Study Overview
Objective. To understand the risk of diabetes, coronary heart disease (CHD), stroke, and death over time by comparing adults who were “metabolically healthy” but obese at baseline and those with baseline cardiometabolic abnormalities, and to understand the stability of metabolic health over time.
Design. Secondary analysis of data from 2 large prospective epidemiologic cohort studies of cardiovascular risk and outcomes.
Setting and participants . This study relied on data from the Atherosclerosis Risk in Communities (ARIC) study and the Coronary Artery Risk Development in Young Adults (CARDIA) study. ARIC includes adults who were recruited in late middle-age (45–64 years at baseline) from several clinical sites in the Southeastern and Midwestern United States and who have been followed over time with multiple examinations to assess cardiovascular risk factors and outcomes. CARDIA similarly assessed cardiovascular risk behaviors and events over time in a large cohort of American men and women, however, it recruited younger participants at baseline (18–30 years old). For the present study, the authors used data from all available ARIC and CARDIA participants who had complete information on body mass index (BMI) and cardiometabolic health status and who had not already developed one of the outcomes of interest at baseline, which led to a final sample of 4990 individuals from CARDIA and 14,685 from ARIC.
The independent variable of interest in this study was twofold, describing baseline status in terms of cardiometabolic risk markers and weight. Cardiometabolic risk was categorized as either “healthy,” “suboptimal,” or “unhealthy” based on the presence or absence of 3 risk factors: (1) elevated blood pressure (with untreated threshold of < 130/85 mm Hg considered negative); (2) elevated blood glucose (with untreated threshold of fasting < 100 mg/dL or hemoglobin A1c < 5.7% considered negative); and (3) dyslipidemia (with untreated total cholesterol < 240 mg/dL and HDL cholesterol > 40 for men or > 50 for women considered negative). Participants who were negative for all 3 risk factors were deemed metabolically healthy, those with 1 or 2 risk factors “suboptimal,” and those with all 3 risk factors “unhealthy.” Participants were then further characterized by baseline weight status as “lean” (BMI < 25), “overweight” (BMI 25–29.9), or “obese” (BMI ≥ 30). Combining a participant’s metabolic and weight status therefore yielded 9 possible exposure categories, ranging from healthy-lean to unhealthy-obesity. The group of greatest interest for this study was participants in the metabolic-ally healthy-obesity (MHO) category—those with a BMI ≥ 30 but with zero of the 3 cardiometabolic risk factors.
Main outcome measures . The investigators assessed both the stability of MHO over time, and the relative contribution of BMI status vs. cardiometabolic abnormalities to key health outcomes.
To assess the stability of MHO over time, the investigators used follow-up data from both studies to create descriptive statistics on the frequency with which patients (1) changed weight status or (2) changed metabolic status, over up to 10 years of follow-up in ARIC and up to 20 years of follow-up in CARDIA.
Among only ARIC participants (the older adults at baseline), risk of several health outcomes over up to 10 years of follow-up was compared across groups. The health outcomes of interest were incident diabetes, incident CHD (myocardial infarction or coronary death), incident stroke, or all-cause mortality. To visually represent incidence of these outcomes over time, the investigators constructed Kaplan-Meier survival curves. To determine whether the risk of an outcome differed significantly according to baseline exposure category, they used Cox proportional hazards modeling, adjusting for covariates including age, sex, race, income, education, and tobacco and alcohol use. All available follow-up time was used from baseline until an outcome of interest developed or a participant was censored. Reasons for censoring a participant were not outlined. Multivariable Cox models were separately conducted for the 4 outcomes of diabetes, CHD, stroke, and mortality across the 9 main exposure categories and across 15 categories in an additional analysis where “suboptimal cardiometabolic health” was split according to whether participants had 1 or 2 baseline risk factors. The reference group for all analyses was patients with MHO. A P value of < 0.05 was specified as statistically significant.
Results. Baseline characteristics were presented only for ARIC patients. Among that group ( n = 14,685), just 2% ( n = 260) were characterized as MHO at baseline. Just over one-quarter (27%) were obese at baseline, and the vast majority of patients with obesity at baseline (94%) had either suboptimal cardiometabolic risk (SO) or metabolically unhealthy obesity (MUO—all 3 risk factors present). Mean follow-up time for ARIC participants was 18.7 years.
Just under half of the ARIC sample were women (45%), 25% were black (the remaining were white), mean age was 54.3 years, and mean BMI was 27.7. Covariates such as education and income were not reported in the table of patient characteristics. No statistical testing was reported comparing exposure categories at baseline, however, within the “healthy”, “suboptimal,” and “unhealthy” categories, increasing weight status appeared to track with increasing blood pressure, fasting glucose, insulin resistance, and waist circumference.
With MHO participants as the reference group, there were no significant differences between baseline weight categories (lean, overweight, obese) of “healthy” (zero risk factors) participants for CHD, stroke, or mortality during follow-up. In other words, baseline weight status did not significantly impact the risk of these 3 outcomes, assuming someone started out metabolically healthy. However, significant differences did emerge among participants with 1 or more risk factor at baseline. For those in the “suboptimal” (SO) category (1 or 2 risk factors), all 3 weight subgroups (lean, overweight, and obese) had significantly higher risk of CHD and stroke during follow-up relative to the MHO participants (hazard ratios [HRs] for CHD: lean 2.3, overweight 2.5, obese 3.0; HRs for stroke: lean 2.6, overweight 2.7, obese 3.0), and mortality risk was higher among lean-SOs (HR 1.4) and obese-SOs (HR 1.7). For those in the unhealthy category at baseline, there was significantly higher risk of CHD, stroke, and mortality across all 3 baseline weight categories relative to participants who were MHO at baseline – that is, even “lean” at baseline patients who had 3 risk factors had significantly higher risk of all of these outcomes than “healthy obese” patients (CHD HR 3.6; stroke HR 2.9; mortality HR 1.9).
Diabetes results differed slightly. For this outcome, participants in the lean-healthy baseline category had about half the risk of developing diabetes during follow-up (HR 0.47) compared to the MHO participants. Those in the overweight-healthy and lean-suboptimal health categories had no difference in risk of diabetes compared to MHO. All other subgroups had higher risk of developing diabetes over time (eg, lean-unhealthy diabetes HR 2.3; obese-unhealthy diabetes HR 5.4) relative to MHO.
Data from both ARIC and CARDIA were used to evaluate the stability of weight and metabolic health during follow-up. Consistent with nationally observed trends in the U.S., many participants gained weight during follow-up, with 17.5% of initially lean and 67.3% of overweight CARDIA patients transitioning to obesity over time. For patients initially categorized as metabolically healthy, a large fraction developed 1 or 2 metabolic abnormalities in follow-up (52% in ARIC and 35% in CARDIA). Very few participants from either study transitioned from a healthy state of 0 risk factors to the unhealthy state of 3 risk factors during follow-up.
Conclusion. The authors conclude that patients with MHO have lower risk for diabetes, CHD, stroke, and mortality than unhealthy subjects regardless of weight status. They did note that obesity increased diabetes risk, even in the absence of detectable baseline abnormalities, relative to lean healthy individuals.
Commentary
Metabolically healthy obesity describes a state where a patient’s body mass index is above 30 kg/m 2 yet the individual lacks traditional measures of cardiometabolic derangement often associated with excess adiposity. The definition of MHO varies between studies but often requires that a patient with obesity display less than 3 metabolic syndrome criteria, sometimes allowing for even fewer abnormalities (eg, 0 or 1) [1]. It is estimated that anywhere between 10% to one-third of adults with obesity may fall into this category of relative metabolic health despite an elevated BMI [1,2]. Some controversy surrounds how the MHO state should be viewed and its practical implications for clinical management. It is unclear whether patients with MHO are simply in a transient state (ie, “pre-metabolic,” akin to “pre-diabetic”) that will later convert to metabolically unhealthy obesity (MUO), or whether they are truly somehow genetically able to handle excess weight without ever developing the sequelae that are so commonly observed in most patients with obesity. This is an important distinction for clinicians, as it may have implications for how aggressively weight loss is pursued and how long-term risks of excess weight are framed for these individuals. Consider a 40-year-old female patient with a BMI of 32 who is otherwise healthy and active, on no BP medications, with an optimal lipid profile and no signs of insulin resistance. Should this patient be encouraged to lose weight? What health risks does she face in the next few decades if she does not?
In this secondary data analysis from 2 large cohort studies of cardiovascular risk, Guo and Garvey conclude that it is the cardiometabolic risk markers of elevated blood pressure, dyslipidemia, and elevated blood glucose that confer far more risk in terms of long-term cardiovascular outcomes than excess weight in and of itself. Their analyses make use of data from 2 large and rigorous cohort studies of cardiovascular outcomes, which lend credibility to the outcomes they aimed to study (ie, we can be confident that if someone is listed as having had a myocardial infarction in ARIC or CARDIA, they probably did) and provided them with the unique ability to study long-term outcomes on a large sample size. In short, this study would have been difficult to do with many other sources of data or methodologies. Their statistical methods for comparing the risk of the outcomes over long-term follow-up appear robust, particularly given the high event rates in some of the groups and therefore inevitable high levels of censoring over time. Importantly, they control for a number of potential confounders in their study, including tobacco use. On the other hand, they perform quite a large number of statistical comparisons, therefore it is possible they may have found fewer significant differences between groups with a more stringent cutoff for their P value (eg, with a Bonferroni correction).
Regarding the stability of the metabolically healthy state over time, it appears there was significant crossover of participants from “healthy” to “suboptimal,” and significant weight gain occurred during follow-up. It is not clear whether an individual’s baseline exposure category was permitted to change over time in the statistical models, which could have impacted their results. Clinically, it is not surprising that there was a lot of movement between categories over up to 20 years of follow-up. It underscores the notion that even if a patient is obese and metabolically healthy cross-sectionally, many of these individuals will not remain metabolically healthy over time. Additionally, although the study abstract describes using data from both ARIC and CARDIA, the health outcomes component relied solely on ARIC participants. These were a group of relatively older adults at baseline who had already made it through much of their adult lives without developing any of the outcomes of interest (diabetes, CHD, stroke or mortality), therefore could have represented a sample that is somehow more metabolically resilient than the general population. As stated by the authors in their limitation section, the assertion that MHO patients are not at increased risk of cardiovascular disease (CVD) outcomes should not be extrapolated to younger patients based on this cohort. This is particularly true because, again, the stability of metabolic health appears relatively low—over half of baseline “healthy” participants in ARIC and over one-third in CARDIA developed 1 or more risk factors in follow-up, and therefore presumably also developed greater risk of CVD than if they had remained “metabolically healthy.” The likelihood that young adults with MHO will go on to develop new risk factors over time is underscored by how rare the MHO state was in the ARIC sample—it represented only 2% of the overall population, and 6% of those with obesity.
Additionally, as the authors noted, while CVD appeared to be much more influenced by the risk factor trio than by obesity alone, obesity did increase diabetes risk even in the “metabolically healthy” group. This finding aligns with prior work suggesting that patients with MHO are at increased risk of diabetes but not CVD, compared with their normal weight metabolically healthy counterparts [3].
Applications for Clinical Practice
Regardless of weight status, patients with risk factors such as elevated blood pressure, glucose, or lipids would benefit from interventions to reduce their long-term cardiovascular risk and mortality. On the other hand, patients with obesity who lack traditional cardiometabolic risk factors represent a clinical population where it is more difficult to advise on some of the potential benefits of weight loss. Adults with MHO can be advised with confidence that weight loss may reduce their risk of developing diabetes, and they may have other important motivations for weight loss that can be supported as well. Importantly, young adults with MHO who are not interested in weight loss should not be assumed to be “in the clear” for cardiovascular risk; they should be monitored for development of new risk factors over time and for the ensuing need for increased intensity of weight loss recommendations and interventions.
—Kristina Lewis, MD, MPH