Alopecia areata and the risk of insulin resistance: a systematic review and meta‐analysis
Bibliographic record
Abstract
The pathogenesis of alopecia areata (AA) remains incompletely understood. Prior research identified an increased odds of metabolic syndrome, hyperinsulinemia, and dyslipidemia in patients with AA.1 In this systematic review and meta-analysis, we aimed to investigate whether insulin resistance was more common in patients with AA compared with controls based on full multivariate analysis. Medline and Embase databases were searched from inception to April 3, 2024, using variations of “alopecia areata” and “insulin resistance,” adhering to PRISMA reporting guidelines.2 PROSPERO protocol was registered (CRD42024532125). Clinical studies that measured the homeostasis model assessment of insulin resistance (HOMA-IR), along with other metabolic biomarkers including insulin, fasting blood glucose (FBG), C-peptide, and body mass index (BMI) in patients with AA and control subjects were included. Non-English, non-published works, review articles, and literature including patients with known metabolic disorders (e.g., diabetes mellitus) were excluded. Statistical analyses were conducted using Review Manager v5.4.1 (Cochrane Collaboration). Continuous outcomes were assessed using the standardized mean difference (SMD) with 95% confidence intervals employing a random-effects model. Heterogeneity among studies was evaluated using the I2 statistic, and publication bias was assessed via funnel plot analysis. Risk of bias across included studies was assessed using the Newcastle–Ottawa Scale (NOS) for nonrandomized studies. The systematic search yielded 799 unique results, of which five met the inclusion criteria for analysis (Fig. 1). A total of 249 patients with AA (mean age 32.7 ± 10.56, 45.8% female), and 187 controls (mean age 32.1 ± 8.7, 47.1% female) were included. The specific AA subtypes included multifocal (62.1%), universalis (18.2%), totalis (11.1%), ophiasis (6.6%), and diffuse (2.0%). Compared with controls, patients with AA had higher HOMA-IR values (SMD = 0.62; 95% CI, 0.20–0.88; P = 0.004), (Fig. 1) higher fasting insulin values (SMD = 0.54; 95% CI, 0.16–0.93; P = 0.006), (Fig. 2), higher FBG (SMD = 0.33; 95% CI, 0.13–0.53; P = 0.001), and higher C-peptide (SMD = 0.67; 95% CI, 0.40–0.95; P < 0.00001). BMI did not differ significantly between groups (SMD = 0.01; 95% CI, −0.18 to 0.20; P = 0.89). Risk of bias assessment was overall heterogeneous with two studies scoring “good,” two studies scoring “fair,” and one study scoring “poor”. Further research is needed to understand the precise role of insulin resistance in the pathogenesis of AA and whether specific treatments that target insulin resistance will impact AA-specific patient outcomes and associated comorbidities. AA and insulin resistance may share similar immunopathogenic mechanisms, including elevated cytokines (e.g., IFN-γ, TNF-α, and IL-1) and disrupted insulin receptor signaling.3 The potential contribution of insulin resistance to AA pathogenesis continues to be investigated. Adiponectin, an adipose tissue-circulating adipokine has effects in promoting insulin sensitivity. Patients with AA have shown lower serum levels of adiponectin than healthy controls, illustrating a potential mechanism for AA pathogenesis.4 Prior genome-wide association studies have suggested a shared genetic foundation between AA and metabolic dysregulation, identifying common genes such as ERBB3, PTPN22, and CTLA4 in both conditions.5 Further research is also needed to better understand which subset of patients with AA are at highest risk for insulin resistance. These patients may benefit from measurement of hemoglobin A1c, FBG, and fasting insulin in addition to their standard blood testing panels. At present, it seems reasonable to consider these tests in patients with known risk factors such as family history, obesity, hypertension, hypercholesterolemia, current heart disease, sleep apnea, smoking history, and known liver disease. Consultation with primary care and/or endocrinology will be important for patients with AA with confirmed insulin resistance. Lifestyle modifications are an important first-line treatment strategy, with pharmacotherapies added to the treatment plan when these lifestyle changes are not successful.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".