Assessment of late gadolinium enhancement in hypertrophic cardiomyopathy improves risk stratification based on current guidelines.

BACKGROUND AND AIMS: Identifying patients with hypertrophic cardiomyopathy (HCM) who are candidates for implantable cardioverter defibrillator (ICD) implantation in primary prevention for sudden cardiac death (SCD) is crucial. The aim of this study was to externally validate the 2022 European Society of Cardiology (ESC) model and other guideline-based ICD class of recommendation (ICD-COR) models and explore the utility of late gadolinium enhancement (LGE) in further risk stratification. METHODS: Seven hundred and seventy-four consecutive patients who underwent cardiac magnetic resonance imaging were retrospectively enrolled. RESULTS: Forty-six (5.9%) patients reached the SCD-related endpoint during 7.4 ± 2.5 years of follow-up. Patients suffering from SCD had higher ESC Risk-SCD score (4.3 ± 2.4% vs. 2.8 ± 2.1%, P < .001) and LGE extent (13.7 ± 9.4% vs. 4.9 ± 6.6%, P < .001). Compared with the 2014 ESC model, the 2022 ESC model showed increased area under the curve (.76 vs. .63), sensitivity (76.1% vs. 43.5%), positive predictive value (16.8% vs. 13.6%), and negative predictive value (98.1% vs. 95.9%). The C-statistics for SCD prediction of 2011 American College of Cardiology (ACC)/American Heart Association (AHA), 2014 ESC, 2020 AHA/ACC, and 2022 ESC models were .68, .64, .76 and .78, respectively. Furthermore, in patients without extensive LGE, LGE ≥5% was responsible for seven-fold SCD risk after multivariable adjustment. Whether in ICD-COR II or ICD-COR III, patients with LGE ≥5% and <15% showed significantly worse prognosis than those with LGE <5% (all P < .001). CONCLUSIONS: The 2022 ESC model performed better than the 2014 ESC model with especially improved sensitivity. LGE enabled further risk stratification based on current guidelines.

Association of the triglyceride glucose index as a measure of insulin resistance with mortality and cardiovascular disease in populations from five continents (PURE study): a prospective cohort study.

BACKGROUND: The triglyceride glucose (TyG) index is an easily accessible surrogate marker of insulin resistance, an important pathway in the development of type 2 diabetes and cardiovascular diseases. However, the association of the TyG index with cardiovascular diseases and mortality has mainly been investigated in Asia, with few data available from other regions of the world. We assessed the association of insulin resistance (as determined by the TyG index) with mortality and cardiovascular diseases in individuals from five continents at different levels of economic development, living in urban or rural areas. We also examined whether the associations differed according to the country's economical development. METHODS: We used the TyG index as a surrogate measure for insulin resistance. Fasting triglycerides and fasting plasma glucose were measured at the baseline visit in 141 243 individuals aged 35-70 years from 22 countries in the Prospective Urban Rural Epidemiology (PURE) study. The TyG index was calculated as Ln (fasting triglycerides [mg/dL] x fasting plasma glucose [mg/dL]/2). We calculated hazard ratios (HRs) using a multivariable Cox frailty model with random effects to test the associations between the TyG index and risk of cardiovascular diseases and mortality. The primary outcome of this analysis was the composite of mortality or major cardiovascular events (defined as death from cardiovascular causes, and non-fatal myocardial infarction, or stroke). Secondary outcomes were non-cardiovascular mortality, cardiovascular mortality, all myocardial infarctions, stroke, and incident diabetes. We also did subgroup analyses to examine the magnitude of associations between insulin resistance (ie, the TyG index) and outcome events according to the income level of the countries. FINDINGS: During a median follow-up of 13·2 years (IQR 11·9-14·6), we recorded 6345 composite cardiovascular diseases events, 2030 cardiovascular deaths, 3038 cases of myocardial infarction, 3291 cases of stroke, and 5191 incident cases of type 2 diabetes. After adjusting for all other variables, the risk of developing cardiovascular diseases increased across tertiles of the baseline TyG index. Compared with the lowest tertile of the TyG index, the highest tertile (tertile 3) was associated with a greater incidence of the composite outcome (HR 1·21; 95% CI 1·13-1·30), myocardial infarction (1·24; 1·12-1·38), stroke (1·16; 1·05-1·28), and incident type 2 diabetes (1·99; 1·82-2·16). No significant association of the TyG index was seen with non-cardiovascular mortality. In low-income countries (LICs) and middle-income countries (MICs), the highest tertile of the TyG index was associated with increased hazards for the composite outcome (LICs: HR 1·31; 95% CI 1·12-1·54; MICs: 1·20; 1·11-1·31; pinteraction=0·01), cardiovascular mortality (LICs: 1·44; 1·15-1·80; pinteraction=0·01), myocardial infarction (LICs: 1·29; 1·06-1·56; MICs: 1·26; 1·10-1·45; pinteraction=0·08), stroke (LICs: 1·35; 1·02-1·78; MICs: 1·17; 1·05-1·30; pinteraction=0·19), and incident diabetes (LICs: 1·64; 1·38-1·94; MICs: 2·68; 2·40-2·99; pinteraction <0·0001). In contrast, in high-income countries, higher TyG index tertiles were only associated with an increased hazard of incident diabetes (2·95; 2·25-3·87; pinteraction <0·0001), but not of cardiovascular diseases or mortality. INTERPRETATION: The TyG index is significantly associated with future cardiovascular mortality, myocardial infarction, stroke, and type 2 diabetes, suggesting that insulin resistance plays a promoting role in the pathogenesis of cardiovascular and metabolic diseases. Potentially, the association between the TyG index and the higher risk of cardiovascular diseases and type 2 diabetes in LICs and MICs might be explained by an increased vulnerability of these populations to the presence of insulin resistance. FUNDING: Full funding sources are listed at the end of the paper (see Acknowledgments).

Socioeconomic status and risk of cardiovascular disease in 20 low-income, middle-income, and high-income countries: the Prospective Urban Rural Epidemiologic (PURE) study.

BACKGROUND: Socioeconomic status is associated with differences in risk factors for cardiovascular disease incidence and outcomes, including mortality. However, it is unclear whether the associations between cardiovascular disease and common measures of socioeconomic status-wealth and education-differ among high-income, middle-income, and low-income countries, and, if so, why these differences exist. We explored the association between education and household wealth and cardiovascular disease and mortality to assess which marker is the stronger predictor of outcomes, and examined whether any differences in cardiovascular disease by socioeconomic status parallel differences in risk factor levels or differences in management. 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).