Background : Obesity and poor cardiorespiratory fitness (CRF) are strong predictors of insulin resistance. The mechanisms underpinning the beneficial effects of CRF on insulin action, however, are not fully understood. Several weight loss intervention trials have suggested that while improving CRF is associated with better insulin sensitivity in adolescents and adults, this association is no longer significant with correction for indices of adiposity. A growing body of literature in the last decade also indicates that CRF with or without changes in BMI/waist circumference enhances insulin sensitivity and that overweight/obese individuals with an adequate level of CRF may consequently be at a reduced risk of diseases associated with insulin resistance. In this project, we investigate whether CRF and degree of obesity have independent associations with insulin resistance in overweight/obese women enrolled in a weight loss trial. Methods : The data are from the MENU Study (Diet Composition and Genetics: Effects on Weight, Inflammation and Biomarkers), a randomized clinical trial funded as part of the TREC initiative at UCSD (Trans-disciplinary Research on Energetics and Cancer (TREC) - NCI U54CA155435). The main objective of the MENU trial is to examine if different macronutrient compositions in the diet affect a differential weight loss response in 234 healthy obese women, depending on insulin resistance status. The current paper utilizes baseline data on the 98 women who are enrolled in the first cohort. The variables used for the current analysis include BMI and waist circumference (WC), as well as insulin sensitivity by HOMA (homeostasis model assessment) approach. CRF was assessed by measuring heart rate during recovery after a 3-minute stepping test. Linear regression models were used to examine associations between CRF, BMI and insulin sensitivity. Results : The findings indicated that CRF was associated with less insulin resistance, independent of BMI and WC. The present study, along with others in recent l.

Autoimmune glial fibrillar acidic protein (GFAP) astrocytopathy is a rare autoimmune neuroinflammatory disorder that affects the central nervous system. We present a case of GFAP astrocytopathy in a middle-aged male who presented with constitutional symptoms, encephalopathy and lower extremity weakness and numbness. Initially MRI of the spine was normal, but he subsequently developed longitudinally extensive myelitis and meningoencephalitis. Workup for infectious aetiologies was negative and the patient's clinical course worsened despite broad antimicrobial coverage. Ultimately, he was found to have anti-GFAP antibodies in his cerebral spinal fluid consistent with GFAP astrocytopathy. He was treated with steroids and plasmapheresis with clinical and radiographic improvement. This case demonstrates the temporal evolution of myelitis on MRI in a case of steroid-refractory GFAP astrocytopathy.

Coccidioidomycosis is a fungal infection endemic to the southwestern United States and Central/South America, and its range is expanding with the warming climate. People with HIV/AIDS are at increased risk of developing disseminated infection, and furthermore are at risk for developing immune reconstitution inflammatory syndrome (IRIS) if they are initiating or re-initiating anti-retroviral therapy (ART). There have been few cases of coccidioidomycosis-related IRIS reported in the literature, and there is no clear guidance on treatment. We present a case of paradoxical IRIS in a patient with AIDS who clinically improved after initiating corticosteroids.

Background

There is a pressing need for digital tools that can leverage big data to help clinicians select effective antibiotic treatments in the absence of timely susceptibility data. Clinical presentation and local epidemiology can inform therapy selection to balance the risk of antimicrobial resistance and patient risk. However, data and clinical expertise must be appropriately integrated into clinical workflows.

Objective

The aim of this study is to leverage available data in electronic health records, to develop a data-driven, user-centered, clinical decision support system to navigate patient safety and population health.

Methods

We analyzed 5 years of susceptibility testing (1,078,510 isolates) and patient data (30,761 patients) across a large academic medical center. After curating the data according to the Clinical and Laboratory Standards Institute guidelines, we analyzed and visualized the impact of risk factors on clinical outcomes. On the basis of this data-driven understanding, we developed a probabilistic algorithm that maps these data to individual cases and implemented iBiogram, a prototype digital empiric antimicrobial clinical decision support system, which we evaluated against actual prescribing outcomes.

Results

We determined patient-specific factors across syndromes and contexts and identified relevant local patterns of antimicrobial resistance by clinical syndrome. Mortality and length of stay differed significantly depending on these factors and could be used to generate heuristic targets for an acceptable risk of underprescription. Combined with the developed remaining risk algorithm, these factors can be used to inform clinicians' reasoning. A retrospective comparison of the iBiogram-suggested therapies versus the actual prescription by physicians showed similar performance for low-risk diseases such as urinary tract infections, whereas iBiogram recognized risk and recommended more appropriate coverage in high mortality conditions such as sepsis.

Conclusions

The application of such data-driven, patient-centered tools may guide empirical prescription for clinicians to balance morbidity and mortality with antimicrobial stewardship.

Background

We assessed the impact of health literacy and hepatitis C (HCV) knowledge on HCV treatment willingness among people living with HIV (PLWH) at an academic HIV clinic.

Methods

Cross-sectional analysis of PLWH coinfected with HCV who completed health literacy, HIV literacy, and HCV knowledge inventories. We estimated the prevalence of low health literacy, HIV knowledge, and HCV knowledge sampled from 3-comparison groups: PLWH not referred for HCV, referred but who "not showed" to the HCV clinic, and referred and attended the HCV clinic. We used mixed-model linear and logistic regression to ascertain predictors of low health literacy, HIV knowledge, HCV knowledge, and predictors of willingness to start HCV treatment.

Results

We enrolled 151 PLWH; 17% were female, 38% non-White, and 60% without a high-school education. Approximately, 68% were men who have sex with men, of whom 62% used intravenous drugs. The prevalence of low health, HIV knowledge, and HCV knowledge was 10%, 32%, and 29%, respectively. Predictors of low health literacy were being Hispanic, cirrhotic, and not completing high-school education. Low HCV knowledge was observed in female, non-White, and those diagnosed with HCV for a decade. In adjusted analyses, PLWH living with HCV for a decade (OR: 0.23) were less likely to be very willing to be treated for HCV. By contrast, those with high HCV knowledge were more likely to be very willing to receive treatment (OR: 1.27).

Conclusion

Low HCV knowledge and living with HCV for at least a decade are under-recognized negative predictors for PLWH's willingness to receive HCV treatment.

Clinical trials registration

ClinicaTrials.gov identifier: NCT20170991.

Background

Weight gain and associated metabolic complications are increasingly prevalent among people with HIV (PWH). Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are incretin-based therapies for diabetes and weight management that have been shown to result in substantial weight loss; however, studies of their effects in PWH are limited.

Methods

A retrospective single-center cohort study was conducted among PWH who were taking GLP-1RAs at UC San Diego Owen Clinic between 2/1/2021 to 2/1/2023. Baseline clinical data were collected and changes in weight, body mass index (BMI), and hemoglobin A1C (A1C) before starting GLP-1RAs compared to the most recent clinic visit were calculated (with a minimum of 3 months follow-up time required). Logistic regression was performed to identify variables associated with >5% of total body weight loss.

Results

A total of 225 patients received on average 13 months of GLP-1RA therapy, with 85 (37.8%) achieving the maximum GLP-1RA dose. GLP-1RA therapy resulted, on average, in a loss of 5.4 kg, decrease in BMI by 1.8 kg/m2, and decrease in A1C by 0.6%. In the multivariable analysis, higher baseline BMI [OR 1.10 (1.03-1.16)], treatment duration of GLP-1RA therapy greater than 6 months [OR 3.12 (1.49-6.49], and use of tirzepatide [OR 5.46 (1.44-20.76)] were significantly more likely to be associated with >5% weight loss.

Conclusions

Use of GLP-1RAs led to declines in weight, BMI, and hemoglobin A1C among PWH and offers an additional strategy to address weight gain and diabetes.

Background

Rising incidence of hepatitis C virus (HCV) among people with HIV (PWH) in San Diego County (SDC) was reported. In 2018, the University of California San Diego (UCSD) launched a micro-elimination initiative among PWH, and in 2020 SDC launched an initiative to reduce HCV incidence by 80% across 2015-2030. We model the impact of observed treatment scale-up on HCV micro-elimination among PWH in SDC.

Methods

A model of HCV transmission among people who inject drugs (PWID) and men who have sex with men (MSM) was calibrated to SDC. The model was additionally stratified by age, gender, and HIV status. The model was calibrated to HCV viremia prevalence among PWH in 2010, 2018, and 2021 (42.1%, 18.5%, and 8.5%, respectively), and HCV seroprevalence among PWID aged 18-39 years, MSM, and MSM with HIV in 2015. We simulate treatment among PWH, weighted by UCSD Owen Clinic (reaching 26% of HCV-infected PWH) and non-UCSD treatment, calibrated to achieve the observed HCV viremia prevalence. We simulated HCV incidence with observed and further treatment scale-up (+/- risk reductions) among PWH.

Results

Observed treatment scale-up from 2018 to 2021 will reduce HCV incidence among PWH in SDC from a mean of 429 infections/year in 2015 to 159 infections/year in 2030. County-wide scale-up to the maximum treatment rate achieved at UCSD Owen Clinic (in 2021) will reduce incidence by 69%, missing the 80% incidence reduction target by 2030 unless accompanied by behavioral risk reductions.

Conclusions

As SDC progresses toward HCV micro-elimination among PWH, a comprehensive treatment and risk reduction approach is necessary to reach 2030 targets.