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Short-term weight gain after antiretroviral therapy initiation and subsequent risk of cardiovascular disease and diabetes: the D:A:D study : ART, weight gain and cardiometabolic outcomes
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Abstract
The aim of the study was to assess the impact of the gain in body mass index (BMI)
observed immediately after antiretroviral therapy (ART) initiation on the subsequent
risk of cardiovascular disease (CVD) and diabetes.
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Most cited references13
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Predicting the risk of cardiovascular disease in HIV-infected patients: the data collection on adverse effects of anti-HIV drugs study.
Rodolphe Thiébaut, Rainer Weber, Matthew Law … (2010)
HIV-infected patients receiving combination antiretroviral therapy may experience metabolic complications, potentially increasing their risk of cardiovascular diseases (CVDs). Furthermore, exposures to some antiretroviral drugs seem to be independently associated with increased CVD risk. We aimed to develop cardiovascular risk-assessment models tailored to HIV-infected patients. Prospective multinational cohort study. The data set included 22,625 HIV-infected patients from 20 countries in Europe and Australia who were free of CVD at entry into the Data collection on Adverse Effects of Anti-HIV Drugs Study. Using cross-validation methods, separate models were developed to predict the risk of myocardial infarction, coronary heart disease, and a composite CVD endpoint. Model performance was compared with the Framingham score. The models included age, sex, systolic blood pressure, smoking status, family history of CVD, diabetes, total cholesterol, HDL cholesterol and indinavir, lopinavir/r and abacavir exposure. The models performed well with area under the receiver operator curve statistics of 0.783 (range 0.642-0.820) for myocardial infarction, 0.776 (0.670-0.818) for coronary heart disease and 0.769 (0.695-0.824) for CVD. The models estimated more accurately the outcomes in the subgroups than the Framingham score. Risk equations developed from a population of HIV-infected patients, incorporating routinely collected cardiovascular risk parameters and exposure to individual antiretroviral therapy drugs, might be more useful in estimating CVD risks in HIV-infected persons than conventional risk prediction models.
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Class of antiretroviral drugs and the risk of myocardial infarction.
Nina Friis-Møller, Peter Reiss, Caroline Sabin … (2007)
We have previously demonstrated an association between combination antiretroviral therapy and the risk of myocardial infarction. It is not clear whether this association differs according to the class of antiretroviral drugs. We conducted a study to investigate the association of cumulative exposure to protease inhibitors and nonnucleoside reverse-transcriptase inhibitors with the risk of myocardial infarction. We analyzed data collected through February 2005 from our prospective observational study of 23,437 patients infected with the human immunodeficiency virus. The incidence rates of myocardial infarction during the follow-up period were calculated, and the associations between myocardial infarction and exposure to protease inhibitors or nonnucleoside reverse-transcriptase inhibitors were determined. Three hundred forty-five patients had a myocardial infarction during 94,469 person-years of observation. The incidence of myocardial infarction increased from 1.53 per 1000 person-years in those not exposed to protease inhibitors to 6.01 per 1000 person-years in those exposed to protease inhibitors for more than 6 years. After adjustment for exposure to the other drug class and established cardiovascular risk factors (excluding lipid levels), the relative rate of myocardial infarction per year of protease-inhibitor exposure was 1.16 (95% confidence interval [CI], 1.10 to 1.23), whereas the relative rate per year of exposure to nonnucleoside reverse-transcriptase inhibitors was 1.05 (95% CI, 0.98 to 1.13). Adjustment for serum lipid levels further reduced the effect of exposure to each drug class to 1.10 (95% CI, 1.04 to 1.18) and 1.00 (95% CI, 0.93 to 1.09), respectively. Increased exposure to protease inhibitors is associated with an increased risk of myocardial infarction, which is partly explained by dyslipidemia. We found no evidence of such an association for nonnucleoside reverse-transcriptase inhibitors; however, the number of person-years of observation for exposure to this class of drug was less than that for exposure to protease inhibitors. Copyright 2007 Massachusetts Medical Society.
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Patterns of Obesity Development before the Diagnosis of Type 2 Diabetes: The Whitehall II Cohort Study
Dorte Vistisen, Daniel Witte, Adam G. Tabak … (2014)
Introduction Obesity is a well-established risk factor for type 2 diabetes; however, it is well-known that patients with type 2 diabetes vary greatly with respect to degree of adiposity at time of diagnosis [1]–[3]. Thus, a better understanding of the heterogeneity of diabetes is important for improving disease prevention and treatment. Recent studies have described trajectories in plasma glucose, insulin sensitivity, beta cell function, and subclinical inflammation related to diabetes before the disease is diagnosed [4]–[6]. These population-level growth curves contribute to aetiological and pathophysiological understanding, but may somewhat oversimplify the complex and heterogeneous disease mechanisms responsible for type 2 diabetes. To facilitate stratified, targeted interventions, identification of population subgroups with similar risk factor patterns seems essential. One way of identifying such groups is to use data-driven statistical methods, such as latent class trajectory analysis [7]. This method identifies distinct classes or subgroups of people who are homogeneous with respect to the development of a given risk factor over time, but heterogeneous as compared with other groups. Although latent class trajectory analysis has been widely used in criminology and behavioural research [8],[9], it is new to health research [10],[11] and has only very recently been applied in a study of diabetes patients [12], but not in relation to diabetes aetiology. In this study, we aimed to identify different patterns of obesity development over a period of 18 years in a population initially free of diabetes. In addition, we examined trajectories of other metabolic risk factors accompanying each pattern of obesity development. Methods Ethics Statement The Whitehall II study was reviewed and approved by the University College London Ethics Committee (85/0938). Written informed consent was obtained from each participant at each phase. The study was conducted according to the principles of the Helsinki Declaration. Study Participants This study uses data from the longitudinal Whitehall II cohort of non-industrial British civil servants. In the original study, a total of 10,308 participants (6,896 men and 3,412 women aged 35–55 years) of mainly white ethnicity who worked in London offices of 20 departments were recruited between 1985 and 1988 (phase 1) and followed at eight subsequent phases ∼2.5 years apart. All study phases included a questionnaire, and every second phase (∼5 years apart) also included a clinical health examination (phases 1, 3, 5, 7, and 9). In the Whitehall II cohort 6,057 men and 2,758 women participated at phase 3 (1991–1993); 5,473 men and 2,397 women at phase 5 (1997–1999); 4,893 men and 2,074 women at phase 7 (2002–2004); and 4,759 men and 2,002 women at phase 9 (2007–2009). The Whitehall II study is described in detail elsewhere [13]. Phase 3 (1991–1993) was the first phase when an oral glucose tolerance test (OGTT) was performed. Therefore, we did not use data from phase 1. With the last follow-up being the phase 9 examination in 2008–2009, this study was based on 9,181 (89.1%) white participants. From these, we excluded 830 (9.0%) participants who were lost to follow-up prior to phase 3, 211 (2.3%) participants with prevalent diabetes at phase 3 (51% based on OGTT and 49% based on a diabetes diagnosis outside the study), 776 (8.5%) participants for which diabetes status could not be assessed at any phase in the study, and another 659 (7.2%) with no measurement of body mass index (BMI) throughout the study. At phases 3, 5, 7, and 9 a standard 2-hour 75 g OGTT was performed in the morning after an overnight fast (≥8 hours of fasting). For a subset of participants, the OGTT was administered in the afternoon after a light fat-free breakfast (≥5 hours of fasting). OGTT measurements for participants with less than 8 hours of fasting were excluded from the analysis. Diabetes was diagnosed by a doctor outside the study (43.1%) or at screening by OGTTs (56.9%). Screen-detected diabetes was ascertained throughout follow-up by OGTTs administered every 5 years and defined according to the OGTT criteria defined by the World Health Organization (WHO) [14]. Thus, the final sample included 6,705 participants (73.0% of the original sample of white ethnicity) with a median follow-up time of 14.1 years (interquartile range [IQR]: 8.7–16.2 years) and 15,269 person-examinations. We identified 645 (9.6%, 1,209 person-examinations) cases of incident diabetes by phase 9. A flow diagram of the participants included at each phase is shown in Figure 1. 10.1371/journal.pmed.1001602.g001 Figure 1 Flow diagram of the participants included at each phase. Study Procedures and Calculations Weight, height, and waist circumference were measured according to standard protocols [13] at phases 3, 5, 7, and 9. Systolic and diastolic blood pressure was measured by manual random zero sphygmomanometer at phases 3 and 5 and by OMRON HEM 907 at phases 7 and 9. Data on ethnicity, smoking status, and family history of diabetes were collected by questionnaire at each phase. During all phases of the study, blood samples were handled according to standardized procedures. Blood glucose was measured with the glucose oxidase method [4], and serum insulin was measured with an in-house human insulin radioimmunoassay (phase 3) and a DAKO insulin ELISA kit (DakoCytomation Ltd) in later phases [15]. Serum triglycerides, total cholesterol, and high-density lipoprotein (HDL) cholesterol were measured using automated enzymatic colorimetric methods at all phases. Interleukin 1 receptor antagonist (IL-1Ra) and total adiponectin serum concentrations were measured with the Quantikine ELISA kit (R&D Systems) in a diabetes case-cohort sample [5],[6]. We used Friedewald's formula to calculate low-density lipoprotein (LDL) cholesterol [16]. The homeostasis model assessment was used to estimate β-cell function (HOMA-%B) and insulin resistance (HOMA-IR) [17]. The absolute 8-year risk of developing type 2 diabetes was calculated in all participants using the Framingham diabetes risk score [18]. Moreover, the Framingham cardiovascular disease (CVD) risk score was used to estimate absolute 10-year risk of developing CVD [19]. Statistical Analysis For comparison of characteristics between groups we used chi-square test for categorical variables and t-tests for continuous data. A level of significance of 5% was used. Latent class trajectory analysis The observation period for retrospective trajectories started at the date of diagnosis for those who developed diabetes, and at the last screening or questionnaire phase for those not developing diabetes (year 0). Date of diabetes diagnosis was set to the date of the OGTT for screen-detected diabetes or to the midpoint between dates of first self-reported diabetes and last diabetes-free screening for patients diagnosed by a doctor outside the study. The latent class trajectory analysis was performed in the population developing diabetes. The model was specified as a linear mixed-effects model with BMI as the dependent variable. The mixed-effects model specification was used to account for the likely correlation of repeated measurements within the same participant. In the analysis we adjusted for age, sex, and study phase in order to identify latent groups with a different BMI development over time not attributed to differences in age, sex, or study phase. We used a cubic specification for trajectory shape, i.e., both linear, quadratic, and cubic terms for the time before diabetes diagnosis was entered as covariates in the model. We assumed the effects of confounders (age, sex, and study phase) to be the same for all latent classes and looked for latent classes with different regression parameters for time. A linear term for time before diagnosis was used to specify the random effects of the model, i.e., the individual variation around the mean trajectory (of the individual's latent class). The “hlme” function in the “lcmm” package in R version 9.15.2 (The R foundation for Statistical Computing) was used to fit the model http://cran.r-project.org/web/packages/lcmm/lcmm.pdf. hlme (BMI ∼ t + t2 + t3 + age + sex + phase, mixture = ∼ t + t2 + t3 , random = ∼ t, subject = ‘id’, ng = 3, data = data) The number of latent classes needs to be specified a priori. The optimal number of latent classes to describe data is assessed by comparing the fit of models with different number of latent classes. With a prior requirement of at least 2% of the diabetes population in each group to warrant clinical relevance, we used the Bayesian Information Criterion (BIC) to evaluate the models and selected the number of classes for which the model had the lowest BIC. In this study, three latent classes were identified. Upon the model fit, a posterior probability of membership to each of the identified latent classes was calculated for each participant, who then was assigned exclusively to the class for which the highest probability was obtained. This class-allocation was used in the subsequent analyses of the accompanying cardiometabolic risk factors. For each identified BMI group, trajectories of the following outcomes were followed backwards in time to the first clinical examination: waist circumference; systolic and diastolic blood pressure; total, HDL and LDL cholesterol; triglycerides; fasting and 2-hour plasma glucose; fasting and 2-hour serum insulin; HOMA-%B and HOMA-IR; the Framingham diabetes and CVD risk scores; adiponectin and IL-1Ra. Prior to analysis, outcomes with highly skewed distributions were log-transformed (fasting and 2-hour serum insulin, HOMA-%B and HOMA-IR, adiponectin and IL-1Ra). For most determinants ≤5% of the values were missing. For plasma glucose and serum insulin the proportions of missing values were slightly higher (7%–18%). The case-cohort sample with measurements of adiponectin and IL-1Ra covered 66% of diabetes cases and 41% of diabetes-free individuals in this study. We used linear mixed-effects models to estimate trajectories for each group. For those developing diabetes, time dependence was allowed to vary across the BMI groups. Quadratic and cubic terms for time were included in the three BMI groups when significant (two-sided 5% significance level). For individuals not developing diabetes, year 0 is merely a time point in a normal life course, and we therefore fitted the trajectories by linear models. All analyses were adjusted for age, sex, and study phase. Analyses of lipids were further adjusted for lipid-lowering treatment, and analyses of blood pressure were further adjusted for anti-hypertensive treatment. We tested pair-wise differences in growth curves between the BMI groups using the F-test by comparing the curve of contrasts between two BMI subgroups to a straight line with zero slope and through the origin (two-sided 5% significance level). Provided p-values therefore relate to curve differences in slope, intercept, or both. Statistical analyses were performed in R version 9.15.2 (The R Foundation for Statistical Computing) and SAS version 9.2 (SAS Institute). Data Sharing Whitehall II data, protocols, and other metadata are available to the scientific community. Please refer to the Whitehall II data sharing policy on: www.ucl.ac.uk/whitehallII/data_sharing/index.htm. Results Patterns of Obesity Development We identified three distinct patterns of BMI development prior to diabetes diagnosis (Figure 2A). The latent class group, representing the majority of individuals who developed diabetes, was characterised by an average BMI in the overweight range during the entire follow-up (n = 604). This group had an average weight gain of 2.3 BMI units during 18 years of follow-up and was labelled “stable overweight,” because mean BMI was within the overweight category as defined by the WHO (average BMI at time of diagnosis: 28.1 kg/m2). Another group had an average BMI in the overweight range more than 15 years before diagnosis, an early weight gain of 8.6 BMI units from 18 to 10 years before diagnosis, then stable BMI in the obese range until 4–5 years before diagnosis. Thereafter, BMI increased to morbid obesity (mean BMI of 41.1 kg/m2), totalling a weight gain of 16.1 BMI units during follow-up. This group was termed “progressive weight gainers” (n = 15). The third group (n = 26), labelled “persistently obese,” was characterised by BMI in the obese range more than 15 years before diagnosis of diabetes (average BMI of 32.7 kg/m2), and with persistent obesity until the time of diagnosis. This group had an average weight gain of 6.0 BMI units during the 18 years of follow-up. 10.1371/journal.pmed.1001602.g002 Figure 2 Trajectories for a hypothetical male of 60 years at time 0 of body mass index (A), waist circumference (B), systolic blood pressure (C), and diastolic blood pressure (D) from 18 years before time of diagnosis/last examination. Trajectories for blood pressure represent a person not on anti-hypertensive treatment. Solid lines indicate estimated trajectories for each group and dashed lines are 95% confidence limits. Black bars at the bottom indicate the relative data distribution over the follow-up period. Trajectories of BMI for a hypothetical female of 50 years of age at time of diagnosis are shown in Figure S1. Light blue, stable overweight; dark blue, progressive weight gain; red, persistently obese; grey, diabetes-free population. The average BMI development in the reference group not developing diabetes was only 1.7 BMI units during the 18 years of follow-up, from 24.5 kg/m2 to 26.2 kg/m2. The trajectories of waist circumference (Figure 2B) followed those of BMI for the three BMI trajectory groups with the stable overweight group being significantly different from the progressive weight gainers (p 0.31), but the levels of IL-1Ra increased exponentially in the group of progressive weight gainers as compared with the other groups (p<0.002 for both) (Figure 6A and 6B). 10.1371/journal.pmed.1001602.g006 Figure 6 Trajectories for a hypothetical male of 60 years at time 0 of adiponectin (A) and IL-1Ra (B) from 18 years before time of diagnosis/last examination. Solid lines indicate estimated trajectories and dashed lines are 95% confidence limits. Black bars at the bottom indicate the relative data distribution over the follow-up period. Light blue, stable overweight; dark blue, progressive weight gain; red, persistently obese; grey, diabetes-free population. Other Characteristics Among those developing diabetes, a significantly higher proportion of the persistently obese than the stable overweight individuals were diagnosed with diabetes by their own general practitioner (p = 0.008) (Table 1). The proportion of women was also higher in the persistently obese group than in the stable overweight or diabetes-free groups (p<0.02). Furthermore, individuals in the stable overweight and progressive weight gain groups were more likely to have a family history of diabetes compared with the diabetes-free population (p<0.04) (Table 1). 10.1371/journal.pmed.1001602.t001 Table 1 Characteristics of study participants at time of diagnosis for the three diabetes groups or at the last clinical examination for the diabetes-free group. Characteristics Individuals Developing Diabetes in the Study Stratified by Latent Class BMI Groups Individuals Free of Diabetes in the Study Stablen Overweight (n = 604) Progressive Weight Gain (n = 15) Persistently Obese (n = 26) Diabetes-Free (n = 6,060) Men (%) 74.5 (70.8–77.9) 53.3 (26.6–78.7) 50.0 (29.9–70.1)a 71.8 (70.6–72.9)b Ever smoker (%) 56.8 (52.7–60.8) 66.7 (38.4–88.2) 57.7 (36.9–76.6) 51.6 (50.3–52.8)a Family history of diabetes (%) 19.9 (16.8–23.3) 26.7 (7.8–55.1) 11.5 (2.4–30.2) 9.8 (9.1–10.6)a , c Diagnosed by GP outside study (%) 42.2 (38.2–46.3) 33.3 (11.8–61.6) 69.2 (48.2–85.7)a , c – Ever on anti-hypertensive treatment (%) 27.6 (24.1–31.4) 20.0 (4.3–48.1) 38.5 (20.2–59.4) 22.9 (21.9–24.0)a Ever on lipid lowering treatment (%) 14.4 (11.7–17.5) 13.3 (1.7–40.5) 15.4 (4.4–34.9) 15.7 (14.8–16.7) Age at diagnosis/last exam. (years) 60.6 (7.8) 61.1 (8.2) 57.9 (7.7) 60.3 (7.9) Data are percentages (95% CI) or means (SD). Test of difference in characteristics between groups: chi-square test for categorical variables and t-test for continuous data, respectively. Significantly different. a Significantly different from stable overweight. b Significantly different from persistently obese. c Significantly different from progressive weight gain. The characteristics of the participants' first clinical examination in the study are shown in Table 2. Since the time span from the first clinical examination to the diagnosis of diabetes differed between the groups, the data should not be compared directly. 10.1371/journal.pmed.1001602.t002 Table 2 Characteristics of study participants at their first clinical examination. Characteristics Individuals Developing Diabetes in the Study Stratified by Latent Class BMI Groups Individuals Free of Diabetes in the Study Stable Overweight (n = 604) Progressive Weight Gain (n = 15) Persistently Obese (n = 26) Diabetes-Free (n = 6,060) Time before diabetes diagnosis/last examination (years) 10.3 (5.6–12.7) 6.6 (5.0–13.0) 10.9 (9.7–15.2) 14.3 (9.0–16.2) Men (%) 74.5 (70.8–77.9) 53.3 (26.6–78.7) 50 (29.9–70.1)a 71.8 (70.6–72.9)b Smoker (%) 14.9 (12.2–18.0) 13.3 (1.7–40.5) 23.1 (9.0–43.6) 12.0 (11.2–12.9)a Family history of diabetes (%) 19.9 (16.8–23.3) 26.7 (7.8–55.1) 11.5 (2.4–30.2) 9.8 (9.1–10.6)a , c Antihypertensive treatment (%) 14.6 (11.9–17.6) 6.7 (0.2–31.9) 23.1 (9.0–43.6) 8.5 (7.9–9.3)a , b Lipid lowering treatment (%) 4.5 (3.0–6.4) 0 0 2.4 (2.0–2.8)a Age (years) 52.7 (7.0) 52.9 (7.3) 50.0 (5.9) 51.6 (7.4)a Fasting plasma glucose (mmol/l) 5.7 (1.1) 5.6 (1.5) 5.6 (0.7) 5.2 (0.5)a , b , c 2-hour plasma glucose (mmol/l) 6.6 (2.3) 7.1 (3.0) 6.6 (2.1) 5.2 (1.4)a , b , c BMI (kg/m2) 27 (3.8) 34.2 (8.7)a 38.2 (5.0)a , c 25.3 (3.6)a , b , c Waist circumference (cm) 92.3 (11.8) 99.6 (18.0)a 112.0 (10.9)a , c 86.8 (11.7)a , b , c Height (cm) 172.4 (9.1) 167.4 (9.4)a 166.5 (10.4)a 172.9 (9.1)b , c Total cholesterol (mmol/l) 6.6 (1.2) 6 (1.2) 6.8 (1.0)c 6.3 (1.2)a , b HDL cholesterol (mmol/l) 1.3 (0.4) 1.3 (0.2) 1.2 (0.4) 1.5 (0.4)a , b LDL cholesterol (mmol/l) 4.4 (1) 3.9 (1.1) 4.5 (0.9) 4.2 (1.1)a Triglycerides (mmol/l) 1.9 (1.3) 1.8 (0.6) 2.8 (2.1)a , c 1.3 (0.9)a , b Systolic blood pressure (mmHg) 125.4 (15.5) 122.7 (22.3) 128.0 (14.9) 120.6 (14.3)a , b Diastolic blood pressure (mmHg) 81.4 (10.3) 80.1 (14.1) 85.5 (9.7)a 78.1 (9.9)a , b Fasting serum insulin (pmol/l) 8.5 (5.4–13.3) 12.5 (7.5–31.5)a 15.9 (10.2–20.7)a 5.8 (3.9–8.7)a , b , c 2-hour serum insulin (pmol/l) 52.0 (29.4–86.4) 56.5 (46.9–96.5) 84.3 (44.8–140.7)a 30.6 (17.6–50.8)a , b , c HOMA-IR 2.2 (1.4–3.5) 3.2 (1.9–7.3)a 3.8 (2.5–5.2)a 1.5 (1.0–2.2)a , b , c HOMA-%B 86.4 (60.5–131.5) 132.8 (123.7–153.3)a 155.5 (109.4–214.8)a 77.3 (56–112)a , b , c Adiponectin (mg/ml) 7.4 (5.3–10.8) 7.4 (7.0–9.7) 7.9 (5.0–12.6) 9.0 (6.6–12.8)a CRP (µmol/L) 1.3 (0.7–2.6) 1.8 (1.4–3.2) 4.4 (2.4–6.6)a 0.8 (0.4–1.7)a , b , c IL-6 (µmol/l) 1.6 (1.2–2.3) 1.6 (1.1–3) 2.5 (2.1–3.8)a 1.3 (1.0–2.0)a , b Data are percentages (95% CI), means (SD) or medians (IQR). Test of difference in characteristics between groups: chi-square test for categorical variables and t-test for continuous data, respectively. a Significantly different from stable overweight. b Significantly different from persistently obese. c Significantly different from progressive weight gain. Time Effects and Posterior Probability Memberships Estimated beta coefficients (SE) for the time effects are shown in Table 3. In the stable overweight group, a cubic specification of time (t3) was only included in the models for fasting and 2-hour plasma glucose, HOMA-IR, and estimated diabetes and CVD risk. The trajectories for diastolic blood pressure, cholesterol, and triglyceride were linear for all groups. 10.1371/journal.pmed.1001602.t003 Table 3 Fixed effects of time dependence for the different outcome variables in the multilevel models of change before the diagnosis of diabetes for the diabetes groups and before the end of follow-up for the diabetes-free population. Stable Overweight Progressive Weight Gainers Persistently Obese Diabetes-Free t t2 t3 t t2 t3 t t2 t3 t BMI 0.095 (0.015) – – 2.285 (0.314) 0.268 (0.054) 0.011 (0.002) 0.016 (0.269) 0.060 (0.042) 0.005 (0.002) 0.056 (0.012) Waist circumference 0.284 (0.048) – – 4.832 (1.238) 0.553 (0.211) 0.024 (0.009) 2.558 (1.074) 0.537 (0.169) 0.026 (0.007) 0.173 (0.035) Systolic BP 0.462 (0.081) – – 1.158 (0.383) – – −0.280 (0.970) −0.072 (0.059) – 0.248 (0.049) Diastolic BP 0.236 (0.055) – – 1.379 (0.266) – – 0.265 (0.228) – – 0.183 (0.032) Total cholesterol 0.001 (0.006) – – −0.010 (0.030) – – −0.016 (0.025) – – 0.022 (0.003) HDL cholesterol −0.006 (0.002) – – −0.008 (0.008) – – −0.007 (0.007) – – −0.006 (0.001) LDL cholesterol 0.002 (0.006) – – −0.023 (0.027) – – −0.026 (0.024) – – 0.019 (0.003) Triglycerides 0.014 (0.005) – – 0.051 (0.024) – – −0.013 (0.019) – – 0.023 (0.003) Fasting glucose 0.444 (0.018) 0.045 (0.003) 0.001 (0.000) 0.898 (0.099) 0.105 (0.017) 0.004 (0.001) 0.471 (0.093) 0.053 (0.014) 0.002 (0.001) 0.006 (0.002) 2-hour glucose 1.391 (0.051) 0.139 (0.008) 0.004 (0.000) 2.443 (0.290) 0.314 (0.050) 0.012 (0.002) 1.403 (0.260) 0.162 (0.040) 0.006 (0.002) 0.012 (0.005) Fasting insulin 0.026 (0.003) – – 0.082 (0.017) – – 0.014 (0.014) – – 0.016 (0.002) 2-hour insulin 0.025 (0.004) – – 0.030 (0.022) – – −0.093 (0.053) –0.006 (0.003) – 0.017 (0.003) HOMA-%B −0.037 (0.008) −0.002 (0.000) – 0.011 (0.046) −0.001 (0.003) – −0.138 (0.034) –0.009 (0.002) – 0.010 (0.002) HOMA-IR 0.104 (0.017) 0.009 (0.003) 0.000 (0.000) 0.376 (0.105) 0.044 (0.018) 0.002 (0.001) 0.156 (0.085) 0.026 (0.013) 0.001 (0.001) 0.012 (0.002) Estimated DM risk −0.171 (0.033) −0.024 (0.005) −0.001 (0.000) 0.124 (0.027) – – 0.060 (0.023) – – 0.023 (0.003) Estimated CVD risk 2.575 (0.099) 0.265 (0.016) 0.009 (0.001) 0.279 (0.087) – – 3.713 (0.500) 0.417 (0.077) 0.014 (0.003) 0.076 (0.012) Adiponectin −0.005 (0.003) – – −0.014 (0.011) – – −0.002 (0.009) – – −0.002 (0.003) IL-1RA 0.051 (0.008) 0.002 (0.000) – 0.385 (0.085) 0.044 (0.015) 0.002 (0.001) 0.232 (0.092) 0.040 (0.014) 0.002 (0.001) 0.013 (0.002) Data are beta-coefficients (SE). BP, blood pressure; DM, diabetes mellitus. The mean posterior probability of class membership for individuals was high for each of the classes (75%–96%) (Table 4). 10.1371/journal.pmed.1001602.t004 Table 4 Average class probabilities by latent classes. Latent Class Mean of Posterior Probabilities Class 1 Class 2 Class 3 Class 1: stable overweight 0.955 0.027 0.018 Class 2: progressive weight gainers 0.113 0.779 0.108 Class 3: persistently obese 0.151 0.103 0.746 Discussion By use of latent class trajectory analysis we identified three distinct patterns of obesity development prior to the diagnosis of type 2 diabetes: (1) a stable overweight group, (2) a group of progressive weight gainers, and (3) a persistently obese group. The patterns of obesity development were accompanied by different trajectories of insulin resistance and other cardiometabolic risk factors, underscoring that type 2 diabetes is a not a single disease entity, but rather a heterogeneous disease with different pathophysiological pathways depending on the level and development of obesity. In contrast to common belief, the great majority of patients diagnosed with diabetes did not have a substantial weight gain prior to diagnosis (stable overweight group). Their average obesity development was comparable to the reference group not developing diabetes, with a slightly higher initial BMI level of 1.2 kg/m2 and ending at a 1.9 unit higher BMI level at time of diagnosis. This group of patients experienced a slightly worsening of beta cell function and insulin sensitivity starting ∼5 years before they were diagnosed with diabetes. A previous study found that leaner patients with type 2 diabetes may have a stronger genetic predisposition than obese patients [20]. Our finding of a higher proportion of individuals with a family history of diabetes among the stable overweight group compared with the diabetes-free group supports this notion. Of particular interest is the finding that the persistently obese group did not have a higher proportion of individuals with a family history of diabetes compared with the diabetes-free group, which may have prevented them from developing diabetes earlier in their life despite their high degree of obesity. Interestingly, the estimated 8-year diabetes risk from the Framingham diabetes risk score remained relatively low and even decreased during the last 5 years prior to diabetes diagnosis in the stable overweight group. Prediction of diabetes in this group may therefore be difficult using established validated models. The finding of a significantly higher proportion of the persistently obese individuals being diagnosed with diabetes by their own general practitioner as compared with the two other diabetes groups supports this notion, and indicates that general practitioners are more likely to screen for diabetes in morbidly obese than in overweight individuals. Indeed, our findings support the “prevention paradox,” proposed by Geoffrey Rose more than 30 years ago, in which he stated that “a large number of people exposed to a low risk is likely to produce more cases than a small number of people exposed to a high risk” [21]. In the context of diabetes prevention, it may therefore not be optimal to focus only on promoting weight loss in the most obese individuals, but also aiming at preventing small weight gains in the entire population (i.e., shifting the entire BMI distribution to the left). This will only give a small benefit to each individual, but may prove effective at the population level in terms of preventing diabetes and CVD events in the future. The progressive weight gainers exhibited a pattern of obesity development with two separate phases of weight gain before type 2 diabetes diagnosis. Individuals with this pattern had an exponential increase in both insulin levels and beta cell function before the diagnosis of diabetes. Of interest, we found increased IL-1Ra concentrations towards diabetes diagnosis in the group of progressive weight gainers, but not in the persistently obese group. A previous analysis of the Whitehall II study found that IL-1Ra concentrations accelerated 6 years before diagnosis of diabetes, and differences between individuals who developed diabetes and those remaining diabetes-free were attenuated by adjustment for BMI or waist circumference [5]. Comparing IL-1RA trajectories with concurrent obesity development in our study, it seems that low-grade inflammation may predominantly be determined by changes in body weight and to a lesser extent by changes in glucose metabolism or actual level of obesity. Whether this applies to the general population needs further investigation. Despite an increase in both obesity and systolic blood pressure over time, the calculated 10-year CVD risk from the Framingham CVD risk score was lower among the progressive weight gainers than among the other two groups at time of diabetes diagnosis, whereas the persistently obese and stable overweight groups had similar levels of estimated CVD risk despite different trajectories of CVD risk factors. These findings question the validity of calculated scores for disease risk in a population with heterogeneous disease development. Thus, future risk prediction models should aim to include knowledge about the heterogeneity of disease development instead of assuming a one-size-fits-all model. The group of persistently obese individuals was on average characterised by class II obesity (35–40 kg/m2) [22] already 18 years before they were diagnosed with type 2 diabetes. Towards diabetes diagnosis this group experienced a pattern of beta cell compensation followed by loss of beta cell function, whereas insulin resistance only increased slightly. This pattern of beta cell dysfunction has for many years been thought to be characteristic for type 2 diabetes development [23]. However, this suggestion was originally based on a cross-sectional study of 82 individuals with long-standing (∼8–32 years) obesity and ∼8 years duration of type 2 diabetes [24], which questions the generalizability to leaner individuals with newly diagnosed diabetes. Thus, as evident from our data, the natural history of beta cell dysfunction in type 2 diabetes seems to be even more complex than previously thought and indeed not similar in all people developing type 2 diabetes [25]. A major strength of the Whitehall II study is its 18-year follow-up period and the detailed phenotypic characterisation of the study participants, which enabled us to relate trajectories of obesity development to trajectories of clinically relevant measures of metabolism and cardiovascular risk. Most previous studies examining BMI development over time have assumed that there is an average pattern of obesity development over time applying to the whole population [11],[26]–[28]. Another often used approach is to classify BMI into pre-defined categories, as currently debated in relation to mortality [29]. Limitations of this approach are that available information is not utilised optimally [30], and it may cause misclassification of individuals, especially near the cut-points for classification [31]. Moreover, the use of such more or less arbitrarily set cut-points keeps research locked in pre-defined concepts and thought patterns, which may prevent research moving forward. Instead of studying changes in predefined BMI categories, we chose to define subgroups of individuals by latent class trajectory analysis. This type of statistical method is useful to explore heterogeneous growth patterns that would not be identified by use of conventional methods. Indeed, latent class trajectory analysis is more flexible, because it models group-specific average patterns of obesity development. One disadvantage of latent class analysis is, however, that it often results in very different sizes of subgroups [11],[32], potentially limiting statistical power as well as interpretation and generalizability of the results. Another factor limiting the generalizability of our results is that this analysis was performed only in white participants. The reason for studying obesity development in an ethnically homogenous population was to avoid identifying differences in BMI patterns mainly attributed to ethnic differences. Therefore, these analyses should be confirmed in other study populations. In conclusion, latent class trajectory analysis identified three distinct patterns of obesity development leading to type 2 diabetes. The accompanying trajectories of insulin resistance and other cardiometabolic risk factors differed between these groups. In general, the majority of individuals developing type 2 diabetes were rather weight stable during follow-up with a slightly higher average BMI than the diabetes-free population, suggesting that strategies focusing on small weight reductions for the entire population may be more beneficial than predominantly focusing on weight loss for high-risk individuals. Supporting Information Figure S1 Trajectories for a hypothetical female of 50 years at time 0 of body mass index from 18 years before time of diagnosis/last examination. Solid lines indicate estimated trajectories for each group and dashed lines are 95% confidence limits. Black bars at the bottom indicate the relative data distribution over the follow-up period. Light blue, stable overweight; dark blue, progressive weight gain; red, persistently obese; grey, diabetes-free population. (TIF) Click here for additional data file.
Author and article information
Journal
Title:
HIV Medicine
Abbreviated Title:
HIV Med
Publisher:
Wiley
ISSN:
14642662
Publication date Created:
April
2016
Publication date
(Print):
April
2016
Publication date
(Electronic):
July
28 2015
Volume: 17
Issue: 4
Pages: 255-268
Affiliations
Article
DOI: 10.1111/hiv.12294
PubMed ID: 26216031
SO-VID: 7d6a1e41-a83f-41a3-bdac-283b074d5111
Copyright © ©
2015
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