This review collects and critically examines data on the levels of tumour necrosis factor-alpha (TNF-α) in lean, overweight and obese subjects, and the effects of intervention with different foods and food products containing bioactive constituents in overweight/obese individuals. We additionally explore the influence of different single nucleotide polymorphisms (SNPs) on TNF-α levels and compare the response to food products with that to some anti-obesity drugs. Our aim was to provide an overview of the variability, consistency, and magnitude of the reported effects of dietary factors on TNF-α, and to envisage the reliability of measuring changes in the levels of this cytokine as a biomarker responsive to food intervention in association with the reduction in body weight. Regarding the circulating levels of TNF-α, we report: (i) a large intra-group variability, with most coefficients of variation (CV%) values being ≥30% and, in many cases, >100%; (ii) a large between-studies variability, with baseline TNF-α values ranging from <1.0 up to several hundred pg/mL; (iii) highly variable effects of the different dietary approaches with both statistically significant and not significant decreases or increases of the protein, and the absolute effect size varying from <0.1 pg/mL up to ≈50 pg/mL. Within this scenario of variability, it was not possible to discern clear differentiating limits in TNF-α between lean, overweight, and obese individuals or a distinct downregulatory effect on this cytokine by any of the different dietary approaches reviewed, i.e., polyunsaturated fatty acids (PUFAs), Vitamin-D (VitD), mixed (micro)nutrients, (poly)phenols or other phytochemicals. Further, there was not a clear relationship between the TNF-α responses and body weight changes. We found similarities between dietary and pharmacological treatments in terms of variability and limited evidence of the TNF-α response. Different factors that contribute to this variability are discussed and some specific recommendations are proposed to reinforce the need to improve future studies looking at this cytokine as a potential biomarker of response to dietary approaches. Tumour necrosis factor-alpha (TNF-α) is a widely investigated multifunctional cytokine that belongs to the TNF/TNFR (tumour necrosis factor receptor) superfamily. It is produced by multiple cells, primarily by circulatory and infiltrating immune cells, such as macrophages, T and B lymphocytes, natural killer cells, and monocytes, as well as by other types of cells including adipocytes, endothelial cells, muscle cells, mast cells, fibroblasts, osteoclasts ,2 Figur. The solTNF-α is also a potent activator of the production of different cytokines promoting a pro-inflammatory status and thus, it has an important function in the inflammatory and immune responses. Multiple molecular and cellular mechanistic studies have pointed to the therapeutic potential of targeting the system TNF/TNFRs for the treatment of inflammatory and immune diseases . In geneIt is now also clear that TNF-α has an impact on healthy metabolism as well as on metabolic diseases, particularly, on obesity-linked glucose metabolism and insulin resistance (IR) . IncreasOverall, TNF-α is a pleiotropic molecule involved in multiple physiological and pathological mechanisms and constitutes an important target in therapies against a diversity of immunologically related disorders. In the context of obesity, a broad range of human intervention dietary studies have investigated the changes on the blood levels of TNF-α in connection with body weight changes. In this manuscript, we present an exploratory and critical revision of those studies looking at the modulation and variability in the levels of this cytokine. We examine a selection of human intervention trials with different dietary approaches including foods and food derived products containing (micro)nutrients and/or bioactive constituents. The number of studies selected and included in each section of this review is indicated in A good diagnostic biomarker has been defined as one that allows for the differentiation between the conditions under investigation , and for identifying an individual within one of those conditions . When reWe reviewed a number of human studies carried out between 1995 and 2022, where the circulatory levels of TNF-α were determined in lean, overweight and obese individuals ,43,44,45p < 0.001) from 2.9 ± 2.2 pg/mL in the lean participants to 6.5 ± 3.1 pg/mL in overweight individuals, 6.8 ± 3.1 pg/mL in participants with obesity, and 7.4 ± 2.6 pg/mL in very obese participants [p < 0.05) [p < 0.001). As an example of (NS) but substantial differences between individuals, in a sample population of Spanish women, the levels of TNF-α ranged from 5.7 ± 9.8 pg/mL in lean participants to 11.6 ± 19.0 pg/mL in obese ones, and 8.6 ± 12.4 pg/mL in very obese participants [The results show that the CV% values were typically >30% and, in some cases, well above 100%, for the lean, overweight and obese subpopulations, indicating a rather high relative intragroup variation. The data also show that the circulating levels of TNF-α displayed a very high between-studies variability. In the lean individuals, reported values expanded from <1.0 pg/mL to ~80 pg/mL; however, in most cases, the values were found in the range 2.0–6.0 pg/mL . In geneicipants . In anot < 0.05) , the dificipants . In addiicipants ,41,43. Licipants . FurtherOverall, the percentage of change from lean to overweight or obese subgroups varied from a (NS) 2% increase  up to a The overall scenario regarding the circulatory levels of TNF-α in humans shows a large intra- and between studies variability with several hundred- and, even up to several thousand-fold differences within the values attributed to each specific group of individuals. Many of the studies included in this section reported an increase in the levels of this cytokine in the overweight/obese sample populations as compared to the lean ones, and the general message conveyed is that there is an association between increased body weight and increased levels of the cytokine. Yet, looking at all the data gathered here, it was not possible to designate clear-cut ranges of TNF-α values that differentiated between lean, overweight and obese individuals, and/or to discern any clear tendency between TNF-α levels and body weight. The majority of the reported values for circulating levels of TNF-α ranged between ~1.0 and 10.0 pg/mL, with apparent independence of the body weight category posing some reasonable doubts on the potentiality of TNF-α as a biomarker to differentiate between body weight conditions.The most common, accessible, and first-line options for the treatment of body weight excess are behavioural changes which consist principally of adopting a healthier lifestyle, including a change in eating habits towards a healthier diet , Mediterranean diet (MD)) ,47. GeneDietary fatty acids (FAs) constitute essential nutrients and an important source of energy, having a considerable range of biological functions including a modulatory role of metabolism and immune responses and inflammation . Dependip = 0.008) reduction of −0.50 pg/mL. Regarding body weight changes, none of the studies showed significant (S) effects from the supplementation with ω-3 PUFAs. In the study by de Luis et al. [In this section we reviewed a series of intervention trials in which overweight/obese participants received supplements containing ω-3 PUFAs, and the changes in body weight and TNF-α levels were investigated ,69,70,71s et al.  that tesSalvia hispanica L.) have been attributed metabolic and immune-regulatory properties partly due to their high content of PUFAs [Nigella sativa L.) oil, also a rich source of PUFAs [p = 0.03) [Dipteryx alata Vog., legume seeds) also constitute a rich source of PUFAs [The results of the supplementation with several foods or oils enriched in PUFAs ,69,70,71of PUFAs . An inteof PUFAs . Similarof PUFAs ,73, did of PUFAs , was tesof PUFAs , and werVitamin-D (VitD) occurs naturally as cholecalciferol or vitamin D3, and ergosterol or vitamin D2. In humans, the quantitatively most important source of vitamin D3 is that synthesized in the skin by the sun’s ultraviolet rays. Natural dietary sources of vitamin D2 are some fungi (sun-dried mushrooms) and yeast, whereas vitamin D3 can be found in salmon, fatty fish, fish liver oil, organs , meat and egg yolks. Since only few foods contain high levels of VitD, fortification of different food products such as margarine, milk and cereals with VitD is common in many countries. Furthermore, the use of oral supplements is widely recommended in order to prevent deficiencies. The recommended intake of VitD is 10 µg/d (400 IU/d). . VitD plIn humans, low plasma concentrations of VitD (hypovitaminosis D) have been consistently reported in overweight/obese people , and thup < 0.05) [We revised the results of several intervention trials in which overweight/obese adults of both sexes were treated with VitD, and changes both in body weight and levels of TNF-α were investigated ,92,93,94 < 0.05) . Consist < 0.05) , the triA series of trials that investigated the effects on body weight and TNF-α in overweight/obese participants following intervention with other bioactive (micro)nutrients have been examined ,99,100 . The stuPhytochemicals cluster a large group of organic non-essential secondary metabolites with rich structural diversity produced principally, but not exclusively, by plants. (Poly)phenols constitute one of the largest and more heterogeneous families of these naturally occurring organic compounds and are characterized by a reactive composition of aromatic rings and one or several phenolic hydroxyl groups . DependiA variety of polyphenol-containing products  have been investigated for a wide range of potential health benefits, including body weight and inflammation regulatory properties. Cell and animal models of obesity have provided extensive supportive evidence of the potential mechanisms by which many of these compounds may exert their activity against obesity, including the inhibition of adipocyte differentiation and proliferation, reduced fat absorption, increased energy expenditure, and increased fat utilization . In addipost-pandrial, −2.1 pg/mL, 28 d) [In this section, we examined the changes in the levels of TNF-α in overweight and obese adults of both sexes following supplementation with different phytochemical-containing products from different sources ,130,131 L, 28 d) . Most ofL, 28 d)  that repL, 28 d) ,121,122.p = 0.02) [p = 0.035) but only in a small subgroup of participants with C-reactive protein (CRP) values ≥3.0 mg/mL [Other mixed products  have also been tested in overweight/obese individuals , i.e., 6 = 0.02) . Also, t.0 mg/mL . None ofp < 0.05) [p < 0.05) and in the subgroup of only overweight individuals  whereas in the obese participants a large but (NS) increase of TNF-α levels was observed (+21.3 pg/mL) [In addition to those studies conducted with phytochemical-containing capsules, extracts or powders, we have collected some examples of dietary intervention with different foods rich in mixed bioactive (poly)phenols and other phytochemicals which have also investigated for their effects on body weight and TNF-α levels ,135,136  < 0.05) . SupplemTNF-α gene at different positions  [p = 0.001, +38.0 pg/mL) [p = 0.045, +50.1 pg/mL) [As already seen in the previous sections, the circulatory levels of TNF-α show a large variability which may be partly due to a substantial genetic contribution to TNF-α regulation. Nonetheless, the consequences of the presence of different genetic variants on the transcription and final levels of TNF-α are complex and not yet understood. Various single nucleotide polymorphisms (SNPs) have been described within the promoter region of the 238 G/A) . One of 238 G/A) . Neverth238 G/A) ,143,144 0 pg/mL) , and in PPARD  [CAT  [TGFB  [ZIP2  [VDR in obese participants  [TMEM182 in normal and obese participants  [We additionally reviewed a number of studies reporting the levels of TNF-α across the different genotypes for other variants associated with different genes related with obesity and metabolic disorders ,157,158 1 pg/mL) , in maln9 pg/mL) , in pati0 pg/mL) , and in 1 pg/mL) . On the 5 pg/mL) , and rs1Throughout the years, a considerable number of drugs has been developed and tested to try to combat obesity. Overall, in clinical trials, many of those anti-obesity drugs promote moderate body weight reductions  when compared with lifestyle modifications. Often, those drugs also promote considerable adverse events leading to poor adherence to the treatments ,160. ProOrlistat, a pancreatic lipase inhibitor , was useA similar limited level of evidence was found for sibutramine, a drug that stimulates satiety . One stuOverall, the data included in this section show weak evidence of the reducing effects on body weight and TNF-α of the tested anti-obesity drugs. The reported changes in TNF-α levels (‘effect size’) were small and mostly (NS). It was not possible to infer that any drug or drug-diet combination reduced the levels of TNF-α consistently and significantly, in association with body weight loss nor that the drugs were more effective than diet restriction.Healthy foods and/or bioactive constituents have been long attributed with many benefits and, in particular, with anti-obesity and anti-inflammatory effects, mostly supported by many cell and animal studies reporting the reduction in the levels (gene and/or protein expression) of related biomarkers such as the pro-inflammatory cytokine TNF-α ,110. DesOverall, the results of our analysis show that: (i) many of the estimated intra-group CV% values were well above 30% and, in some cases, reached values ≥100%; (ii) the circulating levels of TNF-α were highly variable and ranged from less than 1.0 pg/mL up to some examples of several hundred pg/mL; (iii) the reported changes in this cytokine following intervention with the different dietary approaches (effect) included both statistically (S) and (NS) decreases or increases of the protein with independence of the size effect, which ranged from −0.05 pg/mL up to +21.3 pg/mL. Within this intricate scenario of high intra-group and between-studies variability it was not possible to: (1) propose clear limits for the circulating levels of TNF-α as a biomarker to differentiate between lean, overweight, and obese individuals, (2) discern distinct down-regulatory effects of the levels of this cytokine by any of the different dietary approaches investigated nutrients, (poly)phenols or other phytochemicals), and (3) perceive what an effect-size (response) of this cytokine to dietary changes might be. In addition, we found that in most of the revised trials there was not a body weight response (body weight reduction) following intervention, nor a clear association between the body weight and the TNF-α responses. In comparison with the dietary approaches, the studies revised here looking at the effects of different anti-obesity chemical drugs and proteins showed similarities in terms of trials design and variability of the results. In addition, they showed a lack of clear response of the cytokine and of an association with body weight reduction, giving consistent evidence of the difficulty in understanding TNF-α as a responsive biomarker. Nutritional and pharmacological research should go hand-in-hand to further understand variability in the response to treatment, and to move towards more personalized anti-obesity and anti-inflammatory treatments. In the next paragraphs, we discuss some of the factors that contribute to the observed variability, and propose specific recommendations to boost the quality of future intervention trials looking at TNF-α as a biomarker of anti-inflammatory response in relationship with body weight management.d = 0.5), we estimated that the sample size should be ≈35 subjects per group  [d = 0.2), the sample size would increase up to 220 participants per arm. With a few exceptions, most of the studies investigated here were carried out in groups of less than 30 participants and, in some cases, of even less than 10 participants. Despite the well-known difficulties of recruiting and maintaining a large number of participants for this kind of intervention study, we recommend that future intervention dietary studies looking at the regulation of the levels of TNF-α in overweight/obese people must make an effort to increase the number of participants and to incorporate at least a minimum of 35 subjects in the T and C groups. Another highly variable feature regarding intervention trials looking at the beneficial effects of dietary bioactive (micro)nutrients and (or) phytochemicals is that related to the ‘duration of intervention’. It has been indicated that the greatest number of trials investigating the effects of mixed (poly)phenols have been designed as ‘chronic’ to look at the long term effects of these products [A first critical issue is ‘sample size’. It has been repeatedly indicated that ‘given the variability of the results and the generally small size effects observed in dietary interventions with bioactive constituents, larger clinical trials are needed’ ,184,185.% power) ,187. ForIntervention studies looking at the health benefits of the consumption of dietary bioactive constituents have been most commonly conducted when using as the ‘test products’ readily available mixtures of compounds in the form of enriched extracts, oils, foods, beverages. In the particular case of (poly)phenols, the greatest numbers of studies have been performed using (poly)phenol-rich foods or (poly)phenol-rich extracts, mainly those from berries, grapes, cocoa, soy, olive oil, pomegranate, flaxseed, nuts, tea or red wine . The intIt has also been recently shown that the results of the analyses of several cytokines, including TNF-α, were different across various different assays tested . The authors also showed that the enzyme-linked immunosorbent ELISA assay displayed high precision and sensitivity. However, there was not a good correlation between the serum and plasma samples analysed using this method . In the The baseline characterization of the individuals taking part in a particular trial, so that at the point of departure the features of the participants are as homogeneous as possible, is an essential issue not yet fully resolved in studies looking at the beneficial properties of dietary bioactive compounds. It is still rather common to find studies carried out in mixed sample populations in terms of sex, age, body weight and/or disease status. However, each of these features constitute a well-recognised factor contributing to variability. Detailed phenotyping of trial participants for as many as possible of those potentially modulating factors has been already recommended . In our The presence of different SNPs in the regions involved in the regulation of the transcription of a gene might affect gene expression and protein levels, and eventually influence the circulating levels of such protein and constitute a potential associated risk to develop a disease . In the p = 0.0004) only with the highest dose in the subgroup of overweight/obese women with the TT genotype for the MTHFR C677T polymorphism [Even more complex and difficult is demonstrating the influence of genetic variants on the response to dietary intervention. Studies looking at the interaction between genotype and the intake of dietary bioactive (micro)nutrients on TNF-α are scarce and heterogeneous. In the study conducted by Curti et al. , the autmorphism . These rmorphism , the intmorphism . With a p-values for all comparison). Requesting common protocols of reporting results will importantly contribute to the improvement of future comparative studies and the understanding of the overall outcomes.One final and additional point that we would like to reinforce here is the need for and importance of increasing the quality of the data presentation . While rTNF-α has been widely investigated in preclinical studies looking at the metabolic and anti-inflammatory benefits of a wide range of foods and bioactive food constituents yet its validation in human trials remains unsolved. A large variability in the circulating levels of the protein as well as in the response of this important cytokine to dietary intervention is partly attributable to a range of factors related with the design of the studies but also with human intrinsic causes. One such factor is the presence of a complex mixture of genetic variants that may positively and negatively influence the production of TNF-α. Improving and normalizing the study design and incorporating the genotyping of an increasing number of genetic variants into the intervention trials will contribute to understanding the large interindividual variability of the cytokine as well as to improving the comparative analyses of multiple results from different studies, increasing the evidence and validity of TNF-α as a biomarker of response to diet.


Cryptosporidium oocyst shedding, and how shedding may be affected by subtypes and clinical parameters, is limited. Reduced transmission may be a secondary benefit of cryptosporidiosis treatment in high-prevalence areas. We conducted a prospective clinical case series in children of <5 years presenting with diarrhea to a health center and a hospital in Ethiopia over an 18-month period. Stool samples were collected repeatedly from children diagnosed with cryptosporidiosis for up to 60 days. Samples were examined, and Cryptosporidium shedding was quantified, using auramine phenol, immunofluorescent antibody staining, and quantitative PCR (qPCR). In addition, species determination and subtyping were used to attempt to distinguish between new infections and ongoing shedding. Duration and quantity of shedding over time were estimated by time-to-event and quantitative models (sex- and age-adjusted). We also explored how diarrheal severity, acute malnutrition, and Cryptosporidium subtypes correlated with temporal shedding patterns. From 53 confirmed cryptosporidiosis cases, a median of 4 (range 1 to 5) follow-up stool samples were collected and tested for Cryptosporidium. The median duration of oocyst shedding was 31 days  after onset of diarrhea, with similar estimates from the quantitative models . Genotype shift occurred in 5 cases (9%). A 10-fold drop in quantity occurred per week for the first 4 weeks. Prolonged oocyst shedding is common in a pediatric clinical population with cryptosporidiosis. We suggest that future intervention trials should evaluate both clinical efficacy and total parasite shedding duration as trial endpoints.Knowledge on the duration of IMPORTANCE Cryptosporidiosis is an important cause of diarrhea, malnutrition, and deaths in young children in low-income countries. The infection spreads from person to person. After infection, prolonged release of the Cryptosporidium parasite in stool (shedding) may contribute to further spread of the disease. If diagnosis and treatment are made available, diarrhea will be treated and deaths will be reduced. An added benefit may be to reduce transmission to others. However, shedding duration and its characteristics in children is not well known. We therefore investigated the duration of shedding in a group of young children who sought health care for diarrhea in a hospital and health center in Ethiopia. The study followed 53 children with cryptosporidiosis for 2 months. We found that, on average, children released the parasite for 31 days after the diarrhea episode started. Point-of-care treatment of cryptosporidiosis may therefore reduce onward spread of the Cryptosporidium parasite within communities and households. Cryptosporidium hominis. Cryptosporidium parvum is uncommon in livestock in Sub-Saharan Africa, and human C. parvum infections are caused almost exclusively by anthroponotic subtypes in this region  reported a mean shedding duration of 17 days after onset of diarrhea  , a few siarrhea 79. These iarrhea 7. The medTargeted treatment for cryptosporidiosis is currently limited to nitazoxanide, a drug that is not effective in immunocompromised children and regrettably is FDA-approved only for children older than 12 months, although available data indicate no adverse effects in children aged 6 to 11 months –13. Sign–Cryptosporidium shedding, i.e., both overall duration of shedding and quantitative shedding patterns over time, supplemented by subtyping to help distinguish between new infections and ongoing shedding. Cases were identified by near-patient testing using light-emitting diode auramine-phenol fluorescence microscopy (LED-AP), already available in many low-income countries, as the method is used for tuberculosis point-of-care testing . Second, we considered shedding as a quantitative outcome, measured as DNA copies/g of stool in follow-up samples. In addition, cases with a shift in the detected Cryptosporidium DNA was confirmed in 54 of these 56 cases, as two cases had negative Cryptosporidium qPCR in both enrollment and follow-up stool samples (ID34 and ID40). One case that was shedding on enrollment failed to submit any follow-up samples (ID19) and was therefore excluded from the analysis. A median of 4 (range 1 to 5) follow-up stool samples were collected from each of the remaining participants. The lowest positive detection by the qPCR assay was 519 Cryptosporidium copies per gram of stool, corresponding to approximately 130 oocyst equivalents per gram, as there are 4 copies of the cowp gene per oocyst . See Cryptosporidiosis screening was performed by LED-AP microscopy on 878 diarrhea cases; 82 (9%) were positive, and, of these, 56 were eligible and consented to participate in the follow-up study. Shedding of Cryptosporidium genotype occurred in five cases (9%): in three cases, this was reflected by subsequent detections of a different C. hominisgp60 allele family in samples taken 16, 24, and 25 days after onset of diarrhea, respectively , in one case the shift happened after two consecutive negative samples, 34 days after onset of diarrhea (ID 48), and in one case, C. hominisgp60 allele type Ib was detected in a sample obtained at day 37, after three consecutive samples with C. parvum IIc (ID 36). There were no cases where subtype shifted within the same gp60 allele family.Notably, two cases were still shedding oocysts at their last follow-up visits, 63 days after onset of the initial diarrheal episodes. Both were 6- to 11-month-olds, without acute malnutrition, who presented with acute diarrhea; HIV status was unknown for one and the other was HIV negative. A shift in Cryptosporidium declined over time. Cryptosporidium during the follow-up-period.As expected, the proportion of cases shedding Stratification of the time-to-event curves by key subgroups revealed subtle differences by sex and age group, which might confound other comparisons. We therefore fitted a log-logistic parametric time-to-event model  which allowed us to adjust for sex and age. The overall model of the decline in shedding is illustrated by Cryptosporidium, rising to 75% (95% CI 62 to 87) by 6 weeks  had stopped shedding  6 weeks .C. parvum and C. hominis. However, when the shedding curves between the different C. hominisgp60 subtype families were compared, gp60 allele family Id, the second most common gp60 allele family, was associated with a significantly longer duration of shedding than the other C. hominisgp60 allele families.Results from the parametric models, stratified by key predictors, are summarized in Cryptosporidium DNA quantity from 805 copies/g to 158 million copies/g. Quantities declined significantly with days since onset of diarrhea   .Cryptosporidium DNA shedding quantity dropped about 10-fold per week (Table S1). Thereafter, the overall trend line curved gently upwards, likely due to inclusion in the model of two cases who were still shedding after 63 days.The overall trend line was close to a quadratic relationship (effective degrees of freedom [EDF] = 1.9); however, for the first 4 weeks after onset of diarrhea, a striking pattern was seen where C. hominisgp60 allele family Id was associated with a higher quantity of shedding than the other C. hominis allele families during the time window from 11 to 55 days after onset of diarrhea . This yielded an estimate for the duration of Cryptosporidium DNA shedding at 31 days (95% CI from 25 to 37 days).We proceeded to estimate shedding duration by quantity over time by estimating the time point at which Cryptosporidium parasite shedding was investigated by follow-up of individual children presenting to health care with cryptosporidiosis. Shedding duration was approximated using two complementary analytical approaches: first by time-to-event modeling where ongoing shedding was considered a binary outcome, based on microscopic detection of oocysts and/or DNA detection by PCR, and then by quantitative modeling of the drop in DNA quantity over time, using a highly sensitive qPCR assay. Both models yielded remarkably similar estimates for median duration of shedding, at 31 days, with 95% CIs from 26 to 36 days and 25 to 37 days, respectively. The findings demonstrate that prolonged shedding is common in a general pediatric clinical population with cryptosporidiosis. This is a slightly shorter estimate than, but still in keeping with, the 40-day estimate from the MAL-ED study, which was obtained using robust modeling and quantitative PCR for reliable case ascertainment , both of which included confounder adjustment.Third, the observed tendency toward lower shedding in children who were dehydrated on enrollment seems counterintuitive but should at best be interpreted as a trend, as it was not a statistically significant finding in the adjusted models .gp60 subtype as likely ongoing shedding from a single Cryptosporidium acquisition event. This is supported by the overall drop in quantity in repeat detections but is only a plausible assumption; another possibility is that some of the repeat detections represent new acquisitions of the same gp60 genotype, e.g., from a still-present household source. Contact tracing has demonstrated clustering of subtypes in households , we may have missed some low-level mixed subtype infections. Elucidating this further would require the application of techniques to detect gp60 subpopulations that are outside the scope of this analysis, e.g., deep sequencing, vector cloning, single-cell whole-genome amplification  and Serbo Health Center in southwest Ethiopia as a follow-up study of a subset of participants in the CRYPTO-POC diagnostic accuracy study. Details of screening and enrollment have been published previously . In brieCryptosporidium oocysts, stool samples were examined by LED-AP and quantitative immunofluorescent stain antibody test (qIFAT) microscopy, and for Cryptosporidium DNA, stool samples were examined by a well-established quantitative PCR (qPCR) assay targeting the pan-species Cryptosporidium cowp gene. We generated standard curves using 10-fold dilutions of quantitative genomic Cryptosporidium parvum DNA, with final qPCR results expressed as DNA copies/g of wet stool, as described previously (Cryptosporidiosis point-of-care testing was performed by LED-AP microscopy (a semiquantitative test) at both sites, with results communicated back to the study nurse . Positiveviously .Cryptosporidium species qPCR-positive samples, C. parvum and C. hominis species identification was performed in a duplex real-time PCR assay targeting C. parvum- and C. hominis-specific sequences of the lib13 gene, using primer and probe sequences from reference On Cryptosporidium gp60 gene, which codes for a surface glycoprotein that contains a conserved region that allows classification of gp60 allele families and also a hypervariable serine repeat region that allows for classification into gp60 allele family subtypes. The gp60 PCR is a nested PCR, with primary and secondary primer sequences as described in reference gp60 gene sequence data were analyzed directly using the free software CryptoGenotyper  score was calculated based on assessment of general appearance, tears, skin pinch, and respirations; a score of 4 or more was deemed severe dehydration, 2 to 3 as some dehydration, and 0 to 1 as no dehydration .For children aged <6 months, severe acute malnutrition (SAM) was defined as weight-for-height z-score (WHZ) of ≤−3 of the WHO standard curves  and/or pCryptosporidium DNA during the enrollment episode if qPCR was positive in either the enrollment sample or the 1-week follow-up sample. A case was considered no longer shedding Cryptosporidium from the first day, after onset of the diarrheal episode, at which both Cryptosporidium qPCR and oocyst microscopy were negative. Genotype shift was defined as a detection of a new Cryptosporidium genotype  in a later sample. A case was considered having ongoing shedding at the latest time point, in days after onset of diarrhea, with either a positive Cryptosporidium qPCR or positive Cryptosporidium microscopy (LED-AP and/or qIFAT) but excluding those cases where a genotype shift had occurred.A diarrhea case was defined as shedding Time to shedding cessation was the event of interest. The nonparametric maximum likelihood estimate for the time-to-event distribution was approximated by the Turnbull estimator, a generalization of the Kaplan-Meier estimate that accounts for interval-censored data, using the R package interval . We fittCryptosporidium DNA quantity (measured in log10 copies/g of stool) in sequential follow-up samples was plotted against time (in days since onset of diarrhea) for each cryptosporidiosis case. The visual inspection indicated that the temporal drop in quantity followed a nonlinear pattern. We therefore fitted and visualized a trend line  using a generalized additive mixed model (GAMM), using an identity link and thin plate regression splines, with the maximum number of base functions (“knots”) set to 3, using the R package mgcv , sex, disease severity , and Cryptosporidium genotype and by separate plotting of difference smooths with 95% confidence intervals, using the R package itsadug. Overall shedding duration was estimated from DNA quantity, where shedding cessation was defined as the time point at which Cryptosporidium DNA quantity dropped below the reliable detection limit of the qPCR. This was estimated by a separate GAMM where days since onset of diarrhea was the outcome variable and a smoothed trend line for DNA quantity was the main predictor. To all GAMMs were added random effect intercepts for each study participant to account for within-subject clustering and also separate fixed effect linear terms to adjust for confounding by sex and age.age mgcv . The oveJimma University IRB (reference: RPGC/610/2016), the Ethiopian National Research Ethics Review Committee (reference: JU JURPGD/839/2017), and the Regional Committee for Medical and Health Research Ethics of Western Norway (reference: 2016/1096) approved the study.


Inflammation is implicated in the etiology of diverse retinopathies including uveitis, age-related macular degeneration or diabetic retinopathy. Tumor necrosis factor alpha (TNF-α) is a well-known proinflammatory cytokine that is described as a biomarker for inflammation in diverse retinopathies and therefore emerged as an interesting target to treat inflammation in the eye by neutralizing anti-TNF-α antibodies.Recently, we have demonstrated that Adeno-associated virus (AAV)–mediated expression of human TNF-α in the murine eye induces retinal inflammation including vasculitis and fibrosis, thereby mimicking human disease-relevant pathologies. In a proof-of-mechanism study, we now tested whether AAV-TNF-α induced pathologies can be reversed by neutralizing TNF-α antibody treatment.Strikingly, a single intravitreal injection of the TNF-α antibody golimumab reduced AAV-TNF-α–induced retinal inflammation and retinal thickening. Furthermore, AAV-TNF-α–mediated impaired retinal function was partially rescued by golimumab as revealed by electroretinography recordings. Finally, to study TNF-α-induced vasculitis in human in vitro cell culture assays, we established a monocyte-to-endothelium adhesion co-culture system. Indeed, also in vitro TNF-α induced monocyte adhesion to human retinal endothelial cells, which was prevented by golimumab.Overall, our study describes valuable in vitro and in vivo approaches to study the function of TNF-α in retinal inflammation and demonstrated a preclinical proof-of-mechanism treatment with golimumab.The AAV-based model expressing human TNF-α allows us to investigate TNF-α–driven pathologies supporting research in mechanisms of retinal inflammation. A major hindrance in such efforts is the lack of cross-reactivity of most neutralizing TNF-α antibodies between human and disease model species,In this study, we validated our AAV-TNF-α induced, humanized mouse model by using the therapeutic neutralizing TNF-α antibody golimumab (Simponi) as a proof-of-mechanism treatment. Strikingly, a single IVT treatment with golimumab significantly reduced retinal inflammation and rescued the TNF-α-induced increase in retinal thickness. We further evaluated electroretinography (ERG) recordings as a quantifiable readout to measure treatment efficacy and demonstrated improved photoreceptor function after golimumab treatment. Finally, we established a human monocyte adhesion in vitro model that may be used to transfer results from our mouse model to a human setting.UBE3A gene) has been described elsewhere.,,2, 2.5 mM KCl, 10% glycerol, 0.001% Pluronic F-68, pH 7.4).Cloning and production of AAV-TNF-α and the negative control AAV-stuffer  or double-distilled H2O as vehicle control treatment were injected intravitreally with a 34-G needle. Eyes in which the lens or a major blood vessel was injured due to the IVT injection procedure were excluded from analysis. Mice were sacrificed by cervical dislocation, and the eyes were enucleated and snap-frozen in liquid nitrogen or fixed in 4% paraformaldehyde for histological analysis six weeks after injection of AAV-TNF-α or AAV-stuffer. Animal experiments were performed in accordance with the German Animal Welfare Act, the guidelines of the Federation of the European Laboratory Animal Science Association and the ARVO statement for the use of animals in ophthalmic and vision research. Animal experiments performed in this study were reviewed and approved by the local authorities.Male and female C57BL/6J mice six to eight weeks old were purchased from Charles River  and housed in individually ventilated cages. Six mice per group (vehicle or golimumab) were used. Ketamine 60 to 90 mg/kg  and 6 to 8 mg/kg xylazine  were injected intraperitoneally to anesthetize the mice for in vivo imaging and ERG analysis  and phenylephrine . Optical coherence tomography (OCT) recordings were done with a Bioptigen Envisu R2210 device  equipped with a lens designed for mouse eyes (50° field of view). Fundus pictures were recorded with a Micron IV Fundus camera . Pictures of mouse eyes (pupil size) were taken with a Spectralis HRA/OCT device  approximately 20 minutes after pupil dilation as described above. The same eyes were imaged three or four times: Before AAV injection  and one week (AAV-stuffer control only) and three and six weeks after AAV injection.2. The amplitude heights of b-waves for each ERG flash response were calculated using the MATLAB software (MathWorks), and the results were graphically depicted in GraphPad Prism.ERG recordings were performed before injection of AAV  and at one (AAV-stuffer control only), three, and six weeks after AAV injection in the same mice. Mice were dark-adapted over-night and anesthetized as described above with ketamine/xylazine. Pupils were dilated with tropicamide and phenylephrine (see above) and Methocel  and gold electrodes were applied to both eyes. As a reference, a clip was placed in the mouth corner of the mouse, and grounding was ensured by another clip positioned at the tail root of the mouse. ERGs were recorded using an Espion E3 Electroretinography system  and a Full-Field Ganzfeld stimulator  with UV LEDs and a Xenon light. ERG signals were recorded with 0.15 Hz low-frequency and 500 Hz high-frequency cutoffs. The following flash intensities were used for scotopic measurements: 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, and 10 cd ∙ s/mTo quantify the inflammation stage of a mouse eye based on OCT scans, an inflammation grading system was developed: 1 = normal eye; 2 = few (<30) cellular infiltrates in vitreous; 3 = many (>20) cellular infiltrates in vitreous, retinal layers intact; 4 = cellular infiltrates and retinal layers partially disorganized; 5 = retinal layers fully disorganized, regular OCT image recording not possible. Examples on the OCT inflammation grading are shown in + and F4/80+ area was quantified using HALO image analysis software .Eyes were enucleated and fixed in 4% paraformaldehyde  for 48 hours at 4°C. Whole eyeballs were dehydrated and infiltrated with paraffin using a tissue processor  and subsequently embedded into paraffin blocks on a HistoCore Arcadia Embedding Center . Three-micrometer sections were cut at the level of the optical nerve and hematoxylin and eosin and Masson's Trichrome staining was performed according to standard protocols. Immunofluorescence staining was carried out on the automated Leica Bond platform (Leica Biosystems) using the Opal Multiplex IHC Kit . Antibodies used in this study and details about the antigen retrieval are listed in the g and 4°C for 10 minutes. Total protein concentration was determined using a bovine serum albumin protein assay , and lysates were diluted to 5 µg/mL total protein. A customized MSD U-Plex assay  was used to measure human TNF-α and mouse IL-1β, IL-6, CCL2 and MMP-9. MSD assay was performed according to the manufacturer's protocol. SECTOR Imager 6000  was used for quantification.Mouse eye lysates were generated by homogenization in ice-cold lysis buffer  supplemented with proteinase inhibitor Pefabloc SC  with metal beads  in a tissue homogenizer . The supernatant was carefully collected after centrifugation at >16,000Primary human retinal microvascular endothelial cells  were cultivated in Endothelial Cell Growth Medium  on plates precoated with 0.1% gelatin . Immortalized human microglia cells  were cultivated in microglia cell medium . THP-1 cells  were cultivated in RPMI medium (BE12-702F) supplemented with 1% GlutaMax  and 10% FCS. For the TNF-α–induced expression analysis assays, cells were stimulated with 10 ng/mL recombinant human TNF-α  for 24 hours with or without 58.8 nM golimumab .HRMEC cells were cultivated until they reached confluence. Cells were stimulated in a starvation medium containing endothelial basal medium with 0.5% bovine serum albumin with and without TNF-α and golimumab for three hours. THP-1 cells were labeled with the fluorescent live-stain BCECF-AM  at 37°C for one hour and 20,000 THP-1 cells were added onto the HRMEC monolayer and incubated for 30 minutes. Nonadherent THP-1 cells were washed off with phosphate-buffered saline solution and cells were fixed in 4% paraformaldehyde. Imaging and subsequent image analysis was done with the Opera Phenix High-Content Screening System and Harmony software .RNA was extracted using the RNeasy Mini Kit  according to the manufacturer's protocol. cDNA synthesis was done using the High capacity cDNA Archive Kit  and q-RT-PCR was performed with the TaqMan Fast Advanced Master Mix  on a Quant Studio 6 Real-Time PCR system (Applied Biosystems). Relative expression (fold induction) was calculated using the ΔΔCT method and the POLR2A gene was used as a normalization control. The following Taqman probes were used in this study: POLR2A (Hs00172187_m1), CCL2 (Hs00234140_m1), MMP9 (Hs00957562_m1), IL1B (Hs1555410_m1), IL6 (Hs00174131_m1), ICAM1 (Hs00164932_m1).P value: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.GraphPad Prism (Version 9.0.0) was used for statistical analysis. Statistical tests are indicated in the respective figure legends. Bar plot graphs represent the mean values ± SEM. The GraphPad Prism function “Identify outliers” was used to remove outliers. Significance was determined according to the The aim of this proof-of-mechanism study was to test whether the pathologies induced by a chronic AAV-induced expression of human TNF-α may be reversed by a bolus IVT injection of a TNF-α neutralizing antibody. We first injected mice with AAV-TNF-α intravitreally and two weeks later with a single IVT dose of the therapeutic neutralizing TNF-α antibody golimumab A. First,To confirm whether the disorganization of the retinal layers observed by in vivo imaging also impairs retinal function, we used electroretinography (ERG) to analyze and quantify retinal function. Interestingly, AAV-TNF-α induced a decrease in rod-driven b-wave amplitude that was progressing over time compared to the baseline values before IVT injection of AAV-TNF-α D. Altoge+ microglia and macrophages revealed a slight, but not significant trend towards less Iba1+ cells in golimumab treated eyes . To achieve a full rescue of AAV-TNF-α mediated pathologies, long-term exposure of golimumab may be needed in future studies  for sustained release of therapeutic antibodies in the vitreousIn this study, we evaluated whether the AAV-TNF-α induced retinopathy mouse model is susceptible to an anti-inflammatory treatment. AAV-driven expression of approximately 120 pg human TNF-α is stable for at least six weeks,,Aire gene.–,,–Inflammation is a common hallmark in diverse retinopathies that is not limited to uveitis, but also in AMD and DR and has, thus, come into focus for therapeutic approaches. Uveitis-related pathologies are modeled in rodents by endotoxin- or antigen-induced uveitis,Posterior synechia is the adhesion of the iris to the lens and a common complication of chronic inflammation in uveitis patients. Similarly, our AAV-TNF-α induced uveitis-like mouse model also developed posterior synechiae as seen by a smaller pupil size with irregular borders in vivo, which was confirmed by histological cross-sections. In line with our data, the EAU uveitis mouse model,+ myofibroblasts have been found in the subretinal space at the site of the laser damage.Fibrosis has been described in diverse retinal diseases and contributes to disease progression25 similar to the upregulation of several cytokines and chemokines that have been measured in the vitreous humor of uveitis patients.IL1B, IL6, CCL2 and MMP9, which was fully prevented in the presence of golimumab. Our data are in line with a previous transcriptome analysis that demonstrated upregulation of IL1B and CCL2 after TNF-α stimulation in HRMECs,We have shown previously that IVT injection of AAV-TNF-α significantly increases expression of the human transgene itself, as well as proinflammatory cytokines and chemokines IL-1β, IL-6, and CCL2/MCP-1–,Finally, we have shown in this study that AAV-TNF-α induced vasculitis can be translated to a human cell-culture based assay using a monocyte-to-retinal-endothelium adhesion assay. This approach demonstrates the relevance of the observed phenotypes in our AAV-TNF-α induced mouse model for human diseases, allows high-throughput approaches and further minimizes the use of animal models in accordance with animal welfare guidelines. TNF-α-induced monocyte-to-endothelial-cell adhesion assays have a long history and are frequently used, especially in the context of atherosclerosis research. Human umbilical vein endothelial cells or human aortic endothelial cells are the major endothelial cell types used in the literature for TNF-α induced adhesion assays,Altogether, we presented evidence that intravitreal AAV-TNF-α injection induces both morphological but also functional deficits in the mouse retina, which is partially rescued by a single dose of neutralizing TNF-α antibody. Our data suggest that our model may be used in the future to further study the function of TNF-α in retinal inflammation and is susceptible to anti-inflammatory treatments. Furthermore, this study is a proof of concept that AAV-driven expression of a retinopathy-associated gene can be modulated by a single IVT injection of a neutralizing antibody, highlighting the great potential of AAV-induced animal models.


Patient and public involvement (PPI) in health research is required by some funders and publications but we know little about how common it is. In this study we estimated the frequency of PPI inclusion in health research papers and analysed how it varied in relation to research topics, methods, funding sources and geographical regions.Cross-sectional.BMJ Open) that requires a statement on whether studies included PPI. We classified each paper as ‘included PPI’ or ‘did not include PPI’ and analysed the association of this classification with location (country or region of the world), methods used, research topic  and funding source. We used adjusted regression models to estimate incident rate ratios of PPI inclusion in relation to these differences.Our sample consisted of 3000 research papers published in 2020 in a general health-research journal (618 (20.6%) of the papers in our sample included PPI. The proportion of papers including PPI varied in relation to location (from 44.5% (95% CI 40.8% to 48.5%) in papers from the UK to 3.4% (95% CI 1.5% to 5.3%) in papers from China), method (from 38.6% (95% CI 27.1% to 50.1%) of mixed-methods papers to 5.3% (95% CI –1.9% to 12.5%) of simulation papers), topic (from 36.9% (95% CI 29.1% to 44.7%) of papers on mental health to 3.4% (95% CI –1.3% to 8.2%) of papers on medical education and training, and funding source (from 57.2% (95% CI 51.8% to 62.6%) in papers that received funding from the UK’s National Institute for Health Research to 3.4% (95% CI 0.7% to 6.0%) in papers that received funding from a Chinese state funder).Most research papers in our sample did not include PPI and PPI inclusion varied widely in relation to location, methods, topic and funding source. There has been little previous research on how often patient and public involvement (PPI) is included in health research papers and our sample (of 3000 papers) is much larger than that of previous studies.As far as we are aware, this is the first study to analyse associations between PPI inclusion and other aspects of research papers such as topic and methods.Our sample came from a single journal, but it is a general health-research and medical-research journal that includes a broad range of topics and methods and requires authors to provide a statement on PPI inclusion.We relied on published statements provided in research papers and cannot assess their accuracy.with or by people rather than on or about them.12The involvement and engagement of patients and members of the public in the conduct of health research has been endorsed and promoted by a range of government and other funders.However, reviews of PPI have found low levels of involvement and poor-quality reporting.23–27To date, research on the inclusion of PPI has been almost entirely qualitative and we lack information on how widespread PPI is within health research and in what circumstances researchers are more or less likely to include it: information that is important if we are to understand and promote PPI. Our aim in this study was to provide some of this information. We used data on PPI in papers published in BMJ Open, an open-access journal in which PPI reporting is obligatory and authors are required to include a statement about PPI even if they did not include PPI in their study. We looked at all the PPI statements in all research papers published in BMJ Open in a 12-month period. We analysed, first, how common PPI inclusion was and, second, whether and to what extent PPI varied in relation to location, method, topic and funding source.www.proquest.com), to identify all original research papers published in BMJ Open during the 12 months from 1 January 2020 to 31 December 2020 (n=3000), the most recent complete year for which data were available when we started data collection. The data for our study were the full texts of papers plus accompanying metadata . We used a custom-written PerlWe used ProQuest, an online bibliographic database  or following the paper .Location (country or region) from which the paper came, based on the country of the primary affiliation of the first author. Many countries were associated with only a small number of papers and for this reason, as well as for ease and clarity of reporting, we bundled all countries with fewer than 100 associated papers into regions. This left us with seven individual countries and six regions.Method, based on our categorisation of words in the title, from which we identified 18 terms that allowed us to assign each paper to at least one category  in relation to methods. Some papers had more than one method associated with them because they contained multiple terms, for example, both protocol and trial.Research Topic, based on the section of the journal to which the paper was assigned. Most of these relate to substantive topics  and some relate to methods  but each paper is assigned to only one section. The papers in our sample fell into 61 sections but many of these had only small numbers of papers in them. For reporting purposes, below, we provide details of research topics from the 19 sections containing at least 50 papers.Funding source, extracted from the funding section of each paper. We identified all funders associated with each paper but for reporting purposes we only provide details of those with at least 25 papers related to them: there were 12 of these. These more common funders were associated with approximately one-third of the papers in our sample.exposure variables based on this extracted information: location, method, research topic and funding source. Our aim was to understand whether papers that differed in relation to these categories  were also likely to differ in terms of including PPI.We looked at four outcome variable on our interpretation of the PPI statement and categorised each paper as either ‘included PPI’ or ‘did not include PPI’. We used the Perl script to find phrases commonly used in PPI statements, such as ‘no patients involved’, ‘patients and public were not involved’ and ‘patients and/or the public were not invited’. Based on this, the script assigned 1797 of the 3000 papers to the ‘did not include PPI’ group. This assignation helped us reach our final decision on the group in which we should put each paper but did not constitute our decision: we read through each statement to check that we agreed with the categorisation.We based our Deciding what does and does not constitute PPI is conceptually difficult, especially when only limited information—short statements provided by authors—is available. We classed papers as ‘included PPI’ if any form of involvement by one or more non-professional researchers, whether as individuals or in the form of a group, was claimed at any stage of the study. We classed as ‘did not include PPI’ papers in which:PPI was explicitly omitted .29Patient input was taken from previous literature or previous studies  and placebo trials of irritable bowel syndrome were consulted for this study to include patient experiences’).30PPI-type input was provided by experts whose primary role was not to provide knowledge from lived experience or public oversight of the study .31One of the authors (IL) took responsibility for reading all the PPI statements, correcting the script-based assignments where they were wrong, and making a final decision on the categorisation of each paper. IL and two other authors (KB and KL) did initial calibration for this decision-making in concert: all three of us discussed several borderline cases and arrived at a consensus on how to classify these. In 130 of the papers in our data set we could not find a statement or any other information about PPI and we coded these as not including PPI.We analysed our data using Stata/SE V.17.0 and Microsoft Excel. For each exposure category  we calculated descriptive statistics relating to the number of paper assigned to each category and the mean level of PPI in each. For example, in relation to methods, did papers that reported trials, evaluations or systematic reviews differ in terms of how likely they were to include PPI? What about papers that concerned surgery compared with those about anaesthetics or general practice? For the groupings within each category we estimated, with 95% CIs, the proportion of papers in which the authors reported the inclusion of PPI.In the regression models of which we report the results below, we used Stata to estimate the relative risk and CIs of having PPI by means of generalised linear models with a modified Poisson approach and robust error variances.The highest and lowest levels of PPI inclusion were associated, respectively, with papers receiving funding from two national government funders, the UK’s National Institute for Health Research (NIHR) and Chinese state funders. To provide insight into the relationships with PPI inclusion of being based in a specific country and of being supported by the state funder in that country, we conducted additional analyses to look at the differences in PPI inclusion levels in UK papers that were and that were not funded by NIHR, and in Chinese papers that were and were not funded by Chinese state funders.Since this study used publicly available data and involved neither human nor animal subjects, we did not submit it for, and it has not received, formal ethics approval.https://arc-swp.nihr.ac.uk/ppie/groups-we-work-with/penpeg), has been a patient and carer for most of her life, and has remained active in both patient advocacy and PPI for over 25 years. She joined the study after inception and was involved in data analysis and interpretation and in writing the manuscript. From the perspective of the professional coauthors, AK’s most important contribution has been to challenge us on how we have described PPI throughout the paper. She drew our attention to issues around language and power in an early draft and because of this we changed the way in which we described PPI throughout the paper.One of our coauthors, AK, is not a professional researcher but has experience of being involved in research as a patient and carer. She is a member of the Peninsula Patient Engagement Group  as having included PPI. The tables and figures below show the distribution of these papers in relation to the four types of difference we examined: location , method location  came from the UK. Those 272 papers represent 46.0% of the 591 UK papers in our sample, and 59.9% (95% CI 54.1% to 65.7%) of them included PPI. Of the 319 (54.0%) of UK papers whose authors did not report receiving NIHR funding, 31.3% (95% CI 26.2% to 36.5%) included PPI. For UK papers, receipt of NIHR funding was associated with increased PPI inclusion.Of the 178 papers whose authors reported receiving funding from Chinese state funders, 174 (97.8%) came from China. Those 174 papers represent 49.3% of the 352 Chinese papers in our sample, and 3.4% (95% CI 0.7% to 6.2%) of them included PPI. Of the 178 (50.6%) of Chinese papers whose authors did not report receiving Chinese state funding, 3.4% (95% CI 0.7% to 6.0%) included PPI. For Chinese papers, receipt of Chinese state funding was not associated with any difference in PPI inclusion.We had two aims in this study: to assess how common PPI is in a sample of published health research papers, and to measure how much variation there is in PPI inclusion according to known differences between the papers. We addressed these aims by looking at the PPI statements included in 3000 papers published in a general health and medical research journal where authors are required to include a statement about PPI, BMJ Open. In relation to our first aim, we found that approximately one in five (20.6%) of the papers in our sample reported including PPI. In relation to the second, we found marked variations in PPI inclusion across the categories we looked at. For example, papers from the UK were more than 10 times likelier to include PPI than those from China, and there were also marked differences in relation to each of method, topic and funding source.These large differences may indicate where there is potential to increase PPI inclusion in research. For example, funders wishing to increase PPI inclusion might target support and promotion to researchers working in fields with low levels of inclusion. The funding source we found to be associated with the highest level of PPI inclusion was the UK’s NIHR, which has for several years included in its application process a section in which applicants may either apply for funding to include PPI or justify why they are not including it. We note, at the same time, that NIHR lacks a single, unequivocal policy statement about PPI.33Previous work has reviewed PPI reporting in specific topics such as dementia,Our study has some noteworthy limitations. The first of these relates to PPI statements. We took authors’ PPI statements at face value: if the statement said that PPI took place then we took this to be the case, even though there is evidence that what researchers say they will do and what they actually do, in relation to PPI, varies.36A second, related, limitation is that PPI may be understood differently in different places and, as a result, reported differently. We found PPI is more common in health research from the UK than elsewhere. The development and institutionalisation of PPI in the UK can be seen as the outcome of specific national socio-political events and trends. As a result, the term ‘patient and public involvement’ has resonances in UK health research that may be different or absent elsewhere. As a group of authors, we are most familiar with policies and expectations around PPI in the country in which we are based, the UK, so we cannot comment in detail on the ways PPI may be viewed or implemented elsewhere. In other settings there may be other ways of addressing the concerns that PPI is seen as addressing  and a focus on PPI may overlook these. A recent systematic review of attitudes and approaches to PPI in Europeand (b) is open access and (c) is not discipline- or method-specific and publishes papers across a range of health-related topics and range of methods. Using only a single journal also simplified our analysis, since we did not have to consider or attempt to account for variations across journals in terms of editorial policy or other factors. However, peer-reviewed journals are, by definition, selective in the papers in they publish. BMJ Open publishes many study protocols and many papers by authors based in the UK. The journal’s policy regarding PPI may encourage authors to submit papers that include PPI and discourage them from submitting those that lack it. These characteristics may influence the topics and methods submitted to the journal as well as actions concerning PPI.A third limitation relates to our sample. We used a purposeful sample of papers from a single journal, BMJ Open—if this were a clinical study we might call it a case series. We used BMJ Open because, as far as we know, it is the only journal that (a) has a written policy requiring papers to include a PPI statement We believe there is scope for further research on the issues we have begun to explore here. This might include work on the quality of PPI reporting, perhaps based on established criteria; replication of our analyses using a sample of papers taken from elsewhere; and qualitative analysis of the statements made both by authors who report PPI and those who report none. Our dichotomisation of statements into ‘included PPI’ or ‘did not include PPI’ ignores any issues of quality or extent of involvement and this topic merits investigation. Our analysis has looked only at associations between PPI involvement and broad categories such as country of origin or funding body and not micro-level differences such as attitudes, policies or beliefs at the level of research organisations, research groups or individual researchers. We will need different methods to study such influences on how and why PPI in health research occurs—and why it does not.


Higher levels of sTNFr1 were associated with higher sTNFr2 (p < 0.001) and CRP (p < 0.001). Moreover, the concentration of sTNFr2 positively correlated with CRP (p < 0.05) and adiponectin (p < 0.001). Levels of TNF-α were not associated with elevated CRP. Conclusion: This study demonstrated that changes in the concentrations of TNF and its receptors differ between groups of patients with different BMI. These findings suggest that the effective use of anti-TNF therapy is dependent not only on BMI, but also on concentrations of TNF-α receptors and other laboratory parameters.Background. We sought to measure the levels of adipokines, TNF-α and soluble receptors  in heart failure patients with reduced ejection fraction (HFrEF) due to non-ischemic cardiomyopathy (nDCM). Methods. A total of 123 patients with HFrEF due to nDCM were divided into three groups according to BMI: 34 (27.6%) normal weight, 56 (45.5%) overweight and 33 (26.8%) obese. A six-minute walk test, echocardiography and right heart catheterization were performed. Serum concentrations of adiponectin, leptin, NT-proBNP, blood hemoglobin, sodium, creatinine, ALAT, AspAT, bilirubin, CRP, lipids, TNF-α, sTNFr1 and sTNFr2 receptors were measured. Results. Obese patients had the lowest NT-proBNP concentrations, significantly higher leptin levels and higher leptin/adiponectin ratios. The concentration of sTNFr1 was higher in normal-weight patients. In all groups, TNF-α concentrations correlated positively with sTNFr1 ( Heart failure is a clinical syndrome with different etiologies, both at the structural and cellular level. As the disease is still a common and potentially fatal condition, scientists are continually looking for new diagnostic methods and treatments to improve the quality of life and survival of patients. Last year, the new guidelines established a new recommendation for the treatment of chronic heart failure. The new, simplified treatment algorithm for the management of all HFrEF patients includes five groups of drugs . In addiInflammation is known to play an important role in the pathogenesis of HF ,7,8,9 anThese receptors, which play opposite roles in TNF-α signaling, can be released from various cells to generate soluble TNF receptors 1 or 2 (sTNF r1 or r2). Circulating TNF-α may bind to these receptors, which reduces the availability of TNF-α to bind and activate cell membrane receptors for TNF ,17.Although initial trials with high-dose anti-tumor necrosis factor alpha agents were associated with worsening HF ,19, someThis study sought to assess the relationships between serum TNF-α levels, sTNFr1 and sTNFr2 and other biomarkers and some clinical parameters in patients with HFrEF due to non-ischemic cardiomyopathy, stratified according to their BMI. Additionally, we examined the association between the TNF system and adipocytokines, released from the adipose tissue, such as leptin and adiponectin.We enrolled 124 heart failure patients with a reduced ejection fraction due to non-ischemic cardiomyopathy. Patients were admitted to the 2nd Department of Cardiology, Zabrze, Poland, between 2008 and 2010, for periodic right heart catheterization in the management of heart transplant recipients. Dilated cardiomyopathy was diagnosed according to the WHO criteria. Patients were clinically stable, and their therapy had not been changed for at least one month before enrollment. Exclusion criteria were connective tissue disease, active inflammatory or infectious disorders, ischemic or valvular heart disease and alcohol abuse. All patients received optimal conventional HF therapy according to the current guidelines. The study protocol was approved by the Bioethics Committee of the Medical University of Silesia. Written informed consent was obtained from all patients.To assess functional capacity, the NYHA classification and six-minute walk test (6-minWT) were used. Body mass index was calculated by the following formula: weight in kilograms divided by the square of the height in meters (BMI). We performed a physical examination, ECG and echocardiography . Left ventricular end-diastolic volume (EDV) and end-systolic volume (ESV) were obtained from the apical four- and two-chamber views by the modified Simpson’s method. To determine ventricular systolic function, the left ventricular ejection fraction (LVEF) was calculated as follows: [EDV − ESV] × 100/EDV. Obstructive coronary artery disease was excluded by coronary angiography. Right heart catheterization (RHC) was performed with Swan-Ganz catheters (Star Edwards Lifesciences) under local anesthesia (1% Lignocaine) via the right jugular vein into the pulmonary artery. Pulmonary wedge pressure (PWP), systolic pulmonary artery pressure (sPAP), diastolic pulmonary artery pressure (dPAP) and right atrial pressure (RAP) were measured after a 20-min stabilization period. Systolic (sABP) and diastolic (dABP) arterial blood pressure were examined noninvasively. Cardiac output was measured by thermodilution with the use of a rapid bolus injection of 10 cc of cold saline. For the final evaluation, mean values of hemodynamic measurements (performed five times) were used .g, 4 °C), and the serum was immediately separated and transferred into 1 mL cryotubes and stored at −70 °C for later analysis. The human adiponectin level was measured by sandwich enzyme-linked immunosorbent assay (ELISA) with a commercially available kit . The concentration of adiponectin was expressed as μg/mL. The sensitivity of the assay was 26 ng/mL. Assay range was 0.1–10 μg/mL. Intra-assay was 4.9%, inter-assay 6.7%. The human leptin level was measured by sandwich enzyme-linked immunosorbent assay (ELISA) with a commercially available kit . The concentration of leptin was expressed as ng/mL. The sensitivity of the assay was 0.2 ng/mL. Assay range was 1–50 ng/mL. Intra-assay was 5.9%, inter-assay was 5.6%. Human TNF-α, sTNFr1 (60 kDa) and sTNFr2 (80 kDa) levels were measured with a commercially available ELISA kit . The concentration of TNF-α was expressed as pg/mL. The sensitivity of the assay was 2.3 pg/mL. Assay range was 7.8–500 pg/mL. Intra-assay was 6.0%, inter-assay was 7.4%. The concentration of sTNFr1 (60 kDa) was expressed as ng/mL. The sensitivity of the assay was 0.05 ng/mL. Assay range was 0.08–5 ng/mL. Intra-assay was 1.9%, inter-assay was 8.6%. The concentration of sTNFr2 (80 kDa) was expressed as pg/mL. The sensitivity of the assay was 0.10 ng/mL. Assay range was 0.16–10 ng/mL. Intra-assay was 1.4%, inter-assay was 2.0%. All ELISA tests were performed using a BioTek Elx50 reader .Blood samples for laboratory assessments were obtained from all patients on the day of clinical assessment at the start of the study. The samples were collected in the morning before breakfast, after 12 h of overnight fasting. The blood samples (5 mL) were centrifuged .p < 0.05. Statistical analyses were performed using Statistica 10.0 software .The study group was divided into three subgroups depending on their BMI. The normality of the distribution of the continuous data in the entire group of patients and each subgroup was analyzed using the Shapiro–Wilk test. Due to the nonparametric distribution, the continuous data were expressed as medians and first (Q1) and third (Q3) quartiles. Categorical data are presented as numbers and percentages. The significance of differences between groups was determined using the Kruskal–Wallis ANOVA test and, in the case of statistical significance, with the Chi2 with Yates correction and U Mann–Whitney tests as appropriate. The Spearman correlation coefficient was used to determine the correlations between biomarkers. Nonparametric tests were used due to the small sample size and non-normally distributed data. The results were considered statistically significant when 2), 26.8% were obese (BMI: >30.00 kg/m2) and 27.6% were of normal weight (BMI: 18.00–24.99 kg/m2). Patients presented with severe left ventricular systolic dysfunction with median ejection fraction 25.0% (17.5–30.0%). Most patients (69.11%) were in NYHA functional class I or II. The clinical characteristics of the patients divided according to their BMI are summarized in A total of 124 patients , median age 47.8 [35.8–54.6] years, with HFrEF due to dilated cardiomyopathy were enrolled in the study. Only one female (0.1%) was considered underweight and cachectic with BMI = 17.3 and was not included in the analysis. In the entire group, 45.5% of patients were overweight , aldosterone receptor antagonists (MRA), diuretics, digitalis or statins, but there was a small percentage of patients who reached target doses. The number of patients tolerating and treated with the maximum recommended doses of beta-blockers and ACE/ARB is provided in the second line of In the entire group, there were no statistically significant correlations between TNF activity and NT-proBNP, 6-minWT distance, echocardiographic and hemodynamic measurements.p < 0.001). Moreover, higher levels of sTNFr1 were associated with higher sTNFr2  and CRP . In turn, the concentration of sTNFr2 positively correlated with adiponectin . Apart from the correlation with TNFR2, the concentration of adiponectin correlated negatively with the level of leptin . The leptin/adiponectin ratio correlated negatively with sTNFr2 . BMI correlated negatively with adiponectin  and positively with leptin  and leptin/adiponectin ratios In the entire group, TNF-α concentrations correlated positively with sTNFr1 . In the normal-weight group, only sTNFr1 correlated positively and TNF-α/sTNFr1 correlated negatively with NT-proBNP . There was no correlation between TNF activity and NT-proBNP in obese individuals. In normal-weight patients, TNF-α positively correlated with sTNFr1 and sTNFr1, whereas sTNFr2 positively correlated with CRP; in turn, adiponectin correlated negatively with leptin concentrations. In obese patients, adiponectin positively correlated with TNF-α and negatively with leptin and with CRP  were more common in obese patients. On the other hand, patients with a normal body weight were likely to have adverse prognostic factors, such as the lowest sodium concentrations, NYHA class III and IV and the highest NT-proBNP levels.It is generally known that NT-proBNP is a useful biomarker in the diagnosis and prognosis of heart failure and is regarded as the gold standard in heart failure and cardiac dysfunction ,36,37,38Recent evidence indicates also an inverse association between body mass index and NT-proBNP levels ,40. One Another possible explanation for the BNP degradation is the release of proinflammatory cytokines  and resistin from the adipose tissue .In the entire group, but not in overweight patients, there was a positive correlation between TNF-α and sTNFr1 levels.The question remains as to whether the increase in sTNFr1 was compensatory and why it was not observed in overweight patients.The findings of the present study show that elevated TNF-α was associated with elevated sTNFr1 in the entire group. After stratification into subgroups, this correlation was not observed in overweight patients. There was no relationship between TNF-α/sTNFr1 ratios and NTproBNP levels in the entire group or in obese patients. However, the relationship was negative in normal-weight patients and positive in overweight patients. Moreover, the concentration of TNF-α and the TNF-α/sTNFr2 ratio were associated with elevated NT-proBNP levels in overweight patients.The question remains as to whether the increase in sTNFr1 is compensatory and why it is not observed in overweight patients.Kleinborgard et al. found that sTNFr1, but not elevated sTNFr2, was associated with incident HF . SeveralMarti et al.  confirmeSoluble TNF-α receptors seem to be more useful biomarkers because they are more stable than TNF in blood samples .It is also worth mentioning that both sTNFr1 and sTNFr2, but not TNF-α, were associated with increased concentrations of CRP in normal-weight patients in the current study. Therefore, we propose to consider sTNF receptors rather than TNF-α as a biomarker of inflammation associated with heart failure.In addition to analyzing the correlation between TNF and BMI, we assessed the relationship between TNF and adipokines produced by the adipose tissue. This study showed that obese patients had the highest leptin concentrations and leptin/adiponectin ratios. Similar findings were also reported by Wannammethe et al. In their study, leptin levels were significantly higher in overweight and obese patients with heart failure in comparison to subjects with a normal BMI. The investigators concluded that in men with HF, leptin (possibly reflecting cachexia) explained the inverse association between mortality and excess weight . FurtherThe current study found a negative correlation between leptin and sTNFr1 and a positive relationship between adiponectin and sTNFr2 in normal-weight patients. Hyperleptinemia in obesity is probably a result of the downregulation of leptin receptors, leading to leptin resistance. The role of leptin in heart failure is unclear. Some papers show that high concentrations of leptin may have a protective role in HF . HoweverIn obese subjects, adiponectin levels are decreased, and the ability of adiponectin to inhibit the inflammatory processes is limited .In our study, adiponectin was inversely related to CRP in obesity, but, surprisingly, leptin was inversely related to CRP in normal-weight patients. These unexpected results do not align with most previous observations ,59; howesTNFr1 and sTNFr2, but not TNF-α, were associated with increased CRP primarily in normal-weight patients. It is worth noting that patients in this group were least likely to receive statins with documented anti-inflammatory effects . ObesityIn fact, recent reports have confirmed that a reduction in body mass index between the first and the second hospitalization for HF is associated with an increased risk for further hospitalizations and cardiovascular mortality .This report suggests that changes in body weight reflecting cachexia may be more important than BMI. In line with these findings, a single assessment of BMI does not clearly identify patients with TNF disorders who could benefit from anti-TNF therapy.On the other hand, the limitation of this hypothesis is the difficulty in the distinction between purposeful and non-purposeful weight loss ,81. MoreBiological anti-TNF-α therapies are routine medications used in patients with rheumatoid and psoriatic arthritis, psoriasis, ankylosing spondylitis or Crohn’s disease. TNF-α inhibitors represent the fusion protein of TNF-α receptors linked to the Fc region of human antibody (etanercept) or chimeric (infliximab), fully human  or modified human (certolizumab–pegol) anti-TNF-α antibodies . One of Based on the analyses performed, it was found that the behavior of TNF and its receptors differs depending on the BMI. However, based on our assessment, we do not believe that qualification for anti-TNF treatment can be based only on anthropometry . The possibility of a change in body weight, the risk of sarcopenia or cachexia and evidence of inflammation should be taken into account. Moreover, the key to the benefit of this therapy may be determining the appropriate dose for patients with heart failure.Therefore, we believe that future studies with a larger group of patients and with a more accurate assessment of cachexia should be conducted. Moreover, it is necessary to consider a study with patients treated according to the recent guidelines, including ARNI and SGLT-2 inhibitor therapy.Undoubtedly, solutions to the above issues can be provided by studies that, on the one hand, assess the expression of TNF-α and its receptors, and, on the other hand, consider their influence on the cardiovascular system and long-term mortality in patients with heart failure.In conclusion, laboratory tests showed that TNF-α concentrations were similar in patients with a normal body weight, overweight and obesity, whereas sTNFr1 levels were higher in normal-weight patients. There were no differences in the calculated ratios of TNF-α/sTNRr1, TNF-α/sTNFr2 and sTNFr1/sTNFr2 between the groups, either. No correlations between TNF activity and echocardiographic and hemodynamic parameters were found. The lowest NT-proBNP concentrations were detected in obese patients and the highest ones in lean patients. There were no correlations between NT-proBNP and TNF activity in obese patients.This study showed that both sTNFr1 and sTNFr2, but not TNF-α, were associated with increased CRP only in normal-weight patients.Thus, it is reasonable to assume that not only TNF-α concentrations but also the levels of its receptors are required to be measured before the implementation of anti-TNF therapy.We do, however, acknowledge that our study has certain limitations. First, the study group was relatively small. However, the group was homogenous, consisting of subjects with non-ischemic cardiomyopathy, and patients with the exacerbation of HF were not included in the study. Second, there was no information on the breakdown of muscle and fat tissues. Third, there were no data on weight changes prior to study entry.


This letter to the editor responds to remarks on a recently published article aboutdermatologic events associated with enfortumab vedotin and agrees that education on theprevention and management of such events should be a priority. Ingen-Housz-Oro et alEnfortumab vedotin is the first and only therapy known to improve overall survival, comparedwith chemotherapy, in locally advanced/metastatic urothelial cancer (la/mUC) in the postplatinum-based chemotherapy and post-PD-1/PD-L1 inhibitor setting. As such, the role ofappropriate adverse event management to allow patients with limited treatment options toreceive enfortumab vedotin is critical to the safe use of this drug. These strategies havealso been further contextualized by dermatologists and oncologists with relevant experiencemanaging dermatologic events. We believe these practice recommendations have the potential toimprove quality of life and patient outcomes.2 The referenced cases from Dr. Ingen-Housz-Oro and colleagues1 further highlight the importance of closemonitoring for signs and symptoms as early as the first cycle (when skin reactions oftenpresent), the use of recommended dose modifications, including dose holds, and interventionfor patients with these reactions receiving enfortumab vedotin. Close collaboration with adermatologist is also recommended. All of this guidance is detailed further in themanuscript.2Indeed, there is a spectrum of dermatologic manifestations observed with enfortumab vedotin,given that its target, Nectin-4, is normally expressed at the surface of keratinocytes andepithelium of sweat glands and hair follicles.We agree that further research is important. Several clinical trials to investigateenfortumab vedotin in first-line treatment of la/mUC, in muscle-invasive bladder cancer, andin other solid tumors are ongoing. Data from clinical trials, combined with observations fromcase reports and clinical management manuscripts, will allow refinement of best practicerecommendations for optimizing the mitigation and management of skin reactions with enfortumabvedotin, both alone and in combination with other agents.