Immune Phenotype and Postoperative Complications After Elective Surgery

OBJECTIVES

To characterize and quantify accumulating immunologic alterations, pre and postoperatively in patients undergoing elective surgical procedures.

BACKGROUND

Elective surgery is an anticipatable, controlled human injury. Although the human response to injury is generally stereotyped, individual variability exists. This makes surgical outcomes less predictable, even after standardized procedures, and may provoke complications in patients unable to compensate for their injury. One potential source of variation is found in immune cell maturation, with phenotypic changes dependent on an individual's unique, lifelong response to environmental antigens.

METHODS

We enrolled 248 patients in a prospective trial facilitating comprehensive biospecimen and clinical data collection in patients scheduled to undergo elective surgery. Peripheral blood was collected preoperatively, and immediately on return to the postanesthesia care unit. Postoperative complications that occurred within 30 days after surgery were captured.

RESULTS

As this was an elective surgical cohort, outcomes were generally favorable. With a median follow-up of 6 months, the overall survival at 30 days was 100%. However, 20.5% of the cohort experienced a postoperative complication (infection, readmission, or system dysfunction). We identified substantial heterogeneity of immune senescence and terminal differentiation phenotypes in surgical patients. More importantly, phenotypes indicating increased T-cell maturation and senescence were associated with postoperative complications and were evident preoperatively.

CONCLUSIONS

The baseline immune repertoire may define an immune signature of resilience to surgical injury and help predict risk for surgical complications.

Multi-ancestry genome-wide meta-analysis of 56,241 individuals identifies LRRC4C, LHX5-AS1 and nominates ancestry-specific loci PTPRK , GRB14 , and KIAA0825 as novel risk loci for Alzheimer's disease: the Alzheimer's Disease Genetics Consortium

Limited ancestral diversity has impaired our ability to detect risk variants more prevalent in non-European ancestry groups in genome-wide association studies (GWAS). We constructed and analyzed a multi-ancestry GWAS dataset in the Alzheimer's Disease (AD) Genetics Consortium (ADGC) to test for novel shared and ancestry-specific AD susceptibility loci and evaluate underlying genetic architecture in 37,382 non-Hispanic White (NHW), 6,728 African American, 8,899 Hispanic (HIS), and 3,232 East Asian individuals, performing within-ancestry fixed-effects meta-analysis followed by a cross-ancestry random-effects meta-analysis. We identified 13 loci with cross-ancestry associations including known loci at/near CR1 , BIN1 , TREM2 , CD2AP , PTK2B , CLU , SHARPIN , MS4A6A , PICALM , ABCA7 , APOE and two novel loci not previously reported at 11p12 ( LRRC4C ) and 12q24.13 ( LHX5-AS1 ). Reflecting the power of diverse ancestry in GWAS, we observed the SHARPIN locus using 7.1% the sample size of the original discovering single-ancestry GWAS (n=788,989). We additionally identified three GWS ancestry-specific loci at/near ( PTPRK ( P =2.4×10 -8 ) and GRB14 ( P =1.7×10 -8 ) in HIS), and KIAA0825 ( P =2.9×10 -8 in NHW). Pathway analysis implicated multiple amyloid regulation pathways (strongest with P adjusted =1.6×10 -4 ) and the classical complement pathway ( P adjusted =1.3×10 -3 ). Genes at/near our novel loci have known roles in neuronal development ( LRRC4C, LHX5-AS1 , and PTPRK ) and insulin receptor activity regulation ( GRB14 ). These findings provide compelling support for using traditionally-underrepresented populations for gene discovery, even with smaller sample sizes.

Multidimensional machine learning models predicting outcomes after trauma

BACKGROUND

An emerging body of literature supports the role of individualized prognostic tools to guide the management of patients after trauma. The aim of this study was to develop advanced modeling tools from multidimensional data sources, including immunological analytes and clinical and administrative data, to predict outcomes in trauma patients.

METHODS

This was a prospective study of trauma patients at Level 1 centers from 2015 to 2019. Clinical, flow cytometry, and serum cytokine data were collected within 48 hours of admission. Sparse logistic regression models were developed, jointly selecting predictors and estimating the risk of ventilator-associated pneumonia, acute kidney injury, complicated disposition (death, rehabilitation, or nursing facility), and return to the operating room. Model parameters (regularization controlling model sparsity) and performance estimation were obtained via nested leave-one-out cross-validation.

RESULTS

A total of 179 patients were included. The incidences of ventilator-associated pneumonia, acute kidney injury, complicated disposition, and return to the operating room were 17.7%, 28.8%, 22.5%, and 12.3%, respectively. Regarding extensive resource use, 30.7% of patients had prolonged intensive care unit stay, 73.2% had prolonged length of stay, and 23.5% had need for prolonged ventilatory support. The models were developed and cross-validated for ventilator-associated pneumonia, acute kidney injury, complicated dispositions, and return to the operating room, yielding predictive areas under the curve from 0.70 to 0.91. Each model derived its optimal predictive value by combining clinical, administrative, and immunological analyte data.

CONCLUSION

Clinical, immunological, and administrative data can be combined to predict post-traumatic outcomes and resource use. Multidimensional machine learning modeling can identify trauma patients with complicated clinical trajectories and high resource needs.

Can Genetic Analysis of Putative Blood Alzheimer's Disease Biomarkers Lead to Identification of Susceptibility Loci?

Although 24 Alzheimer’s disease (AD) risk loci have been reliably identified, a large portion of the predicted heritability for AD remains unexplained. It is expected that additional loci of small effect will be identified with an increased sample size. However, the cost of a significant increase in Case-Control sample size is prohibitive. The current study tests whether exploring the genetic basis of endophenotypes, in this case based on putative blood biomarkers for AD, can accelerate the identification of susceptibility loci using modest sample sizes. Each endophenotype was used as the outcome variable in an independent GWAS. Endophenotypes were based on circulating concentrations of proteins that contributed significantly to a published blood-based predictive algorithm for AD. Endophenotypes included Monocyte Chemoattractant Protein 1 (MCP1), Vascular Cell Adhesion Molecule 1 (VCAM1), Pancreatic Polypeptide (PP), Beta2 Microglobulin (B2M), Factor VII (F7), Adiponectin (ADN) and Tenascin C (TN-C). Across the seven endophenotypes, 47 SNPs were associated with outcome with a p-value ≤1x10^-7. Each signal was further characterized with respect to known genetic loci associated with AD. Signals for several endophenotypes were observed in the vicinity of CR1, MS4A6A/MS4A4E, PICALM, CLU, and PTK2B. The strongest signal was observed in association with Factor VII levels and was located within the F7 gene. Additional signals were observed in MAP3K13, ZNF320, ATP9B and TREM1. Conditional regression analyses suggested that the SNPs contributed to variation in protein concentration independent of AD status. The identification of two putatively novel AD loci (in the Factor VII and ATP9B genes), which have not been located in previous studies despite massive sample sizes, highlights the benefits of an endophenotypic approach for resolving the genetic basis for complex diseases. The coincidence of several of the endophenotypic signals with known AD loci may point to novel genetic interactions and should be further investigated.

Olfactory copy number association with age at onset of Alzheimer disease

OBJECTIVES

Copy number variants (CNVs) have been recognized as a source of genetic variation that contributes to disease phenotypes. Alzheimer disease (AD) has high heritability for occurrence and age at onset (AAO). We performed a cases-only genome-wide CNV association study for age at onset of AD.

METHODS

The discovery case series (n = 40 subjects with AD) was evaluated using array comparative genome hybridization (aCGH). A replication case series (n = 507 subjects with AD) was evaluated using Affymetrix array (n = 243) and multiplex ligation-dependent probe amplification (n = 264). Hazard models related onset age to CNV.

RESULTS

The discovery sample identified a chromosomal segment on 14q11.2 (19.3-19.5 Mb, NCBI build 36, UCSC hg18 March 2006) as a region of interest (genome-wide adjusted p = 0.032) for association with AAO of AD. This region encompasses a cluster of olfactory receptors. The replication sample confirmed the association (p = 0.035). The association was found for each APOE4 gene dosage (0, 1, and 2).

CONCLUSION

High copy number in the olfactory receptor region on 14q11.2 is associated with younger age at onset of AD.