Genes, depression and life satisfaction

Vulnerability to major depression is linked with how satisfied we are with our lives. This association is largely due to genes.

This is the main finding of a new twin study from the Norwegian Institute of Public Health in collaboration with the University of Oslo. The researchers compared longitudinal information from identical and fraternal twins to determine how vulnerability to major depression is associated with dispositional (overall) lifetime satisfaction.

Previous studies have systematically shown that life satisfaction is considerably stable over time. People who are satisfied at any one point in life are often also satisfied at other times in their lives. This stability—the dispositional life satisfaction—is often said to reflect an underlying positive mood or a positive disposition. Previous studies have also shown that people with such a positive disposition are less depressed, but very few studies have examined the mechanisms behind this relationship.

Results

• Both men and women who met the criteria for lifetime major depression (15.8% and 11.1% respectively) reported lower life satisfaction.

• 74% of the relationship between major depression and life satisfaction could be explained by genes.

• The remaining association (26%) could be explained by unique environmental factors.

• The researchers also calculated the heritability of dispositional life satisfaction and major depression separately. The heritability of dispositional life satisfaction, which has not previously been reported, was estimated to be 72%. In other words, it is largely genes that explain why we differ in our tendency to be satisfied and content with our lives.

• Major depression had a heritability of 34%, which is highly consistent with previous studies.

“The stable tendency to see the bright side of life is associated with lower risk of major depression because some genetic factors influence both conditions”, says researcher Ragnhild Bang Nes from the Division of Mental Health. Genes involved in satisfaction and positivity thus give protection against major depression. Nes is the main author of the study that was recently published in the Journal of Affective Disorders.

Susceptibility to both depression and overall life satisfaction is partly influenced by the same set of genes, but is also influenced by genes that are unique to each.

“The heritability figures mean that 72% of the individual differences in overall satisfaction, and 34% of the differences in depression, are caused by genes. These figures do not provide information on the importance of specific genes for an individual's life satisfaction or risk of major depression. Traits and propensities like dispositional life satisfaction and vulnerability to major depression are not heritable in themselves. Heritability refers to the importance of genes for explaining the differences between people and the estimates may vary across time and place”, explains Nes.

Although the heritability of major depression was lower than that of life satisfaction, this does not necessarily mean that life satisfaction is far more heritable than depression. The researchers used questionnaire data from two time points to measure dispositional life satisfaction, and a single clinical interview to measure the prevalence of lifetime major depression. The use of only a single assessment to measure depression may partly explain why the heritability of depression is so much lower than life satisfaction.

Can we prevent depression by promoting life satisfaction?

“We found that depression and life satisfaction did not share as many environmental factors as genetic factors. This means that environmental factors of importance to life satisfaction (for example, activities and interventions that make you happy and content) only to a small extent protect against depression”, says Nes.

“Although our underlying disposition to life satisfaction and positivity appears to be relatively stable, small actions in our daily lives may provide temporary pleasures, and these are also important. How we spend our time is tremendously important for our happiness and well-being. It is therefore important to encourage and follow up on activities that make us happy”.

Nes adds:

“To some extent, positive experiences may also accumulate over time and create favorable conditions for our quality of life”.

About the study

The analyses were based on approximately 1500 twin pairs (both identical and fraternal) from the NIPH twin panel. Identical twins share 100% of the genetic material, while fraternal on average share 50% of their genes —meaning that they are genetically like other siblings and first-degree relatives. By comparing how similar identical and fraternal co-twin are in life satisfaction and risk of major depression, scientists can determine the extent to which variation and covariation is due to genes and environmental influences. The resulting figures reflect the importance of genetic and environmental influences on differences between individuals and do not provide information about the exact relationship between depression and life satisfaction in individuals. At the individual level genetic and environmental factors are dependent on one another in a complex interaction.
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References:

Norwegian Institute of Public Health. 2012. “Genes, depression and life satisfaction”. Norwegian Institute of Public Health. Posted: October 24, 2012. Available online: http://www.fhi.no/eway/default.aspx?pid=238&trg=Area_5954&MainArea_5811=5895:0:15,4549:1:0:0:::0:0&MainLeft_5895=5954:0:15,4549:1:0:0:::0:0&Area_5954=5825:99897::1:5955:1:::0:0

Journal Reference:

Nes, R.B., Czajkowski, N.O., Røysamb, E., Ørstavik. R.E., Tambs, K., Reichborn-Kjennerud, T. (2012) “Major depression and life satisfaction: A population-based twin study”. Journal of Affective Disorders. DOI: http://dx.doi.org/10.1016/j.jad.2012.05.060

How Do People Value Life?

This is an interesting look at how our culture prioritizes human life.

Abstract:

Who should be saved when health resources are limited? Although bioethicists and policymakers continue to debate which metric should be used to evaluate health interventions, public policy is also subject to public opinion. We investigated how the public values life when evaluating vaccine-allocation policies during a flu epidemic. We found that people’s ratings of the acceptability of policies were dramatically influenced by question framing. When policies were described in terms of lives saved, people judged them on the basis of the number of life years gained. In contrast, when the policies were described in terms of lives lost, people considered the age of the policy’s beneficiaries, taking into account the number of years lived to prioritize young targets for the health intervention. In addition, young targets were judged as more valuable in general, but young participants valued young targets even more than older participants did.
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Imagine having to choose between saving the life of a young person or an old person. Although painful, such decisions are inherent in the appropriation of scarce resources for public- health initiatives. For example, in the event of pandemic flu, who should receive the limited supply of vaccines and antiviral medication (Emanuel & Wertheimer, 2006)? These decisions entail, even if only implicitly, the prioritization of certain individuals’ lives over others. Are all lives equally valuable, and if not, whose lives are more valuable?

In the current article, we put aside the important issues of risk (who is at highest risk of dying?) and efficacy (for whom is the intervention most effective?; Galvani, Medlock, & Chapman, 2006) and instead focus on how people quantify the outcome of health interventions—the metric for valuing life. Potential metrics include the number of lives saved, the number of life years gained, and the number of quality-adjusted life years (QALYs) gained (Pliskin, Shepard, & Weinstein, 1980). These metrics imply different optimal policies. The number-of-lives metric requires that the optimal policy maximize the number of individuals being saved, assuming no priority in saving certain individuals over others. Under a life-years-saved metric, however, one would prioritize younger individuals, to the extent that they have a greater number of years left to live. Some government agencies, such as the Food and Drug Administration, use life years saved to quantify benefits (U.S. Food and Drug Administration, 2006). The QALYs metric is similar but assigns greater value to lives of healthy compared with ill individuals, given the same life expectancy, and greater value to interventions that improve quality of life.

Bioethicists and public-health policymakers have extensively debated which metric to adopt (Evans, 1997; Williams, 1997a, 1997b). The controversy reflects the inherently moral nature of placing a value on life. Aside from these debates, if we accept that public-health policies should reflect the moral values of the public, it is critical to understand how the public thinks life should be valued and the underlying mechanisms that give rise to these value judgments. Such an understanding is also relevant to the debate on what kinds of inequities constitute ageism in health care (Kane & Kane, 2005).

Previous studies indicate that the public values the lives of young people more than those of older people (Busschbach, Hessing, & de Charro, 1993; Cropper, Aydede, & Portney, 1994; Johannesson & Johansson, 1997; Lewis & Chamy, 1989; Ratcliffe, 2000; Rodriguez & Pinto, 2000; Tsuchiya, Dolan, & Shaw, 2000). People may value young people more than older people for a number of reasons (Rodriguez & Pinto, 2000). Not only do young people have more years left to live, and thus receive more benefit from a lifesaving intervention, they also have fewer years lived so far and thus deserve their “fair innings” (Williams, 1997a). For example, a 20-year-old has about 3 times as many years left as a 60-year-old (assuming an average life expectancy of 80 years); however, saving one 20-year-old is viewed as equivalent to saving seven 60-year-olds (Cropper et al., 1994), which indicates greater value for younger individuals even beyond what the life-years-saved metric would predict. This response pattern may stem from a sentiment that the death of a younger person is perceived as more tragic and unjust than the death of an older person (Chasteen & Madey, 2003). A similar message is conveyed in the Chinese saying, “Nothing is sadder than for the gray-haired to see the dark-haired go.”

Do people use a years-left metric, a years-lived metric, or a combination of both, to value life? Under normal circumstances, years left (equivalent to remaining life expectancy) is almost perfectly correlated with years lived (equivalent to age), making it impossible to separate these two bases of evaluating life. In our study, we disentangled years left from years lived by manipulating the life expectancy of individuals in a hypothetical scenario, where individuals of various ages were described as either having a normal life expectancy or having only 2 years left due to a preexisting health condition.

If a years-left metric is adopted, value of life should be a negative linear function of age for individuals with normal life expectancy (because years left depends on age) but should not vary by age for individuals with a fixed 2 more years to live (because years left is independent of age). However, if a years-lived metric is adopted, value of life should be a negative linear function of age (years lived is age), regardless of whether the individuals are expected to live to a normal life expectancy or only 2 more years.

We explored whether the metric people use to evaluate life is influenced by how the question is asked. Past research on decision making has demonstrated the powerful influence of question framing (Tversky & Kahneman, 1981)—two equivalent descriptions can lead to very different preferences. Framing can even influence moral behavior (Kern & Chugh, 2009). We hypothesized that “lives saved” and “lives lost” frames would not merely alter preference between options, as shown in previous studies, but would actually invoke different psychological processes or strategies for evaluating lifesaving interventions.

Specifically, we expect the “lives saved” frame to prompt people to evaluate the benefits of the lifesaving interventions and focus on what the victims stand to gain: the number of life years they are expected to gain from the intervention. In contrast, the “lives lost” frame is expected to prompt people to consider what the victims stand to lose: the loss of life. Consequently, we hypothesize that in the “lives saved” frame, people will use a years-left metric, judging younger victims as more valuable only when they have more years left to live; and those in the “lives lost” frame will adopt a years-lived metric, judging younger victims as more valuable regardless of number of years left, because the death of a young person feels more tragic than the death of an older person (Chasteen & Madey, 2003).

The rest of this article is available online http://pss.sagepub.com/content/21/2/163.full.
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References:

Lil, Meng; Vietril, Jeffrey; Galvani, Alison and Chapman, Gretchen B. 2009. "How Do People Value Life?". Psychological Science. Posted: December 22, 2009. Available online: http://pss.sagepub.com/content/21/2/163.full