Department of Clinical Sciences, Lund University, Skåne University Hospital, CRC, Jan Waldenströms Gata 35, 20502, Malmö, Sweden.
Precision Health, Department of Bioengineering, Graduate School of Engineering, the University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan.
BMC Med. 2024 Apr 23;22(1):173. doi: 10.1186/s12916-024-03392-1.
The molecular pathways linking short and long sleep duration with incident diabetes mellitus (iDM) and incident coronary heart disease (iCHD) are not known. We aimed to identify circulating protein patterns associated with sleep duration and test their impact on incident cardiometabolic disease.
We assessed sleep duration and measured 78 plasma proteins among 3336 participants aged 46-68 years, free from DM and CHD at baseline, and identified cases of iDM and iCHD using national registers. Incident events occurring in the first 3 years of follow-up were excluded from analyses. Tenfold cross-fit partialing-out lasso logistic regression adjusted for age and sex was used to identify proteins that significantly predicted sleep duration quintiles when compared with the referent quintile 3 (Q3). Predictive proteins were weighted and combined into proteomic scores (PS) for sleep duration Q1, Q2, Q4, and Q5. Combinations of PS were included in a linear regression model to identify the best predictors of habitual sleep duration. Cox proportional hazards regression models with sleep duration quintiles and sleep-predictive PS as the main exposures were related to iDM and iCHD after adjustment for known covariates.
Sixteen unique proteomic markers, predominantly reflecting inflammation and apoptosis, predicted sleep duration quintiles. The combination of PSQ1 and PSQ5 best predicted sleep duration. Mean follow-up times for iDM (n = 522) and iCHD (n = 411) were 21.8 and 22.4 years, respectively. Compared with sleep duration Q3, all sleep duration quintiles were positively and significantly associated with iDM. Only sleep duration Q1 was positively and significantly associated with iCHD. Inclusion of PSQ1 and PSQ5 abrogated the association between sleep duration Q1 and iDM. Moreover, PSQ1 was significantly associated with iDM (HR = 1.27, 95% CI: 1.06-1.53). PSQ1 and PSQ5 were not associated with iCHD and did not markedly attenuate the association between sleep duration Q1 with iCHD.
We here identify plasma proteomic fingerprints of sleep duration and suggest that PSQ1 could explain the association between very short sleep duration and incident DM.
目前尚不清楚将短时间和长时间睡眠与新发糖尿病（iDM）和新发冠心病（iCHD）联系起来的分子途径。我们旨在确定与睡眠时间相关的循环蛋白谱，并测试它们对心血管代谢疾病发病的影响。
我们评估了 3336 名年龄在 46-68 岁的参与者的睡眠时间，并测量了他们的 78 种血浆蛋白。在基线时，这些参与者均无糖尿病和冠心病。使用国家登记册确定 iDM 和 iCHD 的发病情况。排除了随访前 3 年内发生的新发事件进行分析。使用 10 倍交叉拟合偏分最小绝对收缩和选择算子（LASSO）逻辑回归，当与参考五分位数 3（Q3）相比时，识别出显著预测睡眠时间五分位数的蛋白质。对预测蛋白进行加权，并将其组合成睡眠时间 Q1、Q2、Q4 和 Q5 的蛋白组评分（PS）。组合 PS 纳入线性回归模型，以确定习惯性睡眠时间的最佳预测因子。Cox 比例风险回归模型以睡眠时间五分位数和睡眠预测 PS 为主要暴露因素，调整已知协变量后，与 iDM 和 iCHD 相关。
16 个独特的蛋白标志物，主要反映炎症和细胞凋亡，可预测睡眠时间五分位数。PSQ1 和 PSQ5 的组合最能预测睡眠时间。新发糖尿病（n=522）和新发冠心病（n=411）的平均随访时间分别为 21.8 年和 22.4 年。与睡眠时间 Q3 相比，所有睡眠时间五分位数与 iDM 均呈正相关且有统计学意义。只有睡眠时间 Q1 与 iCHD 呈正相关且有统计学意义。纳入 PSQ1 和 PSQ5 可消除睡眠时间 Q1 与 iDM 之间的关联。此外，PSQ1 与 iDM 显著相关（HR=1.27，95%CI：1.06-1.53）。PSQ1 和 PSQ5 与 iCHD 无关，且不能明显减弱睡眠时间 Q1 与 iCHD 之间的关联。
我们在这里确定了睡眠时间的血浆蛋白指纹图谱，并提出 PSQ1 可以解释极短睡眠时间与新发糖尿病之间的关联。
