chen, zhuo

Background: Depression is prevalent in almost half of patients with cardiovascular disease (CVD) and is associated with CVD and mortality. We used an artificial intelligence algorithm using ECG that predicts physiological age (ECG-Age) that has been linked to increased total and CVD mortality and CVD risk factors. We hypothesized that depressive symptoms would be associated with physiologic aging, as measured by AI-ECG Methods: A previously validated algorithm was applied to determine ECG-Age to 105,732 consecutive ECGs of individuals age ≥18 who sought care at Mayo Clinic between June 2019 and January 2022 and filled the Patient Health Questionnaire-9 (PHQ-9) within a month of ECG. PHQ-9 ascertains severity and frequency of depressive symptoms with values from 0 - 27 and were categorized as (None≤4, Mild 5-9, Moderate 10-14, Moderately severe 15-19 and Severe ≥20). Delta-Age was defined as ECG-Age minus chronological age and accelerated aging was defined as a Delta-Age >1SD above the mean. Logistic regression analyses associated PHQ-9 scores with positive Delta-Age while adjusting for CVD risk factors Results: Mean chronological age was 57±17 yr and mean ECG-Age 58±14 yr (R2 0.85, P < 0.0001), Delta-Age 0.71±9.40 yr, 58% women, average PHQ-9 5±6, 61% had none, 20% mild, 10 % moderate, 5%moderately severe and 4% had severe depressive symptoms. Delta-Age was 2.10 years higher in those with depressive symptoms, reflecting accelerated aging. Compared to those with no depressive symptoms, those with severe symptoms were 1.5 times more likely to have Delta-Age >1SD (OR 1.52 CI 95%1.73-1.33), independently of CV risk factors and the Charlson comorbidity index. Figure. There is significant attenuation of the association once accounting for age, sex and CVD risk factors Conclusions: Depressive symptoms are associated with physiologic aging, highlighting the role of depression in physiologic aging and the potential of preventive strategies in patients with depression and CVD

Introduction: Metabolically obese normal weight (MONW) refers to metabolic abnormalities despite normal body mass index (BMI). Hypothesis: Because obesity is generally defined by BMI, it is assumed that people with MONW do not have excessive adiposity but still have the metabolic dysregulation associated with adiposity. Methods: We included participants aged 20-65 of the National Health and Nutrition Examination Surveys from 1999-2006 and 2011-2018 with normal BMI (18.5-25 kg/m 2 ), fasting blood samples, and DEXA data. According to the AHA criteria, MONW was defined having ≥3 of the following criteria, besides a normal BMI: Hypertriglyceridemia (≥ 150 mg/dL) or treatment for elevated triglycerides; HDL cholesterol level < 40 mg/dL in men or <50 mg/dL in women or treatment for reduced HDL-C; elevated blood pressure (systolic ≥ 130 mmHg and/or diastolic ≥ 85 mmHg) or antihypertensive treatment or history of hypertension; elevated fasting plasma glucose ≥ 100 mg/dL or insulin/oral hypoglycemic treatment and central obesity by waist circumference (≥ 94 cm in men and ≥ 80 cm in women). High fat percentage was defined as ≥25% in men, ≥35% in women. Weighted logistic regression was performed to test the relation between MONW and high fat%, central obesity. Results: Of the 7,887 participants (53.6% men; mean age±SE was 39.23±0.24 years), 2433 (33.4%) had normal BMI included in the analysis. Among these, 148 had MONW (5%). Mean Fat% was 26.08±0.36% in those with MONW versus 21.90±0.17% in those without MONW for men, and 37.02±0.35% versus 33.31±0.21% for women. MONW subjects were 2.21 times more likely to have either central obesity or high fat % (OR 12.09, 95%CI: 6.18-23.67, p<0.01) and 2.18 times more likely to have high fat % (OR=2.84, 95%CI: 1.72-4.67, p<0.01) (see Figure). Conclusions: Most individuals with MONW have either increased fat content, central deposition of fat, or both. That highlights people with normal BMI may still have metabolic complications of adiposity.

Background: The gap between electrocardiogram (ECG) artificial intelligence-derived age (ECG-Age) and chronological age (Age-Gap) predicts cardiovascular (CV) comorbidities and mortality reflecting physiologic age. Adverse childhood experiences (ACEs) are traumatic and harmful events that impact adulthood CV outcomes. We hypothesized that ACEs would be associated with physiologic age as determined by the Age-Gap. Methods: We applied a previously validated ECG-Age algorithm to the ECGs of 1,988 consecutive individuals aged ≥18, that within 2 years completed an ACEs questionnaire as part of psychological evaluations for weight management. The ACE questionnaire assesses 3 psychosocial domains (i) childhood abuse (psychological, physical or sexual), (ii) household dysfunction (substance abuse, mental illness, female figure treated violently, criminal behavior and parental separation/divorce) and (iii) neglect (emotional or physical) before age 18; results ranged 0 to 10 and were categorized as burden (0, 1-3, and ≥4) and presence (abuse, household dysfunction, neglect). Age-Gap was operationalized as (≤mean or > mean) positive values reflect physiological aging. Logistic regression analyses tested the association between ACE and Age-Gap. Results: Mean ± SD chronological age was 49±13 yr, ECG-Age 54±12 yr, Age-Gap 4.26±8.95 yr,75% women, average ACE 2±2, 32% experienced 0, 44% 1-3, and 24% ≥4 ACEs. Age-Gap was 1.4 years higher in those who experienced at least 1 ACE. Compared to those who didn’t experience ACE those who faced 1-3 ACE were more likely to have an Age-Gap >mean (OR 1.28 95%CI 1.57-1.04) independent of sex and the age-adjusted Charlson comorbidity index Fig A. Also, they were more prone to experiencing neglect and household dysfunction Fig B. Conclusions: Age-gap was associated with ACEs burden and domains, underscoring the need to identify ACEs during clinical evaluations to prevent ACEs at the individual, family, and community levels.