SUSCEPTIBILIDAD GENÉTICA A LA OBESIDAD · KEL LDLR 255 Mb Obq2 ACP1 APO B GCKR POMC PRKAR2 UCP2/3...

SUSCEPTIBILIDAD GENÉTICA

A LA OBESIDAD

Prof. J. Alfredo Martínez

BMR

Physical Activity

Thermogenesis

ENERGY BALANCE

Balance E. = Energy Intake - E Expenditure.

Foods

Drinks fat?

Obesity? Genétics/Lifestyles?

INTAKE NEEDS

Energy Balance

Efferent Signals Afferent Signals

CNS

Neuropeptides

Genétics / habits / neuroendocrine status

mins VITs R

estr

uctu

re

Genética

Introducción

EXCESO DE GRASA

CORPORAL

OBESIDAD

DEFINICIÓN DE OBESIDAD

Fact. ambientales

Estilo de vida

+ + +

INGESTA

ENERGÉTICA GASTO

ENERGÉTICO

Fact.

neuroendocrinos

- -

ADIPOSIDAD

Ácidos grasos

Síndrome metabólico

Intolerancia a la

glucosa

Diabetes mellitus tipo II

Inflamación

Hipertensión

Intolerancia glucosa

Aterosclerosis

Resistencia a

la insulina

Disfunción

endotelial

Introducción

DNA RNA PROTEINS

Transcription Translation

Gene Expression= f(DNA Environmental factors)

GENOTYPE PHENOTYPE

Regulatory Elements Estructural Functions

Proteíns

NUTRIENTS FUNCTIONS

Energy

Nutrients

D:/WINNT/Profiles/Alfredo/Escritorio/SENPE/3-TODO.gif

GENE EXPRESSION NUTRITIONAL CONTROL

Fatty Acids

Retinoids

Vitamin D

Glucose

Energy

Transcriptión mRNA DNA Proteíns Translation

Amino ácids

Iron

Selenium

Postranslation

Minerals

Vitamins

D:/ALFREDO/DIAPOSITIVAS/transcription_startani-mov.gif

D:/WINNT/Profiles/Alfredo/Escritorio/SENPE/2-TRADUCCION.gif

D:/ALFREDO/DIAPOSITIVAS/1-TRANSCRIPCION.gif

D:/ALFREDO/DIAPOSITIVAS/2-TRADUCCION.gif

GENE EXPRESSION CONTROL: MECHANISMS

Cascade

Kinases

Mechanisms : - Receptors (membrane/nucleus)

Nutrients

Nutrients

Nutrients &

Metabolites

- Metabolism intermediate

FT

- Transcription Factors (FT): affinity & concentrations

RE

Mendelian Syndromes

• Autosomal dominant

• Autosómico recessive

•X-linked

Animal models

•Genetically obese animals

•Transgénic Animals

• Q Trait Loci (QTL)

Association and Linkage Studies

• Candidate genes

• Family segregatiobn

GENE OBESITY RESEARCH

GENETIC LINKAGE

Phenotype

Framingham Canada Norway

BMI BMI/Skinfolds BMI

n r2

n r2

n r2

Spouses 1163 0,19 3138 0,12 23936 0,12

Parent/child 4027 0,23 7194 0,20 43586 0,20

Sibblings 992 0,28 3924 0,34 19157 0,24

MENDELIAN SYNDROMES

MIM number Disorder Locus

Autosomal Dominant

100800 Achondroplasia (ACH) 4p16.3

122000 Posterior Polymorphous Corneal Dystrophy (PPCD) 20q11

176270 Prader-Willi Syndrome (PWS) 15q11.2-q12

181450 Schinzel syndrome/Ulnar-mammary syndrome (UMS) 12q23-q24.1

Autosomal Recessive

Bardet-Biedl Syndrome (BBS)

209901 BBS1 11q13

209900 BBS2 16q21

600151 BBS3 3p13-p12

600374 BBS4 15q22.3-q23

216550 Cohen Syndrome (CHS1)

X-LINKED

301900 Borjeson-Forssman-Lehman Syndrome (BFLS) Xq26-q27

303110 Choroideremia with deafness and obesity Xq21

309585 Wilson-Turner Syndrome (WTS) Xq21.1-q22

312870 Simpson-Golabi-Behmel (SGBS) Xq26

PERSONALIZED NUTRITION ? YES, PLEASE

Genetic make-up can determine

unique nutritional requirements

and responses to different

foods.and nutrients

Based on:

- The sequencing of the human genome,

- subsequent analyses of human genetic variation,

-studies that associate gene variants with disease markers

-Impact of nutrition/nutrients on gene expression

PREDICTOR FACTORS FOR CHILDHOOD OBESITY

Ochoa, et al. 2006

0,94

1,74

OR

2,02

Physical

Activity

Sugar,

Sweetened

Beverages

Watching

TV

Family

History

4,18

1

3

4

2

5

Trp64Arg MUTATION OF THE 3

ADRENOCEPTOR

0,83

0,84

0,85

0,86

0,87

0,88

0,89

0,9

0,91

0,92

Un

its

CORBALAN, et al. 2000

0,86

0,90

C.R.

0,87

0,91

TT (-) TA+AA (+)

C/C

PPARg2 x ADR3 INTERACTIONS

0

1

2

3

4

5

6

(-) PPARү (+) PPARү+ADRβ3 (+)

OR

Carriers Ochoa, et al. 2004

Data acquisition

OMICS & GWAS

Sequentiation microarrays

Nontransmissible

Genetic

Cultural

transmission

Genotype Phenotype (Obesity)

Nontransmissible

Cultural

transmission Genetic

45%

Nontransmissible

30% Cultural

transmission

25% Genetic

Nutrition

P. Activity

Hormones

Nervous System

Homeostasis

Environment

GENETICS

HOMEOSTASIS: CAUSAL INTERACTIONS

• ADIPOGENESIS: PPAR, C/EBP5, PKA, AP2,, RXR,...

• THERMOGENESIS: 3AR, 2AR, UCPS,...

• APPETITE: NPY, LEP, CCK, MC4R, POMC, OREX,..

•OTHERS:. FTO, IL6

METABOLISM RELATED GENES

Do1

LEPR

HS

3B1

ATP1A2

D1S202

Nidd/gk6

1 4 6 7 8

155 Mb

bw/gk1 Mob-4

CHS1

ADRB3

LPL

Do6

9 10 11

UMS

12

ESD

2 3 5

236 Mb

PigQTL

ACH

UCP 1

Do6

CPE

203 Mb 183 Mb

TNFir24

Do2

GLO1

Obq2

171Mb

Mob-2 LEP

Do3

145 Mb 144 Mb

Mob-1

Niddm1

APOA4

Nidd/gk5

144 Mb

DRD2

BBS1

SURTUB

Mob-1

Obq1 D11S419

143 Mb

Weight 1

85Mb

114 Mb

13 18

109 Mb

14

Mob-3

Mob-1

98 Mb

BBS4

16 17

92 Mb

Niddm3

19

67 Mb

Do6

Obq1

20

72 Mb

PPCD

Mob-5 ADA

ASIP

21

50 Mb

22

56 Mb

P1 bw/gk1

28 Mb

Y Bw2

Choroideremia

WTS

SGBS

BFLS

X

164 Mb

Bw3

Bw1

KEL

LDLR

255 Mb

Obq2

ACP1

APO B

GCKR

POMC

PRKAR2 UCP2/3

MC4/5R

106 Mb

15

Do2

PWS

BBS4

LIPC

214 Mb

Nidd/gk1

BBS3

APOD

PPAR

194 Mb

GRL

Mob-4

PC1

LATEST: UCP, POMC, LEP, AR, PPAR, AP2, TNF,...

THE HUMAN OBESITY GENE MAP

TOOLS: Microchip Scanning Macronutrient Metabolism

Fold Code Name

~49.2 D45862 Leptin, ob

15.7 J02773 Low molecular weight FABP

7.3 J00713 Carboxypeptidase-a- 5 ~6.7 U64451 Short-branched chain acyl-CoA DH precursor ~4.9 AF063302 Carnitine palmitoyltransferase I beta

~4.6 AF034577 Pyruvate dehydrogenase kinase isoenzyme 4 4.1 M95591 Squalene synthetase 3.3 S69874 Fatty acid-binding protein (FABP) 3.2 K03249 Enoyl-CoA-hydratase-3-hydroxyacyl-CoA DH 3 AB002558 Glycerol 3-phosphate dehydrogenase 2.9 AB005743 Fatty acid transporter 2.8 L07114 Apolipoprotein B 2.5 U20643 Aldolase A

2.5 M26594 Malic enzyme 2.4 M60322 Aldose reductase 2.2 S56481 Beta 3-adrenergic receptor 2.2 S81497 Lysosomal acid lipase 2.2 J02585 Liver stearyl-CoA desaturase 2.2 X15580 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 2.2 L12016 Tricarboxylate transport protein 2.2 U32314 Pyruvate carboxylase

2.2 D10354 Alanine aminotransferase

2.2 L25331 Lysyl hydroxylase 2.1 D43623 Carnitine palmitoyltransferase I like protein 2 D10655 Dihydrolipoamide acetyltransferase

2 AF035943 Uncoupling protein-3

Transcription factor

3.7 AB015724 Nuclear receptor binding factor-1

2.8 X12752 DNA binding protein C/EBP 2.5 S77528 C/EBP-related transcription factor 2.1 AB011365 PPAR-gamma protein 2 AF022081 Small nuclear RING finger protein 2 X13167 NF-1 like DNA-binding protein

Hormone receptor and signal transduction

~10.7 M96159 Adenylyl cyclase type V

4 S79241 Oxytocin receptor 3.6 U93880 Insulin receptor substrate-3 (IRS-3) 3 K03045 Retinol-binding protein (RBP) 2.9 D38036 Truncated TSH receptor 2.9 Z83757 Growth hormone receptor

2 X92069 P2X5 receptor (ATP-gated ion channels) 2.8 E12286 GM2 activator protein

2.7 X17053 Immediate-early serum-responsive JE

2.6 S74351 Protein tyrosine phosphatase 2.6 X06107 Insulin-like growth factor I 2.5 M64300 Signal-related kinase (ERK2) 2.4 D85183 SHPS-1 (protein tyrosine phosphatase)

2.4 L13619 Insulin-induced growth-respons protein 2.4 L35767 Very low density lipoprotein receptor, 2.3 S49003 Short isoform growth hormone receptor

2.3 D85435 Protein kinase C delta-bindig protein 2.2 D89655 Scavenger receptor class B

2.2 U21101 Cyclic GMP stimulated phosphodiesterase 2.2 J03819 Thyroid (T3) hormone receptor 2 AF022952 Vascular endothelial growth factor B 2 S50461 Signal-transducing G protein alpha 12 subunit 2 L25633 Neuroendocrine-specific protein 2 M12492 Type II cAMP-dependent PK regulatory subunit

Cellular cytoskeleton

~4.9 K00512 Myelin basic protein

~4.0 AF004811 Moesin 3.5 X60351 Alpha B-crystallin

3.1 AF041373 Clathrin assembly protein short form 2.7 U50717 Synaptic density protein PSD-93 2.5 M83196 Microtubule-associated protein 1A

corresponds to a transcript absent in the control group (basal line).

Macronutrient Metabolism

Fold Code Name

-22.5 AF001898 Aldehyde dehydrogenase (ALDH)

-22.1 AB009999 CDP-diacylglycerol synthase

~-20.0 AB017260 High-affinity carnitine transporter -19.0 D37920 Squalene epoxidase

-9.1 L25387 Phosphofructokinase C

~-6.4 AB010428 Acyl-CoA hydrolase

-6.0 S68135 GLUT1

-3.8 M18467 Aspartate aminotransferase

-2.9 AF080468 Glycogen storage disease type 1b protein

-2.8 S49760 Diacylglycerol kinase

-2.6 X04979 Apolipoprotein E

-2.4 M93297 Ornithine aminotransferase

-2.2 L07736 Carnitine palmitoyl-transferase I

Redox and stress proteins

-7.8-34.1 S82820 Glutathione S-transferase Yc2 subunit -4.8-6.2 X62660 Glutathione S-transferase subunit 8

-4.2 M11794 Metallothionein-2 and metallothionein-1

-2.6-3.2 X02904 Glutathione S-transferase P subunit

Transcription factor

-25.2 U78102 Krox20 ó EGR-2(early growth response protein 2)

-5.2 X94246 Pax-8 protein

-2.7 M91802 Homeobox protein (Hox 1.11)

Hormone and signal transduction

-117.5 S49491 Proenkephalin ~-92.3 J04488 Prostaglandin D synthetase

~-47.3 D63772 Neuronal high affinity glutamate transporter

~-8.6 M12450 Vitamin D binding protein

-4.1 U57715 FGF receptor activating protein FRAG1

-2.8 U48596 MAP kinase kinase kinase 1 (MEKK1)

-2.3 L06096 Inositol trisphosphate receptor subtype 3 (IP3R-3)

-2.3 U53184 Estrogen-responsive uterine -2.3 X59132 Secretin receptor

-2.2 D64045 Phosphatidylinositol 3-kinase p85 alpha subunit

-2.1 AF014009 Acidic calcium-independent phospholipase A2

-2 M91599 Fibroblast growth factor receptor subtype 4

Cellular cytoskeleton

~-21.4 X81448 Keratin 18

~-12.2 M93638 Keratin 5

~-6.6 AF013247 Beta-A4 crystallin

~-3.8 M59936 Connexin-31

-3.2 X67788 Ezrin p81

-2.3 X81449 Keratin 19

corresponds to a transcript absent in the obese group (basal line).

+ -

NUTRIGENOMICS: Personalized nutrition based on genotype.

?

Nutrigenomics is the study of molecular relationships between nutrition and the response

of genes, with the aim of extrapolating how such subtle changes can affect human health .

http://en.wikipedia.org/wiki/Nutrition

http://en.wikipedia.org/wiki/Gene

http://en.wikipedia.org/wiki/Human_health

GENOTYPE & NUTRITION INTERACTIONS

Phenotype GENE

Expression Nutrition

0

0,5

1

1,5

2

2,5

3

Re

lati

ve

Un

its

UCP3

Control (30 d)

Cafetería (30 d)

**

PPARg2

**

DIET DEPENDENT GENE EXPRESSION

aP2

Margareto, et al. 2001

**

PPARg2

0

0,25

0,5

0,75

1

1,25

rela

tive

un

its

UCP3

Control (8 d)

Cafetería (8 d)

DIET DEPENDENT GENE EXPRESSION

aP2

**

Margareto, et al. 2001

mRNA LEVELS AFTER ENERGY

RESTRICTION

-40

-30

-20

-10

0

10

20

30

40

Leptin HSL UCP2 PGC-1 PAARg2

%

Viguerie, et al. 2005

FAT INTAKE AND mRNA LEVELS AFTER

ENERGY RESTRICTION

Viguerie, et al. 2005

-40

-30

-20

-10

0

10

20

30

40

% high-fat

low-fat

Leptin HSL UCP2 PGC-1 PAARg2

Genotype

Nutrition

Gene

Expression


Phenotype

Nutrition

Status

BMI, FAT INTAKE AND GENOTYPE

INTERACTIONS

-2.5

-1.5

-0.5

0.5

1.5

2.5

3.5

4.5

5.5

6.5

7.5

<2,1 2.3 2.6 2.9 3.1 3.3 3.6 3.9 4.2 >4,2

Ch

an

ge

s in

BM

I

Fat intake (MJ)

Ow/Op Ow=overweight at baseline Ow/Np Nw=normal weight at baseline

Nw/Op Op=obese parents Nw/Np Np=normal weight parents

SORENSEN, et al. 1997


Corbalan, et al. 2002

30

31

32

33

34

35

36

37

38

39

40

<49% E CHO >49% E

CHO

Mut (-)

Mut (+)

BMI

31,5

35,3

33,4

32,4

1 2 3 4 5

Energy Derived from fat (Quintiles)

0

1

2

3

4

5

6

Od

ds

Rati

o

GENOTYPE X NUTRIENT (FAT) INTERACTIONS

Memisoglu, et al. 2003

Non carriers

Carriers (Pro12Ala)

Luan, et al. 2001

>0,66

24

25

26

27

Mcan

BM

I (K

g/m

3)

<0,39 <0,51 <0,66

Quartiles of P:S ratio

Pro/Pro

Ala carriers


APPROACHES AGAINST OBESITY

Nutritional and Diet Theraphy

Drugs Treatments

Physical Activity Programmes

Bariatric Surgery

Others: Personalized.........

....................................Nutrition

POLYMORPHISM IN THE APOLIPOPROTEIN A5 GENE

Aberle, et al. 2005

BMI

(Kg/m2)

23

24

25

26

29

28

27

30

TT

C allele carriers

0 6 12

Time (weeks)

29,1

26,5

25

27,9

28,1 27,8

PERILIPIN GENE AND WEIGHT LOSS

Corella, et al. 2005

10

7,70

GG

0,97

AA

6

2

8

4

Weight

Loss

(%)

LIPC – 514>CT POLYMORPHISM AND FIBRE

INTAKE (TERTILES)

Santos, et al. 2006

1

0,5

0.5

OR

0,5

First

Tertil

Third

Tertil

Second

Tertil

1

UCP3 HAPLOTYPES IMPACT ON WEIGHT LOSS

Cha, et al. 2006

4,3

5,6 6

2

8

4

Weight

Loss

(Kg)

4,7

C/C R/R C/R

PPAR x ADRB2 interaction

E. L. Rosado, et al. 2006

10

5,65

Pro 12 Pro

Glu 27 Glu

8

Pro 12 Ala

Gln 27 Glu

6

2

8

4

Weight

Loss

(Kg)

ADIPOQ – 11377 C>G POLIMORPHISM INFLUENCE ON

WEIGHT LOSS

Sorensen, et al. 2006

1,2

Low fat

1

High-fat

Weight

Loss

(Kg)

Heterozygous Homozygous

100

70

75

80

85

90

95

0 1 2 3 4 5 6 7 8 9 10 11 12

Trp64Arg 3AR

No mutationes A->G UCP-1

Trp64Arg

3AR

And A->G

UCP-1

FOGELHOLM, M. et al. 1998

WEIGHT CHANGES AND GENOTYPE

BODY WEIGHT REDUCTION AND GENETIC VARIANTS:

IL-6 AND PPAR-G2

Goyenechea, et al. 2006

96

Baseline

(day0) Endpoint

(day 70)

92

88

94

90

Body

Weight

(Kg.)

86

98

100

102

1 year after

LED (week 62)

* +

C+Ala-

C+Ala+

C-Ala-

GPS * Energy intake interaction

P= 0.0020

15

20

25

30

35

Pe

rcen

tage

of bo

dy fa

t

1000 2000 3000 4000

TOTAL ENERGY INTAKE

low GPS (<=7 risk alleles) high GPS (>7 risk alleles)

Ajustado por sexo, edad y factor de actividad física

GPS * Protein intake interaction

P= 0.0027

15

20

25

30

Pe

rcen

tage

of bo

dy fa

t

0 50 100 150 200

PROTEIN intake


P= 0.0316

20

22

24

26

28

30

Pe

rcen

tage

of bo

dy fa

t

0 50 100 150 200

ANIMAL PROTEIN intake


P= 0.0034

20

21

22

23

24

25

Pe

rcen

tage

of bo

dy fa

t

10 20 30 40 50 60

VEGETABLE PROTEIN intake


Ajustado por sexo, edad, factor de actividad física e ingesta energética

GPS * Fat intake interaction

P= 0.0292

20

22

24

26

Pe

rcen

tage

of bo

dy fa

t

0 50 100 150 200

FAT intake


P= 0.0151

22

23

24

25

26

Pe

rcen

tage

of bo

dy fa

t

10 20 30 40 50

AGS intake


P= 0.0024

18

20

22

24

26

Pe

rcen

tage

of bo

dy fa

t

0 10 20 30 40

AGP intake



GPS * CHO-Fibre intake interaction

P= 0.0078

21

22

23

24

25

26

Pe

rcen

tage

of bo

dy fa

t

0 100 200 300 400 500

CARBOHYDRATES intake


P= 0.0304

21

22

23

24

25

26

Pe

rcen

tage

of bo

dy fa

t

0 100 200 300

COMPLEX CARBOHYDRATES intake


P= 0.0394

18

20

22

24

26

Pe

rcen

tage

of bo

dy fa

t

10 20 30 40 50 60

FIBER intake



SCORE UNAV

SNP polimorfismo

01 APOA5 (rs662799) g.4430 T>C

02 APOB (rs5742904) c.10580 G>A

03 APOA1 (rs670) g.4926 G>A

04 ESR1 (rs2234693) c.453-387 T>C

05 FTO (rs9939609) c.46-23525 T>A

06 GC (rs2282679) c.*26-796 A>C

07 GCKR (rs1260326) c.1337 C>T

08 GNB3 (rs5443) c. 825 C>T

09 MTNR1B (rs10830963) c.223+5596 C>G

10 MC4R (rs17782313) g.5785109 T>C

11 LPL (rs328) c.1421 C>G

12 LIPG (rs4939883) g.47167214 C>T

13 CETP (rs1800777) c.1403 G>A

14 LIPC (rs1800588) g.4501 C>T

15 NOS3 (rs1799983) c.894 G>T

16 PLIN4 (rs894160) c.772-799 G>A

17 PPARA (rs1800206) c.484 C>G

18 PPARG (rs1801282) c.34 C>G

19 CELSR2 (rs12740374) c.*919 G>T

20 MTHFR (rs1801133) c.665 C>T

21* LCT/MCM6 (rs4988235) c.1917+326 T>C

23 APOE (rs429358) c.388 T>C

24 APOE (rs7412) c.526 C>T

Gen

SNP

SNP

(Alternative

nomenclature)

GPS based on computing risk alleles

Gen SNP Genotipo Score/SNP Score/patolog (minor-minor) (major-minor) (major-major) Major allele (1)Minor allele (2)

OBESIDAD

FTO rs9939609 AA 0,6 4,1 0,6 0,3 0 T A

Prevalencia 15 15 60 25

MC4R rs17782313 CC 2,5 2,5 2 0 T C


MTHFR rs1801133 CC 0 0,2 0 0 C T


DIABETES

FTO rs9939609 AA 2 3,7 2 0,6 0 T A


PPARA rs1800206 CC 0 0,3 0,2 0 C G


PPARG rs1801282 CG 0,3 0,3 0,2 0 C G


MTNR1B rs10830963 CC 0 0,3 0,1 0 C G


GNB3 rs5443 TT 0,4 0,4 0,2 0 C T


HIPERTENSIÓN

MTHFR rs1801133 CC 0 1,4 0,2 0,1 0 C T


NOS3 rs1799983 CT 0 0,2 0 0 C T


GNB3 rs5443 TT 0,4 0,4 0,1 0 C T


INTOLERANCIA A LA LACTOSA

LCT rs4988235 TT -4 -5 4 0 -4 T C


(C;C) 4x lactose intolerance (C;T) 0x lactose intolerance

(T;T) -4x lactose tolerance

SCORE UNAV

Gen SNP Major allele (1)Minor allele (2)INTERACCIONES

APOA1 rs670 G A Si GG (3) Si su dieta es rica en grasas o si su ingesta de grasas monoinsaturadas, como el aceite de oliva, es muy elevada, usted tiene mayor predisposición que la mayoría de la población para desarrollar diabetes tipo 2, obesidad e hipertensión. Le recomendamos una dieta algo más baja en grasas para prevenir estas posibles consecuencias.

APOA1 rs670 G A Si AA (1) Una dieta rica en ácidos grasos de tipo poliinsaturado es especialmente beneficiosa en su caso para incrementar los niveles de colesterol HDL, protectores frente a la enfermedad cardiovascular. Le recomendamos una dieta rica en pescado azul y con presencia de frutos secos, especialmente nueces o avellanas.

LIPC rs1800588 C T Si TT (1) Una dieta rica en grasas monoinsaturadas le ayudará a reducir sus niveles de colesterol LDL y, por lo tanto, el riesgo cardiovascular. Le recomendamos que una parte importante de su ingesta de grasas provenga de aceite de oliva o bien de girasol alto oleico. Algunos frutos secos como avellanas y nueces de macadamia, o el aguacate, también son ricos en este tipo de grasas.

MTHFR rs1801133 C T Si TT (1) Si la ingesta de ácido fólico no es elevada, usted tiene más riesgo que la mayoría de la población de presentar niveles elevados de homocisteína. Este metabolito está implicado en un aumento del riesgo a sufrir enferemedad cardiovascular. Por lo tanto, le recomendamos una dieta rica en ácido fólico (legumbres, verduras y frutas). Además, le puede ayudar que una parte importante de las grasas que consuma sean de tipo poliinsaturado n-3, abundante en el pescado azul y las nueces.

NOS3 rs1799983 C T Si TT (1) Los ácidos grasos de tipo n-3 son particularmente beneficiosos en su caso para mantener bajo control el riesgo de sufrir enfermedad cardiovascular y sus niveles de lípidos en sangre. Le recomendamos una dieta en la que abunde el pescado azul y las nueces.

PLIN rs894160 G A Si AA (1) Una dieta rica en grasas saturadas (de origen animal, en especial carnes grasas y lácteos enteros, y también en productos de bollería) y pobre en hidratos de carbono (pasta, arroz, pan) aumenta su predisposición a desarrollar resitencia a la insulina y diabetes de tipo 2. Le recomendamos que no abuse del consumo de grasas saturadas.

PPARA rs1800206 C G Si GG (1) Si su dieta es rica en grasas y especialmente en grasas saturadas (carnes grasas y lácteos enteros), su predisposición a sufrir enfermedad cardiovascular aumenta más que en el resto de la población. Le recomendamos que en su dieta no abunden este tipo de alimentos. Además, si su dieta es pobre en ácidos grasos polinsaturados aumenta su predisposición a desarrollar hipercolesterolemia. Le recomendamos que incluya en su dieta habitual los aceites de semillas (girasol, soja), el pescado azul y los frutos secos (en especial nueces).

PPARG rs1801282 C G Si GG (1) Si su dieta es pobre en ácidos grasos monoinsaturados (en especial aceite de oliva o girasol alto oléico, pero también frutos secos) y rica en grasas saturadas (carnes grasas y lacteos enteros) tiene mayor predisposición que la mayoría de la población a desarrollar resistencia a la insulina y diabetes tipo 2. Le recomendamos que incremente la proporción de grasas monoinsaturadas en su dieta en detrimento de las saturadas.

SCORE UNAV

GENE-DIET INTERACTIONS

Si su dieta es rica en grasas o si su ingesta de grasas monoinsaturadas, como el aceite de oliva, es muy elevada, usted

tiene mayor predisposición que la mayoría de la población para desarrollar diabetes tipo 2, obesidad e hipertensión.

Le recomendamos una dieta algo más baja en grasas para prevenir estas posibles consecuencias.

Nutriepigenetics

Epigenetic processes modulate gene activity and expression

without changes in the DNA nucleotide sequence

../../Escritorio/Epigenetics.mp4

DNA Methylation and Histone modification. (From Molecular Development - Epigenetics by

Dr Mark Hill.)

Epigenetic modifications

http://www.youtube.com/watch?v=eYrQ0EhVCYA&NR=1

- DNA methylation

Addition or removal of a methyl group (CH3), predominantly where cytosine is

followed by a guanine (CpGs).

-Post-translational modifications on N-terminal tails of histones,

phosphorylation, sumoylation, ubiquitination, acetylation, and methylation

These modifications alter chromatin structure to influence gene expression.

In general, tightly folded chromatin (heterochromatin) tends to be

shut down, or not expressed, while more open Chromatin (euchromatin) is

functional, or expressed.

-Other mechanims

Gene expression maybe modified by miRNA, transposons, etc

Epigenetic Phenomena

Epigenetic modifications

Interactions of Foods/Nutrients & Epigenetic Genes modulate metabolism

Journal of

Physioligy and Biochemistry

8 weeks of Low Calorie Diet

(n=6)

(n=21)

(n=27)

Subcutaneous Adipose Tissue

(SAT)

SAT

Blood

Blood

Low vs. High responders before diet

Epigenetic Biomarkers In Weight Loss

Cordero P et al. J Physiol Biochem (2011) 67:463-70

LEPTIN

TNFa

0

10

20

30

40

50

60

70

Leptina TNF-alfa

Respuesta

No respuesta

*

TBASELINE methylation %

LEPTIN TNFa

Responders

No Responders

Ladder

sample 1 sample 2

met

unmet unmet

met

MSP



8 weeks of hypocaloric diet

Before

After

Illumina Microarray

Milagro FI et al. FASEB J (2011)


Microarray Changes by the diet

70 CpG hypermrth

100 CpG hypometh

Before vs. After diet

(20 % variation p < 0.05)

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

APOA5 PTEN GNAS H19 IL26

Inicial

Final

***

***

***

***

***

(% methylation)


Methylation changes induced by an energy restricted diet

Milagro FI et al. FASEB J (2011).

602 CpG hypermethylation

432 CpG hypomethylation


(>20 % variation, p < 0.05)

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

IL10 AR GNAS SIRT1 CASP8

Respuesta

No respuesta

***

***

***

***

***

(% methylation)




Differential DNA methylation patterns between high and low responders to a weight loss intervention in overweight or obese adolescents: the EVASYON study

Martí et al FASEB J. 2013

TNF-alpha promoter methylation in peripheral white blood cells: Relationship with circulating TNFa, truncal fat and

n-6 PUFA intake in young women.

CLOCK, PER2 and BMAL1 DNA methylation: association with obesity and metabolic

syndrome characteristics and monounsaturated fat intake.

•Milagro et al Chronobiol Int. 2012 Nov;29(9):1180-94

CLOCK, PER2 and BMAL1 DNA methylation: association with obesity and metabolic syndrome

characteristics and monounsaturated fat intake.

•Milagro et al Chronobiol Int. 2012 Nov;29(9):1180-94

•FIGURE 4.

•Partial correlations between the percentages of methylation of CLOCK CpG 1 and CLOCK CpG 8 with the percentages of MUFA (3A) and PUFA (3B) in the diet, after adjusting total fat intake by the BMI of each subject.

High-Throughput Sequencing of microRNAs in Peripheral Blood Mononuclear Cells: Identification of Potential Weight Loss Biomarkers

Martínez et al PLoS One. 2013;8(1)

PROGRAMACIÓN PERINATAL Y ENF. CRÓNICAS

NUTRITION, PERINATAL PROGRAMING AND DISEASE

Genetics Genomics Epigenetics

Nutrition and Health

San Sebastián/Donostia 2009

But genetics/epigenetcs is only the tip of the iceberg

Conclussion?

Rather than there being an „optimal‟ human diet, there are a range of adequate

diets which depend upon individual biological and cultural variation.

EPIGENETIC MARKS GENETIC BACKGROUND

PHYSICAL ACTIVITY

LIKES AND DISLIKES ALLERGIES AND INTOLERANCES

FAMILY HISTORY

PREVIOUS DISEASES CULTURE

Precision diet

PERSONALIZED NUTRITION

Conclussions

Genoma: 30.000 genes (millions polymorphisms)

GENOME

Gene expression = f (DNA environmental factors )

Applications: - Personalized nutrient requirements

- diagnoses of metabolic diseases

- Nutritional prescriptions (Prevention/therapy)

D:/ALFREDO/DIAPOSITIVAS/3-TODO.gif

D:/ALFREDO/DIAPOSITIVAS/3-TODO.gif

PERSONALIZED WEIGHT MANAGEMENT

INDIVIDUALIZED NUTRITION

•Buenos Aires, Argentina, 20-25 October 2017

Personalized Medicine and Nutrition

Genetic make up can determine

unique nutritional requirements

and responses to different foods.

Based on:

- The sequencing of the human genome,

- subsequent analyses of human genetic variation,

-studies that associate gene variants with disease markers

-Impact of nutrition/nutrients on gene expression

0 25 50 75 1000.0

2.5

5.0

7.5

10.0

12.5 Control Diet

HighFat Diet

r=-0.899

p=0.015

r=-0.400

p=0.505

-443 CpG Methylation (%)

Lepti

n (

ng/m

l)

0 25 50 75 100300

400

500

600 Control Diet

HighFat Diet

r=-0.841

p=0.036

r=-0.400

p=0.505

Total Methylation (%)

Fin

al

Body W

eig

ht

(g)

A B

Association between circulating leptin and methylation of the -443 CpG (A),

and between the total methylation of the distal island and rat final body weights (B).

High-fat diet-induced obesity in rodents is able to enhance the methylation

pattern of the leptin promoter in adipocytes.

Those subjects more methylated in the HF diet, showed lower leptin levels

and lower body weight.

DIET MODIFIES DNA METHYLATION

J Physiol Biochem 2009; 65: 1-8

Wonders:

1- How the dietary-induced epigenetic marks could be inherited and influence

the obesity susceptibility and metabolic alterations of the offspring ?.

2- How the diet could influence the epigenetic pattern (DNA methylation)

of gene promoters and epigenetics could be concerned with obesity ?.

3- How the epigenetic marks related with inflammation could be used as

biomarkers of disease risk or weight loss/weight gain susceptibility ?.

Nutriepigenomics

Campión J, Milagro FI and Martinez JA. TNF-alpha promoter methylation as a predictive biomarker for weight-loss response.

Obesity 2009; 17: 1293-7.

Does epigenetically-mediated inflammatory regulation participate in body

weight control?

Could the epigenetic control of inflammation-related gene promoters be

implicated in the susceptibility to lose body weight by a hypocaloric diet?

Is inflammation-related epigenetic status a good marker of weight loss?

hypocaloric diet/exercise

Microarray and validation

PBMCs

White cells

Epigenetic biomarkers in weight loss

Bouchard L et al. Am J Clin Nutr. 2010; 91: 309-20

Epigenetic biomarkers and weight loss

Epigenomic and transcriptomic responses of 14 overweight and obese

postmenopausal women to a caloric restriction intervention.

Epigenetic biomarkers in weight loss

Bouchard L et al. Am J Clin Nutr. 2010; 91: 309-20

Epigenetic biomarkers and weight loss

Subcutaneous adipose tissue

Epigenetic biomarkers in weight loss ( Bouchard et al.)

The carbon atom at the 5' position of cytosines within a CpG site can be methylated by DNA

methyltransferases (DNMTs).

Methylated CpGs are recognized and bound by specific proteins including histone deacetylases (HDACs).

Histone deacetylation alters the nucleosomal structure and decreases the chromatin transcriptional activity.

Subsequent methylation of histone tails by histone methyltransferases (HMTs) enhance the formation of

transcriptionally incompetent heterochromatin.

Lodygin D et al. Cell Research (2005) 15, 237–246.

Relationship between DNA methylation and chromatin structure

May high energy or high-fat diets affect gene promoter methylation,

affecting thus obese “phenotype”?

The methylation pattern of leptin promoter is modified by high fat diet-

induced obesity in rats. Milagro FI, Campión J, García-Díaz DF, Goyenechea E, Paternain L, Martínez JA.

J Physiol Biochem 2009; 65: 1-8.

HYPOTHESIS

Journal of

Physioligy and Biochemistry

8 weeks of Low Calorie Diet

(n=6)

(n=21)

(n=27)

Subcutaneous Adipose Tissue

(SAT)

SAT

Blood

Blood




LEPTIN

TNFa

0

10

20

30

40

50

60

70

Leptina TNF-alfa

Respuesta

No respuesta

*

TBASELINE methylation %

LEPTIN TNFa

Responders

No Responders

Ladder

sample 1 sample 2

met

unmet unmet

met

MSP



24 obese subjects (BMI: 30.5 ± 1.5 kg/m2)

12 men 12 female

Serum: TNF-a Blood, PBMC: DNA

8-week hypocaloric diet

Weigh loss ≥ 5% of initial body

Responders Non-responders

Serum: TNF-a

Experimental Design

8 weeks of hypocaloric diet

Before

After

Illumina Microarray

Milagro FI et al. FASEB J (2011)


Microarray Changes by the diet

70 CpG hypermrth

100 CpG hypometh

Before vs. After diet

(20 % variation p < 0.05)

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9


Inicial

Final

***

***

***

***

***

(% methylation)


Methylation changes induced by an energy restricted diet


602 CpG hypermethylation

432 CpG hypomethylation


(>20 % variation, p < 0.05)

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1


Respuesta

No respuesta

***

***

***

***

***

(% methylation)




Differential DNA methylation patterns between high and low responders to a weight loss intervention in overweight or obese adolescents: the EVASYON study

Martí et al FASEB J. 2013

TNF-alpha promoter methylation in peripheral white blood cells: Relationship with circulating TNFa, truncal fat and

n-6 PUFA intake in young women.

Rather than there being an ‘optimal’ human diet, there are a range of adequate diets

which depend upon individual biological and cultural variation.

EPIGENETIC MARKS GENETIC BACKGROUND

PHYSICAL ACTIVITY

LIKES AND DISLIKES ALLERGIES AND INTOLERANCES

FAMILY HISTORY

PREVIOUS DISEASES CULTURE

Personalized diet

Personalized Nutrition

Conflict of interest regarding this presentation:

I have no conflict of interest to report in relation to this presentation.

Nutriepigenetics

Epigenetic processes modulate gene activity and expression

without changes in the DNA nucleotide sequence

TRATAMIENTO DE LA OBESIDAD

Tratamiento nutricional Modificación de la

conducta alimentaria

Estrategias de aumento

de la actividad física

Tratamiento farmacológico

Tratamiento quirúrgico

1º

2º

3º

Introducción

DIETAS

Pobres en hidratos de

carbono y/o ricas en

grasas/proteínas

Ricas en hidratos de

carbono/ Bajas en grasa

IG

Muy bajo

contenido calórico

400-1000 kcal

Productos milagro

Pastillas, infusiones,

cremas, preparados…

Toma de un

alimento

Espagueti, piña, manzana,

helado, zanahoria…

Introducción

LOW CALORIE DIETS

Fat losses

lean tissue maintenance

Macronutrient distribution

Food Composition

Proteins, 3 fatty acids,

glycemic index, fiber,

amino acids…

Risk factors

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2

Carbohydrates

Lípids

Proteíns

Body weight composition

DEXA, Bio-impedance

Energy expenditure

Indirect Calorimetry

Mitochondrial Oxidation

Breath tests for stable isotopes

Blood Sampling

Lipids, carbohydrates, leptin,…

Personalized Nutrition

Energy - 30%

Macronutrients

Genotype/phenotype

DIET

Anthropometrics

Weight, stature, circumferences,……..

Material y Métodos: Personalización

GENÉTICA Y EPIGENÉTICA DE LA OBESIDAD

J Alfredo Martinez

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