BackgroundHypertension is the primary out-auditory adverse outcome caused due to occupational noise exposure. This study investigated the associations of noise exposure in an occupational setting with blood pressure and risk of hypertension.MethodsA total of 1,390 occupational noise-exposed workers and 1399 frequency matched non-noise-exposed subjects were recruited from a cross-sectional survey of occupational noise-exposed and the general population, respectively. Blood pressure was measured using a mercury sphygmomanometer following a standard protocol. Multiple logistic regression was used to calculate the odds ratio (OR) and 95% confidence interval (CI) of noise exposure adjusted by potential confounders.ResultsNoise-exposed subjects had significantly higher levels of systolic blood pressure(SBP) (125.1 ± 13.9 mm Hg) and diastolic blood pressure (DBP) (77.6 ± 10.7 mm Hg) than control subjects (SBP: 117.2 ± 15.7 mm Hg, DBP: 70.0 ± 10.5 mm Hg) (P < 0.001). Significant correlations were found between noise exposure and blood pressure (SBP and DBP) (P < 0.001). However, the linear regression coefficients with DBP appeared larger than those with SBP.The prevalence of hypertension was 17.8% in subjects with noise exposure and 9.0% in control group (P < 0.001). Compared with the control group, the subjects with noise exposure had the risk of hypertension with an OR of 1.941 (95% CI = 1.471– 2.561) after adjusting for age, sex, smoking, and drinking status. Dose–response relationships were found between noise intensity, years of noise exposure, cumulative noise exposure and the risk of hypertension (all P values < 0.05). No significant difference was found between subjects wearing an earplug and those not wearing an earplug, and between steady and unsteady noise categories (P > 0.05).ConclusionsOccupational noise exposure was associated with higher levels of SBP, DBP, and the risk of hypertension. These findings indicate that effective and feasible measures should be implemented to reduce the risk of hypertension caused by occupational noise exposure.

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Background Primary dysmenorrhoea (PDM), characterized as menstrual pain without pelvic pathology, is associated with pain‐related negative mood and hormone fluctuations. Previous studies strongly supported the link between pain and negative mood in affected individuals; however, it remains largely unknown in patients with PDM. Methods We focused on the effects how spontaneous pain, negative mood and hormone levels played on the central nervous system in 34 PDM women and 33 matched healthy controls across their cycles (periovulatory phase and menstruation phase) by using T1‐weighted and functional imaging. Voxel‐based morphometry and functional connectivity (FC) analyses were performed to evaluate brain structural and functional changes. Hormone concentrations (oestradiol, progesterone and cortisol) were also obtained. Results Abnormal state‐related GM volume in the amygdala was found between periovulatory and menstruation phases in PDM. Furthermore, larger amygdalar volume was observed in patients’ menstruation phase, which was significantly correlated with higher levels of cortisol. In addition, we found increased amygdala‐seeded FC in vlPFC, which may be associated with pain intensity and negative mood in PDM women during the pain state. Conclusions Taken together, we found women with PDM had structural and functional abnormalities in the amygdala, which associated with stress hormone levels, pain intensity and negative mood, may reflect disturbed emotional and pain modulation in women with PDM. Significance Our findings provide further evidence of amygdala‐related abnormalities, which may be associated with pain‐related affective distress and hormonal fluctuations in women with PDM, and complement the brain mechanism investigations for the pathophysiology of PDM.

Neuroimaging studies have demonstrated the critical role of the insula in pain pathways and its close relation with the perceived intensity of nociceptive stimuli. We aimed to identify the structural and functional characteristics of the insula during periovulatory phase in women with primary dysmenorrhea (PDM), and further investigate its association with the intensity of perceived pain during menstruation. Optimized voxel-based morphometry and functional connectivity (FC) analyses were applied by using 3-dimensional T1-weighted and resting functional magnetic resonance imaging (fMRI) in 36 patients at the peri-ovulation phase and 29 age-, education-, and gender-matched healthy controls (HC). A visual analogue scale (VAS) was used to examine the intensity of the abdominal pain at periovulation and menstruation. In our results, PDM patients had significant higher VAS-rating during menstruaion than periovulation. Compared with the HC, PDM patients had lower gray matter density in the left anterior insula (aINS). Taken the left aINS as a seed region, we further found hypoconnectivity between aINS and medial prefrontal cortex (mPFC), which showed negative relation with the VAS during menstruation. As the aINS is a key site of the salience network (SN) and the mPFC is a critical region in the default mode network (DMN), it's implicated a trait-related central-alteration that communications between pain attention and perception networks were disrupted without the ongoing menstrual pain. Moreover, result of correlation analysis, at least in part, suggested a possible role of altered FC (pain-free period) in predicting pain perception (menstruation).

To develop a machine learning model to investigate the discriminative power of whole-brain gray-matter (GM) images derived from primary dysmenorrhea (PDM) women and healthy controls (HCs) during the pain-free phase and further evaluate the predictive ability of contributing features in predicting the variance in menstrual pain intensity. Sixty patients with PDM and 54 matched female HCs were recruited from the local university. All participants underwent the head and pelvic magnetic resonance imaging scans to calculate GM volume and myometrium-apparent diffusion coefficient (ADC) during their periovulatory phase. Questionnaire assessment was also conducted. A support vector machine algorithm was used to develop the classification model. The significance of model performance was determined by the permutation test. Multiple regression analysis was implemented to explore the relationship between discriminative features and intensity of menstrual pain. Demographics and myometrium ADC-based classifications failed to pass the permutation tests. Brain-based classification results demonstrated that 75.44% of subjects were correctly classified, with 83.33% identification of the patients with PDM (P < 0.001). In the regression analysis, demographical indicators and myometrium ADC accounted for a total of 29.37% of the variance in pain intensity. After regressing out these factors, GM features explained 60.33% of the remaining variance. Our results suggested that GM volume can be used to discriminate patients with PDM and HCs during the pain-free phase, and neuroimaging features can further predict the variance in the intensity of menstrual pain, which may provide a potential imaging marker for the assessment of menstrual pain intervention.