Increasing protein and dairy intake may help reduce burden of diabetes: A study

Diabetes – increase in blood glucose levels – is an emerging health problem, especially in developing countries. According to the World Health Organisation, India had 69 million diabetic individuals in 2015 and the number is projected to go up to 98 million by 2030. The problem is more serious for Asians as their genetic make-up puts them at a greater risk of diabetes at a younger age than their European counterparts.

If left untreated, diabetes can intensify to serious health conditions like blindness, kidney failure, heart problems, etc. Apart from genetic factors, the occurrence of diabetes is related to food preferences and lifestyles. Therefore, understanding the link between the consumption of various foods and the prevalence of diabetes in different states can help in devising effective strategies to address the problem.

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A new study has linked the food preferences of individuals to the prevalence of diabetes in various states of India. The study has found that eating calorie-rich food like sugar and honey increases the risk of diabetes while the addition of protein-rich food and dairy products in a meal can help reduce the risk of diabetes.

The study is based on data from the National Family Health Survey-4 (2015– 2016), 2011 census, data on per capita crop production, and consumption figures of different food groups from the 68th round of the National Sample Survey. It evaluated trends of food availability and preferences in various states with diabetes numbers.

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It was found that diabetes was more common in states like West Bengal and Kerala where people consume more non-vegetarian food than the one where the diet is predominantly vegetarian. This is because a vegetarian diet helps in diabetes management by making the body sensitive to the insulin hormone.

Researchers also noted that there were more diabetics in the states where people consumed more sugar and honey though this trend was not valid in the case of Maharashtra, Rajasthan, and Haryana. The study also found that eating protein-rich food such as pulses and nuts decreased the chances of diabetes. Similarly, states, where people ate more dairy products, had a low prevalence of diabetes.

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Diabetes, often dubbed as a lifestyle disorder, was also found to be high in urban districts than in rural areas. This is so because people in cities tend to be less physically active. This makes urbanization an important factor for the growing number of diabetics in the country. Also, diabetes was found to be more common in males than females as has been indicated in the earlier studies.

‘Our study has identified hotspot districts having a high prevalence of diabetes and recommended them to be targeted in public health programs,’ said Dr. Preeti Dhillon, author of the study and Assistant Professor at the International Institute for Population Sciences (IIPS), Mumbai. The analysis showed that 6.9% of adults in India between the age of 15-49 years have diabetes. Among the states and union territories, diabetes is highly prevalent in Andaman and Nicobar Islands, while the least in Rajasthan. The prevalence of disease was found to be more in districts that are close to the coastal areas. ‘The high prevalence of diabetes in Andamans and Lakshadweep is of interest as this has not been reported earlier and it would be worthwhile exploring whether any genetic or environmental factors contribute to this,’ commented Dr. V Mohan, Director, Madras Diabetes Research Foundation. Though he was not associated with the study.

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Dr. Anoop Misra, Vice President, Diabetes Foundation (India), commented that while this study provided new information about dietary context and prevalence of diabetes, it must be understood that diabetes is a multifactorial disease. Genetics, physical activity, alcohol consumption, and other lifestyle factors should be taken into consideration before establishing conclusive links. Food intake is a continuously changing and dynamic process and cannot be completely captured in a cross-sectional snapshot. He agreed, however, that high protein intake is needed for Indians as it boosts glucose metabolism in muscles reducing incidences of diabetes. The research team at IIPS included Koustav Ghosh and Gopal Agrawal apart from Dr. Dhillon. The results of this study have been published in the Journal of Public Health.

Credits: India Science Wire

Artificial Intelligence can help decode epileptic brains. Epilepsy is the fourth most common neurological disorder affecting nearly 65 million people worldwide. The seizures or ‘fits’ as is commonly known, arise due to unusual electrical activity in the brain and is the chief symptom of epilepsy. Neither dependent on age or gender, the onset of the seizure is unpredictable without a set pattern of frequency of occurrence or severity, often posing a challenge to the caregiver.

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Although epilepsy can be related to previous brain injuries or genetic factors, neurologists have found unprovoked, recurrent seizures in healthy individuals too. How and why these seizures occur remains a mystery. However, research has found that the source of seizures is within the brain. In other words, the brain itself is the generator of epilepsy.

Spatial maps of top 10 networks: If the origin is within the brain, then are there any fingerprints that can be detected? Does the brain often tell-tale signs which can be mapped to predict the tendency of epilepsy?

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Seeking answers to these questions, a team of interdisciplinary researchers conducted a study to peep inside epileptic brains. The results indicate that there exist independent neural networks that can carry disease sensitive information about the anomaly. With the help of machine learning models and artificial intelligence, researchers were able to detect and reveal the hidden patterns.

“Epilepsy is not a disorder but the manifesting of something from within the brain’s electrical activity. Interestingly, each one of us has the neural map of epilepsy within our brain. It is only when the network gets fired and manifests externally, in a recurrent manner, it becomes disorder or epilepsy,” explained Dr. Tapan Kumar Gandhi, lead researcher of the study from Indian Institute of TechnologyDelhi, while speaking to India Science Wire.

The usual diagnosing tool for epilepsy is by EEG (Electroencephalography) readings of epileptic patterns and visible symptoms like convulsions, loss of consciousness or sensory disturbances.

Existing studies reveal specific patterns that represent synchronous activities of sensory, auditory, cognitive and other functions. These activities are indicated by the change in blood flow to the brain and seen as BOLD signals or changes in the Blood-Oxygen-Level-Dependent output.

Recent developments in Magnetic Resonance Imaging or MRI help picture these activities in the brain and detect the cause of seizures such as a lesion or scar. However, MRI is not very useful when a seizure flares up. Whereas, functional MRI — another scanning method — can record regional interactions in the brain when a particular task is being performed.

In 1995, Indian researchers had found that the brain shows prominent neural network connections even in its resting state. Termed as resting-state functional MRI or rsfMRI, the images from this scanning indicate neural patterns in an individual’s brain even when no action is performed.

In the present study, the team utilized rsfMRI technique and performed brain scans on individuals with Temporal Lobe Epilepsy (TLE), which is the most common form of epilepsy.

Dr. Gandhi said, “We hypothesized that there could be ‘disease-specific networks’ in epilepsy prone brain that can be identified with the help of the machine learning model.” Machine learning involves artificial intelligence to read live data instead of pre-programmed information. Such a building block of a machine is analogous to a neuron cell in the brain.

Researchers used a tool called Support Vector Machine (SVM) to deal with the complex and non-linear data obtained from the scans. By using another algorithm called Elastic-net based ranking, the relevant features of the neuroimaging data were extracted. The signals were integrated to reveal the patterns.

The team conducted a pilot study on 132 subjects – 42 with epilepsy, and the rest with healthy individuals. Parameters like age, gender, history of epilepsy, genetic predisposition, incidents of injuries, medications and more, were taken into account. The epilepsy patients underwent three rsfMRI while those in the healthy group were scanned once.

In all, 88 independent components or networks were obtained from the whole brain imaging data and fed as input to the SVM. From the patterns, top 10 strong networks were correlated with clinical features using another standard method called Pearson’s Correlation to generate the rsfMRI epileptic neural networks.

From the pattern inputs, the SVM could identify epileptic individuals to an accuracy of 97.5% and specific lobes in the brain responsible for the condition. The model also revealed correlations such as the age of onset, frequency of seizures, or duration of illness.

By this, researchers concluded that the independently derived rsfMRI contains epilepsy-related networks. ‘Our research establishes that with the help of machine learning methods, we can identify these networks, as we had hypothesized. Increased strength in these networks indicates the possibility of a progressing Temporal Lobe Epilepsy’, explained Dr.Gandhi.

The team included Rose Dawn Bharath, Sujas Bharadwaj, Sanjib Sinha, Kenchaiah Raghavendra, Ravindranadh C. Mundlamuri, Arivazhagan Arimappamagan, Malla Bhaskara Rao, Jamuna Rajeshwaran, Kandavel Thennarasu and Parthasarathy Satishchandra (National Institute of Mental Health and Neurosciences, Bengaluru); Tapan K. Gandhi and Jeetu Raj (IIT, Delhi); Rajanikant Panda (Universitè de Liège, Belgium); Ganne Chaitanya (Thomas Jefferson University, USA) and Kaushik K. Majumdar (Indian Statistical Institute, Bengaluru). The study results have been published in the journal European Radiology.

Credits: India Science Wire