In contemporary neuroimaging studies, it has been observed that patients with major depressive disorder (MDD) exhibit aberrant spontaneous neural activity, commonly quantified through the amplitude of low-frequency fluctuations (ALFF). However, the substantial individual heterogeneity among patients poses a challenge to reaching a unified conclusion.

To address this variability, our study adopts a novel framework to parse individualized ALFF abnormalities. We hypothesize that individualized ALFF abnormalities can be portrayed as a unique linear combination of shared differential factors. Our study involved two large multi-center datasets, comprising 2424 patients with MDD and 2183 healthy controls. In patients, individualized ALFF abnormalities were derived through normative modeling and further deconstructed into differential factors using non-negative matrix factorization.

Two positive and two negative factors were identified. These factors were closely linked to clinical characteristics and explained group-level ALFF abnormalities in the two datasets. Moreover, these factors exhibited distinct associations with the distribution of neurotransmitter receptors/transporters, transcriptional profiles of inflammation-related genes, and connectome-informed epicenters, underscoring their neurobiological relevance. Additionally, factor compositions facilitated the identification of four distinct depressive subtypes, each characterized by unique abnormal ALFF patterns and clinical features. Importantly, these findings were successfully replicated in another dataset with different acquisition equipment, protocols, preprocessing strategies, and medication statuses, validating their robustness and generalizability.

This research identifies shared differential factors underlying individual spontaneous neural activity abnormalities in MDD and contributes novel insights into the heterogeneity of spontaneous neural activity abnormalities in MDD.

There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression.

In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis.

In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset.

These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.

To evaluate antibiotic prescribing behavior (APB) among physicians with various specialties in five Asian countries.

Survey of antibiotics prescribing behavior in three stages (initial, on-treatment, and de-escalation stages).

Participants included internists, infectious diseases (ID) specialists, hematologists, intensivists, and surgeons. Participants’ characteristics, patterns of APB, and perceptions of antimicrobial stewardship were collected. A multivariate analysis was conducted to evaluate factors associated with appropriate APB.

There were 367 participants. The survey response rate was 82.5% (367/445). For the initial stage, different specialties had different choices for empiric treatment. For the on-treatment stage, if the patient does not respond to empiric treatment, most respondents will step up to broader-spectrum antibiotics (273/367: 74.39%). For the de-escalation stage, the rate of de-escalation was 10%–60% depending on the specialty. Most respondents would de-escalate antibiotics based on guidelines (250/367: 68.12%). De-escalation was mostly reported by ID specialists (66/106: 62.26%). Respondents who reported that they performed laboratory investigations prior to empirical antibiotic prescriptions (aOR = 2.83) were associated with appropriate use, while respondents who reported ID consultation were associated with appropriate antibiotic management for infections not responding to empiric treatment (aOR = 40.87); adherence with national guidelines (aOR = 2.57) was associated with reported successful carbapenem de-escalation.

This study highlights the variation in practices and gaps in appropriate APB on three stages of antibiotic prescription among different specialties. Education on appropriate investigation, partnership with ID specialist, and availability and adherence with national guidelines are critical to help guide appropriate APB among different specialties.

Mental disorders, including depression, obsessive compulsive disorder (OCD), and schizophrenia, share a common neuropathy of disturbed large-scale coordinated brain maturation. However, high-interindividual heterogeneity hinders the identification of shared and distinct patterns of brain network abnormalities across mental disorders. This study aimed to identify shared and distinct patterns of altered structural covariance across mental disorders.

Subject-level structural covariance aberrance in patients with mental disorders was investigated using individualized differential structural covariance network. This method inferred structural covariance aberrance at the individual level by measuring the degree of structural covariance in patients deviating from matched healthy controls (HCs). T1-weighted anatomical images of 513 participants (105, 98, 190 participants with depression, OCD and schizophrenia, respectively, and 130 age- and sex-matched HCs) were acquired and analyzed.

Patients with mental disorders exhibited notable heterogeneity in terms of altered edges, which were otherwise obscured by group-level analysis. The three disorders shared high difference variability in edges attached to the frontal network and the subcortical-cerebellum network, and they also exhibited disease-specific variability distributions. Despite notable variability, patients with the same disorder shared disease-specific groups of altered edges. Specifically, depression was characterized by altered edges attached to the subcortical-cerebellum network; OCD, by altered edges linking the subcortical-cerebellum and motor networks; and schizophrenia, by altered edges related to the frontal network.

These results have potential implications for understanding heterogeneity and facilitating personalized diagnosis and interventions for mental disorders.

Major depressive disorder (MDD) is a common neuropsychiatry disorder with high prevalence and recurrence rate, but the misdiagnosis rate is inevitable due to the shortage of objective laboratory-based diagnostic criteria. This study is focused on the disturbance of lipid metabolism, providing potential biomarkers for diagnosing.

Lipid metabolism-related molecules in plasma of 42 drug-naïve MDD patients and 49 healthy people were measured by liquid chromatography-mass spectrometry. Further to evaluate the diagnostic values of changed metabolites, these molecules were evaluated by the receiver operating characteristic curve. Based on the significant role of phosphatidylcholine (PC) disturbance in depression, oxidization of PCs, oxidation of 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphocholine (OxPAPC), IL-8 and caspase-3 in hippocampus, and serum of chronic lipopolysaccharide (cLPS) depression mice were detected by ELISA.

Compared with healthy control, MDD patients expressed higher 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (16:0-16:0 PC, DPPC), 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphocholine (16:0-20:4 PC, PAPC), 1-palmitoyl-2-stearoyl-sn-glycero-3-phosphocholine (16:0-18:0 PC), glycocholic acid, taurocholic acid, glycoursodeoxycholic acid, and chenodeoxycholic acid glycine conjugate, and lower 1-heptadecanoyl-2-hydroxy-sn-glycero-3-phosphocholine (LPC 20:0). The 16:0-20:4 PC showed the great diagnostic value for MDD with an area under the curve (AUC) of 0.9519, and combination of 16:0 PC, 16:0-18:0 PC, and 16:0-20:4 PC exhibited the highest diagnostic value with AUC of 0.9602. OxPAPC was certified increase in hippocampus and serum of cLPS depression mice, which further supported PCs disorder participated in depression.

This research offers 16:0-20:4 PC as the latent diagnostic indicator for MDD and hints the important role of PCs in depression.

Elucidating individual aberrance is a critical first step toward precision medicine for heterogeneous disorders such as depression. The neuropathology of depression is related to abnormal inter-regional structural covariance indicating a brain maturational disruption. However, most studies focus on group-level structural covariance aberrance and ignore the interindividual heterogeneity. For that reason, we aimed to identify individualized structural covariance aberrance with the help of individualized differential structural covariance network (IDSCN) analysis.

T1-weighted anatomical images of 195 first-episode untreated patients with depression and matched healthy controls (n = 78) were acquired. We obtained IDSCN for each patient and identified subtypes of depression based on shared differential edges.

As a result, patients with depression demonstrated tremendous heterogeneity in the distribution of differential structural covariance edges. Despite this heterogeneity, altered edges within subcortical-cerebellum network were often shared by most of the patients. Two robust neuroanatomical subtypes were identified. Specifically, patients in subtype 1 often shared decreased motor network-related edges. Patients in subtype 2 often shared decreased subcortical-cerebellum network-related edges. Functional annotation further revealed that differential edges in subtype 2 were mainly implicated in reward/motivation-related functional terms.

In conclusion, we investigated individualized differential structural covariance and identified that decreased edges within subcortical-cerebellum network are often shared by patients with depression. The identified two subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of depression.

The impact of the dietary potential inflammatory effect on diabetic kidney disease (DKD) has not been adequately investigated. The present study aimed to explore the association between dietary inflammatory index (DII) and DKD in US adults.

This is a cross-sectional study.

Data from the National Health and Nutrition Examination Survey (2007–2016) were used. DII was calculated from 24-h dietary recall interviews. DKD was defined as diabetes with albuminuria, impaired glomerular filtration rate or both. Logistic regression and restricted cubic spline models were adopted to evaluate the associations.

Data from the National Health and Nutrition Examination Survey (2007–2016) were used, which can provide the information of participants.

Four thousand two-hundred and sixty-four participants were included in this study. The adjusted OR of DKD was 1·04 (95 % CI 0·81, 1·36) for quartile 2, 1·24 (95 % CI 0·97, 1·59) for quartile 3 and 1·64 (95 % CI 1·24, 2·17) for quartile 4, respectively, compared with the quartile 1 of DII. A linear dose–response pattern was observed between DII and DKD (Pnonlinearity = 0·73). In the stratified analyses, the OR for quartile 4 of DII were significant among adults with higher educational level (OR 1·83, 95 % CI 1·26, 2·66) and overweight or obese participants (OR 1·67, 95 % CI 1·23, 2·28), but not among the corresponding another subgroup. The interaction effects between DII and stratified factors on DKD were not statistically significant (all P values for interactions were >0·05).

Our findings suggest that a pro-inflammatory diet, shown by a higher DII score, is associated with increased odd of DKD.

As a neuroprogressive illness, depression is accompanied by brain structural abnormality that extends to many brain regions. However, the progressive structural alteration pattern remains unknown.

To elaborate the progressive structural alteration of depression according to illness duration, we recruited 195 never-treated first-episode patients with depression and 130 healthy controls (HCs) undergoing T1-weighted MRI scans. Voxel-based morphometry method was adopted to measure gray matter volume (GMV) for each participant. Patients were first divided into three stages according to the length of illness duration, then we explored stage-specific GMV alterations and the causal effect relationship between them using causal structural covariance network (CaSCN) analysis.

Overall, patients with depression presented stage-specific GMV alterations compared with HCs. Regions including the hippocampus, the thalamus and the ventral medial prefrontal cortex (vmPFC) presented GMV alteration at onset of illness. Then as the illness advanced, others regions began to present GMV alterations. These results suggested that GMV alteration originated from the hippocampus, the thalamus and vmPFC then expanded to other brain regions. The results of CaSCN analysis revealed that the hippocampus and the vmPFC corporately exerted causal effect on regions such as nucleus accumbens, the precuneus and the cerebellum. In addition, GMV alteration in the hippocampus was also potentially causally related to that in the dorsolateral frontal gyrus.

Consistent with the neuroprogressive hypothesis, our results reveal progressive morphological alteration originating from the vmPFC and the hippocampus and further elucidate possible details about disease progression of depression.