The study examined the relationship between menopausal symptoms and sleep disturbances and the related influencing factors. Methods. We recruited women aged 40-65 years who attended the menopause clinic at Shanghai Jiao Tong University’s Sixth People’s Hospital from February 2011 to November 2019. The Menopause Rating Scale (MRS) was used to collect women’s menopausal symptoms, and the Pittsburgh Sleep Quality Index (PSQI) was used to evaluate the subjects’ sleep condition. We used logistic regression models to identify the relationship between menopausal symptoms and sleep quality. Results. A total of 1341 participants were recruited in this study. The most frequent three symptoms assessed by MRS were fatigue (72.9%), sleep disturbance (67%), and hot flashes with night sweats (65%). Participants’ age was significantly associated with the severity of menopausal syndrome ( P < 0.01 ). According to the PSQI sleep evaluation, 66.9 percent of participants had sleep disturbances ( PSQI > 5 ). Logistic regression analysis revealed that women with mild, moderate, or severe menopausal syndrome had a 3-, 7-, and 17-fold increased chance of having sleep disturbances compared to women without menopausal syndrome. Conclusion. Women aged 40–65 years were found to have a significantly higher risk of menopausal syndrome and sleep disturbances.

Objective To explore whether functional near‐infrared spectroscopy (fNIRS) can aid in the early detection and diagnosis of postpartum depression. Methods The study was a cross‐sectional survey that invited all women who sought postpartum health examination 42 days after childbirth between August 2020 and January 2021. Personal information, Edinburgh Postnatal Depression Scale (EPDS), and well as fNIRS results were collected. Results In all, 109 individuals agreed to participate and completed the examination in its entirety. The variance in integral and centroid values was not statistically significant across different subgroups of depression (P > 0.05). The difference in diagnosis of postpartum major depression between EPDS and fNIRS was statistically significant (P < 0.001). fNIRS results in postpartum depression diagnosis were substantially associated with gestational diabetes mellitus (P = 0.027), the number of pregnancies (P = 0.001), and postpartum body mass index (P = 0.035). Conclusion fNIRS can provide an objective method for early detection and diagnosis of postpartum depression. Certain clinical conditions can have an effect on brain activity, which may result in postpartum depression. Additional high‐quality study is required to establish strong evidence on the subject.

ObjectiveThis study established a model to predict the risk of diabetic retinopathy (DR) with amino acids selected by partial least squares (PLS) method, and evaluated the effect of metformin on the effect of amino acids on DR in the model.MethodsIn Jinzhou, Liaoning Province, China, we retrieved 1031 patients with type 2 diabetes (T2D) from the First Affiliated Hospital of Liaoning Medical University. After sorting the amino acids using the PLS method, the top 10 amino acids were included in the model. Multivariate logistic regression was used to analyze the relationship between different amino acids and DR. And then the effects of metformin on amino acids were explored through interaction. Finally, Spearman’s rank correlation analysis was used to analyze the correlation between different amino acids.ResultsAfter sorting by PLS, Gly, Pro, Leu, Lyr, Glu, Phe, Tyr, His, Val and Ser were finally included in the DR risk prediction model. The predictive model after adding amino acids was statistically different from the model that only included traditional risk factors (p=0.001). Metformin had a significant effect on the relationship between DR and 7 amino acids (Gly, Glu, Phe, Tyr, His, Val, Ser, p&lt;0.05), and the population who are not using metformin and have high levels of Glu (OR: 0.44, 95%CI: 0.27-0.71) had an additive protection effect for the occurrence of DR. And the similar results can be seen in high levels of Gly (OR: 0.46, 95%CI: 0.29-0.75), Leu (OR: 0.48, 95%CI: 0.29-0.8), His (OR: 0.46, 95%CI: 0.29-0.75), Phe (OR: 0.24, 95%CI: 0.14-0.42) and Tyr (OR: 0.41, 95%CI: 0.24 -0.68) in population who are not using metformin.ConclusionsWe established a prediction model of DR by amino acids and found that the use of metformin reduced the protective effect of amino acids on DR developing, suggesting that amino acids as biomarkers for predicting DR would be affected by metformin use.