HDL Function across the Lifespan: From Childhood, to Pregnancy, to Old Age

The Nonlinear Relationship Between High-Density Lipoprotein and Changes in Pulmonary Structure Function and Pulmonary Function in COPD Patients in China

Background

The previous findings on the correlation between spirometry and high-density lipoprotein (HDL) cholesterol are intriguing yet conflicting. The aim of this research is to evaluate the relationship between HDL levels and spirometry as well as imaging parameters in patients with chronic obstructive pulmonary disease (COPD) in China.

Methods

This study encompasses a total of 907 COPD patients. Participants with complete data from questionnaire interviews, lipid profile examinations, spirometry testing, and computed tomography (CT) scans were included in the analysis. A generalized additive model was employed to identify the non-linear relationship between HDL levels and both spirometry and imaging parameters. In the presence of non-linear correlations, segmented linear regression model was applied to ascertain threshold effects.

Results

After adjusting for various factors, we found a non-linear correlation between HDL levels and spirometry/imaging parameters, with an inflection point at 4.2 (66 mg/dL). When Ln (HDL) was below 4.2, each unit increase correlated significantly with reduced post-bronchodilator FEV1 (0.32L, 95% CI: 0.09-0.55), decreased predicted FEV1% (11.0%, 95% CI: 2.7-19.3), and lowered FEV1/FVC (8.0%, 95% CI: 4.0-12.0), along with notable increases in Ln (LAA-950) by 1.20 (95% CI: 0.60-1.79) and Ln (LAA-856) by 0.77 (95% CI: 0.37-1.17). However, no significant associations were observed when Ln (HDL) was greater than or equal to 4.2.

Conclusion

A non-linear correlation existed between HDL levels with lung function and CT imaging in COPD patients. Prior to reaching 66 mg/dL, an elevation in HDL was significantly associated with impaired lung function, more severe gas trapping and emphysema.

Assessing the risk of prenatal depressive symptoms in Chinese women: an integrated evaluation of serum metabolome, multivitamin supplement intake, and clinical blood indicators

Background

Prenatal depressive symptoms (PDS) is a serious public health problem. This study aimed to develop an integrated panel and nomogram to assess at-risk populations by examining the association of PDS with the serum metabolome, multivitamin supplement intake, and clinical blood indicators.

Methods

This study comprised 221 pregnant women, categorized into PDS and non-PDS groups based on the Edinburgh postnatal depression scale. The participants were divided into training and test sets according to their enrollment time. We conducted logistic regression analysis to identify risk factors, and employed liquid chromatography/high resolution mass spectrometry-based serum metabolome analysis to identify metabolic biomarkers. Multiple factor analysis was used to combine risk factors, clinical blood indicators and key metabolites, and then a nomogram was developed to estimate the probability of PDS.

Results

We identified 36 important differential serum metabolites as PDS biomarkers, mainly involved in amino acid metabolism and lipid metabolism. Multivitamin intake works as a protective factor for PDS. The nomogram model, including multivitamin intake, HDL-C and three key metabolites (histidine, estrone and valylasparagine), exhibited an AUC of 0.855 in the training set and 0.774 in the test set, and the calibration curves showed good agreement, indicating that the model had good stability.

Conclusion

Our approach integrates multiple models to identify metabolic biomarkers for PDS, ensuring their robustness. Furthermore, the inclusion of dietary factors and clinical blood indicators allows for a comprehensive characterization of each participant. The analysis culminated in an intuitive nomogram based on multimodal data, displaying potential performance in initial PDS risk assessment.