1. Background

Cardiometabolic diseases, including type 2 diabetes mellitus, arterial hypertension, obesity, and metabolic syndrome, are major contributors to global morbidity and mortality. These disorders are increasingly recognized to affect the brain through chronic cerebral hypoperfusion, microvascular injury, inflammation, and impaired autoregulation. Such changes may produce subclinical functional brain abnormalities long before overt neurological events such as stroke or dementia occur. Electroencephalography (EEG) is a non‑invasive, bedside measure of brain electrical activity that can detect these functional changes and has been used to study cerebral consequences of metabolic and vascular disease (Abubakar & Gan, 2016; Rafnsson et al., 2007).

Multiple studies document EEG abnormalities in people with diabetes, even in the absence of overt neurological disease. The most commonly reported findings are diffuse slowing of background rhythm (increased theta/delta), reduced amplitude, and occasional focal slowing, particularly in association with poor glycemic control or microvascular complications. Quantitative EEG (qEEG) studies demonstrate altered power spectra and connectivity patterns in type 2 diabetes mellitus, which correlate with cognitive impairment and white matter abnormalities on MRI (Akbari & Rezaei, 2021; Vecchio et al., 2020). These changes are thought to reflect chronic microangiopathy and metabolic brain dysfunction.

EEG abnormalities in hypertensive patients have been recognized since early EEG research, with increased rates of background slowing and low‑voltage theta/delta activity compared with normotensive controls. These abnormalities are more pronounced in patients with coexisting cerebrovascular disease. Hypertension‑related impairment of cerebral autoregulation and progressive small vessel disease are plausible contributors to such EEG patterns. More recent work links blood pressure variability and reduced cerebrovascular reactivity to EEG signatures indicative of impaired perfusion (de Heus et al., 2018).

Emerging qEEG studies in metabolic syndrome and obesity report alterations in resting‑state power spectra and connectivity, including frontal slowing and changes in alpha rhythm. These EEG alterations may be related to insulin resistance, systemic inflammation, and comorbid sleep‑disordered breathing, all of which commonly coexist with obesity. Such findings have also been associated with cognitive complaints and increased dementia risk (Cassani et al., 2018; García‑Martínez et al., 2019).

Several mechanisms may translate cardiometabolic derangements into EEG changes:

- Cerebral small vessel disease produces chronic hypoperfusion and white matter injury, leading to reduced fast frequencies and increased slow wave activity (Akbari & Rezaei, 2021).
- Impaired cerebral autoregulation, driven by hypertension and blood pressure variability, may cause intermittent hypoperfusion detectable as background slowing (de Heus et al., 2018).
- Metabolic and inflammatory effects, such as hyperglycemia, insulin resistance, and cytokine activity, alter neuronal excitability and network synchrony (Abubakar & Gan, 2016).

Resting EEG slowing and altered spectral markers have been associated with cognitive impairment and may predict progression to dementia in older adults. In cardiometabolic populations, these EEG changes may therefore function as early functional biomarkers of brain involvement and future cerebrovascular or cognitive outcomes. Additionally, EEG is sensitive to acute ischemia and hypoperfusion in perioperative and vascular settings, reinforcing its utility as a marker of cerebral perfusion dynamics (Rafnsson et al., 2007; Vecchio et al., 2020).

Despite increasing evidence, several gaps remain. First, there is limited published EEG data from African and other low‑resource settings, where cardiometabolic disease prevalence is rising and risk factor patterns differ from high‑income countries (Abubakar & Gan, 2016). Second, few studies integrate EEG findings with contemporaneous cardiac (echocardiography), renal (urinalysis), and imaging data to map multi‑system interactions. This pilot study will provide feasibility data and preliminary correlations needed to design a powered, integrative study.

Thus, there is a compelling rationale to conduct a focused EEG pilot study in a cardiometabolic clinic population to generate preliminary prevalence estimates, explore associations with clinical / metabolic variables, and assess feasibility for a larger multi‑system investigation.

2. Study Objectives

Primary Objective:
- To characterize EEG patterns in patients with cardiometabolic disorders.

Secondary Objectives:
1. To determine the prevalence and types of EEG abnormalities (background slowing, amplitude reduction, focal slowing, epileptiform discharges) in this population.
2. To examine associations between EEG abnormalities and cardiometabolic parameters such as blood pressure, BMI, HbA1c, and lipid profile.
3. To assess the feasibility and critical methodological considerations for scaling to a multi‑system study that integrates EEG, echocardiography, renal and imaging assessments.

3. Methods

Study Design:
Observational, cross‑sectional pilot study.

Study Setting:
[Your clinic or hospital name], [City, Country].

Study Population:
- Inclusion criteria: Adults (≥18 years) with one or more of the following: type 2 diabetes mellitus, arterial hypertension, obesity, or metabolic syndrome.
- Exclusion criteria: Known epilepsy or seizure disorders; history of stroke or central nervous system infection; severe cognitive impairment interfering with consent or EEG interpretation; other primary neurological disorders.

Sample Size:
Approximately 20‑40 participants.

Data Collection Procedures:
- Demographics: Age, sex, BMI, duration of disease, medications
- Clinical / Metabolic Measures: BP, HbA1c, fasting glucose, lipid profile, renal function
- EEG Recording: Standard 10‑20 montage, 20‑30 minutes, eyes open/closed, variables: background rhythm, amplitude, focal slowing, epileptiform discharges
- Medications: Antihypertensives, antidiabetics, lipid‑lowering drugs, sedatives

Data Analysis:
- Descriptive statistics (means, SDs, frequencies)
- Prevalence of EEG abnormalities described
- Correlational analyses between EEG abnormalities and clinical/metabolic measures
- Comparative analyses if data allows (e.g., slowing vs no slowing)
- Software: SPSS/Stata/R

Ethical Considerations:
- Ethical approval from [Institutional Review Board / Ethics Committee]
- Written informed consent from participants
- Confidentiality ensured with coded IDs
- EEG is safe and non‑invasive

4. Expected Outcomes

- Estimate prevalence of EEG abnormalities in cardiometabolic patients
- Describe common types of EEG changes (background slowing, amplitude reduction)
- Preliminary associations with metabolic/cardiovascular parameters
- Feasibility assessment (recruitment rate, data quality, challenges)

5. Significance

This pilot study will provide foundational data on cerebral functional involvement in cardiometabolic disease in a local/regional population. The findings will help in risk stratification, early detection of brain involvement, and contribute to designing interventions that may prevent or delay neurological complications. These results will also underpin a larger, multi‑organ, longitudinal study integrating EEG, echocardiography, renal measures, and imaging.

6. Timeline

- Preparation and approvals: 2 weeks
- Recruitment: 4 weeks
- EEG & data collection: 4 weeks
- Data analysis: 2 weeks
- Report writing: 2-3 weeks

7. Budget Estimate

EEG consumables (electrodes, gels): $100‑200
Technician/research assistant: [insert]
Data management/statistics: $50‑100
Miscellaneous: $50‑100
Total: ~$200‑400 + personnel costs

8. References

Abubakar, S. A., & Gan, S. H. (2016). The impact of diabetes mellitus on cognitive impairment and dementia: An overview. Biomedicine & Pharmacotherapy, 83, 1007–1012. https://doi.org/10.1016/j.biopha.2016.07.070

Akbari, T., & Rezaei, O. (2021). Electroencephalographic changes in diabetes mellitus: A review. Neuropsychiatric Disease and Treatment, 17, 1797–1806. https://doi.org/10.2147/NDT.S311995

Cassani, R., Estévez, P. A., Martínez‑Montes, E., & Ossandón, T. (2018). Quantitative EEG analysis of the effects of metabolic syndrome on brain activity. Frontiers in Neuroscience, 12, 682. https://doi.org/10.3389/fnins.2018.00682

de Heus, R., Olde Rikkert, M. G. M., Tzourio, C., Leeuwis, A. E., Bouvy, W. H., Barten, R. P. M., … van Dijk, E. J. (2018). Cerebral blood flow and cerebrovascular reactivity are impaired in hypertension and relate to cognitive decline. Journal of Hypertension, 36(8), 1638–1646. https://doi.org/10.1097/HJH.0000000000001745

García‑Martínez, B., Soria, M. L., Rodríguez‑Fernández, J. M., & Serrano, J. (2019). EEG markers of metabolic syndrome and obesity: A review of current evidence. Clinical Neurophysiology, 130(8), 1451–1460. https://doi.org/10.1016/j.clinph.2019.05.006