Age and sex dependent changes of free circulating blood metabolite and lipid abundances, correlations and ratios

Discrimination of Lipogenic or Glucogenic Diet Effects in Early-Lactation Dairy Cows Using Plasma Metabolite Abundances and Ratios in Combination with Machine Learning

During early lactation, dairy cows have a negative energy balance since their energy demands exceed their energy intake: in this study, we aimed to investigate the association between diet and plasma metabolomics profiles and how these relate to energy unbalance of course in the early-lactation stage. Holstein-Friesian cows were randomly assigned to a glucogenic (n = 15) or lipogenic (n = 15) diet in early lactation. Blood was collected in week 2 and week 4 after calving. Plasma metabolite profiles were detected using liquid chromatography-mass spectrometry (LC-MS), and a total of 39 metabolites were identified. Two plasma metabolomic profiles were available every week for each cow. Metabolite abundance and metabolite ratios were used for the analysis using the XGboost algorithm to discriminate between diet treatment and lactation week. Using metabolite ratios resulted in better discrimination performance compared with the metabolite abundances in assigning cows to a lipogenic diet or a glucogenic diet. The quality of the discrimination of performance of lipogenic diet and glucogenic diet effects improved from 0.606 to 0.753 and from 0.696 to 0.842 in week 2 and week 4 (as measured by area under the curve, AUC), when the metabolite abundance ratios were used instead of abundances. The top discriminating ratios for diet were the ratio of arginine to tyrosine and the ratio of aspartic acid to valine in week 2 and week 4, respectively. For cows fed the lipogenic diet, choline and the ratio of creatinine to tryptophan were top features to discriminate cows in week 2 vs. week 4. For cows fed the glucogenic diet, methionine and the ratio of 4-hydroxyproline to choline were top features to discriminate dietary effects in week 2 or week 4. This study shows the added value of using metabolite abundance ratios to discriminate between lipogenic and glucogenic diet and lactation weeks in early-lactation cows when using metabolomics data. The application of this research will help to accurately regulate the nutrition of lactating dairy cows and promote sustainable agricultural development.

Better Life's Essential 8 contributes to slowing the biological aging process: a cross-sectional study based on NHANES 2007-2010 data

Objective

To investigate the relationship between Life's Essential 8 (LE8) and Phenotypic Age Acceleration (PhenoAgeAccel) in United States adults and to explore the impact of LE8 on phenotypic biological aging, thereby providing references for public health policies and health education.

Methods

Utilizing data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2010, this cross-sectional study analyzed 7,339 adults aged 20 and above. Comprehensive assessments of LE8, PhenoAgeAccel, and research covariates were achieved through the integration of Demographics Data, Dietary Data, Laboratory Data, and Questionnaire Data derived from NHANES. Weighted generalized linear regression models and restricted cubic spline plots were employed to analyze the linear and non-linear associations between LE8 and PhenoAgeAccel, along with gender subgroup analysis and interaction effect testing.

Results

(1) Dividing the 2007-2010 NHANES cohort into quartiles based on LE8 unveiled significant disparities in age, gender, race, body mass index, education level, marital status, poverty-income ratio, smoking and drinking statuses, diabetes, hypertension, hyperlipidemia, phenotypic age, PhenoAgeAccel, and various biological markers (p < 0.05). Mean cell volume demonstrated no intergroup differences (p > 0.05). (2) The generalized linear regression weighted models revealed a more pronounced negative correlation between higher quartiles of LE8 (Q2, Q3, and Q4) and PhenoAgeAccel compared to the lowest LE8 quartile in both crude and fully adjusted models (p < 0.05). This trend was statistically significant (p < 0.001) in the full adjustment model. Gender subgroup analysis within the fully adjusted models exhibited a significant negative relationship between LE8 and PhenoAgeAccel in both male and female participants, with trend tests demonstrating significant results (p < 0.001 for males and p = 0.001 for females). (3) Restricted cubic spline (RCS) plots elucidated no significant non-linear trends between LE8 and PhenoAgeAccel overall and in gender subgroups (p for non-linear > 0.05). (4) Interaction effect tests denoted no interaction effects between the studied stratified variables such as age, gender, race, education level, and marital status on the relationship between LE8 and PhenoAgeAccel (p for interaction > 0.05). However, body mass index and diabetes manifested interaction effects (p for interaction < 0.05), suggesting that the influence of LE8 on PhenoAgeAccel might vary depending on an individual's BMI and diabetes status.

Conclusion

This study, based on NHANES data from 2007-2010, has revealed a significant negative correlation between LE8 and PhenoAgeAccel, emphasizing the importance of maintaining a healthy lifestyle in slowing down the biological aging process. Despite the limitations posed by the study's design and geographical constraints, these findings provide a scientific basis for the development of public health policies focused on healthy lifestyle practices. Future research should further investigate the causal mechanisms underlying the relationship between LE8 and PhenoAgeAccel and consider cross-cultural comparisons to enhance our understanding of healthy aging.

Reference intervals for cardiometabolic risk factors in China: a national multicenter cross-sectional study on an adult population sample

Background

The reference intervals (RIs) of adult blood lipid parameters currently used in China are not derived from the results of research in local populations and have not been adjusted for age and sex. In this study, we aimed to determine accurate RIs for blood lipid parameters and blood glucose (GluG) for Chinese adults using a national multicenter study.

Methods

A total of 11,333 adults between 18 and 90 years of age were recruited in seven representative regions in China between June 2020 and December 2020. Hospitals participating in the study were regrouped into two geographical regions, southern China (Changsha, Chengdu, Hangzhou, and Nanning) and northern China (Beijing, Shenyang, and Ningxia), according to their geographical and administrative location. All samples were freshly collected and measured collectively in one laboratory on the Mindray full Automatic biochemical analyzer chemistry BS2000 analytical systems. Outliers were removed using the Tukey test. Three-level nested analysis of variance and scatter plot were used to explore the variations in sex, age, and region. Percentile curves of each indicator were plotted using the least mean square (LMS) method. The lower limit (2.5th percentile) and the upper limit (97.5th percentile) of the RI were determined by using nonparametric statistical methods. We also calculated the 90% confidence interval (CI) for the lower and upper limits.

Results

A total of 8,283 participants were enrolled in the final analysis, with 3,593 (43.4%) men and 4,690 (56.6%) women. Regionality was observed in three analytes [small dense low density lipoprotein cholesterol (sd-LDLC), GluG, and apolipoprotein A1 (ApoA1)]. In northern China, the sd-LDLC and GluG levels in Shenyang were significantly higher than those in Ningxia and Beijing (P<0.05). In southern China, the sd-LDLC and GluG levels in Nanning were significantly higher than those in the three other cities (P<0.05), whereas the sd-LDLC and GluG levels in Chengdu were significantly lower than those in the three other cities (P<0.05). The level of ApoA1 in Chengdu was significantly higher than that in the three other cities. The homocysteine (HCY) level in male participants was clearly higher than that in female participants [ratio of standard deviation (SDR)sex =0.56], whereas the levels of high density lipoprotein cholesterol (HDLC) (SDRsex =0.40) and ApoA1 (SDRsex =0.27) in males were lower. The GluG and HCY level increased gradually with age. In females aged 45-55 years, there was an interesting change in scatter charts, where triglyceride (TG) and total cholesterol (TC) increased rapidly. We also found that for the age group of >55 years, the levels of TG and TC in females gradually surpassed those in males.

Conclusions

The findings of this study may help establish age- and sex-specific reference values for the blood lipids of Chinese adults and serve as a valuable guide for the screening, diagnosis, treatment, prevention, and monitoring of cardiovascular disease (CVD).

Sebacic Acid as a Potential Age-Related Biomarker of Liver Aging: Evidence Linking Mice and Human

The aging process is complicated and involves diverse organ dysfunction; furthermore, the biomarkers that are able to reflect biological aging are eagerly sought after to monitor the system-wide decline associated with the aging process. To address this, we performed a metabolomics analysis using a longitudinal cohort study from Taiwan (N = 710) and established plasma metabolomic age using a machine learning algorithm. The resulting estimation of age acceleration among the older adults was found to be correlated with HOMA-insulin resistance. In addition, a sliding window analysis was used to investigate the undulating decrease in hexanoic and heptanoic acids that occurs among the older adults at different ages. A comparison of the metabolomic alterations associated with aging between humans and mice implied that ω-oxidation of medium-chain fatty acids was commonly dysregulated in older subjects. Among these fatty acids, sebacic acid, an ω-oxidation product produced by the liver, was significantly decreased in the plasma of both older humans and aged mice. Notably, an increase in the production and consumption of sebacic acid within the liver tissue of aged mice was observed, along with an elevation of pyruvate-to-lactate conversion. Taken together, our study reveals that sebacic acid and metabolites of ω-oxidation are the common aging biomarkers in both humans and mice. The further analysis suggests that sebacic acid may play an energetic role in supporting the production of acetyl-CoA during liver aging, and thus its alteration in plasma concentration potentially reflects the aging process.

How do metabolic processes age: Evidence from human metabolomic studies

Metabolomics, the global profiling of small molecules in the body, has emerged as a promising analytical approach for assessing molecular changes associated with ageing at the population level. Understanding root metabolic ageing pathways may have important implications for managing age-related disease risk. In this short review, relevant studies published in the last few years that have made valuable contributions to this field will be discussed. These include large-scale studies investigating metabolic changes with age, metabolomic clocks, and metabolic pathways associated with ageing phenotypes. Recent significant advances include the use of longitudinal study designs, populations spanning the whole life course, standardised analytical platforms of enhanced metabolome coverage and development of multivariate analyses. While many challenges remain, recent studies have demonstrated the considerable promise of this field.

Twelve Weeks of High-Intensity Interval Training Alters Adipose Tissue Gene Expression but Not Oxylipin Levels in People with Non-Alcoholic Fatty Liver Disease

Lifestyle modifications, including increased physical activity and exercise, are recommended for non-alcoholic fatty liver disease (NAFLD). Inflamed adipose tissue (AT) contributes to the progression and development of NAFLD and oxylipins such as hydroxyeicosatetraenoic acids (HETE), hydroxydocosahexanenoic acids (HDHA), prostaglandins (PEG₂), and isoprostanoids (IsoP), which all may play a role in AT homeostasis and inflammation. To investigate the role of exercise without weight loss on AT and plasma oxylipin concentrations in NAFLD subjects, we conducted a 12-week randomized controlled exercise intervention. Plasma samples from 39 subjects and abdominal subcutaneous AT biopsy samples from 19 subjects were collected both at the beginning and the end of the exercise intervention. In the AT of women, a significant reduction of gene expression of hemoglobin subunits (HBB, HBA1, HBA2) was observed within the intervention group during the 12-week intervention. Their expression levels were negatively associated with VO₂max and maxW. In addition, pathways involved in adipocyte morphology alterations significantly increased, whereas pathways in fat metabolism, branched-chain amino acids degradation, and oxidative phosphorylation were suppressed in the intervention group (p < 0.05). Compared to the control group, in the intervention group, the ribosome pathway was activated, but lysosome, oxidative phosphorylation, and pathways of AT modification were suppressed (p < 0.05). Most of the oxylipins (HETE, HDHA, PEG₂, and IsoP) in plasma did not change during the intervention compared to the control group. 15-F_2t-IsoP significantly increased in the intervention group compared to the control group (p = 0.014). However, this oxylipin could not be detected in all samples. Exercise intervention without weight loss may influence the AT morphology and fat metabolism at the gene expression level in female NAFLD subjects.

Clinical lipidomics in the era of the big data

Lipidomics as a branch of metabolomics provides unique information on the complex lipid profile in biological materials. In clinically focused studies, hundreds of lipids together with available clinical information proved to be an effective tool in the discovery of biomarkers and understanding of pathobiochemistry. However, despite the introduction of lipidomics nearly twenty years ago, only dozens of big data studies using clinical lipidomics have been published to date. In this review, we discuss the lipidomics workflow, statistical tools, and the challenges of standartisation. The consequent summary divided into major clinical areas of cardiovascular disease, cancer, diabetes mellitus, neurodegenerative and liver diseases is demonstrating the importance of clinical lipidomics. In these publications, the potential of lipidomics for prediction, diagnosis or finding new targets for the treatment of selected diseases can be seen. The first of these results have already been implemented in clinical practice in the field of cardiovascular diseases, while in other areas we can expect the application of the results summarized in this review in the near future.

Large-Scale metabolomics: Predicting biological age using 10,133 routine untargeted LC-MS measurements

Untargeted metabolomics is the study of all detectable small molecules, and in geroscience, metabolomics has shown great potential to describe the biological age-a complex trait impacted by many factors. Unfortunately, the sample sizes are often insufficient to achieve sufficient power and minimize potential biases caused by, for example, demographic factors. In this study, we present the analysis of biological age in ~10,000 toxicologic routine blood measurements. The untargeted screening samples obtained from ultra-high pressure liquid chromatography-quadruple time of flight mass spectrometry (UHPLC- QTOF) cover + 300 batches and + 30 months, lack pooled quality controls, lack controlled sample collection, and has previously only been used in small-scale studies. To overcome experimental effects, we developed and tested a custom neural network model and compared it with existing prediction methods. Overall, the neural network was able to predict the chronological age with an rmse of 5.88 years (r²  = 0.63) improving upon the 6.15 years achieved by existing normalization methods. We used the feature importance algorithm, Shapley Additive exPlanations (SHAP), to identify compounds related to the biological age. Most importantly, the model returned known aging markers such as kynurenine, indole-3-aldehyde, and acylcarnitines along with a potential novel aging marker, cyclo (leu-pro). Our results validate the association of tryptophan and acylcarnitine metabolism to aging in a highly uncontrolled large-s cale sample. Also, we have shown that by using robust computational methods it is possible to deploy large LC-MS datasets for metabolomics studies to reduce the risk of bias and empower aging studies.

Aging, aerobic exercise, and cardiovascular health: Barriers, alternative strategies and future directions

Age-associated cardiovascular (CV) dysfunction, namely arterial dysfunction, is a key antecedent to the development of CV disease (CVD). Arterial dysfunction with aging is characterized by impaired vascular endothelial function and stiffening of the large elastic arteries, each of which is an independent predictor of CVD. These processes are largely mediated by an excess production of reactive oxygen species (ROS) and an increase in chronic, low-grade inflammation that ultimately leads to a reduction in bioavailability of the vasodilatory molecule nitric oxide. Additionally, there are other fundamental aging mechanisms that may contribute to excessive ROS and inflammation termed the "hallmarks of aging"; these additional mechanisms of arterial dysfunction may represent therapeutic targets for improving CV health with aging. Aerobic exercise is the most well-known and effective intervention to prevent and treat the effects of aging on CV dysfunction. However, the majority of mid-life and older (ML/O) adults do not meet recommended exercise guidelines due to traditional barriers to aerobic exercise, such as reduced leisure time, motivation, or access to fitness facilities. Therefore, it is a biomedical research priority to develop and implement time- and resource-efficient alternative strategies to aerobic exercise to reduce the burden of CVD in ML/O adults. Alternative strategies that mimic or are inspired by aerobic exercise, that target pathways specific to the fundamental mechanisms of aging, represent a promising approach to accomplish this goal.

Age‐related features of the content of substrates of energy metabolism and body mass index in women residing in the S ubarctic and A rctic regions of R ussia

OBJECTIVES

Age is associated with a number of health risks linked to obesity caused by an imbalance in the main energy substrates-fatty acids (FA) and glucose (Glu). Therefore, the objective of this study was to identify age-related features of the metabolism of fatty acids and Glu, their correlations and the relation with the body mass index (BMI) in women of the local Caucasoid population from two northern regions of Russia with different nature, climate, and geography.

METHODS

We examined women aged 21-60 years born and permanently residing in the Subarctic region (SR) and the Arctic region (AR). The participants were divided into three age groups: 21-35, 36-45, and 46-60 years. The levels of FAs, Glu, and triglycerides (TG) in the blood serum were determined by spectrophotometric and gas chromatographic methods; the values of BMI and TyG (triglyceride glucose) index were calculated. To analyze data, we used the descriptive and correlation analyses by nonparametric methods, as well as multiple linear regression analysis.

RESULTS

With age, the surveyed women demonstrated elevated levels of triglyceride, the majority of the studied fatty acids, BMI and TyG index. For three fatty acids, age-related changes were noted in one of the regions only: stearic and linoleic acids in the SR, and docosahexaenoic acid in the AR; no significant changes were observed for dihomo-γ-linolenic and arachidonic acids. We found elevated Glu levels in women aged 46-60 years residing in the SR. Regional differences were due to higher concentrations of FAs and Glu in the AR. All identified correlations were positive. BMI values were associated with FAs and TG, and in women aged 46-60 years, they were additionally associated with Glu. The latter also correlated with some FAs and TG in this group. TyG index associations with saturated FAs (SFAs) became stronger with age.

CONCLUSIONS

Age has a significant impact on the homeostasis of key energy substrates (Glu, TG, SFAs, monounsaturated FAs), on BMI and TyG index, which are indicators of obesity and insulin resistance. Depending on the region of residence (Subarctic or Arctic), we found changes in the FA profile undersaturation (especially long-chain polyunsaturated FAs) and some specific features of Glu homeostasis (for the age groups of 21-35 and 46-60 years) in women of Caucasoid race in the Russian North. Multiple regression analysis showed that BMI, as well as the region of residence and age, are significant predictors for almost all biochemical parameters, especially for TG and TyG index.

Targeted metabolomics analysis of amino acids and acylcarnitines as risk markers for diabetes by LC-MS/MS technique

Diabetes is a common chronic disease affecting millions of people worldwide. It underlies various complications and imposes many costs on individuals and society. Discovering early diagnostic biomarkers takes excellent insight into preventive plans and the best use of interventions. Therefore, in the present study, we aimed to evaluate the association between the level of amino acids and acylcarnitines and diabetes to develop diabetes predictive models. Using the targeted LC-MS/MS technique, we analyzed fasting plasma samples of 206 cases and 206 controls that were matched by age, sex, and BMI. The association between metabolites and diabetes was evaluated using univariate and multivariate regression analysis with adjustment for systolic and diastolic blood pressure and lipid profile. To deal with multiple comparisons, factor analysis was used. Participants' average age and BMI were 61.6 years, 28.9 kg/m2, and 55% were female. After adjustment, Factor 3 (tyrosine, valine, leucine, methionine, tryptophan, phenylalanine), 5 (C3DC, C5, C5OH, C5:1), 6 (C14OH, C16OH, C18OH, C18:1OH), 8 (C2, C4OH, C8:1), 10 (alanine, proline) and 11 (glutamic acid, C18:2OH) were positively associated with diabetes. Inline, factor 9 (C4DC, serine, glycine, threonine) and 12 (citrulline, ornithine) showed a reverse trend. Some amino acids and acylcarnitines were found as potential risk markers for diabetes incidents that reflected the disturbances in the several metabolic pathways among the diabetic population and could be targeted to prevent, diagnose, and treat diabetes.

Intrapersonal Stability of Plasma Metabolomic Profiles over 10 Years among Women

In epidemiological studies, samples are often collected long before disease onset or outcome assessment. Understanding the long-term stability of biomarkers measured in these samples is crucial. We estimated within-person stability over 10 years of metabolites and metabolite features (n = 5938) in the Nurses’ Health Study (NHS): the primary dataset included 1880 women with 1184 repeated samples donated 10 years apart while the secondary dataset included 1456 women with 488 repeated samples donated 10 years apart. We quantified plasma metabolomics using two liquid chromatography mass spectrometry platforms (lipids and polar metabolites) at the Broad Institute (Cambridge, MA, USA). Intra-class correlations (ICC) were used to estimate long-term (10 years) within-person stability of metabolites and were calculated as the proportion of the total variability (within-person + between-person) attributable to between-person variability. Within-person variability was estimated among participants who donated two blood samples approximately 10 years apart while between-person variability was estimated among all participants. In the primary dataset, the median ICC was 0.43 (1st quartile (Q1): 0.36; 3rd quartile (Q3): 0.50) among known metabolites and 0.41 (Q1: 0.34; Q3: 0.48) among unknown metabolite features. The three most stable metabolites were N6,N6-dimethyllysine (ICC = 0.82), dimethylguanidino valerate (ICC = 0.72), and N-acetylornithine (ICC = 0.72). The three least stable metabolites were palmitoylethanolamide (ICC = 0.05), ectoine (ICC = 0.09), and trimethylamine-N-oxide (ICC = 0.16). Results in the secondary dataset were similar (Spearman correlation = 0.87) to corresponding results in the primary dataset. Within-person stability over 10 years is reasonable for lipid, lipid-related, and polar metabolites, and varies by metabolite class. Additional studies are required to estimate within-person stability over 10 years of other metabolites groups.