Prevalent Multimorbidity Combinations Among Middle-Aged and Older Adults Seen in Community Health Centers

BACKGROUND: Multimorbidity (≥ 2 chronic diseases) is associated with greater disability and higher treatment burden, as well as difficulty coordinating self-management tasks for adults with complex multimorbidity patterns. Comparatively little work has focused on assessing multimorbidity patterns among patients seeking care in community health centers (CHCs). OBJECTIVE: To identify and characterize prevalent multimorbidity patterns in a multi-state network of CHCs over a 5-year period. DESIGN: A cohort study of the 2014-2019 ADVANCE multi-state CHC clinical data network. We identified the most prevalent multimorbidity combination patterns and assessed the frequency of patterns throughout a 5-year period as well as the demographic characteristics of patient panels by prevalent patterns. PARTICIPANTS: The study included data from 838,642 patients aged ≥ 45 years who were seen in 337 CHCs across 22 states between 2014 and 2019. MAIN MEASURES: Prevalent multimorbidity patterns of somatic, mental health, and mental-somatic combinations of 22 chronic diseases based on the U.S. Department of Health and Human Services Multiple Chronic Conditions framework: anxiety, arthritis, asthma, autism, cancer, cardiac arrhythmia, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), congestive heart failure, coronary artery disease, dementia, depression, diabetes, hepatitis, human immunodeficiency virus (HIV), hyperlipidemia, hypertension, osteoporosis, post-traumatic stress disorder (PTSD), schizophrenia, substance use disorder, and stroke. KEY RESULTS: Multimorbidity is common among middle-aged and older patients seen in CHCs: 40% have somatic, 6% have mental health, and 24% have mental-somatic multimorbidity patterns. The most frequently occurring pattern across all years is hyperlipidemia-hypertension. The three most frequent patterns are various iterations of hyperlipidemia, hypertension, and diabetes and are consistent in rank of occurrence across all years. CKD-hyperlipidemia-hypertension and anxiety-depression are both more frequent in later study years. CONCLUSIONS: CHCs are increasingly seeing more complex multimorbidity patterns over time; these most often involve mental health morbidity and advanced cardiometabolic-renal morbidity

Histone Modifications at Human Enhancers Reflect Global Cell-Type-Specific Gene Expression

The human body is composed of diverse cell types with distinct functions. Although it is known that lineage specification depends on cell-specific gene expression, which in turn is driven by promoters, enhancers, insulators and other cis-regulatory DNA sequences for each gene1, 2, 3, the relative roles of these regulatory elements in this process are not clear. We have previously developed a chromatin-immunoprecipitation-based microarray method (ChIP-chip) to locate promoters, enhancers and insulators in the human genome4, 5, 6. Here we use the same approach to identify these elements in multiple cell types and investigate their roles in cell-type-specific gene expression. We observed that the chromatin state at promoters and CTCF-binding at insulators is largely invariant across diverse cell types. In contrast, enhancers are marked with highly cell-type-specific histone modification patterns, strongly correlate to cell-type-specific gene expression programs on a global scale, and are functionally active in a cell-type-specific manner. Our results define over 55,000 potential transcriptional enhancers in the human genome, significantly expanding the current catalogue of human enhancers and highlighting the role of these elements in cell-type-specific gene expression

Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome.

Eukaryotic gene transcription is accompanied by acetylation and methylation of nucleosomes near promoters, but the locations and roles of histone modifications elsewhere in the genome remain unclear. We determined the chromatin modification states in high resolution along 30 Mb of the human genome and found that active promoters are marked by trimethylation of Lys4 of histone H3 (H3K4), whereas enhancers are marked by monomethylation, but not trimethylation, of H3K4. We developed computational algorithms using these distinct chromatin signatures to identify new regulatory elements, predicting over 200 promoters and 400 enhancers within the 30-Mb region. This approach accurately predicted the location and function of independently identified regulatory elements with high sensitivity and specificity and uncovered a novel functional enhancer for the carnitine transporter SLC22A5 (OCTN2). Our results give insight into the connections between chromatin modifications and transcriptional regulatory activity and provide a new tool for the functional annotation of the human genome. Activation of eukaryotic gene transcription involves the coordination of a multitude of transcription factors and cofactors on regulatory DNA sequences such as promoters and enhancers and on the chromatin structure containing these elements 1-3 . Promoters are located at the 5¢ ends of genes immediately surrounding the transcriptional start site (TSS) and serve as the point of assembly of the transcriptional machinery and initiation of transcription 4 . Enhancers contribute to the activation of their target genes from positions upstream, downstream or within a target or neighboring gene Recent investigations using chromatin immunoprecipitation (ChIP) and microarray (ChIP-chip) experiments have described the chromatin architecture of transcriptional promoters in yeast, fly and mammalian systems 9 . In a manner largely conserved across species, active promoters are marked by acetylation of various residues of histones H3 and H4 and methylation of H3K4, particularly trimethylation of this residue. Nucleosome depletion is also a general characteristic of active promoters in yeast and flies, although this feature remains to be thoroughly examined in mammalian systems. Although some studies suggest that distal regulatory elements like enhancers may be marked by similar histone modification patterns 10-13 , the distinguishing chromatin features of promoters and enhancers have yet to be determined, hindering our understanding of a predictive histone code for different classes of regulatory elements. Here, we present high-resolution maps of multiple histone modifications and transcriptional regulators in 30 Mb of the human genome, demonstrating that active promoters and enhancers are associated with distinct chromatin signatures that can be used to predict these regulatory elements in the human genome. RESULTS Chromatin architecture and transcription factor localization We performed ChIP-chip analysis 14 to determine the chromatin architecture along 44 human loci selected by the ENCODE consortium as common targets for genomic analysis 15 , totaling 30 Mb

Nocturnal Continuous Glucose and Sleep Stage Data in Adults with Type 1 Diabetes in Real-World Conditions

BackgroundSleep plays an important role in health, and poor sleep is associated with negative impacts on diabetes management, but few studies have objectively evaluated sleep in adults with type 1 diabetes mellitus (T1DM). Nocturnal glycemia and sleep characteristics in T1DM were evaluated using body-worn sensors in real-world conditions.MethodsAnalyses were performed on data collected by the Diabetes Management Integrated Technology Research Initiative pilot study of 17 T1DM subjects: 10 male, 7 female; age 19-61 years; T1DM duration 14.9 ± 11.0 years; hemoglobin A1c (HbA1c) 7.3% ± 1.3% (mean ± standard deviation). Each subject was equipped with a continuous glucose monitor and a wireless sleep monitor (WSM) for four nights. Sleep stages [rapid eye movement (REM), light, and deep sleep] were continuously recorded by the WSM. Nocturnal glycemia (mg/dl) was evaluated as hypoglycemia (<50 mg/dl), low (50-69 mg/dl), euglycemia (70-120 mg/dl), high (121-250 mg/dl), and hyperglycemia (>250 mg/dl) and by several indices of glycemic variability. Glycemia was analyzed within each sleep stage.ResultsSubjects slept 358 ± 48 min per night, with 85 ± 27 min in REM sleep, 207 ± 42 min in light sleep, and 66 ± 30 min in deep sleep (mean ± standard deviation). Increased time in deep sleep was associated with lower HbA1c (R2 = 0.42; F = 9.37; p < .01). Nocturnal glycemia varied widely between and within subjects. Glycemia during REM sleep was hypoglycemia 5.5% ± 18.1%, low 6.6% ± 18.5%, euglycemia 44.6% ± 39.5%, high 37.9% ± 39.7%, and hyperglycemia 5.5% ± 21.2%; glycemia during light sleep was hypoglycemia 4.8% ± 12.4%, low 7.3% ± 12.9%, euglycemia 42.1% ± 33.7%, high 39.2% ± 34.6%, and hyperglycemia 6.5% ± 20.5%; and glycemia during deep sleep was hypoglycemia 0.5% ± 2.2%, low 5.8% ± 14.3%, euglycemia 48.0% ± 37.5%, high 39.5% ± 37.6%, and hyperglycemia 6.2% ± 19.5%. Significantly less time was spent in the hypoglycemic range during deep sleep compared with light sleep (p = .02).ConclusionsIncreased time in deep sleep was associated with lower HbA1c, and less hypoglycemia occurred in deep sleep in T1DM, though this must be further evaluated in larger subsequent studies. Furthermore, the consumer-grade WSM device was useful for objectively studying sleep in a real-world setting