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Adipose gene expression prior to weight loss can differentiate and weakly predict dietary responders.

Mutch DM, Temanni MR, Henegar C, Combes F, Pelloux V, Holst C, Sørensen TI, Astrup A, Martinez JA, Saris WH, Viguerie N, Langin D, Zucker JD, Clément K - PLoS ONE (2007)

Bottom Line: Using a bottom-up (i.e. black-box) approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%+/-8.1% accuracy.Using a top-down approach (i.e. using differentially expressed genes to build a classifier) improved prediction accuracy to 80.9%+/-2.2%.While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition.

View Article: PubMed Central - PubMed

Affiliation: INSERM, Nutriomique U872, Paris, France.

ABSTRACT

Background: The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot.

Methodology/principal findings: The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8-12 kgs weight loss) could always be differentiated from non-responders (<4 kgs weight loss). We also assessed whether this differentiation was sufficient for prediction. Using a bottom-up (i.e. black-box) approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%+/-8.1% accuracy. Using a top-down approach (i.e. using differentially expressed genes to build a classifier) improved prediction accuracy to 80.9%+/-2.2%.

Conclusion: Adipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition.

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Related in: MedlinePlus

Weight loss curves during the 10 week hypocaloric diet.The two groups were defined as responders (i.e. subjects losing between 8–12 kgs) and non-responders (i.e. subjects losing less than 4 kgs). Weight was measured in at least 43 subjects at each weekly time point. Error bars represent the 95% confidence intervals (equal to 1.96 * standard deviation).
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pone-0001344-g001: Weight loss curves during the 10 week hypocaloric diet.The two groups were defined as responders (i.e. subjects losing between 8–12 kgs) and non-responders (i.e. subjects losing less than 4 kgs). Weight was measured in at least 43 subjects at each weekly time point. Error bars represent the 95% confidence intervals (equal to 1.96 * standard deviation).

Mentions: Subcutaneous adipose tissue biopsies were obtained for the majority of the 771 subjects participating in this dietary intervention study (both before and after the dietary intervention). An abdominal subcutaneous fat specimen (∼1 g) was obtained by needle aspiration under local anaesthesia after an overnight fast. Biopsies were washed and stored in RNA later preservative solution (Qiagen, Courtaboeuf, France) at −80°C until analysis. Total RNA was extracted using the RNeasy total RNA Mini kit (Qiagen). When accounting for both drop outs during the intervention study and those biopsies that produced total RNA that did not meet quality and quantity controls, 319 subjects were assessed for weight loss after 10 weeks and subsequently divided into two groups: ‘responders’ (i.e. subjects losing between 8–12 kg) and ‘non-responders’ (subjects losing <4 kg) (Figure 1). Twenty-seven female subjects were randomly selected from each group after careful matching based on weight, height, body mass index (BMI), waist/hip ratio, energy intake, fat, carbohydrate, protein and alcohol energy intakes at baseline (Table 1). Only total RNA from biopsies taken prior to the 10 week dietary intervention was used in the present study.


Adipose gene expression prior to weight loss can differentiate and weakly predict dietary responders.

Mutch DM, Temanni MR, Henegar C, Combes F, Pelloux V, Holst C, Sørensen TI, Astrup A, Martinez JA, Saris WH, Viguerie N, Langin D, Zucker JD, Clément K - PLoS ONE (2007)

Weight loss curves during the 10 week hypocaloric diet.The two groups were defined as responders (i.e. subjects losing between 8–12 kgs) and non-responders (i.e. subjects losing less than 4 kgs). Weight was measured in at least 43 subjects at each weekly time point. Error bars represent the 95% confidence intervals (equal to 1.96 * standard deviation).
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2147074&req=5

pone-0001344-g001: Weight loss curves during the 10 week hypocaloric diet.The two groups were defined as responders (i.e. subjects losing between 8–12 kgs) and non-responders (i.e. subjects losing less than 4 kgs). Weight was measured in at least 43 subjects at each weekly time point. Error bars represent the 95% confidence intervals (equal to 1.96 * standard deviation).
Mentions: Subcutaneous adipose tissue biopsies were obtained for the majority of the 771 subjects participating in this dietary intervention study (both before and after the dietary intervention). An abdominal subcutaneous fat specimen (∼1 g) was obtained by needle aspiration under local anaesthesia after an overnight fast. Biopsies were washed and stored in RNA later preservative solution (Qiagen, Courtaboeuf, France) at −80°C until analysis. Total RNA was extracted using the RNeasy total RNA Mini kit (Qiagen). When accounting for both drop outs during the intervention study and those biopsies that produced total RNA that did not meet quality and quantity controls, 319 subjects were assessed for weight loss after 10 weeks and subsequently divided into two groups: ‘responders’ (i.e. subjects losing between 8–12 kg) and ‘non-responders’ (subjects losing <4 kg) (Figure 1). Twenty-seven female subjects were randomly selected from each group after careful matching based on weight, height, body mass index (BMI), waist/hip ratio, energy intake, fat, carbohydrate, protein and alcohol energy intakes at baseline (Table 1). Only total RNA from biopsies taken prior to the 10 week dietary intervention was used in the present study.

Bottom Line: Using a bottom-up (i.e. black-box) approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%+/-8.1% accuracy.Using a top-down approach (i.e. using differentially expressed genes to build a classifier) improved prediction accuracy to 80.9%+/-2.2%.While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition.

View Article: PubMed Central - PubMed

Affiliation: INSERM, Nutriomique U872, Paris, France.

ABSTRACT

Background: The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot.

Methodology/principal findings: The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8-12 kgs weight loss) could always be differentiated from non-responders (<4 kgs weight loss). We also assessed whether this differentiation was sufficient for prediction. Using a bottom-up (i.e. black-box) approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%+/-8.1% accuracy. Using a top-down approach (i.e. using differentially expressed genes to build a classifier) improved prediction accuracy to 80.9%+/-2.2%.

Conclusion: Adipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition.

Show MeSH
Related in: MedlinePlus