Dietary Glycemic Index and Load and Semen Quality: A Cross-Sectional and Prospective Analysis within the FERTINUTS Trial.

PURPOSE: Infertility is a global health issue and nutrition plays a significant role in fertility outcomes. We aimed to investigate the cross-sectional and prospective associations of glycemic index (GI) and glycemic load (GL) with semen quality parameters in a cohort of healthy young men. MATERIALS AND METHODS: The study included 106 men aged 18-35 years from the FERTINUTS trial. Dietary intake was estimated through 3-day dietary records and several semen parameters were assessed. Multivariable linear regression analysis with the Least Absolute Shrinkage and Selection Operator (LASSO) approach was employed. RESULTS: The cross-sectional analysis revealed positive associations between GI and GL and total sperm count, sperm concentration, and total motility. In the prospective analysis, baseline GI was associated with increases in pH, vitality, immotile sperm or abnormal midpiece and decreases in total sperm count and motility. Conversely, GL was positively associated with changes in vitality and total sperm count. CONCLUSIONS: While these findings suggest that GI may have adverse effects on several sperm quality parameters, the results were not consistently observed in the cross-sectional analysis. However, GL was consistently associated with better sperm quality in both analyses. The impact of carbohydrate quality and quantity on fertility remains uncertain and larger prospective studies are needed.

Effects of a High-Fat Diet on Insulin-Related miRNAs in Plasma and Brain Tissue in APPSwe/PS1dE9 and Wild-Type C57BL/6J Mice.

Insulin resistance (IR)-related miRNAs have been associated with the development and progression of Alzheimer's disease (AD). The dietary modulation of these miRNAs could become a potential strategy to manage AD. The aim of this study was to evaluate the effect of a high-fat diet (HFD), which aggravates AD-related pathogenic processes, on serum, cortex and hippocampus IR-related miRNA expression. C57BL/6J WT and APPSwe/PS1dE9 mice were fed either an HFD or a conventional diet till 6 months of age. The mice fed with the HFD showed a significant increase in body weight and worsening glucose and insulin metabolism. miR-19a-3p was found to be up-regulated in the cortex, hippocampus and serum of APP/PS1 mice and in the serum and hippocampus of WT mice fed with the HFD. miR-34a-5p and miR-146a-5p were up-regulated in the serum of both groups of mice after consuming the HFD. Serum miR-29c-3p was overexpressed after consuming the HFD, along with hippocampal miR-338-3p and miR-125b-5p, only in WT mice. The HFD modulated the expression of peripheral and brain miRNAs related to glucose and insulin metabolism, suggesting the potential role of these miRNAs not only as therapeutic targets of AD but also as peripheral biomarkers for monitoring AD.

Exploring small non-coding RNAs as blood-based biomarkers to predict Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) diagnosis relies on clinical symptoms complemented with biological biomarkers, the Amyloid Tau Neurodegeneration (ATN) framework. Small non-coding RNA (sncRNA) in the blood have emerged as potential predictors of AD. We identified sncRNA signatures specific to ATN and AD, and evaluated both their contribution to improving AD conversion prediction beyond ATN alone. METHODS: This nested case-control study was conducted within the ACE cohort and included MCI patients matched by sex. Patients free of type 2 diabetes underwent cerebrospinal fluid (CSF) and plasma collection and were followed-up for a median of 2.45-years. Plasma sncRNAs were profiled using small RNA-sequencing. Conditional logistic and Cox regression analyses with elastic net penalties were performed to identify sncRNA signatures for A+(T|N)+ and AD. Weighted scores were computed using cross-validation, and the association of these scores with AD risk was assessed using multivariable Cox regression models. Gene ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) enrichment analysis of the identified signatures were performed. RESULTS: The study sample consisted of 192 patients, including 96 A+(T|N)+ and 96 A-T-N- patients. We constructed a classification model based on a 6-miRNAs signature for ATN. The model could classify MCI patients into A-T-N- and A+(T|N)+ groups with an area under the curve of 0.7335 (95% CI, 0.7327 to 0.7342). However, the addition of the model to conventional risk factors did not improve the prediction of AD beyond the conventional model plus ATN status (C-statistic: 0.805 [95% CI, 0.758 to 0.852] compared to 0.829 [95% CI, 0.786, 0.872]). The AD-related 15-sncRNAs signature exhibited better predictive performance than the conventional model plus ATN status (C-statistic: 0.849 [95% CI, 0.808 to 0.890]). When ATN was included in this model, the prediction further improved to 0.875 (95% CI, 0.840 to 0.910). The miRNA-target interaction network and functional analysis, including GO and KEGG pathway enrichment analysis, suggested that the miRNAs in both signatures are involved in neuronal pathways associated with AD. CONCLUSIONS: The AD-related sncRNA signature holds promise in predicting AD conversion, providing insights into early AD development and potential targets for prevention.

Lipoprotein Particle Profiles Associated with Telomere Length and Telomerase Complex Components.

Telomere length (TL) is a well-known marker of age-related diseases. Oxidative stress and inflammation increase the rate of telomere shortening, triggering cellular senescence. Although lipoproteins could have anti-inflammatory and proinflammatory functional properties, the relationship between lipoprotein particles with TL and telomerase activity-related genes has not been investigated much. In this study, we assessed the associations of lipoprotein subfractions with telomere length, TERT, and WRAP53 expression in a total of 54 pre-diabetic subjects from the EPIRDEM study. We regressed TL, TERT, and WRAP53 on 12 lipoprotein subclasses, employing a Gaussian linear regression method with Lasso penalty to determine a lipoprotein profile associated with telomere-related parameters. The covariates included age, sex, body mass index (BMI), dyslipidemia, statin consumption, and physical activity leisure time. We identified a lipoprotein profile composed of four lipoprotein subfractions associated with TL (Pearson r = 0.347, p-value = 0.010), two lipoprotein subfractions associated with TERT expression (Pearson r = 0.316, p-value = 0.020), and five lipoprotein subfractions associated with WRAP53 expression (Pearson r = 0.379, p-value =0.005). After adjusting for known confounding factors, most lipoprotein profiles maintained the association with TL, TERT, and WRAP53. Overall, medium and small-sized HDL particles were associated with shorter telomeres and lower expression of TERT and WRAP53. Large HDL particles were associated with longer telomere and lower expression of WRAP53, but not with TERT. Our results suggest that the lipoprotein profiles are associated with telomere length, TERT, and WRAP53 expression and should be considered when assessing the risk of chronic diseases.

The Mutational Landscape of SARS-CoV-2.

Mutation research is crucial for detecting and treating SARS-CoV-2 and developing vaccines. Using over 5,300,000 sequences from SARS-CoV-2 genomes and custom Python programs, we analyzed the mutational landscape of SARS-CoV-2. Although almost every nucleotide in the SARS-CoV-2 genome has mutated at some time, the substantial differences in the frequency and regularity of mutations warrant further examination. C>U mutations are the most common. They are found in the largest number of variants, pangolin lineages, and countries, which indicates that they are a driving force behind the evolution of SARS-CoV-2. Not all SARS-CoV-2 genes have mutated in the same way. Fewer non-synonymous single nucleotide variations are found in genes that encode proteins with a critical role in virus replication than in genes with ancillary roles. Some genes, such as spike (S) and nucleocapsid (N), show more non-synonymous mutations than others. Although the prevalence of mutations in the target regions of COVID-19 diagnostic RT-qPCR tests is generally low, in some cases, such as for some primers that bind to the N gene, it is significant. Therefore, ongoing monitoring of SARS-CoV-2 mutations is crucial. The SARS-CoV-2 Mutation Portal provides access to a database of SARS-CoV-2 mutations.