Case Study: Personalized Diabetes Treatment Using AI-Driven Genomics

Abstract

This case study focuses on how AI-driven genomics helped develop a personalized treatment plan for Tukaram More, a 51-year-old man struggling to manage his Type 2 diabetes. Like many others, Tukaram followed the traditional treatment route of medications like metformin, but it wasn’t working for him. In Tukaram’s case, AI-based genetic testing revealed that his body processed metformin poorly, leading to high blood sugar levels and gastrointestinal side effects. Based on his genetic profile, a new treatment plan was developed, including the use of an SGLT2 inhibitor, which led to better blood sugar control, fewer side effects, and overall improved health.

Introduction

Type 2 diabetes is a widespread chronic condition that typically requires lifestyle changes and medications like metformin or insulin. However, not all patients respond well to these standard treatments due to genetic factors that influence drug metabolism and efficacy. Advances in genomics and artificial intelligence (AI) now allow for a more personalized approach to diabetes care. This case study details the journey of Tukaram More, who benefited from a tailored treatment plan developed through AI-driven genetic testing, significantly improving his diabetes management.

Patient Information

Name: Tukaram More

Age: 51

Occupation: School teacher

Medical History: Diagnosed with Type 2 diabetes five years ago. He also has hypertension and a family history of diabetes on both sides. He leads a moderately active lifestyle.

Tukaram first noticed his health was off when he started feeling persistently fatigued, was urinating more often than usual, and had blurry vision. Even though he was taking metformin regularly and trying to eat right, he just couldn’t get his blood sugar under control. The ongoing discomfort from the medication, like bloating, made it even harder to stick to his regimen.

Clinical Findings

Tukaram’s diabetes wasn’t well-managed, despite being on the standard treatment plan. His fasting glucose levels ranged between 180-220 mg/dL, and his HbA1c was at 8.7%, well above the ideal target of less than 7%. On top of that, his blood pressure was high (145/90 mmHg) and his LDL cholesterol was elevated (130 mg/dL).

Presenting Symptoms

Tukaram experienced persistent fatigue, frequent urination, blurry vision, and difficulty controlling his blood glucose levels despite adhering to a regimen of metformin, dietary changes, and exercise. He also suffered gastrointestinal discomfort from metformin, which led to inconsistent medication adherence.

Blood Glucose Levels: Fasting glucose consistently between 180-220 mg/dL.

HbA1c: 8.6% (above the target range of <7%)

Blood Pressure: 145/90 mmHg

Lipid Profile: Elevated LDL cholesterol (130 mg/dL), normal triglycerides (100 mg/dL)

Due to his struggles with managing blood sugar levels, Tukaram was referred to a clinic specializing in personalized treatment through genetic testing and AI analysis.

Timeline

Initial Diagnosis (5 years ago): Type 2 diabetes was diagnosed and metformin was prescribed.

Year 3: HbA1c values remained elevated despite increasing metformin doses and lifestyle changes.

Year 5 (Present): Referred to a genetics clinic after not responding to traditional therapy.

Diagnostic Assessment

Tukaram, unable to control his diabetes, decided to have extensive genomic testing done.

Genomic Testing: Tukaram's absorption of metformin was impacted by changes in his genes, specifically CYP2C9 and SLC22A1, as revealed by an AI-powered genetic study.

Pharmacogenomics Profile: Tukaram's DNA profile was examined using AI against huge databases to forecast a more effective treatment method. The results indicated that SGLT2 inhibitors would be more beneficial to him than metformin.

In-depth analysis of health data: Tukaram's health records, behaviors, and blood sugar levels were examined by artificial intelligence algorithms to generate a personalized treatment plan.

Follow-Up and Outcomes

Medication changes: empagliflozin, an SGLT2 inhibitor that lowers blood sugar by promoting glucose excretion through the urine, was substituted for metformin in Tukaram.

Lifestyle Modifications: Based on his genetic markers for fat metabolism and insulin sensitivity, AI tools suggested an increase in moderate exercise and a higher-fiber diet.

Ongoing Monitoring: With the advent of continuous glucose monitoring (CGM), artificial intelligence is now able to optimize prescription dosages and lifestyle recommendations instantly.

Outcome: After six months, Tukaram’s HbA1c dropped from 8.7 percent to 6.9 percent, within the desired target range. His fasting glucose levels stabilized between 120 and 140 mg/dL, and he also reported feeling more energized and having fewer gastrointestinal problems.

Discussion

Tukaram's case shows how AI-powered genomics can revolutionize the treatment of diabetes. Traditionally, diabetes is managed using a one-size-fits-all strategy in which drug dosages are changed through trial and error. Genetic variations, which can have a major impact on treatment outcomes, are not taken into that certain gene variants were preventing him from effectively processing metformin. Improved blood glucose control and fewer side effects resulted from the switch to an SGLT2 inhibitor, which was guided by crucial insights obtained through the use of AI tools.

Takeaway

The success of personalized medicine in treating complicated diseases like Type 2 diabetes is demonstrated by this case. AI and genomics analyze a patient's genetic profile to provide a more personalized treatment plan that improves outcomes and reduces drug side effects.

Patient’s Perspective

Tukaram More: “I always thought diabetes management was just about eating right and taking my pills, but after working with my doctors and seeing how my genetics influenced everything, I finally felt like I had control over my condition,” Tukaram said. “I’m so grateful that there are now tools that can look at me as an individual and not just treat me like any other diabetic patient.”

Conclusion

The remarkable potential of AI-driven genomics in personalized medicine is exemplified by Tukaram's case. Tukaram was prescribed a treatment that was more suited to his genetic makeup through the use of genetic testing and AI analysis, which improved his quality of life and allowed for more effective management of his diabetes. This case study illustrates the personalized treatment plans of the future of precision medicine, which do not rely on data from a general population.

References

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