At the biochemical level, ageing is a continuous and dynamic remodelling process of metabolism and cell function. This chemical reconditioning is heavily influenced by unrepaired accumulation of DNA mutational damage occurring within nuclear DNA45 and mitochondrial DNA46,47 brought about by environmental stressors. Ensuing dysfunction can be translated back to physiological status and contribute to an ageing phenotype. Indeed, population studies have already examined metabolic baseline levels in human health48 and longevity28, showing that metabolomics combined with symptom, biochemical or demographic data can successfully identify distinct biochemical models that were not previously been associated with lifespan in humans. These studies not only indicate modulation of various metabolic pathways such as those within the TCA cycle28 and lipid biosynthesis49,50, but also suggest that large sample sizes (n > 600)48 and precise analytical methodologies, such as those performed within the HUSERMET study48,51, are essential for robust analysis of the data generated. Yet, metabolic studies directly investigating frailty have previously focused primarily on the influence of specific disease states, such as breast cancer52 (n = 79) and sarcopenia53 (n = 139), and have not specifically analysed the broad underlying causal processes relating to the frailty-ageing condition. With the aim of expanding knowledge in this area, our goal was to identify the presence of a potential frailty metabolic phenotype and link it to associated physiological and pathophysiological processes. Using a validated assessment of frailty status in conjunction with standard biochemistry analysis and high-throughput metabolic profiling, we generated a metabolic network that highlights significant areas of metabolism that are associated with the clinically assessed FI. This multi-level approach developed a mROC model that identified 12 metabolites as being highly significant in the differentiation of subjects exhibiting frail and non-frail phenotypes as indicated by their position on the FI. Ultimately, our studies show that global lipid metabolism is changed under the frailty phenotype and down regulation of the carnitine shuttle and vitamin E metabolism show potential in playing a role in modulating cellular energy production. These biochemical observations in turn mirror the reduced state of physical activity observed clinically in frail subjects.

Initial calculations of the frailty indices used within the study generated unimodal right-skewed distributions6 (Supplementary Fig. 1), comparable to those developed in other population scale assessments of frailty30. The operationalization of the Wave 4 index into a range of four discreet classifiers applicable to stratification over mass spectrometry based metabolomics data was achieved by binning FI scores in to four supervisory classes and applying PC-DFA (Fig. 2). Upon analysis, this approach identified only three distinct sub-planes of separation along the 0.1–0.2 axis and the 0.2–0.3 axes of FI scoring. These metabolic level observations correlate with independent FI assessments made in other large scale studies of frailty across the globe, such as in Canada18,54,55 and Taiwan51 in which non-frail, pre-frail and frail discreet classifiers were considered to have equivalent FI scores. Subsequent whole index validation by PLS methodologies (Fig. 3) and individual bin assessment using linear regression (Supplementary Fig. 2) also indicated that correlation between age and frailty actually decreases across the index thus distinguishing it from normal age-related degeneration, further supplying validation to the concept that frailty is in fact a geriatric syndrome within its own right and, although influenced by age, distinct from normal temporal changes.

Prior to metabolomics and pathway analysis, a panel of standard clinical biochemical tests were performed on matched blood samples to investigate how conventional assays, already routinely used within clinical practise, could be used to assess and develop the frailty phenotype. Stable cholesterol, LDL and HDL levels were noted within the non- and pre-frail phenotypes, but sharp decreases were associated with the frail phenotype (Fig. 1c). These results are confirmed by previous experimental data in which serum cholesterol levels have been indicated as a hematologic marker of frailty in older hospitalized patients56,57. LDL/HDL levels have also been demonstrated to decrease with age58, and conversely, high levels of HDL has also been directly associated with better survival rates in very old subjects59. However, a fluctuation in triglyceride levels was observed across the FI range. Within these experimental studies weight loss is identified as the key explanatory variable, which parallels the importance of involuntary weight loss displayed within the frailty phenotype.

Steadily increasing fibrinogen and white blood cell levels were also noted across the FI (Fig. 1d). Fibrinogen, as an essential component of the coagulation cascade and a key regulator of inflammation, which has been implicated as a risk factor for several diseases60, the elevation of which, has previously been associated with increasing frailty level61. In the present study observed white blood cell levels were directly correlated with frailty in older adults, an observation that further supplies evidence for the role of immuno-endocrine cross-talk within62 functional decline. Serum ferritin levels were also noted to decrease over the FI which would initially infer an increase in anaemia. However, previous studies designed to investigate the utility of ferritin as a single indicator of frailty determined it to be of exceedingly low potential63 owing to the complex interactions between serum iron, total iron binding capacity and transferrin saturation ratio severely hampering levels of assay sensitivity. To compound the use of serum ferritin as a biomarker of frailty further, increased levels are associated with an increase in oxidative stress and cellular damage64 which goes against observed values obtained within this study. Dehydroepiandrosterone (DHEAS) levels were also evaluated over the index range, the decline in which was correlated with a higher FI. This correlation is in agreement with previous studies reporting a widely-recognised association between decreasing androgen levels and ageing65,66.

All clinical biochemistry data were also analysed controlling for sex. As a result, an interesting observation was noted within the measured triglyceride levels. Upon stratification, male vs. female triglyceride levels act in a divergent manner (see Supplementary Fig. 10) as FI score increases; females noting a sharp increase and males noting a decrease. The identification of this important role of triglyceride levels has already been documented in the Leiden Longevity Study (n = 1664)63 in which multiple regression models indicated decreased triglyceride levels predicted to serve as an indicator of longevity in females.

Biochemical network activity assessment, in which all m/z features were used as input, detected 25 identified metabolites (Supplementary Table 1) that contributed to dysregulation of four metabolic pathways (Fig. 4)—monosaccharide, kynurenine, vitamin E and carnitine metabolism. All individual pathways contain a link to energy production within eukaryotic cells. In this process, pathways identified as significant can contain individual metabolites that may not be significant on their own—due to their presence within a pathway that has other significant features contained within it. To investigate the role of the individual 25 metabolites in differentiating non-frail and frail metabolic phenotypes, Kruskal-Wallis analysis of variance with subsequent false discovery rate (FDR) testing for multiple comparisons was used to test for significance. In total 12 metabolites (Fig. 5a) were deemed individually statistically significant (>0.05) in differentiating non-frail and frail metabotypes. These feature were then used to develop a Multivariate Receiver Operating Characteristic (mROC) curve (Fig. 5b), to act as a predictive model of frail status. In this process, the final mROC model used 12 metabolites (individual distributions and univariate contributing ROCs shown in Fig. 5c) to generate an AUC of 0.755 (95% CI = 0.708–0.815)—indicating a moderately strong level of performance. Overall results from combining PC-DFA separation, RUSBoost-CART sampling validation, pathway enrichment, univariate descriptive comparison of metabolite means and concluding mROC predictive modelling provide a diverse range of evidence that all support the theory of metabolic dysregulation within the frail metabotype. As further evidence, predictive modelling was also replicated on in a validation subset of 768 samples from the same subjects collected four years later. The presence of 9 out of 12 the metabolites used to generate the wave 4 mROC model were detected within the deconvolved Wave 6 mass spectrometry dataset. The data from these features was then used to generate a mROC model from the validation subset with an AUC of 0.702 (95% CI = 0.63–0.748) (Supplementary Fig. 15) indicating a reproducible result even with slightly reduced data input.

Using the two main pathways identified within pathway analysis, two of the four genetic instruments used in the Mendelian Randomization analysis showed evidence for the causal effect of carnitine levels in frailty. Our instrument SNPs represent the SLC16A9 (solute carrier family 16 member 9) (rs12356193) and SLC22A4 (solute carrier family 22 member 4) (rs419291) genes. These SNPs were strongly associated with carnitine levels in a study of human metabolites (p = 3.69 × 10−63 and p = 3.1 × 10−18, respectively)43. Although our results do not survive strict correction for multiple testing, they are firmly supported by the literature. A recent study measuring common variants (minor allele frequency >5%) using healthy ageing as outcome reported the possible involvement of the SLC22A4 gene, represented by multiple variants, including rs41929167.

Vitamin E analogues, in this case detected tocotrienols, are well-documented due to their lipoperoxyl radical-scavenging abilities in the termination of lipid peroxidation via proton transfer on to lipid free radicals68. However, they are also noted for their ability to scavenge reactive nitrogen species, inhibit cyclooxygenase- and 5-lipoxygenase-catalyzed eicosanoids, and suppress pro-inflammatory signalling, such as NF-κB69. This reduction of free radical-mediated oxidative damage alongside general inflammatory suppression is vital to the maintenance of a healthy lifestyle over time. Breakdown of the endogenous antioxidant system can lead to the accumulation of oxidative damage from lipids that has been linked to ageing, cancer and many other co-morbidities70. The ability of the carnitine shuttle to generate acetyl-CoA is vital for the successful generation of FADH 2 and the regeneration of ATP at the end of the electron transport chain. Breakdown of this mechanism is terminal to the cell. We found that a decrease in the levels of several carnitines at higher levels of frailty could be potential indications that general cell-based lipid metabolism is deteriorating, but it is essential that further experimentation needs to be performed to confirm and validate this hypothesis. The importance of the kynurenine pathway provides a conduit for the consumption of over 99% of ingested tryptophan that is unused in protein synthesis71,72. With an upregulation of tryptophan noted within the frail metabotype, and with age-related sarcopenia known to be an underlying phenotype within frailty, this observation suggests that muscle protein breakdown is a potential contributor to frailty metabolic output. Further along the kynurenine pathway, a bottleneck in the biogenesis of the vital energy co-factor NAD and its associated dysregulation has also been associated to mitochondrial disturbances73, activation in times of stress and immune activation74 alongside links to neurodegenerative diseases75. Conversely, the tryptophan kynurenine pathway is also the starting point for the biosynthesis of two related neurotransmitters; serotonin and melatonin. Previous work has indicated that an over activation of this pathway can lead to activation of the immune system and downstream accumulation of potentially neurotoxic intermediates such as quinolinic acid72 and kynurenic acid76. These metabolites are currently considered to be involved in some way in Alzheimer’s disease, Parkinson’s disease, Huntington’s disease and amyotrophic lateral sclerosis and future works in frailty metabolism should consider them as interesting mechanistic targeted76.

This work exemplifies the high suitability for combined metabolic and pathway analysis to explore and uncover significantly modulated biological pathways within biogerontology. The longitudinal nature of the study, alongside the unselected aspect of the sample cohort are strengths that increases the external validity of the findings. However, several limitations also exist, and these should be considered. While providing results that are consistent with data from previous experimental literature, our findings should be considered hypothesis generating in nature.

This fact, tied to the restricted geography of the cohort (all subjects residing in England), requires that further validation from a range of independent cohorts is essential to test the conserved nature of the results.

Also, to understand fully the complex biological processes that are dysregulated as a component of frailty, a comprehensive systems-based approach is needed to model all dimensions of the process. Further work is needed link metabolic profiles to genotypic expression. Several genetic mutations and markers have already been identified in model organisms77,78 and humans living extremely long lives79,80 and these observations need to be related to RNA, protein and metabolite expression. Candidate gene-association studies on data from the same wave of ELSA have indicated genetic changes effecting lipoprotein receptor-related protein 1 (LRP1) gene on chromosome 1281. This multi ligand receptor has previously been reported to be involved in lipid homeostasis including cholesterol transport; thus, supporting our theory of global lipid imbalance in frailty.

There are also several limitations that need to be considered within the Mendelian Randomization work. Firstly, as frailty is a complex condition and as such is likely to involve multiple genetic variants. The genetic variants typically explain only a small fraction of the total variance in traits; therefore, MR studies require very large sample sizes for sufficient statistical power. Although we chose the two-sample approach to achieve greater power, our sample size with 1500 cases and 3500 controls may not be powerful enough.

Secondly, the instruments we employed may be considered weak, as indicated by the large confidence intervals of the causal estimates. Finally, for individual polymorphisms the variance explained is usually <1%; therefore, it is advisable to combine multiple polymorphisms into a single allele score to maximize the explanatory power of the instrument. However, due to lack of reported significant SNP-metabolite associations, the available number of genetic instruments was a single or maximum two SNPs for the exposures. The lack of multiple genetic instruments also prevented us from carrying out a pleiotropy test. One of the assumptions of MR analysis is that there is no horizontal pleiotropy, i.e., when a genetic variant affecting multiple traits via separate pathways82. The MR-Egger regression method provides valid causal estimates in the presence of some violations of the MR assumptions. However, as this method requires more than two genetic variants assigned to the same exposure, we could not test for this and assumed no pleiotropic effects. Also, in order to obtain more conclusive evidence on the effect of carnitine levels on frailty, studies with sufficiently large sample size are required. While our results should be interpreted with caution, this is an important exercise towards identifying causal relationships.

In summary, our work reveals that the presence of frailty, with an associated increased risk of negative health outcomes in later life, is not only just identifiable through symptomatic presentation but, as predicted by Fried and colleagues21, is multifactorial and subsequently recognisable by a distinct biochemical phenotype. Our results, primarily imply that a deterioration in lipid metabolism is present within those who clinically present as frail: The downstream set of metabolic observations detected within this study (primarily linked to energy dysregulation) are directly linked with the primary clinical description for frailty: a reduction in physiological reserve. Metabolic frailty measurement has the potential to contribute greatly to the standardisation of frailty assessment. In addition, the application of metabolomics in combination with other -omics based technologies (such as we have with Mendelian Randomization) offers the potential for a greater understanding of the biologic basis and complexity of frailty. Knowledge of frailty risk factors and biomarkers offers the scope to yield effective early stage interventions that can be incorporated into standard of care practices and ultimately contribute to healthy ageing.