Validation of Candidate Phospholipid Biomarkers of Chronic Kidney Disease in Hyperglycemic Individuals and Their Organ-Specific Exploration in Leptin Receptor-Deficient db/db Mouse

Huang, Jialing; Covic, M; Huth, Cornelia; Rommel, Martina; Adam, Jonathan; Zukunft, Sven; Prehn, Cornelia; Wang, Li; Nano, Jana; Scheerer, Markus F.; Neschen, Susanne; Kastenmüller, Gabi; Gieger, Christian; Laxy, Michael; Schliess, Freimut; Adamski, Jerzy; Suhre, Karsten; Angelis, Martin Hrabě de; Peters, Annette; Wang-Sattler, Rui

doi:10.3390/metabo11020089

Cited by 15 publications

(10 citation statements)

References 57 publications

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“…Since CKD is a characteristic of disease progression in db/db mice [ 51 , 52 ], we also explored the effects of CMS121 on the kidney. Kidney function was affected in the db/db mice, as evidenced by urinary albuminuria that was increased in the first, third, and fifth experimental months ( Figure 5 A–C).…”

Section: Resultsmentioning

confidence: 99%

The Geroprotective Drug Candidate CMS121 Alleviates Diabetes, Liver Inflammation, and Renal Damage in db/db Leptin Receptor Deficient Mice

Zahid

Dafre

Currais

et al. 2023

IJMS

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db/db mice, which lack leptin receptors and exhibit hyperphagia, show disturbances in energy metabolism and are a model of obesity and type 2 diabetes. The geroneuroprotector drug candidate CMS121 has been shown to be effective in animal models of Alzheimer’s disease and aging through the modulation of metabolism. Thus, the hypothesis was that CMS121 could protect db/db mice from metabolic defects and thereby reduce liver inflammation and kidney damage. The mice were treated with CMS121 in their diet for 6 months. No changes were observed in food and oxygen consumption, body mass, or locomotor activity compared to control db/db mice, but a 5% reduction in body weight was noted. Improved glucose tolerance and reduced HbA1c and insulin levels were also seen. Blood and liver triglycerides and free fatty acids decreased. Improved metabolism was supported by lower levels of fatty acid metabolites in the urine. Markers of liver inflammation, including NF-κB, IL-18, caspase 3, and C reactive protein, were lowered by the CMS121 treatment. Urine markers of kidney damage were improved, as evidenced by lower urinary levels of NGAL, clusterin, and albumin. Urine metabolomics studies provided further evidence for kidney protection. Mitochondrial protein markers were elevated in db/db mice, but CMS121 restored the renal levels of NDUFB8, UQCRC2, and VDAC. Overall, long-term CMS121 treatment alleviated metabolic imbalances, liver inflammation, and reduced markers of kidney damage. Thus, this study provides promising evidence for the potential therapeutic use of CMS121 in treating metabolic disorders.

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Section: Resultsmentioning

confidence: 99%

The Geroprotective Drug Candidate CMS121 Alleviates Diabetes, Liver Inflammation, and Renal Damage in db/db Leptin Receptor Deficient Mice

Zahid

Dafre

Currais

et al. 2023

IJMS

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“…Table We use metabolomics data (N = 2,085) from F4 (𝑡𝑡1) and FF4 (𝑡𝑡2) to construct the KORA metabolomics dataset. After QC [23][24][25] (Methods), 106 targeted metabolites are selected from five analyte classes, namely acylcarnitine (AC), amino acid (AA), glycerophospholipid (GPL), sphingolipid (SL), and monosaccharide (MS). The metabolites are divided into two view groups: 70 metabolites from GPL (𝑣𝑣1) and 36 metabolites from the other four classes (𝑣𝑣2).…”

Section: Resultsmentioning

confidence: 99%

LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer

Han,

Yu,

Shi

et al. 2023

Preprint

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Longitudinal multi-view omics data offer unique insights into the temporal dynamics of individual-level physiology that is instrumental to advancements in personalized healthcare. However, the common occurrence of incomplete view makes inference tasks difficult, and there is a lack of tailored methods for solving this critical issue. Here, we introduce LEOPARD, an approach specifically designed to complete missing views for multi-timepoint omics data. By disentangling longitudinal omics data into content and temporal representations, LEOPARD transfers temporal knowledge to omics-specific content to complete missing views. Compared to generic imputation methods, LEOPARD gives the most robust and reliable results across three longitudinal proteomics and metabolomics datasets. LEOPARD-imputed data also achieve the closest performance to observed data in our regression and classification analyses. Our work takes the first step towards a principled and generalized treatment of missing views in longitudinal omics data, enabling comprehensive exploration of temporal changes within omics data.

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“…We speculated that this might result from the ineptitude of the linear regression in capturing complex data patterns. The raw data of KORA FF4 used in this study are already of quite high quality, thus NF is able to further improve the data quality [ 54 ]. When being applied to a dataset with a large quantity of noises, such as Amide, NF underfits the data and fails to yield satisfactory results.…”

Section: Discussionmentioning

confidence: 99%

TIGER: technical variation elimination for metabolomics data using ensemble learning architecture

Han

Huang

Foppiano

et al. 2022

Briefings in Bioinformatics

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Large metabolomics datasets inevitably contain unwanted technical variations which can obscure meaningful biological signals and affect how this information is applied to personalized healthcare. Many methods have been developed to handle unwanted variations. However, the underlying assumptions of many existing methods only hold for a few specific scenarios. Some tools remove technical variations with models trained on quality control (QC) samples which may not generalize well on subject samples. Additionally, almost none of the existing methods supports datasets with multiple types of QC samples, which greatly limits their performance and flexibility. To address these issues, a non-parametric method TIGER (Technical variation elImination with ensemble learninG architEctuRe) is developed in this study and released as an R package (https://CRAN.R-project.org/package=TIGERr). TIGER integrates the random forest algorithm into an adaptable ensemble learning architecture. Evaluation results show that TIGER outperforms four popular methods with respect to robustness and reliability on three human cohort datasets constructed with targeted or untargeted metabolomics data. Additionally, a case study aiming to identify age-associated metabolites is performed to illustrate how TIGER can be used for cross-kit adjustment in a longitudinal analysis with experimental data of three time-points generated by different analytical kits. A dynamic website is developed to help evaluate the performance of TIGER and examine the patterns revealed in our longitudinal analysis (https://han-siyu.github.io/TIGER_web/). Overall, TIGER is expected to be a powerful tool for metabolomics data analysis.

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Validation of Candidate Phospholipid Biomarkers of Chronic Kidney Disease in Hyperglycemic Individuals and Their Organ-Specific Exploration in Leptin Receptor-Deficient db/db Mouse

Cited by 15 publications

References 57 publications

The Geroprotective Drug Candidate CMS121 Alleviates Diabetes, Liver Inflammation, and Renal Damage in db/db Leptin Receptor Deficient Mice

The Geroprotective Drug Candidate CMS121 Alleviates Diabetes, Liver Inflammation, and Renal Damage in db/db Leptin Receptor Deficient Mice

LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer

TIGER: technical variation elimination for metabolomics data using ensemble learning architecture

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