Metabolic profiling of polycystic ovary syndrome reveals interactions with abdominal obesity.

BACKGROUND: Polycystic ovary syndrome (PCOS) is a common reproductive disorder associated with metabolic disturbances including obesity, insulin resistance and diabetes mellitus. Here we investigate whether changes in the metabolic profile of PCOS women are driven by increased tendency to obesity or are specific features of PCOS related to increased testosterone levels. DESIGN AND METHODS: We conducted an NMR metabolomics association study of PCOS cases (n=145) and controls (n=687) nested in a population-based birth cohort (n=3127). Subjects were 31 years old at examination. The main analyses were adjusted for waist circumference (WC) as a proxy measure of central obesity. Subsequently, metabolite concentrations were compared between cases and controls within pre-defined WC strata. In each stratum, additional metabolomics association analyses with testosterone levels were conducted separately among cases and controls. RESULTS: Overall, women with PCOS showed more adverse metabolite profiles than the controls. Four lipid fractions in different subclasses of very low density lipoprotein (VLDL) were associated with PCOS, after adjusting for WC and correction for multiple testing (P<0.002). In stratified analysis the PCOS women within large WC strata (⩾98 cm) had significantly lower high density lipoprotein (HDL) levels, Apo A1 and albumin values compared with the controls. Testosterone levels were significantly associated with VLDL and serum lipids in PCOS cases with large WC but not in the controls. The higher testosterone levels, adjusted for WC, associated adversely with insulin levels and HOMA IR in cases but not in the controls. CONCLUSIONS: Our findings show that both abdominal obesity and hyperandrogenism contribute to the dyslipidaemia and other metabolic traits of PCOS which all may negatively contribute to the long-term health of women with PCOS.

Bidirectional correlation of NMR and capillary electrophoresis fingerprints: a new approach to investigating Schistosoma mansoni infection in a mouse model.

We demonstrate the statistical integration of nuclear magnetic resonance (NMR) spectroscopy and capillary electrophoresis (CE) data in order to describe a pathological state caused by Schistosoma mansoni infection in a mouse model based on urinary metabolite profiles. Urine samples from mice 53 days post infection with S. mansoni and matched controls were analyzed via NMR spectroscopy and CE. The two sets of metabolic profiles were first processed and analyzed independently and were subsequently integrated using statistical correlation methods in order to facilitate cross assignment of metabolites. Using this approach, metabolites such as 3-ureidopropionate, p-cresol glucuronide, phenylacetylglycine, indoxyl sulfate, isocitrate, and trimethylamine were identified as differentiating between infected and control animals. These correlation analyses facilitated structural elucidation using the identification power of one technique to enhance and validate the other, but also highlighted the enhanced ability to detect functional correlations between metabolites, thereby providing potential for achieving deeper mechanistic insight into the biological process.