Digitally enabled asynchronous remote medical management of anxiety and depression: A cohort study.

OBJECTIVE: To evaluate the clinical outcomes of a remote mental health program for managing anxiety and depression, primarily using asynchronous digital communication. METHODS: This retrospective cohort study examined U.S. adults seeking remote care for anxiety and depression from January 2021 to May 2022. The program involves clinician-led assessment, patient education, medication management, and ongoing monitoring, primarily via text. Anxiety and depression were measured using Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) scores. Outcomes examined were changes in scores, 50% score improvement rate, and remission rate (score <5) at 1, 3, and 6 months. RESULTS: During the period evaluated, 11,844 program participants met the inclusion criteria. Most were female (n = 8328, 70.3%); their age ranged from 18-82 years (median 31 years). At baseline, median PHQ-9 and GAD-7 scores were 13 (IQR 9-17); 67% and 69% met score criteria for depression and anxiety, respectively. Most participants (80%) were prescribed a selective serotonin reuptake inhibitor (SSRI). By one month, average PHQ-9 and GAD-7 scores decreased significantly by 9.2 and 9.1 points (both p < .01). At 1-month follow-up, the 50% score improvement rate was 66% for PHQ-9 and 69% GAD-7 (p < .01). Scores continued to decrease with follow-up. At 3 months, over half achieved remission (percent [95% CI]: 52% [51-54] for anxiety, 53% [52-55] for depression). Similar improvement was observed at 6 months and in sensitivity analyses accounting for loss to follow-up. CONCLUSIONS: Use of a remote mental health program with digital tools was associated with significant clinical improvement in anxiety and depression. Challenges remain in maintaining patient engagement and ensuring appropriate care quality monitoring in digital mental health programs. Additional research comparing remote digital care to traditional in-person models is warranted. Studies should examine long-term outcomes, optimal care protocols, and the challenges to integrating these programs into existing healthcare systems and ensuring equitable access.

The Association between Serum Vitamin D Levels and Helicobacter pylori Presence and Eradication.

BACKGROUND: The success of Helicobacter pylori (H. pylori) eradication depends on several host and treatment factors. Serum vitamin D levels may be associated with H. pylori infection and eradication rates. We investigated the association between vitamin D and H. pylori infection and eradication, using a large electronic database based on medical records from a population-based health maintenance organization. METHODS: Data regarding adults who underwent H. pylori testing and had vitamin D measurements within one month of H. pylori testing were collected. H. pylori infection was ascertained using urea breath or stool antigen tests. A negative H. pylori test following a positive result implied eradication. Multivariate regression models were constructed to assess associations between H. pylori infection, eradication, and vitamin D. RESULTS: Among 150,483 members who underwent H. pylori testing from 2009 to 2018, 27,077 (18%) had vitamin D measurements. Vitamin D levels were inversely associated with H. pylori infection, p < 0.001. The odds of a positive H. pylori test were 31% higher among patients with vitamin D levels <20 ng/mL, compared with those with levels ≥20 ng/mL (OR 1.31, 99% CI 1.22-1.4, p < 0.001). Purchase of vitamin D supplements was associated with a negative subsequent H. pylori test (p < 0.001). Mean vitamin D levels were moderately higher in those with successful vs. failed H. pylori eradication (19.34 ± 9.55 vs. 18.64 ± 9.61, p < 0.001). CONCLUSIONS: Vitamin D levels are associated with H. pylori infection. Increased vitamin D levels are associated with successful H. pylori eradication. Vitamin D may have a role in H. pylori eradication.

A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring.

Immune responses generally decline with age. However, the dynamics of this process at the individual level have not been characterized, hindering quantification of an individual's immune age. Here, we use multiple 'omics' technologies to capture population- and individual-level changes in the human immune system of 135 healthy adult individuals of different ages sampled longitudinally over a nine-year period. We observed high inter-individual variability in the rates of change of cellular frequencies that was dictated by their baseline values, allowing identification of steady-state levels toward which a cell subset converged and the ordered convergence of multiple cell subsets toward an older adult homeostasis. These data form a high-dimensional trajectory of immune aging (IMM-AGE) that describes a person's immune status better than chronological age. We show that the IMM-AGE score predicted all-cause mortality beyond well-established risk factors in the Framingham Heart Study, establishing its potential use in clinics for identification of patients at risk.

Defective Signaling in the JAK-STAT Pathway Tracks with Chronic Inflammation and Cardiovascular Risk in Aging Humans.

Chronic inflammation, a decline in immune responsiveness, and reduced cardiovascular function are all associated with aging, but the relationships among these phenomena remain unclear. Here, we longitudinally profiled a total of 84 signaling conditions in 91 young and older adults and observed an age-related reduction in cytokine responsiveness within four immune cell lineages, most prominently T cells. The phenotype can be partially explained by elevated baseline levels of phosphorylated STAT (pSTAT) proteins and a different response capacity of naive versus memory T cell subsets to interleukin 6 (IL-6), interferon α (IFN-α), and, to a lesser extent, IL-21 and IFN-γ. Baseline pSTAT levels tracked with circulating levels of C-reactive protein (CRP), and we derived a cytokine response score that negatively correlates with measures of cardiovascular disease, specifically diastolic dysfunction and atherosclerotic burden, outperforming CRP. Thus, we identified an immunological link between inflammation, decreased cell responsiveness in the JAK-STAT pathway, and cardiovascular aging. Targeting chronic inflammation may ameliorate this deficiency in cellular responsiveness and improve cardiovascular function.

Aging affects B-cell antigen receptor repertoire diversity in primary and secondary lymphoid tissues.

The elderly immune system is characterized by reduced responses to infections and vaccines, and an increase in the incidence of autoimmune diseases and cancer. Age-related deficits in the immune system may be caused by peripheral homeostatic pressures that limit bone marrow B-cell production or migration to the peripheral lymphoid tissues. Studies of peripheral blood B-cell receptor spectratypes have shown that those of the elderly are characterized by reduced diversity, which is correlated with poor health status. In the present study, we performed for the first time high-throughput sequencing of immunoglobulin genes from archived biopsy samples of primary and secondary lymphoid tissues in old (74 ± 7 years old, range 61-89) versus young (24 ± 5 years old, range 18-45) individuals, analyzed repertoire diversities and compared these to results in peripheral blood. We found reduced repertoire diversity in peripheral blood and lymph node repertoires from old people, while in the old spleen samples the diversity was larger than in the young. There were no differences in somatic hypermutation characteristics between age groups. These results support the hypothesis that age-related immune frailty stems from altered B-cell homeostasis leading to narrower memory B-cell repertoires, rather than changes in somatic hypermutation mechanisms.

Immunoglobulin gene repertoire diversification and selection in the stomach - from gastritis to gastric lymphomas.

Chronic gastritis is characterized by gastric mucosal inflammation due to autoimmune responses or infection, frequently with Helicobacter pylori. Gastritis with H. pylori background can cause gastric mucosa-associated lymphoid tissue lymphoma (MALT-L), which sometimes further transforms into diffuse large B-cell lymphoma (DLBCL). However, gastric DLBCL can also be initiated de novo. The mechanisms underlying transformation into DLBCL are not completely understood. We analyzed immunoglobulin repertoires and clonal trees to investigate whether and how immunoglobulin gene repertoires, clonal diversification, and selection in gastritis, gastric MALT-L, and DLBCL differ from each other and from normal responses. The two gastritis types (positive or negative for H. pylori) had similarly diverse repertoires. MALT-L dominant clones (defined as the largest clones in each sample) presented higher diversification and longer mutational histories compared with all other conditions. DLBCL dominant clones displayed lower clonal diversification, suggesting the transforming events are triggered by similar responses in different patients. These results are surprising, as we expected to find similarities between the dominant clones of gastritis and MALT-L and between those of MALT-L and DLBCL.

BCR CDR3 length distributions differ between blood and spleen and between old and young patients, and TCR distributions can be used to detect myelodysplastic syndrome.

Complementarity-determining region 3 (CDR3) is the most hyper-variable region in B cell receptor (BCR) and T cell receptor (TCR) genes, and the most critical structure in antigen recognition and thereby in determining the fates of developing and responding lymphocytes. There are millions of different TCR Vß chain or BCR heavy chain CDR3 sequences in human blood. Even now, when high-throughput sequencing becomes widely used, CDR3 length distributions (also called spectratypes) are still a much quicker and cheaper method of assessing repertoire diversity. However, distribution complexity and the large amount of information per sample (e.g. 32 distributions of the TCRα chain, and 24 of TCRß) calls for the use of machine learning tools for full exploration. We have examined the ability of supervised machine learning, which uses computational models to find hidden patterns in predefined biological groups, to analyze CDR3 length distributions from various sources, and distinguish between experimental groups. We found that (a) splenic BCR CDR3 length distributions are characterized by low standard deviations and few local maxima, compared to peripheral blood distributions; (b) healthy elderly people's BCR CDR3 length distributions can be distinguished from those of the young; and (c) a machine learning model based on TCR CDR3 distribution features can detect myelodysplastic syndrome with approximately 93% accuracy. Overall, we demonstrate that using supervised machine learning methods can contribute to our understanding of lymphocyte repertoire diversity.

Natural killer cell inhibitory receptor expression in humans and mice: a closer look.

The Natural Killer (NK) cell population is composed of subsets of varying sizes expressing different combinations of inhibitory receptors for MHC class I molecules. Genes within the NK gene complex, including the inhibitory receptors themselves, seem to be the primary intrinsic regulators of inhibitory receptor expression, but the MHC class I background is an additional Modulating factor. In this paper, we have performed a parallel study of the inhibitory receptor repertoire in inbred mice of the C57Bl/6 background and in a cohort of 44 humans. Deviations of subset frequencies from the "product rule (PR)," i.e., differences between observed and expected frequencies of NK cells, were used to identify MHC-independent and MHC-dependent control of receptor expression frequencies. Some deviations from the PR were similar in mice and humans, such as the decreased presence of NK cell subset lacking inhibitory receptors. Others were different, including a role for NKG2A in determining over- or under-representation of specific subsets in humans but not in mice. Thus, while human and murine inhibitory receptor repertoires differed in details, there may also be shared principles governing NK cell repertoire formation in these two species.

Models and methods for analysis of lymphocyte repertoire generation, development, selection and evolution.

T and B cell receptor repertoires are diversified by variable region gene rearrangement and selected based on functionality and lack of self-reactivity. Repertoires can also be defined based on phenotype and function rather than receptor specificity - such as the diversity of T helper cell subsets. Natural killer (NK) cell repertoires, in which each cell expresses a randomly chosen subset of its inhibitory receptor genes, and is educated based on self-MHC recognition by yet unknown mechanisms, are also phenotypic repertoires. Studying the generation, development and selection of lymphocyte repertoires, and their functions during immune responses, is essential for understanding the function of the immune system in healthy individuals and in immune deficient, autoimmune or cancer patients. The study of lymphocyte repertoires will enable clinical immunologists to develop better therapeutic monoclonal antibodies, vaccines, transplantation donor-recipient matching protocols, and other immune intervention strategies. The recent development of high-throughput methods for repertoire data collection - from multicolor flow cytometry through single-cell imaging to deep sequencing - presents us now, for the first time, with the ability to analyze and compare large samples of lymphocyte repertoires in health, aging and disease. The exponential growth of these datasets, however, challenges the theoretical immunology community to develop methods for data organization and analysis. Furthermore, the need to test hypotheses regarding immune function, and generate predictions regarding the outcomes of medical interventions, necessitates the development of complex mathematical and computational models, covering processes on multiple scales, from the genetic and molecular to the cellular and system scales.

Probing natural killer cell education by Ly49 receptor expression analysis and computational modelling in single MHC class I mice.

Murine natural killer (NK) cells express inhibitory Ly49 receptors for MHC class I molecules, which allows for "missing self" recognition of cells that downregulate MHC class I expression. During murine NK cell development, host MHC class I molecules impose an "educating impact" on the NK cell pool. As a result, mice with different MHC class I expression display different frequency distributions of Ly49 receptor combinations on NK cells. Two models have been put forward to explain this impact. The two-step selection model proposes a stochastic Ly49 receptor expression followed by selection for NK cells expressing appropriate receptor combinations. The sequential model, on the other hand, proposes that each NK cell sequentially expresses Ly49 receptors until an interaction of sufficient magnitude with self-class I MHC is reached for the NK cell to mature. With the aim to clarify which one of these models is most likely to reflect the actual biological process, we simulated the two educational schemes by mathematical modelling, and fitted the results to Ly49 expression patterns, which were analyzed in mice expressing single MHC class I molecules. Our results favour the two-step selection model over the sequential model. Furthermore, the MHC class I environment favoured maturation of NK cells expressing one or a few self receptors, suggesting a possible step of positive selection in NK cell education. Based on the predicted Ly49 binding preferences revealed by the model, we also propose, that Ly49 receptors are more promiscuous than previously thought in their interactions with MHC class I molecules, which was supported by functional studies of NK cell subsets expressing individual Ly49 receptors.