Premature ovarian insufficiency (POI) is a disorder of ovarian function, which occurs in approximately 3.7% of women younger than 40 years of age.1 The aetiologies of most cases were unknown and usually manifest as isolated organ senescence at early stage of diagnosis, only with long-term consequences of increased risk of metabolic syndrome or disease.2 We tried to explore the essence of POI from multiple dimensions. First, we found that upregulated genes in POI patients' ovarian granulosa cells were mainly enriched in metabolic pathways. Then, we profiled metabolites in the patients' sera and identified some metabolites with changed levels, including fumarate, arachidonate and acetoacetate, and the combination of hyodeoxycholic acid (HDCA) and acetoacetate may be used as one of potential biomarkers for POI. In addition, we found genes with upregulated transcription levels and differential methylated levels were enriched in oxidative stress pathways, which can be also elucidated from abnormal metabolism.The transcriptomes of patient and control granulosa cells were profiled, and 3966 differentially expressed genes (DEGs) (fold change > 2, Padj < .05) were identified (Table S1). Gene ontology analyses found that downregulated DEGs were mainly related to extracellular matrix/structure organization and different types of junctions. The upregulated DEGs were enriched in various catabolic/metabolic/biosynthetic/processes (Figure 1A). KEGG (Kyoto Encyclopaedia of Genes and Genomes) analysis showed that cytokine–cytokine receptor interaction, PI3K-Akt-signalling pathway and MAPK-signalling pathway were enriched in the downregulated genes, whereas the metabolic pathway was the most enriched in the upregulated genes. Moreover, other pathways, such as amino acid degradation, synthesis, peroxisome and oxidative phosphorylation, were also enriched in upregulated DEGs (Figure 1B). Several previous studies present valuable information about metabolic features of POI, which mainly focus on total cholesterol, lipoprotein cholesterol and glucose metabolism.3-6 Elevated levels of free fatty acids in follicular fluid were also reported as a potential driver of human POI.7 However, many of these results were inconsistent and not so comprehensive, although they have been noteworthy. Therefore, we carried out metabolomic analyses on serum samples from POI patients and controls. The outlined demographic characteristics of all recruited patients and controls were shown (Table 1). Two cohorts were enrolled in the metabolomic analyses in this study (Figure 2A). In the discovery cohort, a total of 6 categories of 181 features were reliably detected. POI patients could be clearly distinguished from controls by conducting multivariate statistical analysis (Figure 2B). Sixty-two features with VIP (variable importance in projection) values greater than 1 were identified and considered as differential metabolites (DMets). Subsequently, 26 of the 63 DMets were found to be significantly different between POI patients and controls using univariate t test analysis (p < .05) (Figure 2C; Table S2). Next, 32 out of the 62 features were validated using the validation cohort (Table S3). The numbers of metabolites in the union and intersections of the DMets identified from the discovery and validation cohorts were 39 and 13, respectively. Pearson's correlation coefficient figure and heat map analysis of the 39 DMets are shown (Figure S1). Enrichment and pathway analysis of the 39 DMets are shown respectively (Figure 2D,E). Out of the 13 validated DMets, 5 (acetoacetate, glutamate, C22:1, arachidonate and succinate) and 4 (HDCA, C22:0, homocitrulline [Hcit] and fumarate) were increased and decreased in the sera of POI patients, respectively. In addition, ROS and LDH (lactic dehydrogenase) were increased, and NADH and ATP were decreased in patients (Figure 2F). Receiver-operating characteristic (ROC) analysis was performed and 8 metabolites had area under curve (AUCs) greater than .7. In addition, the top two combinations with the highest AUCs were NADH plus acetoacetate (.868) and HDCA plus acetoacetate (.955) (Figure 2G,H). Furthermore, we reanalysed the human granulosa cell whole-genome DNA methylation profiling data that we previously generated.7 A number of 536 genes were hypermethylated in their differentially methylated regions (DMRs), whereas 1583 genes with hypomethylated DMRs were identified (Table S4; Figure S2). Integrating our transcriptomic and methylomic data, we found that 240 differentially expressed and methylated genes (DEMGs) were mainly enriched in response to oxidative stress, response to chemical stress, reactive oxygen species and hydrogen peroxide (Table S5; Figure S3A,B). DNMT3, DNMT1, methylenetetrahydrofolate reductase) and 5-methyltetrahydrofolate–homocysteine methyltransferase reductase, involved in methionine cycle, were differently expressed in POI patients' granulosa cells (Figure S3C).
