Sex-Specific Risk Factors Associated With First Acute Myocardial Infarction in Young Adults

Lu, Yuan; Li, Shuxia; Liu, Yuntian; Rodríguez, Fátima; Watson, Karol E.; Dreyer, Rachel P.; Khera, Rohan; Murugiah, Karthik; D’Onofrio, Gail; Spatz, Erica S.; Nasir, Khurram; Krumholz, Harlan M.

doi:10.1001/jamanetworkopen.2022.9953

Cited by 39 publications

(20 citation statements)

References 49 publications

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“…The patterns of risk and strength of association for cardiovascular events were slightly different between men and women and across the various age‐bands. This difference in the strength of association is consistent with prior work in both LMICs [29] and HICs [61]. A plausible explanation could be differences in the cardiovascular risk profile between men and women and between various age‐bands.…”

Section: Discussionsupporting

confidence: 90%

Risk factors for incident cardiovascular events and their population attributable fractions in rural India: The Rishi Valley Prospective Cohort Study

Birhanu,

Zengin,

Joshi

et al. 2024

Tropical Med Int Health

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ObjectiveWe prospectively determined incident cardiovascular events and their association with risk factors in rural India.MethodsWe followed up with 7935 adults from the Rishi Valley Prospective Cohort Study to identify incident cardiovascular events. Using Cox proportional hazards regression, we estimated hazard ratios (HRs) with 95% confidence intervals (95% CI) for associations between potential risk factors and cardiovascular events. Population attributable fractions (PAFs) for risk factors were estimated using R (‘averisk’ package).ResultsOf the 4809 participants without prior cardiovascular disease, 57.7% were women and baseline mean age was 45.3 years. At follow‐up (median of 4.9 years, 23,180 person‐years [PYs]), 202 participants developed cardiovascular events, equating to an incidence of 8.7 cardiovascular events/1000 PYs. Incidence was greater in those with hypertension (hazard ratio [HR] [95% CI] 1.73 [1.21–2.49], adjusted PAF 18%), diabetes (1.96 [1.15–3.36], 4%) or central obesity (1.77 [1.23, 2.54], 9%) which together accounted for 31% of the PAF. Non‐traditional risk factors such as night sleeping hours and number of children accounted for 16% of the PAF.ConclusionsBoth traditional and non‐traditional cardiovascular risk factors are important contributors to incident cardiovascular events in rural India. Interventions targeted to these factors could assist in reducing the incidence of cardiovascular events.

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Section: Discussionsupporting

confidence: 90%

Risk factors for incident cardiovascular events and their population attributable fractions in rural India: The Rishi Valley Prospective Cohort Study

Birhanu,

Zengin,

Joshi

et al. 2024

Tropical Med Int Health

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“…Ischaemic heart disease (IHD), manifested as a myocardial infarction (MI), represents the leading cause of death in women [ 124 ]. In patients under the age of 55, the association of hypertension, depression, diabetes, current smoking, a family history of diabetes, and younger age is stronger for MI in women than men [ 125 ]. However, a meta-analysis of 1.2 million participants and 95 cohorts showed an increased risk of coronary heart disease in obese individuals without significant sex heterogeneity [ 126 ].…”

Section: Sex Dimorphism and Obesity-related Cardiovascular Abnormalitiesmentioning

confidence: 99%

Sex Differences in Adiposity and Cardiovascular Diseases

Konja

Wang

et al. 2022

IJMS

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Body fat distribution is a well-established predictor of adverse medical outcomes, independent of overall adiposity. Studying body fat distribution sheds insights into the causes of obesity and provides valuable information about the development of various comorbidities. Compared to total adiposity, body fat distribution is more closely associated with risks of cardiovascular diseases. The present review specifically focuses on the sexual dimorphism in body fat distribution, the biological clues, as well as the genetic traits that are distinct from overall obesity. Understanding the sex determinations on body fat distribution and adiposity will aid in the improvement of the prevention and treatment of cardiovascular diseases (CVD).

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“…8 In addition, poor CKM health is associated with earlier onset of CVD. [81][82][83] Therefore, the approach in the PREVENT models incorporate age as a time scale, which also allows estimation of longer-term risk (eg, 30-year time horizon) and targeted prevention earlier in the life course. Thus, PRE-VENT addresses the fact that risk for CVD is not able to be calculated for those <40 years of age and is underestimated among younger individuals when relying only on short-term risk.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of the American Heart Association’s PREVENT Equations

Khan,

Matsushita,

Sang

et al. 2024

Circulation

128

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Background: Multivariable equations are recommended by primary prevention guidelines to assess absolute risk of cardiovascular disease (CVD). However, current equations have several limitations. Therefore, we developed and validated the AHA Predicting Risk of CVD EVENTs (PREVENT) equations among US adults aged 30-79 years without known CVD. Methods: The derivation sample included individual-level participant data from 25 datasets (N=3,281,919) between 1992-2017. The primary outcome was CVD (atherosclerotic CVD [ASCVD] and heart failure [HF]). Predictors included traditional risk factors (smoking status, systolic blood pressure, cholesterol, anti-hypertensive or statin use, diabetes) and estimated glomerular filtration rate [eGFR]. Models were sex-specific, race-free, developed on the age-scale, and adjusted for competing risk of non-CVD death. Analyses were conducted in each dataset and meta-analyzed. Discrimination was assessed using Harrell’s C-statistic. Calibration was calculated as the slope of the observed vs. predicted risk by decile. Additional equations to predict each CVD subtype (ASCVD, HF) and include optional predictors (urine albumin-to-creatinine ratio [UACR], hemoglobin A1c [HbA1c]), and social deprivation index [SDI]) were also developed. External validation was performed in 3,330,085 participants from 21 additional datasets. Results: Among 6,612,004 adults included, mean (SD) age was 53 (12) years and 56% were female. Over a mean (SD) follow-up of 4.8 (3.1) years, there were 211,515 incident total CVD events. The median C-statistics in external validation for CVD were 0.794 (interquartile interval [IQI]: 0.763-0.809) in female and 0.757 (0.727-0.778) in male participants. The calibration slopes were 1.03 (IQI 0.81 -1.16) and 0.94 (0.81-1.13) among females and males, respectively. Similar estimates for discrimination and calibration were observed for ASCVD- and HF-specific models. The improvement in discrimination was small but statistically significant when UACR, HbA1c, and SDI were added together to the base model to total CVD (ΔC-statistic [IQI] 0.004 [0.004, 0.005] and 0.005 [0.004, 0.007] among females and males, respectively). Calibration improved significantly when UACR was added to the base model among those with marked albuminuria (>300mg/g) (1.05 [0.84-1.20] vs. 1.39 [1.14-1.65], p=0.01). Conclusions: PREVENT equations accurately and precisely predicted risk for incident CVD and CVD subtypes in a large, diverse, and contemporary sample of US adults using routinely available clinical variables.

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Sex-Specific Risk Factors Associated With First Acute Myocardial Infarction in Young Adults

Abstract: This case-control study assesses the sex-specific associations of demographic, clinical, and psychosocial risk factors with first acute myocardial infarction among adults younger than 55 years, overall, and by infarction subtype.

Cited by 39 publications

References 49 publications

Risk factors for incident cardiovascular events and their population attributable fractions in rural India: The Rishi Valley Prospective Cohort Study

Risk factors for incident cardiovascular events and their population attributable fractions in rural India: The Rishi Valley Prospective Cohort Study

Sex Differences in Adiposity and Cardiovascular Diseases

Development and Validation of the American Heart Association’s PREVENT Equations

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