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> Disorders > Disease or Syndrome > Osteoporosis

Osteoporosis

Osteoporosis is a skeletal disorder characterized by compromised bone strength, predisposing individuals to an increased risk of fracture.
It is a common condition, particularly among postmenopausal women, and is associated with significant morbidity and mortality.
Osteoporosis is a multifactorial disease, with both genetic and environmental factors contributing to its development.
Diagnosis typically involves bone mineral density measurements, while treatment may include pharmacological interventions, lifestyle modifications, and fall prevention strategies.
Ongoing research aims to enhance our understanding of the pathogenesis of osteoporosis and develop more effective preventive and therapeutic approahces.

Most cited protocols related to «Osteoporosis»

1
Gender-specific Grip Strength Centiles
Our main analyses used all available data, including values for individuals who had had grip strength measured at more than one age. We produced gender-specific cross-sectional centiles for grip strength using the Box-Cox Cole and Green (BCCG) distribution (also known as the LMS method [27] (link)) implemented in the Generalised Additive Models for Location, Scale and Shape (GAMLSS) library [28] for the statistical program, R [29] . We used restricted cubic splines to model the relationship between age and each of the three model parameters: the median, variation and skewness. We identified the optimum number of degrees of freedom for each parameter using the GAMLSS command find.hyper. We anticipated a smooth relationship with age and therefore used a maximum number of degrees of freedom of seven and increased the standard penalty. We looked for evidence of kurtosis in the grip strength values by using the Box-Cox power exponential distribution. We modelled the mean and SD of grip at each age using the normal distribution in GAMLSS.
We defined a T-score for grip strength as an individual’s value expressed as a multiple of the number of standard deviations below the peak mean value encountered in young adult life. This is the same as the approach applied to measurements of bone density in the diagnosis of osteoporosis [30] (link), except we used gender-specific peak mean values for grip strength. We explored the gender-specific prevalence of weak grip strength in mid and late adult life in two ways. Firstly, using a T-score for grip strength of equal to or less than −2 as used previously [31] (link), and secondly using a T-score of equal to or less than −2.5, as widely used in the diagnosis of osteoporosis.
We carried out sensitivity analyses by producing further sets of centile curves and comparing these to our main findings. We restricted the data to the first observation for each individual. We produced dynamometer-specific sets of centile curves by allowing the median, variation and skewness curves to vary by dynamometer type. Similarly we considered the impact of the position of grip strength measurement: standing or sitting, with the latter divided into those who were sitting as per protocol and those who chose to sit or were unable to stand. Finally we checked if any one study was unduly influencing the results obtained by excluding each study in turn. To compare each additional model to the main findings, we examined absolute differences for the 10th, median and 90th centiles; we considered that a 10 percent difference or less in the centile values at any given age provided evidence of acceptably similar findings. We carried out data management using Stata version 12.0 [32] .
Dodds R.M., Syddall H.E., Cooper R., Benzeval M., Deary I.J., Dennison E.M., Der G., Gale C.R., Inskip H.M., Jagger C., Kirkwood T.B., Lawlor D.A., Robinson S.M., Starr J.M., Steptoe A., Tilling K., Kuh D., Cooper C, & Sayer A.A. (2014). Grip Strength across the Life Course: Normative Data from Twelve British Studies. PLoS ONE, 9(12), e113637.
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Publication 2014
Adult Bone Density cDNA Library Cuboid Bone Debility Diagnosis Grasp Hypersensitivity Osteoporosis Young Adult
2
Comprehensive Genetic Analysis of Bone Mineral Density
Further analyses were performed for the SNPs carried forward for replication. Each of these analyses is described in detail in the “Supplementary Note”. In brief, we performed: 1) a conditional genome-wide association analysis to examine whether any of the 82 BMD loci harbored additional independent signals; 2) tested gene-by-gene pair-wise interactions between these BMD loci; 3) assessed within the independent setting of the PERF study (for details on study design see Supplementary Tables 20A, 20B & 20C) the predictive ability derived from the cumulative effect of the 63 genome-wide significant autosomal BMD SNPs in relation to BMD levels and osteoporosis risk; and that of the 16 BMD SNPs also associated with fracture risk in relation to fracture risk; 4) identified SNPs having r2 ≥ 0.80 with the lead SNP that were potentially functional (nonsense, nonconservative non-synonymous, synonymous, exonic splicing, transcription factor binding sites, etc) using regional imputation with the 1000 Genomes data (June 2010 release); 5) tested the relationship between gene expression profiles from a) trans-iliacal bone biopsies and BMD in 84 unrelated postmenopausal women49 (link) and b) also examined cis- associations between each of the 55 significant BMD SNPs and expression of nearby genes in different tissues including lymphoblastoid cell lines50 (link)–52 (link), primary human fibroblasts and osteoblasts53 (link), adipose tissue54 (link), whole blood54 (link) and circulating monocytes55 (link); and finally 6) evaluated the connectivity and relationships between identified loci using the literature-based annotation with Gene Relationships across Implicated Loci (GRAIL19 (link)) statistical strategy.
Estrada K., Styrkarsdottir U., Evangelou E., Hsu Y.H., Duncan E.L., Ntzani E.E., Oei L., Albagha O.M., Amin N., Kemp J.P., Koller D.L., Li G., Liu C.T., Minster R.L., Moayyeri A., Vandenput L., Willner D., Xiao S.M., Yerges-Armstrong L.M., Zheng H.F., Alonso N., Eriksson J., Kammerer C.M., Kaptoge S.K., Leo P.J., Thorleifsson G., Wilson S.G., Wilson J.F., Aalto V., Alen M., Aragaki A.K., Aspelund T., Center J.R., Dailiana Z., Duggan D.J., Garcia M., Garcia-Giralt N., Giroux S., Hallmans G., Hocking L.J., Husted L.B., Jameson K.A., Khusainova R., Kim G.S., Kooperberg C., Koromila T., Kruk M., Laaksonen M., Lacroix A.Z., Lee S.H., Leung P.C., Lewis J.R., Masi L., Mencej-Bedrac S., Nguyen T.V., Nogues X., Patel M.S., Prezelj J., Rose L.M., Scollen S., Siggeirsdottir K., Smith A.V., Svensson O., Trompet S., Trummer O., van Schoor N.M., Woo J., Zhu K., Balcells S., Brandi M.L., Buckley B.M., Cheng S., Christiansen C., Cooper C., Dedoussis G., Ford I., Frost M., Goltzman D., González-Macías J., Kähönen M., Karlsson M., Khusnutdinova E., Koh J.M., Kollia P., Langdahl B.L., Leslie W.D., Lips P., Ljunggren Ö., Lorenc R.S., Marc J., Mellström D., Obermayer-Pietsch B., Olmos J.M., Pettersson-Kymmer U., Reid D.M., Riancho J.A., Ridker P.M., Rousseau F., Slagboom P.E., Tang N.L., Urreizti R., Van Hul W., Viikari J., Zarrabeitia M.T., Aulchenko Y.S., Castano-Betancourt M., Grundberg E., Herrera L., Ingvarsson T., Johannsdottir H., Kwan T., Li R., Luben R., Medina-Gómez C., Palsson S.T., Reppe S., Rotter J.I., Sigurdsson G., van Meurs J.B., Verlaan D., Williams F.M., Wood A.R., Zhou Y., Gautvik K.M., Pastinen T., Raychaudhuri S., Cauley J.A., Chasman D.I., Clark G.R., Cummings S.R., Danoy P., Dennison E.M., Eastell R., Eisman J.A., Gudnason V., Hofman A., Jackson R.D., Jones G., Jukema J.W., Khaw K.T., Lehtimäki T., Liu Y., Lorentzon M., McCloskey E., Mitchell B.D., Nandakumar K., Nicholson G.C., Oostra B.A., Peacock M., Pols H.A., Prince R.L., Raitakari O., Reid I.R., Robbins J., Sambrook P.N., Sham P.C., Shuldiner A.R., Tylavsky F.A., van Duijn C.M., Wareham N.J., Cupples L.A., Econs M.J., Evans D.M., Harris T.B., Kung A.W., Psaty B.M., Reeve J., Spector T.D., Streeten E.A., Zillikens M.C., Thorsteinsdottir U., Ohlsson C., Karasik D., Richards J.B., Brown M.A., Stefansson K., Uitterlinden A.G., Ralston S.H., Ioannidis J.P., Kiel D.P, & Rivadeneira F. (2012). Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nature genetics, 44(5), 491-501.
Publication 2012
Binding Sites Biopsy Bones Cells DNA Replication Exons Fibroblasts Fracture, Bone Gene Annotation Gene Expression Genes Genome Genome-Wide Association Study Homo sapiens Mutation, Nonsense Obesity Osteoporosis Single Nucleotide Polymorphism Tissues Transcription Factor
3
Validating a Osteoporosis Assessment Tool
There are multiple methods for testing content validity. This study used one method that involved empirical techniques to calculate the index of content validity (CVI) and the content validity ratio (CVR) and semi-structure cognitive evaluations [15 (link), 16 (link)]. The empirical techniques reviewed in this tool were:

CVI: CVI is the most widely reported approach for content validity in instrument development and can be computed using the Item-CVI (I-CVI) and the Scale-level-CVI (S-CVI) [16 (link)]. I-CVI is computed as the number of experts giving a rating of “very relevant” for each item divided by the total number of experts. Values range from 0 to 1 where I-CVI > 0.79, the item is relevant, between 0.70 and 0.79, the item needs revisions, and if the value is below 0.70 the item is eliminated [16 (link)]. Similarly, S-CVI is calculated using the number of items in a tool that have achieved a rating of “very relevant” [16 (link)]. There are two methods to calculating S-CVI, one is the Universal Agreement (UA) among experts (S-CVI/UA), and the second, the Average CVI (S-CVI/Ave), the latter being a less conservative method [16 (link)]. S-CVI/UA is calculated by adding all items with I-CVI equal to 1 divided by the total number of items, while S-CVI/Ave is calculated by taking the sum of the I-CVIs divided by the total number of items [16 (link)]. A S-CVI/UA ≥ 0.8 and a S-CVI/Ave ≥ 0.9 have excellent content validity [29 ].

CVR: The second type of empirical analysis was CVR, which measures the essentiality of an item [30 (link)]. CVR varies between 1 and −1, and a higher score indicates greater agreement among panel members [16 (link)]. The formula for the CVR is CVR = (Ne – N/2)/(N/2), where Ne is the number of panelists indicating an item as “essential” and N is the total number of panelists [16 (link)].

A cover letter and the PEQ were included with the content validity survey explaining why experts were invited to participate, along with clear and concise instructions on how to rate each item. To evaluate whether items were relevant, clear and essential, experts were given a critical appraisal sheet with the following four inquiries: 1) the relevance of each question in the tool (how important the question is); 2) the clarity of each question (how clear the wording is); 3) the essentiality of each question (how necessary the question is); and 4) recommendations for improvement of each question. The critical appraisal tool that experts used to rate the questionnaire is in Additional file 1: Appendix A. For the relevancy scale, a 4-point Likert scale was used and responses include: 1 = not relevant, 2 = somewhat relevant, 3 = quite relevant, and 4 = very relevant. Ratings of 1 and 2 are considered content invalid while ratings of 3 and 4 are considered content valid [31 ]. A 3-point Likert scale was used for the clarity and essentiality scale since answers can only be trichotomous. The clarity scale was: 1 = not clear, 2 = item needs some revision; and 3 = very clear, and for essentiality: 1 = not essential; 2 = useful, but not essential; and 3 = essential [15 (link), 16 (link)]. Additional comments and recommendations by the experts were written on the hard copy of the questionnaire that was provided with the cover letter.
The recommended number of experts to review an instrument varies from 2 to 20 individuals [15 (link)]. At least 5 people are suggested to review the instrument to have sufficient control over chance agreement [16 (link)]. Content validity was determined using a number of experts (n = 6) that included an athletic therapist and a Ph.D. candidate from the University of Western Ontario, a physiotherapist, a chiropractor, and a family doctor from Toronto, Ontario and an orthopedic surgeon with a research background in osteoporosis from McMaster University. Experts were chosen based on the following guidelines: 1) worked in a medical or rehabilitation setting with patients with osteoporosis; or 2) published at least one article related to the care of patients with osteoporosis.
Rodrigues I.B., Adachi J.D., Beattie K.A, & MacDermid J.C. (2017). Development and validation of a new tool to measure the facilitators, barriers and preferences to exercise in people with osteoporosis. BMC Musculoskeletal Disorders, 18, 540.
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Publication 2017
Cognition Orthopedic Surgeons Osteoporosis Patients Physical Therapist Physicians Rehabilitation
4
Comprehensive Assessment of Centenarian Functioning
Interviews covered four main areas of functioning, including (a) physical functioning, (b) cognitive functioning, (c) social resources, and (d) mental health.

Physical functioning: Physical functioning was indicated by number of diagnoses, subjective health, and functional status. Participants responded “yes or no” to a checklist of common age-related illnesses. Health conditions included: high blood pressure, heart condition, diabetes, chronic lung disease, ulcers or other serious stomach issues, cirrhosis or other liver problems, kidney condition, frequent urinary infections, incontinence, prostate problems, problems with vision or hearing, arthritis, osteoporosis, stroke, cancer, pneumonia, falls, and other. Conditions mentioned as “other” were later coded. Subjective health was measure d with an item asking participants to evaluate their current health status (1 = poor; 5 = excellent). Functional status was measured with the Older Americans Resources and Services (OARS) Multidimensional Functional Assessment Questionnaire [25 ] in which participants were asked how much difficulty they had performing seven personal activities of daily living (PADLs) and seven instrumental activities of daily living (IADLs) using a 3-point rating scale (0 = can’t do without help, to 2 = no difficulty; 0–14 each).

Cognitive functioning: To assess cognitive functioning, we used the following subscales from the Mini-Mental State Examination (MMSE) [26 (link)]: Orientation (range: 0–10 points), Registration (range: 0–3 points), Attention (0–5 points), and Recall (0–3 points), resulting in a maximum total of 21 points. We followed the recommendations by Holtsberg et al. [27 (link)], who proposed using items that were unlikely to be biased by the poor sensory functioning highly prevalent in centenarians. This selection of MMSE items has been used in prior centenarian studies [16 (link)]. As a second cognitive indicator, we used the Global Deterioration Scale (GlobDetScale) [28 (link)], which is an observer’s rating of cognitive status (1 = no memory deficit evident from interview, to 7 = very severe cognitive decline).

Social resources: Number of living children was used as an indicator of social resources. Social contact and support was assessed with the 6-item Social Network Scale [29 (link)]. Items asked for the number of relatives and number of friends to whom one talks to at least once a month, with whom one feels at ease to talk with about private matters (confidants), and to whom one feels close enough to ask for help (SOS contacts; 0 = none, to 5 = nine or more).

Mental health: We used the 15-item version of the Geriatric Depression Scale (GDS) [30 ] to assess depressive symptoms. Items were answered using 1 = yes, and 0 = no and were summed; higher values indicated higher frequency of depressive symptoms (range: 0–15). Life satisfaction was measured with a modified version of the 5-item Satisfaction with Life Scale [31 (link)]. As centenarians with poor cognition had difficulty understanding items formulated as statements (e.g., In most ways, my life is close to my ideal), we reformulated those into questions (e.g., In most ways, is your life close to your ideal?). To further reduce cognitive load, we also limited the answering format to 5 options (0 = not at all, to 4 = very much). Higher mean scores represent greater subjective well-being.

Jopp D.S., Park M.K., Lehrfeld J, & Paggi M.E. (2016). Physical, cognitive, social and mental health in near-centenarians and centenarians living in New York City: findings from the Fordham Centenarian Study. BMC Geriatrics, 16, 1.
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Publication 2016
Arthritis Attention Centenarians Cerebrovascular Accident Child Cognition Depressive Symptoms Diabetes Mellitus Diagnosis Diagnostic Self Evaluation Disease, Chronic Disorders, Cognitive Feelings Friend Heart High Blood Pressures Kidney Diseases Liver Liver Cirrhosis Lung Lung Diseases Malignant Neoplasms Memory Deficits Menopause Mental Health Mental Recall Mini Mental State Examination Osteoporosis Physical Examination Pneumonia Prostate Satisfaction Speech Stomach Ulcer Urinary Tract Infection Urine
5
Fracture Risk Assessment Using FRAX
Individual patient details comprise age (50 to 90 years), sex, weight (in kg) and height (in cm). BMI is automatically computed from height and weight. Dichotomised risk variables are then entered:

A prior fragility fracture (yes/no)

Parental history of hip fracture (yes/no)

Current tobacco smoking (yes/no)

Ever long-term use of oral glucocorticoids (yes/no)

Rheumatoid arthritis (yes/no)

Other causes of secondary osteoporosis (yes/no)

Daily alcohol consumption of three or more units daily (yes/no)

A distinction is made between rheumatoid arthritis and other secondary causes of osteoporosis. Rheumatoid arthritis carries a fracture risk over and above that provided by BMD [11 (link)]. Whereas this may hold true for other secondary causes of osteoporosis, the evidence base is weak. Of the many secondary causes of osteoporosis, the following have been consistently documented to be associated with a significant increase in fracture risk:

Untreated hypogonadism in men and women, e.g., bilateral oophorectomy or orchidectomy, anorexia nervosa, chemotherapy for breast cancer, hypopituitarism [33 (link)–40 (link)]

Inflammatory bowel disease, e.g., Crohn’s disease and ulcerative colitis [41 (link)–43 (link)]. It should be noted that the risk is in part dependent on the use of glucocorticoids, but an independent risk remains after adjustment for glucocorticoid exposure [44 (link)].

Prolonged immobility, e.g., spinal cord injury, Parkinson’s disease, stroke, muscular dystrophy, ankylosing spondylitis [45 (link)–50 (link)]

Organ transplantation [51 –54 (link)]

Type I diabetes [55 (link)–58 (link)]

Thyroid disorders, e.g., untreated hyperthyroidism, over-treated hypothyroidism [59 (link)–63 (link)]

Whereas there is strong evidence for the association of these disorders and fracture risk, the independence of these risk factors from BMD is uncertain. It was conservatively assumed, therefore, that the fracture risk was mediated via low BMD, but with a risk ratio similar to that noted in rheumatoid arthritis. From an operational view, where the field for rheumatoid arthritis is entered as ‘yes’, a risk is computed with and without BMD. If the field for other secondary osteoporosis is also filled as ‘yes’ this does not contribute to the calculation of fracture probability. Conversely, where the field for rheumatoid arthritis entered as ‘no’, and the field for secondary osteoporosis is ‘yes’, the same β coefficients as used for rheumatoid arthritis contribute to the computation of probability where BMD is not entered. In the presence of BMD, however, no additional risk is assumed in the presence of secondary osteoporosis, since its independence of BMD is uncertain.
If any of the fields for dichotomous variables is not completed, a negative response is assumed. Fractures probability can then be calculated. The output (without BMD) comprises the 10-year probability of hip, clinical spine, shoulder or wrist fracture and the 10-year probability of hip fracture (Fig. 1).

Input and output for the FRAX™ model

Femoral neck BMD can additionally be entered either as a Z-score or a T-score. The transformation of Z- to T-score and vice versa is derived for the NHANES III database for female Caucasians aged 20–29 years [64 (link)]. When entered, calculations give the 10-year probabilities as defined above with or without the inclusion of BMD.
Kanis J.A., Johnell O., Oden A., Johansson H, & McCloskey E. (2008). FRAX™ and the assessment of fracture probability in men and women from the UK. Osteoporosis International, 19(4), 385-397.
Publication 2008
Ankylosing Spondylitis Anorexia Nervosa Caucasoid Races Cerebrovascular Accident Crohn Disease Debility Diabetes Mellitus, Insulin-Dependent Female Castrations Fracture, Bone Fracture, Wrist Glucocorticoids Hip Fractures Hyperthyroidism Hypogonadism Hypopituitarism Hypothyroidism Inflammatory Bowel Diseases Malignant Neoplasm of Breast Muscular Dystrophy Neck Orchiectomy Organ Transplantation Osteoporosis Parent Patients Pharmacotherapy Rheumatoid Arthritis Shoulder Spinal Cord Injuries Thyroid Diseases Ulcerative Colitis Vertebral Column Woman

Most recents protocols related to «Osteoporosis»

1
Sociodemographic Factors and USOC/HL Associations
Covariate selection was guided by previous literature on sociodemographic and health characteristics associated with having a USOC or HL (10 (link),11 (link)). These include baseline, age, race/ethnicity (White, Black, Hispanic, and other), sex, marital status (married/living with partner, and single/never married/divorced/widow), education (less than high school, high school diploma or equivalent, and some college or more), household income (under the poverty line, 100%–199% the poverty line, and ≥200% of the poverty line), number of chronic health conditions among heart attack, heart disease, high blood pressure, arthritis, osteoporosis, diabetes, lung disease, stroke, or cancer (0, 1–2, 3–5, or 6+), self-reported health status (Likert scale, 1 = Excellent, …, 5 = Poor), number of activities of daily living (ADLs) for which the respondent reported needing help (none, 1–2 ADLs, and 3≤ ADLs), dementia (probable, possible, and no dementia) (20 ), additional health coverage (Medigap/Medicare supplement, Medicaid, or Tricare), and depression status (based on Patient Health Questionnaire-2 scores ≥3) (21 (link)).
Despite being identified as a risk factor for loss of USOC, experiencing transportation barriers (reporting that a transportation problem restricted any activity participation in the month before the interview) was not included in the main analyses due to data availability, as a total of N = 1 804 participants had missing information.
Garcia Morales E., Assi L., Powell D., Luu K, & Reed N. (2023). The Association Between Self-Reported Hearing Loss and Loss of Usual Source of Health Care Among Older Medicare Beneficiaries: Evidence From the National Health and Aging Trends Study. Innovation in Aging, 7(2), igad002.
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Publication 2023
Arthritis Cerebrovascular Accident Dementia Diabetes Mellitus Dietary Supplements Ethnicity Heart Diseases High Blood Pressures Hispanics Households Insurance, Medigap Lung Diseases Malignant Neoplasms Myocardial Infarction Osteoporosis Training Programs Widow
2
Retrospective Study of Cervical Spondylosis Surgery
This was a retrospective study and was approved by the Ethical Committee of our hospital. We reviewed cervical spondylosis patients undergoing surgery in our hospital between January 2014 and December 2021 in our orthopedic department. The basic information of patients was inquired according to the case system. The disease time was determined according to the patient's complaint in the case system. In this work, the inclusion criteria were as follows: [1 (link)] diagnosis of cervical spondylosis; [2 (link)] patients with preoperative cervical CT and X-ray within 1 week before surgery; and [3 (link)] accept cervical surgery at our orthopedic department. The exclusion criteria were as follows: [1 (link)] patients with spine infection, spine tumor, spine trauma and metabolic bone disease; [2 (link)] merged cervical spine posterior longitudinal ligament ossification or multiple osteosclerosis; [3 (link)] long-term use of hormones or combined with immune diseases; [4 (link)] patient with nervous system disorders such as demyelinating disease; [5 (link)] a history of previous spinal surgery; [6 (link)] diagnosed with osteoporosis and treated with medication; and [7 (link)] incomplete radiologic data.
Wang Z., Zhong Z., Feng H., Mei J., Feng X., Wang B, & Sun L. (2023). The impact of disease time, cervical alignment and range of motion on cervical vertebral Hounsfield unit value in surgery patients with cervical spondylosis. Journal of Orthopaedic Surgery and Research, 18, 187.
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Publication 2023
Cervical Vertebrae Demyelinating Diseases Hormones Immune System Diseases Infection Metabolic Bone Disease Neck Nervous System Disorder Operative Surgical Procedures Orthopedic Surgical Procedures Ossification of Posterior Longitudinal Ligament Osteoporosis Osteosclerosis Patients Pharmaceutical Preparations Radiography Spinal Injuries Spinal Neoplasms Spondylosis, Cervical Vertebral Column
3
Age-Related Respiratory Function Decline
The respiratory system undergoes various anatomical, physiological and immunological changes with age. Ageing is associated with a progressive decline in respiratory function that accompanies changes in the structure of the chest wall due to loss of supporting tissue, increased air trapping and decreased respiratory muscle strength [28 ]. Respiratory function was measured using the CareFusion Microlab Spirometer with the participant seated. Measurements included forced expiratory volume in one second (FEV1, l), forced vital capacity (FVC, l) and forced expiratory flow (FEF) 25–75%. Measures of lung function (FEV1 and FVC) are associated with all-cause and cardiovascular mortality [29 , 30 ]. Low FEV1 is also recognised as an independent predictor of non-cardiopulmonary comorbidities including diabetes, chronic kidney disease, osteoporosis and dementia [31 –34 ]. For the purposes of this manuscript the highest FEV1 and FVC reading was used. A maximum of five attempts were undertaken to obtain three satisfactory readings. Analyses are only based on participants who obtained at least three satisfactory readings.
Neville C.E., Young I.S., Kee F., Hogg R.E., Scott A., Burns F., Woodside J.V, & McGuinness B. (2023). Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA): health assessment protocol, participant profile and patterns of participation. BMC Public Health, 23, 466.
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Publication 2023
Cardiovascular System Chronic Kidney Diseases Dementia Diabetes Mellitus Exhaling Muscle Weakness Osteoporosis physiology Respiratory Physiology Respiratory Rate Respiratory System Spirometry Tissues Volumes, Forced Expiratory Wall, Chest
4
Comprehensive AS Management System Database
The EMRMS was established in November, 2016 to assist rheumatologists in conducting ASDAS assessments and comprehensively evaluating clinical outcomes in all patients with AS attending TCVGH. The EMRMS database contains information necessary to determite ASDAS, including CRP, level and erythrocyte sedimentation rate [ESR], patient comorbidities, patient history, and family history. The reliability and validity of the data have been verified14 (link).Patients with AS were consecutively enrolled in the TCVGH-AS cohort after they received a confirmed AS diagnosis from a TCVGH rheumatologist according to the 1984 modified New York criteria10 (link). The CRP and ESR data were automatically uploaded to the TCVGH healthcare information system (HIS) to reduce human error. The baseline information, which was collected by trained nurses during the initial visit, including clinical characteristics, onset age, comorbidities at presentation (hypertension, diabetes mellitus, hyperlipidemia, hepatitis B, hepatitis C, renal insufficiency, gout, coronary artery disease, stroke, periodontal disease, osteoporosis, and tuberculosis history), periarticular extraspinal features (synovitis, enthesitis, and dactylitis) and nonarticular manifestations (psoriasis, uveitis, and IBD), family history of autoimmune disease, and patient history of arthropathy, obtained through standardized questionnaires and worksheets to ensure reproducibility and adherence to good laboratory practice. The rheumatologist in charge then confirmed patients’ clinical characteristics, and nurses assisted the patients with AS to complete the self-assessment questionnaires for disease evaluation. The following measures were used: global assessment of disease activity on a numerical rating scale (NRS) of 0–10, back pain on an NRS of 0–10, duration of morning stiffness on an NRS of 0–10, and peripheral pain or swelling on an NRS of 0–10. Before every 3-month visiting clinic, the patient would first to have blood examination. Blood reports can be uploaded to EMRMS through the HIS system, trained nurses assist patient fills out the questionnaire on EMRMS, the assessment of disease activity completed before visiting the doctor. All laboratory data, including CRP and ESR, have been uploaded to the HIS. The IT at TCVGH help "feed-forward" the patient reported outcomes to HIS, and do the auto-calculation of ASDAS-ESR, ASDAS-CRP using the ESR, CRP data in HIS, then "feed-back" these data to both HIS and EMRMS, showing the data on the summary overview "dashboard" in the EMRMS, which was shown both in HIS and the devices (iPAD handled by a nurse in charge and smartphones of patients with AS).
Huang P.J., Chen Y.H., Huang W.N., Chen Y.M., Lai K.L., Hsieh T.Y., Hung W.T., Lin C.T., Tseng C.W., Tang K.T., Chou Y.Y., Wu Y.D., Huang C.Y., Hsieh C.W., Chen Y.J., Liao Y.W., Liu Y.T, & Chen H.H. (2023). The electronic medical record management systems may improve monitoring and control of disease activity in patients with ankylosing spondylitis. Scientific Reports, 13, 3957.
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Publication 2023
Arthropathy Autoimmune Diseases Back Pain BLOOD Cerebrovascular Accident Charge Nurses Coronary Artery Disease Diabetes Mellitus Diagnosis Gout Hepatitis B Hepatitis C virus High Blood Pressures Homo sapiens Hyperlipidemia Medical Devices Nurses Osteoporosis Pain Patients Periodontal Diseases Physicians Psoriasis Renal Insufficiency Rheumatologist Sedimentation Rates, Erythrocyte Self-Assessment Synovitis Tuberculosis Uveitis
5
Long-term Conditions in Welsh Residents
Residents of Wales diagnosed for the first time with at least one of 17 long-term conditions between January 2000 and December 2021 were identified using ICD-10 or Read v2 codes (Supplementary Tables S2 and S3). The conditions included were anxiety disorders, asthma, atrial fibrillation, coronary heart disease (CHD), chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), dementia, depression, diabetes mellitus, epilepsy, heart failure, hypertension, inflammatory bowel disease (IBD), osteoporosis, peripheral vascular disease (PVD), rheumatoid arthritis, and stroke and transient ischaemic attack (TIA). These conditions comprise most of the general practice ‘Quality and Outcomes (QOF) Framework’.13 In addition, individuals diagnosed with three diabetes subtypes (type 1, type 2, undetermined) were identified using an algorithm.14 (link) ‘Undetermined type diabetes’ was assigned when criteria for type 1 or type 2 were not met.
The final study dataset excluded records missing week of birth or sex, or where the diagnosis date was before birth or after death dates.
Qi C., Osborne T., Bailey R., Cooper A., Hollinghurst J.P., Akbari A., Crowder R., Peters H., Law R.J., Lewis R., Smith D., Edwards A, & Lyons R.A. (2023). Impact of COVID-19 pandemic on incidence of long-term conditions in Wales: a population data linkage study using primary and secondary care health records. The British Journal of General Practice, 73(730), e332-e339.
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Publication 2023
Anxiety Disorders Asthma Atrial Fibrillation Cerebrovascular Accident Childbirth Chronic Kidney Diseases Chronic Obstructive Airway Disease Congestive Heart Failure Dementia Diabetes Mellitus Diagnosis Epilepsy Heart Disease, Coronary High Blood Pressures Inflammatory Bowel Diseases Osteoporosis Peripheral Vascular Diseases Rheumatoid Arthritis Training Programs Transient Ischemic Attack

Top products related to «Osteoporosis»

1
Lunar prodigy by GE Healthcare
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The Lunar Prodigy is a bone densitometry system designed for the assessment of bone mineral density (BMD) and body composition. It utilizes dual-energy X-ray absorptiometry (DXA) technology to provide accurate and reproducible measurements.
2
Sas version 9 by SAS Institute
Sourced in United States, Austria, Japan, Cameroon, Germany, United Kingdom, Canada, Belgium, Israel, Denmark, Australia, New Caledonia, France, Argentina, Sweden, Ireland, India
SAS version 9.4 is a statistical software package. It provides tools for data management, analysis, and reporting. The software is designed to help users extract insights from data and make informed decisions.
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Qdr 4500 by Hologic
Sourced in United States, United Kingdom, Morocco
The QDR 4500 is a dual-energy X-ray absorptiometry (DXA) system designed for bone mineral density (BMD) measurements. It is a diagnostic device used to assess bone health and detect conditions such as osteoporosis.
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Sas 9 by SAS Institute
Sourced in United States, Austria, Japan, Belgium, United Kingdom, Cameroon, China, Denmark, Canada, Israel, New Caledonia, Germany, Poland, India, France, Ireland, Australia
SAS 9.4 is an integrated software suite for advanced analytics, data management, and business intelligence. It provides a comprehensive platform for data analysis, modeling, and reporting. SAS 9.4 offers a wide range of capabilities, including data manipulation, statistical analysis, predictive modeling, and visual data exploration.
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Qdr 4500a by Hologic
Sourced in United States, United Kingdom, New Zealand
The QDR 4500A is a dual-energy X-ray absorptiometry (DXA) system designed for bone density measurements. It is a diagnostic medical device used to assess bone mineral density (BMD) and evaluate the risk of osteoporosis.
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Spss software by IBM
Sourced in United States, Japan, United Kingdom, Germany, Austria, Belgium, China, Italy, India, Israel, France, Spain, Denmark, Canada, Hong Kong, Poland, Australia
SPSS is a software package used for statistical analysis. It provides a graphical user interface for data manipulation, statistical analysis, and visualization. SPSS offers a wide range of statistical techniques, including regression analysis, factor analysis, and time series analysis.
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Discovery a by Hologic
Sourced in United States, United Kingdom, New Zealand
The Discovery A is a laboratory equipment product designed for molecular diagnostic testing. It is a compact, automated system that performs nucleic acid extraction, amplification, and detection. The Discovery A is capable of processing multiple sample types and is intended for use in clinical and research laboratories.
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Discovery w by Hologic
Sourced in United States, United Kingdom
The Discovery W is a laboratory equipment product designed for scientific research and analysis. It serves as a versatile platform for performing various analytical tasks. The core function of the Discovery W is to provide researchers and scientists with a reliable and efficient tool for their laboratory work.
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Lunar prodigy advance by GE Healthcare
Sourced in United States, United Kingdom, Italy, Belgium, Germany
The Lunar Prodigy Advance is a dual-energy X-ray absorptiometry (DXA) system designed for bone mineral density (BMD) assessment. It utilizes low-dose X-rays to measure the bone density of the spine, hip, and other skeletal sites. The system provides precise and reproducible measurements to aid in the diagnosis and management of osteoporosis and other bone-related conditions.
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Discovery w fan beam densitometer by Hologic
Sourced in United States
The DISCOVERY-W fan-beam densitometer is a laboratory equipment used for bone mineral density (BMD) measurement. It utilizes fan-beam technology to assess bone density, providing data on bone health and structure.

More about "Osteoporosis"

Osteoporosis is a widespread skeletal disorder characterized by diminished bone strength, putting individuals at heightened risk of fractures.
It is particularly prevalent among postmenopausal women, and is associated with significant morbidity and mortality.
This multifactorial disease is influenced by both genetic and environmental factors.
Diagnosis typically involves bone mineral density (BMD) measurements, often conducted using technologies like Lunar Prodigy, QDR 4500, or Discovery A/W densitometers.
Treatment approaches may include pharmacological interventions, lifestyle modifications, and fall prevention strategies.
Ongoing research aims to enhance our understanding of osteoporosis pathogenesis and develop more effective preventive and therapeutic measures.
Explore the latest insights and protocols in osteoporosis research with the help of data-driven tools like PubCompare.ai, which leverage AI to streamline literature review, protocol identification, and product comparisons.
Whether you're using SAS 9.4, SPSS, or other statistical software, these resources can support your efforts to advance the field of osteoporosis management and improve patient outcomes.