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Middle Aged

Middle aged, a period in human life spanning approximatley ages 45 to 64 years.
This stage of life is characterized by declines in certain physiological functions, but is also marked by a wealth of life experience and productivity for many individuals.
Research on the health and well-being of middle aged populations can provide valuable insights to improve outcomes during this pivotal life stage.

Most cited protocols related to «Middle Aged»

1
Automated Segmentation of Brain Structures from 3D MRI

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Publication 2009
Amygdaloid Body Brain Cloning Vectors Cortex, Cerebral ECHO protocol Females Globus Pallidus Gray Matter Hippocampal Formation Middle Aged Neurodegenerative Disorders Nucleus, Caudate Putamen Thalamus Ventricle, Lateral
2
Cardiometabolic Multimorbidity and Mortality
For both the ERFC and the UK Biobank, we categorized participants into the following 8 mutually exclusive groups according to baseline disease: (1) diabetes, (2) stroke, (3) MI, (4) diabetes and MI, (5) diabetes and stroke, (6) stroke and MI, (7) diabetes, stroke, and MI, (8) none of these (reference group). We assessed associations of these baseline groups with the risk of death from any cause.
Hazard ratios (HRs) were calculated using Cox proportional hazards regression models. The principal objective of our study was to estimate reductions in life expectancy associated with having different combinations of cardiometabolic multimorbidity. To this end, our primary analysis calculated HRs stratified by sex and adjusted for age only. A secondary objective was to explore the extent to which markers of some established intermediate pathways (ie, total and high-density lipoprotein cholesterol, blood pressure, body mass index) and lifestyle factors (ie, smoking, diet, socioeconomic status) could explain associations between cardiometabolic multimorbidity and mortality. To this end, subsidiary analyses calculated HRs adjusted for these additional factors. The HRs in the ERFC were calculated using a 2-stage approach, with estimates calculated separately within each study before pooling across studies by random-effects meta-analysis using an extension of the DerSimonian and Laird procedure.16 (link),19 (link)Participants were included in the analyses irrespective of previous nonfatal events. For each specific cause of death, outcomes were censored if a participant was lost to follow-up, died of other causes, or reached the end of the follow-up period. The proportional hazards assumption was satisfied for all-cause mortality (eFigure 2 in the Supplement). We used the I2 statistic to quantify between-study heterogeneity and the Wald test to assess interactions.
Because age-specific mortality rates cannot be directly obtained from a 2-stage approach using Cox regression models (ie, these models estimate instantaneous probability of death), we used a 2-level mixed-effects Poisson regression model with random study intercept adjusted for baseline disease status, sex and age at risk (linear and quadratic terms), and interactions of age at risk with the preceding variables. This Poisson regression model was used to obtain mortality rates adjusted to the age of 60 years (ie, marginal effects).
Detail of the methods used to estimate reductions in life expectancy is in eAppendix 5 in the Supplement. Briefly, estimates of cumulative survival from 40 years of age onward among the 8 baseline disease groups were calculated by applying the HRs for cause-specific mortality from the ERFC (specific to age at risk and sex) to the detailed mortality component of the US Centers for Disease Control and Prevention’s CDC WONDER database, which recorded almost 10 million deaths among more than 305 million individuals during 2007 through 2010.20 ,21 We modeled results throughout middle age and old age, giving specific consideration to the HRs with cardiometabolic multimorbidity recorded by the age of 60 years, the period of life when multimorbidity becomes increasingly common.22 (link) Analyses involved Stata version 12.0 (StataCorp), 2-sided P values, and used a significance level of P < .05.
Association of Cardiometabolic Multimorbidity With Mortality. (2015). JAMA, 314(1), 52-60.
Publication 2015
Blood Pressure Cerebrovascular Accident Diabetes Mellitus Diet Dietary Supplements Genetic Heterogeneity High Density Lipoprotein Cholesterol Index, Body Mass Middle Aged Population at Risk
3
Longitudinal Study of Alzheimer's Disease Risk
WRAP is a longitudinal study of a sample of over 1500 middle-aged adults predominantly between the ages of 40 and 65 years at baseline. In order to increase our power to detect decline (and associated predictors) in middle-age, the WRAP sample is enriched for a family history of Alzheimer's disease with over 70% of WRAP participants having a parent with either autopsy-confirmed or probable AD as defined by National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association research criteria [24 (link)]. Follow-up assessments are underway, with second wave assessments occurring approximately 4 years after baseline and all subsequent waves occurring at approximately 2 year intervals. As shown in Figure 1, WRAP has a very low attrition rate, with less than 5% of the baseline sample being unavailable for cognitive follow-up (e.g., deceased or dropped out). A large subset of the baseline sample had not yet returned for their third wave visit (n=831, 52.9%) and were therefore not included in analyses of our study hypotheses. To be included in testing the first hypothesis, participants must have completed three waves of testing, be free of dementia at or before the third wave of assessment, and be free of neurological conditions (including stroke, meningitis, epilepsy, multiple sclerosis, and Parkinson's Disease); 532 met these inclusion/exclusion criteria (see sample flow chart in Figure 1). To be included in analyses of hypotheses two and three, participants also had to meet aMCI criteria for one or more of the three psychometric aMCI approaches OR meet criteria for “Cognitively Healthy” across all three assessment waves.
Koscik R.L., Rue A.L., Jonaitis E.M., Okonkwo O.C., Johnson S.C., Bendlin B.B., Hermann B.P, & Sager M.A. (2014). Emergence of Mild Cognitive Impairment in Late-Middle-Aged Adults in the Wisconsin Registry for Alzheimer's Prevention. Dementia and geriatric cognitive disorders, 38(0), 16-30.
Publication 2014
Adult Alzheimer's Disease Autopsy Cerebrovascular Accident Cognition Dementia Epilepsy Familial Alzheimer Disease (FAD) Meningitis Middle Aged Multiple Sclerosis Nervous System Disorder Parent Parkinson Disease Psychometrics Tooth Attrition
4
Rhesus Monkey Alcohol Self-Administration
Fifty rhesus monkeys (Macaca mulatta) from the Oregon National Primate Research Center (ONPRC) were used in this study. Animals were both male and female, derived from 7 cohorts, designated as “4,” “5,” “6a,” “6b,” “7a,” “7b,” and “10.” Table 1 displays the sex of the cohorts and their average age and weight at the beginning of 1.5 g/kg ethanol induction and after 2 consecutive 6‐month periods of open‐access drinking. Table 1 also indicates assigned drinking categories.
All animals were born into a pedigreed population and remained with their mothers in a multigenerational troop until weaning at about 2 years of age. All monkeys were continually in a social setting at ONPRC and transitioned to individual cages at least 3 months prior to the onset of ethanol self‐administration according to established protocols (Helms et al., 2014b). Subjects were not members of the cynomolgus (Macaca fascicularis) cohort that initially were defined as either heavy or non heavy drinkers using this model (Grant et al., 2008). Rhesus cohorts 4, 5, 7a, and 7b were used in the previous analyses to identify the 4 drinking categories (Baker et al., 2014) and are used in these analyses along with cohorts 6a, 6b, and 10. The age range encompasses late adolescence to early middle age of captive monkeys, and data from a subset of these monkeys addressing age as a risk factor for chronic alcohol self‐administration have been published (Helms et al., 2014b).
Monkeys were housed individually with 4 cages (1.6 × 0.8 × 0.8 m) on a single rack: 2 cages located above and 2 below. Monkeys that weighed over 10 kg were allowed access to both horizontal cages, but only 1 drinking panel was affixed to the side of the cage. Each cohort was housed in a single room, and all animals had visual and auditory access to other monkeys in the room. Male monkeys were allowed tactile access to adjacent monkeys through grooming grids inserts in the common wall of the cage, and female monkeys were allowed 2‐hour access/d to a common space by removing the horizontal barriers between the cages.
Baker E.J., Walter N.A., Salo A., Rivas Perea P., Moore S., Gonzales S, & Grant K.A. (2017). Identifying Future Drinkers: Behavioral Analysis of Monkeys Initiating Drinking to Intoxication is Predictive of Future Drinking Classification. Alcoholism, Clinical and Experimental Research, 41(3), 626-636.
Publication 2017
Alcoholic Intoxication Alcohols Animals Auditory Perception Childbirth Ethanol Females Macaca fascicularis Macaca mulatta Males Middle Aged Monkeys Mothers Primates Self Administration
5
Racial Segregation and Blood Pressure
CARDIA is a prospective, multi-center investigation of contributors to changes in cardiovascular disease risk factors and cardiovascular disease onset and progression during the transition from young adulthood to middle age. In 1985–1986, 5,115 Blacks and Whites aged 18–30 years were recruited and examined in field centers in four locations: Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; and Oakland, California.10 (link), 11 (link) Participants were re-examined 2, 5, 7, 10, 15, 20, and 25 years later, with high retention rates (91%, 86%, 81%, 79%, 74%, 72%, and 72% of the surviving cohort, respectively). The current study uses data from all the exam years in which participant addresses were geocoded: baseline, 7, 10, 15, 20, and 25 year examinations. Given the unique historical context of residential segregation of Blacks from Whites in the US, and the nearly non-overlapping levels of segregation scores in Black and White CARDIA participants, the current study is limited to Black participants. Of the 2,637 Black baseline participants, 313 were excluded for not having two measures of blood pressure and 44 were excluding for missing data on baseline covariates, leaving a final sample of 2,280. The CARDIA study was approved by the institutional review boards at all study sites and all participants provided written informed consent.
Neighborhood-level racial residential segregation of Blacks from other racial/ethnic groups was measured at each available exam using the local Gi* statistic,12 based on the geocoded addresses of CARDIA participants linked to the nearest US Census: 1980 for baseline, 1990 for years 7 and 10, 2000 for years 15 and 20, and 2010 for year 25. The Gi* statistic returns a Z-score for each neighborhood (census tract), indicating the number of standard deviations the racial composition (in this case, percent Black) in the focal tract and neighboring tracts is from the mean racial composition of some larger areal unit surrounding the tract (in this case the surrounding metropolitan area or county). See the online appendix for further details.
Segregation was modeled continuously for the analyses of all Black participants. Segregation was categorized as high (Gi* >1.96), medium (Gi* 0 – 1.96), and low (Gi* < 0) for the analyses in the subset of participants in the high segregation category at baseline. The cutpoints for the categories were chosen to be consistent with the critical Z-score values for a 95% confidence interval (−1.96 and 1.96), which corresponds to statistical significance at the α=0.05 level.13 (link) No participants lived in areas with Gi* < −1.96, so we used a cutpoint of 0 for the low category to indicate a Z-score equal to or below the mean racial composition of the surrounding metropolitan area/county.
Blood pressure was measured at each CARDIA examination by trained technicians using either a random zero mercury sphygmomanometer (baseline – year 15) or an oscillometer (years 20–25). Oscillometer readings have been calibrated to the sphygmomanometric measures.14 (link) Resting systolic and diastolic blood pressure was measured 3 times at 1-minute intervals; the average of the second and third measurements was used in the analyses. We accounted for treatment effects by adding 10 mm Hg to the observed systolic blood pressure and 5 mm Hg to the observed diastolic blood pressure in treated subjects.15 (link) We also conducted a sensitivity analysis where we adjusted for self-reported medication use as a covariate instead.
Several covariates associated with residential segregation and blood pressure were adjusted for in our analyses as potential confounders or mediators including age, sex, current marital status (dichotomized as married or co-habiting vs. not married/co-habiting), education (categorized as high school graduate or less, some college, and college degree or higher, neighborhood poverty (percent of neighborhood residents who were below the U.S. Census Bureau-defined poverty threshold), and neighborhood population density. Individual income data were not collected at baseline, so we did not include it in our primary analyses. As a sensitivity analyses, we ran all models adjusting for time-varying income using data from the year 5 examination for baseline income.
Cigarette smoking was dichotomized as current vs. not current. Leisure-time physical activity was assessed with the CARDIA physical activity questionnaire, which includes questions on the frequency of participation in 13 categories of sports and exercise during the previous 12 months.16 (link) A score was summed across all activities and expressed continuously in Exercise Units. The reliability and validity of the instrument are comparable to other activity questionnaires.17 (link), 18 Body mass index (BMI) was calculated as weight (in kg) divided by height (in m2). Body weight in light clothing was measured to the nearest 0.5 pound using a balance beam scale. Height without shoes was measured to the nearest 0.5 cm using a vertically mounted centimeter ruler and a metal carpenter’s square.
We calculated descriptive statistics for study variables by examination year and segregation category (high and medium/low). Segregation scores can change over time if participants move to neighborhoods with different levels of segregation or if participants stay in the same place and the level of segregation changes in their neighborhood. To get a better sense of what drove changes in segregation score, we used a paired t-test to calculate the mean within-person change in segregation score between each successive exam separately for movers (those who changed census tracts between exams) and stayers (those who did not). Fixed-effects regression models were used to estimate associations of within-person changes in segregation with within-person changes in blood pressure.19 Fixed-effects models focus only on within-person variation rather than between-person variation. This approach has the advantage over mixed-effects models in that it tightly controls for all measured and unmeasured time-invariant characteristics. In fixed-effects models, characteristics that do not change over time are conditioned out of the estimation process. For this reason, fixed-effects models cannot be used to examine main effects of time-invariant characteristics. However interactions involving these variables can be examined. Baseline time-invariant covariates including age, sex, and field center were tested for interactions with time to allow for different trends in blood pressure over time associated with these characteristics. All statistically significant interactions (α=0.05) were retained in the models.
Our first set of multivariable regression models were designed to assess the overall relationship between within-person changes in neighborhood-level racial residential segregation and within-person changes in blood pressure over the follow-up period. For these analyses, segregation was modeled continuously using the full sample of black participants. We also assessed whether this relationship varied by participant age, neighborhood poverty, education, and income by testing interaction terms between these covariates and segregation. To investigate whether reductions in segregation were associated with reductions in blood pressure among residents who were in highly segregated neighborhoods at baseline, we also examined this relationship among the 1,861 Black participants living in high segregation neighborhoods at baseline (81.6% of total sample). For these analyses, segregation was modeled categorically in order to estimate changes associated with more meaningful changes in levels of segregation. As a sensitivity analyses, we ran this same model excluding participants who changed to lower segregation neighborhoods and changed back to high segregation neighborhoods at any subsequent exam. All analyses were conducted using SAS 9.4 (Cary, NC).
Kershaw K.N., Robinson W.R., Gordon-Larsen P., Hicken M.T., Goff DC J.r., Carnethon M.R., Kiefe C.I., Sidney S, & Diez Roux A.V. (2017). Association of Changes in Neighborhood-Level Racial Residential Segregation With Changes in Blood Pressure Among Black Adults: The CARDIA Study. JAMA Internal Medicine, 177(7), 996-1002.
Publication 2017
BLOOD Blood Pressure Body Weight Cardia Cardiovascular Diseases Disease Progression Ethics Committees, Research Hypersensitivity Index, Body Mass Light Metals Middle Aged Oscillometry Physical Examination Pressure Pressure, Diastolic Racial Groups Retention (Psychology) Sphygmomanometers Systole Systolic Pressure

Most recents protocols related to «Middle Aged»

1
COVID-19 Severity Risk Factors
Results are presented separately for women and men. For this purpose, respondents' information on the sex noted in their birth certificate was used. Information on gender could not be used in the present analyses, since the data for the evaluations are adjusted to the marginal distributions of the official reference statistics [source: Microcensus (95 (link))], which lacks information on gender identity.
Four age groups were formed to capture young adulthood, different stages of middle age, and the ages of an increased risk of severe COVID-19 infection: 18–29, 30–44, 45–64, and 65 years and older.
Educational levels according to the CASMIN classification (“Comparative Analyses of Social Mobility in Industrial Nations”) were used as an indicator of socioeconomic status (96 ). Three groups with low, medium, and high levels of education are distinguished on the basis of school and vocational qualifications.
Mauz E., Walther L., Junker S., Kersjes C., Damerow S., Eicher S., Hölling H., Müters S., Peitz D., Schnitzer S, & Thom J. (2023). Time trends in mental health indicators in Germany's adult population before and during the COVID-19 pandemic. Frontiers in Public Health, 11, 1065938.
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Publication 2023
Age Groups COVID 19 Gender Identity Middle Aged Social Mobility Woman
2
Epistemic Beliefs Across Generations
The responses to the dilemmas were coded for absolutist, multiplist, and evaluativist tendencies based on the participants’ choices of the response options (e.g., Person 1, Person 2, both, one more so than the other) and their qualitative responses to the “why” questions, in case their explanation was in contrast with their choice. However, this conflict between choice and explanation never occurred. The responses were coded independently by two researchers who had been or are currently immersed in Romanian language and culture in order to assure interrater reliability. There was 100% agreement between the coders.
The answers which indicated certainty about one side being right and the other not at all, were categorized as "absolutist.” An example of an absolutist response given by a participant (82-years-old) belonging to the oldest age group was “Sebastian is right because I hate lying”.
The answers that suggested that correct judgment belonged to both characters were coded as "multiplist" if the arguments supported the subjectivity of opinions. An example of a multiplist response by a participant (44-years-old) in the middle age group was “They can both be right, it depends… and how and when the lie is told.”
Finally, in the situation where the participants emphasized the graduated nature of truth, yet determined that one of the characters was more right than the other, depending on several factors (e.g., scientific support, context), the answer was categorized as "evaluativist.” An example of an evaluativist response by a participant (20-years-old) belonging to the youngest age group is “They can both be right. Yes, again, one might be more right than the other, but all depends on the book they have, how scientifically accurate it is.” Further examples can be found in the supplemental materials.
We then conducted quantitative analyses of the distribution of types of epistemic response in each generation and how they related to sociodemographic information. First, we conducted Pearson correlational tests to examine possible correlations between each of our variables. Then, we conducted a multivariate analysis of variance (MANOVA) to examine differences in education, social media, and international travel between the three generations. Additionally, we conducted a two-way ANOVA to test for potential differences in absolutist, multiplist, and evaluativist responses across the three age groups. Finally, we carried out a hierarchical regression to test the effects of each of our sociodemographic variables on absolutist and evaluativist responses. All quantitative analyses were carried out in SPSS.
In contrast, we treated responses to the questions concerning the experience of social change qualitatively. The participant responses to the qualitative questions were generally thematically homogenous, with a focus on the contrast between how much access to new sources of information has expanded since the fall of communism (for the middle and oldest generations), and even in the recent years (including and especially for the youngest generation). A few responses to each of the qualitative questions are described in the results section as examples of the type of general response that was found across all participants of different ages. Some qualitative responses are included in the quantitative results section to elucidate the meaning of a particular quantitative result. Qualitative responses concerning expanded sources of information, the rise of opinions, and generational differences in the volume of opinions are presented in a separate section on qualitative findings.
The internal consistency of the ten dilemmas, calculated by Cronbach’s Alpha coefficient, produces a value equal to .77. That score represents a good level of item intercorrelation. Looking at each cohort separately, good internal consistency is maintained for the 18–29 age group, with a Cronbach’s Alpha equal to .79; the other two age cohorts have acceptable internal consistency: 45–59 years (.68) and over 75 years (.68).
Ionescu A., Furdui R., Gavreliuc A., Greenfield P.M, & Weinstock M. (2023). The effects of sociocultural changes on epistemic thinking across three generations in Romania. PLOS ONE, 18(3), e0281785.
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Publication 2023
Age Groups Character Homozygote Learning Disorders Middle Aged neuro-oncological ventral antigen 2, human
3
Diepkloof Rock Shelter: Prehistoric Habitation
The archaeological site at Diepkloof Rock Shelter (DRS) is located approximately 180 km north of Cape Town [71 ,72 (link)]. Along with the neighbouring shelter, it dominates a largely isolated outcrop of quartzitic sandstone and is situated approximately 120 m above the southern bank of the Verloren Vlei River [72 (link)]. The site is 14 km from the Atlantic coast and the nearby site of Elands Bay Cave [72 (link)] and was first excavated by Cedric Poggenpoel and John Parkington in 1973; however, rock paintings on the shelter walls were discovered in 1960 [71 ,73 (link),74 ]. The Rock Shelter is a storehouse of both Middle Stone Age (MSA) and Later Stone Age (LSA) faunal assemblages [75 (link)], encompassing pre-Stillbay, Stillbay, Howiesons Poort (HP) and post-Howiesons Poort components [72 (link),76 (link)–78 (link)]. Bones from the DRS were recovered from hearths and as such, the skeletal elements were heated/burned to various degrees, but this did not obstruct the histological features [49 (link)]. The DRS tortoise assemblages analysed by us from this site are radiometrically dated to MIS 3 (i.e. 45–65 ka; [76 (link),77 (link)]).
Bhat M.S., Chinsamy A, & Parkington J. (2023). Bone histology of Neogene angulate tortoises (Testudines: Testudinidae) from South Africa: palaeobiological and skeletochronological implications. Royal Society Open Science, 10(3), 230064.
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Publication 2023
Bones Calculi Middle Aged Rivers Skeleton Tortoises
4
Cognitive Aging in Cross-Sectional Design
Given the limited resources for a dissertation project, we used a cross-sectional discrepant age-group design to maximize our power to detect age-related differences. Such designs are commonly used in studies of cognitive aging. Participants were recruited into either a younger (age 18–35 years) or older (age 50+ years) study group. The inclusion of younger individuals up to age 35 years in the younger group allowed us to reach beyond college-aged adults and diversify the educational attainment of the sample. The inclusion of older adults as young as age 50 was in recognition of emerging data showing that cognitive aging may be present during middle age (Lindenberger, 2014 (link)) and aligns with initiatives focusing on brain health in mid-life (e.g., Cognitive Health and Older Adults n.d. ).
Matchanova A., Woods S.P., Neighbors C., Medina L.D., Podell K., Beltran-Najera I., Alex C., Babicz M.A, & Thompson J.L. (2023). Are accuracy discernment and sharing of COVID-19 misinformation associated with older age and lower neurocognitive functioning?. Current Psychology (New Brunswick, N.j.).
Publication 2023
Adult Aged Age Groups Brain Cognition Middle Aged Youth
5
Funding Disparities in Science Careers
Nonparametric t tests and Kruskal-Wallis tests with posthoc Dunn correction for multiple comparisons were used to determine significance between median funding dollar amounts across groups. Multivariable logistic regression was used to determine the relative odds of women and investigators from underrepresented racial and ethnic groups of being an SPI compared with men and White investigators, respectively. Covariates include PI’s highest degree and career stage. Degree was defined as MD, MD/PhD, PhD, or other degrees. PI’s career stage was approximated using investigator’s age, categorized as early (age under 46 years), middle (age 46 to 58 years), and late (age above 58 years), as described previously.1 (link) Finally, we included 3 time periods that delineate significant changes in the NIH budget: 1991-1998 (phase 1) before the first budget increase, 1999-2014 (phase 2) between the first and second budget increase, and 2015-2020 (phase 3) after the second budget increase, and tested the interaction between phase and gender, ethnic, and racial identities to determine whether the relative odds of being an SPI for disadvantaged groups (eg, women, Black, and Hispanic) have changed over time. Adjusted percentage of SPI investigators within each combined subgroup of gender, ethnic, and racial identity was determined from the fully adjusted logistic models. Statistical tests were 2-sided with type 1 error rate of 0.05. All analyses were performed using Stata version 16.1 (Stata Inc).
Nguyen M., Chaudhry S.I., Desai M.M., Dzirasa K., Cavazos J.E, & Boatright D. (2023). Gender, Racial, and Ethnic and Inequities in Receipt of Multiple National Institutes of Health Research Project Grants. JAMA Network Open, 6(2), e230855.
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Publication 2023
Ethnic Groups Hispanics Middle Aged Woman

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More about "Middle Aged"

Exploring the Middle-Aged Population: Insights, Challenges, and Innovative Approaches The middle-aged stage of life, spanning approximately ages 45 to 64, is a pivotal period characterized by both physiological changes and a wealth of life experience.
This phase is marked by declines in certain bodily functions, but also by increased productivity and overall well-being for many individuals.
Researchers delving into the health and well-being of middle-aged populations can uncover valuable insights to enhance outcomes during this crucial stage of life.
Synonyms and related terms for the middle-aged population include middle-life, middle-adulthood, and mid-life.
Abbreviations such as MA or M-A may also be used to refer to this demographic.
Key subtopics within the middle-aged research landscape include, but are not limited to, physical health, mental well-being, chronic disease management, lifestyle factors, and the impact of socioeconomic status.
Enriching the understanding of middle-aged populations, studies involving C57BL/6J mice and C57BL/6 mice have provided valuable insights into age-related physiological changes.
Additionally, the use of statistical software like SPSS version 26 and Stata V.12 has enabled researchers to analyze data and uncover trends within middle-aged cohorts.
The Infinium HumanMethylation450 BeadChip has also been utilized to explore epigenetic modifications associated with the middle-aged stage of life.
In the realm of circadian rhythms, the ClockLab program and telemetry receivers have been employed to monitor the sleep-wake patterns of middle-aged individuals.
Furthermore, the application of DFC500 technology has assisted researchers in investigating the dynamic changes in middle-aged populations over time.
By embracing innovative tools and techniques, researchers can enhance the reproducibility and accuracy of their middle-aged studies, leading to improved research outcomes and a better understanding of this pivotal life stage.
Experienece the power of PubCompare.ai today and discover how its cutting-edge AI platform can streamline your middle-aged research endeavors.