Note: for non-commercial purposes only Systems Science to Guide Implementation of Whole-of-community Childhood Obesity Interventions Matthew W. Gillman, MD, SM EN Power of Programming March 2014
Thanks to… Faculty, Trainees, & Staff Obesity Prevention Program Department of Population Medicine Harvard Medical School/Harvard Pilgrim Health Care Institute
Questions about Obesity • Population trends – What caused/is causing the epidemic? – How can we reverse it? • Not necessarily the same as what caused it • Individual (between-person) variability – Why do some people develop obesity and others not? – How can we use that information to tailor, and evaluate, prevention and treatment • What works, for whom, & under what circumstances
Questions about Obesity • Population trends – What caused/is causing the epidemic? – How can we reverse it? • Individual (between-person) variability – Why do some people develop obesity and others not? – How can we use that information to tailor, and evaluate, prevention and treatment
Questions about Obesity • Population trends – What caused/is causing the epidemic? – How can we reverse it? • Individual (between-person) variability – Why do some people develop obesity and others not? – How can we use that— and other —information to tailor, and evaluate, prevention and treatment
Questions about Obesity • Population trends – What caused/is causing the epidemic? – How can we reverse it? • Individual (between-person) variability – Why do some people develop obesity and others not? – How can we use that information to tailor, and evaluate, prevention and treatment
It’s because of what happened to them in utero and in early childhood • Early (developmental) origins of obesity – [Motivation, Evidence] – How systems science may help • Untangle the complex webs of etiology • Help drive design of prevention
The Childhood Obesity Epidemic US DHHS, 2001; Hedley et al., 2004; Ogden et al., 2006, 2008
…in Younger Children Too Including Infants 15 Prevalence of Overweight 12-23 months 24-71 months 10 5 0-11 months 0 1980 1985 1990 1995 2000 Year Kim et al., Obesity 2006; ~500,000 well child visits in Mass. HMO
Downward trend in BMI since 2004 in 0-6-year-olds Overweight (standardized) 16 Obesity (standardized) 15 14.2 13.8 14 13.5 Boys 13.7 13.1 13.3 13.3 12.9 13.0 13 12.7 12.6 12.1 12.3 11.9 12 Prevalence 11 10.6 10.0 10.2 9.8 10 9.8 9.9 9.8 9.6 9.3 9.1 9.0 9 8.1 8.1 8 7.4 7 6 5 4 1980-1982 1983-1984 1985-1986 1987-1988 1989-1990 1991-1992 1993-1994 1995-1996 1997-1998 1999-2000 2001-2002 2003-2004 2005-2006 2007-2008 Year Standardized for age, race/ethnicity, and HVMA site, using the year 1999-2000 as reference a X. Wen et al. Pediatrics 2012;129:823-831
Overweight (standardized) 16 Obesity (standardized) 15 14 13.3 12.6 12.6 13 12.3 12.3 12.1 11.9 11.7 11.7 11.7 12 Girls 11.4 11.3 11.1 Prevalence 10.9 11 10 8.6 9 8.5 8.5 8.4 8.0 7.8 8 7.5 7.4 7.3 7.0 6.8 7 6.5 6.3 5.8 6 5 4 1980-1982 1983-1984 1985-1986 1987-1988 1989-1990 1991-1992 1993-1994 1995-1996 1997-1998 1999-2000 2001-2002 2003-2004 2005-2006 2007-2008 Year Standardized for age, race/ethnicity, and HVMA site, using the year 1999-2000 as reference a X. Wen et al. Pediatrics 2012;129:823-831
Curious trends in SGA & LGA, U.S. 1990-2005 N = 502,716 low-risk mothers: 37-41 wk, age 25-29 y, white, >13 y educ, married, 1st trim prenatal care, non-smoker, no complications, NSVD, had U/S, GWG 26-35 lb Donahue et al., Obstet Gynecol 2010; 115:357
Curious trends in SGA & LGA, U.S. 1990-2005 N = 502,716 low-risk mothers: 37-41 wk, age 25-29 y, white, >13 y educ, married, 1st trim prenatal care, non-smoker, no complications, NSVD, had U/S, GWG 26-35 lb Donahue et al., Obstet Gynecol 2010; 115:357
Message • The obesity epidemic has spared no age group, not even our youngest children • Once present, obesity tenaciously resists treatment • Prevention must start early
Developmental Origins of Obesity • Usual etiologic epidemiology – 1 determinant at a time – Independent of others • Moving toward systems approach – More than 1 determinant
Developmental Origins of Obesity How Important Can It Be? Prenatal Infancy Maternal GWG Breastfeeding Daily P (Ob) smoking (IOM cat.) duration sleep at 7 y Inadequate/ N 12+ m 12+ h 0.04 Adequate Y <12 m <12 h Excessive 0.28 Adjusted for maternal BMI, education; HH income; child race/ethnicity Gillman , Ludwig. New Engl J Med 2013 (5 Dec); 369:2173-2175
Risk of obesity at age 7-10 y according to combinations of 4 pre/post-natal risk factors 0.40 Probability of obesity 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Combinations of 4 risk factors Combinations of 4 risk factors Smoking – – – + – – + – + – + + – + + + Gest. weight gain – + – – – + + + – – – + + + – + Breastfeeding – – + – – + – – + + – + + – + + Sleep – – – – + – – + – + + – + + + + Prob. obesity 0.04 0.06 0.07 0.07 0.08 0.10 0.10 0.11 0.11 0.13 0.13 0.16 0.18 0.18 0.20 0.28 Pred. BMI-z 0.07 0.24 0.22 0.23 0.31 0.39 0.40 0.48 0.38 0.46 0.47 0.55 0.63 0.64 0.62 0.79 Pred. DXA % fat 23.2 23.0 24.5 24.1 24.4 24.4 24.0 24.2 25.4 25.7 25.3 25.3 25.5 25.2 26.6 26.5 Prevalence in this cohort 6.9% 10.4% 20.3% 0.2% 5.2% 26.6% 0.2% 5.6% 1.1% 7.2% 0.1% 3.5% 9.2% 0.3% 1.5% 1.9% Gillman , Ludwig. New Engl J Med 2013 (5 Dec); 369:2173-2175
Risk of obesity at age 7-10 y according to combinations of 4 pre/post-natal risk factors 0.40 Probability of obesity 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Combinations of 4 risk factors Combinations of 4 risk factors Smoking – – – + – – + – + – + + – + + + Gest. weight gain – + – – – + + + – – – + + + – + Breastfeeding – – + – – + – – + + – + + – + + Sleep – – – – + – – + – + + – + + + + Prob. obesity 0.04 0.06 0.07 0.07 0.08 0.10 0.10 0.11 0.11 0.13 0.13 0.16 0.18 0.18 0.20 0.28 Pred. BMI-z 0.07 0.24 0.22 0.23 0.31 0.39 0.40 0.48 0.38 0.46 0.47 0.55 0.63 0.64 0.62 0.79 Pred. DXA % fat 23.2 23.0 24.5 24.1 24.4 24.4 24.0 24.2 25.4 25.7 25.3 25.3 25.5 25.2 26.6 26.5 Prevalence in this cohort 6.9% 10.4% 20.3% 0.2% 5.2% 26.6% 0.2% 5.6% 1.1% 7.2% 0.1% 3.5% 9.2% 0.3% 1.5% 1.9% PAR% ~ 20-50%
More risk factors • Prenatal – Smoking, GWG, GDM • Perinatal – C-section, leptin • Infancy – Type of feeding, sleep duration, rapid adiposity gain, early intro solids
More risk factors • Prenatal – Smoking, GWG, GDM • Perinatal – C-section, leptin • Infancy – Type of feeding, sleep duration, rapid adiposity gain, early intro solids • Emerging – Epigenetics, toxic environment, microbiota….
Developmental Origins of Obesity • More than 1 determinant • Interacting with each other
Developmental Origins of Obesity • More than 1 determinant • Interacting with each other • Over time (age) – Life course approach
Developmental Origins of Obesity • More than 1 determinant • Interacting with each other • Over time (age) • At multiple levels of influence – Different influences at different developmental periods
Developmental Origins of Obesity • More than 1 determinant • Interacting with each other • Over time (age) • At multiple levels of influence *********************************************** • Dynamic – Feedback loops
Maternal and child inter-generational vicious cycles
Developmental Origins of Obesity • More than 1 determinant • Interacting with each other • Over time (age) • At multiple levels of influence *********************************************** • Dynamic – Feedback loops • That may operate in different directions at different stages of the life course
Hormone feedback loops in older children and adults Tend to impede weight loss
Cord blood leptin predicts slower WFL gain in 1 st 6 mo, lower 3 & 7-y BMI, but ...3-y leptin predicts higher 7-y BMI early sensitive period of leptin action, then tolerance Boeke et al, Obesity 2013;21:1430-7
Predicting metabolic adaptation, body weight change, and energy intake Body Composition Whole Body Total Energy Expenditure Thermic Effect of Feeding Adaptive Thermogenesis Physical Activity Energy Expenditure Resting Metabolic Rate Daily Average Lipolysis Rate Ketone Oxidation Rate Daily Average Ketogenesis Rate Daily Average Ketone Excretion Rate Daily Average Glycogenolysis Rate Glycerol 3-Phosphate Production Rate Gluconeogenesis From Amino Acids De Novo Lipogenesis Rate Macronutrient Oxidation Rates Respiratory Gas Exchange Nutrient Balance Parameter Constraints Carbohydrate Perturbation Constraint Protein Perturbation Constraint Physical Inactivity Constraint Model Parameter Values KD Hall
Recommend
More recommend
