National Center for Global Health and Medicine

Regulated transcription controls the diversity, developmental pathways and spatial organization of the hundreds of cell types that make up a mammal. Using single-molecule cDNA sequencing, we mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body. We find that few genes are truly ‘housekeeping’, whereas many mammalian promoters are composite entities composed of several closely separated TSSs, with independent cell-type-specific expression profiles. TSSs specific to different cell types evolve at different rates, whereas promoters of broadly expressed genes are the most conserved. Promoter-based expression analysis reveals key transcription factors defining cell states and links them to binding-site motifs. The functions of identified novel transcripts can be predicted by coexpression and sample ontology enrichment analyses. The functional annotation of the mammalian genome 5 (FANTOM5) project provides comprehensive expression profiles and functional annotation of mammalian cell-type-specific transcriptomes with wide applications in biomedical research. A study from the FANTOM consortium using single-molecule cDNA sequencing of transcription start sites and their usage in human and mouse primary cells, cell lines and tissues reveals insights into the specificity and diversity of transcription patterns across different mammalian cell types. FANTOM5 (standing for functional annotation of the mammalian genome 5) is the fifth major stage of a major international collaboration that aims to dissect the transcriptional regulatory networks that define every human cell type. Two Articles in this issue of Nature present some of the project's latest results. The first paper uses the FANTOM5 panel of tissue and primary cell samples to define an atlas of active, in vivo bidirectionally transcribed enhancers across the human body. These authors show that bidirectional capped RNAs are a signature feature of active enhancers and identify more than 40,000 enhancer candidates from over 800 human cell and tissue samples. The enhancer atlas is used to compare regulatory programs between different cell types and identify disease-associated regulatory SNPs, and will be a resource for studies on cell-type-specific enhancers. In the second paper, single-molecule sequencing is used to map human and mouse transcription start sites and their usage in a panel of distinct human and mouse primary cells, cell lines and tissues to produce the most comprehensive mammalian gene expression atlas to date. The data provide a plethora of insights into open reading frames and promoters across different cell types in addition to valuable annotation of mammalian cell-type-specific transcriptomes.

OBJECTIVE: To present results of the Kyoto Global Consensus Meeting, which was convened to develop global consensus on (1) classification of chronic gastritis and duodenitis, (2) clinical distinction of dyspepsia caused by Helicobacter pylori from functional dyspepsia, (3) appropriate diagnostic assessment of gastritis and (4) when, whom and how to treat H. pylori gastritis. DESIGN: Twenty-three clinical questions addressing the above-mentioned four domains were drafted for which expert panels were asked to formulate relevant statements. A Delphi method using an anonymous electronic system was adopted to develop the consensus, the level of which was predefined as ≥80%. Final modifications of clinical questions and consensus were achieved at the face-to-face meeting in Kyoto. RESULTS: All 24 statements for 22 clinical questions after extensive modifications and omission of one clinical question were achieved with a consensus level of >80%. To better organise classification of gastritis and duodenitis based on aetiology, a new classification of gastritis and duodenitis is recommended for the 11th international classification. A new category of H. pylori-associated dyspepsia together with a diagnostic algorithm was proposed. The adoption of grading systems for gastric cancer risk stratification, and modern image-enhancing endoscopy for the diagnosis of gastritis, were recommended. Treatment to eradicate H. pylori infection before preneoplastic changes develop, if feasible, was recommended to minimise the risk of more serious complications of the infection. CONCLUSIONS: A global consensus for gastritis was developed for the first time, which will be the basis for an international classification system and for further research on the subject.

Concept of Diabetes Mellitus: Diabetes mellitus is a group of diseases associated with various metabolic disorders, the main feature of which is chronic hyperglycemia due to insufficient insulin action. Its pathogenesis involves both genetic and environmental factors. The long-term persistence of metabolic disorders can cause susceptibility to specific complications and also foster arteriosclerosis. Diabetes mellitus is associated with a broad range of clinical presentations, from being asymptomatic to ketoacidosis or coma, depending on the degree of metabolic disorder. Classification (Tables 1 and 2, and Figure 1): Table 1. Etiological classification of diabetes mellitus and glucose metabolism disorders I. Type 1 (destruction of pancreatic β-cells, usually leading to absolute insulin deficiency) A. Autoimmune B. Idiopathic II. Type 2 (ranging from predominantly insulin secretory defect, to predominantly insulin resistance with varying degrees of insulin secretory defect) III. Due to other specific mechanisms or diseases (see Table 2 for details) A. Those in which specific mutations have been identified as a cause of genetic susceptibility (1) Genetic abnormalities of pancreatic β-cell function (2) Genetic abnormalities of insulin action B. Those associated with other diseases or conditions (1) Diseases of exocrine pancreas (2) Endocrine diseases (3) Liver disease (4) Drug- or chemical-induced (5) Infections (6) Rare forms of immune-mediated diabetes (7) Various genetic syndromes often associated with diabetes IV. Gestational diabetes mellitus Note: Those that cannot at present be classified as any of the above are called unclassifiable. The occurrence of diabetes-specific complications has not been confirmed in some of these conditions. Table 2. Diabetes mellitus and glucose metabolism disorders due to other specific mechanisms and diseases A. Those in which specific mutations have been identified as a cause of genetic susceptibility B. Those associated with other diseases or conditions (1) Genetic abnormalities of pancreatic β-cell functionInsulin gene (abnormal insulinemia, abnormal proinsulinemia, neonatal diabetes mellitus) HNF 4α gene (MODY1) Glucokinase gene (MODY2) HNF 1α gene (MODY3) IPF-1 gene (MODY4) HNF 1β gene (MODY5) Mitochondria DNA (MIDD) NeuroD1 gene (MODY6) Kir6.2 gene (neonatal diabetes mellitus) SUR1 gene (neonatal diabetes mellitus) AmylinOthers(2) Genetic abnormalities of insulin actionInsulin receptor gene (type A insulin resistance, leprechaunism, Rabson–Mendenhall syndrome etc.) Others (1) Diseases of exocrine pancreasPancreatitisTrauma/pancreatectomyNeoplasmHemochromatosisOthers(2) Endocrine diseasesCushing’s syndromeAcromegalyPheochromocytomaGlucagonomaAldosteronismHyperthyroidismSomatostatinomaOthers(3) Liver diseaseChronic hepatitisLiver cirrhosis Others(4) Drug- or chemical-inducedGlucocorticoidsInterferonOthers(5) InfectionsCongenital rubellaCytomegalovirusOthers(6) Rare forms of immune-mediated diabetesAnti-insulin receptor antibodiesStiffman syndromeInsulin autoimmune syndromeOthers(7) Various genetic syndromes often associated with diabetesDown syndromePrader-Willi syndromeTurner syndromeKlinefelter syndromeWerner syndromeWolfram syndromeCeruloplasmin deficiencyLipoatrophic diabetes mellitusMyotonic dystrophyFriedreich ataxiaLaurence-Moon-Biedl syndromeOthers The occurrence of diabetes-specific complications has not been confirmed in some of these conditions. Figure 1Open in figure viewerPowerPoint A scheme of the relationship between etiology (mechanism) and patho-physiological stages (states) of diabetes mellitus. Arrows pointing right represent worsening of glucose metabolism disorders (including onset of diabetes mellitus). Among the arrow lines, indicates the condition classified as ‘diabetes mellitus’. Arrows pointing left represent improvement in the glucose metabolism disorder. The broken lines indicate events of low frequency. For example, in type 2 diabetes mellitus, infection can lead to ketoacidosis and require temporary insulin treatment for survival. Also, once diabetes mellitus has developed, it is treated as diabetes mellitus regardless of improvement in glucose metabolism, therefore, the arrow lines pointing left are filled in black. In such cases, a broken line is used, because complete normalization of glucose metabolism is rare. The classification of glucose metabolism disorders is principally derived from etiology, and includes staging of pathophysiology based on the degree of deficiency of insulin action. These disorders are classified into four groups: (i) type 1 diabetes mellitus; (ii) type 2 diabetes mellitus; (iii) diabetes mellitus due to other specific mechanisms or diseases; and (iv) gestational diabetes mellitus. Type 1 diabetes is characterized by destruction of pancreatic β-cells. Type 2 diabetes is characterized by combinations of decreased insulin secretion and decreased insulin sensitivity (insulin resistance). Glucose metabolism disorders in category (iii) are divided into two subgroups; subgroup A is diabetes in which a genetic abnormality has been identified, and subgroup B is diabetes associated with other pathologic disorders or clinical conditions. The staging of glucose metabolism includes normal, borderline and diabetic stages depending on the degree of hyperglycemia occurring as a result of the lack of insulin action or clinical condition. The diabetic stage is then subdivided into three substages: non-insulin- requiring, insulin-requiring for glycemic control, and insulin-dependent for survival. The two former conditions are called non-insulin-dependent diabetes and the latter is known as insulin-dependent diabetes. In each individual, these stages may vary according to the deterioration or the improvement of the metabolic state, either spontaneously or by treatment. Diagnosis (Tables 3–7 and Figure 2): Table 3. Criteria of fasting plasma glucose levels and 75 g oral glucose tolerance test 2-h value Normal range Diabetic range Fasting value <110 mg/dL (6.1 mmol/L) ≥126 mg/dL (7.0 mmol/L) 75 g OGTT 2-h value <140 mg/dL (7.8 mmol/L) ≥200 mg/dL (11.1 mmol/L) Evaluation of OGTT Normal type: If both values belong to normal range *Diabetic type: If any of the two values falls into diabetic range Borderline typeNeither normal nor diabetic types *Casual plasma glucose ≥200 mg/dL (≥11.1 mmol/L) and HbA1c≥6.5% are also regarded as to indicate diabetic type. Even for normal type, if 1-h value is 180 mg/dL (10.0 mmol/L), the risk of progression to diabetes mellitus is greater than for <180 mg/dL (10.0 mmol/L) and should be treated as with borderline type (follow-up observation, etc.). Fasting plasma glucose level of 100–109 mg/dL (5.5–6.0 mmol/L) is called ‘high-normal’: within the range of normal fasting plasma glucose. Plasma glucose level after glucose load in oral glucose tolerance test (OGTT) is not included in casual plasma glucose levels. The value for HbA1c (%) is indicated with 0.4% added to HbA1c (JDS) (%). Table 4. Procedures for diagnosing diabetes mellitus Clinical diagnosis (1) At initial examination, a ‘diabetic type’ is diagnosed if any of the following criteria are met: (i) fasting plasma glucose level ≥126 mg/dL (7.0 mmol/L), (ii) 75 g OGTT 2-h value ≥200 mg/dL (11.1 mmol/L), (iii) casual plasma glucose level ≥200 mg/dL (11.1 mmol/L) or (iv) *HbA1c≥6.5%. Re-examination is carried out at another date and diabetes mellitus is diagnosed if ‘diabetic type’ is confirmed again**. However, diagnosis cannot be made on the basis of a repeated HbA1c test alone. If the same blood sample is confirmed to be diabetic type by both plasma glucose and HbA1c levels (any of [i] to [iii] plus [iv]), then diabetes mellitus can be diagnosed from the initial test (2) If plasma glucose level shows diabetic type (any of [i] to [iii]) and either of the following conditions exists, diabetes mellitus can be diagnosed immediately at the initial examination• The presence of typical symptoms of diabetes mellitus (thirst, polydipsia, polyuria, weight loss)• The presence of definite diabetic retinopathy (3) If it can be confirmed that either of the above conditions 1 or 2 existed in the past, diabetes mellitus must be diagnosed or suspected even if present test values do not meet the above conditions (4) If diabetes mellitus is suspected but the diagnosis cannot be made by the above (1) to (3), the patient should be followed-up (5) The following points should be kept in mind when selecting the method of determination in initial examination and re-examination• If HbA1c is used at initial examination, another method of determination is required for diagnosis at re-examination. As a rule, both plasma glucose level and HbA1c should be measured• If casual plasma glucose level is ≥200 mg/dL (11.1 mmol/L) at the initial test, a different test method is desirable for re-examination• In the case of disorders and conditions in which HbA1c may be inappropriately low, plasma glucose level should be used for diagnosis (Table 5) Epidemiological study For the purpose of estimating the frequency of diabetes mellitus, determination of ‘diabetic type’ from a single test can be considered to represent ‘diabetes mellitus’. Whenever possible, the criteria to be used are HbA1c≥6.5% or OGTT 2-h value ≥200 mg/dL (11.1 mmol/L) Health screening It is important to detect diabetes mellitus and identify high risk groups without overlooking anyone. Therefore, besides measuring plasma glucose and HbA1c, clinical information such as family history and obesity should be referred *The value for HbA1c (%) is indicated with 0.4% added to HbA1c (JDS) (%). **Hyperglycemia must be confirmed in a non-stressful condition. OGTT, oral glucose tolerance test. Table 5. Disorders and conditions associated with low HbA1c values Anemia Liver disease Dialysis Major hemorrhage Blood transfusion Chronic malaria Hemoglobinopathy Others Table 6. Situations where a 75-g oral glucose tolerance test is recommended Strongly recommended (suspicion of present diabetes mellitus cannot be ruled out) Fasting plasma glucose level is 110–125 mg/dL (6.1–6.9 mmol/L) Casual plasma glucose level is 140–199 mg/dL (7.8–11.0 mmol/L) *HbA1c is 6.0–6.4% (excluding those having overt symptoms of diabetes mellitus) Testing is desirable (high risk of developing diabetes mellitus in the future;Testing is especially advisable for patients with risk factors for arteriosclerosis such as hypertension, dyslipidemia and obesity.) Fasting plasma glucose level is 100–109 mg/dL (5.5–6.0 mmol/L) *HbA1c is 5.6–5.9% Strong family history of diabetes mellitus or present obesity regardless of above criteria *The value for HbA1c (%) is indicated with 0.4% added to HbA1c (JDS) (%). Table 7. Definition and diagnostic criteria of gestational diabetes mellitus Definition of gestational diabetes mellitus Glucose metabolism disorder with first recognition or onset during pregnancy, but that has not developed into diabetes mellitus Diagnostic criteria of gestational diabetes mellitus Diagnosed if one or more of the following criteria is met in a 75 g OGTT Fasting plasma glucose ≥92 mg/dL (5.1 mmol/L) 1-h value ≥180 mg/dL (10.0 mmol/L) 2-h value ≥153 mg/dL (8.5 mmol/L) However, diabetes mellitus that is diagnosed according to ‘Clinical diagnosis’ outlined in Table 4 is excluded from gestational diabetes mellitus (IADPSG Consensus Panel, Reference 42, partly modified with permission of Diabetes Care). Figure 2Open in figure viewerPowerPoint Flow chart outlining steps in the clinical diagnosis of diabetes mellitus. *The value for HbA1c (%) is indicated with 0.4% added to HbA1c (JDS) (%). Categories of the State of Glycemia: Confirmation of chronic hyperglycemia is essential for the diagnosis of diabetes mellitus. When plasma glucose levels are used to determine the categories of glycemia, patients are classified as having a diabetic type if they meet one of the following criteria: (i) fasting plasma glucose level of ≥126 mg/dL (≥7.0 mmol/L); (ii) 2-h value of ≥200 mg/dL (≥11.1 mmol/L) in 75 g oral glucose tolerance test (OGTT); or (iii) casual plasma glucose level of ≥200 mg/dL (≥11.1 mmol/L). Normal type is defined as fasting plasma glucose level of <110 mg/dL (<6.1 mmol/L) and 2-h value of <140 mg/dL (<7.8 mmol/L) in OGTT. Borderline type (neither diabetic nor normal type) is defined as falling between the diabetic and normal values. According to the current revision, in addition to the earlier listed plasma glucose values, hemoglobin A1c (HbA1c) has been given a more prominent position as one of the diagnostic criteria. That is, (iv) HbA1c≥6.5% is now also considered to indicate diabetic type. The value of HbA1c, which is equivalent to the internationally used HbA1c (%) (HbA1c [NGSP]) defined by the NGSP (National Glycohemoglobin Standardization Program), is expressed by adding 0.4% to the HbA1c (JDS) (%) defined by the Japan Diabetes Society (JDS). Subjects with borderline type have a high rate of developing diabetes mellitus, and correspond to the combination of impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) noted by the American Diabetes Association (ADA) and WHO. Although borderline cases show few of the specific complications of diabetes mellitus, the risk of arteriosclerosis is higher than those of normal type. When HbA1c is 6.0–6.4%, suspected diabetes mellitus cannot be excluded, and when HbA1c of 5.6–5.9% is included, it forms a group with a high risk for developing diabetes mellitus in the future, even if they do not have it currently. Clinical Diagnosis: 1 If any of the criteria for diabetic type (i) through to (iv) is observed at the initial examination, the patient is judged to be ‘diabetic type’. Re-examination is conducted on another day, and if ‘diabetic type’ is reconfirmed, diabetes mellitus is diagnosed. However, a diagnosis cannot be made only by the re-examination of HbA1c alone. Moreover, if the plasma glucose values (any of criteria [i], [ii], or [iii]) and the HbA1c (criterion [iv]) in the same blood sample both indicate diabetic type, diabetes mellitus is diagnosed based on the initial examination alone. If HbA1c is used, it is essential that the plasma glucose level (criteria [i], [ii] or [iii]) also indicates diabetic type for a diagnosis of diabetes mellitus. When diabetes mellitus is suspected, HbA1c should be measured at the same time as examination for plasma glucose. 2 If the plasma glucose level indicates diabetic type (any of [i], [ii], or [iii]) and either of the following conditions exists, diabetes mellitus can be diagnosed immediately at the initial examination. • The presence of typical symptoms of diabetes mellitus (thirst, polydipsia, polyuria, weight loss) • The presence of definite diabetic retinopathy 3 If it can be confirmed that the above conditions 1 or 2 existed in the past, diabetes mellitus can be diagnosed or suspected regardless of the current test results. 4 If the diagnosis of diabetes cannot be established by these procedures, the patient is followed up and re-examined after an appropriate interval. 5 The physician should assess not only the presence or absence of diabetes, but also its etiology and glycemic stage, and the presence and absence of diabetic complications or associated conditions. Epidemiological Study: For the purpose of estimating the frequency of diabetes mellitus, ‘diabetes mellitus’ can be substituted for the determination of ‘diabetic type’ from a single examination. In this case, HbA1c≥6.5% alone can be defined as ‘diabetes mellitus’. Health Screening: It is important not to misdiagnose diabetes mellitus, and thus clinical information such as family history and obesity should be referred to at the time of screening in addition to an index for plasma glucose level. Gestational Diabetes Mellitus: There are two hyperglycemic disorders in pregnancy: (i) gestational diabetes mellitus (GDM); and (ii) diabetes mellitus. GDM is diagnosed if one or more of the following criteria is met in a 75 g OGTT during pregnancy: 1 Fasting plasma glucose level of ≥92 mg/dL (5.1 mmol/L) 2 1-h value of ≥180 mg/dL (10.0 mmol/L) 3 2-h value of ≥153 mg/dL (8.5 mmol/L) However, diabetes mellitus that is diagnosed by the clinical diagnosis of diabetes mellitus defined earlier is excluded from GDM. (J Diabetes Invest, doi: 10.1111/j.2040-1124.2010.00074.x, 2010)

A novel betacoronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused a large respiratory outbreak in Wuhan, China in December 2019, is currently spreading across many countries globally. Here, we show that a TMPRSS2-expressing VeroE6 cell line is highly susceptible to SARS-CoV-2 infection, making it useful for isolating and propagating SARS-CoV-2. Our results reveal that, in common with SARS- and Middle East respiratory syndrome-CoV, SARS-CoV-2 infection is enhanced by TMPRSS2.

Lists of authors and their affiliations appear in the online version of the paper Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry 1 . We identified 65 new loci that are associated with overall breast cancer risk at P < 5 10 -8 . The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2-5-fold enriched relative to the genomewide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.

Heavy alcohol consumption is an established risk factor for hypertension; the mechanism by which alcohol consumption impact blood pressure (BP) regulation remains unknown. We hypothesized that a genome-wide association study accounting for gene-alcohol consumption interaction for BP might identify additional BP loci and contribute to the understanding of alcohol-related BP regulation. We conducted a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions. In Stage 1, genome-wide discovery meta-analyses in ≈131K individuals across several ancestry groups yielded 3,514 SNVs (245 loci) with suggestive evidence of association (P < 1.0 x 10-5). In Stage 2, these SNVs were tested for independent external replication in ≈440K individuals across multiple ancestries. We identified and replicated (at Bonferroni correction threshold) five novel BP loci (380 SNVs in 21 genes) and 49 previously reported BP loci (2,159 SNVs in 109 genes) in European ancestry, and in multi-ancestry meta-analyses (P < 5.0 x 10-8). For African ancestry samples, we detected 18 potentially novel BP loci (P < 5.0 x 10-8) in Stage 1 that warrant further replication. Additionally, correlated meta-analysis identified eight novel BP loci (11 genes). Several genes in these loci (e.g., PINX1, GATA4, BLK, FTO and GABBR2) have been previously reported to be associated with alcohol consumption. These findings provide insights into the role of alcohol consumption in the genetic architecture of hypertension.

At the end of 2019, a novel coronavirus (severe acute respiratory syndrome coronavirus 2; SARS-CoV-2) was detected in Wuhan, China, that spread rapidly around the world, with severe consequences for human health and the global economy. Here, we assessed the replicative ability and pathogenesis of SARS-CoV-2 isolates in Syrian hamsters. SARS-CoV-2 isolates replicated efficiently in the lungs of hamsters, causing severe pathological lung lesions following intranasal infection. In addition, microcomputed tomographic imaging revealed severe lung injury that shared characteristics with SARS-CoV-2-infected human lung, including severe, bilateral, peripherally distributed, multilobular ground glass opacity, and regions of lung consolidation. SARS-CoV-2-infected hamsters mounted neutralizing antibody responses and were protected against subsequent rechallenge with SARS-CoV-2. Moreover, passive transfer of convalescent serum to naïve hamsters efficiently suppressed the replication of the virus in the lungs even when the serum was administrated 2 d postinfection of the serum-treated hamsters. Collectively, these findings demonstrate that this Syrian hamster model will be useful for understanding SARS-CoV-2 pathogenesis and testing vaccines and antiviral drugs.

Abstract The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-19 1,2 , host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases 3–7 . They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.

Chicago Classification v4.0 (CCv4.0) is the updated classification scheme for esophageal motility disorders using metrics from high-resolution manometry (HRM). Fifty-two diverse international experts separated into seven working subgroups utilized formal validated methodologies over two-years to develop CCv4.0. Key updates in CCv.4.0 consist of a more rigorous and expansive HRM protocol that incorporates supine and upright test positions as well as provocative testing, a refined definition of esophagogastric junction (EGJ) outflow obstruction (EGJOO), more stringent diagnostic criteria for ineffective esophageal motility and description of baseline EGJ metrics. Further, the CCv4.0 sought to define motility disorder diagnoses as conclusive and inconclusive based on associated symptoms, and findings on provocative testing as well as supportive testing with barium esophagram with tablet and/or functional lumen imaging probe. These changes attempt to minimize ambiguity in prior iterations of Chicago Classification and provide more standardized and rigorous criteria for patterns of disorders of peristalsis and obstruction at the EGJ.

In 1999, the Japan Diabetes Society (JDS) launched the previous version of the diagnostic criteria of diabetes mellitus, in which JDS took initiative in adopting glycated hemoglobin (HbA1c) as an adjunct to the diagnosis of diabetes. In contrast, in 2009 the International Expert Committee composed of the members of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) manifested the recommendation regarding the use of HbA1c in diagnosing diabetes mellitus as an alternative to glucose measurements based on the updated evidence showing that HbA1c has several advantages as a marker of chronic hyperglycemia2–4. The JDS extensively evaluated the usefulness and feasibility of more extended use of HbA1c in the diagnosis of diabetes based on Japanese epidemiological data, and then the ‘Report of the Committee on the Classification and Diagnostic Criteria of Diabetes Mellitus’ was published in the Journal of Diabetes Investigation5 and Diabetology International6. The new diagnostic criterion in Japan came into effect on 1 July 2010. According to the new version of the criteria, HbA1c (JDS) ≥6.1% is now considered to indicate a diabetic type, but the previous diagnosis criteria of high plasma glucose (PG) levels to diagnose diabetes mellitus also need to be confirmed. Those are as follows: (i) FPG ≥126 mg/dL (7.0 mmol/L); (ii) 2-h PG ≥200 mg/dL (11.1 mmol/L) during an oral glucose tolerance test; or (iii) casual PG ≥200 mg/dL (11.1 mmol/L). If both PG criteria and HbA1c in patients have met the diabetic type, those patients are immediately diagnosed to have diabetes mellitus5,6. In the report, the HbA1c measurements in Japan are well calibrated with Japanese-Clinical-Laboratory-Use Certified Reference Material (JCCRM). The certified values are determined by a high-resolution type ion-exchange high performance liquid chromatography (HPLC) (KO 500 method) and certified using the designated comparison method (DCM) of the Japan Society of Clinical Chemistry (JSCC) and the JDS. After incorporating a proportional bias correction to the value anchored to the peptide mapping method of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC), the DCM actually measures β-N-mono-deoxyfructosyl hemoglobin and has an intercept approximately equal to zero against the peptide mapping method of IFCC in measuring fresh raw human blood samples. Furthermore, standardization of HbA1c in Japan was initiated in 1993, and the serial reference materials from JDS Lot 1 to JDS Lot 4 are well certified using the DCM until now. In the new diagnosis criteria5,6, the new cut-point of HbA1c (JDS) for diagnosis of diabetes mellitus is 6.1%, which is equivalent to the internationally-used HbA1c (National Glycohemoglobin Standardization Program [NGSP]) 6.5%, as HbA1c (NGSP)(%) is reported to be equivalent to 1.019 × HbA1c (JDS)% + 0.3%, which is reasonably estimated by the equation of HbA1c (JDS)% + 0.4%, as the difference between the two equations is within error of HbA1c measurements (2∼3%). However, on 1 October 2011, the Reference Material Institute for Clinical Chemistry Standards (ReCCS, Kanagawa, Japan) was certified as an Asian Secondary Reference Laboratory (ASRL) using the KO 500 method and the reference materials JCCRM411-2 (JDS Lot 4) after successful completion of NGSP network laboratory certification. Therefore, the HbA1c unit is now traceable to the Diabetes Control and Complications Trial (DCCT) reference method. The comparison was carried out with the Central Primary Reference Laboratory (CPRL) in the University of Missouri School of Medicine. The conversion equation from HbA1c (JDS) to HbA1c (NGSP) units is officially certified as follows: NGSP (%) = 1.02 × JDS (%) + 0.25%; conversely, JDS (%) = 0.980 × NGSP (%) – 0.245%. Based on this equation, in the range of JDS values ≤4.9%, NGSP (%) = JDS (%) + 0.3%; in the range of JDS 5.0∼9.9%, NGSP (%) = JDS (%) + 0.4%; and in the range of JDS 10∼14.9%, NGSP (%) = JDS (%) + 0.5%. These results show that the previous equation of NGSP (%) = JDS (%) + 0.4% is also confirmed in the present equation, considering a 2∼3% error of HbA1c measurements. The council meeting of the JDS finally decided to use HbA1c (NGSP) values in clinical practice from 1 April 2012, although HbA1c (JDS) values will be included until people become familiar with the new expression. Finally, it is also important to emphasize that the new HbA1c (NGSP) values can be directly measured and printed out from 1 April 2012. However, both new diagnostic reference values and target values of glycemic control have been adjusted to those equivalent values of HbA1c (JDS), as shown in the Table 1.

Abstract Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes 1 . Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel 2 ) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

BACKGROUND: Remarkable financial and political efforts have been focused on the reduction of child mortality during the past few decades. Timely measurements of levels and trends in under-5 mortality are important to assess progress towards the Millennium Development Goal 4 (MDG 4) target of reduction of child mortality by two thirds from 1990 to 2015, and to identify models of success. METHODS: We generated updated estimates of child mortality in early neonatal (age 0-6 days), late neonatal (7-28 days), postneonatal (29-364 days), childhood (1-4 years), and under-5 (0-4 years) age groups for 188 countries from 1970 to 2013, with more than 29,000 survey, census, vital registration, and sample registration datapoints. We used Gaussian process regression with adjustments for bias and non-sampling error to synthesise the data for under-5 mortality for each country, and a separate model to estimate mortality for more detailed age groups. We used explanatory mixed effects regression models to assess the association between under-5 mortality and income per person, maternal education, HIV child death rates, secular shifts, and other factors. To quantify the contribution of these different factors and birth numbers to the change in numbers of deaths in under-5 age groups from 1990 to 2013, we used Shapley decomposition. We used estimated rates of change between 2000 and 2013 to construct under-5 mortality rate scenarios out to 2030. FINDINGS: We estimated that 6·3 million (95% UI 6·0-6·6) children under-5 died in 2013, a 64% reduction from 17·6 million (17·1-18·1) in 1970. In 2013, child mortality rates ranged from 152·5 per 1000 livebirths (130·6-177·4) in Guinea-Bissau to 2·3 (1·8-2·9) per 1000 in Singapore. The annualised rates of change from 1990 to 2013 ranged from -6·8% to 0·1%. 99 of 188 countries, including 43 of 48 countries in sub-Saharan Africa, had faster decreases in child mortality during 2000-13 than during 1990-2000. In 2013, neonatal deaths accounted for 41·6% of under-5 deaths compared with 37·4% in 1990. Compared with 1990, in 2013, rising numbers of births, especially in sub-Saharan Africa, led to 1·4 million more child deaths, and rising income per person and maternal education led to 0·9 million and 2·2 million fewer deaths, respectively. Changes in secular trends led to 4·2 million fewer deaths. Unexplained factors accounted for only -1% of the change in child deaths. In 30 developing countries, decreases since 2000 have been faster than predicted attributable to income, education, and secular shift alone. INTERPRETATION: Only 27 developing countries are expected to achieve MDG 4. Decreases since 2000 in under-5 mortality rates are accelerating in many developing countries, especially in sub-Saharan Africa. The Millennium Declaration and increased development assistance for health might have been a factor in faster decreases in some developing countries. Without further accelerated progress, many countries in west and central Africa will still have high levels of under-5 mortality in 2030. FUNDING: Bill & Melinda Gates Foundation, US Agency for International Development.

Abstract Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities 1,2 . This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity 3–6 . Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017—and more than 80% in some low- and middle-income regions—was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing—and in some countries reversal—of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories.

OBJECTIVE: This trial compared the efficacy and safety of transarterial chemoembolisation (TACE) plus sorafenib with TACE alone using a newly established TACE-specific endpoint and pre-treatment of sorafenib before initial TACE. DESIGN: Patients with unresectable hepatocellular carcinoma (HCC) were randomised to TACE plus sorafenib (n=80) or TACE alone (n=76). Patients in the combination group received sorafenib 400 mg once daily for 2-3 weeks before TACE, followed by 800 mg once daily during on-demand conventional TACE sessions until time to untreatable (unTACEable) progression (TTUP), defined as untreatable tumour progression, transient deterioration to Child-Pugh C or appearance of vascular invasion/extrahepatic spread. Co-primary endpoints were progression-free survival (PFS), which is not a conventional one but defined as TTUP, or time to any cause of death plus overall survival (OS). Multiplicity was adjusted by gatekeeping hierarchical testing. RESULTS: Median PFS was significantly longer in the TACE plus sorafenib than in the TACE alone group (25.2 vs 13.5 months; p=0.006). OS was not analysed because only 73.6% of OS events were reached. Median TTUP (26.7 vs 20.6 months; p=0.02) was also significantly longer in the TACE plus sorafenib group. OS at 1 year and 2 years in TACE plus sorafenib group and TACE alone group were 96.2% and 82.7% and 77.2% and 64.6%, respectively. There were no unexpected toxicities. CONCLUSION: TACE plus sorafenib significantly improved PFS over TACE alone in patients with unresectable HCC. Adverse events were consistent with those of previous TACE combination trials. TRIAL REGISTRATION NUMBER: NCT01217034.

Abstract The recent emergence of B.1.1.529, the Omicron variant 1,2 , has raised concerns of escape from protection by vaccines and therapeutic antibodies. A key test for potential countermeasures against B.1.1.529 is their activity in preclinical rodent models of respiratory tract disease. Here, using the collaborative network of the SARS-CoV-2 Assessment of Viral Evolution (SAVE) programme of the National Institute of Allergy and Infectious Diseases (NIAID), we evaluated the ability of several B.1.1.529 isolates to cause infection and disease in immunocompetent and human ACE2 (hACE2)-expressing mice and hamsters. Despite modelling data indicating that B.1.1.529 spike can bind more avidly to mouse ACE2 (refs. 3,4 ), we observed less infection by B.1.1.529 in 129, C57BL/6, BALB/c and K18-hACE2 transgenic mice than by previous SARS-CoV-2 variants, with limited weight loss and lower viral burden in the upper and lower respiratory tracts. In wild-type and hACE2 transgenic hamsters, lung infection, clinical disease and pathology with B.1.1.529 were also milder than with historical isolates or other SARS-CoV-2 variants of concern. Overall, experiments from the SAVE/NIAID network with several B.1.1.529 isolates demonstrate attenuated lung disease in rodents, which parallels preliminary human clinical data.

Abstract During the current coronavirus disease 2019 (COVID-19) pandemic, a variety of mutations have accumulated in the viral genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and, at the time of writing, four variants of concern are considered to be potentially hazardous to human society 1 . The recently emerged B.1.617.2/Delta variant of concern is closely associated with the COVID-19 surge that occurred in India in the spring of 2021 (ref. 2 ). However, the virological properties of B.1.617.2/Delta remain unclear. Here we show that the B.1.617.2/Delta variant is highly fusogenic and notably more pathogenic than prototypic SARS-CoV-2 in infected hamsters. The P681R mutation in the spike protein, which is highly conserved in this lineage, facilitates cleavage of the spike protein and enhances viral fusogenicity. Moreover, we demonstrate that the P681R-bearing virus exhibits higher pathogenicity compared with its parental virus. Our data suggest that the P681R mutation is a hallmark of the virological phenotype of the B.1.617.2/Delta variant and is associated with enhanced pathogenicity.

Broad-spectrum antiviral drugs against highly pathogenic coronaviruses and other emerging viruses are desirable to enable a rapid response to pandemic threats. Transmembrane protease serine type 2 (TMPRSS2), a protease belonging to the type II transmembrane serine protease family, cleaves the coronavirus spike protein, making it a potential therapeutic target for coronavirus infections. Here, we examined the role of TMPRSS2 using animal models of SARS-CoV and MERS-CoV infection. The results suggest that lack of TMPRSS2 in the airways reduces the severity of lung pathology after infection by SARS-CoV and MERS-CoV. Taken together, the results will facilitate development of novel targets for coronavirus therapy.

The Clinical Practice Manual for Hepatocellular Carcinoma was published based on evidence confirmed by the Evidence-based Clinical Practice Guidelines for Hepatocellular Carcinoma along with consensus opinion among a Japan Society of Hepatology (JSH) expert panel on hepatocellular carcinoma (HCC). Since the JSH Clinical Practice Guidelines are based on original articles with extremely high levels of evidence, expert opinions on HCC management in clinical practice or consensus on newly developed treatments are not included. However, the practice manual incorporates the literature based on clinical data, expert opinion, and real-world clinical practice currently conducted in Japan to facilitate its use by clinicians. Alongside each revision of the JSH Guidelines, we issued an update to the manual, with the first edition of the manual published in 2007, the second edition in 2010, the third edition in 2015, and the fourth edition in 2020, which includes the 2017 edition of the JSH Guideline. This article is an excerpt from the fourth edition of the HCC Clinical Practice Manual focusing on pathology, diagnosis, and treatment of HCC. It is designed as a practical manual different from the latest version of the JSH Clinical Practice Guidelines. This practice manual was written by an expert panel from the JSH, with emphasis on the consensus statements and recommendations for the management of HCC proposed by the JSH expert panel. In this article, we included newly developed clinical practices that are relatively common among Japanese experts in this field, although all of their statements are not associated with a high level of evidence, but these practices are likely to be incorporated into guidelines in the future. To write this article, coauthors from different institutions drafted the content and then critically reviewed each other's work. The revised content was then critically reviewed by the Board of Directors and the Planning and Public Relations Committee of JSH before publication to confirm the consensus statements and recommendations. The consensus statements and recommendations presented in this report represent measures actually being conducted at the highest-level HCC treatment centers in Japan. We hope this article provides insight into the actual situation of HCC practice in Japan, thereby affecting the global practice pattern in the management of HCC.

Small-cell lung cancer (SCLC) is a subgroup of lung cancer with a high frequency of liver metastasis, which is a predictor of poor prognosis. Diffuse liver metastases of SCLC with no visible nodular lesions in the liver when examined using computed tomography (CT) are relatively rare; however, a few cases with rapid progression to acute liver failure that were diagnosed after death have been reported. In this paper, we report a 63-year-old man with diffuse liver metastases of SCLC that were histologically diagnosed using a transjugular liver biopsy while the patient was alive, even though no lesions were visible during a contrast-enhanced CT examination.

BACKGROUND: A growing body of evidence has suggested that metformin potentially reduces the risk of cancer. Our objective was to enhance the precision of estimates of the effect of metformin on the risk of any-site and site-specific cancers in patients with diabetes. METHODS/PRINCIPAL FINDINGS: We performed a search of MEDLINE, EMBASE, ISI Web of Science, Cochrane Library, and ClinicalTrials.gov for pertinent articles published as of October 12, 2011, and included them in a systematic review and meta-analysis. We calculated pooled risk ratios (RRs) for overall cancer mortality and cancer incidence. Of the 21,195 diabetic patients reported in 6 studies (4 cohort studies, 2 RCTs), 991 (4.5%) cases of death from cancer were reported. A total of 11,117 (5.3%) cases of incident cancer at any site were reported among 210,892 patients in 10 studies (2 RCTs, 6 cohort studies, 2 case-control studies). The risks of cancer among metformin users were significantly lower than those among non-metformin users: the pooled RRs (95% confidence interval) were 0.66 (0.49-0.88) for cancer mortality, 0.67 (0.53-0.85) for all-cancer incidence, 0.68 (0.53-0.88) for colorectal cancer (n = 6), 0.20 (0.07-0.59) for hepatocellular cancer (n = 4), 0.67 (0.45-0.99) for lung cancer (n = 3). CONCLUSION/SIGNIFICANCE: The use of metformin in diabetic patients was associated with significantly lower risks of cancer mortality and incidence. However, this analysis is mainly based on observational studies and our findings underscore the more need for long-term RCTs to confirm this potential benefit for individuals with diabetes.