of solely

one nutrient on disease. Furthermore, not only is diet affected by several other factors

such as environment, availability of foods, seasonality of foods, and socioeconomic

status, but disease is also affected by several environmental and lifestyle factors that

may be confounding results. The presence of other underlying disease status is yet

another potential for confounding, therefore multivariate analyses should be

performed in the future analyses of this dataset. Furthermore, the analyses are not

adjusted for physical activity, which is a potential confounder of the observed

relationships between diet and disease. Also, dietary intake data collected by the

three-‐day food record was standardized intake per ‘reference man’, defined as a male

aged 19-‐59 years old, weighing 65 kg and undertaking very light physical activity,

which potentially limited the detection of associations that may have been identified if

all analyses could simply be adjusted for age and gender variables separately. Also,

although the mortality rates were age-‐standardized for males and females separately,

the dietary intake data from the three-‐day food record is presented as a single average

per study area and does not distinguish between male and female data. Dietary

intakes vary greatly with age and gender, therefore the presentation of the data in this

manner is also a potential limitation. Also, data was collected for approximately 8,307

individuals and presentation of individual data, if it is feasible, would have allowed for

better analysis of diet-‐disease relationships.

29

Lastly but not least, this was a geographical correlation study therefore the

potential of ecological fallacy due to confounding variables is a major limitation; in

geographical correlation studies it is not uncommon to observe statistically significant

relationships with a p-‐value of less than 0.001 just by chance.

30

Bibliography

1. Campbell TC, Campbell TM. The China study: the most comprehensive

study of nutrition ever conducted and the startling implications for diet, weight loss,

and long-term health: Benbella Books; 2006.

2. Hulley S, Cummings S, Browner W, Grady D, Newman T. Designing Clinical

Research. Third Edition ed. Philadelphia; 2007.

3. FAO/WHO/UNU. Energy and protein requirements. Report of a joint

FAO/WHO/UNU Expert Consultation. In: WHO Technical Report Series No. 724.

Geneva: World Health Organization; 1985.

4. Chen J, Peto R, Pan W, Liu B, Campbell TC. Mortality, Biochemistry, Diet,

and Lifestyle in Rural China: Geographic study of the characteristics of 69 counties

in mainland China and 16 rural areas in Taiwan: Oxford University Press; 2006.

5. WHO. Obesity: Preventing and Managing the Global Epidemic. In: WHO

Technical Report Series. Geneva: World Health Organization; 2000.

Documents

1.0Introductionraschfoundation.org/wp-content/uploads/Cornell... · Chen J,Peto R ,Pan W ,Liu B ,Campbell TC .Mo rtality, Biochemistry, Diet, and Lifestyle in Rural China: Geographic