The effects of serum granulin levels on anthropometric measures and glucose metabolism in infertile women with different ovarian reserve status.

INTRODUCTION: Granulin (GRN) is an adipokine with proinflammatory features, which plays important role in glucose metabolism and insulin resistance pathogenesis. It has been reported that granulin precursors were localised in developing follicles in animal studies. The purpose of this study was to evaluate the association of granulin levels with anthropometric features, glucose metabolism, and ovarian reserve. MATERIAL AND METHODS: A total of 109 infertile women were included in this cross-sectional, prospective study, who attended a tertiary clinic. All participants were categorised into diminished ovarian reserve (DOR) and normal ovarian reserve groups (NOR), in accordance with Bologna criteria. The demographic characteristics, including age, BMI, waist-hip circumferences, and biochemical parameters, were recorded. Serum granulin level was determined by enzyme-linked immunosorbent assay. RESULTS: No significant difference was observed in the GRN levels (p = 0.229) between the groups. There was a positive correlation between GRN levels and BMI, WC, HC, and 75 g oral glucose tolerance values in NOR group (p < 0.01, p < 0.05, p < 0.01, and p < 0.05, respectively). CONCLUSIONS: Our results suggest that granulin is associated with anthropometric features in infertile patients and might be an important indicator of obesity and impaired glucose metabolism. Elevated levels of granulin may have a diabetogenic effect and predispose women to high glucose levels.

A proteomic clock of human pregnancy.

BACKGROUND: Early detection of maladaptive processes underlying pregnancy-related pathologies is desirable because it will enable targeted interventions ahead of clinical manifestations. The quantitative analysis of plasma proteins features prominently among molecular approaches used to detect deviations from normal pregnancy. However, derivation of proteomic signatures sufficiently predictive of pregnancy-related outcomes has been challenging. An important obstacle hindering such efforts were limitations in assay technology, which prevented the broad examination of the plasma proteome. OBJECTIVE: The recent availability of a highly multiplexed platform affording the simultaneous measurement of 1310 plasma proteins opens the door for a more explorative approach. The major aim of this study was to examine whether analysis of plasma collected during gestation of term pregnancy would allow identifying a set of proteins that tightly track gestational age. Establishing precisely timed plasma proteomic changes during term pregnancy is a critical step in identifying deviations from regular patterns caused by fetal and maternal maladaptations. A second aim was to gain insight into functional attributes of identified proteins and link such attributes to relevant immunological changes. STUDY DESIGN: Pregnant women participated in this longitudinal study. In 2 subsequent sets of 21 (training cohort) and 10 (validation cohort) women, specific blood specimens were collected during the first (7-14 weeks), second (15-20 weeks), and third (24-32 weeks) trimesters and 6 weeks postpartum for analysis with a highly multiplexed aptamer-based platform. An elastic net algorithm was applied to infer a proteomic model predicting gestational age. A bootstrapping procedure and piecewise regression analysis was used to extract the minimum number of proteins required for predicting gestational age without compromising predictive power. Gene ontology analysis was applied to infer enrichment of molecular functions among proteins included in the proteomic model. Changes in abundance of proteins with such functions were linked to immune features predictive of gestational age at the time of sampling in pregnancies delivering at term. RESULTS: An independently validated model consisting of 74 proteins strongly predicted gestational age (P = 3.8 × 10-14, R = 0.97). The model could be reduced to 8 proteins without losing its predictive power (P = 1.7 × 10-3, R = 0.91). The 3 top ranked proteins were glypican 3, chorionic somatomammotropin hormone, and granulins. Proteins activating the Janus kinase and signal transducer and activator of transcription pathway were enriched in the proteomic model, chorionic somatomammotropin hormone being the top-ranked protein. Abundance of chorionic somatomammotropin hormone strongly correlated with signal transducer and activator of transcription-5 signaling activity in CD4 T cells, the endogenous cell-signaling event most predictive of gestational age. CONCLUSION: Results indicate that precisely timed changes in the plasma proteome during term pregnancy mirror a proteomic clock. Importantly, the combined use of several plasma proteins was required for accurate prediction. The exciting promise of such a clock is that deviations from its regular chronological profile may assist in the early diagnoses of pregnancy-related pathologies, and point to underlying pathophysiology. Functional analysis of the proteomic model generated the novel hypothesis that chrionic somatomammotropin hormone may critically regulate T-cell function during pregnancy.