Relationship between dietary pattern and cognitive function in elderly patients with type 2 diabetes mellitus

Abstract

Objective

To analyse the relationships between dietary patterns and cognitive function in elderly patients with type 2 diabetes mellitus (T2DM).

Methods

Patients with T2DM completed a 3-day dietary record and Mini-mental State Examination (MMSE). Dietary patterns were identified by factor analysis.

Results

The study included 73 patients and identified five dietary patterns, one of which was characterized by high loading for vegetables and fish. A higher consumption of vegetables and fish was significantly associated with improved MMSE score (unadjusted model, model adjusted for age and sex, and model adjusted for age, sex, education, diabetic nephropathy and alcohol consumption), and decreased prevalence of suspected mild dementia (unadjusted model, model adjusted for age and sex).

Conclusions

A high score in the vegetables and fish dietary pattern was associated with high MMSE score and low prevalence of suspected mild dementia in elderly patients with T2DM.

Keywords

Dietary pattern suspected mild dementia type 2 diabetes mellitus

Introduction

Patients with type 2 diabetes mellitus (T2DM) are between two and four times more likely to develop cognitive impairment and dementia than individuals without T2DM.¹ Several factors (including age, sex, glycaemic control, disease duration, complications, hypertension, lipid metabolism, education, depression, physical activity and chronic infection) are known to correlate with cognitive impairment in patients with T2DM,^2–6 and there are likely to be additional unrecognized contributing factors.

Intake of certain types of foods (such as fish and vegetables) and nutrients (such as vitamins C, E, B6 and B12, folate, potassium, calcium, magnesium and unsaturated fatty acids) lowers the risk of cognitive impairment^7,8 and/or Alzheimer’s disease,⁹ although the extent of protection varies among studies. Since people do not consume individual foods or single nutrients in real life, the dietary pattern should be taken into consideration as it reflects the complexity of dietary intake, where foods have interactive, synergistic and antagonistic effects.¹⁰ It has been suggested that dietary pattern may be more predictive of disease risks than specific food- and nutrient-based approaches.¹⁰

Adherence to the Mediterranean dietary pattern (characterized by high consumption of plant foods [vegetables, fruits, legumes and cereals], high intake of olive oil, moderate intake of fish, low-to-moderate intake of dairy products and low intake of saturated fats and meat¹¹) is associated with slower cognitive decline¹² and reduced Alzheimer’s disease risk.¹³ Although the Japanese dietary pattern differs widely from the Mediterranean diet, a diet characterized by high intake of soybeans, vegetables, algae, dairy products and low intake of rice was associated with reduced risk of dementia in the general population of elderly Japanese individuals.¹⁴ Taken together, these data suggest that dietary pattern could contribute to the risk of cognitive decline and/or Alzheimer’s disease.

Patients with T2DM have been found to consume higher quantities of fruit, vegetables and meat than healthy control subjects,¹⁵ but the relationship between dietary patterns and cognitive function in patients with T2DM remains largely unexplored. The aim of this cross-sectional study was to analyse these associations using data from elderly Japanese patients with T2DM.

Patients and methods

Study population

This ancillary study was a subanalysis of an original study that investigated the relationship between olfactory function and cognitive function.¹⁶ Patients with T2DM were enrolled from the Diabetes Outpatient Clinic of Juntendo Tokyo Koto Geriatric Medical Centre (Tokyo, Japan) and Juntendo University Hospital (Tokyo, Japan) between October 2012 and December 2013, as described.¹⁶ Inclusion criteria were patients with T2DM who were aged >65 years and free of clinically evident cognitive impairment. Exclusion criteria were: (i) severe infection within the preceding 2 weeks; (ii) any scheduled or performed surgery; (iii) severe trauma; (iv) current psychiatric disorders; (v) partial or complete olfactory dysfunction associated with sinusitis, allergic rhinitis or deviated nasal septum; (vi) history of brain tumour; (vii) MMSE (mini-mental state examination) score <22 points (moderate-to-severe cognitive impairment¹⁷); (ix) patients considered ineligible based on the assessment of clinical investigators.

The study protocol was approved by the Institutional Review Board of Juntendo University Hospital and Juntendo Tokyo Koto Geriatric Medical Centre, and was conducted in accordance with the principles described in the Declaration of Helsinki. Each patient provided written informed consent prior to enrolment.

MMSE

Cognitive status was evaluated using MMSE,^18,19 and was assessed in a separate room by a trained physician. The MMSE scale ranges between 0 and 30, with a higher score indicating a better cognitive performance. Patients were divided into two groups: suspected mild dementia (score 22–26), and normal (score ≥27).¹⁷

Assessment of depression

Depression was assessed in a separate room by a trained physician (H.S.) using the BDI (Beck Depression inventory)-II.²⁰ The BDI-II scale ranges between 0 and 63, with higher scores indicating increasingly severe depression.

Dietary survey

Weighed dietary records were obtained from each patient for three consecutive days just before a scheduled diabetes-related hospital appointment. The participants were also asked to take digital pictures of their meals. A trained nutritionist (M.E.) (who was blinded to participants’ characteristics) checked all the recorded sheets and digital pictures, and interviewed subjects to clarify any ambiguous points and increase the accuracy of reporting. Mean values of the estimated individual food amounts were used in the analysis. Alcohol intake was reported as the quantities of specific types of alcoholic beverage. The consumption of foods and nutrients was estimated by a computerized dietary analysis program (HealthyMaker Pro 501; Mushroom soft Inc., Tokyo, Japan; http://www.msrsoft.com/). For dietary pattern analysis, food items were classified into 19 foods based on the whole food catalogue of the Ministry of Education, Culture, Sports, Science and Technology, Japan.

Clinical parameters

Data including body mass index (BMI; kg/m²), smoking status (nonsmokers, former smokers [no smoking in 6 months prior to study] or current smokers), and presence of retinopathy (simple, preproliferative or proliferative retinopathy as defined by trained ophthalmologists), nephropathy (microalbuminuria [30 mg/g creatinine]) and neuropathy (two or more of: symptoms; absence of ankle tendon reflexes; abnormal vibration perception threshold using a tuning fork) were obtained by patient examination.

Blood samples were obtained after overnight fast. Serum lipids (total cholesterol, high-density lipoprotein [HDL]-cholesterol, low-density lipoprotein [LDL]-cholesterol, triglycerides) and glucose were quantified using routine analyser (LABOSPECT 008; Hitachi High-Technologies, Tokyo, Japan), and HbA1c (National Glycohemoglobin Standardization Program; http://www.ngsp.org/) was quantified using a high-performance liquid chromatography analyser (HLC-723G8; Tosho Bioscience, Tokyo, Japan).

Statistical analyses

No formal sample size justification for this study was performed as it was an ancillary study of an exploratory nature. Data were presented as mean ± SD or median (interquartile range) for continuous variables, or n(%) of patients for categorical variables. Some parameters were logarithmically transformed to approximate normal distribution. Factor analysis with varimax rotation was used to reduce the complexity of dietary patterns. Factors with cumulative coefficient of determination >0.5 were retained. For simplicity, individual metabolites with a factor loading >0.4 are reported as composing that factor. Factor scores for each dietary pattern and each subject were calculated by summing each dietary pattern score, weighted by their factor loadings. Estimated factor scores were categorized into tertiles. Trend association across tertiles was evaluated using linear regression analysis for continuous variables or logistic regression analysis for categorical variables, in an unadjusted model and/or model adjusted for age and sex. Spearman’s correlation coefficient was used to evaluate correlations between MMSE score and possible risk factors for cognitive impairment (BMI, estimated duration of diabetes, glycaemic control, presence of diabetic complications [retinopathy, nephropathy and neuropathy], education, depressive status, smoking, alcohol consumption, food intake, hypertension and hyperlipidaemia). Trend associations across MMSE score and suspected mild dementia tertiles were evaluated by linear regression analysis and logistic regression analysis, respectively. Regression models included statistically significant variables (P < 0.05) from the above Spearman’s correlation analysis, in addition to age and sex. Statistical analyses were performed using SAS® software version 9.3 (SAS Institute, Cary, NC, USA), and two-sided P-values < 0.05 were considered statistically significant.

Results

The study included 73 patients (40 male/33 female; mean age 72.4 ± 5.1 years; age range 65–83 years). Demographic and clinical characteristics of the patients are shown in Table 1.

Table 1.

Demographic and clinical characteristics of patients with type 2 diabetes mellitus included in a study investigating the relationship between cognitive function and dietary patterns (n = 73).

Characteristic	n
Age, years	72.4 ± 5.1
Male sex	40 (54.8)
Body mass index, kg/m²	23.9 ± 3.3
Estimated duration of diabetes, years	15.5 ± 9.5
Current smoker	6 (8.2)
Education, years	11.8 ± 2.8
Beck Depression inventory II score²⁰	9.6 ± 7.2
Energy intake, kcal/day	1728 ± 332
Alcohol consumption, g/day	4.8 ± 11.1
Diabetic retinopathy	28 (38.4)
Diabetic nephropathy	40 (54.8)
Diabetic neuropathy	54 (72.0)
Hypertension	45 (61.6)
Hyperlipidaemia	53 (72.6)
Oral hypoglycaemic drugs	56 (76.7)
Insulin therapy	17 (23.3)
Hypertension medication	34 (46.6)
Hyperlipidaemia medication	38 (52.1)
Antiplatelet agents	18 (24.7)
Systolic blood pressure, mmHg	131.5 ± 15.5
Diastolic blood pressure, mmHg	69.9 ± 12.0
Glycosylated haemoglobin, %	7.3 ± 0.9
Fasting blood glucose, mg/dl	128 ± 32
Total cholesterol, mg/dl	186 ± 36
High-density lipoprotein cholesterol, mg/dl	56 ± 14
Low-density lipoprotein cholesterol, mg/dl	109 ± 29
Triglyceride, mg/dl	83 (63–106)
Mini-mental State Examination score^18,19	26.9 ± 2.2
Suspected cognitive impairment	26 (35.6)

Data presented as mean ± SD, n (%) or median (interquartile range).

Factor analysis with varimax rotation identified five dietary patterns (Table 2): (i) oils, nuts, seeds, sugars and eggs (high loadings for fats and oils, nuts, seeds, sugars and eggs); (ii) cereals and meats (high loadings for cereals, meats, seasonings, spices and mushrooms); (iii) fruits and potatoes (high loadings for fruits, potatoes, cooking and processing, algae, green and dark-yellow vegetables, and milk and milk products); (iv) vegetables and fish (high loadings for green/dark yellow leafy vegetables, fish and seafood, and beverages); (v) pulses (high loading for pulses). Overall, these five patterns accounted for 56% of the variance in food intake.

Table 2.

Dietary patterns of patients with type 2 diabetes mellitus, as determined by factor analysis with varimax rotation of food diary information (n = 73).

Components and item labels	Dietary pattern
Components and item labels	Oils, nuts, seeds, sugars, eggs	Cereals and meats	Fruits and potatoes	Vegetables and fish	Pulses
Cereals	0.36	0.59	−0.04	−0.23	0.16
Potatoes and starch	0.07	0.23	0.62	−0.01	0.24
Sugars	0.78	0.15	0.26	0.00	−0.12
Pulses	0.04	0.12	0.09	0.32	0.73
Nuts and seeds	0.81	−0.31	0.09	0.22	−0.06
Green and dark yellow vegetables	−0.21	0.08	0.45	0.48	0.20
Leafy vegetables	−0.08	0.36	−0.02	0.76	0.03
Fruits	0.09	−0.19	0.71	−0.02	−0.15
Mushrooms	−0.09	0.55	−0.04	0.13	0.03
Algae	−0.08	0.14	0.51	0.32	−0.31
Fish and seafood	0.27	−0.15	−0.10	0.73	0.18
Meats	0.24	0.56	0.01	−0.24	−0.51
Eggs	0.48	0.18	0.05	−0.26	0.33
Milk and milk products	−0.10	−0.63	0.44	0.05	0.02
Fats and oils	0.87	0.04	−0.14	−0.01	−0.11
Snacks	0.07	−0.06	0.07	0.09	−0.44
Beverages	−0.03	−0.32	0.19	0.51	−0.26
Seasonings and spices	−0.15	0.56	0.30	0.13	0.28
Cooking and processing	0.14	−0.27	0.58	−0.04	−0.05
Contribution, %	15	14	12	8	7

Table 3 shows the nutrient intake for each dietary pattern. In the oils, nuts, seeds, sugars and eggs pattern, there was a significant trend across the tertiles for increased intake of carbohydrate, protein, fat, zinc, copper, cholesterol, salt, saturated fatty acids and omega 6 fatty acids (P < 0.05 for each comparison). Across the tertiles in the cereals and meats pattern, there were significant trends for increased consumption of carbohydrate, zinc, copper, fibre and salt (P < 0.05 for each comparison). In the fruits and potatoes pattern, there were significant trends towards increased intake of carbohydrate, vitamin B6, vitamin C, folic acid, calcium, magnesium, zinc, copper and fibre (P < 0.05 for each comparison). In the vegetables and fish pattern, there were significantly higher intakes of protein, fat, vitamin B6, vitamin B12, niacin, vitamin C, vitamin E, folic acid, potassium, calcium, magnesium, zinc, copper, fibre, salt, monounsaturated fatty acids, omega 3 fatty acids and omega 6 fatty acids across the tertiles (P < 0.05 for each comparison). There was a significant trend towards increased cholesterol consumption in the pulses pattern (P < 0.05).

Table 3.

Nutrient intake associated with dietary patterns (identified by factor analysis with varimax rotation of food diary information) in patients with type 2 diabetes mellitus (n = 73), stratified by tertile.

Parameter	Oils, nuts, seeds, sugars, eggs		Cereals and meats		Fruits and potatoes		Vegetables and fish		Pulses
Parameter	Tertile 1	Tertile 3	Tertile 1	Tertile 3	Tertile 1	Tertile 3	Tertile 1	Tertile 3	Tertile 1	Tertile 3
Carbohydrate, g	189 ± 30	250 ± 45***	201 ± 53	243 ± 42*	197 ± 46	243 ± 41***	220 ± 44	221 ± 42	219 ± 48	221 ± 45
Protein, g	66.3 ± 12.7	80.5 ± 11.1**	71.0 ± 16.1	79.1 ± 11.5	70.2 ± 15.3	78.4 ± 12.6	67.0 ± 14.2	81.0 ± 10.8***	72.9 ± 12.2	75.5 ± 14.3
Fat, g	46.5 ± 11.0	65.5 ± 15.0*	58.0 ± 17.5	60.1 ± 18.6	57.0 ± 21.9	58.3 ± 13.1	56.6 ± 15.2	62.6 ± 21.9*	61.0 ± 19.2	56.2 ± 14.9
Vitamin B6, mg	1.30 ± 0.38	1.50 ± 0.54	1.29 ± 0.35	1.55 ± 0.39	1.27 ± 0.54	1.60 ± 0.35**	1.14 ± 0.32	1.63 ± 0.31***	1.44 ± 0.35	1.39 ± 0.39
Vitamin B12, µg	8.69 ± 6.69	9.26 ± 4.62	8.46 ± 4.53	8.10 ± 4.15	9.59 ± 7.00	8.12 ± 4.55	6.14 ± 3.27	9.43 ± 3.93*	8.17 ± 3.90	9.09 ± 7.27
Niacin, mg	29.1 ± 6.1	33.3 ± 7.8	30.0 ± 8.8	33.6 ± 4.9	30.1 ± 9.0	33.5 ± 6.1	26.9 ± 6.3	35.3 ± 5.5***	32.2 ± 5.7	30.5 ± 7.3
Vitamin C, mg	110 ± 44	123 ± 74	113 ± 61	134 ± 58	91 ± 57	152 ± 50***	97 ± 34	153 ± 62***	132 ± 51	118 ± 63
Vitamin E, mg	19.7 ± 5.2	25.8 ± 6.1	23.2 ± 6.4	25.6 ± 9.5	23.8 ± 9.3	25.1 ± 6.4	22.6 ± 6.4	26.6 ± 9.5*	23.3 ± 9.5	24.8 ± 6.5
Folic acid, µg	353 ± 107	393 ± 147	366 ± 137	413 ± 148	341 ± 146	428 ± 121*	319 ± 67	465 ± 159***	409 ± 155	386 ± 136
Potassium, mg	276 ± 156	318 ± 106	259 ± 98	339 ± 159	309 ± 142	357 ± 169	292 ± 133	374 ± 156*	288 ± 133	366 ± 153
Calcium, mg	527 ± 186	622 ± 244	642 ± 239	562 ± 221	480 ± 238	658 ± 207**	466 ± 160	676 ± 218***	564 ± 231	644 ± 209
Magnesium, mg	263 ± 65	303 ± 80	275 ± 70	319 ± 82	262 ± 81	334 ± 80**	250 ± 73	337 ± 67***	283 ± 75	314 ± 82
Zinc, mg	7.19 ± 1.20	8.99 ± 1.80*	7.33 ± 1.59	9.08 ± 1.90**	7.57 ± 1.64	8.87 ± 1.98*	7.85 ± 2.26	8.52 ± 1.43*	8.24 ± 1.73	8.34 ± 2.03
Copper, mg	1.05 ± 0.22	1.31 ± 0.31*	1.09 ± 0.28	1.33 ± 0.32*	1.13 ± 0.25	1.34 ± 0.33*	1.07 ± 0.27	1.34 ± 0.29***	1.16 ± 0.29	1.30 ± 0.35
Fibre, g	14.4 ± 4.4	16.8 ± 4.7	14.4 ± 4.5	18.3 ± 5.0*	13.3 ± 4.4	18.7 ± 5.0***	13.6 ± 3.5	18.5 ± 4.9***	16.2 ± 5.1	16.8 ± 5.2
Cholesterol, mg	249 ± 86	384 ± 138**	296 ± 112	345 ± 133	335 ± 149	303 ± 109	332 ± 127	323 ± 125	284 ± 102	348 ± 154*
Salt, g	9.02 ± 1.89	11.38 ± 1.92**	9.32 ± 2.23	11.50 ± 1.81**	10.07 ± 2.89	10.74 ± 1.63	9.59 ± 2.46	11.00 ± 1.90**	10.00 ± 1.60	10.41 ± 2.50
Saturated fatty acids, g	12.2 ± 4.1	17.0 ± 5.4*	15.5 ± 4.7	14.6 ± 4.1	14.1 ± 6.0	14.6 ± 3.8	14.7 ± 5.2	15.5 ± 5.3	16.0 ± 5.3	14.2 ± 4.4
Monounsaturated fatty acids, g	16.3 ± 4.7	23.2 ± 6.2	20.7 ± 7.5	21.7 ± 8.5	20.6 ± 9.4	20.1 ± 5.8	19.9 ± 6.1	22.7 ± 9.8*	22.6 ± 8.5	18.7 ± 6.5
Polyunsaturated fatty acids, g	10.6 ± 2.8	15.1 ± 3.6	13.1 ± 4.6	14.9 ± 6.2	14.1 ± 6.3	14.0 ± 3.9	12.6 ± 3.4	15.6 ± 6.6**	13.2 ± 6.2	14.0 ± 4.2
Omega 3 fatty acid, g	2.46 ± 1.03	2.90 ± 1.26	2.91 ± 1.52	3.00 ± 1.81	3.07 ± 1.85	2.86 ± 1.34	2.24 ± 0.74	3.74 ± 1.94***	2.87 ± 1.83	2.83 ± 1.41
Omega 6 fatty acid, g	8.10 ± 2.18	12.11 ± 2.73*	10.15 ± 3.41	11.88 ± 4.62	10.98 ± 4.74	11.11 ± 2.91	10.35 ± 2.91	11.77 ± 5.03*	10.26 ± 4.61	11.15 ± 3.08

Data presented as mean ± SD before adjustment for age and sex.

P < 0.05, **P < 0.01, ***P < 0.001 across tertiles; linear regression analysis for continuous variables or logistic regression analysis for categorical variables, adjusted for age and sex.

Table 4 shows the demographic and clinical characteristics of study subjects stratified by dietary pattern score. In the oils, nuts, seeds, sugars and eggs pattern, there was a significant trend towards increased calorie consumption and LDL cholesterol level (P < 0.05 for each comparison). In the cereals and meats pattern, there was a significant trend towards lower HDL cholesterol (P < 0.05). There were significant trends for increased calorie consumption and lower prevalence of diabetic retinopathy in the fruits and potatoes pattern (P < 0.05 for each comparison). Across the tertiles in the vegetables and fish pattern, there were significant trends towards increased calorie and alcohol consumption, higher diastolic blood pressure, and reduced likelihood of antiplatelet agent use (P < 0.05 for each comparison). There was a significant trend towards higher diastolic and systolic blood pressure in the pulses pattern (P < 0.05 for each comparison).

Table 4.

Demographic and clinical characteristics associated with dietary patterns (identified by factor analysis with varimax rotation of food diary information) in patients with type 2 diabetes mellitus (n = 73), stratified by tertile.

Variable	Oils, nuts, seeds, sugars, eggs		Cereals and meats		Fruits and potatoes		Vegetables and fish		Pulses
Variable	Tertile 1	Tertile 3	Tertile 1	Tertile 3	Tertile 1	Tertile 3	Tertile 1	Tertile 3	Tertile 1	Tertile 3
Age, years	72.5 ± 5.7	73.7 ± 5.0	72.1 ± 4.8	71.2 ± 4.5	73.5 ± 4.9	72.3 ± 5.5	73.1 ± 5.3	71.1 ± 4.2	72.4 ± 4.8	73.0 ± 5.2
Male sex	7 (29.2)	22 (88.0)	8 (33.3)	18 (72.0)	13 (54.2)	16 (64.0)	18 (75.0)	13 (52.0)	14 (58.3)	14 (53.8)
Body mass index, kg/m²	24.8 ± 2.8	23.5 ± 3.6	24.4 ± 3.1	24.1 ± 3.2	24.4 ± 3.9	23.7 ± 2.5	24.6 ± 3.9	24.3 ± 3.0	23.6 ± 3.3	24.4 ± 2.9
Estimated duration of diabetes, years	14.8 ± 8.9	16.4 ± 10.0	16.0 ± 8.9	15.7 ± 10.3	16.0 ± 8.4	14.9 ± 9.1	17.2 ± 11.7	14.0 ± 7.2	15.1 ± 9.4	13.8 ± 8.3
Current smoker	1 (4.2)	2 (8.0)	1 (4.2)	2 (8.0)	3 (12.5)	1 (4.0)	4 (16.7)	1 (4.0)	2 (8.3)	2 (7.7)
Education, years	11.7 ± 2.5	12.0 ± 3.5	12.8 ± 3.0	11.9 ± 2.5	11.3 ± 3.3	11.9 ± 2.6	11.4 ± 2.4	12.9 ± 3.2	11.8 ± 3.0	11.7 ± 2.7
Beck Depression inventory II score²⁰	8.4 ± 5.3	9.3 ± 8.2	9.4 ± 7.5	8.9 ± 6.7	11.1 ± 6.7	8.5 ± 5.5	8.9 ± 5.9	9.0 ± 6.8	10.1 ± 8.0	7.9 ± 5.9
Energy, kcal/day	1460 ± 215	1960 ± 221***	1667 ± 402	1863 ± 277	1615 ± 323	1864 ± 317**	1686 ± 311	1831 ± 335**	1746 ± 340	1725 ± 287
Alcohol, g/day	1.9 ± 4.8	5.0 ± 9.4	8.3 ± 16.7	3.6 ± 6.8	3.0 ± 7.3	7.7 ± 15.9	2.4 ± 4.9	8.7 ± 15.6*	3.8 ± 8.9	3.2 ± 6.3
Diabetic retinopathy	9 (37.5)	9 (36.0)	12 (50.0)	7 (28.0)	12 (50.0)	4 (16.0)*	12 (50.0)	7 (28.0)	8 (33.3)	9 (34.6)
Diabetic nephropathy	8 (33.3)	16 (64.0)	10 (41.7)	16 (64.0)	14 (58.3)	13 (52.0)	17 (70.8)	11 (44.0)	11 (45.8)	16 (61.5)
Diabetic neuropathy	18 (75.0)	17 (68.0)	20 (83.3)	21 (84.0)	18 (75.0)	20 (80.0)	18 (75.0)	18 (72.0)	19 (79.2)	20 (76.9)
Hypertension	10 (41.7)	12 (48.0)	16 (66.7)	15 (60.0)	16 (66.7)	13 (52.0)	16 (66.7)	18 (72.0)	13 (54.2)	16 (61.5)
Hyperlipidaemia	16 (66.7)	18 (72.0)	18 (75.0)	16 (64.0)	16 (66.7)	20 (80.0)	19 (79.2)	15 (60.0)	17 (70.8)	20 (76.9)
Oral hypoglycaemic drugs	17 (70.8)	18 (72.0)	21 (87.5)	18 (72.0)	20 (83.3)	18 (72.0)	18 (75.0)	21 (84.0)	19 (79.2)	20 (76.9)
Insulin therapy	6 (25.0)	6 (24.0)	4 (16.7)	9 (36.0)	6 (25.0)	5 (20.0)	6 (25.0)	3 (12.0)	4 (16.7)	7 (26.9)
Hypertension medication	6 (25.0)	11 (44.0)	13 (54.2)	11 (44.0)	15 (62.5)	11 (44.0)	13 (54.2)	11 (44.0)	10 (41.7)	12 (46.2)
Hyperlipidaemia medication	13 (54.2)	10 (40.0)	13 (54.2)	14 (56.0)	13 (54.2)	13 (52.0)	13 (54.2)	11 (44.0)	13 (54.2)	15 (57.7)
Antiplatelet agents	4 (16.7)	4 (16.0)	4 (16.7)	9 (36.0)	6 (25.0)	6 (24.0)	8 (33.3)	1 (4.0)*	5 (20.8)	8 (30.8)
Systolic BP, mmHg	132.2 ± 17.0	127.0 ± 15.3	135.5 ± 17.0	128.6 ± 15.6	130.0 ± 12.1	131.2 ± 12.6	130.7 ± 15.9	134.8 ± 13.9	128.6 ± 12.7	137.4 ± 15.8*
Diastolic BP, mmHg	72.0 ± 13.8	69.8 ± 10.9	69.2 ± 12.3	70.6 ± 11.9	69.1 ± 11.8	72.4 ± 10.6	67.1 ± 9.2	73.4 ± 12.3*	67.5 ± 10.5	77.1 ± 11.2**
Glycosylated haemoglobin, %	7.5 ± 1.2	7.2 ± 0.7	7.4 ± 0.8	7.4 ± 0.8	7.3 ± 0.7	7.5 ± 1.1	7.4 ± 1.0	7.3 ± 0.6	7.3 ± 1.3	7.4 ± 0.6
Fasting blood glucose, mg/dl	125 ± 30	130 ± 38	135 ± 29	127 ± 39	125 ± 27	133 ± 31	128 ± 38	128 ± 24	126 ± 31	129 ± 23
Total cholesterol, mg/dl	181 ± 30	191 ± 39	192 ± 33	177 ± 36	194 ± 31	180 ± 41	175 ± 42	192 ± 31	185 ± 35	180 ± 41
High-density lipoprotein cholesterol, mg/dl	55 ± 13	55 ± 14	61 ± 15	52 ± 13*	54 ± 13	57 ± 17	52 ± 15	59 ± 14	58 ± 14	53 ± 14
Low-density lipoprotein cholesterol, mg/dl	103 ± 24	116 ± 29*	108 ± 28	104 ± 27	114 ± 27	103 ± 31	102 ± 35	110 ± 29	107 ± 27	107 ± 28
Triglyceride, mg/dl	84 (73–96)	78 (60–107)	82 (60–95)	70 (57–86)	104 (74–168)	83 (68–88)	75 (60–112)	83 (67–102)	77 (64–87)	82 (60–102)

Data presented as mean ± SD n (%) or median (interquartile range) before adjustment for age and sex.

P < 0.05, **P < 0.01, ***P < 0.001 across tertiles; linear regression analysis for continuous variables or logistic regression analysis for categorical variables, adjusted for age and sex.

Correlation analysis found significant relationships between MMSE score and education (r = 0.38, P = 0.001), presence of diabetic nephropathy (r = −0.29, P = 0.013) and alcohol consumption (r = 0.24, P = 0.042). Data regarding cognitive function across the tertiles for each dietary pattern are shown in Table 5. A higher consumption of vegetables and fish was significantly associated with improved MMSE score (unadjusted model, P < 0.001; model adjusted for age and sex, P < 0.01; model adjusted for age, sex, education, diabetic nephropathy and alcohol consumption, P < 0.05) and decreased prevalence of suspected mild dementia (unadjusted model, P < 0.05; model adjusted for age and sex, P < 0.05). There were no other significant trends in cognitive function in any other dietary pattern.

Table 5.

Cognitive function in patients with type 2 diabetes mellitus (n = 73), stratified by tertiles of dietary pattern (identified by factor analysis with varimax rotation of food diary information).

Dietary pattern	Variable	Tertile 1	Tertile 2	Tertile 3	Trend estimation
Dietary pattern	Variable	Tertile 1	Tertile 2	Tertile 3	A	B	C
Oils, nuts, seeds, sugars, eggs	MMSE score^18,19	27.0 ± 2.3	27.0 ± 2.1	26.6 ± 2.4	−0.50	0.54	0.44
Oils, nuts, seeds, sugars, eggs	Suspected mild dementia	9 (37.5)	7 (29.2)	10 (40.0)	0.19	−0.62	−0.20
Cereals and meats	MMSE score	27.5 ± 1.8	26.2 ± 2.5	26.9 ± 2.2	−1.02	−0.50	0.37
Cereals and meats	Suspected mild dementia	5 (20.8)	12 (50.0)	9 (36.0)	1.08	0.73	−0.17
Fruits and potatoes	MMSE score	26.4 ± 2.0	26.9 ± 2.2	27.2 ± 2.5	1.30	1.43	0.95
Fruits and potatoes	Suspected mild dementia	10 (41.7)	9 (37.5)	7 (28.0)	−1.00	−1.08	−0.45
Vegetables and fish	MMSE score	25.5 ± 2.3	27.5 ± 1.6	27.6 ± 2.1	3.45***	3.08**	2.17*
Vegetables and fish	Suspected mild dementia	14 (58.3)	6 (25.0)	6 (24.0)	−2.43*	−2.19*	−1.49
Pulses	MMSE score	27.5 ± 2.2	26.2 ± 2.5	26.9 ± 1.8	−0.94	−0.99	−0.79
Pulses	Suspected mild dementia	7 (29.2)	9 (39.1)	10 (38.5)	0.67	0.72	0.74

Data presented as mean ± SD or n (%) before adjustment for age, sex, education, presence of diabetic nephropathy and alcohol consumption.

A, unadjusted model; B, adjusted for age and sex; C, adjusted for age, sex, education, presence of diabetic nephropathy and alcohol consumption.

P < 0.05, **P < 0.01, ***P < 0.001; linear regression analysis (MMSE score) or logistic regression analysis (suspected mild dementia).

MMSE: Mini-mental State Examination.¹⁸

Discussion

The present study identified five dietary patterns, of which a high score in the vegetables and fish pattern was associated with a heightened MMSE score and a reduced risk of suspected mild dementia in elderly patients with T2DM.

A high score in the vegetables and fish pattern was also associated with a heightened intake of various vitamins and minerals, in the present study. These are known to act as antioxidant agents, and oxidative stress is a substantial risk factor for age-related cognitive decline.²¹ The antioxidant rich diet in the vegetables and fish pattern may account for the high MMSE score and low prevalence of mild dementia in these subjects. Consistent with our findings, others have demonstrated an association between a high intake of vegetables and a reduced risk of cognitive decline.^22–24

In the present study, subjects with a high score in the fruits and potatoes pattern also consumed a diet that was high in vitamins and minerals, but there was no association between this dietary pattern and cognitive function. The effect of a high intake of fruits on the prevention of cognitive decline is unclear.²⁴ A significant negative association has been identified between fructose intake and cognitive function among middle-aged and elderly subjects without T2DM,²⁵ and it is possible that high fructose intake may attenuate the beneficial effects of vitamins and minerals from fruits. On the other hand, others have found that high fruit intake correlated with low incidence of diabetic retinopathy,²⁶ suggesting that the antioxidative effect of vitamins may prevent its progression. This is consistent with the present finding of a low prevalence of diabetic retinopathy in the highest tertile of the fruits and potatoes pattern.

Studies have found a correlation between high fish consumption and a reduced risk of cognitive decline,^27–29 although reports are not consistent.^30,31 Generally, fish contains high amount of omega 3 fatty acids in addition to vitamins and minerals. There was a positive correlation between the vegetables and fish pattern and omega 3 fatty acids in the present study, which may be beneficial for brain health via their anti-inflammatory, antioxidative and antithrombotic properties.³² The higher cognitive function and lower use of antiplatelet agents in subjects with a high vegetables and fish score, compared with other subjects in the present study, may be at least in part due to their high intake of omega 3 fatty acids.

Several studies have demonstrated that light-to-moderate alcohol consumption is associated with high cognitive test score and/or risk reductions in the development of dementia,^33–36 although other studies do not support such findings.^37,38 While the mechanisms underlying the relationship between alcohol intake and cognitive function remain largely unknown, moderate alcohol intake may play a role in cardioprotection and/or neuroprotection through the activation of cellular survival pathways,³⁹ leading to reductions in the risk of cardiovascular and/or cerebrovascular diseases,^40,41 and, ultimately, a reduced risk of cognitive decline. Alcohol consumption was very modest in the present study, but correlated with MMSE score. In addition, patients with a high vegetables and fish score consumed significantly more alcohol than those in the lowest tertile. Thus, the consumption of moderate amounts of alcohol (8 g/day) in those subjects could also be associated with better cognitive function. It is interesting to note that the vegetables and fish pattern was associated with high MMSE score, even after adjustment for confounding factors including alcohol intake.

The present study has several limitations. First, the study was a subanalysis of a small sample size study with a cross-sectional design. Such study design may result in selection bias and does not allow inference of a causal relationship between dietary patterns and cognitive function. In particular, it is possible that dietary patterns may have changed as a result of altered cognition; also, we cannot exclude the possibility that dietary patterns found in this study were influenced by unknown factors caused by a decline in cognitive function. Furthermore, some of the negative results may be related to the underpowered sample size. Another potential limitation is that we evaluated dietary patterns and cognitive function via self-reported questionnaires; this method has been widely used in studies, however. It is also necessary to acknowledge that the validity and reproducibility of dietary patterns identified in this study have not been confirmed, although the methods employed have been widely used. In addition, we did not include a group of control subjects without T2DM, and it is therefore impossible to determine whether our findings are general or specific to T2DM. Other lifestyle factors such as physical activity, sleep/wake pattern and metal health, were not evaluated, and we cannot exclude the possibility that subjects with higher scores for vegetables and fish may have had higher health-related literacy than other subjects. Finally, we could not fully consider possible confounding factors associated with cognitive function.

In conclusion, a high score in the vegetables and fish dietary pattern was associated with high MMSE score and low prevalence of suspected mild dementia in elderly patients with T2DM.

Footnotes

Declaration of conflicting interest

T.M. received research funds from MSD, Takeda and Eli Lilly. M.G. has received lecture fees from Novartis Pharmaceuticals and travel fees from Takeda Pharmaceutical Co. H.W. has received lecture fees from Boehringer Ingelheim, Sanofi-Aventis, Ono Pharmaceutical Co., Novo Nordisk Pharma, Novartis Pharmaceuticals, Eli Lilly, Sanwakagaku Kenkyusho, Daiichi Sankyo Inc., Takeda Pharmaceutical Co., MSD, Dainippon Sumitomo Pharm., Kowa Co. and research funds from Boehringer Ingelheim, Pfizer, Mochida Pharmaceutical Co., Sanofi-Aventis, Novo Nordisk Pharma, Novartis Pharmaceuticals, Sanwakagaku Kenkyusho, Terumo Corp. Eli Lilly, Mitsubishi Tanabe Pharma, Daiichi Sankyo Inc., Takeda Pharmaceutical Co., MSD, Shionogi, Pharma, Dainippon Sumitomo Pharma, Kissei Pharma, and Astrazeneca. These funding sources were potentially related to the present study.

Funding

This study was funded by a grant from the Ministry of Education, Sports and Culture of Japan to Chie Ohmura (grant number 23500858).

Acknowledgements

We thank all patients who participated in this study and all the staff at Juntendo University Graduate School of Medicine, Department of Medicine, Metabolism and Endocrinology (Tokyo, Japan), and Juntendo Tokyo Koto Geriatric Medical Centre, Department of Medicine, Diabetology and Endocrinology (Tokyo, Japan).

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