To investigate the association of polypharmacy with adverse health outcomes, in relation to comorbidity and frailty.Excessive polypharmacy (≥ 10 medications) is highly prevalent in older adults at the emergency department and associated with falls, mortality and readmission. Frailty and comorbidity partly drive the association of polypharmacy with adverse health outcomes.Trials that target polypharmacy and inappropriate prescribing are needed to answer the lingering question of causality in the observed polypharmacy–mortality association and to evaluate whether medication review improves health outcomes in older patients at the ED.The online version contains supplementary material available at 10.1007/s41999-022-00664-y. Older adults at the emergency department (ED) with polypharmacy, comorbidity, and frailty are at risk of adverse health outcomes. We investigated the association of polypharmacy with adverse health outcomes, in relation to comorbidity and frailty.This is a prospective cohort study in ED patients  ≥ 70 years. Non-polypharmacy was defined as 0–4 medications, polypharmacy 5–9 and excessive polypharmacy ≥ 10. Comorbidity was classified by the Charlson comorbidity index (CCI). Frailty was defined by the Identification of Seniors At Risk—Hospitalized Patients (ISAR-HP) score. The primary outcome was 3-month mortality. Secondary outcomes were readmission to an ED/hospital ward and a self-reported fall < 3 months. The association between polypharmacy, comorbidity and frailty was analyzed by logistic regression.881 patients were included. 43% had polypharmacy and 18% had excessive polypharmacy. After 3 months, 9% died, 30% were readmitted, and 21% reported a fall. Compared with non-polypharmacy, the odds ratio (OR) for mortality ranged from 2.62 (95% CI 1.39–4.93) in patients with polypharmacy to 3.92 (95% CI 1.95–7.90) in excessive polypharmacy. The OR weakened after adjustment for comorbidity: 1.80 (95% CI 0.92–3.52) and 2.32 (95% CI 1.10–4.90). After adjusting for frailty, the OR weakened to 2.10 (95% CI 1.10–4.00) and OR 2.40 (95% CI 1.15–5.02). No significant association was found for readmission or self-reported fall.Polypharmacy is common in older patients at the ED. Polypharmacy, and especially excessive polypharmacy, is associated with an increased risk of mortality. The observed association is complex given the confounding effect of comorbidity and frailty.The online version contains supplementary material available at 10.1007/s41999-022-00664-y. The proportion of older patients seen at the emergency department (ED) is high and is expected to rise in the next decades . Since tPharmacological therapy is a highly valued and an effective intervention. Nonetheless, the benefits of treatment should outweigh the risks in each individual patient, particularly in older patients with frailty, chronic comorbidity, or those near the end of life. The presence of multiple diseases, and therewith polypharmacy, increases the risk of drug non-compliance, drug–drug interactions, and adverse drug reactions  , 5. OldeStudies investigating the association between polypharmacy and adverse events in older patients in the emergency care setting are sparse, and did not include important confounders such comorbidity and frailty –13. The This prospective cohort study—the Amsterdam Geriatric Emergency Medicine study (AmsterGEM) —was condFor the AmsterGEM study , researcData were collected by chart review and interviews with patients and their caregivers at the ED. All the research students collecting data were extensively trained by a team of geriatric consultants. Sociodemographic data and care-related data were obtained at baseline, including living situation, and number of prescriptions. Physical status was assessed by ‘The Katz Index of Independence in Activities of Daily Living’ , polypharmacy (5–9) and excessive polypharmacy (≥ 10) , 22. Subdid you fall between now and inclusion?’ by telephone and registered a fall as a dichotomous answer (yes/no). If possible, the caregiver was asked to verify the answer. Only the first fall after baseline was taken into consideration. Readmission was defined as a second presentation at the ED and/or readmission to a hospital ward.The primary outcome measures was 3-month mortality. Secondary outcome measures were readmission and a self-reported fall at 3 months. Other follow-up timepoints were at 1 and 6 months. Follow-up data were collected by research students, who were not blinded to baseline data. Follow-up information was obtained by telephone using a standardized charting form. If the patient was unreachable after five attempts by telephone, follow-up data were obtained from the general practitioner. Data on mortality were extracted from the electronic health record and cross-referenced with the general practitioner or caregiver. A fall was defined as an event reported by the person who fell. At each follow-up moment, we asked the patient ‘U test. Differences in dichotomous data were evaluated with a Chi-square test. In all analyses, a p-value of < 0.05 was considered statistically significant. Logistic regression analyses were performed to compare the risk of mortality, readmission, and a self-reported fall in patients with polypharmacy, and excessive polypharmacy, with non-polypharmacy as reference category. Different models were conducted: (1) crude; (2) adjusted for age and gender; (3) adjusted for chronic comorbidity (Charlson comorbidity index) and (4) adjusted for frailty (ISAR-HP). Before conducting the logistic regression analyses, we checked for collinearity among polypharmacy, comorbidity and frailty, using Spearman correlation coefficients and variance inflation factors (VIFs). In our data, there was no substantial overlap between these variables . Additional logistic regression analyses were conducted to evaluate the association between the use of one additional medication and adverse outcomes in all patients, and expressed as odd ratio’s with 95% confidence intervals. A sensitivity analysis was performed for the primary outcome of falls after 3 months in patients without self-reported memory problems. Data were statistically analyzed with IBM SPSS statistics version 25 .Continuous variables were displayed as median with an interquartile range (IQR) given the skewed data. Dichotomous variables were presented as numbers with percentages. Differences between groups were tested with a Mann–Whitney In total, 1601 patients were screened for eligibility and 720 were excluded  older patients had died, 249 (31%) patients were readmitted to the hospital, and 141 (21%) reported a fall (Table The odds ratio (OR) for mortality ranged from 2.62 95% CI 1.39–4.93) in patients with polypharmacy to 3.92 (95% CI 1.95–7.90) in patients with excessive polypharmacy, compared with non-polypharmacy (Table 9–4.93 inThe OR for readmission ranged from 1.46 (95% CI 1.04–2.04) in patients with polypharmacy to 1.37 (95% CI 0.89–2.10) in patients with excessive polypharmacy, compared with non-polypharmacy  diseases. With increasing frailty, medication related problems are more likely because of the reduced functional reserve and impaired homeostatic compensatory mechanisms , 23. TheThe high prevalence of polypharmacy, comorbidity, and frailty along with the high proportion of short-term adverse events in our cohort justify initiatives to improve the prescribing quality and a close medication review in older ED patients . ResultsThe strengths and weaknesses of this study merit careful consideration. Major strengths are its prospective design, large sample size, and well-characterized study population. This is also the first study at the ED that evaluates frailty and comorbidity in relation to polypharmacy and adverse outcomes using the ISAR-HP  and CharWeaknesses should also be acknowledged. First, the possibility of selection bias, as older patients were only included if they were willing and/or able to participate. Second, inaccurate medication recall is common among older patients  and perhThis study in older patients at the ED shows that polypharmacy is highly prevalent and independently associated with mortality. However, this association was attenuated by frailty and comorbidity, illustrating a complex interplay. Trials that target polypharmacy and inappropriate prescribing are needed to address the unanswered question relating to causality in the observed polypharmacy–mortality association and to evaluate whether medication review improves health outcomes in older patients at the ED.Supplementary file1 (DOCX 16 KB)Supplementary file2 (DOCX 19 KB)Below is the link to the electronic supplementary material.


TP73 was recently identified as a novel causative gene for amyotrophic lateral sclerosis (ALS). We aimed to determine the contribution of variations in TP73 in the Chinese ALS population and to further explore the genotype-phenotype correlations.TP73 mutations in a large Chinese ALS cohort and performed association analysis of both rare and common TP73 variations between cases and controls.We screened rare, putative pathogenic TP73 were identified among six unrelated sALS patients. Exon 14 of TP73 might be a mutant hotspot in our cohort. Patients with ALS with only rare, putative pathogenic TP73 mutations exhibited a characteristic clinical profile. Patients harboring multiple mutations in TP73 and other ALS-related genes displayed a significantly earlier onset of ALS. Association analysis revealed that rare TP73 variants in the untranslated regions (UTRs) were enriched among ALS patients; meanwhile, two common variants in the exon-intron boundary were discovered to be associated with ALS.Of the 985 ALS patients studied, six rare, heterozygous putative pathogenic variants in TP73 variations also have contributed to ALS in the Asian population and broaden the genotypic and phenotypic spectrum of TP73 variants in the ALS-frontotemporal dementia (FTD) spectrum. Furthermore, our findings first suggest that TP73 is not only a causative gene, but also exerts a disease-modifying effect. These results may contribute to a better understanding of the molecular mechanism of ALS.We demonstrate that  ALS hasTP73) as a novel causative gene for ALS  axonal branching assay in vivo. Furthermore, they first proposed that apoptosis in motor neurons may be involved in the pathology of ALS. Following that, Pereira and colleagues linked rare mutations in TP73 with frontotemporal dementia (FTD), particularly primary progressive aphasia, which further corroborated the contribution of TP73 variations in the ALS-FTD spectrum . WES was performed on all participants using a previously published method . the frequency was lower than 0.001 in our in-house and the following public database: the 1,000 Genome Project-East Asian (1000G_EAS), the NHLBI Exome Sequencing Project-East Asian (ESP6500s_EAS), the Exome Aggregation Consortium-East Asian (ExAC_EAS), and the Genome Aggregation Database-East Asian (GnomAD_EAS); (2). absent from HCs (3). located in the protein-coding regions; (4). present in a heterozygous state; (5). annotated as missense, stop gain/loss, frameshift, or splice-site variants; and (6). rare missense variants predicted to be pathogenic by at least five of 11 in silico  in all participants in our cohort .TP73 variants with ALS risk in the Chinese population, we conducted the single common variant association test between the ALS and control groups using the Mixed Linear Model (MLM). The extracted top three principal components of population stratification in the principal component analysis (PCA) were included as covariates in our cohort. The possible effect of common variants inside exon-intron boundaries on the TP73 gene expression was assessed by Expression Quantitative Trait Locus (eQTL) analysis  implemented in the R package SKAT  for continuous variables and percentages for categorical variables. The comparison of continuous variables was assessed by Student’s t-test. For categorical variables, the Fisher’s exact test or Chi-square test was used to verify the significant differences between the two compared groups. Statistical analyses were carried out in SPSS (version 26.0) software. All tests were two-tailed, and significance was set at 3.3.1.In all, 985 patients with ALS and 1,224 healthy controls were analyzed in our study. The 985 ALS patients were on average 54.2 years old at the time of onset, and 56.2 years old at the time of sampling; the geographically matched controls were older (mean age 68.47 years). 75.3% (740/985) of our ALS cohort had limb onset, while 19.39% (191/985) had bulbar onset. Detailed demographic information for all individuals is shown in 3.2.TP73 exon regions and their surrounding sequences in each ALS patient. A total of six rare, heterozygous putative pathogenic mutations that fulfilled pathogenicity criteria were discovered among six unrelated sALS patients but not in HCs. The overall frequency of patients with rare pathogenic TP73 mutations was 0.6%. All these six variations were protein-altering missense mutations. And the majority of them substituted amino acids that were highly conserved across species. The six variants were c.187G > A (p.A63T), c.1226C > T (p.P409L), c.1613G > A (p.R538H), c.1628G > A (p.R543Q), c.1679 T > C (p.L560P), and c.1736G > A (p.R579H). The variant, c.187G > A (p.A63T), was novel and absent from all databases. Three of these six variations (50%), namely c.187G > A (p.A63T), c.1226C > T (p.P409L), and c.1613G > A (p.R538H) have never been reported in association with the ALS-FTD spectrum. Interestingly, when investigating the distribution of these six potential pathogenic loci present in TP73, we discovered that a significant portion of them  were located inside exon 14, which differed from earlier studies. Consequently, we hypothesize that exon 14 of TP73 might be a mutant hotspot in our ALS series. The location and pathogenicity information of these six variants identified in this study were summarized in We screened all 3.3.TP73 variants described above, the age at disease onset ranged from 32 to 69 years old. We observed a completely masculine involvement since all six patients were men. In addition, we screened for other pathogenic mutations in the known ALS genes in these patients , and the remaining four patients only had TP73 mutations. Considering the burden of multiple variants in ALS-causing genes on the disease expression, including the age at onset, progression rate, and survival, we separated these six patients into two subgroups for further clinical phenotype analysis  , a shorter survival time (19.25 ± 10.01 months), and invariably limb onset. In the other subgroup, in contrast to patients carrying only TP73 mutations, the age at onset in patients with ALS with multiple mutations is accelerated . Furthermore, one of the six patients, S0423, suffered from cognitive impairment and behavioral problems and was eventually diagnosed with ALS-FTD. Most of these affected individuals exhibited typical symptoms and signs of the simultaneous impairment of upper and lower motor neurons. The clinical features of the six sALS patients were listed in For the six patients carrying potential pathogenic patients . There wd genes) . When thThe variant, c.1226C > T (p.P409L), was identified in patient S0423, diagnosed with ALS-FTD. He was a farmer without specific environmental exposure. At the age of 65, he presented with muscle weakness in his upper limbs initiated from bilateral hands, which quickly extended to all four extremities in only 1 month. He then developed dysarthria and dysphagia 2 months later. Concurrently, his primary care providers complained of his language difficulties, behavioral changes, and cognitive impairment, which included trouble retrieving words, effortful speech restricted to short, simple sentences, hyperphagia, irritability, attacks on others, decreased memory, dropped comprehension, inability to find out things, and failure to understand some instructions. At the time of the first observation after 8 months of onset (at 66 years old), he was unable to walk or talk, and was reliant on family members for his daily requirements. On comprehensive neurological evaluation, he showed signs of upper motor neuron (UMN) damage and lower motor neuron (LMN) depletion features, such as positive palmomental reflex, hyperreflexia, and muscle atrophy of all four extremities. Electromyogram (EMG) revealed abundant and diffuse ongoing denervation  and chronic reinnervation changes in all four segments . Brain magnetic resonance imaging (MRI) scan indicated mild age-related brain atrophy . A batteAnother two male patients, S3513 and S4096, carrying variants c.1736G > A (p.R579H) and c.1628G > A (p.R543Q) separately, had similar clinical manifestations. They all began with muscle weakness in the right hand, at the ages of 61 and 69, respectively. They both developed muscle weakness and atrophy in all four limbs over the next year, along with extensive fasciculation, and dysarthria. Patient S3513 subsequently reported simultaneous involvement of the contralateral arm, and bilateral legs, accompanied by fasciculation, 5 months after disease onset. He developed dysarthria in the seventh month of onset, at which point the weakness of four limbs aggravated: he was unable to lift heavy objects with his upper limbs and had difficulty walking independently. For patient S4096 with a right-hand onset, his left upper limb and bulbar (choking) were reported as the second symptomatic sites. Following that, at the fifth month of the disease course, the weakness continuously progressed to both lower limbs, and dysarthria appeared. Their bilateral deep tendon reflexes were brisk, the Hoffman sign and palmomental reflex were present. EMGs both showed abundant and diffuse ongoing denervation as well as chronic reinnervation alterations at four segments. However, brain MRI revealed that patient S4096 with the c.1628G > A (p.R543Q) variant had mild brain atrophy , whereasAnother patient (S5941), carrying c.1679 T > C (p.L560P) variants, was a male, who complained of progressive muscle weakness and atrophy in his right arm at the 51-year-old. The weakness gradually extended to his bilateral lower limbs, making stair climbing difficult. Besides, he developed autonomic dysfunction, manifesting as sometimes excessive sweating during the day and heat intolerance, with no nocturnal sweating or cold intolerance, as revealed by the Scale for Outcomes in Parkinson’s disease (PD) for Autonomic Symptoms (SCOPA-AUT), a self-reported questionnaire widely used for the assessment of autonomic function in neurodegenerative disease . NeuroloTP73 and other ALS-related genes. Patient A0048 who carried the c.1613G > A (p.R538H) variant, and patient S7918 who had the c.187G > A (p.A63T) variant, were both young men with no family history of the condition, starting with ALS phenotype at 32 and 38-year-old, respectively. Patient A0048 first displayed weakness in his left lower extremity, and then spread fast in the early disease course. By the fourth month from symptom onward, he had weakness in all four limbs; he then gradually experienced bulbar muscle weakness, presenting with dysarthria, dysphagia, and bucking during the next 2 years. Patient S7918 showed bulbar onset, initially with dysarthria and dysphagia, then developed asymmetric weakness and atrophy in bilateral lower limbs and the right arm within 3 years, accompanied by extensive fasciculation. Neurological evaluation and EMGs on them both indicated evident signs of involvement of both upper and lower motor neurons. According to ECAS, patient A0048 had executive function deficits and memory impairments , whereas patient S7918 had no cognitive changes.The remaining two patients, both harbored multiple variants in 3.4.TP73 and ALS. As shown in TP73 variants were identified in our cohort (p was 0.0028 (0.05/18). The resulting data revealed that none of the common variants inside the exon regions displayed significant univariate association with ALS. While two common variants rs2181486 and rs2146657 in the same block at exon-intron boundary, attained Bonferroni-corrected statistical significance (R2 of these two variants is 0.9850), we next chose the rs2181486 mutant locus to search the publicly accessible GTEx database to find if these two common variations impact the expression of TP73. Of note, based on the calculation of the eQTL algorithm, the rs2181486 variants might give rise to a decreased TP73 protein level in the cerebral cortex, spinal cord, as well as skeletal muscles , a DNA binding domain (DBD), an oligomerization domain (OD), a Sterile-alpha motif domain (SAM), and a transactivation inhibitory domain (TID)  . The p73in (TID) . Wetzel  results . Of noteTP73 mutation accounted for 0.60% of Chinese sALS patients, which was similar with previously reported in Russell’s research (0.60% & 0.82%), both implying that it is an uncommon genetic determination in ALS population worldwide. Interestingly, we discovered that exon 14 of the TP73 gene appeared to be a mutant hotspot in the Chinese ALS cohort, unlike the earlier studies where the mutated sites were relatively clustered in exons eight, nine, ten, eleven and twelve (TP73 variants found in HCs that met similar criteria (except for the (2) in the inclusion criteria). The nine rare variants detected in the control group were dispersed across whole coding regions of TP73, with just one mutant site situated in exon 14, which did not exhibit any mutant site clustering (TP73 variants within exon 14 between cases and controls (p = 0.1789). In the current study, we found four of the six (66.7%) variants at exon 14 , the lower disease duration (19.25 months vs. 71 months), and the higher spinal onset rate (100% vs. 76.6%)  . These futations . Based ot of ALS . NeverthTP73 variants among 34 sALS patients in a large Chinese ALS cohort and briefly described the clinical presentation of these patients having rare TP73 mutation: the average age at onset was 54.32 (11.76) years, with a sex ratio of 1.45:1. There was no data on disease progression or survival available. Our findings suggest that patients with ALS with only rare, putative pathogenic TP73 mutations tend to have a late onset, faster progression, and worse prognosis. The following might be the causes of these clinical phenotypic contradictions: Patients and controls in Li, Chunyu et al’s and our studies came from different Chinese areas; The criteria for rare, pathogenic variants in Li, Chunyu et al’s study were inconsistent with those in our study; Two studies varied in the exclusion of patients with ALS with known pathogenic mutations in established ALS genes, making Li, Chunyu et al’ patients’ clinical presentation more complex and variable; However, in the present study, we tightly restricted the rarity and pathogenicity of TP73 variations, and separated these ALS patients with TP73 mutation into two subgroups for further clinical phenotype analysis .A most recent publication by TP73 variants in the exon-intron boundaries associated with ALS, and both can lead to a reduced P73 protein expression in the human brain based on the eQTL analysis. In addition, we noted an enrichment of rare variants in the UTRs of TP73 among our ALS patients. Increasing evidence has shown that variations in the UTRs are strongly linked to human diseases by impacting the transcription of nearby genes and protein expression level by changing the poly(A) motifs, RNA secondary structure, and RNA binding protein-binding sites ; National Key R&D Program of China (No. 2021YFA0805202 and 2018YFC1312003); the Program of the National Natural Science Foundation of China ; the National Key Research and Development Program of China (#2018YFC1312003); the Natural Science Fund for Distinguished Young Scholars of Hunan Province, China (#2020JJ2057); the Project Program of National Clinical Research Center for Geriatric Disorders at Xiangya Hospital (#2020LNJJ13).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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Previous studies have suggested that patients with esophageal squamous cell carcinoma (ESCC) are still at a high risk of developing second primary malignancies (SPMs) after definitive therapies. We evaluated the development of SPMs and explored its risk factors in patients with clinical T1bN0 ESCC.JCOG0502 prospectively compared esophagectomy with definitive chemo-radiotherapy for clinical T1bN0 ESCC. Here, we reviewed all JCOG0502 patients’ data for SPMs and investigated the risk factors for SPMs using uni-variable and multivariable analyses by Fine and Gray model.Among 379 enrolled patients, 213 underwent esophagectomy and 166 received chemo-radiotherapy. Patient characteristics were male (85%); median age  and many LVLs  were significantly associated with the development of SPMs. Sixteen patients died due to the SPMs.The incidence of SPMs was high. The presence of LVLs, which was a predictive factor for SPMs, may be useful for surveillance planning.The online version contains supplementary material available at 10.1007/s00535-022-01870-y. Esophageal cancer is one of the most fatal diseases worldwide, mainly because of its high-grade malignancy . In AsiaWe previously conducted a retrospective analysis of 758 patients with esophageal cancer and found an increased incidence of SPMs in patients with esophageal cancer even after definitive treatment . FurtherJCOG0502, a multicenter phase III trial, compared esophagectomy with definitive chemo-radiotherapy in clinical T1bN0 ESCC . This tr2 (days 1 and 29) and 5-fluorouracil 700 mg/m2/day  combined with 60 Gy/ 30 fr radiotherapy were delivered. Written informed consent was obtained from all the patients prior to enrollment. The study protocol of the trial was approved by the institutional review boards of all institutions. The study was conducted in accordance with the principles of the Helsinki Declaration of 1964 and its later amendments and registered in the clinical trial database (UMIN000000551). Using data from all patients enrolled in JCOG0502, we performed additional analyses for SPMs.The details of JCOG0502 have been described elsewhere . The maiIn the surgical arm, CT scans and tumor marker testing, such as carcinoembryonic antigen and squamous cell carcinoma antigen testing, were performed every 3 months in the first year, every 4 months in the second year, and every 6 months in the third, fourth, and fifth years after completion of surgical resection. Esophagogastroduodenoscopy was performed at the discretion of the investigators. In the chemo-radiotherapy arm, endoscopic examination was mandatory in addition to tumor marker testing, in similar time intervals as CT scan.SPMs were defined as malignancies developing in organs other than the esophagus after enrollment in JCOG0502. SPMs in the esophagus were excluded from this study. Patient characteristics, such as age, body mass index (BMI), history of smoking, and alcohol use, were based on data at the time of enrollment in JCOG0502. LVLs were assessed in the noncancerous esophageal mucosa using electronic images of endoscopic examinations before the initiation of treatment. Three experienced endoscopists who were blinded to the clinical data reviewed the images centrally. Lugol-voiding pattern was graded according to the maximum number of small LVLs in at least one endoscopic field of view as follows: grades A, B, and C referred to no; several (1 to 9); and many (≥ 10) small LVLs, respectively .2), history of alcohol consumption (no vs. 0–25 mL of ethanol per day vs. vs. ≥ 25 mL of ethanol per day), history of smoking (no vs. 1–20 cigarette per day vs. vs. ≥ 20 cigarette per day), LVLs (grade A vs. B vs. C), location of the primary tumor (upper thoracic esophagus vs. middle thoracic esophagus or lower thoracic esophagus), primary tumor length (≥ 4 vs. < 4 cm), and study treatment arm (surgery vs. chemo-radiotherapy). The cut-off value of BMI was determined according to World Health Organization (WHO) criteria, and the cut-off values of alcohol consumption and smoking were set based on previous reports [p value < 0.05 was considered statistically significant. In the case of using a variable selection procedure, backward elimination method with a p value < 0.10 was used in multivariable analyses. Statistical Analysis Software (SAS) version 9.4  was used for all statistical analyses.The cumulative incidence function for SPMs was estimated using death as a competing risk. Since mortality affects the occurrence of SPMs and can be a competing risk, uni-variable and multivariable analyses using the Fine and Gray model were performed to investigate the risk factors for SPMs . The haz reports , 25. AllA total of 379 patients were registered in the JCOG0502 between December 2006 and February 2013. The median observation time was 7.1 years (range 0.0–11.0 years). The patient backgrounds are summarized in Table n = 41), stomach (n = 24), lung (n = 17), urinary tract (n = 10), colon and rectum (n = 9), pancreas (n = 3), liver (n = 3), and leukemia (n = 3)  patients. Cumulative incidences after 3, 5, and 10 years were 9.0% (95% CI: 6.4–12.2); 14.7% (95% CI: 11.3–18.5); and 36.1% (95% CI: 28.9–43.5), respectively , 29 (81%), 4 (11%), and 2 (6%) were detected at endoscopic examination, CT scan, and regular otolaryngological examination, respectively. The development of tumor-related symptoms triggered the detection of only one case of head and neck cancer. Consequently, 26 (72%), 8 (22%), and 2 (6%) cancers were diagnosed with clinical stages 0–I, II–III, and IV, respectively. All stomach cancers were detected at endoscopic examination and with clinical stages 0–I. Among lung cancers, 14 (93%) were detected by CT scans. The remaining one (7%) developed symptoms and was diagnosed with lung cancer. Ten (67%), four (27%), and one (7%) cancer cases were diagnosed with clinical stages 0–I, II–III, and IV, respectively.Sixteen patients died due to SPMs. Among the five most common types of SPMs, there were four and two deaths due to head and neck and lung cancers, respectively. However, there was no death following stomach cancer. Three, two, and one death occurred among patients with pancreatic cancer; leukemia; and myelodysplastic syndrome, colorectal cancer, tracheal cancer, breast cancer, and renal pelvis cancer, respectively.The results of SPM risk analysis are presented in Table The association between SPMs and LVLs was observed in head and neck cancer (Supplementary Table 3). In the multivariable analysis including 284 patients whose baseline background data were not missing, compared to no LVLs, the HRs of several and many LVLs were 8.92 (95% CI 2.49–32.02) and 18.97 (95% CI 3.86–93.17), respectively. Meanwhile, the risk of other malignancies, such as gastric and lung cancers, did not increase in patients with LVLs (Supplementary Table 4). Interestingly, gastric cancer occurred more frequently in the chemo-radio-therapy arm .This study investigated the development of SPMs in patients with clinical T1bN0 ESCC who were enrolled in JCOG0502. To the best of our knowledge, this is the first study to evaluate a second cancer incidence in esophageal cancer patients who underwent surgery or chemo-radiotherapy using data from a study where the patients were prospectively followed up according to a protocol-specified schedule. Although the Japanese Esophageal Cohort (JEC) study also evaluated SPMs in a prospective setting, patients with esophageal cancer who were treated with endoscopic resection were included , 21. BesIn our study, a high incidence of SPMs of 26% was observed, reinforcing the importance of SPMs detection in improving survival outcomes in patients with early-stage ESCC. Among the retrospective studies that assessed SPMs after treatment for esophageal cancer, Sato et al. reported SPM as the most common cause of death in patients with thoracic esophageal cancer whose initial surgically-resected lymph nodes tested negative . In addiSeventeen of 99 (17%) patients who developed SPMs died. Given that the incidence of SPMs was high during the follow-up period, the survival of a considerable number of patients irrespective of SPM in the study population could be explained by the follow-up strategies. In JCOG0502, the protocol specified endoscopic examinations or CT scans during follow-up periods to enable early diagnosis of SPMs, with resultant relatively good prognosis. Of the three most common types of SPMs, 72%, 100%, and 93% were head and neck, stomach, and lung cancers, respectively, detected at clinical stages of 0–I. The rate of early detection in our study was comparable to or higher than that of the report by Yamaguchi, et al. showing 75%, 92%, and 60% were head and neck, stomach, and lung cancers, respectively, detected at clinical stages of 0–I . The outTP53 mutations are frequently observed in the esophageal epithelium with high severity of LVLs grade [Meanwhile, cancer screening requires efficient and cost-effective approaches. In this regard, the identification of risk factors is useful, and that motivated us to conduct this study. Multivariable analyses revealed an association between the development of SPMs and Lugol-voiding patterns. The high prevalence of LVLs has been linked to inactive aldehyde dehydrogenase type 2 (ALDH2) phenotypes . ALDH2 pLs grade . MoreoveLs grade . These dLs grade .In the analyses of prognostic factors, a higher incidence of SPMs in the chemo-radiotherapy arm was observed. However, in JCOG0502, most patients received the study treatment in a non-randomized patient-preference manner. Therefore, there was an imbalance in patient characteristics between the surgical and chemo-radiotherapy arms, which made it difficult to evaluate whether treatment modalities affected the occurrence of SPMs. Furthermore, esophagogastroduodenoscopy was not mandatory in the surgical arm, which could have contributed to differences in detecting gastric cancer. Compared with the surgical arm, more than double stomach cancer incidence rates were observed in the chemo-radiotherapy arm. Although data on the interval of esophagogastroduodenoscopy were not obtained in the present study, it was reported that approximately 10% or less of the hospitals continued follow-up after esophagectomy without using upper gastrointestinal endoscopy . Our resThere are some limitations to the present study. First, the data on social history or LVLs were missing in some patients because the data were not mandatory at enrollment. Iodine staining is sometimes irritable and time-consuming. Since recent studies have reported that image-enhanced endoscopy can replace Lugol’s iodine staining, the optimal method should be determined in further research . MoreoveIn summary, our data show that the incidence of SPMs was high, indicating that careful attention to monitoring and management is crucial after treatment completion. However, the establishment of optimal surveillance planning is undoubtedly required. We plan to further study of SPMs using combined data with other studies, such as the JCOG0508  and the Supplementary file1 (DOCX 53 KB)Supplementary file2 (PPTX 69 KB)Below is the link to the electronic supplementary material.


The expression profiles and molecular mechanisms of CXC chemokine receptors (CXCRs) in Lung adenocarcinoma (LUAD) have been extensively explored. However, the comprehensive prognostic values of CXCR members in LUAD have not yet been clearly identified.Multiple available datasets, including Oncomine datasets, the cancer genome atlas (TCGA), HPA platform, GeneMANIA platform, DAVID platform and the tumor immune estimation resource (TIMER) were used to detect the expression of CXCRs in LUAD, as well as elucidate the significance and value of novel CXCRs-associated genes and signaling pathways in LUAD.P = 1.90e−02), CXCR3 , CXCR4 , CXCR5  and CXCR6  were significantly associated with shorter overall survival (OS), whereas high CXCR7 mRNA expression  was extremely related with shorter OS in patients.The mRNA and/or protein expression of CXCR1, CXCR2, CXCR3, CXCR4, CXCR5 and CXCR6 displayed predominantly decreased in LUAD tissues as compared to normal tissues. On the contrary, compared with the normal tissues, the expression of CXCR7 was significantly increased in LUAD tissues. Subsequently, we constructed a network including CXCR family members and their 20 related genes, and the related GO functions assay showed that CXCRs connected with these genes participated in the process of LUAD through several signal pathways including Chemokine signaling pathway, Cytokine-cytokine receptor interaction and Neuroactive ligand-receptor interaction. TCGA and Timer platform revealed that the mRNA expression of CXCR family members was significantly related to individual cancer stages, cancer subtypes, patient’s gender and the immune infiltration level. Finally, survival analysis showed that low mRNA expression levels of CXCR2 (HR = 0.661, and Log-rank Our findings from public databases provided a unique insight into expression characteristics and prognostic values of CXCR members in LUAD, which would be benefit for the understanding of pathogenesis, diagnosis, prognosis prediction and targeted treatment in LUAD. Lung cancer is the most common type of human malignancy and the leading cause of cancer death . Non-smaChemokines are a class of cytokines associated with cellular secretion and structure; their initial discovery and most important role is to make up the extracellular matrix of tumors and have the ability to directly influence cancer cell proliferation and metastasis. –10 AmongAlthough a series of studies have elucidated the significant prognostic roles of some CXCR members in LUAD, the whole picture of the prognostic values of CXCR members remain inequitably characterized in LUAD. In this study, the clinical significance of CXCR family members in LUAD was analyzed in terms of mRNA expression level, protein expression level, immunity, interaction network, pathway analysis, clinical and prognostic aspects by using multiple databases and platforms based on the study of Xiaojuan Li et al.  in orderhttps://www.oncomine.org/resource/login.html) is an integrated online gene microarray database and data mining platform that provides peer review, powerful analysis methods, and a robust set of analytical capabilities to calculate gene expression levels. [P-value < 0.05, fold change = 1.5, gene rank = 10%, the Benjamini and Hochberg method was used to correct the P value.Oncomine  were analyzed using the UALCAN  platform. The database is a more comprehensive information platform containing gene expression databases and corresponding clinical information data. [The LUAD data in the Cancer Genome Atlas (TCGA) database (on data.  UALCAN ion data.  In this https://www.proteinatlas.org/) is a platform containing Protein expression level data for a wide range of common cancers. [HPA (cancers.  Immunohiwww.genemania.org/) is an interactive, visual online protein interaction prediction tool. [GeneMANIA (http: //on tool.  Given a https://david.ncifcrf.gov/) is a functional enrichment analysis web tool with continuously updated and effectively reduce data redundancy. [DAVID (undancy.  In this https://cistrome.shinyapps.io/timer/) is an online tool for systematical analyses of immune infiltration of various cancers. [P-values were automatically calculated and displayed in the graphs. And the Benjamini and Hochberg method was used to correct the P value.TIMER . The results could be got by selecting R (4.02) integrated into the TCGA database.P < 0.05 was considered statically significant.The TCGA-LUAD gene expression data and clinical data were read separately using R (v4.0.2) software, and the patients were divided into high and low expression groups using the median expression of the samples as the cut-off point. The "survival" package was applied to analyze the survival rate of the high and low gene expression groups. P = 4.71E−7, P = 1.19E−6), CXCR2 , CXCR4 (P = 1.63E−15) and CXCR7  were significantly lower in LUAD than in adjacent tissues.  CXCR1 staining and the CXCR1 staining in lung adenocarcinoma cells was usually not detected (11/11). While the CXCR5 staining in lung alveolar cells was usually not detected (3/6) or low (3/6), the LUAD tissues displayed not detected (10/22), low (6/22), medium (4/22) or high (2/22) CXCR1 staining.  and biological processes . According to the previous literature and our research results, we speculated that CXCRs, expressed by tumor cells and immune cells in the microenvironment, have the ability to regulate the growth, invasion and metastasis of LUAD in a variety of ways, in which the exact molecular mechanisms underlying these need to be further explored.In summary, our results provided supportive evidence for undestanding the complexity of CXCR1-7 and their related biological functions, which may provide a valuable insight for the development of CXCRs-mediated targeted therapy. However, there were some limitations to our study. First, all the data was based on the online databases, further in vivo and in vitro studies are required to verify these findings. Second, the underlying mechanisms of CXCRs in LUAD is still unknown. In addition, the current study was only a retrospective study, future detailed prospective studies need to be further explored.Our findings from public databases provided a unique insight into expression characteristics and prognostic values of CXCR members in LUAD, which would be benefit for the understanding of pathogenesis, diagnosis, prognosis prediction and targeted treatment in LUAD.


Being in the top 10% versus the bottom 90% of a polygenic score for multisite chronic pain had an effect of 3.63 DALYs. Some common variants had a population-level effect comparable to modifiable risk factors such as high sodium intake and low physical activity. Attributable DALYs vary between males and females for some genetic exposures. Genetic risk factors can explain a sizable number of healthy life years lost both at the individual and population level.The impact of genetic variation on overall disease burden has not been comprehensively evaluated. We introduce an approach to estimate the effect of genetic risk factors on disability-adjusted life years . We use genetic information from 735,748 individuals and consider 80 diseases. Rare variants had the highest effect on DALYs at the individual level. Among common variants,  A new analysis combining data from large biobanks and the Global Burden of Disease study estimates that genetic risk factors significantly impact the number of healthy life years lost both at the individual and population level Overall, these results demonstrate widespread pleiotropy (genetic variants associated with more than one trait)2. Studies commonly quantify the impact of genetic variation on a single disease at a time5, or when considering multiple diseases7, do not use a single metric that can capture overall disease burden, apart from studies on lifespan10. It is therefore challenging to assess the impact of genetic variation on overall health and to compare the total impact of different variants.Genome-wide association studies (GWASs) have identified thousands of variants associated with biological traits and diseases11. This type of assessment has not been systematically performed for genetic risk factors. The utility of genetic information for rare disease diagnosis12 and inherited cancer syndromes13 is well established. In contrast, despite major advances and ongoing research investments, the viability, utility and cost-effectiveness of other applications such as genetic screening for common diseases14, risk stratification via polygenic scores (PGSs)18, in vivo gene editing21 and, most controversially, embryo selection26 remain uncertain. A comparative risk assessment framework for genetic risk factors can help to develop and evaluate these attempts to make genetic information actionable.Past efforts in comparative risk assessment involve quantifying the effects of modifiable exposures  on health outcomes to inform public health measures27. Combining both quality of life and mortality into a single metric, DALYs are used to monitor disease burden across hundreds of countries in the Global Burden of Disease (GBD) study27. GBD estimates the yearly amount of DALYs in each country that are attributable to a list of collectively exhaustive and non-overlapping diseases and injuries27. DALYs are the sum of years lived with disability  and years of life lost   alleles, APOE haplotypes and PGSs  and UK Biobank29  with registry-based follow-up of 48.7 and 22.4 years, respectively (Supplementary Table 27)  using a Cox proportional hazards model. Because a single genetic variant is expected to affect only a minority of the considered 80 diseases, we used a shrinkage approach with a spike-and-slab type prior distribution for the log HRs of each genetic exposure for the 80 diseases. We discarded any HRs where the posterior probability of the null model was above 10%.P < 5 × 10−8) and 99% had an association with P < 7.3 × 10−4  were genome-wide significant  fine-mapped30 95% credible set in FinnGen. Additionally, we selected 155 common variants with at least one P < 5 × 10−12 association with six traditional risk factor traits (body mass index (BMI), glycated hemoglobin (HbA1c), high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, systolic blood pressure and cigarettes per day) that had the highest probability of being causal within a SuSiE fine-mapped 95% credible set in UKB31. Last, we included 325 coding variants having a P < 5 × 10−8 association with one of the diseases in FinnGen. Among the 1,044 variants, 34.6% were annotated as missense (n = 335) or putative loss-of-function . The HRs for common variants were comparable between FinnGen and UKB  over 0.01 , with only 56 out of 1,044 (5.4%) variants with over 0.25 attributable DALYs  1.03–1.32) attributable DALYs from carrying one versus zero copies of the C allele  attributable DALYs, mainly due to prostate cancer  attributable DALYs  attributable DALYs, mainly through an increase in the risk of Alzheimer’s disease and other dementias  with longevity in the largest GWAS on lifespan10 versus 18% in the bottom 10%.Given the strong associations between n = 174,379) to estimate attributable DALYs for rare deleterious coding variants (MAF < 0.001). The American College of Medical Genetics and Genomics (ACMG) recommends reporting incidental findings in clinical exome and genome sequencing for 73 genes36. We estimated the attributable DALYs for two types of burdens for these ACMG genes: (1) putative loss-of-function (pLOF) variant burden and (2) ‘pathogenic’ or ‘likely pathogenic’ Clinvar37 variant burden (for BRCA1/2 we used ‘pathogenic’ ENIGMA38 variants instead). We report results for nine genes with at least 35 individuals with a positive burden and at least one disease association.We used whole-exome sequencing data from UKB (LDLR (ischemic heart disease), BRCA2 , MYBPC3 (cardiomyopathy and myocarditis), BRCA1 (breast and ovarian cancer) and MLH1 (colon and rectum cancer). As an example, individuals carrying one pLOF in BRCA1 lose on average 4.08  healthy life years through breast cancer  and ovarian cancer .The five most impactful genes Fig. –9 were L39) to 3.81 (shorter lifespan10)  Fig.  and 12. 10 PGS had the highest impact. Individuals in the top 10% of the PGS are expected to lose 3.81 (CI 3.52–4.13) healthy life years compared to an individual in the bottom 90%  DALYs  and the highest individual attributable DALYs at 6.52  of the common variants , a missense variant in PNPLA3 linked to liver fat accumulation and steatohepatitis41, provides a clarifying example: carrying one versus zero copies of the minor allele resulted in 0.27 (CI 0.24–0.30) attributable DALYs in males and 0.05 (CI 0.03–0.07) DALYs in females (sex difference P = 1.0 × 10−34). The sex difference is in part driven by differences in HRs  had the highest population-attributable DALYs, with minor allele carriers accounting for 447 (CI 420–473) yearly population DALYs per 100,000 in Finland 2019 .Next, for the Finnish population, we estimated the amount of population-attributable DALYs per year per 100,000 from all (heterozygous and homozygous) carriers of the minor allele: the expected amount of healthy life years per year per 100,000 individuals in the population that would be gained if the minor allele were completely removed. 019 Fig. . The lar 11 Fig. , the popors Fig. . Clinica43 have contributed to the enrichment of several functional genetic variants44 otherwise rare in non-Finnish populations. We estimated the population-attributable DALYs that are due to the enrichment in the Finnish population compared to non-Finnish, non-Swedish, non-Estonian European (NFSEE) populations. The largest impact on population-attributable DALYs  contributing to 56.1 (CI 50.1−62.0) yearly population DALYs per 100,000 individuals of Finnish ancestry through increased risk of dementia . The presence of the Finnish-enriched allele in the population contributes to preventing 39.1 (CI 32.0–45.8) yearly population DALYs per 100,000 individuals of Finnish ancestry . Overall, rare deleterious variants tended to have higher effects on DALYs than common variants at the individual level. Genetic exposures increasing the risk of ischemic heart disease tended to be most impactful in terms of DALYs , BRCA2 , MYBPC3 (cardiomyopathy and myocarditis), LDLR (ischemic heart disease) and MLH1 (colon and rectum cancer); however, due to the rarity of these variants, the population impact was, at most, 21 yearly population DALYs per 100,000 for BRCA2 (Supplementary Table rs7859727 (CDKN2B-CDKN2A) where minor allele carriers account for 447 yearly population DALYs per 100,000  or pain/addiction-related traits  and some affected risk of dementia , prostate cancer  or type 2 diabetes . The number of DALYs for a disease in the GBD26 is driven by how common it is, how much it contributes to premature mortality and how much it lowers quality of life. Common diseases that either lead to premature mortality  and/or long periods of living with disability  account for a large number of population DALYs (Supplementary Table rs8040868 in CHRNA5/A3/B4 being a notable exception due to its impact on smoking behavior).Most common variants with the largest effects on DALYs affected ischemic heart disease risk . In quantifying the disease burden, we combine the association results with population DALYs from the GBD27, which reports perhaps the most accurate and unbiased estimates of population-level disease burden. This is important for many diseases for which defining the absolute amount of disease burden relying on available data is biased because of under-ascertainment  or non-representativeness ). Finally, compared to the GBD risk factors approach11, which estimates attributable DALYs relying on effect estimates of the modifiable risk factors based mostly on (non-genetic) observational analyses, genetic exposures that we examine are less likely to be impacted by confounding and other biases, making a causal interpretation of our estimates more credible.There are multiple strengths and limitations to our study. One key strength is that the genetic associations were estimated using individual-level data from two large biobank studies with long registry-based follow-ups. We apply a shrinkage procedure to the associations and thus obtain a conservative overview of the pleiotropic effect of genetic exposures on 80 major diseases accounting for 83.1% of the total DALYs in Finland through all non-communicable diseases in 2019 ref. 2. In quan27 are accurate and that DALYs are a valid and meaningful measure of disease burden (which has been debated50). Second, the DALYs for individuals with a disease are assumed to be the same among those with and without the genetic exposures . Third, we used DALYs estimated for Finland 2019 and estimated genetic associations using data between 1972 and 2020 in Finland and the United Kingdom, so the estimated effects on lifetime individual DALYs for someone born today can change as the disease incidence, medical care and mediating factors  change. Fourth, although we suggest a causal interpretation for the attributable DALYs, there are important caveats. Despite rigorous statistical fine-mapping, the reported variants with the highest posterior probability might only tag underlying causal genetic variation. For example, the rs3798220 (LPA) variant is known to tag copy number variation of the Kringle IV-like domain 2 in the LPA locus, which is the probable mechanism behind the association of rs3798220 with ischemic heart disease51, so rs3798220 is not actually responsible for the effect on DALYs. Another caveat lies in the possibility that a reported variant does not tag one causal variant, but multiple causal variants in linkage disequilibrium (LD); however, using expression quantitative trait loci data, previous work on quantitative lipid traits has shown that a minority of pleiotropic effects at a given locus are explained by this configuration52. Fifth, note that we mainly present attributable DALYs for both sexes in aggregate, which might be misleading for exposures with sex-specific effects. For example, rs183373024 (POU5F1B) affected DALYs only through prostate cancer and benign prostatic hyperplasia . The sex-specific results were generated by estimating the HRs stratified by sex and using sex-specific population DALYs reported by the GBD27. For all error estimates we assumed that there was no uncertainty in frequencies of the exposures  or DALYs reported by the GBD, so we only considered variation from HR estimation in the error estimates.All https://www.finngen.fi/en). The 28. Participants for FinnGen include participants of legacy cohorts, both population-based epidemiological cohorts (initiated as far back as 1992) and disease-based cohorts and volunteers from biobanks. All samples donated to Finnish biobanks are eligible for FinnGen. Information about the opportunity to donate a sample and medical information for biobank research was distributed by leaflets, advertisement campaigns in press and on TV and by dedicated biobank nurses in hospitals.FinnGen is a public-private partnership research project combining genotype data generated from Finnish biobanks and digital health record data from Finnish health registries , Causes of Death , reimbursed medication entitlements and all prescription medication purchases  and the Finnish Cancer Registry (ICD-O-3 codes). The diagnostic accuracy and validity of these registers has been reviewed in many previous publications29 and Bycroft67. We used UKB data for common variants  and rare variants (UKB only).We additionally included participants from the UKB, which is a population-based biobank cohort study that recruited around 500,000 people aged between 40 and 69 years from 2006 to 2010 across the United Kingdom. For details on participants and recruitment see Sudlown = 309,136  with median (IQR) age at start of follow-up of 15.1 (0–26.3) and at end of follow-up 62.2 (47.1–72.9). The median (IQR) follow-up length was 47.3 (41.6–47.7) years  with median (IQR) age at start of follow-up of 47.2 (39.5–52.4) and at end of follow-up 69.1 (61.7–74.2). The median (IQR) follow-up length was 22.3 (22.3–22.3) years  with 27 with data publicly available at https://ghdx.healthdata.org/. DALYs are a metric for measuring population-level disease burden that combines a measure of premature mortality (YLL) and a measure of healthy life years lost due to lowered quality of life (YLD), so DALYs are the sum of YLDs and YLLs .As the measure of disease burden, we used the DALYs from the 2019 GBD study27. The DALYs estimated by GBD avoid double-counting . Because all of the genetic exposures, except rare variants, were measured in FinnGen, we used the 2019 GBD metrics for Finland and present all estimates for Finland  and 1 (death). Yearly YLLs are estimated by multiplying the number of deaths attributable to a disease by the standard life expectancy at age of death27 into FinnGen clinical end points , I9_MI_COMPLICATIONS , I9_POSTAMI , I9_CORATHER  and I9_REVASC . The first occurrence of any of these five end points was coded as the event of ischemic heart disease.For FinnGen, we did not define disease phenotypes just using the ICD-10 codes provided in GBD for multiple reasons. First, ICD-10-based definitions for all disease conditions in GBD are not available . Second, even when GBD presents ICD-10 diagnoses, as follow-up for FinnGen for this study started in 1968, diseases need to also be defined through ICD-8 (in use until Dec 1987) and ICD-9 (in use from Jan 1987 to Dec 1995). Third, the FinnGen clinical end point phenotyping algorithms have been hand-crafted to utilize various sources of registry data in addition to diagnosis codes .For example, migraine is a disease commonly managed in the primary care setting, so relying on diagnosis codes present in hospital and secondary care specialist visits data will lead to serious under-ascertainment of migraine. Also, there is misclassification of other types of headaches as migraine. The FinnGen end point that we used to define migraine (MIGRAINE_TRIPTAN) requires that a patient has at least one purchase of prescribed triptans, capturing, for example, patients who do not have a diagnosis code for migraine in the available registries that do not include primary care.28 relied on information on diagnoses starting from 1972 in the hospital discharge registry65, the causes of death registry and the cancer registry. The drug purchases registry was additionally used starting from 1995 for selected diseases . The cumulative incidence of the diseases varied from 14.93% (cataract) to 0.16% (acute glomerulonephritis) in FinnGen. Phenotyping in UKB was performed via groups of ICD-10 codes mapped from the FinnGen end points, relying on ICD-10 diagnosis codes from the hospital episode statistics, cancer registry and causes of death registry data starting from 1998 (Supplementary Table n = 480 in UKB), Hodgkin lymphoma (n = 464), autism spectrum disorders (n = 164), eating disorders (n = 163), acne vulgaris (n = 106), acute glomerulonephritis (n = 58), ADHD (n = 26) and conduct disorder (n = 13). As both FinnGen and UKB rely mainly on diagnosis codes recorded at hospitals, conditions that are usually managed in the primary or outpatient care setting are under-ascertained, but this does not bias the attributable DALY estimates if the HR estimates are unbiased.Phenotyping in FinnGen68. Participants with ambiguous sex, genotype missingness of over 5%, excess heterozygosity (±4 s.d.) and non-Finnish ancestry were excluded. Variants with over 2% missingness, low Hardy–Weinberg equilibrium P value (<10−6) and minor allele count <3 were excluded. Array data pre-phasing was performed with Eagle v.2.3.527 with default parameters, except the number of conditioning haplotypes was set at 20,000. Genotype imputation was performed with Beagle v.4.128 as described in ref. 69 by using the SISu v.3 reference panel developed from data on 3,775 high-coverage (25–30×) whole-genome sequenced Finns. For more details, please see the FinnGen flagship manuscript28.Samples for FinnGen were genotyped using Illumina and Affymetrix arrays (Illumina and Thermo Fisher Scientific). Genotype calls were made with GenCall or zCall for Illumina and the AxiomGT1 algorithm for Affymetrix data. Chip genotyping data produced with previous chip platforms and reference genome builds were lifted over to build v.38 (GRCh38/hg38) following the protocol described in ref. 67. The genotyping was performed using the Applied Biosystems UK BiLEVE Axiom Array or the Applied Biosystems UKB Axiom Array. The genotype imputation was performed using a combination of the Haplotype Reference Consortium, UK10K and 1000 Genomes Project phase 3 reference panels by IMPUTE4 software. Variants with INFO score ≤ 0.8, MAF ≤ 0.01 and Hardy–Weinberg equilibrium P value ≤ 1 × 10−10 were excluded.For detailed information on genotyping, imputation and quality control for the UKB data, see Bycroft60 (VEP) v.103. For coding variants, we chose a single most severe consequence and corresponding gene among canonical transcripts. We considered stop_gained, frameshift_variant, splice_donor, splice_acceptor, missense_variant, start_lost, stop_lost, inframe_insertion and inframe_deletion as protein-truncating variants.For variant annotation, we utilized the Variant Effect Predictorr2 filter of 0.1.For FinnGen, the principal component analysis (PCA) for population structure was performed using the following approach. First, the following filters were applied: (1) exclusion of chromosome 23; (2) exclusion of variants with INFO score 0.95; (3) exclusion of variants with missingness >0.01; (4) exclusion of variants with MAF < 0.05; and (5) LD pruning with window 500 kb, step 50 kb and n = 309,136).The imputed genotypes were merged with 1000 Genomes Project phase 3 data into a single dataset of 49,451 pruned single-nucleotide polymorphisms (SNPs), on which the principal components (PCs) were estimated. An unsupervised Bayesian algorithm (Aberrant) was used to spot outliers in the PCA space and remove them. While this method automatically detected the 1000 Genomes Project samples with non-European and southern European ancestries as outliers, it did not manage to exclude some samples with western European origins. As the signal from these samples would have been too small to allow a second round to be performed without detecting substructures of the Finnish population, another approach was used. The FinnGen samples that survived the first round were used to compute another PCA. The European and Finnish 1000 Genomes Project samples were projected onto the space generated by the first three PCs. For each sample, the probability of belonging to the EUR/FIN cluster was estimated through using a chi-squared distribution based on the Mahalanobis distance to the centroid of each cluster. Samples whose relative probability of being part of the Finnish cluster was >95% were classified as belonging to the Finnish ancestry and retained in all following analyses for FinnGen (https://pan.ukbb.broadinstitute.org/) and the procedure is described at https://pan.ukbb.broadinstitute.org/docs/qc#ancestry-definitions.PCA and ancestry assignment in UKB followed the procedure of the Pan-UKB analysis  was performed by polymerase chain reaction (PCR)-based methods. HIBAG71 v.1.14.0 with 100 classifiers for each of the seven HLA genes was fitted using the training data of 1,150 individuals to construct an imputation reference for the Finnish population, which was used to impute the HLA alleles in FinnGen70. We analyzed 74 alleles in seven HLA genes  variants within a window of 3 Mb centered at the variant and merging overlapping regions. Summary statistics were obtained using BOLT-LMM73 and SAIGE72. In-sample dosage LD was estimated using LDStore2. The maximum number of causal variants for each locus was ten. We only considered fine-mapping results for six quantitative risk factor traits  and considered variants with P < 10−12 to restrict the number of variants to be selected.The fine-mapping in UKB using SuSiE followed a similar procedure. Regions for fine-mapping were defined by greedily starting with the most significantly associated (highest chi-squared) variant, including all genome-wide significant  were coding variants with a P < 5 × 10−8 association with at least one of the diseases in FinnGen; or (3) had at least one P < 5 × 10−12 association with any of the selected six modifiable risk factor traits  and had the highest probability of being causal within a SuSiE fine-mapped 95% credible set in UKB31. We labeled common variants as Finnish-enriched if they had at least fivefold MAF enrichment in FinnGen compared to NFSEE ancestry MAF in gnomAD59 and the NFSEE MAF was <0.01.We selected an initial list of 2,562 common variants (MAF > 0.01) for inclusion in the analysis that either (1) had at least one 74 v.1.90 to clump all common variants using an r2 threshold of 0.2, a 250-kb clumping window and FinnGen release 4 genotypes as the reference panel to remove SNPs in LD with variants having a smaller minimum P value among the 80 HRs for all examined diseases ; however, if there was a coding variant among the variants in LD, that was instead kept and others were discarded. Additionally, if there was a Finnish-enriched variant among the variants in LD (but no coding variant), the Finnish-enriched variant was kept. This resulted in 1,044 independent common variants to be included in the analyses  from the December 2020 release. Data was preprocessed using hail v.0.2. We used the quality-controlled PLINK74 files provided by UKB. Variants were annotated using the Ensembl Variant Effect Predictor (VEP)60 following the approach in gnomAD59 (gnomadvep.vep_or_lookup_vep). VEP annotations were processed using the function ‘gnomadvep.process_consequences’, consistently with the gnomAD definition of pLOF, missense and synonymous variants, using the canonical transcript. We also extracted ClinVar-annotated37 variants (accessed in November 2020) and germline variants in BRCA1 and BRCA2 (accessed in November 2020) annotated by the ENIGMA consortium38. From ClinVar we considered ‘pathogenic’ and ‘likely-pathogenic’ variants (no filtering on star status or number of submitters) and from ENIGMA we considered ‘pathogenic’ variants. Variants with a frequency >0.001 were excluded because they are less likely to be deleterious. We considered all genes part of the ACMG recommendations for reporting incidental findings in clinical exome and genome sequencing studies36. We formed two types of rare variant burdens for individuals for each gene: (1) the pLOF burden was set as positive if there was at least one variant annotated as pLOF; and (2) the ClinVar/ENIGMA burden was set as positive if there was at least one variant annotated as ‘pathogenic’ in ENIGMA38 for BRCA1 and BRCA2 and for other genes ‘likely pathogenic’ or ‘pathogenic’ in ClinVar. Due to statistical power considerations, we restricted our analysis so that at least 35 individuals had a positive burden, resulting in nine genes for both burden types . We used the PRS-CS-auto algorithm, which learns the global scaling parameter ϕ from the data and performs well with large datasets. Default PRS-CS parameters were used and only HapMap 3 variants were considered (https://github.com/FINNGEN/CS-PRS-pipeline). Scores for lifespan, educational attainment, cognitive performance and intelligence were reversed before analysis to make all scores on net deleterious in terms of DALYs. For defining the exposure for survival models, we coded individuals for each PGS as one if they were in the top 10% of the score distribution and zero if not. Consequently, the individual DALYs can be interpreted as the effect on lifetime DALYs if the average individual in top 10% of the PGS were to have their PGS be that of the average in the bottom 90% of the PGS.We used PRS-CS9 by modeling survival via a Cox proportional hazards model with sex, the 30 PGSs and ten PCs of population structure as predictors. We then extracted the linear predictor for all individuals, while setting sex to 0.5 and the PCs to 0 (the mean) and interpreted these values as a composite PGS of mortality.We additionally formed a composite PGS of mortality using all the 30 PGSs . For the common variants, HRs were estimated both in FinnGen and UKB separately and combined using fixed effects inverse-variance-weighted meta-analysis. A comparison of effect sizes between FinnGen and UKB is provided in Extended Data Fig. 77 v.2.2 only including HapMap 3 variants. Individuals up to a third degree of relatedness were included in the same family. The robust standard errors were estimated using a family indicator to compute robust standard errors by including cluster(family_id) as a covariate in coxph. Compared to the main analysis estimates, robust standard errors were a median 1.0128-times (IQR 1.0044–1.0205) larger. Thus, accounting for relatedness would not meaningfully affect the CIs and P values.As a sensitivity analysis in FinnGen, we examined whether accounting for relatedness would meaningfully change the standard errors of the log HRs. For 2,562 common variant–disease pairs we estimated the log HRs using a survival model clustered by family indicator to generate robust standard errors. We generated a family indicator from genotype data using KING78, we observed age-varying HRs for genetic exposures  for the 50–54-year age group and 1.75 (CI 1.62–1.87) for the 70–74-year age group.As a second sensitivity analysis, we explored whether the effect of the genetic exposures on the diseases was age-dependent by performing age-stratified survival analyses for a subset of genetic exposures. Perhaps unsurprisinglyWe use prior information to apply a shrinkage procedure to the HRs for exposure–disease pairs to reduce the effect of sampling variation at the cost of being more conservative . The possible benefits of shrinkage include: (1) the top-ranking variants suffer less from the Winner’s curse (overestimation due to sampling variation); (2) by reducing the number of diseases through which a variant contributes to DALYs, variants increasing risk of low-DALY diseases do not have their total DALY estimates overshadowed by noisy weak effects through high-DALY diseases reflecting sampling variation; and (3) the shrinkage helps remove possible weak effects due to residual confounding from population stratification.be,d the log HR of genetic exposure e on disease d = 1,..,80. One genetic exposure at a time, we set the prior distribution of be,d, to be a mixture distribution between the point mass at 0 and a 50:50 mixture of two normal distributions with means at −0.3 and 0.3, respectively and with a s.d. of 0.1. We denote the mixture weight of the non-zero component by pe, which can be interpreted as the exposure-specific proportion of non-zero effects across the 80 diseases. We set the prior distribution of pe to a α = 1, β = 19, μ = 0.3 and σ = 0.1.We denoted by e at a time, we used a Markov Chain Monte Carlo procedure with 10,000 iterations to estimate the posterior probabilities of the log HRs  for diseases d = 1,2,…,80 coming from the null model (point mass at zero). We discarded any log HRs where the null probability was over 10% and, for the remaining log HRs, we used the maximum partial likelihood estimates from the Cox proportional hazards model in the downstream analyses.This model implies that, before seeing the data, for each genetic exposure, we expect a non-zero effect for four (= 0.05 × 80) diseases and the direction of the non-zero effects are equally likely to be risk increasing (centered around HR = 1.35) as protective (centered around HR = 0.74). In practice, which effects are shrunk to zero and which are retained as non-zero, does not vary considerably when these prior parameters are varied . The heritability of the phenotypes was randomly sampled from a uniform distribution ranging from 10 to 60%. The phenotypes were consequently binarized based on the disease prevalence observed in FinGen using the function ldscsim.binarize. We considered four π values  meaning that 0.1%, 0.2%, 0.5% and 1% of the 558,240 independent variants had a true underlying effect different from 0.As a sensitivity analysis to explore the performance of the shrinkage method, we used hail v.0.2 to simulate variant–phenotype associations for 80 diseases where the true underlying effects are known. The approach used genetic data from 361,194 individuals from UKB with European ancestry and has the advantage of using realistic variant frequencies and population structure as compared to simulated genetic data. Only 558,240 independent HapMap 3 SNPs were considered in the analysis. Using the ldscsim.simulate_phenotypes function we simulated 80 phenotypes based on a spike-and-slab model with a different probability of SNPs being causal . For this selected group of variants, we applied the same shrinkage procedure as in the main analysis.We then ran a GWAS for each of the phenotypes across the four different π. Thus, the shrinkage approach can identify true causal variants in simulated GWAS data  and effect sizes different from 0 , we can compare how well our procedure shrinks variants from the null model versus does not shrink those from the alternative model. Overall, our approach results in area under the curve values of 0.817 to 0.897 for different values of 11, we used the HRs and frequencies of the exposures to estimate attributable DALYs one disease at a time. We used the multilevel exposure formula79 for the population-attributable fraction :d at exposure level i  relative to reference  and Pi is the fraction of the population at exposure level i and i in the counterfactual scenario , which is 0.061 in this case, so approximately 6.1% of disease cases would be prevented if all with one copy had instead zero copies at birth.As an example, for individuals carrying zero, one and two alleles with respective population frequencies of BRCA1 mutation carriers develop breast cancer earlier and consequently accumulate more DALYs per case. Then, multiplying the population-attributable fraction of DALYs (AFpd) by the population DALYs per year per 100,000 reported by the GBD (DALYd gives the population-attributable DALYs), interpreted in our example as the expected loss of healthy life years per year per 100,000 if the population frequencies of the exposure were Pi .We then assumed that the estimated population-attributable fraction of disease cases can be interpreted as the population-attributable fraction of DALYs, which is true if all disease cases contribute on average the same amount of DALYs independent of whether they have the genetic exposure or not. This assumption does not hold if, for example, deleterious Pi) and multiplying by life expectancy at birth (L):d represents the population DALYs per year per 100,000 through disease d from GBD, Pi is the fraction of population for which the exposure is changed  and L is included to convert yearly DALYs into lifetime estimates. These individual DALYs are interpreted as the expected loss of healthy life years for an individual caused by having the genetic exposure at birth. Finally, both population-attributable DALYs and individual DALYs can be summed up across the 80 diseases to arrive at the total impact of the genetic exposure. Note that the attributable DALYs (or the population-attributable fractions) for multiple exposures cannot be added together to estimate the effect of jointly intervening on multiple exposures80. Thus, summing the attributable DALYs for multiple genetic exposures does not prove a correct estimate for the counterfactual joint intervention on multiple exposures . Also see Witte et al.81 for discussion on how population-attributable fractions relate to other measures of genetic contribution.We further estimated individual-attributable DALYs for binary counterfactuals  by dividing the population-attributable DALYs per 100,000 by the number of individuals with the exposure out of 100,000 , for a single disease both attributable individual and population DALYs are a deterministic function of the HRs between the exposure and the diseases. Therefore, CIs for the effect of a genetic exposure on attributable DALYs through one disease was estimated using the delta method.Estimating the total attributable DALYs through the 80 examined diseases is less straightforward, as the HRs for different diseases are not independent . Bootstrapping was not computationally feasible, so we estimated the uncertainty via resampling the multivariate normal distribution of the log HR estimates.e, let e and the βe denote the estimated vector of Cox model coefficients (log HRs) for the 80 diseases, let Σe denote the covariance matrix of those coefficients, where the diagonal represents the standard errors of the coefficients. The coefficients follow a multivariate normal distribution:Considering a single genetic exposure e in terms of the diagonal matrix De = diag (σe) that has the standard errors of the coefficients C as:We can express the covariance matrix Σβe and σe from the 80 Cox models for each disease . Let Cd × d by taking all the shrunk log HRs (assuming that they represent null effects) between all common variant–disease pairs and calculating the Pearson’s correlation coefficient between the log HRs of two diseases:i, j . We restricted the correlation estimation to unshrunk variants to to make the coefficients reflect sampling variability, not true effects.We estimate e we then resample the vector of log HRs P values via a normal approximation.For each genetic exposure Patients and control participants in FinnGen provided informed consent for biobank research, based on the Finnish Biobank Act. Alternatively, separate research cohorts, collected before the Finnish Biobank Act came into effect (in September 2013) and start of FinnGen (August 2017), were collected based on study-specific consents and later transferred to the Finnish biobanks after approval by Fimea (Finnish Medicines Agency), the National Supervisory Authority for Welfare and Health. Recruitment protocols followed the biobank protocols approved by Fimea. The Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS) statement number for the FinnGen study is HUS/990/2017.The FinnGen study is approved by the Finnish Institute for Health and Welfare , Digital and Population Data Service Agency , the Social Insurance Institution , Findata permit nos. THL/2364/14.02/2020, THL/4055/14.06.00/2020, THL/3433/14.06.00/2020, THL/4432/14.06/2020, THL/5189/14.06/2020, THL/5894/14.06.00/2020, THL/6619/14.06.00/2020, THL/209/14.06.00/2021, THL/688/14.06.00/2021, THL/1284/14.06.00/2021, THL/1965/14.06.00/2021 and THL/5546/14.02.00/2020 and Statistics Finland (permit nos. TK-53-1041-17 and TK/143/07.03.00/2020 (earlier TK-53-90-20)).The Biobank Access Decisions for FinnGen samples and data utilized in FinnGen Data Freeze 7 include: THL Biobank BB2017_55, BB2017_111, BB2018_19, BB_2018_34, BB_2018_67, BB2018_71, BB2019_7, BB2019_8, BB2019_26 and BB2020_1, Finnish Red Cross Blood Service Biobank 7.12.2017, Helsinki Biobank HUS/359/2017, Auria Biobank AB17-5154 and amendment no. 1 (17 August 2020), Biobank Borealis of Northern Finland_2017_1013, Biobank of Eastern Finland 1186/2018 and amendment 22 §/2020, Finnish Clinical Biobank Tampere MH0004 and amendments (21 February 2020 and 6 October 2020), Central Finland Biobank 1-2017 and Terveystalo Biobank STB 2018001.UKB obtained ethics approval from the North West Multicentre Research Ethics Committee, which covers the United Kingdom  and obtained informed consent from all participants. Our analyses were conducted under the UKB application no. 31063.Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41591-022-01957-2.Supplementary InformationFinnGen contributorsReporting SummarySupplementary TablesSupplementary Tables 1−16


Streptococcus pneumoniae is a leading cause of ocular infections including serious and sight-threatening conditions. The use of pneumococcal conjugate vaccines (PCV) has substantially reduced the incidence of pneumonia and invasive pneumococcal diseases, but has had limited impact on ocular infections. Additionally, widespread vaccine use has resulted in ongoing selective pressure and serotype replacement in carriage and disease. To gain insight into the population structure of pneumococcal isolates causing ocular infections in a post-PCV-13 time period, we investigated the genomic epidemiology of ocular S. pneumoniae isolates (n=45) collected at Massachusetts Eye and Ear between 2014 and 2017. By performing a series of molecular typing methods from draft genomes, we found that the population structure of ocular S. pneumoniae is highly diverse with 27 sequence types  and 17 serotypes being identified. Distribution of these lineages diverged according to the site of isolation, with conjunctivitis being commonly caused by isolates grouped in the Epidemic Conjunctivitis Cluster-ECC (60 %), and ST448 (53.3 %) being most frequently identified. Conversely, S. pneumoniae keratitis cases were caused by a highly diverse population of isolates grouping within 15 different clonal complexes. Serotyping inference demonstrated that 95.5 % of the isolates were non-PCV-13 vaccine types. Most of the conjunctivitis isolates (80 %) were unencapsulated, with the remaining belonging to serotypes 15B, 3 and 23B. On the other hand, S. pneumoniae causing keratitis were predominantly encapsulated (95.2 %) with 13 different serotypes identified, mostly being non-vaccine types. Carriage of macrolide resistance genes was common in our ocular S. pneumoniae population (42.2 %), and usually associated with the mefA +msrD genotype (n=15). These genes were located in the Macrolide Efflux Genetic Assembly cassette and were associated with low-level in vitro resistance to 14- and 15-membered macrolides. Less frequently, macrolide-resistant isolates carried an ermB gene (n=4), which was co-located with the tetM gene in a Tn-916-like transposon. Our study demonstrates that the population structure of ocular S. pneumoniae is highly diverse, mainly composed by isolates that escape the PCV-13 vaccine, with patterns of tissue/niche segregation, adaptation and specialization. These findings suggest that the population structure of ocular pneumococcus may be shaped by multiple factors including PCV-13 selective pressure, microbial-related and niche-specific host-associated features. CC, clonal complexes; ECC, epidemic conjunctivitis cluster; IPD, invasive pneumococcal disease; MEE, Massachusetts Eye and Ear; MIC, minimum inhibitory concentration; MLST, multilocus sequence typing; PCV, pneumococcal conjugative vaccine; ST, sequence type.Streptococcus pneumoniae is the leading cause of community-acquired pneumonia, bacteremia and meningitis, as well as non-invasive infections including serious and sight-threatening ocular infectious diseases. Massive administration of vaccines targeting the polysaccharide capsule has substantially reduced morbidity and mortality associated with pneumococcal diseases, but has had limited impact on ocular infection incidence. By performing a series of molecular typing methods from draft genomes we found that the population structure of ocular S. pneumoniae is highly diverse and mainly composed by isolates that scape the 13-valent pneumococcal conjugate vaccine. We also found differences in the microscale biogeography of ocular pneumococcal infections, with conjunctivitis being commonly caused by unencapsulated isolates mainly associated with a distinct cluster of closely related lineages that seem to be confined to the conjunctiva. Conversely, pneumococcal keratitis was caused by a highly diverse population of non-vaccine encapsulated isolates grouping within 15 different clonal complexes that are scattered across the phylogenomic tree, and interspersed with strains originating from other body sites. This work provides new insights into the population structure of ocular S. pneumoniae that will serve as a foundation for performing large-scale studies to better understand the epidemiology of ocular pneumococcal infections, and inform the design of next-generation vaccines to prevent, and help decrease the economical and health burden of ocular pneumococcal infections.This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the BioProject number PRJNA715222.Streptococcus pneumoniae is an inhabitant of the upper respiratory tract and a leading cause of invasive infections including pneumonia, bacteremia and meningitis [S. pneumoniae ranks among the top five most common causes of ocular surface infections, being associated with around 6–7.6 % of bacterial conjunctivitis cases in all age groups [S. pneumoniae is the leading cause of bacterial keratitis being associated with up to 36 % of all cases [ningitis . This orningitis  and oculningitis , keratitningitis  and endoningitis . In the e groups , nearly e groups  and 6–10e groups . The burll cases .S. pneumoniae to evade phagocytosis by inhibiting complement-mediated opsonization and is a key virulence factor in invasive pneumococcal disease (IPD) [Production of a polysaccharide capsule enables se (IPD) . Currentse (IPD) , some ofse (IPD) . PCV-20,se (IPD) , but seese (IPD) .S. pneumoniae has been also associated with several outbreaks of this disease in the USA [S. pneumoniae conjunctivitis is mainly caused by a highly divergent group of unencapsulated strains termed Epidemic Conjunctivitis Cluster (ECC), which are not targeted by current vaccine strategies [In addition to being a common cause of sporadic bacterial conjunctivitis in all age groups, a costly disease that results in high economic burden  S. pne the USA . We prevrategies . ECC strrategies .S. pneumoniae is a leading cause of infectious keratitis [S. pneumoniae diverges according to the site of isolation, with ECC-related isolates being confined to the conjunctiva. Keratitis cases examined here are caused by a highly diverse population of mainly encapsulated S. pneumoniae lineages that escape the currently available PCV vaccines.Because of the ongoing vaccine selective pressure, and because in addition to conjunctivitis, eratitis , we sougS. pneumoniae were isolated from multiple ocular sites of infections  and included in this work. Protocols for obtaining discarded isolates with waived informed consent were approved by the MEE Institutional Review Board. Primary clinical specimens collected from multiple ocular infection sites were obtained by the attending ophthalmologist or resident following institutional guidelines and submitted to the clinical microbiology laboratory for processing. Suspected S. pneumoniae colonies were identified using the MicroScan WalkAway system  following the manufacture’s protocol. Isolates were stored at −80 °C in Microbank cryopreservative tubes (ProLab Diagnostics). Frozen isolates were cultured on 5 % sheep blood agar plates  and incubated at 37 °C with 5 % CO2.From 2014 to 2017, 45 consecutive and non-duplicate S. pneumoniae causing ocular infections, isolates included in this study were submitted to whole-genome sequencing. Total DNA was purified from an overnight pure culture in 5 ml Todd Hewitt broth using the DNeasy DNA extraction kit . DNA quality was verified on a Bio-Tek Synergy two microplate reader  prior to quantification using a Qubit fluorometer and dsDNA High-Sensitivity assay kit . Library preparation for Illumina sequencing was carried out using the Nextera XT DNA Library Preparation kit , according to the manufacturer’s specifications. Quality and quantity of each sample library was measured on a TapeStation instrument . The genomes were sequenced as 2×100 bp or 250 bp reads on an Illumina HiSeq sequencer, according to the manufacture’s specifications with a minimum depth of coverage of 30× . Sequence reads were assembled de novo using CLC Genomics Workbench . Samples with sequence reads below a quality score of 25 at any position and assemblies that were not between 1.8 to 2.4 Mb were re-sequenced. This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the BioProject number PRJNA715222.To understand the population structure of et al. [Streptococcus mitis strain B6 as an outgroup, and random seed number 545212 [To generate a phylogeny, the core genomes of 45 ocular samples and 29 references from Valentino et al.  were aliet al. . Recombiet al. . Before r 545212 . The phyS. pneumoniae), and to identify the pool of acquired antibiotic resistance genes in each genome using the ResFinder algorithm [http://www.phyloviz.net/goeburst/). Determination of newly proposed nomenclature by the GPS project of Global Pneumococcal Sequence Clusters (GPSCs) for internationnaly distributed pneumococcal lineages [https://pathogen.watch/). Capsule serotype was predicted from genome sequences using PneumoCaT  v1.2.1. The genetic Tn916-like and Megacassette environments were built by using Easyfig v2.2.5.We used the Center for Genomic Epidemiology pipeline to obtain confirmation of species identification, sequence type (ST) , conjunctivitis , endophthalmitis , dacryocystitis  and orbital cellulitis . Genome sequence-based multilocus sequence typing (MLST) analysis of isolates from all ocular sites identified 27 unique STs grouped into 20 clonal complexes (CCs), revealing a highly diverse population of isolates (n=9), CC199  and CC558  (n=10) appear highly adapted and confined to the conjunctival mucosa only. Non-typeable ECC isolates were the most common causes of conjunctivitis in our population , with ST448  being most frequently identified (n=9) and belonged to ST448 (n=9) and ST344 (n=1). One ST448 (isolate 58–28) was isolated from the conjunctival discharge of a patient presenting with scleral buckle extrusion and orbital cellulitis, and was not considered as conjunctivitis. A second ST448 isolate (isolate 7 14) was recovered from a corneal scraping of a patient that presented with an initial chief complaint of conjunctivitis that evolved with a small peripheral corneal infiltrate, and was considered a conjunctivitis case with a possible secondary marginal keratitis. Conversely, S. pneumoniae keratitis were caused by a highly diverse population of encapsulated isolates grouping within 15 different CCs that were somewhat evenly distributed across the cases  , Fig. 1.entified . These ihe cases . The divdy sites . Our ECCS. pneumoniae capsular serotypes according to the disease, and to determine the influence of the PCV-13 on this distribution, we extracted presumptive capsular serotyping data from draft genome sequences using PneumoCat. In agreement to our previous findings [S. pneumoniae isolates  were unencapsulated, with the remaining belonging to serotypes 15B, 3 and 23B . The genotypically heterogeneous population of keratitis isolates, as determined by MLST analysis, also presents with a high degree of diversity at the capsule serotype level. Thirteen different capsule operon types were identified among these isolates and included the following inferred serotypes: 15B, 11A, 15A, 9 N, 23A, 22F, 35B, 33F, 29, 21, 19A, 16F and 10A  with non-PCV-13 serotypes , and usually associated with the mefA+msrD (n=15) genotype (mega) cassette (mefA+msrD genes (92.3 %) exhibited low-level in vitro resistance to erythromycin, azithromycin and clarithromycin , encoding ribosomal methylase. ermB carriage resulted in high-level resistance to the macrolides tested (MIC>32 µg ml−1). The percentage of isolates carrying a macrolide resistance element increased over the period of our study from 30.8 % in 2014–33.3 % in 2015, 42.8 % in 2016 and 56.3 % in 2017 (tetM gene (n=4), which confers resistance to tetracycline. In these isolates, the ermB gene was also present and they were localized to a Tn916-class transposon  [S. pneumoniae lineages, but lineages that are also not covered by PCV-13.Pneumococcal ocular infections occur in a variety of eye tissues and compartments, each with their specificities and unique mechanisms of protection . These der (ECC) . BecauseS. pneumoniae isolatesin these eye compartments.The molecular basis for this bifurcation of the population between two ocular surface tissues is not known. Our previous  and currn=10), isolates belonging to ST448 were the most common in our study (n=9). This lineage has been implicated in many outbreaks of pneumococcal conjunctivitis in the USA since 1980 [. The ST448 strain seems to have become stably established in the community and is the major cause of non-outbreak-related pneumococcal conjunctivitis in the USA [Among ECC isolates , 11A (n=2), 33F (n=1), 22F (n=2) and 10A (n=1). Pneumococcal keratitis can be a serious and sight-threatening infection associated with significant morbidity [The widespread use of pneumococcal vaccines has significantly decreased the morbidity and mortality of invasive diseases caused by vaccine serotypes . Howeverisolates . In our orbidity . AlthougS. pneumoniae keratitis population, preventive approaches that rely on the development of updated PCVs targeting the capsule may not be feasible. In addition, the wide range of capsular serotypes that can be expressed by S. pneumoniae colonizing humans associated with its natural transformability and frequent capsule switching events, may further complicate the design of PCVs aiming to reduce the burden of pneumococcal keratitis. Additional designs targeting other central virulence factors, for which a more restricted number of structural variants is biologically conceivable, may result in more universal vaccines that could potentially be less impacted by constant recombination events [Given the high diversity of capsular serotypes in the n events . Since vn events , neutraln events .S. pneumoniae isolated in the USA is relatively common, with rates ranging from 20–40 % [S. pneumoniae population was most commonly mediated by the ABC-type efllux system MefA/MsrD, which was the dominant macrolide resistance genotype of S. pneumoniae in isolates causing conjunctivitis characterized in our previous report (particularly STs 448 and 1186) and also from other infectious sites in the USA [in vitro resistance to erythromycin, azithromycin and clarithromycin, but not to tylosin. Less frequently, macrolide-resistant isolates carried ermB gene (n=4), encoding ribosomal methylases. ermB carriage resulted in high-level resistance to the macrolides tested (MIC>32 µg ml−1), and was co-located with the tetM gene in a Tn-916-like transposon. The genetic environment of this transposon was similar to what we had previously found among unencapsulated strains causing conjunctivitis [Among the antimicrobial classes commonly used for treatment of Gram-positive keratitis, our population was sensitive to the fluoroquinolones and vancomycin, and showed different degrees of resistance to macrolides including erythromycin and azithromycin. Resistance to macrolides in  20–40 % . Over a  the USA . This ef the USA . In agrectivitis . No signS. pneumoniae keratitis were caused by a highly diverse population of isolates grouping within 15 different clonal complexes. This genotypically heterogeneous population of keratitis isolates, also presents with a high degree of diversity at the capsule serotype level, with most of the serotypes not being targeted by PCV-13. Overall, 95.5 % of our ocular S. pneumoniae population was not targeted by existing vaccines. Because of the extensive variation observed, modification of vaccines targeting conventional S. pneumoniae virulence traits such as the polysaccharide capsule may not provide coverage to prevent most of the ocular infections seen in our service. These data will be useful in informing the design of next-generation vaccines targeting other virulence factors that are central in the pathogenesis of ocular infections, which could help decrease the economical and health burden of pneumococcal eye infections.In summary, we found that isolates grouped within the ECC are highly adapted and mainly confined to the conjunctival mucosa with ST448 being most frequently identified. Conversely,