Longitudinal analysis reveals transition barriers between dominant ecological states in the gut microbiome.

The Pioneer 100 Wellness Project involved quantitatively profiling 108 participants' molecular physiology over time, including genomes, gut microbiomes, blood metabolomes, blood proteomes, clinical chemistries, and data from wearable devices. Here, we present a longitudinal analysis focused specifically around the Pioneer 100 gut microbiomes. We distinguished a subpopulation of individuals with reduced gut diversity, elevated relative abundance of the genus Prevotella, and reduced levels of the genus Bacteroides We found that the relative abundances of Bacteroides and Prevotella were significantly correlated with certain serum metabolites, including omega-6 fatty acids. Primary dimensions in distance-based redundancy analysis of clinical chemistries explained 18.5% of the variance in bacterial community composition, and revealed a Bacteroides/Prevotella dichotomy aligned with inflammation and dietary markers. Finally, longitudinal analysis of gut microbiome dynamics within individuals showed that direct transitions between Bacteroides-dominated and Prevotella-dominated communities were rare, suggesting the presence of a barrier between these states. One implication is that interventions seeking to transition between Bacteroides- and Prevotella-dominated communities will need to identify permissible paths through ecological state-space that circumvent this apparent barrier.

Pyruvate carboxylase supports the pulmonary tropism of metastatic breast cancer.

BACKGROUND: Overcoming systemic dormancy and initiating secondary tumor grow under unique microenvironmental conditions is a major rate-limiting step in metastatic progression. Disseminated tumor cells encounter major changes in nutrient supplies and oxidative stresses compared to the primary tumor and must demonstrate significant metabolic plasticity to adapt to specific metastatic sites. Recent studies suggest that differential utilization of pyruvate sits as a critical node in determining the organotropism of metastatic breast cancer. Pyruvate carboxylase (PC) is key enzyme that converts pyruvate into oxaloacetate for utilization in gluconeogenesis and replenishment of the TCA cycle. METHODS: Patient survival was analyzed with respect to gene copy number alterations and differential mRNA expression levels of PC. Expression of PC was analyzed in the MCF-10A, D2-HAN and the 4 T1 breast cancer progression series under in vitro and in vivo growth conditions. PC expression was depleted via shRNAs and the impact on in vitro cell growth, mammary fat pad tumor growth, and pulmonary and non-pulmonary metastasis was assessed by bioluminescent imaging. Changes in glycolytic capacity, oxygen consumption, and response to oxidative stress were quantified upon PC depletion. RESULTS: Genomic copy number increases in PC were observed in 16-30% of metastatic breast cancer patients. High expression of PC mRNA was associated with decreased patient survival in the MCTI and METABRIC patient datasets. Enhanced expression of PC was not recapitulated in breast cancer progression models when analyzed under glucose-rich in vitro culture conditions. In contrast, PC expression was dramatically enhanced upon glucose deprivation and in vivo in pulmonary metastases. Depletion of PC led to a dramatic decrease in 4 T1 pulmonary metastasis, but did not affect orthotopic primary tumor growth. Tail vein inoculations confirmed the role of PC in facilitating pulmonary, but not extrapulmonary tumor initiation. PC-depleted cells demonstrated a decrease in glycolytic capacity and oxygen consumption rates and an enhanced sensitivity to oxidative stress. CONCLUSIONS: Our studies indicate that PC is specifically required for the growth of breast cancer that has disseminated to the lungs. Overall, these findings point to the potential of targeting PC for the treatment of pulmonary metastatic breast cancer.

1α,25-Dihydroxyvitamin D Inhibits the Metastatic Capability of MCF10CA1a and MDA-MB-231 Cells in an In Vitro Model of Breast to Bone Metastasis.

Breast cancer metastasis to the bone continues to be a major health problem, with approximately 80% of advanced breast cancer patients expected to develop bone metastasis. Although the problem of bone metastasis persists, current treatment options for metastatic cancer patients are limited. In this study, we investigated the preventive role of the active vitamin D metabolite, 1α,25-dihydroxyvitamin D (1,25(OH)2D), against the metastatic potential of breast cancer cells using a novel three-dimensional model (rMET) recapitulating multiple steps of the bone metastatic process. Treatment of MCF10CA1a and MDA-MB-231 cells inhibited metastasis in the rMET model by 70% (±5.7%) and 21% (±6%), respectively. In addition, 1,25(OH)2D treatment decreased invasiveness (20 ± 11% of vehicle) and decreased the capability of MCF10CA1a cells to survive in the reconstructed bone environment after successful invasion through the basement membrane (69 ± 5% of vehicle). An essential step in metastasis is epithelial-mesenchymal transition (EMT). Treatment of MCF10CA1a cells with 1,25(OH)2D increased gene (2.04 ± 0.28-fold increase) and protein (1.87 ± 0.20-fold increase) expression of E-cadherin. Additionally, 1,25(OH)2D treatment decreased N-cadherin gene expression (42 ± 8% decrease), a marker for EMT. Collectively, the present study suggests that 1,25(OH)2D inhibits breast cancer cell metastatic capability as well as inhibits EMT, an essential step in the metastatic process.

Generally-healthy individuals with aberrant bowel movement frequencies show enrichment for microbially-derived blood metabolites associated with reduced kidney function.

Bowel movement frequency (BMF) has been linked to changes in the composition of the human gut microbiome and to many chronic conditions, like metabolic disorders, neurodegenerative diseases, chronic kidney disease (CKD), and other intestinal pathologies like irritable bowel syndrome and inflammatory bowel disease. Lower BMF (constipation) can lead to compromised intestinal barrier integrity and a switch from saccharolytic to proteolytic fermentation within the microbiota, giving rise to microbially-derived toxins that may make their way into circulation and cause damage to organ systems. However, the connections between BMF, gut microbial metabolism, and the early-stage development and progression of chronic disease remain underexplored. Here, we examined the phenotypic impact of BMF variation in a cohort of generally-healthy, community dwelling adults with detailed clinical, lifestyle, and multi-omic data. We showed significant differences in microbially-derived blood plasma metabolites, gut bacterial genera, clinical chemistries, and lifestyle factors across BMF groups that have been linked to inflammation, cardiometabolic health, liver function, and CKD severity and progression. We found that the higher plasma levels of 3-indoxyl sulfate (3-IS), a microbially-derived metabolite associated with constipation, was in turn negatively associated with estimated glomerular filtration rate (eGFR), a measure of kidney function. Causal mediation analysis revealed that the effect of BMF on eGFR was significantly mediated by 3-IS. Finally, we identify self-reported diet, lifestyle, and psychological factors associated with BMF variation, which indicate several common-sense strategies for mitigating constipation and diarrhea. Overall, we suggest that aberrant BMF is an underappreciated risk factor in the development of chronic diseases, even in otherwise healthy populations.

Aberrant bowel movement frequencies coincide with increased microbe-derived blood metabolites associated with reduced organ function.

Bowel movement frequency (BMF) directly impacts the gut microbiota and is linked to diseases like chronic kidney disease or dementia. In particular, prior work has shown that constipation is associated with an ecosystem-wide switch from fiber fermentation and short-chain fatty acid production to more detrimental protein fermentation and toxin production. Here, we analyze multi-omic data from generally healthy adults to see how BMF affects their molecular phenotypes, in a pre-disease context. Results show differential abundances of gut microbial genera, blood metabolites, and variation in lifestyle factors across BMF categories. These differences relate to inflammation, heart health, liver function, and kidney function. Causal mediation analysis indicates that the association between lower BMF and reduced kidney function is partially mediated by the microbially derived toxin 3-indoxyl sulfate (3-IS). This result, in a generally healthy context, suggests that the accumulation of microbiota-derived toxins associated with abnormal BMF precede organ damage and may be drivers of chronic, aging-related diseases.

Microbial community-scale metabolic modelling predicts personalized short-chain fatty acid production profiles in the human gut.

Microbially derived short-chain fatty acids (SCFAs) in the human gut are tightly coupled to host metabolism, immune regulation and integrity of the intestinal epithelium. However, the production of SCFAs can vary widely between individuals consuming the same diet, with lower levels often associated with disease. A systems-scale mechanistic understanding of this heterogeneity is lacking. Here we use a microbial community-scale metabolic modelling (MCMM) approach to predict individual-specific SCFA production profiles to assess the impact of different dietary, prebiotic and probiotic inputs. We evaluate the quantitative accuracy of our MCMMs using in vitro and ex vivo data, plus published human cohort data. We find that MCMM SCFA predictions are significantly associated with blood-derived clinical chemistries, including cardiometabolic and immunological health markers, across a large human cohort. Finally, we demonstrate how MCMMs can be leveraged to design personalized dietary, prebiotic and probiotic interventions aimed at optimizing SCFA production in the gut. Our model represents an approach to direct gut microbiome engineering for precision health and nutrition.

The Off-Treatment Effects of Olpasiran on Lipoprotein(a) Lowering: OCEAN(a)-DOSE Extension Period Results.

BACKGROUND: Olpasiran, a small interfering RNA (siRNA), blocks lipoprotein(a) (Lp(a)) production by preventing translation of apolipoprotein(a) mRNA. In phase 2, higher doses of olpasiran every 12 weeks (Q12W) reduced circulating Lp(a) by >95%. OBJECTIVES: This study sought to assess the timing of return of Lp(a) to baseline after discontinuation of olpasiran, as well as longer-term safety. METHODS: OCEAN(a)-DOSE (Olpasiran Trials of Cardiovascular Events And LipoproteiN[a] Reduction-DOSE Finding Study) was a phase 2, dose-finding trial that enrolled 281 participants with atherosclerotic cardiovascular disease and Lp(a) >150 nmol/L to 1 of 4 active doses of olpasiran vs placebo (10 mg, 75 mg, 225 mg Q12W, or an exploratory dose of 225 mg Q24W given subcutaneously). The last dose of olpasiran was administered at week 36; after week 48, there was an extended off-treatment follow-up period for a minimum of 24 weeks. RESULTS: A total of 276 (98.2%) participants entered the off-treatment follow-up period. The median study exposure (treatment combined with off-treatment phases) was 86 weeks (Q1-Q3: 79-99 weeks). For the 75 mg Q12W dose, the off-treatment placebo-adjusted mean percent change from baseline in Lp(a) was -76.2%, -53.0%, -44.0%, and -27.9% at 60, 72, 84, and 96 weeks, respectively (all P < 0.001). The respective off-treatment changes in Lp(a) for the 225 mg Q12W dose were -84.4%, -61.6%, -52.2%, and -36.4% (all P < 0.001). During the extension follow-up phase, no new safety concerns were identified. CONCLUSIONS: Olpasiran is a potent siRNA with prolonged effects on Lp(a) lowering. Participants receiving doses ≥75 mg Q12W sustained a ∼40% to 50% reduction in Lp(a) levels close to 1 year after the last dose. (Olpasiran Trials of Cardiovascular Events And LipoproteiN[a] Reduction-DOSE Finding Study [OCEAN(a)-DOSE]; NCT04270760).

A changing gut virome ecological landscape with longevity.

A recent study (Johansen et al., 2023) shows how the ecological composition of the human gut virome changes with age, showing a decline in core taxa and an enrichment of subdominant taxa, similar to what has been observed in the gut bacteriomes of centenarians.

Microbial community-scale metabolic modeling predicts personalized short chain fatty acid production profiles in the human gut.

Microbially-derived short chain fatty acids (SCFAs) in the human gut are tightly coupled to host metabolism, immune regulation, and integrity of the intestinal epithelium. However, the production of SCFAs can vary widely between individuals consuming the same diet, with lower levels often associated with disease. A systems-scale mechanistic understanding of this heterogeneity is lacking. We present a microbial community-scale metabolic modeling (MCMM) approach to predict individual-specific SCFA production profiles. We assess the quantitative accuracy of our MCMMs using in vitro, ex vivo, and in vivo data. Next, we show how MCMM SCFA predictions are significantly associated with blood-derived clinical chemistries, including cardiometabolic and immunological health markers, across a large human cohort. Finally, we demonstrate how MCMMs can be leveraged to design personalized dietary, prebiotic, and probiotic interventions that optimize SCFA production in the gut. Our results represent an important advance in engineering gut microbiome functional outputs for precision health and nutrition.

Multiomic signatures of body mass index identify heterogeneous health phenotypes and responses to a lifestyle intervention.

Multiomic profiling can reveal population heterogeneity for both health and disease states. Obesity drives a myriad of metabolic perturbations and is a risk factor for multiple chronic diseases. Here we report an atlas of cross-sectional and longitudinal changes in 1,111 blood analytes associated with variation in body mass index (BMI), as well as multiomic associations with host polygenic risk scores and gut microbiome composition, from a cohort of 1,277 individuals enrolled in a wellness program (Arivale). Machine learning model predictions of BMI from blood multiomics captured heterogeneous phenotypic states of host metabolism and gut microbiome composition better than BMI, which was also validated in an external cohort (TwinsUK). Moreover, longitudinal analyses identified variable BMI trajectories for different omics measures in response to a healthy lifestyle intervention; metabolomics-inferred BMI decreased to a greater extent than actual BMI, whereas proteomics-inferred BMI exhibited greater resistance to change. Our analyses further identified blood analyte-analyte associations that were modified by metabolomics-inferred BMI and partially reversed in individuals with metabolic obesity during the intervention. Taken together, our findings provide a blood atlas of the molecular perturbations associated with changes in obesity status, serving as a resource to quantify metabolic health for predictive and preventive medicine.

Lifespan-extending interventions induce consistent patterns of fatty acid oxidation in mouse livers.

Aging manifests as progressive deteriorations in homeostasis, requiring systems-level perspectives to investigate the gradual molecular dysregulation of underlying biological processes. Here, we report systemic changes in the molecular regulation of biological processes under multiple lifespan-extending interventions. Differential Rank Conservation (DIRAC) analyses of mouse liver proteomics and transcriptomics data show that mechanistically distinct lifespan-extending interventions (acarbose, 17α-estradiol, rapamycin, and calorie restriction) generally tighten the regulation of biological modules. These tightening patterns are similar across the interventions, particularly in processes such as fatty acid oxidation, immune response, and stress response. Differences in DIRAC patterns between proteins and transcripts highlight specific modules which may be tightened via augmented cap-independent translation. Moreover, the systemic shifts in fatty acid metabolism are supported through integrated analysis of liver transcriptomics data with a mouse genome-scale metabolic model. Our findings highlight the power of systems-level approaches for identifying and characterizing the biological processes involved in aging and longevity.

Genome-microbiome interplay provides insight into the determinants of the human blood metabolome.

Variation in the blood metabolome is intimately related to human health. However, few details are known about the interplay between genetics and the microbiome in explaining this variation on a metabolite-by-metabolite level. Here, we perform analyses of variance for each of 930 blood metabolites robustly detected across a cohort of 1,569 individuals with paired genomic and microbiome data while controlling for a number of relevant covariates. We find that 595 (64%) of these blood metabolites are significantly associated with either host genetics or the gut microbiome, with 69% of these associations driven solely by the microbiome, 15% driven solely by genetics and 16% under hybrid genome-microbiome control. Additionally, interaction effects, where a metabolite-microbe association is specific to a particular genetic background, are quite common, albeit with modest effect sizes. This knowledge will help to guide targeted interventions designed to alter the composition of the human blood metabolome.

Heterogeneity in statin responses explained by variation in the human gut microbiome.

BACKGROUND: Statins remain one of the most prescribed medications worldwide. While effective in decreasing atherosclerotic cardiovascular disease risk, statin use is associated with adverse effects for a subset of patients, including disrupted metabolic control and increased risk of type 2 diabetes. METHODS: We investigated the potential role of the gut microbiome in modifying patient responses to statin therapy across two independent cohorts (discovery n = 1,848, validation n = 991). Microbiome composition was assessed in these cohorts using stool 16S rRNA amplicon and shotgun metagenomic sequencing, respectively. Microbiome associations with markers of statin on-target and adverse effects were tested via a covariate-adjusted interaction analysis framework, utilizing blood metabolomics, clinical laboratory tests, genomics, and demographics data. FINDINGS: The hydrolyzed substrate for 3-hydroxy-3-methylglutarate-coenzyme-A (HMG-CoA) reductase, HMG, emerged as a promising marker for statin on-target effects in cross-sectional cohorts. Plasma HMG levels reflected both statin therapy intensity and known genetic markers for variable statin responses. Through exploring gut microbiome associations between blood-derived measures of statin effectiveness and adverse metabolic effects of statins, we find that heterogeneity in statin responses was consistently associated with variation in the gut microbiome across two independent cohorts. A Bacteroides-enriched and diversity-depleted gut microbiome was associated with more intense statin responses, both in terms of on-target and adverse effects. CONCLUSIONS: With further study and refinement, gut microbiome monitoring may help inform precision statin treatment. FUNDING: This research was supported by the M.J. Murdock Charitable Trust, WRF, NAM Catalyst Award, and NIH grant U19AG023122 awarded by the NIA.

From taxonomy to metabolic output: what factors define gut microbiome health?

Many studies link the composition of the human gut microbiome to aberrant health states. However, our understanding of what constitutes a 'healthy' gut ecosystem, and how to effectively monitor and maintain it, are only now emerging. Here, we review current approaches to defining and monitoring gut microbiome health, and outline directions for developing targeted ecological therapeutics. We emphasize the importance of identifying which ecological features of the gut microbiome are most resonant with host molecular phenotypes, and highlight certain gut microbial metabolites as potential biomarkers of gut microbiome health. We further discuss how multi-omic measurements of host phenotypes, dietary information, and gut microbiome profiles can be integrated into increasingly sophisticated host-microbiome mechanistic models that can be leveraged to design personalized interventions. Overall, we summarize current progress on defining microbiome health and highlight a number of paths forward for engineering the ecology of the gut to promote wellness.

The geometry of clinical labs and wellness states from deeply phenotyped humans.

Longitudinal multi-omics measurements are highly valuable in studying heterogeneity in health and disease phenotypes. For thousands of people, we have collected longitudinal multi-omics data. To analyze, interpret and visualize this extremely high-dimensional data, we use the Pareto Task Inference (ParTI) method. We find that the clinical labs data fall within a tetrahedron. We then use all other data types to characterize the four archetypes. We find that the tetrahedron comprises three wellness states, defining a wellness triangular plane, and one aberrant health state that captures aspects of commonality in movement away from wellness. We reveal the tradeoffs that shape the data and their hierarchy, and use longitudinal data to observe individual trajectories. We then demonstrate how the movement on the tetrahedron can be used for detecting unexpected trajectories, which might indicate transitions from health to disease and reveal abnormal conditions, even when all individual blood measurements are in the norm.

Towards early risk biomarkers: serum metabolic signature in childhood predicts cardio-metabolic risk in adulthood.

BACKGROUND: Cardiovascular diseases may originate in childhood. Biomarkers identifying individuals with increased risk for disease are needed to support early detection and to optimise prevention strategies. METHODS: In this prospective study, by applying a machine learning to high throughput NMR-based metabolomics data, we identified circulating childhood metabolic predictors of adult cardiovascular disease risk (MetS score) in a cohort of 396 females, followed from childhood (mean age 11·2 years) to early adulthood (mean age 18·1 years). The results obtained from the discovery cohort were validated in a large longitudinal birth cohort of females and males followed from puberty to adulthood (n = 2664) and in four cross-sectional data sets (n = 6341). FINDINGS: The identified childhood metabolic signature included three circulating biomarkers, glycoprotein acetyls (GlycA), large high-density lipoprotein phospholipids (L-HDL-PL), and the ratio of apolipoprotein B to apolipoprotein A-1 (ApoB/ApoA) that were associated with increased cardio-metabolic risk in early adulthood (AUC = 0·641‒0·802, all p<0·01). These associations were confirmed in all validation cohorts with similar effect estimates both in females (AUC = 0·667‒0·905, all p<0·01) and males (AUC = 0·734‒0·889, all p<0·01) as well as in elderly patients with and without type 2 diabetes (AUC = 0·517‒0·700, all p<0·01). We subsequently applied random intercept cross-lagged panel model analysis, which suggested bidirectional causal relationship between metabolic biomarkers and cardio-metabolic risk score from childhood to early adulthood. INTERPRETATION: These results provide evidence for the utility of a circulating metabolomics panel to identify children and adolescents at risk for future cardiovascular disease, to whom preventive measures and follow-up could be indicated. FUNDING: This study was financially supported by the Academy of Finland, Ministry of Education of Finland and University of Jyv€askyl€a, the National Nature Science Foundation of China (Grant 31571219), the 111 Project (B17029), the Shanghai Jiao Tong University Zhiyuan Foundation (Grant CP2014013), China Postdoc Scholarship Council (201806230001), the Food and Health Bureau of Hong Kong SAR's Health and Medical Research Fund (HMRF grants 15162161 and 07181036) and the CUHK Direct Grants for Research (2016¢033 and 2018¢034), and a postdoctoral fellowship from K. Carole Ellison (to T.W.). The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. NFBC1966 received financial support from University of Oulu Grant no. 24000692, Oulu University Hospital Grant no. 24301140, ERDF European Regional Development Fund Grant no. 539/2010 A31592. This work was supported by European Union's Horizon 2020 research and innovation programme LongITools 874739.

Gut microbiome pattern reflects healthy ageing and predicts survival in humans.

The gut microbiome has important effects on human health, yet its importance in human ageing remains unclear. In the present study, we demonstrate that, starting in mid-to-late adulthood, gut microbiomes become increasingly unique to individuals with age. We leverage three independent cohorts comprising over 9,000 individuals and find that compositional uniqueness is strongly associated with microbially produced amino acid derivatives circulating in the bloodstream. In older age (over ~80 years), healthy individuals show continued microbial drift towards a unique compositional state, whereas this drift is absent in less healthy individuals. The identified microbiome pattern of healthy ageing is characterized by a depletion of core genera found across most humans, primarily Bacteroides. Retaining a high Bacteroides dominance into older age, or having a low gut microbiome uniqueness measure, predicts decreased survival in a 4-year follow-up. Our analysis identifies increasing compositional uniqueness of the gut microbiome as a component of healthy ageing, which is characterized by distinct microbial metabolic outputs in the blood.

D3T acts as a pro-oxidant in a cell culture model of diabetes-induced peripheral neuropathy.

Diabetes mellitus is one of the most common chronic diseases in the United States and peripheral neuropathy (PN) affects at least 50% of diabetic patients. Medications available for patients ameliorate symptoms (pain), but do not protect against cellular damage and come with severe side effects, leading to discontinued use. Our research group uses differentiated SH-SY5Y cells treated with advanced glycation end products (AGE) as a model to mimic diabetic conditions and to study the mechanisms of oxidative stress mediated cell damage and antioxidant protection. N-acetylcysteine (NAC), a common antioxidant supplement, was previously shown by our group to fully protect against AGE-induced damage. We have also shown that 3H-1,2-dithiole-3-thione (D3T), a cruciferous vegetable constituent and potent inducer of nuclear factor (erythroid-derived 2)- like 2 (Nrf2), can significantly increase cellular GSH concentrations and protect against oxidant species-induced cell death. Paradoxically, D3T conferred no protection against AGE-induced cell death or neurite degeneration. In the present study we establish a mechanism for this paradox by showing that D3T in combination with AGE increased oxidant species generation and depleted GSH via inhibition of glutathione reductase (GR) activity and increased expression of the NADPH generating enzyme glucose-6-phosphate dehydrogenase (G6PD). Blocking NADPH generation with the G6PD inhibitor dehydroepiandrosterone was found to protect against AGE-induced oxidant species generation, loss of viability, and neurite degeneration. It further reversed the D3T potentiation effect under AGE-treated conditions. Collectively, these results suggest that strategies aimed at combating oxidative stress that rely on upregulation of the endogenous antioxidant defense system via Nrf2 may backfire and promote further damage in diabetic PN.

Blood metabolome predicts gut microbiome α-diversity in humans.

Depleted gut microbiome α-diversity is associated with several human diseases, but the extent to which this is reflected in the host molecular phenotype is poorly understood. We attempted to predict gut microbiome α-diversity from ~1,000 blood analytes (laboratory tests, proteomics and metabolomics) in a cohort enrolled in a consumer wellness program (N = 399). Although 77 standard clinical laboratory tests and 263 plasma proteins could not accurately predict gut α-diversity, we found that 45% of the variance in α-diversity was explained by a subset of 40 plasma metabolites (13 of the 40 of microbial origin). The prediction capacity of these 40 metabolites was confirmed in a separate validation cohort (N = 540) and across disease states, showing that our findings are robust. Several of the metabolite biomarkers that are reported here are linked with cardiovascular disease, diabetes and kidney function. Associations between host metabolites and gut microbiome α-diversity were modified in those with extreme obesity (body mass index ≥ 35), suggesting metabolic perturbation. The ability of the blood metabolome to predict gut microbiome α-diversity could pave the way to the development of clinical tests for monitoring gut microbial health.

Multi-Omic Biological Age Estimation and Its Correlation With Wellness and Disease Phenotypes: A Longitudinal Study of 3,558 Individuals.

Biological age (BA), derived from molecular and physiological measurements, has been proposed to better predict mortality and disease than chronological age (CA). In the present study, a computed estimate of BA was investigated longitudinally in 3,558 individuals using deep phenotyping, which encompassed a broad range of biological processes. The Klemera-Doubal algorithm was applied to longitudinal data consisting of genetic, clinical laboratory, metabolomic, and proteomic assays from individuals undergoing a wellness program. BA was elevated relative to CA in the presence of chronic diseases. We observed a significantly lower rate of change than the expected ~1 year/year (to which the estimation algorithm was constrained) in BA for individuals participating in a wellness program. This observation suggests that BA is modifiable and suggests that a lower BA relative to CA may be a sign of healthy aging. Measures of metabolic health, inflammation, and toxin bioaccumulation were strong predictors of BA. BA estimation from deep phenotyping was seen to change in the direction expected for both positive and negative health conditions. We believe BA represents a general and interpretable "metric for wellness" that may aid in monitoring aging over time.