To examine patterns of cognitive function among a clinical sample of patients seeking treatment for Post-Acute Sequelae of COVID-19 (PASC).

One hundred nineteen patients each completed a baseline neuropsychological evaluation, including clinical diagnostic interview, cognitive assessments, and a comprehensive battery of self-report questionnaires. Patients had a mean age of 50 years (range:18 to 74, SD=10.1) and a mean of 15.5 years (SD=2.54) of formal education. Patients were primarily female (74%) and of White/Caucasian race (75%). Hierarchical agglomerative clustering was used to partition the data into groups based on cognitive performance. Euclidean distance was used as the similarity measure for the continuous variables and within-cluster variance was minimized using Ward’s method. The optimal number of clusters was determined empirically by fitting models with 1 to 15 clusters, with the best number of clusters selected using the silhouette index. All analyses were conducted using the NbClust package, an R package for determining the relevant number of clusters in a data set.

Clustering yielded two distinct clusters of cognitive performance. Group 1 (n=57) performed worse than Group 2 (n=62) on most cognitive variables (including a brief cognitive screener and tests of attention/working memory, executive function, processing speed, learning and delayed recall). Of note, there were no significant differences between groups on an infection severity scale, hospitalizations/ICU admissions, initial or current COVID-19 symptoms, or prior comorbidities. Groups did not differ in age or gender, but Group 1 had a lower education level than Group 2 (M=14.7, SD=2.45 vs. M=16.2, SD=2.42; p=.001). Group 1 also had significantly more minorities than Group 2 (40% vs. 8%; p<.001). No other demographic differences (income, living arrangement, or marital status) were observed. In comparison to Group 2 patients, Group 1 patients self-reported significantly higher levels of anxiety and depression and functional impairment (Functional Activities Questionnaire: M=11.3, SD=8.33 vs. M=7.65, SD=7.97), perceived stress (Perceived Stress Scale: M=24.7, SD=7.90 vs. M=20.3, SD=7.89), insomnia (Insomnia Severity Index: M=16.0, SD=6.50 vs. M=13.1, SD=6.76), and subjective cognitive functioning (Cognitive Failures Questionnaire: M=58.8, SD=16.9 vs. M=50.3, SD=18.6; p’s<.05).

Findings indicate two predominant subtypes of patients seeking treatment for PASC, with one group presenting as more cognitively impaired and reporting greater levels of anxiety, depression, insomnia, perceived stress, functional limitations, and subjective cognitive impairment. Future directions include follow-up assessments with these patients to determine cognitive trajectories over time and tailoring treatment adjuncts to address mood symptoms, insomnia, functional ability, and lifestyle variables. Understanding mechanisms of differences in cognitive and affective symptoms is needed in future work. Limitations to the study were that patients were referred for evaluation based on the complaint of “brain fog” and the sample was a homogenous, highly educated, younger group of individuals who experienced generally mild COVID-19 course.

Despite associations between hypoglycemia and cognitive performance using cross-sectional and experimental methods (e.g., Insulin clamp studies), few studies have evaluated this relationship in a naturalistic setting. This pilot study utilizes an EMA study design in adults with T1D to examine the impact of hypoglycemia and hyperglycemia, measured using CGM, on cognitive performance, measured via ambulatory assessment.

Twenty adults with T1D (mean age 38.9 years, range 26-67; 55% female; 55% bachelor’s degree or higher; mean HbA1c = 8.3%, range 5.4% - 12.5%), were recruited from the Joslin Diabetes Center at SUNY Upstate Medical University. A blinded Dexcom G6 CGM was worn during everyday activities while completing 3-6 daily EMAs using personal smartphones. EMAs were delivered between 9 am and 9 pm, for 15 days. EMAs included 3 brief cognitive tests developed by testmybrain.org and validated for brief mobile administration (Gradual Onset CPT d-prime, Digit Symbol Matching median reaction time, Multiple Object Tracking percent accuracy) and self-reported momentary negative affect. Day-level average scores were calculated for the cognitive and negative affect measures. Hypoglycemia and hyperglycemia were defined as the percentage of time spent with a sensor glucose value <70 mg/dL or > 180 mg/dL, respectively. Daytime (8 am to 9 pm) and nighttime (9 pm to 8 am) glycemic excursions were calculated separately. Multilevel models estimated the between- and within-person association between the night prior to, or the same day, time spent in hypoglycemia or hyperglycemia and cognitive performance (each cognitive test was modeled separately). To evaluate the effect of between-person differences, person-level variables were calculated as the mean across the study and grand-mean centered. To evaluate the effect of within-person fluctuations, day-level variables were calculated as deviations from these person-level means.

Within-person fluctuations in nighttime hypoglycemia were associated with daytime processing speed. Specifically, participants who spent a higher percentage of time in hypoglycemia than their average percentage the night prior to assessment performed slower than their average performance on the processing speed test (Digit Symbol Matching median reaction time, b = 94.16, p = 0.042), while same day variation in hypoglycemia was not associated with variation in Digit Symbol Matching performance. This association remained significant (b = 97.46, p = 0.037) after controlling for within-person and between-person effects of negative affect. There were no significant within-person associations between time spent in hyperglycemia and Digit Symbol Matching, nor day/night hypoglycemia or hyperglycemia and Gradual Onset CPT or Multiple Object Tracking.

Our findings from this EMA study suggest that when individuals with T1D experience more time in hypoglycemia at night (compared to their average), they have slower processing speed the following day, while same day hypoglycemia and hyperglycemia does not similarly impact processing speed performance. These results showcase the power of intensive longitudinal designs using ambulatory cognitive assessment to uncover novel determinants of cognitive variation in real world settings that have direct clinical applications for optimizing cognitive performance. Future research with larger samples is needed to replicate these findings.

Anorexia nervosa (AN) is a psychiatric disorder with complex etiology, with a significant portion of disease risk imparted by genetics. Traditional genome-wide association studies (GWAS) produce principal evidence for the association of genetic variants with disease. Transcriptomic imputation (TI) allows for the translation of those variants into regulatory mechanisms, which can then be used to assess the functional outcome of genetically regulated gene expression (GReX) in a broader setting through the use of phenome-wide association studies (pheWASs) in large and diverse clinical biobank populations with electronic health record phenotypes.

Here, we applied TI using S-PrediXcan to translate the most recent PGC-ED AN GWAS findings into AN-GReX. For significant genes, we imputed AN-GReX in the Mount Sinai BioMe™ Biobank and performed pheWASs on over 2000 outcomes to test the clinical consequences of aberrant expression of these genes. We performed a secondary analysis to assess the impact of body mass index (BMI) and sex on AN-GReX clinical associations.

Our S-PrediXcan analysis identified 53 genes associated with AN, including what is, to our knowledge, the first-genetic association of AN with the major histocompatibility complex. AN-GReX was associated with autoimmune, metabolic, and gastrointestinal diagnoses in our biobank cohort, as well as measures of cholesterol, medications, substance use, and pain. Additionally, our analyses showed moderation of AN-GReX associations with measures of cholesterol and substance use by BMI, and moderation of AN-GReX associations with celiac disease by sex.

Our BMI-stratified results provide potential avenues of functional mechanism for AN-genes to investigate further.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Studies involving clinically recruited samples show that genetic liability to schizophrenia overlaps with that for several psychiatric disorders including bipolar disorder, major depression and, in a population study, anxiety disorder and negative symptoms in adolescence.

We examined whether, at a population level, association between schizophrenia liability and anxiety disorders continues into adulthood, for specific anxiety disorders and as a group. We explored in an epidemiologically based cohort the nature of adult psychopathology sharing liability to schizophrenia.

Schizophrenia polygenic risk scores (PRSs) were calculated for 590 European-descent individuals from the Christchurch Health and Development Study. Logistic regression was used to examine associations between schizophrenia PRS and four anxiety disorders (social phobia, specific phobia, panic disorder and generalised anxiety disorder), schizophrenia/schizophreniform disorder, manic/hypomanic episode, alcohol dependence, major depression, and – using linear regression – total number of anxiety disorders. A novel population-level association with hypomania was tested in a UK birth cohort (Avon Longitudinal Study of Parents and Children).

Schizophrenia PRS was associated with total number of anxiety disorders and with generalised anxiety disorder and panic disorder. We show a novel population-level association between schizophrenia PRS and manic/hypomanic episode.

The relationship between schizophrenia liability and anxiety disorders is not restricted to psychopathology in adolescence but is present in adulthood and specifically linked to generalised anxiety disorder and panic disorder. We suggest that the association between schizophrenia liability and hypomanic/manic episodes found in clinical samples may not be due to bias.

None.

We aimed to determine the frequency of qacA/B chlorhexidine tolerance genes and high-level mupirocin resistance among MRSA isolates before and after the introduction of a chlorhexidine (CHG) daily bathing intervention in a surgical intensive care unit (SICU).

Retrospective cohort study (2005–2012)

A large tertiary-care center

Patients admitted to SICU who had MRSA surveillance cultures of the anterior nares

A random sample of banked MRSA anterior nares isolates recovered during (2005) and after (2006–2012) implementation of a daily CHG bathing protocol was examined for qacA/B genes and high-level mupirocin resistance. Staphylococcal cassette chromosome mec (SCCmec) typing was also performed.

Of the 504 randomly selected isolates (63 per year), 36 (7.1%) were qacA/B positive (+) and 35 (6.9%) were mupirocin resistant. Of these, 184 (36.5%) isolates were SCCmec type IV. There was a significant trend for increasing qacA/B (P=.02; highest prevalence, 16.9% in 2009 and 2010) and SCCmec type IV (P<.001; highest prevalence, 52.4% in 2012) during the study period. qacA/B(+) MRSA isolates were more likely to be mupirocin resistant (9 of 36 [25%] qacA/B(+) vs 26 of 468 [5.6%] qacA/B(−); P=.003).

A long-term, daily CHG bathing protocol was associated with a change in the frequency of qacA/B genes in MRSA isolates recovered from the anterior nares over an 8-year period. This change in the frequency of qacA/B genes is most likely due to patients in those years being exposed in prior admissions. Future studies need to further evaluate the implications of universal CHG daily bathing on MRSA qacA/B genes among hospitalized patients.

Infect Control Hosp Epidemiol 2016;37:590–597