We would like to thank Anne-Thea McGill and Brown et al for their response to our manuscript “Parental history of type 2 diabetes is associated with lower resting energy expenditure in normoglycemic subjects.” The points raised by the commentaries are well taken. However, as stated in the limitations of our study, we performed a cross-sectional study which did not track weight gain A longitudinal study would be required to gain such specific insights. While predictive models are useful, they are not without limitations and the most accurate determination of weight gain arising from lower resting energy expenditure is best done by a longitudinal study. Lower resting expenditure may not always equate to an energy surplus as energy intake could be lower in subjects with lower REE or physical activity energy expenditure may be higher, thus balancing the total energy expenditure.
It’s appreciated for addressing an interesting area of research about the efficacy of medical nutrition treatment based on the Mediterranean Diet (MedDiet).
The study was a secondary analysis of the St Carlos GDM Prevention Study, conducted between January and December 2015 in Hospital Clinico San Carlos (Madrid, Spain). The author used MedDiet-MNT in order to observe its effects on mother’s glycemic level and also the prenatal outcome.
According to this study, there were two groups. Both groups received dietary recommendation to follow MD guideline, the difference was just in intervention group, they added portion for virgin olive oil and nuts. Basically both groups had similar diet recommendation, so further clinical experiment is highly needed to determine the exact effect of adding portion in extra virgin olive oil and nuts on lowering risk of GDM.
Although this diet had several benefits, the use of adding portion on extra virgin oil and pistachios in the intervention group treatment still becomes a question. In the other study, traditional Mediterranean diet had positive effect on lowering risk of GDM in pregnant women (Izadi, 2016), this outcome also occurred in the study conducted by Perez,Ferre (2014) that MD could reduce risk of GDM. So, if the traditional way has been reported successful in lowering GDM risk, is that really necessary to modify the basic guideline of MedDiet?
It’s appreciated for addressing an interesting area of research about the efficacy of medical nutrition treatment based on the Mediterranean Diet (MedDiet).
The study was a secondary analysis of the St Carlos GDM Prevention Study, conducted between January and December 2015 in Hospital Clinico San Carlos (Madrid, Spain). The author used MedDiet-MNT in order to observe its effects on mother’s glycemic level and also the prenatal outcome.
According to this study, there were two groups. Both groups received dietary recommendation to follow MD guideline, the difference was just in intervention group, they added portion for virgin olive oil and nuts. Basically both groups had similar diet recommendation, so further clinical experiment is highly needed to determine the exact effect of adding portion in extra virgin olive oil and nuts on lowering risk of GDM.
Although this diet had several benefits, the use of adding portion on extra virgin oil and pistachios in the intervention group treatment still becomes a question. In the other study, traditional Mediterranean diet had positive effect on lowering risk of GDM in pregnant women (Izadi, 2016), this outcome also occurred in the study conducted by Perez,Ferre (2014) that MD could reduce risk of GDM. So, if the traditional way has been reported successful in lowering GDM risk, is that really necessary to modify the basic guideline of MedDiet?
References:
1. Izadi V, Tehrani H, Haghighatdoost F, et.al. Adherence to the DASH and Mediterranean diets is associated with decreased risk for gestational diabetes mellitus. Nutrition 2016;32:1092-6. doi:10.1016/j.nut.2016.03.006.
2. Perez-Ferre N, Valle LD, Torrej MJ, et.al. Diabetes Mellitus and Abnormal Glucose Tolerance Development After Gestational Diabetes: A Three-year, Prospective, Randomized, Clinical based, Mediterranean lifestyle Interventional Study with Parallel Groups. Clinical Nutrition 2015;34:579-585. doi:10.1016/j.clnu.2014.09.005.
Humans have proportionately large, complex brains that require large amounts of nutrients- energy and micronutrients. There are a number of little recognised co-adaptations to manage this 'brain drain'. Two very important mechanisms to manage this high localised metabolic rate were to - 1) Use the extremely varied and reactive plant chemicals that were increasingly being consumed in the nomadic hunter-gatherer hominins 2) To increase the buffer stores of nutrients by reactivating mammalian genes for subcutaneous fat stores. 3) increase strong drives to acquire high nutrient food predicated on energy density.
The nutrient chemicals are often plant defence (secondary) chemicals) of which the anti yeast polyphenol resveratrol is but one of myriads, act as Michael acceptors. These reactions are much less precise that enzymatic reactions. They shuffle-reshuffle electrons and efficiently manage energy, reducing free radical production and energy loss . There are a number of enhanced anti-oxidant, detoxification, and adaptive and general cell repair pathways coordinated by the NRF2/Keap1/antioxidant response element cell protection systems.
2) As mentioned, the subcutaneaous adipose tissue is a brain nutrient buffer - especially for the intra-uterine and postnatal human brain development. This adipose is not just a fat store but lipids and many other nutrients should be in the stores - those absorbed through the colon after being trafficked there...
Humans have proportionately large, complex brains that require large amounts of nutrients- energy and micronutrients. There are a number of little recognised co-adaptations to manage this 'brain drain'. Two very important mechanisms to manage this high localised metabolic rate were to - 1) Use the extremely varied and reactive plant chemicals that were increasingly being consumed in the nomadic hunter-gatherer hominins 2) To increase the buffer stores of nutrients by reactivating mammalian genes for subcutaneous fat stores. 3) increase strong drives to acquire high nutrient food predicated on energy density.
The nutrient chemicals are often plant defence (secondary) chemicals) of which the anti yeast polyphenol resveratrol is but one of myriads, act as Michael acceptors. These reactions are much less precise that enzymatic reactions. They shuffle-reshuffle electrons and efficiently manage energy, reducing free radical production and energy loss . There are a number of enhanced anti-oxidant, detoxification, and adaptive and general cell repair pathways coordinated by the NRF2/Keap1/antioxidant response element cell protection systems.
2) As mentioned, the subcutaneaous adipose tissue is a brain nutrient buffer - especially for the intra-uterine and postnatal human brain development. This adipose is not just a fat store but lipids and many other nutrients should be in the stores - those absorbed through the colon after being trafficked there by fibre and released from the (non grain) variably fermentable dietary fibre.
A high nutrient varied diet from heritage and wild type immune competent foods has been supplying healthy humans for the few million years of their evolution... until our
3) particularly strong reward mesolimbic system drives for energy dense foods also fostered new abilities to refine and process dietary items with various technologies. Current agribusiness, mass-monocultural food production and processing have excluded the vast range and volume of complex plant chemicals required. In addition, these refined highly bred or genetically altered/edited foods are often grown and processed with foreign, unnecessary (xenobiotic) or frankly toxic chemicals.
SO, unless the total plant food adequacy, lack of contaminants, genetics (polymorphisms, particularly that of adipose distribution type) and epigenetics is known or modelled, each individual's energetics equations will be different. Many studies show very different results in individuals fed different controlled energy diets - where the micronutrient intake is totally ignored. Often the participants are blamed for not recording their food intake or complying in other ways properly. This is the likely reason for such poor predictions thus far. The inability to explain varied and 'equivocal' results in so many studies has not had an adequate explanation. Modelling human energetics in other animals is not useful due to this set of unusual nutritional and metabolic characteristics.
Human metabolism can only be very generally approximated. This will improve if mathematical systems models using dynamic energy budget equations where both macronutrient and micronutrient reserve factors are corrected for.
We read with great interest the article by Gilbert-Ouimet M et al1 recently published in your journal. Such study showed an interesting result that only increased risk of incidence of diabetes occurring among female workers working 45 hours or more per week, and suggested that modification of such risk factors would be helpful to improve prevention strategies and orient policymaking by following up 7065 workers over a 12-year period in Ontario, Canada.
However, we have some concerns. Firstly, in order to find out the potential relationship between long work hours and the incidence of diabetes, several other independent variables were considered in the analysis process such as sociodemographic and health-related covariates, but the information of menopause and menopausal hormone therapy (MHT) among women was not added. Strong association with increased risk of cardiovascular diseases had been confirmed in several studies and data from large randomized-controlled trials have shown that the decrease of incidence of T2D in women could be achieved by MHT with conjugated estrogens 2,3 . Although the clinical evidence still not be sufficient to recommend the use of hormones for prevention of diabetes among women especially with early menopause or premature ovarian insufficiency4, such detail might be helpful to uncover the neglected association between menopause and increased risk of diabetes.
Secondly, compared with the increased risk...
We read with great interest the article by Gilbert-Ouimet M et al1 recently published in your journal. Such study showed an interesting result that only increased risk of incidence of diabetes occurring among female workers working 45 hours or more per week, and suggested that modification of such risk factors would be helpful to improve prevention strategies and orient policymaking by following up 7065 workers over a 12-year period in Ontario, Canada.
However, we have some concerns. Firstly, in order to find out the potential relationship between long work hours and the incidence of diabetes, several other independent variables were considered in the analysis process such as sociodemographic and health-related covariates, but the information of menopause and menopausal hormone therapy (MHT) among women was not added. Strong association with increased risk of cardiovascular diseases had been confirmed in several studies and data from large randomized-controlled trials have shown that the decrease of incidence of T2D in women could be achieved by MHT with conjugated estrogens 2,3 . Although the clinical evidence still not be sufficient to recommend the use of hormones for prevention of diabetes among women especially with early menopause or premature ovarian insufficiency4, such detail might be helpful to uncover the neglected association between menopause and increased risk of diabetes.
Secondly, compared with the increased risk of incidence of diabetes within women, such risk tended to decrease as the number of work hours increased, which was very interesting. A recent meta-analysis of individual data from 68 prospective studies showed that diabetes increased the risk for occlusive vascular mortality nearly three times among women, so could Gilbert-Ouimet M et al's data be translated into the potential relationship of increased risk of occlusive vascular mortality with more work hours? It would deserve in-depth exploration of the raw data of the Ontario study.
In conclusion, we appreciate Gilbert-Ouimet M et al for their study in sex difference of work hours and incidence of diabetes. It indeed expanded our knowledge of effects of working long hours on diabetes, but further studies are still needed to investigate the potential effects of menopause and MHT on the association between work hour and increased risk of incidence of diabetes and cardiovascular events among women.
REFERENCES:
1. Gilbert-Ouimet M, Ma H, Glazier R, et al. Adverse effect of long work hours on incident diabetes in 7065 Ontario workers followed for 12 years. BMJ Open Diab Res Care 2018; 6: e000496.
2. Kanaya AM, Herrington D, Vittinghoff E, et al. Glycemic effects of postmenopausal hormone therapy: the Heart and Estrogen/progestin Replacement Study. A randomized, double-blind, placebo-controlled trial. Ann Intern Med. 2003; 138: 1-9.
3. Manson JE, Chlebowski RT, Stefanick ML, et al. Menopausal hormone therapy and health outcomes during the intervention and extended poststopping phases of the Women’s Health Initiative randomized trials. JAMA, 2013; 310: 1353-68.
4. Mauvais-Jarvis F, Manson JE, Stevenson JC, et al. Menopausal Hormone Therapy and Type 2 Diabetes Prevention: Evidence, Mechanisms, and Clinical Implications. Endocr Rev. 2017; 38: 173-88.
5. Norhammar A. Diabetes and cardiovascular mortality: the impact of sex. Lancet Diabetes Endocrinol. 2018; 6: 517-519.
ACKNOWLEDGEMENT:
This work was supported by Program of Shanghai Academic Research Leader (17XD1405000), Program for Outstanding Medical Academic Leader (LJRC2015-21), NSFC grants (91539118, 81611130092) to C.L., NSFC grants (81473445,2014ZX09301307-016) to Z.W.
Nyenwe et al. (1) address an interesting and important topic of the effects or associations of parental diabetes with offspring outcomes. However, the paper contains an important error that renders one of their conclusions markedly incorrect.
Specifically, having estimated a difference in energy expenditure among offspring of parents with diabetes (which the authors refer to as ‘parental diabetes’) versus offspring of parents without diabetes, the authors project that persons with parental diabetes will, as a result of this difference, steadily gain substantial weight indefinitely. They state:
“According to the data published by Wishnofsky (2), one pound has a caloric value of 3500 kcal or (1 kg=7700 kcal). We derived the estimated weight gain in kg by dividing the projected energy accrual by 7700. When normalized REE is used for this estimation, subjects with parental diabetes had a daily energy surplus of 125 kcal which would translate to ~6 kg weight gain per year.”
This type of estimation is commonly referred to as the 3500 kcal rule or 3500 kcal per pound rule.
This reasoning and calculation is erroneous because it fails to account for the dynamic changes of energy expenditure that occur with weight gain and loss. Wishnofsky himself noted the complexity of estimating energetic equivalents of gaining or losing body weight, specifically addressing the importance of time, nitrogen balance, tissue type, and water loss, among other factors, on...
Nyenwe et al. (1) address an interesting and important topic of the effects or associations of parental diabetes with offspring outcomes. However, the paper contains an important error that renders one of their conclusions markedly incorrect.
Specifically, having estimated a difference in energy expenditure among offspring of parents with diabetes (which the authors refer to as ‘parental diabetes’) versus offspring of parents without diabetes, the authors project that persons with parental diabetes will, as a result of this difference, steadily gain substantial weight indefinitely. They state:
“According to the data published by Wishnofsky (2), one pound has a caloric value of 3500 kcal or (1 kg=7700 kcal). We derived the estimated weight gain in kg by dividing the projected energy accrual by 7700. When normalized REE is used for this estimation, subjects with parental diabetes had a daily energy surplus of 125 kcal which would translate to ~6 kg weight gain per year.”
This type of estimation is commonly referred to as the 3500 kcal rule or 3500 kcal per pound rule.
This reasoning and calculation is erroneous because it fails to account for the dynamic changes of energy expenditure that occur with weight gain and loss. Wishnofsky himself noted the complexity of estimating energetic equivalents of gaining or losing body weight, specifically addressing the importance of time, nitrogen balance, tissue type, and water loss, among other factors, on estimating the energetic equivalent of one pound of body weight (2, 3). He did not imply that 3500 kcal should be used as the equivalent of one pound of weight in all cases and extrapolated indefinitely.
Over very short periods of time, such a linear projection might be a reasonable rough approximation, but over periods of months or years, the 3500 kcal rule can lead to order-of-magnitude overestimates of weight changes in response to energy intake or expenditure variations (4). This has been noted repeatedly in the literature (5-8), including prominent journals such as New England Journal of Medicine (9), The Journal of the American Medical Association (10, 11), and The Lancet (12). This error has resulted in at least one paper being retracted (13).
Validated mathematical models have been developed to account for the dynamic changes in energy expenditure that occur with weight gain and loss (12). Using such a model, we calculated that the observed 125 kcal/d difference in energy expenditure would produce a total weight change of ~6.5 kg, with ~3.9 kg gained in the first year and 95% of the total gained in 3 years. These model calculations incorporated the baseline demographics and anthropometrics reported by Nyenwe et al. and assumed that the subjects had a constant energy intake and a physical activity level of 1.7. Even the simple 10 kcal/d per pound rule of thumb (or equivalently 100 kJ/d per kg) derived from a more detailed mathematical model (12) predicts a total weight change of ~6 kg, with half of the gain occurring in the first year and 95% of the total gain in 3 years. These estimates stand in marked contrast to the predicted ~6 kg of weight gain every year stated by Nyenwe et al.
We encourage the authors to revise their conclusion regarding projected weight effects of the estimated differences in energy expenditure.
References:
1. Nyenwe EA, Ogwo CC, Owei I, et al. Parental history of type 2 diabetes is associated with lower resting energy expenditure in normoglycemic subjects. BMJ Open Diabetes Research & Care 2018;6(1) doi: https://www.doi.org/10.1136/bmjdrc-2018-000511
4. Brown AW, Hall KD, Thomas D, et al. Order of Magnitude Misestimation of Weight Effects of Children's Meal Policy Proposals. Childhood Obesity 2014;10(6):542-5. doi: https://www.doi.org/10.1089/chi.2014.0081
5. Hall KD, Chow CC. Why is the 3500 kcal per pound weight loss rule wrong? International Journal of Obesity 2013;37(12):10.1038/ijo.2013.112. doi: https://www.doi.org/10.1038/ijo.2013.112
6. Thomas DM, Martin CK, Lettieri S, et al. Response to 'why is the 3500 kcal per pound weight loss rule wrong?'. Int J Obes 2013;37(12):1614-5. doi: https://www.doi.org/10.1038/ijo.2013.113
7. Thomas DM, Martin CK, Lettieri S, et al. Can a weight loss of one pound a week be achieved with a 3500-kcal deficit? Commentary on a commonly accepted rule. Int J Obes 2013;37(12):1611-3. doi: https://www.doi.org/10.1038/ijo.2013.51
8. Bohan Brown MM, Brown AW, Allison DB. Linear extrapolation results in erroneous overestimation of plausible stressor-related yearly weight changes. Biological Psychiatry 2014 doi: https://www.doi.org/10.1016/j.biopsych.2014.10.028
9. Casazza K, Fontaine KR, Astrup A, et al. Myths, presumptions, and facts about obesity. N Engl J Med 2013;368(5):446-54. doi: https://www.doi.org/10.1056/NEJMsa1208051
12. Hall KD, Sacks G, Chandramohan D, et al. Quantification of the effect of energy imbalance on bodyweight. Lancet 2011;378(9793):826-37. doi: https://www.doi.org/10.1016/S0140-6736(11)60812-X
13. Retraction of “Modeling Potential Effects of Reduced Calories in Kids' Meals with Toy Giveaways”. Childhood Obesity 2014;10(6):546-46. doi: https://www.doi.org/10.1089/chi.2014.1062
Thank you to the authors for addressing a relevant and interesting area of research (Snorgaard et al., 2017).
The review was well planned, but the methodology lacks detail that enables the reader to understand the processes involved in the completion of the meta-analysis and some study limitations were not described.
Please could the authors clarify why the meta-analyses use both mean change from baseline and mean final value in the same meta-analysis (see Figure 2 and Figure 3 where the lower means indicate change from baseline and the higher means indicate unadjusted final values)? Also, could it be clarified why the selected arms from the three-arm trials were chosen over the arms that were omitted? Although the population, intervention and outcomes were defined in the methodology, the comparator was not.
Furthermore, when using the mean final HbA1c value in the meta-analyses, papers such as Krebs et al. (2012) and Guldbrand et al. (2012) have higher baseline HbA1cs in the lower-carbohydrate group, which was not mentioned in the paper, nor mentioned as a limitation to the meta-analysis. Guldbrand et al. (2012) demonstrate that HbA1c remained the same at two years (the time point the authors refer to) in the lower-carbohydrate arm but increased in the comparator arm by 0.2%. Therefore the low-carbohydrate arm was the superior intervention; however, the forest plot (Figure 3) suggests that the control intervention was slightly but not significant...
Thank you to the authors for addressing a relevant and interesting area of research (Snorgaard et al., 2017).
The review was well planned, but the methodology lacks detail that enables the reader to understand the processes involved in the completion of the meta-analysis and some study limitations were not described.
Please could the authors clarify why the meta-analyses use both mean change from baseline and mean final value in the same meta-analysis (see Figure 2 and Figure 3 where the lower means indicate change from baseline and the higher means indicate unadjusted final values)? Also, could it be clarified why the selected arms from the three-arm trials were chosen over the arms that were omitted? Although the population, intervention and outcomes were defined in the methodology, the comparator was not.
Furthermore, when using the mean final HbA1c value in the meta-analyses, papers such as Krebs et al. (2012) and Guldbrand et al. (2012) have higher baseline HbA1cs in the lower-carbohydrate group, which was not mentioned in the paper, nor mentioned as a limitation to the meta-analysis. Guldbrand et al. (2012) demonstrate that HbA1c remained the same at two years (the time point the authors refer to) in the lower-carbohydrate arm but increased in the comparator arm by 0.2%. Therefore the low-carbohydrate arm was the superior intervention; however, the forest plot (Figure 3) suggests that the control intervention was slightly but not significantly superior. The paper by Krebs et al. (2012) concluded that both groups had an increase in HbA1c of 0.1% at trial end suggesting no superiority of either intervention; however, again the forest plot (Figure 3) indicates that the effect size was slightly but not significantly in favour of the control group. It appears that the inclusion of these data in their current form may limit the reliability of the meta-analysis seen in Figure 3. Could the authors or editor please comment on this.
The study published in a recent volume of the journal by Blauw et al. is an excellent opportunity to highlight an under-examined environmental hypothesis in the pathophysiology of type 2 diabetes.1 In their epidemiological study, the authors used meta-regression models and demonstrated that diabetes incidence rate in the USA has increased with higher outdoor temperatures from 1996 and 2009, after adjustment for most common confounders. They also evidenced an independent association between the prevalence of glucose intolerance worldwide and mean annual temperature on a global scale. The theoretical background for the work mainly stands on the reduction in brown adipose tissue activity due to high ambient temperature that is expected to negatively impact glucose metabolism. This view is plausible, particularly since recent data uncovered potential crosstalk between brown adipose tissue and glucose regulatory pathways,2 but it is important for us to discuss the context of the study.
We are definitely concerned about the burden of consequences of climate change including biodiversity assault, threats to the human species’ safety, health and well-being because of increased risks related to extreme weather events, wildfire, air quality, and other environmental disease carriers. However, isn’t it cynical that the glucose metabolism disturbance observed in warm environmental temperature might become a serious working hypothesis concomitantly with (b...
The study published in a recent volume of the journal by Blauw et al. is an excellent opportunity to highlight an under-examined environmental hypothesis in the pathophysiology of type 2 diabetes.1 In their epidemiological study, the authors used meta-regression models and demonstrated that diabetes incidence rate in the USA has increased with higher outdoor temperatures from 1996 and 2009, after adjustment for most common confounders. They also evidenced an independent association between the prevalence of glucose intolerance worldwide and mean annual temperature on a global scale. The theoretical background for the work mainly stands on the reduction in brown adipose tissue activity due to high ambient temperature that is expected to negatively impact glucose metabolism. This view is plausible, particularly since recent data uncovered potential crosstalk between brown adipose tissue and glucose regulatory pathways,2 but it is important for us to discuss the context of the study.
We are definitely concerned about the burden of consequences of climate change including biodiversity assault, threats to the human species’ safety, health and well-being because of increased risks related to extreme weather events, wildfire, air quality, and other environmental disease carriers. However, isn’t it cynical that the glucose metabolism disturbance observed in warm environmental temperature might become a serious working hypothesis concomitantly with (because of?) rising climate change concerns? The question particularly arises when other questions are considered, such as: Are there earlier pieces of evidence feeding this idea? How discordant were the rare previous studies on the question? How confident and compelling were the authors?
Yes, there have been earlier experimental studies. The potential role of environmental factors in diabetes pathophysiology certainly has emerged from observations of diabetes overprevalence in native American Pima Indians, but also in urbanized Melanesians,3 leading to a cascade of studies with genetic and evolutionary considerations. However, the first landmark observation on the question seems to be the communication by Akanji et al. in 1987.4 They investigated the metabolic effect of the ingestion of a meal or a glucose load in non-diabetic obese and non-obese subjects. The plasma glucose concentration excursion was significantly increased when the ambient temperature was 33°C as compared with 23°C. This result was confirmed and completed by other experimental studies.5-10 The a priori discordant results come from animal model studies. In rodents, heat treatment, typically administered through warm baths, improve glucose tolerance.11 However, the apparent disagreement with results of human studies may fade with the consideration that the cutaneous blood flow of humans and furred animals is incomparable. Our understanding of the human model is that the more or less recent studies displayed very strong agreement in their results, systematically corroborating glucose metabolism impairment in warm environment. Also, another recurrent feature in these studies was the high level of prudence by the authors, respecting the caution required by the scientific method, since the observations were isolated and the number of subjects remained limited. They got cold feet because they had to reduce the (alpha) risk of falsely rejecting the null hypothesis (or type I error) and avoid claiming by error to the world that exposure to warm temperature causes diabetes. Interestingly, some authors even proposed that the phenomenon was only apparent.5,7 If that were the case, which has not been confirmed or denied yet, this is still calling for correction factors for the interpretation of glucose tolerance testing, which remain inexistent so far.
Of course, there have also been previous epidemiological studies. A key observation was the doubling of glucose intolerance rates in pregnant women tested on warmer days as compared with those tested in the coldest period of the year.12 Together with the experimental studies of the same period, these early results were interpreted with extreme caution, calling for confirmation, and have gained little attention, or at least were poorly cited. However, it should be noted that later results of epidemiological studies focusing on seasonality and temperature effects on glucose metabolism are strikingly convergent. Both warm13 and cold14 environments were associated, after adjustments, with higher fasting plasma glucose than moderate temperature (18.1°C) in a large population of individuals tested several times.
So, there are regrets concerning the time it has taken to seriously consider the putative contribution of exposure to warmth in the development of glucose metabolism impairment, in particular for residents in India, the Middle-East, Central America, the Caribbean and Mexico for example. These regions are chronically exposed to heat; they have a very high prevalence of type 2 diabetes, yet this coincidence has long been ignored. It may be that climate change concerns acted as a trigger to improve the community readiness to be convinced by the evidence of glucose metabolism impairment in warm environments.
We wish that the paper by Blauw et al. and all their/our precursors will inspire other teams so as to really improve the understanding of the phenomenon, for a global shared benefit.
References:
1. Blauw LL, Aziz NA, Tannemaat MR, et al. Diabetes incidence and glucose intolerance prevalence increase with higher outdoor temperature. BMJ Open Diabetes Res Care 2017;5:e000317.
2. Lee P, Bova R, Schofield L, et al. Brown adipose tissue exhibits a glucose-responsive thermogenic biorhythm in humans. Cell Metab 2016;23:602-9.
3. Zimmet P, Taylor R, Ram P, et al. Prevalence of diabetes and impaired glucose tolerance in the biracial (Melanesian and Indian) population of Fiji: a rural- urban comparison. Am J Epidemiol 1983;118:673-88.
4. Akanji AO, Bruce M, Frayn K, et al. Oral glucose tolérance and ambient temperature in non-diabetic subjects. Diabetologia 1987;30:431–3.
5. Frayn KN, Whyte PL, Benson HA, Earl DJ, Smith HA. Changes in forearm blood flow at elevated ambient temperature and their role in the apparent impairment of glucose tolerance. Clin. Sci. (Lond). 1989;76:323–8.
6. Akanji AO, Oputa RN. The effect of ambient temperature on glucose tolerance. Diabet Med 1991;8:946–8.
7. Moses RG, Patterson MJ, Regan JM, et al. A non-linear effect of ambient temperature on apparent glucose tolerance. Diabetes Res Clin Pract 1997;36:35–40.
8. Dumke CL, Slivka DR, Cuddy JS, et al. The effect of environmental temperature on glucose and insulin after an oral glucose tolerance test in healthy young men. Wilderness Environ Med 2015;26:335-42.
9. Faure C, Charlot K, Henri S, et al. Impaired glucose tolerance after brief heat exposure: a randomized crossover study in healthy young men. Clin Sci (Lond) 2016;130:1017-25.
10. Antoine-Jonville S, Faure C, Hue O, et al. Ambient temperature-related exaggerated post-prandial insulin response in a young athlete: a case report and implications for climate change. Accepted for publication in the Asia Pacific Journal of Clinical Nutrition.
11. Gupte AA, Bomhoff GL, Touchberry CD, et al. Acute heat treatment improves insulin-stimulated glucose uptake in aged skeletal muscle. J Appl Physiol 1985;110:451–7.
12. Schmidt MI, Matos MC, Branchtein L, et al. Variation in glucose tolerance with ambient temperature. Lancet (London, England) 1994;344:1054–5.
13. Li S, Zhou Y, Williams G, et al. Seasonality and temperature effects on fasting plasma glucose: A population-based longitudinal study in China. Diabetes Metab 2016;42:267-75
14. Suarez L, Barrett-Connor E. Seasonal variation in fasting plasma glucose levels in man. Diabetologia 1982;22:250-3.
We thank colleagues for their critical comments that help to clarify relationships we have studied. We are not concerned with the frequency of what specific genes, high-risk or not, has increased recently. We are just making a general statement that with relaxed natural selection detrimental mutations may accumulate. The paper by Witas et al. cited by our critics uses the same rationale as we do when suggesting changes in type 1 d...
We thank colleagues for their critical comments that help to clarify relationships we have studied. We are not concerned with the frequency of what specific genes, high-risk or not, has increased recently. We are just making a general statement that with relaxed natural selection detrimental mutations may accumulate. The paper by Witas et al. cited by our critics uses the same rationale as we do when suggesting changes in type 1 diabetes (T1D) susceptibility gene frequencies under the operation of natural selection over the last 700 years [1]. For most of that time, excluding the last 100 years or so, natural selection was strong so it eliminated susceptibility genes. Selection only became relaxed in the recent decades. Besides the Human Leukocyte Antigen (HLA) gene group studied in the papers quoted by our critics, we know now that there are some 40-50 alleles located on various chromosomes that may increase susceptibility to type 1 diabetes [2, 3] . Our statements pertain to the genetic susceptibility to T1D that may be caused by all the T1D related genes, not just specific alleles. We argue that T1D related genes may have been accumulated in the whole population rather than in some particular samples. Having studied papers quoted by our critics to support their letter we found a fatal error in their logic. The studies [4-8] cited in the letter used only rather small samples of T1D patients of a limited age range and considered subsets of disease related genes, not all of them. Within those samples of patients, frequencies of alleles considered to confer greatest susceptibility decreased. Meanwhile frequencies of other alleles conferring some T1D susceptibility increased. Without consideration of the frequency of greatest susceptibility alleles, and others, in the total gene pool of the entire population from which limited samples were selected, it is impossible to tell whether the total frequency of susceptibility genes in a population has increased or decreased. The only conclusion that can be drawn from the studies cited by our critics is that frequencies of a limited number of alleles providing different levels of susceptibility to T1D have changed through time among people already suffering from the disease. This tells us nothing about total frequencies of these alleles in a population.
A particular study [6] may have shown that a large proportion of T1D patients have no family history of the disease, but this runs counter to the well-known fact that people of different ethnic ancestry (and thus derived from different gene pools) have vastly different prevalence rates of T1D and to the strong correlation of the disease appearance among monozygotic twins. The genetic component of T1D susceptibility is substantial and, as we have already said, provided by scores of genes from various chromosomes.
Our critics are also wrong in pointing that our conclusions can be based on the fact that prevalence of T1D increases with increasing GDP. In our statistical analyses we have excluded possible influence of GDP by using partial correlation analysis that stabilizes effects of controlled variables statistically, as if they were of the same magnitude in all analysed countries. The correlation between T1D and Ibs we have observed is independent of the correlation between GDP and T1D prevalence.
We are not denying that environmental factors may play some role in increasing prevalence of T1D, we are just pointing, in an appropriately structured statistical analysis of worldwide data, that relaxation of natural selection has an effect on T1D prevalence. Study of relative magnitudes of genetic and environmental influences on T1D increase requires careful quantitative analyses not just rhetorical statements "play much larger role" based on small-scale selective studies.
1. Witas, H., K. J??drychowska???Da??ska, and P. Zawicki, Changes in frequency of IDDM???associated HLA DQB, CTLA4 and INS alleles. International journal of immunogenetics, 2010. 37(3): p. 155-158.
2. Barrett, J.C., et al., Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nature genetics, 2009. 41(6): p. 703-707.
3. Bradfield, J.P., et al., A genome-wide meta-analysis of six type 1 diabetes cohorts identifies multiple associated loci. PLoS Genet, 2011. 7(9): p. e1002293.
4. Spoletini, M., et al., Temporal Trends of HLA, CTLA-4 and PTPN22 Genotype Frequencies among Type 1 Diabetes in Continental Italy. PloS one, 2013. 8(4): p. e61331.
5. Fourlanos, S., et al., The rising incidence of type 1 diabetes is accounted for by cases with lower-risk human leukocyte antigen genotypes. Diabetes care, 2008. 31(8): p. 1546-1549.
6. Parkkola, A., et al., Extended family history of type 1 diabetes and phenotype and genotype of newly diagnosed children. Diabetes care, 2013. 36(2): p. 348-354.
7. Patterson, C., et al., Is childhood-onset type I diabetes a wealth-related disease? An ecological analysis of European incidence rates. Diabetologia, 2001. 44(3): p. B9-B16.
8. Vehik, K., et al., Trends in high-risk HLA susceptibility genes among Colorado youth with type 1 diabetes. Diabetes Care, 2008. 31(7): p. 1392-1396.
In the article, "Type 1 diabetes prevalence increasing globally and regionally: The role of natural selection and life expectancy at birth" (You and Henneberg 2016), the authors find a correlation between worldwide type 1 diabetes prevalence and both life expectancy at birth and the "Biological State Index" (Ibs), a measure of population reproductive success. Based on these findings, they argue that "the correlation of Ibs to the...
In the article, "Type 1 diabetes prevalence increasing globally and regionally: The role of natural selection and life expectancy at birth" (You and Henneberg 2016), the authors find a correlation between worldwide type 1 diabetes prevalence and both life expectancy at birth and the "Biological State Index" (Ibs), a measure of population reproductive success. Based on these findings, they argue that "the correlation of Ibs to the type 1 diabetes prevalence rate has been observed, compatible with suggestion (sic) that lower opportunity for selection allows accumulation of unfavorable genes," and that, "reduced natural selection (Ibs) may be the major contributor to the increasing prevalence of type 1 diabetes worldwide with special regard to European countries." We do not agree with the conclusion that natural selection is playing a role in these correlations, particularly in light of an existing body of evidence that has already examined the question of whether or not the high-risk genes associated with type 1 diabetes have increased over the past decades--evidence that the authors failed to cite.
The existing body of evidence shows that the high-risk genes associated with type 1 diabetes have actually decreased over time, while more people with low-risk genes are now developing the disease. For example, Hermann et al. (2003) found that the high risk genotypes were lower, and protective genotypes were higher, in Finnish type 1 diabetes patients diagnosed more recently (between 1990 and 2001) as compared to those diagnosed between 1939 and 1965. Resic-Lindehammer et al. (2008) found that the high-risk type 1 diabetes genotypes were decreasing over time, and the lower-risk genotypes were increasing in Swedes diagnosed in three time periods between 1986 and 2005. Gillespie et al. (2004) compared the frequency of high- risk genes in UK patients diagnosed with type 1 diabetes more than 50 years ago compared to those more recently diagnosed (1985-2002). They found that, "The proportion of high- risk susceptibility genotypes was increased in the earlier cohort, especially in those diagnosed at age 5 years or younger, which is consistent with the hypothesis that the rise of type 1 diabetes is due to a major environmental effect." The strongest correlations found by You and Henneberg (2016) were in Europe, where these studies were conducted.
Outside of Europe, the patterns are the same. Steck et al. (2011) found that in two large cohorts representing multiple populations, the high-risk genotypes associated with type 1 diabetes decreased between 1965 and 2004-6. Fourlanos et al. (2008) analyzed the genotypes of Australians diagnosed with type 1 diabetes between 1950 and 2005 and found that, "The rising incidence and decreasing age at diagnosis of type 1 diabetes is accounted for by the impact of environment on children with lower-risk ... genes, who previously would not have developed type 1 diabetes in childhood." Vehik et al. (2008) genotyped Colorado youth with type 1 diabetes (diagnosed from 1978-1988, as compared to 2002-4), and found that "high-risk... genotypes are becoming less frequent over time in youth with type 1 diabetes of non-Hispanic white and Hispanic origin. This temporal trend may suggest that increasing environmental exposure is now able to trigger type 1 diabetes in subjects who are less genetically susceptible."
Witas et al. (2010) compared the DNA from bones from the 11th-14th centuries to those of today. They found that, "Contrary to the initial assumptions, genetic predisposition towards type 1 diabetes... is much lower contemporarily than it was approximately 700 years before present."
Moreover, the vast majority of people who develop type 1 diabetes have no family history of the disease. Even when taking into account both first and second degree relatives, the number of people diagnosed with type 1 diabetes without a family history approaches 80% (Parkkola et al. 2013).
Aside from natural selection, there are other reasons that could explain why reproductive success and life expectancy are associated with worldwide type 1 diabetes prevalence, such as the availability of insulin (which the authors do acknowledge) and other medications, technologies, and health care opportunities that are more available in countries with higher life expectancies and reproductive success. Numerous additional environmental factors that are positively associated with GDP, which the authors found was even more strongly associated with type 1 diabetes prevalence than the Biological State Index, could also explain these patterns (Patterson et al. 2001).
The authors' conclusion that "reduced natural selection (Ibs) may be the major contributor to the increasing prevalence of type 1 diabetes worldwide" is not supported by the existing scientific evidence. The studies that have examined this hypothesis via genetic analyses show the genetic risk of type 1 diabetes actually declining over time, and that people with lower genetic risk profiles are now developing type 1 diabetes more often than they did in the past. These patterns suggest that environmental factors play a much larger role than natural selection in the increasing rates of type 1 diabetes.
References Cited
Fourlanos S, Varney MD, Tait BD, Morahan G, Honeyman MC, Colman PG et al. The rising incidence of type 1 diabetes is accounted for by cases with lower-risk human leukocyte antigen genotypes. Diabetes Care 2008; 31(8):1546-1549.
Gillespie KM, Bain SC, Barnett AH, Bingley PJ, Christie MR, Gill GV et al. The rising incidence of childhood type 1 diabetes and reduced contribution of high-risk HLA haplotypes. Lancet 2004; 364(9446):1699-1700.
Hermann R, Knip M, Veijola R, Simell O, Laine AP, Akerblom HK et al. Temporal changes in the frequencies of HLA genotypes in patients with Type 1 diabetes-- indication of an increased environmental pressure? Diabetologia 2003; 46(3):420-425.
Parkkola A, Harkonen T, Ryhanen SJ, Ilonen J, Knip M. Extended family history of type 1 diabetes and phenotype and genotype of newly diagnosed children. Diabetes Care 2013; 36(2):348-354.
Patterson CC, Dahlquist G, Soltesz G, Green A. Is childhood-onset type I diabetes a wealth-related disease? An ecological analysis of European incidence rates. Diabetologia 2001; 44 Suppl 3:B9-16.
Resic-Lindehammer S, Larsson K, Ortqvist E, Carlsson A, Cederwall E, Cilio CM et al. Temporal trends of HLA genotype frequencies of type 1 diabetes patients in Sweden from 1986 to 2005 suggest altered risk. Acta Diabetol 2008; 45(4):231-235.
Steck AK, Armstrong TK, Babu SR, Eisenbarth GS. Stepwise or linear decrease in penetrance of type 1 diabetes with lower-risk HLA genotypes over the past 40 years. Diabetes 2011; 60(3):1045-1049.
Vehik K, Hamman RF, Lezotte D, Norris JM, Klingensmith GJ, Rewers M et al. Trends in high-risk HLA susceptibility genes among Colorado youth with type 1 diabetes. Diabetes Care 2008; 31(7):1392-1396.
Witas HW, Jedrychowska-Danska K, Zawicki P. Changes in frequency of IDDM-associated HLA DQB, CTLA4 and INS alleles. Int J Immunogenet 2010; 37(3):155-158.
You WP, Henneberg M. Type 1 diabetes prevalence increasing globally and regionally: the role of natural selection and life expectancy at birth. BMJ Open Diabetes Res Care 2016; 4(1):e000161.
We will start by emphasizing that we cannot control
what has been written in the media about this study. We
will therefore limit our response to the reviewer's
comments that directly pertain to our manuscript.
The results of this study are clearly enumerated in the
paper, with the supporting data shown in every
instance[1]. We distinguish explicitly be...
We will start by emphasizing that we cannot control
what has been written in the media about this study. We
will therefore limit our response to the reviewer's
comments that directly pertain to our manuscript.
The results of this study are clearly enumerated in the
paper, with the supporting data shown in every
instance[1]. We distinguish explicitly between between-
group and within-group effects, noting that the
between-group effects are primary. In studies that
cannot be blinded, however, there are well-known
Hawthorne effects, which can attenuate statistical
power in unpredictable ways. Thus, isolated within-
group effects, though secondary, are nonetheless
relevant and of interest, and may speak to issues that
warrant reinvestigation in trials with greater
statistical power.
While we considered Hawthorne effects to the greatest
extent possible in our study design, it is very
difficult to anticipate their magnitude in any given
context. We were thus left to conjecture about such
effects in the interpretation of our data.
Interpreting data from different perspectives, rather
than relying on a single approach, helps to overcome
Hawthorne and other potential effects that may obscure
meaningful associations. This, of course, is uniquely
important in lifestyle interventions that cannot be
fully blinded. Presenting only between-group effects
would provide a stunted view, and perhaps a
misrepresentation of our findings. This is
particularly true given the relatively small sample
size. We do not mistake our secondary outcomes for
primary, and nor should our readers. We do, however,
think that these findings warrant readers' attention.
We clearly note the basis for our data interpretation
and the relevant limitations in our manuscript.
References
1 Njike VY, Ayettey R, Petraro P, Treu JA, Katz
DL. Walnut ingestion in adults at risk for diabetes:
effects on body composition, diet quality, and cardiac
risk measures. BMJ Open Diabetes Res Care. 2015 Oct
19;3(1):e000115. doi: 10.1136/bmjdrc-2015-000115.
eCollection 2015.
We would like to thank Anne-Thea McGill and Brown et al for their response to our manuscript “Parental history of type 2 diabetes is associated with lower resting energy expenditure in normoglycemic subjects.” The points raised by the commentaries are well taken. However, as stated in the limitations of our study, we performed a cross-sectional study which did not track weight gain A longitudinal study would be required to gain such specific insights. While predictive models are useful, they are not without limitations and the most accurate determination of weight gain arising from lower resting energy expenditure is best done by a longitudinal study. Lower resting expenditure may not always equate to an energy surplus as energy intake could be lower in subjects with lower REE or physical activity energy expenditure may be higher, thus balancing the total energy expenditure.
It’s appreciated for addressing an interesting area of research about the efficacy of medical nutrition treatment based on the Mediterranean Diet (MedDiet).
The study was a secondary analysis of the St Carlos GDM Prevention Study, conducted between January and December 2015 in Hospital Clinico San Carlos (Madrid, Spain). The author used MedDiet-MNT in order to observe its effects on mother’s glycemic level and also the prenatal outcome.
According to this study, there were two groups. Both groups received dietary recommendation to follow MD guideline, the difference was just in intervention group, they added portion for virgin olive oil and nuts. Basically both groups had similar diet recommendation, so further clinical experiment is highly needed to determine the exact effect of adding portion in extra virgin olive oil and nuts on lowering risk of GDM.
Although this diet had several benefits, the use of adding portion on extra virgin oil and pistachios in the intervention group treatment still becomes a question. In the other study, traditional Mediterranean diet had positive effect on lowering risk of GDM in pregnant women (Izadi, 2016), this outcome also occurred in the study conducted by Perez,Ferre (2014) that MD could reduce risk of GDM. So, if the traditional way has been reported successful in lowering GDM risk, is that really necessary to modify the basic guideline of MedDiet?
References:
Show More1. Izadi V, Tehrani H, Haghighatdoost F, et.a...
Humans have proportionately large, complex brains that require large amounts of nutrients- energy and micronutrients. There are a number of little recognised co-adaptations to manage this 'brain drain'. Two very important mechanisms to manage this high localised metabolic rate were to - 1) Use the extremely varied and reactive plant chemicals that were increasingly being consumed in the nomadic hunter-gatherer hominins 2) To increase the buffer stores of nutrients by reactivating mammalian genes for subcutaneous fat stores. 3) increase strong drives to acquire high nutrient food predicated on energy density.
Show MoreThe nutrient chemicals are often plant defence (secondary) chemicals) of which the anti yeast polyphenol resveratrol is but one of myriads, act as Michael acceptors. These reactions are much less precise that enzymatic reactions. They shuffle-reshuffle electrons and efficiently manage energy, reducing free radical production and energy loss . There are a number of enhanced anti-oxidant, detoxification, and adaptive and general cell repair pathways coordinated by the NRF2/Keap1/antioxidant response element cell protection systems.
2) As mentioned, the subcutaneaous adipose tissue is a brain nutrient buffer - especially for the intra-uterine and postnatal human brain development. This adipose is not just a fat store but lipids and many other nutrients should be in the stores - those absorbed through the colon after being trafficked there...
To the Editor
We read with great interest the article by Gilbert-Ouimet M et al1 recently published in your journal. Such study showed an interesting result that only increased risk of incidence of diabetes occurring among female workers working 45 hours or more per week, and suggested that modification of such risk factors would be helpful to improve prevention strategies and orient policymaking by following up 7065 workers over a 12-year period in Ontario, Canada.
Show MoreHowever, we have some concerns. Firstly, in order to find out the potential relationship between long work hours and the incidence of diabetes, several other independent variables were considered in the analysis process such as sociodemographic and health-related covariates, but the information of menopause and menopausal hormone therapy (MHT) among women was not added. Strong association with increased risk of cardiovascular diseases had been confirmed in several studies and data from large randomized-controlled trials have shown that the decrease of incidence of T2D in women could be achieved by MHT with conjugated estrogens 2,3 . Although the clinical evidence still not be sufficient to recommend the use of hormones for prevention of diabetes among women especially with early menopause or premature ovarian insufficiency4, such detail might be helpful to uncover the neglected association between menopause and increased risk of diabetes.
Secondly, compared with the increased risk...
Nyenwe et al. (1) address an interesting and important topic of the effects or associations of parental diabetes with offspring outcomes. However, the paper contains an important error that renders one of their conclusions markedly incorrect.
Specifically, having estimated a difference in energy expenditure among offspring of parents with diabetes (which the authors refer to as ‘parental diabetes’) versus offspring of parents without diabetes, the authors project that persons with parental diabetes will, as a result of this difference, steadily gain substantial weight indefinitely. They state:
“According to the data published by Wishnofsky (2), one pound has a caloric value of 3500 kcal or (1 kg=7700 kcal). We derived the estimated weight gain in kg by dividing the projected energy accrual by 7700. When normalized REE is used for this estimation, subjects with parental diabetes had a daily energy surplus of 125 kcal which would translate to ~6 kg weight gain per year.”
This type of estimation is commonly referred to as the 3500 kcal rule or 3500 kcal per pound rule.
This reasoning and calculation is erroneous because it fails to account for the dynamic changes of energy expenditure that occur with weight gain and loss. Wishnofsky himself noted the complexity of estimating energetic equivalents of gaining or losing body weight, specifically addressing the importance of time, nitrogen balance, tissue type, and water loss, among other factors, on...
Show MoreThank you to the authors for addressing a relevant and interesting area of research (Snorgaard et al., 2017).
The review was well planned, but the methodology lacks detail that enables the reader to understand the processes involved in the completion of the meta-analysis and some study limitations were not described.
Please could the authors clarify why the meta-analyses use both mean change from baseline and mean final value in the same meta-analysis (see Figure 2 and Figure 3 where the lower means indicate change from baseline and the higher means indicate unadjusted final values)? Also, could it be clarified why the selected arms from the three-arm trials were chosen over the arms that were omitted? Although the population, intervention and outcomes were defined in the methodology, the comparator was not.
Furthermore, when using the mean final HbA1c value in the meta-analyses, papers such as Krebs et al. (2012) and Guldbrand et al. (2012) have higher baseline HbA1cs in the lower-carbohydrate group, which was not mentioned in the paper, nor mentioned as a limitation to the meta-analysis. Guldbrand et al. (2012) demonstrate that HbA1c remained the same at two years (the time point the authors refer to) in the lower-carbohydrate arm but increased in the comparator arm by 0.2%. Therefore the low-carbohydrate arm was the superior intervention; however, the forest plot (Figure 3) suggests that the control intervention was slightly but not significant...
Show MoreDear editor,
The study published in a recent volume of the journal by Blauw et al. is an excellent opportunity to highlight an under-examined environmental hypothesis in the pathophysiology of type 2 diabetes.1 In their epidemiological study, the authors used meta-regression models and demonstrated that diabetes incidence rate in the USA has increased with higher outdoor temperatures from 1996 and 2009, after adjustment for most common confounders. They also evidenced an independent association between the prevalence of glucose intolerance worldwide and mean annual temperature on a global scale. The theoretical background for the work mainly stands on the reduction in brown adipose tissue activity due to high ambient temperature that is expected to negatively impact glucose metabolism. This view is plausible, particularly since recent data uncovered potential crosstalk between brown adipose tissue and glucose regulatory pathways,2 but it is important for us to discuss the context of the study.
We are definitely concerned about the burden of consequences of climate change including biodiversity assault, threats to the human species’ safety, health and well-being because of increased risks related to extreme weather events, wildfire, air quality, and other environmental disease carriers. However, isn’t it cynical that the glucose metabolism disturbance observed in warm environmental temperature might become a serious working hypothesis concomitantly with (b...
Show MoreWe appreciate the attention to our manuscript[1].
We will start by emphasizing that we cannot control what has been written in the media about this study. We will therefore limit our response to the reviewer's comments that directly pertain to our manuscript.
The results of this study are clearly enumerated in the paper, with the supporting data shown in every instance[1]. We distinguish explicitly be...
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