1.6: Effects and Rates of Aging - Biology

1.6: Effects and Rates of Aging - Biology

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Aging is process that begins at maturity and ends with death. An individual’s aging process will be determined by a combination of his genetic makeup and environmental factors.

Each body system will be studied in depth throughout this course and in general each system’s capacity to function is reduced as an individual ages. Additionally, while the decreased functionality of body systems is a normal part of the aging process and does not by itself cause disease the relationship between aging and disease is well known. The older one gets the more likely one is to die from a major disease. This is largely due to the fact that as the body ages it has a reduced capacity tolerate physiological stress and maintain homeostasis.

Maintaining homeostasis requires that the body continuously monitor its internal conditions. From body temperature to blood pressure to levels of certain nutrients, each physiological condition has a particular set point. Aset point is the physiological value around which the normal range fluctuates. A normal range is the restricted set of values that is optimally healthful and stable. For example, the set point for normal human body temperature is approximately 37°C (98.6°F) Physiological parameters, such as body temperature and blood pressure, tend to fluctuate within a normal range a few degrees above and below that point. Control centers in the brain play roles in regulating physiological parameters and keeping them within the normal range. As the body works to maintain homeostasis, any significant deviation from the normal range will be resisted and homeostasis restored through a process called negative feedback. Negative feedback is a mechanism that prevents a physiological response from going beyond the normal range by reversing the action once the normal range is exceeded. The maintenance of homeostasis by negative feedback goes on throughout the body at all times.

Ageing and health

The pace of population ageing around the world is also increasing dramatically. France had almost 150 years to adapt to a change from 10% to 20% in the proportion of the population that was older than 60 years. However, places such as Brazil, China and India will have slightly more than 20 years to make the same adaptation.

While this shift in distribution of a country's population towards older ages &ndash known as population ageing - started in high-income countries (for example in Japan 30% of the population are already over 60 years old), it is now low- and middle-income countries that are experiencing the greatest change. By the middle of the century many countries for e.g. Chile, China, the Islamic Republic of Iran and the Russian Federation will have a similar proportion of older people to Japan.

A longer life brings with it opportunities, not only for older people and their families, but also for societies as a whole. Additional years provide the chance to pursue new activities such as further education, a new career or pursuing a long neglected passion. Older people also contribute in many ways to their families and communities. Yet the extent of these opportunities and contributions depends heavily on one factor: health.

There is, however, little evidence to suggest that older people today are experiencing their later years in better health than their parents. While rates of severe disability have declined in high-income countries over the past 30 years, there has been no significant change in mild to moderate disability over the same period.

If people can experience these extra years of life in good health and if they live in a supportive environment, their ability to do the things they value will be little different from that of a younger person. If these added years are dominated by declines in physical and mental capacity, the implications for older people and for society are more negative.

Ageing explained

At the biological level, ageing results from the impact of the accumulation of a wide variety of molecular and cellular damage over time. This leads to a gradual decrease in physical and mental capacity, a growing risk of disease, and ultimately, death. But these changes are neither linear nor consistent, and they are only loosely associated with a person&rsquos age in years. While some 70-year-olds enjoy extremely good health and functioning, other 70-year-olds are frail and require significant help from others.

Beyond biological changes, ageing is also associated with other life transitions such as retirement, relocation to more appropriate housing, and the death of friends and partners. In developing a public-health response to ageing, it is important not just to consider approaches that ameliorate the losses associated with older age, but also those that may reinforce recovery, adaptation and psychosocial growth.


The authors would like to thank all of the participants for the very lively discussion that occurred within the context of the Biology of Aging Summit in September 2008. In addition, AB acknowledges partial support from NIH grants R01-AG028872 and P01-AG027734 and GBW from the NSF grant PHY 0202180. GBW would also like to acknowledge support from the Thaw Charitable Trust and the Bryan and June Zwan Foundation and discussions with Walter Fontana and Michal Jazwinski.

Sex gap in aging and longevity in non-human animal species

In most animals, males and females show marked differences in their mortality patterns. Whether females outlive males or vice versa differs among taxa. In this section, we present a brief overview of the sex differences in aging and longevity patterns across the tree of life by focusing on the most studied species in that context (i.e., mammals, birds and insects) and in most cases, reviewing studies performed in wild populations.

Mammals and birds

In non-human mammals, sex gaps in longevity have for a long time been measured through comparisons between the maximum longevity of males and females (see Box 1). Similarly, to what we described for human populations, longevity records are in most species held by females although in some species the sex gap is tiny with no obvious differences (Table 1). Again, as observed in human populations, the direction of the sex gap in lifespan appears to be shared among populations of the same species [31], although with different magnitudes. Different magnitudes of the sex gap among populations of a given species indicate that environmental conditions modulate the general picture, as reported in humans. For instance, when comparing three north-American free-ranging populations of bighorn sheep (Ovis canadensis), the sex gap in maximum longevity can change from an almost equal longevity between sexes in some populations, with females outliving males by 5 or 6 years in other populations [32].

In vertebrates, the increasing number of long-term longitudinal studies since the 1960s, which involve individuals monitored from birth to death [33], now allow getting reliable estimates of age-specific survival in both sexes for many wild populations. The availability of such high-quality data makes the detailed study of sex differences in mortality possible by estimating more accurate metrics of aging (such as the age at the onset of aging and the rate of aging, see Table 1) than the rather crudely observed maximum longevity (see [34] for a thorough discussion on aging metrics in mammals). Not surprisingly, in a wide range of mammalian orders, males generally show a higher rate of aging than females [35,36,37] even if in some populations the sex gap in aging patterns is not detected (e.g., [38, 39]). Such striking exceptions constitute valuable biological models to better understand the evolutionary roots of the sex gap in aging patterns. In terms of the sex gap in mortality patterns, birds seem to present a much less constant pattern than mammals. In many avian species, differences in age-dependent mortality trajectories are rather tenuous (Table 1). However, contrary to mammals, large-scale comparisons of the sex gap in aging patterns are still lacking in birds. Indeed, to date, most studies that investigated the sex gap in mortality patterns were based on mean adult mortality ([40, 41], see Table 1).


Contrary to vertebrates, most insect studies on the sex gap in aging have been performed using laboratory-controlled experiments. Although differences in mating treatments and genotypes can make the sex gap in lifespan extremely variable in laboratory-based experiments [2, 42], it is advantageous to study a tremendous number of individuals, which sometimes allow complex sex-specific mortality trajectories to be revealed. In a demographic study involving approximately 600,000 medflies (Ceratitis capitata) of each sex, Carey et al. (1995) showed that mortality is higher in females in early life, then in adults, mortality becomes higher in males between 20 and approximately 60 days of age (i.e., mortality crossover), and finally, mortality does not differ between the sexes later in life [43]. In natural populations of insects, the comparison of survival-related traits between males and females has been mainly done in Diptera (Telostylinus angusticollis), where individuals can be marked shortly after emergence. Free-living males appear to live a dramatically shorter life than females (i.e., maximum lifespan of 18 days for females vs. only 10 days for males) and, even more unexpectedly, aging (measured as the increase rate of mortality with age, see Box 1) was only observed in males [44]. A parallel sex-specific comparison was performed on a laboratory-based population of T. angusticollis (derived from the same natural population) revealed that females also outlive males in controlled conditions even if the sex gap was much less pronounced [44], which suggests that laboratory assessment of sex differences in mortality might sometimes be misleading for extrapolating what is going on in the wild. Sex differences in lifespan have also been studied in Odonates, since it is possible to mark and monitor individuals in species such as damselflies [45]. However, in this taxon, the sex gap in the lifespan differs across species (Table 1). Overall, it is important to note that insects encompass a much wider set of species than mammals and birds combined, and the sex gap in aging/longevity has been studied so far in very few insect species. This prevents us from drawing any firm conclusions on the overall direction and magnitude of the sex gap in aging/longevity in this taxon.

In the two next sections, we discuss the hypotheses that have been proposed to explain these patterns.

Renal system

Renal mass decreases with age [10]. This reflects the reduction in nephrons [11]. Intra-renal vascular changes also occur, consisting of hyalinization of the vascular tuft leading to reduced blood flow in the afferent arterioles in the cortex [12]. No changes in the medullary vasculature are reported with ageing [13]. Both renal plasma flow and glomerular filtration rate decline with age. The decline is not uniform or consistent, however, [14, 15]. Despite the decline in glomerular filtration rate, there is no concomitant increase in plasma creatinine because of age-related loss of muscle mass. Therefore, creatinine is not a reliable indicator of glomerular filtration rate in the elderly subject. Other markers such as serum cystatin C do not provide significant advantages over creatinine for the measurement of creatinine clearance [16].

Acid-base balance is maintained under physiological conditions but a reduced response to stress is revealed by the inability to deal with acid loads, which may be due to defective renal tubular secretion of ammonium ions [17]. The ability to concentrate the urine during water deprivation is reduced. This is probably due to the inability of the declining number of nephrons to deal with an increased solute load or to the increased perfusion of the juxtamedullary glomeruli producing medullary washout [11, 17]. The response to water loading is also impaired but the mechanisms responsible are unclear. Basal vasopressin secretion is probably normal in elderly subjects. However, both normal and reduced responses to water deprivation have been described [18, 19]. Although the ability to conserve salt is maintained, there is a delay in balancing losses [20]. Changes in salt and water regulation also interact with age-related change in thirst mechanisms. Reduced thirst has been reported in elderly subjects during water deprivation [18], despite considerable rises in plasma osmolality. Possible mechanisms include reduced sensation of mouth dryness and increased activity of the renin-angiotensin-aldosterone axis [18, 21].



The study was carried out as a part of the AGEMAP (Atlas of Gene Expression in Mouse Aging Project) at the Intramural Research Program of the National Institute on Aging (NIA/NIH) [74–76]. C57BL/6 mice were purchased from the National Institute on Aging (NIA) Rodent Colony at ages of 3 weeks, 5, 15, and 23 months. The mice were kept under the standard AL and CR conditions [20]. In the CR cohorts, the caloric intake was reduced to the 60% level in a stepwise manner over 3 weeks, beginning at 14 weeks of age. After transferring to the mouse facility of the NIA Intramural Research Program, the mice were kept under the same conditions. Mice were individually housed. The mice were euthanized at 1, 6, 16, and 24 months of age, and organs were quickly removed and stored in RNAlater at -20°C. The CR mice were fed between 9 AM and 11 AM everyday, but were not fed on the final day and euthanized before noon. The AD mice were continuously supplied with an excess of food. The use of mice in this project was approved by the NIA-IRP Animal Care and Use Committee.

Microarray experiments

RNA extraction, labeling, and hybridization on a microarray were performed independently for each mouse with two replications for each combination of age, sex, and diet (14 microarrays were used for ovaries and 14 for testis). Two replications were sufficient for this study because the goal was to depict major trends in the change of gene expression with age and diet rather than to assess the individual variability of gene expression for each gene. The latter task would require many more replications but it was beyond the scope of this work. Genes with high individual variability of their expression appeared not significant in our statistical analysis and therefore were ignored. Most of the analysis (except the age-specific effect of CR) is based on at least four samples which increased the reliability of results. Total RNAs were extracted from entire organ (ovary or testis). The tissue was processed by the Bead Beater (Bio-Spec, Bartlesville, OK) followed by RNA purification using the RNeasy Mini Kit (Qiagen, Valencia, CA Invitrogen). Total RNAs were labeled with Cy3-CTP. Fluorescently labeled microarray targets were prepared from 2.5 μg aliquots of total RNA samples using a Low RNA Input Fluorescent Linear Amplification Kit (Agilent). A reference target (Cy5-CTP-labeled) was produced from Stratagene Universal Mouse Reference RNA. Targets were purified using an RNeasy Mini Kit (Qiagen), and then quantified on a NanoDrop scanning spectrophotometer (NanoDrop Technologies). cRNA was hybridized to the NIA Mouse 44K microarrays v2.1 and v2.2 (whole genome 60-mer oligo arrays manufactured by the Agilent Technology: designs 012799 and 014117, respectively) [77]. All hybridizations compared one Cy3-CTP-labeled experimental target with the single Cy5-CTP-labeled universal mouse reference (Stratagene) target which was used for normalization. Microarrays were hybridized and washed according to the Agilent protocol G4140-90030. Slides were scanned on an Agilent DNA Microarray Scanner, using standard settings, including automatic PMT adjustment. The microarray data discussed in this publication have been deposited in NCBI Gene Expression Omnibus (GEO) [78] and are accessible through GEO Series accession number GSE7502. The data are also available at the NIA Array Analysis software [79, 80].

Statistical analysis

Statistical analysis of microarray results was done using NIA Array Analysis software [79, 80] on the basis of 25,585 non-redundant genes with symbols (Additional file 1). Median center global adjustment was used to remove differences between two batches of replications that were performed with different array versions for the ovary (testis samples were all performed with one version of arrays v2.2). For each gene, we estimated the difference between log-transformed gene expression values obtained with different array versions for samples from mice of the same age and diet, then used the median of these differences as an adjustment to all log-transformed expression values for this gene from arrays of v2.1. If a gene was represented by multiple oligos we selected the oligo with the most significant intensity change. One-factor ANOVA was performed with adjustment of error variances for individual genes so that it did not fall below the moving average error variance for genes with similar intensity, and with FDR method for assessing statistical significance [81]. FDR is already corrected for the number of tested hypotheses. Gene expression difference was considered significant based on the threshold of FDR at most 0.1 and fold change at least 1.5. Effects of age were studied by comparison of gonads in young (1 and 6 months) versus old (16 and 24 months) animals. In addition, we used two-factor linear regression with age and diet as independent variables with a threshold of p = 0.05 applied to the effect of age. Genes were considered age-dependent if they passed both tests. Effects of diet were studied by comparison of gonad samples from animals on CR diet with a group of samples from animals on AL diet. In this analysis we combined data from mice of age from 6 to 24 months which were adjusted for age effects as follows: x adi' = x adi- M a+ M, where x adi' is the adjusted log-transformed gene expression for age a, diet d, and replication i, x adiis the original log-transformed gene expression for age a, diet d, and replication i, M ais the average log-transformed gene expression for age a (diets and replications combined), and M is the average log-transformed gene expression for all ages. In addition, we used standard two-factor ANOVA without interaction effects and with a threshold of p = 0.05 applied to the effect of diet. Genes were considered diet-dependent if they passed both tests. Age-specific effects of CR were detected by pair-wise comparison of means using the error variance from ANOVA for all ages and diets. To reduce false positives, we excluded genes with age-specific effects of CR if these effects were reversed in another age group. Specifically, a gene was excluded if x · x 1 < 0 and |x 1| > 0.3333 · |x|, where x is the log ratio of gene expression CR/AL in the age group where diet had the strongest effect, and x 1 is the log ratio of gene expression CR/AL in any other age group. PCA was used to identify major patterns of gene expression [79]. Analysis of GO annotation terms in a selected list of genes was done using the NIA Mouse Gene Index (ver. mm7) software [82] using FDR at most 0.1 and enrichment ratio at least 1.5 as thresholds. Only nonredundant genes with gene symbols were used for analysis. Statistical significance was assessed using the hypergeometric distribution and FDR method, which was adjusted to account for redundant GO categories as described [83].

RT-PCR analysis

The same microarray hybridization RNA samples together with one additional biological replicate (total, N = 3) were used for quantitative reverse-transcription (RT)-PCR. The total RNA was DNAse treated (DNA-free, Ambion, Austin, TX, USA), annealed with random hexamer and reverse transcribed into cDNA with ThermoScript reverse transcriptase (Invitrogen, Carlsbad, CA, USA). PCR primer pairs were designed using Vector NTI software (Invitrogen) and were tested on ovary or testis cDNA with SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA, USA). Each primer pair was run using a matrix of forward and reverse primers concentrations, and threshold cycle measurements were compared with dissociation curves to determine optimal primer concentrations with high amplicon specificity. Genes were analyzed by quantitative RT-PCR on an ABI 7700 Sequence Detection System (Applied Biosystems). Reactions were set up in a 25 μl volume containing 12.5 μl of SYBR Green PCR Master Mix (Applied Biosystems), 2.0 μl of primers, 0.5 μl of H2O and 10.0 μl of cDNA 5.0 ng/μl. The list of primers and relative concentrations are summarized in Additional file 19 and results are shown in Additional file 20. Thermal cycling was initiated with 2 min incubation at 50°C, followed by a two-step PCR amplification at 95°C for 15 s and 60°C for 40 s repeated 40 times. The amount of target mRNA was determined from the appropriate standard curve and divided by the amount of H2A.2 mRNA for normalization.

DNA Damage as the Primary Cause of Aging

DNA damage appears to be ubiquitous in the biological world, as judged by the variety of organisms which have evolved DNA-repair systems. Previously, it was proposed that germ-line DNA of multicellular organisms may be protected from damage, and consequently from aging, by efficient recombinational repair during meiosis. The somatic line, however, may be vulnerable to the accumulation of DNA damage, and hence undergo aging, owing to relatively less repair. Although DNA Lesions most important in aging are not known yet, these is evidence for several types of endogenous damage. DNA lesions have been shown to interfere with transcription and replication, and so lead to loss of cell function and death. In mammals, there is a progressive decline of function in many different tissues with increasing age. Deterioration of central nervous system functions appears to be a critical part of the aging process. This may be due to the low DNA repair capacity which is found in postmitotic brain tissue, and which could result in the accumulation of DNA lesions in this tissue. Also reviewed is evidence that species longevity is directly related to tissue DNA-repair capacity and that aging may be accelerated by treatment with DNA-damaging agents, or in individuals with genetically defective repair. Although it has been frequently postulated that somatic mutation may be a cause of aging, current evidence suggests that it is probably less important than DNA damage. A prominent theory on the evolution of aging, which attributes special importance to genes that are advantagous in youth but are deleterious later on, is discussed in terms of regulatory genes that reduce DNA repair as cells differentiate to the postmitotic state. Finally, we hypothesize that the factors which determine maximum longevity of individuals in a population are the rate of occurrence of DNA damage, the rate of DNA repair, the degree of cellular redundancy, and the extent of exposure to stress.

Effects of Aging on the Heart and Blood Vessels

As people age, the heart tends to enlarge slightly, developing thicker walls and slightly larger chambers. The increase in size is mainly due to an increase in the size of individual heart muscle cells. The age-related stiffening of the heart walls causes the left ventricle to not fill as well and can sometimes lead to heart failure (called diastolic heart failure or heart failure with preserved ejection fraction), especially in older people with other diseases such as high blood pressure, obesity, and diabetes.

During rest, the older heart functions in almost the same way as a younger heart, except the heart rate (number of times the heart beats within a minute) is slightly lower. Also, during exercise, older people's heart rate does not increase as much as in younger people.

The walls of the arteries and arterioles become thicker, and the space within the arteries expands slightly. Elastic tissue within the walls of the arteries and arterioles is lost. Together, these changes make the vessels stiffer and less resilient. Since the arteries and arterioles are less elastic, blood pressure cannot adjust quickly when people stand, and older people are at risk for dizziness or in some cases fainting when they stand up suddenly.

Because arteries and arterioles become less elastic as people age, they cannot relax as quickly during the rhythmic pumping of the heart. As a result, blood pressure increases more when the heart contracts (during systole)—sometimes above normal—than it does in younger people. Abnormally high blood pressure during systole with normal blood pressure during diastole is very common among older people. This disorder is called isolated systolic hypertension.

Many of the effects of aging on the heart and blood vessels can be reduced by regular exercise. Exercise helps people maintain cardiovascular fitness as well as muscular fitness as they age. Exercise is beneficial regardless of the age at which it is started.


Two subjects from the older age group were excluded from the analyses on the basis of three or more missing blocks of data (N for older = 18 M = 66.3 years, SD = 4.1 10 F 17.9 years of education, SD = 3.1). Two additional subjects were missing data from two or fewer non-consecutive blocks due to technical failure or wrong finger placement on the keyboard. Model-predicted RT and accuracy for the missing blocks were interpolated from surrounding blocks.

Accuracy analysis

Accuracies for each condition (FR, IF) across each session and day were submitted to a linear mixed-effect model after logistic transformation. The maximum random effects model justified by the data included a random effect for intercept, linear slope, and quadratic slope for participants. Significant main effects were detected for day (t(690.8) = 6.909, p < 0.001), linear slope (t(456.9) = 2.368, p < 0.05), age group (t(880.0) = 4.807, p < 0.001), but not pair frequency (t<1). These results indicate that 1) accuracies were higher on day 2 than day 1, 2) participants improved their response accuracy for both conditions within each day, 3) young adults achieved higher accuracies than older adults in general, and 4) accuracies did not differ between frequent pairs (FR) and infrequent pairs (IF) (see Fig 3). In addition, a significant quadratic effect was detected (t(690.7) = 2.419, p < 0.05), indicating that the overall rate of increase in accuracies decreased as each day progressed.

Shaded regions represent standard error. Note in linear model, the time variable (session) was centered so that the linear slope for each group was calculated at the midpoint of each day (session 3).

The accuracy analysis revealed two significant interactions. First, we observed a significant interaction between day and age group (t(690.8) = -3.108, p < 0.01) indicating that overall older adults had higher accuracies on day 2 than day 1, and this difference was significantly greater than the change across days for young adults. Second, there was also a significant interaction between day and quadratic slope (t(691.3) = -2.809, p < 0.01), indicating that the rise in accuracy across sessions diminished significantly more rapidly on day 2 than on day 1. No other interactions were significant. Because the interaction between age group and pair frequency was not statistically significant, this result provides a foundation for assessing configural learning as a relative speeding of RT in frequently practiced pairs compared to infrequently practiced pairs, unconfounded by accuracy differences between the two conditions.

In sum, young adults performed more accurately relative to older adults through day 2. Moreover, young adults were quicker to reach asymptotic performance, which occurred early on day 2. In contrast, older adults continued to show increases in accuracy through day 2. However, for both age groups, accuracies were not statistically different between FR and IF conditions across sessions. That the configural learning effect is not apparent in accuracy scores is consistent with Hazeltine’s previous study [5], and this supports the assessment of configural learning as an RT difference between FR and IF conditions unconfounded by differences in accuracy between the FR and IF conditions.

Motor-skill performance (analysis of frequently performed pairs)

The maximum random effects model justified by the data included random effects for asymptote, the magnitude of RT decrements, and rate of RT change for participants. On day 1, significant differences were observed between groups for the asymptote (t(1310) = - 4.85, p < 0.001), and magnitude (t(1310) = -2.62, p < 0.01), but not rate of change (t(1310) = -0.61, ns). As illustrated in Fig 4, these results indicate that both groups showed statistically significant reductions in RTs across blocks on day 1. The young adults were approaching an asymptote that was 284 ms shorter than the older adults, but the older adults showed significantly greater reductions. Despite these differences the groups were similar in their speed to reach asymptote on day 1.

Only frequently performed pairs are modeled, rather than the comparison of frequent and infrequent pairs this served as a method for assessing motor skill learning (basic response speeding) separately from configural learning. Participants performed 7 blocks of frequent pair responses in each of 5 sessions per day, which are plotted sequentially in the figure above as blocks 1 through 35 for Day 1 and Day 2. Dotted lines represent block-wise group averages shaded region represents standard error solid lines represent model-predicted values.

Since the 3-parameter model is best estimated per day rather than across days, a separate model was estimated for day 2. Significant performance gains were also observed on day 2 the asymptote (t(1304) = 58.95, p < 0.001), magnitude of RT decrements (t(1304) = 8.66, p < 0.001), and rate of change (t(1304) = 7.75, p < 0.001) across groups differed significantly from 0. Differences between groups were observed for asymptote achieved (t(1304) = - 3.54, p < 0.001), magnitude (t(1304) = -2.03, p < 0.05), and rate of change (t(1304) = 2.15, p < 0.05). Similar to day 1, young participants approached a lower model-predicted asymptote than the older participants, but the older participants showed greater decrements in RT. However, unlike day 1, there was a significantly faster rate of RT change for older adults over young adults on day 2. One possible explanation for this pattern of results is that young adults may have continued to experience performance gains on day 2, whereas older adults may have quickly reached their asymptotic performance early in day 2. Alternatively, older adults may have experienced reduced retention on block 1 of day 2 which resulted in a steeper decrease in RTs over subsequent blocks (see Retention and Savings in supplementary materials (S1 Text)).

Configural learning scores

Configural learning scores were submitted to a linear mixed-effects model where the best-fit model representing the data included a random intercept for participant. Note that configural learning scores were computed such that a higher score indicated relatively faster response times for the frequently practiced pairs compared to infrequently practiced pairs. Learning scores were higher on day 2 than day 1 (t(332.8) = 2.691, p < 0.01). Significant learning was detected across sessions (t(333.50) = 2.804, p < 0.01). No quadratic effect or interaction between quadratic slope and day, or quadratic slope and group (ts < 1) was detected. Overall, these results indicate that configural learning followed a linear time course. This was also supported by a graphical comparison of the observed and modeled configural learning scores (see Fig 5). The only significant interaction was a three-way interaction between day, linear slope, and age group (t(333.7) = 2.056, p < 0.05), indicating that on day 2 older adults experienced a significantly larger rate of configural learning gains than young adults. One explanation for this effect is that young adults may have reached their asymptote for frequent pairs at the end of day 1, while older adults continued to improve, which contributed to a higher configural learning score.

Dashed lines connect group average learning scores for each session shaded regions represent standard error solid lines represent model-predicted values. Note in linear model, the time variable (session) was centered so that the linear slope for each group was calculated at the midpoint of each day (session 3).

In sum, although young adults performed more accurately on the configural response task through day 2 compared to older adults, accuracy did not differ between FR and IF conditions for either young or older adults across sessions (Fig 3). This provided a basis for measuring configural learning as a relative speeding of RT in frequently practiced pairs compared to infrequently practiced pairs. Not surprisingly, older adults were generally slower than young adults (Fig 4), however, the configural learning scores of older adults were similar to those of young adults (Fig 5).

Additional file 1:

Supplementary figures that complement the main manuscript. (PDF 2877 kb)

Additional file 2:

Information for the samples with developmental disorders (cases) that were included in the main screen (N = 367). (TSV 216 kb)

Additional file 3:

Information for the healthy control samples that were included in the main screen (N = 1128). (TSV 633 kb)

Additional file 4:

Information about the different blood cell type deconvolution strategies that were benchmarked against the gold standard dataset (GSE77797). (XLSX 13 kb)

Additional file 5:

Information (including the source) about the continuous (epi) genomic features (ChIP-seq and RNA-seq data) that were included in our analysis to annotate the different sets of CpG sites. (CSV 1 kb)

Additional file 6:

DNA methylation (beta value) profiles for the 353 Horvath’s epigenetic clock CpG sites during aging for healthy individuals (gray) and Sotos patients (orange). A linear model (displayed in dark gray) can be fixed to each CpG site to model the changes in beta value with chronological age in the controls (gray). Information about whether the site is a differentially methylated position during aging (aDMP) or in Sotos patients (Sotos DMP) is also provided. Hyper, hypermethylated Hypo, hypomethylated No, not statistically significant after Bonferroni correction. (PDF 2811 kb)

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