Attempts were made to review the literature on diabetic patients who experience complications when they contract COVID-19, and to determine whether ethnicity and other risk factors play an important role in the development of symptoms and their severity, as well as responding to medications. A literature search was performed using five keywords, namely COVID-19, diabetes, ethnicity, medications, and risk factors between January 2019 and December 2020 using electronic databases such as PubMed, Science Direct, Google Scholar, Springer Link, and Scopus. Forty studies were included. The review indicated that diabetes was a significant risk factor for poorer outcomes and increased mortality associated with COVID-19. There were several risk factors for diabetic patients that increased their likelihood of poorer outcomes associated with COVID-19. These included black and Asian ethnicity, male sex with high BMI. In conclusion, patients with diabetes of black or Asian origin with high BMI, male sex, and older age had an increased risk of poorer outcomes associated with COVID-19. This highlights the importance of considering the history of the patient in prioritising care and treatment. In 2019 a new strain of coronavirus was released known as ‘COVID-19’ from Wuhan, China. This new strain of coronavirus, named SARS-COV-2, rapidly spread and the WHO announced a worldwide pandemic within a matter of months. There has been a total of 58,274,308 reported cases and 1,382,745 deaths worldwide and in the United Kingdom a total of 1,512,045 reported cases and 55,024 deaths from COVID-19 up to December 2020 [The Preferred Reporting Items for Systemic Reviews and Meta-Analysis (PRISMA) guidelines were followed. Briefly, a literature search was performed between January 2019 and December 2020. The following databases were searched: PubMed, Science Direct, Google Scholar, Springer Link, and Scopus.The following five keywords were included in the search in all databases: “COVID-19” AND “Diabetes” AND “Ethnicity” AND “Risk factors”. Duplicates were removed using ‘OR’ as appropriate and where possible. Original research articles were included. The exclusion criteria included papers in other languages than English, commentaries, reviews, news reports, and conference abstracts. The process of screening, identification and inclusion for this review is shown in PRISMA chart in Fig. The reviewers independently screened the titles and abstracts, and selected the studies that were within the inclusion and exclusion criteria. Studies were only included if they were original research articles, available as full text, and published between January 2019 to December 2020 in English language. Anything else such as reviews, commentaries, letters to editors or abstracts presented at scientific conferences were excluded.To avoid bias in the study, the inclusion criteria were strictly followed during the search process to provide reliable data. In addition, discussions were conducted to resolve any conflict between the reviewers and consensus was developed.The collected data including authors, year, objectives, key findings, country from eligible articles were included in Table The total number of articles retrieved from each database using the relevant keywords included 463 articles . Duplicates and irrelevant articles were removed according to the inclusion and exclusion criteria, resulting in 156 articles. The 156 articles were screened in compliance with the PRISMA checklist, which identified a total number of 40 relevant articles that were included in the study. In an analysis of 40 papers, two main themes were identified. These are: (a) COVID-19 in diabetic patients is associated with high mortality, and (b) Patients’ characteristics could influence COVID 19 symptoms in diabetic patients. Research by Holman and his colleagues showed that mortality rate of diabetic patients with COVID-19 is higher than non-diabetic patients . AccordiStudies in Scotland indicated that of 319,349 diabetic people, 2724 people contracted COVID-19 and 1082 had severe symptoms. However, of the non-diabetic people, 4081 people (0.1%) from 5,143,951 suffered from severe or fatal COVID-19 [This is supported by a study conducted in a hospital in Iran, where among the 595 COVID-19 patients, 148 patients had diabetes. Patients with diabetes were found to have more comorbidities and complications compared to those without diabetes, which required them to have more respiratory support .Another study reported that 6256 patients were hospitalized with COVID-19 and many patients were patients with type 2 diabetes. These patients were divided into two groups, one group was treated with metformin while the other group was in the non-metformin group. Of the 2333 patients who were in the metformin group, 394 of them died, indicating a mortality rate of 16.9%. 3923 patients were in the non-metformin group and 791 of them died, indicating a mortality rate of 20.2%. As a result, metformin was linked to a decrease in mortality in COVID-19 patients in diabetic patients .The studies showed variations in symptoms in relation to the characteristics of the patients. In this theme, four subthemes were identified: older diabetic patients suffer from severe symptoms, males are more likely to be badly affected, non-white diabetic patients are at higher risk, the higher the BMI, the higher the risk in diabetic patients.The results also showed that age contributes to the severity of COVID-19 symptoms. The results showed that older age and being male increased the chance of fatality from COVID-19 for type 1 and type 2 diabetic patients .Studies have also shown that different underlying health complications and comorbidities can further exacerbate the state of a patient’s disease. A study carried out in hospitalized COVID-19 patients in Mexico showed that of 15,529 patients, 62.6% were over the age of 40, 57.8% were men and a high proportion were diagnosed with diabetes (18.4%), hypertension (21.9%) and obesity (20.9%) –13. ThisPatients who had diabetes were older, more prone to receiving mechanical ventilation and ICU, and had higher mortality. The clinical characteristics of these patients included elevated D-dimer levels, increased white blood cell count, neutrophil count, C-reactive protein as well as having a more severe inflammatory response .In Iran, 595 patients with COVID-19 with diabetes were hospitalized, of these patients were men and the median age was 55 years, 148 of the patients (24.9%) were diabetic and those who had diabetes also had more comorbidities than non-diabetic patients such as hypertension (48.6% vs 22.3%), chronic liver disease (9.4% vs 4%), chronic kidney disease (16.2% vs 7.6%) and cardiovascular disease (27% vs 16.1%) [Ethnicity was seen to have a potential link in the worsening prognosis in COVID-19. The anti-SARS-CoV-2 IgG seroprevalence of South Asian and Black hospital staff was reported to be significantly higher than that in white staff . Some stFurther studies reported that Black individuals had more COVID-19 risk factors than white individuals and that hospitalised Black COVID-19 patients had more chronic diseases when compared to other ethnicities , 36. COVA study carried out on 872 patients with 48.1% Black, 33.7% White, 5.6% Asian and 12.6% mixed/other Asian reported a higher risk ratio of 3.12 in the Black ethnic group compared to the White population which had a hazard ratio of 2.97 with black people between the ages of 45 and 65 having an especially high rate of hospital admissions . MoreoveAt the University Hospitals of Leicester (UHL) NHS Trust, Martin et al. reported that with conditions such as diabetes, 11.6% and 12.7% of Black males and females had the disease, respectively. This percentage was lower in the white male and female groups with 10% and 8.5%, respectively . Higher Other studies further confirmed that patients of Black and Asian background with type 1 diabetes had an increased risk of mortality compared to White people . For typHbA1c and CRP levels, as well as the incidence of diabetes, were higher in Black and Asian individuals than in White individuals, even at a younger age. Patients of Black ethnicity were 4 times as likely to be hospitalized for COVID-19, and Asian and other ethnic groups were twice as likely to be hospitalised for COVID-19, compared to patients who were White . AccordiAttempts were made to review the literature focusing on diabetic patients who experienced complications when they contracted COVID-19, as well as the impact of ethnicity, which may play an important role in terms of the development of symptoms and their severity and the response to medications. Although it was found that diabetes is a risk factor for COVID-19, many people with diabetes also have other underlying health conditions, therefore this should be considered as a possible contributing factor for the worse outcomes of covid-19 in people with diabetes. It was found that at least 40% of patients with diabetes had at least 1 microvascular comorbidity , 79% at least 1 macrovascular comorbidity , and 61% at least 1 non-diabetes-related comorbidity  . In addiBlack and Asian individuals were at increased risk for diabetes and were a risk factor for worse outcomes associated with COVID-19. This suggested that Black and Asian people are likely to be predisposed to worse outcomes and an increased risk of hospitalization with COVID-19 . People Studies also showed that there was a significantly higher number of patients with type 2 diabetes  that died from COVID-19 compared to patients with type 1 diabetes (464 patients) .Interestingly, male gender was a risk factor for COVID-19 mortality and increased oxygen requirements . Males aIn addition, studies have shown that patients that have chronic kidney disease (CKD) are more likely to test positive for COVID-19 and are at an increased risk of mortality from COVID-19 . The kidInterestingly, studies have shown the transmembrane protease serine 2 (TMPRSS2) to be expressed at a higher level within the nasal epithelium in Black people compared to other ethnicities. The SARS-CoV-2 virus uses TMPRSS2 as a host for S protein priming and fusion of viral and host cell membranes, which could explain why there is a higher infection burden amongst Black people .Other published data have also shown that patients with diabetes and hyperglycaemia have a greater risk of being more severely affected by SARS-CoV-2 compared to those with normoglycemia and who are non-diabetic . A studyAlthough this study contains credible information highlighting the impact of COVID-19 in conjunction with diabetes and relevant risk factors and treatment strategies, there were several limitations in this review. First, this study only included original research articles and excluded secondary studies, thus preventing a variety of expert perspectives and insights. The studies included within this review were from different countries, which could insinuate a lack of under reporting and thus impact the quality of data. The predefined inclusion criteria meant that articles published between January 2019 to December 2020 could only be included, which could have meant that we missed out on additional insights and key findings on COVID-19. Furthermore, due to the rapidly changing management of COVID-19, some of the findings and results could be affected. However, despite these limitations, this review does provide key information and insights on the impacts of COVID-19 in correlation with diabetes, and the associated risk factors and therapeutic approaches used to manage and/or prevent progression of the disease and the adverse effects of SARS-CoV-2.The accumulated data confirmed that patients with diabetes are at an increased risk of COVID-19, some of the risk factors for these patients included BAME ethnicity, high BMI, male gender and older age. Patients with these risk factors were predisposed to worse outcomes associated with COVID-19. More research is required to investigate additional risk factors for people with diabetes that may predispose them to worse outcomes associated with COVID-19. Although attempts to vaccinate people have progressed rapidly, further research is needed to consider the speed with which the virus is mutating and the fact that some individuals may not respond well to vaccination.


In view of experts’ warnings about the potential negative mental health consequences of the sudden nationwide lockdowns implemented in many countries to limit the spread of the COVID-19 pandemic, we sought to study the incidence of posttraumatic stress disorder (PTSD) after traumatic events related to this unprecedented lockdown in the French general population.This longitudinal study among adults (aged =18) consisted of two surveys: the first during the last days of the lockdown and the second a month later. We estimated PTSD incidence with the PCL-5 and ran multiple Poisson regression models to identify factors associated with PTSD.Among the 1736 participants, 30.1% reported at least one traumatic event. PTSD incidence was 17.5% . It was higher in participants who reported multiple traumatic events, who had high COVID-19-related media use, who had general anxiety disorder (GAD-7) during the lockdown, and who had GAD, depression (PHQ-9), or sleep problems 1 month later. In addition, 43.1% of people with PTSD reported suicidal thoughts.These results should help clinicians to target people who are at high risk of developing PTSD after a pandemic-related lockdown and could benefit from preventive measures. Collaboration between the media and mental health professionals could be envisioned to inform the population about care resources. Follow-up recommendations should also be disseminated to general practitioners to facilitate PTSD screening and ensure that they are aware of the appropriate management.The online version contains supplementary material available at 10.1186/s12889-022-13880-9. The Coronavirus disease-2019 (COVID-19) emerged in late 2019 in China and spread rapidly in early 2020, and continued to circulate actively for the next 2 years, affecting million people worldwide with more than 535 million confirmed cases and leading to 6 million deaths by June 2022 . In viewTo expand our knowledge of COVID-19’s consequences on mental health in France, we conducted the COCONEL cohort survey to 1) quantify the incidence of PTSD in the general population, 2) characterize the relative frequency of the PTSD symptoms observed, 3) study the sociodemographic, economic, and COVID-19-related factors, including media use to obtain information about the pandemic, associated with PTSD onset, and 4) study PTSD’s comorbidity with generalized anxiety and depression.The COCONEL cohort survey used two successive surveys, one taken during the last days of the first French lockdown May 7-11, 2020) and the other about a month later  among the same representative sample of adults (18+ years old) residing in mainland France. The sample was selected using the quota method from an online research panel of more than 750,000 households, developed and maintained by IFOP . A quota sampling method was applied to obtain a sample representative of the adult general population in France for age, gender, occupation, and rural/urban residence. To limit selection bias, panelists with low response rates  were oversampled relative to the others. Finally, 2003 panelists participated in the first survey and, among them, 1736 (86.7%) responded to the second , a 20-item self-report instrument, validated in French  and corrExperience of a traumatic event during the lockdown was documented during both surveys: respondents were asked whether they had faced such an event (yes/no); those who answered yes were asked if the event was associated with some of the government announcements, their work situation, a relative infected by the coronavirus, travel, conflict with a member of the household, and/or the death of a family member not due to Covid-19 . The repetition of these questions in the second survey allowed construction of a variable measuring the dynamics of the perception of the traumatic event during the lockdown as follows: we defined “acute stress” as reporting a traumatic event during the lockdown in the first survey only; and “persistent stress” as reporting a traumatic event in the second survey, whether participants had or had not reported an event during the first survey.In addition, we used the Patient Health Questionnaire-9  and the General Anxiety Disorder-7  to screen for prevalent depression or generalized anxiety disorder (GAD) during the lockdown (first survey) and during the 2 weeks before the second survey , 19. We During the first survey, participants were also asked how much they worried about becoming infected  and, in the second survey, if they had been diagnosed with COVID-19, and if any relatives had been admitted to an intensive care unit due to the disease. The first survey also addressed participants media consumption for information about COVID-19 during the lockdown, asking them how many hours per day  they had spent looking at information about it from five different media sources  in the past week; we constructed a media exposure indicator according to a previously published method . As answFinally, we collected participants’ gender, age, region of residence, education level, financial  quartiles) and work situations before and during the lockdown, household composition, overcrowded housing (defined as a living area <  18 square meters per person or < 25 sq.m. for a single person), and the lockdown’s perceived impact on the household’s financial situation.p < 0.20 in the univariate analyses were eligible for the multivariate models. To obtain relative risks, we performed two multiple modified Poisson regression models: 1/ in the entire population, and 2/ only among those with a traumatic event, to estimate the PTSD risks associated with the type of traumatic event and the persistence of stress between the two surveys. We checked the risk of multicollinearity and the potential endogeneity bias with a Heckman selection model. Finally, only significant factors are shown, except for gender, which we used as a basic adjustment variable. All statistical analyses were performed with SAS version 9.4 .We weighted data so that the structure of the weighted sample matched that of the French population for age, gender, occupation, and population density in the region of residence. All analyses were performed with these weights. Using Student t-tests and proportion comparison tests, we first calculated PTSD scores  and estimated PTSD incidence by type of traumatic event experienced. We then compared PTSD incidence according to individual characteristics among two different groups: the entire sample, and those who reported a traumatic event in either survey (prerequisite for measuring PTSD). We first used Chi-2 tests to explore associations between individual characteristics and PTSD incidence and then tested them in multivariate analyses. Variables significant at During the first and/or second surveys, 1046 participants reported a traumatic experience during the lockdown: 34.5% only during the first survey, 5.5% during the second, 18.1% during both, 41.9% none, see Additional file p < 0.001, Fig. Among these 1046 participants, 30.1% were classified with PTSD 1 month after the lockdown ended. In the general adult population Table  PTSD in Incidence of PTSD did not differ according to gender Table  but it wPTSD incidence was also higher in those diagnosed with COVID-19 (50.1%) than in those who were not (16.6%) Table . Similarp < 0.001). Finally, among people who had experienced a traumatic event, PTSD incidence was significantly higher in respondents reporting persistent stress due to this event than in those reporting acute stress only.PTSD incidence was significantly higher in participants with anxiety or depression, identified in either survey Table . It was The first multiple regression model  in the general population. The risk of PTSD in the general population was higher in people with high COVID-19-related media use, with at least mild anxiety during the lockdown, and diagnosed with COVID-19. PTSD was also strongly comorbid with anxiety, depression, and sleep problems at follow-up. Among people reporting a traumatic event, young age and exposure to multiple types of traumatic events were associated with increased PTSD risk.Several studies have measured PTSD incidence during this pandemic, with various instruments and definitions. Some measured acute stress, e.g., at the epidemic peak , 11, 22.Most recent review study have highlighted that health professional was concerned but also COVID-19 patients and general population . StudiesAs in recent studies , 15, 25,The collection of data in two waves a month apart allowed us to explore the persistence of the perception of stress related to traumatic events. While acute stress was perceived more frequently than persistent stress, the latter was more highly correlated to incident PTSD, although no longer significantly after comorbidity factors were included in the model. PTSD–depression comorbidity has frequently been noted in the literature, among military personnel (exposed to combat), victims of sexual assault , and eveMore generally, our findings raise the question of the nature of traumatic events in a pandemic containment context. Risk was highest for those reporting several sources of traumatic events. Government announcements alone were not associated with PTSD risk in the multivariate analysis, but were rather mostly cited by people exposed to several types of events during the lockdown. Therefore, in addition to other stressful events, these announcements may have played a role in augmenting the risk. The DSM-5 definition of trauma, however, requires “actual or threatened death, serious injury, or sexual violence”: proximity to death and/or physical violence was infrequent in the traumatic events reported in our study and is reflected in the low prevalence of intrusion symptoms in our results  or/and one month later (second survey), and incidence of PTSD. Table 1. PTSD score according to population characteristics among those who experienced a traumatic event during the lockdown .


Branta canadensis), by comparing metagenomes and metatranscriptomes to simulated metagenomes derived from 16S amplicon sequencing. We found significant differences between the metagenomes, metatranscriptomes, and simulated metagenomes when comparing enzymes, KEGG orthologies (KO), and metabolic pathways. The simulated metagenomes accurately identified the majority (>70%) of the total enzymes, KOs, and pathways. The simulated metagenomes accurately identified the majority of the short-chain fatty acid metabolic pathways crucial to folivores. When narrowed in scope to specific genes of interest, the simulated metagenomes overestimated the number of antimicrobial resistance genes and underestimated the number of genes related to the breakdown of plant matter. Our results suggest that simulated metagenomes should not be used in lieu of empirical sequencing when studying the functional potential of a nonmodel organism’s microbiome. Regarding the function of the Canada goose microbiome, we found unexpected amounts of fermentation pathways, and we found that a few taxa are responsible for large portions of the functional potential of the microbiome.16S rRNA amplicon sequences are predominantly used to identify the taxonomic composition of a microbiome, but they can also be used to generate simulated metagenomes to circumvent costly empirical shotgun sequencing. The effectiveness of using “simulated metagenomes” (shotgun metagenomes simulated from 16S rRNA amplicons using a database of full genomes closely related to the amplicons) in nonmodel systems is poorly known. We sought to determine the accuracy of simulated metagenomes in a nonmodel organism, the Canada goose . Metagenome and metatranscriptome sequencing can determine microbiome function but can be cost prohibitive. Therefore, computational methods have been developed to generate simulated metagenomes derived from 16S rRNA sequences and databases of full-length genomes. Simulated metagenomes can be an effective alternative to empirical sequencing, but accuracy depends on the genomic database used and whether the database contains organisms closely related to the 16S sequences. These tools are effective in well-studied systems, but the accuracy of these predictions in a nonmodel system is less known. Using a nonmodel bird species, we characterized the function of the microbiome and compared the accuracy of 16S-derived simulated metagenomes to sequenced metagenomes. We found that the simulated metagenomes reflect most but not all functions of empirical metagenome sequencing. All vertebrates host microorganisms in and on their bodies. These “host-associated microbiomes” have strong effects on host biology that range from beneficial to detrimental , 2. On t3–10–Numerous microbiome studies use a single marker, the 16S rRNA gene (here 16S), to taxonomically identify the microbiota. 16S sequencing is an effective method to identify what microbes constitute the microbiota , but 16S data are unable to describe the functions  of the microbiome –19. Conv–20–To characterize the functional potential of these communities, the total genomic DNA from the microbiome can be extracted and sequenced. The total genomic DNA is referred to as the metagenome (MG), and it contains the functional potential of the microbiome , 25; it Metagenomes and metatranscriptomes provide functional as well as taxonomic insight into the microbial community but are more expensive than 16S sequencing , which u26–Branta canadensis). Canada geese are a ubiquitous species across North America and are one of the few folivores in the avian clade. They thrive in many urban and periurban environments  and metatranscriptomes (MTs) to compare to simulated metagenomes (simulatedMGs) using a wild, nonmodel bird species, the Canada goose Categorize the Canada goose fecal microbiota by identifying what microbes are present.(ii)Determine the accuracy of the simulated metagenomes by comparing the diversity and number of shared enzymes (enzyme commission numbers [ECs]), KEGG orthologies (KOs), and metabolic pathways in each method.(iii)Identify genes or pathways that may be functionally important based on their abundance in each method.(iv)Look for antimicrobial resistance genes (AMRs) found in Canada geese and see if they are accurately identified by PICRUSt2.(v)Determine the relative contributions of constituent microbes on the functional potential of the microbiome.Specifically, we collected 16S, MG, and MT data on fecal samples from nine Canada geese and identified the taxa, pathways, and functions present. We also used the 16S data to simulate metagenomes using PICRUSt2 and compared them to the MG and MT. The goals of this research were tox̄ = 40.78%) of the reads in the metagenomes and 30.69% (x̄ = 30.69) of the reads in the metatranscriptomes mapped to a known protein sequence. In the MGs and MTs, we identified 1,607,386 unique UniProt IDs; after running the datamining script, we identified 583,903 UniProt IDs that were not associated with prokaryotes. We wrote a datamining script and extracted taxonomic data associated with every UniProt ID, and those that did not contain either “Bacteria” or “Archaea” were removed. Large portions of the MGs and MTs were viruses (The nine metagenomes (MGs) and nine metatranscriptomes (MTs) were cleaned and trimmed, yielding an average of 22,409,670 reads per MG and 21,116,359 reads per MT. After HUMANn2’s organism-agnostic translated search, 40.78% , 2,884 ECs , and 588 pathways (x̄ = 444). The MTs yielded 7,778 KOs , 2,089 ECs , and 436 pathways (x̄ = 275). PICRUSt2 analysis (simulatedMGs) of the complementary 16S sequences from the ZymoBionics extractions identified 6,451 KOs , 2,021 unique ECs , and 388 pathways (x̄ = 338).To characterize the functions of the microbiota, we used KOs (groups of orthologous genes grouped by molecular function) , 44, ECst test (P = 0.6511). There were significant differences in KOs between the simulatedMGs and MTs as well as the MGs and MTs . We detected no significant difference between the number of ECs identified by the simulatedMGs and MGs . There were significant differences between the comparisons of simulatedMGs:MTs and MGs:MTs . There were significant differences in the number of pathways identified in all three methods .Significant differences in the raw number of KOs, ECs, and pathways found in each method were calculated using the Wilcoxon rank-sum t test  to C. Wex̄ = 4,338), 2,409 ECs , and 454 pathways (x̄ = 358) for the MGs. The MTs yielded 3,929 KOs , 1,539 ECs (x̄ = 954), and 318 pathways (x̄ = 217).Because the simulation software only simulates bacteria and archaea, we removed the viral and eukaryotic KOs, ECs, and pathways for a more “fair” comparison. This yielded 6,089 KOs (t test (P = 0.01061). There were significant differences in KOs between the simulatedMGs and MTs as well as the MGs and MTs . We detected no significant difference between the number of ECs identified by the simulatedMGs and MGs . There were significant differences between the comparisons of simulatedMGs:MTs and MGs:MTs . We found that significant differences were still observed between simulatedMGs:MTs (P = 0.0000082) and MGs:MTs (P = 0.00012); however, there was no longer a significant difference in the number of pathways identified between the simulatedMGs and MGs (P = 0.81012).We tested the KOs, ECs, and pathways that belong only to prokaryotes again using the Wilcoxon rank-sum t test  to F. WeP < 0.001, R2 = 0.5194; ECs, P < 0.001, R2 = 0.51433; pathways, P < 0.001, R2 = 0.60249).To see how the samples compared to one another, principal coordinate analysis (PCoA) ordinations were generated to compare the three data types. Visual analysis of the PCoA plots showed that the simulatedMGs, MGs, and MTs cluster separately for KOs , ECs, anP < 0.001, R2 = 0.56167; ECs, P < 0.001, R2 = 0.5370; pathways, P < 0.001, R2 = 0.59299).When viral and eukaryotic KOs, ECs, and pathways were removed, the simulatedMGs, MGs, and MTs clustered separately for the KOs, ECs, and pathways  to D. UsTo identify how many of the KOs, ECs, and pathways were shared versus unique in the total data set and in the prokaryote-only data, we used UpSet plots . In the x̄ = 49.05%; ECs, x̄ = 61.94%; pathways, x̄ = 63.11%). The average percent similarity of the KOs, ECs, and pathways was lowest between all three methods.We calculated the percent similarity of the identified KOs, ECs, and pathways . The greWhen the nonprokaryotic KOs, ECs, and pathways were removed, the intersection of all three data types still contained the largest amount of KOs, ECs, and pathways , and secWe wanted to quantify the effect of the nonprokaryotic data on the averaged percent similarity between the three data types and calculated the change in percent similarity in KOs, ECs, and pathways. The percent similarity of the KOs increased across each pair . The percent similarity for ECs in MGs:simulatedMGs and simulatedMGs:MTs increased ; however, MGs:MTs decreased (Δ−0.40%). The percent similarity of the pathways in simulatedMGs:MGs had the largest change (Δ+8.98%), followed by simulatedMGs:MTs (Δ+6.36%) and MGs:MTs (Δ+1.08%). The similarity between all three data types increased in the KOs, ECs, and pathways .l-alanine fermentation to propanoate and acetate). The simulatedMG samples shared the same pathways, but all nine simulatedMG samples were missing the butyrate pathway GLUDEG-II-PWY (l-glutamate degradation VII to butanoate), and all nine were missing the propionate pathway PROPFERM-PWY . The metatranscriptomes were missing pathways that were present in the MGs and simulatedMGs, and three pathways were found in all of the samples in every group: P161-PWY, PWY-5100, and ANAEROFRUCAT-PWY. Five pathways in the MTs, P162-PWY, P163-PWY, GLUDEG-II-PWY, PWY-5677, and PROPFERM-PWY, were missing in the majority of the samples. The pathways producing lactate were the most abundant; of the five pathways that yield lactate, two were present in all nine samples in each group, two were present in eight of the MTs, and one was present in seven of the MTs. The five lactate-producing pathways were present in all of the MGs and simulatedMGs.Short-chain fatty acids (SCFAs) are the byproduct of microbial-dependent fermentation of indigestible dietary fibers like cellulose and hemicellulose , 48. In t test; MG:MT, P = 0.0012; MG:simulatedMG, P = 0.0027; MT:simulatedMGs, P = 0.0013). We identified 102 ECs; the MGs had a total of 97 ECs, and 41 of those ECs were found in all nine samples. The MTs had 67, but only 6 ECs were found in every MT. The simulatedMGs identified 66, with 48 being present in all the simulatedMGs. We found that 35 ECs were shared by every MG and the simulatedMGs. Only 20 ECs were found in at least half of the MGs, MTs, and simulatedMGs. The MGs had 73 ECs that were present in at least 5 samples, the MTs had 29, and the simulatedMGs had 50. The simulatedMGs only identified 60.27% .We identified ECs that were associated with the breakdown of dietary fibers, specifically cellulose and hemicellulose. We searched the Carbohydrate-Active enZymes Database (CAZy) for glycoside hydrolases (GH) associated with the cellulase and hemicellulase family of enzymes , 50. We d 60.27%  of the Ed 60.27% . See Tabd 60.27% . There wWe specifically looked for cellulase (EC 3.2.1.4), which breaks down the cellulose found in plants. Cellulase was found in all of the MGs and all of the simulatedMGs and in eight of the MTs (Table S3). We also looked into enzymes associated with hemicellulose degradation, EC 3.2.1.8  and EC 3.2.1.37 , which were both found in all the MGs and simulatedMGs. We identified EC 3.2.1.8 in five of the MTs and EC 3.2.1.37 in four of the MTs. Many of the ECs that appear relevant to plant fiber degradation were not consistently identified in all the samples; for example, EC 3.2.1.91  breaks down the nonreducing end of cellulose, cleaving a glucose molecule, and was found in five of the MGs and eight of the simulatedMGs and was absent in the MTs. EC 3.2.1.131  breaks down the chain linking the different strands of xylans found in plants and was found in all of the MGs and one of the MTs and was not identified in any of the simulatedMGs.Antimicrobial resistance (AMR) KOs were pulled from the KEGG database , 44. We The MGs shared the same KOs as the simulatedMGs, except that the MGs had 7 KOs that were missing in the simulatedMGs. These KOs present in MGs and not the simulatedMGs were associated with macrolide-lincosamide-streptogramin, penicillin, and aminoglycoside resistance. There were no KOs that were solely found in the MTs; all 20 KOs from the MTs were found in either the MGs or simulatedMGs. The most prevalent antibiotic KOs were associated with vancomycin, phenicol, and penicillin. The MGs identified 41 KOs that were present in at least five samples . The simBacteroides, Campylobacter, Curtobacterium, Escherichia, Faecalibacterium, Helicobacter, Megamonas, Subdoligranulum, and Turicibacter in the KO data. The relative abundance of those nine genera in each sample comprised 26.8% to 64.1% (x̄ = 44.7%) of the genera identified , Campylobacter (7), Curtobacterium, Faecalibacterium, Megamonas, and Subdoligranulum. Three metagenomes had KOs associated with Escherichia and Helicobacter. One sample had only Turicibacter-associated KOs. The nine genera were responsible for contributing 4.9% to 56.2% (x̄ = 44.7%); see Table S6 for a complete breakdown of each genus’s contribution of KOs.We then compared the KOs that originated from those nine genera and quantified the relative contribution to the total number of KOs detected in the MGs . KOs froUsing the fecal microbiome of the Canada goose, we sought to characterize the function , functional potential , and the taxa  of a complex wild animal’s microbiota. We also sought to determine the accuracy and utility of using metagenome simulation software in a nonmodel organism  and metatranscriptomes (x̄ = 30.69%). The number of KOs, ECs, and pathways is most likely incomplete, and we are only observing a fraction of the functional potential of the microbiome. It is likely that many of the unknown sequences are novel or uncharacterized, and it is reasonable to assume that if these unknown sequences were identified, the number of KOs, ECs, and pathways would likely increase to levels that would far exceed PICRUSt2’s reported KOs, ECs, and pathways.PICRUSt2 is a computational alternative to metagenomic sequencing with high accuracy in many systems ; howeverP < 0.001). These differences were observed regardless of the presence or absence of the nonprokaryotic data. We thus conclude that the nonprokaryotic KOs, ECs, and pathways affect the raw counts (discussed above), but their removal does not change the significant differences observed between the MGs, MTs, and simulatedMGs, as whole samples. The simulatedMGs and MGs shared large portions of KOs, ECs, and pathways  showed distinct and generally nonoverlapping clusters in the MGs, MTs, and simulatedMGs . Signifipathways ; howeverpathways . The MTsl-alanine fermentation to propionate and acetate) and GLUDEG-II-PWY (l-glutamate degradation VII to butanoate) were identified in most of the MGs but were not found in any of the simulatedMGs. The simulatedMGs were less accurate with identifying ECs relevant to fiber digestion and more accurate with identifying complete SCFA pathways.Folivore diets are inherently nutrient poor, and organisms that consume large quantities of grasses and other leafy plants rely on their microbiome to breakdown the cellulolytic or hemicellulolytic foodstuffs into digestible components that the host can then absorb , 49. TheAnser albifrons), the bean goose , and the swan goose (Anser cygnoides) have all shown limited ability to ferment plant matter (Anser cygnoides)   does usegnoides) . It is pAntimicrobial resistance is a growing concern in the medical community, and infection caused by an antibiotic-resistant bacterium can lead to dangerous medical situations and can be lethal . BecauseActinobacteria, Proteobacteria, and Firmicutes. The majority of the MTs were dominated by viruses and were also missing Bacteroidetes, which were found in large quantities in the MGs and simulatedMGs. In Canada geese from Maryland, Ohio, and Ontario, Actinobacteria was a minor phylum in the fecal microbiomes   , 40. Ourironment . Bacteroulations , 40 and Helicobacter, Escherichia/Shigella, Campylobacter, Bacteroides, and Clostridium. One of the more consistent genera across the samples was Subdoligranulum, a close relative of Faecalibacterium, which has been primarily found in humans and is characterized as a butyrate producer , goose adenovirus A (Adenoviridae), avian myelocytomatosis (Retroviridae), and avian sapelovirus (Picornaviridae). We also detected plant viruses, such as peanut stunt virus (Bromoviridae), tobacco ringspot virus (Secoviridae), and tobacco mosaic virus (Virgaviridae) (Table S7). Therefore, Canada geese feces may be a potential vector for agricultural crop viruses. More work is needed to fully assess the vector potential for Canada geese on crop production as well as other avian pathogens.Viral pathogens in Canada goose feces are also of interest to the public and can be identified using -omics methods. Characterization of the viromes in the MGs and MTs  found thTaxa and functions are equally important aspects of a host-associated microbiome, and to fully understand the microbiome, we must determine what methods are best for identifying taxa and function in the microbiomes of nonmodel organisms. In this work, we compared the accuracy of different -omics-based methods to simulated metagenomes generated by PICRUSt2 in the Canada goose fecal microbiome. The Canada goose microbiome had few taxa that contributed a large amount to the microbiome’s functional potential, and we found plant-degrading, fermentation, and antimicrobial resistance genes. We found a significant difference between the simulated metagenomes and the sequenced -omics data at different functional levels . The simulated metagenomes accurately predicted most pathways that were identified in the metagenomes and metatranscriptomes; however, it was less accurate when trying to identify functionally relevant genes for the goose’s digestion (underestimated) or antimicrobial resistance genes (overestimated). The discrepancies discovered here are likely due to deficiencies or biases in public databases and demonstrate the need for more whole-metagenome sequencing from different sources to improve the accuracy of future predictions in nonmodel systems.n = 9, and New York, n = 3; Zymo Research, Irvine, CA, USA). Samples were collected in the summer of 2018 with the Connecticut Department of Energy and Environmental Protection (DEEP) and New York Department of Environmental Management (DEM). Fecal samples were collected from molting resident (nonmigratory) Canada geese during routine annual banding. The sex, age, and location were recorded for each sample . While Canada geese were being held for banding, sterile weigh boats were held under each goose to catch the fecal pellet. Sterile tweezers were used to collect a portion of the fecal pellet. Samples were stored on ice until they could be transferred to a −80°C freezer.For the comparative -omics analyses, 12 fecal samples were collected and stored in 9 mL of ZymoBionics DNA/RNA shield, preserving both DNA and RNA  high-sensitivity assay for Qubit 3.0 . To assess fragmentation, genomic DNA was analyzed on an Agilent TapeStation 4200  using the genomic DNA assay . DNA integrity numbers (DINe) were recorded for each sample. Total RNA was quantified, and purity ratios were determined for each sample using a NanoDrop 2000 spectrophotometer . To assess RNA quality, total RNA was analyzed on an Agilent TapeStation 4200  using the RNA high-sensitivity assay following the manufacturer’s protocol. Ribosomal integrity numbers (RINe) were recorded for each sample. Three samples had insufficient DINe and RINe values for successful metagenomic and metatranscriptomic sequencing.Genomic DNA samples were normalized to 0.2 ng/μL in preparation for whole-genome shotgun library preparation using the Illumina Nextera XT library preparation kit following the manufacturer’s protocol . Libraries were validated for length  and adapter dimer removal using an Agilent TapeStation 4200 D5000 high-sensitivity assay  and then quantified and normalized using the dsDNA high-sensitivity assay for Qubit 3.0 .Total RNA samples  were prepared for prokaryotic/eukaryotic transcriptome sequencing using the Illumina Ribo-Zero total RNA library preparation kit  following the manufacturer’s protocol. Libraries were validated for length and adapter dimer removal using an Agilent TapeStation 4200 D1000 high-sensitivity assay  and then quantified and normalized using the dsDNA high-sensitivity assay for Qubit 3.0 .Sample libraries were prepared for Illumina sequencing by denaturing and diluting the libraries per the manufacturer’s protocol . All samples were combined into one sequencing pool, proportioned according to expected number of reads, and run as one sample pool on the Illumina HiSeq. Target read depth was achieved per sample with paired-end 150-bp reads.The reserved DNA extracts not used for metagenomic sequencing were used to amplify and sequence the V4 region of the 16S rRNA gene at the University of Connecticut Microbial Analysis, Resources and Services center, using the standard protocols. Extracts were quantified using the Quant-iT PicoGreen kit . Partial bacterial 16S rRNA genes  were amThe quality of the nine metagenomes and nine metatranscriptomes was analyzed using FastQC . Low-qua43–Sequence processing for 16S rRNA samples was done using R v.4.1.0 . SequencThe sequence files were converted into biom files in R. PICRUSt2  v2.4.1 wEukaryotic and viral pathways found in the metagenomes and metatranscriptomes were removed to assess the effects of nonprokaryotes between the simulatedMGs and the sequenced data. After merging the pathway tables, all the pathways that were shared by the simulatedMGs and the MGs and MTs were identified. Because the simulatedMGs were derived from 16S data, we assumed that if the same pathways were found in the MGs and the MTs, they were derived from prokaryotes. We then compiled the pathways that were found in either the MGs or MTs and not in the simulatedMGs. We wrote a Python datamining script to extract the taxonomic data from each pathway’s respective HTML file using each pathways’ associated MetaCyc URL. The text files were searched using regular expressions to find all text associated with taxonomic rank. The UniProt IDs and MetaCyc pathways that contained “Bacteria” or “Archaea” were coalesced into our “prokaryotic-only” data set. We were liberal in our classification; if a pathway was found in both prokaryotes and eukaryotes, we kept it with the prokaryotic data. The remaining data included UniProt IDs and MetaCyc pathways that were associated with plants, animals, other eukaryotes, or viruses or did not contain specific taxonomic data. The total data set and prokaryote-only EC, KO, and pathway data were used for downstream analysis. The datamining script used public packages available with Anaconda v.2.1.1 , includiThe three simulatedMGs that did not have complementary MGs or MTs were removed for all comparisons. HUMAnN2 and MetaPhlAn  were uset test.To compare the number of ECs, KOs, and pathways identified in the simulatedMGs to the MGs and MTs, box and whisker plots were generated by summing the number ECs, KOs, and pathways in each sample for both the total and the prokaryote-only data sets. Significant differences were determined using a Wilcoxon rank-sum The EC, KO, and pathway tables were reformatted to be analogous to otu_tables and tax_table used in the R packages phyloseq v.1.36.0  and vegaFor percent similarities between methods , UpSet plots were constructed using the UpSetR package in R v.1.4.0  for botht test, and significance between groups was calculated using a PERMANOVA. Third, antimicrobial resistance (AMR) genes were identified using the KEGG Orthology (KO) database. One hundred and sixty-two KOs were associated with one of the following antimicrobials: aminoglycosides (39), fosfomycin (5), macrolide-lincosamide-streptogramin (19), methicillin (3), penicillin (66), phenicol (8), quinolone (2), rifamycin (4), sulfonamide (3), tetracycline (11), trimethoprim (6), and vancomycin (18). This list was cross-referenced with the KO table, and any matches were extracted for comparisons of the AMR profile. To assess the accuracy of AMR detection, the presence of each antimicrobial was recorded and compared for each of the three methods . The AMR KOs were compared individually , and each sample was merged to be compared between groups (averaged: MGs versus MTs versus simulatedMGs).Based on the Canada goose diet, genes and pathways found in herbivores were selected for additional analysis, as they could be crucial for the breakdown of fibrous plant matter. First, the MetaCyc database was used to find all pathways involved in short-chain fatty acid (SCFA) metabolism, as they are critical in the breakdown of dietary fiber into absorbable fatty acids , 54, 55.HUMAnN2  providesThe Connecticut Department of Energy and Environmental Protection and New York Department of Environmental Management hold all necessary state and federal permits to handle Canada geese. The research was not subject to the University of Connecticut’s Institutional Animal Care and Use Committee because the fecal samples were collected noninvasively.https://github.com/joshuacgil/multi-omics-CAGO-microbiome-code-.git repository (PRJNA879251.All data generated and analyzed during this study are included in this published article, its supplementary information files, and in the pository . Sequenc


Low levels of albumin, prealbumin (on the third day) and high CONUT score (on the third day) showed an association with higher mortality. Nutritional variables were inversely correlated with clinical and inflammatory parameters. Critically ill patients with COVID-19 have poor nutritional status related to a poor prognosis of disease severity and mortality.This study evaluated the clinical and nutritional status, the evolution over three days, and the relationship between nutritional, inflammatory, and clinical parameters of critically ill patients with COVID-19. A longitudinal study was conducted in the Intensive Care Unit of the Virgen de las Nieves University Hospital in Granada (Spain). The study population comprised patients with a positive polymerase chain reaction test for COVID-19 presenting critical clinical involvement. Clinical outcomes were collected, and inflammatory and nutritional parameters  score) were determined. A total of 202 critical patients with COVID-19 were selected, presenting highly altered clinical-nutritional parameters. The evolution experienced by the patients on the third day of admission was a decrease in albumin ( The novel coronavirus disease 2019 (COVID-19) is the condition responsible for the global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The COVID-19 pandemic has caused an enormous health and economic impact and changed how medicine and medical education are practiced. This transformation is based on the following reflections on the negative impact that the COVID-19 pandemic has had on medical practice ; (3) theMalnutrition in hospitalized patients remains a relevant problem in Spain due to its implication in clinical worsening during disease evolution, especially in critical patients, affecting 30–50% . VariousLow albumin levels are related to a negative prognosis, with an increase in the appearance of complications and mortality. However, albumin levels have little sensitivity to changes in nutritional status, so it is not a suitable parameter for short-term nutritional monitoring. Values below 2.1 g/dL indicate a serious malnutrition situation . PrealbuTherefore, the objective of the present study was to investigate the usefulness of some nutritional parameters in the evolution and prognosis of critically ill patients with COVID-19 admitted to the ICU. In addition, we aimed to determine the evolution of nutritional parameters such as albumin, prealbumin, transferrin, transferrin saturation index (TSI), cholesterol, and triglycerides on the third day of admission to the ICU. We also aimed to investigate their usefulness as potential biomarkers of mortality and disease severity.The study design is based on a prospective observational follow-up study of critically ill patients with COVID-19. Patients admitted to the ICU with a diagnosis of COVID-19 from March to December 2020 were recruited for this study. The diagnosis of the patients was based on a positive polymerase chain reaction (PCR) test and subsequent RNA sequencing specific for the SARS-CoV-2. The patients were selected at the Hospital Virgen de las Nieves ICU in Granada (Spain). This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the hospital (Ref. number: 149/TEIH/2016). Informed consent was obtained from the patients or their relatives.Patients over 18 years of age who were admitted to the ICU, stayed for at least 3 days and presented a positive PCR test forSARS-CoV-2 were recruited. Those patients under 18 years, those who were pregnant, and those who did not have a positive PCR test even though they presented symptoms compatible with COVID-19 were excluded. Patients were considered critically ill when they had a respiratory failure requiring mechanical ventilation, needed vasopressor treatment (shock), or presented other complications with organ failure requiring monitoring or treatment in the ICU.2 and PaO2/FiO2 were also obtained. One of the limitations of our study is that we have not been able to calculate other nutritional risk indices due to the lack of data on the patient’s body mass.Basic information on the study patients  and their illness and evolution in the ICU  was collected. The clinical parameters collected on the first and third day of admission to the ICU were obtained in the same way. The clinical parameters collected were: Acute Physiology and Chronic Health Evaluation II (APACHE II) scale score , Sequent® autoanalyzer for biochemistry and immunochemistry” Alinity.” The determination methods were enzymatic colorimetry and immunoassay.Serum samples were obtained from patients included in the study on admission to the ICU and on the third day. The samples were centrifuged, and after obtaining the serum, the parameters were determined immediately. The laboratory variables analyzed were (a) biochemical variables: sodium, potassium, creatinine, total proteins, lactate dehydrogenase (LDH), alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), alkaline phosphatase, creatine kinase (CK); (b) hematological variables: hemoglobin, hematocrit, leukocytes, percentage of neutrophils, percentage of lymphocytes, platelets, International Normalized Ratio (INR) and activated partial thromboplastin time (APTT); (c) nutritional variables: glucose, total proteins, albumin, prealbumin, transferrin, TSI, cholesterol, and triglycerides; (d) other variables such as inflammatory markers: fibrinogen, D-dimer, C-reactive protein (CRP), ferritin, and procalcitonin (PCT). These parameters were analyzed using the Abbott Core LaboratoryThe patients received treatment that included medications , respiratory support, and nutritional support  during their hospital stay. The latter was according to the Clinical Nutrition Units guidelines of the hospitals, based on the American Society for Parenteral and Enteral Nutrition (ASPEN) and the European Society of Parenteral and Enteral Nutrition (ESPEN) guidelines . Parentep-value < 0.05 was considered statistically significant.A database was completed, and the statistical tests were processed using the statistical program SPSS version 21.0. The sample size calculation was performed to evaluate the difference between two means for a paired t-test, an alpha of 0.05, a power of 95% and a medium effect size (0.05). A total of 45 participants were required. However, a total of 202 participants were recruited for the present study; since it is a cross-sectional study, it does not involve any additional intervention for the patient, and therefore, ethical aspects are not compromised. Qualitative variables were presented as frequencies and percentages, and quantitative variables as mean ± standard deviation (SD) or median (Interquartile range). The assumption of normality was tested using the Kolmogorov–Smirnoff–Lilliefors test. The variables treated as non-parametric in this study were alanine aminotransferase, aspartate aminotransferase, glutamyl transferase, lactate dehydrogenase, creatine kinase, and days of mechanical ventilation; these variables were presented as median (interquartile range). The relationship of the dichotomous qualitative variables was found using the Chi-square test. The association of quantitative variables with mortality and mechanical ventilation was performed using the Student’s t-test. Quantitative variables between day 1 and day 3 of admission were compared using the Student’s t-test for paired data to check the evolution of the critically ill patients with COVID-19 in the ICU, and the Wilcoxon test was used for non-parametric variables. Correlations between nutritional parameters and clinical variables were established using Pearson’s correlation coefficient for parametric variables and Spearman’s correlation coefficient for non-parametric variables. A p < 0.001). The mean age (SD) was 60.6 (13.6) years. Most patients diagnosed with COVID-19 presented dry cough, fever, asthenia, myalgia, ageusia, and anosmia. Admission to the ICU was due to respiratory failure in 98% of cases. Most patients had one or more underlying diseases. Among patients with comorbidities, 35.1% had diabetes, 54.1% had hypertension, 29.7% had dyslipidemia, 5.4%had chronic kidney disease, 27.0% had chronic obstructive pulmonary disease, and 16.2%had cardiovascular disease.A total of 202 patients requiring admission to the ICU, staying there for at least 3 days, and meeting all the inclusion criteria were recruited. The study sample comprised 147 (72.8%) men and 55 (27.2%) women. Significant differences were observed in the frequency of men and women with this disease admitted to the ICU, being more frequent in men  length of stay in the ICU was 21.6 (16.6) days and the mean (SD) number of days on mechanical ventilation was 17.3 (16.2) days. A total of 156 (77.2%) patients required mechanical ventilation (these patients received vasoactive support), and 44 (21.8%) patients required a high-flow nasal cannula (HFNC). Therefore, a total of 200 (99.0%) patients required ventilatory support. The kidney also received support in our patients; 10 (5.0%) patients required extrarenal purification techniques. A total of 5 (2.5%) patients required parenteral nutrition, whereas 151 (74.8%) patients required enteral nutrition.In the first 59 cases (until May 2020), all patients received the standard treatment recommended without scientific evidence . However, because of the publication of the RECOVERY study in July 2020 , none of2 was found on the third day compared with the first day of admission. Based on the CONUT score, 53.8% of patients were at moderate risk of malnutrition, and 12.1% were at severe risk of malnutrition, which totals 65.9%.Mortality at 28 days was 35.1% (71/202 patients). An association of nutritional and biochemical variables with mortality at 28 days was performed, as shown in p < 0.039) was obtained, so patients with a higher risk of malnutrition died at a higher percentage than those with a lower risk of malnutrition. The mean survival time was calculated for those patients who died. Survival was measured according to the greater or lesser risk of malnutrition. The risk of malnutrition was estimated with albumin values. A cut-off point was defined at 3.1 g/dL so that patients with albumin values higher than or equal to that value are defined as having a low risk of malnutrition, and patients with values lower than that value are defined as patients with a high risk of malnutrition. The median survival was 48.2 (95% CI 39.6–56.8). The association between the two groups with higher or lower risk of malnutrition and survival was calculated. A chi-square of 4.26  the risk of mortality among critically ill patients with COVID-19 [Previous studies conducted on patients with COVID-19 admitted to the ICU have reported a prevalence of malnutrition consistent with our results, reaching 66.7% , but theCOVID-19 , also seCOVID-19 .Albumin is a parameter that provides information on the nutritional status of the patient but not on the disease progression. Albumin is a protein with a long half-life (20 days). Its levels are altered by non-nutritional factors such as infections, diffusion into the extracellular space, and dilution by infused fluids, which explains that albumin levels do not respond quickly to the nutrient replacement conducted in our study. Serum albumin levels decrease in the acute phase of infections and are associated with the intensity of the inflammatory response against them ,52, as wPrealbumin has a short half-life, between 2 and 3 days, and is catabolized in the kidney. It is less affected by liver disease than other serum proteins. It has one of the highest ratios of essential to non-essential amino acids of any protein in the body, making it a distinctive marker for protein synthesis. Prealbumin is, therefore, the preferred marker of protein malnutrition. Due to its short half-life, prealbumin is a suitable marker to assess and monitor rapid changes, inflammatory burden, and nutritional status. Prealbumin can be used to monitor the patient’s nutritional intake during the hospital stay. In the present study, we found increases in prealbumin levels after three days. The association between mortality and prealbumin levels on the third day could indicate that those patients who have responded better to nutritional treatment, evidenced by the increase in prealbumin levels, have a better prognosis. Low serum prealbumin levels indicate the presence of acute inflammation and/or malnutrition, unlike other biomarkers such as CRP and PCT that predominantly reflect the inflammatory burden ,77,78. IIn short, our study indicates that malnutrition in patients with COVID-19, evidenced by low albumin and prealbumin levels, is associated with clinical severity parameters and inflammatory parameters. Low serum albumin levels and other clinical and demographic characteristics may help identify patients at increased risk of severe disease and transfer to the ICU. Moreover, these levels could guide nutritional therapeutic strategies as an important element in ICU care . FurtherThe main finding of this study was the decrease in albumin and the increase in prealbumin three days after ICU admission of critical patients with COVID-19; in addition, a relationship was found between hypoalbuminemia, decreased serum prealbumin concentrations and CONUT, with high mortality in these patients. Measuring albumin upon admission of patients with COVID-19 would help to identify patients with a higher risk of malnutrition, and support measures could be adapted to the patients, improving the prognosis. The response to this enteral or parenteral nutrition can be monitored with prealbumin levels throughout the ICU stay. Therefore, a collaboration between nutritionists and intensivists is truly important for the detection, treatment, and follow-up of critically ill patients with COVID-19 at risk of malnutrition and, consequently, at risk of death.


Rickettsia occur worldwide. The symptoms of this bacterial infection are similar to those of viral infection, and thus, diagnostic accuracy has special clinical importance. One of the commonly used methods for the diagnosis of tick-borne spotted fever is enzyme-linked immunosorbent assay (ELISA), which is based on estimation of the presence of specific IgM antibodies in blood. However, IgA analysis has not been used for the diagnosis of rickettsial diseases thus far. We investigated the diagnostic value of IgA antibody determination using patient sera collected in the Astrakhan region of Russia, where an isolated site of Astrakhan rickettsial fever (ARF) caused by Rickettsia conorii subsp. caspia is located. Our investigation was performed on serum samples collected from 185 patients diagnosed with Astrakhan rickettsial fever from May to October 2019. Western blot analysis revealed that specific IgA antibodies, as well as IgM antibodies, from patient sera bind to high-molecular-weight pathogen proteins with similar masses. The obtained data show that the determination of IgM alone allows for serological confirmation of diagnosis in only 46.5% of cases but that the determination of both IgM and IgA increases this rate to 66.5%. Taken together, the findings show an important diagnostic value of IgA evaluation for tick-borne spotted fever rickettsiosis.Tick-borne spotted fevers caused by IMPORTANCE Tick-borne spotted fevers caused by Rickettsia occur worldwide. The symptoms of this bacterial infection are similar to the symptoms of viral infection, and thus, diagnostic accuracy has special clinical importance. The most serious spotted fever group rickettsiosis is Rocky Mountain fever in the United States, which is caused by Rickettsia rickettsii, and disease complications can lead to hemiparesis, blindness, or amputation. Rickettsia conorii subsp. caspia causes a rickettsial spotted fever named Astrakhan rickettsial fever (ARF). One of the commonly used methods for the diagnosis of tick-borne spotted fevers is ELISA, which is based on estimation of the presence of specific IgM antibodies in blood, though IgA has not been used for the diagnosis of rickettsial diseases thus far. In this study, we showed that both IgA and IgM should be analyzed in the blood serum samples of patients to significantly enhance the accuracy of diagnostics of tick-borne spotted fever rickettsiosis. Rickettsia genus  consists of Gram-negative rod-like, coccoidal, or pleomorphic bacteria without flagella that are 0.3 to 0.5 μm in diameter and 0.8 to 2.0 μm in length , caused by Rickettsia conorii; and North Asian tick typhus, caused by Rickettsia sibirica. Rickettsia conorii subsp. caspia, a causative agent of Astrakhan rickettsial fever (ARF), which is an SFG member, plays an important role in the epidemiology of rickettsial diseases in the Russian Federation  and the typhus group (TG) , 5. Sevegangrene . Tick-boderation , 8.Rhipicephalus sanguineus, which feeds on and is carried by dogs , complement fixation assay, and Western blotting , 10. HowThe symptoms of rickettsial fevers are similar to those of viral infections . Thus, tIn this study, we used blood serum from patients with Astrakhan rickettsial fever. The disease is endemic to the Astrakhan region of Russia. The Astrakhan region is densely populated and located in southern Russia in the lower Volga River basin, where the river flows into the Caspian Sea, forming the largest river delta in Europe, with marshes and lakes .Coxiella burnetii ELISA kits; Vircell, Spain), and (ii) serum from healthy persons who were seronegative for ARF IgG/IgM and Q fever IgG/IgM, with the fact being determined via commercially available assays from Vircell, Spain.In total, 185 samples of serum from 185 patients with ARF  were evaluated. Blood for analysis was taken at various disease stages (days 1 to 16 after infection onset) from May to October 2019. We also include in the study two control groups of sera: (i) sera from Q-fever-seropositive patients, whose seropositivity was determined using a commercially available assay . As only fractions corresponding to the first chromatography peak had antigenic activity , they were used in further experiments as an antigenic substance.Then, the presence of specific IgM and IgA to The patients’ demographic data and results of serum IgA and IgM evaluation are presented in After the first round of screening, the positive blood samples were diluted up to 1:640. The titer and the number of IgA- and IgM-positive samples according to the day from the onset of disease are shown in In addition to IgA and IgM evaluation, IgG presence was studied in the sera. Obtained data and a Venn diagram showing the percentage of total IgA-positive, IgM-positive, IgG-positive, and double IgA/IgM, IgA/IgG, IgG/IgM-positive sera, as well as IgA/IgM/IgG-positive sera, are presented in Fig. S2.Rickettsia conorii , 295 cases of ARF in the Russian Federation were registered in 2014, with 314 in 2015, 299 in 2016, 176 in 2017, and 290 in 2018 (data for 2019 are not yet available). We used serum from 185 patients with ARF collected in the Astrakhan region, Russia, in 2019. As ARF is endemic to the Astrakhan region, it can be argued that the sera of the majority of patients with ARF who sought medical help in 2019 were included in this study.In Russia, approximately 200 to 300 cases of ARF are registered every year. In accordance with the data of the Rospotrebnadzor, the Russian Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing  specific IgA antibodies against causative agents of ARF are present in the blood from the early stage of disease and (ii) IgA evaluation has an important diagnostic value, with detection of both IgA and IgM allowing for a significant increase in the number of serologically confirmed cases.Rickettsia conorii, the causative agent of ARF, is also responsible for MSF in several parts of Europe, Africa, and Asia . and Asia . Moreoveis group –17, it mPreviously published studies have revealed that specific IgM and IgG both predominantly interact with surface rickettsial proteins of high molecular masses, such as OmpA and OmpB –21. Our –Toxoplasma gondii, the bacterium Mycobacterium tuberculosis, and the obligate intracellular bacterium Chlamydia trachomatis  inoculated into damaged skin or mucous membranes, and this is the main route of transmission for typhus group Rickettsia , 10% 2-propanol . The suspension was sonicated using a Soniprep 150 Plus ultrasonic disintegrator , with 6 rounds of sonication for 30 s at a frequency of 23 kHz with a maximal amplitude, with cooling on ice between rounds of sonication. During sonication, the probe of the ultrasound disintegrator was immersed in a glass of water, where the test tube with the treated antigen suspension was placed. The obtained suspension was centrifuged at 10,000 × g for 10 min at a temperature of 4°C. Size exclusion chromatography was performed using a chromatograph Äkta Explorer 10 system equipped with a spectrophotometer detector and a Superdex 200 prep-grade column  at a temperature of 4°C, with a running buffer consisting of 10 mM Tris-HCl, 0.5 M sodium chloride, 10% isopropanol, pH 7.5. Detection was performed at 280 nm.g and at a temperature of 37°C for 5 min, and the resulting upper layer containing LPS-free protein fraction was collected. The procedure was repeated twice.The removal of LPS from the antigen was performed according to a procedure described earlier with some modifications . The chrR. conorii was propagated in embryonated eggs, and formalin-inactivated and lyophilized bacteria served as a starting material. The lyophilized bacteria were suspended in solution containing 10 mM Tris-HCl, pH 7.5, 2 mM MgCl2  and then sonicated using a Soniprep 150 Plus ultrasonic disintegrator , for 1 min at a frequency of 23 kHz with a maximal amplitude. The LPS extraction was performed according to a previously described method with minor modifications , and supernatant was treated at 37°C firstly with Benzonase at a concentration of 2,500 U/mL  for 2 hours and than proteinase K  at a concentration of 0.2 mg/mL overnight. Then, the solution was incubated for 15 min at 95°C and centrifuged . The resulting supernatant was dialyzed against distilled water.ications . The obtProteins in soluble analytes were quantified using a Bradford method  reagent Limulus amebocyte lysate (LAL) assay  according to the manufacturer’s instructions. Experiments and data analysis were performed using an ELx808 microplate reader  equipped with WinKQCL version 5.3.3 software .Bacterial lipopolysaccharide (endotoxin) quantification was determined using the g). Formalin-inactivated and lyophilized Rickettsia conorii bacteria were resuspended in isotonic phosphate-buffered saline , treated with ultrasound using a Branson S-450D instrument , separated via SDS-PAGE, and stained using EZblue gel stain reagent  or transferred onto nitrocellulose membranes . The membranes were probed with serum samples (1:200), and proteins of interest were detected with a horseradish peroxidase (HRP)-conjugated goat anti-human IgA antibody  or goat anti-human IgM antibody  and visualized using the Optiblot enhanced chemiluminescence (ECL) detection kit  according to the provided protocol. Protein markers  were used in experiments to estimate the molecular masses of proteins.Serum samples were pooled from five samples and centrifuged  guidelines. The ELISA was shown to be linear within the analytical range and specific for diagnosis of spotted rickettsial fever using sera from patients with brucellosis, chlamydiosis, Q-fever, and bartonellosis. Robustness and the precision of the assay were verified in the Laboratory of Zooanthroponosis in the Pasteur Institute of Epidemiology and Microbiology, Saint-Petersburg, Russia. The developed assay sensitivity and precision adequately fulfilled all validation criteria as defined in the ICH guidelines.The chromatographically purified antigen was diluted with 0.05 M carbonate buffer (pH 9.6) to a protein concentration of 10 μg/mL, pipetted into 96-well ELISA plates , and incubated at room temperature overnight. The buffer was prepared using sodium bicarbonate and sodium carbonate . To prevent nonspecific binding, 0.05 M carbonate buffer (pH 9.6) with the addition of 1% casein sodium salt was used .Rickettsia-specific IgM and IgG assay is widely used, and the dilution (1:20) is recommended as a diagnostic titer in a commercially available kit   , 41. PosData were analyzed using Microsoft Excel 2016 software  or GraphPad Prism 4.03 software .


The present findings suggest that familial factors, mainly confined to shared environmental influences during the intrauterine period, seem to contribute to the development of GD.Both genetic and environmental influences have been proposed to contribute to the variance of gender identity and development of gender dysphoria (GD), but the magnitude of the effect of each component remains unclear. We aimed to examine the prevalence of GD among twins and non-twin siblings of individuals with GD, using data derived from a large register-based population in Sweden over the period 2001–2016. Register data was collected from the Statistics Sweden and the National Board of Health and Welfare. The outcome of interest was defined as at least four diagnoses of GD or at least one diagnosis followed by gender-affirming treatment. A total of 2592 full siblings to GD cases were registered, of which 67 were twins; age at first GD diagnosis for the probands ranged from 11.2 to 64.2 years. No same-sex twins that both presented with GD were identified during the study period. The proportion of different-sex twins both presenting with GD (37%) was higher than that in same-sex twins (0%, Fisher’s exact test  Gender incongruence is a condition defined in the 11th Revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-11) as “a pronounced, persistent incongruence between the individual’s experience of gender and the sex assigned”, a change from the category Gender Identity Disorder, used in ICD-103. When this mismatch causes discomfort or distress, it is referred to as gender dysphoria (GD), according to the fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5)4. During the last decades, an increase in the prevalence of GD has been reported5 and multiple studies have shown that people with GD have a higher risk for, among others, depression, anxiety disorders, suicide, and less access to health care facilities6. The etiology of gender incongruence and GD remains mostly unknown. In recent years, there has been mounting evidence on biological, more specifically genetic, factors, in addition to psychosocial factors that may underlie the etiology of GD.Gender identity is defined by the American Psychological Association as a person’s deeply‐felt, inherent sense of being male, female or alternative gender, such as gender queer, gender non-conforming, gender neutral, that may or may not correspond to a person’s sex assigned at birth or to a person’s primary or secondary sex characteristics15. These previous studies were all limited by small samples and unclear generalizability due to the selection of participants, and that most of them focused on gender nonconformity and measures of gender identity rather than GD.The twin study design is a valuable tool for the examination of genetic and environmental influences on physical and behavioral traits. The premise of the twin study is that monozygotic twins (MZ) share the same genotype, whereas dizygotic twins (DZ) share an average of 50% of their genes and are therefore genetically as similar as non-twin siblings. According to the equal environments assumption, both types of twins are equally exposed to environmental factors during upbringing. Twin studies that compare concordance rates for MZ twins to those for DZ twins can therefore provide estimates of heritability, i.e., the degree to which genetic factors as well as shared and non-shared environmental factors contribute to the etiology of a phenotype. The role of genetic factors in the development of GD remains unclear, despite several previous twin studies in the last two decades9. Gender nonconformity has been shown to be a predictor of sexuality rather than gender identity and gender incongruence later in life8. Even though gender identity and gender incongruence are more similar to the construct of GD, they are far from the same, as most people with gender incongruence do not develop GD defined as a term in clinical settings. The few twin studies that have examined outcomes related to GD have shown a possible genetic influence, but with varying heritability or concordance rates. Coolidge et al. estimated heritability at 62%10, while Sasaki et al. found a heritability of 41% for adolescent assigned females (aF) and 11% for adult aF, and no genetic effects for assigned males (aM)14. Heylens et al. reported a concordance of 39.1% in MZ twins and 0% in DZ pairs12. Diamond found that the concordance among same-sex DZ pairs was 33% for aM and 23% for aF11.Gender nonconformity in childhood has been examined by assessing phenotypes such as gender role, expression or behavior, with sometimes non-validated instruments14, heterogeneous population11 and parent rating rather than self-rating10.In addition, these studies have some methodological limitations, such as small sample size16. Likewise, a Spanish study on 995 consecutive transsexual probands reported 12 pairs of GD in non-twin siblings17. This study further showed that the probability that a sibling of a transsexual will also be transsexual was 4.48 times higher for siblings of male-to-female than for siblings of female-to-male transsexual probands, and 3.88 times higher for the brothers than for the sisters of transsexual probands.Beyond twin studies, a study has also reported familial cases of GD in 5 non-twin sibling or father-child pairsOverall, evidence for a genetic etiology of GD remains precarious and is mainly compromised by the large heterogeneity in study design, outcome definitions, measures of assessing GD and the assessment of potential confounders across different studies. In the present study, we aimed to examine the prevalence of GD among same- and different-sex twins compared to a non-twin sibling cohort derived from a large register-based population in Sweden over the period 2001–2016.Our source population consisted of all individuals who had received a GD diagnosis according to the 10th version of the International Classification of Diseases (ICD-10) between January 1, 2001, and December 31, 2016, and were at least 10 years old (N = 4374). Individuals who had also been diagnosed with the previous ICD-8 and ICD-9 GD diagnoses (N = 100) or had previously changed legal sex (N = 22) were excluded, given that these were not incident cases. Using the National Patient Register (NPR) and Prescribed Drugs Register (PDR), individuals with surgical or prescribed hormonal gender-affirming treatment prior to first diagnosis were also excluded (N = 166), in order to achieve higher validity of our definition, as some components of gender-affirming treatment could theoretically be given for indications other than gender dysphoria. The decision to have 2001 as the starting year of the follow-up period was partly because of the introduction of ICD-10 in Sweden in 1997 and partly because the outpatient NPR started in 2001. The study was designed in 2016 and that year was, therefore, the end of the follow-up period.18. The following codes were used: G03B (testosterone), G03C, L02AA, G03D, L02AB, G03H, L02BB, G04CB, C03DA01, L02AE and H01CA (antiandrogens and estrogen), L02AE and H01CA (puberty blockers), HAC10, HAC15, HAC20, HAC99, HAD20, HAD30, HAD35, HAD99 and HAE99 (mastectomy and breast reductions), HAD00, HAD10, HAD99, HAE00, HAE20 and HAE99 (breast reconstruction), KFH50, KGV30, KGW96, KGH96, LCD00, LCD01, LCD04, LCD10, LCD11, LCD96, LCD97, LED00, LEE10, LEE40, LEE96, LFE10, LFE96 and KGC10 , DQD40 (larynx surgery).Gender-affirming interventions consisted of hormonal and surgical treatments. Gender-affirming hormonal treatments, including puberty blockers, were identified by ATC codes in the PDR and gender-affirming surgical treatments were identified by NOMESCO Classification of Surgical Procedures (NCSP) codes in the NPR18, showed to be indicative of clinically relevant GD in the Swedish setting when compared with other register definitions.Individuals with GD were defined as those who received one GD diagnosis  in the NPR on at least four occasions, or those who received at least one GD diagnosis followed by gender-affirming treatment. The first definition was utilized in order to include individuals with clinically significant GD who, for a variety of reasons, did not receive gender-affirming treatment, while the latter was used in order not to miss individuals with treated GD but with too short follow-up time to obtain four diagnoses. The definition, as proposed by Indremo et al.We identified 2592 individuals with GD according to our definition, of which 1536 had at least one full sibling, which yielded 2601 siblings .Register data was collected from the Statistics Sweden (SCB) and the National Board of Health and Welfare. All registers in Sweden use the 10-digit National Registration Number, a unique personal identifier assigned to all Swedish residents to allow linkage between registers. When retrieving data from the registers, all personal identifiers are replaced with consecutive numbers to secure anonymity. From the National Board of Health and Welfare, we retrieved data from the NPR, comprising information on primary and secondary ICD diagnoses from visits to specialist outpatient care since 2001 and inpatient health care since 1964, including surgeries, admission, and discharge dates. The inpatient register has nation-wide coverage since 1987, while the outpatient register started with a coverage between 8 and 18% during its first three years and reached 93% coverage by 2015. Sociodemographic data on birth dates, assigned sex at birth, change of legal sex, country of birth, and education level were retrieved from the Swedish Total Populations Register and linked to the NPR and PDR, that includes data on all redeemed medication since July 1, 2005. Data available from the Swedish Multi-Generation register allow the linkage of individuals born in 1932 and later, to parents, siblings and offspring22 which were conducted in RStudio (R version 4.1.2 (2021-11-01), RStudio 2021.09.1 Build 372, tidyverse 1.3.1, data.table 1.14.2).The percentage of probands with a twin or non-twin sibling with GD, totally and by assigned sex in birth, was calculated along with descriptive statistics of the population. Fisher’s exact test and Mann–Whitney U test were used, and a p-value of 0.05 was set as the level of statistical significance. The statistical software environment R was used for data management and statistical analysisOver the period 2001–2016, 2592 full siblings were registered, of which 67 were twins; one set of triplets (one proband with two “co-twins”) was also identified  with GD according to our definition. There were 40 probands with a same-sex twin and 27 probands with a different-sex twin. All 10 twin siblings with GD were twins to a different-sex proband, i.e., among the twins to a proband of the same assigned sex, none had GD . No same-sex twins (and thus no MZ) with GD were identified. The prevalence in different-sex twin pairs was much higher than the prevalence in same-sex twins, as well as than that in non-twin sibling pairs. The different-sex pairs can, by definition, be only DZ twins, whereas the same-sex twin pairs can be either MZ or DZ. Since there were no twin siblings with GD among the 40 same-sex twin pairs, we can conclude that there are no concordant MZ twins, irrespective of the total number of MZ twins. By contrast, among the different-sex  twins there were 10 (37%) with GD. Interestingly, there were only 4 full siblings with GD (0.16%) among the 2534 non-twin siblings, a striking difference in concordance compared to the different-sex twin pairs, considering that they are genetically similar. This difference cannot be explained by genetics or shared childhood environments, pointing to a possible effect of intrauterine exposures.8. However, the true magnitude of the effect of each component has not been clarified so far. Table 12, while some studies have shown moderate genetic influences in gender variation9, and others have shown that heritability accounts for 11%-70% of gender variance15. The large variability in study design, study period, outcome definitions and age of the studied populations across the different study groups may also underlie the heterogeneous findings of previous studies, thus not allowing a direct comparison with the results presented herein.Both genetic, epigenetic and environmental factors have been proposed to account for the variance in gender identity12 That study, which showed a higher concordance in MZ than in DZ twins, was a hybrid study including 20 previously published case reports and 31 twin pairs that had attended a gender clinic and had been clinically assessed for gender identity disorder (GID), the latter being more comparable setting to our setting. The conclusion of higher concordance in MZ (39.1%) was heavily driven by the case reports, which are subject to publication bias. The 31 twin pairs that had a clinically assessed GID consisted of 6 MZ and 25 DZ pairs and the concordance rate in MZ pairs was 16.7% (1 out of 6 pairs) and 0% in DZ pairs. Most of the twins (N = 25) in that study came from a total of 561 children evaluated between 1976 and 2011 at the Gender Identity Service at the Centre for Addiction and Mental Health in Toronto, while three twin pairs came from the Gender Clinic of Ghent University Hospital during the period 1985–2010, from a pool of about 450 attendees and three twin pairs came from the Child and Adolescent Gender Clinic of Ghent University Hospital12. Interestingly, there were no concordant twins in this sample, including the 18 same-sex twin pairs (4 MZ and 14 DZ).Our study had a different outcome than most other studies that reported on outcomes related to gender identity, gender role or cross-gender behavior, i.e., traits and not clinical diagnoses. Indeed, the only study in the literature that included a clinical definition and a clinical setting for GD, similar to ours, was that by Heylens et al.14. In particular, heritability may be a non-fixed parameter with an attenuated effect on gender identity throughout adolescence and adult life23.Moreover, 28 of the twin pairs the study by Heylens et al. were children, and 25 of those were younger than 12 at the diagnosis of GD. In our study, the mean age at diagnosis was 25 and 32 years for discordant and concordant twin pairs, respectively, which consists an interesting major difference between these studies. Even though the age at diagnosis is by no means the same with age of onset of GD, the vast majority of the twins included in the study by Heylens et al. had a clearly early onset of GD and a GD diagnosis in childhood, which was not the case in our study. Of note is that previous studies have stipulated that the effect of genetics on gender identity may be prone to changes during lifetime14, that used self-assessment of GID with criteria based on DSM-IV. The primary outcomes of the study were prevalence and heritability of GID but the authors also reported on the concordance for GID among MZ, DZ, same- and different-sex twin pairs. That study showed a higher concordance among same-sex twin pairs (20.7%) than different-sex twin pairs (8.8%). Although the outcome was similar to ours, the study was population based and the authors recognize, among other potential limitations, the fact that their definition of GID does not necessarily reflect a clinical GID diagnosis.Another study with a similar outcome was the study by Sasaki et al.26. These organizing structures are activated by sex hormones during puberty. Several studies have shown that sexually dimorphic brain structures seem to be more congruent with gender identity than the sex assigned at birth, possibly as a result of the effect of sex hormones, creating, thus, a restricted potential for change28. Indeed, our results of higher prevalence of GD in different-sex twins than that in same-sex twins and non-twin siblings in the present study, if replicated, provide some evidence that intrauterine factors, such as sex hormones produced by the co-twin, may influence the development of GD.Prenatal sex hormones have been considered to play a significant role in brain sexual differentiation through permanent organizing effects during a critical period of fetal brain organization, especially during the last trimester of the pregnancy30.Some previous twin studies have provided evidence for a strong genetic influence in shaping gender identity. Candidate gene association studies have investigated whether functional variants in genes may alter sex hormone signaling, resulting in atypical sexual differentiation of the developing brains of those who will later experience GD26.However, most studies have not used a stringent definition of GD as used in the present cohort, which may explain the null effect of heritability found herein. Indeed, the potential effect of genetics, if any, may apply to the development of gender identity as a trait, but not necessarily to the clinical diagnosis of GD. Future studies need to clarify the intricate interaction between genetics and environmental influences on gender identity and GD31, as almost one third of all twins are MZ32. The distribution of twin pairs in our study, i.e., the ratio between same-sex (n = 40) and different-sex (n = 27) pairs, was 1.5:1, which is in line with that reported by the Swedish Twin Registry31. The observation time from the first GD diagnosis varied from 1 to 15 years and thus we cannot exclude the possibility that some siblings and co-twins with shorter follow-up time could receive a GD diagnosis after the end of this study period and thus be misclassified as discordant. Lastly, the main outcome of this study was a clinically significant GD diagnosis according to our definition; thus, the present findings cannot provide assumptions on the whole picture of gender identity.In the present study we used, for the first time, registry data from a large Swedish population-based cohort over a long period (2001–2016) to study the familial aggregation of GD in the clinical setting, using a validated definition of GD. Our study with 67 twin pairs is larger than the one by Heylens et al., which is the largest available study with similar design studying clinical GD and which included 31 twin pairs. However, both studies are small and subject to random error. Moreover, the small power of the present study did not allow further sub-analyses, e.g., by age, to be conducted. Another limitation of our study was the lack of information about zygosity. However, it would be safe to assume that among the 40 same-sex pairs, approximately half of them are MZ pairsThe present register-based study aimed to examine the concordance for GD diagnosis utilizing a cohort of twin and non-twin sibling pairs in Sweden. We found no concordance among same-sex twins, as well as higher concordance among different-sex twin than non-twin sibling pairs. Beyond any potential limitations and biases, our findings, if replicated, suggest that familial factors, mainly confined to environmental influences during the intrauterine period, are more likely to explain the development of GD. Future studies are deemed necessary to shed light into the intricate interaction between genetic and environmental factors, as well as into the potentially relevant molecular pathways that underlie these associations, if causal.