We examined parenting stress and mental health status in parents of autistic children and assessed factors associated with such stress. Participants were parents of 188 autistic children diagnosed with DSM-IV criteria and parents of 144 normally developing children. Parents of autistic children reported higher levels of stress, depression, and anxiety than parents of normally developing children. Mothers of autistic children had a higher risk of depression and anxiety than that did parents of normally developing children. Mothers compared to fathers of autistic children were more vulnerable to depression. Age, behavior problems of autistic children, and mothers’ anxiety were significantly associated with parenting stress.
While the disease course of stress-induced cardiomyopathy (SIC) is usually benign, it can be fatal in some cases. The prognostic factors to predict poorer outcome are not well established, however. We analyzed the Acute Physiology And Chronic Health Evaluation (APACHE) II score to assess its value for predicting poor prognosis in patients with SIC. Thirty-seven consecutive patients with SIC were followed prospectively during their hospitalization. Clinical factors, including APACHE II score, coronary angiogram, echocardiography and cardiac enzymes at presentation were analyzed. Of the 37 patients, 27 patients (73%) were women. The mean age was 66.1 ± 15.6 years, and the most common presentation was chest pain (38%). Initial echocardiographic left ventricular ejection fraction (EF) was 42.5 ± 9.3%, and the wall motion score index (WMSI) was 1.9 ± 0.3. Six patients (16%) expired during the follow-up period of hospitalization. Based on the analysis of characteristics and clinical factors, the only predictable variable in prognosis was APACHE II score. According to ROC curve by this result, we divided patients into two groups (APACHE II score > 20, and ≤20). The patients with APACHE II score greater than 20 had tendency to expire than the others (P = 0.001). Based on present study, APACHE II score more than 20, rather than cardiac function, was associated with mortality in patients with SIC.
The primary aim is to compare the effects of a low-fat diet vs a personalized diet on % weight loss at 6-months. Secondary outcomes include body composition (fat mass [FM] and fat free mass [FFM]), resting energy expenditure (REE) and adaptive thermogenesis (AT). The Personal Diet Study was a 6-month, single-center, randomized clinical trial in adults with pre-diabetes and moderately controlled type 2 diabetes who were overweight or obese. Participants were randomized to follow either a hypocaloric low-fat diet, with < 25% energy intake from total fat (Standardized), or a hypocaloric personalized diet determined by a machine learning algorithm which predicts PPGR to meals (Personalized). Participants in both arms received behavioral counseling and logged dietary intake and physical activity into a smartphone app. Participants in the Personalized arm received real-time feedback as color-coded scores based on pre-consumed meals entered into the smartphone app. T-tests were used to assess group differences. A total of 200 adults (Standardized: n = 97 vs. Personalized: n = 103) contributed data (mean [SD]: age, 58 [11] years; 67% female; BMI, 34.0 [4.8] kg/m2; HbA1c, 5.8 [0.6]%; Metformin use, 21.0%). There were no significant group differences in mean % weight loss (Standardized: −4.4 [4.8]% vs Personalized: −3.3 [5.4]%; p = 0.19), mean absolute change in FM (Standardized: −2.7 [3.4] kg vs. Personalized: −1.6 [3.5] kg; p = 0.18), and AT between the two arms (Standardized: −54.7 [177] kcal/d vs. Personalized: 26.2 [199] kcal/d; p = 0.078). However, the Standardized arm lost significantly more FFM (−1.4 [1.6] kg vs. −0.45 [2.0] kg; p = 0.03) and had a greater decrease in REE (−111.0 [195.0] kcal/d vs. 1.93 [215.0] kcal/d; p = 0.02) compared to Personalized. A personalized diet to minimize PPGR had no greater effect on % weight loss compared to a low-fat diet at 6-months. Future precision nutrition trials may require deeper phenotyping of individuals or the development of body weight-specific algorithms. Supported by grants from the American Heart Association 17SFRN33590133.
Abstract Prostate-specific antigen (PSA)-based prostate cancer screening is a preference-sensitive decision for which experts recommend a shared decision making (SDM) approach. This study aimed to examine PSA screening SDM in primary care. Methods included qualitative analysis of audio-recorded patient-provider interactions supplemented by quantitative description. Participants included 5 clinic providers and 13 patients who were: (1) 40–69 years old, (2) Black, (3) male, and (4) attending clinic for routine primary care. Main measures were SDM element themes and “observing patient involvement in decision making” (OPTION) scoring. Some discussions addressed advantages, disadvantages, and/or scientific uncertainty of screening, however, few patients received all SDM elements. Nearly all providers recommended screening, however, only 3 patients were directly asked about screening preferences. Few patients were asked about prostate cancer knowledge (2), urological symptoms (3), or family history (6). Most providers discussed disadvantages (80%) and advantages (80%) of PSA screening. Average OPTION score was 25/100 (range 0–67) per provider. Our study found limited SDM during PSA screening consultations. The counseling that did take place utilized components of SDM but inconsistently and incompletely. We must improve SDM for PSA screening for diverse patient populations to promote health equity. This study highlights the need to improve SDM for PSA screening.
Emerging data suggest that inorganic arsenic exposure and gut microbiome are associated with the risk of cardiovascular disease. The gut microbiome may modify disease risk associated with arsenic exposure. Our aim was to examine the inter-relationships between arsenic exposure, the gut microbiome, and carotid intima-media thickness (IMT)—a surrogate marker for atherosclerosis. We recruited 250 participants from the Health Effects of Arsenic Longitudinal Study in Bangladesh, measured IMT and collected fecal samples in year 2015–2016. 16S rRNA gene sequencing was conducted on microbial DNA extracted from the fecal samples. Arsenic exposure was measured using data on arsenic concentration in drinking water wells over time to derive a time-weighted water arsenic index. Multivariable linear regression models were used to test the inter-relationships between arsenic exposure, relative abundance of selected bacterial taxa from phylum to genus levels, and IMT. We identified nominally significant associations between arsenic exposure, measured using either time-weighted water arsenic or urinary arsenic, and the relative abundances of several bacterial taxa from the phylum Tenericutes, Proteobacteria, and Firmicutes. However, none of the associations retained significance after correction for multiple testing. The relative abundances of the family Aeromonadaceae and genus Citrobacter were significantly associated with IMT after correction for multiple testing (P-value = 0.02 and 0.03, respectively). Every 1% increase in the relative abundance of Aeromonadaceae and Citrobacter was related to an 18.2-μm (95% CI: 7.8, 28.5) and 97.3-μm (95% CI: 42.3, 152.3) difference in IMT, respectively. These two taxa were also the only selected family and genus using the LASSO variable selection method. There was a significant interaction between Citrobacter and time-weighted water arsenic in IMT (P for interaction = 0.04). Our findings suggest a role of Citrobacter in the development of atherosclerosis, especially among individuals with higher levels of arsenic exposure.
To explore the causal relationship between obesity and hypothyroidism and identify risk factors and the predictive value of subclinical hypothyroidism (SCH) in obese patients using Mendelian randomization, this study employed five Mendelian randomization methods (MR Egger, Weighted Median, Inverse Variance Weighted, Simple Mode, and Weighted Mode) to analyze clinical data from 308 obese patients at the People's Hospital of Xinjiang Uygur Autonomous Region, from January 2015 to June 2023. Patients were divided based on thyroid function tests into normal (n = 173) and SCH groups (n = 56). Comparative analyses, along with univariate and multivariate logistic regression, were conducted to identify risk factors for SCH in obese patients. A significant association between obesity and hypothyroidism was established, especially highlighted by the inverse variance weighted method. SCH patients showed higher ages, thyroid-stimulating hormone levels, and thyroid autoantibody positivity rates, with lower T4 and FT4 levels. Age, FT4, thyroid autoantibodies, TPO-Ab, and Tg-Ab were confirmed as risk factors. The predictive value of FT4 levels for SCH in obesity was significant, with an Area Under the Curve (AUC) of 0.632. The study supports a potential causal link between obesity and hypothyroidism, identifying specific risk factors for SCH in obese patients. FT4 level stands out as an independent predictive factor, suggesting its utility in early diagnosis and preventive strategies for SCH.
Increasing evidence shows the importance of the commensal microbe Oxalobacter formigenes in regulating host oxalate homeostasis, with effects against calcium oxalate kidney stone formation, and other oxalate-associated pathological conditions. However, limited understanding of O. formigenes in humans poses difficulties for designing targeted experiments to assess its definitive effects and sustainable interventions in clinical settings. We exploited the large-scale dataset from the American Gut Project (AGP) to study O. formigenes colonization in the human gastrointestinal (GI) tract and to explore O. formigenes-associated ecology and the underlying host–microbe relationships. In >8000 AGP samples, we detected two dominant, co-colonizing O. formigenes operational taxonomic units (OTUs) in fecal specimens. Multivariate analysis suggested that O. formigenes abundance was associated with particular host demographic and clinical features, including age, sex, race, geographical location, BMI, and antibiotic history. Furthermore, we found that O. formigenes presence was an indicator of altered host gut microbiota structure, including higher community diversity, global network connectivity, and stronger resilience to simulated disturbances. Through this study, we identified O. formigenes colonizing patterns in the human GI tract, potential underlying host–microbe relationships, and associated microbial community structures. These insights suggest hypotheses to be tested in future experiments. Additionally, we proposed a systematic framework to study any bacterial taxa of interest to computational biologists, using large-scale public data to yield novel biological insights.
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