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TRANSCRIPT
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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
(SE) Beta Coefficient (SE)
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).
Independent Variables Model 11
MALE Model 22
FEMALE
Beta Coefficient
(SE) Beta Coefficient (SE)
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.
13
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.
Independent Variables Model 1 MALE
Model 22
FEMALE
Beta Coefficient
(SE) Beta Coefficient (SE)
% 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
% E Plant Food Low (73%-96%) High (96%-100%)
3.48
0.96**
-0.40
1.40
% E Processed Starch & Sugar Low (0g-1g) High (2g-22.9g)
-0.66
1.07
1.21
1.42
% E Fiber Low (4.8g-11g) High (12g-38.8g)
1.36
1.00
-0.70
1.35
% E Legumes Low (0g-17g) High (18g-104.6g)
-1.75
0.99#
0.95
1.35
% E Light Colored Vegetables Low (0g-148g) High (149g-510.6g)
-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
% E Meat Low (0g-31g) High (32g-104.4g)
-1.89
0.98#
0.57
1.35
% E Milk No (0g) Yes (1g-292.2g)
0.85
1.27
3.19
1.65#
% E Eggs Low (0g-3g) High (3g-18g)
-1.25
1.00
1.18
1.34
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 5. Linear Regression Beta Coefficients (SE) for RBC Fatty Acids and Intake of Fats and Oils Separately Evaluated for their Association with Diabetes Mortality.
Independent Variables Model 1
MALE Model 22
FEMALE
Beta Coefficient
(SE) Beta Coefficient (SE)
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
1 Adjusted for total energy intake by entering kilocalories as an additional covariate.
*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.
Independent Variables Model 11
MALE Model 22
FEMALE
Beta Coefficient
(SE) Beta Coefficient (SE)
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**
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
16
4.3 Correlates of Hypertensive Disease Mortality Age 35-69
(stand.rate/100,000)(ICD9 401-5)
Linear regression was performed to evaluate the association between risk of
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.
Independent Variables Model 1 MALE
Model 22
FEMALE
Beta Coefficient
(SE) Beta Coefficient (SE)
% 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**
% E Plant Food Low (73%-96%) High (96%-100%)
10.8
6.05#
11.5
4.63*
% E Processed Starch & Sugar Low (0g-1g) High (2g-22.9g)
-17.5
5.94**
-13.3
4.65**
% E Fiber Low (4.8g-11g) High (12g-38.8g)
-3.74
5.95
2.67
4.64
% E Legumes Low (0g-17g) High (18g-104.6g)
-12.8
5.78*
-11.1
4.46*
% E Light Colored Vegetables Low (0g-148g) High (149g-510.6g)
-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**
% E Meat Low (0g-31g) High (32g-104.4g)
-6.85
5.89
-8.13
4.53
% E Milk No (0g) Yes (1g-292.2g)
7.44
7.62
5.26
5.95
% E Eggs Low (0g-3g) High (3g-18g)
-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.
Independent Variables Model 1
MALE Model 22
FEMALE
Beta Coefficient
(SE) Beta Coefficient (SE)
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
1 Adjusted for total energy intake by entering kilocalories as an additional covariate.
*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.
Independent Variables Model 11
MALE Model 22
FEMALE
Beta Coefficient
(SE) Beta Coefficient (SE)
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
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
20
4.4 Correlates of Cancer Mortality (all malignant neoplasms) Age 35-69
(stand.rate/100,000)(ICD9 140-208)
Linear regression was performed to evaluate the association between risk of
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.
Independent Variables Model 1 MALE
Model 22
FEMALE
Beta Coefficient
(SE) Beta Coefficient (SE)
% 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
% E Plant Food Low (73%-96%) High (96%-100%)
0.13
0.69
0.53
0.37
% E Processed Starch & Sugar Low (0g-1g) High (2g-22.9g)
-0.01
0.73
0.08
0.41
% E Fiber Low (4.8g-11g) High (12g-38.8g)
0.15
0.69
0.66
0.38#
% E Legumes Low (0g-17g) High (18g-104.6g)
-0.40
0.69
-0.27
0.39
% E Light Colored Vegetables Low (0g-148g) High (149g-510.6g)
-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
% E Meat Low (0g-31g) High (32g-104.4g)
-0.95
0.68
-0.87
0.37*
% E Milk No (0g) Yes (1g-292.2g)
0.64
0.86
0.33
0.48
% E Eggs Low (0g-3g) High (3g-18g)
0.79
0.68
-0.07
0.38
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
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.
Independent Variables Model 1
MALE Model 22
FEMALE
Beta Coefficient
(SE) Beta Coefficient (SE)
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
1 Adjusted for total energy intake by entering kilocalories as an additional covariate.
*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.
Independent Variables Model 11
MALE Model 22
FEMALE
Beta Coefficient
(SE) Beta Coefficient (SE)
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**
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
24
4.5 Correlates of Lymphoma and Myeloma Mortality Age 35-69
(stand.rate/100,000) (ICD9 200-3)
Linear regression was performed to evaluate the association between risk of
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
% E Plant Food Low (73%-96%) High (96%-100%)
-2.15
1.26#
-1.18
1.32
% E Processed Starch & Sugar Low (0g-1g) High (2g-22.9g)
2.82
1.26*
3.02
1.23*
% E Fiber Low (4.8g-11g) High (12g-38.8g)
-1.64
1.22
-0.35
1.32
% E Legumes Low (0g-17g) High (18g-104.6g)
3.07
1.18*
2.48
1.22*
% E Light Colored Vegetables Low (0g-148g) High (149g-510.6g)
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*
% E Meat Low (0g-31g) High (32g-104.4g)
0.02
1.23
-0.66
1.25
% E Milk No (0g) Yes (1g-292.2g)
-1.56
1.54
-0.80
1.52
% E Eggs Low (0g-3g) High (3g-18g)
2.27
1.20
1.70
1.22
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
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.
Independent Variables Model 11 Model 22
HBsAg, Malaria Beta
Coefficient (SE) Beta Coefficient (SE)
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
1 Adjusted for total energy intake by entering kilocalories as an additional covariate.
*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.
Independent Variables Model 11 Model 22
HBsAg, Malaria Beta
Coefficient (SE) Beta Coefficient (SE)
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**
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
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
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