Body Composition and Cardiometabolic Risks in Patients With Psoriatic Arthritis
Background
Psoriatic arthritis (PsA) is related to both adverse body composition1 and elevated cardiometabolic risk,2 but these relationships are incompletely understood.3 It is unclear which variables most directly and strongly influence cardiometabolic risk in PsA; this uncertainty prevents systematic and reliable cardiovascular risk prediction for patients with PsA.
It is unknown whether global measures of body composition, such as body mass index (BMI), adequately predict cardiometabolic risks or whether more granular analyzers, such as those assessing visceral or muscle-contained fat, provide more useful risk estimates. To address these questions, researchers have begun to use such tools to develop algorithms that predict the risks of coronary heart disease (CHD) and type 2 diabetes (T2D) in the general population. This Journal Club discussion will focus on a study by Ferguson and colleagues that sought to extend risk-evaluation methods to the management of patients with PsA.4
Methods
Patients with PsA who were enrolled in the Immune Metabolic Associations in PsA (IMAPA) study were recruited for the cross-sectional study.4 For 26 study participants, the researchers analyzed body composition data based on magnetic resonance imaging (MRI) studies, including measures of visceral adipose tissue, abdominal subcutaneous adipose tissue, liver fat, fat infiltration of muscle, thigh muscle volume, and weight-to-muscle ratio.
Study investigators similarly analyzed MRI-based body composition data for a control group of 130 participants in the UK Biobank5 who did not have metabolic disease and were matched with participants with PsA for sex, age, and BMI. They also included similar data from a second UK Biobank control group of 454 patients with T2D. Based on a previously-developed machine learning algorithm,6 Ferguson and colleagues then determined the prevalence of and propensity for CHD and T2D across these 3 groups.
Results
The researchers found that body composition in patients with PsA was more similar to patients with T2D without inflammatory arthritis compared with study participants who were metabolic disease-free (MDF). The mean propensity for CHD among participants with PsA was 1.27 times higher than that of participants who were MDF. The mean propensity for T2D in the PsA group was even greater: 1.83 times higher than that of the group that was MDF.4 Given the observed similarity in body habitus between patients with PsA vs T2D and their elevated risk of CHD, Ferguson and colleagues advised clinicians to place high priority on facilitating weight loss in patients with PsA, given the importance of this lifestyle modification in patients with T2D.
Discussion
Allan Gibofsky, MD, JD
Kevin Yip, MD
Genna Braverman, MD
Allan Gibofsky, MD, JD, professor of medicine at Weill Cornell Medicine and an attending rheumatologist at the Hospital for Special Surgery, and his colleagues, Kevin Yip, MD, and Genna Braverman, MD, who are currently pursuing clinical fellowships in rheumatology, discuss this study and the clinical implications of published research in this area.
Allan Gibofsky, MD, JD: An interesting cross-sectional PsA study from the United Kingdom appeared in Rheumatology last year. As we know from previous research, PsA is associated with adverse body composition predictive of greater propensity for both CHD and T2D.1 For their study, Ferguson and colleagues broke down the concept of adverse body composition in patients with PsA. We know that BMI in patients with PsA is significantly different from BMI in patients with other diseases, particularly those with other inflammatory diseases such as rheumatoid arthritis.7 Ferguson et al evaluated the BMI differences a bit further and uncovered additional evidence regarding the adverse coronary events seen in patients with PsA.
We know inflammation is both a harbinger and a sequela of malignancy and cardiovascular disease (CVD) so any new insights into understanding and modifying disease factors related to inflammation would be particularly helpful. It’s also relevant that emerging literature is establishing fairly well that obesity is itself an inflammatory condition.8 So that opens yet another dimension into the kinds of things we can do for our patients with PsA to reduce CV risk and major adverse cardiac events (MACE). In our discussion of the Ferguson paper, we will focus on CVD in PsA as a function of body composition and set aside any discussion of the role of malignancy. Genna, would you take us through the objectives and methodology of this study?
Genna Braverman, MD: This study compared features and CVD risks in 3 participant groups: 1 group of patients with PsA vs 2 other groups. The 2 non-PsA groups were drawn from a large UK Biobank5 dataset. The MDF control group did not have any metabolic disease and the T2D group had self-identified as having been diagnosed with diabetes. The investigators’ objective was to characterize the body composition profile using a variety of imaging parameters based on MRI data. The researchers also used a previously developed algorithm6 to estimate the propensity of the PsA group for CVD and T2D given their anthropometric data and known disease status compared with the UK Biobank participants.
Participants with PsA were selected from enrollees in the IMAPA study9 of open-label use of apremilast. They needed to fulfill the CASPAR criteria10 [ClASsification criteria for Psoriatic Arthritis] for PsA — which we are familiar with in clinical practice in the United States — and they had to be eligible to receive apremilast as outlined in guidelines from the Scottish Medicines Consortium.11 Under those guidelines, apremilast use is restricted to adult patients with active PsA and an inadequate response to at least 2 prior disease-modifying antirheumatic drug (DMARD) therapies (or proven intolerance to those therapies), which is a bit different from how we use apremilast in clinical practice here in the United States.
Patients with other comorbid autoimmune diseases, significant renal disease, recent weight loss, transaminitis, or diabetes; receiving biologics or leflunomide; or who were pregnant were excluded from the study. Given these enrollment criteria, we might question how representative these patients were of a typical patient with PsA who we might see in clinic.
Ferguson and colleagues first matched patients in the MDF control group with patients in the PsA group based on age, sex, and BMI. In the MDF group, patients with CV disease, diabetes, liver disease, or respiratory and gastrointestinal comorbidities were excluded. Then they matched participants with PsA to participants who were MDF in a 1:5 ratio, starting with sex and followed by age and BMI. Patients in the group with T2D had been diagnosed by a doctor at 30 years of age or older, by self-report.
Based on data from UK Biobank and MRI studies of patients with PsA that were performed before they began therapy with apremilast, Ferguson and colleagues analyzed MRI studies for both the MDF and T2D groups. To perform body composition profiling, the study investigators calculated several different clinical parameters. First was a set of central obesity and visceral fat measures, which included assessment of abdominal subcutaneous adipose tissue (ASAT), visceral adipose tissues (VAT), and liver fat. They also calculated measures of sarcopenia, including muscle fat infiltration (MFI), thigh fat-free muscle volume (FFMV), and weight-to-muscle ratio (WMR).4
An earlier article authored by 2 of the current study authors and their colleagues describes the adaptive k-nearest neighbors algorithm6 used in this study, which was to allow for what Ferguson and colleagues described as an individual-centered assessment of metabolic phenotype. Essentially, this was a predictive algorithm to estimate propensity for development of CVD and T2D in patients with PsA.
AG: Right, I think the authors have gone beyond the classical BMI evaluation. They suggest that there may be endotypes of obesity that are not captured by the BMI — hence the need to “subgroup” the BMI. Then, as you pointed out, they are evaluating not only the MDF control population but also the group with T2D. That is, they are assessing patients with the inflammatory disease of PsA and comparing them against patients without an inflammatory disease. They are also comparing the PsA group against a group of patients with diabetes, which of course is yet another inflammatory disease. Their goal is to gain insights into the concept of CV risk as a function of underlying disease and underlying body habitus. Kevin, can you share your perspective on the study data and outcomes?
Kevin Yip, MD: In comparing the PsA patients against the MDF control group, it is evident that the researchers did a good job of matching by age, sex, and BMI. Across the board, when considering the different measures of fat — VAT, ASAT, liver, everything except MFI — patients in the PsA group had worse values compared with healthy patients in the control group, and all of those differences were statistically significant. A large proportion of patients in the PsA group also had signs of sarcopenia, with a higher weight-to-muscle ratio and lower FFMV.
When the PsA group is compared against the T2D group, we see that while patients with T2D was a bit older with a smaller proportion of women, the BMIs of both groups were roughly similar. In terms of the different fat indices, the variations between study groups were not statistically significant overall. Aside from the ASAT score and abdominal fat index, the other scores were also fairly similar.
These results imply that fat distribution in patients with PsA is more similar to that of patients with T2D than it is to healthy controls. Turning to the muscle parameters, we can see that there is probably a higher element of sarcopenia implied by the findings: MFI was a little higher in patients in the T2D group compared with patients in the PsA group, though the difference in thigh fat-free muscle volume was not very significant. However, the weight-to-muscle ratio was lower in patients in the T2D group compared with patients in the PsA group. Across the board, the results indicate that fat distribution is similar between patients with PsA and those with diabetes. In general, patients in the PsA group had less muscle and a bit more fat infiltration compared with patients in the T2D and MDF groups. The supplemental materials for this study show that the PsA group also had a high propensity for both CVD and T2D compared with the healthy MDF controls.
AG: I was intrigued by Figure 1. I typically do not see “spider graphs” like these aside from patient-reported outcomes, but this is a very effective and novel way of demonstrating the phenotypes that the investigators are evaluating. Kevin, how representative is the PsA population in this study? We always talk about how we can extrapolate research findings. This is a group “across the pond,” so to speak. Can you really make use of their results in clinic?
KY: Well, taking it at face value, the UK Biobank seems to be a large population base with over half a million people.5 So that may be a favorable factor. However, I am not quite sure if the investigators selected a group of patients that represents the population of patients with PsA. For starters, UK population is more homogenous compared with the US population. In this paper, the study authors have not described the racial demographics of their patients. This is important because we know that across different ethnicities and racial backgrounds, there is a difference in how BMI and fat are distributed.12
As Genna mentioned earlier, patients from the IMAPA study had all been treated with apremilast and had failed prior DMARD therapies to satisfy the enrollment criteria. This implies that they may have had somewhat more resistant disease than the average patient with PsA walking into our clinic. Another of the exclusion criteria was treatment with a biologic agent or leflunomide, which also tells us a lot about this population of patients with PsA. From the supplementary materials, the median disease activity score (28 joints) with erythrocyte sedimentation rate (DAS28-ESR) was 3.91 and the Psoriasis Area and Severity Index (PASI) score was about 4. You can tell that these patients are on the mild to moderate end of the disease spectrum, which may also be true for most of the reference population. However, they are already taking apremilast. So I do not really know how representative these 26 patients are in comparison to the average patient with PsA who walks into our center.
AG: Genna, before we became rheumatologists, we were internists and treatment considerations such as managing CV risk were the “coin of our realm,” so to speak. Take us through the whole concept of CV risk factors from an internal medicine perspective. What are the traditional risk factors? How do we try and modify them in internal medicine and in the context of the rheumatic diseases?
GB: That’s right, our training began as internists and as internists we spent a lot of time, both in the inpatient and the outpatient setting, considering CV risk factors. A branch point, in thinking about risk stratification and moving forward with treatment and optimization of our patients’ health, is the determination of what risk factors patients have and which of these are modifiable.
Smoking is the first (and second and third) major risk factor that we tend to think about. We also think about comorbid diseases that confer an increased risk for atherosclerotic CVD, including coronary artery disease, peripheral arterial disease, and cerebrovascular disease. In terms of important comorbid diseases, diabetes, hypertension, and hyperlipidemia are the main ones. There is room for clinical improvement by addressing modifiable patient risk factors, such as diet, exercise, and obesity. Less modifiable risk factors would be family history and certain genetic predispositions toward dyslipidemias.
This comes up quite a bit in rheumatology because we know that in many rheumatic diseases, from lupus to rheumatoid arthritis, patients experience accelerated atherosclerosis and — considering the atherogenic potential of inflammation and endovascular or endothelial dysfunction — are at risk of significant morbidity from atherosclerotic disease. Of course, we educate our patients about this in the rheumatology clinic and teach them how to modify their risk profiles. One other thing that I will add regarding risk-profile modification, which is sort of a corollary of diabetes, is something that we often do to patients, which is to initiate treatment with steroids because we see the sequela of glucocorticoid-induced diabetes in clinic very frequently. Another example, albeit a bit less applicable to PsA, would be interleukin-6 blockade13: we must consider the risk posed by drugs that alter lipid metabolism.
In our clinical practice, we are very fortunate to be able to partner at a major academic center with our primary care physicians (PCPs). We also work closely with cardiology and we have support programs such as nutrition services and physical therapy to help mobilize patients and help them understand how best to begin an exercise program. Again, because some of our medications can increase the risk of dyslipidemia, we will often be in the position of checking lipid panels and we always pay attention to a patient’s blood pressure even if we are not directly titrating antihypertensives. I am very frequently in communication with PCPs about the risks vs benefits of statin initiation and whether it would make sense to obtain a coronary artery calcification score. So for our rheumatic patients, we really do partner very closely with our specialists and subspecialists.
AG: As you mentioned, Genna, the 3 of us are fortunate to work at a major academic medical center where there are more cardiologists available than you can shake a stick at, so to speak. Yet I am wondering about, for example, a colleague who might be treating a rancher with PsA in Bozeman, Montana, and what she might face in that context. So Kevin, Genna highlighted patient management from her perspective as an internist. Somewhere and someday soon, you will have a very busy private practice. What will you do and what should the rheumatologist do in the setting of PsA with CV risk factors?
KY: It is a very good question, because although in the last 20 years we have come to appreciate the importance of metabolic syndrome in our patients, no one really “owns” it in terms of patient management. If one piece of the clinical picture is high blood pressure, another is abnormal fasting glucose levels, and yet another is waist circumference, then who owns each piece? There really needs to be a multidisciplinary approach to care, or else 1 clinician will have to own it all. In a piecemeal approach, you may feel as if you are looking after “someone else’s problem” but never addressing what is in front of you.
As rheumatologists, we are well placed to be dealing with this, thanks to our internal medicine training. I suppose it depends on the degree to which you want to take ownership of what is traditionally seen as a “primary care problem.” For example, in addition to addressing the patient’s PsA, are we able to spend time dealing with triglycerides and blood pressure and glucose? I think it does mean that we need to be in very good communication with the primary care doctor, because the flip side of the issue is that our patients could start relying on us as PCPs. That could be the case in Montana, though probably not in New York.
AG: Kevin, are you suggesting that in cases where other specialists are available, they should be primary in modifying CV risk?
KY: I am saying that no one has really taken ownership of it. That is unfortunate because there does not seem to be a clear consensus. I think that if other team members are involved, they all need to be on the same page and be especially careful in managing these patients globally. PsA is a CV risk factor in and of itself,14 but specialists other than rheumatologists may not recognize this fact. Treatment of PsA also poses new risks. Drugs like tocilizumab can change lipid profiles13 and steroids can change lipids levels. Even with Janus kinase (JAK) inhibitors, which we have been increasingly wanting to use for PsA, we need to be a lot more cautious, including with respect to CV risk.15 In light of the findings by Ferguson and colleagues as well as data in the current literature, maybe we should re-evaluate our choices of first-line treatment for PsA.
AG: That is an excellent point. One of our side discussions has been whether we can extrapolate recent JAK inhibitor warnings16 (that are based on their use in a population of patients with rheumatoid arthritis 50 years of age or older and with CV risk factors) to a population of younger patients with PsA who are men. In younger patients under consideration for treatment with a JAK inhibitor, given the extent to which features of their body habitus may predispose them to MACE as a function of phenotype, we should be thinking of CV risk factors other than the traditional ones. I think you correctly identify the potential for a creative tension between who should manage CVD and who will manage CVD. That is going be decided on a case-by-case basis as well. In more rural communities, I think specialists do take on a lot of primary care functions. In urban communities, many tend not to — although here at HSS, as you know, some colleagues will take over the complete care of certain patients while some will just provide specialty care as rheumatologists.
That said, I think this is a very interesting paper, not just in terms of demonstrating what the differences are in a population of patients with PsA, but also in helping us to recognize that “overweight” is not just 1 unified risk factor and patients may be categorized into different subtypes with different risk factors for a CV event. Obviously, when evaluating registry data, one likes to use data to generate hypotheses for future controlled trials; Ferguson and colleagues appropriately suggest that large randomized placebo-controlled trials are needed to prove that weight loss will improve body composition as well as outcomes in patients with PsA. Perhaps the best thing we can do is to tell patients to lose weight — or perhaps as you implied, Genna, telling them to stop smoking is the first, second, and third best approach and losing weight may be the fourth best. Those may be the lifestyle factors over which we have more control.
In summary, I think this paper very effectively demonstrated the association between body habitus and disease activity and outcomes in PsA, especially in terms of implications for CV outcomes and what we can do to mitigate risk for our patients.
Genna and Kevin, do you have any final comments?
KY: One thing that I wanted to point out earlier relates to the question of CVD and T2D propensities. It is my understanding that the data were based purely on the body composition and where the fat lies, correct? I ask because compared with other data on vascular risk in psoriasis,17 the data from Ferguson and colleagues may underestimate vascular risk in PsA.4 Given the inflammatory component and the endothelial dysfunction that are characteristic of PsA, there may be added CV risk, as some investigators have hypothesized.18
AG: Let us not forget disease activity.
KY: Exactly. These estimates may actually be an understatement of risk. This question of whether or not body composition is a strong contributor to CV risk in PsA could be explored in later studies.
AG: Good point, Kevin. Genna, do you have any final comments?
GB: I agree. I was also very interested in measures of actual subclinical CV dysfunction. I do not know if it would be feasible, with this particular biobank and the IMAPA study, to extend the study to those additional outcomes. The authors did a great job of biometrically profiling their patients in terms of body composition so I was curious about their vascular measures.
This article was edited for clarity and length.
Disclosure
Allan Gibofsky, MD, JD, reported affiliations with AbbVie, Inc.; Acquist Therapeutics, Inc.; Amgen Inc.; Biosplice Therapeutics, Inc.; Boehringer Ingelheim Pharmaceuticals, Inc.; Eli Lilly and Company; Pfizer Inc.; Janssen Therapeutics; and Regeneron Pharmaceuticals, Inc.
References
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Reviewed August 2022
