Sleep-Circadian Rhythms, Sleep Disparities, and Healthy Aging: Circadian Disruption, the Lurking “Geriatric Giant”

Background: Older adults exhibit more fragmented circadian rhythm as they age, and undergo age-related changes in sleep patterns that influence sleep duration, timing, and overall satisfaction. Several challenges exist in conducting inclusive research for promoting healthy sleep and aging over the lifespan. Multiple sleep dimensions exist and complicate synthesis of sleep findings. Sleep health disparities also disproportionately affect the same populations that experience overall health disparities, yet diverse groups are underrepresented. Methods: Study 1 examined prospective associations between self-reported sleep from the Hispanic Community Health Study / Study of Latinos (n=10,640) and actigraphy-derived sleep/circadian measures from Sueño (n=1,808) with multimorbidity at follow-up. Study 2 used an ensemble of machine learning techniques to identify accelerometry-derived sleep and circadian profiles; we then examined associations between sleep-circadian profiles, fall risk, and physical functioning among 4,543 diverse older women in OPACH. Study 3 linked accelerometry data, claims, and genetic data to construct two network models to simultaneously evaluate the relationship between multiple sleep rest-activity rhythm measures with each cognitive outcome (e.g., dementia and Alzheimer’s Disease). Results: In study 1, several sleep and circadian measures were associated with comorbidity at follow-up, and we observed effect modification of these associations by US-born and non-US-born status and duration of residency. In study 2, we identified multiple sleep and circadian profiles using machine learning, and these profiles were associated with greater fall risk and lower physical functioning. In study 3, several circadian measures were indirectly associated with dementia and AD and shared a central hub in the network model. Results from adjusted survival models showed consistent associations with the network model. Conclusion: This analysis enhances the field of life course epidemiology and sleep disparities research, having identified sleep and circadian behaviors as risk factors for disease and worse aging at middle and older ages. This study also highlights study designs that promote inclusive research when performing epidemiological studies on sleep

Tobacco Smoke and CYP1A2 Activity in a US Population with Normal Liver Enzyme Levels.

Non-alcoholic fatty liver disease (NAFLD) is common among 30% of American adults. Former and current smokers are at higher risk for NAFLD compared to never smokers. The ratio of urine caffeine metabolites to caffeine intake-namely, urine caffeine metabolite indices-has previously been used as a proxy for CYP1A2 activity, which is one of the main liver metabolizing enzymes. CYP1A2 activity is associated with NAFLD progression. No studies to our knowledge have examined the associations of liver enzymes, smoking intensity, and secondhand smoke (SES) with CYP1A2 activity (using caffeine metabolite indices) across smoking status. We analyzed national representative samples from the 2009-2010 National Health and Nutrition Examination Survey (NHANES). Interestingly, even within a normal range, several liver enzymes were associated with caffeine metabolite indices, and patterns of many of these associations varied by smoking status. For instance, within a normal range, aspartate aminotransferase (AST) in never smokers and bilirubin in current smokers were inversely associated with 1-methyluric acid and 5-acetylamino-6-amino-3-methyluracil (URXAMU). Furthermore, we observed a common pattern: across all smoking statuses, higher AST/alanine aminotransferase (AST/ALT) was associated with 1-methyluric acid and URXAMU. Moreover, in current smokers, increased lifelong smoking intensity was associated with reduced caffeine metabolite indices, but acute cigarette exposure as measured by SES levels was associated with increased caffeine metabolite indices among never smokers. In summary, commonly used liver enzyme tests can reflect the CYP1A2 activity even within a normal range, but the selection of these enzymes depends on the smoking status; the associations between smoking and the CYP1A2 activity not only depend on the intensity but also the duration of tobacco exposure

Tobacco Smoke and CYP1A2 Activity in a US Population with Normal Liver Enzyme Levels

Non-alcoholic fatty liver disease (NAFLD) is common among 30% of American adults. Former and current smokers are at higher risk for NAFLD compared to never smokers. The ratio of urine caffeine metabolites to caffeine intake—namely, urine caffeine metabolite indices—has previously been used as a proxy for CYP1A2 activity, which is one of the main liver metabolizing enzymes. CYP1A2 activity is associated with NAFLD progression. No studies to our knowledge have examined the associations of liver enzymes, smoking intensity, and secondhand smoke (SES) with CYP1A2 activity (using caffeine metabolite indices) across smoking status. We analyzed national representative samples from the 2009–2010 National Health and Nutrition Examination Survey (NHANES). Interestingly, even within a normal range, several liver enzymes were associated with caffeine metabolite indices, and patterns of many of these associations varied by smoking status. For instance, within a normal range, aspartate aminotransferase (AST) in never smokers and bilirubin in current smokers were inversely associated with 1-methyluric acid and 5-acetylamino-6-amino-3-methyluracil (URXAMU). Furthermore, we observed a common pattern: across all smoking statuses, higher AST/alanine aminotransferase (AST/ALT) was associated with 1-methyluric acid and URXAMU. Moreover, in current smokers, increased lifelong smoking intensity was associated with reduced caffeine metabolite indices, but acute cigarette exposure as measured by SES levels was associated with increased caffeine metabolite indices among never smokers. In summary, commonly used liver enzyme tests can reflect the CYP1A2 activity even within a normal range, but the selection of these enzymes depends on the smoking status; the associations between smoking and the CYP1A2 activity not only depend on the intensity but also the duration of tobacco exposure

Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning

A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pathways that can lead to widespread inflammation, tissue damage, and organ failure. A dysregulated immune response can manifest as changes in differentiated immune cell populations and concentrations of circulating biomarkers. To propose an early diagnostic system that enables differentiation and identifies the severity of immune-dysregulated syndromes, we built an artificial intelligence tool that uses input data from single-cell RNA sequencing. In our results, single-cell transcriptomics successfully distinguished between mild and severe sepsis and COVID-19 infections. Moreover, by interpreting the decision patterns of our classification system, we identified that different immune cells upregulating or downregulating the expression of the genes CD3, CD14, CD16, FOSB, S100A12, and TCRɣδ can accurately differentiate between different degrees of infection. Our research has identified genes of significance that effectively distinguish between infections, offering promising prospects as diagnostic markers and providing potential targets for therapeutic intervention

Associations of daily steps and step intensity with incident diabetes in a prospective cohort study of older women : The OPACH Study

OBJECTIVE The primary aim was to assess associations between total steps per day and incident diabetes, whereas the secondary aim was to assess whether the intensity and/or cadence of steps is associated with incident diabetes. RESEARCH DESIGN AND METHODS Women without physician-diagnosed diabetes (n = 4,838; mean [SD] age 78.9 [6.7] years) were followed up to 6.9 years; 395 developed diabetes. Hip-worn ActiGraph GT3X+ accelerometers worn for 1 week enabled measures of total, light-intensity, and moderate- to vigorous-intensity (MV-intensity) steps per day. Using Cox proportional hazards analysis we modeled adjusted change in the hazard rate for incident diabetes associated with total, light-intensity, and MV-intensity steps per day. We further estimated the proportion of the steps-diabetes association mediated by BMI. RESULTS On average, participants took 3,729 (SD 2,114) steps/day, of which 1,875 (791) were light-intensity steps and 1,854 ± 1,762 were MV-intensity. More steps per day were associated with a lower hazard rate for incident diabetes. Confounder-adjusted models for a 2,000 steps/day increment yielded hazard ratio (HR) 0.88 (95% CI 0.78–1.00; P = 0.046). After further adjustment for BMI, HR was 0.90 (95% CI 0.80–1.02; P = 0.11). BMI did not significantly mediate the steps-diabetes association (proportion mediated = 17.7% [95% CI −55.0 to 142.0]; P = 0.09]). The relationship between MV-intensity steps per day (HR 0.86 [95% CI 0.74–1.00]; P = 0.04) and incident diabetes was stronger than for light-intensity steps per day (HR 0.97 [95% CI 0.73–1.29]; P = 0.84). CONCLUSIONS These findings suggest that for older adults, more steps per day are associated with lower incident diabetes and MV-intensity steps are most strongly associated with a lower hazard of diabetes. This evidence supports that regular stepping is an important risk factor for type 2 diabetes prevention in older adults

An artificial neural network classification method employing longitudinally monitored immune biomarkers to predict the clinical outcome of critically ill COVID-19 patients

Background The severe form of COVID-19 can cause a dysregulated host immune syndrome that might lead patients to death. To understand the underlying immune mechanisms that contribute to COVID-19 disease we have examined 28 different biomarkers in two cohorts of COVID-19 patients, aiming to systematically capture, quantify, and algorithmize how immune signals might be associated to the clinical outcome of COVID-19 patients. Methods The longitudinal concentration of 28 biomarkers of 95 COVID-19 patients was measured. We performed a dimensionality reduction analysis to determine meaningful biomarkers for explaining the data variability. The biomarkers were used as input of artificial neural network, random forest, classification and regression trees, k-nearest neighbors and support vector machines. Two different clinical cohorts were used to grant validity to the findings. Results We benchmarked the classification capacity of two COVID-19 clinicals studies with different models and found that artificial neural networks was the best classifier. From it, we could employ different sets of biomarkers to predict the clinical outcome of COVID-19 patients. First, all the biomarkers available yielded a satisfactory classification. Next, we assessed the prediction capacity of each protein separated. With a reduced set of biomarkers, our model presented 94% accuracy, 96.6% precision, 91.6% recall, and 95% of specificity upon the testing data. We used the same model to predict 83% and 87% (recovered and deceased) of unseen data, granting validity to the results obtained. Conclusions In this work, using state-of-the-art computational techniques, we systematically identified an optimal set of biomarkers that are related to a prediction capacity of COVID-19 patients. The screening of such biomarkers might assist in understanding the underlying immune response towards inflammatory diseases

Moderate Alcohol Use Is Associated with Reduced Cardiovascular Risk in Middle-Aged Men Independent of Health, Behavior, Psychosocial, and Earlier Life Factors.

We examined whether the often-reported protective association of alcohol with cardiovascular disease (CVD) risk could arise from confounding. Our sample comprised 908 men (56-67 years), free of prevalent CVD. Participants were categorized into 6 groups: never drinkers, former drinkers, and very light (1-4 drinks in past 14 days), light (5-14 drinks), moderate (15-28 drinks), and at-risk (>28 drinks) drinkers. Generalized linear mixed effect models examined the associations of alcohol use with three established CVD risk scores: The Framingham Risk Score (FRS); the atherosclerotic CVD (ASCVD) risk score; and the Metabolic Syndrome (MetS) Severity score, adjusting for group differences in demographics, body size, and health-related behaviors. In separate models we additionally adjusted for several groups of potentially explanatory factors including socioeconomic status, social support, physical and mental health status, childhood factors, and prior history of alcohol misuse. Results showed lower CVD risk among light and moderate alcohol drinkers, relative to very light drinkers, for all CVD risk scores, independent of demographics, body size, and health-related behaviors. Alcohol-CVD risk associations were robust to further adjustment for several groups of potential explanatory factors. Study limitations include the all-male sample with limited racial and ethnic diversity, and the inability to adjust for sugar consumption and for patterns of alcohol consumption. Although this observational study does not address causation, results show that middle-aged men who consume alcohol in moderation have lower CVD risk and better cardiometabolic health than men who consume little or no alcohol, independent of a variety of health, behavioral, psychosocial, and earlier life factors