scholarly journals Biological Aging, Mortality, and Alzheimer’s Disease Related Biomarkers from Midlife to Old Age

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 666-666
Author(s):  
Carol Franz ◽  
Erik Buchholz ◽  
Amy Yongmei Qin ◽  
Xin Tu ◽  
William Kremen
Keyword(s):  
Learning Algorithm ◽  
Frailty Index ◽  
Community Dwelling ◽  
Physiological Age ◽  
Biological Aging ◽  
Age Difference ◽  
Machine Learning Algorithm ◽  
Neurofilament Light ◽  
Subcortical Regions ◽  
Brain Age

Abstract People age at different rates and in different biological systems that may differentially contribute to accelerated decline. Better understanding of biological aging may contribute to identification of better targets for intervention. In 1005 VETSA participants we created 3 indicators of biological age: physiological age (PA), frailty, and brain age. PA included hemoglobin, glucose, lipids, height, weight, waist, systolic and diastolic blood pressure, and age. PA was calculated using the Klemera and Doubal (2006) method. The frailty index summed 37 health deficits (Jiang et al. 2017). A machine learning algorithm was used to estimate brain age across cortical and subcortical regions (Liem et al, 2017); predicted brain age subtracted from chronological age comprised the predicted brain age difference score (PBAD). Frailty and PBAD were calculated at waves 1, 2 and 3 when participants were average age 56, 62, and 68, respectively. PA markers were only available at waves 2 and 3. Outcome measures included mortality by wave 3 and scores on AD-related plasma biomarkers—Neurofilament light (NFL), Tau, and AB40 and AB42 at wave 3. Frailty at wave 1 and 2 predicted mortality. Frailty at wave 1 was significantly associated with wave 3 NFL, AB42 and AB40. Wave 2 & 3 frailty was associated with all biomarkers. Neither PA nor PBAD predicted biomarkers or mortality. The results are striking given the relatively young age of the sample. Even as early as one’s 50s, frailty in a community-dwelling sample predicted accelerated decline and mortality when the outcome age was only 66-73.

scholarly journals Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter?

2021 ◽  
Vol 310 ◽  
pp. 111270
Author(s):  
Won Hee Lee ◽  
Mathilde Antoniades ◽  
Hugo G Schnack ◽  
Rene S. Kahn ◽  
Sophia Frangou
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Brain Age ◽  
Age Prediction

scholarly journals 1195Brain-predicted age difference is associated with cognitive processing and delayed recall in later life

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Jo Wrigglesworth ◽  
Nurathifah Yaacob ◽  
Phillip Ward ◽  
Robyn Woods ◽  
John McNeil ◽  
...  
Keyword(s):  
Community Dwelling ◽  
Chronological Age ◽  
Accelerated Ageing ◽  
Age Difference ◽  
Psychomotor Speed ◽  
Delayed Recall ◽  
Memory Decline ◽  
Brain Ageing ◽  
Community Dwelling Older Adults ◽  
Brain Age

Abstract Background Brain age is a novel neuroimaging-based marker of ageing that uses machine learning to predict a person’s biological brain age. A higher brain age relative to chronological age (i.e., brain-predicted age difference [brain-PAD]) is considered a sign of accelerated ageing. We examined whether brain-PAD is associated with cognition and the change in cognitive function over time. Methods This study involved 531 cognitively healthy community-dwelling older adults (70+ years). Using a previously trained algorithm, brain age was estimated using T1-weighted structural magnetic resonance images acquired at baseline. Psychomotor speed, delayed recall, verbal fluency and global cognition were assessed at baseline, years 1 and 3. Results At baseline, a significant negative association was observed between brain-PAD and psychomotor speed (r=-0.14, p = 0.001), delayed recall (r=-0.09, p = 0.04), and the three-year change in delayed recall (r=-0.15, p = 0.02), which persisted after adjusting for covariates. Conclusions These findings indicate that accelerated brain ageing in cognitively unimpaired older people is associated with worse psychomotor speed, and delayed recall. This study also provides new evidence that accelerated brain ageing is a risk factor for progressive memory decline. Future research would benefit from further prospective analyses of associations between brain-PAD and cognitive function in community dwelling older adults. Key messages Brain age is a neuroimaging-based marker of biological ageing. A higher estimate of brain age relative to chronological age (i.e., accelerated ageing) is associated with worse psychomotor speed and memory, and memory decline.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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