The critique, authored by Shubhendu Mohanty, Adarsh Jyoti Lakra, Hima Bindu Mantravadi, Prerna Uniyal and Dhanya Dedeepya, does not dispute the value of the underlying cohort. Assembling nearly a hundred well-characterized patients with two rare vasculitides, imaged to a common protocol and tracked over years, is genuinely difficult work, and the descriptive account of vascular distribution and treatment patterns stands as a useful contribution to the literature. The objection is narrower and more technical: it concerns what kind of question this particular study design is capable of answering, and whether the answer the authors reached was baked into the study before the first patient was enrolled.

The heart of the problem lies in the 2022 classification criteria from the American College of Rheumatology and the European Alliance of Associations for Rheumatology, which were used to allocate patients to the two diagnostic groups. Classification criteria are, by design, tools built to discriminate between named diseases. They are constructed from clinical and imaging features known to separate the conditions, and they are validated for that discriminating purpose. When a study then compares the groups on those same features and reports the differences as findings, the analysis becomes circular. The classifier and the outcome are the same variable, and the result is a foregone conclusion dressed in the language of discovery.

Age offers the clearest illustration of this circularity. The Takayasu criteria require disease onset at or below 60 years of age, while the giant cell arteritis criteria require patients to be 50 or older. The Italian study reported a median age of 33 in the Takayasu group against 76 in the giant cell arteritis group, with a p-value below 0.001. But as the commentators point out, this striking difference simply restates the entry rule. No patient over 60 could appear in the Takayasu group, and few under 50 could appear in the giant cell arteritis group. Reporting the age gap as a distinguishing feature of the two diseases adds no new biological information; it merely echoes the arithmetic of the classification scheme.

Vascular distribution presents a more consequential version of the same problem. The study’s methods state explicitly that the differentiation of cranial giant cell arteritis from its large-vessel form, and from Takayasu arteritis, was based on angiographic assessment. Yet the results then compare angiographic vascular distribution between the groups and report involvement of the axillary arteries, the aortic arch, the mesenteric vessels and the renal arteries as findings that distinguish the diseases. Polymyalgia rheumatica, which was present in 37.8 percent of the giant cell arteritis group and in none of the Takayasu group, falls into the same category, because it is a recognized component of the giant cell arteritis phenotype that informs the diagnosis in the first place. In each case, the feature that appears to separate the groups is the feature that was used to separate them.

Crucially, the commentators are not arguing that Takayasu arteritis and giant cell arteritis are the same disease. Their claim is epistemological rather than clinical: a cohort assembled through classification criteria cannot adjudicate whether the two entities are distinct, because the criteria were engineered to make them distinct. The features that could genuinely settle the question are precisely the ones the criteria do not use, and the Italian study actually measured several of them. Erythrocyte sedimentation rate and C-reactive protein, the two classic inflammatory markers, showed no significant difference between the groups, with p-values of 0.722 and 0.448 respectively. Neurologic and pulmonary manifestations likewise did not differ, and the long-term remission rate was reported as not significantly different in the abstract. An analysis restricted to these criteria-independent variables, the commentators suggest, would constitute a real test of the distinctness question, and would arguably make a more interesting paper.

The critique raises a second, independent statistical concern: the sheer number of comparisons. Across Tables 1, 2 and 3, the original study performed roughly thirty, eleven and ten statistical tests respectively, all evaluated at a significance threshold of 0.05 with no adjustment for multiple testing. At that volume of testing, chance alone guarantees that two or three findings will cross the significance threshold even if no true differences exist. Several of the differences that survived into the paper’s conclusion were marginal: dermatologic involvement at p equals 0.04, chronic liver disease at p equals 0.02, axillary artery involvement at p equals 0.02, and low-dose glucocorticoid use at p equals 0.04. The commentators note that Fisher’s exact test, which the authors appropriately chose for the sparse data, validates each individual test but does nothing to address the multiplicity problem. Their proposed remedy is straightforward: designate a small number of prespecified comparisons as primary, and label everything else exploratory.

A third concern involves confounding by referral pattern, a problem the original authors themselves acknowledged in their limitations section. One of the three participating centers is a regional tertiary referral center for Takayasu arteritis, which the authors offered as an explanation for the imbalance in group sizes. But the consequences of that arrangement extend well beyond group size. A referral center concentrates severe and refractory disease, so the Takayasu group was enriched for exactly the patients most likely to receive aggressive treatment. The study reported high-dose glucocorticoid use in 59.3 percent of Takayasu patients against 18.9 percent of giant cell arteritis patients, combination therapy with glucocorticoids plus immunosuppressants in 86.4 percent against 51.4 percent, and interventional vascular procedures in 25.4 percent against 5.4 percent. These figures, the commentators argue, reflect differences between two differently recruited populations at least as much as differences between two diseases, and the conclusion that the conditions respond differently to therapy does not follow from them.

The comment also catalogs a series of numerical inconsistencies that the authors are asked to reconcile at proof stage. The abstract reports the p-value for time from symptom onset to diagnosis as 0.025, while Table 1 and the results text give 0.029. The caption to Table 3 describes a cohort of 84 patients, although the study comprises 96. The subclavian artery row of Table 2 lists the giant cell arteritis proportion as 35140, presumably a typographical error for 35.14 percent. And the proportion of patients followed for at least 60 months appears as 72 percent in the abstract and discussion but 75 percent in the methods. None of these discrepancies is individually decisive, but together they underscore the importance of careful proofreading in studies that will inform clinical classification.

The broader lesson extends well beyond this single cohort. Classification criteria are indispensable tools for enrolling homogeneous patient groups into research studies and for standardizing clinical communication, but they are diagnostic heuristics, not ground truth. When researchers compare groups defined by such criteria on the variables that constitute them, they risk converting a definitional artifact into a purported biological finding. The commentators close with a constructive proposal: the Italian cohort remains a valuable resource, and a comparison restricted to criteria-independent features, governed by a prespecified analysis plan, would allow the data to speak to the question the study set out to answer. Until such an analysis is performed, the claim that Takayasu arteritis and giant cell arteritis require fundamentally different approaches remains plausible, but unproven by this evidence, a distinction that matters for the clinicians treating these rare and potentially devastating diseases of the large arteries.

Subject of Research: Methodological critique of a cross-sectional comparison of Takayasu arteritis and giant cell arteritis

Article Title: Comment on “Takayasu Arteritis and Giant Cell Arteritis: Results From a Cross‐Sectional Study of 96 Italian Patients”

Article References: Mohanty, S., Lakra, A. J., Mantravadi, H. B., Uniyal, P., & Dedeepya, D. (2026). Comment on “Takayasu Arteritis and Giant Cell Arteritis: Results From a Cross‐Sectional Study of 96 Italian Patients”. Immunity, Inflammation and Disease, 14(9), Article e70513. https://doi.org/10.1002/iid3.70513

Image Credits: AI Generated

DOI: 10.1002/iid3.70513

Keywords: Takayasu arteritis, giant cell arteritis, vasculitis, classification criteria, ACR/EULAR 2022, circular reasoning, multiple testing, referral bias, rheumatology, statistical methodology, large-vessel vasculitis, epidemiology

Takayasu arteritis, often called “pulseless disease,” primarily affects the aorta and its major branches. Inflammation can thicken the arterial wall, reduce the diameter of the vessel, and eventually restrict blood flow to the brain, arms, kidneys, or other organs. Neurological complications may include transient ischemic attacks, strokes, dizziness, visual disturbances, and fainting. Because symptoms and laboratory markers do not always reflect the full extent of vascular injury, clinicians increasingly rely on imaging to identify active inflammation and structural narrowing. The new work explores an unusual imaging target: the amygdala, a small almond-shaped structure located in the medial temporal lobe and involved in emotional processing, stress responses, autonomic regulation, and communication with the immune system.

The investigators analyzed data from 303 people with Takayasu arteritis who underwent whole-body ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography, commonly known as ¹⁸F-FDG PET/CT. The radioactive glucose analogue is taken up by metabolically active cells, allowing PET to visualize tissues with increased glucose consumption. In large-vessel vasculitis, inflammatory cells in the arterial wall can accumulate FDG and produce a measurable signal. The researchers also quantified FDG uptake in the amygdala and bone marrow, as well as in affected vessel walls, while collecting clinical information, blood-test results, and vascular imaging findings. Participants were followed for a median of 27 months, during which cerebrovascular and other adverse events were recorded.

The principal result was not uniform across the entire cohort. When all participants were analyzed together, amygdalar standardized uptake values, or SUVs, were not significantly associated with cerebrovascular events. SUV is a semi-quantitative measure that estimates how much tracer has accumulated in a region after accounting for factors such as injected dose and body size. SUVmax represents the highest measured activity within a region of interest, whereas SUVmean reflects the average activity. This distinction matters because a single intense voxel can influence SUVmax, while SUVmean may provide a broader estimate of regional metabolic activity. In the overall study population, neither measurement consistently separated patients who experienced cerebrovascular events from those who remained event-free.

A clearer pattern emerged in the treatment-naïve subgroup. Patients who had suffered cerebrovascular events showed lower amygdalar activity than those without such events. Mean amygdalar SUVmax was 9.3 compared with 10.3 in event-free patients, while mean SUVmean was 6.6 compared with 7.4. The differences were statistically significant, with p values of 0.011 and 0.003, respectively. When the researchers divided patients according to amygdalar metabolic activity, 24.3 percent of people in the low-SUV group had experienced cerebrovascular events, compared with 15.9 percent in the higher-SUV group. The low-activity group also had higher immunoglobulin G and immunoglobulin A levels and lower lymphocyte counts, suggesting that reduced amygdalar uptake may coexist with distinctive systemic immune features.

The relationship became especially notable when the researchers examined structural disease in the arteries supplying the head and neck. Higher amygdalar SUVmax was identified as an independent protective factor against combined carotid and vertebral artery stenosis. The reported odds ratio was 0.876, with a p value of 0.032. An odds ratio below one indicates that, within the statistical model, increasing amygdalar activity was associated with lower odds of the outcome after accounting for other evaluated factors. The carotid arteries deliver blood to much of the brain’s anterior circulation, while the vertebral arteries contribute to the posterior circulation. Narrowing in both systems can substantially reduce cerebral blood flow and increase the risk of ischemic injury.

Follow-up findings provided additional support for the signal, although they also illustrated the complexity of the biology. Patients who later experienced cerebrovascular events had a significantly lower amygdalar SUVmax than a group described as having new-onset symptoms without the same event outcome: 8.2 compared with 10.4. This observation raises the possibility that amygdalar metabolic activity could reflect a brain-body state associated with vascular vulnerability before or during clinically important disease. However, PET uptake is not a direct measurement of stress, emotion, or immune control. It can be influenced by age, medication, glucose levels, scanner characteristics, image-processing methods, brain structure, and other medical conditions. The amygdala is also small, making accurate measurement vulnerable to partial-volume effects, in which limited spatial resolution causes activity from neighboring tissues to blend into the region of interest.

The authors’ interpretation builds on a growing body of research concerning the brain’s role in cardiovascular and immune regulation. Earlier studies in other populations have linked resting amygdalar activity with cardiovascular events, while experimental work has shown that stress-related neural circuits can influence the hypothalamic-pituitary-adrenal axis, sympathetic nervous system, bone marrow activity, and inflammatory signaling. The amygdala communicates with regions that regulate autonomic output and endocrine responses, and these pathways can affect circulating immune cells and the behavior of inflammatory tissues. In Takayasu arteritis, such neuroimmune interactions could theoretically alter the inflammatory environment surrounding the aorta and its branches. The present study does not prove this mechanism, but it adds a new imaging-based association to the emerging concept that vascular inflammation may be shaped by both immune processes and neural activity.

The findings should therefore be viewed as a potential biomarker discovery rather than a clinical test ready for routine use. The study was observational, and its results cannot establish whether reduced amygdalar activity contributes to arterial stenosis, results from chronic vascular disease, or reflects another factor shared by patients with worse outcomes. The absence of a significant association in the full cohort also suggests that treatment exposure and disease history may modify the relationship. In addition, the reported associations came from a single clinical cohort and require confirmation in independent populations using standardized PET acquisition and analysis. Future studies could combine serial brain PET, vascular imaging, inflammatory biomarkers, autonomic measurements, psychological assessments, and long-term clinical follow-up. If the association is reproduced, amygdalar metabolism might eventually help identify patients who need closer neurological surveillance, more detailed carotid and vertebral imaging, or intensified prevention strategies.

For now, the study offers a striking shift in perspective on Takayasu arteritis. The disease is traditionally assessed through arterial anatomy, blood-flow measurements, laboratory inflammation markers, and metabolic activity within the vessel wall. The new results suggest that the brain itself may contain information about the risk of vascular complications. A low amygdalar PET signal cannot yet predict an individual stroke, and it should not replace established clinical evaluation. Nevertheless, the work highlights how a scan originally used to map glucose metabolism can reveal connections between emotional-neural circuitry, systemic immunity, and arterial injury. As researchers continue to decode these pathways, the amygdala may become an important part of the story of how large-vessel inflammation affects the whole body.

Subject of Research: Takayasu arteritis, amygdalar metabolism, cerebrovascular events, and carotid-vertebral artery stenosis

Article Title: Amygdalar metabolic activity associated with cerebrovascular events and carotid-vertebral artery stenosis in takayasu arteritis

Article References: Ma L, Wu B, Wu S, et al. “Amygdalar metabolic activity associated with cerebrovascular events and carotid-vertebral artery stenosis in takayasu arteritis.” European Journal of Nuclear Medicine and Molecular Imaging (2026). References include Tawakol A, Ishai A, Takx RA, et al. “Relation between resting amygdalar activity and cardiovascular events: a longitudinal and cohort study.” The Lancet. 2017;389:834–845.

Image Credits: AI Generated

DOI: 10.1007/s00259-026-08090-z

Keywords: Takayasu arteritis; amygdala; ¹⁸F-FDG PET/CT; cerebrovascular events; carotid artery stenosis; vertebral artery stenosis; neuroimmune interaction; vascular inflammation; brain metabolism; nuclear medicine imaging