Temporal Distribution and Trends of Thyroid Dysfunctions in Gujrat, Pakistan by Linear Regression Model: A Cross-Sectional Analysis
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Background: Thyroid-function abnormalities vary according to sex, age, clinical characteristics, referral patterns, and geographical setting, but temporal laboratory-based data from Gujrat, Pakistan, remain limited. Objective: To describe the annual and sex-specific distribution of biochemical thyroid abnormalities among patients undergoing thyroid-profile testing and to explore temporal changes from January 2020 to July 2025. Methods: This retrospective laboratory record-based observational study included 1,004 thyroid-profile records obtained from Dr. Abdul Rauf Laboratory, Gujrat. The sample comprised 848 females and 156 males. Records were classified as primary hypothyroidism, subclinical hypothyroidism, central hypothyroidism, primary hyperthyroidism, subclinical hyperthyroidism, isolated low T3, or isolated high T3 where available. Frequencies and percentages were summarized by year and sex. Exploratory linear regression estimated annual percentage-point changes for each abnormality. Results: Subclinical hypothyroidism was the most frequently reported abnormality during most study years, decreasing from 22.2% in 2020 to 8.0% during January–July 2025. Its estimated slope was −2.089 percentage points per year (p=0.0283; R²=0.738). Isolated low T3 increased descriptively to 8.8% during January–July 2025, but its trend was not statistically significant (slope=1.383; p=0.2928). Other abnormalities showed no statistically supported linear trends. Females generally had higher reported proportions of hypothyroid abnormalities, whereas male estimates were more variable because of smaller numbers. Conclusion: Subclinical hypothyroidism was the predominant laboratory-detected abnormality and showed an exploratory decreasing trend. The results reflect a tested laboratory population rather than community prevalence and require confirmation using patient-level, multicentre, or population-based data
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