A Randomized, Crossover Trial of a Novel Sound-to-Sleep Mattress Technology in Children with Autism and Sleep Difficulties.

STUDY OBJECTIVES: This preliminary study investigated the tolerability and efficacy of a novel mattress technology-the Sound-To-Sleep (STS) system-in the treatment of sleep problems in children with autism. METHODS: After screening, 45 children, ages 2.5 to 12.9 years, were randomized to order of mattress technology use (On-Off vs. Off-On). Treatment conditions (On vs. Off) lasted two weeks with immediate crossover. Tolerability, including study discontinuation and parent-report of mattress tolerance and ease of use, was tracked throughout the study. Efficacy assessments were obtained at baseline, prior to crossover, and end of study and included measures of autism traits, other psychopathology symptoms, sensory abnormalities, communication difficulties, quality of life, sleep diary parameters, and single-blinded actigraphy-derived sleep parameters. Statistical analyses evaluated differences in tolerability and efficacy when the STS system was on versus off. RESULTS: STS system use was well tolerated (n = 2, 4.4% dropout) and resulted in parent-reported sleep quality improvements (STS off mean = 4.3, 95% CI = 4.05-4.54 vs. on mean = 4.9, 95%CI = 4.67-5.14). The technology was described by parents as very easy to use and child tolerance was rated as good. Parent-diary outcomes indicated improvements in falling asleep and reduced daytime challenging behavior. Actigraphy-derived sleep parameters indicated improved sleep duration and sleep efficiency. Improvements in child and family quality of life were identified on parent questionnaires. CONCLUSIONS: A future large sample phase 2 trial of the STS system is warranted and would benefit from extended study duration, an objective primary efficacy outcome, and careful attention to methodological issues that promote compliance with the intervention and study procedures.

Development of an Objective Autism Risk Index Using Remote Eye Tracking.

OBJECTIVE: Abnormal eye gaze is a hallmark characteristic of autism spectrum disorder (ASD), and numerous studies have identified abnormal attention patterns in ASD. The primary aim of the present study was to create an objective, eye tracking-based autism risk index. METHOD: In initial and replication studies, children were recruited after referral for comprehensive multidisciplinary evaluation of ASD and subsequently grouped by clinical consensus diagnosis (ASD n = 25/15, non-ASD n = 20/19 for initial/replication samples). Remote eye tracking was blinded to diagnosis and included multiple stimuli. Dwell times were recorded to each a priori-defined region of interest (ROI) and averaged across ROIs to create an autism risk index. Receiver operating characteristic curve analyses examined classification accuracy. Correlations with clinical measures evaluated whether the autism risk index was associated with autism symptom severity independent of language ability. RESULTS: In both samples, the autism risk index had high diagnostic accuracy (area under the curve [AUC] = 0.91 and 0.85, 95% CIs = 0.81-0.98 and 0.71-0.96), was strongly associated with Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) severity scores (r = 0.58 and 0.59, p < .001), and not significantly correlated with language ability (r ≤| -0.28|, p > .095). CONCLUSION: The autism risk index may be a useful quantitative and objective measure of risk for autism in at-risk settings. Future research in larger samples is needed to cross-validate these findings. If validated and scaled for clinical use, this measure could inform clinical judgment regarding ASD diagnosis and track symptom improvements.

Metabolite Profiles of the Serum of Patients with Non-Small Cell Carcinoma.

INTRODUCTION: Alterations of serum metabolites may allow us to identify individuals with lung cancer and advance our understanding of the nature and treatment of their cancer. We aimed to identify serum metabolites that differentiate patients with lung cancer from at-risk controls. METHODS: Serum samples from patients with biopsy-confirmed untreated stage I through stage III non-small cell lung cancer and at-risk controls were divided into fractions for analysis by ultrahigh-performance liquid chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry. Compounds were identified by comparison with library entries of purified standards. Differences in concentrations of single metabolites and metabolite ratios were identified. Prediction models were developed. RESULTS: Serum samples from 284 subjects was analyzed. The subjects' mean age was 67 and 48% were female. Ninety-four patients had lung cancer (50 had adenocarcinoma and 44 had squamous cell carcinoma), 44% had stage I disease, 17% had stage II disease, and 39% had stage III disease. The patients with cancer were slightly older than the controls (68.7 versus 66.2 years, p = 0.013). A total of 534 metabolites were identified in eight metabolite superpathways and 73 subpathways. The concentrations of 149 metabolites differed significantly (q values <0.05) between the cancer and control groups (70 were lower in the cancer group and 79 were higher), and 9723 metabolite ratios differed significantly (q values <0.001) between the cancer and control groups. The accuracies of the models (cancer and cancer subtypes versus control) trained on 70% of the subjects and tested on 30% (expressed as C-statistics) ranged from 0.748 to 0.858. CONCLUSIONS: Differences in the serum metabolite profile exist between patients with stage I through stage III non-small cell lung cancer and matched controls.

Accuracy of volatile urine biomarkers for the detection and characterization of lung cancer.

BACKGROUND: The mixture of volatile organic compounds in the headspace gas of urine may be able to distinguish lung cancer patients from relevant control populations. METHODS: Subjects with biopsy confirmed untreated lung cancer, and others at risk for developing lung cancer, provided a urine sample. A colorimetric sensor array was exposed to the headspace gas of neat and pre-treated urine samples. Random forest models were trained from the sensor output of 70% of the study subjects and were tested against the remaining 30%. Models were developed to separate cancer and cancer subgroups from control, and to characterize the cancer. An additional model was developed on the largest clinical subgroup. RESULTS: 90 subjects with lung cancer and 55 control subjects participated. The accuracies, reported as C-statistics, for models of cancer or cancer subgroups vs. control ranged from 0.795 - 0.917. A model of lung cancer vs. control built using only subjects from the largest available clinical subgroup (30 subjects) had a C-statistic of 0.970. Models developed and tested to characterize cancer histology, and to compare early to late stage cancer, had C-statistics of 0.849 and 0.922 respectively. CONCLUSIONS: The colorimetric sensor array signature of volatile organic compounds in the urine headspace may be capable of distinguishing lung cancer patients from clinically relevant controls. The incorporation of clinical phenotypes into the development of this biomarker may optimize its accuracy.

Progress in the development of volatile exhaled breath signatures of lung cancer.

RATIONALE: Volatile organic compounds present in the exhaled breath have shown promise as biomarkers of lung cancer. Advances in colorimetric sensor array technology, breath collection methods, and clinical phenotyping may lead to the development of a more accurate breath biomarker. OBJECTIVES: Perform a discovery-level assessment of the accuracy of a colorimetric sensor array-based volatile breath biomarker. METHODS: Subjects with biopsy-confirmed untreated lung cancer, and others at risk for developing lung cancer, performed tidal breathing into a breath collection instrument designed to expose a colorimetric sensor array to the alveolar portion of the breath. Random forest models were built from the sensor output of 70% of the study subjects and were tested against the remaining 30%. Models were developed to separate cancer and subgroups from control, and to characterize the cancer. Additional models were developed after matching the clinical phenotypes of cancer and control subjects. MEASUREMENTS AND MAIN RESULTS: Ninety-seven subjects with lung cancer and 182 control subjects participated. The accuracies, reported as C-statistics, for models of cancer and subgroups versus control ranged from 0.794 to 0.861. The accuracy was improved by developing models for cancer and control groups selected through propensity matching for clinical variables. A model built using only subjects from the largest available clinical subgroup (49 subjects) had a C-statistic of 0.982. Models developed and tested to characterize cancer histology, and to compare early- with late-stage cancer, had C-statistics of 0.881-0.960. CONCLUSIONS: The colorimetric sensor array signature of exhaled breath volatile organic compounds was capable of distinguishing patients with lung cancer from clinically relevant control subjects in a discovery level trial. The incorporation of clinical phenotypes into the further development of this biomarker may optimize its accuracy.

The effect of metformin and thiazolidinedione use on lung cancer in diabetics.

BACKGROUND: Metformin and the thiazolidinediones (TZDs) may have a protective effect against the development of lung cancer. METHODS: Patients with diabetes mellitus (DM) were identified from the electronic medical records of the Cleveland Clinic. Diabetics with lung cancer were identified then verified by direct review of their records. Control subjects were matched with cancer subjects 1:1 by date of birth, sex, and smoking history. The frequency and duration of diabetic medication use was compared between the groups. The cancer characteristics were compared between those with lung cancer who had and had not been using metformin and/or a TZD. RESULTS: 93,939 patients were identified as having DM. 522 lung cancers in 507 patients were confirmed. The matched control group was more likely to have used metformin and/or a TZD (61.0% vs. 41.2%, p < 0.001 for any use; 55.5% vs. 24.6%, p < 0.001 for >24 months vs. 0-12 months). In the group with lung cancer, those who had used metformin alone had a different histology distribution than those who received neither metformin nor a TZD, were more likely to present with metastatic disease (40.8% vs. 28.2%, p = 0.013), and had a shorter survival from the time of diagnosis (HR 1.47, p < 0.005). CONCLUSIONS: The use of metformin and/or the TZDs is associated with a lower likelihood of developing lung cancer in diabetic patients. Diabetics who develop lung cancer while receiving metformin may have a more aggressive cancer phenotype.

Exhaled breath analysis with a colorimetric sensor array for the identification and characterization of lung cancer.

INTRODUCTION: The pattern of exhaled breath volatile organic compounds represents a metabolic biosignature with the potential to identify and characterize lung cancer. Breath biosignature-based classification of homogeneous subgroups of lung cancer may be more accurate than a global breath signature. Combining breath biosignatures with clinical risk factors may improve the accuracy of the signature. OBJECTIVES: To develop an exhaled breath biosignature of lung cancer using a colorimetric sensor array and to determine the accuracy of breath biosignatures of lung cancer characteristics with and without the inclusion of clinical risk factors. METHODS: The exhaled breath of 229 study subjects, 92 with lung cancer and 137 controls, was drawn across a colorimetric sensor array. Logistic prediction models were developed and statistically validated based on the color changes of the sensor. Age, sex, smoking history, and chronic obstructive pulmonary disease were incorporated in the prediction models. RESULTS: The validated prediction model of the combined breath and clinical biosignature was moderately accurate at distinguishing lung cancer from control subjects (C-statistic 0.811). The accuracy improved when the model focused on only one histology (C-statistic 0.825-0.890). Individuals with different histologies could be accurately distinguished from one another (C-statistic 0.864 for adenocarcinoma versus squamous cell carcinoma). Moderate accuracies were noted for validated breath biosignatures of stage and survival (C-statistic 0.785 and 0.693, respectively). CONCLUSIONS: A colorimetric sensor array is capable of identifying exhaled breath biosignatures of lung cancer. The accuracy of breath biosignatures can be optimized by evaluating specific histologies and incorporating clinical risk factors.