Estimating the Total Variance Explained by Whole-Brain Imaging for Zero-inflated Outcomes.

Zero-inflated outcomes are very common in behavioral data, particularly for responses to psychological questionnaires. Modeling these challenging distributions is further exacerbated by the absence of established statistical models capable of characterizing total signals attributed to whole-brain imaging features, making the accurate assessment of brain-behavior relationships particularly formidable. Given this critical need, we have developed a novel variational Bayes algorithm that characterizes the total signal captured by whole-brain imaging features for zero-inflated outcomes . Our zero-inflated variance (ZIV) estimator robustly estimates the fraction of variance explained (FVE) and the proportion of non-null effects from large-scale imaging data. In simulations, ZIV outperformed other linear prediction algorithms. Applying ZIV to data from one of the largest neuroimaging studies, the Adolescent Brain Cognitive Development SM (ABCD) Study, we found that whole-brain imaging features have a larger FVE for externalizing compared to internalizing behavior. We also demonstrate that the ZIV estimator, especially applied to focal sub-scales, can localize key neurocircuitry associated with human behavior.

A semi-parametric Bayesian model for semi-continuous longitudinal data.

Semi-continuous data present challenges in both model fitting and interpretation. Parametric distributions may be inappropriate for extreme long right tails of the data. Mean effects of covariates, susceptible to extreme values, may fail to capture relevant information for most of the sample. We propose a two-component semi-parametric Bayesian mixture model, with the discrete component captured by a probability mass (typically at zero) and the continuous component of the density modeled by a mixture of B-spline densities that can be flexibly fit to any data distribution. The model includes random effects of subjects to allow for application to longitudinal data. We specify prior distributions on parameters and perform model inference using a Markov chain Monte Carlo (MCMC) Gibbs-sampling algorithm programmed in R. Statistical inference can be made for multiple quantiles of the covariate effects simultaneously providing a comprehensive view. Various MCMC sampling techniques are used to facilitate convergence. We demonstrate the performance and the interpretability of the model via simulations and analyses on the National Consortium on Alcohol and Neurodevelopment in Adolescence study (NCANDA) data on alcohol binge drinking.

Rapid, Large-Scale Wastewater Surveillance and Automated Reporting System Enable Early Detection of Nearly 85% of COVID-19 Cases on a University Campus.

Wastewater-based surveillance has gained prominence and come to the forefront as a leading indicator of forecasting COVID-19 (coronavirus disease 2019) infection dynamics owing to its cost-effectiveness and its ability to inform early public health interventions. A university campus could especially benefit from wastewater surveillance, as universities are characterized by largely asymptomatic populations and are potential hot spots for transmission that necessitate frequent diagnostic testing. In this study, we employed a large-scale GIS (geographic information systems)-enabled building-level wastewater monitoring system associated with the on-campus residences of 7,614 individuals. Sixty-eight automated wastewater samplers were deployed to monitor 239 campus buildings with a focus on residential buildings. Time-weighted composite samples were collected on a daily basis and analyzed on the same day. Sample processing was streamlined significantly through automation, reducing the turnaround time by 20-fold and exceeding the scale of similar surveillance programs by 10- to 100-fold, thereby overcoming one of the biggest bottlenecks in wastewater surveillance. An automated wastewater notification system was developed to alert residents to a positive wastewater sample associated with their residence and to encourage uptake of campus-provided asymptomatic testing at no charge. This system, integrated with the rest of the "Return to Learn" program at the University of California (UC) San Diego-led to the early diagnosis of nearly 85% of all COVID-19 cases on campus. COVID-19 testing rates increased by 1.9 to 13× following wastewater notifications. Our study shows the potential for a robust, efficient wastewater surveillance system to greatly reduce infection risk as college campuses and other high-risk environments reopen. IMPORTANCE Wastewater-based epidemiology can be particularly valuable at university campuses where high-resolution spatial sampling in a well-controlled context could not only provide insight into what affects campus community as well as how those inferences can be extended to a broader city/county context. In the present study, a large-scale wastewater surveillance was successfully implemented on a large university campus enabling early detection of 85% of COVID-19 cases thereby averting potential outbreaks. The highly automated sample processing to reporting system enabled dramatic reduction in the turnaround time to 5 h (sample to result time) for 96 samples. Furthermore, miniaturization of the sample processing pipeline brought down the processing cost significantly ($13/sample). Taken together, these results show that such a system could greatly ameliorate long-term surveillance on such communities as they look to reopen.

Relation of prior statin and anti-hypertensive use to severity of disease among patients hospitalized with COVID-19: Findings from the American Heart Association's COVID-19 Cardiovascular Disease Registry.

BACKGROUND: Statins have anti-inflammatory and immunomodulatory effects that may reduce the severity of coronavirus disease 2019 (COVID-19), in which organ dysfunction is mediated by severe inflammation. Large studies with diverse populations evaluating statin use and outcomes in COVID-19 are lacking. METHODS AND RESULTS: We used data from 10,541 patients hospitalized with COVID-19 through September 2020 at 104 US hospitals enrolled in the American Heart Association's COVID-19 Cardiovascular Disease (CVD) Registry to evaluate the associations between statin use and outcomes. Prior to admission, 42% of subjects (n = 4,449) used statins (7% on statins alone, 35% on statins plus anti-hypertensives). Death (or discharge to hospice) occurred in 2,212 subjects (21%). Outpatient use of statins, either alone or with anti-hypertensives, was associated with a reduced risk of death (adjusted odds ratio [aOR] 0.59, 95% CI 0.50-0.69), adjusting for demographic characteristics, insurance status, hospital site, and concurrent medications by logistic regression. In propensity-matched analyses, use of statins and/or anti-hypertensives was associated with a reduced risk of death among those with a history of CVD and/or hypertension (aOR 0.68, 95% CI 0.58-0.81). An observed 16% reduction in odds of death among those without CVD and/or hypertension was not statistically significant. CONCLUSIONS: Patients taking statins prior to hospitalization for COVID-19 had substantially lower odds of death, primarily among individuals with a history of CVD and/or hypertension. These observations support the continuation and aggressive initiation of statin and anti-hypertensive therapies among patients at risk for COVID-19, if these treatments are indicated based upon underlying medical conditions.

Quantitative mapping of human hair greying and reversal in relation to life stress.

Background: Hair greying is a hallmark of aging generally believed to be irreversible and linked to psychological stress. Methods: Here, we develop an approach to profile hair pigmentation patterns (HPPs) along individual human hair shafts, producing quantifiable physical timescales of rapid greying transitions. Results: Using this method, we show white/grey hairs that naturally regain pigmentation across sex, ethnicities, ages, and body regions, thereby quantitatively defining the reversibility of greying in humans. Molecularly, grey hairs upregulate proteins related to energy metabolism, mitochondria, and antioxidant defenses. Combining HPP profiling and proteomics on single hairs, we also report hair greying and reversal that can occur in parallel with psychological stressors. To generalize these observations, we develop a computational simulation, which suggests a threshold-based mechanism for the temporary reversibility of greying. Conclusions: Overall, this new method to quantitatively map recent life history in HPPs provides an opportunity to longitudinally examine the influence of recent life exposures on human biology. Funding: This work was supported by the Wharton Fund and NIH grants GM119793, MH119336, and AG066828 (MP).

Hair greying is a visible sign of aging that affects everyone. The loss of hair color is due to the loss of melanin, a pigment found in the skin, eyes and hair. Research in mice suggests stress may accelerate hair greying, but there is no definitive research on this in humans. This is because there are no research tools to precisely map stress and hair color over time. But, just like tree rings hold information about past decades, and rocks hold information about past centuries, hairs hold information about past months and years. Hair growth is an active process that happens under the skin inside hair follicles. It demands lots of energy, supplied by structures inside cells called mitochondria. While hairs are growing, cells receive chemical and electrical signals from inside the body, including stress hormones. It is possible that these exposures change proteins and other molecules laid down in the growing hair shaft. As the hair grows out of the scalp, it hardens, preserving these molecules into a stable form. This preservation is visible as patterns of pigmentation. Examining single-hairs and matching the patterns to life events could allow researchers to look back in time through a person's biological history. Rosenberg et al. report a new way to digitize and measure small changes in color along single human hairs. This method revealed that some white hairs naturally regain their color, something that had not been reported in a cohort of healthy individuals before. Aligning the hair pigmentation patterns with recent reports of stress in the hair donors' lives showed striking associations. When one donor reported an increase in stress, a hair lost its pigment. When the donor reported a reduction in stress, the same hair regained its pigment. Rosenberg et al. mapped hundreds of proteins inside the hairs to show that white hairs contained more proteins linked to mitochondria and energy use. This suggests that metabolism and mitochondria may play a role in hair greying. To explore these observations in more detail Rosenberg et al. developed a mathematical model that simulates the greying of a whole head of hair over a lifetime, an experiment impossible to do with living people. The model suggested that there might be a threshold for temporary greying; if hairs are about to go grey anyway, a stressful event might trigger that change earlier. And when the stressful event ends, if a hair is just above the threshold, then it could revert back to dark. The new method for measuring small changes in hair coloring opens up the possibility of using hair pigmentation patterns like tree rings. This could track the influence of past life events on human biology. In the future, monitoring hair pigmentation patterns could provide a way to trace the effectiveness of treatments aimed at reducing stress or slowing the aging process. Understanding how 'old' white hairs regain their 'young' pigmented state could also reveal new information about the malleability of human aging more generally.

Relation of Statin Use Prior to Admission to Severity and Recovery Among COVID-19 Inpatients.

The impact of statins, angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers (ARBs) on coronavirus disease 2019 (COVID-19) severity and recovery is important given their high prevalence of use among individuals at risk for severe COVID-19. We studied the association between use of statin/angiotensin-converting enzyme inhibitors/ARB in the month before hospital admission, with risk of severe outcome, and with time to severe outcome or disease recovery, among patients hospitalized for COVID-19. We performed a retrospective single-center study of all patients hospitalized at University of California San Diego Health between February 10, 2020 and June 17, 2020 (n = 170 hospitalized for COVID-19, n = 5,281 COVID-negative controls). Logistic regression and competing risks analyses were used to investigate progression to severe disease (death or intensive care unit admission), and time to discharge without severe disease. Severe disease occurred in 53% of COVID-positive inpatients. Median time from hospitalization to severe disease was 2 days; median time to recovery was 7 days. Statin use prior to admission was associated with reduced risk of severe COVID-19 (adjusted OR 0.29, 95%CI 0.11 to 0.71, p < 0.01) and faster time to recovery among those without severe disease (adjusted HR for recovery 2.69, 95%CI 1.36 to 5.33, p < 0.01). The association between statin use and severe disease was smaller in the COVID-negative cohort (p for interaction = 0.07). There was potential evidence of faster time to recovery with ARB use (adjusted HR 1.92, 95%CI 0.81 to 4.56). In conclusion, statin use during the 30 days prior to admission for COVID-19 was associated with a lower risk of developing severe COVID-19, and a faster time to recovery among patients without severe disease.

Decelerated DNA methylation age predicts poor prognosis of breast cancer.

BACKGROUND: DNA methylation (DNAm) age was found to be an indicator for all-cause mortality, cancer incidence, and longevity, but no study has involved in the associations of DNAm age with the prognosis of breast cancer. METHODS: We retrieved information of 1076 breast cancer patients from Genomic Data Commons (GDC) data portal on March 30, 2017, including breast cancer DNAm profiling, demographic features, clinicopathological parameters, recurrence, and all-cause fatality. Horvath's method was applied to calculate the DNAm age. Cox proportional hazards regression models were used to test the associations between DNAm age of the cancerous tissues and the prognosis (recurrence of breast cancer and all-cause fatality) with or without adjusting for chronological age and clinicopathological parameters. RESULTS: The DNAm age was markedly decelerated in the patients who were premenopausal, ER or PR negative, HER2-enriched or basal-like than their counterparts. In the first five-year follow-up dataset for survival, every ten-year increase in DNAm age was associated with a 15% decrease in fatality; subjects with DNAm age in the second (HR: 0.52; 95%CI: 0.29-0.92), the third (HR: 0.49; 95%CI: 0.27-0.87) and the fourth quartile (HR: 0.38; 95%CI: 0.20-0.72) had significant longer survival time than those in the first quartile. In the first five-year follow-up dataset for recurrence, every ten-year increase in DNAm age was associated with a 14% decrease of the recurrence; in the categorical analysis, a clear dose-response was shown (P for trend =0.02) and the fourth quartile was associated with a longer recurrence free survival (HR: 0.32; 95%CI: 0.14-0.74). In the full follow-up dataset, similar results were obtained. CONCLUSIONS: DNAm age of breast cancer tissue, which associated with menopausal status and pathological features, was a strong independent predictor of the prognosis. It was suggested that the prognosis of breast cancer was related to intrinsic biological changes and specific molecular targets for treatment of breast cancer may be implicit.

Molecular features in young vs elderly breast cancer patients and the impacts on survival disparities by age at diagnosis.

Young and elderly breast cancer patients are more likely to have a poorer outcome than middle-aged patients. The intrinsic molecular features for this disparity are unclear. We obtained data from the Cancer Genome Atlas (TCGA) on May 15, 2017 to test the potential mediation effects of the molecular features on the association between age and prognosis with a four-step approach. The relative contributions of the molecular features (PAM50 subtype, risk stratification, DNAm age, and mutations in TP53, PIK3CA, MLL3, CDH1, GATA3, and MAP3K1) to age disparities in survival were estimated by Cox proportional hazard models with or without the features. Young patients were significantly more likely to have basal-like subtype, GATA3 mutations, and younger DNA methylation (DNAm) age than middle-aged patients (P < .05). Both the young and elderly patients had a significantly increased risk of breast cancer recurrence after adjusted by race, tumor size, and node status (Hazard ratio [HR] (95% confidence interval [CI]): 2.81 [1.44, 5.45], 2.37 [1.45, 3.89], respectively). This increased risk was weakened in the young patients after further adjustments in the molecular features, particularly basal-like subtype, GATA3 mutations, and DNAm age (HR [95%CI]: 1.87 [0.81, 4.32]), resulting in 33.5% decreased risk of recurrence. Meanwhile, the adjustments of the molecular features did not alter the recurrence risk for the elderly patients. Compared with middle-aged patients of breast cancer, poorer prognosis of elderly patients may be caused by aging, while poorer prognosis of young patients was probably mediated through intrinsic characteristics, such as basal-like subtype, GATA3 mutations, and DNAm age of the cancerous tissues.

Potential factors associated with clinical stage of nasopharyngeal carcinoma at diagnosis: a case-control study.

BACKGROUND: In China, most patients with nasopharyngeal carcinoma (NPC) are diagnosed at a late stage and consequently have a poor prognosis. This study aimed to investigate potential factors associated with the clinical stage of NPC at diagnosis. METHODS: Data were obtained from 118 patients with early-stage NPC and 274 with late-stage NPC who were treated at Sun Yat-sen University Cancer Center between August 2014 and July 2015. Patients were individually matched by age, sex, and residence, and a conditional logistic regression model was applied to assess the associations of clinical stage at diagnosis with socioeconomic status indicators, knowledge of NPC, physical examinations, patient interval, and risk factors for NPC. RESULTS: Although knowledge of early NPC symptoms, smoking cessation, and patient interval were important factors, the number of cigarettes smoked per day, motorbike ownership, and physical examination exhibited the strongest associations with the clinical stage of NPC at diagnosis. Compared with smoking fewer than ten cigarettes a day, smoking 10-30 cigarettes [odds ratio (OR) 4.03; 95% confidence interval (CI) 1.11-14.68] or more than 30 cigarettes (OR 11.46; 95% CI 1.26-103.91) was associated with an increased risk of late diagnosis. Compared with not owning a motorbike, owning a motorbike (OR 0.38; 95% CI 0.23-0.64) was associated with early diagnosis. Subjects who underwent physical examinations were less likely to receive a late diagnosis than those who did not undergo examinations (OR 0.50; 95% CI 0.28-0.89). However, indicators of wealth were not significant factors. CONCLUSIONS: Initiatives to improve NPC patient prognosis should aim to promote knowledge about early symptoms and detection, health awareness, and accessibility to health facilities among all patients, regardless of socioeconomic status.

Polymorphisms in homologous recombination repair genes and the risk and survival of breast cancer.

BACKGROUND: Immunoglobulin (Ig)A antibody of Epstein-Barr virus (EBV) was found to associate with breast cancer (BC), whereas IgA positivity was related to a series of genetic markers in the genes of homologous recombination repair system (HRRs). We assessed the associations of the polymorphisms in HRR genes with the risk and survival of BC. METHODS: A case-control study was conducted with 1551 bc cases and 1605 age-matched healthy controls between October 2008 and March 2012 in the Guangzhou Breast Cancer Study (GZBCS), China, and the case population were followed up until 31 January 2016. Five single nucleotide polymorphisms of candidate genes in HRR system were genotyped. Odds ratios (ORs) and hazards ratios (HRs) were calculated using multivariate logistic regression and Cox proportional hazards regression to estimate the risk and prognostic effect, respectively. RESULTS: RFC1 rs6829064 (AA) was associated with an increased BC risk [OR = 1.35; 95% confidence interval (CI) = 1.06-1.73] compared to the wild genotype (GG). NRM rs1075496 (GT/TT versus GG) was associated with a worse progression-free survival (PFS) and the HR was 1.34 (95% CI = 1.01-1.78), particularly among advanced patients. LIG3 rs1052536 (CT/TT versus CC) was associated with a better PFS and the HR was 0.70 (95% CI = 0.53-0.93). However, RAD54L rs1710286 and RPA1 rs11078676 were not observed to be associated with either the risk or survival of BC. CONCLUSIONS: The findings of the present study suggest that the polymorphisms in HRR genes were associated with BC risk (RFC1 rs6829064) and prognosis (NRM rs1075496 and LIG3 rs1052536), whereas RAD54L rs1710286 and RPA1 rs11078676 had null associations with BC.

Modification effects of genetic polymorphisms in FTO, IL-6, and HSPD1 on the associations of diabetes with breast cancer risk and survival.

The contribution of diabetes to breast cancer remains uncertain among Chinese females, which may result from different genetic factors. We evaluated the associations of diabetes, combined with the polymorphisms in the genes of fat mass and obesity-associated gene (FTO), interleukin 6 (IL-6), and heat shock protein 60 (HSPD1), with breast cancer risk and survival in a Chinese Han population. The information on the history of diabetes was collected from 1551 incident breast cancer cases and 1605 age-frequency matched controls in Guangzhou, China. In total, 1168 cases were followed up. Diabetes was associated with both an increased risk of breast cancer [OR (95%CI): 1.67 (1.11, 2.52)] and a poor overall survival and progression free survival for breast cancer patients [HRs (95%CIs): 2.66 (1.10, 6.44) and 2.46 (1.29, 4.70), respectively]. IL-6 rs1800796 and HSPD1 rs2605039 had interactions with diabetes on breast cancer risk. Among women with CC genotype of IL-6 rs1800796 or GG genotype of HSPD1 rs2605039, diabetic individuals had a remarkably increased risk of breast cancer compared to non-diabetic women with ORs and 95%CIs of 2.53 (1.45, 4.41) and 6.40 (2.29, 17.87), respectively. GT/TT genotypes of HSPD1 rs2605039 was also associated with a better progression free survival for breast cancer patients [HR (95%CI): 0.70 (0.49, 0.99)]. Our results suggest that the contribution of diabetes to breast cancer risk might be modified by IL-6 rs1800796 and HSPD1 rs2605039. Diabetes and HSPD1 rs2605039 might also influence breast cancer prognosis.