Prevalence, treatment rates and control of dyslipidemia among diabetes patients in northern Nigeria: A cross-sectional, multicenter study

This multicenter cross-sectional study in northern Nigeria reveals that dyslipidemia affects nearly 60% of diabetes patients, is independently associated with female gender and proteinuria, and remains poorly managed with only 17.1% of patients achieving cardiovascular risk targets due to limited treatment options and low guideline adherence.

The Gist

Imagine your body as a bustling city. Diabetes is like a traffic jam caused by too much sugar clogging the roads. But there's another invisible problem lurking in this city: Dyslipidemia.

Think of dyslipidemia as a garbage truck crisis. Your blood carries different types of "trash" (fats and cholesterol). When you have dyslipidemia, you have too much heavy, sticky trash (bad cholesterol) clogging the pipes, or not enough of the "cleanup crew" (good cholesterol) to sweep it away. If this trash builds up, it forms "roadblocks" (plaque) that can stop traffic completely, leading to heart attacks or strokes.

This study is like a city inspection report from Northern Nigeria. The researchers went into five major hospitals (the city's main hubs) to check the health of 403 people living with diabetes. They wanted to answer three big questions:

1. How many people have this "garbage truck crisis"?
2. Are they getting the right cleanup crew (medicine)?
3. Is the city actually getting cleaner (are the cholesterol levels down)?

Here is what they found, broken down simply:

1. The "Garbage" is Everywhere

The Finding: Nearly 60% of the people with diabetes also had dyslipidemia.
The Analogy: Imagine walking into a room of 10 people with diabetes. Six of them are carrying a heavy, invisible backpack full of sticky tar that is slowly gluing their arteries shut.
Who is most at risk? The study found that women were much more likely to have this problem than men. It's like the "garbage trucks" were more likely to get stuck in the female half of the city. Also, people with protein in their urine (a sign their kidneys are leaking) were at higher risk. It's as if the kidney's "filter" is broken, letting more trash into the blood.

2. The Cleanup Crew is Missing (or Incomplete)

The Finding: Only about half of the people who needed help were actually taking medicine to fix it.
The Analogy: Even though 6 out of 10 people had the sticky tar problem, only 3 of them had a cleanup crew.
What kind of crew? The ones who were treated were mostly using Statins. Think of Statins as a basic, old-fashioned broom. It works, but it's the only tool most people have.
What was missing? The study found zero people using newer, high-tech tools like PCSK9 inhibitors or Ezetimibe.
Why? Money. In this part of Nigeria, newer medicines are like imported luxury cars—too expensive for most people to buy with their own pockets. They can afford the basic broom (Statins), but not the high-tech vacuum cleaner.

3. The City is Still Dirty

The Finding: Only 17% of all the patients (about 1 in 6) actually reached the "clean city" goal.
The Analogy: The doctors have a target: "Get the sticky tar down to this specific level so the roads are safe." But despite the medicine some people were taking, 83% of the city is still clogged.
It's like trying to clean a room with a broom, but the room is so full of trash that the broom just isn't enough, or people aren't using the broom correctly.

The Big Takeaway

The researchers are saying: "We know the trash is there, and we know who has it, but we aren't cleaning it up fast enough."

• The Problem: Dyslipidemia is very common in diabetic patients in Northern Nigeria, especially women.
• The Gap: Doctors are mostly relying on just one type of medicine (Statins) because the newer, better options are too expensive.
• The Result: Most people are still at high risk for heart attacks and strokes because their "blood roads" are still clogged.

The Solution?
The study suggests that doctors need to stick to the "playbook" (medical guidelines) more strictly, and the community needs to find a way to make the "high-tech cleanup tools" (newer medicines) affordable for everyone. Until then, the "garbage trucks" will keep clogging the roads for too many people.

Technical Summary

1. Problem Statement

Diabetes Mellitus (DM) is a major global driver of cardiovascular disease (CVD), with dyslipidemia acting as a critical mediator of this risk. While the prevalence of dyslipidemia in diabetic populations is known to be high, there is a significant lack of data regarding treatment rates, adherence to guidelines, and the proportion of patients achieving lipid targets in Northern Nigeria.

• Gap: Existing studies in Nigeria have focused on prevalence patterns but have not adequately assessed the efficacy of current lipid-lowering therapies (LLTs) or the gap between clinical guidelines and actual practice.
• Context: Global guidelines recommend statins as first-line therapy, with add-on agents (e.g., ezetimibe, PCSK9 inhibitors) for high-risk patients. However, economic barriers and guideline adherence in low-resource settings like Northern Nigeria remain poorly understood.

2. Methodology

• Study Design: Multicenter, cross-sectional, observational study.
• Setting: Conducted between February 8 and December 15, 2023, across five major teaching hospitals in Northern Nigeria:
  • Abubakar Tafawa Balewa University Teaching Hospital (Bauchi)
  • Ahmadu Bello University Teaching Hospital (Zaria)
  • Aminu Kano Teaching Hospital (Kano)
  • Modibbo Adama University Teaching Hospital (Yola)
  • University of Maiduguri Teaching Hospital (Maiduguri)
• Population: 403 consecutive adult patients attending diabetes clinics (58.8% female).
  • Inclusion: Patients with DM, attending at least two follow-up visits, with available lipid profiles.
  • Exclusion: Patients missing lipid profile tests or second follow-up visits.
• Data Collection:
  • Demographics & History: Sociodemographics, DM type/duration, comorbidities (HTN, CKD, CVD), and medication history.
  • Clinical Measures: BMI, waist circumference, blood pressure, HbA1c, fasting blood glucose.
  • Laboratory: Lipid profile (Total Cholesterol, LDL-C, HDL-C, Triglycerides), renal function (eGFR), and urinalysis (proteinuria).
• Definitions:
  • Dyslipidemia: Defined by NCEP-ATP III guidelines (TC > 6.2, LDL-C > 4.1, TG ≥ 2.3, or HDL-C < 1.0/1.3 mmol/L).
  • Target: LDL-C < 1.8 mmol/L for primary CVD prevention.
• Statistical Analysis: SPSS v25. Used Chi-square/Fisher's exact tests for categorical variables, Student's t-test for continuous variables, and binomial logistic regression to identify independent predictors of dyslipidemia.

3. Key Contributions

• Multicenter Scope: Provides the most comprehensive data to date on lipid management specifically within the diverse Northern Nigerian region, covering five states.
• Guideline Gap Analysis: Explicitly quantifies the disparity between current treatment practices (limited to statins/fibrates) and international guidelines recommending newer agents (PCSK9 inhibitors, ezetimibe, etc.) for high-risk patients.
• Risk Factor Identification: Identifies female gender and proteinuria as independent predictors of dyslipidemia in this specific population, distinct from other factors like CKD (defined by eGFR) or poor glycemic control.
• Economic Context: Highlights the role of cost as a primary barrier to accessing advanced lipid-lowering therapies, noting that treatment was restricted to cheaper generic statins and fibrates.

4. Key Results

• Prevalence:
  • 59.6% of participants had dyslipidemia.
  • Gender Disparity: Dyslipidemia was significantly higher in females (65.8%) compared to males (50.6%).
  • Lipid Patterns: The most common abnormality was Low HDL-C (69.6%), followed by High Total Cholesterol (26.2%), High LDL-C (25.0%), and High Triglycerides (18.7%).
• Predictors:
  • In multivariate logistic regression, Female Gender (OR = 1.74) and Proteinuria (OR = 2.26) were the only independent factors associated with dyslipidemia.
  • Obesity, hypertension, and CKD (by eGFR) showed associations in univariate analysis but were not independent predictors in the final model.
• Treatment Rates:
  • Among those with dyslipidemia, only 51.3% were on lipid-lowering therapy.
  • Medication Types: Treatment was almost exclusively limited to Statins (49.6%) and Clofibrate (1.7%).
  • Zero Usage: No patients were prescribed ezetimibe, bempedoic acid, icosapent ethyl, or PCSK9 inhibitors.
• Control/Targets:
  • Only 17.1% of all participants achieved the LDL-C target (< 1.8 mmol/L) for primary CVD prevention.
  • There was no significant correlation between achieving the target and the use of LLTs, glycemic control, or education levels.

5. Significance and Conclusion

• Clinical Implications: The study reveals a critical failure in the secondary prevention of CVD among diabetic patients in Northern Nigeria. Despite a high prevalence of dyslipidemia (nearly 60%), the majority of patients are either untreated or inadequately treated to reach guideline targets.
• Guideline Adherence: The exclusive reliance on statins and fibrates, without the use of newer agents for high-risk or statin-intolerant patients, indicates a significant gap in physician adherence to international guidelines, likely driven by the high cost of newer drugs in an out-of-pocket payment system.
• Public Health Impact: With only 1 in 6 patients meeting lipid targets, the risk of stroke, myocardial infarction, and premature death remains unacceptably high in this population.
• Recommendations: The authors call for:
  1. Improved physician adherence to clinical practice guidelines.
  2. Policy interventions to improve access to affordable, newer lipid-lowering medications.
  3. Targeted screening and management strategies specifically for female diabetic patients and those with proteinuria, who are at highest risk.

Limitations: The study relied on self-reported history for some CVD events (potential underestimation) and defined dyslipidemia strictly by current lab values rather than historical diagnosis, which may have excluded some treated patients. Additionally, missing data for some anthropometric measures required exclusion from specific denominators.