scholarly journals Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer’s Disease

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Jeffrey R. Petrella ◽  
Wenrui Hao ◽  
Adithi Rao ◽  
P. Murali Doraiswamy
Keyword(s):  
Clinical Trials ◽  
Natural History ◽  
Cognitive Decline ◽  
Computational Model ◽  
Early Onset ◽  
Causal Model ◽  
Late Onset ◽  
Neuronal Loss ◽  
Prior Literature ◽  
Longitudinal Biomarker

Background. Alzheimer’s disease (AD) is a major public health concern, and there is an urgent need to better understand its complex biology and develop effective therapies. AD progression can be tracked in patients through validated imaging and spinal fluid biomarkers of pathology and neuronal loss. We still, however, lack a coherent quantitative model that explains how these biomarkers interact and evolve over time. Such a model could potentially help identify the major drivers of disease in individual patients and simulate response to therapy prior to entry in clinical trials. A current theory of AD biomarker progression, known as the dynamic biomarker cascade model, hypothesizes AD biomarkers evolve in a sequential but temporally overlapping manner. A computational model incorporating assumptions about the underlying biology of this theory and its variations would be useful to test and refine its accuracy with longitudinal biomarker data from clinical trials. Methods. We implemented a causal model to simulate time-dependent biomarker data under the descriptive assumptions of the dynamic biomarker cascade theory. We modeled pathologic biomarkers (beta-amyloid and tau), neuronal loss biomarkers, and cognitive impairment as nonlinear first-order ordinary differential equations (ODEs) to include amyloid-dependent and nondependent neurodegenerative cascades. We tested the feasibility of the model by adjusting its parameters to simulate three specific natural history scenarios in early-onset autosomal dominant AD and late-onset AD and determine whether computed biomarker trajectories agreed with current assumptions of AD biomarker progression. We also simulated the effects of antiamyloid therapy in late-onset AD. Results. The computational model of early-onset AD demonstrated the initial appearance of amyloid, followed by biomarkers of tau and neurodegeneration and the onset of cognitive decline based on cognitive reserve, as predicted by the prior literature. Similarly, the late-onset AD computational models demonstrated the first appearance of amyloid or nonamyloid-related tauopathy, depending on the magnitude of comorbid pathology, and also closely matched the biomarker cascades predicted by the prior literature. Forward simulation of antiamyloid therapy in symptomatic late-onset AD failed to demonstrate any slowing in progression of cognitive decline, consistent with prior failed clinical trials in symptomatic patients. Conclusions. We have developed and computationally implemented a mathematical causal model of the dynamic biomarker cascade theory in AD. We demonstrate the feasibility of this model by simulating biomarker evolution and cognitive decline in early- and late-onset natural history scenarios, as well as in a treatment scenario targeted at core AD pathology. Models resulting from this causal approach can be further developed and refined using patient data from longitudinal biomarker studies and may in the future play a key role in personalizing approaches to treatment.

scholarly journals Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer’s Disease

2018 ◽  
Author(s):  
Jeffrey R. Petrella ◽  
Wenrui Hao ◽  
Adithi Rao ◽  
P. Murali Doraiswamy ◽  
Keyword(s):  
Clinical Trials ◽  
Natural History ◽  
Cognitive Decline ◽  
Computational Model ◽  
Early Onset ◽  
Causal Model ◽  
Late Onset ◽  
Neuronal Loss ◽  
Prior Literature ◽  
Longitudinal Biomarker

AbstractBackgroundAlzheimer’s disease (AD) is a major public health concern and there is an urgent need to better understand its complex biology and develop effective therapies. AD progression can be tracked in patients though validated imaging and spinal fluid biomarkers of pathology and neuronal loss. We still, however, lack a coherent quantitative model that explains how these biomarkers interact and evolve over time. Such a model could potentially help identify the major drivers of disease in individual patients and simulate response to therapy prior to entry in clinical trials. A current theory of AD biomarker progression, known as the dynamic biomarker cascade model, hypothesizes AD biomarkers evolve in a sequential, but temporally overlapping manner. A computational model incorporating assumptions about the underlying biology of this theory and its variations would be useful to test and refine its accuracy with longitudinal biomarker data from clinical trials.MethodsWe implemented a causal model to simulate time-dependent biomarker data under the descriptive assumptions of the dynamic biomarker cascade theory. We modeled pathologic biomarkers (beta-amyloid and tau), neuronal loss biomarkers and cognitive impairment as non-linear first order ordinary differential equations (ODEs) to include amyloid-dependent and non-dependent neurodegenerative cascades. We tested the feasibility of the model by adjusting its parameters to simulate three specific natural history scenarios in early-onset autosomal dominant AD and late-onset AD, and determine whether computed biomarker trajectories agreed with current assumptions of AD biomarker progression. We also simulated the effects of anti-amyloid therapy in late-onset AD.ResultsThe computational model of early-onset AD demonstrated the initial appearance of amyloid, followed by biomarkers of tau and neurodegeneration, followed by onset of cognitive decline based on cognitive reserve, as predicted by prior literature. Similarly, the late-onset AD computational models demonstrated the first appearance of amyloid or non-amyloid-related tauopathy, depending on the magnitude of comorbid pathology, and also closely matched the biomarker cascades predicted by prior literature. Forward simulation of anti-amyloid therapy in symptomatic late-onset AD failed to demonstrate any slowing in progression of cognitive decline, consistent with prior failed clinical trials in symptomatic patients.ConclusionWe have developed and computationally implemented a mathematical causal model of the dynamic biomarker cascade theory in AD. We demonstrate the feasibility of this model by simulating biomarker evolution and cognitive decline in early and late-onset natural history scenarios, as well as in a treatment scenario targeted at core AD pathology. Models resulting from this causal approach can be further developed and refined using patient data from longitudinal biomarker studies, and may in the future play a key role in personalizing approaches to treatment.

scholarly journals Latent profile analysis of cognitive decline and depressive symptoms after intracerebral hemorrhage

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sophia Keins ◽  
Jessica R. Abramson ◽  
Juan Pablo Castello ◽  
Marco Pasi ◽  
Andreas Charidimou ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Intracerebral Hemorrhage ◽  
Cognitive Decline ◽  
Early Onset ◽  
Latent Profile Analysis ◽  
Late Onset ◽  
Profile Analysis ◽  
Clinical Genetic ◽  
Dementia Risk ◽  
Latent Profile

Abstract Background Cognitive impairment and depressive symptoms are highly prevalent after Intracerebral Hemorrhage (ICH). We leveraged Latent Profile Analysis (LPA) to identify profiles for cognitive decline and depression onset after ICH. We also investigated differences in clinical, genetic and neuroimaging characteristics across patients’ profiles. Methods We analyzed data from the ICH study conducted at Massachusetts General Hospital between January 1998 and December 2019. We collected information from electronical health records, follow-up interviews, CT and MRI imaging, and APOE genotype. We conducted LPA and multinomial logistic regression analyses to: 1) identify distinct profiles for cognitive decline and depression onset after ICH; 2) identify clinical, neuroimaging and genetic factors predicting individuals’ likelihood to express a specific profile. Results We followed 784 ICH survivors for a median of 45.8 months. We identified four distinct profiles in cognitive and depressive symptoms after ICH: low depression and dementia risk, early-onset depression and dementia, late-onset depression and dementia, high depression with low dementia risk. Cerebral small vessel disease severity and APOE genotype were specifically associated with the late-onset profile (both p < 0.05). Acute hematoma characteristics (size, intraventricular extension) and functional disability were specifically associated with the early-onset profile (all p < 0.05). Conclusion We identified four distinct profiles for cognitive and depressive symptoms after ICH, each displaying specific associations with individual patients’ clinical, genetic and neuroimaging data. These associations reflect separate biological mechanisms influencing dementia and depression risk after ICH. Our findings support employing LPA in future ICH studies, and is likely applicable to stroke survivors at large.

Most rapid cognitive decline in APOE ε4 negative Alzheimer's disease with early onset

2009 ◽  
Vol 39 (11) ◽  
pp. 1907-1911 ◽  
Author(s):  
A. E. van der Vlies ◽  
E. L. G. E. Koedam ◽  
Y. A. L. Pijnenburg ◽  
J. W. R. Twisk ◽  
P. Scheltens ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Early Onset ◽  
Late Onset ◽  
Age At Onset ◽  
Apoe Genotype ◽  
Apoe Ε4 ◽  
Rapid Cognitive Decline ◽  
State Examination

BackgroundWe aimed to compare the rate of cognitive decline in patients with early and late onset Alzheimer's disease (AD) and to investigate the potentially modifying influence of the apolipoprotein E (APOE) genotype.MethodWe included 99 patients with early onset AD (age ⩽65 years) and 192 patients with late onset AD (age >65 years) who had at least two scores on the Mini-Mental State Examination (MMSE) (range 2–14) obtained at least 1 year apart. Linear mixed models were performed to investigate the rate of cognitive decline dependent on age at onset (AAO) and APOE genotype.ResultsThe mean (s.d.) age for patients with early onset AD was 57.7 (4.5) years, and 74.5 (5.1) years for patients with late onset AD. AAO was not associated with baseline MMSE [β (s.e.)=0.8 (0.5), p=0.14]. However, patients with early onset showed a faster decline on the MMSE [β (s.e.)=2.4 (0.1) points/year] than those with late onset [β (s.e.)=1.7 (0.1) points/year, p=0.00]. After stratification according to APOE genotype, APOE ε4 non-carriers with early onset showed faster cognitive decline than non-carriers with late onset [2.4 (0.3) v. 1.3 (0.3) points/year, p=0.01]. In APOE ε4 carriers, no difference in rate of cognitive decline was found between patients with early and late onset [β (s.e.)=0.2 (0.2), p=0.47].ConclusionPatients with early onset AD show more rapid cognitive decline than patients with late onset, suggesting that early onset AD follows a more aggressive course. Furthermore, this effect seems to be most prominent in patients with early onset who do not carry the genetic APOE ε4 risk factor for AD.

scholarly journals Age of onset of cannabis use and decision making under uncertainty

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5201 ◽  
Author(s):  
Jose Ramón Alameda-Bailén ◽  
Pilar Salguero-Alcañiz ◽  
Ana Merchán-Clavellino ◽  
Susana Paíno-Quesada
Keyword(s):  
Decision Making ◽  
Computational Model ◽  
Loss Aversion ◽  
Early Onset ◽  
Iowa Gambling Task ◽  
Age Of Onset ◽  
Late Onset ◽  
Cannabis Use ◽  
Model Parameters ◽  
And Control

Objective Cannabis, like other substances, negatively affects health, inducing respiratory problems and mental and cognitive alterations. Memory and learning disorders, as well as executive dysfunctions, are also neuropsychological disorders associated to cannabis use. Recent evidence reveals that cannabis use during adolescence may disrupt the normal development of the brain. This study is aimed to analyze possible differences between early-onset and late-onset cannabis consumers. Method We used a task based on a card game with four decks and different programs of gains/losses. A total of 72 subjects (19 women; 53 men) participated in the study; they were selected through a purposive sampling and divided into three groups: early-onset consumers, late-onset consumers, and control (non-consumers). The task used was the “Cartas” program (computerized version based on the Iowa Gambling Task (IGT)), with two versions: direct and inverse. The computational model “Prospect Valence Learning” (PVL) was applied in order to describe the decision according to four characteristics: utility, loss aversion, recency, and consistency. Results The results evidence worst performance in the IGT in the early-onset consumers as compared to late-onset consumers and control. Differences between groups were also found in the PVL computational model parameters, since the process of decision making of the early-onset consumers was more influenced by the magnitude of the gains-losses, and more determined by short-term results without loss aversion. Conclusions Early onset cannabis use may involve decision-making problems, and therefore intervention programs are necessary in order to reduce the prevalence and delay the onset of cannabis use among teenagers.

scholarly journals Early-but Not Late-Onset Hypertension Is Related to Midlife Cognitive Function

Hypertension ◽  
2021 ◽  
Vol 77 (3) ◽  
pp. 972-979
Author(s):  
Karri Suvila ◽  
Joao A.C. Lima ◽  
Yuichiro Yano ◽  
Zaldy S. Tan ◽  
Susan Cheng ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cognitive Function ◽  
Cognitive Decline ◽  
Early Onset ◽  
Cognitive Assessment ◽  
Late Onset ◽  
Montreal Cognitive Assessment ◽  
Digit Symbol Substitution Test ◽  
Digit Symbol ◽  
Symbol Substitution

Hypertension is related to increased risk of cognitive decline in a highly age-dependent manner. However, conflicting evidence exists on the relation between age of hypertension onset and cognition. Our goal was to investigate the association between early- versus late-onset hypertension and midlife cognitive performance in 2946 CARDIA study (Coronary Artery Risk Development in Young Adults) participants (mean age 55±4, 57% women). The participants underwent 9 repeat examinations, including blood pressure measurements, between 1985 to 1986 and 2015 to 2016. The participants underwent brain magnetic resonance imaging and completed Digit Symbol Substitution Test, Rey Auditory Verbal Learning Test, Stroop interference test, and the Montreal Cognitive Assessment to evaluate cognitive function at the year 30 exam. We assessed the relation between age of hypertension onset and cognitive function using linear regression models adjusted for cognitive decline risk factors, including systolic blood pressure. We observed that individuals with early-onset hypertension (onset at <35 years) had 0.24±0.09, 0.22±0.10, 0.27±0.09, and 0.19±0.07 lower standardized Z-scores in Digit Symbol Substitution Test, Stroop test, Montreal Cognitive Assessment, and a composite cognitive score than participants without hypertension ( P <0.05 for all). In contrast, hypertension onset at ≥35 years was not associated with cognitive function ( P  >0.05 for all). In a subgroup of 559 participants, neither early- nor late-onset hypertension was related to macrostructural brain alterations ( P  >0.05 for all). Our results indicate that early-onset hypertension is a potent risk factor for midlife cognitive impairment. Thus, age of hypertension onset assessment in clinical practice could improve risk stratification of cognitive decline in patients with hypertension.

NEONATAL SEPSIS AND IDENTIFICATION OF RISK FACTORS: STUDY IN AVBRH HOSPITAL SAWANGI

2019 ◽  
Vol 3 (6) ◽  
Author(s):  
Pramod P. Singhavi
Keyword(s):  
Risk Factors ◽  
Neonatal Sepsis ◽  
Early Onset ◽  
Late Onset ◽  
Signs And Symptoms ◽  
Premature Rupture ◽  
Maternal Risk ◽  
Late Onset Sepsis ◽  
Early Onset Sepsis ◽  
Meconium Stained Amniotic Fluid

Introduction: India has the highest incidence of clinical sepsis i.e.17,000/ 1,00,000 live births. In Neonatal sepsis septicaemia, pneumonia, meningitis, osteomyelitis, arthritis and urinary tract infections can be included. Mortality in the neonatal period each year account for 41% (3.6 million) of all deaths in children under 5 years and most of these deaths occur in low income countries and about one million of these deaths are due to infectious causes including neonatal sepsis, meningitis, and pneumonia. In early onset neonatal sepsis (EOS) Clinical features are non-specific and are inefficient for identifying neonates with early-onset sepsis. Culture results take up to 48 hours and may give false-positive or low-yield results because of the antenatal antibiotic exposure. Reviews of risk factors has been used globally to guide the development of management guidelines for neonatal sepsis, and it is similarly recommended that such evidence be used to inform guideline development for management of neonatal sepsis. Material and Methods: This study was carried out using institution based cross section study . The total number neonates admitted in the hospital in given study period was 644, of which 234 were diagnosed for neonatal sepsis by the treating pediatrician based on the signs and symptoms during admission. The data was collected: Sociodemographic characteristics; maternal information; and neonatal information for neonatal sepsis like neonatal age on admission, sex, gestational age, birth weight, crying immediately at birth, and resuscitation at birth. Results: Out of 644 neonates admitted 234 (36.34%) were diagnosed for neonatal sepsis by the paediatrician based on the signs and symptoms during admission. Of the 234 neonates, 189 (80.77%) infants were in the age range of 0 to 7 days (Early onset sepsis) while 45 (19.23%) were aged between 8 and 28 days (Late onset sepsis). Male to female ratio in our study was 53.8% and 46% respectively. Out of total 126 male neonates 91(72.2%) were having early onset sepsis while 35 (27.8%) were late onset type. Out of total 108 female neonates 89(82.4%) were having early onset sepsis while 19 (17.6%) were late onset type. Maternal risk factors were identified in 103(57.2%) of early onset sepsis cases while in late onset sepsis cases were 11(20.4%). Foul smelling liquor in early onset sepsis and in late onset sepsis was 10(5.56%) and 2 (3.70%) respectively. In early onset sepsis cases maternal UTI, Meconium stained amniotic fluid, Multipara and Premature rupture of membrane was seen in 21(11.67%), 19 (10.56%), 20(11.11%) and 33 (18.33%) cases respectively. In late onset sepsis cases maternal UTI, Meconium stained amniotic fluid, Multipara and Premature rupture of membrane was seen in 2 (3.70%), 1(1.85%), 3 (5.56%) and 3 (5.56%) cases respectively. Conclusion: Maternal risk identification may help in the early identification and empirical antibiotic treatment in neonatal sepsis and thus mortality and morbidity can be reduced.

Bridging the Knowledge Gaps in Arrhythmogenic Cardiovascular Conditions: The Critical Role of Registries

2016 ◽  
Vol 10 (2) ◽  
pp. 1 ◽  
Author(s):  
Melody Hermel ◽  
Rebecca Duffy ◽  
Alexander Orfanos ◽  
Isabelle Hack ◽  
Shayna McEnteggart ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Natural History ◽  
Risk Stratification ◽  
Critical Role ◽  
Knowledge Gaps ◽  
Level Of Evidence ◽  
The Future

Cardiac registries have filled many gaps in knowledge related to arrhythmogenic cardiovascular conditions. Despite the less robust level of evidence available in registries when compared with clinical trials, registries have contributed a range of clinically useful information. In this review, the authors discuss the role that registries have played – related to diagnosis, natural history, risk stratification, treatment, and genetics of arrhythmogenic cardiovascular conditions – in closing knowledge gaps, and their role in the future.

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