Prevalence and Multivariable Analysis of Risk Factors for Premature Canities Occurring Before the Age of 30 Among Young Adults in Pakistan: A Cross-Sectional Study

Abstract

Background: Premature graying of hair is an early-onset pigmentary change that may reflect genetic susceptibility, oxidative stress, nutritional imbalance, psychological factors, systemic illness, and lifestyle-related exposures. Evidence from Pakistan remains limited, particularly among young adults. Objective: To estimate the prevalence of premature graying before 30 years of age and describe associated demographic, lifestyle, nutritional, psychological, clinical, hair-related, and familial characteristics among young adults in Lahore, Pakistan. Methods: A cross-sectional online survey was conducted from November 2025 to April 2026 among 610 young adults. Data were collected using a structured questionnaire covering sociodemographic characteristics, smoking, diet, physical activity, vitamin or mineral deficiency, hair loss, scalp type, psychological symptoms, comorbidities, medication use, hair-loss treatment practices, and family history of premature graying. Categorical variables were summarized as frequencies and percentages. Results: Premature graying before age 30 years was reported by 337 participants, giving a corrected prevalence of 55.2%. Among affected participants, 47.5% reported 1–10 gray hairs at onset, 19.9% reported 11–100 gray hairs, and 32.6% reported more than 100 gray hairs. Frequently reported characteristics included family history of premature graying (62.5%), vitamin or mineral deficiency (58.2%), hair loss (58.2%), oily scalp (55.9%), anxiety (52.0%), smoking (51.5%), unhealthy diet (50.8%), depression (24.9%), chronic medication use (24.6%), and comorbidities (19.3%). Conclusion: Premature graying was common among young adults in this Lahore-based sample and clustered with familial, nutritional, lifestyle, psychological, and hair-health factors. Corrected multivariable analysis using the cleaned dataset is required to identify independent predictors.