How pleiotropic genetics of the musculoskeletal system can inform genomics and phenomics of aging

Karasik, David

doi:10.1007/s11357-010-9159-3

Cited by 21 publications

(14 citation statements)

References 164 publications

(160 reference statements)

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“…Phenome-wide association studies (PheWAS) studies aim to understand the role of a genetic variant identified from genome-wide association studies (GWAS) in increasing or decreasing the likelihood of observing other diseases in a case-control cohort. PheWAS studies are now revealing the molecular architecture of the pleiotropic nature of genetic variants in mediating multiple diseases 1,8 .…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Hospital Readmission Rates Using Electronic Medical Record-Wide Machine Learning: A Case-Study Using Mount Sinai Heart Failure Cohort

et al. 2016

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Reduction of preventable hospital readmissions that result from chronic or acute conditions like stroke, heart failure, myocardial infarction and pneumonia remains a significant challenge for improving the outcomes and decreasing the cost of healthcare delivery in the United States. Patient readmission rates are relatively high for conditions like heart failure (HF) despite the implementation of high-quality healthcare delivery operation guidelines created by regulatory authorities. Multiple predictive models are currently available to evaluate potential 30-day readmission rates of patients. Most of these models are hypothesis driven and repetitively assess the predictive abilities of the same set of biomarkers as predictive features. In this manuscript, we discuss our attempt to develop a data-driven, electronic-medical record-wide (EMR-wide) feature selection approach and subsequent machine learning to predict readmission probabilities. We have assessed a large repertoire of variables from electronic medical records of heart failure patients in a single center. The cohort included 1,068 patients with 178 patients were readmitted within a 30-day interval (16.66% readmission rate). A total of 4,205 variables were extracted from EMR including diagnosis codes (n=1,763), medications (n=1,028), laboratory measurements (n=846), surgical procedures (n=564) and vital signs (n=4). We designed a multistep modeling strategy using the Naïve Bayes algorithm. In the first step, we created individual models to classify the cases (readmitted) and controls (non-readmitted). In the second step, features contributing to predictive risk from independent models were combined into a composite model using a correlation-based feature selection (CFS) method. All models were trained and tested using a 5-fold cross-validation method, with 70% of the cohort used for training and the remaining 30% for testing. Compared to existing predictive models for HF readmission rates (AUCs in the range of 0.6–0.7), results from our EMR-wide predictive model (AUC=0.78; Accuracy=83.19%) and phenome-wide feature selection strategies are encouraging and reveal the utility of such data-driven machine learning. Fine tuning of the model, replication using multi-center cohorts and prospective clinical trial to evaluate the clinical utility would help the adoption of the model as a clinical decision system for evaluating readmission status.

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Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Hospital Readmission Rates Using Electronic Medical Record-Wide Machine Learning: A Case-Study Using Mount Sinai Heart Failure Cohort

et al. 2016

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“…These novel findings suggest that, although the klotho gene is a susceptibility gene for reduced BMD and lower serum klotho (<575 pg/mL) is related to greater declines in grip strength and higher disability, klotho blood levels were unrelated to bone loss or fracture risk in older adults. A possible explanation for the lack of association is the pleotropic function of the klotho gene . The transmembrane form of klotho, not measured in our study, is the bone‐derived hormone which is thought to be involved in bone regulation because of its coupling with FGF‐23.…”

Section: Discussionmentioning

confidence: 69%

“…A possible explanation for the lack of association is the pleotropic function of the klotho gene. 16 The transmembrane form of klotho, not measured in our study, is the bone-derived hormone which is thought to be involved in bone regulation because of its coupling with FGF-23. However, this form cannot be readily measured.…”

Section: Discussionmentioning

confidence: 74%

Association of Serum Klotho with Loss of Bone Mineral Density and Fracture Risk in Older Adults

Chalhoub

Marques

Meirelles

et al. 2016

J American Geriatrics Society

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OBJECTIVES Klotho deficiency has been previously linked to aging-like phenotypes such as osteoporosis, cognitive impairment, and sarcopenia. Low serum klotho was shown to be related to grip strength and disability. Nonetheless, no previous study has explored the association between serum klotho and fractures. The purpose of this report is to examine the relationship of serum klotho with bone mineral density (BMD) loss and fractures in older adults. DESIGN The Health, Aging, and Body Composition (Health ABC) Study is a longitudinal cohort study of 3,075 community-dwelling older adults. SETTING U.S. clinical centers. PARTICIPANTS 2,776 well-functioning black and white adults aged 70 to 79 years with serum klotho measurements were followed up for a median of 5 years. MEASUREMENTS Percent annualized BMD change and fracture risk were compared across klotho quartiles. A Poisson distribution was used to calculate age-adjusted fracture incidence rates, and Cox proportional hazards models for multivariable-adjusted hazard ratios. RESULTS The annualized percent changes in hip, femoral neck, and vertebral BMD were similar across klotho quartiles. Participants experienced 507 nonspine fractures, 203 hip fractures, and 135 vertebral fractures. The Incidence rate (IR) of nonspine fractures was 17 per 1,000 person-years. The most frequent site was hip (IR=6 per 1,000 person-years) and the IR of vertebral fractures was 3 per 1,000 person-years. There was no association between the lowest quartile of plasma klotho and nonspine (hazard ratio (HR)=1.19, 95% confidence interval (CI)=0.86–1.65) , hip (HR=1.34, 95% CI=0.79–2.27), or vertebral fractures (HR= 1.17, 95% CI=0.65–2.11). CONCLUSION Although klotho gene is a susceptible gene for reduced BMD, klotho blood concentration does not appear to be a predictor of bone loss or fracture risk in well-functioning older adults.

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“…It is becoming apparent that there are several genes involved in the genetic control of maturation, development, and decline of musculoskeletal systems [179–181]. The heritability of lean mass, measured with DXA, has been estimated to vary between 56% and 84% [182] as well as those of bone strength, measured with section modulus of femoral neck, has been reported to be 40 to 55% [183].…”

Section: Evidence For Bone-muscle Interactionmentioning

confidence: 99%

Interaction Between Bone and Muscle in Older Persons with Mobility Limitations

Ferrucci¹,

Baroni²,

Ranchelli³

et al. 2014

CPD

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Aging is associated with a progressive loss of bone-muscle mass and strength. When the decline in mass and strength reaches critical thresholds associated with adverse health outcomes, they are operationally considered geriatric conditions and named, respectively, osteoporosis and sarcopenia. Osteoporosis and sarcopenia share many of the same risk factors and both directly or indirectly cause higher risk of mobility limitations, falls, fractures and disability in activities of daily living. This is not surprising since bones adapt their morphology and strength to the long-term loads exerted by muscle during anti-gravitational and physical activities. Non-mechanical systemic and local factors also modulate the mechanostat effect of muscle on bone by affecting the bidirectional osteocyte-muscle crosstalk, but the specific pathways that regulate these homeostatic mechanisms are not fully understood. More research is required to reach a consensus on cut points in bone and muscle parameters that identify individuals at high risk for adverse health outcomes, including falls, fractures and disability. A better understanding of the muscle-bone physiological interaction may help to develop preventive strategies that reduce the burden of musculoskeletal diseases, the consequent disability in older persons and to limit the financial burden associated with such conditions. In this review, we summarize age-related bone-muscle changes focusing on the biomechanical and homeostatic mechanisms that explain bone-muscle interaction and we speculate about possible pathological events that occur when these mechanisms become impaired. We also report some recent definitions of osteoporosis and sarcopenia that have emerged in the literature and their implications in clinical practice. Finally, we outline the current evidence for the efficacy of available anti-osteoporotic and proposed anti-sarcopenic interventions in older persons.

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How pleiotropic genetics of the musculoskeletal system can inform genomics and phenomics of aging

Cited by 21 publications

References 164 publications

Predictive Modeling of Hospital Readmission Rates Using Electronic Medical Record-Wide Machine Learning: A Case-Study Using Mount Sinai Heart Failure Cohort

Predictive Modeling of Hospital Readmission Rates Using Electronic Medical Record-Wide Machine Learning: A Case-Study Using Mount Sinai Heart Failure Cohort

Association of Serum Klotho with Loss of Bone Mineral Density and Fracture Risk in Older Adults

Interaction Between Bone and Muscle in Older Persons with Mobility Limitations

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