title: Accelerated telomere attrition is associated with relative household income, diet and inflammation in the pSoBid cohort.
abstract: BACKGROUND
  It has previously been hypothesized that lower socio-economic status can accelerate biological ageing, and predispose to early onset of disease. This study investigated the association of socio-economic and lifestyle factors, as well as traditional and novel risk factors, with biological-ageing, as measured by telomere length, in a Glasgow based cohort that included individuals with extreme socio-economic differences.
  
  
  METHODS
  A total of 382 blood samples from the pSoBid study were available for telomere analysis. For each participant, data was available for socio-economic status factors, biochemical parameters and dietary intake. Statistical analyses were undertaken to investigate the association between telomere lengths and these aforementioned parameters.
  
  
  RESULTS
  The rate of age-related telomere attrition was significantly associated with low relative income, housing tenure and poor diet. Notably, telomere length was positively associated with LDL and total cholesterol levels, but inversely correlated to circulating IL-6.
  
  
  CONCLUSIONS
  These data suggest lower socio-economic status and poor diet are relevant to accelerated biological ageing. They also suggest potential associations between elevated circulating IL-6, a measure known to predict cardiovascular disease and diabetes with biological ageing. These observations require further study to tease out potential mechanistic links.
authors.affiliation: Institute of Cancer Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, Scotland. Paul.Shiels@glasgow.ac.uk
authors.forename: Paul G
authors.initials: PG
authors.lastname: Shiels
authors.forename: Liane M
authors.initials: LM
authors.lastname: McGlynn
authors.forename: Alan
authors.initials: A
authors.lastname: MacIntyre
authors.forename: Paul C D
authors.initials: PC
authors.lastname: Johnson
authors.forename: G David
authors.initials: GD
authors.lastname: Batty
authors.forename: Harry
authors.initials: H
authors.lastname: Burns
authors.forename: Jonathan
authors.initials: J
authors.lastname: Cavanagh
authors.forename: Kevin A
authors.initials: KA
authors.lastname: Deans
authors.forename: Ian
authors.initials: I
authors.lastname: Ford
authors.forename: Alex
authors.initials: A
authors.lastname: McConnachie
authors.forename: Agnes
authors.initials: A
authors.lastname: McGinty
authors.forename: Jennifer S
authors.initials: JS
authors.lastname: McLean
authors.forename: Keith
authors.initials: K
authors.lastname: Millar
authors.forename: Naveed
authors.initials: N
authors.lastname: Sattar
authors.forename: Carol
authors.initials: C
authors.lastname: Tannahill
authors.forename: Yoga N
authors.initials: YN
authors.lastname: Velupillai
authors.forename: Chris J
authors.initials: CJ
authors.lastname: Packard
mesh_terms: D000328:Adult; D015415:Biomarkers / Q000378:metabolism; D015331:Cohort Studies; D004032:Diet; D005191:Family Characteristics; D005260:Female; D006798:Housing; D006801:Humans; D007182:Income; D007249:Inflammation / Q000473:pathology*; D015850:Interleukin-6 / Q000378:metabolism; D008019:Life Style; D008297:Male; D008875:Middle Aged; D012044:Regression Analysis; D016615:Telomere / Q000378:metabolism*
chemical_list: D015415:Biomarkers; D015850:Interleukin-6