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Fructose and metabolic syndrome: Fructose and metabolic syndrome: Is there a link?Is there a link?
Baptist Health South Florida, Feb. 6, 2014
Robert H. Lustig, M.D., M.S.L.Robert H. Lustig, M.D., M.S.L.Division of Endocrinology, Department of PediatricsDivision of Endocrinology, Department of Pediatrics
Institute for Health Policy StudiesInstitute for Health Policy StudiesUniversity of California, San FranciscoUniversity of California, San Francisco
Adjunct FacultyAdjunct FacultyUC Hastings College of the LawUC Hastings College of the Law
• No disclosures
PastPast
2001
Currently there are 30% more obese than undernourished people worldwide
(World Health Organization)
371 million diabetics in 2012(5% of the world‘s population)
(International Diabetes Federation)
PresentPresent
Experts predict:
165 million Americans will be obese by 2030(4 part obesity series in Lancet, 8/26/11)
42% of Americans will be obese by 2030 (Finkelstein et al. Am J Prev Med epub 5/7/12)
100 million Americans will have diabetes by 2050(CDC Division of Diabetes Translation, 2011)
MedicareMedicare will will bebe brokebroke byby 20262026
FutureFuture
Obesity is the problem Obesity is the problem (?)
2
Obesity is the problem (?)Obesity is the problem (?) Obesity is the problem (?)Obesity is the problem (?)
Basu et al. PLoS One 8:e58783, 2013
Obesity is the problem (?)Obesity is the problem (?)
• Obesity is increasing worldwide by 1% per year
• Diabetes is increasing worldwide by 4% per year
““ExclusiveExclusive”” view of obesity and view of obesity and metabolic dysfunctionmetabolic dysfunction
Obese (30%)
Normal weight (70%)
240 million adults in U.S.
72 million
168 million
Obese (30%)
Obese and sick
(80% of 30%)
Normal weight (70%)
240 million adults in U.S.
72 million
168 million
Total: 57 million sick
““ExclusiveExclusive”” view of obesity and view of obesity and metabolic dysfunctionmetabolic dysfunction
Obese (30%)
Normal weight (70%)
240 million adults in U.S.
72 million
168 million
““InclusiveInclusive”” view of obesity and view of obesity and metabolic dysfunctionmetabolic dysfunction
3
Obese (30%)
Normal weight (70%)
240 million adults in U.S.
Normal weight,
Metabolic dysfunction
(40% of 70%)
Obese and sick
(80% of 30%)
57 million 67 million
Total: 124 million sick
72 million
168 million
““InclusiveInclusive”” view of obesity and view of obesity and metabolic dysfunctionmetabolic dysfunction
Relation between Relation between visceral and subcutaneous obesity:visceral and subcutaneous obesity:(thin on the outside, fat on the inside)(thin on the outside, fat on the inside)
Thomas et al. Obesity doi: 10.1038/oby.2011.142, 2011
Obesity is not the problemObesity is not the problem Obesity is not the problemObesity is not the problem
Metabolic Syndrome: where all the money goesMetabolic Syndrome: where all the money goes(75% of all healthcare dollars)(75% of all healthcare dollars)
Obesity is not the problemObesity is not the problem
Metabolic Syndrome: where all the money goesMetabolic Syndrome: where all the money goes(75% of all healthcare dollars)(75% of all healthcare dollars)
DiabetesDiabetesHypertensionHypertension
Lipid abnormalitiesLipid abnormalitiesCardiovascular diseaseCardiovascular disease
NonNon--alcoholic fatty liver diseasealcoholic fatty liver diseasePolycystic ovarian diseasePolycystic ovarian disease
CancerCancerDementiaDementia
Metabolic syndrome is difficult to define in adults Metabolic syndrome is difficult to define in adults
• WHO 1998 • AACE 2003
• EGIR 1998 • IDF 2005
• NCEP/ATPIII 2001 • AHA 2005
4
Metabolic syndrome is difficult to define in adults Metabolic syndrome is difficult to define in adults
And even more difficult to define in children And even more difficult to define in children
• WHO 1998 • AACE 2003
• EGIR 1998 • IDF 2005
• NCEP/ATPIII 2001 • AHA 2005
Circulation 119:628, 2009
Because each of these definitions sought to define theBecause each of these definitions sought to define the
metabolic syndrome phenomenologically, with cutoffs metabolic syndrome phenomenologically, with cutoffs
Because each of these definitions sought to define theBecause each of these definitions sought to define the
metabolic syndrome phenomenologically, with cutoffs metabolic syndrome phenomenologically, with cutoffs
It is easier to define the metabolic syndrome mecha nisticallyIt is easier to define the metabolic syndrome mecha nistically
WhereWhere’’s the insulin resistance?s the insulin resistance?
Cytokines
The standard model of insulin resistanceThe standard model of insulin resistance
Familial Partial Lipodystrophy: Dunningan or Type 2Familial Partial Lipodystrophy: Dunningan or Type 2
•X-linked or autosomal dominant•Absence of limb fat
�Easily visible veins�Defined musculature
•Normal or excess facial fat •Cushingoid facies (moon facies)•Dorsocervical fat pad•Acanthosis nigricans•Metabolic Syndrome
Peters et al. Nature Genet 18:292, 1998
• Fat mass
• Leptin• Adiponectin• Inflam. Cytokines• Metabolic Syndrome
Comparison between lipodystrophy and obesityComparison between lipodystrophy and obesity
Asterholm et al. Drug Disc Today Dis Models 4:17, 2007
LD obesity
±++
5
• Fat mass
• Leptin• Adiponectin• Inflam. Cytokines• Metabolic Syndrome
Comparison between lipodystrophy and obesityComparison between lipodystrophy and obesity
Asterholm et al. Drug Disc Today Dis Models 4:17, 2007
LD obesity
±++
So the metabolic syndrome can arise from too much, or too little fatSo the metabolic syndrome can arise from too much, or too little fati.e. iti.e. it’’s not the fat that countss not the fat that counts
Obesity Lipodystrophy
InsulinResistance
Chehab, Endocrinol 149:925, 2008
Obesity and lipodystrophy share insulin resistanceObesity and lipodystrophy share insulin resistance
Relation between obesity, T2DM, and Metabolic Syndr omeRelation between obesity, T2DM, and Metabolic Syndr ome
Steinberger et al. Circulation 119:628, 2009
REFRAMING THE DEBATEREFRAMING THE DEBATE
REFRAMING THE DEBATEREFRAMING THE DEBATE
Obesity doesnObesity doesn’’t CAUSE metabolic syndromet CAUSE metabolic syndrome
Obesity is a MARKER for metabolic syndromeObesity is a MARKER for metabolic syndrome
REFRAMING THE DEBATEREFRAMING THE DEBATE
Obesity doesnObesity doesn’’t CAUSE metabolic syndromet CAUSE metabolic syndrome
Obesity is a MARKER for metabolic syndromeObesity is a MARKER for metabolic syndrome
OBESITY IS A OBESITY IS A ““RED HERRINGRED HERRING””EVERYONE IS AT RISK OF METABOLIC SYNDROMEEVERYONE IS AT RISK OF METABOLIC SYNDROME
6
Obesity isnObesity isn’’t enough!t enough!
Insulin resistance isnInsulin resistance isn’’t enough!t enough!
What kind of obesity?What kind of obesity?
What kind of insulin resistance? What kind of insulin resistance?
In which tissue?In which tissue?
Are all insulin pathways affected?Are all insulin pathways affected?
Intrahepatic fat explains metabolic perturbation Intrahepatic fat explains metabolic perturbation better than visceral fatbetter than visceral fat
Fabbrini et al. Proc Natl Acad Sci 106:15430, 2009
HepaticInsulin
SensitivityIndex
InsulinStimulated
GlucoseDisposal
Rate
InsulinStimulatedPalmitate
SuppressionRate
VLDLSecretion
Rate
ContributionOf Free
Fatty AcidsTo VLDL
Insulin Receptor Knockouts (IRKO)Insulin Receptor Knockouts (IRKO)Kahn Lab, Kahn Lab, JoslinJoslin 19981998--presentpresent
Obesity, Metabolic SyndromeLiver (LIRKO)Brain (NIRKO)
Protected from ObesityMuscle (MIRKO)White Adipose Tissue (FIRKO)Brown Adipose Tissue (BATIRKO)β-cell (βIRKO)Vascular Smooth Muscle (VSMCIRKO)Glomerular Podocyte (PODIRKO)
Biddinger and Kahn, Ann Rev Physiol 68:123, 2006 Brown and Goldstein, Cell Metab 7:95, 2008
Insulin has two effects on the liverInsulin has two effects on the liver
Result: ObesityHyperglycemia, hyperinsulinemia, DMLow TG, VLDLNormal BPNOT Metabolic Syndrome
Result: ObesityHyperglycemia, hyperinsulinemia, DMHigh TG, VLDLLow BPMetabolic Syndrome
7
In order to explain Metabolic Syndrome:
• We are looking for a ubiquitous factor that– promotes obesity (preferably visceral)– promotes hypertension– induces selective hepatic insulin resistance
• blocks Foxo1 to promote gluconeogenesis(hyperglycemia, hyperinsulinemia, and diabetes)
• stimulates de novo lipogenesis(dyslipidemia, atherosclerosis)
U.N. General AssemblyU.N. General AssemblySept 20, 2011Sept 20, 2011
• Non-communicable disease is now a bigger problemthan acute infectious diseases worldwide
• Plan to target, tobacco, alcohol, and diet
U.N. General AssemblyU.N. General AssemblySept 20, 2011Sept 20, 2011
• Non-communicable disease is now a bigger problemthan acute infectious diseases worldwide
• Plan to target, tobacco, alcohol, and diet
• But exactly what about diet?Total calories?Fat?Red meat?Dairy?Carbohydrate?
The FictionThe Fiction“Beating obesity will take action by all of us, based on onesimple common sense fact: All calories count, no matter wherethey come from, including Coca-Cola and everything else wit hcalories…”
-The Coca Cola Company, “Coming Together”, 2013
8
• Some Calories Cause Disease More than Others
• Different Calories are Metabolized Differently
• A Calorie is Not A Calorie
The ScienceThe ScienceHigh Fructose Corn Syrup is 42High Fructose Corn Syrup is 42--55% Fructose;55% Fructose;
Sucrose is 50% FructoseSucrose is 50% Fructose
GlucoseGlucose FructoseFructose
SucroseSucrose
Ventura et al. Obesity 19:868, 2011
Actual fructose content in soft drinksActual fructose content in soft drinks
55%
150150
125
100
75
50
25
0
Grams per day
U.S. Commerce Service 1822-1910, combined with Economic Research Service, USDA 1910-2010
150150
125
100
75
50
25
0
Grams per day
U.S. Commerce Service 1822-1910, combined with Economic Research Service, USDA 1910-2010
Growth ofSugar Industry
StabilizationHFCS +Sugar for Fat
WWII
150150
125
100
75
50
25
0
Grams per day
U.S. Commerce Service 1822-1910, combined with Economic Research Service, USDA 1910-2010
Growth ofSugar Industry
StabilizationHFCS +Sugar for Fat
WWII
Theoreticalthreshold based on EtOH
AHA threshold for CVD
9
150150
125
100
75
50
25
0
Grams per day
U.S. Commerce Service 1822-1910, combined with Economic Research Service, USDA 1910-2010
Growth ofSugar Industry
Stabilization
WWII
Emergence of CVD as health issue 1931
HFCS +Sugar for Fat
Theoreticalthreshold based on EtOH
AHA threshold for CVD
150150
125
100
75
50
25
0
Grams per day
U.S. Commerce Service 1822-1910, combined with Economic Research Service, USDA 1910-2010
Growth ofSugar Industry
Stabilization
WWII
Emergence of CVD as health issue 1931
HFCS +Sugar for Fat
Emergence of Adolescent T2DM as health issue 1988
AHA threshold for CVD
Theoreticalthreshold based on EtOH
Global consumption of sugar/sugarcropsGlobal consumption of sugar/sugarcropsCalories per day, 2007Calories per day, 2007
Data from Food and Agriculture Organization, World Health Organization, 2007 Mozaffarian et al. N Engl J Med 364:2392, 2011
Foods that cause weight gainFoods that cause weight gain
Mozaffarian et al. N Engl J Med 364:2392, 2011
Foods that cause weight gainFoods that cause weight gain
Mozaffarian et al. N Engl J Med 364:2392, 2011
Foods that cause weight gainFoods that cause weight gain
10
Loweringsugar
Raisingsugar
Effects of sugar on obesity (metaEffects of sugar on obesity (meta--analysis)analysis)
Te Morenga et al. BMJ 345:e7492, 2013
Obesity is not the problemObesity is not the problem
Metabolic syndrome is the problemMetabolic syndrome is the problem
Fructose is not glucoseFructose is not glucose
• Fructose is 7 times more likely than glucoseto form Advanced Glycation End-Products (AGE’s)
• Fructose does not suppress ghrelin
• Acute fructose does not stimulate insulin (or leptin)
• Hepatic fructose metabolism is different
• Chronic fructose exposure promotes the metabolic syndrome
Elliot et al. Am J Clin Nutr, 2002Elliot et al. Am J Clin Nutr, 2002Bray et al. Am J Clin Nutr, 2004Bray et al. Am J Clin Nutr, 2004Teff et al. J Clin Endocrinol Metab, 2004Teff et al. J Clin Endocrinol Metab, 2004Gaby, Alt Med Rev, 2005Gaby, Alt Med Rev, 2005
Le and Tappy, Curr Opin Clin Nutr Metab Care, 2006Le and Tappy, Curr Opin Clin Nutr Metab Care, 2006Wei et al. J Nutr Biochem, 2006Wei et al. J Nutr Biochem, 2006Johnson et al. Am J Clin Nutr 2007Johnson et al. Am J Clin Nutr 2007Rutledge and Adeli, Nutr Rev, 2007Rutledge and Adeli, Nutr Rev, 2007Brown et al. Int. J. Obes, 2008Brown et al. Int. J. Obes, 2008
A different model of insulin resistanceA different model of insulin resistance
Cytokines
Fructose
Fatty liver
Sensitivity
Hepatic insulin resistance
11
A different model of insulin resistanceA different model of insulin resistance
Cytokines
Fructose
Fatty liver
Sensitivity
Hepatic insulin resistance
The second problem The second problem
The browning reaction or Maillard reaction The browning reaction or Maillard reaction or nonor non--enzymatic glycationenzymatic glycation
Instead of roasting 1 hour at 375 degreesInstead of roasting 1 hour at 375 degreeswe slow cook at 98.6 degrees for 75 yearswe slow cook at 98.6 degrees for 75 years
Aging and costal cartilage Aging and costal cartilage
Courtesy Dr Baynes
Figueroa-Romero et al. Rev Endo Metab Dis 9:301, 2008
Generation of reactive oxygen species by carbohydra te Generation of reactive oxygen species by carbohydra te
12
The furan ring of fructose is more unstable,so at equilibrium, fructose exists in the linear fo rm
Lim et al. Nat Rev Gastro Hepatol 7:251, 2010
Glucose
Fructose
Days of in vitro glycation
Flu
ores
cenc
e
600
400
200
0
Ahmed and Furth, Clin Chem 38:1301, 1992
Fructose and glycationin vitro
0 8 16 24
Rates of reactivity
Rate Carbonyl (/mM/hr) %
Glucose 0.6 0.002
Galactose 2.8 0.02
Fructose 4.5 0.7
Bunn and Higgins, Science 213:222, 1981
NonNon--enzymatic glycation: fructose >> glucoseenzymatic glycation: fructose >> glucose
Hepatocyte death Hepatocyte death in vitro in vitro upon fructose exposure upon fructose exposure (after generation of H(after generation of H 22OO22))
Treatment ED 50
Fructose 1.5 ± 0.13 M
Glucose >1.5 M
Glycoaldehyde 20 ± 2 mM
Glyoxal 5 ± 0.5 mM
Lee et al. Chemico-biological Interactions 178:332, 2009
Hepatocyte death Hepatocyte death in vitro in vitro upon fructose exposure upon fructose exposure (after generation of H(after generation of H 22OO22))
Treatment ED 50 ED50 (with H 2O2)
Fructose 1.5 ± 0.13 M 12 ± 2 mM
Glucose >1.5 M 1.5 M
Glycoaldehyde 20 ± 2 mM 0.5 ± 0.1 mM
Glyoxal 5 ± 0.5 mM 0.02 ± 0.002 mM
Lee et al. Chemico-biological Interactions 178:332, 2009
Prevented by addition of:antioxidant vitamins (VitB 1, VitB 6, VitC)P450 inhibitorshydroxyl radical and carbonyl scavengers heavy metal chelators
The MethionineThe Methionine--Choline Deficient DietCholine Deficient Diet
Pickens et al. J Lipid Res 50:2072, 2009
Fastest animal model of NASH• sucrose necessary to provide the substrate for steatosis• methionine deficiency reduces glutathione, the hepatic hydroxyl radical scavenger
• choline deficiency reduces phosphatidyl choline, another mechanism of hepatic lipid export
Sucrose is necessary for NAFLD in theSucrose is necessary for NAFLD in theMethionineMethionine--Choline deficient dietCholine deficient diet
Pickens et al. J Lipid Res 50:2072, 2009
13
TUNEL staining in the TUNEL staining in the MethionineMethionine--Choline deficient dietCholine deficient diet
Pickens et al. J Lipid Res 50:2072, 2009
Association of fructose consumption with Association of fructose consumption with severity of steatosis and fibrosisseverity of steatosis and fibrosis
Grade of Steatosis
p =0.06 p < 0.005
Stage of Fibrosis
p < 0.0007
Non NonOccasional OccasionalDaily Daily
Error bar = 95%CI
Abdelmalek et al. Hepatology 51:1961, 2010
10 Most Obese States
> 30% obese
10 Most Obese States 10 Laziest States
> 30% obese < 63% active
10 Most Obese States 10 Laziest States
> 30% obese < 63% active
10 Most Unhappy States 10 Most Obese States 10 Laziest States
> 30% obese < 63% active
10 Most Unhappy States
14
10 Most Obese States 10 Laziest States
> 30% obese < 63% active
10 Most Unhappy States
Adult Heart Disease Rate
10 Most Obese States 10 Laziest States
> 30% obese < 63% active
10 Most Unhappy States
Adult Heart Disease Rate
Figure 1. Adjusted Hazard Ratio of the Usual Percent of Calories from Added Sugar for CVD Mortality Among US Adults Aged >20 Years – NHANES Linked Mortality Files, 1988-2006
Histogram is the distribution of usual percent of calories from added sugar in population. Lines show the adjusted HRs from Cox models. Mid-value of quintile 1 (7.5%) was the reference standard. Model was adjusted for age, sex, race/ethnicity, educational attainment, smoking status, alcohol consumption, physical activity level, family history of CVD, antihypertensive medication use, health eating index score, body mass index, systolic blood pressure, total serum cholesterol and total calories. Solid line indicates point estimates ; dashed lines indicate 95% CIs. CVD indicates cardiovascular disease; HR, hazard ratio; NHANES, National Health and Nutrition Examination Survey.
Yang et al. JAMA Int. Med epub Nov 15, 2013
Hazard ratio for CV mortality based on percent calories as sugar
for US adult population, 1988-2006
Prevalence of diabetes, 2010
Romaguera-Bosch et al. Diabetologia 56:1520, 2013
SSBSSB’’s and BMIs and BMI--adjusted risk of diabetes in adjusted risk of diabetes in EPICEPIC--Interact (Europe)Interact (Europe)
An international longitudinal panel analysis of An international longitudinal panel analysis of diet and diabetesdiet and diabetes
Food and Agriculture Organization (FAO); FAOSTATFood Supply data in kcal/capita/day calculation: Food Supply= ∑Supply Elements - ∑Utilization Elements =
(Production + Import Quantity + Stock Variation – Export Quantity) - (Feed + Seed + Processing + Waste).
Only industrial waste factored in.
Extracted Food Supply data for 2000 and 2007:Total Calories Roots & Tubers, Pulses, Nuts, Vegetables Fruits-Excluding Wine MeatOils CerealsSugar, Sugarcrops & Sweeteners
International Diabetes Federation (IDF)2000 (1st ed) and 2007 (3rd ed)
The World Bank World Development Indicators Databas eGDP expressed in purchasing power parity in 2005 US dollars for
comparability among countries Basu et al. PLoS One, Feb 27, 2013
15
Total 204 countries; complete data for 154 countrie s (50 not different)
An international longitudinal panel analysis of An international longitudinal panel analysis of diet and diabetesdiet and diabetes
Basu et al. PLoS One, Feb 27, 2013
Total 204 countries; complete data for 154 countrie s (50 not different)
Data monitoring and quality
Generalized estimating equations
Conservative fixed effects approach (Hausman test)
Hazard model to control for selection bias (Heckman selection model)
Longitudinal data to determine what preceded diabetes (Granger causality)
Period effects controlled for secular trends that may have occurred as a
result of changes diabetes detection capacity or importation policies.
An international longitudinal panel analysis of An international longitudinal panel analysis of diet and diabetesdiet and diabetes
Basu et al. PLoS One, Feb 27, 2013
Total 204 countries; complete data for 154 countrie s (50 not different)
Data monitoring and quality
Generalized estimating equations
Conservative fixed effects approach (Hausman test)
Hazard model to control for selection bias (Heckman selection model)
Longitudinal data to determine what preceded diabetes (Granger causality)
Period effects controlled for secular trends that may have occurred as a
result of changes diabetes detection capacity or importation policies.
Controlled for:
GDP per capita % population living in urban areas
Obesity % of population over age 65
physical inactivity
An international longitudinal panel analysis of An international longitudinal panel analysis of diet and diabetesdiet and diabetes
Basu et al. PLoS One, Feb 27, 2013
An international longitudinal panel analysis of An international longitudinal panel analysis of diet and diabetesdiet and diabetes
Diabetes prevalence rose from 5.5% to 7.0% for 204 countries 2000-2007
Basu et al. PLoS One, Feb 27, 2013
An international longitudinal panel analysis of An international longitudinal panel analysis of diet and diabetesdiet and diabetes
Diabetes prevalence rose from 5.5% to 7.0% for 204 countries 2000-2007
Sugar
Sugar+controls
Sugar+controls+period
Overall
Model # countries Effect (95% CI)
Basu et al. PLoS One, Feb 27, 2013
An international longitudinal panel analysis of An international longitudinal panel analysis of diet and diabetesdiet and diabetes
Basu et al. PLoS One, Feb 27, 2013
16
An international longitudinal panel analysis of An international longitudinal panel analysis of diet and diabetesdiet and diabetes
Context
Only changes in sugar availability correlated with changes in diabetes prevalence
Every extra 150 calories increased diabetes prevalence by 0.1%
But if those 150 calories were a can of soda, diabetes prevalence
increased 11-fold, by 1.1%; p <0.001)
These data meet the criteria for Causal Medical Inference (Bradford Hill):
— dose — directionality
— duration — precedence
Controlled for many confounders; obesity exacerbated, but did not confound the effect
These data estimate that 25% of diabetes worldwide is explained by sugar
Basu et al. PLoS One, Feb 27, 2013
Limitations
Ecologic, not raw data analysis
Ecologic fallacy: inferences about individuals are based on aggregates
Could the sugar consumers and the diabetes be different people?
Food supply, not food consumption data (wastage, esp. in the U.S.?)
but wastage does not appear to be different based on different foods
And leaving the U.S. out of the analysis did not change the findings
Only one decade (but longitudinal time-series data, not 2 cross-sectional
points in time)
Not a complete dietary analysis
Different techniques used to screen for diabetes in different countries
Different diagnostic criteria for diabetes in different countries
Some countries used self-reported data; many diabetics are undiagnosed
Data includes both Type 1 and Type 2 diabetes
An international longitudinal panel analysis of An international longitudinal panel analysis of diet and diabetesdiet and diabetes
Basu et al. PLoS One, Feb 27, 2013
Foodstuffs and metabolic syndromeFoodstuffs and metabolic syndrome
•• TransfatsTransfats•• Branched chain amino acidsBranched chain amino acids•• EthanolEthanol•• FructoseFructose
•• Liver is the only site for energy metabolismLiver is the only site for energy metabolism•• Not insulin regulatedNot insulin regulated•• No glycogen popoff, mitochondria are overwhelmedNo glycogen popoff, mitochondria are overwhelmed
ROS
FRUCTOSE
ROS
ROS
Mitochondria
Peroxisome
ROS
UPR
Celldeath
FRUCTOSE
Acetyl-CoA
ROSATP
ROS
Cellular/metabolic
dysfunction
NH2
EndoplasmicReticulum
Acyl-CoA Lipiddroplet
pSer-IRS-1
PKCεεεε
JNK1
Insulin resistance
Fat deposition
Insulin Receptor
Toward a unifying hypothesis of metabolic syndromeToward a unifying hypothesis of metabolic syndrome
Bremer et al., Pediatrics 129:557. 2012
No drug targetNo drug target
•• Mitochondrial overload promotes lipogenesis, leading to Mitochondrial overload promotes lipogenesis, leading to hepatic insulin resistance, and metabolic syndromehepatic insulin resistance, and metabolic syndrome
•• Mitochondrial overload releases ROSMitochondrial overload releases ROS’’s, which lead to cell s, which lead to cell dysfunction, aging, and deathdysfunction, aging, and death
•• Only options are:Only options are:-- reduce substrate availability (diet)reduce substrate availability (diet)-- reduce hepatic flux (fiber)reduce hepatic flux (fiber)-- increase clearance (exercise)increase clearance (exercise)
Bremer et al., Pediatrics 129:557, 2012
Recognition at the Recognition at the American Heart AssociationAmerican Heart Association
Johnson et al. Circulation 120:1011, 2009
Recommends reduction in added sugar intake from 22 tsp/day to 9 tsp/day (males) and 6 tsp/day (females)
17
Philpott, Mother Jones 2012 (from Bureau of Labor Statistics)
How our food dollars have been reallocatedHow our food dollars have been reallocated
Question 1:Question 1:
Can our Can our ““toxic food environmenttoxic food environment”” be changed be changed
without government/societal intervention? without government/societal intervention?
Especially when there are potentially addictiveEspecially when there are potentially addictive
substances involved?substances involved?
Question 2:Question 2:
Can we afford to wait to enact public health measur esCan we afford to wait to enact public health measur es
when health care will be bankrupt due to when health care will be bankrupt due to
chronic metabolic disease?chronic metabolic disease? Nat Rev Gastroenterol Hepatol 7:251, 2010
J Am Diet Assoc 110:1305, 2010
Further readingFurther reading
Arterioscler Throm Vasc Biol 25:2451, 2005
Is fast food addictive?
Andrea K. Garber, Robert H. Lustig
Curr Drug Abuse Rev 4:146, 2011
Pediatric Annals 41:23, 2012
Further readingFurther reading
Arterioscler Throm Vasc Biol 25:2451, 2005
Andrew A. Bremer, M.D., Ph.D. a, Michele Mietus-Snyder, M.D. b, Robert H. Lustig, M.D. c*
Pediatrics 129:557, 2012
Nature 487:27, 2012
Advances in Nutrition 4:1, 2013
Annals NY Academy of Sciences, 1, 2013
Further readingFurther reading
18
PLoS One 8:e57873, 2013
Further readingFurther reading
Current Opinion Gastroenterology, 29:170, 2013
We have started a nonWe have started a non--profit to provide profit to provide
medical, nutritional and legal analysis and consult ationmedical, nutritional and legal analysis and consult ation
to promote personal and public health vs. Big Foodto promote personal and public health vs. Big Food
INSTITUTE FOR RESPONSIBLE NUTRITIONINSTITUTE FOR RESPONSIBLE NUTRITIONINSTITUTE FOR RESPONSIBLE NUTRITIONINSTITUTE FOR RESPONSIBLE NUTRITIONINSTITUTE FOR RESPONSIBLE NUTRITIONINSTITUTE FOR RESPONSIBLE NUTRITIONINSTITUTE FOR RESPONSIBLE NUTRITIONINSTITUTE FOR RESPONSIBLE NUTRITION
www.responsiblefoods.orgwww.responsiblefoods.orgwww.responsiblefoods.orgwww.responsiblefoods.orgwww.responsiblefoods.orgwww.responsiblefoods.orgwww.responsiblefoods.orgwww.responsiblefoods.org
Please let me know if you would like more informati on!Please let me know if you would like more informati on!
[email protected]@earthlink.net
UCSF Weight Assessment for Teen and Child Health (W ATCH)Andrea Garber, Ph.D., R.D.Kristine Madsen, M.D., M.P.H.Patrika Tsai, M.D., M.P.H.Stephanie Nguyen, M.D. M.A.S.Emily Perito, M.D.Jung Sub Lim, M.D., Ph.D.
UCSF Dept. of Epidemiology and BiostatisticsNancy Hills, M.S.
Touro University Dept. of BiochemistryJean-Marc Schwarz, Ph.D.
San Francisco General Hospital Depts. of Medicine a nd RadiologySanjay Basu, M.D., Ph.D.Susan Noworolski, Ph.D.Kathleen Mulligan, Ph.D.
UC Berkeley Dept. of Nutritional Sciences and Integ rative BiologyPat Crawford, R.D., Ph.D.Paula Yoffe, B.S.
Vanderbilt University Dept. of PediatricsAndrew Bremer, M.D., Ph.D.
Children’s National Medical CenterMichele Mietus-Snyder, M.D.
CollaboratorsCollaborators