Dietary fat and risk of colon and rectal cancer with aberrant MLH1 expression, APC or KRAS genes

Objective To investigate baseline fat intake and the risk of colon and rectal tumors lacking MLH1 (mutL homolog 1, colon cancer, nonpolyposis type 2) repair gene expression and harboring mutations in the APC (adenomatous polyposis coli) tumor suppressor gene and in the KRAS (v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog) oncogene.
Introduction
Although dietary fat has been implicated in the etiology of colorectal cancer [1], results from epidemiological studies are inconsistent [2, 3] and often do not support an association, as observed recently in the Women’s Health Study [4]. Fortunately, current molecular techniques to detect DNA alterations on a large scale allow studying molecular endpoints for colorectal cancer, characterized by acquired (epi) genetic defects in tumor DNA [5]. This approach may improve our ability to observe associations between dietary factors and cancer that may otherwise remain undetected.
A multistep model linking sporadic colorectal carcinogenesis to molecular aberrations has been proposed [6–8], with DNA repair genes, tumor suppressor genes and oncogenes, operating in multiple genetic pathways. About 10–20% of sporadic colon carcinomas are characterized by microsatellite instability, predominantly due to promoter methylation of the MLH1 (mutL homolog 1, colon cancer, nonpolyposis type 2) DNA mismatch repair gene, which prevents expression of the enzyme [9]. Up to 90% of colon and rectum carcinomas are chromosomally instable [10, 11] and are associated with mutations in the APC (adenomatous polyposis coli) and TP53 (tumor protein 53) tumor suppressor genes and in the KRAS (v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog) oncogene [12]. However, simultaneous occurrence of mutations in these three genes is rare suggesting that, even within this group of chromosomally instable tumors, different genetic pathways to colorectal cancer exist [13, 14]. Mutations in the APC gene are found to occur relatively early in colorectal tumorigenesis and are observed in up to 80% of both adenomas and carcinomas [8, 15]. Mutations in the KRAS gene are observed in approximately 10–20% of small adenomas and 40–50% of larger adenomas and carcinomas, suggesting it to be an important event in the progression of adenoma to carcinoma [15]. Mutations in the TP53 gene are postulated to affect relatively late stages of colorectal carcinogenesis [15].
Breivik et al. proposed that the type of genetic instability in cancer cells reflects the selection pressures exerted by specific carcinogens [16]. Bardelli et al. subsequently tested this hypothesis in immortal genetically stable human cells and concluded that exposure to specific carcinogens can indeed select for tumor cells with distinct forms of genetic instability and vice versa [17]. Therefore, DNA adducts derived from dietary fat metabolism could also be associated with colorectal tumors exhibiting chromosomal instability. This is supported by the observations that malondialdehyde (MDA), generated during lipid peroxidation and arachidonic acid metabolism, can form DNA adducts and induce G→T transversions and G→A transitions in DNA [18, 19]. In addition, higher levels of MDA-DNA adducts have been observed in colorectal tissue of adenoma patients than in tissue of controls [20]. MDA levels are modulated by dietary factors, with polyunsaturated fatty acids, and specifically ω-6 polyunsaturated fatty acids, presumably increasing MDA levels [21]. This is in line with our previous report of a significant association between the intake of linoleic acid, the most abundant ω-6 polyunsaturated fatty acid in the diet, and increased risk of colon carcinomas with a mutated KRAS gene within the Netherlands Cohort Study (NLCS) on diet and cancer [22].
These observations and hypotheses prompted us to investigate the associations between the intake of total fat and different types of fat and the risk of colon and rectal tumors lacking MLH1 expression and with and without APC gene mutations, two early events in colorectal tumorigenesis, independent of tumors harboring KRAS gene mutations.
Materials and methods
Study population
The prospective NLCS was initiated in The Netherlands in September 1986. The study design has been described in detail elsewhere [23]. Briefly, at baseline a total of 58,279 men and 62,573 women, between the ages of 55 and 69 years, completed a self-administered food frequency and lifestyle questionnaire. Incident cancer cases are identified by monitoring of the entire cohort for cancer occurrence through annual record linkage to the National Cancer Registry (NCR), consisting of nine regional cancer registries throughout The Netherlands, and to PALGA, a nationwide network and registry of histo- and cytopathology [24]. The NCR and PALGA together provide a near 100% coverage of the 204 municipalities included in the NLCS.
Accumulation of person-time in the cohort was estimated through biennial vital status follow-up of a subcohort of 3,500 men and women who were randomly selected after baseline exposure measurement [24]. Cases with prevalent cancer other than non-melanoma skin cancer were excluded from the subcohort, which left 3,346 men and women for analysis next to all colorectal cancer cases from the entire cohort. No subcohort members were lost to follow-up. A flow diagram of subcohort members and patients on whom the analyses are based is given in Fig. 1.
Fig. 1Flow diagram of the number of subjects on whom the final statistical analyses were based. aNetherlands Cancer Registry. bPathologisch Anatomisch Landelijk Geautomatiseerd Archief. cPatients with rectosigmoid tumors were not included in the analyses. dmutL homolog 1, colon cancer, nonpolyposis type 2. eAdenomatous polyposis coli. fMutation cluster region. gv-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog. hPatients with rectal tumors were not included in the analysis according to MLH1 expression
The first 2.3 years of follow up were excluded because of possible preclinical disease affecting exposure status and because of incomplete nationwide coverage of PALGA alone (i.e., not in combination with the NCR) in some of the municipalities included in the NLCS. Within this period, 83 subcohort members deceased or were diagnosed with cancer other than non-melanoma skin cancer, leaving 3,263 subcohort members for analysis. From 1989 to 1994, 929 incident cases with histologically confirmed colorectal cancer were identified within the entire cohort, of whom 819 could also be linked to a PALGA report of the lesion. The PALGA reports were used to identify and locate tumor tissues from eligible colorectal cancer patients in 54 pathology laboratories throughout the Netherlands. Cancers were classified according to site as follows, colon: cecum through sigmoid colon (ICD-O codes 153:0, 153.1, 153.2, 153.3, 153.4, 153.5, 153.6, 153.7), proximal colon (ICD-O codes 153.0, 153.1, 153.4, 153.5, 153.6), distal colon (ICD-O codes 153.2, 153,3, `53.7), rectosigmoid (ICD-O code 154.0), and rectum (ICD-O code 154.1).
Tissue samples
Approval for collection of archival tissue samples from colorectal cancer patients was obtained from the Ethical Review Board of University Maastricht, the NCR and PALGA. The tissue specimen collection started in August 1999 and was completed in December of 2001. For five percent of patients, tissue samples could not be retrieved (44/819) due to administrative inconsistencies. Of 775 available tissue samples, 737 (95%) contained sufficient tumor material for molecular analyses of MLH1 expression and mutations in APC and KRAS genes.
Since the rectosigmoid can be considered as a clinically applied term rather than an anatomically defined transitional zone between the colon and rectum, the 85 patients with a rectosigmoid tumor were excluded from data analysis. Moreover, the group of patients with a rectosigmoid tumor was too small for adequate stratified analysis.
MLH1 expression analysis
Formalin-fixed, paraffin-embedded tissues were sectioned at 4 μm and contained tumor tissue and normal adjacent mucosa. Endogeneous peroxidase activity was blocked with 3% H2O2. Slides were submitted to microwave antigen retrieval in 1mM EDTA buffer (pH 8.0) and incubated with 10% normal horse serum for 10 min at room temperature. Then, sections were incubated overnight at 4°C with mouse monoclonal antibodies against MLH1 protein (clone G168–15, PharMingen, San Diego, CA) at a 1:100 dilution. Antibody binding was detected by incubating the sections at room temperature with the peroxidase-labeled DAKO Envision System (DAKO, Carpinteris, CA) and using DAB as a chromogen. Sections were counterstained with haematoxylin.
Lesions were considered to lack MLH1 protein expression when unequivocal absence of nuclear staining of the tumor epithelial cells was observed. Nuclear staining of normal epithelial and stromal cells or lymphocytes served as internal positive control. Two investigators reviewed the immunohistochemical staining independently and discrepancies were re-examined and discussed with a pathologist until consensus was reached. MLH1 expression status was determined successfully in 98% of samples, i.e., 468 colon tumors and 173 rectum tumors (Fig. 1).
APC mutation analysis
The majority of somatic mutations in APC occur within the mutation cluster region. Mutation analysis of the mutation cluster region (codons 1,286–1,520), was performed on archival adenocarcinoma specimens, using macrodissection followed by extraction of tumor DNA. Then nested PCR was used to amplify the mutation cluster region in four overlapping DNA fragments and the purified fragments were sequenced. This procedure has been described in detail elsewhere [25]. In brief, in a first round of PCR, two overlapping fragments were generated, that served as templates for a second round of PCR to amplify four overlapping biotin-labeled PCR fragments that were subsequently used for direct sequencing. The sequence profile was analyzed on ALFexpress DNA Analysis System using ALFwin software (Amersham Biosciences, Roosendaal, The Netherlands). Evaluation of the sequence patterns and data entry were independently performed by two observers. Sensitivity and specificity was assessed by analyzing the mutational status of APC in six colorectal cancer cell lines. Both sensitivity and specificity were regarded to be satisfactory since specific mutations in the mutation cluster region of APC were confirmed in CaCo2 cells, SW480 cells and LOVO cells, as previously described [25, 26], and wild type sequences were confirmed in HCT116, Colo205 and HT29, for the mutation cluster region of APC [25]. In addition, the detection limit was 5% of mutated DNA [25]. Reproducibility of mutation analysis was regarded to be satisfactory since 85% of duplicate analyses, from flank PCR of genomic DNA to sequencing of the four fragments (i.e., 61 out of 72 fragments), revealed identical mutation status of APC [25].
From 47 colon cancer patients and 25 rectum cancer patients, one or more fragments of the APC gene mutation cluster region could not be amplified and these patients were not included in this study, leaving 429 colon and 151 rectum cancer cases with successful analysis of the mutation cluster region of the APC gene (Fig. 1).
KRAS mutation analysis
Mutation analysis of the exon1 fragment of the KRAS oncogene, spanning codons 8–29, was performed on archival adenocarcinoma specimens, using nested PCR, followed by direct sequencing of purified fragments [27]. The detection limit was 5% mutated DNA. Reproducibility was regarded to be satisfactory, since 88% of duplicate analyses, from tissue sectioning to DNA sequencing (i.e., 28 out of 32), revealed identical mutation status of KRAS [27]. Mutation analysis was performed with success on 476 colon and 176 rectum cancer cases (Fig. 1).
Exposure assessment
The 150–item semi-quantitative food frequency questionnaire concentrated on habitual consumption of food and beverages during the year preceding the start of the study. Mean individual nutrient intakes per day were computed using the computerized Dutch food composition table of 1986 [28]. The questionnaire was validated against a 9-day diet record [29]. Crude and energy-gender-adjusted (in parentheses) correlation coefficients were 0.72 (0.52) for total fat, 0.73 (0.58) for saturated fat and 0.73 (0.75) for polyunsaturated fat [29]. For energy intake the correlation coefficient was 0.74. On average, the questionnaire covered 91% of the energy intake assessed by the record intake. Questionnaire data were key-entered twice and processed for all incident cases in the cohort and for all subcohort members in a manner blinded with respect to case/subcohort status.
For 7% of subjects (either cases or subcohort members), dietary data were incomplete or inconsistent, and they were excluded from the analyses. Questionnaires were considered incomplete when either: (1) more than 60 items were left blank and less than 35 items were eaten at least once a month; or (2) one or more item blocks (groups of items, e.g., beverages) were left blank. Additional details are given elsewhere [29]. This resulted in the availability of 3,048 subcohort members, 441 colon cancer cases for whom MLH1 expression status was known, and 414 colon and 136 rectal cancer cases for whom APC and KRAS mutation status was known. No data-analyses were conducted for lack of MLH1 expression in rectal cancer cases since there were only two such cases in the cohort (Fig. 1).
Intake of specific fatty acids was based on a food composition database with specific fatty acids derived from the TRANSFAIR study [30]. For this database, the hundred foods that contributed most to fat intake in the Dutch dietary pattern were sampled and analyzed as methyl esters of the fatty acids present in the foods. In the database, total fat includes triglycerides and other lipids such as phospholipids and sterols. The percentage of triglycerides in total fat is assumed to be 93% on average, but varies across food sources. Daily intakes of total fat (g/day), saturated fat (g/day), monounsaturated fat (g/day), polyunsaturated fat (g/day), and linoleic acid (C18:2, C9, 12) (g/day) and linolenic acid (C18:3, C9, 12, 15) (g/day) as the main constituents of polyunsaturated fat, were used as exposure variables. Linoleic and linolenic acid were used as the most abundant sources of ω-6 polyunsaturated fatty acids and ω-3 polyunsaturated fatty acids in the diet. In all analyses, the values for fat intake variables are adjusted for energy intake by the residual method [31]. For data analyses, quartiles of the intake of fat and different types of fats were computed based on the distribution of subcohort members. Daily intake of dietary fiber (g/day), alcohol (g/day), fruit (g/day), vegetables (g/day) and total energy (kJ/day) and age at baseline (years), sex (men/women), body mass index (kg/m2), non-occupational physical activity (<30 min/day, 30–60 min/day, 60–90 min/day, >90 min/day), family history of colorectal cancer (yes/no) and smoking status (never/ex/current) were regarded as potential confounders.
Statistical analysis
Data analyses were based on study participants for whom data on fat intake and confounding variables were complete, i.e., 2,948 subcohort members, 428 colon cancer patients for whom MLH1 expression status was known, and 401 colon cancer and 130 rectal cancer patients for whom APC and KRAS mutation status was known (Fig. 1).
Data analyses were conducted separately for overall colon and rectal cancer, colon cancer lacking MLH1 expression, colon and rectal cancer with or without a truncating APC mutation, described here as APC+ and APC− tumors respectively. Truncating APC mutations lead to the introduction of a stop codon and result in a truncated and therefore, inactive APC protein. The analyses with truncating APC mutations were also conducted separately for the most common point mutations resulting in the introduction of a stop codon, i.e., C:G → T:A or G:C → T:A point mutations. As indicated previously, associations between fat intake and KRAS mutated tumors have been described in this population previously, and a positive association between the intake of linoleic acid and KRAS mutated colon tumors was observed [22]. Therefore, when (borderline) significant associations were observed with any of the colon tumor endpoints, analyses were repeated excluding tumors harboring mutations in KRAS.
Since tumors may harbor multiple mutations it is difficult to assess whether observed associations are specific for tumors with a particular gene defect. We therefore, conducted additional analyses when (borderline) significant associations were observed. In these analyses subgroups of tumors were formed characterized by either the absence of the three gene defects, or by defects in a single gene, i.e., either only lack of MLH1 expression, only a truncating APC mutation or only an activating KRAS mutations. Activating KRAS mutations are defined as mutations in codons 12 and 13 of the KRAS gene leading to an altered amino acid.
Mean values of the intake of fat variables (g/day), and possible confounding variables including age at baseline (years), dietary fiber (g/day), alcohol (g/day), intake of fruit (g/day), vegetable (g/day), energy (kJ/day), and BMI (kg/m2), as well as distributions of the variables sex, family history of colorectal cancer (yes/no), smoking status (never/ex/current smoker) and physical activity in leisure time (<30, 30–60, 60–90, >90 min/day) were evaluated for subcohort members, colon and rectal cancer patients with or without a truncating APC mutation and colon cancer patients lacking MLH1 expression. Differences in mean values of the continuous variables between patients with or without truncating nonsense or frameshift mutations in the mutation cluster region of the APC gene, and between patients with or without MLH1 expression, were tested using the Mann–Whitney-U-test since the variables were not normally distributed among cases. The distributions of the categorical variables between patients with and without truncating APC mutations were tested with the χ2–test.
Incidence rate ratios (RR) and corresponding 95% confidence intervals (CI) for colon and rectal cancer patients were estimated according to quartiles of intake of fat variables, and one standard deviation increment of intake, using Cox proportional hazards regression models. In addition, associations were estimated for specific molecular endpoints of the tumors.
Standard errors were estimated using the robust Huber–White sandwich estimator to account for additional variance introduced by sampling the subcohort from the entire cohort [32, 33]. The proportional hazards assumption was tested using the scaled Schoenfeld residuals [34]. Tests for dose response trends over the different quartiles and categories of fat intake were estimated by fitting the ordinal exposure variables as continuous variables and evaluated using the Wald test.
The covariates included in the multivariate analyses were those found to significantly (p < 0.05) contribute to the multivariate model for colon and/or rectal cancer (age at baseline, sex, body mass index, family history of colorectal cancer, and smoking status) or to influence the RR by more than ten percent, as well as energy intake.
Results
Lack of expression in MLH1 was observed in 13% (54 out of 428) of tumors from colon cancer patients (Table 1). APC truncating mutations were observed in tumors from 32% of colon cancer patients (127 out of 401) and 44% of rectal cancer patients (57 out of 130) (Table 1). C:G→T:A transitions or G:C→T:A transversions that would result in a stop codon were observed in 10% and 5% of colon cancer patients and 12% and 5% of rectal cancer patients, respectively. These figures are similar to the percentages reported for the total group of colon and rectal cancer patients for whom APC mutation status was available, but for whom dietary intake data were not always complete [25].
Table 1Baseline dietary intake and other characteristics of the subcohort and colon and rectum cancer patients from The Netherlands cohort studySubcohortColon cancer (n = 428)p-valuebColon cancer (n = 401)p-valuebRectal cancer (n = 130)p-valuebMLH1 expressionaNo MLH1 expressionaAPC − cAPC +cAPC −cAPC  +cN2,948374542741277357    Sex (%men)4856410.0452560.4964670.79    Age (years)61.3 ± 4.263.0 ± 4.062.8 ± 4.50.9163.1 ± 4.062.7 ± 4.00.3462.2 ± 4.462.6 ± 3.50.63Fat variables (g/day)d    Total fat83.8 ± 15.885.1 ± 14.784.1 ± 17.50.5785.2 ± 15.084.8 ± 14.90.8085.1 ± 14.286.7 ± 14.80.52    Saturated fat33.2 ± 7.533.5 ± 6.633.3 ± 7.60.6433.3 ± 6.733.5 ± 7.00.9233.0 ± 5.835.5 ± 7.70.05    MUFAe31.4 ± 7.031.9 ± 6.531.1 ± 7.50.4531.8 ± 6.631.9 ± 6.60.9831.8 ± 6.132.8 ± 6.20.37    PUFAf17.3 ± 7.517.9 ± 7.517.4 ± 6.80.8518.2 ± 7.317.8 ± 7.60.4418.6 ± 8.716.5 ± 6.90.27    Linoleic acid16.0 ± 7.516.7 ± 7.516.6 ± 7.00.7617.0 ± 7.416.6 ± 7.70.3617.5 ± 8.815.2 ± 7.20.15    Linolenic acid1.3 ± 0.61.2 ± 0.51.3 ± 0.60.981.2 ± 0.51.3 ± 0.50.571.3 ± 0.61.3 ± 0.60.79Other dietary factors    Fibre (g/day)27.0 ± 8.224.5 ± 8.125.2 ± 7.10.1326.7 ± 7.927.9 ± 8.10.2028.1 ± 7.028.2 ± 9.00.78    Alcohol (g/day)g10.1 ± 14.111.0 ± 14.810.7 ± 17.00.4911.0 ± 14.911.3 ± 16.50.8912.2 ± 14.914.5 ± 18.10.53    Fruit (g/day)177.0 ± 118.0178.8 ± 122.1160.5 ± 137.70.07169.7 ± 121.9187.5 ± 132.20.11197.4 ± 155.1205.0 ± 118.30.21    Vegetables (g/day)193.8 ± 82.2191.3 ± 82.3185.4 ± 73.80.80187.9 ± 80.7192.7 ± 85.20.71186.3 ± 68.5169.0 ± 122.20.69    Energy (kj/day)8,028 ± 2,1648,080 ± 2,0597,505 ± 1,7180.077,845 ± 1,8998,335 ± 2,3060.098,433 ± 1,9248,449 ± 1,6160.92Other characteristics    BMIh (kg/m2)25.1 ± 3.125.6 ± 3.225.6 ± 3.50.5725.5 ± 3.2825.8 ± 3.10.3025.3 ± 3.125.1 ± 2.80.92    Family history of CRCi (% yes)61390.4911110.9810110.86Smoker (%)    Never37373336382633    Ex-smoker35453746434739    Current smoker2818300.1218190.8827280.59Physical activity (%)g    <30 min/day21212219261726    30-60 min/day32332233282832    60-90 min/day21211922182418    >90 min/day2725370.2026270.3132250.43aFor rectal cancer there were only two patients without MLH1 expression, these are not shown separately in this tablebp-value for the difference between cancer patients with and without MLH1 expression and colon and rectal cancer patients with and without a mutation leading to the introduction of a stop codon in APCcAPC −: cancer patients without a mutation in the MCR of the APC gene leading to a stop codon; APC + : cancer patients with a mutation in the MCR of the APC gene leading to a stop codondAdjusted for energy intakeeMonounsaturated fatfPolyunsaturated fatgFor alcohol intake and physical activity the mean levels in the subcohort are based on 2,862 and 2,915 subjects respectively. Four and six colon cancer cases had missing values for alcohol intake and physical activity respectively. Two and one rectal cancer case had missing values for alcohol intake and physical activity respectivelyhBMI: body mass indexiCRC: colorectal cancer
Colon and rectal cancer patients were generally older and more frequently men than subcohort members (Table 1). Colon cancer patients lacking MLH1 expression in their tumor were significantly less often men than patients with expression of the gene (41% vs. 56%). There were no striking differences in fat intake between patients and subcohort members or between patients with or without MLH1 expression or APC mutations in their tumors. Only rectal cancer patients with a tumor harboring a truncating APC mutation had a higher intake of saturated fat than rectal cancer patients without a truncating APC mutation (p = 0.05).
Neither total fat nor different types of fat appeared to be associated with overall colon cancer risk in this population (Table 2). For different subgroups of colon cancer based on absence of MLH1 expression or absence or presence of APC truncating mutations in their tumors, total fat intake and most of the specific types of fat intake variables were also not associated with risk. However, polyunsaturated fat intake, and especially linoleic acid intake, appeared to be associated with an increased risk of colon tumors without MLH1 expression and with colon tumors without APC truncating mutations, but not with colon tumors with APC truncating mutations (Table 2). For colon tumors without MLH1 expression, the RR according to the quartiles of linoleic acid intake were increased, though not significantly, for all the categories of intake above the reference (lowest quartile of intake), i.e., 1.66 (95% CI 0.69–3.98), 2.14 (95% CI 0.91–5.00) and 2.02 (95% CI 0.86–4.76) for the second through the fourth quartiles respectively, and the test for linear trend was borderline significant (p = 0.08). A similar trend was observed for the risk of colon tumors without APC truncating mutations (RR over the quartiles of linoleic acid intake: 1.50 (95% CI 1.02–2.21), 1.68 (95% CI 1.15–2.45) and 1.44 (95% CI 0.99–2.11) respectively, p-trend = 0.05 (Table 2). Additional analyses for subgroups of colon tumors with specific truncating point mutations in APC did not show any associations with the intake of fat or different types of fat (results not shown).
Table 2Adjusted incidence rate ratios and 95% confidence intervals for colon cancer patients overall, without MLH1 expression, and with and without an APC mutation leading to a stop codon, according to the intake of fat variables (The Netherlands Cohort Study)Dietary fat intakeMedian intake (g/day)Person yearsbColon cancerOverallNo MLH1 expressionAPC –aAPC +aMenWomenNumber of patientsRRc(95% CI)cNumber of patientsRRc(95% CI)cNumber of patientsRRc(95% CI)cNumber of patientsRRc(95% CI)cTotal fat    Q178.063.03,5491101.00(reference)111.00(reference)611.00(reference)401.00(reference)    Q290.271.43,5561251.09(0.82–1.44)171.47(0.69–3.14)871.34(0.94–1.90)320.80(0.49–1.28)    Q398.577.63,579890.75(0.55–1.01)110.94(0.40–2.17)550.82(0.56–1.21)240.57(0.33–0.96)    Q4108.885.33,5881100.96(0.72–1.28)151.30(0.59–2.86)711.11(0.77–1.59)310.74(0.46–1.20)    p-valued0.290.820.710.13    1SD incremente1.00(0.88–1.13)1.21(0.82–1.77)1.04(0.89–1.22)0.93(0.76–1.14)Saturated fat    Q128.923.93,567991.00(reference)91.00(reference)591.00(reference)331.00(reference)    Q233.727.83,5501201.20(0.89–1.61)141.48(0.63–3.49)841.38(0.97–1.97)300.94(0.55–1.61)    Q338.330.93,5831151.11(0.82–1.49)191.93(0.86–4.32)731.15(0.79–1.66)331.01(0.60–1.68)    Q445.836.63,5711000.94(0.69–1.27)121.25(0.52–3.00)580.90(0.62–1.32)310.89(0.53–1.48)    p-valued0.540.480.350.72    1SD incremente0.97(0.87–1.08)1.12(0.82–1.51)0.95(0.83–1.09)0.95(0.79–1.14)MUFAf    Q128.222.43,546981.00(reference)111.00(reference)561.00(reference)311.00(reference)    Q233.226.03,5461221.20(0.89–1.61)141.15(0.52–2.53)791.33(0.92–1.91)361.18(0.72–1.95)    Q336.928.93,5801131.12(0.83–1.52)141.15(0.52–2.54)761.30(0.89–1.89)311.03(0.60–1.77)    Q442.533.13,6001010.99(0.73–1.34)151.25(0.58–2.71)631.08(0.74–1.57)290.90(0.54–1.52)    p-valued0.790.590.800.57    1SD incremente0.99(0.88–1.12)1.04(0.73–1.46)1.01(0.87–1.17)0.98(0.81–1.18)PUFAg    Q111.68.83,507911.00(reference)81.00(reference)491.00(reference)321.00(reference)    Q216.012.43,5621181.37(1.02–1.86)162.03(0.86–4.81)731.55(1.06–2.28)331.13(0.68–1.89)    Q320.916.23,6181131.24(0.91–1.68)162.00(0.84–4.76)781.57(1.07–2.31)300.96(0.57–1.62)    Q429.322.53,5801121.21(0.89–1.63)141.75(0.72–4.24)741.47(1.01–2.16)320.98(0.59–1.63)    p-valued0.380.260.060.79    1SD incremente1.03(0.94–1.14)1.07(0.82–1.38)1.09(0.97–1.23)1.00(0.84–1.19)Linoleic acid    Q110.07.53,509861.00(reference)81.00(reference)491.00(reference)261.00(reference)    Q214.811.23,5861221.49(1.10–2.02)131.66(0.69–3.98)711.50(1.02–2.21)401.65(0.99–2.76)    Q319.514.93,5991121.32(0.97–1.79)172.14(0.91–5.00)821.68(1.15–2.45)251.00(0.57–1.76)    Q428.021.23,5741141.30(0.96–1.77)162.02(0.86–4.76)721.44(0.99–2.11)361.35(0.80–2.28)    p-valued0.200.080.050.65    1SD incremente1.06(0.96–1.17)1.15(0.90–1.48)1.12(0.99–1.26)1.02(0.86–1.21)Linolenic acid    Q10.80.63,5181071.00(reference)111.00(reference)681.00(reference)311.00(reference)    Q21.20.93,5741040.95(0.70–1.30)171.32(0.60–2.89)720.99(0.68–1.43)240.84(0.47–1.49)    Q31.51.23,5711171.10(0.82–1.48)110.91(0.40–2.10)620.89(0.62–1.29)431.48(0.91–2.40)    Q42.01.63,6041061.01(0.76–1.36)151.32(0.61–2.84)721.08(0.76–1.53)290.97(0.57–1.65)    p-valued0.680.730.820.52    1SD incremente0.98(0.89–1.09)1.07(0.79–1.44)0.97(0.85–1.10)1.01(0.86–1.19)aAPC −: cancer patients without a mutation in the MCR of the APC gene leading to a stop codon; APC +: cancer patients with a mutation in the MCR of the APC gene leading to a stop codonbPerson years at risk are estimated from the subcohortcIncidence rate ratios (RR) and 95% confidence intervals (95% CI) are adjusted for age, sex, body mass index, smoking, energy intake and family history of colorectal cancerdp-value for trend over the quartiles of intake of fat variableseFor 1 standard deviation of intake of fat in the subcohort (see Table 1); i.e., 15.8 g/day for total fat, 7.5 g/day for saturated fat, 7.0 g/day for monounsaturated fat, 7.5 g/day for polyunsaturated fat, 7.5 g/day for linoleic acid and 0.6 g/day for linolenic acidfMonounsaturated fatgPolyunsaturated fat
For overall rectal cancer, associations with the intake of total fat or different types of fat were not observed (Table 3). Also after taking account of truncating APC mutations in rectal tumors, none of the fat intake variables were significantly associated with risk of rectal cancer. Only the intake of saturated fat appeared to be inversely associated with rectal tumors without APC truncating mutations (RR for the highest versus the lowest quartile of intake: 0.46 (95% CI 0.22–0.97), p-trend = 0.07) (Table 3). For rectal tumors with specific types of APC truncating mutations no associations were observed with any of the fat intake variables (results not shown).
Table 3Adjusted incidence rate ratios and 95% confidence intervals for rectal cancer patients overall, and with and without an APC mutation leading to a stop codon, according to the intake of fat variables. (The Netherlands Cohort Study)Dietary fat intakeMedian intake (g/day)Person yearsbRectal cancerOverallAPC −aAPC +aMenWomenNumber of patientsRRc(95% CI)cNumber of patientsRRc(95% CI)cNumber of patientsRRc(95% CI)cTotal fat    Q178.063.03,549431.00(reference)241.00(reference)151.00(reference)    Q290.271.43,556390.89(0.56–1.39)170.70(0.37–1.34)120.79(0.36–1.70)    Q398.577.63,579330.74(0.46–1.19)160.65(0.33–1.26)140.91(0.43–1.90)    Q4108.885.33,588380.87(0.55–1.36)150.60(0.31–1.16)161.06(0.51–2.19)    p-valued0.420.130.80    1SD incremente0.91(0.76–1.09)0.84(0.65–1.08)0.94(0.69–1.28)Saturated fat    Q128.923.93,567431.00(reference)231.00(reference)141.00(reference)    Q233.727.83,550340.80(0.49–1.29)180.81(0.42–1.56)120.87(0.39–1.91)    Q338.330.93,583441.02(0.66–1.59)210.92(0.49–1.73)120.86(0.40–1.88)    Q445.836.63,571330.74(0.46–1.18)110.46(0.22–0.97)191.30(0.65–2.63)    p-valued0.380.070.47    1SD incremente0.95(0.81–1.12)0.81(0.65–1.02)1.16(0.89–1.50)MUFAf    Q128.222.43,546441.00(reference)231.00(reference)151.00(reference)    Q233.226.03,546310.70(0.44–1.13)180.79(0.41–1.49)90.62(0.27–1.43)    Q336.928.93,580400.91(0.58–1.44)150.67(0.34–1.33)201.36(0.67–2.78)    Q442.533.13,600390.88(0.56–1.37)170.72(0.38–1.36)130.88(0.40–1.90)    p-valued0.820.280.76    1SD incremente0.94(0.78–1.12)0.85(0.67–1.08)1.00(0.76–1.33)PUFAg    Q111.68.83,507451.00(reference)171.00(reference)211.00(reference)    Q216.012.43,562320.74(0.46–1.18)191.17(0.60–2.28)100.49(0.23–1.07)    Q320.916.23,618390.86(0.55–1.34)160.94(0.47–1.88)160.76(0.39–1.48)    Q429.322.53,580380.82(0.53–1.29)211.20(0.63–2.29)100.47(0.22–1.00)    p-valued0.530.730.11    1SD incremente0.98(0.83–1.16)1.06(0.84–1.34)0.78(0.60–1.03)Linoleic acid    Q110.07.53,509391.00(reference)181.00(reference)181.00(reference)    Q214.811.23,586391.03(0.65–1.64)170.98(0.50–1.92)140.80(0.40–1.63)    Q319.514.93,599350.90(0.56–1.44)160.90(0.45–1.79)150.84(0.42–1.68)    Q428.021.23,574411.03(0.65–1.62)221.19(0.63–2.24)100.54(0.25–1.18)    p-valued0.950.650.15    1SD incremente0.99(0.84–1.17)1.09(0.86–1.38)0.79(0.60–1.04)Linolenic acid    Q10.80.63,518421.00(reference)181.00(reference)181.00(reference)    Q21.20.93,574380.92(0.57–1.48)201.16(0.58–2.31)100.55(0.25–1.24)    Q31.51.23,571360.87(0.55–1.38)150.87(0.43–1.75)130.72(0.35–1.48)    Q42.01.63604380.92(0.58–1.44)201.13(0.60–2.14)160.90(0.45–1.80)    p-valued0.680.930.91    1SD incremente0.95(0.81–1.12)0.98(0.77–1.24)1.03(0.81–1.31)aAPC −: cancer patients without a mutation in the MCR of the APC gene leading to a stop codon; APC +: cancer patients with a mutation in the MCR of the APC gene leading to a stop codonbPerson years at risk are estimated from the subcohortcIncidence rate ratios (RR) and 95% confidence intervals (95% CI) are adjusted for age, sex, body mass index, smoking, energy intake and family history of colorectal cancerdp-value for trend over the quartiles of intake of fat variableseFor 1 standard deviation of intake of fat in the subcohort (see Table 1); i.e., 15.8 g/day for total fat, 7.5 g/day for saturated fat, 7.0 g/day for monounsaturated fat, 7.5 g/day for polyunsaturated fat, 7.5 g/day for linoleic acid and 0.6 g/day for linolenic acidfMonounsaturated fatgPolyunsaturated fat
Additional analyses were conducted to assess whether the observed associations of polyunsaturated fat intake, and especially linoleic acid intake, with the increased risk of colon tumors lacking MLH1 expression and with the increased risk of colon tumors without APC truncating mutations, were observed because of an underlying association with colon tumors harboring a KRAS mutation, as previously observed [22]. Excluding tumors with a KRAS mutation resulted in the absence of a statistically significant association of polyunsaturated fat intake and linoleic acid intake with colon tumors lacking MLH1 expression (p-trend = 0.34 and 0.12, respectively) and those lacking APC tuncating mutations (p-trend = 0.77 and 0.99, respectively). Intake of polyunsaturated fat or linoleic acid was neither associated with the risk of colon cancer without any of the three gene defects, nor with the risk of colon cancer only lacking MLH1 expression, nor with the risk of colon cancer with only truncating APC mutations (Table 4). With increasing intake of polyunsaturated fat and of linoleic acid, a strongly increased risk of colon cancer with only activating KRAS mutations was observed (Table 4) (p-trend ≤ 0.001 for both polyunsaturated fat and linoleic acid intake). The RRs for one standard deviation increase in intake were 1.40 (95% CI 1.17–1.68) and 1.41 (95% CI 1.18–1.69), respectively. The RRs for polyunsaturated fat (not shown) and linoleic acid intake were of similar size when estimated separately for men (1.41, 95% CI 1.15–1.72 for 1 standard deviation increase in linoleic acid intake) and women (1.42, 95% CI 0.96–2.10), and were elevated for proximal (1.23, 95% CI 0.99–1.53) and distal colon cancer (1.53, 95% CI 1.21–1.95). Likewise, a positive association was observed for all colorectal cancers (1.24, 95% CI 1.06–1.47 and p-trend = 0.01), based on a total of 87 cases (i.e., including the rectosigmoid).
Table 4Adjusted incidence rate ratios and 95% confidence intervals for colon cancer patients without any of the gene defects or with only a single gene defect, i.e., either lack of MLH1 expression, a truncating APC gene mutation or an activating KRAS gene mutation, according to the intake of polyunsaturated fat and linoleic acid intake (The Netherlands Cohort Study)Dietary fat intakePerson yearseColon cancerNo gene defectsaOnly lack of MLH1 expressionbOnly truncating APC mutationscOnly activating KRAS mutationsdNumber of patientsRRf(95% CI)fNumber of patientsRRf(95% CI)fNumber of patientsRRf(95% CI)fNumber of patientsRRf(95% CI)fPUFAgQ13,507391.00(reference)61.00(reference)181.00(reference)41.00(reference)Q23,562441.15(0.73–1.81)142.48(0.94–6.54)211.31(0.68–2.53)143.76(1.21–11.69)Q33,618451.10(0.70–1.73)111.89(0.69–5.16)150.86(0.43–1.74)195.00(1.67–14.99)Q43,580350.86(0.54–1.38)101.72(0.61–4.81)130.70(0.34–1.45)286.74(2.36–19.51)p-valueh0.510.480.19≤0.0011SD incrementi0.96(0.82–1.13)1.01(0.72–1.40)0.90(0.71–1.14)1.40(1.17–1.68)Linoleic acidQ13,509381.00(reference)71.00(reference)141.00(reference)41.00(reference)Q23,586451.20(0.77–1.90)101.51(0.58–0.93)262.03(1.03–3.99)154.06(1.32–12.46)Q33,599451.15(0.73–1.81)142.08(0.84–5.19)130.99(0.46–2.13)215.61(1.88–16.68)Q43,574350.90(0.56–1.44)101.49(0.56–3.97)140.97(0.46–2.06)256.02(2.09–17.41)p-valueh0.620.300.38≤0.0011SD incrementi0.98(0.84–1.15)1.08(0.79–1.49)0.91(0.73–1.14)1.41(1.18–1.69)aColon cancer patients with MLH1 expression and without truncating APC or activating KRAS gene mutationsbColon cancer patients lacking MLH1 expression but without truncating APC or activating KRAS gene mutationscColon cancer patients with a truncating APC gene mutation but with MLH1 expression and without activating KRAS gene mutationsdColon cancer patients with an activating KRAS gene mutations but with MLH1 expression and without truncating APC gene mutationsePerson years at risk are estimated from the subcohort.fIncidence rate ratios (RR) and 95% confidence intervals (95% CI) are adjusted for age, sex, body mass index, smoking, energy intake and family history of colorectal cancer.gPolyunsaturated fathp-value for trend over the quartiles of intake of fat variablesiFor 1standard deviation if intake of fat in the subcohort (see Table 1); i.e., 7.5 g/day for polyunsaturated fat and 7.5 g/day for linoleic acid
Likewise, additional analyses were conducted for saturated fat intake in relation to risk of rectal cancer without APC truncating mutations, also excluding individuals with a KRAS mutation and lack of MLH1 expression. The association did not change substantially. Again, only the highest level of intake showed a significant reduced risk of cancer compared to the reference category (RR 0.40 95% CI 0.14–1.15, p-trend = 0.09).
Discussion
In this prospective study, we observed that the intake of total, saturated and monounsaturated fat was not associated with the risk of colon cancer, rectal cancer, or the different molecular subgroups of cancer based on lack of MLH1 expression or truncating mutations in the APC gene. This was also found for polyunsaturated fat intake and rectal cancer. However, linoleic acid showed an association with increased risk of colon tumors with only an activated KRAS mutation and no additional truncating APC mutation or lack of MLH1 expression.
None of the other epidemiological studies report on specific fatty acids and the risk of molecular surrogate end-points for colon or rectal cancer or adenomas [35–43]. Some of these studies report on various types of fat depending on saturation level, but the results are inconsistent across the studies [35, 38, 41, 42] including the current study.
Diergaarde et al. observed unsaturated fat intake to be associated with increased colon carcinomas with a truncating APC mutation [38]. No distinction was made between mono- and polyunsaturated fats. In our study, we did not observe any association between various types of fat intake and risk of colon or rectal cancer with or without truncating APC mutations after patients also harboring a KRAS mutation in their tumor were excluded from the analyses. We observed a possible inverse association between saturated fat intake and risk of rectal tumors without a truncating APC mutation. However, the association was weak, did not increase gradually according to the quartiles of intake and was only a result of the reduced risk in the highest category of intake. Furthermore, in absence of a biological explanation for this finding and considering the large number of associations investigated, the observation may best be attributed to chance.
Slattery et al. observed saturated and monounsaturated fats, but not polyunsaturated fat, to be associated with increased risk of colon tumors with specific KRAS mutations, i.e., a G→T transversion at codon 12 [41]. No distinction was made between ω-6 and ω-3 fatty acids. We observed an association between polyunsaturated fat intake, especially linoleic acid, and increased risk of colon tumors with a KRAS mutation, regardless of the type of mutations [22].
Finally, Bautista et al. observed an inverse association between monounsaturated fats, mostly derived from olive oil in the Spanish diet, and risk of colon cancer without KRAS mutations [35]. Since olive oil was rarely consumed by this elderly Dutch population in the years preceding 1986 (the cohort baseline), this could explain the lack of association for this type of fat in our study. However, a recent Dutch case–control study on risk factors for colorectal adenomas showed a significant positive association between monounsaturated fats and adenoma risk [42].
Several factors hamper comparisons between our findings and those of other epidemiological studies and may in part explain observed inconsistencies. First, our study is the first large prospective cohort study incorporating molecular end-points for colon and rectal cancer. One of the other studies was a cross-sectional case–case comparison study [40], and the others were case–control studies of varying size (ranging from 108 to 1,510 cases) [35–39, 41–43]. Second, varying end-points were considered. Three of the other studies focused on adenomas instead of carcinomas [37, 40, 42], and three studies also incorporated rectal tumors but did not distinguish between colon and rectum [35, 37, 42].
Previously, we reported the association between linoleic acid intake and increased risk of colon tumors with KRAS mutations (adjusted RR for one standard deviation of increase 1.22 (95% CI 1.05–1.42)) [22]. Now, we report that the association appears to be confined to those colon tumors with activating KRAS mutations and an otherwise intact APC gene and with MLH1 expression. In addition, the association appears to be robust since RRs clearly increase over the quartiles of linoleic acid intake and the RRs for one standard deviation increase in linoleic acid intake are similar for men and women, and is increased for proximal and distal colon cancer as well as for overall colorectal cancer, including the rectosigmoid. The activating KRAS mutations at codons 12 and 13 are predominantly G→T and G→A mutations [27] which could be a result of MDA DNA adduct formation [18, 19] associated with increased ω-6 polyunsaturated fat intake [21]. Therefore, even though chance cannot be ruled out and verification by others is warranted, the association seems quite plausible.
Still several issues remain puzzling. First, why is the observed association for linoleic acid confined to colon cancer and not rectum cancer with only a KRAS mutation? The multistep model for molecular aberrations underlying colorectal carcinogenesis is likely to apply equally for both tumor subsites [7, 8, 15]. However, lack of MLH1 expression in our study is rare among rectum cancer patients and there is growing evidence for differences in the etiology of colon and rectal tumors [33].
Second, why is the association with linoleic acid observed for KRAS and not for truncating APC gene mutations? It is unlikely that adduct formation selectively occurs in one gene but not in the other. However, KRAS is an oncogene requiring only one mutation for the gene to be activated, whereas APC is a tumor suppressor gene requiring an additional aberration in the other allele for loss of function. In addition, more than half of the patients with an APC mutation had multiple mutations in this gene [25], complicating data analyses and interpretation. Additional analyses for subgroups of colon tumors with specific truncating point mutations in APC did not show any associations with the intake of fat or different types of fat.
This still does not satisfy our third query, i.e., why are the associations specifically confined to this subgroup of colon cancer patients whose tumors are characterized by activating KRAS mutations, and not truncating APC mutations or lack of MLH1 expression? It is speculative, but plausible, that when KRAS is the only one of the three genes affected, the mutation may more likely be the result of exogenous exposure, for example a relatively high linoleic acid intake. In contrast, when a KRAS mutation co-occurs with a mutation in APC or, although more rarely, in addition to a defective MLH1, these other early gene defects also had a role in tumor formation and may have resulted in a mutator phenotype leading to mutations in other genes (such as the KRAS gene) irrespective of exogenous factors. Since there is no information on the timing of genetic aberrations in this type of human studies, we cannot verify this with our data. Aberrations in other genes, not available for this study, but possibly involved in early tumorigenesis of colorectal cancer, could not be accounted for in analyses and may have influenced results. However, a recent systematic sequence analysis of 13,023 exons in individual colorectal cancers showed that the prevalence of mutations other than in APC, KRAS and TP53 is rather low [44], and mutations in TP53 is not an early event in colorectal carcinogenesis. Finally, results are based on relatively small numbers of patients, especially in the reference group of polyunsaturated fat or linoleic acid intake (four patients, see Table 4), and point estimates of RRs for quartiles of intake of polyunstaturated fat or linoleic acid should therefore, be interpreted cautiously. Nevertheless, as discussed previously, the association appears to be robust when regarding the results for one standard deviation increase in linoleic acid intake (based on a total of 65 patients). Therefore, Breivik and Glaudernack’s hypothesis for distinct carcinogens to exert their effect on two proposed types of genetic instability, i.e., microsatellite instability and chromosomal instability [16], may be extended to the potential effect of carcinogens on more specific genetic pathways to colorectal tumorigenesis, as for example the KRAS mutated pathway.
The data from this large prospective cohort study suggest that linoleic acid intake is strongly associated with colon tumors with an aberrant KRAS gene, but an intact APC gene and MLH1 expression. Verification in other studies is warranted. Possibly, tumors revealing the involvement of distinct genetic pathways on the basis of specific genetic aberrations, may have a unique etiology.

dietary fats
epidemiology
molecular
colorectal neoplasms


Metabolic and vascular determinants of impaired cognitive performance and abnormalities on brain magnetic resonance imaging in patients with type 2 diabetes

Aims/hypothesis The determinants of cerebral complications of type 2 diabetes are unclear. The present study aimed to identify metabolic and vascular factors that are associated with impaired cognitive performance and abnormalities on brain MRI in patients with type 2 diabetes.
Introduction
Diabetes mellitus is associated with slowly progressive changes in the brain [1]. Neuropsychological studies show that patients with type 2 diabetes mellitus have mild to moderate impairments in attention and executive functioning, information processing speed and memory (for reviews see [2, 3]). Patients with type 2 diabetes also show changes on brain magnetic resonance imaging (MRI), such as cortical and hippocampal atrophy [4, 5]. We have recently shown that cognitive dysfunction in patients with type 2 diabetes was associated with white matter lesions (WML), (silent) brain infarcts and to a lesser extent with atrophy [6].
The determinants of changes in cognition and abnormalities on brain MRI of patients with type 2 diabetes are uncertain [2]. Some studies report associations with hypertension [3, 4, 7, 8], but this was not supported by others [5, 9, 10]. Associations between impaired cognition and chronic hyperglycaemia have also been noted [9]. Studies on other diabetic complications may provide leads for potentially relevant determinants. Complications like nephropathy, retinopathy and neuropathy are thought to be due to hyperglycaemia-induced microangiopathy [11, 12], with additional involvement of hypertension and macrovascular disease [13–15]. Since atherosclerosis and hypertension are established risk factors for age-related cognitive decline and brain MRI changes in the general population [16–19], we hypothesised that the combined effects of atherosclerotic macrovascular disease, chronic hyperglycaemia and hypertension are involved in the development of cognitive impairments in patients with type 2 diabetes.
The aim of the present study was to identify possible metabolic and vascular determinants of cognitive dysfunction and changes on brain MRI in patients with type 2 diabetes. Given the uncertainty about these determinants, an exploratory design was chosen. A detailed neuropsychological examination and brain MRI were obtained from a large cross-sectional sample of type 2 diabetes patients and related to different measures of glucose metabolism, vascular risk factors, microvascular complications and macrovascular disease.
Methods
Participants The Utrecht Diabetic Encephalopathy Study aims to identify determinants of cognitive impairment in patients with diabetes [6]. Therefore, patients were not selected for the presence or absence of diabetic complications, co-morbid conditions (e.g. hypertension) or exposure to other risk factors (e.g. smoking). For inclusion patients had to be 55 to 80 years of age, functionally independent and speakers of Dutch, with a minimal diabetes duration of 1 year. Exclusion criteria for all participants were: a psychiatric or neurological disorder that could influence cognitive functioning; a history of alcohol or substance abuse and dementia; and, for the control group, a fasting blood glucose ≥7.0 mmol/l [20]. Participants with a history of stroke who were still fully functionally independent were classified as eligible. To increase statistical power for within-group analyses in the type 2 diabetes group, twice as many patients as controls were enrolled.Overall, 122 patients with type 2 diabetes (age 56–80 years) and 56 controls (age 57–78 years) were included in the present study. Patients were recruited through their general practitioners; controls were spouses or acquaintances of the patients. Groups were comparable for age, sex and educational level. The study was approved by the local medical ethics committee and each participant signed an informed consent form. All participants underwent a 2 day protocol, which included brain MRI, a neurological and neuropsychological examination, retinal photography and ultrasonography of the carotid arteries. Fasting blood and urine samples were collected and blood pressure was recorded. In one control person and two type 2 diabetes patients it was not possible to perform the neuropsychological examination. Similarly, MRIs could not be obtained in five controls and nine type 2 diabetes patients, mostly due to MRI contraindications (claustrophobia, pacemaker).
Neuropsychological examination The neuropsychological examination tapped the major cognitive domains in verbal and non-verbal ways. Eleven tasks were administered in a fixed order, taking about 90 min to complete. These tasks were divided into five cognitive domains, as described previously [6]: (1) attention and executive functioning; (2) information processing speed; (3) memory; (4) abstract reasoning; and (5) visuoconstruction. For analysis the test scores were standardised into z scores for each of the five domains, based on the means of the whole group. The mean performance from each participant across the domains is expressed as the composite cognitive z score.Premorbid IQ was assessed with the Dutch version of the National Adult Reading Test. To control for possible effects of mood disturbances or affective disorders a Beck depression inventory [21] was performed.
Brain MRI The MRI investigation (1.5 T; Philips Medical Systems, Best, the Netherlands) consisted of an axial T1-weighted and an axial T2 and T2 fluid-attenuating inverse recovery (FLAIR) scan (TR/TE/TI: 6000/100/2000, field of view 230 mm, matrix 180 × 256, slice thickness 4.0 mm, contiguous, 38 slices).WML were rated according to the Scheltens scale [22] with slight modifications [6]. Periventricular WML (PWML) were rated on a severity scale (0–2) at the frontal and occipital horns and the body of the lateral ventricle on both sides (sum score 0–12). For the rating of deep (subcortical) WML (DWML) the brain was divided into six regions: frontal, parietal, occipital, temporal, basal ganglia and infra-tentorial. Per region the size and number of WML were rated on a scale ranging from 0 to 6. The total score thus ranged from 0 to 36.Cortical atrophy was evaluated by the frontal interhemispheric fissure ratio and the Sylvian fissure ratio [23]. Subcortical atrophy was evaluated by the bifrontal ratio and by the bicaudate ratio [23]. These ratios were converted to z scores: a cortical atrophy z score (mean of z frontal fissure ratio and z Sylvian fissure ratio) and a subcortical atrophy z score (mean of z bicaudate ratio and z bifrontal ratio).All MRI scans were rated by two investigators (S. M. Manschot and G. J. Biessels) blinded for presence or absence of diabetes or other characteristics. In case of disagreement of more than 1 point on the WML scales in a particular region or more than 5 mm (actual size) on any of the atrophy measurements (2 mm for fissure widths), a consensus reading was held (0% of PWML, 4% of DWML and 4% of atrophy ratio readings were thus affected). In all other cases the readings of both raters were averaged.
Diabetes characteristics and glucose metabolism A standardised questionnaire addressed medical history, medication use, diabetes duration and the life-time occurrence of severe hypoglycaemic episodes (defined as episode of hypoglycaemia severe enough to require the assistance of another person, hospitalisation or emergency room visit). BMI was calculated as weight divided by height square.Blood was drawn by venepuncture to assess HbA1c, fasting glucose and insulin levels. Insulin resistance was estimated with the homeostasis model assessment of insulin resistance (HOMA-IR). The HOMA-IR is calculated as fasting glucose (mmol/l) × fasting insulin (mU/l)/22.5 [24]. Because insulin was expressed in pmol/l we used the formula fasting glucose (mmol/l) × fasting insulin (pmol/l)/(22.5 × 6.945) [24].
Vascular risk factors Blood pressure was measured at home at nine fixed time points during the day with an automatic blood pressure machine (705CP; Omron, Mannheim, Germany). These measurements were averaged. In the primary analysis hypertension was defined as a mean systolic blood pressure >160  mmHg or a mean diastolic pressure >95 mmHg or the use of antihypertensive medication. In a second analysis cut-off values for systolic and diastolic blood pressure of 140 and 90 mmHg were used.Smoking habits were classified as ‘current’ and ‘past or never’. Total cholesterol, HDL-cholesterol, LDL-cholesterol and triacylglycerol were assessed in a fasting venous blood sample.
Microvascular disease Following mydriasis with phenylephrine and tropicamide, single-field photographs were taken of both eyes with a 50-degree retinal camera (Zeiss FF 450, Carl Zeiss B.V., Sliedrecht, the Netherlands), centred on the macula. Retinopathy was rated on slides, according to the diabetic retinopathy severity scale (grades 1–7) as used in the Wisconsin Epidemiologic Study of Diabetic Retinopathy [25]. Photocoagulated eyes were rated at grade 5 or higher (severe non-proliferative diabetic retinopathy). Ratings were performed by two investigators, blinded to patient characteristics. In case of disagreement (2%), a third investigator was involved and a consensus was made. Retinopathy was defined as a grade of 1.5 or higher.Neuropathy was rated with the Toronto Clinical Neuropathy Scoring System [26], with a slight modification. A sensory test for temperature was not performed, so that the maximum score was 18 points (severe polyneuropathy) instead of 19. A score of 0–5 indicated no neuropathy, 6–8 indicated mild neuropathy, 9–11 moderate neuropathy and ≥12 severe neuropathy. Neuropathy was defined as a score of ≥6.Urine was collected overnight. Albuminuria was defined as microalbuminuria (albumin 0.03–0.25 g/l) or macroalbuminuria (albumin 0.25 g/l or positive protein dipstick test).
Macrovascular disease Several composite measures of macrovascular disease were defined. ‘Any peripheral arterial disease’ was defined as current complaints of intermittent claudication (assessed with the Rose questionnaire [27]) or a history of surgery or endovascular treatment for arterial disease of the legs or the abdominal aorta. ‘Ischaemic heart disease’ was defined as a history of myocardial infarction or surgery or endovascular treatment for coronary artery disease. ‘Any vascular event’ was defined as a history of myocardial infarction or stroke, or a history of operative or endovascular treatment for coronary, carotid or peripheral (legs, abdominal aorta) artery disease.Brain infarcts were rated on brain MRI, by location (cortical and subcortical), size (lacunar [<1.5 cm] or large) and number. A lesion was considered an infarct if it was hypo-intense on T1 and FLAIR images and if its appearance was unlike a perivascular space.Carotid intima-media thickness (CIMT) was measured in both common carotid arteries as described previously [28] with an ATL Ultramark 9 (Advanced Technology Laboratories, Bothell, WA, USA) equipped with a 10-MHz linear-array transducer. Scanning was performed at three different longitudinal projections (anterior-oblique, lateral and posterior-oblique). The CIMT was measured in a 1 cm section proximal to the beginning of the dilatation of the carotid bulb in all three projections, in both carotid arteries. CIMT was calculated as the average of these six measurements. CIMT readings were not available in six type 2 diabetes patients and one person in the control group.
Statistical analysis The differences between patients and the control group were examined with t test for means, Mann–Whitney U was used for non-parametric data and χ2 test for proportions. In the text and tables, data are shown as mean ± SD or proportions, unless stated otherwise.Within the type 2 diabetes population, cognition (five domains) and brain MRI findings (cortical and subcortical atrophy z scores, PWML, DMWL and infarcts) were related to the different measures of glucose, insulin and lipid metabolism, and to microvascular complications and macrovascular disease by linear or logistic regression analyses, adjusting for age, sex and estimated IQ. In order to limit the number of analyses the ‘composite cognitive z score’ was used as the primary cognitive outcome measure in the regression analyses. For significant associations, post hoc tests were performed per domain. Secondary analyses were performed with information processing speed, the domain most markedly affected by type 2 diabetes. The results were essentially the same as for the composite cognitive z score (data not shown).In the regression analyses, B values >0 indicate that a variable is associated with more severe MR abnormalities; for cognition B values <0 indicate that a variable is associated with more pronounced performance impairments. For the between and within-group analyses, p < 0.05 was considered statistically significant. All variables that reached a significance level of p ≤ 0.1 in the adjusted univariate risk factor analyses were included in a multivariate model that also included age, sex and estimated IQ.
Results
Participant characteristics The age, sex, level of education and estimated IQ in the groups were comparable (Table 1).
Table 1Participant characteristicsCharacteristicType 2 diabetes groupControl groupParticipants (n)12256Sex (male/female)62/6025/31Age (years)66.0 ± 5.865.1 ± 5.2Level of education (1–7)a4 (3–5)4 (3–5)Estimated premorbid IQ99 ± 15101 ± 14Diabetes duration (years)b8.7 ± 6.1Diabetes treatment (%) Diet10 Oral medication alone61 Insulinb29HbA1c (%)b6.9 ± 1.2**, d5.5 ± 0.3Fasting glucose levels (mmol/l)b8.6 ± 2.9**5.5 ± 0.6Fasting insulin levels (pmol/l)b,c120 ± 110**, e (n = 82)76 ± 50 (n = 54)HOMA-IRb,c6.6 ± 6.4**, f2.6 ± 1.8BMI (kg/m2)b28.1 ± 4.427.3 ± 5.3Data are given as number or percentage (as indicated), mean ± SD or ranges**p < 0.01 for type 2 diabetes vs control groupaLevel of education was expressed in seven categories [49]bEntered as explanatory variable in the regression analyses within the type 2 diabetes groupcOnly from participants who were not treated with insulin and did not have antibodies against insulinStatistically significant associations within the type 2 diabetes group: with composite cognitive z scores—dp < 0.05; with MRI abnormalities—ep < 0.01, fp < 0.05Detailed neuropsychological and MRI data have been reported previously [6]. In short, performance of patients with type 2 diabetes was worse than that of the control group across all five cognitive domains, with statistically significant differences on attention and executive functioning (difference mean z scores 0.23 [95% CI 0.03, 0.43]; p = 0.02), information processing speed (0.40 [0.17, 0.63]; p = 0.001) and memory (0.20 [0.05, 0.36]; p = 0.01). Patients with type 2 diabetes had more pronounced cortical atrophy (difference mean z scores 0.62 [95% CI 0.33, 0.91]; p < 0.001) and subcortical atrophy (0.38 [0.07, 0.68]; p = 0.01). They also had more severe DWML (controls, median [range]: 5 [0, 18]; type 2 diabetes: 7 [0.5, 27.5]; p = 0.02), but PWML severity in the two groups was similar (control: 6 [4, 10]; type 2 diabetes: 6 [3, 12]; p = 0.13). Patients with type 2 diabetes also had more (silent) cerebral infarcts than controls (type 2 diabetes 22/113, control 4/54; p = 0.06).
Glucose metabolism HbA1c, fasting glucose and insulin levels were higher (all p < 0.01) in patients with type 2 diabetes than in the control group. BMI was similar in both groups. Only a small proportion (6%) of type 2 diabetes patients had ever experienced a severe hypoglycaemic event (Table 1).In the regression analyses within the type 2 diabetes group, HbA1c levels were significantly related to cognition (composite z score: B [per % HbA1c]: −0.07 [−0.14, 0] p = 0.047; post hoc per domain: information processing speed: B [per % HbA1c]: −0.15 [95% CI: −0.27, −0.2], p = 0.02; abstract reasoning: B: −0.15 [−0.29, −0.01], p = 0.04). Elevated fasting insulin levels and HOMA-IR were related to increased DWML severity (B [per 10 pmol/l insulin]: 0.14 [0.04, 0.26], p = 0.009; B [HOMA-IR]: 0.21 [0.04, 0.39], p = 0.02).
Vascular risk factors Table 2 shows that patients with type 2 diabetes had higher systolic blood pressure (p < 0.01) and pulse pressure (p < 0.05) than controls. They also had hypertension more often (p < 0.01). Total cholesterol was lower in the type 2 diabetes group (p < 0.01), but the proportion of individuals taking lipid-lowering drugs was higher in that group (p < 0.01). There were no statistically significant differences between type 2 diabetic patients and the control group in the proportion of participants who smoked or had dyslipidaemia (Table 2).
Table 2Vascular risk factorsCharacteristicType 2 diabetes group (n = 122)Control group (n = 56)Mean arterial pressure (mmHg)a103 ± 11*,b98 ± 10Pulse pressure (mmHg)a65 ± 15*59 ± 16Hypertension (%)a73**34Antihypertensive drugs (%)70**32Current smoking (%)a2214Total cholesterol (mmol/l)a5.0 ± 0.9**5.8 ± 1.1HDL-cholesterol (mmol/l)a4.3 ± 1.24.4 ± 1.5Triacylglycerol (mmol/l)a1.9 ± 1.01.6 ± 1.1Lipid-lowering drugs (%)a54**,c21Data are given as percentage or mean ± SD*p < 0.05, **p < 0.01 for type 2 diabetes vs control groupaEntered as explanatory variable in the regression analyses within the type 2 diabetes groupbp < 0.05 for association with more severe MRI abnormalitiescp < 0.05 for reverse associationIn the regression analyses within the type 2 diabetes group there were no statistically significant associations with the composite cognitive z score. Non-significant trends (p ≤ 0.10) were observed for associations between both hypertension and current smoking and impaired cognitive performance (hypertension B: −0.19 [−0.38, 0], p = 0.053; smoking B: −0.21 [−0.43, 0], p = 0.051), and between the use of lipid-lowering drugs and better performance (B: 0.15 [−0.03, 0.32], p = 0.10). Reanalysis with cut-off values for hypertension of 140/90 mmHg made the association with the composite cognitive z score less strong. Mean arterial pressure was associated with more severe PWML (B [per 10 mmHg]: 0.28 [0.03, 0.53], p = 0.03). The use of lipid-lowering drugs (statins in all but one patient) was associated with less severe MRI abnormalities (PWML: B: −0.68 [−1.25, −0.12], p = 0.02; cortical atrophy: B: −0.36 [−0.69, −0.03], p = 0.03). These effects were not affected by additional adjustment for the actual cholesterol levels (Table 2).
Microvascular disease In 20 patients with type 2 diabetes and 8 control individuals it was not possible to perform retinal photographs. Mostly due to logistical reasons, overnight urine samples could not be obtained from 21 diabetic patients and 13 controls. Patients with type 2 diabetes had more retinopathy and neuropathy than the control group (both p < 0.01). Although albuminuria was more common in the type 2 diabetic than in the control group, this difference was not statistically significant (Table 3).
Table 3Microvascular diseaseCharacteristicType 2 diabetes group (n = 122)Control group (n = 56)Retinopathy (diabetes n = 112; control n = 48)a37 (33)**,b1 (2) Background 33 1 Severe non-proliferative4 Neuropathya47 (39)**7 (13) Mild neuropathy25 7  Moderate neuropathy18 0 Severe neuropathy4 0Albuminuria (diabetes n = 101; control n = 43)a16 (16)3 (7) Microalbuminuria93 Macroalbuminuria7 0Any microvascular diseasec72 (59)**11 (20)Data are given as values (percentage)**p < 0.01 for type 2 diabetes vs control groupaEntered as explanatory variable in the regression analyses within the type 2 diabetes groupbp < 0.05 for association with MRI abnormalitiescIncludes all patients with albuminuria, neuropathy or retinopathyIn the regression analyses within the type 2 diabetes group, there were no statistically significant associations with the composite cognitive z score. Retinopathy was associated with more pronounced cortical atrophy (B: 0.48 [0.11, 0.85], p = 0.01) (Table 3).
Macrovascular disease Patients with type 2 diabetes were more likely to have had intermittent claudication (p < 0.01) or a history of ischaemic heart disease (p < 0.01). There was no difference between the two groups in the CIMT (Table 4).
Table 4Macrovascular diseaseCharacteristicType 2 diabetes group (n = 122)Control group (n = 56)Any peripheral arterial diseasea18 (15)** 0 Claudicatio intermittens14 (11)**0 Vascular surgery femoral artery4 (3)0 Vascular surgery (AAA)3 (3)0Ischaemic heart diseasea23 (19)**,b2 (4) Myocardial infarction15 (12)*1 (2) CABG13 (11)*1 (2)Brain infarct on MRIa22 (20)*,b,c4 (8)History of brain infarcta7 (6)2 (4)Carotid surgerya2 (2)1 (2)Any vascular event a33 (27)**,b,c4 (7)CIMTa0.093 ± 0.0180.092 ± 0.023Data are given as value (percentage) or mean ± SDAAA, abdominal aortic aneurysm; CABG, coronary artery bypass grafting*p < 0.05, **p < 0.01 for type 2 diabetes vs control groupaEntered as explanatory variable in the regression analyses within the type 2 diabetes groupStatistically significant associations: with MRI abnormalities bp < 0.05; with impaired cognition cP < 0.05In the regression analyses within the type 2 diabetes group, a history of ‘any vascular event’ and the presence of brain infarcts on MRI were associated with an impaired composite cognitive z score as follows: (1) vascular event: composite z score B: −0.25 (−0.44, −0.05), p = 0.01; post hoc per domain: information processing speed B: −0.46 (−0.80, −0.12), p = 0.008; and memory B: −0.23 (−0.41, −0.06), p = 0.01; (2) infarct on MRI: composite z score B: −0.28 (−0.50, −0.06), p = 0.01; post hoc per domain: information processing speed B: −0.77 (−1.14, −0.39), p < 0.001; and abstract reasoning B: −0.41 (−0.82, 0.01), p = 0.06. A history of ‘any vascular event’ was also associated with more pronounced DWML (B: 2.0 [0, 4.1], p = 0.05) and with an increased occurrence of infarcts on MRI (odds ratio: 2.9 [1.1, 7.9], p = 0.04). Patients with a (silent) infarct on MRI tended to have more pronounced PWML (B: 0.7 [0, 1.4], p = 0.06) and cortical atrophy (B: 0.51 [0.10, 0.92], p = 0.02) relative to type 2 diabetes patients without infarcts on MRI (Table 4).Exclusion of patients with a history of stroke attenuated the association between ‘any vascular event’ and the cognitive z score, but not that between ‘any vascular event’ and DWML.
Multivariate analysis For the majority of risk factors the values of the standardised regression coefficients ß were similar in the single risk factor and multivariate models, indicating that interaction between factors was limited. Values for ß for individual factors varied between 0.15 and 0.30, indicative of modest associations (Table 5).
Table 5Multivariate risk factor analyses within the type 2 diabetes group Single factors β (95% CI)Full model β (95% CI)Final model β (95% CI)Cognition Age−0.30 (−0.44, −0.16)***−0.29 (−0.43, −0.14)*** Sex−0.12 (−0.24, 0.02) Estimated IQ0.65 (0.50, 0.79)***0.65 (0.50, 0.80)*** HbA1c−0.14 (−0.28, −0.002)*−0.11 (−0.25, 0.02) Hypertension−0.14 (−0.26, 0.002)−0.12 (−0.26, 0.01)−0.14 (−0.27, −0.001)* Current smoking−0.14 (−0.29, 0.001)−0.15 (−0.29, −0.02)*−0.14 (−0.27, 0.001) Lipid-lowering drugs0.12 (−0.02, 0.26)0.17 (0.03, 0.31)*0.17 (0.03, 0.31)* Any vascular event−0.18 (−0.32, −0.03)*−0.18 (−0.32, −0.03)*−0.19 (−0.34, −0.05)** Brain infarct on MRI a−0.18 (−0.32, −0.04)*Cortical atrophy Sex−0.005 (−0.23, 0.22) Age0.35 (0.18, 0.52)***0.34 (0.18, 0.51)*** Estimated IQ−0.07 (−0.24, 0.10) Lipid-lowering drugs−0.20 (−0.37, −0.02)*−0.19 (−0.36, −0.03)*−0.18 (−0.35, −0.02)* Retinopathy0.24 (0.05, 0.43)*0.21 (0.04, 0.39)*0.22 (0.06, 0.39)** Brain infarct on MRI0.22 (0.04, 0.39)*0.23 (0.06, 0.40)**0.23 (0.06, 0.40)**DWML Sex0.13 (−0.05, 0.30) Age0.20 (0.02, 0.35)*0.22 (0.04, 0.39)* Estimated IQ−0.14 (−0.31, 0.04) Insulin level0.29 (0.08, 0.51)**0.26 (0.04, 0.47)*0.24 (0.03, 0.45)* Any vascular event0.19 (−0.002, 0.37)0.15 (−0.03, 0.34)PWML Sex0.16 (−0.02, 0.34)0.16 (−0.02, 0.34) Age0.25 (0.07, 0.43)**0.24 (0.07, 0.42)** Estimated IQ−0.12 (−0.30, 0.06) Mean arterial pressure0.20 (0.02, 0.38)*0.15 (−0.03, 0.33)0.17 (−0.004, 0.35) Lipid-lowering drugs−0.22 (−0.35, −0.001)*−0.20 (−0.38, −0.02)*−0.18 (−0.35, 0.001)* Brain infarct on MRI0.17 (−0.006, 0.35)0.18 (0.01, 0.36)*0.18 (0.009, 0.36)*β is the standardised regression coefficient B. The first column contains the values of β for all variables that reached a significance level of a least p = 0.10 in the univariate risk factor analyses within the type 2 diabetes patient group, adjusted for age, sex and estimated IQ. These variables were included in a multivariate model that also included age, sex and estimated IQ (second column, full model). The final model (third column) is based on backward elimination of non-significant factors. This final model shows which variables that were related to the outcome measures in the univariate model remained significantly associated with the outcome measures independently of the other factors in the full modelFor cognition β values <0 indicate that a variable is associated with more pronounced performance impairments; for MRI β values >0 indicate that a variable is associated with more severe MRI abnormalities*p < 0.05**p < 0.01***p < 0.001aBecause ‘infarct on MRI’ and ‘history of any vascular event’ were interrelated in the univariate analyses (see Macrovascular disease section) only ‘history of any vascular event’ was entered in the multivariate modelIn the multivariate model, hypertension and a history of vascular events were associated with worse cognitive performance and statin use with better performance. Retinopathy and brain infarction on MRI were associated with more severe cortical atrophy and statin use with less atrophy. A higher insulin level was associated with more DWML, brain infarction on MRI with more PWML and statin use with less PWML. Overall, macrovascular disease (history of macrovascular events or infarct on MRI) were most consistently associated with the different outcome measures (Table 5).
Discussion
Patients with type 2 diabetes had more cortical and subcortical atrophy and more DWML than control participants and their overall performance in the five cognitive domains was worse. As expected, patients with type 2 diabetes had more microvascular complications, more macrovascular (atherosclerotic) disease and more hypertension than the control group. In multivariate regression analyses within the type 2 diabetes group, hypertension and a history of vascular events were associated with worse cognitive performance, while statin use was associated with better performance. Retinopathy and brain infarcts on MRI were associated with more severe cortical atrophy and statin use with less atrophy. Insulin level and brain infarcts were associated with more severe WML and statin use with less severe WML.
Cognitive function in patients with type 2 diabetes has been studied extensively (for reviews see [2, 3]). Performance in the domains verbal memory and information processing speed, and probably also executive functioning and non-verbal memory, is moderately impaired. Our results are in keeping with these findings. Thus far, relatively few studies have specifically addressed brain MRI abnormalities in patients with type 2 diabetes. In agreement with our observations, modest cortical and subcortical atrophy and symptomatic or asymptomatic infarcts have been found more often in type 2 diabetes patients than in control individuals [4, 5, 29]. Results of previous studies on the association between type 2 diabetes and WMLs are less consistent [30]. This might be due to the study populations involved and the use of relatively insensitive WML rating scales [30].
Chronic hyperglycaemia might be a determinant of cerebral changes in patients with type 2 diabetes. In the present study, HbA1c levels were related to the composite cognitive z score, but only in de univariate analysis. Moreover, retinopathy, which is generally considered to be a consequence of chronic exposure to hyperglycaemia [11], was related to cortical atrophy. Previous studies on cognition in patients with type 2 diabetes have also reported an association with HbA1c levels [2, 9, 31]. The relation with fasting blood glucose or duration of diabetes is, however, inconsistent [31, 32]. No previous studies have provided detailed data on the association between glycaemic control and MRI changes in type 2 diabetes. Studies in type 1 diabetes mellitus, however, have shown an association between diabetic retinopathy (as a proxy of chronic hyperglycaemia) and both brain atrophy [33, 34] and cognitive functioning [35]. There are no previous studies on the relation between insulin levels and cerebral complications in type 2 diabetes. The association with WML severity, observed by us in the present study, is of particular interest in the light of recent studies in the general population, which link insulin to vascular abnormalities and degenerative changes in the brain [36, 37].
Previous studies in the general population indicate that risk factors for vascular disease, such as hypertension, dyslipidaemia, increased BMI and smoking, are associated with an increased risk of cognitive decline and dementia and with brain MRI changes, including WML (e.g. [38–41]). Previous studies on the modulating effect of hypertension on cognitive function in type 2 diabetes show conflicting results [7, 9, 42, 43]. In the present study, hypertension was related with impaired cognitive performance and mean arterial pressure with PWML severity. To our knowledge, the relation between other vascular risk factors and both cognition and brain MRI in patients with type 2 diabetes has not been examined previously. The reverse association between the use of statins and both cognition and MRI findings is intriguing. Nevertheless, this observation cannot be regarded as proof of a possible treatment effect. It should be noted that the association between statin use and both cognition and age-related brain MRI changes in the general population is still being debated [44]. The present findings will need to be confirmed by further studies.
Macrovascular atherosclerotic disease appeared to be the most consistent determinant of impaired cognition and brain MRI abnormalities in the type 2 diabetes patients in the present study. We have not found any previous studies that presented detailed data on the relation between macrovascular disease and cerebral changes in people with type 2 diabetes. In the general population, however, several studies have shown that macrovascular atherosclerotic disease is associated with age-related cognitive impairment and changes in brain MRI. In a large cross-sectional study, for example, previous vascular events, presence of plaques in the carotid arteries and presence of peripheral arterial atherosclerotic disease were negatively associated with cognitive performance [17]. In another study, the association between the number of cardiovascular disease conditions and cognitive impairment appeared to show a ‘dose–response’ relationship [18]. With regard to brain MRI changes, a history of stroke or myocardial infarction has been associated with the presence of WML [19] and plaques in the carotid artery with PWML [16, 45].
The strength of our study is that we combined detailed data on cognitive function and brain MRI with detailed data on metabolic and vascular risk factor clusters, thus allowing an accurate assessment of the relation between these factors. Possible limitations include patient selection, the cross-sectional design and the large number of explanatory variables addressed. With regard to patient selection, we aimed to obtain a representative sample of functionally independent patients with type 2 diabetes from the general population. Although the rather demanding testing protocol may have deterred patients with relatively severe mental or physical limitations, the prevalence of microvascular and macrovascular disease, hypertension and smoking habits, as well as the level of metabolic control in our study sample is comparable with those found in other population-based studies in the Netherlands [46–48]. To minimise the effects of lifestyle and socioeconomic factors, control participants were recruited from the direct environment of the type 2 diabetic patients. Consently, the prevalence of risk factors such as hypertension and high BMI was higher than would be expected in the general population in the Netherlands. If anything, this would have decreased the differences in cognition and MRI ratings between the groups. The cross-sectional design of our study precludes inferences about causal relationships. Moreover, the cognitive and imaging outcome measures were probably influenced by a large number of factors, some of which are specific to type 2 diabetes mellitus (e.g. chronic hyperglycaemia, diabetes treatment) and some not (e.g. age, hypertension, atherosclerosis). Our exploratory analysis included a large number of explanatory variables, which has certain drawbacks. First, different explanatory variables might be interrelated. The relatively small regression coefficients and effect sizes affect the evaluation of these interrelations and limit statistical power. This may also explain why some of the variables that reached statistical significance in the univariate analyses dropped out of the multivariate model. Nevertheless, the multivariate analysis as presented in Table 5 does indicate which variables were the strongest independent determinants of cognition and MRI abnormalities in the model used. The second drawback is that the large number of regression analyses can lead to type I errors. Nevertheless, we feel that this first detailed study of cognition and brain MRI in type 2 diabetes patients in relation to metabolic and vascular risk factors does provide important leads that could be further evaluated in future studies. Such studies should: (1) preferably have a longitudinal design; (2) include assessment of cognition and brain MRI in relation to chronic hyperglycaemia and atherosclerotic vascular disease; and (3) allow the assessment of potential confounders (e.g. hypertension).
Type 2 diabetes is associated with modest impairments in cognition, as well as with atrophy and vascular lesions on MRI. This ‘diabetic encephalopathy’ is a multifactorial condition, for which atherosclerotic (macroangiopathic) vascular disease is an important determinant. Chronic hyperglycaemia, hypertension and hyperinsulinaemia may play additional roles.
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The Utrecht Diabetic Encephalopathy Study Group (PDF 12.8 kb)

cognitive performance
cerebral complications
brain mri
type 2 diabetes mellitus
white matter lesions
vascular risk factors
diabetic encephalopathy
brain atrophy
metabolic risk factors
neuropsychological investigation