Mendelian Randomization of Blood Metabolites Suggests Circulating Glutamine Protects Against Late-Onset Alzheimer's Disease.

Background: Late-onset Alzheimer's disease (LOAD) represents a growing health burden. Previous studies suggest that blood metabolite levels influence risk of LOAD. Objective: We used a genetics-based study design which may overcome limitations of other epidemiological studies to assess the influence of metabolite levels on LOAD risk. Methods: We applied Mendelian randomization (MR) to evaluate bi-directional causal effects using summary statistics from the largest genome-wide association studies (GWAS) of 249 blood metabolites (n = 115,082) and GWAS of LOAD (ncase = 21,982, ncontrol = 41,944). Results: MR analysis of metabolites as exposures revealed a negative association of genetically-predicted glutamine levels with LOAD (Odds Ratio (OR) = 0.83, 95% CI = 0.73, 0.92) that was consistent in multiple sensitivity analyses. We also identified a positive association of genetically-predicted free cholesterol levels in small LDL (OR = 1.79, 95% CI = 1.36, 2.22) on LOAD. Using genetically-predicted LOAD as the exposure, we identified associations with phospholipids to total lipids ratio in large LDL (OR = 0.96, 95% CI = 0.94, 0.98), but not with glutamine, suggesting that the relationship between glutamine and LOAD is unidirectional. Conclusions: Our findings support previous evidence that higher circulating levels of glutamine may be a target for protection against LOAD.

Serum per- and polyfluoroalkyl substance concentrations and longitudinal change in post-infection and post-vaccination SARS-CoV-2 antibodies.

Per- and polyfluoroalkyl substances (PFAS) are ubiquitous throughout the United States. Previous studies have shown PFAS exposure to be associated with a reduced immune response. However, the relationship between serum PFAS and antibody levels following SARS-CoV-2 infection or COVID-19 vaccination has not been examined. We examined differences in peak immune response and the longitudinal decline of antibodies following SARS-CoV-2 infection and COVID-19 vaccination by serum PFAS levels in a cohort of essential workers in the United States. We measured serum antibodies using an in-house semi-quantitative enzyme-linked immunosorbent assay (ELISA). Two cohorts contributed blood samples following SARS-CoV-2 infection or COVID-19 vaccination. We used linear mixed regression models, adjusting for age, race/ethnicity, gender, presence of chronic conditions, location, and occupation, to estimate differences in immune response with respect to serum PFAS levels. Our study populations included 153 unvaccinated participants that contributed 316 blood draws over a 14-month period following infection, and 860 participants and 2451 blood draws over a 12-month period following vaccination. Higher perfluorooctane sulfonic acid (PFOS), perfluorohexane sulfonic acid (PFHxS), and perfluorononanoic acid (PFNA) concentrations were associated with a lower peak antibody response after infection (p = 0.009, 0.031, 0.015). Higher PFOS, perfluorooctanoic acid (PFOA), PFHxS, and PFNA concentrations were associated with slower declines in antibodies over time after infection (p = 0.003, 0.014, 0.026, 0.025). PFOA, PFOS, PFHxS, and PFNA serum concentrations prior to vaccination were not associated with differences in peak antibody response after vaccination or with differences in decline of antibodies over time after vaccination. These results suggest that elevated PFAS may impede potential immune response to SARS-CoV-2 infection by blunting peak antibody levels following infection; the same finding was not observed for immune response to vaccination.

Disease and social factors associated with healthcare utilization for the treatment of SARS-CoV-2 infections in a longitudinal cohort of essential workers in Arizona.

BACKGROUND: Demands on health systems due to COVID-19 are substantial, but drivers of healthcare utilization are not well defined in non-severe SARS-CoV-2 infections. Among a prospective cohort of frontline workers from July 2020 to February 2023, we assessed predictors of healthcare utilization during SARS-CoV-2 infection. METHODS: Weekly specimens tested via real-time reverse transcriptase polymerase chain reaction analysis. Participants reported sociodemographic, health status information, and illness experience information. Primary outcome was healthcare utilization during SARS-CoV-2 infection. Predictors included sociodemographic characteristics, baseline health status, and measures of illness severity. Multivariable logistic regression was utilized to generate odds ratios for predictors of healthcare utilization. RESULTS: 1,923 SARS-CoV-2 infections (1,276 first infections and 647 reinfections from 4,208 participants): 1221 (63.5%) individuals were between 40 and 65 years old; 1115 (58.0%) were female; 449 (23.3%) were Hispanic and 1305 (67.9%) non-Hispanic White. 294 (15.3%) individuals sought medical care during first infection, 106 (5.5%) during reinfection. Sociodemographic and baseline health characteristics were not associated with healthcare utilization during infections from any variant for first infections, while age (OR 1.04, 95%CI 1.01-1.07) was during Omicron reinfection. In first infection, number of symptoms (OR 1.16, 95%CI 1.00-1.36 in Origin/Alpha, OR 1.12, 95%CI 1.00-1.49 in Delta, OR 1.09, 95%CI 1.01-1.16 in Omicron), number of days spent in bed (OR 1.13, 95%CI 1.02-1.33 in Origin/Alpha, OR 1.23, 95%CI 1.00-1.59 in Delta, OR 1.12, 95%CI 1.03-1.22 in Omicron), and illness duration (OR 1.01, 95%CI 1.00-1.04 in Origin/Alpha, OR 1.01, 95%CI 1.00-1.03 in Delta, OR 1.01, 95%CI 1.00-1.02 in Omicron) were related to healthcare utilization for all variants. Number of days in bed (OR 1.12, 95%CI 1.01-1.27), illness duration (OR 1.01, 95%CI 1.00-1.02), and hours of work missed (OR 2.24, 95%CI 1.11-4.74) were positively associated with healthcare utilization during Omicron reinfection. CONCLUSION: The main factors associated with healthcare utilization for SARS-CoV-2 infection were symptom severity and duration. Practices and therapeutics aimed at decreasing these factors would be most helpful in easing the burden on health systems.

Loneliness and depression: bidirectional mendelian randomization analyses using data from three large genome-wide association studies.

Major depression (MD) is a serious psychiatric illness afflicting nearly 5% of the world's population. A large correlational literature suggests that loneliness is a prospective risk factor for MD; correlational assocations of this nature may be confounded for a variety of reasons. This report uses Mendelian Randomization (MR) to examine potentially causal associations between loneliness and MD. We report on analyses using summary statistics from three large genome wide association studies (GWAS). MR analyses were conducted using three independent sources of GWAS summary statistics. In the first set of analyses, we used available summary statistics from an extant GWAS of loneliness to predict MD risk. We used two sources of outcome data: the Psychiatric Genomics Consortium (PGC) meta-analysis of MD (PGC-MD; N = 142,646) and the Million Veteran Program (MVP-MD; N = 250,215). Finally, we reversed analyses using data from the MVP and PGC samples to identify risk variants for MD and used loneliness outcome data from UK Biobank. We find robust evidence for a bidirectional causal relationship between loneliness and MD, including between loneliness, depression cases status, and a continuous measure of depressive symptoms. The estimates remained significant across several sensitivity analyses, including models that account for horizontal pleiotropy. This paper provides the first genetically-informed evidence that reducing loneliness may play a causal role in decreasing risk for depressive illness, and these findings support efforts to reduce loneliness in order to prevent or ameliorate MD. Discussion focuses on the public health significance of these findings, especially in light of the SARS-CoV-2 pandemic.

Metabolically favorable adiposity and bone mineral density: a Mendelian randomization analysis.

OBJECTIVE: This analysis assessed the putative causal association between genetically predicted percent body fat and areal bone mineral density (aBMD) and, more specifically, the association between genetically predicted metabolically "favorable adiposity" (MFA) and aBMD at clinically relevant bone sites. METHODS: Mendelian randomization was used to assess the relationship of MFA and percent body fat with whole-body, lumbar spine, femoral neck, and forearm aBMD. Sex-stratified and age-stratified exploratory analyses were conducted. RESULTS: In all MR analyses, genetically predicted MFA was inversely associated with aBMD for the whole body (ß = -0.053, p = 0.0002), lumbar spine (ß = -0.075; p = 0.0001), femoral neck (ß = -0.045; p = 0.008), and forearm (ß = -0.115; p = 0.001). This negative relationship was strongest in older individuals and did not differ by sex. The relationship between genetically predicted percent body fat and aBMD was nonsignificant across all Mendelian randomization analyses. Several loci that were associated at a genome-wide significance level (p < 5 × 10-8 ) in opposite directions with body fat and aBMD measures were also identified. CONCLUSIONS: This study did not support the hypothesis that MFA protects against low aBMD. Instead, it showed that MFA may result in lower aBMD. Further research is needed to understand how MFA affects aBMD and other components of bone health such as bone turnover, bone architecture, and osteoporotic fractures.

Association of sedentary and physical activity behaviours with body composition: a genome-wide association and Mendelian randomisation study.

Objectives: Studies suggest that body composition can be independently improved through physical activity (PA). We performed a Mendelian randomisation (MR) study to test the incremental benefits of sedentary behaviour and various PA exposures on body composition outcomes as assessed by anthropometric indices, lean body mass (kg), body fat (%) and visceral adipose tissue (VAT) (kg). Methods: Genetic instruments were identified for both self-reported and accelerometer-measured sedentary behaviour and PA. Outcomes included anthropometric and dual-energy X-ray absorptiometry measures of adiposity, extracted from the UK Biobank and the largest available consortia. Multivariable MR (MVMR) included educational attainment as a covariate to address potential confounding. Sensitivity analyses were evaluated for weak instrument bias and pleiotropic effects. Results: We did not identify consistent associations between genetically predicted self-reported and accelerometer-measured sedentary behaviour and body composition outcomes. All analyses for self-reported moderate PA were null for body composition outcomes. Genetically predicted PA at higher intensities was protective against VAT in MR and MVMR analyses of both accelerometer-measured vigorous PA (MVMR ß=-0.15, 95% CI: -0.24 to -0.07, p<0.001) and self-reported participation in strenuous sports or other exercises (MVMR ß=-0.27, 95% CI: -0.52 to -0.01, p=0.034) was robust across several sensitivity analyses. Conclusions: We did not identify evidence of a causal relationship between genetically predicted PA and body composition, with the exception of a putatively protective effect of higher-intensity PA on VAT. Protective effects of PA against VAT may support prior evidence of biological pathways through which PA decreases risk of downstream cardiometabolic diseases.

Cross-Sectional Study of Clinical Predictors of Coccidioidomycosis, Arizona, USA.

Demographic and clinical indicators have been described to support identification of coccidioidomycosis; however, the interplay of these conditions has not been explored in a clinical setting. In 2019, we enrolled 392 participants in a cross-sectional study for suspected coccidioidomycosis in emergency departments and inpatient units in Coccidioides-endemic regions. We aimed to develop a predictive model among participants with suspected coccidioidomycosis. We applied a least absolute shrinkage and selection operator to specific coccidioidomycosis predictors and developed univariable and multivariable logistic regression models. Univariable models identified elevated eosinophil count as a statistically significant predictive feature of coccidioidomycosis in both inpatient and outpatient settings. Our multivariable outpatient model also identified rash (adjusted odds ratio 9.74 [95% CI 1.03-92.24]; p = 0.047) as a predictor. Our results suggest preliminary support for developing a coccidioidomycosis prediction model for use in clinical settings.

Contribution of Biologic Response Modifiers to the Risk of Coccidioidomycosis Severity.

BACKGROUND: The risk of coccidioidomycosis (CM) as a life-threatening respiratory illness or disseminated CM (DCM) increases as much as 150-fold in immunosuppressed patients. The safety of biologic response modifiers (BRMs) as treatment for patients with autoimmune disease (AI) in CM-endemic regions is not well defined. We sought to determine that risk in the Tucson and Phoenix areas. METHODS: We conducted a retrospective study reviewing demographics, Arizona residency length, clinical presentations, specific AI diagnoses, CM test results, and BRM treatments in electronic medical records of patients ≥18 years old with International Classification of Diseases (ICD-10) codes for CM and AI from 1 October 2017 to 31 December 2019. RESULTS: We reviewed 944 charts with overlapping ICD-10 codes for CM and AI, of which 138 were confirmed to have both diagnoses. Male sex was associated with more CM (P = .003), and patients with African ancestry were 3 times more likely than those with European ancestry to develop DCM (P < .001). Comparing CM+/AI+ (n = 138) with CM+/AI- (n = 449) patients, there were no significant differences in CM clinical presentations. Patients receiving BRMs had 2.4 times more DCM compared to pulmonary CM (PCM). CONCLUSIONS: AI does not increase the risk of any specific CM clinical presentation, and BRM treatment of most AI patients does not lead to severe CM. However, BRMs significantly increase the risk of DCM, and prospective studies are needed to identify the immunogenetic subset that permits BRM-associated DCM.

Comparison of a Novel Rapid Lateral Flow Assay to Enzyme Immunoassay Results for Early Diagnosis of Coccidioidomycosis.

BACKGROUND: Coccidioidomycosis (CM) is a common cause of community-acquired pneumonia where CM is endemic. Manifestations include self-limited pulmonary infection, chronic fibrocavitary pulmonary disease, and disseminated coccidioidomycosis. Most infections are identified by serological assays including enzyme-linked immunoassay (EIA), complement fixation, and immunodiffusion. These are time-consuming and take days to result, impeding early diagnosis. A new lateral flow assay (LFA; Sona; IMMY, Norman, OK) improves time-to-result to 1 hour. METHODS: We prospectively enrolled 392 patients with suspected CM, compared the LFA with standard EIA and included procalcitonin evaluation. RESULTS: Compared with standard EIA, LFA demonstrates 31% sensitivity (95% confidence interval [CI], 20-44%) and 92% specificity (95% CI, 88-95%). Acute pulmonary disease (74%) was the most common clinical syndrome. Hospitalized patients constituted 75% of subjects, and compared with outpatients, they more frequently had ≥3 previous healthcare facility visits (P = .05), received antibacterials (P < .01), and had >3 antibacterial courses (P < .01). Procalcitonin (PCT) was <0.25 ng/mL in 52 (83%) EIA-positive patients, suggesting infection was not bacterial. CONCLUSIONS: When CM is a possible diagnosis, LFA identified nearly one-third of EIA-positive infections. Combined with PCT <0.25 ng/mL, LFA could reduce unnecessary antibacterial use by 77%.