Causal relationships between anthropometric traits, bone mineral density, osteoarthritis and spinal stenosis: a Mendelian randomization investigation

Background: Spinal stenosis is a common condition among older individuals, with significant morbidity attached. Little is known about its risk factors but degenerative conditions, such as osteoarthritis (OA) have been identified for their mechanistic role. This study aims to explore causal relationships between anthropometric risk factors, osteoarthritis, and spinal stenosis using Mendelian randomization (MR) techniques. Methods: We applied two-sample univariable and multivariable MR to investigate the causal relationships between genetic liability for select risk factors (including adiposity and skeletal traits) and spinal stenosis. Next, we examined the genetic relationship between osteoarthritis and spinal stenosis with LD score regression and CAUSE MR method. Using multivariable MR, osteoarthritis and BMI were then tested as potential mediators of the causal pathways identified. Results: Our analysis revealed strong evidence for the effect of higher BMI (OR=1.54, 95% CI: 1.41-1.69, p-value=2.7 x 10-21), waist (OR=1.43, 95% CI: 1.15-1.79, p-value=1.5 x 10-3) and hip (OR=1.50, 95% CI: 1.27-1.78, p-value=3.3 x 10-6) circumference on spinal stenosis. Strong associations were observed for higher bone mineral density (BMD): total body (OR=1.21, 95% CI: 1.12-1.29, p-value=1.6 x 10-7), femoral neck (OR=1.35, 95% CI: 1.09-1.37, p-value=7.5 x 10-7), and lumbar spine (OR=1.38, 95% CI: 1.25-1.52, p-value=4.4 x 10-11). We detected high genetic correlations between spinal stenosis and osteoarthritis (rg range: 0.47-0.66), with Bayesian CAUSE results supporting a causal effect of osteoarthritis on spinal stenosis (ORall OA=1.6, 95% CI:1.41-1.79). Direct effects of BMI, total body/femoral neck/lumbar spine BMD on spinal stenosis remained after adjusting for osteoarthritis and/or BMI in the multivariable MR. Conclusions: Genetic susceptibility to anthropometric risk factors, particularly higher BMI and bone mineral density can increase the risk of spinal stenosis, independent of osteoarthritis status. These results improve our understanding of spinal stenosis aetiology and may inform preventative strategies and treatments.

[1]  R. Forlano,et al.  A two-sample Mendelian randomization study to identity microbiome signatures in patients with Non-alcoholic fatty liver disease. , 2023, Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver.

[2]  Jacob C. Ulirsch,et al.  FinnGen provides genetic insights from a well-phenotyped isolated population , 2023, Nature.

[3]  D. Berry,et al.  How to Distinguish Correlation From Causation in Orthopaedic Research. , 2022, The Journal of arthroplasty.

[4]  Levent Aydin,et al.  Subcutaneous fat index: a reliable tool for lumbar spine studies , 2022, European Radiology.

[5]  N. Timpson,et al.  Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration , 2021, BMJ.

[6]  Tom R. Gaunt,et al.  Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations , 2021, Cell.

[7]  O. Mäkitie,et al.  An Expert Perspective on Phosphate Dysregulation With a Focus on Chronic Hypophosphatemia , 2021, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[8]  I. Rotter,et al.  The Effects of Calcium, Magnesium, Phosphorus, Fluoride, and Lead on Bone Tissue , 2021, Biomolecules.

[9]  J. V. van Meurs,et al.  Using multivariable Mendelian randomization to estimate the causal effect of bone mineral density on osteoarthritis risk, independently of body mass index , 2021, medRxiv.

[10]  S. J. Molina,et al.  Populations , 2020, River Networks as Ecological Corridors.

[11]  Marco Piñón,et al.  I Overview , 2020, The Diaries and Letters of Lord Woolton 1940-1945.

[12]  V. Mattila,et al.  National trends in lumbar spine decompression and fusion surgery in Finland, 1997–2018 , 2020, Acta orthopaedica.

[13]  Valeriia Haberland,et al.  The MRC IEU OpenGWAS data infrastructure , 2020, bioRxiv.

[14]  Andrew P. Boughton,et al.  Exploring and visualizing large-scale genetic associations by using PheWeb , 2020, Nature Genetics.

[15]  Tom R. Gaunt,et al.  The variant call format provides efficient and robust storage of GWAS summary statistics , 2020, Genome Biology.

[16]  Joseph H. Marcus,et al.  Publisher Correction: Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics , 2020, Nature Genetics.

[17]  N. Sheehan,et al.  The use of two-sample methods for Mendelian randomization analyses on single large datasets , 2020, bioRxiv.

[18]  J. Bowden,et al.  Testing and correcting for weak and pleiotropic instruments in two‐sample multivariable Mendelian randomization , 2020, bioRxiv.

[19]  S. Movérare-Skrtic,et al.  Causal Factors for Knee, Hip, and Hand Osteoarthritis: A Mendelian Randomization Study in the UK Biobank , 2019, Arthritis & rheumatology.

[20]  Joseph H. Marcus,et al.  Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics , 2019, bioRxiv.

[21]  N. Akpolat,et al.  An Update on the Epidemiology, Diagnosis and Treatment of Leprosy , 2018, Hansen's Disease - The Forgotten and Neglected Disease.

[22]  David M. Evans,et al.  An atlas of genetic influences on osteoporosis in humans and mice , 2018, Nature Genetics.

[23]  G. S. Moore,et al.  Human Populations , 2018, Nature.

[24]  H. Woodrow,et al.  : A Review of the , 2018 .

[25]  G. Davey Smith,et al.  Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians , 2018, British Medical Journal.

[26]  B. Neale,et al.  Publisher Correction: Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases , 2018, Nature Genetics.

[27]  B. Neale,et al.  Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases , 2018, Nature Genetics.

[28]  P. Visscher,et al.  Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry , 2018, bioRxiv.

[29]  Valeriia Haberland,et al.  The MR-Base platform supports systematic causal inference across the human phenome , 2018, eLife.

[30]  M. Delgado-Rodríguez,et al.  Systematic review and meta-analysis. , 2017, Medicina intensiva.

[31]  Fernando Pires Hartwig,et al.  Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption , 2017, bioRxiv.

[32]  S. Thompson,et al.  Bias due to participant overlap in two‐sample Mendelian randomization , 2016, Genetic epidemiology.

[33]  David M. Evans,et al.  Using Mendelian randomization to investigate a possible causal relationship between adiposity and increased bone mineral density at different skeletal sites in children , 2016, International journal of epidemiology.

[34]  G. Davey Smith,et al.  Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator , 2016, Genetic epidemiology.

[35]  Gabor T. Marth,et al.  A global reference for human genetic variation , 2015, Nature.

[36]  K. Michaëlsson,et al.  Body Mass Index and Risk for Clinical Lumbar Spinal Stenosis: A Cohort Study , 2015, Spine.

[37]  Beth Wilmot,et al.  Edinburgh Explorer Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture , 2022 .

[38]  David M. Evans,et al.  Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality. , 2015, Annual review of genomics and human genetics.

[39]  M. Daly,et al.  An Atlas of Genetic Correlations across Human Diseases and Traits , 2015, Nature Genetics.

[40]  G. Davey Smith,et al.  Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression , 2015, International journal of epidemiology.

[41]  Yu-hua Huang,et al.  Impact of body mass index on adjacent segment disease after lumbar fusion for degenerative spine disease. , 2015, Neurosurgery.

[42]  Ross M. Fraser,et al.  Genetic studies of body mass index yield new insights for obesity biology , 2015, Nature.

[43]  P. Dieppe,et al.  Osteoarthritis and bone mineral density: are strong bones bad for joints? , 2015, BoneKEy reports.

[44]  Ross M. Fraser,et al.  Defining the role of common variation in the genomic and biological architecture of adult human height , 2014, Nature Genetics.

[45]  S. Yabuki,et al.  Lumbar Spinal Stenosis Has a Negative Impact on Quality of Life Compared with Other Comorbidities: An Epidemiological Cross-Sectional Study of 1862 Community-Dwelling Individuals , 2013, TheScientificWorldJournal.

[46]  P. Visscher,et al.  Calculating statistical power in Mendelian randomization studies. , 2013, International journal of epidemiology.

[47]  P. Gerdhem,et al.  Degenerative changes at the lumbar spine—implications for bone mineral density measurement in elderly women , 2013, Osteoporosis International.

[48]  David Levine,et al.  A high-performance computing toolset for relatedness and principal component analysis of SNP data , 2012, Bioinform..

[49]  R. Delamarter,et al.  Spinal Fusion in the United States: Analysis of Trends From 1998 to 2008 , 2012, Spine.

[50]  J. Clarke,et al.  What is a systematic review? , 2011, Evidence Based Nursing.

[51]  S. Thompson,et al.  Avoiding bias from weak instruments in Mendelian randomization studies. , 2011, International journal of epidemiology.

[52]  S. Thompson,et al.  Bias in causal estimates from Mendelian randomization studies with weak instruments , 2011, Statistics in medicine.

[53]  Sharon R Grossman,et al.  Integrating common and rare genetic variation in diverse human populations , 2010, Nature.

[54]  Jeffrey G Jarvik,et al.  Trends, major medical complications, and charges associated with surgery for lumbar spinal stenosis in older adults. , 2010, JAMA.

[55]  H. Kehlet,et al.  [Lumbar fusion surgery for degenerative conditions in Denmark 2005-2006]. , 2009, Ugeskrift for læger.

[56]  J. Fletcher.  Distribution , 2009, BMJ : British Medical Journal.

[57]  S. Moon,et al.  Bone Metabolism in Postmenopausal Women With Lumbar Spinal Stenosis: Analysis of Bone Mineral Density and Bone Turnover Markers , 2008, Spine.

[58]  R. Luukkonen,et al.  Disc degeneration of the lumbar spine in relation to overweight , 2005, International Journal of Obesity.

[59]  S. Kutateladze.  Traits , 2005, math/0507204.

[60]  R. Deyo,et al.  United States Trends in Lumbar Fusion Surgery for Degenerative Conditions , 2005, Spine.

[61]  Kozo Nakamura,et al.  Impact of degenerative spinal diseases on bone mineral density of the lumbar spine in elderly women , 2004, Osteoporosis International.

[62]  G. Németh,et al.  Spinal stenosis surgery in Sweden 1987–1999 , 2003, European Spine Journal.

[63]  S. Giannini,et al.  Lumbar osteoarthritis, bone mineral density, and quantitative ultrasound , 2000, Aging.

[64]  Y. Maugars,et al.  Lumbar spinal stenosis: a review. , 1997, Revue du rhumatisme.

[65]  R A Deyo,et al.  An Assessment of Surgery for Spinal Stenosis: Time Trends, Geographic Variations, Complications, and Reoperations , 1996, Journal of the American Geriatrics Society.

[66]  J J Anderson,et al.  Effects of weight and body mass index on bone mineral density in men and women: The framingham study , 1993, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[67]  M. Hasue,et al.  Lumbar spinal stenosis , 1977, International Orthopaedics.

[68]  A. Belanger,et al.  The Framingham study. , 1976, British medical journal.

[69]  Chao Qian,et al.  Population , 1940, State Rankings 2020: A Statistical View of America.

[70]  Carmen Domingo.  Biology , 1929, Nature.

[71]  M. Okano,et al.  Cohort Study , 2020, Definitions.

[72]  典子 吉村,et al.  脊椎加齢変性疾患の疫学指標ならびに運動機能との関連 −The Wakayama Spine Study , 2016 .

[73]  石元 優々.  Prevalence of symptomatic lumbar spinal stenosis and its association with physical performance in a population-based cohort in Japan : the Wakayama spine study , 2012 .

[74]  R. Barkmann,et al.  [Quantitative ultrasound]. , 2006, Der Radiologe.

[75]  S. Norby.  [Mendelian randomization]. , 2005, Ugeskrift for laeger.

[76]  and treatment of , 2022 .

[77]  Douglas G. Altman,et al.  Explanation and Elaboration , 2022 .

[78]  O. Franco,et al.  Mendelian Randomization Causal Analysis LDL cholesterol still a problem in old age? A Mendelian randomization study , 2015 .