Knowledge of how human fat tissue is affected by age has long been defined by numerous mouse-based studies.
Researchers at Karolinska Institutet in Sweden have now, for the first time, been able to conduct a prospective study on humans that provides novel insights into how our fat cells reduce lipid metabolism with age. The study is published in the journal Cell Metabolism.
As we age, many changes take place in our organs that affect physiological function. Earlier studies on mice have shown that macrophages in adipose (fat) tissue start to degrade noradrenaline, a hormone that stimulates lipolysis (the breakdown of lipids).
It has long been thought that humans have a similar mechanism, but the new study shows that age-induced changes in human fat metabolism operates differently. Instead of macrophages, which are a type of immune cell, it is the fat cells themselves that degrade noradrenaline with age.
“We were surprised to see this difference between animals and humans,” says Mikael Rydén, senior consultant and professor of clinical and experimental adipose tissue research at Karolinska Institutet and one of the paper’s senior authors.
“The earlier mouse studies are correct, but it’s been hard to do similar work on humans as you need prospective studies that track the same people over time.”
Clear link to age
The foundation of the project was laid many years ago when a diet intervention study was conducted on women between the ages of 30 and 35, in which fat samples were taken before, during and after their diet. Now, over 13 years after the first samples were taken, the same women were contacted for a follow-up study.
“Our findings provide the first insights into the changes in adipose tissue that are controlled by biological age in humans,” explains Rydén. “What we find is that lipolysis in the adipose tissue declines over time.
These changes also seem to be independent of menopause or pregnancy. They are simply the result of aging.”
The breakdown of fat affects body function
A lower rate of lipolysis can contribute to weight gain and the accumulation of fat in other tissue. Atherosclerosis can be a result of this process, for instance, as well as changes in the ability of the body to deal with cold and hunger.
The results of the study are also interesting from a public health perspective with respect to obesity – a growing problem that leaves people susceptible to many diseases.
“It was once thought that the fat cell was fairly inactive, but we suspect that it’s active and controls a lot more than previously thought,” says Niklas Mejhert, co-senior author of the paper and joint group leader with Rydén at the Department of Medicine, Huddinge, Karolinska Institutet. “If we can regulate the accumulation of fat in a more controlled way, it could bring huge advantages.”
Further cell studies planned
The results of the study, which explain why adipose tissue becomes less effective and how lipolysis declines with age, are of interest to the efforts being made to find future treatments able to improve the function of adipose tissue.
“We now plan to examine how different cells within the fat tissue are affected by age,” continues Rydén. “This is particularly interesting when it comes to stem cells, which have the unique and important ability to renew themselves and repair injury. It’s something we’re keen to follow up on.”
Aging is a complex, multifarious process characterized by changes such as stem cell exhaustion, mitochondrial dysfunction, impaired immune function, reduced autophagy, epigenetic alterations, accumulation of somatic and mitochondrial DNA mutations, aberrant intercellular communication, loss of telomeres, altered nutrient sensing, and impaired protein homeostasis (Lopez‐Otin, Blasco, Partridge, Serrano, & Kroemer, 2013; Singh, Demmitt, Nath, & Brunet, 2019).
A large portion of our current, limited understanding of what causes aging comes from lifespan studies in short‐lived model organisms. By identifying genetic, pharmacological, and dietary interventions that both extend and reduce lifespan, we have gleaned that specific molecular mechanisms—like the target of rapamycin (TOR), insulin/insulin‐like growth factor (IGF), and adenosine monophosphate‐activated protein kinase (AMPK) signaling pathways—play integral roles in regulating aging (Singh et al., 2019).
The identification of aging biomarkers that change over time has concomitantly helped us to understand what mechanisms underlie aging. For example, nicotinamide adenine dinucleotide (NAD+) concentrations decrease during aging and high‐fat diets as well as increase in response to caloric restriction, exercise, and fasting (Verdin, 2015).
Moreover, NAD+ supplementation extends lifespan in mice (Zhang et al., 2016) as well as in yeast and worms (Verdin, 2015). This biomarker data (i.e., that NAD+ levels decrease with age) preceded the lifespan data and paved the way for studies exploring the effects of NAD+ repletion on aging.
Due to the sheer amount of time and cost required to validate a study in humans, the bulk of our aging and lifespan data come from shorter‐lived yeast, worms, flies, and rodents. With the exception of research showing that caloric restriction improves health and survival in rhesus monkeys (Mattison et al., 2017), little aging work has been done in longer‐lived organisms.
The bulk of our understanding regarding aging comes from genetic experiments in model organisms (Singh et al., 2019), and we do not yet know how similar or dissimilar human aging is. For example, genome‐wide association studies searching for longevity‐related variants have found a lack of association with many genes known to extend lifespan in simpler animals (de Magalhaes, 2014).
This is likely due to major biological differences between these organisms and humans as well as the limited genetic diversity of laboratory animal strains. As such, it is probable that a large portion of aging interventions proven in the laboratory will not yield significant clinical effects in humans (de Magalhaes, 2014).
Therapies that are evolutionarily conserved between different model organisms are, however, more likely to have a therapeutic effect in Homo sapiens. Caloric restriction, for example, extends lifespan or improves health in every organism tested—including radically disparate animals such as mosquitoes (Joy, Arik, Corby‐Harris, Johnson, & Riehle, 2010) and humans (Kraus et al., 2019; Most, Tosti, Redman, & Fontana, 2017).
Rather than screen every lifespan‐extending intervention in humans to better understand how human aging works, another approach would be to utilize aging biomarkers. Biomarkers that strongly correlate with aging, lifespan, and healthspan can teach us about which processes are involved in human aging.
They can also help us understand, independent of an individual’s chronological age, how old a patient is biologically. Clinically, this could be used as an important health assessor. For example, Fleischer et al recently generated and analyzed a large dataset of genome‐wide RNA‐seq profiles of human dermal fibroblasts (Fleischer et al., 2018).
These fibroblasts were derived from 133 people aged one to 94 years old. By developing an ensemble machine learning method, they were able to estimate an individual’s age to a median error of four years.
Testing in ten progeria patients revealed that this transcriptomic approach was capable of predicting accelerated aging (Fleischer et al., 2018). These data are impactful as they suggest that, with sufficient biomarker knowledge, patient senescence could be accurately measured by looking at objective, computer‐analyzed parameters.
In the clinic, this would enable precision medicine by giving doctors the ability to make patient‐specific decisions based on their aging state. Put differently, a patient’s true biological age could be accurately ascertained instead of making assumptions based on their chronological age.
Currently, generalized recommendations are provided given average outcomes associated with different age groups. Robust biomarkers would also allow us to rapidly test the efficacy of rejuvenative interventions in humans (Mahmoudi, Xu, & Brunet, 2019).
Myriad types of aging biomarkers exist. They can take the form of physiological and clinical data such as white blood cell count, absolute monocyte count, blood pressure, body mass index, resting heart rate, forced expiratory volume, gait speed, and grip strength (Burkle et al., 2015; Sebastiani et al., 2017; Xia, Chen, McDermott, & Han, 2017). As an example of how useful one of these biomarker parameters can be, grip strength is highly predictive of mortality, morbidity, and future disability (Leong et al., 2015).
Biomarkers can also manifest as analyzed genomic, epigenetic, transcriptomic, and proteomic data. The epigenetic marker DNAm PhenoAge, which is comprised of DNA methylation information from 513 different CpGs, was shown to strongly correlate with age in every tissue tested and to be predictive of all‐cause mortality as well as the age‐related diseases cancer and Alzheimer’s disease (Levine et al., 2018).
By performing transcriptomic analyses, this marker was also associated with an increased activation of pro‐inflammatory pathways as well as a decreased activation of DNA damage response genes (Levine et al., 2018).
Efforts are currently underway to initiate a clinical trial that will utilize DNA methylation information to assess the efficacy of various antiaging interventions (Mitteldorf, 2019). Biomarkers can additionally manifest as molecules such as carbohydrates, apolipoproteins, glycoproteins, hormones, cytokines, and lipids (Burkle et al., 2015; Sebastiani et al., 2017; Xia et al., 2017).
Interleukin‐6, for instance, is a pro‐inflammatory cytokine and glycoprotein that increases in concentration with age (Maggio, Guralnik, Longo, & Ferrucci, 2006). This age‐related increase in interleukin‐6 fits into our current understanding that the immune system gets progressively dysregulated with age and that unhealthy inflammation contributes to senescence.
The upregulation of the interferon response pathway, for example, occurs during aging in multiple tissues from mice as well as in other vertebrate species such as rats, African turquoise killifish, and humans (Benayoun et al., 2019).
Ideally, a robust and practical biomarker would be one that incurs a low monetary cost and can be measured safely, repeatedly, and easily. Blood draws are especially appealing because they are inexpensive, simple, low risk, and can be taken as needed throughout a patient’s lifetime.
While several biomarker studies have focused on protein‐based markers, the advancement of metabolomic techniques has made it feasible to look closely into a large array of metabolites. Metabolomic lipids and lipid‐related proteins represent a large, rich source of potential biomarkers that are easily measured in the blood.
Compounds in lipid metabolism can take many forms, such as phospholipids, triglycerides, waxes, steroids, and fatty acids. They also play diverse physiological roles, such as forming cell membranes and lipid rafts (Pike, 2003) as well as exerting powerful cell signaling effects (Sunshine & Iruela‐Arispe, 2017). Lipids are perhaps the most well‐known for the paramount roles they play in both the storage and mobilization of energy.
Although lipids have been traditionally treated as detrimental and as simply associated with age‐related diseases, numerous studies have shown that lipid metabolism potently regulates aging and lifespan. Jové et al, for example, assessed the plasma lipidomic profiles of 11 different mammalian species with longevities varying from 3.5 to 120 years (Jove et al., 2013).
They found that a lipidomic profile could accurately predict an animal’s lifespan and that, in particular, plasma long‐chain free fatty acids, peroxidizability index, and lipid peroxidation‐derived product content are inversely correlated with longevity (Jove et al., 2013).
Similarly, Jobson et al scanned the genomes of 25 different species and reported that genes involved in lipid composition had undergone increased selective pressure in longer‐lived animals (Jobson, Nabholz, & Galtier, 2010). Evidence from animals with extreme longevity also links lipid metabolism to aging.
The ocean quahog clam Arctica islandica, an exceptionally long‐lived animal that can survive for more than 500 years, exhibits a unique resistance to lipid peroxidation in mitochondrial membranes (Munro & Blier, 2012).
The bowhead whale, another complex animal with extreme longevity that can live longer than 200 years, has lens membranes that are especially enriched with phospholipids. This unique enrichment is thought to at least partially underlie its uncanny resistance to the age‐related lens disease of cataracts (Borchman, Stimmelmayr, & George, 2017).
Naked mole rats, which enjoy remarkably long lifespans and healthspans for rodents, have a unique membrane phospholipid composition that has been theorized to contribute to their exceptional longevity (Mitchell, Buffenstein, & Hulbert, 2007). The importance of lipids in lifespan is further confirmed by the ability of lipid‐related interventions to enhance longevity in model organisms (Huang, Withers, & Dickson, 2014).
The goal of this review was to assess the potential of lipids or lipid‐related proteins to function as biomarkers of aging and to affect aging. To do this, we highlight how alterations in lipid metabolism can impact lifespan and age‐related disease.
We discuss how these lipid‐related interventions are distinct from those made by altering canonical aging pathways and also highlight lipid‐associated signatures that correlate with extreme human longevity.
Based on the existing data, we believe that lipids are a promising source of human aging biomarkers and that, clinically, they may be able to effectively determine a patient’s biological age. We also believe that lipid‐related interventions represent a promising clinical strategy for improving human healthspan and ameliorating age‐related disease.
Lastly, we propose aspects of lipid metabolism that could be clinically targeted to elongate the period of healthy life in humans. The ability of lipid‐specific interventions to elongate both lifespan and healthspan in animal models demonstrates that, rather than being simply associated with age‐related disease, lipid metabolism is a direct and potent regulator of aging.
LIPID BIOMARKERS OF AGING IN HUMANS
Genetic lipid signatures correlated with extreme human longevity
Genome‐wide association studies have identified multiple genetic factors that are correlated with exceptional longevity. Given the ability of lipid‐related interventions to modulate lifespan and healthspan in model organisms (Tables (Tables1,1, ,2,2, ,3,3, ,4),4), lipid‐related genes would be expected to be associated with longer lifespans in humans.
This is indeed the case. Work by Atzmon et al. (2006) have shown that, among centenarians, homozygosity for the −641C allele in the APOC3 promoter (rs2542052) is significantly higher compared with controls.
This genotype was associated with lower serum levels of APOC3, a favorable pattern of lipoprotein levels and sizes, a lower prevalence of hypertension, and greater insulin sensitivity (Atzmon et al., 2006). A separate study involving 338 centenarians implicated both ApoE and angiotensin‐converting enzyme in human aging.
The epsilon 4 allele of ApoE was significantly less common in centenarians compared with controls. Conversely, the epsilon 2 allele of ApoE was significantly more common in centenarians (Schachter et al., 1994). Ryu, Atzmon, Barzilai, Raghavachari, and Suh (2016) have shown that the ε3/ε4 ApoE genotype is markedly depleted in centenarians, while the ε2/ε3 genotype is substantially enriched.
The epsilon 4 allele of ApoE is also a risk variant for late‐onset neurodegenerative diseases and is thought to contribute to the pathogenesis of Alzheimer’s disease via multiple different pathways (Yamazaki, Painter, Bu, & Kanekiyo, 2016). Given that overexpression of human ApoD or GLaz, the fly homolog of ApoD, was shown to confer antiaging benefits in flies and mice (Tables (Tables33 and and4),4), it is interesting that apolipoproteins are also associated with greater human longevity. This suggests that the protective effects of apolipoproteins are highly evolutionarily conserved and that clinical interventions targeting apolipoproteins could improve human healthspan.
In addition to ApoE, the PON1 gene encoding paraoxonase/arylesterase 1 is reportedly associated with human longevity and is thought to impact lipid metabolism (Lescai, Marchegiani, & Franceschi, 2009).
Tindale et al compared a group of healthy individuals aged 85 or over to random midlife controls and identified that both the ε4 ApoE allele and the haptoglobin HP2 allele were less common among the healthy, aged group. Moreover, a network analysis of candidate longevity genes revealed that lipid and cholesterol metabolism was a common theme. Although it did not reach statistical significance (p = .052), the lipoprotein A gene LPA showed an interaction with the insulin signaling‐relevant FOXO3 (Tindale, Leach, Spinelli, & Brooks‐Wilson, 2017).
A separate study also identified lipoprotein A as being longevity‐associated (Joshi et al., 2017). Rs7844965, located in an intron of the lipid hydrolase EPHX2, was linked with increased human lifespan in a group of UK Biobank participants (Pilling et al., 2017). Genome‐wide association of 1 million parental lifespans has shown that gene pathways involving lipid proteins and homeostasis, synaptic function, and vesicle‐mediated transport are enriched for lifespan variation (Timmers et al., 2019).
In addition, a group of Ashkenazi Jews with exceptional longevity (mean age of 98.2 years) and their offspring were reported to have an increased frequency of homozygosity for the codon 405 valine allele of the gene CETP, which encodes for cholesteryl ester transfer protein (Barzilai et al., 2003).
Lipidomic analyses of extreme human longevity
A few studies have done broad, lipidomic analysis to assess the relationship between aging and a large array of different lipids. For example, measurements of 128 lipid species using liquid chromatography coupled to mass spectrometry previously identified 19 lipid species associated with familial longevity in women.
The authors compared the plasma lipidome of offspring of nonagenarians (a person between 90 and 99 years) to non‐nonagenarian controls. Although no significant differences were observed for men, female offspring exhibited increased levels of either sphingomyelin or phosphocholine species as well as lower levels of long‐chain triglycerides and phosphoethanolamine.
Longevity was also associated with a higher ratio of MUFAs over PUFAs (Gonzalez‐Covarrubias et al., 2013). Interestingly, many long‐lived organisms or mutants have a decreased ratio of PUFA to MUFA (Papsdorf & Brunet, 2019). Separate work utilizing NMR metabonomics and shot‐gun lipidomics found that centenarians display unique changes in biosynthesis compared with elderly controls.
In particular, phospholipids and sphingolipids were identified as putative markers and modulators of healthy aging (Montoliu et al., 2014). Recent lipidomic work by Jové et al. have reported that a fatty acid profile resistant to lipid peroxidation is associated with extreme longevity.
Extreme longevity was associated with a higher saturated fatty acid content as well as a lower content of unsaturated fatty acids, such as PUFAs.
Longer lifespans were also correlated with a higher average fatty acid chain length. The authors also proposed that specific lipid species of ceramides are biomarkers of extreme longevity (Jove et al., 2017).
Even more recently, Wong et al have used lipidomics to discover that their “oldest old” subjects over 95 years of age exhibited globally low levels of lipids. Women and men also showed sex‐related age differences in their plasma lipid levels.
For example, women had higher levels of LDL cholesterol, HDL cholesterol, and total cholesterol. Sphingomyelin and docosahexaenoic acid‐containing phospholipid levels were also higher in females (Wong et al., 2019).
Although more studies and data are required to better understand how the lipidome changes with age, these few studies harbor shared findings. Two different lipidomic screens reported that sphingomyelin levels increase with age for women (Gonzalez‐Covarrubias et al., 2013; Wong et al., 2019).
Two of these papers reported that human longevity is associated with lower unsaturated fatty acid content (Gonzalez‐Covarrubias et al., 2013; Jove et al., 2017) and two different studies identified sex‐specific age differences in lipid content (Gonzalez‐Covarrubias et al., 2013; Wong et al., 2019).
All three studies linked sphingolipids to aging (Gonzalez‐Covarrubias et al., 2013; Jove et al., 2017; Wong et al., 2019). Broader lipidomic screens are warranted to better understand additional plasma lipids that with age and how plasma lipids uniquely change for men and women.
Triglycerides as blood biomarkers of aging
Other nonlipidomic studies have analyzed whether or not a single lipid or few different lipids are useful as biomarkers of aging or age‐related disease. Multiple papers have identified plasma triglycerides, the lipids stored in fat cells that make up most of body fat, as a potential blood aging biomarker.
For example, Parthasarathy et al have reported that triglycerides levels are inversely correlated with cognitive function in nondemented elderly adults (Parthasarathy et al., 2017). Triglycerides have been reported to increase progressively with age and have been actively proposed as a biomarker of aging (Xia et al., 2017).
Triglyceride levels increase in older patients and are thought to be a significant risk factor for coronary artery disease, particularly in women (LaRosa, 1997). These data suggest that triglycerides have the potential to be a useful lipid biomarker. However, it is important to note that genetically predicted triglyceride levels have been reported to be unassociated with either frailty and longevity in elderly populations (Liu et al., 2017). Ergo, while most studies indicate that triglycerides increase with age (Papsdorf & Brunet, 2019), it remains to be determined whether or not triglyceride levels can accurately predict parameters of aging.
Lipoproteins and cholesterol as blood biomarkers of aging
Given everything mentioned heretofore, it should be unsurprising that ApoE plasma levels have been proposed as a human aging biomarker and have been reported to strongly associate with cardiovascular mortality (Mooijaart et al., 2006).
The related LDL apolipoprotein B and LDL cholesterol reportedly both increase with age, and these increases are linked to a progressively reduced fractional catabolic rate of LDL apolipoprotein B. Stimulation of hepatic LDL receptor expression via the cholesterol‐lowering medication cholestyramine in six old males was sufficient to increase the catabolic rate to levels identified in younger subjects. These data indicate the LDL increase with age occurs due to a reduced capacity for its removal (Ericsson et al., 1991).
Several studies have examined how LDL and HDL change over time, and some of the results are conflictive. A systematic review by Wirth et al. (2017) assessed various markers of human sedentary behavior in the elderly. They identified a positive correlation for LDL and ApoA1 as well as a negative correlation for HDL with older sedentary behavior (Wirth et al., 2017).
Offspring of individuals with exceptional longevity have significantly larger LDL and HDL particle sizes as well as a lower prevalence of cardiovascular disease, hypertension, and metabolic syndrome (Barzilai et al., 2003).
Ashkenazi Jewish offspring of centenarians showed fewer and larger LDL particles compared with their same‐aged partners.
No differences in HDL particle phenotypes were reported (Heijmans et al., 2006). Larger LDL particle sizes as well as lower triglyceride levels were reported in offspring of nonagenarian siblings compared with controls, which were partners of the offspring. LDL particle size was associated with male longevity, while triglyceride levels were associated with female longevity (Vaarhorst et al., 2011).
HDL from older subjects was reported to have an altered composition that impaired its antioxidant properties and overall function. HDL from elderly patients contained less cholesterol and had more sphingomyelin (Holzer et al., 2013).
Relatedly, a progressive decline in plasma HDL concentrations has been associated with cognitive dysfunction in centenarians (Atzmon et al., 2002).
Data also suggest that plasma cholesterol may be a viable human aging blood biomarker, though the data are disparate.
Both female and male offspring of centenarians reportedly have higher plasma levels of HDL cholesterol compared with controls. Men also exhibited significantly lower LDL cholesterol levels (Barzilai, Gabriely, Gabriely, Iankowitz, & Sorkin, 2001).
Work by Weijenberg, Feskens, and Kromhout (1996) found that total cholesterol decreased with age, but HDL cholesterol did not change significantly with age in Dutch men. Kreisberg and Kasim previously concluded that total cholesterol, HDL cholesterol, and LDL cholesterol change uniquely over time for both men and women (Kreisberg & Kasim, 1987).
A biomarker signature comprised of multiple different biomarkers, including total cholesterol, was found to be associated with lower morbidity and mortality as well as better physical function (Sebastiani et al., 2017).
More comprehensive, systematic analyses are required to better understand the relationship between cholesterol and lipoproteins with aging as well as how this relationship differs between men and women.
Fatty acids and lipid peroxidation as blood biomarkers of aging
Fatty acids and lipid peroxidation have additionally been implicated as potential blood lipid biomarkers of human aging. Given that a higher dietary intake and higher circulating levels of eicosapentaenoic acid and docosahexaenoic acid have been associated with a reduced risk of dementia, Tan et al. analyzed red blood cell levels of these ω‐3 PUFAs in 1,575 dementia‐free patients.
Participants with lower docosahexaenoic acid had lower scores on tests for visual memory, abstract thinking, and executive function. Those with docosahexaenoic acid levels in the lowest quartile also had greater white matter hyperintensity and lower total brain volumes.
The authors concluded that this PUFA was therefore a candidate marker of brain aging (Tan et al., 2012). By assessing different fatty acids with age, it was reported that plasma saturated, polyunsaturated, and monounsaturated fatty acids increase with age. Concomitant with this, circulating concentrations of IL‐6 and TNF‐α increased, while IL‐10 and TGF‐β1 decreased over time. Certain saturated fatty acids were reported to be associated with changing levels of TGF‐β1 and TNF‐α (Pararasa et al., 2016).
In normal elderly people, a decrease in antioxidants and an increase in lipid peroxidation were reported compared with younger controls. The lipid peroxidation malondialdehyde was highly elevated in older patients with diabetes and hypertension (Akila, Harishchandra, D’Souza, & D’Souza, 2007).
Yavuzer et al. (2016) have shown that both hypertension and aging are associated with higher lipid peroxidation in humans. In particular, they identified lipid hydroperoxide and thiobarbituric acid‐reactive substances as sensitive markers for both hypertension and aging in elderly patients (Yavuzer et al., 2016).Aging is additionally associated with an increase in lipid peroxidation in cardiac muscle obtained from 59 patient donors (age range of 8–86 years) with a mean age of 56 ± 12 years (Miro et al., 2000).
Sphingolipids and phospholipids as blood biomarkers of aging
Very little work has explicitly assessed whether or not sphingolipids, including ceramides, are candidate human aging blood biomarkers. Plasma sphingolipids have been proposed as biomarkers for Alzheimer’s disease (Mielke & Haughey, 2012) and have also been linked to the age‐related diseases of diabetes, obesity, nonalcoholic fatty liver disease, insulin resistance, and cardiovascular disease (Iqbal, Walsh, Hammad, & Hussain, 2017).The plasma ceramide C16:0 has been associated with a slower gait, an important aging marker of physical function (Wennberg et al., 2018). These data as well as the lipidomic data previously mentioned (Gonzalez‐Covarrubias et al., 2013; Montoliu et al., 2014; Wong et al., 2019) nicely justify further exploring the relationship between blood sphingolipids and human aging. Indeed, a recent study in nondiabetic patients found that higher levels of plasma insulin and an increased HOMA of insulin resistance score were associated with an elevation in plasma ceramides (Lemaitre et al., 2018).
With regard to phospholipids, low plasma levels of lysophosphatidylcholines were found to be associated with impaired mitochondrial oxidative capacity in adults (Semba et al., 2019). Lower levels of blood phospholipids, including the lysophosphatidylcholine 18:2, were separately shown to be highly predictive of memory impairment in older adults (Mapstone et al., 2014). Low plasma levels of lysophosphatidylcholine 18:2 also predict a greater decline of gait speed in the elderly (Gonzalez‐Freire et al., 2019).
Patients with cancer, a classical age‐related disease, analogously show a decrease in the concentration of plasma lysophosphatidylcholine (Taylor, Arends, Hodina, Unger, & Massing, 2007).
Given these data and that both phosphatidylcholine and phosphatidylethanolamine have been reported to decline with age in model organisms (Papsdorf & Brunet, 2019), phospholipids show substantial potential as blood aging biomarkers in humans.
Interestingly, a study by Trabado et al. (2017) have reported that elderly healthy subjects have higher plasma levels of sphingomyelins and phosphatidylcholines compared with young subjects. Although this reinforces the theory that these lipids are connected to aging, it suggests that specific lipids within these families may uniquely increase or decrease with age. It also suggests that other parameters, like patient health or genetic variability, may influence the relationship between a given sphingolipid or phospholipid with age.
More information: Hui Gao et al. Age-Induced Reduction in Human Lipolysis: A Potential Role for Adipocyte Noradrenaline Degradation, Cell Metabolism (2020). DOI: 10.1016/j.cmet.2020.06.007