Julio Licinio - An important role for SAHMRI
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Transcript of Julio Licinio - An important role for SAHMRI
Julio Licinio, MD, FRANZCP South Australian Health and Medical Research Institute
Flinders University, Adelaide, Australia
Depression and obesity A neglected clinical interface
An important role for SAHMRI
Promote integration
Not only across institutions
But also across medical disciplines
Psychiatric patients are often physically ill
Fragmentation between mind and body
Research-wise
Clinically
Consumers are therefore often not well served
Wasted resources due to fragmentation
An example of mind – brain clinical and
research disconnect
Depression and obesity
10 good reasons to study
depression and obesity together
1. The two disorders frequently co-exist
in the same patient
2. Depression can cause obesity
3. Antidepressants cause weight gain
4. Obesity can cause depression
5. Obesity treatments can cause
depression and suiciality
10 good reasons to study
depression and obesity together
6. Chronic stress can cause both.
7. Brain circuits that regulate mood also
regulate food intake.
8. Both obesity and depression
contribute to cardiovascular disease
9. Both contribute to diabetes
10. Having one of these two disorders
makes treatment of the other
particularly challenging
Depression and obesity
Obesity is far more intertwined with depression than
appreciated
Is depression a cause or outcome of obesity?
Are both caused by a chronically dysregulated stress
response?
Is long-term obesity the outcome of antidepressant
treatment or exposure?
Obesity is tightly related to the medical outcomes of
depression
Diabetes
Cardiovascular disease
Cancer
What is the relationship?
Which way do the arrows go?
Stress Depression Obesity
Stress Obesity Depression
Stress Depression Obesity
Stress Obesity Depression
Obesity Stress Depression
Contributors to the obesity epidemic
Food marketing and consumption practices
Reduction in physical activity
Infections
Epigenetics/genetic variations
Maternal age
Reproductive fitness (greater fecundity among people with higher adiposity)
Positive assortative mating (mates are phenotypically more similar)
Sleep debt
Endocrine disrupters (such as industrial chemicals)
Reduction in ambient temperature variations
Intrauterine and intergenerational effects
Drug-induced weight gain
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Chronic stress may contribute to obesity
The rate of
obesity is
escalating in
recent years.
Obesity Trends* Among U.S. Adults
BRFSS, 1985 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
Obesity Trends* Among U.S. Adults
BRFSS, 1986 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
Obesity Trends* Among U.S. Adults
BRFSS, 1987 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
Obesity Trends* Among U.S. Adults
BRFSS, 1988 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
Obesity Trends* Among U.S. Adults
BRFSS, 1989 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
Obesity Trends* Among U.S. Adults
BRFSS, 1990 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14%
Obesity Trends* Among U.S. Adults
BRFSS, 1991 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
Obesity Trends* Among U.S. Adults
BRFSS, 1992 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
Obesity Trends* Among U.S. Adults
BRFSS, 1993 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
Obesity Trends* Among U.S. Adults
BRFSS, 1994 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
Obesity Trends* Among U.S. Adults
BRFSS, 1995 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
Obesity Trends* Among U.S. Adults
BRFSS, 1996 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19%
Obesity Trends* Among U.S. Adults
BRFSS, 1997 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% ≥20%
Obesity Trends* Among U.S. Adults
BRFSS, 1998 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% ≥20%
Obesity Trends* Among U.S. Adults
BRFSS, 1999 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% ≥20%
Obesity Trends* Among U.S. Adults
BRFSS, 2000 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% ≥20%
Obesity Trends* Among U.S. Adults
BRFSS, 2001 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29%
(*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
Obesity Trends* Among U.S. Adults BRFSS, 2002
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29%
Obesity Trends* Among U.S. Adults
BRFSS, 2003 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29%
Obesity Trends* Among U.S. Adults
BRFSS, 2004 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29%
Obesity Trends* Among U.S. Adults
BRFSS, 2005 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Obesity Trends* Among U.S. Adults
BRFSS, 2006 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Obesity Trends* Among U.S. Adults
BRFSS, 2007 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Obesity Trends* Among U.S. Adults
BRFSS, 2008 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Obesity Trends* Among U.S. Adults
BRFSS, 2009 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Obesity Trends* Among U.S. Adults
BRFSS, 2010 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person)
No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%
Prevalence* of Self-Reported Obesity Among U.S. Adults BRFSS, 2011
*Prevalence reflects BRFSS methodological changes in 2011, and these estimates should not be compared to previous years.
15%–<20% 20%–<25% 25%–<30% 30%–<35% ≥35%
The rate of obesity is
escalating in recent years.
Are chronic stress and,
other factors that act in
conjunction with stress
increasing in recent years
to facilitate obesity?
Are the rates of
stress increasing
over time?
Adrenals
Kidney
Posterior Pituitary Gland
Hypothalamus
Anterior Pituitary Gland
ACTH
Stress Circadian
rhythm
CRH
(-)
Glucocorticoids, Catecholamines, etc..
Glucocorticoids, Catecholamines, etc..
Muscle: Net loss of amino Acids (glucose)
Liver: Deamination of
proteins into amino acids,
gluconeogenesis (glucose)
Fat Cells: Free fatty
acid mobilization
Heart rate: Increased
Immune system: altered
Hypothalamopituitary adrenal (HPA) axis
Atrophy of apical dendritic branches in CA3 pyramidal
neurons cognitive impairments
Control Restraint (6 hr/day, 21 days)
• Repeated stress or chronically elevated glucocorticoids can
cause atrophy of apical dendrites in CA3, and reduce
production of new granule cells in dentate gyrus.
• Following repeated stress, there can be less inhibition of the
PVN by the hippocampus. Magarinos and McEwen. Neuroscience (1995) Vol 69, p89-98
Stress and Health Long term effects of stress
Obesity
Heart disease
Immune deficiency
Decreased sexual interest
Growth problems
Reduced bone density
Reduced muscle mass
Blood sugar imbalances
Cancer
Caused by chronically high cortisol levels
How do people cope with
increased chronic stress
and depression?
Antidepressant use has escalated
Milane MS, Marc A.
Suchard MA, Licinio
J, Wong M-L.
PLoS Medicine
2006;3:816-824
Antidepressant use has escalated
Antidepressants and weight gain 2 million fluoxetine (Prozac®) prescriptions in the USA in 1988
164 million antidepressant prescriptions in USA 2009
12.3 million antidepressant scripts for 1.6 million Australians 2008
56% of 7,525 started on antidepressants in Europe stopped by themselves within 4 months
Populations exposed to antidepressants shifts over time
Lifetime prevalence of exposure to antidepressants is very high
Critical to understand complex interactions among the factors associated with increased risk of obesity, such as MDD
Antidepressant use
Highly prevalent high-fat diets.
Major gaps in our understanding include: Interactions that occur with antidepressant exposure in an obesogenic environment
Whether obesity in the context of antidepressant exposure is different from obesity that is related to chronic imbalance between energy intake and expenditure
Whether various antidepressants affect body weight differently
Current situation
Baseline of obesogenic food environment
Chronic stress High cortisol
Depression
Widespread antidepressant use
What are the metabolic consequences of this combination?
Impossible and unethical to test experimentally in individual people
A new animal paradigm.
The SAD paradigm
stress – antidepressant – diet
The SAD paradigm
stress – antidepressant – diet
Chronic repeated stress elicits behavioral features of depression
Depression-like behaviors improve with short-term antidepressants
• Work of Suhyun Lee,
PhD student in our lab
• OP: Obesity prone animals
• Top tercile of weight gain
• Easier to see weight-related
outcome differences
• Exposure to antidepressants
leads to long-term weight
gain
• Provides materials for search
for mechanisms
0 100 200 300 400
200
300
400
500
600
NRCF
R-C
R-FX
R-IM
Day
Bod
y w
eigh
t (g)
NRCF
R-C
R-F
XR-IM
0
1
2
3
**
**
Comparison of average IGF-I/GAPDHgene expression between thetreatment. One tailed t-test: *, p<0.05;
**, p<0.01. NRCF: n=25, R-C: n=9,
R-FX: n=12, R-IM: n=13.
Treatment
Avg
.Rati
o IG
F-I
/GA
PD
H
IGF-1
IGF-1
BDNF
NGF
GH
antidepressant
IGF-1 gene expression
There is a difference in IGF-1 in stressed versus non-stressed
Fat-diet weight gain: lower IGF-1 levels
Stress/antidepressant+fat diet: higher IGF-1 Note that stressed, non-antidepressant treated rats
were studied 10 months after stress (recovery period)
• Weight gain after termination of drug use
plus ongoing high fat diet
• Exposure to antidepressants leads to
long-term weight gain (with high fat diet)
• An epigenetic effect?
• Antidepressants are the second
bestselling drug class
• Lifetime antidepressant exposure:
A contributor to the obesity epidemic?
Antidepressants and weight gain
The spectrum of gene-environmental interactions
in the causation of obesity
Predominance of
nutritional
environment Predominance of
psychological
environment
Predominance of
genetic factors
Within SAHMRI’s Mind & Brain we will:
Foster new talent
Conduct groundbreaking research
Promote integration of efforts across academic,
clinical, and research groups in the city, state, as well
as nationally
Quarterly workshops for stakeholders
Closely collaborate in our research efforts with
those in other themes and areas
Translate results to improved healthcare in SA.