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      <title>Insulin resistance and inflammation predict kinetic body weight changes in response to dietary weight loss and maintenance in overweight and obese subjects by using a Bayesian network approach</title>
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      <namePart type="family">Kong</namePart>
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    <abstract>Background: The ability to identify obese subjects who will lose weight in response to energy restriction is an important strategy in obesity treatment. Objective: We aimed to identify obese subjects who would lose weight and maintain weight loss through 6 wk of energy restriction and 6 wk of weight maintenance. Design: Fifty obese or overweight subjects underwent a 6-wk energy-restricted, high-protein diet followed by another 6 wk of weight maintenance. Network modeling by using combined biological, gut microbiota, and environmental factors was performed to identify predictors of weight trajectories. Results: On the basis of body weight trajectories, 3 subject clusters were identified. Clusters A and B lost more weight during energy restriction. During the stabilization phase, cluster A continued to lose weight, whereas cluster B remained stable. Cluster C lost less and rapidly regained weight during the stabilization period. At baseline, cluster C had the highest plasma insulin, interleukin (IL)-6, adipose tissue inflammation (HAM56+ cells), and Lactobacillus/Leuconostoc/Pediococcus numbers in fecal samples. Weight regain after energy restriction correlated positively with insulin resistance (homeostasis model assessment of insulin resistance: r = 0.5, P = 0.0002) and inflammatory markers (IL-6; r = 0.43, P = 0.002) at baseline. The Bayesian network identified plasma insulin, IL-6, leukocyte number, and adipose tissue (HAM56) at baseline as predictors that were sufficient to characterize the 3 clusters. The prediction accuracy reached 75.5%. Conclusion: The resistance to weight loss and proneness to weight regain could be predicted by the combination of high plasma insulin and inflammatory markers before dietary intervention.</abstract>
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      <titleInfo>
        <title>American Journal of Clinical Nutrition</title>
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      <part>
        <detail type="volume">
          <number>98</number>
        </detail>
        <detail type="volume">
          <number>6</number>
        </detail>
        <extent unit="pages">
          <list> 1385-1394</list>
        </extent>
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      <originInfo>
        <dateIssued>2013</dateIssued>
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      <identifier type="issn">0002-9165</identifier>
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    <identifier type="uri">https://www.documentation.ird.fr/hor/fdi:010061354</identifier>
    <identifier type="doi">10.3945/ajcn.113.058099</identifier>
    <identifier type="issn">0002-9165</identifier>
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      <recordCreationDate encoding="w3cdtf">2014-02-05</recordCreationDate>
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