Study: Asians unable to produce enough insulin

Recently the news reported on a study which suggested that one reason why Asians are more prone to diabetes is due to inadequate insulin production.

Study: Asians unable to produce enough insulin

Local researchers from the National University Hospital (NUH), in collaboration with Janssen Pharmaceuticals, have found that the inability to produce enough insulin could be why Asians are more prone to Type 2 diabetes than their Western counterparts.

The breakthrough finding, based on a study of 140 mostly Chinese participants, will pave the way for better diabetes management for people here and in the region. This includes tailoring dietary advice and a better selection of drugs to treat diabetes, doctors believe.

Another interesting finding from a separate study is that Chinese people are more prone to diabetes at lower BMIs than Caucasians.

According to a previous study, 8 per cent of people of Chinese descent with a Body Mass Index (BMI) of 23 (just outside the healthy weight range) have diabetes. This is four times more than their European counterparts. A BMI of 23 is within the normal weight range for Caucasians.

As I have mentioned in my previous blog post, Caucasians tend to have lower body fat than Asians despite having the same BMI.

Asians have lower body mass index (BMI) but higher percent body fat than do whites: comparisons of anthropometric measurements.

Although Asians had lower BMI, they were fatter than whites of both sexes. The correlations between fat% and BMI varied by BMI and sex and race. Comparisons in anthropometry show that Asians had more subcutaneous fat than did whites and had different fat distributions from whites. Asians had more upper-body subcutaneous fat than did whites. The magnitude of differences between the two races was greater in females than in males.

My theory is that the impaired insulin production is due to the higher body fat percentage that Asians seem to have. A higher body fat percentage translates to a lower fat free mass. Which in turn reduces insulin production. Hence in comparison to Caucasians with the same BMI but lower body fat percentage, Asians will produce less insulin.

This news outlet reported on the same article, but I disagree with something they added.

Most Asians don’t produce enough insulin, more prone to diabetes

In a separate study, it was also discovered that 8 per cent of Chinese participants with a Body Mass Index (BMI) of 23 (this means they’re just outside the healthy range) have diabetes, four times more than those of European descent.

The reason for this? Caucasians generally have more body fat and therefore, a BMI of 23 is considered normal for them.

The part that is in bold is incorrect. The opposite is true, Asians in general have MORE body fat. That is the reason why WHO recommended a LOWER BMI cutoff for Asians.

WHO Expert Consultation: Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies.

Conclusions

On the basis of the available data in Asia, the WHO expert consultation concluded that Asians generally have a higher percentage of body fat than white people of the same age, sex, and BMI. Also, the proportion of Asian people with risk factors for type 2 diabetes and cardiovascular disease is substantial even below the existing WHO BMI cut-off point of 25 kg/m2. Thus, current WHO cut-off points do not provide an adequate basis for taking action on risks related to overweight and obesity in many populations in Asia.

Advertisements

Is BMI a good measure of health?

Recently, my government has increased it’s focus on its citizens adopting a healthy lifestyle. Today, I decided to look into one metric that is commonly used to determine a population’s health.

What is BMI?

Body Mass Index (BMI) is a person’s weight in kilograms divided by the square of height in meters. A high BMI can be an indicator of high body fatness. BMI can be used to screen for weight categories that may lead to health problems but it is not diagnostic of the body fatness or health of an individual.

What is it used for?

BMI is mainly used to assess weight status of a population. It is a simple screening tool to estimate a person’s body fat, but should be used in conjunction with other body composition tests before a diagnosis is to be made. This is because BMI is unable to discern between body fat percentage and lean mass.

How accurate is it?

This is a tricky question to answer. It’s accuracy differs between populations.

For example, in the USA and Singapore, the use of BMI actually under-diagnoses the prevalence of obesity.

In this study, it was observed that the use of BMI underdiagnosed obesity by 30%!
Accuracy of Body Mass Index to Diagnose Obesity In the US Adult Population

BMI-defined obesity (≥ 30 kg/m2) was present in 21% of men and 31% of women, while BF %-defined obesity was present in 50% and 62%, respectively.

Our findings also suggest that the magnitude of the obesity epidemic may be greatly underestimated by the use of BMI as the marker of obesity 35. In our results, BMI showed an unacceptable low sensitivity for detecting body fatness, with more than half of obese subjects (by body fat measurement) being labeled as normal or overweight by BMI. The true prevalence of obesity might be strikingly higher than that estimated by BMI.

In Singapore, it was observed that the accuracy of BMI differs between ethnic groups.
The paradox of low body mass index and high body fat percentage among Chinese, Malays and Indians in Singapore

RESULTS: Compared with body fat percentage (BF%) obtained using the reference method, BF% for the Singaporean Chinese, Malays and Indians were under-predicted by BMI, sex and age when an equation developed in a Caucasian population was used. The mean prediction error ranged from 2.7% to 5.6% body fat. The BMI/BF% relationship was also different among the three Singaporean groups, with Indians having the highest BF% and Chinese the lowest for the same BMI. These differences could be ascribed to differences in body build. It was also found that for the same amount of body fat as Caucasians who have a body mass index (BMI) of 30 kg/m2 (cut-off for obesity as defined by WHO), the BMI cut-off points for obesity would have to be about 27 kg/m2 for Chinese and Malays and 26 kg/m2 for Indians.

If obesity is defined as excess body fat rather than excess weight, the obesity cut-off point for Singaporeans should be 27 kg/m2 instead of 30 kg/m2. The lowering of the cut-off point for obesity would more than double the prevalence figures in Singapore.

In addition, the study authors also noted that the use of BMI underdiagnosed the prevalence of obesity in Singapore by at least 10%.

Generally, if the cut-off point for obesity in Singapore were lowered to 27 kg/m2, this would have immense impact on the prevalence of obesity among the adult Singapore population. Compared to a BMI cut-off point of 30 kg/m2 the prevalence would increase in females from 6.5% to 15.4% and in males from 5.2% to 17.3%.

Interestingly, it is the opposite for Korea. The use of BMI actually over-diagnosed obesity.

Diagnostic Performance of Body Mass Index Using the Western Pacific Regional Office of World Health Organization Reference Standards for Body Fat Percentage

In the present study, obesity was identified in 38.7% of men and 28.1% of women using body mass index (≥25 kg/m2) and in 25.2% of men and 31.1% of women using body fat percentage. A body mass index cut-off ≥25 kg/m2 had high specificity (89%, men; 84%, women) but poor sensitivity (56%, men; 72%, women).

On the basis of BF%, 25.2% of men and 31.1% of women were classified as obese in the present study compared to 50% of men and 62% of women in American populations (10). Thus, BF% better reflects the current status than population-specific cut-off points for BMI for international comparisons of the prevalence of obesity. The prevalence of BF%-defined obesity was higher in women than in men in Korea, which suggests that Korean women have less lean mass than do Korean men.

What is the significance of BMI?

As mentioned earlier it is mainly used a health indicator of a population but not an individual.

High and low BMI have been correlated with increased mortality rates. However, the increased risk of mortality observed in underweight people could at least partly be caused by residual confounding from prediagnostic disease. i.e. It’s a reverse causation, people are underweight due to their pre-existing conditions (eg. cancer).

An increase in BMI has been correlated with an increased risk for a myriad of diseases such as heart disease and diabetes.

Is “Less Sugar” Always Better?

Healthier food costs more because slow demand leads to inadequate economies of scale: Chee Hong Tat

During a parliament session Senior Minister of State (SMS) for Health Chee Hong Tat attributed the increased cost of “heathier” food options to lack of demand.

“During the initial phase when the healthier products are being introduced, they will have to go through this phase where consumers are getting used to it and the demand is not quite picking up,” he said.

“So when you produce it and there’s inadequate economies of scale, the merchant finds it difficult to price it at a very competitive level.”

Mr Chee was responding to Member of Parliament for Bishan-Toa Payoh GRC Chong Kee Hiong, who asked why healthier foods tend to be more expensive than less healthy options.

As for his question on the price disparity, Mr Chong clarified that he was referring to two variations of the same brand of kaya. The low sugar option costs S$1 more, he said.

Out of interest, I went to take a look at the two types of kaya from Fairprice private label:
Fairprice Nonya Kaya – Less Sugar 400G – $3.95
FairPrice Nonya Kaya 410G – $2.65

Below I have made a comparison table of the macronutrients.

Normal Less Sugar
Serving(g) 16 16
Calories(kcal) 48 49
Protein(g) 0.8 0.6
Total Fat(g) 1.4 0.8
Carbohydrate(g) 8 9.9

Not only does the less sugar version contain more calories, it also contains MORE carbohydrates and LESS protein.

To reduce the sugar, they added maltitol, a cheap sugar alcohol that has half the calories of sugar per gram and actually cost less than sugar for the same unit of sweetness
Source: http://www.sugar-and-sweetener-guide.com/maltitol.html

So is the “heathier” option always healthier?

Best to be a smart consumer and always remember to always check the nutrition label! Caveat emptor.

 

running

I read an article recently, titled “Running Is Stupid, So Why Do I Do It?”

And it reminded me of this passage from “The Curse of Lono” by Hunter S. Thompson

There are 30,000 of them now and they all are running for their own reasons. And this is the angle — this is the story: Why do these buggers run? What kind of sick instinct, stroked by countless hours of brutal training, would cause intelligent people to get up at 4 in the morning and stagger through the streets of Honolulu for 26 ball-busting miles in a race that less than a dozen of them have any chance of winning? This is the question we have come to Hawaii to answer — again. They do not enter to win. They enter to survive, and go home with a T-shirt. That was the test and the only ones who failed were those who dropped out.

Most people grapple with the fact that I run as a hobby. For leisure and not punishment. They often ask what am I training for and I answer nothing. I’m essentially a hobby yogger.

I don’t post my runs on social media and neither do I talk about it unless prompted to. I feel that running is a private activity and try to keep it that way. Yes, I do run in public places, but at same time I am by myself. The only participant in this impromptu race to nowhere. Challenging myself to be better.. for no apparent purpose.

I usually run by time or direction and see where my feet take me. I do get lost often and usually have to ask for directions. But I love that. The people I meet are always so helpful, which is a refreshing change from the barbaric hordes you battle with during rush hour.

So if running is stupid and hard, why do I do it?  I honestly don’t know. But I do know not running is harder.

Why do beans have less protein after cooking?

Q. If you boil beans, they lose their protein?

According to google search, 1 cup RAW of pinto beans is 41 grams of protein, but if you boil them they become 1.9g / cup. Why is this so?

The discrepancy in protein per cup is due to the difference in volume between a dried bean and a cooked bean.

When the dried beans are cooked or soaked, they absorb the liquid they are cooked/soaked in, which causes them to expand.

From a quick google search, dried beans can expand up to 2-3 times their original volume after an overnight soak and 3-4 times their original volume after cooking. So if you started with 1 cup of dried beans, you will on average end up with 3 cups of cooked beans. i.e. On average, 1 cup of dried beans will contain 3x the protein of 1 cup of cooked beans.

The same applies to other dried food stuff such as grains, legumes and lentils. The only difference is the amount of water they will absorb. To make it easier and less confusing to track these calories, weigh them raw and log them  based on the raw nutritional information for that ingredient.

Related reading:

Recipe book and cooking advice for beans, legumes and lentils:

http://www.extension.uidaho.edu/adaefnep/Efnep%20pdf/BeansSplitPeasLentils.pdf

Cooking Dried Beans,Peas and Lentils:
https://www.uaf.edu/files/ces/publications-db/catalog/hec/FNH-00360.pdf

Bean conversions:
http://www.reluctantgourmet.com/bean-conversions/

Dried grains to cooked conversions:
http://wholegrainscouncil.org/recipes/cooking-whole-grains

Accuracy of self-reported height, weight, and waist circumference in a general adult

Background

Self-reported height, weight, and waist circumference (WC) are widely used to estimate the prevalence of obesity, which has been increasing rapidly in China, but there is limited evidence for the accuracy of self-reported data and the determinants of self-report bias among the general adult Chinese population.
Methods

Using a multi-stage cluster sampling method, 8399 residents aged 18 or above were interviewed in the Jiangsu Province of China. Information on self-reported height, weight, and WC, together with information on demographic factors and lifestyle behaviors, were collected through structured face-to-face interviews. Anthropometrics were measured by trained staff according to a standard protocol.
Results

Self-reported height was overreported by a mean of 1.1 cm (95 % confidence interval [CI]: 1.0 to 1.2). Self-reported weight, body mass index (BMI), and WC were underreported by −0.1 kg (95 % CI: −0.2 to 0.0), −0.4 kg/m2 (95 % CI: −0.5 to −0.3) and −1.5 cm (95 % CI: −1.7 to −1.3) respectively. Sex, age group, location, education, weight status, fruit/vegetable intake, and smoking significantly affected the extent of self-report bias. According to the self-reported data, 25.5 % of obese people were misclassified into lower BMI categories and 8.7 % of people with elevated WC were misclassified as normal. Besides the accuracy, the distribution of BMI and WC and their cut-off point standards for obesity of a population affected the proportion of obesity misclassification.

TL:DR

  • BMI = measure of “bigness”
  • Waist Circumference =  measure of abdominal adiposity
  • People tend to under-report weight, BMI and WC.
  • People tend to over-report height
  • ~25% of people who classified themselves as within “normal – overweight” BMI based on self-measurements are actually obese based on the directly measured stats.

Use of BMI

The Body Mass Index (BMI)

  • Recognised that it does not differentiate between the muscular and the overweight, except at very high BMIs
  • Does not distinguish between individuals with different types of fat distribution.

Waist circumference (WC)

  • Measure of abdominal obesity
  • Commonly under reported

Self-report vs Actual

  • People tend to over report height, especially as they get older.
  • People tend to under-report weight, BMI and WC across all age group and income groups
  • Women tend to under-report more than men
  • Obese/overweight = over-report height, under-report weight, BMI and WC
  • “Healthier” people (eg. exercise, non-smokers) tend to over-report height and under-report WC

Misclassification

  • ~25% of people who classified themselves as within “normal – overweight” BMI based on self-measurements are actually obese based on the directly measured stats.
  • increased incidence of misclassification amongst women vs men