Mitochondrial sequence variants: testing imputation accuracy and their association with dairy cattle milk traits.

BACKGROUND: Mitochondrial genomes differ from the nuclear genome and in humans it is known that mitochondrial variants contribute to genetic disorders. Prior to genomics, some livestock studies assessed the role of the mitochondrial genome but these were limited and inconclusive. Modern genome sequencing provides an opportunity to re-evaluate the potential impact of mitochondrial variation on livestock traits. This study first evaluated the empirical accuracy of mitochondrial sequence imputation and then used real and imputed mitochondrial sequence genotypes to study the role of mitochondrial variants on milk production traits of dairy cattle. RESULTS: The empirical accuracy of imputation from Single Nucleotide Polymorphism (SNP) panels to mitochondrial sequence genotypes was assessed in 516 test animals of Holstein, Jersey and Red breeds using Beagle software and a sequence reference of 1883 animals. The overall accuracy estimated as the Pearson's correlation squared (R2) between all imputed and real genotypes across all animals was 0.454. The low accuracy was attributed partly to the majority of variants having low minor allele frequency (MAF < 0.005) but also due to variants in the hypervariable D-loop region showing poor imputation accuracy. Beagle software provides an internal estimate of imputation accuracy (DR2), and 10 percent of the total 1927 imputed positions showed DR2 greater than 0.9 (N = 201). There were 151 sites with empirical R2 > 0.9 (of 954 variants segregating in the test animals) and 138 of these overlapped the sites with DR2 > 0.9. This suggests that the DR2 statistic is a reasonable proxy to select sites that are imputed with higher accuracy for downstream analyses. Accordingly, in the second part of the study mitochondrial sequence variants were imputed from real mitochondrial SNP panel genotypes of 9515 Australian Holstein, Jersey and Red dairy cattle. Then, using only sites with DR2 > 0.900 and real genotypes, we undertook a genome-wide association study (GWAS) for milk, fat and protein yields. The GWAS mitochondrial SNP effects were not significant. CONCLUSION: The accuracy of imputation of mitochondrial genotypes from the SNP panel to sequence was generally low. The Beagle DR2 statistic enabled selection of sites imputed with higher empirical accuracy. We recommend building larger reference populations with mitochondrial sequence to improve the accuracy of imputing less common variants and ensuring that SNP panels include common variants in the D-loop region.

Fracture risk assessment in the presence of competing risk of death.

PURPOSE: To identify the optimal statistical approach for predicting the risk of fragility fractures in the presence of competing event of death. METHODS: We used real-world data from the Dubbo Osteoporosis Epidemiology Study that has monitored 3035 elderly participants for bone health and mortality. Fragility fractures were ascertained radiologically. Mortality was confirmed by the State Registry. We considered four statistical models for predicting fracture risk: (i) conventional Cox's proportional hazard model, (ii) cause-specific model, (iii) Fine-Gray sub-distribution model, and (iv) multistate model. These models were fitted and validated in the development (60% of the original sample) and validation (40%) subsets, respectively. The model performance was assessed by discrimination and calibration analyses. RESULTS: During a median follow-up of 11.3 years (IQR: 7.2, 16.2), 628 individuals (34.5%) in the development cohort fractured, and 630 (34.6%) died without a fracture. Neither the discrimination nor the 5-year prediction performance was significantly different among the models, though the conventional model tended to overestimate fracture risk (calibration-in-the-large index = - 0.24; 95% CI: - 0.43, - 0.06). For 10-year risk prediction, the multistate model (calibration-in-the-large index = - 0.05; 95% CI: - 0.20, 0.10) outperformed the cause-specific (- 0.23; - 0.30, - 0.08), Fine-Gray (- 0.31; - 0.46, - 0.16), and conventional model (- 0.54; - 0.70, - 0.39) which significantly overestimated fracture risk. CONCLUSION: Adjustment for competing risk of death has minimum impact on the short-term prediction of fracture. However, the multistate model yields the most accurate prediction of long-term fracture risk and should be considered for predictive research in the elderly, who are also at high mortality risk. Fracture risk assessment might be compromised by the competing event of death. This study, using real-world data found a multistate model was superior to the current competing risk methods in fracture risk assessment. A multistate model is considered an optimal statistical method for predictive research in the elderly.

Multimorbidity clusters potentially superior to individual diseases for stratifying fracture risk in older people: a nationwide cohort study.

RATIONALE: Comorbidities are common in fracture patients, but the interaction between fracture and comorbidities remains unclear. This study aimed to define specific multimorbidity clusters in older adults and quantify the association between the multimorbidity clusters and fracture risk. METHODS: This nationwide cohort study includes 1.7 million adults in Denmark aged ≥50 years who were followed from 2001 through 2014 for an incident low-trauma fracture. Chronic diseases and fractures were identified from the Danish National Hospital Discharge Register. Latent class analysis and Cox's regression were conducted to define the clusters and quantify fracture risk, respectively. RESULTS: The study included 793 815 men (age: 64 ± 10) and 873 524 women (65.5 ± 11), with a third having ≥1 chronic disease. The pre-existent chronic diseases grouped individuals into low-multimorbidity (80.3% in men, 83.6% in women), cardiovascular (12.5%, 10.6%), malignant (4.1%, 3.8%), diabetic (2.4%, 2.0%) and hepatic clusters (0.7%, men only). These clusters distinguished individuals with advanced, complex, or late-stage disease from those having earlier-stage disease. During a median follow-up of 14 years (IQR: 6.5, 14), 95 372 men and 212 498 women sustained an incident fracture. The presence of multimorbidity was associated with a significantly greater risk of fracture, independent of age and sex. Importantly, the multimorbidity clusters had the highest discriminative performance in assessing fracture risk, whereas the strength of their association with fracture risk equalled or exceeded that of both the individual chronic diseases most prevalent in each cluster and of counts-based comorbidity indices. CONCLUSIONS: Future fracture prevention strategies should take comorbidities into account. Multimorbidity clusters may provide greater insight into fracture risk than individual diseases or counts-based comorbidity indices.

Using expression data to fine map QTL associated with fertility in dairy cattle.

BACKGROUND: Female fertility is an important trait in dairy cattle. Identifying putative causal variants associated with fertility may help to improve the accuracy of genomic prediction of fertility. Combining expression data (eQTL) of genes, exons, gene splicing and allele specific expression is a promising approach to fine map QTL to get closer to the causal mutations. Another approach is to identify genomic differences between cows selected for high and low fertility and a selection experiment in New Zealand has created exactly this resource. Our objective was to combine multiple types of expression data, fertility traits and allele frequency in high- (POS) and low-fertility (NEG) cows with a genome-wide association study (GWAS) on calving interval in Australian cows to fine-map QTL associated with fertility in both Australia and New Zealand dairy cattle populations. RESULTS: Variants that were significantly associated with calving interval (CI) were strongly enriched for variants associated with gene, exon, gene splicing and allele-specific expression, indicating that there is substantial overlap between QTL associated with CI and eQTL. We identified 671 genes with significant differential expression between POS and NEG cows, with the largest fold change detected for the CCDC196 gene on chromosome 10. Our results provide numerous candidate genes associated with female fertility in dairy cattle, including GYS2 and TIGAR on chromosome 5 and SYT3 and HSD17B14 on chromosome 18. Multiple QTL regions were located in regions with large numbers of copy number variants (CNV). To identify the causal mutations for these variants, long read sequencing may be useful. CONCLUSIONS: Variants that were significantly associated with CI were highly enriched for eQTL. We detected 671 genes that were differentially expressed between POS and NEG cows. Several QTL detected for CI overlapped with eQTL, providing candidate genes for fertility in dairy cattle.

Development of a shape-based algorithm for identification of asymptomatic vertebral compression fractures: A proof-of-principle study.

Objectives: Vertebral fracture is both common and serious among adults, yet it often goes undiagnosed. This study aimed to develop a shape-based algorithm (SBA) for the automatic identification of vertebral fractures. Methods: The study included 144 participants (50 individuals with a fracture and 94 without a fracture) whose plain thoracolumbar spine X-rays were taken. Clinical diagnosis of vertebral fracture (grade 0 to 3) was made by rheumatologists using Genant's semiquantitative method. The SBA algorithm was developed to determine the ratio of vertebral body height loss. Based on the ratio, SBA classifies a vertebra into 4 classes: 0 = normal, 1 = mild fracture, 2 = moderate fracture, 3 = severe fracture). The concordance between clinical diagnosis and SBA-based classification was assessed at both person and vertebra levels. Results: At the person level, the SBA achieved a sensitivity of 100% and specificity of 62% (95% CI, 51%-72%). At the vertebra level, the SBA achieved a sensitivity of 84% (95% CI, 72%-93%), and a specificity of 88% (95% CI, 85%-90%). On average, the SBA took 0.3 s to assess each X-ray. Conclusions: The SBA developed here is a fast and efficient tool that can be used to systematically screen for asymptomatic vertebral fractures and reduce the workload of healthcare professionals.

Association between Fat Mass and Obesity-Related Transcript Polymorphisms and Osteoporosis Phenotypes.

BACKGROUND: Common variants in the fat mass and obesity-related transcript (FTO) gene are related to body mass index and obesity, suggesting its potential association with bone mineral density (BMD) and fracture risk. This study sought to define the association between FTO gene variants and the following phenotypes: (1) BMD; (2) bone loss; and (3) fracture risk. METHODS: This analysis was based on the Dubbo Osteoporosis Epidemiology Study that included 1,277 postmenopausal women aged ≥60 years living in Dubbo, Australia. BMD at the femoral neck and lumbar spine was measured biennially by dual energy X-ray absorptiometry (GE Lunar). Fractures were radiologically ascertained. Six single nucleotide polymorphisms (SNPs; rs1421085, rs1558902, rs1121980, rs17817449, rs9939609, and rs9930506) of the FTO gene were genotyped using TaqMan assay. RESULTS: Women homozygous for the minor allele (GG) of rs9930506 had a significantly higher risk of hip fracture (adjusted hazard ratio, 1.93; 95% confidence interval, 1.15-3.23) than those homozygous for the major allele (AA) after adjusting for potential confounding effects. Similar associations were also observed for the minor allele of rs1121980. However, there was no significant association between the FTO SNPs and BMD or the rate of bone loss. CONCLUSIONS: Common variations in the FTO gene are associated with a hip fracture risk in women, and the association is not mediated through BMD or bone loss.

Machine learning to detect the SINEs of cancer.

We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a machine learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to 7615 samples from 5178 individuals, 2073 with solid cancer and the remainder without cancer. Samples from patients with cancer and controls were prespecified into four cohorts used for model training, analyte integration, and threshold determination, validation, and reproducibility. A-PLUS alone provided a sensitivity of 40.5% across 11 different cancer types in the validation cohort, at a specificity of 98.5%. Combining A-PLUS with aneuploidy and eight common protein biomarkers detected 51% of the cancers at 98.9% specificity. We found that part of the power of A-PLUS could be ascribed to a single feature-the global reduction of AluS subfamily elements in the circulating DNA of patients with solid cancer. We confirmed this reduction through the analysis of another independent dataset obtained with a different approach (whole-genome sequencing). The evaluation of Alu elements may therefore have the potential to enhance the performance of several methods designed for the earlier detection of cancer.

Prevalence, incidence of and risk factors for vertebral fracture in the community: the Vietnam Osteoporosis Study.

The epidemiology of vertebral fractures (VF) in underrepresented populations is not well-documented. This cohort study was part of a longitudinal osteoporosis research project with the aim of determining the prevalence, incidence, and risk factors for VF. 401 individuals (155 men) aged 50 years and older without a clinical diagnosis of VF were took radiographs at baseline and 2 years later. VF were ascertained using the Genant's semi-quantitative method. Bone mineral density (BMD) of femoral neck and lumbar spine were measured by dual-energy X-ray absorptiometry (Hologic Inc). The association between VF and risk factors was analyzed by the multiple logistic regression. The 95% confidence interval for prevalence and incidence was estimated by exact Poisson test. At baseline, the prevalence of VF was 12.2% (n = 49, 95% CI 9.0-16.2%) and increased with advancing age with one-fifth of those aged 70 and older having a VF. During the follow-up period, we observed 6 new VF, making the incidence of 6.6/1000 person-years (n = 6, 95% CI 2.4-14.3). The risk of prevalent VF was associated with male gender (OR: 2.67; 95% CI 1.28-5.87) and T-score at the femoral neck (OR per one SD decrease: 1.1; 1.03-1.17). These data indicate that VF is common among adults, and that lower femoral neck BMD was a risk factor for VF.

Prevalence of and risk factors for sarcopenia in community-dwelling people: The Vietnam Osteoporosis Study.

BACKGROUND: Sarcopenia is a geriatric disease characterized by the progressive and generalized loss of skeletal lean mass and strength with age. The prevalence of sarcopenia in the Vietnamese population is unknown. This study sought to estimate the prevalence of and risk factors for sarcopenia among community-dwelling individuals in Vietnam. METHODS: This cross-sectional study is part of the ongoing Vietnam Osteoporosis Study project. The study involved 1308 women and 591 men aged 50 years and older as at 2015 (study entry). Whole-body dual-energy X-ray absorptiometry was used to measure the appendicular skeletal lean mass. Anthropometric and clinical data were collected using a structured questionnaire. Sarcopenia was defined according to the criteria proposed by the Asian Working Group for Sarcopenia in 2019. Logistic regression analysis was used to determine the association between potential risk factors and sarcopenia. RESULTS: The prevalence of sarcopenia in women and men was 14% (n = 183) and 16% (n = 83), respectively. Age (odds ratio [OR] per 10 years = 1.37; 95% confidence interval [CI] 1.26-1.48) and being underweight (OR = 1.61; 95% CI 1.00-2.58) were independently associated with increased risk of sarcopenia. The combination of low physical activity, being underweight and advancing age accounted for ~27% of sarcopenic patients. However, most of the attributable fraction was due to ageing. CONCLUSIONS: Sarcopenia is common in community-dwelling Vietnamese adults, particularly those with advancing age, who are underweight and with low physical activity.

BONEcheck: A digital tool for personalized bone health assessment.

Objectives: Osteoporotic fracture is a significant public health burden associated with increased mortality risk and substantial healthcare costs. Accurate and early identification of high-risk individuals and mitigation of their risks is a core part of the treatment and prevention of fractures. Here we introduce a digital tool called 'BONEcheck' for personalized assessment of bone health. Methods: The development of BONEcheck primarily utilized data from the prospective population-based Dubbo Osteoporosis Epidemiology Study and the Danish Nationwide Registry. BONEcheck has 3 modules: input data, risk estimates, and risk context. Input variables include age, gender, prior fracture, fall incidence, bone mineral density (BMD), comorbidities, and genetic variants associated with BMD. Results: Based on the input variables, BONEcheck estimates the probability of any fragility fracture and hip fracture within 5 years, subsequent fracture risk, skeletal age, and time to reach osteoporosis. The probability of fracture is shown in both numeric and human icon array formats. The risk is also contextualized within the framework of treatment and management options on Australian guidelines, with consideration given to the potential fracture risk reduction and survival benefits. Skeletal age was estimated as the sum of chronological age and years of life lost due to a fracture or exposure to risk factors that elevate mortality risk. Conclusions: BONEcheck is an innovative tool that empowers doctors and patients to engage in well-informed discussions and make decisions based on the patient's risk profile. Public access to BONEcheck is available via https://bonecheck.org and in Apple Store (iOS) and Google Play (Android).

Prevention of Hip Fractures: Trade-off between Minor Benefits to Individuals and Large Benefits to the Community.

Goeffrey Rose postulated that a population-based measure bringing a small benefit to each individual can yield large benefits to the community. We aimed to test this axiom by quantifying the relationship between change in bone mineral density (BMD) and hip fracture incidence between two prospective cohorts separated by ~10 years. In this prospective population-based Dubbo Osteoporosis Epidemiology Study (DOES), the participants aged 60+ were recruited in two waves: the initial cohort (1311 women, 842 men) in 1989 to 1992 and the second cohort (974 women, 544 men) in 1999 to 2001. The incident hip fracture was radiologically ascertained. Femoral neck BMD was measured biannually. Multivariable-adjusted Cox's proportional hazards models were adjusted for the predefined covariates such as age, BMI, lifestyle factors, falls, and prior fracture. Compared with the initial cohort, the second cohort had a higher femoral neck BMD by ~0.04 g/cm2 in women and 0.03 g/cm2 in men. However, the prevalence of osteoporosis in the second cohort was halved (prevalence ratio 0.51, 95% CI 0.36 to 0.73 in women; 0.45, 0.24 to 0.84 in men), and its hip fracture incidence was significantly reduced (hazard ratio 0.54, 95% CI, 0.38 to 0.78 in women; 0.39, 0.19 to 0.80 in men). Sensitivity analyses indicated that the "effect" was unlikely due to unmeasured confounders. These findings suggest that a population-wide strategy aimed at enhancing BMD across the entire population could lead to a substantial decrease in the incidence of hip fractures. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

'Skeletal Age' for mapping the impact of fracture on mortality.

Background: Fragility fracture is associated with an increased risk of mortality, but mortality is not part of doctor-patient communication. Here, we introduce a new concept called 'Skeletal Age' as the age of an individual's skeleton resulting from a fragility fracture to convey the combined risk of fracture and fracture-associated mortality for an individual. Methods: We used the Danish National Hospital Discharge Register which includes the whole-country data of 1,667,339 adults in Denmark born on or before January 1, 1950, who were followed up to December 31, 2016 for incident low-trauma fracture and mortality. Skeletal age is defined as the sum of chronological age and the number of years of life lost (YLL) associated with a fracture. Cox's proportional hazards model was employed to determine the hazard of mortality associated with a specific fracture for a given risk profile, and the hazard was then transformed into YLL using the Gompertz law of mortality. Results: During the median follow-up period of 16 years, there had been 307,870 fractures and 122,744 post-fracture deaths. A fracture was associated with between 1 and 7 years of life lost, with the loss being greater in men than women. Hip fractures incurred the greatest loss of life years. For instance, a 60-year-old individual with a hip fracture is estimated to have a skeletal age of 66 for men and 65 for women. Skeletal Age was estimated for each age and fracture site stratified by gender. Conclusions: We propose 'Skeletal Age' as a new metric to assess the impact of a fragility fracture on an individual's life expectancy. This approach will enhance doctor-patient risk communication about the risks associated with osteoporosis. Funding: National Health and Medical Research Council in Australia and Amgen Competitive Grant Program 2019.

Osteoporosis is a 'silent disease' which often has no immediate symptoms but gradually weakens bones and makes them more likely to break. A bone fracture caused by osteoporosis in people over the age of 50 is linked to long-term health decline and in some cases, even early death. However, poor communication of the mortality risk to patients has led to a low uptake of treatment, resulting in a crisis of osteoporosis management. The impact of a fracture on life expectancy is typically conveyed to patients and the public in terms of probability (how likely something is to occur) or the relative risk of death compared to other groups. However, statements such as "Your risk of death over the next 10 years is 5% if you have suffered from a bone fracture" can be difficult to comprehend and can lead to patients underestimating the gravity of the risk. With the aim of devising a new way of conveying risks to patients, Tran et al. analyzed the relationship between fracture and lifespan in over 1.6 million individuals who were 50 years of age or older. The findings showed that one fracture was associated with losing up to 7 years of life, depending on gender, age and fracture site. Based on this finding, Tran et al. proposed the idea of 'skeletal age' as a new metric for quantifying the impact of a fracture on life expectancy. Skeletal age is the sum of the chronological age of a patient and the estimated number of years of life lost following a fracture. For example, a 60-year-old man with a hip fracture is predicted to lose an estimated 6 years of life, resulting in a skeletal age of around 66. Therefore, this individual has the same life expectancy as a 66-year-old person that has not experienced a fracture. Skeletal age can also be used to quantify the benefit of osteoporosis treatments. Some approved treatments substantially reduce the likelihood of post-fracture death and translating this into skeletal age could help communicate this to patients. For instance, telling patients that "This treatment will reduce your skeletal age by 2 years" is easier to understand than "This treatment will reduce your risk of death by 25%". Given the current crisis of osteoporosis management, adopting skeletal age as a new measure of how the skeleton declines after a fracture could enhance doctor-patient communication regarding treatment options and fracture risk assessment. Tran et al. are now developing an online tool called 'BONEcheck.org' to enable health care professionals and the public to calculate skeletal age. Future work should investigate the effectiveness of this new metric in conveying risk to patients, compared with current methods.

Genetic Prediction of Lifetime Risk of Fracture.

CONTEXT: Fragility fracture is a significant public health problem because it is associated with increased mortality. We want to find out whether the risk of fracture can be predicted from the time of birth. OBJECTIVE: To examine the association between a polygenic risk score (PRS) and lifetime fracture risk. METHODS: This population-based prospective study involved 3515 community-dwelling individuals aged 60+ years who have been followed for up to 20 years. Femoral neck bone mineral density (BMD) was measured by dual-energy x-ray absorptiometry. A PRS was created by summing the weighted number of risk alleles for each single nucleotide polymorphism using BMD-associated coefficients. Fragility fractures were radiologically ascertained, whereas mortality was ascertained through a state registry. Residual lifetime risk of fracture (RLRF) was estimated by survival analysis. RESULTS: The mortality-adjusted RLRF for women and men was 36% (95% CI, 34%-39%) and 21% (18%-24%), respectively. Individuals with PRS > 4.24 (median) had a greater risk (1.2-fold in women and 1.1-fold in men) than the population average risk. For hip fracture, the average RLRF was 10% (95% CI, 8%-12%) for women and ∼5% (3%-7%) for men; however, the risk was significantly increased by 1.5-fold and 1.3-fold for women and men with high PRS, respectively. CONCLUSION: A genetic profiling of BMD-associated genetic variants is associated with the residual lifetime risk of fracture, suggesting the potential for incorporating the polygenic risk score in personalized fracture risk assessment.

Detection of rare mutations, copy number alterations, and methylation in the same template DNA molecules.

The analysis of cell-free DNA (cfDNA) from plasma offers great promise for the earlier detection of cancer. At present, changes in DNA sequence, methylation, or copy number are the most sensitive ways to detect the presence of cancer. To further increase the sensitivity of such assays with limited amounts of sample, it would be useful to be able to evaluate the same template molecules for all these changes. Here, we report an approach, called MethylSaferSeqS, that achieves this goal, and can be applied to any standard library preparation method suitable for massively parallel sequencing. The innovative step was to copy both strands of each DNA-barcoded molecule with a primer that allows the subsequent separation of the original strands (retaining their 5-methylcytosine residues) from the copied strands (in which the 5-methylcytosine residues are replaced with unmodified cytosine residues). The epigenetic and genetic alterations present in the DNA molecules can then be obtained from the original and copied strands, respectively. We applied this approach to plasma from 265 individuals, including 198 with cancers of the pancreas, ovary, lung, and colon, and found the expected patterns of mutations, copy number alterations, and methylation. Furthermore, we could determine which original template DNA molecules were methylated and/or mutated. MethylSaferSeqS should be useful for addressing a variety of questions relating genetics and epigenetics.

Longitudinal changes in bone mineral density during perimenopausal transition: the Vietnam Osteoporosis Study.

Based on the prospective cohort of the Vietnam Osteoporosis Study, we show that bone loss occurred before menopause, and that the loss accelerated in the first 5 years post-menopause. PURPOSE: To define the change in bone mineral density (BMD) among women during the menopausal transition. METHODS: The study involved 1062 women aged 40-59 who were participants of the population-based prospective Vietnam Osteoporosis Study. BMD at the femoral neck (FN), lumbar spine (LS), and whole body scan was measured by DXA. Each woman has had two BMD measurements separated by approximately 2 years, and the rate of BMD change was calculated for each woman. Multivariable linear regression models were used to quantify the association between body composition parameters and the rate of BMD change. RESULTS: At FN, there were 3 phases of BMD change: a slight decline before the age of 45-49 (average loss of 0.51%/year); a substantial decline between the ages of 49 and 54 (average loss of 1.39%/year); and then slowed down between the ages of 54 and 59 (average loss of 0.31%/year). The same trend was also observed at LS: a slight decline (- 0.56%/year) among women aged 45-49; then a significant decline between the ages of 50 and 54 (- 1.33%/year); but then slowed down at - 0.31%/year after the age of 55. Changes in BMD were not significantly associated with changes in lean mass or fat mass. CONCLUSIONS: Although bone loss occurred before menopause, the loss accelerated in the early perimenopausal transition (45-50 years of age). This finding suggests that screening for osteoporosis in women should be considered at the age of 45.

In it for the long run: perspectives on exploiting long-read sequencing in livestock for population scale studies of structural variants.

Studies have demonstrated that structural variants (SV) play a substantial role in the evolution of species and have an impact on Mendelian traits in the genome. However, unlike small variants (< 50 bp), it has been challenging to accurately identify and genotype SV at the population scale using short-read sequencing. Long-read sequencing technologies are becoming competitively priced and can address several of the disadvantages of short-read sequencing for the discovery and genotyping of SV. In livestock species, analysis of SV at the population scale still faces challenges due to the lack of resources, high costs, technological barriers, and computational limitations. In this review, we summarize recent progress in the characterization of SV in the major livestock species, the obstacles that still need to be overcome, as well as the future directions in this growing field. It seems timely that research communities pool resources to build global population-scale long-read sequencing consortiums for the major livestock species for which the application of genomic tools has become cost-effective.

Association of Decrease in Body Mass Index With Reduced Incidence and Progression of the Structural Defects of Knee Osteoarthritis: A Prospective Multi-Cohort Study.

OBJECTIVE: To define the association between change in body mass index (BMI) and the incidence and progression of the structural defects of knee osteoarthritis as assessed by radiography. METHODS: Radiographic analyses of knees at baseline and at 4-5 years of follow-up were obtained from the following 3 independent cohort studies: the Osteoarthritis Initiative (OAI) study, the Multicenter Osteoarthritis Study (MOST), and the Cohort Hip and Cohort Knee (CHECK) study. Logistic regression analyses using generalized estimating equations, with clustering of both knees within individuals, were used to investigate the association between change in BMI from baseline to 4-5 years of follow-up and the incidence and progression of knee osteoarthritis. RESULTS: A total of 9,683 knees (from 5,774 participants) in an "incidence cohort" and 6,075 knees (from 3,988 participants) in a "progression cohort" were investigated. Change in BMI was positively associated with both the incidence and progression of the structural defects of knee osteoarthritis. The adjusted odds ratio (OR) for osteoarthritis incidence was 1.05 (95% confidence interval [95% CI] 1.02-1.09), and the adjusted OR for osteoarthritis progression was 1.05 (95% CI 1.01-1.09). Change in BMI was also positively associated with degeneration (i.e., narrowing) of the joint space and with degeneration of the femoral and tibial surfaces (as indicated by osteophytes) on the medial but not on the lateral side of the knee. CONCLUSION: A decrease in BMI was independently associated with lower odds of incidence and progression of the structural defects of knee osteoarthritis and could be a component in preventing the onset or worsening of knee osteoarthritis.

Association of Multimorbidity and Excess Mortality After Fractures Among Danish Adults.

Importance: Limited knowledge about interactions among health disorders impedes optimal patient care. Because comorbidities are common among patients 50 years and older with fractures, these fractures provide a useful setting for studying interactions among disorders. Objective: To define multimorbidity clusters at the time of fracture and quantify the interaction between multimorbidity and fracture in association with postfracture excess mortality. Design, Setting, and Participants: This nationwide cohort study included 307 870 adults in Denmark born on or before January 1, 1951, who had an incident low-trauma fracture between January 1, 2001, and December 31, 2014, and were followed up through December 31, 2016. Data were analyzed from February 1 to March 31, 2022. Main Outcomes and Measures: Fracture and 32 predefined chronic diseases recorded within 5 years before the index fracture were identified from the Danish National Hospital Discharge Register. Death was ascertained from the Danish Register on Causes of Death. Latent class analysis was conducted to identify multimorbidity clusters. Relative survival analysis was used to quantify excess mortality associated with the combination of multimorbidity and fractures at specific sites. Results: Among the 307 870 participants identified with incident fractures, 95 372 were men (31.0%; mean [SD] age at fracture, 72.3 [11.2] years) and 212 498 were women (69.0%; mean [SD] age at fracture, 74.9 [11.2] years). During a median of 6.5 (IQR, 3.0-11.0) years of follow-up, 41 017 men (43.0%) and 81 727 women (38.5%) died. Almost half of patients with fractures (42.9%) had at least 2 comorbidities. Comorbidities at fracture were categorized as low-multimorbidity (60.5% in men and 66.5% in women), cardiovascular (23.7% in men and 23.5% in women), diabetic (5.6% in men and 5.0% in women), malignant (5.1% in men and 5.0% in women), and mixed hepatic and/or inflammatory (5.1% in men only) clusters. These clusters distinguished individuals with advanced, complex, or late-stage disease from those with earlier-stage disease. Multimorbidity and proximal or lower leg fractures were associated with increased mortality risk, with the highest excess mortality found in patients with hip fracture in the malignant cluster (1-year excess mortality: 40.8% [95% CI: 38.1%-43.6%]). The combination of multimorbidity and fracture compounded the association with mortality, conferring much greater risk than either alone. Conclusions and Relevance: Concomitant illnesses were common and clustered into distinct multimorbidity clusters that were associated with excess postfracture mortality. The compound contribution of multimorbidity to postfracture excess mortality highlights the need for more comprehensive approaches in these high-risk patients. The analytical approach applied to fracture could also be used to examine other sentinel health events.