title: Multi-omic signature of body weight change: results from a population-based cohort study.
abstract: 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.
  
  
  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.
authors: Simone
authors: S
authors: Wahl
authors: Susanne
authors: S
authors: Vogt
authors: Ferdinand
authors: F
authors: Stückler
authors: Jan
authors: J
authors: Krumsiek
authors: Jörg
authors: J
authors: Bartel
authors: Tim
authors: T
authors: Kacprowski
authors: Katharina
authors: K
authors: Schramm
authors: Maren
authors: M
authors: Carstensen
authors: Wolfgang
authors: W
authors: Rathmann
authors: Michael
authors: M
authors: Roden
authors: Carolin
authors: C
authors: Jourdan
authors: Antti J
authors: AJ
authors: Kangas
authors: Pasi
authors: P
authors: Soininen
authors: Mika
authors: M
authors: Ala-Korpela
authors: Ute
authors: U
authors: Nöthlings
authors: Heiner
authors: H
authors: Boeing
authors: Fabian J
authors: FJ
authors: Theis
authors: Christa
authors: C
authors: Meisinger
authors: Melanie
authors: M
authors: Waldenberger
authors: Karsten
authors: K
authors: Suhre
authors: Georg
authors: G
authors: Homuth
authors: Christian
authors: C
authors: Gieger
authors: Gabi
authors: G
authors: Kastenmüller
authors: Thomas
authors: T
authors: Illig
authors: Jakob
authors: J
authors: Linseisen
authors: Annette
authors: A
authors: Peters
authors: Holger
authors: H
authors: Prokisch
authors: Christian
authors: C
authors: Herder
authors: Barbara
authors: B
authors: Thorand
authors: Harald
authors: H
authors: Grallert
mesh_terms: D000328:Adult; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D005260:Female; D015870:Gene Expression / Q000502:physiology*; D053263:Gene Regulatory Networks; D006801:Humans; D016014:Linear Models; D055442:Metabolome / Q000502:physiology*; D055432:Metabolomics; D008875:Middle Aged; D011446:Prospective Studies; D012307:Risk Factors; D015430:Weight Gain / Q000502:physiology*