Background This longitudinal study aims to characterize longitudinal body mass index ( BMI ) trajectories during young adulthood (20–40 years) and examine the impact of level‐independent BMI trajectories on hypertension risk. Methods and Results The cohort consisted of 3271 participants (1712 males and 1559 females) who had BMI and blood pressure ( BP ) repeatedly measured 4 to 11 times during 2004 to 2015 and information on incident hypertension. Four distinct trajectory groups were identified using latent class growth mixture model: low‐stable (n=1497), medium‐increasing (n=1421), high‐increasing (n=291), sharp‐increasing (n=62). Model‐estimated levels and linear slopes of BMI at each age point between ages 20 and 40 were calculated in 1‐year intervals using the latent class growth mixture model parameters and their first derivatives, respectively. Compared with the low‐stable group, the hazard ratios and 95% CI were 2.42 (1.88, 3.11), 4.25 (3.08, 5.87), 11.17 (7.60, 16.41) for the 3 increasing groups, respectively. After adjusting for covariates, the standardized odds ratios and 95% CI of model‐estimated BMI level for incident hypertension increased in 20 to 35 years, ranging from 0.80 (0.72–0.90) to 1.59 (1.44–1.75); then decreased gradually to 1.54 (1.42–1.68). The standardized odds ratio s of level‐adjusted linear slopes increased from 1.22 (1.09–1.37) to 1.79 (1.59–2.01) at 20 to 24 years; then decreased rapidly to 1.12 (0.95–1.32). Conclusions These results indicate that the level‐independent BMI trajectories during young adulthood have significant impact on hypertension risk. Age between 20 and 30 years is a crucial period for incident hypertension, which has implications for early prevention.

IntroductionThis longitudinal study aims to characterize distinct body mass index (BMI) trajectories during early to mid-life adulthood and to explore the association between BMI change from young adulthood to midlife and incident diabetes.Research design and methodsThis study included 7289 adults who had repeatedly measured BMI 3–9 times during 1989–2011 and information on incident diabetes. Latent class growth mixed model (LCGMM) was used to identify different BMI trajectories. Cox proportional hazard models were used to investigate the association between the trajectory group membership and incident hyperglycemia, adjusting for covariates. The hyperglycemia group included individuals with prediabetes or diabetes. The model-estimated BMI levels and slopes were calculated at each age point in 1-year intervals according to the model parameters and their first derivatives, respectively. Logistic regression analyses were used to examine the association of model-estimated levels and slopes of BMI at each age point with incident hyperglycemia. The area under the curve (AUC) was computed from longitudinal growth curve models during the follow-up for each individual. Prior to the logistic regression analyses, quartiles of total, baseline, and incremental AUC values were calculated.ResultsThree distinct trajectories were characterized by LCGMM, comprising of low-increasing group (n=5136), medium-increasing group (n=1914), and high-increasing group (n=239). Compared with the low-increasing group, adjusted HRs and 95% CIs were 1.21 (0.99 to 1.48) and 1.56 (1.06 to 2.30) for the medium-increasing and the high-increasing group, respectively. The adjusted standardized ORs of model-estimated BMI levels increased among 20–50 years, ranging from 0.98 (0.87 to 1.10) to 1.19 (1.08 to 1.32). The standardized ORs of level-adjusted linear slopes increased gradually from 1.30 (1.16 to 1.45) to 1.42 (1.21 to 1.67) during 20–29 years, then decreased from 1.41 (1.20 to 1.66) to 1.20 (1.08 to 1.33) during 30–43 years, and finally increased to 1.20 (1.04 to 1.38) until 50 years. The fourth quartile of incremental AUC (OR=1.31, 95% CI 1.03 to 1.66) was significant compared with the first quartile, after adjustment for covariates.ConclusionsThese findings indicate that the BMI trajectories during early adulthood were significantly associated with later-life diabetes. Young adulthood is a crucial period for the development of diabetes, which has implications for early prevention.

BackgroundStudies have demonstrated that high or low haemoglobin increases the risk of stroke. Previous studies, however, performed only a limited number of haemoglobin measurements, while there are dynamic haemoglobin changes over the course of a lifetime. This longitudinal cohort study aimed to classify the long-term trajectory of haemoglobin and examine its association with stroke incidence.MethodsThe cohort consisted of 11,431 participants (6549 men) aged 20 to 50 years whose haemoglobin was repeatedly measured 3–9 times during 2004–2015. A latent class growth mixture model (LCGMM) was used to classify the long-term trajectory of haemoglobin concentrations, and hazard ratios (HRs) and 95% confidence intervals (95% CI) according to the Cox proportional hazard model were used to investigate the association of haemoglobin trajectory types with the risk of stroke.ResultsThree distinct trajectory types, high-stable (n = 5395), normal-stable (n = 5310), and decreasing (n = 726), were identified, with stroke incidence rates of 2.7, 1.9 and 3.2 per 1000 person-years, respectively. Compared to the normal-stable group, after adjusting for the baseline covariates, the decreasing group had a 2.94-fold (95% CI 1.22 to 7.06) increased risk of developing stroke. Strong evidence was observed in men, with an HR (95% CI) of 4.12 (1.50, 11.28), but not in women (HR = 1.66, 95% CI 0.34, 8.19). Individuals in the high-stable group had increased values of baseline covariates, but the adjusted HR (95% CI), at 1.23 (0.77, 1.97), was not significant for the study cohort or for men and women separately.ConclusionsThis study revealed that a decreasing haemoglobin trajectory was associated with an increased risk of stroke in men. These findings suggest that long-term decreasing haemoglobin levels might increase the risk of stroke.

IntroductionTo explore the temporal relationship between blood lipids and insulin resistance in perimenopausal women.Research design and methodsThe longitudinal cohort consisted of 1386 women (mean age 46.4 years at baseline) in the Study of Women’s Health Across the Nation. Exploratory factor analysis was used to identify appropriate latent factors of lipids (total cholesterol (TC); triglyceride (TG); high-density lipoprotein cholesterol (HDL-C); low-density lipoprotein cholesterol (LDL-C); lipoprotein A-I (LpA-I); apolipoprotein A-I (ApoA-I); apolipoprotein B (ApoB)). Cross-lagged path analysis was used to explore the temporal sequence of blood lipids and homeostasis model assessment of insulin resistance (HOMA-IR).ResultsThree latent lipid factors were defined as: the TG factor, the cholesterol transport factor (CT), including TC, LDL-C, and ApoB; the reverse cholesterol transport factor (RCT), including HDL-C, LpA-I, and ApoA-I. The cumulative variance contribution rate of the three factors was 86.3%. The synchronous correlations between baseline TG, RCT, CT, and baseline HOMA-IR were 0.284, −0.174, and 0.112 (p<0.05 for all). After adjusting for age, race, smoking, drinking, body mass index, and follow-up years, the path coefficients of TG→HOMA-IR (0.073, p=0.004), and HOMA-IR→TG (0.057, p=0.006) suggested a bidirectional relationship between TG and HOMA-IR. The path coefficients of RCT→HOMA-IR (−0.091, P < 0.001) and HOMA-IR→RCT (−0.058, p=0.002) were also significant, but the path coefficients of CT→HOMA-IR (0.031, p=0.206) and HOMA-IR→CT (−0.028, p=0.113) were not. The sensitivity analyses showed consistent results.ConclusionsThese findings provide evidence that TG and the reverse cholesterol transport-related lipids are related with insulin resistance bidirectionally, while there is no temporal relationship between the cholesterol transport factor and insulin resistance.

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