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This study evaluates the association of anthropometric indexes and the incidence of type 2 diabetes mellitus (T2DM) after a 5-year follow-up. This analysis included 1091 middle-aged participants (57% women, mean age 47 ± 15 years) who were free of T2DM at baseline and attended two health examinations cycles [cycle 1 (2005–2006) and cycle 2 (2010–2013)]. As expected, the participants who developed T2DM after five years (3.8%) had the worst metabolic profile with higher hypertension, dyslipidemia, and obesity rates. Besides, using mixed-effects logistic regression and adjustment for sex, age, and glucose, we found that one unit increase in body adiposity index (BAI) was associated with an 8% increase in their risk of developing T2DM (odds ratio [OR] = 1.08 [95% CI, 1.02–1.14]) and visceral adiposity index (VAI) was associated with a risk increase of 11% (OR = 1.11 [95% CI, 1.00–1.22]). Moreover, a one-unit increase in the triglycerides-glucose index (TyG) was associated with more than four times the risk of developing T2DM (OR = 4.27 [95% CI, 1.01–17.97]). The interquartile range odds ratio for the continuous predictors showed that TyG had the best discriminating performance. However, when any of them were additionally adjusted for waist circumference (WC) or even body mass index (BMI), all adiposity indexes lost the effect in predicting T2DM. In conclusion, TyG had the most substantial predictive power among all three indexes. However, neither BAI, VAI, nor TyG were superior to WC or BMI for predicting the risk of developing T2DM in a middle-aged normoglycemic sample in this rural Brazilian population.

During the first year of the COVID-19 pandemic, several countries have implemented non-pharmacologic measures, mainly lockdowns and social distancing, to reduce the spread of the SARS-CoV-2 virus. These strategies varied widely across nations, and their efficacy is currently being studied. This study explores demographic, socioeconomic, and epidemiological factors associated with the duration of lockdowns applied in Chile between March 25th and December 25th, 2020. Joint models for longitudinal and time-to-event data were used. In this case, the number of days under lockdown for each Chilean commune and longitudinal information were modeled jointly. Our results indicate that overcrowding, number of active cases, and positivity index are significantly associated with the duration of lockdowns, being identified as risk factors for longer lockdown duration. In short, joint models for longitudinal and time-to-event data permit the identification of factors associated with the duration of lockdowns in Chile. Indeed, our findings suggest that demographic, socioeconomic, and epidemiological factors should be used to define both entering and exiting lockdown.

Hypertension is the leading cause of overall mortality in low‐ and middle‐income countries. In Brazil, there is paucity of data on the determinants of incident hypertension and related risk factors. We aimed to determine the incidence of hypertension in a sample from the Brazilian population and investigate possible relationships with body adiposity indexes. We assessed risk factors associated with cardiovascular disease, including adiposity body indexes and biochemical analysis, in a sample from the Baependi Heart Study before and after a 10‐year follow‐up. Hypertension was defined by the presence of systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg or the use of antihypertensive drugs. From an initial sample of 1693 participants, 498 (56% women; mean age 38 ± 13 years) were eligible to be included. The overall hypertension incidence was 24.3% (22.3% in men and 25.6% in women). Persons who developed hypertension had higher prevalence of obesity, higher levels for blood pressure, higher frequency of dyslipidemia, and higher body adiposity indexes at baseline. The best prediction model for incident hypertension includes age, sex, HDL‐c, SBP, and Body Mass Index (BMI) [AUC = 0.823, OR = 1.58 (95% CI 1.23‐2.04)]. BMI was superior in its predictive capacity when compared to Body Adiposity Index (BAI), Body Roundness Index (BRI), and Visceral Adiposity Index (VAI). Incident hypertension in a sample from the Brazilian population was 24.3% after 10‐year follow‐up and BMI, albeit the simpler index to be calculated, is the best anthropometric index to predict incident hypertension.

Background The association between diabetes and obesity is very well established. Faced with this, several anthropometric indices of adiposity are often involved in studies on diabetes. Our main goal in this paper is to evaluate the association between body adiposity index (BAI) and type 2 diabetes mellitus (T2DM) in a sample of the Brazilian population after 5-year follow-up. Methods The data used come from the Baependi Heart Study cohort, which consists of two periods: cycle 1 (2005–2006) and cycle 2 (2010–2013). Individuals of both sexes (n = 1121) were selected by excluding participants with type 2 diabetes mellitus at baseline or those that were lost to follow-up. Results The diabetic subjects showed higher systolic blood pressure, BAI, body mass index, waist circumference and fasting glucose levels. In addition, using mixed-effects logistic regression, we found that the elevation of a single unit of BAI represented an increase of 8.4% in the risk of a patient developing T2DM (OR = 1.084 [95% CI 1.045–1.124]). Conclusions Obesity is recognised as one of the most important risk factors for T2DM and BAI has proven to be a useful tool in estimating the risk of a patient developing T2DM in a Brazilian population.

Growing awareness of environmental threats has encouraged researchers to increasingly focus on analyzing spatial and temporal patterns of diseases, including vector-borne diseases. A byproduct of this is the also increased interest in cluster analysis. Over the last few decades, the frequency and magnitude of disease outbreaks caused by insects have increased dramatically. In addition to areas that are recurrently affected, outbreaks are spreading into regions that were previously unaffected. Faced with such a scenario, clustering analysis is essential for recognizing areas and times with high disease incidence, thus aiding in intervention planning. Moreover, the increasing availability of large datasets of high quality has culminated in the emergence of more sophisticated statistical models and methods. In response to this need, we have developed some flexible Bayesian approaches whose main goal is to identify and cluster neighboring regions where the infection behaves similarly, and to evaluate how the spatial clustering pattern changes over time. To begin with, we develop a technique for recognizing and grouping regions that display similar time-based patterns for a specific disease. Our method employs product partition models that take into account the influence of neighboring regions to cluster geographical data. This prior is tied to temporal modeling, as it aligns the classification of regions with their time trends. Consequently, the temporal coefficients are common among areas within the same cluster. Furthermore, we introduce a directed acyclic graph structure to manage the spatial dependencies among these regions. As a contribution to the literature on multivariate data, we extend the first approach to jointly modeling multiple diseases, explicitly accounting for potential space-time correlations between them. In this case, we employ a multivariate directed acyclic graph autoregressive framework to capture both spatial and inter-disease dependencies. In the initial two models, the spatial cluster stays unchanged throughout time. However, the challenge of modeling intensifies when we attempt to examine temporal changes across different spatial partitions. To address this, we introduce a model for time-dependent sequences of spatial random partitions, establishing a prior based on product partition models that correlate spatial configurations. By utilizing random spanning trees as a methodological tool, we ease the exploration of the complex partition search space. We validate the properties of all models through simulation studies, demonstrating its competitive performance against alternative approaches. Furthermore, we apply them to mosquito-borne diseases dataset in the Brazilian Southeast region.

This study evaluates the association of anthropometric indexes and the incidence of type 2 diabetes mellitus (T2DM) after a 5-year follow-up. This analysis included 1091 middle-aged participants (57% women, mean age 47 ± 15 years) who were free of T2DM at baseline and attended two health examinations cycles [cycle 1 (2005–2006) and cycle 2 (2010–2013)]. As expected, the participants who developed T2DM after five years (3.8%) had the worst metabolic profile with higher hypertension, dyslipidemia, and obesity rates. Besides, using mixed-effects logistic regression and adjustment for sex, age, and glucose, we found that one unit increase in body adiposity index (BAI) was associated with an 8% increase in their risk of developing T2DM (odds ratio [OR] = 1.08 [95% CI, 1.02–1.14]) and visceral adiposity index (VAI) was associated with a risk increase of 11% (OR = 1.11 [95% CI, 1.00–1.22]). Moreover, a one-unit increase in the triglycerides-glucose index (TyG) was associated with more than four times the risk of developing T2DM (OR = 4.27 [95% CI, 1.01–17.97]). The interquartile range odds ratio for the continuous predictors showed that TyG had the best discriminating performance. However, when any of them were additionally adjusted for waist circumference (WC) or even body mass index (BMI), all adiposity indexes lost the effect in predicting T2DM. In conclusion, TyG had the most substantial predictive power among all three indexes. However, neither BAI, VAI, nor TyG were superior to WC or BMI for predicting the risk of developing T2DM in a middle-aged normoglycemic sample in this rural Brazilian population.

DNA double-strand break (DSB) repair by homologous recombination is confined to the S and G 2 phases of the cell cycle partly due to 53BP1 antagonizing DNA end resection in G 1 phase and non-cycling quiescent (G 0 ) cells where DSBs are predominately repaired by non-homologous end joining (NHEJ). Unexpectedly, we uncovered extensive MRE11- and CtIP-dependent DNA end resection at DSBs in G 0 murine and human cells. A whole genome CRISPR/Cas9 screen revealed the DNA-dependent kinase (DNA-PK) complex as a key factor in promoting DNA end resection in G 0 cells. In agreement, depletion of FBXL12, which promotes ubiquitylation and removal of the KU70/KU80 subunits of DNA-PK from DSBs, promotes even more extensive resection in G 0 cells. In contrast, a requirement for DNA-PK in promoting DNA end resection in proliferating cells at the G 1 or G 2 phase of the cell cycle was not observed. Our findings establish that DNA-PK uniquely promotes DNA end resection in G 0 , but not in G 1 or G 2 phase cells, which has important implications for DNA DSB repair in quiescent cells.

Carrying out health research with schools can be both challenging and highly rewarding. Here we describe lessons learned from a research partnership lasting over 5 years, initially with 84 primary schools in London and Luton, and extended to 35 secondary schools, during our children health cohort study. This period included school closures and societal disruption during the COVID-19 pandemic, creating additional challenges to ongoing school participation. Our study involved annual health assessment visits to schools to test over 3000 participants and parental self-report questionnaires, to assess the potential benefits of air quality improvements arising from London Ultra Low Emission Zone (introduced in April 2019) on children’s lung development and health. Measures included height, weight, pre- and post- bronchodilator spirometry, physical activity monitoring, cognitive assessment, epigenetic markers of disease risk, SARS-CoV-2 IgE and IgM antibody testing, and heavy metals testing. The average annual participant attrition for our study was 11.6%. The acceptable threshold outlined in the initial protocol was 20%. All schools continued to participate in the study for 5 years. Central to the study success have been: shared agreement on the importance of the research topic; early preparatory work with stakeholders, a parallel engaging and innovative air pollution learning and outreach programme, incentivising school/teacher co-operation and parental questionnaire completion to boost response rates and mitigate non-response bias; and continuity of contact with the accessible and flexible research team. These successes form a template for other health research studies planning long-term engagement with schools.

PurposeObesity is a known risk factor for post-menopausal breast cancer and may increase risk for triple negative breast cancer in premenopausal women. Intervention strategies are clearly needed to reduce obesity-associated breast cancer risk.MethodsWe conducted a Phase II double-blind, randomized, placebo-controlled trial of metformin in overweight/obese premenopausal women with components of metabolic syndrome to assess the potential of metformin for primary breast cancer prevention. Eligible participants were randomized to receive metformin (850 mg BID, n = 76) or placebo (n = 75) for 12 months. Outcomes included breast density, assessed by fat/water MRI with change in percent breast density as the primary endpoint, anthropometric measures, and intervention feasibility.ResultsSeventy-six percent in the metformin arm and 83% in the placebo arm (p = 0.182) completed the 12-month intervention. Adherence to study agent was high with more than 80% of participants taking ≥ 80% assigned pills. The most common adverse events reported in the metformin arm were gastrointestinal in nature and subsided over time. Compared to placebo, metformin intervention led to a significant reduction in waist circumference (p < 0.001) and waist-to-hip ratio (p = 0.019). Compared to placebo, metformin did not change percent breast density and dense breast volume but led to a numerical but not significant decrease in non-dense breast volume (p = 0.070).ConclusionWe conclude that metformin intervention resulted in favorable changes in anthropometric measures of adiposity and a borderline decrease in non-dense breast volume in women with metabolic dysregulation. More research is needed to understand the impact of metformin on breast cancer risk reduction.Trial registrationClinicalTrials.gov NCT02028221. Registered January 7, 2014, https://clinicaltrials.gov/ct2/show/NCT02028221

Leishmaniasis is a neglected tropical disease that affects people living in tropical and subtropical areas of the world. There are few therapeutic options for treating this infectious disease, and available drugs induce severe side effects in patients. Different communities have limited access to hospital facilities, as well as classical treatment of leishmaniasis; therefore, they use local natural products as alternative medicines to treat this infectious disease. The present work performed a bibliographic survey worldwide to record plants used by traditional communities to treat leishmaniasis, as well as the uses and peculiarities associated with each plant, which can guide future studies regarding the characterization of new drugs to treat leishmaniasis. A bibliographic survey performed in the PubMed and Scopus databases retrieved 294 articles related to traditional knowledge, medicinal plants and leishmaniasis; however, only 20 were selected based on the traditional use of plants to treat leishmaniasis. Considering such studies, 378 quotes referring to 292 plants (216 species and 76 genera) that have been used to treat leishmaniasis were recorded, which could be grouped into 89 different families. A broad discussion has been presented regarding the most frequent families, including Fabaceae (27 quotes), Araceae (23), Solanaceae and Asteraceae (22 each). Among the available data in the 378 quotes, it was observed that the parts of the plants most frequently used in local medicine were leaves (42.3% of recipes), applied topically (74.6%) and fresh poultices (17.2%). The contribution of Latin America to studies enrolling ethnopharmacological indications to treat leishmaniasis was evident. Of the 292 plants registered, 79 were tested against Leishmania sp. Future studies on leishmanicidal activity could be guided by the 292 plants presented in this study, mainly the five species Carica papaya L. (Caricaceae), Cedrela odorata L. (Meliaceae), Copaifera paupera (Herzog) Dwyer (Fabaceae), Musa × paradisiaca L. (Musaceae), and Nicotiana tabacum L. (Solanaceae), since they are the most frequently cited in articles and by traditional communities.