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Caloric Restriction - latest human data Previous admage have shown that caloric restriction decreases mitochondrial oxygen radical production and restrcition DNA damage in rat organs, which can calotic linked to the slowing of aging rate induced by this regime. Damags two characteristics are Resupply delivery services typical snd long-lived caloric restriction and DNA damage. However, it has never been investigated if those decreases are linked to the decrease in the intake of calories themselves or to decreases in specific dietary components. In this study the possible role of the dietary protein was investigated. The decreases in ROS generation occurred specifically at complex~I. They also occurred without changes in mitochondrial oxygen consumption. Instead, there was a decrease in the percent free radical leak the percentage of total electron flow leading to ROS generation in the respiratory chain.Caloric restriction and DNA damage -
White adipose tissue WAT forms an endocrine organ with both positive and negative effects on metabolism. By secreting adipokines , adipocytes regulate metabolism, energy intake, and fat storage. Adipocytes are known to enlarge during obesity and the ageing process. Several studies demonstrated that increased fat cell size is a significant predictor of altered blood lipid profiles and glucose-insulin homeostasis.
The contribution of visceral adiposity to these associations seems to be of particular importance. Senescence and inflammation are two important mechanisms contributing to ageing and the metabolic consequences of obesity. Inflammation can result from accumulation of macrophages in adipose tissue via production of cytokines such as TNFα and IL Increase in lipolysis has been shown to induce macrophage migration in vitro.
Macrophage numbers in adipose tissue also increase with obesity and ageing where they scavenge dead or senescent adipocytes.
However, inflammatory cytokines and chemokines are also characteristics of the senescence-associated secretory phenotype SASP in senescent cells.
We have shown previously that reactive oxygen species ROS , DNA damage , and mitochondrial dysfunction are instrumental to maintain cellular senescence. Various treatments have been suggested to delay senescence in adipose tissues while obesity and short telomeres exacerbated senescence.
A recent study showed that feeding a high-fat diet ad libitum induced senescence in mouse visceral adipose tissue which could be ameliorated by exercise. However, dietary restriction DR seems to regulate many more genes than exercise in subcutaneous fat in humans.
We have demonstrated previously that short-term dietary restriction in wild type mice decreased the amount of senescent cells in various tissues. We hypothesise that pro-inflammatory cytokines and senescence are also causally related in visceral WAT, increase together during ageing, and might be rescued during DR.
We used visceral WAT from mice of different ages as well as mice on late-onset, short term DR to investigate the changes in adipocyte size, accumulation of DNA damage during ageing and DR, together with the expression of pro-inflammatory cytokines TNFα, IL-6, IL-1β , and senescence markers p16 and p We also analysed AMPK activity which is an important signal transduction pathway implicated in the regulation of physiological processes of DR.
AMPK activation is thought to be able to inhibit inflammatory responses and plays a central role in the regulation of whole body energy homeostasis and functions as a key regulator of intracellular fatty acid metabolism. Our results demonstrate increased senescence and inflammation during ageing in mouse visceral fat while DR was able to ameliorate several of these parameters.
DR was able to significantly reduce adipocyte size and multiple markers of adipocyte senescence significant for DNA damage, p21 and IL-6 expression.
This indicates that DR acts as a senolytic treatment in visceral fat, similar to its effects in other tissues. This highlights the health benefits of a decreased nutritional intake over a relatively short period of time at middle age. Now we need a correct protocol of DR,CR or outright fasting.
Intuitively, it seems that CR can stimulate the autophagy, which can prevent pre-senescsant cells beginning senescent and even killing some weaker cells who don't have get enough energy. On the other hand the human body is easy to complex and the there might be some negative effects too.
That's why a correct portfolio is so needed. I've fasted four times using the Prolon diet over the last 15 months. I don't have any quotes, but I have read somewhere where Valter Longo states that fasting-mimicking diets of five days do clear out stubborn body fat that accumulates with age.
Maybe the stomach fat cells perform apoptosis? Genetically, it would be more beneficial for you if you are homozygous for the good alleles of TNF-a, IL-6, IL-1b, and AMPK genes. I checked my genome and am homozygous for the beneficial SNP allele of all 4 of these genes. About 10 ago I did a 22 day fast and lost closer to 50 pounds.
IV regression is a method commonly used to reduce the impact of confounding in association analysis. Under conditions of nonadherence, traditional ITT analysis can result in a biased estimate of the treatment effect and an IV estimator can provide a complement The ITT estimate may therefore underestimate the effect of CR on biological aging.
The IV approach we used involved two related regressions. The second regression modeled the outcomes changes in measures of biological aging as functions of the predicted treatment dose estimated by the first regression and pretreatment covariates.
The base first-stage regression took the form. Results from this first-stage regression were then included in the second-stage model:.
For final TOT analysis, we included a further instrument in the first-stage regression consisting of the interaction between the baseline level of the aging measure and the CR treatment group. Sensitivity analysis involving re-estimating the IV regression models omitting this final instrument did not change results.
Supplementary Fig. Data met model assumptions. Normality of outcome variables was evaluated by visual inspection of distributions and the Shapiro—Wilk test Equality of variances was evaluated according to the tests proposed by Brown and Forsythe 68 and Markowski and Markowski Models used to test ITT and TOT effects were fitted with heteroskedasticity-robust standard errors.
Normality of distribution of error terms was evaluated by visual inspection of histograms of residuals and the Shapiro—Wilk test.
The clocks we analyzed were developed to predict mortality risk. The age values computed by the clock algorithms correspond to the age at which predicted mortality risk would be approximately normal in the reference population used to develop the clock.
Pace-of-aging measures estimate the rate of biological aging, defined as the rate of decline in overall system integrity. Pace-of-aging values correspond to the years of biological aging experienced during a single calendar year. A value of 1 represents the typical pace of aging in a reference population; values above 1 indicate faster pace of aging; values below 1 indicate slower pace of aging.
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Applications for some types of data may require IRB oversight. Source data for Fig. Kaeberlein, M. Longevity and aging. FPrime Rep.
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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.
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Do dakage want to live a longer life in good health? Simple practices can make some difference, such calofic exercise or calorie Metabolism and metabolism syndrome. But over the long haul all that caloric restriction and DNA damage restrictjon is retriction caloric restriction and DNA damage medicine: building new classes of therapy to repair and reverse the known root causes of aging. The sooner these treatments arrive, the more lives will be saved. Find out how to help ». We know that calorie restriction slows the accumulation of nuclear DNA damage - possibly by enhancing DNA repair mechanisms - just as it slows more or less every other age-related change of interest that scientists have investigated. The degree to which ongoing random damage to nuclear DNA contributes to degenerative aging is debatedhowever:.
Ich denke, dass es die gute Idee ist.