The majority of the genetic variance of systemic lupus erythematosus (SLE) remains unexplained by the common disease-common variant hypothesis. Rare variants, which are not detectable by genome-wide association studies because of their low frequencies, are predicted to explain part of this "missing heritability." However, recent studies identifying rare variants within known disease-susceptibility loci have failed to show genetic associations because of their extremely low frequencies, leading to the questioning of the contribution of rare variants to disease susceptibility. A common (minor allele frequency = 17.4% in cases) nonsynonymous coding variant rs1143679 (R77H) in ITGAM (CD11b), which forms half of the heterodimeric integrin receptor, complement receptor 3 (CR3), is robustly associated with SLE and has been shown to impair CR3-mediated phagocytosis. We resequenced ITGAM in 73 SLE cases and identified two previously unidentified, case-specific nonsynonymous variants, F941V and G1145S. Both variants were genotyped in 2,107 and 949 additional SLE cases, respectively, to estimate their frequencies in a disease population. An in vitro model was used to assess the impact of F941V and G1145S, together with two nonsynonymous ITGAM polymorphisms, A858V (rs1143683) and M441T (rs11861251), on CR3-mediated phagocytosis. A paired two-tailed t test was used to compare the phagocytic capabilities of each variant with that of wild-type CR3. Both rare variants, F941V and G1145S, significantly impair CR3-mediated phagocytosis in an in vitro model (61% reduction, P = 0.006; 26% reduction, P = 0.0232). However, neither of the common variants, M441T and A858V, had an effect on phagocytosis. Neither rare variant was observed again in the genotyping of additional SLE cases, suggesting that their frequencies are extremely low. Our results add further evidence to the functional importance of ITGAM in SLE pathogenesis through impaired phagocytosis. Additionally, this study provides a new example of the identification of rare variants in common-allele-associated loci, which, because of their extremely low frequencies, are not statistically associated. However, the demonstration of their functional effects adds support to their contribution to disease risk, and questions the current notion of dismissing the contribution of very rare variants on purely statistical analyses.
OBJECTIVE There are variable reports of risk of concordance for progression to islet autoantibodies and type 1 diabetes in identical twins after one twin is diagnosed. We examined development of positive autoantibodies and type 1 diabetes and the effects of genetic factors and common environment on autoantibody positivity in identical twins, nonidentical twins, and full siblings. RESEARCH DESIGN AND METHODS Subjects from the TrialNet Pathway to Prevention Study (N = 48,026) were screened from 2004 to 2015 for islet autoantibodies (GAD antibody [GADA], insulinoma-associated antigen 2 [IA-2A], and autoantibodies against insulin [IAA]). Of these subjects, 17,226 (157 identical twins, 283 nonidentical twins, and 16,786 full siblings) were followed for autoantibody positivity or type 1 diabetes for a median of 2.1 years. RESULTS At screening, identical twins were more likely to have positive GADA, IA-2A, and IAA than nonidentical twins or full siblings (all P < 0.0001). Younger age, male sex, and genetic factors were significant factors for expression of IA-2A, IAA, one or more positive autoantibodies, and two or more positive autoantibodies (all P ≤ 0.03). Initially autoantibody-positive identical twins had a 69% risk of diabetes by 3 years compared with 1.5% for initially autoantibody-negative identical twins. In nonidentical twins, type 1 diabetes risk by 3 years was 72% for initially multiple autoantibody–positive, 13% for single autoantibody–positive, and 0% for initially autoantibody-negative nonidentical twins. Full siblings had a 3-year type 1 diabetes risk of 47% for multiple autoantibody–positive, 12% for single autoantibody–positive, and 0.5% for initially autoantibody-negative subjects. CONCLUSIONS Risk of type 1 diabetes at 3 years is high for initially multiple and single autoantibody–positive identical twins and multiple autoantibody–positive nonidentical twins. Genetic predisposition, age, and male sex are significant risk factors for development of positive autoantibodies in twins.
Plasmodium falciparum virulence is linked to its ability to sequester in post-capillary venules in the human host. Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) is the main variant surface antigen implicated in this process. Complete loss of parasite adhesion is linked to a large subtelomeric deletion on chromosome 9 in a number of laboratory strains such as D10 and T9-96. Similar to the cytoadherent reference line FCR3, D10 strain expresses PfEMP1 on the surface of parasitized erythrocytes, however without any detectable cytoadhesion. To investigate which of the deleted subtelomeric genes may be implicated in parasite adhesion, we selected 12 genes for D10 complementation studies that are predicted to code for proteins exported to the red blood cell. We identified a novel single copy gene (PF3D7_0936500) restricted to P. falciparum that restores adhesion to CD36, termed here virulence-associated protein 1 (Pfvap1). Protein knockdown and gene knockout experiments confirmed a role of PfVAP1 in the adhesion process in FCR3 parasites. PfVAP1 is co-exported with PfEMP1 into the host cell via vesicle-like structures called Maurer's clefts. This study identifies a novel highly conserved parasite molecule that contributes to parasite virulence possibly by assisting PfEMP1 to establish functional adhesion at the host cell surface.
A three-arm, randomized, double-masked, placebo-controlled phase 2b trial performed by the Type 1 Diabetes TrialNet Study Group previously demonstrated that low-dose anti-thymocyte globulin (ATG) (2.5 mg/kg) preserved β-cell function and reduced HbA1c for 1 year in new-onset type 1 diabetes. Subjects (N = 89) were randomized to 1) ATG and pegylated granulocyte colony-stimulating factor (GCSF), 2) ATG alone, or 3) placebo. Herein, we report 2-year area under the curve (AUC) C-peptide and HbA1c, prespecified secondary end points, and potential immunologic correlates. The 2-year mean mixed-meal tolerance test–stimulated AUC C-peptide, analyzed by ANCOVA adjusting for baseline C-peptide, age, and sex (n = 82) with significance defined as one-sided P < 0.025, was significantly higher in subjects treated with ATG versus placebo (P = 0.00005) but not ATG/GCSF versus placebo (P = 0.032). HbA1c was significantly reduced at 2 years in subjects treated with ATG (P = 0.011) and ATG/GCSF (P = 0.022) versus placebo. Flow cytometry analyses demonstrated reduced circulating CD4:CD8 ratio, increased regulatory T-cell:conventional CD4 T-cell ratios, and increased PD-1+CD4+ T cells following low-dose ATG and ATG/GCSF. Low-dose ATG partially preserved β-cell function and reduced HbA1c 2 years after therapy in new-onset type 1 diabetes. Future studies should determine whether low-dose ATG might prevent or delay the onset of type 1 diabetes.
Summary Short sleep has been associated with cardiovascular risk. The aim of this study was to determine the impact of short‐term sleep restriction on lipid profiles and resting blood pressure factors in young, normal‐weight individuals (14 men, 13 women). Participants were randomized to five nights of either habitual (9 h) or short (4 h) sleep in a cross‐over design separated by a 3‐week washout period. There was no sleep × day interaction on lipid profile and blood pressure. Short‐term sleep restriction does not alter lipid profiles and resting blood pressure in healthy, normal‐weight individuals. The association between short sleep and increased cardiovascular risk reported in the epidemiological literature may be the result of long‐term sleep restriction and poor lifestyle choices.
Article Figures and data Abstract Editor's evaluation Introduction Methods Results Discussion Data availability References Decision letter Author response Article and author information Abstract Background: Ageing is a heterogenous process characterised by cellular and molecular hallmarks, including changes to haematopoietic stem cells and is a primary risk factor for chronic diseases. X chromosome inactivation (XCI) randomly transcriptionally silences either the maternal or paternal X in each cell of 46, XX females to balance the gene expression with 46, XY males. Age acquired XCI-skew describes the preferential selection of cells across a tissue resulting in an imbalance of XCI, which is particularly prevalent in blood tissues of ageing females, and yet its clinical consequences are unknown. Methods: We assayed XCI in 1575 females from the TwinsUK population cohort using DNA extracted from whole blood. We employed prospective, cross-sectional, and intra-twin study designs to characterise the relationship of XCI-skew with molecular and cellular measures of ageing, cardiovascular disease risk, and cancer diagnosis. Results: We demonstrate that XCI-skew is independent of traditional markers of biological ageing and is associated with a haematopoietic bias towards the myeloid lineage. Using an atherosclerotic cardiovascular disease risk score, which captures traditional risk factors, XCI-skew is associated with an increased cardiovascular disease risk both cross-sectionally and within XCI-skew discordant twin pairs. In a prospective 10 year follow-up study, XCI-skew is predictive of future cancer incidence. Conclusions: Our study demonstrates that age acquired XCI-skew captures changes to the haematopoietic stem cell population and has clinical potential as a unique biomarker of chronic disease risk. Funding: KSS acknowledges funding from the Medical Research Council [MR/M004422/1 and MR/R023131/1]. JTB acknowledges funding from the ESRC [ES/N000404/1]. MM acknowledges funding from the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, Chronic Disease Research Foundation (CDRF), Zoe Global Ltd and the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. Editor's evaluation XCI skewing is affected by age but how this may affect a person's health is not known. Roberts et al., demonstrate that these changes result in an increased risk of cardiovascular disease and cancer. These findings will be of interest to researchers studying the impact of age on health. https://doi.org/10.7554/eLife.78263.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Ageing is a heterogenous process characterised by cellular and molecular hallmarks and can manifest clinically as frailty and multimorbidity (Clegg et al., 2013; López-Otín et al., 2013). Ageing is a primary risk factor for diseases such as cardiovascular disease and cancer, and a better understanding of the biomarkers of ageing promises to reduce the burden of chronic disease which significantly impacts the human healthspan (López-Otín et al., 2013). X chromosome inactivation (XCI) evolved in placental mammals to compensate for the X-linked gene dosage between XX females and XY males. XCI transcriptionally silences either the maternal or paternal X in each cell to equalise the gene expression between 46, XX females and 46, XY males (Lyon, 1961). The selection of which X is silenced is a random process that occurs during development, with the XCI status then clonally inherited by all daughter cells. Therefore, mammalian female tissues are mosaics with respect to XCI status, with an expected ratio of 1:1. However, some individuals display a skewed pattern of XCI (XCI-skew), which is defined as a deviation from the expected 1:1 ratio. Examples of primary XCI-skew have been identified, including stochastic events resulting in XCI-skew across all tissues (Tukiainen et al., 2016) or the preferential selection of cells expressing functioning alleles in heterozygous females with X-linked recessive traits (Busque and Gilliland, 1998; Nyhan et al., 1970). However, secondary or age acquired XCI-skew is more common and refers to increasing XCI-skew with age, particularly in mitotically active blood tissue (Busque et al., 1996; Gale et al., 1997). Within individuals, the correlation of XCI ratios between blood and other tissues diminishes over the life course as the XCI ratios in blood continue to skew with age (Bolduc et al., 2008; Zito et al., 2019). The stability of XCI-skew in blood has been demonstrated over 18–24 months and is thought to be a gradual process affecting the whole haematopoietic stem cell population rather than representing fluctuations in the active stem cell pool (Tonon et al., 1998; van Dijk et al., 2002). Therefore, though XCI-skew is a sex-specific measurable phenotype, it is a potential marker of stem cell depletion or polyclonal expansion of haematopoietic stem cells, which are age-associated traits irrespective of chromosomal sex (Busque et al., 1996; Busque et al., 2012; Gale et al., 1997). Age acquired XCI-skew has previously been linked to autoimmunity, which presents with a stark sex-imbalance (Chabchoub et al., 2009), as well as breast and ovarian cancers, albeit with inconsistent findings (Kristiansen et al., 2002; Lose et al., 2008; Manoukian et al., 2013; Struewing et al., 2006). Yet the consequences of XCI-skew on chronic disease risk in an unselected population have largely been unexplored. Clonal expansion of haematopoietic stem cells is also measurable by somatic mutations shared across blood cells, indicating a common stem cell precursor (Xie et al., 2014). Clonal haematopoiesis of indeterminate potential (CHIP) is a cellular phenotype describing a pre-malignant state in which ≥4% of blood cells harbour the same somatic mutation (Jaiswal and Ebert, 2019), thus representing monoclonal expansion. CHIP is robustly associated with all-cause mortality (Jaiswal et al., 2014), haematological cancers (Genovese et al., 2014), and cardiovascular disease (Jaiswal et al., 2017). XCI-skew can sometimes be a marker of CHIP: XCI-skew was previously used to determine clonality (Busque and Gilliland, 1998), and exome sequencing of females with XCI-skew identified TET2 mutations as drivers of pre-malignant clonal haematopoiesis. However, XCI-skew and CHIP are not completely mutually inclusive (Busque et al., 2012). Given XCI-skew is potentially tagging changes to the haematopoietic stem cell pool, we hypothesised that XCI-skew may be a marker of biological ageing and a risk factor for chronic disease. We tested this hypothesis by assaying XCI-skew in 1575 females from the TwinsUK cohort and employed prospective, cross-sectional, and intra-twin study designs to characterise the relationship of XCI with molecular and cellular measures of ageing, cardiovascular disease risk, and cancer diagnoses. Methods TwinsUK cohort Archival DNA samples derived from whole blood (collected 1997–2017) were selected from individuals of the TwinsUK population cohort (Verdi et al., 2019). Twin pairs were date matched and the final dataset of 1575 samples comprised 423 monozygotic (MZ) twin pairs (nindividuals = 846), 257 dizygotic (DZ) twin pairs (nindividuals = 514), and 215 singletons (Figure 1—figure supplement 2). The age range of the XCI cohort is 19–99, with a median age of 61 (Figure 2A). All samples and information were collected with written and signed informed consent, including consent to publish within the TwinsUK study. TwinsUK has received ethical approval associated with TwinsUK Biobank (19/NW/0187), TwinsUK (EC04/015) or Healthy Ageing Twin Study (HATS) (07 /H0802/84) studies from NHS Research Ethics Service Committees London – Westminster. Human Androgen Receptor Assay (HUMARA) The HUMARA method combines methylation-sensitive restriction enzyme digest and amplification of a highly polymorphic (CAG)n repeat in the first exon of the X-linked AR gene, allowing for the differentiation of the active and inactive chromosomes in heterozygous individuals (Cutler Allen et al., 1992). Here, 625 ng of genomic DNA was divided into three aliquots and incubated for 30 min at 37 °C with (i) the methylation-sensitive enzyme HpaII, (ii) the methylation-insensitive enzyme MspI, or (iii) water (mock digest) in 1 × New England Biolabs CutSmart Buffer. The HpaII digest was followed by an additional 20 min at 80 °C to avoid residual enzymatic activity. Fluorescently labelled PCR primers (FAM, VIC, NED, or PET; Forward primer 5’-dye-GCTGTGAAGGTTGCTGTTCCTCAT-3’, Reverse primer 5’-TCCAGAATCTGTTCCAGAGCGTGC-3’) were used in New England BioLabs One Taq Master Mix to amplify 1.5 μl of digested PCR product. The Mock and HpaII digested DNA were amplified in triplicate (using FAM, VIC, and NED), and the MspI digest, used as control of digestion efficiency, was amplified once (using PET). All PCRs were amplified with an initial denaturation step at 94 °C for 5 min, followed by 30 cycles of 94 °C for 30 s, 60 °C for 1 min, and 72 °C for 2 min, and a final elongation step of 72 °C for 15 min. To minimize technical bias and batch effects, the labelled amplified products were diluted 1:15 with nuclease-free ddH2O and pooled together with the GeneScan 500 LIZ size standard and analysed on an ABI 3730xl. Twin pairs were assayed on the same plate and plates contained a mix of both MZ and DZ pairs. Two replicates were included on each plate and a within-plate correlation of 0.99 was measured. A total of 2382 DNA samples were assayed, with 194 failed samples, and 601 samples were homozygous for the CAG repeat and were therefore uninformative (Figure 1—figure supplement 2). Calculation of XCI Data were analysed using the Microsatellite Analysis Software available on the Thermo Fisher Cloud. The XCI status was calculated in each of the triplicates as follows: Allele Ratio Mock Digestion (Rm)=allele 1 peak height / allele 2 peak height Allele Ratio HpaII Digestion (Rh)=allele 1 peak height / allele 2 peak height Normalized Ratio (Rn)=Rh/Rm XCI percentage = [Rn/(Rn +1)] * 100 A coefficient of variation (CV) was calculated across the triplicates and samples with CV >0.15 were excluded from downstream analysis (n=12; Figure 1—figure supplement 2). A mean XCI percentage (0–100%) was calculated for each sample, where 50% is perfectly balanced XCI and the directionality of XCI away from 50% is uninformative (e.g., both 0% and 100% are considered equal). Therefore, the XCI values are collapsed to a range of 50–100% when XCI is the dependent variable in analyses. XCI-skew categorical variable The XCI percentage data were normalised, and a categorical XCI-skew variable was created from the absolute values of the normalised distribution as follows: standard deviation (SD)<1 from the mean = random XCI (0); 1≤SD<2 = skewed XCI (1); and SD≥2 = extreme skew (2). As such, XCI-skew equated to ≥75% XCI, and extreme XCI-skew equated to ≥91% XCI (Figure 1—figure supplement 1). These thresholds are very similar to previous studies (Busque et al., 1996; Gale et al., 1997), and allowed for linear associations to be tested using the XCI-skew categorical variable. Statistical analysis In all regression models, a linear mixed effects model was used with relatedness and family structure fitted with a random intercept using the lme4 package (Bates et al., 2015). The relevant fixed effects are described in each section below and were specific to each test. All analyses were carried out using R version 4.1.1, and all plots were generated using ggplot (Wickham, 2016). Chronological and biological ageing Datasets were matched to be within 1 year of the XCI DNA sample and the significance threshold after Bonferroni correction was p<0.007 to account for multiple testing across the seven tests in this section. Chronological age was calculated at time of DNA sampling and the association was tested using XCI as the dependent variable. Body Mass Index (BMI) measures were taken during clinical visits and obesity was defined as BMI ≥30. Smoking status was classified based on longitudinal questionnaire answers (Christiansen et al., 2021). The associations were tested with XCI as the dependent variable and obesity (obese/not obese) or smoking (ever/never smoker) as the independent variable, controlling for age as a fixed effect. A Frailty Index (Searle et al., 2008) was calculated based on longitudinal questionnaire data and used as the dependent variable, with XCI-skew as the independent variable and age and BMI as fixed effects. Leukocyte Telomere Length was measured using qPCR as previously described (Codd et al., 2010), and the normalised measures were used as the dependent variable and XCI-skew as the independent variable, with age and smoking as fixed effects. DNA methylation (DNAm) GrimAge was calculated using 450 K methylation data and GrimAge epigenetic age acceleration measures were obtained from regressing epigenetic age on chronological age (Costeira et al., 2021). GrimAge Acceleration was used as the dependent variable and XCI-skew as the independent variable, with no additional covariates included in the model. Whole blood count data Automated whole blood count data were date-matched to the XCI DNA sample, and each of the 10 blood count variables was normalised. The significance threshold after Bonferroni correction was p<0.005 to account for multiple testing across the 10 tests. In addition, Monocyte-to-Lymphocyte Ratio (MLR) and Neutrophil-to-Lymphocyte Ratio (NLR) were calculated by dividing the total monocyte or neutrophil count, respectively, by total lymphocyte count, and a Bonferroni-corrected significance threshold of P<0.025. Associations were tested with XCI-skew as an independent variable after controlling for age, BMI, seasonality, and smoking status as fixed effects in a linear mixed effects model. Cytokines levels and C-reactive protein Serum IL-1β, IL-10, IL-6, and TNF were measured simultaneously using the bead-based high sensitivity human cytokine kit (HSCYTO-60SK, Linco-Millipore) according to the manufacturers’ instructions. CRP concentrations from serum were measured with the Human Cardiovascular Disease Panel 2 LINCOplex Kit (HCVD2-67BK, Linco-Millipore) and with the Extracellular Protein Buffer Reagent Kit (LHB0001, Invitrogen). CRP concentrations were diluted 1:2000 prior to analysis and assayed in duplicate, as previously described (Ligthart et al., 2018). Date-matched data with batch effects regressed out were normalised and associations were assessed using linear mixed models controlling for seasonality and age as fixed effects. The significance threshold after Bonferroni correction was p<0.01 to account for multiple testing across five tests. Atherosclerosis and cardiovascular disease (ASCVD) risk score The ASCVD risk score was calculated for a subset of 228 individuals with date-matched data on age, total cholesterol, HDL cholesterol, smoking, diabetes, systolic blood pressure, and hypertension medication, as previously described (Goff et al., 2014). A linear mixed effects model was used to control for BMI and monocyte abundance as fixed effects (Madjid et al., 2004). Twin pairs discordant for XCI-skew but matched for date of visit and age (n=34 pairs) were used for the intra-twin study, and ASCVD risk scores were compared using a one-sided paired samples Wilcoxon test. Cancer and all-cause mortality Anonymised data were obtained from the National Disease Registration Service. Study entry was the date of DNA sampling and follow-up occurred through to January 2020 (study end date). For the cancer analysis, individuals who had not experienced the event were censored at 10 years, study end date, or date-of-death. All participants with a history of cancer before sampling, or within 6 months of sampling, were excluded from analyses, and reports of non-melanoma skin cancer were filtered, leaving a sample size of 1417. For all-cause mortality, individuals who had not experienced the event were censored at 10 years or study end date. For both analyses, age, relatedness, and zygosity were controlled for in the Cox regression model using R package Survival (Therneau, 2021). Proportional hazards assumptions were assessed using the cox.zph function of the Survival package. Kaplan-Meier plots were used for graphical representation of years until diagnosis. XCI-skew and extreme XCI-skew groups were combined due to the limited number of events. Results XCI-skew in the TwinsUK population cohort We measured XCI in DNA derived from whole blood using the methylation-sensitive PCR-based Human Androgen Receptor Assay (HUMARA), which differentiates between alleles from the active and inactive X (Cutler Allen et al., 1992; Hatakeyama et al., 2004). HUMARA is an extensively used assay which correlates well with transcription-based methods (Bolduc et al., 2008; Zito et al., 2019). The output of the HUMARA assay is a continuous XCI variable from 0–100%, where 50% is perfectly balanced XCI and the directionality of XCI away from 50% is uninformative (e.g., both 0% and 100% are considered equal). We normalised the distribution of the continuous XCI values across the cohort, and defined XCI-skew as measures 1 SD from the mean, corresponding to XCI score ≥75%, and extreme XCI-skew as measures 2 SD from the mean, corresponding to XCI score ≥91% (Figure 1—figure supplement 1). Of note, these values are extremely similar to thresholds previously used in the literature (Bolduc et al., 2008; Busque and Gilliland, 1998). Figure 1 shows representative samples of how the HUMARA assay relates to the three categorical XCI-skew statuses. Figure 1 with 2 supplements see all Download asset Open asset Measuring XCI with the HUMARA assay. The Human Androgen Receptor Assay (HUMARA) uses methylation-sensitive restriction enzyme digest and PCR to measure skewed X-inactivation. The assay estimates XCI-skew by comparing the relative abundance of allele specific fragments from a mock digest to a methylation-sensitive HpaII digest in which only the alleles from the inactive X are amplified. Representative examples are displayed of fragment analysis of the PCR products for samples with random XCI (top), skewed XCI (middle), and extreme skewed XCI (bottom). The x-axis shows the size, and the y-axis represents the abundance, of the PCR products, respectively. The left panel shows the PCR products after a mock digest with water, resulting in amplification of both alleles regardless of chromosomal inactivation. The right panel shows the PCR products after a restriction enzyme digest with methylation-sensitive enzyme HpaII, resulting in amplification of only the alleles deriving from inactive chromosomes. For each sample, the ratio of the HpaII digested allele products (Rh = allele 1/allele 2) is divided by the ratio of the Mock digest allele products (Rm = allele 1/allele 2) to create a Normalized Ratio (Rn). The XCI percentage is then calculated using the formula [Rn/(Rn +1)] * 100. Images were generated using the Microsatellite Analysis Software on the Thermo Fisher Cloud. We defined XCI-skew in 1575 participants (median age = 61; Figure 2A) unselected for chronic disease status from the TwinsUK population cohort (Verdi et al., 2019), which comprised of 423 MZ pairs, 257 DZ pairs, and 215 singletons. In line with previous studies (Vickers et al., 2001), we see increased concordance of XCI-skew within MZ twin pairs compared with DZ twin pairs: 27% of MZ twin pairs (114/423), and 45.5% of DZ twin pairs (117/257), were discordant for their categorical XCI-skew status. We date-matched the XCI data with existing phenotypes from TwinsUK (e.g., blood count data, molecular markers) in the subsets of individuals on whom each phenotype was available (Figure 1—figure supplement 2). Figure 2 Download asset Open asset Age acquired XCI-skew across age groups and time. (A) A histogram displaying the age distribution of the TwinsUK HUMARA cohort (age range: 19–99; median age = 61). (B) The proportions of individuals (y-axis) in each of three XCI-skew categories across increasing age groups (x-axis) are shown (N=1575). (C) A Sankey plot shows the longitudinal changes to XCI in 31 individuals across two measures 15–17 years apart. Colours indicate XCI at visit 1, axis 1 displays the age group of individuals at visit 1, and axis 2 displays XCI at visit 2. Figure 2—source data 1 XCI-skew across age groups. Categorical variable of XCI-skew and corresponding age group of each individual in the study (n=1575). https://cdn.elifesciences.org/articles/78263/elife-78263-fig2-data1-v1.txt Download elife-78263-fig2-data1-v1.txt Cross-sectional and longitudinal changes to XCI-skew with age We assessed changes in frequency of XCI-skew across increasing age groups and identified 12% (9 of 75) of individuals under 40 years old (yrs) displaying XCI-skew (≥75% XCI); 28% (183 of 652) of 40–59 yrs; 37% (185 of 498) of 60–69 yrs; and 44% (132 of 303) of those over 70 yrs (Figure 2B). Proportions of individuals displaying extreme XCI-skew (≥91% XCI) remains consistent at ~3–4% below the age of 60 but increases to 7% of 60–69 yrs and 9% of those over 70 yrs. These results suggest a stepwise increase in prevalence of XCI-skew happening after 40 years of age, then again after 60 years of age, where we also see the first increase in prevalence of extreme XCI-skew (Figure 2B). As expected, after controlling for relatedness and zygosity, we find a significant positive association between age and XCI skewing (p=2.8 × 10–9, N=1575). This result replicates the many existing studies on age acquired XCI-skew and acts as a validation of the TwinsUK HUMARA dataset (Busque et al., 1996; Gale et al., 1997; Hatakeyama et al., 2004; Zito et al., 2019). We assessed change in XCI-skew over time using 31 individuals on whom we had an additional second sample available from 15 to 17 years prior to the main study (Figure 2C. median age at visit 1=55.5; median age at visit 2=72.1). The two individuals who had extreme XCI-skew at visit 1 still displayed extreme XCI-skew at visit 2. Of the eight individuals who had XCI-skew at visit 1, seven remained skewed and one progressed to extreme XCI-skew at visit 2. Of the 21 who had a random pattern of XCI at visit 1, 15 (71.4%) remained the same, and 6 (28.6%) progressed to XCI-skew at visit 2. These longitudinal data indicate that XCI-skew categorisation persists over extended periods of time and increases over the life course. XCI-skew is independent of known markers of biological ageing Ageing is a heterogenous process in which an individual’s biological age can differ from their chronological age. Smoking and obesity are risk factors for accelerated ageing (Tam et al., 2020). Accelerated ageing can be estimated through measures of frailty (Clegg et al., 2013; Chen et al., 2014), and on a molecular level using measures of leukocyte telomere length shortening (Blackburn et al., 2006) and epigenetic ageing clocks such as DNA methylation (DNAm) GrimAge (Lu et al., 2019); all these measures are associated with adverse health outcomes (Blackburn et al., 2006; Hewitt et al., 2020; Lu et al., 2019). Given the robust association with chronological age, we sought to establish whether XCI-skew was associated with biological ageing using measures taken within 1 year of the XCI DNA sample and using linear regression mixed effects models, controlling for relatedness and zygosity as random effects (Figure 3). We observed no association with smoking status (p=0.33, Nnever_smoker=879; Never_smoker = 673; Figure 3A), nor obesity (p=0.88, Nnot_obese=726; Nobese = 165; Figure 3B), after correcting for age. We also found no association with a robust frailty index (p=0.59, Nranodm XCI=398; nSkewed XCI = 177; Nextreme skew = 36; Figure 3C) after correcting for age and BMI, nor with leukocyte telomere length shortening (p=0.9, Nranodm XCI=278; nSkewed XCI = 103; Nextreme skew = 16; Figure 3D), after correcting for age and smoking status. Finally, we see no association with DNAm GrimAge acceleration (p=0.22, Nranodm XCI=101; nSkewed XCI = 30; Nextreme skew = 6; Figure 3E), however, we believe the relationship between XCI-skew and epigenetic ageing would benefit from a follow-up study with a larger sample size particularly given the limited number of samples with extreme XCI-skew in our analysis. Together, these data, ranging from the molecular to organismal level, suggest age acquired XCI-skew is independent of many known markers of biological ageing and is potentially a unique biomarker with unexplored utility. Figure 3 with 1 supplement see all Download asset Open asset Age acquired XCI-skew and markers of biological ageing and blood cell counts. Box plots representing the results of the linear regression mixed effects models to assess (A) Smoking status (p=0.33, Nnever_smoker = 879; Never_smoker = 673) and (B) obesity (p=0.88, Nnot_obese = 726; Nobese = 165) after correcting for age with XCI as the dependent variable, and (C) frailty index (p=0.59, Nranodm XCI = 398; nSkewed XCI = 177; Nextreme skew = 36), after correcting for age and BMI, (D) Leukocyte telomere length shortening (p=0.9, Nranodm XCI = 278; nSkewed XCI = 103; Nextreme skew = 16) after correcting for age and smoking status, and (E) DNAm GrimAge acceleration (p=0.22, Nranodm XCI = 101; nSkewed XCI = 30; Nextreme skew = 6), with XCI-skew as the dependent variable. All boxplots display the median and IQR, and have the residuals of the models on the y-axis. (F) A forest plot of associations with data-matched Complete Blood Count data (top panel) and Myeloid-to-lymphoid ratios (bottom) with effect size and lower and upper confidence intervals indicated. Associations were tested with XCI-skew as an independent variable after controlling for age, BMI, seasonality, and smoking status as fixed effects in a linear mixed effects model (Nranodm XCI = 445; nSkewed XCI = 183; Nextreme skew = 43). The significance threshold after Bonferroni correction was p<0.005 and p<0.023 to account for multiple testing across the 10 tests and 2 tests, respectively. Figure 3—source data 1 XCI-skew and DNAm GrimAge Acceleration. Normalised DNAm GrimAge Acceleration variable and categorical XCI-skew data (n=137). https://cdn.elifesciences.org/articles/78263/elife-78263-fig3-data1-v1.txt Download elife-78263-fig3-data1-v1.txt XCI-skew is associated with increased monocyte abundance and decreased IL-10 levels Changes in blood cell composition can be indicative of ill health or systemic inflammation (Kabat et al., 2017; Madjid et al., 2004; Patel et al., 2009), and a haematopoietic stem cell bias towards the myeloid lineage is observed with ageing (Pang et al., 2011). We tested for associations between XCI-skew and whole blood count data, including white cell differentials, in a subset of individuals with matched data (Nranodm XCI = 445; nSkewed XCI = 183; Nextreme skew = 43, median age = 63). After controlling for age, seasonality, BMI, smoking, relatedness and zygosity in a linear regression mixed effects model, XCI-skew is association with increased monocyte abundance after multiple testing correction (p=0.0038), and we observed nominal increases in abundance across other myeloid cells (Figure 3F). We next tested the hypothesis that XCI-skew was associated with a myeloid lineage bias using the Monocyte-to-Lymphocyte Ratio (MLR) (Chen et al., 2019) and Neutrophil-to-Lymphocyte Ratio (NLR) (Arbel et al., 2012), and detect an association between XCI-skew and MLR (p=0.019), and a nominal association with NLR (p=0.042) (Figure 3F). Though myeloid cells show a greater degree of skewing, thought to be due to the shorter lifespan of these cells (Gale et al., 1997), we do not believe the association seen here between XCI-skew and increasing monocyte numbers is causal: XCI-skew is defined as ≥25% shift in cell mosaicism, whereas monocytes account for only ~10% of white blood cells. ‘Inflammageing’ is the chronic pro-inflammatory phenotype observed in ageing and is considered an altered state of intercellular communication (López-Otín et al., 2013). Markers of inflammageing include cytokines produced by immune cells and C-reactive protein (CRP) produced by liver cells (Salminen et al., 2012). We date-matched the XCI data with serum levels of CRP (Nranodm XCI = 121; nSkewed XCI = 38; Nextreme skew = 6) and a more modest cytokine dataset of interleukin (IL)–6, IL-1B, IL-10, and TNF (Nranodm XCI = 23; nSkewed XCI = 4) and used linear regression mixed effects models to control for age, seasonality, relatedness and zygosity (Figure 3—figure supplement 1). We see no association with primary markers of inflammageing CRP (p=0.41), IL-6 (p=0.41), or TNF (p=0.61), though a nominal association with IL-1β is observed (p=0.02) which does not pass multiple correction, but warrants follow up analysis with a larger sample size. However, we observe a strong negative association with IL-10 (p=0.0008, Figure 3—figure supplement 1). IL-10 is a broadly expressed anti-inflammatory cytokine which can inhibit the proinflammatory responses of both innate and adaptive immune cells (Saraiva and O’Garra, 2010). Though we note that due to minimal overlap in the datasets, we were unable to control for cell
Sleep reduction is associated with increased energy intake and weight gain, though few studies have explored the relationship between sleep architecture and energy balance measures in the context of experimental sleep restriction. Fourteen males and 13 females (body mass index: 22–26 kg/m 2 ) participated in a crossover sleep curtailment study. Participants were studied under two sleep conditions: short (4 h/night; 0100–0500 h) and habitual (9 h/night; 2200–0700 h), for 5 nights each. Sleep was polysomnographically recorded nightly. Outcome measures included resting metabolic rate (RMR), feelings of appetite-satiety, and ad libitum food intake. Short sleep resulted in reductions in stage 2 sleep and rapid eye movement (REM) sleep duration ( P < 0.001), as well as decreased percentage of stage 2 sleep and REM sleep and increased slow wave sleep (SWS) percentage ( P < 0.05). Linear mixed model analysis demonstrated a positive association between stage 2 sleep duration and RMR ( P = 0.051). Inverse associations were observed between REM sleep duration and hunger ( P = 0.031) and between stage 2 sleep duration and appetite for sweet ( P = 0.015) and salty ( P = 0.046) foods. Stage 2 sleep percentage was inversely related to energy consumed ( P = 0.024). Stage 2 sleep ( P = 0.005), SWS ( P = 0.008), and REM sleep ( P = 0.048) percentages were inversely related to fat intake, and SWS ( P = 0.040) and REM sleep ( P = 0.050) were inversely related to carbohydrate intake. This study demonstrates that changes in sleep architecture are associated with markers of positive energy balance and indicate a means by which exposure to short sleep duration and/or an altered sleep architecture profile may lead to excess weight gain over time.
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