Evaluating the Neuroimaging-Genetic Prediction of Symptom Changes in Individuals with ADHD

AbstractIn recent decades, ongoing GWAS findings discovered novel therapeutic modifications such as whole-genome risk prediction in particular. Here, we proposed a method based on integrating the traditional genomic best linear unbiased prediction (gBLUP) approach with GWAS information to boost genetic prediction accuracy and gene-based heritability estimation. This study was conducted in the framework of the Tehran Cardio-metabolic Genetic study (TCGS) containing 14,827 individuals and 649,932 SNP markers. Five SNP subsets were selected based on GWAS results: top 1%, 5%, 10%, 50% significant SNPs, and reported associated SNPs in previous studies. Furthermore, we randomly selected subsets as large as every five subsets. Prediction accuracy has been investigated on lipid profile traits with a tenfold and 10-repeat cross-validation algorithm by the gBLUP method. Our results revealed that genetic prediction based on selected subsets of SNPs obtained from the dataset outperformed the subsets from previously reported SNPs. Selected SNPs’ subsets acquired a more precise prediction than whole SNPs and much higher than randomly selected SNPs. Also, common SNPs with the most captured prediction accuracy in the selected sets caught the highest gene-based heritability. However, it is better to be mindful of the fact that a small number of SNPs obtained from GWAS results could capture a highly notable proportion of variance and prediction accuracy.

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Acute symptoms of mild to moderate COVID-19 are highly heterogeneous across individuals and over time

Open Forum Infectious Diseases ◽

10.1093/ofid/ofab090 ◽

2021 ◽

Author(s):

Thomas L Rodebaugh ◽

Madelyn R Frumkin ◽

Angela M Reiersen ◽

Eric J Lenze ◽

Michael S Avidan ◽

...

Keyword(s):

Temporal Dynamics ◽

Survey Methods ◽

Clinical Care ◽

Clinical Deterioration ◽

Symptom Profiles ◽

Fluctuating Course ◽

The Common ◽

Symptom Changes ◽

Over Time

Abstract Background The symptoms of COVID-19 appear to be heterogenous, and the typical course of these symptoms is unknown. Our objectives were to characterize the common trajectories of COVID-19 symptoms and assess how symptom course predicts other symptom changes as well as clinical deterioration. Methods 162 participants with acute COVID-19 responded to surveys up to 31 times for up to 17 days. Several statistical methods were used to characterize the temporal dynamics of these symptoms. Because nine participants showed clinical deterioration, we explored whether these participants showed any differences in symptom profiles. Results Trajectories varied greatly between individuals, with many having persistently severe symptoms or developing new symptoms several days after being diagnosed. A typical trajectory was for a symptom to improve at a decremental rate, with most symptoms still persisting to some degree at the end of the reporting period. The pattern of symptoms over time suggested a fluctuating course for many patients. Participants who showed clinical deterioration were more likely to present with higher reports of severity of cough and diarrhea. Conclusion The course of symptoms during the initial weeks of COVID-19 is highly heterogeneous and is neither predictable nor easily characterized using typical survey methods. This has implications for clinical care and early-treatment clinical trials. Additional research is needed to determine whether the decelerating improvement pattern seen in our data is related to the phenomenon of patients reporting long-term symptoms, and whether higher symptoms of diarrhea in early illness presages deterioration.

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Understanding symptom appraisal and help-seeking in people with symptoms suggestive of pancreatic cancer: a qualitative study

BMJ Open ◽

10.1136/bmjopen-2016-015682 ◽

2017 ◽

Vol 7 (9) ◽

pp. e015682 ◽

Cited By ~ 6

Author(s):

Katie Mills ◽

Linda Birt ◽

Jon D Emery ◽

Nicola Hall ◽

Jonathan Banks ◽

...

Keyword(s):

Pancreatic Cancer ◽

Help Seeking ◽

Survival Rates ◽

Initial Symptom ◽

Self Medication ◽

Gastrointestinal Conditions ◽

Symptom Appraisal ◽

Age Range ◽

Specific Symptoms ◽

Symptom Changes

ObjectivePancreatic cancer has poor survival rates due to non-specific symptoms leading to later diagnosis. Understanding how patients interpret their symptoms could inform approaches to earlier diagnosis. This study sought to explore symptom appraisal and help-seeking among patients referred to secondary care for symptoms suggestive of pancreatic cancer.DesignQualitative analysis of semistructured in-depth interviews. Data were analysed iteratively and thematically, informed by the Model of Pathways to Treatment.Participants and settingPancreatic cancer occurs rarely in younger adults, therefore patients aged ≥40 years were recruited from nine hospitals after being referred to hospital with symptoms suggestive of pancreatic cancer; all were participants in a cohort study. Interviews were conducted soon after referral, and where possible, before diagnosis.ResultsTwenty-six interviews were conducted (cancer n=13 (pancreas n=9, other intra-abdominal n=4), non-cancer conditions n=13; age range 48–84 years; 14 women). Time from first symptoms to first presentation to healthcare ranged from 1 day to 270 days, median 21 days. We identified three main themes. Initial symptom appraisal usually began with intermittent, non-specific symptoms such as tiredness or appetite changes, attributed to diet and lifestyle, existing gastrointestinal conditions or side effects of medication. Responses to initial symptom appraisal included changes in meal type or frequency, or self-medication. Symptom changes such as alterations in appetite and enjoyment of food or weight loss usually prompted further appraisal. Triggers to seek help included a change or worsening of symptoms, particularly pain, which was often a ‘tipping point’. Help-seeking was often encouraged by others. We found no differences in symptom appraisal and help-seeking between people diagnosed with cancer and those with other conditions.ConclusionsGreater public and healthcare professional awareness of the combinations of subtle and intermittent symptoms, and their evolving nature, is needed to prompt timelier help-seeking and investigation among people with symptoms of pancreatic cancer.

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Mood and Global Symptom Changes among Psychotherapy Clients with Depressive Personality

Depression Research and Treatment ◽

10.1155/2012/208435 ◽

2012 ◽

Vol 2012 ◽

pp. 1-12

Author(s):

Rachel E. Maddux ◽

Lars-Gunnar Lundh

Keyword(s):

Repeated Measures ◽

Mixed Model ◽

Psychological Symptoms ◽

Self Report ◽

Treatment Results ◽

Repeated Measures Analysis ◽

Depressive Personality ◽

Symptom Changes ◽

Psychotherapy Clients ◽

Over Time

The present study assessed the rate of depressive personality (DP), as measured by the self-report instrument depressive personality disorder inventory (DPDI), among 159 clients entering psychotherapy at an outpatient university clinic. The presenting clinical profile was evaluated for those with and without DP, including levels of depressed mood, other psychological symptoms, and global severity of psychopathology. Clients were followed naturalistically over the course of therapy, up to 40 weeks, and reassessed on these variables again after treatment. Results indicated that 44 percent of the sample qualified for DP prior to treatment, and these individuals had a comparatively more severe and complex presenting disposition than those without DP. Mixed-model repeated-measures analysis of variance was used to examine between-groups changes on mood and global severity over time, with those with DP demonstrating larger reductions on both outcome variables, although still showing more symptoms after treatment, than those without DP. Only eleven percent of the sample continued to endorse DP following treatment. These findings suggest that in routine clinical situations, psychotherapy may benefit individuals with DP.

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260 Genetic prediction for first-service conception rate in Angus heifers using a random regression model

Journal of Animal Science ◽

10.1093/jas/skaa278.364 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 197-198

Author(s):

Miguel A Sánchez-Castro ◽

Milt Thomas ◽

Mark Enns ◽

Scott Speidel

Keyword(s):

Conception Rate ◽

Random Regression ◽

State University ◽

Base Function ◽

Genetic Prediction ◽

Colorado State University ◽

Random Regression Model ◽

Service Conception ◽

Available Information ◽

Angus Heifers

Abstract First-service conception rate (FSCR) can be defined as the probability of a heifer conceiving in response to her first artificial insemination (AI). Given the binary nature of its phenotypes, FSCR has been typically evaluated using animal threshold models (ATM). However, susceptibility of these models to the extreme-category problem (ECP) limits their ability to use all available information to calculate Expected Progeny Differences (EPD). Random regression models (RRM) represent an alternative method to evaluate binary traits, and they are not affected by ECP. Nevertheless, RRM were originally developed to analyze longitudinal traits, so their usefulness to evaluate traits with singly observed phenotypes remains unclear. Therefore, objectives herein were to evaluate the feasibility of a RRM genetic prediction for heifer FSCR by comparing its resulting EPD and genetic parameters to those obtained with a traditional ATM. Breeding and ultrasound records of 4,334 Angus heifers (progeny of 354 sires and 1,626 dams) collected between 1992 to 2019 at the Colorado State University Beef Improvement Center were utilized. Observations for FSCR (1, successful; 0, unsuccessful) were defined by fetal age at pregnancy inspections performed approximately 130 d post-AI. Traditional FSCR evaluation was performed using a univariate BLUP threshold animal model, whereas an alternative evaluation was performed by regressing FSCR on age at AI using a linear RRM with Legendre Polynomials as the base function. Heritability estimates were 0.03 ± 0.02 for the ATM and 0.005 ± 0.001 for the average age at AI with the RRM, respectively. Pearson and rank correlations between EPD obtained with each method were 0.63 and 0.60, respectively. The regression coefficient of RRM predictions on those obtained with the ATM was 0.095. In conclusion, these results suggested that although a RRM genetic prediction for FSCR was feasible, a considerable degree of re-ranking occurred between the two methodologies.

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