Genetic variation and covariation of parathyroid hormone levels and bone density in the human population

I. Otremski, D. Karasik, G. Livshits

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12 Scopus citations

Abstract

The present study was an attempt to evaluate the relative importance of familial/genetic factors in interindividual variation of plasma concentrations of parathyroid hormone (PTH) and bone mineral density (BMD). We also examined to what extent common genetic and environmental factors may be involved in covariation between the hormone concentrations and BMD levels. Ninety-five nuclear pedigrees (consisting of 187 males and 168 females, aged 18-91 and 18-86 years old, respectively), from several small villages in the Chuvasha Autonomy, Russia, were assessed for PTH, sex hormones, and BMD. PTH plasma levels were measured in duplicate by immunoradiometric assay using an N-tact PTH SP kit. Standard roentgenography was done from the second and third phalanges of the middle finger on both hands for assessment of compact and cancellous bone BMD separately. The present study clearly confirmed the results of the previous genetic analyses of BMD which indicated that between 47% and 60% of the total variance of BMD, adjusted for sex and age effects, were attributable to genetic factors. Genetic factors also contributed significantly to interindividual variation of PTH. Constraining these additive genetic effects to zero dramatically increased the likelihood ratio (P < 0.001), indicating that at least 30% of the hormone plasma variation was attributable to genetic sources. The results of bivariate decomposition analysis were not clear cut. Two types of bivariate analyses showed that PTH- BMD genetic correlations according to sex and between the opposite sexes were consistently negative, but only marginally significant.

Original languageEnglish
Pages (from-to)168-175
Number of pages8
JournalCalcified Tissue International
Volume66
Issue number3
DOIs
StatePublished - 2000

Keywords

  • Bivariate analysis
  • Bone mineral density
  • Parathyroid hormone
  • Variance decomposition analysis

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