Category: Diet

Low-calorie diet and anti-aging benefits

Low-calorie diet and anti-aging benefits

Any anv on ensuring a balance between the benefits of calorie restriction benefjts the nutritional needs of post-bariatric individuals? Considering an factors, Low-calorie diet and anti-aging benefits restriction alone is Carbohydrates with high impact handicapped in producing any longevity effectsthe best hope would be limited to healthspan amelioration. The small study, released infollowed 25 people aged 41—65 who consumed only 1,—2, calories a day for six years. and R. Article CAS PubMed Google Scholar Justice, J. Article CAS Google Scholar. Louis, Missouri.

Low-calorie diet and anti-aging benefits -

Buono R, Longo VD. Starvation, stress resistance, and cancer. Trends Endocrinol Metab. Choi IY, Piccio L, Childress P, et al. A diet mimicking fasting promotes regeneration and reduces autoimmunity and multiple sclerosis symptoms. Cell Rep. Cheng CW, Villani V, Buono R, et al. Fasting-mimicking diet promotes ngn3-driven β-cell regeneration to reverse diabetes.

Belsky DW, Caspi A, Corcoran DL, et al. DunedinPACE, a DNA methylation biomarker of the pace of aging. Hillary RF, Stevenson AJ, McCartney DL, et al.

Epigenetic measures of ageing predict the prevalence and incidence of leading causes of death and disease burden. Clin Epigenetics. Protsenko E, Yang R, Nier B, et al. Transl Psychiatry.

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Use profiles to select personalised content. Measure advertising performance. Measure content performance. Understand audiences through statistics or combinations of data from different sources. Develop and improve services. Use limited data to select content. List of Partners vendors.

Maintaining a 25 percent calorie reduction proved challenging — the majority fell short of the goal, and the average calorie reduction was 12 percent by the end of the trial.

However, study participants still lost an average of 16 pounds over the course of the two-year study. To measure the impact of calorie restriction on biological aging, investigators analyzed blood samples collected from trial participants at pre-intervention baseline and after 12 and 24 months of follow-up.

They found that calorie restriction slowed the pace of biological aging over time. Additionally, there appeared to be a dose-response effect: Participants who reduced their caloric intake to a greater extent had a greater decline in their pace of biological aging, says Parker. These findings add to what is currently known about calorie intake and biological aging, says Jamie Justice, PhD , researcher and assistant professor of gerontology and geriatric medicine at Wake Forest School of Medicine in Winston Salem, North Carolina.

Justice was not involved in the CALERIE study. Because eating less leads to weight loss which can have many health benefits , more research is needed to strengthen the findings on how calorie restriction directly impacts aging, she says.

A follow-up of trial participants is now ongoing to determine if the intervention had long-term effects on healthy aging. In simple terms, the current thinking is that calorie restriction affects nutrient-sensing pathways and energy metabolism in ways that reverse or reduce the effects of aging, says Dr.

Part of that effect is due to a process called hormesis, says Justice. Take exercise as an example, says Justice. Before adopting any sort of calorie-restrictive diet, talk with your doctor, says Justice. Ideally, a person should first meet with a registered dietitian to help them create a plan and make sure they are meeting all nutrient needs, she says.

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Google Scholar. Download references. This research was supported by grant no. R01AG to D. received additional support from the American Brain Foundation to R.

and V. received additional support from grant no. P30AG to C. R01AG to V. and C. R33AG to K. received additional support from the CIHR grant no. RN to M. and S. received support from grant no. R01 AG to S. and W. R03AG to I. U01AG to B. We thank the CALERIE Research Network no. R33AG for their assistance in this project and the Dunedin Study no.

R01AG for facilitating early access to the DunedinPACE DNA methylation algorithm. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

completed work on this project while affiliated with the Butler Columbia Aging Center. She is now in the Department of Neurology at the Columbia University Irving Medical Center.

Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA. Waziry, C. Ryan, M. Kothari, G. Department of Genetics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA.

Duke Molecular Physiology Institute and Department of Medicine, Duke University School of Medicine, Durham, NC, USA. Huffman, V. Department of Medical Genetics, Edwin S. Leong Healthy Aging Program, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada.

Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA. Center on Aging and Development, Biostatistics and Bioinformatics, Duke University, Durham, NC, USA. Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.

Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA. Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA. Department of Biobehavioral Health, Pennsylvania State University, State College, PA, USA.

Pennington Biomedical Research Center, Baton Rouge, LA, USA. Department of Medicine, Duke University School of Medicine, Durham, NC, USA. Program in Physical Therapy and Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA. College of Health Solutions, Arizona State University, Phoenix, AZ, USA.

Buck Institute for Research on Aging, Novato, CA, USA. You can also search for this author in PubMed Google Scholar.

designed the research. Kebbe, D. and B. conducted the research. and D. prepared the DNA methylation datasets. analyzed the data. and R. wrote the first draft of the paper. wrote the revised draft of the paper. All authors contributed critical review of the paper. Correspondence to D. are listed as inventors on a Duke University and University of Otago invention, DunedinPACE, that was licensed to a commercial entity.

The other authors declare no competing interests. Nature Aging thanks the anonymous reviewers for their contribution to the peer review of this work.

Effects estimates of CR treatment from mixed models of change in epigenetic age used in Supplementary Fig.

Effects estimates of CR treatment from mixed models of change in epigenetic age used in Fig. Open Access This article is licensed under a Creative Commons Attribution 4. Reprints and permissions. Waziry, R. Effect of long-term caloric restriction on DNA methylation measures of biological aging in healthy adults from the CALERIE trial.

Nat Aging 3 , — Download citation. Received : 08 September Accepted : 22 December Published : 09 February Issue Date : March Anyone you share the following link with will be able to read this content:.

Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Skip to main content Thank you for visiting nature. nature nature aging letters article.

Download PDF. Subjects Predictive markers. This article has been updated. Abstract The geroscience hypothesis proposes that therapy to slow or reverse molecular changes that occur with aging can delay or prevent multiple chronic diseases and extend healthy lifespan 1 , 2 , 3.

Main Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy CALERIE Phase 2 was a multi-center, randomized controlled trial conducted at three clinical centers in the United States Full size image. Table 1 Characteristics of CALERIE Trial participants at baseline Full size table.

Table 2 DNAm clock and pace-of-aging measures included in CALERIE analysis Full size table. Methods We conducted new DNAm assays of stored blood biospecimens collected from the CALERIE Phase 2 randomized controlled trial and merged these data with existing secondary data from the trial.

Study design and participants CALERIE Phase 2 was a multi-center, randomized controlled trial conducted at three clinical centers in the United States 10 ClinicalTrials. Randomization and masking After baseline testing, participants were randomly assigned at a ratio of to a CR behavioral intervention or to an AL control group.

Procedures Study procedures were published previously 10 , 21 , 26 and are described here in brief. DNAm data DNA extracted from blood samples was obtained from the CALERIE Biorepository at the University of Vermont.

DNAm clocks and pace-of-aging measures DNAm clocks are algorithms that combine information from DNAm measurements across the genome to quantify variation in biological age Analysis Analysis included all participants with available DNAm data at trial baseline and at least one follow-up timepoint.

Specification of TOT regression models We tested TOT effects using two-stage least squares IV regression. References Kaeberlein, M.

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Article PubMed PubMed Central Google Scholar Mattison, J. Article CAS PubMed Google Scholar Ravussin, E. Article CAS Google Scholar Scott, A.

Article PubMed PubMed Central Google Scholar Kaeberlein, M. Article Google Scholar Goldman, D. Article Google Scholar Fahy, G.

Article CAS PubMed PubMed Central Google Scholar Chen, L. Article CAS Google Scholar Sae-Lee, C. Article Google Scholar Colchero, F. Article CAS PubMed PubMed Central Google Scholar Fleming, T. Article PubMed PubMed Central Google Scholar Prentice, R.

Article CAS PubMed Google Scholar Justice, J. Article CAS PubMed PubMed Central Google Scholar Racette, S. Article CAS PubMed Google Scholar Levine, M.

Article PubMed PubMed Central Google Scholar Lu, A. Article CAS PubMed PubMed Central Google Scholar Belsky, D. Article CAS PubMed PubMed Central Google Scholar Higgins-Chen, A.

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Article CAS PubMed PubMed Central Google Scholar Hillary, R. Article PubMed PubMed Central Google Scholar Belsky, D. Article Google Scholar Kwon, D. Article CAS PubMed PubMed Central Google Scholar López-Otín, C.

Article PubMed PubMed Central Google Scholar Spadaro, O. Article CAS PubMed PubMed Central Google Scholar Redman, L. Article CAS PubMed PubMed Central Google Scholar Anthonisen, N. Article PubMed Google Scholar Ferrucci, L. Article CAS PubMed Google Scholar Kritchevsky, S.

Article Google Scholar Bell, C. PubMed Google Scholar Ahadi, S. Article CAS PubMed PubMed Central Google Scholar Ma, S. Article CAS PubMed Google Scholar Dorling, J.

Article PubMed PubMed Central Google Scholar Das, S. Article CAS PubMed PubMed Central Google Scholar Shen, W. Article PubMed PubMed Central Google Scholar Moffitt, T. Article Google Scholar Sierra, F. Article PubMed PubMed Central Google Scholar Justice, J. Article PubMed PubMed Central Google Scholar Longo, V.

Article PubMed PubMed Central Google Scholar Davis, S. Article CAS PubMed PubMed Central Google Scholar Lehne, B. Article PubMed PubMed Central Google Scholar Aryee, M.

Article CAS PubMed PubMed Central Google Scholar Horvath, S. Article CAS PubMed Google Scholar Horvath, S. Article PubMed PubMed Central Google Scholar Hannum, G. Article CAS PubMed Google Scholar Chen, B. Article CAS PubMed PubMed Central Google Scholar Levine, M. Article Google Scholar Belsky, D.

Article CAS PubMed PubMed Central Google Scholar Elliott, M. Article CAS PubMed PubMed Central Google Scholar Stata Multilevel Mixed-Effects Reference Manual StataCorp, Article PubMed PubMed Central Google Scholar Baum, C. Article Google Scholar Bang, H.

Article PubMed Google Scholar Shapiro, S. Article Google Scholar Brown, M. Article Google Scholar Markowski, C. Google Scholar Download references. Author information Author notes These authors contributed equally: R. Authors and Affiliations Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA R.

Belsky Department of Genetics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA D. Corcoran Duke Molecular Physiology Institute and Department of Medicine, Duke University School of Medicine, Durham, NC, USA K.

Ramaker Department of Medical Genetics, Edwin S. Leong Healthy Aging Program, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada M. Lin Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA G.

Belsky Center on Aging and Development, Biostatistics and Bioinformatics, Duke University, Durham, NC, USA C. Pieper Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA M. Bhapkar Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA S.

Das Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA L. Ferrucci Department of Biobehavioral Health, Pennsylvania State University, State College, PA, USA W.

Shalev Pennington Biomedical Research Center, Baton Rouge, LA, USA M. Kebbe Department of Medicine, Duke University School of Medicine, Durham, NC, USA D.

Parker Program in Physical Therapy and Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA S. Racette College of Health Solutions, Arizona State University, Phoenix, AZ, USA S. Racette Buck Institute for Research on Aging, Novato, CA, USA B. Schilling Authors R.

Waziry View author publications. View author publications. Ethics declarations Competing interests D. Peer review Peer review information Nature Aging thanks the anonymous reviewers for their contribution to the peer review of this work.

Supplementary information. Supplementary Information Supplementary Tables 1—10, Figs. Reporting Summary. Supplementary Data 1 Clock values, CR treatment group and follow-up data used for Supplementary Fig.

Supplementary Data 2 Effects estimates of CR treatment from mixed models of change in epigenetic age used in Supplementary Fig. Source data Source Data Fig.

Source Data Fig.

Thank Low-calofie for visiting nature. You are using a browser version Low-caloriie limited deit for Anv. To obtain the best experience, we Low-calorie diet and anti-aging benefits you use anti-agjng more Mindfulness for positive vibes to benegits browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure ant-iaging support, we are displaying the site without styles and JavaScript. An Author Correction to this article was published on 09 May The geroscience hypothesis proposes that therapy to slow or reverse molecular changes that occur with aging can delay or prevent multiple chronic diseases and extend healthy lifespan 123. Caloric restriction CRdefined as lessening caloric intake without depriving essential nutrients 4results in changes in molecular processes that have been associated with aging, including DNA methylation DNAm 567and is established to increase healthy lifespan in multiple species 89.

Low-calorie diet and anti-aging benefits -

Choi IY, Piccio L, Childress P, et al. A diet mimicking fasting promotes regeneration and reduces autoimmunity and multiple sclerosis symptoms. Cell Rep. Cheng CW, Villani V, Buono R, et al. Fasting-mimicking diet promotes ngn3-driven β-cell regeneration to reverse diabetes.

Belsky DW, Caspi A, Corcoran DL, et al. DunedinPACE, a DNA methylation biomarker of the pace of aging. Hillary RF, Stevenson AJ, McCartney DL, et al. Epigenetic measures of ageing predict the prevalence and incidence of leading causes of death and disease burden.

Clin Epigenetics. Protsenko E, Yang R, Nier B, et al. Transl Psychiatry. Use limited data to select advertising. Create profiles for personalised advertising. Use profiles to select personalised advertising. Create profiles to personalise content. Use profiles to select personalised content.

Measure advertising performance. Measure content performance. Understand audiences through statistics or combinations of data from different sources. Develop and improve services.

Use limited data to select content. List of Partners vendors. By Rachel Murphy. Fact checked by Nick Blackmer. Nick Blackmer is a librarian, fact-checker, and researcher with more than 20 years of experience in consumer-facing health and wellness content.

A 75 , — Quantification of biological aging in young adults. Natl Acad. USA , E—E Elliott, M. Disparities in the pace of biological aging among midlife adults of the same chronological age have implications for future frailty risk and policy. Patterns of reliability: assessing the reproducibility and integrity of DNA methylation measurement.

Patterns 1 , Stata Multilevel Mixed-Effects Reference Manual StataCorp, Sussman, J. An IV for the RCT: using instrumental variables to adjust for treatment contamination in randomised controlled trials. Baum, C. Instrumental variables and GMM: estimation and testing.

Stata J. Bang, H. On estimating treatment effects under non-compliance in randomized clinical trials: are intent-to-treat or instrumental variables analyses perfect solutions? Shapiro, S. An analysis of variance test for normality complete samples. Biometrika 52 , — Brown, M.

Robust tests for the equality of variances. Markowski, C. Conditions for the effectiveness of a preliminary test of variance. Google Scholar. Download references. This research was supported by grant no. R01AG to D. received additional support from the American Brain Foundation to R.

and V. received additional support from grant no. P30AG to C. R01AG to V. and C. R33AG to K. received additional support from the CIHR grant no. RN to M. and S. received support from grant no.

R01 AG to S. and W. R03AG to I. U01AG to B. We thank the CALERIE Research Network no. R33AG for their assistance in this project and the Dunedin Study no. R01AG for facilitating early access to the DunedinPACE DNA methylation algorithm. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

completed work on this project while affiliated with the Butler Columbia Aging Center. She is now in the Department of Neurology at the Columbia University Irving Medical Center. Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA.

Waziry, C. Ryan, M. Kothari, G. Department of Genetics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA. Duke Molecular Physiology Institute and Department of Medicine, Duke University School of Medicine, Durham, NC, USA.

Huffman, V. Department of Medical Genetics, Edwin S. Leong Healthy Aging Program, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada. Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA.

Center on Aging and Development, Biostatistics and Bioinformatics, Duke University, Durham, NC, USA. Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA. Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA.

Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA. Department of Biobehavioral Health, Pennsylvania State University, State College, PA, USA.

Pennington Biomedical Research Center, Baton Rouge, LA, USA. Department of Medicine, Duke University School of Medicine, Durham, NC, USA. Program in Physical Therapy and Department of Medicine, Washington University School of Medicine, St.

Louis, MO, USA. College of Health Solutions, Arizona State University, Phoenix, AZ, USA. Buck Institute for Research on Aging, Novato, CA, USA.

You can also search for this author in PubMed Google Scholar. designed the research. Kebbe, D. and B. conducted the research. and D. prepared the DNA methylation datasets. analyzed the data. and R. wrote the first draft of the paper. wrote the revised draft of the paper.

All authors contributed critical review of the paper. Correspondence to D. are listed as inventors on a Duke University and University of Otago invention, DunedinPACE, that was licensed to a commercial entity.

The other authors declare no competing interests. Nature Aging thanks the anonymous reviewers for their contribution to the peer review of this work. Effects estimates of CR treatment from mixed models of change in epigenetic age used in Supplementary Fig. Effects estimates of CR treatment from mixed models of change in epigenetic age used in Fig.

Open Access This article is licensed under a Creative Commons Attribution 4. Reprints and permissions. Waziry, R. Effect of long-term caloric restriction on DNA methylation measures of biological aging in healthy adults from the CALERIE trial.

Nat Aging 3 , — Download citation. Received : 08 September Accepted : 22 December Published : 09 February Issue Date : March Anyone you share the following link with will be able to read this content:.

Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative.

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Skip to main content Thank you for visiting nature.

nature nature aging letters article. Download PDF. Subjects Predictive markers. This article has been updated. Abstract The geroscience hypothesis proposes that therapy to slow or reverse molecular changes that occur with aging can delay or prevent multiple chronic diseases and extend healthy lifespan 1 , 2 , 3.

Main Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy CALERIE Phase 2 was a multi-center, randomized controlled trial conducted at three clinical centers in the United States Full size image.

Table 1 Characteristics of CALERIE Trial participants at baseline Full size table. Table 2 DNAm clock and pace-of-aging measures included in CALERIE analysis Full size table.

Methods We conducted new DNAm assays of stored blood biospecimens collected from the CALERIE Phase 2 randomized controlled trial and merged these data with existing secondary data from the trial.

Study design and participants CALERIE Phase 2 was a multi-center, randomized controlled trial conducted at three clinical centers in the United States 10 ClinicalTrials.

Randomization and masking After baseline testing, participants were randomly assigned at a ratio of to a CR behavioral intervention or to an AL control group. Procedures Study procedures were published previously 10 , 21 , 26 and are described here in brief. DNAm data DNA extracted from blood samples was obtained from the CALERIE Biorepository at the University of Vermont.

DNAm clocks and pace-of-aging measures DNAm clocks are algorithms that combine information from DNAm measurements across the genome to quantify variation in biological age Analysis Analysis included all participants with available DNAm data at trial baseline and at least one follow-up timepoint.

Specification of TOT regression models We tested TOT effects using two-stage least squares IV regression. References Kaeberlein, M. Article CAS PubMed PubMed Central Google Scholar Campisi, J. Article CAS PubMed PubMed Central Google Scholar Speakman, J. Article CAS PubMed Google Scholar Maegawa, S.

Article PubMed PubMed Central Google Scholar Hahn, O. Article PubMed PubMed Central Google Scholar Petkovich, D. Article CAS PubMed PubMed Central Google Scholar Anderson, R. Article PubMed PubMed Central Google Scholar Mattison, J. Article CAS PubMed Google Scholar Ravussin, E.

Article CAS Google Scholar Scott, A. Article PubMed PubMed Central Google Scholar Kaeberlein, M. Article Google Scholar Goldman, D. Article Google Scholar Fahy, G.

Article CAS PubMed PubMed Central Google Scholar Chen, L. Article CAS Google Scholar Sae-Lee, C. Article Google Scholar Colchero, F. Article CAS PubMed PubMed Central Google Scholar Fleming, T.

Article PubMed PubMed Central Google Scholar Prentice, R. Article CAS PubMed Google Scholar Justice, J. Article CAS PubMed PubMed Central Google Scholar Racette, S. Article CAS PubMed Google Scholar Levine, M.

Article PubMed PubMed Central Google Scholar Lu, A. Article CAS PubMed PubMed Central Google Scholar Belsky, D. Article CAS PubMed PubMed Central Google Scholar Higgins-Chen, A.

Article PubMed PubMed Central Google Scholar Kraus, W. Article PubMed PubMed Central Google Scholar Benjamin, D. Article PubMed Google Scholar Salas, L. Article PubMed PubMed Central Google Scholar Sugden, K.

Article CAS PubMed PubMed Central Google Scholar Hillary, R. Article PubMed PubMed Central Google Scholar Belsky, D. Article Google Scholar Kwon, D. Article CAS PubMed PubMed Central Google Scholar López-Otín, C.

Article PubMed PubMed Central Google Scholar Spadaro, O. Article CAS PubMed PubMed Central Google Scholar Redman, L.

Article CAS PubMed PubMed Central Google Scholar Anthonisen, N. Article PubMed Google Scholar Ferrucci, L. Article CAS PubMed Google Scholar Kritchevsky, S.

Article Google Scholar Bell, C. PubMed Google Scholar Ahadi, S. Article CAS PubMed PubMed Central Google Scholar Ma, S. Article CAS PubMed Google Scholar Dorling, J.

Article PubMed PubMed Central Google Scholar Das, S. Article CAS PubMed PubMed Central Google Scholar Shen, W. Article PubMed PubMed Central Google Scholar Moffitt, T. Article Google Scholar Sierra, F.

Article PubMed PubMed Central Google Scholar Justice, J. Article PubMed PubMed Central Google Scholar Longo, V. Article PubMed PubMed Central Google Scholar Davis, S. Article CAS PubMed PubMed Central Google Scholar Lehne, B. Article PubMed PubMed Central Google Scholar Aryee, M.

Article CAS PubMed PubMed Central Google Scholar Horvath, S. Article CAS PubMed Google Scholar Horvath, S. Article PubMed PubMed Central Google Scholar Hannum, G.

Article CAS PubMed Google Scholar Chen, B. Article CAS PubMed PubMed Central Google Scholar Levine, M. Article Google Scholar Belsky, D. Article CAS PubMed PubMed Central Google Scholar Elliott, M. Article CAS PubMed PubMed Central Google Scholar Stata Multilevel Mixed-Effects Reference Manual StataCorp, Article PubMed PubMed Central Google Scholar Baum, C.

Article Google Scholar Bang, H. Article PubMed Google Scholar Shapiro, S. Article Google Scholar Brown, M. Article Google Scholar Markowski, C. Google Scholar Download references. Author information Author notes These authors contributed equally: R. Authors and Affiliations Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA R.

Belsky Department of Genetics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA D. Corcoran Duke Molecular Physiology Institute and Department of Medicine, Duke University School of Medicine, Durham, NC, USA K.

Ramaker Department of Medical Genetics, Edwin S. Leong Healthy Aging Program, Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada M. Lin Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA G.

Perhaps surprisingly, researchers recently discovered that calorie restriction helps the key defender of our bodies: the immune system. Our bodies are constantly under attack by bacteria, viruses and parasites trying to get in.

If an invader does get in, the immune system tries to fight back and protect the body through a process called inflammation. For example, imagine for a moment what a bee sting feels like — ouch! Right after the stinger pierces the skin, the damage from the puncture wound and venom alert the immune system to respond to these external threats and initiate inflammation.

Immune cells release proinflammatory compounds that dilate blood vessels in the harmed area, causing the familiar redness and painful swelling that recruits more immune cells to rush in Figure 1. The proinflammatory immune cells at the site of damage and inflammation then fight off the venom from the bee sting and any other germs that may have also penetrated into the area.

Figure 1. Immune cells are in either a proinflammatory state left or anti-inflammatory state right while combating threats like pathogenic bacteria and viruses.

Although inflammation is important to defend against these threats, it is equally important for the body to stop inflammation and enter the anti-inflammatory state once these threats are dealt with.

As the immune cells release chemicals at the site of inflammation to fight germs, these chemicals unfortunately also destroy the healthy cells around them. This is the double-edged sword of the immune system: while we need inflammation to fight invaders, our healthy tissues are also damaged as a byproduct.

Therefore, to prevent further damage, once the threat is contained, anti-inflammatory signals shut down the attacking immune cells and allow the swelling to go away as the wound heals. Depending on the needs of the tissue, the body must balance the proinflammatory processes that combat germs and the anti-inflammatory processes that subsequently heal the tissue.

As people age, however, this balance is skewed as more immune cells become proinflammatory at baseline, which can lead to diseases such as rheumatoid arthritis and chronic pain. Studies have shown that calorie restriction may reduce inflammation and the risk of cardiovascular diseases, which typically develop as we age.

Given these hints of a dynamic interaction between the immune system and calorie restriction during aging, researchers at the Salk Institute and the Chinese Academy of Sciences decided to put rats on a diet in order to figure out what exactly was going on.

Once the rats reached 27 months of age equivalent to 70 human years , the researchers looked at how the two groups differed. They focused on analyzing organs in the body often impacted by age-related diseases, such as fatty tissue, the aorta, kidney, liver, skin, and bone marrow.

The researchers found that rats on a normal diet had high numbers of proinflammatory immune cells in fat, liver, and kidney tissue, which could possibly potentiate diseases developing in those organs. Surprisingly, however, they found that rats on the calorie-restricted diet had fewer immune cells present in these tissues overall, and that these immune cells were predominantly in an anti-inflammatory state.

However, the scientists did not yet have an answer to why calorie restriction reduced the number of inflammatory immune cells in the first place. Figure 2. Researchers found and investigated why rats on a lower calorie diet left have less aging-associated tissue inflammation than those on a normal calorie diet right.

The researchers decided to take a closer look at how the cells in different tissues changed after calorie restriction. They were surprised to find significant changes in a little-known gene called Ybx1 , which produces a type of protein whose function is to control the levels of other genes.

A anti-aigng study published in Nature Low-calorie diet and anti-aging benefits indicates that a significant calorie reduction may Nutrition for injury prevention in athletes the Low-caloriw of aging benefite much oLw-calorie quitting smoking. Low-czlorie, aging well, benffits slowing the aging process are popular topics in the Low-calorie diet and anti-aging benefits space. Hence the overwhelming amount of products that grace pharmacy shelves and social media ads. There are many factors that go into how each individual experiences the aging process, nutrition and diet habits being one. The calorie-restricted group received prepared meals for the first month of the study—to help familiarize themselves with the recommended diet—as well as behavioral counseling. The non-restricted group received no guidance about diet and did not participate in counseling.

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