Biochemical, Metabolic, and Cardiovascular Factors Associated With Anemia Severity and Hemoglobin Recovery in Women With Heavy Menstrual Bleeding: A Prospective Cohort Study

Background: Heavy menstrual bleeding (HMB) commonly contributes to anemia in reproductive-aged women.This study investigated clinical, biochemical, metabolic, and cardiovascular factors associated with baseline hemoglobin and subsequent recovery, while describing endometrial histopathology in a clinically selected subgroup.Methodology: In this prospective observational cohort study, 150 women aged 18-49 years with HMBassociated anemia were enrolled at Sandeman Provincial Hospital in Quetta, Pakistan, in March 2026 and followed for 12 weeks.Assessments included pictorial blood loss assessment chart (PBAC) scores, iron status markers, C-reactive protein levels, metabolic indices, and cardiovascular measurements.The primary longitudinal outcome was the absolute change in hemoglobin level.Multivariable linear regression was used to assess associations, and histopathological findings were evaluated descriptively when clinically indicated.Results: Mild, moderate, and severe anemia were present in 34 (22.7%),97 (64.7%), and 19 (12.7%) participants, respectively.Of the 150 women, 140 (93.3%) completed follow-up.Mean hemoglobin levels increased from 9.76 to 11.91 g/dL, and 89 women (63.6%) achieved an increase of at least 2.0 g/dL.Higher PBAC scores and resting heart rates, as well as lower ferritin concentrations, were associated with lower baseline hemoglobin levels.Intravenous iron use and concurrent bleeding-control treatment were associated with greater changes in hemoglobin levels.Comparisons of endometrial histopathological findings were exploratory.Conclusions: Menstrual blood loss and ferritin levels were associated with baseline anemia, whereas an elevated resting heart rate may reflect physiological compensation.Treatment associations were observational and do not establish comparative efficacy.Independent validation is needed before these findings can be applied as predictive criteria.