The most important results are the prevalence of ADHD of 27.4% in the sample, and the surprisingly strong association between ADHD and Obesity III. Nearly half, 42.6%, of patients with Obesity III had ADHD, that is, the OB+ADHD population was concentrated in the obesity class having the highest mortality and morbidity risks, and greatest need for effective treatment.
Moreover, at all levels of obesity patients with ADHD symptoms were less successful at losing weight than non-ADHD peers. Compared to NAD, AD had a significantly higher starting BMI (39.6 vs. 34.2), yet lost less weight (2.6 vs. 4.0 kg/m2). Greater contrast is found in Obesity III, with NAD in that class achieving more than twice the weight loss of AD (7.0 vs. 2.9 kg/m2), while mean weight loss for ADSx did not differ significantly from AD (2.3 vs. 2.6 kg/m2), implying that the presence of even "subthreshhold" ADHD symptoms reduces the effectiveness of obesity treatment. In other words, in OB+ADHD, treatment outcome has stronger association with symptoms of ADHD than level of obesity. Effect size of ADHD for the whole sample was only about 0.2, but in Obesity III patients, effect size was about 0.5, comparable, for example, to the moderate effect size of SSRI drugs on panic symptoms .
Impersistence was not the cause of worse results for AD, who attended more visits than NAD (56.0 vs. 39.4 mean total visits), over a longer span of time. ADSx was no more successful than AD, but had fewer visits, comparable to NAD, in this respect, ADSx showing characteristics intermediate between AD and NAD. The result is AD, ADSx and NAD having similar rates of clinic visits (AD had 1.5 visits/month, ADSx 1.2 and NAD 1.4), while NAD had the highest rate of BMI decrease (NAD lost 0.14 BMI/month, ADSx 0.075 and AD 0.067). However, these statistics have limited utility, given the large variance in number of visits and months of treatment. The "slow" aspect of task performance in ADHD has been previously reported , and could be described as a kind of "inefficiency", that is, taking more time to accomplish less.
The reasons for a strong association between ADHD and obesity (particularly extreme obesity) are unknown, but there are a number of reported findings that appear to be relevant and certainly interesting. For one, evidence exists that variations of dopamine receptor (DR) genes affect both conditions. In obesity, DRD2 and DRD4 [30, 31] genes, and in ADHD, the DRD4 [32, 33] gene, have been implicated in the transmission of, or predisposition to, the disorders, raising the possibility that similar, overlapping or shared DR functioning (or dysfunction) in these disorders is related to their co-co-occurrence.
DRD2 and a range of dopamine and other genes have been associated with a "reward deficiency syndrome"  in which insufficient dopamine-mediated "natural" reward leads to use of "unnatural" rewards, such as substances, gambling, risk taking and inappropriate eating. This syndrome is associated with obesity , and common in ADHD . The DRD4 gene has been associated with "novelty seeking" traits, said to be greater in substance abusers , and individuals with both DRD2 and DRD4 genetic variations may be especially prone to multiple difficulties (e.g., having both ADHD and "reward deficiency syndrome") , further suggesting obesity and ADHD could share neurobiological attributes.
A recent study showed the availability of striatal DRD2 receptors was decreased as a function of increasing BMI , supporting the idea that reward-seeking behavior plays a role in the onset or continuation of obesity. In other studies, administration of D2 agonists resulted in decreases in hyperinsulinemia associated with obesity , and it is known that the brain is richly supplied with insulin receptors, including the cortex and striatal areas , suggesting an intriguing link between insulin resistance, characteristic of obesity, and dopamine-mediated psychiatric symptoms, including ADHD. No doubt, this hypothesis and many far more refined hypotheses will be studied in coming years and will elucidate the complex neurophysiological connections hinted at by the above.
The 27.4% prevalence of ADHD in obese patients is considerably higher than found in the general adult population, reported as 4.7% by Murphy and Barkley . Across studies of other specific populations, prevalence of ADHD or ADHD symptoms was also greater than in general, for example, cocaine abusers 12% , anxiety disorders 16% , panic disorder 22% , and substance abusers 25% . Considering the differing methods and populations, the reported prevalences are difficult to compare, yet are fairly consistent with one another for the most part. The prevalence of OB+ADHD in this report is plausibly within the range of these studies.
The uniformity of the Inattentive subtype of ADHD in this adult population was not unusual or unexpected, considering the well-known attenuation of hyperactive and impulsive symptoms observed as children with ADHD grow into adolescence and adulthood, compared to the much stronger retention of inattentive symptoms . This is not to say that ADHD adults don't behave impulsively, simply that with maturation, continuing hyperactive/impulsive (H/I) behaviors are usually expressed in less obvious ways than among their school-age counterparts. That is, while ADHD adults frequently have impairing H/I symptoms, the number and types of these behaviors seldom meet DSM-IV diagnostic criteria for the H/I or Combined subtype of ADHD. DSM-IV ADHD subtyping, strictly applied, is likely to have limited correlation to neurobiological processes (assuming processes reflect impulsivity or disinhibition), since the level of symptomatic behavior required for diagnosing DSM-IV H/I or Combined subtypes is higher than nearly all ADHD adults will display, even if substantial H/I behavior is evident.
Another factor is that girls, compared to boys, have lower rates of H/I symptoms, which is associated with lower likelihood of being diagnosed in childhood, but if diagnosed, girls more frequently have the Inattentive subtype . It would be expected that this difference would persist into adulthood, augmenting the likelihood that Inattentive symptoms would predominate the clinical presentation. In a sample in which most patients are female and middle-aged, predominance of the Inattentive subtype is predictable.
Inevitably, there are numerous caveats and limitations that apply to this preliminary work. For one, results reflect the origin of data in clinical practice – hardly ideal from a research perspective. Treatment settings may favor a higher case-finding rate because of the opportunity to observe and assess behavior is greater than the methods of prospective research designs. For example, ADHD research instruments have a sensitivity of 70–90% [43, 44], and would likely identify fewer cases of ADHD than the "gold standard" of careful clinical assessment, expected to find most cases. For this report, diagnosis had been made primarily by one interviewer, which could favor consistency, but also leads to potential biases that skew results. In addition, the modification of age of onset criterion for ADHD may increase difficulty of comparing present results to those of studies using the unmodified criterion. Clearly, for the purpose of comparing prevalence among diverse populations, a prospective design is strongly advantageous, but likely not congruent with the goals of clinical practice.
Bias could also be introduced by patient factors, such as comorbid conditions producing symptoms which might not be easily separable from those found in ADHD, and erroneously diagnosed as ADHD. Several disorders common in obese patients fit into this category, e.g., obstructive sleep apnea, depression, and anxiety disorders. While the differential characteristics of these conditions, e.g., relative duration, pervasiveness and continuity of symptoms can help distinguish one from another, the nature and interactions of these comorbidities are not particularly well-delineated. While careful clinical practice requires effort to avoid diagnosing ADHD if symptoms are not sufficiently distinct from those of confounding disorders, reducing error requires further research into the effects of comorbidities intercurrent with ADHD.
Another form of bias originates in patients' predisposition to endorse symptoms because of dysphoria, negative affectivity, or readiness to attribute distress to some preferred, rather than actual, source when queried by the interviewer. For the OB+ADHD population, the idea is that some patients may have had a preference for endorsing ADHD symptoms, instead of accepting more accurate explanations for distress or lack of obesity treatment progress, e.g., failure to lose weight while being unwilling to exercise. ("I can't lose weight because I just get so distracted.")
Some forms of endorsement bias are readily seen in medical practice, for example, patients who would rather talk about somatic events than discuss their anxieties or depressed mood. Implications for the OB+ADHD population are hard to assess, since this form of bias and its effects on research of ADHD prevalence have not received comparable study, nor are there reports of the frequency of encountering this bias in clinical work with adult ADHD patients. Moreover, no special measures were taken to reduce or account for such biases during the course of diagnosing patients in the present sample, rendering it quite difficult to estimate how much and what kind of effect biases have had on the above reported prevalences of OB+ADHD. At this point, it remains an open question, suggesting additional reasons for cautious interpretation of the results given in this report.
The demographic characteristics of the sample population are worth a few comments. Notably, about 90% of the patients were women, which obviously does not reflect the general population, but may not be surprising in this context because more women than men are concerned about weight gain, and are more likely to seek medical care than male contemporaries. The patients as a group were also distinctly middle-aged, no doubt reflecting the fact that older individuals are more likely to be able to afford non-insured medical care (obesity treatment isn't covered under most policies), and the time to devote to their own needs.
Some effects of demographic factors on diagnosis of ADHD were discussed above. Gender and age differences (e.g., younger patients or higher male to female ratio) could conceivably affect the range and intesity of ADHD symptoms that are observed, prevalence findings, and individual as well as aggregate response to obesity treatment.
In addition, generalizability of conclusions based on the data in this report are limited by a modest sample size, and uncertainty that the bariatric patients in the sample were truly representative of the general obese population. Results could be misleading, for example, if the sample had a disproportion of individuals with serious medical or psychiatric problems, higher weight or greater social skill deficits, even if the sample is construed to be typical of those seen in similar practices.