Seasonal Periodicity of Ischemic Heart Disease and Heart Failure

Brain Dopamine-Clock Interactions Regulate Cardiometabolic Physiology: Mechanisms of the Observed Cardioprotective Effects of Circadian-Timed Bromocriptine-QR Therapy in Type 2 Diabetes Subjects

Despite enormous global efforts within clinical research and medical practice to reduce cardiovascular disease(s) (CVD), it still remains the leading cause of death worldwide. While genetic factors clearly contribute to CVD etiology, the preponderance of epidemiological data indicate that a major common denominator among diverse ethnic populations from around the world contributing to CVD is the composite of Western lifestyle cofactors, particularly Western diets (high saturated fat/simple sugar [particularly high fructose and sucrose and to a lesser extent glucose] diets), psychosocial stress, depression, and altered sleep/wake architecture. Such Western lifestyle cofactors are potent drivers for the increased risk of metabolic syndrome and its attendant downstream CVD. The central nervous system (CNS) evolved to respond to and anticipate changes in the external (and internal) environment to adapt survival mechanisms to perceived stresses (challenges to normal biological function), including the aforementioned Western lifestyle cofactors. Within the CNS of vertebrates in the wild, the biological clock circuitry surveils the environment and has evolved mechanisms for the induction of the obese, insulin-resistant state as a survival mechanism against an anticipated ensuing season of low/no food availability. The peripheral tissues utilize fat as an energy source under muscle insulin resistance, while increased hepatic insulin resistance more readily supplies glucose to the brain. This neural clock function also orchestrates the reversal of the obese, insulin-resistant condition when the low food availability season ends. The circadian neural network that produces these seasonal shifts in metabolism is also responsive to Western lifestyle stressors that drive the CNS clock into survival mode. A major component of this natural or Western lifestyle stressor-induced CNS clock neurophysiological shift potentiating the obese, insulin-resistant state is a diminution of the circadian peak of dopaminergic input activity to the pacemaker clock center, suprachiasmatic nucleus. Pharmacologically preventing this loss of circadian peak dopaminergic activity both prevents and reverses existing metabolic syndrome in a wide variety of animal models of the disorder, including high fat-fed animals. Clinically, across a variety of different study designs, circadian-timed bromocriptine-QR (quick release) (a unique formulation of micronized bromocriptine-a dopamine D2 receptor agonist) therapy of type 2 diabetes subjects improved hyperglycemia, hyperlipidemia, hypertension, immune sterile inflammation, and/or adverse cardiovascular event rate. The present review details the seminal circadian science investigations delineating important roles for CNS circadian peak dopaminergic activity in the regulation of peripheral fuel metabolism and cardiovascular biology and also summarizes the clinical study findings of bromocriptine-QR therapy on cardiometabolic outcomes in type 2 diabetes subjects.

A Systematic Review on the Impact of Seasonality on Severe Mental Illness Admissions: Does Seasonal Variation Affect Coercion?

Coercion in psychiatry is associated mainly with involuntary admissions. The purpose of this study was to investigate the associations between hospital admissions of patients suffering from affective and schizophrenic disorders and seasonality. A systematic literature search using PubMed, Scopus and Google Scholar was conducted, including studies with affective and schizophrenia disorder admissions, published from October 1992 to August 2020. A total of 31 studies were included in the review. Four broad severe mental illness admission categories were identified regarding seasonality: affective disorders, schizophrenia disorders, involuntary admission affective disorders and involuntary admission schizophrenia disorders. There was clear and strong evidence for spring and summer peaks for severe mental illness admissions; data provided for age, gender and involuntary admissions was limited. Seasonality may have a significant effect on the onset and exacerbation of psychopathology of severe mental illness and should be considered as a risk factor in psychiatric admissions, violence and the risk of mental health coercion. A better understanding of the impact of seasonality on severe mental illness will help professionals to provide the best practices in mental health services in order to reduce and prevent psychiatric hospitalizations (especially involuntary admissions) resulting in further coercive measures.

Day-night and seasonal variation of human gene expression across tissues

Circadian and circannual cycles trigger physiological changes whose reflection on human transcriptomes remains largely uncharted. We used the time and season of death of 932 individuals from GTEx to jointly investigate transcriptomic changes associated with those cycles across multiple tissues. Overall, most variation across tissues during day-night and among seasons was unique to each cycle. Although all tissues remodeled their transcriptomes, brain and gonadal tissues exhibited the highest seasonality, whereas those in the thoracic cavity showed stronger day-night regulation. Core clock genes displayed marked day-night differences across multiple tissues, which were largely conserved in baboon and mouse, but adapted to their nocturnal or diurnal habits. Seasonal variation of expression affected multiple pathways, and it was enriched among genes associated with the immune response, consistent with the seasonality of viral infections. Furthermore, they unveiled cytoarchitectural changes in brain regions. Altogether, our results provide the first combined atlas of how transcriptomes from human tissues adapt to major cycling environmental conditions. This atlas may have multiple applications; for example, drug targets with day-night or seasonal variation in gene expression may benefit from temporally adjusted doses.

Confounding Effect of Undergraduate Semester-Driven "Academic" Internet Searches on the Ability to Detect True Disease Seasonality in Google Trends Data: Fourier Filter Method Development and Demonstration

Background

Internet search volume for medical information, as tracked by Google Trends, has been used to demonstrate unexpected seasonality in the symptom burden of a variety of medical conditions. However, when more technical medical language is used (eg, diagnoses), we believe that this technique is confounded by the cyclic, school year-driven internet search patterns of health care students.

Objective

This study aimed to (1) demonstrate that artificial "academic cycling" of Google Trends' search volume is present in many health care terms, (2) demonstrate how signal processing techniques can be used to filter academic cycling out of Google Trends data, and (3) apply this filtering technique to some clinically relevant examples.

Methods

We obtained the Google Trends search volume data for a variety of academic terms demonstrating strong academic cycling and used a Fourier analysis technique to (1) identify the frequency domain fingerprint of this modulating pattern in one particularly strong example, and (2) filter that pattern out of the original data. After this illustrative example, we then applied the same filtering technique to internet searches for information on 3 medical conditions believed to have true seasonal modulation (myocardial infarction, hypertension, and depression), and all bacterial genus terms within a common medical microbiology textbook.

Results

Academic cycling explains much of the seasonal variation in internet search volume for many technically oriented search terms, including the bacterial genus term ["Staphylococcus"], for which academic cycling explained 73.8% of the variability in search volume (using the squared Spearman rank correlation coefficient, P<.001). Of the 56 bacterial genus terms examined, 6 displayed sufficiently strong seasonality to warrant further examination post filtering. This included (1) ["Aeromonas" + "Plesiomonas"] (nosocomial infections that were searched for more frequently during the summer), (2) ["Ehrlichia"] (a tick-borne pathogen that was searched for more frequently during late spring), (3) ["Moraxella"] and ["Haemophilus"] (respiratory infections that were searched for more frequently during late winter), (4) ["Legionella"] (searched for more frequently during midsummer), and (5) ["Vibrio"] (which spiked for 2 months during midsummer). The terms ["myocardial infarction"] and ["hypertension"] lacked any obvious seasonal cycling after filtering, whereas ["depression"] maintained an annual cycling pattern.

Conclusions

Although it is reasonable to search for seasonal modulation of medical conditions using Google Trends' internet search volume and lay-appropriate search terms, the variation in more technical search terms may be driven by health care students whose search frequency varies with the academic school year. When this is the case, using Fourier analysis to filter out academic cycling is a potential means to establish whether additional seasonality is present.

Day-night and seasonal variation of human gene expression across tissues

Circadian and circannual cycles trigger physiological changes whose reflection on human transcriptomes remains largely uncharted. We used the time and season of death of 932 individuals from GTEx to jointly investigate transcriptomic changes associated with those cycles across multiple tissues. Overall, most variation across tissues during day-night and among seasons was unique to each cycle. Although all tissues remodeled their transcriptomes, brain and gonadal tissues exhibited the highest seasonality, whereas those in the thoracic cavity showed stronger day-night regulation. Core clock genes displayed marked day-night differences across multiple tissues, which were largely conserved in baboon and mouse, but adapted to their nocturnal or diurnal habits. Seasonal variation of expression affected multiple pathways and it was enriched among genes associated with the immune response, consistent with the seasonality of viral infections. Furthermore, they unveiled cytoarchitectural changes in brain regions. Altogether, our results provide the first combined atlas of how transcriptomes from human tissues adapt to major cycling environmental conditions.

Clinical and endoscopic characteristics of acute esophageal necrosis and severe reflux esophagitis

The similarities and differences between acute esophageal necrosis and severe reflux esophagitis have not been elucidated. We compared Los Angeles classification Grade C reflux esophagitis, Grade D reflux esophagitis, and acute esophageal necrosis to consider the similarities and differences between acute esophageal necrosis and severe reflux esophagitis.We retrospectively reviewed records of patients who underwent esophagogastroduodenoscopy at a tertiary referral center from January 2012 to December 2019. Data on patients diagnosed as Grade C reflux, Grade D reflux, or acute esophageal necrosis for the first time were extracted for analysis.A total of 213 patients were enrolled in the study, composed of 130 Grade C reflux, 74 Grade D reflux, and 9 acute esophageal necrosis patients. Compared to Grade C reflux patients, Grade D reflux and acute esophageal necrosis patients were more likely to be transfused (P = .013 and P = .011, respectively), to have duodenal ulcers (P = .025 and P = .049, respectively), and to have psychiatric illnesses (P = .022 and P = .018, respectively). Compared to both Grade C and D reflux, acute esophageal necrosis patients were more likely to present with shock (P = .003 and P < .001, respectively), have type 1 diabetes (P = .030 and P = .004, respectively), and present in winter (P < .001 and P < .001, respectively). Significant step-wise differences (Grade C < Grade D < acute esophageal necrosis) were observed in the need for admission (P < .001 and P = .009), coffee ground emesis (P < .001 and P = .022), and stigmata of hemorrhage on endoscopy (P = .002 and P < .001). Admission (P = .003) and coffee ground emesis (P = .003) independently predicted either Grade D reflux or acute esophageal necrosis over Grade C reflux on multivariate analysis.Shock, type 1 diabetes, and winter may predict acute esophageal necrosis, while the need for admission and coffee ground emesis may predict Grade D reflux or acute esophageal necrosis.

Associations between acute exposure to ambient air pollution and length of stay for inpatients with ischemic heart disease: a multi-city analysis in central China

Ambient air pollution (AAP) has been widely associated with increased morbidity of ischemic heart disease (IHD). However, no prior studies have investigated the effects of AAP exposure on the length of stay (LOS) due to IHD. Hospital data during 2015-2017 were obtained from hospital information system in five cities of Hubei province, China. We collected daily mean concentrations of air pollutants, including PM_2.5, PM₁₀, SO₂, NO₂, O₃, and CO, and meteorological data during the same time period. Poisson regression was applied to estimate the acute impacts of AAP on the LOS of IHD inpatients. A total of 42,114 inpatients with primary diagnosis of IHD were included, 50.63% of which were chronic IHD inpatients. Annual average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, O₃, and CO were 61.93 μg/m³, 95.47 μg/m³, 18.59 μg/m³, 35.87 μg/m³, 100.30 μg/m³, and 1.117 mg/m³, respectively. After adjusting for temperature, relative humidity, gender, age group, payment method, number of hospital beds, location of hospital, and surgery or not, exposures to PM_2.5, PM₁₀, SO₂, O₃, and CO were associated with increased LOS for all IHD patients in both single- and multi-pollutant models, and stronger associations were observed among chronic IHD patients. In addition, subgroup analyses demonstrated that males and the group aged 65+ years were more vulnerable to air pollution, and the adverse effects were also promoted by low temperature in cold season. This study provides the first investigation of the adverse effects of AAP on the LOS for IHD patients. In order to shorten the LOS of IHD, measures should be taken to strengthen the AAP management and protect the high-risk population.

Seasonal variation in gut microbiota composition: cross-sectional evidence from Ukrainian population

BACKGROUND

Gut microbiota composition is known to depend on environmental (diet, day length, infections, xenobiotic exposure) and lifestyle (alcohol/drug intake, physical activity) factors. All these factors fluctuate seasonally, especially in areas with highly variable climatic conditions between seasons. Seasonal microbiota changes were reported in several previous studies. The purpose of our study was to investigate whether there is a seasonal variability in the gut microbiota composition in Ukrainian population. In contrast to previous studies performed on small-size samples using a longitudinal design, we used cross-sectional design with a large sample size (n = 769). Determination of microbial composition at the level of major microbial phyla was performed by qRT-PCR.

RESULTS

The relative abundance of major taxonomic groups of gut microbiota was found to be affected by month of sampling. Actinobacteria were more abundant and Bacteroidetes were less abundant in summer-derived samples compared to those obtained during other seasons, whereas Firmicutes content was seasonally independent. The Firmicutes to Bacteroidetes (F/B) ratio was significantly higher in summer-derived samples than in winter-derived ones. Odds to have F/B > 1 were 3.3 times higher in summer samples and 1.9 times higher in autumn samples than in winter ones; neither age, nor sex were significant confounding factors.

CONCLUSIONS

Seasonality of sampling could influence results of human microbiome research, thereby potentially biasing estimates. This factor must be taken into consideration in further microbiome research.

Chronic lifestyle diseases display seasonal sensitive comorbid trend in human population evidence from Google Trends

Seasonal and human physiological changes are important factors in the development of many diseases. But, the study of genuine seasonal impact on these diseases is difficult to measure due to many other environment and lifestyle factors which directly affect these diseases. However, several clinical studies have been conducted in different parts of the world, and it has clearly indicated that certain groups of population are highly subjected to seasonal changes, and their maladaptation can possibly lead to several disorders/diseases. Thus, it is crucial to study the significant seasonal sensitive diseases spread across the human population. To narrow down these disorders/diseases, the study hypothesized that high altitude (HA) associated diseases and disorders are of the strong variants of seasonal physiologic changes. It is because, HA is the only geographical condition for which humans can develop very efficient physiological adaptation mechanism called acclimatization. To study this hypothesis, PubMed was used to collect the HA associated symptoms and disorders. Disease Ontology based semantic similarity network (DSN) and disease-drug networks were constructed to narrow down the benchmark diseases and disorders of HA. The DSN which was further subjected to different community structure analysis uncovered the highly associated or possible comorbid diseases of HA. The predicted 12 lifestyle diseases were assumed to be “seasonal (sensitive) comorbid lifestyle diseases (SCLD)”. A time series analyses on Google Search data of the world from 2004–2016 was conducted to investigate whether the 12 lifestyle diseases have seasonal patterns. Because, the trends were sensitive to the term used as benchmark; the temporal relationships among the 12 disease search volumes and their temporal sequences similarity by dynamic time warping analyses was used to predict the comorbid diseases. Among the 12 lifestyle diseases, the study provides an indirect evidence in the existence of severe seasonal comorbidity among hypertension, obesity, asthma and fibrosis diseases, which is widespread in the world population. Thus, the present study has successfully addressed this issue by predicting the SCLD, and indirectly verified them among the world population using Google Search Trend. Furthermore, based on the SCLD seasonal trend, the study also classified them as severe, moderate, and mild. Interestingly, seasonal trends of the severe seasonal comorbid diseases displayed an inverse pattern between USA (Northern hemisphere) and New Zealand (Southern hemisphere). Further, knowledge in the so called “seasonal sensitive populations” physiological response to seasonal triggers such as winter, summer, spring, and autumn become crucial to modulate disease incidence, disease course, or clinical prevention.