Association of lipid accumulation product and the risk of type 2 diabetes; a cross sectional population-based study

doi:10.21203/rs.3.rs-3875246/v1

Background

The Lipid Accumulation Product (LAP) is a measure that indicates the presence of excessive fat accumulation in the body. LAP have been the focus of research in epidemiological studies aimed at forecasting chronic and metabolic illnesses. This study was carried out to examine the association between LAP and type 2 diabetes mellitus (T2DM) in the adult population of western Iran.

Methods

The study included 9,065 adults between the ages of 35 and 65 who were registered baseline phase of the Ravansar non-communicable diseases study (RaNCD) cohort study. Multiple logistic regression models were employed to explore the association between LAP and T2DM. The receiver operating characteristic (ROC) curve was used to evaluate the predictive capability of the LAP for T2DM.

Results

The average LAP was 53.10 ± 36.60 in the healthy group and 75.51 ± 51.34 in the diabetic group (P < 0.001). The multiple regression model indicated that, after controlling for potentially confounding variables, the odds of T2DM in the second quartile of lipids is 1.60 (95%CI: 1.17, 2.18) times higher than in the first quartile. Additionally, in the third and fourth quartiles, it is 2.43 (95%CI: 1.80, 3.28) and 3.36 (95%CI: 2.47, 4.56) times higher than in the first quartile, respectively. The results of ROC analysis for predicting T2DM indicated that the LAP index has (AUC: 0.66, 95%CI: 0.64, 0.68).

Conclusion

The association between high LAP levels and the T2DM was found to be strong in the adult population of western Iran. LAP is suggested as a tool in diabetes susceptibility screening.

Type 2 diabetes

lipid accumulation product

waist circumference

triglyceride

The World Health Organization (WHO) states that non-communicable diseases (NCDs) are the main reason for global mortality [1]. Type 2 diabetes mellitus (T2DM) is a significant NCD and accounts for 2.74% of all global deaths. In Iran, 3% of all deaths are associated with T2DM [2]. The age-standardized mortality rate for T2DM in Iran has been steadily increasing since 2015 and is expected to rise slightly by 2030 [3]. Diabetes is a long-term condition that can lead to microvascular and microvascular complications, such as cardio-cerebrovascular disease (CVD), diabetic nephropathy, and diabetic retinopathy. Additionally, T2DM can result in disability and a decrease in quality of life [4]. In the early stages of T2DM, there are no specific symptoms, making it easy to overlook [5].

Absolutely, early diagnosis of T2DM can lead to early intervention, which in turn can help reduce or prevent the development of complications. This can ultimately lessen the burden of the disease for individuals and healthcare systems [5]. Obesity, especially abdominal obesity, is a risk factor for T2DM, as it results from the accumulation of fat in the central part of the body [6–8]. Disorders in the metabolism of adipose tissue directly effect on the balance of lipids and glucose [9]. It has been suggested that T2DM may be the result of complex metabolic effects caused by excessive accumulation of abnormal lipids or liver fat [10–12]. Furthermore, scientific evidence indicates that waist circumference (WC), as a common indicator of abdominal obesity, and elevated triglyceride (TG) levels are associated with an increased risk of developing T2DM [13–16].

Recently, the lipid accumulation product (LAP) index has emerged as a tool for predicting metabolic diseases. LAP is a combined index of WC and TG levels, used to assess visceral fat. Research conducted in some populations suggests that the LAP index is a reliable predictor for chronic conditions such as metabolic syndrome, CVD, hypertension, and T2DM [17–20]. It appears that there has not been a thorough study in Iran to examine the effectiveness of LAP in predicting T2DM. If LAP has a good predictive power in Iranian populations, it can be used as a simple, cost-effective and accurate tool for T2DM screening in large populations. Therefore, the present study was conducted to investigate the association between the LAP and T2DM in a large population of adults in western Iran.

Study design and participants

The current study is a cross-sectional analysis and the data for this study were obtained from the baseline phase of the Ravansar non-communicable diseases (RaNCD) cohort study [21]. The RaNCD study is a component of the PERSIAN (Prospective Epidemiological Research Studies in Iran) studies [22]. The RaNCD study is being conducted with the aim of investigating NCD and their risk factors in western Iran (Ravansar city of Kermanshah province). The RaNCD study includes 10,000 adults from the urban and rural areas of Ravansar, who will be followed for 15 years. Data for the RaNCD study were collected through medical examinations, biochemical and anthropometric measurements, as well as information from a comprehensive questionnaire. In the present study, participants with cancer and fatty liver, along with pregnant women and subjects with incomplete information, were excluded from the study. Ultimately, 9065 people were included in the study (Fig. 1).

Data collection and measurements

All data was gathered at the RaNCD cohort center in accordance with the standard protocol of Persian studies [21]. The socioeconomic status (SES) was assessed through principal component analysis (PCA), considering factors like education level, asset ownership (e.g., house, car, motorcycle, computer, and internet), household amenities (e.g., washing machine, dishwasher, refrigerator, and freezer), type and frequency of trips, educational attainment, household size, and number of rooms. Following this analysis, the SES was categorized into three groups (poor, medium, and good) [23].

Anthropometric indices including body mass index (BMI), visceral fat area (VFA), and waist circumference (WC) were measured using a Bio-Impedance Analyzer BIA (Inbody 770, Inbody Co, Seoul, Korea). In addition, WC was measured using a flexible measuring tape at a point midway between the lower rib margin and the iliac crest, with measurements recorded to the nearest 0.5 cm. Fasting blood samples of participants were used to evaluate fasting blood sugar (FBS), liver enzymes including aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT) and Alkaline phosphatase (ALP), and lipid profile including triglyceride (TG), Total cholesterol (TC), high-density lipoprotein cholesterol (HDL), low-density lipoprotein (LDL).

Diabetic patients were considered as individuals with a FBS level of 126 mg/dL and/or a history of using medication to manage T2DM [21]. The LAP was calculated using the following formula: [WC (cm) – 65] × TG concentration (mmol/L) for men, and [WC (cm) – 58] × TG concentration (mmol/L) for women [24].

The participants' systolic and diastolic blood pressure (SBP and DBP) was measured while they were seated on a chair using the standard method, following a 10-minute rest period, and measurements were taken for both the right and left arm [25]. The physical activity was evaluated using the standard questionnaire of the PERSIAN cohort. This questionnaire includes inquiries about sports, work, and leisure activities over a 24-hour period, measured in MET/hour per day [21]. Individuals who reported that they had smoked over 100 cigarettes in their lifetime were categorized as current smokers [26].

Statistical analysis

The analysis in this study was performed using Stata version 14.2 software (Stata Corp, College Station, TX, USA). For the report of the descriptive information of the participants, we used the mean ± standard deviation (SD) for quantitative variables, and for the categorical variables, we used frequency (percentage). T-test and Chi-square tests were used to investigate the difference between the basic characteristics of the two diabetic and non-diabetic groups. To compare participant characteristics between LAP quartiles, one-way ANOVA and chi-square tests were conducted. Univariate and multiple binary logistic regression models were used to evaluate the relationship between T2DM and LAP quartiles. The multiple model incorporated adjustments for age, family history of T2DM, BMI, physical activity, SBP, DBP and SES variables. Receiver operating characteristic (ROC) curves were created to assess LAP, WC and TG ability to predict T2DM based on the area under the curves (AUC) with a 95% confidence interval. All reported P-values were two-sided, and statistical significance was defined as p < 0.05.

A total of 9,065 participants, comprising 49.30% men and 50.70% women, with an average age of 47.24 ± 8.27 years, were included in the study. Among the participants, 7.82% had T2DM, 11.97% were smokers, and 5.02% reported alcohol consumption. Anthropometric indices, including BMI, WC, and VFA, were significantly higher in diabetic individuals (P < 0.001). Additionally, liver enzymes, systolic and diastolic blood pressure were significantly higher in diabetic subjects (P < 0.001). Except for LDL, all lipid profile indices were significantly different between the two diabetic and non-diabetic groups (P < 0.001). The average LAP was 53.10 ± 36.60 in the healthy group and 75.51 ± 51.34 in the diabetic group (P < 0.001) (Table 1).

Table 1

Baseline characteristics of participants this study, (n = 9,065)
Parameters	Total (n = 9,065)	Non-T2DM (n = 8,356)	T2DM (n = 709)	P value*
Gender, n (%)
Men	4469 (49.30)	4126 (92.32)	343 (7.68)	0.609
Women	4596 (50.70)	4230 (92.04)	366 (7.96)	0.609
Age (year)	47.24 ± 8.27	46.89 ± 8.24	51.44 ± 7.45	< 0.001
Current smoker, n (%)	1080 (11.97)	1006 (12.09)	74 (10.45)	< 0.001
Alcohol drinking, n (%)	455 (5.02)	425 (5.09)	30 (4.23)	0.317
Physical activity, n (%)
Low	2682 (29.59)	2431 (29.09)	251 (35.40)	< 0.001
Moderate	4287 (47.29)	3952 (47.30)	335 (47.25)
Vigorous	2096 (23.12)	1973 (23.61)	123 (17.35)
Socioeconomic status, n (%)
Low	3031 (33.44)	2803 (33.54)	228 (32.16)	0.230
Moderate	3010 (33.20)	2754 (32.96)	256 (36.11)
Good	3024 (33.36)	2799 (33.50)	225 (31.73)
Body mass index (kg/m²)	27.19 ± 4.49	27.10 ± 4.50	28.47 ± 4.14	< 0.001
Waist circumference (cm)	96.63 ± 10.29	96.35 ± 10.30	100.01 ± 9.56	< 0.001
Visceral fat area (cm²)	118.83 ± 50.28	117.51 ± 50.21	134.51 ± 48.39	< 0.001
Fasting blood sugar (mg/dl)	96.48 ± 29.46	90.23 ± 9.72	170.04 ± 64.14	< 0.001
Triglycerides (mg/dl)	135.01 ± 82.28	131.34 ± 75.22	178.10 ± 133.64	< 0.001
High-density lipoprotein cholesterol (mg/dl)	46.48 ± 11.37	46.7 ± 11.36	44.19 ± 11.20	< 0.001
Low-density lipoprotein cholesterol (mg/dl)	111.37 ± 31.12	111.38 ± 30.85	111.21 ± 34.21	0.443
Total cholesterol (mg/dl)	184.81 ± 37.66	184.30 ± 37.10	190.73 ± 43.48	< 0.001
Systolic blood pressure (mmHg)	108.10 ± 17.04	107.47 ± 16.77	115.23 ± 18.54	< 0.001
Diastolic blood pressure (mmHg)	69.78 ± 9.92	69.52 ± 9.80	72.80 ± 10.83	< 0.001
Alkaline phosphatase (mg/dl)	196.81 ± 62.80	195.01 ± 61.88	218.11 ± 69.25	< 0.001
Aspartate aminotransferase (mg/dl)	21.31 ± 8.95	21.41 ± 8.91	20.24 ± 9.43	< 0.001
Alanine aminotransferase (mg/dl)	24.65 ± 14.65	24.42 ± 14.59	27.37 ± 15.10	< 0.001
Gamma-glutamyl transpeptidase (mg/dl)	24.20 ± 19.35	23.53 ± 18.84	32.10 ± 23.21	< 0.001
Lipid accumulation product	54.82 ± 38.43	53.10 ± 36.60	75.51 ± 51.34	< 0.001
Family history of T2DM, n (%)	2213 (24.42)	1900 (22.74)	313 (44.15)	< 0.001
*P value < 0.005 by t-test or chi² test

The study found that the mean of BMI was significantly higher in the fourth quartile of the LAP compared to the first quartile (23.63 ± 3.48 vs. 30.17 ± 4.31, Ptrend < 0.001). Additionally, WC, TG, LDL and TC showed an increasing trend from the first to the fourth quartile of LAP (Ptrend < 0.001). The average FBS in the fourth quartile of LAP was significantly higher compared to the first quartile (89.80 ± 24.20 vs. 103.85 ± 34.78, Ptrend < 0.001). Across quartiles of LAP, vigorous physical activity has decreased and low physical activity has increased. Liver enzymes were significantly higher in the fourth quartile of LAP compared to the first quartile (P < 0.001) (Table 2).

Table 2

Baseline characteristics of participants according to lipid accumulation product quartiles *P value < 0.005 by one-way ANOVA or chi² tests
Variables	Quartiles of lipid accumulation product
Variables	Q1 (n = 2,267)	Q2 (n = 2,266)	Q3 (n = 2,267 )	Q4 (n = 2,265 )	P value*	P value trend*
Age (year)	46.05 ± 8.34	47.24 ± 8.34	47.68 ± 8.15	48.19 ± 8.10	< 0.001	< 0.001
Body mass index (kg/m²)	23.63 ± 3.48	26.91 ± 3.55	28.59 ± 3.78	30.17 ± 4.31	< 0.001	< 0.001
Waist circumference (cm)	87.01 ± 8.28	95.45 ± 7.27	99.88 ± 7.80	104.18 ± 8.94	< 0.001	< 0.001
Triglycerides (mg/dl)	72.55 ± 24.37	101.64 ± 27.44	138.67 ± 58.97	227.20 ± 93.44	< 0.001	< 0.001
High-density lipoprotein cholesterol (mg/dl)	50.70 ± 11.79	47.67 ± 11.10	45.21 ± 10.93	42.32 ± 9.68	< 0.001	< 0.001
Low-density lipoprotein cholesterol (mg/dl)	100.99 ± 28.27	111.49 ± 29.60	116.74 ± 30.31	116.27 ± 33.22	< 0.001	< 0.001
Total cholesterol (mg/dl)	166.20 ± 32.10	179.45 ± 34.14	190.21 ± 34.75	203.88 ± 39.47	< 0.001	< 0.001
Fasting blood sugar (mg/dl)	89.80 ± 24.20	93.77 ± 25.89	98.52 ± 30.95	103.85 ± 34.78	< 0.001	< 0.001
Systolic blood pressure (mmHg)	103.20 ± 16.28	107.10 ± 15.01	109.55 ± 16.95	112.46 ± 17.55	< 0.001	< 0.001
Diastolic blood pressure (mmHg)	67.39 ± 9.35	69.21 ± 9.47	70.49 ± 10.10	72.10 ± 10.20	< 0.001	< 0.001
Alkaline phosphatase (mg/dl)	184.20 ± 56.36	192.83 ± 56.64	201.99 ± 66.34	208.25 ± 67.62	< 0.001	< 0.001
Aspartate aminotransferase (mg/dl)	21.23 ± 9.24	20.94 ± 10.02	21.13 ± 7.60	21.94 ± 8.60	0.003	0.002
Alanine aminotransferase (mg/dl)	21.01 ± 13.05	23.41 ± 12.72	26.14 ± 12.50	28.76 ± 16.21	< 0.001	< 0.001
Gamma-glutamyl transpeptidase (mg/dl)	19.21 ± 13.51	22.19 ± 17.11	25.73 ± 20.90	30.34 ± 23.65	< 0.001	< 0.001
Current smoker, n (%)	351 (15.57)	254 (11.25)	234 (10.36)	241 (10.68)	0.001	0.090
Alcohol drinking, n (%)	130 (5.73)	108 (4.77)	112 (4.94)	105 (4.64)	0.325	0.128
Physical activity, n (%)
Low	548 (24.17)	634 (27.98)	726 (32.02)	774 (34.17)	0.002	0.001
Moderate	1014 (44.73)	1053 (46.47)	1101 (48.57)	1119 (49.40)
Vigorous	705 (31.10)	579 (25.55)	440 (19.41)	372 (16.42)
Socioeconomic status, n (%)
Low	803 (35.42)	754 (33.27)	679 (29.95)	795 (35.10)	0.001	0.373
Moderate	733 (32.33)	768 (33.89)	761 (33.57)	748 (33.02)
Good	731 (32.25)	744 (32.83)	827 (36.48)	722 (31.88)

*P value<0.005 by one-way ANOVA or chi² tests

The crude model revealed that the odds of T2DM in the second quartile of LAP is 1.9 times, in the third quartile it is 3.20 times and in the fourth quartile it is 4.77 times higher than in the first quartile (P < 0.001). After adjusting for age and history of diabetes in first-degree relatives, it was observed that the odds of diabetes in the second quartile of LAP is 1.74 (95%CI: 1.29, 2.35) times, in the third quartile 2.82 (95% CI: 2.13, 3.74) times, and in the fourth quartile 4.05 (95% CI: 3.10, 5.3) times higher than the first quartile. The multiple regression model indicated that, after controlling for potentially confounding variables, the odds of T2DM in the second quartile of lipids is 1.60 (95% CI: 1.17, 2.18) times higher than in the first quartile. Additionally, in the third and fourth quartiles, it is 2.43 (95% CI: 1.80, 3.28) and 3.36 (95% CI: 2.47, 4.56) times higher than in the first quartile, respectively (Table 3).

Table 3

Association of lipid accumulation product and type 2 diabetes mellitus by logistic regression analysis
Lipid accumulation product quartiles	Model 1		Model 2		Model 3
Lipid accumulation product quartiles	OR (95% CI)	P value	OR (95% CI)	P value	OR (95% CI)	P value
Q1	1.00 (Reference)		1.00 (Reference)		1.00 (Reference)
Q2	1.91 (1.42, 2.57)	< 0.001	1.74 (1.29, 2.35)	< 0.001	1.60 (1.17, 2.18)	0.03
Q3	3.20 (2.43, 4.22)	< 0.001	2.82 (2.13, 3.74)	< 0.001	2.43 (1.80, 3.28)	< 0.001
Q4	4.77 (3.65, 6.23)	< 0.001	4.05 (3.10, 5.3)	< 0.001	3.36 (2.47, 4.56)	< 0.001
P value trend	< 0.001		< 0.001		< 0.001
Model 1: Unadjusted; Model 2: Adjusted for age and family history of T2DM; Model 3: Adjusted for age, family history of T2DM, BMI, physical activity, SBP, DBP and SES

Table 4

ROC curve analysis of lipid accumulation product and its components for predicting of type 2 diabetes mellitus risk
Variables	Cut-points	AUC ROC (95% CI)	Sensitivity (%)	Specificity (%)	Youden J-index
Lipid accumulation product	46.24	0.66 (0.64, 0.68)	0.70	0.57	27%
Triglycerides	120.40	0.64 (0.62, 0.66)	0.65	0.54	19%
Waist circumference	100.2	0.60 (0.58, 0.62)	0.78	0.41	19%

The ROC curve in Fig. 2 illustrates the prediction of T2DM based on LAP, TG, and WC in the population under study. The results of ROC analysis for predicting T2DM indicated that the LAP index has higher predictive power (AUC: 0.66, 95% CI: 0.64, 0.68) than TG (AUC: 0.64, 95% CI: 0.62, 0.66) and WC (AUC:0.60, 95% CI: 0.58, 0.62) in the studied population (Table 3).

In this population-based study, we observed a significant increase in the odds of T2DM with the rise in LAP levels in the adult population of western Iran. Specifically, in the second to fourth quartiles of LAP levels, the odds of T2DM was 1.60, 2.43, and 3.36 times higher than in the first quartile, respectively.

The association between LAP and T2DM has been found to be positive in several demographic and ethnic subgroups. Briefly, in the Japanese population, high LAP have been shown to increase the risk of T2DM by 76% over time [20]. Similar results have been reported in adults Chinese and middle-aged Koreans [27, 28]. Studies have shown that obesity is a risk factor for T2DM [29]. The excessive accumulation of lipids or liver fat leads to a series of complex metabolic consequences, such as non-alcoholic fatty liver disease (NAFLD) and insulin resistance (IR), which ultimately contribute to the development and progression of T2DM [11, 12, 30]. Additionally, WC, relative to BMI, is a more accurate indicator for assessing excess visceral fat in the body. WC does not clearly distinguish between subcutaneous and visceral fat in the abdominal cavity [30]. Furthermore, dyslipidemia, hypertriglyceridemia, and low HDL cholesterol levels have been observed to be related to T2DM [31]. Therefore, LAP, as a composite index of WC and TG, reflects the state of visceral fat and blood lipids. A strong relationship between LAP and metabolic diseases such as CVD and hypertension has also been observed [19, 32]. Generally, the positive relationship between LAP and diabetes is acceptable.

In individuals who are overweight, excessive fat tissue in the body, combined with overeating and chronic inflammation, can result in IR and a disorder in glucose metabolism [33]. IR is a crucial stage in the progression of diabetes [34], and chronic inflammation and mitochondrial dysfunction caused by obesity can both worsen IR [35–37]. Additionally, prolonged exposure to fatty acids in β-cells can decrease glucose-induced insulin secretion, disrupt insulin gene expression, and increase cell death [38, 39]. The onset of T2DM is gradual and may lead to complications for an individual before it is diagnosed, making early diagnosis and prevention particularly important. Considering the change in lifestyle and the increasing trend of T2DM as well as serious complications, reduction in quality of life and heavy economic burden caused by this disease, it is very important to provide a low-cost, easy and calculable index to identify T2DM as soon as possible. Previous studies have shown that LAP has a higher diagnostic value for predicting T2DM than indicators such as BMI, WC, A Body Shape Index (ABSI), Visceral adiposity index (VAI) and waist/height ratio (WHtR) [20, 40]. Similarly, the present study also showed that LAP (AUC = 0.66) has higher diagnostic value than WC (AUC = 0.60) and TG (AUC = 0.64). Furthermore, LAP has been identified as a useful indicator for predicting metabolic diseases like hypertension and CVD [18, 19]. Based on the results of this study in a population of 9,065 participants including men and women and residents of cities and villages, and the results of previous studies, we suggest LAP as an accurate and comprehensive index for T2DM screening in large populations.

The study had a few limitations. It was a cross-sectional analysis, which means that the observed relationships are not causal. However, causal relationships in longitudinal studies have been observed in other population groups. Additionally, the study was conducted on adults in western Iran, so the results should be cautiously generalized to other population groups. It is recommended to carry out this study in other population subgroups. Lastly, the study did not measure HbA1c and oral glucose tolerance test variables. The large sample size is a key advantage of the current study. Additionally, the comprehensive collection of lifestyle, anthropometric, and biochemical variables, as well as the adjustment of a large number of confounding variables, is strength of this study.

The findings of this study revealed a significant increase in the risk of T2DM with rising LAP levels in the adult population of western Iran. Additionally, based on Rock curve analysis, LAP was found to be an accessible and cost-effective tool for screening and monitoring diabetes in the adults, with a higher predictive value than TG levels and obesity.

Authors’ contributions

SS and AA conceived the idea of the study. FN developed the statistical analysis plan and conducted statistical analyses. YP, SS and AA contributed to the interpretation of the results. SS and AA drafted the original manuscript. AA, YP and FN supervised the conduct of this study. All authors reviewed the manuscript draft and revised it critically on intellectual content. All authors approved the final version of the manuscript to be published.

Ethics approval and consent to participate

The study was approved by the ethics committee of Kermanshah University of Medical Sciences (KUMS.REC.1394.318). All methods were carried out in accordance with relevant guidelines and regulations. All the participants were provided oral and written informed consent. All methods were carried out by relevant guidelines and regulations. This study was conducted by the Declaration of Helsinki.

Consent for publication

Not applicable.

Availability of data and materials

The data analyzed in the study are available from the corresponding author upon reasonable request.

Competing interests

The authors declare no conflicts of interest.

Funding

This research was supported by Kermanshah University of Medical Sciences (grant number: 92472). The Iranian Ministry of Health and Medical Education has also contributed to the funding used in the PERSIAN Cohort through Grant no 700/534.

Acknowledgements

The authors thank the PERSIAN cohort Study collaborators and of Kermanshah University of Medical Sciences.

Budreviciute A, Damiati S, Sabir DK, Onder K, Schuller-Goetzburg P, Plakys G, et al. Management and prevention strategies for non-communicable diseases (NCDs) and their risk factors. Frontiers in public health. 2020;8:788.
Institute for Health Metrics and Evaluation (IHME). GBD Compare Seattle, WA: IHME,University of Washington2015 [Available from: https://vizhub.healthdata.org/gbd-compare/.
Khosravi Shadmani F, Farzadfar F, Larijani B, Mirzaei M, Haghdoost AA. Trend and projection of mortality rate due to non-communicable diseases in Iran: A modeling study. PloS one. 2019;14(2):e0211622.
Valencia WM, Florez H. How to prevent the microvascular complications of type 2 diabetes beyond glucose control. Bmj. 2017;356.
Gong Q, Zhang P, Wang J, Ma J, An Y, Chen Y, et al. Morbidity and mortality after lifestyle intervention for people with impaired glucose tolerance: 30-year results of the Da Qing Diabetes Prevention Outcome Study. The lancet Diabetes & endocrinology. 2019;7(6):452-61.
Klein S, Sheard NF, Pi-Sunyer X, Daly A, Wylie-Rosett J, Kulkarni K, et al. Weight management through lifestyle modification for the prevention and management of type 2 diabetes: rationale and strategies. A statement of the American Diabetes Association, the North American Association for the Study of Obesity, and the American Society for Clinical Nutrition. The American journal of clinical nutrition. 2004;80(2):257-63.
Zhang F-L, Ren J-X, Zhang P, Jin H, Qu Y, Yu Y, et al. Strong Association of waist circumference (WC), body mass index (BMI), waist-to-height ratio (WHtR), and waist-to-hip ratio (WHR) with diabetes: A population-based cross-sectional study in Jilin province, China. Journal of diabetes research. 2021;2021.
Wei J, Liu X, Xue H, Wang Y, Shi Z. Comparisons of visceral adiposity index, body shape index, body mass index and waist circumference and their associations with diabetes mellitus in adults. Nutrients. 2019;11(7):1580.
Guilherme A, Virbasius JV, Puri V, Czech MP. Adipocyte dysfunctions linking obesity to insulin resistance and type 2 diabetes. Nature reviews Molecular cell biology. 2008;9(5):367-77.
Kahn HS, Cheng YJ, Thompson TJ, Imperatore G, Gregg EW. Two risk-scoring systems for predicting incident diabetes mellitus in US adults age 45 to 64 years. Annals of internal medicine. 2009;150(11):741-51.
Sattar N, Wannamethee S, Forouhi N. Novel biochemical risk factors for type 2 diabetes: pathogenic insights or prediction possibilities? Diabetologia. 2008;51:926-40.
Unger RH. Reinventing type 2 diabetes: pathogenesis, treatment, and prevention. Jama. 2008;299(10):1185-7.
Cassano PA, Rosner B, Vokonas PS, Weiss ST. Obesity and body fat distribution in relation to the incidence of non-lnsulin-dependent diabetes mellitus: A prospective cohort study of men in the normative aging study. American journal of epidemiology. 1992;136(12):1474-86.
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(8):1921-3.
Yang W, Lu J, Weng J, Jia W, Ji L, Xiao J, et al. Prevalence of diabetes among men and women in China. New England journal of medicine. 2010;362(12):1090-101.
Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nature reviews endocrinology. 2018;14(2):88-98.
Ray L, Ravichandran K, Nanda SK. Comparison of lipid accumulation product index with body mass index and waist circumference as a predictor of metabolic syndrome in Indian population. Metabolic syndrome and related disorders. 2018;16(5):240-5.
Muheiyati G, Mei Y, Tao N. Association of lipid accumulation product and visceral adiposity index with the risk of hypertension among oil workers in Xinjiang, China. PeerJ. 2023;11:e15273.
Vieira JN, Braz MAD, Gomes FO, Silva PRd, Santos OTdM, Rocha IMGd, et al. Cardiovascular risk assessment using the lipid accumulation product index among primary healthcare users: a cross-sectional study. Sao Paulo Medical Journal. 2019;137:126-31.
Liu T, Lu W, Zhao X, Yao T, Song B, Fan H, et al. Relationship between lipid accumulation product and new-onset diabetes in the Japanese population: a retrospective cohort study. Frontiers in Endocrinology. 2023;14:1181941.
Pasdar Y, Najafi F, Moradinazar M, Shakiba E, Karim H, Hamzeh B, et al. Cohort profile: Ravansar Non-Communicable Disease cohort study: the first cohort study in a Kurdish population. International journal of epidemiology. 2019;48(3):682-3f.
Poustchi H, Eghtesad S, Kamangar F, Etemadi A, Keshtkar A-A, Hekmatdoost A, et al. Prospective epidemiological research studies in Iran (the PERSIAN Cohort Study): rationale, objectives, and design. American journal of epidemiology. 2018;187(4):647-55.
Najafi F, Rezaei S, Hajizadeh M, Soofi M, Salimi Y, Kazemi Karyani A, et al. Decomposing socioeconomic inequality in dental caries in Iran: cross-sectional results from the PERSIAN cohort study. Archives of Public Health. 2020;78(1):1-11.
Ching YK, Chin YS, Appukutty M, Gan WY, Chan YM. Comparisons of conventional and novel anthropometric obesity indices to predict metabolic syndrome among vegetarians in Malaysia. Scientific reports. 2020;10(1):20861.
Chobanian AV. National heart, lung, and blood institute joint national committee on prevention, detection, evaluation, and treatment of high blood pressure; national high blood pressure education program coordinating committee. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. Jama. 2003;289:2560-72.
Ryan H, Trosclair A, Gfroerer J. Adult current smoking: differences in definitions and prevalence estimates—NHIS and NSDUH, 2008. Journal of environmental and public health. 2012;2012.
Yan G, Li F, Elia C, Zhao Y, Wang J, Chen Z, et al. Association of lipid accumulation product trajectories with 5-year incidence of type 2 diabetes in Chinese adults: a cohort study. Nutrition & metabolism. 2019;16(1):1-8.
Lee J-W, Lim N-K, Park H-YJBed. The product of fasting plasma glucose and triglycerides improves risk prediction of type 2 diabetes in middle-aged Koreans. 2018;18:1-10.
Rohm TV, Meier DT, Olefsky JM, Donath MY. Inflammation in obesity, diabetes, and related disorders. Immunity. 2022;55(1):31-55.
Kang PS, Neeland IJ. Body Fat Distribution, Diabetes Mellitus, and Cardiovascular Disease: an Update. Current Cardiology Reports. 2023;25(11):1555-64.
Kane JP, Pullinger CR, Goldfine ID, Malloy MJ. Dyslipidemia and diabetes mellitus: Role of lipoprotein species and interrelated pathways of lipid metabolism in diabetes mellitus. Current Opinion in Pharmacology. 2021;61:21-7.
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(6):e0198105.
Lu J, Zhao J, Meng H, Zhang XJFii. Adipose tissue-resident immune cells in obesity and type 2 diabetes. 2019;10:1173.
Hanson P, Weickert MO, Barber TMJTAiE, Metabolism. Obesity: novel and unusual predisposing factors. 2020;11:2042018820922018.
Di Meo S, Iossa S, Venditti PJJoE. Skeletal muscle insulin resistance: role of mitochondria and other ROS sources. 2017;233(1):R15-R42.
Kim O-K, Jun W, Lee JJAoN, Metabolism. Mechanism of ER stress and inflammation for hepatic insulin resistance in obesity. 2015;67(4):218-27.
Shoelson SE, Herrero L, Naaz AJG. Obesity, inflammation, and insulin resistance. 2007;132(6):2169-80.
Jacqueminet S, Briaud I, Rouault C, Reach G, Poitout VJM. Inhibition of insulin gene expression by long-term exposure of pancreatic β cells to palmitate is dependent on the presence of a stimulatory glucose concentration. 2000;49(4):532-6.
Maedler K, Spinas G, Dyntar D, Moritz W, Kaiser N, Donath MYJD. Distinct effects of saturated and monounsaturated fatty acids on β-cell turnover and function. 2001;50(1):69-76.
Yang SH, Yoon J, Lee Y-J, Park B, Jung D-HJD, Metabolic Syndrome, Targets O, et al. Lipid accumulation product index predicts new-onset type 2 diabetes among non-obese koreans: a 12-year longitudinal study. 2022:3729-37.

No competing interests reported.

Association of lipid accumulation product and the risk of type 2 diabetes; a cross sectional population-based study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Methods

Study design and participants

Data collection and measurements

Statistical analysis

Results

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1