OBJECTIVE: Noise exposure in the workplace has been linked to a number of health consequences. Our objectives were to explore the relationship between occupational noise and lipid metabolism and evaluate the possible mediating effect of obesity indices in those relationships with a cross-sectional study design.
METHODS: Cumulative noise exposure (CNE) was used to measure the level of noise exposure. Logistic regression models or generalized linear models were employed to evaluate the association of occupational noise and obesity with lipid metabolism markers. Cross-lagged analysis was conducted to explore temporal associations of obesity with lipid metabolism.
RESULTS: A total of 854 participants were included, with each one-unit increase in CNE, the values of total cholesterol/high-density lipoprotein cholesterol and low-density lipoprotein cholesterol/high-density lipoprotein cholesterol increased by 0.013 (95% confidence interval: 0.006, 0.020) and 0.009 (0.004, 0.014), as well as the prevalence of dyslipidemia increased by 1.030 (1.013, 1.048). Occupational noise and lipid metabolism markers were all positively associated with body mass index (BMI), waist circumference (WC), a Body Shape Index (ABSI) and a Body Shape Index and Body Roundness Index (BRI) (all P < 0.05). Moreover, BMI, WC, ABSI and BRI could mediate the associations of occupational noise with lipid metabolism; the proportions ranged from 21.51 to 24.45%, 23.84 to 30.14%, 4.86 to 5.94% and 25.59 to 28.23%, respectively (all P < 0.05).
CONCLUSIONS: Our study demonstrates a positive association between occupational noise and abnormal lipid metabolism, and obesity may partly mediate the association. Our findings reinforce the need to take practical steps to reduce or even eliminate the health risks associated with occupational noise.

Numerous studies have investigated the causes of falls in the elderly. However, there is little information about metabolic syndrome (MS) as a risk factor for falls in older adults. No evaluations have given a qualitative overview of studies examining the relationship between MS and falls in the elderly. We did a literature search in electronic databases to look for studies that assessed a link between MS and falls among people over the age of 55 years. We found three studies of high quality. These included 2774 people with an average age of 72 years. Even after controlling for other risk factors, two studies found that MS was significantly associated with an older adult\'s 1.3-2.5-fold increased risk of falling. We found that MS and its independent components were strongly linked with falls among the elderly, even after correcting for numerous variables.

BACKGROUND: We previously identified a genetic subtype (C4) of type 2 diabetes (T2D), benefitting from intensive glycemia treatment in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Here, we characterized the population of patients that met the C4 criteria in the UKBiobank cohort.
METHODS: Using our polygenic score (PS), we identified C4 individuals in the UKBiobank and tested C4 status with risk of developing T2D, cardiovascular disease (CVD) outcomes, and differences in T2D medications.
RESULTS: C4 individuals were less likely to develop T2D, were slightly older at T2D diagnosis, had lower HbA1c values, and were less likely to be prescribed T2D medications (P < .05). Genetic variants in MAS1 and IGF2R, major components of the C4 PS, were associated with fewer overall T2D prescriptions.
CONCLUSIONS: We have confirmed C4 individuals are a lower risk subpopulation of patients with T2D.

BACKGROUND: Some recent studies have shown that female subclinical hypothyroidism (SCH) is associated with diminished ovarian reserve (DOR). In this study, we aimed to investigate whether serum-free thyroxine (fT4) concentrations within the reference range are associated with ovarian reserve in women.
METHODS: This cross-sectional study included 4933 infertile women with normal-range fT4 concentrations who received assisted reproductive technology treatment in our clinic. The data of women in different fT4 concentration tertiles (namely 12-15.33, 15.34-18.67, and 18.68-22 pmol/L) were compared with ovarian reserve markers, namely the anti-Müllerian hormone (AMH) concentration, the antral follicle count (AFC), and the number of aspirated oocytes. The primary outcomes were the AMH concentration and the risk of DOR, diagnosed as an AMH concentration < 1.1 ng/mL.
RESULTS: The average ages of women in the low-normal, middle-normal, and high-normal fT4 tertiles were 33.20 (standard deviation [SD]: 5.11), 32.33 (SD: 5.13), and 31.61 (SD: 5.10) years, respectively (p < 0.0001). AMH concentrations (adjusted mean: 3.32 [95% confidence interval {CI}: 3.16 to 3.50] vs. 3.51 [3.40 to 3.62] vs. 3.64 [3.50 to 3.80] ng/mL, p = 0.022) were significantly different between the fT4 concentration tertiles. The risk of DOR was significantly increased in the low-normal (adjusted odds ratio: 1.61 [95% CI: 1.01 to 2.58]) and middle-normal (1.47 [95% CI: 1.00 to 2.16]) tertiles compared with the high-normal tertile. Subgroup analysis showed that AMH concentrations were significantly different among the fT4 concentration tertiles in women aged < 35 years (adjusted mean: 3.94 [95% CI: 3.70 to 4.20] vs. 4.25 [4.11 to 4.39] vs. 4.38 [4.18 to 4.58], p = 0.028), whereas this difference was not significant in women aged ≥ 35 years (p = 0.534). The general additive models using fT4 as a continuous variable indicated that a lower fT4 concentration within the normal range was significantly associated with a lower AMH concentration (p = 0.027), a lower AFC (p = 0.018), a lower number of aspirated oocytes (p = 0.001), and a higher risk of DOR (p = 0.007).
CONCLUSIONS: Low-normal fT4 concentrations are associated with lower ovarian reserve in infertile women.

BACKGROUND: Overweight, often known as obesity, is the abnormal and excessive accumulation of fat that exposes the health of a person at risk by increasing the likelihood that they may experience many chronic conditions. Consequently, obesity has become a global health threat, presenting serious health issues, and attracting a lot of attention in the healthcare profession and the scientific community.
METHODS: This study aims to explore the anti-adipogenic properties of 7-MEGA™ in an attempt to address obesity, using both in vitro and in vivo research. The effects of 7MEGA™ at three distinct concentrations were investigated in obese mice who were given a high-fat diet (HFD) and 3T3-L1 adipocytes.
RESULTS: 7MEGA™ decreased the total fat mass, overall body weight, and the perirenal and subcutaneous white adipose tissue (PWAT and SWAT) contents in HFD mice. Additionally, 7MEGA™ showed promise in improving the metabolic health of individuals with obesity and regulate the levels of insulin hormone, pro-inflammatory cytokines and adipokines. Furthermore, Peroxisome proliferator-activated receptors (PPAR) α and γ, Uncoupling Protein 1 (UCP-1), Sterol Regulatory Element-Binding Protein 1 (SREBP-1), Fatty Acid-Binding Protein 4 (FABP4), Fatty Acid Synthase (FAS), Acetyl-CoA Carboxylase (ACC), Stearoyl-CoA Desaturase-1 (SCD-1) and CCAAT/Enhancer-Binding Protein (C/EBPα) were among the adipogenic regulators that 7MEGA™ could regulate.
CONCLUSIONS: In summary, this study uncovered that 7MEGA™ demonstrates anti-adipogenic and anti-obesity effects, suggesting its potential in combating obesity.

BACKGROUND: Observational studies have indicated that the plasma lipid profiles of patients with atopic dermatitis show significant differences compared to healthy individuals. However, the causal relationship between these differences remains unclear due to the inherent limitations of observational studies. Our objective was to explore the causal effects between 179 plasma lipid species and atopic dermatitis, and to investigate whether circulating inflammatory proteins serve as mediators in this causal pathway.
METHODS: We utilized public genome-wide association studies data to perform a bidirectional two-sample, two-step mendelian randomization study. The inverse variance-weighted method was adopted as the primary analysis technique. MR-Egger and the weighted median were used as supplementary analysis methods. MR-PRESSO, Cochran\'s Q test, and MR-Egger intercept test were applied for sensitivity analyses to ensure the robustness of our findings.
RESULTS: The Mendelian randomization analysis revealed that levels of Phosphatidylcholine (PC) (18:1_20:4) (OR: 0.950, 95% CI: 0.929-0.972, p = 6.65 × 10- 6), Phosphatidylethanolamine (O-18:1_20:4) (OR: 0.938, 95% CI: 0.906-0.971, p = 2.79 × 10- 4), Triacylglycerol (TAG) (56:6) (OR: 0.937, 95% CI: 0.906-0.969, p = 1.48 × 10- 4) and TAG (56:8) (OR: 0.918, 95% CI: 0.876-0.961, p = 2.72 × 10- 4) were inversely correlated with the risk of atopic dermatitis. Conversely, PC (18:1_20:2) (OR: 1.053, 95% CI: 1.028-1.079, p = 2.11 × 10- 5) and PC (O-18:1_20:3) (OR: 1.086, 95% CI: 1.039-1.135, p = 2.47 × 10- 4) were positively correlated with the risk of atopic dermatitis. The results of the reverse directional Mendelian randomization analysis indicated that atopic dermatitis exerted no significant causal influence on 179 plasma lipid species. The level of circulating IL-18R1 was identified as a mediator for the increased risk of atopic dermatitis associated with higher levels of PC (18:1_20:2), accounting for a mediation proportion of 9.07%.
CONCLUSIONS: Our research suggests that plasma lipids can affect circulating inflammatory proteins and may serve as one of the pathogenic factors for atopic dermatitis. Targeting plasma lipid levels as a treatment for atopic dermatitis presents a potentially novel approach.

BACKGROUND: The triglyceride-glucose (TyG) index is a risk marker for arterial stiffness; however, the extent to which the TyG index is associated with arterial stiffness via lipids and inflammation remains unknown. The first aim was to probe the relationship between the TyG index and arterial stiffness in two surveys. The second aim was to clarify whether lipids and inflammation mediate this relationship.
METHODS: The sample size of 13,726 U.S. individuals from the National Examination Survey (NHANES) and 3,964 Chinese individuals from the China Health and Retirement Longitudinal Study (CHARLS 2015) were enrolled. Weighted multivariate logistic and linear regression models, as well as restricted cubic spline (RCS) and mediation analyses, were utilized to estimate complex relationships between the TyG index, arterial stiffness, lipids (non-high-density lipoprotein cholesterol [non-HDL-C]) and inflammation (C-reactive protein [CRP]) biomarkers.
RESULTS: A total of 3,420 U.S. patients and 992 Chinese patients were diagnosed with increased arterial stiffness. Regression analyses demonstrated that higher quartiles of the TyG index were associated with a greater incidence of increased arterial stiffness (NHANES: OR = 2.610, 95% CI = 2.043-3.334, P < 0.001; CHARLS: OR = 1.579, 95% CI = 1.057-2.360, P < 0.001). Participants with a higher TyG index/higher CRP level or with a higher TyG index/higher non-HDL-C level had the highest incidence of increased arterial stiffness in the two surveys. The results were still consistent when the sensitivity analysis was implemented with stricter clinical cut-off values of non-HDL-C. Mediation analysis verified that lipids (mediated effect: β = 0.012, P < 0.001 in NHANES; β = 0.020, P < 0.001 in CHARLS) and inflammation (mediated effect: β = 0.003, P < 0.001 in NHANES; β = 0.006, P < 0.001 in CHARLS) partially mediated this relationship.
CONCLUSIONS: These results indicated a positive linear correlation between the TyG index, non-HDL-C level, CRP level and increased arterial stiffness in two surveys. Furthermore, lipids and inflammation could partly mediate the correlation of the TyG index with arterial stiffness in both surveys.

BACKGROUND: Insulin resistance (IR) induces hyperinsulinemia, which activates downstream signaling pathways such as the phosphatidylinositol-3-kinase/protein kinase B (PI3K/AKT) pathway, ultimately leading to abnormal proliferation and apoptosis of endometrial cells. This is thought to be a key pathogenic mechanism underlying the development of endometrial polyps (EP). This study aims to investigate the relationship between IR and the development of EP, the expression levels of downstream signaling molecules, including PI3K and AKT, and related laboratory parameters were examined.
METHODS: A total of 100 patients who visited the gynecology outpatient clinic of Zhongda Hospital affiliated with Southeast University from May 2021 to March 2023 and were diagnosed with abnormal endometrial echoes by vaginal ultrasound and underwent hysteroscopic diagnostic curettage were enrolled in this study. General data and relevant hematological indicators were compared, and intraoperative specimens were obtained for pathological examination. Possible factors influencing the development of endometrial polyps were analyzed using Pearson correlation analysis and logistic regression analysis.
RESULTS: In terms of body mass index, waist circumference, fasting insulin, insulin resistance index, serum total testosterone, and free testosterone index, women of childbearing age in the endometrial polyp group had higher values than those in the non-polyp group, while sex hormone-binding globulin in the endometrial polyp group was lower than that in the non-polyp group, and the differences were statistically significant (P < 0.05). The expression scores and mRNA expression levels of PI3K and AKT proteins were higher in the EP group than in the non-EP group (p < 0.05). Pearson correlation analysis showed a positive correlation between HOMA-IR and the expression scores of PI3K and AKT proteins (p < 0.01).
CONCLUSIONS: Insulin resistance and abnormal activation of the phosphatidylinositol 3-kinase/protein kinase B signaling pathway may be potential pathogenic mechanisms for the development of endometrial polyps.

BACKGROUND: Obesity is a global health problem, and its connection with social and environmental factors is well-established. Social factors, such as urban segregation, may impact obesity through various mechanisms, including food and physical activity environments, as well as social norms and networks. This multilevel study aims to examine the effect of socio-economic residential segregation of Latin American cities on the obesity of individuals within those cities.
METHODS: We analyzed data from national surveys for a total of 59,340 individuals of 18-70 years of age, conducted in 156 cities across Brazil, Chile, Colombia, and Mexico between 2007 and 2013. We adjusted two-level linear mixed models for body mass index (BMI) stratified by sex and country, controlling for age, educational level and poverty. Separate models were built for dissimilarity and isolation segregation indices.
RESULTS: The relationships between segregation indices and BMI were mostly not statistically significant, and in some cases, they were opposite to what was expected. The only significant relationships were observed in Colombian men, using the dissimilarity index (-7.5 [95% CI: -14.4, -0.5]) and in Colombian women, using the isolation index (-7.9 [95% CI: -14.1, -1.7]).
CONCLUSIONS: While individual-level factors cannot fully explain differences among people in the same city, segregation indices may help. However, we found that in some cases, the relationship between BMI and segregation indices is opposite to what is expected based on prior literature. This should be considered in examining the phenomenon. Further research on obesogenic environments in segregated neighborhoods could provide valuable evidence.

Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore, it is of utmost importance to identify individuals at risk as early as possible to avoid long-term T2DM complications. In this study, we developed an interpretable machine learning model leveraging baseline levels of biomarkers of oxidative stress (OS), inflammation, and mitochondrial dysfunction (MD) for identifying individuals at risk of developing T2DM. In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. Two iForest models were trained and evaluated through ten-fold cross-validation, the first on traditional biomarkers (BMI, blood glucose levels (BGL) and triglycerides) alone and the second including the additional aforementioned biomarkers. The second model outperformed the first across all evaluation metrics, particularly for F1 score and recall, which were increased from 0.61 ± 0.05 to 0.81 ± 0.05 and 0.57 ± 0.06 to 0.81 ± 0.08, respectively. The feature importance scores identified a novel combination of biomarkers, including interleukin-10 (IL-10), 8-isoprostane, humanin (HN), and oxidized glutathione (GSSG), which were revealed to be more influential than the traditional biomarkers in the outcome prediction. These results reveal a promising method for simultaneously predicting and understanding the risk of T2DM development and suggest possible pharmacological intervention to address inflammation and OS early in disease progression.