Fall Risk and Utilization of Balance Training for Adults with Symptomatic Knee Osteoarthritis: Secondary Analysis from a Randomized Clinical Trial

Knee osteoarthritis (KOA) is a common disease that hinders activity participation in older adults. Associated symptoms and physiological changes can increase risk of falling in individuals with KOA. Balance training can decrease fall risks in older adults. Limited evidence exists regarding utilization of balance training in physical therapy (PT) for this population. This secondary data analysis investigated the proportion of participants at high risk for falling in the PhysicAl THerapy vs. INternet-based Exercise Training for Patients with Osteoarthritis (PATH-IN) study and the frequency with which balance training was utilized as an intervention in PT. Methods: PATH-IN study participants (N = 344) performed the Four-Stage Balance Test and the Timed Up and Go (TUG) test during baseline assessment. Participants were randomly allocated to PT, an Internet-based exercise program, or a control group. Participants were classified as being at high risk for falling if they did not progress to the single-leg stance (SLS) during the Four-Stage Balance Test, were unable to maintain SLS for 5 seconds, or took longer than 13.5 seconds to complete the TUG test. The proportion of participants at high risk for falling was calculated for all participants and separately for those allocated to PT. In addition, PT notes were coded for balance training and the frequency of balance training utilization was calculated. Results and Discussion: Upon enrollment, 35.5% (N = 122) of all participants and 36.2% (N = 50) of those allocated to PT were at high risk for falling. Of participants allocated to PT with documentation available for coding (N = 118), 35.5% (N = 42) were at high risk for falling. Balance training was provided to 62.7% (N = 74) during at least one PT session. Of those classified as being at high risk for falling, 33.3% (N = 14) did not receive balance training. Conclusions: The finding of high fall risks in more than one-third of all participants with KOA is consistent with previous reports of a higher risk of falling in this population. Many PT participants did receive some balance training; however, one-third of participants at high risk for falling did not. Balance training for individuals with KOA at high risk for falling may be underutilized

Associations of Comorbid Conditions and Transitions Across States of Knee Osteoarthritis in a Community-Based Cohort

Objective: To examine relationships between knee osteoarthritis (KOA) and obesity, diabetes mellitus (DM), and cardiovascular disease (CVD). Methods: Associations of time-dependent obesity, DM, and CVD with KOA transition states over approximately 18 years were examined among 4093 participants from a community-based cohort. Transition states were 1) no knee symptoms and no radiographic KOA (rKOA; Kellgren-Lawrence grade ≥2 in at least one knee), 2) asymptomatic rKOA, 3) knee symptoms only, 4) symptomatic rKOA (sxKOA; rKOA and symptoms in same knee). Markov multistate models estimated adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for associations between comorbid conditions and transitions across states, adjusting for baseline age, sex, race, education, enrollment cohort, birth year, and time-dependent knee injury history. Results: At baseline, 40% of participants had obesity, 13% had DM, and 22% had CVD (mean age = 61 years; 34% Black; 37% male). Compared with those without obesity, those with obesity had a higher hazard of worsening from no rKOA/no symptoms to asymptomatic rKOA (aHR = 1.7; 95% CI = 1.3-2.2) and from knee symptoms to sxKOA (aHR = 1.7; 95% CI = 1.3-2.3), as well as a lower hazard of symptom resolution from sxKOA to asymptomatic rKOA (aHR = 0.5 [95% = CI 0.4-0.7]). Compared with those without CVD, those with CVD had a higher hazard of worsening from no rKOA/symptoms to knee symptoms (aHR = 1.5; 95% CI = 1.1-2.1). DM was not associated with transitions of rKOA. Conclusion: Prevention of obesity and CVD may limit the development or worsening of rKOA and symptoms

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.Public Health and primary carePrevention, Population and Disease management (PrePoD

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.</p

The power of genetic diversity in genome-wide association studies of lipids (vol 600, pg 675, 2021)

Metabolic health: pathophysiological trajectories and therap

The power of genetic diversity in genome-wide association studies of lipids

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use(1). Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels(2), heart disease remains the leading cause of death worldwide(3). Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS(4-23) have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns(24). Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine(25), we anticipate that increased diversity of participants will lead to more accurate and equitable(26) application of polygenic scores in clinical practice.Prevention, Population and Disease management (PrePoD)Public Health and primary car

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery.Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N=1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism.Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.Diabetes mellitus: pathophysiological changes and therap

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes &lt;sup&gt;1&lt;/sup&gt; . Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel &lt;sup&gt;2&lt;/sup&gt; ) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries