Metabolites

Background/Objectives: Diel reprogramming of central metabolism is well documented in vascular plants; whether diatoms organize carbon and nitrogen metabolism in the same way is unknown for want of absolutely quantified metabolite time series covering a complete light/dark cycle. Methods: Wild-type Phaeodactylum tricornutum was grown under a 12 h light/12 h dark cycle and sampled at the 3rd, 6th, and 12th h of both phases (n = 3 per time point; 18 samples). Ninety metabolites were detected and absolutely quantified by targeted liquid chromatography–tandem mass spectrometry (LC–MS/MS), and the 72 metabolites were retained for downstream analysis after excluding those with extensive missingness; time effects were tested by one-way analysis of variance (ANOVA) (p < 0.05). Results: Forty metabolites (55.6%) changed significantly over time and clustered into five diel patterns. The citrate branch of the tricarboxylic acid (TCA) cycle declined in the light and recovered in the dark, malate and fumarate peaked at early dark (D3), and α-ketoglutarate reached its minimum at D3. Photorespiratory intermediates were generally higher in the light, except glyoxylate (D3 peak). Glutamate-family pools bottomed out at the end of the light phase; ornithine fell monotonically, the largest drop among measured amino acids. Conclusions: Central carbon and nitrogen metabolism of P. tricornutum undergoes phased reallocation over the light/dark cycle. Pool dynamics suggest a diel, bidirectional exchange of carbon skeletons between the TCA cycle and amino acid metabolism via the α-ketoglutarate node, with early-dark four-carbon (C4) accumulation coinciding with amino acid catabolic replenishment (carbon origin awaits ¹³C tracing). Photorespiratory intermediates followed a light-high, dark-low pattern consistent with suppression, but not abolition, of photorespiration by the CO₂-concentrating mechanism (CCM). This dataset provides a metabolite-level, absolutely quantified reference for diatom diel metabolism.

Background: Cardiovascular disease (CVD) remains the leading cause of mortality globally. The associations between composite indices integrating body composition and lipid-related metabolic characteristics and CVD remain insufficiently investigated. We therefore constructed the skeletal muscle-fat distribution metabolic index (SMFDMI) and explored its association with CVD. Methods: Data were derived from the China Health and Retirement Longitudinal Study. The prospective analysis included 6785 participants aged ≥45 years followed from 2011 to 2020, while the cross-sectional analysis included 8279 participants. Cox and logistic regression models, restricted cubic splines, subgroup analyses, and sensitivity analyses were performed. Results: The cohort and cross-sectional analyses included participants with median ages of 58 and 59 years and male proportions of 47.19% and 46.42%, respectively. In Model 3, higher SMFDMI was associated with a lower risk of incident CVD, with a 25% lower risk in Q5 than in Q1 (HR = 0.75, 95% CI: 0.63–0.89). The cross-sectional analysis also showed an inverse association with prevalent CVD. Restricted cubic splines suggested an approximately N-shaped nonlinear relationship between SMFDMI and CVD risk. Subgroup analyses showed a stronger inverse association in males (Q5 vs. Q1: HR = 0.62, 95% CI: 0.48–0.81) and in those without chronic diseases. An alternative index yielded similar findings. Conclusions: Higher SMFDMI was associated with a lower risk of incident CVD in middle-aged and older Chinese adults. SMFDMI may provide additional insights into the relationship between muscle health and cardiovascular health during aging, with potential relevance to primary healthcare.

Background: Bile acids regulate glucose metabolism through enterohepatic signaling and gut microbiota-mediated transformations. However, the role of specific bile acid subtypes, particularly lithocholic acid (LCA) and its derivatives, in pediatric obesity remains incompletely understood. Methods: In this cross-sectional study, 149 children with obesity underwent comprehensive metabolic assessment, including a 75-g oral glucose tolerance test (OGTT) and fasting serum bile acid profiling using liquid chromatography–tandem mass spectrometry (LC–MS/MS). Bile acid-derived indices were used for unsupervised clustering to identify metabolic phenotypes. Associations between LCA-related indices and dysglycemia (defined as HbA1c ≥ 5.7% and/or 2-h plasma glucose ≥ 7.8 mmol/L) were evaluated using multivariable regression models. The LCA derivative sum was analyzed separately using tertile categories and continuous linear and quadratic models. Cluster-stratified patterns were further assessed. Results: Three bile acid-derived clusters with distinct metabolic profiles were identified. Despite similar anthropometric characteristics, clusters differed significantly in glycemic status and insulin dynamics. LCA-related bile acid indices showed a non-monotonic redistribution across clusters. Compared with the lowest tertile, adjusted odds ratios for dysglycemia were 1.44 (95% CI: 0.62–3.37) for the intermediate tertile and 0.33 (0.13–0.83) for the highest tertile. The continuous linear model showed an inverse association per SD increase (OR 0.646 [0.441–0.946]). A separate continuous quadratic model suggested an inverted U-shaped exposure–response pattern, describing variation across the continuous exposure range rather than comparisons between tertile categories. Interaction analyses did not demonstrate a statistically significant modification of the association for the primary outcome, although analyses of continuous glycemic traits suggested potential heterogeneity across clusters. Conclusions: Bile acid-based clustering identifies distinct metabolic phenotypes in pediatric obesity. LCA-related bile acid metabolism shows nonlinear associations with dysglycemia and may exhibit heterogeneity across metabolic phenotypes. These findings highlight the importance of considering bile acid subtype specificity and metabolic context in understanding bile acid–glucose interactions.