Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease.

To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Deep longitudinal multiomics profiling reveals two biological seasonal patterns in California.

The influence of seasons on biological processes is poorly understood. In order to identify biological seasonal patterns based on diverse molecular data, rather than calendar dates, we performed a deep longitudinal multiomics profiling of 105 individuals over 4 years. Here, we report more than 1000 seasonal variations in omics analytes and clinical measures. The different molecules group into two major seasonal patterns which correlate with peaks in late spring and late fall/early winter in California. The two patterns are enriched for molecules involved in human biological processes such as inflammation, immunity, cardiovascular health, as well as neurological and psychiatric conditions. Lastly, we identify molecules and microbes that demonstrate different seasonal patterns in insulin sensitive and insulin resistant individuals. The results of our study have important implications in healthcare and highlight the value of considering seasonality when assessing population wide health risk and management.

Factors Associated with Supportive Care Service Use Among California Alzheimer's Disease Patients and Their Caregivers.

BACKGROUND: Existing literature on factors associated with supportive care service (SCS) use is limited. A better understanding of these factors could help tailor SCS to the needs of frequent users, as well as facilitate targeted outreach to populations that underutilize available services. OBJECTIVE: To investigate the prevalence of SCS use and to identify factors associated with, and barriers to, service use. METHODS: California Alzheimer's Disease Center patients with AD (n = 220) participated in the study from 2006-2009. Patients and their caregivers completed assessments to determine SCS use. Cognitive, functional, and behavioral status of the patients were also assessed. A two-part hurdle analysis identified 1) factors associated with any service use and 2) service use frequency among users. RESULTS: Forty percent of participants reported using at least one SCS. Patients with more impaired cognition and activities of daily living and more of the following: total number of medications, comorbid medical conditions, and years of education were more likely to use any SCS (p < 0.05). Factors associated with more frequent SCS use included younger age, more years of education, older age of AD onset, female gender, and having a spouse or relative for a caregiver (p < 0.05). Caregivers frequently indicated insufficient time as a reason for not receiving enough services. CONCLUSION: Factors associated with any SCS use mostly differed from those associated with SCS frequency, suggesting different characteristics between those who initiate versus those who continue SCS use. Our findings highlight the importance of targeted education on services and identifying barriers to long-term SCS use.

Longitudinal multi-omics of host-microbe dynamics in prediabetes.

Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical T2D. To understand the earliest stages of T2D better, we obtained samples from 106 healthy individuals and individuals with prediabetes over approximately four years and performed deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, as well as changes in the microbiome. This rich longitudinal data set revealed many insights: first, healthy profiles are distinct among individuals while displaying diverse patterns of intra- and/or inter-personal variability. Second, extensive host and microbial changes occur during respiratory viral infections and immunization, and immunization triggers potentially protective responses that are distinct from responses to respiratory viral infections. Moreover, during respiratory viral infections, insulin-resistant participants respond differently than insulin-sensitive participants. Third, global co-association analyses among the thousands of profiled molecules reveal specific host-microbe interactions that differ between insulin-resistant and insulin-sensitive individuals. Last, we identified early personal molecular signatures in one individual that preceded the onset of T2D, including the inflammation markers interleukin-1 receptor agonist (IL-1RA) and high-sensitivity C-reactive protein (CRP) paired with xenobiotic-induced immune signalling. Our study reveals insights into pathways and responses that differ between glucose-dysregulated and healthy individuals during health and disease and provides an open-access data resource to enable further research into healthy, prediabetic and T2D states.

Understanding non-performance reports for instrumental activity of daily living items in population analyses: a cross sectional study.

BACKGROUND: Concerns about using Instrumental Activities of Daily Living (IADLs) in national surveys come up frequently in geriatric and rehabilitation medicine due to high rates of non-performance for reasons other than health. We aim to evaluate the effect of different strategies of classifying "does not do" responses to IADL questions when estimating prevalence of IADL limitations in a national survey. METHODS: Cross-sectional analysis of a nationally representative sample of 13,879 non-institutionalized adult Medicare beneficiaries included in the 2010 Medicare Current Beneficiary Survey (MCBS). Sample persons or proxies were asked about difficulties performing six IADLs. Tested strategies to classify non-performance of IADL(s) for reasons other than health were to 1) derive through multiple imputation, 2) exclude (for incomplete data), 3) classify as "no difficulty," or 4) classify as "difficulty." IADL stage prevalence estimates were compared across these four strategies. RESULTS: In the sample, 1853 sample persons (12.4 % weighted) did not do one or more IADLs for reasons other than physical problems or health. Yet, IADL stage prevalence estimates differed little across the four alternative strategies. Classification as "no difficulty" led to slightly lower, while classification as "difficulty" raised the estimated population prevalence of disability. CONCLUSIONS: These analyses encourage clinicians, researchers, and policy end-users of IADL survey data to be cognizant of possible small differences that can result from alternative ways of handling unrated IADL information. At the population-level, the resulting differences appear trivial when applying MCBS data, providing reassurance that IADL items can be used to estimate the prevalence of activity limitation despite high rates of non-performance.