Food Effect on Cancer 

 
 

 

 

C O N T E N T S

1. Study Basis and Approach

2. Conclusions

3. Food vs. Cancer Graphics

 

4. Complementary Graphics

 

5. Appendix

 

 

 

 

 

 

Study Basis and Approach

  • To evaluate the effect that different kind of food has on Cancer, we used the statistic information of 129 countries, plotting on XY graphics food type consumption versus Cancer Incidence Rate.

 

  • The source used for Cancer Incidence Rate (all places, age adjusted) is GLOBOCAN 2002, IARC (International Agency for Research on Cancer), a part of WHO. The source used for Food Consumption (expressed on calories, proteins and Whole Food) is FAO (Food and Agriculture Organization).

 

  • In the study we did not considered the countries of South Africa because their prevalence level of AIDS affects Cancer statistics. So, from the 153 countries FAO considers, we end up with 129 for this study.

 

  • In order to have a better picture of the trends, we plot only 10 points on the graphics (instead of 129), each point averaging a group of about 13 countries. Groups were defined placing all countries in ascendant order from the least to the biggest consumer of the food type and then divided them in 10 groups. There is a group detail table for each graphic (see appendix).

 

  • The study considers 25 XY graphics relating Food Consumption with Cancer Incidence Rate. Food Consumption is expressed in Calories (Calories/person/day), Proteins (grams/person/day) and Whole Food (grams/person/day). In order to help the analysis of the graphics, we include an Evaluation Table using three factors: An indirect measure of Dispersion of the groups, the increment of Cancer rate between the first and last group, and how linear is the relationship food-disease.

 

  • Additional to the mentioned XY graphics, 7 other graphics are include in different format to emphasize particular ideas related with Cancer causes.

 

  • Using as base reference the food graphic with the biggest influence on cancer (Red Meat plus Dairy Consumption), we identify the countries whose Cancer values are too high or too low with respect to the graphic value. The idea is to know which countries have other important unknown factors that are affecting in a positive or negative way the Cancer rate (see table P2-10a in appendix).

 

  • The scope of this study considers only Total Cancer Incidence Rate. However Cancer statistic information available from GLOBOCAN, permit to establish the relationship between food intake (from FAO) and Particular types of Cancer.

 

 

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C o n c l u s i o n s

1.-

Among the different kind of food considered in the study, consumption of Red Meat and Dairy protein show to be the clearest causal agent of Cancer. (See graphic P2-37).

 

2.-

Although all animal origin food exhibit negative influence on cancer, Fish consumption show little effect as cause of the disease. (See graphic P2-28).

 

3.-

Vegetal origin food consumption shows no positive or negative influence on Cancer Incidence. (See graphic P2-29, P2-30, P2-47).

 

4.-

Other food that shows to be a causal agent of Cancer is Sugar. Highest Cancer Rates are found in countries with high consumption of Animal food and Sugar. (See graphic P2-32, P2-53).

 

5.-

In countries with very low consumption of animal and sugar, Cancer Incidence Rate are the lowest but far from zero. This means there are other important causes of Cancer not included on this study. One of these causal agents is the consumption of Salt. To show the effect salt has on Cancer we are taken a graphic from “World Report on Cancer” by World Health Organization. (See graphic P2-92).

 

6.-

It has been established in some studies a relationship between Cancer Incidence and economic status of the country. To confirm this relation it is included a XY graphic using Health Expenditures as economical status of the countries versus Cancer Incidence. The result of the relationship is poor and not lineal at all. On the other hand, Meat and Dairy consumption, definitely related with economical status, show a lineal and conclusive relationship. (See graphic P2-68).

 

7.-

The effect of Red Meat Protein on Cancer it is not only clear but also expected, as this relationship has been established since many years ago (see ref. 1 and 2). However, our problem is to explain why? This food has been part of mankind evolution so our body should be fully adapted to consume it without any harm. Is it then the grate amount we are consuming? Is it the fact that for first time on history we are eating it on a daily basis? Or it might be the synthetic chemicals (medicines, hormones, etc.) added during fatting? We don’t have the answer.

 

8.-

On the other hand, results of negative influence of Salt and Sugar on Cancer, can be explained due to the fact that in the last century men has increase their consumption far above any other time in history. The reason is because both products are inexpensive and the first choice as preservatives for processed food.

 

 

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Food vs. Cancer Graphics

Evaluation Table

25 Graphics

Comments of Graphics

 

 

 

Table P2-11b  Food Consumption vs. Cancer

Evaluation of Graphics

GRAPHIC   NUMBER    P2-

note   (5)

FOOD  CONSUMPTION

DISPERSION      (1)

TOTAL  INCREMENT   (2)

SMOOTHNESS EVALUATION   (3)

GRAPHIC  EVALUATION  (PLACE) (4) (6) (7)

46

CALORIES

Total Calories

52

154

R

8

53

Animal + Sugar

35

193

VG

1

54

Red Meat + Milk + Sugar

38

181

G

3

60

Animal

37

1

G

4

61

Animal but Fish

39

161

G

5

62

Animal but Fish + Sugar

35

1

G

2

69

Red Meat  

44

158

G

6

70

Red Meat + Milk 

38

167

F

7

37

PROTEIN

Red Meat + Milk

42

168

VG

1

47

Vegetal

62

-42

NC

none

56

Animal

42

166

G

3

58

Animal but Fish

45

168

VG

2

67

Animal / Vegetal Ratio

40

156

F

5

 71

Red Meat

44

158

G

4

23

WHOLE  FOOD

Red Meat

44

158

G

3

28

Fish

62

75

NC

none

29

Fruits and Vegetables

 

72

NC

none

30

Cereals

58

-109

NC

none

31

Milk (Dairy)

45

156

F

6

32

Sugar

53

141

F

7

63

Red Meat + Sugar

43

162

G

4

64

Animal + Sugar

39

1

VG

1

65

Animal

41

173

VG

2

66

Vegetal but Sugar

62

 

NC

none

72

Red Meat + Milk

41

162

F

5

 

 

NOTES:

(1) Graphics are made with 10 points each representing the average of a group of countries

      (from 10 to 13). Dispersion indicate how faraway (bigger or smaller) are the countries´

      individual values from the group average value. To evaluate Dispersion we use the Standard

      Deviation of each group and then we average the ten values.

(2) Total Increment is the difference of the Cancer Incidence value of group 10 minus the

      value of group 1

(3) This is a visual (subjective) evaluation of the smoothness. Because we consider a linear

      effect of the food consumption on the disease, perfect curve would be a straight line.  NC

     means Not Conclusive. R means Regular. F means Fair. G means Good. VG means Very Good.

(4) Base on values of Dispersion, Total Increment and Smoothness, we give a final evaluation for

     each graphic, where 1 would be the more conclusive graphic and the biggest number the less

     conclusive. None means we see no relationship between food consumption and Cancer.

(5)  Food Consumption Tables from FAO are presented in Calories, Proteins and Whole Food. As the

      Table of Whole Food is the most complete, we calculate Calories and Protein from this table to 

      elaborate some of the graphics

(6) Red meat and Dairy Protein show the biggest influence on cancer Incidence. Consumption of

      Sugar has also a clear negative effect on this disease. Fish consumption graphic it is not

      conclusive. On the other hand, Consumption of Cereals, Fruits and Vegetables graphics show

      that these food have no negative influence on Cancer.

(7) Lowest Cancer Incidence Value of a group is about 100. This means that there are other sources

      of negative influence not considered on these group of graphics.

 

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Comments of Food vs. Cancer Graphics

P2-46 Total Calories: Graphic shows some relationship between Energy Consumption and Cancer. However there is a relation between Red Meat Consumption and Energy Consumption that contaminates the result. Also the Dispersion of the groups is too high. We consider this graphic as inconclusive.
P2-53 Animal and Sugar: This is one of the best graphics. The Increment of Cancer Rate from groups 1 to 10 is the highest and group dispersion is the lowest. The Graphic is much better than Animal alone. (P2-60) and Sugar alone (P2-32), what means that Sugar has a clear negative effect on Cancer specially combined with an excess of animal consumption.
P2-54 Meat + Milk + Sugar: Also on this graphic, combination of Sugar with meat shows a synergy effect on Cancer. Meat + Milk Consumption show a more clear effect on cancer when it is expressed on Proteins rather than on Calories.
P2-60 Animal Calories: Group 9 is too close on Cancer Rate to group 10, because at group 10 are gather the coldest countries (see table P2-60) that eat much animal fat (high calories diet). However animal protein is the main causal of Cancer rather than animal fat. We found a more proportioned curve in the Animal Protein Graphic (see P2-56).
P2-61 Animal but Fish: As Fish has not much Calories, this Graphic is similar than All Animal Calories (P2-60)
P2-62 Animal but Fish + Sugar: If we compare this graphic with P2-61, we can see again a clear negative influence of Sugar on Cancer.
P2-69 Red Meat Calories: This is the same graphic as Red Meat Protein because both are calculated from Whole Food Meat Consumption Table but multiplied by different factors.
P2-70 Meat and Milk Calories: The curve is not as smooth as for Red Meat alone. However the factors of Dispersion and Total Increment are much better. We can see a better comparison of both factors on Protein Graphics (P2-37 and P2-71)
P2-37 Red Meat + Milk Protein: This is a conclusive graphic. It shows a clear and strong influence of Red Meat and Milk Protein on Cancer Incidence. It should be noted that graphic starts on a Cancer Rate of about 100. This means that this protein it might be an important cause of Cancer, but it is not the only one.
P2-47 Vegetal Protein: We see no influence of this type of protein on Cancer. The drop on the graphic of group 10 is not because this food has a positive effect on Cancer, but because in these countries eat little animal protein.
P2-56 All Animal Protein: This is also a clear, clean and conclusive curve that speaks for itself. .
P2-58 All Animal except Fish: This graphic was made to see the influence of Fish on Cancer, because the direct relation of Fish Consumption versus Cancer (P2-28) was not conclusive. The curve obtained is even smoother than the one made for All Animal (P2-56). This indicates that the effect of Fish protein on Cancer is small or at least much smaller than Red Meat Protein.
P2-67 Animal over Vegetal Protein Ratio: This curve does not look as good as the one for Animal protein alone, probably because Vegetal Protein has no positive or negative influence on cancer.
P2-71 Red Meat Protein: The Graphic is clear and determinant but not as good as Red Meat + Milk (P2-37). This result is expected as the protein is the same for both types of food.
P2-23 Red Meat Whole Food: Graphics P2-69 and P2-71 are the same as this one but expressed in Calories and Proteins respectably.
P2-28 Fish: The curve is too erratic and dispersion too high. We may conclude from the graphic that if there is an effect of Fish on Cancer, it is not big.
P2-29 Fruits and Vegetables: These foods show no effect on Cancer. The reason groups 1 to 3 have the lowest values of Cancer is because they also have the lowest consumption of Red Meat (they average only 58 grams/person/day).
P2-30 Cereals: We may conclude from the graphic that cereals do not have any important influence on Cancer rate, positive or negative. The reason why group 10 has the lowest Cancer Rate is because it also has the lowest meat consumption (62 grams/person/day). It is clear that countries that eat more cereals and less meat have the lowest Cancer Rates.
P2-31 Milk and Dairy: Even though curve is not smooth, the graphic shows a clear influence of milk on Cancer. The trend of the curve can also be explained because beef and milk eaters come normally together.
P2-32 Sugar: Curve is not smooth but the trend is clear.
P2-63 Meat and Sugar: The curve is as good as the one for Meat alone (P2-23) so we can’t conclude from this graphic the effect of Sugar on Cancer.
P2-64 All Animal and Sugar: this is the best graphic of the whole food group. The idea is to compare it with All Animal alone graphic (P2-65) and see the influence of Sugar. From this graphic we can not say that the effect of Sugar is important. The influence of sugar on cancer is clear on graphic P2-52 where food is expressed as calories.
P2-65 All Animal: This curve is smooth and conclusive. Expressed as Whole Food the graphic of All Animal is better than Meat and Milk (P2-72). However when expressed as Proteins, Meat and Milk Graphic (P2-37) is better than All Animal Food (P2-56). The Conclusion might be that All Animal food has a negative effect on Cancer but among them Red Meat and Dairy have the biggest influence.
P2-66 All Vegetal but Sugar: The graphic shows that vegetal food has no positive or negative influence on Cancer.
P2-72 Meat + Milk: This curve is just fair. Best result on this food combination is obtained when expressed in proteins (P2-37).
 

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Complementary Graphics 

7 Graphics

Comments of Graphics

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Comments of Complementary Graphics

P2-68 Health Expenditures vs. Cancer: There is no proportionality of these two factors at all. We will comment on a later topic that there are in developed societies some general factors that affect body defenses and make more likely chronic diseases. However, on Cancer main causal is the animal food consumption habit.
P2-91 Geographic and Food Region: The graphic average data of the countries in the region regardless the size of the countries. In the regions of low meat consumption the main causes of Cancer are others than the consumption of this protein.
P2-92 Salt Intake: We were not able to find global statistics of salt consumption, but this graphic clearly show the effect of salt on Cancer. In fact, in our opinion, in world regions where consumption of meat is low, salt consumption is the main cause of Cancer.
P2-93 Meat and Milk Consumption Groups: We divided the world in only 4 big groups. Relationship between meat protein consumption and Cancer is clear.
P2-95 Cancer vs. Diabetes: It is commonly said that there is a relationship between Cancer and Diabetes. However, we did not find any connection between both diseases. Groups 1 to 3 gather the countries with the lowest Food Energy, Sugar, and Red Meat and Dairy protein consumption at the same time. This is the reason why they have both, the lowest rates of Diabetes and Cancer at the same time (see Food Consumption vs. Diabetes Study).
P2-96 Cancer in the 20 Wealthiest and 20 Poorest Countries: We can see from the graphic that Health Expenditures have an important influence on death rates. In the wealthiest countries the approximate survival rate (Incidence minus Death divided by Incidence) is 52% and in the poorest with almost no medical attention is 25%. The influence of health care on Death Rate, prevent us using this statistic in the search for causes. Comparing Incidence Rate for both groups, we can easily see that health expenditures on wealthy countries are not properly used for prevention. We can’t avoid noticing the abysmal difference of health expenditures between both groups.
P2-97 Dispersion Effect: The example used is graphic P2-53 but plotting data for all 129 countries and also for the 10 average groups. This procedure of using 10 groups is done for clarity and is valid only as to find the trends. The difference from the data for a particular country and the group curve is the dispersion and represents other unknown causes of cancer. On Table P2-10a at the Index are identified countries and groups of neighbor countries with particular high dispersion.

 

 

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A p p e n d i x

Nota a lectores de la página: No se incluyen estas tablas por tamaño de archivo. Si alguien desea una o todas las tablas, solicítela en el botón de Contacto, con gusto se las enviaremos.

 

 

 

 

Source Tables

P2-01 World Food Consumption Table

P2-02 World Energy Consumption Table

P2-03 General Data Table

 

 

 

 

Calculation Tables

P2-07 Protein Data

P2-08 Calories Data

P2-09 Calories Data

 

 

 

 

Food vs. Cancer Graphic Tables (same number as graphics)

P2-46          P2-37          P2-23

P2-53          P2-47          P2-28

P2-54          P2-56          P2-29

P2-60          P2-58          P2-30

P2-61          P2-67          P2-31

P2-62          P2-71          P2-32

P2-69                             P2-63

P2-70                             P2-64

                                      P2-65

                                      P2-66

                                      P2-72

 

Complementary Graphic Tables (same numbers as graphics)

P2-68

P2-91

P2-93

P2-95

P2-96

P2-97

 

 

 

 

Off Rates Countries Table

P2-10a

 

 

 

References

 

 

 

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