TANUH

Cardiovascular-Kidney-Metabolic (CKM) syndrome describes how heart, kidney and metabolic health are linked: when one system weakens, it puts strain on the others. Obesity, diabetes, hypertension, chronic kidney disease and cardiovascular disease are better seen as one connected continuum than as separate conditions.

Good CKM care means assessing a person's current stage, setting personalised diet and lifestyle plans, supporting treatment and reviewing progress over time. Current care pathways make this hard to deliver.

Our Solution

To address this, TANUH is developing a multilingual, voice-first, human-in-the-loop AI tool. It helps non-physician health workers, general practitioners and clinicians read CKM information consistently. The tool combines diet, physiology, blood and urine biomarkers, and CKM-related medical history from repeated visits. A small language model (SLM) links these four inputs.

Solution 1

An AI-based longitudinal risk deviance score prediction model that projects how a person's CKM health will change.

Goals

Spot people whose CKM health is getting worse before they move to a higher stage
Show the direction of change, not just a one-time snapshot
Give care teams one integrated view of heart, kidney and metabolic risk

Spot people whose CKM health is getting worse before they move to a higher stage
Show the direction of change, not just a one-time snapshot
Give care teams one integrated view of heart, kidney and metabolic risk

Spot people whose CKM health is getting worse before they move to a higher stage
Show the direction of change, not just a one-time snapshot
Give care teams one integrated view of heart, kidney and metabolic risk

Key Features of the Solution:

Combines four inputs: diet, physiology, biomarkers and CKM syndrome data
Models change across repeated visits
Predicts risk trajectory at 6 months and 1 year

Combines four inputs: diet, physiology, biomarkers and CKM syndrome data
Models change across repeated visits
Predicts risk trajectory at 6 months and 1 year

Combines four inputs: diet, physiology, biomarkers and CKM syndrome data
Models change across repeated visits
Predicts risk trajectory at 6 months and 1 year

Solution 2

An AI-based diet and lifestyle recommendation system that prepares personalised priorities for clinical review.

Goals

Make personalised diet guidance available where dietitians are scarce
Give recommendations that are culturally and regionally relevant
Help people stay stable or move back to an earlier stage

Make personalised diet guidance available where dietitians are scarce
Give recommendations that are culturally and regionally relevant
Help people stay stable or move back to an earlier stage

Make personalised diet guidance available where dietitians are scarce
Give recommendations that are culturally and regionally relevant
Help people stay stable or move back to an earlier stage

Key Features of the Solution:

Food-pattern recommendations with regional alternatives and practical next steps
Multilingual and voice-first, so frontline workers can use it
Human review at every clinical cycle: clinical nutritionists review Stages 0–2 and expert clinicians review Stages 3–4 before anything is shared
Supports clinical judgement rather than replacing it, and never recommends changes to prescribed medication

Food-pattern recommendations with regional alternatives and practical next steps
Multilingual and voice-first, so frontline workers can use it
Human review at every clinical cycle: clinical nutritionists review Stages 0–2 and expert clinicians review Stages 3–4 before anything is shared
Supports clinical judgement rather than replacing it, and never recommends changes to prescribed medication

Team & Collaborators

Principal Investigator

Sriram Ganapathy

Associate Professor
Department of Electrical Engineering, IISc

Personal website

Associate Professor
Department of Electrical Engineering, IISc

Personal website

Associate Professor
Department of Electrical Engineering, IISc

Personal website

Co-Principal Investigator

Lakshmi Krishnan

Sr. Program Manager

TANUH

Sr. Program Manager

TANUH

Sr. Program Manager

TANUH

Prabhdeep Kaur

Chair and Professor
Isaac Centre for Public Health, IISc

Personal website

Sundar Swaminathan

Endowed Chair Professor & Head
Department of Nephrology,
Tata-IISc Medical School

Personal website

Team

Arjun Anand

Technical Staff

Ramoju Raghavamsi

ML Intern

Sonie Sait

Clinical Coordinator

Partners & Collaborators

Get Involved

We invite collaborations with cardiologists, nephrologists, endocrinologists and clinical nutritionists; AI researchers and data scientists; public health organisations; hospitals and healthcare institutions; and technology partners.