Evolution of the human lifespan, past, present, and future: phases in the evolution of human life expectancy in relation to the inflammatory load.

INTRODUCTIONThis essay considers environmental and life-style factors in natural selection during the three hundred thousand generations that separate us from a great ape ancestor. Humans have the evolved greatest life expectancy (LE) among the primates (fig. 1). The LE at birth of pre-industrial humans, ca. 30-40 years, is twice that of the four extant great ape species (Finch 2007, 2010a; Finch and Austad 2011). The longer human LE is associated with slower postnatal maturation and with lower mortality as adults than the great apes. While these differences are clearly seated in genetics, recent environmental improvements have further increased LE. Since 1800, during industrialization and economic development, the LE doubled again, reaching 70-85 years in favored populations (Christensen et al. 2009). Not only was survival at early ages enhanced, but the LE at age 70 has also more than doubled (Finch and Crimmins 2005). The recent rapid increases in lifespan within ten generations are consistent with environmental factors, rather than genetic selection. The limited heritability of human lifespans, about 25% in identical twins (Herskind et al. 1996; Finch and Tanzi 1997), also shows the importance of environmental and epigenetic factors in aging processes (Finch and Kirkwood 2000; Kirkwood and Finch 2002; Martin 2011). Infection and inflammation have had recognized roles during the recent increases of LE, which I propose were also important in our evolutionary past. Last, I also consider how global environmental deterioration and emerging climate changes could increase global inflammatory exposure and potentially reverse recent gains in LE.MORTALITY CURVE ANALYSISThe huge recent increases in LE are concurrent with the improvements in hygiene, nutrition, and medicine during the nineteenth and twentieth centuries that reduced mortality from infections at all ages. A hypothesis developed with Eileen Crimmins proposes that these reductions in mortality were enabled by a reduced load of chronic inflammation and infection. The load of infection and inflammation is represented by mortality at early ages, which is dominated by infectious causes (Finch and Crimmins 2004, 2005; Crimmins and Finch 2006a; Finch 2007). Inflammation is closely linked to most chronic diseases of aging, including atherosclerosis and cancer (Finch 2007, 2010a,c; Franceschi 2007; Van Den Biggelaar et al. 2004). Consider two textbook examples: The twentieth-century scourge of tobacco smoking is well understood as causing chronic inflammation with oxidative stress that accelerates vascular aging (carotid thickening) and causes DNA damage and increased cancer. The common gut bacterium Helicobacter pylori, once pandemic and now increasingly restricted, increases cancer risk in association with chronic inflammation and DNA damage.The historical shifts in mortality are graphed as mortality rates by historical cohort, which have a "J-shaped" curve (fig. 2A). Data may be plotted by period, i.e., cross-sectionally, representing all ages present for a given year. Alternatively, tracking a birth cohort across its lifespan reveals additional features of lingering environmental effects. When mortality rates are graphed by cohort, the curves show more distinct trajectories than if plotted by period, with progressive displacement downward across the lifespan during the last two hundred years. The most complete demographic history is from the national data of Sweden since 1750 (Finch and Crimmins 2004; Crimmins and Finch 2006a,b). Four phases can be resolved in human mortality curves: Mortality Phase 1, decreasing mortality from birth to puberty (0-9 years); Phase 2, basal mortality, which is the lowest during the lifespan (10-40 y); Phase 3, exponentially accelerating mortality (40-80 y); Phase 4, asymptoting mortality (>80 y), which approaches a maximum of 0.5/y after 100. The acceleration of mortality in adults, during the long ascending Phase 3, is described by the Gompertz exponential model:m(x) - Aexp(Gx) Eq. …