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Angela C Estampador, Paul W Franks, Precision Medicine in Obesity and Type 2 Diabetes: The Relevance of Early-Life Exposures, Clinical Chemistry, Volume 64, Issue 1, 1 January 2018, Pages 130–141, https://doi.org/10.1373/clinchem.2017.273540
reverses diabetes type 2 born (⭐️ nature journal) | reverses diabetes type 2 reddithow to reverses diabetes type 2 for Type 2 diabetes is highly prevalent and devastating. Obesity is a diabetogenic factor, driving insulin resistance and a compensatory demand for increased insulin secretion from the pancreatic β cells; a failure to address this demand results in diabetes. Accordingly, primary and secondary prevention of obesity are at the core of diabetes prevention programs. The development of obesity and declining β-cell function often span many years or decades before diabetes is clinically manifest. Thus, characterizing the early-life process and risk factors that set disease trajectories may yield novel targets for early intervention and help improve the accuracy of prediction algorithms, factors germane to the emerging field of precision medicine.
Here, we overview the concepts of precision medicine and fetal programming. We discuss the barriers to preventing obesity and type 2 diabetes in adulthood and present the rationale for considering early-life events in this context. In so doing, we discuss proof-of-concept studies and cutting-edge technological developments that are likely to transform current thinking on the etiology and pathogenesis of obesity and type 2 diabetes. We also review the factors hampering progress, including the success and failures of pregnancy intervention trials.
Obesity and type 2 diabetes are among the major health and economic burdens of our time. Defeating these diseases is likely to require life-course approaches, which may include aggressive interventions informed by biomarker profiling undertaken during early life.
The emerging field of precision medicine presents not only new opportunities for tackling many diseases but also new challenges in the form of data integration, health literacy, and data privacy. The incorporation of layers of personal data obtained from biosamples (e.g., genomic, transcriptomic, proteomic, epigenomic, and megagenomic) (1), digital images (e.g., composition and structure of organs and tissues), wearable devices (e.g., that objectively monitor physical activity, stress, and sleep), and conventional data from medical records are key features of the vision. It is anticipated that by doing so, new disease taxonomies will be defined, such that the right treatment can be provided for the right patient, at the right time (1).
Complex diseases such as obesity and type 2 diabetes are obvious targets for precision medicine, as current prevention and treatment strategies vary in their effectiveness from 1 person to the next, raising the possibility that patient characteristics dictate susceptibility to risk factors and response to therapeutics. The latter is presumed to reflect, at least in part, the heterogeneous biological for 1 last update 30 May 2020 nature of the diseases and the need for subclassifications of diagnoses and patient groups for targeted therapies.Complex diseases such as obesity and type 2 diabetes are obvious targets for precision medicine, as current prevention and treatment strategies vary in their effectiveness from 1 person to the next, raising the possibility that patient characteristics dictate susceptibility to risk factors and response to therapeutics. The latter is presumed to reflect, at least in part, the heterogeneous biological nature of the diseases and the need for subclassifications of diagnoses and patient groups for targeted therapies.
Preventing rather than curing type 2 diabetes is far preferable, as the disease carries with it a heavy burden of life-threatening comorbidities. However, although numerous drug and lifestyle paradigms are proven to help slow progression to type 2 diabetes, completely preventing the disease in most high-risk people has proven unattainable. This may be because the (a) underlying pathologies that eventually manifest clinically as diabetes begin many years, perhaps even decades, earlier (2); (b) preventive interventions begin too late in this process; and (c) interventions are simply ineffective in some people owing to features of their biology that block or inhibit the intended effects of the interventions.
Added to this challenge is our inability to adequately distinguish which individuals of those considered high risk based on conventional screening criteria will progress to disease, as the vast majority of people with prediabetic blood glucose concentrations will remain in this intermediate risk state or revert to normoglycemia, with as few as 5% to 10% of those with prediabetes progressing to full-blown diabetes within the next year (3). Indeed, within 5 years, 20% to 30% of people with prediabetic hemoglobin A1c and/or fasting glucose (FG)6 values will have reverted to normoglycemia (4). Thus, intervening at an early stage on the basis of prediabetic blood glucose measures alone would require treating a much larger subgroup of the population than necessary, wasting valuable resources and exposing patients to unnecessary stress, discomfort, or harm if therapeutics have side effects.
reverses diabetes type 2 uk statistics (☑ quote) | reverses diabetes type 2 new zealandhow to reverses diabetes type 2 for If diabetes had few consequences, we might not worry that by the year 2050, about half a billion men, women, and children will have the disease. However, diabetes is no anodyne condition; it is often aggressive, debilitating, and deadly. The complications of diabetes include retinopathies, painful neuropathies in the lower limbs and feet, kidney disease, heart disease and stroke. Many millions die each year of these complications, amounting to 1 death every 7 s, with cardiovascular disease accounting for about half of all fatalities among people with diabetes (5).
Thus, the narrative is compelling and the demand urgent for more precise prediction algorithms and more effectual paradigms for prevention than most contemporary healthcare systems currently deploy. Recognizing this, several major precision medicine initiatives have been launched around the world. The European Union has, for example, engaged the major pharmaceutical companies to jointly fund the Innovative Medicines Initiative (IMI), a 5 billion Euro funding mechanism through which the top academic institutions and drug companies are working together to tackle the most burdensome diseases; for type 2 diabetes, the IMI studies include DIRECT, SUMMIT, and IMIDIA (IMI stage 1 projects) and RHAPSODY and BEATDKD (IMI stage 2 projects), which seek to discover biomarkers for (a) glycemic deterioration before and after the onset of type 2 diabetes, (b) cardiovascular complications, (c) β-cell dysfunction, and (d) diabetic kidney disease. In the US, the Accelerating Medicines Partnership, managed by the Foundation of the NIH, has adopted a similar private–public partnership model to IMI, and in 2016 it set aside 215 million US dollars for precision medicine research. In China, the government recently announced US$9.2 billion funding to support a precision medicine initiative running from 2016 to 2031. There are many other national and regional funding mechanisms for precision medicine research, many of which focus on diabetes. The core objectives of the major precision medicine consortia typically focus on the discovery and credentialization of biomarkers that might help optimize prevention and management of diabetes or its complications, with special emphasis on pharmacotherapies. None currently focuses on the impact of pregnancy, despite the important influence it has on diabetes risk in both mother and offspring.
Body corpulence is a remarkably resilient phenotype, tracking from childhood, through adolescence and into adulthood (6). The extent to which overweight and obesity persist across much of the life span may reflect the influence of biological encoding (e.g., early-life programming events or genetics) and/or chronic exposure to environmental risk factors; the latter is somewhat mitigated by the fairly substantial changes in obesogenic environmental exposures during the past decades (7) and that even with tightly controlled, intensive weight-loss interventions, many people struggle to retain long-term reductions in body weight (8).
reverses diabetes type 2 by race (☑ vegetarian diet) | reverses diabetes type 2 chartshow to reverses diabetes type 2 for According to the UK government, 90% of adults, ages 16 to 54 years have type 2 diabetes or are overweight or obese (9). Intervention trials clearly demonstrate that weight loss slows diabetes progression (10, 11) and is a cardinal feature of the major clinical practice guidelines for diabetes prevention. But the impact of obesity extends much further, with cancers (lung, kidney, and colorectal), coronary heart disease, and osteoarthritis likely to be directly caused by obesity (12), with many other diseases correlated with the condition.
Most weight-loss interventions for the primary or secondary prevention of diabetes focus on drugs, surgery, or lifestyle. The side effects of weight-loss drugs (13–15) and surgery (16) are not inconsiderable, and although all classes of weight-loss intervention can achieve significant short-term weight loss in most individuals, the effects tend to wane with time.
Thus, given the early-life origins of adult obesity, the major negative impact that adult obesity has on morbidity and mortality, and the intractability of the condition, interventions to prevent obesity that are initiated at the beginning of the life course, or even preconception, may be warranted.
reverses diabetes type 2 prevalence (🔥 limits) | reverses diabetes type 2 reddithow to reverses diabetes type 2 for One major objective of diabetes precision medicine is to distinguish people with prediabetes who will progress rapidly to diabetes from those who will not. Although precision medicine is heavily focused on the use of biomarkers for stratification, these are likely to complement, rather than supplant, other patient characteristics that are helpful in risk prediction and patient stratification. Despite being nonmodifiable, ethnicity, family history of diabetes, current age, low or high birth weight, and history of gestational diabetes mellitus (GDM) are each of considerable value for prediction. Modifiable lifestyle factors (body weight, poor diet, smoking, stress, sleep disturbance, and physical inactivity) also impact progression from prediabetes to diabetes, and have been the focus of successful diabetes prevention trials (10, 11, 17–19). Importantly, these factors may also be relevant in precision medicine, as they may interact with genetic susceptibility; for example, in a large nested case–cohort study from Europe, genetic burden was most strongly associated with diabetes incidence in people who were young or lean (20).
Observational studies consistently highlight the importance of the intrauterine environment in determining trajectories for disease in later life. For instance, some fetal complications are associated with intrauterine exposure to maternal obesity and GDM, including neuropsychological development (21), preterm birth, macrosomia, hyperbilirubinemia, hypoglycemia, respiratory distress syndrome, and shoulder dystocia (22). However, a very significant long-term postnatal complication of a diabetic pregnancy is the substantially increased risk of obesity, type 2 diabetes, and other cardiometabolic abnormalities in the offspring later in life (23–27).
Birth weight, which conveys a J-shaped relationship with risk of type 2 diabetes in the offspring, is in part reflective of intrauterine nutrition and an indicator of diabetes risk (28). Maternal glucose tolerance and obesity are strongly correlated with offspring birth weight (28), with women with GDM [odds ratio (OR), 2.19 (1.93–2.47)] or obesity [OR, 1.73 (1.50–2.00)] more likely to deliver a large for gestational age (LGA) baby (defined as birth weight >90th percentile), and in women with both GDM and obesity the odds of an LGA delivery increases further still [OR, 3.62 (3.04–4.32)], indicating that these 2 metabolic insults convey largely independent effects on birth weight (29). Furthermore, ethnic groups in which gestational obesity and GDM are common, which is true for many aboriginal populations in the Americas (30) and Oceania (31), the risks of LGA and future type 2 diabetes in the offspring are often high.
Recently, the largest metaanalysis to date, comprising data from 1.3 million pregnant women, was undertaken to evaluate associations between maternal gestational weight gain and maternal and infant outcomes (32). The authors sought to evaluate the implications of the Institute of Medicine recommendations for gestational weight gain on preterm birth, LGA, and small for gestational age as primary outcomes. The Institute of Medicine guidelines outline ideal weight gains of 12.5 to 18.0 kg for underweight women [body mass index (BMI), <18.5 kg/m2], 11.5 to 16.0 kg for normal-weight women (BMI, 18.5–24.9 kg/m2), 7 to 11 kg for overweight women (BMI, 25–29.9 kg/m2), and 5 to 9 kg for obese women (BMI, ≥30 kg/m2) (32). Gestational weight gains above or below the Institute of Medicine recommendations were differentially associated with increased risks for adverse neonatal outcomes; weight gain above the recommendations conveyed the highest odds [OR, 1.95 (1.79–2.11) for macrosomia; OR, 1.85 (1.76–1.95) for delivering an LGA baby].
Pregnancy is characterized by transient states of insulin resistance and concomitant changes in lipid storage and metabolism (33, 34). These metabolic adaptations help accommodate the energy demands of the developing fetus and placenta.
In healthy pregnancies, blood insulin concentrations progressively increase to levels typically observed in early-stage, nonpregnancy, type 2 diabetes (35). In early pregnancy, some hormonal changes occur, such as increases in estrogen, progesterone, and insulin, which promote the accumulation of triglyceride-rich adipose in the pregnant woman for use later in pregnancy and during the postnatal period, when energy requirements are high (36). Triglyceride accumulation in adipose tissue (adipocyte hypertrophy), along with the effects of placental hormones, is a key driver of peripheral insulin resistance in the pregnant woman (35, 37, 38). In late pregnancy, impaired insulin-mediated glucose transportation into peripheral tissue drives increased lipid metabolism in the woman, which conserves her circulating glucose for fetal nutrition (36, 37).
The pancreatic β cells in healthy pregnant women respond adequately to the demand for increased insulin production, and blood glucose concentrations remain well controlled (35, 39, 40). However, pregnancy is effectively a chronic metabolic stress test, and for women who enter pregnancy with compromised β cells, the metabolic burden can trigger GDM, which when left unchecked can drive broader metabolic dyshomeostasis in mother and fetus. Although many of the consequences for the exposed fetus are apparent from epidemiological studies, the mechanisms that affect risk remain unclear.The pancreatic β cells in healthy pregnant women respond adequately to the demand for increased insulin production, and blood glucose concentrations remain well controlled (35, 39, 40). However, pregnancy is effectively a chronic metabolic stress test, and for women who enter pregnancy with compromised β cells, the metabolic burden can trigger GDM, which when left unchecked can drive broader metabolic dyshomeostasis in mother and fetus. Although many of the consequences for the exposed fetus are apparent from epidemiological studies, the mechanisms that affect risk remain unclear.
Several conceptual frameworks have been put forward implicating pregnancy as an important period for intergenerational propagation of obesity, type 2 diabetes, and related traits in the offspring. In the early 1950s, Pedersen asserted that “maternal hyperglycemia results in fetal hyperglycemia and, hence, in hypertrophy of fetal islet tissue with insulin hypersecretion” (41). Central to his paradigm is excess glucose, delivered to the fetus via the placenta, as the primary driver of increased insulin secretion in the fetus, causing subsequent macrosomia. In 1980, Freinkel sought to test Pedersen''s concept of “fuel-mediated teratogenesis” encompasses the effects of a range of insulin-dependent fuels, including amino acids and lipids, and their subtler effects on structural and functional changes because of GDM (39). More recently, the thrifty phenotype hypothesis, postulated by Hales and Barker, delineated the effects of maternal malnutrition on impaired growth and function of the fetal β cell, and consequent risk of type 2 diabetes (42). Although their original hypothesis was formulated within the context of low birth weight and maternal malnutrition, it has since been extended to include the effects of maternal hyperglycemia as an additional component of fetal malnutrition that fuels intrauterine growth, predisposing the offspring to later metabolic disease (43).
These hypotheses have spurred much research into fetal programming over the past several decades. Compared with the more gradual increase in research on type 2 diabetes and obesity, research on fetal programming and early-life risk factors, which constitute a tiny subgroup of studies within obesity and type 2 diabetes, has increased more rapidly in recent years, with the bulk of articles being produced after the year 2010 (Fig. 1). This trend highlights how pregnancy and early development are increasingly being recognized for their role in the pathogenesis of metabolic disease. The challenge is now to unravel the mechanisms at play during early life that predispose the offspring to future metabolic disease.
Two searches were conducted in Scopus: [(“fetal programming” OR “prenatal programming”) AND (“type 2 diabetes” OR “obesity”)] (red area) and [“type 2 diabetes” OR “obesity”] (gray area). Searches were restricted to the medical field and articles indexed between 1970 and 2016. The plot was created using ggplot2 ((102)) in R (version 3.3.2) ((103)) and shows the percentage of the total number of articles indexed for each search term (y axis; n = 1129 for the first search term and n = 338247 for the second search term) per year (x axis).
reverses diabetes type 2 good foods to eat (👍 treatment side effect) | reverses diabetes type 2 range charthow to reverses diabetes type 2 for Two searches were conducted in Scopus: [(“fetal programming” OR “prenatal programming”) AND (“type 2 diabetes” OR “obesity”)] (red area) and [“type 2 diabetes” OR “obesity”] (gray area). Searches were restricted to the medical field and articles indexed between 1970 and 2016. The plot was created using ggplot2 ((102)) in R (version 3.3.2) ((103)) and shows the percentage of the total number of articles indexed for each search term (y axis; n = 1129 for the first search term and n = 338247 for the second search term) per year (x axis).
Although obesity has a strong genetic basis (44), there are many nongenetic factors that impact the condition. Education and the social and built environments are strongly correlated with obesity predisposition; analyses performed in the Framingham Heart Study (45), for example, indicate that people living in communities where people gain weight, increases the risk they themselves will gain weight, with physical proximity determining the degree of susceptibility independently of biologic relatedness or the selective formation of interpersonal relationships. Access to fast-food outlets also tends to raise risk of obesity (46), whereas the relationship between access to green space, which promotes physical activity, and obesity is equivocal (47).
The inheritance of nuclear DNA variants from 1 generation to the next contributes to variation in birth weight, as well as increased risk of obesity and type 2 diabetes in those carrying birth weight-raising alleles, in offspring of women with obesity during pregnancy (48). Nevertheless, the risk of diabetes in sib-pairs, one born to a diabetic and the other to a nondiabetic pregnancy, suggests that fetal programming occurs to some extent independently of genetic inheritance (49), possibly at the level of the epigenome.
Epigenetics refers to covalent DNA modifications that regulate gene expression without altering the nucleotide sequence itself. Epigenetic modifications are an important regulatory feature of biology, silencing or activating specific genes in organ- and tissue-specific ways. Although the adenosine, thymine, cytosine, and guanine sequence of DNA remains materially unchanged throughout the life span, the epigenome in somatic cells is thought to be modifiable. The epigenome is also mitotically heritable (50), enabling a degree of phenotypic plasticity and/or adaptability in response to environmental effectors (51).
Because the epigenome, which appears to influence important metabolic processes, exhibits far greater between-person variation compared with the nuclear genome (52), characterization of epigenomic variation associated with disease may for 1 last update 30 May 2020 provide opportunities for precision medicine. It is believed that certain phenotypes are epigenetically acquired during critical developmental periods in life when the epigenome is especially malleable, such as during fetal development (53). Although the epigenome'' interventions impacted epigenetic adaptations detectable in these delivery tissues. However, the epigenetic adaptations of greatest interest are likely to be those occurring during fetal growth and development. Assessing such adaptations is extremely challenging in vivo but might in theory be achieved by applying next-generation sequencing to fetal genomic material extracted from blood drawn repeatedly throughout gestation from women with or without GDM, thereby allowing fetal molecular adaptations to be characterized and compared between groups.Because the epigenome, which appears to influence important metabolic processes, exhibits far greater between-person variation compared with the nuclear genome (52), characterization of epigenomic variation associated with disease may provide opportunities for precision medicine. It is believed that certain phenotypes are epigenetically acquired during critical developmental periods in life when the epigenome is especially malleable, such as during fetal development (53). Although the epigenome'' interventions impacted epigenetic adaptations detectable in these delivery tissues. However, the epigenetic adaptations of greatest interest are likely to be those occurring during fetal growth and development. Assessing such adaptations is extremely challenging in vivo but might in theory be achieved by applying next-generation sequencing to fetal genomic material extracted from blood drawn repeatedly throughout gestation from women with or without GDM, thereby allowing fetal molecular adaptations to be characterized and compared between groups.
The Human Genome Project (89) motivated major technological advances in the characterization of human biological variation. Despite this, a temporal appreciation of the molecular adaptions that occur in the fetus during diabetic pregnancies rarely has been attained; this is largely because fetal tissue is hard to safely obtain in vivo. However, recent advancements in molecular phenotyping technologies are opening up new fields of exploration in fetal biology. For example, the discovery of cell-free fetal DNA in the maternal bloodstream (90) has enabled relatively easy access to fetal genetic material and facilitated numerous innovations. Successful applications of cell-free DNA are widespread and traverse a broad range of disciplines. These include noninvasive prenatal testing or noninvasive prenatal diagnosis for rare fetal genetic imbalances, such as aneuploidies [e.g., Trisomy 21 (91) and microdeletions (92)], as well as “liquid biopsies” to monitor treatment response and predict metastasis in breast cancer patients (93) and the early detection (12–16 weeks''s age, and, notably, not the mother''s clinical records. The use of this information for risk prediction might allow interventions to begin much earlier than usual, long before β-cell function has begun to decline. This is important because the conventional clinical biomarkers of diabetes (chronically increased blood glucose and hemoglobin A1c) are usually assessed long after the disease process has begun and are consequences rather than direct readouts of the pathogenic, tissue-specific processes that precede the clinical manifestation of the disease.
It may also be that the optimal strategies for diabetes prevention in people exposed to diabetes in utero are likely to differ from those born to healthy pregnancies. This is because the mechanisms underlying type 2 diabetes may differ depending on when and how the molecular insults that cause the disease occur. Thus, the use of modern molecular phenotyping technologies combined with information about early-life exposures (like diabetic pregnancy or childhood obesity) may help define subgroups of the population within which tailored interventions are likely to prove more effective than a one-size-fits-all standard of care.
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