A new report published in The Lancet Diabetes & Endocrinology challenges the conventional definition of obesity, and urges a shift from the reliance on Body Mass Index (BMI) to a more nuanced approach. This is supported by over 50 global medical experts. The report also recommends splitting the term "obesity" into two categories: "Clinical obesity" and "Pre-clinical obesity". This aims to improve diagnosis and treatment for over a billion people worldwide living with obesity.

Clinically Obese

This applies to individuals whose obesity has progressed to a disease state, manifesting in organ damage, heart disease, type 2 diabetes, or other health complications. These individuals could also experience symptoms like breathlessness, joint pain, or impaired daily functioning. Treatment also involves medical interventions, including weight-loss medications or surgery.

Pre-Clinic Obese

Whereas the term "pre-clinic obese" refers to those who are overweight but not yet exhibiting health issues. While they may be at risk of developing obesity-related conditions, their organ function and overall health remain intact. What they need is preventive care, which includes dietary guidance, counselling, and regular monitoring to avoid and reduce future health risks.

What does the study say?

The study, led by Professor Francesco Rubino from King's College London emphasizes that obesity is not one-size-fits-all condition. This means it should rather be treated as a spectrum as some individuals maintain normal organ function despite being classified as obese. There are others who may face severe health complications too. However, the current method of calculating obesity based on BMI often leads to misdiagnosis or inadequate care.

The report also states that BMI, while is useful for analyzing population trends, is a flawed unit of measuring individual health. Therefore, there is a need to redefine obesity, and healthcare professionals can provide more precise care by distinguishing those who need immediate medical intervention and those who require preventive strategies.

Limitations of BMI, Why It Falls Short?

BMI is used to classify individuals as underweight, healthy weight, overweight, or obese based on their height and weight. It is calculated by dividing weight in kilograms by health in meters squared. However, there are reasons while it falls short.

•Muscle vs Fat: Athletes or muscular individuals often have high BMIs despite the low body fat

•Fat Distribution: BMI does not measure fat around the waist or organs, which could be more dangerous to one's health.

•Individual Health Variation: It also overlooks the specific health conditions such as heart diseases or diabetes, or any other, while evaluating a person's category in terms of weight.

ALSO READ: Is It Time To Say Goodbye To BMI?

Scope Of Study

By redefining obesity, the study could transform the approach to diagnosis and treatment. It can focus on individual health risks rather than BMI alone. Healthcare providers can also offer tailored care. This also will ensure hat weight-loss medications like Wegovy or Mounjaro are prescribed only to those who genuinely require it.

As per Professor Louise Baur from the University of Sydney, a Children's obesity expert said that this redefinition allows both adults and children to receive more appropriate care while reducing over-diagnosis and unnecessary treatments.

Healthcare in India must move beyond curative treatments to include preventive and holistic health, said experts today.

Speaking at a public health event in New Delhi, organized by the Illness to Wellness Foundation, the experts stressed the need to integrate technology, tradition, and lifestyle interventions to build a healthier, more resilient population in the country.

“Healthcare is not limited to curative treatments. It includes preventive, promotive, palliative, and rehabilitative care, much of which happens within the community,” said Rajesh Bhushan, Former Secretary, Ministry of Health & Family Welfare.

He called for building a culture of health-seeking behavior through community-focused programs and technology integration.

“Technologies like artificial intelligence (AI) and machine learning (ML) can improve the efficiency and quality of healthcare delivery, when combined with systems of digital public health infrastructure, including the Ayushman Bharat Digital Mission (ABDM) and the Ayushman Bharat Health Account (ABHA), which enable interoperability, longitudinal health records, and a more integrated healthcare ecosystem,” Bhushan added.

India today stands at a critical juncture in its healthcare journey. Rapid urbanization, changing lifestyles, rising stress levels, and increasing screen time are contributing to a growing burden of chronic conditions.

The experts argued that the real challenge will be to prevent disease and enable people to live healthier, longer, and more balanced lives.

Anil Rajput, Chairperson, Advisory Council, Illness to Wellness Foundation, urged for a more personalized and holistic understanding of health to build effective preventive systems.

Building Awareness On Healthcare

Dr. T S Kler, Chairman & HOD – BLK-Max Heart & Vascular Institute and Chairman Pan Max – Electrophysiology, spoke about the importance of leveraging public healthcare systems not only for treatment, but also for building awareness around health and prevention.

Amid rising cases of premature deaths linked to lifestyle risks and environmental factors, the experts advised keeping health as the foremost priority, far above all else.

"We must move towards an integrated, holistic model that combines allopathy with traditional systems of medicine, ensuring a more balanced and patient-centric approach. Equally important is the need to create greater awareness through continuous dialogue and education, as a lot can be achieved with the resources we already have,” said Dr. Kler, a Padma Bhushan awardee.

“The real shift we need is from managing disease to building a culture of health ownership. As stakeholders across sectors, our role is not just to develop systems, but to create awareness and belief that preventive and person-centric healthcare is achievable,” added Dr. Ravi Gaur, Co-Chair, FICCI Digital Health Task Force.

The event also featured a series of thematic discussions examining multiple dimensions of holistic health and well-being.

These include conversations around mental health as a critical component of productivity and daily life, with a focus on managing stress, addressing burnout, supporting students, and fostering more open and supportive environments across workplaces and educational institutions.

While the statistics for Autism Spectrum Disorders (ASD) are alarming, what is even more alarming is the lack of recognition and timely intervention.

Many parents whose children are diagnosed with ASD have common statements – "I just didn’t ignore the signs, I did not know they were signs", when my child did not respond to his name, I just thought he was busy in play, when he did not talk by a particular age I just felt its ok, kids talk late and if it’s a boy, they always talk late. Many boys in my family spoke late."

While these statements are real, they are alarming and indicate that ASD signs are not obvious and can be missed. Autism spectrum disorder (ASD) affects approximately one in 68 Indian children aged 2-9 years, equating to about 18 million individuals nationwide, with diagnoses often delayed until age 4-5 due to limited screening and awareness gaps.

This lag overlooks subtle early signs amid typical toddler behaviors and cultural norms, forfeiting a prime window for interventions that profoundly influence lifelong development.

Why Early Signs Are Overlooked

The signs for ASD should be recognized before the age of two as there are many indications which parents miss or do not want to address. Parents frequently ignore subtle cues such as reduced eye contact or lack of social smiling by 6 – 9 months sometimes calling their kids shy or introvert.

By 12 months, if a child does not respond to their name or by 14 months, there is an absence of pointing and gesturing, these are signs not to be ignored. Repetitive

behaviors like obsessively lining up toys, spinning wheels, or fixating on objects should be treated, not looked as harmless quirks or strong preferences.

Sensory sensitivities, such as aversion to loud noises, scratchy fabrics, or bright lights, manifest as tantrums or fussiness, especially in bustling Indian homes where joint families normalize varied child responses.

Surveys reveal over 50 percent of Indian cases go undiagnosed before preschool, due to lack of awareness (especially in rural areas) and stigma around developmental labels.

A Real Life Case

Aditya has some signs when he was a kid such as delayed speech, repetitive play patterns, and sensitivity to loud noises. On evaluation, it was recognized that as he grew older when academic needs increased, Aditya began experiencing several issues such as anxiety, meltdowns, and reduced school performance. He became more withdrawn and struggled with group activities.

Critical Need for Screening Before Age Three

The case in point addresses the fact that a child's brain grows super-fast before age three as neuroplasticity peaks, forming millions of connections daily that you can reshape easily.

Thus, it is essential to screen children early, which is critical so that possible signs of autism can be identified and therapies suggested effectively, boosting language, social skills, and independence. Based on the therapies, many children show signs of improvement by 50 percent compared to delayed diagnosis.

We need to understand that delaying of small issues can turn into major behavioural struggles. In India, pediatric doctors now recommend regular checks for toddlers,

especially in smaller towns, so that timely support can be administered to kids making them reach normal milestones instead of facing lifelong hurdles.

Value of Multidisciplinary Therapy

Despite early diagnosis, parents and families need to understand that there is no single approach to treating ASD. It is a combined effort of multidisciplinary teams such as

neurologists, speech therapists, occupational therapists, psychologists, and educators deliver that deliver holistic care. Sensory integration therapy addresses the sensory issues while Applied Behaviour Analysis (ABA) builds daily skills for children and understating routine.

Speech therapy helps the child build communication skills but despite all the therapies, it is the parent’s total understanding and acceptance that will enable success of

the therapies skills. Plexus Neuro Centre exemplifies integrated, evidence-based models prioritizing child-led progress that is backed by the support of the family.

It is important to raise awareness and reduce the spread of myths. Every child should have the right to a healthy and independent childhood, thus we urge parents to trust their instincts and act swiftly. This can be achieved through routine screenings and analysis by pediatric neurologists, participating in the right therapies and encouraging kids to have live an independent life, thereby empowering their futures

A newly developed deep learning-based artificial intelligence (AI) system can read heart MRI scans as accurately as medical specialists.

The deep learning model developed by researchers at the University of Pennsylvania is trained on more than 300,000 MRI video clips from roughly 20,000 patients.

The system matches specialists in assessing heart function and diagnosing many heart conditions, according to a study published in the journal Nature Biomedical Engineering.

“Cardiac MRI is one of the most powerful tools available to cardiologists, but interpreting these scans requires rare expertise, and many hospitals -especially community and rural centers- lack specialists who regularly read complex cardiac MRI studies,” said Rohan Shad, an integrated cardiothoracic surgery resident in the Perelman School of Medicine at the University of Pennsylvania.

How The Deep Learning Model Worked

In the study, the researchers described a foundational vision system for cardiac MRI that is capable of representing the breadth of human cardiovascular disease and health.

The “foundation model” learns by linking MRI videos to their corresponding radiology reports, enabling it to recognize a wide range of conditions without extensive labeled data.

In tests, it estimated ejection fraction with expert‑level accuracy and identified severe heart dysfunction far more effectively than traditional AI methods.

It also diagnosed 39 cardiac conditions — including genetic problems like hypertrophic and dilated cardiomyopathies.

In a real‑world screen of more than 40,000 scans, the AI flagged 112 previously undiagnosed cases of hypertrophic cardiomyopathy.

According to researchers, the system could help hospitals without specialized cardiac imaging expertise detect rare but treatable diseases earlier.

Also read: Women Turning to AI for Health Detection: Helpful Tool or Risky Trend?

What Is A Cardiac MRI

A cardiac MRI is a scan of the heart in which radio waves and magnets create images. It shows the parts of the heart, such as chambers, valves, and muscles, are working ― including how the blood moves.

A cardiac MRI is typically performed to

• decode heart symptoms like chest pain, shortness of breath, or fainting
• diagnose congenital heart disease, heart failure, heart masses, valve disease,
• understand muscle damage, infection, protein buildup, and iron deposits
• check damage post a heart attack

AI And Heart Health

AI-powered techniques such as Machine Learning and Deep Learning are transforming how heart diseases are detected, treated, and managed globally.

Also read: Women Are More Comfortable With AI-Assisted Mammography

The technology has enabled faster analysis of large amounts of medical data—such as ECGs, heart MRIs, and patient records. It also flags patterns that may be missed by humans, and helps in early detection, treatment, and improving survival rates.

In 2024, the American Heart Association issued a scientific statement on the use of AI in improving outcomes in heart disease.

The objective aimed "to enable precision medicine and implementation science in cardiovascular research and clinical care".

However, it urged policymakers to develop principles and ethical guidance for the development and application of AI/ML-based digital health.