PTGS2, HAS2 and GREM1 gene expression correlates to morphological and physiological characteristics and provides a novel approach to predict human embryo development. Ultimately, with better predictors of follicular and embryonic health, higher quality embryos can be selected and transferred, reducing higher order pregnancy rates.

Prediction and improvement of oocyte competence are two critical issues in assisted reproductive technology to improve infertility therapy. The lack of reliable and objective predictors of oocyte developmental competence for oocyte/embryo selection during in vitro fertilization hampers the effectiveness of this technology. Likewise, the low pregnancy rate resulting from in vitro maturation of human oocytes represents a major obstacle for its clinical application. Oocyte competence is progressively acquired during follicular development, and the oocyte plays a dominant role in regulating granulosa cell functions and maintaining the microenvironment appropriate for the development of its competence. Hence, granulosa cell functions are reflective of oocyte competence, and molecular markers of granulosa cells are potentially reliable predictors of oocyte quality. With the advent of the functional genomics era, the transcriptome of granulosa cells has been extensively characterized. Experimental data supporting granulosa cell markers as predictors of oocyte competence are now emerging in both animal models and humans. Future efforts should focus on integrating granulosa cell genetic markers as parameters for oocyte/embryo selection. Moreover, novel in vitro evidence highlights the effectiveness of exogenous oocyte-secreted factors in promoting oocyte developmental competence in animal models. The challenge in evaluating the effect of oocyte-secreted factors on oocyte quality in a clinical setting is to standardize the various preparations of these recombinant proteins and decipher their complex interactions/cooperativity within the germline-somatic cell regulatory loop.

Background: Many CpGs become hyper or hypo-methylated with age. Multiple methods have been developed by Horvath et al. to estimate DNA methylation (DNAm) age including Pan-tissue, Skin & Blood, PhenoAge, and GrimAge. Pan-tissue and Skin & Blood try to estimate chronological age in the normal population whereas PhenoAge and GrimAge use surrogate markers associated with mortality to estimate biological age and its departure from chronological age. Here, we applied Horvath's four methods to calculate and compare DNAm age in 499 subjects with type 1 diabetes (T1D) from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study using DNAm data measured by Illumina EPIC array in the whole blood. Association of the four DNAm ages with development of diabetic complications including cardiovascular diseases (CVD), nephropathy, retinopathy, and neuropathy, and their risk factors were investigated. Results: Pan-tissue and GrimAge were higher whereas Skin & Blood and PhenoAge were lower than chronological age (p < 0.0001). DNAm age was not associated with the risk of CVD or retinopathy over 18-20 years after DNAm measurement. However, higher PhenoAge (β = 0.023, p = 0.007) and GrimAge (β = 0.029, p = 0.002) were associated with higher albumin excretion rate (AER), an indicator of diabetic renal disease, measured over time. GrimAge was also associated with development of both diabetic peripheral neuropathy (OR = 1.07, p = 9.24E−3) and cardiovascular autonomic neuropathy (OR = 1.06, p = 0.011). Both HbA1c (β = 0.38, p = 0.026) and T1D duration (β = 0.01, p = 0.043) were associated with higher PhenoAge. Employment (β = − 1.99, p = 0.045) and leisure time (β = − 0.81, p = 0.022) physical activity were associated with lower Pan-tissue and Skin & Blood, respectively. BMI (β = 0.09, p = 0.048) and current smoking (β = 7.13, p = 9.03E−50) were positively associated with Skin & Blood and GrimAge, respectively. Blood pressure, lipid levels, pulse rate, and alcohol consumption were not associated with DNAm age regardless of the method used. Conclusions: Various methods of measuring DNAm age are sub-optimal in detecting people at higher risk of developing diabetic complications although some work better than the others.

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