RACE, BIOLOGICAL AGE, AND COGNITION: THE SYSTEMATIC ASSESSMENT OF GERIATRIC ELEMENTS IN ATRIAL FIBRILLATION STUDY

Abstract Atrial Fibrillation (AF) is associated with dementia and cognitive decline. AF is less prevalent among Blacks than Whites, although AF-related complications are more common in Blacks. In the general population, all-cause cognitive decline and dementia are more prevalent among Blacks than Whites. Thus, studying diverse populations with AF may advance our understanding of racial disparities in cognitive functioning. We created a measure of multisystem dysregulation (weathering), which includes but is more encompassing than aging, and examined its association with racial differences in cognition using data from the SAGE-AF study, a prospective cohort of >65-year olds with AF, at high stroke risk, and eligible for anticoagulation. Biological (as opposed to chronological) age among 974 participants was calculated using the Klemera and Doubal method using biomarkers representing physiological functioning, metabolism, and blood pressure. We defined weathering as the difference between biological and chronological age (weathering >0 indicates that biological age is higher than chronological age). We measured the association between weathering and the Montreal Cognitive Assessment (MoCA) score. Mean weathering (SD) was -0.7 (11.5) and 4.3 (12.6) for whites and non-whites, respectively. There was an interaction between race/ethnicity and weathering on cognition (P=0.004). In stratified analyses, higher weathering was associated with a lower MoCA score among both Whites and non-Whites but more so among non-whites (B = -0.09, 95% CI: -0.17, -0.02) for Whites (B = -0.03, 95% CI: -0.06, -0.01) for non-whites. Aging-related multisystem dysregulation is more strongly associated with worse cognition in non-whites than in whites.

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Differences in Left Atrial Size and Function and Supraventricular Ectopy Between Black and White Participants in the ARIC Study

Journal of the American Heart Association ◽

10.1161/jaha.121.021723 ◽

2021 ◽

Author(s):

Wendy Wang ◽

Faye L. Norby ◽

Michael J. Zhang ◽

Jorge L. Reyes ◽

Amil M. Shah ◽

...

Keyword(s):

Risk Factors ◽

Atrial Fibrillation ◽

Cardiovascular Risk ◽

Cardiovascular Risk Factors ◽

Racial Differences ◽

Left Atrial ◽

Future Research ◽

Atrial Remodeling ◽

Black And White ◽

The Difference

Background Black Americans have more atrial fibrillation risk factors but lower atrial fibrillation risk than White Americans. Left atrial (LA) enlargement and/or dysfunction, frequent atrial tachycardia (AT), and premature atrial contractions (PAC) are associated with increased atrial fibrillation risk. Racial differences in these factors may exist that could explain the difference in atrial fibrillation risk. Methods and Results We included 2133 ARIC (Atherosclerosis Risk in Communities) study participants (aged 74±4.5 years[mean±SD], 59% women, 27% Black participants) who had echocardiograms in 2011 to 2013 and wore the Zio XT Patch (a 2‐week continuous heart monitor) in 2016 to 2017. Linear regression was used to analyze (1) differences in AT/day or PAC/hour between Black and White participants, (2) differences in LA measures between Black and White participants, and (3) racial differences in the association of LA measures with AT or PAC frequency. Compared with White participants, Black participants had a higher prevalence of cardiovascular risk factors and disease, lower AT frequency, greater LA size, and lower LA function. After multivariable adjustments, Black participants had 37% (95% CI, 24%–47%) fewer AT runs/day than White participants. No difference in PAC between races was noted. Greater LA size and reduced LA function are associated with more AT and PAC runs; however, no race interaction was present. Conclusions Differences in LA measures are unlikely to explain the difference in atrial fibrillation risk between Black and White individuals. Despite more cardiovascular risk factors and greater atrial remodeling, Black participants have lower AT frequency than White participants. Future research is needed to elucidate the protective mechanisms that confer resilience to atrial arrhythmias in Black individuals.

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Relation of Atrial Fibrillation to Cognitive Decline (from the REasons for Geographic and Racial Differences in Stroke [REGARDS] Study)

The American Journal of Cardiology ◽

10.1016/j.amjcard.2021.02.036 ◽

2021 ◽

Author(s):

Margie J. Bailey ◽

Elsayed Z. Soliman ◽

Leslie A. McClure ◽

George Howard ◽

Virginia J. Howard ◽

...

Keyword(s):

Atrial Fibrillation ◽

Cognitive Decline ◽

Racial Differences ◽

Regards Study

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Distributed Counterfactual Modeling Approach for Investigating Hospital-Associated Racial Disparities in COVID-19 Mortality

10.1101/2021.09.14.21263596 ◽

2021 ◽

Author(s):

Mackenzie J Edmondson ◽

Chongliang Luo ◽

Nazmul Islam ◽

David Asch ◽

Jiang J Bian ◽

...

Keyword(s):

Racial Differences ◽

Hospital System ◽

Black And White ◽

Patient Level ◽

Modeling Approach ◽

Black Patients ◽

The Difference ◽

Using Data ◽

The Impact ◽

White Patients

Several studies have found that black patients are more likely than white patients to test positive for or be hospitalized with COVID-19, but many of these same studies have found no difference in in-hospital mortality. These studies may have underestimated racial differences due to reliance on data from a single hospital system, as adequate control of patient-level characteristics requires aggregation of highly granular data from several institutions. Further, one factor thought to contribute to disparities in health outcomes by race is site of care. Several differences between black and white patient populations, such as access to care and referral patterns among clinicians, can lead to patients of different races largely attending different hospitals. We sought to develop a method that could study the potential association between attending hospital and racial disparity in mortality for COVID-19 patients without requiring patient-level data sharing among collaborating institutions. We propose a novel application of a distributed algorithm for generalized linear mixed modeling (GLMM) to perform counterfactual modeling and investigate the role of hospital in differences in COVID-19 mortality by race. Our counterfactual modeling approach uses simulation to randomly assign black patients to hospitals in the same distribution as those attended by white patients, quantifying the difference between observed mortality rates and simulated mortality risk following random hospital assignment. To illustrate our method, we perform a proof-of-concept analysis using data from four hospitals within the OneFlorida Clinical Research Consortium. Our approach can be used by investigators from several institutions to study the impact of admitting hospital on COVID-19 mortality, a critical step in addressing systemic racism in modern healthcare.

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Measurement of the lower canine pulp chamber through periapical radiography for age estimation

Jurnal Radiologi Dentomaksilofasial Indonesia ◽

10.32793/jrdi.v5i2.710 ◽

2021 ◽

Vol 5 (2) ◽

pp. 48

Author(s):

Otty Ratna Wahyuni ◽

Deny Saputra ◽

Nastiti Faradilla Ramadhani ◽

Dennaya Listya Dias

Keyword(s):

Biological Age ◽

Chronological Age ◽

Pulp Chamber ◽

Index Method ◽

Chamber Height ◽

Lower Canine ◽

The Mean ◽

The Difference ◽

Parallel Technique ◽

Periapical Radiography

Objectives: The principle of measurement using the TCI (Tooth Coronal Index) method is to compare the pulp chamber height with a person's chronological age based on the formation of secondary dentin. The purpose of this study is to estimate age based on pulp chamber height in lower canines using periapical radiographs with TCI measurement. Materials and Methods: This study is an observational analytic study using 42 samples of periapical radiographs with the parallel technique of the lower canines. Samples were measured for CH and CPCH heights to determine TCI values and then linear regression was made to determine their biological age. Finally, the difference between biological and chronological age is calculated to determine the approximate age. Results: The mean difference between chronological age and biological age was ± 5.05 years and an average biological age of 29.38 years. Conclusion: TCI method based on pulp chamber height in lower canines using periapical radiographs can be used to estimate age with the difference between chronological age and biological age of ±5.05 years.

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The Interplay of Race/Ethnicity and Education in Fertility Patterns

10.31235/osf.io/wuxsn ◽

2021 ◽

Author(s):

Emma Zang ◽

Chloe Sariego ◽

Anirudh Krishnan

Keyword(s):

Black Mothers ◽

Black And White ◽

Educational Attainments ◽

Race Ethnicity ◽

The Difference ◽

Using Data ◽

Racial Ethnic ◽

College Educated ◽

Second Births ◽

White College

This study examines the racial/ethnic and educational disparities in fertility for U.S. women born during 1960–80. Using data from the National Survey of Family Growth from 2006 to 2017, we apply a regression-based approach to estimate 1) cohort total fertility rates, 2) parity progression ratios, and 3) parity-specific probability of having a birth by age, for non-Hispanic Whites, non-Hispanic Blacks, and Hispanics by educational attainments. We find that compared to their White counterparts, Black and Hispanic women with less than a high school education have higher fertility. However, among college educated women, Blacks have the lowest fertility levels, whereas Hispanics have the highest. The difference in fertility between Black and White college educated women is mainly driven by the smaller proportion of Black mothers having second births. We find little evidence that the observed racial disparities in fertility levels across educational levels are driven by differences in fertility timing.

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Epigenetic age is associated with baseline and 3-year change in frailty in the Canadian Longitudinal Study on Aging

Clinical Epigenetics ◽

10.1186/s13148-021-01150-1 ◽

2021 ◽

Vol 13 (1) ◽

Cited By ~ 1

Author(s):

Chris P. Verschoor ◽

David T. S. Lin ◽

Michael S. Kobor ◽

Oxana Mian ◽

Jinhui Ma ◽

...

Keyword(s):

Longitudinal Study ◽

Healthy Aging ◽

Frailty Index ◽

Sociodemographic Factors ◽

Chronological Age ◽

Short Term ◽

Epigenetic Age ◽

Health Related ◽

The Difference ◽

Using Data

Abstract Background The trajectory of frailty in older adults is important to public health; therefore, markers that may help predict this and other important outcomes could be beneficial. Epigenetic clocks have been developed and are associated with various health-related outcomes and sociodemographic factors, but associations with frailty are poorly described. Further, it is uncertain whether newer generations of epigenetic clocks, trained on variables other than chronological age, would be more strongly associated with frailty than earlier developed clocks. Using data from the Canadian Longitudinal Study on Aging (CLSA), we tested the hypothesis that clocks trained on phenotypic markers of health or mortality (i.e., Dunedin PoAm, GrimAge, PhenoAge and Zhang in Nat Commun 8:14617, 2017) would best predict changes in a 76-item frailty index (FI) over a 3-year interval, as compared to clocks trained on chronological age (i.e., Hannum in Mol Cell 49:359–367, 2013, Horvath in Genome Biol 14:R115, 2013, Lin in Aging 8:394–401, 2016, and Yang Genome Biol 17:205, 2016). Results We show that in 1446 participants, phenotype/mortality-trained clocks outperformed age-trained clocks with regard to the association with baseline frailty (mean = 0.141, SD = 0.075), the greatest of which is GrimAge, where a 1-SD increase in ΔGrimAge (i.e., the difference from chronological age) was associated with a 0.020 increase in frailty (95% CI 0.016, 0.024), or ~ 27% relative to the SD in frailty. Only GrimAge and Hannum (Mol Cell 49:359–367, 2013) were significantly associated with change in frailty over time, where a 1-SD increase in ΔGrimAge and ΔHannum 2013 was associated with a 0.0030 (95% CI 0.0007, 0.0050) and 0.0028 (95% CI 0.0007, 0.0050) increase over 3 years, respectively, or ~ 7% relative to the SD in frailty change. Conclusion Both prevalence and change in frailty are associated with increased epigenetic age. However, not all clocks are equally sensitive to these outcomes and depend on their underlying relationship with chronological age, healthspan and lifespan. Certain clocks were significantly associated with relatively short-term changes in frailty, thereby supporting their utility in initiatives and interventions to promote healthy aging.

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Gambaran Estimasi Usia Biologis dengan Menggunakan Metode Blenkin-Taylor (Modifikasi Sistem Demirjian) di Kota Semarang

e-GIGI ◽

10.35790/eg.9.1.2021.32569 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Niluh R. Woroprobosari ◽

Devina V. Wisaputri ◽

Muhammad H. Ni'am

Keyword(s):

Data File ◽

Biological Age ◽

Chronological Age ◽

Taylor Formula ◽

Cross Sectional ◽

Digital Radiograph ◽

Application Data ◽

Taylor Method ◽

The Difference ◽

Cross Sectional Design

Abstract: Unexpected incident such as natural disaster and accident often occur in many countries including Indonesia which causes many victims with unknown identity. Tooth is one of the indicators to assess and determine a person's identity. Blenkin-Taylor method is used for age estimation of an individual by using teeth. This study was aimed to obtain the estimation of biological age by using Blenkin-Taylor method in Semarang. This was a descriptive study with a cross sectional design. Samples were panoramic digital radiograph data of patients aged 5-15 years, copied in the form of a soft file. The observation and measurement were performed on seven teeth of right lower jaw by using the DICOM RadiAnt application. Data of observations and measurements of maturation scores were calculated and converted into the Blenkin-Taylor formula to determine the biological age. The results showed that the difference between biological and chronological age was ±0.32 years. This value was lower than the Blenkin-Taylor previous study result which was ±0,6 years. In conclusion, by using the Blenkin-Taylor method, there was a difference between biological age and chronological age as many as ±0,32 years in individuals aged 5-15 years old in Semarang.Keywords: biological age, the Blenkin-Taylor method, panoramic radiography Abstrak: Kejadian tidak terduga seperti bencana alam dan kecelakaan sering terjadi di berbagai negara, salah satunya di Indonesia yang menimbulkan banyak korban jiwa yang tidak diketahui identitasnya. Gigi merupakan salah satu indikator untuk menilai dan menentukan identitas seseorang. Salah satu metode dalam menentukan estimasi usia dengan menggunakan gigi ialah metode Blenkin-Taylor. Penelitian ini bertujuan untuk mendapatkan gambaran estimasi usia biologis dengan menggunakan metode Blenkin-Taylor di Kota Semarang. Jenis penelitian ialah deskriptif dengan desain potong lintang. Sampel penelitian ialah data file digital radiograf panoramik pasien berusia 5-15 tahun yang disalin ke dalam bentuk soft file, kemudian dilakukan pengamatan dan pengukuran pada 7 gigi regio kanan rahang bawah dengan menggunakan aplikasi RadiAnt DICOM. Hasil pengamatan dan pengukuran skor maturasi dihitung dan dikonversikan ke dalam rumus metode Blenkin-Taylor untuk menentukan usia biologis. Hasil penelitian menunjukkan bahwa selisih usia biologis dan usia kronologis sebesar 0,32 tahun. Hal ini lebih kecil dibandingkan penelitian Blenkin-Taylor terdahulu sebesar 0,6 tahun. Simpulan penelitian ini ialah dengan mengggunakan metode Blenkin-Taylor terdapat selisih rerata usia kronologis dan usia biologis sebesar ± 0,32 tahun pada individu usia 5-15 tahun di Kota Semarang.Kata kunci: usia biologis, metode Blenkin-Taylor, radiograf panoramik

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Racial and Urban-Rural Difference in the Frequency of Ischemic Stroke as Initial Manifestation of Atrial Fibrillation

Frontiers in Public Health ◽

10.3389/fpubh.2021.780185 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jingchuan Guo ◽

Nico Gabriel ◽

Jared W. Magnani ◽

Utibe R. Essien ◽

Walid F. Gellad ◽

...

Keyword(s):

Atrial Fibrillation ◽

Ischemic Stroke ◽

Racial Differences ◽

Urban Residents ◽

Newly Diagnosed ◽

Initial Manifestation ◽

Black Patients ◽

Increased Risk ◽

Urban Rural ◽

Race Ethnicity

Objective: Atrial fibrillation (AF) may remain undiagnosed until the development of complications. We aimed to examine the epidemiology and racial/ethnic and rural/urban differences in the frequency of newly diagnosed AF manifesting as ischemic stroke in a nationally representative sample of Medicare beneficiaries.Methods: We used a 5% random sample of Medicare claims to identify patients newly diagnosed with AF in 2016. The primary dependent variable was stroke or transient ischemic attack (TIA) in the 7 days prior to the first AF diagnosis, i.e., stroke or TIA as the initial manifestation of AF. We constructed a multivariable logistic regression to quantify the association between race/ethnicity, urban/rural residence, and the primary dependent variable.Results: Among 39,409 patients newly diagnosed with AF (mean age 77 ± 10 years; 58% women; 7.2% Black, 87.8% White, 5.1% others), 2,819 (7.2%) had ischemic stroke or TIA in the 7 days prior to AF diagnosis. Black patients (adjusted OR [95% CI]: 1.21 [1.05, 1.40], vs. White) and urban residents (1.21 [1.08, 1.35], vs. rural) were at increased risk of stroke as the initial manifestation of AF. Racial differences were larger among patients aged ≥75 years, with adjusted ORs of 1.43 (1.19, 1.73) for Black vs. White patients, but non-significant for those aged <75 (P for interaction = 0.03).Conclusion: We observed significant and important differences in the risk of stroke as initial manifestation of AF between White and Black patients and between rural and urban residents. Our results suggest potential disparities in the identification AF across race/ethnicity groups and urban/rural areas.

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Extracting biological age from biomedical data via deep learning: too much of a good thing?

10.1101/219162 ◽

2017 ◽

Author(s):

Tim Pyrkov ◽

Konstantin Slipensky ◽

Mikhail Barg ◽

Alexey Kondrashin ◽

Boris Zhurov ◽

...

Keyword(s):

Deep Learning ◽

Proportional Hazards ◽

Wearable Sensors ◽

Learning Technologies ◽

Biological Age ◽

Chronological Age ◽

Biomedical Data ◽

Care Providers ◽

Proportional Hazards Models ◽

The Difference

Aging-related physiological changes are systemic and, at least in humans, are linearly associated with age. Therefore, linear combinations of physiological measures trained to estimate chronological age have recently emerged as a practical way to quantify aging in the form of biological age. Aging acceleration, defined as the difference between the predicted and chronological age was found to be elevated in patients with major diseases and is predictive of mortality. In this work, we compare three increasingly accurate biological age models: metrics derived from unsupervised Principal Components Analysis (PCA), alongside two supervised biological age models; a multivariate linear regression and a state-of-the-art deep convolution neural network (CNN). All predictions were made using one-week long locomotor activity records from a 2003-2006 National Health and Nutrition Examination Survey (NHANES) dataset. We found that application of the supervised approaches improves the accuracy of the chronological age estimation at the expense of a loss of the association between the aging acceleration predicted by the model and all-cause mortality. Instead, we turned to the NHANES death register and introduced a novel way to train parametric proportional hazards models in a form suitable for out-of-the-box implementation with any modern machine learning software. Finally, we characterized a proof-of-concept example, a separate deep CNN trained to predict mortality risks that outperformed any of the biological age or simple linear proportional hazards models. Our findings demonstrate the emerging potential of combined wearable sensors and deep learning technologies for applications involving continuous health risk monitoring and real-time feedback to patients and care providers.

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Racial Inequalities in Alcohol Use Disorder Diagnosis in a Sample of 700,000 Veterans

10.1101/2021.07.14.21256113 ◽

2021 ◽

Author(s):

Rachel A. Vickers-Smith ◽

Amy C. Justice ◽

William C. Becker ◽

Christopher T. Rentsch ◽

Brenda Curtis ◽

...

Keyword(s):

Alcohol Consumption ◽

Alcohol Use ◽

Racial Differences ◽

Alcohol Use Disorder ◽

Diagnostic Methods ◽

Similar Distribution ◽

Racial Inequalities ◽

Black And White ◽

Race Ethnicity ◽

The Difference

Background: Studies show that Black and Hispanic Veterans have a higher prevalence of alcohol use disorder (AUD) than White Veterans. We examined whether the relationship between self–reported race/ethnicity and AUD diagnosis varies by self–reported alcohol consumption. Methods: The sample included 700,013 Black, Hispanic, and White Veterans enrolled in the Million Veteran Program cohort. Alcohol consumption was defined as an individual's maximum score on the Alcohol Use Disorders Identification Test–Consumption (AUDIT–C) questionnaire, a screen for hazardous or harmful drinking. The primary outcome, AUD, was defined by the presence of ICD–9/10 codes in the electronic health record. We used logistic regression with interactions to assess the association between race/ethnicity and AUD by maximum AUDIT–C score. Results: Black and Hispanic Veterans were more likely to have an AUD diagnosis than White Veterans despite similar levels of alcohol consumption. The difference was greatest between Black and White men. At all but the lowest and highest levels of alcohol consumption, Black men had 24%–111% greater odds of an AUD diagnosis. The association between race/ethnicity and AUD diagnosis remained after adjustment for alcohol consumption, alcohol–related disorders, and other potential confounders. Conclusions: The large discrepancy in AUD diagnosis across groups despite a similar distribution of alcohol consumption measures suggests that Veterans are differentially assigned an AUD diagnosis by race/ethnicity. Efforts are needed to examine the causes of the observed differences and to implement changes, such as structured diagnostic methods, to address a likely contributor to racial differences (i.e., bias) in AUD diagnosis.

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