Effects of Lifestyle Intervention in Tissue-Specific Lipidomic Profile of Formerly Obese Mice

A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age

BACKGROUND

Metabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health.

METHODS

Utilising comprehensive lipidomic datasets from two large independent population cohorts in Australia (n = 14,833, including 6630 males, 8203 females), we employed different machine learning models, to predict age, and calculated metabolic age scores (mAge). Furthermore, we defined the difference between mAge and age, termed mAgeΔ, which allow us to identify individuals sharing similar age but differing in their metabolic health status.

FINDINGS

Upon stratification of the population into quintiles by mAgeΔ, we observed that participants in the top quintile group (Q5) were more likely to have cardiovascular disease (OR = 2.13, 95% CI = 1.62-2.83), had a 2.01-fold increased risk of 12-year incident cardiovascular events (HR = 2.01, 95% CI = 1.45-2.57), and a 1.56-fold increased risk of 17-year all-cause mortality (HR = 1.56, 95% CI = 1.34-1.79), relative to the individuals in the bottom quintile group (Q1). Survival analysis further revealed that men in the Q5 group faced the challenge of reaching a median survival rate due to cardiovascular events more than six years earlier and reaching a median survival rate due to all-cause mortality more than four years earlier than men in the Q1 group.

INTERPRETATION

Our findings demonstrate that the mAge score captures age-related metabolic changes, predicts health outcomes, and has the potential to identify individuals at increased risk of metabolic diseases.

FUNDING

The specific funding of this article is provided in the acknowledgements section.

A Workflow for Meaningful Interpretation of Classification Results from Handheld Ambient Mass Spectrometry Analysis Probes

While untargeted analysis of biological tissues with ambient mass spectrometry analysis probes has been widely reported in the literature, there are currently no guidelines to standardize the workflows for the experimental design, creation, and validation of molecular models that are utilized in these methods to perform class predictions. By drawing parallels with hurdles that are faced in the field of food fraud detection with untargeted mass spectrometry, we provide a stepwise workflow for the creation, refinement, evaluation, and assessment of the robustness of molecular models, aimed at meaningful interpretation of mass spectrometry-based tissue classification results. We propose strategies to obtain a sufficient number of samples for the creation of molecular models and discuss the potential overfitting of data, emphasizing both the need for model validation using an independent cohort of test samples, as well as the use of a fully characterized feature-based approach that verifies the biological relevance of the features that are used to avoid false discoveries. We additionally highlight the need to treat molecular models as "dynamic" and "living" entities and to further refine them as new knowledge concerning disease pathways and classifier feature noise becomes apparent in large(r) population studies. Where appropriate, we have provided a discussion of the challenges that we faced in our development of a 10 s cancer classification method using picosecond infrared laser mass spectrometry (PIRL-MS) to facilitate clinical decision-making at the bedside.

Lipidomic-Based Approach to 10 s Classification of Major Pediatric Brain Cancer Types with Picosecond Infrared Laser Mass Spectrometry

Picosecond infrared laser mass spectrometry (PIRL-MS) is shown, through a retrospective patient tissue study, to differentiate medulloblastoma cancers from pilocytic astrocytoma and two molecular subtypes of ependymoma (PF-EPN-A, ST-EPN-RELA) using laser-extracted lipids profiled with PIRL-MS in 10 s of sampling and analysis time. The average sensitivity and specificity values for this classification, taking genomic profiling data as standard, were 96.41 and 99.54%, and this classification used many molecular features resolvable in 10 s PIRL-MS spectra. Data analysis and liquid chromatography coupled with tandem high-resolution mass spectrometry (LC-MS/MS) further allowed us to reduce the molecular feature list to only 18 metabolic lipid markers most strongly involved in this classification. The identified 'metabolite array' was comprised of a variety of phosphatidic and fatty acids, ceramides, and phosphatidylcholine/ethanolamine and could mediate the above-mentioned classification with average sensitivity and specificity values of 94.39 and 98.78%, respectively, at a 95% confidence in prediction probability threshold. Therefore, a rapid and accurate pathology classification of select pediatric brain cancer types from 10 s PIRL-MS analysis using known metabolic biomarkers can now be available to the neurosurgeon. Based on retrospective mining of 'survival' versus 'extent-of-resection' data, we further identified pediatric cancer types that may benefit from actionable 10 s PIRL-MS pathology feedback. In such cases, aggressiveness of the surgical resection can be optimized in a manner that is expected to benefit the patient's overall or progression-free survival. PIRL-MS is a promising tool to drive such personalized decision-making in the operating theater.

Effect of resistance training on lipid profile in postmenopausal women: A systematic review and meta-analysis of randomized controlled trials

OBJECTIVE

Physical exercise decreases cardiovascular risk and can alter the lipid profile in postmenopausal women. Although it is believed that resistance training can potentially decrease serum lipid levels in postmenopausal females, the evidence remains inconclusive. The aim of this systematic review and meta-analysis of randomized controlled trials (RCTs) was to clarify the impact of resistance training on the lipid profile in postmenopausal women.

METHODS

Web of Science, Scopus, PubMed/Medline and Embase were searched. RCTs that evaluated the effect of resistance training on total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglyceride (TG) levels were included in this review. Effect size was estimated using the random effects model. Subgroup analyses based on age, duration of intervention, pre-enrolment serum lipid levels and body mass index were performed.

RESULTS

Data pooled from 19 RCTs revealed that resistance training can reduce TC [weighted mean difference (WMD) -11.47 mg/dl; p = 0.002], LDL-C (WMD -8.48 mg/dl; p = 0.01) and TG (WMD -6.61 mg/dl; p = 0.043) levels. TC levels decreased particularly in subjects aged < 60 years (WMD -10.77 mg/dl; p = 0.003), in RCTs lasting < 16 weeks (WMD -15.70 mg/dl; p = 0.048), and in subjects with hypercholesterolaemia (WMD -12.36 mg/dl; p = 0.001) or obesity (WMD -19.35 mg/dl; p = 0.006) before RCT enrolment. There was a significant decrease in LDL-C (WMD -14.38 mg/dl; p = 0.002) levels in patients with LDL-C ≥ 130 mg/dl before trial enrolment. Resistance training reduced HDL-C (WMD -2.97 mg/dl; p = 0.01) levels particularly in subjects with obesity. TG (WMD -10.71 mg/dl; p = 0.01) levels decreased particularly when the intervention lasted < 16 weeks.

CONCLUSION

Resistance training can decrease TC, LDL-C and TG levels in postmenopausal females. The impact of resistance training on HDL-C levels was small, and was only observed in individuals with obesity. The effect of resistance training on the lipid profile was more notable in short-term interventions and in postmenopausal women with dyslipidaemia or obesity before trial enrolment.

Switching from high-fat diet to normal diet ameliorate BTB integrity and improve fertility potential in obese male mice

Obesity is a prominent risk factor for male infertility, and a high-fat diet is an important cause of obesity. Therefore, diet control can reduce body weight and regulate blood glucose and lipids, but it remains unclear whether it can improve male fertility and its mechanism. This study explores the effects of switching from a high-fat diet (HFD) to a normal diet (ND) on the fertility potential of obese male mice and its related mechanisms. In our study, male mice were separated into three groups: normal diet group (NN), continuous high-fat diet group (HH), and return to normal diet group (HN). The reproductive potential of mice was tested through cohabitation. Enzymatic methods and ELISA assays were used to measure metabolic indicators, follicle-stimulating hormone (FSH) levels and intratesticular testosterone levels. Transmission electron microscopy and immunofluorescence with biotin tracers assessed the integrity of the blood-testis barrier (BTB). Malondialdehyde (MDA), superoxide dismutase (SOD), and reactive oxygen species (ROS) were inspected for the assessment of oxidative stress. The expression and localization of BTB-related proteins were detected through the immunoblot and immunofluorescence. The mice in the high-fat diet group indicated increased body weight and epididymal fat weight, elevated serum TC, HDL, LDL, and glucose, decreased serum FSH, and dramatic lipid deposition in the testicular interstitium. Analysis of fertility potential revealed that the fertility rate of female mice and the number of pups per litter in the HH group were significantly reduced. After the fat intake was controlled by switching to a normal diet, body weight and epididymal fat weight were significantly reduced, serum glucose and lipid levels were lowered, serum FSH level was elevated and the deposition of interstitial lipids in the testicles was also decreased. Most significantly, the number of offspring of male mice returning to a normal diet was significantly increased. Following further mechanistic analysis, the mice in the sustained high-fat diet group had disrupted testicular BTB integrity, elevated levels of oxidative stress, and abnormal expression of BTB-related proteins, whereas the restoration of the normal diet significantly ameliorated the above indicators in the mice. Our study confirms diet control by switching from a high-fat diet to a normal diet can effectively reduce body weight, ameliorate testicular lipotoxicity and BTB integrity in male mice, and improve fertility potential, providing an effective treatment option for obese male infertility.

Brain N-Glycosylation and Lipidomic Profile Changes Induced by a High-Fat Diet in Dyslipidemic Hamsters

The consumption of diets rich in saturated fats is known to be associated with higher mortality. The adoption of healthy habits, for instance adhering to a Mediterranean diet, has proved to exert a preventive effect towards cardiovascular diseases and dyslipidemia. Little is known about how a suboptimal diet can affect brain function, structure, and the mechanisms involved. The aims of this study were to examine how a high-fat diet can alter the brain N-glycan and lipid profile in male Golden Syrian hamsters and to evaluate the potential of a Mediterranean-like diet to reverse this situation. During twelve weeks, hamsters were fed a normal fat diet (CTRL group), a high-fat diet (HFD group), and a high-fat diet followed by a Mediterranean-like diet (MED group). Out of seventy-two identified N-glycans, fourteen were significant (p < 0.05) between HFD and CTRL groups, nine between MED and CTRL groups, and one between MED and HFD groups. Moreover, forty-nine lipids were altered between HFD and CTRL groups, seven between MED and CTRL groups, and five between MED and HFD groups. Our results suggest that brain N-glycan composition in high-fat diet-fed hamsters can produce events comparable to those found in some neurodegenerative diseases, and may promote brain ageing.

Lipid Profiling in Cancer Diagnosis with Hand-Held Ambient Mass Spectrometry Probes: Addressing the Late-Stage Performance Concerns

Untargeted lipid fingerprinting with hand-held ambient mass spectrometry (MS) probes without chromatographic separation has shown promise in the rapid characterization of cancers. As human cancers present significant molecular heterogeneities, careful molecular modeling and data validation strategies are required to minimize late-stage performance variations of these models across a large population. This review utilizes parallels from the pitfalls of conventional protein biomarkers in reaching bedside utility and provides recommendations for robust modeling as well as validation strategies that could enable the next logical steps in large scale assessment of the utility of ambient MS profiling for cancer diagnosis. Six recommendations are provided that range from careful initial determination of clinical added value to moving beyond just statistical associations to validate lipid involvements in disease processes mechanistically. Further guidelines for careful selection of suitable samples to capture expected and unexpected intragroup variance are provided and discussed in the context of demographic heterogeneities in the lipidome, further influenced by lifestyle factors, diet, and potential intersect with cancer lipid pathways probed in ambient mass spectrometry profiling studies.