- 學位論文
青少年肥胖成長趨勢相關的基因變異及與成年人早發性糖尿病的關聯性
Adolescent growth trajectory: the associated genetic factors and the risk of diabetes in early adulthood
摘要
背景與目標： 糖尿病目前在全球各國仍造成很大的負擔，其中早發性糖尿病因為病程時間較長，引發併發症的機率更高。青少年時期以身體質量指數所定義的肥胖已被證實受到某些基因變異的影響且和成年人的糖尿病有關，但新興肥胖指標體重立方質量指數及肥胖成長趨勢是否能對早發性糖尿病提供更好的預測能力，過去研究仍未提供足夠定論。本研究將比較兩種不同肥胖指標所建構的肥胖成長趨勢和成年人早期新診斷糖尿病的關聯性，並探討基因變異和肥胖成長趨勢之間的關聯性。 方法： 我們先使用台北市立聯合醫院輻射屋體檢資料庫中所收集的資料，用每年所收集到的標準化身體質量指數和體重立方質量指數以Growth mixture modeling分別建構出13-18歲之間的肥胖成長趨勢軌跡，再使用Cox’s proportional hazard model來比較不同分群之間早發性糖尿病的風險。此外我們會使用台灣青春期孩童世代追蹤研究中所召募的個案，採集口腔黏膜檢體的表皮細胞以取得個案肥胖相關的基因變異，以多分類羅吉斯回歸尋求此基因變異對不同肥胖成長軌跡的影響；最後透過飲食內容分析，探討基因變異的影響力是否和飲食中總熱量及三種主要營養素攝取習慣的差異間存有交互作用。 結果： 我們從台北市立聯合醫院輻射屋體檢資料庫中的1387個個案，以標準化身體質量指數和體重立方質量指數分別建構出五種不同的肥胖成長趨勢軌跡。在近20400人年的追蹤後，共發生了33個糖尿病的個案。再校正過孩童時期的肥胖其及他干擾因素後，體重立方質量指數在青少年時期呈現持續增加的個案，對成年人早期糖尿病的發生，仍存在顯著的風險 (Hazard ratio: 2.85，95%信賴區間: 1.01-8.09)；在標準化身體質量指數所建構的成長趨勢中則無此發現。在台灣青春期孩童世代追蹤研究中，我們以體重立方質量指數建構出三種肥胖成長趨勢軌跡，當中肥胖程度持續向上的約占有9.7%。在校正過研究基期的肥胖程度和其他相關干擾因子後，FTO/rs7206790的變異仍和青少年時期體重立方質量指數持續增加有關 (odds ratio: 2.13，95%信賴區間: 1.08-4.21)。我們以4基因變異(FTO/ rs7206790, ADCY9/ rs2531995, TFAP2B/ rs4715210, and TMEM18/ rs6548238)所組成的基因風險分數10分為切點所定義的高低風險族群中，體重立方質量指數增加的青少年比例有明顯的差異 (11.66% vs. 3.24%)。而在不同飲食習慣的分群當中，可發現此基因影響的效力，在較低熱量攝取的青少年身上較為顯著。 結論： 青少年時期呈現體重立方質量指數持續增加趨勢的族群，是防治成年人早發型糖尿病重要的目標；而某些特殊的基因變異及在不同飲食習慣上的差異表現，可以協助我們辨認出這些高危險族群，提供個人化的介入與衛教。
並列摘要
Background: Diabetes mellitus is a chronic metabolic disease with a growing global health burden. Studies have reported the influence of adolescent obesity on development of early-onset type II diabetes, but the effect of the growth pattern during this period has rarely been explored. Besides, tri-ponderal mass index (TMI) was thought to be a better estimation of adolescent body fat levels than the body mass index (BMI), so we seek to investigate whether growth trajectories derived by these two indices could predict incident diabetes. At last, we will also explore the effect of obesity related genetic variations on adolescent growth pattern and examine the interaction between dietary fat components. Methods: Taipei City Hospital Radiation Building Database is a longitudinal cohort established from 1996 until now. Physical exam results including blood test results were collected annually and the BMI z-score/TMI growth trajectory groups during 13–18 years of age were identified using growth mixture modeling. A Cox proportional hazard model for incident diabetes was used to examine the risk of baseline obese status and different BMI/TMI growth trajectories. In addition, we conducted Taiwan Puberty Longitudinal Study since 2018 and recruited female adolescents aged 6-14 years-old and male adolescents aged 9-17 years-old. Anthropometric measurements, nutrition assessment, Tanner’s stages, physical activity level and genetic sampling were collected while the first visit. A multinomial logistic regression model for different growth trajectories was used to examine the effect of candidate SNPs, and the most related SNPs were used to establish the genetic risk score. We then explored the effect of the genetic risk score in subgroup analysis according to dietary calories and different dietary consumption patterns. Result: Five growth trajectory groups were identified for the BMI z-score and the TMI from Taipei City Hospital Radiation Building Database. During approximately 20 400 person-years follow-up, 33 of 1387 participants developed diabetes. Baseline obesity defined by the BMI z-score and the TMI were both related to adult diabetes. The persistent increase TMI growth trajectory exhibited a significantly increased risk of diabetes after adjusting for baseline obese status and other correlated covariates (hazard ratio: 2.85, 95% confidence interval: 1.01-8.09). There was no association between BMI growth trajectory groups and incident diabetes. Besides, three TMI-based growth trajectory groups were identified among adolescents in Taiwan Puberty Longitudinal Study. The “increased weight” trajectory group accounted for approximately 9.7% of the participants. FTO/rs7206790 was associated with the increased weight growth trajectory after adjusting for the baseline TMI and other correlated covariates (odds ratio: 2.13, 95% confidence interval: 1.08–4.21). We generated the genetic risk score by using 4 SNPs (FTO/rs7206790, ADCY9/rs2531995, TFAP2B/rs4715210, and TMEM18/rs6548238) and selected the threshold of 10 points to define risk categories. There were 11.66% and 3.24% of participants belonged to the increased weight trajectory in high- and low-risk groups, respectively; and the predictive ability of the genetic risk score was notable among low calories intake participants (odds ratio: 1.90, 95% confidence interval: 1.18-3.05 vs. odds ratio: 1.17, 95% confidence interval: 0.78-1.75 in high calories intake group). Conclusion: A specific TMI growth trajectory pattern during adolescence might be critical for diabetes prevention efforts. Our results offer a new perspective on the genetic and dietary basis of changes in adolescent obesity status. Individualized interventions for obesity prevention may be considered among high-risk children.