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Human growth and body weight dynamics: an integrative systems model.

Rahmandad H - PLoS ONE (2014)

Bottom Line: For example I find that even without further rise in obesity, the gap between healthy and actual Body Mass Indexes (BMIs) has embedded, for different population groups, a surplus of 14%-24% in energy intake which will be a source of significant inertia in obesity trends.In another analysis, energy deficit percentage needed to reduce BMI by one unit is found to be relatively constant across ages.Accompanying documented and freely available simulation model facilitates diverse applications customized to different sub-populations.

View Article: PubMed Central - PubMed

Affiliation: Industrial and Systems Engineering Department, Virginia Tech, Falls Church, Virginia, United States of America; Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

ABSTRACT
Quantifying human weight and height dynamics due to growth, aging, and energy balance can inform clinical practice and policy analysis. This paper presents the first mechanism-based model spanning full individual life and capturing changes in body weight, composition and height. Integrating previous empirical and modeling findings and validated against several additional empirical studies, the model replicates key trends in human growth including A) Changes in energy requirements from birth to old ages. B) Short and long-term dynamics of body weight and composition. C) Stunted growth with chronic malnutrition and potential for catch up growth. From obesity policy analysis to treating malnutrition and tracking growth trajectories, the model can address diverse policy questions. For example I find that even without further rise in obesity, the gap between healthy and actual Body Mass Indexes (BMIs) has embedded, for different population groups, a surplus of 14%-24% in energy intake which will be a source of significant inertia in obesity trends. In another analysis, energy deficit percentage needed to reduce BMI by one unit is found to be relatively constant across ages. Accompanying documented and freely available simulation model facilitates diverse applications customized to different sub-populations.

No MeSH data available.


Related in: MedlinePlus

Growth trajectory for simulated subjects.Tracking 3rd, 5th, 10th, 25th, 50th, 75th, 85th, 90th, 95th, and 97th CDC BMI percentiles. Median curves are bold and pink. Male and Female results for (A & E) Energy intake; (B & F) BMI; (C & G) Fat percentage; (D & H) Height (only 3rd, 5th, 10th, and 50th percentiles are shown).
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pone-0114609-g004: Growth trajectory for simulated subjects.Tracking 3rd, 5th, 10th, 25th, 50th, 75th, 85th, 90th, 95th, and 97th CDC BMI percentiles. Median curves are bold and pink. Male and Female results for (A & E) Energy intake; (B & F) BMI; (C & G) Fat percentage; (D & H) Height (only 3rd, 5th, 10th, and 50th percentiles are shown).

Mentions: This model can be used for various applications. Two sets of analyses provide examples. First, the current model can be used to provide sample age, gender, and BMI specific reference body composition and energy intake curves. Whereas BMI percentile curves are readily available, reference curves for the corresponding body composition and energy intake for individuals not on the typical BMI reference are harder to obtain. The model can provide customized curves for individuals and groups. In Figure 4 EI, FM, BMI, and height trajectories (from left to right) are graphed for simulated individuals (none-hispanic white male (top row) and female (bottom row)) following different CDC BMI percentile curves [14]. EI for the 97th BMI percentile individual grows to twice the 3rd percentile female by age 20. Male variations are also significant, but more limited due to the smaller range in reference BMI curves. The corresponding FM curves show even more variation than BMI because the majority of weight difference is reflected in the changes in FM. Height curves show the impact of energy restriction on height, most notably for the 3rd percentile female subject. Here, sustained reduction in energy intake required to keep the individual at the lower end of BMI distribution leads to stunted growth in height. While variations in EI over the years allows real subjects to benefit from catch up growth in periods of energy surplus, the simulated subject faces continuous energy deficit that significantly hurts her height growth.


Human growth and body weight dynamics: an integrative systems model.

Rahmandad H - PLoS ONE (2014)

Growth trajectory for simulated subjects.Tracking 3rd, 5th, 10th, 25th, 50th, 75th, 85th, 90th, 95th, and 97th CDC BMI percentiles. Median curves are bold and pink. Male and Female results for (A & E) Energy intake; (B & F) BMI; (C & G) Fat percentage; (D & H) Height (only 3rd, 5th, 10th, and 50th percentiles are shown).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4257729&req=5

pone-0114609-g004: Growth trajectory for simulated subjects.Tracking 3rd, 5th, 10th, 25th, 50th, 75th, 85th, 90th, 95th, and 97th CDC BMI percentiles. Median curves are bold and pink. Male and Female results for (A & E) Energy intake; (B & F) BMI; (C & G) Fat percentage; (D & H) Height (only 3rd, 5th, 10th, and 50th percentiles are shown).
Mentions: This model can be used for various applications. Two sets of analyses provide examples. First, the current model can be used to provide sample age, gender, and BMI specific reference body composition and energy intake curves. Whereas BMI percentile curves are readily available, reference curves for the corresponding body composition and energy intake for individuals not on the typical BMI reference are harder to obtain. The model can provide customized curves for individuals and groups. In Figure 4 EI, FM, BMI, and height trajectories (from left to right) are graphed for simulated individuals (none-hispanic white male (top row) and female (bottom row)) following different CDC BMI percentile curves [14]. EI for the 97th BMI percentile individual grows to twice the 3rd percentile female by age 20. Male variations are also significant, but more limited due to the smaller range in reference BMI curves. The corresponding FM curves show even more variation than BMI because the majority of weight difference is reflected in the changes in FM. Height curves show the impact of energy restriction on height, most notably for the 3rd percentile female subject. Here, sustained reduction in energy intake required to keep the individual at the lower end of BMI distribution leads to stunted growth in height. While variations in EI over the years allows real subjects to benefit from catch up growth in periods of energy surplus, the simulated subject faces continuous energy deficit that significantly hurts her height growth.

Bottom Line: For example I find that even without further rise in obesity, the gap between healthy and actual Body Mass Indexes (BMIs) has embedded, for different population groups, a surplus of 14%-24% in energy intake which will be a source of significant inertia in obesity trends.In another analysis, energy deficit percentage needed to reduce BMI by one unit is found to be relatively constant across ages.Accompanying documented and freely available simulation model facilitates diverse applications customized to different sub-populations.

View Article: PubMed Central - PubMed

Affiliation: Industrial and Systems Engineering Department, Virginia Tech, Falls Church, Virginia, United States of America; Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

ABSTRACT
Quantifying human weight and height dynamics due to growth, aging, and energy balance can inform clinical practice and policy analysis. This paper presents the first mechanism-based model spanning full individual life and capturing changes in body weight, composition and height. Integrating previous empirical and modeling findings and validated against several additional empirical studies, the model replicates key trends in human growth including A) Changes in energy requirements from birth to old ages. B) Short and long-term dynamics of body weight and composition. C) Stunted growth with chronic malnutrition and potential for catch up growth. From obesity policy analysis to treating malnutrition and tracking growth trajectories, the model can address diverse policy questions. For example I find that even without further rise in obesity, the gap between healthy and actual Body Mass Indexes (BMIs) has embedded, for different population groups, a surplus of 14%-24% in energy intake which will be a source of significant inertia in obesity trends. In another analysis, energy deficit percentage needed to reduce BMI by one unit is found to be relatively constant across ages. Accompanying documented and freely available simulation model facilitates diverse applications customized to different sub-populations.

No MeSH data available.


Related in: MedlinePlus