Impact of common cardio-metabolic risk factors on fatal and non-fatal cardiovascular disease in Latin America and the Caribbean: an individual-level pooled analysis of 31 cohort studies

Background: Estimates of the burden of cardio-metabolic risk factors in Latin America and the Caribbean (LAC) rely on relative risks (RRs) from non-LAC countries. Whether these RRs apply to LAC remains un- known. Methods: We pooled LAC cohorts. We estimated RRs per unit of exposure to body mass index (BMI), systolic blood pressure (SBP), fasting plasma glucose (FPG), total cholesterol (TC) and non-HDL cholesterol on fatal (31 cohorts, n = 168,287) and non-fatal (13 cohorts, n = 27,554) cardiovascular diseases, adjusting for regression dilution bias. We used these RRs and national data on mean risk factor levels to estimate the number of cardiovascular deaths attributable to non-optimal levels of each risk factor. Results: Our RRs for SBP, FPG and TC were like those observed in cohorts conducted in high-income countries; however, for BMI, our RRs were consistently smaller in people below 75 years of age. Across risk factors, we observed smaller RRs among older ages. Non-optimal SBP was responsible for the largest number of attributable cardiovascular deaths ranging from 38 per 10 0,0 0 0 women and 54 men in Peru, to 261 (Dominica, women) and 282 (Guyana, men). For non-HDL cholesterol, the lowest attributable rate was for women in Peru (21) and men in Guatemala (25), and the largest in men (158) and women (142) from Guyana. Interpretation: RRs for BMI from studies conducted in high-income countries may overestimate disease burden metrics in LAC; conversely, RRs for SBP, FPG and TC from LAC cohorts are similar to those esti- mated from cohorts in high-income countries

Retinopatia hipertensiva: revisão Hypertensive retinopathy: review

O presente estudo faz uma revisão do tema retinopatia hipertensiva. Para tanto propôs-se uma breve revisão dos dados históricos da retinopatia hipertensiva. Este estudo relata as alterações clássicas da retinopatia hipertensiva e suas classificações, bem como os achados mais recentes associados à hipertensão arterial sistêmica, os prováveis mecanismos fisiopatológicos e as alterações histológicas associadas à retinopatia hipertensiva. Abordamos, ainda, os diversos métodos utilizados para a investigação, suas vantagens e desvantagens; uma visão crítica da interpretação dos sinais do envolvimento do bulbo ocular pela hipertensão arterial sistêmica; ainda, baseado na diversidade das metodologias de investigação da retinopatia, comenta-se a repercussão desta, na prevalência da retinopatia hipertensiva e suas implicações, como órgão-alvo da hipertensão arterial sistêmica, em um contexto atualizado da síndrome metabólica e de outros fatores associados à fisiopatologia da HAS, como a leptina e a endotelina.<br>The present study carries out a review of the theme hypertensive retinopathy. Thus it presents a brief review of the historical data on hypertensive retinopathy. The study reports the classical alterations of hypertensive retinopathy and its classifications, as well as the most recent findings associated with systemic arterial hypertension, the likely patho-physiological mechanisms, and the several methods used for investigation, their advantages and disadvantages; a critical view of the interpretation of signs of the ocular bulb involvement by systemic arterial hypertension; furthermore, based on the diversity of methodologies used in the investigation of retinopathy, comments are made on its reverberation, in the prevalence of hypertensive retinopathy and its implications, as a target organ of systemic arterial hypertension, in an updated context of the metabolic syndrome and of other elements associated with systemic arterial hypertension, such as leptin and endothelin

Association of PvuII and XbaI polymorphisms on estrogen receptor alpha (ESR1) gene to changes into serum lipid profile of post-menopausal women: Effects of aging, body mass index and breast cancer incidence - Fig 2

<p>A: Linear regression graph comparing serum Total Lipids and age. Each lines slopes are significantly different (p = 0.015). Goodness of fit (r²): XX = 0.097; Xx = 0.007. B: Linear regression graph comparing serum Triglycerides and age. Each lines slopes are significantly different (p<0.05). Goodness of fit (r²): XX = 0.060; Xx = 0.002. C: Linear regression graph comparing serum Total Lipid and BMI. Each lines slopes are not significantly different (p>0.05). Goodness of fit (r²): XX = 0.072; Xx = 0.004. D: Linear regression graph comparing serum triglycerides and BMI. Each lines slopes are significantly different (p<0.05). Goodness of fit (r²): XX = 0.106; Xx = 0.017.</p

Serum lipid profile for each genotype.

<p>Serum lipid profile for each genotype.</p

Population characteristics.

<p>Population characteristics.</p