Comprehensive assessment of the combined impact of dyslipidemia and inflammation on chronic kidney disease development: A prospective cohort study

BACKGROUND

There remains a limited comprehensive understanding of how dyslipidemia and chronic inflammation collectively contribute to the development of chronic kidney disease (CKD).

OBJECTIVE

We aimed to identify clusters of individuals with five variables, including lipid profiles and C-reactive protein (CRP) levels, and to assess whether the clusters were associated with incident CKD risk.

METHODS

We used the Korean Genome and Epidemiology Study-Ansan and Ansung data. K-means clustering analysis was performed to identify distinct clusters based on total cholesterol, triglyceride, non-high-density lipoprotein (HDL)-C, HDL-C, and CRP levels. Cox proportional hazards models were used to examine the association between incident CKD risk and the different clusters.

RESULTS

During the mean 10-year follow-up period, CKD developed in 1,645 participants (690 men and 955 women) among a total of 8,053 participants with a mean age of 51.8 years. Four distinct clusters were identified: C1, low cholesterol group (LC); C2, high-density lipoprotein cholesterol group (HC); C3, insulin resistance and inflammation group (IIC); and C4, dyslipidemia and inflammation group (DIC). Cluster 4 had a significantly higher risk of incident CKD compared to clusters 2 (hazard ratio (HR) 1.455 [95% confidence interval (CI) 1.234-1.715]; p < 0.001) and cluster 1 (HR 1.264 [95% CI 1.067-1.498]; p = 0.007) after adjusting for confounders. Cluster 3 had a significantly higher risk of incident CKD compared to clusters 2 and 1.

CONCLUSION

Clusters 4 and 3 had higher risk of incident CKD compared to clusters 2 and 1. The combination of dyslipidemia with inflammation or insulin resistance with inflammation appears to be pivotal in the development of incident CKD.

Diurnal variation characteristics and meteorological causes of autumn ozone in the Pearl River Delta, China

This study investigates the diurnal variation of ozone (O3) in the Pearl River Delta (PRD) during autumn from 2016 to 2021, focusing on the main O3 modes and their relationship with meteorological conditions. Utilizing K-means clustering, four patterns of O3 variation were identified: Cluster 1 (extremely low O3), Cluster 2 (close to autumn average), Cluster 3 (abnormally high O3 at night), and Cluster 4 (extremely high O3). In Cluster 1, the PRD was situated on the northwest side of the western Pacific subtropical high (WPSH), resulting in increased cloud cover, weakened radiation, and the lowest O3 growth rate during the day, with weak nighttime changes. Cluster 2 presents O3 changes under normal autumn conditions, closely resembling the autumn average. In Cluster 3, the PRD was located between continental high pressure and the low-pressure system over the South China Sea. The enhanced horizontal pressure gradient led to an increase in the horizontal wind speed, promoting the formation of a low-level jet (LLJ). The LLJ caused decoupling between the residual layer and stable boundary layer at night, leading to increased surface O3 concentration and a higher background O3 concentration before sunrise the next day. In Cluster 4, favorable meteorological conditions for O3 generation and accumulation were created by the influence of the WPSH and peripheral tropical cyclones. O3 rapidly increased during the day, reaching extremely high values in the afternoon, with an exceedance rate of 80 %. Comparing the four diurnal patterns and their meteorological conditions highlights the significance of meteorological processes in O3 variations.

Analysis of Physiological Variations and Genetic Architecture for Photosynthetic Capacity of Japanese Soybean Germplasm

The culmination of conventional yield improving parameters has widened the margin between food demand and crop yield, leaving the potential yield productivity to be bridged by the manipulation of photosynthetic processes in plants. Efficient strategies to assess photosynthetic capacity in crops need to be developed to identify suitable targets that have the potential to improve photosynthetic efficiencies. Here, we assessed the photosynthetic capacity of the Japanese soybean mini core collection (GmJMC) using a newly developed high-throughput photosynthesis measurement system "MIC-100" to analyze physiological mechanisms and genetic architecture underpinning photosynthesis. K-means clustering of light-saturated photosynthesis (A_sat ) classified GmJMC accessions into four distinct clusters with Cluster2 comprised of highly photosynthesizing accessions. Genome-wide association analysis based on the variation of A_sat revealed a significant association with a single nucleotide polymorphism (SNP) on chromosome 17. Among the candidate genes related to photosynthesis in the genomic region, variation in expression of a gene encoding G protein alpha subunit 1 (GPA1) showed a strong correlation (r = 0.72, p < 0.01) with that of A_sat . Among GmJMC accessions, GmJMC47 was characterized by the highest A_sat , stomatal conductance (g_s ), stomatal density (S_Density ), electron transfer rate (ETR), and light use efficiency of photosystem II (Fv'/Fm') and the lowest non-photochemical quenching [NPQ(t)], indicating that GmJMC47 has greater CO₂ supply and efficient light-harvesting systems. These results provide strong evidence that exploration of plant germplasm is a useful strategy to unlock the potential of resource use efficiencies for photosynthesis.

Discovering hidden spatial patterns and their associations with controlling factors for potentially toxic elements in topsoil using hot spot analysis and K-means clustering analysis

The understanding of sources and controlling factors of potentially toxic elements (PTEs) in soils plays an important role in the improvement of environmental management. With the rapid growth of data volume, effective methods are required for data analytics for the large geochemical data sets. In recent years, spatial machine learning technologies have been proven to have the potential to reveal hidden spatial patterns in order to extract geochemical information. In this study, two spatial clustering techniques of Getis-Ord G_i* statistic and K-means clustering analysis were performed on 15 PTEs in 6,862 topsoil samples from the Tellus datasets of Northern Ireland to investigate the hidden spatial patterns and association with their controlling factors. The spatial clustering patterns of hot spots (high values) and cold spots (low values) for the 15 PTEs were revealed, showing clear association with geological features, especially peat and basalt. Peat was associated with high concentrations of Bi, Pb, Sb and Sn, while basalt was associated with high concentrations of Co, Cr, Cu, Mn, Ni, V and Zn. The high concentrations of As, Ba, Mo and U were associated with mixture of various lithologies, indicating the complicated influences on them. In addition, three hidden patterns in the 6,862 soil samples were revealed by K-means clustering analysis. The soil samples in the first and second clusters were overlaid on the peatland and basalt formation, respectively, while the samples in the third cluster were overlaid on the mixture of the other lithologies. These hidden patterns of soil samples were consistent with the spatial clustering patterns for PTEs, highlighting the dominant control of peat and basalt in the topsoil of Northern Ireland. This study demonstrates the power of spatial machine learning techniques in identifying hidden spatial patterns, providing evidences to extract geochemical knowledge in environmental studies.

Temporal phenotyping by mining healthcare data to derive lines of therapy for cancer

Lines of therapy (LOT) derived from real-world healthcare data not only depict real-world cancer treatment sequences, but also help define patient phenotypes along the course of disease progression and therapeutic interventions. The sequence of prescribed anticancer therapies can be defined as temporal phenotyping resulting from changes in morphological (tumor staging), biochemical (biomarker testing), physiological (disease progression), and behavioral (physician prescribing and patient adherence) parameters. We introduce a novel methodology that is a two-part approach: 1) create an algorithm to derive patient-level LOT and 2) aggregate LOT information via clustering to derive temporal phenotypes, in conjunction with visualization techniques, within a large insurance claims dataset. We demonstrated the methodology using two examples: metastatic non-small cell lung cancer and metastatic melanoma. First, we generated a longitudinal patient cohort for each cancer type and applied a set of rules to derive patient-level LOT. Then the LOT algorithm outputs for each cancer type were visualized using Sankey plots and K-means clusters based on durations of LOT and of gaps in therapy between LOT. We found differential distribution of temporal phenotypes across clusters. Our approach to identify temporal patient phenotypes can increase the quality and utility of analyses conducted using claims datasets, with the potential for application to multiple oncology disease areas across diverse healthcare data sources. The understanding of LOT as defining patients' temporal phenotypes can contribute to continuous health learning of disease progression and its interaction with different treatment pathways; in addition, this understanding can provide new insights that can be applied by tailoring treatment sequences for the patient phenotypes who will benefit.

Confocal Raman spectroscopic imaging for in vitro monitoring of active ingredient penetration and distribution in reconstructed human epidermis model

Topically applied active cosmetic ingredients (ACI) or active pharmaceutical ingredients (API) efficacy is directly related to their efficiency of penetration in the skin. In vitro reconstructed human epidermis surrogate models offer in vivo like skin samples for transdermal studies. Using Delipidol®, an ACI currently used in the cosmetics industry, the capabilities to deliver accurate distribution maps and penetration profiles of this molecule by means of confocal Raman spectroscopic imaging have been demonstrated. Using a non-negative constrained least squares (NCLS) approach, contribution of specific molecules can be estimated at each point of spectral maps in order to deliver semi-quantitative heat maps representing the ACI levels in the different skin layers. The concentration profiles obtained are approximately single exponential for all 3 time points evaluated, with a consistent decay constant, which is independent of the sublayer structure. Notably, however, there is no significant penetration into the lower basal layers until a critical concentration is built up, after 3 hours. Combination of Raman confocal imaging with spectral unmixing methods such as NCLS is demonstrated to be a relevant approach for in vitro biological evaluation of cosmetic and pharmaceutical active ingredients and could easily be implemented as a screening tool for industrial use.

Exploratory Investigation of Bacteroides fragilis Transcriptional Response during In vitro Exposure to Subinhibitory Concentration of Metronidazole

Bacteroides fragilis, member from commensal gut microbiota, is an important pathogen associated to endogenous infections and metronidazole remains a valuable antibiotic for the treatment of these infections, although bacterial resistance is widely reported. Considering the need of a better understanding on the global mechanisms by which B. fragilis survive upon metronidazole exposure, we performed a RNA-seq transcriptomic approach with validation of gene expression results by qPCR. Bacteria strains were selected after in vitro subcultures with subinhibitory concentration (SIC) of the drug. From a wild type B. fragilis ATCC 43859 four derivative strains were selected: first and fourth subcultures under metronidazole exposure and first and fourth subcultures after drug removal. According to global gene expression analysis, 2,146 protein coding genes were identified, of which a total of 1,618 (77%) were assigned to a Gene Ontology term (GO), indicating that most known cellular functions were taken. Among these 2,146 protein coding genes, 377 were shared among all strains, suggesting that they are critical for B. fragilis survival. In order to identify distinct expression patterns, we also performed a K-means clustering analysis set to 15 groups. This analysis allowed us to detect the major activated or repressed genes encoding for enzymes which act in several metabolic pathways involved in metronidazole response such as drug activation, defense mechanisms against superoxide ions, high expression level of multidrug efflux pumps, and DNA repair. The strains collected after metronidazole removal were functionally more similar to those cultured under drug pressure, reinforcing that drug-exposure lead to drastic persistent changes in the B. fragilis gene expression patterns. These results may help to elucidate B. fragilis response during metronidazole exposure, mainly at SIC, contributing with information about bacterial survival strategies under stress conditions in their environment.