Communities Study Research Articles

Research on the climate change and prediction of the temperature in Beijing

Abstract. Over the past few years, global warming has become a growing threat to the human living environment. Thus, preventing extreme temperature changes is a crucial area of study for the global community. However, temperature changes have diverse causes, making it hard to accurately calculate the data that impacts subsequent temperature forecasts. The ARIMA model is widely favoured by researchers as a typical time series prediction model. Additionally, it holds significant research value in forecasting dynamic models. This paper will use the ARIMA model to forecast temperature changes in Beijing for the next month, starting on 21 July. The findings show that the temperature remains relatively stable in the upcoming month, with minimal fluctuations. Based on historical data, this is the peak temperature in Beijing for the year. So, if the temperatures are not extremely high now, they might not get higher later. This forecast can be used as a guide for managing extreme temperatures in Beijing in the future.

Duration of diabetes, glycemic control, and low heart rate variability: The atherosclerosis risk in communities (ARIC) study

Persons with long-standing, poorly controlled diabetes or recent hyperglycemia had the highest burden of cardiac autonomic neuropathy. Cardiac autonomic neuropathy contributed to elevated long-term incidence of cardiovascular disease and mortality even in persons with well-controlled diabetes.

Identifying a broad range of psychological characteristics associated with unsafe driving behaviors

Unsafe driving behaviors (UDBs) represent one of the most consequential classes of behavior in public health. The present study aims to identify a greater range of psychological and behavioral characteristics associated with UDBs, with a special focus on identifying characteristics varying in their associations with different UDBs. Data was analyzed from the Eugene-Springfield Community Study (ESCS) linking self-ratings of the 2539-item International Personality Item Pool (IPIP; Goldberg et al., 2006) to self-reports of four UDBs: speeding, driving after drinking alcohol, driving without a seatbelt, and consuming food or drinks while driving. A subset of 176 IPIP items were identified as having non-trivial relations with the four UDBs, which were then organized into 20 more specific clusters. Some groups were commonly predictive of all four UDBs, such as tendencies to break rules, to view cheating as acceptable, or to talk impulsively. Others were mainly predictive of one UDB but not others. For instance, religiosity was uniquely predictive of lower tendencies to drive after drinking alcohol, whereas negative self-perceptions were uniquely predictive of greater tendencies to drive without a seatbelt. The results provide a more molecular, fine-grained picture of the characteristics associated with UDBs, which can inform larger models of why people engage in UDBs, and why they perform some but not others.

Development of a brief stigma and perceptions questionnaire for pharmacists: An exploratory factor analysis approach in New York state counties enrolled in the healing communities study

IntroductionLittle is known about how pharmacists' attitudes and stigma toward naloxone and Medication for opioid use disorder (MOUD) influence effective linkage to treatment. We examine the psychometrics of a new Pharmacist Opioid Use Disorder Perceptions Questionnaire (P-OUDP-Q), a multidimensional measure to examine pharmacists' stigma and perceptions related to MOUD in the New York State (NYS) site of the HEALing Communities Study. MethodsThe study recruited a sample of 324 pharmacists from 16 counties in NYS between January and June 2022. A 74-item questionnaire assessed pharmacists' familiarity with opioid-related medications, protocols, policies and attitudes regarding their role, confidence, and beliefs centered around delivery of MOUD and naloxone in the community. Exploratory factor analysis assessed individual and community-level factors associated with four underlying constructs. Factor scores were compared across the demographic predictors. Variables factor loadings <0.4 were eliminated from the factor analysis and the process was reiterated. ResultsEighty-six percent (n = 280) of the pharmacists were white. A little over half, 57 % (n = 186), were female, 35 % (n = 113) were 30–35 years old. The mean number of years practicing (SD) was 18 (SD: 13). Exploratory factor analysis identified four underlying constructs: (1) practice confidence, (2) practice familiarity, (3) practice attitudes, and (4) methadone attitudes. Statistically significant (p < .05) mean factor scale score differences by race were observed for practice familiarity (white reporting higher than non-white); by pharmacy size for practice familiarity (across all groups; non-significant Tukey post-hoc) and practice attitudes (hospital/clinic greater than big chain pharmacies); by gender (males greater than females) for practice familiarity and methadone attitudes; by poverty quartile for practice attitudes (lowest less than highest quartile); and urban versus rural pharmacist county setting for practice familiarity (rural greater than urban). ConclusionsFindings show the P-OUDP-Q is a concise measure of pharmacists' perceptions of their role in dispensing MOUD and naloxone, including distinct “stigma” dimensions, which is valuable for use with pharmacists in communities highly impacted by the opioid epidemic. The development and validation of a reliable measure to assess pharmacists' perceptions of stigma and barriers represents a valuable contribution to the field, to inform the design/implementation of targeted interventions and support systems.

The Office of Shaykh al-‘Aql of the Druze in Lebanon

This book forum centers on Said Abou Zaki's Arabic book, “The Office of Shaykh al-‘Aql of the Druze in Lebanon.” It includes contributions from various scholars, each offering a unique perspective on the historical and institutional analysis of Druze religious leadership in Lebanon: Tuba Yildiz, Yusri Hazran, Akram Khater, and Abdul Rahman M. Chamseddine. Yildiz acknowledges the lack of historical readings and erroneous information in early 20th-century works on the Druze. She appreciates Abou Zaki's comprehensive study of Lebanese and Ottoman perspectives, offering a broader understanding of the Druze religious institution's development and its connection with the Ottoman Empire's socio-religious policies. Hazran commends the book for providing a comprehensive historical study of the Druze community's religious and spiritual leadership, shedding light on its evolution from the 11th century through to the Ottoman Empire's fall. Although the book excels in its research methods and historical insights, Hazran suggests strengthening it by addressing specific methodological and historical issues, such as placing the discussion on existing narratives and substantiating Emir Bashir Shihab II's role in the schism. Khater highlights the internal tensions in Abou Zaki's book arising from its threefold goals: serving as a historiographical essay, an origins story, and a history of Mashyakhat al-‘Aql. Khater commends the work's critical review of existing literature and acknowledges the book's significant contribution to understanding the position of Shaykh al-‘Aql amidst political crises. Chamseddine praised Abou Zaki's critical historiographical approach and genealogical method. Chamseddine highlights the importance of integrating oral histories and local traditions into academic research. He notes that Abou Zaki's work sheds light on the Druze community's religious evolution, although he critiques the book's occasional lack of coherence due to abrupt transitions and unexplained terminology. The reviewers concluded that the book constitutes a significant contribution to Druze Studies.

Evaluating off-target drug effects seen in RCTs using proteomics and mendelian randomisation

Abstract Introduction Serious adverse drug effects are often not detected until late in drug development after thousands of patients have been exposed to an investigational drug in long and costly clinical trials. More efficient approaches are desirable. Proteomics-informed Mendelian randomisation (MR) can identify potential molecular mechanisms underlying off-target effects of pharmaceuticals and drug repurposing opportunities. Purpose As a demonstration of this approach, we used findings from a previous plasma proteomics study nested within a clinical trial of torcetrapib, which was terminated early because of an increased risk of cardiac events and mortality, in which 200 plasma proteins significantly increased (n=68) or decreased (n=132) after three months of torcetrapib use. We surveyed potential plasma protein-mediated causal effecs of torcetrapib on a wide array of relevant phenotypes in three broad domains: cardiovascular(n=10), immunological(n=9) and cancer(n=4), representing major categories of adverse outcomes that were more common in the treatment arm. Methods We performed three-sample MR on torcetrapib-associated proteins. First, we identified independent cis-pQTL instruments for these proteins in the Atherosclerosis Risk in Communities (ARIC) Study. Then, we used pQTL effect estimates from the deCODE study, to reduce the potential for bias due to winner’s curse and to increase precision. Lastly, we conducted MR analyses on disease-relevant phenotypes using a debiased-IVW estimator and summary results from the largest available GWAS on these phenotypes: coronary artery disease, ischaemic stroke, blood pressure, lipids, heart rate, QT interval, eGFR, serious infections, blood cell counts, immune cell fractions, and several cancers. The significance level for causal protein-phenotype associations was p=1.85E-5 (0.05/(104*26)), correcting for the 104 proteins with valid instruments and the 26 tested outcomes. Results Thirty-three proteins had evidence of a causal association with at least one phenotype. Seventeen proteins were associated with cardiovascular outcomes, including 8 for blood pressure and 9 for lipid levels. We identified more proteins, 25, associated with immune-related phenotypes. One protein was associated with cancer. Of note, 9 proteins had pleiotropic effects across several domains. Conclusions Our approach of using Mendelian randomisation to investigate the causal effects of plasma proteins impacted by medication with known off-target adverse effects revealed several novel causal relationships, highlighting the role of immune function in the development of cardiovascular disease. These results support the use of intermediate omics evaluation to identify potential biomarkers for safety monitoring for drug development and reveal avenues of investigation for drug repurposing.

Left atrial function for predicting atrial fibrillation: a machine learning approach

Abstract Background Common risk scores for atrial fibrillation (AF) are based on clinical risk factors, and little is known about the contribution of left atrial (LA) function parameters to risk assessment. Purpose To determine whether a machine learning-based model that includes LA function parameters outperforms a commonly used risk scores, the CHARGE-AF, in predicting AF. Methods Participants in the Atherosclerosis Risk in Communities (ARIC) Study (a community-based cohort study in the USA) who attended visit 5 (2011-2013) and without a history of AF were included in this analysis. Incident AF was ascertained from ECG during study visits, hospitalization discharge codes, and death certificates. Random survival forest methodology was used to build a prediction model for AF; candidate variables included traditional clinical risk factors and measures of LA structure and function. Dataset was randomly split into a training (75%) and testing (25%) cohort. For each variable, importance was measured based on the reduction in Harrell’s c-statistic if the variable was randomly permuted prior to obtaining predicted values. Variables significantly associated with AF were included in the final model. Model calibration was tested using the Greenwood-Nam-D'Agostino (GND) test. Discrimination ability of our model was assessed in the testing cohort using Harrell’s c-statistic and compared with that of the CHARGE-AF model. Results 4,909 participants (59% female, 22% Black, mean age 75 years) were included. During a median follow-up of 7.0 years (1st-3rd quartile: 5.4-7.8), 650 participants developed AF. Variables significantly associated with incident AF were race, triglycerides, systolic blood pressure, coronary heart disease, age, LA maximal volume index, NT-proBNP, and LA reservoir strain (Figure 1). LA reservoir strain was the variable most strongly associated with AF. The final model showed good discrimination (c-statistic (95% CI): 0.734 (0.698-0.768)). The GND test did not detect statistically significant miscalibration (GND p=0.08). Compared with our model, CHARGE-AF had lower discrimination ability (c-statistic (95% CI): 0.635 (0.594-0.677)) and the difference was statistically significant (difference in c-statistic (95% CI): 0.099 (0.043-0.152)). Conclusion A machine learning-based model with few variables including LA function outperforms a commonly used risk model in predicting AF. Incorporation of LA functional assessment may help inform clinicians regarding individual risk of AF.

A novel electrocardiogram-based model for prediction of dementia: the Atherosclerosis Risk in Communities (ARIC) study

Abstract Background Current dementia prediction models, such as the UK Biobank Dementia Risk Score (UKBDRS, 12 variables), are limited by having variables that are difficult to obtain, subjectively measured, and self-reported. Although individual ECG parameters are linked to higher dementia risk, no study has combined multiple ECG parameters to create an ECG model for dementia prediction. Purpose Using data from the ARIC study, a prospective community-based cohort of Black and White participants in the USA that started in 1987-89, we created an ECG model (based on 7 pre-defined ECG parameters linked to dementia) and compared its model discrimination with the UKBDRS. Methods Participants without prevalent dementia from 2 baselines were included: Visit 4 (V4, 1996-98) and V5 (2011-13). Seven ECG parameters were examined: heart rate, abnormal P-wave terminal force in lead V1 (PTFV1), prolonged P wave duration, abnormal P wave axis, advanced interatrial block, left ventricular hypertrophy (LVH, Cornell voltage criteria), and corrected QT interval (QTc). Dementia cases were identified through 2019 using in-person and phone cognitive assessments, hospitalisation codes, and death certificates. Using a backward selection method (keeping variables with P&amp;lt;0.10 and age, since it is an important dementia risk factor), parsimonious Cox regression models were constructed for prediction of dementia over the longest available follow-up (V4: 23 years, V5: 8 years). Model discrimination of the ECG models and UKBDRS variables was evaluated by the Harrell’s C-statistic (95% CI). Results At V4, 11,520 participants (mean age, 62.8 ± 5.7 years; 56% women; 22% Black) were included; 2,427 developed dementia. At V5, 5,667 participants (mean age, 75.4 ± 5.1 years; 59% women; 22% Black) were included; 830 developed dementia. Figures 1 and 2 show the C-statistic (95%) and area under the ROC curve for the ECG model and UKBDRS variables at V4 and V5, respectively. The C-statistic (95% CI) for both ECG models were &amp;gt;0.70 and comparable with the corresponding UKBDRS models. Conclusion For middle-aged and older adults, a dementia prediction model comprising age and 4 ECG variables has good discrimination for dementia that is comparable with the 12-variable UKBDRS model. Since ECG variables are easily obtainable and objectively measured, these ECG models will be easier to adopt than the UKBDRS model. Further work to validate this model in an external cohort and develop a risk score will facilitate clinical translation.

A genome-wide association study identifies genetic determinants of hemoglobin glycation index with implications across sex and ethnicity.

We investigated the genetic determinants of variation in the hemoglobin glycation index (HGI), an emerging biomarker for the risk of diabetes complications. We conducted a genome-wide association study (GWAS) for HGI in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (N = 7,913) using linear regression and additive genotype encoding on variants with minor allele frequency greater than 3%. We conducted replication analyses of top findings in the Atherosclerosis Risk in Communities (ARIC) study with inverse variance-weighted meta-analysis. We followed up with stratified GWAS analyses by sex and self-reported race. In ACCORD, we identified single nucleotide polymorphisms (SNPs) associated with HGI, including a peak with the strongest association at the intergenic SNP rs73407935 (7q11.22) (P = 5.8e-10) with a local replication in ARIC. In black individuals, the variant rs10739419 on chromosome 9 in the Whirlin (WHRN) gene formally replicated (meta-P = 2.2e-9). The SNP-based heritability of HGI was 0.39 (P< 1e-10). HGI had significant sex-specific associations with SNPs in or near GALNT11 in women and HECW2 in men. Finally, in Hispanic participants, we observed genome-wide significant associations with variants near USF1 and NXNL2/SPIN1. Many HGI-associated SNPs were distinct from those associated with fasting plasma glucose or HbA1c, lending further support for HGI as a distinct biomarker of diabetes complications. The results of this first evaluation of the genetic etiology of HGI indicate that it is highly heritable and point to heterogeneity by sex and race.

Cultivating dispersed collectivity: How communities between organizations sustain employee activism

Pushing for social change at work is frustrating and precarious. Many employee activists therefore seek support in communities that form around their aspirations and reside ‘between’ organizations. This article advances our understanding of how community participation shapes employee activists’ experiences of their change agency as they return to and pursue their social purpose in their corporate lives. Grounded in an in-depth qualitative study of an inter-organizational community of employee activists, we introduce the notion of ‘dispersed collectivity’: employee activists generate a shared sense of collectivity that they maintain even as they disperse into their workplaces. Dispersed collectivity enables subtle agentic experiences by emboldening employee activists to endure their often-challenging corporate lives, unsettle corporate norms, and detach from their corporate positions. Even without mobilizing a collective push for change across firms, communities can thus play a critical role in sustaining employee activism. Our study contributes a more nuanced account of employee activists’ change agency and offers new theoretical insights into the role of inter-organizational communities in social change, the practices they can use to build collective momentum and empathic connections, and their impact on employee activists’ determination to drive social change from within.

Genealogy in social media: navigating time and space

ABSTRACT Driven by the increasing use of social media by people seeking to reconstruct family histories and preserve historical, cultural, and societal legacy, genealogy-based online communities are experiencing a surge in popularity. This study explores how trauma-affected communities use online communities to reconstruct their identities and histories, demonstrating the resilience and adaptability of these groups in the digital age. Based on the theory of Knowledge Co-Creation, this paper presents a novel conceptual framework designed to categorize the content in such communities. This framework is composed of two axes. The first axis, ‘Time,’ reflects the evolving character of participants’ involvement, starting with an exploration of immediate family history and gradually expanding to encompass a broader interest in ancestral and communal history of the past. The second axis, ‘Space,’ explores the interplay between online interactions and offline activities. To demonstrate the applicability of this framework, the paper focuses on the case study of the WhatsApp community dedicated to collaborative research of the descendants of annihilated Jewish communities of the Upper Silesia region of southwestern Poland.This research contributes to the literature on digital genealogy and online communities by offering a structured approach to analyzing the dynamics of trauma-affected populations engaging in collaborative genealogical research.

An equation for estimating low-density lipoprotein-triglyceride content and its use for cardiovascular disease risk stratification.

The triglyceride (TG) content of low-density lipoprotein (LDL-TG) has been shown to be more predictive of atherosclerotic cardiovascular disease (ASCVD) events than the cholesterol content of LDL (LDL-C). The goal of our study was to develop an equation for estimating LDL-TG (eLDL-TG) based on the standard lipid panel and to compare it to estimated LDL-C as an ASCVD risk biomarker. Using least-square regression analysis, the following eLDL-TG equation was developed: . LDL-TG was measured by the β-quantification (BQ) reference method (N = 40,202). LDL-C was calculated by the Sampson-NIH equation. The association of LDL-C and eLDL-TG with ASCVD risk markers was performed in the National Heart and Nutrition Examination Survey (NHANES) (N = 37,053) and with ASCVD events in a primary prevention cohort from the UK Biobank (UKB) (N = 429,367) and the Atherosclerosis Risk in Communities (ARIC) study (N = 14,632). eLDL-TG showed better ASCVD risk stratification of UKB participants than LDL-C (Wilcoxon Chi-Square: 2,099.6 vs. 418.7, respectively). Receiving-operating characteristics analysis revealed that eLDL-TG had a stronger association with ASCVD events than LDL-C (AUC: 0.596 vs. 0.542, respectively) and other conventional lipid markers. Similar findings were found in ARIC. Discordance analysis in UKB showed that the group with low LDL-C/high eLDL-TG had a similar risk as the high LDL-C/high eLDL-TG group. Furthermore, these same two groups with the highest eLDL-TG levels and the highest ASCVD event rate also had higher mean levels of systolic blood pressure, Body Mass Index, hemoglobin A1C, and C-reactive protein than the two lower eLDL-TG groups. Using eLDL-TG > 44.6 mg/dl (80th percentile) as a cut-point leads to a hazard ratio of 1.32 (95% CI, 1.29-1.36) for ASCVD events, which remained significant after adjustment for LDL-C and apoB. Furthemore, using eLDL-TG as a risk-enhancer test leads to reclassification of 50% more high-risk individuals than current lipid-enhancer test rules. Like LDL-C, LDL-TG can also be calculated from the results of the standard lipid panel. Compared to estimated LDL-C, eLDL-TG was a better risk marker for primary prevention and hence could improve initial ASCVD risk stratification.

Assessing Socio-economic Dynamics and Willingness to Pay for Improved Ecosystem: A Study of Coastal Communities of Korapuzha Estuary, Kerala, India

Assessing Socio-economic Dynamics and Willingness to Pay for Improved Ecosystem: A Study of Coastal Communities of Korapuzha Estuary, Kerala, India

The loss of rural vital places: A case study using the social determinants of health framework

Healthy food sources, recreational facilities, and social spaces serve as vital places for supporting health. Less is known about what happens when vital places are lost, especially in rural contexts that have experienced disinvestment and population decline. In this paper we use a case study of a small rural community in Pennsylvania that lost a substantial share of its vital places over a short period of time to explore the experiences of local residents. Using qualitative interviews with 26 local residents we show that the loss of vital places, which are part of the neighborhood and built environment, intersected with other social determinants of health with potential consequences for health and well-being. This was often because vital places were essential (i.e., few or no alternatives existed) and multifunctional (i.e., supporting health through multiple behavioral and social mechanisms). Examining experiences of local residents following the loss of vital places through a social determinants of health lens helps to identify consequences and inform adaptation strategies. We also use the findings to refine the vital places framework for rural contexts.

PERUBAHAN FUNGSI POS JAGA: DARI PENJAGAAN KE INOVASI USAHA UNTUK EKONOMI KREATIF

This community field study aimed to repair the damage to the business post on Rt 6 Wisma Permai Barat, which is an important source of income for local residents. The aim of this activity was to improve the functionality of the business post and increase the comfort of its occupants. The project involved initial site surveys, renovation planning, and post-renovation work. Seven community service program students from Darma Cendika University and several local residents collaborated on this project from March to July 2024 (5 months). The main activities carried out included painting, adding canopies and decorations, filling holes in walls, and leveling the expanded land with paving and plastering. Students and residents participated in the smooth running of this project. The result of this project is that the business post now looks cleaner and more attractive due to the new color scheme. The extension area becomes more comfortable, with additional shade and flatness. This community service project shows the significant impact of collaborative efforts between students and local communities in improving public infrastructure. This project was undertaken so that the upgraded business outpost would hopefully better fulfill its purpose and provide a more enjoyable environment for the business and its customers.

Multimorbidity and Person-Centred care in a socioeconomically deprived community: a qualitative study.

Multimorbidity (>2 long-term conditions) has poorer outcomes in areas of high socioeconomic deprivation (SED). High-quality Person-Centred Care (PCC) is important in multimorbidity but socially vulnerable populations have not informed current PCC models. To explore how wider community factors influence management of multimorbidity in the context of high SED, how high-quality PCC is defined by patients, and whether this influences healthcare management. Ethnographically informed case study in community experiencing high SED in Scotland. Participant observation (hours = 142) within 4 community groups who also took part in 2 participatory workshops. 25 in-depth interviews with people with multimorbidity, recruited from local General Practices; emerging findings discussed with interviewees in one focus group. Field notes/transcripts analysed using inductive thematic analysis. Key aspects of PCC were "patient as person", "therapeutic relationship", "co-ordination" and "power-sharing"; power sharing was particularly enabling but rarely happened (barriers often unseen by practitioners). Shared community experiences of "being known", "stigma" and "none of the systems working" influenced how people approached health services, and healthcare decisions. High quality PCC may have been particularly effective in this setting because of its influence on ameliorating wider shared negative community experiences. In a high SED setting PCC is important and can enhance engagement. Wider community factors have a critical influence on engagement with healthcare in areas of high SED and PCC may be particularly important in this context because of its influence ameliorating these. Policy makers should prioritise and resource PCC.

Estimation of bacterio‐, picophyto‐ and nanophytoplankton abundance in the Middle Caspian Sea by flow cytometry

Aim. For the first time, flow cytometry has been used to study the structure and functional characteristics of bacterio‐, pico‐ and nano‐ phytoplankton in the Middle Caspian during the bloom period in early September 2022.Water samples were taken at different horizons (from the surface to the bottom layer) at a series of stations along the western coast of the Caspian Sea on the border between its northern and central parts to quantitatively assess heterotrophic bacterioplankton, picophyto‐ and nanophy‐ toplankton. Sampling was undertaken in early September 2022 under summer hydrological conditions with water surface temperatures ranging from 24.7°C to 26.7°C. Sample analysis was performed using flow cytometry.Extremely high concentrations of picophytoplankton (up to 1.8×105 cells/mL), probably of Synechococcus picocyanobacteria, were detected off the northwestern coast of the Middle Caspian. Very high values of nanophytoplankton abundance (up to 1.6×105 cells/mL), dominated by small flagellates, were also found. A clear spatial uncoupling of pico‐ and nanophytoplankton was revealed. Maximum levels of nanophytoplankton were confined to the area of intensive phytoplankton blooming with high concentrations of chlorophyll a, while picophytoplankton reached peak abundance further south, outside this area. The influence of the thermocline on the vertical structure of microbial communities was detected. The concentrations of picophytoplankton and the proportion of physiologically active HNA‐bacteria decreased sharply under the thermocline.The results obtained indicate the leading role of the smallest phototrophs in the Caspian pelagial and emphasise the need for further comprehensive studies of Caspian microbial communities using modern methods.

Serum metabolite signature of the modified Mediterranean-DASH intervention for neurodegenerative delay (MIND) diet.

There is a lack of biomarkers of clinically important diets, such as the Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet. Our study explored serum metabolites associated with adherence to the MIND diet. In 3,908 Atherosclerosis Risk in Communities (ARIC) study participants, we calculated a modified MIND diet score based on a 66-item self-reported food frequency questionnaire (FFQ). The modified score did not include berries and olive oil, as these items were not assessed in the FFQ. We used multivariable linear regression models in 2 subgroups of ARIC study participants and meta-analyzed results using fixed effects regression to identify significant metabolites after Bonferroni correction. We also examined associations between these metabolites and food components of the modified MIND diet. C-statistics evaluated the prediction of high modified MIND diet adherence using significant metabolites beyond participant characteristics. Of 360 metabolites analyzed, 27 metabolites (15 positive, 12 negative) were significantly associated with the modified MIND diet score (lipids, n = 13; amino acids, n = 5; xenobiotics, n = 3; cofactors and vitamins, n = 3; carbohydrates n = 2; nucleotide n = 1). The top 4 metabolites that improved the prediction of high dietary adherence to the modified MIND diet were 7-methylxanthine, theobromine, docosahexaenoate (DHA), and 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (CMPF). Twenty-seven metabolomic markers were correlated with the modified MIND diet. The biomarkers, if further validated, could be useful to objectively assess adherence to the MIND diet.

Genotoxic effects in island populations of Cartagena de Indias, Colombia due to environmental exposure to mercury and cadmium

Background Contamination of Cartagena Bay, Colombia with heavy metals such as mercury (Hg) and cadmium (Cd) presents a major environmental and public health concern, particularly for human communities residing on nearby islands and coastal areas. These populations face enhanced exposure risks owing to their traditional fishing practices and continuous interactions with polluted marine environments. This study aimed to evaluate the genotoxic effects of environmental exposure to Hg and Cd in populations from the island zone of the Cartagena district, Bolívar. Methods Ninety-four individuals from the four island communities (study group) and 30 individuals from the urban area of Cartagena (control group) participated in this study. The blood samples were collected to measure total mercury (T-Hg) and Cd concentrations, and a Buccal Micronucleus Cytome (BMCyt) assay was used to evaluate exposure effects. Results Cadmiun levels in the blood of the study group were within the normal range and comparable to those of the control group (p &gt; 0.05). However, the study group exhibited significantly higher T-Hg levels (7.34 μg/L) compared to the control group (2.01 μg/L), surpassing the accepted limit. Moreover, the study group showed a higher incidence of DNA damage and cell death biomarkers (p &lt; 0.05). Additionally, significant correlations were observed between total blood Hg levels and the frequencies of micronuclei, karyorrhexis, and karyolysis. Conclusion These results suggest that island populations of Cartagena are exposed to high levels of Hg and exhibit genotoxic damage, indicating a problem that must be addressed.

Synergies between diabetes and hyperhomocysteinaemia: New insights to predict and prevent adverse cardiovascular effects.

To explore the association of hyperhomocysteinaemia (HHcy) and diabetes synergies with cardiovascular events in the adult population of northern China. Data were collected from the Asymptomatic Polyvascular Abnomalities Community study for 2010 to 2019. Serum homocysteine (Hcy) levels were determined by enzyme-linked immunosorbent assay. The participants were categorized into four groups based on their Hcy levels and diabetes status: non-diabetes/non-HHcy, non-diabetes/HHcy, diabetes/non-HHcy and diabetes/HHcy. The composite endpoint consisted of the occurrence of first-ever stroke, myocardial infraction (MI) or all-cause mortality. Cox regression analyses were performed to evaluate the associations of diabetes and HHcy with cardiovascular disease (CVD) events. In total, 5278 participants were eligible (average age 55.1 years, 60% male). Over a follow-up of 9.1 years, 618 events were identified, 202 stroke, 52 MI and 406 all-cause deaths. Compared with the non-diabetes/non-HHcy group, hazard ratios with 95% confidence intervals in the diabetes/HHcy group for stroke, MI, major adverse cardiovascular event (MACE), all-cause death and composite endpoint were 1.85 (1.12-3.04), 1.33 (0.42-4.23), 1.78 (1.13-2.80), 2.24 (1.56-3.23) and 1.97 (1.47-2.65), respectively. Significant interactions between HHcy and diabetes status were found for stroke, MI and MACE (P for interaction = .002, .027 and .044, respectively). In addition, the association of diabetes/HHcy with stroke was modified by age (< 60 and ≥ 60 years; P for interaction = .016). The findings highlight the synergistic impact of diabetes and HHcy on CVD. Joint assessments of diabetes and Hcy levels should be emphasized for risk stratification and primary prevention of CVD.