Association of Variability in Body Mass Index and Metabolic Health With Cardiometabolic Disease Risk

Higher intraindividual variability of body mass index is associated with elevated risk of COVID-19 related hospitalization and post-COVID conditions

BACKGROUND

Cardiometabolic diseases are risk factors for COVID-19 severity. The extent that cardiometabolic health represents a modifiable factor to mitigate the short- and long-term consequences from SARS-CoV-2 remains unclear. Our objective was to evaluate the associations between intraindividual variability of cardiometabolic health indicators and COVID-19 related hospitalizations and post-COVID conditions (PCC) among a relatively healthy population.

METHODS

This retrospective, multi-site cohort study was a post-hoc analysis among individuals with cardiometabolic health data collected during routine blood donation visits in 24 US states (2009-2018) and who responded to COVID-19 questionnaires (2021-2023). Intraindividual variability of blood pressure (systolic, diastolic), total circulating cholesterol, and body mass index (BMI) were defined as the coefficient of variation (CV) across all available donation timepoints (ranging from 3 to 74); participants were categorized into CV quartiles. Associations were evaluated by multivariable binomial regressions.

RESULTS

Overall, 3344 participants provided 42,090 donations (median 9 [IQR 5, 17]). The median age was 48 years (38, 56) at the first study donation. 1.2% (N = 40) were hospitalized due to COVID-19 and 15.5% (N = 519) had PCC. Higher BMI variability was associated with greater risk of COVID-19 hospitalization (4th quartile aRR 4.15 [95% CI 1.31, 13.11], p = 0.02; 3rd quartile aRR 3.41 [95% CI 1.09, 10.69], p = 0.04). Participants with higher variability of BMI had greater risk of PCC (4th quartile aRR 1.29 [95% CI 1.02, 1.64]; p = 0.04). Intraindividual variability of blood pressure (systolic, diastolic) and total circulating cholesterol were not associated with COVID-19 hospitalization or PCC risk (all p > 0.05). From causal mediation analysis, the association between the highest quartiles of BMI variability and PCC was not mediated by hospitalization (p > 0.05).

CONCLUSIONS

Higher intraindividual variability of BMI was associated with COVID-19 hospitalization and PCC risk. Our findings underscore the need for further elucidating mechanisms that explain these associations and importance for consistent maintenance of body weight.

BMI variability and cardiovascular outcomes within clinical trial and real-world environments in type 2 diabetes: an IMI2 SOPHIA study

BACKGROUND

BMI variability has been associated with increased cardiovascular disease risk in individuals with type 2 diabetes, however comparison between clinical studies and real-world observational evidence has been lacking. Furthermore, it is not known whether BMI variability has an effect independent of HbA1c variability.

METHODS

We investigated the association between BMI variability and 3P-MACE risk in the Harmony Outcomes trial (n = 9198), and further analysed placebo arms of REWIND (n = 4440) and EMPA-REG OUTCOME (n = 2333) trials, followed by real-world data from the Tayside Bioresource (n = 6980) using Cox regression modelling. BMI variability was determined using average successive variability (ASV), with first major adverse cardiovascular event of non-fatal stroke, non-fatal myocardial infarction, and cardiovascular death (3P-MACE) as the primary outcome.

RESULTS

After adjusting for cardiovascular risk factors, a + 1 SD increase in BMI variability was associated with increased 3P-MACE risk in Harmony Outcomes (HR 1.12, 95% CI 1.08-1.17, P < 0.001). The most variable quartile of participants experienced an 87% higher risk of 3P-MACE (P < 0.001) relative to the least variable. Similar associations were found in REWIND and Tayside Bioresource. Further analyses in the EMPA-REG OUTCOME trial did not replicate this association. BMI variability's impact on 3P-MACE risk was independent of HbA1c variability.

CONCLUSIONS

In individuals with type 2 diabetes, increased BMI variability was found to be an independent risk factor for 3P-MACE across cardiovascular outcome trials and real-world datasets. Future research should attempt to establish a causal relationship between BMI variability and cardiovascular outcomes.

Exploring metabolically healthy obesity: prevalence, characteristics, and cardiovascular risk in the Iranian population based on the STEPS 2021

Background

Regarding the rapidly increasing prevalence of obesity throughout the globe, it remains a serious public health concern. A subgroup of obesity that does not meet metabolic syndrome criteria is called metabolically healthy obesity (MHO). However, whether the MHO phenotype increases cardiovascular disease (CVD) risk is controversial. This study aimed to evaluate the prevalence of MHO and its 10-year CVD risk in Iranian populations.

Methods

Based on the STEPS 2021 project in Iran, we collected data on 18119 Iranians 25 years and older from all 31 provinces after applying many statistical factors. Using the Framingham score, we evaluated the 10-year cardiovascular risk associated with the various MHO definition criteria for Iranian populations.

Results

The prevalence of MHO was 6.42% (5.93-6.91) at the national level according to the AHA-NHLBI definition, and 23.29% of obese women and 24.55% of obese men were classified as MHOs. Moreover, the MHO group was younger than the metabolically unhealthy obesity (MUO) group based on all definitions (p < 0.001). The odds ratio of MUO individuals being classified as high-risk individuals by the Framingham criteria for CVD was significantly higher than that of MHO individuals by all definitions, with a crude odds ratio of 3.55:1 based on AHA-NHLBI definition.

Conclusion

This study reveals a significant prevalence of MHO in the Iranian population, with approximately 25% of obese individuals classified as MHO. While MHO is associated with a lower risk of cardiovascular disease compared to MUO, MHO carries the potential for transitioning to an unhealthy state.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-023-01364-5.

The association of body mass index variability with cardiovascular disease and mortality: a mediation analysis of pooled cohorts

Aim

We aimed to investigate the effect of BMI variability on CVD and mortality and to explore the mediation effects of the main cardiovascular risk factors contributing to this association.

Method

Participants aged 40-65 years were pooled from three cohort studies(ARIC [Atherosclerosis Risk in Communities], MESA [Multi-ethnic Study of Atherosclerosis], and TLGS [Tehran Lipid and Glucose Study]. We employed root mean squared error of the fractional mixed model to calculate BMI variability in the measurement period. In the event assessment period, the hazard ratios for CVD and mortality were estimated using Cox proportional hazard regression models. In the next step, the mediation and interaction effects of fasting plasma glucose, total cholesterol, and systolic blood pressure were determined.

Results

A total of 19073 participants were included in this pooled analysis. During a median of 20.7 years of follow-up, 3900 (20.44%) CVD and 6480 (33.97%) all-cause mortality events were recorded. After adjusting for potential confounders, BMI variability was linked to the 1.3 (1.2-1.4) and 1.7 (1.6-1.8) increased risk of CVD and mortality, respectively. Fasting plasma glucose mediated approximately 24% and 8% of the effect of BMI variability on CVD and mortality, respectively. However, systolic blood pressure and total cholesterol did not have mediation effects in this association.

Conclusion

High BMI variability is independently associated with the development of CVD and mortality. This association is partly mediated through fasting plasma glucose. Modern cardiometabolic therapies that lower fasting glucose may reduce the risk of future CVD and mortality in individuals with high BMI variability.

Factors influencing the bariatric surgery treatment of bariatric surgery candidates in underdeveloped areas of China

BACKGROUND

From year to year, the proportion of people living with overweight and obesity in China rises, along with the prevalence of diseases linked to obesity. Although bariatric surgery is gaining popularity, there are still several issues with its promotion compared to Western nations. Since less developed places in China are more widespread due to disparities in the development of different regions, there has been little exploration of the factors that might be related to acceptance of bariatric surgery in these regions.

METHODS

Patients who visited the Department of Gastrointestinal Surgery at the North Sichuan Medical College Affiliated Hospital from 2018 to 2022 and had obesity or other relevant metabolic problems were surveyed using a questionnaire. The relationship between demographic factors, socioeconomic status, and acceptance of bariatric surgery was analyzed.

RESULTS

Of 334 patients, 171 had bariatric surgery. BMI, education level, marriage history, medical insurance, family support, and a history of type 2 diabetes were all linked to having bariatric surgery, according to a univariate analysis. In a multivariate analysis, BMI (P = 0.02), education (P = 0.02), family support (P<0.001), medical insurance coverage (P<0.001), and history of type 2 diabetes (P = 0.004) were all positively associated with a willingness to have bariatric surgery. Among 163 non-bariatric patients with obesity, 15.3% were not opposed to surgery but preferred trying medication first, 54.6% leaned towards medical therapy, and 30% were hesitant. Additionally, a majority of patients (48.55%) often lacked adequate knowledge about weight reduction therapy. Age, height, gender, smoking, drinking, family history of type 2 diabetes, education, and marital status did not significantly differ (P > 0.05).

CONCLUSIONS

Many patients are concerned about the safety of surgical treatment and the possibility of regaining weight. Due to the relatively high cost of bariatric surgery, they tend to choose medical treatment. To enhance the acceptance of bariatric surgery in underdeveloped regions of China, it is crucial to focus on disseminating knowledge about bariatric surgery, offer pertinent health education to the community, and foster support from patients' families. The government should pay more attention to obesity and provide support in the form of medical insurance.

Effects of body weight variability on risks of macro- and microvascular outcomes in individuals with type 2 diabetes: The Rio de Janeiro type 2 diabetes cohort

AIMS

To investigate the effects of body weight variability (BWV) on macro- and microvascular outcomes in a type 2 diabetes cohort.

METHODS

BWV parameters were assessed in 684 individuals. Multivariable Cox regressions examined associations between BWV parameters and cardiovascular outcomes (total cardiovascular events [CVEs], major CVEs [MACEs], cardiovascular deaths),all-cause mortality and microvascular outcomes. Interaction/subgroup analyses were performed according to being physically-active/sedentary and having/not lost ≥ 5 % of weight.

RESULTS

Median follow-up was 11 years over which 194 total CVEs (174 MACEs), and 223 all-cause deaths (110 cardiovascular), occurred. There were 215 renal, 152 retinopathy and 167 peripheral neuropathy development/worsening outcomes. In general, increased BWV was associated with higher risks of CVEs, MACEs, all-cause mortality, advanced renal failure and peripheral neuropathy outcomes, but not of microalbuminuria and retinopathy outcomes. On interaction/subgroup analyses, increased BWV was associated with higher risks of outcomes in sedentary individuals and in those who did not lose ≥ 5 % of body weight. In physically-active participants or in those who lost ≥ 5 % weight, the adjusted risks were null or protective.

CONCLUSIONS

Increased BWV was associated with most adverse outcomes; however, in those who were physically-active or consistently losing weight, it was not hazardous and might be even beneficial.

Higher metabolic variability increases the risk of depressive disorder in type 2 diabetes mellitus: a longitudinal nationwide cohort study

BACKGROUND AND OBJECTIVES

While variabilities in metabolic parameters (METv) have been linked to adverse health outcomes in type 2 DM, their association with depression is yet to be studied. This research aimed to investigate the association between METv and depressive disorder in patients with type 2 DM.

METHODS

The study involved a nationwide cohort of 1,119,631 type 2 DM patients who had undergone three or more serial health examinations between 2005 and 2012. At each visit, body mass index (BMI), fasting glucose (FG), systolic blood pressure (BP), and total cholesterol (TC) were measured and stratified into quartiles, with Q4 being the highest and Q1 the lowest. The risk of depressive disorder was evaluated using Cox proportional hazard regression models, which accounted for METs in the indexes, after adjusting for sex, income status, lifestyle habits, medical comorbidities, DM severity, and baseline levels of BMI, FG, BP, and TC.

RESULTS

During a mean follow-up period of 6.00 ± 2.42 years, 239,477 (21.4%) cases of type 2 DM patients developed depressive disorder. The risk of developing depressive disorder was gradually increased as the number of METv increased (HR 1.18; 95% CI 1.13, 1.23 for the group with the highest METv in all parameters compared to those with the lowest METv in all parameters). In the subgroup analysis, the risk of developing depressive disorder was 43% higher in men (HR 1.43; 95% CI 1.34, 1.51), and 31% higher in those younger than 65 years of age (HR 1.31; 95% CI 1.23, 1.39) in the group with the highest number of METv compared to the group with the lowest number of METv.

CONCLUSION

In type 2 DM, higher METv was an independent risk factor for depressive disorder. This risk is notably elevated in men and individuals under the age of 65 years.

Weight variability and diabetes complications

Body weight is a recognized risk factor for cardiovascular diseases (CVD). More recently, weight variability, i.e. the oscillation of body weight over time, has also been suggested to be independently associated with development of CVD and mortality in subjects without diabetes and in people with both type 1 and type 2 diabetes (T2D). In T2D, weight variability emerged as one of the most relevant risk factors for CVD and it was suggested to interact with the variability of other risk factors to identify people at high cardiovascular risk. In addition, weight variability seems also to confer a higher risk for microvascular complications in people with T2D. While the exact mechanism linking weight variability to CVD is unknown, evidence from experimental models suggests that weight cycling promote an enduring pro-inflammatory program in the adipose tissue. Here we review the clinical evidence relative to the association of weight variability with CVD and microvascular complications of diabetes. We then briefly summarize the alterations proposed to explain this association. Finally, we synthesize the possible strategies, e.g. specific dietetic regimens and available glucose-lowering drugs, to minimize weight fluctuations.

Weight variability and cardiovascular outcomes: a systematic review and meta-analysis

The association between body weight variability and the risk of cardiovascular disease (CVD) has been investigated previously with mixed findings. However, there has been no extensive study which systematically evaluates the current evidence. Furthermore, the impact of ethnicity and type 2 diabetes on this phenomena has not yet been investigated. Therefore, the aim of this study was to comprehensively evaluate the effect of weight variability on risk of CVD (any cardiovascular (CV) event, composite CV outcome, CV death, Stroke, Myocardial Infarction) and the influence of ethnicity and type 2 diabetes status on the observed association. A systematic review and meta-analysis was performed according to the meta-analyses of observational studies in epidemiology (MOOSE) guidelines. The electronic databases PubMed, Web of Science, and the Cochrane Library were searched for studies that investigated the relationship between body weight or BMI variability and CV diseases using Medical Subject Headings (MeSH) terms and keywords. The relative risks (RRs) for the outcomes were collected from studies, pooled, and analysed using a random-effects model to estimate the overall relative risk. Of 5645 articles screened, 23 studies with a total population of 15,382,537 fulfilled the prespecified criteria and were included. Individuals in the highest strata of body weight variability were found to have significantly increased risk of any CV event (RR = 1.27; 95% Confidence Interval (CI) 1.17-1.38; P < 0.0001; I² = 97.28%), cardiovascular death (RR = 1.29; 95% CI 1.03-1.60; P < 0.0001; I² = 55.16%), myocardial infarction (RR = 1.32; 95% CI 1.09-1.59; P = 0.0037; I² = 97.14%), stroke (RR = 1.21; 95% CI 1.19-1.24; P < 0.0001; I² = 0.06%), and compound CVD outcomes (RR = 1.36; 95% CI 1.08-1.73; P = 0.01; I² = 92.41%). Similar RRs were observed regarding BMI variability and per unit standard deviation (SD) increase in body weight variability. Comparable effects were seen in people with and without diabetes, in White Europeans and Asians. In conclusion, body weight variability is associated with increased risk of CV diseases regardless of ethnicity or diabetes status. Future research is needed to prove a causative link between weight variability and CVD risk, as appropriate interventions to maintain stable weight could positively influence CVD.

Weight fluctuation, mortality, and cardiovascular disease in adults in 18 years of follow-up: Tehran Lipid and Glucose Study

PURPOSE

Controversies exist in the effect of body weight loss and fluctuation on cardiovascular disease (CVD) and mortality. This study aims to assess the effect of weight variability on CVD and all-cause and cardiovascular mortality in the Tehran Lipid and Glucose Study (TLGS) cohort.

METHOD

Participants aged  ≥ 40 year at the baseline period with at least 3 BMI measurements were included in this study. After excluding individuals with cancer, CVD, end-stage renal disease, systemic use of glucocorticoids, pregnancy, and missing covariates at the baseline, a total of 3461 participants were enrolled and followed for 18 years. BMI variability was defined using root mean squared error (RMSE) and average successive variability (ASV). In the RMSE method, BMI variability was calculated using the best-fitting model for BMI trend of each subject. Multivariate Cox proportional hazard models were applied to assess BMI variability's effect on CVD and mortality.

RESULTS

Among the 3461 participants in this study, the group with the highest weight variability had an increased risk of death for all-cause (HR 1.65; 95% CI 1.21-2.25), non-cardiovascular (HR 1.77; 95% CI 1.24-2.53), and non-cancer (HR 1.77; 95% CI 1.25-2.50) mortality. However, BMI variability showed to be protective against CVD (HR 0.76; 95% CI 0.6-0.97). These findings were significant in males, non-smokers, participants with age  ≤ 60 year, BMI < 30, negative BMI slope, and both diabetic and non-diabetic subjects.

CONCLUSION

High BMI variability is associated with increased risk of all-cause, non-CVD, and non-cancer mortality, although protective for the CVD event. Appropriate strategies for body weight maintenance after weight loss could be adopted to avoid weight variability, particularly in non-obese subjects.

Recent Updates on Associations among Various Obesity Metrics and Cognitive Impairment: from Body Mass Index to Sarcopenic Obesity

Obesity and obesity-associated morbidity continues to be a major public health issue worldwide. Dementia is also a major health concern in aging societies and its prevalence has increased rapidly. Many epidemiologic studies have shown an association between obesity and cognitive impairment, but this relationship is not as well established as other comorbidities. Conflicting results related to the age and sex of participants, and the methodology used to define obesity and dementia may account for the uncertainty in whether obesity is a modifiable risk factor for dementia. More recently, sarcopenia and sarcopenic obesity have been reported to be associated with cognitive impairment. In addition, new mediators such as the muscle-myokine-brain axis and gut-microbiota-brain axis have been suggested and are attracting interest. In this review, we summarize recent evidence on the link between obesity and cognitive impairment, especially dementia. In particular, we focus on various metrics of obesity, from body mass index to sarcopenia and sarcopenic obesity.

Risk of Heart Failure between Different Metabolic States of Health and Weight: A Meta-Analysis of Cohort Studies

We conducted a systematic review of cohort studies comparing the risk of heart failure in people with differing metabolic health and obesity statuses. We searched three electronic databases (PubMed, Web of Science, Scopus), where the studies of the relationships of metabolic health and obesity statuses with heart failure were included. Fixed-effects or random-effects models were used to estimate the summary relative risks [RRs]. Ten cohort studies were selected. Compared with individuals with normal metabolic health and body mass, the pooled RRs (95% confidence intervals) for heart failure were 1.23 (1.17, 1.29) for metabolic healthy overweight individuals, 1.52 (1.40, 1.64) for metabolic healthy individuals with obesity, 1.56 (1.30, 1.87) for metabolically unhealthy normal-weight individuals, 1.75 (1.55, 1.98) for metabolically unhealthy overweight individuals, and 2.28 (1.96, 2.66) for metabolic unhealthy individuals with obesity. A sensitivity analysis suggested that no single study had a substantial effect on the results. The Egger's and Begg's tests showed no evidence of publication bias. People with overweight or obesity were at a higher risk of heart failure, even if metabolically healthy. In addition, compared with metabolically healthy normal-weight individuals; metabolically unhealthy normal-weight individuals, and those with overweight or and obesity, were at higher risk of heart failure.

Independent effect of body mass index variation on amyloid-β positivity

Objectives

The relationship of body mass index (BMI) changes and variability with amyloid-β (Aβ) deposition remained unclear, although there were growing evidence that BMI is associated with the risk of developing cognitive impairment or AD dementia. To determine whether BMI changes and BMI variability affected Aβ positivity, we investigated the association of BMI changes and BMI variability with Aβ positivity, as assessed by PET in a non-demented population.

Methods

We retrospectively recruited 1,035 non-demented participants ≥50 years of age who underwent Aβ PET and had at least three BMI measurements in the memory clinic at Samsung Medical Center. To investigate the association between BMI change and variability with Aβ deposition, we performed multivariable logistic regression. Further distinctive underlying features of BMI subgroups were examined by employing a cluster analysis model.

Results

Decreased (odds ratio [OR] = 1.68, 95% confidence interval [CI] 1.16–2.42) or increased BMI (OR = 1.60, 95% CI 1.11–2.32) was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI variability. A greater BMI variability (OR = 1.73, 95% CI 1.07–2.80) was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI change. We also identified BMI subgroups showing a greater risk of Aβ positivity.

Conclusion

Our findings suggest that participants with BMI change, especially those with greater BMI variability, are more vulnerable to Aβ deposition regardless of baseline BMI. Furthermore, our results may contribute to the design of strategies to prevent Aβ deposition with respect to weight control.

Body Weight Variability and Risk of Cardiovascular Outcomes and Death in the Context of Weight Loss Intervention Among Patients With Type 2 Diabetes

IMPORTANCE

Body weight fluctuation is associated with greater risks of adverse health outcomes. Whether intensive weight loss interventions affect the association of variability in adiposity measures with adverse health outcomes in individuals with type 2 diabetes has not been studied previously.

OBJECTIVE

To evaluate the associations of long-term variability in adiposity indices with cardiovascular disease (CVD) outcomes and whether these associations are affected by an intensive lifestyle intervention among adults with type 2 diabetes.

DESIGN, SETTING, AND PARTICIPANTS

This prospective cohort study included participants in the Action for Health in Diabetes (Look AHEAD) trial without CVD at baseline (August 2001 to April 2004). The Look AHEAD study included 16 centers in the United States. Data analysis was performed from December 2020 to June 2021.

EXPOSURES

Variability of body mass index (BMI) and waist circumference (WC) across 4 annual visits, assessed using the coefficient of variation (CV), variability independent of the mean (VIM), and standard deviation (SD).

MAIN OUTCOMES AND MEASURES

Main outcomes were (1) all-cause mortality, (2) cardiovascular deaths (deaths from myocardial infarction [MI] or stroke), and (3) CVD events (MI, stroke, and/or death from cardiovascular causes).

RESULTS

Among 3604 study participants (mean [SD] age, 58.4 [6.6] years; 2240 [62.3%] women; 1364 [37.7%] Black participants; 2404 [66%] White participants), there were 216 CVD events, 33 CVD deaths, and 166 deaths over a median of 6.7 years. In the control group, the hazard ratios (HRs) for the highest quartile (quartile 4) compared with the lowest quartile (quartile 1) of CV of BMI were 4.06 (95% CI, 2.17-7.57), 15.28 (95% CI, 2.89-80.90), and 2.16 (95% CI, 1.21-3.87) for all-cause mortality, CVD mortality, and cardiovascular events, respectively. In the intervention group, the corresponding HRs were 0.99 (95% CI, 0.45-2.16), 1.14 (95% CI, 0.12-10.53), and 0.77 (95% CI, 0.40-1.49) for quartile 4 vs quartile 1. Regarding WC, in the control group, HRs for quartile 4 vs quartile 1 were 1.84 (95% CI, 1.01-3.35), 6.46 (95% CI, 1.16-36.01), and 1.28 (95% CI, 0.72-2.29). In the intervention group, HRs were 1.23 (95% CI, 0.61-2.46), 0.55 (95% CI, 0.15-2.11), and 0.70 (95% CI, 0.39-1.25) for quartile 4 vs quartile 1.

CONCLUSIONS AND RELEVANCE

In this cohort study of individuals with type 2 diabetes, higher variability of adiposity indices was associated with significantly increased risk of CVD outcomes and death in the control group but not in the intensive lifestyle intervention group.

Subclinical cardiovascular disease and utility of coronary artery calcium score

ASCVD are the leading causes of mortality and morbidity among Globe. Evaluation of patients' comprehensive and personalized risk provides risk management strategies and preventive interventions to achieve gain for patients. Framingham Risk Score (FRS) and Systemic Coronary Risk Evaluation Score (SCORE) are two well studied risk scoring models, however, can miss some (20-35%) of future cardiovascular events. To obtain more accurate risk assessment recalibrating risk models through utilizing novel risk markers have been studied in last 3 decades and both ESC and AHA recommends assessing Family History, hs-CRP, CACS, ABI, and CIMT. Subclinical Cardiovascular Disease (SCVD) has been conceptually developed for investigating gradually progressing asymptomatic development of atherosclerosis and among these novel risk markers it has been well established by literature that CACS having highest improvement in risk assessment. This review study mainly selectively discussing studies with CACS measurement. A CACS = 0 can down-stratify risk of patients otherwise treated or treatment eligible before test and can reduce unnecessary interventions and cost, whereas CACS ≥ 100 is equivalent to statin treatment threshold of ≥ 7.5% risk level otherwise statin ineligible before test. Since inflammation, insulin resistance, oxidative stress, dyslipidemia and ongoing endothelial damage due to hypertension could lead to CAC, ASCVD linked with comorbidities. Recent cohort studies have shown a CACS 100-300 as a sign of increased cancer risk. Physical activity, dietary factors, cigarette use, alcohol consumption, metabolic health, family history of CHD, aging, exposures of neighborhood environment and non-cardiovascular comorbidities can determine CACs changes.

Plasma adrenomedullin level and year-by-year variability of body mass index in the general population

Plasma levels of the hypotensive peptides of adrenomedullin and atrial and B-type natriuretic peptides (AM, ANP, BNP) are possible biomarkers for cardiovascular diseases. Increased variability of body mass index (BMI) over a certain period of time has been reported to be associated with cardiovascular morbidity or mortality. The aim of this study is to examine clinical significance of those hypotensive peptides as biomarkers by analyzing the relationship between plasma levels of the peptides and year-by-year variability of BMI in the general population without overt cardiovascular diseases. The subjects were 427 local residents (141 males and 286 females) attending their annual health check-up, who had been examined at least 5 times over the preceding period of 10 years. They were divided into two groups of low or high variability by the median of coefficient of variation (CV) of BMI values for each gender. Plasma AM levels of those with high year-by-year variability of BMI were significantly increased, as compared to the group with low variability, in both genders; meanwhile, such a difference was not noted in plasma levels of the natriuretic peptides. No significant differences were found in the basal parameters, which could affect plasma AM level, such as age, BMI, blood pressure or serum creatinine, between two groups. In conclusion, increase in plasma AM was associated with high year-by-year variability of BMI in the general population without overt heart disease. This relationship between the two suggests that increased plasma AM level is a cardiovascular risk marker.

Body weight gain rather than body weight variability associated with increased risk of nonalcoholic fatty liver disease

Weight loss, the most established therapy for nonalcoholic fatty liver disease (NAFLD), is frequently followed by weight regain and fluctuation. The aim of this study was to investigate whether body weight change and variability were independent risk factors for incident NAFLD. We conducted a longitudinal cohort study. Among the 1907 participants, incident NAFLD occurred in 420 (22.0%) cases during median follow-up of 5.6 years. In the multivariate analysis, there was no significant association between weight variability and the risk of incident NAFLD. The risk of incident NAFLD was significantly higher in subjects with weight gain ≥ 10% and 7% < gain ≤ 10% [hazard ratios (HR), 2.43; 95% confidence intervals (CI), 1.65-3.58 and HR, 1.73; 95% CI, 1.26-2.39, respectively], while the risk of incident NAFLD was significantly lower in those with -7% < weight loss ≤ --3% (HR, 0.33; 95% CI, 0.22-0.51). Overall body weight gain rather than bodyweight variability was independently associated with the risk of incident NAFLD. Understanding the association between body weight variability and incident NAFLD may have future clinical implications for the quantification of weight loss as a treatment for patients with NAFLD.

Abdominal periaortic and renal sinus fat attenuation indices measured on computed tomography are associated with metabolic syndrome

OBJECTIVES

To investigate the association between abdominal periaortic (APA) and renal sinus (RS) fat attenuation index (FAI) measured on MDCT and metabolic syndrome in non-obese and obese individuals.

METHODS

Visceral, subcutaneous, RS, and APA adipose tissue were measured in preoperative abdominal CT scans of individuals who underwent donor nephrectomy (n = 84) or bariatric surgery (n = 155). FAI was defined as the mean attenuation of measured fat volume. Participants were categorized into four groups: non-obese without metabolic syndrome (n = 64), non-obese with metabolic syndrome (n = 25), obese without metabolic syndrome (n = 21), and obese with metabolic syndrome (n = 129). The volume and FAI of each fat segment were compared among the groups. Receiver operator characteristics curve analysis was used to assess the association between the FAIs and metabolic syndrome.

RESULTS

FAIs of all abdominal fat segments were significantly lower in the obese group than in the non-obese group (p < 0.001). RS, APA, and the visceral adipose tissue FAIs were significantly lower in participants with metabolic syndrome than in those without metabolic syndrome in the non-obese group (p < 0.001, p = 0.006, and p < 0.001, respectively). The area under the curve for predicting metabolic syndrome was significantly higher for APA FAI (0.790) than subcutaneous, visceral, and RS FAI in all groups (0.649, 0.647, and 0.655, respectively).

CONCLUSION

Both metabolic syndrome and obesity were associated with lower RS and APA adipose tissue FAI, and APA FAI performed best for predicting metabolic syndrome.

KEY POINTS

• The volume and FAI of RS, APA, and visceral adipose tissue showed opposite trends with regard to metabolic syndrome or obesity. • Both metabolic syndrome and obesity were associated with lower RS FAI and APA FAI. • APA FAI performed best for predicting metabolic syndrome among FAIs of abdominal fat segments.

Long-term body mass trajectories and hypertension by sex among Chinese adults: a 24-year open cohort study

Evidence was limited on trajectory of body mass index (BMI) through adulthood and its association with hypertension. We aimed to evaluate their association by sex in large-scale study. Data were obtained from the China Health and Nutrition Survey (CHNS) from 1991 to 2015. Latent class trajectory analysis (LCTA) was used to capture BMI change trajectories. Hazard risks (HRs) were estimated from Cox proportion hazard regression. Among 14,262 participants (mean age, 38.8; 47.8% men), 5138 hypertension occurred (2687 men and 2451 women) occurred during a mean follow-up 9.6 years. Four body mass trajectory groups were identified as BMI loss, stable, moderate and substantial gain. Appropriately half of participants (48.0%) followed 1 of the 2 BMI gain trajectories, where BMI increased at least 3 kg/m2 overtime. Compared with participants with stable BMI, those gaining BMI substantially had higher risk of hypertension by 65% (HR 1.65, 95% CI 1.45-1.86) in male and 83% (HR 1.83, 95% CI 1.58-2.12) in female. The HRs in BMI loss patterns were 0.74 (0.62-0.89) in men and 0.87 (0.75-1.00) in women. Our findings imply that majority of Chinese adults transited up to a higher BMI level during follow-up. Avoiding excessive weight gain and maintaining stable weight might be important for hypertension prevention.

PROMIS-29 survey confirms major impact of fatigue on health-related quality of life in common variable immunodeficiency

Health-related quality of life (HRQOL) is an emerging topic of interest in patients with immunodeficiency. Information about HRQOL in common variable immunodeficiency (CVID) is limited. The primary objective was to compare primary immunodeficiency disease (PIDD) patients with and without common variable immunodeficiency (CVID) on HRQOL domains using Patient-Reported Outcomes Measurement Information System (PROMIS-29) survey data from the United States Immunodeficiency Network (USIDNET) registry. The primary endpoint variables were scores on 7 HRQOL domains. The USIDNET registry was used to select patients with baseline PROMIS-29 data collected between 2015 and 2018. Descriptive statistics, Fisher's exact test, and Student's two-sample t test were used to compare patients with CVID versus patients with non-CVID on demographic and clinical characteristics. The single-sample t test was used to compare sample means to the normed population mean of 50. A general linear model approach to multiple regression with backward selection was used to remove factors that did not contribute significant information to the multivariable models, while controlling for multiple testing. Potential explanatory variables included group (CVID/non-CVID), sex, age, and BMI. Among 184 PIDD patients, 146 (79%) were diagnosed with CVID. Patients had a mean (SD) age of 53 (13.8), were predominantly female (83%), and were Caucasian (98%). PROMIS-29 results revealed a significant effect of group (CVID/non-CVID) on the anxiety, fatigue, and social participation domains, with fatigue being the most statistically significant. Fatigue, anxiety, and social participation may be key factors influencing HRQOL among patients with CVID. Future prospective longitudinal studies using PROMIS-29 will be needed to confirm these findings and to determine the mechanisms through which these factors develop in CVID, and how they can be improved.

Body weight variability is not associated with changes in risk factors for cardiometabolic disease

CONTEXT

Weight loss is known to improve health, however the influence of variability in body weight around the overall trajectory on these outcomes is unknown. Few studies have measured body weight frequently enough to accurately estimate the variability component.

OBJECTIVE

To investigate the association of 12-month weight variability and concurrent weight change with changes in health markers and body composition.

METHODS

This study was a secondary analysis of the NoHoW trial, a 2 × 2 factorial randomised controlled trial promoting evidence-based behaviour change for weight loss maintenance. Outcome measurements related to cardiometabolic health and body composition were taken at 0, 6 and 12 months. Participants were provided with Wi-Fi connected smart scales (Fitbit Aria 2) and asked to self-weigh regularly over this period. Associations of weight variability and weight change with change in outcomes were investigated using multiple linear regression with multiple levels of adjustment in 955 participants.

RESULTS

Twelve models were generated for each health marker. Associations between weight variability and changes in health markers were inconsistent between models and showed no evidence of a consistent relationship, with all effects explaining <1% of the outcome, and most 0%. Weight loss was consistently associated with improvements in health and body composition, with the greatest effects seen in percent body fat (R2 = 10.4-11.1%) followed by changes in diastolic (4.2-4.7%) and systolic (3-4%) blood pressure.

CONCLUSION

Over 12-months, weight variability was not consistently associated with any measure of cardiometabolic health or body composition, however weight loss consistently improved all outcomes.

TRIAL REGISTRATION NUMBER

ISRCTN88405328.

Range of adiposity and cardiorenal syndrome

Obesity and obesity-related co-morbidities, diabetes mellitus, and hypertension are among the fastest-growing risk factors of heart failure and kidney disease worldwide. Obesity, which is not a unitary concept, or a static process, ranges from alterations in distribution to the amount of adiposity. Visceral adiposity, which includes intraabdominal visceral fat mass and ectopic fat deposition such as hepatic, cardiac, or renal, was robustly associated with a greater risk for cardiorenal morbidity than subcutaneous adiposity. In addition, morbid obesity has also demonstrated a negative effect on cardiac and renal functioning. The mechanisms by which adipose tissue is linked with the cardiorenal syndrome (CRS) are hemodynamic and mechanical changes, as well neurohumoral pathways such as insulin resistance, endothelial dysfunction, nitric oxide bioavailability, renin-angiotensin-aldosterone, oxidative stress, sympathetic nervous systems, natriuretic peptides, adipokines and inflammation. Adiposity and other associated co-morbidities induce adverse cardiac remodeling and interstitial fibrosis. Heart failure with preserved ejection fraction has been associated with obesity-related functional and structural abnormalities. Obesity might also impair kidney function through hyperfiltration, increased glomerular capillary wall tension, and podocyte dysfunction, which leads to tubulointerstitial fibrosis and loss of nephrons and, finally, chronic kidney disease. The development of new treatments with renal and cardiac effects in the context of type 2 diabetes, which improves mortality outcome, has highlighted the importance of CRS and its prevalence. Increased body fat triggers cellular, neuro-humoral and metabolic pathways, which create a phenotype of the CRS with specific cellular and biochemical biomarkers. Obesity has become a single cardiorenal umbrella or type of cardiorenal metabolic syndrome. This review article provides a clinical overview of the available data on the relationship between a range of adiposity and CRS, the support for obesity as a single cardiorenal umbrella, and the most relevant studies on the recent therapeutic approaches.

The combined treatment with lentil protein hydrolysate and a mixed training protocol is an efficient lifestyle intervention to manage cardiovascular and renal alterations in obese Zucker rats

PURPOSE

Functional and structural changes in cardiovascular and renal systems resulting from obesity and metabolic syndrome represent a severe risk to human health. Lifestyle interventions such as combining healthy diet with adequate physical exercise protocols are good strategies to manage these pathologies. In this research, the effects of lentil protein hydrolysate administration, combined or not with a mixed training protocol, on insulin resistance, cardiovascular and renal functionality were studied in the obese Zucker rat experimental model.

METHODS

Thirty-two rats (16 lean and 16 obese subdivided in sedentary and trained animals) were administered lentil protein hydrolysate, whereas another 32 subdivided in the same experimental design were administered placebo. The experiment lasted for 8 weeks. At the end of the experimental period, insulin resistance and different parameters of cardiovascular and renal functionality were measured.

RESULTS

The individual or combined interventions with lentil protein hydrolysate and mixed training protocol were efficient at counteracting some of the metabolic, cardiovascular and renal alterations characterizing the obese Zucker rat. Specifically, lentil protein hydrolysate decreased hyperphagia, amplitude of QRS complex, plasma ACE and selectin E expression in aorta, while increasing urinary volume and pH. Exercise showed beneficial actions on HOMA-IR, QRS amplitude, QTc interval, urinary volume, kidney weight and Mn-SOD activity. Interestingly, most of the mentioned benefits of exercise were more consistent when protein hydrolysate was also administered.

CONCLUSION

The interesting synergies between the two interventions assessed qualify them as alternative therapeutic strategies to treat cardiovascular and kidney diseases associated to the metabolic syndrome.

Metabolically healthy obesity increased diabetes incidence in a middle-aged and elderly Chinese population

BACKGROUND

We examined the association between metabolically healthy obese (MHO) and diabetes incidence in a middle-aged and elderly population and whether the association differed by the presence of nonalcoholic fatty liver disease (NAFLD).

METHODS

We examined 17 801 participants without diabetes at study entry (7980 males and 9821 females with a mean age of 63.2 years) derived from the Dongfeng-Tongji cohort study (median follow-up: 4.6 years). Participants were divided into six groups based on BMI (normal weight, overweight, or obese) and metabolic health (healthy/unhealthy) defined by the Adult Treatment Panel III criteria. The MHO was defined as BMI greater than 28.0 kg/m² with 0 or 1 of four metabolic abnormalities (elevated blood pressure, triglyceridaemia, hyperglycaemia, low HDL cholesterol). The hazard ratio (HR) and 95% confidence interval (CI) for incident diabetes were derived from the Cox proportional hazard regression model.

RESULTS

During 79 843 person-years of follow-up, 1453 individuals developed diabetes. Compared with metabolically healthy normal weight (MH-NW) individuals, the multivariable-adjusted HRs (95% CI) were 1.74 (1.16-2.59) for MHO and 2.15 (1.65-2.81) for metabolically unhealthy obese subjects after adjusting for age, sex, smoking, alcohol drinking, physical activity, fruit and vegetable consumption, family history of diabetes, fasting glucose, waist circumference, and NAFLD. Among those without NAFLD, MHO individuals showed higher incidence of diabetes (multivariate-adjusted HR = 2.71, 95% CI: 1.47-5.00) than MH-NW individuals.

CONCLUSIONS

The MHO phenotype was associated with increased incidence of diabetes in a middle-aged and elderly population, and the association did not differ by the presence or absence of NAFLD.

Central But Not General Obesity Is Positively Associated with the Risk of Hyperhomocysteinemia in Middle-Aged Women

Objective: Obesity and homocysteine (Hcy) are two important risk factors for cardiovascular disease (CVD). However, evidence on the association between obesity and Hcy concentration was conflicting. The aim of our study is to explore the associations of general and central obesity with hyperhomocysteinemia (HHcy) in middle-aged women. Methods: The current analysis was based on data from 11,007 women aged 40–60 years. Height, weight, and waist circumference (WC) were measured and serum homocysteine was determined. Multiple logistic regression models were used to assess the associations of the risk of hyperhomocysteinemia (HHcy, Hcy > 15 μmol/L) with BMI and WC. Results: 13.71% women had HHcy. The prevalences of BMI-based general obesity and WC-based central obesity were 11.17% and 22.88%, respectively. Compared with non-obese women, the mean serum Hcy concentration was significantly higher in WC-based central obese women (p = 0.002), but not in BMI-based general obese women (p > 0.05). In the multiple logistic regression models, central obesity was positively related to the risk of HHcy (OR = 1.30, 95% CI = 1.10 to 1.52), while general obesity was inversely related to the risk of HHcy (OR = 0.82, 95% CI = 0.72 to 0.93 and OR = 0.71, 95% CI = 0.57 to 0.89). Conclusions: Central obesity was positively related to the risk of HHcy, while general obesity was negatively related. Menopause showed no effect modification on these associations.

Association of Variability in Body Mass Index and Metabolic Health With Cardiometabolic Disease Risk

Background Metabolic syndrome is associated with high risk of cardiovascular disease, although risk may differ according to the specific conditions present and variability in those conditions. Methods and Results We defined obesity (body mass index [BMI] ≥30 kg/m2) and metabolic health (<2 nonobesity National Cholesterol Education Program Adult Treatment Panel III conditions) among 3632 Framingham Heart Study offspring cohort participants (mean age, 50.8 years; 53.8% women) who were followed up from 1987 to 2014. We defined participants whose variance independent of the mean for a metabolic syndrome-associated measure was in the top quintile as being "variable" for that measure. Variable metabolic health was defined as ≥2 variable nonobesity metabolic syndrome components. We investigated the interaction between obesity and metabolic health in their associations with cardiometabolic disease and cardiovascular disease using Cox proportional hazards regression. In addition, we estimated the associations of BMI variability and variable metabolic health with study outcomes within categories of obesity and metabolic health status, respectively. We observed 567 incident obesity (41 439 person-years), 771 incident metabolically unhealthy state (25 765 person-years), 272 incident diabetes mellitus (56 233 person-years), 503 incident hypertension (12 957 person-years), 589 cardiovascular disease (60 300 person-years), and 195 chronic kidney disease (47 370 person-years) events on follow-up. Obesity and being metabolically unhealthy were independently and positively associated with all outcomes. BMI variability, compared with stable BMI, was associated with 163%, 67%, 58%, and 74% higher risks of having obesity, becoming metabolically unhealthy, having diabetes mellitus, and having hypertension, respectively, among nonobese participants. Variable metabolic health, compared with stable metabolic health, was associated with a 28% higher risk of cardiovascular disease, among metabolically healthy participants. Conclusions We did not observe evidence for a positive interaction between obesity and metabolic health status with regard to study outcomes. BMI and metabolic health variability are associated with adverse health outcomes.