A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of large-scale data sharing for mental health research. Thus, effective and early treatment is crucial. Unfortunately, current strategies for treating MDD, which do not take neurobiological markers into consideration, have not been particularly effective6.Major depressive disorder (MDD) is the largest contributor to the disease burden caused by poor mental health worldwide owing to its high prevalence, high recurrence rates, chronicity, and comorbidity with physical illness7; Kempton et al.8), progress has still been limited in part by underpowered studies and a lack of reproducible findings . Many research studies in the field have been restricted by small samples, resulting in a lower probability of finding “true” effects  and an inflated estimate of the effect size when a true effect is discovered12. This is particularly problematic when the true effect size is modest, which is often the case for differences in brain imaging measures between patients and controls. Underpowered studies also make it difficult to reproduce significant findings, leading to inconsistent and poorly replicated neuroimaging findings in depression14.Over the past several decades, technical advances in neuroimaging have provided the impetus for identifying measurable indicators of brain processes associated with MDD in order to detect individuals at risk for the disorder, to facilitate the development of novel interventions, and to evaluate treatment effects. Although several neuroimaging markers have been found that differentiate patients with MDD from healthy controls 15, are yielding key insights into brain mechanisms involved in MDD . However, recruiting large samples is not always feasible because of limited access to patient populations at any one site or limited availability of scanning facilities and the financial costs of scanning hundreds or even thousands of participants. Moreover, large-scale population-based samples also typically focus on individuals from a single geographic region or country within a restricted age range, thereby limiting the generalizability of findings across countries, cultures, and developmental stages. Issues with retrospective meta-analyses include the potential over-representation of positive findings in the published literature (publication bias) and a lack of harmonization of data processing and statistical analysis methods across the different studies included in the meta-analysis.Larger samples and meta-analytic approaches represent good strategies to overcome issues associated with small sample sizes. Large-scale data collection initiatives with harmonized assessments (including neuroimaging), such as the population-based UK Biobank study  makes optimal use of valuable and costly existing data sets from individual studies; (2) collates large data sets at a relatively low cost; (3) allows coordinated analysis using standardized protocols for data processing and analysis; and (4) combines expertize of hundreds of professionals in the fields of neuroimaging, psychiatry, statistics, and mathematics. Here, we discuss the work of the worldwide Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Major Depressive Disorder (MDD) consortium.21. Because most major mental illnesses have a high-dimensional genetic architecture with polygenic influences, epistasis and gene by environment interactions, by focusing on intermediate phenotypes—or endophenotypes—at the level of MRI-derived brain measures, it was thought that researchers would be better able to identify the neurobiological underpinnings of psychiatric disorders24. Therefore, the ENIGMA consortium was launched to combine existing genomic and neuroimaging data around the world to conduct well-powered GWAS analyses. ENIGMA has since published the largest genetic studies of the brain, in partnership with other consortia30, mapping genome-wide effects of over a million genetic loci in over 30,000–50,000 brain MRI scans .The MDD Working Group was founded in 2012 as part of the ENIGMA consortium. ENIGMA was initiated in 2009 to boost statistical power in genome-wide association studies (GWAS) that aimed to identify common genetic variants that affect brain structureBuilding on ENIGMA’s initial successes in imaging genetics, disease working groups were formed to study patterns of brain abnormalities in major psychiatric, neurodevelopmental, neurological, and neurogenetic disorders. ENIGMA MDD was established with the initial aim to (1) identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across many different samples worldwide; and (2) identify demographic, genetic, clinical, psychological, and environmental factors that affect these associations.http://enigma.ini.usc.edu/wp-content/uploads/2019/10/List_members_oct2019-2.pdf. To date, participating researchers have shared demographic, clinical and neuroimaging data from 9788 healthy individuals and 4372 individuals with MDD.Since it was established, ENIGMA MDD has grown to 35 participating research institutions (including 45 study cohorts) from 14 different countries across six continents in September 2019 , quality assurance (http://enigma.ini.usc.edu/protocols/imaging-protocols/) and statistical analysis protocols were run locally on data from participating cohorts. In addition, the scope of retrospective meta-analyses in terms of examined brain regions is limited to those reported in the original studies, as many published studies adopted a hypothesis-driven approach and focused on specific regions of interest (ROIs). The large sample size available in ENIGMA MDD ensures the statistical power needed to investigate whole-brain structural brain alterations. For the IPD-based meta-analyses, summary statistics of each site were shared to be included in a random effects meta-analysis to examine differences in structural brain measures between MDD patients and controls.The initial studies conducted with data available through the ENIGMA MDD consortium focused on identifying associations between MDD and structural brain measures that could reliably be detected across many samples worldwide. Because inconsistent findings across previous retrospective meta-analyses may be owing in part to differences in data processing and statistical analyses among the primary studies, we conducted individual participant data (IPD)-based (or prospective) meta-analyses to identify differences in subcortical volume, subcortical shape, cortical thickness, cortical surface area, and white matter integrity between patients with MDD and healthy controls, and to examine the effects of demographic and clinical characteristics40. However, the advantages of a mega-analytic versus meta-analytic approach include (1) greater flexibility in the control of confounders at the level of individual patients and studies; and (2) not having to assume within-study normality and known within-study variances40, as these assumptions can be especially problematic with smaller samples41. Moreover, pooling all data in a single statistical model may boost statistical power to detect certain effects42, such as higher-level interactions, when the phenotype of interest is rare  or when the range of continuous variables is limited  in individual studies. All ENIGMA MDD studies published to date are summarized in Table A few ENIGMA MDD studies have started to adopt a mega-analytic approach, where individual-level measures derived from the harmonized imaging processing protocols are pooled across sites and regression analyses are conducted on this pooled dataset while correcting for confounding site effects . Key advantages of a meta-analytic versus mega-analytic approach include: 1) allowing the analysis of individual studies to account for local population substructure; (2) allowing analysis of study-specific covariates that may be better dealt with within each study; and (3) allowing analyses to be conducted within each participating site and results to be shared through a central site without requiring individual-level data to be shared allowingN = 1728) and healthy controls (N = 7199)35. Consistent with prior studies and retrospective meta-analyses45, in this IPD-based meta-analysis we found significantly lower hippocampal volumes in individuals with MDD compared with healthy controls. This effect was also consistently observed across individual cohorts, although the overall effect size was modest (Cohen’s d = −0.14). The hippocampal volume deficit was greater in MDD patients with recurrent episodes , compared with healthy controls, whereas no hippocampal volume alterations were observed in first-episode patients (N = 583). Our findings may suggest that depression-related reductions in hippocampal volume are a result of longer illness duration or greater number of episodes, instead of a premorbid vulnerability factor. This is consistent with prior longitudinal studies showing greater hippocampal atrophy in individuals with persistent, recurring, or worsening of depressive symptoms over time49. Nonetheless, it is unclear whether hippocampal atrophy associated with prolonged illness duration or recurrence represents a state marker instead of a permanent scar. Fortunately, hippocampal alterations may normalize as hippocampal enlargement has been observed following remission or treatment of MDD51. We also found smaller hippocampal volumes in patients with an adolescent onset of MDD , compared with controls, whereas no differences were observed in those with an adult onset of MDD (N = 997)  of the adolescent-onset patients had a recurrent episode of MDD, adolescent disease onset may, in part, have an independent association with hippocampal volumes. Smaller hippocampal volumes may precede disease onset, especially in this early-onset group, perhaps as a result of factors commonly associated with early-onset MDD including childhood adversity59 and genetic influences60. Longitudinal studies designed to track hippocampal volume changes prior to disease onset and over the disease course are required to elucidate whether hippocampal abnormalities result from a prolonged duration of chronic stress associated with depressive episodes, represent a vulnerability factor for MDD, or both.The first ENIGMA MDD project focused on differences in subcortical volume between MDD patients (97) Fig. . This is62. Nonetheless, associations with MDD may still be present for functionally distinct subregions within these broader subcortical regions. Patterns of depression-related alterations in subregions of subcortical surfaces have been difficult to identify as there are few identifiable surface landmarks, in contrast with the landmarks consistently found in cortical surfaces . In this context, subcortical shape analysis may be a more sensitive method to identify more localized effects in subdivisions in subcortical regions that were not captured by the volumetric analysis of subcortical regions in the first paper from the ENIGMA MDD consortium35. To address this, we conducted an additional multi-site meta-analytic investigation to test whether MDD patients, and specific subgroups of MDD based on important clinical characteristics, differ from healthy controls in subcortical shape. Specifically, we applied meta-analytic models on effect sizes generated from 1781 patients with MDD and 2953 healthy controls across 10 study cohorts. Consistent with the findings from our first meta-analysis, we found that relative to healthy controls (N = 2879), patients with an adolescent onset of MDD (N = 476) had lower thickness (Cohen’s d = −0.17) and smaller surface area (Cohen’s d = −0.18) in the hippocampus, with most pronounced effects in the subiculum and cornu ammonis (CA) subfields two and three of the hippocampus37  and smaller surface area (Cohen’s d = −0.17) in the amygdala in adolescent-onset patients, specifically within the basolateral subdivision of the amygdala37 . Previous reports have varied regarding volume abnormalities in subcortical regions other than the hippocampus, such as the amygdalas37 Fig. . Extendia37 Fig. . These s36. Because more research groups joined ENIGMA MDD after the publication of our subcortical volume meta-analysis study, we were able to conduct separate analyses in young people (⩽21 years) and adults (>21 years). Most published studies to date have focused on regional cortical volume, which is a function of cortical thickness and surface area. Advances in neuroimaging data processing have made it possible to separate cortical surface area and cortical thickness, which is important to do in the context of understanding brain correlates of MDD, given that these neural characteristics are genetically and phenotypically distinct65. In adults, we observed subtle cortical thickness alterations in 13 of 68 cortical regions in patients with MDD (N = 1902) compared with healthy controls (N = 7658) (Cohen’s d’s between −0.10 and −0.14), including lower thickness of the bilateral medial orbitofrontal cortex (OFC), fusiform gyrus, insula, rostral anterior (ACC) and posterior cingulate cortex (PCC) and unilaterally in the left middle temporal gyrus, right inferior temporal gyrus and right caudal ACC , posterior cingulate cortex and insula. These cortico-limbic thickness alterations may contribute to the broad spectrum of emotional, cognitive, and behavioral disturbances in MDD.Following these studies of subcortical morphology, we examined cortical thickness and surface area in relation to MDD and clinical characteristics in a meta-analysis of data from 20 participating ENIGMA MDD cohortsACC Fig. . Our finN = 1214; Cohen’s d −0.11 to −0.18) relative to controls. No cortical surface area differences were found among the adult groups. We speculated that the more pronounced effects in adult patients with an adult age of onset may be driven in part by their older age compared with adult patients with an adolescent age of onset of their first depressive episode, which was confirmed by a post hoc moderator analysis with mean age of patients in each sample. This suggests that mental illness has a greater impact on cortical thickness in the context of aging. Indeed, cortical thickness has been shown to be a more sensitive indicator of aging than is surface area or volume72. Because our meta-analytic approach did not allow us to pool all data across samples, we were not able to investigate age-by-diagnosis effects across the entire age range . Therefore, future mega-analyses could further elucidate these dynamic relations with development and aging.The largest effect size was observed in the medial OFC, which—in contrast to lower hippocampal volume—was already detectable in first-episode patients. In contrast to the hippocampal volume finding that were driven by adult patients with an adolescent onset of their first depressive episode, the lower cortical thickness findings were driven mostly by adult patients with an adult onset of MDD  than the cortical thickness alterations observed in adults. Cortical surface area has been understudied in the context of MDD. Nonetheless, a recent longitudinal study showed that lower surface area was specifically observed in young people experiencing depressive symptoms in early adolescence but not in those developing depressive symptoms later in adolescence, and that lower surface area was already observable in young people with subclinical depressive symptoms, not all of whom will develop a full-threshold MDD diagnosis36. Thus, cortical surface area reductions may represent an early developing subtype of depressive disorder, shaped by genetic factors or early life adversity , and potentially precede the onset of MDD. This hypothesis is consistent with the observation that, compared with cortical thickness, cortical surface area has a higher genetic heritability78, has a genetic correlation with MDD and depressive symptoms , is determined earlier in development, and is less strongly affected by later environmental influences79.Surprisingly, in contrast to adults with MDD, adolescents with MDD showed no cortical thickness alterations, but rather, alterations in global cortical surface area Fig. ; the eff81, and in individuals at high risk for and/or transitioning to psychosis83. Longitudinal studies are required to test the hypothesis that cortical surface area alteration is a pre-existing risk factor for the development of MDD, and to investigate the subsequent clinical course of these depressed young people with global surface area reductions.Importantly and paradoxically, no differences in surface area were observed in adult patients with adolescent-onset MDD. This might be explained by (1) normalization of cortical surface area when transitioning into adulthood; (2) cortical surface area alterations being only present in a specific subgroup (subtype) of adult patients with adolescent-onset MDD, which we were unable to detect; or (3) those with cortical surface area alterations in early adolescence may be at higher risk for transitioning from MDD to other mental disorders over time. This latter possibility is consistent with reports of lower cortical surface area in adolescents and adults with psychosis or schizophrenia84, but only a few studies have investigated structural asymmetry86. In our studies, the majority of cortical thickness or surface area alterations were observed across both hemispheres, with a few regions showing only unilateral differences36. However, we did not explicitly test whether the effect sizes of our findings differed significantly by hemisphere. Therefore, in a separate study, we investigated structural asymmetries by investigating asymmetry indices ((left − right)/(left + right)) for local and global cortical and subcortical brain regions in individuals with MDD (N = 2256) compared with healthy controls (N = 3504)87. The results showed no significant differences in brain structural asymmetry between individuals with MDD and controls for any of the structural brain measures, nor any associations with clinical characteristics. These findings suggest that altered brain structural asymmetry is of little relevance to the pathophysiology of MDD, although functional asymmetries may still play a role.Altered brain asymmetry may have a role in MDD, especially at a functional level89. Although aging is associated with loss of gray matter, depression may accelerate age-related brain atrophy90. Therefore, we examined deviations from normative brain aging in adults with MDD and associated clinical heterogeneity by pooling data from >6900 healthy controls and individuals with MDD from 19 different scanners participating in the ENIGMA MDD consortium91. Normative brain aging was estimated by predicting chronological age (18–75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and ICV measures using Ridge Regression, separately in 952 male and 1236 female controls. We showed that our brain age prediction model generalized to unseen hold-out samples  ranged from 6.32 to 7.18 years), as well as to completely independent samples from different scanning sites 91.MDD is associated with an increased risk of aging-related medical illnesses such as cardiovascular disease and cancer92. We found that, at the group level, MDD patients had a + 1.08 years  greater discrepancy between their predicted and actual age compared with control participants. In other words, individuals with MDD were estimated to be ~ 1 year older than expected based on the brain age model. The brain age model relied mostly on cortical thickness measures  in order to make good age predictions. Brain-PAD differences were observed in all subgroups of patients compared with controls, with no significant differences in brain-PAD between the patient groups.Brain-predicted age difference (brain-PAD) was computed from the difference between predicted “brain age” and chronological age93. Inflammatory biomarkers are commonly dysregulated in MDD and negative relationships between levels of inflammatory cytokine  and cortical thickness have been found in medication-free, first-episode MDD patients94, suggesting that inflammation may be a common biological mechanism between MDD and brain aging. Notably, brain-PAD has been shown to be a general predictor of psychiatric and neurological disorders, with low clinical disease specificity95.As many of the MDD patients did not show advanced brain aging compared with controls, the clinical significance of the observed higher brain-PAD in MDD patients may be limited. However, there may still be a subgroup of MDD patients with more extreme patterns of brain aging, which would be important to identify as accelerated brain aging may be reversed with targeted treatment. For example, one study showed that brain-PAD was temporarily reduced by 1.1 years in healthy controls owing to the acute anti-inflammatory effects of ibuprofen38. The ENIGMA protocol for diffusion tensor imaging (DTI)33 calculates fractional anisotropy (FA) for 25 atlas-defined white matter tracts of interest. FA is a commonly used measure in DTI analysis, and higher values indicate directionally constrained diffusion of water molecules within the white matter, which is mostly interpreted as higher degree of myelin integrity. In addition to FA, the ENIGMA DTI protocols also yield the following diffusivity metrics: axial diffusivity (AD), which is thought to represent the number, caliber, and organization of axons, radial diffusivity (RD), which may be a measure of myelination, and mean diffusivity (MD), which is often considered a measure of membrane density96. As maturation of white matter tracts continues through adolescence and young adulthood, adolescent (age ≤21 years) and adult (age >21 years) patients and controls were analyzed separately. The meta-analysis showed subtle but widespread changes in FA, with lower FA in adult MDD patients observed in 16 out of 25 ROIs (Cohen’s d between 0.12 and 0.26) , potentially reflecting changes in the morphology of glial cells or myelination98. These alterations in FA and RD appeared to be global effects, as after correction for average FA and RD across the white matter skeleton respectively, these effects were no longer significant. Nevertheless, and in accordance with previous studies, the strongest regional changes in FA were observed in the genu and body of the corpus callosum and the corona radiata100. The corpus callosum connects brain regions in both hemispheres, including regions involved in mood regulation such as the anterior cingulate cortices and orbitofrontal cortices, whereas the corona radiata is part of the limbic-thalamo-cortical circuitry and is also implicated in mood regulation101.We also examined white matter microstructure in 1305 MDD patients, and 1602 healthy controls from 20 samples participating in ENIGMA MDD worldwide, again using a meta-analytic approach26) Fig. . Adult M36. Also, in line with the previous cortical and subcortical meta-analysis findings, the widespread alterations in FA in adult patients were driven by MDD patients with recurrent episodes (N = 645), as there were no significant differences between first-episode patients (N = 169) and controls (N = 816). This again suggests that these alterations may reflect the cumulative effect of stress on brain morphology, rather than a vulnerability factor for MDD, although the reduced statistical power for the first-episode patients comparison may also explain this difference. In line with the cortical meta-analysis findings, but in contrast with findings on subcortical volume, we observed lower FA in patients with an adult age of onset (N = 399) compared with controls (N = 869)  compared with controls (N = 853). We hypothesized that MDD may interact with the normal aging process of white matter, which was in line with findings from our diagnosis-by-age interaction analysis, in which we observed that MDD was associated with an accelerated decline in overall FA with increasing age compared to controls.The effect sizes for case–control differences in adults were small, but very similar to the effect sizes reported in the meta-analysis of subcortical volume and cortical morphology69) Fig. , but no N = 2096 patients and 3275 healthy controls), which may be related to lower severity of MDD symptoms in the UK Biobank sample or subtle differences in image processing. In addition, there were no significant differences in FA or diffusivity measures between adolescent patients and controls in the ENIGMA MDD sample after correction for multiple testing, with smaller effects in adolescents potentially related to lower disease duration and number of episodes in adolescent patients compared with adult patients. However, we cannot rule out that our sample of adolescent participants may still have been too small to detect subtle effects .We could not replicate the case–control differences in white matter microstructure in adults in a sample from the UK Biobank  advanced brain aging that we observed in adults with MDD was not different for male versus female patients91. Nonetheless, sex differences in structural brain alterations may be present during specific sensitive periods of brain development, such as adolescence or more specifically, during puberty102. We did indeed found higher RD only in adolescent males with MDD, but not females, compared with adolescent controls38. However, we did not observe sex differences in the cortical alterations in the adolescent MDD group36. This could perhaps be explained by the observation that sex differences in white matter volumes increase from birth, through adolescence, until males and females reach adulthood, whereas sex differences in gray matter remain relatively stable across development103. Future research would also benefit from a separation between gender and sex analyses.Major depression is more than twice as prevalent and the disease burden of MDD is 50% higher in females than in males105 and is associated with an increased risk of a multitude of psychiatric illnesses, including MDD106. In addition to epidemiological and clinical evidence linking childhood maltreatment and MDD, recent neuroimaging studies show that brain structures affected by childhood maltreatment are also implicated in the etiology and expression of MDD symptoms109.Of central relevance to understanding the role of environmental factors contributing to neurophenotypes of MDD is research focused on childhood adversity and maltreatment. Indeed, childhood maltreatment is relatively common in the general population110. In the two largest studies to date examining associations between childhood maltreatment and brain structure, Frodl et al. and Tozzi et al. examined the association between severity of childhood maltreatment—including emotional, physical and sexual abuse, or emotional and physical neglect as assessed with the childhood trauma questionnaire (CTQ)—and brain morphometry in a total of 3036 and 3872 individuals with and without MDD, respectively. Across all individuals, and correcting for MDD diagnosis, greater exposure to childhood maltreatment was associated with lower cortical thickness of the banks of the superior temporal sulcus (STS) and supramarginal gyrus (SMG), and with lower surface area across the whole brain and in the middle temporal gyrus , and especially those with an adult age of onset of MDD, had lower cortical thickness in the bilateral OFC, ACC, PCC, insula, frontal, and temporal lobe regions. It is thus possible, then, that in adults these depression-related cortical regions are explained by the severity of childhood maltreatment.Two studies from the ENIGMA MDD consortium examined the effects of childhood maltreatment on brain structure in depressed and non-depressed individuals using a mega-analytic approachrus Fig. . Childho110. This result stands in contrast to the first paper from the ENIGMA MDD consortium, where we found that MDD was associated with smaller hippocampal volumes, but not smaller caudate volumes, regardless of sex. As part of the dorsal striatum, the caudate is involved in motor planning, procedural learning, and reward-based reinforcement learning112. Specifically, the caudate codes representations of expectation violation and reward prediction errors that underlie approach and avoidance behaviors and reward-based learning, all of which are significantly altered in individuals exposed to childhood adversity and maltreatment114. While keeping in mind the heterogeneity of MDD and the limitations of retrospective reports of childhood maltreatment115, in the context of understanding neurophenotypes of MDD thus far, our results may suggest that smaller hippocampal volumes result from (stress-related) mechanisms directly associated with MDD, whereas smaller caudate volumes may result from exposure to stress during sensitive periods of development . Of note, MDD was no longer associated with smaller hippocampal volumes when corrected for childhood maltreatment110, although the overall sample included in the subcortical meta-analysis35 was larger than the sample included in this childhood maltreatment mega-analysis and there was also a difference in the composition of the cohorts evaluated. Future longitudinal studies in youth exposed to childhood maltreatment are needed to disentangle primary consequences of childhood maltreatment on brain integrity, from secondary associations caused by prolonged stress experiences and/or the development of maltreatment-associated psychiatric diseases such as MDD.The effects of childhood maltreatment on subcortical structures in MDD and healthy controls, however, were distinct from the effects of MDD on subcortical structures. Notably, in females only—although the same pattern showed a trend towards significance in males—greater maltreatment severity was associated with smaller caudate volumes116. The lifetime probability of suicide attempts is 20–25% among people with major mood disorders117. Prior studies had identified structural brain alterations in individuals with MDD and a history of suicidal thoughts and behaviors, with most consistent evidence for structural deficits in the ventromedial and ventrolateral PFC, dorsomedial and dorsolateral PFC, ACC, insula and posterior structures including PCC, temporal regions, and the cerebellum . Findings of structural alterations in subcortical regions, including the amygdala, hippocampus and striatal regions have been less-consistent across studies. However, published studies have been primarily conducted using small sample sizes . Therefore, we performed an IPD-based meta-analysis of subcortical volumes, lateral ventricle volumes, and total ICV using data from N = 1101 people with MDD  and N = 1996 healthy controls from seven research cohorts participating in the ENIGMA MDD consortium124. Groups were identified based on the presence of suicidal ideation, defined as thinking about suicide or taking one’s life, but without making any specific plan or acting upon those thoughts; suicidal planning, or the systematic formulation of a program of action that has the potential to lead to a suicide attempt; and a suicide attempt, defined as any self-initiated action aimed at terminating one’s life, regardless of the method or degree of its consequences. Because the number of suicide attempters (N = 14) was too small to allow any cross-group comparison, the MDD individuals with suicide attempts were grouped with those MDD individuals with suicide planning into a single category .Many individuals with MDD experience suicidal thoughts, and major depressive episodes account for at least half of suicide deaths124. MDD patients reporting suicidal plans or attempts did show a 2.87% smaller ICV (Cohen’s d = −0.284) than controls, but no significant differences were found when compared with the MDD patients with only suicidal ideation without a plan or those without suicidal ideation and behavior. These null findings with regard to subcortical volumes could perhaps be explained if additional involvement of subcortical regions, and especially the hippocampus35, in suicidal thoughts and behaviors beyond their role in MDD is subtle and only apparent in studies with very large sample sizes. Alternatively, given the highly heterogeneous nature of both MDD and suicidality, subcortical structural alterations may only become apparent in specific subgroups of people with suicidal thoughts and behaviors. Finally, it may be that cortical structural alterations play a greater role than subcortical alterations in suicidal thoughts and behaviors. Efforts to identify cortical structural alterations associated with suicidal thoughts and behaviors are currently ongoing within ENIGMA MDD in a sample with a higher prevalence of suicide attempts.No significant association of suicidal thoughts and behavior with any of the subcortical volumes was found36. These findings are counterintuitive as antidepressant treatment has been associated with reduced hippocampal atrophy by putatively enhancing synaptic plasticity and neurogenesis125. However, as the majority of the ENIGMA MDD cohorts did not collect detailed information on the history of antidepressant use, the duration of use, time since last antidepressant treatment and dose of the antidepressant, and given the cross-sectional nature of the studies, these findings cannot be interpreted as direct effects of antidepressant medication use. MDD patients taking antidepressants at the time of scanning were likely the most severe/chronic or recurrent patients in the sample, so the results are likely to be confounded by the severity or course of the disorder. Potential neuroprotective effects of antidepressant medication are more consistent with our cortical surface area findings in adolescents with MDD, showing lower cortical surface area in several regions in antidepressant-free adolescent patients compared with healthy controls but no differences between adolescent patients taking antidepressants and healthy adolescents36. Confounding effects of recurrent or chronic illness were also minimized in this group given their earlier stage of illness.With regard to antidepressant medication use at the time of scanning, patients taking antidepressants tended to show greater structural alterations than antidepressant-free patients, both in the subcortical and cortical ENIGMA MDD meta-analysis studies38. Differences in white matter microstructure were, however, present in adults with MDD who were antidepressant-free at the time of scanning compared with controls38. This finding was unexpected as the meta-analysis of white matter microstructure and the meta-analysis of cortical thickness were both performed in a partly overlapping sample of adults with MDD with a similar prevalence of recurrent episode patients (79% versus 71%). Therefore, if the greater and more widespread cortical thickness alterations were driven by a more severe course of the disorder, a similar effect would have been expected with regard to white matter microstructure. These findings raise the question of whether antidepressant medication may have differential effects on different characteristics of the brain  within the same individuals. Effects of antidepressant medication use on measures of gray matter and white matter microstructure require further investigation in a sample with more detailed and comprehensive information on antidepressant treatment  and simultaneous use of other medications . In addition, although many human and animal studies have examined the effects of short-term antidepressant medication use on brain structure, there is limited information on the effects of long-term antidepressant medication use on brain structure. In this respect, to disentangle indirectly associated phenomena from causal effects of antidepressants, longitudinal studies are needed, preferably with a focus on long-term exposure and at different stages of brain development and aging.In contrast to associations between antidepressant use and more pronounced cortical thickness and hippocampal volume abnormalities in adults with MDD, the meta-analysis of white matter microstructure revealed no differences between adult patients who were taking antidepressants at the time of scanning and healthy individualsOur findings were computed from many data sets combined, which has provided a more reliable estimate of effect sizes of structural brain alterations associated with MDD than have individual small sample studies. This inclusion of large-scale, diverse samples also enabled us to calculate and report how reproducible these structural brain alterations are across data sets and how well findings generalize to cohorts with different ages of onset, duration of illness, and with different geographic origins. Critically, most of our findings were based on a meta-analytic approach, which increases rigor. Moreover, we also extend retrospective meta-analyses of published studies by including data that have not been previously published owing to publication bias and by using harmonized data processing and statistical analysis protocols across all data sets.Our work has identified subtle structural brain alterations that are associated with specific demographic and clinical characteristics of MDD. In particular, specific features of brain structure were differentially associated with MDD at different stages of life and different stages of illness. Specifically, the associations with hippocampal and amygdala volumes/shapes and cortical surface area were documented in adults with an adolescent-onset MDD and in adolescents with MDD, respectively. In contrast, cortical thickness reductions and white matter abnormalities were associated specifically with adult-onset MDD and with older age in individuals with MDD and childhood maltreatment Fig. . Moreove126). Indeed, large-scale studies, including studies that pool existing data such as ENIGMA as well as large population-representative samples127, are beginning to show that variability in structural and functional brain imaging accounts for only a small percentage of the explained variance of clinical phenotypes. Thus, similar to genetics literature, it appears that individual measures of structural brain alterations account for limited variance in complex phenotypes such as depression.Many of the structural brain alterations identified in the ENIGMA MDD studies have smaller effect sizes than had been assumed based on previously published studies, even in more homogeneous subgroups of MDD patients. However, many of the larger effect sizes observed in prior studies may have been owing to small sample sizes and publication bias. Effect sizes of neuroimaging measures have been shown to have noticeable instability up to as many as 1000–2000 subjects  could explain more variance in the depressive phenotype, with the ultimate goal of developing clinically useful diagnostic or predictive tools.These findings have important implications for our theoretical understanding of MDD; small effect sizes make it unlikely that MDD can be explained by a generic disease process, which is perhaps not surprising given the multi-causal nature of this highly complex disorder. In addition, from a clinical perspective, these small effect sizes may make it unlikely for individual structural brain measures to provide diagnostic biomarkers. Effect sizes between a Cohen’s 129. Future collaborations between the ENIGMA MDD and REST-meta-MDD consortia will be important for identifying potential cultural differences in brain alterations associated with MDD.The ENIGMA MDD consortium is a dynamically evolving consortium, in which new research groups continue to join and new projects are continually being initiated. Our first studies have mainly focused on case–control differences in structural brain measures that can reliably be identified and replicated across many samples worldwide. An important next step within ENIGMA MDD is investigating higher dimensional structural brain measures , which may be better able to detect subtle regional structural brain alterations in MDD, with potentially larger effect sizes. Future work to identify potential histological, genetic, and environmental mechanisms underlying these structural brain alterations is also underway. Furthermore, as can be seen in Fig. Future plans of the ENIGMA MDD consortium also include (but are not limited to): (1) parsing the heterogeneity of MDD, (2) moving beyond structural brain measures to include functional brain alterations, and (3) elucidating whether the identified neuroimaging markers are unique to MDD or shared across mental disorders, which are further discussed below.By pooling data across many samples worldwide, the ENIGMA MDD consortium performs studies encompassing a range of depressive phenotypes—from very mild to severe and a broad range of previous treatments received. This broad spectrum of depressive phenotypes combined with a very large sample size provides the opportunity to study the phenotypic and neurobiological heterogeneity of MDD. Analyses within subtypes, rather than across a heterogeneous diagnosis-based sample, could reveal more pronounced changes in brain structure and function. For example, large samples allow the stratification or clustering patients into different subgroups while preserving sufficient statistical power within each subgroup.In addition, heterogeneity could be addressed by examining individual differences, for which large samples are required to capture the full range of variation in the phenotype. Such approaches may reveal clues for the development of treatments tailored to subtypes or individual differences. At present, there are various ongoing ENIGMA MDD projects that aim to address this heterogeneity by examining associations between brain alterations and depressive symptom subtypes  and the presence or absence of phenotypes closely related to MDD .130, as well as their response to treatment . Therefore, several ongoing projects in ENIGMA MDD have taken a dimensional approach to identify neural correlates of between-subject differences in the severity of individual symptoms .In addition, it is also important to investigate associations with individual symptoms, as individual symptoms differ in their impact on impairment of functioning, their response to specific life events, their risk factors132, but also see Dinga et al.133 for limitations associated with this approach), we aim to investigate potential biotypes and their replicability across multiple cohorts in ENIGMA MDD.Moreover, the pathophysiology of MDD is also likely highly heterogeneous. Different pathophysiological mechanisms can result in similar symptoms for different individuals  and the same underlying biological risk factors may result in a different expression of a certain disorder depending on an interaction with the environment and genetic vulnerability . In line with recent studies using brain imaging markers to identify subtypes of MDD defined by different profiles of biological markers, so-called “biotypes” 136. As potential case–control differences with small effect sizes  may be omitted in such meta-analyses and previous negative findings may have not been published, it will be important to expand this work through applying IPD-based meta- and mega-analyses. Therefore, we will conduct voxel- (or vertex-)wise meta-analyses, which have been shown to be superior to coordinate-based meta-analyses138. A caveat is whether different versions of task paradigms—or even different tasks probing the same functional domain—can be combined meaningfully in a meta- or mega-analysis. This needs to be further confirmed empirically, although preliminary results from the ENIGMA task-based fMRI workgroup are promising139.In addition to rsfMRI, future endeavors will also include task fMRI. In the spirit of the Research Domain Criteria frameworkVariance introduced by the different scanner types and acquisition parameters at each of the contributing sites cannot be avoided, but we can overcome the apparent heterogeneity in preprocessing, feature extraction, and statistical testing of fMRI data by harmonizing software packages, preprocessing settings, task contrasts, seed masks, parcellation atlases, network templates, and statistical models across participating sites. Similarly, the same rigorous quality assessment (QA) procedures should be employed across participating sites, judging data quality against centrally defined criteria. Following the example of using standardized analytical tools and QA procedures for structural analyses in ENIGMA, such procedures and tools are currently developed within the ENIGMA task-based and ENIGMA resting state fMRI methods working groups.80, bipolar disorder (BD)140, and MDD36 patients are all characterized by lower prefrontal and temporal cortical thickness relative to healthy control subjects. However, effect sizes differed between disorders, the largest (up to Cohen’s d 0.5) having been observed in SCZ, followed by BD (Cohen’s d 0.3) and MDD (Cohen’s d 0.15). A similar gradient was observed for hippocampal volume across these disorders142, suggesting an “affective-psychotic severity continuum” 146. The presence of suicidal thoughts and behaviors and childhood maltreatment are also relevant, as these constitute transdiagnostic constructs.Given that all ENIGMA psychiatric disease working groups use the same standardized preprocessing pipelines and analysis protocols, ENIGMA is well positioned to perform cross-disorder comparison studies. Several cross-disorder initiatives are ongoing, including comparisons of brain morphology across SCZ, BD, and MDD as well as across neurodevelopmental disorders 147.Worldwide data-sharing initiatives such as ENIGMA are not without their challenges. Some of these challenges encompass ethical and computational issues with regard to data sharing, as well as science and data sharing policies that vary from one research institute to another, from country to country or even from continent to continent and may change over time. This may restrict some researchers from sharing raw neuroimaging data, although sharing de-identified, individual-level data may still be feasible. In addition, several challenges need to be addressed in translating the small to moderate effect sizes observed throughout ENIGMA MDD to the individualized and generalizable prediction of MDD-related phenotypes. The first is the need for rigorous testing and validation of predictive models in multiple independent samples. A challenge in this respect is the relative unavailability of deeply characterized phenotypes and longitudinal data. To date, ENIGMA MDD has largely relied on existing data, which implies a degree of heterogeneity with respect to phenotyping including clinical assessments, limiting the analysis of sources of clinical heterogeneity. In addition, the current focus of ENIGMA MDD is on cross-sectional studies. Consequently, our findings require further investigation in longitudinal studies to elucidate, for example, influences of brain development and aging, medication effects, and the clinical relevance of the observed structural brain alterations in MDD. Combining longitudinal samples is not without its challenges, but has already been successfully done for healthy individuals by the ENIGMA Plasticity working groupAnother limitation is that neuroimaging data were collected using different MRI scanners, different sequences, different brain coverage and, for functional analyses, different paradigm versions or acquisition lengths, which may all introduce noise and further complicate the search for robust biological markers of MDD. Efforts to develop post-processing methods to reduce noise associated with differences in scanning and other characteristics between cohorts will be important, especially in the context of machine-learning analysis.Finally, it could be the case that certain findings regarding the neurobiology of MDD will not be obtained by ever larger meta-analyses of existing samples; we may need alternative methods of data collection or new data types that are sensitive to effects that are undetected today. We stress the need for a many-pronged approach using novel data collection and the coordinated analysis of the data already available, as well as the development of new approaches.Over the past 7 years since its initiation, ENIGMA MDD has brought together research groups across the world with broad expertize to work together to gain a better understanding of brain abnormalities associated with MDD. By addressing issues of underpowered studies, our work has provided more reliable estimates of the extent of structural brain abnormalities in depression, showing that variability in structural brain alterations may only account for a small percentage of the depression phenotype. Future work is underway that aims to address the heterogeneity of depression and to integrate across data modalities to better understand the multi-causal nature of depression, with the ultimate goal to help develop or select more effective treatments for MDD.


Biologists are drawn to the most extraordinary adaptations in the natural world, often referred to as evolutionary novelties, yet rarely do we understand the microevolutionary context underlying the origins of novel traits, behaviors, or ecological niches. Here we discuss insights gained into the origins of novelty from a research program spanning biological levels of organization from genotype to fitness in Caribbean pupfishes. We focus on a case study of the origins of novel trophic specialists on San Salvador Island, Bahamas and place this radiation in the context of other rapid radiations. We highlight questions that can be addressed about the origins of novelty at different biological levels, such as measuring the isolation of novel phenotypes on the fitness landscape, locating the spatial and temporal origins of adaptive variation contributing to novelty, detecting dysfunctional gene regulation due to adaptive divergence, and connecting behaviors with novel traits. Evolutionary novelties are rare, almost by definition, and we conclude that integrative case studies can provide insights into this rarity relative to the dynamics of adaptation to more common ecological niches and repeated parallel speciation, such as the relative isolation of novel phenotypes on fitness landscapes and the transient availability of ecological, genetic, and behavioral opportunities. One of the most fundamental biodiversity patterns across the tree of life is the highly uneven distribution of evolutionary novelty , i.e., nde novo genes  which provides a landmark study of the evolution of novel citrate-feeding in cultures . This secultures . Exampleic range , idiosynic range , multi-tig trees , highly ig trees , novel eig trees , and arcig trees . In contAntilles  or haploAntilles ), microeMicroendemic adaptive radiations of novel specialists are rare because novelty is rare and most isolated environments containing sufficient levels of ecological opportunity for adaptive radiation to occur are large and unique, rather than highly replicated. Most examples of novel ecological specialists are either ancient taxa  or speciCyprinodon desquamator; Martin and Wainwright 2013) and a molluscivore , and a third generalist species, C. variegatus, which feeds on macroinvertebrates and algae  strikes biting scales and protein-rich mucus from other fishes, usually generalist pupfish which make up 95% of the fish community . This spd shells .Gambusia hubbsi and Atherinomorus stipes as on San Salvador [The San Salvador radiation exhibits oral jaw diversification rates over 1400 times faster than generalist pupfish populations on neighboring Bahamian islands , similarSalvador ). Lake aCyprinodon trophic specialists endemic to Laguna Chichancanab in the central Yucatán, Mexico. Similarly, this saline lake contains at least five endemic pupfish species but a different set of trophic specialists, including a large piscivore C. maya and a small open-water zooplanktivore C. simus (Chara spp.) and one competing fish species, Gambusia sexradiata (Astyanax sp.) and all trophic specialists may now be extinct in the wild . Indeed, we conceptualize novelty as accessing novel fitness peaks or new adaptive zones, collections of similar ecological niches in new regions of phenotypic space . TheoretE. coli initially climbed one or more similar fitness peaks for adapting to a uniform suspension environment in the laboratory. However, a duplication event enabled one strain to cross a fitness valley and access a new higher fitness peak for citrate metabolism  spatially rare because they arise in a single population/location and 2) temporally rare because the waiting times for relevant de novo mutations are expected to be long –Dobzhanksy–Muller incompatibilities (DMIs) within the genetic regulatory networks shaping novel traits . For exacis- and trans-acting elements in different species of pupfish, even those coexisting within the same environment. Indeed, this has broad implications for ecological speciation in general if divergent selection on adaptive traits may also result in reproductive isolation due to gene misexpression in hybrids. Interestingly, the high level of hybrid misexpression observed between our novel trophic specialists appears to stand out relative to other speciation systems of similar age , reaching approximately 19% of genes . This wage e.g., , suggestGene regulation is one of the fundamental ways that evolution shapes existing structures for new functions. However, studies of differential expression between species or novel tissue types rarely result in a tractable list of candidate genes. Furthermore, existing studies of rapid radiations and speciation show a large range of differential gene expression and no clear associations with novelty . ConversBehavior plays a fundamental role in adaptation to new ecological niches, the origins of evolutionary innovations, and novel traits. There is still debate about the relative roles of behavior and morphology in driving the origins of novel ecological niches and ecological speciation, i.e., behavior-first or morphology-first adaptation to novel resources . This haThe origins of scale-eating are still unknown; however, there is no shortage of hypotheses, all of which involve behavior-first explanations. The three main hypotheses are 1) the algae-grazer hypothesis, 2) the cleaner hypothesis, and 3) the aggression hypothesis. The algae-grazer hypothesis proposes that scale-eating arose due to incidental ingestion of scales while foraging on algae . This hyThe scale-eating pupfish is an excellent species to investigate the potential behavioral origins of scale-eating for two reasons. First, it is the youngest known scale-eating specialist at 10,000 years old , providiThe aggression hypothesis makes a clear and easily testable prediction: scale-eating pupfish should be more aggressive than generalist or molluscivore pupfish. We tested this using behavioral assays to measure aggressive behaviors  toward a mirror image for generalist, scale-eater, and molluscivore pupfishes. This simple and widely used assay may not always elicit the same response as a live opponent , 2018b, rnf14 and crebrf) were differentially expressed in both specialists compared with generalists. However, all seven genes have pleiotropic effects on other divergent traits in this system, including craniofacial morphology, protein degradation pathways, and melanin pigmentation  helping to reconstruct the origins of behaviors associated with novelty using phylogenetic comparative methods. For example, novel behaviors, such as increased aggression during scale-eating, are present in a non-sister trophic specialist species , thus shifting the role of aggression in scale-eating to an association with trophic specialization in general. Conversely, 2) consideration of pleiotropy in candidate genes for observed behavioral differences can suggest whether behavioral phenotypes may be an indirect effect of selection on other adaptive phenotypes. Testing the causal effects of behavior on the origins of novelty remains a formidable challenge, but behavioral ecology studies coupled with an understanding of the pleiotropic effects of gene function on behavior and other phenotypes are necessary components of these investigations.In this review, we examined processes underlying the origins of novelty at the level of fitness, genetic variation, gene expression, and behavior. These perspectives and emerging results suggest distinct predictions for investigations of novelty in other systems. Here we speculate about general patterns underlying the origins of evolutionary novelties based on the unusual features and biological processes observed in our case study of the origins of novel trophic specialists in San Salvador pupfishes.First, we predict that novel traits, behaviors, and ecological niches may be more isolated on the fitness landscape, i.e., these phenotypes may be separated by wider and deeper fitness valleys from other phenotypes occupying more common ecological niches. Further investigation of the relative isolation of performance optima through biomechanical and kinematic models  and fitnde novo variation or adaptive introgression from specific source populations rather than widespread standing genetic variation. This is expected if larger fitness valleys isolate novel phenotypes, selecting for alleles of larger effect size to cross these valleys which may also exhibit strong negative sign epistasis (de novo mutation to adaptive phenotypes is an empirical question (Second, ancient balancing selection is now frequently associated with ecological divergence between populations and repeated parallel speciation . Howeverpistasis . Indeed,pistasis . The relquestion  that wilquestion . Thus, uThird, we predict that adaptive divergence of novel traits may result in more dysfunction in genetic regulatory networks in hybrids, detected as gene misexpression in F1 hybrids, than more typical ecological and morphological divergence observed during local adaptation or repeated parallel speciation. This prediction is related to the snowball effect for genetic incompatibilities with increasing genetic divergence between populations : as the Fourth, we predict that behaviors often associated with the origins of novel traits and ecological niches may be incidental effects of selection on highly pleiotropic genetic networks. Thus, causality is difficult to establish without fitness, performance, and behavioral ecology studies to understand the direct and indirect targets of selection. The numerous pathways with indirect effects on behavior (and the omnigenic model in general: Clearly, much work remains to establish general patterns about the origins of evolutionary novelties at genomic, gene regulation, behavioral, and fitness landscape scales. The persistence of ecological specialist species across temporal scales is also currently unknown. Although some ecological specialists may be viewed as ephemeral due to stochastically shifting environments  and inco


Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital signs–based, aggregate-weighted early warning systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results.This study aimed to identify, summarize, and evaluate the available research, current state of utility, and challenges with machine learning–based early warning systems using vital signs to predict the risk of physiological deterioration in acutely ill patients, across acute and ambulatory care settings.PubMed, CINAHL, Cochrane Library, Web of Science, Embase, and Google Scholar were searched for peer-reviewed, original studies with keywords related to “vital signs,” “clinical deterioration,” and “machine learning.” Included studies used patient vital signs along with demographics and described a machine learning model for predicting an outcome in acute and ambulatory care settings. Data were extracted following PRISMA, TRIPOD, and Cochrane Collaboration guidelines.We identified 24 peer-reviewed studies from 417 articles for inclusion; 23 studies were retrospective, while 1 was prospective in nature. Care settings included general wards, intensive care units, emergency departments, step-down units, medical assessment units, postanesthetic wards, and home care. Machine learning models including logistic regression, tree-based methods, kernel-based methods, and neural networks were most commonly used to predict the risk of deterioration. The area under the curve for models ranged from 0.57 to 0.97.In studies that compared performance, reported results suggest that machine learning–based early warning systems can achieve greater accuracy than aggregate-weighted early warning systems but several areas for further research were identified. While these models have the potential to provide clinical decision support, there is a need for standardized outcome measures to allow for rigorous evaluation of performance across models. Further research needs to address the interpretability of model outputs by clinicians, clinical efficacy of these systems through prospective study design, and their potential impact in different clinical settings. Patient deterioration and adverse outcomes are often preceded by abnormal vital signs -3. These2), temperature, and sometimes the level of consciousness [EWS typically employ heart rate (HR), respiratory rate (RR), blood pressure (BP), peripheral oxygen saturation  , NationaThe predictive ability of aggregate-weighted EWS has limitations. First, the scores indicate the present risk of the patient but do not incorporate trends nor provide information about the possible risk trajectory ; thus, tA newer approach to EWS relies on machine learning (ML). ML models learn patterns and relationships directly from data rather than relying on a rule-based system . Unlike Two systematic reviews in 2019 ,15 evaluThe review conducted by Linnen et al  comparedThese are important findings, but to date no review has systematically reviewed the evidence from studies using ML-based EWS using vital sign measurements of varying frequencies, across different care settings and clinical outcomes in order to identify common methodological trends and limitations with current approaches to generate recommendations for future research in this area.The objective of this study was to scope the state of research in ML-based EWS using vital signs data for predicting the risk of physiological deterioration in patients across acute and ambulatory care settings and to identify directions for future research in this area.A systematic scoping review was conducted by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews (PRISMA-ScR) framework . This prWe searched PubMed, CINAHL, Cochrane Library, Web of Science, Embase, and Google Scholar for peer-reviewed studies without using any filters for study design and language. Searches were also conducted without any date restrictions. The reference lists of all studies that met the inclusion criteria were screened for additional articles. The search strategy involved a series of searches using a combination of relevant keywords and synonyms, including “vital signs,” “clinical deterioration,” and “machine learning.” See The inclusion criteria covered the following:Peer-reviewed studies evaluating continuous or intermittent vital sign monitoring in adult patients so that all data collection or sampling frequencies  wedre taken into consideration;Studies conducted using data gathered from all acute and ambulatory care settings including medical or surgical hospital wards, ICUs, step-down units, ED, and in-home care;Quantitative, observational, retrospective, and prospective cohort studies and randomized controlled trials;Studies that involved ML or multivariable statistical or ML models and reported some model performance measure  ;Studies that reported mortality or any outcomes related to clinical deterioration so that EWS models and performance can be examined for all explored outcomes.The exclusion criteria included the following:Studies that used any laboratory values as predictors for the ML-based EWS, as this review focuses on examining time-sensitive predictions of clinical deterioration using patient parameters that are readily available across all care settings;Studies involving pediatric or obstetric populations due to these patients having different or altered physiologies that cannot be compared to standard adult patients;Qualitative studies, reviews, preprints, case reports, commentaries, or conference proceedings.References from the preliminary searches were handled using Mendeley reference management software. After duplicates were removed, titles and abstracts were screened to assess preliminary eligibility. Eligible studies were then read in full length to be assessed against the inclusion and exclusion criteria.Data were extracted from eligible studies using an extraction sheet that followed the PRISMA  and CochThe search for “vital signs” AND “clinical deterioration” AND “machine learning” using the same query terms and filters identified 417 studies after duplicate removal. During the title and abstract screening process, 386 studies were excluded. Of the 31 full-text articles that were assessed, 7 studies were excluded for not meeting the eligibility criteria: 2 studies did not use ML models to predict deterioration, 3 studies included vital sign measurements in addition to laboratory values as predictors, 1 study focused on a cohort of pregnant women, and 1 study did not meet the criteria for model performance measures. A review of the reference lists of the 24 selected studies did not yield any additional studies fulfilling the eligibility criteria refer to .Of the selected studies, 23 conducted a retrospective analysis of the vital signs data, while 1 study  used a pStudies were conducted in a variety of settings within hospitals while the study by Larburu et al  was cond2, body temperature, level of consciousness through either the Glasgow Coma Score or the AVPU scale, and mean arterial pressure. Measurement frequencies for these variables ranged from once every 5 seconds [The most commonly used vital sign predictors were HR, RR, systolic BP, diastolic BP, SpO seconds  in hospi seconds  in an amThe outcomes being predicted in most studies focused on cardiorespiratory insufficiency–related events. Cardiac arrest was the primary outcome in 7 ,38,42,45Outcomes were first identified, and baseline models were created using predefined parameter thresholds (ground truth) consistent with the MEWS ,26,35 orAll included studies consider the prediction of deterioration risk to be a classification task and therefore use different types of classification models in the process, including tree-based models, linear models, kernel-based methods, and neural networks (refer to Measures used to assess model performance varied across the studies. The most common measure was the area under the receiver operator characteristic (AUROC) along with model accuracy, sensitivity, and specificity. Area under the precision-recall, F-score, Hamming’s score, and precision  were reported less commonly.Prediction windows ranged from 30 minutes to 30 days before an event.Model performance varied substantially based on outcome measure being predicted , ML method used , and prediction window .Nine studies compared the performance of ML-based EWS with aggregate-weighted EWS. Studies exploring cardiorespiratory outcomes, general physiological deterioration, or mortality carried out comparisons with NEWS ,45, MEWSIn all 9 studies, the ML models performed better than the aggregate-weighted EWS systems for all clinical outcomes except for cardiac arrest in the study by Badriyah et al . For exaBased on this scoping review, ML-based EWS models show considerable promise, but there exist several important avenues for future research if these models are to be effectively implemented in clinical practice.A model’s prediction window refers to how far in advance a model is predicting an adverse event. Most studies included in our review used a prediction window between 30 minutes  and 72 hThe studies included in this review focused on ML model development and did not explore how the output of these models would be communicated to clinicians. Since many ML models are “black boxes” ,47, it mNearly all the studies included in this review were conducted in inpatient settings. While EWS are highly valuable in an inpatient context, there is also considerable need in the ambulatory setting, particularly postdischarge. For example, the VISION study  found thAll but one study  includedA key observation from this review is the lack of an agreed-upon standard among the research community for reporting performance measures across studies. This makes meaningful comparison between the outcomes of these studies difficult, and where there is overlap, it is not clear that the most clinically relevant metrics have been chosen. The majority of the studies in this review report the AUROC as the main performance metric, reflecting a common practice in the ML literature. However, AUROC may not be adequate for evaluating the performance of the EWS in a clinical setting .As Romero-Brufau et al  discusseThe performance of an EWS depends on the tradeoff between 2 goals: early detection of outcomes versus issuance of fewer false-positive alerts to prevent alarm fatigue . SensitiOn a related note, only 9 of the studies included in our review made comparisons between their ML-based models and a “gold standard” aggregate-weighted EWS, such as MEWS or NEWS. Future research in the area should report a commonly used aggregate-weighted EWS as a baseline model, which would aid in making effective comparisons between them. NEWS may be particularly well suited to this area of research as its input variables can all be measured automatically and continuously via devices.The search strategy was comprehensive while not being too focused on specific clinical outcomes, sampling frequencies, or filtering for time. This allowed for the identification of as many studies as possible that examined the use of ML models and vital signs to predict the risk of patient deterioration. No additional studies were identified through citation tracking after the original search, indicating our search strategy was comprehensive. Unlike previous reviews, inclusion criteria for the review supported the examination of findings from studies conducted across a variety of clinical settings including specialty units or wards and ambulatory care. This helped in characterizing the use of ML-based prediction models in different patient-care environments with varying clinical endpoints. Wherever the original studies provided the data, comparisons were drawn between the performance of the ML models and that of aggregate-weighted EWS. This gives an indication of the differences in accuracy of the models in predicting clinical deterioration.The findings within this review are subject to some limitations. First, the literature search, assessment of eligibility of full-text articles, inclusion in the review, and extraction of study data were carried out by only 1 author. Second, only the findings from published studies were included in this scoping review, which may affect the results due to publication bias. While studies from a variety of settings were included, the generalizability of our findings may be limited due to the heterogeneity of patient populations, clinical practices, and study methodologies. Sampling procedures and frequencies varied across studies from single to multiple observations of patient vital signs, and clinical outcome definitions were based on different criteria or aggregate-weighted EWS. Finally, due to this variation in ML methods, prediction windows, and outcome reporting, a meta-analysis was not feasible.Our findings suggest that ML-based EWS models incorporating easily accessible vital sign measurements are effective in predicting physiological deterioration in patients. Improved prediction performance was also observed with these models when compared to traditional aggregate-based risk stratification tools. The clinical impact of these ML-based EWS could be significant for clinical staff and patients due to decreased false alerts and increased early detection of warning signs for timely intervention, though further development of these models is needed and the necessary prospective research to establish actual clinical utility does not yet exist.


The history of modern oncology started around eighty years ago with the introduction of cytotoxic agents such as nitrogen mustard into the clinic, followed by multi-agent chemotherapy protocols. Early success in radiation therapy in Hodgkin lymphoma gave birth to the introduction of radiation therapy into different cancer treatment protocols. Along with better understanding of cancer biology, we developed drugs targeting cancer-related cellular and genetic aberrancies. Discovery of the crucial role of vasculature in maintenance, survival, and growth of a tumor opened the way to the development of anti-angiogenic agents. A better understanding of T-cell regulatory pathways advanced immunotherapy. Awareness of stem-like cancer cells and their role in cancer metastasis and local recurrence led to the development of drugs targeting them. At the same time, sequential and rapidly accelerating advances in imaging and surgical technology have markedly increased our ability to safely remove ≥90% of tumor cells. While we have advanced our ability to kill cells from multiple directions, we have still failed to stop most types of cancer from recurring. Here we analyze the tactics employed in cancer evolution; namely, chromosomal instability (CIN), intra-tumoral heterogeneity (ITH), and cancer-specific metabolism. These tactics govern the resistance to current cancer therapeutics. It is time to focus on maximally delaying the time to recurrence, with drugs that target these fundamental tactics of cancer evolution. Understanding the control of CIN and the optimal state of ITH as the most important tactics in cancer evolution could facilitate the development of improved cancer therapeutic strategies designed to transform cancer into a manageable chronic disease. One could historically consider the birth of the modern era of cancer therapeutics to be the serendipitous discovery of nitrogen mustard during the second world war . The surThe thinking at that time was that nitrogen mustard could kill the rapidly dividing malignant lymphocytes. This prompted the need for understanding the mechanism of action, dosimetry, and minimum duration of treatment ,7. ShortAs knowledge regarding intracellular pathways grew, awareness of mechanism of action was taken to a higher level. Discovery of fluorinated pyrimidines during Heidelberger’s experiments on hepatoma cell lines in 1950s , broughtThe concept of multi-agent chemotherapy was born following better understanding of intracellular communication networks, as well as cell growth and division . The NatRadiation therapy also came to be recognized as another cytotoxic therapy, and through time found its way into cancer treatment protocols. Its surprisingly positive effect in Hodgkin lymphoma in 1960s expedited this process . DiscoveThe discovery of double helix DNA by Watson and Crick in early 1950s generated the next footstep in the pathway of the evolution of cancer therapeutics thinking. Cancer was soon regarded as a disorder of genes. Inactivating mutations of tumor suppressor genes and activating mutations of oncogenes, and the resulting distortions of cellular signaling pathways gave further support to that thinking . The disThe next step in the evolution of cancer therapeutics was guided by the discovery of growth promoting and inhibitory pathways, cell cycle kinetics, including cyclin dependent kinases, oncogenes, tumor suppressor genes, gene regulatory machineries, and nuclear receptors. This was followed by discovery of epigenome, and micro-RNA network . While still using chemotherapy in cancer clinic  and oncoPublications about cancer treatment have recently started to move away from chemotherapy, and focus more on targeted therapeutics agents, and cell-based immunotherapy, tumor vaccines , and mosHelicobacter Pylori induced gastric maltoma. Vaccination against specific human papillomavirus serotypes has significantly reduced the incidence of squamous cell carcinoma of cervix. Because of immune dysregulation, low level of tumor antigen presentation, cross reactivity with self-antigens, and poor immune response among many other pitfalls, vaccination against the vast majority of malignancies has continued to face major challenges. High dose IL2, with or without lymphokine or anti-CD3 activated killer cells in the treatment of melanoma in 1990s led to minor response with major and life threatening toxicities. Alfa-interferon showed similar results and had similar problems. Around one in six cancers are thought to have their roots in infection. Some of the well-known examples include hepatitis B and C induced hepatocellular carcinoma, human herpesvirus-8 induced kaposi sarcoma in acquired immunodeficiency syndrome, epstein-Barr virus induced Burkitt lymphoma in sub Saharan Africa, human papillomavirus induced carcinoma of cervix and oral cavity and Immunotherapy for cancer came into focus around 30 years ago, with mononuclear cells from the peripheral blood, activated ex vivo, and then re-infused into patients with tumor. This treatment failed to achieve long-term responses . Our fruThe limits of immunotherapy arise from major similarities between normal cells and cancer cells, especially cancer stem cells, with little differences of their surface antigens. Cancer stem cells could easily repopulate the tumor following escape from current immunotherapeutic measures. However, the new generation of immunotherapy is a significant step in the evolution of cancer therapy, simply because we are recruiting the body’s natural defense to fight cancer. We are also trying to avoid toxicities associated with chemotherapy and radiation therapy, including generation of destructive mutations originating from the therapies themselves . HoweverThe role of blood vessels in tumor progression has been investigated for more than a century . FolkmanAlthough most of the blood vessels in tumor are derived from angiogenesis, the leaky blood vessels in tumors, namely vasculogenic mimicry (VM), support rapidly growing tumors. They arise from tumor cells that have undergone endothelial trans-differentiation. With this process, neoplastic cells take on endothelial features and form abnormal blood vessels to help supply the tumor. This is an important part of tumor vascularization associated with cancer progression ,59,60,61Recognition of cancer as a disease of genes and dysregulated molecular pathways of cell growth guided the development of targeted therapies. Identification and characterization of a small population within neoplastic cells, described as cancer stem cells (CSC), and their role in cancer evolution and cancer recurrence ,72,73,74In contrast to the disappointing history of treating cancer patients with drugs, surgical removal of tumor remains the most effective treatment for most patients with solid cancer. Surgery is usually offered as first-line therapy for localized disease and in non-CNS locations can be curative when cancer is caught early. In the CNS in general, and other organs when cancer is not detected early enough, individual cancer cells are infiltrating into surround normal tissue and surgery for cure is no longer likely. In these circumstances, maximum tumor cytoreduction continues to have the largest intervention effect size, especially for the CNS. Below is a review of the advances in the resection of brain tumors, which reflects the advances in surgical oncology.Over the past hundred years, glioma surgery has been greatly advanced, from essentially “exploratory” surgeries by neurosurgeons using a finger trying to discern underlying firmness or abnormal ballottability, to guided surgery by computerized tomography (CT), magnetic resonance (MR) imaging. The application of microscopic magnification and lighting visualization ,77 and dHowever, this combination still required the surgeon to determine the tumor-brain interface visually and/or through tactile/haptic appreciation. Further incremental advances in this area included the development of intra-operative frameless stereotactic neuronavigation in the 1990s based on pre-operative neuro-imaging, first with proprioceptive jointed arm , and theFunctional (Fx) MR imaging and diffusion tensor (DTI) tractography, coupled with real-time somatosensory evoked potential (SEP) motor strip cortical mapping and continuous intra-operative monitoring with SEP’s and intermittent monitoring with motor-evoked potentials (MEP) is currently the best aide to safe maximal surgical tumor cytoreduction efforts in patients under general anesthesia. Among the modalities utilized to treat patients with malignant gliomas , surgical cytoreduction has the largest effect size. In properly selected patients based on thorough pre-operative treatment planning analysis, we can now routinely achieve a “one log kill” (90% volumetric resection) of the enhancing tumor volume. Studies have shown that significantly improved glioma outcomes begin at the 70–78% volumetric resection threshold ,86, but Our failure in curing cancer necessitates rethinking our strategy for future cancer therapeutics. If one looks at the grand scheme of the evolution of cancer therapeutics in the last almost eighty years, one comes to realize that we have gone the path of becoming more sophisticated cancer cell killers. Unfortunately, we still face seemingly insurmountable barriers, as witnessed by the fact that the vast majority of metastatic cancer patients succumb to their disease or complications of the treatment given to them ,92. AlmoNo two cancer cells among billions of cancer cells comprising a tumor mass are exactly the same . There iCIN refers chromosome segregation errors in cell division, resulting in numerical and structural chromosomal abnormalities of daughter cells. Aneuploidy refers to karyotype of cells with alteration of chromosome number that is not a multiple of the haploid complement. Hence, it is different from polyploidy, which is a multiple of their haploid karyotype. Since early 50s, and with more focused studies in the recent decade, cancer has been perceived as a disease process involving chromosomes, based on the observation that most cancer cells are aneuploid ,102. ChrMounting evidence links CIN to aggressive tumor behavior ,118,119.Deep in the matrix of living cells, mitochondrial energetics has been linked to a wide range of diseases, including cancer ,131. MetWe began to realize that exploiting cancer metabolism for clinical benefit necessitates defining the pathways that limit cancer progression and understanding the context specificity of metabolic preferences and vulnerabilities in malignant cells . AntimetIn addition to differences in metabolism between normal and neoplastic tissues, metabolic differences exist among different tumor subpopulations that affects their interactions with tumor microenvironment ,143. ThiCancer cells utilize gene regulatory and micro-RNA networks, which are temporally and spatially controlled during embryonic development ,145,146.Aneuploidy is a manifestation of the same fundamental change, deep in the matrix of the living cell. In cancer cells, the rate of mutational processes and chromosomal instability is affected by constant variations and increase in these changes over time, and following chemotherapy, or radiation therapy, which act as geno-toxic agents . InsightCIN and aneuploidy come from errors in cell division, including mitotic checkpoint defects, aberrations in centrosome duplication cycle, altered kinetochore function, microtubule attachment defects, chromosome cohesion defects, and mutations building further genomic instability . Based oThough more important in terms of achieving control of cancer plasticity and therapeutic resistance, CIN-reducing drugs are less explored. It is due to limited knowledge about how cancer, as a whole biological entity, manages to control CIN rate of heterogeneous cancer cells in an ever-changing microenvironment, in the path of cancer initiation and progression. A study suggested that replication stress causes CIN in colorectal cancer cells with silencing of CIN-suppressor genes . Sansregret and colleagues show that one mechanism to restrain excessive CIN in tumor cells and increase fitness is through mutations in the anaphase promoting complex/cyclosome . A recenFailure of cancer therapeutics in the past and present clearly indicates that future cancer therapeutics will not win through a simple cancer-killing strategy. Future cancer therapeutics should target the natural strategies of cancer evolution, e.g., the mechanisms employed by cancer to maintain intra-tumoral heterogeneity that underlies resistance to targeted therapeutics. This would establish an equilibrium that converts the overall tumor population into an indolent and chronic neoplasm in the human body.As shown in Extracellular proteins playing a dual function in cancer are particularly interesting at it relates to natural cancer evolution tactics. They could differentially modulate cancer cell populations within a tumor that are different, in karyotype and metabolism, to synergistically optimize a tumor’s opposing and balanced “grow” (cellular proliferation and neo-angiogenic dominant) or “go” (angiogenesis-independent single cell invasion and migration dominant) cellular behavior in response to changes in local microenvironment, such as deprivation of nutrients and oxygen. The cell-context dependent dual function of EFEMP1 is one such example. EFEMP1 is an important extracellular protein employed in cancer evolution via differential regulation of gene expression in different populations to achieve cell-context dependent dual function . WeaponiThe goal of future cancer therapeutics by targeting and manipulating naturally occurring cancer evolutionary tactics is not the elimination of all cancer cells. Instead, the goal is to establish a steady state between tumor population and microenvironment thus preventing the emergence of rapidly progressive disease, which often follows therapeutic interventions, such as cyto-reductive therapy, targeted therapy, and immunotherapy.Currently targeting one population leads to escape of some cells in the same population and conversion into another in terms of functionality in tumor development. Alternatively, the resistant clones, e.g., STIC, following targeting TMC, could convert to TMC later. Consequently, there are multiple collateral and parallel escape and survival pathways available to re-form tumor mass, following current treatment strategies. The surviving cells would replenish and recapitulate the tumor mass and tumor subpopulations at a higher level of complexity and adapt to new environment . If one We have come a long way since the serendipitous discovery of nitrogen mustard some eighty years ago. We have developed multi-agent chemotherapy protocols. We have brought radiation therapy into our cancer treatment protocols. We have developed targeted therapeutic agents, addressing a diverse category of cancer cell survival and proliferation pathways. We have tried anti-angiogenic agents, proteasome inhibitors, oncolytic viruses, as well as antibodies against inhibitors of cancer cell genetic suicide machinery. One by one, we have become more sophisticated cancer cell killers, following our natural instinct that killing the cancer cell is the answer to the problem. Unfortunately, we are facing insurmountable barriers, as our metastatic as well as locally recurrent cancer patients remain incurable.Tumor resection remains as the most effective front-line treatment of solid cancers, which has been significantly improved in maximizing both tumor resection and patient’s safety. Recent advances in other fields, such as computer science and robotics, are opening the door on a new path in the evolution of surgical oncology. By arming our future generation surgeons with virtual reality technology and tagging the tumor cells with fluorescent antibodies, we are developing the capability of precision surgical oncology alongside precision medicine in early detection of cancer, prognosis of cancer progression, and personalization of treatment with targeted therapeutic agents and the new generation of immunotherapy. Future efforts really need to address maximizing extent of surgical resection as the intervention with the largest therapeutic effect size, without excluding patients from clinical trials as they take 1–3 months to recover functional status resulting from maximal aggressive, but ultimately safe surgery.Advances in technology  have allowed early detection and monitoring of cancer progression. Next generation sequencing has become commonplace in community cancer clinics . Knowled


Renal fibrosis is an inevitable consequence of parenchymal scarring and is the common final pathway that mediates almost all progressive renal diseases. Adiponectin, a hormone produced by adipose tissue, possesses potent anti-insulin, anti-inflammatory, and anti-fibrotic properties. Reportedly, adiponectin serves as an important messenger that facilitates complex interactions between adipose tissue and other metabolically related organs. In recent years, a growing body of evidence supports adiponectin involvement in renal fibrosis. These studies provide a deeper understanding of the molecular mechanism of action of adiponectin in renal fibrosis and also offer a potential preventive and therapeutic target for renal fibrosis. In this review, the physiological role of adiponectin is briefly introduced, and then the mechanism of adiponectin-mediated renal fibrosis and the related signaling pathways are described. Finally, we summarize the findings regarding the clinical value of adiponectin in renal fibrotic diseases and prospected its application potential. Chronic kidney disease (CKD) is a growing public health issue worldwide with an annual increase in incidence. Renal fibrosis is a well-established final common pathological pathway in almost all cases of chronic kidney injury . InflammMuch research has focused on the study of adipose tissue and it is no longer considered an inert fat store of the body. Adipose tissue is now known to be an endocrine organ associated with the synthesis and secretion of several hormones . In thisHerein, we summarize the recent findings regarding the role of adiponectin in renal fibrosis and update the current comprehensive knowledge regarding the usefulness of adiponectin-based treatments in renal fibrosis disease. Additionally, we explored the clinical setting of renal fibrosis to focus on adiponectin as a marker of disease progression or a therapeutic target.APN, an adipocyte-specific plasma protein, consists of 244 amino acid residues and is mainly secreted by white adipose tissue. It is encoded by the ADIOPOQ gene located on chromosome 3q27 . APN exiThe physiological effects of APN occur secondary to binding of APN to specific receptors on target cell membranes . CurrentClinically, conditions such as diabetic nephropathy, immunoglobulin A nephropathy (IgAN), and lupus nephritis (LN), which are associated with CKD can precipitate renal fibrosis , 22. In The pathomechanism of renal fibrosis involves several factors, including oxidative stress and related inflammation, disturbances of glucose metabolism, and hemodynamic abnormalities. Many studies have confirmed that APN is involved in reducing renal fibrosis, and its specific mechanisms include reducing renal toxicity, reducing renal cell damage, resisting fibrosis, and reducing proteinuria to protect the glomerular filter .A large body of evidence suggests that disorders of lipid metabolism in the kidney may precipitate kidney injury and dysfunction . APN is Previous studies have shown that elevated glucose levels increase the mammalian target of rapamycin (mTOR) expression, and overexpression of the mTOR protein can cause kidney injury. Based on in vitro experiments, Sajjad Esmaeili et al. confirmed that APN reduces oxidative stress and associated kidney cell injury by inhibiting mTOR . NotablyThe following in vitro studies report that APN may reduce angiotensin II (ANG II)-induced renal fibrosis. Min Tan et al. confirmed that APN inhibits the expression of transforming growth factor beta 1 (TGF- β1) and fibronectin in Ang II- stimulated human renal mesangial cells (HRMCs), thereby reducing the synthesis of extracellular matrix in these cells. This suggests that APN may attenuate Ang II-induced glomerular sclerosis and renal insufficiency . Fei FanInjury to the glomerular filtration membrane increases its permeability and affects the barrier function, with consequent renal fibrosis. Endothelial cell and podocyte injury is the chief contributor to glomerular filtration membrane destruction , 40. ProRenal fibrosis involves a complex interplay between several molecular mechanisms; signaling pathways play an important role in the pathogenesis of this condition . The comAMPK is a protein kinase that occurs in most mammalian tissues and acts as an important metabolic switch. Studies have shown that AMPK activation is essential to maintain normal kidney physiology and its function includes activation of the ATP process, inhibition of oxidative stress, and reduction of ROS production , 45. SevPPARs are members of the nuclear receptor superfamily, mainly comprising PPAR-γ and PPAR-α, which regulate several biological functions, such as cell growth, differentiation, and apoptosis . PPARs dIn addition to the therefore mentioned signaling pathways, adiponectin may also mediate others signaling pathways. However, there is limited evidence in this regard. Recent studies have reported that APN triggers the mTOR/p mTOR/SIRT1 signaling pathway to prevent cell death in the human kidney . AdditioClinical and basic studies have confirmed the renoprotective action of APN in patients with renal fibrosis. Therefore, attention is presently being focused on the clinical value of APN in the treatment of renal diseases. The current research mainly focuses on two aspects: The first is that changes in plasma or urine APN can be used as biomarkers of renal injury levels . Many reDiabetic nephropathy is the most common and serious etiopathogenetic contributor to end-stage renal disease (ESRD) . SeveralLN is the most serious form of SLE, which is also one of the most common causes of renal fibrosis . Rovin eIgAN is a common condition associated with renal fibrosis; however, there is limited evidence to conclusively prove the association between APN and IgAN. Studies have confirmed increased urinary APN excretion in patients with IgAN showing massive proteinuria . FurtherAccumulated evidence in the available literature highlights the renoprotective action of APN, which is increasingly being viewed as a novel therapeutic target for renal fibrosis via interventions to increase serum APN levels or enhance APN sensitivity by activating APN receptors . NumerouAlthough most current studies report the renoprotective effect of APN, a few authors have expressed differing opinions. Jun Yang et al. describe a novel mechanism by which APN mediates the pathogenesis of renal fibrosis. In response to renal tissue injury, APNincreases Th2 cytokine production and monocyte-to-fibroblast transition, which contribute significantly to the pathogenesis of renal interstitial fibrosis. These data suggest that inhibition of APN/AMPK signaling mayserve as a novel therapeutic approach in patients with CKD . APN secAPN (a type of adipocytokine) plays a key role in the onset and progression of renal fibrosis in several diseases. Overwhelming evidence suggests the renoprotective actions of APN, which binds the APN-related receptors, triggers several signaling pathways, reduces oxidative stress and inflammation, regulates lipid and glucose metabolism, and consequently reduces renal fibrosis. However, a few clinical studies have reported that high serum APN levels are associated with hyperalbuminuria and worsened renal function. This finding could be attributed to a compensatory endogenous effort of the body to salvage the injured kidney by producing more protective factors. Currently, APN has attracted much attention in this regard; however, its exact role in renal fibrosis remains unclear. Further clinical studies are warranted to translate the experimental evidence regarding the renoprotective role of APN into real-world clinical practice to offer newer therapeutic targets for renal fibrosis.