Towards Metabolomics-Guided Healthy and Anti-Aging Nutrition

Abstract

Background: Metabolomic studies have generated extensive data on metabolic changes in aging and disease, yet translating this data into practical nutrition guidelines remains challenging. Recent analysis identified pathways common to both processes, termed the metapathway. As a network, it features key central metabolites that most representatively reflect its state. The manageable number of these key metabolites provides a practical basis for translating complex metabolomic data into actionable nutritional information. Methods: We developed a conceptual framework for precision nutrition approach involving: (1) selecting an initial (baseline) diet with minimal impact on key metapathway metabolites, (2) defining dietary modifications using foods and supplements that are capable of elevating them, and (3) implementing mass spectrometry-based metabolome fingerprinting to assess individual responses. This capability was evaluated using blood plasma and dried blood spot samples. Results: A promising precision nutrition was created, consisting of a selected baseline diet and its metabolomics-guided modification. The metabolic fingerprinting demonstrated the possibility of determining the diet outcome by identifying biological age change with an accuracy of 1 month. Conclusions: The fully metabolomics-guided nutrition strategy has been developed and is ready for further human testing to validate its translational potential and health benefits.

1. Introduction

An important aspect of medicine is maintaining human health, thereby increasing lifespan. Worldwide, this has led to a continuous rise in life expectancy [1], a trend associated with population aging and a growing burden of aging-related diseases [2,3]. To effectively extend lifespan and mitigate the negative effects of aging, it is necessary to apply discoveries obtained at the molecular level [4,5].

Aging involves changes across all molecular levels, from the genome to the metabolome. It is known that longevity is heritable, with heritability ranging from 20% to about 50% [6,7,8,9,10]. However, lifespan is also influenced by numerous “post-genomic” factors [11,12,13,14]. These include epigenetic alterations, loss of protein (proteostasis) and stem cell (stem cell exhaustion) function, altered intercellular communication (inflammation), cell senescence, and a deregulated response to nutrients (nutrient sensing) [15]. For instance, epigenetic changes, specifically DNA methylation patterns, are closely related to person’s age, enabling the development of accurate epigenetic clocks [16]. Yet, these epigenetic data have limited power in predicting individual health outcomes and time of death. The next, phenotype level, exhibits this property more strongly [16]. Phenotypic clocks, which use readily measurable biological markers to quantify aging and disease-related mortality, can predict mortality more accurately than chronological age itself.

The molecular phenotype is primarily represented by metabolites: low-molecular-weight compounds that serve as substrates, intermediates, and products of metabolic processes. Collectively, these metabolites constitute the metabolome [4,17]. Metabolomic analysis can identify markers of aging and describe their relationship with pathological processes [18,19,20,21,22]. Databases such as the Human Metabolome Database (HMDB) [23] and MetaboAge [24] compile key findings from these studies, cataloging age-related metabolites and their associated pathways. By detecting variations in metabolites, metabolomics guides interventions and helps assess biological age [22]. Thus, for developing health and anti-aging interventions, including healthy nutrition, and for assessing the body’s response, the metabolomic level appears to be the most promising among all levels of molecular organization.

Alongside physical activity, stress management, and avoiding harmful habits, healthy nutrition is a cornerstone of longevity [25,26,27]. In recent years, the concept of precision nutrition has evolved, which involves tailoring specific dietary recommendations based on an individual’s metabolism [28]. The potential of using metabolomics to inform such personalized nutrition strategies has already been demonstrated [29,30,31,32,33], including applications in food intake biomarker discovery, monitoring metabolic responses to dietary interventions, assessing the health impacts of specific foods, and identifying subgroups (metabotypes) for personalized dietary advice based on metabolomic profiles [32,34,35]. Therefore, leveraging modern metabolomic advances to assess health and slow down aging through precision nutrition is highly relevant.

However, the direct application of metabolomic data in human nutrition by scientists, physicians, and nutritionists is not straightforward. Translational research, which serves as a bridge between fundamental science and practical medicine [36,37,38], is required to transform the abundance of available data into an applicable nutritional guidance. Therefore, selecting metabolomics findings with translational potential is the first step.

Recently, a comparative analysis of untargeted metabolomics studies of aging across various animal models (from nematodes to mammals) and humans has been conducted [39]. The results showed that metabolites significantly differing between age groups are related to carbohydrates, amino acids, carnitines, biogenic amines, and lipids. Notably, the age-associated metabolites identified across different models are largely linked to the same metabolic pathways. This commonality at the metabolomic level has prompted further study of the metabolic connection between aging and disease. It has been established that human aging and pathological conditions affect identical metabolic pathways with a high probability (p-value = 0.9996) [40]. By combining these pathways, a single pathway, termed metapathway, was compiled, which captured changes simultaneously linked to health and aging. The identification of this metapathway holds translational potential for developing health and anti-aging interventions, including nutritional strategies [41]. Figure 1 illustrates our translational approach, which uses the identified metapathway to transform accumulated metabolomic data on aging and disease into practical nutrition recommendations.

This study aligns with the T1-phase of translational research [42]. Using the “metapathway” concept, which consolidates age- and disease-related metabolic changes, we convert the abundance of metabolomic data into a limited number of key metabolites. Measuring and interpreting this focused subset is significantly simpler and more practical than analyzing the full, original dataset. This simplification enables the development of practical, metabolomics-based nutritional guidelines.

Thus, the aim of this work is to design a conceptual framework for healthy and anti-aging metabolomics-guided precision nutrition. This framework integrates initial, metabolomics data-based dietary prescriptions with ongoing monitoring of metabolic response and guided dietary adjustments using metabolomic fingerprinting.

2. Methods

2.1. Scientific Background for Translational Study

In a prior study [40], a meta-analysis of six human untargeted metabolomic studies on aging [43,44,45,46,47,48] and metabolomic data for pathological conditions from the Human Metabolome Database (HMDB) was performed. Combinatorial analysis revealed a significant overlap between metabolic pathways associated with human aging and those linked to pathological conditions. This association was highly non-random, with a high probability (p = 0.9996) that the seven identified aging-related pathways were presented among disease-related pathways.

These findings demonstrate a high degree of identity between aging and disease at the metabolic level and allow to identify a set of seven metabolic pathways that simultaneously reflect aging-related and health-related changes:

• Arginine biosynthesis pathway;
• Valine, leucine, and isoleucine biosynthesis pathway;
• Alanine, aspartate, and glutamate metabolism pathway;
• Butanoate metabolism pathway;
• Glyoxylate and dicarboxylate metabolism pathway;
• Phenylalanine, tyrosine, and tryptophan biosynthesis pathway;
• Aminoacyl-tRNA biosynthesis pathway.

Therefore, in prior work, these pathways were consolidated into a single, integrated metapathway (Figure 2a) providing the basis for the practical application of the original metabolomic data. As a network, the metapathway contains key nodal metabolites that are highly representative of its overall state. These metabolites were termed biomarkers of metapathway state (BMS). Manipulating this limited set of metabolites is feasible for practical application. Thus, in the present work, the metapathway existence was considered a scientific background, and nutritional guidance was designed around the known or expected effects of nutrients on its central node metabolites.

Precision nutrition uses a dynamic approach, where dietary recommendations are adjusted based on an individual’s response, rather than a static plan applied to a wide group. To enable this, metabolic fingerprinting was proposed to monitor changes in biological age following dietary modification. The selection of specific foods to modulate BMS levels in the body is suggested based on the fundamental principle of nutritional science: consuming foods enriched with the target compounds or their precursors to increase BMS levels, while vice versa to decrease them.

2.2. Selection of Biomarkers of Metapathway State (BMS)

To select BMS, a metabolite–metabolite interaction network previously constructed for the metapathway was used (Figure 2b) [40]. To build the metabolite–metabolite interaction network and determine the degree of its nodes, the network analysis module of the MetaboAnalyst 6 (option ‘metabolite–metabolite interaction network’; layout Fruchterman–Reingold; accessed on 8 September 2024) was used. The chemical–chemical associations for the metabolites were extracted from STITCH [49] so that only confident interactions were used for this. The central nodes of network were considered key metabolites (

Table 1. Metabolites with the highest centrality (central nodes) in the metabolite–metabolite interaction network constructed for the metapathway. Adapted from [40].

Metabolite Name	Degree (Connections of Node)
L-Glutamic acid	57
Oxoglutaric acid	53
Adenosine triphosphate (ATP)	51
Pyruvic acid	50
NADP	46
CO2	46
NADH	44
O2	40
On average for all metabolites in the network
	24

) that most effectively reflect the state of the overall metapathway [50]. Essentially, metabolites that participate in the greatest number of reactions occurring in the metapathway were selected, which makes them the best representatives of the metapathway state. To define central nodes, the degree, as the direct metric to characterize the centrality of nodes in network analysis, was used. Metabolites with the highest centrality, i.e., central nodes, were identified as BMS, which can be used to monitor the metapathway state and to select foods, supplements, and a baseline diet for precision nutrition design.

2.3. Scientific Evidence Linking BMS to Health and Longevity

To confirm that metabolomics-derived BMS indeed reflect health and longevity, the published literature was reviewed for supporting evidence.

2.4. Implementation of Metabolomics-Guided Precision Nutrition

Precision nutrition is an individualized approach that involves assessing a person’s response to a diet and then adjusting it accordingly. In this framework, the initial baseline diet is modified using selected foods and supplements. To quantify the individual’s response, the use of blood plasma metabolome fingerprinting to measure changes in biological age was proposed.

2.5. Selection of a Baseline Diet

A baseline diet for metabolomics-guided nutrition was selected from an analysis of popular diets, including the Mediterranean, ketogenic, paleolithic, and vegan diets, as well as intermittent fasting. The selection prioritized evidence from human studies over animal or theoretical data. The chosen diet was required to have the smallest effect on BMS. Furthermore, any observed effect on BMS had to be beneficial and weaker than the effects of other diets.

2.6. Selection of Foods and Food Supplements for Modifying the Baseline Diet

To modify the baseline diet, it is proposed to incorporate specific foods and food supplements rich in BMS or their metabolic precursors, which is in line with the standard nutritional approach to diet formulation [51], specifically, increasing the level of target compounds in the body through the consumption of foods enriched with such compounds or their precursors. Since the choice of the baseline diet is based on minimizing the impact on BMS levels, adding foods and supplements to the baseline diet is aimed specifically at increasing them in organism. Dietary changes aimed at reducing BMS levels were not considered because they are more difficult to implement and are not consistent with scientific data indicating the advisability of increasing their levels in the organism (see Section 3.5).

2.7. Assessment of Nutrition Outcome by Metabolome Fingerprinting

Blood metabolome fingerprinting using direct mass spectrometry was selected as the method for routine monitoring of nutrition outcomes. In this case, the fingerprint is formed by a set of mass spectrometric peak intensities and is characteristic for the individual. The fingerprint’s characteristics, specifically its changes with age, were established in a cohort of patients of varying ages and genders. Furthermore, the feasibility of using this fingerprinting approach for assessing biological age, specifically its precision, technical variability, and biological reproducibility, were evaluated.

2.7.1. Blood Samples

Blood plasma samples (Set 1) from Caucasian healthy subjects (n = 190) of different ages (from 18 to 81 years old) and body mass index (BMI) in the normal range were taken from the previously conducted metabolomic study [52] (

Table 2. Characteristics of the sample sets used in this study.

Set of Samples	Age (Years)	Subjects (Samples)	Gender (Male/Female)	Sample Type	Purpose of Set of Samples
Set 1	18–81	190 (190)	96/94	Blood plasma	To build and characterize age-related metabolic curve.
Set 1.1	36	1 (10)	1/0	Blood plasma	For technical variability calculation.
Set 2	18–66	124 (124)	51/72	Blood plasma	To build age-related metabolic curve for the “metabolic clock” test.
Set 2.1	18–54	15 (30)	6/9	Blood plasma	For testing biological age calculation by comparing with chronological age (“metabolic clock” test).
Set 3	18–81	100 (100)	60/40	DBS	To build age-related metabolic curve for DBS samples.
Set 4	25, 29	2 (6)	0/2	DBS	To demonstrate the measurement of biological age change at different time intervals.
Set 5	25, 29	2 (7)	0/2	DBS	To calculate the biological reproducibility of biological age change measurement.
Set 6	43, 43	2 (4)	1/1	DBS	To demonstrate the influence of food intake on the measurement of biological age.

).

Blood plasma samples from Caucasian healthy subjects used to construct an age-related metabolic curve for the “metabolic clock” testing (Set 2: curve-building samples including 30 samples obtained from the same subjects at intervals of 3, 6, 12, and 18 months) were taken from the previous study [53]. Dried blood spot samples (Set 3, Caucasian subjects) were taken from another previously conducted study [54,55]. Dried blood spot samples (Sets 4, 5, and 6) were provided by Caucasian healthy volunteers of normal BMI after signing informed consent and permission to publish personal metabolomic data. For sample Set 6, a man and a woman provided blood samples on an empty stomach and 1 h after the morning meal (mashed potatoes (200 g, composition: potatoes, milk (2.5%), butter, salt), cutlet (100 g, composition: ground meat (pork/beef), onion, wheat bread, chicken egg, salt, pepper), and tea with sugar (200 mL)).

The characteristics of the subjects for sample sets are presented in Supplementary Materials (Tables S1–S4).

2.7.2. Blood Plasma Sample Preparation

Blood samples were collected in the morning after overnight fasting into EDTA Vacutainer plasma tubes (BD, Franklin Lakes, NJ, USA) and cooled down at 4 °C immediately. Blood plasma was separated by centrifugation according to the manufacturer’s instructions (4000 rpm for 10 min at 4 °C), transferred into a clean 2 mL Eppendorf, and immediately stored at −80 °C until analysis. For analysis, the frozen plasma samples were thawed on ice, and an aliquot (10 µL) was mixed with 80 µL pre-cooled methanol (J.T. Baker, Gliwice, Poland) and 10 µL water (Sigma-Aldrich, St. Louis, MO, USA). The mixture was incubated for 10 min (on ice with periodical shaking) and centrifuged (13,000× g, 4 °C, 15 min). The supernatant was transferred to a clean 2 mL Eppendorf, and 10 µL of the supernatant was mixed with fifty volumes of methanol containing 0.1% formic acid (Fluka, Munich, Germany). As an internal standard, 0.4 µL (5 mg/L) of losartan solution was added. The resulting solutions were analyzed by direct infusion mass spectrometry.

2.7.3. DBS Samples Preparation

For metabolite extraction, the circles with dried blood spots (Whatman™ 903 Proteinsaver Snap-Apart Card, Cytiva, Marlborough, MA, USA) were cut out and divided in half, one part of which was placed in clean Eppendorf ™ tubes, where 40 µL of water (LiChrosolv; Merck KGaA, Darmstadt, Germany) and 160 µL of methanol (Fluka, Munich, Germany) were added and mixed. After 10 min of incubation at room temperature, samples were centrifuged at 13,000× g (Centrifuge 5804R; Eppendorf AG, Hamburg, Germany) for 15 min. The supernatant was then transferred to clean plastic Eppendorf ™ tubes, and fifty volumes of methanol containing 0.1% formic acid (Fluka) were added to each tube. The resulting solutions were subjected to direct mass spectrometry analysis.

2.7.4. Mass Spectrometry

Samples were analyzed by direct infusion mass spectrometry (DIMS) using a hybrid quadrupole time-of-flight mass spectrometer (maXis Impact, Bruker Daltonics, Billerica, MA, USA) equipped with an electrospray ionization (ESI) source. The mass spectrometer was set up to prioritize the detection of ions with a mass-to-charge ratio (m/z) ranging from 45 to 900, with a mass accuracy of 1–3 parts per million (ppm). The spectra were recorded in the positive ion charge detection mode. The samples were injected into the ESI source using a glass syringe (Hamilton Bonaduz AG, Bonaduz, Switzerland) connected to a syringe injection pump (KD Scientific, Holliston, MA, USA). The rate of sample flow to the ionization source was 180 µL/h. Mass spectra were obtained using DataAnalysis version 4.1 (Bruker Daltonics) to summarize one-minute signals.

2.7.5. Mass Spectra Processing

Peak detection, recalibration, and peak intensity calculation of mass spectra were carried out automatically by DataAnalysis software. Masses of compounds were determined from the mass spectrum peaks obtained using the following parameters: peak width, 2; signal-to-noise ratio, 1; relative and absolute threshold intensity, 0.01% and 100, respectively. For recalibration of all the peak m/z values, the internal standard losartan (m/z 423.169) was used. Alignment of the m/z values of the mass peaks between different mass spectra was performed as described previously [56]. The alignment algorithm used was previously specially developed and tested for the high-resolution mass spectra of blood metabolites obtained by DIMS and implemented as an iterative process based on the detection of correlation of mass spectrometry peak patterns.

2.8. Age-Related Trajectory of the Blood Metabolome

To obtain the age-related trajectory of the blood plasma metabolome, a $\overline{Z}$-score curve was constructed. To do this, age-related mass peaks were first identified (feature extraction). For the mass peak intensities having non-zero values in at least 15% of the mass spectra, the Spearman correlation probability was calculated (p-values for testing the hypothesis of no correlation against the alternative hypothesis of a nonzero correlation; corr function, Matlab ver. R2019b, MathWorks, Natick, MA, USA). Mass spectrometry peaks positively and negatively correlated with age (adjusted p-value < 0.01; FDR adjusted by mafdr function) were converted to Z-scores (3245 peaks for men and 1838 peaks for women). The Z-score is a common way of representing data on a unitless scale and is the data minus the mean divided by the standard deviation of the data. The data with Z-scores were sorted by the age of the subjects, and the Z-scores in the direction of age were smoothed by averaging over 7 years (moving average with a sliding window of ±3 years). The smoothed Z-scores for each subject were averaged, and the resulting values ($\overline{Z}$-scores) were plotted according to the subject’s age. Since many subjects were of the same age, the average of their $\overline{Z}$-scores was plotted. To build a curve for plotted data, a smoothing spline with piecewise polynomial was used (Curve fitting toolbox ver. 3.5.10, Matlab). To demonstrate the stability of the $\overline{Z}$-score curve shape depending on its construction parameters, the adjusted p-value was varied, which affects feature extraction efficacy and the number of mass spectrometric peaks used in calculating the $\overline{Z}$-score curve (Figure S1). To exclude the influence of missing values on the $\overline{Z}$-score curve shape, the percentage of mass spectrometry peaks with non-zero intensity values used to construct the curve was calculated (Figure S2).

Mass spectra for DBS samples from sample Set 2 were used to construct an age-related trajectory of the whole blood metabolome in a similar manner.

2.9. Biological Age Change Determination

The Euclidean distances from an individual’s metabolic fingerprints to points on the age-related metabolomic curve were measured (pdist function, Matlab). Where the similarity is greatest, the resulting distance curve has a minimal value and indicates the biological age of the individual. Given that the age-related curve has a resolution of 1 year, polynomial extrapolation (empirically selected) from three points forming the minimum of the distance curve was used to accurately determine biological age. To determine the change in biological age, the difference between two points of time was measured. The quality of biological age change measurement by the proposed way was validated by chronological age (“metabolic clock” test). For this, an age-related curve was built (sample Set 2, n = 124), and changes in biological age for samples collected at intervals of 3, 6, 12, and 18 months (sample Set 2.1; n = 30) were compared with changes in chronological age. When using samples from Set 2.1, they were excluded one by one from Set 2 to eliminate their influence on the age-related curve construction. The coefficient of determination of linear extrapolation was used as a criterion for the coincidence of changes in biological and chronological ages.

Technical variability for biological age measurement was assessed by acquiring mass spectra ten times from the same blood plasma sample (male, 36 years old; sample Set 1.1), and the standard deviation, standard error of the measurement (SEM), and mean absolute difference (MAD) were defined.

To describe the use of DBS for measuring biological age change, an age-related metabolomic curve was constructed using DBS samples (Set 3; n = 100), and DBS samples were used to measure changes in biological age (Set 4, n = 6; samples from women aged 25 and 29 years collected with a gap of three months and one week). Samples collected with a gap of one week from the same women (sample Set 5; n = 6) were used to measure biological reproducibility of the DBS approach for measuring biological age. The sample Set 7 was used to assess food intake influence on biological age measurement.

The minimum detectable change (MDC) of biological age change at a 95% confidence level represents the smallest change in a measurement that is unlikely to have occurred due to random error (i.e., precision of measurement). MDC was calculated as 1.96 × $\sqrt{2}$ × SEM, where 1.96 is a statistical constant for the 95% confidence level [57].

3. Results

3.1. Scientific Evidence Linking BMS to Health and Longevity

To confirm that metabolomics-derived BMS are associated with health and longevity, the published literature was reviewed, and the supporting evidence was summarized. Figure 3 shows the involvement of BMS in metabolic processes and their relation to disease. Scientific evidence for the involvement of BMS in aging-related processes is summarized in Figure 4, which confirms that metabolomics-derived BMS are integrated in the main aging-related events in the organism. The data underlying this figure are presented in the Appendix A.

3.2. Selection of a Baseline Diet from Popular Diets

To implement precision nutrition, a baseline diet must be selected for subsequent adjustment based on metabolic response. For this purpose, we focused on widely recognized diets—the Mediterranean, ketogenic, paleolithic, and vegan diets, as well as intermittent fasting—as their extensive literature allows for a systematic assessment of their effects on BMS. The following several sections provide a brief description of these diets, with a focus on their documented effects on BMS.

3.2.1. Mediterranean Diet

The Mediterranean diet emphasizes the consumption of whole foods, such as fruits, vegetables, whole grains, legumes, nuts, olive oil, and moderate amounts of fish, poultry, and dairy [58]. Red meat and processed foods are limited. The Mediterranean diet might mitigate the imbalance of glutamine and glutamate and associated risk of disease. A case-cohort study (n = 892) found that one year of the Mediterranean diet did not significantly alter glutamate levels compared to a control diet [59]. A separate metabolomic study identified pyruvic acid as one of the top five most variable biomarkers in response to the same diet, showing a −21% change over one year [60].

While direct experimental measurements of other BMS changes under the Mediterranean diet are lacking, indirect evidence suggests its components can modulate these key metabolites. For instance, polyphenols and unsaturated fats may support NAD biosynthesis by providing precursors (e.g., tryptophan, vitamin B3) or activating sirtuins [61,62]. The diet may also enhance mitochondrial efficiency and reduce oxidative stress, thereby indirectly promoting ATP production [61,63]. Specific components like extra virgin olive oil, nuts, and berries have been shown to improve mitochondrial respiration and reduce reactive oxygen species (ROS) [61,64].

Although a direct effect of the Mediterranean diet on NADPH levels has not been established, its documented ability to reduce oxidative stress and inflammation may indirectly support the function of NADPH-dependent physiological systems [65].

From an environmental perspective, numerous studies have quantified that higher adherence to the Mediterranean diet is associated with lower dietary CO₂ emissions, typically in the range of 0.9–6.88 kg CO₂/day [66,67]. It is important to note that this refers to environmental carbon footprint, not internal physiological CO₂ levels (e.g., blood partial pressure of CO₂).

Regarding O₂, several studies report that the Mediterranean diet increases maximal oxygen consumption (VO₂max) [68,69]. This improvement in VO₂max serves as an indirect indicator of enhanced oxygen utilization. Therefore, the diet appears to promote efficient cellular respiration.

3.2.2. Ketogenic Diet

The ketogenic diet is high in fat and very low in carbohydrates (usually less than 50 g per day) to induce ketosis, in which the body burns fat for fuel instead of carbohydrates [70]. Rapid weight loss is common with this diet. Human data on glutamate levels under the ketogenic diet are limited. Existing evidence, derived solely from cerebrospinal fluid studies in epilepsy patients, shows no significant change in glutamate [71]. However, this may not reflect the general population. In fact, research indicates that ketone body metabolism can supply up to 30% of the brain’s glutamate, potentially increasing its release from the nervous system [72]. This is consistent with animal studies, where findings are mixed: some report no change in extracellular glutamate [73], while others show an increase in overall brain tissue glutamate [74,75]. Direct measurements of BMS like oxoglutarate and pyruvate in humans are still an emerging and data-scarce area. It is hypothesized that oxoglutaric acid levels may fluctuate as the Krebs cycle adapts to ketosis and decreased glucose availability [76].

As glycolysis decreases due to low carbohydrate intake, pyruvate levels decline. ATP production may drop initially during the adaptation phase but can later increase with enhanced fat oxidation [77]. Consistent with this, the ketogenic diet has shown a significant increase in brain ATP levels in rodent models [78].

Most data on NADH during the ketogenic diet are derived from brain tissue (hippocampus or occipital lobe), while systemic measurements in blood are lacking. Studies in both rodent models and humans consistently show that the diet increases the NAD/NADH ratio in the brain [79]. This shift is supported by a recent ³¹P-MRS study in healthy humans, where a ketogenic drink significantly increased brain NAD levels by 3.4% and decreased NADH by 13%, resulting in an 18% increase in the NAD/NADH ratio [80]. Furthermore, a ketone body, such as β-hydroxybutyrate, can induce antioxidant defense by reducing the cytoplasmic NADP/NADPH ratio [81,82,83]. Collectively, these changes in NAD redox states are linked to neuroprotective and metabolic benefits.

The ketogenic diet reduces CO₂ levels in the body, primarily through metabolic acidosis and increased respiratory excretion. These effects have been observed in both animal models and humans [84,85]. A 20-day ketogenic diet affected respiratory parameters in healthy individuals by reducing carbon dioxide production (VCO₂) and end-tidal CO₂ concentration (PETCO₂) with p < 0.05 [85].

3.2.3. Paleolithic Diet

The paleolithic diet mimics the eating patterns of ancient humans by focusing on whole foods, such as lean meats, fish, fruits, vegetables, nuts, and seeds while excluding modern processed foods, grains, dairy, and legumes [86]. To date, no direct experimental data exist on the diet’s effects on BMS. This gap in the literature is likely because existing research has prioritized broader health outcomes over detailed metabolic profiling.

Based on its nutritional composition, several indirect effects on BMS can be hypothesized. The diet’s high protein content (from meat and fish) is expected to maintain adequate levels of glutamic acid [87]. Impact on oxoglutaric acid levels is likely to be indirect, stemming from changes in the availability of substrates for the Krebs cycle.

Furthermore, moderate carbohydrate intake from fruits and vegetables results in improving insulin sensitivity and stabilizing blood sugar levels, thereby promoting balanced pyruvate levels. Enhanced ATP production is plausible through the metabolism of proteins and fats [88], while Krebs cycle function and NADH production are likely sustained. The diet may indirectly influence NADP levels through its impact on oxidative stress [89,90]. Collectively, these factors are posited to support efficient cellular respiration. In its dietary structure, the paleolithic diet is less restrictive than the ketogenic diet, but more restrictive than the Mediterranean diet, with a characteristically higher in protein and lower in carbohydrate profile.

3.2.4. Vegan Diet

The vegan diet excludes all animal products, including meat, dairy, eggs, and honey, and is composed exclusively of plant-based foods, including fruits, vegetables, grains, nuts, seeds, and legumes [91]. The diet is high in complex carbohydrates (fruits, vegetables, grains, and legumes provide stable glucose for energy). Excess fiber slows down digestion and promotes stable blood sugar levels. The diet can improve insulin sensitivity and reduce inflammation, supporting a healthy metabolism [92]. Unlike the ketogenic diet, it relies heavily on carbohydrates for energy. Adequate levels of glutamic acid come from plant proteins (legumes, nuts, seeds) [93]. A study comparing vegans and omnivores found that vegans had higher plasma glutamate levels (+13.1%) compared to omnivores [94]. Another study observed a progressive decrease in plasma glutamic acid levels combined with an increase in glutamine levels with a partially plant-based diet (including fish) [95].

Direct evidence regarding the diet’s impact on other BMS is limited. However, its metabolic profile allows for several inferences. As the diet relies heavily on carbohydrates for energy, pyruvate levels are likely to be stable. Furthermore, the abundant supply of substrates from fruits, vegetables, and grains is expected to support the Krebs cycle, promoting balanced levels of oxoglutarate. Consequently, NADH production from carbohydrate metabolism is likely enhanced, supporting efficient ATP production [96]. The diet’s high content of antioxidant-rich foods may help maintain high NADPH levels, crucial for redox defense [89,90]. The combined effects of high fiber, antioxidants, and efficient substrate utilization are also posited to contribute to efficient cellular respiration.

Despite the lack of data on the dynamics of physiological CO₂ (e.g., blood pCO₂, bicarbonate, or end-tidal CO₂) in response to the vegan diet, many studies have quantified the carbon footprint of the vegan diet in terms of greenhouse gas emissions (e.g., kg CO₂ equivalent per day or per calorie). Vegan diets had a daily carbon footprint of 1.38 kg CO₂-eq in a Polish study [97] and 2.6 versus 5.3 kg CO₂-eq/day in an Icelandic study [98], compared with higher values for omnivorous diets. A US study found that the vegan diet generated 0.69 kg CO₂ equivalent per 1000 kcal, which was lower (p < 0.05) than the pescatarian (1.66), omnivore (2.23), paleo (2.62), or keto (2.91) diets [69].

Regarding O₂, a recent study [99] compared the cardiovascular fitness of nine habitual vegan with sixteen habitual omnivorous young, healthy men by assessing the relative and absolute VO₂max on a cycle ergometer. The data indicated no difference between groups for both relative and absolute VO₂max.

Blancquaert and colleagues [100] assigned 40 healthy female omnivores to either an omnivorous group (n = 10), a vegetarian group that was supplemented with creatine and-alanine (n = 15), or a vegetarian group that received a placebo (n = 15) over a period of six months. At baseline, 3 months, and 6 months, the subjects performed an incremental cycling test to assess VO₂max. VO₂max did not differ between groups at baseline, nor did it change during the 6-month intervention period.

Hietavala and co-authors conducted a cross-over design study with nine healthy recreationally active men [101]. Subjects were assigned to both the low-protein vegetarian and the omnivorous diet for four days each. After the low-protein vegetarian diet, VO₂ was significantly higher at 40% (p = 0.035), 60% (p < 0.001), and 80% (p < 0.001) of VO₂max compared to the omnivorous diet.

Another study was carried out on patients with type 2 diabetes [102]. In this study, 37 participants were assigned to a hypocaloric (500 kcal) vegetarian or hypocaloric omnivorous diet group. Both groups performed aerobic exercises three times a week for 12 weeks. The results revealed an increase in VO₂max by 12% (p < 0.001) in the vegetarian diet group but no significant changes in the omnivorous diet group.

3.2.5. Intermittent Fasting

Intermittent fasting is not about what you eat, but when you eat it, such as 16 h of fasting followed by 8 h of eating [103]. The diet focuses on timing rather than food choices. By restricting calories and allowing the body to switch between burning glucose and fat for energy, the diet promotes metabolic flexibility. This increases insulin sensitivity and reduces blood sugar spikes. During fasting periods, the body can increase autophagy (cellular repair) [104] and improve mitochondrial efficiency [105]. The diet can be combined with other diets (such as the Mediterranean or ketogenic diets) for additional metabolic benefits.

In a study on rats subjected to intermittent fasting (24 h fasting periods for 1, 7, or 15 days), regional brain glutamate levels were measured. Glutamate levels were found to be significantly reduced in the midbrain, thalamus/hypothalamus, and hippocampus after one day of dieting but returned to the baseline or fluctuated with prolonged fasting [106]. These results cannot be extrapolated to systemic levels due to compartmentalized metabolism (e.g., splanchnic sequestration of dietary glutamate) [107].

An untargeted metabolomic analysis of human blood during 58 h of fasting identified 44 metabolites that increased significantly, including TCA cycle-related metabolites [108]. An approximately two-fold increase in 2-oxoglutarate was observed.

Despite the lack of data on pyruvate during intermittent fasting, there are results from fasting. Pyruvate shows a consistent decrease during water-only fasting (with p < 0.01 on days 3 and 5 of fasting) [109], and a significant decrease in pyruvate is observed after Ramadan fasting (p < 0.019) [110].

Direct measurements of ATP, NADH, and NADP in humans are lacking in the available literature. The hypothesis paper suggests that intermittent fasting may increase NAD levels by activating AMPK, increasing NAMPT expression, or improving the NAD/NADH ratio [111]. However, these are mechanistic speculations not supported by direct experimental measurements. A study on mice subjected to a 24 h fasting showed an increase in liver NAD levels and NAMPT activity, but this was a single prolonged fasting, not intermittent fasting [112].

Other hypothesis papers suggest that intermittent fasting may influence NADPH levels through ketosis-induced citrate export. Fasting increases mitochondrial export of citrate, which is formed from ketone bodies and is metabolized in the cytoplasm by isocitrate dehydrogenase 1 (IDH1) to form NADPH [113]. Studies in aged mice have not found significant changes in the NADPH in the liver cytoplasm with dietary restriction [114].

For CO₂ and O₂, there are only indirect measurements obtained during fasting. Numerous studies have measured the respiratory exchange ratio (RER) during fasting, which reflects the ratio of CO₂ produced to O₂. A 60 h fasting study found lower RER (closer to 0.7) during fasting [115]. A 21-day fasting study reported that the RER tended toward fat metabolism (decreasing to ~0.7), indicating reduced CO₂ production relative to O₂ consumption [116]. An 8-week time-restricted feeding (TRF) study noted a significant decrease in respiratory ratio (equivalent to RER) in the TRF group, suggesting enhanced fat oxidation [117]. A large study using the Lumen device (a breath analyzer) measured the percentage of CO₂ in an exhaled breath after fasting and found that longer fasting was associated with lower levels of % CO₂ in an exhaled breath [118].

Based on available data (

Table 3. Summary of available data on the effects of diets on biomarkers of metapathway state (BMS).

BMS	Diet
	Mediterranean	Ketogenic	Paleolithic	Vegan	Intermittent Fasting
Glutamic Acid	No change (direct) [59]	No change (direct, in CSF of epilepsy patients) [71]; increase (indirect, theoretical) [72]	No data; expected stable (indirect) [87]	Increase (direct) [110]	Decrease (indirect, in specific brain regions in rats) [106]; fluctuates (indirect) [106]
Oxoglutaric Acid	No data	Fluctuates (indirect, theoretical) [76]	No data; expected stable (indirect)	Expected stable (indirect)	Increase (direct) [108]
ATP	Increase (indirect, theoretical) [61,63]	Increase (indirect, in rodent brain) [78]; drops then increases (indirect, theoretical) [77]	Increase (indirect, theoretical) [88]	Increase (indirect, theoretical) [96]	No data
Pyruvic Acid	Decrease (direct) [60]	Decrease (indirect, theoretical)	Expected stable (indirect)	Expected stable (indirect)	Decrease (direct) [109,110]
NADH	Increase (indirect, theoretical) [77,78]	NAD/NADH ratio increase (direct, in human brain) [80]	No data; expected stable (indirect)	No data	Increase (indirect, theoretical) [111]
NADP	No data; indirect support of function (indirect) [65]	NADP/NADPH ratio decrease (indirect, theoretical) [81,82,83]	No data; indirect influence (indirect) [89,90]	Expected high (indirect) [89,90]	No change (indirect, in aged mice) [114]; increase (indirect, theoretical) [113]
CO2 emission	Lower dietary emissions (direct) [66,67]	Decrease (direct) [84,85]	No data	No data on physiological levels; lower dietary emissions (direct) [69,97,98]	Decrease (indirect, via RER) [115,116,118]
O2 (VO2max)	Increase (direct) [68,69]	No data	No data	No change (direct) [99,100]; increase (direct, at submaximal levels) [101]; increase (direct, in diabetic patients) [102]	No data; fat oxidation increases (indirect, via RER) [115,116,117]

BMS: biomarkers of metapathway state. Direct: data obtained from human studies. Indirect: data from animal studies, theoretical assumptions, or inferred from related metabolic effects. CSF: cerebrospinal fluid. RER: respiratory exchange ratio (CO₂ produced/O₂ consumed). A lower RER indicates higher fat oxidation.

), only the Mediterranean diet offers the most direct human evidence for stable glutamic acid levels. It is also associated with a decrease in pyruvate, which is linked to its documented health benefits. A similar effect can be expected from the keto diet and fasting, but these remain largely hypothetical due to a lack of direct human data. For other BMS—including oxoglutarate, NADH, NADP, and ATP—direct measurements in humans are still lacking. However, the Mediterranean diet, with its balanced nutrient profile and proven health benefits, suggests that it supports BMS levels through overall metabolic balance.

Regarding CO₂, all diets affect its excretion from the body, but differently. A US study found that the vegan diet had the lowest CO₂ equivalent per calorie (0.69 kg/1000 kcal), significantly lower than the pescatarian (1.66), omnivore (2.23), paleo (2.62), or keto (2.91) diets [69]. While this study did not include the Mediterranean diet, separate data indicates it is also a lower-impact option. Adherence to the Mediterranean diet was associated with lower odds of high dietary CO₂ emissions, with a dose-response relationship showing progressively lower odds for higher emission quartiles [67]. The moderate metabolic activity associated with this diet is consistent with data showing a corresponding level of oxygen consumption.

Based on the available evidence, the Mediterranean diet emerges as the most justified choice for the baseline diet. This conclusion rests on three key points: First, it is the only diet reviewed with direct human evidence showing stable glutamate levels and a beneficial reduction in pyruvate. Second, its environmental impact is moderate, associated with lower CO₂ emissions than several other diets, which aligns with physiological data on efficient metabolic activity (reflected in O₂ consumption). Finally, while direct human data for other BMS are lacking, the diet’s proven health benefits and balanced nutrient composition provide a strong indirect argument for its ability to support overall metabolic equilibrium, in contrast to other, more extreme diets.

3.3. Foods Selected to Increase the Level of BMS

To modify the baseline diet for elevating BMS levels in the organism, one can select foods enriched with BMS or their metabolic precursors. This is consistent with the core tenet of nutritional science, which holds that food composition is essential for diet development and assessment [51]. However, given the specific nature of this work, this principle was extended by explicitly incorporating metabolic knowledge. We propose a framework where understanding metabolic connections—such as precursor relationships, enzymatic cofactors, and regulatory effects—informs the selection of foods to translate metabolomic insights into dietary instructions.

3.3.1. Glutamic Acid

Glutamic acid is a non-essential amino acid found in high-protein foods. Animal proteins and fermented foods are particularly rich sources of this amino acid. The source of such animal proteins can be eggs, chicken, beef, and fish (for example, salmon and tuna). Plant proteins as a source of glutamine are found in soybeans, lentils, chickpeas, and spirulina. Among fermented foods, miso, tempeh, and soy sauce can be distinguished [119,120].

3.3.2. Oxoglutaric Acid

Oxoglutaric acid is a key metabolite of the Krebs cycle (Figure 3). Its level depends on the availability of precursors and cofactors involved in metabolism. Glutamate is the main source of nitrogen for the synthesis of oxoglutaric acid [121], and glutamate is deaminated with the formation of this acid. Products rich in glutamine and glutamate [122], such as meat (beef, chicken), fish (salmon, tuna), eggs, dairy products (cottage cheese, cheese), spinach, parsley, tofu, and cabbage, can increase the level of oxoglutaric acid. Also, the level of oxoglutaric acid is affected by foods such as whole grains, nuts (almonds, walnuts), seeds (sunflower, chia), legumes, liver, and eggs, which are rich in B vitamins that act as cofactors for the enzymes of the Krebs cycle.

Products rich in magnesium, such as spinach, cashews, almonds, bananas, and dark chocolate, accelerate the conversion of oxoglutaric acid into the following metabolites, as magnesium activates α-ketoglutarate dehydrogenase [123]. Citrus fruits (oranges, lemons), kiwi, bell peppers, broccoli, and avocado contain antioxidants (vitamin C and glutathione), which protect mitochondria from oxidative stress, maintaining the efficiency of the Krebs cycle and the production of oxoglutaric acid. Products with citric acid (lemons, limes, oranges, and grapefruits) support the overall speed of the cycle since they contain citric acid—the starting substrate of the Krebs cycle.

3.3.3. Pyruvic Acid

Since pyruvate is a product of glycolysis, foods rich in complex carbohydrates will provide the glucose needed for its production. Whole grains best suited for this are: oats, barley, quinoa (provide glucose for glycolysis), legumes (lentils, chickpeas, black beans provide complex carbohydrates and protein), fruits (apples, berries, bananas (natural sugars for glycolysis)), and dairy products (yogurt, kefir).

3.3.4. ATP

ATP levels depend on its production and are determined by the efficiency of glycolysis, the Krebs cycle, and oxidative phosphorylation. Foods rich in magnesium, B vitamins, and healthy fats optimize these processes. The best foods for this are fatty fish, such as salmon, mackerel, and sardines (rich in omega-3 for mitochondrial health); whole grains: quinoa, brown rice, and oats (provide complex carbohydrates for glycolysis); nuts and seeds: almonds, walnuts, and chia seeds (rich in magnesium, a cofactor for ATP synthesis [124]); and leafy greens, such as spinach and kale (rich in magnesium and iron).

3.3.5. NADH

While a high-fat/sugar diet causes energy overload, culminating in reduced NAD/NADH ratio [125] and decreased NAD levels [126,127], the level of NAD can be increased by products rich in B vitamins [128] and complex carbohydrates, which will enhance its production in glycolysis and the Krebs cycle. Suitable for this are fatty fish, which support the function of mitochondria (salmon, mackerel, sardines), meat by-products rich in B vitamins (liver, kidneys), whole grains (quinoa, oats, barley; provide glucose for glycolysis), and legumes (lentils, chickpeas; complex carbohydrates and B vitamins).

3.3.6. NADP

NADP is critical for anabolic reactions and antioxidant protection [129]. Foods rich in niacin (vitamin B3) and folate support its synthesis. Such foods include leafy greens (spinach, kale, and arugula, which are rich in folate and antioxidants), nuts and seeds (almonds, sunflower seeds) rich in niacin, and whole grains rich in B vitamins (brown rice and whole wheat). Among animal products, niacin-rich proteins from chicken, turkey, and fish are suitable.

3.3.7. CO₂

CO₂ is produced as a result of cellular respiration. Foods that fuel glycolysis and the Krebs cycle increase CO₂ production as part of energy metabolism [130]. As previously stated, carbohydrate-rich foods provide fuel for glycolysis and the Krebs cycle (whole grains, fruits, vegetables), and protein-rich foods (eggs, fish, legumes) support amino acid metabolism.

3.3.8. O₂

Efficient oxygen utilization depends on hemoglobin (Ferrum) levels and, conversely, the severity of oxidative stress. Ferrum-rich foods (spinach, red meat, lentils, tofu) support hemoglobin production [131]. In addition, nitrate-rich foods (beets, arugula, celery) improve blood flow and oxygen delivery.

Table 4. Foods selected for elevating biomarkers of metapathway state (BMS) levels.

BMS	Foods
Glutamic Acid	Eggs, chicken, soybeans.
Oxoglutaric Acid	Citrus fruits, spinach, almonds, liver.
ATP	Salmon, quinoa, almonds, spinach.
Pyruvic Acid	Oats, lentils, apples, yogurt.
NADH	Salmon, liver, quinoa, lentils.
NADP	Spinach, almonds, brown rice, chicken.
CO2	Whole grains, fruits, eggs, legumes.
O2	Spinach, red meat, blueberries, beets.

BMS: biomarkers of metapathway state.

summarizes top foods that have been selected for inclusion in the baseline diet for elevating BMS in the body.

3.4. Food Supplements Selected to Increase the Level of BMS

To increase the levels of BMS in the body, specific food supplements can be used. These supplements provide BMS, their precursors, cofactors, or substrates for related biochemical reactions.

Table 5. Food supplements selected for elevating biomarker of metapathway state (BMS) levels.

BMS	Food Supplements	Mechanisms of Action
Glutamic acid	L-glutamine	A precursor to glutamic acid, supporting amino acid metabolism and neurotransmitter synthesis.
	Whey protein	Rich in glutamine and glutamic acid [132].
	Spirulina	A plant-based source of glutamic acid and other amino acids [133].
Oxoglutaric acid	Oxoglutaric acid	Directly supplements oxoglutaric acid.
	B-vitamin complex	Enhance the oxoglutaric acid metabolism by supporting the Krebs cycle by providing cofactors (e.g., B1, B2, B3, B5).
	Magnesium	Enhance the oxoglutaric acid metabolism providing a cofactor (magnesium) for enzymes in the Krebs cycle.
ATP	CoQ10	Supports mitochondrial ATP production [134].
	D-ribose	A sugar that serves as a backbone for ATP synthesis [135].
	Creatine monohydrate	Enhances ATP regeneration, especially in muscle cells [136].
	Magnesium	A cofactor for ATP synthesis and utilization.
Pyruvic acid	Calcium pyruvate	Directly supplements pyruvate, supporting glycolysis and energy production.
	B-vitamin complex	Supports glycolysis and the conversion of pyruvate to acetyl-CoA.
	Magnesium	A cofactor for enzymes in glycolysis.
NADH	Nicotinamide riboside	Increases NAD levels, which are converted to NADH in energy metabolism.
	Nicotinamide mononucleotide	A precursor to NAD, supporting NADH production.
	B-vitamin complex	Provides cofactors for NADH production in glycolysis and the Krebs cycle.
NADP	Niacin (vitamin B3)	A precursor for NADP synthesis.
	Nicotinamide riboside	Elevate NADP levels by increasing NAD availability.
	Folate (vitamin B9)	Supports NADP-dependent reactions in anabolic pathways.
CO2	Bicarbonate supplements	Support CO2 buffering and transport in the blood.
	Citrate	Supports the Krebs cycle and CO2 production.
	B-vitamin complex	Enhances metabolic pathways that produce CO2.
O2	Ferrum	Supports hemoglobin production, improving oxygen transport.
	CoQ10	Enhances mitochondrial function and oxygen utilization.
	Beetroot powder	Rich in nitrates, which improve blood flow and oxygen delivery [137].
	Antioxidants (vitamin C and E)	Reduce oxidative stress, improving oxygen efficiency.

BMS: biomarkers of metapathway state.

contains a list of supplements and their mechanisms of action.

3.5. Scientific Evidence Supporting the Direction and Safety of BMS Level Changes

To confirm the proposed modulation of BMS levels in the direction of increase and to assess the associated risk, an analysis of relevant scientific data was conducted.

A 2024 double-blind, placebo-controlled randomized clinical trial investigated the tolerability of glutamine supplementation in older adults (mean age 77 years) [138]. Participants received a daily dose of 12.4 g of oral glutamine for 60 days. The study concluded that this dosage was well tolerated and safe, with no adverse effects reported, supporting its potential as a viable intervention for maintaining health in aging individuals.

A state-of-the-art review summarizes the biological effects of oxoglutaric acid from a healthy aging perspective [139]. The review highlights that blood concentrations of oxoglutaric acid can decrease as much as 10-fold with age (from 40 to 80 years). Supplementing with oxoglutaric acid has been shown in various in vivo and in vitro studies to positively influence protein synthesis and absorption, bone structure and strength, age-related muscle loss and weakness, and cholesterol regulation.

Decreased ATP levels are a key marker of aging, leading to impaired cell regeneration and the development of age-related diseases. Two recent human studies provide evidence for ATP’s role in healthy aging. A 10-month randomized study on middle-aged individuals (45–72 years) showed that intervention with a functional food significantly increased red blood cell ATP levels [140]. This was associated with a counteraction of age-related endothelial dysfunction, redox dysregulation, and bioenergetic decline. Two human clinical trials presented in 2025 demonstrated that supplementing with a patented form of ATP (PEAK ATP^®) significantly enhanced amino acid absorption from dietary protein [141]. This improved protein bioavailability is crucial for maintaining muscle mass and supporting metabolic health in older adults. Additionally, oral ATP administration has been shown to prevent exercise-induced declines in ATP and its metabolites, while enhancing peak power and muscular excitability [142].

With age, the level of pyruvic acid in the blood and tissues tends to decrease, while lactate content increases, indicating metabolic shifts and a possible decline in the efficiency of tissue respiration [143]. Chronic pyruvate supplementation increases exploratory activity and brain energy reserves in young and middle-aged mice [144]. Today, pyruvate is being considered as an alternative to popular anti-aging drugs (NAD precursors and senolytics), as it may act as a substitute for them. Scientists suggest that pyruvate-enriched fluids (e.g., oral rehydration salt solutions) could become a novel intervention for age-related diseases. However, direct evidence of pyruvate’s benefits for healthy aging is currently limited, and further intensive research is needed [145]. A 2024 review emphasizes that the long-term effects of pyruvate supplementation in healthy individuals (e.g., on physical performance) have not been confirmed in well-controlled studies [146].

A decrease in NAD levels is also a key marker of aging, and increasing its levels is currently one of the key anti-aging strategies [147,148]. NADP(H) levels also decline with age due to a decrease in the overall activity of NAD(P)-dependent enzymes and a decline in energy metabolism, which is accompanied by a reduced ability of cells to resist oxidative stress and impaired tissue repair. Currently, directly increasing NADP levels is not used as an anti-aging treatment. However, this is not a limiting factor for our metabolomics-guided nutrition, as it does not directly supplement with this substance (Table 3).

With age, oxygen consumption and carbon dioxide production decrease due to reduced muscle mass, slower metabolism, and decreased elasticity of lung tissue [149]. Increased oxygen consumption (VO₂) and carbon dioxide production (VCO₂) are physiologically beneficial for the body (excluding pathologically related hypercapnia). This is observed during beneficial aerobic exercise and the EPOC (excess post-exercise oxygen consumption) state, which restores energy reserves, enhances protein synthesis, and utilizes metabolic byproducts (e.g., lactate) [150]. Increased oxygen consumption activates cognitive activity, as the brain is the primary consumer of oxygen. An increase in carbon dioxide levels (within reasonable limits) indicates an increase in “waste release,” enhances oxygen release by hemoglobin into tissues (the Bohr effect), and acts as a natural vasodilator, improving blood flow.

Thus, the strategy of the proposed nutrition aimed at increasing BMS levels corresponds to the modern scientific concepts and can be implemented safely for humans.

3.6. Evaluation of Nutrition Effeciency

3.6.1. Age-Related Metabolomic Curve

Direct mass spectrometry was selected as the most straightforward method for obtaining blood metabolome fingerprints, which were processed to obtain the age-related metabolomic curves ($\overline{Z}$-score versus age) for both genders (Figure 5a–c).

The figure visualizes the nonlinear, age-related dynamics of the metabolome. Changes occur in distinct phases or “waves”. After 50 years in men and 47 years in women, more intense near-linear changes lead to fluctuations in the metabolome at a new level. Notably, the nonlinear dynamics of the metabolome with age and the timing of the waves are consistent with recently published data [151,152]. Missing values in the mass spectrometry data and moderate variations in the curve construction parameters did not affect its shape (Figures S1 and S2).

The superposition of the individual’s metabolome fingerprints on all points of the age-related metabolomic curve (Figure 5d) shows that, despite the presence of a wave in the $\overline{Z}$-score curve, the metabolome fingerprints have age specificity, which allows them to be used for biological age determination across the entire age range.

Figure 5c shows that the age-related metabolomic curve for the DBS samples differs slightly from that obtained with plasma, but the overall waveform is preserved.

3.6.2. Biological Age Change Determination

Measuring the change in an individual’s biological age using an age-related metabolomic curve is proposed in the most simplified way. The distance (Euclidean distance) from an individual’s metabolic fingerprint to all points on the curve is measured. Where the similarity is greatest, the distance is minimal, indicating the biological age of the individual. An example of such a distance measurement is shown in Figure 6a. Given that the age-related curve is constructed with a resolution of 1 year, extrapolation from three points forming the minimum was used to accurately determine biological age (Figure 6b). The technical variability of this approach is quite low, and the MAD is only 12 days (CI 95% 8.9–15.3) (Figure 6b). Figure 6c confirms the accuracy of biological age measurements, as they are linearly related to changes in chronological age (the R² coefficient of determination for linear extrapolation was 0.99). Figure 6d shows an example of measuring biological age change as the difference in its values between two time points.

Figure 7 shows an example of implementing this approach for DBS of two individuals, each with a long (3-month) and two following short-term sample collections. The measurements were characterized by different precision, indicating interindividual variability, likely related to individual metabolome properties, since the measurements were performed identically.

3.7. Food Intake Influence on the Biological Age Measurement

Given the profound influence of food on the blood metabolome, the extent of food’s influence on biological age determinations was assessed. Figure 8 shows that food intake has a moderate effect, close to biological variability. This feature is characteristic of the measurement method, which confirms the need to use fasting blood samples.

4. Discussion

4.1. Scientific Evidence Linking BMS to Health and Longevity

The summary of the involvement of BMS in metabolic processes and their relation to disease (Figure 3) shows that the main processes involving BMS occur in mitochondria [153]. Oxoglutaric acid (α-ketoglutarate) and pyruvic acid (pyruvate) occur there as key intermediates in the Krebs cycle (citric acid cycle). Glutamic acid, a precursor of oxoglutaric acid, links amino acid metabolism to the Krebs cycle. ATP is the end product of oxidative phosphorylation that occurs in mitochondria. ATP is the main energy currency of the cell, and its decline is associated with aging [154]. NAD is a key electron carrier in the electron transport chain that helps generate ATP. Decreased NAD levels and associated mitochondrial dysfunction are also associated with aging-related diseases [155]. NADP and its reduced form NADPH are critical for combating oxidative stress [156]. NADPH participates in the regeneration of antioxidants, such as glutathione, which protect cells from damage. Oxygen is needed to produce ATP and is a source of reactive oxygen species (ROS), which contribute to aging and aging-related diseases [157]. CO₂ is a byproduct of the Krebs cycle that is eliminated from the body. Dysregulation of CO₂ levels can affect pH [158] and cell function [159], contributing to aging.

Scientific evidence for the involvement of BMS in aging-related processes (Figure 4) confirms that metabolomics-derived BMS are integrated the in main aging-related events in the organism. Thus, the BMS-centric design of healthy and anti-aging nutrition is not only metabolomically sound but also justified in terms of accumulated today scientific evidence.

4.2. Selection of a Baseline Diet for Precision Nutrition

To implement precision nutrition, a baseline diet must be selected for subsequent adjustment based on metabolic response. For this purpose, we focused on widely recognized diets, as their extensive literature allows for a systematic assessment of their effects on BMS. Based on the available evidence, the Mediterranean diet emerges as the most justified choice for the baseline diet (Table 3). It is important to note that the availability of scientific data on it and the information gaps regarding other, less popular diets influence the choice. This implies that as additional information becomes available, the baseline diet will be modified. Nevertheless, for now, its selection as the baseline diet can be considered well founded.

4.3. Foods Selected to Increase the Level of BMS

Data on the specific changes in BMS concentrations following food consumption are scarce. This gap exists because such precise measurements—for example, tracking blood level changes after ingestion—are typically applied to pure, well-characterized substances like pharmaceuticals to determine pharmacokinetic parameters. In contrast, foods are complex, unstandardized mixtures of numerous compounds that interact with one another. Furthermore, their composition is highly variable, and they are consumed as part of a mixed meal, leading to significant variations in the bioaccessibility and bioavailability of their constituents [160]. The only possible option in this case is to simply use information on the composition of foods and the known scheme for their use. Following this principle, foods enriched with BMS or their metabolic precursors were suggested to modify the baseline diet for elevating BMS levels in the organism (Table 4). The list of these foods can be supplemented with foods with known composition, for example, from FoodData Central (https://fdc.nal.usda.gov, accessed on 22 March 2026 ) [119] or the Food Database (FooDB; https://foodb.ca) [120].

For baseline dietary change, the overarching scheme, represented by the Nutrition Care Process (NCP), which is a standardized model used by registered dietitians, is proposed. Medical history, current health status, anthropometrics (height, weight, BMI), dietary intake history, food preferences, cultural practices, socioeconomic status, and psychological factors are considered. It should also be noted that the uncontrolled use of food supplements leads to their excess in the body, which may cause side effects and dangerous interactions with medications, as well as toxicity, a significant healthcare issue [161,162,163,164].

4.4. Implementation of Metabolomic-Guided Precision Nutrition

Precision nutrition involves providing dynamic dietary recommendations tailored to individual metabolic characteristics. In this study, recommendations are based on metabolic age (biological age estimated from metabolic data) and, more specifically, its change in response to dietary intervention. To implement this precision nutrition framework, the baseline diet (Mediterranean diet) is proposed to be dynamically adjusted according to individual change in the metabolic age (Figure 9). Depending on the individual’s response, the diet is step-by-step personalized by incorporating foods relevant to BMS (from Table 4) and specific food supplements (from Table 5).

4.5. Evaluation of Nutritional Efficiency

Since the proposed nutrition is aimed at improving health and slowing down aging by influencing the metapathway, the metapathway state can provide surrogate endpoints of such nutrition. To assess the metapathway state, two approaches can be distinguished. The first is to measure BMS, as they are representative of the metapathway (Figure 10a). Non-volatile BMS can be measured by laboratory assays. Oxygen and CO₂ can be assessed indirectly by respirometry. Since the measurement of individual substances for evaluating the diet does not involve metabolomics, can be achieved through laboratory assays, and concerns surrogate endpoints, this approach was not explored in this study.

In general, BMS deviations from normal levels are not expected. If such deviations are observed, they may be associated with pathology or other factors, which would likely require consultation with a physician to correct their levels rather than the use of antiaging nutrition. Otherwise, the use of surrogate endpoints appears appropriate for: (i) confirming the mechanism of the diet’s antiaging effect (metabolomic fingerprinting data are confirmed by changes in BMS levels); (ii) identifying the cause of the diet’s ineffectiveness (a negative metabolomic fingerprinting result is combined with no change in BMS levels); and (iii) monitoring BMS levels to prevent them from exceeding the normal range in response to excessive antiaging nutrition, which could have negative health consequences.

To assess the state of the entire metapathway (normal state, upregulated, downregulated), it is possible to use metabolite set enrichment analysis (MSEA) (Figure 10b). In metabolomics, enrichment analysis is extremely common. However, the implementation of the MSEA for a large metabolic pathway, such as the metapathway, is problematic. MSEA requires the identification and accurate quantification of many metabolites by metabolome profiling of blood plasma. Such an analysis is carried out by a group of scientists specializing in metabolomics and, in terms of time and cost, is related to a full-fledged scientific study, which seems unjustified for a routine assessment of nutrition. Therefore, this approach is mentioned here as an impractical but well-described available option.

The last option, metabolomic fingerprinting, represents the most promising approach for assessing the nutritional outcome (Figure 10c). Since the metapathway was originally identified through multiple untargeted (panoramic) metabolomic analyses, employing a fingerprint as a comprehensive metabolomic metric is a consistent choice. Furthermore, within the framework of anti-aging precision nutrition, metabolomic fingerprinting directly evaluates the anti-aging effect by measuring the primary endpoint—change in metabolic age—unlike the surrogate endpoints discussed above.

The fingerprinting approach was also selected for its practical advantages. A fingerprint is defined as multivariate characteristics derived from the set of mass spectrometric peak intensities. This makes fingerprinting a form of metabolome mining that does not require metabolite identification. By forgoing metabolite identification, the method is radically simplified. Furthermore, utilizing the combined signal from many metabolites allows to buffer fluctuations in any single metabolite; this averaging process generates a stable and robust composite signal.

Traditionally, biological age measurements are validated by chronological age. In our case, we used the change in chronological age, as it is closer to assessing biological age in response to dietary changes. Figure 6c validates biological age measurements by metabolomic fingerprinting, as they are linearly related to changes in chronological age (R² = 0.99).

The translational nature of the study makes it appropriate to evaluate DBS samples for biological age measurements. DBS is more complementary to dietary adjustments, as blood samples are collected at home, stored, and transported to the laboratory in a dried form at room temperature. An assessment of biological variability of biological age determination by metabolic fingerprinting yielded an MDC of 27.7 days for using DBS (Figure 7), which corresponds to the precision of the measurement. Therefore, the anti-aging effect of the diet can be recorded if it consists of a change in biological age of one month or more. In such a situation, it is advisable to monitor biological age change using DBS no more than once a month. It is noteworthy that the obtained MDC value does not contradict the larger MAD value obtained for biological reproducibility. The MDC is the smallest change in a measurable value that reliably exceeds random measurement error or system “noise”. MAD is a measure of the dispersion of data around a center (usually the mean) and characterizes the variability of the data itself.

It should be noted that the obtained precision for biological age measurement cannot be considered fully representative, as it depends not only on the properties of the blood metabolome but also on mass spectrometry (sensitivity of the used mass spectrometer, the number of detected mass peaks, the quality of mass spectra processing, and feature extraction options). In the case of routine use of metabolome analysis, the degree of influence of biological confounders (see Section 4.6) and how they were mitigated will also affect the reproducibility of the data. The reproducibility parameters measured in this study should be considered as an achievable reference, which may vary for the worse in further human tests, or, perhaps, for the better with targeted efforts.

4.6. Biological Confounders in Metabolomics-Guided Nutrition

The proposed metabolomics-guided nutrition must account for biological factors that influence outcomes, especially those that affect blood metabolome. Previous research has shown that genetics, gut microbiome, clinical parameters, nutrition, lifestyle, and anthropometric measurements collectively explain more than 76% of the variance in blood metabolites [165]. Among these, nutrition and microbiome exert the strongest influence, accounting for over 50% of the observed metabolite variation. In the context of an intervention for an individual, the influence of genetics can be considered constant and ignored. Similarly, lifestyle and anthropometric factors are expected to remain stable during short-term dietary modifications.

The pronounced influence of food on the blood metabolome prompted us to evaluate the impact of food on biological age determination. It was found that food intake has a moderate effect, close to biological variability (Figure 8). This may be due to the multiparametric nature of the metabolome fingerprint, which smooths out fluctuations in individual metabolites. To avoid such influence, it seems sufficient to take fasting blood samples for measurements.

Since nutrition and microbiome are primary determinants of blood metabolome, the biological age response must be carefully considered whenever dietary adjustments are made. It is advisable to monitor biological age dynamics following dietary adjustments until a new steady-state value is reached. The true effect of nutritional intervention on biological age should then be assessed from this stabilized point. Notably, because foods are complex mixtures with varying nutrient bioavailability, and due to individual differences in intestinal absorption, metabolism, and initial microbiome composition, the lag time between dietary change and metabolome stabilization is expected to vary across individuals. Generalizable patterns regarding the biological age change and the time required to reach a new steady state can be established through further human tests of the proposed nutritional framework.

4.7. Limitations

In this work, the metabolomic datasets on human aging and disease were translated by focusing on a limited set of central metabolites within the metapathway. These metabolites, i.e., BMS, were selected based on their high centrality rank. However, the optimal number of metabolites for metabolomics-guided nutrition remains an open question; a smaller set might simplify dietary guidance without losing efficacy, while a larger one could improve it. Our final selection was guided by the availability of scientific data on their role in biochemical processes and aging, the existence of human measurement techniques, and information on their presence in foods and supplements.

Regarding the baseline diet and recommendations, no restrictions are foreseen other than the well-known ones, such as the presence of diseases requiring special nutrition, elderly people, and the data presented being applied to adults. In addition, the usefulness of the Mediterranean diet for healthy longevity is well known and can be recommended to a wide range of people. This work further supports its usefulness based on metabolomic data.

Among the limitations of the presented nutrition is its reliance on supplementation with foods and food supplements to personalize the baseline diet for an anti-aging effect. However, it is possible that, based on human testing, an anti-aging effect will be observed with a reduction in BMS levels. In this case, it is recommended to reduce the consumption of the foods listed in Section 3 and Table 4 if they have been included in the baseline diet.

The influence of gut microbiota on the precision of biological age measurement remains an important and unaccounted factor. A previous study using an identical protocol for blood metabolomic fingerprinting by DIMS demonstrated the significant impact of changes in even individual groups of gut microbiome microorganisms on the results [166]. Therefore, a separate study linking changes in the gut microbiome to the precision of biological age measurement using blood metabolomic fingerprinting appears highly relevant in the near future.

The conceptual nature of this study dictated limited testing of metabolomic fingerprinting as a means of monitoring dietary effectiveness. The tests confirmed its efficacy, but with a limited number of samples. Specifically, the DBS sample set did not include elderly individuals, and biological reproducibility was studied using samples only from middle-aged individuals. Although the test results support the concept of metabolomics-based nutrition, questions remain about its effectiveness in older adults, since the impact of confounders in older individuals may differ significantly. For example, the gut microbiota, which influences the blood metabolome, exhibits significant differences in older individuals and is characterized by a decrease in beneficial microorganisms and an increase in opportunistic microorganisms (so-called “age-related dysbiosis”). Therefore, the effectiveness of metabolomic fingerprinting in older adults as a means of monitoring the proposed nutrition requires further research.

The key limitation concerns metabolomic control and subsequent adjustment of the proposed diet. Although the accuracy of the biological age determination from the metabolome is measured in years [167], it can precisely estimate the change in biological age within an individual. This approach is most precise, since it excludes the influence of interindividual biological variability.

4.8. Final Notes

A distinctive feature of the proposed nutrition is its use of accumulated scientific metabolomic data on diseases and aging, which has not previously been observed in diets or other metabolomics-based nutritional approaches. This is the main achievement of this study and makes the proposed precise nutrition new. Furthermore, the precise assessment of biological age change for assessing the effectiveness of nutrition also has not previously been used.

The long-term significance of this study is largely determined by the ability to objectively measure the health benefits of diet, individual foods, and food supplements. This may ultimately lead to a renewed understanding of their healing properties and the development of new, more effective diets.

The next phase of research is a T2-stage translational study (translation to patients; Figure 1). This phase aims to evaluate the efficacy and effectiveness of the proposed nutritional intervention in the target population and to inform evidence-based guidelines. Specifically, this stage will:

• Confirm the translational potential of the underlying scientific basis;
• Clinically validate proposed nutrition (assess its strength, reproducibility, and ability to account for biological confounders);
• Test, refine, or adjust the accuracy of measuring changes in biological age using metabolome fingerprints in response to dietary modifications;
• Identify practical limitations;
• Evaluate the overall feasibility of implementing the proposed nutrition, considering its labor intensity and the cost of implementation.

5. Conclusions

The role of nutrition in health, healthy aging, and longevity is undeniable. However, both dietary practices and scientific understanding of them continually evolve. Consequently, a pressing challenge is the translation of the latest scientific discoveries into actionable nutritional strategies for health improvement. The recent identification of a unified metapathway underlying aging and disease provided a translational foundation for this modernization. The existence of a limited number of representative BMS for the overall metapathway enabled the design of BMS-centric dietary instructions, forming the basis for metabolomics-guided precision nutrition. The proposed nutrition is now ready for the next phases of translation, which include developing evidence-based guidelines and establishing its effectiveness in humans.