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A commonly used drug may be the secret to a longer, healthier life, new research suggests. Aging is a complex process, which many of us consider to be an inevitable consequence of life on Earth. But thanks to modern technology, many scientists now consider aging to be a disease that we can treat, or at least delay. “You can think about life span like a candle it depends on how fast it burns,” Michael Lisanti, a professor in cancer research and metabolism…..Story continues….
Source: Newsweek
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Critics:
The heritability of lifespan is estimated to be less than 10%, meaning the majority of variation in lifespan is attributable due to differences in environment rather than genetic variation. However, researchers have identified regions of the genome which can influence the length of life and the number of years lived in good health.
For example, a genome-wide association study of 1 million lifespans found 12 genetic loci which influenced lifespan by modifying susceptibility to cardiovascular and smoking-related disease. The locus with the largest effect is APOE. Carriers of the APOE ε4 allele live approximately one year less than average (per copy of the ε4 allele), mainly due to increased risk of Alzheimer’s disease.
In July 2020, scientists identified 10 genomic loci with consistent effects across multiple lifespan-related traits, including healthspan, lifespan, and longevity. The genes affected by variation in these loci highlighted haem metabolism as a promising candidate for further research within the field. This study suggests that high levels of iron in the blood likely reduce, and genes involved in metabolising iron likely increase healthy years of life in humans.
A follow-up study which investigated the genetics of frailty and self-rated health in addition to healthspan, lifespan, and longevity also highlighted haem metabolism as an important pathway, and found genetic variants which lower blood protein levels of LPA and VCAM1 were associated with increased healthy lifespan.
In developed countries, the number of centenarians is increasing at approximately 5.5% per year, which means doubling the centenarian population every 13 years, pushing it from some 455,000 in 2009 to 4.1 million in 2050. Japan is the country with the highest ratio of centenarians (347 for every 1 million inhabitants in September 2010). Shimane Prefecture had an estimated 743 centenarians per million inhabitants.
In the United States, the number of centenarians grew from 32,194 in 1980 to 71,944 in November 2010 (232 centenarians per million inhabitants). Mental illness is reported to occur in approximately 18% of the average American population. The mentally ill have been shown to have a 10- to 25-year reduction in life expectancy.
Generally, the reduction of lifespan in the mentally ill population compared to the mentally stable population has been studied and documented. The greater mortality of people with mental disorders may be due to death from injury, from co-morbid conditions, or medication side effects. For instance, psychiatric medications can increase the risk of developing diabetes.
It has been shown that the psychiatric medication olanzapine can increase risk of developing agranulocytosis, among other comorbidities. Psychiatric medicines also affect the gastrointestinal tract; the mentally ill have a four times risk of gastrointestinal disease. As of 2020 and the COVID-19 pandemic, researchers have found an increased risk of death in the mentally ill.
The life expectancy of people with diabetes, which is 9.3% of the U.S. population, is reduced by roughly 10–20 years. People over 60 years old with Alzheimer’s disease have about a 50% life expectancy of 3–10 years. Other demographics that tend to have a lower life expectancy than average include transplant recipients and the obese.
Education on all levels has been shown to be strongly associated with increased life expectancy. This association may be due partly to higher income, which can lead to increased life expectancy. Despite the association, among identical twin pairs with different education levels, there is only weak evidence of a relationship between educational attainment and adult mortality.
According to a paper from 2015, the mortality rate for the Caucasian population in the United States from 1993 to 2001 is four times higher for those who did not complete high school compared to those who have at least 16 years of education. In fact, within the U.S. adult population, people with less than a high school education have the shortest life expectancies.
Preschool education also plays a large role in life expectancy. It was found that high-quality early-stage childhood education had positive effects on health. Researchers discovered this by analyzing the results of the Carolina Abecedarian Project, finding that the disadvantaged children who were randomly assigned to treatment had lower instances of risk factors for cardiovascular and metabolic diseases in their mid-30s.
Various species of plants and animals, including humans, have different lifespans. Evolutionary theory states that organisms which—by virtue of their defenses or lifestyle—live for long periods and avoid accidents, disease, predation, etc. are likely to have genes that code for slow aging, which often translates to good cellular repair. One theory is that if predation or accidental deaths prevent most individuals from living to an old age, there will be less natural selection to increase the intrinsic life span.
That finding was supported in a classic study of opossums by Austad;however, the opposite relationship was found in an equally prominent study of guppies by Reznick. One prominent and very popular theory states that lifespan can be lengthened by a tight budget for food energy called caloric restriction. Caloric restriction observed in many animals (most notably mice and rats) shows a near doubling of life span from a very limited calorific intake. Support for the theory has been bolstered by several new studies linking lower
basal metabolic rate to increased life expectancy. That is the key to why animals like giant tortoises can live so long. Studies of humans with life spans of at least 100 have shown a link to decreased thyroid activity, resulting in their lowered metabolic rate. The ability of skin fibroblasts to perform DNA repair after UV irradiation was measured in shrew, mouse, rat, hamster, cow, elephant and human. It was found that DNA repair capability increased systematically with species life span
Since this original study in 1974, at least 14 additional studies were performed on mammals to test this correlation. In all, but two of these studies, lifespan correlated with DNA repair levels, suggesting that DNA repair capability contributes to life expectancy. See DNA damage theory of aging. In a broad survey of zoo animals, no relationship was found between investment of the animal in reproduction and its life span.
Life expectancy forecasting is usually based on one of two different approaches:
1. Forecasting the life expectancy directly, generally using ARIMA or other time-series extrapolation procedures. This has the advantage of simplicity, but it cannot account for changes in mortality at specific ages, and the forecast number cannot be used to derive other life table results. Analyses and forecasts using this approach can be done with any common statistical/mathematical software package, like EViews, R, SAS, Stata, Matlab, or SPSS.
2. Forecasting age-specific death rates and computing the life expectancy from the results with life table methods. This is usually more complex than simply forecasting life expectancy because the analyst must deal with correlated age-specific mortality rates, but it seems to be more robust than simple one-dimensional time series approaches. It also yields a set of age-specific rates that may be used to derive other measures, such as survival curves or life expectancies at different ages.
The most important approach in this group is the Lee-Carter model, which uses the singular value decomposition on a set of transformed age-specific mortality rates to reduce their dimensionality to a single time series, forecasts that time series, and then recovers a full set of age-specific mortality rates from that forecasted value. The software includes Professor Rob J. Hyndman’s R package called ‘demography’ and UC Berkeley’s LCFIT system
