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IntroductionThere is conflicting evidence whether high-density lipoprotein cholesterol (HDL-C) is a risk factor for Alzheimer's disease (AD) and dementia. Genetic variation in the cholesteryl ester transfer protein (CETP) locus is associated with altered HDL-C. We aimed to assess AD risk by genetically predicted HDL-C.MethodsTen single nucleotide polymorphisms within the CETP locus predicting HDL-C were applied to the International Genomics of Alzheimer's Project (IGAP) exome chip stage 1 results in up 16,097 late onset AD cases and 18,077 cognitively normal elderly controls. We performed instrumental variables analysis using inverse variance weighting, weighted median, and MR-Egger.ResultsBased on 10 single nucleotide polymorphisms distinctly predicting HDL-C in the CETP locus, we found that HDL-C was not associated with risk of AD (P > .7).DiscussionOur study does not support the role of HDL-C on risk of AD through HDL-C altered by CETP. This study does not rule out other mechanisms by which HDL-C affects risk of AD.

Introduction: Transplantation of kidneys from expanded criteria donors (ECD), including after circulatory death (DCD), is associated with a higher risk of adverse events compared to kidneys from standard criteria donors. In previous studies, improvements in renal transplant outcomes have been seen when kidneys were perfused with gaseous oxygen during preservation (persufflation, PSF). In the present study, we assessed ex-vivo renal function from a Diffusion Contrast Enhanced (DCE)-MRI estimation of glomerular filtration rate (eGFR); and metabolic sufficiency from whole-organ oxygen consumption (WOOCR) and lactate production rates., Methods: Using a porcine model of DCD, we assigned one kidney to antegrade PSF, and the contralateral kidney to static cold storage (SCS), both maintained for 24 h at 4°C. Post-preservation organ quality assessments, including eGFR, WOOCR and lactate production, were measured under cold perfusion conditions, and biopsies were subsequently taken for histopathological analysis., Results: A significantly higher eGFR (36.6 ± 12.1 vs. 11.8 ± 4.3 ml/min, p  < 0.05), WOOCR (182 ± 33 vs. 132 ± 21 nmol/min*g, p  < 0.05), and lower rates of lactate production were observed in persufflated kidneys. No overt morphological differences were observed between the two preservation methods., Conclusion: These data suggest that antegrade PSF is more effective in preserving renal function than conventional SCS. Further studies in large animal models of transplantation are required to investigate whether integration with PSF of WOOCR, eGFR or lactate production measurements before transplantation are predictive of post-transplantation renal function and clinical outcomes., Competing Interests: BW and MT formed Sylvatica Biotech, Inc., after the investigations reported herein were completed. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (© 2024 Min, Galons, Lynch, Steyn, Price, Weegman, Taylor, Pandey, Harland, Martin, Besselsen, Putnam and Papas.)

Breast cancer imposes a significant burden globally. While the survival rate is steadily improving, much remains to be elucidated. This observational, single time point, multiomic study utilizing genomics, proteomics, targeted and untargeted metabolomics, and metagenomics in a breast cancer survivor (BCS) and age-matched healthy control cohort (N = 100) provides deep molecular phenotyping of breast cancer survivors. In this study, the BCS cohort had significantly higher polygenic risk scores for breast cancer than the control group. Carnitine and hexanoyl carnitine were significantly different. Several bile acid and fatty acid metabolites were significantly dissimilar, most notably the Omega-3 Index (O3I) (significantly lower in BCS). Proteomic and metagenomic analyses identified group and pathway differences, which warrant further investigation. The database built from this study contributes a wealth of data on breast cancer survivorship where there has been a paucity, affording the ability to identify patterns and novel insights that can drive new hypotheses and inform future research. Expansion of this database in the treatment-naïve, newly diagnosed, controlling for treatment confounders, and through the disease progression, can be leveraged to profile and contextualize breast cancer and breast cancer survivorship, potentially leading to the development of new strategies to combat this disease and improve the quality of life for its victims.

Objective: There is uncertainty around the safety of SSRIs for treating depression during pregnancy. Nevertheless, the use of SSRIs has been gradually increasing, especially during the COVID-19 pandemic period. We aimed to (1) characterize maternal depression rate and use of SSRIs in a recent 10-year period, (2) address confounding by indication, as well as socioeconomic and environmental factors, and (3) evaluate associations of the timing of SSRI exposure in pregnancy with risk for preterm birth (PTB), low birthweight (LBW), and small for gestational age (SGA) infants among women with depression before pregnancy., Methods: We conducted propensity score-adjusted regression to calculate odds ratios (ORs) of PTB, LBW, and SGA. We accounted for maternal/pregnancy characteristics, comorbidity, depression severity, time of delivery, social vulnerability, and rural residence., Results: There were 50.3% and 40.3% increases in the prevalence rate of prenatal depression and prenatal SSRI prescription rate during the pandemic. We identified women with depression ≤180 days before pregnancy ( n  = 8406). Women with no SSRI order during pregnancy ( n  = 3760) constituted the unexposed group. The late SSRI exposure group consisted of women with an SSRI order after the first trimester ( n  = 3759). The early-only SSRI exposure group consisted of women with SSRI orders only in the first trimester ( n  = 887). The late SSRI exposure group had an increased risk of PTB of OR = 1.5 ([1.2,1.8]) and LBW of OR = 1.5 ([1.2,2.0]), relative to the unexposed group. Associations between late SSRI exposure and risk of PTB/LBW were similar among a subsample of patients who delivered during the pandemic., Conclusions: These findings suggest an association between PTB/LBW and SSRI exposure is dependent on exposure timing during pregnancy. Small for gestational age is not associated with SSRI exposure.

Gene regulation is essential to placental function and fetal development. We built a genome-scale transcriptional regulatory network (TRN) of the human placenta using digital genomic footprinting and transcriptomic data. We integrated 475 transcriptomes and 12 DNase hypersensitivity datasets from placental samples to globally and quantitatively map transcription factor (TF)-target gene interactions. In an independent dataset, the TRN model predicted target gene expression with an out-of-sample R 2 greater than 0.25 for 73% of target genes. We performed siRNA knockdowns of four TFs and achieved concordance between the predicted gene targets in our TRN and differences in expression of knockdowns with an accuracy of >0.7 for three of the four TFs. Our final model contained 113,158 interactions across 391 TFs and 7712 target genes and is publicly available. We identified 29 TFs which were significantly enriched as regulators for genes previously associated with preterm birth, and eight of these TFs were decreased in preterm placentas.

For many cancer survivors, toxic side effects of treatment, lingering effects of the aftermath of disease and cancer recurrence adversely affect quality of life (QoL) and reduce healthspan. Data-driven approaches for quantifying and improving wellness in healthy individuals hold great promise for improving the lives of cancer survivors. The data-driven strategy will also guide personalized nutrition and exercise recommendations that may help prevent cancer recurrence and secondary malignancies in survivors., Competing Interests: Author NP was employed by company Thorne HealthTech. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hariharan, Hood and Price.)

Studies of asthma and allergy are generating increasing volumes of omics data for analysis and interpretation. The National Institute of Allergy and Infectious Diseases (NIAID) assembled a workshop comprising investigators studying asthma and allergic diseases using omics approaches, omics investigators from outside the field, and NIAID medical and scientific officers to discuss the following areas in asthma and allergy research: genomics, epigenomics, transcriptomics, microbiomics, metabolomics, proteomics, lipidomics, integrative omics, systems biology, and causal inference. Current states of the art, present challenges, novel and emerging strategies, and priorities for progress were presented and discussed for each area. This workshop report summarizes the major points and conclusions from this NIAID workshop. As a group, the investigators underscored the imperatives for rigorous analytic frameworks, integration of different omics data types, cross-disciplinary interaction, strategies for overcoming current limitations, and the overarching goal to improve scientific understanding and care of asthma and allergic diseases., (Copyright © 2024 American Academy of Allergy, Asthma & Immunology. All rights reserved.)

Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health., (© 2023. Springer Nature Limited.)

Background: Placental dysfunction, a root cause of common syndromes affecting human pregnancy, such as preeclampsia (PE), fetal growth restriction (FGR), and spontaneous preterm delivery (sPTD), remains poorly defined. These common, yet clinically disparate obstetrical syndromes share similar placental histopathologic patterns, while individuals within each syndrome present distinct molecular changes, challenging our understanding and hindering our ability to prevent and treat these syndromes., Methods: Using our extensive biobank, we identified women with severe PE (n = 75), FGR (n = 40), FGR with a hypertensive disorder (FGR + HDP; n = 33), sPTD (n = 72), and two uncomplicated control groups, term (n = 113), and preterm without PE, FGR, or sPTD (n = 16). We used placental biopsies for transcriptomics, proteomics, metabolomics data, and histological evaluation. After conventional pairwise comparison, we deployed an unbiased, AI-based similarity network fusion (SNF) to integrate the datatypes and identify omics-defined placental clusters. We used Bayesian model selection to compare the association between the histopathological features and disease conditions vs SNF clusters., Results: Pairwise, disease-based comparisons exhibited relatively few differences, likely reflecting the heterogeneity of the clinical syndromes. Therefore, we deployed the unbiased, omics-based SNF method. Our analysis resulted in four distinct clusters, which were mostly dominated by a specific syndrome. Notably, the cluster dominated by early-onset PE exhibited strong placental dysfunction patterns, with weaker injury patterns in the cluster dominated by sPTD. The SNF-defined clusters exhibited better correlation with the histopathology than the predefined disease groups., Conclusions: Our results demonstrate that integrated omics-based SNF distinctively reclassifies placental dysfunction patterns underlying the common obstetrical syndromes, improves our understanding of the pathological processes, and could promote a search for more personalized interventions., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background: COVID-19 in pregnant people increases the risk for poor maternal-fetal outcomes. However, COVID-19 vaccination hesitancy remains due to concerns over the vaccine's potential effects on maternal-fetal outcomes. Here we examine the impact of COVID-19 vaccination and boosters on maternal SARS-CoV-2 infections and birth outcomes., Methods: This was a retrospective multicentre cohort study on the impact of COVID-19 vaccination on maternal-fetal outcomes for people who delivered (n=106 428) at Providence St Joseph Health across seven western US states from Jan 26, 2021 to Oct 26, 2022. Cohorts were defined by vaccination status at delivery: vaccinated (n=35 926; two or more doses of mRNA-1273 Moderna or BNT162b2 Pfizer-BioNTech), unvaccinated (n=55 878), unvaccinated propensity score matched (n=16 771), boosted (n=10 927; three or more doses), vaccinated unboosted (n=13 243; two doses only), and vaccinated unboosted with propensity score matching (n=4414). We built supervised machine learning classification models, which we used to determine which people were more likely to be vaccinated or boosted at delivery. The primary outcome was maternal SARS-CoV-2 infection. COVID-19 vaccination status at delivery, COVID-19-related health care, preterm birth, stillbirth, and very low birthweight were evaluated as secondary outcomes., Findings: Vaccinated people were more likely to conceive later in the pandemic, have commercial insurance, be older, live in areas with lower household composition vulnerability, and have a higher BMI than unvaccinated people. Boosted people were more likely to have more days since receiving the second COVID-19 vaccine dose, conceive earlier in the pandemic, have commercial insurance, be older, and live in areas with lower household composition vulnerability than vaccinated unboosted people. Vaccinated pregnant people had lower rates of COVID-19 during pregnancy (4·0%) compared with unvaccinated matched people (5·3%; p<0·0001). COVID-19 rates were even lower in boosted people (3·2%) compared with vaccinated unboosted matched people (5·6%; p<0·0001). Vaccinated people were also less likely to have a preterm birth (7·9%; p<0·0001), stillbirth (0·3%; p<0·0002), or very low birthweight neonate (1·0%; p<0·0001) compared with unvaccinated matched people (preterm birth 9·4%; stillbirth 0·6%; very low birthweight 1·5%). Boosted people were less likely to have a stillbirth (0·3%; p<0·025) and have no differences in rates of preterm birth (7·6%; p=0·090) or very low birthweight neonates (0·8%; p=0·092) compared with vaccinated unboosted matched people (stillbirth 0·5%; preterm birth 8·4%; very low birthweight 1·1%)., Interpretation: COVID-19 vaccination protects against adverse maternal-fetal outcomes, with booster doses conferring additional protection. Pregnant people should be high priority for vaccination and stay up to date with their COVID-19 vaccination schedule., Funding: National Institute for Child Health & Human Development and the William O and K Carole Ellison Foundation., Competing Interests: Declaration of interests LH and NDP are scientific advisors for Sera Prognostics, a pregnancy diagnostics company, and NDP holds stock options. The company is not associated with this study or any of the findings. NDP is the Chief Science Officer of Thorne HealthTech. JJH has received grant funding from Pfizer and Novartis and contract funding from Janssen, Gilead, and Bristol Myers Squibb for research unrelated to this work. All other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

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., (© 2023. The Author(s).)

In Type 1 diabetes patients, even ultra-rapid acting insulins injected subcutaneously reach peak concentrations in 45 minutes or longer. The lag time between dosing and peak concentration, as well as intra- and inter-subject variability, render prandial glucose control and dose consistency difficult. We postulated that insulin absorption from subcutaneously implantable vascularizing microchambers would be significantly faster than conventional subcutaneous injection. Male athymic nude R. norvegicus rendered diabetic with streptozotocin were implanted with vascularizing microchambers (single chamber; 1.5 cm2 surface area per side; nominal volume, 22.5 μl). Plasma insulin was assayed after a single dose (1.5 U/kg) of diluted insulin human (Humulin®R U-100), injected subcutaneously or via microchamber. Microchambers were also implanted in additional animals and retrieved at intervals for histologic assessment of vascularity. Following conventional subcutaneous injection, the mean peak insulin concentration was 22.7 (SD 14.2) minutes. By contrast, when identical doses of insulin were injected via subcutaneous microchamber 28 days after implantation, the mean peak insulin time was shortened to 7.50 (SD 4.52) minutes. Peak insulin concentrations were similar by either route; however, inter-subject variability was reduced when insulin was administered via microchamber. Histologic examination of tissue surrounding microchambers showed mature vascularization on days 21 and 40 post-implantation. Implantable vascularizing microchambers of similar design may prove clinically useful for insulin dosing, either intermittently by needle, or continuously by pump including in "closed loop" systems, such as the artificial pancreas., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: [R.J. and KKP are co-founders of and have financial interests in Procyon Technologies LLC.]. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2023 Steyn et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., (© 2023. The Author(s).)

The rapidly evolving COVID-19 pandemic has resulted in an upsurge of scientific productivity to help address the global health crisis. One area of active research is the impact of COVID-19 on pregnancy. Here, we provide an epidemiological overview about what is known about the effects of maternal SARS-CoV-2 infection and COVID-19 vaccination on maternal-fetal outcomes, and identify gaps in knowledge. Pregnant people are at increased risk for severe COVID-19, and maternal SARS-CoV-2 infection increases the risk of negative maternal-fetal outcomes. Despite this elevated risk, there have been high rates of vaccine hesitancy, heightened by the initial lack of safety and efficacy data for COVID-19 vaccination in pregnancy. In response, retrospective cohort studies were performed to examine the impact of COVID-19 vaccination during pregnancy. Here, we report the vaccine's efficacy during pregnancy and its impact on maternal-fetal outcomes, as well as an overview of initial studies on booster shots in pregnancy. We found that pregnant people are at risk for more severe COVID-19 outcomes, maternal SARS-CoV-2 infection is associated with worse birth outcomes, COVID-19 vaccine hesitancy remains prevalent in the pregnant population, and COVID-19 vaccination and boosters promote better maternal-fetal outcomes. The results should help reduce vaccine hesitancy by alleviating concerns about the safety and efficacy of administering the COVID-19 vaccine during pregnancy. Overall, this review provides an introduction to COVID-19 during pregnancy. It is expected to help consolidate current knowledge, accelerate research of COVID-19 during pregnancy and inform clinical, policy, and research decisions regarding COVID-19 vaccination in pregnant people., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Nathan Price reports that financial support was provided by the National Institute of Child Health and Human Development. Samantha Piekos reports that financial support was provided by the William O. and K. Carole Ellison Foundation. Nathan Price reports a relationship with Thorne HealthTech, New York, NY, 10019, USA that includes: employment, equity or stocks, and travel reimbursement. Nathan Price, Leroy Hood reports a relationship with Sera Prognostics, Inc. that includes: consulting or advisory. Jennifer Hadlock has received grant funding from Pfizer Inc. and Novartis Pharmaceutical Corporation for research unrelated to this review., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Dysregulation of sphingomyelin and ceramide metabolism have been implicated in Alzheimer's disease. Genome-wide and transcriptome-wide association studies have identified various genes and genetic variants in lipid metabolism that are associated with Alzheimer's disease. However, the molecular mechanisms of sphingomyelin and ceramide disruption remain to be determined. We focus on the sphingolipid pathway and carry out multi-omics analyses to identify central and peripheral metabolic changes in Alzheimer's patients, correlating them to imaging features. Our multi-omics approach is based on (a) 2114 human post-mortem brain transcriptomics to identify differentially expressed genes; (b) in silico metabolic flux analysis on context-specific metabolic networks identified differential reaction fluxes; (c) multimodal neuroimaging analysis on 1576 participants to associate genetic variants in sphingomyelin pathway with Alzheimer's disease pathogenesis; (d) plasma metabolomic and lipidomic analysis to identify associations of lipid species with dysregulation in Alzheimer's; and (e) metabolite genome-wide association studies to define receptors within the pathway as a potential drug target. We validate our hypothesis in amyloidogenic APP/PS1 mice and show prolonged exposure to fingolimod alleviated synaptic plasticity and cognitive impairment in mice. Our integrative multi-omics approach identifies potential targets in the sphingomyelin pathway and suggests modulators of S1P metabolism as possible candidates for Alzheimer's disease treatment., (© 2022. The Author(s).)

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., Competing Interests: Declaration of interests Arivale, which closed in May 2019, partially funded this study. At the time this study was conceived and designed, J.L., S.A.K., A.M., N.P., and L.H. held positions and/or held stock options in the company. The authors declare no ongoing financial interests in Arivale. The authors declare no other competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

While a range of methods for stool collection exist, many require complicated, self-directed protocols and stool transfer. In this study, we introduce and validate a novel, wipe-based approach to fecal sample collection and stabilization for metagenomics analysis. A total of 72 samples were collected across four different preservation types: freezing at -20°C, room temperature storage, a commercial DNA preservation kit, and a dissolvable wipe used with DESS (dimethyl sulfoxide, ethylenediaminetetraacetic acid, sodium chloride) solution. These samples were sequenced and analyzed for taxonomic abundance metrics, bacterial metabolic pathway classification, and diversity analysis. Overall, the DESS wipe results validated the use of a wipe-based capture method to collect stool samples for microbiome analysis, showing an R 2 of 0.96 for species across all kingdoms, as well as exhibiting a maintenance of Shannon diversity (3.1-3.3) and species richness (151-159) compared to frozen samples. Moreover, DESS showed comparable performance to the commercially available preservation kit (R 2 of 0.98), and samples consistently clustered by subject across each method. These data support that the DESS wipe method can be used for stable, room temperature collection and transport of human stool specimens., Competing Interests: All authors except for CD’A are employees of Thorne HealthTech. The remaining author declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hua, Meydan, Afshin, Lili, D’Adamo, Rickard, Dudley, Price, Zhang and Mason.)

Risk stratification for hospitalized adults with COVID-19 is essential to inform decisions about individual patients and allocation of resources. So far, risk models for severe COVID outcomes have included age but have not been optimized to best serve the needs of either older or younger adults. Models also need to be updated to reflect improvements in COVID-19 treatments. This retrospective study analyzed data from 6906 hospitalized adults with COVID-19 from a community health system across five states in the western United States. Risk models were developed to predict mechanical ventilation illness or death across one to 56 days of hospitalization, using clinical data available within the first hour after either admission with COVID-19 or a first positive SARS-CoV-2 test. For the seven-day interval, models for age ≥ 18 and < 50 years reached AUROC 0.81 (95% CI 0.71-0.91) and models for age ≥ 50 years reached AUROC 0.82 (95% CI 0.77-0.86). Models revealed differences in the statistical significance and relative predictive value of risk factors between older and younger patients including age, BMI, vital signs, and laboratory results. In addition, for hospitalized patients, sex and chronic comorbidities had lower predictive value than vital signs and laboratory results., (© 2022. The Author(s).)

Genetics play an important role in late-onset Alzheimer's Disease (AD) etiology and dozens of genetic variants have been implicated in AD risk through large-scale GWAS meta-analyses. However, the precise mechanistic effects of most of these variants have yet to be determined. Deeply phenotyped cohort data can reveal physiological changes associated with genetic risk for AD across an age spectrum that may provide clues to the biology of the disease. We utilized over 2000 high-quality quantitative measurements obtained from blood of 2831 cognitively normal adult clients of a consumer-based scientific wellness company, each with CLIA-certified whole-genome sequencing data. Measurements included: clinical laboratory blood tests, targeted chip-based proteomics, and metabolomics. We performed a phenome-wide association study utilizing this diverse blood marker data and 25 known AD genetic variants and an AD-specific polygenic risk score (PGRS), adjusting for sex, age, vendor (for clinical labs), and the first four genetic principal components; sex-SNP interactions were also assessed. We observed statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D, and APOE), with effects detectable from early adulthood. The ABCA7 SNP and the APOE2 and APOE4 encoding alleles were associated with lipid variability, as seen in previous studies; in addition, six novel proteins were associated with the e2 allele. The most statistically significant finding was between the NYAP1 variant and PILRA and PILRB protein levels, supporting previous functional genomic studies in the identification of a putative causal variant within the PILRA gene. We did not observe associations between the PGRS and any analyte. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. In post-hoc analysis, sex-stratified GWAS results from an independent AD case-control meta-analysis supported sex-specific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable. Known AD genetic variation influenced lipid metabolism and immune response systems in a population of non-AD individuals, with associations observed from early adulthood onward. Further research is needed to determine whether and how these effects are implicated in early-stage biological pathways to AD. These analyses aim to complement ongoing work on the functional interpretation of AD-associated genetic variants., (© 2022. The Author(s).)

Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific auto-antibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8 + T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes, exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time, leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies., Competing Interests: Declaration of interests J.R.H. and A.R. are founders and board members of PACT Pharma. J.R.H. is a board member of Isoplexis, and A.R. is the scientific advisor to Isoplexis. M.M.D. is a member of the Scientific Advisory Board of PACT Pharma. J.A.B. is a member of the Scientific Advisory Boards of Arcus, Solid, and VIR. J.A.B. is a member of the Board of Directors of Gilead and Provention. J.A.B. is the CEO of Sonoma Biotherapeutics. L.L.L. is on the scientific advisory boards of Alector, Atreca, Dragonfly, DrenBio, Nkarta, Obsidian Therapeutics, and SBI Biotech. R.G. has received consulting income from Juno Therapeutics, Takeda, Infotech Soft, Celgene, and Merck, has received research support from Janssen Pharmaceuticals and Juno Therapeutics, and declares ownership in CellSpace Biosciences. P.D.G. is on the Scientific Advisory Board of Celsius, Earli, Elpiscience, Immunoscape, Rapt, and Nextech, was a scientific founder of Juno Therapeutics, and receives research support from Lonza. J.D.G. declared contracted research with Gilead, Lilly, and Regeneron. J.A.H. received consulting fees or honoraria from Gilead Sciences, Amplyx, Allovir, Allogene therapeutics, CRISPR therapeutics, CSL Behring, OptumHealth, Octapharma, and Takeda and research funding from Takeda, Allovir, Karius, and Gilead Sciences. Q.D., D.H.M., R.T., R.M.G., M.K., and T.M.S. have employment and equity ownership with Adaptive Biotechnologies. The remaining authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: The impact of maternal SARS-CoV-2 infection remains unclear. In this study, we evaluated the risk of maternal SARS-CoV-2 infection on birth outcomes and how this is modulated by the pregnancy trimester in which the infection occurs. We also developed models to predict gestational age at delivery for people following a SARS-CoV-2 infection during pregnancy., Methods: We did a retrospective cohort study of the impact of maternal SARS-CoV-2 infection on birth outcomes. We used clinical data from Providence St Joseph Health electronic health records for pregnant people who delivered in the USA at the Providence, Swedish, or Kadlec sites in Alaska, California, Montana, Oregon, or Washington. The SARS-CoV-2 positive cohort included people who had a positive SARS-CoV-2 PCR-based test during pregnancy, subdivided by trimester of infection. No one in this cohort had been vaccinated for COVID-19 at time of infection. The SARS-CoV-2 negative cohort were people with at least one negative SARS-CoV-2 PCR-based test and no positive tests during pregnancy. Cohorts were matched on common covariates impacting birth outcomes, and univariate and multivariate analysis were done to investigate risk factors and predict outcomes. The primary outcome was gestational age at delivery with annotation of preterm birth classification. We trained multiple supervised learning models on 24 features of the SARS-CoV-2 positive cohort to evaluate performance and feature importance for each model and discuss the impact of SARS-CoV-2 infection on gestational age at delivery., Findings: Between March 5, 2020, and July 4, 2021, 73 666 pregnant people delivered, 18 335 of whom had at least one SARS-CoV-2 test during pregnancy before Feb 14, 2021. We observed 882 people infected with SARS-CoV-2 during their pregnancy (first trimester n=85; second trimester n=226; and third trimester n=571) and 19 769 people who have never tested positive for SARS-CoV-2 and received at least one negative SARS-CoV-2 test during their pregnancy. SARS-CoV-2 infection indicated an increased risk of preterm delivery (p<0·05) and stillbirth (p<0·05), accounted for primarily by first and second trimester SARS-CoV-2 infections. Gestational age at SARS-CoV-2 infection was correlated with gestational age at delivery (p<0·01) and had the greatest impact on predicting gestational age at delivery. The people in this study had mild or moderate SARS-CoV-2 infections and acute COVID-19 severity was not correlated with gestational age at delivery (p=0·31)., Interpretation: These results suggest that pregnant people would benefit from increased monitoring and enhanced prenatal care after first or second trimester SARS-CoV-2 infection, regardless of acute COVID-19 severity., Funding: US National Institutes of Health., Competing Interests: Declaration of interests LH and NDP are scientific advisors for Sera Prognostics, a pregnancy diagnostics company, and hold stock options. The company is not associated with this study or any of the findings. All other authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Vast numbers of differentially expressed genes and perturbed networks have been identified in Alzheimer's disease (AD), however, neither disease nor brain region specificity of these transcriptome alterations has been explored. Using RNA-Seq data from 231 temporal cortex and 224 cerebellum samples from patients with AD and progressive supranuclear palsy (PSP), a tauopathy, we identified a striking correlation in the directionality and magnitude of gene expression changes between these 2 neurodegenerative proteinopathies. Further, the transcriptomic changes in AD and PSP brains ware highly conserved between the temporal and cerebellar cortices, indicating that highly similar transcriptional changes occur in pathologically affected and grossly less affected, albeit functionally connected, areas of the brain. Shared up- or downregulated genes in AD and PSP are enriched in biological pathways. Many of these genes also have concordant protein changes and evidence of epigenetic control. These conserved transcriptomic alterations of 2 distinct proteinopathies in brain regions with and without significant gross neuropathology have broad implications. AD and other neurodegenerative diseases are likely characterized by common disease or compensatory pathways with widespread perturbations in the whole brain. These findings can be leveraged to develop multifaceted therapies and biomarkers that address these common, complex, and ubiquitous molecular alterations in neurodegenerative diseases.

A better understanding of the metabolic alterations in immune cells during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may elucidate the wide diversity of clinical symptoms experienced by individuals with coronavirus disease 2019 (COVID-19). Here, we report the metabolic changes associated with the peripheral immune response of 198 individuals with COVID-19 through an integrated analysis of plasma metabolite and protein levels as well as single-cell multiomics analyses from serial blood draws collected during the first week after clinical diagnosis. We document the emergence of rare but metabolically dominant T cell subpopulations and find that increasing disease severity correlates with a bifurcation of monocytes into two metabolically distinct subsets. This integrated analysis reveals a robust interplay between plasma metabolites and cell-type-specific metabolic reprogramming networks that is associated with disease severity and could predict survival., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The dramatic convergence of molecular biology, genomics, proteomics, metabolomics, bioinformatics, and artificial intelligence has provided a substrate for deep understanding of the biological basis of health and disease. Systems biology is a holistic, dynamic, integrative, cross-disciplinary approach to biological complexity that embraces experimentation, technology, computation, and clinical translation. Systems Medicine integrates genome analyses and longitudinal deep phenotyping with biological pathways and networks to understand mechanisms of disease, identify relevant blood biomarkers, define druggable molecular targets, and enhance the maintenance or restoration of wellness. Two programs initiated our understanding of data-driven population-based wellness. The Pioneer 100 Study of Scientific Wellness and the much larger Arivale commercial program that followed had two spectacular results: demonstrating the feasibility and utility of collecting longitudinal multiomic data, and then generating dense, dynamic data clouds for each individual to utilize actionable metrics for promoting health and preventing disease when combined with personalized coaching. Future developments in these domains will enable better population health and personal, preventive, predictive, participatory (P4) health care., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Recent human feeding studies have shown how the baseline taxonomic composition of the gut microbiome can determine responses to weight loss interventions. However, the functional determinants underlying this phenomenon remain unclear. We report a weight loss response analysis on a cohort of 105 individuals selected from a larger population enrolled in a commercial wellness program, which included healthy lifestyle coaching. Each individual in the cohort had baseline blood metabolomics, blood proteomics, clinical labs, dietary questionnaires, stool 16S rRNA gene sequencing data, and follow-up data on weight change. We generated additional targeted proteomics data on obesity-associated proteins in blood before and after intervention, along with baseline stool metagenomic data, for a subset of 25 individuals who showed the most extreme weight change phenotypes. We built regression models to identify baseline blood, stool, and dietary features associated with weight loss, independent of age, sex, and baseline body mass index (BMI). Many features were independently associated with baseline BMI, but few were independently associated with weight loss. Baseline diet was not associated with weight loss, and only one blood analyte was associated with changes in weight. However, 31 baseline stool metagenomic functional features, including complex polysaccharide and protein degradation genes, stress-response genes, respiration-related genes, and cell wall synthesis genes, along with gut bacterial replication rates, were associated with weight loss responses after controlling for age, sex, and baseline BMI. Together, these results provide a set of compelling hypotheses for how commensal gut microbiota influence weight loss outcomes in humans. IMPORTANCE Recent human feeding studies have shown how the baseline taxonomic composition of the gut microbiome can determine responses to dietary interventions, but the exact functional determinants underlying this phenomenon remain unclear. In this study, we set out to better understand interactions between baseline BMI, metabolic health, diet, gut microbiome functional profiles, and subsequent weight changes in a human cohort that underwent a healthy lifestyle intervention. Overall, our results suggest that the microbiota may influence host weight loss responses through variable bacterial growth rates, dietary energy harvest efficiency, and immunomodulation.

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., Competing Interests: Declaration of Competing Interest The authors declared no conflict of interest., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Neurodegenerative diseases (NDDs) encompass a wide range of conditions that arise owing to progressive degeneration and the ultimate loss of nerve cells in the brain and peripheral nervous system. NDDs such as Alzheimer's, Parkinson's, and Huntington's diseases negatively impact both length and quality of life, due to lack of effective disease-modifying treatments. Herein, we review the use of genome-scale metabolic models, network-based approaches, and integration with multiomics data to identify key biological processes that characterize NDDs. We describe powerful systems biology approaches for modeling NDD pathophysiology by leveraging in silico models that are informed by patient-derived multiomics data. These approaches can enable mechanistic insights into NDD-specific metabolic dysregulations that can be leveraged to identify potential metabolic markers of disease and predisease states., Competing Interests: Conflict of interest statement Nothing declared., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: Phthalates are commonly used endocrine-disrupting chemicals that are ubiquitous in the general population. Prenatal phthalate exposure may alter placental physiology and fetal development, leading to adverse perinatal and childhood health outcomes., Objective: We examined associations between prenatal phthalate exposure in the second and third trimesters and the placental transcriptome at birth, including genes and long noncoding RNAs (lncRNAs), to gain insight into potential mechanisms of action during fetal development., Methods: The ECHO PATHWAYs consortium quantified 21 urinary phthalate metabolites from 760 women enrolled in the CANDLE study (Shelby County, TN) using high-performance liquid chromatography-tandem mass spectrometry. Placental transcriptomic data were obtained using paired-end RNA sequencing. Linear models were fitted to estimate separate associations between maternal urinary phthalate metabolite concentration during the second and third trimester and placental gene expression at birth, adjusted for confounding variables. Genes were considered differentially expressed at a Benjamini-Hochberg false discovery rate (FDR) p < 0.05 . Associations between phthalate metabolites and biological pathways were identified using self-contained gene set testing and considered significantly altered with an FDR-adjusted p < 0.2 ., Results: We observed significant associations between second-trimester phthalate metabolites mono (carboxyisooctyl) phthalate (MCIOP), mono-2-ethyl-5-carboxypentyl phthalate, and mono-2-ethyl-5-oxohexyl phthalate and 18 genes in total, including four lncRNAs. Specifically, placental expression of NEAT1 was associated with multiple phthalate metabolites. Third-trimester MCIOP and mono-isobutyl phthalate concentrations were significantly associated with placental expression of 18 genes and two genes, respectively. Expression of genes within 27 biological pathways was associated with mono-methyl phthalate, MCIOP, and monoethyl phthalate concentrations., Discussion: To our knowledge, this is the first genome-wide assessment of the relationship between the placental transcriptome at birth and prenatal phthalate exposure in a large and diverse birth cohort. We identified numerous genes and lncRNAs associated with prenatal phthalate exposure. These associations mirror findings from other epidemiological and in vitro analyses and may provide insight into biological pathways affected in utero by phthalate exposure. https://doi.org/10.1289/EHP8973.

Introduction: There is an urgent need to develop effective interventional treatments for people with Alzheimer's disease (AD). AD results from a complex multi-decade interplay of multiple interacting dysfunctional biological systems that have not yet been fully elucidated. Epidemiological studies have linked several modifiable lifestyle factors with increased incidence for AD. Because monotherapies have failed to prevent or ameliorate AD, interventional studies should deploy multiple, targeted interventions that address the dysfunctional systems that give rise to AD., Methods: This randomized controlled trial (RCT) will examine the efficacy of a 12-month personalized, multimodal, lifestyle intervention in 60 mild cognitive impairment (MCI) and early stage AD patients (aged 50+, amyloid positivity). Both groups receive data-driven, lifestyle recommendations designed to target multiple systemic pathways implicated in AD. One group receives these personalized recommendations without coaching. The other group receives personalized recommendations with health coaching, dietary counseling, exercise training, cognitive stimulation, and nutritional supplements. We collect clinical, proteomic, metabolomic, neuroimaging, and genetic data to fuel systems-biology analyses. We will examine effects on cognition and hippocampal volume. The overarching goal of the study is to longitudinally track biological systems implicated in AD to reveal the dynamics between these systems during the intervention to understand differences in treatment response., Results: We have developed and implemented a protocol for a personalized, multimodal intervention program for early AD patients. We began enrollment in September 2019; we have enrolled a third of our target (20 of 60) with a 95% retention and 86% compliance rate., Discussion: This study presents a paradigm shift in designing multimodal, lifestyle interventions to reduce cognitive decline, and how to elucidate the biological systems being targeted. Analytical efforts to explain mechanistic or causal underpinnings of individual trajectories and the interplay between multi-omic variables will inform the design of future hypotheses and development of effective precision medicine trials., Competing Interests: None., (© 2021 The Authors. Alzheimer's & Dementia: Translational Research & Clinical Interventions published by Wiley Periodicals, Inc. on behalf of Alzheimer's Association.)

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.

Intron retention (IR) has been implicated in the pathogenesis of complex diseases such as cancers; its association with Alzheimer's disease (AD) remains unexplored. We performed genome-wide analysis of IR through integrating genetic, transcriptomic, and proteomic data of AD subjects and mouse models from the Accelerating Medicines Partnership-Alzheimer's Disease project. We identified 4535 and 4086 IR events in 2173 human and 1736 mouse genes, respectively. Quantitation of IR enabled the identification of differentially expressed genes that conventional exon-level approaches did not reveal. There were significant correlations of intron expression within innate immune genes, like HMBOX1, with AD in humans. Peptides with a high probability of translation from intron-retained mRNAs were identified using mass spectrometry. Further, we established AD-specific intron expression Quantitative Trait Loci, and identified splicing-related genes that may regulate IR. Our analysis provides a novel resource for the search for new AD biomarkers and pathological mechanisms., (© 2021 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Introduction: Passive immunotherapies targeting Aβ continue to be evaluated as Alzheimer's disease (AD) therapeutics, but there remains debate over the mechanisms by which these immunotherapies work. Besides the amount of preexisting Aβ deposition and the type of deposit (compact or diffuse), there is little data concerning what factors, independent of those intrinsic to the antibody, might influence efficacy. Here we (i) explored how constitutive priming of the underlying innate activation states by Il10 and Il6 might influence passive Aβ immunotherapy and (ii) evaluated transcriptomic data generated in the AMP-AD initiative to inform how these two cytokines and their receptors' mRNA levels are altered in human AD and an APP mouse model., Methods: rAAV2/1 encoding EGFP, Il6 or Il10 were delivered by somatic brain transgenesis to neonatal (P0) TgCRND8 APP mice. Then, at 2 months of age, the mice were treated bi-weekly with a high-affinity anti-Aβ1-16 mAb5 monoclonal antibody or control mouse IgG until 6 months of age. rAAV mediated transgene expression, amyloid accumulation, Aβ levels and gliosis were assessed. Extensive transcriptomic data was used to evaluate the mRNA expression levels of IL10 and IL6 and their receptors in the postmortem human AD temporal cortex and in the brains of TgCRND8 mice, the later at multiple ages., Results: Priming TgCRND8 mice with Il10 increases Aβ loads and blocks efficacy of subsequent mAb5 passive immunotherapy, whereas priming with Il6 priming reduces Aβ loads by itself and subsequent Aβ immunotherapy shows only a slightly additive effect. Transcriptomic data shows that (i) there are significant increases in the mRNA levels of Il6 and Il10 receptors in the TgCRND8 mouse model and temporal cortex of humans with AD and (ii) there is a great deal of variance in individual mouse brain and the human temporal cortex of these interleukins and their receptors., Conclusions: The underlying immune activation state can markedly affect the efficacy of passive Aβ immunotherapy. These results have important implications for ongoing human AD immunotherapy trials, as they indicate that underlying immune activation states within the brain, which may be highly variable, may influence the ability for passive immunotherapy to alter Aβ deposition.

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.

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.

Cancer cells are adept at reprogramming energy metabolism, and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism, and reactive oxygen species (ROS) detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

We present an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention., Competing Interests: Declaration of Interests J.R.H. is founder and board member of Isoplexis and PACT Pharma. M.M.D. is a member of the Scientific Advisory Board of PACT Pharma. J.A.B. is a member of the Scientific Advisory Boards of Arcus, Celsius, and VIR. J.A.B. is a member of the Board of Directors of Rheos and Provention. J.A.B. has recently joined Sonoma Biotherapeutics as President and CEO. Sonoma Biotherapeutics is involved in developing novel Treg-based cell therapies for the treatment of autoimmune diseases. R.G. has received consulting income from Juno Therapeutics, Takeda, Infotech Soft, Celgene, Merck and has received research support from Janssen Pharmaceuticals and Juno Therapeutics, and declares ownership in CellSpace Biosciences. P.D.G is on the Scientific Advisory Board of Celsius, Earli, Elpiscience, Immunoscape, Rapt, and Nextech, was a scientific founder of Juno Therapeutics, and receives research support from Lonza. J.D.G. declared contracted research with Gilead, Lilly, and Regeneron. The remaining authors declare no competing interests., (Published by Elsevier Inc.)

Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of ~30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC = 0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD.

Increasing evidence suggests Alzheimer's disease (AD) pathophysiology is influenced by primary and secondary bile acids, the end product of cholesterol metabolism. We analyze 2,114 post-mortem brain transcriptomes and identify genes in the alternative bile acid synthesis pathway to be expressed in the brain. A targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals supports these results. Our metabolic network analysis suggests that taurine transport, bile acid synthesis, and cholesterol metabolism differ in AD and cognitively normal individuals. We also identify putative transcription factors regulating metabolic genes and influencing altered metabolism in AD. Intriguingly, some bile acids measured in brain tissue cannot be explained by the presence of enzymes responsible for their synthesis, suggesting that they may originate from the gut microbiome and are transported to the brain. These findings motivate further research into bile acid metabolism in AD to elucidate their possible connection to cognitive decline., Competing Interests: The authors declare no competing interests., (© 2020 The Authors.)

Variation in the human gut microbiome can reflect host lifestyle and behaviors and influence disease biomarker levels in the blood. Understanding the relationships between gut microbes and host phenotypes are critical for understanding wellness and disease. Here, we examine associations between the gut microbiota and ~150 host phenotypic features across ~3,400 individuals. We identify major axes of taxonomic variance in the gut and a putative diversity maximum along the Firmicutes-to-Bacteroidetes axis. Our analyses reveal both known and unknown associations between microbiome composition and host clinical markers and lifestyle factors, including host-microbe associations that are composition-specific. These results suggest potential opportunities for targeted interventions that alter the composition of the microbiome to improve host health. By uncovering the interrelationships between host diet and lifestyle factors, clinical blood markers, and the human gut microbiome at the population-scale, our results serve as a roadmap for future studies on host-microbe interactions and interventions.

We analyzed 1196 proteins in longitudinal plasma samples from participants in a commercial wellness program, including samples collected pre-diagnosis from ten cancer patients and 69 controls. For three individuals ultimately diagnosed with metastatic breast, lung, or pancreatic cancer, CEACAM5 was a persistent longitudinal outlier as early as 26.5 months pre-diagnosis. CALCA, a biomarker for medullary thyroid cancer, was hypersecreted in metastatic pancreatic cancer at least 16.5 months pre-diagnosis. ERBB2 levels spiked in metastatic breast cancer between 10.0 and 4.0 months pre-diagnosis. Our results support the value of deep phenotyping seemingly healthy individuals in prospectively inferring disease transitions.

Transitions from health to disease are characterized by dysregulation of biological networks under the influence of genetic and environmental factors, often over the course of years to decades before clinical symptoms appear. Understanding these dynamics has important implications for preventive medicine. However, progress has been hindered both by the difficulty of identifying individuals who will eventually go on to develop a particular disease and by the inaccessibility of most disease-relevant tissues in living individuals. Here we developed an alternative approach using polygenic risk scores (PRSs) based on genome-wide association studies (GWAS) for 54 diseases and complex traits coupled with multiomic profiling and found that these PRSs were associated with 766 detectable alterations in proteomic, metabolomic, and standard clinical laboratory measurements (clinical labs) from blood plasma across several thousand mostly healthy individuals. We recapitulated a variety of known relationships (e.g., glutamatergic neurotransmission and inflammation with depression, IL-33 with asthma) and found associations directly suggesting therapeutic strategies (e.g., Ω-6 supplementation and IL-13 inhibition for amyotrophic lateral sclerosis) and influences on longevity (leukemia inhibitory factor, ceramides). Analytes altered in high-genetic-risk individuals showed concordant changes in disease cases, supporting the notion that PRS-associated analytes represent presymptomatic disease alterations. Our results provide insights into the molecular pathophysiology of a range of traits and suggest avenues for the prevention of health-to-disease transitions., Competing Interests: The authors declare no competing interest.