Multi-omic signature of body weight change: results from a population-based cohort study

Simone Wahl, Susanne Vogt, Ferdinand Stückler, Jan Krumsiek, Jörg Bartel, Tim Kacprowski, Katharina Schramm, Maren Carstensen, Wolfgang Rathmann, Michael Roden, Carolin Jourdan, Antti J. Kangas, Pasi Soininen, Mika Ala-Korpela, Ute Nöthlings, Heiner Boeing, Fabian J. Theis, Christa Meisinger, Melanie Waldenberger, Karsten SuhreGeorg Homuth, Christian Gieger, Gabi Kastenmüller, Thomas Illig, Jakob Linseisen, Annette Peters, Holger Prokisch, Christian Herder, Barbara Thorand, Harald Grallert

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

RESULTS: Four metabolite and two gene expression modules were significantly and stably associated with body weight change (P-values ranging from 1.9 × 10(-4) to 1.2 × 10(-24)). The four metabolite modules covered major branches of metabolism, with VLDL, LDL and large HDL subclasses, triglycerides, branched-chain amino acids and markers of energy metabolism among the main representative molecules. One gene expression module suggests a role of weight change in red blood cell development. The other gene expression module largely overlaps with the lipid-leukocyte (LL) module previously reported to interact with serum metabolites, for which we identify additional co-expressed genes. The omics modules were interrelated and showed cross-sectional associations with clinical traits. Moreover, weight gain and weight loss showed largely opposing associations with the omics modules.

CONCLUSIONS: Long-term weight change in the general population globally associates with serum metabolite concentrations. An integrated metabolomics and transcriptomics approach improved the understanding of molecular mechanisms underlying the association of weight gain with changes in lipid and amino acid metabolism, insulin sensitivity, mitochondrial function as well as blood cell development and function.

BACKGROUND: Excess body weight is a major risk factor for cardiometabolic diseases. The complex molecular mechanisms of body weight change-induced metabolic perturbations are not fully understood. Specifically, in-depth molecular characterization of long-term body weight change in the general population is lacking. Here, we pursued a multi-omic approach to comprehensively study metabolic consequences of body weight change during a seven-year follow-up in a large prospective study.

METHODS: We used data from the population-based Cooperative Health Research in the Region of Augsburg (KORA) S4/F4 cohort. At follow-up (F4), two-platform serum metabolomics and whole blood gene expression measurements were obtained for 1,631 and 689 participants, respectively. Using weighted correlation network analysis, omics data were clustered into modules of closely connected molecules, followed by the formation of a partial correlation network from the modules. Association of the omics modules with previous annual percentage weight change was then determined using linear models. In addition, we performed pathway enrichment analyses, stability analyses, and assessed the relation of the omics modules with clinical traits.

Original languageEnglish
Pages (from-to)48
Number of pages1
JournalBMC Medicine
Volume13
DOIs
Publication statusPublished - 2015
Externally publishedYes

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Body Weight Changes
Gene Regulatory Networks
Cohort Studies
Gene Expression
Metabolomics
Weights and Measures
Population
Weight Gain
Serum
Lipids
Branched Chain Amino Acids
Energy Metabolism
Insulin Resistance
Weight Loss
Linear Models
Blood Cells
Triglycerides
Leukocytes
Erythrocytes
Body Weight

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Wahl, S., Vogt, S., Stückler, F., Krumsiek, J., Bartel, J., Kacprowski, T., ... Grallert, H. (2015). Multi-omic signature of body weight change: results from a population-based cohort study. BMC Medicine, 13, 48. https://doi.org/10.1186/s12916-015-0282-y

Multi-omic signature of body weight change : results from a population-based cohort study. / Wahl, Simone; Vogt, Susanne; Stückler, Ferdinand; Krumsiek, Jan; Bartel, Jörg; Kacprowski, Tim; Schramm, Katharina; Carstensen, Maren; Rathmann, Wolfgang; Roden, Michael; Jourdan, Carolin; Kangas, Antti J.; Soininen, Pasi; Ala-Korpela, Mika; Nöthlings, Ute; Boeing, Heiner; Theis, Fabian J.; Meisinger, Christa; Waldenberger, Melanie; Suhre, Karsten; Homuth, Georg; Gieger, Christian; Kastenmüller, Gabi; Illig, Thomas; Linseisen, Jakob; Peters, Annette; Prokisch, Holger; Herder, Christian; Thorand, Barbara; Grallert, Harald.

In: BMC Medicine, Vol. 13, 2015, p. 48.

Research output: Contribution to journalArticle

Wahl, S, Vogt, S, Stückler, F, Krumsiek, J, Bartel, J, Kacprowski, T, Schramm, K, Carstensen, M, Rathmann, W, Roden, M, Jourdan, C, Kangas, AJ, Soininen, P, Ala-Korpela, M, Nöthlings, U, Boeing, H, Theis, FJ, Meisinger, C, Waldenberger, M, Suhre, K, Homuth, G, Gieger, C, Kastenmüller, G, Illig, T, Linseisen, J, Peters, A, Prokisch, H, Herder, C, Thorand, B & Grallert, H 2015, 'Multi-omic signature of body weight change: results from a population-based cohort study', BMC Medicine, vol. 13, pp. 48. https://doi.org/10.1186/s12916-015-0282-y
Wahl, Simone ; Vogt, Susanne ; Stückler, Ferdinand ; Krumsiek, Jan ; Bartel, Jörg ; Kacprowski, Tim ; Schramm, Katharina ; Carstensen, Maren ; Rathmann, Wolfgang ; Roden, Michael ; Jourdan, Carolin ; Kangas, Antti J. ; Soininen, Pasi ; Ala-Korpela, Mika ; Nöthlings, Ute ; Boeing, Heiner ; Theis, Fabian J. ; Meisinger, Christa ; Waldenberger, Melanie ; Suhre, Karsten ; Homuth, Georg ; Gieger, Christian ; Kastenmüller, Gabi ; Illig, Thomas ; Linseisen, Jakob ; Peters, Annette ; Prokisch, Holger ; Herder, Christian ; Thorand, Barbara ; Grallert, Harald. / Multi-omic signature of body weight change : results from a population-based cohort study. In: BMC Medicine. 2015 ; Vol. 13. pp. 48.
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abstract = "RESULTS: Four metabolite and two gene expression modules were significantly and stably associated with body weight change (P-values ranging from 1.9 × 10(-4) to 1.2 × 10(-24)). The four metabolite modules covered major branches of metabolism, with VLDL, LDL and large HDL subclasses, triglycerides, branched-chain amino acids and markers of energy metabolism among the main representative molecules. One gene expression module suggests a role of weight change in red blood cell development. The other gene expression module largely overlaps with the lipid-leukocyte (LL) module previously reported to interact with serum metabolites, for which we identify additional co-expressed genes. The omics modules were interrelated and showed cross-sectional associations with clinical traits. Moreover, weight gain and weight loss showed largely opposing associations with the omics modules.CONCLUSIONS: Long-term weight change in the general population globally associates with serum metabolite concentrations. An integrated metabolomics and transcriptomics approach improved the understanding of molecular mechanisms underlying the association of weight gain with changes in lipid and amino acid metabolism, insulin sensitivity, mitochondrial function as well as blood cell development and function.BACKGROUND: Excess body weight is a major risk factor for cardiometabolic diseases. The complex molecular mechanisms of body weight change-induced metabolic perturbations are not fully understood. Specifically, in-depth molecular characterization of long-term body weight change in the general population is lacking. Here, we pursued a multi-omic approach to comprehensively study metabolic consequences of body weight change during a seven-year follow-up in a large prospective study.METHODS: We used data from the population-based Cooperative Health Research in the Region of Augsburg (KORA) S4/F4 cohort. At follow-up (F4), two-platform serum metabolomics and whole blood gene expression measurements were obtained for 1,631 and 689 participants, respectively. Using weighted correlation network analysis, omics data were clustered into modules of closely connected molecules, followed by the formation of a partial correlation network from the modules. Association of the omics modules with previous annual percentage weight change was then determined using linear models. In addition, we performed pathway enrichment analyses, stability analyses, and assessed the relation of the omics modules with clinical traits.",
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T1 - Multi-omic signature of body weight change

T2 - results from a population-based cohort study

AU - Wahl, Simone

AU - Vogt, Susanne

AU - Stückler, Ferdinand

AU - Krumsiek, Jan

AU - Bartel, Jörg

AU - Kacprowski, Tim

AU - Schramm, Katharina

AU - Carstensen, Maren

AU - Rathmann, Wolfgang

AU - Roden, Michael

AU - Jourdan, Carolin

AU - Kangas, Antti J.

AU - Soininen, Pasi

AU - Ala-Korpela, Mika

AU - Nöthlings, Ute

AU - Boeing, Heiner

AU - Theis, Fabian J.

AU - Meisinger, Christa

AU - Waldenberger, Melanie

AU - Suhre, Karsten

AU - Homuth, Georg

AU - Gieger, Christian

AU - Kastenmüller, Gabi

AU - Illig, Thomas

AU - Linseisen, Jakob

AU - Peters, Annette

AU - Prokisch, Holger

AU - Herder, Christian

AU - Thorand, Barbara

AU - Grallert, Harald

PY - 2015

Y1 - 2015

N2 - RESULTS: Four metabolite and two gene expression modules were significantly and stably associated with body weight change (P-values ranging from 1.9 × 10(-4) to 1.2 × 10(-24)). The four metabolite modules covered major branches of metabolism, with VLDL, LDL and large HDL subclasses, triglycerides, branched-chain amino acids and markers of energy metabolism among the main representative molecules. One gene expression module suggests a role of weight change in red blood cell development. The other gene expression module largely overlaps with the lipid-leukocyte (LL) module previously reported to interact with serum metabolites, for which we identify additional co-expressed genes. The omics modules were interrelated and showed cross-sectional associations with clinical traits. Moreover, weight gain and weight loss showed largely opposing associations with the omics modules.CONCLUSIONS: Long-term weight change in the general population globally associates with serum metabolite concentrations. An integrated metabolomics and transcriptomics approach improved the understanding of molecular mechanisms underlying the association of weight gain with changes in lipid and amino acid metabolism, insulin sensitivity, mitochondrial function as well as blood cell development and function.BACKGROUND: Excess body weight is a major risk factor for cardiometabolic diseases. The complex molecular mechanisms of body weight change-induced metabolic perturbations are not fully understood. Specifically, in-depth molecular characterization of long-term body weight change in the general population is lacking. Here, we pursued a multi-omic approach to comprehensively study metabolic consequences of body weight change during a seven-year follow-up in a large prospective study.METHODS: We used data from the population-based Cooperative Health Research in the Region of Augsburg (KORA) S4/F4 cohort. At follow-up (F4), two-platform serum metabolomics and whole blood gene expression measurements were obtained for 1,631 and 689 participants, respectively. Using weighted correlation network analysis, omics data were clustered into modules of closely connected molecules, followed by the formation of a partial correlation network from the modules. Association of the omics modules with previous annual percentage weight change was then determined using linear models. In addition, we performed pathway enrichment analyses, stability analyses, and assessed the relation of the omics modules with clinical traits.

AB - RESULTS: Four metabolite and two gene expression modules were significantly and stably associated with body weight change (P-values ranging from 1.9 × 10(-4) to 1.2 × 10(-24)). The four metabolite modules covered major branches of metabolism, with VLDL, LDL and large HDL subclasses, triglycerides, branched-chain amino acids and markers of energy metabolism among the main representative molecules. One gene expression module suggests a role of weight change in red blood cell development. The other gene expression module largely overlaps with the lipid-leukocyte (LL) module previously reported to interact with serum metabolites, for which we identify additional co-expressed genes. The omics modules were interrelated and showed cross-sectional associations with clinical traits. Moreover, weight gain and weight loss showed largely opposing associations with the omics modules.CONCLUSIONS: Long-term weight change in the general population globally associates with serum metabolite concentrations. An integrated metabolomics and transcriptomics approach improved the understanding of molecular mechanisms underlying the association of weight gain with changes in lipid and amino acid metabolism, insulin sensitivity, mitochondrial function as well as blood cell development and function.BACKGROUND: Excess body weight is a major risk factor for cardiometabolic diseases. The complex molecular mechanisms of body weight change-induced metabolic perturbations are not fully understood. Specifically, in-depth molecular characterization of long-term body weight change in the general population is lacking. Here, we pursued a multi-omic approach to comprehensively study metabolic consequences of body weight change during a seven-year follow-up in a large prospective study.METHODS: We used data from the population-based Cooperative Health Research in the Region of Augsburg (KORA) S4/F4 cohort. At follow-up (F4), two-platform serum metabolomics and whole blood gene expression measurements were obtained for 1,631 and 689 participants, respectively. Using weighted correlation network analysis, omics data were clustered into modules of closely connected molecules, followed by the formation of a partial correlation network from the modules. Association of the omics modules with previous annual percentage weight change was then determined using linear models. In addition, we performed pathway enrichment analyses, stability analyses, and assessed the relation of the omics modules with clinical traits.

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