Lactylation, a new epigenetic modification, is an important way in which lactate exerts physiological functions. There is a close relationship between increased lactylations caused by lactate and glycolysis, which can interact and play a role in disease through lactate as an intermediate mediator. Current research on lactylations has focused on histone lactylation, but non-histone lactylation also has greater research potential. Due to the ubiquity of lactate modifications in mammalian cells, an increasing number of studies have found that lactate modifications play important roles in tumour cell metabolism, gene transcription and immunity.

A systematic literature search was carried out using search key terms and synonyms. Full-paper screening was performed based on specific inclusion and exclusion criteria.

Many literatures have reported that the lactylation of protein plays an important role in human diseases and is involved in the occurrence and development of human diseases.

This article summary the correlation between lactylation and glycolysis, histones and non-histone proteins; the relationship between lactonation modifications and tumour development; and the current existence of lactylation-related inhibitors, with a view to provide new basic research ideas and clinical therapeutic tools for lactylation-related diseases.

Pediatric bipolar disorder (PBD) is characterized by abnormal functional connectivity among distributed brain regions. Increasing evidence suggests a role for the limbic network (LN) and the triple network model in the pathophysiology of bipolar disorder (BD). However, the specific relationship between the LN and the triple network in PBD remains unclear. This study aimed to investigate the aberrant causal connections among these four core networks in PBD.

Resting-state functional MRI scans from 92 PBD patients and 40 healthy controls (HCs) were analyzed. Dynamic Causal Modeling (DCM) was employed to assess effective connectivity (EC) among the four core networks. Parametric empirical Bayes (PEB) analysis was conducted to identify ECs associated with group differences, as well as depression and mania severity. Leave-one-out cross-validation (LOOCV) was used to test predictive accuracy.

Compared to HCs, PBD patients exhibited primarily excitatory bottom-up connections from the LN to the salience network (SN) and bidirectional excitatory connections between the default mode network (DMN) and SN. In PBD, top-down connectivity from the triple network to the LN was excitatory in individuals with higher depression severity but inhibitory in those with higher mania severity. LOOCV identified dysconnectivity circuits involving the caudate and hippocampus as being associated with mania and depression severity, respectively.

Disrupted bottom-up connections from the LN to the triple network distinguish PBD patients from healthy controls, while top-down disruptions from the triple network to LN relate to mood state differences. These findings offer insight into the neural mechanisms of PBD.

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.

COVID-19 has long-term impacts on public mental health, while few research studies incorporate multidimensional methods to thoroughly characterise the psychological profile of general population and little detailed guidance exists for mental health management during the pandemic. This research aims to capture long-term psychological profile of general population following COVID-19 by integrating trajectory modelling approaches, latent trajectory pattern identification and network analyses.

Longitudinal data were collected from a nationwide sample of 18 804 adults in 12 months after COVID-19 outbreak in China. Patient Health Questionnaire-9, Generalised Anxiety Disorder-7 and Insomnia Severity Index were used to measure depression, anxiety and insomnia, respectively. The unconditional and conditional latent growth curve models were fitted to investigate trajectories and long-term predictors for psychological symptoms. We employed latent growth mixture model to identify the major psychological symptom trajectory patterns, and ran sparse Gaussian graphical models with graphical lasso to explore the evolution of psychopathological network.

At 12 months after COVID-19 outbreak, psychological symptoms generally alleviated, and five psychological symptom trajectories with different demographics were identified: normal stable (63.4%), mild stable (15.3%), mild-increase to decrease (11.7%), mild-decrease to increase (4.0%) and moderate/severe stable (5.5%). The finding indicated that there were still about 5% individuals showing consistently severe distress and approximately 16% following fluctuating psychological trajectories, who should be continuously monitored. For individuals with persistently severe trajectories and those with fluctuating trajectories, central or bridge symptoms in the network were mainly ‘motor abnormality’ and ‘sad mood’, respectively. Compared with initial peak and late COVID-19 phase, aftermath of initial peak might be a psychologically vulnerable period with highest network connectivity. The central and bridge symptoms for aftermath of initial peak (‘appetite change’ and ‘trouble of relaxing’) were totally different from those at other pandemic phases (‘sad mood’).

This research identified the overall growing trend, long-term predictors, trajectory classes and evolutionary pattern of psychopathological network of psychological symptoms in 12 months after COVID-19 outbreak. It provides a multidimensional long-term psychological profile of the general population after COVID-19 outbreak, and accentuates the essentiality of continuous psychological monitoring, as well as population- and time-specific psychological management after COVID-19. We believe our findings can offer reference for long-term psychological management after pandemics.

T long-term effects of cognitive therapy and behavior therapy (CTBT) for menopausal symptoms are unknown, and whether the effects are different between natural menopause and treatment-induced menopause are currently unclear. Therefore, we sought to conduct an accurate estimate of the efficacy of CTBT for menopausal symptoms.

We conducted searches of Cochrane Library, EMBASE, PsycINFO, PubMed, and Web of Science databases for studies from 1 January 1977 to 1 November 2021. Randomized controlled trials (RCTs) comparing intervention groups to control groups for menopausal symptoms were included. Hedge's g was used as the standardized between-group effect size with a random-effects model.

We included 14 RCTs comprising 1618 patients with a mean sample size of 116. CTBT significantly outperformed control groups in terms of reducing hot flushes [g = 0.39, 95% confidence interval (CI) 0.23–0.55, I2 = 45], night sweats, depression (g = 0.50, 95% CI 0.34–0.66, I2 = 51), anxiety (g = 0.38, 95% CI 0.23–0.54, I2 = 49), fatigue, and quality of life. Egger's test indicated no publication bias.

CTBT is an effective psychological treatment for menopausal symptoms, with predominantly small to moderate effects. The efficacy is sustained long-term, although it declines somewhat over time. The efficacy was stronger for natural menopause symptoms, such as vasomotor symptoms, than for treatment-induced menopause symptoms. These findings provide support for treatment guidelines recommending CTBT as a treatment option for menopausal symptoms.

The present work was performed to analyse the association of dietary patterns with glycaemic control (Hb A1c < 7 %) in a large group of Chinese adults aged between 45 and 59 years.

Habitual dietary intakes in the preceding 12 months were assessed by well-trained interviewers using a validated semi-quantitative FFQ. Factor analysis with principal component method was used to obtain the dietary patterns, and the associations between dietary patterns and glycaemic control were determined using multivariable logistic regression models. Poor glycaemic control was defined as HbA1c ≥ 7·0.

Despite decades of research, data on the relationship between dietary patterns and glycaemic control (HbA1c < 7 %) in China are sparse.

A total of 1739 participants aged 45–59 years from Hangzhou were included in the final analysis.

Three dietary patterns were ascertained and labelled as traditional southern Chinese, Western and grains-vegetables patterns. After controlling of the possible confounders, participants in the highest quartile of Western pattern scores had greater OR for HbA1c ≥ 7·0 (OR = 1·05; (95 % CI 1·000, 1·095); P = 0·048) than did those in the lowest quartile. Compared with those in the lowest quartile of grains-vegetables pattern, participants in the highest quartile had lower OR for HbA1c ≥ 7·0 (OR = 0·82; (95 % CI 0·720, 0·949); P = 0·038). Besides, no significant relationship between the traditional southern Chinese pattern and HbA1c ≥ 7·0 was observed (P > 0·05).

This study indicated that the Western pattern was associated with a higher risk, and the grains-vegetables pattern was associated with a lower risk for HbA1c ≥ 7·0. Future prospective studies are needed to confirm our findings.

To determine if specific dietary patterns are associated with breast cancer (BC) risk in Chinese women.

Latent class analysis (LCA) was performed to identify generic dietary patterns based on daily food-frequency data.

The Chinese Wuxi Exposure and Breast Cancer Study (2013–2014).

A population-based case–control study (695 cases, 804 controls).

Four dietary patterns were identified, Prudent, Chinese traditional, Western and Picky; the proportion in the controls and cases was 0·30/0·32/0·16/0·23 and 0·29/0·26/0·11/0·33, respectively. Women in Picky class were characterised by higher extreme probabilities of non-consumption of specific foods, the highest probabilities of consumption of pickled foods and the lowest probabilities of consumption of cereals, soya foods and nuts. Compared with Prudent class, Picky class was associated with a higher risk (OR = 1·42, 95 % CI 1·06, 1·90), while the relevant association was only in post- (OR = 1·44, 95 % CI 1·01, 2·05) but not in premenopausal women. The Western class characterised by high-protein, high-fat and high-sugar foods, and the Chinese traditional class characterised by typical consumption of soya foods and white meat over red meat, both of them showed no difference in BC risk compared with Prudent class did.

LCA captures the heterogeneity of individuals embedded in the population and could be a useful approach in the study of dietary pattern and disease. Our results indicated that the Picky class might have a positive association with the risk of BC.

To identify the association of the glucokinase gene (GCK) rs4607517 polymorphism with gestational diabetes mellitus (GDM) and determine whether sweets consumption could interact with the polymorphism on GDM in Chinese women.

We conducted a case–control study at a hospital including 1015 participants (562 GDM cases and 453 controls). We collected the data of pre-pregnancy BMI, sweets consumption and performed genotyping of the GCK rs4607517 polymorphism. Logistic regression was performed to test the association between the rs4607517 polymorphism and GDM, and the stratified analyses by sweets consumption were conducted, using an additive genetic model.

A case–control study of women at a hospital in Beijing, China.

One thousand and fifteen Chinese women.

The GCK rs4607517 A allele was significantly associated with GDM (OR 1·35, 95 % CI 1·03, 1·77; P = 0·028). Furthermore, stratified analyses showed that the A allele increased the risk of GDM only in women who had a habitual consumption of sweet foods (sweets consumption ≥ once per week) (OR 1·61, 95 % CI 1·17, 2·21; P = 0·003). Significant interaction on GDM was found between the rs4607517 A allele and sweets consumption (P = 0·004).

This study for the first time reported the interaction between the GCK rs4607517 polymorphism and sweets consumption on GDM. The results provided novel evidence for risk assessment and personalised prevention of GDM.

The present study investigated the association between dietary patterns and hypertension applying the Chinese Dietary Balance Index-07 (DBI-07).

A cross-sectional study on adult nutrition and chronic disease in Inner Mongolia. Dietary data were collected using 24 h recall over three consecutive days and weighing method. Dietary patterns were identified using principal components analysis. Generalized linear models and multivariate logistic regression models were used to examine the associations between DBI-07 and dietary patterns, and between dietary patterns and hypertension.

Inner Mongolia (n 1861).

A representative sample of adults aged ≥18 years in Inner Mongolia.

Four major dietary patterns were identified: ‘high protein’, ‘traditional northern’, ‘modern’ and ‘condiments’. Generalized linear models showed higher factor scores in the ‘high protein’ pattern were associated with lower DBI-07 (βLBS = −1·993, βHBS = −0·206, βDQD = −2·199; all P < 0·001); the opposite in the ‘condiments’ pattern (βLBS = 0·967, βHBS = 0·751, βDQD = 1·718; all P < 0·001). OR for hypertension in the highest quartile of the ‘high protein’ pattern compared with the lowest was 0·374 (95 % CI 0·244, 0·573; Ptrend < 0·001) in males. OR for hypertension in the ‘condiments’ pattern was 1·663 (95 % CI 1·113, 2·483; Ptrend < 0·001) in males, 1·788 (95 % CI 1·155, 2·766; Ptrend < 0·001) in females.

Our findings suggested a higher-quality dietary pattern evaluated by DBI-07 was related to decreased risk for hypertension, whereas a lower-quality dietary pattern was related to increased risk for hypertension in Inner Mongolia.