LIFE AHEAD - Its BASIS and METHOD
This section is intended for researchers and others that wish to know details about how Life Ahead was developed. For the usual health-interested person what is included here can be briefly summarized in two paragraphs following:
Abstract: Life Ahead starts with the rates of 19 key categories of disease and death for an average US population from the US vital statistics. Each of these basic rates is adjusted by year of age and gender for the effect of cigarette smoking based on results of the two largest studies of smoking and health. The effect on risk of disease and life of each factor is taken from a difference in an individual's factor vs. that of this average population. For example, if cholesterol is 250 at an age of 50, a risk of heart disease will be computed as higher than that of the 213 cholesterol of the average person of age and sex in US population at this age If it is 150, risk will be computed as lower than that of this average level of 213. If a factor as say of blood pressure, Life Ahead will assumed as default an average population value. One that smokes cigarettes will have much higher risks than that of this non-smoking population. The program thus compares each person's risks with those at age and gender for this base US population of non-smokers in deriving its overall result.
Life Ahead derives a difference in the probable biochemical processes and rates at which these diseases progress during life, and develops complete new life tables for each year to life expectancy for each factor or for change in a factor from a starting age. A change in risk of one disease will result in changes in the development of all other diseases in this life cycle model. Or a starting or stopping of a new habit (as smoking or use of a vitamin) will initiate an annual increase or decrease in risk with its duration of its use. The risks of disease and death computed by Life Ahead should approximate the real values that populations will experience as a result of almost any combination of diet, exercise, or other habits. A problem with this an all such models, is that the program only can compute risks of a population. The actual risks of individuals will vary from that of the population due to individual genetics and family related risks that still cannot be valued accurately. But Life Ahead does compute genetic risks of some factors as cholesterol and cardiofitness if sufficient information is provided.
The Life Ahead Base File: Life Ahead initiates with health results for populations of men and women having the death rates of the US population of 2002, the latest information found during 2005. Nineteen categories of death and/or disease are included, two for cardiovascular diseases as Heart Disease and Stroke; six for cancer as Breast, Lung, Stomach, Genital (Prostate for men, all Genital for women), Colorectal, and All other Cancer; plus Respiratory Disease (COPD), Diabetes, Flu and Pneumonia, Nephritis (Kidney), Dementia including Alzheimer's, Arthritis, Macular Degeneration, All other Diseases; Motor Vehicles; and All other non-disease.
Actual data by disease were available from the US National Health Statistics (NIH) to about age 93, and life expectancy values were found to age 100. Death rates for this population were extended and interpolated to annual values by year from age 1 to age 110. This primary model was adjusted to be consistent with survival rates and life expectancy from ages 50 to 100 as given in the US vital statistics. Estimates for ages above 100 are approximations from information on centenarians.
Available NIH statistics are for populations of men and women that smoked differing amounts of cigarettes at various ages. Thus an adjusted model was developed by subtracting out the probable deaths due to smoking for each disease category. (See Smoking in the Health Research Library). NIH data for the percentages of US men and women that smoked, and for the number of cigarettes they smoked in each age and gender category were used in this adjustment. Thus the primary set of death rate statistics used in Life Ahead is that of non-smoking US populations of men and women. Starting from this non-smoking basis, the risk of any smoking individual can be readily computed. Data on smoking risks were obtained from the two large American Cancer Society Studies of about a million people each. These adjustments for smoking are important and needed because the US Death rate statistics are confused by smoking habits that varied substantially with age and gender. As example, most deaths and disease for lung cancer and COPD were experienced by smokers.
Although primary results of the model rely on these death rates, estimates of disease incidence were included for informative reasons. The disease rates for cancer were taken from the Seer cancer statistics. Disease rates for cardiovascular and all other diseases were estimated from a variety of sources and are less accurate than are those for cancer. But errors in rates of disease will have only a small effect on the Well Days computations. In addition to the adjustment for smoking, the primary set of death rates used by Life Ahead also is for a population that is assumed to not take diet supplements, and that does not take advantage of a few other smaller risk factors as physical examinations, mammograms, etc. This makes valuation of these options more direct and accurate. The death rates in the model should forecast actual US values for year 2002 life expectancy at average population risks for men and women to within about one year from ages 50 to age 100.
The Life Ahead Inputs: Life Ahead first asks for sex, age, height, weight, and for up to 100 additional factors that might affect present and future health. Most values can be entered easily into seven or eight entry screens, and need entry only once. Information can be added or edited any time, but some information that should be gathered in advance includes recent and any past measurements of cholesterol factors (Total, and if available LDL, HDL, Triglycerides), recent Blood Pressures, the bottles of any Diet Supplements used so that amounts from their labels can be entered. Results about diet can be so important that everyone should complete one or more diet entries. A cardiofitness test will be very useful if facilities for this test are available. A first diet entry should represent a most usual recent weekly diet. The Demo program includes 11 described diets. Life Ahead can explain how each diet entered can be improved to produce lowest risk of major diseases..
The Life Table Method: Cancer and the atherosclerosis that causes most Cardiovascular diseases are biochemical processes that proceed gradually over the decades of a lifetime. (See Atherosclerosis - a Chemical Process and Cancer - a Chemical Process) To reflect this properly, Life Ahead as a Life Cycle Model computes an incidence of each death and disease year by year, a population survival at year, and constructs a new life table to life expectancy for every combination of or change in health risk factors at each year of age above age 18. . Because the major health risk factors of our population change with age, life tables of the values of major risk factors for the base US population accompany the model. Major risk factor values now included for all population age years and gender are Body Weight, Total Serum Cholesterol, LDL Cholesterol, HDL Cholesterol, Triglycerides, Cardiofitness/exercise, and Systolic and Diastolic Blood Pressures. Values of these factors at age for men and women are mostly based on statistics published by NIH. . Values at population age for Cardiofitness were developed as part of the Life Ahead project. (See information on Cardiofitness). The actual average or base values for populations assumed for these major risk factors are listed on this website by age and gender in some of the Life Ahead analyses by factor in the Research Library.
A risk of each major health factor is taken in reference to these average life table values. For example, the life table values of total serum cholesterol and weight for a man of age 50 are 213 mg/dl and 171 pounds. These life table values at age all are assigned a risk for each involved disease of 1.00. Any less favorable value of a factor is assigned an appropriately higher risk, any more favorable value is assigned a lower risk of disease. Thus the model starts with the actual life risks of the non-smoking US population and computes differing death rates only in relation to how some new life risks vary from those of the average population. These table values also are default risks. If no values for these major risk factors and other factors are entered into the program, Life Ahead simply computes the lifetime rates of disease by year, life expectancy and Well Days as those of the average US population of non-smokers. As noted above, an approximation is that this average population also is assumed to take no diet supplements or not to use a few health options such as physical exams, mammograms or female hormones.
This method thus provides risks of death and disease that should approximate actual real population values. Importantly, Life Ahead recognizes that risks accrue as part of a gradual biochemical progress of disease during life. As example, total serum cholesterol and other cholesterol values determine in part a rate of atherosclerosis that gradually clogs arteries during life. A man having a usual 260 total cholesterol probably will have substantially narrowed coronary arteries by age 50. If cholesterol is then lowered to 200, this narrowing will not change immediately but probably will persist for many future years. Another man having usual cholesterol during life of 200 usually will have only modest artery narrowing at age 50. Conventional risk factors or models assign each of these men the same risk, that of a snapshot at year of a cholesterol of 200. But the man with the narrowed arteries really will have a much higher risk. Life Ahead computes an actual life profile of serum cholesterol during life from the inclusion of as many earlier measurements as possible. It then recognizes a difference in risk via the Artery Blockage Model that computes likely artery narrowing based on cholesterol values at each prior year.
Some factors in Life Ahead are valued by duration of that factor, for differing values of factors at different years of age, and for differing times after a health factor is changed. Conventional statistical health models do not have this needed capability. As example, the risk of smoking accumulates gradually as years of smoking increase, and this risk can persist in part for up to two decades after smoking is stopped. Other factors that can depend importantly on time and duration include use of diet and diet supplements, antioxidants, mammograms, female hormones, and use of cholesterol drugs. The risk of some factors as cholesterol and smoking also vary substantially with age. These changes in risk also are recognized in Life Ahead via differing age-related risk factors applied at each successive year of life. More on how each factor is valued is provided in the Health Research Library. Antioxidants appear to modify the rate of atherosclerosis, and thus their risk depends directly on duration of exposure. Life Ahead computes risk from durations of exposure using compounded risks for periods up to 20 years. Thus a health risk of today depends not just on habits of today, but also may in part depend on habits of the past and the prior age at which habits were changed.
Conventional Life Expectancy Estimates from Models are Not Useful: A common approach in developing presumed models of life is to assign some number of years to various lifestyles, as for example some years for 'exercise', or 'portions of meat or vegetables eaten', etc. This approach is too inaccurate to be useful. A serious life cycle model of population deaths reveals that the pathway to life expectancy is extremely complex, with risk of each disease interdependent on risks of all other diseases at each age. Values of "Years of life" for any habit can vary ten fold depending on user age, number of habits included and other factors. These 'Years of Life' values are not additive, and accumulations of such for numbers of habits can produce absurd values of life expectancy as for example above age 150. The author was not able to find any other serious method for computing life expectancy from user habits documented and published. Most Health Models and Health Risk Appraisals appear to be undocumented and probably are constructs of statistical equation sets that are useful in identifying user health problems, but cannot adequately identify what must be done to correct these problems beyond generalities such as 'exercise more.'
Life Ahead in accord with logical biochemical engineering shows that health factors and risks in the human boy are highly dynamic and interdependent. For example, if a risk of heart disease is reduced for any reason at some age, as for example by exercise or diet, an increasingly larger remainder of healthy people will survive that will become at risk to suffer all other diseases. This means that every change in one disease and death risk will affect the risk of every other risk. A life cycle model such as Life Ahead that re-computes risks of disease and death at every age of life is required to reflect this important reality.
Research Data Used: After careful consideration, it was concluded that a proper scientific approach required the use of all available useful research studies found published. To be useful for the Life Ahead Model research must show a quantified relationship between a risk and and a factor. A problem is that the measurements of these associations have very high error margins averaging about 50%, with only a few largest studies having error margins of less than 30%. Thus a mean or average risk ratio of say 0.70 will typically have 5-95% result range of from about 0.45 to 0.95. Although the 0.7 risk is quoted 'statistically significant' and is inferred to be a hard number, this is very misleading. The result remains an estimate probably somewhere between 0.45 and 0.95. A series of repeat studies having such an error margin could produce mean values ranging from below 0.45 to above 0.95. A fourth to a third of such studies that have mean values above about 0.75 would not be 'statistically significant', and with present health research dogma be classified as showing 'No effect found." This leads to the inevitable statistical confusion about results that continually plagues reviews and media reports about such research. We need multiple studies with consistent results to obtain risk ratios of needed accuracy.
Research studies commonly claim to be 'Better" or more "Rigorous" that those previously published. And supposed health experts often seem to be aware of only a few recent studies on a subject. But many studies have been published on most subjects included in Life Ahead.. And there seems to be little difference in their study margins of error over time. The most important and comprehensive studies on such major health factors as exercise, smoking, cholesterol and weight were published from four to two decades ago. Rejection of potentially useful research on some technicality results in study selection, and study selection can support preconceived positions and destroy objectivity. Selection of only "statistically significant' studies biases an average risk as too high. The analyses used in Life Ahead thus attempt to use all pertinent research found, but usually identify separate risks for men and women, of those initially free of and patients of a disease, and those from clinical and observation studies. Locating and analyzing the mass of research used in the present Life Ahead Version required several years of full time study and analysis. Few analyses were found published on any subject that provided the useful quantified results needed for use in this program. .
About Race and Origin: Standard vital statistics report rates of disease and death by race, as for example White, Black, Non-White, Asians, etc. Life Ahead now uses total population values and includes no segregation of results by race. A problem here is that differing races can have quite differing life style habits, health factor profiles, and other listed and valued health factors in addition to what may be differing genetic risks. Thus use of such raw vital statistics by race would be invalid in a general population model such as Life Ahead. We will need to know the usual lifestyle habits and other risks of these populations in order to separate any genetic risk from those expected to occur from their habits.
It is well established that as various world populations move to the US and adopt US style lifestyles their previously differing health risks move toward those of the US. The Life Ahead technology provides what may be a useful basis for identifying true differences in the genetic risks of populations by its method for adjusting for effects of multiple differences in identified risk factors, diet, exercise, other life style factors. Yet it seems likely that even if some true genetic health risks of different races exist, the effect of changes in diet and exercise and other lifestyle habits on health and longevity will remain similar for those of differing races. Thus Life Ahead probably will provide useful health guides for people of all races.
A Truly Basic and complete Life Cycle Model should produce health risks for ALL WORLD POPULATIONS: Such a model should be able to reproduce not just the US population death rates, but should be able back-forecast US population death rates for years in the past. It should be capable of forecasting rates of death and disease into the future from differences in health factor levels that are proposed to develop. The program also should be able to explain the widely differing death rates of disease in the various countries of the world and how these vary over the years. It is unlikely that the present model will have this important capability yet, but any verified failure of the model to do this on a population will identify either a defect that should be remedied or a true genetic difference in risk. .
A brief review suggests that the present model will roughly back-forecast most of the large increase in coronary heart disease of the US population from 1900 to 1960 as due to changes in population smoking, serum cholesterol and cardiofitness from physical activity. The subsequent decline in risk since 1960 is due mostly to reductions in serum cholesterol and smoking. The diet model does explain long puzzling facts as example for the much lower rates of coronary disease obtained from the so-called Mediterranean and other diets. Hopefully, others will undertake to learn if the present model can explain results in other countries, and if not, advise what factors in the model need improvement.
The previous section “What Life Ahead Computes” describes more about this. See also Some Concepts: Biochemical Engineering vs. Statistical Methods.
