Cognitive Performance at Late Adolescence and the Risk for Impaired Fasting Glucose Among Young Adults

Context:

Although dysglycemia is a risk factor for cognitive decline, it is unknown whether cognitive performance among young and apparently healthy adults affect the risk for impaired fasting glucose (IFG).

Objective:

This study aimed to characterize the relationship between cognitive function and the risk for IFG among young adults.

Design and Setting:

This was a retrospective cohort study utilizing data collected at pre-military recruitment assessments with information collected at the screening center of Israeli Army Medical Corps.

Participants:

Normoglycemic adults (n = 17 348) (free of IFG and diabetes; mean age 31.0 ± 5.6 y; 87% men) of the Metabolic Lifestyle and Nutrition Assessment in Young Adults (MELANY) cohort with data regarding their General Intelligence Score (GIS), a comprehensive measure of cognitive function, at age 17 y.

Interventions:

Fasting plasma glucose was assessed every 3–5 y at scheduled visits. Cox proportional hazards models were applied.

Main Outcomes Measures:

The main outcome of the study was incident IFG (≥100 mg/dL and <126 mg/dL) at scheduled visits.

Results:

During a median followup of 6.6 y, 1478 cases of IFG were recorded (1402 men). After adjustment for age and sex, participants in the lowest GIS category had a 1.9-fold greater risk for incident IFG compared with those in the highest GIS category. In multivariable analysis adjusted for age, sex, body mass index, fasting plasma glucose, family history of diabetes, country of origin, socioeconomic status, education, physical activity, smoking status, alcohol consumption, breakfast consumption, triglyceride level, white blood cell count, the risk for IFG was nearly doubled in the lowest GIS category compared with the highest GIS category (hazard ratio, 1.8; 95% confidence interval, 1.4–2.3; P < .001). These results persisted when GIS was treated as a continuous variable and when the model was adjusted also for body mass index at the end of followup.

Conclusions:

This study demonstrates that lower cognitive function at late adolescence is independently associated with an elevated risk IFG in both men and women.

The number of people with diabetes throughout the world has increased from 153 million in 1980 to 347 million in 2008 (1). This phenomenon is linked to the increasing rates of obesity as well an increasingly sedentary lifestyle. Evidence from the last several years has also linked psycho-cognitive-social factors to incident diabetes. For example, epidemiological studies have reported a bidirectional association between depression and type 2 diabetes with each diagnosis being a risk factor for the incident cases of the other one (2). The same may be true for cognitive function and diabetes. Both diabetes (3–5) and elevated glucose levels (6) accelerate the rate of cognitive decline and increase the risk for dementia. Moreover, we recently reported (7) an inverse relationship between cognitive function at late adolescence and incident diabetes in young men. Whether this is also true for IFG is unknown.

The Metabolic, Lifestyle and Nutrition Assessment in Young Adults (MELANY) cohort of the Israeli Defense Forces has been used to assess a variety of risk factors for cardiovascular disease (8–11) and diabetes (12–17) among young men. This cohort also provided extensive cognitive performance data prior to military recruitment (at age 17 y), and repeated assessment of glucose status during periodic medical assessments up to late adulthood. In this study we analyzed data from the MELANY cohort and assessed the relationship between cognitive function at late adolescence and the risk for subsequent development of IFG during young adulthood.

Materials and Methods

The MELANY cohort

The MELANY cohort is an ongoing investigation of the Israel Defense Forces Medical Corps (9, 14, 18). Army personnel older than age 25 y who remain in military service beyond 2–3 years of mandatory service have a standardized health examination and laboratory assessment every approximately 5 years. At each visit participants complete a detailed questionnaire assessing demographic, social, nutritional, lifestyle, and medical factors. Blood samples are drawn following a 14-hour fast and immediately analyzed. Height and weight are measured, and a physician at the center performs a complete physical examination. All of the collected data are recorded in one central database (independent of scheduled visits), thereby facilitating ongoing, and uniform followup as described previously (15). The results of an obligatory general intelligence test (GIS, detailed below) administered at age 17 years prior to enlistment in the military are also recorded in that database. All participants in MELANY, independent of their rank and position, have similar access to medical services, which are provided free of charge.

Study population

As noted in Supplemental Figure 1, people who had a normal FPG (<100 mg/dL) at the first visit and who had at least two routine assessments (≥2 y apart) between January 1, 1995 and January 1, 2011 were included. Individuals with diabetes or IFG at or before the first visit (N = 1458 men and 179 women) were excluded. A total of 15 099 men and 2249 women were included in the current study. The Institutional Review Board of the Israel Defense Forces Medical Corps approved this study on the basis of strict maintenance of participants' anonymity during database analyses.

Outcomes and followup

The primary outcome was incident IFG defined as a FPG at least 100 mg/dL and less than 126 mg/dL detected during a scheduled visit. Secondary outcomes were metabolic abnormalities known to be related to IFG including: 1) hypertriglyceridemia, defined as a triglyceride (TG) level at least 150 mg/dL or the use of lipid-lowering drugs; 2) low high-density lipoprotein cholesterol (HDL-C), defined as a value less than or equal to 40 mg/dL for men, no more than 50 mg/dL for women, or the use of lipid-lowering drugs; 3) elevated blood pressure (BP), defined as a systolic BP at least 130 mm Hg or diastolic BP at least 85 mm Hg, or the use of a BP-lowering drug; and 4) obesity, defined as a body mass index (BMI) at least 30 kg/m². Followup began at the first visit to the screening center and ended at a visit in which a FPG at least 100 mg/dL was measured, last visit to the screening center (March 8, 2011 the latest), death, or retirement from military service, whichever came first.

Assessing the General Intelligence Score

Intelligence assessment was conducted as part of the military recruitment evaluation. The score given for this evaluation, the General Intelligence Score (GIS), has been described and used as an investigative tool extensively with a correlation above 0.8 with the Wechsler Adult Intelligence Scale (WAIS) total intelligence quotient (19–23). The intelligence assessment includes four subtests: the Otis-R, which is a measure of the ability to understand and carry out verbal instructions; Similarities-R, which assesses verbal abstraction and categorization; Arithmetic-R, which assesses mathematical reasoning, concentration, and concept manipulation; and Raven's Progressive Matrices-R, which measures nonverbal abstract reasoning and visual-spatial problem-solving abilities (24). The sum of the scores is expressed on a scale of 1 to 9 (25).

Additional variables collected

Age, BMI, TG level, FPG, and white blood cell (WBC) count at enrollment were treated as continuous variables. Smoking status (current smoker, ex-smoker, never smoked), physical activity (≥150 min/wk, <150 min/wk, or inactivity), family history of diabetes (yes, no), regular alcohol consumption (yes/no), and breakfast consumption (frequent, sometimes or none) were treated as categorical variables. Socioeconomic status (SES) data based on place of residence were obtained from records of the Israeli Ministry of Interior, which is coded on a scale of 1 to 10 based on the Israeli Central Bureau of Statistics. This scoring system stratifies all municipalities into 10 ranks taking into account variables that might affect SES (7). SES was coded into three groups: low (SES, 1–4), medium (SES, 5–7) and high (SES, 8–10) as reported previously (26). Education was modeled as a categorical variable divided into low and high levels at a threshold of 11 full years of school education. This cutoff was chosen because it represents the maximum potential school instruction at the time of GIS assessment. Country of origin (classified by the father's or grandfather's country of birth) was categorized into five geographical areas: former Union of Soviet Socialist Republics (USSR) countries, Asia (non-USSR), Africa (excluding South Africa), Western (comprised of non-USSR Europe, North and South America, South Africa, Australia, and New Zealand), and Israel. Birth countries were classified in a similar manner.

Laboratory methods

All laboratory studies were performed on fresh samples, in an ISO-9002 quality–assured, core facility laboratory. The levels of glucose were measured at an on-site laboratory with the use of fresh blood samples collected in test tubes containing sodium fluoride following 14-hour fast. With the exception of low-density lipoproteins, lipids were measured directly. Biochemical markers were measured with an automated analyzer (BM/Hitachi 917, Boehringer Mannheim).

Statistical analysis

Continuous variables were summarized using means and SD and/or medians with interquartile ranges. Counts with percentages were used for binary variables.

GIS was analyzed as a continuous and a categorical variable (1–3, 4–7, 8–9) with the highest GIS group used as the reference. ANOVA and χ² tests were used to measure differences between the continuous and categorical variables, respectively, at enrolment among the study groups. Cox proportional hazard models were used to estimate the hazard ratios (HR) and 95% confidence intervals (CIs) for developing IFG across the three GIS categories and for every 1-point decrement when GIS was treated as a continuous variable. Several models were used to assess the GIS-IFG association after adjusting for various variables that were used previously to characterize the GIS-diabetes association (7): model 1: age, sex; model 2: age, sex, BMI, FPG; model 3: age, sex, BMI, FPG, and sociogenetic risk factors (family history of diabetes, country of origin, SES, education); model 4: age, sex, BMI, FPG, and lifestyle risk factor (physical activity, smoking status, TG level, alcohol consumption, breakfast consumption); model 5: age, sex, BMI, FPG, WBC count (14), and sociogenetic and lifestyle risk factors for dysglycemia. To account for the possibility that IFG status during followup simply reflected changes in BMI, the BMI at the last visit was included in the highly adjusted model (model 5). Differences in the relationship between the GIS and IFG in subgroups were assessed by including an interaction term for sex and GIS (in model 5). In addition, differences in the relationship between the GIS and IFG in metabolically healthy (normal BMI, lipid status [TGs and high-density lipoprotein cholesterol (HDL-C)], normal BP, and normal FPG) vs those that were not was assessed by including an interaction term for metabolic status and GIS in all models. All covariates were tested for colinearity using Pearson's correlation, with a maximal value of 0.36 between BMI and TG. The relationship between the GIS and incident IFG, elevated BP, hyperlipidemia, and obesity in the subset of 6251 metabolically healthy (ie, nomoglycemic, normotensive, normolipidemic with normal weight) people was analyzed using Cox proportional hazard models adjusted for the variables in model 5. Sensitivity analysis was conducted to assess the potential effect of an unknown confounder (U) on the relationship (27). Thus, a set of assumptions were made regarding the difference in prevalence of this confounder in those in the highest GIS group vs those in the lowest GIS group, and regarding the difference in IFG incidence in those with and without such an hypothesized confounder. Subjects with missing data were excluded from multivariable analysis. Reported values throughout this report are mean ± SD unless mentioned otherwise. Analyses were performed with SPSS statistical software (version 21.0).

Results

This analysis pertains to 17 348 participants (15 099 men) who were normoglycemic at beginning of followup. Median followup was 6.6 years (interquartile range, 5.1–9.4 y) with 46 008 number of scheduled visits during this time at the screening center. Over 82% of study participants were born in Israel. Table 1 presents the baseline characteristics distributed according to GIS score at age 17 years. As can be seen, those in the lower GIS group attended the screening center at a younger age than those in the higher GIS group. Compared with highest GIS group, participants in lower GIS groups had a lower SES, level of education, and higher prevalence of a family history of diabetes.

Table 1.

Baseline Characteristics of the Cohort According to GIS at the First Visit to the Screening Center (Beginning of followup) and at the Age of 17 Years

	GIS			Total or Mean ± sd	P
	Low (GIS, 1–3)	Medium (GIS, 4–7)	High (GIS, 8–9)
N (fraction of men)	1332 (97%)	12 752 (88%)	3264 (80%)	17 348 (87%)
Age, y	30.4 ± 5.4	32.0 ± 5.6	32.2 ± 5.0	31.9 ± 5.5	<0.001
Birth country, %					<0.001
Israel	85.0	83.6	82.3	83.5
USSR	3.2	4.8	7.0	5.1
Asia	2.0	2.6	1.4	2.3
Africa	9.6	6.4	1.9	5.8
West	0.4	2.6	7.5	3.3
Country of origin, %					<0.001
Israel	1.6	3.1	4.9	3.3
USSR	4.2	7.7	14.8	8.7
Asia	25.7	30.0	20.3	27.9
Africa	60.6	38.4	16.2	35.9
West	7.8	20.8	43.9	24.2
Education > 10 y	95.0	99.5	99.7		<0.001
SES					<0.001
Low	44.6	30.6	20.2	29.7
Intermediate	49.6	56.2	54.9	55.5
High	5.8	13.2	24.9	14.8
BMI at age 17 y, kg/m2	21.2 ± 3.1	21.3 ± 2.9	21.4 ± 2.8	21.3 ± 2.9	0.218
Height at age 17 y, cm	171.3 ± 6.8	171.9 ± 7.5	172.8 ± 8.2	172.0 ± 7.6	<0.001
BMI, kg/m2	25.3 ± 4.2	25.3 ± 3.9	25.1 ± 3.8	25.2 ± 3.9	0.001
BMI categories, kg/m2					<0.001
BMI < 25	51.9	51.7	55.8	52.5
25 ≤ BMI < 30	35.1	36.8	35.0	36.4
BMI ≥ 30	13.0	11.4	9.2	11.1
BPSystolic/BPDiastolic	115.3 ± 12.4/	115.1 ± 12.7/	114.5 ± 12.6/	115.0 ± 12.7/	0.057/
Mean ± sd, mm Hg	74.0 ± 9.6	74.0 ± 9.7	73.6 ± 9.6	74.0 ± 9.7	0.056
Fasting glucose level, mg/dL	88.1 ± 6.8	87.8 ± 6.7	87.4 ± 6.7	87.7 ± 6.7	0.003
HDL-C, mg/dL	46.7 ± 11.5	47.8 ± 12.4	50.0 ± 12.8	48.2 ± 12.4	<0.001
LDL, mg/dL	116.5 ± 32.8	119.0 ± 33.3	119.0 ± 32.8	118.8 ± 33.1	0.045
TG, mg/dL (25th; 75th)	118 (70; 144)	119.5 (71; 145)	111.1 (67; 135)	117.9 (70; 143)	<0.001
Frequent breakfast consumption, %	15.5	16.2	25.0	17.8	<0.001
Regular alcohol consumption	4.4	4.2	6.1	4.6	<0.001
Physical inactivity	63.7	67.7	64.7	66.8	<0.001
Family history of diabetes	29.3	26.2	21.0	7.0	<0.001
Smoking history					<0.001
Never	46.7	56.6	71.9	58.7
Ex smoker	12.8	13.7	11.5	13.3
Current smoker	40.5	29.7	16.6	28.1
WBC count, cells/mm3	6.61 ± 1.5	6.60 ± 1.5	6.51 ± 1.4	6.58 ± 1.5	0.006

Abbreviation: LDL, low-density lipoprotein.

Categorical variables are presented by %. Mean differences in continuous variables among the GIS categories were tested with ANOVA. χ² test was used to assess the association between categorical variables and GIS groups. For continuous variables, the mean (SD) is given.

GIS and the incidence of IFG levels

There were 1478 new cases of IFG (1402 men and 76 women) during 122 688 person-years of followup. The number of scheduled visits to the screening center among those who developed IFG and those remained normoglycemic throughout study period was similar; 2.65 ± 0.72 and 2.65 ± 0.68, respectively (P = .96). There was an inverse relationship between GIS scores and the incidence of IFG with higher GIS scores predicting a lower incidence and this relationship was retained after adjusting for different clusters of dysglycemia risk factors (Table 2). Compared with the highest GIS group, the hazard for incident IFG was 1.93 (95% CI, 1.56–2.38; P < .001) for the lowest GIS group and 1.32 (95% CI, 1.14–1.54; P < .001) for the intermediate group in univariate analysis (model 1). These results persisted in models that were further adjusted for BMI, FPG, family history, country of origin, education, physical activity, smoking status, TG level, alcohol consumption, breakfast consumption, and WBC (Table 2). This is also depicted in the Cox survival curve (Figure 1).

Figure 1.

Cognitive performance at age 17 y is associated with the onset of IFG.

The incidence of IFG over time is shown for different GIS categories. Cox models were adjusted for age, sex, BMI, FPG, family history, country of origin, SES, education, physical activity, smoking status, TG level, alcohol consumption, breakfast consumption, and WBC count (model 5; Table 2).

Table 2.

HR for Developing Dysglycemia Across 3 GIS Categories for Various Models

	GIS			Total
	Low (GIS, 1–3)	Medium (GIS, 4–7)	High (GIS, 8–9)
N	1332	12 752	3264	17 348
Total new cases of dysglyemia (men/women)	156 (156/0)	1117 (1054/63)	205 (192/13)	1478 (1402/76)
Mean followup, y	7.25 ± 2.8	7.06 ± 2.8	7.01 ± 2.8	7.07 ± 2.8
Median followup, y	6.64	6.58	6.54	6.57
Person years of followup	9668	90 136	22 884	122 688
Rate, 1/1000 person years	16.1	12.3	8.9	3.49
Model 1: Age, sex
HR	1.93	1.32	1 (ref)
95% CI	1.56–2.38	1.14–1.54
P	<.001	<.001
Model 2: Age, sex, BMI, FPG
HR	1.80	1.29	1 (ref)
95% CI	1.46–2.23	1.11–1.50
P	<.001	.001
Model 3: Age, sex, BMI, FPG, family history of diabetes, country of origin, SES, education
HR	1.76	1.28	1 (ref)
95% CI	1.41–2.21	1.10–1.50
P	<.001	.001
Model 4: Age, sex, BMI, FPG, physical activity, smoking status, TG level, alcohol consumption, breakfast consumption
HR	1.82	1.34	1 (ref)
95% CI	1.43–2.32	1.13–1.59
P	<.001	.001
Model 5: Age, sex, BMI, FPG, family history of diabetes, country of origin, SES, education, physical activity, smoking status, TG level, alcohol consumption, breakfast consumption, WBC count
HR	1.81	1.33	1 (ref)
95% CI	1.40–2.34	1.11–1.59
P	<.001	.002

The cognition-IFG association was also assessed using GIS as a continuous variable in the above mentioned models (Table 2). In all these models, there was an approximately 11% increase in IFG risk for every point decrement in GIS (model 5: HR, 1.11; 95% CI, 1.06–1.1; P < .001). When BMI at the last visit to the screening center was used instead of baseline BMI similar results were obtained (HR, 1.11; 95% CI, 1.06–1.15; P < .001). During the study period, 165 cases of diabetes were recorded despite normal FPG in repetitive measurements at the screening center. In sensitivity analysis in which data regarding these were added to the analysis similar risk estimates were found (model 5; HR, 1.11; 95% CI, 1.08–1.16; P < .001). The GIS-IFG association also persisted when only participants with at least 5- or 8-year followup were included in the analyses (5-year: HR, 1.13; 95% CI, 1.07–1.19; 8-year: HR, 1.23; 95% CI, 1.04–1.44). Additional sensitivity analysis was conducted using the approach described by Vanderweele and Arah (27), in which a set of assumptions were made regarding a potential unmeasured confounder. Thus, to null the association described a dichotomous unmeasured confounder U with a 1) prevalence difference greater than 0.4 in the highest vs lowest GIS score category group, and 2) difference greater than 4 in the incidence of the outcome among those with and without the unmeasured confounder would have to be assumed.

Relationship between GIS score and incident IFG in subgroup of participants

There was no difference in the cognition-IFG relationship between men and women (model 5, P for interaction = .177), or between individuals with normal weight, lipids, BP, and FPG (“metabolically healthy”) vs those that were not (model 5; P for interaction = 0.348). When the metabolically healthy individuals were analyzed (N = 6251), a 1-point reduction in GIS was associated with a 19% increased risk of IFG after multivariable adjustment (HR, 1.19; 95% CI, 1.10–1.29; P < .001; Figure 2 and Supplemental Table 1). Notably, when this model was used to analyze the relationship between GIS and the incidence of other metabolic abnormalities, no relationship between GIS and incident obesity, elevated BP, or hypertriglyceridemia was noted in univariate model (Figure 2; Supplemental Table 1, respectively). Finally, a modest (ie, 3.5%) increased risk for incident abnormal HDL-C levels for every one point decrement in GIS (95% CI, 1.00–1.07; P = .041) in the univariate model was no longer significant in the multivariate model (Supplemental Table 1; Figure 2).

Figure 2.

The association between cognitive performance at age 17 y with traits of the metabolic syndrome.

The HR (± 95% CI) for every point decrement in GIS was measured for each of the study outcomes using data of 6251 participants with normal weight (BMI < 25 kg/m²), lipid (men, HDL-C > 40 mg/dL or women, HDL-C > 50 mg/dL and TG < 150 mg/dL), BP (BP_Systolic < 130 mm Hg and BP_Diastolic < 85 mm Hg). Values in parentheses represent the incident number of cases. Univariate analysis was adjusted for sex and age. Multivariate analysis was adjusted for age, sex, BMI, FPG, family history, country of origin, SES, education, physical activity, smoking status, TG level, alcohol consumption, breakfast consumption, and WBC count (model 5; Table 2). Detailed followup and cases incidence for each of the outcomes is shown in Supplemental Table 1.

Discussion

This analysis of 17 438 men and women with a followup of 122 688 person-years demonstrates an inverse relationship between cognitive function at late adolescence and incident impaired fasting glucose (IFG) at young adulthood. Individuals with the lowest cognitive scores at age 17 had an approximately 2-fold greater risk for the development of IFG compared with those with the highest cognitive scores. Every 1-point decrement in GIS was associated with 11% greater risk for the development of IFG. The fact that these associations persisted in models that were adjusted for age, sex, country of origin, socioeconomic status, education, BMI, FPG, TG level, physical activity, smoking status, and family history of diabetes supports the hypothesis of an independent relationship between premorbid cognitive function and incident IFG. In a recent study we reported a relationship between GIS score at age 17 years and the development of diabetes among men in young adulthood, with an approximately 10% increase in the incidence of diabetes for every 1 point lower score on the GIS (7). The results of the current study further show that the relationship exists across the whole dysglycemia spectrum, including IFG with a similar pattern in both men and women.

This study has several limitations. First, this is a retrospective cohort study in which data were collected during visits, thus raising the possibility that the relationship observed is merely a reflection of the frequency of attendance. The fact that all army personal must attend the scheduled visits and that similar visit frequency to the screening center were measured among those who remained normoglycemic and those who developed IFG minimizes this possibility but does not eliminate it. Second, a limited number of variables that might be associated with IFG and cognitive function (confounders) were collected (SES, education, country of birth and origin, and BMI). As such, it might be that some confounders (for example individual level SES) were not measured, and thus, were not adjusted for. However, the minor changes in effect size in response to adding a variety of measured confounders (including BMI at enrolment or at the end of followup) and the different models of analysis lower the probability for this possibility. In addition, sensitivity analysis using the approach described by Vanderweele and Arah (27) demonstrated that relatively extreme assumptions would have to be made regarding an unmeasured confounder to null the association described. Thus, in the setup of the cohort study where participants are career army personnel and are exposed to relatively similar ambient conditions, such an effect of a confounder is highly unlikely. Finally, all participants in the MELANY cohort, independent of rank and position, had equal access to free medical services, thus lowering the potential effect of followup environmental variables. Third, Israel is considered a “young” country with a relatively high rate of immigration. This analysis pertains to a wide range of people from different backgrounds and origins. The strong relationship observed despite this limitation and after adjustment for country of origin supports the robustness of the results.

The finding of an inverse relationship between cognitive function in young adulthood and subsequent development of IFG has several possible explanations. First, as cognitive function is associated with education and SES, it is possible that the relationship merely reflects the already recognized relationship between these two variables and the subsequent risk for IFG. Second, lower GIS might be associated with a more diabetogenic lifestyle. Although we did control for physical activity, and TGs [that are associated with activity (12)], other unmeasured factors may have been involved. The observation that in participants without any of the components of the metabolic syndrome at baseline the only relationship that retained statistical significance was between cognitive function and IFG makes this explanation less likely as the sole explanation. Finally, it could be that the relationship observed suggests an origin(s) or pathway common to decreased cognitive function and increased incidence of IFG (28). These might include, among others, mitochondrial (dys)function (29, 30), the sortilin pathway (31) activation of the hypothalamic-pituitary-adrenal axis, inflammation, or brain and systemic insulin signaling (32–34). There are many insulin receptors in the brain that some have a role in glucose transport, whereas others are considered to have a function in cognitive processes (35). Prospective studies have reported a relationship between insulin sensitivity/resistance indices in midlife and the risk for cognitive impairment in older age (36). Collectively, our study supports the possibility that a mild decrease in cognitive function is an early manifestation of a decrease in brain insulin homeostasis.

To conclude, this analysis of 17 438 men and women followed for 122 688 person-years demonstrates a clear inverse relationship between cognitive function at late adolescence and risk for dysglycemia during young adulthood. On a clinical level, along with family history of diabetes, FPG, TG level, WBC counts, and BMI, assessment of cognitive function may serve as a marker of overall health and aid in the identification of those at increased risk for dysglycemia. On a research level, these findings may be the basis for further research exploring the mechanistic routes that underlie the cognitive-dysglycemia relationship.

Acknowledgments

Author Contributions: T.C.-Y., G.T.: study concept and design, acquisition and interpretation of data, data analysis and drafting of the manuscript; M.K.-M., H.C.G.: interpretation of the data and critical revision of the manuscript; E.D.: study concept and design, statistical analysis and critical revision of the manuscript; D.T.: data acquisition; E.F., A.A., A.K.: critical revision of the manuscript; G.T. had supervised the study and had full access to all the data in the study and takes full responsibility for the integrity of the data and the accuracy of the data analysis.

This work was supported by a research grant of the Israel Defense Forces Medical Corp and the Israeli ministry of defense (G.T.). G.T. was partially supported by a grant from the Pinchas Borenstein Talpiot Medical Leadership Program, Chaim Sheba Medical Center, Tel Hashomer, Israel.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Disclosure Summary: All authors have nothing to declare.

Abbreviations

 
• BMI

  body mass index

 
• BP

  blood pressure

 
• CI

  confidence interval

 
• FPG

  fasting plasma glucose

 
• GIS

  General Intelligence Score

 
• HDL-C

  high-density lipoprotein cholesterol

 
• HR

  hazard ratio

 
• IFG

  impaired fasting glucose

 
• MELANY

  Metabolic Lifestyle and Nutrition Assessment in Young Adults

 
• SES

  socioeconomic status

 
• TG

  triglyceride

 
• USSR

  Union of Soviet Socialist Republics

 
• WAIS

  Wechsler Adult Intelligence Scale

 
• WBC

  white blood cell.