289
19
From: Contemporary Endocrinology: Evidence-Based Endocrinology Edited by: V. M. Montori © Mayo Foundation for Medical Education and Research
The Patient at Risk for Diabetes
Considering Prevention
Sarah E. Capes, MD, MS
C, FRPC
C
ONTENTSI
NTRODUCTIONW
HO IS ATR
ISK FORD
IABETES? C
AND
IABETES BEP
REVENTED? P
REVENTINGD
IABETESC
ONCLUSIONSR
EFERENCESINTRODUCTION
More than 170 million people worldwide have diabetes, and the World Health Orga- nization (WHO) projects that this number will more than double by the year 2030.
Most of the increase is expected to occur in developing countries, where diabetes already affects people in their most productive years, the 45- to 64-yr age bracket (1).
In the United States, an estimated 17.7 million people have diabetes, and this number is expected to increase to 30.3 million by 2030 (2). Thus, diabetes is poised to become one of the primary causes of disability and death worldwide within the next 25 yr.
From these data, it is clear that there is an urgent need to develop and implement effec- tive strategies to prevent diabetes. This chapter will review the evidence regarding pre- dictors of diabetes and therapies to prevent diabetes, and will discuss how diabetes prevention strategies can be applied in the “real world.”
WHO IS AT RISK FOR DIABETES?
Predisposition to diabetes is likely to be caused by a complex interaction among genetic, environmental/lifestyle factors, and possibly perinatal factors. Large cohort studies have identified several key predictors of diabetes.
Age
In most populations, type 2 diabetes is rare before age 30 but increases with age. In
6000 men enrolled in the Usual Care group of the Multiple Risk Factor Intervention
Trial (MRFIT), the incidence of diabetes over 5 yr follow-up increased by 30% for each 5-yr increment in age (3). In NHANES III, the prevalence of diabetes (diagnosed and undiagnosed) in the United States rose from 1–2% at ages 20–39 to 18–20% at ages 60–74, with a plateau after age 75 (4).
Obesity and Central Fat Distribution
Risk of type 2 diabetes also increases with increasing body mass index (BMI), defined as weight in kilograms per height in meters squared) (Table 2). In men in the MRFIT Usual Care cohort, the risk of diabetes almost doubled for each 5 kg/m
2increase in BMI (3). The risk may extend into the “normal” range of BMI (20–25 kg/m
2); for women in the Nurses’ Health Study, the risk of self-reported diabetes doubled for BMI 22–22.9 kg/m
2, tripled for BMI 23–23.9 kg/m
2, and was five times higher for BMI 25–26.9 kg/m
2compared to BMI under 22 kg/m
2(5). Central fat distribution further increases the risk of type 2 diabetes for a given BMI (6–8). In American men, waist circumference >40 in. (101 cm) or waist-to-hip ratio >0.99 were associated with a significantly increased risk of self-reported diabetes (7). In American women, waist cir- cumference >29 in. (76 cm) or waist-to-hip ratio >0.75 indicated an increased risk (8).
Weight gain in adulthood of more than 10 kg after age 18 in women (5) or more than 8 kg after age 21 in men (7) was also associated with an increased risk of clinical diabetes independent of BMI in early adulthood.
Ethnicity
The prevalence of type 2 diabetes varies substantially among different ethnic groups.
The most accurate estimates of prevalence come from large population-based surveys
Table 1Risk Factors for Type 2 Diabetes Definite risk factors
Increasing age Obesity
Central fat distribution Weight gain in adulthood Ethnicity
Low birthweight Sedentary lifestyle Family history of diabetes
Impaired glucose tolerance and impaired fasting glucose Hypertension
Dyslipidemia (high triglyceride, low HDL cholesterol, high VLDL cholesterol levels) Polycystic ovary syndrome
Probable risk factors Gestational diabetes Diet: High-glycemic load
Low-cereal fiber content Possible risk factors
Abstention from alcohol Cigarette smoking Schizophrenia
Table 2 Clinical Trials of Diabetes Prevention Reference (no.)Study populationInterventionResults Finnish Diabetes Prevention 522 middle-aged men and women Lifestyle modification Mean 3.2 yr incidence of DM 11% Study(62)with IGT. Mean BMI 31 kg/m2.(individualized counseling to (95%; CI 6–15%) in intervention reduce weight and fat intake,group vs 23% (95%; CI 17–29%) and increase fiber intake and in control group. physical activity)RRR = 58% (p< 0.001). Diabetes Prevention Program 3234 nondiabetic men and Lifestyle modification (16-lesson After mean follow-up 2.8 yr, Study(63)women,mean BMI 34.0 kg/m2curriculum and individualized incidence of DM 4.8/100 + fasting hyperglycemia support to achieve ≥7% weight person-yr in the lifestyle group vs (5.3–6.9 mmol/L) and IGT.loss through low-calorie and 11.0/100 person-yr in placebo group. low-fat diet + physical activity RRR = 58% (95%; CI 48–66%) ≥150 min/wk)vs placebo. Metformin 850 mg bidIncidence of DM 7.3/100 person-yr in metformin group. RRR = 31% (95%; CI 17–43%) vs placebo. STOP-NIDDM Study (64)1429 patients with fasting Acarbose 100 mg tidAfter mean follow-up 3.3 yr, hyperglycemia (5.6–7.7 mmol/L) incidence of DM 32% in and IGTacarbose group vs 42% in placebo group. RRR = 25% (95%; CI 10–37%). XENDOS Study (65)3305 obese men and women Lifestyle modification + orlistat DM incidence over 4 yr = 6.2% (BMI≥30 kg/m2) with either 120 mg tidin orlistat group vs 9.0% in normal or impaired glucose placebo group. toleranceRRR = 37.3% (95%; CI 13.7–54.5%). Preventive effect of orlistat seen only in pts with IGT at baseline (RRR = 52%,p= 0.02). TRIPOD Study (66)236 Hispanic women TroglitazoneAfter 2.2 years follow-up,RRR = 55% with past GDM(95% CI 17% –75% with troglitazone vs placebo. Abbr:I GT,impaired glucose tolerance; DM,type 2 diabetes mellitus; GDM,gestational diabetes; RRR,relative risk reduction.
291
of randomly selected individuals in whom diabetes was diagnosed by plasma glucose levels 2 h after a 75 g oral glucose load. Using these standardized criteria, striking dif- ferences in the prevalence of diabetes mellitus (DM) are observed between countries and ethnic groups (1). In many traditional communities in the least industrialized coun- tries, such as sub-Saharan Africa, rural China, and rural India, the prevalence of diabetes is extremely low (<3%). A moderate prevalence of DM (5–10%) is observed among the people of Tunisia, Thailand, and among people of European origin who live in Europe and North America. A high prevalence (>20%) of DM is observed among migrant South Asians, Arab populations in the Middle East, and Chinese migrants living in Mauritius. Groups with extremely high rates of DM include aboriginal populations who have experienced marked changes in their energy consumption and physical activity patterns such as the Pima and Papago Indians of Arizona (50%), the Micronesian Nau- rans (41%), Oji-Cree Aboriginals of Northern Canada (26%), and the Australian Abo- rigines (24%). The effect that changes in lifestyle have on the prevalence of DM is also highlighted by intra-ethnic group comparisons. For example, among people of South Asian origin, the relative prevalence of DM compared with the rural dwelling South Asians (1.0), is 3- to 6-fold among urban South Asians in India, and increases to 6- to 12-fold among South Asians living in urban areas of Fiji. Similar increases in the relative prevalence of DM are observed among Chinese, Black African, and Japanese migrants. In general, the prevalence of diabetes in urban areas of developing countries is about two to four times higher than that of rural areas in the same country (2,9).
The reasons why selected populations appear “protected” from developing diabetes while others are at “high risk” are difficult to discern, and are likely attributable to unique gene-environment interactions. Comparisons between and within certain ethnic groups reveal that populations who have replaced a traditional lifestyle with an urban lifestyle are at high risk. The common risk factors associated with urbanization are decreased physical activity, increased energy consumption, and increased body weight.
Among migrants, the “thrifty genotype” theory is widely accepted and hypothesizes that populations who are exposed to periods of famine develop insulin resistance as a protective mechanism to use the least energy expending mechanism to store energy as fat (10). However, when exposed to an abundance of energy (as is found in urban envi- ronments), this once protective gene action becomes deleterious, and results in increased abdominal obesity and DM.
Family History of Diabetes
First-degree relatives of people with type 2 diabetes have a twofold increased risk of diabetes (11–13). In the United States, the prevalence of type 2 diabetes in adults with one diabetic parent is four times as high as in adults without a parental history of dia- betes (6.0 vs 1.5%); the risk is almost doubled again if both parents have diabetes (14).
Impaired Glucose Tolerance and Impaired Fasting Glucose
Impaired glucose tolerance (IGT) (i.e., plasma glucose level 2 h after a 75 g oral glu- cose load ≥7.8 mmol/L but <11.1 mmol/L [ 15]) and impaired fasting glucose (IFG) (i.e., fasting plasma glucose ≥5.6 but <7.0 mmol/L [ 16]) strongly predict the subsequent development of type 2 diabetes. In the Paris Prospective Study, subjects with IGT (defined as fasting glucose <7.8 mM and 2-h glucose after a 75 g glucose load ≥7.8 and
<11.1 mM) had a 9.6-fold increased risk of diabetes, whereas subjects with IFG (defined
as fasting glucose >6.1 and <7.8 mM, and 2-h glucose <7.8 mM) had a 5.6-fold increased risk of diabetes compared to subjects with normal glucose tolerance (17). In an analysis of six prospective studies, the risk of conversion from IGT to type 2 diabetes ranged from 3.6–8.7% per year. Higher fasting plasma glucose (with a sharp increase in risk above 6.0 mM), higher 2-h post-challenge glucose, and increased BMI (>27), waist circumference and waist-to-hip ratio predicted conversion from IGT to diabetes (18).
Gestational Diabetes
There is general consensus that a diagnosis of gestational diabetes also predicts the development of type 2 diabetes in women. Estimates of the rate at which women with previous gestational DM (GDM) develop type 2 diabetes vary greatly. A published sys- tematic review of the literature found crude rates of conversion from GDM to type 2 DM ranging from 2.6 to 70% over follow-up periods ranging from 6 wk to 28 yr post- partum in 28 cohort studies (19). Much of the variability among studies could be attrib- uted to important differences in factors such as completeness of follow-up, duration of follow-up after the index GDM pregnancy, diagnostic criteria used to diagnose dia- betes, and selection of the study cohort. Predictors of higher risk of conversion from GDM to type 2 diabetes included higher fasting glucose at the time of diagnosis of GDM (20), higher maternal BMI before pregnancy (20,21) and at diagnosis of GDM (21), preterm delivery (20), abnormal glucose tolerance in the first 2 mo postpartum (20), longer time since the index GDM pregnancy (21) and more weight gain during that time (21), use of insulin in pregnancy (21), and diabetes in a first degree relative (21). Of course, some cases of GDM may represent new onset, or new recognition, of type 1 diabetes. For example, a study of 298 Danish women with a previous diagnosis of GDM found that more than 20% of those women who developed diabetes postpar- tum actually had type 1 diabetes; these women were leaner and younger than those who developed type 2 diabetes (20).
Birthweight and Other Early Life Influences
Low birthweight (<2500 g) has been associated with the development of type 2 dia- betes in adulthood in Pima Indians (22), British men (23), Swedish men (24), and American men (25) and women (26). This association is further supported by the obser- vation that in monozygotic twins discordant for diabetes, birthweight was significantly lower in the diabetic twin than in the nondiabetic individual (27). Although the mech- anism of the association between diabetes and low birth weight is uncertain, a plausi- ble hypothesis is that fetal under-nutrition impairs pancreatic development, leading to inadequate β-cell function in the face of dietary abundance later in life ( 22). This theory, known as the “thrifty phenotype” hypothesis, also provides an alternative explanation for the high incidence of diabetes in migrants from underdeveloped countries to more affluent countries; however, it does not explain the low prevalence of diabetes in those populations who do not migrate and maintain a traditional lifestyle. Another explana- tion that has been proposed for the increased incidence of type 2 diabetes in people with low birthweight is that low-birthweight infants who survive to adulthood have some genetic feature (such as a predisposition to insulin resistance), which allows them to survive, but also predisposes to diabetes later in life (10).
Among Pima Indians, high birthweight (>4500 g) is also associated with the subse- quent development of diabetes. This association is no longer significant, however, after
Chapter 19 / Risk for Diabetes 293
controlling for maternal diabetes in pregnancy (10). Further observations that offspring of Pima Indian women who were diabetic during pregnancy have a higher risk of dia- betes than offspring of nondiabetic mothers or mothers who developed diabetes after pregnancy suggest that the diabetic environment in utero may itself be a risk factor for the subsequent development of diabetes (28).
Many other influences in infancy and childhood may affect an individual’s diabetes risk. These include nutrition, childhood growth, and physical activity in childhood, all of which interact with an individual’s genetic susceptibility to affect diabetes risk. For example, among Pima Indians, people who were breast-fed for at least 2 mo were half as likely to develop diabetes in adulthood as those who were exclusively bottle-fed (29).
In another study of 7086 Finnish adults aged 67–76, the highest risk of diabetes was seen in adults with birthweight less than 3000 g and accelerated growth between ages 7 and 15 (30). Several cross-sectional studies have demonstrated a correlation between lack of physical activity and increased risk of obesity in children, but there are few data address- ing how quantitative aspects of physical activity in children (such as frequency and inten- sity) affect body composition and health, including diabetes risk. Further, parent–child interactions and the home environment have been shown to affect behaviors related to obesity and diabetes risk. For example, children who eat dinner at home with their family watch less television, eat a better diet (less fried food, less trans and saturated fat, lower glycemic load carbohydrates, more fiber, fewer soft drinks, and more fruits and vegeta- bles), and have increased social support, which positively correlates with participation in physical activity (31). Behaviors established in early life are likely to persist into adult- hood and are likely to influence the long-term risk of type 2 diabetes.
Diet
The hypothesis that poor diet might lead to type 2 diabetes was proposed centuries ago and remains an attractive hypothesis because diet is more easily modified than many other risk factors. Older studies of the association between diet and type 2 dia- betes showed contradictory results, with no relationship reported in several prospec- tive studies (32–34). Recently, however, two large prospective studies in American men and women found that a diet containing both a high glycemic load (i.e., high in easily digestible carbohydrates, which produce a marked rise in blood glucose and insulin levels) and a low-cereal fiber content increased the risk of self-reported type 2 diabetes up to 2.5-fold (35,36). Similarly, a prospective study of 31,641 Australian men and women showed that the risk of developing type 2 diabetes was positively associated with increasing glycemic index of the diet (37). High-glycemic diets are thought to create a chronically high demand for insulin. Patients with a genetic predisposition to type 2 diabetes may initially be able to produce enough insulin to meet these demands and maintain a normal blood glucose level. Diabetes may develop, however, if the abil- ity of the pancreatic β-cell to secrete these large amounts of insulin fails. Total energy intake was not related to the risk of type 2 diabetes in these studies after adjustment for age, BMI, physical activity, alcohol intake, smoking, and family history of diabetes.
Interestingly, magnesium intake was inversely related to the risk of type 2 diabetes.
This is consistent with the observation that magnesium increases insulin sensitivity in
vitro (38). In both of these studies, the risk of diabetes fell with increasing alcohol
intake. Men with moderate alcohol intake (30–49.9 g/d) and women with alcohol intake
of >15 g/d had about a 40% lower risk of diabetes than nondrinkers (39,40). In contrast,
alcohol intake of more than 176 g of alcohol per week (or more than 25 g/d) signifi- cantly increased the risk of diabetes in men but not in women in the Rancho Bernardo study (41). Although the discrepancies between these studies remain to be resolved, increased insulin sensitivity has been demonstrated in moderate drinkers compared to abstainers (42) and might contribute to a protective effect of moderate alcohol intake.
Sedentary Lifestyle
A sedentary lifestyle (i.e., vigourous exercise less than once per week) increases the risk of type 2 diabetes in men and women by 20–40% in both obese and nonobese individuals, independent of BMI (43,44). The amount of daily physical activity appears to have a continuous inverse relationship with the risk of type 2 diabetes, with a reduc- tion in risk of 10% for each 500 kcal increase in leisure-time energy expenditure (36,38,45,46). Exercise may be beneficial both by promoting weight loss and by increasing insulin sensitivity even in the absence of weight loss (47).
Smoking
Cigarette smoking has been associated with an increased risk of diabetes in some cohort studies (3,48,49) but others have failed to show this association (50–52).
Hypertension and Other Cardiovascular Risk Factors
A positive association between systolic blood pressure (51,53,54) or use of antihy- pertensive drugs (55,56) and the subsequent risk of diabetes has been noted in several prospective studies. On the other hand, treatment with certain antihypertensive drugs (e.g., angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and cal- cium antagonists) have been associated with lower risk of developing diabetes com- pared to β-blockers and diuretics ( 57). High triglyceride (47,48), low HDL cholesterol, and high VLDL cholesterol (51) levels have also been associated with an increased risk of diabetes in some studies.
Polycystic Ovary Syndrome
Women with polycystic ovary syndrome clearly are at increased risk of type 2 dia- betes. In a cross-sectional study of 254 American women with polycystic ovary syn- drome, aged 14–44, 39% had abnormal glucose tolerance on a 75-g oral glucose tolerance test. Three percent of these women had previously undiagnosed diabetes, 31% had impaired glucose tolerance, and 5% had impaired fasting glucose based on American Diabetes Association (ADA) criteria (58). Furthermore, in the Nurses’ Health Study (a prospective study of 116,671 mostly Caucasian female nurses aged 24–43 at study inception in 1989), women with highly irregular or long ( ≥40 d) menstrual cycles had twice the risk of developing diabetes compared to women with regular, 26–31 d cycles (RR 2.1 [95% CI 1.6–2.7] after adjustment for other risk factors) (59).
Schizophrenia and Antipsychotic Drugs
The prevalence of type 2 diabetes in people with schizophrenia may be two- to four-fold higher than in the general population. Although the precise prevalence of diabetes in this population is difficult to determine from the existing literature, recent studies suggest that approx 15% of people with schizophrenia may have diabetes and an equal proportion may have impaired glucose tolerance (60). Adverse metabolic
Chapter 19 / Risk for Diabetes 295
effects of antipsychotic drugs may increase the risk, although more prospective stud- ies are needed (61).
Summary of Diabetes Risk Factors
Epidemiologic studies have identified a number of clinical risk factors for diabetes, including increasing age and BMI, weight gain in adulthood, central fat distribution, ethnicity, family history of diabetes, low birthweight, sedentary lifestyle, higher systolic blood pressure, impaired glucose tolerance and impaired fasting glucose, and poly- cystic ovary syndrome. Diets rich in easily digestible carbohydrates and low in cereal fiber may increase the risk of type 2 diabetes; moderate alcohol intake may be protective.
Gestational diabetes and schizophrenia may also be risk factors for type 2 diabetes.
CAN DIABETES BE PREVENTED?
This question has been addressed by a number of recent, large clinical trials. All of these trials have focused on preventing diabetes in adults with strong risk factors.
Lifestyle Interventions
Two large, randomized controlled trials have shown unequivocally that a program of
lifestyle modification, incorporating regular exercise and modest weight loss, can pre-
vent or delay progression of impaired glucose tolerance to type 2 diabetes. The Fin-
nish Diabetes Prevention Study enrolled 522 middle-aged men and women with mean
BMI of 31 kg/m
2and impaired glucose tolerance (IGT) according to WHO criteria
(i.e., fasting glucose <7.8 mmol/L and 2-h glucose after a 75 g glucose challenge of
7.8–11.0 mmol/L). Patients were randomly assigned to either a lifestyle intervention group
(consisting of individualized counseling to reduce weight and fat intake, and increase
fiber intake and physical activity) or to a control group. After 2 yr, the lifestyle inter-
vention group lost a mean of 3.5–5.5 kg compared to 0.8–4.4 kg in the control group
(p < 0.001). After mean follow-up of 3.2 yr, the cumulative incidence of diabetes in the
intervention group was 11% (95%; confidence interval [CI] 6–15%) compared to cumu-
lative incidence of 23% (95%; CI 17–29%) in the control group. The risk of incident dia-
betes was reduced by 58% (p < 0.001) in the lifestyle intervention group (62). The
Diabetes Prevention Program (DPP) Study similarly showed that sustained healthy
lifestyle changes reduce the incidence of diabetes in high-risk individuals. In the DPP
Study, a group of 3234 ethnically diverse, nondiabetic men and women with mean BMI
34.0 kg/m
2plus fasting hyperglycemia (5.3–6.9 mmol/L) and IGT (glucose level 7.8–11.0
mmol/L 2 h after a 75 g glucose load) were randomly assigned to one of three interven-
tions: standard lifestyle recommendations plus metformin 850 mg twice daily; standard
lifestyle recommendations plus placebo twice daily; or an intensive lifestyle intervention
(consisting of a 16-lesson curriculum and individualized support to help patients achieve
the goals of weight reduction of at least 7% of initial body weight through healthy, low-
calorie, and low-fat diet, as well as physical activity for at least 150 min/wk). After mean
follow-up of 2.8 yr, the incidence of diabetes was 4.8 cases per 100 person-years in the
lifestyle group vs 11.0 cases per 100 person-years in the placebo group. The lifestyle
intervention reduced the incidence of diabetes by 58% (95%; CI 48–66%) compared to
placebo; results for the metformin group are described next (63).
Pharmacotherapy
There is strong clinical trial evidence that pharmaceutical agents—including met- formin, acarbose, and orlistat—may also prevent progression to type 2 diabetes in high- risk individuals. Patients with high fasting glucose and IGT who were treated with metformin in the DPP Study showed a diabetes incidence of 7.8 cases per 100 person- year, representing a risk reduction of 31% (95%; CI 17–43%) compared to placebo.
Metformin was most effective in preventing diabetes in patients with higher BMI and higher fasting glucose levels. Nevertheless, in the entire study group, metformin was less effective than lifestyle modification at preventing diabetes: the lifestyle intervention resulted in a 39% lower incidence of diabetes (95%; CI 24–51%) than treatment with metformin. The advantage of lifestyle intervention over metformin was most pro- nounced in older people and in those with lower body-mass index (63).
In the STOP-NIDDM Trial, 1429 patients with high fasting glucose (5.6–7.7 mmol/L) and IGT (2-h plasma glucose of 7.8–11.0 mmol/L after a 75 g glucose load) were ran- domized to treatment with acarbose 100 mg three times daily, or placebo. Patients treated with acarbose had a 25% lower risk of developing diabetes than those on placebo (32 vs 42%; relative risk 0.75 [95%; CI 0.63–0.90], p = 0.0015) over mean follow-up of 3.3 yr. However, mild to moderate gastrointestinal side effects (commonly flatulence and diarrhea) were significantly more common in patients treated with acar- bose compared to placebo (p < 0.0001) and almost one-third of patients treated with acarbose discontinued treatment early (64).
In the XENDOS Study, 3305 obese men and women (BMI ≥30 kg/m
2) with either normal or impaired glucose tolerance were randomized to lifestyle changes plus either orlistat 120 mg or placebo, three times per day. The cumulative incidence of diabetes over 4-yr follow-up was 6.2% in patients treated with orlistat, compared to 9.0% in patients on placebo. Treatment with orlistat reduced the incidence of diabetes by 37.3% (95%; CI 13.7–54.5%, p = 0.0032). Exploratory analyses showed that the preventive effect of orlistat was confined to subjects with IGT at baseline (21% of the group), within whom treatment with orlistat reduced the incidence of diabetes by 52% (65). Thiazolidinedione drugs also may prevent diabetes in individuals at high risk. In the TRIPOD Study, 236 Hispanic women with previous history of gestational diabetes were randomized to treatment with troglitazone (a thiazolidine- dione drug that was withdrawn from the market owing to liver toxicity) vs placebo.
After treatment for 2.5 yr, the cumulative risk of developing diabetes was reduced by 55% (relative risk reduction 0.55 [95%; CI 0.17–0.75], p = 0.009) in patients treated with troglitazone compared to placebo (66). Clinical trials are underway to assess whether other thiazolidinedione drugs may have similar effects in preventing diabetes (67).
Analysis of secondary endpoints from cardiovascular trials have suggested that ACE inhibitors (68), angiotensin receptor blockers (69), and lipid-lowering agents (70) also may prove to lower the risk of incident diabetes. Further studies are underway to explore the possible role of these agents in diabetes prevention (71).
Surgical Approaches
At least one observational study has suggested that surgical treatment for obesity (gastric banding, vertical banded gastroplasty, or gastric bypass) might reduce the inci-
Chapter 19 / Risk for Diabetes 297
dence of diabetes in obese patients, compared to matched controls who were not treated surgically (72). Liposuction does not appear to have beneficial effects on glucose metabolism (73).
PREVENTING DIABETES
Research described above has clearly shown that intensive lifestyle modification and medications can prevent or delay onset of type 2 diabetes. Potential benefits of translating these research findings to the community include lower population rates of cardiovascular disease, renal failure, blindness, and premature mortality. For example, an epidemiologic analysis projected that if all diabetes could be avoided in white American males through effective primary prevention, the rate of all-cause and CVD mortality in the American population could be reduced by up to 6.2 and 9.0%, respec- tively (74). The challenge for clinicians, and public health providers, will be in find- ing the most effective ways of implementing diabetes prevention strategies within a practice or community.
Diabetes prevention strategies can be implemented at a number of different levels.
These include: (1) directing diabetes prevention initiatives at high-risk subgroups of a population (such as high-risk ethnic groups); (2) promoting healthy lifestyle changes in the general population to lower the population risk of diabetes; and (3) identifying and treating high-risk individuals (such as those with IGT or strong family history of dia- betes) with interventions proven to lower diabetes risk. To date, there has been a paucity of research into the first two approaches. Indeed, a systematic review of community- based interventions to delay or prevent diabetes was able to identify only 16 relevant reports in peer-reviewed journals, most of which lacked a rigorous research design. Most of the studies examined the effect of community-based interventions on intermediate outcomes (such as healthy eating behaviors and physical activity) rather than plasma glucose levels or other diabetes risk factors. Results of the interventions were varied, with no study showing consistent positive effects on the outcomes of interest (75).
The third approach to diabetes prevention, which involves identifying and treating high-risk individuals, is most familiar to clinicians in the context of everyday practice.
Both the Canadian Diabetes Association (CDA) and the ADA have recommended rou- tine screening and intervention for pre-diabetes in primary care. The ADA recommends routine screening for diabetes every 3 yr for individuals 45 yr old and above, particu- larly those with BMI ≥25 kg/m
2. Early or more frequent testing is suggested for over- weight individuals who have one or more of the other risk factors for diabetes. The ADA recommends counseling to promote weight loss and increased physical activity in people with pre-diabetes, but does not recommend the routine use of drugs to prevent diabetes due to lack of information about their cost-effectiveness (76). Similarly, the CDA recommends screening every 3 yr for people 40 yr old and older, with imple- mentation of strategies to prevent diabetes and modify CVD risk factors in those with pre-diabetes (77). However, a number of challenges are inherent in this “high-risk”
approach. For example, the “high-risk” approach requires screening for risk factors or
for pre-diabetes, which may take place either in a primary care office or in a community
setting. If screening is done in a community setting, strategies must be put in place to pro-
vide adequate follow-up and treatment of people that are identified as high-risk. On the
other hand, opportunistic screening in primary-care settings may miss individuals who
lack access to health care. Once identified, people at high risk for diabetes must have access to adequate professional and nonprofessional resources to support lifestyle change, and to sustain the changes in the long term. Furthermore, the cost-effectiveness of these interventions in “real world” settings needs to be evaluated. The challenges sur- rounding the implementation of diabetes prevention research in the community have been highlighted in recent reviews (78,79).
CONCLUSIONS
Predisposition to diabetes is likely to be caused by a complex interaction among genes, perinatal and early life influences, and environmental/lifestyle factors in adult- hood. Recent research has focused on treatments to prevent diabetes in adults at high risk, in whom both lifestyle modification and pharmaceutical agents have been shown to prevent or delay the onset of diabetes. There has been less research into interventions in children and adolescents that might prevent the onset of diabetes later in life. More efforts need to be made to translate the research findings into effective, “real life” dia- betes prevention strategies.
REFERENCES
1. From World Health Organization website http://www.who.int/diabetes/facts/world_figures/en/index.
html.
2. Wild S, Roglic G, Green A, et al. Global prevalence of diabetes. Diabetes Care 2004;27:1047–1053.
3. Shaten BJ, Davey Smith G, Kuller LH, Neaton JD. Risk factors for the development of type II diabetes among men enrolled in the Usual Care group of the Multiple Risk Factor Intervention Trial.
Diabetes Care 1993;16:1331–1339.
4. Harris MI, Flegal KM, Cowie CC, et al. Prevalence of diabetes, impaired fasting glucose, and impaired glucose tolerance in U.S. adults. Diabetes Care 1998;21:518–524.
5. Colditz GA, Willett WC, Stampfer MJ, et al. Weight as a risk factor for clinical diabetes in women.
Am J Epidemiol 1990;132:501–513.
6. Cassano PA, Rosner B, Vokonas PS, Weiss ST. Obesity and body fat distribution in relation to the inci- dence of non-insulin-dependent diabetes mellitus. Am J Epidemiol 1992;136:1474–1486.
7. Chan JM, Rimm EB, Colditz GA, Stampfer MJ, Willett WC. Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men. Diabetes Care 1994;17:961-969.
8. Carey VJ, Walters EE, Colditz GA, et al. Body fat distribution and risk of non-insulin-dependent diabetes mellitus in women. Am J Epidemiol 1997;145:614–619.
9. Ramachandran A, Snehalatha C, Latha E et al. Impacts of urbanization on the lifestyle and on the prevalence of diabetes in native Asian Indian population. Diabetes Res Clin Pract 1999;44:
207–213.
10. Neel V. Diabetes mellitus: a thrifty genotype rendered detrimental by progress? Am J Hum Genetics 1962;14:353–362.
11. Ohlson L-O, Larsson B, Bjorntorp P, et al. Risk factors for Type 2 (non-insulin-dependent) diabetes mellitus. Thirteen and one-half years of follow-up of the participants in a study of Swedish men born in 1913. Diabetologia 1988;31:798–805.
12. Kawakami N, Takatsuka N, Shimizu H, Ishibashi H. Effects of smoking on the incidence of non- insulin-dependent diabetes mellitus. Am J Epidemiol 1997;145:103–109.
13. Anand S, Yusuf S. Ethnicity and vascular disease. In: Salim Yusuf ed. Evidence Based Cardiology.
BMJ, London, 1998, pp. 329–352.
14. Rewers M, Hamman RF. Risk factors for non-insulin-dependent diabetes. In: National Diabetes Data Group, ed. Diabetes in America, 2nd ed. National Institutes of Health/NIDDK, Bethesda, MD, 1995, pp. 179–220.
15. Report of The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diab Care 1997;20:1183–1197.
Chapter 19 / Risk for Diabetes 299
16. Genuth S, Alberti KG, Bennett P et al. Follow-up report on the diagnosis of diabetes mellitus. Dia- betes Care 2003;26:3160–3167.
17. Charles MA, Fontbonne A, Thibult N, Warnet J-M, Rosselin GE, Eschwege E. Risk factors for NIDDM in white population. Paris Prospective Study. Diabetes 1991;40:796–799.
18. Edelstein SL, Knowler WC, Bain RP, et al. Predictors of progression from impaired glucose tolerance to NIDDM. An analysis of six prospective studies. Diabetes 1997;46:701–710.
19. Kim C, Newton KM, Knopp RH. Gestational diabetes and the incidence of type 2 diabetes. Diabetes Care 2002;25:1862–1867
20. Damm P, Kuhl C, Bertelsen A, Molsted-Pedersen L. Predictive factors for the development of diabetes in women with previous gestational diabetes. Am J Obstet Gynecol 1992;167:607–616.
21. Coustan DR, Carpenter MW, O’Sullivan PS, Carr SR. Gestational diabetes: predictors of subsequent disordered glucose metabolism. Am J Obstet Gynecol 1993;168:1139–1145.
22. McCance DR, Pettitt DJ, Hanson RL, Jacobsson TH, Knowler WC, Bennett PH. Birth weight and non-insulin-dependent diabetes: thrifty genotype, thrifty phenotype, or surviving small baby geno- type? BMJ 1994;308:942–945.
23. Hales CN, Barker DJP, Clark PMS, et al. Fetal growth and impaired glucose tolerance at age 64.
BMJ 1991;303:1019–1022.
24. Carlsson S, Persson P-G, Alvarsson M, et al. Low birth weight, family history of diabetes, and glu- cose intolerance in Swedish middle-aged men. Diabetes Care 1999; 22:1043–1047.
25. Curhan GC, Willett WC, Rimm EB, Spiegelman D, Ascherio AL, Stampfer MJ. Birth weight and adult hypertension, diabetes mellitus, and obesity in US men. Circulation 1996;94:3246–3250.
26. Rich-Edwards JW, Colditz GA, Stampfer MJ, et al. Birthweight and the risk of type 2 diabetes mel- litus in adult women. Ann Intern Med 1999;130:278–284.
27. Poulsen P, Vaag AA, Moller Jensen D, Beck-Nielsen H. Low birth weight is associated with NIDDM in discordant monozygotic and dizygotic twin pairs. Diabetologia 1997;40:439–446.
28. Pettitt DJ, Aleck KA, Baird HR, Carraher MJ, Bennett PH, Knowler WC. Congenital susceptibility to NIDDM: role of intrauterine environment. Diabetes 1988;37:622–628.
29. Pettitt DJ, Forman MR, Hanson RL, Knowler WC, Bennett PH. Breastfeeding and incidence of non- insulin-dependent diabetes mellitus in Pima Indians. Lancet 1997;350:166–168.
30. Forsen T, Eriksson J, Tuomilehto J, Reunanen A, Osmond C, Barker D. The fetal and childhood growth of persons who develop type 2 diabetes. Ann Intern Med 2000;133:176–182.
31. Ebbeling CB, Pawiak DB, Ludwig DS. Childhood obesity: public-health crisis, common sense cure.
Lancet 2002;360:473–482.
32. Feskens EJM, Kromhout D. Cardiovascular risk factors and the 25-year incidence of diabetes melli- tus in middle-aged men. Am J Epidemiol 1989;130:1101–1108.
33. Lundgren H, Bengtsson C, Blohme G, et al. Dietary habits and incidence of noninsulin-dependent diabetes mellitus in a population study of women in Gothenburg, Sweden. Am J Clin Nutr 1989;49:
708–712.
34. Medalie JH, Papier CM, Goldbourt U, Herman JB. Major factors in the development of diabetes mel- litus in 10 000 men. Arch Intern Med 1975;135:811–817.
35. Salmeron J, Manson JE, Stampfer MJ, Colditz GA, Wing AL, Willett WC. Dietary fiber, glycemic load, and risk of non-insulin-dependent diabetes mellitus in women. JAMA 1997;277:472–477.
36. Salmeron J, Ascherio A, Rimm EB, et al. Dietary fiber, glycemic load, and risk of NIDDM in men.
Diabetes Care 1997;20(4):545–550.
37. Hodge AM, English DR, O’Dea K, Giles GG. Glycemic index and dietary fiber and the risk of type 2 diabetes. Diabetes Care 2004;27:2701–2706.
38. Resnick LM. Ionic basis of hypertension, insulin resistance, vascular disease, and related disorders: the mechanism of syndrome X. Am J Hypertens 1993;6:123s–134s.
39. Rimm EB, Chan J, Stampfer MJ, Colditz GA, Willett WC. Prospective study of cigarette smoking, alcohol use, and the risk of diabetes in men. BMJ 1995;310:555–559.
40. Stampfer MJ, Colditz GA, Willett WC, et al. A prospective study of moderate alcohol drinking and risk of diabetes in women. Am J Epidemiol 1988;128:549–558.
41. Holbrook TL, Barrett-Connor E, Wingard DL. A prospective population-based study of alcohol use and non-insulin-dependent diabetes mellitus. Am J Epidemiol 1990;132:902–909.
42. Facchini F, Chen Y-DI, Reaven GM. Light-to-moderate alcohol intake is associated with enhanced insulin sensitivity. Diabetes Care 1994;17:115–119.
43. Manson JE, Rimm EB, Stampfer MJ, et al. Physical activity and incidence of non-insulin-dependent diabetes mellitus in women. Lancet 1991;338:774–778.
44. Manson JE, Nathan DM, Krolewski AS, Stampfer MJ, Willett WC, Hennekens CH. A prospec- tive study of exercise and incidence of diabetes among US male physicians. JAMA 1992;268:
63–67.
45. Perry IJ, Wannamethee SG, Walker MK, Thomson AG, Whincup PH, Shaper AG. Prospective study of risk factors for development of non-insulin dependent diabetes in middle aged British men. BMJ 1995;310:560–564.
46. Helmrich SP, Ragland DR, Leung RW, Paffenbarger RS. Physical activity and reduced occurrence of non-insulin-dependent diabetes mellitus. N Engl J Med 1991;325:147–152.
47. Ruderman N, Apelian AZ, Schneider SH. Exercise in therapy and prevention of type II diabetes:
implications for blacks. Diabetes Care 1990;13: 1163–1168.
48. Rimm EB, Manson JE, Stampfer MJ, et al. Cigarette smoking and the risk of diabetes in women. Am J Public Health 1993;83:211–214.
49. Rimm EB, Chan J, Stampfer MJ, Colditz GA, Willett WC. Prospective study of cigarette smoking, alcohol use, and the risk of diabetes in men. BMJ 1995;310: 555–559.
50. Njolstad I, Arnesen E, Lund-Larsen PG. Sex differences in risk factors for clinical diabetes mel- litus in a general population: a 12-year follow-up of the Finnmark Study. Am J Epidemiol 1998;147:
49–58.
51. Stolk RP, van Splunder IP, Schouten JSAG, Witteman JCM, Hofman A, Grobbee DE. High blood pressure and the incidence of non-insulin dependent diabetes mellitus: findings in a 11.5 year follw- up study in the Netherlands. Eur J Epidemiol 1993;9:134–139.
52. Perry IJ, Wannamethee SG, Walker MK, thomson AG, Whincup PH, Shaper AG. Prospective study of risk factors for development of non-insulin dependent diabetes in middle aged British men. BMJ 1995;310:560–564.
53. McPhillips JB, Barrett-Connor E, Wingard DL. Cardiovascular disease risk factors prior to the diag- nosis of impaired glucose tolerance and non-insulin-dependent diabetes mellitus in a community of older adults. Am J Epidemiol 1990;131:443–453.
54. Balkau B, King H, Zimmet P, Raper LR. Factors associated with the development of diabetes in the Micronesian population of Nauru. Am J Epidemiol 1985;122:594–605.
55. Skarfors ET, Selinus KI, Lithell HO. Risk factors for developing non-insulin dependent diabetes: a 10 year follow up of men in Uppsala. BMJ 1991;303:755–760.
56. Wilson PWF, Anderson KM, Kannel WB. Epidemiology of diabetes mellitus in the elderly. The Fram- ingham Study. Am J Med 1986;80:3–9.
57. Pepine CJ, Cooper-DeHoff RM. Cardiovascular therapies and risk for development of diabetes. J Am Coll Cardiol 2004;44:509–12.
58. Legro RS, Kunselman AR, Dodson WC, Dunaif A. Prevalence and predictors of risk for type 2 dia- betes mellitus and impaired glucose tolerance in polycystic ovary syndrome: a prospective, controlled study in 254 affected women. J Clin Endocrinol Metab 1999;84:165–169.
59. Solomon CG, Hu FB, Dunaif A, et al. Long or highly irregular menstrual cycles as a marker for risk of type 2 diabetes mellitus. JAMA 2001;286:2421.
60. Bushe C, Holt R. Prevalence of diabetes and impaired glucose tolerance in patients with schizophre- nia. Br J Psychiatry Suppl 2004;47:s67–s71.
61. “Schizophrenia and Diabetes 2003” Expert Consensus Meeting, Dublin, 2-3 October 2003: consensus summary. Br J Psychiatry Suppl 2004;184:s112–s114.
62. Tuomilehto J, Lindström J, Eriksson JG, et al. Prevention of type 2 diabetes by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 2001;344:1343–1350.
63. Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 2002;346:393–403.
64. Chiasson J-L, Josse RG, Gomis R, et al. Acarbose for prevention of type 2 diabetes mellitus: the STOP-NIDDM randomised trial. Lancet 2002;359:2072–2077.
65. Torgerson JS, Hauptman J, Boldrin MN, Sjöström L. XENical in the prevention of diabetes in obese subjects (XENDOS) Study. Diab Care 2004;27:155–161.
66. Buchanan TA, Xiang AH, Peters RK et al. Preservation of pancreatic β-cell function and prevention of type 2 diabetes by pharmacological treatment of insulin resistance in high-risk Hispanic women.
Diabetes 2002;51:2796–2803.
Chapter 19 / Risk for Diabetes 301
67. The DREAM Trial Investigators. Rationale, design and recruitment characteristics of a large, simple international trial of diabetes prevention: the DREAM trial. Diabetologia 2004;47:1519–1527.
68. Heart Outcome Protection Evaluation Study Investigators. Effects of ramipril on cardiovascular and microvascular outcomes in people with diabetes mellitus: results of the HOPE study and MICRO- HOPE substudy. Lancet 2000;355:253–9.
69. Lindholm LH, Ibsen H, Dahlof B et al. Cardiovascular morbidity and mortality in patients with dia- betes in the Losartan Intervention For Endpoint reduction in hypertension study (LIFE): a randomised trial against atenolol. Lancet 2002;359:1004–1010.
70. Freeman DJ, Norrie J, Sattar N et al. Pravastatin and the development of diabetes mellitus: evidence for a protective treatment effect in the West of Scotland Coronary Prevention Study. Circulations 2001;103:357–362.
71. Simpson RW, Shaw JE, Zimmet PZ. The prevention of type 2 diabetes- lifestyle change or pharma- cotherapy? A challenge for the 21st century. Diabetes Res Clin Pract 2003;59:165–180.
72. Torgerson JS, Sjöström L. The Swedish Obese Subjects (SOS) study- rationale and results. Int J Obe- sity 2001;25 Suppl 1:S2–S4.
73. Klein S, Fontana L, Young VL et al. Absence of an effect of liposuction on insulin action and risk factors for coronary heart disease. N Engl J Med 2004;350:2549–2457.
74. Narayan KM, Thompson TJ, Boyle JP et al. The use of population attributable risk to estimate the impact of prevention and early detection of type 2 diabetes on population-wide mortality risk in US males. Health Care Manag Sci 1999;2:223–227.
75. Satterfield DW, Volansky M, Caspersen CJ et al. Community-based lifestyle interventions to prevent type 2 diabetes. Diabetes Care 26:2643-2652, 2003.
76. American Diabetes Association. Standards of medical care in diabetes. Diabetes Care 2005;28:
S4–S36.
77. Canadian Diabetes Association 2003 Clinical Practice Guidelines for the Prevention and Manage- ment of Diabetes in Canada. Can J Diab 2003;27:S10–S13.
78. Centers for Disease Control and Prevention Primary Prevention Working Group. Primary prevention of type 2 diabetes mellitus by lifestyle intervention: implications for health policy. Ann Int Med 2004;
140:951–957.
79. Zimmet P, Shaw J, Alberti KGMM. Preventing type 2 diabetes and the dysmetabolic syndrome in the real world: a realistic view. Diabet Med 2003;20:693–702.
