Even as kids growing up in the 1930-40s we knew smoking wasn’t good for you. In fact, we said if you smoked a cigarette you were driving another nail in your coffin. Sure enough after years of research it was decided that smoking does cause lung cancer and other problems. But, the question for today is does it increase breast cancer?
In a new paper just published the answer is: Yes and No and Maybe (Xue et al 2011 Arch Intern Med 171 125). As usual, this epidemiological study presents marginal data some of which they say show a “modest” increase risk of breast cancer associated with smoking.
For example: for women who never smoked the absolute risk of breast cancer was 0.28% compared to those who smoked, 0.30% or an increase of 0.02%. Or put another way the odds are 28/10,000 if you don’t smoke and 30/10,000 if you do. Such a small difference cannot be considered significant, even though they say it is.
Other subgroup studies indicated some more marginal, just barely significant data. The more interesting subgroup study suggested that smoking before menopause was associated marginally with a slight increase incidence of breast cancer, whereas smoking during the postmenopausal period was associated with a consistent and significant decrease.
The most interesting data which the authors fail to discuss is in a subgroup consisting of women who smoked and took postmenopausal hormone therapy: The surprising answer: Whether they smoked or not there was no increase in breast cancer. How could the authors not mention that this finding contradicts the prevailing and incorrect assumption that postmenopausal hormone treatment increases the incidence of breast cancer??? Could it be that they are embarrassed by the possibility that they were wrong about this important point???
At any rate, the paper is just another example of the kind of epidemiological data which should never have been published. All of you smokers out there need not worry about breast cancer, that is the least of your worries.
Wednesday, January 26, 2011
Tuesday, December 21, 2010
WHAT IS THE TRUTH ABOUT FAT AND DEATH
One day you read in the paper or hear on TV that being overweight will cause you to die earlier. The following week you hear just the opposite. So what should you believe? Well, it is not easy unless you read the original epidemiological literature and analyze the results yourself.
But, you don’t have to do that because I have already done it.
Recently I posted a blog concerning body weight and death in which I summarized two recent studies which showed that bodyweight and death were related by a U-shaped curve (shown below). That is, if you are too skinny or too fat (obese) you are at a greater risk of death and that just being overweight did not increase the risk.
The data shown in the figure below was taken form McGee DL (2005) Ann Epidemiol 15:87 and Adams KF (2006) N Engl J Med 355:763
As you can see, if you weight too much or too little your risk of death is increased by approximately 1 to 2 percent as compared with the normal weight people. The group numbers indicate bodyweight category: 1. Underweight, 2-4. Normal weight, 5-7 Overweight and 8-10 Obese. These data are for men but the data for women are very similar.
It is clear from these data that people in the overweight groups of 5-7 are not at any greater risk of death than the normal weight groups 3-4. But there is a greater risk for being underweight (groups 1 and 2) or obese (groups 9 and 10).
NEW STUDY
Now a new study says that both overweight and obese people are at greater risk of death (de Gonzalez et al 2010 N Engl J Med 363 2211). So what should you believe?
As noted above, in the field of epidemiology, which is where all of these kinds of studies come from, disagreement almost always exists. In the case of bodyweight and death the disagreement level is greater than usual and downright ridiculous. For example:
• AT LEAST 4 STUDIES HAVE PROPOSED THAT THERE IS NO DISTINCT RELATION.
• OTHERS HAVE PROPOSED THAT THE RELATIONSHIP IS J-SHAPED (7 STUDIES)
• SEVEN STUDIES SAY THE CURVE IS U-SHAPED AS I DESCRIBED IN MY PREVIOUS BLOG
• AND 6 STUDIES SAY THE RELATIONSKIP IS LINEAR AND 4 SAY ITS INVERSELY LINEAR
So if the data and conclusions are so contradictory why go on? Why not just accept that the fact that the truth about fat and death will never be clear? Because very recently a paper was published which claims it has uncovered the truth and all other studies should be ignored (de Gonzalez et al 2010 N Engl J Med 363 2211). Right off the bat you realize, it can’t be true, so what is the real story.
This group has combined a large study which made up of 57 other studies (Prospective Studies Collaboration 2009 Lancet 373 1083) and added four more in order to come up with their own set of data from which they conclude that being overweight or obese will cause you to kick off sooner.
However, The Prospective Study showed the same U-shaped curve as we have already discussed. The only difference is that more groups were added in the Obese Category which made the curve slightly J-shaped. The values for Normal Weight average 9.6/yearly deaths /1000 and 9.8/yearly deaths/1000 for the Overweight group. Clearly not different. Hence being overweight poses no addition risk of death. The real skinny and obese groups were at a greater risk of early death but the overweight were not.
The deGonzalez group has taken the above data and combined it with four other studies and the combination was analyzed. Of the four studies listed two had no useable data and the other two are discussed below.
One of these by Hu et al (2004 N Engl J Med 351 2694) shows a linear trend of increase risk of death as a function of body mass index.
This is an example of an upward linear relationship and does not agree with most other studies. However the inclusion of these data in the de Gonzalez study decreases the upward curve of the underweight people and makes it appear that if there is a risk in this group it is very low. Notice that the difference between normal weight (2-3) and overweight (4-6) is 0.51-0.34= 0.17% which is not significant.
The study by Baik et al (2000 Am J Epidemiol 152 264) indicates that underweight people are at somewhat greater risk of death and that normal weight and overweight people are have no significant increase in risk. Whereas obese people are at somewhat greater risk.
Curiously, the study by Klenk et al (2009 Eur J Epidemiol 24 83) was not added to the list. Could it be because the data in Klenk et al 2009 show a U-shaped curve with conclusions about overweight people similar to those discussed above and obtained from McGee 2005 and Adams 2006.
In their final analysis de Gonzalez et al conclude that overweight and obese people are at greater risk of death. However, the small differences between overweight and normal groups are very small and of no biological significance. The authors are able to claim statistical significance because the number of subjects is so large. Thus many studies (some good, some poor, and some bad) many of which are not statistically significant can be brought together and suddenly statistical significance pops out. It is my opinion and the opinion of many others that such lumping of diverse studies into one analysis is a crock of statistical crap.
Obviously I believe that the U-shaped curve holds and overweight people are not at any greater risk of death than normal weight people.
But, you don’t have to do that because I have already done it.
Recently I posted a blog concerning body weight and death in which I summarized two recent studies which showed that bodyweight and death were related by a U-shaped curve (shown below). That is, if you are too skinny or too fat (obese) you are at a greater risk of death and that just being overweight did not increase the risk.
The data shown in the figure below was taken form McGee DL (2005) Ann Epidemiol 15:87 and Adams KF (2006) N Engl J Med 355:763
As you can see, if you weight too much or too little your risk of death is increased by approximately 1 to 2 percent as compared with the normal weight people. The group numbers indicate bodyweight category: 1. Underweight, 2-4. Normal weight, 5-7 Overweight and 8-10 Obese. These data are for men but the data for women are very similar.
It is clear from these data that people in the overweight groups of 5-7 are not at any greater risk of death than the normal weight groups 3-4. But there is a greater risk for being underweight (groups 1 and 2) or obese (groups 9 and 10).
NEW STUDY
Now a new study says that both overweight and obese people are at greater risk of death (de Gonzalez et al 2010 N Engl J Med 363 2211). So what should you believe?
As noted above, in the field of epidemiology, which is where all of these kinds of studies come from, disagreement almost always exists. In the case of bodyweight and death the disagreement level is greater than usual and downright ridiculous. For example:
• AT LEAST 4 STUDIES HAVE PROPOSED THAT THERE IS NO DISTINCT RELATION.
• OTHERS HAVE PROPOSED THAT THE RELATIONSHIP IS J-SHAPED (7 STUDIES)
• SEVEN STUDIES SAY THE CURVE IS U-SHAPED AS I DESCRIBED IN MY PREVIOUS BLOG
• AND 6 STUDIES SAY THE RELATIONSKIP IS LINEAR AND 4 SAY ITS INVERSELY LINEAR
So if the data and conclusions are so contradictory why go on? Why not just accept that the fact that the truth about fat and death will never be clear? Because very recently a paper was published which claims it has uncovered the truth and all other studies should be ignored (de Gonzalez et al 2010 N Engl J Med 363 2211). Right off the bat you realize, it can’t be true, so what is the real story.
This group has combined a large study which made up of 57 other studies (Prospective Studies Collaboration 2009 Lancet 373 1083) and added four more in order to come up with their own set of data from which they conclude that being overweight or obese will cause you to kick off sooner.
However, The Prospective Study showed the same U-shaped curve as we have already discussed. The only difference is that more groups were added in the Obese Category which made the curve slightly J-shaped. The values for Normal Weight average 9.6/yearly deaths /1000 and 9.8/yearly deaths/1000 for the Overweight group. Clearly not different. Hence being overweight poses no addition risk of death. The real skinny and obese groups were at a greater risk of early death but the overweight were not.
The deGonzalez group has taken the above data and combined it with four other studies and the combination was analyzed. Of the four studies listed two had no useable data and the other two are discussed below.
One of these by Hu et al (2004 N Engl J Med 351 2694) shows a linear trend of increase risk of death as a function of body mass index.
This is an example of an upward linear relationship and does not agree with most other studies. However the inclusion of these data in the de Gonzalez study decreases the upward curve of the underweight people and makes it appear that if there is a risk in this group it is very low. Notice that the difference between normal weight (2-3) and overweight (4-6) is 0.51-0.34= 0.17% which is not significant.
The study by Baik et al (2000 Am J Epidemiol 152 264) indicates that underweight people are at somewhat greater risk of death and that normal weight and overweight people are have no significant increase in risk. Whereas obese people are at somewhat greater risk.
Curiously, the study by Klenk et al (2009 Eur J Epidemiol 24 83) was not added to the list. Could it be because the data in Klenk et al 2009 show a U-shaped curve with conclusions about overweight people similar to those discussed above and obtained from McGee 2005 and Adams 2006.
In their final analysis de Gonzalez et al conclude that overweight and obese people are at greater risk of death. However, the small differences between overweight and normal groups are very small and of no biological significance. The authors are able to claim statistical significance because the number of subjects is so large. Thus many studies (some good, some poor, and some bad) many of which are not statistically significant can be brought together and suddenly statistical significance pops out. It is my opinion and the opinion of many others that such lumping of diverse studies into one analysis is a crock of statistical crap.
Obviously I believe that the U-shaped curve holds and overweight people are not at any greater risk of death than normal weight people.
Saturday, December 18, 2010
DETECTION OF BREAST CANCER IS NOT THE SAME AS INCREASED RISK (PART 1)
By James H. Clark
The following is a short summary of my paper which explains why postmenopausal hormones do not cause breast cancer, but instead, increase the level of detection which is a good thing.
Almost all of the studies published concerning postmenopausal hormone replacement therapy (HRT) use the phrase: The risk of breast cancer was increased as a result of HRT. The implication is that during the study HRT caused the cancer to form and grow to a size which could be seen by mammographic screening. This is very unlikely because breast cancers require from 10 to 20 years or more to reach a size that is detectable. Since studies of HRT are usually 5 years or shorter it would be impossible to observe a hormone initiated cancer.
So what are these scientists observing? They are detecting hidden tumors. In some women breast tumors exist in a hidden state (occult) and are not big enough to be detected. However, since some of these tumors are hormone sensitive they grow from the occult size to a size which can be detected by screening and are mistakenly labeled hormone induced cancer.
So every time you read in the paper that some HRT increases the risk of breast cancer, just substitute detection for risk. HRT is not causing breast cancer it is just detecting hidden cancers which can be a good thing.
The following paper explains in more detail the summary points made above.
POSTMENOPAUSAL HORMONE TREATMENT DOES NOT INCREASE THE RISK OF BREAST CANCER (PART 2)
By James H. Clark
Most papers published on the relationship between estrogen and progestin replacement therapy (EPHRT) of postmenopausal women indicate that such treatment increases slightly the risk of breast cancer. The implication being that these hormones cause breast cancer. Although these results have been discounted by several investigators, their conclusions continued to be quoted as fact in the scientific, news and television media. The purpose of this paper is to show that such studies cannot claim that EPHRT increases the risk of breast cancer. Instead what they have been studying is the small increases in the growth of preexisting cancers which were too small to be detected at the beginning of the studies. Therefore, the only claim they can make is the EPHRT may increase the detection of occult cancers and this may be a good thing.
BREAST CANCER GROWTH: TIME BETWEEN INCEPTION AND DETECTION
The length of most EPHRT studies is too short for an increased incidence of BC to be detected if EPHRT were the cause of this increase. This is true because the time between initiation of breast cancer and the time that cancer can be detected varies between 10 to 20 years (Dietel et al. 2005; von Fournier et al. 1980; Koscielny et al. 1985). Since most EPHRT studies are between 5 and 10 yrs any tumor that was initiated by hormone treatment would not be apparent for at least 10 yrs and probably more.
As pointed out by Dietel et al (2005) these estimates of time between inception and detection are based on calculations of tumor doubling time (TDT) which has been reported to be between 23 and 209 days (Spratt et al., 1977; Shackney et al., 1978; Spratt and Spratt, 1985; Haskell, 1985). All things considered the average TDT is considered to be 50-100 days (Spratt et al., 1995). Since a mammary tumor cannot be detected until it reaches approximately 1 cm and would contain approximately 10 billion cells, the time to produce this number of cells would be between 5 and 10 years. This time period is an underestimate because the loss of cells due to apoptosis and the length of the carcinoma in situ, which is very variable and can be several years, are not taken into consideration.
RELATIOSHIP BETWEEN ESTROGEN ONLY (ERT) AND ESTROGEN PLUS PROGESTIN (EPRT)
Most studies of EHRT have shown no increased detection of breast canceer and some have shown a decrease. In contrast, EPHRT studies consistently show a small or marginal increase in detection of breast cancer. This probably occurs as a result of estrogen plus progesterone stimulating the more highly differentiated occult cancers which contain receptors for these two hormones. Progesterone appears to have angiogenic effects which may be the cause of an additional effect on growth which is not seen with estrogen alone (Liang 2007 Cancer Res 67 9929; 32: Hyder 1998 Cancer Res 58 392). Whereas, estrogen only exposure may not produce a significant growth response because the tumor is less well differentiated and is incapable of a growth response.
HRT, CANCER CHARACTERISTICS AND MORTALITY
Increased detection as a result of EPHRT means that tumors could be treated sooner and this potentially could reduce morality. This may be the case since these tumors have favorable characteristics which are associated with decreased mortality. Many investigators have found a such characteristics associated with decreased mortality (Kerlinkowske 2003; Newcomb et al 2008)
The increased detection could be due to more frequent screening of women who use HRT than nonusers. However even in studies which adjust for screening bias the tumors HRT tend to be smaller ( Bonnier 1995; Magnusson 1996) of lower grade ( Harding 1996), less advanced stage (Holli 1998; Christante 2008) have fewer positive axillary lymph nodes (Bonnier 1995; Magnusson 1996, Hardin 1996; Squitieeri 1994), lower tumor cell proliferation rate (Oestreicher 2004; Holli 1998, and have other clinically more favorable features (Schnitt 2001; Holli 1998; Chen 2004; Rosenberg 2008; Newcomb 2008).
In contrast to the above studies Chlebowski 2010 found that mortality was increased by EPHRT in the WHI studies. The WHI authors explain that other studies are not as valid as those of the WHI because they were not randomized placebo controlled trials. However it is difficult to disregard the many studies which do not agree with WHI especially since some of them are just as valid, if not more so, than those of the WHI. The WHI studies were far from perfect and have been criticized at length by many scientists.
For instance: Many investigators agree that the WHI study does not even qualify as a randomized placebo-controlled study which is supposed to be superior to other types of studies. The reasons for this statement are: 1. Following randomization the women were free to decide whether to continue their assigned treatment or whether to undergo diagnostic procedures. 2. Almost half of the women were aware of their treatment so there was no valid placebo group. 3. Several warnings were sent to the participants about the detection of increased risks of myocardial infarction, stroke and pulmonary embolism during the study. These problems make the WHI study no better than any observational study with all of their limitations (Clark JH 2006).
DECLINE IN BREAST CANCER DETECTION AFTER WITHDRAWAL FROM HRT
Following the release of the WHI results in 2002 there was a decline in the number of detected breast cancers which was associated with a decrease in the number of women taking EPRT. Many investigators have taken this as evidence that EPRT causes breast cancer. However, it is much more likely that the decreased number of breast cancer was due to a decreased detection of occult tumors which would have been observed if EPRT had continued.
As Berry and Ravdin (2007) explained: If EPRT caused breast cancer and millions of women stopped taking EPRT there would be a long slow decline in the number of breast cancers, not a rapid decline between 2002 and 2003 as has been reported. Breast cancer has a long preclinical period that varies in duration from one tumor to another. So a drop in breast cancer incidence would be gradual and not discernable for several years after EPRT was stopped.
A more likely possibility is that a cessation of EPRT removes the hormonal stimulation of receptor positive cancers and their growth rate decreases. If this were the case a discernable and rapid drop in breast cancer numbers would be observed. To use a modified example from Berry and Ravidin (2007): Consider 30 to 40/100,000 women with receptor positive cancers that were growing and destined to be detected in 2003. They had negative mammograms in 2002 and then when the WHI results were announced they stopped taking EPRT. Without the hormonal stimulation their cancers stopped growing and were not detected by the mammograms in 2003. So the overall decrease on breast cancer incidence rates would be immediate and noticeable. This seems to be the case since the rate of decline of breast cancers was rapid between 2002 and 2003 and was parallel to receptor positive cancers (Ravdin 2007).
SUMMARY
The preponderance of evidence indicates that EPHRT stimulates some occult breast cancers to grow to a size which is detectable by screening. Such tumors were not caused by the hormonal exposure because the length of time of most studies is too short for tumors to grow to a detectable size. Therefore these hormone responsive tumors are being detected before they would be in nonusers of hormone therapy. This appears to be a good thing since these tumors have favorable characteristics and are associated with a decreased rate of mortality.
NOTE IN ADDED PROOF
The Endocrine Society just published a 65 page review of HRT which agrees and substantiates my views (Santen 2010 J Clin Endocrinol Metabol 95 S1-S66).
REFERENCES
Berry DA, Ravdin PM (2007) Breast cancer trials: A marriage between clinical trial evidence and epidemiology. JNCI 99: 1139-1141
Bonnier et al.(1995) Clinical and biologic prognostic factors in breast cancer diagnosed during postmenopausal hormone replacement therapy. Obstet Gynecol 85:11–7
Chen et al (2004) Association of hormone replacement therapy to estrogen and progesterone receptor status in invaseive breast carcinoma. Cancer 101 1490
Chlebowski et al 2010 Estrogen Plus Progestin and Breast Cancer Incidence and Mortality in Postmenopausal Women. JAMA 304 1684
Clark (2006) A critique of the Women’s Health Initiative Studies: estrogen plus progestin (Prempro). Nuclear Receptor Signaling 4, e023
Dietel et al. (2005) Hormone replacement therapy: pathobiological aspects of hormone-sensitive cancers in women relevant to epidemiological studies on HRT: a mini-review. Human Reproduction 20:2052-2060.
Harding et al.(1996) Hormone replacement therapy and tumour grade in breast cancer: prospective study in screening unit. BMJ 312:1646–7.
Haskell CM (1985) Thorax, Unknown Primary—Breast Cancer in Cancer Treatment, 2nd edn. WB Saunders Company
Holli (1998) Low biologic aggressiveness in breast cancer in women using hormone replacement therapy. Clin Oncol 16(9):3115-20.
Hyder et al.(1998) Progestin regulation of vascular endothelial growth factor in human breast cancer cells. Cancer Res 58 392
Kerlikowske et al. (2003) Prognostic characteristics of breast cancer among postmenopausal hormone users in a screened population. J Clin Oncol 21:4314–21.
Koscielny S (1985) A simulation model of the natural history of human breast cancer. Br J Cancer 52:515-524.
Liang et al. (2007) Progestin dependent progression of human breast tumor xenograpfts : a novel model for evaluating antitumor therapeutics. Cancer Res 67 9929
Oestreiche (2004) Hormonal factors and breast tumor proliferation: do factors that affect cancer risk also affect tumor growth? Breast Cancer Res Treat 85:133–42
O'Meara (2001) Hormone replacement therapy after a diagnosis of breast cancer in relation to recurrence and mortality. J Natl Cancer Inst 93:754–61.
Ravdin (2007) et al. The decrease in breast-cancer incidence in 2003 in the United States . NEngl J Med 356 : 1670 – 4 .
Schuetz (2007) Reduced incidence of distant metastases and lower mortality in 1072 patients with breast cancer with a history of hormone replacement therapy. Am J Obstet Gynecol. 196(4):342.e1-9.
Sener (2009) The effects of hormone replacement therapy on postmenopausal breast cancer biology and survival. Am J Surg. 197(3):403-7.
Schnitt (2001) Traditional and newer pathologic factors. J Natl Cancer Inst Monogr 30:22–6.
Shackney et al.(1978) Growth rate patterns of solid tumors and their relation to responsiveness to therapy: an analytical review. Ann Intern Med 89, 107–121.
Spratt JS and Spratt JA (1985) What is breast cancer doing before we can detect it? J Surg Oncol 30, 156–160.
Spratt (1977) Cytokinetic definition of acute and chronic breast cancer. Cancer Res 37, 226–230.
Spratt et al (1995) Rates of growth of human solid neoplasms: part I. J Surg Oncol 60, 137–146.
Squitieri et al. (1994) Carcinoma of the breast in postmenopausal hormone user and nonuser control groups. J Am Coll Surg 178:167–70.
Rosenberg et al. (2008) Menopausal hormone therapy in relation to breast cancer characteristics and prognosis: a cohort study. Breast Cancer Research 10;R78 (doi:10.1186/bcr2145)
Newcomb et al (2008) Prediagonistic use of hormone therapy and mortality after breast cancer. Canceer Epidemiol Biomaarkers Prev 17:864-871.
von Fournier et al. (1980)Growth rate of 147 mammary carcinomas. Cancer 45:2198-2207.
The following is a short summary of my paper which explains why postmenopausal hormones do not cause breast cancer, but instead, increase the level of detection which is a good thing.
Almost all of the studies published concerning postmenopausal hormone replacement therapy (HRT) use the phrase: The risk of breast cancer was increased as a result of HRT. The implication is that during the study HRT caused the cancer to form and grow to a size which could be seen by mammographic screening. This is very unlikely because breast cancers require from 10 to 20 years or more to reach a size that is detectable. Since studies of HRT are usually 5 years or shorter it would be impossible to observe a hormone initiated cancer.
So what are these scientists observing? They are detecting hidden tumors. In some women breast tumors exist in a hidden state (occult) and are not big enough to be detected. However, since some of these tumors are hormone sensitive they grow from the occult size to a size which can be detected by screening and are mistakenly labeled hormone induced cancer.
So every time you read in the paper that some HRT increases the risk of breast cancer, just substitute detection for risk. HRT is not causing breast cancer it is just detecting hidden cancers which can be a good thing.
The following paper explains in more detail the summary points made above.
POSTMENOPAUSAL HORMONE TREATMENT DOES NOT INCREASE THE RISK OF BREAST CANCER (PART 2)
By James H. Clark
Most papers published on the relationship between estrogen and progestin replacement therapy (EPHRT) of postmenopausal women indicate that such treatment increases slightly the risk of breast cancer. The implication being that these hormones cause breast cancer. Although these results have been discounted by several investigators, their conclusions continued to be quoted as fact in the scientific, news and television media. The purpose of this paper is to show that such studies cannot claim that EPHRT increases the risk of breast cancer. Instead what they have been studying is the small increases in the growth of preexisting cancers which were too small to be detected at the beginning of the studies. Therefore, the only claim they can make is the EPHRT may increase the detection of occult cancers and this may be a good thing.
BREAST CANCER GROWTH: TIME BETWEEN INCEPTION AND DETECTION
The length of most EPHRT studies is too short for an increased incidence of BC to be detected if EPHRT were the cause of this increase. This is true because the time between initiation of breast cancer and the time that cancer can be detected varies between 10 to 20 years (Dietel et al. 2005; von Fournier et al. 1980; Koscielny et al. 1985). Since most EPHRT studies are between 5 and 10 yrs any tumor that was initiated by hormone treatment would not be apparent for at least 10 yrs and probably more.
As pointed out by Dietel et al (2005) these estimates of time between inception and detection are based on calculations of tumor doubling time (TDT) which has been reported to be between 23 and 209 days (Spratt et al., 1977; Shackney et al., 1978; Spratt and Spratt, 1985; Haskell, 1985). All things considered the average TDT is considered to be 50-100 days (Spratt et al., 1995). Since a mammary tumor cannot be detected until it reaches approximately 1 cm and would contain approximately 10 billion cells, the time to produce this number of cells would be between 5 and 10 years. This time period is an underestimate because the loss of cells due to apoptosis and the length of the carcinoma in situ, which is very variable and can be several years, are not taken into consideration.
RELATIOSHIP BETWEEN ESTROGEN ONLY (ERT) AND ESTROGEN PLUS PROGESTIN (EPRT)
Most studies of EHRT have shown no increased detection of breast canceer and some have shown a decrease. In contrast, EPHRT studies consistently show a small or marginal increase in detection of breast cancer. This probably occurs as a result of estrogen plus progesterone stimulating the more highly differentiated occult cancers which contain receptors for these two hormones. Progesterone appears to have angiogenic effects which may be the cause of an additional effect on growth which is not seen with estrogen alone (Liang 2007 Cancer Res 67 9929; 32: Hyder 1998 Cancer Res 58 392). Whereas, estrogen only exposure may not produce a significant growth response because the tumor is less well differentiated and is incapable of a growth response.
HRT, CANCER CHARACTERISTICS AND MORTALITY
Increased detection as a result of EPHRT means that tumors could be treated sooner and this potentially could reduce morality. This may be the case since these tumors have favorable characteristics which are associated with decreased mortality. Many investigators have found a such characteristics associated with decreased mortality (Kerlinkowske 2003; Newcomb et al 2008)
The increased detection could be due to more frequent screening of women who use HRT than nonusers. However even in studies which adjust for screening bias the tumors HRT tend to be smaller ( Bonnier 1995; Magnusson 1996) of lower grade ( Harding 1996), less advanced stage (Holli 1998; Christante 2008) have fewer positive axillary lymph nodes (Bonnier 1995; Magnusson 1996, Hardin 1996; Squitieeri 1994), lower tumor cell proliferation rate (Oestreicher 2004; Holli 1998, and have other clinically more favorable features (Schnitt 2001; Holli 1998; Chen 2004; Rosenberg 2008; Newcomb 2008).
In contrast to the above studies Chlebowski 2010 found that mortality was increased by EPHRT in the WHI studies. The WHI authors explain that other studies are not as valid as those of the WHI because they were not randomized placebo controlled trials. However it is difficult to disregard the many studies which do not agree with WHI especially since some of them are just as valid, if not more so, than those of the WHI. The WHI studies were far from perfect and have been criticized at length by many scientists.
For instance: Many investigators agree that the WHI study does not even qualify as a randomized placebo-controlled study which is supposed to be superior to other types of studies. The reasons for this statement are: 1. Following randomization the women were free to decide whether to continue their assigned treatment or whether to undergo diagnostic procedures. 2. Almost half of the women were aware of their treatment so there was no valid placebo group. 3. Several warnings were sent to the participants about the detection of increased risks of myocardial infarction, stroke and pulmonary embolism during the study. These problems make the WHI study no better than any observational study with all of their limitations (Clark JH 2006).
DECLINE IN BREAST CANCER DETECTION AFTER WITHDRAWAL FROM HRT
Following the release of the WHI results in 2002 there was a decline in the number of detected breast cancers which was associated with a decrease in the number of women taking EPRT. Many investigators have taken this as evidence that EPRT causes breast cancer. However, it is much more likely that the decreased number of breast cancer was due to a decreased detection of occult tumors which would have been observed if EPRT had continued.
As Berry and Ravdin (2007) explained: If EPRT caused breast cancer and millions of women stopped taking EPRT there would be a long slow decline in the number of breast cancers, not a rapid decline between 2002 and 2003 as has been reported. Breast cancer has a long preclinical period that varies in duration from one tumor to another. So a drop in breast cancer incidence would be gradual and not discernable for several years after EPRT was stopped.
A more likely possibility is that a cessation of EPRT removes the hormonal stimulation of receptor positive cancers and their growth rate decreases. If this were the case a discernable and rapid drop in breast cancer numbers would be observed. To use a modified example from Berry and Ravidin (2007): Consider 30 to 40/100,000 women with receptor positive cancers that were growing and destined to be detected in 2003. They had negative mammograms in 2002 and then when the WHI results were announced they stopped taking EPRT. Without the hormonal stimulation their cancers stopped growing and were not detected by the mammograms in 2003. So the overall decrease on breast cancer incidence rates would be immediate and noticeable. This seems to be the case since the rate of decline of breast cancers was rapid between 2002 and 2003 and was parallel to receptor positive cancers (Ravdin 2007).
SUMMARY
The preponderance of evidence indicates that EPHRT stimulates some occult breast cancers to grow to a size which is detectable by screening. Such tumors were not caused by the hormonal exposure because the length of time of most studies is too short for tumors to grow to a detectable size. Therefore these hormone responsive tumors are being detected before they would be in nonusers of hormone therapy. This appears to be a good thing since these tumors have favorable characteristics and are associated with a decreased rate of mortality.
NOTE IN ADDED PROOF
The Endocrine Society just published a 65 page review of HRT which agrees and substantiates my views (Santen 2010 J Clin Endocrinol Metabol 95 S1-S66).
REFERENCES
Berry DA, Ravdin PM (2007) Breast cancer trials: A marriage between clinical trial evidence and epidemiology. JNCI 99: 1139-1141
Bonnier et al.(1995) Clinical and biologic prognostic factors in breast cancer diagnosed during postmenopausal hormone replacement therapy. Obstet Gynecol 85:11–7
Chen et al (2004) Association of hormone replacement therapy to estrogen and progesterone receptor status in invaseive breast carcinoma. Cancer 101 1490
Chlebowski et al 2010 Estrogen Plus Progestin and Breast Cancer Incidence and Mortality in Postmenopausal Women. JAMA 304 1684
Clark (2006) A critique of the Women’s Health Initiative Studies: estrogen plus progestin (Prempro). Nuclear Receptor Signaling 4, e023
Dietel et al. (2005) Hormone replacement therapy: pathobiological aspects of hormone-sensitive cancers in women relevant to epidemiological studies on HRT: a mini-review. Human Reproduction 20:2052-2060.
Harding et al.(1996) Hormone replacement therapy and tumour grade in breast cancer: prospective study in screening unit. BMJ 312:1646–7.
Haskell CM (1985) Thorax, Unknown Primary—Breast Cancer in Cancer Treatment, 2nd edn. WB Saunders Company
Holli (1998) Low biologic aggressiveness in breast cancer in women using hormone replacement therapy. Clin Oncol 16(9):3115-20.
Hyder et al.(1998) Progestin regulation of vascular endothelial growth factor in human breast cancer cells. Cancer Res 58 392
Kerlikowske et al. (2003) Prognostic characteristics of breast cancer among postmenopausal hormone users in a screened population. J Clin Oncol 21:4314–21.
Koscielny S (1985) A simulation model of the natural history of human breast cancer. Br J Cancer 52:515-524.
Liang et al. (2007) Progestin dependent progression of human breast tumor xenograpfts : a novel model for evaluating antitumor therapeutics. Cancer Res 67 9929
Oestreiche (2004) Hormonal factors and breast tumor proliferation: do factors that affect cancer risk also affect tumor growth? Breast Cancer Res Treat 85:133–42
O'Meara (2001) Hormone replacement therapy after a diagnosis of breast cancer in relation to recurrence and mortality. J Natl Cancer Inst 93:754–61.
Ravdin (2007) et al. The decrease in breast-cancer incidence in 2003 in the United States . NEngl J Med 356 : 1670 – 4 .
Schuetz (2007) Reduced incidence of distant metastases and lower mortality in 1072 patients with breast cancer with a history of hormone replacement therapy. Am J Obstet Gynecol. 196(4):342.e1-9.
Sener (2009) The effects of hormone replacement therapy on postmenopausal breast cancer biology and survival. Am J Surg. 197(3):403-7.
Schnitt (2001) Traditional and newer pathologic factors. J Natl Cancer Inst Monogr 30:22–6.
Shackney et al.(1978) Growth rate patterns of solid tumors and their relation to responsiveness to therapy: an analytical review. Ann Intern Med 89, 107–121.
Spratt JS and Spratt JA (1985) What is breast cancer doing before we can detect it? J Surg Oncol 30, 156–160.
Spratt (1977) Cytokinetic definition of acute and chronic breast cancer. Cancer Res 37, 226–230.
Spratt et al (1995) Rates of growth of human solid neoplasms: part I. J Surg Oncol 60, 137–146.
Squitieri et al. (1994) Carcinoma of the breast in postmenopausal hormone user and nonuser control groups. J Am Coll Surg 178:167–70.
Rosenberg et al. (2008) Menopausal hormone therapy in relation to breast cancer characteristics and prognosis: a cohort study. Breast Cancer Research 10;R78 (doi:10.1186/bcr2145)
Newcomb et al (2008) Prediagonistic use of hormone therapy and mortality after breast cancer. Canceer Epidemiol Biomaarkers Prev 17:864-871.
von Fournier et al. (1980)Growth rate of 147 mammary carcinomas. Cancer 45:2198-2207.
Sunday, October 24, 2010
DEATH AND HORMONES: WATCH OUT FOR THE SCARY WHI MONSTER
Just in time for Halloween the Women’s Health Initiative (WHI) investigators are trying to scare women again (Chlebowski et al 2010 JAMA 304 1684). They did this in 2002 with the help of unknowing press and TV journalists who spread the word that hormone replacement therapy (HRT, estrogen plus progestin, Prempro) was really bad for you and you should stop taking it. These authors were wrong then and they are wrong now. The new article says that HRT not only increases the risk of breast cancer but it also causes greater mortality.
For one thing the data in this paper are what polite scientist call marginal which translates to: through it out and forget it. All of the risk data are just barely statistically significant and the absolute risks are very small which means they are of no importance to any individual woman who has taken HRT.
These very weak findings of increased risk of death are in contrast to the many studies which say just the opposite. The WHI authors explain that other studies are not as good as the WHI studies because they are not randomized placebo controlled trials, so just forget them. Well it is hard to forget 15 to 20 studies which disagree with the WHI especially since some of them are just as good as those of the WHI. The WHI people would do well to remember that their studies are far from perfect and have been criticized at length by many scientists.
For instance: Many investigators agree that the WHI study does not even qualify as a randomized placebo-controlled study which is supposed to be superior to other types of studies. The reasons for this statement are: 1. Following randomization the women were free to decide whether to continue their assigned treatment or whether to undergo diagnostic procedures. 2. Almost half of the women were aware of their treatment so there was no valid placebo group. 3. Several warnings were sent to the participants about the detection of increased risks of myocardial infarction, stroke and pulmonary embolism during the study. These problems make the WHI study no better than any observational study with all of their limitations.
In addition to all of these problems, the women in these WHI studies were 12-15 years past the onset of menopause. Thus they were without their pre-menopausal levels of estrogen and progesterone long enough to bring about changes in various bodily functions which are the precursors of disease or of undiagnosed disease. For instance, ovarian hormones are important for maintaining normal structure and function of the blood vascular system. Once vascular disease has begun hormone treatment is not likely to reverse the effects. Proper bone strength is maintained by estrogen and when estrogen is no longer secreted at menopause bones begin to lose calcium and the first stages of osteoporosis begin.
So if anyone you know is or has taken HRT tell them not to be scared. It’s only the big bad WHI monster who makes a loud noise but has no claws.
For more information see my critique of the WHI studies from 2002-2006: Clark JH (2006) Nuclear Receptor Signaling 4, e023
For one thing the data in this paper are what polite scientist call marginal which translates to: through it out and forget it. All of the risk data are just barely statistically significant and the absolute risks are very small which means they are of no importance to any individual woman who has taken HRT.
These very weak findings of increased risk of death are in contrast to the many studies which say just the opposite. The WHI authors explain that other studies are not as good as the WHI studies because they are not randomized placebo controlled trials, so just forget them. Well it is hard to forget 15 to 20 studies which disagree with the WHI especially since some of them are just as good as those of the WHI. The WHI people would do well to remember that their studies are far from perfect and have been criticized at length by many scientists.
For instance: Many investigators agree that the WHI study does not even qualify as a randomized placebo-controlled study which is supposed to be superior to other types of studies. The reasons for this statement are: 1. Following randomization the women were free to decide whether to continue their assigned treatment or whether to undergo diagnostic procedures. 2. Almost half of the women were aware of their treatment so there was no valid placebo group. 3. Several warnings were sent to the participants about the detection of increased risks of myocardial infarction, stroke and pulmonary embolism during the study. These problems make the WHI study no better than any observational study with all of their limitations.
In addition to all of these problems, the women in these WHI studies were 12-15 years past the onset of menopause. Thus they were without their pre-menopausal levels of estrogen and progesterone long enough to bring about changes in various bodily functions which are the precursors of disease or of undiagnosed disease. For instance, ovarian hormones are important for maintaining normal structure and function of the blood vascular system. Once vascular disease has begun hormone treatment is not likely to reverse the effects. Proper bone strength is maintained by estrogen and when estrogen is no longer secreted at menopause bones begin to lose calcium and the first stages of osteoporosis begin.
So if anyone you know is or has taken HRT tell them not to be scared. It’s only the big bad WHI monster who makes a loud noise but has no claws.
For more information see my critique of the WHI studies from 2002-2006: Clark JH (2006) Nuclear Receptor Signaling 4, e023
Tuesday, September 28, 2010
HORMONES ARE NOT BAD FOR YOU BUT BAD SCIENCE IS
In 2002 some scientists published a paper (see ref at end of this article) which said taking hormones after menopause increased risks of heart problems, breast cancer, stroke and blood clots. The news media were quick to exclaim to the world that: BIG BAD HORMONES WERE NOT GOOD FOR YOU.
This caused quite a stir and many physicians advised patients to stop taking their hormone drugs which contained estrogen and progesterone. This is too bad because the conclusions of the Woman’s Health Initiative (WHI) scientist were wrong for the following reasons:
THEY USED THE WRONG CONFIDENCE INTERVAL TO DETERMINE STATISTICAL SIGNIFICANCE
In their first paper, the one in 2002 that caused most of the problems, the WHI authors used a measurement of variation called the nominal 95% confidence interval and they should have used an adjusted 95% confidence interval. Confidence intervals (CIs) are used in tests for statistical significance.
Nominal CIs can be used when a single outcome or result is being tested but in the WHI studies there were multiple outcomes being tested, so they should have used CIs which are adjusted to take into consideration the possible error introduced by multiple testing. Nominal CIs are smaller than adjusted CIs, so when they are used incorrectly they make numbers look significant when they are not.
For example: The relative risk (RR) and nominal CI for risk of breast cancer was 1.26 (1.0-1.59) which is barely significant ; however, when the correct adjusted CI is used (0.83-1.92) we see that it includes the no effect level of 1.0 and would be judged as not significant. This incorrect use of nominal CIs was used throughout the paper.
Such incorrect use of statistical techniques should have disqualified this paper from publication.
ACTUAL AND RELATIVE RISKS
One of the biggest contributions to inaccuracies and exaggerations in these studies is the failure to state and emphasize that the actual or absolute risks are small. How can this be? It is really very simple as I have explained in my two earlier Blogs entitled: What is the truth about health risks? and How to understand blog graphs.
The bottom line is you can have very small actual risks accompanied by very large relative risks. Since the relative risk numbers are large they are the ones that show up in the news media. If the small actual risk numbers were released no one would pay any attention to them.
For more comparisons between relative and actual risk see the table below. Remember these claimed increased risks were not significant in the first place so they don’t count.
FAILURE TO EXAMINE THEIR DATA CAREFULLY
A good example of this lack of attention was their failure to see that the apparent increased risk of heart disease and breast cancer during a critical time (yr 5) was not caused by an increased number of women with these diseases but a chance decrease in the number of women in the placebo control group. There was no real or significant increase in risk in heart disease or breast cancer but these mistakes were the main reason for stopping the study.
Increases and decreases in risks may occur by chance or there may be some underlying cause. The proper statistical analysis tells us whether these differences are real or just due to chance. All too often in these kinds of studies the authors will make statements like: The risk of breast cancer was increased by hormone treatment. Then they add: although the increase was not significant. In other words the differences could have occurred by chance and there is no real increase. The only part of the above which is published in the press is: Breast Cancer Risks Increased by Hormone Treatment. The headline should have been: Breast Cancer Risks Not Increased by Hormone Treatment.
In a subsequent more detailed paper on the effects of hormone treatment these authors indicate that no significant increased risks of breast cancer were observed (Chlebowski et al 2003). This revised conclusion was not announced by the news media and no retraction of the original incorrect conclusions was made. Most doctors and researchers either do not know of this correction or choose to ignore it.
THE WOMEN IN THE STUDY WERE TOO OLD WHEN THE STUDY BEGAN
The WHI studies used data from women who were 12-15 years past menopause before they started taking hormones. This means these women were without their normal levels of estrogen and progesterone long enough to bring about changes in various bodily functions which are the precursors of disease. For instance, ovarian hormones are important for maintaining the blood vascular system in good shape. Once vascular disease has begun as a result of menopause hormone treatment is not likely to reverse the effects. Proper bone strength is maintained by estrogen and when it is no longer secreted at menopause bones begin to lose calcium and the first stages of osteoporosis begin. Most reproductive scientists believe that post menopausal hormones should be used as preventives not corrective therapy.
WHI STUDIES WERE NOT REALLY DOUBLE BLIND
The best way to do a clinical study of this kind is to do it so that neither the participants nor the doctors know who is getting the drug and who is getting the placebo. This was supposed to be one of the strengths of the WHI studies; unfortunately, the women did not remain blind and as many as 45% of them were not only told what group they were in but also given warnings about the possibility of increased risks for heart disease, breast cancer etc. This destroys the credibility of such a study and when this happen the entire study should have been stopped because of these flaws.
ESTROGEN ALONE DID NOT INCREASE RISK OF BREAST CANCER
The WHI investigators also evaluated disease outcomes in women who took estrogen alone as a post-menopausal treatment. The authors concluded that estrogen alone did not increase the risk of breast cancer or heart disease and decreased the risks of hip fracture. They also concluded that estrogen alone did increase the risk of stroke. These four papers, most of which were published in 2006, suffer from the same problems and bad sciences as outlined above. They are loaded with extreme variability in the treated and control groups which were not taken into consideration. Once again, they used the wrong measure of variability to test for significance, and called the increase risk of stroke to be significant when it was not. In addition, these estrogen only studies have all of the other problems discussed for the estrogen plus progestin studies.
CONCLUSIONS
Most people, including physicians, do not realize that the conclusions in the original 2002 paper were incorrect. Although the WHI investigators indicate in their later papers that no increased risks were found, they fail to point out clearly that their conclusions were incorrect in 2002 and that the study should not have been stopped. Little has been said in the popular media. No retraction, no admission that the WHI study should have been continued, no attempt to set the record straight. So most women and many physicians keep plodding along believing hormones increase risks for these diseases, when they don’t.
REFERENCES
Chlebowski RT et al (2003) Influence of estrogen plus progestin on breast cancer and mammography in healthy postmenopausal women. JAMA 289: 3243
Writing Group for the Women’s Health Initiative Investigators. (2002) Risks and Benefits of Estrogen Plus Progestin in Healthy Postmenopausal Women: Principal Results From the Women’s Health Initiative Randomized Controlled Trial. JAMA:288:321-333.
Sunday, September 26, 2010
DIETARY FIBER AND HEART PROBLEMS
Surely high fiber diets are good for you? I always thought so, but I began to wonder when I looked into the evidence. This concept has suggested by many people.
One of these beliefs is that high fiber diets will reduce the risk of heart attacks (myocardial infarcts, MI) and other heart diseases(see Mozaffarian et al for references). This belief comes from a paper by Rimm et al (1996) who claimed to demonstrate such an inverse relationship. This seemed to be a reasonable assumption and one I would like to believe, but after analyzing the data I am not convinced.
In the study by Rimm et al (1996) a total of 43,757 male health professionals 40 to 75 years of age were studied for 6 years. During this time 734 cases of myocardial infarction were documented and these were analyzed with respect to dietary intake of fiber. The results of this study are shown in Figure 1. You can see that no significant effect on MI is seen in any group but group 5 which was the highest intake category (28.9 grams/day).
So if you are willing to spend most of you time eating fiber containing foods all day you might be less likely to have a heart attack. However, anytime you have data points which suddenly go from insignificant, as in category 4, to significant, as in category 5 you should worry that the value in category 5 may be a fluke.
Figure 1. Relative risk of myocardial infarct (MI) and dietary intake of fiber.
Each intake category represents increasing intake of total dietary fiber. The data points are relative risks and 95% confidence intervals taken form the data in table 2 of Rimm et al 1996. The quantity of fiber in the diet increases from 1 to 5.
The next study from this same group involved 39,876 women health professionals who were followed for 6 years (Liu et al 2002). The incidence of cardiovascular disease (CVD) and MI were examined in this study and their results for total dietary fiber intake are shown in Fig 2. No significant associations were observed between total fiber intake and CVD (Fig 2 A), and the women in category 4 showed marginal significant decrease in MI (Fig 2 B). Since all other RR are insignificant these data do not support the contention that dietary fiber intake is associated with a decreased risk of CVD or MI.
Figure 2. A. Relative risk of cardiovascular disease (CVD) and B. myocardial infarct (MI) as a result of dietary intake of total fiber. Data taken from tables 3 and 4 in Liu et al 2002.
These authors also examined the association between the various sources of dietary fiber. If any type of fiber would show a protective effect one would think it would be vegetable fiber. Think again. The data in Figure 3 demonstrated that no decrease or increase in relative risks of CVD or MI result from the consumption of various amounts of vegetable fiber.
Figure 3. Relative risk of cardiovascular disease (CVD) and myocardial infarct (MI) as a result of dietary intake of vegetable fiber. Data taken from table 3 and 4 in Liu et al 2002.
If no associations could be shown with total fiber or vegetable fiber, then surely these would be found with soluble fiber. After all, soluble fiber was thought to be primarily responsible for the cholesterol lowering effect of dietary fiber, hence one would expect great things. Several studies claimed this to be true and were summarized in a meta-analysis by Brown et al 1999. However, if you read this paper carefully you would not expect much effect from soluble fiber. Brown et al 1999 found that the concentrations of total cholesterol and LDL cholesterol in the blood were decreased marginally and no effects on HDL cholesterol and triglycerides were found. The reduction of total cholesterol and LDL cholesterol were minus 0.045 mM ( 0.9%)and 0.057 mM ( 2.7%) respectively which are very small and probably physiologically insignificant effects. The authors point out that increasing soluble fiber intake can make only a small contribution to lowering plasma cholesterol. Little did they know, the effect was not just small but it was without consequence as a factor in decreasing the risk of CVD or MI (Figure 4).
Figure 4 . Relative risk of cardiovascular disease (CVD) and myocardial infarct (MI) as a result of dietary intake of soluble fiber. Data taken from tables 3 and 4 in Liu et al 2002.
Other types of fiber which were included in this study were cereal fiber, fruit fiber and insoluble fiber. These produced no significant reduction in the risk of CVD or MI.
CONCLUSIONS
Try as they might, all of the kings men could not put the story of fiber and heart problems together again. No evidence of a protective effect on CVD or MI from eating even very large quantities of any kind of fiber was found in these studies.
REFERENCES
Brown L, Rosner B, Willett WW, Sacks FM 1999 Cholesterol lowering effects of dietary fiber: a meta-analysis. Am J Clin Nutr 69:30-42.
Liu S et al 2002 A prospective study of dietary fiber intake and risk of cardiovascular disease among women. J Am Coll Cardiol 39:49-56.
Mozaffarian et al 2006 Trans fatty acids and cardiovascular disease. NEJM 354:1601-1613.
Rimm EB et al 1996 Vegetable, fruit and cereal fiber intake and risk of coronary heart disease among men. JAMA 275: 447-51/
One of these beliefs is that high fiber diets will reduce the risk of heart attacks (myocardial infarcts, MI) and other heart diseases(see Mozaffarian et al for references). This belief comes from a paper by Rimm et al (1996) who claimed to demonstrate such an inverse relationship. This seemed to be a reasonable assumption and one I would like to believe, but after analyzing the data I am not convinced.
In the study by Rimm et al (1996) a total of 43,757 male health professionals 40 to 75 years of age were studied for 6 years. During this time 734 cases of myocardial infarction were documented and these were analyzed with respect to dietary intake of fiber. The results of this study are shown in Figure 1. You can see that no significant effect on MI is seen in any group but group 5 which was the highest intake category (28.9 grams/day).
So if you are willing to spend most of you time eating fiber containing foods all day you might be less likely to have a heart attack. However, anytime you have data points which suddenly go from insignificant, as in category 4, to significant, as in category 5 you should worry that the value in category 5 may be a fluke.
Figure 1. Relative risk of myocardial infarct (MI) and dietary intake of fiber.
Each intake category represents increasing intake of total dietary fiber. The data points are relative risks and 95% confidence intervals taken form the data in table 2 of Rimm et al 1996. The quantity of fiber in the diet increases from 1 to 5.
The next study from this same group involved 39,876 women health professionals who were followed for 6 years (Liu et al 2002). The incidence of cardiovascular disease (CVD) and MI were examined in this study and their results for total dietary fiber intake are shown in Fig 2. No significant associations were observed between total fiber intake and CVD (Fig 2 A), and the women in category 4 showed marginal significant decrease in MI (Fig 2 B). Since all other RR are insignificant these data do not support the contention that dietary fiber intake is associated with a decreased risk of CVD or MI.
Figure 2. A. Relative risk of cardiovascular disease (CVD) and B. myocardial infarct (MI) as a result of dietary intake of total fiber. Data taken from tables 3 and 4 in Liu et al 2002.
These authors also examined the association between the various sources of dietary fiber. If any type of fiber would show a protective effect one would think it would be vegetable fiber. Think again. The data in Figure 3 demonstrated that no decrease or increase in relative risks of CVD or MI result from the consumption of various amounts of vegetable fiber.
If no associations could be shown with total fiber or vegetable fiber, then surely these would be found with soluble fiber. After all, soluble fiber was thought to be primarily responsible for the cholesterol lowering effect of dietary fiber, hence one would expect great things. Several studies claimed this to be true and were summarized in a meta-analysis by Brown et al 1999. However, if you read this paper carefully you would not expect much effect from soluble fiber. Brown et al 1999 found that the concentrations of total cholesterol and LDL cholesterol in the blood were decreased marginally and no effects on HDL cholesterol and triglycerides were found. The reduction of total cholesterol and LDL cholesterol were minus 0.045 mM ( 0.9%)and 0.057 mM ( 2.7%) respectively which are very small and probably physiologically insignificant effects. The authors point out that increasing soluble fiber intake can make only a small contribution to lowering plasma cholesterol. Little did they know, the effect was not just small but it was without consequence as a factor in decreasing the risk of CVD or MI (Figure 4).
Figure 4 . Relative risk of cardiovascular disease (CVD) and myocardial infarct (MI) as a result of dietary intake of soluble fiber. Data taken from tables 3 and 4 in Liu et al 2002.
Other types of fiber which were included in this study were cereal fiber, fruit fiber and insoluble fiber. These produced no significant reduction in the risk of CVD or MI.
CONCLUSIONS
Try as they might, all of the kings men could not put the story of fiber and heart problems together again. No evidence of a protective effect on CVD or MI from eating even very large quantities of any kind of fiber was found in these studies.
REFERENCES
Brown L, Rosner B, Willett WW, Sacks FM 1999 Cholesterol lowering effects of dietary fiber: a meta-analysis. Am J Clin Nutr 69:30-42.
Liu S et al 2002 A prospective study of dietary fiber intake and risk of cardiovascular disease among women. J Am Coll Cardiol 39:49-56.
Mozaffarian et al 2006 Trans fatty acids and cardiovascular disease. NEJM 354:1601-1613.
Rimm EB et al 1996 Vegetable, fruit and cereal fiber intake and risk of coronary heart disease among men. JAMA 275: 447-51/
Sunday, August 15, 2010
DO FAT PEOPLE DIE EARLIER THAN SKINNY PEOPLE?
Overweight and obese people are generally thought to be unhealthy and have more diseases and higher death rate than people of normal weight. However, such information is usually found in articles written by authors who have not examined the actual medical evidence which support these statements. The purpose of this paper is to take a close look at the evidence for some of these claims.
There is no agreement among epidemiologists concerning the increased risk of death in overweight and obese individuals. Some studies indicate that no relationship exist and some that the relationship is the reverse. Some studies agree that obese people are more likely to die than people of normal weight, but there is disagreement concerning overweight people and increased risk of death. Some say yes and others say no. These studies have been summarized by McGee (2005) who concluded that overweight people do not have a significantly higher rate of death than normal weight people.
A recent study claims that even overweight women or men are at a greater risk of dieing than people of normal weight (Adams et al 2006). In their study the relative and actual risks were elevated for under weight, obese men and surprisingly men and women in the normal weight categories 2 and 3 (Table 1). These conclusions are taken from relative risk ratio statistics in column 2. In this table I have added the relative risk as percent (column 3) and the actual risk as percent (column 4) for comparison. It is apparent that the actual risk of death is not great even for obese men in which the increase is only 1.9% greater than the normal reference body weight of 1.3%. Underweight men have an elevated actual risk (3.5%) which is similar to those who are overweight (3.2%). Similar risks were found for all women studied (results not shown).
TABLE 1 RELATIONSHIP BETWEEN BODY MASS INDEX BMI), RELATIVE RISKS AND ACTUAL RISK OF MORTALITY IN ALL MEN*
*Similar results were found for women. **BMI or Body Mass Index is
equal to the body weight in kg divided by height in meters squared.
A BMI calculator can be found at http://www.nhlbisupport.com/bmi/bmicalc.htm
The conclusions that can be drawn from Table 1 are:
1. Underweight and some low normal weight individuals are at an increased risk of death. This important point rarely gets a mention in the news media or fashion magazines.
2. Overweight people are not at an elevated risk of death.
3. Obese people have an elevated risk of death similar to underweight individuals.
INFLUENCE OFSMOKING AND BODY WEIGHT
In order to examine the effect of bodyweight on risk of death the influence of smoking was considered by Adams et al (2006). To do this data were grouped into the following groups: Never Smoked, Current Smoker and Former Smoker. For the sake of simplicity I have made a table which shows data from never smokers and current smokers (Table 2).
TABLE 2 RISK OF DEATH IN MEN WHO NEVER SMOKED AND CURRENT SMOKERS
* Actual risk was calculated by dividing the number of individual in each category by 100,000. Data were obtained from table 2 in Adams et al (2006).
If you look only at the results of relative risks and compare never smokers with current smokers you could draw the incorrect conclusions that it is better to be an obese smoker than an obese non-smoker. For example, in group 10 the relative risk for the never smoker is 159%, whereas for the current smoker in this group the risk is 42%. This is a good example of how relative risks can be misleading and why actual risks should be used in these kinds of studies. Comparing the actual risks in these two groups provides the correct answers. The actual risk of death in category 10 for the never smoker group is 2.6% compared to 5.0% for the current smoker group. So just as we suspected being obese and smoking is not good for you.
The reason for the reversal seen with relative risk analysis is that the reference category 4 is a lower number (0.78) in the never smoker group than that of the current smoker group (3.08). Since all weight categories are compared to these numbers the relative risk increases in the smoker groups are lower. For example: in the never smoker group the percent in category 10 is 2.6 compared to 0.78 in the reference group. Therefore the relative risk is 3.25 times as great as the reference value of 0,78 for never smokers and only 1.61 times as great for current smokers (5.0/3.08 = 1.61).
The influence of smoking on the actual risk of death is shown in figure 1. It is clear from these data that current smoking increases the actual risk of death in all BMI categories. The data for women are similar to men (data not shown). In this figure and in table 2 we see that men in the never smoked group show similar actual risks in all categories except the obese groups 9 and 10 and these are not great but they were judged to be significant by Adams et al (2006). The former smokers have a small actual increase in risk in categories 3 – 8 and elevated risks in the underweight, low normal and obese categories. There is no evidence from these data that being overweight increased your risk of death.
Figure 1 Actual risk of death as function of BMI groups in smokers and non-smokers.
STUDIES ANALYZED BY MCGEE 2005
My conclusion that being overweight does not increase the risk of death agrees with that of McGee (2005) who analyzed 26 large population studies which included 388,622 individuals.
TABLE 3 RELATIVE RISK OF DEATH DUE TO VARIOUS CAUSES IN OVERWEIGHT AND OBESE WOMEN AND MEN.
Data taken form McGee 2005. The data are expressed as relative risks and the 95% confidence interval in parentheses. All groups were compared to normal weight individual defined as having a BMI of 18.5 to 25. Over weight defined as BMI between 25 and 30 and obese as BMI of 30 or greater. Abbreviations: CHD, coronary heart disease; CVD, cardiovascular disease.
From table 3 it is clear that even the relative risks of death for overweight individuals is not different from those of normal weight. Actual risks could not be calculated because insufficient data. These data suggest that obese men and women are at a greater risk of death from all causes, CHD and CVD but not cancer. The lack of any increased risk of cancer in overweight women supports my contention that heavier women do not have higher risk of breast cancer.
CONCLUSIONS
1. Underweight and some low normal weight individuals are at an increased risk of death.
2. Overweight people are not at an elevated risk of death.
3. Obese people have an elevated risk of death similar to underweight individuals.
4. Current smoking in obese and under weight individuals increases their risk of death. This is also true for those in the former and never smokers but the risks are not as great.
5. Obesity increases the risk death from heart and vascular diseases but not cancer.
The finding that overweight men and women show no significant increased risk for heart and vascular diseases or death casts doubt on the continual bombardment from the news media, nutrition experts, many physicians, drug companies and food manufactures that being over weight will increase the incidence of many diseases.
REFERENCES
McGee DL (2005) Ann Epidemiol 15:87
Adams KF (2006) N Engl J Med 355:763
There is no agreement among epidemiologists concerning the increased risk of death in overweight and obese individuals. Some studies indicate that no relationship exist and some that the relationship is the reverse. Some studies agree that obese people are more likely to die than people of normal weight, but there is disagreement concerning overweight people and increased risk of death. Some say yes and others say no. These studies have been summarized by McGee (2005) who concluded that overweight people do not have a significantly higher rate of death than normal weight people.
A recent study claims that even overweight women or men are at a greater risk of dieing than people of normal weight (Adams et al 2006). In their study the relative and actual risks were elevated for under weight, obese men and surprisingly men and women in the normal weight categories 2 and 3 (Table 1). These conclusions are taken from relative risk ratio statistics in column 2. In this table I have added the relative risk as percent (column 3) and the actual risk as percent (column 4) for comparison. It is apparent that the actual risk of death is not great even for obese men in which the increase is only 1.9% greater than the normal reference body weight of 1.3%. Underweight men have an elevated actual risk (3.5%) which is similar to those who are overweight (3.2%). Similar risks were found for all women studied (results not shown).
TABLE 1 RELATIONSHIP BETWEEN BODY MASS INDEX BMI), RELATIVE RISKS AND ACTUAL RISK OF MORTALITY IN ALL MEN*
*Similar results were found for women. **BMI or Body Mass Index is
equal to the body weight in kg divided by height in meters squared.
A BMI calculator can be found at http://www.nhlbisupport.com/bmi/bmicalc.htm
The conclusions that can be drawn from Table 1 are:
1. Underweight and some low normal weight individuals are at an increased risk of death. This important point rarely gets a mention in the news media or fashion magazines.
2. Overweight people are not at an elevated risk of death.
3. Obese people have an elevated risk of death similar to underweight individuals.
INFLUENCE OFSMOKING AND BODY WEIGHT
In order to examine the effect of bodyweight on risk of death the influence of smoking was considered by Adams et al (2006). To do this data were grouped into the following groups: Never Smoked, Current Smoker and Former Smoker. For the sake of simplicity I have made a table which shows data from never smokers and current smokers (Table 2).
TABLE 2 RISK OF DEATH IN MEN WHO NEVER SMOKED AND CURRENT SMOKERS
If you look only at the results of relative risks and compare never smokers with current smokers you could draw the incorrect conclusions that it is better to be an obese smoker than an obese non-smoker. For example, in group 10 the relative risk for the never smoker is 159%, whereas for the current smoker in this group the risk is 42%. This is a good example of how relative risks can be misleading and why actual risks should be used in these kinds of studies. Comparing the actual risks in these two groups provides the correct answers. The actual risk of death in category 10 for the never smoker group is 2.6% compared to 5.0% for the current smoker group. So just as we suspected being obese and smoking is not good for you.
The reason for the reversal seen with relative risk analysis is that the reference category 4 is a lower number (0.78) in the never smoker group than that of the current smoker group (3.08). Since all weight categories are compared to these numbers the relative risk increases in the smoker groups are lower. For example: in the never smoker group the percent in category 10 is 2.6 compared to 0.78 in the reference group. Therefore the relative risk is 3.25 times as great as the reference value of 0,78 for never smokers and only 1.61 times as great for current smokers (5.0/3.08 = 1.61).
The influence of smoking on the actual risk of death is shown in figure 1. It is clear from these data that current smoking increases the actual risk of death in all BMI categories. The data for women are similar to men (data not shown). In this figure and in table 2 we see that men in the never smoked group show similar actual risks in all categories except the obese groups 9 and 10 and these are not great but they were judged to be significant by Adams et al (2006). The former smokers have a small actual increase in risk in categories 3 – 8 and elevated risks in the underweight, low normal and obese categories. There is no evidence from these data that being overweight increased your risk of death.
Figure 1 Actual risk of death as function of BMI groups in smokers and non-smokers.
STUDIES ANALYZED BY MCGEE 2005
My conclusion that being overweight does not increase the risk of death agrees with that of McGee (2005) who analyzed 26 large population studies which included 388,622 individuals.
TABLE 3 RELATIVE RISK OF DEATH DUE TO VARIOUS CAUSES IN OVERWEIGHT AND OBESE WOMEN AND MEN.
Data taken form McGee 2005. The data are expressed as relative risks and the 95% confidence interval in parentheses. All groups were compared to normal weight individual defined as having a BMI of 18.5 to 25. Over weight defined as BMI between 25 and 30 and obese as BMI of 30 or greater. Abbreviations: CHD, coronary heart disease; CVD, cardiovascular disease.
From table 3 it is clear that even the relative risks of death for overweight individuals is not different from those of normal weight. Actual risks could not be calculated because insufficient data. These data suggest that obese men and women are at a greater risk of death from all causes, CHD and CVD but not cancer. The lack of any increased risk of cancer in overweight women supports my contention that heavier women do not have higher risk of breast cancer.
CONCLUSIONS
1. Underweight and some low normal weight individuals are at an increased risk of death.
2. Overweight people are not at an elevated risk of death.
3. Obese people have an elevated risk of death similar to underweight individuals.
4. Current smoking in obese and under weight individuals increases their risk of death. This is also true for those in the former and never smokers but the risks are not as great.
5. Obesity increases the risk death from heart and vascular diseases but not cancer.
The finding that overweight men and women show no significant increased risk for heart and vascular diseases or death casts doubt on the continual bombardment from the news media, nutrition experts, many physicians, drug companies and food manufactures that being over weight will increase the incidence of many diseases.
REFERENCES
McGee DL (2005) Ann Epidemiol 15:87
Adams KF (2006) N Engl J Med 355:763
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