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. Author manuscript; available in PMC: 2023 Aug 1.
Published in final edited form as: Maturitas. 2022 Apr 29;162:31–36. doi: 10.1016/j.maturitas.2022.04.002

Better Cognitive Function in Younger Generations-Insights from Two Cohort Studies of Middle-Aged to Older Adults in Wisconsin

Natascha Merten a,b,c, A Alex Pinto d, Adam J Paulsen d, Yanjun Chen d, Carla R Schubert d, Karen J Cruickshanks a,d
PMCID: PMC9233025  NIHMSID: NIHMS1795515  PMID: 35526325

Abstract

Background.

Understanding generational trends in dementia and cognitive decline is essential to quantify future healthcare needs and may help identify interventions and preventions. We aimed to determine whether individuals from more recent generations showed better neurocognitive function.

Methods.

This cross-sectional study combined data from 4439 participants (mean age 64 years (SD=13), 57% were women) from the Epidemiology of Hearing Loss Study and Beaver Dam Offspring Study. We assessed participants’ birth cohort (1901–1924: Greatest Generation;1925–1945: Silent Generation;1946–1964: Baby Boom Generation;1965–1984: Generation X) and neurocognition (Trail-Making Tests A and B, Digit Symbol Substitution Test, Auditory Verbal Learning Test, Verbal Fluency Test). Multivariable linear regression models were utilized.

Results.

Adjusted for age, sex, education, and known cognitive decline risk factors, more recent generations showed better processing speed, executive function, attention, and verbal fluency than the Greatest Generation. Largest benefits were found in the Baby Boom Generation. Compared to the Greatest Generation, individuals from the Baby Boom Generation performed better on Trail-Making Tests A (−0.21 ln(time in sec); 95% confidence interval (CI)-0.29,−0.13) and B (−0.31 ln(time in sec); 95%CI −0.40,−0.22), Digit Symbol Substitution Test (6.07 numbers correct; 95%CI 3.61,8.52) and Verbal Fluency Test (8.75 numbers correct; 95%CI 5.07,12.42 in women; 5.28 numbers correct; 95%CI 0.79,9.78 in men), with effect sizes similar to effects of 11–15 years of less aging.

Conclusions.

This indicates that some benefits of younger generations might be related to yet unknown and potentially modifiable environmental, health-related or lifestyle factors and motivates research of such underlying factors to promote healthy cognitive aging.

Keywords: Cognition, Birth Cohort, Aging, Executive Function, Processing Speed, Memory

1. INTRODUCTION

Cognitive impairment and dementia are public health challenges given their expected increase in prevalence rates in the aging populations.[1] However, promisingly, several studies have reported declining age-specific prevalence and incidence rates of dementia in more recent birth cohorts,[2,3] particularly in Western countries. Secular trends and improvements in risk factors for dementia over the past century, such as increasing education, improved medical care, such as better treatment of cardiovascular disease and adoption of healthier lifestyles (e.g. increases in physical activity) have been considered as underlying factors for these changes.[3–11] Understanding generational trends and differences in age-related diseases is essential to quantify future burden and healthcare needs in our aging populations and may help to identify areas for intervention and prevention. Neurodegenerative diseases and dementia have a long preclinical phase with onset of changes starting early in midlife[12] and the study of early cognitive changes in midlife and cognitive decline can help to identify individuals at risk for decline early.

Epidemiological cohort studies have used varying birth cohort definitions and different statistical approaches to study secular trends and revealed inconsistent results. Existing research on secular trends in cognitive function mainly focused on older adults and older generations, investigating individuals from the Greatest to the Silent Generations.[11,13–18] Most studies reported an improvement in more recent generations as compared to individuals from earlier birth years,[5,11,14–18] with one exception.[13] One Swedish study,[5] one UK study[19] and two US studies[20,21] also included individuals from the Baby Boom Generation and showed large inconsistencies. Two of these studies reported a continuing improvement in the Baby Boom Generation.[5,19] One study found no difference studying individuals of the Silent and the Baby Boom Generation[20] and one study determined an improvement from the Greatest Generation to the Silent Generation, but then significant declines in cognitive function starting in the Early-Baby Boomers.[21] Moreover, most studies used verbal-only and/or cognitive screening tests and more comprehensive and robust assessments of different cognitive functions are lacking. Therefore, the aim of this study on middle-aged to older adults was to determine whether individuals from more recent generations show better cognitive function.

2. METHODS

2.2. Study Population

This cross-sectional study included participants of the Epidemiology of Hearing Loss Study (EHLS) and the Beaver Dam Offspring Study (BOSS), two prospective studies of aging in Beaver Dam, Wisconsin. Participants of the baseline Beaver Dam Eye Study (1987–1988) were eligible for participation in EHLS. The baseline EHLS examination was conducted 1993–1995 (participants aged 48–92 years), with follow-up examinations every 5 years (1998–2000; 2003–2005; 2009–2010; 2013–2015).[22,23] Adult offspring of the population-based EHLS participants were eligible for the baseline BOSS examination (2005–2008) and have been followed every 5 years.[24] To increase the overlap in age at examination among different birth cohorts, we included all BOSS 10-year follow-up and all EHLS 15-year follow-up participants who had cognitive data. To increase the sample size, we then also included all participants from the BOSS 5-year follow-up and EHLS 20-year follow-up who were not already included. Some participants had participated in both the EHLS and BOSS (n=100). For those participants, we used their EHLS visit data (first/youngest age at examination). The studies were approved by the University of Wisconsin Health Sciences Institutional Review Board with written informed consent from all participants before each examination.

2.3. Measurements

2.3.1. Generation

Generation was based on birth year and categorized by commonly used sociodemographic descriptors (Greatest Generation: 1901–1924, Silent Generation: 1925–1945, Baby Boom Generation: 1946–1964, Generation X: 1965–1984).

2.3.2. Cognitive Measures

The neurocognitive test battery consisted of: Trail-Making Tests A and B (TMTA, TMTB), Digit Symbol Substitution Test (DSST), modified Rey Auditory Verbal Learning Test (AVLT) and Verbal Fluency Test (VFT) assessing the cognitive domains of attention, speed, executive function, memory and verbal fluency.[25] In TMT, the participant had to connect consecutive numbers (TMTA) and alternatingly consecutive numbers and letters (TMTB) on a sheet of paper with a pencil. Completion time in seconds was the outcome. Longer durations indicated worse performance. Inability to complete each subtest in 5 minutes resulted in a score of 301 seconds.[25] In the DSST, participants had to convert numbers to symbols based on a key. The number of correctly converted numbers in 90 seconds served as outcome score. In the AVLT, subjects were asked to recall as many words as they could from a list of 15 verbally presented words. The same 15-word list was administered three times followed by a new 15-word distractor list. Immediately after the distractor list recall, the participant was asked to recall as many words as they could from the first word list. The number of words correctly recalled from the first list in the final trial served as the memory function outcome.[25] In the VFT, the task was to produce as many words as possible in one minute beginning with each letter F,A and S. The sum of the numbers of correct words provided for each of the three letters was the test outcome.[25]

2.3.3. Other Variables

We assessed participants’ age, sex, education, body mass index (BMI; measured weight/height in kg/m2), smoking status, weekly alcohol consumption in the past year (average grams per week), regular exercise (at least once a week and long enough to work up a sweat), diabetes (history of diabetes diagnosis and/or glycated hemoglobin≥6.5%), history of cardiovascular disease (CVD; history of stroke, myocardial infarction, angina, congestive heart failure, transient ischemic attack, peripheral vascular disease, thrombosis, angioplasty or a stent operation, coronary bypass, and/or carotid arteries surgery), hypertension (systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90mmHg, and/or self-reported diagnosis of high blood pressure with current antihypertensive medication use), history of head injury, statin use, depressive symptoms (Center for Epidemiological Studies Depression Scale score ≥16[26]) and mean intima-media thickness (IMT; based on carotid artery ultrasound scans[27]). In both studies, we also measured hearing and defined impairment as the pure-tone average of air-conduction thresholds at 0.5,1,2 and 4kHz in the worse ear >25 decibel hearing level and olfaction with impairment defined as <6 out of 8 correctly identified odors on the San Diego Odor Identification Test.[25]

2.4. Statistical Analyses

Statistical analyses were conducted using SAS software v.9.4 (SAS Institute, Inc, Cary, NC).

In preparation for the linear models, right-skew data of TMTA and B were natural log-transformed. We used multivariable linear regression models to assess generational differences in the different cognitive function test outcomes (TMTA,TMTB,DSST,AVLT,VFT). Models were adjusted for age, sex and education and repeated additionally adjusting for factors known to be associated with cognitive outcomes: BMI, smoking, alcohol intake, exercise, diabetes, CVD, hypertension, head injury, statin use, depressive symptoms and IMT. We then also repeated models with additional adjustments for hearing and olfaction loss. We tested for sex interactions and report results stratified if there were any.

To visualize generational differences, we plotted the association of age with TMTB by generations (Supplementary Figure S1). We also generated forest plots of the generational differences from the fully-adjusted models in all cognitive outcomes (TMTA,TMTB,DSST,AVLT,VFT) (Supplementary Figure S2–S7).

Due to our observations of a stagnation of the benefit in the Generation X, in secondary analyses, we repeated models testing for generational differences between Generation X and the Baby Boom Generation.

3. RESULTS

Participants were on average 64 years old (SD=13, range=28–100), 2507(57%) were women and 1345(31%) had a bachelor’s degree or higher (Table 1). There were n=262 in the Greatest Generation (mean age 89, range 84–100), n=1819 in the Silent Generation (mean age 73, range 63–89), n=1717 in Baby Boom Generation (mean age 58, range 46–70) and n=641 in Generation X (mean age 45, range 28–52).

Table 1.

Characteristics of the Analytic Sample (N=4439)

Age, yrs, M(SD) 64.3(12.8)
Sex, n(%)
 Women 2507(56.5)
 Men 1932(43.5)
Education, n(%)
 0–12 years 1855(42.1)
 13–15 years 1209(27.4)
 16 years and more 1345(30.5)
BMI, n(%)
 <25 711(17.7)
 25–29.9 1296(32.2)
 >30 2015(50.1)
Smoking, n(%)
 Never 2282(51.8)
 Former 1656(37.6)
 Current 468(10.6)
Weekly Alcohol consumption, n(%)
 0 820(18.8)
 0–14 g/week 1641(37.7)
 15–74 g/week 934(21.4)
 75–140 g/week 531(12.2)
 More than 140 g/week 430(9.9)
CVD, n(%) 850(19.4)
Hypertension, n(%) 2291(56.1)
Statin use, n(%) 1448(35.0)
Diabetes, n(%) 711(16.1)
Depressive symptoms, n(%) 624(15.3)
Exercise at least once a week, n(%) 2488(53.7)
History of head injury, n(%) 1584(36.0)
Mean IMT, mm, n(%) 0.8(0.2)
Hearing impairment, PTA worse ear >25 dB HL, n(%) 1695(41.4)
Olfactory impairment, n(%) 612(15.1)
TMTA, s, M(SD) 37.2(26.2)
TMTB, s, M(SD) 93.3(57.8)
DSST, number correct, M(SD) 50.0(14.4)
VFT, number correct, M(SD) 37.5(12.8)
AVLT, number correct, M(SD) 6.4(3.1)
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Note: AVLT, modified Rey Auditory Verbal Learning Test; BMI, body mass index; CVD, cardiovascular disease; DSST, Digit Symbol Substitution Test; IMT, intima-media thickness; TMT, Trail-Making Tests; VFT, Verbal Fluency Test.

Note. Sample sizes differ slightly due to missing data.

TMT performance was better in more recent generations as compared to the Greatest Generation in age-sex-education adjusted and fully-adjusted models (Table 2, Supplementary Figure S1–S3). Effect sizes for TMTA were slightly decreased with adjustment and marginally decreased for TMTB. In fully-adjusted models, participants of more recent generations performed faster on the TMTA with benefits in the Silent Generation (−0.18; 95 % confidence interval (CI) −0.25,−0.12), Baby Boom Generation (−0.21; 95%CI −0.29,−0.13) and Generation X (−0.14; 95%CI −0.25,−0.04) and on the TMTB (Silent Generation: −0.20; 95%CI −0.27,−0.12; Baby Boom Generation: −0.31; 95%CI −0.40,−0.22; Generation X: −0.18; 95%CI −0.30,−0.07.). These differences were comparable to age effects of 8 (Generation X) to 11 (Baby Boom Generation) years for TMTA and 9 (Generation X) to 15 (Baby Boom Generation) years for TMTB.

Table 2.

Generational Differences in TMTA and TMTB Beta (95% Confidence Interval)

TMTAa (ln(time in sec) TMTBa (ln(time in sec)
Model 1 Model 2 Model 1 Model 2
Greatest REF - - - -
Silent −0.27
(−0.33,−0.22)
p<.001		 −0.18
(−0.25,−0.12)
p<.001		 −0.22
(−0.28,−0.15)
p<.001		 −0.20
(−0.27,−0.12)
p<.001
Baby Boom −0.28
(−0.36,−0.20)
p<.001		 −0.21
(−0.29,−0.13)
p<.001		 −0.33
(−0.42,−0.25)
p<.001		 −0.31
(−0.40,−0.22)
p <.001
Gen X −0.19
(−0.29,−0.09)
p<.001		 −0.14
(−0.25,−0.04)
p=.008		 −0.19
(−0.30,−0.08)
p<.001		 −0.18
(−0.30,−0.07)
p=.002
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Note: TMT, Trail-Making Tests.

a

Due to skewness in the data trail-making test scores were log-transformed.

Model 1: Linear regression model adjusted for age, sex and education.

Model 2: Linear regression model adjusted for age, sex, education, smoking, body mass index, alcohol grams, cardiovascular disease, hypertension, statin use, diabetes, depression, regular exercise, ever head injury and mean intima-media thickness.

More recent generations also performed better on the DSST. Effect sizes were substantially smaller after full adjustment but remained significant (benefits in Silent Generation: 2.27; 95%CI 0.31,4.23; Baby Boom Generation: 6.07; 95%CI 3.61,8.52; Generation X: 5.96; 95%CI 2.80,9.13, Table 3, Supplementary Figure S4). These effects compared to differences of 5 (Silent Generation) to 12 (Baby Boom Generation) years of aging.

Table 3.

Generational Differences in DSST and AVLT, Beta (95% Confidence Interval)

DSST (# correct) AVLT (# correct)
Model 1 Model 2 Model 1 Model 2
Greatest REF - - - -
Silent 4.74
(2.99,6.49)
p<.001		 2.27
(0.31,4.23)
p=.02		 0.68
(0.24,1.13)
p=.003		 0.39
(−0.14,0.91)
p=.15
Baby Boom 8.54
(6.24,10.83)
p<.001		 6.07
(3.61,8.52)
p<.001		 0.91
(0.32,1.49)
p=.003		 0.62
(−0.04,1.28)
p=.06
Gen X 7.79
(4.77,10.81)
p<.001		 5.96
(2.80,9.13)
p<.001		 0.42
(−0.36,1.20)
p=.29		 0.09
(−0.76,0.94)
p=.84
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Note: AVLT, modified Rey Auditory Verbal Learning Test; DSST, Digit Symbol Substitution Test.

Model 1: Linear regression model adjusted for age, sex and education.

Model 2: Linear regression model adjusted for age, sex, education, smoking, body mass index, alcohol grams, cardiovascular disease, hypertension, statin use, diabetes, depression, regular exercise, ever head injury and mean intima-media thickness.

For the AVLT, we found generational differences in the age-sex-education adjusted models which were no longer statistically significant in fully adjusted models (Table 3, Supplementary Figure S5).

Given the significant sex interaction in VFT (p=.01), we conducted VFT analyses stratified by sex. There were advantages of all younger generations as compared to the Greatest Generation in women (Silent Generation: 4.35; 95%CI 1.54,7.16; Baby Boom Generation: 8.75; 95%CI 5.07,12.42; Generation X: 5.42; 95%CI 0.58,10.26; Table 4, Supplementary Figure S6). Effect sizes were smaller in men and only significant for the Baby Boom Generation (5.28; 95%CI 0.79,9.78).

Table 4.

Generational Differences in VFT in Women and Men, Beta (95% Confidence Interval)

VFT (# correct)
Model 1 Model 2
Greatest REF - -
Silent W: 4.58 (2.20,6.95); p<.001
M: 2.16 (−1.03,5.36); p=.18		 W: 4.35; (1.54,7.16); p=.002
M: 1.03; (−2.65,4.70), p=.58
Baby Boom W: 8.65 (5.32,11.98); p<.001
M: 6.84 (2.80,10.89); p<.001		 W: 8.75; (5.07,12.42); p<.001
M: 5.28; (0.79,9.78), p=.021
Gen X W: 5.25 (0.74,9.76); p=.02
M: 7.54 (2.31,12.77; p=.005		 W: 5.42; (0.58,10.26). p=.028
M: 5.25; (−0.45,10.95), p=.071
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Note: M, men; VFT, Verbal Fluency Test; W, women.

Model 1: Linear regression models adjusted for age and education.

Model 2: Linear regression models adjusted for age, sex, education, smoking, body mass index, alcohol grams, cardiovascular disease, hypertension, statin use, diabetes, depression, regular exercise, ever head injury and mean intima-media thickness

Results did not substantially change when we additionally adjusted for hearing and olfaction loss.

Descriptively, Generation X participants showed equal or worse performance compared to the Baby Boom Generation on all cognitive tests (Table 2–4 and Supplementary Figure S1–S7). However, the difference was only significant for the TMTB (0.06; 95%CI 0.01,0.11; p=.02), which compared to an effect of 3.8 years of aging.

4. DISCUSSION

We found better age-adjusted performance in the cognitive domains of processing speed, executive function, attention and verbal fluency in more recent generations compared to the Greatest Generation. The generational differences were stable even after adjusting for age, sex, education and other health-related mediator variables, indicating that a certain degree of the cohort effect might be unrelated to known generational differences in socio-economic, health-related or lifestyle factors. The largest benefits were found in the Baby Boom Generation while there was a lack of continued improvement in Generation X. This study extends existing research to the study of middle-aged US adults of more recent generations, and we were able to adjust for many known risk factors of cognitive decline, which facilitates our understanding of potential underlying contributors to these cohort effects.

Our results of increasing cognitive function in individuals from the Greatest to the Silent Generations are in line with the majority of cohort comparisons studies in older adults of earlier generations,[11,14–18,21] although two studies found contradicting results.[13,20] Research on secular trends in cognition of younger adults of more recent generations is limited. Few studies included individuals from the Baby Boom Generation,[5,19–21] of which two were US cohorts[20,21] and two were European cohorts.[5,19] One of the two US studies found no generational difference[20] and one study reports a performance worsening within the youngest subgroup of the Baby Boom Generation as compared to older generations.[21] While we found the largest improvement in cognition in the Baby Boom Generation, we determined a plateau, i.e. no further increase in improvement in Generation X, the most recent generation in our dataset. This trend seems to align with the trend shown by Zheng and colleagues.[21] However, the different birth cohort definitions, cognitive test measures and statistical approaches used to determine secular trends make it difficult to conduct a more elaborate comparison between studies. As compared to the other studies of midlife cognition,[5,19–21] we did not use a verbally conducted cognitive impairment screener but a neurocognitive test battery that included verbal and non-verbal tests, making our assessment more robust against confounding e.g. by deficits in sensory function.

We found the strongest generational effects on cognitive tests of TMTA, TMTB, and DSST, which are measures of speed, attention, and executive functioning. It has been shown that processing speed in particular starts to decline early in life[28] and speed tests might thus be more prone to detect early changes. There were weaker effects for the memory test (AVLT), which did not remain in the fully adjusted model and we found sex differences, i.e. smaller cohort differences, in language function (VFT) in men. Sex differences in cognition have been documented previously; women have often been reported to outperform men in verbal and in memory tests.[29] Consistent with our VFT results, a recent study found that sex differences in fluency scores were smaller in later birth cohorts. Disparities between sexes might thus be likely multifactorial and sex differences in secular increases in educational opportunities may play an important underlying role.[30]

Multiple risk and protective factors over the life course of an individual contribute to their physical, mental and cognitive health, and might thereby delay the onset of cognitive impairment and reduce dementia risk.[3] We were able to adjust for a number of risk factors of cognitive decline and mediator variables in our models. Effect sizes slightly decreased while remaining generally stable after adjustment. Thus, some of the benefits of more recent cohorts were due to known improvements in education, medical care and healthier lifestyles, which have been previously reported.[3] Such factors include an increase in level of education,[4,5,9,11,31] which is a protective factor for dementia and expected to improve cognitive reserve and slow cognitive decline.[32] Other health-related improvements include lower prevalence and better treatment of cardiovascular diseases and associated risk factors[6–10,33–35] as well as lifestyle changes such as decreasing smoking prevalence rates[7] starting in the mid-1960s and an uptake of physical activity,[8] which were all identified as risk factors for cognitive decline and dementia.[36–40]

We identified a lack of continued improvement in the Generation X. While descriptively, the Generation X performed worse than the Baby Boom Generation on multiple cognitive domains, this trend was only significant for TMTB performance, and the difference was small. One explanation may be that the effects of improvements in education, cardiovascular risk factors and healthy lifestyle may have reached its peak and do not promote further improvement in Generation X’s cognitive function. Others suggest that there are counteracting and disadvantageous processes in the most recent generations, given the increasing prevalence of obesity and diabetes,[21,41] which are major risk factors for cognitive impairment and dementia.[42] Also effects of increased loneliness, depression and anxiety have been discussed.[21,43,44] However, in our models, we adjusted for diabetes, obesity and depressive symptoms. Other generational changes in environments and lifestyles, such as increases in exposure to microplastics, consumption of processed food and increased sitting may be additional contributing sources to these negative trends that need further investigation.

It is unknown if the improvements (in health factors, lifestyle, environment etc.) over the past decades can continue to expand lifespans and years without disability in a similar fashion and when we might reach a plateau. The question about how much rise in life expectancy with continuing health improvements is biologically plausible is still largely under debate.[45] Further longitudinal studies on cognitive decline and studies with longer follow-up of younger generations will be needed to monitor secular trends and estimate future burden in our aging populations.

Importantly, even after comprehensive adjustment, some of the more recent generations’ benefits remained. This emphasizes that there may be other areas of improvement within the environment, health care system, living conditions and/or lifestyle contributing to cohort differences. Availability of vaccinations against infectious disease and more widely use of antibiotics over the course of the 20th century might be one explanation. It has been suggested that chronic (or even some acute) infections may cause inflammation and potentiate atherosclerosis,[46] which is a risk factor for cognitive impairment and dementia.[27,47] Implementations of environmental surveillance and regulations of pollution and toxin levels in food, water and air have led to environmental improvements,[48] limiting environmental risks for neurodegeneration and dementia.[49,50] Finally, more recent generations might be more likely to have cognitively stimulating occupations, obtain further education later in life, and engage in more lifelong learning and stimulation activities than older generations, which are considered to increase cognitive reserve.[32]

The study of generational differences is complicated due to accumulating effects in ontogenesis and later in life. Longitudinal, well-phenotyped cohort studies will be particularly relevant to investigate cohort differences in underlying pathways and modifiable risk factors that are likely responsible for the observed generational differences. Identifying underlying differences in environments and lifestyle has great potential to determine strategies for future treatment and prevention methods.

4.1. Limitations and Strengths

The BOSS and EHLS cohorts are primarily non-Hispanic White, which may limit the generalizability of these results to other populations. Due to the cross-sectional design, we could not determine differences in cognitive change over time. A common limitation in cohort studies on generational effects is a limited amount of age-specific overlap, i.e., there are fewer individuals of older age in the younger generations and younger age in the older generations (e.g.[21,29]). While the age adjustment in our analyses allows the comparison for differing age distributions, precaution is warranted when extrapolating these trends across the age continuum. To increase power and age-specific overlap, we included data from the later exam phase in the younger BOSS cohort and the earlier exam phase in the older EHLS cohort.

Strengths of the study are a large well-characterized middle to older aged sample that underwent standardized objective assessments and allowed us to adjust for and study a variety of potential mediator variables. Due to our offspring design, we have a larger homogeneity in genetics as compared to other studies using other sampling approaches, which makes it more likely that the observed generational differences are due to environmental or lifestyle factors than a genetic origin.

4.2. Conclusion

More recent generations show better cognition, and this trend is not solely explained by known generational risk factor differences in socio-economic, health-related or lifestyle factors. This indicates that some of the benefits of younger generations might be related to not yet known and potentially modifiable environmental and/or lifestyle factors and motivates research to identify such underlying factors to inform future prevention and intervention strategies for healthy cognitive aging.

Supplementary Material

Supplementary Figures

Supplementary Figure S1. Associatino of Age with TMTB by Generation.

Supplementary Figure S2. Generational Differences in TMTA

Supplementary Figure S3. Generational Differences in TMTB

Supplementary Figure S4. Generational Differences in DSST

Supplementary Figure S5. Generational Differences in AVLT

Supplementary Figure S6. Generational Differences in VFT in Women

Supplementary Figure S7. Generational Differences in VFT in Men

Highlights.

  • More recent generations show better cognitive function in multiple cognitive domains.

  • Trends remain after adjusting for age, sex, education, and known cognitive decline risk factors.

  • Some younger generations’ benefits might be due to yet unknown and possibly modifiable environmental, health-related or lifestyle factors.

  • This may help quantify future burden and healthcare needs in our aging populations and inform research on interventions and preventions.

ACKNOWLEDGEMENTS

Funding

This work was supported by the National Institutes of Health [R37AG011099, R01AG021917, RF1AG066837]; an unrestricted grant from Research to Prevent Blindness, Inc to the Department of Ophthalmology and Visual Sciences; the University of Wisconsin-Madison Office of the Vice Chancellor for Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation. The content is solely the responsibility of the authors and does not necessarily reflect the official views of the funding sources.

Footnotes

Conflict of Interest

The authors have no conflicts.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Figures

Supplementary Figure S1. Associatino of Age with TMTB by Generation.

Supplementary Figure S2. Generational Differences in TMTA

Supplementary Figure S3. Generational Differences in TMTB

Supplementary Figure S4. Generational Differences in DSST

Supplementary Figure S5. Generational Differences in AVLT

Supplementary Figure S6. Generational Differences in VFT in Women

Supplementary Figure S7. Generational Differences in VFT in Men

NIHMS1795515-supplement-Supplementary_Figures.docx (105.9KB, docx)

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