METHODS: In this large-scale prospective cohort study, we recruited adults aged between 35 years and 70 years from 367 urban and 302 rural communities in 20 countries. We collected data on families and households in two questionnaires, and data on cardiovascular risk factors in a third questionnaire, which was supplemented with physical examination. We assessed socioeconomic status using education and a household wealth index. Education was categorised as no or primary school education only, secondary school education, or higher education, defined as completion of trade school, college, or university. Household wealth, calculated at the household level and with household data, was defined by an index on the basis of ownership of assets and housing characteristics. Primary outcomes were major cardiovascular disease (a composite of cardiovascular deaths, strokes, myocardial infarction, and heart failure), cardiovascular mortality, and all-cause mortality. Information on specific events was obtained from participants or their family.
FINDINGS: Recruitment to the study began on Jan 12, 2001, with most participants enrolled between Jan 6, 2005, and Dec 4, 2014. 160 299 (87·9%) of 182 375 participants with baseline data had available follow-up event data and were eligible for inclusion. After exclusion of 6130 (3·8%) participants without complete baseline or follow-up data, 154 169 individuals remained for analysis, from five low-income, 11 middle-income, and four high-income countries. Participants were followed-up for a mean of 7·5 years. Major cardiovascular events were more common among those with low levels of education in all types of country studied, but much more so in low-income countries. After adjustment for wealth and other factors, the HR (low level of education vs high level of education) was 1·23 (95% CI 0·96-1·58) for high-income countries, 1·59 (1·42-1·78) in middle-income countries, and 2·23 (1·79-2·77) in low-income countries (pinteraction<0·0001). We observed similar results for all-cause mortality, with HRs of 1·50 (1·14-1·98) for high-income countries, 1·80 (1·58-2·06) in middle-income countries, and 2·76 (2·29-3·31) in low-income countries (pinteraction<0·0001). By contrast, we found no or weak associations between wealth and these two outcomes. Differences in outcomes between educational groups were not explained by differences in risk factors, which decreased as the level of education increased in high-income countries, but increased as the level of education increased in low-income countries (pinteraction<0·0001). Medical care (eg, management of hypertension, diabetes, and secondary prevention) seemed to play an important part in adverse cardiovascular disease outcomes because such care is likely to be poorer in people with the lowest levels of education compared to those with higher levels of education in low-income countries; however, we observed less marked differences in care based on level of education in middle-income countries and no or minor differences in high-income countries.
INTERPRETATION: Although people with a lower level of education in low-income and middle-income countries have higher incidence of and mortality from cardiovascular disease, they have better overall risk factor profiles. However, these individuals have markedly poorer health care. Policies to reduce health inequities globally must include strategies to overcome barriers to care, especially for those with lower levels of education.
FUNDING: Full funding sources are listed at the end of the paper (see Acknowledgments).
METHODS: Reverse-transcription-polymerase chain reaction was employed to measure the expression of plasmacytoma variant translocation 1 (PVT1), microRNAs (miRNAs), and SIRT3, and the dual-luciferase assay was used to determine their interaction. Electron microscopy observes autophagosomes, green fluorescent protein-microtubule-associated protein 1 light chain 3 (GFP-LC3) staining, and immunoblot analysis with antibodies against LC3，beclin-1, and P62 were conducted to measure autophagy. Cellular senescence was determined using immunoblot analysis with anti-phosphorylated retinoblastoma and senescence-associated β-galactosidase staining.
RESULTS: Women with higher estrogen levels (during the 10-13th day of the menstrual cycle or premenopausal) exhibit markedly higher serum levels of PVT1 than women with lower estrogen levels (during the menstrual period or postmenopausal). The dual-luciferase assay showed that PVT1 acts as a sponge for miR-31, and miR-31 binds to its target gene, SIRT3. The 17β-E2 treatment increased the expression of PVT1 and SIRT3 and downregulated miR-31 expression in human umbilical vein endothelial cells (HUVECs). Consistently, PVT1 overexpression suppresses miR-31 expression, promotes 17β-E2-induced autophagy, and inhibits H2O2-induced senescence. miR-31 inhibitor increases SIRT3 expression and leads to activation of 17β-E2-induced autophagy and suppression of H2O2-induced senescence.
CONCLUSION: Our findings demonstrated that 17β-E2 upregulates PVT1 gene expression and PVT1 functions as a sponge to inhibit miR-31, resulting in the upregulation of SIRT3 expression and activation of autophagy and subsequent inhibition of H2O2-induced senescence in HUVECs.