Is there an increased cardiovascular risk in metabolically healthy obese individuals? Lessons from the HUNT (Nord-Trøndelag Health) study

Metabolic syndrome and associated factors among severely ill psychiatric and non-psychiatric patients: a comparative cross-sectional study in Eastern Ethiopia

BACKGROUND

Metabolic syndrome is a major public health challenge in both developed and developing countries. The burden of this disease is high, even in patients with psychiatric disorders. However, very little is known about the association between metabolic syndrome and psychiatric illness in Ethiopia. Therefore, the aim of this study was to investigate the magnitude of metabolic syndrome and its components among psychiatric clients.

METHODS

A comparative cross-sectional study was undertaken between psychiatric patients and age-and sex-matched non-psychiatric controls at the Dilchora referral hospital. The study included 192 study participants (96 psychiatric patients and 96 non- psychiatric controls from general medical and surgical patients). The National Cholesterol Education Program: Adult Treatment Panel III criteria were used to diagnose metabolic syndromes. The data were cleaned and analyzed using the Statistical Package for Social Sciences, Version 21. All intergroup comparisons for continuous data were performed using an independent sample t-test, whereas categorical data were analyzed using the Chi-square test. Logistic regression analysis was used to identify the association between metabolic syndrome and the associated variables.

RESULTS

The magnitude of metabolic syndrome among psychiatric patients was 36.5% (95%CI: 27.6, 47.4) compared to non-psychiatric control patients, 21.9% (95%CI: 13.5, 30.3), p = 0.02. The prevalence of MetS components, such as waist circumference (25.0% vs. 14.3%), lower-high density lipoprotein level (35.4% vs. 20.8%), higher systolic blood pressure (41.7% vs. 29.2%) and higher fasting blood glucose (40.6% vs. 18.8%) showed statistically significant differences between the exposed and non-exposed groups. Age greater than 50 years (AOR: 2.8, CI: 1.14, 20.0, p < 0.05); being female (AOR: 7.4, CI: 2.0, 27.6, p < 0.05), being urban residence (AOR: 6.4, CI: 2.2, 20.6, p < 0.05), ever alcohol intake (AOR: 5.3, CI: 1.3, 21.2), being physically inactive (AOR: 3.52, CI: 1.1, 12.9, p < 0.05) and family history of hypertension (AOR: 2.52, CI: 1.1, 12.2, p < 0.05) were independent predictors of metabolic syndrome (p < 0.05).

CONCLUSIONS

There is a high burden of metabolic syndrome and its components in patients with severe psychiatric disorders. Therefore, screening and mitigation strategies for metabolic syndrome and their components should be implemented in the management of psychiatric disorders.

Changes in Obesity Phenotype Distribution in Mixed-ancestry South Africans in Cape Town Between 2008/09 and 2014/16

Background: The concept of obesity phenotypes encompasses a different approach to evaluating the relationship between obesity and cardiometabolic diseases. Considering the minimal research on obesity phenotypes in Africa, we investigated these changes from 2008/09 to 2014/16 in the mixed ancestry population in Cape Town, South Africa. Methods: In all, 928 (2008/09) and 1969 (2014/16) ≥20 year old participants were included in two community-based cross-sectional studies. For obesity phenotype classification, a combination of body mass index (BMI) categories and prevalent cardiometabolic disease risk factors were used, with the presence of ≥2 cardiometabolic abnormalities defining abnormal metabolic status. Interaction tests were used to investigate changes in their distribution across the years of study. Results: Distribution of BMI categories differed significantly between the 2 years; normal weight, overweight and obese: 27.4, 27.4, and 45.3% in 2008/09 vs. 34.2, 23.6, and 42.2% in 2014/16 (p = 0.001). There was no differential effect in the distribution of obesity phenotypes pattern across the two time-points (interaction p = 0.126). Across BMI categories, levels of cardiometabolic risk factors linearly deteriorated in both metabolically healthy and abnormal participants (all p ≤ 0.018 for linear trends). Findings were not sensitive to the number of metabolic abnormalities included in the definition of obesity phenotypes. Conclusions: Our study showed negligible differences in obesity phenotypes over time, but a high burden of metabolic abnormalities among normal weight participants, and a significant proportion of metabolically health obese individuals. Further investigation is needed to improve risk stratification and cost-effective identification of individuals at high risk for cardiometabolic diseases.

Long-term metabolic risk for the metabolically healthy overweight/obese phenotype

BACKGROUND/OBJECTIVES

The clinical relevance of the metabolically healthy overweight/obese (MHO) phenotype is controversial and the relationships between weight change and the development of cardiometabolic risk factors is unknown. Therefore, we aim to: (1) Assess the long-term risk of developing one or more components of the metabolic syndrome in MHO adults compared with metabolically healthy normal weight (MHNW); (2) Evaluate risk of a composite of death, cardiovascular disease (CVD), and risk of developing type 2 diabetes between adults defined according to baseline body mass index and metabolic health.

SUBJECTS/METHODS

Retrospective cohort study of adults 18-65 years of age seen at our institution between 1998 and 2000 who lived in Olmsted County. Metabolically healthy was defined as the absence of all components of the metabolic syndrome (except for waist circumference). Main outcome was the development of metabolic risk factors. The secondary outcome was a composite of mortality, CVD and heart failure.

RESULTS

Of the 18 070 individuals with complete data at baseline, 1805 (10%) were MHO (mean age 38±11 years) and 3047 were MHNW (mean age 35±11 years). After a median follow-up of 15 years, interquartile range 10-17, 80% of MHO vs 68% of MHNW developed at least one cardiometabolic risk factor (P<0.001). In multivariate analysis, MHO individuals who gained ⩾10% of their body weight were more likely to have developed metabolic complications compared to MHO individuals that did not gain weight (P=0.001 for 10-15%, P<0.001 for >15% weight gain). The risk for the secondary composite end point was similar between MHO and MHNW, number of events 218/1805 vs 217/3048 for MHO and MHNW, respectively, (hazard ratio: 1.16, 95% confidence interval: 0.96-1.40).

CONCLUSIONS

MHO are more likely to develop metabolic complications than MHNW, especially if they gain weight.

Obesity, metabolic syndrome and cardiovascular prognosis: from the Partners coronary computed tomography angiography registry

Objective

To investigate the relationship among body mass index (BMI), cardiometabolic risk and coronary artery disease (CAD) among patients undergoing coronary computed tomography angiography (CTA).

Methods

Retrospective cohort study of 1118 patients, who underwent coronary CTA at two centers from September 2004 to October 2011. Coronary CTA were categorized as normal, nonobstructive CAD (<50%), or obstructive CAD (≥50%) in addition to segment involvement (SIS) and stenosis scores. Extensive CAD was defined as SIS > 4. Association of BMI with cardiovascular prognosis was evaluated using multivariable fractional polynomial models.

Results

Mean age of the cohort was 57 ± 13 years with median follow-up of 3.2 years. Increasing BMI was associated with MetS (OR 1.28 per 1 kg/m², p < 0.001) and burden of CAD on a univariable basis, but not after multivariable adjustment. Prognosis demonstrated a J-shaped relationship with BMI. For BMI from 20–39.9 kg/m², after adjustment for age, gender, and smoking, MetS (HR 2.23, p = 0.009) was more strongly associated with adverse events.

Conclusions

Compared to normal BMI, there was an increased burden of CAD for BMI > 25 kg/m². Within each BMI category, metabolically unhealthy patients had greater extent of CAD, as measured by CCTA, compared to metabolically healthy patients.

Stroke risk assessment in atrial fibrillation: risk factors and markers of atrial myopathy

Atrial fibrillation (AF) is a complex phenomenon associated with electrical, mechanical, and structural abnormalities of the atria. Ischaemic stroke in AF is only partially understood, but the mechanisms are known to be related to the atrial substrate as well as the atrial rhythm. The temporal dissociation between timing of AF and occurrence of stroke has led to the hypothesis that fibrotic, prothrombotic atrial tissue is an important cause of thrombus formation in patients with AF, independent of the atrial rhythm. Current stroke risk scores are practical, but limited in their capacity to predict stroke risk accurately in individual patients. Stroke prediction might be improved by the addition of emerging risk factors, many of which are expressions of atrial fibrosis. The use of novel parameters, including clinical criteria, biomarkers, and imaging data, might improve stroke risk prediction and inform on optimal treatment for patients with AF and perhaps individuals only at risk of AF.

Obesity, metabolic abnormality, and health-related quality of life by gender: a cross-sectional study in Korean adults

PURPOSE

This study sought to compare the association between health-related quality of life (HRQoL) and four body health types by gender.

METHODS

The study included 6217 men and 8243 women over 30 years of age chosen from a population-based survey. Participants were grouped by body mass index and metabolic abnormality into four types: metabolically healthy normal weight, metabolically abnormal but normal weight (MANW), metabolically healthy obesity (MHO), and metabolically abnormal obesity (MAO). HRQoL was measured using the EQ-5D health questionnaire. The outcomes encompassed five dimensions (mobility, self-care, usual activity, pain/discomfort, and anxiety/depression), and the impaired HRQoL dichotomized by the EQ-5D preference score. Complex sample multivariate binary logistic regression analyses were conducted to adjust for sociodemographic variables, lifestyle factors, and disease comorbidity.

RESULTS

Among men, those in the MANW group presented worse conditions on all dimensions and the impaired HRQoL compared to other men. However, no significant effect remained after adjusting for relevant covariates. For women, those in the MAO group had the most adversely affected HRQoL followed by those females in the MHO group. The domain of mobility and impaired HRQoL variable of the MAO and MHO groups remained significant when controlling for all covariates in the model.

CONCLUSIONS

The MANW is the least favorable condition of HRQoL for men, suggesting that metabolic health may associate with HRQoL more than obesity for males. In women, the MAO and MHO groups had the most adversely affected HRQoL, implying that MHO is not a favorable health condition and that obesity, in general, may be strongly associated with HRQoL in women.