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Aims: To examine the association of HbA1c and glucose levels with incident diabetic retinopathy according to black African or white European ancestry. Methods: In this retrospective cohort study of 202 500 US Veterans with diabetes (2000–2014), measures included HbA1c, outpatient random serum/plasma glucose, and incident retinopathy [conversion from negative to ≥2 positive evaluations (ICD‐9 codes), without a subsequent negative]. Results: At baseline, the study population had a mean age of 59.3 years, their mean BMI was 31.9 kg/m2, HbA1c level was 57 mmol/mol (7.4%) and glucose level was 8.8 mmol/l, and 77% were of white European ancestry (white individuals) and 21% of black African ancestry (black individuals). HbA1c was 0.3% higher in black vs white individuals (P < 0.001), adjusting for baseline age, sex, BMI, estimated glomerular filtration rate (eGFR), haemoglobin, and average systolic blood pressure and glucose. Over 11 years, incident retinopathy occurred in 9% of black and 7% of white individuals, but black individuals had higher HbA1c, glucose, and systolic blood pressure (all P < 0.001); adjusted for these factors, incident retinopathy was reduced in black vs white individuals (P < 0.001). The population incidence of retinopathy (7%) was associated with higher mean baseline HbA1c in individuals with black vs white ancestry [63 mmol/mol (7.9%) vs 58 mmol/mol (7.5%); P < 0.001)], but with similar baseline glucose levels (9.0 vs 9.0 mmol/l; P = 0.660, all adjusted for baseline age, sex and BMI). Conclusions: Since retinopathy occurs at higher HbA1c levels in black people for a given level of average plasma glucose, strategies may be needed to individualize the interpretation of HbA1c measurements. What's new?: Evidence suggests that HbA1c levels are 3–5 mmol/mol (0.3–0.5%) higher in people with black African vs white European ancestry at the same plasma glucose concentration.We examined the association of HbA1c and glucose levels with incident diabetic retinopathy in a retrospective cohort study of 202 500 US veterans with diabetes, 2000–2014.Adjusted for baseline age, sex and BMI, a higher baseline HbA1c was associated with retinopathy in black compared to white people, whereas baseline glucose was similar.Retinopathy occurs at higher HbA1c levels relative to glucose in those with black ancestry, suggesting a need to individualize the interpretation of HbA1c measurements. [ABSTRACT FROM AUTHOR]

Aims To test the hypothesis that a 50-g oral glucose challenge test with 1-h glucose measurement would have superior performance compared with other opportunistic screening methods. Methods In this prospective study in a Veterans Health Administration primary care clinic, the following test performances, measured by area under receiver-operating characteristic curves, were compared: 50-g oral glucose challenge test; random glucose; and HbA1c level, using a 75-g oral glucose tolerance test as the 'gold standard'. Results The study population was comprised of 1535 people (mean age 56 years, BMI 30.3 kg/m2, 94% men, 74% black). By oral glucose tolerance test criteria, diabetes was present in 10% and high-risk prediabetes was present in 22% of participants. The plasma glucose challenge test provided area under receiver-operating characteristic curves of 0.85 (95% CI 0.78-0.91) to detect diabetes and 0.76 (95% CI 0.72-0.80) to detect high-risk dysglycaemia (diabetes or high-risk prediabetes), while area under receiver-operating characteristic curves for the capillary glucose challenge test were 0.82 (95% CI 0.75-0.89) and 0.73 (95% CI 0.69-0.77) for diabetes and high-risk dysglycaemia, respectively. Random glucose performed less well [plasma: 0.76 (95% CI 0.69-0.82) and 0.66 (95% CI 0.62-0.71), respectively; capillary: 0.72 (95% CI 0.65-0.80) and 0.64 (95% CI 0.59-0.68), respectively], and HbA1c performed even less well [0.67 (95% CI 0.57-0.76) and 0.63 (95% CI 0.58-0.68), respectively]. The cost of identifying one case of high-risk dysglycaemia with a plasma glucose challenge test would be $42 from a Veterans Health Administration perspective, and $55 from a US Medicare perspective. Conclusions Glucose challenge test screening, followed, if abnormal, by an oral glucose tolerance test, would be convenient and more accurate than other opportunistic tests. Use of glucose challenge test screening could improve management by permitting earlier therapy. [ABSTRACT FROM AUTHOR]

Metabolic reprogramming is a hallmark of cancer, enabling tumor cells to adapt to and exploit their microenvironment for sustained growth. The liver is a common site of metastasis, but the interactions between tumor cells and hepatocytes remain poorly understood. In the context of liver metastasis, these interactions play a crucial role in promoting tumor survival and progression. This study leverages multiomics coverage of the microenvironment via liquid chromatography and high-resolution, high-mass accuracy mass spectrometry-based untargeted metabolomics, 13 C-stable isotope tracing, and RNA sequencing to uncover the metabolic impact of co-localized primary hepatocytes and a colon adenocarcinoma cell line, SW480, using a 2D co-culture model. Metabolic profiling revealed disrupted Warburg metabolism with an 80% decrease in glucose consumption and 94% decrease in lactate production by hepatocyte-SW480 co-cultures relative to SW480 control cultures. Decreased glucose consumption was coupled with alterations in glutamine and ketone body metabolism, suggesting a possible fuel switch upon co-culturing. Further, integrated multiomic analysis indicates that disruptions in metabolic pathways, including nucleoside biosynthesis, amino acids, and TCA cycle, correlate with altered SW480 transcriptional profiles and highlight the importance of redox homeostasis in tumor adaptation. Finally, these findings were replicated in 3-dimensional microtissue organoids. Taken together, these studies support a bioinformatic approach to study metabolic crosstalk and discovery of potential therapeutic targets in preclinical models of the tumor microenvironment.

Individually, the COVID-19 and HIV pandemics have differentially impacted minoritized groups due to the role of social determinants of health (SDoH) in the U.S. Little is known how the collision of these two pandemics may have exacerbated adverse health outcomes. We evaluated county-level SDoH and associations with hospitalization after a COVID-19 diagnosis among people with (PWH) and without HIV (PWOH) by racial/ethnic groups. We used the U.S. National COVID Cohort Collaborative (January 2020-November 2023), a nationally-sampled electronic health record repository, to identify adults who were diagnosed with COVID-19 with HIV (n = 22,491) and without HIV (n = 2,220,660). We aggregated SDoH measures at the county-level and categorized racial/ethnic groups as Non-Hispanic (NH) White, NH-Black, Hispanic/Latinx, NH-Asian and Pacific Islander (AAPI), and NH-American Indian or Alaskan Native (AIAN). To estimate associations of county-level SDoH with hospitalization after a COVID-19 diagnosis, we used multilevel, multivariable logistic regressions, calculating adjusted relative risks (aRR) with 95% confidence intervals (95% CI). COVID-19 related hospitalization occurred among 11% of PWH and 7% of PWOH, with the highest proportion among NH-Black PWH (15%). In evaluating county-level SDoH among PWH, we found higher average household size was associated with lower risk of COVID-19 related hospitalization across racial/ethnic groups. Higher mean commute time (aRR: 1.76; 95% CI 1.10-2.62) and higher proportion of adults without health insurance (aRR: 1.40; 95% CI 1.04-1.84) was associated with a higher risk of COVID-19 hospitalization among NH-Black PWH, however, NH-Black PWOH did not demonstrate these associations. Differences by race and ethnicity exist in associations of adverse county-level SDoH with COVID-19 outcomes among people with and without HIV in the U.S., (© 2024. The Author(s).)

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction., Competing Interests: Declaration of interests The authors declare no competing interests.

Background: Time series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types., Methods: We first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data., Results: First, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of "suboptimal" data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data., Conclusions: We propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to declare., (Copyright © 2024. Published by Elsevier Inc.)

Competing Interests: Competing interests: None declared.

Background: Understanding the genetic interplay between human hosts and infectious pathogens is crucial for how we interpret virulence factors. Here, we tested for associations between HIV and host genetics, and interactive genetic effects on viral load (VL) in HIV-positive antiretroviral treatment-naive clinical trial participants., Methods: HIV genomes were sequenced and the encoded amino acid (AA) variants were associated with VL, human single nucleotide polymorphisms (SNPs), and imputed HLA alleles using generalized linear models with Bonferroni correction., Results: Human (388 501 SNPs) and HIV (3010 variants) genetic data were available for 2122 persons. Four HIV variants were associated with VL (P < 1.66 × 10-5). Twelve HIV variants were associated with a range of 1-512 human SNPs (P < 4.28 × 10-11). We found 46 associations between HLA alleles and HIV variants (P < 1.29 × 10-7). HIV variants and immunotypes when analyzed separately were associated with lower VL, whereas the opposite was true when analyzed in concert. Epitope binding predictions supported our observations., Conclusions: Our results show the importance of immunotype specificity on viral antigenic determinants, and the identified genetic interplay emphasizes that viral and human genetics should be studied in the context of each other.Clinical Trials Registration: NCT00867048., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Self-reported data suggest American adults with multiple chronic conditions account for 25.7% to 60% of the population. Despite emphasis on information technology to improve quality in health care, data addressing outcomes of clinically focused, provider-oriented dashboards are limited. To explore integrating performance platforms into clinical care, the authors designed a platform-based intervention to address 2 prevalent chronic conditions with significant long-term burden. This study used a performance platform to enhance clinicians' management of patients with diabetes and osteoporosis. Descriptive statistics were used to summarize patients' surveys and quality metrics, and to analyze clinicians' knowledge, attitudes, and beliefs in the pre and post time frames. The frequency of screening for osteoporosis in women improved post intervention (40% vs. 44%, P  < 0.0001), whereas other quality metrics did not. Clinician respondents were primarily physicians (82%), white (73%), internal medicine specialists (58%), with an average of 18 years' experience, and nearly equally male and female. Their percent of correctly answered knowledge questions increased slightly in the postintervention phase for osteoporosis and hypoglycemia (0.53 and 1.74, respectively); however, results were not statistically significant ( P  > 0.4). Post intervention, clinicians reported that their attitudes and beliefs regarding disease management had changed in the past 6 months in a positive direction. Although few outcomes studied changed over time, results suggest that performance platforms may have a role to play in managing chronic conditions. However, their efficacy must continue to be evaluated in order to improve understanding of optimal approaches to integrating technology into patient care.

Importance: Coronavirus disease 2019 (COVID-19) has infected more than 8.1 million US residents and killed more than 221 000. There is a dearth of research on epidemiology and clinical outcomes in US patients with COVID-19., Objectives: To characterize patients with COVID-19 treated in US hospitals and to examine risk factors associated with in-hospital mortality., Design, Setting, and Participants: This cohort study was conducted using Premier Healthcare Database, a large geographically diverse all-payer hospital administrative database including 592 acute care hospitals in the United States. Inpatient and hospital-based outpatient visits with a principal or secondary discharge diagnosis of COVID-19 (International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis code, U07.1) between April 1 and May 31, 2020, were included., Exposures: Characteristics of patients were reported by inpatient/outpatient and survival status. Risk factors associated with death examined included patient characteristics, acute complications, comorbidities, and medications., Main Outcomes and Measures: In-hospital mortality, intensive care unit (ICU) admission, use of invasive mechanical ventilation, total hospital length of stay (LOS), ICU LOS, acute complications, and treatment patterns., Results: Overall, 64 781 patients with COVID-19 (29 479 [45.5%] outpatients; 35 302 [54.5%] inpatients) were analyzed. The median (interquartile range [IQR]) age was 46 (33-59) years for outpatients and 65 (52-77) years for inpatients; 31 968 (49.3%) were men, 25 841 (39.9%) were White US residents, and 14 340 (22.1%) were Black US residents. In-hospital mortality was 20.3% among inpatients (7164 patients). A total of 5625 inpatients (15.9%) received invasive mechanical ventilation, and 6849 (19.4%) were admitted to the ICU. Median (IQR) inpatient LOS was 6 (3-10) days. Median (IQR) ICU LOS was 5 (2-10) days. Common acute complications among inpatients included acute respiratory failure (19 706 [55.8%]), acute kidney failure (11 971 [33.9%]), and sepsis (11 910 [33.7%]). Older age was the risk factor most strongly associated with death (eg, age ≥80 years vs 18-34 years: odds ratio [OR], 16.20; 95% CI, 11.58-22.67; P < .001). Receipt of statins (OR, 0.60; 95% CI, 0.56-0.65; P < .001), angiotensin-converting enzyme inhibitors (OR, 0.53; 95% CI, 0.46-0.60; P < .001), and calcium channel blockers (OR, 0.73; 95% CI, 0.68-0.79; P < .001) was associated with decreased odds of death. Compared with patients with no hydroxychloroquine or azithromycin, patients with both azithromycin and hydroxychloroquine had increased odds of death (OR, 1.21; 95% CI, 1.11-1.31; P < .001)., Conclusions and Relevance: In this cohort study of patients with COVID-19 infection in US acute care hospitals, COVID-19 was associated with high ICU admission and in-hospital mortality rates. Use of statins, angiotensin-converting enzyme inhibitors, and calcium channel blockers were associated with decreased odds of death. Understanding the potential benefits of unproven treatments will require future randomized trials.

Background: Extensive research suggests that positive patient experience leads to improvement in patient health outcomes. Patient experience is particularly important in ambulatory care, where a patient builds a long-term relationship with a provider to manage his/her chronic illness over the span of years. Despite these known benefits, patient experience and its impact on health outcomes is poorly understood in low- and middle-income countries, where resources may be limited and primary care infrastructure spotty., Objectives: This paper aims to better characterize patient experience in a tertiary teaching hospital in Accra, Ghana., Methods: Forty qualitative interviews were conducted in the Outpatient Medical Clinic at Korle-Bu Teaching Hospital in Accra, Ghana. All interviews were transcribed and a qualitative analysis of central themes was evaluated by the study team., Findings: We found patients eager to share their views on clinical care in an ambulatory clinic in Ghana's largest tertiary care center. Patients voiced desires for decreasing patient wait times, increasing wayfinding resources to navigate the clinic, creating appointment times, and implementing continuity of care with a single physician. The majority of patients also reported feeling actively engaged in their clinical care and emphasized their positive interpersonal interactions with providers., Conclusions: These findings suggest that patients described positive interpersonal experiences with providers at this ambulatory clinic, but identified numerous operational changes that could be made to vastly improve patient experience., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2020 The Author(s).)

Despite being a promising prevention strategy for populations at risk for HIV acquisition, there has not been rapid uptake of HIV antiretroviral pre-exposure prophylaxis (PrEP). Yet, HIV clinics within the Mount Sinai Health System in New York City have successfully incorporated PrEP. HIV care providers ( n  = 18), who practice in these clinics and were early PrEP adopters, participated in a survey and semistructured interview. Qualitative thematic analysis revealed that barriers and facilitators to PrEP uptake were identified on multiple levels from broader systemic to provider-and-patient-level concerns. The following themes were identified: (1) to reach a greater proportion of patients at risk for HIV and address racial/ethnic and gender disparities, PrEP should be available in a variety of settings and provided by different types of providers within proximity to affected populations; (2) financial support is needed beyond addressing medication cost; and (3) multidisciplinary teams and population-specific clinic protocols can assist providers in conducting high-quality visits and addressing these barriers to PrEP.

Aims: Clinical trials show lifestyle change programs are beneficial, yet large-scale, successful translation of these programs is scarce. We investigated the association between participation in the largest U.S. lifestyle change program, MOVE!, and diabetes control outcomes., Methods: This longitudinal, retrospective cohort study used Veterans Health Administration databases of patients with diabetes who participated in MOVE! between 2005 and 2012, or met eligibility criteria (BMI ≥25kg/m 2 ) but did not participate. Main outcomes were diabetic eye disease, renal disease, and medication intensification., Results: There were 400,170 eligible patients with diabetes, including 87,366 (22%) MOVE!, Participants: Included patients were 96% male, 77% white, with mean age 58years and BMI 34kg/m 2 . Controlling for baseline measurements and age, race, sex, BMI, and antidiabetes medications, MOVE! participants had lower body weight (-0.6kg), random plasma glucose (-2.8mg/dL), and HbA1c (-0.1%) at 12months compared to nonparticipants (each p<0.001). In multivariable Cox models, MOVE! participants had lower incidence of eye disease (hazard ratio 0.80, 95% CI 0.75-0.84) and renal disease (HR 0.89, 95% CI 0.86-0.92) and reduced medication intensification (HR 0.82, 95% CI 0.80-0.84)., Conclusions: If able to overcome participation challenges, lifestyle change programs in U.S. health systems may improve health among the growing patient population with diabetes., (Published by Elsevier Inc.)

Introduction: Lifestyle change programs implemented within healthcare systems could reach many Americans, but their impact on cardiovascular disease (CVD) remains unclear. The MOVE! program is the largest lifestyle change program implemented in a healthcare setting in the U.S. This study aimed to determine whether MOVE! participation was associated with reduced CVD incidence., Methods: This retrospective cohort study, analyzed in 2013-2015, used national Veterans Health Administration databases to identify MOVE! participants and eligible non-participants for comparison (2005-2012). Patients eligible for MOVE!-obese or overweight with a weight-related health condition, and no baseline CVD-were examined (N=1,463,003). Of these, 169,248 (12%) were MOVE!, Participants: Patients were 92% male, 76% white, with mean age 52 years and BMI of 32. The main outcome was incidence of CVD (ICD-9 and procedure codes for coronary artery disease, cerebrovascular disease, peripheral vascular disease, and heart failure)., Results: Adjusting for age, race, sex, BMI, statin use, and baseline comorbidities, over a mean 4.9 years of follow-up, MOVE! participation was associated with lower incidence of total CVD (hazard ratio [HR]=0.83, 95% CI=0.80, 0.86); coronary artery disease (HR=0.81, 95% CI=0.77, 0.86); cerebrovascular disease (HR=0.87, 95% CI=0.82, 0.92); peripheral vascular disease (HR=0.89, 95% CI=0.83, 0.94); and heart failure (HR=0.78, 95% CI=0.74, 0.83). The association between MOVE! participation and CVD incidence remained significant when examined across categories of race/ethnicity, BMI, diabetes, hypertension, smoking status, and statin use., Conclusions: Although participation was limited, MOVE! was associated with reduced CVD incidence in a nationwide healthcare setting., (Published by Elsevier Inc.)

Background: Community advisory boards (CAB) are proposed as one mechanism to carry out successful community based participatory research (CBPR), but the presence of CABs may be insufficient to optimize academic-community partnerships., Methods: We conducted semi-structured interviews with minority members of a CAB partnered with a HIV/AIDS research center and identified three themes., Results: First, lack of trust in researchers included two subthemes: researchers' lacked respect for community-based organizations' (CBO's) interests and paid inadequate attention to building trust. Second, power imbalance included three subthemes: CAB members felt like inferior "token" members, felt that a lack of communication led to disempowerment, and held preconceived beliefs of researchers that led to perceived power imbalance. Third, CAB members suggested best practices, including using collaborations to build trust, actively allocating power, and sharing tangible research benefits with CBOs., Conclusions: Our findings indicate that CABs must be founded on trust and instilled with power to meet the tenets of CBPR.

Stable radicals 2-(6-uradinyl)-4,4,5,5-tetramethyl-4,5-dihydro-1H-imidazole-1-oxyl (Ur6IN) and 4-(p-tert-butylaminoxylphenyl)-2,6-di(propylamido)pyridine (DAPPN) form heterospin radical pair complexes due to complementary multi-point hydrogen-bonds.