o*******y
发帖数: 1121 | 1 去上班，然后下班再步行一小时，尽量快地步行，会不会减肥？
来美国以后胖了10磅，在健身房里做什么运动都减不了。吃得不算多，也不喝碳酸饮料
。 | b***i
发帖数: 10018 | 2 对普通人来说，快走的确能够帮助减肥。
【在 o*******y 的大作中提到】
: 去上班，然后下班再步行一小时，尽量快地步行，会不会减肥？
: 来美国以后胖了10磅，在健身房里做什么运动都减不了。吃得不算多，也不喝碳酸饮料
: 。
| j**f
发帖数: 7403 | 3 减肥，不一定。
身体健康：肯定会提高。
stress level : 很可能会降低
睡眠状况： 很可能会变好
皮肤： 很可能会变差
去上班，然后下班再步行一小时，尽量快地步行，会不会减肥？
来美国以后胖了10磅，在健身房里做什么运动都减不了。吃得不算多，也不喝碳酸饮料
。
【在 o*******y 的大作中提到】
: 去上班，然后下班再步行一小时，尽量快地步行，会不会减肥？
: 来美国以后胖了10磅，在健身房里做什么运动都减不了。吃得不算多，也不喝碳酸饮料
: 。
| s******u
发帖数: 125 | 4 I think it will work if you stick to this routine long enough and are
careful about what you eat.
【在 o*******y 的大作中提到】
: 去上班，然后下班再步行一小时，尽量快地步行，会不会减肥？
: 来美国以后胖了10磅，在健身房里做什么运动都减不了。吃得不算多，也不喝碳酸饮料
: 。
| j**f
发帖数: 7403 | 5 问题是，楼主来美国后增加了10磅，是更接近正常体重了，还是超重了？
很多女生，来美国之前都严重低体重。来美国后涨１０磅都未必达到了标准
体重。越运动，越接近标准体重。 而不是“越运动越减肥”。
I think it will work if you stick to this routine long enough and are
careful about what you eat.
【在 s******u 的大作中提到】
: I think it will work if you stick to this routine long enough and are
: careful about what you eat.
| s******u
发帖数: 125 | 6 What you asked makes sense.
【在 j**f 的大作中提到】
: 问题是，楼主来美国后增加了10磅，是更接近正常体重了，还是超重了？
: 很多女生，来美国之前都严重低体重。来美国后涨１０磅都未必达到了标准
: 体重。越运动，越接近标准体重。 而不是“越运动越减肥”。
:
: I think it will work if you stick to this routine long enough and are
: careful about what you eat.
| o*******y
发帖数: 1121 | 7 皮肤会变差，这个有点可怕。
【在 j**f 的大作中提到】
: 减肥，不一定。
: 身体健康：肯定会提高。
: stress level : 很可能会降低
: 睡眠状况： 很可能会变好
: 皮肤： 很可能会变差
:
: 去上班，然后下班再步行一小时，尽量快地步行，会不会减肥？
: 来美国以后胖了10磅，在健身房里做什么运动都减不了。吃得不算多，也不喝碳酸饮料
: 。
| o*******y
发帖数: 1121 | 8 应该是超重了，158cm, 原来重50kg,现在快55kg了，还是喜欢原来的样子。
不是很短时间胖起来的，当然有年龄增长和新陈代谢下降的因素。
【在 j**f 的大作中提到】
: 问题是，楼主来美国后增加了10磅，是更接近正常体重了，还是超重了？
: 很多女生，来美国之前都严重低体重。来美国后涨１０磅都未必达到了标准
: 体重。越运动，越接近标准体重。 而不是“越运动越减肥”。
:
: I think it will work if you stick to this routine long enough and are
: careful about what you eat.
| o*******y
发帖数: 1121 | 9 会尽量注意健康饮食，但恐怕不能节食，我是一饿就头晕眼花受不了那种。
【在 s******u 的大作中提到】
: I think it will work if you stick to this routine long enough and are
: careful about what you eat.
| b***i
发帖数: 10018 | 10 注意饮食不等于节食...要多吃蔬果，多吃粗粮，多喝水，少吃精制食品
【在 o*******y 的大作中提到】
: 会尽量注意健康饮食，但恐怕不能节食，我是一饿就头晕眼花受不了那种。
| | | o*******y
发帖数: 1121 | 11 好，谢谢。 白米饭属不属于精制食品? 好像大家都说少吃白米白面。
【在 b***i 的大作中提到】
: 注意饮食不等于节食...要多吃蔬果，多吃粗粮，多喝水，少吃精制食品
| z********0
发帖数: 9013 | 12 brown rice is better
【在 o*******y 的大作中提到】
: 好，谢谢。 白米饭属不属于精制食品? 好像大家都说少吃白米白面。
| j**f
发帖数: 7403 | 13 就是120磅？ 比我瘦，没事。不用减。
应该是超重了，158cm, 原来重50kg,现在快55kg了，还是喜欢原来的样子。
不是很短时间胖起来的，当然有年龄增长和新陈代谢下降的因素。
【在 o*******y 的大作中提到】
: 应该是超重了，158cm, 原来重50kg,现在快55kg了，还是喜欢原来的样子。
: 不是很短时间胖起来的，当然有年龄增长和新陈代谢下降的因素。
| T*U
发帖数: 22634 | 14 减
一天两小时无氧。
【在 o*******y 的大作中提到】
: 去上班，然后下班再步行一小时，尽量快地步行，会不会减肥？
: 来美国以后胖了10磅，在健身房里做什么运动都减不了。吃得不算多，也不喝碳酸饮料
: 。
| b**********e
发帖数: 5571 | 15 我觉的还是要减，你和我一样高，比我重了将近20磅，当然你要是都是肌肉我就没话说
，不过不要把我当参照物，我是骨架小，我觉得体重在100-110磅左右都算健康吧，关
键看你多出来的是肥肉还是肌肉了，少吃碳水，多吃肉和蔬菜，快走和跑步都会有效果
的，给自己几个月，不要太着急，慢慢会瘦下来的。
【在 o*******y 的大作中提到】
: 应该是超重了，158cm, 原来重50kg,现在快55kg了，还是喜欢原来的样子。
: 不是很短时间胖起来的，当然有年龄增长和新陈代谢下降的因素。
| o*******y
发帖数: 1121 | 16 天，才知道自己真的胖。我的目标是减10磅， 20磅看来很困难。
弱弱地问一声，少吃碳水是指少吃些什么?
【在 b**********e 的大作中提到】
: 我觉的还是要减，你和我一样高，比我重了将近20磅，当然你要是都是肌肉我就没话说
: ，不过不要把我当参照物，我是骨架小，我觉得体重在100-110磅左右都算健康吧，关
: 键看你多出来的是肥肉还是肌肉了，少吃碳水，多吃肉和蔬菜，快走和跑步都会有效果
: 的，给自己几个月，不要太着急，慢慢会瘦下来的。
| b**********e
发帖数: 5571 | 17 我建议你是以健身为目标，顺便减肥，所以不要把体重看得太重，我现在上下个三磅根
本就不管，关键是看三围，身体曲线和肌肉线条，整体好看就行了。
少吃精细食品，米饭啊，面食，面包吃全麦的，然后可以用红薯代替米饭，我最初就是
这么瘦下来的，当然要配合有氧运动，不然肯定反弹，等你运动习惯了，饮食结构也调
整好了，想反弹都很难。
【在 o*******y 的大作中提到】
: 天，才知道自己真的胖。我的目标是减10磅， 20磅看来很困难。
: 弱弱地问一声，少吃碳水是指少吃些什么?
| d*i
发帖数: 9453 | 18 你说的这个不太可信啊，你都在健身房里面作什么运动了？
多久作一次？每次多长时间？强度如何？还有坚持了多长时间得出不减的结论？
另外你来美国之前，身高体重是什么情况？
【在 o*******y 的大作中提到】
: 去上班，然后下班再步行一小时，尽量快地步行，会不会减肥？
: 来美国以后胖了10磅，在健身房里做什么运动都减不了。吃得不算多，也不喝碳酸饮料
: 。
| d*i
发帖数: 9453 | 19 大家要搞清楚减脂和减体重的区别，肌肉和脂肪重量是不一样的。
当然，国内刚出来的女孩是搞不清楚这个的，
都觉得体重减了就是减脂了，体重增了就是增肥了。
顶多再知道个减/增的可能是水，至于肌肉的事情完全搞不懂。
【在 j**f 的大作中提到】
: 就是120磅？ 比我瘦，没事。不用减。
:
: 应该是超重了，158cm, 原来重50kg,现在快55kg了，还是喜欢原来的样子。
: 不是很短时间胖起来的，当然有年龄增长和新陈代谢下降的因素。
| J*L
发帖数: 2659 | | | | j**f
发帖数: 7403 | 21 我严重不支持“少吃碳水，多吃肉和蔬菜”。
肉，是个很TRICKY的东西。如果能是完全的LEAN PROTEIN，还好。如果不是，
那么，还是多吃碳水去吧。
多吃粗粮，小分量多次的吃，其实比“多吃肉”健康多了。
全世界最长寿的民族是啥？ 他们怎么吃东西？全世界吃肉多的国家是哪个？
他们健康情况如何？
我觉的还是要减，你和我一样高，比我重了将近20磅，当然你要是都是肌肉我就没话说
，不过不要把我当参照物，我是骨架小，我觉得体重在100-110磅左右都算健康吧，关
键看你多出来的是肥肉还是肌肉了，少吃碳水，多吃肉和蔬菜，快走和跑步都会有效果
的，给自己几个月，不要太着急，慢慢会瘦下来的。
【在 b**********e 的大作中提到】
: 我觉的还是要减，你和我一样高，比我重了将近20磅，当然你要是都是肌肉我就没话说
: ，不过不要把我当参照物，我是骨架小，我觉得体重在100-110磅左右都算健康吧，关
: 键看你多出来的是肥肉还是肌肉了，少吃碳水，多吃肉和蔬菜，快走和跑步都会有效果
: 的，给自己几个月，不要太着急，慢慢会瘦下来的。
| j**f
发帖数: 7403 | 22 你不一定是胖。找人借个BODY FAT的称。称一下。如果25%以内，就还好了，
不胖。
天，才知道自己真的胖。我的目标是减10磅， 20磅看来很困难。
弱弱地问一声，少吃碳水是指少吃些什么?
【在 o*******y 的大作中提到】
: 天，才知道自己真的胖。我的目标是减10磅， 20磅看来很困难。
: 弱弱地问一声，少吃碳水是指少吃些什么?
| J***n
发帖数: 210 | 23
吃肉最多的：
1. Danmark - 321.7 lb/person yearly
life expectancy 78.47 years (ranked 47th)
2. New Zealand - 313.3 lb/person yearly
life expectancy 80.48 years (ranked 22th)
3. Luxemburg - 312.4 lb/person yearly
life expectancy 79.48 (ranked 35th)
吃肉最少的：
1. Bhutan - 6.6 lb/person yearly
life expectancy 66.71 159th
2. Bangladesh - 6.8 lb/person yearly
life expectancy 69.44 149th
3. Burundi - 7.7lb /person yearly
life expectancy 58.29 192th
结论，吃肉多了没大问题，一天一磅天天吃也还好。
资料来源：
吃肉资料：
http://awesome.good.is/transparency/web/0909/let-them-eat-
meat/flash.html
寿命资料：
The World Factbook:
https://www.cia.gov/library/publications/the-world-
factbook/fields/2102.html
【在 j**f 的大作中提到】
: 我严重不支持“少吃碳水，多吃肉和蔬菜”。
: 肉，是个很TRICKY的东西。如果能是完全的LEAN PROTEIN，还好。如果不是，
: 那么，还是多吃碳水去吧。
: 多吃粗粮，小分量多次的吃，其实比“多吃肉”健康多了。
: 全世界最长寿的民族是啥？ 他们怎么吃东西？全世界吃肉多的国家是哪个？
: 他们健康情况如何？
:
: 我觉的还是要减，你和我一样高，比我重了将近20磅，当然你要是都是肌肉我就没话说
: ，不过不要把我当参照物，我是骨架小，我觉得体重在100-110磅左右都算健康吧，关
: 键看你多出来的是肥肉还是肌肉了，少吃碳水，多吃肉和蔬菜，快走和跑步都会有效果
| b***i
发帖数: 10018 | 24 The largest study ever of diet vs longevity and a host of western
diseases was the China Project, a "survey of death rates for twelve
different kinds of cancer for more than 2,400 counties and 880 million
(96%) of their citizens" combined to study the relationship between
various mortality rates and several dietary, lifestyle, and
environmental characteristics in 65 mostly rural counties in China
conducted jointly by Cornell University, Oxford University, and the
Chinese Academy of Preventive Medicine over the course of twenty years.
A strong dose-response relationship was found between the amount of
animal foods in the diet, and the top causes of mortality in the West:
heart disease, diabetes, and cancer.
http://www.ctsu.ox.ac.uk/projects/cecology1989/
【在 J***n 的大作中提到】
:
: 吃肉最多的：
: 1. Danmark - 321.7 lb/person yearly
: life expectancy 78.47 years (ranked 47th)
: 2. New Zealand - 313.3 lb/person yearly
: life expectancy 80.48 years (ranked 22th)
: 3. Luxemburg - 312.4 lb/person yearly
: life expectancy 79.48 (ranked 35th)
: 吃肉最少的：
: 1. Bhutan - 6.6 lb/person yearly
| z********0
发帖数: 9013 | 25 这几个B开头的国家医疗水平，生活质量也不怎么样吧
【在 J***n 的大作中提到】
:
: 吃肉最多的：
: 1. Danmark - 321.7 lb/person yearly
: life expectancy 78.47 years (ranked 47th)
: 2. New Zealand - 313.3 lb/person yearly
: life expectancy 80.48 years (ranked 22th)
: 3. Luxemburg - 312.4 lb/person yearly
: life expectancy 79.48 (ranked 35th)
: 吃肉最少的：
: 1. Bhutan - 6.6 lb/person yearly
| J***n
发帖数: 210 | 26
years.
这个Colin Campbell和他的China Project是有很大争议的。
下面这个是一个比较有名的反驳文章:
The China Study: Fact or Fallacy?
http://rawfoodsos.com/2010/07/07/the-china-study-fact-or-fallac
全文在这里：
http://rawfoodsos.com/2010/08/06/final-china-study-response-htm
或
http://rawfoodsos.files.wordpress.com/2010/08/minger_formal_res
f
反驳的这位美女主要从统计学批评他的。比如，蛋白摄入和胆固醇正相关，胆固醇和某
些癌症正相
关，是否就可以推出蛋白质摄入和癌症正相关呢。Campbell干嘛不去直接统计蛋白质摄
入和癌症的
相关系数呢？呵呵，很简单，他不能够。他自己的数据显示:
Lymphoma: -18
Penis cancer: -16
Rectal cancer: -12
Bladder cancer: -9
Colorectal cancer: -8
Leukemia: -5
Nasopharyngeal: -4
Cervix cancer: -4
Colon cancer: -3
Liver cancer: -3
Oesophageal cancer: +2
Brain cancer: +5
Breast cancer: +12
这部大部分是负相关的吗？！
其他还有很多批评，很多人说Campbell自己有目的的。
【在 b***i 的大作中提到】
: The largest study ever of diet vs longevity and a host of western
: diseases was the China Project, a "survey of death rates for twelve
: different kinds of cancer for more than 2,400 counties and 880 million
: (96%) of their citizens" combined to study the relationship between
: various mortality rates and several dietary, lifestyle, and
: environmental characteristics in 65 mostly rural counties in China
: conducted jointly by Cornell University, Oxford University, and the
: Chinese Academy of Preventive Medicine over the course of twenty years.
: A strong dose-response relationship was found between the amount of
: animal foods in the diet, and the top causes of mortality in the West:
| b***i
发帖数: 10018 | 27 貌似这位美女也是有目的的...
http://www.vegsource.
com/news/2010/07/china-study-author-colin-campbell-slaps-down-critic-den
ise-minger.html
Reply to Denise Minger
by Dr. T. Colin Campbell, PhD, author The China Study
Ms. Denise Minger has published a critique of our book, The China Study,
as follows.
The China Study: Fact or Fallacy? by Denise Minger - article found at:
http://bit.ly/9s5pn8
It is both interesting and gratifying that there has been such a huge
response, both on her blog and on those of others. This is a welcome
development because it gives this topic an airing that has long been
hidden in the halls and annals of science. It is time that this
discussion begin to reach a much larger audience, including both
supporters and skeptics.
I hope at some point to be able to read all of the discussions and the
questions that have been raised, but present deadlines and long-standing
commitments have forced me, for now, to focus on the most common
concerns and questions, in order to respond in a timely manner here.
the-china-study2.jpgKudos to Ms. Minger for having the interest, and
taking the time, to do considerable analysis, and for describing her
findings in readily accessible language. And kudos to her for being
clear and admitting, right up front, that she is neither a
statistician nor an epidemiologist, but an English major with a love for
writing and an interest in nutrition. We need more people with this
kind of interest.
I am the first to admit that background and academic credentials are
certainly not everything, and many interesting discoveries and
contributions have been made by "outsiders" or newcomers in various
fields. On the other hand, background, time in the field, and especially
peer review, all do give one a kind of perspective and insight that is,
in my experience, not attainable in any other way. I will try to make
clear in my comments below when this is particularly relevant.
My response can be divided into three parts, mostly addressing her
argument's lack of proportionality--what's important and what's not.
* Misunderstanding our book's objectives and my research findings
* Excessive reliance on the use of unadjusted correlations in the
China database
* Failure to note the broader implications of choosing the right
dietary lifestyle
Before proceeding further, however, I would like to make a general
comment about my approach in responding to Denise. I believe Denise
is a very intelligent person, and I can see how she might reach the
conclusions she did; this is easy to do for someone without extensive
scientific research experience. Having said this, there are fundamental
flaws in her reasoning, and it is these flaws that I will address in
this paper. Some might wonder, "Why didn't he go through her laundry
list of claims and address each one in the same order?" The answer is
simple: these claims are derived from the same faulty reasoning, so it
is this underlying problem that I will address. I do in fact illustrate
this point by addressing one of her claims regarding wheat, and the
reader can assume that one could go through a similar exercise with
all her claims.
A. Not understanding the book's objectives.
The findings described in the book are not solely based on the China
survey data, even if this survey was the most comprehensive (not the
largest) human study of its kind. As explained in the book, I draw my
conclusions from several kinds of findings and it is the consistency
among these various findings that matter most.
First and foremost, our extensive work on the biochemical fundamentals
of the casein effect on experimental cancer in laboratory animals
(only partly described in our book) was prominent because these findings
led to my suggestion of fundamental principles and concepts that
apply to the broader effects of nutrition on cancer development. These
principles were so compelling that they should apply to different
species, many nutrients, many cancers and an almost unlimited list of
health and disease responses (e.g., nutritional control of gene
expression, multi-mechanistic causation, reversal of cancer promotion
but not reversal of initiation, rapidity of nutritional response, etc.).
These principles also collectively and substantially inferred major
health benefits of whole plant-based foods.
This earlier laboratory work, extensively published in the very best
peer-reviewed journals, preceded the survey in China. These findings
established the essence of what can be called biological plausibility,
one of the most important pillars establishing the reliability of
epidemiological research. [Biological plausibility represents
established evidence showing how a cause-effect relationship works at
the biological level, one of the principles of epidemiology research
established by the epidemiology pioneer, Sir Bradford Hill.]
Unfortunately, this issue of biological plausibility too often escapes
the attention of statisticians and epidemiologists, who are more
familiar with 'number crunching' than with biological phenomena. The
first 15-20 years of our work was not, as some have speculated, an
investigation specifically focused on the carcinogenic effects of
casein. It was primarily a series of studies intended to understand
the basic biology of cancer and the role of nutrition in this disease.
The protein effect, of course, was remarkable, and for this reason, it
was a very useful tool to give us a novel insight into the workings of
the cancer process. [Nonetheless, the casein effect, which was studied
in great depth and, if judged by the formal criteria for
experimentally determining which chemicals classify as carcinogens,
places casein in the category of being the most relevant carcinogen ever
identified.]
Second, this survey in rural China, based on a very unique population
and experimental format (from several perspectives), resulted in the
collection of an exceptionally comprehensive database that, to a
considerable extent, permitted the testing of hypotheses and
principles learned in the laboratory, both mine and others. By
'testing', I mean questioning whether any evidence existed in the
China database to support a protective effect characterized by the
nutritional composition of a plant-based diet. I was not sure what might
be found but nonetheless became impressed with what was eventually
shown.
The China project data afforded an opportunity to consider the
collective interplay and effects of many potentially causative factors
with many disease outcomes--the very definition of nutrition (my
definition of nutrition is not about the isolated effects of individuals
nutrients, or even foods for that matter). The China project encouraged
us not to rely on independent statistical correlations with little or
no consideration of biological plausibility. In the book, I drew my
conclusions from six prior models of investigation to illustrate this
approach: breast cancer, liver cancer, colon cancer (minimally),
energy utilization/body weight control, affluent disease-poverty disease
and protein vs. body growth rates. Using this strategy, I first
inquired whether a collection of variables in the China survey
(ranging from univariate correlations to more sophisticated analyses)
could consistently and internally support each of these biologically
plausible models and, second, I determined whether the findings for each
of these models were consistent with the overarching hypothesis that
a whole food, plant-based diet promotes health--I could not discuss much
of this rationale in a page-limited book intended for the public.
Most importantly, I cannot emphasize enough that the findings from the
China project, standing alone, do not solely determine my final views
expressed in the book. That's why only one chapter of 18 was devoted
to the China survey project, which is only one link in a chain of
experimental approaches. I was simply asking the question whether
there were biologically plausible data in the China database to
support the findings gained in our laboratory, among others. Because
of the uniqueness of the China database, I believed that the evidence
was highly supportive. One of the unique characteristics of this
survey was the traditional dietary practices of this cohort of people.
Mostly, they were already consuming a diet largely comprised of
plant-based foods, thus limiting our ability to detect an hypothesized
plant-based food effect--thus making our final observations that much
more impressive.
Third, in the book, we summarized findings from other research groups
for a variety of diseases to determine the consistency of our model with
their findings, according to my principles and concepts. One of the
most compelling parts of this exercise was the fact that so many of
their findings, although published in good peer-reviewed journals, had
been and were continuing to be ignored and/or distorted, a very
disturbing and puzzling phenomenon. This posed for me the question, why?
My participation in extensive reviews of the work of others during my
20-year stint working on or as a member of expert committees gave me a
particularly rich opportunity to consider these previously published
studies. There still is, and for a long time has been, an intentional
effort at various levels of science hierarchy to denigrate studies
that speak to the more fundamental biology of plant-based diets. The
fact that there has been resistance, oftentimes hostile and personal
in the lay community, speaks volumes to me.
Fourth, and most importantly, there is the enormously impressive
findings of my physician colleagues, which came to my attention near the
end of the China project data collection period and which were
showing remarkable health benefits of plant-based nutrition, involving
not only disease prevention but also disease treatment (alphabetically:
Diehl, Esselstyn, Goldhamer, Klaper, McDougall, Ornish, Shintani-and
many others since the book's publication: T. Barnard, N. Barnard, Corso,
Fuhrman, Lederman, Montgomery, Popper, Pulde, Schulz, Shewman). I
cannot overemphasize the remarkable accomplishments of these primary
care physicians. In effect, their work affirmed my earlier laboratory
research. I should add that I knew none of them or their work during
my career in the laboratory, thus was not motivated or biased to find
ways to affirm their work.
It was the combination of these various lines of inquiry that made so
compelling the larger story told in the book, at least for me. Denise
mostly ignores these fundamental but highly consistent parts of my
story. In that vein, I strongly believe that the findings of no single
study in biology or even a group of similar studies should be taken
too seriously until context is established. Biology is not for engineers
and number crunchers, as important as they may be, because, compared to
their systems, biological response is much more complex and dynamic.
B. The use of 'raw' univariate correlations.
In a study like this survey in China (ecologic, cross-sectional),
univariate correlations represent one-to-one associations of two
variables, one perhaps causal, the other perhaps effect. Use of these
correlations (about 100,000 in this database) should only be done with
caution, that is, being careful not to infer one-to-one causal
associations. Even though this project provided impressive and highly
unique experimental features, using univariate correlations to
identify specific food vs. specific disease associations is not one of
these redeeming features, for several reasons. First, a variable may
reflect the effects of other factors that change along with the variable
under study. Therefore, this requires adjustment for confounding
factors--mostly, this was not done by Denise. Second, for a variable
to have information of value (as in making a correlation), it must
exhibit a sufficient range. If, for example, a variable is measured in
65 counties (as in China), there must be a distribution of values over a
sufficiently broad range for it to be useful. Third, the variables
should represent exposures representative of prior years when the
diseases in question are developing. I see little or no indication
that Denise systematically considered each of these requirements.
I should point out that when we were deciding to publish these data in
the original monograph, we decided to do something highly unusual in
science--to publish the uninterpreted raw correlations, hoping that
future researchers would know how to use or not use them. We felt that
this highly unusual decision was necessary because we were wary of those
in the West who might have doubted the validity of data collected in
China--we had several experiences to suspect this. But also, we
believe that research should be as transparent as possible, simply for
the sake of transparency, thus minimizing suspicion of hidden agendas.
We knew that taking this approach was a risk because there could be
those who, knowing little or nothing about experimentation of this type,
might wish to use the data for their own questionable purposes.
Nonetheless, we decided to be generous and, in order advise future users
of these data, we added our words of caution--written about 1988--as
part of our 894-page monograph. I also have repeated this caution in
other publications of mine. It seems that Denise missed reading this
material in the monograph.
As I was writing this, I discovered this comment from a self-described
professional epidemiologist (PhD, cancer epidemiology) on one of the
blogs (A Cancer Epidemiologist refutes Denise Mingers China Study Claims
due to incorrect data analysis - 30 Bananas a Day!)--a comment that
is relevant to the point that I am now addressing in this response.
I do not know this person but did find her comment interesting. After
reviewing Denise's critique, she wrote the following for her
(Denise's) blog, only then to see it quickly and mysteriously
disappear.
"Your analysis is completely OVER-SIMPLIFIED. Every good
epidemiologist/statistician will tell you that a correlation does NOT
equal an association. By running a series of correlations, you've merely
pointed out linear, non-directional, and unadjusted relationships
between two factors. I suggest you pick up a basic biostatistics book,
download a free copy of "R" (an open-source statistical software
program), and learn how to analyze data properly. I'm a PhD cancer
epidemiologist, and would be happy to help you do this properly. While
I'm impressed by your crude, and - at best - preliminary analyses, it is
quite irresponsible of you to draw conclusions based on these results
alone. At the very least, you need to model the data using regression
analyses so that you can account for multiple factors at one time."
This blogger is making the same point that I am making but I am
puzzled why was it deleted from Denise's blog?
Lest it be forgotten, the main value of the China data set is its
descriptive nature, thus providing a baseline against which other data
sets can be broadly compared, either over time or over geographic space.
I must emphasize: the correlations published in our monograph CANNOT be
blindly used to infer causality--at least for specific cause-effect
associations having no biological plausibility. Nonetheless, they do
offer a rich trove of opportunities to generate interesting hypotheses,
relatively few of which have been explored to date. In contrast,
using models representing biological plausibility, which was
determined from prior research, I simply wanted to see if they were
consistent with the China survey data.
For the sake of understanding the downside risk of using univariate
correlations, I'll use this imaginary conversation involving a few
correlations that Denise thought were relevant to her personal allergy
to wheat, although many other examples from Denise's treatise could
serve the same purpose.
Denise makes a point concerning a highly significant (but unadjusted)
univariate correlation between wheat flour consumption and two
cardiovascular diseases plus a couple other diseases. In doing so, she
infers that wheat flour causes these cardiovascular diseases. She also
makes the point that "none of these correlations appear to be tangled
with any risk-heightening variables, either." And further, she implies
that I ignored this potentially important correlation, perhaps
intentionally, because of my alleged bias against meat. I use this
particular example here because others who very much dislike my views
have pointed out on the Internet that this example cited by Denise
represents evidence of my lack of integrity.
The conversation goes like this, after Denise reminds me of these
univariate correlations.
"Denise, that correlation of wheat flour and heart disease is
interesting but I am not aware of any prior and biologically plausible
and convincing evidence to support an hypothesis that wheat causes these
diseases, as you infer."
"Did you, by any chance, look for evidence whether there might be
other variables confounding the wheat flour correlation, variables
that change in parallel with wheat flour consumption? I presume you
did because you said that 'none of these correlations appear to be
tangled with any risk-heightening variables.'
"But just a minute, I found some, and they're all highly statistically
significant (p<0.01 to p<0.001)."
"Higher wheat flour consumption, for example, is correlated, as
univariate correlations, with
* lower green vegetable consumption (many of these people live in
northern, arid regions where they often consume meat based diets with
little no consumption of vegetables). [By the way, Tuoli county data, to
which you refer as my "sin of omission" intentionally were excluded
from virtually all our analyses on meat consumption because this
county ranked very high when meat consumption was documented at survey
time, but much lower when responding to the questionnaire on frequency
of meat consumption. That is, these nomadic people migrate for part of
the year to valleys, where they consume more vegetables and fruits.]
* lower serum levels of monounsaturated fats (possibly increasing
risk of heart disease?)
* higher serum levels of urea (a biomarker of protein consumption)
* greater body weight (higher risk of heart disease?)"
"Interestingly, you might be interested to know that all of these
variables are known from prior knowledge, i.e., biological plausibility,
to associate with higher risk for heart disease."
"Denise, this is quite an oversight that could suggest the opposite
conclusion from the one that you intended to convey. Or was this bias
reflecting your personal preference for eating raw meat and avoiding
wheat flour? Any thoughts?"
"Why did you highlight this relationship as a key example of my "sin
of omission", being even more 'troubling than the distorted facts in The
China Study and the details that (I) leave out?'"
Incidentally, aside from Denise's claiming there were no confounding
factors, I might have taken her seriously when she posed a possible
effect of wheat flour on heart disease, because it may be possible to
gather prior evidence that could be considered as supporting the
opposite point of view. In fact, this would be a proper use of
univariate correlations, simply searching for those correlations that
might hint of supporting evidence for such an hypothesis. If
sufficiently convincing, then we could design a more analytical type
of study. This exercise is called hypothesis generation, which is one of
the virtues of the China data set. But Denise is doing something
different, coming very close to almost randomly inferring causality
without adjusting for confounding factors, without scanning the
variables for analytical authenticity and without--to my
knowledge--having prior evidence of biological plausibility for such
an hypothesis.
Then, she uses this example as evidence of a "sin of omission" and a
"distorted fact" on my part. Using these rather inflammatory words
infers serious personal indiscretion on my part. Does she really mean
this?
There are different ways of using univariate correlations in a
database like this. It is not that these correlations are useless and
should be ignored. Rather, it is a question of using them intelligently.
By this, I mean first adjusting these correlations for confounding
factors (if and when possible) then examining the individual variables
of the correlations for authenticity. Depending on the reliability of
these correlations, they may be used to guide whether a hypothetical,
cause-effect model, perhaps having preliminary evidence of biological
plausibility, is on the right track. The most critical expertise
needed for their use is knowing the underlying biology, which is so
often missing among trained statisticians.
The six models to which I referred in our book are those evaluated in
this manner. Yes, when possible, I also used univariate correlations
(along with statistical significance) in support of these models but
only after we had preliminary supportive data for the model (only
brief summarized in the book). Here are a few representative
publications of those supportive data for the six models that we
explored in our book:
Breast cancer (Marshall JR, Qu Y, Chen J, Parpia B, Campbell TC.
Additional ecologic evidence: lipids and breast cancer mortality among
women age 55 and over in China. Europ. J. Cancer 1991;28A:1720-1727; Key
TJA, Chen J, Wang DY, Pike MC, Boreham J. Sex hormones in women in
rural China and in Britain. Brit. J. Cancer 1990;62:631-636.)
Liver cancer (Campbell TC, Chen J, Liu C, Li J, Parpia B.
Non-association of aflatoxin with primary liver cancer in a
cross-sectional ecologic survey in the People's Republic of China.
Cancer Res. 1990;50:6882-6893; .Youngman LD, Campbell TC. Inhibition
of aflatoxin B1-induced gamma-glutamyl transpeptidase positive (GGT+)
hepatic preneoplastic foci and tumors by low protein diets: evidence
that altered GGT+ foci indicate neoplastic potential. Carcinogenesis
1992;13:1607-1613).
Energy utilization (Horio F, Youngman LD, Bell RC, Campbell TC.
Thermogenesis, low-protein diets, and decreased development of
AFB1-induced preneoplastic foci in rat liver. Nutr. Cancer 1991;16:
31-41:Campbell TC. Energy balance: interpretation of data from rural
China. Toxicological Sciences 1999;52:87-94).
Colon cancer (Campbell, T.C., Wang G., Chen J., Robertson, J., Chao, Z.
and Parpia, B. Dietary fiber intake and colon cancer mortality in
The People's Republic of China. In: Dietary Fiber, Chemistry Physiology
and Health Effects, (Ed. Kritchevsky, D., Bonfield, C., Anderson, W.),
Plenum Press, New York, 473-480, 1990).
Affluent-Poverty Diseases (Campbell TC, Chen J, Brun T, et al. China:
from diseases of poverty to diseases of affluence. Policy
implications of the epidemiological transition. Ecol. Food Nutr. 1992;
27:133-144).
Protein-growth rate (Campbell TC, Chen J. Diet and chronic
degenerative diseases: a summary of results from an ecologic study in
rural China. In: Temple NJ, Burkitt DP, eds. Western diseases: their
dietary prevention and reversibility. Totowa, NJ: Humana Press, 1994:
67-118; Campbell TC, Junshi C. Diet and chronic degenerative
diseases"perspectives from China. Am. J. Clin. Nutr. 1994;59:
1153S-1161S).
As I previously said, one of my interests in the China database was
simply to see if there was evidence supporting the health benefits of
a plant-based diet for these various models (and many more). The fact
that we observed a slew of statistically significant results
supporting this proposition, especially for a dietary experience
having such low total fat and animal based foods, was quite remarkable.
Did every correlation among our 100,000 show the expected? This was
my comment, verbatim, already published in our book (that Denise did not
acknowledge in her critique):
"Do I think the China Study findings constitute absolute scientific
proof? Of course not. Does it provide enough information to inform
some practical decision-making? Absolutely. An impressive and
informative web of information was emerging from this study. But does
every potential strand (or association) in this mammoth study fit
perfectly into this web of information? No. Although most
statistically significant strands readily fit into the web, there were a
few surprises. Most, but not all, have since been explained."
In summary, Denise's critique lacks a sense of proportionality. She
gives (with considerable hyperbole, at times) the analyses of the
China data more weight than they deserve by ignoring the remaining
evidence discussed in the other 17 chapters in the book. The China
research project was a cornerstone study, yes, but it was NOT the sole
determinant of my views (as I have repeated, almost ad nauseum in my
lectures). In doing so, and except for a few denigrating remarks on
our experimental animal research, she also ignores the remaining
findings that I presented in our book. She seems not to understand
what our laboratory research was showing. Using univariate
correlations mostly without adjustment for confounding factors,
qualification of variable authenticity, and/or biological plausibility
can lead to haphazard evidence, subject to the whims of personal bias.
Also, univariate correlations of this type can lead to too much emphasis
on individual nutrients and foods as potential causes of events.
Also, as I already mentioned, she questions our omission of the Tuoli
County data as if this was some sort of sleight of hand on my part (in
addition to my comments above, I already explained this omission in
one of my papers and on my preliminary blog). She over-interprets our
very limited 'dairy' data which only includes 3 counties (of 65) that
use a very different product from what we consider to be dairy. And
she continues to characterize my views in reference to veganism and
vegetarianism (I don't even use these words) as if I were motivated by
an ideology instead of by my consideration of empirical data and
biological plausibility.
Not only does Denise misrepresent and misunderstand the rationale for
the science in The China Study, her choice of words do not facilitate
what she hopes to achieve. Her overall message, often embellished with
adjectives and subjective remarks, appeals to some questionable
characters sympathetic to or subservient to the Weston A Price
Foundation, a farm lobbying group whose advocates and apologists have
accused me of being a "fraud", a "liar", a "buffoon" and (earlier) an
associate of a "terrorist" organization. I doubt that this is what she
wanted to achieve. These individuals, for much too long, have been
carelessly using or even ignoring science to further their own
interests, such as advocating for the use of a very high fat, high
protein diet mostly consistent with the diet that has caused us so
much difficulty.
This name calling means nothing to me personally but it does indicate
their desperation with our message. They would be well advised not to
use such tactics because it reflects on them, not me. Whether Denise
intended this is not clear, but the results of her critique is clearly
apparent.
I must repeat for emphasis that no single study (or even a group of
similar studies) is perfect in its design, in its data collection or
in its interpretation of results. From the perspective of developing a
research career, I see two possible paths that a researcher may follow.
One option proceeds from experiment to experiment by probing ever
deeper into the details of one of those experiments that they may happen
upon where precision of measurement matters deeply and where the
findings can become useful at some future time-indeed, they may
"happen upon" an observation that becomes their life's work very early
in this process, maybe even at the outset.
A second option proceeds 'outwardly' to better understand the broader
implications of a series of findings, or experiments. I did some of
the first but eventually preferred the second, taking each finding not
as something to refine into 'perfection' but to ask whether it was
sufficiently compelling to suggest the next obvious experiment that
eventually might lead to an important network of findings. Having done
both, I strongly prefer the latter option because the whole, indicated
by a network of findings, is often far more useful than its parts. I
also believed that this second option had more potential to meet the
interests of the public who funded our research. I also am very much
motivated by the fact that there are far too many individuals needlessly
paying a heavy personal price in their health for not having access
to information of this kind that could have saved their lives, a moral
issue for me. Under no circumstances was I controlled by what my
personal preferences might have been!
In the case of our project in China, I believe that its design, its
uniqueness and its execution are virtually without parallel in its
quality-thanks very much to my colleagues. However, as trained people
know, making specific inferences about causality is not appropriate in a
study of this kind. The concept of 'ecologic fallacy', wherein a
univariate correlation is improperly used to diagnose or to treat an
individual person, is well known. In contrast, if one initially has a
reasonably convincing and biologically plausible body of data and if the
data are appropriately qualified, then using a study like to this to
see if there is consistency, is appropriate This is appropriate in my
opinion if the hypothesis being addressed represents a comprehensive
causal effect where many factors are acting in concert and where there
may be multiple ways of examining the data (e.g., multiple factors being
consumed, multiple clinical biomarkers of factor tissue status,
multiple methods of measurement and, perhaps, even multiple outcomes).
This is what we did. We began with a collection of previously
developed cause-effect models (previously published) that we could
test for consistency with the China data. We found on balance
considerable support in the China database for these models. As I've
said many times, not all the evidence in the China database supported
this conclusion, although the large majority did. To find this degree of
consistency in a population mostly using a low fat, high fiber, whole
plant-based foods with little or no processed foods--where I had thought
that we would see little or nothing--was impressive. One cannot, as
Denise has done, rely on univariate correlations to make conclusions,
especially when they are focused on specific foods for specific
diseases--it is too easy to find what one wants to find.
I know that this discussion between Denise and me is difficult to
judge by readers of this exchange without having access to the raw
data base and without knowing how to use or interpret it. Accepting
this, therefore, I suggest that, in the final analysis, the
reliability of any conclusion about complex cause-effect issues should
be judged by its ability to predict health outcomes. In this case, the
results of people using a diet of whole, plant-based foods, as shown
by physician colleagues (previously mentioned, McDougall, Esselstyn,
Ornish, Barnard, Fuhrman, et al) as well as by many of the readers of
our book are nothing less than incredible. There is nothing else in
medicine like it!
C. Denise's failure to note the broader implications of choosing the
right dietary lifestyle.
I suggest that those people who are so hostile to this message take
another look at their reasoning. There is far more to this story than
the interpretation of the scientific data alone. There are major
issues of health care and health care costs, there are serious
environmental issues that have not been adequately communicated to the
public, and there are political, social and ethical issues that must
be considered. Of most importance, there are people who deserve to
hear this message--namely, the taxpayers who funded this work. For me to
do anything less than to report on these findings is both immoral and
unethical. In the current discussions about this issue, I would urge
that it is vitally important that all of us keep these ideas in mind,
while being very careful not to promote ideas simply for the sake of
defending one's own personal preferences. I strongly believe that
discussion of these issues focus outwardly for the sake of all of us,
not just inwardly for the satisfaction of personal ego.
My greatest mistake throughout this process may have been our
acquiescence to our publisher's choice for our book's title. We
suggested 200 possible titles, not one of which was 'The China Study'.
But when we objected, he said that we already had signed the contract
and this was his right and responsibility. We felt locked in, especially
because we had already explored publication with about 10 other
publishers, some of whom had offered advances (one very large), if we
did it their way. Because we had refused to accept their suggestions
(including at least half the book as recipes, going easy on the
references and 'dumbing down' the language), it seemed clear that we had
no other choice than to go along with our new publisher who accepted
our way of telling this story.
Obviously, the title of our book has been misleading for some because of
the inappropriate weight suggested by the China project itself. When
these rather novel data are considered both in reference to biologically
plausible, multi-factor models of causation and in reference to the
large body of other kinds of studies discussed in the book, the China
project database becomes very important. But relying on the results of
this study in isolation, especially when unadjusted univariate
correlations are used, is not appropriate.
I should conclude by noting the suggestion of the professional
epidemiologist, cited above, who suggested that ultimately Denise may
wish to publish her findings in a peer-reviewed journal but who
presently felt strongly that the current version would not be accepted.
I concur.
【在 J***n 的大作中提到】
:
: years.
: 这个Colin Campbell和他的China Project是有很大争议的。
: 下面这个是一个比较有名的反驳文章:
: The China Study: Fact or Fallacy?
: http://rawfoodsos.com/2010/07/07/the-china-study-fact-or-fallac
: 全文在这里：
: http://rawfoodsos.com/2010/08/06/final-china-study-response-htm
: 或
: http://rawfoodsos.files.wordpress.com/2010/08/minger_formal_res
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