Second People's Hospital of Nanning

To examine the association of individual and multi-nutrient intake with inflammation indicators, developing an interpretable machine learning model to assess nutrient impacts on systemic inflammation in a Chinese population.

A total of 874 participants were recruited in this cross-sectional study. Information of dietary intake was obtained via face-to-face interviews on 24-hour food intake and dietary consumption frequency using validated questionnaires. Systemic Inflammatory indices were assessed based on the peripheral blood count. The associations were estimated by linear regression models or restricted cubic splines (RCS) approach. Six machine learning models were developed to evaluate the contribution of nutrient on systemic inflammation, with the optimal model interpreted via SHapley additive explanation (SHAP).

Regression analysis showed higher intake of Cu and Zn was positively associated with higher value of lymphocyte-to-monocyte ratio (LMR), an anti-inflammatory index (β = 0.06. 95% CI: 0.02-0.10; β = 0.07. 95% CI: 0.01-0.13, respectively). A dose-dependent manner was observed for the Cu-LMR association. The random forest model demonstrated superior performance for LMR prediction (R²=0.32). SHAP interpretation identified sex, Cu intake, and age as the top predictors, with higher intake of Cu, Zn, carbohydrates, vitamin B3, dietary fiber, and iron correlating with elevated LMR predictions.

Higher dietary intake of Cu and Zn were significantly associated with lower levels of systemic inflammation. The findings integrating machine learning highlights the potential benefits of optimizing dietary Cu and Zn intake to mitigate systemic inflammation.

Adrenocortical hormones, particularly 11-oxygenated androgens, are pivotal in female reproductive health and fertility. Standardized detection kits and population-specific reference intervals are lacking in China, hindering related clinical applications.

A HPLC-tandem mass spectrometry (HPLC-MS/MS) pipeline was developed, rigorously validated, and applied to simultaneously quantify corticosterone, cortisone, cortisol, 18-OH cortisol, androstenedione (A4), 11β-hydroxyandrostenedione (11-OH A4), dehydroepiandrosterone, and dehydroepiandrosterone sulfate in serum samples from 455 reproductive-aged women (18-45 yrs) in Guangxi, China. Age-dependent concentration trends were analyzed, and reference intervals stratified by age (2.5th to 97.5th percentiles) were established. Correlations with body-composition metrics, ethnicity, and the menstrual cycle were investigated.

The HPLC-MS/MS method demonstrated high precision (intra- and inter-assay CVs <15%), accuracy, and sensitivity. All eight hormones exhibited significant age-related declines (P <0.001 for seven hormones; P =0.001 for 11-OH A4). Notably, 11-OH A4 levels were significantly lower in the 35-45-yr (3.05 nmol/L) and 25-34-yr (3.09 nmol/L) age groups than in the 18-24-yr (3.57 nmol/L) age group, whereas no significant difference was observed between the 35-45-yr and 25-34-yr age groups. Weak negative correlations were observed between the body mass index and corticosterone and cortisone levels, whereas ethnicity and the menstrual cycle showed no significant associations with hormone levels.

We developed an HPLC-MS/MS-based method for simultaneously quantifying eight adrenocortical hormones, including 11-OH A4, and defined age-specific reference intervals for reproductive-aged Chinese women. These findings advance the clinical utility of adrenocortical hormones in diagnosing and managing reproductive disorders.

Psoriatic arthritis (PsA) and Crohn disease (CD) are chronic immune-mediated illnesses that afflict a growing number of adults and children worldwide. Observational studies have revealed a link between PsA and CD, but the modifiable risk variables that mediate the causal effects remain unknown. We aim to look into the relationship between PsA and CD, and to see if circulating metabolites have a role in the pathophysiology of both disorders. By scanning the whole genome of a large number of people and detecting millions of genetic variation sites in the genome, we take advantage of the random assignment of genotypes in nature and use single nucleotide polymorphisms as instrumental variables to simulate the environment of randomized controlled trials, so as to infer the causal relationship between exposure factors and outcomes. Using summary statistics from genome-wide association studies of predominantly European ancestry, we used two-sample Mendelian randomization (MR) to estimate the effects of PsA on CD (1637 cases/212,242 controls; 1401 cases/461,532 controls), and two-step MR to assess the association with 1400 circulating metabolites. Genetic predisposition to PsA was associated with a higher risk of CD (pooled odds ratio, 1.00051; 95% confidence interval [CI], 1.00023–1.00078; P  < .001). The statistical association between PsA and CD was mediated by the concentrations of homocitrulline, 5-hydroxyhexanoate, carnitine C14, gamma-glutamylthreonine, furaneol sulfate, and trans-urocanate, which accounted for 4.23% (95% CI, ‐0.68% to 9.15%, P  = 4.58 × 10 ‐2 ), 7.85% (95% CI, ‐1.74% to 17.44%, P  = 9.91 × 10 ‐3 ), 6.09% (95% CI, ‐1.70% to 13.88%, P  = 3.09 × 10 ‐2 ), 7.45% (95% CI, ‐1.21% to 16.11%, P  = 1.31 × 10 ‐3 ), 8.88% (95% CI, 0.23% to 17.54%, P  = 8.39 × 10 ‐3 ), and 3.96% (95% CI, ‐0.48% to 8.40%, P  = 2.29 × 10 ‐2 ) of the total effect, respectively. Our MR analysis identified significant PsA–CD associations mediated through specific metabolites, particularly the gut microbiome-derived furaneol sulfate (showing highest mediation), supporting gut–joint axis involvement. These findings establish a crucial theoretical framework for guiding clinical practice in the management of PsA and CD.