Elevated and fluctuating TyG and LAP trajectories are associated with cardiometabolic multimorbidity development in midlife: the CARDIA study

Elevated and fluctuating TyG and LAP trajectories are associated with cardiometabolic multimorbidity development in midlife: the CARDIA study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Elevated and fluctuating TyG and LAP trajectories are associated with cardiometabolic multimorbidity development in midlife: the CARDIA study Lingqu Zhou, Junjie Wang, Zirui Zhou, Liangjiao Wang, Qi Guo, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5436679/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 May, 2025 Read the published version in Cardiovascular Diabetology → Version 1 posted 15 You are reading this latest preprint version Abstract Background Insulin resistance and central obesity are major risk factors for cardiometabolic diseases. The triglyceride-glucose index (TyG) and lipid accumulation product (LAP) are markers that independently predict cardiometabolic risk. However, their combined long-term trajectories and impact on cardiometabolic multimorbidity (CMM) development remain unclear. Methods This cohort study utilized data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, which tracked 3,467 participants at baseline. Dual-trajectory of TyG and LAP were identified using a group-based dual-trajectory model. Cox proportional hazards models were employed to assess the relationships between dual-trajectory groups and primary cardiometabolic outcomes, including first cardiometabolic disease (FCMD), CMM (two or more conditions such as type 2 diabetes, coronary heart disease, or stroke), and all-cause mortality. Multi-state models were performed to assess the associations of dual-trajectory with CMM development. Results The study included 3,467 participants with a mean age of 25.08 years (SD = 3.59). Of these, 43.4% (n = 1,505) were male, and 53.2% (n = 1,561) were White. Three distinct dual-trajectory groups were identified: low-increasing (61.5%), high-amplitude fluctuation (7.6%), and high-increasing (30.9%). After multivariate adjustment, compared with the low-increasing group, the high-amplitude fluctuation group exhibited significantly higher risks for FCMD (hazard ratio [HR] 1.38, 95% confidence interval [CI]: 1.08–1.77), CMM (HR 2.63, 95% CI: 1.21–5.71), and all-cause mortality (HR 2.16, 95% CI: 1.30–3.56), as well as elevated risks for transitions from baseline to FCMD (HR: 1.39, 95% CI: 1.09–1.78), FCMD to CMM (HR: 2.31, 95% CI: 1.16–4.62), CMM to death (HR: 3.45, 95% CI: 1.13–10.51). The high-increasing group showed similar results. Conclusions Elevated and fluctuating trajectories of TyG and LAP from early adulthood are associated with increased risks of CMM development in midlife. Triglyceride-glucose index Lipid accumulation product Dual-trajectory Cardiometabolic multimorbidity Cohort study Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Multimorbidity, characterized by an individual having at least two chronic metabolic conditions, has emerged as a critical global health challenge due to its substantial impact on individuals, families, healthcare systems, and society as a whole[ 1 – 3 ]. Among the different forms of multimorbidity, cardiometabolic multimorbidity (CMM), which involving the simultaneous combination of two or more cardiometabolic diseases (CMDs) such as stroke, type 2 diabetes (T2D), and coronary heart disease (CHD), is particularly worrisome[ 4 ]. A Canadian study reported that 22% of individuals with diabetes, 32.2% of those with heart disease, and 48.4% of stroke patients have one additional cardiometabolic condition[ 5 ]. Moreover, multiple studies have demonstrated that the coexistence of multiple CMDs significantly escalates mortality risk and markedly reduces life expectancy compared to the presence of a single CMD[ 3 , 6 ]. Therefore, early identification of potential risk factors contributing to CMM development is crucial. Insulin resistance (IR) is a key risk factor linked to numerous cardiovascular and metabolic diseases, including CHD, stroke, hypertension, atherosclerosis, T2D, and atrial fibrillation[ 4 , 7 ]. Recently, the triglyceride-glucose index (TyG), derived from fasting triglyceride (TG) and glucose levels, has emerged as a reliable indicator of insulin resistance (IR) and its progression[ 8 ]. Numerous studies have validated the TyG's utility in predicting stroke risk among individuals aged 45 and above [ 9 ], the incidence of diabetes in the general population[ 10 ], cardiovascular disease (CVD) risk in Middle-aged and older Chinese population[ 11 ], and adverse cardiovascular outcomes in patients with hypertension, CHD, and T2D complicated by acute myocardial infarction[ 12 , 13 ]. Moreover, obesity, particularly central obesity, is another known contributor to CMDs, with strong associations to premature mortality[ 14 , 15 ]. Although body mass index (BMI) is widely employed to evaluate obesity, it has significant limitations: it cannot distinguish between lean body mass and fat mass, nor does it accurately reflect abdominal fat distribution[ 15 , 16 ]. In response to these limitations, the lipid accumulation product (LAP), introduced by Henry Kahn in 2005, integrates waist circumference (WC) and fasting TG levels and has emerged as a reliable indicator of lipid overaccumulation and cardiometabolic risk[ 17 ]. Empirical evidence further supports the utility of LAP in predicting conditions such as metabolic syndrome and diabetes, underscoring its value as a crucial metric for improving survival assessments in obese populations[ 18 , 19 ]. However, previous research has predominantly concentrated on the independent associations of TyG and LAP with cardiometabolic risk, while the potential utility of combining these two markers to comprehensively evaluate CMM progression remains largely underexplored. Furthermore, most existing studies have focused on older populations, often neglecting younger individuals. Nevertheless, metabolic changes during young adulthood have a major impact on future cardiometabolic outcomes, underscoring the importance of focusing on younger cohorts. Additionally, the dynamic nature of TyG and LAP over time suggests that static, single-point assessments may provide only limited insights. Trajectory modeling, in contrast, allows for the examination of temporal changes, the identification of distinct risk trajectories, and the facilitation of more precise, individualized prevention and intervention strategies[ 20 ]. Finally, prior studies have predominantly examined the coexistence of one or two CMDs, thereby failing to fully address the complex interactions involved in the progression of CMM, which limits the comprehensive utility of TyG and LAP in assessing cardiometabolic disease risk. In light of these gaps, this study aimed to utilize data from the Coronary Artery Risk Development in Young Adults (CARDIA) study to describe the longitudinal trajectory patterns of TyG and LAP levels during young adulthood, and to evaluate their combined effect on CMM development in middle age. 2. Methods 2.1 Study design and population Between 1985 and 1986 (year 0), the CARDIA study recruited over 5,115 participants aged 18 to 30 from urban areas in four U.S. cities: Minneapolis (Minnesota), Birmingham (Alabama), Oakland (California), and Chicago (Illinois). As a prospective, multi-center study, CARDIA was established to track CVD risk progression and contributing factors from young adulthood to midlife. Data have been collected over nine follow-up intervals, starting with the initial baseline assessment and continuing with further exams at 2, 5, 7, 10, 15, 20, 25, and 30 years. Detailed methodology and examination procedures are documented in previously published reports[ 21 ]. For this study, participants with prevalent diabetes, stroke or CHD (n = 356) at baseline, missing baseline waist, fast plasma glucose (FPG) and TG values (n = 121), missing following waist, FPG and TG data (n = 831) and missing other covariates (n = 168) were excluded. We also excluded individuals with prevalent cancer (n = 172) at baseline to ensure data reliability by minimizing confounding factors, competing risks, and metabolic effects related to cancer and its treatments[ 22 , 23 ]. A total of 3,467 participants were ultimately included to study the association between TyG and LAP dual-trajectory and CMM development (Fig. 1 ). 2.2 Assessment of the TyG and LAP The FPG was measured using the hexokinase UV method[ 21 ]. TG concentrations in fasting sample blood were assessed by calibration and enzymatic analysis[ 21 ]. The TyG was computed using the following formula: Ln [fasting TG (mg/dL) × FPG (mg/dL)/2][ 24 ]. Measurements of weight, height, and WC were gathered according to standardized procedures outlined in earlier studies [ 25 ]. WC was measured at the midpoint between the iliac crest and the lowest rib laterally, and between the xiphoid process and the umbilicus anteriorly, with measurements recorded to the nearest 0.5 cm. LAP was calculated using the formula (WC(cm)-65) × TG (mmol/L) for man, and (WC(cm)-58) × TG (mmol/L) for women[ 26 ]. 2.3 Other covariates At baseline, demographic data and cardiometabolic risk factors—including age, sex, race, education, physical activity, smoking and drinking status, and use of antihypertensive medications—were gathered using standardized protocols[ 21 ]. Blood pressure was measured three times following a 5 minutes rest period. Hypertension was defined as having a systolic blood pressure (SBP) of 140 mmHg or above, a diastolic blood pressure (DBP) of 90 mmHg or above, or the use of antihypertensive drugs. BMI was derived by dividing body weight (kg) by the square of height (m), expressed as kg/m². Protocols for measuring serum total cholesterol, HDL-C (high-density lipoprotein cholesterol), and LDL-C (low-density lipoprotein cholesterol) were detailed in prior studies[ 27 ]. Smoking status was categorized into three classes: current, former, or never. Physical activity was measured using the validated CARDIA questionnaire, which quantified 13 exercise categories over the past year and converted them into exercise units (EU), with 300 EU equivalent to 150 minutes of moderate-intensity exercise weekly [ 28 ]. 2.4 Outcomes The primary outcomes in this study were defined as first cardiometabolic disease (FCMD), CMM and all-cause mortality. In accordance with established criteria from previous research, we defined CMM as the coexistence of at least two of the following three CMDs—T2D, CHD, and stroke—with the first occurring CMD identified as the FCMD [ 29 , 30 ]. To define T2D, criteria included an FPG level reaching 126 mg/dL or more, a 2-hour post-challenge glucose level of ≥ 11.1 mmol/L (200 mg/dL), HbA1c at 48 mmol/mol (6.5%) or higher, or the administration of antidiabetic drugs. Participants were confirmed to be free of diabetes by Year 0, based on assessments of medication use and fasting glucose levels conducted at baseline. Tracking of cardiovascular and cerebrovascular incidents, which including CHD and stroke, as well as mortality, was conducted from the initial assessment until August 31, 2014. For individuals who underwent hospitalization or outpatient vascular procedures, corresponding medical documentation was collected. Vital status updates were obtained every six months, with next-of-kin consent acquired for access to medical and death records as needed. Each reported event was reviewed independently by two physicians from the CARDIA Endpoints Surveillance and Adjudication Subcommittee, adhering to predefined criteria for cardiovascular incidents described in prior publications[ 31 – 33 ]. In cases of disagreement, the full committee conducted a review. Participants without events who remained in the study were censored as of August 31, 2014. 2.5 Statistical Analysis Continuous variables were reported as mean ± SD, and categorical variables as frequency (percentage). Participants were divided into quartiles according to their baseline TyG and LAP levels. Group differences were analyzed using ANOVA, Kruskal-Wallis test, and χ² test, as appropriate. To explore the relationships between baseline TyG and LAP levels and FCMD, CMM, and all-cause mortality, multivariable logistic regression models were employed. The fully adjusted models accounted for baseline age, sex, race, BMI, education, physical activity, SBP, hypertension, antihypertensive medication use, smoking status, alcohol consumption, and LDL-C. A group-based dual-trajectory model with a semi-parametric approach was used to examine the temporal trends of TyG and LAP levels over the follow-up duration (from year 0 to year 25). This method enables the simultaneous analysis of both indicators' dynamics, evaluating the likelihood of LAP trajectories corresponding to specific TyG trajectories, and suggesting they may be interconnected through a shared underlying etiological process without prior assumptions. According to recommendations of Nagin[ 34 ], to select the optimal model, a two-stage approach was applied. Firstly, we identified the optimal number of trajectories for the model, exploring options from 2 to 5 clusters. In the following stage, the trajectory shapes were refined by adjusting the polynomial order, specifying them as linear, quadratic, or cubic. Selection of the best-fit dual-trajectory model was guided by three main criteria [ 35 ]: (1) minimum Bayesian Information Criterion (BIC) value; (2) each trajectory group included at least 5% of the participants; and (3) mean posterior probability greater than 0.7. Participants were further grouped by dual-trajectory of TyG and LAP. We employed Cox proportional hazards models to assess the associations of dual-trajectory groups with FCMD, CMM, and all-cause mortality, and calculated hazard ratios (HRs) and 95% confidence intervals (CIs) to evaluate risk. The Cox models included the same set of baseline covariates for full adjustment as in the logistic regression models. Subsequently, multi-state models, an extension of Cox proportional hazards models, were utilized to investigate the role of dual-trajectory groups at multiple phases of CMM progression, beginning from a baseline without CMDs to the development of FCMD, progression to CMM, and ultimately, mortality. The primary advantage of multi-state models lies in their ability to incorporate multiple sequential or competing events as transition states, enabling a comprehensive evaluation of risk factors across various phases of disease progression with consideration for competing risks.[ 36 , 37 ]. In accordance with prior research[ 23 , 38 ], five key transition stages were identified(Fig. 2 ): (1) baseline to FCMD (21.2%), (2) FCMD to CMM (10.7%), (3) baseline to death (1.6%), (4) FCMD to death (8.0%), and (5) CMM to death (26.6%). The initiation of CMM was defined by the occurrence date of a second CMD. Analyses were carried out in R (version 4.1.3), with the group-based dual-trajectory model fitted by the “lcmm” package and the multi-state models by “mstate”. All statistical tests were two-sided, with p-values below 0.05 considered statistically significant in all analyses. 3. Results 3.1 Baseline characteristics of TyG and LAP quartiles and outcomes This study included 3,467 participants with a baseline mean age of 25.08 ± 3.59 years, among whom 43.4% were male and 53.2% were white. Participants were separated into four quartile groups by TyG and LAP levels. In the TyG quartile grouping (Additional file 1: Table S1 ), participants with higher TyG levels were older and more likely to be male, White, smokers, and daily alcohol consumers. They also had greater WC, BMI, SBP, DBP, TG, TC, LDL-C, FPG, LAP, and a higher prevalence of hypertension, while HDL-C was significantly lower. The LAP quartiles showed a similar trend (Additional file 1: Table S2). With increasing LAP levels, participants demonstrated higher age, a greater proportion of males, and higher rates of smoking and alcohol intake. They also exhibited elevated WC, BMI, SBP, DBP, TG, TC, LDL-C, FPG, TyG, as well as a higher prevalence of hypertension and antihypertensive medication use, with notably reduced HDL-C levels. Furthermore, (Additional file 1: Figure S1 ) illustrates the associations between TyG levels, LAP levels, and outcomes including FCMD, CMM, and all-cause mortality. Elevated baseline TyG levels correlated with an increased incidence of FCMD (β = 0.39, P < 0.001), CMM (β = 0.72, P < 0.001), and all-cause mortality (β = 0.37, P = 0.032). Similarly, elevated baseline LAP levels were positively associated with FCMD (β = 0.014; P < 0.001), CMM (β = 0.009; P = 0.009), and all-cause mortality (β = 0.008; P = 0.002). 3.2 Baseline characteristics based on dual-trajectory groups In dual-trajectory analysis, a three-group model was identified as the best-fit pattern. (Additional file 1: Table S3). We identified 3 discrete dual-trajectory groups, denoted as low-increasing group (group 1, 61.5%), high-amplitude fluctuation group (group 2, 7.6%), and high-increasing group (group 3, 30.9%). The mean posterior probabilities for Groups 1, 2, and 3 were 0.86, 0.87, and 0.92, respectively. As shown in Fig. 3 , these groups displayed distinct trajectories throughout the follow-up period. Group 1 demonstrated a stable and gradual increase in TyG and LAP levels. Group 2 showed pronounced fluctuations, characterized by an initial rapid increase reaching a peak around Year 5, followed by a marked decline to a nadir between Years 15 and 20, and subsequently rebounding with a sharp upward trend. In contrast, group 3 displayed a consistently rapid and steady increase in TyG and LAP levels over the entire follow-up. The median (interquartile range) changes in TyG and LAP levels from Year 0 to Year 25 were 0.5 (0.46–0.57) for the low-increasing TyG group, 0.7 (0.7–0.92) for the high-amplitude fluctuation TyG group, and 0.71 (0.62–0.79) for the high-increasing TyG group. For LAP levels, the changes over this period were 20.27 (11.28–35.71) in the low-increasing group, 33.12 (19.16–64.1) in the high-amplitude fluctuation group, and 29.49 (16.88–48.02) in the high-increasing group (Additional file 1: Table S4). Table 1 shows the baseline characteristics of dual-trajectory groups. Compared with group 1, participants in groups 2 and 3 were more often male and exhibited higher values in cardiometabolic markers, including WC, BMI, SBP, DBP, TC, TG, and LDL-C, while having significantly lower HDL-C levels. Additionally, group 2 had the highest rates of smoking (38.0%, P = 0.001) and alcohol consumption ( P = 0.045). In contrast, group 1 exhibited the lowest levels of the aforementioned metabolic burden and unhealthy lifestyle factors. Table 1 Baseline characteristics of participants stratified by dual-trajectory groups. Characteristics Dual-trajectory group Total (n = 3467) Group 1 (n = 2133) Group 2 (n = 263) Group 3 (n = 1071) P value Age, mean (SD), years 25.08 (3.59) 25.05 (3.58) 25.30 (3.57) 25.08 (3.62) 0.585 Male, no (%) 1505 (43.40) 763 (35.80) 143 (54.40) 599 (55.90) < 0.001 White, no (%) 1845 (53.20) 1151 (54.00) 120 (45.60) 574 (53.60) 0.036 Waist circumference, mean (SD), cm 77.49 (10.70) 76.59 (10.68) 80.83 (11.27) 78.46 (10.36) < 0.001 BMI, mean (SD), kg/m² 24.42 (4,71) 24.19 (4.65) 25.61 (4.96) 24.59 (4.71) < 0.001 SBP, mean (SD), mmHg 109.82 (10.65) 109.02 (10.44) 111.70 (11.67) 110.96 (10.66) < 0.001 DBP, mean (SD), mmHg 68.05 (9.19) 67.71 (9.05) 69.35 (9.43) 68.39 (9.38) 0.008 Smoking status, No. (%) 0.001 Current smoker 942 (27.20) 547 (25.60) 100 (38.00) 295 (27.50) Former smoker 1040 (30.00) 662 (31.00) 71 (27.00) 307 (28.70) Never smoker 1485 (42.80) 924 (43.30) 92 (35.00) 469 (43.80) Alcohol consumption, median (SD), ml/day 11.47 (19.87) 11.09 (19.93) 14.32 (20.32) 11.55 (19.59) 0.045 Educational level, mean (SD), year 13.82 (1.83) 13.89 (1.82) 13.22 (1.86) 13.83 (1.83) < 0.001 Physical activity, mean (SD), EU 419.07 (297.39) 410.89 (294.60) 432.29 (286.74) 432.12 (305.09) 0.123 TC, mean (SD), mg/dL 177.53 (32.96) 176.95 (32.88) 180.77 (35.99) 177.88 (32.33) 0.189 TG, mean (SD), mg/dL 72.62 (47.82) 73.08 (46.06) 83.01 (66.31) 69.15 (45.44) < 0.001 LDL-C, mean (SD), mg/dL 109.81 (30.87) 108.38 (30.66) 113.25 (33.04) 111.81 (30.57) 0.002 HDL-C, mean (SD), mg/dL 53.20 (12.84) 53.97 (12.73) 50.92 (12.41) 52.24 (13.05) < 0.001 FPG, mean (SD), mg/dL 81.93 (10.87) 81.98 (10.39) 83.19 (19.69) 81.53 (8.50) 0.083 TyG, mean (SD) 7.86 (0.51) 7.87 (0.50) 7.95 (0.61) 7.80 (0.51) < 0.001 LAP, mean (SD) 15.01 (19.07) 14.56 (17.29) 21.35 (31.85) 14.36 (17.97) < 0.001 Hypertension, no (%) 300 (8.70) 183 (8.60) 27 (10.30) 90 (8.40) 0.617 Antihypertensive medication, no (%) 71 (2.00) 49 (2.30) 7 (2.70) 15 (1.40) 0.183 Group 1: Low-increasing trajectory group; Group 2: High-amplitude fluctuation trajectory group; Group 3: High-increasing trajectory group. Abbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglyceride; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; CAC, coronary artery calcium; FPG, fasting plasma glucose; TyG, triglyceride-glucose index; LAP, lipid accumulation product. 3.3 Dual-trajectory and outcomes Over a mean follow-up of 24.04 ± 3.32 years, 736(21.2%) individuals were identified as FCMD, 79 (2.3%) as CMM and 137 (4.0%) as mortality. The Cox proportional hazards models revealed significant positive associations between dual-trajectory groups and risks of all three aforementioned outcomes. (Table 2 ). Compared with the low-increasing group, risks for all three outcomes were significantly elevated in the high-amplitude fluctuation group. For instance, in the unadjusted model, the HR for FCMD in the high-amplitude fluctuation group was 1.78 (95% CI: 1.40–2.26, P < 0.001); after adjusting for demographics and cardiometabolic risk factors (model 2), the HR decreased to 1.39 (95% CI: 1.09–1.78, P = 0.008), and further adjustment for baseline TyG and LAP levels (model 3) yielded an HR of 1.38(95% CI: 1.08–1.77, P = 0.01). For CMM risk, HRs (95% CI) for the high-amplitude fluctuation group across models 1 to 3 were 3.27(1.68–6.38), 2.61(1.27–5.34), and 2.63(1.21–5.71), respectively ( P < 0.05). Similarly, in terms of mortality, the HRs (95% CI) were 3.05(1.87–4.98), 2.17(1.32–3.58), and 2.16(1.30–3.56) across the three models ( P < 0.05). Table 2 Risk of FCMD, CMM and all-cause mortality for dual-trajectory groups. Dual-trajectory groups No. events/total Model 1 Model 2 Model 3 HR (95%CI) P value HR (95%CI) P value HR (95%CI) P value FCMD Low-increasing 382/2133 Reference 1.0 Reference 1.0 Reference 1.0 High-amplitude fluctuation 80/263 1.78(1.40–2.26) < 0.001 1.39(1.09–1.78) 0.008 1.38(1.08–1.77) 0.01 High-increasing 274/1071 1.54(1.31–1.79) < 0.001 1.48(1.27–1.74) < 0.001 1.59(1.36–1.87) < 0.001 CMM Low-increasing 33/2133 Reference 1.0 Reference 1.0 Reference 1.0 High-amplitude fluctuation 12/263 3.27(1.68–6.38) < 0.001 2.61(1.27–5.34) 0.008 2.63(1.21–5.71) 0.01 High-increasing 34/1071 2.00(1.23–3.26) 0.005 2.33(1.38–3.91) 0.001 2.68(1.57–4.56) < 0.001 Death Low-increasing 60/2133 Reference 1.0 Reference 1.0 Reference 1.0 High-amplitude fluctuation 22/263 3.05(1.87–4.98) < 0.001 2.17(1.32–3.58) 0.002 2.16(1.30–3.56) 0.003 High-increasing 55/1071 1.85(1.28–2.66) 0.001 1.68(1.16–2.45) 0.006 1.77(1.21–2.59) 0.003 Model 1: Unadjusted. Model 2: Adjusted for baseline age, race, sex, body mass index, education, physical activity, systolic blood pressure, hypertension, antihypertensive medication use, smoking status, alcohol consumption and low-density lipoprotein cholesterol. Model 3: Adjusted for model 2 covariates plus TyG and LAP at year 0. Abbreviation: HR, hazard ratio; CI, confidence interval; FCMD, first cardiometabolic disease; CMM, cardiometabolic multimorbidity. The high-increasing group also demonstrated significantly higher risks than the low-increasing group. The HR (95%CI) for FCMD increased from 1.54(1.31–1.79) in model 1 to 1.59(1.36–1.87) in model 3( P < 0.05). For CMM, HR (95%CI) rose from 2.00(1.23–3.26) in model 1 to 2.68(1.57–4.56) in model 3. Similarly, for all-cause mortality, the HR decreased slightly from 1.85 (1.28–2.66) in model 1 to 1.77 (1.21–2.59) in model 3 ( P < 0.05). 3.4 Multi-state analysis A multi-state analysis was utilized to investigate the role of dual-trajectory groups in shaping transition dynamics across each stage of CMM development. Figure 2 illustrates a gradual increase in mortality risk with the progression of CMM. Table 3 further details the transition risks across trajectory groups. Compared with the low-increasing group, the high-amplitude fluctuation group exhibited significantly higher risks at all stages, including baseline to FCMD (HR: 1.39, 95% CI: 1.09–1.78, P = 0.008), FCMD to CMM (HR: 2.31, 95% CI: 1.16–4.62, P = 0.017), CMM to death (HR: 3.45, 95% CI: 1.13–10.51, P = 0.030), and FCMD to death (HR: 3.07, 95% CI: 1.50–6.27, P = 0.002). In contrast, the high-increasing group exhibited elevated risk primarily in the earlier stages, including baseline to FCMD (HR: 1.48, 95% CI: 1.27–1.74, P < 0.001) and FCMD to CMM (HR: 1.80, 95% CI: 1.07–3.01, P = 0.026), with no significant differences observed in subsequent mortality stages (CMM to death, HR: 1.17, P = 0.776; FCMD to death, HR: 1.63, P = 0.104). Table 3 Associations between the dual-trajectory groups and CMM transition patterns. Transition pattern Case HR (95%CI) P value Low-increasing Baseline → FCMD 382 Reference 1.0 FCMD → CMM 33 Reference 1.0 CMM → Death 9 Reference 1.0 FCMD → Death 24 Reference 1.0 Baseline → Death 30 Reference 1.0 High-amplitude fluctuation Baseline → FCMD 80 1.39 (1.09–1.78) 0.008 FCMD → CMM 12 2.31 (1.16–4.62) 0.017 CMM → Death 6 3.45 (1.13–10.51) 0.030 FCMD → Death 12 3.07 (1.50–6.27) 0.002 Baseline → Death 9 1.62 (0.79–3.30) 0.188 High-increasing Baseline → FCMD 274 1.48 (1.27–1.74) < 0.001 FCMD → CMM 34 1.80 (1.07–3.01) 0.026 CMM → Death 6 1.17 (0.39–3.43) 0.776 FCMD → Death 23 1.63 (0.90–2.93) 0.104 Baseline → Death 18 1.59 (0.98–2.58) 0.063 Models adjusted for baseline age, race, sex, body mass index, education, physical activity, systolic blood pressure, hypertension, antihypertensive medication use, smoking status, alcohol consumption and low-density lipoprotein cholesterol. Abbreviations as in Table 2 . 3.5 Sensitivity Analysis In participants whose baseline LDL-C was < 4.144 mmol/L, the risks for FCMD, CMM, and all-cause mortality remained significantly elevated in the high-amplitude fluctuation and high-increasing groups, in alignment with the primary analysis (Additional file 1: Table S5). Second, individuals on antihypertensive, cardiac, or both medications were excluded, and this exclusion did not alter the risk patterns observed across dual-trajectory groups(Additional file 1: Table S6). Furthermore, including baseline cancer patients in the analysis showed that elevated risks for FCMD, CMM, and all-cause mortality remained significant in high-amplitude fluctuation and high-increasing groups, underscoring the robustness of these findings(Additional file 1: Table S7). 3.6 Subgroup Analysis After stratifying participants sex, race, BMI, smoking status, lipid levels, and hypertension revealed that the link between dual-trajectory groups and outcomes remained similar (Fig. 4 ). For CMM risk, all subgroup interaction effects were non-significant except for the LDL-C subgroup, which showed a significant interaction ( P = 0.01). Similarly, subgroup analysis for all-cause mortality risk showed high stability, with non-significant interaction P -values across all subgroups ( P > 0.05). However, FCMD risk exhibited some variability within the BMI and race subgroups, with both showing significant interaction P -values ( P < 0.001). 4. Discussion In this study, based on data from a prospective cohort, we found that elevated baseline TyG and LAP levels in young adulthood were associated with increased risks of FCMD, CMM, and mortality in middle age. We also identified three distinct dual-trajectory groups for TyG and LAP levels in young adults: low-increasing, high-amplitude fluctuation, and high-increasing. We further found that high-amplitude fluctuation trajectories and high-increasing trajectories of TyG and LAP are significantly associated with the increased risk of FCMD, CMM, and mortality. Unlike traditional single-time measurements, trajectory patterns more intuitively capture the dynamic changes in metabolic status, emphasizing the cumulative impact of insulin resistance and lipid metabolism disorders. Our findings align with previous studies that have shown elevated TyG and LAP levels to be predictive of cardiometabolic disorders[ 12 , 17 , 19 , 39 ]. However, few studies have clarified the co-evolution patterns of these two markers. Given that each of these markers independently predicts cardiometabolic risk, exploring their combined trajectory is crucial to understanding the full extent of metabolic dysregulation. Our research identified a synchronous trend in the dual trajectories of TyG and LAP, which revealed a systemic nature of metabolic imbalance that went beyond single measurements. Several mechanisms may partially account for this synchronous trend. IR disrupts lipid and glucose metabolic pathways, leading to visceral fat accumulation, elevated circulating free fatty acids (FFA), and persistently high levels of pro-inflammatory cytokines like TNF-α and IL-6[ 8 , 40 ]. This metabolic dysregulation accelerates triglyceride and glucose production, thereby raising TyG levels, while FFA accumulation further promotes visceral fat deposition, increasing the LAP[ 41 ]. Additionally, chronic low-grade inflammation induced by IR exacerbates systemic lipid and glucose metabolic disturbances, reinforcing the link between TyG and LAP [ 8 , 42 ]. Addressing these disturbances in glucose and lipid metabolism may be crucial for interrupting this cycle and preventing CMM development. These findings underscore the combined impact of TyG and LAP, highlighting the importance of dynamic monitoring of blood glucose, lipids, and fat distribution for effective cardiometabolic health management. Numerous studies have demonstrated a link between higher long-term trajectory of TyG and LAP and adverse cardiovascular outcomes [ 43 – 48 ]. However, these studies have primarily concentrated on a single disease stage, without assessing the impact of long-term trajectory of TyG and LAP across various transition stages in the entire progression of CMM-namely, from being CMD-free to developing FCMD, progressing to CMM, and eventually leading to mortality. To overcome these limitations, we utilized a multi-state model that accounts for competing risks as well as transitions across different cardiometabolic stages. Our findings suggest that both the high-amplitude fluctuation and high-increasing groups could impact entire progression of CMM. Furthermore, we found distinct risk distribution patterns between the high-amplitude fluctuation and high-increasing groups. The high-amplitude fluctuation group presents a higher risk that intensifies in all stages of disease progression, while the high-increasing group has a greater impact on the earlier stages. This distinction is likely due to the instability, cumulative metabolic damage, and lack of gradual adaptation caused by metabolic fluctuations. A prospective cohort study revealed that revealed greater TyG variability were causally related to higher incidence of CVD[ 49 ]. The underlying mechanism between the high-amplitude fluctuation group and the overall progression of CMM is not fully understood, and we have hypothesized several plausible mechanisms. Frequent fluctuations result in significant changes in insulin resistance, blood glucose, and lipid levels, which place the cardiovascular system in a prolonged state of stress. Thereby the likelihood of systemic inflammation, oxidative stress, endothelial dysfunction and plaque instability is increased which raise the risk of CMM and all-cause mortality[ 50 , 51 ]. With disease progression, cumulative metabolic damage increases, leading individuals in the high-amplitude fluctuation group to experience multiple fluctuation cycles, repeated stress, and metabolic disorders. Consequently, they are more prone to severe complications, organ failure, and a significantly higher risk of death[ 52 ]. In contrast, although the metabolic indicators of individuals in the high-increasing group continued to rise, the steady upward trend enabled the body to gradually adapt to this metabolic burden, reducing the accumulated inflammation and stress[ 53 ]. Therefore, the risk in the high-increasing group is mainly concentrated in the early stages when the cardiovascular system had not fully adapted to metabolic stress, and FCMD and CMM were more likely to occur. With disease progression, metabolic adaptation provides some protection in later stages, resulting in no significant difference in the progression from CMM to mortality or FCMD to mortality. This finding suggests that late-stage intervention has limited effects, emphasizing the importance of early intervention to address metabolic abnormalities and maintain a stable metabolic state, thereby reducing the risks of cardiometabolic disease and all-cause mortality. Adjusting lifestyle factors (e.g., diet management, increased exercise, stable daily routine) and pharmacological intervention to improve IR and reduce visceral fat accumulation may help delay the progression of cardiometabolic disease. Given the high metabolic fluctuation in some individuals, a single time-point intervention may be insufficient for long-term effects. Thus, a dynamic monitoring and individualized management approach is recommended to stabilize metabolic fluctuations and mitigate cumulative systemic stress. Our analysis also indicates that individuals with higher baseline TyG and LAP levels, as well as those with high-increasing and high-amplitude fluctuation trajectories, display a significantly higher proportion of males, highlighting gender differences in metabolic trajectories. Males are more susceptible to fat accumulation in the visceral area, a pattern linked to lipid metabolism disorders and elevated LAP[ 54 ]. Additionally, androgens, such as testosterone, can promote visceral fat accumulation and increase pro-inflammatory factors, thus exacerbating IR and elevating TyG levels[ 55 – 57 ]. Female estrogen, to a certain extent, inhibits the accumulation of visceral fat and plays a protective role[ 56 ]. Males also tend to consume a high-calorie diet, which can lead to visceral fat accumulation and metabolic burden[ 58 ]. In response to environmental stress or dietary changes, males exhibit greater sensitivity to IR and inflammation, leading to higher metabolic fluctuations[ 59 , 60 ]. These sex-specific metabolic responses and lifestyle differences jointly contribute to the higher likelihood of males following high-risk metabolic trajectories under increased metabolic load. This study has several limitations. Firstly, as an observational cohort study, although adjustments were made for multiple confounding factors, some unmeasured or unknown factors might still introduce potential bias, which could affect causal interpretations. Additionally, since data were drawn from a specific cohort, the sample population is relatively homogeneous, possibly affecting the generalizability of the findings; populations from other regions, ethnic backgrounds, or health statuses may display different metabolic patterns. Moreover, dynamic data on lifestyle changes and dietary habits, which could influence metabolic indicators, were not available for analysis. Similarly, the absence of additional metabolic and inflammatory markers (e.g., CRP, IL-6) may have limited a more comprehensive examination of metabolic health. Overall, these considerations suggest that future studies could benefit from verifying findings across diverse populations and incorporating more comprehensive biomarker and lifestyle data to further elucidate the relationship between TyG and LAP trajectories and cardiometabolic multimorbidity risks. 5. Conclusion This study demonstrates that higher TyG and LAP levels in early adulthood are associated with an increased risk of FCMD, CMM, and mortality by midlife. Additionally, chronic exposure to elevated and fluctuating TyG and LAP levels in young adulthood is associated with increased CMM risk, with fluctuating TyG and LAP levels showing higher risks across all stages of CMM development, while consistently high levels primarily impact earlier stages. These findings emphasize the critical role of early intervention and sustained monitoring of insulin resistance and lipid accumulation to mitigate long-term cardiometabolic risks. Abbreviations TyG Triglyceride-glucose index LAP Lipid Accumulation Product CHD Coronary Heart Disease T2D Type 2 Diabetes CMDs Cardiometabolic Diseases CMM Cardiometabolic Multimorbidity FCMD First Cardiometabolic Disease CARDIA Coronary Artery Risk Development in Young Adults CVD Cardiovascular Disease SBP Systolic Blood Pressure DBP Diastolic Blood Pressure BMI Body Mass Index WC Waist Circumference HDL-C High-Density Lipoprotein Cholesterol LDL-C Low-Density Lipoprotein Cholesterol TG Triglyceride FPG Fasting Plasma Glucose CRP C-Reactive Protein IR Insulin Resistance HR Hazard Ratio CI Confidence Interval Declarations Data availability The data that support the findings of this study are available from the corresponding author, Yinyin Zhang, upon reasonable request. Acknowledgements The authors thank the staff and participants of the CARDIA (Coronary Artery Risk Development in Young Adults) study for their contributions. Funding This research was funded by the Youth Fund of the National Natural Science Foundation of China, Grant/Award Number: 81900443. Author information Lingqu Zhou, Junjie Wang and Zirui Zhou have contributed equally to this work and are co-first authors. Authors and Affiliations Department of Ultrasonography and Electrocardiograms, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat‑sen University Cancer Center, Guangzhou, China Lingqu Zhou, Liangjiao Wang, Hui Zeng & Ziyue Zhong Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China Zirui Zhou, Qi Guo & Yinyin Zhang Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China Junjie Wang Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China Junjie Wang Contributions YYZ had full access to all of the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. YYZ, LQZ and JJW contributed to the study design and inception. LQZ, JJW, and ZRZ contributed to the acquisition, analysis, interpretation of data, and drafted the manuscript. QG and LJW contributed to the analysis of the data and interpretation. HZ and ZYZ revised the manuscript. All authors provided a revision of the manuscript for critically important intellectual content and approved the final version of the manuscript. Corresponding author Correspondence to Yinyin Zhang Ethics declarations Ethics approval and consent to participate The study was performed according to the guidelines of the Helsinki Declaration and was approved by the Institutional Review Board at Sun Yat-sen Memorial Hospital. Written informed consent was obtained from all participants for data collection. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Skou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, Boyd CM, Pati S, Mtenga S, Smith SM. Multimorbidity. Nat Rev Dis Primers. 2022;8(1):48. 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Supplementary Files GraphicalAbstract.jpg Additionalfile1.docx Cite Share Download PDF Status: Published Journal Publication published 09 May, 2025 Read the published version in Cardiovascular Diabetology → Version 1 posted Editorial decision: Revision requested 16 Dec, 2024 Reviews received at journal 16 Dec, 2024 Reviews received at journal 11 Dec, 2024 Reviews received at journal 07 Dec, 2024 Reviews received at journal 06 Dec, 2024 Reviewers agreed at journal 23 Nov, 2024 Reviewers agreed at journal 23 Nov, 2024 Reviewers agreed at journal 22 Nov, 2024 Reviews received at journal 22 Nov, 2024 Reviewers agreed at journal 22 Nov, 2024 Reviewers agreed at journal 21 Nov, 2024 Reviewers invited by journal 21 Nov, 2024 Editor assigned by journal 12 Nov, 2024 Submission checks completed at journal 12 Nov, 2024 First submitted to journal 12 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5436679","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":383453220,"identity":"662cdeb2-1dab-4060-b9fd-107978747597","order_by":0,"name":"Lingqu Zhou","email":"","orcid":"","institution":"Sun Yat‑sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Lingqu","middleName":"","lastName":"Zhou","suffix":""},{"id":383453221,"identity":"dfa58040-8977-4438-86ba-0d4e2d207c0a","order_by":1,"name":"Junjie Wang","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Wang","suffix":""},{"id":383453222,"identity":"f8b7bc34-cfec-42e1-ae6b-10bee6460417","order_by":2,"name":"Zirui Zhou","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Zirui","middleName":"","lastName":"Zhou","suffix":""},{"id":383453223,"identity":"fca7b32e-fe9e-4310-ad4b-cfd2e10bb83f","order_by":3,"name":"Liangjiao Wang","email":"","orcid":"","institution":"Sun Yat‑sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Liangjiao","middleName":"","lastName":"Wang","suffix":""},{"id":383453224,"identity":"5d03ce26-367b-47f1-9f6c-e7fe0f0726d4","order_by":4,"name":"Qi Guo","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Guo","suffix":""},{"id":383453225,"identity":"156b7536-2ed1-49cc-8fa8-d6ee5119736e","order_by":5,"name":"Hui Zeng","email":"","orcid":"","institution":"Sun Yat‑sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zeng","suffix":""},{"id":383453226,"identity":"2ed1fc6c-b150-4be0-9eef-a05ff58b175e","order_by":6,"name":"Ziyue Zhong","email":"","orcid":"","institution":"Sun Yat‑sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Ziyue","middleName":"","lastName":"Zhong","suffix":""},{"id":383453227,"identity":"6c640585-4bea-4010-8dd6-c7cf601d4cdc","order_by":7,"name":"Yinyin Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYDACCQaGAyCSH8g+8AAmykOMFskGICOBWC1gYADUyECUFvnZPYaHC9ss8oyvHX4ItKUucf6MBMYHb9sY5M1xaGGcc8bg8Mw2iWKz22kGQC2HEzfcSGA2nNvGYLizAbsWZokcg8O8bRKJ224ngLQcSNwgkcAmzdvGkAB2KhbABtOyeXb6B5jD2H/j08ID07JBOgdkC3Niw40ENmZ8WiQk0goO85yTSJxxO6fgQILBYeMNZx42S845J2G4AYcW+RnJmz/zlNUl9s9O3/zhQ0Wd7Pz25IMf3pTZyOOyhYGBw4CBkQ3GMWBwbGBgbGBAxBc2wP6AgeEPgmuPR+koGAWjYBSMUAAA+2VgBZRBJpQAAAAASUVORK5CYII=","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Yinyin","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-11-12 06:23:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5436679/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5436679/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12933-025-02761-1","type":"published","date":"2025-05-09T15:57:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71630497,"identity":"03fc0214-7eea-4229-b0ab-fcc08d500d97","added_by":"auto","created_at":"2024-12-17 09:27:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":121885,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of participant selection\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5436679/v1/55040390504a2ee9022d010f.png"},{"id":71629181,"identity":"45e2d956-a85d-47de-8121-8756b4b8bcb4","added_by":"auto","created_at":"2024-12-17 09:19:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82710,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCounts and percentages of participants in the five transition stages of cardiometabolic outcomes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviation: FCMD, first cardiometabolic disease; CMM, cardiometabolic multimorbidity.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5436679/v1/545cd074dee5c0c219ae0d7c.png"},{"id":71630498,"identity":"30482ecc-71d3-4ec8-a2af-234c154ae282","added_by":"auto","created_at":"2024-12-17 09:27:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":463712,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDual-trajectory of TyG and LAP levels over 25 years.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5436679/v1/7f7a514a96c097a42091416d.png"},{"id":71629185,"identity":"65cfd925-93de-4347-a4c7-9b0741e4e3e4","added_by":"auto","created_at":"2024-12-17 09:19:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":473646,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between TyG and LAP dual-trajectory and cardiometabolic outcomes in subgroup analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea. subgroup analysis of the association between Dual-trajectory and FCMD.\u003c/p\u003e\n\u003cp\u003eb. subgroup analysis of the association between Dual-trajectory and CMM.\u003c/p\u003e\n\u003cp\u003ec. subgroup analysis of the association between Dual-trajectory and all-cause mortality.\u003c/p\u003e\n\u003cp\u003eModels were adjusted for baseline age, race, sex, body mass index, education, physical activity, systolic blood pressure, hypertension, antihypertensive medication use, smoking status, alcohol consumption and low-density lipoprotein cholesterol.\u003c/p\u003e\n\u003cp\u003e*HR and 95% CI were derived from Cox regression models and low Low-increasing trajectory group was used as the reference in each subgroup analysis.\u003c/p\u003e\n\u003cp\u003eAbbreviation: LDL-C, low-density lipoprotein cholesterol; BMI, body mass index; HR, hazard ratio; CI, confidence interval; FCMD, first cardiometabolic disease; CMM, cardiometabolic multimorbidity.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5436679/v1/491abe84a1a71c6eb7bffa4a.png"},{"id":82537565,"identity":"46fc3254-d110-47d5-936d-928b1ba4729e","added_by":"auto","created_at":"2025-05-12 16:08:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2214616,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5436679/v1/4aee7e63-9b64-4b96-b9b8-e42294a25490.pdf"},{"id":71629179,"identity":"451e05e0-968d-43bf-b3d6-fcd6a26261fb","added_by":"auto","created_at":"2024-12-17 09:19:58","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":714085,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5436679/v1/bfba5ab4f145969071171f71.jpg"},{"id":71629183,"identity":"868e11c1-48a8-4bd9-8353-f174017ed2fe","added_by":"auto","created_at":"2024-12-17 09:19:58","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":215782,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5436679/v1/e28c7b23a3fd5816a2fa75a4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elevated and fluctuating TyG and LAP trajectories are associated with cardiometabolic multimorbidity development in midlife: the CARDIA study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMultimorbidity, characterized by an individual having at least two chronic metabolic conditions, has emerged as a critical global health challenge due to its substantial impact on individuals, families, healthcare systems, and society as a whole[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Among the different forms of multimorbidity, cardiometabolic multimorbidity (CMM), which involving the simultaneous combination of two or more cardiometabolic diseases (CMDs) such as stroke, type 2 diabetes (T2D), and coronary heart disease (CHD), is particularly worrisome[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A Canadian study reported that 22% of individuals with diabetes, 32.2% of those with heart disease, and 48.4% of stroke patients have one additional cardiometabolic condition[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, multiple studies have demonstrated that the coexistence of multiple CMDs significantly escalates mortality risk and markedly reduces life expectancy compared to the presence of a single CMD[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, early identification of potential risk factors contributing to CMM development is crucial.\u003c/p\u003e \u003cp\u003eInsulin resistance (IR) is a key risk factor linked to numerous cardiovascular and metabolic diseases, including CHD, stroke, hypertension, atherosclerosis, T2D, and atrial fibrillation[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Recently, the triglyceride-glucose index (TyG), derived from fasting triglyceride (TG) and glucose levels, has emerged as a reliable indicator of insulin resistance (IR) and its progression[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Numerous studies have validated the TyG's utility in predicting stroke risk among individuals aged 45 and above [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the incidence of diabetes in the general population[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], cardiovascular disease (CVD) risk in Middle-aged and older Chinese population[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and adverse cardiovascular outcomes in patients with hypertension, CHD, and T2D complicated by acute myocardial infarction[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, obesity, particularly central obesity, is another known contributor to CMDs, with strong associations to premature mortality[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Although body mass index (BMI) is widely employed to evaluate obesity, it has significant limitations: it cannot distinguish between lean body mass and fat mass, nor does it accurately reflect abdominal fat distribution[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In response to these limitations, the lipid accumulation product (LAP), introduced by Henry Kahn in 2005, integrates waist circumference (WC) and fasting TG levels and has emerged as a reliable indicator of lipid overaccumulation and cardiometabolic risk[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Empirical evidence further supports the utility of LAP in predicting conditions such as metabolic syndrome and diabetes, underscoring its value as a crucial metric for improving survival assessments in obese populations[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, previous research has predominantly concentrated on the independent associations of TyG and LAP with cardiometabolic risk, while the potential utility of combining these two markers to comprehensively evaluate CMM progression remains largely underexplored. Furthermore, most existing studies have focused on older populations, often neglecting younger individuals. Nevertheless, metabolic changes during young adulthood have a major impact on future cardiometabolic outcomes, underscoring the importance of focusing on younger cohorts. Additionally, the dynamic nature of TyG and LAP over time suggests that static, single-point assessments may provide only limited insights. Trajectory modeling, in contrast, allows for the examination of temporal changes, the identification of distinct risk trajectories, and the facilitation of more precise, individualized prevention and intervention strategies[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Finally, prior studies have predominantly examined the coexistence of one or two CMDs, thereby failing to fully address the complex interactions involved in the progression of CMM, which limits the comprehensive utility of TyG and LAP in assessing cardiometabolic disease risk.\u003c/p\u003e \u003cp\u003eIn light of these gaps, this study aimed to utilize data from the Coronary Artery Risk Development in Young Adults (CARDIA) study to describe the longitudinal trajectory patterns of TyG and LAP levels during young adulthood, and to evaluate their combined effect on CMM development in middle age.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and population\u003c/h2\u003e \u003cp\u003eBetween 1985 and 1986 (year 0), the CARDIA study recruited over 5,115 participants aged 18 to 30 from urban areas in four U.S. cities: Minneapolis (Minnesota), Birmingham (Alabama), Oakland (California), and Chicago (Illinois). As a prospective, multi-center study, CARDIA was established to track CVD risk progression and contributing factors from young adulthood to midlife. Data have been collected over nine follow-up intervals, starting with the initial baseline assessment and continuing with further exams at 2, 5, 7, 10, 15, 20, 25, and 30 years. Detailed methodology and examination procedures are documented in previously published reports[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor this study, participants with prevalent diabetes, stroke or CHD (n\u0026thinsp;=\u0026thinsp;356) at baseline, missing baseline waist, fast plasma glucose (FPG) and TG values (n\u0026thinsp;=\u0026thinsp;121), missing following waist, FPG and TG data (n\u0026thinsp;=\u0026thinsp;831) and missing other covariates (n\u0026thinsp;=\u0026thinsp;168) were excluded. We also excluded individuals with prevalent cancer (n\u0026thinsp;=\u0026thinsp;172) at baseline to ensure data reliability by minimizing confounding factors, competing risks, and metabolic effects related to cancer and its treatments[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A total of 3,467 participants were ultimately included to study the association between TyG and LAP dual-trajectory and CMM development (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Assessment of the TyG and LAP\u003c/h2\u003e \u003cp\u003eThe FPG was measured using the hexokinase UV method[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. TG concentrations in fasting sample blood were assessed by calibration and enzymatic analysis[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The TyG was computed using the following formula: Ln [fasting TG (mg/dL) \u0026times; FPG (mg/dL)/2][\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMeasurements of weight, height, and WC were gathered according to standardized procedures outlined in earlier studies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. WC was measured at the midpoint between the iliac crest and the lowest rib laterally, and between the xiphoid process and the umbilicus anteriorly, with measurements recorded to the nearest 0.5 cm. LAP was calculated using the formula (WC(cm)-65) \u0026times; TG (mmol/L) for man, and (WC(cm)-58) \u0026times; TG (mmol/L) for women[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Other covariates\u003c/h2\u003e \u003cp\u003eAt baseline, demographic data and cardiometabolic risk factors\u0026mdash;including age, sex, race, education, physical activity, smoking and drinking status, and use of antihypertensive medications\u0026mdash;were gathered using standardized protocols[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBlood pressure was measured three times following a 5 minutes rest period. Hypertension was defined as having a systolic blood pressure (SBP) of 140 mmHg or above, a diastolic blood pressure (DBP) of 90 mmHg or above, or the use of antihypertensive drugs. BMI was derived by dividing body weight (kg) by the square of height (m), expressed as kg/m\u0026sup2;. Protocols for measuring serum total cholesterol, HDL-C (high-density lipoprotein cholesterol), and LDL-C (low-density lipoprotein cholesterol) were detailed in prior studies[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Smoking status was categorized into three classes: current, former, or never. Physical activity was measured using the validated CARDIA questionnaire, which quantified 13 exercise categories over the past year and converted them into exercise units (EU), with 300 EU equivalent to 150 minutes of moderate-intensity exercise weekly [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Outcomes\u003c/h2\u003e \u003cp\u003eThe primary outcomes in this study were defined as first cardiometabolic disease (FCMD), CMM and all-cause mortality.\u003c/p\u003e \u003cp\u003eIn accordance with established criteria from previous research, we defined CMM as the coexistence of at least two of the following three CMDs\u0026mdash;T2D, CHD, and stroke\u0026mdash;with the first occurring CMD identified as the FCMD [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. To define T2D, criteria included an FPG level reaching 126 mg/dL or more, a 2-hour post-challenge glucose level of \u0026ge;\u0026thinsp;11.1 mmol/L (200 mg/dL), HbA1c at 48 mmol/mol (6.5%) or higher, or the administration of antidiabetic drugs. Participants were confirmed to be free of diabetes by Year 0, based on assessments of medication use and fasting glucose levels conducted at baseline.\u003c/p\u003e \u003cp\u003eTracking of cardiovascular and cerebrovascular incidents, which including CHD and stroke, as well as mortality, was conducted from the initial assessment until August 31, 2014. For individuals who underwent hospitalization or outpatient vascular procedures, corresponding medical documentation was collected. Vital status updates were obtained every six months, with next-of-kin consent acquired for access to medical and death records as needed. Each reported event was reviewed independently by two physicians from the CARDIA Endpoints Surveillance and Adjudication Subcommittee, adhering to predefined criteria for cardiovascular incidents described in prior publications[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In cases of disagreement, the full committee conducted a review. Participants without events who remained in the study were censored as of August 31, 2014.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, and categorical variables as frequency (percentage). Participants were divided into quartiles according to their baseline TyG and LAP levels. Group differences were analyzed using ANOVA, Kruskal-Wallis test, and χ\u0026sup2; test, as appropriate.\u003c/p\u003e \u003cp\u003eTo explore the relationships between baseline TyG and LAP levels and FCMD, CMM, and all-cause mortality, multivariable logistic regression models were employed. The fully adjusted models accounted for baseline age, sex, race, BMI, education, physical activity, SBP, hypertension, antihypertensive medication use, smoking status, alcohol consumption, and LDL-C.\u003c/p\u003e \u003cp\u003eA group-based dual-trajectory model with a semi-parametric approach was used to examine the temporal trends of TyG and LAP levels over the follow-up duration (from year 0 to year 25). This method enables the simultaneous analysis of both indicators' dynamics, evaluating the likelihood of LAP trajectories corresponding to specific TyG trajectories, and suggesting they may be interconnected through a shared underlying etiological process without prior assumptions. According to recommendations of Nagin[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], to select the optimal model, a two-stage approach was applied. Firstly, we identified the optimal number of trajectories for the model, exploring options from 2 to 5 clusters. In the following stage, the trajectory shapes were refined by adjusting the polynomial order, specifying them as linear, quadratic, or cubic. Selection of the best-fit dual-trajectory model was guided by three main criteria [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]: (1) minimum Bayesian Information Criterion (BIC) value; (2) each trajectory group included at least 5% of the participants; and (3) mean posterior probability greater than 0.7.\u003c/p\u003e \u003cp\u003eParticipants were further grouped by dual-trajectory of TyG and LAP. We employed Cox proportional hazards models to assess the associations of dual-trajectory groups with FCMD, CMM, and all-cause mortality, and calculated hazard ratios (HRs) and 95% confidence intervals (CIs) to evaluate risk. The Cox models included the same set of baseline covariates for full adjustment as in the logistic regression models.\u003c/p\u003e \u003cp\u003eSubsequently, multi-state models, an extension of Cox proportional hazards models, were utilized to investigate the role of dual-trajectory groups at multiple phases of CMM progression, beginning from a baseline without CMDs to the development of FCMD, progression to CMM, and ultimately, mortality. The primary advantage of multi-state models lies in their ability to incorporate multiple sequential or competing events as transition states, enabling a comprehensive evaluation of risk factors across various phases of disease progression with consideration for competing risks.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In accordance with prior research[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], five key transition stages were identified(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): (1) baseline to FCMD (21.2%), (2) FCMD to CMM (10.7%), (3) baseline to death (1.6%), (4) FCMD to death (8.0%), and (5) CMM to death (26.6%). The initiation of CMM was defined by the occurrence date of a second CMD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalyses were carried out in R (version 4.1.3), with the group-based dual-trajectory model fitted by the \u0026ldquo;lcmm\u0026rdquo; package and the multi-state models by \u0026ldquo;mstate\u0026rdquo;. All statistical tests were two-sided, with p-values below 0.05 considered statistically significant in all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics of TyG and LAP quartiles and outcomes\u003c/h2\u003e \u003cp\u003eThis study included 3,467 participants with a baseline mean age of 25.08\u0026thinsp;\u0026plusmn;\u0026thinsp;3.59 years, among whom 43.4% were male and 53.2% were white. Participants were separated into four quartile groups by TyG and LAP levels.\u003c/p\u003e \u003cp\u003eIn the TyG quartile grouping (Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), participants with higher TyG levels were older and more likely to be male, White, smokers, and daily alcohol consumers. They also had greater WC, BMI, SBP, DBP, TG, TC, LDL-C, FPG, LAP, and a higher prevalence of hypertension, while HDL-C was significantly lower. The LAP quartiles showed a similar trend (Additional file 1: Table S2). With increasing LAP levels, participants demonstrated higher age, a greater proportion of males, and higher rates of smoking and alcohol intake. They also exhibited elevated WC, BMI, SBP, DBP, TG, TC, LDL-C, FPG, TyG, as well as a higher prevalence of hypertension and antihypertensive medication use, with notably reduced HDL-C levels.\u003c/p\u003e \u003cp\u003eFurthermore, (Additional file 1: Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) illustrates the associations between TyG levels, LAP levels, and outcomes including FCMD, CMM, and all-cause mortality. Elevated baseline TyG levels correlated with an increased incidence of FCMD (β\u0026thinsp;=\u0026thinsp;0.39, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CMM (β\u0026thinsp;=\u0026thinsp;0.72, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and all-cause mortality (β\u0026thinsp;=\u0026thinsp;0.37, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032). Similarly, elevated baseline LAP levels were positively associated with FCMD (β\u0026thinsp;=\u0026thinsp;0.014; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CMM (β\u0026thinsp;=\u0026thinsp;0.009; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), and all-cause mortality (β\u0026thinsp;=\u0026thinsp;0.008; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Baseline characteristics based on dual-trajectory groups\u003c/h2\u003e \u003cp\u003eIn dual-trajectory analysis, a three-group model was identified as the best-fit pattern. (Additional file 1: Table S3). We identified 3 discrete dual-trajectory groups, denoted as low-increasing group (group 1, 61.5%), high-amplitude fluctuation group (group 2, 7.6%), and high-increasing group (group 3, 30.9%). The mean posterior probabilities for Groups 1, 2, and 3 were 0.86, 0.87, and 0.92, respectively. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, these groups displayed distinct trajectories throughout the follow-up period. Group 1 demonstrated a stable and gradual increase in TyG and LAP levels. Group 2 showed pronounced fluctuations, characterized by an initial rapid increase reaching a peak around Year 5, followed by a marked decline to a nadir between Years 15 and 20, and subsequently rebounding with a sharp upward trend. In contrast, group 3 displayed a consistently rapid and steady increase in TyG and LAP levels over the entire follow-up. The median (interquartile range) changes in TyG and LAP levels from Year 0 to Year 25 were 0.5 (0.46\u0026ndash;0.57) for the low-increasing TyG group, 0.7 (0.7\u0026ndash;0.92) for the high-amplitude fluctuation TyG group, and 0.71 (0.62\u0026ndash;0.79) for the high-increasing TyG group. For LAP levels, the changes over this period were 20.27 (11.28\u0026ndash;35.71) in the low-increasing group, 33.12 (19.16\u0026ndash;64.1) in the high-amplitude fluctuation group, and 29.49 (16.88\u0026ndash;48.02) in the high-increasing group (Additional file 1: Table S4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline characteristics of dual-trajectory groups. Compared with group 1, participants in groups 2 and 3 were more often male and exhibited higher values in cardiometabolic markers, including WC, BMI, SBP, DBP, TC, TG, and LDL-C, while having significantly lower HDL-C levels. Additionally, group 2 had the highest rates of smoking (38.0%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and alcohol consumption (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045). In contrast, group 1 exhibited the lowest levels of the aforementioned metabolic burden and unhealthy lifestyle factors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants stratified by dual-trajectory groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eDual-trajectory group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3467)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 1\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2133)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;263)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup 3\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1071)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean (SD), years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.08 (3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.05 (3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.30 (3.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.08 (3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, no (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1505 (43.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e763 (35.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e143 (54.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e599 (55.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite, no (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1845 (53.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1151 (54.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120 (45.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e574 (53.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference,\u003c/p\u003e \u003cp\u003emean (SD), cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77.49 (10.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.59 (10.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.83 (11.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.46 (10.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, mean (SD), kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.42 (4,71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.19 (4.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.61 (4.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.59 (4.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mean (SD), mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109.82 (10.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109.02 (10.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111.70 (11.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e110.96 (10.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mean (SD), mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.05 (9.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.71 (9.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.35 (9.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.39 (9.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e942 (27.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e547 (25.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100 (38.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e295 (27.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1040 (30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e662 (31.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71 (27.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e307 (28.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1485 (42.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e924 (43.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92 (35.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e469 (43.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption, median (SD), ml/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.47 (19.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.09 (19.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.32 (20.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.55 (19.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level,\u003c/p\u003e \u003cp\u003emean (SD), year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.82 (1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.89 (1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.22 (1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.83 (1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity,\u003c/p\u003e \u003cp\u003emean (SD), EU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e419.07 (297.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e410.89 (294.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e432.29 (286.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e432.12 (305.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mean (SD), mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e177.53 (32.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e176.95 (32.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180.77 (35.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e177.88 (32.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mean (SD), mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.62 (47.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.08 (46.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.01 (66.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.15 (45.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C, mean (SD), mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109.81 (30.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108.38 (30.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113.25 (33.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e111.81 (30.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C, mean (SD), mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53.20 (12.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.97 (12.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.92 (12.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.24 (13.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG, mean (SD), mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.93 (10.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.98 (10.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.19 (19.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81.53 (8.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.86 (0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.87 (0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.95 (0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.80 (0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAP, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.01 (19.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.56 (17.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.35 (31.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.36 (17.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, no (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300 (8.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e183 (8.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27 (10.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90 (8.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive medication, no (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71 (2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49 (2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15 (1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eGroup 1: Low-increasing trajectory group; Group 2: High-amplitude fluctuation trajectory group; Group 3: High-increasing trajectory group.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglyceride; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; CAC, coronary artery calcium; FPG, fasting plasma glucose; TyG, triglyceride-glucose index; LAP, lipid accumulation product.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Dual-trajectory and outcomes\u003c/h2\u003e \u003cp\u003eOver a mean follow-up of 24.04\u0026thinsp;\u0026plusmn;\u0026thinsp;3.32 years, 736(21.2%) individuals were identified as FCMD, 79 (2.3%) as CMM and 137 (4.0%) as mortality. The Cox proportional hazards models revealed significant positive associations between dual-trajectory groups and risks of all three aforementioned outcomes. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared with the low-increasing group, risks for all three outcomes were significantly elevated in the high-amplitude fluctuation group. For instance, in the unadjusted model, the HR for FCMD in the high-amplitude fluctuation group was 1.78 (95% CI: 1.40\u0026ndash;2.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); after adjusting for demographics and cardiometabolic risk factors (model 2), the HR decreased to 1.39 (95% CI: 1.09\u0026ndash;1.78, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), and further adjustment for baseline TyG and LAP levels (model 3) yielded an HR of 1.38(95% CI: 1.08\u0026ndash;1.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). For CMM risk, HRs (95% CI) for the high-amplitude fluctuation group across models 1 to 3 were 3.27(1.68\u0026ndash;6.38), 2.61(1.27\u0026ndash;5.34), and 2.63(1.21\u0026ndash;5.71), respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, in terms of mortality, the HRs (95% CI) were 3.05(1.87\u0026ndash;4.98), 2.17(1.32\u0026ndash;3.58), and 2.16(1.30\u0026ndash;3.56) across the three models (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk of FCMD, CMM and all-cause mortality for dual-trajectory groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDual-trajectory groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo. events/total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eFCMD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-increasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e382/2133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-amplitude fluctuation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80/263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.78(1.40\u0026ndash;2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.39(1.09\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.38(1.08\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-increasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274/1071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.54(1.31\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.48(1.27\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.59(1.36\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCMM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-increasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33/2133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-amplitude fluctuation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12/263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.27(1.68\u0026ndash;6.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.61(1.27\u0026ndash;5.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.63(1.21\u0026ndash;5.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-increasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34/1071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00(1.23\u0026ndash;3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.33(1.38\u0026ndash;3.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.68(1.57\u0026ndash;4.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeath\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-increasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60/2133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-amplitude fluctuation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22/263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.05(1.87\u0026ndash;4.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.17(1.32\u0026ndash;3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.16(1.30\u0026ndash;3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-increasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55/1071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85(1.28\u0026ndash;2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.68(1.16\u0026ndash;2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.77(1.21\u0026ndash;2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eModel 1: Unadjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eModel 2: Adjusted for baseline age, race, sex, body mass index, education, physical activity, systolic blood pressure, hypertension, antihypertensive medication use, smoking status, alcohol consumption and low-density lipoprotein cholesterol.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eModel 3: Adjusted for model 2 covariates plus TyG and LAP at year 0.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eAbbreviation: HR, hazard ratio; CI, confidence interval; FCMD, first cardiometabolic disease; CMM, cardiometabolic multimorbidity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe high-increasing group also demonstrated significantly higher risks than the low-increasing group. The HR (95%CI) for FCMD increased from 1.54(1.31\u0026ndash;1.79) in model 1 to 1.59(1.36\u0026ndash;1.87) in model 3(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For CMM, HR (95%CI) rose from 2.00(1.23\u0026ndash;3.26) in model 1 to 2.68(1.57\u0026ndash;4.56) in model 3. Similarly, for all-cause mortality, the HR decreased slightly from 1.85 (1.28\u0026ndash;2.66) in model 1 to 1.77 (1.21\u0026ndash;2.59) in model 3 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Multi-state analysis\u003c/h2\u003e \u003cp\u003eA multi-state analysis was utilized to investigate the role of dual-trajectory groups in shaping transition dynamics across each stage of CMM development. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates a gradual increase in mortality risk with the progression of CMM. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e further details the transition risks across trajectory groups. Compared with the low-increasing group, the high-amplitude fluctuation group exhibited significantly higher risks at all stages, including baseline to FCMD (HR: 1.39, 95% CI: 1.09\u0026ndash;1.78, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), FCMD to CMM (HR: 2.31, 95% CI: 1.16\u0026ndash;4.62, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017), CMM to death (HR: 3.45, 95% CI: 1.13\u0026ndash;10.51, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030), and FCMD to death (HR: 3.07, 95% CI: 1.50\u0026ndash;6.27, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). In contrast, the high-increasing group exhibited elevated risk primarily in the earlier stages, including baseline to FCMD (HR: 1.48, 95% CI: 1.27\u0026ndash;1.74, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and FCMD to CMM (HR: 1.80, 95% CI: 1.07\u0026ndash;3.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026), with no significant differences observed in subsequent mortality stages (CMM to death, HR: 1.17, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.776; FCMD to death, HR: 1.63, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.104).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between the dual-trajectory groups and CMM transition patterns.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransition pattern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eLow-increasing\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline \u0026rarr; FCMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCMD \u0026rarr; CMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMM \u0026rarr; Death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCMD \u0026rarr; Death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline \u0026rarr; Death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh-amplitude fluctuation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline \u0026rarr; FCMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39 (1.09\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCMD \u0026rarr; CMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.31 (1.16\u0026ndash;4.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMM \u0026rarr; Death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.45 (1.13\u0026ndash;10.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCMD \u0026rarr; Death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.07 (1.50\u0026ndash;6.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline \u0026rarr; Death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62 (0.79\u0026ndash;3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh-increasing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline \u0026rarr; FCMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48 (1.27\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCMD \u0026rarr; CMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.80 (1.07\u0026ndash;3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMM \u0026rarr; Death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (0.39\u0026ndash;3.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCMD \u0026rarr; Death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63 (0.90\u0026ndash;2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline \u0026rarr; Death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59 (0.98\u0026ndash;2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModels adjusted for baseline age, race, sex, body mass index, education, physical activity, systolic blood pressure, hypertension, antihypertensive medication use, smoking status, alcohol consumption and low-density lipoprotein cholesterol. Abbreviations as in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eIn participants whose baseline LDL-C was \u0026lt;\u0026thinsp;4.144 mmol/L, the risks for FCMD, CMM, and all-cause mortality remained significantly elevated in the high-amplitude fluctuation and high-increasing groups, in alignment with the primary analysis (Additional file 1: Table S5). Second, individuals on antihypertensive, cardiac, or both medications were excluded, and this exclusion did not alter the risk patterns observed across dual-trajectory groups(Additional file 1: Table S6). Furthermore, including baseline cancer patients in the analysis showed that elevated risks for FCMD, CMM, and all-cause mortality remained significant in high-amplitude fluctuation and high-increasing groups, underscoring the robustness of these findings(Additional file 1: Table S7).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Subgroup Analysis\u003c/h2\u003e \u003cp\u003eAfter stratifying participants sex, race, BMI, smoking status, lipid levels, and hypertension revealed that the link between dual-trajectory groups and outcomes remained similar (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For CMM risk, all subgroup interaction effects were non-significant except for the LDL-C subgroup, which showed a significant interaction (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). Similarly, subgroup analysis for all-cause mortality risk showed high stability, with non-significant interaction \u003cem\u003eP\u003c/em\u003e-values across all subgroups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, FCMD risk exhibited some variability within the BMI and race subgroups, with both showing significant interaction \u003cem\u003eP\u003c/em\u003e-values (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, based on data from a prospective cohort, we found that elevated baseline TyG and LAP levels in young adulthood were associated with increased risks of FCMD, CMM, and mortality in middle age. We also identified three distinct dual-trajectory groups for TyG and LAP levels in young adults: low-increasing, high-amplitude fluctuation, and high-increasing. We further found that high-amplitude fluctuation trajectories and high-increasing trajectories of TyG and LAP are significantly associated with the increased risk of FCMD, CMM, and mortality. Unlike traditional single-time measurements, trajectory patterns more intuitively capture the dynamic changes in metabolic status, emphasizing the cumulative impact of insulin resistance and lipid metabolism disorders.\u003c/p\u003e \u003cp\u003eOur findings align with previous studies that have shown elevated TyG and LAP levels to be predictive of cardiometabolic disorders[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. However, few studies have clarified the co-evolution patterns of these two markers. Given that each of these markers independently predicts cardiometabolic risk, exploring their combined trajectory is crucial to understanding the full extent of metabolic dysregulation. Our research identified a synchronous trend in the dual trajectories of TyG and LAP, which revealed a systemic nature of metabolic imbalance that went beyond single measurements. Several mechanisms may partially account for this synchronous trend. IR disrupts lipid and glucose metabolic pathways, leading to visceral fat accumulation, elevated circulating free fatty acids (FFA), and persistently high levels of pro-inflammatory cytokines like TNF-α and IL-6[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This metabolic dysregulation accelerates triglyceride and glucose production, thereby raising TyG levels, while FFA accumulation further promotes visceral fat deposition, increasing the LAP[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Additionally, chronic low-grade inflammation induced by IR exacerbates systemic lipid and glucose metabolic disturbances, reinforcing the link between TyG and LAP [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Addressing these disturbances in glucose and lipid metabolism may be crucial for interrupting this cycle and preventing CMM development. These findings underscore the combined impact of TyG and LAP, highlighting the importance of dynamic monitoring of blood glucose, lipids, and fat distribution for effective cardiometabolic health management.\u003c/p\u003e \u003cp\u003eNumerous studies have demonstrated a link between higher long-term trajectory of TyG and LAP and adverse cardiovascular outcomes [\u003cspan additionalcitationids=\"CR44 CR45 CR46 CR47\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. However, these studies have primarily concentrated on a single disease stage, without assessing the impact of long-term trajectory of TyG and LAP across various transition stages in the entire progression of CMM-namely, from being CMD-free to developing FCMD, progressing to CMM, and eventually leading to mortality. To overcome these limitations, we utilized a multi-state model that accounts for competing risks as well as transitions across different cardiometabolic stages. Our findings suggest that both the high-amplitude fluctuation and high-increasing groups could impact entire progression of CMM. Furthermore, we found distinct risk distribution patterns between the high-amplitude fluctuation and high-increasing groups. The high-amplitude fluctuation group presents a higher risk that intensifies in all stages of disease progression, while the high-increasing group has a greater impact on the earlier stages. This distinction is likely due to the instability, cumulative metabolic damage, and lack of gradual adaptation caused by metabolic fluctuations. A prospective cohort study revealed that revealed greater TyG variability were causally related to higher incidence of CVD[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The underlying mechanism between the high-amplitude fluctuation group and the overall progression of CMM is not fully understood, and we have hypothesized several plausible mechanisms. Frequent fluctuations result in significant changes in insulin resistance, blood glucose, and lipid levels, which place the cardiovascular system in a prolonged state of stress. Thereby the likelihood of systemic inflammation, oxidative stress, endothelial dysfunction and plaque instability is increased which raise the risk of CMM and all-cause mortality[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. With disease progression, cumulative metabolic damage increases, leading individuals in the high-amplitude fluctuation group to experience multiple fluctuation cycles, repeated stress, and metabolic disorders. Consequently, they are more prone to severe complications, organ failure, and a significantly higher risk of death[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In contrast, although the metabolic indicators of individuals in the high-increasing group continued to rise, the steady upward trend enabled the body to gradually adapt to this metabolic burden, reducing the accumulated inflammation and stress[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Therefore, the risk in the high-increasing group is mainly concentrated in the early stages when the cardiovascular system had not fully adapted to metabolic stress, and FCMD and CMM were more likely to occur. With disease progression, metabolic adaptation provides some protection in later stages, resulting in no significant difference in the progression from CMM to mortality or FCMD to mortality. This finding suggests that late-stage intervention has limited effects, emphasizing the importance of early intervention to address metabolic abnormalities and maintain a stable metabolic state, thereby reducing the risks of cardiometabolic disease and all-cause mortality. Adjusting lifestyle factors (e.g., diet management, increased exercise, stable daily routine) and pharmacological intervention to improve IR and reduce visceral fat accumulation may help delay the progression of cardiometabolic disease. Given the high metabolic fluctuation in some individuals, a single time-point intervention may be insufficient for long-term effects. Thus, a dynamic monitoring and individualized management approach is recommended to stabilize metabolic fluctuations and mitigate cumulative systemic stress.\u003c/p\u003e \u003cp\u003eOur analysis also indicates that individuals with higher baseline TyG and LAP levels, as well as those with high-increasing and high-amplitude fluctuation trajectories, display a significantly higher proportion of males, highlighting gender differences in metabolic trajectories. Males are more susceptible to fat accumulation in the visceral area, a pattern linked to lipid metabolism disorders and elevated LAP[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Additionally, androgens, such as testosterone, can promote visceral fat accumulation and increase pro-inflammatory factors, thus exacerbating IR and elevating TyG levels[\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Female estrogen, to a certain extent, inhibits the accumulation of visceral fat and plays a protective role[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Males also tend to consume a high-calorie diet, which can lead to visceral fat accumulation and metabolic burden[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. In response to environmental stress or dietary changes, males exhibit greater sensitivity to IR and inflammation, leading to higher metabolic fluctuations[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. These sex-specific metabolic responses and lifestyle differences jointly contribute to the higher likelihood of males following high-risk metabolic trajectories under increased metabolic load.\u003c/p\u003e \u003cp\u003eThis study has several limitations. Firstly, as an observational cohort study, although adjustments were made for multiple confounding factors, some unmeasured or unknown factors might still introduce potential bias, which could affect causal interpretations. Additionally, since data were drawn from a specific cohort, the sample population is relatively homogeneous, possibly affecting the generalizability of the findings; populations from other regions, ethnic backgrounds, or health statuses may display different metabolic patterns. Moreover, dynamic data on lifestyle changes and dietary habits, which could influence metabolic indicators, were not available for analysis. Similarly, the absence of additional metabolic and inflammatory markers (e.g., CRP, IL-6) may have limited a more comprehensive examination of metabolic health. Overall, these considerations suggest that future studies could benefit from verifying findings across diverse populations and incorporating more comprehensive biomarker and lifestyle data to further elucidate the relationship between TyG and LAP trajectories and cardiometabolic multimorbidity risks.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates that higher TyG and LAP levels in early adulthood are associated with an increased risk of FCMD, CMM, and mortality by midlife. Additionally, chronic exposure to elevated and fluctuating TyG and LAP levels in young adulthood is associated with increased CMM risk, with fluctuating TyG and LAP levels showing higher risks across all stages of CMM development, while consistently high levels primarily impact earlier stages. These findings emphasize the critical role of early intervention and sustained monitoring of insulin resistance and lipid accumulation to mitigate long-term cardiometabolic risks.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTyG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglyceride-glucose index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLAP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLipid Accumulation Product\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCHD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronary Heart Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eT2D\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eType 2 Diabetes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCMDs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiometabolic Diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCMM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiometabolic Multimorbidity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFCMD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFirst Cardiometabolic Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCARDIA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronary Artery Risk Development in Young Adults\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCVD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSBP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystolic Blood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDBP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiastolic Blood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist Circumference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHDL-C\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-Density Lipoprotein Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLDL-C\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-Density Lipoprotein Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFPG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFasting Plasma Glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCRP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-Reactive Protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInsulin Resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, Yinyin Zhang, upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the staff and participants of the CARDIA (Coronary Artery Risk Development in Young Adults) study for their contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Youth Fund of the National Natural Science Foundation of China, Grant/Award Number: 81900443.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLingqu Zhou, Junjie Wang and Zirui Zhou have contributed equally to this work and are co-first authors.\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eDepartment of Ultrasonography and Electrocardiograms, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat‑sen University Cancer Center, Guangzhou, China\u003cbr\u003e\u0026nbsp;Lingqu Zhou, Liangjiao Wang,\u0026nbsp;Hui Zeng \u0026amp; Ziyue Zhong\u003c/p\u003e\n\u003cp\u003eDepartment of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China\u003cbr\u003e\u0026nbsp;Zirui Zhou, Qi Guo \u0026amp; Yinyin Zhang\u003c/p\u003e\n\u003cp\u003eDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China\u003c/p\u003e\n\u003cp\u003eJunjie Wang\u003c/p\u003e\n\u003cp\u003eGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China\u003c/p\u003e\n\u003cp\u003eJunjie Wang\u003c/p\u003e\n\u003cp\u003eContributions\u003c/p\u003e\n\u003cp\u003eYYZ had full access to all of the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. YYZ, LQZ and JJW contributed to the study design and inception. LQZ, JJW, and ZRZ contributed to the acquisition, analysis, interpretation of data, and drafted the manuscript. QG and LJW contributed to the analysis of the data and interpretation. HZ and ZYZ revised the manuscript. All authors provided a revision of the manuscript for critically important intellectual content and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003eCorresponding author\u003c/p\u003e\n\u003cp\u003eCorrespondence to\u0026nbsp;Yinyin Zhang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study was performed according to the guidelines of the Helsinki Declaration and was approved by the Institutional Review Board at Sun Yat-sen Memorial Hospital. Written informed consent was obtained from all participants for data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSkou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, Boyd CM, Pati S, Mtenga S, Smith SM. Multimorbidity. Nat Rev Dis Primers. 2022;8(1):48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalisbury C. Multimorbidity: redesigning health care for people who use it. Lancet. 2012;380(9836):7\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Angelantonio E, Kaptoge S, Wormser D, Willeit P, Butterworth AS, Bansal N, O'Keeffe LM, Gao P, Wood AM, Burgess S, et al. 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Combined lifestyle factors and cardiovascular disease mortality in Chinese men and women: the Singapore Chinese health study. Circulation. 2011;124(25):2847\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan Y, Hu Y, Yu C, Guo Y, Pei P, Yang L, Chen Y, Du H, Sun D, Pang Y, et al. Lifestyle, cardiometabolic disease, and multimorbidity in a prospective Chinese study. Eur Heart J. 2021;42(34):3374\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuerrero-Romero F, Simental-Mend\u0026iacute;a LE, Gonz\u0026aacute;lez-Ortiz M, Mart\u0026iacute;nez-Abundis E, Ramos-Zavala MG, Hern\u0026aacute;ndez-Gonz\u0026aacute;lez SO, Jacques-Camarena O, Rodr\u0026iacute;guez-Mor\u0026aacute;n M. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab. 2010;95(7):3347\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCutter GR, Burke GL, Dyer AR, Friedman GD, Hilner JE, Hughes GH, Hulley SB, Jacobs DR, Liu K, Manolio TA. Cardiovascular risk factors in young adults. The CARDIA baseline monograph. Control Clin Trials 1991, 12(1 Suppl).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQiao T, Luo T, Pei H, Yimingniyazi B, Aili D, Aimudula A, Zhao H, Zhang H, Dai J, Wang D. Association between abdominal obesity indices and risk of cardiovascular events in Chinese populations with type 2 diabetes: a prospective cohort study. Cardiovasc Diabetol. 2022;21(1):225.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkwuosa TM, Greenland P, Burke GL, Eng J, Cushman M, Michos ED, Ning H, Lloyd-Jones DM. Prediction of coronary artery calcium progression in individuals with low Framingham Risk Score: the Multi-Ethnic Study of Atherosclerosis. JACC Cardiovasc Imaging. 2012;5(2):144\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaddu DR, Rana JS, Murillo R, Sorel ME, Quesenberry CP, Allen NB, Gabriel KP, Carnethon MR, Liu K, Reis JP, et al. 25-Year Physical Activity Trajectories and Development of Subclinical Coronary Artery Disease as Measured by Coronary Artery Calcium: The Coronary Artery Risk Development in Young Adults (CARDIA) Study. Mayo Clin Proc. 2017;92(11):1660\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo Y, He L, Ma T, Li J, Bai Y, Cheng X, Zhang G. Associations between consumption of three types of beverages and risk of cardiometabolic multimorbidity in UK Biobank participants: a prospective cohort study. BMC Med. 2022;20(1):273.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan Y, Hu Y, Yu C, Guo Y, Pei P, Yang L, Chen Y, Du H, Sun D, Pang Y, et al. Lifestyle, cardiometabolic disease, and multimorbidity in a prospective Chinese study. Eur Heart J. 2021;42(34):3374\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdams HP, Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL, Marsh EE. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke. 1993;24(1):35\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEaston JD, Saver JL, Albers GW, Alberts MJ, Chaturvedi S, Feldmann E, Hatsukami TS, Higashida RT, Johnston SC, Kidwell CS, professionals from the American Heart Association/American Stroke Association Stroke Council; Council on Cardiovascular Surgery, the Interdisciplinary Council on Peripheral Vascular Disease. The American Academy of Neurology affirms the value of this statement as an educational tool for neurologists. Stroke. 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Metabolites as regulators of insulin sensitivity and metabolism. Nat Rev Mol Cell Biol. 2018;19(10):654\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan G, Li F, Elia C, Zhao Y, Wang J, Chen Z, Yuan H, Lu Y. Association of lipid accumulation product trajectories with 5-year incidence of type 2 diabetes in Chinese adults: a cohort study. Nutr Metab (Lond). 2019;16:72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmouzegar A, Honarvar M, Masoumi S, Tohidi M, Mehran L, Azizi F. Sex-specific Trajectories of Insulin Resistance Markers and Reduced Renal Function During 18 Years of Follow-up: TLGS. J Clin Endocrinol Metab. 2023;108(6):e230\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan Y, Wang D, Sun Y, Ma Q, Wang K, Liao Y, Chen C, Jia H, Chu C, Zheng W, et al. Triglyceride-glucose index trajectory and arterial stiffness: results from Hanzhong Adolescent Hypertension Cohort Study. 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Environ Int. 2024;188:108780.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Triglyceride-glucose index, Lipid accumulation product, Dual-trajectory, Cardiometabolic multimorbidity, Cohort study","lastPublishedDoi":"10.21203/rs.3.rs-5436679/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5436679/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInsulin resistance and central obesity are major risk factors for cardiometabolic diseases. The triglyceride-glucose index (TyG) and lipid accumulation product (LAP) are markers that independently predict cardiometabolic risk. However, their combined long-term trajectories and impact on cardiometabolic multimorbidity (CMM) development remain unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cohort study utilized data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, which tracked 3,467 participants at baseline. Dual-trajectory of TyG and LAP were identified using a group-based dual-trajectory model. Cox proportional hazards models were employed to assess the relationships between dual-trajectory groups and primary cardiometabolic outcomes, including first cardiometabolic disease (FCMD), CMM (two or more conditions such as type 2 diabetes, coronary heart disease, or stroke), and all-cause mortality. Multi-state models were performed to assess the associations of dual-trajectory with CMM development.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 3,467 participants with a mean age of 25.08 years (SD\u0026thinsp;=\u0026thinsp;3.59). Of these, 43.4% (n\u0026thinsp;=\u0026thinsp;1,505) were male, and 53.2% (n\u0026thinsp;=\u0026thinsp;1,561) were White. Three distinct dual-trajectory groups were identified: low-increasing (61.5%), high-amplitude fluctuation (7.6%), and high-increasing (30.9%). After multivariate adjustment, compared with the low-increasing group, the high-amplitude fluctuation group exhibited significantly higher risks for FCMD (hazard ratio [HR] 1.38, 95% confidence interval [CI]: 1.08\u0026ndash;1.77), CMM (HR 2.63, 95% CI: 1.21\u0026ndash;5.71), and all-cause mortality (HR 2.16, 95% CI: 1.30\u0026ndash;3.56), as well as elevated risks for transitions from baseline to FCMD (HR: 1.39, 95% CI: 1.09\u0026ndash;1.78), FCMD to CMM (HR: 2.31, 95% CI: 1.16\u0026ndash;4.62), CMM to death (HR: 3.45, 95% CI: 1.13\u0026ndash;10.51). The high-increasing group showed similar results.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eElevated and fluctuating trajectories of TyG and LAP from early adulthood are associated with increased risks of CMM development in midlife.\u003c/p\u003e","manuscriptTitle":"Elevated and fluctuating TyG and LAP trajectories are associated with cardiometabolic multimorbidity development in midlife: the CARDIA study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 09:19:53","doi":"10.21203/rs.3.rs-5436679/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-16T05:34:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-16T05:16:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-11T21:47:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-07T21:32:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-07T04:48:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170004927354488332900545787436463804141","date":"2024-11-24T00:15:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77873665840525647945146302276461477091","date":"2024-11-23T15:24:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300671971851462335940363718003905690589","date":"2024-11-23T04:00:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-22T17:13:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146438090835669848778617610413567601989","date":"2024-11-22T09:03:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220721971489844550536300673387069382592","date":"2024-11-21T22:42:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-21T19:52:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-12T07:20:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-12T06:38:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2024-11-12T06:17:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9ed656e4-f6ec-4c09-ab52-29e04346ebe6","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-12T16:03:26+00:00","versionOfRecord":{"articleIdentity":"rs-5436679","link":"https://doi.org/10.1186/s12933-025-02761-1","journal":{"identity":"cardiovascular-diabetology","isVorOnly":false,"title":"Cardiovascular Diabetology"},"publishedOn":"2025-05-09 15:57:04","publishedOnDateReadable":"May 9th, 2025"},"versionCreatedAt":"2024-12-17 09:19:53","video":"","vorDoi":"10.1186/s12933-025-02761-1","vorDoiUrl":"https://doi.org/10.1186/s12933-025-02761-1","workflowStages":[]},"version":"v1","identity":"rs-5436679","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5436679","identity":"rs-5436679","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}