Computationally Modeling Lipid Metabolism and Aging: A Mini-review

LipidSIM: Inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, Markov modeling framework

Lipid metabolism is a complex and dynamic system involving numerous enzymes at the junction of multiple metabolic pathways. Disruption of these pathways leads to systematic dyslipidemia, a hallmark of many pathological developments, such as nonalcoholic steatohepatitis and diabetes. Recent advances in computational tools can provide insights into the dysregulation of lipid biosynthesis, but limitations remain due to the complexity of lipidomic data, limited knowledge of interactions among involved enzymes, and technical challenges in standardizing across different lipid types. Here, we present a low-parameter, biologically interpretable framework named Lipid Synthesis Investigative Markov model (LipidSIM), which models and predicts the source of perturbations in lipid biosynthesis from lipidomic data. LipidSIM achieves this by accounting for the interdependency between the lipid species via the lipid biosynthesis network and generates testable hypotheses regarding changes in lipid biosynthetic reactions. This feature allows the integration of lipidomics with other omics types, such as transcriptomics, to elucidate the direct driving mechanisms of altered lipidomes due to treatments or disease progression. To demonstrate the value of LipidSIM, we first applied it to hepatic lipidomics following Keap1 knockdown and found that changes in mRNA expression of the lipid pathways were consistent with the LipidSIM-predicted fluxes. Second, we used it to study lipidomic changes following intraperitoneal injection of CCl4 to induce fast NAFLD/NASH development and the progression of fibrosis and hepatic cancer. Finally, to show the power of LipidSIM for classifying samples with dyslipidemia, we used a Dgat2-knockdown study dataset. Thus, we show that as it demands no a priori knowledge of enzyme kinetics, LipidSIM is a valuable and intuitive framework for extracting biological insights from complex lipidomic data.

Life's Essential 8: Optimizing Health in Older Adults

The population worldwide is getting older as a result of advances in public health, medicine, and technology. Older individuals are living longer with a higher prevalence of subclinical and clinical cardiovascular disease (CVD). In 2010, the American Heart Association introduced a list of key prevention targets, known as "Life's Simple 7" to increase CVD-free survival, longevity, and quality of life. In 2022, sleep health was added to expand the recommendations to "Life's Essential 8" (eat better, be more active, stop smoking, get adequate sleep, manage weight, manage cholesterol, manage blood pressure, and manage diabetes). These prevention targets are intended to apply regardless of chronologic age. During this same time, the understanding of aging biology and goals of care for older adults further enhanced the relevance of prevention across the range of functions. From a biological perspective, aging is a complex cellular process characterized by genomic instability, telomere attrition, loss of proteostasis, inflammation, deregulated nutrient-sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication. These aging hallmarks are triggered by and enhanced by traditional CVD risk factors leading to geriatric syndromes (eg, frailty, sarcopenia, functional limitation, and cognitive impairment) which complicate efforts toward prevention. Therefore, we review Life's Essential 8 through the lens of aging biology, geroscience, and geriatric precepts to guide clinicians taking care of older adults.

Computationally Modelling Cholesterol Metabolism and Atherosclerosis

Cardiovascular disease (CVD) is the leading cause of death globally. The underlying pathological driver of CVD is atherosclerosis. The primary risk factor for atherosclerosis is elevated low-density lipoprotein cholesterol (LDL-C). Dysregulation of cholesterol metabolism is synonymous with a rise in LDL-C. Due to the complexity of cholesterol metabolism and atherosclerosis mathematical models are routinely used to explore their non-trivial dynamics. Mathematical modelling has generated a wealth of useful biological insights, which have deepened our understanding of these processes. To date however, no model has been developed which fully captures how whole-body cholesterol metabolism intersects with atherosclerosis. The main reason for this is one of scale. Whole body cholesterol metabolism is defined by macroscale physiological processes, while atherosclerosis operates mainly at a microscale. This work describes how a model of cholesterol metabolism was combined with a model of atherosclerotic plaque formation. This new model is capable of reproducing the output from its parent models. Using the new model, we demonstrate how this system can be utilized to identify interventions that lower LDL-C and abrogate plaque formation.

Conversion of fat to cellular fuel-Fatty acids β-oxidation model

β-oxidation of fatty acids plays a significant role in the energy metabolism of the cell. This paper presents a β-oxidation model of fatty acids based on queueing theory. It uses Michaelis-Menten enzyme kinetics, and literature data on metabolites' concentration and enzymatic constants. A genetic algorithm was used to optimize the parameters for the pathway reactions. The model enables real-time tracking of changes in the concentrations of metabolites with different carbon chain lengths. Another application of the presented model is to predict the changes caused by system disturbance, such as altered enzyme activity or abnormal fatty acid concentration. The model has been validated against experimental data. There are diseases that change the metabolism of fatty acids and the presented model can be used to understand the cause of these changes, analyze metabolites abnormalities, and determine the initial target of treatment.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

The Interplay between Finasteride-Induced Androgen Imbalance, Endoplasmic Reticulum Stress, Oxidative Stress, and Liver Disorders in Paternal and Filial Generation

Finasteride (Fin) causes androgen imbalance by inhibiting the conversion of testosterone (T) to its more active metabolite, dihydrotestosterone (DHT). Androgen receptors (AR) are present (e.g., in hepatocytes), which have well-developed endoplasmic reticulum (ERet). Cellular protein quality control is carried out by ERet in two paths: (i) unfolded protein response (UPR) and/or (ii) endoplasmic reticulum associated degradation (ERAD). ERet under continuous stress can generate changes in the UPR and can direct the cell on the pathway of life or death. It has been demonstrated that genes involved in ERet stress are among the genes controlled by androgens in some tissues. Oxidative stress is also one of the factors affecting the functions of ERet and androgens are one of the regulators of antioxidant enzyme activity. In this paper, we discuss/analyze a possible relationship between androgen imbalance in paternal generation with ERet stress and liver disorders in both paternal and filial generation. In our rat model, hyperglycemia and subsequent higher accumulation of hepatic glycogen were observed in all filial generation obtained from females fertilized by Fin-treated males (F1:Fin). Importantly, genes encoding enzymes involved in glucose and glycogen metabolism have been previously recognized among UPR targets.

Altered Basal Lipid Metabolism Underlies the Functional Impairment of Naive CD8+ T Cells in Elderly Humans

Aging is associated with functional deficits in the naive T cell compartment, which compromise the generation of de novo immune responses against previously unencountered Ags. The mechanisms that underlie this phenomenon have nonetheless remained unclear. We found that naive CD8+ T cells in elderly humans were prone to apoptosis and proliferated suboptimally in response to stimulation via the TCR. These abnormalities were associated with dysregulated lipid metabolism under homeostatic conditions and enhanced levels of basal activation. Importantly, reversal of the bioenergetic anomalies with lipid-altering drugs, such as rosiglitazone, almost completely restored the Ag responsiveness of naive CD8+ T cells. Interventions that favor lipid catabolism may therefore find utility as adjunctive therapies in the elderly to promote vaccine-induced immunity against targetable cancers and emerging pathogens, such as seasonal influenza viruses and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Modeling cholesterol metabolism and atherosclerosis

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of morbidity and mortality among Western populations. Many risk factors have been identified for ASCVD; however, elevated low-density lipoprotein cholesterol (LDL-C) remains the gold standard. Cholesterol metabolism at the cellular and whole-body level is maintained by an array of interacting components. These regulatory mechanisms have complex behavior. Likewise, the mechanisms which underpin atherogenesis are nontrivial and multifaceted. To help overcome the challenge of investigating these processes mathematical modeling, which is a core constituent of the systems biology paradigm has played a pivotal role in deciphering their dynamics. In so doing models have revealed new insights about the key drivers of ASCVD. The aim of this review is fourfold; to provide an overview of cholesterol metabolism and atherosclerosis, to briefly introduce mathematical approaches used in this field, to critically discuss models of cholesterol metabolism and atherosclerosis, and to highlight areas where mathematical modeling could help to investigate in the future. This article is categorized under: Cardiovascular Diseases > Computational Models.

A quantitative systems pharmacology modeling platform for evaluating triglyceride profiles in patients with high triglycerides receiving evinacumab

A model to quantitatively characterize the effect of evinacumab, an investigational monoclonal antibody against angiopoietin‐like protein 3 (ANGPTL3) on lipid trafficking is needed. A quantitative systems pharmacology (QSP) approach was developed to predict the transient responses of different triglyceride (TG)‐rich lipoprotein particles in response to evinacumab administration. A previously published hepatic lipid model was modified to address specific queries relevant to the mechanism of evinacumab and its effect on lipid metabolism. Modifications included the addition of intermediate‐density lipoprotein and low‐density lipoprotein compartments to address the modulation of lipoprotein lipase (LPL) activity by evinacumab, ANGPTL3 biosynthesis and clearance, and a target‐mediated drug disposition model. A sensitivity analysis guided the creation of virtual patients (VPs). The drug‐free QSP model was found to agree well with clinical data published with the initial hepatic liver model over simulations ranging from 20 to 365 days in duration. The QSP model, including the interaction between LPL and ANGPTL3, was validated against clinical data for total evinacumab, total ANGPTL3, and TG concentrations as well as inhibition of apolipoprotein CIII. Free ANGPTL3 concentration and LPL activity were also modeled. In total, seven VPs were created; the lipid levels of the VPs were found to match the range of responses observed in evinacumab clinical trial data. The QSP model results agreed with clinical data for various subjects and was shown to characterize known TG physiology and drug effects in a range of patient populations with varying levels of TGs, enabling hypothesis testing of evinacumab effects on lipid metabolism.

Physiologically Based Modeling of Food Digestion and Intestinal Microbiota: State of the Art and Future Challenges. An INFOGEST Review

This review focuses on modeling methodologies of the gastrointestinal tract during digestion that have adopted a systems-view approach and, more particularly, on physiologically based compartmental models of food digestion and host-diet-microbiota interactions. This type of modeling appears very promising for integrating the complex stream of mechanisms that must be considered and retrieving a full picture of the digestion process from mouth to colon. We may expect these approaches to become more and more accurate in the future and to serve as a useful means of understanding the physicochemical processes occurring in the gastrointestinaltract, interpreting postprandial in vivo data, making relevant predictions, and designing healthier foods. This review intends to provide a scientific and historical background of this field of research, before discussing the future challenges and potential benefits of the establishment of such a model to study and predict food digestion and absorption in humans.

Brain aging and garbage cleaning : Modelling the role of sleep, glymphatic system, and microglia senescence in the propagation of inflammaging

Brain aging is a complex process involving many functions of our body and described by the interplay of a sleep pattern and changes in the metabolic waste concentration regulated by the microglial function and the glymphatic system. We review the existing modelling approaches to this topic and derive a novel mathematical model to describe the crosstalk between these components within the conceptual framework of inflammaging. Analysis of the model gives insight into the dynamics of garbage concentration and linked microglial senescence process resulting from a normal or disrupted sleep pattern, hence, explaining an underlying mechanism behind healthy or unhealthy brain aging. The model incorporates accumulation and elimination of garbage, induction of glial activation by garbage, and glial senescence by over-activation, as well as the production of pro-inflammatory molecules by their senescence-associated secretory phenotype (SASP). Assuming that insufficient sleep leads to the increase of garbage concentration and promotes senescence, the model predicts that if the accumulation of senescent glia overcomes an inflammaging threshold, further progression of senescence becomes unstoppable even if a normal sleep pattern is restored. Inverting this process by "rejuvenating the brain" is only possible via a reset of concentration of senescent glia below this threshold. Our model approach enables analysis of space-time dynamics of senescence, and in this way, we show that heterogeneous patterns of inflammation will accelerate the propagation of senescence profile through a network, confirming a negative effect of heterogeneity.

Trends in LDL-C and Non-HDL-C Levels with Age

Understanding how blood lipid levels change with age in the general population is a precondition to defining dyslipidemia. To explore age-related trends in LDL-C and non-HDL-C levels in the general population, a large-scale cross-sectional study with 49,201 males and 35,084 females was adopted. Trends of non-HDL-C and LDL-C levels were plotted against each age (18 to 85 years old, one-year increments); the trends, as well as the influence of confounding factors on the trends, were validated and adjusted by linear regression modeling. The trajectory of LDL-C and non-HDL-C levels by age displayed a nonlinear correlation trend. Further multivariate linear regression modeling that incorporated sex-specific age phases showed that age was positively associated with LDL-C and non-HDL-C levels, with coefficients of 0.018 and 0.031, respectively, in females aged ≥18 to ≤56 years and negatively associated with LDL-C and non-HDL-C levels, with coefficients of -0·013 and -0.015, respectively, in females aged ≥57 years. The LDL-C and non-HDL-C levels increased with age in males ≥18 to ≤33 years of age, with coefficients of 0.025 and 0.053, respectively; the lipid levels plateaued at ≥34 to ≤56 years of age and subsequently decreased in those ≥57 years of age, with coefficients of -0.008 and -0.018, respectively. In contrast, pooled analyses without age stratification concealed these details. In conclusion, fluctuating increasing and decreasing lipid levels occurred with phases of aging in both sexes. Well-grounded age stratification is necessary to improve lipid-related pathophysiological studies.

Cholesterol Homeostasis: An In Silico Investigation into How Aging Disrupts Its Key Hepatic Regulatory Mechanisms

The dysregulation of intracellular cholesterol homeostasis is associated with several age-related diseases, most notably cardiovascular disease (CVD). Research in this area has benefitted from using computational modelling to study the inherent complexity associated with the regulation of this system. In addition to facilitating hypothesis exploration, the utility of modelling lies in its ability to represent an array of rate limiting enzymatic reactions, together with multiple feedback loops, which collectively define the dynamics of cholesterol homeostasis. However, to date no model has specifically investigated the effects aging has on this system. This work addresses this shortcoming by explicitly focusing on the impact of aging on hepatic intracellular cholesterol homeostasis. The model was used to investigate the experimental findings that reactive oxygen species induce the total activation of 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase (HMGCR). Moreover, the model explored the impact of an age-related decrease in hepatic acetyl-CoA acetyltransferase 2 (ACAT2). The model suggested that an increase in the activity of HMGCR does not have as significant an impact on cholesterol homeostasis as a decrease in hepatic ACAT2 activity. According to the model, a decrease in the activity of hepatic ACAT2 raises free cholesterol (FC) and decreases low-density lipoprotein cholesterol (LDL-C) levels. Increased acetyl CoA synthesis resulted in a reduction in the number of hepatic low-density lipoprotein receptors, and increased LDL-C, FC, and cholesterol esters. The rise in LDL-C was restricted by elevated hepatic FC accumulation. Taken together these findings have important implications for healthspan. This is because emerging clinical data suggest hepatic FC accumulation is relevant to the pathogenesis of non-alcoholic fatty liver disease (NAFLD), which is associated with an increased risk of CVD. These pathophysiological changes could, in part, help to explain the phenomenon of increased mortality associated with low levels of LDL-C which have been observed in certain studies involving the oldest old (≥85 years).

Effects of obesity on cholesterol metabolism and its implications for healthy ageing

The last few decades have witnessed a global rise in the number of older individuals. Despite this demographic shift, morbidity within this population group is high. Many factors influence healthspan; however, an obesity pandemic is emerging as a significant determinant of older people's health. It is well established that obesity adversely affects several metabolic systems. However, due to its close association with overall cardiometabolic health, the impact that obesity has on cholesterol metabolism needs to be recognised. The aim of the present review is to critically discuss the effects that obesity has on cholesterol metabolism and to reveal its significance for healthy ageing.

Omega-3 PUFA metabolism and brain modifications during aging

In Canada, 5.5 million (16% of Canadians) adults are >65 years old and projections suggest this number will be approximately 20% of Canadians by 2024. A major concern regarding old age is a decline in health, especially if this entails a loss of self-sufficiency and independence caused by a decline in cognition. The brain contains 60% of fat and is one of the most concentrated organs in long chain omega-3 fatty acids such as docosahexaenoic acid (DHA). During aging, there are physiological modifications in the metabolism of lipids that could also have consequences on brain structure and levels of DHA. This review will hence discuss the physiological modifications in the metabolism of lipids during aging with a focus on long chain omega-3 and omega-6 fatty acids and also outline the structural and functional modifications of the brain during aging including brain lipid modifications and its relation to higher levels of DHA and cognition. Therefore, in this review, we outline the importance of collecting more data on the biology of aging since it might highly improve our understanding about what are «normal» modifications occurring during aging and what can become pathological.

Hallmarks of Aging in the Liver

While the liver demonstrates remarkable resilience during aging, there is growing evidence that it undergoes all the cellular hallmarks of aging, which increases the risk of liver and systemic disease. The aging process in the liver is driven by alterations of the genome and epigenome that contribute to dysregulation of mitochondrial function and nutrient sensing pathways, leading to cellular senescence and low-grade inflammation. These changes promote multiple phenotypic changes in all liver cells (hepatocytes, liver sinusoidal endothelial, hepatic stellate and Küpffer cells) and impairment of hepatic function. In particular, age-related changes in the liver sinusoidal endothelial cells are a significant but under-recognized risk factor for the development of age-related cardiometabolic disease.

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Liver aging is driven by transcription and metabolic epigenome alterations.

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This leads to cellular senescence and low-grade inflammation.

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Hepatocyte, sinusoidal endothelial, stellate and Küpffer cells undergoes the hallmarks of aging.

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Each cell type demonstrates phenotypical cellular changes with age.

Reactive oxygen species-induced changes in glucose and lipid metabolism contribute to the accumulation of cholesterol in the liver during aging

Aging is a major risk factor for many chronic diseases due to increased vulnerability to external stress and susceptibility to disease. Aging is associated with metabolic liver disease such as nonalcoholic fatty liver. In this study, we investigated changes in lipid metabolism during aging in mice and the mechanisms involved. Lipid accumulation was increased in liver tissues of aged mice, particularly cholesterol. Increased uptake of both cholesterol and glucose was observed in hepatocytes of aged mice as compared with younger mice. The mRNA expression of GLUT2, GK, SREBP2, HMGCR, and HMGCS, genes for cholesterol synthesis, was gradually increased in liver tissues during aging. Reactive oxygen species (ROS) increase with aging and are closely related to various aging‐related diseases. When we treated HepG2 cells and primary hepatocytes with the ROS inducer, H₂O₂, lipid accumulation increased significantly compared to the case for untreated HepG2 cells. H₂O₂ treatment significantly increased glucose uptake and acetyl‐CoA production, which results in glycolysis and lipid synthesis. Treatment with H₂O₂ significantly increased the expression of mRNA for genes related to cholesterol synthesis and uptake. These results suggest that ROS play an important role in altering cholesterol metabolism and consequently contribute to the accumulation of cholesterol in the liver during the aging process.

Plasma lipidome variation during the second half of the human lifespan is associated with age and sex but minimally with BMI

Recent advances in mass spectrometry-based techniques have inspired research into lipidomics, a subfield of ‘–omics’, which aims to identify and quantify large numbers of lipids in biological extracts. Although lipidomics is becoming increasingly popular as a screening tool for understanding disease mechanisms, it is largely unknown how the lipidome naturally varies by age and sex in healthy individuals. We aimed to identify cross-sectional associations of the human lipidome with ‘physiological’ ageing, using plasma from 100 subjects with an apolipoprotein E (APOE) E3/E3 genotype, and aged between 56 to 100 years. Untargeted analysis was performed by liquid chromatography coupled-mass spectrometry (LC-MS/MS) and data processing using LipidSearch software. Regression analyses confirmed a strong negative association of age with the levels of various lipid, which was stronger in males than females. Sex-related differences include higher LDL-C, HDL-C, total cholesterol, particular sphingomyelins (SM), and docosahexaenoic acid (DHA)-containing phospholipid levels in females. Surprisingly, we found a minimal relationship between lipid levels and body mass index (BMI). In conclusion, our results suggest substantial age and sex-related variation in the plasma lipidome of healthy individuals during the second half of the human lifespan. In particular, globally low levels of blood lipids in the ‘oldest old’ subjects over 95 years could signify a unique lipidome associated with extreme longevity.

Computational modelling folate metabolism and DNA methylation: implications for understanding health and ageing

Dietary folates have a key role to play in health, as deficiencies in the intake of these B vitamins have been implicated in a wide variety of clinical conditions. The reason for this is folates function as single carbon donors in the synthesis of methionine and nucleotides. Moreover, folates have a vital role to play in the epigenetics of mammalian cells by supplying methyl groups for DNA methylation reactions. Intriguingly, a growing body of experimental evidence suggests that DNA methylation status could be a central modulator of the ageing process. This has important health implications because the methylation status of the human genome could be used to infer age-related disease risk. Thus, it is imperative we further our understanding of the processes which underpin DNA methylation and how these intersect with folate metabolism and ageing. The biochemical and molecular mechanisms, which underpin these processes, are complex. However, computational modelling offers an ideal framework for handling this complexity. A number of computational models have been assembled over the years, but to date, no model has represented the full scope of the interaction between the folate cycle and the reactions, which governs the DNA methylation cycle. In this review, we will discuss several of the models, which have been developed to represent these systems. In addition, we will present a rationale for developing a combined model of folate metabolism and the DNA methylation cycle.

The burden of dyslipidaemia and factors associated with lipid levels among adults in rural northern Ghana: An AWI-Gen sub-study

Dyslipidaemia is a primary risk factor for cardiometabolic disease, causing over 17 million deaths globally in 2015. However, the burden of dyslipidaemia and factors associated with lipid levels remain unknown in many rural African populations. Therefore, this study evaluated the association of socio-demographic, anthropometric and behavioural factors with lipid levels in rural Ghana. The prevalence of hypercholesterolaemia, hypertriglyceridaemia and elevated LDL-C in the total population of 1839 (846 men and 993 women) was 4.02%, 2.12%, and 5.55% respectively and did not differ between genders. The prevalence of low HDL-C levels was 60.30% and differed (p = 0.005) between men (56.86%) and women (63.24%). Subcutaneous abdominal fat was associated with TC (β = 0.067, p = 0.015) and TG (β = 0.137, p<0.001) among women and LDL-C (β = 0.139, p = 0.006) and TC (β = 0.071, p = 0.048) among men. Body mass index was associated with TC (β = 0.010, p = 0.043) among men while waist circumference was associated with LDL-C (β = 0.116, p<0.001) and TG (β = 0.094, p<0.001) among women. Hip circumference was negatively associated (β = -0.053, p = 0.043) while visceral fat was positively associated with TG (β = 0.033, p = 0.022) among women. Socioeconomic status, education, being unmarried and employment were associated with HDL-C (β = 0.081, p = 0.004), LDL-C (β = 0.095, p = 0.004) and TG (β = 0.095, p = 0.001) all among women, and TC (β = 0.070, p = 0.010) among men, respectively. Nankana women had lower TC (β = -0.069, p = 0.001), and men lower TG levels (β = -0.084, p = 0.008) than the other ethnic groups. Tobacco smoking (β = 0.066, p = 0.024) and alcohol intake (β = 0.084, p = 0.001) were associated with HDL-C levels among men and women respectively. Further studies are required to investigate whether high prevalence of low HDL-C levels in this population presents with any adverse cardiovascular disease outcomes. Associations of education, employment and adiposity with lipid levels suggest that future societal advances and increases in the prevalence of obesity may lead to associated adverse health consequences. Monitoring and interventions are required to limit these effects.

Optical monitoring of adipose tissue destruction under encapsulated lipase action

Enzymatic destruction of adipose tissue has been achieved by encapsulation of lipase into the polymeric microcapsules. Adipose tissue destruction was delayed while lipase is encapsulated comparing with the direct lipase action as demonstrated by optical microscopy and optical coherence tomography in in vitro studies. Raman spectroscopy confirms that triglycerides in fat tissue were cleaved into free fatty acids, glycerol, and possible di- and monoglyceride residues. The results underpin the concept of local and controlled treatment of tissues via encapsulation. Effect of lipase encapsulation into the polymeric microcapsules on adipose tissue destruction compared to free lipase application.

A review of inflammatory mechanism in airway diseases

BACKGROUND

Inflammation in the lung is the body's natural response to injury. It acts to remove harmful stimuli such as pathogens, irritants, and damaged cells and initiate the healing process. Acute and chronic pulmonary inflammation are seen in different respiratory diseases such as; acute respiratory distress syndrome, chronic obstructive pulmonary disease (COPD), asthma, and cystic fibrosis (CF).

FINDINGS

In this review, we found that inflammatory response in COPD is determined by the activation of epithelial cells and macrophages in the respiratory tract. Epithelial cells and macrophages discharge transforming growth factor-β (TGF-β), which trigger fibroblast proliferation and tissue remodeling. Asthma leads to airway hyper-responsiveness, obstruction, mucus hyper-production, and airway-wall remodeling. Cytokines, allergens, chemokines, and infectious agents are the main stimuli that activate signaling pathways in epithelial cells in asthma. Mutation of the CF transmembrane conductance regulator (CFTR) gene results in CF. Mutations in CFTR influence the lung epithelial innate immune function that leads to exaggerated and ineffective airway inflammation that fails to abolish pulmonary pathogens. We present mechanistic computational models (based on ordinary differential equations, partial differential equations and agent-based models) that have been applied in studying the complex physiological and pathological mechanisms of chronic inflammation in different airway diseases.

CONCLUSION

The scope of the present review is to explore the inflammatory mechanism in airway diseases and highlight the influence of aging on airways' inflammation mechanism. The main goal of this review is to encourage research collaborations between experimentalist and modelers to promote our understanding of the physiological and pathological mechanisms that control inflammation in different airway diseases.

The role of DNA methylation in ageing and cancer

The aim of the present review paper is to survey the literature related to DNA methylation, and its association with cancer and ageing. The review will outline the key factors, including diet, which modulate DNA methylation. Our rationale for conducting this review is that ageing and diseases, including cancer, are often accompanied by aberrant DNA methylation, a key epigenetic process, which is crucial to the regulation of gene expression. Significantly, it has been observed that with age and certain disease states, DNA methylation status can become disrupted. For instance, a broad array of cancers are associated with promoter-specific hypermethylation and concomitant gene silencing. This review highlights that hypermethylation, and gene silencing, of the EN1 gene promoter, a crucial homeobox gene, has been detected in various forms of cancer. This has led to this region being proposed as a potential biomarker for diseases such as cancer. We conclude the review by describing a recently developed novel electrochemical method that can be used to quantify the level of methylation within the EN1 promoter and emphasise the growing trend in the use of electrochemical techniques for the detection of aberrant DNA methylation.

The Interplay Between Cholesterol Metabolism and Intrinsic Ageing

The last few decades have witnessed remarkable progress in our understanding of ageing. From an evolutionary standpoint it is generally accepted that ageing is a non-adaptive process which is underscored by a decrease in the force of natural selection with time. From a mechanistic perspective ageing is characterized by a wide variety of cellular mechanisms, including processes such as cellular senescence, telomere attrition, oxidative damage, molecular chaperone activity, and the regulation of biochemical pathways by sirtuins. These biological findings have been accompanied by an unrelenting rise in both life expectancy and the number of older people globally. However, despite age being recognized demographically as a risk factor for healthspan, the processes associated with ageing are routinely overlooked in disease mechanisms. Thus, a central goal of biogerontology is to understand how diseases such as cardiovascular disease (CVD) are shaped by ageing. This challenge cannot be ignored because CVD is the main cause of morbidity in older people. A worthwhile way to examine how ageing intersects with CVD is to consider the effects ageing has on cholesterol metabolism, because dysregualted cholesterol metabolism is the key factor which underpins the pathology of CVD. The aim of this chapter is to outline a hypothesis which accounts for how ageing intersects with intracellular cholesterol metabolism. Moreover, we discuss the implications of this relationship for the onset of disease in the 'oldest old' (individuals ≥85 years of age). We conclude the chapter by discussing the important role mathematical modelling has to play in improving our understanding of cholesterol metabolism and ageing.

LDL-C levels in older people: Cholesterol homeostasis and the free radical theory of ageing converge

The cardiovascular disease (CVD) risk factor, low density lipoprotein cholesterol (LDL-C) increases with age, up until the midpoint of life in males and females. However, LDL-C can decrease with age in older men and women. Intriguingly, a recent systematic review also revealed an inverse association between LDL-C levels and cardiovascular mortality in older people; low levels of LDL-C were associated with reduced risk of mortality. Such findings are puzzling and require a biological explanation. In this paper a hypothesis is proposed to explain these observations. We hypothesize that the free radical theory of ageing (FRTA) together with disrupted cholesterol homeostasis can account for these observations. Based on this hypothesis, dysregulated hepatic cholesterol homeostasis in older people is characterised by two distinct metabolic states. The first state accounts for an older person who has elevated plasma LDL-C. This state is underpinned by the FRTA which suggests there is a decrease in cellular antioxidant capacity with age. This deficiency enables hepatic reactive oxidative species (ROS) to induce the total activation of HMG-CoA reductase, the key rate limiting enzyme in cholesterol biosynthesis. An increase in cholesterol synthesis elicits a corresponding rise in LDL-C, due to the downregulation of LDL receptor synthesis, and increased production of very low density lipoprotein cholesterol (VLDL-C). In the second state of dysregulation, ROS also trigger the total activation of HMG-CoA reductase. However, due to an age associated decrease in the activity of cholesterol-esterifying enzyme, acyl CoA: cholesterol acyltransferase, there is restricted conversion of excess free cholesterol (FC) to cholesterol esters. Consequently, the secretion of VLDL-C drops, and there is a corresponding decrease in LDL-C. As intracellular levels of FC accumulate, this state progresses to a pathophysiological condition akin to nonalcoholic fatty liver disease. It is our conjecture this deleterious state has the potential to account for the inverse association between LDL-C level and CVD risk observed in older people.

Effects of exogenous vitamins A, C, and E and NADH supplementation on proliferation, cytokines release, and cell redox status of lymphocytes from healthy aged subjects

Aging is an inevitable biological event that is associated with immune alterations. These alterations are related to increased cellular oxidative stress and micronutrient deficiency. Antioxidant supplementation could improve these age-related abnormalities. The aim of this study was to determine in vitro effects of vitamin A, vitamin C, vitamin E, and nicotinamide adenine dinucleotide (NADH) on T cell proliferation, cytokine release, and cell redox status in the elderly compared with young adults. Peripheral blood lymphocytes were isolated using a density gradient of Histopaque. They were cultured in vitro and stimulated with concanavalin A in the presence or absence of vitamins. Cell proliferation was determined by conducting MTT assays, and based on interleukin-2 and interleukin-4 secretions. Cell oxidant/antioxidant balance was assessed by assaying reduced glutathione (GSH), malondialdehyde, carbonyl protein levels, and catalase activity. The present study demonstrated that T-lymphocyte proliferation was decreased with aging and was associated with cytokine secretion alterations, GSH depletion, and intracellular oxidative stress. In the elderly, vitamin C, vitamin E, and NADH significantly improved lymphocyte proliferation and mitigated cellular oxidative stress, whereas vitamin A did not affect cell proliferation or cell redox status. In conclusion, vitamin C, vitamin E, and NADH supplementation improved T-lymphocytes response in the elderly, and could contribute to the prevention of age-related immune alterations. Consumption of food items containing these vitamins is recommended, and further investigation is necessary to evaluate the effect of vitamin supplementation in vivo.

Budding Yeast: An Ideal Backdrop for In vivo Lipid Biochemistry

Biological membranes are non-covalent assembly of lipids and proteins. Lipids play critical role in determining membrane physical properties and regulate the function of membrane associated proteins. Budding yeast Saccharomyces cerevisiae offers an exceptional advantage to understand the lipid-protein interactions since lipid metabolism and homeostasis are relatively simple and well characterized as compared to other eukaryotes. In addition, a vast array of genetic and cell biological tools are available to determine and understand the role of a particular lipid in various lipid metabolic disorders. Budding yeast has been instrumental in delineating mechanisms related to lipid metabolism, trafficking and their localization in different subcellular compartments at various cell cycle stages. Further, availability of tools and enormous potential for the development of useful reagents and novel technologies to localize a particular lipid in different subcellular compartments in yeast makes it a formidable system to carry out lipid biology. Taken together, yeast provides an outstanding backdrop to characterize lipid metabolic changes under various physiological conditions.

Investigating cholesterol metabolism and ageing using a systems biology approach

CVD accounted for 27 % of all deaths in the UK in 2014, and was responsible for 1·7 million hospital admissions in 2013/2014. This condition becomes increasingly prevalent with age, affecting 34·1 and 29·8 % of males and females over 75 years of age respectively in 2011. The dysregulation of cholesterol metabolism with age, often observed as a rise in LDL-cholesterol, has been associated with the pathogenesis of CVD. To compound this problem, it is estimated by 2050, 22 % of the world's population will be over 60 years of age, in culmination with a growing resistance and intolerance to pre-existing cholesterol regulating drugs such as statins. Therefore, it is apparent research into additional therapies for hypercholesterolaemia and CVD prevention is a growing necessity. However, it is also imperative to recognise this complex biological system cannot be studied using a reductionist approach; rather its biological uniqueness necessitates a more integrated methodology, such as that offered by systems biology. In this review, we firstly discuss cholesterol metabolism and how it is affected by diet and the ageing process. Next, we describe therapeutic strategies for hypercholesterolaemia, and finally how the systems biology paradigm can be utilised to investigate how ageing interacts with complex systems such as cholesterol metabolism. We conclude by emphasising the need for nutritionists to work in parallel with the systems biology community, to develop novel approaches to studying cholesterol metabolism and its interaction with ageing.

Obesity and the dysregulation of fatty acid metabolism: implications for healthy aging

The population of the world is aging. In 2010, an estimated 524 million people were aged 65 years or older representing eight percent of the global population. By 2050, this number is expected to nearly triple to approximately 1.5 billion, 16 percent of the world's population. Although people are living longer, the quality of their lives are often compromised due to ill-health. Areas covered: Of the conditions which compromise health as we age, obesity is at the forefront. Over half of the global older population were overweight or obese in 2010, significantly increasing the risk of a range of metabolic diseases. Although, it is well recognised excessive calorie intake is a fundamental driver of adipose tissue dysfunction, the relationship between obesity; intrinsic aging; and fat metabolism is less understood. In this review we discuss the intersection between obesity, aging and the factors which contribute to the dysregulation of whole-body fat metabolism. Expert commentary: Being obese disrupts an array of physiological systems and there is significant crosstalk among these. Moreover it is imperative to acknowledge the contribution intrinsic aging makes to the dysregulation of these systems and the onset of disease.

Computational Modeling of Lipid Metabolism in Yeast

Lipid metabolism is essential for all major cell functions and has recently gained increasing attention in research and health studies. However, mathematical modeling by means of classical approaches such as stoichiometric networks and ordinary differential equation systems has not yet provided satisfactory insights, due to the complexity of lipid metabolism characterized by many different species with only slight differences and by promiscuous multifunctional enzymes. Here, we present an object-oriented stochastic model approach as a way to cope with the complex lipid metabolic network. While all lipid species are treated objects in the model, they can be modified by the respective converting reactions based on reaction rules, a hybrid method that integrates benefits of agent-based and classical stochastic simulation. This approach allows to follow the dynamics of all lipid species with different fatty acids, different degrees of saturation and different headgroups over time and to analyze the effect of parameter changes, potential mutations in the catalyzing enzymes or provision of different precursors. Applied to yeast metabolism during one cell cycle period, we could analyze the distribution of all lipids to the various membranes in time-dependent manner. The presented approach allows to efficiently treat the complexity of cellular lipid metabolism and to derive conclusions on the time- and location-dependent distributions of lipid species and their properties such as saturation. It is widely applicable, easily extendable and will provide further insights in healthy and diseased states of cell metabolism.

Cholesterol metabolism: A review of how ageing disrupts the biological mechanisms responsible for its regulation

Cholesterol plays a vital role in the human body as a precursor of steroid hormones and bile acids, in addition to providing structure to cell membranes. Whole body cholesterol metabolism is maintained by a highly coordinated balancing act between cholesterol ingestion, synthesis, absorption, and excretion. The aim of this review is to discuss how ageing interacts with these processes. Firstly, we will present an overview of cholesterol metabolism. Following this, we discuss how the biological mechanisms which underpin cholesterol metabolism are effected by ageing. Included in this discussion are lipoprotein dynamics, cholesterol absorption/synthesis and the enterohepatic circulation/synthesis of bile acids. Moreover, we discuss the role of oxidative stress in the pathological progression of atherosclerosis and also discuss how cholesterol biosynthesis is effected by both the mammalian target of rapamycin and sirtuin pathways. Next, we examine how diet and alterations to the gut microbiome can be used to mitigate the impact ageing has on cholesterol metabolism. We conclude by discussing how mathematical models of cholesterol metabolism can be used to identify therapeutic interventions.

Aging and computational systems biology

Aging research is undergoing a paradigm shift, which has led to new and innovative methods of exploring this complex phenomenon. The systems biology approach endeavors to understand biological systems in a holistic manner, by taking account of intrinsic interactions, while also attempting to account for the impact of external inputs, such as diet. A key technique employed in systems biology is computational modeling, which involves mathematically describing and simulating the dynamics of biological systems. Although a large number of computational models have been developed in recent years, these models have focused on various discrete components of the aging process, and to date no model has succeeded in completely representing the full scope of aging. Combining existing models or developing new models may help to address this need and in so doing could help achieve an improved understanding of the intrinsic mechanisms which underpin aging.

Integration of 'omics' data in aging research: from biomarkers to systems biology

Age is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age-related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high-throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so-called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age-related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

Mathematical modelling of metabolic regulation in aging

The underlying cellular mechanisms that characterize aging are complex and multifaceted. However, it is emerging that aging could be regulated by two distinct metabolic hubs. These hubs are the pathway defined by the mammalian target of rapamycin (mTOR) and that defined by the NAD+-dependent deacetylase enzyme, SIRT1. Recent experimental evidence suggests that there is crosstalk between these two important pathways; however, the mechanisms underpinning their interaction(s) remains poorly understood. In this review, we propose using computational modelling in tandem with experimentation to delineate the mechanism(s). We briefly discuss the main modelling frameworks that could be used to disentangle this relationship and present a reduced reaction pathway that could be modelled. We conclude by outlining the limitations of computational modelling and by discussing opportunities for future progress in this area.