NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

National Research Council (US) Committee on Advances in Collecting and Utilizing Biological Indicators and Genetic Information in Social Science Surveys; Weinstein M, Vaupel JW, Wachter KW, editors. Biosocial Surveys. Washington (DC): National Academies Press (US); 2008.

Cover of Biosocial Surveys

Biosocial Surveys.

Show details

1Biological Indicators and Genetic Information in Danish Twin and Oldest-Old Surveys

, , and .

Over the last 10 years, biological indicators and genetic information have been added to ongoing nationwide longitudinal surveys among twins and elderly people in Denmark. This chapter summarizes experiences and some important results obtained by adding both traditional and new biological indicators to these surveys in order to get a better understanding of the predictors and determinants of healthy aging.

The overall strategy on biological indicators in these Danish studies was designed to achieve two goals. First, we decided to collect a few easily obtainable biological indicators from nearly all participants in the surveys to be able to make valid and statistically powerful tests of new (or old) findings from other studies—in particular, genetic association studies, which constitute a research field that suffers from many false positive findings due to multiple testing in many small-scale studies. Second, we decided to obtain more detailed biological indicators from individuals who were identified through the survey as particularly informative for a given research purpose, meaning that we used the survey as a screening instrument for follow-up ancillary studies.

The chapter starts with a short description of the challenges and biases in the search for biological and nonbiological determinants of healthy aging. An overview of the Danish surveys follows, with a focus on the limited impact the inclusion of biological indicators in the surveys had on response rates and the experience that the small amount of DNA obtained by a finger prick or a cheek brush probably is enough for hundreds (if not thousands) of genotypes. Then examples of cross-sectional and longitudinal large-scale genetic association studies in the Danish cohort are given, focusing on physical and cognitive functioning and survival. Studies using more detailed biological indicators for particularly interesting subsets of the survey participants, such as twin pairs, will also be covered. Finally, a new biological indicator, perceived age based on photographs, is described and we show how this biological indicator—which is at the other end of the biological spectrum from molecular indicators of aging—correlates to environmental exposures and predicts survival.

CHALLENGES IN THE SEARCH FOR DETERMINANTS OF HEALTHY AGING AND LONGEVITY

A large ongoing research effort is under way to identify the genetic, environmental, and behavioral determinants of extreme survival (longevity) by comparing exceptional survivors (often centenarians) with younger cohorts—that is, case-control or association studies. A characteristic that is more common in centenarians than in the younger control group is interpreted as a determinant for exceptional longevity. A weakness of this approach is that, for all unfixed characteristics, it cannot be distinguished whether an observed characteristic among exceptional long-lived persons is a cause or an adaptive change. Another reason for the limited success of centenarian studies in identifying factors (even fixed characteristics such as genotype) of importance for survival until extreme ages may also be the lack of an appropriate control group, as cohort-specific characteristics may confound the comparison between the centenarians and younger cohorts.

The interference from an observation from such cross-sectional studies (e.g., that a fixed characteristic like a gene variant is decreasing in frequency with age) is dependent on a stable population with little migration into or out of it—that is, no population stratification (Lewis and Brunner, 2004). Remote islands or other isolated populations will therefore often be very well suited for such cross-sectional age dependency studies (also because of better preserved linkage disequilibrium between markers and putative causal mutations). Immigrant countries like the United States and Australia are less suited due to greater genetic and environmental heterogeneity. The case-control studies may also suffer from publication bias because many of the reported associations are often found in subgroups (defined by age or sex) and usually without an a priori hypothesis of which polymorphism is the advantageous one. Consequently, most associations fail to be replicated in independent studies (Ioannidis, 2003). Large longitudinal studies in genetically rather homogeneous populations, such as that in Denmark, avoid many of these biases.

THE DANISH SURVEYS OF TWINS AND THE OLDEST-OLD

The Longitudinal Study of Aging Danish Twins (LSADT) and the Danish 1905 Cohort Study are longitudinal surveys aimed at understanding the determinants of healthy aging and exceptional survival. They have been conducted in parallel and are both nationwide in scope and population based.

Longitudinal Study of Aging Danish Twins

LSADT began in 1995 with the assessment of members of same-sex twin pairs born in Denmark prior to 1920 (i.e., at least 75 years old at the beginning of 1995). The surviving members of the initial cohort were followed up every two years in 1997, 1999, 2001, 2003, and 2005. Additional cohorts were added to the 1997, 1999, and 2001 assessments and subsequently followed at two-year intervals in 2003 and 2005. Twin pairs, in which both were alive and born between 1920 and 1923 (i.e., at least 73 years old at the beginning of 1997), were added in 1997; twins born between 1921 and 1928 (i.e., at least 70 years old at the beginning of 1999) were added in 1999; and twins born between 1929 and 1930 (i.e., at least 70 years old at the beginning of 2001) were added in 2001. An overview of the LSADT cohort-sequential design is given in Figure 1-1. These studies comprised a home-based two-hour multidimensional interview focusing on health issues, assessment of functional and cognitive abilities, and DNA sampling.

FIGURE 1-1. Overview of the Longitudinal Study of Aging Danish Twins (LSADT).

FIGURE 1-1

Overview of the Longitudinal Study of Aging Danish Twins (LSADT). Participation rates are given in percentage in the white boxes.

Biological Indicators in LSADT

Full Blood Samples on Selected Individuals in 1997

Shortly after the interview in 1997, a trained phlebotomist collected a blood sample from all same-sex twin pairs in which both members were alive and willing to participate. In all a total of 689 subjects donated a blood sample, including 290 same-sex twin pairs and 109 pairs from whom we received blood from only one twin. The samples were sent by ordinary mail from all over Denmark and each sample, within two days of receipt, was separated into cells and plasma layers by centrifugation.

DNA Samples on All Nonproxy Participants from 1999

For the 1999 survey, all participants (except those who participated by proxy—spouse, children, or caretaker) were asked to provide a DNA sample by means of a finger prick blood spot sample. The blood was stored on filter paper, and the procedure is described in detail in Christensen (2000, pp. 56-60). The blood spot card was the preferred DNA collection method; however, the participants could provide biological material by using cheek brushes if they disliked the idea of a finger prick. A total of 90 percent of the nonproxy participants provided a biological sample, of which 91 percent were blood spots. The same procedure was repeated in all subsequent waves (2001, 2003, and 2005) in order to study changes over time and to obtain more DNA. As seen in Figure 1-1, the inclusion of blood spots/cheek brushes did not affect the participation rates in a negative way. Furthermore, more demanding measurements, such as grip strength and pulmonary functioning, were also included for the first time in 1999, still with no apparent influence on the participation rate.

Photographs of All Participants from 2001

When assessing health, physicians traditionally compare perceived and chronological age. Among adults, “looking old for your age” is often interpreted as an indicator of poor health and is used in clinical practice as a biomarker of aging. The sparse data available on the relationship between perceived age and survival indicate an inverse association (Borkan, Bachman, and Norris, 1982). Starting with the 2001 LSADT survey (and continuing in 2003 and 2005), we obtained a digital photograph of each subject in order to explore this association further.

The Danish 1905 Cohort Study

We surveyed the complete Danish 1905 cohort in 1998 when 3,600 individuals from this cohort were still alive and 2,262 participated in the survey that included an interview, physical and cognitive tests, as well as collection of biological material. Subsequently we made three in-person follow-up studies of the participating survivors in 2001, 2003, and 2005 (Figure 1-2) establishing the first large-scale centenarian study in which both environmental and genetic information is available from a noncentenarian control group from the same birth cohort. The participants were age 92-93 when they entered the study, which is only “halfway to becoming a centenarian” in terms of selection: only about 1 in 20 of the 1905 cohort made it to age 92-93, and only about 1 in 20 of these survivors at age 92-93 lived to celebrate their 100th birthday. Hence, the Danish 1905 cohort provides a powerful and unique opportunity for studying the determinants of survival in the second leg of the long trip to becoming a centenarian.

FIGURE 1-2. Overview of the Danish 1905 Cohort Study.

FIGURE 1-2

Overview of the Danish 1905 Cohort Study. All nonproxy participants were eligible for blood spots/cheek brushes at all waves. Participation rates are given in percentage in the white boxes.

DNA Samples from All Participants in All Waves

All nonproxy participants, in all waves, were asked to provide blood spots or cheek brushes, and approximately 90 percent of the nonproxy respondents agreed. Of these, around 80 percent agreed to blood spots and 20 percent to cheek brushes.

Full Blood Samples from All Participants in 2005

To obtain comparable data to a Danish centenarian study conducted 10 years earlier, that of the 1895-1896 cohort (Andersen-Ranberg, Schroll, and Jeune, 2001), full blood samples were taken in 2005 (shortly after the interview) from all participants who consented. Also to enhance comparability to the previous Danish study, we recontacted previous nonresponders (a group comprised of both nonresponders and study dropouts) and were able to add 90 additional subjects to our study (Figure 1-2). The participation in 2005 of the former nonresponders in the 1905 cohort is likely to be influenced by the common misconception among the elderly (and their relatives) that, whereas studies of centenarians are interesting and important for science, this is not the case for studies of 93-year-olds.

How Many Genotypes Can Be Obtained by Blood Spots and Cheek Brushes?

Dried blood spot specimens are made by carefully placing a few drops of blood, freshly drawn by finger prick with a lancet, onto specially manufactured absorbent specimen collection (filter) paper. Cheek brushes collect cells by gently rubbing the inside of the cheek (Christensen, 2000). For both DNA sampling methods, the amount of collected cells and hence the amount of isolated DNA are very variable, especially in the elderly. Using ordinary methods based on polymerase chain reactions, we have until now genotyped the entire 1905 cohort for 20 different single-nucleotide polymorphisms (SNPs, microsatellites and smaller insertions/deletions). We estimate that it is possible to genotype at least 2,000 SNPs from a fully filled blood spot card, whereas each cheek brush is estimated to provide DNA for 200 genotypes if the analyses are carefully optimized for the use of a low DNA content. However, the development of whole genome amplification techniques may perhaps in the future make it feasible to conduct large genetic epidemiological studies using small DNA sources, such as blood spots and cheek brushes. Several reports have shown that whole genome amplification methods are indeed applicable for low DNA yield samples; however, a higher rate of genotyping errors has been reported when using these low DNA yield samples, possibly due to allele dropout. The use of these techniques requires increased attention to genotyping quality control and caution when interpreting results (Silander et al., 2005).

IMPORTANT FINDINGS DUE TO INCLUSION OF BIOLOGICAL INDICATORS IN THE DANISH SURVEYS

Studies of Candidate Genes for Healthy Aging Using the Full Surveys

A very large number of candidate genes have been investigated for putative associations with human aging and longevity (Christensen, Johnson, and Vaupel, 2006). Based on the promising results from animal studies, genes from the insulin/IGF-1 pathway, stress response genes (heat and oxidative stress), and genes influencing mitochondrial functioning have been obvious candidates. So have immune system–regulating genes (e.g., interleukines), as it is a biological system with a sufficiently broad spectrum of functions and hence likely to be associated with aging and survival (Finch and Crimmins, 2004; Franceschi et al., 2000). Other candidates have been selected based on known downstream functioning and association with early or late-in-life pathology and diseases, such as metabolic genes (e.g., lipoproteins), iron metabolism–regulating genes (e.g., HFE), genes affecting the cardiovascular system (e.g., ACE), and tumor suppressor genes called p53. A distinct group of candidates are the genes for the premature aging syndromes, such as the DNA repair helicase Werner syndrome (Yu et al., 1996) in which “leaky mutations” are considered—that is, the syndrome-causing gene is investigated to test if “milder” common mutations could be associated with aging and survival generally.

One candidate gene, microsomal triglyceride transfer protein (MTP) was identified after a genome-wide linkage scan in long-lived families had provided evidence for a locus for longevity on chromosome 4 (Puca et al., 2001; Geesaman et al., 2003). This finding received considerable coverage in both the scientific and the lay communities as a promising “longevity gene.” As illustrated by the examples below, one of the most important aspects of having biological material in surveys like the Danish is that they can readily provide valid and powerful statistical tests of such findings on putative determinants of healthy aging or exceptional survival.

Microsomal Triglyceride Transfer Protein

A genome-wide linkage scan in long-lived families provided evidence for a locus for longevity at chromosome 4 near microsatellite marker D4S1564 (Puca et al., 2001; Reed, Dick, Uniacke, Foroud, and Nichols, 2004), although a follow-up study in a French population did not confirm this observation (Geesaman et al., 2003). Fine mapping of the region identified MTP as the gene most probably responsible for the chromosome 4 linkage peak (Geesaman et al., 2003). The two SNPs found to account for the majority of the variation at the locus are rs2866164—an SNP in complete linkage disequilibrium with rs1800591 (-493G/T), an MTP promoter mutation—and MTP Q/H—a semiconservative mutation in exon 3 of MTP (glutamine to histidine at aminoacid 95) (Geesaman et al., 2003). The rs2866164-G allele and the haplotype composed of rs2866164-G/MTP-Q9 had a significantly lower frequency in long-lived individuals compared with a group of younger controls in the initial study.

MTP is necessary for the assembly of apolipoprotein B-containing lipoproteins in the liver and small intestine (Swift et al., 2003) and lack of the protein leads to abetalipoproteinemia, a rare genetic disorder that is characterized by an inability to produce chylomicrons and very low-density lipoproteins. MTP is a good candidate for a gene involved in human longevity. Its functions and critical position in lipoprotein have made it the basis for a new generation of lipid-lowering drugs that have reached trials in animal models (Wierzbicki, 2003). However, we genotyped the 1,905 cohort, and even after seven years of follow-up with more than 80 percent of the population deceased, we were not able to detect any association between MTP and survival (Bathum et al., 2005) (Figure 1-3). Furthermore, a large case-control study of German centenarians (Nebel et al., 2005) and an association study in Dutch nonagenarians failed to confirm any association of MTP variants with longevity. Finally, in 2006, a meta-analysis of all published studies, analyzing data from 4,915 long-lived cases older than 85 years and 3,002 younger controls, resulted in an odds ratio of 0.93 (95% CI, 0.77-1.14), thus confirming the lack of association between MTP and longevity and illustrating the necessity of replication studies in identifying genes that affect human longevity (Beekman et al., 2006).

FIGURE 1-3. Kaplan-Meier survival estimates in the 1905 Cohort Study (N = 1,651) divided into the three haplotype groups for microsomal triglyceride transfer protein (MTP).

FIGURE 1-3

Kaplan-Meier survival estimates in the 1905 Cohort Study (N = 1,651) divided into the three haplotype groups for microsomal triglyceride transfer protein (MTP). Haplotype 2 is the suggested risk haplotype composed of the rs2866164-G and Q95 allele and (more...)

Apolipoprotein E

Apolipoprotein E plays an important role in lipoprotein metabolism, and the protein is found in three common variants (ApoE-2, ApoE-3, and ApoE-4) that were known at the protein level before the gene was cloned. The ApoE-4 variant has been consistently associated with a moderately increased risk of both cardiovascular diseases and Alzheimer disease, while the ApoE-2 is protective (Corder et al., 1996; Panza et al., 2004; Lewis and Brunner, 2004). Not only has ApoE-4 been shown to be a risk factor for cardiovascular diseases and Alzheimer disease per se, but also several studies have found that the ApoE-4 carrier is more susceptible to some environmental exposures, making this an example of a gene-environment interaction.

For example, an increased risk of chronic brain injury after head trauma has been observed for individuals who carry the ApoE-4 gene variant, compared with non-ApoE-4 carriers (Jordan et al., 1997). ApoE-4 also seems to modulate the effect of other risk factors for cognitive decline. Individuals with ApoE-4 in combination with atherosclerosis, peripheral vascular disease, or diabetes mellitus have a substantially higher risk of cognitive decline than those without ApoE-4 (Haan, Shemanski, Jagust, Manolio, and Kuller, 1999).

Cross-sectional studies of allele frequency differences between age groups for ApoE have been remarkably consistent from study to study, in contrast to other candidate genes (Ewbank, 2004). The ApoE-4 frequency varies considerably among populations of younger adults (about 25 percent among Finns, 17-20 percent among Danes, and about 10 percent among French, Italians, and Japanese), but in all these populations the frequency among centenarians is about half the value of the younger adults (Figure 1-4). Although these changes in ApoE allele frequency with age are substantial, it is compatible with a scenario in which ApoE-2 carriers in adulthood who have an estimated average mortality risk that is only 4-12 percent lower than in ApoE-3 and ApoE-4 carriers have a risk that is only 10-14 percent higher throughout adulthood (Gerdes, Jeune, Ranberg, Nybo, and Vaupel, 2000), making ApoE a “frailty gene” rather than a “longevity gene.” The longitudinal Danish 1905 cohort showed that the ApoE genotype has an association with cognitive functioning and cognitive decline (Figure 1-5) in this age group, albeit of only borderline significance. However, when defining a well-functioning group, that is, those still alive, without interview by proxy or extreme decline in the Mini Mental State Examination at follow-up, there was a significant decrease in the frequency of ApoE-4 positive subjects from intake to first and second follow-up. So ApoE genotypes have a statistically significant effect on the probability of remaining a well-functioning nonagenarian (Bathum et al., 2005). While the impact on survival is minor, the difference in survival between ApoE-4 positive and negative subjects did become statistically significant in January 2006 after more than seven years of follow-up and more than 1,200 deaths in the cohort. Preliminary analyses suggest that, at intake at age 92-93, the participants (both ApoE-4 positive and ApoE-4 negative) have mortality rates below the population average, confirming that terminally ill individuals are overrepresented among the nonparticipants and the proxy-responders. As follow-up time increases, the difference in survival becomes clear, showing that ApoE-4 positive individuals have higher mortality rates than the overall population, whereas ApoE-4 negative individuals have lower mortality rates. This highlights the need for careful analyses of the nonparticipants. In Denmark such analyses can be carried out through linkage to national health registers.

FIGURE 1-4. The frequencies of apolipoprotein E alleles vary with age.

FIGURE 1-4

The frequencies of apolipoprotein E alleles vary with age. Frequencies of the three common ApoE alleles—ApoE-2, ApoE-3, and ApoE-4—are shown, taken from data in 13 published studies. Each line connects the frequencies in various age groups (more...)

FIGURE 1-5. Decline in cognitive performance in 1,551 nonagenarians, - E-4 carriers compared to non-E-4 carriers.

FIGURE 1-5

Decline in cognitive performance in 1,551 nonagenarians, - E-4 carriers compared to non-E-4 carriers. The genotype difference in rate of decline is borderline significant, p = 0.07.

Angiotensin I-Converting Enzyme (ACE)

The insertion/deletion polymorphism in intron 16 in the gene coding for angiotensin I-converting enzyme (ACE) is also a biologically plausible candidate for successful aging and longevity. This is because the genotype accounts for approximately half of the variance in the circulating ACE level and from the II (homozygous insertion) to the DD (homozygous deletion) genotype the presence of each D allele is associated with an additive effect on ACE activity (50 percent higher in the DD compared with the II genotype) (Rigat et al., 1990). The cleavage of angiotensin I by ACE produces the octapeptide angiotensin II, which is a potent vasoconstrictor. The association between ACE genotypes, in particular DD, and the occurrence of cardiovascular and renal diseases has therefore been thoroughly studied in the past decade. The results have been inconsistent, and, for ischemic heart disease, publication bias has been shown to be a likely explanation for the discrepancies (Keavney et al., 2000).

In recent years the ACE gene polymorphism has also been associated with the outcome of physical exercise, especially extreme endurance and performance. Presence of the II genotype has been found to be associated with improved performance (Montgomery et al., 1998; Williams et al., 2000; Gayagay et al., 1998; Myerson et al., 1999). However, the study subjects have mostly been from highly selected populations (e.g., elite athletes and military recruits) and the samples were small. The associations could not be confirmed in LSADT (Frederiksen et al., 2003), but a recent paper suggests an interaction between ACE genotype and mobility limitations among those age 70-79 who exercise (Kritchevsky et al., 2005). Again, no a priori hypothesis about existence of the interaction was found.

ACE genotype has been hypothesized to be associated with longevity since a German study found an increased frequency of the DD genotype in octogenarians (Luft, 1999), which was to some degree supported in LSADT data (Frederiksen et al., 2003). This finding could not, however, be confirmed in two large studies of centenarians and younger controls (Bladbjerg et al., 1999; Blanche, Cabanne, Sahbatou, and Thomas, 2001). ACE genotyping is currently being performed in the 1905 cohort.

Interleukin 6

Chronic low-grade inflammatory activity has been suggested to play a central role in aging processes, as it seems to be implicated in the pathology of several age-related diseases, consequently leading to increased mortality (Bruunsgaard, Pedersen, and Pedersen, 2001; Harris et al., 1999). The multifunctional cytokine interleukin 6 (IL-6) is one of the inflammatory markers central to low-grade inflammation. IL-6 is overexpressed in many of the stress conditions that are characteristic features of the aging process (Ershler and Keller, 2000; Bruunsgaard et al., 2001; Cohen, Pieper, Harris, Rao, and Currie, 1997).

It has been proposed that an increase in IL-6 is a risk factor for the development of age-related diseases, such as rheumatoid arthritis, osteoporosis, Alzheimer disease, cardiovascular disease, and type 2 diabetes (Pradhan, Manson, Rifai, Buring, and Ridker, 2001; Ershler and Keller, 2000; Licastro et al., 2000; Bonaccorso et al., 1998; Robak, Gladalska, Stepien, and Robak, 1998), as well as for functional decline (Barbieri et al., 2003; Ferrucci et al., 1999; Cohen et al., 1997) and mortality (Harris et al., 1999). However, Petersen and Pedersen (2005) suggest that IL-6 is a marker of metabolic syndrome rather than a cause and that IL-6 is actually a “myokine” released by contracting skeletal muscle fibers and disseminates the beneficial effects of exercise to other organs (Petersen and Pedersen, 2005). Twin studies have shown that interindividual variations in IL-6 expression have a substantial genetic component (de Maat et al., 2004; de Craen et al., 2005), and these differences may be a result of base variations located in the promoter region of the IL-6 gene.

Three single-point polymorphisms (-597G/A, -572G/C, and -174G/ C) and an AT-stretch polymorphism (-373(A)n(T)m) have been identified in the IL-6 promoter (Georges et al., 2001; Terry, Loukaci, and Green, 2000; Fishman et al., 1998). The potential significance of the -174G/C polymorphism for both the IL-6 level and hence disease susceptibility and mortality risk has been investigated in a large number of studies involving a variety of diseases. The results have, however, been conflicting (Nauck et al., 2002; Humphries, Luong, Ogg, Hawe, and Miller, 2001; Rauramaa et al., 2000).

Recent independent findings of a modest, but significant, increase in the frequency of interleukin 6 -174GG homozygotes both in Finland (Hume, 2005) and in our Danish surveys (Christiansen, Bathum, Andersen-Ranberg, Jeune, and Christensen, 2004) suggest that this genotype is advantageous for longevity. However, a very recent study in Italy (Ravaglia et al., 2005) was unable to confirm this.

Paraoxonase 1

Stress-response genes, such as paraoxonase, mediate protection against oxidative damage and are therefore excellent candidates for studies of successful aging and longevity. Paraoxonase 1 (PON1) is found to be associated with high-density lipoproteins (HDLs) in plasma. One physiological function of the enzyme appears to be the elimination of toxic oxidized lipids in lipoproteins, whereby it confers protection against atherosclerosis and coronary heart disease. Recent studies have demonstrated a progressive decrease of PON1 activity with age, which may be related to oxidative stress conditions developing with increased age. In general, there is a 40-fold variation in PON1 activity among individuals, which is partly explained by genetic variations in both the coding region and the promoter of the gene, although environmental influences, for example, tobacco smoking, also exert an effect (Costa, Vitalone, Cole, and Furlong, 2005; Seres, Paragh, Deschene, Fulop, and Khalil, 2004).

Investigating the influence of the two coding sequence polymorphisms, L55M and Q192R, on the efficacy of protection of low-density lipoproteins (LDL) lipid oxidation, Mackness et al. (1998) demonstrated that individuals with the PON1 192QQ/55MM genotype were most effective in protecting LDL against lipid oxidation, whereas PON1 192RR/55LL homozygotes were least effective. A meta-analysis of 43 studies involving 11,212 cases and 12,786 controls suggested a weak overall association between 192Q/R and coronary heart disease.

We explored the association between the PON1 192Q/R polymorphism both in LSADT and in the 1905 cohort. Using the longitudinal study design, we first found that 192RR homozygotes had a significantly poorer survival compared with QQ homozygotes in LSADT. We extended this study by testing an independent sample of 541 individuals from the 1905 cohort and confirmed the initial findings. In both samples the effect was most pronounced in women, suggesting that PON1 192RR homozygosity is associated with increased mortality in women in the second half of life (Christiansen et al., 2004). We just completed the PON1 genotyping of the whole 1905 cohort, and the seven years of follow-up (Figure 1-6) further corroborated the initial finding. One of the biggest challenges to genetic association studies are false positive findings (Ioannidis, 2003) and one of the major strengths of including biological material in both LSADT and the Danish 1905 Cohort Study has been that independent replication can be performed.

FIGURE 1-6. Kaplan-Meier survival estimates in the combined group of participants from the 1905 cohort (N = 1,265) divided into the three genotype groups for PON1 192.

FIGURE 1-6

Kaplan-Meier survival estimates in the combined group of participants from the 1905 cohort (N = 1,265) divided into the three genotype groups for PON1 192. The analysis time unit is days (p < 0.05).

STUDIES OF BIOLOGICAL INDICATORS FOR HEALTHY AGING USING SELECTED SUBGROUPS IN THE SURVEYS

Biological indicators from elderly twin pairs provide a number of unique research opportunities, and therefore we included a full blood sample in the LSADT 1997 wave for the “intact twin pairs” in the survey. Among the key uses of the samples were to investigate telomere length, X inactivation, and gene expression.

Telomere Length

The consistent findings of a negative correlation between telomere length and the replicative potential of cultured cells as well as a decreasing telomere length in a number of different tissues in humans with age (Cherif, Tarry, Ozanne, and Hales, 2003) have led to the suggestion that telomeres play a role in cellular aging in vivo, and ultimately even in organismal aging. A possible association between telomere length and mortality late in life in humans was indicated by Cawthon, Smith, O'Brien, Sivatchenko, and Kerber (2003), who measured telomere length on blood samples drawn about 20 years ago from 147 healthy individuals (ages 60-97). They found that, corrected for age, individuals with shorter telomeres had a poorer survival than those with longer telomeres. However, a larger independent study failed to confirm this finding and showed that telomere measurements are fluctuating over time in blood cells (Martin-Ruiz, Gussekloo, van Heemst, von Zglinicki, and Westendorp, 2005). We analyzed the 1997 LSADT samples (Bischoff et al., 2006) and also found that telomere length is not a predictor for remaining life span, once age is controlled for. The sample provided a unique opportunity to do intrapair comparisons among twins, in which the effect of the genetic factor is controlled for (100 percent for monozygotic and 50 percent for dizygotic pairs). Also, this approach did not reveal evidence for an association between telomere length and survival among the elderly. Currently we are reexamining the blood samples using new and improved methods of measurement of telomere length to test if the lack of association could be due to methodological problems.

X Inactivation

Although females have two X chromosomes, only one of them is active in each cell; the other is inactivated in early embryonic life and stays so throughout life (a phenomenon called X inactivation). The random X inactivation makes females mosaics for two cell populations, usually with an approximate 1:1 distribution. Males have only one cell line because they receive only one X chromosome (from their mother) and one Y chromosome (from their father).

Cross-sectional studies have shown that, among younger females, it is very rare to have a skewed distribution of X inactivation, while for females over age 60, more than a third have a predominance of one of the cell lines in their blood, and among centenarian females the majority has a predominant cell line (Christensen et al., 2000). The skewing of this distribution in peripheral blood could be due to either depletion of hematopoietic stem cells followed by random differentiation or selection processes based on X-linked genetic factors.

It has been difficult to make animal experiments or human observations that could critically test the random versus selection hypotheses. The female twins who provided full blood in the LSADT survey, however, provided an excellent opportunity. On one hand, if the often observed predominance of one of the two cell lines in peripheral blood in elderly females was determined by a stochastic process with no selection, then one would expect little correlation in the X inactivation patterns between two monozygotic co-twins. A selection process based on X-linked genetic factors, on the other hand, would create a tendency for the same cell line to become predominant in two monozygotic co-twins. Our studies indicate that the peripheral blood cells from LSADT monozygotic female twins show a strong tendency for the same cell line to become predominant in two co-twins, which suggests that X-linked genetic factors influence human hematopoietic stem cell kinetics and potentially organismal survival. The fact that females have two cell lines with different potentials could be one of the reasons why women live longer than men (Christensen et al., 2000).

Gene Expression

Microarray analyses in animal and humans of gene expression and changes with age are still few, but many believe that a better understanding of gene expression will lead to insight into the mechanisms of aging. We used LSADT to identify three pairs of healthy 80+ year-old female monozygotic and dizygotic twins (six pairs in total), and even in this age group on this small sample we were able to detect a substantial genetic component to the variation in gene expression (Tan, Kruse, and Christensen, 2006).

SURVEYS AS A SCREENING INSTRUMENT TO IDENTIFY CASES FOR ANCILLARY STUDIES

The twin surveys have also been used as a screening instrument for a variety of conditions involving a two-stage study design with initial screening through the LSADT assessment and a follow-up assessment by a specialist. This has been done for movement disorders, in which the LSADT participants performed a so-called Archimedes spiral test—a drawing test to disclose individuals with movement disorders, such as essential tremor and Parkinson's disease. Individuals whose spiral test was positive were recontacted, and 119 twin pairs were interviewed and examined by a movement disorder specialist. The study revealed a nearly complete concordance for monozygotic twins for essential tremor and a substantial lower concordance rate for dizygotic twins, indicating a high heritability for essential tremor, and hence a good candidate for a phenotype to be used in linkage studies (Lorenz et al., 2004).

In Europe there is also an extensive collaboration between the major twin registers (Skytthe et al., 2003), which together have information on more than half a million twin pairs. A collaborative effort including LSADT has been used to identify particularly informative pairs, for example, dizygotic twin pairs extremely concordant or discordant for continuous traits that are especially powerful in genetic linkage studies (Risch and Zhang, 1995). Currently studies of twin pairs extremely concordant or discordant for body mass index are being conducted.

An outstanding example of the potentials of exceptional twin pairs is the cloning of the genes for Van der Woude syndrome (a syndrome with cleft lip and palate and lip pits). Family studies have for many years indicated that this monogenic disease is caused by a gene on chromosome 1, but despite large collaborative efforts, identification of the actual gene has failed. The identification of one monozygotic twin pair discordant for the disease led to the identification. The discordance could be due to either reduced penetrance or alternatively a somatic mutation after a split of a two-cell-stage zygote. A sequencing of the candidate region revealed one genetic difference between the two monozygotic twins in the gene coding for interferon-regulatory factor 6 (IRF-6). With this information in hand, the previously identified families were reinvestigated. It became clear that it was indeed mutations in this gene, IRF-6, that was causing Van der Woude syndrome (Kondo et al., 2002).

A NEW BIOLOGICAL INDICATOR OF AGING: PERCEIVED AGE

Modern medicine includes an increasing number of physiological parameters, biomarkers, and other “objective” measures through body fluids and increasingly sophisticated imaging tools. However, when assessing health, physicians in many countries traditionally still compare perceived and chronological age and record this in the patient's record. Among adults, “looking old for your age” is often interpreted as an indicator of poor health. Hence in the clinic, perceived age is used as a “biomarker of aging,” yet little validation of this biomarker has been performed. The sparse data available on the relation between perceived age and survival indicate an inverse association (Christensen et al., 2004). It is not known whether looking old for your age is primarily a result of lifestyle and other environmental factors or whether genetic factors play an important role.

We used the 2001 LSADT survey to test this: 91 percent of cognitively intact participants agreed to have their picture taken (digital camera, 0.6 meter distance, neutral background, if possible). For a total of 387 same-sex twin pairs we obtained high-quality pictures of both twins (Figure 1-7). We engaged 20 female nurses (ages 25-46) to estimate the twins' ages. The nurses were not informed beforehand about the age range of the twin pairs, and they assessed, based on the digital pictures, the ages of the first-born and the second-born twins on two different days. The mean of the nurses' age estimates for each twin was used as the twin's perceived age. The reliability of the mean age rating was estimated at .94 from a one-way analysis of variance. The correlation between real age and perceived age was 0.4 (p < 0.001), and the nurses' estimates regressed towards a mean of 77 years.

FIGURE 1-7. Longitudinal studies have demonstrated that perceived age is a predictor of survival among the elderly and that it is associated with a number of environmental exposures.

FIGURE 1-7

Longitudinal studies have demonstrated that perceived age is a predictor of survival among the elderly and that it is associated with a number of environmental exposures.

The correlation between the nurses' estimates for monozygotic twins (r ≈ 0.6, p < 0.01) is about twice the correlation for dizygotic twins (r ≈ 0.3, p < 0.01). This indicates an effect of additive genetic factors influencing perceived age. Biometrical models confirmed that the twin similarity is best explained by a model including additive genetic factors and nonfamily environment, and that the heritability (i.e., the proportion of the variance explained by genetic factors) of perceived age is about 60 percent, with no sex or age differences.

By January 2003, nearly two years after having been photographed, one of the pictured twins in 49 pairs had died. Among these 49 pairs, the longer surviving twin was rated as looking younger on average than his or her co-twin (mean of 1.15 years, SD = 3.63, t = 2.22 on 48 df, p < 0.02). This significant difference, however, owed entirely to those twin pairs who were perceived to be discrepant in age. Among the 26 pairs for which perceived age differed by two or more years, the oldest looking twin died first in 19 (73 percent) cases (p < 0.01), verifying that perceived age is associated with mortality. We are currently making a follow-up study of five years' survival, and the preliminary analyses strongly support the initial findings (also using raters of different age, gender, and education).

We have furthermore studied the determinants of perceived age and showed that when age and gender are controlled for factors like smoking, socioeconomic status, body mass index, marital status, and depression symptomatology, all affect perceived age in the expected direction (Rexbye et al., 2006). We did follow-up pictures in 2003 and 2005. We also made high-quality pictures of 100 selected pairs, including facial imprint of the facial structure, to shed light on which immediate and distant factors are the basis for perceived age and its association with health and survival.

OTHER BIOMARKERS

In the Danish surveys of twins and the oldest-old, we have used a number of validated biomarkers, such as grip strength, spirometry, and walk test, as well as molecular markers based on blood samples and cheek swabs. Here we report on what we consider the key findings, but the rapid development within the biomarker field is likely to provide new (combinations of) biomarkers, in particular from the neuroendocrine and the immune system (Goldman et al., 2006).

Finally, the ever-increasing possibilities in imaging and especially neuroimaging could yield major breakthroughs in the biomarker research in the coming years (Winterer et al., 2005), although neuroimaging is not likely to be included in large field surveys with 100+ interviewers in any foreseeable future.

Another promising research direction is the study of gene-environment interaction in aging, and how environmental factors, including social environment, can modify the gene effects and the biomarkers (Ryff and Singer, 2005).

CONCLUSION

The inclusion of biological markers in the Danish surveys of twins and the oldest-old has provided great leverage for these studies by making them highly interdisciplinary in nature and very productive in terms of results. Some 70 papers based on LSADT and the Danish 1905 Cohort Study have been published in international peer-reviewed journals, and half of the papers include biological indicators, even though their inclusion was not started until some years after the study had been launched. LSADT and the Danish 1905 Cohort Study aim at identifying the determinants of healthy aging and longevity. Several studies—including the traditional twin analyses in LSADT—have shown that there is indeed a genetic influence on human lifespan, longevity, and cognitive and physical functioning among the oldest, providing support for the search for candidate genes involved in aging (Hjelmborg et al., 2006; Frederiksen and Christensen, 2003; McGue and Christensen, 2001, 2002; Herskind et al., 1996; Hjelmborg et al., 2006).

We have so far examined several candidate genes for healthy aging and longevity and corroborated some findings and refuted others in large-scale studies with substantial power. In this area, our future studies will continue to focus on promising candidate genes identified in other studies or through biological insight. We intend to use haplotype-based analyses to thoroughly investigate the relevance of each candidate gene, including the regulatory areas surrounding it. Furthermore we intend to broaden our attention toward regulatory changes in the noncoding DNA, which could be functionally important. Also, we will extend our testing of biologically plausible gene-environment interaction (a research area in which we expected major findings five years ago but which has yielded few up to this point). Our studies so far have shown that small amounts of DNA from population-based studies are sufficient to perform simple genotypings on a large scale: at least 2,000 genotypings can be performed on a fully filled blood spot card. We expect that new techniques will evolve that may enable us to further widen the use of the collected material. Using the surveys as a screening instrument for ancillary studies has proved to be particular fruitful, and we intend to further explore this option. We feel that our most important research is a result of extending our surveys beyond the traditional items, and hence we feel that supplementing our epidemiological surveys with the collection of biological markers and indicators has brought us new insight and has helped us to think outside the box in our research field of healthy aging and longevity.

ACKNOWLEDGMENTS

This research was supported by a grant from the Danish National Research Foundation and the National Institute on Aging (P01-AG08761). Karen Andersen-Ranberg, Henrik Frederiksen, Frans Bødker, and Gitte Bay Christensen coordinated the surveys. Matt McGue, Bernard Jeune, and James W. Vaupel played a central role in planning and analyzing these studies.

REFERENCES

  1. Andersen-Ranberg K, Schroll M, Jeune B. Healthy centenarians do not exist, but autonomous do: A population-based study of morbidity among Danish centenarians. Journal of the American Geriatrics Society. 2001;49:900–908. [PubMed: 11527481]
  2. Barbieri M, Ferrucci L, Ragno E, Corsi A, Bandinelli S, Bonafe M, Olivieri F, Giovagnetti S, Franceschi C, Guralnik JM, Paolisso G. Chronic inflammation and the effect of IGF-I on muscle strength and power in older persons. American Journal of Physiology, Endocrinology, and Metabolism. 2003;284:E481–E487. [PubMed: 12419777]
  3. Bathum L, Christiansen L, Tan Q, Vaupel J, Jeune B, Christensen K. No evidence for an association between extreme longevity and microsomal transfer protein polymorphisms in a longitudinal study of 1651 nonagenarians. European Journal of Human Genetics. 2005;13:1154–1158. [PubMed: 16015282]
  4. Beekman M, Blauw GJ, Houwing-Duistermaat JJ, Brandt BW, Westendorp RG, Slagboom PE. Chromosome 4q25, microsomal transfer protein gene, and human longevity: novel data and a meta-analysis of association studies. Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 2006;61(4):355–362. [PubMed: 16611701]
  5. Bischoff C, Petersen HC, Graakjaer J, Andersen-Ranberg K, Vaupel JW, Bohr VA, Kølvraa S, Christensen K. No association between telomere length and survival among the elderly and oldest-old. Epidemiology. 2006;17(2):90–194. [PubMed: 16477260]
  6. Bladbjerg EM, Andersen-Ranberg K, de Maat MP, Kristensen SR, Jeune B, Gram J, Jespersen J. Longevity is independent of common variations in genes associated with cardiovascular risk. Journal of Thrombosis and Haemostasis. 1999;82:1100–1105. [PubMed: 10494771]
  7. Blanche H, Cabanne L, Sahbatou M, Thomas G. A study of French centenarians: Are ACE and APOE associated with longevity? Comptes Fendus de l'Académie des Sciences Série III. 2001;324:129–135. [PubMed: 11280044]
  8. Bonaccorso S, Lin A, Song C, Verkerk R, Kenis G, Bosmans E, Scharpe S, Vandewoude M, Dossche A, Maes M. Serotonin-immune interactions in elderly volunteers and in patients with Alzheimer's disease (DAT): Lower plasma tryptophan availability to the brain in the elderly and increased serum interleukin-6 in DAT. Aging (Milano) 1998;10:316–323. [PubMed: 9825023]
  9. Borkan GA, Bachman SS, Norris AH. Comparison of visually estimated age with physiologically predicted age as indicators of rates of aging. Social Science and Medicine. 1982;16:197–204. [PubMed: 7100970]
  10. Bruunsgaard H, Pedersen M, Pedersen BK. Aging and proinflammatory cytokines. Current Opinion in Hematology. 2001;8:131–136. [PubMed: 11303144]
  11. Cawthon RM, Smith KR, O'Brien E, Sivatchenko A, Kerber RA. Association between telomere length in blood and mortality in people aged 60 years or older. The Lancet. 2003;361:393–395. [PubMed: 12573379]
  12. Cherif H, Tarry JL, Ozanne SE, Hales CN. Ageing and telomeres: A study into organ- and gender-specific telomere shortening. Nucleic Acids Research. 2003;31:1576–1583. [PMC free article: PMC149817] [PubMed: 12595567]
  13. Christensen K. National Research Council; Commission on Behavioral and Social Sciences and Education. Biological material in household surveys: The interface between epidemiology and genetics. In: Committee on PopulationFinch CE, Vaupel JW, Kinsella K, editors. Cells and surveys: Should biological measures be included in social science research? Washington, DC: National Academy Press; 2000. pp. 42–63.
  14. Christensen K, Iachina M, Rexbye H, Tomassini C, Frederiksen H, McGue M, Vaupel JW. Looking old for your age: Genetics and mortality. Epidemiology. 2004;15:251–252. [PubMed: 15127920]
  15. Christensen K, Johnson TE, Vaupel JW. The quest for genetic determinants of human longevity: Challenges and insights. Nature Reviews Genetics. 2006;7(6):436–448. [PMC free article: PMC2726954] [PubMed: 16708071]
  16. Christensen K, Kristiansen M, Hagen-Larsen H, Skytthe A, Bathum L, Jeune B, Andersen-Ranberg K, Vaupel JW, Ørstavik KH. X-linked genetic factors regulate hematopoietic stem-cell kinetics in females. Blood. 2000;95:2449–2451. [PubMed: 10733522]
  17. Christiansen L, Bathum L, Andersen-Ranberg K, Jeune B, Christensen K. Modest implication of interleukin-6 promoter polymorphisms in longevity. Mechanisms of Ageing and Development. 2004;125(5):391–395. [PubMed: 15130757]
  18. Cohen HJ, Pieper CF, Harris T, Rao KM, Currie MS. The association of plasma IL-6 levels with functional disability in community-dwelling elderly. Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 1997;52:M201–M208. [PubMed: 9224431]
  19. Corder EH, Lannfelt L, Viitanen M, Corder LS, Manton KG, Winblad B, Basun H. Apolipoprotein E genotype determines survival in the oldest old (85 years or older) who have good cognition. Archives of Neurology. 1996;53:418–422. [PubMed: 8624216]
  20. Costa LG, Vitalone A, Cole TB, Furlong CE. Modulation of paraoxonase (PON1) activity. Biochemical Pharmacology. 2005;69(4):541–550. [PubMed: 15670573]
  21. de Craen AJ. Genetic influence of innate immunity: An extended twin study. Genes and Immunity. 2005;6:167–170. [PubMed: 15674372]
  22. de Maat MP, Bladbjerg EM, Hjelmborg JB, Bathum L, Jespersen J, Christensen K. Genetic influence on inflammation variables in the elderly. Arteriosclerosis, Thrombosis, and Vascular Biology. 2004;24:2168–2173. [PubMed: 15345506]
  23. Ershler WB, Keller ET. Age-associated increased interleukin-6 gene expression, late-life diseases, and frailty. Annual Review of Medicine. 2000;51:245–270. [PubMed: 10774463]
  24. Ewbank DC. The APOE gene and differences in life expectancy in Europe. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 2004;50:B16–B20. [PubMed: 14718482]
  25. Ferrucci L, Harris TB, Guralnik JM, Tracy RP, Corti MC, Cohen HJ, Penninx B, Pahor M, Wallace R, Havlik RJ. Serum IL-6 level and the development of disability in older persons. Journal of the American Geriatrics Society. 1999;47:639–646. [PubMed: 10366160]
  26. Finch CE, Crimmins EM. Inflammatory exposure and historical changes in human life-spans. Science. 2004;305:1736–1739. [PubMed: 15375259]
  27. Fishman D, Faulds G, Jeffery R, Mohamed-Ali V, Yudkin JS, Humphries S, Woo P. The effect of novel polymorphisms in the interleukin-6 (IL-6) gene on IL-6 transcription and plasma IL-6 levels, and an association with systemic-onset juvenile chronic arthritis. Journal of Clinical Investigation. 1998;102:1369–1376. [PMC free article: PMC508984] [PubMed: 9769329]
  28. Franceschi C, Bonafe M, Valensin S, Olivieri F, De Luca M, Ottaviani E, de Benedictis G. Infamm-aging. An evolutionary perspective on immunosenescence. Annals of the New York Academy of Sciences. 2000;908:244–254. [PubMed: 10911963]
  29. Frederiksen H, Christensen K. The influence of genetic factors on physical functioning and exercise in second half of life. Scandinavian Journal of Medicine and Science in Sports. 2003;13(1):9–18. [PubMed: 12535312]
  30. Frederiksen H, Gaist D, Bathum L, Andersen K, McGue M, Vaupel JW, Christensen K. Angiotensin I-converting enzyme (ACE) gene polymorphism in relation to physical performance, cognition, and survival: A follow-up study of elderly Danish twins. Annals of Epidemiology. 2003;13:57–65. [PubMed: 12547486]
  31. Gayagay G, Yu B, Hambly B, Boston T, Hahn A, Celermajer DS, Trent RJ. Elite endurance athletes and the ACE I allele: The role of genes in athletic performance. American Journal of Human Genetics. 1998;103:48–50. [PubMed: 9737775]
  32. Geesaman BJ, Benson E, Brewster SJ, Kunkel LM, Blanche H, Thomas G, Perls TT, Daly MJ, Puca AA. Haplotype-based identification of a microsomal transfer protein marker associated with the human lifespan. Proceedings of the National Academy of Sciences, USA. 2003;100:14115–14120. [PMC free article: PMC283555] [PubMed: 14615589]
  33. Georges JL, Loukaci V, Poirier O, Evans A, Luc G, Arveiler D, Ruidavets JB, Cambien F, Tiret L. Interleukin-6 gene polymorphisms and susceptibility to myocardial infarction: The ECTIM study. (Etude Cas-Temoin de l'Infarctus du Myocarde) International Journal of Molecular Medicine. 2001;79:300–305. [PubMed: 11485024]
  34. Gerdes LU, Jeune B, Ranberg KA, Nybo H, Vaupel JW. Estimation of apolipoprotein E genotype-specific relative mortality risks from the distribution of genotypes in centenarians and middle-aged men: Apolipoprotein E gene is a frailty gene, not a longevity gene. Genetic Epidemiology. 2000;19:202–210. [PubMed: 11015124]
  35. Goldman N, Turra CM, Glei DA, Seplaki CL, Lin Y, Weinstein M. Predicting mortality from clinical and nonclinical biomarkers. Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 2006;61A(10):1070–1074. [PubMed: 17077201]
  36. Haan MN, Shemanski L, Jagust WJ, Manolio TA, Kuller L. The role of APOE epsilon4 in modulating effects of other risk factors for cognitive decline in elderly persons. Journal of the American Medical Association. 1999;282:40–46. [PubMed: 10404910]
  37. Harris TB, Ferrucci L, Tracy RP, Corti MC, Wacholder S, Ettinger WH Jr, Heimovitz H, Cohen HJ, Wallace R. Associations of elevated interleukin-6 and C-reactive protein levels with mortality in the elderly. American Journal of Medicine. 1999;106:506–512. [PubMed: 10335721]
  38. Herskind AM, McGue M, Holm NV, Sorensen TI, Harvald B, Vaupel JW. The heritability of human longevity: A population-based study of 2872 Danish twin pairs born 1870-1900. American Journal of Human Genetics. 1996;97(3):319–323. [PubMed: 8786073]
  39. Hjelmborg JB, Iachine I, Skytthe A, Vaupel JW, McGue M, Koskenvuo M, Kaprio J, Pedersen NL, Christensen K. Genetic influence on human lifespan and longevity. American Journal of Human Genetics. 2006;119(3):312–321. [PubMed: 16463022]
  40. Hume AL. Safe and effective drug therapy in older adults. Medicine and Health Rhode Island. 2005;88:15–17. [PubMed: 15779550]
  41. Humphries SE, Luong LA, Ogg MS, Hawe E, Miller GJ. The interleukin-6-174 G/C promoter polymorphism is associated with risk of coronary heart disease and systolic blood pressure in healthy men. European Heart Journal. 2001;22:2243–2252. [PubMed: 11728144]
  42. Ioannidis JP. Genetic associations: False or true? Trends in Molecular Medicine. 2003;9(4):135–138. [PubMed: 12727138]
  43. Jordan BD, Relkin NR, Ravdin LD, Jacobs AR, Bennett A, Gandy S. Apolipoprotein E epsilon4 associated with chronic traumatic brain injury in boxing. Journal of the American Medical Association. 1997;278:136–140. [PubMed: 9214529]
  44. Keavney B, McKenzie C, Parish S, Palmer A, Clark S, Youngman L, Delepine M, Lathrop M, Peto R, Collins R. Large-scale test of hypothesised associations between the angiotensin-converting-enzyme insertion/deletion polymorphism and myocardial infarction in about 5000 cases and 6000 controls. (International Studies of Infarct Survival (ISIS) Collaborators) Lancet. 2000;355:434–442. [PubMed: 10841123]
  45. Kondo S, Schutte BC, Richardson RJ, Bjork BC, Knight AS, Watanabe Y, Howard E, de Lima RL, Daack-Hirsch S, Sander A, McDonald-McGinn DM, Zackai EH, Lammer EJ, Aylsworth AS, Ardinger HH, Lidral AC, Pober BR, Moreno L, Arcos-Burgos M, Valencia C, Houdayer C, Bahuau M, Moretti-Ferreira D, Richieri-Costa A, Dixon MJ, Murray JC. Mutations in IRF6 cause Van der Woude and popliteal pterygium syndromes. Nature Genetics. 2002;32:285–289. [PMC free article: PMC3169431] [PubMed: 12219090]
  46. Kritchevsky SB, Nicklas BJ, Visser M, Simonsick EM, Newman AB, Harris TB, Lange EM, Penninx BW, Goodpaster BH, Satterfield S, Colbert LH, Rubin SM, Pahor M. Angiotensin-converting enzyme insertion/deletion genotype, exercise, and physical decline. Journal of the American Medical Association. 2005;294:691–698. [PubMed: 16091571]
  47. Lewis SJ, Brunner EJ. Methodological problems in genetic association studies of longevity: The apolipoprotein E gene as an example. International Journal of Epidemiology. 2004;33:962–970. [PubMed: 15319409]
  48. Licastro F, Pedrini S, Caputo L, Annoni G, Davis LJ, Ferri C, Casadei V, Grimaldi LM. Increased plasma levels of interleukin-1, interleukin-6 and alpha-1-anti-chymotrypsin in patients with Alzheimer's disease: Peripheral inflammation or signals from the brain? Journal of Neuroimmunology. 2000;103:97–102. [PubMed: 10674995]
  49. Lorenz D, Frederiksen H, Moises H, Kopper F, Deuschl G, Christensen K. High concordance for essential tremor in monozygotic twins of old age. Neurology. 2004;62(2):208–211. [PubMed: 14745055]
  50. Luft FC. Bad genes, good people, association, linkage, longevity, and the prevention of cardiovascular disease. Clinical and Experimental Pharmacology and Physiology. 1999;26:576–579. [PubMed: 10405794]
  51. Mackness B, Mackness MI, Arrol S, Turkie W, Durrington PN. Effect of the human serum paraoxonase 55 and 192 genetic polymorphisms on the protection by high-density lipoprotein against low-density lipoprotein oxidative modification. FEBS Letters. 1998;423(1):57–60. [PubMed: 9506841]
  52. Martin-Ruiz CM, Gussekloo J, van Heemst D, von Zglinicki T, Westendorp RG. Telomere length in white blood cells is not associated with morbidity or mortality in the oldest old: A population-based study. Aging Cell. 2005;4:287–290. [PubMed: 16300480]
  53. McGue M, Christensen K. The heritability of cognitive functioning in very old adults: Evidence from Danish twins aged 75 and older. Psychology and Aging. 2001;16:272–280. [PubMed: 11405315]
  54. McGue M, Christensen K. The heritability of level and rate-of-change in cognitive functioning in Danish twins aged 70 years and older. Experimental Aging Research. 2002;28(4):435–451. [PubMed: 12227922]
  55. Montgomery HE, Marshall R, Hemingway H, Myerson S, Clarkson P, Dollery C, Hayward M, Holliman DE, Jubb M, World M, Thomas EL, Brynes AE, Saeed N, Barnard M, Bell JD, Prasad K, Rayson M, Talmud PJ, Humphries SE. Human gene for physical performance. Nature. 1998;393:221–222. [PubMed: 9607758]
  56. Myerson S, Hemingway H, Budget R, Martin J, Humphries S, Montgomery H. Human angiotensin I-converting enzyme gene and endurance performance. Journal of Applied Physiology. 1999;87:1313–1316. [PubMed: 10517757]
  57. Nauck M, Winkelmann BR, Hoffmann MM, Bohm BO, Wieland H, Marz W. The interleukin-6 G(-174)C promoter polymorphism in the LURIC cohort: No association with plasma interleukin-6, coronary artery disease, and myocardial infarction. International Journal of Molecular Medicine. 2002;80:507–513. [PubMed: 12185451]
  58. Nebel A, Croucher PJ, Stiegeler R, Nikolaus S, Krawczak M, Schreiber S. No association between microsomal triglyceride transfer protein (MTP) haplotype and longevity in humans. Proceedings of the National Academy of Sciences, USA. 2005;102:7906–7909. [PMC free article: PMC1142355] [PubMed: 15911777]
  59. Panza F, D'Introno A, Colacicco AM, Capurso C, Capurso S, Kehoe PG, Capurso A, Solfrizzi V. Vascular genetic factors and human longevity. Mechanisms of Ageing and Development. 2004;125:169–178. [PubMed: 15013661]
  60. Petersen AM, Pedersen BK. The anti-inflammatory effect of exercise. Journal of Applied Physiology. 2005;98:1154–1162. [PubMed: 15772055]
  61. Pradhan AD, Manson JE, Rifai N, Buring JE, Ridker PM. C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus. Journal of the American Medical Association. 2001;286:327–334. [PubMed: 11466099]
  62. Puca AA, Daly MJ, Brewster SJ, Matise TC, Barrett J, Shea-Drinkwater M, Kang S, Joyce E, Nicoli J, Benson E, Kunkel LM, Perls T. A genome-wide scan for linkage to human exceptional longevity identifies a locus on chromosome 4. Proceedings of the National Academy of Sciences, USA. 2001;98:10505–10508. [PMC free article: PMC56990] [PubMed: 11526246]
  63. Rauramaa R, Vaisanen SB, Luong LA, Schmidt-Trucksass A, Penttila IM, Bouchard C, Toyry J, Humphries SE. Stromelysin-1 and interleukin-6 gene promoter polymorphisms are determinants of asymptomatic carotid artery atherosclerosis. Arteriosclerosis, Thrombosis, and Vascular Biology. 2000;20:2657–2662. [PubMed: 11116068]
  64. Ravaglia G, Forti P, Maioli F, Chiappelli M, Dolzani P, Martelli M, Bianchin M, Mariani E, Bolondi L, Licastro F. Associations of the-174 g/c interleukin-6 gene promoter polymorphism with serum interleukin 6 and mortality in the elderly. Biogerontology. 2005;6(6):415–423. [PubMed: 16518703]
  65. Reed T, Dick DM, Uniacke SK, Foroud T, Nichols WC. Genome-wide scan for a healthy aging phenotype provides support for a locus near D4S1564 promoting healthy aging. Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 2004;59:227–232. [PubMed: 15031306]
  66. Rexbye H, Petersen I, Johansen M, Klitkou L, Jeune B, Christensen K. Influence of environmental factors on facial ageing. Age and Ageing. 2006;35:110–115. [PubMed: 16407433]
  67. Rigat B, Hubert C, Alhenc-Gelas F, Cambien F, Corvol P, Soubrier F. An insertion/deletion polymorphism in the angiotensin I-converting enzyme gene accounting for half the variance of serum enzyme levels. Journal of Clinical Investigation. 1990;86:1343–1346. [PMC free article: PMC296868] [PubMed: 1976655]
  68. Risch N, Zhang H. Extreme sib pairs for mapping quantitative trait loci in humans. Science. 1995;268(5217):1584–1589. [PubMed: 7777857]
  69. Robak T, Gladalska A, Stepien H, Robak E. Serum levels of interleukin-6 type cytokines and soluble interleukin-6 receptor in patients with rheumatoid arthritis. Mediators of Inflammation. 1998;7:347–353. [PMC free article: PMC1781865] [PubMed: 9883970]
  70. Ryff CD, Singer BH. Social environments and the genetics of aging: Advancing knowledge of protective health mechanisms. Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2005;60(Spec No I):12–23. [PubMed: 15863706]
  71. Seres I, Paragh G, Deschene E, Fulop T Jr, Khalil A. Study of factors influencing the decreased HDL associated PON1 activity with aging. Experimental Gerontology. 2004;39(1):59–66. [PubMed: 14724065]
  72. Silander K, Komulainen K, Ellonen P, Jussila M, Alanne M, Levander M, Tainola P, Kuulasmaa K, Salomaa V, Perola M, Peltonen L, Saarela J. Evaluating whole genome amplification via multiply-primed rolling circle amplification for SNP genotyping of samples with low DNA yield. Twin Research and Human Genetics. 2005;8(4):368–375. [PubMed: 16176722]
  73. Skytthe A, Pedersen NL, Kaprio J, Stazi MA, Hjelmborg J, Iachine I, Vaupel JW, Christensen K. Longevity studies in GenomEUtwin. Twin Research and Human Genetics. 2003;6(5):448–454. [PubMed: 14624729]
  74. Swift LL, Zhu MY, Kakkad B, Jovanovska A, Neely MD, Valyi-Nagy K, Roberts RL, Ong DE, Jerome WG. Subcellular localization of microsomal triglyceride transfer protein. Journal of Lipid Research. 2003;44:1841–1849. [PubMed: 12837846]
  75. Tan Q, Kruse TA, Christensen K. Design and analysis in genetic studies of human ageing and longevity. Ageing Research Reviews. 2006;5(4):371–387. [PubMed: 16337437]
  76. Terry CF, Loukaci V, Green FR. Cooperative influence of genetic polymorphisms on interleukin 6 transcriptional regulation. Journal of Biological Chemistry. 2000;275:18138–18144. [PubMed: 10747905]
  77. Wierzbicki AS. New lipid-lowering agents. Expert Opinion on Emerging Drugs. 2003;8:365–376. [PubMed: 14661995]
  78. Williams AG, Rayson MP, Jubb M, World M, Woods DR, Hayward M, Martin J, Humphries SE, Montgomery HE. The ACE gene and muscle performance. Nature. 2000;403:614. [PubMed: 10688186]
  79. Winterer G, Hariri AR, Goldman D, Weinberger DR. Neuroimaging and human genetics. International Review of Neurobiology. 2005;67:325–383. [PubMed: 16291027]
  80. Yu CE, Oshima J, Fu YH, Wijsman EM, Hisama F, Alisch R, Matthews S, Nakura J, Miki T, Ouais S, Martin GM, Mulligan J, Schellenberg GD. Positional cloning of the Werner's syndrome gene. Science. 1996;272:258–262. [PubMed: 8602509]
Copyright © 2008, National Academy of Sciences.
Bookshelf ID: NBK62441

Views

  • PubReader
  • Print View
  • Cite this Page
  • PDF version of this title (7.4M)

Related information

  • PMC
    PubMed Central citations
  • PubMed
    Links to PubMed

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...