Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts.

Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power. We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84-197 subjects in each study with 562-888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as "prior information" into the next study, thereby improving the overall assessment of the likelihood of finding specific differences between plasma metabolite levels. Using classic statistics and Benjamini-Hochberg FDR-corrections, Study 1 detected 18 metabolic differences and Study 2 detected no differences. Using Bayesian statistics on the same data, we found a high likelihood that 97 compounds were altered in concentration in Study 2, after using the results of Study 1 as the prior distributions. These findings included lower levels of peroxisome-produced ether-lipids, higher levels of long-chain unsaturated triacylglycerides, and the presence of exposome compounds that are explained by the difference in diet and medication between healthy subjects and ME/CFS patients. Although Study 3 reported only 92 compounds in common with the other two studies, these major differences were confirmed. We also found that prostaglandin F2alpha, a lipid mediator of physiological relevance, was reduced in ME/CFS patients across all three studies. The use of Bayesian statistics led to biological conclusions from metabolomic data that were not found through frequentist approaches. We propose that Bayesian statistics is highly useful for studies with similar research designs if similar metabolomic assays are used.

Zinc treatment reverses and anti-Zn-regulated miRs suppress esophageal carcinomas in vivo.

Esophageal squamous cell carcinoma (ESCC) is a deadly disease with few prevention or treatment options. ESCC development in humans and rodents is associated with Zn deficiency (ZD), inflammation, and overexpression of oncogenic microRNAs: miR-31 and miR-21. In a ZD-promoted ESCC rat model with upregulation of these miRs, systemic antimiR-31 suppresses the miR-31-EGLN3/STK40-NF-κB-controlled inflammatory pathway and ESCC. In this model, systemic delivery of Zn-regulated antimiR-31, followed by antimiR-21, restored expression of tumor-suppressor proteins targeted by these specific miRs: STK40/EGLN3 (miR-31), PDCD4 (miR-21), suppressing inflammation, promoting apoptosis, and inhibiting ESCC development. Moreover, ESCC-bearing Zn-deficient (ZD) rats receiving Zn medication showed a 47% decrease in ESCC incidence vs. Zn-untreated controls. Zn treatment eliminated ESCCs by affecting a spectrum of biological processes that included downregulation of expression of the two miRs and miR-31-controlled inflammatory pathway, stimulation of miR-21-PDCD4 axis apoptosis, and reversal of the ESCC metabolome: with decrease in putrescine, increase in glucose, accompanied by downregulation of metabolite enzymes ODC and HK2. Thus, Zn treatment or miR-31/21 silencing are effective therapeutic strategies for ESCC in this rodent model and should be examined in the human counterpart exhibiting the same biological processes.

The Effect of COVID-19 on Adolescents' Mental Health, Social Lives, and Academic Achievement.

The aim of this study was to discover the effects of COVID-19 on the mental health, academic life, social life of students and discover their interrelationships at a boarding school in New Jersey, USA. A majority of participants reported that COVID-19 had a negative effect on their mental health and social lives, felt well informed about COVID-19 cases on campus, and were unconcerned about getting COVID-19 at school. Given the correlations and differences observed, it is likely that some groups of adolescents may be at greater risk of impacted mental health as a result of the COVID-19 pandemic. .

Application of Machine Learning to Metabolomic Profile Characterization in Glioblastoma Patients Undergoing Concurrent Chemoradiation.

We here characterize changes in metabolite patterns in glioblastoma patients undergoing surgery and concurrent chemoradiation using machine learning (ML) algorithms to characterize metabolic changes during different stages of the treatment protocol. We examined 105 plasma specimens (before surgery, 2 days after surgical resection, before starting concurrent chemoradiation, and immediately after chemoradiation) from 36 patients with isocitrate dehydrogenase (IDH) wildtype glioblastoma. Untargeted GC-TOF mass spectrometry-based metabolomics was used given its superiority in identifying and quantitating small metabolites; this yielded 157 structurally identified metabolites. Using Multinomial Logistic Regression (MLR) and GradientBoostingClassifier (GB Classifier), ML models classified specimens based on metabolic changes. The classification performance of these models was evaluated using performance metrics and area under the curve (AUC) scores. Comparing post-radiation to pre-radiation showed increased levels of 15 metabolites: glycine, serine, threonine, oxoproline, 6-deoxyglucose, gluconic acid, glycerol-alpha-phosphate, ethanolamine, propyleneglycol, triethanolamine, xylitol, succinic acid, arachidonic acid, linoleic acid, and fumaric acid. After chemoradiation, a significant decrease was detected in 3-aminopiperidine 2,6-dione. An MLR classification of the treatment phases was performed with 78% accuracy and 75% precision (AUC = 0.89). The alternative GB Classifier algorithm achieved 75% accuracy and 77% precision (AUC = 0.91). Finally, we investigated specific patterns for metabolite changes in highly correlated metabolites. We identified metabolites with characteristic changing patterns between pre-surgery and post-surgery and post-radiation samples. To the best of our knowledge, this is the first study to describe blood metabolic signatures using ML algorithms during different treatment phases in patients with glioblastoma. A larger study is needed to validate the results and the potential application of this algorithm for the characterization of treatment responses.

Gut Microbiome-Linked Metabolites in the Pathobiology of Major Depression With or Without Anxiety-A Role for Bile Acids.

Background: The gut microbiome may play a role in the pathogenesis of neuropsychiatric diseases including major depressive disorder (MDD). Bile acids (BAs) are steroid acids that are synthesized in the liver from cholesterol and further processed by gut-bacterial enzymes, thus requiring both human and gut microbiome enzymatic processes in their metabolism. BAs participate in a range of important host functions such as lipid transport and metabolism, cellular signaling and regulation of energy homeostasis. BAs have recently been implicated in the pathophysiology of Alzheimer's and several other neuropsychiatric diseases, but the biochemical underpinnings of these gut microbiome-linked metabolites in the pathophysiology of depression and anxiety remains largely unknown. Method: Using targeted metabolomics, we profiled primary and secondary BAs in the baseline serum samples of 208 untreated outpatients with MDD. We assessed the relationship of BA concentrations and the severity of depressive and anxiety symptoms as defined by the 17-item Hamilton Depression Rating Scale (HRSD17) and the 14-item Hamilton Anxiety Rating Scale (HRSA-Total), respectively. We also evaluated whether the baseline metabolic profile of BA informs about treatment outcomes. Results: The concentration of the primary BA chenodeoxycholic acid (CDCA) was significantly lower at baseline in both severely depressed (log2 fold difference (LFD) = -0.48; p = 0.021) and highly anxious (LFD = -0.43; p = 0.021) participants compared to participants with less severe symptoms. The gut bacteria-derived secondary BAs produced from CDCA such as lithocholic acid (LCA) and several of its metabolites, and their ratios to primary BAs, were significantly higher in the more anxious participants (LFD's range = [0.23, 1.36]; p's range = [6.85E-6, 1.86E-2]). The interaction analysis of HRSD17 and HRSA-Total suggested that the BA concentration differences were more strongly correlated to the symptoms of anxiety than depression. Significant differences in baseline CDCA (LFD = -0.87, p = 0.0009), isoLCA (LFD = -1.08, p = 0.016) and several BA ratios (LFD's range [0.46, 1.66], p's range [0.0003, 0.049]) differentiated treatment failures from remitters. Conclusion: In patients with MDD, BA profiles representing changes in gut microbiome compositions are associated with higher levels of anxiety and increased probability of first-line treatment failure. If confirmed, these findings suggest the possibility of developing gut microbiome-directed therapies for MDD characterized by gut dysbiosis.

Metabolomic Evidence for Peroxisomal Dysfunction in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic and debilitating disease characterized by unexplained physical fatigue, cognitive and sensory dysfunction, sleeping disturbances, orthostatic intolerance, and gastrointestinal problems. People with ME/CFS often report a prodrome consistent with infections. Using regression, Bayesian and enrichment analyses, we conducted targeted and untargeted metabolomic analysis of plasma from 106 ME/CFS cases and 91 frequency-matched healthy controls. Subjects in the ME/CFS group had significantly decreased levels of plasmalogens and phospholipid ethers (p < 0.001), phosphatidylcholines (p < 0.001) and sphingomyelins (p < 0.001), and elevated levels of dicarboxylic acids (p = 0.013). Using machine learning algorithms, we were able to differentiate ME/CFS or subgroups of ME/CFS from controls with area under the receiver operating characteristic curve (AUC) values up to 0.873. Our findings provide the first metabolomic evidence of peroxisomal dysfunction, and are consistent with dysregulation of lipid remodeling and the tricarboxylic acid cycle. These findings, if validated in other cohorts, could provide new insights into the pathogenesis of ME/CFS and highlight the potential use of the plasma metabolome as a source of biomarkers for the disease.

Metabolomic and inflammatory signatures of symptom dimensions in major depression.

BACKGROUND: Major depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions. METHODS: Using three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism. RESULTS: The IMD clinical dimension and the inflammatory index were positively correlated (r = 0.19, p = 0.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids. CONCLUSION: The IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.

Evidence for Peroxisomal Dysfunction and Dysregulation of the CDP-Choline Pathway in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic and debilitating disease that is characterized by unexplained physical fatigue unrelieved by rest. Symptoms also include cognitive and sensory dysfunction, sleeping disturbances, orthostatic intolerance, and gastrointestinal problems. A syndrome clinically similar to ME/CFS has been reported following well-documented infections with the coronaviruses SARS-CoV and MERS-CoV. At least 10% of COVID-19 survivors develop post acute sequelae of SARS-CoV-2 infection (PASC). Although many individuals with PASC have evidence of structural organ damage, a subset have symptoms consistent with ME/CFS including fatigue, post exertional malaise, cognitive dysfunction, gastrointestinal disturbances, and postural orthostatic intolerance. These common features in ME/CFS and PASC suggest that insights into the pathogenesis of either may enrich our understanding of both syndromes, and could expedite the development of strategies for identifying those at risk and interventions that prevent or mitigate disease. METHODS: Using regression, Bayesian and enrichment analyses, we conducted targeted and untargeted metabolomic analysis of 888 metabolic analytes in plasma samples of 106 ME/CFS cases and 91 frequency-matched healthy controls. RESULTS: In ME/CFS cases, regression, Bayesian and enrichment analyses revealed evidence of peroxisomal dysfunction with decreased levels of plasmalogens. Other findings included decreased levels of several membrane lipids, including phosphatidylcholines and sphingomyelins, that may indicate dysregulation of the cytidine-5’-diphosphocholine pathway. Enrichment analyses revealed decreased levels of choline, ceramides and carnitines, and increased levels of long chain triglycerides (TG) and hydroxy-eicosapentaenoic acid. Elevated levels of dicarboxylic acids were consistent with abnormalities in the tricarboxylic acid cycle. Using machine learning algorithms with selected metabolites as predictors, we were able to differentiate female ME/CFS cases from female controls (highest AUC=0.794) and ME/CFS cases without self-reported irritable bowel syndrome (sr-IBS) from controls without sr-IBS (highest AUC=0.873). CONCLUSION: Our findings are consistent with earlier ME/CFS work indicating compromised energy metabolism and redox imbalance, and highlight new abnormalities that may provide insights into the pathogenesis of ME/CFS. ONE SENTENCE SUMMARY: Plasma levels of plasmalogens are decreased in patients with myalgic encephalomyelitis/chronic fatigue syndrome suggesting peroxisome dysfunction.

A metabolome atlas of the aging mouse brain.

The mammalian brain relies on neurochemistry to fulfill its functions. Yet, the complexity of the brain metabolome and its changes during diseases or aging remain poorly understood. Here, we generate a metabolome atlas of the aging wildtype mouse brain from 10 anatomical regions spanning from adolescence to old age. We combine data from three assays and structurally annotate 1,547 metabolites. Almost all metabolites significantly differ between brain regions or age groups, but not by sex. A shift in sphingolipid patterns during aging related to myelin remodeling is accompanied by large changes in other metabolic pathways. Functionally related brain regions (brain stem, cerebrum and cerebellum) are also metabolically similar. In cerebrum, metabolic correlations markedly weaken between adolescence and adulthood, whereas at old age, cross-region correlation patterns reflect decreased brain segregation. We show that metabolic changes can be mapped to existing gene and protein brain atlases. The brain metabolome atlas is publicly available ( https://mouse.atlas.metabolomics.us/ ) and serves as a foundation dataset for future metabolomic studies.

Indoxyl sulfate, a gut microbiome-derived uremic toxin, is associated with psychic anxiety and its functional magnetic resonance imaging-based neurologic signature.

It is unknown whether indoles, metabolites of tryptophan that are derived entirely from bacterial metabolism in the gut, are associated with symptoms of depression and anxiety. Serum samples (baseline, 12 weeks) were drawn from participants (n = 196) randomized to treatment with cognitive behavioral therapy (CBT), escitalopram, or duloxetine for major depressive disorder. Baseline indoxyl sulfate abundance was positively correlated with severity of psychic anxiety and total anxiety and with resting state functional connectivity to a network that processes aversive stimuli (which includes the subcallosal cingulate cortex (SCC-FC), bilateral anterior insula, right anterior midcingulate cortex, and the right premotor areas). The relation between indoxyl sulfate and psychic anxiety was mediated only through the metabolite's effect on the SCC-FC with the premotor area. Baseline indole abundances were unrelated to post-treatment outcome measures, and changes in symptoms were not correlated with changes in indole concentrations. These results suggest that CBT and antidepressant medications relieve anxiety via mechanisms unrelated to modulation of indoles derived from gut microbiota; it remains possible that treatment-related improvement stems from their impact on other aspects of the gut microbiome. A peripheral gut microbiome-derived metabolite was associated with altered neural processing and with psychiatric symptom (anxiety) in humans, which provides further evidence that gut microbiome disruption can contribute to neuropsychiatric disorders that may require different therapeutic approaches. Given the exploratory nature of this study, findings should be replicated in confirmatory studies.Clinical trial NCT00360399 "Predictors of Antidepressant Treatment Response: The Emory CIDAR" https://clinicaltrials.gov/ct2/show/NCT00360399 .

Evaluating the Consistency of Subjective Activity Assessments and Their Relation to Cognition in Older Adults.

(1) Background: Research examining whether activity engagement is related to cognitive functioning in older adults has been limited to using retrospective reports of activity which may be affected by biases. This study compared two measurements (estimated weekly versus reported daily), and whether these activity assessments were related to cognition in older adults; (2) Methods: Participants from US (n = 199) and Australian (n = 170) samples completed a weekly estimate of activity, followed by 7 consecutive days of daily reporting. Differences between weekly estimates and daily reports were found, such that estimations at the weekly level were lower than self-reported daily information. Multivariate multiple regression was used to determine whether total activity, activity domains and the discrepancy between assessment types (i.e., weekly/daily) predicted cognitive performance across three cognitive domains (fluid, verbal, memory); (3) Results: When activity assessments were totaled, neither predicted cognition; however, when activity was grouped by domain (cognitive, social, physical), different domains predicted different cognitive outcomes. Daily reported cognitive activity significantly predicted verbal performance (ß = 1.63, p = 0.005), while weekly estimated social activity predicted memory performance (ß = -1.81, p = 0.050). Further, while the magnitude of discrepancy in total activity did not significantly predict cognitive performance, domain specific differences did. Differences in physical activity reported across assessments predicted fluid performance (ß = -1.16, p = 0.033); (4) Conclusions: The significant discrepancy between the measurement types shows that it is important to recognize potential biases in responding when conducting activity and cognition research.

Working Memory and Intraindividual Variability in Response Time Mediate Fluid Intelligence Deficits Associated With ADHD Symptomology.

Objective: To determine if decreased fluid intelligence was associated with ADHD, and was mediated by deficits in working memory and intraindividual variability in motor responding. Method: The present study tested 142 young adults from the general population on a range of working memory, response time, and fluid intelligence tasks, and an ADHD self-report symptoms questionnaire. Results: Total and hyperactive ADHD symptoms correlated significantly and negatively with fluid intelligence, but this association was fully mediated by both working memory and intraindividual variability in response time. However, inattentive symptoms were not associated with fluid intelligence. Conclusion: These results have important implications for clinicians using speeded psychometric tests as part of their assessment battery, working memory interventions for ADHD patients that focus on performance improvement without controlling for response consistency, and also demonstrate potential differences in the neuropsychological profiles of ADHD subtypes.

Using Cognitive Intraindividual Variability to Measure Intervention Effectiveness: Results from the Baltimore Experience Corps Trial.

OBJECTIVES: Studies investigating the effectiveness of intervention programs on cognitive ability in older adults are inconsistent; however, these studies generally focus on traditional measures of cognition, and therefore may miss some improvements by not utilizing alternate measures. We evaluate the potential for intraindividual variability in cognitive speed (IIV), a demonstrated sensitive indicator of cognitive functioning, to be used as an index of cognitive plasticity from an intervention. The current study evaluated whether older adults in a school volunteering program showed a reduction in IIV, compared to a low-activity control group over 2 years of exposure. METHOD: Nondemented older adults (n = 336) participated in the Baltimore Experience Corps Trial, an evaluation of a volunteering program conducted at elementary schools designed to increase older adults' physical, cognitive, and social engagement. Participants completed a cognitive battery that included a Stroop task at baseline and after 12 and 24 months. RESULTS: Traditional intent-to-treat analyses did not report significant improvements. Participants who complied at the 80th percentile or above showed a significant reduction in IIV at 24 months, with an additional trend of improved IIV with increased compliance to the treatment protocol, both at 12 months, and at 24 months. Men also showed dose-dependent improvements after 12 months. DISCUSSION: The Experience Corps program resulted in an improvement in cognitive performance as measured by IIV. Analyzing previously collected data with nontraditional measures of cognition, such as IIV, may be a potentially fruitful and cost-effective method for understanding how interventions impact cognition in aging populations.

Coronavirus-Related Anxiety, Social Isolation, and Loneliness in Older Adults in Northern California during the Stay-at-Home Order.

This study aimed to determine the prevalence of and associations between anxiety, social isolation, and loneliness in a sample of older adults in Northern California during the stay-at-home order enacted during the COVID-19 pandemic. 514 older adults completed a 24-item survey. Perceived isolation and loneliness were reported in 56.4% and 36.0% of participants, respectively. Loneliness was found to be associated with both social isolation and COVID-19-related anxiety; however, social isolation and coronavirus-related anxiety were unrelated. Healthcare providers, social service providers, and families are encouraged to maintain or increase contact with older adults during the COVID-19 pandemic.

Interference control in working memory: Evidence for discriminant validity between removal and inhibition tasks.

Working memory (WM) is a system for maintenance of and access to a limited number of goal-relevant representations in the service of higher cognition. Because of its limited capacity, WM requires interference-control processes, allowing us to avoid being distracted by irrelevant information. Recent research has proposed two interference-control processes, which are conceptually similar: (1) an active, item-wise removal process assumed to remove no-longer relevant information from WM, and (2) an inhibitory process assumed to suppress the activation of distractors against competing, goal-relevant representations. The purpose of this study was to determine the extent to which the tasks used to assess removal and inhibition measure the same interference-control construct. Results showed acceptable to good reliabilities for nearly all measures. Similar to previous studies, a structural equation modeling approach identified a reliable latent variable of removal. However, also similar to some previous studies, no latent variable of inhibition could be established. This was the case even when the correlation matrix used to compute the latent variable of inhibition was disattenuated for imperfect reliability. Critically, the individual measures of inhibition were unrelated to the latent variable of removal. These results provide tentative support for the notion that removal is not related to the interference-control processes assessed in inhibition tasks. This suggests that the removal process should be conceptualized as a process independent of the concept of inhibition, as proposed in computational WM models that implement removal as the "unbinding" of a WM item from the context in which it occurred.

Fast fronto-parietal cortical dynamics of conflict detection and context updating in a flanker task.

Recent research has found that the traditional target P3 consists of a family of P3-like positivities that can be functionally and topographically dissociated from one another. The current study examined target N2 and P3-like subcomponents indexing conflict detection and context updating at low- and high-order levels in the neural hierarchy during cognitive control. Electroencephalographic signals were recorded from 45 young adults while they completed a hybrid go/nogo flanker task, and Residue Iteration Decomposition (RIDE) was applied to functionally dissociate these peaks. Analyses showed a stimulus-locked frontal N2 revealing early detection and fast perceptual categorization of nogo, congruent and incongruent trials, resulting in frontal P3-like activity elicited by nogo trials in the latency-variable RIDE cluster, and by incongruent trials in the response-locked cluster. The congruent trials did not elicit frontal P3-like activity. These findings suggest that behavioral incongruency effects are related to intermediate and later stages of motor response re-programming.

Using intraindividual variability as an indicator of cognitive improvement in a physical exercise intervention of older women with mild cognitive impairment.

OBJECTIVES: Intervention programs designed to improve cognitive ability in older adults with mild cognitive impairment (MCI) have often focused on physical exercise as a means to improve traditional measures of cognition, with mixed success. Individuals with MCI show high levels of intraindividual variability (IIV) in response speed, and IIV may be sensitive to intervention-related changes. The current study evaluated if participants who participated in a physical activity intervention (aerobic or resistance training) showed a reduction in IIV, compared to a balance and tone control group. METHOD: This study was a secondary analysis of the EXercise for Cognition and Everyday Living (EXCEL) Study. Women Aged 70-80 years with probable MCI (n = 86) participated in a 6-month randomized controlled trial designed to investigate the effects of different physical exercises on cognitive ability. Participants completed 1-back, task switching, and spatial working memory tasks at baseline, 13 weeks (midpoint) and upon completion of the program. RESULTS: Analyses were conducted following both the intent-to-treat principle and complier average casual effect (CACE) modeling. Participants in the intervention group who complied with the program showed reduced IIV on task switching in the CACE models. The intent-to-treat analyses were all nonsignificant. CONCLUSIONS: Physical exercise resulted in improved IIV in older adults with probable MCI, showing that IIV is modifiable by lifestyle engagement. IIV may be a useful complementary index of cognitive plasticity particularly among those with cognitive impairment. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

An Examination of Effect Sizes and Statistical Power in Speech, Language, and Hearing Research.

Purpose The purpose was to examine and determine effect size distributions reported in published audiology and speech-language pathology research in order to provide researchers and clinicians with more relevant guidelines for the interpretation of potentially clinically meaningful findings. Method Cohen's d, Hedges' g, Pearson r, and sample sizes (n = 1,387) were extracted from 32 meta-analyses in journals in speech-language pathology and audiology. Percentile ranks (25th, 50th, 75th) were calculated to determine estimates for small, medium, and large effect sizes, respectively. The median sample size was also used to explore statistical power for small, medium, and large effect sizes. Results For individual differences research, effect sizes of Pearson r = .24, .41, and .64 were found. For group differences, Cohen's d/Hedges' g = 0.25, 0.55, and 0.93. These values can be interpreted as small, medium, and large effect sizes in speech-language pathology and audiology. The majority of published research was inadequately powered to detect a medium effect size. Conclusions Effect size interpretations from published research in audiology and speech-language pathology were found to be underestimated based on Cohen's (1988, 1992) guidelines. Researchers in the field should consider using Pearson r = .25, .40, and .65 and Cohen's d/Hedges' g = 0.25, 0.55, and 0.95 as small, medium, and large effect sizes, respectively, and collect larger sample sizes to ensure that both significant and nonsignificant findings are robust and replicable.