Thursday, January 7, 2016


Today I want to learn about the Pennington Weight Loss Predictor which was mentioned in the 3500 Calorie Myth article from WebMD that I referenced in yesterday's blog post. The first step on this journey of discovery is to find out what is Pennington. Here is their 'About Us' information from their webpage.
LSU’s Pennington Biomedical Research Center puts science to work for a healthier Louisiana. A world research leader right here in Louisiana, our mission is to discover the triggers of chronic diseases through innovative research that improves human health across the lifespan. At the forefront of medical discovery as it relates to understanding the causes of obesity, diabetes, cardiovascular disease, cancer and dementia, Pennington Biomedical is a campus of Louisiana State University and conducts basic, clinical and population research. The research enterprise at the center includes approximately 70 faculty and more than 15 post-doctoral fellows who comprise a network of 40 laboratories supported by lab technicians, nurses, dietitians and support personnel, and 13 highly specialized core service facilities. The center’s 450 employees perform research activities in state-of-the-art facilities on the 222-acre campus located in Baton Rouge, Louisiana.

The Pennington Weight Loss Predictor applet can be found on the Pennington website.  One enters their gender, age, height, weight and the number of calories to be subtracted from one's diet.
I entered male, 58, 218lbs, and 73inches and 500 calorie reduction.

The app assumed that a male 58 years old, 73" tall and 218 lbs that was neither gaining or losing weight was likely consuming 3457 calories.  The Predictor therefore predicts, that someone at this starting point who reduced their daily caloric intake by 500 calories could expect to lose nearly 15 lbs in one year.  The website give the formula used and I have posted it at the bottom of this post... It makes E=mc2 seem like kindergarten mathematics.

I punched in my gender, age, weight and height with a 600 calorie daily reductions and the Predictor indicates that I would finally dip below 200 lbs in one year.

Current intake is 3457 calories.
MonthWeight (lb)Wt. Loss% Loss

FREQUENTLY ASKED QUESTIONS (taken directly from the Pennington site)

Q: How is my current calorie intake at the bottom of the calculator determined?  
A: This estimate is determined using a formula that depends on your age, height, gender, and current weight. The formula assumes you are weight stable.  This means that you're not currently losing or gaining weight. 

Q: The current calorie intake at the bottom of the calculator seems a lot higher than what I think it should be.  Why?
A: The current intake formula was fit to data from a large group of individuals that varied in age, gender, height, and weight. However, despite the quality of the database, the formula still only explains about 60% of the variation in intake between individuals. What this means is that remaining 40% of the variation in calorie intake between individuals is due to factors other than age, height, gender, and weight. For example, variation may be due to how much routine physical activity you conduct on a daily basis or fluctuations in your weight that deviate from weight stability. With a physical examination and more direct clinical measurements, we could provide improved estimates than the one provided here with only knowledge of your age, height, weight, and gender.

Q: Does the calculator include the effect of my metabolism slowing during weight loss?
A: Yes, it does.  However, keep in mind that that the magnitude of calories conserved due to the slowing of your metabolism, referred to as “metabolic adaptation”, is not very large and ranges between  50 and 100 Calories per day.

Q: If the lack of weight loss I see is not due to the slowing of my metabolism, why am I not losing weight?
A: This is a challenging question whose answer may be dependent on an individual’s characteristics  that are out of the scope of a model's capacity to predict.  The best recommendation is to contact your healthcare provider and discuss your weight patterns with them..
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another successful 92 minute workout on the elliptical machine

Your Food Diary For:

Wednesday, January 6, 2016

BREAKFAST Calories Carbs Fat Protein Sodium Sugar
Malt O Meal - Honey Nut Scooters 600 120 8 15 1,050 45

600 120 8 15 1,050 45
Generic - Teriyaki Salmon Steaks, 6 oz 270 3 8 47 365 5
Generic - Grilled Brussel Sprouts 65 13 3 6 23 0
Generic - Southwest Roasted Corn 160 17 9 3 260 3

495 33 20 55 648 8
Trader Joe's - Pumpkin Soup 200 36 5 2 940 20
Brad's Raw Crunchy Kale - Nacho 330 30 23 13 350 5
Akmak - Cracker, 20 cracker 440 80 8 20 560 0

970 146 36 35 1,850 25
Malt O Meal - Honey Nut Scooters 480 96 6 12 840 36

860 152 6 16 1,200 74
Totals 2,925 451 69 121 4,748 152
Your Daily Goal 4,056 507 135 203 2,300 152
Remaining 1,131 56 66 82 -2,448 0
Calories Carbs Fat Protein Sodium Sugar
*You've earned 2,126 extra calories from exercise today         


       Your Exercise Diary for:

Wednesday, January 6, 2016

Cardiovascular Minutes Calories Burned
MFP iOS calorie adjustment Ic_i N/A -47

Daily Total / Goal 93 / 30 2,126 / 590  
Weekly Total / Goal 354 / 210 6,865 / 4,130             
total calories consumed 2925 calories
total calories burned (92 minutes elliptical) 2173 calories
total net calories 752 calories

fitbit day 125
12089 steps
. . . . . . . . . . .
I honestly wonder how many human beings on earth can look at this formula and know what it all means.  In case you are wondering...I am not one of them.

This was reproduced directly from the Pennington website...

About the Model...

A validated dynamic mathematical energy balance model that predicts weight change (1) was developed from the energy balance equation based on the first law of thermodynamics (2) which states that the rate of energy stored/lost, ES, is equal to the difference of rate of energy intake, EI, and the rate of energy expended,EE,
ES = EI - EE
The model considered the rate of energy stored/lost as the rate of change of fat free mass (FFM) energy and fat mass energy (FM). The energy densities of FFM and FM, derived from chemical tissue analysis, is estimated as 1020 kcal/kg and 9500 kcal/kg respectively (3,4)
ES = 1020dFFM+9500dFM
EE was modeled as the sum of resting metabolic rate (RMR), voluntary physical activity (PA), dietary induced thermogenesis (DIT), and spontaneous physical activity (SPA)
The non-linear function of weight, gender, and age proposed by Livingston and Kohlstadt (5) was applied for the RMR term (Table 1):
RMR = ci Wpi -yiA
where ci ,pi ,yi are constants depending on gender: i = F,M. The Livingston-Kohlstadt model was developed using cross-sectional RMR subject data (N>600) and validated on over 700 subject data points (R2 > 0.71).
PA is modeled by a term that is directly proportional to weight:
PA = mW
and DIT is modeled as a direct proportion of energy intake (6) :
SPA was related to total energy expenditures using both overfeeding and underfeeding experimental conclusions. Specifically, it was observed that
during weight loss (7-9) and
during weight gain (10).
Combining all terms yields the full one dimensional differential equation energy balance model:

FFM-FM equations developed from NHANES data (N>10,000) (13).
FFM = -72.1+ 2.5FM - 0.04+ 0.7H - 0.002FM(A) - 0.01FM(H) - 0.04FM2 + 0.00003FM2+ 0.0000004FM4 + 0.0002FM3 + 0.0003FM2H - 0.000002FM3H
FFM = -71.7+ 3.6FM - 0.04A + 0.7H - 0.002FM(A) - 0.01FM(H) - 0.07FM2 + 0.00003FM2A - 0.000002FM4 + 0.0006FM3 + 0.0003FM2H - 0.000002FM3H
FFM is related to FM through a model derived from the recently released National Health and Nutrition Assessment Survey (NHANES) (13) which contained over 10,000 dual energy X-ray absorptiometry (DXA) measured body composition values, along with subject age, height race and gender (Table 1). We developed the FFM-FM relationship for specific use within the Heymsfield energy balance equation (15).
The model has been recently applied and validated as a tool to assess energy intake during weight loss (16).
  1. D. M. Thomas, C. K. Martin, S. B. Heymsfield, L. M. Redman, D. A. Schoeller, et al, A simple model predicting individual weight change in humans. J. Biol. Dyn., in press (2010).
  2. W.D. McArdle, F.J. Katch, V.L. Katch (eds). Exercise Physiology (Williams & Wilkins, Baltimore, MD, 2009) [seventh edition].
  3. S.B. Heymsfield, M. Waki, J. Kehayias, S. Lichtman S, F.A. Dilmanian, Y. Kamen, J. Wang J, R.N. Pierson Jr. Chemical and elemental analysis of humans in vivo using improved body composition models. Am. J. Physiol. 261, 191-198 (1991).
  4. Y. Schutz, Glossary of energy terms and factors used for calculations of energy metabolism in human studies. Human Energy Metabolism: Physical Activity and Energy Expenditure Measurements in Epidemiological Research Based Upon Direct and Indirect Calorimetry. A. J. H. van Es, Ed. (The Hague, The Netherlands: Koninklijke Bibliotheek, 1984). pp. 169-181.
  5. E.H. Livingston, I. Kohlstadt, Simplified resting metabolic rate—predicting formulas for normal-sized and obese individuals. Obes. Res. 13, 1255-1262 (2005).
  6. K.R. Westerterp, KR. Diet induced thermogenesis. Nutr. Metab. 1, 1-5 (2004).
  7. S.B Roberts, I. Rosenberg, Nutrition and aging:changes in the regulation of energy metabolism with aging. Physiol. Rev. 86, 651-667 (2005).
  8. L.G. Bandini, D.A. Schoeller, J. Edwards, V.R. Young, S.H. Oh, W.H. Dietz, Energy expenditure during carbohydrate overfeeding in obese and nonobese adolescents. Am. J. Clin. Nutr. 256, E357-E36 (1989).
  9. E.O. Diaz, A.M. Prentice, G.R. Goldberg, P.R. Murgatroyd, W.A. Coward, Metabolic response to experimental overfeeding in lean and overweight healthy volunteers. Am. J. Clin. Nutr. 56, 641-655 (1993).
  10. J.A Levine, L.M. Lanningham-Foster, S.K. McCrady, A.C. Krizan, L.R. Olson, P.H. Kane, M.D. Jensen, M.M. Clark, Interindividual variation in posture allocation: possible role in human obesity. Science 307, 530-531 (2005). 
  11. L.K. Heilbronn, L. de Jonge, M.I. Frisard, J.P. DeLany, D.E. Larson-Meyer, J. Rood, T. Nguyen, C.K. Martin, J. Volaufova, M.M. Most, F.L Greenway, S.R. Smith, W.A. Deutsch, D.A. Williamson, E. Ravussin and Team, Pennington CALERIE. Effect of 6-Month Calorie Restriction on Biomarkers of Longevity, Metabolic Adaptation, and Oxidative Stress in Overweight Individuals. JAMA 295, 1539-48 (2006). 
  12. S.B Racette, D.A. Schoeller, R.F. Kushner, K.M. Neil, K. Herling-Iaffaldano K. Effects of aerobicexercise and dietary carbohydrate on energy expenditure and body composition during weight reduction in obese women. Am. J. Clin. Nutr. 61, 486-494 (1995).
  13. G.L. Blackburn, National Health and Nutrition Examination Survey: where nutrition meets medicine for the benefit of health. Am. J. Clin. Nutr. 78, 197 – 198 (2003).
  14. D. Thomas, S. Das, J. Levine, C.K. Martin. L. Mayer, A. McDougall B.J. Strauss, S.B. Heymsfield, New fat free mass - fat mass model for use in physiological energy balance equations. Nutr. Metab. 7, 1-11 (2010).
  15. D. M. Thomas, C. K. Martin, S. B. Heymsfield, L. M. Redman, D. A. Schoeller, et al, A simple model predicting individual weight change in humans. J. Biol. Dyn., in press (2010).
  16. D. M. Thomas, D. A. Schoeller, L. A. Redman, C. K. Martin, J. A. Levine, et al, A computational model to determine energy intake during weight loss. Am. J. Clin. Nutr., doi:10.3945/ajcn.2010.29687 (2010). 
Disclaimer: Information provided by this site is for educational purposes only and is not intended to be a substitute for professional medical advice specific to the reader's particular situation. The information is not to be used for diagnosing or treating any health concerns you may have. The reader is advised to seek prompt professional medical advice from a doctor or other healthcare practitioner about any health question, symptom, treatment, disease, or medical condition.

The java applet was made by Carl Bredlau and Steven Lettieri.

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