BackgroundBone mineral density (BMD) loss is a major complication of menopause, and this loss is closely associated with Fat mass (FM). The relationship between FM, fat distribution (FD), and BMD in postmenopausal women, however, remains incompletely understood. The present study was thus developed to explore these associations between body fat accumulation, FD, and BMD among non-obese postmenopausal women over the age of 60.MethodsThis was a cross-sectional analysis of 357 healthy postmenopausal women between the ages of 60.2 and 86.7 years. Dual-energy X-ray absorptiometry (DXA) was utilized to measure total and regional BMD as well as fat-related parameters including total FM, android and gynoid fat, body fat percentage (BF%), and total lean mass (LM) for all subjects. The android-to-gynoid fat ratio (AOI) was used to assess FD. Pearson’s correlation testing and multiple regression analyses were used to explore relationships among AOI, LM, FM, and BMD.ResultsBoth LM and FM were positively correlated with total and regional BMD in univariate analysis (all P < 0.01), whereas BMD was not significantly associated with AOI in any analyzed site other than the head. Multivariate linear regression models corrected for age, height, and years post-menopause, revealed a sustained independent positive relationship between FM and BMD (standard β range: 0.141 – 0.343, P < 0.01). The relationship between FM and BMD was unaffected by adjustment for LM (standard β range: 0.132 – 0.258, P < 0.01), whereas AOI had an adverse impact on BMD at most analyzed skeletal sites (total body, hip, femoral neck, arm, leg, and head) (standard β range: −0.093 to −0.232, P < 0.05). These findings were unaffected by using BF% in place of FM (standard β range: −0.100 to −0.232, P < 0.05).ConclusionsIn this cohort of non-obese postmenopausal women over the age of 60 from China, total FM was positively associated with BMD, while AOI was negatively correlated with BMD. As such, a combination of proper weight gain and the control of central obesity may benefit the overall bone health of women after menopause.

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease associated with exposure to repetitive head impacts, which is susceptible in elderly people with declined mobility, athletes of full contact sports, military personnel and victims of domestic violence. It has been pathologically diagnosed in brain donors with a history of repetitive mild traumatic brain injury (rmTBI), but cannot be clinically diagnosed for a long time. By the continuous efforts by neuropathologists, neurologists and neuroscientists in recent 10 years, an expert consensus for the diagnostic framework of CTE was proposed in 2021 funded by the National Institute of Neurological Disorders and Stroke. The new consensus contributes to facilitating research in the field. However, it still needs to incorporate in vivo biomarkers to further refine and validate the clinical diagnostic criteria. From this, a single-center, observational cohort study has been being conducted by Tianjin Medical University General Hospital since 2021. As a pilot study of this clinical trial, the present research recruited 12 pairs of gender- and age-matched rmTBI patients with healthy subjects. Their blood samples were collected for exosome isolation, and multi-omics screening to explore potential diagnostic biomarkers in blood and its exosomes. The expression level of CHL1 protein, KIF2A mRNA, LIN7C mRNA, miR-297, and miR-1183 in serum and exosomes were found to be differentially expressed between groups. Besides, serum and exosomal CHL1, KIF2A, and miR-1183, as well as exosomal miR-297 were further verified as potential biomarkers for CTE by low-throughput assays. They are expected to contribute to establishing a novel set of CTE diagnostic signatures with classic neurodegenerative indicators in our future study, thereby updating the consensus diagnostic criteria for CTE by incorporating new evidence of the in vivo biomarkers.

Objective: The issue of when to start treatment in patients with hyperuricemia (HUA) without gout and chronic kidney disease (CKD) is both important and controversial. In this study, Raman spectroscopy (RS) was used to analyze urine samples, and key genes expressed differentially CKD were identified using bioinformatics. The biological functions and regulatory pathways of these key genes were preliminarily analyzed, and the relationship between them as well as the heterogeneity of the urine components of HUA was evaluated. This study provides new ideas for the rapid evaluation of renal function in patients with HUA and CKD, while providing an important reference for the new treatment strategy of HUA disease.Methods: A physically examined population in 2021 was recruited as the research subjects. There were 10 cases with normal blood uric acid level and 31 cases with asymptomatic HUA diagnosis. The general clinical data were collected and the urine samples were analyzed by Raman spectroscopy. An identification model was also established by using the multidimensional multivariate method of orthogonal partial least squares discriminant analysis (OPLS-DA) model for statistical analysis of the data, key genes associated with CKD were identified using the Gene Expression Omnibus (GEO) database, and key biological pathways associated with renal function damage in CKD patients with HUA were analyzed.Results: The Raman spectra showed significant differences in the levels of uric acid (640 cm−1), urea, creatinine (1,608, 1,706 cm−1), proteins/amino acids (642, 828, 1,556, 1,585, 1,587, 1,596, 1,603, 1,615 cm−1), and ketone body (1,643 cm−1) (p < 0.05). The top 10 differentially expressed genes (DEGs) associated with CKD (ALB, MYC, IL10, FOS, TOP2A, PLG, REN, FGA, CCNA2, and BUB1) were identified. Compared with the differential peak positions analyzed by the OPLS-DA model, it was found that the peak positions of glutathione, tryptophan and tyrosine may be important markers for the diagnosis and progression of CKD.Conclusion: The progression of CKD was related to the expression of the ALB, MYC, IL10, PLG, REN, and FGA genes. Patients with HUA may have abnormalities in glutathione, tryptophan, tyrosine, and energy metabolism. The application of Raman spectroscopy to analyze urine samples and interpret the heterogeneity of the internal environment of asymptomatic HUA patients can be combined with the OPLS-DA model to mine the massive clinical and biochemical examination information on HUA patients. The results can also provide a reference for identifying the right time for intervention for uric acid as well as assist the early detection of changes in the internal environment of the body. Finally, this approach provides a useful technical supplement for exploring a low-cost, rapid evaluation and improving the timeliness of screening. Precise intervention of abnormal signal levels of internal environment and energy metabolism may be a potential way to delay renal injury in patients with HUA.