Obesity and Longevity Across Generations

父母和早期生活因素的分布变化是否改变了肥胖和长寿之间的关系？ 美国肥胖症的日益流行是否会逆转美国预期寿命的增长？什么让 有些人比其他人更容易受到肥胖缩短寿命的影响？回答这些 问题需要更好地理解肥胖和长寿之间的复杂关系;特别是， 早期环境和相关的发育因素可能对儿童的发育产生强大的改变作用， 肥胖和长寿之间的联系 我们测试了四个相互关联的假设，关于人口统计学如何改变身体质量指数的分布 (BMI)可能会影响当代人的寿命，并改变肥胖对下一代人寿命的影响。 代这些假设是建立在实验模型生物和人类的发现之上的。 流行病学，生命史理论和进化生物学的发育可塑性，以及社会学 为理解代际变化和肥胖耻辱的健康后果提供了框架。 依靠大量、丰富和互补的二级数据集，我们将完成以下工作 目的：目的1 -检验相对BMI位置在一个人的世代或队列BMI分布的假设 预测寿命高于和超过一个人的绝对BMI。目标2 -测试假设， 与后代发育和BMI相关的因素-父母BMI，后代肥胖基因组风险评分 （BMIGP）和家庭共同环境肥胖风险，发挥跨代效应，并预测（和 因此可能受到影响）后代死亡率与后代BMI无关。目标3 -检验假设 后代对特定肥胖水平的生理倾向与实际肥胖水平之间的“不匹配”， 在其他条件相同的情况下，后代肥胖将与寿命缩短有关。这一假设借鉴了 适应性发展可塑性的想法，其中个人可以在生理上“校准”， 早期发育，以实现最佳功能和长寿的表型，在以后的生活中， 在早期发育过程中受到环境和遗传倾向的影响。目标4 -估计 调整了父母BMI、后代出生体重和后代BMIGP分布的变化， 解释了在过去的半个世纪中BMI-寿命关系的长期变化。 一个由统计学家、老龄化研究人员、肥胖研究人员、社会学家、遗传学家和 进化和生殖生物学家们被精心召集起来进行这项工作。了解如何和 为什么肥胖与寿命缩短有关，以及谁最容易受到健康风险的影响， 为公共政策提供信息，预测人口的健康需求。此外，识别早期生命特征 环境条件加剧了肥胖对健康和寿命的影响， 关键时期和人群，生物医学或行为预防干预措施可以最有效。

Are changing distributions of parental and early life factors altering the relation between obesity and longevity? Will the increasing prevalence of obesity in the US reverse recent gains in US life expectancy? What makes some individuals more susceptible to the life-shortening effects of obesity than others? Answering these questions requires a better understanding of the complex relations between obesity and longevity; in particular, the potential for the early environment and related developmental factors to exert powerful modifying effects on the association between obesity and longevity. We test four interrelated hypotheses about how demographic changes in the distribution of body mass index (BMI) may affect the current generation's lifespan and modify obesity's effect on lifespan in subsequent generations. These hypotheses are built on findings from experimental model organisms and human epidemiology, theories of life histories and developmental plasticity from evolutionary biology, and sociologic frameworks for understanding both intergenerational change and the health consequences of obesity's stigma. Relying on a large, rich, and complementary collection of secondary datasets, we will complete the following aims: Aim 1 - Test the hypothesis that relative BMI position in one's generational or cohort BMI distribution predicts longevity above and beyond one's absolute BMI. Aim 2 - Test the hypotheses that each of several factors related to offspring development and BMI—parental BMI, offspring genome risk score for obesity (BMIGP), and family common environment risk for obesity, exert transgenerational effects and predict (and hence plausibly influence) offspring mortality rate independent of offspring BMI. Aim 3 - Test the hypotheses that a “mismatch” between an offspring's physiological predisposition for a given level of obesity and actual offspring obesity will be, all else being equal, associated with decreased longevity. This hypothesis draws on the idea of adaptive developmental plasticity, in which individuals may be physiologically `calibrated' during early development to achieve best function and longevity for a phenotype in later life that would be predicted by their environmental and genetic predisposition during early development. Aim 4 - Estimate the extent to which adjusting for changes in the distributions of parental BMI, offspring birth weight, and offspring BMIGP accounts for secular changes in the BMI-longevity relation that have occurred in the last half century. An interdisciplinary team of statisticians, aging researchers, obesity researchers, sociologists, geneticists, and evolutionary and reproductive biologists has been carefully assembled for this work. Understanding how and why obesity is associated with reduced longevity and who is most vulnerable to its health risks is vital to informing public policies and anticipating population health needs. Further, identifying early life characteristics and environmental conditions which exacerbate obesity's effects on health and longevity is vital to identifying critical periods and populations where biomedical or behavioral preventive interventions can be most effective.