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Here we introduce a new endpoint "census population size" to evaluate the epidemiology and control of Plasmodium falciparum infections, where the parasite, rather than the infected human host, is the unit of measurement. To calculate census population size, we rely on a definition of parasite variation known as multiplicity of infection (MOI _var ), based on the hyper-diversity of the var multigene family. We present a Bayesian approach to estimate MOI _var from sequencing and counting the number of unique DBLα tags (or DBLα types) of var genes, and derive from it census population size by summation of MOI _var in the human population. We track changes in this parasite population size and structure through sequential malaria interventions by indoor residual spraying (IRS) and seasonal malaria chemoprevention (SMC) from 2012 to 2017 in an area of high-seasonal malaria transmission in northern Ghana. Following IRS, which reduced transmission intensity by > 90% and decreased parasite prevalence by ~40-50%, significant reductions in var diversity, MOI _var , and population size were observed in ~2,000 humans across all ages. These changes, consistent with the loss of diverse parasite genomes, were short lived and 32-months after IRS was discontinued and SMC was introduced, var diversity and population size rebounded in all age groups except for the younger children (1-5 years) targeted by SMC. Despite major perturbations from IRS and SMC interventions, the parasite population remained very large and retained the var population genetic characteristics of a high-transmission system (high var diversity; low var repertoire similarity) demonstrating the resilience of P. falciparum to short-term interventions in high-burden countries of sub-Saharan Africa.

Objective: This study aims to describe the establishment of the Mexican Parkinson's Research Network (MEX-PD), a consortium dedicated to investigating the clinical, genetic, environmental, and neurophysiological underpinnings of phenotypic diversity in Mexican Parkinson's disease (PD) patients and to present preliminary clinical and genetic outcomes.

Methods: PD patients and control participants were recruited from medical centers across Mexico. The initial recruitment phase involved comprehensive neurological evaluations, cognitive assessments, and DNA collection. We conducted classical statistical analyses on clinical variables. Secondly, following genotyping with NeuroBooster array, quality control and imputation, preliminary analysis of ancestry composition, allele frequency calculation and association analysis was carried out for 294 samples.

Results: The cohort consisted of 530 control participants and 470 PD patients, with a mean age of diagnosis of 59.9 ± 11.52 years. Among the PD patients, 21.2% were identified as having early-onset PD (<50 years old). Ancestry composition analysis revealed that the main components were European (49.8% in cases and 42.4% in controls) and Native American (46.1% in cases and 54.3% in controls). Variants in genes such as NOTCH, LRRK2, MTHFR and KPNA1 emerged as relevant to be prioritized in future studies.

Conclusions: The MEX-PD consortium will contribute to the understanding of PD within the Mexican population. The data collected will enable a deeper comprehension of the specific contributions of genetic and environmental factors to these outcomes.

Significance: This research advocates for the development of personalized treatments and aims to improve the quality of life for Mexican PD patients.

A central clinical goal for patients with HPV-positive oropharynx cancer has been to reduce radiation doses while maintaining cure rates. Recent results of Phase 3 prospective trial HN005 demonstrated that RT dose de-escalation can not be safely done based on clinical factors alone. We have previously shown that the genomic adjusted radiation dose (GARD) is predictive of radiation treatment benefit and can be used to guide RT dose selection. We hypothesize that GARD can be used to guide RT dose de-escalation in HPV-positive OPSCC patients. Gene expression was analyzed for 191 formalin-fixed paraffin-embedded samples from HPV-positive OPSCC patients within an international, multi-institutional, prospective/retrospective observational study including patients with AJCC 8th edition stage I-III. Two RT dose fractionations were utilized for the majority of primary RT cases (70 Gy in 35 fractions or 69.96 Gy in 33 fractions). Median RT dose was 70 Gy (range 51.0-74.0), survival at 36 months and 60 months was 94.1% and 87.3%, respectively. Cox proportional hazards analyses were performed with GARD as a continuous variable and time-dependent ROC analyses compared the performance of GARD to clinical variables alone. Despite near-uniform RT dosing, GARD reveals significant heterogeneity (range 15.4 - 71.7) in predicted effect. In univariate analysis, GARD was associated with an improvement in OS (HR = 0.941 (0.888, 0.998), p = 0.041). In multivariable analysis, each unit increase in GARD was associated with an improvement in OS (HR = 0.943 (0.891, 0.999), p = 0.046) where stage was not (T stage HR = 1.992 (0.711-5.576), p=0.190, N stage HR = 2.367 (0.867-6.460), p=0.093). ROC analysis for GARD at 36 months yielded an AUC of 78.26 (65.14, 91.38) compared with 71.20 (54.47, 87.93) for standard clinical variables. We identify two GARD-based strategies to RT dose personalization which are predicted to yield improved clinical outcomes, while delivering an average lower RT dose. In this multi-institutional cohort of patients with HPV-positive OPSCC, GARD associates with OS, outperforms standard clinical variables and provides a novel genomic strategy to RT dose personalization. We propose that GARD should be incorporated in the diagnostic workup of HPV-positive OPSCC patients.

Recent work leveraging artificial intelligence has offered promise to dissect disease heterogeneity by identifying complex intermediate brain phenotypes, called dimensional neuroimaging endophenotypes (DNEs). We advance the argument that these DNEs capture the degree of expression of respective neuroanatomical patterns measured, offering a dimensional neuroanatomical representation for studying disease heterogeneity and similarities of neurologic and neuropsychiatric diseases. We investigate the presence of nine DNEs derived from independent yet harmonized studies on Alzheimer's disease, autism spectrum disorder, late-life depression, and schizophrenia in the UK Biobank study. Phenome-wide associations align with genome-wide associations, revealing 31 genomic loci (P-value<5×10^-8/9) associated with the nine DNEs.The nine DNEs, along with their polygenic risk scores, significantly enhanced the predictive accuracy for 14 systemic disease categories, particularly for conditions related to mental health and the central nervous system, as well as mortality outcomes. These findings underscore the potential of the nine DNEs to capture the expression of disease-related brain phenotypes in individuals of the general population and to relate such measures with genetics, lifestyle factors, and chronic diseases.

The loss of melanized neurons in the substantia nigra pars compacta (SNc) is a hallmark pathology in Parkinson's disease (PD). Melanized neurons in SNc can be visualized in vivo using magnetization transfer (MT) effects. Nigral volume was extracted in data acquired with a MT-prepared gradient echo sequence in 50 controls, 90 non-manifest carriers (46 LRRK2 and 44 GBA1 nonmanifest carriers), 217 prodromal hyposmic participants, 76 participants with rapid eye movement sleep behavior disorder (RBD), 194 de novo PD patients and 26 moderate PD patients from the Parkinson's Progressive Markers Initiative. No difference in nigral volume was seen between controls and LRRK2 and GBA1 non-manifest carriers ( F =0.732; P =0.483). A significant main effect in group was observed between controls, prodromal hyposmic participants, RBD participants, and overt PD patients ( F =9.882; P <10 ^-3 ). This study shows that nigral depigmentation can be robustly detected in prodromal and overt PD populations.

Acute alcohol intake decreases brain glucose metabolism and increases brain uptake of acetate, a metabolite of alcohol. This shift in energy utilization persists beyond acute intoxication in individuals with alcohol use disorder (AUD), and may contribute to alcohol craving. We recently found that ketone therapies decrease alcohol withdrawal and alcohol craving in AUD. Here, we studied the effects of a single-dose ketone ester (KE) supplement on brain energy metabolism and alcohol craving. Five AUD and five healthy control (HC) participants underwent two ¹⁸ F-fluorodeoxyglucose positron emission tomography (PET) scans, after consumption of 395 mg/kg KE or without (baseline), in randomized order. In the AUD group, KE reduced alcohol craving scores compared to baseline. KE decreased blood glucose levels and elevated blood β-hydroxybutyrate (BHB) levels compared to baseline in both groups. Whole-brain voxel-wise maps of the cerebral metabolic rate of glucose (CMRglc) decreased by 17% in both groups, with the largest KE-induced CMRglc reductions in the frontal, occipital, and cingulate cortices, hippocampus, amygdala, and insula. There were no group differences between AUD and HC in blood or FDG measures, and no correlations between reductions in craving with CMRglc. Cingulate BHB levels, as assessed with ¹ H-magnetic resonance spectroscopy in 5 participant with AUD, increased 3-fold with KE compared to baseleline. In sum, administration of a single dose of KE rapidly shifted brain energetics from glucose to ketone metabolism in HC and AUD. KE also reduced ratings of alcohol craving, demonstrating its potential clinical effectiveness for supporting brain health and alcohol craving in AUD.

Stroke remains a leading cause of complications and mortality in heart failure patients treated with a Left Ventricular Assist Device (LVAD). Hemodynamics plays a central role underlying post-LVAD stroke risk and etiology. Yet, detailed quantitative assessment of hemodynamic variables and their relation to stroke outcomes in patients on LVAD support remains a challenge. Modalities for pre-implantation assessment of post-implantation hemodynamics can help address this challenge. We present an in silico hemodynamics analysis for a digital twin cohort 12 patients on LVAD support; 6 with reported stroke outcomes and 6 without. For each patient we created a post-implant twin with the LVAD outflow graft reconstructed from cardiac-gated CT images; and a pre-implant twin of an estimated baseline flow by removing the LVAD outflow graft and driving flow from the aortic valve opening. Hemodynamics was characterized using descriptors for helical flow, vortex generation, and wall shear stress. We observed higher average values for descriptors of positive helical flow, vortex generation, and wall shear stress, across the 6 cases with stroke outcomes when compared with cases without stroke. When the descriptors for LVAD-driven flow were compared against estimated pre-implantation flow, extent of positive helicity was higher, and vorticity and wall shear were lower in cases with stroke compared to those without. Our study suggests that quantitative analysis of hemodynamics after LVAD implantation; and hemodynamic alterations from a pre-implant flow scenario, can potentially reveal hidden information linked to stroke outcomes during LVAD support. This has broad implications on understanding stroke etiology; and using patient digital twins for LVAD treatment planning, surgical optimization, and efficacy assessment.

Some clinical algorithms incorporate an individual's race, ethnicity, or both as an input variable or predictor in determining diagnoses, prognoses, treatment plans, or risk assessments. Inappropriate use of race and ethnicity in clinical algorithms at the point of care may exacerbate health disparities and promote harmful practices of race-based medicine. We identified 42 risk calculators that use race as a predictor, five laboratory test results with different reference ranges recommended for different races, one therapy recommendation based on race, 15 medications with guidelines for initiation and monitoring based on race, and four medical devices with differential racial performance. Information on these clinical algorithms are freely available at http://www.clinical-algorithms-with-race-and-ethnicity.org. This resource aims to raise awareness about the use of race in clinical algorithms and to track the progress made toward eliminating its inappropriate use. The database will be actively updated to include clinical algorithms based on race that were missed, along with additional characteristics of these algorithms.

Summary-data-based multivariable Mendelian randomization (MVMR) methods, such as MVMR-Egger, MVMR-IVW, MVMR median-based, and MVMR-PRESSO, assess the causal effects of multiple risk factors on disease. However, accounting for variances in summary statistics related to risk factors remains a challenge. We propose a linear mixed model with measurement error correction (LMM-MEC) that accounts for the variance of summary statistics for both disease outcomes and risk factors. In step I, a linear mixed model is applied to account for the variance in disease summary statistics. Specifically, if heterogeneity is present in disease summary statistics, we treat it as a random effect and adopt an iteratively re-weighted least squares algorithm to estimate causal effects. In step II, we treat the variance in the summary statistics of risk factors as multiple measurement errors and apply a regression calibration method for simultaneous multiple measurement error correction. In a simulation study, when using independent genetic variants as instrumental variables (IV), our method showed comparable performance to existing MVMR methods under conditions of no pleiotropy or balanced pleiotropy with the outcome, and it exhibited higher coverage rates and power under directional pleiotropy. Similar findings were observed when using genetic variants with low to moderate linkage disequilibrium (LD) (0 < ρ ² ≤ 0.3) as IVs, although coverage rates reduced for all methods compared to using independent genetic variants as IVs. In the application study, we examined causal associations between correlated cholesterol biomarkers and longevity. By including 739 genetic variants selected based on P values <5×10 ^-5 from GWAS and allowing for low LD ( ρ ² ≤ 0.1), our method identified that large LDL-c were causally associated with lower likelihood of achieving longevity.

There are many well-established relationships between pathogens and human disease, but far fewer when focusing on non-communicable diseases (NCDs). We leverage data from The UK Biobank and TriNetX to perform a systematic survey across 20 pathogens and 426 diseases, primarily NCDs. To this end, we assess the association between disease status and infection history proxies. We identify 206 pathogen-disease pairs that replicate in both cohorts. We replicate many established relationships, including Helicobacter pylori with several gastroenterological diseases and connections between Epstein-Barr virus with multiple sclerosis and lupus. Overall, our approach identified evidence of association for 15 pathogens and 96 distinct diseases, including a currently controversial link between human cytomegalovirus (CMV) and ulcerative colitis (UC). We validate this connection through two orthogonal analyses, revealing increased CMV gene expression in UC patients and enrichment for UC genetic risk signal near human genes that have altered expression upon CMV infection. Collectively, these results form a foundation for future investigations into mechanistic roles played by pathogens in NCDs. All results are easily accessible on our website, https://tf.cchmc.org/pathogen-disease.