Electroencephalogram microstates are recurrent scalp potential configurations that remain stable for around 90 ms. The dynamics of two of the four canonical classes of microstates, commonly labeled as C and D, have been suggested as a potential endophenotype for schizophrenia. For endophenotypes, unaffected relatives of patients must show abnormalities compared to controls. Here, we examined microstate dynamics in resting-state recordings of unaffected siblings of patients with schizophrenia, patients with schizophrenia, healthy controls, and patients with first episodes of psychosis (FEP). Patients with schizophrenia and their siblings showed increased presence of microstate class C and decreased presence of microstate class D compared to controls. No difference was found between FEP and chronic patients. Our findings suggest that the dynamics of microstate classes C and D are a candidate endophenotype for schizophrenia. EEG microstate abnormalities have been reported in patients with schizophrenia. Here the authors demonstrate that patients and their siblings show similar microstate abnormalities compared to healthy controls. For this reason, endophenotypes are of crucial interest. Endophenotypes are associated with the illness, state-independent, co-segregate within families, and found in unaffected relatives of individuals with the disorder at a higher prevalence than in the general population3. Abnormal temporal dynamics of electroencephalogram (EEG) microstates were proposed as an endophenotype for schizophrenia6. Microstates are global patterns of scalp potential topographies that remain quasi-stable for around 60–120 ms before changing to a different topography that remains quasi-stable again, suggesting semi-simultaneity of activity of large-scale brain networks7.Schizophrenia is a heterogeneous disease strongly determined by genetics. However, no strong genetic correlates have been found yet8. This allows the use of clustering algorithms to group microstates into a finite set of classes based on their topographical similarity10. Even though there is still no general consensus on how to determine the optimal number of microstate classes13 and the optimal number of classes of microstates may depend on the dataset analyzed14, in clinical research, usually, four classes of microstates, labeled A, B, C, and D, are used15 based on pioneering work18. These four dominant classes of microstates are consistently observed in resting-state EEG , explaining 65–84% of the global variance of the data5. Here, we used these four canonical classes of microstates because this allows comparison between studies.EEG microstates are highly reproducible, both within and across participants22. A direct comparison of the findings is difficult due to the differences in data acquisition and processing as well as the number of microstate classes used and the way the microstates analyses were performed. Nonetheless, these studies indicate that EEG microstates are closely related to resting-state networks (RSNs) commonly found in resting-state functional magnetic resonance imaging (fMRI) despite the different time resolutions of the two modalities (see5 for a review). Among the above-mentioned studies, the one by Britz et al.16 is usually referred to when discussing fMRI correlates of EEG microstates since it used an approach more closely related to the conventional EEG microstate analysis. Britz et al. found that microstate class A was associated with the auditory RSN, microstate class B with the visual RSN, microstate class C with the salience RSN, and microstate class D with the attention RSN. Similar results were found using source localization of the EEG microstate classes13, providing further evidence for these associations. However, since both fMRI RSNs and EEG microstates are still lacking a full understanding of their significance in terms of the functional organization of brain networks, one should be cautious when interpreting these associations.Several studies have attempted to identify the brain sources underlying these classes of microstates5. A meta-analysis comprising seven studies from 1999 to 201515 revealed that microstate class C occurred more frequently and for longer durations in patients than in controls, whereas microstate class D occurred less frequently and for shorter durations. Microstate class B was shorter in patients than controls, but the effect was not significant after correction for multiple comparisons.In schizophrenia research, numerous studies have reported abnormalities in the temporal dynamics of EEG microstates measured in patients with schizophrenia compared with controls23. These results have prompted researchers to suggest that the abnormal EEG microstates dynamics may be an endophenotype for schizophrenia6. For an endophenotype, it is important that the siblings of the patients also show abnormal patterns. No such study exists for microstates.Similar abnormalities were also observed in adolescents with 22q11.2 deletion syndrome, a population that has a 30% risk of developing psychosisHere, we analyze the microstates dynamics in unaffected siblings of patients with schizophrenia, patients with schizophrenia, and healthy controls. To preface our results, siblings show abnormalities in microstate classes C and D, similar to patients. Surprisingly, siblings also show increased presence of microstate class B compared with patients. We interpret this increased presence of microstate class B as a compensation signal, which might prevent unaffected siblings to develop the disorder even if there is a genetic predisposition.In a second study, we investigate whether patients with a first episode of psychosis (FEP) show similar microstates dynamics as chronic patients with schizophrenia or have the compensation signal as the siblings , since the disorder has not fully blown. Moreover, we test FEP three times throughout 1 year to assess whether the microstates dynamics change with the progression on the disorder.Finally, we perform a meta-analysis over studies investigating the EEG microstates dynamics in the schizophrenia spectrum to provide an up-to-date estimate of the overall effect sizes of microstate anomalies in schizophrenia.24. The four microstate classes for patients, siblings, and controls are shown in Fig. 5: two microstate classes (A and B) with diagonal axis orientations of the topographic map field, one class (C) with anterior–posterior orientation, and one class (D) with a fronto-central extreme location.We examined 5 min resting-state EEG data of 101 patients with schizophrenia, 43 siblings of patients with schizophrenia, and 75 healthy controls, and we estimated the dynamics of the four canonical EEG microstate classes using CartoolFor each participant, three per class microstate parameters were computed: mean duration, time coverage, and frequency of occurrence (occurrence). Mean duration (in ms) is the average time that a given microstate was uninterruptedly present. Time coverage (in %) is the percentage of the total time spent in a given microstate. Occurrence is the mean number of times a given microstate is occurring per second. Group average statistics are depicted in Fig. F = 1.653, p = 0.176, η2 = 0.007, 90% CI ), time of coverage =2.063, p = 0.104, η2 = 0.010, 90% CI ), and occurrence  = 1.136, p = 0.334, η2 = 0.005, 90% CI ). The analyses also yielded significant Microstate Class × Group interaction effects for mean duration  = 16.246, p = 4.219e − 10, η2 = 0.071, 90% CI ), time of coverage  = 17.458, p = 8.316e − 11, η2 = 0.086, 90% CI ), and occurrence  = 8.477, p = 1.664e − 5, η2 = 0.035, 90% CI ). These interactions indicate that group differences depend on the microstate class. Post hoc pairwise group comparisons  ANOVAs showed nonsignificant Gender × Microstate Class × Group interaction for mean duration  = 8.061, p = 3.310e − 5, η2 = 0.041, 90% CI ), time of coverage  = 9.073, p = 8.472e − 6, η2 = 0.048, 90% CI ), and occurrence  = 6.938, p = 1.511e − 4, η2 = 0.031, 90% CI ). Post hoc pairwise group comparisons  = 0.360, p = 0.043, pholm = 0.473; occurrence of microstate class C: r(30) = 0.430, p = 0.014, pholm = 0.168). We also compared the microstates dynamics of all patients compared with all siblings (Supplementary Table n = 69) against all siblings , we paired these 32 patients to their siblings and compared their difference score (Δ) for each microstate parameter and class against 0 with one sample r(86) = 0.236, p = 0.027, pholm = 0.324).We correlated the values of the computed microstate parameters of patients with patients’ medication intake (chlorpromazine (CPZ) equivalent), Scales for the Assessment of Negative Symptoms (SANS) and Scales for the Assessment of Positive Symptoms (SAPS) scores, and illness duration (Supplementary Table 5), might be due to the diverse diagnosis of the FEP group  to match the 22 FEP as closely as possible, regarding to gender, age, and education). The microstates analysis was the same as in “Study 1”. The four microstate classes for the FEP group are shown in Fig. F = 0.821, p = 0.485, η2 = 0.011, 90% CI ), time of coverage  = 0.633, p = 0.595, η2 = 0.007, 90% CI ), and occurrence  = 0.860, p = 0.464, η2 = 0.009, 90% CI ), as well as nonsignificant group differences for mean duration  = 1.819, p = 0.185, η2 = 0.042, 90% CI ), time of coverage  = 0.051, p = 0.823, η2 = 0.001, 90% CI ), and occurrence  = 0.484, p = 0.490, η2 = 0.011, 90% CI ).We found no statistically significant differences between the FEP and Patients_22 groups for any of the computed microstates parameters. Two-way rm-ANOVAs yielded nonsignificant group (FEP and Patients_22) × microstate class  interactions for mean duration  = −0.516, p = 0.014, pholm = 0.168; occurrence of microstate class C: r(20) = −0.429, p = 0.046, pholm = 0.506).We correlated the values of the computed microstate parameters in FEP with FEP’s medication intake , SANS and SAPS scores, and illness duration . Out of these 16, 11 were tested 6 months later on a third session FEP_3). Summary statistics of the computed microstates parameters for the FEP group for the three measurements throughout 1 year (FEP_2 and FEP_3) is shown in Fig. . SummaryF = 0.433, p = 0.730, η2 = 0.004, 90% CI ), time of coverage  = 0.512, p = 0.676, η2 = 0.005, 90% CI ), and occurrence  = 1.060, p = 0.375, η2 = 0.009, 90% CI ), as well as nonsignificant testing session differences for mean duration  = 3.416, p = 0.084, η2 = 0.001, 90% CI ), time of coverage  = 1.000, p = 0.333, η2 = 4.729e − 14, 90% CI ), and occurrence  = 0.171, p = 0.685, η2 = 2.812e − 4, 90% CI ). JZS Bayes factor ANOVAs with default prior scales revealed that the main effects models were preferred to the interaction model by Bayes factors of 4.739, 7.957, and 5.824, for mean duration, time of coverage, and occurrence, respectively. Moreover, the analyses revealed that models without the main effect of Testing Session were preferred to the main effects models by Bayes factors of 8.440, 5.464, and 5.051, for mean duration, time of coverage, and occurrence, respectively.For the FEP_2 comparison, two-way rm-ANOVAs yielded no nonsignificant Testing Session (First and Second Session) × Microstate Class  interactions for mean duration  = 0.513, p = 0.796, η2 = 0.009, 90% CI ), time of coverage  = 0.210, p = 0.972, η2 = 0.003, 90% CI ), and occurrence  = 0.255, p = 0.955, η2 = 0.004, 90% CI ), as well as nonsignificant testing session differences for mean duration  = 0.885, p = 0.428, η2 = 0.002, 90% CI ), time of coverage  = 0.443, p = 0.648, η2 = 3.214e − 14, 90% CI ), and occurrence  = 0.289, p = 0.752, η2 = 0.001, 90% CI ). JZS Bayes factor ANOVAs with default prior scales revealed that the main effects models were preferred to the interaction model by Bayes factors of 14.333, 24.000, and 22.500, for mean duration, time of coverage, and occurrence, respectively. Moreover, the analyses revealed that models without the main effect of Testing Session were preferred to the main effects models by Bayes factors of 11.628, 13.889, and 11.111, for mean duration, time of coverage, and occurrence, respectively.For the FEP_3 comparison, two-way rm-ANOVAs yielded no nonsignificant Testing Session  × Microstate Class  interactions for mean duration  and microstate class  are shown in Supplementary Figs. 15, we found consistently increased time coverage  and occurrence  of microstate class C in patients compared with controls, as well as decreased time coverage  and mean duration  of microstate class D in patients compared with controls. We also found a decreased mean duration of microstate class B in patients compared with controls; however, the effect was not significant after correction for multiple comparisons . No consistent group differences were found for microstate class A.Our literature search identified eight independent studies comparing the resting-state dynamics of the four canonical microstate classes in patients belonging to the schizophrenia spectrum against a control group36. Similar patterns were found in patients with 22q11.2 deletion syndrome23. Based on these findings, Tomescu et al. suggested that alterations in the temporal dynamics of EEG microstates are a candidate endophenotype for schizophrenia6. For an endophenotype, it is important that unaffected relatives also show abnormalities, pointing to the genetic underpinnings of the disease3.Several studies have consistently identified abnormal temporal dynamics of EEG microstates in patients with schizophreniaHere, we showed that siblings and patients show similar microstates dynamics: increased presence of microstate class C and decreased presence of microstate class D compared with controls. These results suggest that microstate classes C and D capture some genetic component shared by the patients and their unaffected siblings. For microstate class B, patients showed decreased mean durations compared with controls. Surprisingly, microstate class B was more present in siblings compared with patients. No statistically significant group differences were found for microstate class A.3.We also analyzed the EEG microstates of 22 FEP patients and a subset of 22 chronic patients with schizophrenia, selected pseudo-randomly to match the FEP patients’ demographics as close as possible. We found no evidence for differences between the two groups in any of the microstate parameters of any of the microstate classes. We re-tested FEP patients two other times, separated by 6 months, and found that, in general, the microstates dynamics remained stable. However, this interpretation should be taken with care since only a subset of the initial 22 FEP (16 in the second testing and 11 in the third testing) participated in the three tests. Nevertheless, these results suggest that the microstates abnormalities are present at the beginning of the disease and remain stable until chronicity is established, which is important for fulfilling the requirements of an endophenotype15. Namely, increased presence of microstate class C and decreased presence of microstate class D in patients compared with controls, with medium effect sizes. Decreased mean duration of microstate class B in patients compared with controls, with small effect size; though this effect was not significant after correcting for multiple comparisons.Finally, we conducted a meta-analysis over nine studies investigating the EEG microstates dynamics in the schizophrenia spectrum (including the current study) to provide an up-to-date estimate of the overall effect sizes of microstate dynamics abnormalities in schizophrenia. In general, the results of our meta-analysis were similar as the ones reported in a previous meta-analysis3 and further practicability and explicability criteria proposed by Turetsky et al.37, discussed one-by-one below. Association with the disease: abnormalities in the temporal metrics of microstate classes C and D have been associated with schizophrenia for almost 20 years, with medium effect sizes15. Relatives: here, we showed that unaffected siblings show similar abnormalities as their ill relatives. State independency: here, we showed that FEP show similar microstates dynamics as chronic patients and that, in FEP, the dynamics remain stable throughout 1 year. We did not directly compare the FEP against healthy controls or the effects of medication, but several other studies have done so and found that FEP and un-medicated chronic patients also show similar microstate classes C and D deviations36. Practicability: resting-state EEG is easily recorded in a 5 min session, and EEG montages with as low as 19 electrodes can be used for microstates analysis38. Moreover, even though differences in EEG preprocessing and temporal smoothing parameters might influence the results of microstate analysis, it has been shown that microstate analysis has a high test–retest reliability, independently of the clustering algorithms applied and the number of electrodes used8. Explicability: the abnormal microstates dynamics in schizophrenia are viewed as an imbalance between processes that load on saliency (microstate class C), which are increased, and processes that integrate contextual information (microstate class D), which are reduced15. This interpretation goes in line with the view of schizophrenia as a state of abnormal assignment of saliency39 and a disorder affecting attentional processes, context update, and executive control40. Heritability: we currently have no information on the heritability of the patterns of microstate dynamics. However, we found positive correlations between the values of microstate parameters in patients and their siblings for occurrence of microstate class C and mean duration of microstate class B. Although the current study was not conceived to study heritability and these correlations did not survive multiple comparisons, these correlations provide some weak evidence that patterns of microstate dynamics might be heritable. Further studies, designed to address the heritability issue, might provide further evidence for this hypothesis.In sum, the dynamics of resting-state EEG microstates, particularly classes C and D, are a potential endophenotype for schizophrenia since it meets most of the major criteria proposed by Gottesman and Gould16. In healthy participants, it is the shortest and least frequent microstate from adolescence on41. Moreover, the visual network is expected to reach maturation much earlier than higher order cognitive networks42. Combined together, these observations suggest that the dynamics of microstate class B might be an early marker to discriminate people that are at risk to develop schizophrenia from those that might compensate for their vulnerability.We speculate that EEG microstate dynamics are not only a candidate endophenotype, but also, as our results suggest, they might reveal a potential compensation signal in unaffected siblings of patients with schizophrenia. We associate this compensation signal with the increased presence of microstate class B present in this population. More specifically, even though patients and siblings share similar traits, e.g., dynamics of microstate classes C and D, which might indicate vulnerability for schizophrenia, siblings can somehow counteract these traits by having an increased presence of microstate class B. Little is known about microstate class B. It has been related to a resting-state visual network in fMRIr(99) = −0.144, p = 0.150; time coverage: r(99) = −0.190, p = 0.057; occurrence: r(99) = −0.183, p = 0.067; Supplementary Table 18 and with acute hallucination experiences43 in patients with schizophrenia. More recently, it has been reported that the time coverage of microstate class A correlated positively with avolition–apathy scores, even though there were no group differences between patients and controls30. Finally, in a sample of adolescents with 22q11.2 deletion syndrome, the mean durations of microstate class C were associated with increased hallucination subscores of the structured interview for prodromal syndromes23. These results suggest that there might be an association between the microstate dynamics and psychopathological symptoms; however, the results in the literature are too heterogeneous to make firm conclusions at this point.Regarding associations between psychopathological symptoms and microstate dynamics, in FEP, we observed negative correlations between the SANS scores and the time coverage of microstate class D as well as between the SANS scores and the occurrence of microstate class D; however, the correlations were not significant after correcting for multiple comparisons. For chronic patients with schizophrenia, we found no significant correlations between psychopathological symptoms and the microstate parameters for any of the microstate classes. Nonetheless, the coefficients of the Pearson correlation between the SANS scores of patients with schizophrenia and their microstate parameters for microstate class D were negative as in FEP  in patients that respond well to antipsychotic treatment33. While most of the studies included in our meta-analysis only investigated the microstates dynamics in medication naïve patients, few studies investigated patients taking antipsychotic medication. One of these studies found that microstate class D was decreased in FEP compared with controls31, while another found increased duration and time coverage of microstate class C in patients with schizophrenia compared with controls30. In addition, a recent study with FEP also identified decreased mean durations of microstate class A in FEP compared with controls, a result that does not align with the literature29. However, since most of the studies of EEG microstates in schizophrenia have small samples (n < 30), it is expected that, due to sampling error and the heterogeneity of the disorder, some effects might not be significant in some studies and even reversed in a few studies if the effect sizes are small.Most of our patients were medicated and, in chronic patients with schizophrenia, we found a positive association with medication intake  and the occurrence of microstate class C , providing evidence that medication interact with microstates dynamics. This potential interaction is also supported by previous work that has shown that perospirone (an antipsychotic drug) can increase the duration of microstate class D in healthy controls45 and transcranial magnetic stimulation46, these results open avenues for the development of new treatments for the disorder.In conclusion, this is the first study on the temporal dynamics of the four canonical EEG microstates in siblings of patients with schizophrenia. Results indicate that the dynamics of resting-state EEG microstates, particularly classes C and D, is a potential endophenotype for schizophrenia. Since the dynamics of microstates can be altered by neurofeedbackParticipants were no older than 55 years old. All participants have participated in a previous study on masking and evoked-related potentials (ERPs). Masking and ERP data of some participants have been already published, while data of other participants have not been analyzed yet. All participants signed informed consent and were informed that they could quit the experiments at any time. All procedures complied with the Declaration of Helsinki and were approved by the Ethical Committee of Institute of Postgraduate Medical Education and Continuous Professional Development (Georgia). Protocol number: 09/07. Title: “Genetic polymorphisms and early information processing in schizophrenia”.n = 101), unaffected siblings of patients with schizophrenia (n = 43), and healthy controls (n = 75). Resting microstate dynamics data of 27 patients and 27 controls have already been published in previous work6. Masking and ERP data of 89 patients, 39 siblings, and 63 controls have already been published49. Patients with schizophrenia and their siblings were recruited from the Tbilisi Mental Health Hospital or the psycho-social rehabilitation center. Patients participated in the study when they had recovered sufficiently from an acute psychotic episode. Thirty-one were inpatients; 70 were outpatients. Patients were diagnosed using the Diagnostic and Statistical Manual of Mental Disorders Fourth Edition (DSM-IV) by means of an interview based on the Structured Clinical Interview for DSM-IV, Clinician Version, information from staff, and study of patients’ records. Psychopathology of patients with schizophrenia was assessed by an experienced psychiatrist using the SANS and SAPS. Out of the 101 patients, 88 were receiving neuroleptic medication. CPZ equivalents are indicated in Table X2(1) = 35.762, p = 2.229e − 9) and education (t(174) = −3.915, p = 1.297e − 4), but not in terms of age (t(174) = 1.399, p = 0.164) nor handedness (X2(1) = 0.030, p = 0.863), gender was used as a factor while education was used as a covariate in subsequent analyses. Siblings and controls had similar characteristics: gender (X2(1) = 0.109, p = 0.741), age (t(116) = −1.976, p = 0.051), education (t(174) = −1.653, p = 0.101), and handedness (X2(1) = 0.026, p = 0.871).Three groups of participants joined study 1: chronic patients with schizophrenia . The remaining 11 siblings were siblings of patients that performed a battery of tests but did not participate in the current EEG experiment. Group characteristics of patients_32 and siblings_32 are presented in Table 50. FEP were recruited from the Tbilisi Mental Health Hospital or the Acute Psychiatric Departments of Multiprofile Clinics. FEP selection, exclusion criteria, and psychopathological assessment were the same as for chronic patients with schizophrenia, see “Participants of Study 1”. Out of the 22 FEP, 20 were receiving neuroleptic medication: 4 were inpatients; 18 were outpatients. From our pool of 101 chronic patients with schizophrenia (see “Participants of Study 1”), we pseudo-randomly selected 22 patients (Patients_22), to match the 22 FEP as closely as possible, regarding gender (X2(1) = 0.026, p = 0.871), age (t(42) = −0.563, p = 0.576), and education (t(42) = −0.780, p = 0.440). Group characteristics are shown in Table t(42) = −5.838, p = 6.786e − 7), SANS (t(42) = −2.271, p = 0.028) and SAPS (t(42) = −2.433, p = 0.019) scores. The two groups did not significantly differ in terms of CPZ equivalent (t(36) = −0.305, p = 0.762) nor handedness (X2(1) = 0.000, p = 1.000).Twenty-two FEP participated in the study. Masking and ERP data of 21 of them have been published in previous workWe tested the FEP group three times throughout 1 year to assess whether the microstates dynamics changed with the progression of the disease. Out of the 22 patients, 16 participated 6 months later on a second session (FEP_2). Out of these 16, 11 were tested 6 months later on a third session (FEP_3). All the 22 FEP were invited to participate in all three session, but six of them dropped out after the first session and the other five patients dropped out after the second session. At the second testing, 10 out of the 16 FEP_2 were receiving neuroleptic medication, and they were all outpatients. At the third testing, 6 out of the 11 FEP_3 were receiving neuroleptic medication, and they were all outpatients. Group characteristics of the FEP_2 and FEP_3 patients are shown in Table 51: filtering with a bandpass of 1–40 Hz; removal of powerline noise; re-referencing to the biweight estimate of the mean of all channels; removal and 3D spline interpolation of bad channels; removal of bad EEG periods; independent component analysis to remove eye movement-, muscular- and bad channel-related artifacts; re-referencing to common average reference. The proportion of interpolated electrodes was <5% for each participant. The amount of removed EEG periods was 5.56% ± 3.78 for patients, 4.65% ± 3.42 for siblings and 4.96% ± 3.76 for controls. A one-way ANOVA revealed nonsignificant effect of group on the amount of removed EEG periods  = 0.810, p = 0.446). For Patients_22 and FEP, the amount of removed EEG periods was 5.03% ± 4.53 and 3.46% ± 2.08, for each group, respectively. An independent samples t-test showed that the amount of removed EEG periods was not significantly different between Patients_22 and FEP (t(42) = 0.145, p = 0.145).Participants were sitting in a dim lit room. They were instructed to keep their eyes closed and to relax for 5 min. Resting-state EEG was recorded before participants participated in a masking experiment and using a BioSemi Active 2 system (Biosemi) with 64 Ag-AgCl sintered active electrodes, referenced to the common mode sense electrode. The recording sampling rate was 2048 Hz. Offline data were downsampled to 128 Hz and preprocessed using an automatic pipeline (APP)52. Since EEG map topographies remain stable around the GFP peaks and these are the best representatives of the topographic maps regarding signal-to-noise ratio38, only EEG topographies at the GFP peaks were submitted to further analysis. The GFP-reduced data were submitted to k-means clustering24 to identify the most dominant topographies as classes of microstates present in the recordings. The clustering analysis was first done at the individual level and then across participants in each group. To have equal contributions of microstates per participant, each participant contributed to the group k-means clustering with his/hers four most dominant microstates. To compare our results with previous studies, we selected four microstates for each group, and labeled them A–D according to their similarities to the previously reported microstate classes5. To ensure that the four selected microstates were similar across groups, we computed spatial correlation53 for each of the four microstate classes for each pair of groups. High spatial correlation coefficients indicated that the microstate classes were similar between groups  of the preprocessed EEG data was determined for each participant. GFP is an instantaneous reference-independent measure of neuronal activity throughout the brain, and it is calculated as the standard deviation of the electrical potential across all electrodes at each time point10. For each subject, three per class microstate parameters were computed: mean duration, time coverage, and frequency of occurrence (occurrence). Mean duration (in ms) is the average time that a given microstate was uninterruptedly present. Time coverage (in %) is the percentage of the total analysis time spent in a given microstate. Occurrence is the mean number of times a given microstate is occurring per second. Microstates analysis was performed using Cartool24 (version 3.70).Subsequently, for each group, we compared the four microstates with the instantaneous scalp potential maps in each participant’s artifact-correct EEG using a competitive fitting procedure. For each time point of the individual EEG, the scalp topography was compared with each microstate class using spatial correlation. The time point was then labeled according to the microstate that exhibited the greatest correlation. Temporal smoothing  size = 5, strength (Besag) = 10, rejection of microstates with durations of one time frame) was applied to ensure that noise during low GFP periods did not interrupt segments of quasi-stable topographies6, a study with data from 27 patients and 27 controls included in our sample. As mentioned above, in the current study, we rejected microstates with durations of one time frame, while Tomescu et al. did not. This resulted in the microstate mean durations in the current study to be longer than the ones reported by Tomescu et al. However, in a subsequent work from the same group of researchers41, rejection of microstates with durations of one time frame was applied, which led to microstate mean durations similar to the ones in current study.The temporal smoothing in the current study was different from the one performed by Tomescu et al.We did not remove potentially truncated microstates before evaluating the microstate parameters. Since the groups did not significantly differ in the amount of EEG periods removed, this does not pose a problem in the overall group comparisons.15. We identified 28 relevant studies. For our meta-analysis, we selected studies according to the following criteria:Criterion 1. The study reported original data from a group of patients belonging to the psychosis spectrum as well as a healthy control group.t-, F-, or p values had to be sufficiently detailed in order to compute effect sizes estimates and their variances. If the relevant information was not provided, we contacted the corresponding authors of the studies and asked for additional information. This was the case for two studies, Koenig et al.18 and Murphy et al.29. For these, Thomas Koenig and Michael Murphy, authors of18 and29, respectively, provided the summary statistics, via e-mail.Criterion 2. The reported sample sizes, summary statistics, or Criterion 3. The EEG montage employed the standard 10–20 system.5.Criterion 4. Four microstate classes  were considered, since this is the number of microstate classes most frequently used in the literatureCriterion 5. The study was a resting-state state study, i.e., participants were not engaged in any particular task.Criterion 6. The study reported at least one of the following three microstate parameters: mean duration, time coverage, and occurrence.A literature search was conducted for papers published before 29 November 2019 via PubMed, to identify studies investigating EEG microstate dynamics in schizophrenia. The keywords were “schizophreni*” in conjunction with “microstate*”, in order to get schizophrenia, schizophrenic, and schizophrenics as well as microstate and microstates. Furthermore, a prior meta-analysis and two reviews on EEG microstates were inspected for potentially missed studies6 because it consisted of a subset of participants of the current study. Hence, we included a total of nine studies in our meta-analysis. A comprehensive list of all identified studies, with a short explanation for exclusion (if applicable), is presented in Supplementary Table Only nine of the initial identified 28 studies met these six criteria. In addition, we included the current study  and removed one study32 did not report the time coverage, the study by Giordano et al.30 did not report the occurrence, while the study by Murphy et al.29 only reported the mean duration . For each study, we calculated Cohen’s d as the mean difference between patients and controls divided by the within group standard deviation, for each available microstate parameter and for each microstate class. For the current study , we used the Cohen’s d values reported in Table g was calculated using Cohen’s d multiplied by the coefficient J, which is a correction for small samples54.Apart from three studies, all the other studies reported the three relevant microstate parameters. The study by Nishida et al.t-test against 0. For each of the three analyses , pairwise group comparisons for all microstate parameters and for each microstate class were corrected for multiple comparisons with Bonferroni–Holm for 12 comparisons (3 parameters × 4 classes).In study 1, for patients vs. controls, for each of the computed microstate parameters , we performed a three-way rm-ANOVA, with Group (patients and controls), Microstate Class , Gender as factors and Education as a covariate. For siblings vs. controls, for each of the computed microstate parameters, we performed a two-way rm-ANOVA with Group (siblings and controls) and Microstate Class as factors. For patients_32 vs. siblings_32 pairs, their difference scores (Δ) for each microstate parameter and microstate class were submitted to a two-sided one-sample In study 2, for FEP vs. Patients_22, for each computed microstate parameter, we performed a two-way rm-ANOVA with group and microstate class as factors. To investigate whether the computed microstates changed throughout 1 year for the FEPs, we divided the analysis in two parts. First, we analyzed the microstate parameters in the FEP_2 (FEP that completed the first and second testing session). Then, we analyzed the microstate parameters in the FEP_3 . In both cases, for each computed microstate parameter, we computed a two-way rm-ANOVA with testing session  and microstate class as factors.g values were introduced as a generic effect size in the OpenMeta Analyst software (http://www.cebm.brown.edu/openmeta/) with the corresponding standard error. We used the continuous random-effect analysis with the restricted maximum likelihood method. The meta-analysis software computed the effect sizes, with 95% confidence intervals (CI) and the pooled effect size g*. Z-tests were conducted to test the significance of the of the pooled effect size g*. p values were corrected for 12 comparisons (3 microstate parameters × 4 microstate classes) using Bonferroni–Holm correction.For the meta-analysis, Hedges’ p value was below 0.05 after correction for multiple comparisons using Bonferroni–Holm correction (pholm). As estimates of effect size, we report Cohen’s d with 95% CI and η2 with 90% CI.Where applicable, statistical tests were always two-sided. We considered a statistical test to be significant when the 55 software (version 0.12.1) and R56 (version 3.6.1).Statistical tests were performed with JASPFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Background: Acquired resistance of chemotherapy, especially cisplatin, is a major challenge in lung cancer treatment. We conducted this study to examine whether a cyclin D kinase 4/6 (CDK4/6) inhibitor, PD 0332991, could reverse cisplatin resistance in human lung cancer cells. In addition, we explored the underlying mechanisms.Method: We used CCK‐8 assay to got the IC50 of PD‐0332991 and cisplatin in A549 and A549/CDDP respectively. CCK‐8 assay, CalcuSyn 2.0 software, cell cycle distribution and apoptosis used to identify PD‐0332991 could reverse the acquired resistance of cisplatin. At last, western‐blot used to show the mechanism of PD‐0332991 enhances the effects of cisplatin.Results: We found that PD‐0332991 potentiated cisplatin‐induced growth inhibition in both cisplatin‐sensitive (A549) and cisplatin‐resistant (A549/CDDP) cells via downregulation of the proliferation, induction of apoptosis , and G0/G1 cell cycle arrest . Western blot analysis revealed that PD‐0332991 enhance the effect of cisplatin through inhibit Rb‐E2Fs pathway.Conclusions: These findings suggest that PD‐0332991 could reverse the acquired resistance of cisplatin in lung cancer cells and provide a novel treatment strategy for lung cancer patients with cisplatin resistance. Acquired resistance of cisplatin is associated with dysregulation of cell cycle. CDK4/6 inhibitor, PD‐0332991, could reverse the resistance of cisplatin. Among all types of cancer, lung cancer has the highest morbidity and mortality rates in China. As per recent reports, the number of lung cancer patients in China has exceeded 700 000 and the number of deaths has reached 600 000 annually.Our previous research revealed that when an anticancer drug is administered, a small proportion of the cells survives, escapes from the cell cycle, and enters into a quiescent stage (G0). In certain situations, the quiescent cancer cells return to the cell cycle from the G0 phase. This is termed the “cell cycle re‐entry” theory; we successfully applied this theory in epidermal growth factor receptor‐tyrosine kinase inhibitors (EGFR‐TKI)‐acquired resistance.While as the theory we elucidate above about “cell cycle re‐entry” theory, we also thought targeting the cell cycle could reverse cisplatin resistance as done previously in EGFR‐TKI. Thus, we used palbociclib (PD‐0332991),Human lung adenocarcinoma A549 cell line ; human lung adenocarcinoma A549/CDDP (Tianjin Lung Cancer Institute); RPMI‐1640 Medium, FBS, and DMSO (Gibco); AnnexinV‐FITC/PI (KGI Bio); BCA Protein Assay Kit and Cell Counting Kit‐8 (Biyuntian Biotechnology); TEMED, glycine, Tris–HCl, SDS, and Tween‐20 (Beijing Dingguo Changsheng Bio); flow cytometer (American BD); full‐wavelength microplate reader (Molecular Devices); Western blot instrument (German Bole Company); cisplatin ; PD‐0332991 (American ApexBio); CDK4, CDK6, cyclin D1, Rb, Phospho‐Rb, Bcl‐2, E2F1, and caspase‐3 antibody ; protein membrane (PALL Corporation); cell culture dish and 6‐well and 96‐well plates (Greiner Bio‐One); and EP tube, centrifuge tube, and pipette (Axygen).2 saturated humidity in the A549/CDDP cell culture medium. The drug resistance of the A549/CDDP cells was maintained by adding 1 mg/ml of cisplatin. When the density of cells reached 80%, the cells were digested with trypsin and selected in the logarithmic growth phase for subsequent experiments.A549 (human lung adenocarcinoma cell line obtained from the ATCC) and A549/CDDP (cisplatin resistance cell line incubated by our institute) cell lines were cultured in an RPMI‐1640 medium containing 10% fetal bovine serum and incubated at 37 °C at 5% COA549 and A549/CDDP cell proliferation was determined using a Cell Counting Kit‐8  according to the manufacturer's instructions. For the CCK‐8 assay, 2 × 104 cells/well were seeded in a 96‐well plate for 12 h and added drugs in different concentrations  and PD‐0332991 . After 24 h, we added the medium containing 10% CCK‐8 into each well. Then the 96‐well plates were incubated for 2 h at 37 °C, and the absorbencies of each group were measured at 450 nm by a microplate reader. All experiments were biologically repeated at least three times. The obtained results were calculated using the GraphPad Prism 5.0 software for the IC50 of each group.To determine the effects of cisplatin and/or PD‐0332991 on the cell cycle, 1.5 × 105 cells/well were seeded in 6‐well plates and incubated for 12 h. Cells were synchronized by starving them in serum‐free RPMI‐1640 medium for 24 h. Cells were then incubated with or without cisplatin and/or PD‐ 0332991 for 24 h each, trypsinized, and fixed in 70% ice‐cold ethanol overnight. Cells were then treated with DNase‐free ribonuclease (Takara), stained with propidium iodide (PI) (Sigma–Aldrich), and analyzed using a FACSAriaTM flow cytometer (BD Bioscience) and ModFit LT software (Topsham).Cells (2 × 105 cells/well) were seeded into 6‐well plates and cultured for 12 h. Cisplatin and/or PD‐0332991 were added at various concentrations and cells were cultured for another 24 h. Cells were stained using an Annexin V‐FITC Apoptosis Analysis Kit (BD Bioscience) and analyzed with a FACSAriaTM flow cytometer (BD Bioscience).A549 and A549/CDDP cells were treated with 1/2 IC50 of cisplatin and/or PD‐0332991 for 24 h. Then cells were homogenized in RIPA buffer , and proteins quantified using the bicinchoninic acid (BCA) protein assay kit (Pierce). A total of 10–50 μg of proteins was fractionated on 10–12% gels using SDS‐PAGE, transferred to nitrocellulose membranes (Amersham Biosciences) under wet conditions, then immunoblotted with the appropriate antibodies.t‐tests were used to identify the statistical significance between groups. ANOVAs were used for analysis of more than two groups of data in all experiments. Statistical significance: p < 0.05.All data were analyzed by using the Statistical Package for Social Sciences . Student's First, we used CCK‐8 to obtain the IC50 of cisplatin and PD‐0332991 in the A549 and A549/CDDP cells Figure . After eTo observe the synergistic effect of PD‐0332991 and cisplatin, the A549 and A549/CDDP cells were treated with 11.5 umol/L and 15.35 umol/L respectively. Cell viability was detected using CCK8. We found that the inhibitory effect on lung cancer cells was significantly enhanced when the two drugs were combined Figure , and theTo further prove the synergistic effect of PD‐0332991 and cisplatin on the A549 and A549/CDDP cells, we used CalcuSyn 2.0 software. The results revealed that low doses of PD‐0332991 and cisplatin exerted a more synergistic effect compared with high doses on the A549 cell line Figure . In contFirst, we employed PI staining to evaluate the cell cycle distribution. As presented in Figure Next, we employed AnnexinV/7AAD staining to evaluate cell apoptosis caused by treatment with PD 0332991. As presented in Figure To detect the underlying mechanism of the synergistic effect of PD‐0332991 and ciplatin in A549 and A549/CDDP. We employed western blot analysis to determine the relationship between cell cycle proteins and apoptotic proteins, as presented in Figure Apoptotic proteins were detected via western blot analysis, as presented in the lower panels in Figure Currently, chemotherapy is the main method employed for the treatment of lung cancer. As a first‐line treatment, platinum‐based chemotherapy can inhibit tumor proliferation and metastasis. However, the drug resistance of lung cancer cells is the major cause of chemotherapy failure and disease progression.In most normal cells, the Rb protein is in a state of low phosphorylation. However, in rapidly proliferating tumor cells, the Rb protein is in a high phosphorylation state. Previous studies demonstrated that the phosphorylation of Rb is related to the G1 to S phase of the cell cycle.Furthermore, E2F is a cell cycle‐associated transcription factor, whereas E2F1 is an important transcription factor in cell cycle progression. As a positive regulator of the cell cycle, it can promote the cell cycle from the G1 to the S phase.+ from the endoplasmic reticulum, and the release of Ca2+ is the initiator in apoptosis.In the combination group, the expression of the apoptosis‐related protein caspase‐3 significantly increased. Caspase‐3 plays a significant positive regulatory role in apoptosis. Apoptosis‐related protein can cleave poly ADP‐ribose polymerase (PARP), and the enzyme is cleaved into two fragments that bind the zinc finger structure and carboxyl group in the PARP. The phase separation of the catalytic region leads to the inactivation of the polymerase. However, it can increase endonuclease activity and cleave DNA, which results in apoptosis.Studies have demonstrated that the Wnt/β‐catenin signaling pathway plays a significant role in tumor chemotherapy resistance. The upregulation of the Wnt/β‐catenin signaling pathway is associated with cisplatin resistance.In conclusion, our previous studies demonstrated that PD‐0332991 could reverse EGFR‐TKI‐acquired resistance via downregulation of proliferation, apoptosis induction, and G0/G1 cell cycle arrest. In the clinical setting, cisplatin resistance affects the survival duration of lung cancer patients. We found that the function of cisplatin is associated with the cell cycle. We also attempted to use a CDK4/6 inhibitor, PD‐0332991, combined with cisplatin to reverse cisplatin resistance. Our research revealed that PD‐0332991 could reverse cisplatin resistance using CCK‐8 and the CalcuSyn2.0 software. PD‐0332991 combined with cisplatin can cause cell apoptosis and cell cycle arrest in the G1 phase. Western blot analysis verified the regulation with the cyclinD1/CDK4/6‐Rb/E2F1 pathway. The results are similar to the findings of our previous study.M.L., H.L., and J.C. wrote the manuscript. M.L., L.C., X.L., C.X., Y.L., F.R., and R.W. performed the experiments and collected the data. All authors analyzed the data, discussed the data, and revised the article. All authors read and approved the final manuscript.The authors declare no conflicts of interest.


Facility‐based HIV testing services (HTS) have been less acceptable and accessible by adolescents, men and key populations in South Africa. Community‐based HIV counselling and testing (CBCT) modalities, including mobile unit and home‐based testing, have been proposed to decrease barriers to HIV testing uptake. CBCT modalities and approaches may be differentially acceptable to men and women based on age. Implementation of multimodal CBCT services may improve HIV testing rates among adolescents and men, and support the roll‐out of prevention services.A cross‐sectional analysis was conducted using aggregate, routine programmatic data collected from 1 October 2015 through 31 March 2017 from a multimodal, at‐scale CBCT programme implemented in 12 high‐burden districts throughout South Africa. Data collection tools were aligned to reporting standards for the National Department of Health and donor requirements. HIV testing rates  and testing proportions by modality were stratified by sex, age groups and heath districts. Descriptive statistics were performed using STATA 13.0.Overall, 944,487 tests were performed during the 1.5‐year testing period reported. More tests were conducted among females than males (53.6% vs. 46.4%). Overall, 8206 tests per 100,000 population (95% CI: 8190.2 to 8221.9) were performed; female‐to‐male (F:M) testing ratio was 1.11. Testing rates were highest among young women age 20 to 24 years  and adolescent girls aged 15 to 19 years . Home‐based testing accounted for 61.3% of HIV tests, followed by near‐home mobile unit testing (30.2%) and workplace mobile unit testing (4.7%). More women received HTS via home‐based testing (F:M ratio = 1.29), whereas more men accessed work‐place mobile testing (M:F ratio = 1.35). No sex differential was observed among those accessing near‐home mobile testing (F:M ratio = 0.98).Concurrent implementation of multiple, targeted CBCT modalities can reduce sex disparities in HIV testing in South Africa. Given the acceptability and accessibility of these CBCT services to adolescent girls and young women, evident from their high testing rates, leveraging community‐based services delivery platforms to increase access to HIV prevention services, including pre‐exposure prophylaxis (PrEP), should be considered. ACommunity‐based HIV counselling and testing (CBCT) has been shown to be acceptable to both men and women, and is an accepted platform to improve HTS coverage and uptake in South Africa . Common 2We conducted an analysis of aggregate, HIV testing rates using programmatic data from a multimodal CBCT programme offered in South Africa.2.1From January 2014 through November 2018, a comprehensive CBCT programme was implemented in 24 sub‐districts located in 12 “Focus for Impact” districts/local municipalities in South Africa Figure  [20]. DiUsing aggregate, clinic‐level ART headcounts and HIV test positivity rates, heat‐maps were developed to identify and prioritize clinic catchment areas with the highest presumed HIV prevalence. Based on a sub‐district’s sociodemographic and economic profile, the clinic catchment area being targeted, and in consultation with local health departments, a combination of targeted and complementary CBCT testing teams, resources and modalities were concurrently implemented (i.e. combination implementation) to achieve the highest estimated HIV testing rates and positivity. Per South African National guidelines , any ind2.2As previously described , paper‐b2.3# tests performed on women per 100,000 population/# tests performed on men per 100,000 population. Districts were stratified into urban, peri‐urban and rural areas as described and defined by Statistics South Africa [Aggregate, programmatic testing data collected from 1 October 2015 through 31 March 2017 were used in our analysis. South African National Census of 2011 data at sub‐district level were used as our denominators for testing rates, as mid‐year population estimates are only calculated to the provincial level . Descriph Africa . Where a2.4Prior to receiving an HIV test, all individuals were counselled and consented per South African National HIV testing guidelines . The Fou33.1A total of 944,487 HIV tests were performed over the 1.5‐year period reported. The plurality of HIV tests were performed among clients aged 25 to 49 years  with testing conducted among those aged one to four years , five to fourteen years , 15 to 19 years , 20 to 24 years  and 50+ years  , followed by those aged 15 to 19 years   followed by peri‐urban  and then urban setting  Table . This pa3.2Home‐based testing accounted for the majority  of HIV tests performed, followed by near‐home mobile unit testing . Workplace mobile unit testing , index testing  and twilight testing  accounted for a small proportion of the total tests performed  in African settings are reluctant to use health facilities , 44. Our5Given the programmatic nature of our work, there are a number of limitations regarding the interpretation or generalization of our findings. Due to differential allocation of CBCT resources to meet donor targets in each district, we were unable to compare testing rates and proportions across testing modalities and districts. Given that communities were not presented with all possible modalities at the same time, we are limited in our ability to speak to the unique profiles of people that would prefer one CBCT modality over the other, or rigorously assess which modalities may have worked best given the sociodemographic and socioeconomic profiles of a district. Our use of aggregated testing data, not unique clients tested, likely results in some overestimation of the testing rates per 100,000 population. Moreover, given that mid‐year population estimates are only available at a provincial level, not at the district or sub‐district level, we are limited in our ability to adjust for population changes (i.e. growth or migration rates) since 2011. Moreover, given the lack of information on HIV prevalence estimates at sub‐district level or the number of first‐time or repeat testers, the testing rates may be slightly overestimated. Finally, no data were available for a cost‐effectiveness analysis to fully understand the economic value of each modality.6The success of this CBCT programme in reaching such high testing rates in such a short amount of time is likely due in part to the significant diversity and intensity of community‐based testing modalities implemented. Given the costs associated with this programme, replicating its success may be difficult. However, given the consultations with community and health leaders in determining which modalities may be best to utilize in their communities, significant improvements in the accessibility, and thus uptake of testing, can be replicated elsewhere. Our multimodal CBCT programme was effective at reaching adolescent girls and young women, and decreasing the testing gap between men and women. Given the anticipated at‐scale roll out of pre‐exposure prophylaxis (PrEP) in South Africa, the South African National Department of Health, and its supporting donors, should consider leveraging community‐based delivery platforms for PrEP services to maximize young women’s access and uptake of HIV prevention services. Although we found that some testing modalities seemed to be more readily acceptable and accessible to men, CBCT programmes should be further tailored to optimize testing uptake by males to address their HIV prevention and treatment barriers. Ultimately, implementing multiple CBCT modalities will help men, women and adolescents navigate specific HIV testing barriers and ensure reach and uptake so to meet the 90‐90‐90 targets in South Africa.The authors have declared no conflict of interest.S. J. and N. N. secured funding. T. F, J. S., G. G., S. J. and N. N. implemented the programme. J. S. and G. G. managed and curated databases. A. M. M. developed data analysis plan. A. M. M., S. J., N. N. and T. F. performed analysis. A. M. M., L. G. B. and S. J. interpreted and contextualized findings. A. M. M, J. D., D. B. and R. P. wrote the paper. All authors have read and approved the final manuscript.Table S1. High HIV burden sub‐districts where multimodal community‐based HIV counselling and testing services were implemented, October 2015 to March 2017Click here for additional data file.


The European Treatment and Outcome Study (EUTOS) long‐term survival (ELTS) score predicts disease‐specific death in patients with chronic myeloid leukemia (CML) being treated with imatinib during the chronic phase (CP) of the disease. However, it is unclear whether the ELTS score predicts CML‐related events or treatment responses. This study evaluated the predictive value of the ELTS score regarding prognosis and treatment response in patients with CML‐CP. Clinical data were retrospectively obtained from patients enrolled in the CML Cooperative Study Group (CML‐CSG), which included patients diagnosed with CML‐CP from April 2001 to January 2016, and treated with any tyrosine kinase inhibitor (TKI) as first‐line therapy. Among 342 eligible patients, the ELTS scores indicated low‐, intermediate‐, and high‐risk in 74%, 21%, and 5% of patients, respectively. Patients with high ELTS scores had significantly higher disease‐specific mortality and worse event‐free survival, progression‐free survival, and overall survival. Among four risk scores, including the Sokal, Hasford, EUTOS, and ELTS scores, risk stratification by the ELTS score had the highest predictive value in assessing patient prognosis, and also in treatment responses. In fact, the EUTOS and ELTS scores were able to predict the major molecular response within 12 months. Most importantly, the ELTS score was the only scoring system that predicted deep molecular response at any time, regardless of risk level . Compared to other risk scores, the ELTS score was the most sensitive risk classification tool for the four endpoints of interest in this study, as well as molecular responses in patients with CML‐CP. In this real‐world TKIs treated CML‐CP patients cohort, the EUTOS long‐term survival (ELTS) score was the most sensitive risk classification tool to predict CML‐specific deaths, EFS, PFS, and OS compared to the Sokal, Hasford, and EUTOS scoring systems. Notably, the ELTS score was the only scoring system that predicted cumulative incidence of deep molecular response. In fact, according to recent reports, the life expectancy of CML patients is comparable to that of the general population.Before the TKI era, two prognostic tools were predominantly used for assessing risk in CML‐CP patients; the Sokal and Hasford scores. These scores were established in 1984 and 1998, during the conventional chemotherapy and splenectomy era, and the interferon era, respectively. After the introduction of imatinib, the European Treatment and Outcome Study (EUTOS) score, published in 2011, was developed to predict a complete cytogenetic response (CCyR) at 18 months after treatment initiation, which was used as a surrogate marker for survival.The EUTOS long‐term survival (ELTS) score was established in 2016 for the identification and prediction of potent “leukemic death” in patients treated with imatinib.In April 2009, 2G‐TKIs were approved for use in imatinib‐resistant or intolerant patients. Subsequently, after April 2011, 2G‐TKIs were approved as initial therapy agents for CML‐CP patients in Japan. Therefore, we wonder whether the ELTS score can accurately predict CML‐related death, EFS, PFS, and OS, as well as the achievement rates of major molecular response (MMR) and deep molecular response (DMR) in CML‐CP patients. This study aimed to validate the use of the ELTS score by comparing it with other prognostic scores in a cohort of patients recruited in a real‐world setting.22.1Data from patients registered in the CML Cooperative Study Group (CML‐CSG), which is a collaboration of four university hospitals and five university branch hospitals, were retrospectively reviewed. Patients were eligible for inclusion if they were aged 15 years or older, diagnosed with CML‐CP from April 2001 to January 2016, and treated with any TKI as initial therapy. Since April 2011, 2G‐TKIs have been available as initial therapy for CML‐CP patients in Japan. The study protocol was approved by the research ethics board of each participating institution and conducted in accordance with the principles of the Declaration of Helsinki.2.2BCR‐ABL1 transcript using real‐time quantitative PCR or a transcription‐mediated amplification and hybridization protection (TMA) assay. BCR‐ABL1 transcript levels ≤0.1% according to the International Scale (IS), or ≤100 copies/μg RNA as determined using the TMA assay, were considered an MMR, while transcript levels ≤0.0032% were considered a DMR as described previously.In the present study, disease progression was defined as the transformation to the accelerated phase/blastic phase (AP/BP) of the disease. Incidence of any event included loss of treatment efficacy, including loss of complete hematologic response (CHR) after CHR had previously been achieved, loss of partial cytogenetic response (PCyR) after PCyR had previously been achieved, or loss of CCyR after CCyR had previously been achieved, disease progression, and death due to any cause. The molecular response was evaluated by measuring the 2.3For every patient, PFS was defined as the time from initial treatment with TKIs to confirmed disease progression or the last follow‐up consultation. EFS was defined as the time from initial treatment with TKIs to the first occurrence of any of the events of interest or the last follow‐up consultation. The OS was defined as the time from initial treatment with TKIs to death due to any cause or the last follow‐up consultation. In the analysis of CML‐specific mortality, deaths unrelated to CML were censored at the time of demise. The Kaplan‐Meier method and the log‐rank test were utilized to estimate and compare the PFS, EFS, OS, and CML‐specific mortality rates. Fisher's exact test was utilized to determine statistical significance in each group. Statistical analyses were performed using EZR , a graphical user interface for the R programming language .33.1There were 342 eligible patients newly diagnosed with CML‐CP during the study period. Patient characteristics are shown in Table 3.2p < 0.0001), Hasford score , EUTOS score , and ELTS score . Therefore, the ELTS score was the most sensitive predictor of CML‐related death in this study . The EUTOS score could also predict MMR attainment at 12 months , whereas the Sokal and Hasford scores could not predict this outcome.Then, we assessed molecular responses stratified by these scoring systems. As shown in Table p = 0.0000569. The Sokal, Hasford, and EUTOS scores were not able to differentiate the attainment of DMR.Most importantly, the ELTS score was the only scoring system able to predict the attainment of DMR at any time; 65.0% in low‐, 43.7% in intermediate‐, and 23.5% in high‐risk groups; Furthermore, we compared the concordance and discordance of MMR attainment rates at 12 months among the risk groups between the ELTS and other scoring systems. Table 3.5In this study, an event of interest occurred in 36 patients, including 16 who died of a cause unrelated to CML, and 11 who progressed to AP/BP. Among patients who progressed to AP/BP, nine subsequently died of leukemia. As shown in Table 4, patients defined as high‐risk by the ELTS score particularly had a higher frequency of disease progression (4/17), although disease progression was seen in some low‐ and intermediate‐risk patients also (3/254 in low and 4/71 in intermediate‐risk groups). The numbers of patients who died of leukemia were 2/254 (0.79%), 3/71 (4.22%), and 4/17 (23.5%) in low‐, intermediate‐, and high‐risk patients by the ELTS score, respectively. Furthermore, low‐risk patients by the ELTS score were unlikely to progress to AP/BP, regardless of classification as high‐risk according to other scoring systems.3.6Finally, we investigated whether the choice of first‐line therapy affected the predictability of the risk stratification for CML‐related deaths according to initial therapy. As shown in Figure 4Several factors influence the survival of CML‐CP patients, including clonal chromosomal abnormalities (CCA) in Philadelphia chromosome‐positive cells, BCR‐ABL1 kinase domain mutations, non‐ABL kinase dependencies, and multidrug resistance polymorphisms. However, these examinations, excluding CCA, are not commonly available in clinical practice.In the present study, the ELTS score accurately predicted survival indicators of interest, specifically, CML‐specific mortality, EFS, PFS, and OS. In this study cohort, 11 out of 349 patients progressed to AP/BP; among them, nine died of leukemia. Among patients in the low‐, intermediate‐, and high‐risk groups based on the ELTS score, 2/254 (0.79%), 3/71 (4.22%), and 4/17 (23.5%) died of leukemia, respectively. Furthermore, low‐risk patients based on the ELTS score were unlikely to progress to AP/BP, regardless of whether they were classified as high‐risk according to other scoring systems. Therefore, these findings suggest that ELTS can accurately assess the risk of disease‐specific mortality among high‐risk patients. A previous study conducted with 339 imatinib‐treated patients and 78 2G‐TKIs‐treated patients validated the capability of the ELTS score compared with other scores, and the ELTS score allows for a more precise stratification of the risk of CML‐related death.p = 0.0000569). With this scoring system, not only could we infer the high possibility of CML‐related death in high‐risk patients, but we could also deduce treatment‐free capable patients who had been treated with TKIs and are in remission from the low‐risk group.Next, we investigated whether the ELTS score could anticipate molecular response rates; specifically, MMR at 12 months and DMR at any time after treatment initiation. Among patients defined as low‐ and intermediate‐risk by the ELTS score, the MMR rates at 12 months were 37.5‐60.0%, even if the patients were classified as high risk by other scoring systems. However, the MMR rates were much lower (0‐25.0%) for patients who were classified as high‐risk by the ELTS score. The ELTS score was able to predict the MMR rate, which is considered a surrogate measure of the risk of CML‐specific death, while other risk assessment tools could not. This finding was consistent with findings from a previous study, which analyzed 709 patients receiving imatinib and 244 patients receiving 2G‐TKI as first‐line treatment.The limitations of our study include its retrospective nature, the small sample size, and the unavailability of treatment compliance details. Validation in prospective studies would be needed to further refine the prognostic scoring systems. Another limitation is a possible selection bias since all patients were not analyzed, as the scoring for risk stratification was unavailable in 18 patients with CML‐CP enrolled in the database.In conclusion, the findings from the present study support the predictive value of the ELTS score for CML‐CP patients who were started on treatment with all available first‐line TKIs. Our results showed the ELTS score most accurately predicts CML‐specific deaths and molecular responses in a real‐world clinical setting. Patients identified by the ELTS score as high‐risk might benefit from treatment with 2G‐TKI to faster achieve DMR. Our study provided positive evidence to support that the ELTS score is a better risk stratification tool for CML‐CP patients, and it aids clinicians to make therapeutic decisions on the appropriate first‐line treatment.5N Iriyama received honoraria and speaker fees from Bristol‐Myers Squibb, Novartis Pharma K.K., Otsuka Pharmaceutical, and Pfizer Inc. M Tokuhira received honoraria and speaker fees from Pfizer Inc. and Bristol‐Myers Squibb. M Kizaki received honoraria and speaker fees from Bristol‐Myers Squibb and Novartis Pharma K.K. T Takaku and Y Hatta received honoraria from Bristol‐Myers Squibb and Novartis Pharma K.K. T Kawaguchi received honoraria and speaker fees from Bristol‐Myers Squibb, Novartis Pharma K.K., and Pfizer Inc. The remaining authors declare no competing financial interests.6


TNFα, MCP‐1, CD40, and CTSS) was upregulated in GDM‐derived AMSCs, whereas anti‐inflammatory IL‐33 was downregulated. Macrophages isolated from the amniotic membrane of GDM mothers consistently showed higher expression of MCP‐1 as well. In vitro studies in which AMSCs from healthy control women were exposed to hyperglycemia, hyperinsulinemia, and palmitic acid confirmed these results. Finally, genes involved in the inflammatory response were associated with maternal insulin sensitivity and prepregnancy body mass index, as well as with fetal metabolic parameters. These results suggest that the GDM environment could program stem cells and subsequently favor metabolic dysfunction later in life. Fetal adaptive programming in the setting of GDM might have a direct negative impact on insulin resistance of offspring.Fetal programming has been proposed as a key mechanism underlying the association between intrauterine exposure to maternal diabetes and negative health outcomes in offspring. To determine whether gestational diabetes mellitus (GDM) might leave an imprint in fetal precursors of the amniotic membrane and whether it might be related to adverse outcomes in offspring, a prospective case‐control study was conducted, in which amniotic mesenchymal stem cells (AMSCs) and resident macrophages were isolated from pregnant patients, with either GDM or normal glucose tolerance, scheduled for cesarean section. After characterization, functional characteristics of AMSCs were analyzed and correlated with anthropometrical and clinical variables from both mother and offspring. GDM‐derived AMSCs displayed an impaired proliferation and osteogenic potential when compared with control cells, accompanied by superior invasive and chemotactic capacity. The expression of genes involved in the inflammatory response (  Signatures of metabolic deregulation seem to remain in cells early in development. Given the location on the inner side of the placenta, amniotic membrane stem cells might be a good indicator of how the intrauterine environment impacts the fetus. To the best of authors' knowledge, this study showed for the first time how gestational diabetes disturbs both the phenotype and the functional characteristics of amniotic mesenchymal stem cells, and these alterations are related to maternal and fetal metabolic status, suggesting that fetal adaptive programming in the setting of gestational diabetes might have a direct impact on offspring.1Gestational diabetes mellitus (GDM) is associated both with short‐term adverse obstetric and perinatal complications and with long‐term metabolic health consequences for offspring.As a natural interface between mother and fetus, the functional integrity of the placenta is crucial to support fetal growth and development as well as to adapt to the maternal nutritional and metabolic status.The amniotic membrane is the innermost layer of the placenta and possesses anti‐inflammatory and antibacterial properties that contribute to materno‐fetal tolerance. It also exerts many metabolic functions, such as the transport of water and soluble molecules and the production of bioactive factors.22.1Eighteen pregnant women with a singleton pregnancy  scheduled for cesarean delivery were included in this study, which was performed at the Hospital Universitari de Tarragona Joan XXIII (HUJ23) according to the tenets of the Helsinki Declaration. The institutional review board (CEIm) approved the study protocol and all patients gave written informed consent before participating in the study. All mothers diagnosed with GDM were recruited at the Diabetes and Pregnancy Clinic of the HUJ23, while control subjects were recruited at the delivery suite. GDM was diagnosed according to the current criteria of the Spanish Diabetes in Pregnancy guidelines, which followed the National Data Group Criteria.2.22. GWG was calculated as −(pregravid weight). Neonatal length, weight, and waist circumference were measured at birth and the waist circumference/length ratio was calculated. Suprailiac skinfold thickness was measured at least three times using a Holtain skinfold caliper  to obtain a consistent and stable reading.Upon inclusion, demographic and historical data were collected paying special attention to third trimester HbA1c in GDM women, 1‐hour 50‐g glucose challenge test (GCT), pregravid weight, gestational weight gain (GWG), and gestational age at delivery. Maternal anthropometric measurements of height (measured to the nearest 0.5 cm) and weight (measured to the nearest 0.1 kg) were obtained using a medical scale. Body mass index (BMI) was calculated using the formula BMI = weight (in kilograms)/height (in meters)2.3Maternal blood samples were collected in the morning after an 8 hours fast and immediately before cesarean section, and umbilical vein cord blood was obtained at the time of delivery. Serum was immediately separated by centrifugation and stored at −80°C until analysis. Full‐term placentas (37–39 weeks gestation) were collected after delivery and immediately processed under sterile conditions. The amniotic membrane was mechanically peeled free from underlying chorion and washed with phosphate‐buffered saline (PBS) containing antibiotics.2.4Glucose, cholesterol, and triglycerides levels were determined using ADVIA 1800 and 2400  autoanalyzers by standard enzymatic methods. Fasting plasma insulin was determined by immunoassay on the Centaur XP platform (Siemens AG). Homeostasis model assessment‐insulin resistance (HOMA‐IR) was determined according to the equation HOMA‐IR = fasting plasma glucose (mmol/L) × fasting plasma insulin (μU/mL)/22.5. HbA1c was estimated by high‐performance liquid chromatography‐based ion exchange chromatography .2.5Tissue sections from the maternal (facing the decidua) and fetal (facing the amniotic cavity) sides of the placentas were collected, washed, fixed in a 4% paraformaldehyde, embebbed in paraffin, cut into slices using a rotator microtome and mounted onto glass microscope slides. Tissue sections were then dewaxed and stained with Masson's trichrome to highlight connective tissue and collagen fibers of the extracellular matrix. Slides were observed microscopically at ×2.5 magnification using a Leica DM LB2 bright‐field microscope . Ten different nonoverlapping fields randomly selected from each slide were visualized and captured at ×40 magnification using a Leica DFC320 Digital Camera (Leica Microsystems). Morphometric measurements were performed using Image‐Pro Plus 5.0 software  programmed for multistep algorithms: one for the detection of the total area of the villous/image (in pixels) and a second for the detection of the total trichrome‐positive green pixels/image (fibrotic area). The ratio between the fibrotic area and the total area of the villous was calculated for each image. Finally the average of the ratios of the 10 images was calculated for each case.2.6AMSCs were isolated as previously described.2.7Macrophages were purified by adherence to tissue culture plates following an established procedure that results in >90% purity.CD80, CD86, CD163, CD206, and CD209 were compared in amniotic macrophages and AMSCs.Since amniotic macrophages and AMSCs are isolated from the same primary sample, in order to assess their purity, the expression levels of typical markers of monocyte‐lineage and antigen presenting cells, such as 2.85) were incubated with a panel of primary antibodies  and their surface markers expression was analyzed by flow cytometry . Data analysis was performed using the FACSDiva software (BD Biosciences).To assess mesenchymal lineage features and to determine whether they met the minimum criteria defined by the International Society of Cell Therapy,2.9To determine the multilineage differentiation capacity of AMSCs, specific differentiation conditions were used to trigger cell differentiation into adipocytes, chondrocytes, and osteocytes as described.2.104) suspended in the same medium were added to the upper chambers. Transwells were then incubated for 24 hours at 37°C. Cells that migrated to the lower chamber were then collected and counted using the BioRad TC 10 Automated Cell Counter .The migratory capacity of AMSCs was determined using a Transwell system , as described.2.114) were added to the upper chambers and incubated for 24 hours at 37°C and those ones that invaded into the lower chamber were collected and counted as above.The invasive capacity of AMSCs was determined as for the migration assay except that Transwell membranes were firstly coated with Matrigel (Corning) in DMEM high glucose 0.2% BSA for 2 hours at 37°C. Cells (8 × 102.124) and allowed to attach for 24 hours. Cells were then stimulated for 24 hours with glucose, insulin and/or palmitic acid (PA) to a final concentration of 30 mM, 100 nM, and 0.4 mM, respectively. Absolute ethanol (EtOH) was used as vehicle to dissolve palmitic acid so a control of EtOH 0.4 mM was added in all the experiments. All conditions including PA were relativized to that control.AMSCs from control pregnant women were cultured in 6‐well plates (5 × 102.135) suspended in DMEM/F12, 0.1% BSA were then added to the upper chambers and incubated overnight at 37°C. Cells that migrated to the lower chambers were collected and counted as above.The migratory response of human monocytes (THP‐1 cell line) and human T lymphocytes (Jurkat cell line) to the conditioned medium of AMSCs was determined as for the migration assay, except that a 5‐μm polycarbonate membrane was used. Lower chambers were filled with 24‐hour conditioned medium without FBS. In the stimulation experiments, 24‐hour conditioned medium was collected 24 hours after removing the stimuli. THP‐1 or Jurkat cells (1 × 102.145) suspended in DMEM with 0.2% BSA were then added to the upper chambers and incubated overnight at 37°C. Cells that migrated to the lower chambers were collected and counted as mentioned above.MCP‐1 neutralization was performed as per the chemotaxis experiments with THP‐1 cells, except that the 24‐hour conditioned media without FBS were incubated with 20 μg/mL of an antibody against MCP‐1  for 30 minutes at room temperature before being added to the lower chamber of the Transwell System. A negative epitope control  was included in each experiment. THP‐1 cells (1 × 102.153) were cultured in 96‐well plates and allowed to attach for 24 hours. A MTT assay at day 1 was performed to count the initial number of cells. After 5 days, a second MTT assay was performed (day 7) and the difference in absorbance between day 7 and day 1 was considered the proliferation rate. In the AMSCs stimulation studies, the proliferation rate was measured at 24 hours after the addition of the stimuli. Absorbance was measured by spectrophotometry at 540 nm in all cases.Proliferation rate of AMSCs was determined by standard colorimetric 3‐‐2,5‐diphenyltetra‐zolium bromide (MTT) incorporation experiments. Cells (1.6 × 102.162 (PGE2) concentrations were measured, at 24 hours, in the conditioned medium of AMSCs by ELISA (R&D Systems) following the manufacturer's instructions.Prostaglandin E2.1718S (Hs03928985_g1), ACACA (Hs01046047_m1), ALP (Hs01029144_m1), CCL3 (Hs00234142_m1), CD40 (Hs01002915_g1), CD74 (Hs00269961_m1), CD80 (Hs01045161_m1), CD86 (Hs01567026), CD163 (Hs00174705), CD209 (Hs01588349), COL1a1 (Hs00164004_m1), COL2a1 (Hs00264051_m1), COMP (Hs00164359_m1), CTSB (Hs00947439_m1), CTSS (Hs00175407_m1), FABP4 (Hs01086177_m1), IL‐1β (Hs01555410_m1), IL‐6 (Hs00174131_m1), IL‐10 (Hs00961622_m1), IL‐12b (Hs01011518_m1), IL‐33 (Hs04931857_m1), LPL (Hs00173425_m1), MCP‐1 (Hs00234140_m1), MRC‐1 (Hs00267207), PPARγ (Hs01115513_m1), PTGS2 (Hs00153133), TGFβ1 (Hs00998133_m1), TNFα (Hs00174128_m1).Total RNA was isolated from cells using the RNeasy Mini kit  and its quality was assessed by the OD260/OD280 ratio. For gene expression analysis, RNA was transcribed into cDNA with random primers using the Reverse Transcription System . Quantitative gene expression was evaluated by real‐time polymerase chain reaction (RT‐PCR) on a 7900HT Fast Real‐Time PCR System using the following predesigned TaqMan primers (Applied Biosystems): 2.18t test was used to compare the mean values of continuous variables. To analyze differences in nominal variables between groups we used the chi‐squared test. Pearson's correlation coefficient was used to analyze the univariate correlation between gene expression and clinical and metabolic parameters. A P‐value <.05 was considered statistically significant in all analyses.Data were analyzed with SPSS software version 21.0 . The one‐sample Kolmogorov‐Smirnov test was performed to verify normal distribution of the quantitative variables. Quantitative variables are expressed as mean ± SD or SEM as indicated and categorical variables are reported as number (percentages). Student's 33.1AMSCs were isolated from the amniotic membrane of mothers with a healthy pregnancy  and with GDM (n = 9). Clinical, anthropometric, and biochemical data from mothers and offspring are presented in Table P = .0173) whereas it was higher on the fetal side compared the controls (P = .0067)  and collagen type I alpha 1 chain (COL1a1)  Table . No sign) Figure B,C.3.3TNFα) and the chemokine monocyte chemoattractant protein 1 (MCP‐1), as well as in genes involved in the inflammatory response such as CD40 and cathepsin S (CTSS) compared to controls , transforming growth factor beta 1 (TGFβ1), CD74, CD80, or cathepsin B (CTSB) (data not shown). By contrast, GDM‐AMSC showed a significant reduction in the expression of IL‐33, a cytokine with anti‐inflammatory properties, and also of prostaglandin‐endoperoxide synthase 2 (PTGS2), a key enzyme in prostaglandin biosynthesis   phenotype , CD163, CD209, peroxisome proliferator‐activated receptor gamma (PPARy), and IL‐10 between groups . On the other hand, in the GDM group, the correlations of MCP‐1 with cord blood insulin  and GCT , became stronger, whereas its association with maternal HOMA‐IR  and prepregnancy BMI  followed a trend. The associations between CD40 with maternal prepregnancy BMI  and GCT  remained significant. Overall, our data indicate that maternal metabolic phenotype during pregnancy could determine the biological characteristics of fetal precursors, which may be linked to metabolic characteristics in the offspring. These results become stronger in the context of GDM.Next, we analyzed the groups separately and regarding the control group, all associations observed disappear except for 4It has been proposed that signatures of metabolic deregulation remain in cells early in development.MCP‐1 is associated with maternal anthropometry and with maternal and fetal metabolic parameters.The novelty and the consistency of the clinical and experimental results are some of the strengths of our study. We have been able to reproduce our findings in vitro. AMSCs obtained from control women displayed an inflammatory gene expression profile similar to the pattern observed in GDM women when exposed to a GDM‐like environment. Furthermore, their migration and chemotactic capacities were affected. It is important to note that all placentas were obtained from a well‐characterized cohort of women scheduled for cesarean delivery in order to avoid any uncontrolled inflammatory stimuli linked to labor. Amniotic membrane samples were collected from the placental disk, halfway between the cord insertion point and the lateral edge, avoiding interregional differences. The sample size was similar to or even larger than other studies analyzing the plasticity of placental or umbilical cord mesenchymal stem cells.Although usually discarded following birth, placental membranes are an easily accessible and nontraumatic source of mesenchymal stem cells (MSCs),MCP‐1 (and a notably trend in other pro‐inflammatory genes) than those obtained from controls, suggesting they have a more pro‐inflammatory profile predisposition, even in GDM women with a good metabolic control according to their third trimester HbA1c concentrations. Consistent with previous findings, the levels of PGE2, a potent immunomodulatory molecule secreted by mesenchymal stem cells deeply implied in their anti‐inflammatory and immunosuppressive capacities,PTGS2 in AMSCs from GDM women, a key enzyme involved in the PGE2 synthesis pathway.Previous reports showed that the conditioned medium from non‐GDM AMSCs induced macrophages to become M2‐like (anti‐inflammatory or pro‐repair) macrophages,TNFα expression is increased in placental tissue obtained from pregnancies complicated by GDMMCP‐1 in AMSCs isolated from diabetic mothers along with an increased chemotactic capacity over monocytes and T‐lymphocytes through their conditioned medium. These phenomena may participate in the onset of placental inflammation that accompanies GDM. In addition to TNFα and MCP‐1, AMSCs isolated from GDM women showed an upregulation of genes involved in the inflammatory and immune response such as CD40 and CTSS. Cathepsin is a cysteine protease implicated in the regulation of inflammatory activity and it has been postulated as a potential biomarker of the development of insulin resistance and type 2 diabetes.CTSS, TNFα, and MCP‐1 in AMSCs isolated from GDM mothers, suggesting intrauterine programming/imprinting.A pro‐inflammatory state is known to be associated with the development of insulin resistance, obesity, type 2 diabetes, and atherosclerosis.IL‐33 expression in GDM‐derived AMSCs whereas in vitro control AMSCs exposed to a GDM‐like milieu showed an upward trend. Dalmas et alLess consistent is the behavior of IL‐33, an interleukin involved in the maintenance of tissue homeostasis, resolution of inflammation, and repair of tissue damage.Our in vitro results confirm that AMSCs exposed to high concentrations of insulin, glucose and palmitic acid, simulating the hyperinsulinemia, hyperglycemia, and dyslipidemia observed in metabolic syndromeMCP‐1 gene expression was positively correlated with both cord blood insulin and triglyceride concentrations, but not with birth weight or newborn adiposity. The lack of association between gene expression profiling and neonatal anthropometric parameters could be due, in part, to the sample size, the effect of nutritional therapy which normalizes fetal growth,MCP‐1 and CD40 by the adverse maternal environment seems to be specific to pregnancies complicated by GDM and suggests that maternal nutritional and metabolic status could induce changes in the placental gene expression profile even when they are adequately controlled. It suggests that normalizing birth weight and metabolic control is unable to revert the placental insult. Based on this evidence, we speculate that GDM environment could program stem cells and subsequently favor metabolic dysfunction later in life.According to the fetal programming hypothesis,5For the first time, we demonstrate that maternal metabolic derangements in pregnancies complicated by GDM disturb both the phenotype and biological features of AMSC which are ultimately related to maternal and fetal nutritional and metabolic status, supporting the notion that fetal adaptive programming in the setting of GDM might have a direct impact on offspring. Our results also suggest that AMSCs might be a powerful tool for the indirect study of fetal cells in the context of hyperglycemia and insulin resistance, opening the possibility of new predictor or diagnostic approaches.The authors indicated no potential conflicts of interest.F.A.‐C.: conception and design, provision of study material or patients, collection and/or assembly of data, data analysis and interpretation, manuscript writing, final approval of manuscript; E.M.‐M.: conception and design, provision of study material or patients, collection and/or assembly of data, data analysis and interpretation, final approval of manuscript; M.E., R.B.: collection and/or assembly of data, data analysis and interpretation; M.B.: conception and design, provision of study material or patients, collection and/or assembly of data, final approval of manuscript, administrative support; G.L.: data analysis and interpretation; C.L., C.G.: collection and/or assembly of data, administrative support; A.G.: provision of study material or patients, collection and/or assembly of data; C.S.: data analysis and interpretation, article revision and final approval of manuscript; L.M.‐G.: provision of study material or patients, collection and/or assembly of data, article revision and final approval of manuscript; J.V.: final approval of manuscript, data analysis and interpretation, final approval of manuscript, administrative support, financial support; A.M.: conception and design, provision of study material or patients, collection and/or assembly of data, data analysis and interpretation, manuscript writing, final approval of manuscript, administrative support, financial support; S.F.‐V.: conception and design, data analysis and interpretation, manuscript writing, final approval of manuscript, administrative support, financial support.Supplementary Figure 1 Gene expression analysis of pro‐repair markers  in amniotic membrane‐resident macrophages obtained from pregnant control and GDM women (n = 6–8 per group).Click here for additional data file.


Research from Western populations describes abdominal obesity as a low‐grade inflammatory disease; less is known from tropical areas with high pathogen burden.This cross‐sectional study investigated whether obesity contributes to low‐grade inflammation in 587 individuals from randomly selected households in Zanzibar.The Association between obesity indices , leptin, and inflammatory markers  was investigated using multinomial logistic regression analysis, accounting for ordinal outcome variables with four categories; 1st–4th quartile.Study participants were between 5 and 95 years; 49.6% were male. Mean serum levels were; leptin: 4.3 ± 5.2 ng/ml, CRP: 0.19 ± 0.42 µg/ml, IL‐6: 2.8 ± 5 pg/ml, and TNF‐α: 5.3 ± 5.2 pg/ml. Obesity indices were associated with leptin and CRP in the third and fourth quartiles in single models. In combined models, associations were observed between BMI ; WC ; and %BF  and leptin in the fourth quartile; also between %BF and CRP in the third quartile .Total body fat was associated with low‐grade inflammation in this tropical population rather than body fat distribution such as abdominal obesity. This may increase the risk of insulin resistance and other obesity‐related metabolic and cardiovascular health endpoints. It comprises two main Islands, Unguja and Pemba, and has a population of 1.3 million people, almost 63% of whom live in Unguja.Participants for this cross‐sectional study were randomly selected in 2013, in a representative population in Unguja Island, with households serving as sampling units.2.2Socio‐economic and demographic indicators were assessed at household level.2.3N = 8) were excluded as an acute inflammation was presumed. A further N = 27 individuals in the highest quartiles of IL‐8 as well as CRP were removed from the descriptive analysis as this indicated an acute infection. As extremely high IL‐8 levels were observed in many individuals, the variable IL‐8 was excluded from the final analysis. Four complete data sets were created: one for leptin and three for each of the inflammatory markers. In order to exclude individuals with a possible acute infection not related to obesity, we excluded 10% of the top extreme values in each data set, thus the final sample sizes included in the final analyses were as follows: CRP = 509; IL‐6 = 447; TNF‐α = 429; and Leptin = 465.In total, 616 participants provided complete blood samples for the analysis of leptin, TNF‐α, IL‐6, IL‐8, and CRP. Participants with serum levels outside the detection limits for leptin and each inflammatory marker were excluded. For the overall study analysis, 587 participants additionally fulfilled the inclusion criteria  and provided complete covariate information . Of these, data from 71 participants were excluded due to pregnancy, nonfasting status, and medication use. All individuals with a high CRP concentration >10 µg/ml  and obesity (BMI >95th percentile) according to the WHO centile curves.2 as recommended by the WHO.Bodyweight, body fat percentage , height and WC were measured.2.52–5 × 104 pg/ml (leptin), 2 × 10−2–2 × 10 μg/ml (CRP), 2 × 10−1–6 × 102 pg/ml (IL‐6), 1 × 10−1–5 × 102 pg/ml (IL‐8), and 3 × 10−1–3 × 102 pg/ml (TNF‐α).Venous blood was collected from eligible participants in an overnight fasting status. From each participant, 10 ml (6 ml for children) of venous blood was collected in a clot activator vacutainer PET tube  and kept at 4°C until serum was prepared by centrifugation on the same day.2.6Statistical analysis was performed using SAS 9.3 (SAS Institute). Mean and standard deviation for continuous variables were calculated, stratified by weight status. Leptin, CRP, IL6, and TNF‐α levels were categorized using sex‐ and age‐group‐specific quartile ranges Q1–Q4). For obesity indices, very severe thinness and severe thinness were merged into one category    and above 45 years . The proportion of participants with Leptin and CRP levels in the fourth quartile was highest among those with obesity Table . Pearson3.2Table 4In the present study, the association between obesity indices , leptin and low‐grade inflammation  in a Zanzibari population of individuals aged 5 years and above was investigated. Similar to other studies,The observed association in the individual models between overweight/obesity and leptin is in agreement with previous studies.Similar to other studies,Furthermore, a US study reported significant associations between CRP and BMI of young adults,Overall, a positive correlation was observed between serum concentration of leptin and CRP independent of the obesity indices, as reported previously.Previous studies have reported associations between obesity and high IL‐8 concentrationsThe overall mean IL‐6 concentration in the present study (2.9 pg/ml) was slightly higher than that of males (2.5 pg/ml) and females (2.3 pg/ml) reported in a West African (Ghana and Nigeria) study population,Our unexpected results regarding the higher mean concentration of IL‐6 and TNF‐α in underweight/normal weight participants and the fact that no significant association was observed in overweight/obese participants may be due to underlying causes (infection‐weak immune system) or malnutrition,The major strength of this study is that it represents the first population‐based study in Zanzibar, Tanzania, enrolling nonhospitalized individuals to investigate the association of three different obesity‐related indices with leptin and different inflammatory markers in a sample including children from 5 years, adolescents, younger, middle age, and older adults.N = 77, 21.4 %). The overall study was powered to investigate the prevalence of malnutrition in the Zanzibari population and resulted in a higher sample size compared to the current sample size used for this analysis. While we acknowledge that the decreased sample size of the current analysis is a limitation that may lessen the scope of the results obtained, we are convinced that our results contribute to the literature of sub‐Saharan African studies and provide insight into associations in developing economies with tropical climates. This study is further limited by the cross‐sectional design, which may not accurately reflect longitudinal associations between changes in body fat and the long‐term inflammation status of the study population.The random selection of the study participants, the use of standardized anthropometrical and laboratory measurements, and the exclusion of participants with extreme values of leptin and inflammatory markers/indications of infection enhanced the quality of the data used and the statistical analyses. The overall sample size in this study was similar to other studies,5Adipocytes dysfunction—due to adipose expansion—may have local or systemic effects on inflammatory responses, which may then contribute to the initiation and progression of obesity‐induced metabolic and cardiovascular risk factors, such as type 2 diabetes mellitus. High %BF in this population was associated with higher leptin and higher CRP concentrations, which have been closely linked to insulin resistance, a known risk factor for noncommunicable diseases. The results of this study add important information for the development of obesity prevention interventions promoting healthy diets and healthy lifestyles that will consequently help to reduce cardiovascular risk in this study population.This study contributes to the general understanding on the association between obesity—measured as fat mass—, leptin and low‐grade inflammation in the sub‐Saharan African population. Additionally, the results provide important information for public health stakeholders, policy makers, and researchers in similar contexts.The authors declare no conflict of interest.This manuscript represents original work that has not been published previously and is currently not considered by another journal. The authors' responsibilities were as follows: Antje Hebestreit and Maria Adam Nyangasa developed the idea for this manuscript and analysis strategy. Maria Adam Nyangasa and Christoph Buck conducted statistical analyses interpreted the data; Maria Adam Nyangasa wrote the manuscript and had primary responsibility for final content and submitting the manuscript for publication; Maria Adam Nyangasa, Antje Hebestreit, Christoph Buck, and Soerge Kelm revised the manuscript critically for important intellectual content; Maria Adam Nyangasa, Christoph Buck, Soerge Kelm, Mohammed Ali Sheikh, Kathrin Günther, and Antje Hebestreit were responsible for revisions and final approval of the manuscript.