Building a vertically integrated genomic learning health system: The biobank at the Colorado Center for Personalized Medicine.

Precision medicine initiatives across the globe have led to a revolution of repositories linking large-scale genomic data with electronic health records, enabling genomic analyses across the entire phenome. Many of these initiatives focus solely on research insights, leading to limited direct benefit to patients. We describe the biobank at the Colorado Center for Personalized Medicine (CCPM Biobank) that was jointly developed by the University of Colorado Anschutz Medical Campus and UCHealth to serve as a unique, dual-purpose research and clinical resource accelerating personalized medicine. This living resource currently has more than 200,000 participants with ongoing recruitment. We highlight the clinical, laboratory, regulatory, and HIPAA-compliant informatics infrastructure along with our stakeholder engagement, consent, recontact, and participant engagement strategies. We characterize aspects of genetic and geographic diversity unique to the Rocky Mountain region, the primary catchment area for CCPM Biobank participants. We leverage linked health and demographic information of the CCPM Biobank participant population to demonstrate the utility of the CCPM Biobank to replicate complex trait associations in the first 33,674 genotyped individuals across multiple disease domains. Finally, we describe our current efforts toward return of clinical genetic test results, including high-impact pathogenic variants and pharmacogenetic information, and our broader goals as the CCPM Biobank continues to grow. Bringing clinical and research interests together fosters unique clinical and translational questions that can be addressed from the large EHR-linked CCPM Biobank resource within a HIPAA- and CLIA-certified environment.

Lesion characteristics are associated with bowel, bladder, and overall independence following cervical spinal cord injury.

CONTEXT/OBJECTIVE: There is a growing global interest in quantifying spinal cord lesions and spared neural tissue using magnetic resonance imaging (MRI) in individuals with spinal cord injury (SCI). The primary objective of this study was to assess the relationships between spinal cord lesion characteristics assessed on MRI and bowel, bladder, and overall independence following SCI. DESIGN: Retrospective, exploratory study. PARTICIPANTS: 93 individuals with cervical SCI who were enrolled in a local United States Model Systems SCI database from 2010 to 2017. METHODS: Clinical and MRI data were obtained for potential participants, and MRIs of eligible participants were analyzed. Explanatory variables, captured on MRIs, included intramedullary lesion length (IMLL), midsagittal ventral tissue bridge width (VTBW), midsagittal dorsal tissue bridge width (DTBW), and axial damage ratio (ADR). OUTCOME MEASURES: Bowel and bladder management scale of the Functional Independence Measure (FIM) and FIM total motor score. RESULTS: When accounting for all four variables, only ADR was significantly associated with bowel independence (OR = 0.970, 95% CI: 0.942-0.997, P = 0.030), and both ADR and IMLL were strongly associated with bladder independence (OR = 0.967, 95% CI: 0.936-0.999, P = 0.046 and OR = 0.948, 95% CI: 0.919-0.978, P = 0.0007, respectively). 32% of the variation in overall independence scores were explained by all four predictive variables, but only ADR was significantly associated with overall independence after accounting for all other predictive variables (ß = -0.469, 95% CI: -0.719, -0.218, P = 0.0004). CONCLUSIONS: Our results suggest that the MRI-measured extent of spinal cord lesion may be predictive of bowel, bladder, and overall independence following cervical SCI.

Periprocedural management of ruptured blister aneurysms treated with pipeline flow diversion.

Background: Blister aneurysms are high-risk intracranial vascular lesions. Definitive treatment of these lesions has been challenging. Severe disability or mortality rates are as high as 55% when these lesions are treated with open surgery. Recent data show that flow diversion is a safe and effective alternative treatment for blister aneurysms. Rerupture of the functionally unsecured lesion remains a concern as flow diversion does not immediately exclude the aneurysm from the circulation. Methods: A retrospective review was performed of any patients with ruptured blister aneurysms treated with a pipeline embolization device between 2010 and 2020 at the University of Colorado. Results: In this paper, we present the results of the intensive care management of ruptured intracranial blister aneurysms after flow-diverting stent placement. Conclusion: Despite the need for dual antiplatelet therapy and the delayed occlusion of blister aneurysms treated with flow diversion, we did not find an increase in periprocedural complications.

Characterizing variability of electronic health record-driven phenotype definitions.

OBJECTIVE: The aim of this study was to analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the variability of logical constructs used. MATERIALS AND METHODS: A sample of 33 preexisting phenotype definitions used in research that are represented using Fast Healthcare Interoperability Resources and Clinical Quality Language (CQL) was analyzed using automated analysis of the computable representation of the CQL libraries. RESULTS: Most of the phenotype definitions include narrative descriptions and flowcharts, while few provide pseudocode or executable artifacts. Most use 4 or fewer medical terminologies. The number of codes used ranges from 5 to 6865, and value sets from 1 to 19. We found that the most common expressions used were literal, data, and logical expressions. Aggregate and arithmetic expressions are the least common. Expression depth ranges from 4 to 27. DISCUSSION: Despite the range of conditions, we found that all of the phenotype definitions consisted of logical criteria, representing both clinical and operational logic, and tabular data, consisting of codes from standard terminologies and keywords for natural language processing. The total number and variety of expressions are low, which may be to simplify implementation, or authors may limit complexity due to data availability constraints. CONCLUSIONS: The phenotype definitions analyzed show significant variation in specific logical, arithmetic, and other operators but are all composed of the same high-level components, namely tabular data and logical expressions. A standard representation for phenotype definitions should support these formats and be modular to support localization and shared logic.

Enclosing a pen in a postal questionnaire follow-up to increase response rate: a study within a trial.

Background: Poor response rates to follow-up questionnaires can adversely affect the progress of a randomised controlled trial and the validity of its results. This embedded 'study within a trial' aimed to investigate the impact of including a pen with the postal 3-month questionnaire completed by the trial participants on the response rates to this questionnaire. Methods: This study was a two-armed randomised controlled trial nested in the Gentle Years Yoga (GYY) trial. Participants in the intervention group of the GYY trial were allocated 1:1 using simple randomisation to either receive a pen (intervention) or no pen with their 3-month questionnaire (control). The primary outcome was the proportion of participants sent a 3-month questionnaire who returned it. Secondary outcomes were time taken to return the questionnaire, proportion of participants sent a reminder to return the questionnaire, and completeness of the questionnaire. Binary outcomes were analysed using logistic regression, time to return by Cox Proportional Hazards regression and number of items completed by linear regression. Results: There were 111 participants randomised to the pen group and 118 to the no pen group who were sent a 3-month questionnaire. There was no evidence of a difference in return rates between the two groups (pen 107 (96.4%), no pen 117 (99.2%); OR 0.23, 95% CI 0.02 to 2.19, p=0.20). Furthermore, there was no evidence of a difference between the two groups in terms of time to return the questionnaire (HR 0.90, 95% CI 0.69 to 1.18, p=0.47), the proportion of participants sent a reminder (OR 0.85, 95% CI 0.48 to 1.53, p=0.60) nor the number of items completed (mean difference 0.51, 95% CI -0.04 to 1.06, p=0.07). Conclusion: The inclusion of a pen with the postal 3-month follow-up questionnaire did not have a statistically significant effect on response rate.

ReviewR: a light-weight and extensible tool for manual review of clinical records.

Objectives: Manual record review is a crucial step for electronic health record (EHR)-based research, but it has poor workflows and is error prone. We sought to build a tool that provides a unified environment for data review and chart abstraction data entry. Materials and Methods: ReviewR is an open-source R Shiny application that can be deployed on a single machine or made available to multiple users. It supports multiple data models and database systems, and integrates with the REDCap API for storing abstraction results. Results: We describe 2 real-world uses and extensions of ReviewR. Since its release in April 2021 as a package on CRAN it has been downloaded 2204 times. Discussion and Conclusion: ReviewR provides an easily accessible review interface for clinical data warehouses. Its modular, extensible, and open source nature afford future expansion by other researchers.

Developing and Deploying a Scalable Computing Platform to Support MOOC Education in Clinical Data Science.

One of the challenges of teaching applied data science courses is managing individual students' local computing environment. This is especially challenging when teaching massively open online courses (MOOCs) where students come from across the globe and have a variety of access to and types of computing systems. There are additional challenges with using sensitive health information for clinical data science education. Here we describe the development and performance of a computing platform developed to support a series of MOOCs in clinical data science. This platform was designed to restrict and log all access to health datasets while also being scalable, accessible, secure, privacy preserving, and easy to access. Over the 19 months the platform has been live it has supported the computation of more than 2300 students from 101 countries.

Assessing the utility and accuracy of ICD10-CM non-traumatic subarachnoid hemorrhage codes for intracranial aneurysm research.

BACKGROUND: The 10th revision of International Classification of Disease, Clinical Modification (ICD10-CM) increased the number of codes to identify non-traumatic subarachnoid hemorrhage from 1 to 22. ICD10-CM codes are able to specify the location of aneurysms causing subarachnoid hemorrhage (aSAH); however, it is not clear how frequently or accurately these codes are being used in practice. OBJECTIVE: To systematically evaluate the usage and accuracy of location-specific ICD10-CM codes for aSAH. METHODS: We extracted all uses of ICD10-CM codes for non-traumatic subarachnoid hemorrhage (I60.x) during the first 3 years following the implementation of ICD10-CM from the billing module of the electronic health record (EHR) for UCHealth. For those codes that specified aSAH location (I60.0-I60.6), EHR documentation was reviewed to determine whether there was an active aSAH, any patient history of aSAH, or unruptured intracranial aneurysm/s and the locations of those outcomes. RESULTS: Between 1 October 2015 and 30 September 2018, there were 3119 instances of non-traumatic subarachnoid hemorrhage ICD10-CM codes (I60.00-I60.9), of which 297 (9.5%) code instances identified aSAH location (I60.0-I60.6). The usage of location-specific codes increased from 5.7% in 2015 to 11.2% in 2018. These codes accurately identified current aSAH (64%), any patient history of aSAH (84%), and any patient history of intracranial aneurysm (87%). The accuracy of identified outcome location was 53% in current aSAH, 72% for any history of aSAH, and 76% for any history of an intracranial aneurysm. CONCLUSIONS: Researchers should use ICD10-CM codes with caution when attempting to detect active aSAH and/or aneurysm location.

A 2x2 randomised factorial SWAT of the use of a pen and small, financial incentive to improve recruitment in a randomised controlled trial of yoga for older adults with multimorbidity.

Background: Monetary and other incentives may increase recruitment to randomised controlled trials. Methods: This was a 2x2 factorial 'study within a trial' of including a pen and/or £5 with a postal recruitment pack to improve randomisation rate (primary outcome) into the host Gentle Years Yoga trial in older adults with multimorbidity. Secondary outcomes: return, and time to return, of screening form, and the cost per additional participant recruited. Binary data were analysed using logistic regression and time to return data using Cox proportional hazards regression.  Results: 818 potential host trial participants included. Between those sent a pen (n=409) and not sent a pen (n=409), there was no evidence of a difference in the likelihood of being randomised (15 (3.7%) versus 11 (2.7%); OR 1.38, 95% CI 0.63-3.04), in returning a screening form (66 (16.1%) versus 61 (14.9%); OR 1.10, 95% CI 0.75-1.61) nor in time to return the screening form (HR 1.09, 95% CI 0.77-1.55). There was evidence of improved screening return rates (77 (18.8%) versus 50 (12.2%); OR 1.67, 95% CI 1.13-2.45) and time to return screening form (HR 1.56, 95% CI 1.09-2.22) but not randomisation (14 (3.4%) versus 12 (2.9%); OR 1.18, 95% CI 0.54-2.57) in those sent £5 (n=409) compared with those not sent £5 (n=409). No significant interaction effects between the interventions were observed. The cost per additional participant recruited was £32 for the pen and £1000 for the £5 incentive. Conclusion: Including a small, monetary incentive encouraged increased and faster response to the recruitment invitation but did not result in more participants being randomised into the host trial. Since it is relatively costly, we do not recommend this intervention for use to increase recruitment in this population. Pens are cheaper but did not provide evidence of benefit. Further studies may be required.

Yoga for older adults with multimorbidity (the Gentle Years Yoga Trial): study protocol for a randomised controlled trial.

BACKGROUND: Multimorbidity is common in older adults and associated with high levels of illness burden and healthcare expenditure. The evidence base for how to manage older adults with multimorbidity is weak. Yoga might be a useful intervention because it has the potential to improve health-related quality of life, physical functioning, and several medical conditions. The British Wheel of Yoga's Gentle Years Yoga© (GYY) programme was developed specifically for older adults, including those with chronic medical conditions. Data from a pilot trial suggested feasibility of using GYY in this population, but its effectiveness and cost-effectiveness remain uncertain. METHODS: This is a multi-site, individually randomised, superiority trial with an embedded process evaluation and an economic analysis of cost-effectiveness. The trial will compare an experimental strategy of offering a 12-week GYY programme against a control strategy of no offer in community-dwelling adults aged 65 or over who have multimorbidity, defined as having two or more chronic conditions from a predefined list. The primary outcome is health-related quality of life measured using the EQ-5D-5L, the primary endpoint being the overall difference over 12 months. Both groups will continue to be able to access their usual care from primary, secondary, community, and social services. Participants, care providers, and yoga teachers will not be blinded to the allocated intervention. Outcome measures are primarily self-reported. The analysis will follow intention-to-treat principles. DISCUSSION: This pragmatic randomised controlled trial will demonstrate if the GYY programme is an effective, cost-effective, and viable addition to the management of older adults with multimorbidity. TRIAL REGISTRATION: ISRCTN ISRCTN13567538 . Registered on 18 March 2019.

Opportunity for Genotype-Guided Prescribing Among Adult Patients in 11 US Health Systems.

The value of utilizing a multigene pharmacogenetic panel to tailor pharmacotherapy is contingent on the prevalence of prescribed medications with an actionable pharmacogenetic association. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has categorized over 35 gene-drug pairs as "level A," for which there is sufficiently strong evidence to recommend that genetic information be used to guide drug prescribing. The opportunity to use genetic information to tailor pharmacotherapy among adult patients was determined by elucidating the exposure to CPIC level A drugs among 11 Implementing Genomics In Practice Network (IGNITE)-affiliated health systems across the US. Inpatient and/or outpatient electronic-prescribing data were collected between January 1, 2011 and December 31, 2016 for patients ≥ 18 years of age who had at least one medical encounter that was eligible for drug prescribing in a calendar year. A median of ~ 7.2 million adult patients was available for assessment of drug prescribing per year. From 2011 to 2016, the annual estimated prevalence of exposure to at least one CPIC level A drug prescribed to unique patients ranged between 15,719 (95% confidence interval (CI): 15,658-15,781) in 2011 to 17,335 (CI: 17,283-17,386) in 2016 per 100,000 patients. The estimated annual exposure to at least 2 drugs was above 7,200 per 100,000 patients in most years of the study, reaching an apex of 7,660 (CI: 7,632-7,687) per 100,000 patients in 2014. An estimated 4,748 per 100,000 prescribing events were potentially eligible for a genotype-guided intervention. Results from this study show that a significant portion of adults treated at medical institutions across the United States is exposed to medications for which genetic information, if available, should be used to guide prescribing.

Predictors of 90-Day Readmission Rate After Unruptured Intracranial Aneurysm Repair.

OBJECTIVE: The repair of unruptured intracranial aneurysms has increased since 2000. In this study, we analyzed the Nationwide Readmission Database (NRD) to determine the rate of 90-day readmission. Our objective is to examine readmission trends after unruptured aneurysm repair. METHODS: This study used the 2013 and 2014 NRD. Patient data included standard demographic, comorbidity, and payer information. We selected patients who had undergone microsurgical or endovascular repair for a nonruptured aneurysm. We excluded patients who were under 18 years of age, had a subarachnoid hemorrhage, or were discharged to home the same day. Readmission was calculated by counting the number of days between the end of the index visit and earliest readmission date. RESULTS: A total of 2180 of 29,694 patients (7.34%) were readmitted within 90 days of their initial hospitalization. They were younger (mean, 52.6 years; 95% confidence interval [CI], 51.4-53.8) than patients not readmitted (mean, 57.4 years; 95% CI, 57.1-57.8; P < 0.0001). In total, endovascular repair was more frequent than microsurgery (79.8% vs. 20.2%, respectively). Mean days to readmission was 41.8 (95% CI, 39.7-43.9) and was higher for women (P < 0.0001). The odds ratio for readmission after an endovascular repair was 1.54 (95% CI, 1.27-1.86). CONCLUSIONS: In this study of over 28,000 patients treated for an unruptured aneurysm, the 90-day readmission rate was 7.34%. Endovascular patients had higher odds of readmission than microsurgical patients. Patients with common medical comorbidities (hypertension, obesity, renal failure, and diabetes) were less likely to be readmitted than patients without those conditions.

Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity.

Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories. To provide clarity and a common framework for patient similarity, a workshop at the American Medical Informatics Association 2019 Annual Meeting was convened. This workshop included invited discussants from academics, the biotechnology industry, the FDA, and private practice oncology groups. Drawing from a broad range of backgrounds, workshop participants were able to coalesce around 4 major patient similarity classes: (1) feature, (2) outcome, (3) exposure, and (4) mixed-class. This perspective expands into these 4 subtypes more critically and offers the medical informatics community a means of communicating their work on this important topic.

The diversity and disparity in biomedical informatics (DDBI) workshop.

The Diversity and Disparity in Biomedical Informatics (DDBI) workshop will be focused on complementary and critical issues concerned with enhancing diversity in the informatics workforce as well as diversity in patient cohorts. According to the National Institute of Minority Health and Health Disparities (NIMHD) at the NIH, diversity refers to the inclusion of the following traditionally underrepresented groups: African Americans/Blacks, Asians (>30 countries), American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, Latino or Hispanic (20 countries). Gender, culture, and socioeconomic status are also important dimensions of diversity, which may define some underrepresented groups. The under-representation of specific groups in both the biomedical informatics workforce as well as in the patient-derived data that is being used for research purposes has contributed to an ongoing disparity; these groups have not experienced equity in contributing to or benefiting from advancements in informatics research. This workshop will highlight innovative efforts to increase the pool of minority informaticians and discuss examples of informatics research that addresses the health concerns that impact minority populations. This workshop topics will provide insight into overcoming pipeline issues in the development of minority informaticians while emphasizing the importance of minority participation in health related research. The DDBI workshop will occur in two parts. Part I will discuss specific minority health & health disparities research topics and Part II will cover discussions related to overcoming pipeline issues in the training of minority informaticians.

Opportunities and obstacles for deep learning in biology and medicine.

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

STRATEGIES FOR EQUITABLE PHARMACOGENOMIC-GUIDED WARFARIN DOSING AMONG EUROPEAN AND AFRICAN AMERICAN INDIVIDUALS IN A CLINICAL POPULATION.

The blood thinner warfarin has a narrow therapeutic range and high inter- and intra-patient variability in therapeutic doses. Several studies have shown that pharmacogenomic variants help predict stable warfarin dosing. However, retrospective and randomized controlled trials that employ dosing algorithms incorporating pharmacogenomic variants under perform in African Americans. This study sought to determine if: 1) including additional variants associated with warfarin dose in African Americans, 2) predicting within single ancestry groups rather than a combined population, or 3) using percentage African ancestry rather than observed race, would improve warfarin dosing algorithms in African Americans. Using BioVU, the Vanderbilt University Medical Center biobank linked to electronic medical records, we compared 25 modeling strategies to existing algorithms using a cohort of 2,181 warfarin users (1,928 whites, 253 blacks). We found that approaches incorporating additional variants increased model accuracy, but not in clinically significant ways. Race stratification increased model fidelity for African Americans, but the improvement was small and not likely to be clinically significant. Use of percent African ancestry improved model fit in the context of race misclassification.

Harnessing next-generation informatics for personalizing medicine: a report from AMIA's 2014 Health Policy Invitational Meeting.

The American Medical Informatics Association convened the 2014 Health Policy Invitational Meeting to develop recommendations for updates to current policies and to establish an informatics research agenda for personalizing medicine. In particular, the meeting focused on discussing informatics challenges related to personalizing care through the integration of genomic or other high-volume biomolecular data with data from clinical systems to make health care more efficient and effective. This report summarizes the findings (n = 6) and recommendations (n = 15) from the policy meeting, which were clustered into 3 broad areas: (1) policies governing data access for research and personalization of care; (2) policy and research needs for evolving data interpretation and knowledge representation; and (3) policy and research needs to ensure data integrity and preservation. The meeting outcome underscored the need to address a number of important policy and technical considerations in order to realize the potential of personalized or precision medicine in actual clinical contexts.

Phenotyping Adverse Drug Reactions: Statin-Related Myotoxicity.

It is unclear the extent to which best practices for phenotyping disease states from electronic medical records (EMRs) translate to phenotyping adverse drug events. Here we use statin-induced myotoxicity as a case study to identify best practices in this area. We compared multiple phenotyping algorithms using administrative codes, laboratory measurements, and full-text keyword matching to identify statin-related myopathy from EMRs. Manual review of 300 deidentified EMRs with exposure to at least one statin, created a gold standard set of 124 cases and 176 controls. We tested algorithms using ICD-9 billing codes, laboratory measurements of creatine kinase (CK) and keyword searches of clinical notes and allergy lists. The combined keyword algorithms produced were the most accurate (PPV=86%, NPV=91%). Unlike in most disease phenotyping algorithms, addition of ICD9 codes or laboratory data did not appreciably increase algorithm accuracy. We conclude that phenotype algorithms for adverse drug events should consider text based approaches.

Reactive oxygen species production and mitochondrial dysfunction in white blood cells are not valid biomarkers of ageing in the very old.

Reliable and valid biomarkers of ageing (BoA) are needed to understand mechanisms, test interventions and predict the timing of adverse health events associated with ageing. Since increased reactive oxygen species (ROS) production and mitochondrial dysfunction are consequences of cellular senescence and may contribute causally to the ageing of organisms, we focused on these parameters as candidate BoA. Superoxide levels, mitochondrial mass and mitochondrial membrane potential in human peripheral blood mononuclear cells (PBMCs) and subpopulations (lymphocytes and monocytes) were measured in participants from the Newcastle 85+ study, a population-based study of the very old (aged 85 years and older). The intra- and inter-assay precision expressed as coefficient of variation (CV) for all parameters was acceptable (3% to 12% and 5 to 22% respectively). All parameters were stable in the short-term (1 week interval) in a sample of control individuals in the PBMCs and lymphocyte subpopulation, however they were unstable in the monocyte subpopulation; this rendered monocytes unreliable for further analysis. There was a significant association between superoxide levels and mitochondrial mass (positive in lymphocytes, p = 0.01) and between superoxide levels and mitochondrial membrane potential (negative in PBMCs, p = 0.01; positive in lymphocytes, p = 0.05). There were also significant associations between superoxide levels and mitochondrial parameters with other markers of oxidative stress-induced cellular senescence (p≤0.04), however some were in the opposite direction to expected. No associations were found between the measured parameters and age-related outcomes, including cognitive impairment, disability, co-morbidity and survival - questioning the validity of these parameters as candidate BoA in the very old.

Gender and telomere length: systematic review and meta-analysis.

BACKGROUND: It is widely believed that females have longer telomeres than males, although results from studies have been contradictory. METHODS: We carried out a systematic review and meta-analyses to test the hypothesis that in humans, females have longer telomeres than males and that this association becomes stronger with increasing age. Searches were conducted in EMBASE and MEDLINE (by November 2009) and additional datasets were obtained from study investigators. Eligible observational studies measured telomeres for both females and males of any age, had a minimum sample size of 100 and included participants not part of a diseased group. We calculated summary estimates using random-effects meta-analyses. Heterogeneity between studies was investigated using sub-group analysis and meta-regression. RESULTS: Meta-analyses from 36 cohorts (36,230 participants) showed that on average females had longer telomeres than males (standardised difference in telomere length between females and males 0.090, 95% CI 0.015, 0.166; age-adjusted). There was little evidence that these associations varied by age group (p=1.00) or cell type (p=0.29). However, the size of this difference did vary by measurement methods, with only Southern blot but neither real-time PCR nor Flow-FISH showing a significant difference. This difference was not associated with random measurement error. CONCLUSIONS: Telomere length is longer in females than males, although this difference was not universally found in studies that did not use Southern blot methods. Further research on explanations for the methodological differences is required.