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If you do not allow these cookies we will not know when you have visited our site, and will not be able to monitor its performance. Immunology Immune Fitness: working towards a resilient immune system. A resilient immune system has a positive effect on lifelong health A resilient immune system has a positive effect on lifelong health The immune system maintains good health and helps prevent disease by protecting us from harmful substances such bacteria, viruses and toxins, and by removing malignant cells from our system.

Immune fitness refers to a resilient immune system, with an inbuilt capacity to adapt to challenges by establishing, maintaining and regulating an appropriate immune response.

View References 1, 6 Pawankar R, et al. Published on , Wisconsin 2 West CE, et al. Published on Jan; 1 3 Weizman Z. Published on 4 Azad MB, et al. Published on ;45 3 5 Abrahamsson TR, et al.

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Pawankar R, et al. West CE, et al. Published on Jan; 1 Weizman Z. The difference, they suggest, may be explained in part by a new measure of immunity they call immune resilience — the ability of the immune system to rapidly launch attacks that defend effectively against infectious invaders and respond appropriately to other types of inflammatory stressors, including aging or other health conditions, and then quickly recover, while keeping potentially damaging inflammation under wraps.

The findings in the journal Nature Communications come from an international team led by Sunil Ahuja, University of Texas Health Science Center and the Department of Veterans Affairs Center for Personalized Medicine, both in San Antonio.

To understand the role of immune resilience and its effect on longevity and health outcomes, the researchers looked at multiple other studies including healthy individuals and those with a range of health conditions that challenged their immune systems.

By looking at multiple studies in varied infectious and other contexts, they hoped to find clues as to why some people remain healthier even in the face of varied inflammatory stressors, ranging from mild to more severe.

But to understand how immune resilience influences health outcomes, they first needed a way to measure or grade this immune attribute.

The researchers developed two methods for measuring immune resilience. IHG-I denotes the best balance tracking the highest level of resilience, and IHG-IV denotes the worst balance tracking the lowest level of immune resilience.

An imbalance between the levels of these T-cell types is observed in many people as they age, when they get sick, and in people with autoimmune diseases and other conditions. The researchers also developed a second metric that looks for two patterns of expression of a select set of genes.

One pattern associated with survival and the other with death. The mortality-associated genes are closely related to inflammation, a process through which the immune system eliminates pathogens and begins the healing process but that also underlies many disease states. Their studies have shown that high expression of the survival-associated genes and lower expression of mortality-associated genes indicate optimal immune resilience, correlating with a longer lifespan.

The opposite pattern indicates poor resilience and a greater risk of premature death. When both sets of genes are either low or high at the same time, immune resilience and mortality risks are more moderate. In the newly reported study initiated in , Ahuja and his colleagues set out to assess immune resilience in a collection of about 48, people, with or without various acute, repetitive, or chronic challenges to their immune systems.

In an earlier study, the researchers showed that this novel way to measure immune status and resilience predicted hospitalization and mortality during acute COVID across a wide age spectrum [2].

The investigators have analyzed stored blood samples and publicly available data representing people, many of whom were healthy volunteers, who had enrolled in different studies conducted in Africa, Europe, and North America. Volunteers ranged in age from 9 to years.

They also evaluated participants in the Framingham Heart Study, a long-term effort to identify common factors and characteristics that contribute to cardiovascular disease.

To examine people with a wide range of health challenges and associated stresses on their immune systems, the team also included participants who had influenza or COVID, and people living with HIV.

The short answer is that immune resilience, longevity, and better health outcomes tracked together well. Those with metrics indicating optimal immune resilience generally had better health outcomes and lived longer than those who had lower scores on the immunity grading scale.

Indeed, those with optimal immune resilience were more likely to:. The study also revealed other interesting findings. Some people also maintain higher levels of immune resilience despite the presence of inflammatory stress to their immune systems such as during HIV infection or acute COVID

These findings were published in Nature Communications. The study was supported by a MERIT award and other grants from the National Institute of Allergy and Infectious Diseases NIAID , part of the National Institutes of Health; awards from the U.

Veterans Health Administration; and a Distinguished Clinical Scientist Award from the Doris Duke Charitable Foundation. Ahuja, MD, professor at UT Health Science Center San Antonio with a specialty in infectious diseases. He is director of the Veterans Administration VA Center for Personalized Medicine, a national center within the South Texas Veterans Health Care System.

The laboratory tests developed to assess levels of immune resilience were evaluated in nearly 50, persons of differing ages and types of challenges to their immune systems.

This evaluation demonstrated that individuals with optimal levels of immune resilience were more likely to:. Lee, PharmD, PhD, research investigator at the VA Center for Personalized Medicine and assistant professor at The University of Texas at Austin College of Pharmacy.

The study introduces the novel concept of immune resilience, which looks at the balance between immunocompetence and inflammation as a critical contributor to health outcomes, regardless of age. Muthu Saravanan Manoharan, MS, coauthor and senior research scientist at the VA Center for Personalized Medicine and UT Health Science Center San Antonio, noted that the study team divided participants from the Framingham Heart Study into four groups based on the gene expression markers of immune resilience.

The study team also examined gene expression markers of immune resilience in a population of healthy college students and individuals in the community, all under age 50, who had blood drawn before the influenza season started.

On the day of the first symptoms, most participants, including those with optimal immune resilience before the flu-like illness, had gene expression profiles indicating low immunocompetence and high inflammation, which is noted in persons with shorter life spans.

Many people restored their initial level of immune resilience during recovery; however, even some of those who had optimal immune resilience before influenza infection failed to do so. The study examined female sex workers from Kenya.

During a long-term study, the immune health grades of those with unprotected sex decreased. McKinnon, PhD, coauthor and associate professor in the Max Rady College of Medicine, Department of Medical Microbiology and Infectious Diseases, at the University of Manitoba, Canada. In one of the cohorts, the authors observed a rare ability to maintain a high level of immunocompetence with a low level of inflammation despite chronic inflammatory stress, termed elite immune health status.

Okulicz, MD, U. Air Force infectious disease physician and senior member of the study team. Remarkably, we found that after starting antiviral therapy early, some HIV-positive persons manifested markers of optimal immune resilience typically observed in younger adults without HIV infection.

The association between immune resilience and the response to infection was noted during other infections. Immune resilience was also measured in kidney transplant recipients, who have a fold excess risk of developing skin cancer.

Each of the participants had developed this cancer once after transplant. Bottomley, MD, DPhil, academic clinical lecturer in the Nuffield Department of Surgical Sciences, University of Oxford.

In collaboration with investigators from Sardinia, the authors examined blood immune cell profiles of nearly 4, otherwise healthy individuals. Some younger persons with poor immune resilience had the same signatures and immune health grades commonly seen in older persons.

This finding suggests that the ability to restore and maintain immunocompetence at younger ages may be linked to life span. Another factor noted across the populations and species was that higher levels of optimal immune resilience were observed more often in females than males.

Genetic studies in humans and evaluation of mice with a genetic basis to have lower immune resilience suggest that immune resilience may be calibrated by variations in genes. Notably, mice with lower immune resilience were most susceptible to severe Ebola infection.

Public health ramifications of immune checkups could be significant, Ahuja said. These assessments have utility for understanding who might be at greater risk for developing diseases that affect the immune system, how individuals are responding to treatment, and whether, as well as to what extent, they will recover.

This research was supported by the following funders: 1 National Institute of Allergy and Infectious Diseases NIAID through grant number R37AI MERIT award ; 2 the U.

The Global Fortification Data Exchange. Emerging Initiatives. Improving Child Diets. Mighty Nutrients Coalition. Nutrition for Growth Summit.

The U. Food Systems Summit. Nutrition for Resilience Conference Website. CONNECTED Cancun Ethiopia Nutrition Matters. Nutrition for Resilience. Investment Case for Micronutrients.

Global Hidden Hunger Estimates. Micronutrients Micronutrient Solutions. Stay Informed. All cutoffs and statistical tests were determined pre hoc. The log-rank test was used to evaluate for overall significance.

Details of Pearson vs. Spearman correlation coefficient are provided in Supplementary Information Section Follow-up times and analyses were prespecified. Boxplots center line, median; box, the interquartile range IQR ; whiskers, rest of the data distribution ±1.

Line plots were used to represent proportions of indicated variables. Kaplan-Meier plots were used to represent proportion survived over time since score calculation baseline by indicated groups.

Heatmaps were used to represent correlations of gene signature scores and continuous age. Stacked barplots or barplots were used to represent proportions or correlation coefficients of indicated variables.

Pie charts were used to represent proportions of indicated variables. In the COVID cohort, a Cox proportional hazards model, adjusted for sex and age as a continuous variable, was used to determine whether the gene scores associated with day survival.

In the FHS offspring cohort, a Cox proportional hazards model, adjusted for sex and age as a continuous variable, was used to determine whether the gene scores associated with survival.

Kaplan-Meier survival plots of the FHS offspring cohort are accompanied by P values determined by log-rank test. Grades of antigenic stimulation and IR metrics were used as predictors.

For determining the association between level of antigenic stimulation and IHG status in HIV— persons, proxies were used to grade this level and quantify host antigenic burden accumulated: 1 age was considered as a proxy for repetitive, low-grade antigenic experiences accrued during natural aging; 2 a BAS based on behavioral risk factors condom use, number of clients, number of condoms used per client and a total STI score based on direct [syphilis rapid plasma reagin test and gonorrhea] and indirect vaginal discharge, abdominal pain, genital ulcer, dysuria, and vulvar itch indicators of STI were used as proxies in HIV— FSWs for whom this information was available; and 3 S.

haematobium egg count in the urine was a proxy in children with this infection. ANOVA-based linear regression model was used to evaluate the overall differences between 3 or more groups.

For comparison of groups with multiple samples from the same individuals, we used a linear generalized estimating equation GEE model based on the normal distribution with an exchangeable correlation structure unless otherwise stated.

For the association of gene scores with outcomes, linear regression linear model was used to test them, instead of nonparametric tests as highlighted below in the panel-by-panel detailed statistical methods for each of the figures.

For comparison of groups with multiple samples from the same individuals, we used a linear GEE model based on the normal distribution with an exchangeable correlation structure unless otherwise stated.

For meta-analyses e. All datasets were filtered for common probes. Then, an expression matrix of the probes and samples was created and concurrently normalized as stated in Supplementary Information Section 9.

Example: if dataset 1 provided log 2 values and dataset 2 was quantile normalized, dataset 1 would be un-log transformed by exponentiation with the base 2 before combining with dataset 2 for concurrent normalization and computation of scores. The phenotype groups for plots were determined from the phenotype data deposited in the GEO or ArrayExpress along with the dataset.

The phenotype groups were classified based on the hypothesis evaluated. The transcriptomic signature score is a relative term within a dataset, and it is challenging to compare the score across different datasets. For the meta-analyses, we used a series of criteria as described in Supplementary Information Section 9.

Different RNA microarray or RNA-seq platforms have differences in the availability of gene probes corresponding to the genes in a given transcriptomic signature score. Thus, we indicated the gene count range in each dataset Supplementary Data 13b. As the overall median IQR percentage of available genes is high, In addition, we stress that transcriptomic signature scores were defined in relative terms and caution is needed for cross-dataset comparisons.

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Individual level raw data files of the VA COVID cohort cannot be shared publicly due to data protection and confidentiality requirements.

South Texas Veterans Health Care System STVHCS at San Antonio, Texas, is the data holder for the COVID data used in this study. Data can be made available to approved researchers for analysis after securing relevant permissions via review by the IRB for use of the data collected under this protocol.

Inquiries regarding data availability should be directed to the corresponding author. Accession links to all data generated or analyzed during this study are included in Supplementary Data 13a. Source data are provided with this paper. p11 , phs Aggregate data presented for these cohorts in the current study are provided in the source data file.

Immunophenotyping data from the SardiNiA cohort used in Fig. doi: Data from RTRs are derived and sourced from Bottomley et al. The sources of the data for the literature survey Fig. html ] was used for download and analyses of GEO datasets, and a script from vignette of ArrayExpress R package was used for download and analyses of ArrayExpress datasets.

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The two main sources of funding for the data presented herein are those awarded to S. and J. was supported by grants from the Veterans Affairs VA [VA Research Center for AIDS and HIV Infection, VA Center for Personalized Medicine IP1 CXA1 , and a VA MERIT award]; the National Institutes of Health NIH MERIT award R37AI ; the Doris Duke Distinguished Clinical Scientist Award; the Elizabeth Glaser Pediatric AIDS Foundation; the Burroughs Wellcome Clinical Scientist Award in Translational Research; and the Senior Scholar Award from the Max and Minnie Tomerlin Voelcker Fund.

and M. A portion of the material presented is based on research sponsored by the U. Air Force under agreement number FA United States Air Force 59th Medical Wing Intramural Award to J.

This study was also supported by the Infectious Disease Clinical Research Program IDCRP , a Department of Defense program executed by the Uniformed Services University of the Health Sciences through a cooperative agreement with The Henry M.

Jackson Foundation for the Advancement of Military Medicine, Inc. The Kenya Majengo Observational female sex worker Cohort Study was supported by grants from the NIH R01 AI , the Canadian Institutes of Health Research HOP , the Bill and Melinda Gates Foundation , and the CIHR through the Grand Challenges in Global Health Initiative to F.

The HIV- UCSD cohort was supported by National Institute of Mental Health NIMH P30 grant PI: R. Heaton, MH , MARC from National Institute on Drug Abuse P50 grant PI: I. Grant DA , and ProM from NIMH R01 grant PI: S. Woods, MH was supported by K24 MH from the National Institute of Mental Health.

The renal transplant recipient cohort and MJB were supported by grants from the Wellcome Trust Clinical Training Fellowship and Oxford Hospitals Research Services Committee.

acknowledges the support of the UK National Institute for Health Research through the Local Clinical Research Network. The HIV- Kenyan Schistosoma haematobium children cohort was supported by NIH grant AI C.

The primary HIV infection cohort and D. were supported by NIH grants AI, AI, AI, and MH; Inter-Agency Agreement Y1-AI; and the California HIV Research Program RNSD The Sooty mangabey cohort and GS were supported by NIH grant A1 R The Chinese rhesus macaque study was supported by the National Basic Research Program of China CBA , the Knowledge Innovation Program of CAS KSCX2-EW-R , the National Natural Science Foundation of China , , U , and the Key Scientific and Technological Program of China ZX, ZX The CC mice study and J.

were supported by NIH grants AI, AI, and AI AMS was supported by the NIH T32DE COSTAR institutional research training grant.

This work was also supported by NIH grant 1UL1 TR Clinical and Translational Science Award to RAC. was supported by the NIH KAG We thank participants of the cohorts, other members of the Ahuja lab that contributed to the study, Dr.

Kimberly Summers for help with study approvals, and Donna Thordsen for critical reading of the manuscript.

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