Skip to main content

Obesity paradox in people newly diagnosed with type 2 diabetes

Recent studies have raised the issue of ‘obesity paradox’ in patients with type 2 diabetes mellitus (T2DM). Sanjoy Paul and colleagues evaluated the cardiovascular and mortality risks associated with normal and overweight patients compared to obese at diagnosis of diabetes, separately for patients with and without cardiovascular disease (CVD) before diagnosis. The study was published in the journal Diabetes, Obesity and Metabolism.

They carried out a retrospective study with two study cohorts with/without prior CVD with complete measures of body mass index (BMI) at diagnosis of T2DM from UK General Practice Research Database. Primary outcomes were long-term risks of cardiovascular events (CVEs) and all-cause mortality in patients with normal weight, overweight and obesity at diagnosis.

They reported that mortality rates per 1000 person-years in normal weight, overweight and obese patients among patients without prior CVD were 13.1, 8.6 and 6.0, respectively, during 5 years of median follow-up. For patients with prior CVD, these estimates were 30.1, 21.1 and 15.5, respectively. Among patients without and with prior CVD, normal weight patients had 47% (hazard ratio, HR CI: 1.29, 1.69) and 30% (HR CI: 1.11, 1.53) increased mortality risk respectively compared to obese patients. In the cohort without prior CVD, compared to obese patients, those with normal body weight did not have increased CVE risk. Interactions between age, HbA1c and BMI at diagnosis were observed in both cohorts.

They concluded that adults with normal weight at the time of diagnosis of T2DM have significantly higher mortality risk compared to those who are obese, with significant interactions between age, BMI and HbA1c. Elevated cardiovascular risk was not observed in normal weight patients without prior CVD.

Comments

Popular posts from this blog

What is the difference between primordial prevention and primary prevention?

Primordial prevention and primary prevention are both crucial strategies for promoting health, but they operate at different levels. Primordial prevention aims to address the root causes of health problems and improve the wider determinants of health. It focuses on preventing the emergence of risk factors in the first place by tackling the underlying social, economic, and environmental determinants of health. This involves broad, population-wide interventions such as: Policies that promote healthy food choices: Think about initiatives like taxing sugary drinks to discourage unhealthy consumption, or providing subsidies for fruits and vegetables to make them more accessible. Urban planning that prioritises well-being: This could include creating walkable neighborhoods with safe cycling routes, ensuring access to green spaces for recreation and relaxation, and designing communities that foster social connections. Social programs that address inequality: Initiatives aimed at reducing pov...

UK Covid-19 Inquiry - The Importance of a Strong Primary Healthcare System

In my witness statement for Module 10 of the UK Covid-19 Inquiry, I discuss the pandemic's impact through from the perspectives of  primary care and public health, drawing on my extensive experience as a senior academic at Imperial College London and as a practising GP and NHS Public Health Specialist.  I emphasise that the pandemic disproportionately affected people who were clinically vulnerable, the disabled, ethnic minority communities and those living in deprived areas. The pandemic highlighted how structural inequalities, multigenerational housing, and employment in high-risk frontline roles exacerbated health disparities.  My statement also critiques the weakening of public health infrastructure - particularly for the control of infectious diseases - and the lack of integrated health data systems to identify at-risk groups such as the clinically vulnerable. I also advocated for a more robust preventive healthcare model that prioritises community-based primary care ...

Relevance Over Recall: Rethinking How AI Uses Clinical Data

Our article in the Journal of the Royal Society of Medicine argues that safe and effective AI in healthcare must incorporate mechanisms that emulate human judgement - down-weighting old, inaccurate or superseded information and prioritising what is recent, clinically relevant and reaffirmed - so that AI supports, rather than disrupts, high-quality patient care.  Clinicians constantly revise, reinterpret and filter past information so that only what is relevant, accurate and timely shapes present-day management decisions; medical records function as dynamic “working tools” rather than fixed archives. By contrast, many AI systems lack this capacity for selective forgetting and often treat all historical data as equally meaningful.  This can lead to outdated or low-confidence diagnoses being repeatedly resurfaced, persistent labels influencing clinical expectations, and irrelevant, long-resolved events cluttering summaries and decision-support outputs. Such indiscriminate recall...