Association Between Lipid Accumulation Product and Mild Cognitive Impairment in Patients with Type 2 Diabetes

Abstract

Background:

Diabetes may increase the risk of conversion of mild cognitive impairment (MCI) to dementia. Lipid accumulation product (LAP), an index of visceral obesity, has been shown to be a powerful predictor of insulin resistance and type 2 diabetes (T2D). However, little attention has been paid to the relationship between LAP and MCI in T2D.

Objective:

We aimed to investigate the association between the LAP index and MCI in patients with T2D.

Methods:

In total, 220 hospitalized patients with T2D, including 113 MCI patients and 107 patients with normal cognition, were enrolled in this cross-sectional study. We collected demographic, anthropometric, and biochemical data on each subject. The LAP index was calculated according to the following formulas: [waist circumference (WC) (cm) – 65]×triglyceride (TG) (mmol/L) for males and [WC (cm) – 58] ×TG (mmol/L) for females.

Results:

Compared with patients with normal cognition, MCI patients were older and had a higher LAP index, WC, body mass index, and glycosylated hemoglobin A1c level, as well as a lower Montreal Cognitive Assessment score and education level (p < 0.05). After adjusting for confounding factors, LAP index was associated with MCI (OR = 1.047, 95% CI = 1.031–1.063, p < 0.01). The area under the ROC curve (AUC) for the LAP index was higher than that for WC and BMI.

Conclusion:

A high LAP index is associated with an increased risk of MCI in T2D patients. The LAP index appears to be a good indicator of risk of MCI in patients with T2D.

Keywords

Lipid accumulation product mild cognitive impairment type 2 diabetes visceral obesity

INTRODUCTION

According to statistics, 35.6 million people were diagnosed with dementia in 2010, and this number is predicted to increase to 115.4 million in 2050 [1]. With the aging population in China, the number of Chinese patients with dementia is increasing rapidly. Wang et al. estimated that the pooled prevalence rate of dementia in China was 4.9% between 1985 to 2018, while the prevalence rate from 2015 to 2018 was 7.4% [2]. In addition, the latest research from the Institute for Health Metrics and Evaluation (IHME) of the University of Washington indicated that there were 10.427 million dementia patients in China in 2015 [3]. Dementia places a serious economic burden on families and society. In recent years, researchers have focused on the characteristics of the early stages of cognitive impairment based on the premise that there may be a transition period between normal aging and early dementia. Petersen was the first to define this state as ‘mild cognitive impairment’ [4]. Mild cognitive impairment (MCI) is the transitional stage from normal cognition to dementia in which patients have mild impairment of cognitive function, while activities of daily living are preserved to a large extent [5]. Epidemiological studies have demonstrated that diabetes is associated with an increased risk of MCI and dementia [6, 7]. A quantitative meta-analysis from China found that, compared to patients without diabetes, the relative risks (RRs) of Alzheimer’s disease (AD), vascular dementia, any dementia, and MCI in patients with diabetes were 1.46, 2.48, 1.51, and 1.21, respectively [8]. Cognitive dysfunction and dementia are recognized complications of diabetes [9]. Most MCI patients are at increased risk of developing dementia [10]; surprisingly, diabetes increases this conversion risk by 1.5 3 times [7]. Given that diabetes patients with MCI are at significantly increased risk of progression to dementia, early diagnosis and intervention for MCI are critical.

Type 2 diabetes (T2D) is often accompanied by visceral obesity. Visceral obesity, characterized by the excessive accumulation of visceral adipose tissue, is also a risk factor for cognitive impairment and dementia [11, 12]. A cross-sectional study from China demonstrated that the interaction between T2D and visceral obesity increases the risk of cognitive impairment by more than 2 times [13]. There are many limitations in traditional anthropometric measurements used to evaluate visceral obesity. Body mass index (BMI), a measurement of obesity based on height and weight, cannot distinguish between fat and lean tissue, nor can it assess individual fat distribution. Waist circumference (WC) can reflect abdominal adipose tissue, but it cannot differentiate between subcutaneous adipose tissue and visceral adipose tissue [14, 15]. Imaging studies are highly accurate for the evaluation of visceral adipose tissue, but they are expensive. Kahn et al. first argued that the lipid accumulation product (LAP), a combination of waist circumference and triglycerides, provides a simple and low-cost approach to assess lipid over accumulation in adults [16]. Recent studies have confirmed that the LAP index is more reliable than traditional anthropometric measurements in predicting cardiovascular risk factors [17], insulin resistance [14], metabolic syndrome [18], and diabetes [15]. Recently, a community-based survey in Chinese patients with T2D indicated that a higher LAP index was associated with a lower risk of diabetic retinopathy [19]. Thus, the clinical significance of the LAP index has gradually garnered the attention of researchers and clinicians.

Although extensive research has been carried out on the LAP index, no single study has clarified the relationship between the LAP index and MCI in T2D patients. Therefore, the purpose of our study was to explore the association between the LAP index and MCI in Chinese patients with T2D.

METHODS

Subjects

A total of 220 patients with T2D, who were hospitalized in the endocrinology department of the First Affiliated Hospital of Harbin Medical University between March 2018 and February 2019, were recruited into this study. The diagnosis of T2D was based on the 1999 World Health Organization criteria [20]. Subjects with the following conditions were excluded: 1) acute diabetic complications and history of severe hypoglycemia; 2) autoimmune disease, acute and chronic inflammatory diseases; 3) neurological diseases that affect cognitive function; 4) hearing or visual impairment, psychiatric disorder; 5) participants who refused to participate in the study and those with incomplete data. This study was approved by the Ethics Committee of the First Affiliated Hospital of Harbin Medical University. Informed consent was obtained from all participants.

Data collection

A pre-designed questionnaire was used to collect demographic characteristics, educational level, lifestyle risk factors, duration of diabetes, diabetic complications, and diabetes therapy. Blood pressure (BP) was measured by professional nurses in a quiet environment. Experienced nurses performed anthropometric measurements such as height, weight, and waist circumference on subjects wearing light clothing and no shoes. BMI was calculated as weight (kg) divided by the square of height (m²). Fasting C-peptide, total cholesterol (TC), glycosylated hemoglobin A1c (HbA1c), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), blood urea nitrogen (BUN), creatinine (Cr), and uric acid (UA) were measured in all subjects after overnight fasting. The LAP index was calculated according to the following equations: LAP = [WC (cm) – 65]×TG (mmol/L) for males and [WC (cm) – 58]×TG (mmol/L) for females (65 and 58 represent the estimated minimum WC values for men and women, respectively) [16].

Cognitive function assessment

The diagnostic criteria for MCI employed in this study conformed to the MCI diagnostic guidelines developed by the National Institute on Aging and the Alzheimer’s Association [21]: 1) concerns regarding a change in cognition from patients or their families; 2) objective evidence of impairment in one or more cognitive domains, which in this study, was assessed by professional neurologists using the Beijing version of the Montreal Cognitive Assessment (MoCA-BJ); 3) preservation of independence in daily functional abilities; 4) absence of dementia. The MoCA-BJ is a highly sensitive cognitive screening tool that can quickly detect MCI and distinguishes patients with MCI from cognitively normal individuals [22]. The MoCA-BJ examines seven cognitive domains: visuospatial/executive function, naming, attention, language, abstraction, delayed memory, and orientation [23]. A normal MoCA score is ≥26. MCI in this study was defined as a score <26 [22, 23].

Statistical analysis

Continuous variables were expressed as means±standard deviations or medians (Interquartile Range, IQR). The two-independent samples t-test was employed for normally distributed variables. Skewed variables were compared using the Mann–Whitney U test. Categorical variables were expressed as percentages and compared between groups using the Chi-square test. The relationships between the MoCA score and other variables were quantified with Spearman correlation analysis. Logistic regression analysis was used to explore the association between the LAP index and MCI. In order to evaluate the ability of the LAP index to identify MCI, a receiver operating characteristic curve (ROC) was constructed and the area under the curve (AUC) was calculated. A p-value <0.05 was regarded as statistically significant. All statistical analyses were completed in SPSS version 25.0.

RESULTS

Subject baseline characteristics

Clinical and biochemical characteristics of the T2D patients with normal cognitive function (T2D-NCF) and the T2D patients with MCI (T2D-MCI) are shown in Table 1. Of the 220 T2D subjects (age range 30–80 years), 113 presented with MCI (63 men and 50 women; MoCA score < 26) and 107 exhibited normal cognitive function (69 men and 38 women; MoCA score≥26). Compared with the T2D-NCF group, the patients in the T2D-MCI group were older and had significantly higher BMI, WC, TC, TG, LDL, and LAP index (p < 0.01). HbA1c in the T2D-MCI group was higher than that in the T2D-NCF group (p < 0.05). In addition, the incidence rate of diabetic peripheral neuropathy (DPN) and the rate of use of statins in the T2D-MCI group were significantly higher than those in the T2D-NCF group, while the education level and MoCA score of the T2D-MCI group were significantly lower than those in the T2D-NCF group (p < 0.01). There were no significant differences in sex, duration of diabetes, SBP, DBP, smoking and drinking history, fasting C-peptide, HDL, UA, Cr, BUN, diabetic retinopathy, diabetic nephropathy, lower limb atherosclerosis, carotid atherosclerosis, the use of injectable insulin, and the use of oral antidiabetic drugs between the two groups (p > 0.05).

Table 1

Clinical and biochemical characteristics between T2D patients with normal cognition and T2D patients with MCI

Variables	T2D-NCF group (N = 107)	T2D-MCI group (N = 113)	p
Age (y)	52.02±9.10	55.79±9.86	0.004
Gender (Male, %)	69 (64.5%)	63 (55.8%)	0.186
Duration of diabetes (y)	7 (3–12)	9 (4–15.5)	0.072
Current smoking (%)	39 (36.4%)	41 (36.3%)	0.98
Habitual alcohol drinking (%)	42 (39.3%)	45 (39.8%)	0.931
BMI (kg/m²)	25.39±2.68	26.57±2.61	0.007
Waist circumference (cm)	89.74±7.49	94.52±7.69	<0.001
SBP (mmHg)	137 (126–154)	141 (128.5–155.5)	0.313
DBP (mmHg)	84.60±12.21	83.48±12.22	0.497
TG (mmol/L)	1.56 (1.20 –2.36)	2.50 (1.96–3.60)	<0.001
LAP index	46.32 (28.67–71.53)	80.34 (64.12 –118.75)	<0.001
Fasting C-peptide (ng/ml)	1.40 (0.90–2.00)	1.40 (0.90–2.15)	0.910
HbA1C (%)	8.50 (6.70–10.00)	9.30 (7.60–10.35)	0.012
TC (mmol/L)	4.76 (4.21–5.72)	5.31 (4.61–6.47)	0.001
HDL (mmol/L)	1.21(1.04–1.39)	1.17 (1.06–1.31)	0.427
LDL (mmol/L)	2.83 (2.42–3.38)	3.13 (2.71–3.60)	0.008
UA (μmol/L)	305.00 (267.60 –357.50)	336.20 (272.65–401.50)	0.064
Cr (μmol/L)	61.00 (50.00–71.10)	59.45 (50.55–70.05)	0.887
BUN (mmol/L)	5.41 (4.69 –6.47)	5.56 (4.67–6.69)	0.551
Education level (y)	12 (9–16)	9 (7–12)	<0.001
MoCA score	27 (26–28)	22 (20–24)	<0.001
Lower limb atherosclerosis (%)	88 (82.8%)	97 (85.8%)	0.466
Carotid atherosclerosis (%)	68 (63.5%)	81 (71.6%)	0.197
Diabetic peripheral neuropathy (%)	9 (8.4%)	26 (23.0%)	0.003
Diabetic retinopathy (%)	44 (41.1%)	58 (51.3%)	0.129
Diabetic nephropathy (%)	18 (16.8%)	25 (22.1%)	0.322
Insulin use (%)	91 (85.0%)	101 (89.4%)	0.335
Metformin use (%)	76 (71.0%)	73 (64.6%)	0.308
Acarbose use (%)	13 (12.1%)	17 (15.0%)	0.532
DPP-4 inhibitor use (%)	36 (33.6%)	52 (46.0%)	0.061
Statins use (%)	44 (41.1%)	72 (63.7%)	0.001

NCF, normal cognitive function; MCI, mild cognitive impairment; T2D, type 2 diabetes; BMI, body mass index; DBP, diastolic blood pressure; SBP, systolic blood pressure; TG, triglyceride; LAP, lipid accumulation product; HbA1c, hemoglobin A1c; TC, total cholesterol; HDL, high density lipoprotein; LDL, low density lipoprotein; UA, uric acid; Cr, creatinine; BUN, blood urea nitrogen; MoCA, Montreal Cognitive Assessment.

Associations between MoCA score and other variables

Table 2 shows that age (r = –0.269, p < 0.01), LAP index (r = –0.397, p < 0.01), WC (r = –0.241, p < 0.01), TG (r = –0.313, p < 0.01), and HbA1c (r = –0.165, p < 0.05) were significantly negatively correlated with MoCA score, while education level was significantly positively correlated with MoCA score (r = 0.420, p < 0.01). There were no associations between BMI, LDL, HDL, TC, and the MoCA score (p > 0.05).

Table 2

Associations between MoCA score and other variables

	r	p
Age	–0.269	<0.001
HDL	0.036	0.594
LDL	–0.109	0.108
BMI	–0.13	0.053
WC	–0.241	<0.001
TG	–0.313	<0.001
LAP index	–0.397	<0.001
HbA1C	–0.165	0.015
TC	–0.105	0.120
Education level	0.420	<0.001

HbA1c, hemoglobin A1c; TG, triglyceride; TC, total cholesterol; HDL, high density lipoprotein; LDL, low density lipoprotein; LAP, lipid accumulation product; BMI, body mass index; WC, waist circumference.

Associations between the LAP index and MCI

As shown in Table 3, univariate and multivariate logistic analysis revealed a relationship between the LAP index and MCI. Univariate logistic analysis demonstrated that the risk of MCI was higher with an increased LAP index (OR = 1.037, 95% CI = 1.025 –1.050, p < 0.01). After adjusting for age, gender, lifestyle risk factors, duration of DM, LDL, HbA1c, education level, insulin use, statins use, and diabetic peripheral neuropathy, a high LAP index was still associated with an increased risk of MCI (OR = 1.047, 95% CI = 1.031–1.063, p < 0.01).

Table 3

Associations between LAP index and MCI

	OR	95% CI	p
Model 1	1.037	1.025 –1.050	<0.001
Model 2	1.040	1.027–1.053	<0.001
Model 3	1.047	1.031–1.063	<0.001

Notes: Model 1: Crude model; Model 2: adjust for age and gender; Model 3: Model 2+ duration of DM, HbA1c, education level, current smoking, habitual alcohol drinking, LDL, insulin use, statins use, and diabetic peripheral neuropathy.

Parameters for diagnosing MCI

Among MCI subjects, the AUC for the LAP index was 0.808 (95% CI:0.751–0.864), for WC it was 0.666 (95% CI:0.595–0.737), and for BMI it was 0.597 (95% CI:0.522–0.671). The optimal cut-off points for the diagnosis of MCI for the different markers were 52.87 for the LAP index (sensitivity: 88.5%, specificity: 61.7%), 94.25 for WC (sensitivity: 49.6%, specificity: 76.6%), and 27.29 for BMI (sensitivity: 37.2%, specificity: 81.3%) (Fig. 1).

Fig.1

ROC curves for LAP index, WC, and BMI.

DISCUSSION

This study focused on the relationship between the LAP index and MCI in T2D patients. The important findings are summarized here: 1) compared with the T2D-NCF group, the subjects in the T2D-MCI group had a higher LAP index; 2) there was a negative correlation between the LAP index and the MoCA score; 3) a high LAP index was an independent risk factor for MCI; 4) the LAP index was better than BMI and WC for screening for MCI.

It has been reported that excessive TG and WC contribute to cognitive decline [24, 25]. Similarly, this study confirmed that high TG and WC were associated with MCI in T2D patients. The LAP index, which is based on the combination of these two measurements, is designed to reflect the comprehensive anatomic and physiologic changes associated with lipid overaccumulation in adults [16]. In the present study, we confirmed that the LAP index had higher diagnostic accuracy for MCI than WC. This may be because WC cannot distinguish between subcutaneous adipose tissue and visceral adipose tissue, while the LAP index provides a good reflection of the accumulation of visceral adipose tissue. Visceral adipose tissue, which is considered to be a pathogenic adipose tissue compartment, is closely related to cognitive impairment [12].

Another important finding of our research is that there was no correlation between BMI and MoCA score, and BMI performed poorly in diagnosing MCI. This result indicates that BMI may not be closely related to MCI, which is in line with some previous research results [13]. In fact, studies on the relationship between BMI and cognitive impairment have been mixed. Research suggests that higher BMI in middle age is associated with lower cognitive function during old age [26, 27]. Some studies have reported that high BMI is associated with cognitive decline in the elderly [12, 28], while other studies have reported that overweight is associated with a reduced risk of cognitive impairment in the elderly [29, 30]. Possible reasons for these conflicting findings include study selection criteria and variation in countries and incomes of the subjects. Further, the elderly usually suffer from muscle weight loss while fat weight increases; however, BMI is unable to reflect the distribution and accumulation of fat tissue [29]. As a result, compared with BMI and WC, the LAP index can provide more information for screening for cognitive impairment in T2D patients.

Previous imaging studies have found that excessive abdominal visceral fat is closely related to cognitive dysfunction [11, 12]. Importantly, the LAP index is cheaper and more readily available than imaging studies. In this research, a high LAP index, an indicator of visceral obesity, was associated with an increased risk of MCI. Therefore, the LAP index may be an easy and convenient tool to screen for MCI in T2D patients. The potential mechanism of this association may be related to the common pathways involved in cognitive impairment caused by T2D and that caused by visceral obesity. First, both conditions are associated with insulin resistance. Insulin resistance increases the accumulation of amyloid-β protein (Aβ) and tau hyperphosphorylation, thereby causing cognitive impairment [31, 32]. In addition, a common pathological manifestation of visceral obesity and T2D is chronic low-grade inflammation [33]. Pro-inflammatory cytokines (IL-1β, IL-6, and TNF-α) have been shown to disrupt cognitive function by crossing the blood-brain barrier and causing inflammation of the hippocampus [33, 34]. However, an autopsy study from Brazil found that abdominal visceral fat was negatively correlated with cognitive impairment [29]. This contradiction may be related to differences in race, income, and educational level between studies.

Hospitalized patients with T2D commonly exhibit poor glycemic control, which has been shown to lead to cognitive dysfunction [35]. An epidemiological study from Denmark showed that high HbA1c level was associated with increased risk of cognitive impairment [36]. Similarly, this study also found that HbA1c in the T2D-MCI group was higher than that in the T2D-NCF group and HbA1c was negatively correlated with MoCA score. These results suggest that elevated HbA1c may be related to cognitive damage. Thus, strict control of average blood glucose levels may delay cognitive decline. Insulin administration has also been shown to affect cognitive function [37]. In order to improve blood glucose levels, a high proportion of patients in this study received insulin administration. However, in this study, there was no significant difference in insulin use between the T2D-NCF group and the T2D-MCI group. A future longitudinal study with a larger sample size and longer follow-up period may be able to clarify the effects of insulin administration on cognitive function in patients with T2D. Furthermore, we also found a higher incidence of DPN in the T2D-MCI group. Similarly, DPN was positively correlated with cognitive impairment in Chinese patients with T1D. This may be because DPN is associated with gray and white matter damage, which may increase the risk of cognitive impairment [38].

UA is increasingly considered a double-edged sword for cognitive function, as it not only acts as an antioxidant to delay cognitive decline but also acts as a pro-inflammatory compound, increasing the risk of cognitive impairment [39, 40]. To date, the relationship between UA and cognitive impairment has not been clarified. The present study revealed that there was no significant difference in UA level between patients with MCI and those with normal cognitive function. This is in contrast to previous studies. A population-based cross-sectional study found that elevated UA was associated with poor cognitive performance [39]. On the other hand, Tuven et al. found that a high level of UA was a protective factor for cognitive function [40]. A recent pioneering study showed a U-shaped relationship between UA and cognitive function in people with T2D. They further confirmed that each additional UA unit was associated with a 0.7% reduction in MCI risk [41]. In order to better illustrate this contradiction, a longitudinal study should be conducted to further explore the association between UA and MCI in patients with diabetes.

We evaluated the relationship between visceral obesity and cognitive impairment through a new anthropometric index that makes up for the deficiencies in traditional anthropometric indices. Furthermore, we analyzed the cross-sectional associations between some commonly used clinical indicators and cognitive impairment, which can provide new ideas for clinical research into MCI. However, this study also has several limitations that should be noted. Firstly, this was a cross-sectional study. We could only examine the correlation between LAP and MCI; causal inferences cannot be made. Second, the sample size was small and patient selection bias might exist; this may limit the application of our results to other populations. Moreover, although the MoCA scale is more accurate than the Mini-Mental State Examination (MMSE) scale in screening for MCI in T2D patients, it is only a screening tool and has some limitations in evaluating specific cognitive functions. In addition, the current research did not further classify MCI patients as amnestic MCI or non-amnestic MCI. The relationship between the LAP index and different types of MCI should be further explored in future research. Finally, while the current study adjusted for demographic data, poor lifestyle, and diabetes medications, other potential confounding factors such as exercise and dietary habits were not considered.

Conclusion

In conclusion, the current research is the first to confirm that a high LAP index is closely related to MCI in T2D patients. The LAP index is a powerful indicator for identifying MCI, as compared to WC and BMI. A future longitudinal study with a larger sample size is needed to further explore this association.

Footnotes

ACKNOWLEDGMENTS

This work was supported by a grant from the Natural Foundation of Heilongjiang Province [grant number H2015057], China.

Authors’ disclosures available online ().

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