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Epidemiology and Risk Factors of Type 2 Diabetes

  • Sylvia H. Ley
  • James B. MeigsEmail author
Living reference work entry
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Part of the Endocrinology book series (ENDOCR)

Abstract

Type 2 diabetes has become a serious public health concern. It has multiple behavioral, metabolic, and genetic risk factors. Excess body fat, especially central obesity, is the strongest risk factor for type 2 diabetes. Diets favoring higher intake of whole grains, green leafy vegetables, and coffee and lower intake of refined grains, red and processed meat, and sugar-sweetened beverages have been associated with a lower risk of type 2 diabetes. Regular physical activity, ranging from brisk walking to higher-intensity endurance or resistance training, has been associated with a lower risk of type 2 diabetes. Novel biomarkers, such as adipokines and inflammatory cytokines, and intermediate conditions, such as metabolic syndrome, have offered the potential to improve diabetes prediction. Multiple diabetes genetic variants have been identified, and the collaborative efforts are made to investigate gene-environment interactions. Continued work to prevent diabetes is warranted through development of precision health interventions and public health strategies targeting these risk factors.

Keywords

Type 2 diabetes Epidemiology Risk factors 

Introduction

Type 2 diabetes has become a major public health concern globally and in the United States (US) (International Diabetes Federation 2015). Type 2 diabetes is a heterogeneous disease involving multiple risk factors. Large prospective studies have improved the understanding of modifiable risk factors for type 2 diabetes (Ley et al. 2014). However, individual responses to these environmental risk factors vary, potentially explained by individual differences in intervention adherence and complex gene-environment interactions (Cornelis and Hu 2012). Research on novel biomarkers and intermediate conditions associated with diabetes risk has improved understanding on risk factors for type 2 diabetes and the disease progress and etiology (Meigs 2010).

Epidemiology

The global estimate of adults living with diabetes is 415 million in 2015 and is projected to rise to 642 million by 2040 (International Diabetes Federation 2015) (Fig. 1). Approximately 12% of global health expenditure is spent on diabetes-related treatments, and 46.5% of adults with diabetes is undiagnosed (International Diabetes Federation 2015). Based on the 2011–2012 National Health and Nutrition Examination Survey (NHANES), the unadjusted prevalence in US population was 14.3% for total diabetes, 9.1% for diagnosed diabetes, 5.2% for undiagnosed diabetes, and 38% for prediabetes (Menke et al. 2015). Type 2 diabetes has become an important global health priority in recent decades, and considerable efforts have been made to identify effective preventive strategies (Hu 2011).
Fig. 1

Estimated number of adults aged 20–79 years with diabetes globally and per region in 2015 and 2040 (Adapted from International Diabetes Federation (2015))

Demographic Risk Factors

Based on NHANES data, the prevalence of diabetes increases with age (Centers for Disease Control and Prevention 2011). In most populations, the incidence of type 2 diabetes is low before age 30 years but increases rapidly and continuously with aging (Geiss et al. 2006; González et al. 2009). This is a particular concern at a time when life expectancy is increasing. In the European countries, higher risk of diabetes in men compared with women was observed (The InterAct Consortium 2011a). However, this was not as consistently evident in the US population; the incidence of diabetes among men compared to women was higher in 2010 but lower in 2013 based on the National Health Interview Survey (NHIS) data (Centers for Disease Control and Prevention 2016). The age-standardized prevalence of diabetes was higher among non-Hispanic black (21.8%), non-Hispanic Asian (20.6%), and Hispanic (22.6%) compared with non-Hispanic white (11.3%) in the NHANES 2011–2012 population (Menke et al. 2015). Ethnic differences can be explained only in part by differences in the prevalence of obesity, behavioral risk factors, and socioeconomic status (SES). Asian, Hispanic, and black ethnicity were each associated with higher diabetes risk compared to white participants after adjustment for differences in age, body mass index (BMI), family history of diabetes, and lifestyle risk factors (i.e., alcohol consumption, smoking, physical activity, and diet) in the Nurses’ Health Study (NHS) (Shai et al. 2006). In the Multiethnic Cohort Study of volunteers living in Hawaii and California, diabetes risk for Japanese Americans and Pacific Islanders remained higher compared to white participants after adjustment for BMI and education (Maskarinec et al. 2009).

Genetic Risk Factors

Early efforts to identify genetic variants for type 2 diabetes heritability in epidemiologic studies involved genome-wide linkage and candidate gene approaches. With the introduction of studies incorporating high-throughput, parallel genotyping technologies including genome-wide association studies (GWAS), the field has advanced rapidly. Further, global collaborative efforts have been made to detect small effects of common variants. For example, a collaboration of 23 studies from populations of European ancestry comprising 27,206 type 2 diabetes cases and 57,574 controls led to the identification and fine mapping of numerous new loci for type 2 diabetes (Gaulton et al. 2015). Over 250 genetic loci have been identified for monogenic, syndromic, or common forms of type 2 diabetes and obesity (Qi et al. 2011).

Gene-Environment Interactions

These genetic variations may influence modifiable risk factors for type 2 diabetes evidenced by variations in individual responses to environmental risk factors. Therefore, understanding gene-environment interactions has the potential to benefit strategies for the prevention of type 2 diabetes. However, gene-environment interaction studies have experienced methodological challenges when investigating small effects of common gene variants (Franks 2011). Collaborative efforts have been made to address these challenges, including the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. The consortium conducted a meta-analysis of 14 cohort studies comprising about 48,000 participants of European descent and reported a nominal interaction between GCKR (rs780094) variant and whole grain intake on fasting insulin (Nettleton et al. 2010). Constructing a risk score is another approach that has been used to investigate gene-environment interactions. In a nested case-control study within the Health Professionals Follow-up Study (HPFS) and the NHS, a genetic risk score was calculated on the basis of ten polymorphisms in nine loci (Cornelis et al. 2009). The association between genetic score and type 2 diabetes was strengthened with the BMI increase in this study of individuals of European descent (Cornelis et al. 2009). In the Diabetes Prevention Program (Hivert et al. 2011a), a high genetic risk score was associated with increased risk of developing type 2 diabetes, but a lifestyle intervention attenuated this risk. Although information on common genetic variations may not improve prediction of diabetes (Meigs et al. 2008) (Fig. 2), better understanding of genetic susceptibility to the disease and its influence on environmental risk factors may assist in developing future prevention strategies for type 2 diabetes.
Fig. 2

Association between body mass index and type 2 diabetes (Adapted from DeFronzo et al. (2015))

Behavioral and Lifestyle Risk Factors

Obesity

Excessive body fat is the single largest risk factor for type 2 diabetes (Hu et al. 2001a). Excessive body fat is frequently assessed by BMI (the ratio of body weight in kilograms to squared height in meters) or anthropometric indicators such as a waist circumference or skinfold thickness in epidemiologic studies. Clinical risk categories for obesity measured using BMI (normal BMI 18.5–24.9 kg/m2, overweight 25–29.9 kg/m2, and obesity ≥30 kg/m2) are associated with a stepwise increase in diabetes risk (DeFronzo et al. 2015) (Fig. 3). Duration of overweight/obesity is also an important risk factor because each 2 extra years of being obese increased type 2 diabetes risk by 14% (Hu et al. 2014). Further, weight gain during early adulthood aged 25–40 years was more strongly associated with diabetes risk compared with weight gain during later adulthood aged 40–55 years in the European Prospective Investigation into Cancer and Nutrtion (EPIC)-Potsdam Study (Schienkiewitz et al. 2006).
Fig. 3

A genetic risk score of 18 SNPs predicts type 2 diabetes. (a) Panel shows the distribution of an 18-SNP genetic risk score, where Framingham Heart Study individuals were scored with a zero if they had no risk alleles at a given SNP, one if they were heterozygous, and two if they were homozygous for the risk allele. Individuals who developed type 2 diabetes over 28 years of follow-up have about 0.6 more risk alleles than those who remain free of diabetes. (b) Panel shows that risk for type 2 diabetes increases with increasing genetic risk burden. Those with 16–20 risk alleles have a risk of diabetes of 1.6, and those with ≥21 risk alleles have a risk of 2.5 relative to those with ≤15 risk alleles. However, the genetic risk score does not discriminate those who will develop type 2 diabetes from those who will not after accounting for common clinical risk factors (age, sex, family history of diabetes, and metabolic syndrome traits). (c) Panel shows that the area under the receiver operating characteristic curve (the C statistic) was 0.900 for a simple clinical model including clinical risk factors (gray line) and 0.901 for a model including clinical risk factors and the genetic risk score (black line) (Adapted from Meigs (2010))

Central obesity captured as a higher waist circumference or waist-to-hip ratio was associated with type 2 diabetes risk in the NHS (Carey et al. 1997). In a meta-analysis of prospective observational studies, the risk associated with a higher waist circumference was slightly stronger than that associated with higher BMI (Vazquez et al. 2007), indicating that central obesity was more important. However, the association of waist-to-hip ratio with type 2 diabetes was slightly weaker compared to BMI. In the prospective EPIC Study, individuals in the overweight BMI and abdominal obesity (waist circumference ≥102 cm among men, ≥88 cm among women) had a similar risk compared to obese individuals (BMI ≥30 kg/m2) (The InterAct Consortium 2012). Therefore, measuring waist circumference in addition to BMI may allow for additional stratification for diabetes risk among overweight individuals.

Diet

Dietary intake has been suspected as a major risk factor for type 2 diabetes for a while, but evidence from prospective studies evaluating diet in relation to the incidence of diabetes was vastly accumulated in the past couple of decades (Ley et al. 2014).

Dietary Fat and Carbohydrate

Prospective cohort studies demonstrated that total fat intake is not associated with diabetes risk (Hu et al. 2001b). In the Women’s Health Initiative (WHI), the incidence of treated diabetes was not different among women who consumed a low-fat diet (24% energy from fat) compared to women who consumed a standard US diet (35% energy from fat) (Tinker et al. 2008). Therefore, the specific type of fat may be more important than the total intake. Diets that favor plant fats over animal fats are advantageous (Hu et al. 2001b; Melanson et al. 2009). A higher intake of polyunsaturated fatty acids (PUFA) was associated with a lower diabetes risk (Meyer et al. 2001; Salmerón et al. 2001). However, the association between the quantity of long-chain n-3 PUFA intake and diabetes risk has been inconsistent, and a meta-analysis including 16 prospective cohorts with 440,873 participants and 21,512 cases of incident diabetes reported nonsignificant association (Wu et al. 2012). Exchanging saturated fatty acids with PUFA was associated with a lower risk (Hu et al. 2001b).

Similarly, the quality of carbohydrate is likely more important than the quantity for diabetes prevention. The relative carbohydrate proportion of the diet did not influence diabetes risk (Hauner et al. 2012). Prospective cohort studies investigating carbohydrate substitutions with other macronutrients reported heterogeneous results (Schulze et al. 2004, 2008). A meta-analysis of eight prospective cohort studies, including five from the US and one each from Finland, Australia, and Germany, demonstrated an inverse association of dietary fiber intake from cereal products with risk of type 2 diabetes (Schulze et al. 2007). However, total fiber or fiber from fruits or vegetables was not associated with diabetes risk in this study (Schulze et al. 2007). The protective impact of cereal fiber was evidenced in several other studies (Hopping et al. 2010; Krishnan et al. 2007), but a few studies did not detect such beneficial impact (Barclay et al. 2007; Wannamethee et al. 2009). Carbohydrate quality can be determined by glycemic index and glycemic load by evaluating the physiologic response to carbohydrate-rich foods. High-quality carbohydrate diets low in average glycemic index and glycemic load are associated with a lower risk for diabetes (Bhupathiraju et al. 2014; Dong et al. 2011b; Liu and Chou 2010), independent of the amount of dietary fiber in the diet.

Vitamins and Minerals

In a meta-analysis of four prospective studies, greater heme-iron intake was associated with a higher risk of type 2 diabetes (Zhao et al. 2012). Further, higher iron stores, reflected by elevated ferritin concentrations, were associated with increased risk of developing type 2 diabetes. A meta-analysis of five prospective studies provided evidence that magnesium intake was inversely associated with type 2 diabetes risk (Dong et al. 2011a). This association was more pronounced among overweight and obese participants (BMI ≥25 kg/m2) but was not significant among those with BMI <25 kg/m2 (Dong et al. 2011a). In the Framingham Offspring Study, higher levels of 25-OH vitamin D were associated with lower incidence of type 2 diabetes after accounting for potential confounders (Liu et al. 2010). This potentially protective effect was also reported in the NHS but mainly in the upper levels of circulating 25-OH vitamin D and with a stronger effect in overweight/obese women (Pittas et al. 2010). However, 25-OH vitamin D levels were not associated with type 2 diabetes incidence in the WHI (Robinson et al. 2011). Further, intervention trials investigating the impact of vitamin D supplementation have been mainly inconclusive (Mitri et al. 2011). Therefore, the role of vitamin D in diabetes prevention is currently inconclusive. Further, specific nutrient-based associations with type 2 diabetes may have been confounded by other unaccounted nutrients in food since these nutrients are consumed in combination as food items. For example, dairy products are not only rich in vitamin D but rich in macronutrients and other micronutrients such as magnesium (Dong et al. 2011a).

Food and Beverages

Specific food and beverages have been investigated as risk factors for type 2 diabetes (Ley et al. 2014) (Fig. 4). Whole grain intake was associated with a lower risk of diabetes (Aune et al. 2013; de Munter et al. 2007), even after adjustment for potential confounders including obesity. Conversely, greater intake of white rice which is a form of processed grain was associated with a higher risk for type 2 diabetes (Hu et al. 2012), especially among Asian populations with markedly higher amounts of white rice consumption. Frequent consumption of total red meat and processed meats was associated with higher diabetes risk (Pan et al. 2011b). In a meta-analysis of 13 prospective studies, fish and/or seafood consumption was not significantly associated with a higher risk of type 2 diabetes (Wu et al. 2012). Greater fish/seafood consumption was associated with a higher risk of type 2 diabetes in North America and Europe, while it was associated with a lower risk in Asia (Wallin et al. 2012; Wu et al. 2012). Variations in types of fish consumed and cooking preparation methods used within different geographical locations may explain the differences in results. Total dairy consumption was not associated with type 2 diabetes risk, but greater intake of yogurt was associated with a lower risk of type 2 diabetes (Chen et al. 2014). Although total consumption of fruits and vegetable quantity was not associated with type 2 diabetes, greater green leafy vegetable intake was associated with a lower risk (Carter et al. 2010; Cooper et al. 2012). In NHS cohorts, greater intake of whole fruits rich in anthocyanin such as blueberries, grapes, and apples/pears was also associated with a lower risk of type 2 diabetes (Muraki et al. 2013; Wedick et al. 2012). Nut consumption was associated with a lower risk of diabetes (Jiang et al. 2002; Kendall et al. 2010). In the Prevención con Dieta Mediterránea (PREDIMED) trial, nut supplementation in addition to Mediterranean diet reduced the incidence of type 2 diabetes (Salas-Salvadó et al. 2011).
Fig. 4

Summary of meta-analyses of prospective cohort studies on food and beverage intake and type 2 diabetes. Relative risks (RR) are comparison of extreme categories, except for processed meat (per 50 g/day increase), unprocessed red meat and fish/sea food (per 100 g/day), white rice (per each serving/day), whole grains (per three servings/day), sugar-sweetened beverages in European cohorts (per 336 g/day), and alcohol (abstainers with 22 g/day for men and with 24 g/day for women) (Adapted from Ley et al. (2014))

Based on meta-analyses (Ding et al. 2014; Huxley et al. 2009), total coffee, caffeinated, and decaffeinated coffee consumption was associated with a lower risk of type 2 diabetes. Greater sugar-sweetened beverage consumption was associated with a higher risk of type 2 diabetes (Malik et al. 2010; The InterAct Consortium 2013). Since higher sugar-sweetened beverage intake was associated with more pronounced genetic predisposition to increased BMI and risk for obesity (Qi et al. 2012), this association is likely mediated through weight gain and obesity.

A U-shaped relationship between alcohol consumption and type 2 diabetes was observed with the lowest risk of type 2 diabetes in the moderate range of consumption of about one and half US standard drinks per day (Baliunas et al. 2009; Wannamethee et al. 2003). However, alcohol became harmful at a consumption level above four US standard drinks per day (50 g/day in women and 60 g/day in men) (Baliunas et al. 2009).

Dietary Patterns

Several healthful dietary patterns have been associated with a lower risk of type 2 diabetes (Ley et al. 2014). Mediterranean-style diets were associated with a lower risk of type 2 diabetes (Esposito et al. 2010; Salas-Salvadó et al. 2011, 2014; The InterAct Consortium 2011b). Alternative Healthy Eating Index (AHEI) (Chiuve et al. 2012) and the Dietary Approaches to Stop Hypertension (DASH) diets were also associated with lower diabetes risk (de Koning et al. 2011; Liese et al. 2009a). Vegetarian diets were associated with a lower diabetes risk (Tonstad et al. 2013). Further, prospective studies using exploratory methods to define dietary patterns supported dietary patterns favoring fruits, vegetables, whole grains, and legumes at the expense of red meats, refined grains, and sugar-sweetened beverages for type 2 diabetes prevention (Fung et al. 2004; Heidemann et al. 2005; Imamura et al. 2009; Liese et al. 2009b; McNaughton et al. 2008; Schulze et al. 2005). Several other characteristics of eating patterns such as skipping breakfast (Mekary et al. 2012) and frequent fried food consumption (Cahill et al. 2014) were associated with a higher risk of type 2 diabetes.

Physical Inactivity

Sedentary behaviors such as higher television viewing time are a risk factor for type 2 diabetes (Grøntved and Hu 2011). Physical inactivity defined as insufficient physical activity to meet present global recommendations by the World Health Organization is responsible for 7% of the global burden of type 2 diabetes (Lee et al. 2012). Physical activity of moderate intensity can lower the risk of type 2 diabetes based on a meta-analysis of ten prospective cohort studies (Jeon et al. 2007). Regular walking defined as ≥2.5 h/week of brisk walking was associated with a lower risk for type 2 diabetes compared to almost no walking (Jeon et al. 2007). Moderate- to high-intensity exercise is well known to be beneficial for type 2 diabetes prevention (Manson et al. 1991; Meisinger et al. 2005). In addition to aerobic exercise (e.g., brisk walking, jogging, running, bicycling, swimming, tennis, squash, and rowing), weight training was associated with a lower risk of type 2 diabetes (Grøntved et al. 2012). Engaging in weight training or aerobic exercise for ≥150 min/week was associated with 34–52% reduced risk of developing type 2 diabetes in men (Grøntved et al. 2012). In women, engaging in both aerobic moderate to vigorous physical activity and muscle-strengthening activity including toning, yoga, and resistance training was associated with a lower risk of type 2 diabetes (Grøntved et al. 2014).

Early-Life Environment

Children who experienced intrauterine exposure to maternal diabetes are more likely to have large for gestational age birth weight (Reece et al. 2009), childhood overweight (Lawlor et al. 2011), and impaired glucose tolerance (IGT) in early adulthood (Silverman et al. 1995). Among individuals born around the time of famine in the Netherlands during 1944–1945, prenatal exposure to famine especially during late gestation was associated with compromised glucose tolerance in adulthood (Ravelli et al. 1998). Fetal exposure to the severe Chinese famine during 1959–1961 was also associated with a higher risk of hyperglycemia in adulthood (Li et al. 2010). The association is exacerbated by a nutritionally rich environment in later life (Li et al. 2010). Birth weight is associated with a risk of type 2 diabetes later in life in a U-shaped fashion (Harder et al. 2007; Whincup et al. 2008). However, evidence suggests that most type 2 diabetes cases can be prevented by the adaptation of a healthier lifestyle later in life although birth weight may influence diabetes risk (Li et al. 2015). Further, early postnatal behavioral exposures such as breastfeeding may have a long-term protective effect against obesity and type 2 diabetes later in life (Arenz et al. 2004; Owen et al. 2005, 2006). For example, breastfeeding during early life has a protective effect on obesity (Arenz et al. 2004; Owen et al. 2005) and type 2 diabetes later in life (Owen et al. 2006). However, the multiple potential confounding factors including demographic, socioeconomic, educational, ethnic, cultural, and psychological factors for these associations remain to be clarified (Kramer et al. 2009).

Socioeconomic Status (SES)

In a meta-analysis of 23 prospective case-control and cohort studies (Agardh et al. 2011), the overall risk of developing type 2 diabetes was increased among those in a lower socioeconomic position including lower levels of education (relative risk [RR] 1.41), occupation (RR 1.31), and income (RR 1.40) (Agardh et al. 2011). In the Black Women’s Health Study (Krishnan et al. 2010), lower education, household income, and neighborhood SES were associated with a higher risk of developing type 2 diabetes. However, these associations were attenuated after adjustment for BMI indicating that BMI might be a key intermediate factor in the pathway between SES and diabetes. SES may also contribute to the development of type 2 diabetes through processes involving lack of access to health-care services, healthy foods, places to exercise, and occupational opportunities, leading to unhealthy lifestyle practices (Brown et al. 2004).

Migration and Acculturation

Urbanization and Westernization associated with inter- and intra-country migration is a contributing risk factor for type 2 diabetes (Zimmet 2000; Zimmet et al. 2001). Stepwise increases across the sociocultural gradient were reported on the prevalence of obesity (5% in Nigeria, 23% in Jamaica, and 39% in the USA) (Luke et al. 2001) and type 2 diabetes (1%, 12%, and 13%, respectively) (Rotimi et al. 1999) among African descents. However, acculturation is a complex and multidirectional process. The prevalence of diabetes varies by country of origin based on NHIS 2000–2005 data within the Hispanic ethnic group (Pabon-Nau et al. 2010). Acculturation within a migrant population can vary in degrees of retaining their cultural roots and integrating the local mainstream culture (Pérez-Escamilla and Putnik 2007). Further, the study participant selection process may introduce bias and may not reflect general representation of the source population.

Sleep

Habitual sleep disturbances are associated with risk of developing type 2 diabetes (Cappuccio et al. 2010). Obstructive sleep apnea is highly prevalent among obese adults (Young et al. 2005). In a meta-analysis of six prospective cohort studies, moderate to severe obstructive sleep apnea was associated with a higher risk for type 2 diabetes (Wang et al. 2013). In another meta-analysis of ten prospective cohorts, shorter duration of sleep (≤5–6 h/night) was associated with a higher risk of type 2 diabetes (RR 1.28), while longer duration of sleep (>8–9 h/night) was also associated with the risk (RR 1.48) (Cappuccio et al. 2010). Type 2 diabetes risk was also increased among those with difficulty in initiating or maintaining sleep (Cappuccio et al. 2010). Lower melatonin secretion measured from first-morning urine samples as an indicator of sleep disruption was also associated with a higher risk of type 2 diabetes (McMullan et al. 2013). Performing night shift work for an extended period was associated with a higher risk of type 2 diabetes (Pan et al. 2011a). Other sleep quality measures such as regular snoring (Al-Delaimy et al. 2002) and difficulty falling or staying asleep were associated with type 2 diabetes risk (Li et al. 2016).

Depression and Antidepressant Medications

The relation between depression and type 2 diabetes is bidirectional (Pan et al. 2010). In a meta-analysis of 13 studies, baseline depression was associated with incident diabetes (RR 1.60), while baseline diabetes was also associated with incident depression (RR 1.15) (Mezuk et al. 2008). In addition, the use of antidepressant medication was associated with a higher risk of type 2 diabetes (Pan et al. 2012).

Smoking

In a meta-analysis of 25 prospective cohort studies, active smokers were at a higher risk for developing type 2 diabetes compared with nonsmokers (RR 1.44) (Willi et al. 2007). Further, heavier active smokers had higher risk for type 2 diabetes (RR 1.61), while the associations were weaker for lighter active smokers (RR 1.29) and former smokers (RR 1.23). Smoking cessation was associated with a short-term increased risk of diabetes likely mediated through weight gain (Yeh et al. 2010). Exposure to passive smoking at work or home was also associated with a higher risk of diabetes (Zhang et al. 2011).

To summarize lifestyle risk factors for type 2 diabetes, more than 90% of type 2 diabetes cases could have been prevented by following a healthy diet, having a healthy body weight, exercising for at least 30 min a day, avoiding smoking, and consuming alcohol in moderation (Hu et al. 2001a). Therefore, combinations of these behavioral factors could be utilized to develop optimal strategies to prevent diabetes.

Metabolic Factors Associated with Risk of Type 2 Diabetes

Biomarkers

Prediabetes and insulin resistance are increasingly characterized by a subclinical pro-inflammatory condition derived from adipose tissue dysregulation. With accumulation of excess weight, macrophages infiltrate adipose tissue leading to secretion of pro-inflammatory cytokines and impaired secretion of adipokines secreted by the adipose tissue. The liver is involved in the process through secretion of C-reactive protein (CRP) and liver enzymes. The inflammatory process is also linked with endothelial dysfunction markers.

Adiponectin

Adiponectin is an adipokine mainly produced by adipocytes (Scherer et al. 1995) and has anti-inflammatory and insulin-sensitizing effects (Berg et al. 2001; Kadowaki et al. 2006; Ouchi et al. 2000). In a meta-analysis reviewing 13 prospective studies, lower adiponectin concentrations were consistently associated with a higher risk of type 2 diabetes in populations from various ethnic backgrounds and wide ranges of age, sex, and baseline glucose tolerance (Li et al. 2009). Low adiponectin concentrations were associated with a higher risk of type 2 diabetes among individuals who were insulin resistant at baseline (upper quartile of homeostatic model assessment-insulin resistance [HOMA-IR]) in the Framingham Offspring Study and in the Cooperative Health Research in the Region of Augsburg Study (Hivert et al. 2011b), while the association between lower adiponectin concentrations and a higher risk of type 2 diabetes was stronger in individuals with lower HOMA-IR in the Cardiovascular Health Study (Kizer et al. 2012). Using more refined measures of insulin sensitivity based on intravenous glucose tolerance test, the association between adiponectin levels and the type 2 diabetes incidence was no longer significant after adjusting for Si in the Insulin Resistance Atherosclerosis Study (IRAS) suggesting that insulin sensitivity mediated the association (Hanley et al. 2011).

Pro-inflammatory Cytokines

Pro-inflammatory markers such as tumor necrosis factor-alpha (TNFα), interleukin (IL)-6, and CRP were associated with a higher risk of type 2. When these three biomarkers were mutually adjusted for each other, only CRP remained significantly associated with type 2 diabetes incidence in the NHS (Hu et al. 2004) and WHI Observational Study (Liu et al. 2007). In the EPIC cohort, the association between higher CRP and type 2 diabetes risk was attenuated and became no longer significant after multiple adjustment for waist-to-hip ratio, serum gamma-glutamyltransferase (GGT), and serum adiponectin (Lee et al. 2009). In the Multi-Ethnic Study of Atherosclerosis (MESA), IL-6 and CRP were associated with a risk of type 2 diabetes in white, black, and Hispanic individuals but not in individuals of Chinese origin (Bertoni et al. 2010). In a meta-analysis of 16 prospective studies from various regions and populations from Europe, Asia, and America, higher CRP concentrations were associated with a higher risk of type 2 diabetes (Lee et al. 2009). IL-18 is another cytokine likely involved in pro-inflammatory and insulin resistance pathways. Higher IL-18 concentrations were associated with higher risk of type 2 diabetes in the NHS (Hivert et al. 2009). In the Atherosclerosis Risk in Communities (ARIC) Study, higher IL-18 concentrations were associated with a higher risk of type 2 diabetes in whites, but not in African American descent participants (Negi et al. 2012), suggesting a potential difference between these ethnic backgrounds.

Coagulation and Endothelial Dysfunction Markers

Plasminogen activator inhibitor-1 (PAI-1) is primarily produced by endothelial cells but also secreted by adipose tissues. In the Framingham Offspring Study, higher concentrations of PAI-1 and von Willebrand factor were associated with a higher risk of type 2 diabetes in multivariable models including major diabetes clinical risk factors in addition to CRP levels (Meigs et al. 2006). PAI-1 concentrations were also strongly associated with type 2 diabetes risk in the Health, Aging, and Body Composition Study of black and white older adults (Kanaya et al. 2006) and in the IRAS cohort (Festa et al. 2002).

Endothelial dysfunction can be detected by measurement of elevated plasma concentrations of cellular adhesion molecules, including E-selectin, intercellular adhesion molecule 1, and vascular cell adhesion molecule 1. These markers were associated with type 2 diabetes risk in the NHS (Meigs et al. 2004) and the multiethnic WHI Observational Study (Song et al. 2007).

Liver Markers

Higher liver enzyme concentrations were associated with type 2 diabetes risk in a meta-analysis of prospective cohorts from various countries in Europe and Asia in addition to the USA (Fraser et al. 2009). Higher baseline GGT or alanine aminotransferase (ALT) concentrations were associated with diabetes status at follow-up in the Bogalusa Heart Study (Nguyen et al. 2011) and in the Coronary Artery Risk Development in Young Adults Study higher GGT concentrations at baseline (Lee et al. 2003). In a cross-sectional analysis of NHANES III, an interaction between BMI and GGT concentrations was reported demonstrating the association between GGT and diabetes prevalence among participants with higher BMI only (Lim et al. 2007). Higher concentrations of fetuin-A, a glycoprotein secreted by the liver, were associated with a higher risk of type 2 diabetes in a meta-analysis of four prospective studies (Sun et al. 2013). In the NHS, the positive association between fetuin-A and diabetes remained after adjustment for liver enzymes.

Insulin-Like Growth Factor Axis

Insulin-like growth factor (IGF)-1 shares structural homology with insulin (Rajpathak et al. 2009). However, total IGF-1 concentrations were not significantly associated with type 2 diabetes risk in the NHS (Rajpathak et al. 2012). A statistically significant interaction was observed. Free IGF-1 was inversely associated with type 2 diabetes risk among women with higher (above median, 4.6 μU/mL) insulin concentrations, while it was positively associated with type 2 diabetes risk among those with lower insulin concentrations (Rajpathak et al. 2012). Further, lower IGF binding protein (IGFBP)-1 and IGFBP-2 and higher IGFBP-3 were associated with a higher risk for diabetes (Rajpathak et al. 2012).

Sex Hormones

In a meta-analysis, low testosterone in men was associated with risk of type 2 diabetes (Ding et al. 2006). In the NHANES III, men in the lowest tertile of bioavailable testosterone were about four times more likely to have type 2 diabetes compared with men in the upper tertile (Selvin et al. 2007). In the Rancho Bernardo Study, higher bioavailable testosterone concentrations in postmenopausal women were associated with higher risk of type 2 diabetes (Oh et al. 2002). Higher concentrations of bioavailable estradiol were associated with higher risk of type 2 diabetes in women (Oh et al. 2002) but not in men (Haffner et al. 1996; Oh et al. 2002). Further, lower concentrations of sex hormone-binding globulin were associated with higher risk of type 2 diabetes (Ding et al. 2006).

Metabolic Syndrome

Metabolic traits that cluster in metabolic syndrome include central obesity, high fasting glucose, high blood pressure, high triglyceride, and/or low HDL cholesterol levels (Alberti et al. 2006; Grundy et al. 2005). Metabolic syndrome is a strong risk factor of type 2 diabetes (Ford et al. 2008). The presence versus the absence of National Cholesterol Education Program’s Adult Treatment Panel III (ATP III) metabolic syndrome among individuals without diabetes at baseline was associated with a 5.3-fold higher risk of developing type 2 diabetes. In community studies, the metabolic syndrome was associated with a higher risk for type 2 diabetes in white participants (Wilson et al. 2005), black participants (Schmidt et al. 2005), Mexican Americans (Lorenzo et al. 2007), Native Americans (Hanson et al. 2002; Russell et al. 2007), and Native Canadians (Ley et al. 2009). Further, metabolic syndrome demonstrates a positive dose-response gradient between the number of metabolic syndrome traits and diabetes risk (Wilson et al. 2005). Women with one or two traits had a sixfold higher risk of type 2 diabetes, and those with three or more traits had a 30-fold higher risk of diabetes in the Framingham Offspring Study. When combined in prediction models for type 2 diabetes risk, metabolic syndrome traits have excellent discriminatory capacity (Wilson et al. 2005, 2007) (Fig. 5).
Fig. 5

Metabolic syndrome is a risk factor for both type 2 diabetes (T2D) and cardiovascular disease (CVD). (a) Panel shows that among men in the Framingham Heart Study, the 7- to 11-year risk for CVD increases from 1.5 for those with one or two metabolic syndrome risk factors to 4.0 for those with three or more relative to those with no metabolic syndrome risk factors, even after accounting for other CVD-specific risk factors. The bars in the figure represent the odds ratio and its 95% confidence bounds. The relative risk for CVD is 2.9 comparing metabolic syndrome versus no metabolic syndrome. Risk for type 2 diabetes increases from 4.2 for men with one or two metabolic syndrome risk factors to 24 for those with metabolic syndrome relative to those with no metabolic syndrome risk factors, even after accounting for other type 2 diabetes-specific risk factors. The relative risk for type 2 diabetes is 6.9 comparing metabolic syndrome versus no metabolic syndrome. Patterns are similar for Framingham Heart Study women. Risk rises steadily in a dose-response relationship as the number of component traits increases and is increased regardless of which of the various heterogeneous combinations of specific traits are present and even in the absence of impaired glycemia (b) (Adapted from Meigs (2010)). BP blood pressure, FG fasting glucose, TG triglycerides

Summary

Large prospective cohort studies have improved our understanding on environmental risk factors for type 2 diabetes. However, variations between individual responses to risk factor interventions are likely explained by genetic and individual physiologic differences. Therefore, advancement in the knowledge of gene-environment interactions, biomarkers, and intermediate conditions would contribute to the progress of targeted prevention strategies for type 2 diabetes. Continued efforts are warranted to improve the understanding of type 2 diabetes risk to develop optimal strategies for type 2 diabetes prevention with a long-term goal of addressing this major public health concern.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of MedicineBrigham and Women’s Hospital and Harvard Medical SchoolBostonUSA
  2. 2.Department of NutritionHarvard T.H. Chan School of Public HealthBostonUSA
  3. 3.Department of MedicineMassachusetts General Hospital and Harvard Medical SchoolBostonUSA

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