Nutrition and lifestyle have been long established as risk factors for colorectal cancer (CRC). Modifiable lifestyle behaviours bear potential to minimize long-term CRC risk; however, translation of lifestyle information into individualized CRC risk assessment has not been implemented. Lifestyle-based risk models may aid the identification of high-risk individuals, guide referral to screening and motivate behaviour change. We therefore developed and validated a lifestyle-based CRC risk prediction algorithm in an asymptomatic European population.The model was based on data from 255,482 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) study aged 19 to 70 years who were free of cancer at study baseline (1992–2000) and were followed up to 31 September 2010. The model was validated in a sample comprising 74,403 participants selected among five EPIC centres. Over a median follow-up time of 15 years, there were 3645 and 981 colorectal cancer cases in the derivation and validation samples, respectively. Variable selection algorithms in Cox proportional hazard regression and random survival forest (RSF) were used to identify the best predictors among plausible predictor variables. Measures of discrimination and calibration were calculated in derivation and validation samples. To facilitate model communication, a nomogram and a web-based application were developed.The final selection model included age, waist circumference, height, smoking, alcohol consumption, physical activity, vegetables, dairy products, processed meat, and sugar and confectionary. The risk score demonstrated good discrimination overall and in sex-specific models. Harrell’s C-index was 0.710 in the derivation cohort and 0.714 in the validation cohort. The model was well calibrated and showed strong agreement between predicted and observed risk. Random survival forest analysis suggested high model robustness. Beyond age, lifestyle data led to improved model performance overall (continuous net reclassification improvement = 0.307 (95% CI 0.264–0.352)), and especially for young individuals below 45 years (continuous net reclassification improvement = 0.364 (95% CI 0.084–0.575)).LiFeCRC score based on age and lifestyle data accurately identifies individuals at risk for incident colorectal cancer in European populations and could contribute to improved prevention through motivating lifestyle change at an individual level. Colorectal cancer accounted for over 1.8 million new cases or 10% of all new cases of cancer worldwide in 2018 . TargeteLifestyle behaviours such as smoking, alcohol consumption, and poor diet have long been recognized to be associated with a higher risk of colorectal cancer –15. UpdaA number of colorectal cancer risk prediction models have been published over the last decade –21. MostIn this context, we aimed to develop and validate a lifestyle-based risk prediction model for the prevention of colorectal cancer in a population-based European cohort. We further aimed to construct a simple and widely applicable user-friendly risk calculator offering an estimate of colorectal cancer risk based on individual’s personal data.The lifestyle-based prediction model for colorectal cancer risk (LiFeCRC score) was developed using data collected within EPIC, a multicentre prospective cohort study comprising 521,324 participants aged 17 to 98 years at study baseline (predominantly 35 to 70 years) recruited between 1992 and 2000 across 23 centres in 10 European countries . ParticiThe primary outcome was incident colorectal cancer. Cancer cases were identified through population cancer registries in Denmark, Italy, the Netherlands, Spain, Sweden and the UK. In France, Germany and Greece, a combination of methods was used including health insurance records, cancer pathology registries and active follow-up of study participants. Follow-up began at the date of enrolment and ended at the date of diagnosis of colorectal cancer, death or last complete follow-up. The last update of endpoint information was done up to 31 September 2010. Colon and rectal cancers were defined according to the 10th Revision of the International Statistical Classification of Diseases, Injuries and Causes of Death (ICD-10), proximal colon tumours include tumours in the cecum, cecal appendix, ascending colon, hepatic flexure, transverse colon and splenic flexure (ICD-10 codes C18.0–18.5); distal colon tumours include those in the descending colon (ICD-10 code C18.6) and sigmoid colon (ICD-10 code C18.7); and rectal tumours are those occurring at the rectosigmoid junction (ICD-10 code C19) or in the rectum (ICD-10 code C20). Only the first primary neoplasm was included in the analysis; non-melanoma skin cancer was excluded.N = 255,482) and a validation cohort  on a non-random principle following the TRIPOD recommendations [Figure ndations . The derAt baseline, participants completed extensive medical, dietary and lifestyle questionnaires, including questions on alcohol use, smoking status, physical activity, education and previous illnesses. Body weight, height and waist circumference were measured in all centres except for EPIC-Oxford  and EPIC-France where anthropometric measurements were self-reported . Usual fThe model development and model validation were performed and reported following the TRIPOD guidelines , 31 . The selected parameters were then used to bootstrap the elastic net regularization of each predictor’s Cox regression coefficient with 1000 replications. Based on all bootstrap replications, mean coefficient values and 95% confidence intervals were calculated for each predictor coefficient. Predictors with confidence intervals including zero were removed. All remaining predictors were then used to generate reduced elastic net penalized Cox regression models. The model selection was conducted for colorectal cancer as a single endpoint (LiFeCRC score) and according to sex and cancer subsite (colon/rectum). Variable selection and Cox regression modeling were performed using R 3.6.1 (R Core Team) [glmnet (version 2.0-18) [survival (version 2.44-1.1) [Predictor variable selection was performed using bootstrapped elastic net regularization . Elasticre Team) , and the 2.0-18)  and surv.44-1.1)  packagesP (10y) for colorectal cancer was calculated using the following formula:The individual 10-year absolute risk Sm (10y) was calculated for average predictor variable values. The average Risk Scorem and the individual Risk Scorei were computed using the following formulas:The 10-year survival function estimate j index stands for a predictor variable of a Cox regression model and βj is the beta estimate.The In additional analyses, the study population was stratified according to predefined risk categories of low, intermediate and high risk, based the 50th and 90th percentile of predicted risk in the derivation cohort. Incidence rates and model selection characteristics across the so defined risk categories in both the derivation and validation samples have been assessed.rms (version 5.1-3.1) [Model discrimination was assessed based on Harrell’s C-index as a measure similar to the receiver operating characteristic statistic that takes the censored nature of data into account. This value represents the odds of the predicted probability of developing colorectal cancer being higher for those who actually develop colorectal cancer compared to those who do not develop the disease. To account for model optimism in terms of overfitting, bootstrapping with 1000 replications was performed. In bootstrapping, entries are randomly drawn with replacement from a data set until the bootstrap sample has the size as the original dataset. For each bootstrap sample, an elastic net penalized Cox regression model was fitted. Harrell’s C-index of each bootstrap model was then calculated for the bootstrap sample and the original data in each bootstrap replication. The difference of these values was averaged over all 1000 bootstrap replications to calculate the amount of optimism for the C-index of the original model, which was used to calculate an optimism-corrected C-index. This analysis was performed in R  with the5.1-3.1) .Calibration plots of estimated individual predicted risks of developing colorectal cancer in the next 10 years were derived from the penalized Cox regression model. These values were divided into deciles, and each decile’s mean value was computed. The Kaplan-Meier survival function at 10 years with 95% confidence interval was calculated for each decile group. Subsequently, the trend of the mean predicted risks and the observed complement of the Kaplan-Meier survival of each decile was visually compared as a measure of calibration. Model performance, including Harrell’s C-index and calibration plots, was also evaluated in the validation cohort.rms (version 5.1-3.1) [shiny (version 1.2.0) [shinydashboard (version 0.7.1) [In order to assist the translation of the generated statistical model into an individual risk prediction equation, we created a 10-year risk assessment nomogram as a graphical model representation that allows risk estimation. For this purpose, we used the R  package 5.1-3.1) . In addi5.1-3.1)  packagesn 1.2.0)  and shinn 0.7.1)  that canRandom survival forest was used as an alternative machine learning method in order to prove model robustness, i.e. assess whether the same set of predictors will be selected. Each random survival forest was generated with a total number of 500 decision trees with 100 unique data points on average in each terminal node and a maximum of 10 possible random split points to consider at each branch of a decision tree. A variable importance measure for each predictor variable, describing the impact of using randomly permuted values of this variable instead of observed values for the prediction of known entries, was then extracted from the random survival forest. For the computation of random survival forests, the package “randomForestSRC” (version 2.6.1) was used. Model performance was evaluated in the derivation and validation cohort using Harrell’s C-index and calibration plots.> 0); (2) improvement in model calibration based on comparison of calibration plots and (3) net benefit of the model based on decision curve analysis. We also stratified the study population in the derivation and validation sample according to age groups: < 45 years; 45–65 years; > 65 years and calculated model performance characteristics (Harrell’s C-index and NRI> 0) for the lifestyle-based model across these categories. In addition, we also calculated the predicted 10-year absolute risk of colorectal cancer for a predefined “healthy” and “unhealthy” lifestyle pattern across different age groups and a constant body height. In subsample of the derivation cohort with available information, Harrell’s C-index was compared between models with and without inclusion of NSAID use or family history. To address model generalizability, we further evaluated model performance across subgroups by selected variables, i.e. waist circumference, education, smoking status (including level of smoking intensity) and level of alcohol consumption. Finally, to account for the potential influence of competing risk of death , we calculated the cumulative incidence adjusted for mortality and evaluated the discrimination of the reduced model based on Fine-Gray subdistribution hazard regression [In sensitivity analyses, we evaluated the added predictive value of lifestyle data beyond age, using the following statistics: (1) improvement in model discrimination—based on goodness of fit (likelihood ratio test), estimated net change in Harrell’s C-index and continuous net reclassification improvement  and Risk Scorem are given in Table Sm in the formula accordingly. The survival function estimates for timespans between 0 and 20 years are shown in Supplementary Fig. 2, Additional File Values for Sm 0 years a. The use of the nomogram is simple and involves 3 steps. First, on the scale for each variable, the value corresponding to a specific individual is read and the point scale is used to calculate the points for all variable values. Second, the total number of points is calculated by adding up all the points obtained in the previous step, and its value is identified on the total point scale. Finally, the probability of an event corresponding to the total points of the individual is represented on the risk scale. As a practical example, we estimated the 10-year risk of colorectal cancer, for individuals with two different combinations of ages and lifestyle factors, representing low-risk and high-risk extremes: individual 1 was 45 years old (50 points) with a body height of 166 cm (7.5 points), a waist circumference of 70 cm (3 points) and healthy lifestyle behaviour , non-smoker (0 points), physically active (0 points), 430 g daily vegetable intake (7 points), 630 g daily dairy products intake (2.5 points), 0 g daily processed meat intake (0 points), and 5 g daily sugar and confectionary intake (0 points)), and individual 2 was 65 years old (90 points) with a body height of 166 cm (7.5 points), a waist circumference of 100 cm (12 points) and rather unhealthy lifestyle behaviour , smoker (5 points), physically inactive (2.5 points), 80 g daily vegetable intake (14.5 points), 70 g daily dairy products intake (5 points), 60 g daily processed meat intake (2.5 points), and 90 g daily sugar and confectionary intake (1.5 points)). The total number of points of the various prediction indicators was ~ 70 and ~ 143.5 and the corresponding absolute predicted 10-year risk of colorectal cancer was ~ 0.2% (risk score of ~ 5.7) and ~ 3–3.5% (risk score of ~ 8.6) for individual 1 and individual 2, respectively.Figure As an alternative approach to model communication, we developed a web-based calculator for the estimation of a personalized colorectal cancer risk based on the validated LiFeCRC score. A graphical illustration of the application layout with predicted and absolute risk values for a modifiable time span is presented in Fig. Results of random survival forest-based relative variable importance for colorectal cancer risk prediction are presented in Supplementary Fig. 3, Additional File p < 0.001). The estimated NRI> 0 was 0.307  indicating an improvement in model performance. Supplementary Fig. 5, Additional File > 0, suggesting relative importance of lifestyle data assessment for risk prediction at younger ages (< 45 years), i.e. NRI> 0 = 0.364  . A user-friendly colorectal cancer risk nomogram and a web calculator were developed to facilitate model communication.Currently, the target population for colorectal cancer screening is mainly selected based on age alone (i.e. 50 years or above). Although age is undoubtedly an important predictor of colorectal cancer as shown in our data, information on modifiable lifestyle factors allows provision of preventive health recommendations for individuals at risk . LifestyA number of previous models incorporated lifestyle data with common covariates including self-reported BMI (body mass index), alcohol consumption and smoking –21. ReceThe elaborated phenotyping and detailed assessment of nutritional data in the EPIC cohort allowed selection of several factors not commonly depicted in previous colorectal cancer risk prediction models. Compared to previous models that used data on self-reported BMI, in the EPIC cohort data was available on waist circumference measurements and these were among the main predictors , 54. UnlThe selected model performed similarly well as the model with the full list of predictors, suggesting that it can be used as a simpler approach for determining high-risk individuals. Thus, individuals and health professionals would need to inquire about fewer lifestyle factors, avoiding the use of long questionnaires and minimizing the burden of data collection on both the patient and clinician side. However, for a comprehensive lifestyle recommendation, all healthy behaviours could be considered in additional counselling. The model performance among women was modest, and better in men, likely because some risk factors were more strongly associated with risk among men. The general distribution and influence of risk factors may differ geographically across populations and additional model elaboration and adaptation of country-specific risk models should be further considered. Ultimately, research is needed to assess the feasibility and effectiveness of the current lifestyle-based risk assessment tool on health behaviour modification, colorectal cancer risk factor improvement, and overall potential for colorectal cancer prevention when incorporated into the primary care setting, particularly as a pre-screening instrument of high-risk patients. More work is also warranted for the refinement of the risk communication tool before its general integration into practice. Finally, in future research, additional predictors, including relevant biomarker and genetic variables, should be further explored on the way towards improved precision prevention of colorectal cancer. For example, in a systematic review of 29 studies, addition of common single nucleotide polymorphisms (SNPs) to other risk factors in models developed in asymptomatic individuals in the general population increased model discrimination by 0.01 to 0.06 . OverallOur work has several strengths. The EPIC study provided an ideal setting to develop a lifestyle-based colorectal cancer risk prediction model, given its large sample size, various population backgrounds and a long follow-up time of over 20 years. Furthermore, the study provided a variety of objectively measured anthropometric data along with dietary and lifestyle information. Therefore, the current model is the first developed on a European-wide study population sample, allowing assessment of risk across a broad range of diet and lifestyle behaviours. Given the large sample size, we were also able to validate the risk scores in an independent subset of the EPIC populations. Additionally, we derived the colorectal cancer risk estimates empirically following state-of-the-art and novel machine learning approaches, i.e. random survival forest, considering various predictors simultaneously and the gradient in risk across the full distribution of risk levels. Finally, we considered model application and suggested a nomogram and a web-tool to enable risk communication. Several potential limitations of our study warrant discussion. First, we derived the risk equations based on a study population comprising of volunteers. Volunteer-based studies are prone to include individuals who are often more likely to have favourable exposure and health profiles compared to those who do not. Thus, higher prevalence of healthy behaviours in our sample as compared to the general population could have resulted in overestimated absolute risk estimates. Second, with the exception of age and the anthropometric measures, we relied on data of self-reported predictors and routinely collected cancer outcomes. Though any risk prediction tool made publicly available online would also rely on self-reported data, more accurate risk factor ascertainment would possibly improve overall model discrimination and calibration. Nevertheless, our model has shown a good discrimination and excellent calibration. Third, dietary data was collected using food frequency questionnaires as a commonly applied dietary assessment method in epidemiology, however future model application should consider further adaptation and feasibility assessment to facilitate model communication in practice. Fourth, we based analyses on lifestyle information collected at study baseline and, therefore, could not account for potential behavioural changes during study follow-up. Finally, the model was developed based on data available in the EPIC cohort and did not include some potentially important predictors, such as NSAID use or family history of colorectal cancer. However, we have conducted a sensitivity analysis using data from study centres that collected these data and the model performance was not altered.Despite being one of the leading causes of cancer morbidity and mortality, colorectal cancer is largely preventable. LiFeCRC score based on age and lifestyle data accurately identifies individuals at risk for incident colorectal cancer in European populations and could contribute to improved prevention through motivating lifestyle change at the individual level.Additional file 1:Supplementary Table 1. Baseline characteristics of participants with available information on NSAID use and colorectal cancer family history. Supplementary Table 2. TRIPOD Checklist - Prediction Model Development and Validation. Supplementary Table 3. Factors considered for inclusion in the LiFeCRC score. Supplementary Table 4. LifeCRC model selection characteristics across pre-defined risk categories in the derivation and validation samples. Supplementary Table 5. Added predictive performance for age and lifestyle-based (LiFeCRC) colorectal cancer risk prediction models. Results are stratified by age groups. Supplementary Table 6. Sensitivity analysis by subgroups.Additional file 2: Supplementary Figure 1. Workflow of risk model development and validation. Supplementary Figure 2. Survival function of average predictor values of the derivation cohort. Supplementary Figure 3. Discrimination and relative variable importance based on Random Survival Forest models for colorectal cancer prediction. Supplementary Figure 4. Random Survival Forest colorectal cancer full model calibration. Supplementary Figure 5. Model performance comparison the LiFeCRC score and a colorectal cancer risk model including only age. (a) Calibration plot of predicted 10-year colorectal cancer risk for a model that included only age and the LiFeCRC score model with additional lifestyle predictors . (b) Decision curves illustrating net benefit of prediction models for a range of colorectal cancer risk thresholds, used to decide about further treatment or intervention. Decisions curves are shown for different models: none treatment, all treatment, treatment based on the age-model, treatment based on the LiFeCRC model. Supplementary Figure 6. Predicted 10-year absolute risk of colorectal cancer for a healthy and unhealthy lifestyle. Risk across different age-groups and a constant body height of 166 cm. Unhealthy lifestyle: waist circumference of 100 cm, high daily alcohol consumption, smoker, physically inactive, 80 g daily vegetable intake, 70 g daily dairy products intake, 60 g daily processed meat intake, and 90 g daily sugar and confectionary intake. Healthy lifestyle: waist circumference of 70 cm, low daily alcohol consumption, non-smoker, physically active, 430 g daily vegetable intake, 630 g daily dairy products intake, 0 g daily processed meat intake, and 5 g daily sugar and confectionary intake. Supplementary Figure 7. Full model performance including NSAID use and family history of colorectal cancer.


Downesia species are leaf-mining beetles mainly feed on Poaceae plants in the tropical and subtropical areas in Asia. In this study, we firstly sequenced and reported the complete mitochondrial genome for the genus. The complete mitogenome of Downesia tarsata is 18,557 bp in length, including 13 protein-coding genes (PCG), 22 transfer RNA (tRNA), two ribosomal RNA (rRNA), and one AT-rich region. Phylogenomic analysis indicated that D. tarsata is closely related to Agonita chinensis, and the two species belong to the same tribe of Gonophorini. The complete mitochondrial genome of D. tarsata could help clarify the phylogenetic relationship among Cassidinae species. Downesia species (Chrysomelidae: Coleoptera: Cassidinae) are leaf-mining beetles, their host plants are mainly Poaceae, and they distribute mainly in the tropical and subtropical areas in Asia (Staines Downesia tarsata was only found in Guangdong and Hong Kong in China (Staines Miscanthus floridulus (Poaceae) in Jiangxi. However, no genome information of 41 Downesia species has not been reported at present. In this study, the complete mitochondrial genome of D. tarsata was sequenced and annotated, which may help understand its evolutionary history and population genetics. Staines . Downesi Staines , but we Downesia tarsata samples were collected from Doushuihu, Jiangxi Province, China . The catalog number is 2016XI29002 and 20161120905 for the insects and their host plants, respectively. Voucher specimens are deposited in Nanling Herbarium, Gannan Normal University (GNNU). The sampling adults were all stored in 100% ethanol at −80 °C. The whole genomic DNA was obtained from a single specimen’s head tissue following the manufacturer’s protocol of the TIA Namp Genomic DNA kit . DNA was preserved at −20 °C and sent to Shanghai Personal Biotechnology Co., Ltd. for mitochondrial genome sequencing.D. tarsata was obtained by the next-generation sequencing (NGS) with the whole-genome shotgun (WGS) strategy on the Illumina MiSeq platform. It is 18,557 bp in length containing A 42.45%, C 12.47%, G 8.90%, and T 36.18% respectively. It has 13 protein-coding genes (PCG), 22 transfer RNA (tRNA), two ribosomal RNA (rRNA), and an AT-rich region. Three kinds of start codons  are found in 13 PCGs; six genes end with TAA, two genes end with TAG, and the other genes have an incomplete stop codon, three are TA– and two are T––. The lengths of 22 tRNA genes range from 58 bp to 70 bp, having clover-leaf structures except for trnSer1, which lost the DHU arm. The rRNA genes, rrnS, rrnL, were found between trnSer1 and the control region. The 727-bp rrnS has an A + T content of 78.68%, while the 1195-bp rrnL length has an A + T content of 79.33%. Among the 37 genes, 23 genes (9 PCGs and 14 tRNAs) are located on the major strand (N-strand) and 14 genes  on the minor strand (J-strand) just as in other Cassidinae species  of D. tarsata is closely related to Agonita chinensis (D. tarsata could provide useful genetic information for further study on the evolutionary relationship of Cassidinae species.The other four partial genomes and seven complete genomes of Chrysomeloidea species from Genbank are downloaded and aligned with the MAFFT program (Katoh and Standley, hinensis , and the


Acute kidney injury (AKI) is a common complication among human immunodeficiency virus (HIV)-infected patients resulting in increased morbidity and mortality. Continuous renal replacement therapy (CRRT) is a useful method and instrument in critically ill patients with fluid overload and metabolic disarray, especially in those who are unable to tolerate the intermittent hemodialysis. However, the epidemiology, influence factors of CRRT and mortality in patients with HIV/AIDS are still unclear in China. This study aims to study the HIV-infected patients admitted in Intensive Care Unit (ICU) and explore the influence factors correlated with CRRT and their prognosis.We performed a retrospective case-control study in the ICU of the Beijing Ditan Hospital Capital Medical University. From June 1, 2005 to May 31, 2017, 225 cases were enrolled in this clinical study.p = 0.015), higher likelihood of diagnosis for septic shock , and higher likelihood to use vasoactive agents , longer mechanical ventilation duration , higher likelihood for diagnosis for PCP , higher SOFA score at ICU admission , longer duration of CRRT  contributed to a higher mortality at ICU. The Cox Analysis for the cumulative survival of AKI 3 patients between the CRRT and non-CRRT groups shows no significant differences (p = 0.595).122 (54.2%) patients were diagnosed with AKI during their stay in ICU, the number and percentage of AKI stage 1, 2 and 3 were 38 (31.1%), 21(17.2%) and 63(51.7%), respectively. 26.2% of AKI patients received CRRT during the stay of ICU. 56.25% CRRT patients died in ICU. The 28-day mortality was 62.5%, and the 90-day mortality was 75%. By univariate logistics analysis, it showed that higher likelihood of diagnosis for respiratory failure (OR = 7.333,95% CI 1.467–36.664, There is a high incidence of AKI in HIV-infected patients admitted in our ICU. Patients with severe AKI were more prone to be admitted for CRRT and have a consequent poor prognosis. Acute kidney injury (AKI) is a common disease in critical patients and affects more than 13 million people all around the world annually , 3. 66% The study is a retrospective research in ICU of the Beijing Ditan Hospital Capital Medical University which is one of the top three hospital for infectious diseases. About five hundred patients were admitted in this general ICU and there were 20-bed ward for both medical and surgical patients each year. We reviewed the Hospital Information management System (HIS) from June 1, 2005 to May 31, 2017 in Beijing Ditan Hospital Capital Medical University for this study. All of the databases were recorded and analyzed by infectiology specialists from our team. Patients who were clearly diagnosed with HIV and older than18 years old were included in this study. Patients who underwent RRT before admission, stayed in ICU less than 48 h and were admitted in ICU more than once within 6 months, were excluded from this research. This study only recorded the first admission for patients who were admitted at ICU more than once within 6 months.There were 284 cases admitted at ICU of Beijing Ditan Hospital Capital Medical University from June 1, 2005 to May 31, 2017. Forty seven cases were excluded because of the length of stay at ICU less than 48 h. Four cases were excluded by accepting renal replacement therapy before admission. Meanwhile 8 patients were recorded the first admission because of admitting at ICU twice within 6 months. A total of 225 cases were considered for this study.No trial of any specific therapeutic or prophylactic intervention was carried out and only observational data were used for our analysis. This study was approved by the Ethics Committee of Beijing Ditan Hospital, Capital Medical University, No. 2018–005-01.We used the Kidney Disease Improving Global Outcomes (KDIGO) criteria for the diagnosis of AKI. As defined by the KDIGO, AKI would be diagnosed if there is a 50% rise in serum creatinine from baseline at any point during the patient’s prior 7-day ICU admission; a rise in serum creatinine of > 26.5 mmol/L within a 48-h period; a fall in urine output of < 0.5 mL/kg/h for more than 6 h; an absolute serum creatinine of > 353.6 mmol/L; or an RRT is initiated [Qualitative HIV NAT (DNA or RNA)– Quantitative HIV NAT HIV-1 p24 antigen testHIV isolation  orHIV nucleotide sequence (genotype) As defined by the US Centers for Disease Control and Prevention (CDC) in a revised surveillance case definition for HIV infection in 2014, HIV infection is diagnosed when a positive result from HIV antibody or combination of an antigen/antibody test is obtained, and another positive result is obtained from a supplemental different HIV test, performed in parallel or subsequently to the initial test . AlternaThe following demographic, clinical, laboratory data, complication and multiple organs support therapy were collected from the patient medical manuals and an electronic hospital database by four investigators : age, sex, body mass index, lymphocyte count, hemoglobin, albumin, t-bil, serum calcium, lactate, sodium bicarbonate, serum phosphate, Glomerular Filtration Rate (GRF), Mean Platelet Volume (MPV), Alanine Transaminase (ALT), glutamic oxalacetic transaminase (AST), Lactate Dehydrogenase (LDH),CD4 count at ICU admission, lowest CD4 count in ICU, Highly Active Anti-Retroviral Therapy (HAART), Pneumocystis Carinii Pneumonia (PCP), septic shock, respiratory failure, the use of vasoactive agents, duration of mechanical ventilation, duration of CRRT.The severity of illness was evaluated by the Sequential Organ Failure Assessment (SOFA) and the Acute Physiology and Chronic Health Evaluation (APACHE)II score, which calculation was based on the worst variables recorded within the first 24 h of ICU admission.All patients received CRRT through continuous venovenous hemodiafiltration (CVVHDF) (therapeutic dose of 30 ml/kg) and the related data were not recorded in this study.Length of ICU stay and hospital were recorded in the Electronic Hospital Database.Outcome was recorded by the HIS of Beijing Ditan Hospital Capital Medical University. Follow-up visit was conducted by infectious diseases specialists by telephone in this study.p < 0.05) in the univariate analyses were later included in a multivariate analysis. Univariate and multivariate analyses were performed using a logistic regression. The life table method was used to determine survival curves, and a Cox proportional hazards model was used to evaluate the statistical differences between the survival and non-survival curves. Data are presented as mean ± standard deviation, median (interquartile range), percentage of number of cases, odds ratios (ORs) with 95% confidence intervals. A two-tailed p < 0.05 was considered significant. Statistical analyses were performed using the SPSS version 22.0 software .Categorical variables were compared using a chi-squared test for trends. Risk factors were assessed by univariate analyses, and variables that were statistically significant (n = 18) of patients who received CRRT died at ICU.Among the 225 patients, 205 91.1%) were male. The average age of the total numbers was 42 ± 13 years. 122(54.2%) patients were diagnosed with AKI during their stay at ICU. As shown in Fig. .1% were p = 0.048 and p = 0.043, respectively). As per complications, the incidence of Pneumocystis carinii pneumonia (PCP) and respiratory failure showed a statistical significant difference . In severity of illness, patients with CRRT were even worse both in SOFA and APACHE-II score . Patients with CRRT stayed in the hospital for a shorter period than those without (p = 0.022).Comparison between AKI patients with CRRT and without are shown in Table As shown in Fig. Table p = 0.015), higher likelihood of diagnosis for septic shock , and higher likelihood to use vasoactive agents , longer mechanical ventilation duration , higher likelihood for diagnosis for PCP , higher SOFA score at ICU admission , longer duration of CRRT  contributed to a higher mortality at ICU.Higher likelihood of diagnosis for respiratory failure . In severity of illness, patients who received CRRT had a higher APACHE-II scores (p = 0.015). Even with CRRT, the main outcome, such as the mortality at ICU, and the renal recovery in the two groups did not show a significant difference . Patients who received CRRT probably had a longer stay tendency at ICU (p = 0.679), but a shorter hospital stay (p = 0.029).As shown in Fig. p = 0.595). As determined by the Cox Analysis of the cumulative survival of 3 AKI patients between the CRRT and non-CRRT groups .As shown by the cumulative survival curves of HIV-infected patients who were diagnosed with AKI 3 admitted to the intensive care unit  . MeanwhiThis study suggests that a longer duration of CRRT can contribute to patients’ death at ICU. Longer duration of CRRT can increase the likelihood of a catheter related bloodstream infection (CRSBI) and more vulnerability of CRRT related complications. Scott found that there was no difference of pediatric survival rate between long and short duration of CRRT . The lonThe 28-day mortality of patients with CRRT was 62.5% in this study, matching with the 62% found in sepsis-induced acute kidney injury patients undergoing CRRT in previous reports [As shown in Fig. With the extensively use of highly active antiretroviral therapy (HAART), the survival of HIV-infected patients have been significantly improved . The usep = 0.043) between the CRRT group 37 (13–122) cells/ml and the non-CRRT group 15(7–69) cells/ml. But there was no significant difference (p = 0.464) between the CRRT survival group 97 (25–156) cells/ml and the CRRT non-survival group 40 (15–110) cells/ml. CD4 count < 200 cells/ml can lead to immune depression, however, there was no significant difference (p = 0.245 and p = 0.863) between the CRRT group and the non-CRRT group for sepsis and septic shock. The reason why there was no significant difference between the two groups on CD4 count is probably because the patients admitted at ICU were both suffering for severe immunosuppression and easier to be infected by different pathogens. Franceschini et al. concluded that with the decreasing of patients’ CD4 count, the possibility of morbidity and mortality extensively increased [The count of CD4+ cells reflects the immune status of HIV-infected patients. A previous study has proven that CD4 count < 200 cells/ml was an independent predictor of experiencing AKI . Low CD4ncreased .p = 0.595). Most likely, CRRT had a determinant effect in maintaining the stability of the internal environment and decreasing pre load of heart. While CRRT was a maintenance intervention, other critical conditions except renal injury might affect the outcome. But as the cumulative survival curves illustrated in Fig. p = 0.015. Data in the Fig. By using the Cox Analysis for the cumulative survival of AKI 3 patients between the CRRT and non-CRRT groups in Fig. The number of CRRT events is relatively small, which limits statistical power of analyses. This is a retrospective study. Thus, a large, prospective, randomized, controlled study is needed. Further investigations on different modes and therapeutic dose should be conducted.There is a high incidence of AKI in HIV-infected patients admitted in our ICU. Patients with severe AKI were more prone to be admitted for CRRT and have a consequent poor prognosis.


Candida colonization and saliva properties.To evaluate the efficacy of an edible artificial saliva gel, oral moisturizing jelly (OMJ), and a topical commercial gel (GC dry mouth gel) on Candida colonization, stimulated salivary flow rate (SSFR), saliva pH, and buffering capacity (BC) were measured at 0, 1, and 2 months after each intervention. Candida colonization was quantified by colony counts and species identified by Candida Chromagar, polymerase chain reaction, and API 20C AUX system. Statistical significance level was 0.05.This study was a secondary analysis of a single-blinded randomized controlled trial conducted in xerostomic post-radiotherapy head and neck cancer patients. N = 30) and GC (N = 26) groups completed the study. OMJ significantly increased saliva pH (p = 0.042) and BC (p = 0.013) after 1-month use, while GC only improved saliva pH (p = 0.027). Both interventions tended to increase SSFR but only GC had a significant increase at 2 months (p = 0.015). GC and OMJ significantly decreased the number of Candida species at 1 and 2 months, respectively. Both groups tended to reduce Candida counts but not significant.A total of 56 participants in OMJ (Candida species. OMJ is superior to GC in its buffering capacity, while GC may better improve salivary flow rate. Long-term and large-scale study is warranted to test the efficacy of artificial saliva in oral health improvement.Both OMJ and GC saliva gels could improve saliva pH and decrease the number of Candida species and improve saliva properties in post-radiation xerostomic patients.OMJ and GC gel could decrease the number of Clinicaltrials.gov NCT03035825. Date of registration: 25th January 2017. Radiotherapy for head and neck cancer commonly causes destruction and fibrosis of salivary glands leading to hyposalivation , 2. The Candida infection, xerostomic post-radiotherapy head and neck cancer patients were reported to have increased Candida colonization [Candida albicans is the most common oral species detected in both healthy and xerostomic individuals [albicans species or multiple Candida species, leading to a more complex oral environment and treatment difficulty [Among important oral health problems, such as dental caries, atrophic mucosa, altered taste sensation, and traumatic ulcer, candidiasis is one of the most common oral sequelae in post-radiotherapy head and neck cancer patients –7. Even nization , 8. The nization , 10. Canividuals –14. Howefficulty , 16.Candida colonization are unclear.Current management strategies for hyposalivation  include systemic and topical options. However, systemic therapies have side effects and are ineffective for patients with impaired salivary tissue. Topical treatments, including the use of artificial saliva, are therefore more commonly recommended , 17, 18.Candida colonization and saliva properties of xerostomic post-radiotherapy head and neck cancer patients are unknown.A novel artificial saliva called oral moisturizing jelly (OMJ) was developed by the Dental Innovation Foundation under Royal Patronage, a non-profit organization in Thailand. The product used food-grade ingredients without any preservative agents. Therefore, it can be swallowed and provides lubrication from the oral cavity through the throat, similarly to natural saliva. Our previous pre-post study evaluated the efficacy of OMJ in xerostomic elderly patients with systemic diseases. The results demonstrated that continuous intake of OMJ for 1–2 months significantly reduced signs and symptoms of dry mouth, and achieved more than 80% satisfaction . In addiCandida colonization in xerostomic post-radiotherapy head and neck cancer patients.This report was a secondary analysis of a single-blinded randomized controlled trial in xerostomic post-radiotherapy head and neck cancer patients . We prevtrial.gov .This was a secondary analysis of a single-blinded randomized controlled trial conducted as previously described . The stuN = 37 for OMJ and 35 for GC (control) groups) [The trial included 72 post-radiotherapy head and neck cancer patients with xerostomic problems ( groups) . However groups) , 26. ExcThe protocol of the trial was described in Nuchit et al. . BrieflyCandida colonization were performed using G power. A power of 0.87, 0.9, and 0.92 was obtained for the data of saliva pH, buffering capacity, and the number of Candida species, respectively.The sample size of this study was calculated as described . To ensuCandida counts, and Candida species, were evaluated at 0, 1, and 2 months after interventions. Demographic data, including medical history, cancer sites, detail of cancer treatment, and history of antibiotic and antifungal drug use, were retrieved from treatment records and by interviewing the participants. Subjective dry mouth scores were obtained by using a validated questionnaire as described [The outcome measures, including salivary flow rates, saliva pH and buffering capacity, escribed , 26.Stimulated saliva was collected as described previously , 25. ParCandida colonization was evaluated from oral rinse samples. Participants were asked to orally gargle with 10 ml of 0.01-M sterile phosphate-buffered saline, pH 7.2 for 5 min as described previously [Candida , respectively. The number of colony-forming units (CFUs) and colony color and morphology were recorded. Plates exhibiting no growth were incubated for an additional 24 h to confirm the absence of Candida colonies.eviously . After cCandida species were initially evaluated by color as appeared on CHROMagar Candida . The colonies were then isolated for polymerase chain reaction (PCR) to identify the following species using species-specific primers as specified in the parentheses according to previous studies [C. albicans (CAL5-NL4CAL), Candida glabrata (CGL1-NL4CGL1), Candida parapsilosis (CTA4-NL4LEL1), and Candida dubliniensis (CDU2-NL4CAL). The remaining species were identified by the API 20C AUX yeast identification system . studies , 28, 29:t test or Mann-Whitney U test for continuous data, as specified. Comparisons of numerical outcome measures at baseline and first and second follow-up visits of the same group were analyzed by repeated measure ANOVA or related samples Friedman’s test with Bonferroni correction for multiple comparisons. Comparisons of categorical outcome measures among baseline and first and second follow-up visits of the same group were analyzed by chi-square test or Fisher’s exact test. The prevalence of Candida species was analyzed using descriptive statistics. Correlation between logCFU and saliva properties was analyzed by Pearson correlation analysis. All analyses were performed with the IBM SPSS statistics version 22 and GraphPad Prism version 8. A p value of less than 0.05 was considered statistically significant. Post hoc power analysis was performed by the G Power version 3.1.9.3. The normality of data distributions was analyzed by the Shapiro-Wilk test. Parametric statistical tests were used only when the data passed normality test (p > 0.05).Comparisons of baseline data between OMJ (study) and GC (control) groups were evaluated by Chi-square test or Fisher’s exact test for categorical data, and independent N = 37 for OMJ and 35 for GC) were initially recruited and randomized in the trial as described [Candida status, of the participants in OMJ and GC groups were not statistically different (p > 0.05).As shown in Fig. escribed . Sixteenp = 0.015). Nevertheless, there was no statistically significant difference in the salivary flow rates between groups at each time point.The stimulated salivary flow rates of participants in both OMJ and GC groups showed an increasing trend after 1 and 2 months of interventions . However, the saliva pH did not reach neutral value (pH 6.8) and remained acidic. There was no statistically significant difference in saliva pH between groups at each time point.As shown in Fig. p = 0.013). On the other hand, saliva buffering capacity of those in the GC group remained low without significant change.Likewise, the average saliva buffering capacity of participants with abnormal buffering capacity in the OMJ group showed an improvement from low (less than 4) to moderate level at 1 and 2 months as shown in Fig. p = 0.029). Moreover, the proportion of participants with moderate buffering capacity was significantly increased in the OMJ group at 1 and 2 months . On the other hand, participants in the GC group did not reveal significant changes in buffering capacity.When analyzing changes in each subject, the OMJ group had significantly higher percentage of improved buffering capacity at 1 month than the GC group  among Candida carriers in both OMJ and GC groups slightly decreased at 1 and 2 months after using the products  as shown in Fig. Candida quantity than baseline in the OMJ group appeared greater than that in the GC group; however, the difference was not statistically significant. Taken together, these data suggested that continuous use of artificial saliva might decrease the amount of Candida colonization.The prevalence of cts Fig. . HoweverCandida species were reported to affect the complexity of infection and treatment, we also identified Candida species colonized in the oral cavity of the participants who were Candida carriers. As shown in Fig. Candida species at 1 and 2 months (p < 0.0001 and p < 0.01), respectively. Participants in the OMJ group had a reduced number of Candida species from 1–2 species to no detectable Candida species, while the GC group had reduced mostly from 2 to 1 species.Because multiple Candida species in each individual, Fig. Candida species at 2 months after intervention, compared with that of the OMJ group (p < 0.05).In addition, when evaluating the changes in the number of Candida species detected in the participants and changes during the 2-month period of the trial. The most common Candida species colonized in the participants was C. albicans, followed by C. tropicalis, C. glabrata, and C. dubliniensis. After 2 months, the OMJ group demonstrated a slight decrease in the prevalence of C. albicans (10%) and C. glabrata (10%), while a small increase in C. dubliniensis (6.67%) and no change in C. tropicalis were found. Participants in the GC group demonstrated a decrease in C. tropicalis (11.5%) and C. glabrata (15.4%) at 2 months, while no change in C. dubliniensis was observed. Interestingly, certain species, such as C. parapsilosis and Kodamaea ohmeri, were detected in both groups, but the presence may be transient.Figure Candida colonization (logCFU) in Candida carriers, correlation analysis was performed , suggesting an increase in Candida colonization upon acidic environment. In addition, saliva pH had significant positive correlation with salivary flow rate at baseline and 1-month follow-up (p < 0.01). Interestingly, after 2-month interventions, significant negative correlation was found between Candida quantity (logCFU) and salivary flow rate (p = 0.001), but not saliva pH. No significant correlation was detected between Candida quantity and other saliva properties. The data suggested that the alteration of oral environment by the interventions could affect Candida colonization.To investigate the relationship between saliva properties  and the quantity of med Fig. . At baseCandida species has never been documented. In this study, we reported for the first time that saliva gels (both OMJ and GC) could reduce the number of Candida species in xerostomic cancer patients. Since the higher the number of Candida species, the more difficult treatment can be [Candida count. These findings suggested that modification of saliva properties may influence Candida colonization. Artificial saliva gel may not only alleviate dry mouth signs and symptoms but also improve saliva properties and reduce the risk of Candida infection.Artificial saliva is commonly used to relieve dry mouth in various groups including post-radiotherapy head and neck cancer patients –19. Thout can be , 15, 16,t can be , 35. In Candida species. Taken all together, these evidences support the recommendation of OMJ for clinical treatment of xerostomia in elderly and cancer patients.The favorable efficacy and safety of OMJ were demonstrated in xerostomic elderly . ConsistDue to ethical reasons, we used a commercially available product with gel-like properties, GC dry mouth gel, as a comparison group rather than a placebo. OMJ and GC dry mouth gels have similar ingredients, such as water, carboxymethylcellulose (CMC), glycerol, and flavoring agents. Although both products were CMC-based, their physical and chemical properties are different. OMJ is made from food-grade ingredients and processed by heat treatment (ultra-high temperature (UHT)). The main ingredient of OMJ is buffered water, and its viscosity is close to natural saliva. In contrast, GC dry mouth gel is more viscous and contains preservative. This is the reason why the gel is recommended for topical application to the oral mucosa and not recommended to be swallowed. On the other hand, OMJ could be held in the mouth and then swallowed to provide moisture both in the oral cavity and in the throat. Interestingly, the average saliva buffering capacity in OMJ users significantly improved at 1 month and shifted from low to moderate buffering capacity. In addition, a significantly higher proportion of participants in the OMJ group had improved buffering capacity than in the GC group. The average saliva buffering capacity of GC users remained low throughout the study. This could be because OMJ contains phosphate-buffering agent as its ingredient while GC gel does not. The improved buffering capacity was also observed in elderly patients after using OMJ . In addiThe result of this study showed no significant difference between OMJ and the commercially available GC dry mouth gel in many aspects. First of all, salivary flow rates of participants in both groups appeared to improve after 1 and 2 months, although statistical significance was detected only in the GC group at 2 months. Similar finding was reported earlier in a study testing artificial saliva Optimoist in Sjogren’s syndrome and post-radiotherapy head and neck cancer patients  as well Candida colonization [Secondly, our results showed that saliva pH significantly increased in both OMJ and GC groups at 1 month, although not to a neutral value. This was in concordance with previous studies that evaluated the effects of Bioxtra gel in head and neck cancers and OMJ in elderly participants , 35. In nization –44.Candida carriage was detected in post-radiotherapy head and neck cancer patients in our study. We also found higher percentages of non-albicans species at baseline compared with healthy populations in other studies [Candida logCFU at 1 and 2 months. OMJ group appeared to have a higher percentage of users with decreased logCFU than the GC group. However, the changes/differences did not reach statistical significance. The decrease in Candida colonization after the use of artificial saliva had been reported by a few studies [Candida count in xerostomic post-radiotherapy cancer patients [Candida quantity has not been investigated. Both OMJ and GC dry mouth gel do not contain any antimicrobial agent. The possible explanation for reduced Candida counts in our study could be due to increased salivary flow rate and saliva pH, and, therefore, better oral clearance [Candida quantity and low salivary flow rate. Both interventions led to increased salivary flow rate and saliva pH . Although we obtained adequate power for major outcome analyses, the sample size may not be enough to detect significant differences in certain variables such as Candida species, and a decreasing trend in Candida counts. OMJ achieves many properties of suitable artificial saliva, including lubrication, pH neutralization, and likely increase oral clearance. There were no statistically significant differences in these outcomes when OMJ was compared with the commercially available GC dry mouth gel. Further investigations on the long-term effects of OMJ on oral and dental health will be beneficial. The information is important for future development of artificial saliva and the management of xerostomic patients.To conclude, this study showed that OMJ and GC usage over a period of 2 months led to an improvement of saliva properties, a reduction in the number of


This meta-analysis was conducted to estimate the prevalence of hypothyroidism among pregnant women in India. I2 statistics was used to measure heterogeneity across the studies. Heterogeneity in the pooled estimates was further explored with subgroup analyses and meta-regression analysis.  We searched PubMed, Web of Science, Scopus, Google Scholar, and Shodhganga (Indian thesis repository) for observational studies, providing prevalence of hypothyroidism among pregnant women in India. Systematic study selection and data extraction procedures were followed. Quality assessment of each study was done using JBI critical appraisal checklist. The random effects model was used for pooling the effect sizes. Publication bias was assessed using the funnel plot and rank correlation test. I2 = 99%). Pooled prevalence estimates of subclinical and overt hypothyroidism are 9.51%  and 2.74% .  Sixty-one studies were found eligible and included in this review. The pooled estimate of the prevalence of hypothyroidism in pregnant women was 11.07% (95% CI: 8.79–13.84,  We documented 11.07% pooled prevalence of hypothyroidism in pregnant women in India. Pregnancy has a significant effect on the thyroid gland and its functioning . HypothyWorldwide, several studies have reported 1.5%–4% prevalence of hypothyroidism in pregnant women. Among them, 0.3% to 0.5% had overt hypothyroidism (OH), and the rest had subclinical hypothyroidism (SCH) , 7.Globally, the leading cause of hypothyroidism in pregnancy is iodine deficiency, and in iodine sufficient areas, most common cause is autoimmune thyroiditis , 5, 8. OIn pregnant women, untreated overt hypothyroidism is associated with gestational hypertension, abruptio placenta, anemia, gestational diabetes, and postpartum hemorrhage –13. In oEarlier studies have also proved that most of these complications can be prevented by high-risk screening pregnant women for thyroid status and providing treatment in the form of levothyroxine (LT4) . To effeHence, we conducted this meta-analysis with the aim to estimate the prevalence of hypothyroidism among pregnant women in India.This meta-analysis article is written in accordance with the PRISMA guidelines  and is r  Inclusion criteria: (1) community or hospital-based studies; (2) studies, providing prevalence of hypothyroidism ; (3) studies conducted in India; (4) type of studies: cross-sectional studies, cohort studies, or data-based analysis; (5) diagnosis based on TSH level; (6) singleton pregnancy  Exclusion criteria: (1) studies conducted in the special population groups like the diabetic mothers or mothers with pregnancy losses, etc.; (2) studies which have not reported screening methods; (3) diagnosis other than TSH levels; (4) case-control studies or experimental studiesWe included the studies reporting the prevalence of hypothyroidism in pregnant women in India.https://shodhganga.inflibnet.ac.in/). We included articles published up to December 2019. No date or language restrictions were imposed. The cross-references of the included studies were explored for additional studies. Keywords were identified with discussion among reviewers, and search query was developed separately for each database.We performed searches in PubMed, Web of Science, and Scopus using selected keywords. These results were supplemented by relevant studies obtained form Google Scholar and Shodhganga—Indian thesis repository (The main themes of search terms were thyroid (hypothyroidism OR thyroid disorder); geographic area (India); pregnancy ; and prevalence . To develop a robust search strategy, we used controlled descriptors (such as MeSH terms) and Boolean operators.https://rayyan.qcri.org). Rayyan QCRI helped in ensuring a systematic and comprehensive search and selection process. A third reviewer (VY) managed Rayyan QCRI software, who identified and removed the duplicate citations and ensured independent review of titles and abstracts by blinding the decisions of both reviewers.Two reviewers (ADG and DD) independently conducted searches on all information sources. Then, all search results were uploaded to Rayyan QCRI online software  reviewed the full text of articles and resolved the discrepancies by consensus. If needed, the arbitration was done by the third reviewer (VY).https://www.mendeley.com) for Windows was used to store, organize, cite, and manage all the included articles.We documented the reasons for the exclusion of studies explored as full text. If any study is reported as multiple publications, all publications were obtained, and data were extracted from all the publications to collect maximum relevant information. The study inclusion process is presented using the PRISMA flowchart . The refAfter selecting eligible studies, both reviewers (ADG and DD) independently performed data extraction of relevant information. Data were extracted regarding author, year of publication, study location, site , study type, trimester, whether prepregnancy thyroid disorder patients were excluded, sample size, diagnostic criteria, and prevalence of hypothyroidism . We contacted authors of included articles for additional data whenever required. Inconsistencies in data were resolved by either consensus or seeking further information from the authors of the study. In case of disagreement between two reviewers, arbitration was done by the third reviewer (VY).Two reviewers (ADG and DD) assessed each study's methodological quality independently using JBI critical appraisal checklist  for crosThe effect sizes were calculated for each of the included studies comprising the prevalence estimates of overall hypothyroidism, OH, and SCH among pregnant women in India. Pooled estimates were calculated separately for each of them. All the analysis was done in R software using Meta and Metafor packages , 26.Logit transformation  of proportions was implemented to calculate all pooled estimates, as it is the preferred method for calculating effect size for proportions .I squared (I2) statistic and Cochran's Q test. Due to significant heterogeneity between the studies, we used random effects models for pooling the estimates and they were reported as a proportion with 95% confidence interval. Restricted Maximum-Likelihood Estimator (REML) method was used to calculate Tau squared. Confidence intervals for individual studies were calculated using the Clopper–Pearson method. We determined the presence of publication bias by visual inspection of funnel plots and rank correlation test. A funnel plot was made between transformed proportions and standard error of transformed proportions. Statistical significance was considered as the p value of less than 0.05. Subgroup analysis of estimate of the prevalence of total hypothyroidism was done for “trimester” of participants gestational period , whether participants having prepregnancy thyroid disorder were excluded (excluded/not excluded/not mentioned), and the setting of study . India is a federal union that comprises 28 states and 8 union territories. Therefore, state-wise subgroup analysis was also conducted to get state-wise estimates of the prevalence of hypothyroidism. Another subgroup analysis was conducted to analyze the difference of prevalence in studies undertaken in coastal states  as compared to noncoastal states . Sensitivity analysis was done to exclude studies with a lower sample size (sample size less than 200) and to exclude studies with a quality score of less than six.Heterogeneity between studies was examined by n = 96), Scopus (n = 54), Web of Science (n = 29), Google Scholar (n = 473), and Shodhganga—reservoir of Indian theses (n = 424), a total of 1076 articles were identified related to maternal hypothyroidism.On searching PubMed , “not conducted in humans” (n = 77), “not about hypothyroidism in pregnancy” (n = 65), “not conducted in India” (n = 57), “not conducted in pregnant women” (n = 56), and “review article” (n = 28).An additional 42 articles were found eligible for inclusion through search of cross-references. After removal of 305 duplicate articles, a total of 813 articles were screened for inclusion. Based on their titles and abstract, 320 articles were excluded. Thus, the full texts of 493 articles were assessed for eligibility for the systematic review and meta-analysis. The various reasons for exclusion of studies were “not a prevalence study” (n = 61) provided the prevalence of overall hypothyroidism    [6, 7, 20,066  [6 60,066) .4 and FT3) were varied across studies. Upper cutoff limit of TSH for defining hypothyroidism in pregnancy varied from 2.5 to 4.5 mIU/L. For OH, the lower cutoff limits for FT4 and FT3 varied extensively among selected studies , subclinical hypothyroidism , and overt hypothyroidism (elevated TSH with low free thyroid hormone) was similar across the selected studies, but the reference cutoffs for TSH and free thyroid hormones (FT studies . Thirty-I2 = 99%). Pooled prevalence estimates of subclinical and overt hypothyroidism were 9.51%  and 2.74% , respectively  of the prevalence of overall hypothyroidism in pregnant women was 11.07% .Studies that included only women with second-trimester pregnancy documented a higher prevalence of hypothyroidism  as compared to studies with women in first trimester , or any trimester of pregnancy . However, these differences across various subgroups were not statistically significant  as compared to those studies where they were included  in the analysis.p < 0.001).Studies conducted in hospital settings reported higher pooled prevalence of hypothyroidism among pregnant women  as compared to secondary data  and community-based settings  (p=0.059)  .p=0.024).Studies conducted using ATA 2011 criteria reported higher prevalence  as compared to studies used other criteria  for diagnosis and this difference was statistically significant  after omitting studies with a quality score of less than six on JBI criteria. Another sensitivity analysis was executed to assess the effect of studies with lower sample-sizes on pooled prevalence. This analysis was performed by excluding smaller sample size studies (sample size <200) and pooled prevalence estimate was 11.22 . Both small sample size and low-quality studies did not affect much the prevalence estimate.The sensitivity analysis was conducted by removing lower-quality studies. Prevalence estimate was 10.23  also confirm absence of publication bias.Regarding the publication bias of pooled estimate of overall hypothyroidism, funnel plot is reasonably symmetrical on visual inspection . Rank coThe sixty-one studies included in the current review have reported prevalence of maternal hypothyroidism ranging from 1.2% to 67.0% [This meta-analysis estimates the prevalence of hypothyroidism in pregnant women in India to be 11.07% (95% CI: 8.79–13.84) from sixty-one studies across 60,066 study subjects. Pooled prevalence estimates of subclinical and overt hypothyroidism were 9.52% (95% CI: 7.48–12.04) and 2.74% (95% CI: 2.09–3.58), respectively.Sepasi et al. conducted a meta-analysis to report prevalence of hypothyroidism clinical and subclinical hypothyroidism in Iranian pregnant women. Prevalence estimate for hypothyroidism, clinical hypothyroidism, and subclinical hypothyroidism was 13.01% (95% CI: 9.15–18.17), 1.35% (95% CI: 0.97–1.86), and 11.90% (95% CI: 7.40–18.57), respectively, which are very similar to our study . Howeverp=0.059). Unnikrishnan et al. conducted a survey of hypothyroidism among adult population in 8 urban cities of India [In this review, we found prevalence of hypothyroidism was lower among coastal areas as compared to studies conducted in noncoastal areas, though difference was nonsignificant (of India . They reof India –90.p=0.024). This may be due to higher cutoffs of TSH for diagnosis, used by other stidies.In our study, we documented higher prevalence of hypothyroidism in studies that used ATA 2011 criteria as compared to studies that used other criteria, and this difference is statistically significant  which has normal free T4 levels and overt hypothyroidism (OH) which has decreased free T4 levels. This differentiation is crucial as it has clinical and management implications . MaternaData pertaining to effects of early management of SCH in pregnancy are emerging in latest research. Rao et al. in 2019 identified that levothyroxine supplementation significantly decreased pregnancy loss and prematurity in women with SCH . HoweverPregnancy is a state of increased thyroid hormone requirement. The majority 50–85%) of previously hypothyroid women (on treatment) need to increase their dose of thyroid supplements post conception [0–85% of TSH levels during pregnancy are lower as compared to the nonpregnant state. As per American Thyroid Association recommendations 2011 as well as endocrinology society guidelines for pregnant women, TSH levels should be within the limits of 0.1 to 2.5 mIU/L during the first, 0.2–3.0 mIU/L in second, and 0.3 to 3.0 mIU/L in the third trimester [Optimal TSH cutoffs have been a topic of controversy since long and may have a bearing on prevalence estimates . EuropeaHowever, in Indian setting, the National Guidelines for Screening of Hypothyroidism during Pregnancy 2014 have considered ATA 2011 guidelines for defining cutoffs . A recenA rigorous and comprehensive search strategy was undertaken, which minimizes the possibility of changes in inferences drawn from our study, but there are limitations too. The review exhibits heterogeneity in terms of data. The prevalence varies with the geographic locations, ethnic disparity, and the nutritional intake of the study population. Another aspect that limits our research is the use of nonstandardized TSH cutoffs for diagnosis by studies, as only 35 studies used ATA 2011 TSH cutoffs [This review estimated that every 1 in 10 pregnant Indian women suffers from hypothyroidism. The present study has paved the way for the acceptance of universal screening in pregnant women, especially in the Indian context. The prevalence of hypothyroidism in pregnant women varies across states in India, but data is insufficient. Moreover, there are no agreed-upon guidelines for treating subclinical hypothyroidism in pregnant women. Therefore, further research is needed to fill these gaps regarding the diagnosis and management of hypothyroidism in pregnant women in a heterogeneous country like India.


Dermanyssus gallinae) and the northern fowl mite (Ornithonyssus sylviarum) are considered to be the cause of high economic losses endured by the poultry industry in the Holarctic region, with O. sylviarum predominating in North America and D. gallinae in Europe. Both species have a short life-cycle , a wide range of hosts, synanthropic presence and the ability to bite humans. The aim of this study was to analyze dermanyssoid mite specimens, collected in two human dwellings and two racing pigeon premises in different urban areas in Hungary, with molecular–phylogenetic methods.Among Dermanyssoidea, the chicken red mite (c oxidase subunit I (cox1) and 28S rRNA (28S) genes.Mite species were identified morphologically. This was followed by DNA extraction and molecular–phylogenetic analyses of selected mites, based on the cytochrome D. gallinae special lineage L1. Specimens collected at all other sampling sites were identified as O. sylviarum, including mites that had invaded a home from a house martin (Delichon urbicum) nest, as well as those which were collected from racing pigeons. House martin- or pigeon-associated O. sylviarum specimens showed the highest sequence identity and closest phylogenetic relationship with conspecific mites reported in GenBank from Israel or Canada, respectively.Mites that had invaded a home from a pigeon nest and were linked to human dermatitis were morphologically and molecularly identified as D. gallinae lineage L1 confirmed its status as a cryptic species within D. gallinae (s.l.). Taking into account the well-documented latitudinal migratory routes of house martins between Hungary and Africa, O. sylviarum associated with this bird species most likely arrived on its host from the eastern Mediterranean region. On the other hand, mites collected from pigeons in Hungary showed cox1 genetic homogeneity with North American O. sylviarum, which can only be explained by a long-distance  connection of birds and their mites as part of human activity (e.g. transportation to exhibitions or trading). In summary, this is the first molecularly confirmed and phylogenetically analyzed case of O. sylviarum infestation of birds in Hungary, implicating urban environment and involving distant parts of the country. This is also the first report of D. gallinae lineage L1 in central Europe. Detailed morphological and molecular–phylogenetic analyses of  Dermanyssus gallinae (family Dermanyssidae), and the northern fowl mite, Ornithonyssus sylviarum (family Macronyssidae), are responsible for high economic losses [The Dermanyssoidea (Acari: Parasitiformes: Mesostigmata) is an extremely diverse lineage of mites that are found as free-living predators as well as facultative or obligate parasites of mammals, birds, reptiles and various arthropod groups . Among tc losses , 6 in thc losses , 7.O. sylviarum regarded as the most important mite pest in poultry farms in North America [D. gallinae playing a similar role in Europe [O. sylviarum being disseminated throughout the poultry industry in Europe has increased over the years, with several countries, including France [O. sylviarum, mostly on wild birds but also on poultry in some countries [D. gallinae and O. sylviarum are characterized by a very short life-cycle , ally 1 week . Howeverparasite  that tenparasite  while O.its host , thus alits host .O. sylviarum is able to move short distances off-host, during which time it can survive for several weeks [D. gallinae can endure starvation for up to 8.5 months in the environment, where it moves rapidly, with speeds of up to 120 m/h [D. gallinae and O. sylviarum may parasitize a broad range of such hosts, most commonly birds (from multiple avian orders), but occasionally also mammals [D. gallinae is capable of starting blood-feeding on humans within 3 h [O. sylviarum.Despite its long-term attachment to its host, al weeks . In cont 120 m/h . This me mammals . Among t mammals , 7, causthin 3 h , unlike D. gallinae or O. sylviarum. Although both species are significant ectoparasites of pigeons (order Columbiformes) [D. gallinae in Europe [O. sylviarum. This hypothesis has not yet been fully explored using phylogenetic methods.Knowledge of the life-cycle, host preference and synanthropic presence of these two dermanyssoid mites raises the question of whether long-distance natural bird migration and/or human activity  may be responsible for the local introduction of iformes)  and migriformes) , based on Europe . This isD. gallinae has only been reported from poultry. In one study, four farms were examined, all of which had mite problems despite frequent chemical control [Ornithonyssus sylviarum was also documented in Hungary, when low level of infestation of house sparrows (Passer domesticus) was observed in a rural area near the eastern border of the country [In Hungary,  control . Ornitho country . HoweverColumba livia) nest attached to the outside wall under the eaves. Mites invaded during the night through the window. The young couple living in the house were suffering from gamasoidosis  from a home in the third district of Budapest, which had an occupied pigeon (Delichon urbicum) nest above the front door. Mites had invaded the home. A young, pregnant woman was present, but there was no evidence of bites.Mites collected in 2018 (August) from a home in the first district of Budapest, which had a house martin (Mites collected from racing pigeons in 2020 (March) in the third district of Budapest (north Hungary) and in Sellye (southern Hungary), respectively.The following specimens were used in this study:Dermanyssus gallinae lineage L1 was identified molecularly. Detailed morphologic analyses  were performed on its females (n = 4).In each of the above cases up to 20 mites were collected and preserved in 96% ethanol. Mite species were identified morphologically according to standard keys –21 afterc oxidase subunit I (cox1) sequences [cox1 [Depending on their size, up to three mites were used for DNA extraction, taking into account previous studies in which three locally collected specimens had identical cytochrome equences , one to equences  or poolses [cox1 . Prior tD. gallinae (s.s.) of unknown origin was included in the 28S rRNA gene (28S) analysis, as well as Echinolaelaps echidninus and Laelaps hilaris collected from a mouse and two rats in Malta (August 2017).In addition, a former Hungarian sample of cox1 gene and a 930-bp fragment of the 28S gene were amplified with the primer pairs HCO2198 (5ʹ-TAA ACT TCA GGG TGA CCA AAA AAT CA-3ʹ)/LCO1490 (5ʹ-GGT CAA CAA ATC ATA AAG ATA TTG G-3ʹ), and 43F (5ʹ-GCT GCG AGT GAA CTG GAA TCA AGC CT-3ʹ)/929R (5ʹ-AGG TCA CCA TCT TTC GGG TC-3ʹ), respectively, as reported previously [In order to achieve phylogenetic trees at both species- and genus/family-level resolution, an approximately 710-bp part of the eviously , 24.cox1), MT813463-MT813469 (28S). Sequences (retrieved from GenBank) were only included in the phylogenetic analyses if they had nearly 100% coverage  compared to the sequences from this study. This dataset was resampled 1000 times to generate bootstrap values. Phylogenetic analyses of cox1 and 28S rRNA sequences were conducted with the maximum likelihood method (GTR model) and neighbor-joining method (p-distance model), respectively, by using the software program MEGA 7.0.Purification and sequencing of the PCR products were performed by a commercial laboratory . New sequences were submitted to GenBank, under accession numbers MT812940–MT812943  and cases 3 and 4 (associated with racing pigeons) were identified as O. sylviarum. Within D. gallinae, special lineage L1 was identified molecularly (see following sections).Specimens from case 1 (associated with nesting pigeons) were identified as The following are approximate measurements. The dorsal shield is 920–940 μm long, 360–370 μm wide at the apical end of the peritreme, and 290–330 μm wide at the level of coxae IV. The dorsal shield bears 15 pairs of needle-like setae (20–28 μm long), and the apical part of the dorsal shield is covered by a fine reticulate sculptural pattern. Eighteen pairs of longer (55–62 μm), needle-like setae are situated on the membranous cuticle around the dorsal shield. The sternal shield is quadrangular (70–75 μm long and 180–195 μm wide), has a fine reticulate sculptural pattern and bears two pairs of needle-like setae (50–65 μm long). Two other pairs of sternal setae, situated on the membranous cuticle posterior to the sternal shield, are longer (90–95 μm). The genital shield is linguiform, bearing one pair of pores and a pair of genital setae, its surface smooth. The anal shield is triangular (160–165 μm wide on the anterior margin and 170–175 μm long), the anterior margin is straight and the surface without a sculptural pattern. The anal opening is oval, 40–45 μm long and 30–33 μm wide. The adanal setae are 37–40 μm long and needle-like.Setae around the anal shield are needle-like and 47–54 μm long; three pairs of needle-like setae lateral to the genital shield are shorter (35–40 μm). The membranous cuticle forms a U-shaped bend posterior to coxae IV. Peritreme extends to the posterior margin of coxae I Fig. .Fig. 2Drcox1 sequence of D. gallinae lineage L1 associated with pigeons in Hungary had the highest (95–99%) identity (but only 48–66% coverage) with reported sequences of this lineage from France , Italy (LT714694) and the USA (HQ842554). However, in comparison with D. gallinae (s.s.), it had only 566–568 bp of 629 bp (90–90.3%) identity with sequences reported from Israel  and 567 bp of 629 bp (90.1%) identity with sequences from Croatia (MH983735) and South Korea (MN648058). The 28S rRNA sequence of D. gallinae lineage L1 had the highest, i.e. 838 bp of 840 bp (99.8%), sequence identity to a corresponding sequence of D. gallinae (s.s.) (FJ911771).The D. gallinae lineage L1 and D. gallinae (s.s.) were well-reflected by the topology of the cox1 phylogenetic tree, because their separation received high (95%) bootstrap support  from D. gallinae (s.s.)  as did the latter from D. hirundinis (FJ911770)  sequence identity with O. sylviarum sequences from Israel , while it had only 97.1% (610 of 628 bp) identity with conspecific sequences from Canada . On the contrary, two identical cox1 sequences of O. sylviarum collected from pigeons in two distant locations in Hungary showed the highest  sequence identity with O. sylviarum sequences from Canada (e.g. MN351895), but considerably lower  identity with the above sequences of O. sylviarum from Israel. These comparisons imply that there was a significant  intraspecific sequence divergence between O. sylviarum collected from different avian host species in Hungary.The cox1 phylogenetic tree the separation of two divergent Hungarian O. sylviarum lineages, associated with house martins or pigeons, was highly supported (with 100% bootstrap), and these clustered together with sequences from Israel and Canada, respectively  has not been previously reported in Hungary . We repoe  and D. gallinae special lineage L1 (i.e. approximately 10%), which is well within the range of 8–18% difference in the cox1 sequence that separates Dermanyssus species [D. gallinae lineage L1 with D. gallinae (s.s.) [In the present study, significant sequence divergence was found between  species . This see (s.s.) , 27 revee (s.s.) , 26. At D. gallinae, animal transportation (and not bird migration) is thought to be responsible for the dispersal of this mite species in Europe [Based on molecular evidence and aspects of the life-cycle of n Europe . As refln Europe , 28, takn Europe ; Austrian Europe ), which  via the eastern Mediterranean region, including Israel [O. sylviarum, as demonstrated here, strongly supports our hypothesis that (at least in Hungary) long-distance dispersal and “synanthropic arrival” should be highly relevant in the case of the permanently parasitic O. sylviarum via natural bird migration . Moreover, actively migrating house martin populations use communal roosting sites along their migratory route where birds belonging to distant populations may mix [It is well-documented that house martins in Hungary migrate to and from Africag Israel . Thus, t may mix . Birds s may mix . This becox1 sequence of O. sylviarum collected from pigeons in Hungary had 100% identity to that of mites recently reported from Canada  context.Surprisingly, the  Canada . This is also the first molecularly confirmed and phylogenetically analyzed urban case of O. sylviarum infestation of birds in Hungary. Taking into account the well-documented latitudinal migratory routes of house martins between Hungary and Africa, O. sylviarum associated with this bird species most likely arrived on its host from the eastern Mediterranean region. In contrast, mites collected from pigeons in Hungary showed cox1 genetic homogeneity with North American O. sylviarum, which can only be explained by a long-distance connection of birds and their mites as part of human activity (e.g. transportation to exhibitions or trading).This is the first report of


Aging is characterized by chronic inflammation plus loss of muscle mass and strength, termed sarcopenia. Human leukocyte antigen (HLA) types are drivers of autoimmune disease, although with limited penetrance. We tested whether autoimmune diagnoses are associated with sarcopenia, and whether HLA types and related genetic variants are associated with sarcopenia in autoimmune disease-free older people.Data were collected from 181,301 UK Biobank European descent volunteers aged 60–70 with measured hand grip strength and impedance. Logistic regression analysis estimated HLA type and sarcopenia associations, adjusted for confounders and multiple testing.p = 4.0*10−125). After excluding autoimmune diagnoses, 6 of 100 HLA types  were associated with sarcopenia (low grip strength and muscle mass). Having two HLA-DQA1*03:01 alleles increased odds of sarcopenia by 19.3% , compared to no alleles. Having ≥6 of the 12 HLA alleles increased sarcopenia odds by 23% . Of 658 HLA region non-coding genetic variants previously implicated in disease, 4 were associated with sarcopenia, including rs41268896 and rs29268645 . Some HLA associations with sarcopenia were greater in female participants.Having any autoimmune diagnosis was associated with sarcopenia  1.74–1.92, Autoimmune diagnoses are strongly associated with sarcopenia in 60- to 70-year olds. Variation in specific HLA types and non-coding single nucleotide polymorphisms is also associated with sarcopenia in older carriers free of diagnosed autoimmune diseases. Patients with sarcopenia might benefit from targeted treatment of autoimmune processes. The We aimed to test associations between diagnosed autoimmune conditions and sarcopenia in the exceptionally large UK Biobank study. We also aimed to test associations between common HLA types and sarcopenia in individuals independently of any diagnosed autoimmune condition, using three definitions of sarcopenia . We hypothesize that the presence of certain HLA types are more common in sarcopenic individuals in later life, even in the absence of diagnosed autoimmune disease.http://www.ukbiobank.ac.uk).We used data from 451,447 UK Biobank participants of European descent, confirmed by principal components analyses of genome-wide genetic information from a selection of participants who self-identified as “white European” after filtering for age (60–70) and any diagnosis of autoimmune disease, 181,301 participants were included in the final analysis . The UK Sarcopenia was classified using the EWGSOP definition , based oSkeletal muscle mass (SMM) was calculated using the following equation from Janssen and coworkers :R is Bioelectrical Impedance Analysis (BIA) resistance in ohms for the whole body taken by Tanita BC418MA body composition analyzer at the initial assessment visit; for sex, men = 1 and women = 0; and age is in years. SMI was then calculated from the SMM.where Ht is standing height in centimeters measured at the initial assessment; SDs from the mean .Low SMI was defined by Janssen and coworkers as under 8.87 in males and under 6.42 in females. Analysis was restricted to the age range of 60–70. We excluded a small number of participants with maximum grip strength or lean mass (from BIA) greater than 4 Three definitions of sarcopenia were developed based on the EWGSOP classification: both low grip strength and lean muscle mass ; low grip strength only ; and low muscle mass only . While dual-energy X-ray absorptiometry (DXA) is the preferred alternative to computed tomography and magnetic resonance imaging for estimating muscle mass, EWGSOP deemed that BIA may be considered as a portable alternative to DXA.The Foundation for the National Institutes of Health (FNIH) definition of sarcopenia  based onSome autoimmune diseases are known to influence muscle loss. We aimed to determine whether HLA types were associated with sarcopenia in the absence of diagnosed autoimmune disease, and we therefore excluded participants with a diagnosis at the baseline assessment .A list of 135 (including subgroups) ICD-10 codes for autoimmune diseases were generated from the previous review articles  and the Logistic regression was performed on a selected subset of more common autoimmune conditions such as rheumatoid arthritis, multiple sclerosis, coeliac disease, type 1 diabetes, psoriasis, and ulcerative colitis. In order to include as many participants as possible with each condition, all ICD-10 codes belonging to the superclass of each condition were included , as well as any reported in the UK Biobank data-field, 20002: Non-cancer illness code, self-reported  and self-reported fields.HLA types were modeled first assuming an additive effect, and secondly comparing participants with two alleles to those with no alleles. Participants with imprecise HLA imputations were recorded for the second, categorical analysis . Correction for multiple testing was applied using the Benjamini–Hochberg method, for the additive and recessive models separately as these were independent hypotheses. Statistical analyses were performed in STATA (v14.1) and R (v3.3.2). Charts and figures were generated with package metafor (v2.0).After analyzing each HLA type for its association with sarcopenia, we performed a literature search to identify any autoimmune diseases implicated by the significant HLA types not already included in the autoimmune exclusion criteria. HLA type associations with sarcopenia were reanalyzed after participants with a diagnosis of these further autoimmune conditions had been excluded ; these aHLA types associated with the three definitions of sarcopenia were analyzed with sex-specific cohorts, after the removal of participants with autoimmune conditions. We tested for a statistical interaction between sex and HLA type on sarcopenia, in the models described previously.Sensitivity analysis was performed on the HLA types associated with sarcopenia across the three phenotypes, for smoking status (UK Biobank data field: 20116) and a combination of height and weight.p-value < 5*10–8; n = 658). These were tested for their association with sarcopenia using the methods described above for HLA type analysis. We used the Bonferroni adjusted cutoff for 658 tests of p-value less than 7.6*10−5. Associations between these SNPs and sarcopenia-associated HLA types were also calculated. The GTEx database (–8 (0.05/4 independent lead SNPs * 341316 GTEx v7 eGenes) was used to select the most significant expression quantitative trait loci (eQTLs).The HLA types assessed so far were based on the protein-coding sequence of the HLA protein expressed. We also investigated non-coding single nucleotide polymorphisms (SNPs) within the HLA region that have previously been associated with traits in the genome-wide association study (GWAS) catalogue; SNPs in the HLA region  between database  was usedp = 4.0*10–125; n = 2,833): mean grip strength in this group was 22.1 (SD 11.3) kg compared to 31.0 (SD 10.8) kg (p < .001) for all other participants in the European 60–70 age group. Associations were also present for multiple sclerosis, coeliac disease, and psoriasis, but not for type 1 diabetes or ulcerative colitis.We selected 196,099 UK Biobank participants of European descent aged 60–70 with complete phenotype, diagnosis, and genotype data for investigation. Of these, 14,798 participants had at least one diagnosed autoimmune disease . Participants with a diagnosed autoimmune disease had increased odds of meeting the EWGSOP sarcopenia criteria  increased likelihood of sarcopenia, compared to those without HLA-DQA1*03:01 (p = 2.66*10–5). When the six HLA types were combined, participants with at least 6 alleles  had 23% increased likelihood of sarcopenia .Of 100 HLA types with allele frequency of more than 1%, 6 were associated with sarcopenia (EWGSOP definition) in logistic regression models adjusted for age, sex, genotyping array type, and population structure , after accounting for multiple testing  . We compA1*03:01  and HLA-p = 1.64*10–5; HLA-C*01:02 OR 1.093, 95% CI 1.05–1.13, p = 1.65*10–6, and HLA-C*02:02 OR 1.059, 95% CI 1.02–1.10, p = 1.46*10–3) were distinct from those associated with the EWGSOP definition of sarcopenia.Seven HLA types were associated with the sarcopenia definition based on grip strength alone, and six were associated with the sarcopenia muscle mass definition, with no overlap between the alleles associated with the two phenotypes . All sigp = .032) and HLA-DRB1*04:04 (p = .038), were nominally associated with sarcopenia (EWGSOP’s combined definition), indicating that the effect of these HLA types is significantly greater in females, compared to males. The other interactions were not significant, although we cannot rule out lack of statistical power due to the relatively low numbers of men with sarcopenia , which were not identified in the analysis including both sexes (FDR-adjusted p > .05). In the analysis of males only, there were no significant (FDR-adjusted p > .05) associations between HLA types and sarcopenia.There were three additional HLA types associated with the EWGSOP’s combined definition of sarcopenia in the female-only analysis  in the combined cohort. In the female-only cohort, the protective HLA type DRB1*15:01 was the only HLA type to lose significance. The lean muscle mass-only phenotype maintained the significance of the previously observed HLA types, except in the case of HLA-C*01:02 in the female-only cohort which lost its significant association with sarcopenia.In sensitivity analyses, we investigated the effect of adjusting for height and weight; all the reported risks, increasing associations between HLA types and sarcopenia, remained significant, with nominal changes to effect sizes . HoweverAdjustment for smoking status had no impact on the associations between HLA types and sarcopenia, for any of the three phenotypes analyzed or across the three cohorts of all participants, female only and male only .p < 5*10–8) available in the UK Biobank genotype data. We found 219 SNPs nominally (p < .05) associated with the EWGSOP definition of sarcopenia .We analyzed 658 non-coding SNPs  within the HLA region previously implicated in human traits at genome-wide significance , in addition to other genes with plausible mechanisms of action in sarcopenia . We also searched for protein QTLs in a recent paper by Sun and coworkers (r 2 = .8 in UK Biobank study) with rs8111, a pQTL for ATF6A.We interrogated the GTEx eQTL (expression quantitative trait loci) database of SNP-expression associations to determine the likely genes affected by these four genetic variants .We also investigated the combined effect of the four non-coding SNPs; participants with at least four effect alleles (of a possible eight) showed an increased likelihood of sarcopenia of 11% , plus multiple sclerosis, coeliac disease, and psoriasis, but not for type 1 diabetes or ulcerative colitis.After excluding participants with autoimmune diagnoses, we identified six HLA types associated with sarcopenia in 181,301 UK Biobank participants of European descent aged 60–70. Although these have modest effect sizes , they are common in the study population, ranging in frequency from 3.7% to 24.8%. In combination, participants with more than six of the HLA types had markedly increased likelihood of sarcopenia . We observed markedly different prevalence of sarcopenia in males and females (4% vs 12%); although a recent systematic review found that the average sarcopenia prevalence in males and females was approximately 10% , the indHLA genes, in multiple tissues including skeletal muscle. Although more evidence is required for a clear causal link, this suggests that regulation in expression of HLA genes may also be an important driver of sarcopenia, not just the protein sequence (HLA types). When taken in combination, participants with more than four of the effect alleles had an 11% increased likelihood of sarcopenia.We also identified four non-coding genetic variants in the HLA region of chromosome six associated with sarcopenia. These are known to affect the expression of multiple genes, including The EWGSOP 2010 definition of sarcopenia , which cp = 1.5*10–131, moderate linkage disequilibrium with HLA-DRB1*04:01 r2 = .5).HLA types associated with sarcopenia are also associated with conditions linked to joint pain and loss of function, such as rheumatoid arthritis (DRB1*04:01), ankylosing spondylitis (C*15:02), Behcet’s disease  stress and there has been speculation that ER stress may impair autophagy and myogenesis activity resulting in sarcopenia (r2 = .8), known to affect protein levels of ATF6A  decreases ATF6A protein levels, suggesting a complex relationship; more work is required to understand this. The BAG6 protein product is also involved in elimination of misfolded proteins, including class I HLA products  axis, and disruption to the HPA has been linked to decline in physical function and aging , and the Endoplasmic Reticulum unfolded protein stress response (AFT6B), has shown an association with these variants and sarcopenia. Further studies into the long-term effects of HLA variation are required. More work is needed on whether some patients with sarcopenia have autoimmune processes and might benefit from targeted treatment.http://dx.doi.org/10.13039/501100000265) MR/M023095/1. D.M. and L.C.P. were supported by the University of Exeter Medical School. (http://dx.doi.org/10.13039/501100000737) Input from C.-L.K. and G.K. was supported by the University of Connecticut Health Center (http://dx.doi.org/10.13039/100007710). L.F. was supported by the Intramural Research Program of the National Institute on Aging, U.S. National Institutes of Health (http://dx.doi.org/10.13039/100000002). This work was supported by an IPA Assignment Agreement with Dr. Luigi Ferrucci at the National Institute on Aging (http://dx.doi.org/10.13039/100000002) (#20170526).This work was generously funded by an award to D.M. by the Medical Research Council (None reported.glz042_suppl_Supplementary_Table_1Click here for additional data file.glz042_suppl_Supplementary_Table_2Click here for additional data file.glz042_suppl_Supplementary_Table_3Click here for additional data file.glz042_suppl_Supplementary_Table_4Click here for additional data file.glz042_suppl_Supplementary_Table_5Click here for additional data file.glz042_suppl_Supplementary_Table_6Click here for additional data file.glz042_suppl_Supplementary_Table_7Click here for additional data file.glz042_suppl_Supplementary_Table_8Click here for additional data file.glz042_suppl_Supplementary_Table_9Click here for additional data file.glz042_suppl_Supplementary_Table_10Click here for additional data file.