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Abstract Background Despite extensive research, the identification of effective biomarkers for early prediction of preterm birth (PTB) continues to be a challenging endeavor. This study aims to identify amniotic fluid (AF) protein biomarkers useful for the early diagnosis of PTB. Methods We initially identified the protein expression profiles in the AF of women with PTB (n = 22) and full-term birth (FTB, n = 22), from the First People’s Hospital of Yunnan Province who underwent amniocentesis from November 2019 to February 2020, using mass spectrometry employing the data-independent acquisition (DIA) technique, and then analyzed differentially expressed proteins (DEPs). Subsequently, the least absolute shrinkage and selection operator (LASSO) and random forest analysis were employed to further screen the key proteins for PTB biomarker identification. The receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analyses (DCA) were utilized to assess the discrimination and calibration of the key biomarkers. Results A total of 25 DEPs were identified between the PTB and FTB groups, comprising 13 up-regulated and 12 down-regulated proteins. Three key protein biomarkers for early PTB diagnosis were identified: IL1RL1 (interleukin-1 receptor-like 1), APOE (apolipoprotein E), and NECTIN4 (nectin cell adhesion molecule 4). The results of the ROC analysis showed that the area under the curve (AUC) of the three proteins combined as a biomarker for early diagnosis of PTB was 0.913 (95% CI: 0.823-1.000), with a sensitivity of 0.864 and a specificity of 0.955, both superior to those of the individual biomarkers. Bootstrap internal validation revealed a concordance index (C-index) of 0.878, with a sensitivity of 0.812 and a specificity of 0.773, indicating the robust predictive performance of these biomarkers. Conclusions We identified three previously unexplored yet potentially useful protein biomarkers in AF for early PTB diagnosis: IL1RL1, APOE, and NECTIN4.

IntroductionLow skeletal muscle mass and high adipose tissue coexist across the body weight spectrum and independently predict the survival ratio of colorectal cancer (CRC) patients. This combination may lead to a mutually exacerbating vicious cycle. Tumor-associated metabolic conditions primarily affect subcutaneous adipose tissue, but the nature and direction of its relationship with skeletal muscle are unclear. This study aims to examine the bidirectional causal relationship between skeletal muscle index (SMI) and subcutaneous fat index (SFI) during the perioperative period in CRC patients; as well as to validate the association between perioperative SMI, SFI, and CRC prognosis.MethodsThis population-based retrospective cohort study included patients with stage I-III colorectal cancer who underwent radical resection at the Third Affiliated Hospital of Kunming Medical University between September 2012 and February 2019. Based on inclusion and exclusion criteria, 1,448 patients were analyzed. Preoperative (P1), 2 months postoperative (P2), and 5 months postoperative (P3) CT scans were collected to evaluate the skeletal muscle index (SMI; muscle area at the third lumbar vertebra divided by height squared) and subcutaneous fat index (SFI; subcutaneous fat area at the third lumbar vertebra divided by height squared). A random intercept cross-lagged panel model (RI-CLPM) was used to examine the intra-individual relationship between SMI and SFI, and Cox regression was employed to assess the association between SMI, SFI, recurrence-free survival (RFS), and overall survival (OS).ResultsThe median age at diagnosis was 59.00 years (IQR: 51.00–66.00), and 587 patients (40.54%) were female. RI-CLPM analysis revealed a negative correlation between SFI and subsequent SMI at the individual level: P1-P2 (β = −0.372, p = 0.038) and P2-P3 (β = −0.363, p = 0.001). SMI and SFI showed a negative correlation during P1-P2 (β = −0.363, p = 0.001) but a positive correlation during P2-P3 (β = 0.357, p = 0.006). No significant correlation was found between the random intercepts of SFI and SMI at the between-person level (r = 0.157, p = 0.603). The Cox proportional hazards multivariate regression model identified that patients with elevated SFI had poorer recurrence-free survival (HR, 1.24; 95% CI: 1.00–1.55). Compared to patients with normal preoperative SMI and SFI, those with low SMI or high SFI had poorer recurrence-free survival (HR, 1.26; 95% CI: 1.03–1.55) and overall survival (HR, 1.39; 95% CI: 1.04–1.87). However, no significant association between SMI and SFI and the prognosis of colorectal cancer patients was observed postoperatively.ConclusionIn CRC patients, preoperative muscle loss leads to postoperative fat accumulation, exacerbating muscle loss in a feedback loop. Elevated preoperative SFI predicts poorer survival outcomes. Monitoring SMI and SFI is crucial as prognostic indicators, despite non-significant postoperative associations. Further research is needed to improve patient outcomes.

BackgroundInsulin resistance (IR) is closely related to the development of cardiovascular diseases. Triglyceride-glucose-body mass index (TyG-BMI) has been proven to be a reliable surrogate of IR, but the relationship between TyG-BMI and acute myocardial infarction (AMI) is unknown. The present study aims to determine the effects of TyG-BMI on the clinical prognosis of critically ill patients with AMI.MethodsThe data of AMI patients were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. All patients were divided into four groups according to the TyG-BMI quartile. Outcomes were defined as 30-, 90-, 180-, and 365-day all-cause mortality. Kaplan–Meier (K-M) curve was used to compare survival rate between groups. Meanwhile, Cox regression analysis and restricted cubic splines (RCS) were used to explore the relationship between TyG-BMI index and outcome events.ResultsA total of 1,188 critically ill patients with AMI were included in this study. They were divided into four groups according to TyG-BMI quartiles, there were significant differences in 90-, 180-, and 365-day all-cause mortality while there was no difference in 30-day all-cause mortality. Interestingly, with the increase of TyG-BMI, the 90-, 180-, and 365-day survival rate increased first and then gradually decreased, but the survival rate after decreasing was still higher than that in the group with the lowest TyG-BMI. U-shaped relationships between TyG-BMI index and 90-, 180-, and 365-day all-cause mortality were identified using RCS curve and the inflection point was 311.1, 316.5, and 320.1, respectively, whereas the TyG-BMI index was not non-linearly associated with 30-day all-cause mortality. The results of Cox proportional hazard regression analysis are consistent with those of RCS analysis.ConclusionU-shaped relationships are existed between the TyG-BMI index and 90-, 180-, and 365-day all-cause mortality in critically ill patients with AMI, but not 30-day all-cause mortality. The TyG-BMI index can be used as an effective index for early prevention of critically ill patients with AMI.