CR weight loss response variation

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Hi All,

Do some CRers respond differentially to their diets?

Maybe so, suggests the pdf-available below? Genes matter?

Sesti G, Perego L, Cardellini M, Andreozzi F, Ricasoli C, Vedani P, Guzzi V,

Marchi

M, Paganelli M, Ferla G, Pontiroli AE, Letizia Hribal M, Folli F.

Impact of Common Polymorphisms in Candidate Genes for insulin resistance and

obesity

on Weight Loss of Morbidly Obese Subjects after Laparoscopic Adjustable Gastric

Banding and hypocaloric diet.

J Clin Endocrinol Metab. 2005 Jun 28; [Epub ahead of print]

PMID: 15985484

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve & db=pubmed & dopt=Abstra\

ct & list_uids=15985484 & query_hl=13

Abbreviations: UCP2, uncoupling protein-2; OGTT, glucose tolerance test; NGT,

normal

glucose tolerance; IFG, impaired fasting glucose; IGT, impaired glucose

tolerance;

BMI,

body mass index; HbA1c, glycosylated haemoglobin; HOMA, homeostasis model

assessment; ANOVA, analysis of variance.

.... EXPERIMENTAL SUBJECTS

The study group consisted of 167 Caucasian subjects with morbid obesity, i.e.

grade

3

obesity according to World Health Organization criteria (23) consecutively

recruited

at the

San Raffaele Hospital, Milan, Italy from November 2002 to March 2004. Clinical

characteristics of the study subjects are provided in Table 1. Morbidly obese

subjects were

considered eligible for laparoscopic adjustable gastric banding (LAGB) when

fulfilling the

following criteria: age, 18–66 yr inclusive; BMI, greater than 40 kg/m 2 , or

greater than 35.0

kg/m 2 in the presence of comorbidities (2), and history of at least two

previous

attempts to

lose weight with dietary and medical measures followed by relapse of obesity.

Exclusion

criteria were: obesity secondary to endocrinopathies (Cushing’s disease or

syndrome,

hypothyroidism), gastrointestinal inflammatory diseases, risk of upper

gastrointestinal

bleeding, pregnancy, alcohol or drug addiction, and previous or current

malignancies. All

measurements were made in the morning after a 12-h fast using standardized

methods.

The

obese subjects were advised to continue their normal diet and avoid alcohol

intake

and

vigorous exercise before the visit. Weight was measured with electric scales.

.... For the first month after LAGB, a semi-liquid diet

of 800 and 950 kcal/d in women and men, respectively, was prescribed (33%

proteins,

19%

lipids, 48% carbohydrates). One month after LAGB, a solid diet was reintroduced,

and

by the

third month the suggested diet was 970 and 1090 kcal/d in women and men,

respectively; iron

was supplemented on the basis of blood examinations performed during the second

month.

Diet included 48% carbohydrates (starch or bread), 33% proteins (fat free parts

of

different

animals and fishes), and 19% lipids (olive oil); sweets, cakes, sweetened

drinks,

alcohol, and

animal lipids were forbidden. Patients were advised to eat slowly, to avoid

liquids

during

meals, to use vegetables at each meal and meat or fish at least once a day, and

to

stop eating

when feeling a sense of satiety. All foods had to be cooked without oil, butter,

or

other lipids.

Once or twice weekly, eggs or lean Parma ham or ricotta cheese were allowed in

proper

quantities. Diets were given by a dietician and a physician with a specific

training

in obesity

treatment and nutrition. The patients were also suggested to take 30-min

physical

aerobic

activity every day to avoid muscular loss, with a gradual increase over a

1-month

period.

Physical activity could be gymnastics, swimming, or dancing. Patients were

instructed to eat

only allowed nutrients and to keep a record of ingested foods, physical

activity,

and problems

encountered.

.... RESULTS

Table 1 shows baseline clinical characteristics including gender, age, BMI,

waist

circumference, waist/hip ratio, and metabolic variables of the study group

consisting of 167

unrelated morbidly obese subjects. Of this group, 62.9% of the subjects had

normal

glucose

tolerance, 1.8% of subjects had IFG, 18% of the subjects had IGT, and 17.4% of

subjects had

type 2 diabetes. At 6 months follow-up after LAGB, BMI changed from 44.6±6.5

kg/m 2

to an

average value of 38.6±5.7 kg/m 2 (Table 1). In addition, all anthropometric and

metabolic

variables significantly improved compared with baseline, with the exception of

total

cholesterol levels (Table 1). Of the 29 subjects who had type 2 diabetes at

baseline, 9 became

normal glucose tolerant at 6 months follow-up, 5 became IGT, 4 became IFG and

the 11

remained diabetics. Of the 30 subjects who were IGT, 13 became normal glucose

tolerant at 6

months follow-up, 3 became diabetics,1 became IFG, and 13 remained IGT. During

the 6

months follow-up, the mean number of visits for re-evaluation by a dietician and

a

physician

was 5±1, and this did not differ between the genotypes of any of the

polymorphisms

examined. Baseline clinical characteristics including gender, age, BMI, waist

circumference,

waist/hip ratio, fasting and 120-min levels of glucose, HOMA and metabolic

status

did not

differ between the genotypes of any of the polymorphisms investigated (data not

shown).

Table 2 shows baseline BMI and BMI change at the 6-month follow-up, calculated

as

([bMI6-

month follow-up - BMIbaseline]/BMIbaseline) x 100, according to the four

genotypes

investigated. The

frequency of the Pro12Ala PPARG genotype was 14.1%, and no subjects were

homozygous

for the Ala12 allele. No difference in body weight loss between the two

genotypes

was

observed at 6 months follow-up after LAGB (Table 2).

The frequency of the Gly972Arg IRS1 genotype was 9.0%, and no subjects were

homozygous for the Arg972 allele. Weight loss at 6 months follow-up after LAGB

was

slightly lower in carriers of the Gly972Arg IRS1 genotype as compared with

Gly972Gly

carriers, but this difference was not statistical significant (P=0.06).

The frequencies of the different genotypes at G-174C of the human IL6 gene were

as

follows: 45.5% GG, 46.7% GC, and 7.9% CC. Weight loss at 6 months follow-up

after

LAGB increased according to the dosage of the G allele. Thus, carriers of the

G-174G

genotype lost more weight as compared with G-174C or C-174C genotype (ANOVA,

P=0.037) (Table 2). The difference in weight loss remained significant after

adjustment for

age, gender, and baseline BMI (P=0.028). The difference in weight loss remained

significant

between carriers of the G-174G genotype and carriers of the C-174C genotype

after

Bonferroni correction for multiple comparisons (P=0.05) (Table 2). After pooling

subjects

carrying G-174C and C-174C genotype, carriers of the G-174G genotype displayed

significantly higher weight loss in comparison with carriers of the C allele (%

BMI

change

17.5±7.1 vs. 15.1±6.6, respectively; P=0.034).

The frequencies of the different genotypes at G-866A of the human UCP2 gene were

as

follows: 51.8% GG, 40.2% GA, and 7.9% AA. After adjustment for age, gender, and

baseline

BMI, weight loss at 6 months follow-up after LAGB was significantly different

among

the

three genotypes (P=0.025) (Table 2). After Bonferroni correction for multiple

comparisons,

carriers of the A-866A genotype lost more weight as compared with G-866G

(P=0.05)

(Table

2), suggesting a recessive effect of the A allele. Subjects simultaneously

having

the UCP2 A-866A/

IL-6 G-174G genotypes displayed the highest weight loss as compared with pooled

subjects without these favourable genotypes (unpaired Student’s t test P=0.04).

There was no

interaction between the UCP2 and IL6 genotypes as assessed by GLM. The

differences

among the genotypes were also evaluated by the univariate ANOVA with post hoc

LSD or

Bonferroni correction for multiple comparisons (Table 3). Subjects carrying the

UCP2

A-866A/

IL6 G-174G genotypes showed the highest weight loss as compared with subjects

with

the other genotypes which reached the statistical significance after correction

for

multiple

comparisons with LSD, but not with Bonferroni, test. To estimate the independent

contribution of the four polymorphisms to weight loss, we carried out a linear

regression

analysis in a model which also included gender, age, baseline BMI, and glucose

tolerance

status (Table 4). The results of the multivariate analysis revealed that only

the

C-174G

polymorphism of the IL6 gene (P<0.004), and G-866A polymorphism of UCP2 gene

(P<0.035) were independently associated with weight loss. The model accounted

for

22.8% of

the variation in BMI change after LAGB.

CONCLUSIONS

Laparoscopic adjustable gastric banding (LAGB) is a minimally invasive

surgical procedure that is now regularly applied in a few European centers, and

it

has been

approved by the Food and Drug Administration in 2001 for use in the United

States.

LAGB is

indicated for patients with morbid obesity, i.e. grade 3 obesity according to

World

Health

Organization criteria, and results in a significant reduction of body weight,

accompanied by

improvement of several risk factors for cardiovascular disease (2). A recent

meta-analysis has

revealed that LAGB is as effective in inducing weight loss, at least up to 4

years,

as vertical-banded

gastroplasty, and Roux-en-Y gastric bypass (29). However, a significant

proportion

of

obese subjects who underwent LAGB had only a modest weight loss (2). This

failure

has been

attributed to low compliance to dietary instructions. However, the possibility

that

genetic

factors, which are thought to play an important role in the regulation of body

weight, may

account for the differences in the therapeutic effects of LAGB remains

unsettled. It

is

important to note that previous studies have shown that during the first 6

months

after LAGB,

the patients experienced the most dramatic weight loss, due to the fact that

they

were very

compliant to the administered diet (2, 29) thus allowing to reveal the true

impact

of a given

genetic polymorphism on weight loss after LAGB in grade III obesity. Significant

differences

in weight change were found between the genotypes investigated in this study

where

there

was a power of 0.80 at á = 0.05 to detect a difference in reduction in body

weight

of

approximately 5% between genotype groups. We found that subjects who underwent

LAGB

lost more weight, if they possessed the protective genotypes for obesity in the

IL6

gene (G-174G)

and the UCP2 gene (A-866A). The A allele at -866 of the UCP2 gene has been

associated with enhanced transcriptional activity and decreased risk of obesity

(16). It is

therefore conceivable that increased uncoupling activity associated with the

A-866A

genotype

of the UCP2 gene may result in enhanced energy expenditure thus facilitating

weight

loss.

Several mechanisms may explain the association of the C-174G polymorphism and

reduced

weight loss after LAGB. It has been shown that subjects with the G allele at

-174 of

the IL6

gene have higher energy expenditure than subjects with the C allele, and thus it

was

not

unexpected that they lost more weight (22). IL6 can regulate energy expenditure

centrally, as

it is expressed in hypothalamus. In knockout mice lacking IL6, a central

injection

of IL6

resulted in a significant increase in energy expenditure that was not mediated

by

peripheral

injection (19). In humans, a subcutaneous injection of IL6 increased resting

metabolic rate

and hypothalamic-pituitary-adrenal axis activity in a dose-dependent manner,

suggesting that

hypothalamic corticotrophin–releasing hormone may mediate both of these effects

(30).

Additionally, IL6 may affect energy expenditure by enhancing adrenergic

stimulation

as

sympathetic neurons have been shown to secrete IL6, express IL6 receptors, and

respond to

IL6 (31). Indeed, IL6 has been shown to increase heart rate and norepinephrine

levels (32)

and to stimulate the sympathetic nervous system (33), which is the primary

efferent

pathway

regulating energy expenditure. Finally, it is possible that IL6 may exert its

effects on body

composition by modulating the reaction of aromatase, a key regulatory enzyme for

estrogen

metabolism, influencing satiety and adipose tissue distribution (34).

We also observed that obese subjects carrying the Gly972Arg polymorphism of the

IRS1 gene tended to lose less weight as compared with Gly972Gly carriers but

this

difference

was not statistically significance. Furthermore, it is important to note that

multivariate

regression analysis in a model including BMI change after LAGB as the dependent

variable

and the four polymorphisms, gender, age, baseline BMI, and glucose tolerance

status

as the

independent variables revealed that only the C-174G polymorphism of the IL6

gene,

and G-866A

polymorphism of UCP2 gene were independently associated with BMI change after

LAGB thus indicating that IRS1 polymorphism does not have a significant impact

on

weight

loss.

We did not find any effect of the Pro12Ala polymorphism of the PPARG gene on

weight loss after LAGB. These data are consistent with those of two lifestyle

intervention

studies in which no differences in weight change between carriers of the

Pro12Pro

and

Pro12Ala genotype were reported (5,6). Indeed, in the Finnish Diabetes

Prevention

study,

subjects with the rare Ala12Ala genotype were shown to lose more weight during

the

follow-up

as compared with the two other genotypes (Pro12Pro and Pro12Ala). Since we did

not

find

any homozygous Ala12Ala, we cannot exclude the possibility that this genotype

might

affect

weight loss after LAGB.

In conclusion, this study provide evidence that promoter polymorphisms of IL6

(G-174C) and

UCP2 (A-866G) genes are associated with increased weight loss in morbidly obese

subjects at

6-months follow-up after LAGB. This implies that LAGB was less effective if the

subjects

were carrying risk genotypes for obesity. ...

.... Table 2. BMI change at 6 months follow-up after LABG according to the

genotypes.

.............................

Pro12Ala polymorphism of the PPAR-gamma gene Baseline BMI BMI change (%) at 6

months

follow-up

...............................

Pro12Pro (n=141) 44.3±6.2 -15.9±6.9

Pro12Ala (n=23) 46.5±8.3 -16.4±7.1

Gly972Arg polymorphism of the IRS-1 gene

Gly972Gly (n=152) 44.6±7.0 -16.4±7.0

Gly972Arg (n=15) 44.5±4.9 -12.9±4.9

G-174C polymorphism of the IL-6 gene

G-174G (n=76) 44.0±5.3 -17.5±7.1*

G-174C (n=77) 45.3±7.4 -15.5±6.5

C-174C (n=13) 44.6±7.4 -12.4±6.5

G-866A polymorphism of the UCP2 gene

G-866G (n=85) 45.5±6.7 -15.3±7.1†

G-866A (n=66) 43.9±5.9 -15.7±6.0

A-866A (n=13) 41.9±7.5 -20.1±6.0

..........................................

BMI change at the 6-month follow-up, calculated as ([bMI6-month follow-up -

BMIbaseline]/BMIbaseline) x 100. Data are means ± SD.

*P =0.05 vs. C-174C, and †P=0.05 vs. A-866A genotype after Bonferroni correction

for

multiple comparisons.

Table 3. BMI change at 6 months follow-up after LABG according to UCP2 and IL-6

combined genotypes.

......................................

----Baseline BMI BMI change (%) at 6 months follow-up (95% CI) P*

......................................

UCP2 A-866A/IL-6 G-174G (n=8) 42.6±9.2 - 20.5±6.3 (15.5-25.7)

UCP2 A-866A/IL-6 G-174C (n=5) 40.2±4.2 - 19.3±6.2 (11.5-27.0) 0.745

UCP2 G-866A/IL-6 C-174C (n=6) 45.2±8.1 - 12.7±5.8 (6.6-18.8) 0.031

UCP2 G-866A/IL-6 G-174C (n=34) 43.8±6.2 - 15.2 ±5.8 (12.2-17.1) 0.044

UCP2 G-866A/IL-6 G-174G (n=25) 43.2±5.2 - 17.4±5.9 (15.0-19.9) 0.261

UCP2 G-866G/IL-6 C-174C (n=7) 43.2±7.3 - 12.3±7.5 (5.3-19.2) 0.017

UCP2 G-866G/IL-6 G-174C (n=36) 46.9±8.0 - 15.1±7.3 (12.6-17.6) 0.039

UCP2 G-866G/IL-6 G-174G (n=42) 44.2±4.2 - 16.0±6.8 (13.9-18.2) 0.083

...........................................

BMI change at the 6-month follow-up, calculated as ([bMI6-month follow-up -

BMIbaseline]/BMIbaseline) x

100. Data are means ± SD. CI denotes 95% Confidence Interval. *P vs. UCP2

A-866A/IL-6 G-174G

genotype after LSD correction for multiple comparisons.

Table 4. Independent contribution of the four polymorphisms to BMI change after

LAGB

multivariate linear regression analysis model also including gender, age,

baseline

BMI, glucose tolerance status.

...........................................

Dependent variable Standardized beta-coefficients P

...........................................

Gly972Arg polymorphism of IRS1 -0.087 0.23

Pro12Ala polymorphism of PPARG 0.047 0.51

C-174G polymorphism of IL6 -0.154 0.035

G-866A polymorphism of UCP2 0.213 0.004

Baseline BMI 0.152 0.043

Age -0.134 0.08

Gender 0.322 0.0001

Glucose tolerance status -0.155 0.053

............................................

Standardized beta-coefficients of multiple regressions on BMI change after LAGB

are

presented.

Al Pater, PhD; email: old542000@...

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