scholarly journals Using a genetic risk score to estimate the earliest age of Alzheimer’s disease-related physiologic change in Body Mass Index

2019 ◽  
Author(s):  
Willa D. Brenowitz ◽  
Scott C. Zimmerman ◽  
Teresa J. Filshtein ◽  
Kristine Yaffe ◽  
Stefan Walter ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Body Mass Index ◽  
Weight Loss ◽  
Body Mass ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Genetic Ancestry ◽  
Uk Biobank

AbstractObjectivesWeight loss is common in the years before an Alzheimer’s disease (AD) diagnosis, likely due to changes in appetite and diet. The age at which this change in body mass index (BMI) emerges is unclear but may point to the earliest manifestations of AD, timing that may be important for identifying windows of intervention or risk reduction. We examined the association between AD genetic risk and cross-sectional BMI across adults in mid-to late-life as an innovative approach to determine the age at which BMI changes and may indicate preclinical AD.DesignObservational studySettingUK BiobankParticipants407,386 UK Biobank non-demented participants aged 39-70 with Caucasian genetic ancestry enrolled 2007-2010.Main Outcome MeasuresBMI (kg/m2) was constructed from height and weight measured during the initial visit. A genetic risk score for AD (AD-GRS) was calculated as a weighted sum of 23 genetic variants previously confirmed to be genome-wide significant predictors of AD (Z-scored). We evaluated whether the association of AD-GRS with BMI differed by age using linear regression with adjustment for sex and genetic ancestry, stratified by age grouping (40-60, 61+). We calculated the earliest age at which high AD-GRS predicted divergence in BMI compared to normal age-related BMI trends with linear and quadratic terms for age and interactions with AD-GRS.ResultsIn 39-49 year olds, AD-GRS was not significantly associated with lower BMI (0.00 kg/m2 per SD in AD-GRS; 95%CI: -0.03,0.03). In 50-59 year olds AD-GRS was associated with lower BMI (-0.03 kg/m2 per 1 SD in AD-GRS; 95%CI:-0.06,-0.01) and this association was stronger in 60-70 year olds (-0.09 kg/m2 per 1 SD in AD-GRS; 95%CI:-0.12,-0.07). Model-based BMI age-curves for people with high versus low AD-GRS scores began to diverge after age 47.InterpretationGenetic factors that increase AD risk begin to predict lower BMI in adults by age 50, with greater effect later in older ages. Weight loss may manifest as an early pathophysiologic change associated with AD.

Extension of Mendelian Randomization to Identify Earliest Manifestations of Alzheimer’s Disease: Genetic Risk Score for Alzheimer’s Disease Reduces BMI by Age 50

2021 ◽  
Author(s):  
Willa D Brenowitz ◽  
Scott C Zimmerman ◽  
Teresa J Filshtein ◽  
Kristine Yaffe ◽  
Stefan Walter ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Weight Loss ◽  
Risk Score ◽  
Genetic Risk ◽  
Mendelian Randomization ◽  
Late Onset ◽  
Causal Effect ◽  
Genetic Risk Score ◽  
Sensitivity Analyses

Abstract Weight loss or lower Body Mass Index (BMI) may be an early symptom of Alzheimer’s disease (AD) but when this begins to emerge is difficult to estimate with traditional observational data. In an extension of Mendelian randomization, we used genetic risk for late-onset AD risk to estimate the causal effect of AD on BMI and the earliest ages at which AD-related weight loss (or lower BMI as a proxy) occurs. 407,386 UK Biobank participants enrolled 2007-2010 without dementia, aged 39-73, with Caucasian genetic ancestry, with BMI (kg/m2), and an AD genetic risk score (AD-GRS) based on 23 genetic variants. Using linear regressions, we tested the association of AD-GRS with BMI stratified by decade and calculated the age of divergence in BMI-trends between low and high AD-GRS. AD-GRS was not associated with BMI in 39-49 year-olds (β:0.00;95%CI:-0.03,0.03). AD-GRS was associated with lower BMI in 50-59 year-olds (β:-0.03; 95%CI:-0.06,-0.01) and 60-73 year-olds (β:-0.09;95%CI:-0.12,-0.07). Model-based BMI age-curves for high versus low AD-GRS began to diverge after age 47. Sensitivity analyses found no evidence for pleiotropy or survival bias. Longitudinal replication is needed; however, our findings suggest that AD genes may begin to reduce BMI decades prior to dementia diagnosis.

scholarly journals Genetic risk score for Alzheimer’s disease is associated with poor hearing

2019 ◽  
Author(s):  
Willa D. Brenowitz ◽  
Teresa J. Filshtein ◽  
Kristine Yaffe ◽  
Stefan Walter ◽  
Thomas J. Hoffmann ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hearing Loss ◽  
Risk Score ◽  
Genetic Risk ◽  
Background Noise ◽  
Genetic Risk Score ◽  
Genetic Ancestry ◽  
Logistic Regression Models ◽  
Objectively Measured

AbstractIntroductionIncipient dementia or shared genetics may partly explain the association between hearing loss and dementia. We evaluated whether genetic variants known to increase Alzheimer’s disease (AD) also influence hearing difficulty.MethodsUK Biobank participants aged 56+ with Caucasian genetic ancestry self-reported difficulty hearing and hearing with background noise (n=244,915) and underwent objectively measured hearing assessments (n=80,074). Poor objective hearing was defined as >-5.5 dB speech reception threshold on a Digit Triplet Test. We evaluated whether an AD genetic risk score (AD-GRS; range −1.2 to 1.9), the weighted sum of 23 previously identified AD-related polymorphisms, predicted objective or self-reported poor hearing, using age, sex, and genetic ancestry adjusted logistic regression models.ResultsHigher AD-GRS predicted objectively measured poor hearing (OR=1.06; 95% CI: 1.01, 1.11) and self-reported problems hearing with background noise (OR=1.03; 95% CI: 1.00, 1.05).DiscussionUsing novel methods, we found evidence that AD genetic risk influences hearing loss.

scholarly journals Phenome‐wide associations with an Alzheimer’s disease genetic risk score in UK Biobank: Identifying early indicators of disease

2020 ◽  
Vol 16 (S10) ◽  
Author(s):  
Scott C Zimmerman ◽  
Sarah F Ackley ◽  
Willa D Brenowitz ◽  
Rebecca E Graff ◽  
M Maria Glymour
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Uk Biobank ◽  
Early Indicators

scholarly journals Association of a Genetic Risk Score With Body Mass Index

JAMA ◽  
2016 ◽  
Vol 316 (17) ◽  
pp. 1825
Author(s):  
Marcus R. Munafò ◽  
Kate Tilling ◽  
George Davey Smith
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score

scholarly journals Association of a Genetic Risk Score With Body Mass Index Across Different Birth Cohorts

JAMA ◽  
2016 ◽  
Vol 316 (1) ◽  
pp. 63 ◽  
Author(s):  
Stefan Walter ◽  
Iván Mejía-Guevara ◽  
Karol Estrada ◽  
Sze Y. Liu ◽  
M. Maria Glymour
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Birth Cohorts

Association of a Genetic Risk Score With Body Mass Index

JAMA ◽  
2016 ◽  
Vol 316 (17) ◽  
pp. 1826
Author(s):  
Hexuan Liu ◽  
Guang Guo
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score

scholarly journals 1152 Genetic Risk Of Alzheimer’S Disease Is Linked To Short Sleep Duration

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A439-A439
Author(s):  
Y Leng ◽  
K Yaffe ◽  
S Ackley ◽  
M Glymour ◽  
W Brenowitz
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Sleep Duration ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Genetic Ancestry ◽  
European Ancestry ◽  
Short Sleep Duration ◽  
Short Sleep

Abstract Introduction Sleep disturbances including short sleep duration are common in older adults, especially in those with Alzheimer’s disease (AD). However, it is unclear to what extent sleep duration is a manifestation of AD disease process. We examined whether genetic variants related to AD influence sleep duration in middle-aged and older adults and estimated the causal effects of AD on sleep duration using a mendelian randomization (MR) analysis. Methods We examined 406,687 UK Biobank participants with Caucasian genetic ancestry who self-reported sleep duration at baseline (2006-2010). Sleep duration was assessed by asking: “About how many hours sleep do you get in every 24 hours? (please include naps).” A genetic risk score for AD (AD-GRS) was calculated as a weighted sum of 23 previously identified AD-related single nucleotide polymorphisms in individuals of European ancestry. We evaluated whether AD-GRS predicted sleep duration using linear regression, adjusting for age, sex and principle components for genetic ancestry. We also stratified the analysis by age at baseline (≤55y or >55y) and conducted a MR analysis to estimate the effect of AD (ICD-9/10 codes for AD/dementia diagnosis) on sleep duration. Results The participants (aged 56.91±8.00y) had an average sleep duration of 7.2 (Standard deviation [SD]=1.1) hours and AD-GRS of 0.11 (SD=0.40) (range: -1.15~1.85). Higher AD-GRS score predicted shorter sleep duration (b= -0.013, 95%CI:-0.022,-0.005), mainly among those aged over 55y (b= -0.023, 95%CI:-0.034,-0.012) and not in those 55y or younger (b= 0.006, 95%CI:-0.012,0.013); p for interaction by age=0.02. MR analysis using AD-GRS as an instrumental variable suggested that AD was associated with 1.76 hrs (b=-1.76, -2.62~ -0.90) shorter sleep duration in those aged >55y. Conclusion Using a novel analytical approach, we found that higher genetic risk for AD predicted shorter sleep duration among older adults. This suggests shared genetic pathways; the biologic processes that lead to AD may also affect sleep duration. Support Dr. Leng received support from the National Institute on Aging (NIA) 1K99AG056598, and from GBHI, Alzheimer’s Association, and Alzheimer’s Society (GBHI ALZ UK-19-591141).

scholarly journals Interaction of a genetic risk score with physical activity, physical inactivity, and body mass index in relation to venous thromboembolism risk

2018 ◽  
Vol 42 (4) ◽  
pp. 354-365 ◽  
Author(s):  
Jihye Kim ◽  
Peter Kraft ◽  
Kaitlin A. Hagan ◽  
Laura B. Harrington ◽  
Sara Lindstroem ◽  
...  
Keyword(s):  
Physical Activity ◽  
Body Mass Index ◽  
Venous Thromboembolism ◽  
Body Mass ◽  
Risk Score ◽  
Genetic Risk ◽  
Physical Inactivity ◽  
Genetic Risk Score
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