Skip to main content

Association of lipid accumulation product trajectories with 5-year incidence of type 2 diabetes in Chinese adults: a cohort study



Lipid accumulation product (LAP) is an index describing the overaccumulation of lipid. Baseline LAP was used for type 2 diabetes (T2D) prediction in previous studies. But the longitudinal trajectories of LAP, which reflect the efficacy of patients’ lipid-lowering treatment and lifestyle improvement, have rarely been studied. The aim of this study is to explore the association of lipid accumulation product trajectories with 5-year incidence of type 2 diabetes.


This cohort study included 4508 non-diabetic participants with a median age of 42 years. Using the group-based trajectory modeling (GBTM), LAP from 2011 to 2016 were determined and identified as three trajectories: low (n = 3639), moderate (n = 800), and high (n = 69). Baseline LAP was divided into groups by percentiles and tertiles respectively for the comparison of LAP trajectories. The associations between 5-year T2D incidence and LAP trajectories and baseline LAP were both assessed by generalized linear models.


From 2011 to 2016, 169 participants developed T2D (the 5-year incidence of 3.8%). For participants with low, moderate, and high trajectories, the incidence of T2D was 2.1, 10.0, and 15.9%, respectively. A significant trend was observed in the relative risks (RRs) of 5-year incident T2D in participants with moderate (RR, 1.95; 95% CI: 1.41–2.70) and high LAP trajectory (RR, 2.20; 95% CI: 1.12–4.30) in the fully adjusted model (p for trend< 0.001). However, there were no statically significant trends in RRs in different tertiles of baseline LAP found after full adjustments.


The trajectories of LAP has an independent effect on 5-year T2D incidence beyond LAP measured at baseline.


Dysfunctions in adipose tissue metabolism have a direct impact on lipid and glucose homeostasis [1]. In the latest decades, a view emerged that type 2 diabetes (T2D) may reflect the complex metabolic consequences caused by the overaccumulation of ectopic lipids or hepatic fat [2,3,4]. And multiple epidemiological surveys have revealed that waist circumference, a routine measurement for abdominal obesity, along with hypertriglyceridemia are risk factors of T2D [5,6,7,8].

Therefore, lipid accumulation product (LAP) which calculated by waist circumference (WC) and fasting triglycerides (TG) concentration is proposed to describe lipid overaccumulation [9] and has been found higher in diabetic patients [10, 11]. Studies revealed that LAP had the potentials to predicting the risk of diseases such as cardiovascular disease (CVD) [12,13,14], non-alcoholic fatty liver disease [15] and metabolic syndrome [16, 17] in some healthy populations. Furthermore, LAP had been proved to be a strong predictor of diabetes occurrence in non-diabetic populations [18, 19].

Multiple cross-sectional studies [10, 11, 20], as well as some cohorts [21,22,23], have been conducted to investigate the associations between LAP and diabetes, but only baseline LAP was studied. The longitudinal trajectories of LAP, which may reflect the long-term effect of lipid overaccumulation, had not been reported yet, especially when concerning its association of T2D incidence. The aim of this cohort study was to explore the association of LAP trajectories with the 5-year incidence of T2D in a Chinese population.


Study population

In this study, we retrospectively recruited 5004 participants over 18 years old from the institutions and had completed a medical examination annually from 2011 to 2016 in the Health Management Center of Third Xiangya Hospital, which is one of the largest medical examination centers in China. The exclusion criteria (Fig. 1) were as follows: 1) participants with missing data on the diagnosis of diabetes or confirmed diagnosis of diabetes in 2011, 2) participants with missing data on the diagnosis of diabetes in 2016, 3) participants with less than two records of both WC and TG.

Fig. 1
figure 1

A flow diagram of participant screening and enrollment

The present study was in accordance with the guidelines of the Declaration of Helsinki and was approved by the Medical Ethics Committee of Third Xiangya Hospital. All the participants have signed an informed consent form and agreed to share their health information for medical research.

Diagnosis of type 2 diabetes

Blood samples from all participants were collected in the morning after overnight fasting for 8–12 h and then stored at EDTA-containing vacuum tubes. All blood samples were stored in freezers at − 20 °C until analyzed. The concentration of fasting blood glucose (FBG) was measured by enzymatic colorimetric assay using an automated analyzer (Hitachi 7600–110; Hitachi, Tokyo, Japan) at the central laboratory of the Third Xiangya Hospital. T2D was defined by the presence of any of the following: 1) self-reported doctor-diagnosed type 2 diabetes, 2) current use of insulin or oral hypoglycemic agents, and 3) fasting blood glucose ≥7.0 mmol/L [24].

Assessment of LAP

WC was measured at the navel level using an un-stretched tape measure and without applying any pressure to the body surface [15]. The concentration of fasting TG was measured from the same blood samples and by the same automated analyzer mentioned above. LAP was calculated using the formula [WC (cm) – 65] × TG concentration (mmol/L) for men, and [WC (cm) – 58] × TG concentration (mmol/L) for women [9].

Assessment of other potential covariates

All demographic data (age, sex, race, and marital status), lifestyle factors (cigarette smoking and alcohol consumption), and medical history were obtained by well-trained interviewers using standardized questionnaires. Eleven racial groups, including Han, Miao, Tujia, Hui, Zhuang, Bai, Dong, Man, Yao, Mongolian, and Xibo, were identified in our study and divided into two categories: Han and all other minorities. Marital status was defined as married and unmarried. And all widowed or divorced subjects were defined as married.

Bodyweight and height were measured to the nearest 0.1 kg or 0.1 cm, with participants wearing light clothes and no shoes. Body mass index (BMI) was calculated as body weight in kilograms (kg) divided by the square of height in meters (m2). Blood pressure (BP) was measured from participants in a sitting position after a 10-min rest. Using a corrected mercury sphygmomanometer, two readings were obtained with an interval of 30 s for both systolic and diastolic BP. The mean of the two readings was considered as the participant’s BP. If the two readings differed by > 5 mmHg, BP was re-measured, and the participant’s BP was finally calculated as the average of the three readings. Hypertension was defined as 1) systolic BP ≥140 mmHg and/or diastolic BP ≥ 90 mmHg, 2) self-reported or doctor-diagnosed hypertension, 3) current use of antihypertensive agents [25].

All the other biochemical measurements, including total cholesterol, hemoglobin, creatinine, and uric acid, were measured from the same blood samples and by the same automated analyzer mentioned above [15, 25].

Statistical analysis

All statistical analyses were conducted using Stata version 14.0 (Stata Corp., College Station, TX). The LAP trajectories were formed by group-based trajectory modeling (GBTM), which we also fitted in conjunction with the user-written “traj” program in Stata [26, 27]. GBTM is considered to be a potential growth curve analysis that identifies individuals with a similar trajectory that tracks a single indicator [26]. LAP was modeled by a censored normal distribution. The model with the best fitness was chosen by the following steps [28]: 1) choose an optimal number of trajectories from models with the same shapes but different numbers of trajectories. 2) adjust the shape orders of the model with the optimal number of trajectories and then choose the best-fit model, 3) draw a plot of trajectories from the best-fit model and identify the trajectories. The number and shapes of the LAP trajectories (i.e., linear, quadratic, and cubic) were chosen by following criteria: a) highest value of Bayesian Information Criterion, b) high posterior probability (> 0.7), c) enough membership in allocated groups (at least 1%). A plot of trajectories will be drawn for the best-fit model, and trajectories will be identified according to their different levels.

The baseline characteristics of this study were summarized as the median (interquartile range) for continuous variables and the number (percentage) for categorical variables. The Kruskal-Wallis test for continuous variables and the chi-square test for categorical variables were used to compare to the baseline characteristics of the patients with different LAP trajectories.

Missing values of potential covariables that occurred during the investigation were processed by multiple imputations with chained equations [29]. Thirty imputed datasets were generated for the imputed models since approximately 30% of the study samples had missing values in at least one covariable [29]. And all variables of this study were added into the imputation models in order to achieve better imputation [29].

To compare with the LAP trajectories, baseline LAP was divided into two methods: tertiles and percentiles. Percentiles of baseline LAP was decided by the proportions of LAP trajectories (e.g., if the proportion of LAP trajectories was 20, 40, and 40%, the percentiles groups should be P0 - P20, P20 - P60, and P60 - P100). Baseline LAP and LAP trajectories were analyzed simultaneously to determine the associations with 5-year T2D. The relative risk (RR) of new-onset diabetes in 2016 was used as the parameter of interest to correct for the overestimation in estimates of effect observed with odds ratios when the outcome rate exceeds 10% [30]. Therefore, generalized linear models (GLM) with log-link function, Poisson distributions, and robust error variance were used in this study for yielding RR estimates [31]. The goodness-of-fit was measured as averaged Akaike information criterion (AIC) of GLM models in 30 imputed datasets [32], and analysis of variance (ANOVA) was used to examine the differences among GLM models.


Baseline characteristics by three trajectories of LAP

Totally 4508 participants (54.1% males) were enrolled in the study with a median age of 42.0 years old. The baseline characteristics of the participants are presented in Table 1. The mean BMI and LAP in all participants was 23.4 kg/m2 and 19.7 respectively. And the average fasting blood concentrations of glucose, total cholesterol (TC) triglycerides (TG) and high-density lipoprotein cholesterol (HDL) was 5.0, 4.7, 1.14 and 1.40 mmol/L respectively (Table 1).

Table 1 Baseline characteristics according to LAP trajectories

After the examination of all fitting results (Additional file 1: Tables S1 and S2), a best-fit model with three trajectories is determined and identified as low, moderate, and high. From 2011 to 2016, the low and moderate LAP trajectory presented a slight elevation, while the high LAP trajectory rose at first and then declined (Fig. 2). Models with 2 or 4 trajectories were omitted since they were either poor in Bayesian Information Criterion or lacked subjects in the trajectory group.

Fig. 2
figure 2

Three LAP trajectories determined by the best-fit model from 2011 to 2016

Participants in low trajectory group (80.7% of participants were in this group), compared to those in moderate (17.8%) or high trajectory group (1.5%), were less likely to be male, had lower BMI at the baseline, and were not prone to be current smoker nor alcohol drinker nor hypertensive. Higher FBG, TC, TG, creatinine and uric acid and lower HDL were found in moderate or high trajectory group, compared with low trajectory group.

Association between the 5-year incidence of T2D and LAP trajectories

From 2011 to 2016, 169 participants developed T2D, and the overall incidence of T2D was 3.8% (Table 2). The incidence of T2D progressively increased at higher levels of LAP trajectories. For participants with low, moderate and high trajectories, the incidence of T2D was 2.1, 10.0, and 15.9%, respectively (Table 2).

Table 2 Relative risk and 95% confidence interval for the 5-year incidence of type 2 diabetes according to LAP trajectories and baseline LAP

Compared with the low trajectory, significant trends were observed in RRs of 5-year incident T2D in participants with moderate (RR, 2.56; 95% confidence interval [CI]: 1.78–3.70) and high LAP trajectories (RR, 5.20; 95% CI: 2.77–9.76), respectively (p for trend < 0.001) in GLM adjusted for sociodemographic variables, behavioral factors, hypertension, lipid profile, hemoglobin, creatinine and uric acid (Table 2, Model 2). These associations remained significant after further adjustment for baseline FBG and family histories of diabetes. The RR for moderate trajectory was 1.95 (95% CI: 1.41–2.70) and for high trajectory was 2.20 (95% CI: 1.12–4.30) compared with the low trajectory (p for trend < 0.001) (Table 2, Model 3). The goodness-of-fit was improved from model 1 to model 3 (Additional file 1: Table S3).

Association between the 5-year incidence of T2D and baseline LAP

GLM was performed in groups determined by percentiles (P80.6 and P98.5) and tertiles according to baseline LAP. After adjusted for sociodemographic variables, behavioral factors, hypertension, lipid profile, hemoglobin, creatinine and uric acid (Table 2, Model 2), a significant trend (p for trend < 0.001) in relative risks for 5-year incident T2D over follow-up was observed in second group (RR, 2.56; 95% CI: 1.76–3.73) and third group (RR, 2.61; 95% CI: 1.15–5.95), compared with the first group respectively. And a significant trend (p for trend < 0.001) in relative risks was also found in second tertile (RR, 1.04; 95% CI: 0.52–2.07) and third tertile (RR, 2.93; 95% CI: 1.52–5.65), compared with the first tertile group (Table 2, Model2). However, significant associations between baseline LAP and 5-year incident T2D disappeared after further adjustment for baseline blood glucose and family history of diabetes (p for trend was 0.006 and 0.007 respectively) (Table 2, Model3). The goodness-of-fit was also improved from model 1 to model 3 (Additional file 1: Table S3).


In the current cohort study, we found that higher LAP trajectories were associated with an increased risk of diabetes. Compared to baseline LAP, trajectories of longitudinal LAP had a stronger relationship with incident T2D, especially after adjusting for more confounders.

Overaccumulation of ectopic lipids or hepatic fat causes serials of complex metabolic consequences [2,3,4] such as non-alcoholic fatty liver disease and insulin resistance, which will finally promote the development of diabetes. Recent studies have focused on diabetic correlations in longitudinal changes in multiple indices reflecting obesity and lipid metabolism, such as BMI [33, 34], visceral adiposity index [35], and lipids [36]. LAP combines waist measurements and fasting triglyceride (TG) levels, reflecting both the anatomic and physiological changes associated with lipid overaccumulation. LAP has been confirmed to be closely associated with diabetes, metabolic syndrome, and cardiovascular diseases, and outperformed BMI for identifying these diseases [9, 11, 18, 19]. Accordingly, it is plausible that the longitudinal change of LAP is significantly associated with T2D.

The association between baseline LAP and the incidence of diabetes has been investigated in several different ethnic cohorts. A cohort study of 4083 Chinese subjects indicated that higher LAP was associated with increased RR of diabetes (p for trend < 0.05 from Q1 to Q4 in both male and female) [22]. A Korean cohort study reported that the adjusted ORs for the highest quartiles of LAP, compared to the lowest, was 2.47 (95% CI: 1.82–3.34) in men and 2.44 (95% CI: 1.82–3.26) in women respectively [22]. The result in a 6-year follow-up study of Tehran suggested that OR for incident diabetes with 1 standard deviation increment (log-scale) of LAP was 2.3 (95% CI: 1.8–3.0) for males and 3.2 (95% CI: 2.6–4.1) for females at the age of 20–49 [19]. Similar results had also been reported in Korea [20], Montenegro [37], Italy [38], Germany [21], and the United States [9].

Though the association between baseline LAP and diabetic incidence has been studied, little was known about how the longitudinal effect of LAP trajectories on T2D incidence. The longitudinal level of LAP, as well as the risk of diabetes, are both influenced by many factors. In our current study, moderate (81.0%) and high (97.1%) LAP trajectory consisted of a great majority of males with a higher incidence of diabetes. Similar to this result, Wakabayashi et al. reported that prevalence of high LAP was higher in males than in females before the age of 50 (males vs. females, 34.4% vs. 40.8% at age of 35–39 years; 13.1% vs. 18.1% at age of 40–49 years), but this difference was narrowed by aging, even leading a reversed result in the age group of 60–70 years (38.4% vs. 35.6%) [39]. Besides, elevated LAP in postmenopausal women with diabetes, considering the decline of estrogen, would lead to fat redistribution [21]. Therefore, only baseline LAP may not be sufficient to reflect changes in the longitudinal LAP and its relationship to incident diabetes. In our study, the association between LAP trajectories and T2D incidence was firstly reported that participants with moderate and high LAP trajectories were at higher risks of T2D, compared to those in low trajectories. Our study indicated that compared with higher baseline LAP, higher longitudinal trajectories of LAP were more closely associated with T2D incidence, which implied the insufficiency in focusing baseline LAP alone.

The strengths of our study included a longitudinal design, large-sample size with repeated LAP values over time and the use of GBTM. As a novel index, LAP is radiation-free, simple, cheap, and easy to perform, compared with CT (computed tomography) and MRI (magnetic resonance imaging) for body fat measurement [18]. However, this study has some limitations. Firstly, lacking the data of using oral glucose tolerance tests to diagnose diabetes may lead to underestimation of diabetes events. Secondly, due to the relatively small sample size, further analyses with stratification by gender (only two females in high trajectory group) couldn’t be conducted.


Our study identified three distinct trajectories (low, moderate, and high) of LAP and the trajectories of LAP has an independent effect on T2D incidence beyond LAP measured at baseline. The results suggest that the longitudinal trajectories of LAP may be an important risk factor of diabetes.

Availability of data and materials

The datasets analyzed during the current study are available from the corresponding author on reasonable request.



Akaike information criterion


Analysis of variance


Body mass index


Blood pressure


Confidence interval


Computed tomography


Cardiovascular disease


Fasting blood glucose


Group-based trajectory modeling


Generalized linear model


High density lipoprotein


Hazard ratio


Lipid accumulation product


Magnetic resonance imaging


Odds ratio


Relative risk


Type 2 diabetes


Total cholesterol




Waist circumference


  1. Guilherme A, Virbasius JV, Puri V, Czech MP. Adipocyte dysfunctions linking obesity to insulin resistance and type 2 diabetes. Nat Rev Mol Cell Biol. 2008;9:367–77.

    Article  CAS  Google Scholar 

  2. Unger RH. Reinventing type 2 diabetes: pathogenesis, treatment, and prevention. JAMA. 2008;299:1185–7.

    Article  CAS  Google Scholar 

  3. Sattar N, Wannamethee SG, Forouhi NG. Novel biochemical risk factors for type 2 diabetes: pathogenic insights or prediction possibilities? Diabetologia. 2008;51:926–40.

    Article  CAS  Google Scholar 

  4. Kahn HS, Cheng YJ, Thompson TJ, Imperatore G, Gregg EW. Two risk-scoring systems for predicting incident diabetes mellitus in U.S. adults age 45 to 64 years. Ann Intern Med. 2009;150:741–51.

    Article  Google Scholar 

  5. Cassano PA, Rosner B, Vokonas PS, Weiss ST. Obesity and body fat distribution in relation to the incidence of non-insulin-dependent diabetes mellitus: a prospective cohort study of men in the normative aging study. Am J Epidemiol. 1992;136:1474–86.

    Article  CAS  Google Scholar 

  6. Schulze MB, Heidemann C, Schienkiewitz A, Bergmann MM, Hoffmann K, Boeing H. Comparison of anthropometric characteristics in predicting the incidence of type 2 diabetes in the EPIC-Potsdam study. Diabetes Care. 2006;29:1921–3.

    Article  Google Scholar 

  7. Yang W, Lu J, Weng J, Jia W, Ji L, Xiao J, et al. Prevalence of diabetes among men and women in China. N Engl J Med. 2010;362:1090–101.

    Article  CAS  Google Scholar 

  8. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14:88–98.

    Article  Google Scholar 

  9. Kahn HS. The “lipid accumulation product” performs better than the body mass index for recognizing cardiovascular risk: a population-based comparison. BMC Cardiovasc Disord. 2005;5:26.

    Article  Google Scholar 

  10. Shen YY, Chen JC, Li G, Cao J, Li JX, Huang JF, et al. Relationship of lipid accumulation product with hypertension and diabetes among Beijing residents study. Chin J Prev Med. 2017;51:415–20.

    CAS  Google Scholar 

  11. Wakabayashi I, Daimon T. A strong association between lipid accumulation product and diabetes mellitus in Japanese women and men. J Atheroscler Thromb. 2014;21:282–8.

    Article  Google Scholar 

  12. Hosseinpanah F, Barzin M, Mirbolouk M, Abtahi H, Cheraghi L, Azizi F. Lipid accumulation product and incident cardiovascular events in a normal weight population: Tehran lipid and glucose study. Eur J Prev Cardiol. 2016;23:187–93.

    Article  Google Scholar 

  13. Kyrou I, Panagiotakos DB, Kouli GM, Georgousopoulou E, Chrysohoou C, Tsigos C, et al. Lipid accumulation product in relation to 10-year cardiovascular disease incidence in Caucasian adults: the ATTICA study. Atherosclerosis. 2018;279:10–6.

    Article  CAS  Google Scholar 

  14. Song J, Zhao Y, Nie S, Chen X, Wu X, Mi J. The effect of lipid accumulation product and its interaction with other factors on hypertension risk in Chinese Han population: a cross-sectional study. PLoS One. 2018;13:e0198105.

    Article  Google Scholar 

  15. Dai H, Wang W, Chen R, Chen Z, Lu Y, Yuan H. Lipid accumulation product is a powerful tool to predict non-alcoholic fatty liver disease in Chinese adults. Nutr Metab. 2017;14:49.

    Article  Google Scholar 

  16. Nascimento-Ferreira MV, Rendo-Urteaga T, Vilanova-Campelo RC, Carvalho HB, da Paz OG, Paes Landim MB, et al. The lipid accumulation product is a powerful tool to predict metabolic syndrome in undiagnosed Brazilian adults. Clin Nutr. 2017;36:1693–700.

    Article  CAS  Google Scholar 

  17. Ma CM, Lu N, Wang R, Liu XL, Lu Q, Yin FZ. Three novel obese indicators perform better in monitoring management of metabolic syndrome in type 2 diabetes. Sci Rep. 2017;7:9843.

    Article  Google Scholar 

  18. Kahn HS. The lipid accumulation product is better than BMI for identifying diabetes: a population-based comparison. Diabetes Care. 2006;29:151–3.

    Article  Google Scholar 

  19. Bozorgmanesh M, Hadaegh F, Azizi F. Diabetes prediction, lipid accumulation product, and adiposity measures; 6-year follow-up: Tehran lipid and glucose study. Lipids Health Dis. 2010;9:45.

    Article  Google Scholar 

  20. Oh JY, Sung YA, Lee HJ. The lipid accumulation product as a useful index for identifying abnormal glucose regulation in young Korean women. Diabet Med. 2013;30:436–42.

    Article  CAS  Google Scholar 

  21. Wehr E, Pilz S, Boehm BO, März W, Obermayer-Pietsch B. The lipid accumulation product is associated with increased mortality in normal weight postmenopausal women. Obesity. 2011;19:1873–80.

    Article  CAS  Google Scholar 

  22. Lee JW, Lim NK, Park HY. The product of fasting plasma glucose and triglycerides improves risk prediction of type 2 diabetes in middle-aged Koreans. BMC Endocr Disord. 2018;18:33.

    Article  Google Scholar 

  23. Yang C, Guo ZR, Hu XS, Zhou ZY, Wu M. A prospective study on the association between lipid accumulation product or body mass index and diabetes. Chin J Epidemiol. 2010;31:5–8.

    Google Scholar 

  24. Sun L, Liang L, Gao X, Zhang H, Yao P, Hu Y, et al. Early prediction of developing type 2 diabetes by plasma acylcarnitines: a population-based study. Diabetes Care. 2016;39:1563–70.

    Article  Google Scholar 

  25. Dai H, Wang W, Tang X, Chen R, Chen Z, Lu Y, et al. Association between homocysteine and non-alcoholic fatty liver disease in Chinese adults: a cross-sectional study. Nutr J. 2016;15:102.

    Article  Google Scholar 

  26. Nagin DS, Jones BL, Passos VL, Tremblay RE. Group-based multi-trajectory modeling. Stat Methods Med Res. 2018;27:2015–23.

    Article  Google Scholar 

  27. Jones BL, Nagin DS. A note on a Stata plugin for estimating group-based trajectory models. Sociol Methods Res. 2013;42:608–13.

    Article  Google Scholar 

  28. Nagin DS. Group-based modeling of development. Cambridge: Harvard University Press; 2005. p. 61–77.

    Book  Google Scholar 

  29. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30:377–99.

    Article  Google Scholar 

  30. Greenland S. Model-based estimation of relative risks and other epidemiologic measures in studies of common outcomes and in case-control studies. Am J Epidemiol. 2004;160:301–5.

    Article  Google Scholar 

  31. Zou G. A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159:702–6.

    Article  Google Scholar 

  32. Heraud-Bousquet V, Lot F, Esvan M, Cazein F, Laurent C, Warszawski J, et al. A three-source capture-recapture estimate of the number of new HIV diagnoses in children in France from 2003-2006 with multiple imputation of a variable of heterogeneous catchability. BMC Infect Dis. 2012;12:251.

    Article  Google Scholar 

  33. Chiu CJ, Li SL, Wu CH, Du YF. BMI trajectories as a harbinger of pre-diabetes or underdiagnosed diabetes: an 18-year retrospective cohort study in Taiwan. J Gen Intern Med. 2016;31:1156–63.

    Article  Google Scholar 

  34. Peter RS, Keller F, Klenk J, Concin H, Nagel G. Body mass trajectories, diabetes mellitus, and mortality in a large cohort of Austrian adults. Medicine. 2016;95:e5608.

    Article  Google Scholar 

  35. Zhang M, Zheng L, Li P, Zhu Y, Chang H, Wang X, et al. 4-year trajectory of visceral adiposity index in the development of type 2 diabetes: a prospective cohort study. Ann Nutr Metab. 2016;69:142–9.

    Article  CAS  Google Scholar 

  36. Tsai CW, Huang HC, Chiang HY, Chung CW, Chang SN, Chu PL, et al. Longitudinal lipid trends and adverse outcomes in patients with CKD: a 13-year observational cohort study. J Lipid Res. 2019;60:648–60.

    Article  CAS  Google Scholar 

  37. Kavaric N, Klisic A, Ninic A. Are visceral adiposity index and lipid accumulation product reliable indices for metabolic disturbances in patients with type 2 diabetes mellitus? J Clin Lab Anal. 2018;32:e22283.

    Article  Google Scholar 

  38. Malavazos AE, Cereda E, Ermetici F, Caccialanza R, Briganti S, Rondanelli M, et al. The “lipid accumulation product” is associated with 2-hour postload glucose outcomes in overweight/obese subjects with nondiabetic fasting glucose. Int J Endocrinol. 2015;2015:836941.

    Article  Google Scholar 

  39. Wakabayashi I. Influence of age and gender on lipid accumulation product and its relation to diabetes mellitus in Japanese. Clin Chim Acta. 2014;431:221–6.

    Article  CAS  Google Scholar 

Download references


We acknowledge the invaluable support of participants and the staff in the Health Management Center of the Third Xiangya Hospital.


The study was funded by National Natural Science Foundation of China (81800393), Natural Science Foundation of Hunan Province (2018JJ3783, 2018JJ6050) and Fundamental Research Funds for Central Universities of Central South University (No. 202211807).

Author information

Authors and Affiliations



Study design: HY, YL, and GY. Acquisition of the data: JW, YZ, and ZC. Data analysis and interpretation of the data: CE, GY, and FL. Drafting the manuscript or revising it critically for important intellectual content: GY and FL. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yao Lu.

Ethics declarations

Ethics approval and consent to participate

The study protocol was approved by the Medical Ethics Committee of the Third Xiangya Hospital and all subjects signed a written consent form.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1: Table S1.

Model fit statistics by the numbers of trajectories. Table S2. Model fit statistics of 27 combinations of shape orders (model with 3 trajectories). Table S3. AICa for model 1 to model 3 performed in 30 datasets generated from multiple imputation. Table S4. Summary of baseline LAP by LAP trajectory groups, percentiles groups of baseline LAP, and tertiles of baseline LAP.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, G., Li, F., Elia, C. et al. Association of lipid accumulation product trajectories with 5-year incidence of type 2 diabetes in Chinese adults: a cohort study. Nutr Metab (Lond) 16, 72 (2019).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: