The purpose of this study was to assess whether the retail food environment, measured by multiple indicators around the home and in activity space, was associated with the nutritional quality of food purchases.This cross-sectional study included 462 households from a quota sampling survey conducted in the south of France (Montpellier Metropolitan Area). The revised Healthy Purchase Index was implemented in order to assess the nutritional quality of food purchases. Food environment indicators  were calculated around the home and in activity space using a geographical information system. Six different types of food outlets were studied: supermarkets, markets, greengrocers, bakeries, other specialized food stores  and small grocery stores. Associations between food environment and the nutritional quality of food purchases were assessed using multilevel models, and geographically weighted regressions to account for spatial non-stationarity. Models were adjusted for households’ socioeconomic and demographic characteristics.The nutritional quality of food purchases was positively associated with the number of greengrocers around the home , but negatively associated with the number of markets around the home . These associations varied across space in the area studied. For lower income households, the number of greengrocers in activity space was positively associated with the nutritional quality of food purchases .Greengrocers might be an effective type of food store for promoting healthier dietary behaviors. Further studies, particularly interventional studies, are needed to confirm these results in order to guide public health policies in actions designed to improve the food environment. Eating behaviors and dietary intake result from complex interactions between individual and contextual characteristics. Environmental features such as the food environment or “foodscape” –defined Conclusions from previous literature reviews on the associations between food environment and overall diet quality draw mixed results –6. To ciThe heterogeneity of these results might be due to methodological limitations . The greThe majority of works evaluating relationships between food environment and eating behaviors have been conducted in the United States . StudiesThe Montpellier Metropolitan Area (MMA), an urban area in the south of France, presented itself as an interesting study field to investigate the impact of = food environment on dietary practices given the support of local politicians and interest in this specific issue. Accordingly, we aimed to examine whether retail food environment (RFE) was associated with the overall nutritional quality of food purchases in the MMA. In this study, the RFE was measured using multiple indicators , and food purchases were assessed by objective purchasing data in French households. Beyond the scientific value of this work, which uses an original approach to assess RFE around the home and in activity space, our study might help guide cities looking to improve their RFE.https://www.foodscapes.fr/en), which was designed to assess the impacts of urban foodscapes on peoples’ food styles in the MMA, the seventh-largest urban area in France. Analyses were conducted using data from the Mont’Panier cross-sectional study (https://www.etude-montpanier.com) and from the JArDinS study [This study was carried out as a part of the Surfood-Foodscapes project (nS study . ParticiINSEE), we performed quota sampling based on household composition , and age of head of household .In order to be included in the study, participants had to be 18 years or older, live in the MMA and participate at least to some degree in grocery shopping. Using sociodemographic data on the MMA from the French National Institute of Statistics  and were registered to the In this study, we focused on six different types of food outlet: supermarkets, markets (indoor and outdoor markets), greengrocers, bakeries, other specialized food stores  and small grocery stores. The classification of food outlet type was carried out based on SIRENE’s food store classification. SIRENE is a French national business and establishment register database that is managed by the INSEE, recording the identity of all active companies and their establishments in France. In France, most meals are consumed at home, as highlighted in the Third French Individual and National Food Consumption report (INCA 3) , which iThe location of food stores was obtained through the SIRENE database. The SIRENE database provides information on the French directory of active companies and establishments. This database was first cross-checked using OpenStreetMaps (OSM). OSM provides open data on companies and establishments that can be updated and enriched by external contributors. Online searches on Google Maps, company websites of the major food retailers, and city websites  were then performed to further verify the database. Final verifications were carried out through field observations on around 5% of the area studied: the information provided on location and type of food outlet by the database was validated through ground-truthing in the city-region of Montpellier between May 2018 and January 2019 .For this study, only household members who participated in grocery shopping (even if they participated only a little) would be considered for the geocoding step. The addresses of their current home and places of main activity (i.e. work and/or other places they reported visiting at least once a week) were collected through an online questionnaire. In addition, mode of transport  both to and from these addresses was also obtained for activity space calculations, i.e. to determine the buffer size of journeys (explained below). The addresses collected were then geocoded using QGIS v3.4.7. With the geocoded addresses of food outlets, homes and household members’ places of main activity, RFE was assessed in two exposure environments using geographical information systems. Firstly, RFE was assessed around homes, and secondly, RFE was assessed in the activity space of households, defined as the exposure environment including areas around the home, around household members’ places of main activity, and commuting routes between those places.RFE around homes was estimated using four types of indicator: number, presence and proximity of each food outlet type, and relative density of food stores selling FV. In France, general food stores, namely supermarkets, small grocery stores and greengrocers sell FV and are thus considered as such in our study. The proximity of food stores was calculated by assessing the shortest road network distance between the nearest food outlet relative to each home address. The number of each type of food outlet was calculated within a 500-meter (or 1000-meter) road network distance around each home address. As a large proportion of households had no supermarket, market or greengrocer within 500 meters of the home, a larger and more relevant buffer of 1000 meters was used for these types of outlet . The numRFE in the activity space of households was estimated using three types of indicator: number and presence of each type of food outlet, and relative density of food stores selling FV. Proximity, being commonly related to a specific place, was not estimated in the activity space of households which is composed of areas around multiple places. The number of each food outlet type in the activity space of households was calculated within a 500-meter road network distance around the home and other places of activity, as well as 100 meters or 300 meters along commuting routes between those places. Commuting routes were modelled based on the shortest street network distance and a specific buffer was used depending on the modes of transport declared in the online questionnaire; we used a 100-meter buffer for walking and cycling journeys, and a 300-meter buffer for car journeys.Given the non-normal distribution of RFE indicators, mainly because of the high amount of zeros, categorization of RFE variables was undertaken according to the distribution of each variable, and in such a manner that the interpretation of results would make sense. The number of food outlet variables were categorized into three groups: none, one, and more than one food outlet, except for bakeries, other specialized food stores and small grocery stores when considered in the activity space of households. These three variables were then categorized into three other groups , because distributions with none, one, and more than one categories were too uneven (e.g. no household with no or only one bakery in its activity space given the high amount of bakeries). The proximity of food outlets was also categorized into three groups: <500 meters; 500–1000 meters; >1000 meters.In order to assess the food supplies of households, participants were asked to complete a food supply diary, in which they had to list all foods and drinks purchased (including online purchases), harvested, or received for at-home consumption, over a one-month period. Step-by-step instructions on how to complete the food supply diary was given to participants and also specified at the front of the diary. For each food purchase made, participants were asked to collect grocery cash register receipts. When grocery cash register receipts were not available, participants were asked to provide details of foods purchased, received or harvested . Missing data concerning expenses for food items were imputed using the INSEE database and price were found on food store websites. Mean prices were found on food store websites. Mean prices per kg were multiplied by the quantity (kg) when quantity was available and, when quantity was missing, quantity was first imputed per mean quantity of the given food item. We included imputed received and harvested food expenses in the r-HPI calculation in order to consider all foods that were available for at-home consumption; in addition, such practices might interfere with the relationships between the food environment and food purchases, as they might constitute an alternative food supply source to the commercial food offer. All food items were then classified into 11 groups and 27 subgroups in order to assess the share of expenses of major food groups. The nutritional quality of households’ food purchases was calculated by implementing the revised-Heathy Purchase Index (r-HPI).The HPI is an index, based on share of expenses for specific food groups and sub-groups, developed to evaluate the nutritional quality of households’ monthly food purchases . The r-HSocioeconomic and demographic characteristics were obtained from the online questionnaire. Analyses accounted for household composition , income per unit of consumption , age group of heads of household , and away-from-home food consumption. The away-from-home food consumption variable was calculated using the self-reported answers of each household member to four questions concerning the frequency of meals (per month) consumed away from home according to the type of place . Days of away-from-home food consumption per household was calculated with the total number of meals per month reported as consumed away from home for each household member divided by the number of household members and divided by two .Descriptive statistics were expressed as means (standard deviation) and percentages. In order to examine the associations between households’ RFE and the nutritional quality of households’ food purchases, multivariate multilevel regression models with a random intercept were performed. Multilevel modelling provides the advantage of accounting for the hierarchical structure of the data and spatial dependency that typically characterize observations within spatial units . MunicipOnly covariates and RFE indicators associated with r-HPI at a 0.2 significance level in bi-variate analyses (linear models or multilevel models) were retained for inclusion in the subsequent multivariate models. Interaction effects between income level and RFE indicators associated with r-HPI were tested and were included in multivariate models at a 0.1 significance level.Separate multilevel regression analyses were performed for each type of RFE indicator, adjusted for socioeconomic and demographic household characteristics, away-from-home food consumption, and relevant significant interactions; seven sets of multivariate models were developed.The first four models included the previously selected RFE indicators calculated around the home: a first model with indicators assessing the number of food store types, a second model with presence indicators, a third model with relative density indicators and a fourth model with proximity indicators.The last three multivariate models were conducted with RFE indicators calculated in the activity space of households: a first model with number indicators, a second model with presence indicators, and a third model with relative density indicators. As stated above, we did not develop a model with proximity indicators in activity space, since proximity is commonly related to a specific place and not multiple places.In this paper, we chose to focus on results with indicators of number over those of presence so as to avoid redundancy and because indicators of number enable us to assess a potential gradual association–taking into account a possible diversification of food offer and prices for the same type of food outlet. Only models with number, relative density and proximity indicators will be described, the results of models with presence indicators will, however, be available in supplementary materials.Geographically weighted regression (GWR) models were performed in order to consider the possible spatial variations of the relationships between RFE around the home and the nutritional quality of food purchases . GWR was not performed for associations with RFE indicators calculated in the activity space of households, since local regression models are estimated using unique coordinate points (i.e. home addresses). Multilevel models and GWR are complementary statistical analysis methods. GWR enabled us to explore the associations studied on a finer spatial scale without the constraint of administrative boundaries, while multilevel models compensate for the limited statistical inferences of GWR models that lower their explanatory power . GWR enaSensitivity analyses were performed on the 415 households of the Mont’Panier study, excluding the 47 participants of the JArDinS study. As the results of the sensitivity analyses, namely multivariate multilevel regressions, were very similar to those presented in this paper (i.e. significant results remained significant and the direction of associations remained the same), we did not include them in this paper.All analyses were conducted using SAS, version 9.4  and R (version 4.0.4). GWR models were performed using R package GWmodel . The thrTwo sets of data were used for this paper, 415 households participating in the Mont’Panier survey and 47 households in the JArDinS study . AnalyseHouseholds composed of several adults without children were the most represented household structure in our study sample, income per consumption unit levels were relatively well distributed among the study sample, and over one third of head of households were aged between 30 and 50 years . The disThe distribution of the sample according to the REF indicators is described in Concerning the nutritional quality of households’ food purchases, mean r-HPI was 9.2 (SD: 2.7), median r-HPI was 9.5, with a minimum score of minus two points and a maximum score of 15 points (r-HPI score ranges from a minimum score of -8 to a maximum score of 17 points).Household composition, income per unit of consumption, and head of household’s age group were significantly associated with the nutritional quality of food purchases. The results of bivariate linear models are presented in Adjusted associations between the number of food stores around the home and the nutritional quality of food purchases showed that the number of markets was significantly and inversely associated with the nutritional quality of food purchases, whereas the number of greengrocers was positively associated with the nutritional quality of food purchases . These aThe results of GWR for the associations of nutritional quality of households’ food purchases with number of greengrocers are mapped in The results of the adjusted associations between the presence of food stores around the home and the nutritional quality of food purchases are shown in Bivariate association analysis between the relative density of food stores selling FV around households’ homes and the nutritional quality of food purchases was not statistically significant .Adjusted associations between the proximity of food store types around households’ homes and the nutritional quality of food purchases were also not significant . Only asAnalyses of associations between the number of food stores in the activity space of households and the nutritional quality of food purchases were stratified for income level, because an interaction between the number of greengrocers and households’ income levels was found . Associations showed that the number of greengrocers in the activity space of households was significantly and positively associated with the nutritional quality of food purchases in households with lower incomes . ResultsThe results of adjusted associations between the presence of food stores in the activity space of households and the nutritional quality of food purchases are presented in Bivariate association analysis between the relative density of food stores selling FV in the activity space of households and the nutritional quality of food purchases was not significant .To the best of our knowledge, this is the first study that explores the relationships between food environment and dietary behaviors by assessing the overall nutritional quality of households’ food purchases, and two scales of the RFE (around households’ homes and in their activity space). This study found that having greengrocers around the home and in activity space was associated with higher nutritional quality of food purchases; this association varied according to income level for greengrocers in activity space. Besides, the number of markets around households’ homes was associated with lower nutritional quality of food purchases, while no significant association was found for other types of food outlet.There was no association between the proximity of food stores around households’ homes and nutritional quality of households’ food purchases. Nor did we find any associations between the relative density of food stores selling FV and the nutritional quality of food purchases. Only indicators of number and presence of food outlets showed significant associations with the nutritional quality of households’ food purchases. Types of spatial exposure measurement were compared in a recent systematic review , revealiRegarding household socioeconomic and demographic characteristics, households composed of multiple adults, with older heads of household or higher incomes had food purchases of better nutritional quality (results of bivariate linear models); similar results have been found in other studies , 33.As in ours, another study reported significant associations between greater access to greengrocers and better dietary quality . LikewisIn our study, GWR models, adjusted on socioeconomic and demographic characteristics and away-from-home food consumption, revealed that locally estimated coefficients varied across space with a south-north gradient in the MMA. The Olympic Regeneration in East London Study also found nonstationary relationships between food environment and dietary behavior (FV intake) . This spFurthermore, multilevel analyses, stratified by income level, revealed that among low-income households, those exposed to a greengrocer in their activity space made monthly food purchases of better nutritional quality. These findings are consistent with previous research stating that low-income populations are more easily influenced by their surroundings than high-income populations –46.While we did not find any other studies reporting associations between the availability of markets and the nutritional quality of food purchases, we did find studies reporting positive associations between access to mobile food markets and FV intake , 48. HowWith today’s competitive food environment contributing to rising obesity rates and, thus, to the increasing risk of chronic diseases, it is crucial for public health policies to establish food environments that promote healthy food choices, which consequently could turn out to be a substantial public health payoff in the future. Studies like this could help guide public health policies, facilitating the decision-making process to improve the food environment. Even though we did not find any statistically significant association between supermarkets and the nutritional quality of food purchases in our study, big food supply chains remain the main food supply source even in France , and areThe strengths of our study include the use of objective data on dietary behavior (households’ monthly food purchases) based on the collection of households’ receipts over a one-month period. This allowed us to reduce the known declaration biases , often fThis study also has limitations. Firstly, caution is needed regarding extrapolation of these results to the entire French population as this study was limited to a metropolitan area located in the South of France. Secondly, the study’s cross-sectional design does not allow for drawing any conclusions on the causal relationships between food environment and the nutritional quality of food purchases. In addition, neighborhood self-selection (i.e. people selecting neighborhoods to live in which have the facilities and resources to suit their preferred lifestyle) was not taken into account in this study, which can lead to biased estimates of the associations between the food environment and eating behaviors  and brinThis paper presents an original approach when studying the relationships between food purchasing behaviors and food environment exposure assessed in activity space, taking into account exposure around the home, other places of activity (e.g. work place) and along commuting routes. Our study showed that households exposed to greengrocers make monthly food purchases of better nutritional quality, especially in low-income households. Only RFE indicators of number and presence of food outlets showed significant associations with the nutritional quality of households’ food purchases, suggesting that type and concentration of food outlet may have a greater influence on diet than distance to the closest food outlet.Future studies should consider using multiple RFE indicators and take into account diverse types of food supply source in order to allow for study comparison. In addition, exposure to the food environment should be considered in activity space and not solely around the home. Furthermore, we recommend future studies in order to assess how RFE influences food purchases for at-home as well as away-from-home food consumption. Finally, natural experiment studies designed to explore causality should be conducted in order to assess the suggested effect of greengrocers on dietary behaviors. Consecutively, such research may help guide public health policies to implement actions designed to improve RFE, including actions that might contribute to decreased social inequalities with regard to diet.S1 Tablea Except where specified; b Fresh fruits, canned fruits, stewed fruits. c Fresh vegetables, vegetable soups, canned vegetables. d Potatoes, legumes, wholegrain products, bread rolls, fresh bread, pasta, rice, flour. e Milks & yoghurts, cheeses. f Red meat, processed meat, eggs & poultry, fish. g Vegetables, fruits, dried fruits & nuts. h Hard cheese, soft cheese, cream cheese. i Refrigerated and long-life milk, plain yoghurt, sweetened yoghurt, fruit yoghurt, yoghurt drink. j Hard-boiled egg, fried egg, omelet, chicken, duck, turkey. k Fresh fish, canned fish, shellfish, surimi. l Beef, pork, lamb. m Cured and cooked ham, sausages, bacon, pâté. n Vegetable fats: vegetable oil, margarine, salad dressing; animal fats: cream, butter. o Savory snacks, sugar sweetened beverages, calorie free beverages, fruit juices, sugared cereals, dairy desserts, sweet snacks, sauces. TF: Total Fats.(DOCX)Click here for additional data file.S2 Tablea Other specialized food stores include butcher’s, fishmonger’s and dairy stores. b The proximity of food stores was calculated by assessing the shortest road network distance between the nearest food outlets relative to each home address. c The number of food stores was measures within a 1000-meter road network distance around the home for supermarkets, markets and greengrocers and within a 500-meter road network distance around the home for bakeries, other specialized food stores and small grocery stores. d The number of food stores in activity space was calculated within a 500-meter road network distance around the home and other places of activity, as well as 100 meters or 300 meters along commuting routes between those places. A 100-meter buffer was used for walking and cycling journeys while a 300-meter buffer was used for car journeys. e Relative density of food stores selling fruits and vegetables was measured within a 500-meter road network distance around the home and calculated by dividing the number of food stores selling fruits and vegetables by the total number of food stores.The results for presence of food outlets can be derived from the results for number of food outlets (categories 0 and 1 combined with >1). (DOCX)Click here for additional data file.S3 Tablea CI = Confidence Interval.(DOCX)Click here for additional data file.S4 Tablea CI = Confidence Interval; the presence of supermarkets and other specialized food stores around the home was not included in this multivariate model because it had p-values >0.2 in bivariate analyses.(DOCX)Click here for additional data file.S5 Tablea CI = Confidence Interval; the number of supermarkets, markets, bakeries, other specialized stores and small grocery stores in activity space was not included in this multivariate model because it had p-values >0.2 in bivariate analyses. The activity space includes areas around the home, around household members’ places of main activity, and commuting routes between those places.(DOCX)Click here for additional data file.S6 Tablea CI = Confidence Interval; the presence of markets, bakeries, other specialized stores  and small grocery stores in activity space was not included in this multivariate model because it had p-values >0.2 in bivariate analyses. The activity space includes areas around the home, around household members’ places of main activity and commuting routes between those places.(DOCX)Click here for additional data file.S7 Tablea CI = Confidence Interval; the presence of markets, bakeries, other specialized stores  and small grocery stores in activity space was not included in this multivariate model because it had p-values >0.2 in bivariate analyses. The activity space includes areas around the home, around household members’ places of main activity and commuting routes between those places.(DOCX)Click here for additional data file.


This network primarily focuses on the common age-related musculoskeletal disorder, osteoporosis. One of the main outputs of this network has been to produce six review articles describing the state of the art in osteoporosis genetics research, as summarised below .The Genomics of Musculoskeletal traits Translational Network (GEMSTONE), funded by a European Cooperation in Science and Technology (COST) Action “aims to bring together multiple disciplines currently active in the field of musculoskeletal research under a coordinated effort, which allows translating the emerging wealth of genetic discoveries into palpable clinical applications that can help setting the ground for personalized medicine” Koromani et al. describes the origins, rationale, organisation and prospects for GEMSTONE. Over the last decade, genome wide association studies (GWAS) studies have led to the identification of many new genetic influences on intermediary traits for osteoporosis such as bone mineral density (BMD). However, the function and mechanisms by which such genetic factors affect these traits is unknown. GEMSTONE brings together multidisciplinary expertise to identify these mechanisms, and ultimately translate this understanding for the benefit of patient care. In order to achieve these goals, GEMSTONE has brought together a range of experts, who are organized across six working groups (WGs) namely  “Study populations and expertise groups” (Trajanoska and Rivadeneira), “Phenotyping” , “Monogenic conditions- human knockout (KO) models” , “Functional investigations” , “Bioinformatics” and  “Translational outreach”. These working groups have aimed to describe the state-of-the-art in these different areas; where complete this has led to papers presented herein.The paper by Trajanoska and Rivadeneira further sets the scene for GEMSTONE by summarizing the achievements of genetic studies in the bone field to date, and discussing how these could be translated for the benefit of patients. Many mutations are now recognised to cause a range of monogenic bone disorders including Osteogenesis Imperfecta and Osteopetrosis, as well as several sclerosing bone disorders. The latter include sclerosteosis, where identification of the underlying genetic mutation led to development of romosozumab, a new monoclonal antibody treatment for osteoporosis via sclerostin inhibition. On the other hand, GWAS studies of complex traits such as BMD have identified many hundreds of genetic influences, of which some are related to known bone regulatory pathways, whereas others are entirely novel. These mechanisms might provide drug targets for further treatments for osteoporosis, either in the form of novel therapies, or by providing a rationale for re-purposing drugs already used for other conditions. Understanding the genetic basis of osteoporosis could also lay the foundation for pharmacogenomic studies to identify patients who respond particularly well or poorly to a specific treatment, or are more susceptible to adverse effects. Such data can also provide the basis for polygenic risk scores for osteoporosis, intended to complement other methods for individualised fracture risk assessment.The paper by Foessl et al., on behalf of WG2, reviews the different methods used for phenotyping the human skeleton, as well as how these have been applied to mammalian animal models and zebrafish. The comprehensive description of clinical phenotyping methods comprises (i) features that should be sought from clinical history taking and examination, (ii) widely available imaging and laboratory tests such as X-rays, DXA and bone turnover markers, (iii) patient reported outcome measures from questionnaires, and (iv) more specialized methods more commonly used in the context of research studies such as HRpQCT and trans-iliac bone biopsy. The detailed review of imaging methods divides these into 2D and 3D modalities, comparing applications between humans, rodents and zebrafish. A number of methods for ex-vivo analysis of bone tissue also exist including histomorphometry, compositional bone matrix analysis using backscattered electron microscopy imaging, and immunohistochemistry. The papeE Kague et al. (published in Rising Stars in Bone Research 2021), represents a further output of WG2, by illustrating how analysis of cranial sutures in zebrafish, which unlike in humans remain open during lifetime, can be used in functional validation of osteoporosis-related genes. The rationale for this approach stems from the finding that, as well as several known BMD loci, a recent GWAS of skull BMD identified a number of novel genetic factors. Moreover, a significant overlap was identified between genetic influences on skull BMD, and craniosynostosis, a rare congenital disorder involving premature fusion of the skull sutures. The authors subsequently evaluated the impact of targeting novel genes identified from their skull BMD GWAS in zebrafish by CRISPR/Cas9 to produce loss of function. In all cases, targeting these genes led to marked abnormalities in suture growth, confirming the utility of certain skeletal phenotypes in zebrafish in functional validation of osteoporosis genes identified from GWAS studies.The paper by Formosa et al., on behalf of the WG3, examines the utility of monogenic low and high bone mass disorders for identifying novel therapeutic targets for osteoporosis. This paper provides a comprehensive description of monogenic disorders associated with alterations in bone mass, dividing these according to whether BMD is either increased or decreased. A number of steps are involved in identifying new disorders of this type, including careful clinical phenotyping, and emerging bioinformatic approaches to evaluating whole exome/whole genome sequencing data. Cell based, rodent and zebrafish models are widely used for functional validation of putative genetic mutations. Ultimately, findings from such studies can be used to identify therapeutic targets for both osteoporosis and rare bone diseases, as exemplified by romosozumab for the treatment of osteoporosis discussed above.The paper by Rauner et al., on behalf of WG4, describes the different methods used for identifying causal genes underlying association signals identified in human genetic studies. This is pertinent for analysing both GWAS studies of traits such as BMD, and putative genetic mutations associated with rare skeletal disease. In GWAS studies, a Mendelian Randomisation framework can be used to link a genetic association signal to an mRNA expression quantitative trait locus eQTL (eQTL) or protein expression quantitative trait locus (pQTL) related to a specific gene. Another important tool is the annotation of genetic sequences in coding and non-coding regions. Whereas monogenic disorders most commonly arise from altered protein coding, the great majority of signals identified in GWAS studies are in non-coding regions. Genetic variation in non-coding regions can affect gene transcription via a wide range of mechanisms, including altered DNA methylation, binding of transcriptional enhancers and repressors, and non-coding RNAs (ncRNAs) including microRNAs (miRNAs). This paper also describes the various laboratory methods used for functional follow up of genetic associations. 17 research teams from the GEMSTONE consortium provide a range of cellular resources derived from different tissues represented in bone, from distinct species. In terms of animal models, though the mouse has been the most widely studied historically, zebrafish are being increasingly used as they offer certain advantages such as more rapid throughput. Animal models enable evaluation of the impact of over-expression or deletion of a given gene, either at the level of the whole organism or limited to bone using the Cre/loxP recombination system. As well as perturbation of the entire gene, gene-editing technologies enable the insertion of specific genetic variants in order to evaluate the impact of specific coding changes linked to rare skeletal disorders. To bring together these various resources in order to interrogate any given genetic signal of interest, the musculoskeletal knowledge portal (MSK-KP) has recently been created by the International Federation of MSK Research Societies (IFMRS) [https://msk.hugeamp.org/].Finally, the paper by Together, these six papers provide a comprehensive description of the state-of-the art in genetic research in osteoporosis and related disorders, highlight the important directions and challenges for future research, and will be of interest to researchers and clinicians alike.The author confirms being the sole contributor of this work and has approved it for publication.


Patients with Alzheimer's disease (AD), Parkinson's disease (PD), traumatic brain injury (TBI), stroke, and postoperative neurocognitive disorder (POND) are commonly faced with neurocognitive disorders with limited therapeutic options. Some non-coding ribonucleic acids (ncRNAs) are involved in the development of various brain cognitive disorders. Circular RNAs (circRNAs), a typical group of ncRNAs, can function as competitive endogenous RNAs (ceRNAs) to dysregulate shared microRNAs (miRNAs) at post-transcription level, inhibiting regulation of miRNAs on their targeted messenger RNAs (mRNAs). circRNAs are abundant in central nervous system (CNS) diseases and cause brain disorders, but the exact roles of circRNAs are unclear. The crosstalk between circRNA, miRNA, and mRNA plays an important role in the pathogenesis of these neurocognitive dysfunction diseases and abnormal conditions including AD, PD, stroke, TBI, and POND. In this review, we summarized the participation of circRNA in neuroglial damage and inflammation. Finally, we aimed to highlight the regulatory mechanisms of circRNA–miRNA–mRNA networks in the development of various brain cognitive disorders and provide new insights into the therapeutics of these diseases. CircHtra1 serves as a miR-3960 sponge, upregulates the levels of GRB10, inhibits cell proliferation, and promotes neuronal apoptosis and NK cell infiltration after TBI  globally  in ischemic stroke tissues from transient middle cerebral artery occlusion (tMCAO) mice and in plasma samples from patients with acute ischemic stroke (AIS) are significantly increased. In tMCAO mice, the knockdown of circHectd1 can significantly decrease cerebral infarct size, neuronal deficits, and astrocyte activation, though miR-142 targets TCDD inducible poly[ADP-ribose] polymerase (TIPARP) and inhibits the astrocyte activation via circDLGAP4/miR-143/Hectd1 axes in tMCAO in a mouse stroke model  after elective arthroplasty in an aging population  through one or more circRNA–miRNA–mRNA networks indicates the complicated and dynamic mechanisms of brain cognitive disorders including AD  play important roles in discovering the pathogenesis and development of brain disorders in the future. Recently, some studies indicate that a subset of circRNAs has been considered as codes for proteins , the Health and Family Planning Commission of Yunnan province (H-2017047), and partially by the National Natural Science Foundation of China (81860208).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. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Patient-reported measures are an important emerging metric in outcome monitoring; however, they remain ill-defined and underutilized in bariatric clinical practice. This study aimed to determine the characteristics of patient-reported measures employed in bariatric practices across Australia and Aotearoa New Zealand, including barriers to their implementation and to what extent clinicians are receptive to their use.n = 176). Participants reported their use of patient-reported measures and identified the most important and useful outcomes of patient-reported data for clinical practice.An online survey was distributed to all bariatric surgeons actively contributing to the Australian and Aotearoa New Zealand Bariatric Surgery Registry , citing insufficient staff time or resources as the primary barrier to the collection of both patient-reported experience measures  and patient-reported outcome measures . Participants indicated data collection by the Registry would be useful , highlighting the most valuable application to be a monitoring tool, facilitating increased understanding of patient health needs, increased reporting of symptoms, and enhanced patient-physician communication.Despite the current lack of patient-reported measures, there is consensus that such data would be valuable in bariatric practices. Widespread collection of patient-reported measures by registries could improve the collective quality of the data, while avoiding implementation barriers faced by individual surgeons and hospitals.The online version contains supplementary material available at 10.1007/s11695-022-06237-z. The efficacy of bariatric (metabolic) surgery has been well established in the treatment of obesity and associated medical problems, with the popularity of such procedures escalating in Australia and internationally –4. The rAn important emerging component of reporting is the use of patient-reported measures (PRMs) to capture and quantify aspects of the patient experience and outcomes, particularly surrounding quality of life and psychosocial wellbeing . In addiThe Australian and Aotearoa New Zealand Bariatric Surgery Registry (ANZ BSR) is a clinical quality and safety registry capturing clinical data for patients undergoing bariatric surgery across public and private hospitals in Australia and Aotearoa New Zealand. The ANZ BSR captures data from 89% of eligible hospitals and 82% of eligible surgeons, making it well situated to facilitate unified and consistent collection of PRMs. In aid of developing centralized PRMs, it is pertinent to first understand where and to what extent PROMs and PREMs are currently used in routine clinical practice throughout Australia and Aotearoa New Zealand. Our primary objectives were to (1) determine the characteristics and distribution of PRMs employed in bariatric practices, and (2) examine clinician engagement with the development of centralized PRMs by the ANZ BSR, and identify the most important and useful outcomes for clinical practice. It was hypothesized that a higher patient burden may impact a surgeons’ capacity to collect PRMs in clinical practice. Australia has a larger population with many more patients undergoing bariatric surgery compared to Aotearoa New Zealand, while certain states/territories within Australia  also have a higher patient volume than other jurisdictions. Differences between countries, and jurisdictions within Australia, as well as between surgeons with a higher number of practices or larger patient volume, were analyzed to elucidate any effect of population and/or patient burden on implementation and use of PRMs.A cross-sectional survey was designed to examine current practice for PREM and PROM collection within bariatric practices across Australia and Aotearoa New Zealand, including the extent of PRM measurement within clinical practice, how and why this data was collected and subsequently used, and the opinions of survey participants as to the utility of registry-based PRM data.n = 176 at time of invitation).The ANZ BSR captures data from 197 surgeons in 133 public and private hospitals across Australia and Aotearoa New Zealand. The majority of sites and surgeons are located in Australia , with more populous Australian jurisdictions  representing a higher proportion. Bariatric procedures are predominately performed in women in private hospitals in both Australia  and Aotearoa New Zealand . The mean age for primary procedures is 42.4 years in Australia and 45.8 years in Aotearoa New Zealand with Sleeve Gastrectomy being the dominant procedure in both countries  . All surSurvey questions were adapted from the survey developed by Morton et al.  and refiFollow-up emails were sent at 1, 4, and 12 weeks following initial invitation including options for a nominated delegate to complete the survey on behalf of the surgeon, as well as to complete a short-form of the survey. The short-form survey was released 4 weeks after first invitation to invite additional participant responses. The short-form survey asked participants to divide their response into public and/or private practices, rather than providing responses for each individual practice. This simplification of the questionnaire was employed to avoid surgeons with multiple practices abandoning the survey prior to completing the questionnaire for all of their practices.Surveys were completed via the secure online platform Qualtrics .Surgeon responses were organized into participant responses and practice responses. Participant responses represent the collective answers provided by each surgeon, while practice responses represent the specific answers provided for an individual practice. For example, a surgeon reporting on 3 separate practices would repeat Part 1 of the questionnaire 3 times, once for each practice. This would generate 1 participant response and 3 practice responses. Partial responses of full-form surveys without a single complete practice response and short-form survey responses without a complete public or private practice response were excluded from analysis. Responses from full-form and short-form surveys were crosschecked for repetition of respondents and collapsed into individual practice responses as appropriate. Any practice that was reported by more than one participant was checked for consistency and collapsed as appropriate. If any differences were observed between responses, they were maintained as separate practice responses. This reflects multiple surgeons operating at a single center, but providing individual services which may differ in their approach.In addition to survey responses, the number of procedures performed by each responding surgeon in the year preceding the survey was obtained from the ANZ BSR database as a measure of patient volume.χ2 tests were used to assess differences in proportions between public and private practices as well as between countries and jurisdictions. Mann–Whitney U and Kruskal–Wallis tests were used to assess whether there were any differences in the number of practices or number of procedures between countries, jurisdictions, public/private practices, and PRM use. Related-samples Friedman’s analysis of variance (ANOVA) and post hoc multiple comparisons with Bonferroni correction were used to assess differences in ranked items. Qualitative data were analyzed thematically to identify any barriers to registry-based PRM administration, preferences for data collection, and utility of data in the clinical setting. Statistical analysis was performed using SPSS , with statistical significance inferred at a p value of < 0.05.Descriptive statistics (frequencies and percentages) were reported for each participant and practice response, to each survey item. p > 0.05).Responses to the PRMs ANZ Survey were received from 69 surgeons, a response rate of 39.2% .Overall, most participants reported no collection of any PRMs  or public and private practices . The most common reasons for not collecting PREMs were insufficient staff time or resources , being unaware of available PREMs , PREMs not a local priority , and unsure of how to collect or use PREM data . Other reasons for not collecting PREMs included PREMs not being considered useful or impactful and that PREMs either had been discontinued or were being planned for implementation , with no difference between Australian and Aotearoa New Zealand , annually (15.4%), or ad hoc/as needed 23.1%) for the purposes of monitoring and improving quality of service  and for research  (Table .1% for tp = 0.913) or public and private practices . The primary reasons PROMs were not collected included insufficient time or resources , being unaware of available PROMs , unsure how to collect or use PROM data , and PROMs not being a local priority  , with no difference between Australian and Aotearoa New Zealand  or post-operatively only . When used pre-operatively, PROMs were collected only once (100%), while post-operatively they were collected once , at each clinic appointment  or at regular time points . Two validated instruments were routinely used: the Beck Depression Inventory (BDI) in 7 of 33 practices (21.2%) and the SF-36 in 8 of 33 practices (24.2%). Most commonly, bespoke, practice-specific PROMs were used , measuring outcomes such as weight, changes in associated medical problems, and quality of life. The primary purpose for collecting these PROMs was to audit clinical practice , screen surgical candidates , directly inform clinical care , and for research purposes  , eight were unsure (14%), and two believed it would not be useful (3.5%). For those who answered no or unsure, reasoning provided was primarily regarding a lack of empirical evidence as to their utility, concern for their ability to change clinical practice, and previous experience with PRMs that were not useful.p < 0.001) and outcomes (p < 0.001). The mean and median ranks for each application and outcome are listed in Table Respondents were asked to rank five potential applications of registry-based PRMs from most to least useful and six potential outcomes of registry-based PRMs from most to least important. The results of the Friedman tests indicated there was a statistically significant difference in rank across the applications  employed within a representative sample of bariatric practices across Australia and Aotearoa New Zealand. The majority of participants reported no collection of any PRMs, with time and resources being the primary barrier to their use in clinical practice. PROMs were collected more often than PREMs; however, the majority of both PREMs and PROMs collected were non-specific or non-validated measures, collected at only one time point pre- or post-surgically. There were no differences found in the use of PREMs or PROMs between countries, jurisdictions, practice number, or patient volume, highlighting the lack of PRM implementation is ubiquitous across the study population and was not dictated by patient burden. Despite a clear lack of current uptake, participants agreed PRM data would be useful, highlighting the most valuable application would be as a monitoring tool, and the most important outcomes to be increased understanding of patient health needs and quality of life, increased recognition and reporting of symptoms, and enhanced patient-physician communication.The PRMs identified within our survey are inconsistent and varied, with a notable lack of validated, reliable instruments specific to the experiences and outcomes of bariatric patients. A recent systematic review highlighted this variability, identifying 68 different validated measures across 86 bariatric surgery studies . As in oIn our survey, respondents identified a monitoring tool to be the most useful application of patient-reported data. PRM monitoring tools provide regular, ongoing feedback to clinicians, allowing them, and their patients, to reflect on their progress and respond to changes. Studies of PRMs used as monitoring tools found they increased early detection and discussion of problems by clinicians, with an impact on subsequent clinician intervention provided the PRM was specific to the condition or treatment being assessed , 28. DesIn Australia and Aotearoa New Zealand, the bi-national Bariatric Surgery Registry may be perfectly situated to harmonize the heterogeneous collection of PRMs, while avoiding implementation barriers faced by individual practices or hospitals. PRMs are increasingly being incorporated into registries across Australia and internationally to understand the trajectory of patients’ symptom burden and quality of life over the course of disease and/or treatments , 20, 21.This study was strengthened by collecting multiple responses from each participant and differentiating between public and private practices. This enabled the expansion of data beyond the individual and facilitated important insight into any disparities between the public and private sectors. This is also the first review of PRM use in bariatric practices in the Australian and Aotearoa New Zealand context, providing an important baseline to guide continued development and implementation of PRMs, ensuring the results are relevant and meaningful to clinical practice.The response rate of 39% is a considerable limitation of this study, and the results may not be reflective of the entire population of surgeons across Australia and Aotearoa New Zealand. However, the response rate is comparable to similar surveys of surgeons in Australia and Aotearoa New Zealand –37 as weIn completing the short-form survey, surgeons reported their practices as only public or private, and any duplication of specific practice sites cannot be accounted for. Finally, the results are specific to the Australian and Aotearoa New Zealand context and may not be applicable to international populations; however, the paucity of bariatric-specific PRM collection , 22, barThere is an evident lack of patient-reported measures used in Australian and Aotearoa New Zealand bariatric practices. The majority of practices did not collect any PRMs, with the primary barrier to PRM uptake being a lack of knowledge regarding available PRMs, or insufficient time and resources to implement them. Those that do employ PRMs most commonly use non-specific, or non-validated measures, collected at only one time point. Despite this, surgeons indicated that registry-based PRM data would be useful, highlighting the most useful application would be as a monitoring tool, and the most important outcomes to be increased understanding of patient health needs and quality of life, increased recognition and reporting of symptoms, and enhanced patient-physician communication.Supplementary file1 (DOCX 462 KB)Below is the link to the electronic supplementary material.


Population aging and the universality of the aging process offers individuals and society the opportunity to consider strategies to optimize older adults’ well-being and proactively prepare across their aging trajectory. Understanding aging through a holistic lens is essential to promote healthy aging in light of the risk for increasing chronic conditions, frailty, and disability that affect older adults. Planning for aging and frailty encompasses five domains that entail change as individuals age , and provides foci for a proactive approach to planning across life course. The aims of this research were to examine the stages of change through the Transtheoretical Model within the domains of aging, experiences , and associations between contextual factors and stages of change for readiness to plan for aging and frailty. Using a cross-sectional design, 252 community dwelling-adults (aged 50-80) completed a survey on planning for aging and frailty. Results revealed a distribution of stages of change in readiness across domains of planning for aging and frailty with highest levels of planning in the financial domain (68.7%), and lowest levels in cognitive domain (28.2%). Participants reported increased experiences with others compared to personal experiences. Factors most indicative of planning include: older age, marital status, living situation, social support, and vulnerability. Planning for aging and frailty is an innovative concept that takes a comprehensive approach to the aging process. Future study is needed to promote planning across one’s life course and develop interventions focused on enhancing well-being.


In the present study, the effects of using a corrugated porous layer on the forced convection of a hybrid nanofluid flow over a 3D backward facing step are analyzed under the coupled effects of magnetic field and surface rotation. The thermal analysis is conducted for different values of the Reynolds number (Re between 100 and 500), the rotational Reynolds number (Rew between 0 and 2000), the Hartmann number (Ha between 0 and 15), the permeability of the porous layer (the Darcy number, Da between  Flow separation characteristics are important to be considered in many engineering practices. Dynamics of drag forces, heat transfer (HT) and performance features are affected by the flow separation in various systems, and examples can be found in aerodynamics, turbo-machinery, building energy saving, power generation systems, cooling, thermal management and many more. Flow over a backward/forward facing step (BFS/FFS) includes separated flow regions, and the interaction between the main flow and established complex vortex patterns becomes complicated. The size of the vortex behind the step and separation dynamics are affected by many factors such as the Reynolds number (Re), step size, expansion ratio of the channel and other geometrical and operating parameters. In the experimental work of Armaly et al. , an LDA Many different techniques including active, passive and hybrid ones have been proposed for the convective HT control considering separated flow over BFS. Flow pulsations ,13,14, iThe application of a magnetic field (MaF) is relevant in many engineering practices such as in geothermal energy extraction, microfluidic pumps, coolers of nuclear reactors, metals processing, biomedical applications, convection and many more systems . In the Porous inserts can also be utilized in convection control and HT performance improvements, while applications may be encountered in drying, chemical processes, filtering, thermal management and cooling . AnalytiThe present study focuses on analyzing the coupled effects of surface rotation, magnetic field and installation of a porous layer on the convective HT for flow over 3D BFS geometry. The porous layer is a wavy one installed near the abrupt area change, while it has a rectangular type corrugation. Flow separation dynamics and convective HT performance are analyzed under coupled MaF effects, surface rotation and hybrid nanofluids. The mentioned methods may be already available in the system such as rotating heat exchanger with area expansion, or MaF effects may also be present. In the literature, convective HT control by using the combined effects of MaF, rotations and installation of a wavy porous layer has never been considered for flow in a channel with area expansion. A neural modeling approach is used for fast thermal predictions of HT and pressure coefficients with different influencing input parameters for assisting the high fidelity computational fluid dynamics (CFD) simulations. As thermal management and flow control for channels with area change are important to be considered in many thermal engineering systems such as heat exchangers, electronic cooling, micro-fluidic devices and many more, the outcomes of the present work will be helpful for optimization and energy efficient product development in various energy system technologies.The effects of using a wavy PL and surface rotation on the flow separation and HT characteristics for hybrid nanofluid flow over 3D BFS is analyzed under MaF effects.  in Ref. . We consForced convection of hybrid nanofluid for flow over 3D BFS is considered, while effects of thermal radiation, free convection and viscous dissipation are neglected. The induced MaF effects, joule heating and displacement currents effects are also ignored. In the PL domain, the Brinkman-extended Darcy porous model is considered. The governing equations in compact form are stated as in the following ,85:(1)∇.In the above given equations, Lorentz forces due to the imposed MaF are obtained. A uniform MaF acting only in the radial direction is considered with contributions as :(4)Fm,r=Non-dimensional parameters are the Re, Rew, Da and Ha which are described as in the following:The solid volume fraction of particles in the hybrid nanofluid is taken as 0.02. The temperature and velocity at the inlet are uniform with the values of The GWR-finite element method (FEM) is selected as the solution method. The foundations and theory of FEM for fluid flow and HT problems can be found in several reference textbooks ,88,89. Sg) approximation is made by using with different ordered Lagrange FEM as:W denoting the weight function and R as the residual. To handle local numerical instabilities, artificial diffusion with the streamline upwind Petrov–Galerkin method (SUPG) is considered, while the Biconjugate gradient stabilized iterative method solver (BICGStab) is utilized for fluid flow and heat transfer modules of the solver. Convergence criteria of 10In the method, field variables , the rotational Reynolds number (Rew: 0–2000), MaF strength (Ha: 0–15), permeability of the medium  and other soft computing methods are commonly used in thermal engineering systems for fast performance predictions and in assisting the high fidelity parametric computational fluid dynamics (CFD) simulations. Correlations for the HT and pressure coefficients can be devolved based on neural modeling. In the present study, the trends in the curves of average Nu and pressure coefficient (Cp) for varying Re, Rew, Da and Ha are captured by using a mathematical model based on NNs. The range of parameters are taken as: (Re between 100–500), (Rew between 0–2000), (Ha between 0–15),  and two outputs  is considered. Fast and accurate thermal predictions are made with neural modeling of convective flow over 3D BFS considering the coupled effects of MaF, surface rotation and installation of a wavy porous layer.Effects of surface rotation, MaF and installation of a wavy porous layer on the convective HT of hybrid nanofluid flow in a 3D channel with area expansion are examined. The following conclusions can be drawn:The present work can be extended to include different wave forms, non-uniform MaF effects, and channel expansion ratio effects. Different soft computing techniques such as ANFIS (Adaptive-Network Based Fuzzy Inference Systems), SVM (Support Vector Machines) can also be used and their performances can be compared with ANN model performance.