Welcome back to the journal review. This is the June twenty twenty-six issue of the Journal of the American Academy of Dermatology, and we've got four pieces worth your time this month — a pharmacovigilance brief report on blood pressure medications and skin cancer signals, a spirited exchange of letters over how to properly analyze geographic melanoma data, and an original brief report giving you an actual clinical tool for telling apart two look-alike facial tumor syndromes. Let's get into it.

First up is a brief report titled "Antihypertensives and skin cancer: evidence from the FDA Adverse Event Reporting System." This is a pharmacovigilance study, which is its own methodological animal, so it's worth spending a moment on what that means before we get to the findings. The background here is the emerging concept of "onco-hypertension" — hypertension affects something like a third of adults worldwide and requires lifelong drug therapy, and there's growing concern that some of these agents, particularly hydrochlorothiazide, which got an actual FDA skin cancer warning back in twenty twenty, may promote cutaneous carcinogenesis through a photosensitization mechanism. The proposed biology is what they call photo-nitroso-carcinogenesis — ultraviolet light interacting with photosensitizing drugs or nitrosamine contaminants to drive keratinocyte DNA damage. But the observational literature on this has been messy and inconsistent, so this Chinese pharmacy group turned to FAERS, the FDA's spontaneous adverse event reporting database, to look for disproportionality signals.

Methodologically, this is a classic disproportionality analysis — they pulled every adverse event report from twenty four to early twenty twenty-five, looked at five major first-line antihypertensive classes, and calculated a reporting odds ratio for skin cancer terms against all other reports in the database. A signal was called positive if there were at least three reports and the lower bound of the ninety-five percent confidence interval crossed above one. It's important to understand what this design can and can't tell you — it is not incidence, it is not risk, it's simply whether a drug is reported alongside a skin cancer diagnosis more often than you'd expect by chance across this massive spontaneous reporting system. The appeal of FAERS for a question like this is scale: skin cancer signals tied to any single antihypertensive are rare and likely underreported, so you need a huge denominator to have any hope of detecting a disproportionality signal at all, which a prospective cohort simply couldn't offer at this stage.

The results: out of nearly fourteen million total adverse event reports, they found signals for melanoma with amlodipine among the calcium channel blockers, terazosin and atenolol and nebivolol among the beta and alpha blockers, hydrochlorothiazide as expected, and a whole cluster of ACE inhibitors and ARBs — lisinopril, ramipril, losartan, irbesartan, olmesartan, telmisartan, valsartan. For the broader category of non-melanoma malignant or unspecified skin neoplasms, the signal list was even longer, spanning multiple drugs in every one of the five classes they studied. So essentially, this is a broad, class-spanning signal, not something isolated to one diuretic.

The discussion leans on two competing mechanisms to explain the heterogeneity — the photosensitization and nitrosamine hypothesis on one hand, and on the other, a potentially protective counter-mechanism where angiotensin II blockade suppresses VEGF-driven angiogenesis, which might actually offset carcinogenic signaling for some renin-angiotensin system drugs. The authors are appropriately restrained about what this means. They explicitly flag that FAERS data carries reporting bias, confounding by indication, and heavy co-prescription — patients on antihypertensives are usually on several drugs and have significant comorbidity burden — so causation absolutely cannot be inferred from a disproportionality signal alone.

For your practice, I'd call this hypothesis-generating rather than practice-changing. It doesn't tell you to switch a patient off amlodipine or an ARB. What it should reinforce is something you're probably already doing — treating long-term antihypertensive users, especially those on hydrochlorothiazide, as a photosensitive population that warrants extra sun-protection counseling and a lower threshold for skin surveillance, particularly your fair-skinned, high cumulative UV-exposure patients who are on these drugs for decades.

Next, we've got a linked pair of letters that are worth taking together, even though an original brief report sits between them in the issue — a commentary on a prior JAAD paper by Adler and colleagues on climate and UV variables and melanoma incidence across US counties, followed later in the issue by that same group's formal response. Neither of these is an original study in the full sense — they're Notes and Comments, a scientific back-and-forth about methodology, so there's no new results-and-limitations arc here, just an argument about analytic rigor that actually matters for how you interpret ecological, county-level cancer epidemiology.

The commentary's critique is essentially this: the original paper used simple linear regression to relate UV and climate variables to county-level melanoma incidence, and the commentators argue that approach ignores spatial autocorrelation — the statistical fact that neighboring counties tend to resemble each other, violating the independence assumption that ordinary least squares regression requires. To make their point, they redid the analysis using the same SEER and NPCR incidence data, but ran spatial lag and spatial error models alongside the original ordinary least squares approach, using queen contiguity weighting and Moran's I testing for clustering. They found strong, statistically significant spatial clustering of melanoma incidence nationally, and when they added spatial terms, model fit improved substantially — a large drop in Akaike Information Criterion and meaningfully higher R-squared. Under the spatial error model, UV irradiance actually flipped to a negative association with total melanoma incidence, while it remained a positive association specifically for non-Hispanic White melanoma incidence. Their takeaway is that ignoring spatial structure likely biased the original estimates, and that public health targeting should focus on geographic clusters where high UV overlaps with high incidence, something the original linear approach couldn't reveal.

Now to the response from Adler and colleagues, the original authors. Their pushback is methodologically substantive rather than defensive hand-waving. They argue that detecting spatial autocorrelation doesn't automatically mean the original coefficients were invalid — they cite prior literature suggesting that at broad, national geographic scale, ordinary least squares coefficients aren't meaningfully distorted by spatial autocorrelation, and that spatial correction mostly matters when you're trying to resolve small-scale local variation, which wasn't their study's goal. They also make a sharp point back at the commentary: the fact that the association between UV and melanoma flipped direction between the total population model and the non-Hispanic White subgroup model actually undercuts confidence in the spatial approach as some kind of ground truth — if your corrected model is that sensitive to which subgroup you run it on, that's a reason for caution, not certainty. Their conclusion is that spatial models are a complementary lens, not a corrective one, and that the deeper limitation for both papers is the lack of individual-level staging and socioeconomic data in county-aggregated datasets.

For your purposes, neither letter changes anything about how you counsel patients or triage referrals. The real value here is methodological literacy — if you're reading county-level or geographic ecological melanoma data anywhere else, this exchange is a good primer on why spatial autocorrelation matters, and equally, why detecting it doesn't automatically crown one modeling approach as correct. File it as an analytic caution, not a clinical signal.

Now to the piece with genuine clinical teeth this month — an original brief report titled "Facial tumor mapping distinguishes Birt-Hogg-Dubé syndrome from CYLD cutaneous syndrome." This is a small but elegant original study, and given how these two genodermatoses can produce a visually overlapping crop of small facial papules, it's directly relevant to anyone doing skin cancer risk-stratification and referral triage.

The clinical problem: both Birt-Hogg-Dubé syndrome and CYLD cutaneous syndrome present with multiple benign-appearing facial follicular tumors, but they carry very different systemic implications — Birt-Hogg-Dubé for renal cell carcinoma risk, CYLD cutaneous syndrome for its own cutaneous and other malignancy risk. Distinguishing them by eye has been described anecdotally, but the anatomic distribution had never been objectively mapped before this study.

Methodologically, this was a photographic mapping study — genuinely a smart low-cost design for a rare-disease question where you'll never get a large prospective cohort. The authors took facial photographs from seventeen genotyped Birt-Hogg-Dubé patients and thirteen genotyped CYLD cutaneous syndrome patients across two centers, the NIH and the Royal Victoria Infirmary in the UK, with at least one tumor per patient histologically confirmed and the rest clinically diagnosed. They manually traced every visible tumor and normalized it onto a standardized facial template, then divided the face into four anatomic zones and used Mann-Whitney testing to compare tumor density by zone, plus Spearman correlation and negative binomial regression to look at how tumor number tracked with age. Given how rare these syndromes are, manual annotation and image-based mapping was really the only feasible way to generate a quantitative, poolable dataset — you're not going to run a multi-hundred-patient trial here.

The results are strikingly clean. They mapped over thirteen hundred tumors across the Birt-Hogg-Dubé cohort, averaging around seventy-seven tumors per patient, and just over a thousand tumors in the CYLD cohort, averaging around eighty per patient. The spatial distributions diverged sharply. CYLD cutaneous syndrome tumors clustered heavily in what they call zone one — the medial canthi, medial superior orbital rim, and glabella — with a striking thirty-fold increase in density there compared to Birt-Hogg-Dubé, plus a secondary cluster in zone two, the nasofacial sulci and upper lip, at roughly two and a half times the density. Birt-Hogg-Dubé, by contrast, favored the malar and mid-cheek zone, at roughly seven times the density seen in CYLD syndrome. And there was a clear age effect that differed between the two conditions: in Birt-Hogg-Dubé, each additional year of age was associated with about a five percent increase in tumor count, a significant and progressive relationship. In CYLD cutaneous syndrome, there was no significant relationship between age and tumor burden at all.

The authors describe the CYLD pattern as an "hourglass" distribution running from the glabella down to the upper lip, and propose this reflects specific facial hair follicles that become predisposed to tumor formation around adrenarche, essentially a fixed anatomic template that doesn't accumulate further with age. The Birt-Hogg-Dubé malar pattern, on the other hand, paired with its clear age-dependent accumulation, points toward a UV-driven mechanism, and they draw an analogy to the UV mutational signature described in TSC1 and TSC2 in tuberous sclerosis, which shows a similar central-face and nasal predilection. The limitations are honestly stated: small sample sizes reflecting genuine disease rarity, potential selection bias from specialized referral centers, and the fact that this study only examined two syndromes — they explicitly note that SUFU-associated nevoid basal cell carcinoma syndrome could potentially mimic the CYLD facial pattern and wasn't included here.

This one I would call genuinely practice-relevant, not just interesting. If you're looking at a patient with multiple small facial follicular papules and trying to decide which genetic pathway to chase, the distribution itself is a usable bedside clue: a central, hourglass-shaped cluster around the glabella, medial canthi, and upper lip in a younger patient without strong age-progression should raise your suspicion for CYLD cutaneous syndrome and prompt the relevant surveillance conversation; a malar and mid-cheek predominant pattern that's clearly accumulating with age points toward Birt-Hogg-Dubé and the renal cell carcinoma surveillance pathway. It won't replace genetic confirmation, but as a triage heuristic for who you refer and how urgently, this is exactly the kind of pattern-recognition tool worth internalizing.

That wraps our four articles for this issue — a hypothesis-generating pharmacovigilance signal on antihypertensives worth filing away rather than acting on, a methodological tug-of-war over spatial statistics in melanoma epidemiology that sharpens how you read ecological data without changing your practice, and a small, well-designed mapping study that hands you an actual clinical differentiator for two rare but consequential facial tumor syndromes. Thanks for listening, and we'll see you next month.