Citizen Petition Regarding Vitamin K2

Citizen Petition asking the National Academy of Sciences to set a designated reference index for vitamin K2 of 200 micrograms/day

February 20, 2016

Dr. Stephen Ostroff, M.D.

Acting Commissioner, Food and Drug Administration
Department of Health and Human Services
5630 Fishers Lane, Room 1061
Rockville, MD 20852

Dear Dr. Ostroff,

The undersigned submits this petition under the Federal Food, Drug, and Cosmetic Act 21 U.S.C. Section 355 (e) (3), and 21 C.F.R. 10.30 to request the Food and Nutrition Board (FNB) at the Institute of Medicine of the National Academies to designate that an adequate Intake (AI) of vitamin K2 (menaquinones MK-4 – MK-13).

Specifics of the action requested

The FNB should designate the AIs of vitamin K2 as the following:

Table 1: Proposed adequate Intakes (AIs) for Vitamin K2 (menaquinones MK-4 – MK13) 
Age Male Female Pregnancy Lactation
Birth to 6 months 4.0 mcg 4.0 mcg
7–12 months 5.0 mcg 5.0 mcg
1–3 years 60 mcg 60 mcg
4–8 years  110 mcg 110 mcg
9–13 years 120 mcg 120 mcg
14–18 years 150 mcg 150 mcg 150 mcg 150 mcg
19+ years 240 mcg 160 mcg 160 mcg 160 mcg

 

 

Statement of grounds

The request for the designation of the AI for vitamin K2 is based on the following research article:

Vitamin K, sodium, macronutrient profiles, exercise, biometrics, socio-economic status, tobacco, and gender related to early cardiovascular death in 167 countries

David K. Cundiff

 

Abstract

Background In animal and human studies, vitamin K2 (e.g., menaquinone-4) deficiency has recently been found to possibly contribute to cardiovascular disease (CVD) by calcifying coronary arteries and other vessels. Unlike vitamin K1 (phylloquinone), which comes largely from vegetables, vitamin K2 comes from fermented plants, meat, dairy, and eggs. A small portion of ingested K1 is metabolized by bacteria to a K2 precursor (menadione) in the gut.

Methods Using publicly available sources, I collected food commodity availability data and derived nutrient profiles for people from 167 countries. I also collected female and male cohort data on early death from CVD (ages 15-64 years), insufficient physical activity, tobacco, biometric CVD risk markers, socioeconomic risk factors for CVD, and gender. The main measures were (1) univariate correlations of early death from CVD with each risk factor, (2) multiple regression derived formulas relating early death from CVD (dependent variable) to these selected nutrients and the other risk factors (independent variables), and (3) for each risk factor appearing in the multiple regression formula, the percentages of attributable CVD risk

 

.

 

Results Vitamin K1 and vitamin K2 were both inversely correlated with early CVD death (r= – 0.17, P < 0.0001 and r= – 0.35, P < 0.0001, respectively). I derived a variable by multiple regression to maximize the inverse correlation of the combination of K1 and K2: K2-Total=0.09347 * vitamin K1/1000 kcals/d + K2/1000 kcals/d (r= – 0.37, P < 0.0001), representing dietary K2 + 9.347% of dietary K1 being converted to K2 via the gut. People in countries with K2-Total < 10µg  per 1000 kilocalories/percapita/day (kcals/day) had about 2.75 times the rate of early CVD deaths as people in countries with > 25 µg/day of K2-Total per 1000 kcals/day. A multiple regression derived formula relating early death from CVD to dietary nutrients and other risk factors accounted for 56% of the variance between cohorts in early CVD death. The attributable risks of the variables in the formula were the following: too little protein (1.09%), high total fat/PUFA ratio (0.74%), alcohol (0.42%), too little K-Total (5.91%), too few kcals of animal products in diet (7.09%), tobacco (7.23%), diabetes (1.07%), hypertension (9.25%), air pollution (9.49%), infancy and early childhood nutritional and other stress (3.72%), poverty (7.92%) and too much physical activity (2.27%).

Conclusions The addition of K2-Total to the analysis of CVD risk appears to explain the paradox of people in poor countries that consume a largely plant-based diet out of economic necessity having more than twice the incidence of early cardiovascular death compared with people in wealthy countries that consume more K2-laden animal products. Public health programs to increase the intake of vitamin K2 foods, especially fermented plants like sauerkraut and kombucha, should be considered.

 


Introduction

In complex and poorly understood ways; cardiovascular disease (CVD) risk is determined by the interaction between diet, physical inactivity, possibly alcohol, tobacco, biometric risk factors, socioeconomic risks, gender, race, age and by as yet unquantifiable environmental, cultural, and genetic influences.

 

Vitamin K2 deficiency has not been well studied as a contributor to CVD largely because it has been hard to quantify intakes of vitamin K2 and relate the intakes with CVD outcomes. However, vitamin K inhibition with warfarin has been shown to accelerate vascular calcification in rats and in people.[1-3]

 

Biometric markers that correlate with CVD events  include elevated total cholesterol / high density lipoprotein cholesterol ratio (TC/HDL-C),[4, 5] diabetes (i.e., high fasting blood sugar (FBS) or hemoglobin A1c),[6, 7]  high body mass index (BMI),[8, 9] and elevated systolic blood pressure (SBP).[10] Socioeconomic risk factors such as dropping out of school, poverty, and certain occupations have been correlated with cardiovascular disease.[11, 12] Nutritional and other stress on infants and young children has been found to be associated with higher later CVD death rates. Studies of infants in utero during the influenza pandemic 1918,[13] during the Dutch famine of 1944,[14] and in the rapid development of the American South after the Second World War show that these individuals suffer increased rates of CVD deaths in later life.[15] Early childhood mortality (ages 0-5 years) provides a reasonable index of nutritional and other stresses on in utero infants and young children that might correlate with later early mortality from CVD.

 

According to a World Health Organization report, outdoor and indoor air pollution accounted for almost 5 million CVD death in 2012 (approximately 28% of worldwide CVD deaths).16 According to the World Heart Federation analysis worldwide, CVD deaths can be attributed to the following: hypertension—13%, tobacco use—9%, raised blood glucose—6%, physical inactivity—6%, and overweight and obesity—5%.17 Regarding overweight and obesity, at least in the USA, epidemiologic studies have shown the paradoxical finding that overweight people live longer than normal weight people.18 Men develop early CVD (i.e., ages 15-64 years old) at much higher rates than women.19

 

In relation to rates of early CVD deaths, this paper will highlight the role of vitamin K2 deficiency in conjunction with CVD risk factors for which worldwide data are available for use in univariate and multivariate analyses. In this study, data from worldwide countries on early death from CVD (dependent variable) will be related by multiple regression analysis to diet (macronutrient profile, vitamin K1 and K2, and sodium), insufficient physical activity, tobacco use, alcohol consumption, biometric indices (BMI, FBS, serum cholesterol, SBP), GDP, socioeconomic indices (mortality of children 0-5 years old, air pollution (indoor and outdoor pollution combined), years of schooling,[28]) and gender (independent variables). Multiple regression formulas will also be derived that capture the interaction between BMI, (dependent variable) and the nutritional and other risk factors. Likewise for FBS, serum cholesterols, and SBP and independent variables.

 

 


Methods

For this study, data from countries around the world will be provided by the World Health Organization (WHO),[18-26]  Food and Agriculture Organization (FAO),[27] and the Institute of Health Metrics and Evaluation (IHME).[28-32] From these WHO/FAO/IHME databases, data were available for female and male cohorts regarding the following health risk factors:

Countries         Cohorts

Food commodities[27]                                                175                  350

Fasting blood sugar (FBS)[20]                                   187                  374

Serum cholesterol[24]                                                 187                  374

Mean BMI for ages>15 years (BMI)[19]                   188                  376

Mean systolic blood pressure (SBP)[25]                     187                  374

Insufficient physical activity[26]                                142                  284

Tobacco use[22]                                                          182                  363

Alcohol consumption[23]                                           187                  374

Mean percapita gross domestic product (GDP)[18]   200                  400

Child mortality (0 – 5 years old)[21]                          173                  346

Air pollution (all causes)[29, 30]                                 185                  370

Years of education[28]                                               173                  346

Death by CVD (ages 15-64)[32]                                185                  370

 

If both food commodity data and early mortality from CVD data were available, I included countries in the analysis.

 

The FAO provided data on plant- and animal-based food commodities as kilocalories available per capita/day (kcals/d). I evaluated the following plant-based and animal-based commodities:

 

  1. Cereals
  2. Starchy vegetables
  3. Sweets and sugar added sweets
  4. Pulses (legumes)
  5. Tree nuts
  6. Oils other than vegetable oils
  7. Vegetable oils
  8. Vegetables
  9. Fruits and fruit juices
  10. Alcohol (g) consumed by female and male cohorts obtained from the WHO
  11. Beef
  12. Butter
  13. Cheese
  14. Eggs
  15. Milk products
  16. Mutton
  17. Pork
  18. Poultry
  19. Fish and sea foods
  20. Total kcals/d
  21. Total animal products
  22. Total plant foods

 

These food commodities accounted for > 97% of the total kcals/d available for the average country. For the analysis, I used the average of the food commodity data for the years 1991, 2001, and 2011.

 

I utilized the United States Department of Agriculture Nutrient Database to derive average values for the macronutrient profiles for each of these food commodities. These included

 

  1. protein (g),
  2. carbohydrates (g)
  3. dietary fiber (g),
  4. total fat (g),
  5. saturated fatty acids (g),
  6. monounsaturated fatty acids (g),
  7. polyunsaturated fatty acids (g) (PUFA),
  8. trans fatty acids (g), and
  9. alcohol (g). For the analysis, I used WHO data on alcohol consumption data by gender rather than the alcohol availability data by country from the FAO.

 

I used the mean availability of each food commodity (kcals/day) and the USDA nutrient database to derive the availability of vitamin K1 (µg/1000 kcals/d), vitamin K2 (µg/1000 kcals/d), vitamin K3 (dihydrophylloquinone: µg/1000 kcals/d), and sodium (mg/day). This methodology is described in more detail in a previous paper.[35] Basically, for each of the 19 food categories above, I found all available food items from the USDA nutrient database to create a reference profile of the average quantity for each nutrient under study—i.e., macronutrients (kcals/100 g portion), vitamin K1 (µg/100 g portion), vitamin K2 (µg/100 g portion), vitamin dihydroxyphylloquinone (DK2:µg/100 g portion), and sodium (mg/100 g portion). Using the reference nutrient profile for each of the 19 food components, I determined the nutrient profile for each country with the following steps:

 

  • For each country, I determined the kcals allotted to each food category.
  • For each food category, I used the USDA nutrient database to determine the average nutrient profile in kcals of a 100 g portion of foods in that category (i.e. g of protein, g of carbohydrates, dietary fiber g/ 1000 kcals, g of fat, etc.)
  • For instance, 100 g of pork from the USDA nutrient database averaged 400 kcals and had 40 g of protein, 2 g kcals of carbohydrate, 0 g of dietary fiber, and 30 g of total fat, 5 µg of vitamin K2, etc.
  •  For each country, I then divided the total kcals available in each food group by the kcals per 100 g of that food category, giving a multiplyer ∑119X for each food commodity.
  •  For each country, I then multiplied the kcals for each food commodity by the corresponding multiplyer for each component of the nutrient profile.
  • Finally, I added the values for each nutrient from each of the 19 food groups to form the nutrient profile of that country.

 

Table A shows the template for the nutrient profiles of a 100 g portion of each of the 19 food groups of the USDA nutrient database. Table B shows the example of the resulting nutrient profile of the USA.

 

The WHO evaluated physical activity of females and males in countries worldwide with the variable “insufficient physical activity” (0%-100% scale). According to the WHO, “adults aged 18–64 should do at least 150 minutes of moderate-intensity aerobic physical activity throughout the week or do at least 75 minutes of vigorous-intensity aerobic physical activity throughout the week or an equivalent combination of moderate- and vigorous-intensity activity.”[26] The WHO defined “insufficient physical activity” as less than this recommended level of physical activity. For the 44 cohorts from 22 countries for which “insufficient physical activity” data were absent but food commodities and early death from CVD were available, I imputed estimates based on multiple regression modeling as described previously.[33]

 

For countries that had data on early death from CVD and on food commodities, the WHO provided data on tobacco use (percent of female and male cohorts that used tobacco) for 322 cohorts,[22] childhood mortality for 316 cohorts,[21, 34]  and years of education for 316 cohorts. As with the missing physical activity data, I imputed tobacco use, child mortality, and years of education estimates from multiple regression formulas as previously described.[35]

 

Statistical methods

Pearson correlation coefficient analyses determined the positive or negative associations of early death by CVD with the risk factors for CVD.

 

To assess the interaction of macronutrients, alcohol consumption, physical activity, tobacco use, biometric indices, socioeconomic risks, and gender with early death by CVD, I utilized multiple regression analysis with the non-experimental regression method. The multiple regression analysis was done empirically in stages to maximize the inclusion of as many risk factors as possible. If a nutritional variable comprising a relatively small proportion of the macronutrient intake appeared in the formula and this caused a variable comprising a large proportion of the macronutrient intake to be excluded (e.g., trans fatty acid ≈1% of kcals excluding total fats ≈ 35% of kcals), the variable accounting for the smaller portion of calories was omitted. To maximize the variables included, I began with the risk factors having the weaker univariate associations with early death by CVD and proceeded to risk factors with strong univariate correlations in later stages. If a risk factor directly correlated with early death from CVD in the univariate analysis and indirectly correlated in the formula (or vice versa), it was excluded. Gender was always analyzed last so as to emphasize the influence of the other variables with big gender differences (e.g., tobacco and alcohol use).

 

By multiplying each significant variable in the multiple regression by its parameter estimate and then summing the contributions of each variable, these formulas quantified the overall relationship of early death by CVD with the interaction of CVD risk factors.

 

The percentages of risk of early death from CVD attributable to individual risk factors in the multiple regression derived formula were determined by the following steps:

 

  1. For the multiple regression derived formula, determine the variance (i.e., R2).
  2. Determine the individual variances of each of the risk factors in the formula (i.e. individual R2s)
  3. Total the individual R2s for the risk factors in the formula.
  4. Divide the R2 of the multiple regression derived formula by the sum of the R2s of the individual risk factors to generate a multiplyer.
  5. Use the multiplyer to multiply times the R2s of the individual risk factors to determine the individual portion of the overall R2 attributable to each risk factor.
  6. To convert from R2 to percent of attributable risk, multiply times 100.

 

I performed the data analysis with SAS statistical software 9.1 (SAS Institute, Cary, NC).

Results

Vitamin K1 and vitamin K2 were both inversely associated with early CVD death (r= –0.17, P < 0.0001 and r= –0.35, P < 0.0001, respectively). I derived a variable by multiple regression to maximize the inverse correlation of the combination of K1 and K2 with early CVD death: K2-Total = 0.09347 * vitamin K1/1000 kcals/d + K2/1000 kcals/d (r= – 0.37, P < 0.0001). This was consistent with the medical literature derived estimate that about 10% of K1 is converted to K2. Cohorts in countries with K2-Total < 10 µg/1000 kcals/day had about 2.75 times the rate of early CVD deaths as cohorts in countries with > 25 µg/1000 kcals/day of K2-Total (i.e., early CVD death/100,000/year = 1956, 90% CI: 611-4267 (n=44) versus 711, 90% CI: 167-1740 (n=54)). Table 1 shows the correlations between early CVD death and K2-Total with nutrient variables.

 

K2-Total appears to dominate the univariate associations in Table 1. It is more strongly associated with early death from CVD than any other nutrient and with any food commodity other than cheese and eggs. Significantly, when the food commodities and nutrients are positively associated with K2-Total, the commodities and nutrients are negatively associated with early CVD death and vice versa. The only exception is alcohol, which is weakly positively associated with CVD death despite being directly associated with K2-Total.

 

Table 2 shows the correlations with early CVD death and with K2-Total for other risk factors. Similar to the analysis of food commodities and nutrients in Table 1, the association of K2-Total with early CVD death is usually discordant with the association of the risk factors with early CVD death. Only tobacco use and FBS are exceptions.

Table 1. Correlations of early death from CVD (ages 15-64) and K2-Total with food commodities both plant and animal based (n=334, female and male cohorts from 167 countries)

Macronutrients and other variables (% of Kcals/percapita/day) Kcals/per 1000 kcals per capita/

day

SD Early CVD death r (P) K2-total (µg/day/per 1000 kcals/ day

r (P)

Early CVD death adjusted for K2-total

r (P)

Butter /1000 kcals/day      8.62 11.17 –0.25 (<0.0001) 0.64 (<0.0001) –0.02 (0.77)
Cheese /1000 kcals/day      13.08 17.21 –0.44 (<0.0001) 0.69 (<0.0001) –0.29 (<0.0001)
Milk /1000 kcals/day 60.79 43.61 –0.35 (<0.0001) 0.57 (<0.0001) –0.18 (0.09)
Eggs/1000 kcals/day      8.54 6.46 –0.47 (<0.0001) 0.59 (<0.0001) –0.32 (<0.0001)
Meat /1000 kcals/day    77.88 50.21 –0.32 (<0.0001) 0.68 (<0.0001) –0.10 (0.0626)
Fish /1000 kcals/day      12.81 14.89 –0.10 (0.08) 0.16 (0.0026) 0.02 (0.75)
Animal products overall/1000 kcals/day    189.7 112.3 –0.41 (<0.0001) 0.75 (<0.0001) –0.06 (0.25)
Cereals and grains/1000 kcals/day 445.9 176.3 0.06 (0.25) –0.58 (<0.0001) –0.20 (0.64)
Starchy vegetables /1000 kcals/day 72.61 92.44 0.11 (0.0432) –0.33 (<0.0001) –0.02 (0.77)
Sugar and sweets/1000 kcals/day   105.4 54.95 –0.41 (<0.0001) 0.50 (<0.0001) –0.27 (<0.0001)
Pulses/1000 kcals/day 23.25 23.00 –0.12 (0.0322) –0.40 (0.0236) –0.33 (0.0033)
Tree nuts /1000 kcals/day      4.56 5.30 –0.25 (<0.0001) 0.22 (<0.0001) –0.17 (0.0023)
Oils (olive, avocado, etc) /1000 kcals/day      23.61 38.43 0.18 (0.0012) –0.14 (<0.0014) 0.14 (0.0137)
Vegetable oils (e.g., soy) /1000 kcals/day    96.25 46.87 –0.43 (<0.0001) 0.44  (<0.0001) –0.31 (<0.0001)
Vegetables/1000 kcals/day       20.92 12.89 –0.25 (0.0004) 0.31 (<0.0001) –0.31 (<0.0001)
Fruits (including juices) /1000 kcals/day     40.36 31.04 –0.19 (0.0009) 0.10 (0.0610) –0.17 (<0.0001)
Alcohol (g/day)    6.59 5.82 0.10 (0.07) 0.33 (<0.0001) 0.25 (0.0033)
Protein % of kcals 14.0 2.79 –0.16 (0.0033) 0.58 (<0.0001) 0.01 (0.86)
Carbohydrate % of kcals 57.6 8.92 0.31 (<0.0001) –0.76 (<0.0001) 0.03 (0.54)
Dietary fiber g /1000 kcals 11.9 2.61 0.24 (<0.0001) –0.74 (<0.0001) –0.03 (0.64)
PUFA % of kcals    7.54 3.04 –0.23(<0.0001) 0.28 (<0.0001) –0.11 (0.0338)
MUFA % of kcals 11.0 7.37 –0.10 (0.0575) 0.28 (<0.0001) 0.00 (0.94)
SFA % of kcals    9.70 7.18 –0.13 (0.0220) 0.39 (<0.0001) 0.00 (0.97)
Trans fatty acids % of kcals      0.333 0.200 –0.15 (0.0057) 0.54 (<0.0001) 0.02 (0.65)
Total fat % of kcals 29.7 17.4 –0.13 (0.0161) 0.33 (<0.0001) –0.02(0.76)
Sodium mg/1000 kcals   1856 318 –0.30 (<0.0001) 0.53 (<0.0001) –0.09(0.0879)
Vitamin K1 µg/1000kcals  82.4 23.4 –0.17 (<0.0001) 0.16 (0.0041) 0.00(0.94)
Vitamin K2 µg/1000kcals      9.95 5.96 –0.35 (<0.0001) 0.00(0.94)
Vitamin K2+0.09347* vitamin K1 17.34 6.69 –0.37 (<0.0001)

 

 

 


Table 2. Selected correlations of early death from CVD (ages 15-64) and K2-Total selected CVD risk factors other than food (female and male cohorts from 167 countries: WHO/IHME data, n=334)

CVD early death risk factors Mean SD Early death from CVD r (P) K2-Total (µg/day/

per 1000 kcals/ day

Early CVD death adjusted for K2-Total

r (P)

Insufficient physical activity (%)    25.3 12.4 –0.27 (<0.0001) 0.24 (<0.0001) –0.16 (0.0037)
Tobacco use (portion of cohorts) 0.206 0.159 0.41 (<0.0001) 0.12 (0.0267) 0.50 (<0.0001)
BMI (kg/m2)     25.6 2.30 –0.17 (0.0024) 0.60 (<0.0001) 0.08 (0.17)
Serum cholesterol (mmol/dl)        4.71 0.416 –0.40 (<0.0001) 0.75 (<0.0001) –0.20 (0.0003)
SBP (mmHg)      126.4 5.09 0.47 (<0.0001) –0.31 (<0.0001) 0.40 (<0.0001)
FBS (mmol/dl)           5.49 5.49 0.16 (0.0037) 0.29 (<0.0001) 0.30 (<0.0001)
Sex (female=1, male=0)         0.50 –0.39 (<0.0001) –0.42 (<0.0001)
GDP 2009 ($) 11,135 16,487 –0.43 (<0.0001) 0.65 (<0.0001) –0.27 (<0.0001)
Early childhood mortality (ages 0-5 per 100,000)         41.7 43.3 0.30 (<0.0001) –0.68 (<0.0001) 0.36 (<0.0001)
Years in school (#)           7.94 3.54 –0.20 (<0.0001) 0.71 (<0.0001) 0.09 (0.09)
Air Pollution (all causes)         52.9 31.4 0.47 (<0.0001) –0.46 (<0.0001) 0.36 (<0.0001)
Early CVD mortality/year (deaths/year/100,000)     1449 1040 –0.37 (<0.0001)

 

 

The multiple regression derived overall CVD risk formula combining dietary and other CVD risk factors using the analysis techniques described in the methods is the following:

 

Early death by CVD = (((– protein (g) * 34,193 + total carbohydrates (g) 23,359 – dietary fiber (g) * 89,429 +total fat (g) * 53,300 – PUFA (g) * 214,229 – K2-Total (µg) * 31,242 / kcals /d + alcohol (g consumed /day)) * 48.874) * 0.81278 – (animal foods (kcals/day) * 1548.64 / kcals/day) * 0.36402 + childhood mortality * 3.3967 + FBS * 643.29 + tobacco * 2230.8 – GDP * 0.00951 + BMI * 41.611 + SBP * 18.731 + Air pollution * 9.3648 – insufficient physical activity * 5.3868) * 1.3275 – 7294 (n = 334 cohorts, R2=0.56);

 

The multiple regression derived formula for early death from CVD allows us to attribute proportions of risks to the variables in the formula in relationship to the overall 56% of the variation in risk accounted for by the formula, as described in the Methods. (See Table 3).

 

I used the same multiple regression methodology as with relating CVD early deaths with risk factors to derive formulas for BMI, FBS, SBP, and serum cholesterol and used these formulas to derive the attributable risk of each factor (Table 3):

 

 

BMI formula = (((total carbohydrates * 26.18 – dietary fiber (g) * 242.45 + total fat * 52.65 – PUFA *220.95 + K2-Total (µg) * 197.89) / kcals/day * 0.20460 + insufficient physical activity * 0.01042 + FBS *3.1495 – childhood mortality * 0.00896 – Air pollution * 0.01086 + years of education * 0.04603) *1.0057 + gender (female=1, male=0) * 0.76092; (n=334 cohorts, R2=0.70);

 

FBS formula = ((total carbohydrates (g) * 4.772 – dietary fiber (g) * 38.6 + total fat (g) * 17.55 – PUFA (g) * 57.77 + K2-Total (µg) * 21.09) / kcals/d) * 0.27639 + tobacco * 0.14268 + insufficient physical activity * 0.00411 – childhood mortality * 0.00079473)*0.99421) – gender (female=1, male=0) * 0.06494 (n=334 cohorts, R2=0.44);

 

Systolic BP formula = ((– K2-Total (µg) * 128.53 – animal products (kcals/1000 kcals * 17.29) / kcals/day + alcohol (g) * 0.38622) * 0.72008 + tobacco use * 4.5914 + childhood mortality * 0.01870 – GDP * 0.00004588) * 0.80691 – gender (female=1, male=0) * 2.1264; (n=334 cohorts, R2=0.51);

 

Serum cholesterol formula = ((protein (g) * 12.788 – total carbohydrates (g) * 2.728 – dietary fiber (g) * 31.69 – PUFA (g) * 19.728 + K2-Total (µg) * 28.46) / kcals/d) * 0.49768 + insufficient physical activity * 0.00205 – early childhood mortality * 0.00469 + GDP ($) * 0.00000786; (n=334 cohorts, R2=0.80);

 

Using the steps described in the methods, Table 3 also shows the attributable risks of each risk factor that entered the above formulas.


Table 3. Attributable risks for early death from CVD and associated biometric indices

CVD risk factors CVD death

Attributable risk (%)

BMI

Attributable risk (%)

FBS

Attributable risk (%)

SBP

Attributable risk (%)

Cholesterol

Attributable risk (%)

Protein (–)1.09       (+)8.32
Total carbs/ fiber   (+)1.54 (+)0.49   (+)2.54
Total fat / PUFA ratio (+)0.74 (+)3.30 (+)4.55    
Alcohol (+)0.42     (+)4.02  
K2-Total (–)5.91 (+)11.14 (+)5.43 (–)4.33 (+)16.58
Animal products kcals/1000kcals/day (–)7.09   (+)0.43 (–)8.55 (+)17.00
Little physical activity (–)2.27 (+)2.96 (+)3.14   (+)2.41
Tobacco use (+)7.23   (+)0.98 (+)2.88  
Serum cholesterol   (+)9.70      
FBS (+)1.07 (+)12.31      
BMI     (+)24.87    
SBP (+)9.25        
Gender (F=1, M=0)   (+)0.64 (–)0.71 (–)11.67  
Air pollution (+)9.49 (–)6.97 (–)3.46    
Early childhood  death (+)3.72 (–)12.15   (+)6.27 (–)19.27
GDP (–)7.92     (–)6.19 (+)14.61
Years of education   (+)9.08      
Total 56.22 69.79 44.05 43.91 80.74

 

 


Discussion

Regarding the attributable risk for CVD early deaths of the dietary variables, animal products per 1000 kcals/day accounted for the largest proportion, followed by K2-Total (Table 3). K2-Total directly correlated with BMI, FBS, and cholesterol and inversely correlated with SBP and early CVD death. This is consistent with the worldwide picture of people in wealthy countries with diets high in meat, dairy, and eggs having more obesity, type 2 diabetes, and hypercholesterolemia and lower rates of hypertension and early CVD deaths compared with people in developing countries. As people in developing countries adopt the diets and lifestyles of Western countries, they have become increasingly susceptible to obesity and type 2 diabetes like people in wealthy countries. Worldwide, the kcals/day available as animal products rose from 462 kcals/d in 1991 to 538 kcals/d in 2011 (16% increase). It appears that the prices we pay for the K2 in animal products that reduces the rate of early CVD deaths and SBP are the worldwide epidemics of obesity and type 2 diabetes.

 

Because of these findings, health regulatory agencies of countries should more inclusively measure K2 levels in foods and should set recommended daily amounts of K2. In wealthy countries, given the health concerns with excessive meat, dairy, and eggs, optimally boosting K2 and K2-Total should probably come from fermented food rather than more animal products. While over 40% of deaths in developed countries are due to CVD, people in poor countries with much lower K2-Total levels suffer much higher rates of early CVD death (e.g., Bangladesh: K2-Total/1000 kcal available=5.94 µg/day and CVD early deaths in men= 2320/100,000/year or Cambodia: K2-Total/1000 kcals available=6.82µg/day and CVD early deaths in men=3033/100,000/year). The only cohorts with less than 10µg K2/1000 kcals/day available that had less than 600/100,000 early CVD deaths/year were females in Kenya (540/100,000 early CVD deaths) and females in Uganda (497/100,000 early CVD deaths). Women in these countries didn’t smoke, drank little alcohol and had much below average levels of BMI and FBS. For people in poor countries, increasing intake of fermented foods would be the most cost effective and healthy way to boost vitamin K2 in the diet.

 

Paradoxically, insufficient physical activity correlated positively with early CVD death and made no difference in SBP. However, the multiple regression formulas modeling BMI and FBS—which contribute to early death from CVD—show that sufficient physical activity is beneficial on those biometrics. In the FAO/WHO/IHME participants, the potential beneficial effects of exercise on preventing obesity, type 2 diabetes, and CVD may be underestimated because of the low physical activity levels (only 30 minutes of moderate intensity aerobic exercise 5 days per week).

 

Alcohol trended with early CVD (r=0.10, P = 0.0689) despite positively correlating with vitamin K2/1000 kcals/day (r=0.33, P < 0.0001). The correlation of alcohol with tobacco use (r=0.47 (<0.0001) and SBP (r=0.47 (<0.0001) may have also affected the association of alcohol with CVD death. In any case, the early CVD death risk attributable to alcohol was relatively small (0.42%, Table 3).  A systematic review and meta-analysis regarding alcohol and CVD found that “light to moderate” alcohol consumption is associated with a reduced risk of adverse cardiovascular outcomes.[36, 37] However, other analyses found that some or all of the apparent cardiovascular benefits of alcohol in observational studies may be due to biases, reverse causality; variations in alcohol intake over time, and residual or unmeasured confounding.[38, 39] The potential effect of alcohol consumption on CVD risk in people from other than relatively wealthy western countries has not been well documented.

 

The risk of early CVD death attributable to socio-economic indices—poverty, as measured by the percapita GDP, air pollution, and early childhood mortality (8.48%, 3.99% and 10.15%, respectively)—should be a call to action to aid development in poor countries, to reduce economic inequality in wealthy countries.

 

The relatively strong and counterintuitive direct correlation of serum cholesterol with early death from CVD (r=–0.40, P < 0.0001) most likely resulted from the strong correlation of serum cholesterol with dietary vitamin K2 (r=0.76, P < 0.0001) and with the availability of animal products (r=0.83, P < 0.0001) which provide the bulk of the vitamin K2. Only in wealthy countries with widely available and affordable meat, dairy, and eggs is high cholesterol a major risk factor for heart disease.

 

Limitations of this FAO/WHO/IHME analysis include (1) tracking percapita food availability rather than food consumption, (2) changes in physical activity and tobacco use over generations were not assessed, (3) kcals/d available by gender and consequently food group availability by gender were estimated based on National Health and Nutrition Examination Survey data which found that males consume about 50% more calories than females[40], (4) imputed data on physical activity, tobacco use, early childhood mortality, and years of education were used.

 

These multiple-regression equations relating early CVD death to diet and other risk factors should be confirmed with similar analyses from other databases and in prospective studies.

Acknowledgements

This analysis was not supported by public or private funding. The creation of the interactive website containing the formulas predicting BMI, FBS, and cholesterol—the precursor to this analysis—was partially supported by a grant from the Pollination Project (http://thepollinationproject.org/). Pollination Project personnel had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

 

Dr. George Lundberg suggested that I pay attention to Micki Jacobs, who initially detailed me on the potential impact of K2 deficiency in cardiovascular health.

DKC has no conflicts of interest.

DKC had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.


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| Uncategorized | | February 14, 2016 • 12:21 am

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