Prevalence and correlates of normal body mass index central obesity among people with cardiovascular diseases in Australia

Association of combined body mass index and central obesity with cardiovascular disease in middle-aged and older adults: a population-based prospective cohort study

BACKGROUND

Cardiovascular diseases (CVDs) pose a significant threat to public health. Evidence indicates that the combination of central obesity and normal body mass index (BMI) is associated with an increased risk of cardiovascular disease and mortality. However, limited evidences exists in middle aged and elderly adults in China.

METHODS

This was a prospective cohort study that utilized a nationally representative sample of 6,494 adults aged 45 years and above. These individuals participated in the China Health and Retirement Longitudinal Study spanning from 2011 to 2018. Height, weight and waist circumference (WC) were measured, and BMI was calculated by height and weight. Other variables were obtained through self-reported questionnaires. Association analysis was conducted using Cox proportional hazard regression models.

RESULTS

A total of 10,186 participants were investigated, with 57,185 person-years of follow-up. During this period, 1,571 CVDs occurred, including 1,173 heart diseases and 527 strokes. After adjusting for various factors including age, gender, education, marital status, smoking status, alcohol intake, social activity, hypertension, dyslipidemia, diabetes, cancer, chronic lung diseases, liver disease, kidney disease, digestive disease, ENP(emotional, nervous, or psychiatric problems), memory related disease, arthritis or rheumatism, asthma, self-rated health and depression, the results revealed that compared to those with normal WC normal body mass index (BMI), individuals with central obesity normal BMI had a 27.9% higher risk of CVD incidence (95% confidence interval [CI]:1.074-1.524), and a 33.4% higher risk of heart disease incidence (95% CI:1.095-1.625), while no significant association was found with stroke. Additionally, those with normal WC high BMI showed a 24.6% higher risk of CVD incidence (95% CI:1.046-1.483), and a 29.1% higher risk of heart disease incidence (95% CI:1.045-1.594), again with no significant association with stroke. Finally, individuals with central obesity high BMI exhibited a 49.3% higher risk of CVD incidence (95% CI:1.273-1.751), a 61% higher risk of heart disease incidence (95% CI:1.342-1.931), and a 34.2% higher risk of stroke incidence (95% CI:1.008-1.786). Age- and sex- specific analyses further revealed varying trends in these associations.

CONCLUSIONS

We discovered that the combined association of body mass index(BMI) and central obesity with CVD incidence exhibited a significantly enhanced predictive value. Specifically, a high BMI with central obesity was notably linked to an increased risk of CVD incidence. Additionally, central obesity with a normal BMI or a normal WC coupled with a high BMI significantly augmented the risk of heart disease incidence, but not stroke. Notably, male and middle-aged adults demonstrated a greater propensity for heart disease incidence. Our study underscores the importance of maintaining an optimal BMI and preventing abdominal obesity in promoting cardiovascular health.

Ten-Year Risk Equations for Incident Heart Failure in Established Atherosclerotic Cardiovascular Disease Populations

BACKGROUND

Ten-year risk equations for incident heart failure (HF) are available for the general population, but not for patients with established atherosclerotic cardiovascular disease (ASCVD), which is highly prevalent in HF cohorts. This study aimed to develop and validate 10-year risk equations for incident HF in patients with known ASCVD.

METHODS AND RESULTS

Ten-year risk equations for incident HF were developed using the United Kingdom Biobank cohort (recruitment 2006-2010) including participants with established ASCVD but free from HF at baseline. Model performance was validated using the Australian Baker Heart and Diabetes Institute Biobank cohort (recruitment 2000-2011) and compared with the performance of general population risk models. Incident HF occurred in 13.7% of the development cohort (n=31 446, median 63 years, 35% women, follow-up 10.7±2.7 years) and in 21.3% of the validation cohort (n=1659, median age 65 years, 25% women, follow-up 9.4±3.7 years). Predictors of HF included in the sex-specific models were age, body mass index, systolic blood pressure (treated or untreated), glucose (treated or untreated), cholesterol, smoking status, QRS duration, kidney disease, myocardial infarction, and atrial fibrillation. ASCVD-HF equations had good discrimination and calibration in development and validation cohorts, with superior performance to general population risk equations.

CONCLUSIONS

ASCVD-specific 10-year risk equations for HF outperform general population risk models in individuals with established ASCVD. The ASCVD-HF equations can be calculated from readily available clinical data and could facilitate screening and preventative treatment decisions in this high-risk group.

Cardiometabolic risk stratification using a novel obesity phenotyping system based on body adiposity and waist circumference

BACKGROUND

The estimation of obesity-associated cardiometabolic risk does not usually take into account body composition or the distribution of adiposity. The aim of the present study was to assess the clinical usefulness of a novel obesity phenotyping system based on the combination of actual body fat percentage (BF%) and waist circumference (WC) according to the cardiometabolic risk estimation.

METHODS

A classification matrix combining BF% and WC as measures of both amount and distribution of adiposity establishing nine body phenotypes (3 BF% x 3 WC) was developed. Individuals were grouped in five different cardiometabolic risk phenotypes. We conducted a validation study in a large cohort of White subjects from both genders representing a wide range of ages and adiposity (n = 12,754; 65 % females, aged 18-88 years).

RESULTS

The five risk groups using the matrix combination of BF% and WC exhibited a robust linear distribution regarding cardiometabolic risk, estimated by the Metabolic Syndrome Severity Score, showing a continuous increase between groups with significant differences (P < 0.001) among them, as well as in other cardiometabolic risk factors. An additional 24 % of patients at very high risk was detected with the new classification system proposed (P < 0.001) as compared to an equivalent matrix using BMI and WC instead of BF% and WC.

CONCLUSIONS

A more detailed phenotyping should be a priority in the diagnosis and management of patients with obesity. Our classification system allows to gradually estimate the cardiometabolic risk according to BF% and WC, thus representing a novel and useful tool for both research and clinical practice.

Differences in predictors of incident heart failure according to atherosclerotic cardiovascular disease status

AIMS

Heart failure (HF) is a common cause of morbidity and mortality, related to a broad range of sociodemographic, lifestyle, cardiometabolic, and comorbidity risk factors, which may differ according to the presence of atherosclerotic cardiovascular disease (ASCVD). We assessed the association between incident HF with baseline status across these domains, overall and separated according to ASCVD status.

METHODS AND RESULTS

We included 5758 participants from the Baker Biobank cohort without HF at baseline enrolled between January 2000 and December 2011. The primary endpoint was incident HF, defined as hospital admission or HF-related death, determined through linkage with state-wide administrative databases (median follow-up 12.2 years). Regression models were fitted adjusted for sociodemographic variables, alcohol intake, smoking status, measures of adiposity, cardiometabolic profile measures, and individual comorbidities. During 65 987 person-years (median age 59 years, 38% women), incident HF occurred among 784 participants (13.6%) overall. Rates of incident HF were higher among patients with ASCVD (624/1929, 32.4%) compared with those without ASCVD (160/3829, 4.2%). Incident HF was associated with age, socio-economic status, alcohol intake, smoking status, body mass index (BMI), waist circumference, waist-hip ratio, systolic blood pressure (SBP), and low- and high-density lipoprotein cholesterol (LDL-C and HDL-C), with non-linear relationships observed for age, alcohol intake, BMI, waist circumference, waist-hip ratio, SBP, LDL-C, and HDL-C. Risk factors for incident HF were largely consistent regardless of ASCVD status, although diabetes status had a greater association with incident HF among patients without ASCVD.

CONCLUSIONS

Incident HF is associated with a broad range of baseline sociodemographic, lifestyle, cardiometabolic, and comorbidity factors, which are mostly consistent regardless of ASCVD status. These data could be useful in efforts towards developing risk prediction models that can be used in patients with ASCVD.

Prognostic Value of Arm Circumference for Cardiac Damage and Major Adverse Cardiovascular Events: A Friend or a Foe? A 2-Year Follow-Up in the Northern Shanghai Study

Background

The high prevalence of cardiovascular diseases globally causes a great social burden and much individual suffering. The effective recognition of high-risk subjects is critical for primary prevention in the general population. In the elderly cohort, anthropometric measurements may have different prognostic values. Our study aimed to find convincing anthropometric measures to supplement conventional risk factors for major adverse cardiovascular events (MACEs) in the elderly cohort.

Materials and Methods

A total of 1,576 elderly participants (44.5% male, aged 72.0 ± 6.0 years) recruited into the Northern Shanghai Study (2014–2015) were followed up between 2016 and 2017. Following the standard guideline for cardiovascular risk evaluation, all conventional cardiovascular risk factors were assessed. The body measures were made up of body weight, body height, hip circumference, waist circumference, and middle-upper arm circumference (MUAC). Organ damage (OD) markers for cardiac, vascular, and renal diseases will be evaluated by the standardized methods.

Results

After the average 571 (±135) days of follow-up, a total of 90 MACEs (5.7%) occurred, i.e., 13 non-fatal myocardial infarction, 68 non-fatal stroke, and 9 cardiovascular deaths. Univariable COX survival analysis revealed that only MUAC could validly predict MACEs among anthropometric characters [adjusted hazard ratio (HR) 0.89; 95% confidence interval (CI) 0.82–0.96]. In Kaplan-Meier analysis, the group of high MUAC showed the lowest MACE risk (log-rank p = 0.01). Based on OD analysis, MUAC was independently linked to higher risk of left ventricular hypertrophy (LVH) in women and left ventricular diastolic dysfunction (LVDD) in both men and women. In adjusted COX analysis, only MUAC indicated statistical significance, but all other anthropometric parameters such as BMI, waist circumference, and waist-to-hip ratio (WHR) did not indicate significance. The higher level of MUAC remained a protective factor in fully adjusted models (HR: 0.73; 95% CI: 0.59–0.91), with p-values markedly significant in men (HR: 0.69; 95% CI: 0.49–0.97) and marginally significant in women (HR: 0.0.77; 95% CI: 0.59–1.01). After considering all factors (i.e., cardiovascular risk factors, MUAC, BMI, and WHR), the fully adjusted COX regression analysis demonstrated that the increased MUAC level was linked to decreased MACE risk in both men (HR: 0.57; 95% CI: 0.37–0.88) and women (aHR: 0.64; 95% CI: 0.46–0.93).

Conclusion

Despite being associated with a higher rate of cardiac damage, higher MUAC independently and significantly conferred protection against the MACE, in the elderly cohort.

Prevalence of central obesity according to different definitions in normal weight adults of two cross-sectional studies in Panama

Background

Central Obesity (CO) might arise among individuals with normal body mass index (BMI). We aim to estimate the prevalence of Normal Weight CO (NWCO), using different definitions, and to compare its association with cardiometabolic risk factors in the adult population of Panama.

Methods

Data from two population-based studies conducted in Panama in 2010 and 2019 were used. Using standard definitions, normal weight was defined as a BMI between 18·5 and 24·9 while CO was defined as a Waist-to-Height Ratio (WHtR) ≥ 0·5 in both sexes or a Waist Circumference (WC) ≥ 90, ≥94, or ≥102 cm for men, and 80 or 88 cm for women. Unconditional logistic regression models were used to estimate the association between each CO definition and dyslipidemia, high blood pressure (HBP), diabetes, and clusters of cardiovascular risk factors.

Findings

Recent CO prevalence ranged between 3·9% (WC ≥ 102 cm for men and WC ≥ 88 cm for women) and 43·9% (WHtR ≥ 0·5) among individuals classify as normal weigh according to the BMI. Different cardiovascular risk factors were present in this normal weight population but among men the threshold of WC ≥ 102 cm screened less than 1·0%.

Interpretation

NWCO was associated with cardiovascular risk factors, particularly with elevated concentration of triglycerides. CO evaluation at the primary health care level may be a useful technique to identify normal weight people with metabolically obese characteristics.

Funding

Gorgas Memorial Institute for Health studies via Ministry of Economy and Finance of Panama and Inter-American Development Bank.

The Weight Problem: Overview of the Most Common Concepts for Body Mass and Fat Distribution and Critical Consideration of Their Usefulness for Risk Assessment and Practice

The prevalence of obesity already reached epidemic proportions many years ago and more people may die from this pandemic than from COVID-19. However, the figures depend on which measure of fat mass is used. The determination of the associated health risk also depends on the applied measure. Therefore, we will examine the most common measures for their significance, their contribution to risk assessment and their applicability. The following categories are reported: indices of increased accumulation of body fat; weight indices and mortality; weight indices and risk of disease; normal weight obesity and normal weight abdominal obesity; metabolically healthy obesity; the obesity paradox. It appears that BMI is still the most common measure for determining weight categories, followed by measures of abdominal fat distribution. Newer measures, unlike BMI, take fat distribution into account but often lack validated cut-off values or have limited applicability. Given the high prevalence of obesity and the associated risk of disease and mortality, it is important for a targeted approach to identify risk groups and determine individual risk. Therefore, in addition to BMI, a measure of fat distribution should always be used to ensure that less obvious but risky manifestations such as normal weight obesity are identified.