The aim of this study was to identify in asymptomatic patients with repaired tetralogy of Fallot the prevalence and determinants of impaired left‐sided cardiac function and adverse ventricular remodeling and the relation of left ventricular (LV) dysfunction and remodeling with cardiopulmonary exercise capacity.P=0.001; 49% below age‐adjusted fifth percentile for controls), lower LV mass index (P=0.003), lower LV mass/volume ratio (P<0.01), and impaired left atrial function. Right ventricular mass/volume ratio was the best predictor for LV systolic dysfunction and for a lower LV mass/volume ratio. Compared with controls, LV extracellular volume was higher (P<0.001), particularly in female patients, and associated with subnormal peak oxygen consumption (P=0.037). A peak oxygen consumption below the third percentile reference level was more likely with decreasing LV ejection fraction (P=0.008), and lower LV mass index (P=0.024), but independent of right ventricular ejection fraction.In a cross‐sectional study, 103 patients with tetralogy of Fallot  in New York Heart Association class 1, with surgical repair at a median age of 1.1 years, and 63 age‐matched controls were studied. LV, right ventricular function and geometry, LV myocardial extracellular volume (n=57), and left atrial function were quantified with cardiac magnetic resonance. Peak oxygen consumption was measured by a standardized cardiopulmonary exercise test (n=70). Patients with tetralogy of Fallot had lower LV ejection fraction (In New York Heart Association class 1 patients with tetralogy of Fallot, frequent impaired systolic and diastolic LV function, LV adverse remodeling with LV atrophy, a decreased mass/volume ratio, and extracellular matrix expansion suggest cardiomyopathic changes. The best predictor for LV systolic dysfunction was the right ventricular mass/volume ratio. The subnormal peak oxygen consumption indicates that monitoring of LV status may be important for long‐term prognosis. Impaired systolic and diastolic left ventricular (LV) function, adverse LV remodeling with extracellular matrix expansion, reduced cardiomyocyte mass (atrophy), and a decreased mass/volume ratio have been reported in young to middle‐aged patients with tetralogy of Fallot at a median of 14 years after repair, suggesting LV cardiomyopathic changes, despite the absence of any limitations to normal physical activity (New York Heart Association class 1).Markers of LV systolic dysfunction and adverse tissue remodeling (reduced cardiomyocyte mass), measured by cardiac magnetic resonance imaging, were associated with subnormal peak oxygen consumption during cardiopulmonary exercise testing, an important risk predictor in tetralogy of Fallot.This study recommends imaging‐based surveillance of the LV in patients with repaired tetralogy of Fallot to monitor adverse remodeling.Timely initiation of surgical reintervention for pulmonary insufficiency if present, or medical heart failure therapy, may delay progression of adverse remodeling and improve the long‐term prognosis.In tetralogy of Fallot (ToF), the main clinical focus after surgical repair is directed to the right ventricle (RV) .Indeed, a recent study identified LV systolic dysfunction as an outcome predictor for death and ventricular tachycardia in ToF.Cardiac magnetic resonance (CMR) is optimally suited to simultaneously assess adverse RV and LV volumes and function.The goals of the present study were as follows: (1) to determine the prevalence of LV dysfunction in NHYA class 1 patients with ToF and to identify associations between right heart markers of pathologic and LV remodeling; and (2) to determine, in a subset of patients n=70) with cardiopulmonary exercise testing, if LV dysfunction and remodeling have an impact on age‐ and sex‐adjusted peak exercise capacity, a potentially important risk predictor for adverse events in ToF.0 with caThe data that support the findings of this study are available from the corresponding author on reasonable request.The cohort for this prospective cross‐sectional study consisted of consecutive patients with ToF without overt symptoms, who underwent CMR imaging between January 2012 and August 2016. Patients with clinical symptoms of New York Heart Association functional class >1 as well as patients taking cardiac medications, like angiotensin‐converting enzyme inhibitors and β blockers, were excluded. The Institutional Review Board of our hospital approved the study (protocol No. A168/07), and informed written consent was obtained from either the patients or their guardians. Age‐matched healthy controls were recruited for comparison with the ToF cohort among patients scheduled to undergo a clinically indicated, contrast‐enhanced neurological magnetic resonance imaging or an magnetic resonance imaging study to rule out a vascular abnormality within the thorax. All individuals in the control group had a normal CMR examination result and no history of left‐sided heart disease or cardiomyopathy.All CMR studies were performed with a 1.5‐T scanner  using a phased‐array coil for cardiac imaging. In patients aged <7 years, sedation with propofol and midazolam was necessary. Heart rate, oxygen saturation, and noninvasive blood pressure were assessed with a CMR‐compatible monitor . Dedicated CMR software  was used to analyze the images.max), minimal LA volume at mitral valve closure (LAmin), and LA volume immediately before atrial contraction (LAbac). LA volumes and LA functional parameters  were calculated as described previously.max and LAmin, and was divided into LA passive emptying volume and LA contractile volume. LA contractile volume was the difference between LAbac and LAmin, and LA passive emptying volume was the difference between LAmax and LAbac. LA functional parameters were calculated according to the following formulas:Short‐axis cine imaging was performed to measure LV and RV volumes at end systole (ES) and end diastole (ED), mass at ED, and ejection fraction (EF), per standard guidelines.A retrospectively gated, phase‐contrast cine pulse sequence, with through‐plane velocity encoding, was applied to measure flow  in the main pulmonary artery, with the slice positioned ≈1.5 cm above the pulmonary valve. Pulmonary regurgitation was calculated as the fraction of forward flow that returned to the ventricle in diastole.Late gadolinium enhancement (LGE) imaging was performed using the contrast agent gadopentetate dimeglumine , with a contrast bolus of 0.1 mmol/kg. Approximately 15 minutes after contrast administration, LGE images were acquired with an inversion‐recovery–prepared 3‐dimensional gradient echo sequence  adjusted to null normal myocardium) with respiratory navigator technique. The presence and location of LGE was identified qualitatively by signal enhancement within the myocardium. Some patients or their guardians either declined to receive contrast or woke up from sedation and did not receive contrast agent (36%). Heart‐healthy control subjects did not receive any contrast agent, except for 12 subjects who were scanned for a neurological indication.Cardiac T1 measurements were performed using a cine Look‐Locker technique2 max) was measured by a standard exercise test2 max was obtained in mL kg−1 min−1, and expressed as age‐ and sex‐adjusted percentile ranking on the basis of previously published cardiopulmonary exercise testing (CPET) reference data from 548 female and 647 male patients within 4 to 75 years of age.2 max lower than the third percentile levels for a normal reference population.Peak oxygen consumption , and the R language and environment for statistical computing .As most LV parameters in normal controls show a significant association with age, we defined their normal ranges as a function of age. Smooth curves, corresponding to the 5th and 95th percentile levels for LV parameters measured in healthy volunteers, were calculated with generalized additive models for location scale and shape, using a spline for age as single predictor (package “gamlss” in R). As all controls who had ECV measurements in our study were aged <18 years, we used published referenced data2 max (in mL/kg per minute) included age, sex, LV EF, and cardiomyocyte mass index as predictors . (LV mass was not included as simultaneous predictor because of a relatively strong collinearity with cardiomyocyte mass index.) Preliminary analysis suggested that some of the relationships with VO2 max appeared to be nonlinear. Therefore, we used a generalized additive model and represented age, LV EF, and cardiomyocyte mass index by cubic regression splines to account for these nonlinearities. Multivariate logistic regression analysis was used to identify predictors of abnormally low VO2 max.Multivariate regression models were built for LV EF and LV mass/volume ratio, to determine which measures of right‐sided dysfunction were associated with LV dysfunction and LV remodeling. Age, sex, BSA‐indexed RV volumes at ED and ES, RV mass, RV mass/volume ratio, LV/RV mass ratio, and RV peak pressure from a contemporaneous Doppler‐echocardiography examination were considered as predictors. A best model was chosen by step‐wise forward and backward predictor selection, using the Akaike information criterion. Associations between the prevalence of eccentric LV remodeling and continuous RV parameters were analyzed by logistic regression analysis. A multivariate model for VO2 max value.A total of 103 patients with ToF  were studied at 15.1±8.7 years  after surgical repair of ToF. The patient characteristics are summarized in Table r=0.36, P<0.001). Patients with ToF and controls had similar heart rates  and RV mass/volume ratio . Using the categories of LV systolic dysfunction used in the study of patients with ToF by Broberg et al,P=0.002 for Jonckheere nonparametric test for increasing RV mass/volume ratio across ordered categories of LV systolic dysfunction). The association between LV systolic dysfunction and RV mass/volume ratio remained significant in a multivariate model for RV mass/volume ratio, with LV EF and age (P=0.33 for age effect) as simultaneous independent predictors. Figure P=0.002 for Jonckheere nonparametric test). The probability of less than mild LV dysfunction decreased by 0.38:1 for a 0.1 increase of the RV mass/volume ratio (P=0.009).In ToF, LV EF correlated with RV EF . LV mass/volume ratio correlated with RV mass/volume ratio , as shown in Figure r=0.42; P<0.001). Approximately 28% of all patients with ToF had a LV mass/volume ratio below the age‐specific fifth percentile level in healthy controls, as illustrated in Figure LV mass/volume ratio, a parameter characterizing LV remodeling, was significantly lower in patients with ToF than in healthy controls . Total and passive LA EFs were negatively associated with age, unlike in healthy controls, in whom they remained nearly constant.LA volumes and function were decreased compared with controls Table . LAmax, LGE imaging was performed in 65 patients: 62 (95%) of those had LGE at the site of ventricular septal defect patching, 30 (46.1%) showed LGE in the RV outflow tract, and 9 (13.8%) had small spots of LGE that were detected in other areas of the heart but not the LV free wall.P<0.01). Female patients with ToF showed a significantly higher ECV than male patients (P=0.033). Even when the patients with ToF and LGE in the LV were excluded, ECV was still significantly higher compared with controls . For patients with ToF and abnormally low LV EF, ECV was significantly higher in the septum, compared with patients with ToF and LV EF within the normal range (P=0.02). The LV mass index correlated negatively with ECV .Myocardial ECV was obtained in 57 (55%) of 103 patients with ToF, from the time when the T1 sequence was available on the scanner. It was significantly elevated compared with controls  in 20 patients (29%), and there was no significant difference in the proportion of abnormal VO2 max between male and female patients with ToF .CPET results were available for 70 patients with ToF (68%) who reached the anaerobic threshold. VO2 max, impaired LV resting systolic function and adverse LV remodeling were more frequent than in patients with VO2 max above the third percentile level for controls. In univariate logistic regression models, the likelihood of an abnormally low VO2 max increased with lower LV EF  and a lower LV mass index . LV EF and LV mass index remained significant predictors of an abnormally low VO2 max, when RV mass/volume ratio, RV ESV index, and RV EF were each added as additional predictor in these 2 models, suggesting that LV dysfunction increases the likelihood of impaired exercise capacity, independent of the degree of RV systolic dysfunction and remodeling. A comparison of the patients with ToF and abnormally low VO2 max and normal VO2 max showed that they had a lower LV EF  difference for the free wall ECV average between patients with ToF with normal and abnormal VO2 max.In patients with ToF and abnormally low VOF Figure A, a loweF Figure B, and a F Figure C. To fur2 max (in mL/kg per minute), the cardiomyocyte mass had a significant association with VO2 max (P=0.014), with simultaneous adjustment by sex (P=0.06), age (P=0.25), and LV EF (P=0.057). The predicted dependence of VO2 max on cardiomyocyte mass index is shown in Figure In a multivariate generalized additive model for VOEF P=0.05. The prelower‐than‐average RV mass, RV dilation , and a lower RV mass/volume ratio, are associated with a lower LV mass/volume ratio. The diminished LV mass observed in ToF, in combination with the expansion of the ECV in the LV, implies a loss of total cardiomyocyte mass indicative of LV atrophy. LV systolic dysfunction and the loss of LV mass correlated with impaired exercise tolerance, independently of the RV parameters that predict LV remodeling (RV mass/volume ratio) and LV dysfunction (RV ESVi and RV EF). In a multivariate analysis, LV atrophy had a stronger effect on VO2 max than LV EF, suggesting that LV cardiomyocyte mass may be an important parameter for monitoring LV remodeling in patients with ToF, with the advantage, compared with LV EF, that it is not dependent on hemodynamic loading conditions. In light of previous studies,In this study, clinically asymptomatic patients with repaired ToF frequently show left‐sided diastolic and systolic dysfunction and a lower than normal LV mass/volume ratio that is consistent with a previously described pattern of eccentric LV remodeling.Histological studies have shown increased interstitial collagen content in both the RV and LV walls of patients with ToF.ECV was negatively associated with the LV mass index, suggesting that interstitial fibrosis is linked with LV atrophy in ToF. The negative correlation in ToF of ECV with the LV mass index is opposite to a positive correlation observed in pathological hypertrophy resulting from hypertensive heart diseaseApproximately two thirds of patients with ToF had mild‐to‐moderate LV systolic dysfunction, and in nearly half of them the LV EF was below the age‐adjusted fifth percentile for normal controls Table . This is2 max sets in for LV cardiomyocyte mass indexes <≈39 g/m2 in male patients and <≈30 g/m2 in female patients, indicative for LV atrophy. For comparison, assume a healthy 20‐year‐old man with a myocardial ECV fraction of 0.25 ×LV mass index, would range from ≈45 to 70 g/m2. The observed decline of VO2 max around a level of 39 g/m2 is, therefore, consistent with the expected threshold for LV atrophy on the basis of published reference values for LV mass index. A higher ECV, indicative of diffuse fibrosis, would lower this threshold further. The investigated parameters of LV dysfunction  may, therefore, have a functional significance for predicting impaired exercise capacity that goes beyond the changes in RV volumes and function. In the statistical analysis, the significance of LV EF for prediction of impaired exercise capacity remained with adjustment by RV parameters, equally suggesting that LV EF in this context should not be interpreted as surrogate for RV markers of dysfunction. In contrast to previous studies,P<0.001) correlation between RV EF and LV EF was observed in our study , whereas our cohort only included asymptomatic patients with ToF. In adults with acquired heart disease and compromised RV EF,The subclinical LV abnormalities reported in our study  may contribute to the reported risk of later clinical left‐sided heart failure and cardiac death in ToF.z score <−2.0), an RV mass/volume ratio ≥0.3 g/mL, and history of atrial tachyarrhythmias; however, more important, RV EF was not a predictor. These findings in an older population  with signs of overt heart failure underscore the potential importance of LV systolic dysfunction for adverse outcomes. Bokma et alValente et al previously investigated in patients with ToF the association between markers of RV and LV remodeling/dysfunction and outcome (death and ventricular tachycardia).Diastolic dysfunction may be one of the earliest manifestations of adverse LV remodeling in ToF. We examined LA function by CMR cine volumetry,The results of this study should be viewed in the context of its design. Given that this is a cross‐sectional study, we cannot relate causes and effects in the interplay of LV function and structure, intrinsic tissue‐level alterations (by ECV), RV‐LV interdependence, diastolic properties (as reflected by LA function), or vascular stiffness. Patients aged <7 years (15% of ToF cohort) were sedated during the CMR studies, which may have depressed cardiac function. The statistical significance of the main findings  and had a lower body mass index (18.4±4.82 versus 21.9±4.7 kg/m2), but showed no difference in sex distribution.The availability of CPET for only a subset of 70 (68%) of patients with ToF may have introduced a selection bias in the analysis of the associations between abnormally low VO2 max, an important risk predictor in ToF.This multiparametric CMR study on LV remodeling in asymptomatic patients with ToF at a median of 14 years after surgical repair revealed the following: (1) frequent (49%) impaired systolic function over the full age range, abnormal diastolic relaxation, a decreased LV mass, and interstitial matrix expansion (as reflected by ECV), which are all evidence of adverse LV remodeling and a cardiomyopathic phenotype (the best predictor for LV systolic dysfunction was the RV mass/volume ratio); and (2) CMR markers of LV systolic dysfunction and adverse tissue remodeling (reduced cardiomyocyte mass) correlated with subnormal VOTherefore, early surgical reintervention for pulmonary insufficiency if present, or medical heart failure therapy, both guided by CMR‐guided surveillance of the LV, may delay progression of adverse remodeling and improve the long‐term prognosis.http://www.kinderherzen-wollen-leben.de/).This work was supported by a postdoctoral fellowship grant (6619‐10‐0) for Dr Andrade from the Coordination of Improvement of Higher Education Personnel of Brazil and by a research grant from the foundation “Kinderherzen–wollen‐leben e.V.” for Dr Pham (None.


S‐adenosylmethionine (SAM) and plasma S‐adenosylhomocysteine (SAH), with normal urine adenosine levels. Some of the elevations persisted for over 1 year. Sequencing of the ADK and AHCY genes was negative for causative variants. Plasma methionine normalized 1 year after ascertainment, but SAM and SAH continued to be elevated for six more months before normalization, and aminotransferases remained mildly elevated. Whole exome sequencing demonstrated a homozygous pathogenic variant; NM_018297.3(NGLY1):c.1405C>T (p.Arg469*) in exon 9 of the NGLY1 gene, for which both parents were heterozygous. To our knowledge, this is the first report of NGLY1 deficiency with elevations in plasma methionine, SAM and SAH and a slight elevation of tHcy. Less than 20 patients have been reported with NGLY1 deficiency worldwide and this case expands on the biochemical phenotype of this newly discovered inborn error of metabolism.We report on a 5‐year‐old female born to consanguineous parents, ascertained at the age of 23 months for an elevated plasma methionine level, a mildly abnormal total plasma homocysteine (tHcy), and elevated aminotransferases. She had global developmental delay, microcephaly, dysmorphic facial features, hypotonia, nystagmus and tremor in her upper extremities. Metabolic investigations demonstrated elevations in plasma methionine, plasma  Her features included global developmental delay, microcephaly, dysmorphic facial features, hypotonia, hypolacrima, and tremor in the upper extremities. Novel findings included initially persistent, but resolving elevations in plasma methionine, plasma S‐adenosylmethionine (SAM) and plasma S‐adenosylhomocysteine (SAH), but with normal urine adenosine levels. To our knowledge, fewer than 20 patients have been reported with NGLY1 deficiency worldwide. Our patient highlights an unreported presentation of NGLY1 deficiency and suggests possible additional pathways for investigation.Herein we describe a patient found to be homozygous for a pathogenic variant NM_018297.3(NGLY1):c.1405C>T (p.Arg469*) in the 2+2 weeks gestation, via Caesarian section, for decreased fetal heart rate. Birthweight was 1255 g (10th percentile), length was 38 cm (10th percentile), and head circumference was 28.5 cm (45th percentile). Her parents are of Libyan descent and first cousins. Both parents were healthy, as were the proband's two older male siblings. The patient required an 8‐week stay in the NICU for management of feeding and growth difficulties. Aminotransferases in the newborn period were normal.The patient was born to a healthy 30‐year‐old Gravida 3 Para 2 mother at 32Elevated aminotransferases were first noted at 23 months of age as part of a workup for failure to thrive; AST of 358 U/L  and ALT of 853 U/L  and GGT of 101 U/L . Alpha Fetoprotein was within normal limits for her age. An initial search for alpha‐1 antitrypsin deficiency, cystic fibrosis, infectious and autoimmune etiologies was negative, and a liver ultrasound for structural changes was normal. Liver biopsy was not performed. At 3 years, the GGT normalized but aminotransferases remained elevated, with AST 216 U/L at 3 years and 58 U/L at 5 years of age, and ALT 453 U/L at 3 years and 57 U/L at 5 years of age.1H‐magnetic resonance spectroscopy was normal.A brain MRI at 2 years of age demonstrated age‐appropriate myelination with mildly prominent cerebral sulci, lateral and third ventricles, consistent with cerebral atrophy Figure A. There Z score of −2.1 (0.379 g/cm2). She was small for age, with measurements consistently at or just above the third percentile for height, weight, and head circumference.An echocardiogram performed at 1 month of age demonstrated small atrial septal defect, patent ductus arteriosus, and mild right and left ventricular hypertrophy, with normalization on repeat. Electrocardiogram was normal. Complete blood counts and coagulation studies, albumin, immunoglobins, and thyroid studies were normal. There was no history of lactic acidosis. She had two fractures of the left lower extremity, after trauma. A bone age performed at 4 years; 4 months was within normal limits. Bone mineral density was reported as low, with a lumbar At 5 years of age, development was delayed globally. She was unable to walk unassisted until 4 years of age. She was able to walk up and down stairs with assistance. She had between 15 and 20 words. She was able to follow two‐step commands, and primarily communicated by taking one of her parents' hands or pointing. Fine motor development was delayed, but she was able to finger feed and hold a cup and a pencil with a fisted grasp. Social development was notable for social anxiety, but she demonstrated good eye contact and a social smile. She was not yet toilet trained.On examination at 5 years of age, her weight was 18.9 kg (50th percentile), height 107 cm (25th percentile), and head circumference 48 cm (3rd percentile), consistent with microcephaly. She had dysmorphic facial features including deep‐set eyes with long eyelashes, short palpebral fissures, and synophrys with full, arched eyebrows. Extremities were remarkable for bilateral 5th finger clinodactyly and fetal pads on all fingers. Cardiac, respiratory, and abdominal examination was unremarkable. There was no scoliosis. On neurological exam, she had increased tone with slight catching in the upper extremities bilaterally, and peripheral tremor in her upper extremities. Tone in the lower extremities was normal. She had decreased deep tendon reflexes throughout both upper and lower extremities. She was able to walk independently with a forward‐leaning and wide‐based gait but was unable to run.33.1Comparative genomic hybridization (CGH) oligoarray using the platform (CytoChipTM ISCA 8x60K v2.0) was performed on DNA extracted from peripheral whole blood.3.2Plasma amino acids were quantitated after post‐column ninhydrin derivatization using a Biochrom 30+ amino acid analyzer . Total homocysteine was measured using a heavy‐isotope dilution method following dithiothreitol reduction of disulfide bonds by liquid chromatography (LC)‐ tandem mass spectrometry (MS/MS) as previously described.3.3AHCY gene was performed at Baylor Miraca Genetics Laboratory . Gene sequencing of the ADK gene was performed at Centogene/Life Labs Laboratory . XomeDxSlice analysis of the ADA, GNMT, MAT1A, MTHFR, and SLC6A8 genes was performed at GeneDx Laboratory .Gene sequencing of the Whole exome sequencing (WES) trio after slice was performed at GeneDx Laboratory . Using genomic DNA extracted from peripheral lymphocytes, the Agilent Clinical Research Exome kit was used to target the exonic regions and flanking splice junctions of the genome. The targeted regions were sequenced simultaneously by massively parallel (NextGen) sequencing on an Illumina HiSeq sequencing system with 100 bp paired‐end reads. Bi‐directional sequence was assembled, aligned to reference gene sequences based on human genome build GRCh37/UCSC hg19, and analyzed for sequencing variants using a custom‐developed analysis tool . The mean depth of coverage was 180× and quality threshold was 97.9%. Sanger sequencing was performed in the laboratory on the proband's and parents' DNA for variant confirmation.44.1The array CGH was reported as normal female.4.2Plasma amino acid analysis at 23 months of age demonstrated an isolated 4.7‐fold elevation in plasma methionine at 212 μmol/L , and marginally elevated plasma total homocysteine level of 14.7 μmol/L . Plasma SAM had a 3.8‐fold elevation at 505 nmol/L , and plasma SAH had a 6.4‐fold elevation at 186 nmol/L . In addition, urine creatine was elevated at 1500 mmol/mol creatinine  and urine guanidinoacetoacetate was normal at 124 mmol/mol creatinine . She had normal urine adenosine levels. Creatine kinase (CK) was normal at 97 IU/L.Plasma amino acids were repeated at 2 years, 2 months of age and she continued to have a 1.8‐fold elevation in plasma methionine (but within the context of other elevated amino acids suggestive of a nonfasted specimen), but with a normal plasma total homocysteine at 6.3 μmol/L. There remained a 2.3‐fold elevation of SAM, and an 8.1‐fold elevation in SAH. At 3 years of age, there was normalization of the plasma amino acids but persistence of elevated SAM (1.6‐fold) and SAH (2.4‐fold). At 4 years, 7 months of age, SAM was still marginally elevated, and SAH normalized. SAM‐to‐SAH ratios ranged from 1.31 to 3 between 23 months and 3 years of age and normalized by 4.7 years of age. Urine creatine and guanidinoacetoacetate levels normalized by 5 years of age as well.4.2.1ADK, ACHY, ADA, GNMT, MAT1A, MTHFR, and SLC6A8 genes. WES trio performed on the proband and both parents demonstrated a homozygous NM_018297.3(NGLY1):c.1405C>T (p.Arg469*) pathogenic variant in NGLY1 in the proband, and heterozygosity for this NGLY1 variant in the patient's mother and father. The variant had been reported in the homozygous state in an individual with unclassified epilepsy−5 in ExAC, and no homozygotes reported.No pathogenic variants were found in the 5NGLY1‐CDDG presents with abnormal tear production, choreoathetosis, liver dysfunction, developmental delay, hypotonia, peripheral neuropathy, EEG abnormalities, and microcephaly . All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation  and with the Helsinki declaration of 1975, as revised in 2000 (5). Written informed consent was obtained from the patient's parents prior to the case publication, for being included in the study, and is available for review upon request. This article does not contain any studies with animal subjects performed by any of the authors.The patient was seen at the Alberta Children's Hospital in Calgary, Alberta, Canada, and written parental consent was obtained for publication.


Narcissus is assembled and annotated in this study. The total length of the N. poeticus chloroplast genome is 160,099 bp and comprises the large single copy (LSC) spanning 86,445 bp, the small single copy (SSC) spanning 16,434 bp, and two inverted repeat regions each of 28,610 bp length. The truncated copy of ycf1 before the junction between IRB and SSC was 1277–2428 bp longer than in other included Asparagales samples. A potential pseudogene, cemA, was also identified. This is the first reported plastome for Amaryllidaceae subfamily Amaryllidoideae.The first complete chloroplast genome sequence for  Narcissus L. is a member of Amaryllidaceae, a family comprising several horticulturally important plant genera . Herbarium voucher specimen was deposited at WSY (WSY0108940). Total genomic DNA was extracted using the QIAGEN DNeasy Plant Mini Kit . Library development and 150 bp PE sequencing on 1/16 of an Illumina HiSeq 4000 lane were done at the Oxford Genomics Centre . The chloroplast genome was assembled with Fast-Plast v1.2.6 (McKain and Wilson ndhF sequence of N. poeticus (KT124416) was used as the starting seed and memory was limited to 8 Gb. All other parameters were unchanged. Junctions of the inverted repeats were confirmed by Sanger sequencing.Leaf material was collected in silica gel from Hesperoyucca whipplei – KX931459; Hosta ventricosa – KX931460; Yucca schridigera – KX931469; Cocos nucifera – KF285453). The N. poeticus cpDNA genome sequence was aligned with 13 published Asparagales plastome sequences using MAFFT  is 160,099 bp, comprises the large single copy (LSC) spanning 86,445 bp, the small single copy (SSC) spanning 16,434 bp, and two inverted repeat regions each of 28,610 bp length. The junction between IRB and SSC (JSB) is 34 bp within the ndhF gene. The junction between SSC and IRA (JSA) is within the ycf1 gene, which is 5,346 bp long, of which 2737 bp lies in the inverted repeat. Therefore, there is a 2737 bp long truncated version of ycf1 at JSB. This is 1277–2428 bp longer than the truncated copy of ycf1 in other Asparagales sequences analyzed here, identified by IRScope (Amiryousefi et al. cemA shifts the ORF potentially inactivating this gene. The Amaryllidaceae samples form a clade with N. poeticus sister to Allium cepa (The chloroplast genome sequence of ium cepa  topologiium cepa .


Multivariate analysis with logistic regression specified that age over 47 years-old was predictive to have higher NcirDNA concentration . McirDNA concentration was independent of age and gender in healthy individuals. In mCRC patients, NcirDNA and McirDNA levels were independent of age, gender, delay between food intake and blood collection, and plasma aspect, either with univariate or multivariate analysis. Nonetheless, ad hoc study suggested that menopause and blood collection time might have tendency to influence cirDNA quantification. In addition, high significant statistical differences were found between mCRC patients and healthy individuals for NcirDNA (p < 0.0001), McirDNA (p < 0.0001) and McirDNA/NcirDNA ratio (p < 0.0001). NcirDNA and McirDNA levels do not vary in the same way with regards to cancer vs healthy status, pre-analytical and demographic factors.To our knowledge, this is the first comprehensive study on the influence of several pre-analytical and demographic parameters that could be a source of variability in the quantification of nuclear and mitochondrial circulating DNA (NcirDNA and McirDNA). We report data from a total of 222 subjects, 104 healthy individuals and 118 metastatic colorectal cancer (mCRC) patients. Approximately 50,000 and 3,000-fold more mitochondrial than nuclear genome copies were found in the plasma of healthy individuals and mCRC patients, respectively. In healthy individuals, NcirDNA concentration was statistically influenced by age ( Despite scant early consideration, interest in the feasibility of cirDNA analysis has increased exponentially, over the last decade, among researchers working on a large range of disorders. CirDNA was first clinically implemented in prenatal diagnosis of sex-determination and pregnancy-associated disorders by assaying fetal DNA in maternal plasma13. The main sources of cirDNA are cell death, either by necrosis or apoptosis, and active release by viable cells, including exocytosis and NETosis15. Note, cirDNA may derive from either nuclear (NcirDNA) or mitochondrial DNA (McirDNA). To date, research and development of cirDNA analysis has focused on the qualitative rather than the quantitative information provided. For example, cirDNA analysis is now clinically validated for detecting specific sequences or mutations to guide the oncologist toward the most appropriate treatment. CirDNA analysis is also performed for prenatal and embryo-culture genetic testing. There have been several years of intensive studies validating cirDNA quantitation in different clinical scenarios, including sepsis, transplant recipients and immune disorders. CirDNA quantification is also now taken into consideration in oncology as, it was recently shown that, the level of mutant cirDNA is useful for following-up cancer patients to detect minimal residual disease and to monitor response to therapy and disease recurrence. Although total cirDNA levels were first examined in the early phase of cirDNA research and development, it is now not considered as a single biomarker because of its lack of specificity. Nevertheless, all cirDNA analysis relies on the optimal quantification of total amount of cirDNA. Biological biomarkers should be highly dynamic, and their diagnostic performance may vary depending on internal and external changes. The clinical efficacy of cirDNA will require the identification and the control of various patient-related confounders that may affect its measurement16. Human cellular aging is usually marked by senescence and cell death. Similarly, it often features a phenomenon, originally called “Inflamm-aging”, that induces a chronic or low-grade inflammatory state20. Indeed, it was demonstrated that tissue damage and a pro-inflammatory environment increase release of cirDNA into the blood stream14. Additionally, we may speculate that sex-based differences, such as genetic dissimilarity and steroid hormones levels, could cause differences in cirDNA concentration between men and women. Likewise, blood component concentrations may vary with the circadian clock and upon food intake and potentially influencing cirDNA concentration21. Despite outstanding research in the field of cirDNA, relatively few studies have examined the effect of pre-analytical and demographic parameters as sources of intra- and inter-individual variability in cirDNA levels. There is currently no single operating procedure and there are relatively few clinical guidelines in the literature25. This study aims first at defining a framework for cirDNA analysis, to harmonize NcirDNA and McirDNA quantification and to explore potential sources of variability that could cause interpretation errors. Previous studies already documented various pre-analytical limitations and specified conditions for cirDNA analysis, including specific collection tubes, and specific plasma isolation and extraction protocols, including storage condition variables and limits on the number of freeze-thaw cycles26. Here, we study the influence of various pre-analytical  and demographic variables such age and gender in NcirDNA and McirDNA concentration determination, from plasma of 104 healthy donors and 118 mCRC patients . The McirDNA assay can detect down to one copy of the mitochondrial MT-CO3 gene per 1.7 microliter of plasma . Note, the targeted MT-CO3 gene sequence was selected as being not mutated in the mitochondrial genome of cancer patients. Copy number calculation is performed by using a specific equation that eliminates bias in the nuclear or mitochondrial DNA calibration curves to allow simultaneous calculation of their real relative proportions. For example, in the cohort of 104 healthy individuals, we found a median NcirDNA and McirDNA plasma concentration of 1.64 × 103 and 8.32 × 107 copies/mL, respectively, corresponding to 5.43 and 1.36 ng/mL of plasma.We used serial dilutions of genomic DNA and of mitochondrial plasmid DNA to validate a reproducible, linear and sensitive assay to quantify, both nuclear and mitochondrial, circulating DNA (NcirDNA and McirDNA). The NcirDNA assay is based upon the qPCR detection of a sequence of the Mann-Whitney U test, P value < 0.0001) and equally in mCRC patients . We next compared McirDNA and NcirDNA concentration expressed in ng/ml, in healthy individuals and mCRC patients groups. Here, also, we observed a considerable statistical difference between NcirDNA and McirDNA concentrations in healthy individuals  and mCRC patients . McirDNA concentration is significantly higher than NcirDNA concentration when measured in units of copy number/ml, and conversely, the NcirDNA concentration was significantly higher than the McirDNA concentration when measured in ng/ml, either in healthy individuals and mCRC patients.We compared NcirDNA and McirDNA concentration, in healthy individuals and mCRC patients groups, expressed either in copy number/ml and ng/ml of plasma. A supplementary table summarizes all quantification data and comparative tests in detail (Supplementary Table Age (n = 104): We dichotomized the healthy individuals cohort in two groups around the median age, which was 47 .Age n = 04: We diGender (n = 104): The median NcirDNA copy number in the healthy male group (n = 62) and in the group of healthy females (n = 42) were 1.69 × 103 and 1.48 × 103 copies/ml, respectively. This difference in the NcirDNA copy number between healthy males and females was statistically significant .Multivariate analysis: Logistic regression analysis including age and gender specified that age over 47 years-old was predictive to have higher NcirDNA concentration . Results of multivariate analysis and Odds ratios (OR) with 95% confidence intervals (CIs) are summarized in Supplementary Table Age (n = 104): The median McirDNA copy number in the <47 (n = 52) and in the ≥47 year-old group (n = 52) were 7.77 × 107 and 8.40 × 107 copies/ml, respectively. There was no statistical difference in the McirDNA copy number between these groups .Gender (n = 104): The median McirDNA copy number in the healthy male group (n = 62) and in the group of healthy women (n = 42) were 8.03 × 107 and 9.39 × 107 copies/ml, respectively. No statistical difference in McirDNA copy number was observed between healthy males and females .Multivariate analysis: Logistic regression analysis including age and gender confirmed no statistically significant difference between studied groups : We first compared two groups, 27 abnormal plasmas (icteric and/or opaque plasmas) and 79 normal plasmas. There was no statistical difference in NcirDNA levels between abnormal plasmas and normal plasmas groups . However, the median NcirDNA amount determined in abnormal plasmas group was slightly lower than in normal plasmas (3.62 × 103 vs 5.22 × 103 copies/ml).Delay between blood collection time and last food intake (n = 89): We then compared two groups, “1 h < delay < 5 h” (n = 69) and “other delays” (n = 20). There was no statistical difference in NcirDNA levels between “1 h < delay < 5 h” and “other delays” . Nonetheless, the “1 h < delay < 5 h” group showed lower median NcirDNA amount than the group “other delays” (3.68 × 103 vs 5.72 × 103 copies/ml).Age (n = 118): We also dichotomized the mCRC cohort into two groups around the median age of 65 . A comparative study using the same cut-off for both healthy and mCRC cohorts as the median age of all individuals tested here  confirmed the statistical difference between young (N = 79) and older (N = 25) healthy individuals groups , and also the no statistical difference between young (N = 25) and older (N = 93) mCRC groups  : The median NcirDNA copy number in the mCRC males (n = 68) and in and females (n = 50) mCRC patients were 4.65 × 103 and 5.40 × 103 copies/ml, respectively. No statistical difference was found .Multivariate analysis: Logistic regression analysis including all the parameters  confirmed no statistical significant results : We next compared the abnormal plasmas (n = 11) and normal plasmas (n = 35) groups. There was no statistical difference in NcirDNA levels between abnormal and normal plasmas groups . However, the median McirDNA amount determined in abnormal plasmas was slightly lower than in normal plasmas (6.64 × 106 vs 1.19 × 107 copies/ml).Delay between day-time of blood draw and the last food intake (n = 34): There was no statistical difference in McirDNA copy number between the “1 h < delay < 5 h” group (n = 25) and the “other delay” group (n = 9) . The “1 h < delay < 5 h” group showed higher median NcirDNA amount than the group “other delay” (1.68 × 107 vs 1.19 × 107 copies/ml).Age (n = 50): The median McirDNA copy number in the < 65 year-old (n = 23) and ≥ 65 year-old (n = 27) groups were 1.12 × 107 and 1.84 × 107 copies/ml, respectively. There was no statistical difference in the McirDNA copy number between the groups .Gender (n = 50):The median McirDNA copy number in the mCRC males group (n = 25) and in the group of mCRC women (n = 25) were 1.19 × 107 and 1.68 × 107 copies/ml, respectively. We did not found any statistical difference in the McirDNA copy number between mCRC males and females .Multivariate analysis: Logistic regression analysis including all the parameters  confirmed no statistically significant results .We compared the median NcirDNA amount between healthy individuals (n = 104) and mCRC patients (n = 118). The median NcirDNA concentration in healthy individuals and mCRC patients was 1.64 × 107 and 1.44 × 107 copies/ml, respectively .Next we compared the median McirDNA amount between healthy individuals (n = 104) and mCRC group (n = 50). The median McirDNA concentration in healthy individuals and mCRC patients was statistically different at 8.32 × 104 and 2.70 × 103 copies/ml, respectively . The median McirDNA/NcirDNA ratio was 20-fold higher in the healthy individuals group than in the mCRC patients group. We did the same comparison using McirDNA and NcirDNA concentrations expressed in ng/ml and we obtained the same results .Finally we compared the median McirDNA/NcirDNA ratio between healthy individuals (n = 104) and mCRC patients (n = 50). The median McirDNA/NcirDNA ratio in healthy individuals and mCRC patients were statistically different at 5.41 × 10). In contrast, there was no difference between days 0, 2, 5 and 7 following blood collection in Cell-Free DNA BCT® STRECK (BCT) tubes , 12.00 PM (2 hours after breakfast), 3.00 PM (2 hours after lunch) and 6.00 PM, the median NcirDNA copy numbers were 1500, 480, 715 and 709 copies/ml respectively. An additional figure shows this in more detail . Solid tumor mass is composed of a variety of cells, mostly consisting of malignant/cancer, stromal, endothelial and immunological cells. In order to avoid any confusion in nomenclature, we define as ‘tumor cells’ all the cells composing the tumor mass. The concentration values presented in this study correspond to total cell-free DNA in plasma, either of nuclear or mitochondrial origin. Our observations are summarized in Table 00. Solid22; and adapted from Chiu et al., DNA extraction and sample handling27. The accuracy of the cirDNA concentration measurement in this study is supported by two assessments: (i) total cirDNA concentration by targeting a KRAS sequence was routinely controlled by quantifying a BRAF sequence. In addition, this control quality enable to detect and exclude sample with loss of heterozygosity (LOH) or gene amplification which have been reported in CRC patients28. Moreover, since KRAS amplification is an infrequent event in CRC (0.67%)28, this level will not modify our observations or the values described in our manuscript; and (ii) the study of cirDNA measurement under Poisson law distribution revealed single copy detection of nuclear cirDNA.This study design relies on the analytical performance of the assay. We used here a qPCR-based method with an unmatched combination, of reproducibility, sensitivity and specificity for quantifying plasma cirDNA (see Methods section). Furthermore, our assay benefits from a clinically-validated optimal pre-analytic process that we previously set up for plasma preparation9 bp), and also the high number of mitochondrial genome copies within a cell. Each human cell, depending on its type, contains a number of mitochondria ranging from 500 to 2,000 and each mitochondria holds between 2 and 10 mitochondrial DNA molecules. Therefore, each cell may contain approximatively 1,000 to 20,000 mitochondrial DNA molecules and only one nuclear genome equivalent, which is consistent with our results. Our method of calculation, described here, appears as the most rigorous means to simultaneously quantify mitochondrial and nuclear DNA concentrations. A higher coefficient of variation was routinely observed for mitochondrial than for nuclear DNA, despite the higher analytical signal for McirDNA copy number. The median concentration levels we found here appear to be similar to the average values observed in several studies29. In addition, performance of our assay support the accuracy of the values presented throughout this study such as 5.43 and 1.36 ng/mL in healthy subject plasma of N- and McirDNA, respectively. Considering the significant percentage of McirDNA  among total cirDNA mass existing in plasma, we propose to always specify the origin of cirDNA when examining total circulating WT DNA.Respective NcirDNA and McirDNA proportions inverted when the plasma cirDNA concentrations were calculated according to copy number or mass Fig. . For exa32. A few studies showed opposite observations35. Here, we observed an influence of age on NcirDNA plasma concentration in the healthy individual cohort as a whole (p = 0.009). Our data also revealed a statistical difference in NcirDNA amount between healthy males and females (p = 0.048). We performed logistic regression for multivariate analysis including age and gender and showed that age ≥ 47 was predictive of a high NcirDNA concentration . This observation is in accordance with a study by Jylhävä et al. that consisted of 12 nonagenarian women (age > 90 years) and 11 healthy control female (22 < age < 37 years) that showed a higher concentration of NcirDNA in nonagenarians than in control women33. The authors explained this increase in the amount of cirDNA with age, as the accentuation of a senescence phenomenon and cell death, caused by an inflammation associated with age and even by decreased clearance and phagocytic capacity. Zhong et al. showed a significant increase in total plasma cirDNA concentration in women over 60, compared to younger women, which appears to be consistent with Jylhävä’s study. We may speculate that menopause could be an explanation of the statistically higher NcirDNA concentration in healthy males as compared to healthy females (p = 0.048), whereas no difference between mCRC males and females was observed (p = 0.971). This speculation is based on two observations: (i) Median menopausal age in the European population is 51 years-old; 10–15% of women go into menopause before the age of 45, and globally 90–95% reach menopause by 55 years of age. By applying these categories of age to our women cohorts, we observed no statistical difference and no tendency between mCRC women <45, 45–54 and ≥55 year-old  while we showed a statistical difference between healthy women <45, 45–54 and ≥55 year-old, with a concentration gradient: 1275, 1440 and 2610 median copy number, respectively ; and (ii) CRC females are at a high risk of chemotherapy-induced menopause or menstrual disorders like amenorrhea or a decrease of steroid hormone levels37, and metachronous mCRC patients may attain a menopausal state earlier. An additional figure shows these results in more detail  had higher levels of cirDNA in plasma than younger patients (p = 0.018)38. Conversely, our data showed no statistical influence of age (p = 0.757) and gender (p = 0.971) on NcirDNA concentration in mCRC patients and these results was confirmed with a multivariate analysis using logistic regression. We also demonstrated no influence of pre-analytical factors like plasma aspect (p = 0.266) or delay between last food intake and blood collection (p = 0.220). On the other hand, our results also confirm those previously published by our laboratory25 and by many teams40, namely that NcirDNA concentration is significantly higher cancer patients than in healthy individuals (p < 0.0001), whether male or female. For more than a decade it was suggested that total NcirDNA could be a cancer biomarker42. However, previous attempts to apply total cirDNA quantity as a screening test for cancer lacked a strong statistical demonstration43. High standards, with regard to pre-analytical factors and quantification, could lead to its use as one marker, among other, for tumor burden.Despite the large number of studies that aimed at determining if age and gender might influence NcirDNA levels, no clear results have been demonstrated35. For Pinti et al. however, McirDNA concentration would increase with age18. In this study, McirDNA content was analyzed in 831 plasma samples from subjects with different healthy status, aged from 1 to 104 years; McirDNA content significantly increased after fifty years-of-age and it peaked in nonagenarians. Elevated McirDNA levels might help maintain the low-grade chronic inflammation that is common in elderly individuals. With regards to McirDNA level in cancer patients, Mengel-From et al. measured McirDNA copy number in blood cells from 1,067 subjects aged 18 to 93 and conversely, observed a tendency for lower mitochondrial DNA copy number with advanced age44. These findings are consistent with other studies, performed on different types of tissue, such as skeletal muscle and pancreatic islets46. This age-related tissue-specific depletion of cellular mitochondrial DNA could lead to a proportional reduction of McirDNA copy-number in plasma. Inversely, there was no association between serum McirDNA levels and demographic parameters (age/gender) in urological malignancies48. Likewise, in breast cancer, there was no significant difference in McirDNA content in blood samples of stage I patients with respect to their age49. Our data revealed no influence of age and gender on McirDNA concentration, either in plasma of healthy individuals or mCRC patients. These results were confirmed in multivariate analysis. We also reported no significant influence of plasma aspect (p = 0.263) and delay between last food intake and blood collection (p = 0.509). Nonetheless, our data revealed a significant higher McirDNA than NcirDNA concentration in plasma, whether for mCRC patients, healthy individuals, male or female, and regardless of the age of the subject. Note, while median NcirDNA concentration is much higher in mCRC patients than in healthy individuals, median McirDNA concentration is conversely lower in mCRC patients, revealing a proportionally lower McirDNA release from cancer cells. This might be explained by the fact that cancer cells, in comparison to healthy cells, may have, among other differences, fewer mitochondria per cell and less DNA within their mitochondria50. However, this is still controversial and explanation of our striking observation is under active investigation in our team. Nevertheless, we may speculate that the McirDNA/NcirDNA ratio might have some power in discriminating healthy individuals from cancer patients. McirDNA/NcirDNA ratio is undergoing clinical validation as potential biomarker for tumor burden or diagnosis in a large study involving broader scope of cancer patients with various malignancies and stages. In light of the high copy number of McirDNA and its tendency to be mutated in cancer51, our observations confirm the gradual acceptance of McirDNA as a new potent diagnostic and prognostic biomarker for many solid tumors52.Relatively few reports have quantitatively analyzed McirDNA and there are discrepancies among them. In one study there was no significant difference between young and aged healthy subjects53, NcirDNA concentration determined from blood collected in EDTA tubes increased with time highlighting release of genomic DNA resulting from blood cell lysis when stored at room temperature or +4 °C and consequently to contamination of cell-derived DNA. Note, whole blood stored in EDTA tubes at +4 C° showed no change in cirDNA concentration for up to one day suggesting their potential use within this time period (data not shown)53. We propose routine clinical analyses use plasma stored in EDTA tubes for up to 6 hours, given the uncertainty of maintaining the temperature of samples in the course of blood processing, as we earlier described54. Conversely, BCT tubes appeared to conserve blood cell integrity, since no DNA concentration increase was observed up to 7 days following blood collection55. Thus, BCT tubes maintain the true cirDNA concentration and are good tools to conserve/stabilize blood for optimal quantification of NcirDNA for up to 7 days following collection. Cell-preserving tubes greatly allows postal shipment of whole-blood within this time period and it allows interventional analysis as well as enabling clinical centers that lack lab facilities to immediately prepare plasma. While being cheaper by themselves, use of EDTA tubes necessitates plasma preparation within a short time frame and immediate subsequent storage under frozen conditions until analysis requiring costly shipment when plasma originate from a single patient. We first reported that McirDNA concentration determined from blood collected in EDTA tubes strongly increased with time. We may assume that this results as well blood cell lysis and blood cell-derived mitochondrial DNA contamination.As previously reported56. Second, NcirDNA concentration was lower in opaque than in non-opaque plasmas. Multivariate analysis including age, gender, plasma aspect and delay between food intake and blood collection revealed no statistical influence but abnormal plasma aspect showed a clear tendency to have lower NcirDNA concentration . These observations are all consistent with postprandial effects. Food intake with high lipid content may result in hyperlipidemia which can be characterized by opaque plasma and high triglyceride concentrations. However, despite the large examined cohort data, we cannot state that postprandial is the explanation since no statistical difference was found. This may be due to various factors: (i) blood triglyceride levels largely depend on fat distribution and body weight, lifestyle choices, and also genetic factors57; (ii) there were considerable within- and between-subject variations in non-fasting plasma triglycerides58; and (iii) the subjects had a chronic illness, mCRC. We speculate that the postprandial effect could occur because the presence of lipids or proteins may interfere with DNA extraction yield from plasma. We cannot exclude the possibility that NcirDNA levels depend on circadian clocks and metabolism, resulting in more elevated concentration in the morning. Moreover, we cannot exclude the possibility that other metabolic changes during fasting/feeding alter cirDNA yield. Nevertheless, our data suggest that fasting blood samples should be included when studying or clinically examining cirDNA to improve its diagnostic performance, especially when low mutation frequency in cancer patients or prenatal testing is considered. Note, in addition to opaque plasma, we remarked on various occasions that, icteric plasma had aberrant cirDNA concentration when qPCR was the analytical method. Therefore, we propose observation of icteric, hemolytic and opaque plasma as criteria of blood sample exclusion.There is currently no indication in the literature on the optimal time for blood collection when analyzing cirDNA. Our data seem to indicate that NcirDNA median levels are 2- to 3-fold higher at 9.00 AM, which is the earliest time-point examined, compared to later blood-collection time-points  when the NcirDNA level stabilizes. Decrease from 9.00 AM to 12.00 PM might be explained by the postprandial effect of the breakfast being taken at 10.00 AM. This hypothesis is supported by several observations. First, we reported that NcirDNA plasma levels in blood collected between one and five hours after food intake were lower than in blood collected on patients under fasting conditions. It was previously showed that plasma triglyceride increased one hour after food intake, peaked ≈3 hours after intake of a test meal and baseline values were restored back to initial values after 5 hoursad hoc study is limited by the low number of tested individuals since ad hoc study experiments are cumbersome and time-consuming. Thus, blood stability and blood collection time was only carried out on a few number of healthy volunteers (n = 5) and not on mCRC patients. This not allowed us to provide statistical analysis while the results showed clear tendencies. A specific study should be performed to statistically confirm these results on a larger cohort of healthy volunteers as well as mCRC patients. Although we routinely experienced that abnormal plasmas resulted in lowering cirDNA concentration values, we cannot fully discriminate the implication of postprandial effects like triglycerides serum level, to the possible involvement of biological changes dues to the circadian rhythm. In order to definitively address this issue, it would be interesting to compare NcirDNA levels at 9.00 AM and 12.00 AM, with and without breakfast, in order to determine the impact of food intake and circadian rhythm, respectively. In addition, the influence of the menopause on cirDNA concentration with regard to our observations of gender, age and pathological status is only speculative. A specific study on the difference of NcirDNA concentration between postmenopausal and premenopausal healthy females, as well as the difference between premenstrual and postmenstrual young women, should be performed to definitively address this issue. Conclusions drawn here in respect to cancer plasma samples should be restricted to mCRC patients and extension to other malignancies or even to localized disease is speculative.While the study was carried out with statistically sufficient number of subjects to support the observations, the In conclusion, the levels of mitochondrial and nuclear circulating DNA differently vary with regards to pre-analytical and demographic factors. Those variables should be taken into consideration when evaluating cirDNA analysis in clinical setting and perhaps in the future clinical practice when cirDNA quantification is directly or indirectly used as a biomarker. In addition, our study highlights the potential for combining the analysis of NcirDNA and McirDNA since examining their respective levels may have diagnostic value.KRAS exon 2 and BRAF V600E mutations by circulating DNA (cirDNA) analysis to conventional detection by tumor tissue analysis59. This study (KPLEX2) was performed and presented under the STARD criteria. 140 patients have been included in 11 clinical centers in France, over a period of 12 months. Eligible patients were male or female, aged ≥18 years, with a proven histologically mCRC, a measurable disease as defined by response evaluation criteria in solid tumors (RECIST v1.1) and untreated by radiotherapy or chemotherapy in the last 4 weeks before inclusion. There is no possibility that cirDNA from mCRC patients can be affected by therapy since eligible patients were untreated by radiotherapy or chemotherapy in the last 4 weeks before blood collection (inclusion criteria). Written consent was obtained from the part of all patients. Inclusion criteria were described previously59. 19 were excluded from analysis for no respect of the inclusion criteria (due to various inclusion criteria) and 3 were excluded due to lack of data collected on age or gender. NcirDNA analysis was performed on 118 patients and McirDNA analysis on 50/118 mCRC patients. While study on age and gender effect were carried out on all the included mCRC patients for NcirDNA (N = 118), cohort patient number varied when studying plasma aspect and delay for NcirDNA, or age and gender for McirDNA because of two main reasons: non-reported information for delay and plasma aspect for to NcirDNA and insufficient plasma volume needed to carry out supplementary analysis for McirDNA. All data on age and gender were collected. Plasma aspect  was noted for 106/118 mCRC patients and delay between time of blood draw and the last food intake was informed for 89/118 mCRC patients.Blood samples collected from 104 healthy donors were provided by the Etablissement Français du Sang (E.F.S), the blood transfusion center of Montpellier (Convention EFS-PM N° 21PLER2015-0013). Blood samples collected by the E.F.S were analyzed . If an anomaly is detected, the sample is ruled out and the donor is warned then by mail. NcirDNA concentration and McirDNA copy number were determined for all healthy individuals. Data on age and gender were collected for all healthy individuals. mCRC patients data are taken from a study comparing the detection of Blood samples collected from 104 healthy donors were provided by the Etablissement Français du Sang (E.F.S), the blood transfusion center of Montpellier (Convention EFS-PM N° 21PLER2015-0013). Plasma samples from mCRC patients were obtained from the Kplex2 study registration number EUDRACT 2016-001490-33 with ethic committee approval . All methods were performed in accordance with the relevant guidelines and regulations. The study obtained informed consent from all participants for the study.7 samples for each healthy donor were collected at 9.00 a.m. on Day 0 (fasted state): 3 with EDTA K2 tubes and 4 with Cell-Free DNA BCT® STRECK tubes. Each tube was processed as we early described, at day 0, 2 and 5 for EDTA tubes and day 0, 2, 5 and 7 for BCT tubes. EDTA tubes were stored at +4 °C and BCT tubes were conserved at room temperature before isolation. Healthy individual N°1 (HI1) and N°2 (HI2) are 57 year-old and 29 year-old men, respectively, both with no known disease. Healthy individual N°3 (HI3), N°4 (HI4) and N°5 (HI5) are 25 year-old, 24 year-old and 28 year-old women, respectively, both with no known diseases. NcirDNA analysis for HI1 was not performed due to clotting in BCT tubes.4 blood collection times were defined 9.00 a.m. (fasted state), 12.00 p.m. (2 hours after breakfast), 3.00 p.m. (2 hours after lunch) and 6.00 p.m. (4 hours after lunch). One EDTA K2 and one Cell-Free DNA BCT® STRECK tube per day-time were collected for each donor. Healthy individual N°6 (HI6) and N°7 (HI7) are 29 year-old and 27 year-old men, respectively, both with no known diseases. Healthy individual N°8 (HI8) and N°9 (HI9) are 28 year-old and 30 year-old women respectively, both with no known diseases. Each donors took the same meal during breakfast: one butter croissant, one chocolate croissant and one coffee with sugar; and for the lunch: a dish of tomato rice with sausage, bread and one coffee with sugar.22. In summary, blood was collected in EDTA K3 tubes and plasma was isolated within 2 hours. The isolation technique consist of a double centrifugation. Initially, tubes were centrifuged for 10 minutes at 4 °C and 1,200 g in a Heraeus Multifuge LR centrifuge. The supernatant was collected while carefully avoiding the buffy-coat. The second centrifugation was conducted for 10 minutes at 4 °C and 16,000 g. The supernatant was transferred to 1.5 ml tubes before performing the extraction of cirDNA or being stored at −20 °C. CirDNA extraction was performed with the Qiagen Blood Mini kit, following all steps of the protocol. In all, 0.2 to 1 ml of plasma was extracted in several successive passes on a column. The final elution volume was 80 to 130 µl and eluates were frozen at −20 °C prior to analysis by qPCR. Freeze-thaw cycles should be avoided to reduce the phenomenon of cirDNA fragmentation and the extracts are not kept longer than 3 months at −20 °C.Samples were collected and treated in accordance with a pre-analytical guideline previously established by our group60. The method is based on an innovative design of short amplicons (60–100 bp ± 10 bp) targeting a wild-type sequence of the gene, . Quantification of this amplicon gives an estimation of the total NcirDNA and McirDNA concentration, respectively. For the quantification of NcirDNA, we amplified of a 67 bp-length sequence of the KRAS gene with the following primers: forward (5′ CCTTGGGTTTCAAGTTATATG 3′) and reverse (5′ CCCTGACATACTCCCAAGGA 3′). For McirDNA, we amplified a 67 bp-length sequence of the cytochrome oxidase sub-unit 3 mitochondrial gene with the following primers: forward (5′ GACCCACCAATCACATGC 3′) and reverse (5′ TGAGAGGGCCCCTGTTAG 3′). These primers were designed using the Primer 3 software according to the following requirements: (i) Tm ranging from 50 to 64 °C; (ii) GC-content between 40 and 60%; (iii) size from 18 to 23 NT; (iv) amplicon size ranging from 60 to 100 bp. We performed local-alignment analyses with the BLAST program to confirm the specificity of the designed primers. All sequences were checked for self- or inter-molecular annealing with nucleic-acid-folding software . Oligonucleotides were synthesized and HPLC-purified by Eurofins  and quality control of the oligonucleotides was performed by MALDI-TOF. For all analyses, negative controls and standard curves were used. All tests are performed in triplicate with 5 µl of DNA extract in a 25 µl reaction volume, on a CFX96 instrument using CFX manager software (Bio-Rad). This method  and technical validation have been described previously25. The mCRC patient’s blood samples were excluded if the total cirDNA concentration, due to a problem of pre-analytic treatment or even for unknown reasons, was below a quality threshold.CirDNA analysis was performed by a qPCR technique developed in our laboratory, and clinically validated previously61. The measurement of the total cirDNA concentration by targeting a KRAS sequence was routinely controlled by quantifying a BRAF sequence. Control quality is acceptable when the KRAS-based value is 1.3 to 1.8-fold higher than that of the BRAF-based value when using the reported Q-PCR primer systems; otherwise, a second analysis is performed. Note, BRAF analysis data from 33 healthy individuals were not available. In addition, we excluded 26 patient plasmas  in which the KRAS/BRAF ratio was over 3 or below 0.5. Data revealed that the KRAS-based concentration value was positively correlated with the BRAF-based concentration values in the 62 healthy individuals  and in the 101 mCRC patients  25.The KRAS and BRAF genes are monogenic and poorly amplified in both healthy and cancer individuals62. Supplementary section figures present NcirDNA concentration determined from targeting BRAF with using a primer set of similar size. Data revealed that the same observations could be made: the NcirDNA amount, as determined using BRAF sequence targeting, is statistically different in healthy (N = 62) and mCRC individuals (N = 101) , and fully correlates with our observation based on KRAS sequence targeting. We may therefore indicate that amplification of the KRAS gene will not have any influence on the observations and conclusions made in this study. Valtorta et al. detected KRAS amplification in 7/1,039 (0.67%) evaluable CRC specimens, demonstrating that KRAS amplification is an infrequent event in CRC28. Thus this level will not modify significantly the observations or values described in our manuscript.The DNA concentration quality threshold was 3 ng/mL (about 900 copies/mL) for cancer patients. Note, this value corresponds to about the half of the median concentration found for healthy individuals . In addition to quantifying cirDNA by targeting two different sequences on two different chromosomes, an experiment based on Poisson law distribution showed the accuracy of our cirDNA amount measurement . The standard curve was used to determine the NcirDNA concentration of the mCRC patients and healthy individuals and calculate the NcirDNA copy number per milliliter of plasma.A genomic DNA extract from human wild-type MT-CO3 insert was obtained from ABM good® (accession no.YP_003024032) and used for the McirDNA calibration assay. Initial vector solution concentration and purity were determined by measuring optic density at λ = 260 nm, 230 nm and 280 nm, with an Eppendorf BioPhotometer® D30. Starting vector concentration was adjusted at 1800 pg/µl for the first dilution point, according to optic density measurement at λ = 260 nm. A qPCR standard curve was obtained by 6 successive dilutions of the vector solution . The standard curve was used to determine the McirDNA concentration of the mCRC patients and healthy individuals and calculate the McirDNA copy number per milliliter of plasma.A 3382-pb human ORF vector with a 786-pb NcirDNA copy number per milliliter of plasma, in all analyses, was determined with the following calculation:nuclear is the NcirDNA copy number per milliliter, c is the NcirDNA concentration (pg/µl) determined by qPCR targeting the nuclear KRAS gene sequence and 3.3 pg is the human haploid genome mass. Velution is the volume of cirDNA extract (µl) and Vplasma is the volume of plasma used for the extraction (ml).QMcirDNA copy number per milliliter of plasma, in all analyses, was determined with the following calculation:mitochondrial is the McirDNA copy number per milliliter, ‘c’ is the McirDNA mass concentration (g/µl) determined by a qPCR targeting the mitochondrial MT-CO3 gene. NA is Avogadro’s number (6.02 * 1023 molecules per mole), Lvector is the plasmid length (nucleotides) and MW is the molecular weight of one nucleotide (g/mol). Velution is the elution volume of cirDNA extract (µl) and Vplasma is the volume of plasma used for the extraction (ml).QFor continuous variables, median and range were computed. To investigate their associations with the biologic parameters, univariate statistical analyses were performed using Mann-Whitney U test or Kruskal-Wallis rank test for continuous variables. Moreover, multivariate analyses were carried out using logistic regressions, with a stepwise selection procedure, to investigate known predictive. Odds ratio (OR) with 95% confidence intervals (CIs) are presented. The power of analysis was reduced due to all patients did not have measurements for all variables. All P values reported are two sided. A probability of ≤0.05 was considered to be statistically significant; *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001. Statistical analysis was performed using the STATA 13.1 software .Supplementary information


We aimed to review the epidemiologic literature examining lifestyle and metabolic risk factors, and blood‐based biomarkers including multi‐omics  and to discuss how these predictive markers can inform early diagnosis of pancreatic ductal adenocarcinoma (PDAC). A search of the PubMed database was conducted in June 2018 to review epidemiologic studies of (i) lifestyle and metabolic risk factors for PDAC, genome‐wide association studies, and risk prediction models incorporating these factors and (ii) blood‐based biomarkers for PDAC . Prospective cohort studies have reported at least 20 possible risk factors for PDAC, including smoking, heavy alcohol drinking, adiposity, diabetes, and pancreatitis, but the relative risks and population attributable fractions of individual risk factors are small (mostly < 10%). High‐throughput technologies have continued to yield promising genetic, metabolic, and protein biomarkers in addition to conventional biomarkers such as carbohydrate antigen 19‐9. Nonetheless, most studies have utilized a hospital‐based case–control design, and the diagnostic accuracy is low in studies that collected pre‐diagnostic samples. Risk prediction models incorporating lifestyle and metabolic factors as well as other clinical parameters have shown good discrimination and calibration. Combination of traditional risk factors, genomics, and blood‐based biomarkers can help identify high‐risk populations and inform clinical decisions. Multi‐omics investigations can provide valuable insights into disease etiology, but prospective cohort studies that collect pre‐diagnostic samples and validation in independent studies are warranted. Pancreatic ductal adenocarcinoma (PDAC) has the highest case fatality rate of all cancers.Prospective studies so far have reported over 20 potential risk factors for PDAC, primarily lifestyle and metabolic risk factors, while case–control studies have suggested the clinical utility of genomics, proteomics, and metabolomics assays in early diagnosis of PDAC. An integrated approach of traditional risk factors and biomarkers may improve our understanding of risk prediction and early diagnosis of PDAC. This review gives a timely overview in these areas and can be particularly helpful to inform large‐scale population‐based studies given the readily available resources. In this article, we will review (i) epidemiologic studies of lifestyle and metabolic risk factors for PDAC , genome‐wide association studies (GWAS), and risk prediction models incorporating these factors and (ii) epidemiologic studies of blood‐based biomarkers for PDAC . As summarized in Figure Pancreatic ductal adenocarcinoma has a multifactorial etiology, and several risk factors have been reported.versus 20 g/day of red meat intake,versus 20 g/day of processed meat intake.Lifestyle risk factors including smoking, alcohol drinking, and diet have been investigated in relation to risk of PDAC. Among these lifestyle factors, smoking is the most well‐established one. A meta‐analysis of 35 prospective cohort studies with 14 236 PDAC cases reported a 70% and 20% excess risk among current and former smokers, respectively.n = 8353).versus low categories was 0.91 (0.69–1.19) for total and 0.95 (0.90–1.01) for leisure‐time physical activity.In addition to lifestyle factors, metabolic risk factors that are related to the insulin resistance syndrome may play a role in the etiology of PDAC. Physical activity is associated with improved insulin sensitivity, lower blood glucose, and lower risk of developing type 2 diabetes.2 higher adulthood BMI .2 higher young adulthood BMI .2 higher BMI),Adiposity is an established risk factor for PDAC, and the WCRF has judged that evidence that body fatness is a cause of PDAC is convincing.Diabetes is associated with a 1.5‐fold to 2.5‐fold higher risk of PDAC.Pancreatitis, an inflammatory disease of the pancreas, is also associated with risk of PDAC.Helicobacter pylori infection and periodontitis are associated with higher risks of PDAC,H. pylori with PDAC risk have not been consistent.Apart from these lifestyle and metabolic risk factors mentioned earlier, other risk factors for PDAC have been reported, including hepatitis B virus (RR 1.2–1.4) and non‐O blood group (RR 1.3–1.4). Although the evidence has been inconclusive, history of allergy is associated with lower risk of PDAC, while regular use of aspirin and nonsteroidal anti‐inflammatory drugs is not associated with risk of PDAC.While Mendelian randomization studies utilize genetic instruments for putative risk factors to assess the causality of metabolic risk factors in relation to the disease, large‐scale, trans‐ethnic GWAS have continued to identify common genetic variants that influence disease risk. For sporadic PDAC, previous GWAS have identified at least 22 common variants in primarily European populations . Recent On the other hand, the genetic basis for familial PDAC remains poorly understood, although several genetic factors for PDAC have been identified. A positive family history of PDAC has been reported to be associated with an 80–200% higher risk.Blood sampling through venesection is a noninvasive and cost‐effective approach that can provide high‐throughput diagnostic information.Carbohydrate antigen 19‐9 (CA 19‐9) is a Lewis antigen of the mucin 1 protein class, a well‐established blood test for the early detection of PDAC.Apart from CA 19‐9, alternative biomarkers have been investigated in early detection of PDAC, including tumor markers , cytokines/chemokines , cell adhesion molecules (e.g. intercellular adhesion molecule 1), proteases/inhibitors in extracellular matrix degradation , acute‐phase reactants (e.g. C‐reactive protein and serum amyloid A), and other biomarkers (e.g. osteoprotegerin and IGF‐binding proteins [IGFBP2 and IGFBP3]).In recent years, advances have been made in proteomics assays (mainly antibody microarrays) to capture the systemic immune response to cancer.At least 15 case–control studies have assessed protein panels mostly consisting of two to five biomarkers Tables ,S3. Theset al. presented the first multiplex serum biomarker signature in a case–control study of 34 PDAC patients and 30 healthy controls, as well as 16 CP and 23 autoimmune pancreatitis patients.Previous studies demonstrated the benefit of a larger panel of protein biomarkers in both Caucasians and Asians, using a case–control study design .49, 57, In a recent study, the same study group identified stage‐ associated biomarkers by comparing stage I–IV patients and demonstrated the possibility for diagnosis of PDAC in earlier disease stages .59 The ivs 0.63).P < 0.01).Although case–control studies have demonstrated good diagnostic accuracy of multiplex protein signatures, several prospective studies using pre‐diagnostic samples have yielded low discrimination Table .51, 52, In addition to studies investigating proteomics alone, recent evidence has shown that the combination of protein biomarkers and ctDNA can reach a sensitivity of ~70% and specificity of > 99% in distinguishing PDAC cases and healthy controls .61, 62 CMetabolomics is the comprehensive characterization of small low‐molecular‐weight metabolites in biological samples,et al. used both untargeted and targeted MS‐based approaches including lipidomics and showed that PDAC patients could be distinguished from CP patients, as well as from healthy controls.n = 271), CP (n = 282), liver cirrhosis (n = 100), and healthy and non‐pancreatic disease controls (n = 261) were recruited in three consecutive studies. Of the 474 metabolites measured, a biomarker signature . This elevated risk was independent of known predisposing factors, with the strongest association observed among subjects with samples collected 2 to 5 years before diagnosis. Another nested case–control study within the Japan Public Health Center‐based Prospective Study with 170 cases quantified 12 targeted metabolites and showed that, among patients diagnosed in the first 6 years of follow‐up, higher levels of 1,5‐anhydroglucitol , asparagine, tyrosine, and uric acid were associated with decreased risk of PDAC after adjustment for potential confounders (P for trend 0.02–0.04).P for trend 0.06 and 0.07, respectively). Recently, a nested case–control study in the Shanghai Men's Health Study and the Shanghai Women's Health Study with 226 cases identified 10 metabolites that were associated with risk of PDAC, including seven glycerophospholipids.On the other hand, three nested case–control studies within prospective cohort studies investigated the performance of metabolomics in pre‐diagnostic blood samples Table . Using lLifestyle and metabolic risk factors, as well as genomics and blood‐based biomarkers, are predictive of risk of PDAC. However, blood‐based biomarkers have been rarely investigated in relation to risk prediction in the general population as the majority of previous studies are hospital‐based case–control studies. The primary goal of risk prediction model is to develop a tool for identifying individuals at a high risk of PDAC. Well‐developed models can provide accurate risk assessment for individuals and can inform screening decisions. Although imaging (endoscopic ultrasonography and/or magnetic resonance cholangiopancreatography) has been recommended for initial screening among high‐risk populations, there is no consensus on screening modalities and intervals for follow‐up imaging.PancPRO is a Mendelian model for PDAC risk prediction for identifying high‐risk individuals in those with familial PDAC, built using a Bayesian modeling framework, and was validated using an independent cohort in the National Family Pancreas Tumor Registry (961 families and 26 incident PDAC cases).For risk prediction models developed in the general population, the majority of previous studies developed risk prediction models using routinely collected data and traditional regression models Table ,69, 70, Despite the large number of risk factors suggested by observational studies, the magnitude of RRs is overall small and the PAFs are low. Risk prediction models incorporating these lifestyle and metabolic risk factors as well as other factors routinely collected by health insurance systems have achieved satisfying sensitivity and specificity. Genetic studies of very large samples sizes are required for the discovery of genetic variants ranging from rare, low‐frequency, and common variants in order to inform risk prediction models. Novel blood‐based biomarkers, particularly proteomics and metabolomics, can inform early diagnosis of PDAC. However, prospective cohort studies that collect pre‐diagnostic samples and validation in independent studies are warranted. In this context, prospective biobank studies with samples collected prior to disease onset are valuable resources. For example, the China Kadoorie Biobank has proposed a multi‐omic approach  to investigate novel biomarkers relevant for risk prediction and early diagnosis of PDAC. Similarly, the European Prospective Investigation into Cancer and Nutrition has included metabolomics in their ongoing research topics for PDAC.Because of the lack of noninvasive and low‐cost screening tools, the current recommendation is that screening the general population for PDAC is not feasible, and screening will need to be restricted to people at high risk of PDAC.Table S1. Reviews of ctDNA, cell‐free non‐coding RNA, exosomes, microbiome, circulating tumor cells, and PDAC.Table S2. Study information of GWAS studies of PDAC.Table S3. Study information of case–control studies of proteomics and PDAC.Table S4. Study information of case–control studies of metabolomics and PDAC.Table S5. Case–control studies of metabolomics and PDAC reporting on phospholipids.Table S6. Prospective studies of blood lipids and risk of PDAC.Click here for additional data file.