
==== Front
medRxiv
MEDRXIV
medRxiv
Cold Spring Harbor Laboratory

10.1101/2024.09.03.24313022
preprint
1
Article
Predictors of white matter hyperintensities in the elderly Congolese population
Yohe Emile Omba
Alonso Alvaro http://orcid.org/0000-0002-2225-8323

Drane Daniel L.
Patel Saranya Sundaram http://orcid.org/0000-0003-1070-6421

Schwinne Megan
Epenge Emmanuel
Gikelekele Guy
Herve Esambo
Kavugho Immaculee
Tshengele Nathan
Mampunza Samuel
Mananga Lelo
Zhao Liping
Qiu Deqiang
Stringer Anthony
Saindane Amit M
Ikanga Jean
04 9 2024
2024.09.03.24313022https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
http://medrxiv.org/lookup/doi/10.1101/2024.09.03.24313022
nihpp-2024.09.03.24313022.pdf
ABSTRACT

Background

White matter hyperintensities (WMHs) are strongly linked to cardiovascular risk factors and other health conditions such as Alzheimer’s disease. However, there is a dearth of research on this topic in low-income countries and underserved populations, especially in the Democratic Republic of Congo (DRC) where the population is aging rapidly with increasing cardiovascular risk factors and dementia-related diseases. This study evaluates health factors associated with WMH in the elderly Sub-Saharan Africa (SSA), specifically Congolese adults.

Methods

In a cross-sectional study of 77 people from the DRC, participants underwent neuroimaging to analyze WMH volume and completed clinical evaluation, laboratory-based blood exams, self-reported questionnaires, and interviews. A simple linear regression model was conducted to test the association between WMH and potential predictors (neurological status, age, sex, hypertension, diabetes, tobacco abuse, stroke, high cholesterol, cardiovascular medication, and alcohol abuse). Stepwise selection and backward elimination analyses were performed to obtain the final model. Finally, a multiple linear regression model was conducted to assess the association between WMH and variables retained in the final model (neurological status, sex, and age).

Results

Of the 77 individuals, 47 (61%) had dementia, 40 (52.6%) were males, and the mean age was 73 years (± 8.0 years standard deviation). In simple linear regression models, WMH was significantly associated with dementia (expβ1=1.75, 95% CI=1.14 – 2.71, p-value=0.01) though it had a weak association with age (expβ1=1.03, 95% CI=1.00 – 1.05, p-value=0.05) and sex (male) (expβ1=0.66, 95% CI=0.43 – 1.01, p-value=0.05). In multiple linear regression models, WMH was statistically significantly associated with dementia (expβ1=1.97, 95% CI=1.31 – 2.95, p-value =0.001), male sex (expβ2=0.54, 95% CI=0.36 – 0.80, p-value=0.003), and age (expβ3=1.03, 95% CI=1.00 – 1.06, p-value=0.03). However, WMH was not significantly associated with common cardiovascular risk factors, such as high blood pressure, diabetes, tobacco use, obesity, and high cholesterol levels.

Conclusion

WMH is significantly associated with neurological status, sex, and age in the Congolese population. Understanding these predictors may improve our ability to diagnose, assess, and develop preventative treatments for white matter disease in SSA/DRC populations, where neuroimaging is difficult to obtain.
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