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Traumatic brain injury (TBI) is a leading cause of injury-related disability among older adults, and there is increasing interest in post-discharge management as this population grows. We evaluated the association between TBI and long-term nursing home (NH) entry among a nationally representative sample of older adults. We identified 207,355 adults aged ≥65 years who received a diagnosis of either a TBI, non-TBI trauma, or were uninjured between January 2008 and June 2015 from a 5% sample of Medicare beneficiaries. The NH entry was operationalized as the first NH admission that resulted in a stay ≥100 days. Time to NH entry was calculated as the difference between the NH entry date and the index date (the date of TBI, non-TBI trauma, or inpatient/outpatient visit in the uninjured group). We used cause-specific Cox proportional hazards models with stabilized inverse probability of exposure weights to model time to NH entry as a function of injury in the presence of death as a competing risk and generated hazard ratios (HR) and 95% confidence intervals (CI). After excluding beneficiaries living in a NH at index, there were 60,600 TBI, 63,762 non-TBI trauma, and 69,893 uninjured beneficiaries in the sample. In weighted models, beneficiaries with TBI entered NHs at higher rates relative to the non-TBI trauma (HR 1.15; 95% CI 1.10, 1.20) and uninjured (HR 1.67; 95% CI 1.60, 1.74) groups. Future research should focus on interventions to retain older adult TBI survivors within the community.

BACKGROUND: Hormonal factors behind weight regain (WR) after surgical weight loss remain inadequately understood. Growth/differentiation factor 15 (GDF15) has emerged as a potential therapeutic target in obesity treatment. Cortisol, another stress hormone, has also been associated with weight gain at both low and high circulating concentrations. We aimed to compare meal-stimulated GDF15 and cortisol response in adults with and without WR after sleeve gastrectomy (SG). We hypothesized that GDF15 and cortisol response to meal tolerance test (MTT) will be lower in those with versus without WR after SG. METHODS: Cross-sectional study comprised 21 adults without diabetes, who underwent SG. WR was defined as 100 × (current weight − nadir)/(preoperative weight − nadir) > 10%. GDF15, cortisol, insulin, glucose, and incretins (total glucagon-like peptide (GLP)-1 and glucose-dependent insulinotropic polypeptide (GIP) circulating concentrations) were measured during MTT (0–240 min) after 3–6 years post-bariatric surgery. RESULTS: All participants were 48% White, 85% female, with mean (SD) age: 43(10) years, and BMI: 36.2(7.6) kg/m(2). Compared to the non-WR group (n = 6), the WR group (n = 15) had significantly higher BMI (WR: 38.6 ± 7.6 kg/m(2), non-WR: 30.3 ± 3.5 kg/m(2), p = 0.02) and showed lower GDF15 response (WR AUC vs non-WR AUC (116143 ± 13973 vs 185798 ± 38884 ng*min/L, p = 0.047)) and lower cortisol response (WR AUC vs non-WR AUC (3492 ± 210 vs 4880 ± 655 μg*min/dL, p = 0.015)). Incretin response did not differ between the groups. CONCLUSIONS: GDF15 and cortisol responses to MTT were lower in those who regained the weight after SG compared to those who did not, suggesting that dysregulation in GDF15 and cortisol response following bariatric surgery.

BackgroundALactobacillus-dominated vaginal microbiome provides the first line of defense against numerous adverse genital tract health outcomes. However, there is limited understanding of the mechanisms by which the vaginal microbiome modulates protection, as prior work mostly described its composition through morphologic assessment and marker gene sequencing methods that do not capture functional information. To address this limitation, we developed metagenomic community state types (mgCSTs) which uses metagenomic sequences to describe and define vaginal microbiomes based on both composition and function.ResultsMgCSTs are categories of microbiomes classified using taxonomy and the functional potential encoded in their metagenomes. MgCSTs reflect unique combinations of metagenomic subspecies (mgSs), which are assemblages of bacterial strains of the same species, within a microbiome. We demonstrate that mgCSTs are associated with demographics such as age and race, as well as vaginal pH and Gram stain assessment of vaginal smears. Importantly, these associations varied between mgCSTs predominated by the same bacterial species. A subset of mgCSTs, including three of the six predominated byGardnerellamgSs, as well as a mgSs ofL. iners, were associated with a greater likelihood of Amsel bacterial vaginosis diagnosis. ThisL. inersmgSs, among other functional features, encoded enhanced genetic capabilities for epithelial cell attachment that could facilitate cytotoxin-mediated cell lysis. Finally, we report a mgSs and mgCST classifier as an easily applied, standardized method for use by the microbiome research community.ConclusionsMgCSTs are a novel and easily implemented approach to reducing the dimension of complex metagenomic datasets, while maintaining their functional uniqueness. MgCSTs enable investigation of multiple strains of the same species and the functional diversity in that species. Future investigations of functional diversity may be key to unraveling the pathways by which the vaginal microbiome modulates protection to the genital tract. Importantly, our findings support the hypothesis that functional differences between vaginal microbiomes, including those that may look compositionally similar, are critical considerations in vaginal health. Ultimately, mgCSTs may lead to novel hypotheses concerning the role of the vaginal microbiome in promoting health and disease, and identify targets for novel prognostic, diagnostic, and therapeutic strategies to improve women’s genital health.

Background Growing evidence suggests bidirectional links between gut microbiota and sleep quality as shared contributors to health. Little is known about the relationship between microbiota and sleep among older persons. Methods We used 16S rRNA sequencing to characterize stool microbiota among men (n = 606, mean [standard deviation] age = 83.9 [3.8]) enrolled in the Osteoporotic Fractures in Men (MrOS) study from 2014 to 2016. Sleep was assessed concurrently by a questionnaire (Pittsburgh Sleep Quality index [PSQI]), and activity monitor to examine timing (acrophase) and regularity of patterns (F-statistic). Alpha diversity was measured using Faith’s phylogenetic diversity (PD). Beta diversity was calculated with robust Aitchison distance with matrix completion (RPCA) and phylogenetic-RPCA (PRPCA). Their association with sleep variables was tested with partial distance-based redundancy analysis (dbRDA). Predictive-ratio biomarkers associated with sleep measurements were identified with CoDaCoRe. Results In unadjusted analyses, men with poor sleep (PSQI >5) tended to have lower alpha diversity compared to men with normal sleep (Faith’s PD, beta = −0.15; 95% confidence interval [CI]: −0.30 to 0.01, p = .06). Sleep regularity was significantly associated with RPCA and PRPCA, even after adjusting for site, batch, age, ethnicity, body mass index, diabetes, antidepressant and sleep medication use, and health behaviors (RPCA/PRPCA dbRDA; p = .033/.002). In taxonomic analysis, ratios of 7:6 bacteria for better regularity (p = .0004) and 4:7 for worse self-reported sleep (p = .005) were differentially abundant: some butyrate-producing bacteria were associated with better sleep characteristics. Conclusions Subjective and objective indicators of sleep quality suggest that older men with better sleep patterns are more likely to harbor butyrate-producing bacteria associated with better health.

Comorbid depression in knee osteoarthritis (OA) is associated with declines in physical activity, but how persistent depressive symptoms impact physical activity over time remains unclear. We aimed to determine how the persistence of depressive symptoms affects physical activity in knee OA. Participants (n=2,222) from the Osteoarthritis Initiative had radiographic disease (Kellgren-Lawrence grade ≥ 2) in at least one knee. The Center for Epidemiologic Studies Depression Scale (CES-D; range = 0-60) assessed depressive symptoms from baseline through the first three annual follow-up visits, and persistence was operationalized using the cumulative average severity of symptoms over time. Self-reported physical activity was measured from the first to fourth annual follow-up visit using the Physical Activity Scale for the Elderly (PASE; range = 0-793). The primary method of analysis utilized marginal structural models and included exposure by time interactions in the structural outcome model. Baseline depressive symptoms negatively impacted physical activity at the first follow-up (β = -0.7279; 95% CI: -1.1645, -0.2912), but at later time points, effect estimates were closer to the null and not statistically significant. The association between time-averaged CES-D scores from baseline through the first follow-up and physical activity at year two was -0.1410 (95% CI: -0.7105, 0.4286); and 0.2578 (95% CI: -0.3261, 0.8415) for average CES-D scores through follow-up visit two and physical activity at year three. Thus, the negative influence of persistent depressive symptoms on physical activity decreased over time. Physical activity may not consistently decline with persistent depressive symptoms in adults with knee OA.

The sub-discipline of gerontologic biostatistics (GBS) was introduced in 2010 to emphasize the special challenges encountered in the design and analysis of research studies of older persons. These challenges center on the multifactorial nature of human aging, characterized by the parallel and progressive deterioration of diverse organ and cellular systems that eventually results in death. Ten years after the introduction of GBS, which initially focused on important aspects of design and analysis that ensure their statistical validity, we update how GBS has been enriched by evolving practices. We present individual sessions on three seminal developments in the practice of GBS: integration of data science and multiple streams of data, including those automated and or multidisciplinary in nature; enhanced methods of addressing the heterogeneity of treatment effects from health-related interventions for older patients; and how interactive visualization can help specific patients locate themselves along the continuum of individualized treatment effects. We conclude our presentation with a session that reviews three prominent trends in the validation of the heterogeneity inherent to the assessment of health among older adults. Reflecting this era of big gerontological data, we discuss several established modeling approaches for validation, the proliferation of signal intensive behavior phenotypes, and the deep characterization of phenotypes through OMICS studies and multimodal approaches. All talks discuss pitfalls and areas of future development and draw from published studies. We are submitting as an interest group collaborative panel submission between two interest groups: Epidemiology of Aging and Measurement, Statistics and Design.