Role of Gut Microbe Composition in Psychosocial Symptom Response to Exercise Training in Breast Cancer Survivors (ROME) study: protocol for a randomised controlled trial.

INTRODUCTION: Breast cancer survivors have an increased risk for chronic fatigue and altered gut microbiota composition, both with negative health and quality of life affects. Exercise modestly improves fatigue and is linked to gut microbial diversity and production of beneficial metabolites. Studies suggest that gut microbiota composition is a potential mechanism underlying fatigue response to exercise. Randomised controlled trials testing the effects of exercise on the gut microbiome are limited and there is a scarcity of findings specific to breast cancer survivors. The objective of this study is to determine if fitness-related modifications to gut microbiota occur and, if so, mediate the effects of aerobic exercise on fatigue response. METHODS AND ANALYSIS: The research is a randomised controlled trial among breast cancer survivors aged 18-74 with fatigue. The primary aim is to determine the effects of aerobic exercise training compared with an attention control on gut microbiota composition. The secondary study aims are to test if exercise training (1) affects the gut microbiota composition directly and/or indirectly through inflammation (serum cytokines), autonomic nervous system (heart rate variability) or hypothalamic-pituitary-adrenal axis mediators (hair cortisol assays), and (2) effects on fatigue are direct and/or indirect through changes in the gut microbiota composition. All participants receive a standardised controlled diet. Assessments occur at baseline, 5 weeks, 10 weeks and 15 weeks (5 weeks post intervention completion). Faecal samples collect the gut microbiome and 16S gene sequencing will identify the microbiome. Fatigue is measured by a 13-item multidimensional fatigue scale. ETHICS AND DISSEMINATION: The University of Alabama at Birmingham Institutional Review Board (IRB) approved this study on 15 May 2019, UAB IRB#30000320. A Data and Safety Monitoring Board convenes annually or more often if indicated. Findings will be disseminated in peer-reviewed journals and conference presentations. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov, NCT04088708.

The value of machine learning based on CT radiomics in the preoperative identification of peripheral nerve invasion in colorectal cancer: a two-center study.

BACKGROUND: We aimed to explore the application value of various machine learning (ML) algorithms based on multicenter CT radiomics in identifying peripheral nerve invasion (PNI) of colorectal cancer (CRC). METHODS: A total of 268 patients with colorectal cancer who underwent CT examination in two hospitals from January 2016 to December 2022 were considered. Imaging and clinicopathological data were collected through the Picture Archiving and Communication System (PACS). The Feature Explorer software (FAE) was used to identify the peripheral nerve invasion of colorectal patients in center 1, and the best feature selection and classification channels were selected. Finally, the best feature selection and classifier pipeline were verified in center 2. RESULTS: The six-feature models using RFE feature selection and GP classifier had the highest AUC values, which were 0.610, 0.699, and 0.640, respectively. FAE generated a more concise model based on one feature (wavelet-HLL-glszm-LargeAreaHighGrayLevelEmphasis) and achieved AUC values of 0.614 and 0.663 on the validation and test sets, respectively, using the "one standard error" rule. Using ANOVA feature selection, the GP classifier had the best AUC value in a one-feature model, with AUC values of 0.611, 0.663, and 0.643 on the validation, internal test, and external test sets, respectively. Similarly, when using the "one standard error" rule, the model based on one feature (wave-let-HLL-glszm-LargeAreaHighGrayLevelEmphasis) achieved AUC values of 0.614 and 0.663 on the validation and test sets, respectively. CONCLUSIONS: Combining artificial intelligence and radiomics features is a promising approach for identifying peripheral nerve invasion in colorectal cancer. This innovative technique holds significant potential for clinical medicine, offering broader application prospects in the field. CRITICAL RELEVANCE STATEMENT: The multi-channel ML method based on CT radiomics has a simple operation process and can be used to assist in the clinical screening of patients with CRC accompanied by PNI. KEY POINTS: • Multi-channel ML in the identification of peripheral nerve invasion in CRC. • Multi-channel ML method based on CT-radiomics can detect the PNI of CRC. • Early preoperative identification of PNI in CRC is helpful to improve the formulation of treatment strategies and the prognosis of patients.

Gene-based association tests in family samples using GWAS summary statistics.

Genome-wide association studies (GWAS) have led to rapid growth in detecting genetic variants associated with various phenotypes. Owing to a great number of publicly accessible GWAS summary statistics, and the difficulty in obtaining individual-level genotype data, many existing gene-based association tests have been adapted to require only GWAS summary statistics rather than individual-level data. However, these association tests are restricted to unrelated individuals and thus do not apply to family samples directly. Moreover, due to its flexibility and effectiveness, the linear mixed model has been increasingly utilized in GWAS to handle correlated data, such as family samples. However, it remains unknown how to perform gene-based association tests in family samples using the GWAS summary statistics estimated from the linear mixed model. In this study, we show that, when family size is negligible compared to the total sample size, the diagonal block structure of the kinship matrix makes it possible to approximate the correlation matrix of marginal Z scores by linkage disequilibrium matrix. Based on this result, current methods utilizing summary statistics for unrelated individuals can be directly applied to family data without any modifications. Our simulation results demonstrate that this proposed strategy controls the type 1 error rate well in various situations. Finally, we exemplify the usefulness of the proposed approach with a dental caries GWAS data set.

Individualized driver amplitude in liver MR elastography: a linear regression study.

BACKGROUND: Current liver magnetic resonance elastography (MRE) scans often require adjustments to driver amplitude to produce acceptable images. This could lead to time wastage and the potential loss of an opportunity to capture a high-quality image. PURPOSE: To construct a linear regression model of individualized driver amplitude to improve liver MRE image quality. MATERIAL AND METHODS: Data from 95 liver MRE scans of 61 participants, including abdominal missing volume ratio (AMVR), breath-holding status, the distance from the passive driver on the skin surface to the liver edge (Dd-l), body mass index (BMI), and lateral deflection of the passive driver with respect to the human sagittal plane (Angle α), were continuously collected. The Spearman correlation analysis and lasso regression were conducted to screen the independent variables. Multiple linear regression equations were developed to determine the optimal amplitude prediction model. RESULTS: The optimal formula for linear regression models: driver amplitude (%) = -16.80 + 78.59 × AMVR - 11.12 × breath-holding (end of expiration = 1, end of inspiration = 0) + 3.16 × Dd-l + 1.94 × BMI + 0.34 × angle α, with the model passing the F test (F = 22.455, P <0.001) and R2 value of 0.558. CONCLUSION: The individualized amplitude prediction model based on AMVR, breath-holding status, Dd-l, BMI, and angle α is a valuable tool in liver MRE examination.

SGLT2 Inhibitor Use and Risk of Breast Cancer Among Adult Women with Type 2 Diabetes.

INTRODUCTION: Sodium-glucose cotransporter 2 (SGLT2) inhibitors are a relatively new class of antihyperglycemic agents, with the potential to inhibit breast cancer development. However, the association between SGLT2 inhibitors and risk of breast cancer in human studies is unclear. OBJECTIVE: The aim of our study is to use a large national claims database to assess the association between SGLT2 inhibitor use and risk of breast cancer. METHODS: We considered a study population of 158,483 adult women with type 2 diabetes who newly initiated SGLT2 inhibitors or dipeptidyl peptidase 4 (DPP4) inhibitors using Optum's deidentified Clinformatics Data Mart Database between 1 January 2013 and 31 March 2022. The association between SGLT2 inhibitor use and risk of breast cancer was examined using Cox proportional hazard models stratified by age in the overall sample and in a subsample based on propensity score and medication initiation time matching. The effect of medication use duration was explored. RESULTS: With an average follow-up of 2.2 years, 2154 breast cancer cases were identified. There was no significant association between SGLT2 inhibitor use and the risk of breast cancer in overall sample (HR = 0.96; 95% CI 0.87, 1.06), in women younger than 51 years old (HR = 0.88; 95% CI 0.59, 1.32), or in women aged 51 years or older (HR = 0.95; 95% CI 0.86, 1.04). The results remained nonsignificant using matching, medication use duration, and sensitivity analyses. CONCLUSION: Our findings suggest SGLT2 inhibitors use was not associated with breast cancer risk compared with DPP4 inhibitors use. Studies with longer follow-up and better adjustments are needed to confirm the finding.

Allostatic load and risk of invasive breast cancer among postmenopausal women in the U.S.

OBJECTIVE: Allostatic load can reflect the body's response to chronic stress. However, little is known about the association between allostatic load and risk of breast cancer in postmenopausal women. This study used a large prospective cohort in the United States to examine the relationship between allostatic load and invasive breast cancer risk, and to evaluate the relationship by racial and ethnic identity and breast cancer subtypes. METHODS: Among 161,808 postmenopausal participants in Women's Health Initiative, eligible were a subsample of 27,393 postmenopausal women aged 50-79 years old, who enrolled from 1993 to 1998, had serum test biomarkers, and were followed for breast cancer incidence through February 2022. Allostatic load at enrollment was computed based on eight biomarkers from lab serum tests and a questionnaire about participants' prescription drug use. The associations between allostatic scores and risk of breast cancer (overall and by subtypes) were assessed using Cox proportional hazards models. The race and ethnic differences were examined. RESULTS: Over a median follow-up time of 17.24 years, 1722 invasive breast cancer cases were identified. High allostatic load was associated with an increased risk of breast cancer (HR = 1.36, 95%CI: 1.20, 1.54 for third tertile vs first tertile, Ptrend < 0.0001). Similar trends were found in White women and non-Hispanic women. Higher allostatic load was associated with hormone receptor-positive and HER2/Neu-negative breast cancer (HR = 1.54, 95%CI: 1.30, 1.80 for third tertile vs first tertile, Ptrend < 0.0001). CONCLUSION: In this study, we found that higher allostatic load was significantly associated with an increased risk of breast cancer in postmenopausal women.

Genetic Associations with Dynamic Placental Proteins Identify Causal Biomarkers for Hypertension in Pregnancy.

Preeclampsia (PE), a gestational hypertensive disorder, ranks as the second leading cause of maternal mortality worldwide. While PE is considered a multifactorial disease, placental insufficiency is believed to drive its progression. To noninvasively study placental physiology related to adverse pregnancy outcomes (APOs) and predict these outcomes before symptom onset, we measured nine placental protein levels in first- and second-trimester serum samples from 2,352 nulliparous pregnant women in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers- to-Be (nuMoM2b) study. The proteins analyzed include VEGF, PlGF, ENG, sFlt-1, ADAM-12, PAPP-A, fßHCG, INHA, and AFP. Currently, little is known about the genetic variants contributing to the heritability of these proteins during pregnancy, and no studies have explored the causal relationships between early pregnancy proteins and gestational hypertensive disorders. Our study has three objectives. First, we conducted genome-wide association study (GWAS) of nine placental proteins in maternal serum during the first and second trimesters and the difference between time points to understand how genetics may influence placental proteins in early pregnancy. Second, we examined whether early pregnancy placental proteins are causal factors for PE and gestational hypertension (gHTN). Lastly, we investigated the causal relationship between PE/gHTN and long-term HTN. In conclusion, our study discovered significant genetic associations with placental proteins ADAM-12, VEGF, and sFlt-1, offering insights into their regulation during pregnancy. Mendelian randomization (MR) analyses demonstrated evidence of causal relationships between placental proteins, particularly ADAM-12, and gHTN, potentially informing prevention and treatment strategies. Our findings suggest that placental proteins like ADAM-12 could serve as biomarkers for postpartum HTN risk.

A gene-based association test of interactions for maternal-fetal genotypes identifies genes associated with nonsyndromic congenital heart defects.

The risk of congenital heart defects (CHDs) may be influenced by maternal genes, fetal genes, and their interactions. Existing methods commonly test the effects of maternal and fetal variants one-at-a-time and may have reduced statistical power to detect genetic variants with low minor allele frequencies. In this article, we propose a gene-based association test of interactions for maternal-fetal genotypes (GATI-MFG) using a case-mother and control-mother design. GATI-MFG can integrate the effects of multiple variants within a gene or genomic region and evaluate the joint effect of maternal and fetal genotypes while allowing for their interactions. In simulation studies, GATI-MFG had improved statistical power over alternative methods, such as the single-variant test and functional data analysis (FDA) under various disease scenarios. We further applied GATI-MFG to a two-phase genome-wide association study of CHDs for the testing of both common variants and rare variants using 947 CHD case mother-infant pairs and 1306 control mother-infant pairs from the National Birth Defects Prevention Study (NBDPS). After Bonferroni adjustment for 23,035 genes, two genes on chromosome 17, TMEM107 (p = 1.64e-06) and CTC1 (p = 2.0e-06), were identified for significant association with CHD in common variants analysis. Gene TMEM107 regulates ciliogenesis and ciliary protein composition and was found to be associated with heterotaxy. Gene CTC1 plays an essential role in protecting telomeres from degradation, which was suggested to be associated with cardiogenesis. Overall, GATI-MFG outperformed the single-variant test and FDA in the simulations, and the results of application to NBDPS samples are consistent with existing literature supporting the association of TMEM107 and CTC1 with CHDs.

Conditioning on parental mating types can reduce necessary assumptions for Mendelian randomization.

Mendelian randomization (MR) has become a common tool used in epidemiological studies. However, when confounding variables are correlated with the instrumental variable (in this case, a genetic/variant/marker), the estimation can remain biased even with MR. We propose conditioning on parental mating types (a function of parental genotypes) in MR to eliminate the need for one set of assumptions, thereby plausibly reducing such bias. We illustrate a situation in which the instrumental variable and confounding variables are correlated using two unlinked diallelic genetic loci: one, an instrumental variable and the other, a confounding variable. Assortative mating or population admixture can create an association between the two unlinked loci, which can violate one of the necessary assumptions for MR. We simulated datasets involving assortative mating and population admixture and analyzed them using three different methods: 1) conventional MR, 2) MR conditioning on parental genotypes, and 3) MR conditioning on parental mating types. We demonstrated that conventional MR leads to type I error rate inflation and biased estimates for cases with assortative mating or population admixtures. In the presence of non-additive effects, MR with an adjustment for parental genotypes only partially reduced the type I error rate inflation and bias. In contrast, conditioning on parental mating types in MR eliminated the type I error inflation and bias under these circumstances. Conditioning on parental mating types is a useful strategy to reduce the burden of assumptions and the potential bias in MR when the correlation between the instrument variable and confounders is due to assortative mating or population stratification but not linkage.

MOVER-R and Penalized MOVER-R Confidence Intervals for the Ratio of Two Quantities.

Developing a confidence interval for the ratio of two quantities is an important task in statistics because of its omnipresence in real world applications. For such a problem, the MOVER-R (method of variance recovery for the ratio) technique, which is based on the recovery of variance estimates from confidence limits of the numerator and the denominator separately, was proposed as a useful and efficient approach. However, this method implicitly assumes that the confidence interval for the denominator never includes zero, which might be violated in practice. In this article, we first use a new framework to derive the MOVER-R confidence interval, which does not require the above assumption and covers the whole parameter space. We find that MOVER-R can produce an unbounded confidence interval, just like the well-known Fieller method. To overcome this issue, we further propose the penalized MOVER-R. We prove that the new method differs from MOVER-R only at the second order. It, however, always gives a bounded and analytic confidence interval. Through simulation studies and a real data application, we show that the penalized MOVER-R generally provides a better confidence interval than MOVER-R in terms of controlling the coverage probability and the median width.

Progressive reduction in circulating levels of carotenoids and other micronutrients in patients with chronic pancreatitis.

BACKGROUND: Although micronutrients modulate immunity and inflammation, it remains elusive whether they are implicated in the development and progression of chronic pancreatitis (CP). This study aimed to investigate differences in the circulating levels of selected carotenoids and vitamins between CP and controls and trends in the levels of these micronutrients across controls, early CP, and definite CP. METHODS: Demographic and lifestyle data were extracted from medical records for 53 patients with CP (13 early and 38 definite) and obtained using a questionnaire for 52 controls. Plasma ß-carotene, lycopene, cryptoxanthin, zeaxanthin, and α-tocopherol and serum 25(OH)D, folate, IL-6, TNF-α, and MCP-1 were measured with state-of-the-art methods. RESULTS: The levels of all micronutrients (except folate) were significantly lower in CP than in controls. There was a progressive decrease in the levels of these micronutrients across controls, early CP, and definite CP (all p values for trend: ≤0.0012); e.g., plasma lycopene was 36.6, 21.5, and 14.5 µg/dL for controls, early CP, and definite CP, respectively. After adjustment for confounders, there were strong, inverse associations between the levels of all micronutrients (except folate) and CP (e.g., OR (95% CI) for ≥ median vs.

Random field modeling of multi-trait multi-locus association for detecting methylation quantitative trait loci.

MOTIVATION: CpG sites within the same genomic region often share similar methylation patterns and tend to be co-regulated by multiple genetic variants that may interact with one another. RESULTS: We propose a multi-trait methylation random field (multi-MRF) method to evaluate the joint association between a set of CpG sites and a set of genetic variants. The proposed method has several advantages. First, it is a multi-trait method that allows flexible correlation structures between neighboring CpG sites (e.g. distance-based correlation). Second, it is also a multi-locus method that integrates the effect of multiple common and rare genetic variants. Third, it models the methylation traits with a beta distribution to characterize their bimodal and interval properties. Through simulations, we demonstrated that the proposed method had improved power over some existing methods under various disease scenarios. We further illustrated the proposed method via an application to a study of congenital heart defects (CHDs) with 83 cardiac tissue samples. Our results suggested that gene BACE2, a methylation quantitative trait locus (QTL) candidate, colocalized with expression QTLs in artery tibial and harbored genetic variants with nominal significant associations in two genome-wide association studies of CHD. AVAILABILITY AND IMPLEMENTATION: https://github.com/chenlyu2656/Multi-MRF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Detecting methylation quantitative trait loci using a methylation random field method.

DNA methylation may be regulated by genetic variants within a genomic region, referred to as methylation quantitative trait loci (mQTLs). The changes of methylation levels can further lead to alterations of gene expression, and influence the risk of various complex human diseases. Detecting mQTLs may provide insights into the underlying mechanism of how genotypic variations may influence the disease risk. In this article, we propose a methylation random field (MRF) method to detect mQTLs by testing the association between the methylation level of a CpG site and a set of genetic variants within a genomic region. The proposed MRF has two major advantages over existing approaches. First, it uses a beta distribution to characterize the bimodal and interval properties of the methylation trait at a CpG site. Second, it considers multiple common and rare genetic variants within a genomic region to identify mQTLs. Through simulations, we demonstrated that the MRF had improved power over other existing methods in detecting rare variants of relatively large effect, especially when the sample size is small. We further applied our method to a study of congenital heart defects with 83 cardiac tissue samples and identified two mQTL regions, MRPS10 and PSORS1C1, which were colocalized with expression QTL in cardiac tissue. In conclusion, the proposed MRF is a useful tool to identify novel mQTLs, especially for studies with limited sample sizes.

Mapping methylation quantitative trait loci in cardiac tissues nominates risk loci and biological pathways in congenital heart disease.

BACKGROUND: Most congenital heart defects (CHDs) result from complex interactions among genetic susceptibilities, epigenetic modifications, and maternal environmental exposures. Characterizing the complex relationship between genetic, epigenetic, and transcriptomic variation will enhance our understanding of pathogenesis in this important type of congenital disorder. We investigated cis-acting effects of genetic single nucleotide polymorphisms (SNPs) on local DNA methylation patterns within 83 cardiac tissue samples and prioritized their contributions to CHD risk by leveraging results of CHD genome-wide association studies (GWAS) and their effects on cardiac gene expression. RESULTS: We identified 13,901 potential methylation quantitative trait loci (mQTLs) with a false discovery threshold of 5%. Further co-localization analyses and Mendelian randomization indicated that genetic variants near the HLA-DRB6 gene on chromosome 6 may contribute to CHD risk by regulating the methylation status of nearby CpG sites. Additional SNPs in genomic regions on chromosome 10 (TNKS2-AS1 gene) and chromosome 14 (LINC01629 gene) may simultaneously influence epigenetic and transcriptomic variations within cardiac tissues. CONCLUSIONS: Our results support the hypothesis that genetic variants may influence the risk of CHDs through regulating the changes of DNA methylation and gene expression. Our results can serve as an important source of information that can be integrated with other genetic studies of heart diseases, especially CHDs.

Mediation analysis of racial disparities in triple-negative breast cancer incidence among postmenopausal women.

BACKGROUND: Triple-negative breast cancer (TNBC) is disproportionately higher in Black women relative to White women. The objective of this study was to examine to what extent the association between race/ethnicity and risk of TNBC is mediated by potentially modifiable factors. METHODS: A total of 128,623 Black and White women aged 50-79 years from the Women's Health Initiative were followed for a mean of 15.8 years. 643 incident TNBC cases (92 Black women and 551 White women) were confirmed by medical record review. Mediation analyses were conducted using an approach under a counterfactual framework. RESULTS: Black women had approximately twofold higher risk of TNBC compared with white women (HR = 1.93, 95% CI 1.52-2.45). We observed that 48% of the racial disparity was mediated by metabolic dysfunction defined by having 3 or more cardiometabolic risk factors including elevated waist circumference, having history of diabetes, high cholesterol and hypertension. The racial disparity was not significantly mediated by other factors studied, including socioeconomic, lifestyle or reproductive factors. CONCLUSION: Our study observed that approximately half of the racial disparity between postmenopausal Black and White women in TNBC incidence was driven by metabolic dysfunction.

Skeletal muscle mitochondrial DNA copy number and mitochondrial DNA deletion mutation frequency as predictors of physical performance in older men and women.

Mitochondrial DNA (mtDNA) quality and quantity relate to two hallmarks of aging-genomic instability and mitochondrial dysfunction. Physical performance relies on mitochondrial integrity and declines with age, yet the interactions between mtDNA quantity, quality, and physical performance are unclear. Using a validated digital PCR assay specific for mtDNA deletions, we tested the hypothesis that skeletal muscle mtDNA deletion mutation frequency (i.e., a measure of mtDNA quality) or mtDNA copy number predicts physical performance in older adults. Total DNA was isolated from vastus lateralis muscle biopsies and used to quantitate mtDNA copy number and mtDNA deletion frequency by digital PCR. The biopsies were obtained from a cross-sectional cohort of 53 adults aged 50 to 86 years. Before the biopsy procedure, physical performance measurements were collected, including VO2max, modified physical performance test score, 6-min walk distance, gait speed, grip strength, and total lean and leg mass. Linear regression models were used to evaluate the relationships between age, sex, and the outcomes. We found that mtDNA deletion mutation frequency increased exponentially with advancing age. On average from ages 50 to 86, deletion frequency increased from 0.008 to 0.15%, an 18-fold increase. Females may have lower deletion frequencies than males at older ages. We also measured declines in VO2max and mtDNA copy number with age in both sexes. The mtDNA deletion frequency measured from single skeletal muscle biopsies predicted 13.3% of the variation in VO2max. Copy number explained 22.6% of the variation in mtDNA deletion frequency and 10.4% of the lean mass variation. We found predictive relationships between age, mtDNA deletion mutation frequency, mtDNA copy number, and physical performance. These data are consistent with a role for mitochondrial function and genome integrity in maintaining physical performance with age. Analyses of mtDNA quality and quantity in larger cohorts and longitudinal studies could extend our understanding of the importance of mitochondrial DNA in human aging and longevity.

Penalized Fieller's confidence interval for the ratio of bivariate normal means.

Constructing a confidence interval for the ratio of bivariate normal means is a classical problem in statistics. Several methods have been proposed in the literature. The Fieller method is known as an exact method, but can produce an unbounded confidence interval if the denominator of the ratio is not significantly deviated from 0; while the delta and some numeric methods are all bounded, they are only first-order correct. Motivated by a real-world problem, we propose the penalized Fieller method, which employs the same principle as the Fieller method, but adopts a penalized likelihood approach to estimate the denominator. The proposed method has a simple closed form, and can always produce a bounded confidence interval by selecting a suitable penalty parameter. Moreover, the new method is shown to be second-order correct under the bivariate normality assumption, that is, its coverage probability will converge to the nominal level faster than other bounded methods. Simulation results show that our proposed method generally outperforms the existing methods in terms of controlling the coverage probability and the confidence width and is particularly useful when the denominator does not have adequate power to reject being 0. Finally, we apply the proposed approach to the interval estimation of the median response dose in pharmacology studies to show its practical usefulness.

Mitochondrial DNA deletion mutations increase exponentially with age in human skeletal muscle.

BACKGROUND: Mitochondrial DNA (mtDNA) deletion mutations lead to electron transport chain-deficient cells and age-induced cell loss in multiple tissues and mammalian species. Accurate quantitation of somatic mtDNA deletion mutations could serve as an index of age-induced cell loss. Quantitation of mtDNA deletion molecules is confounded by their low abundance in tissue homogenates, the diversity of deletion breakpoints, stochastic accumulation in single cells, and mosaic distribution between cells. AIMS: Translate a pre-clinical assay to quantitate mtDNA deletions for use in human DNA samples, with technical and biological validation, and test this assay on human subjects of different ages. METHODS: We developed and validated a high-throughput droplet digital PCR assay that quantitates human mtDNA deletion frequency. RESULTS: Analysis of human quadriceps muscle samples from 14 male subjects demonstrated that mtDNA deletion frequency increases exponentially with age-on average, a 98-fold increase from age 20-80. Sequence analysis of amplification products confirmed the specificity of the assay for human mtDNA deletion breakpoints. Titration of synthetic mutation mixtures found a lower limit of detection of at least 0.6 parts per million. Using muscle DNA from 6-month-old mtDNA mutator mice, we measured a 6.4-fold increase in mtDNA deletion frequency (i.e., compared to wild-type mice), biologically validating the approach. DISCUSSION/CONCLUSIONS: The exponential increase in mtDNA deletion frequency is concomitant with the known muscle fiber loss and accelerating mortality that occurs with age. The improved assay permits the accurate and sensitive quantification of deletion mutations from DNA samples and is sufficient to measure changes in mtDNA deletion mutation frequency in healthy individuals across the lifespan and, therefore, patients with suspected mitochondrial diseases.

Hysterectomy, Oophorectomy, and Risk of Renal Cell Carcinoma.

BACKGROUND: Female hormones may play roles during renal cell carcinoma (RCC) carcinogenesis. The aims of this study were to investigate associations between hysterectomy, oophorectomy, and risk of RCC and to assess whether the associations were modified by exogenous estrogen, commonly used among women who have undergone hysterectomy. METHODS: Postmenopausal women (n = 144,599) ages 50-79 years at enrollment (1993-1998) in the Women's Health Initiative were followed for a mean of 15.9 years. Hysterectomy and oophorectomy were self-reported. Incident RCC cases were confirmed by physician review of medical records and pathology reports. Multivariable Cox proportional hazards modeling was used to estimate hazard ratios (HR) and 95% confidence intervals (CI), adjusting for potential confounders. RESULTS: A total of 583 women developed RCC during follow-up. We observed that hysterectomy, regardless of oophorectomy status, was significantly associated with an increased risk of RCC (HR, 1.28; 95% CI, 1.03-1.60). The association appeared to be more pronounced in women with age at hysterectomy younger than 40 years (HR, 1.34; 95% CI, 1.01-1.80) or older than 55 years (HR, 1.52; 95% CI, 1.01-2.29). Oophorectomy was not significantly associated with risk of RCC. There was no evidence that exogenous estrogen use modified the association between hysterectomy and risk of RCC. CONCLUSIONS: In this large prospective study, we showed that women with a history of hysterectomy had 28% increased risk of RCC, and this finding was not modified by exogenous hormone use. IMPACT: If our findings are confirmed, women should be made aware of increased risk of RCC when considering hysterectomy.

Mitochondrial DNA alterations in aged macrophage migration inhibitory factor-knockout mice.

The age-induced, exponential accumulation of mitochondrial DNA (mtDNA) deletion mutations contributes to muscle fiber loss. The causes of these mutations are not known. Systemic inflammation is associated with decreased muscle mass in older adults and is implicated in the formation of sporadic mtDNA deletions. Macrophage migration inhibitory factor knockout (MIF-KO) mice are long-lived with decreased inflammation. We hypothesized that aged MIF-KO mice would have lower mtDNA deletion frequencies and fewer electron transport chain (ETC) deficient fibers. We measured mtDNA copy number and mutation frequency as well as the number and length of ETC deficient fibers in 22-month old MIF-KO and F2 hybrid control mice. We also measured mtDNA copy number and deletion frequency in female UM-HET3 mice, a strain whose lifespan matches the MIF-KO mice. We did not observe a significant effect of MIF ablation on muscle mtDNA deletion frequency. There was a significantly lower mtDNA copy number in the MIF-KO mice and the lifespan-matched UM-HET3 mice compared to the F2 hybrids, suggesting the importance of genetic background in mtDNA copy number control. Our data do not support a definitive role for MIF in age-induced mtDNA deletions.