331 THE USE OF BODY MASS INDEX IN PREDICTING ORTHOSTATIC HYPOTENSION IN OLDER ADULTS. (25th October 2022)

• Record Type: Journal Article
• Authors: Chen, H; Morrison, L; Sheehy, T; Costelloe, A; Griffin, M; Quinn, C; O'Connor, M; Peters, C; Lyons, D
• Is Part Of: Age and ageing. Volume 51(2022)Supplement 3
• Publication Date: 2022-10-25
• Subjects: Aging -- Periodicals; Geriatrics -- Periodicals; 618.97
• DOI: 10.1093/ageing/afac218.289
• Languages: English
• ISSNs: 0002-0729

Abstract

Abstract: Background: The presence of Orthostatic Hypotension (OH) is known to be associated with an increased mortality risk. Previous Irish research has shown that elevated Body Mass Index (BMI) may be protective against OH, with overweight and obese patients having significantly smaller Systolic Blood Pressure (SBP) drops during Head-Up-Tilt (HUT) Testing. Methods: Demographics, including age, height and weight, were obtained retrospectively from all head up tilt testing performed in a tertiary Irish hospital between 2000 and 2021. All incomplete records were excluded. A total of 4, 717 patients were analysed. Linear regression models were used to examine the relationship between BMI and change in tilt SBP. Results: 2, 089 males and 2, 628 females over the age of 60 years old were examined. The mean age is 77 years ± 7.8 (S.D.), with majority (51.5%) of the cohort overweight or obese. 69.7% of individuals demonstrated OH. The mean change in tilt SBP was –7mmHg in the underweight and healthy weight group, and –10mmHg in the overweight or obese group. The linear regression model established that BMI significantly predicted a change in tilt SBP (beta=0.394, 95% CI: 0.235 to 0.554, p<0.001), but remains a poor predictive variable (R2=0.004) for this cohort. This correlation was similar for both genders (male: r=0.08, female: r=0.07). Conclusion: Our findings confirmed a correlation between BMI and its predictive impact on OH in older adults. Future studies should explore targeted populations with multivariate analysis, taking into consideration age and gender, to reduce the heterogeneity of data.