mt DNA heteroplasmy level and copy number indicate disease burden in m.3243A>G mitochondrial disease

Research Article7 May 2018Open Access Transparent process mtDNA heteroplasmy level and copy number indicate disease burden in m.3243A>G mitochondrial disease John P Grady John P Grady Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Sarah J Pickett Corresponding Author Sarah J Pickett [email protected] orcid.org/0000-0002-1242-2927 Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Yi Shiau Ng Yi Shiau Ng Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Charlotte L Alston Charlotte L Alston Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK NHS Highly Specialised Mitochondrial Diagnostic Laboratory, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK Search for more papers by this author Emma L Blakely Emma L Blakely Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK NHS Highly Specialised Mitochondrial Diagnostic Laboratory, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK Search for more papers by this author Steven A Hardy Steven A Hardy Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK NHS Highly Specialised Mitochondrial Diagnostic Laboratory, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK Search for more papers by this author Catherine L Feeney Catherine L Feeney Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Alexandra A Bright Alexandra A Bright Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Andrew M Schaefer Andrew M Schaefer Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Gráinne S Gorman Gráinne S Gorman Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Richard JQ McNally Richard JQ McNally Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Robert W Taylor Robert W Taylor Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK NHS Highly Specialised Mitochondrial Diagnostic Laboratory, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK Search for more papers by this author Doug M Turnbull Doug M Turnbull Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Robert McFarland Corresponding Author Robert McFarland [email protected] orcid.org/0000-0002-8833-2688 Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author John P Grady John P Grady Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Sarah J Pickett Corresponding Author Sarah J Pickett [email protected] orcid.org/0000-0002-1242-2927 Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Yi Shiau Ng Yi Shiau Ng Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Charlotte L Alston Charlotte L Alston Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK NHS Highly Specialised Mitochondrial Diagnostic Laboratory, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK Search for more papers by this author Emma L Blakely Emma L Blakely Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK NHS Highly Specialised Mitochondrial Diagnostic Laboratory, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK Search for more papers by this author Steven A Hardy Steven A Hardy Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK NHS Highly Specialised Mitochondrial Diagnostic Laboratory, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK Search for more papers by this author Catherine L Feeney Catherine L Feeney Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Alexandra A Bright Alexandra A Bright Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Andrew M Schaefer Andrew M Schaefer Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Gráinne S Gorman Gráinne S Gorman Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Richard JQ McNally Richard JQ McNally Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Robert W Taylor Robert W Taylor Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK NHS Highly Specialised Mitochondrial Diagnostic Laboratory, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK Search for more papers by this author Doug M Turnbull Doug M Turnbull Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Robert McFarland Corresponding Author Robert McFarland [email protected] orcid.org/0000-0002-8833-2688 Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK Search for more papers by this author Author Information John P Grady1,4,‡, Sarah J Pickett *,1,‡, Yi Shiau Ng1, Charlotte L Alston1,2, Emma L Blakely1,2, Steven A Hardy1,2, Catherine L Feeney1, Alexandra A Bright1, Andrew M Schaefer1, Gráinne S Gorman1, Richard JQ McNally3, Robert W Taylor1,2, Doug M Turnbull1 and Robert McFarland *,1 1Wellcome Centre for Mitochondrial Research, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK 2NHS Highly Specialised Mitochondrial Diagnostic Laboratory, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK 3Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK 4Present address: Kinghorn Centre for Clinical Genomics, Garvan Institute, Sydney, NSW, Australia ‡These authors contributed equally to this work *Corresponding author. Tel: +44 191 2085397; E-mail: [email protected] *Corresponding author. Tel: +44 191 2820340; E-mail: [email protected] EMBO Mol Med (2018)10:e8262https://doi.org/10.15252/emmm.201708262 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Mitochondrial disease associated with the pathogenic m.3243A>G variant is a common, clinically heterogeneous, neurogenetic disorder. Using multiple linear regression and linear mixed modelling, we evaluated which commonly assayed tissue (blood N = 231, urine N = 235, skeletal muscle N = 77) represents the m.3243A>G mutation load and mitochondrial DNA (mtDNA) copy number most strongly associated with disease burden and progression. m.3243A>G levels are correlated in blood, muscle and urine (R2 = 0.61–0.73). Blood heteroplasmy declines by ~2.3%/year; we have extended previously published methodology to adjust for age. In urine, males have higher mtDNA copy number and ~20% higher m.3243A>G mutation load; we present formulas to adjust for this. Blood is the most highly correlated mutation measure for disease burden and progression in m.3243A>G-harbouring individuals; increasing age and heteroplasmy contribute (R2 = 0.27, P < 0.001). In muscle, heteroplasmy, age and mtDNA copy number explain a higher proportion of variability in disease burden (R2 = 0.40, P < 0.001), although activity level and disease severity are likely to affect copy number. Whilst our data indicate that age-corrected blood m.3243A>G heteroplasmy is the most convenient and reliable measure for routine clinical assessment, additional factors such as mtDNA copy number may also influence disease severity. Synopsis The m.3243A>G pathogenic mtDNA variant is associated with a highly heterogeneous multisystem disorder and varying mutation levels across tissues. In this study, mutation levels were characterised in three commonly sampled tissues - blood, urine, skeletal muscle - and correlated with disease burden. Urine m.3243A>G heteroplasmy levels display more variability than blood levels and must be corrected for a ˜20% lower level in females. Blood m.3243A>G heteroplasmy levels must be corrected for a decline of ˜2.3% per year. Disease burden and progression are more strongly associated with blood m.3243A>G heteroplasmy levels than urine levels. 27% of the variance in disease burden can be attributed to blood m.3243A>G heteroplasmy and age. Age, m.3243A>G heteroplasmy level and mtDNA copy number in skeletal muscle explain 40% of the variance in disease burden. Introduction The pathogenic mitochondrial DNA (mtDNA) m.3243A>G variant in the MT-TL1 gene (encoding mt-tRNALeu(UUR)) is the most common heteroplasmic mtDNA disease genotype (Goto et al, 1990; Elliott et al, 2008; Gorman et al, 2015, 2016). Disease burden and progression vary greatly between individuals, and the associated clinical spectrum is broad (Nesbitt et al, 2013). The cause of this heterogeneity is poorly understood; consequently, disease burden and long-term prognosis are very difficult to predict (Chinnery et al, 1997; Kaufmann et al, 2011; Mancuso et al, 2014; Weiduschat et al, 2014; Fayssoil et al, 2017). In individuals harbouring m.3243A>G, mutant and wild-type mtDNA molecules coexist within the same cell, a situation termed heteroplasmy. Tissue segregation patterns of m.3243A>G vary widely; post-mitotic tissues such as muscle have higher levels of mutant mtDNA, whilst levels in blood decrease significantly over time (Sue et al, 1998; Rahman et al, 2001; Pyle et al, 2007; Rajasimha et al, 2008; Mehrazin et al, 2009; de Laat et al, 2012). Although patients with very severe disease tend to have a high proportion of mutant mtDNA, the relationship between heteroplasmy level and clinical phenotype is not straightforward and such intra-individual variation between tissues adds to the uncertainty (Chinnery et al, 1997; Fayssoil et al, 2017). We aimed to determine which putative prognostic factors are most associated with m.3243A>G-related mitochondrial disease burden and progression. To answer this question, we characterised the levels of heteroplasmy and mtDNA copy number in three commonly sampled tissues: blood, urine and skeletal muscle, in 242 m.3243A>G carriers (including 195 symptomatic patients) from the MRC Mitochondrial Disease Patient Cohort UK. Using multiple linear regression, we ascertained which m.3243A>G heteroplasmy measure and mtDNA copy number were the most associated with total disease burden, as determined by the Newcastle Mitochondrial Disease Adult Scale (NMDAS) (Schaefer et al, 2006). We used linear mixed modelling to investigate disease progression. Results m.3243A>G heteroplasmy measures in all three tissues are correlated In the first instance, we sought to characterise the three measurements of heteroplasmy; all are significantly correlated. The strongest relationship is between blood and urine heteroplasmy levels (Fig 1A; R2 = 0.73, P < 0.001; interaction with age included for a linear fit, P < 0.001), followed by muscle and blood heteroplasmy levels (Fig 1B; R2 = 0.64, P < 0.001, interaction with age included, P = 0.029). Finally, urine and muscle levels are correlated (Fig 1C; R2 = 0.61, P < 0.001, sex term included, see below). Urine levels are significantly lower than muscle (slope = 0.75, 95% CI = 0.60–0.91, P < 0.001). Figure 1. Correlations between heteroplasmy measurements and their relationship with age Urine and blood heteroplasmy correlation (N = 224, R2 = 0.73, P < 0.001; interaction with age included, P < 0.001) Muscle and blood heteroplasmy correlation (N = 74, R2 = 0.64, P < 0.001; interaction with age included, P = 0.029). Muscle and urine heteroplasmy correlation, showing different intercepts for males and females (N = 75, R2 = 0.61, P < 0.001). The relationship between age and muscle heteroplasmy levels (N = 77, R2 = 0.0, P = 0.491). The relationship between age and blood heteroplasmy levels (N = 231, R2 = 0.32, slope = −0.56, 95% CI = −0.67, −0.45, P < 0.001). The relationship between age and urine heteroplasmy levels (N = 235, R2 = 0.22, slope = −0.42, 95% CI = −0.62, −0.22, P < 0.001). Data information: Points represent mean heteroplasmy level for each individual, and linear regression lines are shown with 95% confidence intervals. Download figure Download PowerPoint Blood and urine m.3243A>G heteroplasmy levels are negatively correlated with age The correlation between muscle and blood heteroplasmy (Fig 1A) shows that younger individuals tend to have higher blood heteroplasmy levels, including age in the model increased R2 from 0.38 to 0.64 (P < 0.001), consistent with previous reports of a decline in blood levels with age (Rahman et al, 2001; Pyle et al, 2007; Rajasimha et al, 2008). We therefore examined the relationship of all tissue heteroplasmy measures with age; both blood and urine heteroplasmy levels show significant negative correlations, although this is greater for blood than urine. Muscle heteroplasmy is not correlated with age (Fig 1D–F). Blood m.3243A>G heteroplasmy levels decline with time To evaluate reports of an exponential decline in blood m.3243A>G heteroplasmy level, we examined levels in all patients with multiple measurements (Fig 2). Blood levels have declined in the majority of patients observed for 4 years or more but this is not universal and some individuals appear to reach a terminal nonzero plateau of heteroplasmy; therefore, decline is not universally exponential. The rate of heteroplasmy change in blood is not associated with disease burden (P = 0.977), and individuals whose level increased or remained stationary had a similar disease burden to those whose levels decreased. Figure 2. Decline of blood m.3243A>G heteroplasmy levelLongitudinal blood heteroplasmy levels in individuals with multiple measurements (N = 96). Each point represents one heteroplasmy measurement; points joined by a line representing individual patients. Patients observed over 4 years or more are highlighted in blue for those showing a decline (N = 21) or red for those who show no change or an increase (N = 14). Download figure Download PowerPoint We regressed the rate of heteroplasmy change against initial mutation level, estimating a continuous decline of −0.0185/year (95% CI = −0.0421 to 0.0052), consistent with previous methodology (Rajasimha et al, 2008). However, the model is a poor fit to our data (R2 = 0.06, P = 0.120); this is improved if age at first measurement is included (R2 = 0.42, P = 0.002), with older individuals having a lower rate of decline. Blood m.3243A>G heteroplasmy levels must be adjusted for age To understand the decline in blood m.3243A>G levels across the whole cohort, we modelled the decline in blood heteroplasmy using adjusted urine heteroplasmy levels as an estimate of initial mutation level (N = 204, see below) and validated our model using muscle heteroplasmy levels. We propose the following formula for age correction of blood heteroplasmy levels which represents a compound decline of ~2.3% a year with an added age adjuster to account for a rapid reduction in mutation load in early life (Appendix Supplementary Method 2). (1) Mean age-adjusted blood heteroplasmy is significantly correlated with mean muscle heteroplasmy (Fig 3A). This is an improvement on unadjusted blood levels (R2 = 0.48 compared to 0.38), and the two levels can be more easily compared (slope = 1.03, 95% CI = 0.78–1.28). Adjusted levels are also correlated with urine heteroplasmy (R2 = 0.60, P < 0.001, N = 224) and sex-adjusted urine heteroplasmy (R2 = 0.64, P < 0.001, N = 224). There is a relationship between age-adjusted blood heteroplasmy and age (Fig 3B), but this is weaker and only seen in females. Figure 3. Sex-adjusted urine and age-adjusted blood m.3243A>G heteroplasmy levels Regression of age-adjusted blood heteroplasmy against muscle heteroplasmy (N = 74, R2 = 0.48, P < 0.001). Regression of age-adjusted blood heteroplasmy against age (N = 231, R2 = 0.06, P < 0.001) showing an interaction with sex (P = 0.022). Regression of sex-adjusted urine heteroplasmy against muscle heteroplasmy (N = 75, R2 = 0.55, P < 0.001). Regression of sex-adjusted urine heteroplasmy against age (N = 235, R2 = 0.07, slope = −0.50, 95% CI = −0.73, −0.27, P < 0.001). Data information: Linear regression lines and 95% confidence intervals are shown. Download figure Download PowerPoint Urine m.3243A>G heteroplasmy levels are dependent on sex The correlation between urine and muscle heteroplasmy level shows a clear effect of sex; males have, on average, 19.2% higher urine heteroplasmy (Fig 1C; 95% CI = 12.6–25.8%). Including sex in the model increases R2 from 0.43 to 0.61 (P < 0.001). Consistent with this, a similar effect size is seen in Fig 1F; (male levels are 18.4% higher, 95% CI = 12.7–24.1%, P < 0.001). No relationship with sex is seen for blood (P = 0.288) or muscle heteroplasmy levels (P = 0.245). Urine m.3243A>G heteroplasmy levels must be adjusted for sex Given the ~20% difference in m.3243A>G heteroplasmy levels between males and females, we used the relationship between urine and muscle levels to derive a method to adjust urine levels for sex (Appendix Supplementary Method 1). Summary of transformation: Male adjusted urine level = logit−1((logit (Urine heteroplasmy)/0.791)−0.625) Female adjusted urine level = logit−1((logit (Urine heteroplasmy)/0.791)+0.608) The adjusted levels are significantly correlated with muscle levels (Fig 3C), an improvement on the correlation between uncorrected urine and muscle levels (R2 = 0.55 compared to 0.43; sex not included models). There is still a relationship with age, of a similar magnitude to that seen in Fig 1F (Fig 3D). For 22 individuals, m.3243A>G mutation load in muscle is divergent from urine levels by > 20%; if adjusted urine levels are used, this drops to 12, and if adjusted blood levels are also taken into account, there are only five. Urine m.3243A>G heteroplasmy measures have thehighest variability We were interested in the intra-individual variability of measurements. Urine heteroplasmy levels appear to have much greater variability than the other tissues; levels in a number of individuals differ by 20–33%, and one individual shows a 55% change. In contrast, the biggest change in blood is 15%. To quantify the variability, we calculated the coefficient of variation (CV) for each individual with at least three repeated measurements, allowing us to compare the variability across all tissues (excluding muscle where N = 1). Age-adjusted blood shows the smallest variability (N = 24, mean CV = 0.091, 95% CI = 0.051–0.131), followed by unadjusted blood (N = 24, mean CV = 0.128, 95% CI = 0.084–0.171), although the difference is not significant (P = 0.157). The highest variability is seen in urine (N = 39, mean CV = 0.189, 95% CI = 0.148–0.230) and sex-adjusted urine (N = 39, mean CV = 0.213, 95% CI = 0.166, 0.259), which are significantly different from age-adjusted blood levels (both P < 0.001) and blood levels (Purine = 0.034, Psex-adjusted urine = 0.015). Total disease burden is most strongly associated with blood heteroplasmy level To determine which heteroplasmy measurement is most strongly associated with total disease burden, we performed separate linear regression using each heteroplasmy measure. All are significantly associated, although the total variation explained by heteroplasmy, age and sex is low (R2 range = 0.15–0.27; Table 1). Table 1. Comparisons of different m.3243A>G heteroplasmy measures in association with total disease burden Heteroplasmy measure R 2 P-value Larger cohort (N = 210) Blood 0.2744 < 0.001 Age-adjusted blood 0.2539 < 0.001 Urine 0.1809 < 0.001 Sex-adjusted urine 0.1989 < 0.001 Mean adjusted blood and urine 0.2461 < 0.001 Smaller cohort (N = 69) Blood 0.1547 0.001 Age-adjusted blood 0.1809 < 0.001 Urine 0.1963 < 0.001 Sex-adjusted urine 0.2005 < 0.001 Skeletal muscle 0.2024 < 0.001 Mean adjusted blood and urine 0.1955 < 0.001 Age-adjusted blood (Fig 4A) and unadjusted blood heteroplasmy levels are more highly associated with total disease burden than adjusted and unadjusted urine levels (Table 1). The difference between using these two measures is small, failing to reach significance (P = 0.173; Appendix Table S1); however, as age was also included in the model, this is not surprising and provides further validation of our method of adjustment. Sex-adjusted urine level is the next most associated factor and better than uncorrected urine levels (Table 1), although this difference is not significant (P = 0.166; Appendix Table S1A). A composite measure of adjusted blood and urine m.3243A>G levels explained no more of the variance than using age-adjusted blood levels alone. Interestingly, blood heteroplasmy is significantly more associated with total disease burden than urine heteroplasmy (P = 0.007) and this holds when both corrected measures are used (P = 0.036; Appendix Table S1A). Figure 4. Association of total disease burden and disease progression with age-adjusted blood heteroplasmyPoints are coloured according to mean age-adjusted blood heteroplasmy level. Dashed lines and shading represent scores and 95% confidence intervals predicted using a linear model (with the square root of NMDAS score as the dependent variable and age and age-adjusted blood heteroplasmy as independent variables) for individuals with 10% (blue), 50% (orange) and 90% (red) heteroplasmy levels. Association of total disease burden with age-adjusted blood heteroplasmy. Points represent mean NMDAS score per individual. Association of and disease progression with age-adjusted blood heteroplasmy. Each point represents one NMDAS assessment with points from each individual connected by a solid line (N = 210). Download figure Download PowerPoint In individuals with available muscle heteroplasmy data (Table 1), using blood or urine heteroplasmy levels are just as good as muscle (Appendix Table S1A). Disease progression rate is most strongly associated with age-adjusted blood heteroplasmy level We then asked whether heteroplasmy level is associated with disease progression; we used a linear mixed model to examine the role of heteroplasmy in the rate of NMDAS development. Disease progression is most strongly associated with age-adjusted blood levels, significantly more than urine (P = 0.010; Appendix Table S1B) and sex-adjusted urine levels (P = 0.037). Although the combination of adjusted blood and adjusted urine levels was marginally more associated than adjusted blood levels alone, this difference failed to reach significance (P = 0.624). Crucially, in the cohort with available muscle heteroplasmy data, muscle m.3243A>G levels were not more strongly associated with disease progression than age-adjusted blood (P = 0.189), sex-adjusted urine (P = 0.297), unadjusted urine levels (P = 0.320) or a combination of adjusted blood and urine levels (P = 0.696). Increasing age and the interaction between age and increasing age-adjusted blood heteroplasmy level are significantly positively associated with disease progression (P < 0.001); however, inter-individual variation is extremely high; the standard deviation of the variance due to individual is 2.35 (on NMDAS0.5 scale) compared to 0.04 for age. Although individuals with high heteroplasmy levels tend to have a higher disease burden and rate of progression and vice versa, there is a large spread in the data as well as considerable overlap (Fig 4B). Total mtDNA copy number is affected by m.3243A>G heteroplasmy level (in blood) and sex (in urine) As only 27% of the total disease burden can be explained by m.3243A>G heteroplasmy level and age, we reasoned that the absolute quantity of wild-type mtDNA could affect clinical phenotype. We assessed mtDNA copy number in all three available tissues and determined the relationship between mtDNA copy number, sex, m.3243A>G heteroplasmy level and age. Standardised mtDNA copy number was highest in muscle (median = 3523, IQR = 1708, range = 678–10,610, N = 66) and lowest in blood (median = 144, IQR = 81, range = 24–323, N = 197), whilst urine mtDNA copy number showed the greatest range (median = 1181, IQR = 2870, range = 77–24,730, N = 165). Blood mtDNA copy number shows a weak positive correlation with unadjusted (R2 = 0.03, P = 0.007) and age-adjusted (R2 = 0.02, P = 0.016) m.3243A>G heteroplasmy level (N = 197). We observed no association with sex (P = 0.088) or age (P = 0.163). Urine mtDNA copy number is significantly higher in males (R2 =0.12, P < 0.001, N = 176; medianmale = 3,733, IQRmale = 5,630, medianfemale = 881, IQRfemale = 1,468), which is unsurprising given the differences seen in m.3243A>G heteroplasmy level and is likely to be due to differences in cellular composition. No association with age (P = 0.628), unadjusted (P = 0.113) or sex-adjusted (P = 0.163) urine heteroplasmy was observed. Muscle mtDNA copy number shows a slight downward trend with age, although this does not reach significance (R2 = 0.04, P = 0.060, N = 67). No association with sex (P = 0.450) or muscle m.3243A>G heteroplasmy (P = 0.725) was detected. We saw no correlation between copy number in the three tissues studied (blood*-urine** P = 0.372, N = 148; blood*-muscle P = 0.641, N = 59; urine**-muscle P = 0.493, N = 52; *age-adjusted blood m.3243A>G heteroplasmy or **sex included in model). Low total mtDNA copy number in muscle is an indicator of disease burden To determine the effect of mtDNA copy number with disease burden, we performed separate linear regression models for each tissue, including age, m.3243A>G heteroplasmy level (assessed from the same sample as copy number) and sex (only for urine) as covariates. We found that higher skeletal muscle mtDNA copy number is significantly associated with reduced total disease burden in both single and multiple linear regression (PmtDNA copy number < 0.001; N = 66; Fig 5). We estimate the effect of an increase in mtDNA copy number of 100 is a decrease in NMDAS0.5 by 0.055 (95% CI = 0.079–0.031), which is similar to the effect of decreasing muscle m.3243A>G heteroplasmy by 1% (0.027, 95% CI = 0.008–0.046, P = 0.006). Indeed, mtDNA copy number, muscle heteroplasmy and age account for 40% of the variance in disease burden (P < 0.001); only 21% is explained when mtDNA copy number is not included. No significant associations were found for mtDNA copy number in either blood (Pblood = 0.551, Page-adjusted blood = 0.586; N = 197) or urine (Purine = 0.144, Psex-adjusted urine = 0.491; N = 165). The interaction between age and muscle mtDNA copy number is associated with disease progression (P = 0.0177, N = 63), but no association was seen for blood (P = 0.2491, N = 197) or urine (P = 0.1726, N = 159). Figure 5. Association of total disease burden with age, m.3243A>G heteroplasmy level and mtDNA copy number in skeletal muscleLines represent scores predicted using a linear model (with the square root of NMDAS score as the dependent variable and age, skeletal muscle heteroplasmy and mtDNA copy number as independent variables). Individuals with 10% (blue), 50% (orange) and 90% (red) heteroplasmy levels and low standardised mtDNA copy number (2,000 copies/nucleus; solid line), medium copy number (3,500 copies/nucleus; dashed line) and high copy number (5,000 copies/nucleus; dotted line) are represented. Download figure Download PowerPoint Discussion We have characterised m.3243A>G heteroplasmy levels and mtDNA copy number in blood, urinary sediment and skeletal muscle, describing previously unreported sexual divergences in urine heteroplasmy and mtDNA copy number and confirming a longitudinal and continuous decline in blood heteroplasmy level in the majority of individuals. We have developed formulas to adjust urine m.3243A>G het