1. LE Kelemen, LH Kushi, DR Jacobs Jr., JR Cerhan
Substitution of Dietary Protein for Carbohydrate: Associations of Disease and Mortality in a Prospective Study of Postmenopausal Women
LE Kelemen, LH Kushi, DR Jacobs Jr., JR Cerhan
Mayo Clinic College of Medicine, Rochester, MN
University of Minnesota, Minneapolis, MN
Kaiser Permanente, Oakland, CA

2. Background
Popular high protein (HP) diets extol benefits for weight loss
Often do not discriminate among protein types
Effect of protein & protein type on long term health outcomes not widely studied
Popular high protein diets extol benefits for weight loss, but they often do not discriminate among protein sources, treating the putative beneficial effects of animal and vegetable proteins equally.
The effect of protein and protein type on long term health outcomes has not been widely studied.

3. Objectives Using multivariable nutrient density models:
To estimate the effect of an isoenergetic substitution of total protein for total carbohydrate with cancer incidence and mortality from cancer, CHD and all causes in the IWHS
To estimate the effect of an isoenergetic substitution of one type of protein for another type of protein with these outcomes
Our objectives in this investigation were to use multivariable nutrient density models
To estimate the effect of an isoenergetic substitution of total protein for total carbohydrate with cancer incidence and mortality from cancer, CHD and all causes in the Iowa Women’s Health Study [a change that would be expected while adhering to a higher-protein diet]
We also wanted to estimate the effect of an isoenergetic substitution of one type of protein for another type with these outcomes

4. Study Population In 1986: 99,826 Iowa women aged 55-69 yrs (now 71-86)
Identified from randomly selected driver’s licenses
Mailed questionnaire
diet (FFQ)
self-reported lifestyle, medical & reproductive history
41,836 (41.9%) enrolled
In 1986, approximately 100,000 postmenopausal Iowa women were randomly selected from that state’s licensed drivers’ registry and mailed a questionnaire to assess diet, lifestyle factors, medical and reproductive history
The 42% who responded form the cohort under study

5. Dietary Assessment Semi-quantitative Harvard FFQ
Validation study, 1988, 44 Iowa women
average of five 24-hour recalls over 2 months:
r = 0.16 ( protein)
r = 0.45 (carbohydrate)
r = (fats)
Reproducibility (2.5 yrs)
r = 0.59 (protein)
r = 0.53 (carbohydrate)
r = (fats)
We assessed baseline diet in 1986 with a semi-quantitative FFQ developed by Harvard & modified slightly
Daily intakes of nutrients were calculated by summing across all food items the product of the fq of consumption of the specified serving of each food by the nutrient content of that unit of food
We also collected info on vitamin supplement use
The validity and reproducibility of the FFQ was assessed in a subset of Iowa women and, although the correlation coefficient for protein in our cohort was somewhat low, it ranged from to in the Harvard cohorts when compared to a greater number of days of dietary recording than what we used

6. Follow-Up Questionnaires mailed in 1987, 1989, 1992 and 1997
Incident cancers identified by linkage to Iowa SEER cancer registry
Deceased non-respondents & cause of death identified by linkage to National Death Index
15 years follow-up
4 follow-up questionnaires were mailed subsequently to establish vital status and change of address
Incident cancers were identified by linkage to the Iowa SEER cancer registry and deceased nonrespondents were identified by linkage to the National Death Index
The analyses under investigation represent 15 years of follow-up

7. Eligibility Criteria Excluded 29, 017 eligible women
Premenopausal women (n=569)
Prior history of cancer (n=3,881)
Known heart disease (n=5,116)
Known diabetes (n=2,675)
Diet
 30 blanks on FFQ
total energy (kcal/d)  600 or  5,000 (n=3,096)
29, 017 eligible women
For this analysis, we excluded women who, at baseline, were
Premenopausal
Had a prior history of cancer other than skin cancer
Had a prior hx of HD
Had known diabetes
Had implausibly low or high total energy intake, or left blank a large number of questions on the FFQ
This left approx. 29,000 eligible women at risk in this analysis

8. Data Analysis Dietary exposures
Macronutrients expressed as nutrient densities (i.e. % of energy from protein, carbohydrate and fats)
Micronutrient covariates were energy-adjusted (Willett & Stampfer 1986)
Categorized into quintiles
RR (95% CI) estimated using Cox proportional hazards with lowest intake category as referent; age as time metric
We expressed protein, carbohydrate and fats as a percentage of total energy
Other nutrients treated as covariates were energy-adjusted using the regression method
All nutrients were categorized into quintiles of intake, and modeled categorically
Risk Ratios and 95% CI were estimated using Cox proportional hazards and modeled as a function of age with the lowest intake category as referent

9. Data Analysis
Multivariable-adjusted nutrient density models (Willett 2nd ed 1998 p 295; Hu AJE 1999; Willett AJCN 1997)
Estimate associations from an increase in the % energy from protein intake
By forcing total energy and other intake (i.e., dietary fats) to be constant, and by excluding carbohydrate from the model, modeling the effects of an increase in protein intake, by definition, statistically results in a decrease in carbohydrate intake
Thus, the effect estimates of protein assume a substitution interpretation
The % of energy from protein that is “substituted” for carbohydrate is the difference between the median intake in the highest and lowest quintiles
Models also adjusted for other risk factors
We assessed the relation between dietary protein and each outcome with multivariable-adjusted nutrient density models.
These models allow estimation of the effect on each outcome of an increase in the percentage of energy from protein intake
By forcing total energy and other intake (such as, dietary fats) to be constant, and by excluding carbohydrate from the model, modeling the effects of an increase in protein intake, by definition, statistically results in a decrease in carbohydrate intake
Thus, the effect estimates of protein assume a substitution interpretation
This study was not an intervention; we did not ask women to consume higher protein in place of carbohydrate; rather we used a statistical modeling technique with already-collected dietary data to estimate the effect of a protein for carbohydrate substitution
The % of energy from protein that is “substituted” for carbohydrate is the difference between the median intake in the highest and lowest quintiles
The models were also adjusted for other risk factors
______________________________________________________________
[We also evaluated the effect of an isoenergetic substitution of various intakes of high-protein foods in place of carbohydrate-dense foods, and expressed as servings per 1,000 kcalories per day]

10. Covariates Known/suspected confounders & risk factors: Total energy
Fats (saturated, poly-, mono- & trans) (all quintiles & expressed as % of energy)
Total fiber, dietary cholesterol, dietary methionine (all quintiles & energy-adjusted)
Alcohol (≤14 vs > 14 g/d)
Smoking (never, former, current)
Activity level (active vs not active)
BMI (5 levels)
History of HTN
PM hormone use
Education (≤ high school vs > high school)
Family history of cancer
Multivitamin use
Vitamin E supplement use
Variables were chosen for inclusion in the statistical models based on their reported or suspected associations as independent risk factors or potential confounders
As mentioned, total energy & dietary fats were forced to be constant in the models
A variable was retained in the final model, as all of these were, if it was statistically significant or, if it was not statistically significant and its removal from the model resulted in 10% or greater change in the effect estimates of protein, in which case it was considered a confounder and retained
______________________________________________________________
- Although it may seem counter-intuitive to include components of dietary protein, such as dietary methionine, into the model with total protein, we did so with the attempt to isolate the effects of the protein component on outcome independent of known risk factors. For example, methionine has been reported to be the dietary precursor of homocysteine, a potential risk factor for CVD

11. Results 475,755 person-years Outcomes: 4,843 incident cancers
739 CHD deaths
1,676 cancer deaths
3,978 deaths from all causes
Through December 31, 2000, representing almost 500,000 person-years of follow-up, we documented
4,843 new cancers
739 deaths from CHD
1,676 deaths from cancer, and
3,978 deaths from all causes combined.

12. Quintiles of total protein
Table 1 Distribution of baseline characteristics by quintiles of total protein among 29,017 Iowa women, 1986
Quintiles of total protein
(% of total energy)
Subject characteristics 1
(14.1) 2
(16.3) 3
(17.8) 4
(19.4) 5
(22.0)
Age, y 76 75
Education > high school, % 34 39 42 44
Physical activity, active % 23 26 29
Current smokers, % 19 14
Alcohol >14 g/d, % 13 9 8 6
Body mass index, kg/m2 25 27
Vitamin E supplement use, % 15 16
Table 1 shows the distribution of non-dietary variables and vitamin supplement intake across quintiles of total protein intake, expressed as a % of energy
Increasing protein intake was positively associated with higher education and physical activity,
and inversely associated with the proportion of current smokers and intakes of alcohol > 14 g/d (approximately one drink),
Slight or no associations were observed with body mass index (BMI), WHR, multi- or vitamin E supplement intake, hypertension and postmenopausal hormone use

13. Quintiles of total protein
Table 1 cont.
Quintiles of total protein
(% of total energy)
Nutrient intakes 1
(14.1) 2
(16.3) 3
(17.8) 4
(19.4) 5
(22.0)
Carbohydrates, % energy
53.7
50.8
48.9
46.8
43.9
Total fat, % energy
33.1
33.9
34.2
34.7
34.5
Saturated fat, % energy
10.9
11.5
11.7
12.1
12.3
Polyunsat. fat, % energy
6.3
6.1
5.9
5.7
5.5
Monounsat. fat, % energy
12.7
12.9
13.0
13.2
trans fat, % of energy
1.9
1.7
1.6
1.4
Cholesterol, mg/d
205
239
261
272
297
Total fiber, g/d
19.6
20.4
20.3
19.4
18.2
Methionine, g/d
0.27
0.45
0.55
0.65
0.79
For nutrient intakes,
Increasing protein intake was inversely associated with carbohydrate and
positively associated with dietary cholesterol and methionine
Modest associations were observed with dietary fats and fiber

14. Quintiles of total protein (% of total energy)
Table 1 cont.
Quintiles of total protein (% of total energy)
Food intakes, servings / 1,000 kcals 1
(14.1) 2
(16.3) 3
(17.8) 4
(19.4) 5
(22.0)
Processed & red meat A
0.52
0.62
0.67
0.72
0.73
Chicken & poultry
0.07
0.08
0.09
0.11
0.19
Fish & seafood
0.06
0.16
Dairy products B
1.00
1.13
1.24
1.34
1.45
Eggs
0.12
0.14
Nuts, tofu and legumes
0.13
Whole grains C
0.56
0.69
0.68
Refined grains D
2.86
2.52
2.26
1.99
1.49
Sweets and desserts
0.79
0.63
0.43
0.29
Fruits and vegetables
2.44
2.67
2.72
2.81
3.01
For foods, expressed as servings per 1000 kcals,
Increasing protein intake was positively associated with servings of red meat, poultry, fish, dairy products and eggs, and
inversely associated with servings of refined grains [specifically white bread] and sweets and desserts.
Weak or modest associations were observed with servings of nuts/tofu/legumes, whole grains, and fruits and vegetables
A composite of beef, pork, processed meat
B composite of milk, cream, ice-cream, yogurt, cheese
C composite of dark bread, brown rice, oatmeal, whole grain cereal, bran, wheat germ & other grains (bulgar, kasha, couscous)
D composite of rice, pasta, potatoes, refined cold breakfast cereal, muffins, snack foods, sweetened sodas, pizza, chocolate, cakes, cookies

15. *adjusted for dietary fats, total energy plus other covariates
Table 2 RR (95% CI) for CHD mortality by quintiles of total protein intake (% energy) substituted for isoenergetic amount of carbohydrate, IWHS 1986 to 2001
Quintiles of intake P 1 2 3 4
5 (95% CI)
trend
Total protein
Median (% energy)
14.1
16.3
17.8
19.4
22.0
Multivariable RR*
0.72
0.56
0.71
0.84 (0.39, 1.79)
0.62
Animal protein
8.9
11.3
12.9
14.7
17.5
Multivariable RR
0.99
0.88
0.81
0.88 (0.42, 1.86)
0.29
Vegetable protein
3.7
4.3
4.8
5.3
6.1
0.86
0.75
0.70 (0.49, 0.99)
0.02
For CHD mortality, the substitution of total protein (protein from all sources combined) for the same amount of energy from total carbohydrate was not statistically significant in multivariable adjusted models
the substitution of animal protein (protein from meat, poultry, dairy, fish and eggs) for the same amount of energy from total carbohydrate was also not statistically significantly associated with CHD mortality
However, a 30% decreased risk was observed in the multivariable-adjusted analyses (95% CI, ; P for trend, 0.02) among women in the highest compared to the lowest quintile of vegetable protein intake (representing a difference of 2.4% of total energy) when substituted in place of the same % of total energy from carbohydrate.
*adjusted for dietary fats, total energy plus other covariates

16. *adjusted for dietary fats, total energy plus other covariates
Table 3 RR (95% CI) for cancer incidence by quintiles of total protein intake (% energy) substituted for isoenergetic amount of carbohydrate, IWHS
Quintiles of intake P 1 2 3 4
5 (95% CI)
trend
Total protein
Median (% energy)
14.1
16.3
17.8
19.4
22.0
Multivariable RR*
0.99
1.01
1.08
1.24 (0.92, 1.67)
0.18
Animal protein
8.9
11.3
12.9
14.7
17.5
Multivariable RR
0.96
0.92
1.02 (0.76, 1.37)
0.95
Vegetable protein
3.7
4.3
4.8
5.3
6.1
0.94
0.99 (0.87, 1.14)
For cancer incidence, following multivariable adjustment, none of the associations of protein or protein type were statistically significant when substituted for carbohydrate
*adjusted for dietary fats, total energy plus other covariates

17. *adjusted for dietary fats, total energy plus other covariates
Table 4 RR (95% CI) for cancer mortality by quintiles of total protein intake (% energy) substituted for isoenergetic amount of carbohydrate, IWHS
Quintiles of intake P 1 2 3 4
5 (95% CI)
trend
Total protein
Median (% energy)
14.1
16.3
17.8
19.4
22.0
Multivariable RR*
1.00
1.02
1.03
1.07 (0.64, 1.79)
0.81
Animal protein
8.9
11.3
12.9
14.7
17.5
Multivariable RR
0.94
0.80
0.76
0.77 (0.47, 1.27)
0.31
Vegetable protein
3.7
4.3
4.8
5.3
6.1
0.97
1.04
1.07
1.04 (0.83, 1.32)
0.43
For cancer mortality, following multivariable adjustment, none of the associations of protein or protein type were statistically significant when substituted for carbohydrate
*adjusted for dietary fats, total energy plus other covariates

18. *adjusted for dietary fats, total energy plus other covariates
Table 5 RR (95% CI) for all cause mortality by quintiles of total protein intake (% energy) substituted for isoenergetic amount of carbohydrate, IWHS
Quintiles of intake P 1 2 3 4
5 (95% CI)
trend
Total protein
Median (% energy)
14.1
16.3
17.8
19.4
22.0
Multivariable RR*
0.95
0.81
0.84
0.99 (0.71, 1.38)
0.67
Animal protein
8.9
11.3
12.9
14.7
17.5
Multivariable RR
0.93
0.83
0.79
0.82 (0.59, 1.13)
0.24
Vegetable protein
3.7
4.3
4.8
5.3
6.1
0.90
0.95 (0.82, 1.10)
0.74
Finally, for all cause mortality, none of the associations of protein or protein type were statistically significant when substituted for carbohydrate
*adjusted for dietary fats, total energy plus other covariates

19. Quintiles of vegetable protein intake
Table 6 RR (95% CI) of vegetable protein intake (% of energy) substituted for isoenergetic amount of animal protein for different outcomes
Quintiles of vegetable protein intake P 1 2 3 4
5 (95% CI)
trend
Median (% energy)
3.7
4.3
4.8
5.3
6.1
CHD mortality
Multivariable RR*
0.86
0.83
0.74
0.70 (0.51, 0.98)
0.02
Cancer incidence
Multivariable RR
0.94
0.96
0.99 (0.87, 1.13)
0.92
Cancer mortality
1.00
1.09
1.13
1.11 (0.89, 1.38)
0.29
All cause mortality
0.93
0.98
0.99 (0.86, 1.14)
0.82
In order to compare protein sources with the different outcomes, we evaluated the association of substituting vegetable protein for animal protein [a change that would be expected if one were following a vegetarian diet]
Only the association with CHD mortality was statistically significant, and resulted in a 30% decreased risk among women in the highest compared to lowest category of vegetable protein intake, representing a substitution of 2.4% of total energy from vegetable for animal protein (95% CI, ; P for trend, 0.02).
While the use of nutrient values was necessary to evaluate our hypotheses of protein for carbohydrate substitutions on various outcomes, realistically most individuals interchange foods when implementing dietary changes. Therefore, we also examined the association of different food sources of protein in place of carbohydrate-rich foods, standardized as servings per 1,000 calories, while holding constant total energy, dietary fats and other covariates.
*adjusted for carbohydrate, dietary fats, total energy, plus other covariates

20. Table 7 Multivariable RR
Table 7 Multivariable RR* for protein foods substituted for an isoenergetic amount of carbohydrate foods (svgs/1000 kcals) for different outcomes
Quintiles of intake P
Δ Svg/1,000 kcals Q5:Q1
Svgs/1,000 kcals 1 2 3 4
5 (95% CI)
trend
CHD mortality
Legumes
0.89
0.91
0.81
0.83 (0.65, 1.07)
0.08
0.5
Dairy
1.13
1.26
1.41 (1.07, 1.87)
0.02
2.1
Red meats
1.10
1.09
1.29
1.44 (1.06, 1.94)
0.9
Cancer mortality
1.17
1.14
1.23
1.23 (1.04, 1.46)
0.98
1.08
0.87
0.97 (0.80, 1.17)
0.43
0.93
0.92
1.04 (0.85, 1.27)
0.52
All cause mortality
1.03
0.99
1.10 (0.99, 1.23)
0.09
1.05
1.10 (0.97, 1.24)
0.36
0.97
1.16 (1.02, 1.32)
Of the protein food groups examined, only the results for legumes, dairy and red meats were statistically significant and are shown here
For CHD mortality, there was an increased risk of 41% for dairy and 44% for red meat servings when comparing the highest to lowest quintiles of intake in place of an isoenergetic number of servings of carbohydrate-rich foods
We also observed a 17% decreased risk for the highest compared to lowest quintile of legume intake in place of carbohydrate-rich foods, although the association was only marginally statistically significant (P for trend, 0.08)
In other words, a daily substitution of ~ 1 serving of red meat per 1,000 calories in place of the same number of servings of carbohydrate-rich foods was associated with a 44% increased risk of CHD mortality
No associations were observed with cancer incidence
For cancer mortality, a 0.5 serving per 1000 kcals of legumes when substituted for the same quantity of carbohydrate food servings was associated with a 23% increased risk (P for trend, 0.02).
For all cause mortality, ~ 1 serving of red meat per 1000 kcals was associated with a 16% increased risk when substituted for the same # of servings of carbohydrate foods (P for trend, 0.02).
*adjusted for dietary fats, total energy, other covariates & quintiles of svgs/1000kcals: fruits & veg, eggs, poultry, fish, legumes, dairy, red meats

21. Summary
Similar ↓ in risk of CHD mortality when vegetable protein substituted for carbohydrate or animal protein
suggests animal protein & carbohydrate may have similar potentially adverse effects on CHD mortality
Animal protein not associated with any outcome
↑ risk of CHD mortality for red/processed meat servings (RR=1.44) and dairy servings (RR=1.41) when substituted for carbohydrate foods
Modest risk of red/processed meat servings with all cause mortality (RR=1.16)
Modest risk of legume servings with cancer mortality (RR=1.23) but not with cancer incidence
No associations with cancer incidence
To summarize,
Similar decreases in risk of CHD mortality were observed when vegetable protein was substituted for either carbohydrate and animal protein
This suggests that both animal protein & carbohydrate may have similar potentially adverse effects on CHD mortality compared to vegetable protein
Although animal protein, per se, was not associated with any outcome when substituted for CHO, a composite of red & processed meat servings, in place of CHO food servings, was associated with a 44% increased risk of CHD mortality, and a similar increase in risk was observed for dairy food servings (RR=1.41)
A modest increased risk of red & processed meat servings in place of CHO foods was also observed with all cause mortality (RR=1.16)
The apparent discrepancy between the animal protein and food group analyses may be from the inability to differentiate among the effects of proteins derived from different animal sources when using the nutrient value, suggesting that protein sources may differ in their metabolic effects.
A modest increased risk of legume servings in place of CHO foods was observed with cancer mortality (RR=1.23). This finding is inconsistent with our results for CHD mortality, where an inverse trend was observed for legumes, and may be a spurious association
No significant associations were observed with cancer incidence

22. Strengths & Limitations
Prospective
Large # of events
Adjust for large # of covariates
Limitations
Baseline diet only
No blood samples
Food substitution analyses: measuring non-protein components?
Red meat & CHD – consistent with others’ findings (Snowdon 1984, Hu 1999, Liu 2004)
Our study has several strengths.
The prospective nature of our data collection eliminates any biases associated with recall of diet and lifestyle behaviors.
The use of a large, well-defined sample derived from a general population permits the generalizability of our findings, at least to older Caucasian females.
Further, we were able to adjust for multiple confounding and potential risk factors.
Our study also has potential limitations.
First, diet was assessed once in 1986 and dietary changes may have occurred from the baseline period to the present, possibly biasing our associations. However, if this were the case we would not have expected to see different associations of protein intake for the different outcomes.
Second, we were not able to assess CHD incidence because the Iowa Women’s Health Study has traditionally focused on collecting information on cancer incidence.
Third, we were unable to examine whether intermediate variables, such as serum lipid, glucose or insulin concentrations, could account for the associations we found, as reported in the short-term metabolic studies that replaced dietary protein for carbohydrate
Fourth, in performing food substitution analyses, we may also be measuring the effect on CHD mortality and other outcomes of non-protein components of these foods. We controlled for dietary fats, cholesterol, methionine, fiber, as well as multivitamin & vitamin E supplement use to better isolate the effect on our outcomes of the protein in these foods separate from the effects of other nutrients that have established associations with our outcomes
Despite the limitations of this approach, we performed these analyses because these substitutions realistically represent typical dietary changes.

23. Conclusions
Dietary protein from animal and vegetable sources appear to be differentially associated with mortality from CHD & all causes when substituted in the diet
Long-term adherence to popular HP diets, without discrimination toward protein source, may have potentially adverse health consequences
In conclusion,
Dietary protein from animal and vegetable sources appear to be differentially associated with mortality from CHD & all causes when substituted for CHO in the diet
Long-term adherence to popular HP diets, without discrimination toward protein source, may have potential adverse health consequences

24. Appendix - Protein Food Groupings
Legumes/nuts/tofu
composite of tofu, dried beans, nuts and peanut butter
Dairy
composite of milk, cream, ice-cream, yogurt and cheese
Eggs
Red meats
composite of beef, pork and processed meat
Poultry
composite of chicken and turkey
Fish
composite of fresh fish, canned fish and seafood
Fruits & Vegetables
Including juices, excluding potatoes
Carbohydrate foods = referent
composite of refined carbohydrates (rice, pasta, potatoes, refined cold breakfast cereal, muffins, snack foods, sweetened sodas, pizza, chocolate, cakes, cookies), and
whole grain carbohydrates (dark bread, brown rice, oatmeal, whole grain breakfast cereal, bran, wheat germ and other grains such as bulgar, kasha and couscous)
The protein food groups that we examined were:
Legumes/nuts/tofu
Dairy
Eggs
Red meats
Poultry
Fish
Fruits and vegetables were not examined, because their protein content is low; instead we controlled for this group in the statistical models
The carbohydrate foods were used as the referent, and consisted of both refined and whole grains because some of the high-protein diets do not discriminate among these sources
In sensitivity analyses, we used refined grains as the referent and simultaneously adjusted for whole grains, and the results did not change appreciably, probably because this cohort consumed much lower intakes of whole compared to refined grains, as shown in Table 1