Next generation sequencing with copy number variant detection expands the phenotypic spectrum of HSD17B4-deficiency

Background: D-bifunctional protein deficiency, caused by recessive mutations in HSD17B4, is a severe, infantile-onset disorder of peroxisomal fatty acid oxidation. Few affected patients survive past two years of age. Compound heterozygous mutations in HSD17B4 have also been reported in two sisters diagnosed with Perrault syndrome (MIM # 233400), who presented in adolescence with ovarian dysgenesis, hearing loss, and ataxia. Case presentation: An adult male presented with cerebellar ataxia, peripheral neuropathy, hearing loss, and azoospermia. The clinical presentation, in combination with biochemical findings in serum, urine, and muscle biopsy, suggested a mitochondrial disorder. Commercial genetic testing of 18 ataxia and mitochondrial disease genes was negative. Targeted exome sequencing followed by analysis of single nucleotide variants and small insertions/deletions failed to reveal a genetic basis of disease. Application of a computational algorithm to infer copy number variants (CNVs) from exome data revealed a heterozygous 12 kb deletion of exons 10–13 of HSD17B4 that was compounded with a rare missense variant (p.A196V) at a highly conserved residue. Retrospective review of patient records revealed mildly elevated ratios of pristanic:phytanic acid and arachidonic:docosahexaenoic acid, consistent with dysfunctional peroxisomal fatty acid oxidation. Conclusion: Our case expands the phenotypic spectrum of HSD17B4-deficiency, representing the first male case reported with infertility. Furthermore, it points to crosstalk between mitochondria and peroxisomes in HSD17B4-deficiency and Perrault syndrome

Racial/ethnic differences in job loss for women with breast cancer

IntroductionWe examined race/ethnic differences in treatment-related job loss and the financial impact of treatment-related job loss, in a population-based sample of women diagnosed with breast cancer.MethodsThree thousand two hundred fifty two women with non-metastatic breast cancer diagnosed (August 2005-February 2007) within the Los Angeles County and Detroit Metropolitan Surveillance Epidemiology and End Results registries, were identified and asked to complete a survey (mean time from diagnosis = 8.9 months). Latina and African American women were over-sampled (n = 2268, eligible response rate 72.1%).ResultsOne thousand one hundred eleven women (69.6%) of working age (<65 years) were working for pay at time of diagnosis. Of these women, 10.4% (24.1% Latina, 10.1% African American, 6.9% White, p < 0.001) reported that they lost or quit their job since diagnosis due to breast cancer or its treatment (defined as job loss). Latina women were more likely to experience job loss compared to White women (OR = 2.0, p = 0.013)), independent of sociodemographic factors. There were no significant differences in job loss between African American and White women, independent of sociodemographic factors. Additional adjustments for clinical and treatment factors revealed a significant interaction between race/ethnicity and chemotherapy (p = 0.007). Among women who received chemotherapy, Latina women were more likely to lose their job compared to White women (OR = 3.2, p < 0.001), however, there were no significant differences between Latina and White women among those who did not receive chemotherapy. Women who lost their job were more likely to experience financial strain (e.g. difficulty paying bills 27% vs. 11%, p < 0.001).ConclusionJob loss is a serious consequence of treatment for women with breast cancer. Clinicians and staff need to be aware of aspects of treatment course that place women at higher risk for job loss, especially ethnic minorities receiving chemotherapy

Subsequent Surgery After Revision Anterior Cruciate Ligament Reconstruction: Rates and Risk Factors From a Multicenter Cohort

BACKGROUND: While revision anterior cruciate ligament reconstruction (ACLR) can be performed to restore knee stability and improve patient activity levels, outcomes after this surgery are reported to be inferior to those after primary ACLR. Further reoperations after revision ACLR can have an even more profound effect on patient satisfaction and outcomes. However, there is a current lack of information regarding the rate and risk factors for subsequent surgery after revision ACLR. PURPOSE: To report the rate of reoperations, procedures performed, and risk factors for a reoperation 2 years after revision ACLR. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: A total of 1205 patients who underwent revision ACLR were enrolled in the Multicenter ACL Revision Study (MARS) between 2006 and 2011, composing the prospective cohort. Two-year questionnaire follow-up was obtained for 989 patients (82%), while telephone follow-up was obtained for 1112 patients (92%). If a patient reported having undergone subsequent surgery, operative reports detailing the subsequent procedure(s) were obtained and categorized. Multivariate regression analysis was performed to determine independent risk factors for a reoperation. RESULTS: Of the 1112 patients included in the analysis, 122 patients (11%) underwent a total of 172 subsequent procedures on the ipsilateral knee at 2-year follow-up. Of the reoperations, 27% were meniscal procedures (69% meniscectomy, 26% repair), 19% were subsequent revision ACLR, 17% were cartilage procedures (61% chondroplasty, 17% microfracture, 13% mosaicplasty), 11% were hardware removal, and 9% were procedures for arthrofibrosis. Multivariate analysis revealed that patients aged <20 years had twice the odds of patients aged 20 to 29 years to undergo a reoperation. The use of an allograft at the time of revision ACLR (odds ratio [OR], 1.79; P = .007) was a significant predictor for reoperations at 2 years, while staged revision (bone grafting of tunnels before revision ACLR) (OR, 1.93; P = .052) did not reach significance. Patients with grade 4 cartilage damage seen during revision ACLR were 78% less likely to undergo subsequent operations within 2 years. Sex, body mass index, smoking history, Marx activity score, technique for femoral tunnel placement, and meniscal tearing or meniscal treatment at the time of revision ACLR showed no significant effect on the reoperation rate. CONCLUSION: There was a significant reoperation rate after revision ACLR at 2 years (11%), with meniscal procedures most commonly involved. Independent risk factors for subsequent surgery on the ipsilateral knee included age <20 years and the use of allograft tissue at the time of revision ACLR

METHODS FOR CLUSTERING TIME SERIES DATA ACQUIRED FROM MOBILE HEALTH APPS

In our recent Asthma Mobile Health Study (AMHS), thousands of asthma patients across the country contributed medical data through the iPhone Asthma Health App on a daily basis for an extended period of time. The collected data included daily self-reported asthma symptoms, symptom triggers, and real time geographic location information. The AMHS is just one of many studies occurring in the context of now many thousands of mobile health apps aimed at improving wellness and better managing chronic disease conditions, leveraging the passive and active collection of data from mobile, handheld smart devices. The ability to identify patient groups or patterns of symptoms that might predict adverse outcomes such as asthma exacerbations or hospitalizations from these types of large, prospectively collected data sets, would be of significant general interest. However, conventional clustering methods cannot be applied to these types of longitudinally collected data, especially survey data actively collected from app users, given heterogeneous patterns of missing values due to: 1) varying survey response rates among different users, 2) varying survey response rates over time of each user, and 3) non-overlapping periods of enrollment among different users. To handle such complicated missing data structure, we proposed a probability imputation model to infer missing data. We also employed a consensus clustering strategy in tandem with the multiple imputation procedure. Through simulation studies under a range of scenarios reflecting real data conditions, we identified favorable performance of the proposed method over other strategies that impute the missing value through low-rank matrix completion. When applying the proposed new method to study asthma triggers and symptoms collected as part of the AMHS, we identified several patient groups with distinct phenotype patterns. Further validation of the methods described in this paper might be used to identify clinically important patterns in large data sets with complicated missing data structure, improving the ability to use such data sets to identify at-risk populations for potential intervention

Mutations in MTFMT underlie a human disorder of formylation causing impaired mitochondrial translation

The metazoan mitochondrial translation machinery is unusual in having a single tRNA(Met) that fulfills the dual role of the initiator and elongator tRNA(Met). A portion of the Met-tRNA(Met) pool is formylated by mitochondrial methionyl-tRNA formyltransferase (MTFMT) to generate N-formylmethioninetRNA(Met) (fMet-tRNA(met)), which is used for translation initiation; however, the requirement of formylation for initiation in human mitochondria is still under debate. Using targeted sequencing of the mtDNA and nuclear exons encoding the mitochondrial proteome (MitoExome), we identified compound heterozygous mutations in MTFMT in two unrelated children presenting with Leigh syndrome and combined OXPHOS deficiency. Patient fibroblasts exhibit severe defects in mitochondrial translation that can be rescued by exogenous expression of MTFMT. Furthermore, patient fibroblasts have dramatically reduced fMet-tRNA(Met) levels and an abnormal formylation profile of mitochondrially translated COX1. Our findings demonstrate that MTFMT is critical for efficient human mitochondrial translation and reveal a human disorder of Met-tRNA(Met) formylation

Remote Mobile Outpatient Monitoring in Transplant (Reboot) 2.0: Protocol for a Randomized Controlled Trial

BackgroundThe number of solid organ transplants in Canada has increased 33% over the past decade. Hospital readmissions are common within the first year after transplant and are linked to increased morbidity and mortality. Nearly half of these admissions to the hospital appear to be preventable. Mobile health (mHealth) technologies hold promise to reduce admission to the hospital and improve patient outcomes, as they allow real-time monitoring and timely clinical intervention. ObjectiveThis study aims to determine whether an innovative mHealth intervention can reduce hospital readmission and unscheduled visits to the emergency department or transplant clinic. Our second objective is to assess the use of clinical and continuous ambulatory physiologic data to develop machine learning algorithms to predict the risk of infection, organ rejection, and early mortality in adult heart, kidney, and liver transplant recipients. MethodsRemote Mobile Outpatient Monitoring in Transplant (Reboot) 2.0 is a two-phased single-center study to be conducted at the University Health Network in Toronto, Canada. Phase one will consist of a 1-year concealed randomized controlled trial of 400 adult heart, kidney, and liver transplant recipients. Participants will be randomized to receive either personalized communication using an mHealth app in addition to standard of care phone communication (intervention group) or standard of care communication only (control group). In phase two, the prior collected data set will be used to develop machine learning algorithms to identify early markers of rejection, infection, and graft dysfunction posttransplantation. The primary outcome will be a composite of any unscheduled hospital admission, visits to the emergency department or transplant clinic, following discharge from the index admission. Secondary outcomes will include patient-reported outcomes using validated self-administered questionnaires, 1-year graft survival rate, 1-year patient survival rate, and the number of standard of care phone voice messages. ResultsAt the time of this paper’s completion, no results are available. ConclusionsBuilding from previous work, this project will aim to leverage an innovative mHealth app to improve outcomes and reduce hospital readmission in adult solid organ transplant recipients. Additionally, the development of machine learning algorithms to better predict adverse health outcomes will allow for personalized medicine to tailor clinician-patient interactions and mitigate the health care burden of a growing patient population. Trial RegistrationClinicalTrials.gov NCT04721288; https://www.clinicaltrials.gov/ct2/show/NCT04721288 International Registered Report Identifier (IRRID)PRR1-10.2196/2681