Loneliness is associated with several adverse mental and physical health outcomes in older adults. Previous studies have shown that a variety of individual-level and perceived area-level characteristics are associated with loneliness. This study examined the associations of objectively measured social and physical neighbourhood characteristics with loneliness.We used cross-sectional data from 1959 older adults (63–98 years) who participated in the Longitudinal Ageing Study Amsterdam  and the Health and Living Conditions of the Population of Eindhoven and Surroundings study  in the Netherlands. Study-specific loneliness scores were harmonised across both cohort studies and divided into tertiles denoting low, medium and high levels of loneliness. Objectively measured neighbourhood characteristics, including area-level percentages of low educated residents, social security beneficiaries and unoccupied dwellings, average income, crime levels and land use mix, were linked to individual-level data. Multinomial logistic regression analyses were conducted to examine the associations of interest.medium=0.54, 95% CI=0.18–1.67; ORhigh=0.67, 95% CI=0.21–2.11).There was no statistical evidence for an association of the included neighbourhood characteristics with loneliness. Although not statistically significant, the observed associations suggested that participants living in neighbourhoods with more heterogeneous land use mix were less likely to have a medium and high level of loneliness than those living in more homogeneous neighbourhoods in terms of land use mix (ORThe results indicate that the included objectively measured social and physical neighbourhood characteristics are not associated with loneliness in old age. The results of such studies may provide additional guidance for urban planners and policymakers in designing more appropriate and contextualised strategies for interventions to prevent or reduce loneliness.Although a large body of evidence on individual-level determinants of loneliness, such as low socioeconomic position and poor functional status, exists, only a few recent studies have focused on the associations between environmental characteristics and loneliness in older adults.2 15–17 On the other hand, the homogeneous composition of socioeconomically deprived neighbourhoods  may enhance social cohesion and interactions among residents, which may protect against loneliness.18 Neighbourhood crime may undermine older adults’ feelings of trust and may lead them to feel alienated from other people.8 Neighbourhood crime is therefore expected to be positively associated with loneliness in old age. Access to neighbourhood resources, such as supermarkets, sport facilities and parks, has been shown to be important for older adults in performing activities to meet daily needs, and contributes to initiating and maintaining social interactions with community members.19 Indeed, people who use more resources report less loneliness.11 Higher levels of land use mix, reflecting the availability of various destinations and neighbourhood resources in the local living environment, are therefore expected to be associated with lower levels of loneliness among older adults.20A variety of objectively measured social and physical neighbourhood characteristics, such as socioeconomic conditions, crime and land use mix, has been suggested to affect loneliness in old age. However, empirical evidence on the strength and direction of these associations is lacking. Older adults living in socioeconomically deprived areas, characterised by lower area-levels of education and income and higher percentages of social security beneficiaries and unoccupied dwellings, may be faced with lower levels of neighbourhood quality and other environmental challenges, such as nuisance, in their local living environment. This may make them less likely to participate in the community and to invest in social relationships.This study extends previous research by examining initial cross-sectional associations of objectively measured social and physical neighbourhood characteristics, including area-level percentages of low educated residents, social security beneficiaries and unoccupied dwellings, average income, crime levels, and land use mix, with loneliness in older adults, independent of individual-level characteristics.21–24 In short, the MINDMAP project aims to identify the opportunities and challenges posed by urban environmental characteristics for the promotion and management of mental well-being and cognitive functioning of older individuals, by bringing together longitudinal studies from various countries, enriched with area-level environmental exposures and social and urban policy indicators.21 For this study, LASA (wave 2011/12) and GLOBE (wave 2014) were selected, because harmonised data on loneliness and the environment were available in the MINDMAP project for these waves in these two Dutch cohort studies. To our best knowledge, nothing major changed in terms of loneliness interventions/policies during the 2-year period between the LASA and GLOBE wave. The LASA study was started in 1992, and is an ongoing longitudinal cohort study in the Netherlands that studies the determinants, trajectories and consequences of physical, cognitive, emotional and social functioning in older adults.22 23 The GLOBE study was initiated in 1991, and is an ongoing longitudinal cohort study that has been designed to assess mechanisms and factors explaining socioeconomic inequalities in health in the Netherlands.24We used data from older individuals enrolled in the Longitudinal Ageing Study Amsterdam (LASA) and the Health and Living Conditions of the Population of Eindhoven and Surroundings (GLOBE) study, two Dutch cohorts participating in the MINDMAP  project. The MINDMAP project, LASA and GLOBE have been described in detail previously.25 Because this study focuses on older adults and the LASA respondents were at least 63 years old at wave 2011/12, 2059 GLOBE respondents aged 63 years and older were eligible for inclusion in this study. Of all 3367 eligible individuals, 1408 (41.8%) individuals were excluded due to missing data on loneliness (n=110), on at least one neighbourhood characteristic (n=918) and on at least one confounder (n=380). As a result, the analysed sample consisted of 1959 individuals with complete data. The included and excluded group did not significantly differ in loneliness, neighbourhood income and crime. On average, area-level percentages of low-educated residents and unoccupied dwellings and land use mix were higher in the neighbourhoods of the excluded group, while area-level percentage of social security beneficiaries was higher in the neighbourhoods of the included group. Together, these data do not suggest underrepresentation of, for example, relatively deprived groups. The MINDMAP project, LASA and GLOBE were approved by the Ethical Review Boards of their respected institutions and are conformed to the principles embodied in the Declaration of Helsinki.LASA wave 2011/12 and GLOBE wave 2014 included 1522 and 4851 respondents, respectively. A total of 1308 LASA-respondents (63–102 years) who participated in the main face-to-face interview were included in the MINDMAP project.26 27 The study-specific scale score was harmonised using tertiles and dummy-coded  into: low level of loneliness (reference category), medium level of loneliness, and high level of loneliness.In LASA and GLOBE, loneliness was measured using the 11-item and six-item version of the De Jong Gierveld Loneliness Scale, respectively.In LASA, the scale score ranged from 11 to 33 and tertiles were defined as low (score=11), medium (score=12–14), and high (score=15–33). Scores were calculated for respondents with data on at least nine items. If one or two items were missing, scores on the completed items were summed and divided by the number of items completed and multiplied by 11. If respondents missed three or more items, scores were not calculated and coded as missing.In GLOBE, the scale score ranged from 6 to 30 and tertiles were defined as low (score=6–9), medium (score=10–12), and high (13–30). Scores were calculated for respondents with data on at least five items. If one item was missing, scores on the completed items were summed and divided by five, then multiplied by six. If respondents missed two or more items, scores were not calculated and coded as missing.28–32 In the Netherlands, six-digit postal code areas (average area size: 0.0025 km2), four-digit postal code areas (average area size: 8.3 km2) and neighbourhoods (average area size: 3.1 km2) are geographically delineated areas and include, on average, approximately 15, 1870 and 630 households, respectively.33 Data on average income, percentage of social security beneficiaries, and percentage of unoccupied dwellings were retrieved at the neighbourhood-level. As described elsewhere, Geographic Information System software  was used to link these neighbourhood data to individual-level cohort data using residential six-digit postal codes of respondents.33 Data on the percentage of social security beneficiaries and crime were also retrieved at the neighbourhood-level, and were linked to individual-level cohort data using neighbourhood codes of respondents. The area-level data on educational level were retrieved at the four-digit postal code area-level and were linked to individual-level cohort data using residential four-digit postal codes of respondents. The area-level data on income and crime were collected for the year 2011. The area-level data on social security benefits, unoccupied dwellings and educational level were collected for the years 2010, 2012 and 2014, respectively.Nationwide data on social neighbourhood characteristics were retrieved from Statistics Netherlands.Educational level was measured as the percentage of residents in four-digit postal code areas who attained a low educational level .28Average income was measured as the rounded average gross year income in €1000,- of residents in the neighbourhood.29Percentage of social security beneficiaries was measured as the percentage of residents in the neighbourhood receiving general social assistance according to the Social Assistance Act and Work and Social Assistance Act.30Percentage of unoccupied dwellings was calculated by dividing the number of unoccupied dwellings in the neighbourhood by the total neighbourhood housing stock, and multiplied by 100.31Neighbourhood crime was measured as the number of criminal offences  per 1000 residents in the neighbourhood.32Land use mix was calculated in 1000-m buffer zones around the respondent’s home address using the following entropy formula:20 34 The area size of an 1000-m buffer zone is 3.1 km2. This area size corresponds with the average area size of a Dutch neighbourhood.33in which p is the proportion of land area within the 1000-m buffer zone devoted to a specific land use and N is the total number of land use categories within the 1000-m buffer zone, which was 10 in this study.35 The land use mix score represents a measure of heterogeneity and ranges from 0 to 1, with 0 representing no land use mix and 1 representing a perfect mix of land use categories.20 34For the assessment of the land use mix score, the proportions of the following 10 land use categories within the 1000-m buffer zone were assessed using Urban Atlas 2012 data in QGIS Desktop : built-up areas, industrial and commercial areas, infrastructure, ports, urban green areas, sports and leisure facilities, agricultural land, other natural areas, blue spaces, and other areas.Age in years, sex , study , partner status, educational level and household income were included as individual-level confounders in the analyses.Partner status of respondents was assessed by asking whether they were married or living with a spouse or a partner in a common household . Educational level was assessed as the highest level of education completed by the respondent in accordance with the International Standard Classification of Education .36Household income was measured by indicating whether the net annual household income was above or below the net mean household income  in the Netherlands .37Population density was included as area-level confounder in the analyses. Population density was measured as the number of residents per square kilometre in the neighbourhood and categorised into quartiles, with higher quartiles representing higher population density.28Characteristics of the study sample and the neighbourhoods were presented using descriptive statistics. Pearson correlations were assessed between all neighbourhood characteristics.Pooled multinomial logistic regression analyses were conducted to examine the association between each neighbourhood characteristic and loneliness, separately. All associations were examined in models constructed step by step. In Model 1, the associations were adjusted for age, sex and study. In Model 2, the associations were additionally adjusted for all other individual-level characteristics . In Model 3, the associations were additionally adjusted for area-level population density. All statistical analyses were performed in RStudio .LASA=417 and nGLOBE=1542) participants was 72.8 (SD=6.7) years with an age-range of 63–98 years , 1.8% (SD=1.6%), and 4.2% (SD=3.2%), respectively . The meamedium=1.02, 95% CI=1.00–1.03; ORhigh=1.02, 95% CI=1.00–1.03). After additional adjustment for all individual-level confounders  and population density , these associations were attenuated.Results from the multinomial regression analyses are presented in high=0.96, 95% CI=0.94–0.98). After additional adjustment for all individual-level confounders  and population density , this association was attenuated.Participants who lived in neighbourhoods with a high average income were less likely to have a high level of loneliness than a low level of loneliness . After additional adjustment for all individual-level confounders  and population density , this association was attenuated.Participants who lived in neighbourhoods with relatively many social security beneficiaries were more likely to have a high level of loneliness than a low level of loneliness . This was also found after additional adjustment for all individual-level confounders  and population density .Although not statistically significant, the age-, sex- and study-adjusted associations suggested that participants who lived in areas with higher levels of land use mix were less likely to have a medium and high level of loneliness than a low level of loneliness .This study aimed to examine the associations between objectively measured social and physical neighbourhood characteristics and loneliness in older adults. No statistical evidence was found for an association of objectively measured social neighbourhood characteristics, including area-level percentages of low educated residents, social security beneficiaries and unoccupied dwellings, average income, and crime levels, with loneliness in old age. Although not statistically significant, the observed associations suggest that older adults living in neighbourhoods with more heterogeneous land use mix were less likely to have a medium and high level of loneliness than those living in more homogeneous neighbourhoods in terms of land use mix.21Compared with previous studies, which have mainly focused on individual-level and perceived area-level determinants of loneliness, an innovative aspect of this study is the focus on objectively measured social and physical environmental characteristics in relation to loneliness in older adults. An additional strength of this study is the use of data from the MINDMAP data platform that allowed us to include harmonised data from two large-scale population-based cohort studies in the Netherlands, which have increased the statistical power of the analyses.28–32 These assessments might not exactly characterise the spaces within which participants actually move and therefore might create spatial uncertainty relating to actual exposure, as well as not accounting for within- and between-person heterogeneity in spatial habits.38 This may have led to an underestimation of the associations and may have concealed associations between social neighbourhood characteristics and loneliness. If the social-environmental data would have been examined in personalised exposure areas, as has been done for land use mix in 1000-m buffer zones around participant’s home addresses, we might have found stronger associations between the social neighbourhood characteristics and loneliness. Another caveat concerns temporal mismatches between area-level data and cohort data. However, it is not likely that these mismatches have led to an underestimation of associations in the present study, because the included area-level data are considered to be stable over a time window of 4 years.39There are several caveats. First, the social-environmental data that were used in this study are related to administrative areas.38 a more substantial explanation for our null findings could be that, in particular, perceptions of social neighbourhood conditions are more important determinants of loneliness or behaviour and feelings that affect loneliness, such as social participation and feelings of trust, safety, social cohesion and neighbourhood attachment, than objective neighbourhood conditions of which individuals may not always be aware of.40The results show that the social neighbourhood characteristics included in our analyses are not associated with loneliness in older adults, after adjusting for individual-level socioeconomic and household indicators, and area-level population density. In addition to methodological issues related to exposure,11 The findings of this study suggest that older adults living in neighbourhoods with higher levels of land use mix, which means that various resources and destinations are available in their local living environment, are less likely to have stronger feelings of loneliness. Access to neighbourhood resources invites people to use them and to participate in the community and to initiate and maintain social interactions among community members, which may protect against loneliness.19 Although the association of land use mix with loneliness was not statistically significant, it might be worthwhile to investigate this association in future studies and to examine the pathways between land use mix and loneliness.Previous research found that older adults who use more neighbourhood resources are less lonely.This cross-sectional study shows that the included social and physical neighbourhood characteristics are not associated with loneliness in old age. Studies with a longitudinal design are needed to assess whether changes in environmental characteristics are actually associated with changes in loneliness in old age. By combining Geographic Information System data and Global Positioning System data, future studies could assess the actual activity space and environmental exposure of participants, which could help to better examine the actual impact of environmental characteristics on loneliness in older adults. Furthermore, to obtain a more comprehensive understanding of these associations, future research could examine how objective and perceived neighbourhood characteristics interact and determine loneliness in old age. Additionally, to examine whether the findings of this study are generalisable to other contexts, future research could be conducted in neighbourhoods across different countries and cultures.The prevalence of loneliness in old age is substantial, and loneliness is associated with several adverse mental and physical health outcomes in older adults. A variety of individual-level and perceived area-level characteristics are associated with loneliness.The present study extends the current literature by examining associations of objectively measured social and physical neighbourhood characteristics with loneliness in older adults. This study did not find statistical evidence for an association of objectively measured social neighbourhood characteristics, including area-level percentages of low educated residents, social security beneficiaries and unoccupied dwellings, average income, and crime levels, with loneliness in old age. Although not statistically significant, the observed associations suggest that older adults living in neighbourhoods with higher levels of land use mix, which means that various resources and destinations are available in their local living environment, are less likely to have stronger feelings of loneliness.


Age, gender, civil status, education, employment, place of work and living area were all significant factors for psychosocial health across the countries. Differences in mental health, quality of life, well-being and loneliness were found between the countries in both April and November 2020, while time did not contribute to reducing the toll in any of the four countries over the nine-month period.This cross-national study explored stability and change in mental health, quality of life, well-being and loneliness during the early stage and nine months after the implementation of COVID-19 pandemic social distancing measures and periodic lockdowns as adjusted by demographic variables. In the USA, the UK, Australia and Norway, 7284 individuals responded to the invitation to take part in two cross-sectional web-based surveys (April and November 2020), including questions about sociodemographic variables and psychosocial outcomes. Independent  During the COVID-19 pandemic, the main policy for public behavior has been periodic lockdowns and social distancing . Many peA review article based on 19 cross-sectional studies that focused on the association between the COVID-19 pandemic and mental health revealed that being female was associated with a higher degree of mental distress than among men  and thatAccording to Xiong et al. , the COVIn the initial phase after the COVID-19 outbreak, Wang et al.  performeHowever, most studies with a focus on mental health, quality of life and well-being due to the COVID-19 outbreak are cross-sectional studies that do not address how time may impact psychosocial health. Based on this and the lack of knowledge related to the importance of time on psychosocial health, we performed two consecutive cross-sectional studies that allowed for a comparison between timepoints using appropriate statistical tools.The aim of this study was to explore stability and change in mental health, quality of life, well-being and loneliness from the early stage and nine months after the COVID-19 pandemic outbreak as adjusted by demographic variables in two cross-national samples.The study used a cross-sectional survey design; the survey was distributed through social media in each of the involved countries in April (T1) and November (T2) 2020. The same survey was used at both timepoints with minor adjustments for the second administration of the survey ,9. The lTo be included in the study, participants had to be 18 years or older, understand Norwegian or English, live in Norway, the USA, the UK or Australia and be able to access the electronic survey. There were no exclusion criteria. Altogether, 7284 individuals participated by responding to the online form. Sociodemographic variables included country of residence ; gender , female (2)); age (18–24 years (1), 25–34 years (2), 35–44 years (3), 45–54 years (4), 55–64 years (5), 65–74 years (6), over 75 years (7)); highest completed level of education (Bachelor’s degree or below (1), Master’s/Doctoral degree (2)); civil status , yes (1); having children, yes (1), no (0)); employment (full-time (1) or part-time employment (2)), place of employment/workplace , trade industry (2), construction industry (3), education (4), public safety (5), other (6)) and living area , town/suburb (2), city (3)) .General Health Questionnaire 12 (GHQ-12) is widely used as a self-report measure of mental health ,16,17. ACantril’s self-anchoring ladder (CL) is a self-administered overall quality of life (QoL) questionnaire with one question, ‘How is your life?’, asking the person to rate his or her present experience of life on a scale anchored by their own identified values  and usedPsychosocial well-being (PSW) assesses an individual’s psychological experience of well-being and consists of ten items. The measure includes five positive and five negative statements with item scores ranging between 1 (best well-being) and 5 (worst well-being) . CronbacThe Loneliness Scale  consistsMental health, overall quality of life, psychosocial well-being and overall loneliness were calculated for each included demographic subgroup  within each country at each timepoint. t-test. The Pearson correlation coefficient r was used to assess the strength between categorical data. Depending on the number of group categories, group differences were examined using the one-way analysis of variance (ANOVA) and the independent In order to examine whether changes in mental health, overall quality of life, psychosocial well-being and loneliness were different between the four countries, the time × country variable was computed in SPSS and entered as a predictor (Step 2) in the regression analysis. The sociodemographic variables gender, age, civil status, having children, education and employment were included in Step 1. In cases of significant interaction effects (time × country), the subsequent regression analyses were performed separately for each country.Generalized linear models (GLM) were used to conduct multiple linear regression analyses on psychosocial measures  by demographic variables and time (November vs. April) in each country. Estimated marginal means were used to present mean responses by time after adjusting for all demographic variables in the model. Unstandardized beta (B) and standard error (ES) were reported for measures of the effect of associations between the independent and dependent variables. Statistical significance was set at <0.05. Missing values were handled by case-wise deletion.The data collected in this study were anonymous. The researchers adhered to all relevant regulations in their respective countries concerning ethics and data protection. The study was approved by OsloMet (20/03676) and the regional committees for medical and health research ethics  in Norway, reviewed by the University of Michigan’s Institutional Review Board for Health Sciences and Behavioral Sciences (IRB HSBS) and designated as exempt (HUM00180296) in the USA, by Northumbria University Health Research Ethics Committee (HSR1920-080) in the UK, as well as in Australia (HSR1920-080 2020000956). The total sample from Survey 1 (T1) was 3810 , includin = 547, 16%), the USA , the UK  and Australia ; altogether, 7284 individuals from both surveys. Overall, there were more women than men in the total sample  and the individuals who responded were spread across age groups. Sixty percent lived with a spouse or partner, 60% had children and 64% had a Bachelor’s degree or lower levels of education. Sixty-eight percent had either a full-time or a part-time job, and of these individuals, 18% worked in healthcare, 4%—in trade industry, 14%—in education and 7%—in other critical functions in the society ; 24% reported other areas of work. Eleven percent lived in rural areas, 51%—in a town or a suburb, while 38% lived in a city.The participants included in Survey 2 (T2) were 3474 individuals from Norway (p < 0.001) and poorer well-being (p < 0.001) than men. The lower the age of the individual, the higher the level of emotional distress (p < 0.001), poorer quality of life (p < 0.001) and well-being (p < 0.001) and higher the feeling of loneliness (p < 0.001). Participants living together with a spouse or partner reported less emotional distress (p < 0.001), better quality of life (p < 0.001) and well-being (p < 0.001) and less loneliness (p < 0.001) than their counterparts. The same overall pattern was found among those who had children , had a Master’s degree or higher education  and were in full-time employment . People working in trade industry exhibited the highest level of emotional distress (p < 0.001) and felt most lonely (p < 0.01), while those living in rural or farming areas reported less emotional distress (p < 0.001), better quality of life (p < 0.001) and a higher level of well-being (p < 0.001) than those living in towns/suburbs or cities.p < 0.001) and at the same level in the three other countries in November 2020 compared to April 2020 (p < 0.001). Alternatively, a higher number of individuals reported poorer quality of life on the case level for quality of life in November compared to April , and in line with this, the mean scores of quality of life (CL) were significantly reduced in all countries (p < 0.001) except the UK. Psychosocial well-being was reported significantly poorer in the UK and Norway in November compared to April (p < 0.001), while the Australian and USA samples reported significantly better well-being  in the same timeframe  when controlled for the included demographic variables as described above (data not shown).When assessing whether changes in the outcomes across the two timepoints differed between the countries, regression analysis showed that the time by country interaction effects were significant for all outcomes  were used to conduct multiple linear regression analyses on the psychosocial measures  by demographic variables and time (November vs. April) in each country, both demographic variables and time of survey explained a significant effect of the values of GHQ, CL, PSW and Loneliness . The analysis showed that age and civil status were the independent factors that showed the strongest unique effects across all the countries. p < 0.01 and p < 0.01, respectively) and of poorer well-being in the UK samples (p < 0.01)  . The estimated marginal mean responses by time after adjusting for all demographic variables in the model are shown in The most important findings in this paper were that the levels of emotional distress, quality of life, well-being and loneliness either got poorer or were stable but at a poor level over time. The Norwegian sample reported a higher level of emotional distress, decreased quality of life, poorer well-being and higher level of loneliness over the time on the group level. All outcome variables were mainly stable in the three other countries in the same time period. Nonetheless, the Norwegian sample had significantly better scores in all outcome measures than the comparing countries. In ordinary times, a high level of emotional distress (GHQ) may indicate reduced functional impairment equal to a mental disorder. However, in this particular study, it is clear that these findings are associated with the ongoing COVID-19 pandemic. Even if the level of caseness is reduced compared to the reports in April 2020, it is still much higher than the 20% within a year in all countries due to mental distress before the COVID-19 outbreak ,43,44,45According to the OECD , the aveThe fact that the Norwegian sample reported a higher level of emotional distress in November than in April does not remove the significant differences between the countries in November. While the Norwegian sample still reported significantly less emotional distress than the other countries, it might have been attributed to cultural differences and the level of supports provided in the welfare systems. Differences in perceived trust in how political systems, government and health authorities handle the pandemic and meet challenges due to the COVID-19 pandemic may also contribute to these results similar to what is described in previous research .Prior research, e.g., by Rosenfiled et al. , underscAge and civil status in particular were variables associated with significant contribution to emotional distress, quality of life, well-being and loneliness across the countries. Higher age seems to be a buffer against higher emotional distress, poorer quality of life, well-being and loneliness. The impact on mental health seems to take a higher toll on younger people and this finding is in line with research that points out that younger people have more negative emotions and experience stressful events as more unpleasant than older people ,7, and oLiving with a spouse or a partner was associated with less emotional distress, better quality of life and well-being and less loneliness and could simply be explained by the support of having a close person to confide in, and that living with a spouse or a partner is a buffer against psychosocial stress. This study did not examine mental health with regard to living with other people or a pet. It is to be assumed that for some of those not living with a spouse or a partner living with someone or having a pet would be of big importance. Respondents with a Bachelor’s degree or below exhibited higher levels of emotional distress, poorer quality of life and well-being and more loneliness than those with a Master’s degree or higher education. However, when controlled for other demographic variables, education had a significant contribution to mental health, well-being and loneliness in the Australian sample, and to well-being and loneliness in the USA sample. While education did not contribute to any significant outcomes in the Norwegian sample, it could be explained by an overall higher educational level in the Norwegian population than, for example, in the sample from the USA. An alternative speculation may be that health inequalities by education level are less pronounced in Norway. On the general level, education is assumed to buffer the effect of a disability, in this case associated with a global situation. A reason for this might be the more educated are more equipped, including cognitive skills, to cope with the consequences of a particular cause of stress .The individuals who were employed full-time exhibited less emotional distress, better well-being and less loneliness than those who were employed part-time. In the multiple hierarchical regression, employment had a significant unique contribution to reduced emotional distress in all the countries over time, which is in line with former cross-sectional studies  and formHealthcare workers exhibited a high level of emotional distress, reduced well-being and loneliness, which is consistent with former studies , but in Interestingly, living in a rural or farming area had a positive impact on emotional distress, quality of life and well-being. This does not necessarily mean that there were no infections in these areas, but it may suggest that the toll of the pandemic was smaller in rural areas than in suburbs or cities Time contributed significantly to emotional distress (GHQ). This may be understood as the Norwegian sample was the only country with significantly different scores between April and November 2020. In the countries with higher but stable scores, time had no significant impact. On the other hand, it is to be concluded that, overall, time did not contribute to lowered levels and reduce the toll on mental health, quality of life, well-being or loneliness in any of the four countries.The pandemic is still causing social distance, quarantine, and isolation protocols in many places around the world. More research is needed on how COVID-19-related risk variables and concerns are associated with time so that appropriate support systems could be put in place to improve public psychosocial health. In light of this, health and social workers should be aware of the importance of using professional skills to promote better psychosocial health in meeting individuals with needs, such as young people and those living alone. For example, and as underscored by Dorado et al. , social The strength of this study was the cross-national comparison and the relatively high number of respondents. However, some limitations should be taken into consideration. First, the data were collected using cross-sectional online surveys, which means assumptions about causations cannot be made, only associations. Secondly, the respondents received invitations to participate through social media, and the responses thereby do not include individuals that do not use social media. This may limit the ability to generalize the results to the general population in the four countries included in the study. The sample included respondents from all age groups, but fewer from the oldest age group, as well as more women than men. This may represent a skewed population. To avoid this, in future studies, more sophisticated sampling methods could be used. Third, our findings imply that social distancing policies are significant for psychological distress, quality of life, well-being and loneliness. The degree of disease outbreak and social distancing policies differed between the countries and the states in, for example, the USA and may have included additional factors on individual levels, as well as on the regional and country level. For example, the restriction levels in the four countries at T1 and T2 varied between moderate and high levels, including closures of schools and universities, variations of “stay at home” orders and closedowns, restrictions regarding gatherings of people from different households, closed borders, directions to stay at home other than for essential reasons and no unnecessary travel. Both in April and November, the number of infected people and deaths varied between the countries with the USA and the UK having higher rates than Norway and Australia, and both surveys, T1 and T2, took place before the vaccination rollouts. These issues could have influenced the respondents’ answers. Overall, significant differences in psychosocial health across Norway, the USA, the UK and Australia emerged in this study. Individuals in the younger age groups struggled more than the older groups with mental health, quality of life, well-being and loneliness. Being female, not married or cohabiting, lower level of education, unemployed, working in healthcare or the trade industry and living in urban areas are all factors significantly associated with poorer psychosocial health across the countries. However, when adjusting for months into the pandemic (time) and other demographic variables, age and living together with a spouse or a partner contributed most to the psychosocial outcomes in all the countries. Differences in mental health, quality of life, well-being and loneliness were found between the countries in both April and November 2020. The toll of the COVID-19 pandemic on mental health did not reduce in any of the four countries between April and November 2020.


The Neutrophil-to-Lymphocyte Ratio (NLR) and the Platelet-to-Lymphocyte Ratio (PLR) are inflammatory biomarkers for several diseases, such as cancer and cardiovascular morbidities; however, there are currently few studies on kidney diseases. We aimed to evaluate nondialysis patients and determine the association of NLR and PLR with inflammation in these patients.  A prospective cross-sectional study was conducted with 85 patients at different stages of chronic kidney disease (CKD), treated at the Kidney Disease Prevention Center of the University Hospital of the Federal University of Maranhão. This study included adult nondialysis patients diagnosed with CKD. The participants' blood samples were collected for a high-sensitivity C-reactive protein (hs-CRP) test and blood count. They were divided into two groups according to the presence or absence of inflammation based on the hs-CRP value (<0.5 mg/dL). NLR and PLR were calculated based on the absolute number of neutrophils, lymphocytes, and platelets and were compared between them and with hs-CRP. Statistical analysis was performed using the Stata software, with the Shapiro–Wilk, Mann–Whitney, Spearman's Correlation, and receiver operating characteristic curve tests. This study was approved by the local ethics committee. n = 64) and without inflammation (n = 21). The mean age was 61.43 ± 14.63 y. The NLR and PLR values were significantly different between the groups with and without inflammation . However, only PLR showed a significant positive correlation with hs-CRP (p=0.015). The best cutoff point for NLR to detect inflammation was 1.98, with 76.19% sensitivity and 48.44% specificity. For PLR, it was 116.07, with 85.71% sensitivity and 51.56% specificity. There was no significant difference between the area under the NLR and PLR curve  for this population.  The participants were categorized into two groups: with inflammation ( This study showed that PLR was positively correlated with hs-CRP in nondialysis CKD patients and can be used to identify inflammation in this population. Chronic kidney disease (CKD) is characterized by progressive and irreversible loss of renal function. It is a major health issue worldwide, which leads to end-stage renal failure (ESRD) . A systeCKD has high morbidity and is associated with increased cardiovascular mortality, with 5 to 10 million annual deaths worldwide, followed by infections , 4. InflThe chronic systemic inflammation in the CKD, sometimes referred to as low-grade chronic inflammation, is characterized by 2-3-fold increase of acute-phase protein and inflammatory mediators, slow developing, persistent, and of multifactorial origin , 8.While the source(s) of chronic inflammation in CKD can vary, the negative implications of elevated inflammatory markers are clear, such as reduced renal function and high chances of mortality. Within the predialysis CKD population, the prevalence of inflammation is great and is an important indicator of patient health and outcome , 6.α [It is predicted that early and specific detection of inflammation might improve the quality of life of those and decrease the rate of mortality and morbidity . Nowadayα , 10.However, in the present socioeconomic status, it is important that we seek cost-effective biological markers. Previous studies have demonstrated that the neutrophil-to-lymphocyte (NLR) ratio and platelet-to-lymphocyte ratio (PLR) have begun to be used with an indicator of systemic inflammation and have been widely studied in malignancies –15, hypeNLR and PLR are inexpensive, convenient, and measured easily and have demonstrated utility in stratifying mortality from cardiac events  and progTherefore, the present study was designed to evaluate nondialysis patients and sought to determine the relationship of NLR and PLR with inflammation in these patients.This was a cross-sectional study conducted with nondialysis chronic kidney disease (CKD) patients treated at the Kidney Disease Prevention Center (CPDR) at the University Hospital of the Federal University of Maranhão (HUUFMA).A nonprobabilistic sample was formed by male and female adult and elderly patients with a diagnosis of CKD who were not on dialysis and were treated at the CPDR outpatient clinic during a 12-month period (September 2016 to August 2017). The exclusion criteria were pregnant women, patients with amputations, with only one kidney, with a history of hospitalization and surgeries  in the 3 months prior to the beginning of data collection, and history of dialysis, liver failure, chronic consuming diseases , and/or infectious diseases.Participants, regardless of skin color or financial status, were selected by a doctor during outpatient care and referred to pharmaceutical assistance. During the pharmaceutical appointment, patients were informed about the study and invited to participate. A total of 101 patients of both sexes with a diagnosis of nondialysis CKD agreed to participate in this study . HoweverThis study was conducted in two stages. The first stage was an interview to collect demographic, socioeconomic, lifestyle, and comorbidities data. In the second stage, data were collected from electronic medical records and blood samples to perform the hs-CRP and complete blood count test.The hs-CRP test was performed using serum with the immunoturbidimetry methodology with a Roche Cobas 6000 analyzer . The complete blood count was performed using the Advia 2120 Hematology System .The levels of urea and creatinine were obtained from the HUUFMA electronic records. The glomerular filtration rate value was calculated using the Chronic Kidney Disease Epidemiology Collaboration Creatinine Equation (2009) through the mobile application eGFR , based on data from the National Kidney Foundation. NLR and PLR were obtained by dividing the absolute neutrophil count by lymphocytes and between platelets and lymphocytes, respectively.Participants were categorized according to the presence of low-grade inflammation based on the hs-CRP value, a gold-standard method for the detection of inflammation. As the cutoff point for hs-CRP, participants with no inflammation were those who obtained a result <0.5 mg/dL .The comorbidities found were arterial hypertension, diabetes mellitus, and cardiovascular disease and were determined by previous self-reported diagnosis.U test for the evaluation of NLR and PLR as inflammatory markers in people with CKD, and the coefficient of Spearman's correlation to compare the numerical distributions of each marker with the hs-CRP. The Youden test was used in the receiver operating characteristic (ROC) curve to identify the cutoff points of the markers with the best sensitivity and specificity. The area under the curve (AUC) was used to assess the markers' performance and comparison. The level of significance used for the statistical tests was 5%. The data are presented in tables and graphs.Data were tabulated in Microsoft Excel , and the statistical analysis was performed using Stata software . Initially, descriptive analyses were performed using absolute and relative frequencies for categorical and mean variables, standard deviation for numerical variables with normal and median distribution, and interquartile range for those without normal distribution. Normality was assessed using the Shapiro–Wilk test. For inferential analysis, the chi-square and Fisher's exact tests were used to compare categorical variables between groups with and without inflammation, the Mann–Whitney This study was approved by the local ethics committee under number 2.015.866, with all participants signing an informed consent form.The final sample consisted of 85 participants categorized into two groups according to the presence of inflammation (24.7%) and absence of inflammation (75.3%). The mean age was 61.43 ± 14.63 y, with a predominance of female patients (55.29%). The most prevalent comorbidity found was arterial hypertension (92.94%) .p < 0.001). The levels of urea and creatinine and the glomerular filtration rate did not show significant differences between the groups. NLR and PLR showed median levels of 2.11 (1.41–3.36) and 121.95 (94.72–157.45), respectively, with a statistically significant difference between groups for NLR (p=0.045) and PLR (p=0.004)  was used as a cutoff point to separate the group with and without inflammation, as described in the methodology. The median values of the group without inflammation were 0.09 mg/dL and 0.88 mg/dL for the group with inflammation (p=0.004) .  Table p=0.045 and p=0.004, respectively), confirming that both are markers that alter or stand out in inflammatory processes for nondialysis patients with CKD, as shown in The results showed a statistically significant difference between the groups with and without inflammation based on NLR and PLR values . However, this correlation did not occur for NLR and hs-CRP , as shown in A statistically significant positive correlation was found between PLR and hs-CRP (p=0.186)  .NLR is a parameter that provides information on both inflammation and the stress response. As for PLR, a high platelet count and a low level of lymphocytes are associated with different cardiovascular outcomes . In thisThe objective of this study was to evaluate nondialysis CKD patients and determine the association of NLR and PLR with inflammation in these patients, and the results showed that both biomarker values significantly increased in patients with inflammation.The study by Chávez Valencia  pointed Some studies that focused only on NLR outlined its advantages compared to other inflammatory markers. Malhotra et al.  concludeIn some studies, NRL stood out as an inflammatory marker, reaching higher levels than other markers such as hs-CPR and interleukins and in some outcomes as an indicator of acute kidney injury in patients with sepsis and as an indicator of cardiovascular events in patients with end-stage renal disease , 37. YosThe data presented here corroborate the findings of these authors, showing that NLR changes in the group with nondialysis CKD, but in the studied population, it was not positively correlated with hs-CPR.On the other hand, few studies have shown evidence of the use of PLR as an inflammatory marker in this population. It is considered a recently defined hematological parameter, which is associated with both aggregation and inflammation pathways and may be more valuable than the isolated platelet or lymphocyte count .There was a positive correlation between PLR and hs-CPR, a gold-standard method for the detection of inflammation in this population. Similar findings were presented in a study conducted in Turkey, where patients on hemodialysis and peritoneal dialysis had higher PLR than NLR, in addition to being positively correlated with other inflammatory markers, such as cytokines and tumor necrosis factor-alpha , highligChávez Valencia et al.  concludeIn this context, NLR and PLR show a great advantage compared to other markers in the evaluation of inflammation since it is a simple, relatively inexpensive, and universally available method  that couAs for the determination of the markers' cutoff points, the best sensitivity and specificity values were 1.98 for NLR and 116.07 for PLR. The literature presents great variability as for cutoff values for these markers. Tonyali et al.  found a As for PLR, a cutoff value of 136.85 was found in patients who underwent cardiovascular surgery . The stuThe difference of performance and comparison between the markers was not significant in this study. Li , who alsThese findings indicate that studies with larger, randomized, and multicenter samples are still needed to better assess the performance of these markers in nondialysis renal disease patients.The present study has as limitations the fact that it is cross sectional with local sampling that was nonprobabilistic and relatively small for some statistical analyses, and a cause-and-effect relationship cannot be defined in the findings of this study. On the other hand, this study was conducted in a reference center for the prevention and treatment of CKD in a capital city that concentrates a representative sample of the population of the state. In summary, the data from the present study were able to confirm the association between the values of NLR and PLR and the presence of inflammation in this population. Nevertheless, the literature has few studies on this theme, showing the need for larger, controlled studies to analyze the use of these markers in patients with CKD and their implementation in clinical practice.The study showed that PLR was positively correlated with hs-CPR in nondialysis CKD patients and, thus, can be used to identify inflammation in this population.


The importance of statins in cancer has been discussed in many studies. They are known for their anticancer properties against solid tumors of the liver or lung, as well as diffuse cancers, such as multiple myeloma or leukemia. Currently, the most commonly used statins are simvastatin, rosuvastatin and atorvastatin. The anti-tumor activity of statins is largely related to their ability to induce apoptosis by targeting cancer cells with high selectivity. Statins are also involved in the regulation of the histone acetylation level, the disturbance of which can lead to abnormal activity of genes involved in the regulation of proliferation, differentiation and apoptosis. As a result, tumor growth and its invasion may be promoted, which is associated with a poor prognosis. High levels of histone deacetylases are observed in many cancers; therefore, one of the therapeutic strategies is to use their inhibitors. Combining statins with histone deacetylase inhibitors can induce a synergistic anticancer effect. Their effectiveness in preventing the development of cardiovascular diseases makes statins one of the most widely used drugs . While tThere are currently many observational studies available that aim to assess the risk of individual cancers in long-term statin users. However, only some studies have found that statin use has lasted longer than 5 years . None ofThe aim of this paper is to present the current state of knowledge on the potential mechanisms of statin action in reducing the risk of cancer and its therapy.Cholesterol is one of the basic building elements of cell membranes in the human body, as well as the substrate for steroid hormone production. Its synthesis under physiological conditions is regulated at many levels; however, the key point is the enzymatic conversion of HMG-CoA to mevalonate, catalyzed by HMG-CoA reductase. Physiologically, this reaction can be limited by mevalonate derivatives and sterol regulatory element-binding proteins (SREBPs) . OverexpStatins are one of the key drugs used in the course of hypercholesterolemia, the first of which is lovastatin, which was introduced to the market in 1987 . NowadayIt has been shown that taking lipophilic statins such as simvastatin, atorvastatin, lovastatin, fluvastatin and pitavastatin, which can penetrate into cancer cells, is associated with a better prognosis in patients with breast cancer. On the other hand, improved survival in patients with hepatocellular carcinoma, colon cancer or prostate cancer is visible after the use of any statin . The broThe integrity and permeability of blood vessels play an important role in metastasis. Statins have been shown to induce the expression of endothelial cadherins (including VE-cadherin), which are responsible for tight intercellular junctions. Increasing the number of cadherins is associated with strengthening vessels tightness, which limits the expansion of cancer cells in the body ,28. MoreStatins also regulate autophagy, which is triggered as a cellular defense mechanism when the elimination of toxic stress-inducing components or elements can ensure cell survival. It has been shown that autophagy can participate in both tumor suppression and its progression. On the one hand, it reduces the viability of cancer cells, is extremely important in the early stages of cancer promotion and represents a critical mechanism, and on the other hand, it stimulates aging processes caused by oncogenes. The tumor promoting effect of autophagy is mainly exerted in the later stages of cancer development. The dynamic proliferation of cancer cells requires large energy resources, which autophagy provides by recycling cell substrates . MoreoveAn interesting phenomenon is that statins can also induce ferroptosis, which is another type of programmed cell death. This process is triggered by metabolic changes, and it is characterized by iron overload, accumulation of reactive oxygen species and lipid peroxidation, which can be used in statin therapy of cancer cells resistant to chemotherapy . AnotherBased on model breast cancer and malignant glioma cell lines, it has been proven that statins reducing the invasiveness of pathological cells act through changes in the level of protein phosphorylation and the activation of protein kinases in Rho/Rho kinase (ROCK) and Rho/Focal adhesion kinase (FAK)/Protein kinase B (Akt) pathways. As a consequence, signal flow is blocked at the FAK and Akt kinase level, possibly contributing to the activity changes of transcription factors Jun proto-oncogene, AP-1 transcription factor subunit (AP-1), β-catenin and nuclear factor kappa B (NF-kB), mediating the expression of genes involved in the multi-level cascade of metastatic events .Epigenetics refers to all changes in the functioning of genes that are inherited without interfering with the DNA sequence. Interestingly, these changes are reversible during embryonic development as well as during cell proliferation . EpigeneXie et al. observed HDAC1 overexpression in human hepatocellular carcinoma and liver cancer cell lines. Its knockdown resulted in the induction of p21 and p27 expression and the suppression of cyclin D1 and CDK2 expression. As a consequence, the G1/S cell-cycle transition was impaired, leading to autophagic cell death . SimilarOtahal and his team proved that fluvastatin or pitavastatin combined with erlotinib led to apoptosis in human lung adenocarcinoma cells with a mutated or overexpressed epidermal growth factor receptor (EGFR) ,39,40. IExperimental studies have shown that low doses (0.005–0.01 μmol/L) of cerivastatin and atorvastatin stimulate vascular endothelial cells to create new vascular structures . On the The Increased proliferation of cancer cells is caused by elevated cholesterol levels in these cells . In vitrIn vitro pro-apoptotic activity has been documented in studies on cancer cell lines, including cancer of the cervix, pancreas, thyroid gland, prostate, colon, breast and larynx, as well as multiple myeloma or leukemia ,34,35,36One of the key mechanisms of the antitumor activity of statins is also the angiostatin effect in neoplastic lesions ,65. It iThe clear presence of metalloproteinases in tumor masses, through the remodeling of extracellular matrix and basement membrane, has an unfavorable prognosis . OrganizStudies have shown that statins, in this case simvastatin, induce the phosphorylation of endothelial nitric oxide synthase, inhibiting apoptosis and promoting Akt-dependent angiogenesis in the ischemic limbs of normocholesterolemic rabbits . LipophiIn in vivo studies in mice, pitavastatin was injected intraperitoneally, which inhibited the growth of subcutaneous glioma cells . SimvastThe importance of statins in the context of cancer has been discussed in many studies, the results of which can be contradictory ,85,86,87One of the larger meta-analyzes carried out recently included a study on the use of statins in oncology in the Danish population ,88. MetaThe antitumor effect of statins is largely related to their ability to induce apoptosis, targeting cancer cells with high selectivity . Mechanip < 0.05), the beneficial effect of statins was noted in 10 out of 18 examined tumors. However, when the evidence strength was taken into account, there was no significant effect of statin use on cancer risk. The authors of this study emphasized that the benefits of pharmacotherapy with statins should be evaluated individually in each case [One of the largest observational studies on statins use in cancer prevention is an analysis based on the Danish Cancer Registry. In a group of 300,000 patients, it was reported that statins significantly reduced cancer incidence  . Kuoppalach case .Long-term RCTs play an important role in research . In one,In recent years, due to the increasing access to population data sets deposited in repositories, the number of pharmacoepidemiological studies is growing ,107,108.p = 0003) and did not correlate with the advancement of proliferative changes [p = 0.09) [p = 0.002 vs. OR 0.69; 95% CI 0.51–0.93; p = 0.01) [Analyzes taking into account Mendel’s randomization methods, examining the influence of genetic variants found in populations on the occurrence of a specific effect, are an important type of research. It is based on the well-established knowledge that the polymorphism of the relevant genes is related to specific behaviors. The study of genetic variants avoids confounding factors and errors associated with conducting observational studies. The inclusion of this randomization has opened an era of genomic research. It is used not only to identify links between gene groups and disease occurrence but can also be an alternative or complement to RCTs aimed at establishing a cause-and-effect relationship between exposure to a given factor and disease occurrence. According to Mendel’s second law, gene variants (polymorphisms) are randomly distributed among reproductive cells (sperm and ova), and therefore natural randomization occurs. These phenomena underlie the term “Mendelian randomization” ,105,106. = 0.01) . Therefo = 0.01) ,109. TabThe antitumor effect of statins is largely related to their ability to induce apoptosis, targeting cancer cells with high selectivity . Mechanip = 0.003) and did not correlate with the advancement of proliferative changes [p = 0.09) [p = 0.002 vs. OR 0.69; 95% CI 0.51–0.93; p = 0.01) [Orho-Melander et al. assessed 26–41 single nucleotide polymorphisms (SNPs) of genes encoding proteins involved in the synthesis of cholesterol and its fractions, as well as the determined impact of the rs12916 HMGCR polymorphism, encoding protein targeted by statins, on the risk of cancer development. They confirmed the relationship between serum triglyceride concentration and cancer risk, and that HMGCR polymorphism may be associated with the risk of prostate and breast cancer . In turn changes . Additio = 0.09) . On the  = 0.01) . Therefo = 0.01) ,124. TabDespite the recent development of diagnostics and therapy, cancer remains an ongoing problem. Studies are carried out to identify novel molecular markers as well as therapeutic strategies to diagnose cancer as early as possible and implement appropriate treatment. Attention is also paid to factors lowering the risk of cancer as well as preventing it. One of them may be the use of statins. Statins exhibit pleiotropic activity, which in the case of cancer, may be manifested by the induction of cancer cell apoptosis with high selectivity. Research shows that statins can also induce other types of programmed cell death, including ferroptosis. In addition, their participation in the regulation of autophagy is indicated. Therefore, the use of statins in the treatment of cancer is promising. Interestingly, the combination of statins together with different drugs, including histone deacetylase inhibitors, seems to give better results than using them alone. This review summarizes the in vitro and in vivo studies, meta-analyzes, observational studies and randomized controlled trials which help us to understand the crosstalk between statin use and cancer.


Gestational diabetes mellitus (GDM) is a common metabolic disorder syndrome in women during pregnancy. If effective measures are not taken to intervene in the early stage of GDM, severe effects will damage maternal and infant health. ABO is the most important human blood group system. A large number of studies have displayed that ABO blood group is associated with many diseases. At present, the risk relationship between ABO blood group and GDM is controversial. The purpose of this study is to explore the risk relationship between ABO blood group and GDM by meta-analysis, thus providing basis for the prevention and treatment of GDM.An electronic database, including Embase, Cochrane Library, Pubmed, Chinese databases SinoMed, Chinese National Knowledge Infrastructure, Chinese Scientific Journals Database and Wanfang Data, will be used to search for studies of ABO blood group and GDM. The language will be limited to Chinese and English. The two reviewers will be responsible for the selection of the study, the extraction of data and the evaluation of the quality of the research. All statistical analyses will be carried out using Review Manager 5.3.The results of this meta-analysis will be published in peer-reviewed journals.This study will provide evidence to support the relationship between ABO blood group and the risk of GDMThe private information from individuals will not be published. This systematic review also will not involve endangering participant rights. Ethical approval is not required. The results may be published in a peer-reviewed journal or disseminated in relevant conferences.DOI 10.17605/OSF.IO/W6QSX. The diagnosis is generally based on the results of oral glucose tolerance test in the second trimester of pregnancy. If effective measures are not taken to intervene in the early stage of GDM, severe effects will damage maternal and infant health. GDM increases various risks, such as preeclampsia, fetal death, fetal malformation, macrosomia, dystocia and cesarean section.–6 Women suffering from GDM also had a higher risk of diabetes within 10 years after delivery. The incidence of GDM in women of childbearing age has increased due to the continuous prevalence of obesity and diabetes in the past decade. Therefore, early monitoring of the risk of GDM is of great significance to maternal and infant health.In addition to type 1 diabetes and type 2 diabetes, gestational diabetes mellitus (GDM) is another kind of diabetes. In general, human blood type does not change from the time of embryo formation. Many studies have revealed that ABO blood group is related to infection, cancer, cardiovascular disease, nervous system disease, and so on.–13 Previous studies have also explored the relationship between ABO blood group and pregnancy complications. However, the potential link between ABO blood type and adverse pregnancy outcomes is controversial, including preeclampsia and related diseases, venous thromboembolism, postpartum hemorrhage, and GDM.ABO is the most important human blood group system.,16 Some studies have explored the relationship between ABO blood type and diabetes, but the findings are mostly inconsistent.,17,18 However, most of the studies on ABO blood group and its relationship with GDM are also inconsistent.ABO antigens influence several biomarkers and is closely related to insulin resistance and the development of type 2 diabetes, such as E-selectin, P-selectin, tumor necrosis factor-α, soluble intercellular adhesion molecule-1 and interleukin-6. some studies have reported that AB blood type increases the risk of GDM.,21 On the other hand, in a study carried out in Thailand, there was no relationship between ABO blood group and GDM. To date, the evidence for the relationship between ABO blood group and GDM is still limited and inconsistent. We conducted a meta-analysis to further confirm the relationship between ABO blood group and GDM.Although it has been reported that AB blood type is a protective risk factor of GDM,22.1The protocol was registered in Open Science Framework (OSF) (registration number: DOI 10.17605/OSF.IO/W6QSX). It was reported that this systematic review and meta-analysis are in conformed with the preferred reporting items for systematic reviews and meta-analysis protocols (PRISMA-P) 2015.2.2The review does not involve the assessment of patients’ individual information or rights, so there is no need to obtain approval from an ethical institution.2.3Studies would be included in this meta-analysis based on following criteria:1)Study types: All studies related to ABO blood type and GDM susceptibility should be included.2)Participant type: GDM should be included in the meta-analysis.3)Outcome: GDM risk comparisons.2.4According to the following criteria, studies should be excluded from the meta-analysis: conference summaries, incomplete data studies, repeated published studies, and case series.2.5Embase, Cochrane Library, Pubmed, Chinese databases SinoMed, Chinese National Knowledge Infrastructure (CNKI), Chinese Scientific Journals Database (VIP) and Wanfang Data were searched. The details of PubMed's search strategy are illustrated in Table 2.62.6.1Two researchers independently complete the literature screening, exclude the studies that obviously do not meet the inclusion criteria, and further read the abstracts and the full texts, to determine whether they meet the inclusion criteria. The data included in the literature will be extracted and cross-checked. Disagreement should be solved by consulting a third researcher, thus reaching a consensus. The screening flow chart of this study is displayed in Figure 2.6.2We extracted data from literatures that are conformed to the meta-analysis. The data include the first author, year of publication, country, race of each study population, number of cases and controls, average age, ABO blood type and number of cases of GDM, etc.2.6.3,25 Literatures with a total score of more than 6 are considered to be high quality.Two researchers independently applied Newcastle-Ottawa scale (NOS) to evaluate literatures included in the analysis, and adopted third-party opinions if there exist any differences.2.6.4The research focuses on the defects of the original data. We contacted the author by email to ask for the original data. If the original data is not available, then we would analyze the existing data.2.72. I2 < 50% revealed that the studies exhibited homogeneity, so fixed effects model was adopted. Otherwise, the random effects model was adopted. In the presence of heterogeneity, sensitivity analyses and subgroup analysis would be conducted to investigate the heterogeneity sources.A meta-analysis was performed using RevMan 5.3 (provided by Cochrane Collaboration). Odds ratio (OR) is the statistic of effect analysis, and each effect dose provides 95% of confidence interval (CI). Heterogeneity was ascertained using I2.8According to patient race, sample size, history of diabetes, and so on, we carried out subgroup analysis.2.9In order to test the stability of meta-analysis results of indicators, a one-by-one elimination method will be adopted for sensitivity analysis.2.10,27If more than 10 studies are included, a funnel chart would be applied to assess the report bias.3 The incidence of GDM ranges from 2% to 14%, and the incidence is as high as 40% among obese people. With the increase of the obesity rate of women of childbearing age at this stage, the incidence of GDM is increasing day by day.As a common disease, GDM is defined as various degrees of impaired glucose tolerance due to seizures or first recognition during pregnancy, thus affecting about 5 per cent of pregnancies worldwide. GDM is not only associated with adverse pregnancy outcomes, such as macrosomia, dystocia, birth trauma and neonatal metabolic complications, but also with a powerful predictor of postpartum transition to obvious diabetes. are still insufficient. The red blood cell count of pregnant women with AB blood group was significantly higher than that of pregnant women with A and O blood groups, and the levels of fasting blood glucose, urea and serum creatinine of pregnant women with AB blood group were remarkably higher than those of pregnant women with A blood group. These results indicated that ABO blood type may be associated with some adverse pregnancy outcomes. Karagoz et al discovered that patients with AB and O blood groups had a higher risk of gestational diabetes. Shimodaira proposed that AB blood type is a risk factor for GDM. Zhang et al put forward that women with type B or O blood groups were associated with the increased risk of GDM, and type AB blood was a protective factor for GDM in pregnant women. Therefore, in view of the inconsistency of the above results. In this study, we systematically evaluate the risk relationship between ABO blood group and GDM, to provide evidence-based medicine for future clinical guidance.The association between ABO blood group and DM has been observed in a number of epidemiological and genetic studies, thereby resulting in inconsistent findings. However, in the literature, the researches on the relationship between ABO blood group and GDMConceptualization: Xiaohong Li, Dongyun Chen.Data curation: Dongyun Chen.Funding acquisition: Xiaohong Li.Investigation: Dongyun Chen.Project administration: Xiaohong Li.Resources: Dongyun Chen.Software: Lili Lin.Validation: Lili Lin, Qiong Hong.Visualization: Lili Lin, Qiong Hong.Writing – original draft: Xiaohong Li, Dongyun Chen.Writing – review & editing: Xiaohong Li, Dongyun Chen.


Fear of cancer recurrence (FCR) is a significant issue for most cancer patients. Until now, a detailed investigation of the structure of FCR and the interaction among its constituent elements is lacking. This study aims to investigate the phenomenon of FCR by means of network analysis in Chinese cancer patients.This is a multi-center, cross-sectional study that included 996 cancer patients from southern China. All participants were assessed by the 7-item Chinese version Fear of Cancer Recurrence Scale (FCR-7). Multivariate logistic regression, and network analyses were conducted. Central symptoms (nodes) in the FCR network were identified.P = 0.016), and childhood severe illness experience  were positively associated with high FCR, while higher monthly income  was negative associated with high FCR. The node #FCR2 (Worried/anxious about recurrence) was the most central node within the FCR network (Strength = 1.190), while node #FCR6  was the least central node (Strength = 0.373). The edge FCR1-FCR2 (“Afraid”-“Worried/anxious”) was the thickest and most saturated edge in the network. After controlling for age and gender, an almost identical network was obtained with respect to edges magnitude and strength.Among the 996 patients, 543 (54.52%) reported moderate FCR, and 137 (13.76%) reported high FCR. Chemotherapy  and cognitive component , emotional component  is more central to identify FCR and might be potential targets for further interventions. Fear of cancer recurrence (FCR) is a significant issue for most cancer patients, and it is a key unmet need among this population , 2. As oRecent studies have consistently found that younger age , female FCR is a complex phenomenon among cancer patients with significant clinical consequences, however, little is known about the inner structure of FCR. Previous studies usually focused on FCR total score singularly. However, examining the associations among individual FCR symptoms/items may provide new insights for us. Network analysis estimates the unique associations between each pair of measured symptoms while controlling for all other symptoms and shrinking potentially spurious associations to zero . AnalyziAs an integrative approach to study the structure of psychopathology, in recent years, network analysis has been increasingly used in the field of psychology and psychiatry among different populations. For example, Mullarkey et al., used network analysis to study adolescent depression using the Children's Depression Inventory, and found that “self-hatred”, “loneliness”, “sadness”, and “pessimism” were the most central symptoms among adolescents , while MFCR has received growing attention in research with many cancer survivors of various cancer diagnoses. However, a detailed investigation of the structure of FCR and the interaction among its constituent elements is still lacking. Therefore, the main aim of the current study is to investigate the phenomenon of FCR by network analysis among Chinese cancer patients. Meanwhile, the prevalence and associates of FCR will be examined. To do so, we aim to closely study the 7-item Fear of Cancer Recurrence scale (FCR7), which is a well-developed and widely-used tool for FCR in different cancer populations . In the Study participants were newly diagnosed cancer patients who were seen consecutively at the cancer center in Southern Medical University Nanfang Hospital, Guangdong Provincial People's Hospital, and Guangzhou Women and Children's Medical Center. Participants were recruited from September 2018 to September 2019. Inclusion criteria were: (1) age ≥ 18 years, (2) have a diagnosis of cancer within 6 months, (3) be able to read and write Mandarin or Cantonese; (4) provide written informed consent. Participants were excluded if they had severe physical and/or cognitive impairment. This study was performed in accordance with the Helsinki standard and approved by the Hospital Ethics Committee (Ref No: NFEC-2018-038). Detailed study procedure has been introduced elsewhere .Basic demographic and clinical information were gathered using a specially-designed case record form (CRF), such as current age, gender, marital status, education, personal income level, type of cancer, primary treatment , family cancer history, medical comorbidities, childhood adversity experience  and childhood severe illness experience .The 7-item FCR questionnaire is used to assess cancer patient's recurrence fears and has been used with patients with breast, colorectal and head and neck cancer in a variety of clinical centers in the UK . The relStatistical analyses were performed using SPSS version 24.0 . To characterize all study variables, descriptive statistics were first calculated. Then, multivariate logistic regression analysis with “enter” method was used to identify potential variables associated with high FCR. High FCR  was the dependent variable, and all tested variables were entered as independent variables. Alpha was set at 0.05 (two-tailed).bootnet”  the links between two symptoms were not spurious because of other nodes , and (2) small link/edge coefficients were reduced to zero, hence allowing the network model to be interpreted with relative ease.The network structure of FCR was estimated using the Enhanced Least Absolute Shrinkage and Selection Operator (eLASSO) method . Furtherfunction  to assesStrength, Betweenness and Closeness indices were calculated. Strength is the sum of the weight of all direct connections between a specific node and others, indicating the importance of an individual variable. Betweenness indicates the number of shortest paths connecting any two symptoms, reflecting the importance of the connection of a variable to other variables, while Closeness is computed as the inverse of the sum of the total length of all the shortest paths between a specific node and the rest of the network; therefore, it reflects the strength of the indirect connections of a variable to other variables. Centrality plots were created to represent these indices. For each index, high values reflect great centrality in the network. In the current study, centrality indicators were calculated with the “centralityPlot” function, and they are reported as standardized values (z scores).To highlight which symptoms may be most influential in the FCR network, We tested the stability and accuracy of our network via both case-dropping bootstrap test for centrality stability and bootstrap confidence intervals (CIs) for the accuracy of network edges. First, we applied a case-dropping subset bootstrap to compute the correlation stability (CS) coefficient . It has been suggested that the correlation coefficients between the original indices and indices based on a case-subset network should be at least 0.7 (default) or higher (with 95% probability) . A CS coPrevious evidence indicated that age and gender might influence FCR. Younger age and female gender are significant risk factors of high FCR , 9, 10. n = 996) vs. the rest of the invited sample (n = 223) revealed no significant differences in gender and age. Demographic and clinical characteristics of included participants are reported in In total 1,219 cancer patients were invited to participate in the study with 996 providing consent and completing all assessments. Comparison of patients with complete data (P = 0.016), and childhood severe illness experience  were positively associated with high FCR, while higher monthly income  was negative associated with high FCR  was the thickest and most saturated edge in the network. Worried/anxious about recurrence”, Strength = 1.190, Betweenness = 4, and Closeness = 0.026) was the most central node within the network (with more direct connections with other nodes). Node #FCR6  was situated at the periphery of the model and was the least central node in the network   .Afraid of recurrence”-“Worried/anxious about recurrence”) was the thickest and most saturated edge in the network and was significantly stronger than most other edges in the model (Worried/anxious about recurrence”) was the most influential node and was significantly stronger than other nodes in the network (he model  19). Me. MeAfrai network .r = 0.988 ], and strength [r = 0.995 ] ] , 5.This is the first study to investigate the phenomenon of FCR by means of network analysis. First, we found that 54.52% patients reported “moderate level” of FCR, and 13.76% reported “high” FCR. Until now, no similar studies using the 7-item FCR questionnaire were conducted in China, therefore, direct comparisons were difficult to be made. Prevalence of FCR have varied greatly across studies due to different assessment times, measurement instruments, cut-offs, and socio-economic background or clinical status of participants. For example, a study in Hong Kong using the Fear of Cancer Recurrence Inventory-Short Form (FCRI-SF) found that about 26.0% of the participants reported subclinical FCR, and 11.2% of them reported clinically significant FCR , while aWe found that chemotherapy and childhood severe illness experience was positively associated with high FCR. It has been consistently found that patients with chemotherapy , 25 or p(“Worried/anxious about recurrence”) was the central node in the network, while node #FCR6  was the least central node in the model. Our findings are consistent with the key characteristics of clinical FCR, that is, “persistent and high levels of preoccupation and/or worry”  and cognitive component , emotional component  is more central to identify FCR and might be potential targets for further interventions. Patients with chemotherapy and childhood severe illness experience were more vulnerable and should be particularly monitored. As patients with high FCR could be influenced in their well-being, health-related quality of life, and emotional and social functioning, regular FCR assessment should be adopted. Flexible clinical interventions, such as community reinforcement, and psychotherapy focusing on the processes of emotion and cognition, could be beneficial for patients with high FCR.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Southern Medical University Nanfang Hospital (Ref No. NFEC-2018-038). The patients/participants provided their written informed consent to participate in this study.XL, YY, and BZ: study design. WL, YC, HS, TL, and JZ: data collection, analysis and interpretation. XL, WL, YC, and HS: manuscript drafting. GH, YY, and BZ: manuscript revision. All co-authors approved the final version for publication.This research was supported in part by the National Natural Science Foundation of China (Nos. 72101107 and 82071488), Guangzhou Science and Technology Project (Nos. 201904010326 and 201804010132), Clinical Research Program of Nanfang Hospital, Southern Medical University (2021CR014), Construction project of Team Construction in Guangdong Province Teaching Quality and Teaching Reform Project ([2020]19), College Innovation and Entrepreneurship (Employment) Education Project in Guangzhou ([2019]15), and Guangdong Province Degree and Postgraduate Education Reform Research Project (2021JGXM026).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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Background and Objective: Hyperuricemia is closely related to chronic kidney disease (CKD). The effects of urate-lowering therapy (ULT) on renal outcomes are uncertain, and whether it is warranted in CKD patients is currently unclear. The aim of our meta-analysis of randomized clinical trials (RCTs) was to assess the effectiveness and safety of ULT for improving kidney function in patients with CKD.Methods: RCTs were retrieved from the PubMed, Embase, MEDLINE and Cochrane Central Register of Controlled Trials databases. The meta-analysis was performed using Review Manager and Stata/SE software. The outcomes were changes in renal function and serum uric acid (SUA), serum creatinine, and adverse events.Results: Twelve RCTs with 1,469 participants were included in the meta-analysis. ULT was found to effectively lower SUA (standard mean difference (SMD): -2.70; 95% confidence interval (CI): -3.71, -1.69) but the renoprotective effects were not superior to those of control therapy , which were stable in the subgroup analyses and sensitivity analyses. Regarding adverse events, their risks did not increase in the ULT group compared with the control group and were stable in the sensitivity analyses.Conclusion: The findings of our meta-analysis suggested that ULT can effectively lower SUA, but there is insufficient evidence to support the renoprotective effects of ULT in CKD patients. In addition, ULT is safe for patients with CKD.Systematic Review Registration:https://clinicaltrials.gov/, identifier PROSPERO (CRD42020200550). Chronic kidney disease (CKD) is defined as abnormalities in kidney structure or function that are present for more than 3 months and have an impact on health. These abnormalities include a decreased glomerular filtration rate and increased urinary albumin-creatinine ratio . CKD proHyperuricemia is also closely related to CKD, but the causality is still not very clear (2). Previous studies suggested that SUA was likely a risk factor for CKD . At presThus, we performed a systematic review and meta-analysis of RCTs to evaluate the efficacy and safety of ULT in patients with CKD.This systematic review was performed according to the Cochrane Handbook for Systematic Reviews of Interventions and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement . It was Studies were included in our meta-analysis if they met the following criteria: 1) RCTs involved patients with CKD; 2) the dosage and duration of the intervention (ULT) and control groups  were adequately documented; and 3) changes in uric acid, estimated glomerular filtrate rate (eGFR), creatinine, or proteinuria were reported. Studies were excluded if they 1) lacked a control group , 2) were not published in English, 3) had a follow-up time <3 months. Study selection was performed independently by two authors (XPL and DL). Any disagreement was resolved by the third reviewer (XL).Changes in the USA, eGFR, serum creatinine and adverse events including deterioration of kidney function, liver dysfunction, cardiovascular events, gastrointestinal symptoms, and hypersensitivity (such as rash) were assessed in our article.Two researchers (XPL and DL) independently extracted the information, including title, first author, year of publication, study design, country of origin, inclusion and exclusion criteria, sample size, study population characteristics, drug type and dosage, intervention period and duration, outcomes, and adverse events. Outcomes including adverse events, changes in SUA, serum creatinine, and eGFR were used to evaluate the effects and safety of ULT. When the above data were unavailable, they were extracted from figures and/or tables as far as possible. Any inconsistency between the two researchers was resolved by the third researcher (XL) to reach an agreement.The risk of bias of the included RCTs was assessed independently by two researchers (XPL and XL) according to the revised Cochrane risk-of-bias tool for randomized trials . The risp < 0.05 indicated a significant difference. We used the Q-test and I statistic to examine heterogeneity. If studies were heterogeneous (p < 0.1 or I2 > 50%), the random-effects model was used. Otherwise, the fixed-effects model was applied  and 95% CIs. We considered that  applied . We also applied  and subgA total of 1,245 studies were identified during the initial search, and we eliminated 1,211 studies based on our screening of titles and abstracts. After reading the full texts of 34 studies in detail, 12 RCTs with 1,469 participants were included in this meta-analysis The essential information of the 12 RCTs is shown in 2 and hyperuricemia included, the results were consistent.  . In the sistent. .We performed subgroup analysis according to the treatment duration (6 months) and the ULT drugs, including febuxostat and allopurinol. The analysis of studies with treatment durations of 6 months or more showed the consistent results above , as did 2 and hyperuricemia were included, the changes were still not statistically significant  . When onnificant . The subnificant . Regardinificant .The major concern about febuxostat is its cardiovascular side effect supported by the RCT performed by White et al., which demonstrated that febuxostat had higher all-cause mortality and cardiovascular mortality than allopurinol , and thiThe risk of bias of each study is shown in p = 0.312). However, the funnel plot for the outcome of eGFR in this meta-analysis showed asymmetry (Egger’s test assessing bias for our main primary outcome (the change in eGFR) was not significant . ULT has been shown to effectively lower SUA, and the result was consistent in subgroup analyses and the sensitivity analysis. However, ULT was not found to have renoprotective effects, in terms of eGFR or serum creatinine, each of which was stable in the subgroup analyses and sensitivity analyses.Our study is the largest meta-analysis to evaluate the effectiveness of renal protection between the ULT and control groups. We included 12 RCTs with 1,469 participants in all and ultimately assessed the change in the level of eGFR in 10 RCTs with 1,157 participants, which demonstrated no difference between the ULT and control groups. This was in agreement with some RCTs suggesting that there were no renoprotective effects with ULT. For example, Badve et al.  performeAt present, many studies including clinical and mechanistic research, have explored the relationships among uric acid, cardiovascular diseases and the effect of ULT, and the causality among these variables is very controversial. A cross-sectional study in patients with unilateral small kidney or renal agenesis conducted by Yazici et al. demonstrated that the SUA level was positively correlated with arterial stiffness . MechaniThere are some strengths in our review. First, we included only RCTs with a treatment duration of more than 3 months. Second, the number of included RCTs and patients was relatively appreciable. Furthermore, we performed subgroup analysis by CKD stage and treatment duration, and changes in renal function, including eGFR and serum creatinine, were comprehensively evaluated. The adverse effects incorporating deterioration of renal or liver function, cardiovascular events, gastrointestinal symptoms and hypersensitivity were also analyzed. However, our study also has some limitations. First, the risk of bias of 4 studies was high and this was reflected in the risk of participant or investigator blinding. Second, the evaluation of progression to end-stage renal disease was insufficient (no data). Third, the sample sizes of the studies included were quite small, ranging from 40 to 351.The findings of our meta-analysis suggest that the evidence is insufficient to support the renoprotective effects of ULT in CKD patients. Furthermore, ULT shows a superior decrease in SUA and ULT is safe for patients with CKD. However, a well-designed, large-scale, controlled study is required for further clarification.