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Future body mass index modelling based on macronutrient profiles and physical activity.
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PMID:  23106911     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
BACKGROUND: An accurate system of determining the relationship of macronutrient profiles of foods and beverages to the long-term weight impacts of foods is necessary for evidence-based, unbiased front-of-the-package food labels.
METHODS: Data sets on diet, physical activity, and BMI came from the Food and Agriculture Organization (FAO), the World Health Organization (WHO), the Diabetes Control and Complications Trial (DCCT), and Epidemiology Diabetes Intervention and Complications (EDIC). To predict future BMI of individuals, multiple regression derived FAO/WHO and DCCT/EDIC formulas related macronutrient profiles and physical activity (independent variables) to BMI change/year (dependent variable). Similar formulas without physical activity related macronutrient profiles of individual foods and beverages to four-year weight impacts of those items and compared those forecasts to published food group profiling estimates from three large prospective studies by Harvard nutritional epidemiologists.
RESULTS: FAO/WHO food and beverage formula: four-year weight impact (pounds)=(0.07710 alcohol g+11.95 (381.7+carbohydrates g per serving)*4/(2,613+kilocalories per serving)-304.9 (30.38+dietary fiber g per serving)/(2,613+kilocalories per serving)+19.73 (84.44+total fat g)*9/(2,613+kilocalories per serving)-68.57 (20.45+PUFA g per serving)*9/(2,613+kilocalories per serving))*2.941-12.78 (n=334, R(2)=0.29, P < 0.0001). DCCT/EDIC formula for four-year weight impact (pounds)=(0.898 (102.2+protein g per serving)*4/(2,297+kilocalories per serving)+1.063 (264.2+carbohydrates g per serving)*4/(2,297+ kilocalories per serving)-13.19 (24.29+dietary fiber g per serving)/ (2,297+kilocalories per serving)+ 0.973 (74.59+(total fat g per serving-PUFA g per serving)*9/(2,297+kilocalories per serving))*85.82-68.11 (n=1,055, R(2)=0.03, P < 0.0001). (FAO/WHO+ DCCT/EDIC formula forecasts averaged correlated strongly with published food group profiling findings except for potatoes and dairy foods (n=12, r=0.85, P = 0.0004). Formula predictions did not correlate with food group profiling findings for potatoes and dairy products (n=10, r= -0.33 P=0.36). A formula based diet and exercise analysis tool is available to researchers and individuals: http://thehealtheconomy.com/healthTool/.
CONCLUSIONS: Two multiple regression derived formulas from dissimilar databases produced markedly similar estimates of future BMI for 1,055 individuals with type 1 diabetes and female and male cohorts from 167 countries. These formulas predicted the long-term weight impacts of foods and beverages, closely corresponding with most food group profiling estimates from three other databases. If discrepancies with potatoes and dairy products can be resolved, these formulas present a potential basis for a front-of-the-package weight impact rating system.
Authors:
David K Cundiff; Nikunj Raghuvanshi
Publication Detail:
Type:  Journal Article     Date:  2012-10-29
Journal Detail:
Title:  Theoretical biology & medical modelling     Volume:  9     ISSN:  1742-4682     ISO Abbreviation:  Theor Biol Med Model     Publication Date:  2012  
Date Detail:
Created Date:  2013-01-23     Completed Date:  2013-06-24     Revised Date:  2013-07-11    
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Nlm Unique ID:  101224383     Medline TA:  Theor Biol Med Model     Country:  England    
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Languages:  eng     Pagination:  43     Citation Subset:  IM    
Affiliation:
dkcundiff@thehealtheconomy.com
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MeSH Terms
Descriptor/Qualifier:
Adolescent
Adult
Body Mass Index*
Cohort Studies
Diet / standards*,  trends
Exercise*
Female
Humans
Male
Middle Aged
Models, Biological*
Motor Activity*
Young Adult
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Journal ID (nlm-ta): Theor Biol Med Model
Journal ID (iso-abbrev): Theor Biol Med Model
ISSN: 1742-4682
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Copyright ©2012 Cundiff and Raghuvanshi.; licensee BioMed Central Ltd.
open-access:
Received Day: 23 Month: 7 Year: 2012
Accepted Day: 10 Month: 10 Year: 2012
collection publication date: Year: 2012
Electronic publication date: Day: 29 Month: 10 Year: 2012
Volume: 9First Page: 43 Last Page: 43
PubMed Id: 23106911
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Publisher Id: 1742-4682-9-43
DOI: 10.1186/1742-4682-9-43

Future body mass index modelling based on macronutrient profiles and physical activity
David K Cundiff1 Email: dkcundiff@thehealtheconomy.com
Nikunj Raghuvanshi2 Email: nikunjr@gmail.com
1Independent researcher, 333 Orizaba Avenue, Long Beach, CA, 90814, USA
2Researcher, Microsoft Research (this article is not job-related), 8935 160th Ave NE, Apt B415, Redmond, WA, 98052, USA

Introduction

Previous mathematical modelling approaches to predict weight change have largely looked at short term results of changes of energy intake and expenditure [1,2]. As noted in one article using USA data, "A small persistent average daily energy imbalance gap between intake and expenditure of about 30 kJ per day underlies the observed average weight gain "[3]. The effect of the macronutrient profile, independent of daily energy intake, has been given less attention. Many researchers have characterized the etiology of the worldwide obesity epidemic as much more complex that simply an imbalance of calories in versus calories out. There are both complex economic causes and consequences of obesity [4]. For example, the food industry in Europe invested €1-billion in a campaign to block evidence-based health warnings on food [5]. Producing refined and processed convenience foods, high in sugar and saturated fats, is lucrative. To defend profits from sales of unhealthy foods, the food industry has adapted techniques long used by the tobacco industry to defend cigarettes. For example, they seek to instill doubt in the public about scientific evidence linking certain foods and eating patterns to obesity, emphasize personal responsibility, hire scientists to counteract obesity research, make self-regulatory pledges, lobby to stop government public health anti-obesity programs, and, of course, heavily advertise unhealthy foods [6]. At a macroscopic level in western countries, the overconsumption of value added foods (i.e., processed and refined) is a predictable outcome of market economies predicated on consumption-based growth [7].

Evidence-based public health strategies are needed to better understand the dietary and physical activity factors implicated in the development of obesity and to guide interventions and policies that can curb or reverse the increase in BMI globally [8]. Only with a quantitative understanding of the factors leading to excessive weight gain guiding the implementation of weight control education campaigns and practical public health strategies will the obesity epidemic be curbed. No methodology for relating macronutrient intake and exercise to long-term BMI change/year or future BMI has yet been scientifically validated.

A 2007 European Union regulation on nutrition and health claims made for foods provides for the use of nutrient profiles to determine which foods may bear claims but does not specify what the profiles should be or how they should be developed [9]. An in depth analysis by the French Food Safety Agency of existing nutrient profiling schemes based on indicator foods [10] found fairly good concordance between (1) The UK Food Standards Agency (FSA) model (2), The Dutch Tripartite classification model, and (3) The USA Food and Drug Administration (FDA) model but concluded, “… further improvement of the "indicator foods" approach is needed if it is to serve as a "gold standard" [9,10]. The British Heart Foundation Health Promotion Research Group compared eight nutrient profiling models with a standard ranking of 120 foods. They found good correlations with opinions of nutrition professionals about both the continuous models of nutrient profiling (Spearman's rho = 0.6-0.8) and categorical models of food profiling (high chi squared results) [11]. However, these correlations are expert opinion-based rather than evidence-based and therefore subject to challenges that they are biased.

In a consensus report entitled, “Front-of-Package Nutrition Rating Systems and Symbols: Promoting Healthier Choices,” a USA Institute of Medicine committee concluded, “…it is time for a move away from front-of-package systems that mostly provide nutrition information on foods or beverages but don’t give clear guidance about their healthfulness, and toward one that encourages healthier choices through simplicity, visual clarity, and the ability to convey meaning without written information. The report recommends that the FDA develop, test, and implement a single, standard front-of-package symbol system to appear on all food and beverage products, in place of other systems already in use” [12].

Food and Agriculture Organization (FAO) and World Health Organization (WHO) data worldwide [13,14] show large variations in diets, physical activity, and mean BMIs for female and male cohorts from countries around the world. This provides an opportunity to derive multiple regression formulas capturing the relationship between diet, physical activity, and mean BMIs of adults. Similarly, diet, exercise, and BMI data from the Diabetes Control and Complications Trial (DCCT) and the DCCT follow up study, Epidemiology Diabetes Intervention and Complications study (EDIC), can be used to generate multiple regression formulas predicting BMI change/year. By omitting the physical activity or exercise components of the formulas, the macronutrient components can be used to predict the long-term weight impacts of individual foods and beverages.

In the first and only genuinely evidence-based long-term weight impact assessment methodology of foods and beverages ever published, Harvard nutritional epidemiologists led by Dr. Dariush Mozaffarian analysed the statistical relationship between increases and decreases in servings per day of specific foods and beverages and 4 four year changes in weight of subjects from three large studies of diet and lifestyle [15]. The food group profiling component (categorical profiling) of this pioneering Harvard diet and lifestyle study serves as the comparator for results from the FAO/WHO and DCCT/EDIC macronutrient (continuous profiling) formulas for future BMI predictions for 4 four year weight impacts of individual foods and beverages.

This paper will explore whether these macronutrient and physical activity profiling multiple regression formulas are validated by comparison with the evidence-based Harvard nutritional epidemiology food group profiling study and correlated enough with each other to provide a generalisable model to predict long-term BMI change/year for diverse individuals, populations, foods, and beverages. If so, these future BMI continuous model prediction formulas could inform obesity prevention public health policies for countries and weight control strategies for clinicians and individuals.

Increased physical activity (FAO/WHO database) and exercise (DCCT/EDIC database) were hypothesized to reduce weight gain over time. Regarding the relationship between food group availability/macronutrient availability and BMI change/year of cohorts of female and male adults from countries around the world and macronutrient consumption of individuals with type 1 diabetes, an exploratory hypothesis was put forth.


Methods
FAO and WHO data

Of 200 countries in the Global Health Observatory Data Repository of the WHO and the FAO databases, 112 countries have complete data on plant and animal food commodity availability per capita [14], physical activity [16], and mean BMI (kg/m2) of adults aged 25+ in 2008 [17]. For an additional 55 countries, physical activity data was absent but diet and BMI were available. Imputed estimates of the WHO variable “insufficient physical activity” of these 55 female and male cohorts were obtained by using multiple regression analysis with insufficient physical activity of the 112 countries as the dependent variable and food group availability profile, gender, and country percapita GDP as independent variables. In all, 334 cohorts from 167 countries served as the subjects for these univariate and multivariate statistical analyses.

FAO food group availability data and derived macronutrient profiles

The FAO supplied data on food commodity availability in kilocalories (kcals) per capita per day. These food commodity availability data were broken down to cereals (e.g., rice, maize, and corn), vegetable oils (e.g., soy, rapeseed, mustard seed, and palm), sugar and sweeteners (e.g., sucrose and fructose from sugar cane, corn, beets, and honey), meat (e.g., cow, pig, sheep, goat, offals), animal fats, roots and tubers (e.g., potatoes and cassavas), fruit (including juices), pulses (e.g., beans and lentils), milk, cheese, and eggs. The percent of total available kcals percapita per day for each food group in each country comprised that country’s food group profile. Data for percapita alcohol “consumption”, in contrast with “availability,” came as the variable “g/day consumed” from the WHO [18], rather than as percent of total available kcals.

Macronutrients included for univariate analysis with mean adult BMI were protein (g and % of kcals), carbohydrates (g and % of kcals), dietary fiber (g and g/1,000 kcals), polyunsaturated fatty acids (PUFA: g and % of kcals), monounsaturated fatty acids (MUFA: g and % of kcals), saturated fatty acids (SFA: g and % of kcals), and total fats (g and % of kcals). In addition, percapita daily total kcals was assessed for each cohort.

To generate the FAO/WHO macronutrient profiling formula, the FAO food commodity availability data were broken down to macronutrient availability by analyzing samples of each commodity using the United States Department of Agriculture (USDA) Nutrient Database for Standard Reference, Release 24 [19]. Correlations of the mean BMIs of female and male cohorts from different countries with the formula estimates served as a measure of the adequacy of the method of transforming food group data to macronutrient profile data (i.e., #1 physical activity and food group profiling versus #2 physical activity and macronutrient profiling).

WHO physical activity data

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” [20]. The WHO defined “insufficient physical activity” as less than this recommended level of physical activity.

The DCCT/EDIC study

Diabetes Control and Complications Trial (DCCT) and its follow-up the Epidemiology of Diabetes Interventions and Complications (EDIC) study were conducted by the DCCT/EDIC Research Group and supported by National Institute of Health grants and contracts and by the General Clinical Research Center Program, NCRR. The data [and samples] from the DCCT/EDIC study were supplied by the NIDDK Central Repositories. This manuscript was not prepared under the auspices of the DCCT/EDIC study and does not represent analyses or conclusions of the DCCT/EDIC study group, the NIDDK Central Repositories, or the NIH.

The DCCT eligibility criteria and screening methods and the baseline characteristics of the study subjects have been reported in detail [21-23]. From 1983 and 1989, investigators recruited 1,441 participants between 13 and 39 years of age (mean = 26.8 years) that were C peptide-deficient and in good general health. The range of follow-up for all subjects was 3.5 to 9 years, with a mean of 6.5 years at the end of the trial in 1993 [24]. EDIC data from the National Institute of Diabetes and Diseases of the Kidney (NIDDK) were obtained in collaboration with the Endocrinology Department of the University of Pittsburgh. The EDIC study collected data over the 10 years immediately following the DCCT (1993–2003) [25], bringing the total length of follow up of yearly BMI records to 14–19 years.

DCCT macronutrient profiling

Under the guidance of registered dietitians trained in collecting nutrient consumption data, all DCCT participants submitted detailed accounts of their food intake during the week previous to entry into the study. Subsequently, the dietitians obtained follow-up diet histories at years two and five and at the end of the trial [21]. With these diet histories transformed into intakes of 99 macro and micronutrients, statisticians generated a nutrient consumption data set. This nutritional analysis instrument had a high reproducibility on repeated assessments of the diet history [26]. After screening the data set for nutrients correlating with BMI change/year, univariate correlations were performed on food energy expressed as total kcals per day percapita and the same macronutrient variables as were included as in the FAO macronutrient analysis. To include as much of the macronutrient profile spectrum as possible, the variable “total fat – PUFA” was derived and included in the multiple regression analyses in place of the variables total fat, MUFA, and SFA. Otherwise only the MUFA variable would enter the formula.

For each DCCT participant, kcals and macronutrient intakes on entry, at years two and five, and on completion of the study were averaged.

Glycemic control of DCCT participants

Since people with type 1 diabetes comprised the DCCT database, poor glycemic control could confound the relationship of diet with BMI change/year. Glycosuria due to serum blood sugar levels chronically greater than nine mmol/l reduces the BMI at the expense of increased complications of diabetes for patients. Using HbA1c <= 9.5 as the cutoff for inclusion resulted in no significant correlation between the mean HbA1c and BMI change/year (n = 1,055, r = 0.02, P = 0.42). However, with HbA1c > 9.5, BMI change/year decreased significantly as HbA1c rose (n = 137, r = −0.19, P = 0.0304). Consequently, HbA1c ≤ 9.5 was selected as the cutoff for participant inclusion in the analysis.

Exercise in DCCT participants

In the DCCT data set, the variable “exercise” was on a four point scale (approximate scale gradation: 1 = sedentary, 2 = mild activity (i.e., brisk walking about 30 minutes/day on average or the equivalent), 3 = moderate activity (i.e., brisk walking or running about 60 minutes/day on average or the equivalent), and 4 = strenuous activity (i.e., rigorous aerobic exercise ≥ 90 minutes/day on average). Mean exercise levels while on study were the following: < 1.5: 22.2%, 1.5 - < 2.5: 70.6%, 2.5 - < 3.5: 6.0% and 3.5 - 4.0: 1.2%. For each DCCT participant, yearly exercise assessments were averaged.

Imputing values for “insufficient physical activity” for cohorts with no data from WHO

The mean percent of insufficient physical activity varied markedly in cohorts of females and males from 112 countries (females: range = 6.6% to 76.2%, mean [90% CI] = 37.8 [10.9 to 69.7], median = 37.4 and males: range = 2.7% to 70.7%, mean [90% CI] = 31.0 [10.8 to 60.7], median = 29.3). To impute the level of adult physical activity of the 55 female and 55 male cohorts from countries without insufficient physical activity data from countries with insufficient physical activity data, a multiple regression formula was generated with insufficient physical activity as the dependent variable and the 26 food and beverage groups, mean BMI, gender, and country per capita GDP as independent variables. A constant was added to adjust the lowest imputed insufficient physical activity estimate to “2.7,” equating with the lowest of the range of reported insufficient physical activity values (Bangladeshi males). The resulting formula is as follows:

"Imputed insufficient physical activity = 1.01691 meat + 0.36409 wheat −0.14531 rice + 1.18572 Palm Oil – 2.85570 Sheep and Goats – 2.13059 Potatoes – 3.05422 Cheese + 0.00025787 Country percapita GDP in 2009 + 5.16467 Female gender + 2.65466 mean adult BMI – 38.14 (R2 = 0.48);"

The mean [SD] of the known values for insufficient physical activity and the imputed values were fairly similar (224 cohorts with known values for insufficient physical activity, mean [SD] = 34.4 (17.0) versus 110 cohorts with imputed values for insufficient physical activity, mean [SD] = 38.3 (11.8).

Comparison of FAO/WHO macronutrient profiling formula results with the formula from DCCT/EDIC participants

For the purpose of comparing the FAO/WHO formula predictions of mean BMI of adults with the BMI change/year formula from the DCCT/EDIC trial, the WHO variable “insufficient physical activity” (scale: 0% – 100% of the sample) was converted to the DCCT exercise variable (1–4 scale). The formula for this conversion is as follows: Y (DCCT 1–4 exercise scale) = 1.82 (100% – Z% (WHO insufficient physical activity scale: 0% – 100%))/59.55%. “Z%”, the WHO “insufficient physical activity” score, was transformed to “Y”, the DCCT exercise value used for compatibility of the two formulas. For North American DCCT participants, “1.82” was the mean exercise level on the DCCT 1–4 scale. The divisor, “59.55%”, represents the mean amount of “sufficient physical activity” of males and females in the USA (WHO “insufficient physical activity” scores for USA males and females were 33.5% and 47.4%, respectively. The mean “insufficient physical activity” in North America was 33.5% + 47.4% = 80.9÷2 = 40.45%. Therefore, “sufficient physical activity” = 100% – 40.45% = 59.55%). The conversion of WHO activity scores to the DCCT exercise (1–4 scale) gave the following estimated means and ranges of scores for country female and male cohorts: females: mean DCCT score = 1.86, range 0.073 to 2.87 and males: mean DCCT score = 2.08, range 0.90 to 2.97. As expected due to the formula deriving DCCT “exercise” from WHO “insufficient physical activity,” these variables were negatively correlated (r= −1.0).

Evaluation of the long-term weight impact of foods and beverages

To evaluate the long-term weight impact of individual foods and beverages, each item was assessed by adding the macronutrient profile data of one serving of that food or beverage to the mean macronutrient values of cohorts/individuals from the respective databases. To compose a weight impact prediction formula based on macronutrient profiling for individual foods and beverages comparable to the four-year weight impact (in pounds) of foods profiling analysis of Mozaffarian and his nutritional epidemiology colleagues authoring the Harvard diet and lifestyle study [15], the following adjustments were made to the FAO/WHO and DCCT/EDIC macronutrient and exercise profiling BMI change/year prediction formulas:

1. Eliminate the exercise variable

2. Switch from macronutrients as “% of kcals” to g

3. Multiply b weights of protein, carbohydrates and fats by 100 because of the change from % (0–100) to these variables expressed as a fraction of the total kcals (i.e., 0.00 - 1.00)

4. Multiply protein and total carbohydrates by “4” because they contain 4 kcals/g

5. Multiply fats by “9” because they contain 9 kcals/g

6. Multiply alcohol by “7” because it contains 7 kcals/g (DCCT/EDIC only)

7. Retain alcohol in grams as a variable in the FAO/WHO database because alcohol “consumption” data (WHO) and not “availability” data (FAO) was used

8. Multiply b weights of dietary fiber by 1,000 because of the change from dietary fiber g/1,000 kcal to dietary fiber g

9. Adjust the standard deviations (SDs) of the FAO/WHO and DCCT/EDIC formulas to the SD of the Mozaffarian predictions of the 22 food and beverage groups (SD=0.88734) by multiplying each formula by 0.88734 ÷ (the initial SD of the respective formula estimates for the 22 items).

10. Set the output of each multiple regression formula for a hypothetical food with 0.1 kcal and no macronutrients at “0.00” weight impact in four-years by adding a constant, thereby centering the weight impact of each formula at 0.00 pounds in four-years.

Assuming all macronutrient variables would enter the multiple regression macronutrient profiling formulas to predict BMI change/year (although not all macronutrient variables do enter the formulas), the formulas would have the following format:

"Weight impact over 4 years (pounds) = (b weight of kcals * kcals per serving of the food or beverage + b weight of protein * 4 (kcal/g protein) * 100 (conversion from percentages to 0.00 - 1.00 portions) * protein (g protein per serving + mean intake/day protein g)/(kcals per serving + average kcals/day) + b weight of carbohydrates * 4 (kcal/g carbohydrate) * 100 * carbohydrate (g carbohydrates per serving + mean intake/day carbohydrates g) /(kcals per serving + average kcals/day) + b weight of dietary fiber * dietary fiber (g per 1,000 kcals) * 1,000/(kcals per serving + average kcals/day) + b weight of PUFA * 9 (kcal/g PUFA) * 100 * PUFA (g PUFA per serving + mean intake/day PUFA g) /(kcals per serving + average kcals/day) + b weight of MUFA * 9 (kcal/g MUFA) * 100 * MUFA (g MUFA per serving + mean intake/day MUFA g) /(kcals per serving + average kcals/day) + b weight of SFA * 9 (kcal/g SFA) * 100 * SFA (g SFA per serving + mean intake/day SFA g) /(kcals per serving + average kcals/day) + b weight of total fat * 9 (kcal/g total fat) * 100 * total fat (g total fat per serving + mean intake/day total fat g) /(kcals per serving + average kcals/day) + b weight of alcohol * 7 (kcal/g alcohol) * 100 * alcohol (g alcohol per serving + mean intake/day alcohol g) /(kcals per serving + average kcals/day)) * 0.88734 (the SD of the weight impacts of the Mozaffarian predictions of the 22 food and beverage groups) ÷ (the initial SD of the FAO/WHO and DCCT/EDIC formula estimates, respectively, for the 22 foods and beverages) + constant;"

Statistical analysis

Pearson correlations related food group availability (% of total kcals for each food group), availability of kcals percapita overall, and exercise (WHO insufficient physical activity data transformed to DCCT 1–4 scale) to mean BMI of females and males in each country. Similarly for the derived FAO macronutrient profiling analysis, Pearson correlations related mean BMI in each country to exercise (DCCT 1–4 scale), availability of kcals overall, protein (g and % of total kcals), carbohydrates (g and % of total kcals), dietary fiber (g and g per 1,000 kcals), PUFA (g and % of total kcals), MUFA (g and % of total kcals), SFA (g and % of total kcals), total fat (g and % of total kcals), and total fat – PUFA (g and % of total kcals). Alcohol (g/day) consumption, not availability, was obtained from the WHO. Consequently, alcohol (% of kcals) relative to the other macronutrients was not known or estimated.

Based on the Bonferonni correction for univariate correlations of food group and exercise variables related to mean BMI, P values less than 0.002 were considered significant (i.e., for 27 food groups and exercise: 0.05/28 = 0.00179, rounded off to 0.002).

In the multivariate analyses, WHO data on mean BMIs from female and male adults 25+ years old in 2008 from 167 countries (criterion variable) were correlated with FAO food commodity availability/macronutrient availability data and exercise (DCCT 1–4 scale) data (predictor variables).

In the DCCT analysis, BMI change/year equaled (BMI at the end of the trial – the initial BMI) ÷ years on trial. Pearson correlations were computed for food energy (kcals), macronutrients and exercise with BMI change/year. Based on the Bonferonni correction for macronutrients and exercise of both the FAO/WHO and DCCT/EDIC data sets, P values less than 0.002 were considered significant in univariate correlations (i.e., for 21 macronutrient and exercise variables: 0.05/21 = 0.0024).

With DCCT/EDIC data, multiple regression analysis generated a formula quantifying the relationship of BMI change/year (the criterion variable) with macronutrient intake and exercise (predictor variables).

For the FAO/WHO multiple regression analyses, predictor variables gained entry in the formulas if P < 0.10 and remained if P < 0.10. For the DCCT/EDIC formula, predictor variables gained entry in the formulas and remained if P < 0.25. To derive the formulas, multipliers of each significant predictor variable were the non-standardized coefficients (i.e., b weight). Constants centered each macronutrient profiling formula.

The USDA Nutrient Database for Standard Reference Release 24 [19] served as the macronutrient composition reference for the 22 categories of food and beverage groups included in the Harvard diet lifestyle study. These profiles were plugged into the two formulas to generate four-year weight impact estimates for each of these 22 foods and beverages. Pearson correlations compared Harvard diet and lifestyle study weight impact estimate data on food and beverage groups with results of the FAO/WHO four-year weight impact formula, the DCCT/EDIC four-year weight impact formula, and the average of the two formulas. Additionally, the FAO/WHO 4 four year weight impact formula and the DCCT/EDIC four-year weight impact formula were compared with each other.

SAS statistical software (release 9.1, SAS Institute, Cary, NC) was used in the performance of the data analysis.


Results

Table 1 shows selected FAO plant and animal food group availability data (percentage of total kcals available from each major food group) of female and male cohorts from the 167 countries. Kcal intake of males was equated to 1.2 times the mean population kcal intake and for females 0.8 times the mean, in accordance with USA data from the National Health and Nutrition Examination Survey [27].

Table 2 displays the percent of “insufficient physical activity” (WHO female and male cohort data from 2009: mean [95% CI of the mean] on a 0% to 100% scale), the mean [95% CI of the mean] BMI of female and male cohorts, and the percapita GDP of the 167 countries.

Table 3 presents the FAO/WHO univariate relationships between mean BMI and availability of 27 plant and animal food products in percent of total kcals of food available, percent “insufficient physical activity” and percapita GDP of the countries.

The FAO/WHO multiple regression derived formula relating mean BMI (dependent variable) to kcals, food group availability by percent of total kcals, exercise (DCCT 1–4 scale substituted for WHO “insufficient physical activity”), female gender, and percapita GDP of each country (independent variables) is below:

"Mean BMI (kg/m2) all (n=334) = 0.00131 kcals – 0.05980 cereals – 0.10346 all sugar and sweets + 0.04154 roots and tubers – 0.12157 cassava – 0.10796 + 0.02689 wheat + 0.03856 maize + 0.16379 sugar (raw) + 0.34879 poultry + 0.35408 sheep and goats – 1.21990 offals – 0.00002930 GDP percapita in 2009 + 1.89265 female gender – 1.14996 exercise (1–4 DCCT scale) + 24.598 (n=334 male and female cohorts, R2 = 0.74, P < 0.0001)."

In the univariate analysis, GDP percapita in 2009 is correlated with mean adult BMI (r=0.36, P < 0.0001). However, when GDP percapita in 2009 is included among the independent variables of the multiple regression, increasing GDP tends to decrease mean BMI. So GDP is not a factor in determining the level of adult BMI independent of macronutrient profile, exercise, and gender. Kcals percapita is not an important factor relating to mean adult BMI, accounting for only about 2% of the variance (R2 = 0.02 out of R2 = 0.74).

Breaking down the 334 cohorts to females, males, lower half of percapita GDP, and upper half of percapita GDP yields the following formulas:

"Mean BMI (kg/m2) females (n=167) = 0.00159 kcals + 0.10808 roots and tubers – 0.12987 pulses – 0.01983 rice + 0.02450 maize + 0.14742 sugar (raw) + 0.51772 poultry + 0.43560 sheep and goats + 1.41375 offals – 0.14879 cassava – 0.00003924 GDP percapita in 2009 – 1.30350 exercise + 24.8403; (R2 = 0.73, P < 0.0001);"

"Mean BMI (kg/m2) males (n=167) 0.00106 kcals – 0.07293 cereals – 0.08208 pulses + 0.02227 wheat + 0.02350 maize + 0.26662 poultry + 0.27629 sheep and goats – 1.17870 offals – 0.07866 cassava – 0.95467 exercise – 0.00001452 GDP percapita in 2009 + 25.94667; (R2 = 0.80, P < 0.0001)"

"Mean BMI (kg/m2) lower half countries in percapita GDP 2009 (n=166) = 0.00097484 kcals – 0.08496 cereals – 0.06629 vegetable oil + 0.34149 animal fat – 0.16668 pulses + 0.02239 wheat + 0.02516 maize + 0.17003 soy oil – 1.08248 rape oil + 0.54829 sheep and goats – 3.84885 offals – 0.09109 cassava – 0.11750 milk + 1.10932 cheese + 1.80429 female – 1.27746 exercise + 28.7249; (R2 = 0.81, P < 0.0001)"

"Mean BMI (kg/m2) upper GDP 2009 half countries in percapita GDP (n=166) = 0.03909 cereals – 0.08294 roots and tubers – 0.06886 rice + 0.07593 sugar (raw) + 0.32613 poultry + 0.18367 potatoes + 0.24919 sheep and goats – 0.89450 offals 0.67179 eggs – 0.71995 exercise + 25.78858; (R2 = 0.50, P < 0.0001);"

Table 4 shows the fairly strong correlations of the multiple regression derived formula predicting mean BMI in 2008 (n=334 female and male cohorts) with similarly derived formulas relating to single sex cohorts (female and male) and percapita 2009 GDP (below and above the median). These strong correlations suggest that the food group profiles and exercise levels determine the mean adult BMI virtually independently from gender or country per capita GDP.

Table 5 shows the template for using the USDA Nutrient Database for Standard Reference, Release 24 [19] to convert FAO food group data to macronutrient profiles for each country. As an example of using this template, Table 6 demonstrates the breakdown of food availability by food group in the USA transformed into the macronutrient profile. Table 7 shows the resulting template derived macronutrient availability data in percent of kcals (g per 1,000 kcals for dietary fiber) for male and female cohorts from the 167 countries.

Table 8 presents the univariate relationships between mean BMI and macronutrients available as g and percentages of total kcals of food available in the 167 countries.

The multiple regression derived FAO/WHO formula relating mean adult BMI to macronutrient availability and exercise (DCCT 1–4 scale) is below:

"Mean BMI (kg/m2) for female and male cohorts (n=334) = 0.07710 alcohol (g) + 0.11947 carbohydrates (% of kcals) – 0.30486 dietary fiber (g/1000 kcals) + 0.19733 total fat (% of kcals) – 0.68567 PUFA (% of kcals) – 2.14356 exercise (DCCT 1–4 scale) + 24.64; R2=0.55"

In the initial FAO/WHO multiple regression analysis, protein, kcals and MUFA appeared in the formula, and carbohydrates, total fat, PUFA, SFA and alcohol did not appear. To increase the spectrum of the macronutrient profile covered by the formula, protein (about 10% of kcals), MUFA, SFA, and kcals were omitted, resulting in carbohydrates (about 60% of kcals), total fat, PUFA, alcohol (mean=6.66 g/day consumed) appearing. The b-weight of the alcohol (g) variable was doubled because alcohol did not appear in the DCCT/EDIC macronutrient and exercise formula. The multiple correlations coefficient was almost identical whether protein, MUFA, SFA, and kcals were or were not included as independent variables (R2=0.56 versus R2=0.54).

Formulas of sub cohorts broken down by gender and percapita GDP are below:

"Mean BMI (kg/m2) female cohorts only (n=167) = 0.00147 kcals + 0.06522 carbohydrates (% of kcals) – 0.37905 dietary fiber (g/1,000 kcals) – 0.46653 PUFA −2.25919 exercise + 30.63; R2=0.53"

"Mean BMI (kg/m2) male cohorts only (n=167) = 0.00142 kcals + 0.08247 carbohydrates (% of kcals) + 0.30007 total fat – 0.85814 PUFA % of kcals – 1.44131 exercise (DCCT 1–4 scale) + 15.95; R2 =0.71"

"Mean BMI (kg/m2) percapita GDP below the median (n=166) = 0.05741 alcohol (g/day) + 0.4099 carbohydrates – 0.26010 dietary fiber (g/1,000 kcals) + 0.67133 total fat – 1.50540 PUFA – 2.18068 exercise (DCCT 1–4 scale) – 0.06; R2=0.57"

"Mean BMI (kg/m2) percapita GDP above the median (n=166) = 0.08058 carbohydrates (% of kcals) – 0.36953 dietary fiber (g/1,000 kcals) – 0.98044 exercise (DCCT 1–4 scale) + 27.59; R2=0.23"

Table 9 shows the strong correlations of the BMI in 2008 with the various BMI prediction formulas and the correlations of the formulas with each other.

DCCT/EDIC analysis

Table 10 shows the baseline and on study age, sex, BMI, HbA1c, and exercise levels of the 1,055 DCCT/EDIC participants with average HbA1c levels < 9.5. Table 11 compares the Pearson correlations of macronutrients with BMI change/year.

The DCCT/EDIC participant BMI change/year correlated inversely with baseline age (r = −0.10, P = 0.0012), however, age and caloric intake were also negatively correlated (r = −0.15, P < 0.0001). While BMI change/year did not correlate significantly with sex (r = 0.04, P = 0.24), the caloric intake of males exceeded that of females by 51% (mean [90% CI] = 2,732 [1,869 to 3,827] kcals versus 1,804 [1,231 to 2,527] kcals). As with the FAO/WHO analysis, to compensate for the marked influence of age and sex on macronutrient intake while there was an inverse correlation of age and no influence of sex on BMI change/year, the percentages of the kcals contributed by each macronutrient were evaluated in addition to grams of each macronutrient (e.g., (protein (g) × 4 kcals/g/ kcals) × 100 = percentage of kcals as protein). Dietary fiber was expressed as g/1,000 kcals. Since the sum of SFA % kcals, MUFA % kcals, and trans fats % kcals (i.e., total fat – PUFA) directly correlated with BMI change/year in the univariate analysis (r = 0.07, P = 0.0331, from Table 11), these three macronutrients were combined as a single variable. This facilitated the generation of the following formula including about 97% of the macronutrient profile:

"BMI change/year DCCT/EDIC formula= (0.00898 protein (% of kcals) + 0.01063 carbohydrates (% of kcals) – 0.01319 dietary fiber (g/1,000 kcal) + 0.00973 (total fats – PUFA) (% of kcals) – 0.04468 exercise (1–4 scale)) * 0.545 – 0.119; R2 = 0.03, P < 0.0001)."

The initial formula was multiplied by 0.545 to adjust the SD of the formula from 0.416 to the SD of the DCCT/EDIC subjects (0.227). A constant adjusted the mean output to the mean BMI change/year of the DCCT/EDIC subjects (0.268).

Comparisons of FAO/WHO and DCCT/EDIC formulas

To facilitate the comparison of predictions of future BMIs from the FAO/WHO and DCCT/EDIC formulas, both formulas were adjusted, as described in the methods, to convert macronutrient variables from percent of total kcals to grams of macronutrients, as would enable the use of the interactive web-based future BMI prediction tool. The adjustment also involved changing the output of the FAO/WHO formula from “mean BMI” to “BMI change/year.” This conversion was made by designating the mean BMI of 20 year old people in the USA (mean BMI=22.4, according to the Center for Disease Control [28]) as the baseline adult BMI. US Census Bureau data were used to estimate the median age of adults over 25 years old in the USA (median age of adults > 25 years old = 48.9 years old [29,30]). For adults ≥ 25 years old, median BMI in the USA in 2008 was 28.4 (averaging females and males, Table 2). Using these figures, the average USA person had a BMI change/year over baseline adult BMI = 0.123 BMI change/year (28.4 – 22.4 = 6.0 BMI units above baseline; 6.0/48.9 (48.9 = median age of adults > 25 years old) = 0.123 BMI change/year). To derive the SD (σ) of the BMI change/year above the baseline adult BMI, the BMI change/year for obese people was calculated (BMI ≥ 30 – 22.4 ≥ 7.6 BMI units above baseline; 7.6/48.9 = 0.155 BMI change/year) and the USA incidence of adult obesity (33%) ascertained from the literature [31]. These values were plugged into the formula for a normal distribution [30]: px=12πσexp−x−μ22σ2 Consequently, where

1. the mean age of adults over 25 years old = 48.9 years,

2. μ = 0.123 BMI change/year,

3. the BMI change/year above baseline BMI required for adult obesity (x) is ≥ 0.155 BMI change/year,

4. and the USA adult obesity rate (p(x)) = 33%;

the formula SD (σ) = 0.075. The SD of the FAO/WHO formula for female and male cohorts from all 167 countries was also derived from the normal distribution formula. Where

1. the mean age of adults over 25 years old = 38.9 years [29,32],

2. μ = 0.0720 BMI change/year (25.2 (mean adult BMI in 2008 for 334 cohorts) – 22.4 (baseline BMI) = 2.8; 2.8/38.9 years = 0.0720),

3. the BMI change/year above baseline required for adult obesity (x) is ≥ 0.155 BMI change/year, and

4. the weighted mean adult obesity rate in WHO countries (p(x)) = 14.1% [33];

the normal distribution formula yields an almost identical SD (σ) = 0.077 for the FAO/WHO formula.

To translate the FAO/WHO formula for mean adult BMI in the 167 countries to a FAO/WHO formula for BMI change/year, the SD was equated to 0.077 by multiplying the FAO/WHO mean adult BMI formula by 0.03882 (i.e., 1.984 (SD of FAO/WHO adult BMI prediction formula) * 0.03882 = 0.077). The b-weight of alcohol (g) in the FAO/WHO formula was doubled to compensate for alcohol not appearing in the DCCT/EDIC formula. With this adjustment for alcohol, the mean output of the weight impact of alcohol of the two formulas should better reflect the data. Finally, the output was centered at 0.07289 BMI change/year above baseline BMI per WHO data [17] by adding a constant. This gives the adjusted FAO/WHO formula below:

"BMI change/year FAO/WHO formula = (0.07710 alcohol (g) + 11.95 carbohydrates g * 4 (g/kcal)/kcals – 304.85 dietary fiber g/kcals + 19.7433 total fat g – 63.567 PUFA g * 9 (g/kcal)/kcals – 2.14356 exercise (DCCT 1–4 scale)) * 0.04115 – 0.05033; R2=0.54"

In order to synchronize the output of the two formulas, the DCCT/EDIC formula was adjusted to correspond with the FAO/WHO formula—i.e., the SD changed to 0.077 BMI change/year, and a constant added to change the mean output to 0.07289 BMI change/year above the baseline (BMI=22.4):

"Synchronized DCCT/EDIC BMI change/year formula = (0.898 protein g * 4/kcals + 1.063 carbohydrates g * 4 (g/kcal)/kcals – 13.19 dietary fiber g/kcals + 0.973 (total fat g – PUFA g) * 9 (g/kcal)/kcals – 0.04468 exercise (DCCT 1–4 scale) * 1.574 – 1.001;"

Correlations of the FAO/WHO and DCCT/EDIC diet and exercise profiling formulas

The continuous model macronutrient and exercise formula for BMI change/year correlated strongly with the FAO/WHO categorical model food groups and exercise BMI change/year formula (r = 0.86, P < 0.0001). Testing with both the FAO/WHO and DCCT/EDIC datasets, the FAO/WHO and DCCT/EDIC macronutrient and exercise formulas also correlated with each other (r = 0.79, P < 0.0001 and r = 0.81, P < 0.0001, respectively, Tables 12 and 13).

Predicting weight impacts of individual foods and beverages

The FAO/WHO macronutrient (continuous model profiling) BMI change/year formula above with macronutrients expressed in g, the b-weight of alcohol doubled, and the exercise variable omitted is as follows:

"FAO/WHO adult cohort BMI change/year prediction = (0.07710 alcohol (g) + 11.95 carbohydrates g * 4 (g/kcal)/kcals – 304.86 dietary fiber g/kcals + 19.733 total fat g * 9 (g/kcal)/kcals – 63.567 PUFA g * 9 (g/kcal)/kcals)) * 0.04115 – 0.05033; R2=0.29"

As described in the methods, the above FAO/WHO adult BMI prediction formula was transformed into a weight impact of macronutrients formula (continuous profiling) for individual foods and beverages:

"FAO/WHO formula for four-year weight impact (pounds) = (0.07710 alcohol g + 11.95 (381.7 + carbohydrates g per serving) * 4 / (2,613 + kilocalories per serving) – 304.9 (30.38 + dietary fiber g per serving) / (2,613 + kilocalories per serving) + 19.73 (84.44 + total fat g per serving) * 9/(2,613 + kilocalories per serving) – 68.57 (20.45 + PUFA g per serving) * 9/(2,613 + kilocalories per serving) ) * 2.941 – 12.78 (n=334, R2=0.29, P < 0.0001)."

Likewise, the DCCT/EDIC formula for predicting weight impacts of individual foods and beverages is as follows:

"DCCT/EDIC formula for four-year weight impact (pounds) = (0.898 (102.2 + protein g per serving) * 4/(2,297 + kilocalories per serving) + 1.063 (264.2 + carbohydrates g per serving) * 4/(2,297 + kilocalories per serving) – 13.19 (24.29 + dietary fiber g per serving) /(2,297 + kilocalories per serving) + 0.973 (74.59 + (total fat g per serving – PUFA g per serving) * 9/(2,297 + kilocalories per serving)) * 85.82 – 68.12 (n=1,055, R2=0.03, P < 0.0001)."

Of note, kcals did not enter either formula. Independent of the macronutrient profile, calorie intake did not predict long term weight. The type of foods and beverages consumed rather than the number of kcals determined the long term weight impact.

Using data from the USDA National Nutrient Database for Standard Reference Release 24 [19], Table 14 details the kcals and macronutrients in average servings of 22 categories of foods and beverages reported by Mozaffarian and colleagues from the Harvard nutritional epidemiology team [15]. Table 15 shows the FAO/WHO and DCCT/EDIC formula predicted weight impacts of the 22 selected foods and beverages in pounds/4 years based on the macronutrient formula predictions compared with the Harvard nutritional epidemiology team food group profiling study data. Generally, for high carbohydrate foods and beverages, if the total carbohydrate/dietary fiber ratio is < 10, the item tended to reduce weight according to the FAO/WHO and DCCT/EDIC formula predictions (e.g., fruits, vegetables, and whole grains). For high fat foods (e.g., nuts, meat, and dairy), if the ratio of total fat/PUFA < 6, the FAO/WHO and DCCT/EDIC formulas predicted a lower weight in 4 years (e.g., nuts). For a total carbohydrate/dietary fiber ratio > 10 or a total fat/PUFA > 6, an increase in weight was predicted.

Overall, the FAO/WHO and DCCT/EDIC formula predictions had no significant correlation with the food group profiling predictions or with each other (Table 16). Alcohol consumption in grams entered the FAO/WHO future BMI prediction formula, but not the DCCT/EDIC formula. Consequently, the FAO/WHO and DCCT/EDIC formula estimates for the four-year weight impact of one drink per day of alcohol (averaging macronutrient profiles of beer, wine, and spirits) were markedly different (i.e., FAO/WHO = + 2.93 pounds/4 years and DCCT/EDIC= − 2.57 pounds/4 years, Table 15). However, the average value of the two formulas corresponds with the Harvard food and beverage profiling estimate of the 4 year weight impact of 1 drink (i.e., 0.18 versus 0.41 pounds, Table 15). This single divergent data point causes the two formulas to have no significant overall correlation (r = 0.11, P = 0.64, Table 16). The mean FAO/WHO and DCCT/EDIC four-year weight impact estimates of beer, wine, and spirits are 0.58, 0.10, and −0.03, respectively.

The average of the FAO/WHO and DCCT/EDIC formula predictions correlated strongly with 12 food group profiling findings of Mozaffarian and colleagues (r = 0.85, P < 0.0001, Table 17). However, formula predictions trended towards a negative correlation with the Mozaffarian food group profiling findings for potatoes and dairy products (Table 18).

Web based health tool utilizing the multiple regression derived formula

To allow individuals, health professionals, and nutrition researchers to assess and monitor diet and lifestyle patterns by means of the macronutrient and exercise profiling formulas from the FAO/WHO and the DCCT/EDIC, NR designed a simple-to-use web-based tool [24]. Predicated on sustaining the inputted macronutrient profile and physical activity pattern on average over time, future BMI predictions are made. This long-term BMI prediction tool requires little nutritional or computer expertise on part of the user.


Discussion

These univariate and multivariate analyses support the thesis that disproportionate weight gain is due primarily to lack of exercise and excessive availability/consumption of foods in Table 3 with r > 0 and not enough availability/consumption of foods with r < 0. In Table 3, the r values of the breakdown of items under a broad food group heading probably have less significance that the r value of the broad heading. For instance, individual cereals vary significantly in r values (i.e., broad heading of cereals: r = −0.46, P < 0.0001, and subheadings: rice (r = −0.41, P < 0.0001), maize (r = −0.25, P < 0.0001), and wheat (r = 0.41, P < 0.0001)). This probably indicates that low BMI countries eat more rice and maize and high BMI countries eat more wheat, and much of the wheat in high BMI countries is likely refined into white (low fiber) flour. Overall in country populations, a high proportion of kcals as cereal contributes significantly to relatively lower mean BMIs.

It may be counterintuitive that fruit should be associated with excessive weight gain (i.e., fruit: r=0.22, P < 0.0001 in Table 3). Data from the diet and lifestyle study by Harvard nutritional epidemiologists showed that fruit consumption was associated with significantly decreased weight over a four year span while 100% fruit juices correlated with substantial weight gain (Table 15) [15]. In that study of over 120,000 USA participants, the mean intake of fruit juices was about half of the mean intake of fruit (0.73 juice servings/day versus 1.43 whole fruit servings per day). Under the FAO food group category, “FRUITS AND DERIVED PRODUCTS,” is the following explanation, “Fruit crops are consumed directly as food and are processed into dried fruit, fruit juice, canned fruit, frozen fruit, jam, alcoholic beverages, etc.” [34]. In the USA, US Department of Agriculture data show that about 40% of fruit availability is in the form of juices [35]. Consequently, whole unprocessed fruit likely correlates with normal BMIs while fruit juices likely correlate with overweight and obesity.

Based on both of these BMI change/year formulas, the adage, “eat less and exercise more” should be clarified to “eat more cereals, fruits, vegetables, pulses, roots, and tubers and exercise more” or “eat more high fiber carbs and more high PUFA fats and exercise more.”

In discussing the counterintuitive prediction that one serving per day of low-fat yogurt correlated with the largest weight loss of any food or beverage in their study (−0.82 pounds/4 years), Mozaffarian and the Harvard nutritional epidemiology team allowed for the possibility of “an unmeasured confounding factor that tracks with yogurt consumption” [15]. The paradox of low-fat yogurt associated with weight loss in the Harvard study while potato consumption correlated with increased weight may be due to confounding in three ways:

1. the association of dairy product consumption, particularly low-fat yogurt, with fruits, vegetables, nuts, and whole grains and with above average exercise in educated, relatively affluent, health-conscious people,

2. the association of potato consumption with oil or fat (e.g., butter, sour cream, etc.), and

3. the greater affordability and therefore consumption of inexpensive foods like French Fries, potato chips, sugar sweetened drinks, hamburgers, etc. for the lower socio-economic classes with higher rates of obesity.

Among the many organizations extolling the health benefits of low-fat yogurt are Cleveland Clinic [36], Mayo Clinic [37], Center for Science in the Public Interest [38], American Heart Association [39], FDA and USDA [40]. Due to these endorsements and the heavy advertising of dairy products; educated, health conscious, relatively more affluent people may respond by consuming more low-fat yogurt (and other dairy products) compared with less health conscious people that drink less expensive sugar sweetened beverages.

Data for analyzing the overall diet and exercise pattern associated with dairy foods consumption come from the “CARDIA Study,” a general community sample from four U.S. metropolitan areas [41]. CARDIA Study participants were partitioned into terciles according to consumption of dairy foods. Dairy consumption correlated with 18% more physical activity (the highest tercile in dairy consumption overall averaged about 18% more physical activity than the lowest dairy consuming tercile). Similarly, dietary profile comparisons of the highest and lowest dairy product consuming terciles showed that the highest tercile dairy consumers averaged 68% more whole grains, 13% more fruits and vegetables, and 46% less sugar-sweetened beverages than the tercile consuming the least dairy foods. Estimating conservatively, the highest dairy product tercile consumed at least 40% more dietary fiber/day (i.e., ≥ 10 g/day more) than the lowest. Using the FAO/WHO database, plugging these fiber and exercise values (i.e., 10 g/day more fiber and 18% more exercise) into the two formulas yielded an average prediction that the highest tercile of dairy consumers will gain 0.059 BMI units/year less than the lowest tercile (FAO/WHO formula: 0.065 BMI units/year less and DCCT/EDIC formula: 0.054 BMI units/year less). A similar formula calculation using the DCCT/EDIC database predicted that the highest tercile of dairy consumers will gain 0.069 BMI units/year less than the lowest tercile (FAO/WHO formula: 0.076 BMI units/year less and DCCT/EDIC formula: 0.063 BMI units/year less). An increase of 0.059 - 0.069 BMI units/year is in the range of overall development of the obesity epidemic (i.e., 2.95 – 3.45 extra BMI units in 50 years).

The National Health and Nutrition Examination Survey data show that average gains per year in BMI of the USA adult population ranges from 0.087 BMI units - 0.137 BMI units [42]. These data suggest that the relatively healthy overall diet and exercise pattern associated with low-fat yogurt may reduce long-term weight gain, but low-fat yogurt itself, and similarly other dairy products, more likely increase weight.

Rates of obesity in the U.S. and other developed countries are much higher in the food-insecure lower socio-economic classes [43]. Potatoes (including French Fries and potato chips), sugary foods, low PUFA meats, and refined grains provide dietary energy at the lowest cost and are chosen by poor people out of necessity [44]. As suggested by Drewnowski, food group (categorical) profiling studies linking inexpensive potatoes with obesity may be confounded because low income people, who carry higher risks of obesity, eat more potatoes [45]. FAO/WHO and DCCT/EDIC macronutrient profiling analyses (continuous model profiling) would not be subject to these kinds of biases related to food selections shaped by the income or health consciousness of the consumer.

FAO/WHO formula estimates for dairy products and potatoes accorded with DCCT/EDIC formula predictions (Table 18: n = 10, r = 0.68 P = 0.0311). Both macronutrient profiling formulas disagreed with the Harvard nutritional epidemiology team food group profiling estimates for dairy products and potatoes (r = −0.12, P = 0.73 and r = −0.56, P = 0.09 for the FAO/WHO and DCCT/EDIC versus Harvard weight impact predictions, respectively, Table 18). This supports the view that the health conscious public’s misperceptions of the long-term weight effect of low-fat yogurt and other dairy products and greater affordability of potatoes for low income people confounded the Harvard nutritional epidemiology team’s predictions concerning dairy products and potatoes.

While this analysis has potential confounders, it is, hopefully, a valuable first step with the methodology of comparing long-term data on BMI change/year to macronutrient and exercise profiles in different databases. Limitations of this study include: (1) food availability (FAO) is used rather than food consumption for the diet variables in countries, (2) only 167/200 FAO/WHO countries provided sufficient data on which to base an analysis, (3) imputed physical activity data was used for 55 female and 55 male cohorts, (4) data is lacking in the FAO database on nuts, seeds, and vegetables, (5) the DCCT/EDIC data included subjects with a relatively narrow range of variability in macronutrient intake and exercise level, (6) people with type 1 diabetes are not typical of the population for many reasons, so the univariate and multivariate correlations of DCCT/EDIC participants cannot be assumed to be the same as other populations, (7) the foregoing DCCT/EDIC factors probably led to a weak multiple variables correlation with BMI change/year (R2 = 0.03, P < 0.0001), (8) unequal access to various foods, cultural differences, and other factors may also confound the results of this analysis, and (9) these food group/macronutrient and exercise profiling formulas, although validated by the strong correlation with the food group profiling data from the Harvard nutritional epidemiology group, still require further verification from other databases relating macronutrient and exercise profiles to BMI change/year or adult BMI or from prospective diet and exercise profiling studies.

Inferring the changes in BMI based on the diet and physical activity parameters using the data from these studies may not be optimal, but it is reasonable given that the FAO kcal and macronutrient availability data for each entire country’s population would not be expected to change radically over 50 years for most countries. The exceptions will be part of the noise in the data. The DCCT diet analyses were conducted 2–5 times over the 4–9 years on trial and, unfortunately, not repeated during the EDIC 10 year follow-up. Ideally, we should have diet data on a yearly or monthly basis over decades. However, such data do not yet exist.

In countries with mean BMI levels already in overweight or obese categories or projected to increase into these categories, policymakers, nutrition professionals, and the public should consider that these formulas might inform strategies to combat the obesity epidemic. This should stimulate discussion about strategies to increase physical activity and adjust the availability and consumption of foods that increase BMI relative to BMI decreasing foods to avoid excessive weight gain and the associated health problems for individuals and populations.

Policymakers, dietary professionals, and individuals could also consider using or promoting the use of the website health tool offered in this article to base a “nudge” for people. According to the popular book, Nudge: improving decisions about health, wealth, and happiness by Richard Thaler and Cass Sunstein, the concept of nudging describes “any aspect of the choice architecture that alters people’s behavior in a predictable way without forbidding any options or significantly changing their economic incentives” [46]. Nudging uses “libertarian paternalism,” a political/social philosophy in which people’s choices are actively guided in their best interests but they remain at liberty to behave differently [47]. Regular analysis and monitoring of the long-term weight impacts of one’s diet and physical activity choices with this tool could nudge people to adopt healthier lifestyles in accordance with their own perceived best interests.

The correlations between these two formulas and the validation of the formulas by comparison with the food group profiling data of the three databases used by Mozaffarian and colleagues raise the possibility that multiple regression formulas derived from all other databases may also have a similar format:

"Possible general format for BMI change/year prediction formulas = (A * carbohydrates g * 4 (g/kcal)/kcals – B * dietary fiber g/kcals + C * total fat g * 9 (g/kcal)/kcals – D * PUFA g * 9 (g/kcal)/kcals – E * exercise) * F + G;"

While protein and alcohol were each only in one formula, the univariate correlations of both of these macronutrients in the FAO/WHO database suggest that they tend to increase weight (i.e., r > 0, Table 8). The statistical findings of this analysis support previous recommendations to encourage consumption of mostly unprocessed plant-based commodities (fruits, vegetables, cereals, pulses, roots/tubers, etc.) to combat the obesity epidemic. The formulas in this study may facilitate strategies by individuals and policy makers to nudge the patterns of food consumption in a healthy direction. Further, immediate feedback on the predicted long-term effect of exercise from the health tool should be combined with strategies to promote regular physical activity at population levels (e.g., in schools, worksites, etc.) and to incentivize regular physical activity in the health care system.

Utilizing the website tool offered in this article could provide a welcome “nudge” to motivated users to adopt diet and exercise habits in line with their wishes for long-term weight control. Academic nutrition researchers should consider partnering with the authors in undertaking prospective observational/interventional studies of individuals that use the future BMI prediction interactive website to prevent or treat obesity.


Abbreviations

*: Multiplied by; BMI: Body mass index; carbs: Carbohydrates; DCCT: Diabetes Control and Complications Trial; EDIC: Epidemiology Diabetes Intervention and Complications; FAO: Food and Agriculture Organization; g: Grams; HbA1c: Hemoglobin A 1c; Kcals-Kilocalories: kg/m2, Kilograms per meter squared; PUFA: Polyunsaturated fatty acids; MUFA: Monounsaturated fatty acids; R2: Coefficient of multiple correlations; r: Coefficient of correlation; SD (σ): Standard deviation; SFA: Saturated fatty acids; USDA: United States Department of Agriculture; WHO: World Health Organization.


Competing interests

This analysis was not supported by public or private funding. DKC and NR have no conflicts of interest. They have completed the Unified Competing Interest form at http://www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declares that (1) DKC and NR have received no financial support for this article; (2) DKC and NR have no relationships that might have an interest in the submitted work in the previous three-years; (3) their partners or children have no financial relationships that may be relevant to the submitted work; and (4) DKC and NR have no non-financial interests that may be relevant to the submitted work.


Authors’ contributions

DKC obtained the DCCT/EDIC data sets from the National Technical Information Service in collaboration with the University of Pittsburgh Endocrinology Department and the FAO/WHO databases from the Internet. He wrote the statistical investigation plan, cleaned and analyzed the data, and drafted and revised the paper. He had full access to all of the data in the study and takes responsibility as guarantor for the integrity of the data and the accuracy of the data analysis. NR designed an interactive website termed a “health tool” to allow the public and healthcare professionals to analyze the long-term BMI change/year impact of individual diet and lifestyle patterns according to these FAO/WHO and DCCT/EDIC data derived formulas. He also critiqued the paper and drafted the narrative description of the web-based health tool. All authors read and approved the final manuscript.


Acknowledgements

For critiquing the article and providing comments, thanks go to nutrition scientist Claudio Nigg, PhD and biostatistician John Pezzullo, PhD. Brent Hill enhanced the functionality of the interactive website BMI prediction tool with his Java Script coding skills. Thanks also go to three peer reviewers and the editor of the American Journal of Clinical Nutrition who critiqued two different drafts of the article and provided many excellent comments and suggestions. No compensation was received for critiquing, Java Script coding, or other assistance. Data sharing: SAS statistical code used for this analysis is available from the author at dkcundiff@thehealtheconomy.com. In the surveys conducted by the FAO and WHO, participants’ consent for data sharing was implied so the publicly available data resulting may be freely utilized here.


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Tables
[TableWrap ID: T1] Table 1 

Selected FAO food availability data as percent of total available Kcals from worldwide countries[14]


Country Kcal Cereals Vege Oil Sugars and sweets Meat and offals Roots Tubers Milk, Eggs, Fish Fruit Animal Fat Pulses ETOHG/day
Afghanistan
.
.
.
.
.
.
.
.
.
.
0.04
Albania
2,904
41.99
5.89
6.95
7.78
2.13
19.67
9.15
1.99
1.55
8.75
Algeria
3,104
55.94
10.68
9.37
3.06
3.43
6.92
6.93
0.51
1.94
0.79
Andorra
.
.
.
.
.
.
.
.
.
.
12.20
Angola
1,949
36.64
10.91
8.22
5.26
26.57
2.48
2.71
0.45
2.86
6.68
Antigua and Barbuda
2,319
28.02
10.17
13.43
14.70
1.41
14.72
9.14
2.56
1.11
9.80
Argentina
3,001
34.79
9.52
15.69
17.05
3.22
8.56
4.54
2.38
0.33
11.22
Armenia
2,250
52.07
3.93
8.09
6.71
6.40
8.41
8.74
3.03
0.00
16.39
Australia
3,186
22.45
14.87
13.65
16.65
2.90
11.33
6.26
4.61
0.17
12.25
Austria
3,760
25.05
12.12
12.43
13.30
3.11
8.90
6.48
8.42
0.16
14.88
Azerbaijan
2,996
54.93
4.79
7.81
4.59
5.96
8.96
6.87
1.64
0.00
16.01
Bahamas
2,701
27.68
5.88
15.37
18.05
1.92
7.92
9.02
5.64
0.52
10.38
Bahrain
.
.
.
.
.
.
.
.
.
.
5.03
Bangladesh
2,250
79.90
6.40
3.75
0.67
2.15
2.37
1.25
0.27
1.84
0.20
Barbados
3,055
30.82
8.24
17.46
11.65
3.11
9.71
5.83
1.82
2.21
7.70
Belarus
3,086
33.05
8.02
10.56
10.78
11.43
11.36
4.72
5.10
0.00
22.62
Belgium
3,690
22.05
14.62
13.95
7.90
3.89
12.27
5.68
11.12
0.54
12.49
Belize
2,719
35.56
3.18
15.83
8.16
1.96
6.76
13.10
4.35
3.76
7.10
Benin
2,512
39.03
8.12
1.88
2.22
32.30
1.61
2.69
0.62
3.78
2.50
Bermuda
.
.
.
.
.
.
.
.
.
.
.
Bhutan
.
.
.
.
.
.
.
.
.
.
0.65
Bolivia
2,093
46.00
3.20
13.00
10.90
6.70
3.30
7.20
2.80
1.20
6.94
Bosnia and Herzegovina
3,084
48.05
6.14
4.78
3.95
5.38
9.55
7.75
1.14
2.05
11.52
Botswana
2,235
45.14
8.88
12.68
4.42
7.06
6.98
1.78
1.54
6.18
8.36
Brazil
3,099
32.85
12.88
13.17
12.34
4.33
7.48
4.73
1.84
4.84
12.10
Brunei Darussalam
2,987
45.16
8.57
11.55
8.46
1.47
10.45
5.18
1.28
0.71
2.23
Bulgaria
2,761
37.20
15.92
11.11
7.70
2.36
10.79
3.99
2.51
1.13
13.68
Burkina Faso
2,669
72.53
4.58
1.74
3.40
0.68
1.50
0.69
0.57
4.12
8.78
Burundi
1,680
17.28
0.97
1.63
1.23
36.12
0.67
15.76
0.25
18.85
11.58
Cambodia
2,245
73.16
2.34
3.89
5.75
3.48
2.90
2.23
0.83
0.94
5.65
Cameroon
2,259
39.25
9.29
4.39
3.08
17.46
2.48
9.47
0.28
5.96
9.48
Canada
3,530
24.27
15.79
14.75
10.62
3.92
8.27
5.79
7.21
1.98
12.24
Cape Verde
2,549
47.76
8.49
9.66
8.28
3.80
8.64
3.69
2.33
2.64
5.98
Central African Republic
1,956
23.12
14.04
4.58
8.45
30.97
1.96
4.16
1.09
3.37
3.80
Chad
2,040
53.15
5.38
3.98
3.17
8.43
2.79
1.21
0.47
4.48
5.27
Chile
2,957
38.62
10.96
14.62
12.73
3.46
7.61
5.14
2.30
1.43
10.57
China
2,974
50.79
6.10
2.25
14.66
6.35
4.78
7.81
1.36
0.38
6.67
Colombia
2,662
33.56
10.34
18.64
6.73
6.07
9.11
9.14
1.20
2.07
7.91
Comoros
1,857
37.12
11.07
5.98
2.53
15.47
3.86
8.02
0.20
8.64
0.34
Congo
2,513
26.55
14.36
6.16
3.69
33.38
3.29
6.19
0.13
1.43
5.35
Congo, Dem Rep
1,585
20.24
7.45
1.88
1.25
56.24
0.86
3.73
0.11
2.09
4.07
Costa Rica
2,813
33.71
11.48
19.84
4.99
1.88
11.62
5.51
2.74
3.47
6.97
Cote d'Ivoire
2,515
30.55
12.51
4.48
2.18
32.61
2.04
8.39
0.13
0.70
7.76
Croatia
2,987
30.41
12.45
15.61
6.21
5.08
11.44
5.49
2.85
0.71
18.00
Cuba
3,295
40.72
5.37
16.73
4.68
7.60
4.77
9.01
0.65
6.80
6.14
Cyprus
3,199
23.27
12.03
14.24
13.35
2.53
13.58
7.47
1.45
1.34
10.61
Czech Republic
3,317
28.66
12.57
13.26
10.31
4.12
9.77
4.27
4.90
0.69
19.76
Denmark
3,397
23.95
4.34
14.28
13.11
3.99
11.74
6.09
12.65
0.29
14.42
Djibouti
.
.
.
.
.
.
.
.
.
.
2.24
Dominica
3,115
24.56
5.35
13.90
11.25
9.00
10.79
11.54
1.22
1.67
10.42
Dominican Republic
2,263
28.86
16.56
16.08
7.04
2.55
7.13
10.01
1.47
3.61
7.54
Ecuador
2,304
33.33
15.19
8.00
9.68
3.20
8.32
14.03
3.76
1.84
11.32
Egypt
3,163
63.70
4.20
8.25
2.86
1.70
3.56
8.44
1.51
2.53
0.38
El Salvador
2,585
49.55
4.96
15.09
3.51
2.03
7.83
5.27
2.54
4.84
4.79
Equatorial guinea
.
.
.
.
.
.
.
.
.
.
7.34
Eritrea
1,587
67.58
10.12
3.25
3.07
3.76
1.73
0.42
0.54
7.54
1.97
Estonia
3,129
26.81
6.22
16.38
8.69
6.18
14.46
4.85
4.20
0.48
20.69
Ethiopia
1,952
66.15
1.92
2.40
2.62
13.62
2.04
1.45
0.81
6.70
4.92
Faeroe Islands
.
.
.
.
.
.
.
.
.
.
.
Fiji
3,033
40.64
8.80
11.37
8.17
7.87
5.24
2.33
4.09
2.87
3.31
Finland
3,215
27.65
7.99
10.40
16.00
4.25
16.45
4.49
4.20
0.36
15.72
France
3,553
25.18
12.53
10.52
14.19
3.25
14.02
5.50
7.35
0.50
14.98
French Polynesia
2,904
30.28
11.21
7.20
16.41
3.87
10.58
3.79
3.95
0.98
.
Gabon
2,730
33.24
5.85
5.46
8.01
17.71
5.05
13.83
0.63
0.27
11.35
Gambia
2,345
52.53
20.41
11.14
1.51
1.08
4.41
1.10
0.18
1.41
4.30
Georgia
2,813
56.11
4.41
6.56
6.69
4.13
11.50
4.76
2.10
0.00
7.99
Germany
3,530
24.92
11.52
13.95
10.21
4.03
11.41
5.04
9.04
0.18
14.57
Ghana
2,849
29.85
6.35
3.22
1.43
39.61
2.57
10.75
0.21
0.19
3.73
Greece
3,700
28.87
16.69
9.10
8.61
3.70
12.66
10.01
1.14
1.18
13.21
Greenland
.
.
.
.
.
.
.
.
.
.
.
Grenada
.
.
.
.
.
.
.
.
.
.
12.85
Guatemala
2,171
52.13
8.10
17.56
3.96
0.82
4.13
4.68
1.03
3.96
8.52
Guinea
2,529
47.45
14.26
4.67
1.45
14.08
1.97
10.70
0.19
2.25
0.95
Guinea-Bissau
2,288
60.13
12.51
4.01
4.50
9.13
1.43
4.03
0.50
0.61
4.68
Guyana
2,753
45.56
5.50
12.55
6.09
4.17
9.68
5.29
0.24
2.19
10.44
Haiti
1,848
49.45
5.59
11.30
4.38
8.05
2.30
6.96
0.89
4.73
7.10
Honduras
2,601
45.55
9.47
16.72
5.74
0.59
7.29
6.50
1.92
3.91
5.32
Hong Kong
.
.
.
.
.
.
.
.
.
.
.
Hungary
3,438
25.61
12.43
13.03
10.24
3.53
8.66
5.84
11.55
1.09
19.34
Iceland
3,330
19.05
5.64
14.00
16.45
2.89
23.00
4.83
5.42
0.30
8.86
India
2,301
58.22
10.39
9.84
0.94
1.76
4.71
3.64
2.41
4.56
3.23
Indonesia
2,535
63.92
6.79
6.37
2.41
6.09
2.88
4.23
0.35
0.52
0.67
Iran
3,042
55.85
6.12
8.50
4.60
3.66
4.72
10.79
1.92
2.15
1.24
Iraq
.
.
.
.
.
.
.
.
.
.
0.56
Ireland
3,532
25.80
10.13
12.18
12.80
5.47
13.91
4.55
4.63
0.67
17.90
Isle of Man
.
.
.
.
.
.
.
.
.
.
.
Israel
3,540
33.09
18.77
8.02
11.80
2.44
7.71
9.17
1.43
1.73
3.02
Italy
3,657
31.21
17.84
8.21
11.03
1.98
10.36
7.79
4.41
1.41
11.66
Jamaica
2,848
31.70
10.65
17.65
8.83
5.57
8.62
7.97
2.32
0.96
6.20
Japan
2,806
38.55
12.17
10.19
6.42
2.43
12.60
4.65
1.35
0.62
9.35
Jordan
2,977
44.87
16.15
14.97
5.04
1.74
6.26
5.10
0.36
2.68
0.78
Kazakhstan
3,359
43.21
8.30
10.25
9.27
6.10
14.03
3.74
1.75
0.11
13.32
Kenya
2,060
50.19
7.77
9.08
4.09
6.08
7.40
5.91
0.38
6.71
4.66
Kiribati
.
.
.
.
.
.
.
.
.
.
3.24
Korea, Dem. Rep.
2,146
61.03
5.61
1.99
4.71
6.88
1.89
7.43
0.50
5.43
5.21
Korea, Rep.
3,074
44.33
11.56
11.08
7.28
1.15
5.54
8.02
1.94
0.48
17.77
Kosovo
.
.
.
.
.
.
.
.
.
.
.
Kuwait
3,038
40.48
16.21
11.90
11.03
1.07
6.37
5.26
1.87
2.10
0.12
Kyrgyz Republic
2,672
56.27
3.08
5.88
6.72
8.38
11.37
4.40
0.30
1.47
5.66
Laos
2,227
72.24
1.45
1.99
5.01
3.14
2.35
5.57
0.63
1.17
8.39
Latvia
3,019
28.73
11.26
10.77
8.50
7.13
14.54
4.66
6.76
0.00
16.14
Lebanon
3,107
33.65
13.69
10.50
8.06
5.96
6.60
9.23
1.88
2.62
2.76
Lesotho
2,468
79.45
1.79
6.21
3.49
3.38
1.36
1.00
0.40
1.70
6.67
Liberia
2,163
39.98
19.98
2.43
2.08
24.16
0.91
4.93
0.11
1.34
6.08
Libya
3,144
43.36
17.15
11.92
4.15
2.15
7.60
7.50
0.27
1.35
0.12
Liechtenstein
.
.
.
.
.
.
.
.
.
.
.
Lithuania
3,419
35.12
8.26
9.91
9.78
6.51
12.91
4.60
4.51
0.79
19.56
Luxembourg
3,685
21.52
9.09
7.39
21.33
2.16
15.45
6.14
0.99
0.23
15.41
Macao SAR, China
.
.
.
.
.
.
.
.
.
.
.
Macedonia, FYR
2,983
36.03
13.30
12.03
7.39
3.42
5.72
7.79
5.63
1.91
10.73
Madagascar
2,133
58.28
3.91
3.18
3.78
20.38
3.01
3.47
0.44
1.95
1.58
Malawi
2,127
55.86
2.65
5.58
1.52
18.04
0.94
5.85
0.17
6.10
1.73
Malaysia
2,908
45.43
13.13
12.12
8.85
2.07
8.69
3.70
0.80
0.92
1.04
Maldives
.
.
.
.
.
.
.
.
.
.
.
Mali
2,579
67.43
7.63
4.71
4.19
1.91
5.38
2.11
0.44
4.46
1.19
Malta
3,592
33.40
4.88
15.21
9.16
3.22
12.00
7.44
5.52
1.47
4.92
Marshall Islands
.
.
.
.
.
.
.
.
.
.
.
Mauritania
2,823
46.77
11.68
15.45
5.58
0.52
11.94
1.71
1.09
4.50
0.13
Mauritius
2,936
47.39
13.62
11.84
6.01
1.19
7.74
3.47
0.87
3.29
4.24
Mexico
3,245
43.93
7.82
15.41
9.36
0.98
7.73
4.95
2.24
3.86
10.26
Micronesia
.
.
.
.
.
.
.
.
.
.
6.30
Moldova
2,907
48.25
5.01
9.31
4.95
4.70
11.25
5.26
2.04
0.09
27.61
Monaco
.
.
.
.
.
.
.
.
.
.
.
Mongolia
2,254
45.41
6.25
7.07
18.72
3.45
10.73
1.52
3.21
0.16
4.09
Montenegro
2,445
.
.
.
.
.
.
.
.
.
.
Morocco
3,230
61.58
7.97
11.22
3.02
2.45
2.65
5.96
1.39
1.93
1.49
Mozambique
2,071
44.73
8.67
3.21
1.40
34.18
0.65
1.11
0.13
3.91
2.72
Myanmar
2,438
60.51
9.08
5.60
4.84
1.11
3.58
4.47
0.68
5.61
0.70
Namibia
2,349
45.17
5.63
10.32
6.75
13.88
6.15
1.40
2.29
2.92
13.75
Nepal
2,349
68.20
8.35
1.80
1.72
4.05
3.66
3.60
1.23
3.15
2.90
Netherlands
3,243
19.09
13.30
14.00
10.49
4.91
17.43
7.71
4.77
0.48
11.70
New Caledonia
2,789
32.18
13.04
8.28
12.32
5.16
10.28
3.80
2.88
0.69
.
New Zealand
3,150
23.73
6.61
18.43
14.78
3.72
9.38
7.47
8.35
1.38
11.99
Nicaragua
2,400
53.51
8.56
15.52
3.95
1.24
6.06
1.65
0.66
6.35
6.25
Niger
2,306
65.96
5.56
3.37
2.40
1.81
2.32
2.46
0.90
8.79
0.41
Nigeria
2,708
44.44
13.21
3.81
1.45
19.34
1.47
4.72
0.23
3.10
15.26
Norway
3,455
28.12
11.57
12.32
11.20
4.03
13.81
5.13
6.59
0.39
10.02
Oman
.
.
.
.
.
.
.
.
.
.
1.10
Pakistan
2,251
48.91
11.16
11.58
2.59
1.14
12.07
3.02
5.03
2.67
0.06
Palau
.
.
.
.
.
.
.
.
.
.
11.83
Panama
2,451
43.34
8.55
12.32
8.24
2.37
9.00
5.64
3.21
1.98
8.76
Papua New Guinea
.
.
.
.
.
.
.
.
.
.
4.37
Paraguay
2,622
28.97
13.90
9.46
8.80
13.38
7.02
3.71
2.97
4.43
9.49
Peru
2,426
44.32
5.25
8.84
5.08
14.35
5.73
7.32
0.83
2.72
7.84
Philippines
2,518
54.80
4.11
10.79
8.91
3.07
4.52
7.58
1.85
0.57
7.30
Poland
3,397
34.98
8.56
12.81
11.23
7.08
8.92
4.13
6.43
0.47
17.32
Portugal
3,583
28.72
11.49
8.32
11.07
4.21
11.47
7.11
6.67
1.00
16.67
Qatar
.
.
.
.
.
.
.
.
.
.
1.55
Romania
3,510
40.07
9.95
7.62
7.72
5.27
14.41
6.55
2.05
0.63
19.38
Russian Federation
3,272
37.70
8.50
12.32
7.83
7.55
11.41
3.91
3.30
0.52
19.48
Rwanda
2,054
16.37
3.55
0.84
1.43
39.30
1.50
18.47
0.28
11.79
11.99
Samoa
2,878
19.73
5.52
9.39
16.99
9.56
6.01
8.04
4.66
0.01
5.41
San Marino
.
.
.
.
.
.
.
.
.
.
.
Sao Tome and Principe
2,662
33.79
7.93
7.31
2.36
15.20
4.07
11.74
0.09
2.24
10.14
Saudi Arabia
3,133
48.28
12.11
10.24
7.25
1.09
6.10
10.03
1.14
1.25
0.41
Senegal
2,318
61.89
14.47
4.89
2.79
2.99
4.86
2.49
0.60
0.83
0.61
Serbia and Montenegro
2,703
23.4
10.8
10.2
16.3
3.0
10.7
8.0
7.8
2.7
8.59
Seychelles
2,426
41.40
8.20
10.30
6.10
1.60
11.00
6.40
2.40
2.20
14.53
Sierra Leone
2,128
49.80
14.70
2.80
1.30
9.90
3.00
3.30
0.10
5.30
11.38
Singapore
.
.
.
.
.
.
.
.
.
.
1.85
Slovakia
2,885
33.04
12.17
12.45
9.10
4.36
6.83
4.15
8.78
0.57
15.97
Slovenia
3,220
32.69
11.87
6.13
12.23
3.26
10.94
7.89
6.45
0.63
17.93
Solomon Islands
2,434
35.74
2.82
2.76
2.69
35.83
3.42
2.61
0.74
3.13
1.64
Somalia
1,762
41.36
3.97
14.27
8.44
1.55
22.35
2.30
2.55
1.09
0.60
South Africa
2,986
52.53
11.14
10.90
8.50
1.95
4.14
2.73
0.51
1.05
12.19
Spain
3,271
21.46
20.24
9.71
13.70
3.96
11.86
6.80
2.16
1.39
14.27
Sri Lanka
2,392
56.04
2.21
12.33
1.19
2.24
4.49
3.81
0.37
2.78
0.97
St. Kitts and Nevis
2,452
25.71
9.16
17.17
15.80
2.29
11.21
4.73
1.48
2.22
12.74
St. Lucia
2,744
30.16
4.19
12.41
16.70
2.97
10.86
7.89
2.91
1.91
14.46
St. Vincent and the Grenadines
2,806
34.72
9.25
16.04
12.30
4.88
5.98
6.85
0.51
1.83
5.99
Sudan
2,266
48.75
5.18
9.53
5.51
1.18
18.33
3.65
0.86
3.24
1.43
Suriname
2,468
41.40
13.72
17.90
7.09
2.11
4.33
5.67
0.73
0.63
7.87
Swaziland
2,307
46.27
4.50
13.49
7.93
5.15
6.94
3.71
1.00
2.80
6.06
Sweden
3,116
25.09
11.19
13.34
10.85
3.23
17.61
5.46
6.31
0.48
11.98
Switzerland
3,421
21.80
12.49
16.88
14.01
2.37
13.01
4.72
5.95
0.41
13.69
Syrian Arab Rep.
3,049
45.42
13.36
11.68
4.46
1.68
7.77
5.70
2.30
2.53
1.79
Tajikistan
2,127
63.00
11.30
7.20
3.80
2.80
5.20
4.70
0.10
0.50
4.07
Tanzania
2,017
53.21
6.68
4.40
2.60
17.39
3.03
3.35
0.48
4.77
9.43
Thailand
2,529
48.03
6.32
13.12
6.61
1.83
5.27
5.81
0.55
1.17
8.50
Timor-Leste
2,016
65.50
1.52
1.94
8.59
12.41
3.13
1.64
0.49
1.85
0.89
Togo
2,146
48.55
9.55
2.62
1.64
25.63
1.27
1.21
0.20
3.57
2.30
Tonga
.
.
.
.
.
.
.
.
.
.
4.70
Trinidad and Tobago
2,713
35.76
10.55
20.76
6.47
2.35
7.94
4.22
2.96
3.34
7.39
Tunisia
3,312
49.46
15.35
10.14
3.47
1.82
6.31
7.01
0.68
2.54
1.26
Turkey
3,482
49.05
13.72
7.59
2.66
3.05
7.35
8.17
1.36
3.19
3.62
Turkmenistan
2,754
.
.
.
.
.
.
.
.
.
6.00
Tuvalu
.
.
.
.
.
.
.
.
.
.
2.57
Uganda
2,247
21.28
6.22
3.44
3.07
22.20
2.65
17.84
0.38
8.12
19.37
Ukraine
3,230
38.93
9.40
13.61
5.92
8.17
10.68
4.06
3.39
0.74
20.96
United Arab Emirates
3,138
43.41
6.60
11.97
10.79
0.74
8.57
7.96
1.40
3.58
0.62
United Kingdom
3,442
25.31
11.80
11.25
13.73
6.17
12.42
5.58
4.32
0.99
15.89
USA
3,770
21.71
17.84
17.06
12.07
2.75
12.36
5.07
2.86
1.10
11.64
Uruguay
2,818
41.53
7.09
12.18
13.51
3.75
9.70
4.35
2.16
0.98
10.79
Uzbekistan
2,525
58.17
11.45
2.29
6.32
2.34
11.42
5.42
0.77
0.01
4.33
Vanuatu
2,722
32.10
4.67
5.40
8.44
18.60
3.93
6.61
1.46
0.00
1.15
Venezuela
2,582
37.59
15.44
15.46
8.36
2.68
6.28
5.61
1.32
1.67
9.12
Viet Nam
2,769
67.55
2.37
4.57
10.53
1.39
2.55
4.74
1.15
0.93
4.69
Yemen
2,032
58.79
10.48
13.24
4.19
0.94
3.67
3.77
0.86
2.37
0.24
Zambia
1,885
61.91
6.21
6.28
3.24
14.16
1.97
1.47
0.38
1.35
4.27
Zimbabwe 2,207 55.81 10.62 12.59 3.85 2.26 1.65 1.04 2.00 2.34 5.95

[TableWrap ID: T2] Table 2 

“Insufficient physical activity” and mean BMI from countries and per capita gross domestic product[17]


Country Insufficient activity males [95% CI of the mean] *= imputed Insufficient activity females [95% CI of the mean] *= imputed Mean BMI for adult males 2008 95% CI of the mean Mean BMI for adult males 2008 95% CI of the mean Per-capita GDP 2009 $
Afghanistan
 
 
20.7
18.3 – 23.1
21
18.0 – 24.1
425
Albania
41.5*
44.0*
26.6
25.0 – 28.3
25.6
23.6 – 27.7
3,773
Algeria
31.9 [29.5, 34.3]
49.2 [47.2-51.2]
24.6
23.6 – 25.7
26.4
25.0 – 27.9
4,022
Andorra
 
 
27.6
25.4 – 29.8
26.4
23.5 – 29.2
44,952
Angola
30.5*
39.2*
22.2
19.6 – 24.8
23.5
20.3 – 26.6
4,069
Antigua and Barbuda
49.0*
59.0*
26
23.6 – 28.3
27.8
24.7 – 30.8
14,273
Argentina
65.8 [31.8-86.6]
70.9 [36.3-89.0]
27.5
26.3 – 28.7
27.5
26.0 – 29.0
7,665
Armenia
35.3*
47.1*
25.4
24.5 – 26.2
27.3
26.2 – 28.4
2,803
Australia
35.9 [14.9-68.5]
39.9 [16.6-72.2]
27.6
27.0 – 28.1
26.9
26.2 – 27.6
42,131
Austria
30.3 [11.7-63.8]
39.2 [15.9-72.5]
26.4
24.7 – 28.2
25.1
22.7 – 27.5
45,638
Azerbaijan
31.4*
42.1*
25.6
24.9 – 26.4
27.7
26.6 – 28.7
4,950
Bahamas
51.2*
61.7*
27.4
25.1 – 29.7
29.4
26.4 – 32.4
20,916
Bahrain
 
 
27.8
26.6 – 29.0
28.7
27.2 – 30.3
17,609
Bangladesh
2.7 [2.3-3.3]
6.6 [5.9-7.3]
20.5
19.1 – 21.9
20.5
19.8 – 21.3
608
Barbados
38.3 [34.4-42.0]
55.6 [51.2-60.0]
26.6
25.0 – 28.2
29.7
27.5 – 31.9
13,181
Belarus
24.7*
31.2*
26.2
23.8 – 28.7
26.7
23.5 – 29.8
5,183
Belgium
40.4 [17.4-72.4]
45.0 [21.4-76.9]
26.8
25.6 – 27.9
25.2
23.6 – 26.8
43,799
Belize
43.4*
56.0*
27.1
26.2 – 28.0
29.9
28.8 – 31.1
4,049
Benin
7.1 [6.3-8.0]
11.2 [10.2-12.3]
22.4
21.8 – 23.0
23.7
23.0 – 24.5
772
Bermuda
 
 
.
.
.
.
88,747
Bhutan
 
 
22.8
21.4-24.3
22.9
21.1-24.6
1,772
Bolivia
31.0*
42.6*
24.5
23.0-26.1
26.9
26.0-27.7
1,774
Bosnia and Herzegovina
30.3 [10.8-60.3]
37.0 [15.1-70.4]
26.8
25.7-27.8
26.4
25.0-27.7
4,525
Botswana
26.3 [24.2-28.4]
44.1 [41.3-46.9]
22.1
21.4-22.9
26.1
25.1-27.0
5,790
Brazil
47.2 [20.4-77.1]
51.6 [22.6-79.9]
25.8
25.2-26.4
26
25.3-26.7
8,251
Brunei Darussalam
45.3*
47.0*
24.2
21.7-26.6
22.9
19.7-26.2
27,390
Bulgaria
24.7 [9.0-58.5]
28.8 [11.1-62.4]
26.6
25.7-27.6
25.5
24.2-26.8
6,403
Burkina Faso
14.6 [4.6-37.9]
16.3 [5.4-40.5]
21.2
19.7-22.8
21.6
20.6-22.7
509
Burundi
21.9*
26.0*
21.7
19.2-24.2
21.3
18.3-24.3
6,403
Cambodia
11.4 [10.4-12.5]
11.1 [9.7-12.5]
21.3
20.6-21.9
21.6
20.9-22.2
744
Cameroon
33.0 [9.2-54.4]
48.3 [18.7-73.8]
23.7
22.6-24.8
24.9
24.0-25.9
1,157
Canada
32.3 [17.7-65.9]
35.4 [14.2-69.2]
27.5
27.0-28.0
26.7
26.0-27.4
39,644
Cape Verde
12.1 [9.4-14.8]
29.4 [26.9-31.1]
23.5
22.8-24.2
25
24.0-26.0
3,228
Central African Republic
26.6*
35.2*
21.1
18.6-23.7
22.4
20.3-24.5
459
Chad
22.8 [8.1-54.6]
26.2 [9.8-59.7]
21.5
20.1-22.9
22
20.9-23.0
625
Chile
48.1*
55.9*
27
26.1-28.0
28
26.8-29.1
9,487
China
29.7 [28.6-30.8]
32.3 [31.2-33.3]
22.9
22.5-23.4
22.9
22.3-23.6
3,749
Colombia
39.7*
48.0*
25
24.4-25.6
26.3
25.6-27.0
5,166
Comoros
6.1 [1.5-17.6]
10.6 [3.4-30.0]
22.2
19.9-24.4
22.4
20.7-24.2
748
Congo
44.4 [17.5-74.5]
52.9 [24.8-81.5]
21.9
20.3-23.5
23.2
21.9-24.4
2,431
Congo, Dem Rep
44.4*
52.9*
21.8
20.3-23.3
21.7
20.8-22.6
175
Costa Rica
41.5*
48.3*
26.5
25.6-27.5
27.1
25.9-28.2
6,373
Cote d'Ivoire
28.8 [12.0, 63.8]
36.9 [15.6-71.8]
22.6
21.2-24.0
23.8
22.5-25.1
1,191
Croatia
26.2 [9.9-59.7]
21.0 [8.1-50.2]
26.8
25.4-28.2
25.2
23.3-27.1
14,323
Cuba
32.4*
42.1*
25.2
24.0-26.4
26.9
25.3-28.5
5,565
Cyprus
48.1 [20.9-78.0]
62.6 [29.5-85.6]
27.5
26.3-28.6
25.9
24.3-27.5
29,428
Czech Republic
27.6 [10.8-56.2]
22.3 [8.2-49.3]
28
27.2-28.8
26.6
25.6-27.6
18,137
Denmark
34.8 [13.6-68.5]
35.4 [14.1-69.3]
26.1
24.9-27.3
25.1
23.4-26.9
55,933
Djibouti
 
 
23.4
21.0-25.7
24.3
21.3-27.4
1,203
Dominica
14.3 [10.5-18.4]
34.4 [31.3-37.9]
24.6
23.9-25.4
28.9
27.8-29.9
6,861
Dominican Republic
57.0 [26.7-83.2]
62.9 [30.0-85.5]
25.4
24.1-26.8
27.3
25.7-28.9
4,776
Ecuador
37.0 [14.7-70.9]
48.3 [20.3-78.1]
25.6
24.0-27.3
27.1
25.8-28.4
3,648
Egypt
39.1*
53.1*
26.8
25.8-27.7
30.1
29.5-30.7
2,371
El Salvador
34.3*
43.5*
26.4
24.9-27.9
27.9
27.0-28.8
3,354
Equatorial guinea
.
.
23.7
21.0-26.5
24.5
21.1-27.9
17,944
Eritrea
26.0 [23.0-29.2]
54.8 [51.4-58.0]
20.9
20.0-21.8
21.1
20.0-22.2
364
Estonia
15.7 [6.1-44.4]
18.8 [7.0-49.3]
26.3
24.9-27.7
25.3
23.4-27.1
14,375
Ethiopia
16.5 [5.2-43.0]
22.1 [7.5-52.4]
20.3
19.2-21.4
20.7
19.9-21.6
394
Faeroe Islands
.
.
.
.
.
.
45,206
Fiji
37.3*
50.2*
26.5
25.4-27.6
29.4
27.9-30.9
3,377
Finland
40.8 [16.9-73.1]
34.9 [14.0-68.3]
26.8
26.0-27.5
25.6
24.7-26.6
45,085
France
27.7 [25.5-30.1]
37.2 [35.0-39.5]
25.9
25.2-26.5
24.8
24.0-25.7
40,663
French Polynesia
.
.
.
.
.
.
.
Gabon
26.8 [6.1-42.3]
46.4 [15.0-68.2]
24.1
23.2-25.1
26
25.0-27.0
7,411
Gambia
20.4 [18.5-22.3]
28.7 [26.8-30.6]
21.7
20.3-23.0
24.8
23.1-26.6
436
Georgia
21.1 [19.3-23.0]
23.5 [22.3-24.7]
25.6
23.2-27.9
26.5
23.4-29.6
2,441
Germany
27.5 [10.3-60.6]
28.5 [10.7-62.3]
27.2
26.4-28.0
25.7
24.7-26.7
40,275
Ghana
14.4 [13.0-15.8]
20.8 [19.1-22.5]
22.9
22.3-23.4
24.3
23.8-24.9
1,098
Greece
16.7 [5.4-37.8]
14.5 [4.7-41.4]
26.4
25.3-27.5
25
23.5-26.4
28,521
Greenland
 
 
.
.
.
.
22,508
Grenada
 
 
25.4
23.1-27.7
27.6
24.7-30.6
7,311
Guatemala
15.3 [5.0-42.9]
17.0 [5.7-46.4]
25.3
24.1-26.5
26.8
25.6-28.0
2,685
Guinea
6.1 [1.9-18.6]
18.1 [5.6-37.5]
22.5
20.2-24.7
22.5
21.4-23.5
427
Guinea-Bissau
23.9*
32.0*
21.6
19.4-23.9
22.9
19.9-25.9
562
Guyana
32.1*
45.0*
23.9
21.4-26.2
26.8
23.7-29.9
2,690
Haiti
30.8*
34.9 *
23.9
21.5-26.2
23.5
22.4-24.5
657
Honduras
45.0*
44.4*
25.1
23.6-26.7
26.7
25.8-27.7
1,903
Hong Kong
 
 
.
.
.
.
29,882
Hungary
26.4 [10.7-60.5]
25.6 [10.4-58.7]
27.3
25.8-28.9
25.9
23.6-28.2
12,635
Iceland
23.5*
25.5*
27.2
25.8-28.8
26
23.9-28.1
38,033
India
12.7 11.8-13.7]
18.4 [17.5-19.3]
21
20.5-21.5
21.3
20.8-21.9
1,192
Indonesia
31.5 [28.4-34.8]
28.1 [25.4-30.9]
21.9
21.2-22.6
23
22.1-23.9
2,272
Iran
27.1 [24.4-27.8]
47.0 [46.2-47.8]
25.3
24.8-25.8
27.2
26.6-27.9
4,526
Iraq
62.8 [60.6-64.9]
54.0 [52.0-55.9]
26.7
25.9-27.5
28.4
27.2-29.6
2,097
Ireland
47.8 [20.7-78.3]
58.5 [27.0-83.4]
27.7
27.1-28.2
26.6
25.8-27.4
49,738
Isle of Man
 
 
.
.
.
.
50,191
Israel
50.5*
56.2*
27.1
26.4-27.9
27.3
26.3-28.2
26,102
Italy
49.6 [21.9-79.6]
59.8 [28.3-84.8]
26.5
25.8-27.2
24.8
24.0-25.7
35,073
Jamaica
43.6 [18.5-75.2]
51.5 [23.2-80.1]
24.5
23.9-25.2
28.7
27.8-29.6
4,665
Japan
58.9 [24.6-81.1]
61.6 [26.3-83.7]
23.5
23.1-23.9
21.9
21.3-22.4
39,456
Jordan
47.2*
56.9*
27.5
26.8-28.2
29.2
28.7-29.8
4,242
Kazakhstan
32.0 [12.8-66.3]
31.0 [12.7-65.7]
26.3
24.3-28.3
26.8
25.0-28.6
7,241
Kenya
15.1 [4.6-40.4]
18.0 [6.1-46.8]
21.6
19.7-23.6
23
22.2-23.8
744
Kiribati
38.4 [34.8-42.1]
54.9 [51.7-58.2]
29.2
28.2-30.2
31.3
30.0-32.7
1,306
Korea, Dem. Republic
13.0*
16.1*
22.2
19.6-24.8
21.3
17.8-24.7
200
Korea, Rep.
38.0*
41.5*
24.0
23.5-24.5
23.3
22.6-24.1
17,110
Kosovo
 
 
.
.
.
.
3,011
Kuwait
56.9 [54.4-59.2]
72.1 [68.8-75.3]
29.1
28.4-29.9
31.1
30.2-32.1
41,365
Kyrgyz Republic
28.2*
36.0*
24.8
23.1-26.5
25.8
23.9-27.6
881
Laos
16.7 [5.3-43.5]
21.0 [7.3-51.0]
21.1
19.8-22.5
22
20.9-23.2
966
Latvia
28.1 [10.9-60.9]
35.9 [14.0-69.7]
26.5
25.1-28.0
25.6
23.7-27.6
11,476
Lebanon
51.9 [48.5-55.2]
41.7 [38.8-44.7]
27.4
26.8-28.0
27.2
26.4-28.0
8,321
Lesotho
20.5*
38.6*
21.9
19.5-24.3
26.8
25.6-28.0
800
Liberia
39.8*
48.4*
21.9
19.6-24.2
23.2
22.2-24.3
229
Libya
37.3 [34.9-39.7]
54.4 [52.1-56.7]
26.5
25.9-27.2
29.2
28.3-30.1
9,957
Liechtenstein
 
 
.
.
.
.
134,916
Lithuania
20.3 [16.9-24.4]
24.9 [21.4-28.8]
26.9
25.5-28.4
26.1
24.1-28.1
11,034
Luxembourg
49.9 [21.3-81.2]
45.5 [18.4-77.6]
27.4
25.6-29.2
26.1
23.6-28.6
104,354
Macao SAR, China
 
 
.
.
.
.
40,105
Macedonia, FYR
35.6*
40.8*
26.6
24.3-28.9
25.4
23.3-27.4
4,528
Madagascar
18.3 [16.6-20.1]
28.3 [26.4-30.3]
21.5
20.5-22.4
20.7
20.1-21.4
422
Malawi
7.3 [6.0-8.6]
13.2 [11.9-14.6]
22
21.4-22.7
22.9
22.3-23.6
327
Malaysia
57.3 [54.3-60.4]
65.6 [63.1-67.9]
24.7
24.1-25.3
25.4
24.7-26.2
6,902
Maldives
36.6 [10.3-59.9
41.3 [14.7-69.0]
23.3
20.7-25.9
26.4
24.8-28.1
5,587
Mali
17.9 [5.6-45.7]
23.8 [8.8-56.7]
21.8
20.7-22.9
23
22.2-24.0
601
Malta
70.7 [32.1-88.5]
73.1 [36.7-89.1]
27.7
26.0-29.4
27.1
24.6-29.5
19,727
Marshall Islands
43.5 [40.6-46.3]
55.7 [53.3-58.1]
29.4
28.2-30.6
31.4
29.8-33.0
2,838
Mauritania
40.0 [19.1-77.6]
47.6 [29.6-86.3]
22.6
21.2-24.0
26.3
25.0-27.5
896
Mauritius
38.2 [15.0-70.0]
39.1 [15.5-70.6]
25.1
23.8-26.4
26.1
24.4-27.6
6,951
Mexico
37.1 [14.8-70.0]
38.4 [15.5-71.7]
27.4
26.8-28.1
28.8
27.9-29.6
7,852
Micronesia
58.2 [55.2-61.1]
74.4 [70.2-78.6]
28.1
26.9-29.4
31.3
29.7-32.9
2,528
Moldova
25.4*
37.2*
24.3
21.7-26.
27.0
25.8-28.2
1,526
Monaco
 
 
.
.
.
.
172,676
Mongolia
9.3 [8.3-10.5]
9.5 [8.6-10.4]
24.9
24.4-25.4
25.7
25.1-26.4
1,690
Montenegro
 
 
26.8
24.4-29.1
25.7
22.6-28.7
6,569
Morocco
39.8*
48.7*
24.8
23.7-25.9
26.2
25.2-27.2
2,828
Mozambique
6.7 [5.4-8.2]
7.4 [6.3-8.7]
22
21.1-22.8
23.3
22.3-24.3
426
Myanmar
10.4 [9.3-11.6]
14.9 [13.9-15.9]
21.6
21.0-22.2
22.8
21.8-23.7
.
Namibia
51.9 [22.7-80.2]
65.1 [31.7-86.4]
22.7
21.8-23.8
25.2
24.3-26.0
4,096
Nepal
13.9 [4.5-38.3]
17.0 [5.9-44.1]
20.8
19.0-22.6
20.7
19.8-21.6
438
Netherlands
21.3 [8.2-52.5]
15.2 [5.2-43.6]
25.9
25.2-26.5
25.2
24.3-26.2
47,998
New Caledonia
52.8*
61.7*
.
.
.
.
36,311
New Zealand
45.0 [43.6-46.3]
50.4 [49.2-51.6]
27.8
27.1-28.4
27.4
26.5-28.3
29,352
Nicaragua
33.3*
43.3*
25.8
24.3-27.3
27.6
26.7-28.4
3,000
Niger
24.4 [22.1-26.8]
34.2 [31.6-36.9]
21.2
20.5-21.9
21.9
21.1-22.8
351
Nigeria
29.5*
36.5*
23
21.9-24.2
23.7
23.0-24.4
1,091
Norway
43.4 [18.7-75.5]
45.0 [18.9-76.6]
27
26.2-27.7
25.7
24.7-26.7
76,764
Oman
 
 
26.2
25.2-27.3
26.7
25.3-28.0
17,280
Pakistan
32.7 [12.7-65.7]
48.1 [20.1-76.6]
22.4
21.1-23.6
23.4
21.9-25.0
949
Palau
 
 
30.3
27.9-32.7
31.8
28.8-35.0
8,095
Panama
35.7*
44.6*
26.3
25.2-27.4
27.7
26.2-29.3
7,138
Papua New Guinea
17.2 [15.2-19.3]
21.5 [19.7-23.4]
25
24.3-25.8
25.8
24.6-27.0
1,181
Paraguay
40.7 [16.6-72.9]
42.0 [17.2-73.9]
25.6
23.0-28.2
25.9
22.5-29.4
2,245
Peru
16.8*
25.2*
24.8
24.2-25.4
26
25.4-26.7
4,412
Philippines
21.2 [7.6-53.6]
26.2 [9.8-60.4]
22.9
22.1-23.7
23.4
22.4-24.5
1,836
Poland
23.5 [8.7-56.8]
31.6 [12.3-66.1]
26.7
26.1-27.4
25.9
25.0-26.8
11,285
Portugal
47.5 [23.1-79.3]
54.4 [24.3-81.5]
26.7
25.8-27.6
26.2
25.1-27.4
22,027
Qatar
 
 
28.1
27.4-28.9
28.9
27.9-29.9
61,532
Romania
31.2 [11.9-65.5]
46.2 [18.5-76.4]
25.6
24.3-26.9
25.3
23.6-27.0
7,500
Russian Federation
22.7 [8.2-54.7]
18.8 [6.7-46.1]
26
25.5-26.6
27.2
26.5-27.9
8,615
Rwanda
2.7*
6.0*
22.7
20.2-25.2
22
21.0-23.0
510
Samoa
36.8 [34.3-39.3]
65.4 [62.7-68.1]
30.4
29.2-31.5
33.6
32.1-35.2
2,880
San Marino
 
 
.
.
.
.
60,895
Sao Tome and Principe
11.6 [9.3-14.1]
26.3 [24.5-28.4]
23.5
22.7-24.3
24.9
23.8-26.0
1,169
Saudi Arabia
61.5 [59.4-63.5]
76.2 [74.3-77.9]
27.9
27.2-28.6
29.6
28.7-30.4
13,901
Senegal
19.1 [6.1-48.0]
23.7 [8.0-55.0]
21.9
20.1-23.8
24.3
23.3-25.3
1,056
Serbia and Montenegro
63.2 [30.7-85.5]
73.3 [37.5-89.4]
27.2
26.5-27.9
25.4
24.4-26.3
5,484
Seychelles
22.4 [19.5-25.6]
22.4 [19.1-26.1]
25.5
24.6-26.5
27.9
26.7-29.1
9,028
Sierra Leone
16.2 [14.1-18.5]
23.6 [21.6-25.7]
22.5
21.9-23.2
23.9
23.2-24.7
323
Singapore
 
 
23.9
23.2-24.5
22.9
22.1-23.7
37,790
Slovakia
23.3 [8.2-55.6]
21.2 [7.5-53.1]
27.1
24.8-29.5
26.3
23.3-29.3
16,126
Slovenia
26.5 [9.6-59.9]
33.6 [13.2-68.0]
27.6
25.3-30.0
26.6
23.5-29.7
24,051
Solomon Islands
38.0 [35.3-40.8]
49.5 [47.0-52.0]
27.2
26.1-28.2
28.9
27.4-30.3
1,147
Somalia
22.6*
29.1*
22.1
19.8-24.4
22.6
19.7-25.6
333
South Africa
48.4 [46.0-50.9]
56.5 [54.4-58.5]
26.8
26.1-27.4
29.5
28.7-30.2
5,733
Spain
47.4 [20.1-78.1]
53.1 [23.6-81.0]
27.5
26.8-28.2
26.3
25.5-27.2
31,891
Sri Lanka
18.5 [17.5-19.5]
33.3 [32.2-34.5]
22
21.4-22.7
23.1
22.2-24.0
2,035
St. Kitts and Nevis
28.7 [24.8-32.8]
47.9 [44.5-51.2]
28.3
27.2-29.4
30.6
29.3-31.9
13,003
St. Lucia
42.5*
55.1*
24.9
23.0-26.7
27.7
25.3-30.0
6,423
St. Vincent and the Grenadines
44.4*
55.4*
25.7
23.4-27.9
27.9
24.8-30.9
6,398
Sudan
21.6*
28.8*
22.4
20.1-24.7
23.2
20.2-26.1
1,286
Suriname
37.1*
48.6*
25.7
23.3-28.0
28.1
25.2-31.1
6,254
Swaziland
65.9 [32.1-86.9]
72.1 [36.8-89.0]
23.2
20.8-25.6
28.5
27.4-29.6
2,513
Sweden
44.1 [18.9-75.9]
44.3 [19.3-75.8]
26.4
25.6-27.2
25.2
24.1-26.2
43,472
Switzerland
48.9*
48.5*
26.2
25.0-27.4
24.1
22.5-25.6
63,568
Syrian Arab Rep.
37.9*
48.1*
26.9
25.9-27.9
28.8
27.5-30.2
2,692
Tajikistan
38.0*
43.4*
23.8
21.4-26.1
23.9
22.0-25.8
734
Tanzania
26.3*
30.9*
22.4
21.0-23.8
22.2
21.0-23.4
503
Thailand
17.1 [16.3-17.8]
21.4 [20.6-22.1]
23
22.6-23.5
24.3
23.7-24.9
3,835
Timor-Leste
20.7*
27.4*
20.8
18.4-23.2
21.5
18.5-24.5
544
Togo
25.1*
32.6*
21.9
19.7-24.1
22.8
21.1-24.3
535
Tonga
31.8 [27.1-36.7]
51.9 [47.6-56.2]
31
29.9-32.0
34.2
32.8-35.7
3,011
Trinidad and Tobago
41.7*
52.4*
26.6
24.7-28.4
28.7
26.3-31.0
14,748
Tunisia
31.5 [12.4-63.8]
40.3 [17.0-72.1]
25.2
24.0-26.4
27.9
26.3-29.5
4,169
Turkey
49.5 [21.6-78.8]
62.5 [29.7-84.9]
26.7
26.3-27.1
28.3
27.6-28.9
8,554
Turkmenistan
 
 
25.2
22.9-27.6
24.7
23.0-26.4
3,745
Tuvalu
 
 
 
 
 
 
2,664
Uganda
24.1*
29.0*
22.5
20.0-25.0
22.4
21.5-23.4
488
Ukraine
20.4 [7.2-52.4]
16.3 [6.2-44.6]
25.5
23.0-27.9
26.3
24.5-28.1
2,545
United Arab Emirates
56.1 [25.8-82.9]
68.9 [34.6-88.3]
28
27.0-29.1
29.5
28.2-30.9
38,960
United Kingdom
58.0 [56.8-59.1]
68.6 [67.6-69.6]
27.4
26.9-27.9
27
26.3-27.6
35,163
USA
33.5 [31.9-35.2]
47.4 [45.7-49.1]
28.5
28.0-28.9
28.3
27.7-28.9
45,758
Uruguay
28.0 [24.5-31.6]
40.2 [37.5-42.9]
26.4
25.5-27.3
26.6
25.3-27.8
9,364
Uzbekistan
42.2*
47.9*
25.3
24.3-26.4
25.5
24.1-26.9
1,182
Vanuatu
38.4*
48.1*
26.8
25.7-27.9
28.5
27.2-29.8
2,526
Venezuela
48.6*
55.6*
27.5
26.2-28.7
28.2
26.6-29.8
11,490
Viet Nam
14.6 [4.5-39.5]
15.9 [5.2-41.8]
21
20.2-21.7
21.1
20.2-22.0
1,130
Yemen
48.6*
57.7*
24.5
22.2-26.7
26
23.0-29.2
1,130
Zambia
15.1 [4.8-39.5]
19.3 [6.6-48.2]
20.7
19.5-21.9
23.1
22.3-23.8
1,006
Zimbabwe 21.8 [7.9-51.8] 25.8 [9.5-58.9] 22.1 21.2-23.0 24.7 23.8-25.6 468

[TableWrap ID: T3] Table 3 

Selected correlations of availability of foods and food and beverage groups to mean BMI from 167 Countries


Food Groups and kcals Mean % of kcals SD BMI 2008 r (P)
Cereals
41.7
14.2
−0.46 (<0.0001)
  Wheat
18.3
11.5
0.41 (<0.0001)
  Rice
11.5
14.4
−0.41 (<0.0001)
  Maize
7.56
10.7
−0.25 (<0.0001)
Vegetable oils
9.31
4.27
0.24 (<0.0001)
  Soy
2.47
2.74
0.30 (<0.0001)
  Rape and mustard seed
0.61
1.47
0.13 (0.0156)
  Palm
1.57
2.34
−0.22 (<0.0001)
Sugar and sweets
9.90
4.70
0.60 (<0.0001)
Raw sugar equivalents
9.05
4.44
0.60 (<0.0001)
Meat and offals
7.46
4.48
0.54 (<0.0001)
  Poultry
2.01
1.83
0.60 (<0.0001)
  Cow
1.77
1.53
0.28 (<0.0001)
  Pig
2.46
2.71
0.23 (<0.0001)
  Sheep/Goat
0.64
1.14
0.20 (0.0002)
  Offals
0.31
0.28
0.23 (<0.0001)
Roots and Tubers
7.37
9.24
−0.35 (<0.0001)
  Potatoes
2.23
2.29
0.24 (<0.0001)
  Cassava
2.85
6.78
−0.41 (<0.0001)
Milk, eggs, and Fish
7.58
4.59
0.44 (<0.0001)
  Milk
3.99
3.35
0.26 (<0.0001)
  Cheese
1.18
1.63
0.37 (<0.0001)
  Eggs
0.76
0.61
0.41 (<0.0001)
Fruit
5.54
3.09
0.22 (<0.0001)
Animal fats
2.34
2.45
0.38 (<0.0001)
Pulses
2.30
2.40
−0.34 (<0.0001)
Other food groups
6.32
3.43
0.21 (0.0001)
Alcohol (g/d)
6.93
5.18
0.23 (<0.0001)
Kcals
2,612
717
0.28 (<0.0001)
% Insufficient physical activity excluding imputed estimates (n=224)
34.4
17.0
0.56 (<0.0001)
% Insufficient physical activity all (n=334)
35.7
15.6
0.59 (<0.0001)
Exercise (DCCT 1–4 scale)
1.97
0.48
−0.59 (<0.0001)
GDP 2009 (n=334)
11,261
16,558
0.35 (<0.0001)
Female gender
0.50
0.50
0.16 (0.0032)
BMI 2008 25.19 2.43 1.0

[TableWrap ID: T4] Table 4 

Correlations of FAO/WHO mean female and male cohort BMIs in 2008 with formulas predicting mean BMI


  BMI 2008 r (P) BMI 2008 formula all cohorts r (P) BMI 2008 formula females r (P) BMI 2008 formula males r (P) BMI 2008 formula lower half GDP 2009 r (P) BMI 2008 formula upper half GDP 2009 r (P)
BMI 2008 formula all cohorts
0.86 (< 0.0001)
1.0
0.854 (< 0.0001)
0.88 (< 0.0001)
0.75 (< 0.0001)
0.73 (< 0.0001)
BMI 2008 formula females
0.74 (< 0.0001)
0.85 (< 0.0001)
1.0
0.90 (< 0.0001)
0.56 (< 0.0001)
0.72 (< 0.0001)
BMI 2008 formula males
0.76 (< 0.0001)
0.88 (< 0.0001)
0.90 (< 0.0001)
1.0
0.74 (< 0.0001)
0.66 (< 0.0001)
BMI 2008 formula lower half GDP 2009
0.65 (< 0.0001)
075 (< 0.0001)
0.56 (< 0.0001)
0.74 (< 0.0001)
1.0
0.50 (< 0.0001)
BMI 2008 formula upper half GDP 2009 0.63 (< 0.0001) 0.73 (< 0.0001) 0.72 (< 0.0001) 0.66 (< 0.0001) 0.50 (< 0.0001) 1.0

[TableWrap ID: T5] Table 5 

Template used for converting FAO food group availability data in percent of total kcals to macronutrients (g)


FAO Food groups Kcals Protein Carbs Dietary fiber PUFA MUFA SFA Total fat Alcohol
Cereals
100
2.58
19.2
1.61
0.64
0.56
0.29
1.42
0.00
Vegetable oil
100
0.00
0.00
0.00
3.09
4.27
3.24
11.1
0.00
Sugar and sweeteners
100
0.00
25.0
0.00
0.00
0.00
0.00
0.00
0.00
Meats
100
6.40
0.33
0.00
0.70
4.14
2.71
8.12
0.00
Roots and tubers
100
1.53
23.2
2.87
0.07
0.02
0.04
0.13
0.00
Milk, eggs, fish
100
9.74
4.00
0.00
0.88
2.05
1.69
5.00
0.00
Fruits
100
1.11
21.3
2.63
0.21
0.89
0.18
1.15
0.00
Animal fat
100
0.00
0.00
0.00
1.20
5.46
3.74
11.1
0.00
Pulses
100
5.64
12.9
4.23
1.08
1.49
0.42
2.87
0.00
Alcohol with the associated carbohydrates from wine (fruit) and beer (cereals) 100 0.00 12.1 0.00 0.00 0.00 0.00 0.00 14.30

[TableWrap ID: T6] Table 6 

Converting FAO food group availability data in kcals to macronutrients (g) for the USA


FAO Food groups Kcals Protein Carbs Dietary fiber PUFA MUFA SFA Total fat Alcohol
Cereals
821
22.0
164
13.2
5.50
4.60
2.38
12.2
0.00
Vegetable oil
667
0.00
0.00
0.00
19.5
25.9
20.0
74.3
0.00
Sugar and sweeteners
638
0.00
161
0.00
0.00
0.00
0.00
0.00
0.00
Meats
455
28.5
1.50
0.00
3.46
19.8
13.0
36.2
0.00
Roots and tubers
96
1.51
22.8
2.76
0.06
0.01
0.03
0.12
0.00
Milk, eggs, fish
466
45.4
15.0
0.00
4.61
10.4
8.67
23.6
0.00
Fruits
193
2.40
46.2
5.09
0.44
1.82
0.37
2.47
0.00
Animal fat
109
0.00
0.00
0.00
1.50
6.68
4.59
11.9
0.00
Pulses
41
2.43
5.55
1.75
0.51
0.68
0.19
1.24
0.00
Alcohol
124
0.00
5.56
0.00
0.00
0.00
0.00
0.00
14.2
Totals 3,659 102 422 22.8 35.6 69.9 49.3 162 14.2

[TableWrap ID: T7] Table 7 

Macronutrient availability as percent of total available kcals from 167 countries[14](Fiber is in g/1,000 kcals/d)


Country Kcals Protein Carbs Fiber PUFA MUFA SFA Total fat
Afghanistan
 

.
.
.
.
.
.
Albania
2,832
11.2
61.1
12.9
6.0
11.8
7.3
25.8
Algeria
3,075
10.3
64.2
12.8
7.4
10.5
6.8
25.2
Andorra
.
.
.
.
.
.
.
.
Angola
1,918
8.7
66.2
15.6
6.3
9.5
6.3
22.8
Antigua and Barbuda
2,270
11.4
52.2
9.5
6.9
15.4
10.0
33.7
Argentina
2,967
10.5
54.5
9.0
6.6
14.7
9.6
32.2
Armenia
2,266
11.2
62.3
12.3
5.7
10.3
6.6
23.2
Australia
3,030
10.2
46.9
7.9
8.1
18.3
12.3
40.6
Austria
3,494
9.8
46.8
7.9
7.8
18.2
12.3
40.2
Azerbaijan
2,937
10.5
65.7
13.2
5.8
9.3
5.8
21.3
Bahamas
2,569
12.2
49.6
7.7
6.2
16.3
10.8
35.0
Bahrain
.
.
.
.
.
.
.
.
Bangladesh
2,220
9.7
71.3
15.1
6.8
7.6
4.5
18.9
Barbados
2,824
10.3
59.0
10.1
6.4
13.1
8.4
28.9
Belarus
3,038
9.4
58.1
11.8
6.1
13.3
8.6
29.1
Belgium
3,471
7.9
49.2
8.6
8.3
18.3
12.3
40.8
Belize
2,562
12.9
58.3
10.2
5.8
12.4
8.0
27.2
Benin
2,332
9.2
70.7
18.9
5.9
8.0
5.2
19.5
Bermuda
.
.
.
.
.
.
.
.
Bhutan
.
.
.
.
.
.
.
.
Bolivia
2,010
11.6
61.8
11.1
5.5
11.2
7.2
24.8
Bosnia and Herzegovina
2,806
11.2
62.9
13.9
6.4
10.2
6.4
23.5
Botswana
2,172
8.9
65.3
14.1
6.6
10.1
6.2
23.3
Brazil
2,991
10.4
54.1
10.9
7.7
14.7
9.6
33.3
Brunei Darussalam
2,787
10.2
62.0
11.5
6.8
12.0
7.7
27.2
Bulgaria
2,647
8.6
54.7
10.4
8.3
14.4
9.5
33.4
Burkina Faso
2,446
10.9
66.7
15.1
6.8
8.6
5.0
20.4
Burundi
1,625
15.2
63.3
22.0
4.8
7.6
4.5
17.3
Cambodia
2,178
10.8
68.8
14.4
5.8
8.2
4.8
18.9
Cameroon
2,124
11.8
61.2
15.4
7.0
10.2
6.7
24.6
Canada
3,337
9.0
48.8
8.5
8.5
17.8
12.0
40.1
Cape Verde
2,463
10.0
61.4
12.6
6.8
12.0
7.6
27.2
Central African Republic
1,818
9.6
59.5
15.7
7.3
12.6
8.6
29.7
Chad
1,725
10.0
68.1
16.1
6.4
8.5
5.1
20.2
Chile
2,925
10.2
56.4
9.9
7.1
13.9
9.1
31.4
China
2,851
13.3
55.2
11.9
6.7
13.5
8.8
30.1
Colombia
2,622
10.1
61.4
10.6
6.6
11.6
7.7
26.8
Comoros
1,727
11.5
62.3
16.2
7.6
10.9
7.0
26.1
Congo
2,421
8.9
64.5
15.9
6.9
10.3
7.2
25.4
Congo, Dem Republic
1,511
8.2
75.5
21.5
4.4
5.8
4.1
14.7
Costa Rica
2,718
8.6
62.2
10.9
7.0
12.1
7.8
27.7
Cote d'Ivoire
2,399
9.7
64.7
15.8
6.7
9.5
6.8
23.8
Croatia
2,802
8.4
58.4
10.3
7.1
12.9
8.5
29.5
Cuba
3,213
11.4
65.8
13.2
5.8
9.4
5.9
21.6
Cyprus
2,920
10.7
52.7
9.4
7.3
15.5
10.1
34.3
Czech Republic
3,053
8.8
52.8
9.4
7.5
15.4
10.2
34.6
Denmark
3,166
9.7
51.2
8.8
5.9
17.3
11.2
36.2
Djibouti
.
.
.
.
.
.
.
.
Dominica
2,836
12.4
58.4
11.1
5.6
12.4
8.1
27.2
Dominican Republic
2,157
10.5
52.7
9.2
8.6
14.8
10.0
34.7
Ecuador
2,243
12.7
48.3
9.5
8.9
16.9
11.6
39.0
Egypt
3,063
11.8
66.6
13.2
6.3
9.1
5.7
21.5
El Salvador
2,497
10.0
67.4
13.1
6.0
9.5
5.7
21.6
Equatorial guinea
.
.
.
.
.
.
.
.
Eritrea
1,566
10.1
65.0
15.8
7.9
10.1
6.1
24.2
Estonia
2,870
8.7
61.2
11.0
5.5
12.2
7.7
26.2
Ethiopia
1,935
10.6
72.0
18.1
5.5
6.9
3.8
16.0
Faeroe Islands
.
.
.
.
.
.
.
.
Fiji
2,789
9.3
61.3
12.4
6.9
12.8
8.2
28.8
Finland
3,039
10.3
53.7
10.7
6.4
15.5
9.9
33.1
France
3,401
9.7
48.2
9.3
7.7
18.0
11.8
39.2
French Polynesia
2,628
13.3
43.9
8.2
8.1
18.0
12.3
40.3
Gabon
2,523
13.2
58.6
12.8
5.9
11.1
7.6
25.5
Gambia
2,222
7.2
60.4
11.1
9.6
12.5
8.5
31.4
Georgia
2,753
10.3
65.6
13.5
5.8
10.1
6.1
22.4
Germany
3,277
8.6
51.3
8.9
7.4
17.0
11.3
37.4
Ghana
2,683
10.9
71.1
18.0
5.0
7.3
5.2
18.1
Greece
3,478
10.8
50.9
10.1
8.7
15.6
10.5
36.1
Greenland
.
.
.
.
.
.
.
.
Grenada
.
.
.
.
.
.
.
.
Guatemala
2,121
9.6
66.3
11.7
6.6
9.7
6.1
22.8
Guinea
2,459
11.2
60.5
13.5
8.2
11.3
7.8
28.1
Guinea-Bissau
2,242
9.9
62.2
13.2
7.9
10.9
7.3
26.8
Guyana
2,579
10.1
65.9
12.8
5.8
9.4
5.8
21.4
Haiti
1,777
11.0
64.9
13.4
6.1
9.2
5.8
21.5
Honduras
2,572
10.1
62.3
11.2
6.9
11.4
7.3
26.4
Hong Kong
.
.
.
.
.
.
.
.
Hungary
3,276
8.8
47.2
8.3
7.9
18.3
12.3
40.5
Iceland
3,093
10.0
56.1
10.8
5.5
15.8
9.8
32.5
India
2,238
9.6
64.2
13.1
7.6
10.5
6.7
25.3
Indonesia
2,376
9.6
69.9
14.3
6.5
8.4
5.2
20.3
Iran
2,998
10.1
66.9
14.0
6.3
10.0
6.1
22.7
Iraq
.
.
.
.
.
.
.
.
Ireland
3,285
13.0
47.0
7.7
7.4
16.4
11.3
37.0
Isle of Man
.
.
.
.
.
.
.
.
Israel
3,353
11.0
48.4
9.7
9.5
17.3
11.7
40.1
Italy
3,631
11.6
45.1
9.6
9.4
18.0
12.1
41.2
Jamaica
2,720
10.3
58.6
9.6
6.9
13.1
8.7
29.8
Japan
2,553
12.1
53.3
9.2
8.0
13.7
9.4
32.4
Jordan
2,898
9.6
59.5
10.5
8.5
12.8
8.6
30.8
Kazakhstan
3,327
13.0
55.3
9.8
6.9
12.7
8.7
29.4
Kenya
2,040
11.4
63.7
14.4
6.8
10.1
6.2
23.5
Kiribati
.
.
.
.
.
.
.
.
Korea, Dem. Republic
2,073
10.6
67.1
16.6
6.5
9.2
5.3
21.2
Korea, Republic
2,832
9.9
58.9
10.6
7.7
13.1
8.6
30.3
Kosovo
.
.
.
.
.
.
.
.
Kuwait
2,926
10.6
52.7
9.4
8.9
15.7
10.6
36.6
Kyrgyz Republic
2,651
13.1
63.8
13.3
5.8
9.4
6.0
21.7
Laos
2,131
10.8
69.2
15.1
5.6
7.7
4.4
17.7
Latvia
2,875
12.0
48.2
8.3
7.7
16.1
11.2
36.8
Lebanon
2,880
10.2
56.4
11.5
8.0
14.2
9.4
32.8
Lesotho
2,477
10.2
72.2
14.7
5.6
6.7
3.8
16.0
Liberia
2,110
7.2
62.2
15.6
8.6
11.6
8.0
29.0
Libya
3,001
9.7
58.3
10.6
8.8
13.2
9.0
32.0
Liechtenstein
.
.
.
.
.
.
.
.
Lithuania
3,264
12.5
51.8
9.5
7.0
14.3
9.7
32.4
Luxembourg
3,198
16.3
39.2
6.7
7.6
18.9
12.9
41.6
Macao SAR, China
.
.
.
.
.
.
.
.
Macedonia, FYR
2,781
9.5
55.3
10.3
8.2
15.4
10.3
35.2
Madagascar
2,108
10.1
72.2
17.2
5.4
7.3
4.4
17.2
Malawi
2,069
9.5
75.6
18.8
5.1
6.1
3.3
14.3
Malaysia
2,789
11.3
56.4
9.7
8.1
13.6
9.2
32.1
Maldives
.
.
.
.
.
.
.
.
Mali
2,541
11.5
64.3
14.0
7.3
10.0
6.2
23.9
Malta
3,345
12.2
56.9
9.5
6.1
13.6
9.0
30.0
Marshall Islands
.
.
.
.
.
.
.
.
Mauritania
2,802
12.1
58.1
10.1
7.9
12.5
8.4
29.8
Mauritius
2,828
10.8
57.5
10.7
8.3
12.9
8.6
30.8
Mexico
3,183
11.3
58.8
10.5
6.7
12.4
8.0
28.0
Micronesia
.
.
.
.
.
.
.
.
Moldova
2,770
11.7
60.0
11.1
6.0
10.2
6.7
23.6
Monaco
.
.
.
.
.
.
.
.
Mongolia
2,198
14.3
49.8
9.0
7.0
15.8
10.6
34.9
Montenegro
.
.
.
.
.
.
.
.
Morocco
3,181
9.2
68.4
13.2
6.7
9.3
5.8
22.1
Mozambique
2,046
8.4
73.3
19.2
5.8
7.1
4.5
17.6
Myanmar
2,331
10.9
63.3
14.2
7.6
10.9
6.7
25.7
Namibia
2,287
10.6
63.4
13.2
5.9
10.0
6.5
23.1
Nepal
2,266
10.4
65.6
15.0
7.4
9.6
6.0
23.2
Netherlands
3,060
12.9
45.6
7.1
8.0
17.2
12.0
39.2
New Caledonia
2,515
12.3
47.9
8.8
8.4
16.6
11.4
38.0
New Zealand
3,024
11.2
50.9
7.7
6.3
16.5
11.0
35.6
Nicaragua
2,377
10.6
64.1
12.2
7.0
10.0
6.3
23.7
Niger
2,160
11.2
67.0
16.6
7.1
9.2
5.3
21.6
Nigeria
2,571
8.0
65.1
16.1
7.3
9.5
6.2
23.6
Norway
3,273
12.4
47.1
7.6
7.9
17.2
11.9
38.9
Oman
.
.
.
.
.
.
.
.
Pakistan
2,210
11.4
57.2
10.3
8.0
13.3
9.0
31.4
Palau
.
.
.
.
.
.
.
.
Panama
2,368
11.3
57.2
10.3
7.0
12.9
8.5
29.5
Papua New Guinea
.
.
.
.
.
.
.
.
Paraguay
2,429
10.8
54.9
12.3
8.1
14.8
10.0
34.4
Peru
2,291
10.5
68.7
15.2
5.7
9.1
5.6
24.3
Philippines
2,467
10.6
64.7
12.2
5.7
10.3
6.4
23.0
Poland
3,307
11.0
53.0
9.2
6.8
14.8
10.0
33.2
Portugal
3,329
11.9
46.1
8.7
8.0
17.3
11.8
38.9
Qatar
.
.
.
.
.
.
.
.
Romania
3,416
12.8
53.1
10.2
7.3
13.4
9.1
31.0
Russian Federation
3,154
11.5
55.8
9.8
6.7
12.7
8.6
29.2
Rwanda
1,990
8.8
74.3
24.5
4.0
6.5
3.1
13.5
Samoa
2,333
11.9
51.2
9.9
6.1
16.7
11.0
35.5
San Marino
.
.
.
.
.
.
.
.
Sao Tome and Principe
2,317
8.8
68.0
15.9
5.9
8.8
5.4
20.5
Saudi Arabia
3,056
10.3
60.1
11.5
7.7
12.8
8.3
29.6
Senegal
2,225
9.9
61.1
12.3
8.7
11.7
7.8
28.9
Serbia and Montenegro
2,576
10.4
36.1
6.9
6.6
16.0
10.7
34.9
Seychelles
2,248
11.9
56.2
10.5
7.0
12.4
8.2
28.7
Sierra Leone
1,987
9.2
60.3
15.0
8.5
11.0
7.1
27.1
Singapore
.
.
.
.
.
.
.
.
Slovakia
2,732
9.5
49.6
8.3
7.9
16.6
11.3
37.5
Slovenia
3,072
12.0
45.3
9.0
8.1
17.5
11.8
39.2
Solomon Islands
2,196
9.7
75.2
20.0
4.4
6.2
3.8
15.7
Somalia
1,728
15.9
54.8
8.3
6.3
12.7
8.8
29.1
South Africa
2,857
10.1
59.3
10.6
7.5
12.0
7.9
28.2
Spain
3,067
11.9
41.0
7.4
9.8
19.1
13.3
44.4
Sri Lanka
2,050
10.2
73.4
13.8
5.4
6.8
3.9
16.1
St. Kitts and Nevis
2,267
12.9
50.0
7.6
6.9
15.3
10.3
34.2
St. Lucia
2,553
13.5
51.6
9.2
5.9
14.7
9.6
31.6
St. Vincent and the Grenadines
2,630
10.8
58.9
10.2
6.6
12.8
8.4
28.9
Sudan
2,198
15.0
57.6
10.8
6.7
11.4
7.6
26.6
Suriname
2,352
8.7
60.5
9.5
7.6
12.3
8.2
29.0
Swaziland
2,157
11.4
63.8
11.9
5.8
10.1
6.4
23.0
Sweden
2,987
13.3
45.4
6.9
7.8
17.2
12.0
38.9
Switzerland
3,213
12.1
45.1
6.0
7.9
18.0
12.5
40.4
Syrian Arab Republic
2,904
10.3
58.4
10.9
8.2
13.1
8.7
31.0
Tajikistan
2,121
10.0
63.6
12.4
7.6
10.4
6.8
25.3
Tanzania
1,982
9.8
68.5
16.7
6.1
8.1
5.0
19.3
Thailand
2,293
10.3
64.2
11.3
6.2
10.1
6.4
23.3
Timor-Leste
1,963
11.7
68.6
15.7
5.4
8.5
5.1
19.4
Togo
2,036
8.8
70.4
17.9
6.5
8.1
5.2
20.1
Tonga
.
.
.
.
.
.
.
.
Trinidad and Tobago
2,603
9.9
59.5
9.3
7.0
12.5
8.3
28.9
Tunisia
3,214
9.7
60.0
11.8
8.5
12.4
8.2
30.0
Turkey
3,368
10.2
59.9
12.7
8.3
12.2
8.0
29.3
Turkmenistan
.
.
.
.
.
.
.
.
Tuvalu
.
.
.
.
.
.
.
.
Uganda
1,998
9.0
67.8
20.2
5.2
8.8
4.9
19.1
Ukraine
3,175
10.7
57.7
10.2
6.7
12.1
8.2
28.1
United Arab Emirates
2,986
12.4
59.0
11.4
6.7
12.7
8.1
28.4
United Kingdom
3,246
12.5
46.5
8.2
7.7
16.9
11.6
38.1
USA
3,565
11.4
45.8
6.4
9.0
17.6
12.4
40.9
Uruguay
2,741
12.5
55.0
9.6
6.6
13.6
9.0
30.4
Uzbekistan
2,504
12.6
56.2
11.6
8.1
12.7
8.5
30.3
Vanuatu
2,217
10.4
65.9
15.0
5.4
10.5
6.8
23.4
Venezuela
2,496
9.6
55.5
9.3
8.2
13.9
9.4
32.6
Viet Nam
2,678
11.5
65.0
13.4
5.9
10.1
6.1
22.5
Yemen
1,999
9.5
65.5
11.9
7.4
10.4
6.7
25.0
Zambia
1,855
9.4
70.4
15.2
6.0
7.7
4.8
18.7
Zimbabwe 2,070 8.6 64.0 11.6 7.5 10.7 6.9 25.7

[TableWrap ID: T8] Table 8 

Correlations of macronutrient availability with mean BMI 2008 for female and male cohorts from 167 countries


Macronutrients and kcals available Mean intake (90% CI) BMI 2008 r (P)
Kcals
2,613 (1,855-3,353)
0.28 (<0.0001)
Protein g
70.2 (38.5-111)
0.30 (<0.0001)
Protein % of kcals
10.7 (8.60-13.3)
0.13 (0.0184)
Carbohydrate g
382 (248–536)
0.04 (0.44)
Carbohydrate % of kcals
59.3 (45.8-72.0)
−0.52 (<0.0001)
Dietary fiber g
30.4 (18.2-46.2)
−0.27 (<0.0001)
Dietary fiber g per 1,000 kcals
12.0 (7.60-18.1)
−0.60 (<0.0001)
PUFA g
20.4 (10.5-33.9)
0.31 (<0.0001)
PUFA % of kcals
6.97 (5.39-8.84)
0.20 (<0.0001)
MUFA g
36.8 (14.7-72.9)
0.46 (<0.0001)
MUFA % of kcals
12.3 (7.28-18.0)
0.55 (<0.0001)
SFA g
24.2 (8.62-49.4)
0.47 (<0.0001)
SFA % of kcals
8.06 (4.42-12.3)
0.54 (<0.0001)
Total fat g
84.5 (34.0-164)
0.45 (<0.0001)
Total fat % of kcals
28.3 (17.3-40.4)
0.52 (<0.0001)
Total fat g – PUFA g
64.0 (44.5-130)
0.47 (<0.0001)
Total fat % of kcals – PUFA % of kcals
21.3 (10.3-31.6)
0.55 (<0.0001)
Alcohol g 6.92 (0.27-16.7) 0.23 (<0.0001)

[TableWrap ID: T9] Table 9 

Correlations of BMI 2008 with macronutrient profiles and exercise (DCCT 1–4 scale) N=334


  BMI 2008 r (P) BMI 2008 formula all cohorts r (P) BMI 2008 formula females r (P) BMI 2008 formula males r (P) BMI 2008 formula lower half GDP 2009 r (P) BMI 2008 formula upper half GDP 2009 r (P)
BMI 2008 formula all cohorts
0.74 (< 0.0001)
1.0
0.85 (< 0.0001)
0.83 (< 0.0001)
0.93 (< 0.0001)
0.89 (< 0.0001)
BMI 2008 formula females
0.65 (< 0.0001)
0.85 (< 0.0001)
1.0
0.87 (< 0.0001)
0.84 (< 0.0001)
0.88 (< 0.0001)
BMI 2008 formula males
0.63 (< 0.0001)
0.83 (< 0.0001)
0.87 (< 0.0001)
1.0
0.75 (< 0.0001)
0.68 (< 0.0001)
BMI 2008 formula lower half GDP 2009
0.69 (< 0.0001)
0.95 (< 0.0001)
0.84 (< 0.0001)
0.75 (< 0.0001)
1.0
0.79 (< 0.0001)
BMI 2008 formula upper half GDP 2009 0.66 (< 0.0001) 0.89 (< 0.0001) 0.88 (< 0.0001) 0.87 (< 0.0001) 0.89 (< 0.0001) 1.0

[TableWrap ID: T10] Table 10 

Baseline characteristics and lifestyle factor data of 1,055 DCCT participants with HbA1c ≤ 9.5


Variable Baseline mean [90% CI] On trial results mean [90% CI] Correlation with BMI change/ year r (P)
Age (years)
27.4 [15.0 to 38.0]
42.8 [31.2 to 53.2] (closeout)
−0.09 (0.0025)
BMI
23.4 [19.4 to 27.9]
27.6 [21.6 to 35.7] (closeout)
−0.02 (0.52) (re baseline BMI)
BMI change/year
-
0.27 [−0.06 to 0.70]
1.0
Female %
46.9 [−]
 
−0.04 (0.24)
Exercise: 1–4 scale
-
1.83 [1.00 to 2.67]
−0.10 (0.0012)
HbA1c - weighted mean of DCCT and EDIC - 7.85 [6.43 to 9.23] 0.02 (0.42)

[TableWrap ID: T11] Table 11 

Nutrient intake related to BMI change/year for DCCT/EDIC mean follow up of 16.4 years


Variable Mean intake in g or % kcals [90% CI] BMI change/year vs. % kcals r P
Food energy (kcals)
2,297
0.04
0.22
Protein g
102 [62.1-160]
0.03
0.34
Protein % of kcals
17.9 [14.5-21.7 ]
−0.02
0.47
Carbohydrates g
264 [159–405]
0.02
0.46
Carbohydrates % of kcals
46.5 [37.3-55.9]
−0.03
0.40
Dietary fiber g
24.3 [13.6-39.7]
−0.07
0.0263
Dietary fiber g /1000 kcals
10.9 [6.79-16.2]
−0.12
0.0002
PUFA g
19.1 [9.05-33.2]
−0.00
0.92
PUFA % of kcals
7.45 [4.97-10.3]
−0.06
0.06
MUFA g
35.5 [16.5-62.9]
0.06
0.0416
MUFA % of kcals
13.7 [9.50-17.5]
0.07
0.0318
SFA g
32.3 [15.0-58.7]
0.06
0.07
SFA % of kcals
12.4 [8.46-16.4]
0.06
0.0469
Trans fat g (Total fat – SFA–MUFA–PUFA g)
6.81 [3.84-11.0]
0.04
0.25
Trans fat % kcals (Total fat – SFA–MUFA–-PUFA % kcals)
2.66 [2.21-3.12]
0.00
0.88
Total Fat g
93.7 [45.9-165]
0.05
0.11
Total Fat % of kcals
36.2 [26.6-45.1]
0.04
0.22
Total fat – PUFA g
74.6 [36.1-133]
0.06
0.06
Total fat – PUFA % of kcals
28.8 [20.8-36.2]
0.07
0.0331
Alcohol g
3.35 [0.00-15.9]
−0.04
0.15
Alcohol % of kcals 0.96 [0.00-4.16] −0.06 0.0549

[TableWrap ID: T12] Table 12 

Correlations of FAO/WHO food group availability/macronutrient availability and exercise profiling formulas and DCCT/EDIC formula (n=334 cohorts)


Outcome or formula FAO/WHO mean BMI formula from food group availability and DCCT exercise r(P) FAO/WHO formula from macro-nutrient availability and DCCT exercise r(P) DCCT BMI change per year formula for macro-nutrient availability and exercise r (P) FAO/WHO and DCCT macro-nutrient formulas averaged r (P)
Mean BMI FAO/WHO countries
0.84 (<0.0001)
0.74 (<0.0001)
0.65 (<0.0001)
0.73 (<0.0001)
FAO/WHO mean BMI formula from food group availability and DCCT exercise (1–4 scale)
1.0
0.87 (<0.0001)
0.80 (<0.0001)
0.87 (<0.0001)
FAO/WHO mean BMI formula from macronutrient availability and DCCT exercise (1–4 scale)
0.87 (<0.0001)
1.0
0.87 (<0.0001)
0.98 (<0.0001)
DCCT BMI change/year formula for macronutrient availability and DCCT exercise (1–4 scale) 0.80 (<0.0001) 0.87 (<0.0001) 1.0 0.95 (<0.0001)

[TableWrap ID: T13] Table 13 

Correlations of DCCT cohort (n=1,055) BMI change/year with profiling formulas


Outcome or formula BMI change/year DCCT cohort n = 1,055 r(P) FAO/WHO formula from macronutrient availability and DCCT exercise r(P) FAO/WHO and DCCT macronutrient formulas averaged r(P)
FAO/WHO mean BMI formula from macronutrient availability and DCCT exercise (1–4 scale)
0.15 (<0.0001)
1.0
0.98 (<0.0001)
DCCT BMI change/year formula for macronutrient availability and DCCT exercise (1–4 scale)
0.18 (<0.0001)
0.80 (<0.0001)
0.91 (<0.0001)
FAO/WHO and DCCT macro-nutrient formulas averaged 0.17 (<0.0001) 0.98 (<0.0001) 1.0

[TableWrap ID: T14] Table 14 

Nutrient composition from USDA National Nutrient Database for Standard Reference Release 24[19]of foods and beverages profiled by Mozaffarian and colleagues[15]


Dietary category Serv-ing (g) Kcals Protein (g) Total carbs (g) Fiber (g) PUFA (g) MUFA (g) SFA (g) Total fat (g) Alco-hol
Fruits
154
68.5
0.86
15.8
2.25
0.16
0.60
0.15
1.04
0.00
Vegetables
116
40.8
2.62
7.33
2.39
0.26
0.11
0.09
0.58
0.00
Nuts
28
172
3.51
5.25
2.46
4.43
8.44
2.87
16.5
0.00
Whole grains
50
142
5.90
27.0
4.71
0.95
0.55
0.38
2.12
0.00
Refined grains
50
128
3.57
21.6
1.06
1.12
1.05
0.59
2.99
0.00
Sugary sodas
368
150
0.00
38.7
0.00
0.00
0.00
0.00
0.00
0.00
100% fruit juice
245
116
1.02
28.5
0.40
0.09
0.04
0.04
0.30
0.00
Non caloric soda water
360
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Sweets or desserts
62
204
2.87
25.1
1.51
1.61
4.12
3.91
10.7
0.00
Unprocessed meats
86
283
20.5
0.00
0.00
1.63
9.16
8.37
22.0
0.00
Processed meats
41.3
341
18.0
2.21
0.00
2.69
13.2
10.1
28.4
0.00
Whole-fat dairy
 
104
6.67
3.15
0.00
0.25
1.90
4.35
7.24
0.00
Butter (1 tbsp)
15
108
0.12
0.00
0.00
0.45
3.33
7.65
12.2
0.00
Cheese
28
107
7.72
2.13
0.00
0.25
1.93
4.46
7.54
0.00
Whole milk
244
156
8.00
11.0
0.00
0.33
2.58
5.56
8.93
0.00
Low-fat dairy
 
96.6
7.59
12.2
0.00
0.07
0.54
1.21
1.92
0.00
Low-fat or skim milk
244
108
8.64
12.2
0.00
0.10
0.76
1.66
2.66
0.00
Yogurt
113
79.7
6.02
12.1
0.00
0.02
0.22
0.52
0.81
0.00
Potato chips
28
157
2.02
16.7
1.32
1.05
3.84
3.84
9.59
0.00
Potato with skin
173
171
4.08
39.3
3.90
0.09
0.00
0.04
0.19
0.00
Potatoes French fried
117
180
2.78
29.2
2.57
0.40
3.18
1.95
6.03
0.00
Alcohol (1 average drink of beer, wine, or spirits) 182 124 0.58 5.56 0.00 0.00 0.00 0.00 0.00 14.2

[TableWrap ID: T15] Table 15 

Formula predicted change in pounds/4 years of selected foods based on the DCCT and FAO/WHO macronutrient intake analyses versus the Harvard diet lifestyle study data


Dietary category Kcals Total carbs /fiber ratio Total fats/PUFA ratio FAO/WHO pounds change/4 years DCCT pounds change/4 yrs Average DCCT and FAO/WHO estimates Harvard study pounds change /4 yrs
Fruits
68.5
5.67
6.16
−0.15
−0.21
−0.18
−0.49
Vegetables
40.8
4.06
2.33
−0.68
−0.75
−0.71
−0.22
Nuts
172
3.06
3.51
−1.11
−0.98
−1.04
−0.57
Whole grains
142
6.43
3.12
−1.04
−1.00
−1.02
−0.37
Refined grains
128
27.7
3.35
0.01
0.20
0.09
0.39
Sugary beverages (12 oz)
150

NA
1.27
1.60
1.44
1.00
100% fruit juice
116
50.0
3.80
0.79
1.05
0.92
0.31
Non caloric diet soda (12 oz)
0
NA
NA
0.00
0.00
0.00
−0.11
Sweets or desserts
204
37.1
15.8
0.79
0.51
0.65
0.41
Unprocessed meats
283
NA
13.4
0.75
0.61
0.68
0.95
Processed meats
341
35
10.8
0.71
0.37
0.54
0.93
Whole-fat dairy foods
104

28.5
0.90
0.57
0.74
0.10
Butter (1 tbsp)
108
NA
27.1
1.53
0.63
1.08
0.30
Cheese
107

30.2
0.89
0.56
0.73
0.02
Whole-fat milk
156

18.8
1.32
1.00
1.16
−0.06
Low-fat dairy foods
96.6

30.7
0.44
0.66
0.55
−0.05
Low-fat or skim milk
108

27.4
0.50
0.68
0.59
0.06
Low-fat yogurt
79.7

30.0
0.40
0.61
0.51
−0.82
Potato chips
157
13.5
7.27
0.81
0.38
0.60
1.69
Potato with skin
171
10.1
2.22
−0.08
−0.15
−0.12
0.57
Potatoes French fried
180
11.4
15.1
0.50
0.15
0.32
3.35
Alcohol (14.2 g) 124 - 2.93 −2.57 0.18 0.41

[TableWrap ID: T16] Table 16 

Correlations of FAO/WHO and DCCT/EDIC formulas and the Harvard categorical food profiling results for 22 foods and beverages


Food profiling system FAO/WHO formula r (P) DCCT BMI formula r (P) DCCT and FAO/WHO formulas averaged r (P)
FAO/WHO mean BMI for countries formula adjusted for weight change/4 years
1.0
0.11 (0.64)
0.74 (<0.0001)
DCCT BMI change/year formula adjusted for weight change/4 years
0.11 (0.64)
1.0
0.74 (<0.0001)
Harvard diet and lifestyle food profiling scheme 0.29 (0.20) 0.16 (0.48) 0.30 (0.18)

[TableWrap ID: T17] Table 17 

Correlations of FAO/WHO and DCCT/EDIC formulas and 12 Harvard categorical food profiling results excluding potatoes and dairy products


Food profiling system FAO/WHO formula r (P) DCCT/EDIC BMI formula r (P) FAO/WHO and DCCT/EDIC formulas averaged r (P)
FAO/WHO Mean BMI for countries adjusted for Weight change/4 years
1.0
0.03 (0.92)
0.70 (0.0115)
DCCT/EDIC BMI change/year formula adjusted for weight change/4 years
0.03 (0.92)
1.0
0.74 (0.0126)
Harvard Diet and Lifestyle Food Profiling Scheme 0.80 (0.0019) 0.57 (0.0507) 0.85 (<0.0004)

[TableWrap ID: T18] Table 18 

Correlations of FAO/WHO and DCCT/EDIC macronutrient profiling formulas and the Harvard categorical food profiling results for 10 potato and dairy products


Food profiling system FAO/WHO formula r (P) DCCT BMI formula r (P) DCCT and FAO/WHO formulas averaged r (P)
FAO/WHO mean BMI for countries adjusted for weight change/4 years
1.0
0.68 (0.0311)
0.95 (< 0.0001)
DCCT BMI change/year formula adjusted for weight change/4 years
0.68 (0.0311)
1.0
0.88 (0.0008)
Harvard diet and lifestyle food profiling scheme −0.12 (0.73) −0.56 (0.09) −0.33 (0.36)


Article Categories:
  • Research

Keywords: Obesity, Obesity epidemic, Obesity prevention, Macronutrient profiling, Body mass index, Type 1 diabetes.

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